Streamflow predictions in Alpine Catchments by using artificial neural networks. Application in the Alto Genil Basin (South Spain)

NASA Astrophysics Data System (ADS)

Jimeno-Saez, Patricia; Pegalajar-Cuellar, Manuel; Pulido-Velazquez, David

2017-04-01

This study explores techniques of modeling water inflow series, focusing on techniques of short-term steamflow prediction. An appropriate estimation of streamflow in advance is necessary to anticipate measures to mitigate the impacts and risks related to drought conditions. This study analyzes the prediction of future streamflow of nineteen subbasins in the Alto-Genil basin in Granada (Southeast of Spain). Some of these basin streamflow have an important component of snowmelt due to part of the system is located in Sierra Nevada Mountain Range, the highest mountain of continental Spain. Streamflow prediction models have been calibrated using time series of historical natural streamflows. The available streamflow measurements have been downloaded from several public data sources. These original data have been preprocessed to turn them to the original natural regime, removing the anthropic effects. The missing values in the adopted horizon period to calibrate the prediction models have been estimated by using a Temez hydrological balance model, approaching the snowmelt processes with a hybrid degree day method. In the experimentation, ARIMA models are used as baseline method, and recurrent neural networks ELMAN and nonlinear autoregressive neural network (NAR) to test if the prediction accuracy can be improved. After performing the multiple experiments with these models, non-parametric statistical tests are applied to select the best of these techniques. In the experiments carried out with ARIMA, it is concluded that ARIMA models are not adequate in this case study due to the existence of a nonlinear component that cannot be modeled. Secondly, ELMAN and NAR neural networks with multi-start training is performed with each network structure to deal with the local optimum problem, since in neural network training there is a very strong dependence on the initial weights of the network. The obtained results suggest that both neural networks are efficient for the short term prediction, surpassing the limitations of the ARIMA models and, in general, the experiments showed that NAR networks are the ones with the greatest generalization capability. Therefore, NAR networks are chosen as the starting point for other works, in which we study the streamflow predictions incorporating exogenous variables (as the Snow Cover Area), the sensitivity of the prediction to the initial conditions, multivariate streamflow predictions considering the spatial correlation between the sub-basins streamflow and the synthetic generations to assess droughts statistic. This research has been partially supported by the CGL2013-48424-C2-2-R (MINECO) and the PMAFI/06/14 (UCAM) projects.

Attributing uncertainty in streamflow simulations due to variable inputs via the Quantile Flow Deviation metric

NASA Astrophysics Data System (ADS)

Shoaib, Syed Abu; Marshall, Lucy; Sharma, Ashish

2018-06-01

Every model to characterise a real world process is affected by uncertainty. Selecting a suitable model is a vital aspect of engineering planning and design. Observation or input errors make the prediction of modelled responses more uncertain. By way of a recently developed attribution metric, this study is aimed at developing a method for analysing variability in model inputs together with model structure variability to quantify their relative contributions in typical hydrological modelling applications. The Quantile Flow Deviation (QFD) metric is used to assess these alternate sources of uncertainty. The Australian Water Availability Project (AWAP) precipitation data for four different Australian catchments is used to analyse the impact of spatial rainfall variability on simulated streamflow variability via the QFD. The QFD metric attributes the variability in flow ensembles to uncertainty associated with the selection of a model structure and input time series. For the case study catchments, the relative contribution of input uncertainty due to rainfall is higher than that due to potential evapotranspiration, and overall input uncertainty is significant compared to model structure and parameter uncertainty. Overall, this study investigates the propagation of input uncertainty in a daily streamflow modelling scenario and demonstrates how input errors manifest across different streamflow magnitudes.

A retrospective streamflow ensemble forecast for an extreme hydrologic event: a case study of Hurricane Irene and on the Hudson River basin

NASA Astrophysics Data System (ADS)

Saleh, Firas; Ramaswamy, Venkatsundar; Georgas, Nickitas; Blumberg, Alan F.; Pullen, Julie

2016-07-01

This paper investigates the uncertainties in hourly streamflow ensemble forecasts for an extreme hydrological event using a hydrological model forced with short-range ensemble weather prediction models. A state-of-the art, automated, short-term hydrologic prediction framework was implemented using GIS and a regional scale hydrological model (HEC-HMS). The hydrologic framework was applied to the Hudson River basin ( ˜ 36 000 km2) in the United States using gridded precipitation data from the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) and was validated against streamflow observations from the United States Geologic Survey (USGS). Finally, 21 precipitation ensemble members of the latest Global Ensemble Forecast System (GEFS/R) were forced into HEC-HMS to generate a retrospective streamflow ensemble forecast for an extreme hydrological event, Hurricane Irene. The work shows that ensemble stream discharge forecasts provide improved predictions and useful information about associated uncertainties, thus improving the assessment of risks when compared with deterministic forecasts. The uncertainties in weather inputs may result in false warnings and missed river flooding events, reducing the potential to effectively mitigate flood damage. The findings demonstrate how errors in the ensemble median streamflow forecast and time of peak, as well as the ensemble spread (uncertainty) are reduced 48 h pre-event by utilizing the ensemble framework. The methodology and implications of this work benefit efforts of short-term streamflow forecasts at regional scales, notably regarding the peak timing of an extreme hydrologic event when combined with a flood threshold exceedance diagram. Although the modeling framework was implemented on the Hudson River basin, it is flexible and applicable in other parts of the world where atmospheric reanalysis products and streamflow data are available.

Parameterisation of rainfall-runoff models for forecasting low and average flows, I: Conceptual modelling

NASA Astrophysics Data System (ADS)

Castiglioni, S.; Toth, E.

2009-04-01

In the calibration procedure of continuously-simulating models, the hydrologist has to choose which part of the observed hydrograph is most important to fit, either implicitly, through the visual agreement in manual calibration, or explicitly, through the choice of the objective function(s). Changing the objective functions it is in fact possible to emphasise different kind of errors, giving them more weight in the calibration phase. The objective functions used for calibrating hydrological models are generally of the quadratic type (mean squared error, correlation coefficient, coefficient of determination, etc) and are therefore oversensitive to high and extreme error values, that typically correspond to high and extreme streamflow values. This is appropriate when, like in the majority of streamflow forecasting applications, the focus is on the ability to reproduce potentially dangerous flood events; on the contrary, if the aim of the modelling is the reproduction of low and average flows, as it is the case in water resource management problems, this may result in a deterioration of the forecasting performance. This contribution presents the results of a series of automatic calibration experiments of a continuously-simulating rainfall-runoff model applied over several real-world case-studies, where the objective function is chosen so to highlight the fit of average and low flows. In this work a simple conceptual model will be used, of the lumped type, with a relatively low number of parameters to be calibrated. The experiments will be carried out for a set of case-study watersheds in Central Italy, covering an extremely wide range of geo-morphologic conditions and for whom at least five years of contemporary daily series of streamflow, precipitation and evapotranspiration estimates are available. Different objective functions will be tested in calibration and the results will be compared, over validation data, against those obtained with traditional squared functions. A companion work presents the results, over the same case-study watersheds and observation periods, of a system-theoretic model, again calibrated for reproducing average and low streamflows.

Propagation of stage measurement uncertainties to streamflow time series

NASA Astrophysics Data System (ADS)

Horner, Ivan; Le Coz, Jérôme; Renard, Benjamin; Branger, Flora; McMillan, Hilary

2016-04-01

Streamflow uncertainties due to stage measurements errors are generally overlooked in the promising probabilistic approaches that have emerged in the last decade. We introduce an original error model for propagating stage uncertainties through a stage-discharge rating curve within a Bayesian probabilistic framework. The method takes into account both rating curve (parametric errors and structural errors) and stage uncertainty (systematic and non-systematic errors). Practical ways to estimate the different types of stage errors are also presented: (1) non-systematic errors due to instrument resolution and precision and non-stationary waves and (2) systematic errors due to gauge calibration against the staff gauge. The method is illustrated at a site where the rating-curve-derived streamflow can be compared with an accurate streamflow reference. The agreement between the two time series is overall satisfying. Moreover, the quantification of uncertainty is also satisfying since the streamflow reference is compatible with the streamflow uncertainty intervals derived from the rating curve and the stage uncertainties. Illustrations from other sites are also presented. Results are much contrasted depending on the site features. In some cases, streamflow uncertainty is mainly due to stage measurement errors. The results also show the importance of discriminating systematic and non-systematic stage errors, especially for long term flow averages. Perspectives for improving and validating the streamflow uncertainty estimates are eventually discussed.

Effect of monthly areal rainfall uncertainty on streamflow simulation

NASA Astrophysics Data System (ADS)

Ndiritu, J. G.; Mkhize, N.

2017-08-01

Areal rainfall is mostly obtained from point rainfall measurements that are sparsely located and several studies have shown that this results in large areal rainfall uncertainties at the daily time step. However, water resources assessment is often carried out a monthly time step and streamflow simulation is usually an essential component of this assessment. This study set out to quantify monthly areal rainfall uncertainties and assess their effect on streamflow simulation. This was achieved by; i) quantifying areal rainfall uncertainties and using these to generate stochastic monthly areal rainfalls, and ii) finding out how the quality of monthly streamflow simulation and streamflow variability change if stochastic areal rainfalls are used instead of historic areal rainfalls. Tests on monthly rainfall uncertainty were carried out using data from two South African catchments while streamflow simulation was confined to one of them. A non-parametric model that had been applied at a daily time step was used for stochastic areal rainfall generation and the Pitman catchment model calibrated using the SCE-UA optimizer was used for streamflow simulation. 100 randomly-initialised calibration-validation runs using 100 stochastic areal rainfalls were compared with 100 runs obtained using the single historic areal rainfall series. By using 4 rain gauges alternately to obtain areal rainfall, the resulting differences in areal rainfall averaged to 20% of the mean monthly areal rainfall and rainfall uncertainty was therefore highly significant. Pitman model simulations obtained coefficient of efficiencies averaging 0.66 and 0.64 in calibration and validation using historic rainfalls while the respective values using stochastic areal rainfalls were 0.59 and 0.57. Average bias was less than 5% in all cases. The streamflow ranges using historic rainfalls averaged to 29% of the mean naturalised flow in calibration and validation and the respective average ranges using stochastic monthly rainfalls were 86 and 90% of the mean naturalised streamflow. In calibration, 33% of the naturalised flow located within the streamflow ranges with historic rainfall simulations and using stochastic rainfalls increased this to 66%. In validation the respective percentages of naturalised flows located within the simulated streamflow ranges were 32 and 72% respectively. The analysis reveals that monthly areal rainfall uncertainty is significant and incorporating it into streamflow simulation would add validity to the results.

Stochastic model stationarization by eliminating the periodic term and its effect on time series prediction

NASA Astrophysics Data System (ADS)

Moeeni, Hamid; Bonakdari, Hossein; Fatemi, Seyed Ehsan

2017-04-01

Because time series stationarization has a key role in stochastic modeling results, three methods are analyzed in this study. The methods are seasonal differencing, seasonal standardization and spectral analysis to eliminate the periodic effect on time series stationarity. First, six time series including 4 streamflow series and 2 water temperature series are stationarized. The stochastic term for these series obtained with ARIMA is subsequently modeled. For the analysis, 9228 models are introduced. It is observed that seasonal standardization and spectral analysis eliminate the periodic term completely, while seasonal differencing maintains seasonal correlation structures. The obtained results indicate that all three methods present acceptable performance overall. However, model accuracy in monthly streamflow prediction is higher with seasonal differencing than with the other two methods. Another advantage of seasonal differencing over the other methods is that the monthly streamflow is never estimated as negative. Standardization is the best method for predicting monthly water temperature although it is quite similar to seasonal differencing, while spectral analysis performed the weakest in all cases. It is concluded that for each monthly seasonal series, seasonal differencing is the best stationarization method in terms of periodic effect elimination. Moreover, the monthly water temperature is predicted with more accuracy than monthly streamflow. The criteria of the average stochastic term divided by the amplitude of the periodic term obtained for monthly streamflow and monthly water temperature were 0.19 and 0.30, 0.21 and 0.13, and 0.07 and 0.04 respectively. As a result, the periodic term is more dominant than the stochastic term for water temperature in the monthly water temperature series compared to streamflow series.

Streamflow Bias Correction for Climate Change Impact Studies: Harmless Correction or Wrecking Ball?

NASA Astrophysics Data System (ADS)

Nijssen, B.; Chegwidden, O.

2017-12-01

Projections of the hydrologic impacts of climate change rely on a modeling chain that includes estimates of future greenhouse gas emissions, global climate models, and hydrologic models. The resulting streamflow time series are used in turn as input to impact studies. While these flows can sometimes be used directly in these impact studies, many applications require additional post-processing to remove model errors. Water resources models and regulation studies are a prime example of this type of application. These models rely on specific flows and reservoir levels to trigger reservoir releases and diversions and do not function well if the unregulated streamflow inputs are significantly biased in time and/or amount. This post-processing step is typically referred to as bias-correction, even though this step corrects not just the mean but the entire distribution of flows. Various quantile-mapping approaches have been developed that adjust the modeled flows to match a reference distribution for some historic period. Simulations of future flows are then post-processed using this same mapping to remove hydrologic model errors. These streamflow bias-correction methods have received far less scrutiny than the downscaling and bias-correction methods that are used for climate model output, mostly because they are less widely used. However, some of these methods introduce large artifacts in the resulting flow series, in some cases severely distorting the climate change signal that is present in future flows. In this presentation, we discuss our experience with streamflow bias-correction methods as part of a climate change impact study in the Columbia River basin in the Pacific Northwest region of the United States. To support this discussion, we present a novel way to assess whether a streamflow bias-correction method is merely a harmless correction or is more akin to taking a wrecking ball to the climate change signal.

Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq

NASA Astrophysics Data System (ADS)

Yaseen, Zaher Mundher; Jaafar, Othman; Deo, Ravinesh C.; Kisi, Ozgur; Adamowski, Jan; Quilty, John; El-Shafie, Ahmed

2016-11-01

Monthly stream-flow forecasting can yield important information for hydrological applications including sustainable design of rural and urban water management systems, optimization of water resource allocations, water use, pricing and water quality assessment, and agriculture and irrigation operations. The motivation for exploring and developing expert predictive models is an ongoing endeavor for hydrological applications. In this study, the potential of a relatively new data-driven method, namely the extreme learning machine (ELM) method, was explored for forecasting monthly stream-flow discharge rates in the Tigris River, Iraq. The ELM algorithm is a single-layer feedforward neural network (SLFNs) which randomly selects the input weights, hidden layer biases and analytically determines the output weights of the SLFNs. Based on the partial autocorrelation functions of historical stream-flow data, a set of five input combinations with lagged stream-flow values are employed to establish the best forecasting model. A comparative investigation is conducted to evaluate the performance of the ELM compared to other data-driven models: support vector regression (SVR) and generalized regression neural network (GRNN). The forecasting metrics defined as the correlation coefficient (r), Nash-Sutcliffe efficiency (ENS), Willmott's Index (WI), root-mean-square error (RMSE) and mean absolute error (MAE) computed between the observed and forecasted stream-flow data are employed to assess the ELM model's effectiveness. The results revealed that the ELM model outperformed the SVR and the GRNN models across a number of statistical measures. In quantitative terms, superiority of ELM over SVR and GRNN models was exhibited by ENS = 0.578, 0.378 and 0.144, r = 0.799, 0.761 and 0.468 and WI = 0.853, 0.802 and 0.689, respectively and the ELM model attained lower RMSE value by approximately 21.3% (relative to SVR) and by approximately 44.7% (relative to GRNN). Based on the findings of this study, several recommendations were suggested for further exploration of the ELM model in hydrological forecasting problems.

Ensemble streamflow assimilation with the National Water Model.

NASA Astrophysics Data System (ADS)

Rafieeinasab, A.; McCreight, J. L.; Noh, S.; Seo, D. J.; Gochis, D.

2017-12-01

Through case studies of flooding across the US, we compare the performance of the National Water Model (NWM) data assimilation (DA) scheme to that of a newly implemented ensemble Kalman filter approach. The NOAA National Water Model (NWM) is an operational implementation of the community WRF-Hydro modeling system. As of August 2016, the NWM forecasts of distributed hydrologic states and fluxes (including soil moisture, snowpack, ET, and ponded water) over the contiguous United States have been publicly disseminated by the National Center for Environmental Prediction (NCEP) . It also provides streamflow forecasts at more than 2.7 million river reaches up to 30 days in advance. The NWM employs a nudging scheme to assimilate more than 6,000 USGS streamflow observations and provide initial conditions for its forecasts. A problem with nudging is how the forecasts relax quickly to open-loop bias in the forecast. This has been partially addressed by an experimental bias correction approach which was found to have issues with phase errors during flooding events. In this work, we present an ensemble streamflow data assimilation approach combining new channel-only capabilities of the NWM and HydroDART (a coupling of the offline WRF-Hydro model and NCAR's Data Assimilation Research Testbed; DART). Our approach focuses on the single model state of discharge and incorporates error distributions on channel-influxes (overland and groundwater) in the assimilation via an ensemble Kalman filter (EnKF). In order to avoid filter degeneracy associated with a limited number of ensemble at large scale, DART's covariance inflation (Anderson, 2009) and localization capabilities are implemented and evaluated. The current NWM data assimilation scheme is compared to preliminary results from the EnKF application for several flooding case studies across the US.

Integrating satellite actual evapotranspiration patterns into distributed model parametrization and evaluation for a mesoscale catchment

NASA Astrophysics Data System (ADS)

Demirel, M. C.; Mai, J.; Stisen, S.; Mendiguren González, G.; Koch, J.; Samaniego, L. E.

2016-12-01

Distributed hydrologic models are traditionally calibrated and evaluated against observations of streamflow. Spatially distributed remote sensing observations offer a great opportunity to enhance spatial model calibration schemes. For that it is important to identify the model parameters that can change spatial patterns before the satellite based hydrologic model calibration. Our study is based on two main pillars: first we use spatial sensitivity analysis to identify the key parameters controlling the spatial distribution of actual evapotranspiration (AET). Second, we investigate the potential benefits of incorporating spatial patterns from MODIS data to calibrate the mesoscale Hydrologic Model (mHM). This distributed model is selected as it allows for a change in the spatial distribution of key soil parameters through the calibration of pedo-transfer function parameters and includes options for using fully distributed daily Leaf Area Index (LAI) directly as input. In addition the simulated AET can be estimated at the spatial resolution suitable for comparison to the spatial patterns observed using MODIS data. We introduce a new dynamic scaling function employing remotely sensed vegetation to downscale coarse reference evapotranspiration. In total, 17 parameters of 47 mHM parameters are identified using both sequential screening and Latin hypercube one-at-a-time sampling methods. The spatial patterns are found to be sensitive to the vegetation parameters whereas streamflow dynamics are sensitive to the PTF parameters. The results of multi-objective model calibration show that calibration of mHM against observed streamflow does not reduce the spatial errors in AET while they improve only the streamflow simulations. We will further examine the results of model calibration using only multi spatial objective functions measuring the association between observed AET and simulated AET maps and another case including spatial and streamflow metrics together.

Evaluation of a new satellite-based precipitation dataset for climate studies in the Xiang River basin, Southern China

NASA Astrophysics Data System (ADS)

Zhu, Q.; Xu, Y. P.; Hsu, K. L.

2017-12-01

A new satellite-based precipitation dataset, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) with long-term time series dating back to 1983 can be one valuable dataset for climate studies. This study investigates the feasibility of using PERSIANN-CDR as a reference dataset for climate studies. Sixteen CMIP5 models are evaluated over the Xiang River basin, southern China, by comparing their performance on precipitation projection and streamflow simulation, particularly on extreme precipitation and streamflow events. The results show PERSIANN-CDR is a valuable dataset for climate studies, even on extreme precipitation events. The precipitation estimates and their extreme events from CMIP5 models are improved significantly compared with rain gauge observations after bias-correction by the PERSIANN-CDR precipitation estimates. Given streamflows simulated with raw and bias-corrected precipitation estimates from 16 CMIP5 models, 10 out of 16 are improved after bias-correction. The impact of bias-correction on extreme events for streamflow simulations are unstable, with eight out of 16 models can be clearly claimed they are improved after the bias-correction. Concerning the performance of raw CMIP5 models on precipitation, IPSL-CM5A-MR excels the other CMIP5 models, while MRI-CGCM3 outperforms on extreme events with its better performance on six extreme precipitation metrics. Case studies also show that raw CCSM4, CESM1-CAM5, and MRI-CGCM3 outperform other models on streamflow simulation, while MIROC5-ESM-CHEM, MIROC5-ESM and IPSL-CM5A-MR behaves better than the other models after bias-correction.

Analysis of streamflow response to land use and land cover changes using satellite data and hydrological modelling: case study of Dinder and Rahad tributaries of the Blue Nile (Ethiopia-Sudan)

NASA Astrophysics Data System (ADS)

Hassaballah, Khalid; Mohamed, Yasir; Uhlenbrook, Stefan; Biro, Khalid

2017-10-01

Understanding the land use and land cover changes (LULCCs) and their implication on surface hydrology of the Dinder and Rahad basins (D&R, approximately 77 504 km2) is vital for the management and utilization of water resources in the basins. Although there are many studies on LULCC in the Blue Nile Basin, specific studies on LULCC in the D&R are still missing. Hence, its impact on streamflow is unknown. The objective of this paper is to understand the LULCC in the Dinder and Rahad and its implications on streamflow response using satellite data and hydrological modelling. The hydrological model has been derived by different sets of land use and land cover maps from 1972, 1986, 1998 and 2011. Catchment topography, land cover and soil maps are derived from satellite images and serve to estimate model parameters. Results of LULCC detection between 1972 and 2011 indicate a significant decrease in woodland and an increase in cropland. Woodland decreased from 42 to 14 % and from 35 to 14 % for Dinder and Rahad, respectively. Cropland increased from 14 to 47 % and from 18 to 68 % in Dinder and Rahad, respectively. The model results indicate that streamflow is affected by LULCC in both the Dinder and the Rahad rivers. The effect of LULCC on streamflow is significant during 1986 and 2011. This could be attributed to the severe drought during the mid-1980s and the recent large expansion in cropland.

Genetic Algorithm Based Framework for Automation of Stochastic Modeling of Multi-Season Streamflows

NASA Astrophysics Data System (ADS)

Srivastav, R. K.; Srinivasan, K.; Sudheer, K.

2009-05-01

Synthetic streamflow data generation involves the synthesis of likely streamflow patterns that are statistically indistinguishable from the observed streamflow data. The various kinds of stochastic models adopted for multi-season streamflow generation in hydrology are: i) parametric models which hypothesize the form of the periodic dependence structure and the distributional form a priori (examples are PAR, PARMA); disaggregation models that aim to preserve the correlation structure at the periodic level and the aggregated annual level; ii) Nonparametric models (examples are bootstrap/kernel based methods), which characterize the laws of chance, describing the stream flow process, without recourse to prior assumptions as to the form or structure of these laws; (k-nearest neighbor (k-NN), matched block bootstrap (MABB)); non-parametric disaggregation model. iii) Hybrid models which blend both parametric and non-parametric models advantageously to model the streamflows effectively. Despite many of these developments that have taken place in the field of stochastic modeling of streamflows over the last four decades, accurate prediction of the storage and the critical drought characteristics has been posing a persistent challenge to the stochastic modeler. This is partly because, usually, the stochastic streamflow model parameters are estimated by minimizing a statistically based objective function (such as maximum likelihood (MLE) or least squares (LS) estimation) and subsequently the efficacy of the models is being validated based on the accuracy of prediction of the estimates of the water-use characteristics, which requires large number of trial simulations and inspection of many plots and tables. Still accurate prediction of the storage and the critical drought characteristics may not be ensured. In this study a multi-objective optimization framework is proposed to find the optimal hybrid model (blend of a simple parametric model, PAR(1) model and matched block bootstrap (MABB) ) based on the explicit objective functions of minimizing the relative bias and relative root mean square error in estimating the storage capacity of the reservoir. The optimal parameter set of the hybrid model is obtained based on the search over a multi- dimensional parameter space (involving simultaneous exploration of the parametric (PAR(1)) as well as the non-parametric (MABB) components). This is achieved using the efficient evolutionary search based optimization tool namely, non-dominated sorting genetic algorithm - II (NSGA-II). This approach helps in reducing the drudgery involved in the process of manual selection of the hybrid model, in addition to predicting the basic summary statistics dependence structure, marginal distribution and water-use characteristics accurately. The proposed optimization framework is used to model the multi-season streamflows of River Beaver and River Weber of USA. In case of both the rivers, the proposed GA-based hybrid model yields a much better prediction of the storage capacity (where simultaneous exploration of both parametric and non-parametric components is done) when compared with the MLE-based hybrid models (where the hybrid model selection is done in two stages, thus probably resulting in a sub-optimal model). This framework can be further extended to include different linear/non-linear hybrid stochastic models at other temporal and spatial scales as well.

The role of glacier changes and threshold definition in the characterisation of future streamflow droughts in glacierised catchments

NASA Astrophysics Data System (ADS)

Van Tiel, Marit; Teuling, Adriaan J.; Wanders, Niko; Vis, Marc J. P.; Stahl, Kerstin; Van Loon, Anne F.

2018-01-01

Glaciers are essential hydrological reservoirs, storing and releasing water at various timescales. Short-term variability in glacier melt is one of the causes of streamflow droughts, here defined as deficiencies from the flow regime. Streamflow droughts in glacierised catchments have a wide range of interlinked causing factors related to precipitation and temperature on short and long timescales. Climate change affects glacier storage capacity, with resulting consequences for discharge regimes and streamflow drought. Future projections of streamflow drought in glacierised basins can, however, strongly depend on the modelling strategies and analysis approaches applied. Here, we examine the effect of different approaches, concerning the glacier modelling and the drought threshold, on the characterisation of streamflow droughts in glacierised catchments. Streamflow is simulated with the Hydrologiska Byråns Vattenbalansavdelning (HBV-light) model for two case study catchments, the Nigardsbreen catchment in Norway and the Wolverine catchment in Alaska, and two future climate change scenarios (RCP4.5 and RCP8.5). Two types of glacier modelling are applied, a constant and dynamic glacier area conceptualisation. Streamflow droughts are identified with the variable threshold level method and their characteristics are compared between two periods, a historical (1975-2004) and future (2071-2100) period. Two existing threshold approaches to define future droughts are employed: (1) the threshold from the historical period; (2) a transient threshold approach, whereby the threshold adapts every year in the future to the changing regimes. Results show that drought characteristics differ among the combinations of glacier area modelling and thresholds. The historical threshold combined with a dynamic glacier area projects extreme increases in drought severity in the future, caused by the regime shift due to a reduction in glacier area. The historical threshold combined with a constant glacier area results in a drastic decrease of the number of droughts. The drought characteristics between future and historical periods are more similar when the transient threshold is used, for both glacier area conceptualisations. With the transient threshold, factors causing future droughts can be analysed. This study revealed the different effects of methodological choices on future streamflow drought projections and it highlights how the options can be used to analyse different aspects of future droughts: the transient threshold for analysing future drought processes, the historical threshold to assess changes between periods, the constant glacier area to analyse the effect of short-term climate variability on droughts and the dynamic glacier area to model more realistic future discharges under climate change.

A Case Study: Climate Change Decision Support for the Apalachicola, Chattahoochee, Flint Basins

NASA Astrophysics Data System (ADS)

Day, G. N.; McMahon, G.; Friesen, N.; Carney, S.

2011-12-01

Riverside Technology, inc. has developed a Climate Change Decision Support System (DSS) to provide water managers with a tool to explore a range of current Global Climate Model (GCM) projections to evaluate their potential impacts on streamflow and the reliability of future water supplies. The system was developed as part of a National Oceanic and Atmospheric Administration (NOAA) Small Business Innovation Research (SBIR) project. The DSS uses downscaled GCM data as input to small-scale watershed models to produce time series of projected undepleted streamflow for various emission scenarios and GCM simulations. Until recently, water managers relied on historical streamflow data for water resources planning. In many parts of the country, great effort has been put into estimating long-term historical undepleted streamflow accounting for regulation, diversions, and return flows to support planning and water rights administration. In some cases, longer flow records have been constructed using paleohydrologic data in an attempt to capture climate variability beyond what is evident during the observed historical record. Now, many water managers are recognizing that historical data may not be representative of an uncertain climate future, and they have begun to explore the use of climate projections in their water resources planning. The Climate Change DSS was developed to support water managers in planning by accounting for both climate variability and potential climate change. In order to use the information for impact analysis, the projected streamflow time series can be exported and substituted for the historical streamflow data traditionally applied in their system operations models for water supply planning. This paper presents a case study in which climate-adjusted flows are coupled with the U.S. Army Corps of Engineers (USACE) ResSim model for the Apalachicola, Chattahoochee, and Flint (ACF) River basins. The study demonstrates how climate scenarios can be used with existing or proposed operating rules to explore the range of potential climate impacts on lake levels, drought trigger frequency, hydropower generation, and low-flow statistics. Initial system implementation of the Climate Change DSS was focused in the State of Colorado working with water supply agencies in the Front Range to assess local water supply vulnerability to climate change. To facilitate national implementation, the system capitalizes on National Weather Service (NWS) watershed models currently used for operational river forecasting. These models are well calibrated and available for the entire country. The system has been extended to include the ACF and the Sacramento River basins because of the importance of the water resources in these basins. Plans are now being made to expand coverage to include the Baltimore-Washington, D.C. water supply area. The DSS is operational and publicly available (www.climatechangedss.com).

Assimilating uncertain, dynamic and intermittent streamflow observations in hydrological models

NASA Astrophysics Data System (ADS)

Mazzoleni, Maurizio; Alfonso, Leonardo; Chacon-Hurtado, Juan; Solomatine, Dimitri

2015-09-01

Catastrophic floods cause significant socio-economical losses. Non-structural measures, such as real-time flood forecasting, can potentially reduce flood risk. To this end, data assimilation methods have been used to improve flood forecasts by integrating static ground observations, and in some cases also remote sensing observations, within water models. Current hydrologic and hydraulic research works consider assimilation of observations coming from traditional, static sensors. At the same time, low-cost, mobile sensors and mobile communication devices are becoming also increasingly available. The main goal and innovation of this study is to demonstrate the usefulness of assimilating uncertain streamflow observations that are dynamic in space and intermittent in time in the context of two different semi-distributed hydrological model structures. The developed method is applied to the Brue basin, where the dynamic observations are imitated by the synthetic observations of discharge. The results of this study show how model structures and sensors locations affect in different ways the assimilation of streamflow observations. In addition, it proves how assimilation of such uncertain observations from dynamic sensors can provide model improvements similar to those of streamflow observations coming from a non-optimal network of static physical sensors. This can be a potential application of recent efforts to build citizen observatories of water, which can make the citizens an active part in information capturing, evaluation and communication, helping simultaneously to improvement of model-based flood forecasting.

Partitioning uncertainty in streamflow projections under nonstationary model conditions

NASA Astrophysics Data System (ADS)

Chawla, Ila; Mujumdar, P. P.

2018-02-01

Assessing the impacts of Land Use (LU) and climate change on future streamflow projections is necessary for efficient management of water resources. However, model projections are burdened with significant uncertainty arising from various sources. Most of the previous studies have considered climate models and scenarios as major sources of uncertainty, but uncertainties introduced by land use change and hydrologic model assumptions are rarely investigated. In this paper an attempt is made to segregate the contribution from (i) general circulation models (GCMs), (ii) emission scenarios, (iii) land use scenarios, (iv) stationarity assumption of the hydrologic model, and (v) internal variability of the processes, to overall uncertainty in streamflow projections using analysis of variance (ANOVA) approach. Generally, most of the impact assessment studies are carried out with unchanging hydrologic model parameters in future. It is, however, necessary to address the nonstationarity in model parameters with changing land use and climate. In this paper, a regression based methodology is presented to obtain the hydrologic model parameters with changing land use and climate scenarios in future. The Upper Ganga Basin (UGB) in India is used as a case study to demonstrate the methodology. The semi-distributed Variable Infiltration Capacity (VIC) model is set-up over the basin, under nonstationary conditions. Results indicate that model parameters vary with time, thereby invalidating the often-used assumption of model stationarity. The streamflow in UGB under the nonstationary model condition is found to reduce in future. The flows are also found to be sensitive to changes in land use. Segregation results suggest that model stationarity assumption and GCMs along with their interactions with emission scenarios, act as dominant sources of uncertainty. This paper provides a generalized framework for hydrologists to examine stationarity assumption of models before considering them for future streamflow projections and segregate the contribution of various sources to the uncertainty.

Accessing the capability of TRMM 3B42 V7 to simulate streamflow during extreme rain events: Case study for a Himalayan River Basin

NASA Astrophysics Data System (ADS)

Kumar, Brijesh; Lakshmi, Venkat

2018-03-01

The paper examines the quality of Tropical Rainfall Monitoring Mission (TRMM) 3B42 V7 precipitation product to simulate the streamflow using Soil Water Assessment Tool (SWAT) model for various rainfall intensities over the Himalayan region. The SWAT model has been set up for Gandak River Basin with 41 sub-basins and 420 HRUs. Five stream gauge locations are used to simulate the streamflow for a time span of 10 years (2000-2010). Daily streamflow for the simulation period is collected from Central Water Commission (CWC), India and Department of Hydrology and Meteorology (DHM), Nepal. The simulation results are found good in terms of Nash-Sutcliffe efficiency (NSE) {>}0.65, coefficient of determination (R2) {>}0.67 and Percentage Bias (PBIAS){<}15%, at each stream gauge sites. Thereafter, we have calculated the PBIAS and RMSE-observations standard deviation ratio (RSR) statistics between TRMM simulated and observed streamflow for various rainfall intensity classes, viz., light ({<}7.5 mm/d), moderate (7.5 to 35.4 mm/d), heavy (35.5 to 124.4 mm/d) and extremely heavy ({>}124.4 mm/d). The PBIAS and RSR show that TRMM simulated streamflow is suitable for moderate to heavy rainfall intensities. However, it does not perform well for light- and extremely-heavy rainfall intensities. The finding of the present work is useful for the problems related to water resources management, irrigation planning and hazard analysis over the Himalayan regions.

Regime Behavior in Paleo-Reconstructed Streamflow: Attributions to Atmospheric Dynamics, Synoptic Circulation and Large-Scale Climate Teleconnection Patterns

NASA Astrophysics Data System (ADS)

Ravindranath, A.; Devineni, N.

2017-12-01

Studies have shown that streamflow behavior and dynamics have a significant link with climate and climate variability. Patterns of persistent regime behavior from extended streamflow records in many watersheds justify investigating large-scale climate mechanisms as potential drivers of hydrologic regime behavior and streamflow variability. Understanding such streamflow-climate relationships is crucial to forecasting/simulation systems and the planning and management of water resources. In this study, hidden Markov models are used with reconstructed streamflow to detect regime-like behaviors - the hidden states - and state transition phenomena. Individual extreme events and their spatial variability across the basin are then verified with the identified states. Wavelet analysis is performed to examine the signals over time in the streamflow records. Joint analyses of the climatic data in the 20th century and the identified states are undertaken to better understand the hydroclimatic connections within the basin as well as important teleconnections that influence water supply. Compositing techniques are used to identify atmospheric circulation patterns associated with identified states of streamflow. The grouping of such synoptic patterns and their frequency are then examined. Sliding time-window correlation analysis and cross-wavelet spectral analysis are performed to establish the synchronicity of basin flows to the identified synoptic and teleconnection patterns. The Missouri River Basin (MRB) is examined in this study, both as a means of better understanding the synoptic climate controls in this important watershed and as a case study for the techniques developed here. Initial wavelet analyses of reconstructed streamflow at major gauges in the MRB show multidecadal cycles in regime behavior.

Quantifying uncertainties in streamflow predictions through signature based inference of hydrological model parameters

NASA Astrophysics Data System (ADS)

Fenicia, Fabrizio; Reichert, Peter; Kavetski, Dmitri; Albert, Calro

2016-04-01

The calibration of hydrological models based on signatures (e.g. Flow Duration Curves - FDCs) is often advocated as an alternative to model calibration based on the full time series of system responses (e.g. hydrographs). Signature based calibration is motivated by various arguments. From a conceptual perspective, calibration on signatures is a way to filter out errors that are difficult to represent when calibrating on the full time series. Such errors may for example occur when observed and simulated hydrographs are shifted, either on the "time" axis (i.e. left or right), or on the "streamflow" axis (i.e. above or below). These shifts may be due to errors in the precipitation input (time or amount), and if not properly accounted in the likelihood function, may cause biased parameter estimates (e.g. estimated model parameters that do not reproduce the recession characteristics of a hydrograph). From a practical perspective, signature based calibration is seen as a possible solution for making predictions in ungauged basins. Where streamflow data are not available, it may in fact be possible to reliably estimate streamflow signatures. Previous research has for example shown how FDCs can be reliably estimated at ungauged locations based on climatic and physiographic influence factors. Typically, the goal of signature based calibration is not the prediction of the signatures themselves, but the prediction of the system responses. Ideally, the prediction of system responses should be accompanied by a reliable quantification of the associated uncertainties. Previous approaches for signature based calibration, however, do not allow reliable estimates of streamflow predictive distributions. Here, we illustrate how the Bayesian approach can be employed to obtain reliable streamflow predictive distributions based on signatures. A case study is presented, where a hydrological model is calibrated on FDCs and additional signatures. We propose an approach where the likelihood function for the signatures is derived from the likelihood for streamflow (rather than using an "ad-hoc" likelihood for the signatures as done in previous approaches). This likelihood is not easily tractable analytically and we therefore cannot apply "simple" MCMC methods. This numerical problem is solved using Approximate Bayesian Computation (ABC). Our result indicate that the proposed approach is suitable for producing reliable streamflow predictive distributions based on calibration to signature data. Moreover, our results provide indications on which signatures are more appropriate to represent the information content of the hydrograph.

Evaluation of model-based seasonal streamflow and water allocation forecasts for the Elqui Valley, Chile

NASA Astrophysics Data System (ADS)

Delorit, Justin; Cristian Gonzalez Ortuya, Edmundo; Block, Paul

2017-09-01

In many semi-arid regions, multisectoral demands often stress available water supplies. Such is the case in the Elqui River valley of northern Chile, which draws on a limited-capacity reservoir to allocate 25 000 water rights. Delayed infrastructure investment forces water managers to address demand-based allocation strategies, particularly in dry years, which are realized through reductions in the volume associated with each water right. Skillful season-ahead streamflow forecasts have the potential to inform managers with an indication of future conditions to guide reservoir allocations. This work evaluates season-ahead statistical prediction models of October-January (growing season) streamflow at multiple lead times associated with manager and user decision points, and links predictions with a reservoir allocation tool. Skillful results (streamflow forecasts outperform climatology) are produced for short lead times (1 September: ranked probability skill score (RPSS) of 0.31, categorical hit skill score of 61 %). At longer lead times, climatological skill exceeds forecast skill due to fewer observations of precipitation. However, coupling the 1 September statistical forecast model with a sea surface temperature phase and strength statistical model allows for equally skillful categorical streamflow forecasts to be produced for a 1 May lead, triggered for 60 % of years (1950-2015), suggesting forecasts need not be strictly deterministic to be useful for water rights holders. An early (1 May) categorical indication of expected conditions is reinforced with a deterministic forecast (1 September) as more observations of local variables become available. The reservoir allocation model is skillful at the 1 September lead (categorical hit skill score of 53 %); skill improves to 79 % when categorical allocation prediction certainty exceeds 80 %. This result implies that allocation efficiency may improve when forecasts are integrated into reservoir decision frameworks. The methods applied here advance the understanding of the mechanisms and timing responsible for moisture transport to the Elqui Valley and provide a unique application of streamflow forecasting in the prediction of water right allocations.

Assessing the impact of managed aquifer recharge on seasonal low flows in a semi-arid alluvial river

NASA Astrophysics Data System (ADS)

Ronayne, M. J.; Roudebush, J. A.; Stednick, J. D.

2016-12-01

Managed aquifer recharge (MAR) is one strategy that can be used to augment seasonal low flows in alluvial rivers. Successful implementation requires an understanding of spatio-temporal groundwater-surface water exchange. In this study we conducted numerical groundwater modeling to analyze the performance of an existing MAR system in the South Platte River Valley in northeastern Colorado (USA). The engineered system involves a spatial reallocation of water during the winter months; alluvial groundwater is extracted near the river and pumped to upgradient recharge ponds, with the intent of producing a delayed hydraulic response that increases the riparian zone water table (and therefore streamflow) during summer months. Higher flows during the summer are required to improve riverine habitat for threatened species in the Platte River. Modeling scenarios were constrained by surface (streamflow gaging) and subsurface (well data) measurements throughout the study area. We compare two scenarios to analyze the impact of MAR: a natural base case scenario and an active management scenario that includes groundwater pumping and managed recharge. Steady-periodic solutions are used to evaluate the long-term stabilized behavior of the stream-aquifer system with and without pumping/recharge. Streamflow routing is included in the model, which permits quantification of the timing and location of streamflow accretion (increased streamflow associated with MAR). An analysis framework utilizing capture concepts is developed to interpret seasonal changes in head-dependent flows to/from the aquifer, including groundwater-surface water exchange that impacts streamflow. Results demonstrate that accretion occurs during the target low-flow period but is not limited to those months, highlighting an inefficiency that is a function of the aquifer geometry and hydraulic properties. The results of this study offer guidance for other flow augmentation projects that rely on water storage in shallow alluvial aquifers.

Does internal climate variability overwhelm climate change signals in streamflow? The upper Po and Rhone basin case studies.

PubMed

Fatichi, S; Rimkus, S; Burlando, P; Bordoy, R

2014-09-15

Projections of climate change effects in streamflow are increasingly required to plan water management strategies. These projections are however largely uncertain due to the spread among climate model realizations, internal climate variability, and difficulties in transferring climate model results at the spatial and temporal scales required by catchment hydrology. A combination of a stochastic downscaling methodology and distributed hydrological modeling was used in the ACQWA project to provide projections of future streamflow (up to year 2050) for the upper Po and Rhone basins, respectively located in northern Italy and south-western Switzerland. Results suggest that internal (stochastic) climate variability is a fundamental source of uncertainty, typically comparable or larger than the projected climate change signal. Therefore, climate change effects in streamflow mean, frequency, and seasonality can be masked by natural climatic fluctuations in large parts of the analyzed regions. An exception to the overwhelming role of stochastic variability is represented by high elevation catchments fed by glaciers where streamflow is expected to be considerably reduced due to glacier retreat, with consequences appreciable in the main downstream rivers in August and September. Simulations also identify regions (west upper Rhone and Toce, Ticino river basins) where a strong precipitation increase in the February to April period projects streamflow beyond the range of natural climate variability during the melting season. This study emphasizes the importance of including internal climate variability in climate change analyses, especially when compared to the limited uncertainty that would be accounted for by few deterministic projections. The presented results could be useful in guiding more specific impact studies, although design or management decisions should be better based on reliability and vulnerability criteria as suggested by recent literature. Copyright © 2013 Elsevier B.V. All rights reserved.

Climate model assessment of changes in winter-spring streamflow timing over North America

USGS Publications Warehouse

Kam, Jonghun; Knutson, Thomas R.; Milly, Paul C. D.

2018-01-01

Over regions where snow-melt runoff substantially contributes to winter-spring streamflows, warming can accelerate snow melt and reduce dry-season streamflows. However, conclusive detection of changes and attribution to anthropogenic forcing is hindered by brevity of observational records, model uncertainty, and uncertainty concerning internal variability. In this study, a detection/attribution of changes in mid-latitude North American winter-spring streamflow timing is examined using nine global climate models under multiple forcing scenarios. In this study, robustness across models, start/end dates for trends, and assumptions about internal variability is evaluated. Marginal evidence for an emerging detectable anthropogenic influence (according to four or five of nine models) is found in the north-central U.S., where winter-spring streamflows have been coming earlier. Weaker indications of detectable anthropogenic influence (three of nine models) are found in the mountainous western U.S./southwestern Canada and in extreme northeastern U.S./Canadian Maritimes. In the former region, a recent shift toward later streamflows has rendered the full-record trend toward earlier streamflows only marginally significant, with possible implications for previously published climate change detection findings for streamflow timing in this region. In the latter region, no forced model shows as large a shift toward earlier streamflow timing as the detectable observed shift. In other (including warm, snow-free) regions, observed trends are typically not detectable, although in the U.S. central plains we find detectable delays in streamflow, which are inconsistent with forced model experiments.

Cumulative uncertainty in measured streamflow and water quality data for small watersheds

USGS Publications Warehouse

Harmel, R.D.; Cooper, R.J.; Slade, R.M.; Haney, R.L.; Arnold, J.G.

2006-01-01

The scientific community has not established an adequate understanding of the uncertainty inherent in measured water quality data, which is introduced by four procedural categories: streamflow measurement, sample collection, sample preservation/storage, and laboratory analysis. Although previous research has produced valuable information on relative differences in procedures within these categories, little information is available that compares the procedural categories or presents the cumulative uncertainty in resulting water quality data. As a result, quality control emphasis is often misdirected, and data uncertainty is typically either ignored or accounted for with an arbitrary margin of safety. Faced with the need for scientifically defensible estimates of data uncertainty to support water resource management, the objectives of this research were to: (1) compile selected published information on uncertainty related to measured streamflow and water quality data for small watersheds, (2) use a root mean square error propagation method to compare the uncertainty introduced by each procedural category, and (3) use the error propagation method to determine the cumulative probable uncertainty in measured streamflow, sediment, and nutrient data. Best case, typical, and worst case "data quality" scenarios were examined. Averaged across all constituents, the calculated cumulative probable uncertainty (??%) contributed under typical scenarios ranged from 6% to 19% for streamflow measurement, from 4% to 48% for sample collection, from 2% to 16% for sample preservation/storage, and from 5% to 21% for laboratory analysis. Under typical conditions, errors in storm loads ranged from 8% to 104% for dissolved nutrients, from 8% to 110% for total N and P, and from 7% to 53% for TSS. Results indicated that uncertainty can increase substantially under poor measurement conditions and limited quality control effort. This research provides introductory scientific estimates of uncertainty in measured water quality data. The results and procedures presented should also assist modelers in quantifying the "quality"of calibration and evaluation data sets, determining model accuracy goals, and evaluating model performance.

Comparing large-scale hydrological model predictions with observed streamflow in the Pacific Northwest: effects of climate and groundwater

Treesearch

Mohammad Safeeq; Guillaume S. Mauger; Gordon E. Grant; Ivan Arismendi; Alan F. Hamlet; Se-Yeun Lee

2014-01-01

Assessing uncertainties in hydrologic models can improve accuracy in predicting future streamflow. Here, simulated streamflows using the Variable Infiltration Capacity (VIC) model at coarse (1/16°) and fine (1/120°) spatial resolutions were evaluated against observed streamflows from 217 watersheds. In...

Adjusting Wavelet-based Multiresolution Analysis Boundary Conditions for Robust Long-term Streamflow Forecasting Model

NASA Astrophysics Data System (ADS)

Maslova, I.; Ticlavilca, A. M.; McKee, M.

2012-12-01

There has been an increased interest in wavelet-based streamflow forecasting models in recent years. Often overlooked in this approach are the circularity assumptions of the wavelet transform. We propose a novel technique for minimizing the wavelet decomposition boundary condition effect to produce long-term, up to 12 months ahead, forecasts of streamflow. A simulation study is performed to evaluate the effects of different wavelet boundary rules using synthetic and real streamflow data. A hybrid wavelet-multivariate relevance vector machine model is developed for forecasting the streamflow in real-time for Yellowstone River, Uinta Basin, Utah, USA. The inputs of the model utilize only the past monthly streamflow records. They are decomposed into components formulated in terms of wavelet multiresolution analysis. It is shown that the model model accuracy can be increased by using the wavelet boundary rule introduced in this study. This long-term streamflow modeling and forecasting methodology would enable better decision-making and managing water availability risk.

Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modelling heteroscedastic residual errors

NASA Astrophysics Data System (ADS)

David, McInerney; Mark, Thyer; Dmitri, Kavetski; George, Kuczera

2017-04-01

This study provides guidance to hydrological researchers which enables them to provide probabilistic predictions of daily streamflow with the best reliability and precision for different catchment types (e.g. high/low degree of ephemerality). Reliable and precise probabilistic prediction of daily catchment-scale streamflow requires statistical characterization of residual errors of hydrological models. It is commonly known that hydrological model residual errors are heteroscedastic, i.e. there is a pattern of larger errors in higher streamflow predictions. Although multiple approaches exist for representing this heteroscedasticity, few studies have undertaken a comprehensive evaluation and comparison of these approaches. This study fills this research gap by evaluating 8 common residual error schemes, including standard and weighted least squares, the Box-Cox transformation (with fixed and calibrated power parameter, lambda) and the log-sinh transformation. Case studies include 17 perennial and 6 ephemeral catchments in Australia and USA, and two lumped hydrological models. We find the choice of heteroscedastic error modelling approach significantly impacts on predictive performance, though no single scheme simultaneously optimizes all performance metrics. The set of Pareto optimal schemes, reflecting performance trade-offs, comprises Box-Cox schemes with lambda of 0.2 and 0.5, and the log scheme (lambda=0, perennial catchments only). These schemes significantly outperform even the average-performing remaining schemes (e.g., across ephemeral catchments, median precision tightens from 105% to 40% of observed streamflow, and median biases decrease from 25% to 4%). Theoretical interpretations of empirical results highlight the importance of capturing the skew/kurtosis of raw residuals and reproducing zero flows. Recommendations for researchers and practitioners seeking robust residual error schemes for practical work are provided.

Application of ANN and fuzzy logic algorithms for streamflow modelling of Savitri catchment

NASA Astrophysics Data System (ADS)

Kothari, Mahesh; Gharde, K. D.

2015-07-01

The streamflow prediction is an essentially important aspect of any watershed modelling. The black box models (soft computing techniques) have proven to be an efficient alternative to physical (traditional) methods for simulating streamflow and sediment yield of the catchments. The present study focusses on development of models using ANN and fuzzy logic (FL) algorithm for predicting the streamflow for catchment of Savitri River Basin. The input vector to these models were daily rainfall, mean daily evaporation, mean daily temperature and lag streamflow used. In the present study, 20 years (1992-2011) rainfall and other hydrological data were considered, of which 13 years (1992-2004) was for training and rest 7 years (2005-2011) for validation of the models. The mode performance was evaluated by R, RMSE, EV, CE, and MAD statistical parameters. It was found that, ANN model performance improved with increasing input vectors. The results with fuzzy logic models predict the streamflow with single input as rainfall better in comparison to multiple input vectors. While comparing both ANN and FL algorithms for prediction of streamflow, ANN model performance is quite superior.

A Linear Dynamical Systems Approach to Streamflow Reconstruction Reveals History of Regime Shifts in Northern Thailand

NASA Astrophysics Data System (ADS)

Nguyen, Hung T. T.; Galelli, Stefano

2018-03-01

Catchment dynamics is not often modeled in streamflow reconstruction studies; yet, the streamflow generation process depends on both catchment state and climatic inputs. To explicitly account for this interaction, we contribute a linear dynamic model, in which streamflow is a function of both catchment state (i.e., wet/dry) and paleoclimatic proxies. The model is learned using a novel variant of the Expectation-Maximization algorithm, and it is used with a paleo drought record—the Monsoon Asia Drought Atlas—to reconstruct 406 years of streamflow for the Ping River (northern Thailand). Results for the instrumental period show that the dynamic model has higher accuracy than conventional linear regression; all performance scores improve by 45-497%. Furthermore, the reconstructed trajectory of the state variable provides valuable insights about the catchment history—e.g., regime-like behavior—thereby complementing the information contained in the reconstructed streamflow time series. The proposed technique can replace linear regression, since it only requires information on streamflow and climatic proxies (e.g., tree-rings, drought indices); furthermore, it is capable of readily generating stochastic streamflow replicates. With a marginal increase in computational requirements, the dynamic model brings more desirable features and value to streamflow reconstructions.

Human influences on streamflow drought characteristics in England and Wales

NASA Astrophysics Data System (ADS)

Tijdeman, Erik; Hannaford, Jamie; Stahl, Kerstin

2018-02-01

Human influences can affect streamflow drought characteristics and propagation. The question is where, when and why? To answer these questions, the impact of different human influences on streamflow droughts were assessed in England and Wales, across a broad range of climate and catchments conditions. We used a dataset consisting of catchments with near-natural flow as well as catchments for which different human influences have been indicated in the metadata (Factors Affecting Runoff) of the UK National River Flow Archive (NRFA). A screening approach was applied on the streamflow records to identify human-influenced records with drought characteristics that deviated from those found for catchments with near-natural flow. Three different deviations were considered, specifically deviations in (1) the relationship between streamflow drought duration and the base flow index, BFI (specifically, BFIHOST, the BFI predicted from the hydrological properties of soils), (2) the correlation between streamflow and precipitation and (3) the temporal occurrence of streamflow droughts compared to precipitation droughts, i.e. an increase or decrease in streamflow drought months relative to precipitation drought months over the period of record. The identified deviations were then related to the indicated human influences. Results showed that the majority of catchments for which human influences were indicated did not show streamflow drought characteristics that deviated from those expected under near-natural conditions. For the catchments that did show deviating streamflow drought characteristics, prolonged streamflow drought durations were found in some of the catchments affected by groundwater abstractions. Weaker correlations between streamflow and precipitation were found for some of the catchments with reservoirs, water transfers or groundwater augmentation schemes. An increase in streamflow drought occurrence towards the end of their records was found for some of the catchments affected by groundwater abstractions and a decrease in streamflow drought occurrence for some of the catchments with either reservoirs or groundwater abstractions. In conclusion, the proposed screening approaches were sometimes successful in identifying streamflow records with deviating drought characteristics that are likely related to different human influences. However, a quantitative attribution of the impact of human influences on streamflow drought characteristics requires more detailed case-by-case information about the type and degree of all different human influences. Given that, in many countries, such information is often not readily accessible, the approaches adopted here could provide useful in targeting future efforts. In England and Wales specifically, the catchments with deviating streamflow drought characteristics identified in this study could serve as the starting point of detailed case study research.

Spatial patterns of March and September streamflow trends in Pacific Northwest Streams, 1958-2008

USGS Publications Warehouse

Chang, Heejun; Jung, Il-Won; Steele, Madeline; Gannett, Marshall

2012-01-01

Summer streamflow is a vital water resource for municipal and domestic water supplies, irrigation, salmonid habitat, recreation, and water-related ecosystem services in the Pacific Northwest (PNW) in the United States. This study detects significant negative trends in September absolute streamflow in a majority of 68 stream-gauging stations located on unregulated streams in the PNW from 1958 to 2008. The proportion of March streamflow to annual streamflow increases in most stations over 1,000 m elevation, with a baseflow index of less than 50, while absolute March streamflow does not increase in most stations. The declining trends of September absolute streamflow are strongly associated with seven-day low flow, January–March maximum temperature trends, and the size of the basin (19–7,260 km2), while the increasing trends of the fraction of March streamflow are associated with elevation, April 1 snow water equivalent, March precipitation, center timing of streamflow, and October–December minimum temperature trends. Compared with ordinary least squares (OLS) estimated regression models, spatial error regression and geographically weighted regression (GWR) models effectively remove spatial autocorrelation in residuals. The GWR model results show spatial gradients of local R 2 values with consistently higher local R 2 values in the northern Cascades. This finding illustrates that different hydrologic landscape factors, such as geology and seasonal distribution of precipitation, also influence streamflow trends in the PNW. In addition, our spatial analysis model results show that considering various geographic factors help clarify the dynamics of streamflow trends over a large geographical area, supporting a spatial analysis approach over aspatial OLS-estimated regression models for predicting streamflow trends. Results indicate that transitional rain–snow surface water-dominated basins are likely to have reduced summer streamflow under warming scenarios. Consequently, a better understanding of the relationships among summer streamflow, precipitation, snowmelt, elevation, and geology can help water managers predict the response of regional summer streamflow to global warming.

A precipitation-runoff model for simulating natural streamflow conditions in the Smith River watershed, Montana, water years 1996-2008

USGS Publications Warehouse

Chase, Katherine J.; Caldwell, Rodney R.; Stanley, Andrea K.

2014-01-01

This report documents the construction of a precipitation-runoff model for simulating natural streamflow in the Smith River watershed, Montana. This Precipitation-Runoff Modeling System model, constructed in cooperation with the Meagher County Conservation District, can be used to examine the general hydrologic framework of the Smith River watershed, including quantification of precipitation, evapotranspiration, and streamflow; partitioning of streamflow between surface runoff and subsurface flow; and quantifying contributions to streamflow from several parts of the watershed. The model was constructed by using spatial datasets describing watershed topography, the streams, and the hydrologic characteristics of the basin soils and vegetation. Time-series data (daily total precipitation, and daily minimum and maximum temperature) were input to the model to simulate daily streamflow. The model was calibrated for water years 2002–2007 and evaluated for water years 1996–2001. Though water year 2008 was included in the study period to evaluate water-budget components, calibration and evaluation data were unavailable for that year. During the calibration and evaluation periods, simulated-natural flow values were compared to reconstructed-natural streamflow data. These reconstructed-natural streamflow data were calculated by adding Bureau of Reclamation’s depletions data to the observed streamflows. Reconstructed-natural streamflows represent estimates of streamflows for water years 1996–2007 assuming there was no agricultural water-resources development in the watershed. Additional calibration targets were basin mean monthly solar radiation and potential evapotranspiration. The model estimated the hydrologic processes in the Smith River watershed during the calibration and evaluation periods. Simulated-natural mean annual and mean monthly flows generally were the same or higher than the reconstructed-natural streamflow values during the calibration period, whereas they were lower during the evaluation period. The shape of the annual hydrographs for the simulated-natural daily streamflow values matched the shape of the hydrographs for the reconstructed-natural values for most of the calibration period, but daily streamflow values were underestimated during the evaluation period for water years 1996–1998. The model enabled a detailed evaluation of the components of the water budget within the Smith River watershed during the water year 1996–2008 study period. During this study period, simulated mean annual precipitation across the Smith River watershed was 16 inches, out of which 14 inches evaporated or transpired and 2 inches left the basin as streamflow. Per the precipitation-runoff model simulations, during most of the year, surface runoff rarely (less than 2 percent of the time during water years 2002–2008) makes up more than 10 percent of the total streamflow. Subsurface flow (the combination of interflow and groundwater flow) makes up most of the total streamflow (99 or more percent of total streamflow for 71 percent of the time during water years 2002–2008).

Regional Climate and Streamflow Projections in North America Under IPCC CMIP5 Scenarios

NASA Astrophysics Data System (ADS)

Chang, H. I.; Castro, C. L.; Troch, P. A. A.; Mukherjee, R.

2014-12-01

The Colorado River system is the predominant source of water supply for the Southwest U.S. and is already fully allocated, making the region's environmental and economic health particularly sensitive to annual and multi-year streamflow variability. Observed streamflow declines in the Colorado Basin in recent years are likely due to synergistic combination of anthropogenic global warming and natural climate variability, which are creating an overall warmer and more extreme climate. IPCC assessment reports have projected warmer and drier conditions in arid to semi-arid regions (e.g. Solomon et al. 2007). The NAM-related precipitation contributes to substantial Colorado streamflows. Recent climate change studies for the Southwest U.S. region project a dire future, with chronic drought, and substantially reduced Colorado River flows. These regional effects reflect the general observation that climate is being more extreme globally, with areas climatologically favored to be wet getting wetter and areas favored to be dry getting drier (Wang et al. 2012). Multi-scale downscaling modeling experiments are designed using recent IPCC AR5 global climate projections, which incorporate regional climate and hydrologic modeling components. The Weather Research and Forecasting model (WRF) has been selected as the main regional modeling tool; the Variable Infiltration Capacity model (VIC) will be used to generate streamflow projections for the Colorado River Basin. The WRF domain is set up to follow the CORDEX-North America guideline with 25km grid spacing, and VIC model is individually calibrated for upper and lower Colorado River basins in 1/8° resolution. The multi-scale climate and hydrology study aims to characterize how the combination of climate change and natural climate variability is changing cool and warm season precipitation. Further, to preserve the downscaled RCM sensitivity and maintain a reasonable climatology mean based on observed record, a new bias correction technique is applied when using the RCM climatology to the streamflow model. Of specific interest is how major droughts associated with La Niña-like conditions may worsen in the future, as these are the times when the Colorado River system is most critically stressed and would define the "worst case" scenario for water resource planning.

Estimating current and future streamflow characteristics at ungaged sites, central and eastern Montana, with application to evaluating effects of climate change on fish populations

USGS Publications Warehouse

Sando, Roy; Chase, Katherine J.

2017-03-23

A common statistical procedure for estimating streamflow statistics at ungaged locations is to develop a relational model between streamflow and drainage basin characteristics at gaged locations using least squares regression analysis; however, least squares regression methods are parametric and make constraining assumptions about the data distribution. The random forest regression method provides an alternative nonparametric method for estimating streamflow characteristics at ungaged sites and requires that the data meet fewer statistical conditions than least squares regression methods.Random forest regression analysis was used to develop predictive models for 89 streamflow characteristics using Precipitation-Runoff Modeling System simulated streamflow data and drainage basin characteristics at 179 sites in central and eastern Montana. The predictive models were developed from streamflow data simulated for current (baseline, water years 1982–99) conditions and three future periods (water years 2021–38, 2046–63, and 2071–88) under three different climate-change scenarios. These predictive models were then used to predict streamflow characteristics for baseline conditions and three future periods at 1,707 fish sampling sites in central and eastern Montana. The average root mean square error for all predictive models was about 50 percent. When streamflow predictions at 23 fish sampling sites were compared to nearby locations with simulated data, the mean relative percent difference was about 43 percent. When predictions were compared to streamflow data recorded at 21 U.S. Geological Survey streamflow-gaging stations outside of the calibration basins, the average mean absolute percent error was about 73 percent.

Scale effects on information theory-based measures applied to streamflow patterns in two rural watersheds

NASA Astrophysics Data System (ADS)

Pan, Feng; Pachepsky, Yakov A.; Guber, Andrey K.; McPherson, Brian J.; Hill, Robert L.

2012-01-01

SummaryUnderstanding streamflow patterns in space and time is important for improving flood and drought forecasting, water resources management, and predictions of ecological changes. Objectives of this work include (a) to characterize the spatial and temporal patterns of streamflow using information theory-based measures at two thoroughly-monitored agricultural watersheds located in different hydroclimatic zones with similar land use, and (b) to elucidate and quantify temporal and spatial scale effects on those measures. We selected two USDA experimental watersheds to serve as case study examples, including the Little River experimental watershed (LREW) in Tifton, Georgia and the Sleepers River experimental watershed (SREW) in North Danville, Vermont. Both watersheds possess several nested sub-watersheds and more than 30 years of continuous data records of precipitation and streamflow. Information content measures (metric entropy and mean information gain) and complexity measures (effective measure complexity and fluctuation complexity) were computed based on the binary encoding of 5-year streamflow and precipitation time series data. We quantified patterns of streamflow using probabilities of joint or sequential appearances of the binary symbol sequences. Results of our analysis illustrate that information content measures of streamflow time series are much smaller than those for precipitation data, and the streamflow data also exhibit higher complexity, suggesting that the watersheds effectively act as filters of the precipitation information that leads to the observed additional complexity in streamflow measures. Correlation coefficients between the information-theory-based measures and time intervals are close to 0.9, demonstrating the significance of temporal scale effects on streamflow patterns. Moderate spatial scale effects on streamflow patterns are observed with absolute values of correlation coefficients between the measures and sub-watershed area varying from 0.2 to 0.6 in the two watersheds. We conclude that temporal effects must be evaluated and accounted for when the information theory-based methods are used for performance evaluation and comparison of hydrological models.

Stochastic Watershed Models for Risk Based Decision Making

NASA Astrophysics Data System (ADS)

Vogel, R. M.

2017-12-01

Over half a century ago, the Harvard Water Program introduced the field of operational or synthetic hydrology providing stochastic streamflow models (SSMs), which could generate ensembles of synthetic streamflow traces useful for hydrologic risk management. The application of SSMs, based on streamflow observations alone, revolutionized water resources planning activities, yet has fallen out of favor due, in part, to their inability to account for the now nearly ubiquitous anthropogenic influences on streamflow. This commentary advances the modern equivalent of SSMs, termed `stochastic watershed models' (SWMs) useful as input to nearly all modern risk based water resource decision making approaches. SWMs are deterministic watershed models implemented using stochastic meteorological series, model parameters and model errors, to generate ensembles of streamflow traces that represent the variability in possible future streamflows. SWMs combine deterministic watershed models, which are ideally suited to accounting for anthropogenic influences, with recent developments in uncertainty analysis and principles of stochastic simulation

Modeling Source Water TOC Using Hydroclimate Variables and Local Polynomial Regression.

PubMed

Samson, Carleigh C; Rajagopalan, Balaji; Summers, R Scott

2016-04-19

To control disinfection byproduct (DBP) formation in drinking water, an understanding of the source water total organic carbon (TOC) concentration variability can be critical. Previously, TOC concentrations in water treatment plant source waters have been modeled using streamflow data. However, the lack of streamflow data or unimpaired flow scenarios makes it difficult to model TOC. In addition, TOC variability under climate change further exacerbates the problem. Here we proposed a modeling approach based on local polynomial regression that uses climate, e.g. temperature, and land surface, e.g., soil moisture, variables as predictors of TOC concentration, obviating the need for streamflow. The local polynomial approach has the ability to capture non-Gaussian and nonlinear features that might be present in the relationships. The utility of the methodology is demonstrated using source water quality and climate data in three case study locations with surface source waters including river and reservoir sources. The models show good predictive skill in general at these locations, with lower skills at locations with the most anthropogenic influences in their streams. Source water TOC predictive models can provide water treatment utilities important information for making treatment decisions for DBP regulation compliance under future climate scenarios.

Development of a Precipitation-Runoff Model to Simulate Unregulated Streamflow in the Salmon Creek Basin, Okanogan County, Washington

USGS Publications Warehouse

van Heeswijk, Marijke

2006-01-01

Surface water has been diverted from the Salmon Creek Basin for irrigation purposes since the early 1900s, when the Bureau of Reclamation built the Okanogan Project. Spring snowmelt runoff is stored in two reservoirs, Conconully Reservoir and Salmon Lake Reservoir, and gradually released during the growing season. As a result of the out-of-basin streamflow diversions, the lower 4.3 miles of Salmon Creek typically has been a dry creek bed for almost 100 years, except during the spring snowmelt season during years of high runoff. To continue meeting the water needs of irrigators but also leave water in lower Salmon Creek for fish passage and to help restore the natural ecosystem, changes are being considered in how the Okanogan Project is operated. This report documents development of a precipitation-runoff model for the Salmon Creek Basin that can be used to simulate daily unregulated streamflows. The precipitation-runoff model is a component of a Decision Support System (DSS) that includes a water-operations model the Bureau of Reclamation plans to develop to study the water resources of the Salmon Creek Basin. The DSS will be similar to the DSS that the Bureau of Reclamation and the U.S. Geological Survey developed previously for the Yakima River Basin in central southern Washington. The precipitation-runoff model was calibrated for water years 1950-89 and tested for water years 1990-96. The model was used to simulate daily streamflows that were aggregated on a monthly basis and calibrated against historical monthly streamflows for Salmon Creek at Conconully Dam. Additional calibration data were provided by the snowpack water-equivalent record for a SNOTEL station in the basin. Model input time series of daily precipitation and minimum and maximum air temperatures were based on data from climate stations in the study area. Historical records of unregulated streamflow for Salmon Creek at Conconully Dam do not exist for water years 1950-96. Instead, estimates of historical monthly mean unregulated streamflow based on reservoir outflows and storage changes were used as a surrogate for the missing data and to calibrate and test the model. The estimated unregulated streamflows were corrected for evaporative losses from Conconully Reservoir (about 1 ft3/s) and ground-water losses from the basin (about 2 ft3/s). The total of the corrections was about 9 percent of the mean uncorrected streamflow of 32.2 ft3/s (23,300 acre-ft/yr) for water years 1949-96. For the calibration period, the basinwide mean annual evapotranspiration was simulated to be 19.1 inches, or about 83 percent of the mean annual precipitation of 23.1 inches. Model calibration and testing indicated that the daily streamflows simulated using the precipitation-runoff model should be used only to analyze historical and forecasted annual mean and April-July mean streamflows for Salmon Creek at Conconully Dam. Because of the paucity of model input data and uncertainty in the estimated unregulated streamflows, the model is not adequately calibrated and tested to estimate monthly mean streamflows for individual months, such as during low-flow periods, or for shorter periods such as during peak flows. No data were available to test the accuracy of simulated streamflows for lower Salmon Creek. As a result, simulated streamflows for lower Salmon Creek should be used with caution. For the calibration period (water years 1950-89), both the simulated mean annual streamflow and the simulated mean April-July streamflow compared well with the estimated uncorrected unregulated streamflow (UUS) and corrected unregulated streamflow (CUS). The simulated mean annual streamflow exceeded UUS by 5.9 percent and was less than CUS by 2.7 percent. Similarly, the simulated mean April-July streamflow exceeded UUS by 1.8 percent and was less than CUS by 3.1 percent. However, streamflow was significantly undersimulated during the low-flow, baseflow-dominated months of November through F

Moving Beyond Streamflow Observations: Lessons From A Multi-Objective Calibration Experiment in the Mississippi Basin

NASA Astrophysics Data System (ADS)

Koppa, A.; Gebremichael, M.; Yeh, W. W. G.

2017-12-01

Calibrating hydrologic models in large catchments using a sparse network of streamflow gauges adversely affects the spatial and temporal accuracy of other water balance components which are important for climate-change, land-use and drought studies. This study combines remote sensing data and the concept of Pareto-Optimality to address the following questions: 1) What is the impact of streamflow (SF) calibration on the spatio-temporal accuracy of Evapotranspiration (ET), near-surface Soil Moisture (SM) and Total Water Storage (TWS)? 2) What is the best combination of fluxes that can be used to calibrate complex hydrological models such that both the accuracy of streamflow and the spatio-temporal accuracy of ET, SM and TWS is preserved? The study area is the Mississippi Basin in the United States (encompassing HUC-2 regions 5,6,7,9,10 and 11). 2003 and 2004, two climatologically average years are chosen for calibration and validation of the Noah-MP hydrologic model. Remotely sensed ET data is sourced from GLEAM, SM from ESA-CCI and TWS from GRACE. Single objective calibration is carried out using DDS Algorithm. For Multi objective calibration PA-DDS is used. First, the Noah-MP model is calibrated using a single objective function (Minimize Mean Square Error) for the outflow from the 6 HUC-2 sub-basins for 2003. Spatial correlograms are used to compare the spatial structure of ET, SM and TWS between the model and the remote sensing data. Spatial maps of RMSE and Mean Error are used to quantify the impact of calibrating streamflow on the accuracy of ET, SM and TWS estimates. Next, a multi-objective calibration experiment is setup to determine the pareto optimal parameter sets (pareto front) for the following cases - 1) SF and ET, 2) SF and SM, 3) SF and TWS, 4) SF, ET and SM, 5) SF, ET and TWS, 6) SF, SM and TWS, 7) SF, ET, SM and TWS. The best combination of fluxes that provides the optimal trade-off between accurate streamflow and preserving the spatio-temporal structure of ET, SM and TWS is then determined by validating the model outputs for the pareto-optimal parameter sets. Results from single-objective calibration experiment with streamflow shows that it does indeed negatively impact the accuracy of ET, SM and TWS estimates.

Free internet datasets for streamflow modelling using SWAT in the Johor river basin, Malaysia

NASA Astrophysics Data System (ADS)

Tan, M. L.

2014-02-01

Streamflow modelling is a mathematical computational approach that represents terrestrial hydrology cycle digitally and is used for water resources assessment. However, such modelling endeavours require a large amount of data. Generally, governmental departments produce and maintain these data sets which make it difficult to obtain this data due to bureaucratic constraints. In some countries, the availability and quality of geospatial and climate datasets remain a critical issue due to many factors such as lacking of ground station, expertise, technology, financial support and war time. To overcome this problem, this research used public domain datasets from the Internet as "input" to a streamflow model. The intention is simulate daily and monthly streamflow of the Johor River Basin in Malaysia. The model used is the Soil and Water Assessment Tool (SWAT). As input free data including a digital elevation model (DEM), land use information, soil and climate data were used. The model was validated by in-situ streamflow information obtained from Rantau Panjang station for the year 2006. The coefficient of determination and Nash-Sutcliffe efficiency were 0.35/0.02 for daily simulated streamflow and 0.92/0.21 for monthly simulated streamflow, respectively. The results show that free data can provide a better simulation at a monthly scale compared to a daily basis in a tropical region. A sensitivity analysis and calibration procedure should be conducted in order to maximize the "goodness-of-fit" between simulated and observed streamflow. The application of Internet datasets promises an acceptable performance of streamflow modelling. This research demonstrates that public domain data is suitable for streamflow modelling in a tropical river basin within acceptable accuracy.

Predicting streamflow regime metrics for ungauged streamsin Colorado, Washington, and Oregon

NASA Astrophysics Data System (ADS)

Sanborn, Stephen C.; Bledsoe, Brian P.

2006-06-01

Streamflow prediction in ungauged basins provides essential information for water resources planning and management and ecohydrological studies yet remains a fundamental challenge to the hydrological sciences. A methodology is presented for stratifying streamflow regimes of gauged locations, classifying the regimes of ungauged streams, and developing models for predicting a suite of ecologically pertinent streamflow metrics for these streams. Eighty-four streamflow metrics characterizing various flow regime attributes were computed along with physical and climatic drainage basin characteristics for 150 streams with little or no streamflow modification in Colorado, Washington, and Oregon. The diverse hydroclimatology of the study area necessitates flow regime stratification and geographically independent clusters were identified and used to develop separate predictive models for each flow regime type. Multiple regression models for flow magnitude, timing, and rate of change metrics were quite accurate with many adjusted R2 values exceeding 0.80, while models describing streamflow variability did not perform as well. Separate stratification schemes for high, low, and average flows did not considerably improve models for metrics describing those particular aspects of the regime over a scheme based on the entire flow regime. Models for streams identified as 'snowmelt' type were improved if sites in Colorado and the Pacific Northwest were separated to better stratify the processes driving streamflow in these regions thus revealing limitations of geographically independent streamflow clusters. This study demonstrates that a broad suite of ecologically relevant streamflow characteristics can be accurately modeled across large heterogeneous regions using this framework. Applications of the resulting models include stratifying biomonitoring sites and quantifying linkages between specific aspects of flow regimes and aquatic community structure. In particular, the results bode well for modeling ecological processes related to high-flow magnitude, timing, and rate of change such as the recruitment of fish and riparian vegetation across large regions.

Simulation of streamflow and estimation of streamflow constituent loads in the San Antonio River watershed, Bexar County, Texas, 1997-2001

USGS Publications Warehouse

Ockerman, Darwin J.; McNamara, Kenna C.

2003-01-01

The U.S. Geological Survey developed watershed models (Hydrological Simulation Program—FORTRAN) to simulate streamflow and estimate streamflow constituent loads from five basins that compose the San Antonio River watershed in Bexar County, Texas. Rainfall and streamflow data collected during 1997–2001 were used to calibrate and test the model. The model was configured so that runoff from various land uses and discharges from other sources (such as wastewater recycling facilities) could be accounted for to indicate sources of streamflow. Simulated streamflow volumes were used with land-use-specific, water-quality data to compute streamflow loads of selected constituents from the various streamflow sources.Model simulations for 1997–2001 indicate that inflow from the upper Medina River (originating outside Bexar County) represents about 22 percent of total streamflow. Recycled wastewater discharges account for about 20 percent and base flow (ground-water inflow to streams) about 18 percent. Storm runoff from various land uses represents about 33 percent. Estimates of sources of streamflow constituent loads indicate recycled wastewater as the largest source of dissolved solids and nitrate plus nitrite nitrogen (about 38 and 66 percent, respectively, of the total loads) during 1997–2001. Stormwater runoff from urban land produced about 49 percent of the 1997–2001 total suspended solids load. Stormwater runoff from residential and commercial land (about 23 percent of the land area) produced about 70 percent of the total lead streamflow load during 1997–2001.

Evaluation of multiple hydraulic models in generating design/near-real time flood inundation extents under various geophysical settings

NASA Astrophysics Data System (ADS)

Liu, Z.; Rajib, M. A.; Jafarzadegan, K.; Merwade, V.

2015-12-01

Application of land surface/hydrologic models within an operational flood forecasting system can provide probable time of occurrence and magnitude of streamflow at specific locations along a stream. Creating time-varying spatial extent of flood inundation and depth requires the use of a hydraulic or hydrodynamic model. Models differ in representing river geometry and surface roughness which can lead to different output depending on the particular model being used. The result from a single hydraulic model provides just one possible realization of the flood extent without capturing the uncertainty associated with the input or the model parameters. The objective of this study is to compare multiple hydraulic models toward generating ensemble flood inundation extents. Specifically, relative performances of four hydraulic models, including AutoRoute, HEC-RAS, HEC-RAS 2D, and LISFLOOD are evaluated under different geophysical conditions in several locations across the United States. By using streamflow output from the same hydrologic model (SWAT in this case), hydraulic simulations are conducted for three configurations: (i) hindcasting mode by using past observed weather data at daily time scale in which models are being calibrated against USGS streamflow observations, (ii) validation mode using near real-time weather data at sub-daily time scale, and (iii) design mode with extreme streamflow data having specific return periods. Model generated inundation maps for observed flood events both from hindcasting and validation modes are compared with remotely sensed images, whereas the design mode outcomes are compared with corresponding FEMA generated flood hazard maps. The comparisons presented here will give insights on probable model-specific nature of biases and their relative advantages/disadvantages as components of an operational flood forecasting system.

Implementation of remote sensing data for flood forecasting

NASA Astrophysics Data System (ADS)

Grimaldi, S.; Li, Y.; Pauwels, V. R. N.; Walker, J. P.; Wright, A. J.

2016-12-01

Flooding is one of the most frequent and destructive natural disasters. A timely, accurate and reliable flood forecast can provide vital information for flood preparedness, warning delivery, and emergency response. An operational flood forecasting system typically consists of a hydrologic model, which simulates runoff generation and concentration, and a hydraulic model, which models riverine flood wave routing and floodplain inundation. However, these two types of models suffer from various sources of uncertainties, e.g., forcing data initial conditions, model structure and parameters. To reduce those uncertainties, current forecasting systems are typically calibrated and/or updated using streamflow measurements, and such applications are limited in well-gauged areas. The recent increasing availability of spatially distributed Remote Sensing (RS) data offers new opportunities for flood events investigation and forecast. Based on an Australian case study, this presentation will discuss the use 1) of RS soil moisture data to constrain a hydrologic model, and 2) of RS-derived flood extent and level to constrain a hydraulic model. The hydrological model is based on a semi-distributed system coupled with a two-soil-layer rainfall-runoff model GRKAL and a linear Muskingum routing model. Model calibration was performed using either 1) streamflow data only or 2) both streamflow and RS soil moisture data. The model was then further constrained through the integration of real-time soil moisture data. The hydraulic model is based on LISFLOOD-FP which solves the 2D inertial approximation of the Shallow Water Equations. Streamflow data and RS-derived flood extent and levels were used to apply a multi-objective calibration protocol. The effectiveness with which each data source or combination of data sources constrained the parameter space was quantified and discussed.

Improving Hydrological Simulations by Incorporating GRACE Data for Parameter Calibration

NASA Astrophysics Data System (ADS)

Bai, P.

2017-12-01

Hydrological model parameters are commonly calibrated by observed streamflow data. This calibration strategy is questioned when the modeled hydrological variables of interest are not limited to streamflow. Well-performed streamflow simulations do not guarantee the reliable reproduction of other hydrological variables. One of the reasons is that hydrological model parameters are not reasonably identified. The Gravity Recovery and Climate Experiment (GRACE) satellite-derived total water storage change (TWSC) data provide an opportunity to constrain hydrological model parameterizations in combination with streamflow observations. We constructed a multi-objective calibration scheme based on GRACE-derived TWSC and streamflow observations, with the aim of improving the parameterizations of hydrological models. The multi-objective calibration scheme was compared with the traditional single-objective calibration scheme, which is based only on streamflow observations. Two monthly hydrological models were employed on 22 Chinese catchments with different hydroclimatic conditions. The model evaluation was performed using observed streamflows, GRACE-derived TWSC, and evapotranspiraiton (ET) estimates from flux towers and from the water balance approach. Results showed that the multi-objective calibration provided more reliable TWSC and ET simulations without significant deterioration in the accuracy of streamflow simulations than the single-objective calibration. In addition, the improvements of TWSC and ET simulations were more significant in relatively dry catchments than in relatively wet catchments. This study highlights the importance of including additional constraints besides streamflow observations in the parameter estimation to improve the performances of hydrological models.

Panel regressions to estimate low-flow response to rainfall variability in ungaged basins

USGS Publications Warehouse

Bassiouni, Maoya; Vogel, Richard M.; Archfield, Stacey A.

2016-01-01

Multicollinearity and omitted-variable bias are major limitations to developing multiple linear regression models to estimate streamflow characteristics in ungaged areas and varying rainfall conditions. Panel regression is used to overcome limitations of traditional regression methods, and obtain reliable model coefficients, in particular to understand the elasticity of streamflow to rainfall. Using annual rainfall and selected basin characteristics at 86 gaged streams in the Hawaiian Islands, regional regression models for three stream classes were developed to estimate the annual low-flow duration discharges. Three panel-regression structures (random effects, fixed effects, and pooled) were compared to traditional regression methods, in which space is substituted for time. Results indicated that panel regression generally was able to reproduce the temporal behavior of streamflow and reduce the standard errors of model coefficients compared to traditional regression, even for models in which the unobserved heterogeneity between streams is significant and the variance inflation factor for rainfall is much greater than 10. This is because both spatial and temporal variability were better characterized in panel regression. In a case study, regional rainfall elasticities estimated from panel regressions were applied to ungaged basins on Maui, using available rainfall projections to estimate plausible changes in surface-water availability and usable stream habitat for native species. The presented panel-regression framework is shown to offer benefits over existing traditional hydrologic regression methods for developing robust regional relations to investigate streamflow response in a changing climate.

Panel regressions to estimate low-flow response to rainfall variability in ungaged basins

NASA Astrophysics Data System (ADS)

Bassiouni, Maoya; Vogel, Richard M.; Archfield, Stacey A.

2016-12-01

Multicollinearity and omitted-variable bias are major limitations to developing multiple linear regression models to estimate streamflow characteristics in ungaged areas and varying rainfall conditions. Panel regression is used to overcome limitations of traditional regression methods, and obtain reliable model coefficients, in particular to understand the elasticity of streamflow to rainfall. Using annual rainfall and selected basin characteristics at 86 gaged streams in the Hawaiian Islands, regional regression models for three stream classes were developed to estimate the annual low-flow duration discharges. Three panel-regression structures (random effects, fixed effects, and pooled) were compared to traditional regression methods, in which space is substituted for time. Results indicated that panel regression generally was able to reproduce the temporal behavior of streamflow and reduce the standard errors of model coefficients compared to traditional regression, even for models in which the unobserved heterogeneity between streams is significant and the variance inflation factor for rainfall is much greater than 10. This is because both spatial and temporal variability were better characterized in panel regression. In a case study, regional rainfall elasticities estimated from panel regressions were applied to ungaged basins on Maui, using available rainfall projections to estimate plausible changes in surface-water availability and usable stream habitat for native species. The presented panel-regression framework is shown to offer benefits over existing traditional hydrologic regression methods for developing robust regional relations to investigate streamflow response in a changing climate.

Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modeling heteroscedastic residual errors

NASA Astrophysics Data System (ADS)

McInerney, David; Thyer, Mark; Kavetski, Dmitri; Lerat, Julien; Kuczera, George

2017-03-01

Reliable and precise probabilistic prediction of daily catchment-scale streamflow requires statistical characterization of residual errors of hydrological models. This study focuses on approaches for representing error heteroscedasticity with respect to simulated streamflow, i.e., the pattern of larger errors in higher streamflow predictions. We evaluate eight common residual error schemes, including standard and weighted least squares, the Box-Cox transformation (with fixed and calibrated power parameter λ) and the log-sinh transformation. Case studies include 17 perennial and 6 ephemeral catchments in Australia and the United States, and two lumped hydrological models. Performance is quantified using predictive reliability, precision, and volumetric bias metrics. We find the choice of heteroscedastic error modeling approach significantly impacts on predictive performance, though no single scheme simultaneously optimizes all performance metrics. The set of Pareto optimal schemes, reflecting performance trade-offs, comprises Box-Cox schemes with λ of 0.2 and 0.5, and the log scheme (λ = 0, perennial catchments only). These schemes significantly outperform even the average-performing remaining schemes (e.g., across ephemeral catchments, median precision tightens from 105% to 40% of observed streamflow, and median biases decrease from 25% to 4%). Theoretical interpretations of empirical results highlight the importance of capturing the skew/kurtosis of raw residuals and reproducing zero flows. Paradoxically, calibration of λ is often counterproductive: in perennial catchments, it tends to overfit low flows at the expense of abysmal precision in high flows. The log-sinh transformation is dominated by the simpler Pareto optimal schemes listed above. Recommendations for researchers and practitioners seeking robust residual error schemes for practical work are provided.

Variance analysis of forecasted streamflow maxima in a wet temperate climate

NASA Astrophysics Data System (ADS)

Al Aamery, Nabil; Fox, James F.; Snyder, Mark; Chandramouli, Chandra V.

2018-05-01

Coupling global climate models, hydrologic models and extreme value analysis provides a method to forecast streamflow maxima, however the elusive variance structure of the results hinders confidence in application. Directly correcting the bias of forecasts using the relative change between forecast and control simulations has been shown to marginalize hydrologic uncertainty, reduce model bias, and remove systematic variance when predicting mean monthly and mean annual streamflow, prompting our investigation for maxima streamflow. We assess the variance structure of streamflow maxima using realizations of emission scenario, global climate model type and project phase, downscaling methods, bias correction, extreme value methods, and hydrologic model inputs and parameterization. Results show that the relative change of streamflow maxima was not dependent on systematic variance from the annual maxima versus peak over threshold method applied, albeit we stress that researchers strictly adhere to rules from extreme value theory when applying the peak over threshold method. Regardless of which method is applied, extreme value model fitting does add variance to the projection, and the variance is an increasing function of the return period. Unlike the relative change of mean streamflow, results show that the variance of the maxima's relative change was dependent on all climate model factors tested as well as hydrologic model inputs and calibration. Ensemble projections forecast an increase of streamflow maxima for 2050 with pronounced forecast standard error, including an increase of +30(±21), +38(±34) and +51(±85)% for 2, 20 and 100 year streamflow events for the wet temperate region studied. The variance of maxima projections was dominated by climate model factors and extreme value analyses.

A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites

NASA Astrophysics Data System (ADS)

Wang, Q. J.; Robertson, D. E.; Chiew, F. H. S.

2009-05-01

Seasonal forecasting of streamflows can be highly valuable for water resources management. In this paper, a Bayesian joint probability (BJP) modeling approach for seasonal forecasting of streamflows at multiple sites is presented. A Box-Cox transformed multivariate normal distribution is proposed to model the joint distribution of future streamflows and their predictors such as antecedent streamflows and El Niño-Southern Oscillation indices and other climate indicators. Bayesian inference of model parameters and uncertainties is implemented using Markov chain Monte Carlo sampling, leading to joint probabilistic forecasts of streamflows at multiple sites. The model provides a parametric structure for quantifying relationships between variables, including intersite correlations. The Box-Cox transformed multivariate normal distribution has considerable flexibility for modeling a wide range of predictors and predictands. The Bayesian inference formulated allows the use of data that contain nonconcurrent and missing records. The model flexibility and data-handling ability means that the BJP modeling approach is potentially of wide practical application. The paper also presents a number of statistical measures and graphical methods for verification of probabilistic forecasts of continuous variables. Results for streamflows at three river gauges in the Murrumbidgee River catchment in southeast Australia show that the BJP modeling approach has good forecast quality and that the fitted model is consistent with observed data.

Streamflow variability and classification using false nearest neighbor method

NASA Astrophysics Data System (ADS)

Vignesh, R.; Jothiprakash, V.; Sivakumar, B.

2015-12-01

Understanding regional streamflow dynamics and patterns continues to be a challenging problem. The present study introduces the false nearest neighbor (FNN) algorithm, a nonlinear dynamic-based method, to examine the spatial variability of streamflow over a region. The FNN method is a dimensionality-based approach, where the dimension of the time series represents its variability. The method uses phase space reconstruction and nearest neighbor concepts, and identifies false neighbors in the reconstructed phase space. The FNN method is applied to monthly streamflow data monitored over a period of 53 years (1950-2002) in an extensive network of 639 stations in the contiguous United States (US). Since selection of delay time in phase space reconstruction may influence the FNN outcomes, analysis is carried out for five different delay time values: monthly, seasonal, and annual separation of data as well as delay time values obtained using autocorrelation function (ACF) and average mutual information (AMI) methods. The FNN dimensions for the 639 streamflow series are generally identified to range from 4 to 12 (with very few exceptional cases), indicating a wide range of variability in the dynamics of streamflow across the contiguous US. However, the FNN dimensions for a majority of the streamflow series are found to be low (less than or equal to 6), suggesting low level of complexity in streamflow dynamics in most of the individual stations and over many sub-regions. The FNN dimension estimates also reveal that streamflow dynamics in the western parts of the US (including far west, northwestern, and southwestern parts) generally exhibit much greater variability compared to that in the eastern parts of the US (including far east, northeastern, and southeastern parts), although there are also differences among 'pockets' within these regions. These results are useful for identification of appropriate model complexity at individual stations, patterns across regions and sub-regions, interpolation and extrapolation of data, and catchment classification. An attempt is also made to relate the FNN dimensions with catchment characteristics and streamflow statistical properties.

CrowdWater - Can people observe what models need?

NASA Astrophysics Data System (ADS)

van Meerveld, I. H. J.; Seibert, J.; Vis, M.; Etter, S.; Strobl, B.

2017-12-01

CrowdWater (www.crowdwater.ch) is a citizen science project that explores the usefulness of crowd-sourced data for hydrological model calibration and prediction. Hydrological models are usually calibrated based on observed streamflow data but it is likely easier for people to estimate relative stream water levels, such as the water level above or below a rock, than streamflow. Relative stream water levels may, therefore, be a more suitable variable for citizen science projects than streamflow. In order to test this assumption, we held surveys near seven different sized rivers in Switzerland and asked more than 450 volunteers to estimate the water level class based on a picture with a virtual staff gauge. The results show that people can generally estimate the relative water level well, although there were also a few outliers. We also asked the volunteers to estimate streamflow based on the stick method. The median estimated streamflow was close to the observed streamflow but the spread in the streamflow estimates was large and there were very large outliers, suggesting that crowd-based streamflow data is highly uncertain. In order to determine the potential value of water level class data for model calibration, we converted streamflow time series for 100 catchments in the US to stream level class time series and used these to calibrate the HBV model. The model was then validated using the streamflow data. The results of this modeling exercise show that stream level class data are useful for constraining a simple runoff model. Time series of only two stream level classes, e.g. above or below a rock in the stream, were already informative, especially when the class boundary was chosen towards the highest stream levels. There was hardly any improvement in model performance when more than five water level classes were used. This suggests that if crowd-sourced stream level observations are available for otherwise ungauged catchments, these data can be used to constrain a simple runoff model and to generate simulated streamflow time series from the level observations.

Streamflow characterization using functional data analysis of the Potomac River

NASA Astrophysics Data System (ADS)

Zelmanow, A.; Maslova, I.; Ticlavilca, A. M.; McKee, M.

2013-12-01

Flooding and droughts are extreme hydrological events that affect the United States economically and socially. The severity and unpredictability of flooding has caused billions of dollars in damage and the loss of lives in the eastern United States. In this context, there is an urgent need to build a firm scientific basis for adaptation by developing and applying new modeling techniques for accurate streamflow characterization and reliable hydrological forecasting. The goal of this analysis is to use numerical streamflow characteristics in order to classify, model, and estimate the likelihood of extreme events in the eastern United States, mainly the Potomac River. Functional data analysis techniques are used to study yearly streamflow patterns, with the extreme streamflow events characterized via functional principal component analysis. These methods are merged with more classical techniques such as cluster analysis, classification analysis, and time series modeling. The developed functional data analysis approach is used to model continuous streamflow hydrographs. The forecasting potential of this technique is explored by incorporating climate factors to produce a yearly streamflow outlook.

Wavelet-linear genetic programming: A new approach for modeling monthly streamflow

NASA Astrophysics Data System (ADS)

Ravansalar, Masoud; Rajaee, Taher; Kisi, Ozgur

2017-06-01

The streamflows are important and effective factors in stream ecosystems and its accurate prediction is an essential and important issue in water resources and environmental engineering systems. A hybrid wavelet-linear genetic programming (WLGP) model, which includes a discrete wavelet transform (DWT) and a linear genetic programming (LGP) to predict the monthly streamflow (Q) in two gauging stations, Pataveh and Shahmokhtar, on the Beshar River at the Yasuj, Iran were used in this study. In the proposed WLGP model, the wavelet analysis was linked to the LGP model where the original time series of streamflow were decomposed into the sub-time series comprising wavelet coefficients. The results were compared with the single LGP, artificial neural network (ANN), a hybrid wavelet-ANN (WANN) and Multi Linear Regression (MLR) models. The comparisons were done by some of the commonly utilized relevant physical statistics. The Nash coefficients (E) were found as 0.877 and 0.817 for the WLGP model, for the Pataveh and Shahmokhtar stations, respectively. The comparison of the results showed that the WLGP model could significantly increase the streamflow prediction accuracy in both stations. Since, the results demonstrate a closer approximation of the peak streamflow values by the WLGP model, this model could be utilized for the simulation of cumulative streamflow data prediction in one month ahead.

Simulation of streamflow and estimation of recharge to the Edwards aquifer in the Hondo Creek, Verde Creek, and San Geronimo Creek watersheds, south-central Texas, 1951-2003

USGS Publications Warehouse

Ockerman, Darwin J.

2005-01-01

The U.S. Geological Survey, in cooperation with the San Antonio Water System, constructed three watershed models using the Hydrological Simulation Program—FORTRAN (HSPF) to simulate streamflow and estimate recharge to the Edwards aquifer in the Hondo Creek, Verde Creek, and San Geronimo Creek watersheds in south-central Texas. The three models were calibrated and tested with available data collected during 1992–2003. Simulations of streamflow and recharge were done for 1951–2003. The approach to construct the models was to first calibrate the Hondo Creek model (with an hourly time step) using 1992–99 data and test the model using 2000–2003 data. The Hondo Creek model parameters then were applied to the Verde Creek and San Geronimo Creek watersheds to construct the Verde Creek and San Geronimo Creek models. The simulated streamflows for Hondo Creek are considered acceptable. Annual, monthly, and daily simulated streamflows adequately match measured values, but simulated hourly streamflows do not. The accuracy of streamflow simulations for Verde Creek is uncertain. For San Geronimo Creek, the match of measured and simulated annual and monthly streamflows is acceptable (or nearly so); but for daily and hourly streamflows, the calibration is relatively poor. Simulated average annual total streamflow for 1951–2003 to Hondo Creek, Verde Creek, and San Geronimo Creek is 45,400; 32,400; and 11,100 acre-feet, respectively. Simulated average annual streamflow at the respective watershed outlets is 13,000; 16,200; and 6,920 acre-feet. The difference between total streamflow and streamflow at the watershed outlet is streamflow lost to channel infiltration. Estimated average annual Edwards aquifer recharge for Hondo Creek, Verde Creek, and San Geronimo Creek watersheds for 1951–2003 is 37,900 acrefeet (5.04 inches), 26,000 acre-feet (3.36 inches), and 5,940 acre-feet (1.97 inches), respectively. Most of the recharge (about 77 percent for the three watersheds together) occurs as streamflow channel infiltration. Diffuse recharge (direct infiltration of rainfall to the aquifer) accounts for the remaining 23 percent of recharge. For the Hondo Creek watershed, the HSPF recharge estimates for 1992–2003 averaged about 22 percent less than those estimated by the Puente method, a method the U.S. Geological Survey has used to compute annual recharge to the Edwards aquifer since 1978. HSPF recharge estimates for the Verde Creek watershed average about 40 percent less than those estimated by the Puente method.

Reduction of forecast uncertainty in the context of hydropower production: a case study for two catchment in Lac-St-Jean, Canada

NASA Astrophysics Data System (ADS)

Brisson, Cathy; Boucher, Marie-Amélie; Latraverse, Marco

2014-05-01

This research focuses on the improvement of streamflow forecasts for two subcatchments in the Lac-St-Jean area, a northern part of the province of Quebec in Canada. Those two subcatchments, named Manouane and Passes-Dangereuses, are part of a bigger system, which comprises many reservoirs and six hydropower plants. This system is managed by Rio Tinto Alcan, an aluminium producer who needs this energy for its processes. Optimal management of the hydropower plants highly depends on the reliability of the inflow forecasts to the reservoirs and also on the reliability of observed streamflow. The latter are not directly measured, but rather deduced from the computation of a water balance. This water balance includes streamflow computation based on rating curves for river sections and upstream reservoirs and a modelling process using CEQUEAU hydrological model (Morin et al., 1981). In addition, mostly during the winter, the model has to account for a transfer of water from Lac Manouane reservoir to Passes-Dangereuses through Bonnard channel. Winter flow though Bonnard channel is controlled by a spillway, and represented in CEQUEAU by a transfer function and a fixed time delay (2 days). However, it is suspected that the evacuation function, as it is currently computed, is inaccurate. The main objective of this work is to reduce predictive uncertainty for Lac Manouane and Passes-Dangereuses catchment, for the one-day ahead horizon. This objective is twofold. First, the uncertainty related to the parameterization of the hydrological model had never been evaluated. It was to be investigated whether it is better to spatialize the calibration of the hydrological model. In its actual form, the calibration of the hydrological model CEQUEAU (Morin et al., 1981) is based exclusively on the downstream outflow. There is, however, intermediate streamflow measurements data available for an intermediate location. Our study shows that calibrating the model using streamflows for both locations (intermediate location and downstream) leads to improved forecasts, as measured by the Nash-Sutcliffe efficiency criterion. The parameter sets thus determined best represent the phenomena of exchange and runoff in the watershed. Second, this study aims at reducing the uncertainty associated to the evacuation function for the Bonnard channel as well as the time delay related to this transfer. Instead of using a fixed 2-day time delay for the transfer, it was attempted to represent the channel in the hydrological model CEQUEAU and compute the time delay from this model. The results show that hydrological modelling does not improve the results and that the 2-day time delay is adequate, especially for first days of opening and few days after closure of the gate. In addition, this research shows that the evacuation function of Bonnard spillway is inexact for large streamflows. It is considered the main source of uncertainty for the prediction of inflows to the reservoirs. We also show that the evacuated streamflows can be successfully corrected by hydrological modelling. This case study shows that a careful revision of the inflow forecasting process for those important watersheds can help reduce predictive uncertainty. Although the application is specific to the Lac-St-Jean area, we believe that our experience could serve other users and water managers with similar issues regarding inflow uncertainty. Reference Morin, G., J.-P. Fortin, J.-P. Lardeau, W. Sochanska and S. Paquette. 1981. Modèle CEQUEAU : Manuel d'utilisation. Rapport de recherche no R-93, INRS-Eau, Sainte-Foy

Use of medium-range numerical weather prediction model output to produce forecasts of streamflow

USGS Publications Warehouse

Clark, M.P.; Hay, L.E.

2004-01-01

This paper examines an archive containing over 40 years of 8-day atmospheric forecasts over the contiguous United States from the NCEP reanalysis project to assess the possibilities for using medium-range numerical weather prediction model output for predictions of streamflow. This analysis shows the biases in the NCEP forecasts to be quite extreme. In many regions, systematic precipitation biases exceed 100% of the mean, with temperature biases exceeding 3??C. In some locations, biases are even higher. The accuracy of NCEP precipitation and 2-m maximum temperature forecasts is computed by interpolating the NCEP model output for each forecast day to the location of each station in the NWS cooperative network and computing the correlation with station observations. Results show that the accuracy of the NCEP forecasts is rather low in many areas of the country. Most apparent is the generally low skill in precipitation forecasts (particularly in July) and low skill in temperature forecasts in the western United States, the eastern seaboard, and the southern tier of states. These results outline a clear need for additional processing of the NCEP Medium-Range Forecast Model (MRF) output before it is used for hydrologic predictions. Techniques of model output statistics (MOS) are used in this paper to downscale the NCEP forecasts to station locations. Forecasted atmospheric variables (e.g., total column precipitable water, 2-m air temperature) are used as predictors in a forward screening multiple linear regression model to improve forecasts of precipitation and temperature for stations in the National Weather Service cooperative network. This procedure effectively removes all systematic biases in the raw NCEP precipitation and temperature forecasts. MOS guidance also results in substantial improvements in the accuracy of maximum and minimum temperature forecasts throughout the country. For precipitation, forecast improvements were less impressive. MOS guidance increases he accuracy of precipitation forecasts over the northeastern United States, but overall, the accuracy of MOS-based precipitation forecasts is slightly lower than the raw NCEP forecasts. Four basins in the United States were chosen as case studies to evaluate the value of MRF output for predictions of streamflow. Streamflow forecasts using MRF output were generated for one rainfall-dominated basin (Alapaha River at Statenville, Georgia) and three snowmelt-dominated basins (Animas River at Durango, Colorado: East Fork of the Carson River near Gardnerville, Nevada: and Cle Elum River near Roslyn, Washington). Hydrologic model output forced with measured-station data were used as "truth" to focus attention on the hydrologic effects of errors in the MRF forecasts. Eight-day streamflow forecasts produced using the MOS-corrected MRF output as input (MOS) were compared with those produced using the climatic Ensemble Streamflow Prediction (ESP) technique. MOS-based streamflow forecasts showed increased skill in the snowmelt-dominated river basins, where daily variations in streamflow are strongly forced by temperature. In contrast, the skill of MOS forecasts in the rainfall-dominated basin (the Alapaha River) were equivalent to the skill of the ESP forecasts. Further improvements in streamflow forecasts require more accurate local-scale forecasts of precipitation and temperature, more accurate specification of basin initial conditions, and more accurate model simulations of streamflow. ?? 2004 American Meteorological Society.

Calibrated models as management tools for stream-aquifer systems: the case of central Kansas, USA

NASA Astrophysics Data System (ADS)

Sophocleous, Marios; Perkins, Samuel P.

1993-12-01

We address the problem of declining streamflows in interconnected stream-aquifer systems and explore possible management options to address the problem for two areas of central Kansas: the Arkansas River valley from Kinsley to Great Bend and the lower Rattlesnake Creek-Quivira National Wildlife Refuge area. The approach we followed implements, calibrates, and partially validates for the study areas a stream-aquifer numerical model combined with a parameter estimation package and sensitivity analysis. Hydrologic budgets for both predevelopment and developed conditions indicate significant differences in the hydrologic components of the study areas resulting from development. The predevelopment water budgets give an estimate of natural ground-water recharge, whereas the budgets for developed conditions give an estimate of induced recharge, indicating that major ground-water development changes the recharge-discharge regime of the model areas with time. Such stream-aquifer models serve to link proposed actions to hydrologic effects, as is clearly demonstrated by the effects of various management alternatives on the streamflows of the Arkansas River and Rattlesnake Creek. Thus we show that a possible means of restoring specified streamflows in the area is to implement protective stream corridors with restricted ground-water extraction.

Calibrated models as management tools for stream-aquifer systems: the case of central Kansas, USA

USGS Publications Warehouse

Sophocleous, M.; Perkins, S.P.

1993-01-01

We address the problem of declining streamflows in interconnected stream-aquifer systems and explore possible management options to address the problem for two areas of central Kansas: the Arkansas River valley from Kinsley to Great Bend and the lower Rattlesnake Creek-Quivira National Wildlife Refuge area. The approach we followed implements, calibrates, and partially validates for the study areas a stream-aquifer numerical model combined with a parameter estimation package and sensitivity analysis. Hydrologic budgets for both predevelopment and developed conditions indicate significant differences in the hydrologic components of the study areas resulting from development. The predevelopment water budgets give an estimate of natural ground-water recharge, whereas the budgets for developed conditions give an estimate of induced recharge, indicating that major ground-water development changes the recharge-discharge regime of the model areas with time. Such stream-aquifer models serve to link proposed actions to hydrologic effects, as is clearly demonstrated by the effects of various management alternatives on the streamflows of the Arkansas River and Rattlesnake Creek. Thus we show that a possible means of restoring specified streamflows in the area is to implement protective stream corridors with restricted ground-water extraction. ?? 1993.

Disentangling the response of streamflow to forest management and climate

NASA Astrophysics Data System (ADS)

Dymond, S.; Miniat, C.; Bladon, K. D.; Keppeler, E.; Caldwell, P. V.

2016-12-01

Paired watershed studies have showcased the relationships between forests, management, and streamflow. However, classical analyses of paired-watershed studies have done little to disentangle the effects of management from overarching climatic signals, potentially masking the interaction between management and climate. Such approaches may confound our understanding of how forest management impacts streamflow. Here we use a 50-year record of streamflow and climate data from the Caspar Creek Experimental Watersheds (CCEW), California, USA to separate the effects of forest management and climate on streamflow. CCEW has two treatment watersheds that have been harvested in the past 50 years. We used a nonlinear mixed model to combine the pre-treatment relationship between streamflow and climate and the post-treatment relationship via an interaction between climate and management into one equation. Our results show that precipitation and potential evapotranspiration alone can account for >95% of the variability in pre-treatment streamflow. Including management scenarios into the model explained most of the variability in streamflow (R2 > 0.98). While forest harvesting altered streamflow in both of our modeled watersheds, removing 66% of the vegetation via selection logging using a tractor yarding system over the entire watershed had a more substantial impact on streamflow than clearcutting small portions of a watershed using cable-yarding. These results suggest that forest harvesting may result in differing impacts on streamflow and highlights the need to incorporate climate into streamflow analyses of paired-watershed studies.

Natural streamflow simulation for two largest river basins in Poland: a baseline for identification of flow alterations

NASA Astrophysics Data System (ADS)

Piniewski, Mikołaj

2016-05-01

The objective of this study was to apply a previously developed large-scale and high-resolution SWAT model of the Vistula and the Odra basins, calibrated with the focus of natural flow simulation, in order to assess the impact of three different dam reservoirs on streamflow using the Indicators of Hydrologic Alteration (IHA). A tailored spatial calibration approach was designed, in which calibration was focused on a large set of relatively small non-nested sub-catchments with semi-natural flow regime. These were classified into calibration clusters based on the flow statistics similarity. After performing calibration and validation that gave overall positive results, the calibrated parameter values were transferred to the remaining part of the basins using an approach based on hydrological similarity of donor and target catchments. The calibrated model was applied in three case studies with the purpose of assessing the effect of dam reservoirs (Włocławek, Siemianówka and Czorsztyn Reservoirs) on streamflow alteration. Both the assessment based on gauged streamflow (Before-After design) and the one based on simulated natural streamflow showed large alterations in selected flow statistics related to magnitude, duration, high and low flow pulses and rate of change. Some benefits of using a large-scale and high-resolution hydrological model for the assessment of streamflow alteration include: (1) providing an alternative or complementary approach to the classical Before-After designs, (2) isolating the climate variability effect from the dam (or any other source of alteration) effect, (3) providing a practical tool that can be applied at a range of spatial scales over large area such as a country, in a uniform way. Thus, presented approach can be applied for designing more natural flow regimes, which is crucial for river and floodplain ecosystem restoration in the context of the European Union's policy on environmental flows.

Impacts of land use change on watershed streamflow and sediment yield: An assessment using hydrologic modelling and partial least squares regression

NASA Astrophysics Data System (ADS)

Yan, B.; Fang, N. F.; Zhang, P. C.; Shi, Z. H.

2013-03-01

SummaryUnderstanding how changes in individual land use types influence the dynamics of streamflow and sediment yield would greatly improve the predictability of the hydrological consequences of land use changes and could thus help stakeholders to make better decisions. Multivariate statistics are commonly used to compare individual land use types to control the dynamics of streamflow or sediment yields. However, one issue with the use of conventional statistical methods to address relationships between land use types and streamflow or sediment yield is multicollinearity. In this study, an integrated approach involving hydrological modelling and partial least squares regression (PLSR) was used to quantify the contributions of changes in individual land use types to changes in streamflow and sediment yield. In a case study, hydrological modelling was conducted using land use maps from four time periods (1978, 1987, 1999, and 2007) for the Upper Du watershed (8973 km2) in China using the Soil and Water Assessment Tool (SWAT). Changes in streamflow and sediment yield across the two simulations conducted using the land use maps from 2007 to 1978 were found to be related to land use changes according to a PLSR, which was used to quantify the effect of this influence at the sub-basin scale. The major land use changes that affected streamflow in the studied catchment areas were related to changes in the farmland, forest and urban areas between 1978 and 2007; the corresponding regression coefficients were 0.232, -0.147 and 1.256, respectively, and the Variable Influence on Projection (VIP) was greater than 1. The dominant first-order factors affecting the changes in sediment yield in our study were: farmland (the VIP and regression coefficient were 1.762 and 14.343, respectively) and forest (the VIP and regression coefficient were 1.517 and -7.746, respectively). The PLSR methodology presented in this paper is beneficial and novel, as it partially eliminates the co-dependency of the variables and facilitates a more unbiased view of the contribution of the changes in individual land use types to changes in streamflow and sediment yield. This practicable and simple approach could be applied to a variety of other watersheds for which time-sequenced digital land use maps are available.

Uncertainties in Forecasting Streamflow using Entropy Theory

NASA Astrophysics Data System (ADS)

Cui, H.; Singh, V. P.

2017-12-01

Streamflow forecasting is essential in river restoration, reservoir operation, power generation, irrigation, navigation, and water management. However, there is always uncertainties accompanied in forecast, which may affect the forecasting results and lead to large variations. Therefore, uncertainties must be considered and be assessed properly when forecasting streamflow for water management. The aim of our work is to quantify the uncertainties involved in forecasting streamflow and provide reliable streamflow forecast. Despite that streamflow time series are stochastic, they exhibit seasonal and periodic patterns. Therefore, streamflow forecasting entails modeling seasonality, periodicity, and its correlation structure, and assessing uncertainties. This study applies entropy theory to forecast streamflow and measure uncertainties during the forecasting process. To apply entropy theory for streamflow forecasting, spectral analysis is combined to time series analysis, as spectral analysis can be employed to characterize patterns of streamflow variation and identify the periodicity of streamflow. That is, it permits to extract significant information for understanding the streamflow process and prediction thereof. Application of entropy theory for streamflow forecasting involves determination of spectral density, determination of parameters, and extension of autocorrelation function. The uncertainties brought by precipitation input, forecasting model and forecasted results are measured separately using entropy. With information theory, how these uncertainties transported and aggregated during these processes will be described.

On the Performance of Alternate Conceptual Ecohydrological Models for Streamflow Prediction

NASA Astrophysics Data System (ADS)

Naseem, Bushra; Ajami, Hoori; Cordery, Ian; Sharma, Ashish

2016-04-01

A merging of a lumped conceptual hydrological model with two conceptual dynamic vegetation models is presented to assess the performance of these models for simultaneous simulations of streamflow and leaf area index (LAI). Two conceptual dynamic vegetation models with differing representation of ecological processes are merged with a lumped conceptual hydrological model (HYMOD) to predict catchment scale streamflow and LAI. The merged RR-LAI-I model computes relative leaf biomass based on transpiration rates while the RR-LAI-II model computes above ground green and dead biomass based on net primary productivity and water use efficiency in response to soil moisture dynamics. To assess the performance of these models, daily discharge and 8-day MODIS LAI product for 27 catchments of 90 - 1600km2 in size located in the Murray - Darling Basin in Australia are used. Our results illustrate that when single-objective optimisation was focussed on maximizing the objective function for streamflow or LAI, the other un-calibrated predicted outcome (LAI if streamflow is the focus) was consistently compromised. Thus, single-objective optimization cannot take into account the essence of all processes in the conceptual ecohydrological models. However, multi-objective optimisation showed great strength for streamflow and LAI predictions. Both response outputs were better simulated by RR-LAI-II than RR-LAI-I due to better representation of physical processes such as net primary productivity (NPP) in RR-LAI-II. Our results highlight that simultaneous calibration of streamflow and LAI using a multi-objective algorithm proves to be an attractive tool for improved streamflow predictions.

Stochastic or statistic? Comparing flow duration curve models in ungauged basins and changing climates

NASA Astrophysics Data System (ADS)

Müller, M. F.; Thompson, S. E.

2015-09-01

The prediction of flow duration curves (FDCs) in ungauged basins remains an important task for hydrologists given the practical relevance of FDCs for water management and infrastructure design. Predicting FDCs in ungauged basins typically requires spatial interpolation of statistical or model parameters. This task is complicated if climate becomes non-stationary, as the prediction challenge now also requires extrapolation through time. In this context, process-based models for FDCs that mechanistically link the streamflow distribution to climate and landscape factors may have an advantage over purely statistical methods to predict FDCs. This study compares a stochastic (process-based) and statistical method for FDC prediction in both stationary and non-stationary contexts, using Nepal as a case study. Under contemporary conditions, both models perform well in predicting FDCs, with Nash-Sutcliffe coefficients above 0.80 in 75 % of the tested catchments. The main drives of uncertainty differ between the models: parameter interpolation was the main source of error for the statistical model, while violations of the assumptions of the process-based model represented the main source of its error. The process-based approach performed better than the statistical approach in numerical simulations with non-stationary climate drivers. The predictions of the statistical method under non-stationary rainfall conditions were poor if (i) local runoff coefficients were not accurately determined from the gauge network, or (ii) streamflow variability was strongly affected by changes in rainfall. A Monte Carlo analysis shows that the streamflow regimes in catchments characterized by a strong wet-season runoff and a rapid, strongly non-linear hydrologic response are particularly sensitive to changes in rainfall statistics. In these cases, process-based prediction approaches are strongly favored over statistical models.

Comparing statistical and process-based flow duration curve models in ungauged basins and changing rain regimes

NASA Astrophysics Data System (ADS)

Müller, M. F.; Thompson, S. E.

2016-02-01

The prediction of flow duration curves (FDCs) in ungauged basins remains an important task for hydrologists given the practical relevance of FDCs for water management and infrastructure design. Predicting FDCs in ungauged basins typically requires spatial interpolation of statistical or model parameters. This task is complicated if climate becomes non-stationary, as the prediction challenge now also requires extrapolation through time. In this context, process-based models for FDCs that mechanistically link the streamflow distribution to climate and landscape factors may have an advantage over purely statistical methods to predict FDCs. This study compares a stochastic (process-based) and statistical method for FDC prediction in both stationary and non-stationary contexts, using Nepal as a case study. Under contemporary conditions, both models perform well in predicting FDCs, with Nash-Sutcliffe coefficients above 0.80 in 75 % of the tested catchments. The main drivers of uncertainty differ between the models: parameter interpolation was the main source of error for the statistical model, while violations of the assumptions of the process-based model represented the main source of its error. The process-based approach performed better than the statistical approach in numerical simulations with non-stationary climate drivers. The predictions of the statistical method under non-stationary rainfall conditions were poor if (i) local runoff coefficients were not accurately determined from the gauge network, or (ii) streamflow variability was strongly affected by changes in rainfall. A Monte Carlo analysis shows that the streamflow regimes in catchments characterized by frequent wet-season runoff and a rapid, strongly non-linear hydrologic response are particularly sensitive to changes in rainfall statistics. In these cases, process-based prediction approaches are favored over statistical models.

Assessing the Use of Remote Sensing and a Crop Growth Model to Improve Modeled Streamflow in Central Asia

NASA Astrophysics Data System (ADS)

Richey, A. S.; Richey, J. E.; Tan, A.; Liu, M.; Adam, J. C.; Sokolov, V.

2015-12-01

Central Asia presents a perfect case study to understand the dynamic, and often conflicting, linkages between food, energy, and water in natural systems. The destruction of the Aral Sea is a well-known environmental disaster, largely driven by increased irrigation demand on the rivers that feed the endorheic sea. Continued reliance on these rivers, the Amu Darya and Syr Darya, often place available water resources at odds between hydropower demands upstream and irrigation requirements downstream. A combination of tools is required to understand these linkages and how they may change in the future as a function of climate change and population growth. In addition, the region is geopolitically complex as the former Soviet basin states develop management strategies to sustainably manage shared resources. This complexity increases the importance of relying upon publically available information sources and tools. Preliminary work has shown potential for the Variable Infiltration Capacity (VIC) model to recreate the natural water balance in the Amu Darya and Syr Darya basins by comparing results to total terrestrial water storage changes observed from NASA's Gravity Recovery and Climate Experiment (GRACE) satellite mission. Modeled streamflow is well correlated to observed streamflow at upstream gauges prior to the large-scale expansion of irrigation and hydropower. However, current modeled results are unable to capture the human influence of water use on downstream flow. This study examines the utility of a crop simulation model, CropSyst, to represent irrigation demand and GRACE to improve modeled streamflow estimates in the Amu Darya and Syr Darya basins. Specifically we determine crop water demand with CropSyst utilizing available data on irrigation schemes and cropping patterns. We determine how this demand can be met either by surface water, modeled by VIC with a reservoir operation scheme, and/or by groundwater derived from GRACE. Finally, we assess how the inclusion of CropSyst and groundwater to model and meet irrigation demand improves modeled streamflow from VIC throughout the basins. The results of this work are integrated into a decision support platform to assist the basin states in understanding water availability and the impact of management decisions on available resources.

Effects of groundwater pumping in the lower Apalachicola-Chattahoochee-Flint River basin

USGS Publications Warehouse

Jones, L. Elliott

2012-01-01

USGS developed a groundwater-flow model of the Upper Floridan aquifer in lower Apalachicola-Chattahoochee-Flint River basin in southwest Georgia and adjacent parts of Alabama and Florida to determine the effect of agricultural groundwater pumping on aquifer/stream flow within the basin. Aquifer/stream flow is the sum of groundwater outflow to and inflow from streams, and is an important consideration for water managers in the development of water-allocation and operating plans. Specifically, the model was used to evaluate how agricultural pumping relates to 7Q10 low streamflow, a statistical low flow indicative of drought conditions that would occur during seven consecutive days, on average, once every 10 years. Argus ONETM, a software package that combines a geographic information system (GIS) and numerical modeling in an Open Numerical Environment, facilitated the design of a detailed finite-element mesh to represent the complex geometry of the stream system in the lower basin as a groundwater-model boundary. To determine the effects on aquifer/stream flow of pumping at different locations within the model area, a pumping rate equivalent to a typical center-pivot irrigation system (50,000 ft3/d) was applied individually at each of the 18,951 model nodes in repeated steady-state simulations that were compared to a base case representing drought conditions during October 1999. Effects of nodal pumping on aquifer/stream flow and other boundary flows, as compared with the base-case simulation, were computed and stored in a response matrix. Queries to the response matrix were designed to determine the sensitivity of targeted stream reaches to agricultural pumping. Argus ONE enabled creation of contour plots of query results to illustrate the spatial variation across the model area of simulated aquifer/streamflow reductions, expressed as a percentage of the long-term 7Q10 low streamflow at key USGS gaging stations in the basin. These results would enable water managers to assess the relative impact of agricultural pumping and drought conditions on streamflow throughout the basin, and to develop mitigation strategies to conserve water resources and preserve aquatic habitat.

Methods for estimating tributary streamflow in the Chattahoochee River basin between Buford Dam and Franklin, Georgia

USGS Publications Warehouse

Stamey, Timothy C.

1998-01-01

Simple and reliable methods for estimating hourly streamflow are needed for the calibration and verification of a Chattahoochee River basin model between Buford Dam and Franklin, Ga. The river basin model is being developed by Georgia Department of Natural Resources, Environmental Protection Division, as part of their Chattahoochee River Modeling Project. Concurrent streamflow data collected at 19 continuous-record, and 31 partial-record streamflow stations, were used in ordinary least-squares linear regression analyses to define estimating equations, and in verifying drainage-area prorations. The resulting regression or drainage-area ratio estimating equations were used to compute hourly streamflow at the partial-record stations. The coefficients of determination (r-squared values) for the regression estimating equations ranged from 0.90 to 0.99. Observed and estimated hourly and daily streamflow data were computed for May 1, 1995, through October 31, 1995. Comparisons of observed and estimated daily streamflow data for 12 continuous-record tributary stations, that had available streamflow data for all or part of the period from May 1, 1995, to October 31, 1995, indicate that the mean error of estimate for the daily streamflow was about 25 percent.

A nonparametric stochastic method for generating daily climate-adjusted streamflows

NASA Astrophysics Data System (ADS)

Stagge, J. H.; Moglen, G. E.

2013-10-01

A daily stochastic streamflow generation model is presented, which successfully replicates statistics of the historical streamflow record and can produce climate-adjusted daily time series. A monthly climate model relates general circulation model (GCM)-scale climate indicators to discrete climate-streamflow states, which in turn control parameters in a daily streamflow generation model. Daily flow is generated by a two-state (increasing/decreasing) Markov chain, with rising limb increments randomly sampled from a Weibull distribution and the falling limb modeled as exponential recession. When applied to the Potomac River, a 38,000 km2 basin in the Mid-Atlantic United States, the model reproduces the daily, monthly, and annual distribution and dynamics of the historical streamflow record, including extreme low flows. This method can be used as part of water resources planning, vulnerability, and adaptation studies and offers the advantage of a parsimonious model, requiring only a sufficiently long historical streamflow record and large-scale climate data. Simulation of Potomac streamflows subject to the Special Report on Emissions Scenarios (SRES) A1b, A2, and B1 emission scenarios predict a slight increase in mean annual flows over the next century, with the majority of this increase occurring during the winter and early spring. Conversely, mean summer flows are projected to decrease due to climate change, caused by a shift to shorter, more sporadic rain events. Date of the minimum annual flow is projected to shift 2-5 days earlier by the 2070-2099 period.

Simulation of daily streamflows at gaged and ungaged locations within the Cedar River Basin, Iowa, using a Precipitation-Runoff Modeling System model

USGS Publications Warehouse

Christiansen, Daniel E.

2012-01-01

The U.S. Geological Survey, in cooperation with the Iowa Department of Natural Resources, conducted a study to examine techniques for estimation of daily streamflows using hydrological models and statistical methods. This report focuses on the use of a hydrologic model, the U.S. Geological Survey's Precipitation-Runoff Modeling System, to estimate daily streamflows at gaged and ungaged locations. The Precipitation-Runoff Modeling System is a modular, physically based, distributed-parameter modeling system developed to evaluate the impacts of various combinations of precipitation, climate, and land use on surface-water runoff and general basin hydrology. The Cedar River Basin was selected to construct a Precipitation-Runoff Modeling System model that simulates the period from January 1, 2000, to December 31, 2010. The calibration period was from January 1, 2000, to December 31, 2004, and the validation periods were from January 1, 2005, to December 31, 2010 and January 1, 2000 to December 31, 2010. A Geographic Information System tool was used to delineate the Cedar River Basin and subbasins for the Precipitation-Runoff Modeling System model and to derive parameters based on the physical geographical features. Calibration of the Precipitation-Runoff Modeling System model was completed using a U.S. Geological Survey calibration software tool. The main objective of the calibration was to match the daily streamflow simulated by the Precipitation-Runoff Modeling System model with streamflow measured at U.S. Geological Survey streamflow gages. The Cedar River Basin daily streamflow model performed with a Nash-Sutcliffe efficiency ranged from 0.82 to 0.33 during the calibration period, and a Nash-Sutcliffe efficiency ranged from 0.77 to -0.04 during the validation period. The Cedar River Basin model is meeting the criteria of greater than 0.50 Nash-Sutcliffe and is a good fit for streamflow conditions for the calibration period at all but one location, Austin, Minnesota. The Precipitation-Runoff Modeling System model accurately simulated streamflow at four of six uncalibrated sites within the basin. Overall, there was good agreement between simulated and measured seasonal and annual volumes throughout the basin for calibration and validation sites. The calibration period ranged from 0.2 to 20.8 percent difference, and the validation period ranged from 0.0 to 19.5 percent difference across all seasons and total annual runoff. The Precipitation-Runoff Modeling System model tended to underestimate lower streamflows compared to the observed streamflow values. This is an indication that the Precipitation-Runoff Modeling model needs more detailed groundwater and storage information to properly model the low-flow conditions in the Cedar River Basin.

A unified approach for process-based hydrologic modeling: Part 2. Model implementation and case studies

USDA-ARS?s Scientific Manuscript database

Understanding and prediction of snowmelt-generated streamflow at sub-daily time scales is important for reservoir scheduling and climate change characterization. This is particularly important in the Western U.S. where over 50% of water supply is provided by snowmelt during the melting period. Previ...

Estimating natural monthly streamflows in California and the likelihood of anthropogenic modification

USGS Publications Warehouse

Carlisle, Daren M.; Wolock, David M.; Howard, Jeannette K.; Grantham, Theodore E.; Fesenmyer, Kurt; Wieczorek, Michael

2016-12-12

Because natural patterns of streamflow are a fundamental property of the health of streams, there is a critical need to quantify the degree to which human activities have modified natural streamflows. A requirement for assessing streamflow modification in a given stream is a reliable estimate of flows expected in the absence of human influences. Although there are many techniques to predict streamflows in specific river basins, there is a lack of approaches for making predictions of natural conditions across large regions and over many decades. In this study conducted by the U.S. Geological Survey, in cooperation with The Nature Conservancy and Trout Unlimited, the primary objective was to develop empirical models that predict natural (that is, unaffected by land use or water management) monthly streamflows from 1950 to 2012 for all stream segments in California. Models were developed using measured streamflow data from the existing network of streams where daily flow monitoring occurs, but where the drainage basins have minimal human influences. Widely available data on monthly weather conditions and the physical attributes of river basins were used as predictor variables. Performance of regional-scale models was comparable to that of published mechanistic models for specific river basins, indicating the models can be reliably used to estimate natural monthly flows in most California streams. A second objective was to develop a model that predicts the likelihood that streams experience modified hydrology. New models were developed to predict modified streamflows at 558 streamflow monitoring sites in California where human activities affect the hydrology, using basin-scale geospatial indicators of land use and water management. Performance of these models was less reliable than that for the natural-flow models, but results indicate the models could be used to provide a simple screening tool for identifying, across the State of California, which streams may be experiencing anthropogenic flow modification.

Selective Tree-ring Models: A Novel Method for Reconstructing Streamflow Using Tree Rings

NASA Astrophysics Data System (ADS)

Foard, M. B.; Nelson, A. S.; Harley, G. L.

2017-12-01

Surface water is among the most instrumental and vulnerable resources in the Northwest United States (NW). Recent observations show that overall water quantity is declining in streams across the region, while extreme flooding events occur more frequently. Historical streamflow models inform probabilities of extreme flow events (flood or drought) by describing frequency and duration of past events. There are numerous examples of tree-rings being utilized to reconstruct streamflow in the NW. These models confirm that tree-rings are highly accurate at predicting streamflow, however there are many nuances that limit their applicability through time and space. For example, most models predict streamflow from hydrologically altered rivers (e.g. dammed, channelized) which may hinder our ability to predict natural prehistoric flow. They also have a tendency to over/under-predict extreme flow events. Moreover, they often neglect to capture the changing relationships between tree-growth and streamflow over time and space. To address these limitations, we utilized national tree-ring and streamflow archives to investigate the relationships between the growth of multiple coniferous species and free-flowing streams across the NW using novel species-and site-specific streamflow models - a term we coined"selective tree-ring models." Correlation function analysis and regression modeling were used to evaluate the strengths and directions of the flow-growth relationships. Species with significant relationships in the same direction were identified as strong candidates for selective models. Temporal and spatial patterns of these relationships were examined using running correlations and inverse distance weighting interpolation, respectively. Our early results indicate that (1) species adapted to extreme climates (e.g. hot-dry, cold-wet) exhibit the most consistent relationships across space, (2) these relationships weaken in locations with mild climatic variability, and (3) some species appear to be strong candidates for predicting high flow events, while others may be better at pridicting drought. These findings indicate that selective models may outperform traditional models when reconstructing distinctive aspects of streamflow.

Simulation of effects of wastewater discharges on Sand Creek and lower Caddo Creek near Ardmore, Oklahoma

USGS Publications Warehouse

Wesolowski, Edwin A.

1999-01-01

A streamflow and water-quality model was developed for reaches of Sand and Caddo Creeks in south-central Oklahoma to simulate the effects of wastewater discharge from a refinery and a municipal treatment plant.The purpose of the model was to simulate conditions during low streamflow when the conditions controlling dissolved-oxygen concentrations are most severe. Data collected to calibrate and verify the streamflow and water-quality model include continuously monitored streamflow and water-quality data at two gaging stations and three temporary monitoring stations; wastewater discharge from two wastewater plants; two sets each of five water-quality samples at nine sites during a 24-hour period; dye and propane samples; periphyton samples; and sediment oxygen demand measurements. The water-quality sampling, at a 6-hour frequency, was based on a Lagrangian reference frame in which the same volume of water was sampled at each site. To represent the unsteady streamflows and the dynamic water-quality conditions, a transport modeling system was used that included both a model to route streamflow and a model to transport dissolved conservative constituents with linkage to reaction kinetics similar to the U.S. Environmental Protection Agency QUAL2E model to simulate nonconservative constituents. These model codes are the Diffusion Analogy Streamflow Routing Model (DAFLOW) and the branched Lagrangian transport model (BLTM) and BLTM/QUAL2E that, collectively, as calibrated models, are referred to as the Ardmore Water-Quality Model.The Ardmore DAFLOW model was calibrated with three sets of streamflows that collectively ranged from 16 to 3,456 cubic feet per second. The model uses only one set of calibrated coefficients and exponents to simulate streamflow over this range. The Ardmore BLTM was calibrated for transport by simulating dye concentrations collected during a tracer study when streamflows ranged from 16 to 23 cubic feet per second. Therefore, the model is expected to be most useful for low streamflow simulations. The Ardmore BLTM/QUAL2E model was calibrated and verified with water-quality data from nine sites where two sets of five samples were collected. The streamflow during the water-quality sampling in Caddo Creek at site 7 ranged from 8.4 to 20 cubic feet per second, of which about 5.0 to 9.7 cubic feet per second was contributed by Sand Creek. The model simulates the fate and transport of 10 water-quality constituents. The model was verified by running it using data that were not used in calibration; only phytoplankton were not verified.Measured and simulated concentrations of dissolved oxygen exhibited a marked daily pattern that was attributable to waste loading and algal activity. Dissolved-oxygen measurements during this study and simulated dissolved-oxygen concentrations using the Ardmore Water-Quality Model, for the conditions of this study, illustrate that the dissolved-oxygen sag curve caused by the upstream wastewater discharges is confined to Sand Creek.

Daily Streamflow Predictions in an Ungauged Watershed in Northern California Using the Precipitation-Runoff Modeling System (PRMS): Calibration Challenges when nearby Gauged Watersheds are Hydrologically Dissimilar

NASA Astrophysics Data System (ADS)

Dhakal, A. S.; Adera, S.

2017-12-01

Accurate daily streamflow prediction in ungauged watersheds with sparse information is challenging. The ability of a hydrologic model calibrated using nearby gauged watersheds to predict streamflow accurately depends on hydrologic similarities between the gauged and ungauged watersheds. This study examines daily streamflow predictions using the Precipitation-Runoff Modeling System (PRMS) for the largely ungauged San Antonio Creek watershed, a 96 km2 sub-watershed of the Alameda Creek watershed in Northern California. The process-based PRMS model is being used to improve the accuracy of recent San Antonio Creek streamflow predictions generated by two empirical methods. Although San Antonio Creek watershed is largely ungauged, daily streamflow data exists for hydrologic years (HY) 1913 - 1930. PRMS was calibrated for HY 1913 - 1930 using streamflow data, modern-day land use and PRISM precipitation distribution, and gauged precipitation and temperature data from a nearby watershed. The PRMS model was then used to generate daily streamflows for HY 1996-2013, during which the watershed was ungauged, and hydrologic responses were compared to two nearby gauged sub-watersheds of Alameda Creek. Finally, the PRMS-predicted daily flows between HY 1996-2013 were compared to the two empirically-predicted streamflow time series: (1) the reservoir mass balance method and (2) correlation of historical streamflows from 80 - 100 years ago between San Antonio Creek and a nearby sub-watershed located in Alameda Creek. While the mass balance approach using reservoir storage and transfers is helpful for estimating inflows to the reservoir, large discrepancies in daily streamflow estimation can arise. Similarly, correlation-based predicted daily flows which rely on a relationship from flows collected 80-100 years ago may not represent current watershed hydrologic conditions. This study aims to develop a method of streamflow prediction in the San Antonio Creek watershed by examining PRMS's model outputs as well as empirically generated flow data for their use in water resources management decisions. PRMS is also being used to better understand the streamflow patterns in the San Antonio Creek watershed for a variety of antecedent soil moisture conditions as the creek is generally dry between late Spring and early Fall.

Methods for estimating drought streamflow probabilities for Virginia streams

USGS Publications Warehouse

Austin, Samuel H.

2014-01-01

Maximum likelihood logistic regression model equations used to estimate drought flow probabilities for Virginia streams are presented for 259 hydrologic basins in Virginia. Winter streamflows were used to estimate the likelihood of streamflows during the subsequent drought-prone summer months. The maximum likelihood logistic regression models identify probable streamflows from 5 to 8 months in advance. More than 5 million streamflow daily values collected over the period of record (January 1, 1900 through May 16, 2012) were compiled and analyzed over a minimum 10-year (maximum 112-year) period of record. The analysis yielded the 46,704 equations with statistically significant fit statistics and parameter ranges published in two tables in this report. These model equations produce summer month (July, August, and September) drought flow threshold probabilities as a function of streamflows during the previous winter months (November, December, January, and February). Example calculations are provided, demonstrating how to use the equations to estimate probable streamflows as much as 8 months in advance.

Effect of Tree-to-Shrub Type Conversion in Lower Montane Forests of the Sierra Nevada (USA) on Streamflow

PubMed Central

Tague, Christina L.; Moritz, Max A.

2016-01-01

Higher global temperatures and increased levels of disturbance are contributing to greater tree mortality in many forest ecosystems. These same drivers can also limit forest regeneration, leading to vegetation type conversion. For the Sierra Nevada of California, little is known about how type conversion may affect streamflow, a critical source of water supply for urban, agriculture and environmental purposes. In this paper, we examined the effects of tree-to-shrub type conversion, in combination with climate change, on streamflow in two lower montane forest watersheds in the Sierra Nevada. A spatially distributed ecohydrologic model was used to simulate changes in streamflow, evaporation, and transpiration following type conversion, with an explicit focus on the role of vegetation size and aspect. Model results indicated that streamflow may show negligible change or small decreases following type conversion when the difference between tree and shrub leaf areas is small, partly due to the higher stomatal conductivity and the deep rooting depth of shrubs. In contrast, streamflow may increase when post-conversion shrubs have a small leaf area relative to trees. Model estimates also suggested that vegetation change could have a greater impact on streamflow magnitude than the direct hydrologic impacts of increased temperatures. Temperature increases, however, may have a greater impact on streamflow timing. Tree-to-shrub type conversion increased streamflow only marginally during dry years (annual precipitation < 800 mm), with most streamflow change observed during wetter years. These modeling results underscore the importance of accounting for changes in vegetation communities to accurately characterize future hydrologic regimes for the Sierra Nevada. PMID:27575592

Effect of Tree-to-Shrub Type Conversion in Lower Montane Forests of the Sierra Nevada (USA) on Streamflow.

PubMed

Bart, Ryan R; Tague, Christina L; Moritz, Max A

2016-01-01

Higher global temperatures and increased levels of disturbance are contributing to greater tree mortality in many forest ecosystems. These same drivers can also limit forest regeneration, leading to vegetation type conversion. For the Sierra Nevada of California, little is known about how type conversion may affect streamflow, a critical source of water supply for urban, agriculture and environmental purposes. In this paper, we examined the effects of tree-to-shrub type conversion, in combination with climate change, on streamflow in two lower montane forest watersheds in the Sierra Nevada. A spatially distributed ecohydrologic model was used to simulate changes in streamflow, evaporation, and transpiration following type conversion, with an explicit focus on the role of vegetation size and aspect. Model results indicated that streamflow may show negligible change or small decreases following type conversion when the difference between tree and shrub leaf areas is small, partly due to the higher stomatal conductivity and the deep rooting depth of shrubs. In contrast, streamflow may increase when post-conversion shrubs have a small leaf area relative to trees. Model estimates also suggested that vegetation change could have a greater impact on streamflow magnitude than the direct hydrologic impacts of increased temperatures. Temperature increases, however, may have a greater impact on streamflow timing. Tree-to-shrub type conversion increased streamflow only marginally during dry years (annual precipitation < 800 mm), with most streamflow change observed during wetter years. These modeling results underscore the importance of accounting for changes in vegetation communities to accurately characterize future hydrologic regimes for the Sierra Nevada.

How does spatial variability of climate affect catchment streamflow predictions?

EPA Science Inventory

Spatial variability of climate can negatively affect catchment streamflow predictions if it is not explicitly accounted for in hydrologic models. In this paper, we examine the changes in streamflow predictability when a hydrologic model is run with spatially variable (distribute...

A Regionalized Flow Duration Curve Method to Predict Streamflow for Ungauaged Basins: A Case Study of the Rappahannock Watershed in Virginia, USA

EPA Science Inventory

A method to predict streamflow for ungauged basins of the Mid-Atlantic Region, USA was applied to the Rappahannock watershed in Virginia, USA. The method separates streamflow time series into magnitude and time sequence components. It uses the regionalized flow duration curve (RF...

Determining the importance of model calibration for forecasting absolute/relative changes in streamflow from LULC and climate changes

USGS Publications Warehouse

Niraula, Rewati; Meixner, Thomas; Norman, Laura M.

2015-01-01

Land use/land cover (LULC) and climate changes are important drivers of change in streamflow. Assessing the impact of LULC and climate changes on streamflow is typically done with a calibrated and validated watershed model. However, there is a debate on the degree of calibration required. The objective of this study was to quantify the variation in estimated relative and absolute changes in streamflow associated with LULC and climate changes with different calibration approaches. The Soil and Water Assessment Tool (SWAT) was applied in an uncalibrated (UC), single outlet calibrated (OC), and spatially-calibrated (SC) mode to compare the relative and absolute changes in streamflow at 14 gaging stations within the Santa Cruz River Watershed in southern Arizona, USA. For this purpose, the effect of 3 LULC, 3 precipitation (P), and 3 temperature (T) scenarios were tested individually. For the validation period, Percent Bias (PBIAS) values were >100% with the UC model for all gages, the values were between 0% and 100% with the OC model and within 20% with the SC model. Changes in streamflow predicted with the UC and OC models were compared with those of the SC model. This approach implicitly assumes that the SC model is “ideal”. Results indicated that the magnitude of both absolute and relative changes in streamflow due to LULC predicted with the UC and OC results were different than those of the SC model. The magnitude of absolute changes predicted with the UC and SC models due to climate change (both P and T) were also significantly different, but were not different for OC and SC models. Results clearly indicated that relative changes due to climate change predicted with the UC and OC were not significantly different than that predicted with the SC models. This result suggests that it is important to calibrate the model spatially to analyze the effect of LULC change but not as important for analyzing the relative change in streamflow due to climate change. This study also indicated that model calibration in not necessary to determine the direction of change in streamflow due to LULC and climate change.

Estimated monthly streamflows for selected locations on the Kabul and Logar Rivers, Aynak copper, cobalt, and chromium area of interest, Afghanistan, 1951-2010

USGS Publications Warehouse

Vining, Kevin C.; Vecchia, Aldo V.

2014-01-01

The U.S. Geological Survey, in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations, used the stochastic monthly water-balance model and existing climate data to estimate monthly streamflows for 1951–2010 for selected streamgaging stations located within the Aynak copper, cobalt, and chromium area of interest in Afghanistan. The model used physically based, nondeterministic methods to estimate the monthly volumetric water-balance components of a watershed. A comparison of estimated and recorded monthly streamflows for the streamgaging stations Kabul River at Maidan and Kabul River at Tangi-Saidan indicated that the stochastic water-balance model was able to provide satisfactory estimates of monthly streamflows for high-flow months and low-flow months even though withdrawals for irrigation likely occurred. A comparison of estimated and recorded monthly streamflows for the streamgaging stations Logar River at Shekhabad and Logar River at Sangi-Naweshta also indicated that the stochastic water-balance model was able to provide reasonable estimates of monthly streamflows for the high-flow months; however, for the upstream streamgaging station, the model overestimated monthly streamflows during periods when summer irrigation withdrawals likely occurred. Results from the stochastic water-balance model indicate that the model should be able to produce satisfactory estimates of monthly streamflows for locations along the Kabul and Logar Rivers. This information could be used by Afghanistan authorities to make decisions about surface-water resources for the Aynak copper, cobalt, and chromium area of interest.

Geologic and climatic controls on streamflow generation processes in a complex eogenetic karst basin

NASA Astrophysics Data System (ADS)

Vibhava, F.; Graham, W. D.; Maxwell, R. M.

2012-12-01

Streamflow at any given location and time is representative of surface and subsurface contributions from various sources. The ability to fully identify the factors controlling these contributions is key to successfully understanding the transport of contaminants through the system. In this study we developed a fully integrated 3D surface water-groundwater-land surface model, PARFLOW, to evaluate geologic and climatic controls on streamflow generation processes in a complex eogenetic karst basin in North Central Florida. In addition to traditional model evaluation criterion, such as comparing field observations to model simulated streamflow and groundwater elevations, we quantitatively evaluated the model's predictions of surface-groundwater interactions over space and time using a suite of binary end-member mixing models that were developed using observed specific conductivity differences among surface and groundwater sources throughout the domain. Analysis of model predictions showed that geologic heterogeneity exerts a strong control on both streamflow generation processes and land atmospheric fluxes in this watershed. In the upper basin, where the karst aquifer is overlain by a thick confining layer, approximately 92% of streamflow is "young" event flow, produced by near stream rainfall. Throughout the upper basin the confining layer produces a persistent high surficial water table which results in high evapotranspiration, low groundwater recharge and thus negligible "inter-event" streamflow. In the lower basin, where the karst aquifer is unconfined, deeper water tables result in less evapotranspiration. Thus, over 80% of the streamflow is "old" subsurface flow produced by diffuse infiltration through the epikarst throughout the lower basin, and all surface contributions to streamflow originate in the upper confined basin. Climatic variability provides a secondary control on surface-subsurface and land-atmosphere fluxes, producing significant seasonal and interannual variability in these processes. Spatial and temporal patterns of evapotranspiration, groundwater recharge and streamflow generation processes reveal potential hot spots and hot moments for surface and groundwater contamination in this basin.

Variational assimilation of streamflow into operational distributed hydrologic models: effect of spatiotemporal adjustment scale

NASA Astrophysics Data System (ADS)

Lee, H.; Seo, D.-J.; Liu, Y.; Koren, V.; McKee, P.; Corby, R.

2012-01-01

State updating of distributed rainfall-runoff models via streamflow assimilation is subject to overfitting because large dimensionality of the state space of the model may render the assimilation problem seriously under-determined. To examine the issue in the context of operational hydrology, we carry out a set of real-world experiments in which streamflow data is assimilated into gridded Sacramento Soil Moisture Accounting (SAC-SMA) and kinematic-wave routing models of the US National Weather Service (NWS) Research Distributed Hydrologic Model (RDHM) with the variational data assimilation technique. Study basins include four basins in Oklahoma and five basins in Texas. To assess the sensitivity of data assimilation performance to dimensionality reduction in the control vector, we used nine different spatiotemporal adjustment scales, where state variables are adjusted in a lumped, semi-distributed, or distributed fashion and biases in precipitation and potential evaporation (PE) are adjusted hourly, 6-hourly, or kept time-invariant. For each adjustment scale, three different streamflow assimilation scenarios are explored, where streamflow observations at basin interior points, at the basin outlet, or at both interior points and the outlet are assimilated. The streamflow assimilation experiments with nine different basins show that the optimum spatiotemporal adjustment scale varies from one basin to another and may be different for streamflow analysis and prediction in all of the three streamflow assimilation scenarios. The most preferred adjustment scale for seven out of nine basins is found to be the distributed, hourly scale, despite the fact that several independent validation results at this adjustment scale indicated the occurrence of overfitting. Basins with highly correlated interior and outlet flows tend to be less sensitive to the adjustment scale and could benefit more from streamflow assimilation. In comparison to outlet flow assimilation, interior flow assimilation at any adjustment scale produces streamflow predictions with a spatial correlation structure more consistent with that of streamflow observations. We also describe diagnosing the complexity of the assimilation problem using the spatial correlation information associated with the streamflow process, and discuss the effect of timing errors in a simulated hydrograph on the performance of the data assimilation procedure.

Determination of streamflow of the Arkansas River near Bentley in south-central Kansas

USGS Publications Warehouse

Perry, Charles A.

2012-01-01

The Kansas Department of Agriculture, Division of Water Resources, requires that the streamflow of the Arkansas River just upstream from Bentley in south-central Kansas be measured or calculated before groundwater can be pumped from the well field. When the daily streamflow of the Arkansas River near Bentley is less than 165 cubic feet per second (ft3/s), pumping must be curtailed. Daily streamflow near Bentley was calculated by determining the relations between streamflow data from two reference streamgages with a concurrent record of 24 years, one located 17.2 miles (mi) upstream and one located 10.9 mi downstream, and streamflow at a temporary gage located just upstream from Bentley (Arkansas River near Bentley, Kansas). Flow-duration curves for the two reference streamgages indicate that during 1988?2011, the mean daily streamflow was less than 165 ft3/s 30 to 35 percent of the time. During extreme low-flow (drought) conditions, the reach of the Arkansas River between Hutchinson and Maize can lose flow to the adjacent alluvial aquifer, with streamflow losses as much as 1.6 cubic feet per second per mile. Three models were developed to calculate the streamflow of the Arkansas River near Bentley, Kansas. The model chosen depends on the data available and on whether the reach of the Arkansas River between Hutchinson and Maize is gaining or losing groundwater from or to the adjacent alluvial aquifer. The first model was a pair of equations developed from linear regressions of the relation between daily streamflow data from the Bentley streamgage and daily streamflow data from either the Arkansas River near Hutchinson, Kansas, station (station number 07143330) or the Arkansas River near Maize, Kansas, station (station number 07143375). The standard error of the Hutchinson-only equation was 22.8 ft3/s, and the standard error of the Maize-only equation was 22.3 ft3/s. The single-station model would be used if only one streamgage was available. In the second model, the flow gradient between the streamflow near Hutchinson and the streamflow near Maize was used to calculate the streamflow at the Bentley streamgage. This equation resulted in a standard error of 26.7 ft3/s. In the third model, a multiple regression analysis between both the daily streamflow of the Arkansas River near Hutchinson, Kansas, and the daily streamflow of the Arkansas River near Maize, Kansas, was used to calculate the streamflow at the Bentley streamgage. The multiple regression equation had a standard error of 21.2 ft3/s, which was the smallest of the standard errors for all the models. An analysis of the number of low-flow days and the number of days when the reach between Hutchinson and Maize loses flow to the adjacent alluvial aquifer indicates that the long-term trend is toward fewer days of losing conditions. This trend may indicate a long-term increase in water levels in the alluvial aquifer, which could be caused by one or more of several conditions, including an increase in rainfall, a decrease in pumping, a decrease in temperature, and an increase in streamflow upstream from the Hutchinson-to-Maize reach of the Arkansas River.

Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes

NASA Astrophysics Data System (ADS)

Alvarez-Garreton, C.; Ryu, D.; Western, A. W.; Su, C.-H.; Crow, W. T.; Robertson, D. E.; Leahy, C.

2014-09-01

Assimilation of remotely sensed soil moisture data (SM-DA) to correct soil water stores of rainfall-runoff models has shown skill in improving streamflow prediction. In the case of large and sparsely monitored catchments, SM-DA is a particularly attractive tool. Within this context, we assimilate active and passive satellite soil moisture (SSM) retrievals using an ensemble Kalman filter to improve operational flood prediction within a large semi-arid catchment in Australia (>40 000 km2). We assess the importance of accounting for channel routing and the spatial distribution of forcing data by applying SM-DA to a lumped and a semi-distributed scheme of the probability distributed model (PDM). Our scheme also accounts for model error representation and seasonal biases and errors in the satellite data. Before assimilation, the semi-distributed model provided more accurate streamflow prediction (Nash-Sutcliffe efficiency, NS = 0.77) than the lumped model (NS = 0.67) at the catchment outlet. However, this did not ensure good performance at the "ungauged" inner catchments. After SM-DA, the streamflow ensemble prediction at the outlet was improved in both the lumped and the semi-distributed schemes: the root mean square error of the ensemble was reduced by 27 and 31%, respectively; the NS of the ensemble mean increased by 7 and 38%, respectively; the false alarm ratio was reduced by 15 and 25%, respectively; and the ensemble prediction spread was reduced while its reliability was maintained. Our findings imply that even when rainfall is the main driver of flooding in semi-arid catchments, adequately processed SSM can be used to reduce errors in the model soil moisture, which in turn provides better streamflow ensemble prediction. We demonstrate that SM-DA efficacy is enhanced when the spatial distribution in forcing data and routing processes are accounted for. At ungauged locations, SM-DA is effective at improving streamflow ensemble prediction, however, the updated prediction is still poor since SM-DA does not address systematic errors in the model.

Application of the Precipitation-Runoff Modeling System (PRMS) in the Apalachicola-Chattahoochee-Flint River Basin in the southeastern United States

USGS Publications Warehouse

LaFontaine, Jacob H.; Hay, Lauren E.; Viger, Roland J.; Markstrom, Steve L.; Regan, R. Steve; Elliott, Caroline M.; Jones, John W.

2013-01-01

A hydrologic model of the Apalachicola–Chattahoochee–Flint River Basin (ACFB) has been developed as part of a U.S. Geological Survey (USGS) National Climate Change and Wildlife Science Center effort to provide integrated science that helps resource managers understand the effect of climate change on a range of ecosystem responses. The hydrologic model was developed as part of the Southeast Regional Assessment Project using the Precipitation Runoff Modeling System (PRMS), a deterministic, distributed-parameter, process-based system that simulates the effects of precipitation, temperature, and land use on basin hydrology. The ACFB PRMS model simulates streamflow throughout the approximately 50,700 square-kilometer basin on a daily time step for the period 1950–99 using gridded climate forcings of air temperature and precipitation, and parameters derived from spatial data layers of altitude, land cover, soils, surficial geology, depression storage (small water bodies), and data from 56 USGS streamgages. Measured streamflow data from 35 of the 56 USGS streamgages were used to calibrate and evaluate simulated basin streamflow; the remaining gage locations were used for model delineation only. The model matched measured daily streamflow at 31 of the 35 calibration gages with Nash-Sutcliffe Model Efficiency Index (NS) greater than 0.6. Streamflow data for some calibration gages were augmented for regulation and water use effects to represent more natural flow volumes. Time-static parameters describing land cover limited the ability of the simulation to match historical runoff in the more developed subbasins. Overall, the PRMS simulation of the ACFB provides a good representation of basin hydrology on annual and monthly time steps. Calibration subbasins were analyzed by separating the 35 subbasins into five classes based on physiography, land use, and stream type (tributary or mainstem). The lowest NS values were rarely below 0.6, whereas the median NS for all five classes was within 0.74 to 0.96 for annual mean streamflow, 0.89 to 0.98 for mean monthly streamflow, and 0.82 to 0.98 for monthly mean streamflow. The median bias for all five classes was within –4.3 to 0.8 percent for annual mean streamflow, –6.3 to 0.5 percent for mean monthly streamflow, and –9.3 to 1.3 percent for monthly mean streamflow. The NS results combined with the percent bias results indicated a good to very good streamflow volume simulation for all subbasins. This simulation of the ACFB provides a foundation for future modeling and interpretive studies. Streamflow and other components of the hydrologic cycle simulated by PRMS can be used to inform other types of simulations; water-temperature, hydrodynamic, and ecosystem-dynamics simulations are three examples. In addition, possible future hydrologic conditions could be studied using this model in combination with land cover projections and downscaled general circulation model results.

Moving horizon estimation for assimilating H-SAF remote sensing data into the HBV hydrological model

NASA Astrophysics Data System (ADS)

Montero, Rodolfo Alvarado; Schwanenberg, Dirk; Krahe, Peter; Lisniak, Dmytro; Sensoy, Aynur; Sorman, A. Arda; Akkol, Bulut

2016-06-01

Remote sensing information has been extensively developed over the past few years including spatially distributed data for hydrological applications at high resolution. The implementation of these products in operational flow forecasting systems is still an active field of research, wherein data assimilation plays a vital role on the improvement of initial conditions of streamflow forecasts. We present a novel implementation of a variational method based on Moving Horizon Estimation (MHE), in application to the conceptual rainfall-runoff model HBV, to simultaneously assimilate remotely sensed snow covered area (SCA), snow water equivalent (SWE), soil moisture (SM) and in situ measurements of streamflow data using large assimilation windows of up to one year. This innovative application of the MHE approach allows to simultaneously update precipitation, temperature, soil moisture as well as upper and lower zones water storages of the conceptual model, within the assimilation window, without an explicit formulation of error covariance matrixes and it enables a highly flexible formulation of distance metrics for the agreement of simulated and observed variables. The framework is tested in two data-dense sites in Germany and one data-sparse environment in Turkey. Results show a potential improvement of the lead time performance of streamflow forecasts by using perfect time series of state variables generated by the simulation of the conceptual rainfall-runoff model itself. The framework is also tested using new operational data products from the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) of EUMETSAT. This study is the first application of H-SAF products to hydrological forecasting systems and it verifies their added value. Results from assimilating H-SAF observations lead to a slight reduction of the streamflow forecast skill in all three cases compared to the assimilation of streamflow data only. On the other hand, the forecast skill of soil moisture shows a significant improvement.

Case study applications of the BASINS climate assessment tool (CAT)

EPA Science Inventory

This EPA report will illustrate the application of different climate assessment capabilities within EPA’s BASINS modeling system for assessing a range of potential questions about the effects of climate change on streamflow and water quality in different watershed settings and us...

How soil moisture mediates the influence of transpiration on streamflow at hourly to interannual scales in a forested catchment

Treesearch

G.W. Moore; J.A. Jones; B.J. Bond

2011-01-01

The water balance equation dictates that streamflow may be reduced by transpiration. Yet temporal disequilibrium weakens the relationship between transpiration and streamflow in many cases where inputs and outputs are unbalanced. We address two critical knowledge barriers in ecohydrology with respect to time, scale dependence and lags. Study objectives were to...

Development of a precipitation-runoff model to simulate unregulated streamflow in the South Fork Flathead River Basin, Montana

USGS Publications Warehouse

Chase, K.J.

2011-01-01

This report documents the development of a precipitation-runoff model for the South Fork Flathead River Basin, Mont. The Precipitation-Runoff Modeling System model, developed in cooperation with the Bureau of Reclamation, can be used to simulate daily mean unregulated streamflow upstream and downstream from Hungry Horse Reservoir for water-resources planning. Two input files are required to run the model. The time-series data file contains daily precipitation data and daily minimum and maximum air-temperature data from climate stations in and near the South Fork Flathead River Basin. The parameter file contains values of parameters that describe the basin topography, the flow network, the distribution of the precipitation and temperature data, and the hydrologic characteristics of the basin soils and vegetation. A primary-parameter file was created for simulating streamflow during the study period (water years 1967-2005). The model was calibrated for water years 1991-2005 using the primary-parameter file. This calibration was further refined using snow-covered area data for water years 2001-05. The model then was tested for water years 1967-90. Calibration targets included mean monthly and daily mean unregulated streamflow upstream from Hungry Horse Reservoir, mean monthly unregulated streamflow downstream from Hungry Horse Reservoir, basin mean monthly solar radiation and potential evapotranspiration, and daily snapshots of basin snow-covered area. Simulated streamflow generally was in better agreement with observed streamflow at the upstream gage than at the downstream gage. Upstream from the reservoir, simulated mean annual streamflow was within 0.0 percent of observed mean annual streamflow for the calibration period and was about 2 percent higher than observed mean annual streamflow for the test period. Simulated mean April-July streamflow upstream from the reservoir was about 1 percent lower than observed streamflow for the calibration period and about 4 percent higher than observed for the test period. Downstream from the reservoir, simulated mean annual streamflow was 17 percent lower than observed streamflow for the calibration period and 12 percent lower than observed streamflow for the test period. Simulated mean April-July streamflow downstream from the reservoir was 13 percent lower than observed streamflow for the calibration period and 6 percent lower than observed streamflow for the test period. Calibrating to solar radiation, potential evapotranspiration, and snow-covered area improved the model representation of evapotranspiration, snow accumulation, and snowmelt processes. Simulated basin mean monthly solar radiation values for both the calibration and test periods were within 9 percent of observed values except during the month of December (28 percent different). Simulated basin potential evapotranspiration values for both the calibration and test periods were within 10 percent of observed values except during the months of January (100 percent different) and February (13 percent different). The larger percent errors in simulated potential evaporation occurred in the winter months when observed potential evapotranspiration values were very small; in January the observed value was 0.000 inches and in February the observed value was 0.009 inches. Simulated start of melting of the snowpack occurred at about the same time as observed start of melting. The simulated snowpack accumulated to 90-100 percent snow-covered area 1 to 3 months earlier than observed snowpack. This overestimated snowpack during the winter corresponded to underestimated streamflow during the same period. In addition to the primary-parameter file, four other parameter files were created: for a "recent" period (1991-2005), a historical period (1967-90), a "wet" period (1989-97), and a "dry" period (1998-2005). For each data file of projected precipitation and air temperature, a single parameter file can be used to simulate a s

MODFLOW-LGR-Modifications to the streamflow-routing package (SFR2) to route streamflow through locally refined grids

USGS Publications Warehouse

Mehl, Steffen W.; Hill, Mary C.

2011-01-01

This report documents modifications to the Streamflow-Routing Package (SFR2) to route streamflow through grids constructed using the multiple-refined-areas capability of shared node Local Grid Refinement (LGR) of MODFLOW-2005. MODFLOW-2005 is the U.S. Geological Survey modular, three-dimensional, finite-difference groundwater-flow model. LGR provides the capability to simulate groundwater flow by using one or more block-shaped, higher resolution local grids (child model) within a coarser grid (parent model). LGR accomplishes this by iteratively coupling separate MODFLOW-2005 models such that heads and fluxes are balanced across the shared interfacing boundaries. Compatibility with SFR2 allows for streamflow routing across grids. LGR can be used in two- and three-dimensional, steady-state and transient simulations and for simulations of confined and unconfined groundwater systems.

Mitigating the Impacts of Climate Nonstationarity on Seasonal Streamflow Predictability in the U.S. Southwest

NASA Astrophysics Data System (ADS)

Lehner, Flavio; Wood, Andrew W.; Llewellyn, Dagmar; Blatchford, Douglas B.; Goodbody, Angus G.; Pappenberger, Florian

2017-12-01

Seasonal streamflow predictions provide a critical management tool for water managers in the American Southwest. In recent decades, persistent prediction errors for spring and summer runoff volumes have been observed in a number of watersheds in the American Southwest. While mostly driven by decadal precipitation trends, these errors also relate to the influence of increasing temperature on streamflow in these basins. Here we show that incorporating seasonal temperature forecasts from operational global climate prediction models into streamflow forecasting models adds prediction skill for watersheds in the headwaters of the Colorado and Rio Grande River basins. Current dynamical seasonal temperature forecasts now show sufficient skill to reduce streamflow forecast errors in snowmelt-driven regions. Such predictions can increase the resilience of streamflow forecasting and water management systems in the face of continuing warming as well as decadal-scale temperature variability and thus help to mitigate the impacts of climate nonstationarity on streamflow predictability.

Evaluation of statistical and rainfall-runoff models for predicting historical daily streamflow time series in the Des Moines and Iowa River watersheds

USGS Publications Warehouse

Farmer, William H.; Knight, Rodney R.; Eash, David A.; Kasey J. Hutchinson,; Linhart, S. Mike; Christiansen, Daniel E.; Archfield, Stacey A.; Over, Thomas M.; Kiang, Julie E.

2015-08-24

Daily records of streamflow are essential to understanding hydrologic systems and managing the interactions between human and natural systems. Many watersheds and locations lack streamgages to provide accurate and reliable records of daily streamflow. In such ungaged watersheds, statistical tools and rainfall-runoff models are used to estimate daily streamflow. Previous work compared 19 different techniques for predicting daily streamflow records in the southeastern United States. Here, five of the better-performing methods are compared in a different hydroclimatic region of the United States, in Iowa. The methods fall into three classes: (1) drainage-area ratio methods, (2) nonlinear spatial interpolations using flow duration curves, and (3) mechanistic rainfall-runoff models. The first two classes are each applied with nearest-neighbor and map-correlated index streamgages. Using a threefold validation and robust rank-based evaluation, the methods are assessed for overall goodness of fit of the hydrograph of daily streamflow, the ability to reproduce a daily, no-fail storage-yield curve, and the ability to reproduce key streamflow statistics. As in the Southeast study, a nonlinear spatial interpolation of daily streamflow using flow duration curves is found to be a method with the best predictive accuracy. Comparisons with previous work in Iowa show that the accuracy of mechanistic models with at-site calibration is substantially degraded in the ungaged framework.

Improving Streamflow Forecasts Using Predefined Sea Surface Temperature

NASA Astrophysics Data System (ADS)

Kalra, A.; Ahmad, S.

2011-12-01

With the increasing evidence of climate variability, water resources managers in the western United States are faced with greater challenges of developing long range streamflow forecast. This is further aggravated by the increases in climate extremes such as floods and drought caused by climate variability. Over the years, climatologists have identified several modes of climatic variability and their relationship with streamflow. These climate modes have the potential of being used as predictor in models for improving the streamflow lead time. With this as the motivation, the current research focuses on increasing the streamflow lead time using predefine climate indices. A data driven model i.e. Support Vector Machine (SVM) based on the statistical learning theory is used to predict annual streamflow volume 3-year in advance. The SVM model is a learning system that uses a hypothesis space of linear functions in a Kernel induced higher dimensional feature space, and is trained with a learning algorithm from the optimization theory. Annual oceanic-atmospheric indices, comprising of Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), El Niño-Southern Oscillations (ENSO), and a new Sea Surface Temperature (SST) data set of "Hondo" Region for a period of 1906-2005 are used to generate annual streamflow volumes. The SVM model is applied to three gages i.e. Cisco, Green River, and Lees Ferry in the Upper Colorado River Basin in the western United States. Based on the performance measures the model shows very good forecasts, and the forecast are in good agreement with measured streamflow volumes. Previous research has identified NAO and ENSO as main drivers for extending streamflow forecast lead-time in the UCRB. Inclusion of "Hondo Region" SST information further improve the model's forecasting ability. The overall results of this study revealed that the annual streamflow of the UCRB is significantly influenced by predefine climate modes and the proposed SVM modeling technique incorporating oceanic-atmospheric oscillations is expected to be useful to water managers in the long-term management of the water resources within the UCRB.

Interactive effects of water diversion and climate change for juvenile chinook salmon in the lemhi river basin (USA.).

PubMed

Walters, Annika W; Bartz, Krista K; McClure, Michelle M

2013-12-01

The combined effects of water diversion and climate change are a major conservation challenge for freshwater ecosystems. In the Lemhi Basin, Idaho (U.S.A.), water diversion causes changes in streamflow, and climate change will further affect streamflow and temperature. Shifts in streamflow and temperature regimes can affect juvenile salmon growth, movement, and survival. We examined the potential effects of water diversion and climate change on juvenile Chinook salmon (Oncorhynchus tshawytscha), a species listed as threatened under the U.S. Endangered Species Act (ESA). To examine the effects for juvenile survival, we created a model relating 19 years of juvenile survival data to streamflow and temperature and found spring streamflow and summer temperature were good predictors of juvenile survival. We used these models to project juvenile survival for 15 diversion and climate-change scenarios. Projected survival was 42-58% lower when streamflows were diverted than when streamflows were undiverted. For diverted streamflows, 2040 climate-change scenarios (ECHO-G and CGCM3.1 T47) resulted in an additional 11-39% decrease in survival. We also created models relating habitat carrying capacity to streamflow and made projections for diversion and climate-change scenarios. Habitat carrying capacity estimated for diverted streamflows was 17-58% lower than for undiverted streamflows. Climate-change scenarios resulted in additional decreases in carrying capacity for the dry (ECHO-G) climate model. Our results indicate climate change will likely pose an additional stressor that should be considered when evaluating the effects of anthropogenic actions on salmon population status. Thus, this type of analysis will be especially important for evaluating effects of specific actions on a particular species. Efectos Interactivos de la Desviación del Agua y el Cambio Climático en Individuos Juveniles de Salmón Chinook en la Cuenca del Río Lemhi (E.U.A.). Conservation Biology © 2013 Society for Conservation Biology No claim to original US government works.

Numerical simulation of groundwater flow for the Yakima River basin aquifer system, Washington

USGS Publications Warehouse

Ely, D.M.; Bachmann, M.P.; Vaccaro, J.J.

2011-01-01

Five applications (scenarios) of the model were completed to obtain a better understanding of the relation between pumpage and surface-water resources and groundwater levels. For the first three scenarios, the calibrated transient model was used to simulate conditions without: (1) pumpage from all hydrogeologic units, (2) pumpage from basalt hydrogeologic units, and (3) exempt-well pumpage. The simulation results indicated potential streamflow capture by the existing pumpage from 1960 through 2001. The quantity of streamflow capture generally was inversely related to the total quantity of pumpage eliminated in the model scenarios. For the fourth scenario, the model simulated 1994 through 2001 under existing conditions with additional pumpage estimated for pending groundwater applications. The differences between the calibrated model streamflow and this scenario indicated additional decreases in streamflow of 91 cubic feet per second in the model domain. Existing conditions representing 1994 through 2001 were projected through 2025 for the fifth scenario and indicated additional streamflow decreases of 38 cubic feet per second and groundwater-level declines.

Skilful seasonal forecasts of streamflow over Europe?

NASA Astrophysics Data System (ADS)

Arnal, Louise; Cloke, Hannah L.; Stephens, Elisabeth; Wetterhall, Fredrik; Prudhomme, Christel; Neumann, Jessica; Krzeminski, Blazej; Pappenberger, Florian

2018-04-01

This paper considers whether there is any added value in using seasonal climate forecasts instead of historical meteorological observations for forecasting streamflow on seasonal timescales over Europe. A Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts (produced by forcing the Lisflood model with the ECMWF System 4 seasonal climate forecasts), benchmarked against the ensemble streamflow prediction (ESP) forecasting approach (produced by forcing the Lisflood model with historical meteorological observations), is undertaken. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only (in terms of hindcast accuracy, sharpness and overall performance). However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to 7 months of lead time, for certain months within a season. In terms of hindcast reliability, the EFAS seasonal streamflow hindcasts are on average less skilful than the ESP for all lead times. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making (measured in terms of the hindcast discrimination for the lower and upper terciles of the simulated streamflow). Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for almost 40 % of Europe. Patterns in the EFAS seasonal streamflow hindcast skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting at the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim of improving climate-model-based seasonal streamflow forecasting.

A hydrogeologic framework for characterizing summer streamflow sensitivity to climate warming in the Pacific Northwest, USA

NASA Astrophysics Data System (ADS)

Safeeq, M.; Grant, G. E.; Lewis, S. L.; Kramer, M. G.; Staab, B.

2014-09-01

Summer streamflows in the Pacific Northwest are largely derived from melting snow and groundwater discharge. As the climate warms, diminishing snowpack and earlier snowmelt will cause reductions in summer streamflow. Most regional-scale assessments of climate change impacts on streamflow use downscaled temperature and precipitation projections from general circulation models (GCMs) coupled with large-scale hydrologic models. Here we develop and apply an analytical hydrogeologic framework for characterizing summer streamflow sensitivity to a change in the timing and magnitude of recharge in a spatially explicit fashion. In particular, we incorporate the role of deep groundwater, which large-scale hydrologic models generally fail to capture, into streamflow sensitivity assessments. We validate our analytical streamflow sensitivities against two empirical measures of sensitivity derived using historical observations of temperature, precipitation, and streamflow from 217 watersheds. In general, empirically and analytically derived streamflow sensitivity values correspond. Although the selected watersheds cover a range of hydrologic regimes (e.g., rain-dominated, mixture of rain and snow, and snow-dominated), sensitivity validation was primarily driven by the snow-dominated watersheds, which are subjected to a wider range of change in recharge timing and magnitude as a result of increased temperature. Overall, two patterns emerge from this analysis: first, areas with high streamflow sensitivity also have higher summer streamflows as compared to low-sensitivity areas. Second, the level of sensitivity and spatial extent of highly sensitive areas diminishes over time as the summer progresses. Results of this analysis point to a robust, practical, and scalable approach that can help assess risk at the landscape scale, complement the downscaling approach, be applied to any climate scenario of interest, and provide a framework to assist land and water managers in adapting to an uncertain and potentially challenging future.

Effects of Urbanization and Climate Change on Hydrological Processes over the San Antonio River Basin, Texas

NASA Astrophysics Data System (ADS)

Zhao, G.; Gao, H.; Cuo, L.

2014-12-01

With the rapid population growth and economic development in the State of Texas, a fast urbanization process has occurred over the past several decades. The direct consequences of the increased impervious area are greater surface runoff and higher flood peaks. Meanwhile, climate change has led to more frequent extreme events. Therefore, a thorough understanding of the hydrological processes under urbanization and climate change is indispensable for sustainable water management. In this investigation, a case study was conducted by applying the Distributed Hydrology Soil Vegetation Model (DHSVM) to the San Antonio River Basin (SARB), Texas. Hosting the seventh largest city in the U.S. (i.e., City of San Antonio), the SARB is vulnerable to both floods and droughts. A set of historical and future land cover maps were assembled to represent the urbanization process. Two forcing datasets were employed to drive the DHSVM model. The first is a long-term observation based dataset (1915-2011), which was used as inputs for calibrating and validating DHSVM, as well as evaluating the urbanization effect. The second is the statistically downscaled climate simulations (1950-2099) from the Coupled Model Intercomparison Project Phase 5 (CMIP5), which were applied for understanding impacts related to climate change. Results show that urbanization exerts a much larger influence on streamflow than climate change does. Under the same observed forcings, annual average streamflow increased from 993.0 cfs (with 1929 land cover) to 1777.7 cfs (with 2011 land cover). As for climate change, results suggest that it will exacerbate the drought severity — with reduced evapotranspiration and soil moisture caused by decreased precipitation. However, the projected future streamflow does not show a clear increasing or decreasing trend. Regarding the combined effect from urbanization and climate change, the results indicate that the seasonal streamflow pattern will be notably changed (i.e., streamflow in October will be significantly increased, which makes it a second flow peak in addition to May). Furthermore, with significantly decreased evapotranspiration and slightly increased soil moisture, more water will be available for streamflow, increasing the possibility of flood risk in the region.

Estimating daily time series of streamflow using hydrological model calibrated based on satellite observations of river water surface width: Toward real world applications.

PubMed

Sun, Wenchao; Ishidaira, Hiroshi; Bastola, Satish; Yu, Jingshan

2015-05-01

Lacking observation data for calibration constrains applications of hydrological models to estimate daily time series of streamflow. Recent improvements in remote sensing enable detection of river water-surface width from satellite observations, making possible the tracking of streamflow from space. In this study, a method calibrating hydrological models using river width derived from remote sensing is demonstrated through application to the ungauged Irrawaddy Basin in Myanmar. Generalized likelihood uncertainty estimation (GLUE) is selected as a tool for automatic calibration and uncertainty analysis. Of 50,000 randomly generated parameter sets, 997 are identified as behavioral, based on comparing model simulation with satellite observations. The uncertainty band of streamflow simulation can span most of 10-year average monthly observed streamflow for moderate and high flow conditions. Nash-Sutcliffe efficiency is 95.7% for the simulated streamflow at the 50% quantile. These results indicate that application to the target basin is generally successful. Beyond evaluating the method in a basin lacking streamflow data, difficulties and possible solutions for applications in the real world are addressed to promote future use of the proposed method in more ungauged basins. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Modeling summer month hydrological drought probabilities in the United States using antecedent flow conditions

USGS Publications Warehouse

Austin, Samuel H.; Nelms, David L.

2017-01-01

Climate change raises concern that risks of hydrological drought may be increasing. We estimate hydrological drought probabilities for rivers and streams in the United States (U.S.) using maximum likelihood logistic regression (MLLR). Streamflow data from winter months are used to estimate the chance of hydrological drought during summer months. Daily streamflow data collected from 9,144 stream gages from January 1, 1884 through January 9, 2014 provide hydrological drought streamflow probabilities for July, August, and September as functions of streamflows during October, November, December, January, and February, estimating outcomes 5-11 months ahead of their occurrence. Few drought prediction methods exploit temporal links among streamflows. We find MLLR modeling of drought streamflow probabilities exploits the explanatory power of temporally linked water flows. MLLR models with strong correct classification rates were produced for streams throughout the U.S. One ad hoc test of correct prediction rates of September 2013 hydrological droughts exceeded 90% correct classification. Some of the best-performing models coincide with areas of high concern including the West, the Midwest, Texas, the Southeast, and the Mid-Atlantic. Using hydrological drought MLLR probability estimates in a water management context can inform understanding of drought streamflow conditions, provide warning of future drought conditions, and aid water management decision making.

Decomposition of Sources of Errors in Seasonal Streamflow Forecasts in a Rainfall-Runoff Dominated Basin

NASA Astrophysics Data System (ADS)

Sinha, T.; Arumugam, S.

2012-12-01

Seasonal streamflow forecasts contingent on climate forecasts can be effectively utilized in updating water management plans and optimize generation of hydroelectric power. Streamflow in the rainfall-runoff dominated basins critically depend on forecasted precipitation in contrast to snow dominated basins, where initial hydrological conditions (IHCs) are more important. Since precipitation forecasts from Atmosphere-Ocean-General Circulation Models are available at coarse scale (~2.8° by 2.8°), spatial and temporal downscaling of such forecasts are required to implement land surface models, which typically runs on finer spatial and temporal scales. Consequently, multiple sources are introduced at various stages in predicting seasonal streamflow. Therefore, in this study, we addresses the following science questions: 1) How do we attribute the errors in monthly streamflow forecasts to various sources - (i) model errors, (ii) spatio-temporal downscaling, (iii) imprecise initial conditions, iv) no forecasts, and (iv) imprecise forecasts? and 2) How does monthly streamflow forecast errors propagate with different lead time over various seasons? In this study, the Variable Infiltration Capacity (VIC) model is calibrated over Apalachicola River at Chattahoochee, FL in the southeastern US and implemented with observed 1/8° daily forcings to estimate reference streamflow during 1981 to 2010. The VIC model is then forced with different schemes under updated IHCs prior to forecasting period to estimate relative mean square errors due to: a) temporally disaggregation, b) spatial downscaling, c) Reverse Ensemble Streamflow Prediction (imprecise IHCs), d) ESP (no forecasts), and e) ECHAM4.5 precipitation forecasts. Finally, error propagation under different schemes are analyzed with different lead time over different seasons.

Streamflow forecasting and data assimilation: bias in precipitation, soil moisture states, and groundwater fluxes.

NASA Astrophysics Data System (ADS)

McCreight, J. L.; Gochis, D. J.; Hoar, T.; Dugger, A. L.; Yu, W.

2014-12-01

Uncertainty in precipitation forcing, soil moisture states, and model groundwater fluxes are first-order sources of error in streamflow forecasting. While near-surface estimates of soil moisture are now available from satellite, very few soil moisture observations below 5 cm depth or groundwater discharge estimates are available for operational forecasting. Radar precipitation estimates are subject to large biases, particularly during extreme events (e.g. Steiner et al., 2010) and their correction is not typically available in real-time. Streamflow data, however, are readily available in near-real-time and can be assimilated operationally to help constrain uncertainty in these uncertain states and improve streamflow forecasts. We examine the ability of streamflow observations to diagnose bias in the three most uncertain variables: precipitation forcing, soil moisture states, and groundwater fluxes. We investigate strategies for their subsequent bias correction. These include spinup and calibration strategies with and without the use of data assimilation and the determination of the proper spinup timescales. Global and spatially distributed multipliers on the uncertain states included in the assimilation state vector (e.g. Seo et al., 2003) will also be evaluated. We examine real cases and observing system simulation experiments for both normal and extreme rainfall events. One of our test cases considers the Colorado Front Range flood of September 2013 where the range of disagreement amongst five precipitation estimates spanned a factor of five with only one exhibiting appreciable positive bias (Gochis et al, submitted). Our experiments are conducted using the WRF-Hydro model with the NoahMP land surface component and the data assimilation research testbed (DART). A variety of ensemble data assimilation approaches (filters) are considered. ReferencesGochis, DJ, et al. "The Great Colorado Flood of September 2013" BAMS (Submitted 4-7-14). Seo, DJ, V Koren, and N Cajina. "Real-time variational assimilation of hydrologic and hydrometeorological data into operational hydrologic forecasting." J Hydromet (2003). Steiner, Matthias, JA Smith, SJ Burges, CV Alonso, and RW Darden. "Effect of bias adjustment and rain gauge data quality control on radar rainfall estimation." WRR (1999).

Assessing the value of variational assimilation of streamflow data into distributed hydrologic models for improved streamflow monitoring and prediction at ungauged and gauged locations in the catchment

NASA Astrophysics Data System (ADS)

Lee, Hak Su; Seo, Dong-Jun; Liu, Yuqiong; McKee, Paul; Corby, Robert

2010-05-01

State updating of distributed hydrologic models via assimilation of streamflow data is subject to "overfitting" because large dimensionality of the state space of the model may render the assimilation problem seriously underdetermined. To examine the issue in the context of operational hydrology, we carried out a set of real-world experiments in which we assimilate streamflow data at interior and/or outlet locations into gridded SAC and kinematic-wave routing models of the U.S. National Weather Service (NWS) Research Distributed Hydrologic Model (RDHM). We used for the experiments nine basins in the southern plains of the U.S. The experiments consist of selectively assimilating streamflow at different gauge locations, outlet and/or interior, and carrying out both dependent and independent validation. To assess the sensitivity of the quality of assimilation-aided streamflow simulation to the reduced dimensionality of the state space, we carried out data assimilation at spatially semi-distributed or lumped scale and by adjusting biases in precipitation and potential evaporation at a 6-hourly or larger scale. In this talk, we present the results and findings.

The effects of changing land cover on streamflow simulation in Puerto Rico

USGS Publications Warehouse

Van Beusekom, Ashley E.; Hay, Lauren E.; Viger, Roland; Gould, William A.; Collazo, Jaime; Henareh Khalyani, Azad

2014-01-01

This study quantitatively explores whether land cover changes have a substantive impact on simulated streamflow within the tropical island setting of Puerto Rico. The Precipitation Runoff Modeling System (PRMS) was used to compare streamflow simulations based on five static parameterizations of land cover with those based on dynamically varying parameters derived from four land cover scenes for the period 1953-2012. The PRMS simulations based on static land cover illustrated consistent differences in simulated streamflow across the island. It was determined that the scale of the analysis makes a difference: large regions with localized areas that have undergone dramatic land cover change may show negligible difference in total streamflow, but streamflow simulations using dynamic land cover parameters for a highly altered subwatershed clearly demonstrate the effects of changing land cover on simulated streamflow. Incorporating dynamic parameterization in these highly altered watersheds can reduce the predictive uncertainty in simulations of streamflow using PRMS. Hydrologic models that do not consider the projected changes in land cover may be inadequate for water resource management planning for future conditions.

The Role of Multimodel Combination in Improving Streamflow Prediction

NASA Astrophysics Data System (ADS)

Arumugam, S.; Li, W.

2008-12-01

Model errors are the inevitable part in any prediction exercise. One approach that is currently gaining attention to reduce model errors is by optimally combining multiple models to develop improved predictions. The rationale behind this approach primarily lies on the premise that optimal weights could be derived for each model so that the developed multimodel predictions will result in improved predictability. In this study, we present a new approach to combine multiple hydrological models by evaluating their predictability contingent on the predictor state. We combine two hydrological models, 'abcd' model and Variable Infiltration Capacity (VIC) model, with each model's parameter being estimated by two different objective functions to develop multimodel streamflow predictions. The performance of multimodel predictions is compared with individual model predictions using correlation, root mean square error and Nash-Sutcliffe coefficient. To quantify precisely under what conditions the multimodel predictions result in improved predictions, we evaluate the proposed algorithm by testing it against streamflow generated from a known model ('abcd' model or VIC model) with errors being homoscedastic or heteroscedastic. Results from the study show that streamflow simulated from individual models performed better than multimodels under almost no model error. Under increased model error, the multimodel consistently performed better than the single model prediction in terms of all performance measures. The study also evaluates the proposed algorithm for streamflow predictions in two humid river basins from NC as well as in two arid basins from Arizona. Through detailed validation in these four sites, the study shows that multimodel approach better predicts the observed streamflow in comparison to the single model predictions.

Future streamflow droughts in glacierized catchments: the impact of dynamic glacier modelling and changing thresholds

NASA Astrophysics Data System (ADS)

Van Tiel, Marit; Van Loon, Anne; Wanders, Niko; Vis, Marc; Teuling, Ryan; Stahl, Kerstin

2017-04-01

In glacierized catchments, snowpack and glaciers function as an important storage of water and hydrographs of highly glacierized catchments in mid- and high latitudes thus show a clear seasonality with low flows in winter and high flows in summer. Due to the ongoing climate change we expect this type of storage capacity to decrease with resultant consequences for the discharge regime. In this study we focus on streamflow droughts, here defined as below average water availability specifically in the high flow season, and which methods are most suitable to characterize future streamflow droughts as regimes change. Two glacierized catchments, Nigardsbreen (Norway) and Wolverine (Alaska), are used as case study and streamflow droughts are compared between two periods, 1975-2004 and 2071-2100. Streamflow is simulated with the HBV light model, calibrated on observed discharge and seasonal glacier mass balances, for two climate change scenarios (RCP 4.5 & RCP 8.5). In studies on future streamflow drought often the same variable threshold of the past has been applied to the future, but in regions where a regime shift is expected this method gives severe "droughts" in the historic high-flow period. We applied the new alternative transient variable threshold, a threshold that adapts to the changing hydrological regime and is thus better able to cope with this issue, but has never been thoroughly tested in glacierized catchments. As the glacier area representation in the hydrological modelling can also influence the modelled discharge and the derived streamflow droughts, we evaluated in this study both the difference between the historical variable threshold (HVT) and transient variable threshold (TVT) and two different glacier area conceptualisations (constant area (C) and dynamical area (D)), resulting in four scenarios: HVT-C, HVT-D, TVT-C and TVT-D. Results show a drastic decrease in the number of droughts in the HVT-C scenario due to increased glacier melt. The deficit volume is expected to be up to almost eight times larger in the future compared to the historical period (Wolverine, +674%) in the HVT-D scenario, caused by the regime shift. Using the TVT the drought characteristics between the C and D scenarios and between future and historic droughts are more similar. However, when using the TVT, causing factors of future droughts, anomalies in temperature and/or precipitation, can be analysed. This study highlights the different conclusions that may be drawn on future streamflow droughts in glacierized catchments depending on methodological choices. They could be used to answer different questions: the TVT for analysing drought processes in the future, the HVT to assess changes between historical and future periods, the constant area conceptualisation to analyse the effect of short term climate variability and the dynamical glacier area to model realistic future discharges in glacierized catchments.

Simulation of daily streamflow for nine river basins in eastern Iowa using the Precipitation-Runoff Modeling System

USGS Publications Warehouse

Haj, Adel E.; Christiansen, Daniel E.; Hutchinson, Kasey J.

2015-10-14

The accuracy of Precipitation-Runoff Modeling System model streamflow estimates of nine river basins in eastern Iowa as compared to measured values at U.S. Geological Survey streamflow-gaging stations varied. The Precipitation-Runoff Modeling System models of nine river basins in eastern Iowa were satisfactory at estimating daily streamflow at 57 of the 79 calibration sites and 13 of the 14 validation sites based on statistical results. Unsatisfactory performance can be contributed to several factors: (1) low flow, no flow, and flashy flow conditions in headwater subbasins having a small drainage area; (2) poor representation of the groundwater and storage components of flow within a basin; (3) lack of accounting for basin withdrawals and water use; and (4) the availability and accuracy of meteorological input data. The Precipitation- Runoff Modeling System models of nine river basins in eastern Iowa will provide water-resource managers with a consistent and documented method for estimating streamflow at ungaged sites and aid in environmental studies, hydraulic design, water management, and water-quality projects.

CEREF: A hybrid data-driven model for forecasting annual streamflow from a socio-hydrological system

NASA Astrophysics Data System (ADS)

Zhang, Hongbo; Singh, Vijay P.; Wang, Bin; Yu, Yinghao

2016-09-01

Hydrological forecasting is complicated by flow regime alterations in a coupled socio-hydrologic system, encountering increasingly non-stationary, nonlinear and irregular changes, which make decision support difficult for future water resources management. Currently, many hybrid data-driven models, based on the decomposition-prediction-reconstruction principle, have been developed to improve the ability to make predictions of annual streamflow. However, there exist many problems that require further investigation, the chief among which is the direction of trend components decomposed from annual streamflow series and is always difficult to ascertain. In this paper, a hybrid data-driven model was proposed to capture this issue, which combined empirical mode decomposition (EMD), radial basis function neural networks (RBFNN), and external forces (EF) variable, also called the CEREF model. The hybrid model employed EMD for decomposition and RBFNN for intrinsic mode function (IMF) forecasting, and determined future trend component directions by regression with EF as basin water demand representing the social component in the socio-hydrologic system. The Wuding River basin was considered for the case study, and two standard statistical measures, root mean squared error (RMSE) and mean absolute error (MAE), were used to evaluate the performance of CEREF model and compare with other models: the autoregressive (AR), RBFNN and EMD-RBFNN. Results indicated that the CEREF model had lower RMSE and MAE statistics, 42.8% and 7.6%, respectively, than did other models, and provided a superior alternative for forecasting annual runoff in the Wuding River basin. Moreover, the CEREF model can enlarge the effective intervals of streamflow forecasting compared to the EMD-RBFNN model by introducing the water demand planned by the government department to improve long-term prediction accuracy. In addition, we considered the high-frequency component, a frequent subject of concern in EMD-based forecasting, and results showed that removing high-frequency component is an effective measure to improve forecasting precision and is suggested for use with the CEREF model for better performance. Finally, the study concluded that the CEREF model can be used to forecast non-stationary annual streamflow change as a co-evolution of hydrologic and social systems with better accuracy. Also, the modification about removing high-frequency can further improve the performance of the CEREF model. It should be noted that the CEREF model is beneficial for data-driven hydrologic forecasting in complex socio-hydrologic systems, and as a simple data-driven socio-hydrologic forecasting model, deserves more attention.

Skills of General Circulation and Earth System Models in reproducing streamflow to the ocean: the case of Congo river

NASA Astrophysics Data System (ADS)

Santini, M.; Caporaso, L.

2017-12-01

Although the importance of water resources in the context of climate change, it is still difficult to correctly simulate the freshwater cycle over the land via General Circulation and Earth System Models (GCMs and ESMs). Existing efforts from the Climate Model Intercomparison Project 5 (CMIP5) were mainly devoted to the validation of atmospheric variables like temperature and precipitation, with low attention to discharge.Here we investigate the present-day performances of GCMs and ESMs participating to CMIP5 in simulating the discharge of the river Congo to the sea thanks to: i) the long-term availability of discharge data for the Kinshasa hydrological station representative of more than 95% of the water flowing in the whole catchment; and ii) the River's still low influence by human intervention, which enables comparison with the (mostly) natural streamflow simulated within CMIP5.Our findings suggest how most of models appear overestimating the streamflow in terms of seasonal cycle, especially in the late winter and spring, while overestimation and variability across models are lower in late summer. Weighted ensemble means are also calculated, based on simulations' performances given by several metrics, showing some improvements of results.Although simulated inter-monthly and inter-annual percent anomalies do not appear significantly different from those in observed data, when translated into well consolidated indicators of drought attributes (frequency, magnitude, timing, duration), usually adopted for more immediate communication to stakeholders and decision makers, such anomalies can be misleading.These inconsistencies produce incorrect assessments towards water management planning and infrastructures (e.g. dams or irrigated areas), especially if models are used instead of measurements, as in case of ungauged basins or for basins with insufficient data, as well as when relying on models for future estimates without a preliminary quantification of model biases.

Simulation of daily streamflow for 12 river basins in western Iowa using the Precipitation-Runoff Modeling System

USGS Publications Warehouse

Christiansen, Daniel E.; Haj, Adel E.; Risley, John C.

2017-10-24

The U.S. Geological Survey, in cooperation with the Iowa Department of Natural Resources, constructed Precipitation-Runoff Modeling System models to estimate daily streamflow for 12 river basins in western Iowa that drain into the Missouri River. The Precipitation-Runoff Modeling System is a deterministic, distributed-parameter, physical-process-based modeling system developed to evaluate the response of streamflow and general drainage basin hydrology to various combinations of climate and land use. Calibration periods for each basin varied depending on the period of record available for daily mean streamflow measurements at U.S. Geological Survey streamflow-gaging stations.A geographic information system tool was used to delineate each basin and estimate initial values for model parameters based on basin physical and geographical features. A U.S. Geological Survey automatic calibration tool that uses a shuffled complex evolution algorithm was used for initial calibration, and then manual modifications were made to parameter values to complete the calibration of each basin model. The main objective of the calibration was to match daily discharge values of simulated streamflow to measured daily discharge values. The Precipitation-Runoff Modeling System model was calibrated at 42 sites located in the 12 river basins in western Iowa.The accuracy of the simulated daily streamflow values at the 42 calibration sites varied by river and by site. The models were satisfactory at 36 of the sites based on statistical results. Unsatisfactory performance at the six other sites can be attributed to several factors: (1) low flow, no flow, and flashy flow conditions in headwater subbasins having a small drainage area; (2) poor representation of the groundwater and storage components of flow within a basin; (3) lack of accounting for basin withdrawals and water use; and (4) limited availability and accuracy of meteorological input data. The Precipitation-Runoff Modeling System models of 12 river basins in western Iowa will provide water-resource managers with a consistent and documented method for estimating streamflow at ungaged sites and aid in environmental studies, hydraulic design, water management, and water-quality projects.

Analysis of Future Streamflow Regimes under Global Change Scenarios in Central Chile for Ecosystem Sustainability

NASA Astrophysics Data System (ADS)

Henriquez Dole, L. E.; Gironas, J. A.; Vicuna, S.

2015-12-01

Given the critical role of the streamflow regime for ecosystem sustainability, modeling long term effects of climate change and land use change on streamflow is important to predict possible impacts in stream ecosystems. Because flow duration curves are largely used to characterize the streamflow regime and define indices of ecosystem health, they were used to represent and analyze in this study the stream regime in the Maipo River Basin in Central Chile. Water and Environmental Assessment and Planning (WEAP) model and the Plant Growth Model (PGM) were used to simulate water distribution, consumption in rural areas and stream flows on a weekly basis. Historical data (1990-2014), future land use scenarios (2030/2050) and climate change scenarios were included in the process. Historical data show a declining trend in flows mainly by unprecedented climatic conditions, increasing interest among users on future streamflow scenarios. In the future, under an expected decline in water availability coupled with changes in crop water demand, water users will be forced to adapt by changing water allocation rules. Such adaptation actions would in turns affect the streamflow regime. Future scenarios for streamflow regime show dramatic changes in water availability and temporal distribution. Annual weekly mean flows can reduce in 19% in the worst scenario and increase in 3.3% in the best of them, and variability in streamflow increases nearly 90% in all scenarios under evaluation. The occurrence of maximum and minimum monthly flows changes, as June instead of July becomes the driest month, and December instead of January becomes the month with maximum flows. Overall, results show that under future scenarios streamflow is affected and altered by water allocation rules to satisfy water demands, and thus decisions will need to consider the streamflow regime (and habitat) in order to be sustainable.

Modeling the effect of glacier recession on streamflow response using a coupled glacio-hydrological model

DOE PAGES

Frans, Chris D.; Clarke, Garry K. C.; Burns, P.; ...

2014-02-27

Here, we describe an integrated spatially distributed hydrologic and glacier dynamic model, and use it to investigate the effect of glacier recession on streamflow variations for the Upper Bow River basin, a tributary of the South Saskatchewan River. Several recent studies have suggested that observed decreases in summer flows in the South Saskatchewan River are partly due to the retreat of glaciers in the river's headwaters. Modeling the effect of glacier changes on streamflow response in river basins such as the South Saskatchewan is complicated due to the inability of most existing physically-based distributed hydrologic models to represent glacier dynamics.more » We compare predicted variations in glacier extent, snow water equivalent and streamflow discharge made with the integrated model with satellite estimates of glacier area and terminus position, observed streamflow and snow water equivalent measurements over the period of 1980 2007. Simulations with the coupled hydrology-glacier model reduce the uncertainty in streamflow predictions. Our results suggested that on average, the glacier melt contribution to the Bow River flow upstream of Lake Louise is about 30% in summer. For warm and dry years, however, the glacier melt contribution can be as large as 50% in August, whereas for cold years, it can be as small as 20% and the timing of glacier melt signature can be delayed by a month.« less

Simulation of the Quantity, Variability, and Timing of Streamflow in the Dennys River Basin, Maine, by Use of a Precipitation-Runoff Watershed Model

USGS Publications Warehouse

Dudley, Robert W.

2008-01-01

The U.S. Geological Survey (USGS), in cooperation with the Maine Department of Marine Resources Bureau of Sea Run Fisheries and Habitat, began a study in 2004 to characterize the quantity, variability, and timing of streamflow in the Dennys River. The study included a synoptic summary of historical streamflow data at a long-term streamflow gage, collecting data from an additional four short-term streamflow gages, and the development and evaluation of a distributed-parameter watershed model for the Dennys River Basin. The watershed model used in this investigation was the USGS Precipitation-Runoff Modeling System (PRMS). The Geographic Information System (GIS) Weasel was used to delineate the Dennys River Basin and subbasins and derive parameters for their physical geographic features. Calibration of the models used in this investigation involved a four-step procedure in which model output was evaluated against four calibration data sets using computed objective functions for solar radiation, potential evapotranspiration, annual and seasonal water budgets, and daily streamflows. The calibration procedure involved thousands of model runs and was carried out using the USGS software application Luca (Let us calibrate). Luca uses the Shuffled Complex Evolution (SCE) global search algorithm to calibrate the model parameters. The SCE method reliably produces satisfactory solutions for large, complex optimization problems. The primary calibration effort went into the Dennys main stem watershed model. Calibrated parameter values obtained for the Dennys main stem model were transferred to the Cathance Stream model, and a similar four-step SCE calibration procedure was performed; this effort was undertaken to determine the potential to transfer modeling information to a nearby basin in the same region. The calibrated Dennys main stem watershed model performed with Nash-Sutcliffe efficiency (NSE) statistic values for the calibration period and evaluation period of 0.79 and 0.76, respectively. The Cathance Stream model had an NSE value of 0.68. The Dennys River Basin models make use of limited streamflow-gaging station data and provide information to characterize subbasin hydrology. The calibrated PRMS watershed models of the Dennys River Basin provide simulated daily streamflow time series from October 1, 1985, through September 30, 2006, for nearly any location within the basin. These models enable natural-resources managers to characterize the timing and quantity of water moving through the basin to support many endeavors including geochemical calculations, water-use assessment, Atlantic salmon population dynamics and migration modeling, habitat modeling and assessment, and other resource-management scenario evaluations. Characterizing streamflow contributions from subbasins in the basin and the relative amounts of surface- and ground-water contributions to streamflow throughout the basin will lead to a better understanding of water quantity and quality in the basin. Improved water-resources information will support Atlantic salmon protection efforts.

Application of the Streamflow Prediction Tool to Estimate Sediment Dredging Volumes in Texas Coastal Waterways

NASA Astrophysics Data System (ADS)

Yeates, E.; Dreaper, G.; Afshari, S.; Tavakoly, A. A.

2017-12-01

Over the past six fiscal years, the United States Army Corps of Engineers (USACE) has contracted an average of about a billion dollars per year for navigation channel dredging. To execute these funds effectively, USACE Districts must determine which navigation channels need to be dredged in a given year. Improving this prioritization process results in more efficient waterway maintenance. This study uses the Streamflow Prediction Tool, a runoff routing model based on global weather forecast ensembles, to estimate dredged volumes. This study establishes regional linear relationships between cumulative flow and dredged volumes over a long-term simulation covering 30 years (1985-2015), using drainage area and shoaling parameters. The study framework integrates the National Hydrography Dataset (NHDPlus Dataset) with parameters from the Corps Shoaling Analysis Tool (CSAT) and dredging record data from USACE District records. Results in the test cases of the Houston Ship Channel and the Sabine and Port Arthur Harbor waterways in Texas indicate positive correlation between the simulated streamflows and actual dredging records.

Simulation of groundwater and surface-water resources and evaluation of water-management alternatives for the Chamokane Creek basin, Stevens County, Washington

USGS Publications Warehouse

Ely, D. Matthew; Kahle, Sue C.

2012-01-01

A three-dimensional, transient numerical model of groundwater and surface-water flow was constructed for Chamokane Creek basin to better understand the groundwater-flow system and its relation to surface-water resources. The model described in this report can be used as a tool by water-management agencies and other stakeholders to quantitatively evaluate the effects of potential increases in groundwater pumping on groundwater and surface-water resources in the basin. The Chamokane Creek model was constructed using the U.S. Geological Survey (USGS) integrated model, GSFLOW. GSFLOW was developed to simulate coupled groundwater and surface-water resources. The model uses 1,000-foot grid cells that subdivide the model domain by 102 rows and 106 columns. Six hydrogeologic units in the model are represented using eight model layers. Daily precipitation and temperature were spatially distributed and subsequent groundwater recharge was computed within GSFLOW. Streamflows in Chamokane Creek and its major tributaries are simulated in the model by routing streamflow within a stream network that is coupled to the groundwater-flow system. Groundwater pumpage and surface-water diversions and returns specified in the model were derived from monthly and annual pumpage values previously estimated from another component of this study and new data reported by study partners. The model simulation period is water years 1980-2010 (October 1, 1979, to September 30, 2010), but the model was calibrated to the transient conditions for water years 1999-2010 (October 1, 1998, to September 30, 2010). Calibration was completed by using traditional trial-and-error methods and automated parameter-estimation techniques. The model adequately reproduces the measured time-series groundwater levels and daily streamflows. At well observation points, the mean difference between simulated and measured hydraulic heads is 7 feet with a root-mean-square error divided by the total difference in water levels of 4.7 percent. Simulated streamflow was compared to measured streamflow at the USGS streamflow-gaging station-Chamokane Creek below Falls, near Long Lake (12433200). Annual differences between measured and simulated streamflow for the site ranged from -63 to 22 percent. Calibrated model output includes a 31-year estimate of monthly water budget components for the hydrologic system. Five model applications (scenarios) were completed to obtain a better understanding of the relation between groundwater pumping and surface-water resources. The calibrated transient model was used to evaluate: (1) the connection between the upper- and middle-basin groundwater systems, (2) the effect of surface-water and groundwater uses in the middle basin, (3) the cumulative impacts of claims registry use and permit-exempt wells on Chamokane Creek streamflow, (4) the frequency of regulation due to impacted streamflow, and (5) the levels of domestic and stockwater use that can be regulated. The simulation results indicated that streamflow is affected by existing groundwater pumping in the upper and middle basins. Simulated water-management scenarios show streamflow increased relative to historical conditions as groundwater and surface-water withdrawals decreased.

Potential effects of climate change on streamflow for seven watersheds in eastern and central Montana

USGS Publications Warehouse

Chase, Katherine J.; Haj, Adel E.; Regan, R. Steven; Viger, Roland J.

2016-01-01

Study regionEastern and central Montana.Study focusFish in Northern Great Plains streams tolerate extreme conditions including heat, cold, floods, and drought; however changes in streamflow associated with long-term climate change may render some prairie streams uninhabitable for current fish species. To better understand future hydrology of these prairie streams, the Precipitation-Runoff Modeling System model and output from the RegCM3 Regional Climate model were used to simulate streamflow for seven watersheds in eastern and central Montana, for a baseline period (water years 1982–1999) and three future periods: water years 2021–2038 (2030 period), 2046–2063 (2055 period), and 2071–2088 (2080 period).New hydrological insights for the regionProjected changes in mean annual and mean monthly streamflow vary by the RegCM3 model selected, by watershed, and by future period. Mean annual streamflows for all future periods are projected to increase (11–21%) for two of the four central Montana watersheds: Middle Musselshell River and Cottonwood Creek. Mean annual streamflows for all future periods are projected to decrease (changes of −24 to −75%) for Redwater River watershed in eastern Montana. Mean annual streamflows are projected to increase slightly (2–15%) for the 2030 period and decrease (changes of −16 to −44%) for the 2080 period for the four remaining watersheds.

Progress report on daily flow-routing simulation for the Carson River, California and Nevada

USGS Publications Warehouse

Hess, G.W.

1996-01-01

A physically based flow-routing model using Hydrological Simulation Program-FORTRAN (HSPF) was constructed for modeling streamflow in the Carson River at daily time intervals as part of the Truckee-Carson Program of the U.S. Geological Survey (USGS). Daily streamflow data for water years 1978-92 for the mainstem river, tributaries, and irrigation ditches from the East Fork Carson River near Markleeville and West Fork Carson River at Woodfords down to the mainstem Carson River at Fort Churchill upstream from Lahontan Reservoir were obtained from several agencies and were compiled into a comprehensive data base. No previous physically based flow-routing model of the Carson River has incorporated multi-agency streamflow data into a single data base and simulated flow at a daily time interval. Where streamflow data were unavailable or incomplete, hydrologic techniques were used to estimate some flows. For modeling purposes, the Carson River was divided into six segments, which correspond to those used in the Alpine Decree that governs water rights along the river. Hydraulic characteristics were defined for 48 individual stream reaches based on cross-sectional survey data obtained from field surveys and previous studies. Simulation results from the model were compared with available observed and estimated streamflow data. Model testing demonstrated that hydraulic characteristics of the Carson River are adequately represented in the models for a range of flow regimes. Differences between simulated and observed streamflow result mostly from inadequate data characterizing inflow and outflow from the river. Because irrigation return flows are largely unknown, irrigation return flow percentages were used as a calibration parameter to minimize differences between observed and simulated streamflows. Observed and simulated streamflow were compared for daily periods for the full modeled length of the Carson River and for two major subreaches modeled with more detailed input data. Hydrographs and statistics presented in this report describe these differences. A sensitivity analysis of four estimated components of the hydrologic system evaluated which components were significant in the model. Estimated ungaged tributary streamflow is not a significant component of the model during low runoff, but is significant during high runoff. The sensitivity analysis indicates that changes in the estimated irrigation diversion and estimated return flow creates a noticeable change in the statistics. The modeling for this study is preliminary. Results of the model are constrained by current availability and accuracy of observed hydrologic data. Several inflows and outflows of the Carson River are not described by time-series data and therefore are not represented in the model.

Time-varying parameter models for catchments with land use change: the importance of model structure

NASA Astrophysics Data System (ADS)

Pathiraja, Sahani; Anghileri, Daniela; Burlando, Paolo; Sharma, Ashish; Marshall, Lucy; Moradkhani, Hamid

2018-05-01

Rapid population and economic growth in Southeast Asia has been accompanied by extensive land use change with consequent impacts on catchment hydrology. Modeling methodologies capable of handling changing land use conditions are therefore becoming ever more important and are receiving increasing attention from hydrologists. A recently developed data-assimilation-based framework that allows model parameters to vary through time in response to signals of change in observations is considered for a medium-sized catchment (2880 km2) in northern Vietnam experiencing substantial but gradual land cover change. We investigate the efficacy of the method as well as the importance of the chosen model structure in ensuring the success of a time-varying parameter method. The method was used with two lumped daily conceptual models (HBV and HyMOD) that gave good-quality streamflow predictions during pre-change conditions. Although both time-varying parameter models gave improved streamflow predictions under changed conditions compared to the time-invariant parameter model, persistent biases for low flows were apparent in the HyMOD case. It was found that HyMOD was not suited to representing the modified baseflow conditions, resulting in extreme and unrealistic time-varying parameter estimates. This work shows that the chosen model can be critical for ensuring the time-varying parameter framework successfully models streamflow under changing land cover conditions. It can also be used to determine whether land cover changes (and not just meteorological factors) contribute to the observed hydrologic changes in retrospective studies where the lack of a paired control catchment precludes such an assessment.

Statistical downscaling for winter streamflow in Douro River

NASA Astrophysics Data System (ADS)

Jesús Esteban Parra, María; Hidalgo Muñoz, José Manuel; García-Valdecasas-Ojeda, Matilde; Raquel Gámiz Fortis, Sonia; Castro Díez, Yolanda

2015-04-01

In this paper we have obtained climate change projections for winter flow of the Douro River in the period 2071-2100 by applying the technique of Partial Regression and various General Circulation Models of CMIP5. The streamflow data base used has been provided by the Center for Studies and Experimentation of Public Works, CEDEX. Series from gauing stations and reservoirs with less than 10% of missing data (filled by regression with well correlated neighboring stations) have been considered. The homogeneity of these series has been evaluated through the Pettit test and degree of human alteration by the Common Area Index. The application of these criteria led to the selection of 42 streamflow time series homogeneously distributed over the basin, covering the period 1951-2011. For these streamflow data, winter seasonal values were obtained by averaging the monthly values from January to March. Statistical downscaling models for the streamflow have been fitted using as predictors the main atmospheric modes of variability over the North Atlantic region. These modes have been obtained using winter sea level pressure data of the NCEP reanalysis, averaged for the months from December to February. Period 1951-1995 was used for calibration, while 1996-2011 period was used in validating the adjusted models. In general, these models are able to reproduce about 70% of the variability of the winter streamflow of the Douro River. Finally, the obtained statistical models have been applied to obtain projections for 2071-2100 period, using outputs from different CMIP5 models under the RPC8.5 scenario. The results for the end of the century show modest declines of winter streamflow in this river for most of the models. Keywords: Statistical downscaling, streamflow, Douro River, climate change. ACKNOWLEDGEMENTS This work has been financed by the projects P11-RNM-7941 (Junta de Andalucía-Spain) and CGL2013-48539-R (MINECO-Spain, FEDER).

Two Topics in Seasonal Streamflow Forecasting: Soil Moisture Initialization Error and Precipitation Downscaling

NASA Technical Reports Server (NTRS)

Koster, Randal; Walker, Greg; Mahanama, Sarith; Reichle, Rolf

2012-01-01

Continental-scale offline simulations with a land surface model are used to address two important issues in the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which the downscaling of seasonal precipitation forecasts, if it could be done accurately, would improve streamflow forecasts. The reduction in streamflow forecast skill (with forecasted streamflow measured against observations) associated with adding noise to a soil moisture field is found to be, to first order, proportional to the average reduction in the accuracy of the soil moisture field itself. This result has implications for streamflow forecast improvement under satellite-based soil moisture measurement programs. In the second and more idealized ("perfect model") analysis, precipitation downscaling is found to have an impact on large-scale streamflow forecasts only if two conditions are met: (i) evaporation variance is significant relative to the precipitation variance, and (ii) the subgrid spatial variance of precipitation is adequately large. In the large-scale continental region studied (the conterminous United States), these two conditions are met in only a somewhat limited area.

Attribution of Observed Streamflow Changes in Key British Columbia Drainage Basins

NASA Astrophysics Data System (ADS)

Najafi, Mohammad Reza; Zwiers, Francis W.; Gillett, Nathan P.

2017-11-01

We study the observed decline in summer streamflow in four key river basins in British Columbia (BC), Canada, using a formal detection and attribution (D&A) analysis procedure. Reconstructed and simulated streamflow is generated using the semidistributed variable infiltration capacity hydrologic model, which is driven by 1/16° gridded observations and downscaled climate model data from the Coupled Model Intercomparison Project phase 5 (CMIP5), respectively. The internal variability of the regional hydrologic components using 5100 years of streamflow was simulated using CMIP5 preindustrial control runs. Results show that the observed changes in summer streamflow are inconsistent with simulations representing the responses to natural forcing factors alone, while the response to anthropogenic and natural forcing factors combined is detected in these changes. A two-signal D&A analysis indicates that the effects of anthropogenic (ANT) forcing factors are discernable from natural forcing in BC, albeit with large uncertainties.

Simulation of streamflow in the Pleasant, Narraguagus, Sheepscot, and Royal Rivers, Maine, using watershed models

USGS Publications Warehouse

Dudley, Robert W.; Nielsen, Martha G.

2011-01-01

The U.S. Geological Survey (USGS) began a study in 2008 to investigate anticipated changes in summer streamflows and stream temperatures in four coastal Maine river basins and the potential effects of those changes on populations of endangered Atlantic salmon. To achieve this purpose, it was necessary to characterize the quantity and timing of streamflow in these rivers by developing and evaluating a distributed-parameter watershed model for a part of each river basin by using the USGS Precipitation-Runoff Modeling System (PRMS). The GIS (geographic information system) Weasel, a USGS software application, was used to delineate the four study basins and their many subbasins, and to derive parameters for their geographic features. The models were calibrated using a four-step optimization procedure in which model output was evaluated against four datasets for calibrating solar radiation, potential evapotranspiration, annual and seasonal water balances, and daily streamflows. The calibration procedure involved thousands of model runs that used the USGS software application Luca (Let us calibrate). Luca uses the Shuffled Complex Evolution (SCE) global search algorithm to calibrate the model parameters. The calibrated watershed models performed satisfactorily, in that Nash-Sutcliffe efficiency (NSE) statistic values for the calibration periods ranged from 0.59 to 0.75 (on a scale of negative infinity to 1) and NSE statistic values for the evaluation periods ranged from 0.55 to 0.73. The calibrated watershed models simulate daily streamflow at many locations in each study basin. These models enable natural resources managers to characterize the timing and amount of streamflow in order to support a variety of water-resources efforts including water-quality calculations, assessments of water use, modeling of population dynamics and migration of Atlantic salmon, modeling and assessment of habitat, and simulation of anticipated changes to streamflow and water temperature resulting from changes forecast for air temperature and precipitation.

Predicting the likelihood of altered streamflows at ungauged rivers across the conterminous United States

USGS Publications Warehouse

Eng, Kenny; Carlisle, Daren M.; Wolock, David M.; Falcone, James A.

2013-01-01

An approach is presented in this study to aid water-resource managers in characterizing streamflow alteration at ungauged rivers. Such approaches can be used to take advantage of the substantial amounts of biological data collected at ungauged rivers to evaluate the potential ecological consequences of altered streamflows. National-scale random forest statistical models are developed to predict the likelihood that ungauged rivers have altered streamflows (relative to expected natural condition) for five hydrologic metrics (HMs) representing different aspects of the streamflow regime. The models use human disturbance variables, such as number of dams and road density, to predict the likelihood of streamflow alteration. For each HM, separate models are derived to predict the likelihood that the observed metric is greater than (‘inflated’) or less than (‘diminished’) natural conditions. The utility of these models is demonstrated by applying them to all river segments in the South Platte River in Colorado, USA, and for all 10-digit hydrologic units in the conterminous United States. In general, the models successfully predicted the likelihood of alteration to the five HMs at the national scale as well as in the South Platte River basin. However, the models predicting the likelihood of diminished HMs consistently outperformed models predicting inflated HMs, possibly because of fewer sites across the conterminous United States where HMs are inflated. The results of these analyses suggest that the primary predictors of altered streamflow regimes across the Nation are (i) the residence time of annual runoff held in storage in reservoirs, (ii) the degree of urbanization measured by road density and (iii) the extent of agricultural land cover in the river basin.

Comparison of Strategies for Climate Change Adaptation of Water Supply and Flood Control Reservoirs

NASA Astrophysics Data System (ADS)

Ng, T. L.; Yang, P.; Bhushan, R.

2016-12-01

With climate change, streamflows are expected to become more fluctuating, with more frequent and intense floods and droughts. This complicates reservoir operation, which is highly sensitive to inflow variability. We make a comparative evaluation of three strategies for adapting reservoirs to climate-induced shifts in streamflow patterns. Specifically, we examine the effectiveness of (i) expanding the capacities of reservoirs by way of new off-stream reservoirs, (ii) introducing wastewater reclamation to augment supplies, and (iii) improving real-time streamflow forecasts for more optimal decision-making. The first two are hard strategies involving major infrastructure modifications, while the third a soft strategy entailing adjusting the system operation. A comprehensive side-by-side comparison of the three strategies is as yet lacking in the literature despite the many past studies investigating the strategies individually. To this end, we developed an adaptive forward-looking linear program that solves to yield the optimal decisions for the current time as a function of an ensemble forecast of future streamflows. Solving the model repeatedly on a rolling basis with regular updating of the streamflow forecast simulates the system behavior over the entire operating horizon. Results are generated for two hypothetical water supply and flood control reservoirs of differing inflows and demands. Preliminary findings suggest that of the three strategies, improving streamflow forecasts to be most effective in mitigating the effects of climate change. We also found that, in average terms, both additional reservoir capacity and wastewater reclamation have potential to reduce water shortage and downstream flooding. However, in the worst case, the potential of the former to reduce water shortage is limited, and similarly so the potential of the latter to reduce downstream flooding.

Measuring real-time streamflow using emerging technologies: Radar, hydroacoustics, and the probability concept

NASA Astrophysics Data System (ADS)

Fulton, John; Ostrowski, Joseph

2008-07-01

SummaryForecasting streamflow during extreme hydrologic events such as floods can be problematic. This is particularly true when flow is unsteady, and river forecasts rely on models that require uniform-flow rating curves to route water from one forecast point to another. As a result, alternative methods for measuring streamflow are needed to properly route flood waves and account for inertial and pressure forces in natural channels dominated by nonuniform-flow conditions such as mild water surface slopes, backwater, tributary inflows, and reservoir operations. The objective of the demonstration was to use emerging technologies to measure instantaneous streamflow in open channels at two existing US Geological Survey streamflow-gaging stations in Pennsylvania. Surface-water and instream-point velocities were measured using hand-held radar and hydroacoustics. Streamflow was computed using the probability concept, which requires velocity data from a single vertical containing the maximum instream velocity. The percent difference in streamflow at the Susquehanna River at Bloomsburg, PA ranged from 0% to 8% with an average difference of 4% and standard deviation of 8.81 m 3/s. The percent difference in streamflow at Chartiers Creek at Carnegie, PA ranged from 0% to 11% with an average difference of 5% and standard deviation of 0.28 m 3/s. New generation equipment is being tested and developed to advance the use of radar-derived surface-water velocity and instantaneous streamflow to facilitate the collection and transmission of real-time streamflow that can be used to parameterize hydraulic routing models.

Measuring real-time streamflow using emerging technologies: Radar, hydroacoustics, and the probability concept

USGS Publications Warehouse

Fulton, J.; Ostrowski, J.

2008-01-01

Forecasting streamflow during extreme hydrologic events such as floods can be problematic. This is particularly true when flow is unsteady, and river forecasts rely on models that require uniform-flow rating curves to route water from one forecast point to another. As a result, alternative methods for measuring streamflow are needed to properly route flood waves and account for inertial and pressure forces in natural channels dominated by nonuniform-flow conditions such as mild water surface slopes, backwater, tributary inflows, and reservoir operations. The objective of the demonstration was to use emerging technologies to measure instantaneous streamflow in open channels at two existing US Geological Survey streamflow-gaging stations in Pennsylvania. Surface-water and instream-point velocities were measured using hand-held radar and hydroacoustics. Streamflow was computed using the probability concept, which requires velocity data from a single vertical containing the maximum instream velocity. The percent difference in streamflow at the Susquehanna River at Bloomsburg, PA ranged from 0% to 8% with an average difference of 4% and standard deviation of 8.81 m3/s. The percent difference in streamflow at Chartiers Creek at Carnegie, PA ranged from 0% to 11% with an average difference of 5% and standard deviation of 0.28 m3/s. New generation equipment is being tested and developed to advance the use of radar-derived surface-water velocity and instantaneous streamflow to facilitate the collection and transmission of real-time streamflow that can be used to parameterize hydraulic routing models.

Soil Moisture Initialization Error and Subgrid Variability of Precipitation in Seasonal Streamflow Forecasting

NASA Technical Reports Server (NTRS)

Koster, Randal D.; Walker, Gregory K.; Mahanama, Sarith P.; Reichle, Rolf H.

2013-01-01

Offline simulations over the conterminous United States (CONUS) with a land surface model are used to address two issues relevant to the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which a realistic increase in the spatial resolution of forecasted precipitation would improve streamflow forecasts. The addition of error to a soil moisture initialization field is found to lead to a nearly proportional reduction in streamflow forecast skill. The linearity of the response allows the determination of a lower bound for the increase in streamflow forecast skill achievable through improved soil moisture estimation, e.g., through satellite-based soil moisture measurements. An increase in the resolution of precipitation is found to have an impact on large-scale streamflow forecasts only when evaporation variance is significant relative to the precipitation variance. This condition is met only in the western half of the CONUS domain. Taken together, the two studies demonstrate the utility of a continental-scale land surface modeling system as a tool for addressing the science of hydrological prediction.

Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China

NASA Astrophysics Data System (ADS)

Fang, G. H.; Yang, J.; Chen, Y. N.; Zammit, C.

2015-06-01

Water resources are essential to the ecosystem and social economy in the desert and oasis of the arid Tarim River basin, northwestern China, and expected to be vulnerable to climate change. It has been demonstrated that regional climate models (RCMs) provide more reliable results for a regional impact study of climate change (e.g., on water resources) than general circulation models (GCMs). However, due to their considerable bias it is still necessary to apply bias correction before they are used for water resources research. In this paper, after a sensitivity analysis on input meteorological variables based on the Sobol' method, we compared five precipitation correction methods and three temperature correction methods in downscaling RCM simulations applied over the Kaidu River basin, one of the headwaters of the Tarim River basin. Precipitation correction methods applied include linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), distribution mapping (DM) and quantile mapping (QM), while temperature correction methods are LS, variance scaling (VARI) and DM. The corrected precipitation and temperature were compared to the observed meteorological data, prior to being used as meteorological inputs of a distributed hydrologic model to study their impacts on streamflow. The results show (1) streamflows are sensitive to precipitation, temperature and solar radiation but not to relative humidity and wind speed; (2) raw RCM simulations are heavily biased from observed meteorological data, and its use for streamflow simulations results in large biases from observed streamflow, and all bias correction methods effectively improved these simulations; (3) for precipitation, PT and QM methods performed equally best in correcting the frequency-based indices (e.g., standard deviation, percentile values) while the LOCI method performed best in terms of the time-series-based indices (e.g., Nash-Sutcliffe coefficient, R2); (4) for temperature, all correction methods performed equally well in correcting raw temperature; and (5) for simulated streamflow, precipitation correction methods have more significant influence than temperature correction methods and the performances of streamflow simulations are consistent with those of corrected precipitation; i.e., the PT and QM methods performed equally best in correcting flow duration curve and peak flow while the LOCI method performed best in terms of the time-series-based indices. The case study is for an arid area in China based on a specific RCM and hydrologic model, but the methodology and some results can be applied to other areas and models.

A Hydrological Modeling Framework for Flood Risk Assessment for Japan

NASA Astrophysics Data System (ADS)

Ashouri, H.; Chinnayakanahalli, K.; Chowdhary, H.; Sen Gupta, A.

2016-12-01

Flooding has been the most frequent natural disaster that claims lives and imposes significant economic losses to human societies worldwide. Japan, with an annual rainfall of up to approximately 4000 mm is extremely vulnerable to flooding. The focus of this research is to develop a macroscale hydrologic model for simulating flooding toward an improved understanding and assessment of flood risk across Japan. The framework employs a conceptual hydrological model, known as the Probability Distributed Model (PDM), as well as the Muskingum-Cunge flood routing procedure for simulating streamflow. In addition, a Temperature-Index model is incorporated to account for snowmelt and its contribution to streamflow. For an efficient calibration of the model, in terms of computational timing and convergence of the parameters, a set of A Priori parameters is obtained based on the relationships between the model parameters and the physical properties of watersheds. In this regard, we have implemented a particle tracking algorithm and a statistical model which use high resolution Digital Terrain Models to estimate different time related parameters of the model such as time to peak of the unit hydrograph. In addition, global soil moisture and depth data are used to generate A Priori estimation of maximum soil moisture capacity, an important parameter of the PDM model. Once the model is calibrated, its performance is examined during the Typhoon Nabi which struck Japan in September 2005 and caused severe flooding throughout the country. The model is also validated for the extreme precipitation event in 2012 which affected Kyushu. In both cases, quantitative measures show that simulated streamflow depicts good agreement with gauge-based observations. The model is employed to simulate thousands of possible flood events for the entire Japan which makes a basis for a comprehensive flood risk assessment and loss estimation for the flood insurance industry.

Documentation of a computer program to simulate stream-aquifer relations using a modular, finite-difference, ground-water flow model

USGS Publications Warehouse

Prudic, David E.

1989-01-01

Computer models are widely used to simulate groundwater flow for evaluating and managing the groundwater resource of many aquifers, but few are designed to also account for surface flow in streams. A computer program was written for use in the US Geological Survey modular finite difference groundwater flow model to account for the amount of flow in streams and to simulate the interaction between surface streams and groundwater. The new program is called the Streamflow-Routing Package. The Streamflow-Routing Package is not a true surface water flow model, but rather is an accounting program that tracks the flow in one or more streams which interact with groundwater. The program limits the amount of groundwater recharge to the available streamflow. It permits two or more streams to merge into one with flow in the merged stream equal to the sum of the tributary flows. The program also permits diversions from streams. The groundwater flow model with the Streamflow-Routing Package has an advantage over the analytical solution in simulating the interaction between aquifer and stream because it can be used to simulate complex systems that cannot be readily solved analytically. The Streamflow-Routing Package does not include a time function for streamflow but rather streamflow entering the modeled area is assumed to be instantly available to downstream reaches during each time period. This assumption is generally reasonable because of the relatively slow rate of groundwater flow. Another assumption is that leakage between streams and aquifers is instantaneous. This assumption may not be reasonable if the streams and aquifers are separated by a thick unsaturated zone. Documentation of the Streamflow-Routing Package includes data input instructions; flow charts, narratives, and listings of the computer program for each of four modules; and input data sets and printed results for two test problems, and one example problem. (Lantz-PTT)

Effect of Streamflow Forecast Uncertainty on Real-Time Reservoir Operation

NASA Astrophysics Data System (ADS)

Zhao, T.; Cai, X.; Yang, D.

2010-12-01

Various hydrological forecast products have been applied to real-time reservoir operation, including deterministic streamflow forecast (DSF), DSF-based probabilistic streamflow forecast (DPSF), and ensemble streamflow forecast (ESF), which represent forecast uncertainty in the form of deterministic forecast error, deterministic forecast error-based uncertainty distribution, and ensemble forecast errors, respectively. Compared to previous studies that treat these forecast products as ad hoc inputs for reservoir operation models, this paper attempts to model the uncertainties involved in the various forecast products and explores their effect on real-time reservoir operation decisions. In hydrology, there are various indices reflecting the magnitude of streamflow forecast uncertainty; meanwhile, few models illustrate the forecast uncertainty evolution process. This research introduces Martingale Model of Forecast Evolution (MMFE) from supply chain management and justifies its assumptions for quantifying the evolution of uncertainty in streamflow forecast as time progresses. Based on MMFE, this research simulates the evolution of forecast uncertainty in DSF, DPSF, and ESF, and applies the reservoir operation models (dynamic programming, DP; stochastic dynamic programming, SDP; and standard operation policy, SOP) to assess the effect of different forms of forecast uncertainty on real-time reservoir operation. Through a hypothetical single-objective real-time reservoir operation model, the results illustrate that forecast uncertainty exerts significant effects. Reservoir operation efficiency, as measured by a utility function, decreases as the forecast uncertainty increases. Meanwhile, these effects also depend on the type of forecast product being used. In general, the utility of reservoir operation with ESF is nearly as high as the utility obtained with a perfect forecast; the utilities of DSF and DPSF are similar to each other but not as efficient as ESF. Moreover, streamflow variability and reservoir capacity can change the magnitude of the effects of forecast uncertainty, but not the relative merit of DSF, DPSF, and ESF. Schematic diagram of the increase in forecast uncertainty with forecast lead-time and the dynamic updating property of real-time streamflow forecast

Model simulation of the Manasquan water-supply system in Monmouth County, New Jersey

USGS Publications Warehouse

Chang, Ming; Tasker, Gary D.; Nieswand, Steven

2001-01-01

Model simulation of the Manasquan Water Supply System in Monmouth County, New Jersey, was completed using historic hydrologic data to evaluate the effects of operational and withdrawal alternatives on the Manasquan reservoir and pumping system. Changes in the system operations can be simulated with the model using precipitation forecasts. The Manasquan Reservoir system model operates by using daily streamflow values, which were reconstructed from historical U.S. Geological Survey streamflow-gaging station records. The model is able to run in two modes--General Risk analysis Model (GRAM) and Position Analysis Model (POSA). The GRAM simulation procedure uses reconstructed historical streamflow records to provide probability estimates of certain events, such as reservoir storage levels declining below a specific level, when given an assumed set of operating rules and withdrawal rates. POSA can be used to forecast the likelihood of specified outcomes, such as streamflows falling below statutory passing flows, associated with a specific working plan for the water-supply system over a period of months. The user can manipulate the model and generate graphs and tables of streamflows and storage, for example. This model can be used as a management tool to facilitate the development of drought warning and drought emergency rule curves and safe yield values for the water-supply system.

Streamflow changes in the Sierra Nevada, California, simulated using a statistically downscaled general circulation model scenario of climate change

USGS Publications Warehouse

Wilby, Robert L.; Dettinger, Michael D.

2000-01-01

Simulations of future climate using general circulation models (GCMs) suggest that rising concentrations of greenhouse gases may have significant consequences for the global climate. Of less certainty is the extent to which regional scale (i.e., sub-GCM grid) environmental processes will be affected. In this chapter, a range of downscaling techniques are critiqued. Then a relatively simple (yet robust) statistical downscaling technique and its use in the modelling of future runoff scenarios for three river basins in the Sierra Nevada, California, is described. This region was selected because GCM experiments driven by combined greenhouse-gas and sulphate-aerosol forcings consistently show major changes in the hydro-climate of the southwest United States by the end of the 21st century. The regression-based downscaling method was used to simulate daily rainfall and temperature series for streamflow modelling in three Californian river basins under current-and future-climate conditions. The downscaling involved just three predictor variables (specific humidity, zonal velocity component of airflow, and 500 hPa geopotential heights) supplied by the U.K. Meteorological Office couple ocean-atmosphere model (HadCM2) for the grid point nearest the target basins. When evaluated using independent data, the model showed reasonable skill at reproducing observed area-average precipitation, temperature, and concomitant streamflow variations. Overall, the downscaled data resulted in slight underestimates of mean annual streamflow due to underestimates of precipitation in spring and positive temperature biases in winter. Differences in the skill of simulated streamflows amongst the three basins were attributed to the smoothing effects of snowpack on streamflow responses to climate forcing. The Merced and American River basins drain the western, windward slope of the Sierra Nevada and are snowmelt dominated, whereas the Carson River drains the eastern, leeward slope and is a mix of rainfall runoff and snowmelt runoff. Simulated streamflow in the American River responds rapidly and sensitively to daily-scale temperature and precipitation fluctuations and errors; in the Merced and Carson Rivers, the response to the same short-term influences is much less. Consequently, the skill of simulated flows was significantly lower in the American River model than in the Carson and Merced. The physiography of the three basins also accounts for differences in their sensitivities to future climate change. Increases in winter precipitation exceeding +100% coupled with mean temperature rises greater than +2°C result in increased winter streamflows in all three basins. In the Merced and Carson basins, these streamflow increases reflect large changes in winter snowpack, whereas the streamflow changes in the lower elevation American basin are driven primarily by rainfall runoff. Furthermore, reductions in winter snowpack in the American River basin, owing to less precipitation falling as snow and earlier melting of snow at middle elevations, lead to less spring and summer streamflow. Taken collectively, the downscaling results suggest significant changes to both the timing and magnitude of streamflows in the Sierra Nevada by the end of the 21st Century. In the higher elevation basins, the HadCM2 scenario implies more annual streamflow and more streamflow during the spring and summer months that are critical for water-resources management in California. Depending on the relative significance of rainfall runoff and snowmelt, each basin responds in its own way to regional climate forcing. Generally, then, climate scenarios need to be specified — by whatever means — with sufficient temporal and spatial resolution to capture subtle orographic influences if projections of climate-change responses are to be useful and reproducible.

Does model structure limit the use of satellite data as hydrologic forcing for distributed operational models?

NASA Astrophysics Data System (ADS)

Bowman, A. L.; Franz, K.; Hogue, T. S.

2015-12-01

We are investigating the implications for use of satellite data in operational streamflow prediction. Specifically, the consequence of potential hydrologic model structure deficiencies on the ability to achieve improved forecast accuracy through the use of satellite data. We want to understand why advanced data do not lead to improved streamflow simulations by exploring how various fluxes and states differ among models of increasing complexity. In a series of prior studies, we investigated the use of a daily satellite-derived potential evapotranspiration (PET) estimate as input to the National Weather Service (NWS) streamflow forecast models for watersheds in the Upper Mississippi and Red river basins. Although the spatial PET product appears to represent the day-to-day variability in PET more realistically than current climatological methods used by the NWS, the impact of the satellite data on streamflow simulations results in slightly poorer model efficiency overall. Analysis of the model states indicates the model progresses differently between simulations with baseline PET and the satellite-derived PET input, though variation in streamflow simulations overall is negligible. For instance, the upper zone states, responsible for the high flows of a hydrograph, show a profound difference, while simulation of the peak flows tend to show little variation in the timing and magnitude. Using the spatial PET input, the lower zone states show improvement with simulating the recession limb and baseflow portion of the hydrograph. We anticipate that through a better understanding of the relationship between model structure, model states, and simulated streamflow we will be able to diagnose why simulations of discharge from the forecast model have failed to improve when provided seemingly more representative input data. Identifying model limitations are critical to demonstrating the full benefit of a satellite data for operational use.

Guidelines and Procedures for Computing Time-Series Suspended-Sediment Concentrations and Loads from In-Stream Turbidity-Sensor and Streamflow Data

USGS Publications Warehouse

Rasmussen, Patrick P.; Gray, John R.; Glysson, G. Douglas; Ziegler, Andrew C.

2009-01-01

In-stream continuous turbidity and streamflow data, calibrated with measured suspended-sediment concentration data, can be used to compute a time series of suspended-sediment concentration and load at a stream site. Development of a simple linear (ordinary least squares) regression model for computing suspended-sediment concentrations from instantaneous turbidity data is the first step in the computation process. If the model standard percentage error (MSPE) of the simple linear regression model meets a minimum criterion, this model should be used to compute a time series of suspended-sediment concentrations. Otherwise, a multiple linear regression model using paired instantaneous turbidity and streamflow data is developed and compared to the simple regression model. If the inclusion of the streamflow variable proves to be statistically significant and the uncertainty associated with the multiple regression model results in an improvement over that for the simple linear model, the turbidity-streamflow multiple linear regression model should be used to compute a suspended-sediment concentration time series. The computed concentration time series is subsequently used with its paired streamflow time series to compute suspended-sediment loads by standard U.S. Geological Survey techniques. Once an acceptable regression model is developed, it can be used to compute suspended-sediment concentration beyond the period of record used in model development with proper ongoing collection and analysis of calibration samples. Regression models to compute suspended-sediment concentrations are generally site specific and should never be considered static, but they represent a set period in a continually dynamic system in which additional data will help verify any change in sediment load, type, and source.

Simulation of streamflow in small drainage basins in the southern Yampa River basin, Colorado

USGS Publications Warehouse

Parker, R.S.; Norris, J.M.

1989-01-01

Coal mining operations in northwestern Colorado commonly are located in areas that have minimal available water-resource information. Drainage-basin models can be a method for extending water-resource information to include periods for which there are no records or to transfer the information to areas that have no streamflow-gaging stations. To evaluate the magnitude and variability of the components of the water balance in the small drainage basins monitored, and to provide some method for transfer of hydrologic data, the U.S. Geological Survey 's Precipitation-Runoff Modeling System was used for small drainage basins in the southern Yampa River basin to simulate daily mean streamflow using daily precipitation and air-temperature data. The study area was divided into three hydrologic regions, and in each of these regions, three drainage basins were monitored. Two of the drainage basins in each region were used to calibrate the Precipitation-Runoff Modeling System. The model was not calibrated for the third drainage basin in each region; instead, parameter values were transferred from the model that was calibrated for the two drainage basins. For all of the drainage basins except one, period of record used for calibration and verification included water years 1976-81. Simulated annual volumes of streamflow for drainage basins used in calibration compared well with observed values; individual hydrographs indicated timing differences between the observed and simulated daily mean streamflow. Observed and simulated annual average streamflows compared well for the periods of record, but values of simulated high and low streamflows were different than observed values. Similar results were obtained when calibrated model parameter values were transferred to drainage basins that were uncalibrated. (USGS)

Predicting streamflow response to fire-induced landcover change: implications of parameter uncertainty in the MIKE SHE model.

PubMed

McMichael, Christine E; Hope, Allen S

2007-08-01

Fire is a primary agent of landcover transformation in California semi-arid shrubland watersheds, however few studies have examined the impacts of fire and post-fire succession on streamflow dynamics in these basins. While it may seem intuitive that larger fires will have a greater impact on streamflow response than smaller fires in these watersheds, the nature of these relationships has not been determined. The effects of fire size on seasonal and annual streamflow responses were investigated for a medium-sized basin in central California using a modified version of the MIKE SHE model which had been previously calibrated and tested for this watershed using the Generalized Likelihood Uncertainty Estimation methodology. Model simulations were made for two contrasting periods, wet and dry, in order to assess whether fire size effects varied with weather regime. Results indicated that seasonal and annual streamflow response increased nearly linearly with fire size in a given year under both regimes. Annual flow response was generally higher in wetter years for both weather regimes, however a clear trend was confounded by the effect of stand age. These results expand our understanding of the effects of fire size on hydrologic response in chaparral watersheds, but it is important to note that the majority of model predictions were largely indistinguishable from the predictive uncertainty associated with the calibrated model - a key finding that highlights the importance of analyzing hydrologic predictions for altered landcover conditions in the context of model uncertainty. Future work is needed to examine how alternative decisions (e.g., different likelihood measures) may influence GLUE-based MIKE SHE streamflow predictions following different size fires, and how the effect of fire size on streamflow varies with other factors such as fire location.

2011 Souris River flood—Will it happen again?

USGS Publications Warehouse

Nustad, Rochelle A.; Kolars, Kelsey A.; Vecchia, Aldo V.; Ryberg, Karen R.

2016-09-29

The Souris River Basin is a 61,000 square kilometer basin in the provinces of Saskatchewan and Manitoba and the state of North Dakota. Record setting rains in May and June of 2011 led to record flooding with peak annual streamflow values (762 cubic meters per second [m3/s]) more than twice that of any previously recorded peak streamflow and more than five times the estimated 100 year postregulation streamflow (142 m3/s) at the U.S. Geological Survey (USGS) streamflow-gaging station above Minot, North Dakota. Upstream from Minot, N. Dak., the Souris River is regulated by three reservoirs in Saskatchewan (Rafferty, Boundary, and Alameda) and Lake Darling in North Dakota. During the 2011 flood, the city of Minot, N. Dak., experienced devastating damages with more than 4,000 homes flooded and 11,000 evacuated. As a result, the Souris River Basin Task Force recommended the U.S. Geological Survey (in cooperation with the North Dakota State Water Commission) develop a model for estimating the probabilities of future flooding and drought. The model that was developed took on four parts: (1) looking at past climate, (2) predicting future climate, (3) developing a streamflow model in response to certain climatic variables, and (4) combining future climate estimates with the streamflow model to predict future streamflow events. By taking into consideration historical climate record and trends in basin response to various climatic conditions, it was determined flood risk will remain high in the Souris River Basin until the wet climate state ends.

Simulation of groundwater conditions and streamflow depletion to evaluate water availability in a Freeport, Maine, watershed

USGS Publications Warehouse

Nielsen, Martha G.; Locke, Daniel B.

2012-01-01

In order to evaluate water availability in the State of Maine, the U.S. Geological Survey (USGS) and the Maine Geological Survey began a cooperative investigation to provide the first rigorous evaluation of watersheds deemed "at risk" because of the combination of instream flow requirements and proportionally large water withdrawals. The study area for this investigation includes the Harvey and Merrill Brook watersheds and the Freeport aquifer in the towns of Freeport, Pownal, and Yarmouth, Maine. A numerical groundwater- flow model was used to evaluate groundwater withdrawals, groundwater-surface-water interactions, and the effect of water-management practices on streamflow. The water budget illustrates the effect that groundwater withdrawals have on streamflow and the movement of water within the system. Streamflow measurements were made following standard USGS techniques, from May through September 2009 at one site in the Merrill Brook watershed and four sites in the Harvey Brook watershed. A record-extension technique was applied to estimate long-term monthly streamflows at each of the five sites. The conceptual model of the groundwater system consists of a deep, confined aquifer (the Freeport aquifer) in a buried valley that trends through the middle of the study area, covered by a discontinuous confining unit, and topped by a thin upper saturated zone that is a mixture of sandy units, till, and weathered clay. Harvey and Merrill Brooks flow southward through the study area, and receive groundwater discharge from the upper saturated zone and from the deep aquifer through previously unknown discontinuities in the confining unit. The Freeport aquifer gets most of its recharge from local seepage around the edges of the confining unit, the remainder is received as inflow from the north within the buried valley. Groundwater withdrawals from the Freeport aquifer in the study area were obtained from the local water utility and estimated for other categories. Overall, the public-supply withdrawals (105.5 million gallons per year (Mgal/yr)) were much greater than those for any other category, being almost 7 times greater than all domestic well withdrawals (15.3 Mgal/yr). Industrial withdrawals in the study area (2.0 Mgal/yr) are mostly by a company that withdraws from an aquifer at the edge of the Merrill Brook watershed. Commercial withdrawals are very small (1.0 Mgal/yr), and no irrigation or other agricultural withdrawals were identified in this study area. A three-dimensional, steady-state groundwater-flow model was developed to evaluate stream-aquifer interactions and streamflow depletion from pumping, to help refine the conceptual model, and to predict changes in streamflow resulting from changes in pumping and recharge. Groundwater levels and flow in the Freeport aquifer study area were simulated with the three-dimensional, finite-difference groundwater-flow modeling code, MODFLOW-2005. Study area hydrology was simulated with a 3-layer model, under steady-state conditions. The groundwater model was used to evaluate changes that could occur in the water budgets of three parts of the local hydrologic system (the Harvey Brook watershed, the Merrill Brook watershed, and the buried aquifer from which pumping occurs) under several different climatic and pumping scenarios. The scenarios were (1) no pumping well withdrawals; (2) current (2009) pumping, but simulated drought conditions (20-percent reduction in recharge); (3) current (2009) recharge, but a 50-percent increase in pumping well withdrawals for public supply; and (4) drought conditions and increased pumping combined. In simulated drought situations, the overall recharge to the buried valley is about 15 percent less and the total amount of streamflow in the model area is reduced by about 19 percent. Without pumping, infiltration to the buried valley aquifer around the confining unit decreased by a small amount (0.05 million gallons per day (Mgal/d)), and discharge to the streams increased by about 8 percent (0.3 Mgal/d). A 50-percent increase in pumping resulted in a simulated decrease in streamflow discharge of about 4 percent (0.14 Mgal/d). Streamflow depletion in Harvey Brook was evaluated by use of the numerical groundwater-flow model and an analytical model. The analytical model estimated negligible depletion from Harvey Brook under current (2009) pumping conditions, whereas the numerical model estimated that flow to Harvey Brook decreased 0.38 cubic feet per second (ft3/s) because of the pumping well withdrawals. A sensitivity analysis of the analytical model method showed that conducting a cursory evaluation using an analytical model of streamflow depletion using available information may result in a very wide range in results, depending on how well the hydraulic conductivity variables and aquifer geometry of the system are known, and how well the aquifer fits the assumptions of the model. Using the analytical model to evaluate the streamflow depletion with an incomplete understanding of the hydrologic system gave results that seem unlikely to reflect actual streamflow depletion in the Freeport aquifer study area. In contrast, the groundwater-flow model was a more robust method of evaluating the amount of streamflow depletion that results from withdrawals in the Freeport aquifer, and could be used to evaluate streamflow depletion in both streams. Simulations of streamflow without pumping for each measurement site were compared to the calibratedmodel streamflow (with pumping), the difference in the total being streamflow depletion. Simulations without pumping resulted in a simulated increase in the steady-state flow rate of 0.38 ft3/s in Harvey Brook and 0.01 ft3/s in Merrill Brook. This translates into a streamflow-depletion amount equal to about 8.5 percent of the steady-state base flow in Harvey Brook, and an unmeasurable amount of depletion in Merrill Brook. If pumping was increased by 50 percent and recharge reduced by 20 percent, the amount of streamflow depletion in Harvey Brook could reach 1.41 ft3/s.

Precipitation-runoff and streamflow-routing models for the Willamette River basin, Oregon

USGS Publications Warehouse

Laenen, Antonius; Risley, John C.

1997-01-01

With an input of current streamflow, precipitation, and air temperature data the combined runoff and routing models can provide current estimates of streamflow at almost 500 locations on the main stem and major tributaries of the Willamette River with a high degree of accuracy. Relative contributions of surface runoff, subsurface flow, and ground-water flow can be assessed for 1 to 10 HRU classes in each of 253 subbasins identified for precipitation-runoff modeling. Model outputs were used with a water-quality model to simulate the movement of dye in the Pudding River as an example

Global Flood Response Using Satellite Rainfall Information Coupled with Land Surface and Routing Models

NASA Astrophysics Data System (ADS)

Adler, R. F.; Wu, H.

2016-12-01

The Global Flood Monitoring System (GFMS) (http://flood.umd.edu) has been developed and used in recent years to provide real-time flood detection, streamflow estimates and inundation calculations for most of the globe. The GFMS is driven by satellite-based precipitation, with the accuracy of the flood estimates being primarily dependent on the accuracy of the precipitation analyses and the land surface and routing models used. The routing calculations are done at both 12 km and 1 km resolution. Users of GFMS results include international and national flood response organizations. The devastating floods in October 2015 in South Carolina are analyzed indicating that the GFMS estimated streamflow is accurate and useful indicating significant flooding in the upstream basins. Further downstream the GFMS streamflow underestimates due to the presence of dams which are not accounted for in GFMS. Other examples are given for Yemen and Somalia and for Sri Lanka and southern India. A forecast flood event associated with a typhoon hitting Taiwan is also examined. One-kilometer resolution inundation mapping from GFMS holds the promise of highly useful information for flood disaster response. The algorithm is briefly described and examples are shown for recent cases where inundation estimates available from optical and Synthetic Aperture Radar (SAR) satellite sensors are available. For a case of significant flooding in Texas in May and June along the Brazos River the GFMS calculated streamflow compares favorably with the observed. Available Landsat-based (May 28) and MODIS-based (June 2) inundation analyses from U. of Colorado shows generally good agreement with the GFMS inundation calculation in most of the area where skies were clear and the optical techniques could be applied. The GFMS provides very useful disaster response information on a timely basis. However, there is still significant room for improvement, including improved precipitation information from NASA's Global Precipitation Measurement (GPM) mission, inclusion of dam algorithms in the routing model and integration with or assimilation of observed flood extent from satellite optical and SAR sensors.

Performance assessment of a Bayesian Forecasting System (BFS) for real-time flood forecasting

NASA Astrophysics Data System (ADS)

Biondi, D.; De Luca, D. L.

2013-02-01

SummaryThe paper evaluates, for a number of flood events, the performance of a Bayesian Forecasting System (BFS), with the aim of evaluating total uncertainty in real-time flood forecasting. The predictive uncertainty of future streamflow is estimated through the Bayesian integration of two separate processors. The former evaluates the propagation of input uncertainty on simulated river discharge, the latter computes the hydrological uncertainty of actual river discharge associated with all other possible sources of error. A stochastic model and a distributed rainfall-runoff model were assumed, respectively, for rainfall and hydrological response simulations. A case study was carried out for a small basin in the Calabria region (southern Italy). The performance assessment of the BFS was performed with adequate verification tools suited for probabilistic forecasts of continuous variables such as streamflow. Graphical tools and scalar metrics were used to evaluate several attributes of the forecast quality of the entire time-varying predictive distributions: calibration, sharpness, accuracy, and continuous ranked probability score (CRPS). Besides the overall system, which incorporates both sources of uncertainty, other hypotheses resulting from the BFS properties were examined, corresponding to (i) a perfect hydrological model; (ii) a non-informative rainfall forecast for predicting streamflow; and (iii) a perfect input forecast. The results emphasize the importance of using different diagnostic approaches to perform comprehensive analyses of predictive distributions, to arrive at a multifaceted view of the attributes of the prediction. For the case study, the selected criteria revealed the interaction of the different sources of error, in particular the crucial role of the hydrological uncertainty processor when compensating, at the cost of wider forecast intervals, for the unreliable and biased predictive distribution resulting from the Precipitation Uncertainty Processor.

The Application of Censored Regression Models in Low Streamflow Analyses

NASA Astrophysics Data System (ADS)

Kroll, C.; Luz, J.

2003-12-01

Estimation of low streamflow statistics at gauged and ungauged river sites is often a daunting task. This process is further confounded by the presence of intermittent streamflows, where streamflow is sometimes reported as zero, within a region. Streamflows recorded as zero may be zero, or may be less than the measurement detection limit. Such data is often referred to as censored data. Numerous methods have been developed to characterize intermittent streamflow series. Logit regression has been proposed to develop regional models of the probability annual lowflows series (such as 7-day lowflows) are zero. In addition, Tobit regression, a method of regression that allows for censored dependent variables, has been proposed for lowflow regional regression models in regions where the lowflow statistic of interest estimated as zero at some sites in the region. While these methods have been proposed, their use in practice has been limited. Here a delete-one jackknife simulation is presented to examine the performance of Logit and Tobit models of 7-day annual minimum flows in 6 USGS water resource regions in the United States. For the Logit model, an assessment is made of whether sites are correctly classified as having at least 10% of 7-day annual lowflows equal to zero. In such a situation, the 7-day, 10-year lowflow (Q710), a commonly employed low streamflow statistic, would be reported as zero. For the Tobit model, a comparison is made between results from the Tobit model, and from performing either ordinary least squares (OLS) or principal component regression (PCR) after the zero sites are dropped from the analysis. Initial results for the Logit model indicate this method to have a high probability of correctly classifying sites into groups with Q710s as zero and non-zero. Initial results also indicate the Tobit model produces better results than PCR and OLS when more than 5% of the sites in the region have Q710 values calculated as zero.

Temporal rainfall estimation using input data reduction and model inversion

NASA Astrophysics Data System (ADS)

Wright, A. J.; Vrugt, J. A.; Walker, J. P.; Pauwels, V. R. N.

2016-12-01

Floods are devastating natural hazards. To provide accurate, precise and timely flood forecasts there is a need to understand the uncertainties associated with temporal rainfall and model parameters. The estimation of temporal rainfall and model parameter distributions from streamflow observations in complex dynamic catchments adds skill to current areal rainfall estimation methods, allows for the uncertainty of rainfall input to be considered when estimating model parameters and provides the ability to estimate rainfall from poorly gauged catchments. Current methods to estimate temporal rainfall distributions from streamflow are unable to adequately explain and invert complex non-linear hydrologic systems. This study uses the Discrete Wavelet Transform (DWT) to reduce rainfall dimensionality for the catchment of Warwick, Queensland, Australia. The reduction of rainfall to DWT coefficients allows the input rainfall time series to be simultaneously estimated along with model parameters. The estimation process is conducted using multi-chain Markov chain Monte Carlo simulation with the DREAMZS algorithm. The use of a likelihood function that considers both rainfall and streamflow error allows for model parameter and temporal rainfall distributions to be estimated. Estimation of the wavelet approximation coefficients of lower order decomposition structures was able to estimate the most realistic temporal rainfall distributions. These rainfall estimates were all able to simulate streamflow that was superior to the results of a traditional calibration approach. It is shown that the choice of wavelet has a considerable impact on the robustness of the inversion. The results demonstrate that streamflow data contains sufficient information to estimate temporal rainfall and model parameter distributions. The extent and variance of rainfall time series that are able to simulate streamflow that is superior to that simulated by a traditional calibration approach is a demonstration of equifinality. The use of a likelihood function that considers both rainfall and streamflow error combined with the use of the DWT as a model data reduction technique allows the joint inference of hydrologic model parameters along with rainfall.

The value of model averaging and dynamical climate model predictions for improving statistical seasonal streamflow forecasts over Australia

NASA Astrophysics Data System (ADS)

Pokhrel, Prafulla; Wang, Q. J.; Robertson, David E.

2013-10-01

Seasonal streamflow forecasts are valuable for planning and allocation of water resources. In Australia, the Bureau of Meteorology employs a statistical method to forecast seasonal streamflows. The method uses predictors that are related to catchment wetness at the start of a forecast period and to climate during the forecast period. For the latter, a predictor is selected among a number of lagged climate indices as candidates to give the "best" model in terms of model performance in cross validation. This study investigates two strategies for further improvement in seasonal streamflow forecasts. The first is to combine, through Bayesian model averaging, multiple candidate models with different lagged climate indices as predictors, to take advantage of different predictive strengths of the multiple models. The second strategy is to introduce additional candidate models, using rainfall and sea surface temperature predictions from a global climate model as predictors. This is to take advantage of the direct simulations of various dynamic processes. The results show that combining forecasts from multiple statistical models generally yields more skillful forecasts than using only the best model and appears to moderate the worst forecast errors. The use of rainfall predictions from the dynamical climate model marginally improves the streamflow forecasts when viewed over all the study catchments and seasons, but the use of sea surface temperature predictions provide little additional benefit.

Regional variability in the accuracy of statistical reproductions of historical time series of daily streamflow at ungaged locations

NASA Astrophysics Data System (ADS)

Farmer, W. H.; Archfield, S. A.; Over, T. M.; Kiang, J. E.

2015-12-01

In the United States and across the globe, the majority of stream reaches and rivers are substantially impacted by water use or remain ungaged. The result is large gaps in the availability of natural streamflow records from which to infer hydrologic understanding and inform water resources management. From basin-specific to continent-wide scales, many efforts have been undertaken to develop methods to estimate ungaged streamflow. This work applies and contrasts several statistical models of daily streamflow to more than 1,700 reference-quality streamgages across the conterminous United States using a cross-validation methodology. The variability of streamflow simulation performance across the country exhibits a pattern familiar to other continental scale modeling efforts performed for the United States. For portions of the West Coast and the dense, relatively homogeneous and humid regions of the eastern United States models produce reliable estimates of daily streamflow using many different prediction methods. Model performance for the middle portion of the United States, marked by more heterogeneous and arid conditions, and with larger contributing areas and sparser networks of streamgages, is consistently poor. A discussion of the difficulty of statistical interpolation and regionalization in these regions raises additional questions of data availability and quality, hydrologic process representation and dominance, and intrinsic variability.

Bayesian Models for Streamflow and River Network Reconstruction using Tree Rings

NASA Astrophysics Data System (ADS)

Ravindranath, A.; Devineni, N.

2016-12-01

Water systems face non-stationary, dynamically shifting risks due to shifting societal conditions and systematic long-term variations in climate manifesting as quasi-periodic behavior on multi-decadal time scales. Water systems are thus vulnerable to long periods of wet or dry hydroclimatic conditions. Streamflow is a major component of water systems and a primary means by which water is transported to serve ecosystems' and human needs. Thus, our concern is in understanding streamflow variability. Climate variability and impacts on water resources are crucial factors affecting streamflow, and multi-scale variability increases risk to water sustainability and systems. Dam operations are necessary for collecting water brought by streamflow while maintaining downstream ecological health. Rules governing dam operations are based on streamflow records that are woefully short compared to periods of systematic variation present in the climatic factors driving streamflow variability and non-stationarity. We use hierarchical Bayesian regression methods in order to reconstruct paleo-streamflow records for dams within a basin using paleoclimate proxies (e.g. tree rings) to guide the reconstructions. The riverine flow network for the entire basin is subsequently modeled hierarchically using feeder stream and tributary flows. This is a starting point in analyzing streamflow variability and risks to water systems, and developing a scientifically-informed dynamic risk management framework for formulating dam operations and water policies to best hedge such risks. We will apply this work to the Missouri and Delaware River Basins (DRB). Preliminary results of streamflow reconstructions for eight dams in the upper DRB using standard Gaussian regression with regional tree ring chronologies give streamflow records that now span two to two and a half centuries, and modestly smoothed versions of these reconstructed flows indicate physically-justifiable trends in the time series.

Reconstructing pre-instrumental streamflow in Eastern Australia using a water balance approach

NASA Astrophysics Data System (ADS)

Tozer, C. R.; Kiem, A. S.; Vance, T. R.; Roberts, J. L.; Curran, M. A. J.; Moy, A. D.

2018-03-01

Streamflow reconstructions based on paleoclimate proxies provide much longer records than the short instrumental period records on which water resource management plans are currently based. In Australia there is a lack of in-situ high resolution paleoclimate proxy records, but remote proxies with teleconnections to Australian climate have utility in producing streamflow reconstructions. Here we investigate, via a case study for a catchment in eastern Australia, the novel use of an Antarctic ice-core based rainfall reconstruction within a Budyko-framework to reconstruct ∼1000 years of annual streamflow. The resulting streamflow reconstruction captures interannual to decadal variability in the instrumental streamflow, validating both the use of the ice core rainfall proxy record and the Budyko-framework method. In the preinstrumental era the streamflow reconstruction shows longer wet and dry epochs and periods of streamflow variability that are higher than observed in the instrumental era. Importantly, for both the instrumental record and preinstrumental reconstructions, the wet (dry) epochs in the rainfall record are shorter (longer) in the streamflow record and this non-linearity must be considered when inferring hydroclimatic risk or historical water availability directly from rainfall proxy records alone. These insights provide a better understanding of present infrastructure vulnerability in the context of past climate variability for eastern Australia. The streamflow reconstruction presented here also provides a better understanding of the range of hydroclimatic variability possible, and therefore represents a more realistic baseline on which to quantify the potential impacts of anthropogenic climate change on water security.

IOD and ENSO impacts on the extreme stream-flows of Citarum river in Indonesia

NASA Astrophysics Data System (ADS)

Sahu, Netrananda; Behera, Swadhin K.; Yamashiki, Yosuke; Takara, Kaoru; Yamagata, Toshio

2012-10-01

Extreme stream-flow events of Citarum River are derived from the daily stream-flows at the Nanjung gauge station. Those events are identified based on their persistently extreme flows for 6 or more days during boreal fall when the seasonal mean stream-flow starts peaking-up from the lowest seasonal flows of June-August. Most of the extreme events of high-streamflows were related to La Niña conditions of tropical Pacific. A few of them were also associated with the negative phases of IOD and the newly identified El Niño Modoki. Unlike the cases of extreme high streamflows, extreme low streamflow events are seen to be associated with the positive IODs. Nevertheless, it was also found that the low-stream-flow events related to positive IOD events were also associated with El Niño events except for one independent event of 1977. Because the occurrence season coincides the peak season of IOD, not only the picked extreme events are seen to fall under the IOD seasons but also there exists a statistically significant correlation of 0.51 between the seasonal IOD index and the seasonal streamflows. There also exists a significant lag correlation when IOD of June-August season leads the streamflows of September-November. A significant but lower correlation coefficient (0.39) is also found between the seasonal streamflow and El Niño for September-November season only.

Assessment and Reduction of Model Parametric Uncertainties: A Case Study with A Distributed Hydrological Model

NASA Astrophysics Data System (ADS)

Gan, Y.; Liang, X. Z.; Duan, Q.; Xu, J.; Zhao, P.; Hong, Y.

2017-12-01

The uncertainties associated with the parameters of a hydrological model need to be quantified and reduced for it to be useful for operational hydrological forecasting and decision support. An uncertainty quantification framework is presented to facilitate practical assessment and reduction of model parametric uncertainties. A case study, using the distributed hydrological model CREST for daily streamflow simulation during the period 2008-2010 over ten watershed, was used to demonstrate the performance of this new framework. Model behaviors across watersheds were analyzed by a two-stage stepwise sensitivity analysis procedure, using LH-OAT method for screening out insensitive parameters, followed by MARS-based Sobol' sensitivity indices for quantifying each parameter's contribution to the response variance due to its first-order and higher-order effects. Pareto optimal sets of the influential parameters were then found by the adaptive surrogate-based multi-objective optimization procedure, using MARS model for approximating the parameter-response relationship and SCE-UA algorithm for searching the optimal parameter sets of the adaptively updated surrogate model. The final optimal parameter sets were validated against the daily streamflow simulation of the same watersheds during the period 2011-2012. The stepwise sensitivity analysis procedure efficiently reduced the number of parameters that need to be calibrated from twelve to seven, which helps to limit the dimensionality of calibration problem and serves to enhance the efficiency of parameter calibration. The adaptive MARS-based multi-objective calibration exercise provided satisfactory solutions to the reproduction of the observed streamflow for all watersheds. The final optimal solutions showed significant improvement when compared to the default solutions, with about 65-90% reduction in 1-NSE and 60-95% reduction in |RB|. The validation exercise indicated a large improvement in model performance with about 40-85% reduction in 1-NSE, and 35-90% reduction in |RB|. Overall, this uncertainty quantification framework is robust, effective and efficient for parametric uncertainty analysis, the results of which provide useful information that helps to understand the model behaviors and improve the model simulations.

Comparison between two statistically based methods, and two physically based models developed to compute daily mean streamflow at ungaged locations in the Cedar River Basin, Iowa

USGS Publications Warehouse

Linhart, S. Mike; Nania, Jon F.; Christiansen, Daniel E.; Hutchinson, Kasey J.; Sanders, Curtis L.; Archfield, Stacey A.

2013-01-01

A variety of individuals from water resource managers to recreational users need streamflow information for planning and decisionmaking at locations where there are no streamgages. To address this problem, two statistically based methods, the Flow Duration Curve Transfer method and the Flow Anywhere method, were developed for statewide application and the two physically based models, the Precipitation Runoff Modeling-System and the Soil and Water Assessment Tool, were only developed for application for the Cedar River Basin. Observed and estimated streamflows for the two methods and models were compared for goodness of fit at 13 streamgages modeled in the Cedar River Basin by using the Nash-Sutcliffe and the percent-bias efficiency values. Based on median and mean Nash-Sutcliffe values for the 13 streamgages the Precipitation Runoff Modeling-System and Soil and Water Assessment Tool models appear to have performed similarly and better than Flow Duration Curve Transfer and Flow Anywhere methods. Based on median and mean percent bias values, the Soil and Water Assessment Tool model appears to have generally overestimated daily mean streamflows, whereas the Precipitation Runoff Modeling-System model and statistical methods appear to have underestimated daily mean streamflows. The Flow Duration Curve Transfer method produced the lowest median and mean percent bias values and appears to perform better than the other models.

Multi-site Stochastic Simulation of Daily Streamflow with Markov Chain and KNN Algorithm

NASA Astrophysics Data System (ADS)

Mathai, J.; Mujumdar, P.

2017-12-01

A key focus of this study is to develop a method which is physically consistent with the hydrologic processes that can capture short-term characteristics of daily hydrograph as well as the correlation of streamflow in temporal and spatial domains. In complex water resource systems, flow fluctuations at small time intervals require that discretisation be done at small time scales such as daily scales. Also, simultaneous generation of synthetic flows at different sites in the same basin are required. We propose a method to equip water managers with a streamflow generator within a stochastic streamflow simulation framework. The motivation for the proposed method is to generate sequences that extend beyond the variability represented in the historical record of streamflow time series. The method has two steps: In step 1, daily flow is generated independently at each station by a two-state Markov chain, with rising limb increments randomly sampled from a Gamma distribution and the falling limb modelled as exponential recession and in step 2, the streamflow generated in step 1 is input to a nonparametric K-nearest neighbor (KNN) time series bootstrap resampler. The KNN model, being data driven, does not require assumptions on the dependence structure of the time series. A major limitation of KNN based streamflow generators is that they do not produce new values, but merely reshuffle the historical data to generate realistic streamflow sequences. However, daily flow generated using the Markov chain approach is capable of generating a rich variety of streamflow sequences. Furthermore, the rising and falling limbs of daily hydrograph represent different physical processes, and hence they need to be modelled individually. Thus, our method combines the strengths of the two approaches. We show the utility of the method and improvement over the traditional KNN by simulating daily streamflow sequences at 7 locations in the Godavari River basin in India.

Recent tree die-off has little effect on streamflow in contrast to expected increases from historical studies

NASA Astrophysics Data System (ADS)

Biederman, Joel A.; Somor, Andrew J.; Harpold, Adrian A.; Gutmann, Ethan D.; Breshears, David D.; Troch, Peter A.; Gochis, David J.; Scott, Russell L.; Meddens, Arjan J. H.; Brooks, Paul D.

2015-12-01

Recent bark beetle epidemics have caused regional-scale tree mortality in many snowmelt-dominated headwater catchments of western North America. Initial expectations of increased streamflow have not been supported by observations, and the basin-scale response of annual streamflow is largely unknown. Here we quantified annual streamflow responses during the decade following tree die-off in eight infested catchments in the Colorado River headwaters and one nearby control catchment. We employed three alternative empirical methods: (i) double-mass comparison between impacted and control catchments, (ii) runoff ratio comparison before and after die-off, and (iii) time-trend analysis using climate-driven linear models. In contrast to streamflow increases predicted by historical paired catchment studies and recent modeling, we did not detect streamflow changes in most basins following die-off, while one basin consistently showed decreased streamflow. The three analysis methods produced generally consistent results, with time-trend analysis showing precipitation was the strongest predictor of streamflow variability (R2 = 74-96%). Time-trend analysis revealed post-die-off streamflow decreased in three catchments by 11-29%, with no change in the other five catchments. Although counter to initial expectations, these results are consistent with increased transpiration by surviving vegetation and the growing body of literature documenting increased snow sublimation and evaporation from the subcanopy following die-off in water-limited, snow-dominated forests. The observations presented here challenge the widespread expectation that streamflow will increase following beetle-induced forest die-off and highlight the need to better understand the processes driving hydrologic response to forest disturbance.

Cool-Season Moisture Delivery and Multi-Basin Streamflow Anomalies in the Western United States

NASA Astrophysics Data System (ADS)

Malevich, Steven B.

Widespread droughts can have a significant impact on western United States streamflow, but the causes of these events are not fully understood. This dissertation examines streamflow from multiple western US basins and establishes the robust, leading modes of variability in interannual streamflow throughout the past century. I show that approximately 50% of this variability is associated with spatially widespread streamflow anomalies that are statistically independent from streamflow's response to the El Nino-Southern Oscillation (ENSO). The ENSO-teleconnection accounts for approximately 25% of the interannual variability in streamflow, across this network. These atmospheric circulation anomalies associated with the most spatially widespread variability are associated with the Aleutian low and the persistent coastal atmospheric ridge in the Pacific Northwest. I use a watershed segmentation algorithm to explicitly track the position and intensity of these features and compare their variability to the multi-basin streamflow variability. Results show that latitudinal shifts in the coastal atmospheric ridge are more strongly associated with streamflow's north-south dipole response to ENSO variability while more spatially widespread anomalies in streamflow most strongly relate to seasonal changes in the coastal ridge intensity. This likely reflects persistent coastal ridge blocking of cool-season precipitation into western US river basins. I utilize the 35 model runs of the Community Earth System Model Large Ensemble (CESMLE) to determine whether the model ensemble simulates the anomalously strong coastal ridges and extreme widespread wintertime precipitation anomalies found in the observation record. Though there is considerable bias in the CESMLE, the CESMLE runs simulate extremely widespread dry precipitation anomalies with a frequency of approximately one extreme event per century during the historical simulations (1920 - 2005). These extremely widespread dry events correspond significantly with anomalously intense coastal atmospheric ridges. The results from these three papers connect widespread interannual streamflow anomalies in the western US--and especially extremely widespread streamflow droughts--with semi-permanent atmospheric ridge anomalies near the coastal Pacific Northwest. This is important to western US water managers because these widespread events appear to have been a robust feature of the past century. The semi-permanent atmospheric features associated with these widespread dry streamflow anomalies are projected to change position significantly in the next century as a response to global climate change. This may change widespread streamflow anomaly characteristic in the western US, though my results do not show evidence of these changes within the instrument record of last century.

Factors Affecting Firm Yield and the Estimation of Firm Yield for Selected Streamflow-Dominated Drinking-Water-Supply Reservoirs in Massachusetts

USGS Publications Warehouse

Waldron, Marcus C.; Archfield, Stacey A.

2006-01-01

Factors affecting reservoir firm yield, as determined by application of the Massachusetts Department of Environmental Protection's Firm Yield Estimator (FYE) model, were evaluated, modified, and tested on 46 streamflow-dominated reservoirs representing 15 Massachusetts drinking-water supplies. The model uses a mass-balance approach to determine the maximum average daily withdrawal rate that can be sustained during a period of record that includes the 1960s drought-of-record. The FYE methodology to estimate streamflow to the reservoir at an ungaged site was tested by simulating streamflow at two streamflow-gaging stations in Massachusetts and comparing the simulated streamflow to the observed streamflow. In general, the FYE-simulated flows agreed well with observed flows. There were substantial deviations from the measured values for extreme high and low flows. A sensitivity analysis determined that the model's streamflow estimates are most sensitive to input values for average annual precipitation, reservoir drainage area, and the soil-retention number-a term that describes the amount of precipitation retained by the soil in the basin. The FYE model currently provides the option of using a 1,000-year synthetic record constructed by randomly sampling 2-year blocks of concurrent streamflow and precipitation records 500 times; however, the synthetic record has the potential to generate records of precipitation and streamflow that do not reflect the worst historical drought in Massachusetts. For reservoirs that do not have periods of drawdown greater than 2 years, the bootstrap does not offer any additional information about the firm yield of a reservoir than the historical record does. For some reservoirs, the use of a synthetic record to determine firm yield resulted in as much as a 30-percent difference between firm-yield values from one simulation to the next. Furthermore, the assumption that the synthetic traces of streamflow are statistically equivalent to the historical record is not valid. For multiple-reservoir systems, the firm-yield estimate was dependent on the reservoir system's configuration. The firm yield of a system is sensitive to how the water is transferred from one reservoir to another, the capacity of the connection between the reservoirs, and how seasonal variations in demand are represented in the FYE model. Firm yields for 25 (14 single-reservoir systems and 11 multiple-reservoir systems) reservoir systems were determined by using the historical records of streamflow and precipitation. Current water-use data indicate that, on average, 20 of the 25 reservoir systems in the study were operating below their estimated firm yield; during months with peak demands, withdrawals exceeded the firm yield for 8 reservoir systems.

Statistical downscaling of GCM simulations to streamflow using relevance vector machine

NASA Astrophysics Data System (ADS)

Ghosh, Subimal; Mujumdar, P. P.

2008-01-01

General circulation models (GCMs), the climate models often used in assessing the impact of climate change, operate on a coarse scale and thus the simulation results obtained from GCMs are not particularly useful in a comparatively smaller river basin scale hydrology. The article presents a methodology of statistical downscaling based on sparse Bayesian learning and Relevance Vector Machine (RVM) to model streamflow at river basin scale for monsoon period (June, July, August, September) using GCM simulated climatic variables. NCEP/NCAR reanalysis data have been used for training the model to establish a statistical relationship between streamflow and climatic variables. The relationship thus obtained is used to project the future streamflow from GCM simulations. The statistical methodology involves principal component analysis, fuzzy clustering and RVM. Different kernel functions are used for comparison purpose. The model is applied to Mahanadi river basin in India. The results obtained using RVM are compared with those of state-of-the-art Support Vector Machine (SVM) to present the advantages of RVMs over SVMs. A decreasing trend is observed for monsoon streamflow of Mahanadi due to high surface warming in future, with the CCSR/NIES GCM and B2 scenario.

Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes

USDA-ARS?s Scientific Manuscript database

Assimilation of remotely sensed soil moisture data (SM-DA) to correct soil water stores of rainfall-runoff models has shown skill in improving streamflow prediction. In the case of large and sparsely monitored catchments, SM-DA is a particularly attractive tool.Within this context, we assimilate act...

Spatiotemporal patterns of precipitation inferred from streamflow observations across the Sierra Nevada mountain range

NASA Astrophysics Data System (ADS)

Henn, Brian; Clark, Martyn P.; Kavetski, Dmitri; Newman, Andrew J.; Hughes, Mimi; McGurk, Bruce; Lundquist, Jessica D.

2018-01-01

Given uncertainty in precipitation gauge-based gridded datasets over complex terrain, we use multiple streamflow observations as an additional source of information about precipitation, in order to identify spatial and temporal differences between a gridded precipitation dataset and precipitation inferred from streamflow. We test whether gridded datasets capture across-crest and regional spatial patterns of variability, as well as year-to-year variability and trends in precipitation, in comparison to precipitation inferred from streamflow. We use a Bayesian model calibration routine with multiple lumped hydrologic model structures to infer the most likely basin-mean, water-year total precipitation for 56 basins with long-term (>30 year) streamflow records in the Sierra Nevada mountain range of California. We compare basin-mean precipitation derived from this approach with basin-mean precipitation from a precipitation gauge-based, 1/16° gridded dataset that has been used to simulate and evaluate trends in Western United States streamflow and snowpack over the 20th century. We find that the long-term average spatial patterns differ: in particular, there is less precipitation in the gridded dataset in higher-elevation basins whose aspect faces prevailing cool-season winds, as compared to precipitation inferred from streamflow. In a few years and basins, there is less gridded precipitation than there is observed streamflow. Lower-elevation, southern, and east-of-crest basins show better agreement between gridded and inferred precipitation. Implied actual evapotranspiration (calculated as precipitation minus streamflow) then also varies between the streamflow-based estimates and the gridded dataset. Absolute uncertainty in precipitation inferred from streamflow is substantial, but the signal of basin-to-basin and year-to-year differences are likely more robust. The findings suggest that considering streamflow when spatially distributing precipitation in complex terrain may improve its representation, particularly for basins whose orientations (e.g., windward-facing) are favored for orographic precipitation enhancement.

Assessment of wildland fire impacts on watershed annual water yield: Analytical framework and case studies in the United States

DOE PAGES

Hallema, Dennis W.; Sun, Ge; Caldwell, Peter V.; ...

2016-11-29

More than 50% of water supplies in the conterminous United States originate on forestland or rangeland and are potentially under increasing stress as a result of larger and more severe wildfires. Little is known, however, about the long-term impacts of fire on annual water yield and the role of climate variability within this context. We here propose a framework for evaluating wildland fire impacts on streamflow that combines double-mass analysis with new methods (change point analysis, climate elasticity modeling, and process-based modeling) to distinguish between multiyear fire and climate impacts. The framework captures a wide range of fire types, watershedsmore » characteristics, and climate conditions using streamflow data, as opposed to other approaches requiring paired watersheds. The process is illustrated with three case studies. A watershed in Arizona experienced a +266% increase in annual water yield in the 5 years after a wildfire, where +219% was attributed to wildfire and +24% to precipitation trends. In contrast, a California watershed had a lower (–64%) post-fire net water yield, comprised of enhanced flow (+38%) attributed to wildfire offset (–102%) by lower precipitation in the post-fire period. Changes in streamflow within a watershed in South Carolina had no apparent link to periods of prescribed burning but matched a very wet winter and reports of storm damage. As a result, the presented framework is unique in its ability to detect and quantify fire or other disturbances, even if the date or nature of the disturbance event is uncertain, and regardless of precipitation trends.« less

Assessment of wildland fire impacts on watershed annual water yield: Analytical framework and case studies in the United States

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hallema, Dennis W.; Sun, Ge; Caldwell, Peter V.

More than 50% of water supplies in the conterminous United States originate on forestland or rangeland and are potentially under increasing stress as a result of larger and more severe wildfires. Little is known, however, about the long-term impacts of fire on annual water yield and the role of climate variability within this context. We here propose a framework for evaluating wildland fire impacts on streamflow that combines double-mass analysis with new methods (change point analysis, climate elasticity modeling, and process-based modeling) to distinguish between multiyear fire and climate impacts. The framework captures a wide range of fire types, watershedsmore » characteristics, and climate conditions using streamflow data, as opposed to other approaches requiring paired watersheds. The process is illustrated with three case studies. A watershed in Arizona experienced a +266% increase in annual water yield in the 5 years after a wildfire, where +219% was attributed to wildfire and +24% to precipitation trends. In contrast, a California watershed had a lower (–64%) post-fire net water yield, comprised of enhanced flow (+38%) attributed to wildfire offset (–102%) by lower precipitation in the post-fire period. Changes in streamflow within a watershed in South Carolina had no apparent link to periods of prescribed burning but matched a very wet winter and reports of storm damage. As a result, the presented framework is unique in its ability to detect and quantify fire or other disturbances, even if the date or nature of the disturbance event is uncertain, and regardless of precipitation trends.« less

Evaluation of different parameterizations of the spatial heterogeneity of subsurface storage capacity for hourly runoff simulation in boreal mountainous watershed

NASA Astrophysics Data System (ADS)

Hailegeorgis, Teklu T.; Alfredsen, Knut; Abdella, Yisak S.; Kolberg, Sjur

2015-03-01

Identification of proper parameterizations of spatial heterogeneity is required for precipitation-runoff models. However, relevant studies with a specific aim at hourly runoff simulation in boreal mountainous catchments are not common. We conducted calibration and evaluation of hourly runoff simulation in a boreal mountainous watershed based on six different parameterizations of the spatial heterogeneity of subsurface storage capacity for a semi-distributed (subcatchments hereafter called elements) and distributed (1 × 1 km2 grid) setup. We evaluated representation of element-to-element, grid-to-grid, and probabilistic subcatchment/subbasin, subelement and subgrid heterogeneities. The parameterization cases satisfactorily reproduced the streamflow hydrographs with Nash-Sutcliffe efficiency values for the calibration and validation periods up to 0.84 and 0.86 respectively, and similarly for the log-transformed streamflow up to 0.85 and 0.90. The parameterizations reproduced the flow duration curves, but predictive reliability in terms of quantile-quantile (Q-Q) plots indicated marked over and under predictions. The simple and parsimonious parameterizations with no subelement or no subgrid heterogeneities provided equivalent simulation performance compared to the more complex cases. The results indicated that (i) identification of parameterizations require measurements from denser precipitation stations than what is required for acceptable calibration of the precipitation-streamflow relationships, (ii) there is challenges in the identification of parameterizations based on only calibration to catchment integrated streamflow observations and (iii) a potential preference for the simple and parsimonious parameterizations for operational forecast contingent on their equivalent simulation performance for the available input data. In addition, the effects of non-identifiability of parameters (interactions and equifinality) can contribute to the non-identifiability of the parameterizations.

An evaluation of the accuracy of modeled and computed streamflow time-series data for the Ohio River at Hannibal Lock and Dam and at a location upstream from Sardis, Ohio

USGS Publications Warehouse

Koltun, G.F.

2015-01-01

Streamflow hydrographs were plotted for modeled/computed time series for the Ohio River near the USGS Sardis gage and the Ohio River at the Hannibal Lock and Dam. In general, the time series at these two locations compared well. Some notable differences include the exclusive presence of short periods of negative streamflows in the USGS 15-minute time-series data for the gage on the Ohio River above Sardis, Ohio, and the occurrence of several peak streamflows in the USACE gate/hydropower time series for the Hannibal Lock and Dam that were appreciably larger than corresponding peaks in the other time series, including those modeled/computed for the downstream Sardis gage

THE WATER BALANCE OF THE SUSQUEHANNA RIVER BASIN AND ITS RESPONSE TO CLIMATE CHANGE. (R824995)

EPA Science Inventory

Abstract

Historical precipitation, temperature and streamflow data for the Susquehanna River Basin (SRB) are analyzed with the objective of developing simple statistical and water balance models of streamflow at the watershed's outlet. Annual streamflow is highly corre...

Hydroclimate temporal variability in a coastal Mediterranean watershed: the Tafna basin, North-West Algeria

NASA Astrophysics Data System (ADS)

Boulariah, Ouafik; Longobardi, Antonia; Meddi, Mohamed

2017-04-01

One of the major challenges scientists, practitioners and stakeholders are nowadays involved in, is to provide the worldwide population with reliable water supplies, protecting, at the same time, the freshwater ecosystems quality and quantity. Climate and land use changes undermine the balance between water demand and water availability, causing alteration of rivers flow regime. Knowledge of hydro-climate variables temporal and spatial variability is clearly helpful to plan drought and flood hazard mitigation strategies but also to adapt them to future environmental scenarios. The present study relates to the coastal semi-arid Tafna catchment, located in the North-West of Algeria, within the Mediterranean basin. The aim is the investigation of streamflow and rainfall indices temporal variability in six sub-basins of the large catchment Tafna, attempting to relate streamflow and rainfall changes. Rainfall and streamflow time series have been preliminary tested for data quality and homogeneity, through the coupled application of two-tailed t test, Pettitt test and Cumsum tests (significance level of 0.1, 0.05 and 0.01). Subsequently maximum annual daily rainfall and streamflow and average daily annual rainfall and streamflow time series have been derived and tested for temporal variability, through the application of the Mann Kendall and Sen's test. Overall maximum annual daily streamflow time series exhibit a negative trend which is however significant for only 30% of the station. Maximum annual daily rainfall also e exhibit a negative trend which is intend significant for the 80% of the stations. In the case of average daily annual streamflow and rainfall, the tendency for decrease in time is unclear and, in both cases, appear significant for 60% of stations.

Effects of climate change on streamflow extremes and implications for reservoir inflow in the United States

NASA Astrophysics Data System (ADS)

Naz, Bibi S.; Kao, Shih-Chieh; Ashfaq, Moetasim; Gao, Huilin; Rastogi, Deeksha; Gangrade, Sudershan

2018-01-01

The magnitude and frequency of hydrometeorological extremes are expected to increase in the conterminous United States (CONUS) over the rest of this century, and their increase will significantly impact water resource management. In this study, we evaluated the large-scale climate change effects on extreme hydrological events and their implications for reservoir inflows in 138 headwater subbasins located upstream of reservoirs across CONUS using the Variable Infiltration Capacity (VIC) hydrologic model. The VIC model was forced with a 10-member ensemble of global circulation models under the Representative Concentration Pathway 8.5 that were dynamically downscaled using a regional climate model (RegCM4) and bias-corrected to 1/24° grid cell resolution. Four commonly used indices, including mean annual flow, annual center timing, 100-year daily high streamflow, and 10-year 7-day average low streamflow were used for evaluation. The results projected an increase in the high streamflow by 44% for a majority of subbasins upstream of flood control reservoirs in the central United States (US) and a decrease in the low streamflow by 11% for subbasins upstream of hydropower reservoirs across the western US. In the eastern US, frequencies of both high and low streamflow were projected to increase in the majority of subbasins upstream of both hydropower and flood control reservoirs. Increased frequencies of both high and low streamflow events can potentially make reservoirs across CONUS more vulnerable to future climate conditions. This study estimates reservoir inflow changes over the next several decades, which can be used to optimize water supply management downstream.

Spatiotemporal impacts of LULC changes on hydrology from the perspective of runoff generation mechanism using SWAT model with evolving parameters

NASA Astrophysics Data System (ADS)

Li, Y.; Chang, J.; Luo, L.

2017-12-01

It is of great importance for water resources management to model the truly hydrological process under changing environment, especially under significant changes of underlying surfaces like the Wei River Bain (WRB) where the subsurface hydrology is highly influenced by human activities, and to systematically investigate the interactions among LULC change, streamflow variation and changes in runoff generation process. Therefore, we proposed the idea of evolving parameters in hydrological model (SWAT) to reflect the changes in physical environment with different LULC conditions. Then with these evolving parameters, the spatiotemporal impacts of LULC changes on streamflow were quantified, and qualitative analysis was conducted to further explore how LULC changes affect the streamflow from the perspective of runoff generation mechanism. Results indicate the following: 1) evolving parameter calibration is not only effective but necessary to ensure the validity of the model when dealing with significant changes in underlying surfaces due to human activities. 2) compared to the baseline period, the streamflow in wet seasons increased in the 1990s but decreased in the 2000s. While at yearly and dry seasonal scales, the streamflow decreased in both two decades; 3) the expansion of cropland is the major contributor to the reduction of surface water component, thus causing the decline in streamflow at yearly and dry seasonal scales. While compared to the 1990s, the expansions of woodland in the middle stream and grassland in the downstream are the main stressors that increased the soil water component, thus leading to the more decline of the streamflow in the 2000s.

NASA-modified precipitation products to improve USEPA nonpoint source water quality modeling for the Chesapeake Bay.

PubMed

Nigro, Joseph; Toll, David; Partington, Ed; Ni-Meister, Wenge; Lee, Shihyan; Gutierrez-Magness, Angelica; Engman, Ted; Arsenault, Kristi

2010-01-01

The USEPA has estimated that over 20,000 water bodies within the United States do not meet water quality standards. One of the regulations in the Clean Water Act of 1972 requires states to monitor the total maximum daily load, or the amount of pollution that can be carried by a water body before it is determined to be "polluted," for any watershed in the United States (Copeland, 2005). In response to this mandate, the USEPA developed Better Assessment Science Integrating Nonpoint Sources (BASINS) as a decision support tool for assessing pollution and to guide the decision-making process for improving water quality. One of the models in BASINS, the Hydrological Simulation Program-Fortran (HSPF), computes continuous streamflow rates and pollutant concentration at each basin outlet. By design, precipitation and other meteorological data from weather stations serve as standard model input. In practice, these stations may be unable to capture the spatial heterogeneity of precipitation events, especially if they are few and far between. An attempt was made to resolve this issue by substituting station data with NASA-modified/NOAA precipitation data. Using these data within HSPF, streamflow was calculated for seven watersheds in the Chesapeake Bay Basin during low flow periods, convective storm periods, and annual flows. In almost every case, the modeling performance of HSPF increased when using the NASA-modified precipitation data, resulting in better streamflow statistics and, potentially, in improved water quality assessment.

Impacts of uncertainties in weather and streamflow observations in calibration and evaluation of an elevation distributed HBV-model

NASA Astrophysics Data System (ADS)

Engeland, K.; Steinsland, I.; Petersen-Øverleir, A.; Johansen, S.

2012-04-01

The aim of this study is to assess the uncertainties in streamflow simulations when uncertainties in both observed inputs (precipitation and temperature) and streamflow observations used in the calibration of the hydrological model are explicitly accounted for. To achieve this goal we applied the elevation distributed HBV model operating on daily time steps to a small catchment in high elevation in Southern Norway where the seasonal snow cover is important. The uncertainties in precipitation inputs were quantified using conditional simulation. This procedure accounts for the uncertainty related to the density of the precipitation network, but neglects uncertainties related to measurement bias/errors and eventual elevation gradients in precipitation. The uncertainties in temperature inputs were quantified using a Bayesian temperature interpolation procedure where the temperature lapse rate is re-estimated every day. The uncertainty in the lapse rate was accounted for whereas the sampling uncertainty related to network density was neglected. For every day a random sample of precipitation and temperature inputs were drawn to be applied as inputs to the hydrologic model. The uncertainties in observed streamflow were assessed based on the uncertainties in the rating curve model. A Bayesian procedure was applied to estimate the probability for rating curve models with 1 to 3 segments and the uncertainties in their parameters. This method neglects uncertainties related to errors in observed water levels. Note that one rating curve was drawn to make one realisation of a whole time series of streamflow, thus the rating curve errors lead to a systematic bias in the streamflow observations. All these uncertainty sources were linked together in both calibration and evaluation of the hydrologic model using a DREAM based MCMC routine. Effects of having less information (e.g. missing one streamflow measurement for defining the rating curve or missing one precipitation station) was also investigated.

Groundwater Pumping and Streamflow in the Yuba Basin, Sacramento Valley, California

NASA Astrophysics Data System (ADS)

Moss, D. R.; Fogg, G. E.; Wallender, W. W.

2011-12-01

Water transfers during drought in California's Sacramento Valley can lead to increased groundwater pumping, and as yet unknown effects on stream baseflow. Two existing groundwater models of the greater Sacramento Valley together with localized, monitoring of groundwater level fluctuations adjacent to the Bear, Feather, and Yuba Rivers, indicate cause and effect relations between the pumping and streamflow. The models are the Central Valley Hydrologic Model (CVHM) developed by the U.S. Geological Survey and C2VSIM developed by Department of Water Resources. Using two models which have similar complexity and data but differing approaches to the agricultural water boundary condition illuminates both the water budget and its uncertainty. Water budget and flux data for localized areas can be obtained from the models allowing for parameters such as precipitation, irrigation recharge, and streamflow to be compared to pumping on different temporal scales. Continuous groundwater level measurements at nested, near-stream piezometers show seasonal variations in streamflow and groundwater levels as well as the timing and magnitude of recharge and pumping. Preliminary results indicate that during years with relatively wet conditions 65 - 70% of the surface recharge for the groundwater system comes from irrigation and precipitation and 30 - 35% comes from streamflow losses. The models further indicate that during years with relatively dry conditions, 55 - 60% of the surface recharge for the groundwater system comes from irrigation and precipitation while 40 - 45% comes from streamflow losses. The models irrigation water demand, surface-water and groundwater supply, and deep percolation are integrated producing values for irrigation pumping. Groundwater extractions during the growing season, approximately between April and October, increase by almost 200%. The effects of increased pumping seasonally are not readily evident in stream stage measurements. However, during dry time periods net streamflow gains are about half of the gains seen during wet period.

On the performance of updating Stochastic Dynamic Programming policy using Ensemble Streamflow Prediction in a snow-covered region

NASA Astrophysics Data System (ADS)

Martin, A.; Pascal, C.; Leconte, R.

2014-12-01

Stochastic Dynamic Programming (SDP) is known to be an effective technique to find the optimal operating policy of hydropower systems. In order to improve the performance of SDP, this project evaluates the impact of re-updating the policy at every time step by using Ensemble Streamflow Prediction (ESP). We present a case study of the Kemano's hydropower system on the Nechako River in British Columbia, Canada. Managed by Rio Tinto Alcan (RTA), this system is subject to large streamflow volumes in spring due to important amount of snow depth during the winter season. Therefore, the operating policy should not only maximize production but also minimize the risk of flooding. The hydrological behavior of the system is simulated with CEQUEAU, a distributed and deterministic hydrological model developed by the Institut national de la recherche scientifique - Eau, Terre et Environnement (INRS-ETE) in Quebec, Canada. On each decision time step, CEQUEAU is used to generate ESP scenarios based on historical meteorological sequences and the current state of the hydrological model. These scenarios are used into the SDP to optimize the new release policy for the next time steps. This routine is then repeated over the entire simulation period. Results are compared with those obtained by using SDP on historical inflow scenarios.

Model calibration criteria for estimating ecological flow characteristics

USGS Publications Warehouse

Vis, Marc; Knight, Rodney; Poole, Sandra; Wolfe, William J.; Seibert, Jan; Breuer, Lutz; Kraft, Philipp

2016-01-01

Quantification of streamflow characteristics in ungauged catchments remains a challenge. Hydrological modeling is often used to derive flow time series and to calculate streamflow characteristics for subsequent applications that may differ from those envisioned by the modelers. While the estimation of model parameters for ungauged catchments is a challenging research task in itself, it is important to evaluate whether simulated time series preserve critical aspects of the streamflow hydrograph. To address this question, seven calibration objective functions were evaluated for their ability to preserve ecologically relevant streamflow characteristics of the average annual hydrograph using a runoff model, HBV-light, at 27 catchments in the southeastern United States. Calibration trials were repeated 100 times to reduce parameter uncertainty effects on the results, and 12 ecological flow characteristics were computed for comparison. Our results showed that the most suitable calibration strategy varied according to streamflow characteristic. Combined objective functions generally gave the best results, though a clear underprediction bias was observed. The occurrence of low prediction errors for certain combinations of objective function and flow characteristic suggests that (1) incorporating multiple ecological flow characteristics into a single objective function would increase model accuracy, potentially benefitting decision-making processes; and (2) there may be a need to have different objective functions available to address specific applications of the predicted time series.

Potential Impact of Climate Change on Streamflow of Major Ethiopian Rivers

NASA Astrophysics Data System (ADS)

Gizaw, M. S.; Zhang, S.; Biftu, G. F.; Gan, T. Y.; Tan, X.; Moges, S. A.; Koivusalo, H.

2017-12-01

In this study, HSPF (Hydrologic Simulation Program-FORTRAN) was used to analyze the potential impact of climate change on the streamflow of four major river basins in Ethiopia: Awash, Baro, Genale and Tekeze. The calibrated and validated HSPF model was forced with daily climate data of 10 CMIP5 (Coupled Model Intercomparison Project phase 5) Global Climate Models (GCMs) for the 1971-2000 control period and the RCP4.5 and RCP8.5 climate projections of 2041-2070 (2050s) and 2071-2100 (2080s). The ensemble median of these 10 GCMs projects the temperature in the four study areas to increase by about 2.3 ˚C (3.3 ˚C) in 2050s (2080s) whereas the mean annual precipitation is projected to increase by about 6% (9%) in 2050s (2080s). This results in about 3% (6%) increase in the projected annual streamflow in Awash, Baro and Tekeze rivers whereas the annual streamflow of Genale river is projected to increase by about 18% (33%) in the 2050s (2080s). However, such projected increase in the mean annual streamflow due to increasing precipitation over Ethiopia contradicts the decreasing trends in mean annual precipitation observed in recent decades. Regional climate models of high resolutions could provide more realistic climate projections for Ethiopia's complex topography, thus reducing the uncertainties in future streamflow projections.

Simulated Water-Management Alternatives Using the Modular Modeling System for the Methow River Basin, Washington

USGS Publications Warehouse

Konrad, Christopher P.

2004-01-01

A precipitation-runoff model for the Methow River Basin was used to simulate six alternatives: (1) baseline of current flow, (2) line irrigation canals to limit seepage losses, (3) increase surface-water diversions through unlined canals for aquifer recharge, (4) convert from surface-water to ground-water resources to supply water for irrigation, and (5) reduce tree density in forested headwater catchments, and (6) natural flow. Daily streamflow from October 1, 1959, to September 30, 2001 (water years 1960?2001) was simulated. Lining irrigation canals (alternative 2) increased flows in the Chewuch, Twisp, and the Methow (upstream and at Twisp) Rivers during September because of lower diversion rates, but not in the Methow River near Pateros. Increasing diversions for aquifer recharge (alternative 3) increased streamflow from September into January, but reduced streamflow earlier in the summer. Conversion of surface-water diversions to ground-water wells (alternative 4) resulted in the largest increase in September streamflow of any alternative, but also marginally lower January flows (at most -8 percent in the 90-percent exceedence value). Forest-cover reduction (alternative 5) produced large increases in streamflow during high-flow periods in May and June and earlier onset of high flows and small increases in January streamflows. September streamflows were largely unaffected by alternative 5. Natural streamflow (alternative 6) was higher in September and lower in January than the baseline alternative.

Rainfall-runoff characteristics and effects of increased urban density on streamflow and infiltration in the eastern part of the San Jacinto River basin, Riverside County, California

USGS Publications Warehouse

Guay, Joel R.

2002-01-01

To better understand the rainfall-runoff characteristics of the eastern part of the San Jacinto River Basin and to estimate the effects of increased urbanization on streamflow, channel infiltration, and land-surface infiltration, a long-term (1950?98) time series of monthly flows in and out of the channels and land surfaces were simulated using the Hydrologic Simulation Program- FORTRAN (HSPF) rainfall-runoff model. Channel and land-surface infiltration includes rainfall or runoff that infiltrates past the zone of evapotranspiration and may become ground-water recharge. The study area encompasses about 256 square miles of the San Jacinto River drainage basin in Riverside County, California. Daily streamflow (for periods with available data between 1950 and 1998), and daily rainfall and evaporation (1950?98) data; monthly reservoir storage data (1961?98); and estimated mean annual reservoir inflow data (for 1974 conditions) were used to calibrate the rainfall-runoff model. Measured and simulated mean annual streamflows for the San Jacinto River near San Jacinto streamflow-gaging station (North-South Fork subbasin) for 1950?91 and 1997?98 were 14,000 and 14,200 acre-feet, respectively, a difference of 1.4 percent. The standard error of the mean for measured and simulated annual streamflow in the North-South Fork subbasin was 3,520 and 3,160 acre-feet, respectively. Measured and simulated mean annual streamflows for the Bautista Creek streamflow-gaging station (Bautista Creek subbasin) for 1950?98 were 980 acre-feet and 991 acre-feet, respectively, a difference of 1.1 percent. The standard error of the mean for measured and simulated annual streamflow in the Bautista Creek subbasin was 299 and 217 acre-feet, respectively. Measured and simulated annual streamflows for the San Jacinto River above State Street near San Jacinto streamflow-gaging station (Poppet subbasin) for 1998 were 23,400 and 23,500 acre-feet, respectively, a difference of 0.4 percent. The simulated mean annual streamflow for the State Street gaging station at the outlet of the study basin and the simulated mean annual basin infiltration (combined infiltration from all the channels and land surfaces) were 8,720 and 41,600 acre-feet, respectively, for water years 1950-98. Simulated annual streamflow at the State Street gaging station ranged from 16.8 acre-feet in water year 1961 to 70,400 acre-feet in water year 1993, and simulated basin infiltration ranged from 2,770 acre-feet in water year 1961 to 149,000 acre-feet in water year 1983.The effects of increased urbanization on the hydrology of the study basin were evaluated by increasing the size of the effective impervious and non-effective impervious urban areas simulated in the calibrated rainfall-runoff model by 50 and 100 percent, respectively. The rainfall-runoff model simulated a long-term time series of monthly flows in and out of the channels and land surfaces using daily rainfall and potential evaporation data for water years 1950?98. Increasing the effective impervious and non-effective impervious urban areas by 100 percent resulted in a 5-percent increase in simulated mean annual streamflow at the State Street gaging station, and a 2.2-percent increase in simulated basin infiltration. Results of a frequency analysis of the simulated annual streamflow at the State Street gaging station showed that when effective impervious and non-effective impervious areas were increased 100 percent, simulated annual streamflow increased about 100 percent for low-flow conditions and was unchanged for high-flow conditions. The simulated increase in streamflow at the State Street gaging station potentially could infiltrate along the stream channel further downstream, outside of the model area.

Calibration and use of an interactive-accounting model to simulate dissolved solids, streamflow, and water-supply operations in the Arkansas River basin, Colorado

USGS Publications Warehouse

Burns, A.W.

1989-01-01

An interactive-accounting model was used to simulate dissolved solids, streamflow, and water supply operations in the Arkansas River basin, Colorado. Model calibration of specific conductance to streamflow relations at three sites enabled computation of dissolved-solids loads throughout the basin. To simulate streamflow only, all water supply operations were incorporated in the regression relations for streamflow. Calibration for 1940-85 resulted in coefficients of determination that ranged from 0.89 to 0.58, and values in excess of 0.80 were determined for 16 of 20 nodes. The model then incorporated 74 water users and 11 reservoirs to simulate the water supply operations for two periods, 1943-74 and 1975-85. For the 1943-74 calibration, coefficients of determination for streamflow ranged from 0.87 to 0.02. Calibration of the water supply operations resulted in coefficients of determination that ranged from 0.87 to negative for simulated irrigation diversions of 37 selected water users. Calibration for 1975-85 was not evaluated statistically, but average values and plots of reservoir contents indicated reasonableness of the simulation. To demonstrate the utility of the model, six specific alternatives were simulated to consider effects of potential enlargement of Pueblo Reservoir. Three general major alternatives were simulated: the 1975-85 calibrated model data, the calibrated model data with an addition of 30 cu ft/sec in Fountain Creek flows, and the calibrated model data plus additional municipal water in storage. These three major alternatives considered the options of reservoir enlargement or no enlargement. A 40,000-acre-foot reservoir enlargement resulted in average increases of 2,500 acre-ft in transmountain diversions, of 800 acre-ft in storage diversions, and of 100 acre-ft in winter-water storage. (USGS)

A Statistical Weather-Driven Streamflow Model: Enabling future flow predictions in data-scarce headwater streams

NASA Astrophysics Data System (ADS)

Rosner, A.; Letcher, B. H.; Vogel, R. M.

2014-12-01

Predicting streamflow in headwaters and over a broad spatial scale pose unique challenges due to limited data availability. Flow observation gages for headwaters streams are less common than for larger rivers, and gages with records lengths of ten year or more are even more scarce. Thus, there is a great need for estimating streamflows in ungaged or sparsely-gaged headwaters. Further, there is often insufficient basin information to develop rainfall-runoff models that could be used to predict future flows under various climate scenarios. Headwaters in the northeastern U.S. are of particular concern to aquatic biologists, as these stream serve as essential habitat for native coldwater fish. In order to understand fish response to past or future environmental drivers, estimates of seasonal streamflow are needed. While there is limited flow data, there is a wealth of data for historic weather conditions. Observed data has been modeled to interpolate a spatially continuous historic weather dataset. (Mauer et al 2002). We present a statistical model developed by pairing streamflow observations with precipitation and temperature information for the same and preceding time-steps. We demonstrate this model's use to predict flow metrics at the seasonal time-step. While not a physical model, this statistical model represents the weather drivers. Since this model can predict flows not directly tied to reference gages, we can generate flow estimates for historic as well as potential future conditions.

A Streamflow Statistics (StreamStats) Web Application for Ohio

USGS Publications Warehouse

Koltun, G.F.; Kula, Stephanie P.; Puskas, Barry M.

2006-01-01

A StreamStats Web application was developed for Ohio that implements equations for estimating a variety of streamflow statistics including the 2-, 5-, 10-, 25-, 50-, 100-, and 500-year peak streamflows, mean annual streamflow, mean monthly streamflows, harmonic mean streamflow, and 25th-, 50th-, and 75th-percentile streamflows. StreamStats is a Web-based geographic information system application designed to facilitate the estimation of streamflow statistics at ungaged locations on streams. StreamStats can also serve precomputed streamflow statistics determined from streamflow-gaging station data. The basic structure, use, and limitations of StreamStats are described in this report. To facilitate the level of automation required for Ohio's StreamStats application, the technique used by Koltun (2003)1 for computing main-channel slope was replaced with a new computationally robust technique. The new channel-slope characteristic, referred to as SL10-85, differed from the National Hydrography Data based channel slope values (SL) reported by Koltun (2003)1 by an average of -28.3 percent, with the median change being -13.2 percent. In spite of the differences, the two slope measures are strongly correlated. The change in channel slope values resulting from the change in computational method necessitated revision of the full-model equations for flood-peak discharges originally presented by Koltun (2003)1. Average standard errors of prediction for the revised full-model equations presented in this report increased by a small amount over those reported by Koltun (2003)1, with increases ranging from 0.7 to 0.9 percent. Mean percentage changes in the revised regression and weighted flood-frequency estimates relative to regression and weighted estimates reported by Koltun (2003)1 were small, ranging from -0.72 to -0.25 percent and -0.22 to 0.07 percent, respectively.

Quantifying streamflow change caused by forest disturbance at a large spatial scale: A single watershed study

NASA Astrophysics Data System (ADS)

Wei, Xiaohua; Zhang, Mingfang

2010-12-01

Climatic variability and forest disturbance are commonly recognized as two major drivers influencing streamflow change in large-scale forested watersheds. The greatest challenge in evaluating quantitative hydrological effects of forest disturbance is the removal of climatic effect on hydrology. In this paper, a method was designed to quantify respective contributions of large-scale forest disturbance and climatic variability on streamflow using the Willow River watershed (2860 km2) located in the central part of British Columbia, Canada. Long-term (>50 years) data on hydrology, climate, and timber harvesting history represented by equivalent clear-cutting area (ECA) were available to discern climatic and forestry influences on streamflow by three steps. First, effective precipitation, an integrated climatic index, was generated by subtracting evapotranspiration from precipitation. Second, modified double mass curves were developed by plotting accumulated annual streamflow against annual effective precipitation, which presented a much clearer picture of the cumulative effects of forest disturbance on streamflow following removal of climatic influence. The average annual streamflow changes that were attributed to forest disturbances and climatic variability were then estimated to be +58.7 and -72.4 mm, respectively. The positive (increasing) and negative (decreasing) values in streamflow change indicated opposite change directions, which suggest an offsetting effect between forest disturbance and climatic variability in the study watershed. Finally, a multivariate Autoregressive Integrated Moving Average (ARIMA) model was generated to establish quantitative relationships between accumulated annual streamflow deviation attributed to forest disturbances and annual ECA. The model was then used to project streamflow change under various timber harvesting scenarios. The methodology can be effectively applied to any large-scale single watershed where long-term data (>50 years) are available.

Treating pre-instrumental data as "missing" data: using a tree-ring-based paleoclimate record and imputations to reconstruct streamflow in the Missouri River Basin

NASA Astrophysics Data System (ADS)

Ho, M. W.; Lall, U.; Cook, E. R.

2015-12-01

Advances in paleoclimatology in the past few decades have provided opportunities to expand the temporal perspective of the hydrological and climatological variability across the world. The North American region is particularly fortunate in this respect where a relatively dense network of high resolution paleoclimate proxy records have been assembled. One such network is the annually-resolved Living Blended Drought Atlas (LBDA): a paleoclimate reconstruction of the Palmer Drought Severity Index (PDSI) that covers North America on a 0.5° × 0.5° grid based on tree-ring chronologies. However, the use of the LBDA to assess North American streamflow variability requires a model by which streamflow may be reconstructed. Paleoclimate reconstructions have typically used models that first seek to quantify the relationship between the paleoclimate variable and the environmental variable of interest before extrapolating the relationship back in time. In contrast, the pre-instrumental streamflow is here considered as "missing" data. A method of imputing the "missing" streamflow data, prior to the instrumental record, is applied through multiple imputation using chained equations for streamflow in the Missouri River Basin. In this method, the distribution of the instrumental streamflow and LBDA is used to estimate sets of plausible values for the "missing" streamflow data resulting in a ~600 year-long streamflow reconstruction. Past research into external climate forcings, oceanic-atmospheric variability and its teleconnections, and assessments of rare multi-centennial instrumental records demonstrate that large temporal oscillations in hydrological conditions are unlikely to be captured in most instrumental records. The reconstruction of multi-centennial records of streamflow will enable comprehensive assessments of current and future water resource infrastructure and operations under the existing scope of natural climate variability.

Impacts of changes in climate and land use/land cover under IPCC RCP scenarios on streamflow in the Hoeya River Basin, Korea.

PubMed

Kim, Jinsoo; Choi, Jisun; Choi, Chuluong; Park, Soyoung

2013-05-01

This study examined the separate and combined impacts of future changes in climate and land use/land cover (LULC) on streamflow in the Hoeya River Basin, South Korea, using the representative concentration pathway (RCP) 4.5 and 8.5 scenarios of the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). First, a LULC change model was developed using RCP 4.5 and RCP 8.5 storylines and logistic regression. Three scenarios (climate change only, LULC change only, and climate and LULC change combined) were established, and the streamflow in future periods under these scenarios was simulated by the Soil and Water Assessment Tool (SWAT) model. Each scenario showed distinct seasonal variations in streamflow. Under climate change only, streamflow increased in spring and winter but decreased in summer and autumn, whereas LULC change increased high flow during wet periods but decreased low flow in dry periods. Although the LULC change had less effect than climate change on the changes in streamflow, the effect of LULC change on streamflow was significant. The result for the combined scenario was similar to that of the climate change only scenario, but with larger seasonal changes in streamflow. Although the effects of LULC change were smaller than those caused by climate change, LULC changes may heighten the problems of increased seasonal variability in streamflow caused by climate change. The results obtained in this study provide further insight into the availability of future streamflow and can aid in water resource management planning in the study area. Copyright © 2013 Elsevier B.V. All rights reserved.

Regionalization of harmonic-mean streamflows in Kentucky

USGS Publications Warehouse

Martin, Gary R.; Ruhl, Kevin J.

1993-01-01

Harmonic-mean streamflow (Qh), defined as the reciprocal of the arithmetic mean of the reciprocal daily streamflow values, was determined for selected stream sites in Kentucky. Daily mean discharges for the available period of record through the 1989 water year at 230 continuous record streamflow-gaging stations located in and adjacent to Kentucky were used in the analysis. Periods of record affected by regulation were identified and analyzed separately from periods of record unaffected by regulation. Record-extension procedures were applied to short-term stations to reducetime-sampling error and, thus, improve estimates of the long-term Qh. Techniques to estimate the Qh at ungaged stream sites in Kentucky were developed. A regression model relating Qh to total drainage area and streamflow-variability index was presented with example applications. The regression model has a standard error of estimate of 76 percent and a standard error of prediction of 78 percent.

Hydrologic and hydraulic flood forecasting constrained by remote sensing data

NASA Astrophysics Data System (ADS)

Li, Y.; Grimaldi, S.; Pauwels, V. R. N.; Walker, J. P.; Wright, A. J.

2017-12-01

Flooding is one of the most destructive natural disasters, resulting in many deaths and billions of dollars of damages each year. An indispensable tool to mitigate the effect of floods is to provide accurate and timely forecasts. An operational flood forecasting system typically consists of a hydrologic model, converting rainfall data into flood volumes entering the river system, and a hydraulic model, converting these flood volumes into water levels and flood extents. Such a system is prone to various sources of uncertainties from the initial conditions, meteorological forcing, topographic data, model parameters and model structure. To reduce those uncertainties, current forecasting systems are typically calibrated and/or updated using ground-based streamflow measurements, and such applications are limited to well-gauged areas. The recent increasing availability of spatially distributed remote sensing (RS) data offers new opportunities to improve flood forecasting skill. Based on an Australian case study, this presentation will discuss the use of 1) RS soil moisture to constrain a hydrologic model, and 2) RS flood extent and level to constrain a hydraulic model.The GRKAL hydrological model is calibrated through a joint calibration scheme using both ground-based streamflow and RS soil moisture observations. A lag-aware data assimilation approach is tested through a set of synthetic experiments to integrate RS soil moisture to constrain the streamflow forecasting in real-time.The hydraulic model is LISFLOOD-FP which solves the 2-dimensional inertial approximation of the Shallow Water Equations. Gauged water level time series and RS-derived flood extent and levels are used to apply a multi-objective calibration protocol. The effectiveness with which each data source or combination of data sources constrained the parameter space will be discussed.

Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations.

NASA Astrophysics Data System (ADS)

López López, Patricia; Wanders, Niko; Sutanudjaja, Edwin; Renzullo, Luigi; Sterk, Geert; Schellekens, Jaap; Bierkens, Marc

2015-04-01

The coarse spatial resolution of global hydrological models (typically > 0.25o) often limits their ability to resolve key water balance processes for many river basins and thus compromises their suitability for water resources management, especially when compared to locally-tunes river models. A possible solution to the problem may be to drive the coarse resolution models with high-resolution meteorological data as well as to assimilate ground-based and remotely-sensed observations of key water cycle variables. While this would improve the modelling resolution of the global model, the impact of prediction accuracy remains largely an open question. In this study we investigated the impact that assimilating streamflow and satellite soil moisture observations have on global hydrological model estimation, driven by coarse- and high-resolution meteorological observations, for the Murrumbidgee river basin in Australia. The PCR-GLOBWB global hydrological model is forced with downscaled global climatological data (from 0.5o downscaled to 0.1o resolution) obtained from the WATCH Forcing Data (WFDEI) and local high resolution gauging station based gridded datasets (0.05o), sourced from the Australian Bureau of Meteorology. Downscaled satellite derived soil moisture (from 0.5o downscaled to 0.1o resolution) from AMSR-E and streamflow observations collected from 25 gauging stations are assimilated using an ensemble Kalman filter. Several scenarios are analysed to explore the added value of data assimilation considering both local and global climatological data. Results show that the assimilation of streamflow observations result in the largest improvement of the model estimates. The joint assimilation of both streamflow and downscaled soil moisture observations leads to further improved in streamflow simulations (10% reduction in RMSE), mainly in the headwater catchments (up to 10,000 km2). Results also show that the added contribution of data assimilation, for both soil moisture and streamflow, is more pronounced when the global meteorological data are used to force the models. This is caused by the higher uncertainty and coarser resolution of the global forcing. This study demonstrates that it is possible to improve hydrological simulations forced by coarse resolution meteorological data with downscaled satellite soil moisture and streamflow observations and bring them closer to a hydrological model forced with local climatological data. These findings are important in light of the efforts that are currently done to go to global hyper-resolution modelling and can significantly help to advance this research.

A hybrid model to assess the impact of climate variability on streamflow for an ungauged mountainous basin

NASA Astrophysics Data System (ADS)

Wang, Chong; Xu, Jianhua; Chen, Yaning; Bai, Ling; Chen, Zhongsheng

2018-04-01

To quantitatively assess the impact of climate variability on streamflow in an ungauged mountainous basin is a difficult and challenging work. In this study, a hybrid model combing downscaling method based on earth data products, back propagation artificial neural networks (BPANN) and weights connection method was developed to explore an approach for solving this problem. To validate the applicability of the hybrid model, the Kumarik River and Toshkan River, two headwaters of the Aksu River, were employed to assess the impact of climate variability on streamflow by using this hybrid model. The conclusion is that the hybrid model presented a good performance, and the quantitative assessment results for the two headwaters are: (1) the precipitation respectively increased by 48.5 and 41.0 mm in the Kumarik catchment and Toshkan catchment, and the average annual temperature both increased by 0.1 °C in the two catchments during each decade from 1980 to 2012; (2) with the warming and wetting climate, the streamflow respectively increased 1.5 × 108 and 3.3 × 108 m3 per decade in the Kumarik River and the Toshkan River; and (3) the contribution of the temperature and precipitation to the streamflow, which were 64.01 ± 7.34, 35.99 ± 7.34 and 47.72 ± 8.10, 52.26 ± 8.10%, respectively in the Kumarik catchment and Toshkan catchment. Our study introduced a feasible hybrid model for the assessment of the impact of climate variability on streamflow, which can be used in the ungauged mountainous basin of Northwest China.

Using oceanic-atmospheric oscillations for long lead time streamflow forecasting

NASA Astrophysics Data System (ADS)

Kalra, Ajay; Ahmad, Sajjad

2009-03-01

We present a data-driven model, Support Vector Machine (SVM), for long lead time streamflow forecasting using oceanic-atmospheric oscillations. The SVM is based on statistical learning theory that uses a hypothesis space of linear functions based on Kernel approach and has been used to predict a quantity forward in time on the basis of training from past data. The strength of SVM lies in minimizing the empirical classification error and maximizing the geometric margin by solving inverse problem. The SVM model is applied to three gages, i.e., Cisco, Green River, and Lees Ferry in the Upper Colorado River Basin in the western United States. Annual oceanic-atmospheric indices, comprising Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), and El Nino-Southern Oscillations (ENSO) for a period of 1906-2001 are used to generate annual streamflow volumes with 3 years lead time. The SVM model is trained with 86 years of data (1906-1991) and tested with 10 years of data (1992-2001). On the basis of correlation coefficient, root means square error, and Nash Sutcliffe Efficiency Coefficient the model shows satisfactory results, and the predictions are in good agreement with measured streamflow volumes. Sensitivity analysis, performed to evaluate the effect of individual and coupled oscillations, reveals a strong signal for ENSO and NAO indices as compared to PDO and AMO indices for the long lead time streamflow forecast. Streamflow predictions from the SVM model are found to be better when compared with the predictions obtained from feedforward back propagation artificial neural network model and linear regression.

Hydrological effects of cropland and climatic changes in arid and semi-arid river basins: A case study from the Yellow River basin, China

NASA Astrophysics Data System (ADS)

Li, Huazhen; Zhang, Qiang; Singh, Vijay P.; Shi, Peijun; Sun, Peng

2017-06-01

The Yellow River basin is a typical semi-arid river basin in northern China. Serious water shortages have negative impacts on regional socioeconomic development. Recent years have witnessed changes in streamflow processes due to increasing human activities, such as agricultural activities and construction of dams and water reservoirs, and climatic changes, e.g. precipitation and temperature. This study attempts to investigate factors potentially driving changes in different streamflow components defined by different quantiles. The data used were daily streamflow data for the 1959-2005 period from 5 hydrological stations, daily precipitation and temperature data from 77 meteorological stations and data pertaining to cropland and large reservoirs. Results indicate a general decrease in streamflow across the Yellow River basin. Moreover significant decreasing streamflow has been observed in the middle and lower Yellow River basin with change points during the mid-1980s till the mid-1990s. The changes of cropland affect the streamflow components and also the cumulative effects on streamflow variations. Recent years have witnessed moderate cropland variations which result in moderate streamflow changes. Further, precipitation also plays a critical role in changes of streamflow components and human activities, i.e. cropland changes, temperature changes and building of water reservoirs, tend to have increasing impacts on hydrological processes across the Yellow River basin. This study provides a theoretical framework for the study of the hydrological effects of human activities and climatic changes on basins over the globe.

A comparison of hydrologic models for ecological flows and water availability

Treesearch

Peter V. Caldwell; Jonathan G. Kennen; Ge Sun; Julie E. Kiang; Jon B. Butcher; Michele C. Eddy; Lauren E. Hay; Jacob H. LaFontaine; Ernie F. Hain; Stacy A. C. Nelson; Steve G. McNulty

2015-01-01

Robust hydrologic models are needed to help manage water resources for healthy aquatic ecosystems and reliable water supplies for people, but there is a lack of comprehensive model comparison studies that quantify differences in streamflow predictions among model applications developed to answer management questions. We assessed differences in daily streamflow...

Streamflow response to increasing precipitation extremes altered by forest management

Treesearch

Charlene N. Kelly; Kevin J. McGuire; Chelcy Ford Miniat; James M. Vose

2016-01-01

Increases in extreme precipitation events of floods and droughts are expected to occur worldwide. The increase in extreme events will result in changes in streamflow that are expected to affect water availability for human consumption and aquatic ecosystem function. We present an analysis that may greatly improve current streamflow models by quantifying the...

Multi-scale streamflow variability responses to precipitation over the headwater catchments in southern China

NASA Astrophysics Data System (ADS)

Niu, Jun; Chen, Ji; Wang, Keyi; Sivakumar, Bellie

2017-08-01

This paper examines the multi-scale streamflow variability responses to precipitation over 16 headwater catchments in the Pearl River basin, South China. The long-term daily streamflow data (1952-2000), obtained using a macro-scale hydrological model, the Variable Infiltration Capacity (VIC) model, and a routing scheme, are studied. Temporal features of streamflow variability at 10 different timescales, ranging from 6 days to 8.4 years, are revealed with the Haar wavelet transform. The principal component analysis (PCA) is performed to categorize the headwater catchments with the coherent modes of multi-scale wavelet spectra. The results indicate that three distinct modes, with different variability distributions at small timescales and seasonal scales, can explain 95% of the streamflow variability. A large majority of the catchments (i.e. 12 out of 16) exhibit consistent mode feature on multi-scale variability throughout three sub-periods (1952-1968, 1969-1984, and 1985-2000). The multi-scale streamflow variability responses to precipitation are identified to be associated with the regional flood and drought tendency over the headwater catchments in southern China.

Climate Change and the Snowmelt-runoff Relationship in the Upper Rio Grande Basin

NASA Astrophysics Data System (ADS)

Chavarria, S. B.; Gutzler, D. S.

2016-12-01

Drought and rising temperatures have resulted in reduced snowpack and low flows in recent years for the Rio Grande, a vital source of surface water in three southwestern states and northern Mexico. We assess monthly and seasonal changes in streamflow volume on the upper Rio Grande (URG) near its headwaters in southern Colorado for water years 1958-2015. We use gage data from the U.S. Geological Survey, naturalized streamflows from the U.S. Natural Resources Conservation Service, and observed temperature, precipitation and snowpack data in the URG. Trends in discharge and downstream gains/losses are examined together with covariations in snow water equivalent, and surface climate variables. We test the hypothesis that climate change is already affecting the streamflow volume derived from snow accumulation in ways consistent with CMIP-based model projections of 21st Century streamflow, and we attempt to separate climate-related streamflow signals from variability due to reservoir releases or diversions. Preliminary results indicate that decreasing snowpack and resulting diminution of springtime streamflow in the URG are detectable in both observed and naturalized flow data beginning in the mid to late 1980s, despite the absence of significant decrease in total flow. Correlations between warm and cold season fluctuations in streamflow and temperature or precipitation are being evaluated and will be compared to model projections. Our study will provide information that may be useful for validating hydroclimatic models and improving seasonal water supply outlooks, essential tools for water management.

Groundwater and surface-water interaction and effects of pumping in a complex glacial-sediment aquifer, phase 2, east-central Massachusetts

USGS Publications Warehouse

Eggleston, Jack R.; Zarriello, Phillip J.; Carlson, Carl S.

2015-12-31

Model simulations indicate that under average base-flow conditions, the Birch Road wells have a small effect on flow in the Sudbury River during most months, even at the maximum pumping rate of 4.9 ft3/s (3.17 Mgal/d). Maximum percent streamflow depletion in the Sudbury River caused by simulated pumping takes place during simulated drought conditions, when streamflow decreased by as much as 21 percent under maximum continuous pumping. Simulations also indicate that groundwater withdrawals at the Birch Road site could be managed so that adverse streamflow impacts are substantially ameliorated. Under the most ecologically conservative simulated drought conditions, simulated streamflow depletion was reduced from 21 percent to 3 percent by pumping at the maximum rate for 6 months rather than for 12 months. Simulations that return 10 percent of the Birch Road well withdrawals to Pod Meadow Pond indicate a modest reduction in the Sudbury River streamflow depletion and provide a larger percentage increase to streamflow just downstream of the pond. The groundwater model also indicates that well locations can have a large effect on the sustainable pumping rate and so should be chosen carefully. The model provides a tool for evaluating alternative pumping rates and schedules not included in this analysis.

In ecoregions across western USA streamflow increases during post-wildfire recovery

NASA Astrophysics Data System (ADS)

Wine, Michael L.; Cadol, Daniel; Makhnin, Oleg

2018-01-01

Continued growth of the human population on Earth will increase pressure on already stressed terrestrial water resources required for drinking water, agriculture, and industry. This stress demands improved understanding of critical controls on water resource availability, particularly in water-limited regions. Mechanistic predictions of future water resource availability are needed because non-stationary conditions exist in the form of changing climatic conditions, land management paradigms, and ecological disturbance regimes. While historically ecological disturbances have been small and could be neglected relative to climatic effects, evidence is accumulating that ecological disturbances, particularly wildfire, can increase regional water availability. However, wildfire hydrologic impacts are typically estimated locally and at small spatial scales, via disparate measurement methods and analysis techniques, and outside the context of climate change projections. Consequently, the relative importance of climate change driven versus wildfire driven impacts on streamflow remains unknown across the western USA. Here we show that considering wildfire in modeling streamflow significantly improves model predictions. Mixed effects modeling attributed 2%-14% of long-term annual streamflow to wildfire effects. The importance of this wildfire-linked streamflow relative to predicted climate change-induced streamflow reductions ranged from 20%-370% of the streamflow decrease predicted to occur by 2050. The rate of post-wildfire vegetation recovery and the proportion of watershed area burned controlled the wildfire effect. Our results demonstrate that in large areas of the western USA affected by wildfire, regional predictions of future water availability are subject to greater structural uncertainty than previously thought. These results suggest that future streamflows may be underestimated in areas affected by increased prevalence of hydrologically relevant ecological disturbances such as wildfire.

Assessing the relative importance of parameter and forcing uncertainty and their interactions in conceptual hydrological model simulations

NASA Astrophysics Data System (ADS)

Mockler, E. M.; Chun, K. P.; Sapriza-Azuri, G.; Bruen, M.; Wheater, H. S.

2016-11-01

Predictions of river flow dynamics provide vital information for many aspects of water management including water resource planning, climate adaptation, and flood and drought assessments. Many of the subjective choices that modellers make including model and criteria selection can have a significant impact on the magnitude and distribution of the output uncertainty. Hydrological modellers are tasked with understanding and minimising the uncertainty surrounding streamflow predictions before communicating the overall uncertainty to decision makers. Parameter uncertainty in conceptual rainfall-runoff models has been widely investigated, and model structural uncertainty and forcing data have been receiving increasing attention. This study aimed to assess uncertainties in streamflow predictions due to forcing data and the identification of behavioural parameter sets in 31 Irish catchments. By combining stochastic rainfall ensembles and multiple parameter sets for three conceptual rainfall-runoff models, an analysis of variance model was used to decompose the total uncertainty in streamflow simulations into contributions from (i) forcing data, (ii) identification of model parameters and (iii) interactions between the two. The analysis illustrates that, for our subjective choices, hydrological model selection had a greater contribution to overall uncertainty, while performance criteria selection influenced the relative intra-annual uncertainties in streamflow predictions. Uncertainties in streamflow predictions due to the method of determining parameters were relatively lower for wetter catchments, and more evenly distributed throughout the year when the Nash-Sutcliffe Efficiency of logarithmic values of flow (lnNSE) was the evaluation criterion.

Climatic change projections for winter streamflow in Guadalquivir river

NASA Astrophysics Data System (ADS)

Jesús Esteban Parra, María; Hidalgo Muñoz, José Manuel; García-Valdecasas-Ojeda, Matilde; Raquel Gámiz Fortis, Sonia; Castro Díez, Yolanda

2015-04-01

In this work we have obtained climate change projections for winter streamflow of the Guadalquivir River in the period 2071-2100 using the Principal Component Regression (PCR) method. The streamflow data base used has been provided by the Center for Studies and Experimentation of Public Works, CEDEX. Series from gauging stations and reservoirs with less than 10% of missing data (filled by regression with well correlated neighboring stations) have been considered. The homogeneity of these series has been evaluated through the Pettit test and degree of human alteration by the Common Area Index. The application of these criteria led to the selection of 13 streamflow time series homogeneously distributed over the basin, covering the period 1952-2011. For this streamflow data, winter seasonal values were obtained by averaging the monthly values from January to March. The PCR method has been applied using the Principal Components of the mean anomalies of sea level pressure (SLP) in winter (December to February averaged) as predictors of streamflow for the development of a downscaled statistical model. The SLP database is the NCEP reanalysis covering the North Atlantic region, and the calibration and validation periods used for fitting and evaluating the ability of the model are 1952-1992 and 1993-2011, respectively. In general, using four Principal Components, regression models are able to explain up to 70% of the variance of the streamflow data. Finally, the statistical model obtained for the observational data was applied to the SLP data for the period 2071-2100, using the outputs of different GCMs of the CMIP5 under the RPC8.5 scenario. The results found for the end of the century show no significant changes or moderate decrease in the streamflow of this river for most GCMs in winter, but for some of them the decrease is very strong. Keywords: Statistical downscaling, streamflow, Guadalquivir River, climate change. ACKNOWLEDGEMENTS This work has been financed by the projects P11-RNM-7941 (Junta de Andalucía-Spain) and CGL2013-48539-R (MINECO-Spain, FEDER).

Climate change streamflow scenarios designed for critical period water resources planning studies

NASA Astrophysics Data System (ADS)

Hamlet, A. F.; Snover, A. K.; Lettenmaier, D. P.

2003-04-01

Long-range water planning in the United States is usually conducted by individual water management agencies using a critical period planning exercise based on a particular period of the observed streamflow record and a suite of internally-developed simulation tools representing the water system. In the context of planning for climate change, such an approach is flawed in that it assumes that the future climate will be like the historic record. Although more sophisticated planning methods will probably be required as time goes on, a short term strategy for incorporating climate uncertainty into long-range water planning as soon as possible is to create alternate inputs to existing planning methods that account for climate uncertainty as it affects both supply and demand. We describe a straight-forward technique for constructing streamflow scenarios based on the historic record that include the broad-based effects of changed regional climate simulated by several global climate models (GCMs). The streamflow scenarios are based on hydrologic simulations driven by historic climate data perturbed according to regional climate signals from four GCMs using the simple "delta" method. Further data processing then removes systematic hydrologic model bias using a quantile-based bias correction scheme, and lastly, the effects of random errors in the raw hydrologic simulations are removed. These techniques produce streamflow scenarios that are consistent in time and space with the historic streamflow record while incorporating fundamental changes in temperature and precipitation from the GCM scenarios. Planning model simulations based on these climate change streamflow scenarios can therefore be compared directly to planning model simulations based on the historic record of streamflows to help planners understand the potential impacts of climate uncertainty. The methods are currently being tested and refined in two large-scale planning exercises currently being conducted in the Pacific Northwest (PNW) region of the US, and the resulting streamflow scenarios will be made freely available on the internet for a large number of sites in the PNW to help defray the costs of including climate change information in other studies.

Inferring Soil Moisture Memory from Streamflow Observations Using a Simple Water Balance Model

NASA Technical Reports Server (NTRS)

Orth, Rene; Koster, Randal Dean; Seneviratne, Sonia I.

2013-01-01

Soil moisture is known for its integrative behavior and resulting memory characteristics. Soil moisture anomalies can persist for weeks or even months into the future, making initial soil moisture a potentially important contributor to skill in weather forecasting. A major difficulty when investigating soil moisture and its memory using observations is the sparse availability of long-term measurements and their limited spatial representativeness. In contrast, there is an abundance of long-term streamflow measurements for catchments of various sizes across the world. We investigate in this study whether such streamflow measurements can be used to infer and characterize soil moisture memory in respective catchments. Our approach uses a simple water balance model in which evapotranspiration and runoff ratios are expressed as simple functions of soil moisture; optimized functions for the model are determined using streamflow observations, and the optimized model in turn provides information on soil moisture memory on the catchment scale. The validity of the approach is demonstrated with data from three heavily monitored catchments. The approach is then applied to streamflow data in several small catchments across Switzerland to obtain a spatially distributed description of soil moisture memory and to show how memory varies, for example, with altitude and topography.

HYDRORECESSION: A toolbox for streamflow recession analysis

NASA Astrophysics Data System (ADS)

Arciniega, S.

2015-12-01

Streamflow recession curves are hydrological signatures allowing to study the relationship between groundwater storage and baseflow and/or low flows at the catchment scale. Recent studies have showed that streamflow recession analysis can be quite sensitive to the combination of different models, extraction techniques and parameter estimation methods. In order to better characterize streamflow recession curves, new methodologies combining multiple approaches have been recommended. The HYDRORECESSION toolbox, presented here, is a Matlab graphical user interface developed to analyse streamflow recession time series with the support of different tools allowing to parameterize linear and nonlinear storage-outflow relationships through four of the most useful recession models (Maillet, Boussinesq, Coutagne and Wittenberg). The toolbox includes four parameter-fitting techniques (linear regression, lower envelope, data binning and mean squared error) and three different methods to extract hydrograph recessions segments (Vogel, Brutsaert and Aksoy). In addition, the toolbox has a module that separates the baseflow component from the observed hydrograph using the inverse reservoir algorithm. Potential applications provided by HYDRORECESSION include model parameter analysis, hydrological regionalization and classification, baseflow index estimates, catchment-scale recharge and low-flows modelling, among others. HYDRORECESSION is freely available for non-commercial and academic purposes.

Artificial intelligence based models for stream-flow forecasting: 2000-2015

NASA Astrophysics Data System (ADS)

Yaseen, Zaher Mundher; El-shafie, Ahmed; Jaafar, Othman; Afan, Haitham Abdulmohsin; Sayl, Khamis Naba

2015-11-01

The use of Artificial Intelligence (AI) has increased since the middle of the 20th century as seen in its application in a wide range of engineering and science problems. The last two decades, for example, has seen a dramatic increase in the development and application of various types of AI approaches for stream-flow forecasting. Generally speaking, AI has exhibited significant progress in forecasting and modeling non-linear hydrological applications and in capturing the noise complexity in the dataset. This paper explores the state-of-the-art application of AI in stream-flow forecasting, focusing on defining the data-driven of AI, the advantages of complementary models, as well as the literature and their possible future application in modeling and forecasting stream-flow. The review also identifies the major challenges and opportunities for prospective research, including, a new scheme for modeling the inflow, a novel method for preprocessing time series frequency based on Fast Orthogonal Search (FOS) techniques, and Swarm Intelligence (SI) as an optimization approach.

Linking Statistically- and Physically-Based Models for Improved Streamflow Simulation in Gaged and Ungaged Areas

NASA Astrophysics Data System (ADS)

Lafontaine, J.; Hay, L.; Archfield, S. A.; Farmer, W. H.; Kiang, J. E.

2014-12-01

The U.S. Geological Survey (USGS) has developed a National Hydrologic Model (NHM) to support coordinated, comprehensive and consistent hydrologic model development, and facilitate the application of hydrologic simulations within the continental US. The portion of the NHM located within the Gulf Coastal Plains and Ozarks Landscape Conservation Cooperative (GCPO LCC) is being used to test the feasibility of improving streamflow simulations in gaged and ungaged watersheds by linking statistically- and physically-based hydrologic models. The GCPO LCC covers part or all of 12 states and 5 sub-geographies, totaling approximately 726,000 km2, and is centered on the lower Mississippi Alluvial Valley. A total of 346 USGS streamgages in the GCPO LCC region were selected to evaluate the performance of this new calibration methodology for the period 1980 to 2013. Initially, the physically-based models are calibrated to measured streamflow data to provide a baseline for comparison. An enhanced calibration procedure then is used to calibrate the physically-based models in the gaged and ungaged areas of the GCPO LCC using statistically-based estimates of streamflow. For this application, the calibration procedure is adjusted to address the limitations of the statistically generated time series to reproduce measured streamflow in gaged basins, primarily by incorporating error and bias estimates. As part of this effort, estimates of uncertainty in the model simulations are also computed for the gaged and ungaged watersheds.

Predicting the natural flow regime: Models for assessing hydrological alteration in streams

USGS Publications Warehouse

Carlisle, D.M.; Falcone, J.; Wolock, D.M.; Meador, M.R.; Norris, R.H.

2009-01-01

Understanding the extent to which natural streamflow characteristics have been altered is an important consideration for ecological assessments of streams. Assessing hydrologic condition requires that we quantify the attributes of the flow regime that would be expected in the absence of anthropogenic modifications. The objective of this study was to evaluate whether selected streamflow characteristics could be predicted at regional and national scales using geospatial data. Long-term, gaged river basins distributed throughout the contiguous US that had streamflow characteristics representing least disturbed or near pristine conditions were identified. Thirteen metrics of the magnitude, frequency, duration, timing and rate of change of streamflow were calculated using a 20-50 year period of record for each site. We used random forests (RF), a robust statistical modelling approach, to develop models that predicted the value for each streamflow metric using natural watershed characteristics. We compared the performance (i.e. bias and precision) of national- and regional-scale predictive models to that of models based on landscape classifications, including major river basins, ecoregions and hydrologic landscape regions (HLR). For all hydrologic metrics, landscape stratification models produced estimates that were less biased and more precise than a null model that accounted for no natural variability. Predictive models at the national and regional scale performed equally well, and substantially improved predictions of all hydrologic metrics relative to landscape stratification models. Prediction error rates ranged from 15 to 40%, but were 25% for most metrics. We selected three gaged, non-reference sites to illustrate how predictive models could be used to assess hydrologic condition. These examples show how the models accurately estimate predisturbance conditions and are sensitive to changes in streamflow variability associated with long-term land-use change. We also demonstrate how the models can be applied to predict expected natural flow characteristics at ungaged sites. ?? 2009 John Wiley & Sons, Ltd.

The significance of spatial variability of rainfall on streamflow: A synthetic analysis at the Upper Lee catchment, UK

NASA Astrophysics Data System (ADS)

Pechlivanidis, Ilias; McIntyre, Neil; Wheater, Howard

2017-04-01

Rainfall, one of the main inputs in hydrological modeling, is a highly heterogeneous process over a wide range of scales in space, and hence the ignorance of the spatial rainfall information could affect the simulated streamflow. Calibration of hydrological model parameters is rarely a straightforward task due to parameter equifinality and parameters' 'nature' to compensate for other uncertainties, i.e. structural and forcing input. In here, we analyse the significance of spatial variability of rainfall on streamflow as a function of catchment scale and type, and antecedent conditions using the continuous time, semi-distributed PDM hydrological model at the Upper Lee catchment, UK. The impact of catchment scale and type is assessed using 11 nested catchments ranging in scale from 25 to 1040 km2, and further assessed by artificially changing the catchment characteristics and translating these to model parameters with uncertainty using model regionalisation. Synthetic rainfall events are introduced to directly relate the change in simulated streamflow to the spatial variability of rainfall. Overall, we conclude that the antecedent catchment wetness and catchment type play an important role in controlling the significance of the spatial distribution of rainfall on streamflow. Results show a relationship between hydrograph characteristics (streamflow peak and volume) and the degree of spatial variability of rainfall for the impermeable catchments under dry antecedent conditions, although this decreases at larger scales; however this sensitivity is significantly undermined under wet antecedent conditions. Although there is indication that the impact of spatial rainfall on streamflow varies as a function of catchment scale, the variability of antecedent conditions between the synthetic catchments seems to mask this significance. Finally, hydrograph responses to different spatial patterns in rainfall depend on assumptions used for model parameter estimation and also the spatial variation in parameters indicating the need of an uncertainty framework in such investigation.

Precipitation-Runoff Modeling System (PRMS) and Streamflow Response to Spatially Distributed Precipitation in Two Large Watersheds in Northern California

NASA Astrophysics Data System (ADS)

Dhakal, A. S.; Adera, S.; Niswonger, R. G.; Gardner, M.

2016-12-01

The ability of the Precipitation-Runoff Modeling System (PRMS) to predict peak intensity, peak timing, base flow, and volume of streamflow was examined in Arroyo Hondo (180 km2) and Upper Alameda Creek (85 km2), two sub-watersheds of the Alameda Creek watershed in Northern California. Rainfall-runoff volume ratios vary widely, and can exceed 0.85 during mid-winter flashy rainstorm events. Due to dry antecedent soil moisture conditions, the first storms of the hydrologic year often produce smaller rainfall-runoff volume ratios. Runoff response in this watershed is highly hysteretic; large precipitation events are required to generate runoff following a 4-week period without precipitation. After about 150 mm of cumulative rainfall, streamflow responds quickly to subsequent storms, with variations depending on rainstorm intensity. Inputs to PRMS included precipitation, temperature, topography, vegetation, soils, and land cover data. The data was prepared for input into PRMS using a suite of data processing Python scripts written by the Desert Research Institute and U.S. Geological Survey. PRMS was calibrated by comparing simulated streamflow to measured streamflow at a daily time step during the period 1995 - 2014. The PRMS model is being used to better understand the different patterns of streamflow observed in the Alameda Creek watershed. Although Arroyo Hondo receives more rainfall than Upper Alameda Creek, it is not clear whether the differences in streamflow patterns are a result of differences in rainfall or other variables, such as geology, slope and aspect. We investigate the ability of PRMS to simulate daily streamflow in the two sub-watersheds for a variety of antecedent soil moisture conditions and rainfall intensities. After successful simulation of watershed runoff processes, the model will be expanded using GSFLOW to simulate integrated surface water and groundwater to support water resources planning and management in the Alameda Creek watershed.

Response of streamflow to climate change in a sub-basin of the source region of the Yellow River based on a tank model

NASA Astrophysics Data System (ADS)

Wu, Pan; Wang, Xu-Sheng; Liang, Sihai

2018-06-01

Though extensive researches were conducted in the source region of the Yellow River (SRYR) to analyse climate change influence on streamflow, however, few researches concentrate on streamflow of the sub-basin above the Huangheyan station in the SRYR (HSRYR) where a water retaining dam was built in the outlet in 1999. To improve the reservoir regulation strategies, this study analysed streamflow change of the HSRYR in a mesoscale. A tank model (TM) was proposed and calibrated with monthly observation streamflow from 1991 to 1998. In the validation period, though there is a simulation deviation during the water storage and power generation period, simulated streamflow agrees favourably with observation data from 2008 to 2013. The model was further validated by two inside lakes area obtained from Landsat 5, 7, 8 datasets from 2000 to 2014, and significant correlations were found between the simulated lake outlet runoff and respective lake area. Then 21 Global Climate Models (GCM) ensembled data of three emission scenarios (SRA2, SRA1B and SRB1) were downscaled and used as input to the TM to simulate the runoff change of three benchmark periods 2011-2030 (2020s), 2046-2065 (2050s), 2080-2099 (2090s), respectively. Though temperature increase dramatically, these projected results similarly indicated that streamflow shows an increase trend in the long term. Runoff increase is mainly caused by increasing precipitation and decreasing evaporation. Water resources distribution is projected to change from summer-autumn dominant to autumn winter dominant. Annual lowest runoff will occur in May caused by earlier snow melting and increasing evaporation in March. According to the obtained results, winter runoff should be artificially stored by reservoir regulation in the future to prevent zero-flow occurrent in May. This research is helpful for water resources management and provides a better understand of streamflow change caused by climate change in the future.

21st Century Projections of High Streamflow Events in the UK and Germany

NASA Astrophysics Data System (ADS)

Cioffi, Francesco; Rosario Conticello, Federico; Lall, Upmanu; Merz, Bruno

2017-04-01

Radiative effects of anthropogenic changes in atmospheric composition are expected to enhance the hydrological cycle leading to more frequent and intense floods. To explore if there will be an increased risk of river flooding in the future, 21st century projections under global warming scenarios of High Streamflow Events (HSEs) for UK and German rivers are carried out, using a model that statistically relates large-scale atmospheric predictors - 850 hPa Geopotential Height (GPH850) and Integrated Water Vapor Transport (IVT) - to the occurrence of HSEs in one or simultaneously in several streamflow gauges. Here, HSE is defined as the streamflow exceeding the 99th percentile of daily flowrate time series measured at streamflow gauges. For the common period 1960-2012, historical data from 57 streamflow gauges in UK and 61 streamflow gauges in Germany, as well as, reanalysis data of GPH850 and IVT fields, bounded from 90W to 70E and from 20N to 80N are used. The link between GPH850 configurations and HSEs, and more precisely, identification of the GPH850 states potentially able to generate HSEs, is performed by a combined Kohonen Networks (Self Organized Map, SOM) and Event Syncronization approach. Complex network and modularity methods are used to cluster streamflow gauges that share common GPH850 configurations. Then a model based on a conditional Poisson distribution, in which the parameter of the Poisson distribution is assumed to be a nonlinear function of GPH850 and IVT, allows for the identification of GPH850 state and threshold of IVT beyond which there is the HSE highest probability. Using that model, projections of 21st century changes in frequency of HSEs occurrence in UK and Germany are estimated using the simulated fields of GPH850 and IVT from selected GCMs belonging to the Coupled Model Inter-comparison Project Phase 5 (CMIP5). Among the different GCMs, those are selected whose retrospective predictor fields have consistent statistics with the corresponding reanalysis data.

Application of the US Geological Survey's precipitation-runoff modeling system to Williams Draw and Bush Draw basins, Jackson County, Colorado

USGS Publications Warehouse

Kuhn, Gerhard

1988-01-01

The U.S. Geological Survey 's precipitation-runoff modeling system was calibrated for this study by using daily streamflow data for April through September, 1980 and 1981, from the Williams Draw basin in Jackson County, Colorado. The calibrated model then was verified by using daily streamflow data for April through September, 1982 and 1983. Transferability of the model was tested by application to adjoining Bush Draw basin by using daily streamflow data for April through September, 1981 through 1983. Four model parameters were optimized in the calibration: (1) BST, base air temperature used to determine the form of precipitation (rain, snow, or a mixture); (2) SMAX, maximum available water-holding capacity of the soil zone; (3) TRNCF, transmission coefficient for the vegetation canopy over the snowpack; and (4) DSCOR, daily precipitation correction factor for snow. For calibration and verification, volume and timing of simulated streamflow were reasonably close to recorded streamflow; differences were least during years that had considerable snowpack accumulation and were most during years that had minimal or no snowpack accumulation. Calibration and optimization of parameters were facilitated by snowpack water-equivalent data. Application of the model to Bush Draw basin to test for transferability indicated inaccurate results in simulation of streamflow volume. Weighted values of SMAX, TRNCF, and DSCOR from the calibration basin were used for Bush Draw. The inadequate results obtained by use of weighted parameters indicate that snowpack water-equivalent data are needed for successful application of the precipitation-runoff modeling system in this area, because frequent windy conditions cause variations in snowpack accumulation. (USGS)

Assessment of Climate Change and Agricultural Land Use Change on Streamflow Input to Devils Lake: A Case Study of the Mauvais Coulee Sub-basin

NASA Astrophysics Data System (ADS)

Jackson, C.; Todhunter, P. E.

2017-12-01

Since 1993, Devils Lake in North Dakota has experienced a prolonged rise in lake level and flooding of the lake's neighboring areas within the closed basin system. Understanding the relative contribution of climate change and land use change is needed to explain the historical rise in lake level, and to evaluate the potential impact of anthropogenic climate change upon future lake conditions and management. Four methodologies were considered to examine the relative contribution of climatic and human landscape drivers to streamflow variations: statistical, ecohydrologic, physically-based modeling, and elasticity of streamflow; for this study, ecohydrologic and climate elasticity were selected. Agricultural statistics determined that Towner and Ramsey counties underwent a crop conversion from small grains to row crops within the last 30 years. Through the Topographic Wetness Index (TWI), a 10 meter resolution DEM confirmed the presence of innumerable wetland depressions within the non-contributing area of the Mauvais Coulee Sub-basin. Although the ecohydrologic and climate elasticity methodologies are the most commonly used in literature, they make assumptions that are not applicable to basin conditions. A modified and more informed approach to the use of these methods was applied to account for these unique sub-basin characteristics. Ultimately, hydroclimatic variability was determined as the largest driver to streamflow variation in Mauvais Coulee and Devils Lake.

Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model

NASA Astrophysics Data System (ADS)

Yaseen, Zaher Mundher; Ebtehaj, Isa; Bonakdari, Hossein; Deo, Ravinesh C.; Danandeh Mehr, Ali; Mohtar, Wan Hanna Melini Wan; Diop, Lamine; El-shafie, Ahmed; Singh, Vijay P.

2017-11-01

The present study proposes a new hybrid evolutionary Adaptive Neuro-Fuzzy Inference Systems (ANFIS) approach for monthly streamflow forecasting. The proposed method is a novel combination of the ANFIS model with the firefly algorithm as an optimizer tool to construct a hybrid ANFIS-FFA model. The results of the ANFIS-FFA model is compared with the classical ANFIS model, which utilizes the fuzzy c-means (FCM) clustering method in the Fuzzy Inference Systems (FIS) generation. The historical monthly streamflow data for Pahang River, which is a major river system in Malaysia that characterized by highly stochastic hydrological patterns, is used in the study. Sixteen different input combinations with one to five time-lagged input variables are incorporated into the ANFIS-FFA and ANFIS models to consider the antecedent seasonal variations in historical streamflow data. The mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (r) are used to evaluate the forecasting performance of ANFIS-FFA model. In conjunction with these metrics, the refined Willmott's Index (Drefined), Nash-Sutcliffe coefficient (ENS) and Legates and McCabes Index (ELM) are also utilized as the normalized goodness-of-fit metrics. Comparison of the results reveals that the FFA is able to improve the forecasting accuracy of the hybrid ANFIS-FFA model (r = 1; RMSE = 0.984; MAE = 0.364; ENS = 1; ELM = 0.988; Drefined = 0.994) applied for the monthly streamflow forecasting in comparison with the traditional ANFIS model (r = 0.998; RMSE = 3.276; MAE = 1.553; ENS = 0.995; ELM = 0.950; Drefined = 0.975). The results also show that the ANFIS-FFA is not only superior to the ANFIS model but also exhibits a parsimonious modelling framework for streamflow forecasting by incorporating a smaller number of input variables required to yield the comparatively better performance. It is construed that the FFA optimizer can thus surpass the accuracy of the traditional ANFIS model in general, and is able to remove the false (inaccurately) forecasted data in the ANFIS model for extremely low flows. The present results have wider implications not only for streamflow forecasting purposes, but also for other hydro-meteorological forecasting variables requiring only the historical data input data, and attaining a greater level of predictive accuracy with the incorporation of the FFA algorithm as an optimization tool in an ANFIS model.

Stochastic model for simulating Souris River Basin precipitation, evapotranspiration, and natural streamflow

USGS Publications Warehouse

Kolars, Kelsey A.; Vecchia, Aldo V.; Ryberg, Karen R.

2016-02-24

The Souris River Basin is a 61,000-square-kilometer basin in the Provinces of Saskatchewan and Manitoba and the State of North Dakota. In May and June of 2011, record-setting rains were seen in the headwater areas of the basin. Emergency spillways of major reservoirs were discharging at full or nearly full capacity, and extensive flooding was seen in numerous downstream communities. To determine the probability of future extreme floods and droughts, the U.S. Geological Survey, in cooperation with the North Dakota State Water Commission, developed a stochastic model for simulating Souris River Basin precipitation, evapotranspiration, and natural (unregulated) streamflow. Simulations from the model can be used in future studies to simulate regulated streamflow, design levees, and other structures; and to complete economic cost/benefit analyses.Long-term climatic variability was analyzed using tree-ring chronologies to hindcast precipitation to the early 1700s and compare recent wet and dry conditions to earlier extreme conditions. The extended precipitation record was consistent with findings from the Devils Lake and Red River of the North Basins (southeast of the Souris River Basin), supporting the idea that regional climatic patterns for many centuries have consisted of alternating wet and dry climate states.A stochastic climate simulation model for precipitation, temperature, and potential evapotranspiration for the Souris River Basin was developed using recorded meteorological data and extended precipitation records provided through tree-ring analysis. A significant climate transition was seen around1970, with 1912–69 representing a dry climate state and 1970–2011 representing a wet climate state. Although there were some distinct subpatterns within the basin, the predominant differences between the two states were higher spring through early fall precipitation and higher spring potential evapotranspiration for the wet compared to the dry state.A water-balance model was developed for simulating monthly natural (unregulated) mean streamflow based on precipitation, temperature, and potential evapotranspiration at select streamflow-gaging stations. The model was calibrated using streamflow data from the U.S. Geological Survey and Environment Canada, along with natural (unregulated) streamflow data from the U.S. Army Corps of Engineers. Correlation coefficients between simulated and natural (unregulated) flows generally were high (greater than 0.8), and the seasonal means and standard deviations of the simulated flows closely matched the means and standard deviations of the natural (unregulated) flows. After calibrating the model for a monthly time step, monthly streamflow for each subbasin was disaggregated into three values per month, or an approximately 10-day time step, and a separate routing model was developed for simulating 10-day streamflow for downstream gages.The stochastic climate simulation model for precipitation, temperature, and potential evapotranspiration was combined with the water-balance model to simulate potential future sequences of 10-day mean streamflow for each of the streamflow-gaging station locations. Flood risk, as determined by equilibrium flow-frequency distributions for the dry (1912–69) and wet (1970–2011) climate states, was considerably higher for the wet state compared to the dry state. Future flood risk will remain high until the wet climate state ends, and for several years after that, because there may be a long lag-time between the return of drier conditions and the onset of a lower soil-moisture storage equilibrium.

Simulation of streamflow and water quality in the Leon Creek watershed, Bexar County, Texas, 1997-2004

USGS Publications Warehouse

Ockerman, Darwin J.; Roussel, Meghan C.

2009-01-01

The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers and the San Antonio River Authority, configured, calibrated, and tested a Hydrological Simulation Program ? FORTRAN watershed model for the approximately 238-square-mile Leon Creek watershed in Bexar County, Texas, and used the model to simulate streamflow and water quality (focusing on loads and yields of selected constituents). Streamflow in the model was calibrated and tested with available data from five U.S. Geological Survey streamflow-gaging stations for 1997-2004. Simulated streamflow volumes closely matched measured streamflow volumes at all streamflow-gaging stations. Total simulated streamflow volumes were within 10 percent of measured values. Streamflow volumes are greatly influenced by large storms. Two months that included major floods accounted for about 50 percent of all the streamflow measured at the most downstream gaging station during 1997-2004. Water-quality properties and constituents (water temperature, dissolved oxygen, suspended sediment, dissolved ammonia nitrogen, dissolved nitrate nitrogen, and dissolved and total lead and zinc) in the model were calibrated using available data from 13 sites in and near the Leon Creek watershed for varying periods of record during 1992-2005. Average simulated daily mean water temperature and dissolved oxygen at the most downstream gaging station during 1997-2000 were within 1 percent of average measured daily mean water temperature and dissolved oxygen. Simulated suspended-sediment load at the most downstream gaging station during 2001-04 (excluding July 2002 because of major storms) was 77,700 tons compared with 74,600 tons estimated from a streamflow-load regression relation (coefficient of determination = .869). Simulated concentrations of dissolved ammonia nitrogen and dissolved nitrate nitrogen closely matched measured concentrations after calibration. At the most downstream gaging station, average simulated monthly mean concentrations of dissolved ammonia and nitrate concentrations during 1997-2004 were 0.03 and 0.37 milligram per liter, respectively. For the most downstream station, the measured and simulated concentrations of dissolved and total lead and zinc for stormflows during 1993-97 after calibration do not match particularly closely. For base-flow conditions during 1997-2004 at the most downstream station, the simulated/measured match is better. For example, median simulated concentration of total lead (for 2,041 days) was 0.96 microgram per liter, and median measured concentration (for nine samples) of total lead was 1.0 microgram per liter. To demonstrate an application of the Leon Creek watershed model, streamflow constituent loads and yields for suspended sediment, dissolved nitrate nitrogen, and total lead were simulated at the mouth of Leon Creek (outlet of the watershed) for 1997-2004. The average suspended-sediment load was 51,800 tons per year. The average suspended-sediment yield was 0.34 ton per acre per year. The average load of dissolved nitrate at the outlet of the watershed was 802 tons per year. The corresponding yield was 10.5 pounds per acre per year. The average load of lead at the outlet was 3,900 pounds per year. The average lead yield was 0.026 pound per acre per year. The degree to which available rainfall data represent actual rainfall is potentially the most serious source of measurement error associated with the Leon Creek model. Major storms contribute most of the streamflow loads for certain constituents. For example, the three largest stormflows contributed about 64 percent of the entire suspended-sediment load at the most downstream station during 1997-2004.

Snow Cover and Precipitation Impacts on Dry Season Streamflow in the Lower Mekong Basin

NASA Technical Reports Server (NTRS)

Cook, Benjamin I.; Bell, A. R.; Anchukaitis, K. J.; Buckley, B. M.

2012-01-01

Climate change impacts on dry season streamflow in the Mekong River are relatively understudied, despite the fact that water availability during this time is critically important for agricultural and ecological systems. Analyses of two gauging stations (Vientiane and Kratie) in the Lower Mekong Basin (LMB) show significant positive correlations between dry season (March through May, MAM) discharge and upper basin snow cover and local precipitation. Using snow cover, precipitation, and upstream discharge as predictors, we develop skillful regression models for MAM streamflow at Vientiane and Kratie, and force these models with output from a suite of general circulation model (GCM) experiments for the twentieth and twenty-first centuries. The GCM simulations predict divergent trends in snow cover (decreasing) and precipitation (increasing) over the twenty-first century, driving overall negligible long-term trends in dry season streamflow. Our study demonstrates how future changes in dry season streamflow in the LMB will depend on changes in snow cover and precipitation, factors that will need to be considered when assessing the full basin response to other climatic and non-climatic drivers.

Reconciling Streamflow Uncertainty Estimation and River Bed Morphology Dynamics. Insights from a Probabilistic Assessment of Streamflow Uncertainties Using a Reliability Diagram

NASA Astrophysics Data System (ADS)

Morlot, T.; Mathevet, T.; Perret, C.; Favre Pugin, A. C.

2014-12-01

Streamflow uncertainty estimation has recently received a large attention in the literature. A dynamic rating curve assessment method has been introduced (Morlot et al., 2014). This dynamic method allows to compute a rating curve for each gauging and a continuous streamflow time-series, while calculating streamflow uncertainties. Streamflow uncertainty takes into account many sources of uncertainty (water level, rating curve interpolation and extrapolation, gauging aging, etc.) and produces an estimated distribution of streamflow for each days. In order to caracterise streamflow uncertainty, a probabilistic framework has been applied on a large sample of hydrometric stations of the Division Technique Générale (DTG) of Électricité de France (EDF) hydrometric network (>250 stations) in France. A reliability diagram (Wilks, 1995) has been constructed for some stations, based on the streamflow distribution estimated for a given day and compared to a real streamflow observation estimated via a gauging. To build a reliability diagram, we computed the probability of an observed streamflow (gauging), given the streamflow distribution. Then, the reliability diagram allows to check that the distribution of probabilities of non-exceedance of the gaugings follows a uniform law (i.e., quantiles should be equipropables). Given the shape of the reliability diagram, the probabilistic calibration is caracterised (underdispersion, overdispersion, bias) (Thyer et al., 2009). In this paper, we present case studies where reliability diagrams have different statistical properties for different periods. Compared to our knowledge of river bed morphology dynamic of these hydrometric stations, we show how reliability diagram gives us invaluable information on river bed movements, like a continuous digging or backfilling of the hydraulic control due to erosion or sedimentation processes. Hence, the careful analysis of reliability diagrams allows to reconcile statistics and long-term river bed morphology processes. This knowledge improves our real-time management of hydrometric stations, given a better caracterisation of erosion/sedimentation processes and the stability of hydrometric station hydraulic control.

Contrasting SWAT predictions of watershed-level streamflow and nutrient loss resulting from static versus dynamic atmospheric CO2 inputs

USDA-ARS?s Scientific Manuscript database

Past climate observations have indicated a rapid increase in global atmospheric CO2 concentration during late 20th century (13 ppm/decade), and models project further rise throughout the 21st century (24 ppm/decade and 69 ppm/decade in the best and worst case scenario, respectively). We modified SWA...

Being an honest broker of hydrology: Uncovering, communicating and addressing model error in a climate change streamflow dataset

NASA Astrophysics Data System (ADS)

Chegwidden, O.; Nijssen, B.; Pytlak, E.

2017-12-01

Any model simulation has errors, including errors in meteorological data, process understanding, model structure, and model parameters. These errors may express themselves as bias, timing lags, and differences in sensitivity between the model and the physical world. The evaluation and handling of these errors can greatly affect the legitimacy, validity and usefulness of the resulting scientific product. In this presentation we will discuss a case study of handling and communicating model errors during the development of a hydrologic climate change dataset for the Pacific Northwestern United States. The dataset was the result of a four-year collaboration between the University of Washington, Oregon State University, the Bonneville Power Administration, the United States Army Corps of Engineers and the Bureau of Reclamation. Along the way, the partnership facilitated the discovery of multiple systematic errors in the streamflow dataset. Through an iterative review process, some of those errors could be resolved. For the errors that remained, honest communication of the shortcomings promoted the dataset's legitimacy. Thoroughly explaining errors also improved ways in which the dataset would be used in follow-on impact studies. Finally, we will discuss the development of the "streamflow bias-correction" step often applied to climate change datasets that will be used in impact modeling contexts. We will describe the development of a series of bias-correction techniques through close collaboration among universities and stakeholders. Through that process, both universities and stakeholders learned about the others' expectations and workflows. This mutual learning process allowed for the development of methods that accommodated the stakeholders' specific engineering requirements. The iterative revision process also produced a functional and actionable dataset while preserving its scientific merit. We will describe how encountering earlier techniques' pitfalls allowed us to develop improved methods for scientists and practitioners alike.

A precipitation-runoff model for the analysis of the effects of water withdrawals and land-use change on streamflow in the Usquepaug-Queen River Basin, Rhode Island

USGS Publications Warehouse

Zarriello, Phillip J.; Bent, Gardner C.

2004-01-01

The 36.1-square-mile UsquepaugQueen River Basin in south-central Rhode Island is an important water resource. Streamflow records indicate that withdrawals may have diminished flows enough to affect aquatic habitat. Concern over the effect of withdrawals on streamflow and aquatic habitat prompted the development of a Hydrologic Simulation ProgramFORTRAN (HSPF) model to evaluate the water-management alternatives and land-use change in the basin. Climate, streamflow, and water-use data were collected to support the model development. A logistic-regression equation was developed for long-term simulations to predict the likelihood of irrigation, the primary water use in the basin, from antecedent potential evapotranspiration and precipitation for generating irrigation demands. The HSPF model represented the basin by 13 pervious-area and 2 impervious-area land-use segments and 20 stream reaches. The model was calibrated to the period January 1, 2000 to September 30, 2001, at three continuous streamflow-gaging stations that monitor flow from 10, 54, and 100 percent of the basin drainage area. Hydrographs and flow-duration curves of observed and simulated discharges, along with statistics compiled for various model-fit metrics, indicate a satisfactory model performance. The calibrated HSPF model was modified to evaluate streamflow (1) under no withdrawals to streamflow under current (200001) withdrawal conditions under long-term (19602001) climatic conditions, (2) under withdrawals by the former Ladd School water-supply wells, and (3) under fully developed land use. The effects of converting from direct-stream withdrawals to ground-water withdrawals were evaluated outside of the HSPF model by use of the STRMDEPL program, which calculates the time delayed response of ground-water withdrawals on streamflow depletion. Simulated effects of current withdrawals relative to no withdrawals indicate about a 20-percent decrease in the lowest mean daily streamflows at the basin outlet, but withdrawals have little effect on flows that are exceeded less than about 90 percent of the time. Tests of alternative model structures to evaluate model uncertainty indicate that the lowest mean daily flows ranged between 3 and 5 cubic feet per second (ft3/s) without withdrawals and 2.2 to 4 ft3/s with withdrawals. Changes in the minimum daily streamflows are more pronounced, however; at the upstream streamflow-gaging station, a minimum daily flow of 0.2 ft3/s was sustained without withdrawals, but simulations with withdrawals indicate that the reach would stop flowing part of a day about 5 percent of the time. The effect on streamflow of potential ground-water withdrawals of 0.20, 0.90, and 1.78 million gallons per day (Mgal/d) at the former Ladd School near the central part of the basin were evaluated. The lowest daily mean flows in model reach 3, the main stem of the Queen River closest to the pumped wells, decreased by about 50 percent for withdrawals of 0.20 Mgal/d (from about 0.4 to 0.2 ft3/s) in comparison to current withdrawals. Reach 3 would occasionally stop flowing during part of the day at the 0.20-Mgal/d withdrawal rate because of diurnal fluctuation in streamflow. The higher withdrawal rates (0.90 and 1.78 Mgal/d) would cause reach 3 to stop flowing about 10 to 20 percent of the time, but the effects of pumping rapidly diminished downstream because of tributary inflows. Simulation results indicate little change in the annual 1-, 7-, and 30-day low flows at the 0.20 Mgal/d pumping rate, but at the 1.78 Mgal/d pumping rate, reach 3 stopped flowing for nearly a 7-day period every year and for a 30-day period about every other year. At the 0.90 Mgal/d pumping rate, reach 3 stopped flowing about every other year for a 7-day period and about once every 5 years for a 30-day period. Land-use change was simulated by converting model hydrologic-response units (HRUs) representing undeveloped areas to HRUs representing developed areas o

Modelling hydrologic responses in a small forested catchment (Panola Mountain, Georgia, USA): A comparison of the original and a new dynamic TOPMODEL

USGS Publications Warehouse

Peters, N.E.; Freer, J.; Beven, K.

2003-01-01

Preliminary modelling results for a new version of the rainfall-runoff model TOPMODEL, dynamic TOPMODEL, are compared with those of the original TOPMODEL formulation for predicting streamflow at the Panola Mountain Research Watershed, Georgia. Dynamic TOPMODEL uses a kinematic wave routing of subsurface flow, which allows for dynamically variable upslope contributing areas, while retaining the concept of hydrological similarity to increase computational efficiency. Model performance in predicting discharge was assessed for the original TOPMODEL and for one landscape unit (LU) and three LU versions of the dynamic TOPMODEL (a bare rock area, hillslope with regolith <1 m, and a riparian zone with regolith ???5 m). All simulations used a 30 min time step for each of three water years. Each 1-LU model underpredicted the peak streamflow, and generally overpredicted recession streamflow during wet periods and underpredicted during dry periods. The difference between predicted recession streamflow generally was less for the dynamic TOPMODEL and smallest for the 3-LU model. Bayesian combination of results for different water years within the GLUE methodology left no behavioural original or 1-LU dynamic models and only 168 (of 96 000 sample parameter sets) for the 3-LU model. The efficiency for the streamflow prediction of the best 3-LU model was 0.83 for an individual year, but the results suggest that further improvements could be made. ?? 2003 John Wiley & Sons, Ltd.

Long-Term Interactions of Streamflow Generation and River Basin Morphology

NASA Astrophysics Data System (ADS)

Huang, X.; Niemann, J.

2005-12-01

It is well known that the spatial patterns and dynamics of streamflow generation processes depend on river basin topography, but the impact of streamflow generation processes on the long-term evolution of river basins has not drawn as much attention. Fluvial erosion processes are driven by streamflow, which can be produced by Horton runoff, Dunne runoff, and groundwater discharge. In this analysis, we hypothesize that the dominant streamflow generation process in a basin affects the spatial patterns of fluvial erosion and that the nature of these patterns changes for storm events with differing return periods. Furthermore, we hypothesize that differences in the erosion patterns modify the topography over the long term in a way that promotes and/or inhibits the other streamflow generation mechanisms. In order to test these hypotheses, a detailed hydrologic model is imbedded into an existing landscape evolution model. Precipitation events are simulated with a Poisson process and have random intensities and durations. The precipitation is partitioned between Horton runoff and infiltration to groundwater using a specified infiltration capacity. Groundwater flow is described by a two-dimensional Dupuit equation for a homogeneous, isotropic, unconfined aquifer with an irregular underlying impervious layer. Dunne runoff occurs when precipitation falls on locations where the water table reaches the land surface. The combined hydrologic/geomorphic model is applied to the WE-38 basin, an experimental watershed in Pennsylvania that has substantial available hydrologic data. First, the hydrologic model is calibrated to reproduce the observed streamflow for 1990 using the observed rainfall as the input. Then, the relative roles of Horton runoff, Dunne runoff, and groundwater discharge are controlled by varying the infiltration capacity of the soil. For each infiltration capacity, the hydrologic and geomorphic behavior of the current topography is analyzed and the long-term evolution of the basin is simulated. The results indicate that the topography can be divided into three types of locations (unsaturated, saturated, and intermittently saturated) which control the patterns of streamflow generation for events with different return periods. The results also indicate that the streamflow generation processes can produce different geomorphic effective events at upstream and downstream locations. The model also suggests that a topography dominated by groundwater discharge evolves over a long period of time to a shape that tends to inhibit the development of saturated areas and Dunne runoff.

The impact of climate and land use changes on water resources. The application of the integrated hydrological modelling system, IHMS (Invited)

NASA Astrophysics Data System (ADS)

Ragab, R.; Bromley, J.; Dörflinger, G.; Katsikides, S.; D'Agostino, D. R.; Lamaddalena, N.; Trisorio, G. L.; Montenegro, S. G.; Montenegro, A.

2010-12-01

An Integrated Hydrological Modelling System, IHMS has been developed to study the impact of climate and land use changes on water resources. The system comprises three packages: the DiCaSM, MODFLOW and SWI models. The Distributed Catchment Scale Model DiCaSM, produces the recharge data for MODFLOW which in turn produces the head distribution for the Sea Water Intrusion model, SWI. These models can run separately. The DiCaSM model simulates the water balance and produces values of evapotranspiration, rainfall interception, infiltration, transpiration, soil water content, groundwater recharge, streamflow and surface runoff. In the 1st example of application, the IHMS was applied on Kouris and Akrotiri catchments in Cyprus. The system was successfully tested against the streamflow and groundwater levels data. Further, the model showed that by 2050, groundwater and surface water would decrease by 35% and 24% for Kouris and 20% and 17% for Akrotiri, respectively. In the 2nd example, the reliability of DiCaSM application on Candelaro catchment in the Apulia region, southern Italy was assessed and the uncertainty of the results were investigated using GLUE (Generalised Likelihood Uncertainty Estimation) methodology. In the 3rd example, DiCaSM model was applied on Tapacurá catchment in the NE of Brazil. The model successfully simulated streamflow and the soil moisture. The climate change scenarios indicated a possible reduction in surface water availability by -13.9%, -22.63% and -32.91% in groundwater recharge and by -4.98%, -14.28% and -20.58% in surface flows for the time spans 2010-2039, 2040-2069, 2070-2099, respectively. Changing the land use by reforestation of part of the catchment area, i.e. replacing current use of arable land would decrease groundwater recharge by -4.2% and streamflow by -2.7%. Changing land use from vegetables to sugar cane would result in decreasing groundwater recharge by around -10%, and increasing stream flow by 5%. In the 4th example, the DiCaSM model has been applied on Mimoso catchment in the Brazilian NE region. The model successfully simulated streamflows (2000 -2008) and forecasted a reduction of 27% to 71%, for ground water recharge, and 26% to 67%, for streamflow. Introducing castor beans would increase the groundwater recharge and streamflow, if the caatinga areas would be converted into castor beans. Changing an area of 1000 ha from caatinga to castor beans would increase the groundwater recharge by 46% and streamflow by 3%. If the same area of pasture is converted into castor beans, there would be an increase of groundwater recharge and streamflow by 24% and 5%, respectively. The examples suggest that IHMS is an effective tool for the authorities to help balance water demand and supply under the climate and land use changes.

Simulating the effects of ground-water withdrawals on streamflow in a precipitation-runoff model

USGS Publications Warehouse

Zarriello, Philip J.; Barlow, P.M.; Duda, P.B.

2004-01-01

Precipitation-runoff models are used to assess the effects of water use and management alternatives on streamflow. Often, ground-water withdrawals are a major water-use component that affect streamflow, but the ability of surface-water models to simulate ground-water withdrawals is limited. As part of a Hydrologic Simulation Program-FORTRAN (HSPF) precipitation-runoff model developed to analyze the effect of ground-water and surface-water withdrawals on streamflow in the Ipswich River in northeastern Massachusetts, an analytical technique (STRMDEPL) was developed for calculating the effects of pumped wells on streamflow. STRMDEPL is a FORTRAN program based on two analytical solutions that solve equations for ground-water flow to a well completed in a semi-infinite, homogeneous, and isotropic aquifer in direct hydraulic connection to a fully penetrating stream. One analytical method calculates unimpeded flow at the stream-aquifer boundary and the other method calculates the resistance to flow caused by semipervious streambed and streambank material. The principle of superposition is used with these analytical equations to calculate time-varying streamflow depletions due to daily pumping. The HSPF model can readily incorporate streamflow depletions caused by a well or surface-water withdrawal, or by multiple wells or surface-water withdrawals, or both, as a combined time-varying outflow demand from affected channel reaches. These demands are stored as a time series in the Watershed Data Management (WDM) file. This time-series data is read into the model as an external source used to specify flow from the first outflow gate in the reach where these withdrawals are located. Although the STRMDEPL program can be run independently of the HSPF model, an extension was developed to run this program within GenScn, a scenario generator and graphical user interface developed for use with the HSPF model. This extension requires that actual pumping rates for each well be stored in a unique WDM dataset identified by an attribute that associates each well with the model reach from which water is withdrawn. Other attributes identify the type and characteristics of the data. The interface allows users to easily add new pumping wells, delete exiting pumping wells, or change properties of the simulated aquifer or well. Development of this application enhanced the ability of the HSPF model to simulate complex water-use conditions in the Ipswich River Basin. The STRMDEPL program and the GenScn extension provide a valuable tool for water managers to evaluate the effects of pumped wells on streamflow and to test alternative water-use scenarios. Copyright ASCE 2004.

Towards reliable ET estimates in the semi-arid Júcar region in Spain.

NASA Astrophysics Data System (ADS)

Brenner, Johannes; Zink, Matthias; Schrön, Martin; Thober, Stephan; Rakovec, Oldrich; Cuntz, Matthias; Merz, Ralf; Samaniego, Luis

2017-04-01

Current research indicated the potential for improving evapotranspiration (ET) estimates in state-of-the-art hydrologic models such as the mesoscale Hydrological Model (mHM, www.ufz.de/mhm). Most models exhibit deficiencies to estimate the ET flux in semi-arid regions. Possible reasons for poor performance may be related to the low resolution of the forcings, the estimation of the PET, which is in most cases based on temperature only, the joint estimation of the transpiration and evaporation through the Feddes equation, poor process parameterizations, among others. In this study, we aim at sequential hypothesis-based experiments to uncover the main reasons of these deficiencies at the Júcar basin in Spain. We plan the following experiments: 1) Use the high resolution meteorological forcing (P and T) provided by local authorities to estimate its effects on ET and streamflow. 2) Use local ET measurements at seven eddy covariance stations to estimate evaporation related parameters. 3) Test the influence of the PET formulations (Hargreaves-Samani, Priestley-Taylor, Penman-Montheith). 4) Estimate evaporation and transpiration separately based on equations proposed by Bohn and Vivoni (2016) 5) Incorporate local soil moisture measurements to re-estimate ET and soil moisture related parameters. We set-up mHM for seven eddy-covariance sites at the local scale (100 × 100 m2). This resolution was chosen because it is representative for the footprint of the latent heat estimation at the eddy-covariance station. In the second experiment, for example, a parameter set is to be found as a compromised solution between ET measured at local stations and the streamflow observations at eight sub-basins of the Júcar river. Preliminary results indicate that higher model performance regarding streamflow can be achieved using local high-resolution meteorology. ET performance is, however, still deficient. On the contrary, using ET site calibrations alone increase performance in ET but yields in poor performance in streamflow. Results suggest the need of multi-variable, simultaneous calibration schemes to reliable estimate ET and streamflow in the Júcar basin. Penman-Montheith appears to be the best performing PET formulation. Experiments 4 and 5 should reveal the benefits of separating evaporation from bare soil and transpiration in semi-arid regions using mHM. Further research in this direction is foreseen by incorporating neutron counts from Cosmic Ray Neutron Sensing technology in the calibration/validation procedure of mHM.

The effects of changing land cover on streamflow simulation in Puerto Rico

Treesearch

A.E. Van Beusekom; L.E. Hay; R.J. Viger; W.A. Gould; J.A. Collazo; A. Henareh Khalyani

2014-01-01

This study quantitatively explores whether land cover changes have a substantive impact on simulated streamflow within the tropical island setting of Puerto Rico. The Precipitation Runoff Modeling System (PRMS) was used to compare streamflow simulations based on five static parameterizations of land cover with those based on dynamically varying parameters derived from...

Deep groundwater mediates streamflow response to climate warming in the Oregon Cascades

Treesearch

Christina Tague; Gordon Grant; Mike Farrell; Janet Choate; Anne Jefferson

2008-01-01

Recent studies predict that projected climate change will lead to significant reductions in summer streamflow in the mountainous regions of the Western United States. Hydrologic modeling directed at quantifying these potential changes has focused on the magnitude and timing of spring snowmelt as the key control on the spatial temporal pattern of summer streamflow. We...

A Comparison of Turbidity-Based and Streamflow-Based Estimates of Suspended-Sediment Concentrations in Three Chesapeake Bay Tributaries

USGS Publications Warehouse

Jastram, John D.; Moyer, Douglas; Hyer, Kenneth

2009-01-01

Fluvial transport of sediment into the Chesapeake Bay estuary is a persistent water-quality issue with major implications for the overall health of the bay ecosystem. Accurately and precisely estimating the suspended-sediment concentrations (SSC) and loads that are delivered to the bay, however, remains challenging. Although manual sampling of SSC produces an accurate series of point-in-time measurements, robust extrapolation to unmeasured periods (especially highflow periods) has proven to be difficult. Sediment concentrations typically have been estimated using regression relations between individual SSC values and associated streamflow values; however, suspended-sediment transport during storm events is extremely variable, and it is often difficult to relate a unique SSC to a given streamflow. With this limitation for estimating SSC, innovative approaches for generating detailed records of suspended-sediment transport are needed. One effective method for improved suspended-sediment determination involves the continuous monitoring of turbidity as a surrogate for SSC. Turbidity measurements are theoretically well correlated to SSC because turbidity represents a measure of water clarity that is directly influenced by suspended sediments; thus, turbidity-based estimation models typically are effective tools for generating SSC data. The U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency Chesapeake Bay Program and Virginia Department of Environmental Quality, initiated continuous turbidity monitoring on three major tributaries of the bay - the James, Rappahannock, and North Fork Shenandoah Rivers - to evaluate the use of turbidity as a sediment surrogate in rivers that deliver sediment to the bay. Results of this surrogate approach were compared to the traditionally applied streamflow-based approach for estimating SSC. Additionally, evaluation and comparison of these two approaches were conducted for nutrient estimations. Results demonstrate that the application of turbidity-based estimation models provides an improved method for generating a continuous record of SSC, relative to the classical approach that uses streamflow as a surrogate for SSC. Turbidity-based estimates of SSC were found to be more accurate and precise than SSC estimates from streamflow-based approaches. The turbidity-based SSC estimation models explained 92 to 98 percent of the variability in SSC, while streamflow-based models explained 74 to 88 percent of the variability in SSC. Furthermore, the mean absolute error of turbidity-based SSC estimates was 50 to 87 percent less than the corresponding values from the streamflow-based models. Statistically significant differences were detected between the distributions of residual errors and estimates from the two approaches, indicating that the turbidity-based approach yields estimates of SSC with greater precision than the streamflow-based approach. Similar improvements were identified for turbidity-based estimates of total phosphorus, which is strongly related to turbidity because total phosphorus occurs predominantly in particulate form. Total nitrogen estimation models based on turbidity and streamflow generated estimates of similar quality, with the turbidity-based models providing slight improvements in the quality of estimations. This result is attributed to the understanding that nitrogen transport is dominated by dissolved forms that relate less directly to streamflow and turbidity. Improvements in concentration estimation resulted in improved estimates of load. Turbidity-based suspended-sediment loads estimated for the James River at Cartersville, VA, monitoring station exhibited tighter confidence interval bounds and a coefficient of variation of 12 percent, compared with a coefficient of variation of 38 percent for the streamflow-based load.

Water resources management: Hydrologic characterization through hydrograph simulation may bias streamflow statistics

NASA Astrophysics Data System (ADS)

Farmer, W. H.; Kiang, J. E.

2017-12-01

The development, deployment and maintenance of water resources management infrastructure and practices rely on hydrologic characterization, which requires an understanding of local hydrology. With regards to streamflow, this understanding is typically quantified with statistics derived from long-term streamgage records. However, a fundamental problem is how to characterize local hydrology without the luxury of streamgage records, a problem that complicates water resources management at ungaged locations and for long-term future projections. This problem has typically been addressed through the development of point estimators, such as regression equations, to estimate particular statistics. Physically-based precipitation-runoff models, which are capable of producing simulated hydrographs, offer an alternative to point estimators. The advantage of simulated hydrographs is that they can be used to compute any number of streamflow statistics from a single source (the simulated hydrograph) rather than relying on a diverse set of point estimators. However, the use of simulated hydrographs introduces a degree of model uncertainty that is propagated through to estimated streamflow statistics and may have drastic effects on management decisions. We compare the accuracy and precision of streamflow statistics (e.g. the mean annual streamflow, the annual maximum streamflow exceeded in 10% of years, and the minimum seven-day average streamflow exceeded in 90% of years, among others) derived from point estimators (e.g. regressions, kriging, machine learning) to that of statistics derived from simulated hydrographs across the continental United States. Initial results suggest that the error introduced through hydrograph simulation may substantially bias the resulting hydrologic characterization.

Improving Streamflow Simulation in Gaged and Ungaged Areas Using a Multi-Model Synthesis Combined with Remotely-Sensed Data and Estimates of Uncertainty

NASA Astrophysics Data System (ADS)

Lafontaine, J.; Hay, L.

2015-12-01

The United States Geological Survey (USGS) has developed a National Hydrologic Model (NHM) to support coordinated, comprehensive and consistent hydrologic model development, and facilitate the application of hydrologic simulations within the conterminous United States (CONUS). More than 1,700 gaged watersheds across the CONUS were modeled to test the feasibility of improving streamflow simulations in gaged and ungaged watersheds by linking statistically- and physically-based hydrologic models with remotely-sensed data products (i.e. - snow water equivalent) and estimates of uncertainty. Initially, the physically-based models were calibrated to measured streamflow data to provide a baseline for comparison. As many stream reaches in the CONUS are either not gaged, or are substantially impacted by water use or flow regulation, ancillary information must be used to determine reasonable parameter estimations for streamflow simulations. In addition, not all ancillary datasets are appropriate for application to all parts of the CONUS (e.g. - snow water equivalent in the southeastern U.S., where snow is a rarity). As it is not expected that any one data product or model simulation will be sufficient for representing hydrologic behavior across the entire CONUS, a systematic evaluation of which data products improve simulations of streamflow for various regions across the CONUS was performed. The resulting portfolio of calibration strategies can be used to guide selection of an appropriate combination of simulated and measured information for model development and calibration at a given location of interest. In addition, these calibration strategies have been developed to be flexible so that new data products or simulated information can be assimilated. This analysis provides a foundation to understand how well models work when streamflow data is either not available or is limited and could be used to further inform hydrologic model parameter development for ungaged areas.

Effect of year-to-year variability of leaf area index on variable infiltration capacity model performance and simulation of streamflow during drought

NASA Astrophysics Data System (ADS)

Tesemma, Z. K.; Wei, Y.; Peel, M. C.; Western, A. W.

2014-09-01

This study assessed the effect of using observed monthly leaf area index (LAI) on hydrologic model performance and the simulation of streamflow during drought using the variable infiltration capacity (VIC) hydrological model in the Goulburn-Broken catchment of Australia, which has heterogeneous vegetation, soil and climate zones. VIC was calibrated with both observed monthly LAI and long-term mean monthly LAI, which were derived from the Global Land Surface Satellite (GLASS) observed monthly LAI dataset covering the period from 1982 to 2012. The model performance under wet and dry climates for the two different LAI inputs was assessed using three criteria, the classical Nash-Sutcliffe efficiency, the logarithm transformed flow Nash-Sutcliffe efficiency and the percentage bias. Finally, the percentage deviation of the simulated monthly streamflow using the observed monthly LAI from simulated streamflow using long-term mean monthly LAI was computed. The VIC model predicted monthly streamflow in the selected sub-catchments with model efficiencies ranging from 61.5 to 95.9% during calibration (1982-1997) and 59 to 92.4% during validation (1998-2012). Our results suggest systematic improvements from 4 to 25% in the Nash-Sutcliffe efficiency in pasture dominated catchments when the VIC model was calibrated with the observed monthly LAI instead of the long-term mean monthly LAI. There was limited systematic improvement in tree dominated catchments. The results also suggest that the model overestimation or underestimation of streamflow during wet and dry periods can be reduced to some extent by including the year-to-year variability of LAI in the model, thus reflecting the responses of vegetation to fluctuations in climate and other factors. Hence, the year-to-year variability in LAI should not be neglected; rather it should be included in model calibration as well as simulation of monthly water balance.

Post-processing of multi-hydrologic model simulations for improved streamflow projections

NASA Astrophysics Data System (ADS)

khajehei, sepideh; Ahmadalipour, Ali; Moradkhani, Hamid

2016-04-01

Hydrologic model outputs are prone to bias and uncertainty due to knowledge deficiency in model and data. Uncertainty in hydroclimatic projections arises due to uncertainty in hydrologic model as well as the epistemic or aleatory uncertainties in GCM parameterization and development. This study is conducted to: 1) evaluate the recently developed multi-variate post-processing method for historical simulations and 2) assess the effect of post-processing on uncertainty and reliability of future streamflow projections in both high-flow and low-flow conditions. The first objective is performed for historical period of 1970-1999. Future streamflow projections are generated for 10 statistically downscaled GCMs from two widely used downscaling methods: Bias Corrected Statistically Downscaled (BCSD) and Multivariate Adaptive Constructed Analogs (MACA), over the period of 2010-2099 for two representative concentration pathways of RCP4.5 and RCP8.5. Three semi-distributed hydrologic models were employed and calibrated at 1/16 degree latitude-longitude resolution for over 100 points across the Columbia River Basin (CRB) in the pacific northwest USA. Streamflow outputs are post-processed through a Bayesian framework based on copula functions. The post-processing approach is relying on a transfer function developed based on bivariate joint distribution between the observation and simulation in historical period. Results show that application of post-processing technique leads to considerably higher accuracy in historical simulations and also reducing model uncertainty in future streamflow projections.

Monthly streamflow forecasting with auto-regressive integrated moving average

NASA Astrophysics Data System (ADS)

Nasir, Najah; Samsudin, Ruhaidah; Shabri, Ani

2017-09-01

Forecasting of streamflow is one of the many ways that can contribute to better decision making for water resource management. The auto-regressive integrated moving average (ARIMA) model was selected in this research for monthly streamflow forecasting with enhancement made by pre-processing the data using singular spectrum analysis (SSA). This study also proposed an extension of the SSA technique to include a step where clustering was performed on the eigenvector pairs before reconstruction of the time series. The monthly streamflow data of Sungai Muda at Jeniang, Sungai Muda at Jambatan Syed Omar and Sungai Ketil at Kuala Pegang was gathered from the Department of Irrigation and Drainage Malaysia. A ratio of 9:1 was used to divide the data into training and testing sets. The ARIMA, SSA-ARIMA and Clustered SSA-ARIMA models were all developed in R software. Results from the proposed model are then compared to a conventional auto-regressive integrated moving average model using the root-mean-square error and mean absolute error values. It was found that the proposed model can outperform the conventional model.

Physically Based Mountain Hydrological Modelling using Reanalysis Data in Patagonia

NASA Astrophysics Data System (ADS)

Krogh, S.; Pomeroy, J. W.; McPhee, J. P.

2013-05-01

Remote regions in South America are often characterized by insufficient observations of meteorology for robust hydrological model operation. Yet water resources must be quantified, understood and predicted in order to develop effective water management policies. Here, we developed a physically based hydrological model for a major river in Patagonia using the modular Cold Regions Hydrological Modelling Platform (CRHM) in order to better understand hydrological processes leading to streamflow generation in this remote region. The Baker River -with the largest mean annual streamflow in Chile-, drains snowy mountains, glaciers, wet forests, peat and semi-arid pampas into a large lake. Meteorology over the basin is poorly monitored in that there are no high elevation weather stations and stations at low elevations are sparsely distributed, only measure temperature and rainfall and are poorly maintained. Streamflow in the basin is gauged at several points where there are high quality hydrometric stations. In order to quantify the impact of meteorological data scarcity on prediction, two additional data sources were used: the ERA-Interim (ECMWF Re-analyses) and CFSR (Climate Forecast System Reanalysis) atmospheric reanalyses. Precipitation temporal distribution and magnitude from the models and observations were compared and the reanalysis data was found to have about three times the number of days with precipitation than the observations did. Better synchronization between measured peak streamflows and modeled precipitation was found compared to observed precipitation. These differences are attributed to: (i) lack of any snowfall observations (so precipitation records does not consider snowfall events) and (ii) available rainfall observations are all located at low altitude (<500 m a.s.l), and miss the occurrence of high altitude precipitation events. CRHM parameterization was undertaken by using local physiographic and vegetation characteristics where available and transferring locally unknown hydrological process parameters from cold regions mountain environments in Canada. Some soil moisture parameters were calibrated from streamflow observations. Model performance was estimated through comparison with observed streamflow records. Simulations using observed precipitation had negligible representativeness of streamflow (Nash-Sutcliffe coefficient, NS ≈ 0.2), while those using any of the two reanalyses as forcing data had reasonable model performance (NS ≈ 0.7). In spite of the better spatial resolution of the CFSR, the ability to simulate streamflow were not significantly different using either CFSR or ERA-Interim. The modeled water balance shows that snowfall is about 30% of the total precipitation input, but snowmelt superficial runoff comprises about 10% of total runoff. About 75% of all precipitation is infiltrated, and approximately 15% of the losses are attributed to evapotranspiration from soil and lake evaporation.

Climate Change Impacts on Stream Temperature in Regulated River Systems: A Case Study in the Southeastern United States

NASA Astrophysics Data System (ADS)

Cheng, Y.; Niemeyer, R. J.; Zhang, X.; Yearsley, J. R.; Voisin, N.; Nijssen, B.

2017-12-01

Climate change and associated changes in air temperature and precipitation are projected to impact natural water resources quantity, quality and timing. In the past century, over 280 major dams were built in the Southeastern United States (SEUS) (GRanD database). Regulation of the river system greatly alters natural streamflow as well as stream temperature. Understanding the impacts of climate change on regulated systems, particularly within the context of the Clean Water Act, can inform stakeholders how to maintain and adapt water operations (e.g. regulation, withdrawals). In this study, we use a new modeling framework to study climate change impacts on stream temperatures of a regulated river system. We simulate runoff with the Variable Infiltration Capacity (VIC) macroscale hydrological model, regulated streamflow and reservoir operations with a large-scale river routing-reservoir model (MOSART-WM), and stream temperature using the River Basin Model (RBM). We enhanced RBM with a two-layer thermal stratification reservoir module. This modeling framework captures both the impact of reservoir regulation on streamflow and the reservoir stratification effects on downstream temperatures. We evaluate changes in flow and stream temperatures based on climate projections from two representative concentration pathways (RCPs; RCP4.5 and RCP8.5) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). We simulate river temperature with meteorological forcings that have been downscaled with the Multivariate Constructed Analogs (MACA) method. We are specifically interested in analyzing extreme periods during which stream temperature exceeds water quality standards. In this study, we focus on identifying whether these extreme temperature periods coincide with low flows, and whether the frequency and duration of these operationally-relevant periods will increase under future climate change.

Constraining the JULES land-surface model for different land-use types using citizen-science generated hydrological data

NASA Astrophysics Data System (ADS)

Chou, H. K.; Ochoa-Tocachi, B. F.; Buytaert, W.

2017-12-01

Community land surface models such as JULES are increasingly used for hydrological assessment because of their state-of-the-art representation of land-surface processes. However, a major weakness of JULES and other land surface models is the limited number of land surface parameterizations that is available. Therefore, this study explores the use of data from a network of catchments under homogeneous land-use to generate parameter "libraries" to extent the land surface parameterizations of JULES. The network (called iMHEA) is part of a grassroots initiative to characterise the hydrological response of different Andean ecosystems, and collects data on streamflow, precipitation, and several weather variables at a high temporal resolution. The tropical Andes are a useful case study because of the complexity of meteorological and geographical conditions combined with extremely heterogeneous land-use that result in a wide range of hydrological responses. We then calibrated JULES for each land-use represented in the iMHEA dataset. For the individual land-use types, the results show improved simulations of streamflow when using the calibrated parameters with respect to default values. In particular, the partitioning between surface and subsurface flows can be improved. But also, on a regional scale, hydrological modelling was greatly benefitted from constraining parameters using such distributed citizen-science generated streamflow data. This study demonstrates the modelling and prediction on regional hydrology by integrating citizen science and land surface model. In the context of hydrological study, the limitation of data scarcity could be solved indeed by using this framework. Improved predictions of such impacts could be leveraged by catchment managers to guide watershed interventions, to evaluate their effectiveness, and to minimize risks.

Estimation of streamflow for selected sites on the Carson and Truckee rivers in California and Nevada, 1944-80

USGS Publications Warehouse

Blodgett, J.C.; Oltmann, R.N.; Poeschel, K.R.

1984-01-01

Daily mean and monthly discharges were estimated for 10 sites on the Carson and Truckee Rivers for periods of incomplete records and for tributary sites affected by reservoir regulation. On the basis of the hydrologic characteristics, stream-flow data for a water year were grouped by month or season for subsequent regression analysis. In most cases, simple linear regressions adequately defined a relation of streamflow between gaging stations, but in some instances a nonlinear relation for several months of the water year was derived. Statistical data are presented to indicate the reliability of the estimated streamflow data. Records of discharges including historical and estimated data for the gaging stations for the water years 1944-80 are presented. (USGS)

A physical framework for evaluating net effects of wet meadow restoration on late summer streamflow

NASA Astrophysics Data System (ADS)

Grant, G.; Nash, C.; Selker, J. S.; Lewis, S.; Noël, P.

2017-12-01

Restoration of degraded wet meadows that develop on upland valley floors is intended to achieve a range of ecological benefits. A widely cited benefit is the potential for meadow restoration to augment late-season streamflow; however, there has been little field data demonstrating increased summer flows following restoration. Instead, the hydrologic consequences of restoration have typically been explored using coupled groundwater and surface water flow models at instrumented sites. The expected magnitude and direction of change provided by models has, however, been inconclusive. Here, we assess the streamflow benefit that can be obtained by wet meadow restoration using a parsimonious, physically-based approach. We use a one-dimensional linearized Boussinesq equation with a superimposed solution for changes in storage due to groundwater upwelling and and explicitly calculate evapotranspiration using the White Method. The model accurately predicts water table elevations from field data in the Middle Fork John Day watershed in Oregon, USA. The full solution shows that while raising channel beds can increase total water storage via increases in water table elevation in upland valley bottoms, the contributions of both lateral and longitudinal drainage from restored floodplains to late summer streamflow are undetectably small, while losses in streamflow due to greater transpiration, lower hydraulic gradients, and less drainable pore volume are substantial. Although late-summer streamflow increases should not be expected as a direct result of wet meadow restoration, these approaches offer benefits for improving the quality and health of riparian and meadow vegetation that would warrant considering such measures, even at the cost of increased water demand and reduced streamflow.

Application of AFINCH as a tool for evaluating the effects of streamflow-gaging-network size and composition on the accuracy and precision of streamflow estimates at ungaged locations in the southeast Lake Michigan hydrologic subregion

USGS Publications Warehouse

Koltun, G.F.; Holtschlag, David J.

2010-01-01

Bootstrapping techniques employing random subsampling were used with the AFINCH (Analysis of Flows In Networks of CHannels) model to gain insights into the effects of variation in streamflow-gaging-network size and composition on the accuracy and precision of streamflow estimates at ungaged locations in the 0405 (Southeast Lake Michigan) hydrologic subregion. AFINCH uses stepwise-regression techniques to estimate monthly water yields from catchments based on geospatial-climate and land-cover data in combination with available streamflow and water-use data. Calculations are performed on a hydrologic-subregion scale for each catchment and stream reach contained in a National Hydrography Dataset Plus (NHDPlus) subregion. Water yields from contributing catchments are multiplied by catchment areas and resulting flow values are accumulated to compute streamflows in stream reaches which are referred to as flow lines. AFINCH imposes constraints on water yields to ensure that observed streamflows are conserved at gaged locations.  Data from the 0405 hydrologic subregion (referred to as Southeast Lake Michigan) were used for the analyses. Daily streamflow data were measured in the subregion for 1 or more years at a total of 75 streamflow-gaging stations during the analysis period which spanned water years 1971–2003. The number of streamflow gages in operation each year during the analysis period ranged from 42 to 56 and averaged 47. Six sets (one set for each censoring level), each composed of 30 random subsets of the 75 streamflow gages, were created by censoring (removing) approximately 10, 20, 30, 40, 50, and 75 percent of the streamflow gages (the actual percentage of operating streamflow gages censored for each set varied from year to year, and within the year from subset to subset, but averaged approximately the indicated percentages).Streamflow estimates for six flow lines each were aggregated by censoring level, and results were analyzed to assess (a) how the size and composition of the streamflow-gaging network affected the average apparent errors and variability of the estimated flows and (b) whether results for certain months were more variable than for others. The six flow lines were categorized into one of three types depending upon their network topology and position relative to operating streamflow-gaging stations.    Statistical analysis of the model results indicates that (1) less precise (that is, more variable) estimates resulted from smaller streamflow-gaging networks as compared to larger streamflow-gaging networks, (2) precision of AFINCH flow estimates at an ungaged flow line is improved by operation of one or more streamflow gages upstream and (or) downstream in the enclosing basin, (3) no consistent seasonal trend in estimate variability was evident, and (4) flow lines from ungaged basins appeared to exhibit the smallest absolute apparent percent errors (APEs) and smallest changes in average APE as a function of increasing censoring level. The counterintuitive results described in item (4) above likely reflect both the nature of the base-streamflow estimate from which the errors were computed and insensitivity in the average model-derived estimates to changes in the streamflow-gaging-network size and composition. Another analysis demonstrated that errors for flow lines in ungaged basins have the potential to be much larger than indicated by their APEs if measured relative to their true (but unknown) flows.     “Missing gage” analyses, based on examination of censoring subset results where the streamflow gage of interest was omitted from the calibration data set, were done to better understand the true error characteristics for ungaged flow lines as a function of network size. Results examined for 2 water years indicated that the probability of computing a monthly streamflow estimate within 10 percent of the true value with AFINCH decreased from greater than 0.9 at about a 10-percent network-censoring level to less than 0.6 as the censoring level approached 75 percent. In addition, estimates for typically dry months tended to be characterized by larger percent errors than typically wetter months.

A multi-site reconstruction algorithm for bottom-up vulnerability assessment of water resource systems to changing streamflow conditions

NASA Astrophysics Data System (ADS)

Nazemi, A.; Zaerpour, M.

2016-12-01

Current paradigm for assessing the vulnerability of water resource systems to changing streamflow conditions often involves a cascade application of climate and hydrological models to project the future states of streamflow regime, entering to a given water resource system. It is widely warned, however, that the overall uncertainty in this "top-down" modeling enterprise can be large due to the limitations in representing natural and anthropogenic processes that affect future streamflow variability and change. To address this, various types of stress-tests are suggested to assess the vulnerability of water resources systems under a wide range of possible changes in streamflow conditions. The scope of such "bottom-up" assessments can go well beyond top-down projections and therefore provide a basis for monitoring different response modes, under which water resource systems become vulnerable. Despite methodological differences, all bottom-up assessments are equipped with a systematic sampling procedure, with which different possibilities for future climate and/or streamflow conditions can be realized. Regardless of recent developments, currently available streamflow sampling algorithms are still limited, particularly in regional contexts, for which accurate representation of spatiotemporal dependencies in streamflow regime are of major importance. In this presentation, we introduce a new development that enables handling temporal and spatial dependencies in regional streamflow regimes through a unified stochastic reconstruction algorithm. We demonstrate the application of this algorithm accross various Canadian regions. By considering a real-world regional water resources system, we show how the new multi-site reconstruction algorithm can extend the practical utility of bottom-up vulnerability assessment and improve quantifying the associated risk in natural and anthropogenic water systems under unknown future conditions.

Waning habitats due to climate change: the effects of changes in streamflow and temperature at the rear edge of the distribution of a cold-water fish

NASA Astrophysics Data System (ADS)

María Santiago, José; Muñoz-Mas, Rafael; Solana-Gutiérrez, Joaquín; García de Jalón, Diego; Alonso, Carlos; Martínez-Capel, Francisco; Pórtoles, Javier; Monjo, Robert; Ribalaygua, Jaime

2017-08-01

Climate changes affect aquatic ecosystems by altering temperatures and precipitation patterns, and the rear edges of the distributions of cold-water species are especially sensitive to these effects. The main goal of this study was to predict in detail how changes in air temperature and precipitation will affect streamflow, the thermal habitat of a cold-water fish (the brown trout, Salmo trutta), and the synergistic relationships among these variables at the rear edge of the natural distribution of brown trout. Thirty-one sites in 14 mountain rivers and streams were studied in central Spain. Models of streamflow were built for several of these sites using M5 model trees, and a non-linear regression method was used to estimate stream temperatures. Nine global climate models simulations for Representative Concentration Pathways RCP4.5 and RCP8.5 scenarios were downscaled to the local level. Significant reductions in streamflow were predicted to occur in all of the basins (max. -49 %) by the year 2099, and seasonal differences were noted between the basins. The stream temperature models showed relationships between the model parameters, geology and hydrologic responses. Temperature was sensitive to streamflow in one set of streams, and summer reductions in streamflow contributed to additional stream temperature increases (max. 3.6 °C), although the sites that are most dependent on deep aquifers will likely resist warming to a greater degree. The predicted increases in water temperatures were as high as 4.0 °C. Temperature and streamflow changes will cause a shift in the rear edge of the distribution of this species. However, geology will affect the extent of this shift. Approaches like the one used herein have proven to be useful in planning the prevention and mitigation of the negative effects of climate change by differentiating areas based on the risk level and viability of fish populations.

Using a predictive model to evaluate spatiotemporal variability in streamflow permanence across the Pacific Northwest region

NASA Astrophysics Data System (ADS)

Jaeger, K. L.

2017-12-01

The U.S. Geological Survey (USGS) has developed the PRObability Of Streamflow PERmanence (PROSPER) model, a GIS-based empirical model that provides predictions of the annual probability of a stream channel having year-round flow (Streamflow permanence probability; SPP) for any unregulated and minimally-impaired stream channel in the Pacific Northwest (Washington, Oregon, Idaho, western Montana). The model provides annual predictions for 2004-2016 at a 30-m spatial resolution based on monthly or annually updated values of climatic conditions, and static physiographic variables associated with the upstream basin. Prediction locations correspond to the channel network consistent with the National Hydrography Dataset stream grid and are publicly available through the USGS StreamStats platform (https://water.usgs.gov/osw/streamstats/). In snowmelt-driven systems, the most informative predictor variable was mean upstream snow water equivalent on May 1, which highlights the influence of late spring snow cover for supporting streamflow in mountain river networks. In non-snowmelt-driven systems, the most informative variable was mean annual precipitation. Streamflow permanence probabilities varied across the study area by geography and from year-to-year. Notably lower SPP corresponded to the climatically drier subregions of the study area. Higher SPP were concentrated in coastal and higher elevation mountain regions. In addition, SPP appeared to trend with average hydroclimatic conditions, which were also geographically coherent. The year-to-year variability lends support for the growing recognition of the spatiotemporal dynamism of streamflow permanence. An analysis of three focus basins located in contrasting geographical and hydroclimatic settings demonstrates differences in the sensitivity of streamflow permanence to antecedent climate conditions as a function of geography. Consequently, results suggest that PROSPER model can be a useful tool to evaluate regions of the landscape that may be resilient or sensitive to drought conditions, allowing for targeted management efforts to protect critical reaches.

Can longer forest harvest intervals increase summer streamflow for salmon recovery?

EPA Science Inventory

The Mashel Streamflow Modeling Project in the Mashel River Basin, Washington, is using a watershed-scale ecohydrological model to assess whether longer forest harvest intervals can remediate summer low flow conditions that have contributed to sharply reduced runs of spawning Chin...

Conjunctive-management models for sustained yield of stream-aquifer systems

USGS Publications Warehouse

Barlow, P.M.; Ahlfeld, D.P.; Dickerman, D.C.

2003-01-01

Conjunctive-management models that couple numerical simulation with linear optimization were developed to evaluate trade-offs between groundwater withdrawals and streamflow depletions for alluvial-valley stream-aquifer systems representative of those of the northeastern United States. A conjunctive-management model developed for a hypothetical stream-aquifer system was used to assess the effect of interannual hydrologic variability on minimum monthly streamflow requirements. The conjunctive-management model was applied to the Hunt-Annaquatucket-Pettaquamscutt stream-aquifer system of central Rhode Island. Results show that it is possible to increase the amount of current withdrawal from the aquifer by as much as 50% by modifying current withdrawal schedules, modifying the number and configuration of wells in the supply-well network, or allowing increased streamflow depletion in the Annaquatucket and Pettaquamscutt rivers. Alternatively, it is possible to reduce current rates of streamflow depletion in the Hunt River by as much as 35% during the summer, but such reductions would result increases in groundwater withdrawals.

Monthly paleostreamflow reconstruction from annual tree-ring chronologies

NASA Astrophysics Data System (ADS)

Stagge, J. H.; Rosenberg, D. E.; DeRose, R. J.; Rittenour, T. M.

2018-02-01

Paleoclimate reconstructions are increasingly used to characterize annual climate variability prior to the instrumental record, to improve estimates of climate extremes, and to provide a baseline for climate-change projections. To date, paleoclimate records have seen limited engineering use to estimate hydrologic risks because water systems models and managers usually require streamflow input at the monthly scale. This study explores the hypothesis that monthly streamflows can be adequately modeled by statistically decomposing annual flow reconstructions. To test this hypothesis, a multiple linear regression model for monthly streamflow reconstruction is presented that expands the set of predictors to include annual streamflow reconstructions, reconstructions of global circulation, and potential differences among regional tree-ring chronologies related to tree species and geographic location. This approach is used to reconstruct 600 years of monthly streamflows at two sites on the Bear and Logan rivers in northern Utah. Nash-Sutcliffe Efficiencies remain above zero (0.26-0.60) for all months except April and Pearson's correlation coefficients (R) are 0.94 and 0.88 for the Bear and Logan rivers, respectively, confirming that the model can adequately reproduce monthly flows during the reference period (10/1942 to 9/2015). Incorporating a flexible transition between the previous and concurrent annual reconstructed flows was the most important factor for model skill. Expanding the model to include global climate indices and regional tree-ring chronologies produced smaller, but still significant improvements in model fit. The model presented here is the only approach currently available to reconstruct monthly streamflows directly from tree-ring chronologies and climate reconstructions, rather than using resampling of the observed record. With reasonable estimates of monthly flow that extend back in time many centuries, water managers can challenge systems models with a larger range of natural variability in drought and pluvial events and better evaluate extreme events with recurrence intervals longer than the observed record. Establishing this natural baseline is critical when estimating future hydrologic risks under conditions of a non-stationary climate.

Evaluating Impacts of climate and land use changes on streamflow using SWAT and land use models based CESM1-CAM5 Climate scenarios

NASA Astrophysics Data System (ADS)

Lin, Tzu Ping; Lin, Yu Pin; Lien, Wan Yu

2015-04-01

Climate change projects have various levels of impacts on hydrological cycles around the world. The impact of climate change and uncertainty of climate projections from general circulation models (GCMs) from the Coupled Model Intercomparison Project (CMIP5) which has been just be released in Taiwan, 2014. Since the streamflow run into ocean directly due to the steep terrain and the rainfall difference between wet and dry seasons is apparent; as a result, the allocation water resource reasonable is very challenge in Taiwan, particularly under climate change. The purpose of this study was to evaluate the impacts of climate and land use changes on a small watershed in Taiwan. The AR5 General Circulation Models(GCM) output data was adopted in this study and was downscaled from the monthly to the daily weather data as the input data of hydrological model such as Soil and Water Assessment Tool (SWAT) model in this study. The spatially explicit land uses change model, the Conservation of Land Use and its Effects at Small regional extent (CLUE-s), was applied to simulate land use scenarios in 2020-2039. Combined climate and land use change scenarios were adopted as input data of the hydrological model, the SWAT model, to estimate the future streamflows. With the increasing precipitation, increasing urban area and decreasing agricultural and grass land, the annual streamflow in the most of twenty-three subbasins were also increased. Besides, due to the increasing rainfall in wet season and decreasing rainfall in dry season, the difference of streamflow between wet season and dry season are also increased. This result indicates a more stringent challenge on the water resource management in future. Therefore, impacts on water resource caused by climate change and land use change should be considered in water resource planning for the Datuan river watershed. Keywords: SWAT, GCM, CLUE-s, streamflow, climate change, land use change

ArgoEcoSystem-watershed (AgES-W) model evaluation for streamflow and nitrogen/sediment dynamics on a midwest agricultural watershed

USDA-ARS?s Scientific Manuscript database

AgroEcoSystem-Watershed (AgES-W) is a modular, Java-based spatially distributed model which implements hydrologic/water quality simulation components under the Object Modeling System Version 3 (OMS3). The AgES-W model was previously evaluated for streamflow and recently has been enhanced with the ad...

An Analytical Solution for the Impact of Vegetation Changes on Hydrological Partitioning Within the Budyko Framework

NASA Astrophysics Data System (ADS)

Zhang, Shulei; Yang, Yuting; McVicar, Tim R.; Yang, Dawen

2018-01-01

Vegetation change is a critical factor that profoundly affects the terrestrial water cycle. Here we derive an analytical solution for the impact of vegetation changes on hydrological partitioning within the Budyko framework. This is achieved by deriving an analytical expression between leaf area index (LAI) change and the Budyko land surface parameter (n) change, through the combination of a steady state ecohydrological model with an analytical carbon cost-benefit model for plant rooting depth. Using China where vegetation coverage has experienced dramatic changes over the past two decades as a study case, we quantify the impact of LAI changes on the hydrological partitioning during 1982-2010 and predict the future influence of these changes for the 21st century using climate model projections. Results show that LAI change exhibits an increasing importance on altering hydrological partitioning as climate becomes drier. In semiarid and arid China, increased LAI has led to substantial streamflow reductions over the past three decades (on average -8.5% in 1990s and -11.7% in 2000s compared to the 1980s baseline), and this decreasing trend in streamflow is projected to continue toward the end of this century due to predicted LAI increases. Our result calls for caution regarding the large-scale revegetation activities currently being implemented in arid and semiarid China, which may result in serious future water scarcity issues here. The analytical model developed here is physically based and suitable for simultaneously assessing both vegetation changes and climate change induced changes to streamflow globally.

Rainier Mesa CAU Infiltration Model using INFILv3

DOE Office of Scientific and Technical Information (OSTI.GOV)

Levitt, Daniel G.; Kwicklis, Edward M.

The outline of this presentation are: (1) Model Inputs - DEM, Precipitation, Air temp, Soil props, Surface geology, Vegetation; (2) Model Pre-processing - Runoff Routing and sinks, Slope and Azimuth, Soil Ksat reduction with slope (to mitigate bathtub ring), Soil-Bedrock Interface permeabilities; (3) Model Calibration - ET using PEST, Chloride mass balance data, Streamflow using PEST; (4) Model Validation - Streamflow data not used for calibration; (5) Uncertainty Analysis; and (6) Results. Conclusions are: (1) Average annual infiltration rates =11 to 18 mm/year for RM domain; (2) Average annual infiltration rates = 7 to 11 mm/year for SM domain; (3)more » ET = 70% of precipitation for both domains; (4) Runoff = 8-9% for RM; and 22-24% for SM - Apparently high average runoff is caused by the truncation of the lowerelevation portions of watersheds where much of the infiltration of runoff waters would otherwise occur; (5) Model results are calibrated to measured ET, CMB data, and streamflow observations; (6) Model results are validated using streamflow observations discovered after model calibration was complete; (7) Use of soil Ksat reduction with slope to mitigate bathtub ring was successful (based on calibration results); and (8) Soil-bedrock K{_}interface is innovative approach.« less

Escherichia coli bacteria density in relation to turbidity, streamflow characteristics, and season in the Chattahoochee River near Atlanta, Georgia, October 2000 through September 2008—Description, statistical analysis, and predictive modeling

USGS Publications Warehouse

Lawrence, Stephen J.

2012-01-01

Regression analyses show that E. coli density in samples was strongly related to turbidity, streamflow characteristics, and season at both sites. The regression equation chosen for the Norcross data showed that 78 percent of the variability in E. coli density (in log base 10 units) was explained by the variability in turbidity values (in log base 10 units), streamflow event (dry-weather flow or stormflow), season (cool or warm), and an interaction term that is the cross product of streamflow event and turbidity. The regression equation chosen for the Atlanta data showed that 76 percent of the variability in E. coli density (in log base 10 units) was explained by the variability in turbidity values (in log base 10 units), water temperature, streamflow event, and an interaction term that is the cross product of streamflow event and turbidity. Residual analysis and model confirmation using new data indicated the regression equations selected at both sites predicted E. coli density within the 90 percent prediction intervals of the equations and could be used to predict E. coli density in real time at both sites.

Ground-Water Flow Model of the Sierra Vista Subwatershed and Sonoran Portions of the Upper San Pedro Basin, Southeastern Arizona, United States, and Northern Sonora, Mexico

USGS Publications Warehouse

Pool, D.R.; Dickinson, Jesse

2007-01-01

A numerical ground-water model was developed to simulate seasonal and long-term variations in ground-water flow in the Sierra Vista subwatershed, Arizona, United States, and Sonora, Mexico, portions of the Upper San Pedro Basin. This model includes the simulation of details of the groundwater flow system that were not simulated by previous models, such as ground-water flow in the sedimentary rocks that surround and underlie the alluvial basin deposits, withdrawals for dewatering purposes at the Tombstone mine, discharge to springs in the Huachuca Mountains, thick low-permeability intervals of silt and clay that separate the ground-water flow system into deep-confined and shallow-unconfined systems, ephemeral-channel recharge, and seasonal variations in ground-water discharge by wells and evapotranspiration. Steady-state and transient conditions during 1902-2003 were simulated by using a five-layer numerical ground- water flow model representing multiple hydrogeologic units. Hydraulic properties of model layers, streamflow, and evapotranspiration rates were estimated as part of the calibration process by using observed water levels, vertical hydraulic gradients, streamflow, and estimated evapotranspiration rates as constraints. Simulations approximate observed water-level trends throughout most of the model area and streamflow trends at the Charleston streamflow-gaging station on the San Pedro River. Differences in observed and simulated water levels, streamflow, and evapotranspiration could be reduced through simulation of climate-related variations in recharge rates and recharge from flood-flow infiltration.

A Precipitation-Runoff Model for the Blackstone River Basin, Massachusetts and Rhode Island

USGS Publications Warehouse

Barbaro, Jeffrey R.; Zarriello, Phillip J.

2007-01-01

A Hydrological Simulation Program-FORTRAN (HSPF) precipitation-runoff model of the Blackstone River Basin was developed and calibrated to study the effects of changing land- and water-use patterns on water resources. The 474.5 mi2 Blackstone River Basin in southeastern Massachusetts and northern Rhode Island is experiencing rapid population and commercial growth throughout much of its area. This growth and the corresponding changes in land-use patterns are increasing stress on water resources and raising concerns about the future availability of water to meet residential and commercial needs. Increased withdrawals and wastewater-return flows also could adversely affect aquatic habitat, water quality, and the recreational value of the streams in the basin. The Blackstone River Basin was represented by 19 hydrologic response units (HRUs): 17 types of pervious areas (PERLNDs) established from combinations of surficial geology, land-use categories, and the distribution of public water and public sewer systems, and two types of impervious areas (IMPLNDs). Wetlands were combined with open water and simulated as stream reaches that receive runoff from surrounding pervious and impervious areas. This approach was taken to achieve greater flexibility in calibrating evapotranspiration losses from wetlands during the growing season. The basin was segmented into 50 reaches (RCHRES) to represent junctions at tributaries, major lakes and reservoirs, and drainage areas to streamflow-gaging stations. Climatological, streamflow, water-withdrawal, and wastewater-return data were collected during the study to develop the HSPF model. Climatological data collected at Worcester Regional Airport in Worcester, Massachusetts and T.F. Green Airport in Warwick, Rhode Island, were used for model calibration. A total of 15 streamflow-gaging stations were used in the calibration. Streamflow was measured at eight continuous-record streamflow-gaging stations that are part of the U.S. Geological Survey cooperative streamflow-gaging network, and at seven partial-record stations installed in 2004 for this study. Because the model-calibration period preceded data collection at the partial-record stations, a continuous streamflow record was estimated at these stations by correlation with flows at nearby continuous-record stations to provide additional streamflow data for model calibration. Water-use information was compiled for 1996-2001 and included municipal and commercial/industrial withdrawals, private residential withdrawals, golf-course withdrawals, municipal wastewater-return flows, and on-site septic effluent return flows. Streamflow depletion was computed for all time-varying ground-water withdrawals prior to simulation. Water-use data were included in the model to represent the net effect of water use on simulated hydrographs. Consequently, the calibrated values of the hydrologic parameters better represent the hydrologic response of the basin to precipitation. The model was calibrated for 1997-2001 to coincide with the land-use and water-use data compiled for the study. Four long-term stations (Nipmuc River near Harrisville, Rhode Island; Quinsigamond River at North Grafton, Massachusetts; Branch River at Forestdale, Rhode Island; and Blackstone River at Woonsocket, Rhode Island) that monitor flow at 3.3, 5.4, 19, and 88 percent of the total basin area, respectively, provided the primary model-calibration points. Hydrographs, scatter plots, and flow-duration curves of observed and simulated discharges, along with various model-fit statistics, indicated that the model performed well over a range of hydrologic conditions. For example, the total runoff volume for the calibration period simulated at the Nipmuc River near Harrisville, Rhode Island; Quinsigamond River at North Grafton, Massachusetts; Branch River at Forestdale, Rhode Island; and Blackstone River at Woonsocket, Rhode Island streamflow-gaging stations differed from the observed runoff v

A proposed streamflow-data program for Utah

USGS Publications Warehouse

Whitaker, G.L.

1970-01-01

An evaluation of the streamflow data available in Utah was made to provide guidelines for planning future programs. The basic steps in the evaluation procedure were (1) definition of the long- term goals of the streamflow-data program in quantitative form, (2) examination and analysis of all available data to determine which goals have already been met, and (3) consideration of alternate programs and techniques to meet the remaining objectives. The principal goals are (1) to provide current streamflow data where needed for water management and (2) to define streamflow characteristics at any point on any stream within a specified accuracy. It was found that the first goal generally is being satisfied but that flow characteristics at ungaged sites cannot be estimated within the specified accuracy by regression analysis with the existing data and model now available. This latter finding indicates the need for some changes in the present data program so that the accuracy goals can be approached by alternate methods. The regression method may be more successful at a future time if a more suitable model can be developed, and if an adequate sample of streamflow records can be obtained in all areas. In the meantime, methods of transferring flow characteristics which require some information at the ungaged site may be used. A modified streamflow-data program based on this study is proposed.

Geospatial tools effectively estimate nonexceedance probabilities of daily streamflow at ungauged and intermittently gauged locations in Ohio

USGS Publications Warehouse

Farmer, William H.; Koltun, Greg

2017-01-01

Study regionThe state of Ohio in the United States, a humid, continental climate.Study focusThe estimation of nonexceedance probabilities of daily streamflows as an alternative means of establishing the relative magnitudes of streamflows associated with hydrologic and water-quality observations.New hydrological insights for the regionSeveral methods for estimating nonexceedance probabilities of daily mean streamflows are explored, including single-index methodologies (nearest-neighboring index) and geospatial tools (kriging and topological kriging). These methods were evaluated by conducting leave-one-out cross-validations based on analyses of nearly 7 years of daily streamflow data from 79 unregulated streamgages in Ohio and neighboring states. The pooled, ordinary kriging model, with a median Nash–Sutcliffe performance of 0.87, was superior to the single-site index methods, though there was some bias in the tails of the probability distribution. Incorporating network structure through topological kriging did not improve performance. The pooled, ordinary kriging model was applied to 118 locations without systematic streamgaging across Ohio where instantaneous streamflow measurements had been made concurrent with water-quality sampling on at least 3 separate days. Spearman rank correlations between estimated nonexceedance probabilities and measured streamflows were high, with a median value of 0.76. In consideration of application, the degree of regulation in a set of sample sites helped to specify the streamgages required to implement kriging approaches successfully.

Diagnosis of the hydrology of a small Arctic basin at the tundra-taiga transition using a physically based hydrological model

NASA Astrophysics Data System (ADS)

Krogh, Sebastian A.; Pomeroy, John W.; Marsh, Philip

2017-07-01

A better understanding of cold regions hydrological processes and regimes in transitional environments is critical for predicting future Arctic freshwater fluxes under climate and vegetation change. A physically based hydrological model using the Cold Regions Hydrological Model platform was created for a small Arctic basin in the tundra-taiga transition region. The model represents snow redistribution and sublimation by wind and vegetation, snowmelt energy budget, evapotranspiration, subsurface flow through organic terrain, infiltration to frozen soils, freezing and thawing of soils, permafrost and streamflow routing. The model was used to reconstruct the basin water cycle over 28 years to understand and quantify the mass fluxes controlling its hydrological regime. Model structure and parameters were set from the current understanding of Arctic hydrology, remote sensing, field research in the basin and region, and calibration against streamflow observations. Calibration was restricted to subsurface hydraulic and storage parameters. Multi-objective evaluation of the model using observed streamflow, snow accumulation and ground freeze/thaw state showed adequate simulation. Significant spatial variability in the winter mass fluxes was found between tundra, shrubs and forested sites, particularly due to the substantial blowing snow redistribution and sublimation from the wind-swept upper basin, as well as sublimation of canopy intercepted snow from the forest (about 17% of snowfall). At the basin scale, the model showed that evapotranspiration is the largest loss of water (47%), followed by streamflow (39%) and sublimation (14%). The models streamflow performance sensitivity to a set of parameter was analysed, as well as the mean annual mass balance uncertainty associated with these parameters.

Applications systems verification and transfer project. Volume 5: Operational applications of satellite snow-cover observations, northwest United States

NASA Technical Reports Server (NTRS)

Dillard, J. P.

1981-01-01

The study objective was to develop or modify methods in an operational framework that would allow incorporation of satellite derived snow cover observations for prediction of snowmelt derived runoff. Data were reviewed and verified for five basins in the Pacific Northwest. The data were analyzed for up to a 6-year period ending July 1978, and in all cases cover a low, average, and high snow cover/runoff year. Cloud cover is a major problem in these springtime runoff analyses and have hampered data collection for periods of up to 52 days. Tree cover and terrain are sufficiently dense and rugged to have caused problems. The interpretation of snowlines from satellite data was compared with conventional ground truth data and tested in operational streamflow forecasting models. When the satellite snow-covered area (SCA) data are incorporated in the SSARR (Streamflow Synthesis and Reservoir Regulation) model, there is a definite but minor improvement.

Simulation of flow and sediment mobility using a multidimensional flow model for the White Sturgeon critical-habitat reach, Kootenai River near Bonners Ferry, Idaho

USGS Publications Warehouse

Barton, Gary J.; McDonald, Richard R.; Nelson, Jonathan M.; Dinehart, Randal L.

2005-01-01

In 1994, the Kootenai River white sturgeon (Acipenser transmontanus) was listed as an Endangered Species as a direct result of two related observations. First, biologists observed that the white sturgeon population in the Kootenai River was declining. Second, they observed a decline in recruitment of juvenile sturgeon beginning in the 1950s with an almost total absence of recruitment since 1974, following the closure of Libby Dam in 1972. This second observation was attributed to changes in spawning and (or) rearing habitat resulting from alterations in the physical habitat, including flow regime, sediment-transport regime, and bed morphology of the river. The Kootenai River White Sturgeon Recovery Team was established to find and implement ways to improve spawning and rearing habitat used by white sturgeon. They identified the need to develop and apply a multidimensional flow model to certain reaches of the river to quantify physical habitat in a spatially distributed manner. The U.S. Geological Survey has addressed these needs by developing, calibrating, and validating a multidimensional flow model used to simulate streamflow and sediment mobility in the white sturgeon critical-habitat reach of the Kootenai River. This report describes the model and limitations, presents the results of a few simple simulations, and demonstrates how the model can be used to link physical characteristics of streamflow to biological or other habitat data. This study was conducted in cooperation with the Kootenai Tribe of Idaho along a 23-kilometer reach of the Kootenai River, including the white sturgeon spawning reach near Bonners Ferry, Idaho that is about 108 to 131 kilometers below Libby Dam. U.S. Geological Survey's MultiDimensional Surface-Water Modeling System was used to construct a flow model for the critical-habitat reach of the Kootenai River white sturgeon, between river kilometers 228.4 and 245.9. Given streamflow, bed roughness, and downstream water-surface elevation, the model computes the velocity field, water-surface elevations, and boundary shear stress throughout the modeled reach. The 17.5 kilometer model reach was subdivided into two segments on the basis of predominant grain size: a straight reach with a sand, gravel, and cobble substrate located between the upstream model boundary at river kilometer 245.9 and the upstream end of Ambush Rock at river kilometer 244.6, and a meandering reach with a predominately sand substrate located between upstream end of Ambush Rock and the downstream model boundary at river kilometer 228.4. Model cell size in the x and y (horizontal) dimensions is 5 meters by 5 meters along the computational grid centerline with 15 nodes in the z (vertical) dimension. The model was calibrated to historical streamflows evenly distributed between 141.6 and 2,548.9 cubic meters per second. The model was validated by comparing simulated velocities with velocities measured at 15 cross sections during steady streamflow. These 15 cross sections were each measured multiple (7-13) times to obtain velocities suitable for comparison to the model results. Comparison of modeled and measured velocities suggests that the model does a good job of reproducing flow patterns in the river, although some discrepancies were noted. The model was used to simulate water-surface elevation, depth, velocity, bed shear stress, and sediment mobility for Kootenai River streamflows of 170, 566, 1,130, 1,700, and 2,270 cubic meters per second (6,000, 20,000, 40,000, 60,000, and 80,000 cubic feet per second). The three lowest streamflow simulations represent a range of typical river conditions before and since the construction of Libby Dam, and the highest streamflow simulation (2,270 cubic meters per second) is approximately equal to the annual median peak streamflow prior to emplacement of Libby Dam in 1972. Streamflow greater than 566 cubic meters per second were incrementally increased by 570 cubic meters per second. For each

Using 3D dynamic cartography and hydrological modelling for linear streamflow mapping

NASA Astrophysics Data System (ADS)

Drogue, G.; Pfister, L.; Leviandier, T.; Humbert, J.; Hoffmann, L.; El Idrissi, A.; Iffly, J.-F.

2002-10-01

This paper presents a regionalization methodology and an original representation of the downstream variation of daily streamflow using a conceptual rainfall-runoff model (HRM) and the 3D visualization tools of the GIS ArcView. The regionalization of the parameters of the HRM model was obtained by fitting simultaneously the runoff series from five sub-basins of the Alzette river basin (Grand-Duchy of Luxembourg) according to the permeability of geological formations. After validating the transposability of the regional parameter values on five test basins, streamflow series were simulated with the model at ungauged sites in one medium size geologically contrasted test basin and interpolated assuming a linear increase of streamflow between modelling points. 3D spatio-temporal cartography of mean annual and high raw and specific discharges are illustrated. During a severe flooding, the propagation of the flood waves in the different parts of the stream network shows an important contribution of sub-basins lying on impervious geological formations (direct runoff) compared with those including permeable geological formations which have a more contrasted hydrological response. The effect of spatial variability of rainfall is clearly perceptible.

Continental U.S. streamflow trends from 1940 to 2009 and their relationships with watershed spatial characteristics

NASA Astrophysics Data System (ADS)

Rice, Joshua S.; Emanuel, Ryan E.; Vose, James M.; Nelson, Stacy A. C.

2015-08-01

Changes in streamflow are an important area of ongoing research in the hydrologic sciences. To better understand spatial patterns in past changes in streamflow, we examined relationships between watershed-scale spatial characteristics and trends in streamflow. Trends in streamflow were identified by analyzing mean daily flow observations between 1940 and 2009 from 967 U.S. Geological Survey stream gages. Results indicated that streamflow across the continental U.S., as a whole, increased while becoming less extreme between 1940 and 2009. However, substantial departures from the continental U.S. (CONUS) scale pattern occurred at the regional scale, including increased annual maxima, decreased annual minima, overall drying trends, and changes in streamflow variability. A subset of watersheds belonging to a reference data set exhibited significantly smaller trend magnitudes than those observed in nonreference watersheds. Boosted regression tree models were applied to examine the influence of watershed characteristics on streamflow trend magnitudes at both the CONUS and regional scale. Geographic location was found to be of particular importance at the CONUS scale while local variability in hydroclimate and topography tended to have a strong influence on regional-scale patterns in streamflow trends. This methodology facilitates detailed, data-driven analyses of how the characteristics of individual watersheds interact with large-scale hydroclimate forces to influence how changes in streamflow manifest.

Comparison of Two Conceptually Different Physically-based Hydrological Models - Looking Beyond Streamflows

NASA Astrophysics Data System (ADS)

Rousseau, A. N.; Álvarez; Yu, X.; Savary, S.; Duffy, C.

2015-12-01

Most physically-based hydrological models simulate to various extents the relevant watershed processes occurring at different spatiotemporal scales. These models use different physical domain representations (e.g., hydrological response units, discretized control volumes) and numerical solution techniques (e.g., finite difference method, finite element method) as well as a variety of approximations for representing the physical processes. Despite the fact that several models have been developed so far, very few inter-comparison studies have been conducted to check beyond streamflows whether different modeling approaches could simulate in a similar fashion the other processes at the watershed scale. In this study, PIHM (Qu and Duffy, 2007), a fully coupled, distributed model, and HYDROTEL (Fortin et al., 2001; Turcotte et al., 2003, 2007), a pseudo-coupled, semi-distributed model, were compared to check whether the models could corroborate observed streamflows while equally representing other processes as well such as evapotranspiration, snow accumulation/melt or infiltration, etc. For this study, the Young Womans Creek watershed, PA, was used to compare: streamflows (channel routing), actual evapotranspiration, snow water equivalent (snow accumulation and melt), infiltration, recharge, shallow water depth above the soil surface (surface flow), lateral flow into the river (surface and subsurface flow) and height of the saturated soil column (subsurface flow). Despite a lack of observed data for contrasting most of the simulated processes, it can be said that the two models can be used as simulation tools for streamflows, actual evapotranspiration, infiltration, lateral flows into the river, and height of the saturated soil column. However, each process presents particular differences as a result of the physical parameters and the modeling approaches used by each model. Potentially, these differences should be object of further analyses to definitively confirm or reject modeling hypotheses.

BASINs and WEPP Climate Assessment Tools (CAT): Case ...

EPA Pesticide Factsheets

EPA announced the release of the final report, BASINs and WEPP Climate Assessment Tools (CAT): Case Study Guide to Potential Applications. This report supports application of two recently developed water modeling tools, the Better Assessment Science Integrating point & Non-point Sources (BASINS) and the Water Erosion Prediction Project Climate Assessment Tool (WEPPCAT). The report presents a series of short case studies designed to illustrate the capabilities of these tools for conducting scenario based assessments of the potential effects of climate change on streamflow and water quality. This report presents a series of short, illustrative case studies using the BASINS and WEPP climate assessment tools.

The Mississippi Embayment Regional Aquifer Study (MERAS): Documentation of a Groundwater-Flow Model Constructed to Assess Water Availability in the Mississippi Embayment

USGS Publications Warehouse

Clark, Brian R.; Hart, Rheannon M.

2009-01-01

The Mississippi Embayment Regional Aquifer Study (MERAS) was conducted with support from the Groundwater Resources Program of the U.S. Geological Survey Office of Groundwater. This report documents the construction and calibration of a finite-difference groundwater model for use as a tool to quantify groundwater availability within the Mississippi embayment. To approximate the differential equation, the MERAS model was constructed with the U.S. Geological Survey's modular three-dimensional finite-difference code, MODFLOW-2005; the preconditioned conjugate gradient solver within MODFLOW-2005 was used for the numerical solution technique. The model area boundary is approximately 78,000 square miles and includes eight States with approximately 6,900 miles of simulated streams, 70,000 well locations, and 10 primary hydrogeologic units. The finite-difference grid consists of 414 rows, 397 columns, and 13 layers. Each model cell is 1 square mile with varying thickness by cell and by layer. The simulation period extends from January 1, 1870, to April 1, 2007, for a total of 137 years and 69 stress periods. The first stress period is simulated as steady state to represent predevelopment conditions. Areal recharge is applied throughout the MERAS model area using the MODFLOW-2005 Recharge Package. Irrigation, municipal, and industrial wells are simulated using the Multi-Node Well Package. There are 43 streams simulated by the MERAS model. Each stream or river in the model area was simulated using the Streamflow-Routing Package. The perimeter of the model area and the base of the flow system are represented as no-flow boundaries. The downgradient limit of each model layer is a no-flow boundary, which approximates the extent of water with less than 10,000 milligrams per liter of dissolved solids. The MERAS model was calibrated by making manual changes to parameter values and examining residuals for hydraulic heads and streamflow. Additional calibration was achieved through alternate use of UCODE-2005 and PEST. Simulated heads were compared to 55,786 hydraulic-head measurements from 3,245 wells in the MERAS model area. Values of root mean square error between simulated and observed hydraulic heads of all observations ranged from 8.33 feet in 1919 to 47.65 feet in 1951, though only six root mean square error values are greater than 40 feet for the entire simulation period. Simulated streamflow generally is lower than measured streamflow for streams with streamflow less than 1,000 cubic feet per second, and greater than measured streamflow for streams with streamflow more than 1,000 cubic feet per second. Simulated streamflow is underpredicted for 18 observations and overpredicted for 10 observations in the model. These differences in streamflow illustrate the large uncertainty in model inputs such as predevelopment recharge, overland flow, pumpage (from stream and aquifer), precipitation, and observation weights. The groundwater-flow budget indicates changes in flow into (inflows) and out of (outflows) the model area during the pregroundwater-irrigation period (pre-1870) to 2007. Total flow (sum of inflows or outflows) through the model ranged from about 600 million gallons per day prior to development to 18,197 million gallons per day near the end of the simulation. The pumpage from wells represents the largest outflow components with a net rate of 18,197 million gallons per day near the end of the model simulation in 2006. Groundwater outflows are offset primarily by inflow from aquifer storage and recharge.

Identifying influential data points in hydrological model calibration and their impact on streamflow predictions

NASA Astrophysics Data System (ADS)

Wright, David; Thyer, Mark; Westra, Seth

2015-04-01

Highly influential data points are those that have a disproportionately large impact on model performance, parameters and predictions. However, in current hydrological modelling practice the relative influence of individual data points on hydrological model calibration is not commonly evaluated. This presentation illustrates and evaluates several influence diagnostics tools that hydrological modellers can use to assess the relative influence of data. The feasibility and importance of including influence detection diagnostics as a standard tool in hydrological model calibration is discussed. Two classes of influence diagnostics are evaluated: (1) computationally demanding numerical "case deletion" diagnostics; and (2) computationally efficient analytical diagnostics, based on Cook's distance. These diagnostics are compared against hydrologically orientated diagnostics that describe changes in the model parameters (measured through the Mahalanobis distance), performance (objective function displacement) and predictions (mean and maximum streamflow). These influence diagnostics are applied to two case studies: a stage/discharge rating curve model, and a conceptual rainfall-runoff model (GR4J). Removing a single data point from the calibration resulted in differences to mean flow predictions of up to 6% for the rating curve model, and differences to mean and maximum flow predictions of up to 10% and 17%, respectively, for the hydrological model. When using the Nash-Sutcliffe efficiency in calibration, the computationally cheaper Cook's distance metrics produce similar results to the case-deletion metrics at a fraction of the computational cost. However, Cooks distance is adapted from linear regression with inherit assumptions on the data and is therefore less flexible than case deletion. Influential point detection diagnostics show great potential to improve current hydrological modelling practices by identifying highly influential data points. The findings of this study establish the feasibility and importance of including influential point detection diagnostics as a standard tool in hydrological model calibration. They provide the hydrologist with important information on whether model calibration is susceptible to a small number of highly influent data points. This enables the hydrologist to make a more informed decision of whether to (1) remove/retain the calibration data; (2) adjust the calibration strategy and/or hydrological model to reduce the susceptibility of model predictions to a small number of influential observations.

A statistical analysis of the daily streamflow hydrograph

NASA Astrophysics Data System (ADS)

Kavvas, M. L.; Delleur, J. W.

1984-03-01

In this study a periodic statistical analysis of daily streamflow data in Indiana, U.S.A., was performed to gain some new insight into the stochastic structure which describes the daily streamflow process. This analysis was performed by the periodic mean and covariance functions of the daily streamflows, by the time and peak discharge -dependent recession limb of the daily streamflow hydrograph, by the time and discharge exceedance level (DEL) -dependent probability distribution of the hydrograph peak interarrival time, and by the time-dependent probability distribution of the time to peak discharge. Some new statistical estimators were developed and used in this study. In general features, this study has shown that: (a) the persistence properties of daily flows depend on the storage state of the basin at the specified time origin of the flow process; (b) the daily streamflow process is time irreversible; (c) the probability distribution of the daily hydrograph peak interarrival time depends both on the occurrence time of the peak from which the inter-arrival time originates and on the discharge exceedance level; and (d) if the daily streamflow process is modeled as the release from a linear watershed storage, this release should depend on the state of the storage and on the time of the release as the persistence properties and the recession limb decay rates were observed to change with the state of the watershed storage and time. Therefore, a time-varying reservoir system needs to be considered if the daily streamflow process is to be modeled as the release from a linear watershed storage.

Calibration of a dissolved-solids model for the Yampa River basin between Steamboat Springs and Maybell, northwestern Colorado

USGS Publications Warehouse

Parker, R.S.; Litke, D.W.

1987-01-01

The cumulative effects of changes in dissolved solids from a number of coal mines are needed to evaluate effects on downstream water use. A model for determining cumulative effects of streamflow, dissolved-solids concentration, and dissolved-solids load was calibrated for the Yampa River and its tributaries in northwestern Colorado. The model uses accounting principles. It establishes nodes on the stream system and sums water quantity and quality from node to node in the downstream direction. The model operates on a monthly time step for the study period that includes water years 1976 through 1981. Output is monthly mean streamflow, dissolved-solids concentration, and dissolved-solids load. Streamflow and dissolved-solids data from streamflow-gaging stations and other data-collection sites were used to define input data sets to initiate and to calibrate the model. The model was calibrated at four nodes and generally was within 10 percent of the observed values. The calibrated model can compute changes in dissolved-solids concentration or load resulting from the cumulative effects of new coal mines or the expansion of old coal mines in the Yampa River basin. (USGS)

Predicting long-term streamflow variability in moist eucalypt forests using forest growth models and a sapwood area index

NASA Astrophysics Data System (ADS)

Jaskierniak, D.; Kuczera, G.; Benyon, R.

2016-04-01

A major challenge in surface hydrology involves predicting streamflow in ungauged catchments with heterogeneous vegetation and spatiotemporally varying evapotranspiration (ET) rates. We present a top-down approach for quantifying the influence of broad-scale changes in forest structure on ET and hence streamflow. Across three catchments between 18 and 100 km2 in size and with regenerating Eucalyptus regnans and E. delegatensis forest, we demonstrate how variation in ET can be mapped in space and over time using LiDAR data and commonly available forest inventory data. The model scales plot-level sapwood area (SA) to the catchment-level using basal area (BA) and tree stocking density (N) estimates in forest growth models. The SA estimates over a 69 year regeneration period are used in a relationship between SA and vegetation induced streamflow loss (L) to predict annual streamflow (Q) with annual rainfall (P) estimates. Without calibrating P, BA, N, SA, and L to Q data, we predict annual Q with R2 between 0.68 and 0.75 and Nash Sutcliffe efficiency (NSE) between 0.44 and 0.48. To remove bias, the model was extended to allow for runoff carry-over into the following year as well as minor correction to rainfall bias, which produced R2 values between 0.72 and 0.79, and NSE between 0.70 and 0.79. The model under-predicts streamflow during drought periods as it lacks representation of ecohydrological processes that reduce L with either reduced growth rates or rainfall interception during drought. Refining the relationship between sapwood thickness and forest inventory variables is likely to further improve results.

The importance of warm season warming to western U.S. streamflow changes

USGS Publications Warehouse

Das, T.; Pierce, D.W.; Cayan, D.R.; Vano, J.A.; Lettenmaier, D.P.

2011-01-01

Warm season climate warming will be a key driver of annual streamflow changes in four major river basins of the western U.S., as shown by hydrological model simulations using fixed precipitation and idealized seasonal temperature changes based on climate projections with SRES A2 forcing. Warm season (April-September) warming reduces streamflow throughout the year; streamflow declines both immediately and in the subsequent cool season. Cool season (October-March) warming, by contrast, increases streamflow immediately, partially compensating for streamflow reductions during the subsequent warm season. A uniform warm season warming of 3C drives a wide range of annual flow declines across the basins: 13.3%, 7.2%, 1.8%, and 3.6% in the Colorado, Columbia, Northern and Southern Sierra basins, respectively. The same warming applied during the cool season gives annual declines of only 3.5%, 1.7%, 2.1%, and 3.1%, respectively. Copyright 2011 by the American Geophysical Union.

Evaluating the performance of real-time streamflow forecasting using multi-satellite precipitation products in the Upper Zambezi, Africa

NASA Astrophysics Data System (ADS)

Demaria, E. M.; Valdes, J. B.; Wi, S.; Serrat-Capdevila, A.; Valdés-Pineda, R.; Durcik, M.

2016-12-01

In under-instrumented basins around the world, accurate and timely forecasts of river streamflows have the potential of assisting water and natural resource managers in their management decisions. The Upper Zambezi river basin is the largest basin in southern Africa and its water resources are critical to sustainable economic growth and poverty reduction in eight riparian countries. We present a real-time streamflow forecast for the basin using a multi-model-multi-satellite approach that allows accounting for model and input uncertainties. Three distributed hydrologic models with different levels of complexity: VIC, HYMOD_DS, and HBV_DS are setup at a daily time step and a 0.25 degree spatial resolution for the basin. The hydrologic models are calibrated against daily observed streamflows at the Katima-Mulilo station using a Genetic Algorithm. Three real-time satellite products: Climate Prediction Center's morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Tropical Rainfall Measuring Mission (TRMM-3B42RT) are bias-corrected with daily CHIRPS estimates. Uncertainty bounds for predicted flows are estimated with the Inverse Variance Weighting method. Because concentration times in the basin range from a few days to more than a week, we include the use of precipitation forecasts from the Global Forecasting System (GFS) to predict daily streamflows in the basin with a 10-days lead time. The skill of GFS-predicted streamflows is evaluated and the usefulness of the forecasts for short term water allocations is presented.

An Integrated Modeling System for Estimating Glacier and Snow Melt Driven Streamflow from Remote Sensing and Earth System Data Products in the Himalayas

NASA Technical Reports Server (NTRS)

Brown, M. E.; Racoviteanu, A. E.; Tarboton, D. G.; Sen Gupta, A.; Nigro, J.; Policelli, F.; Habib, S.; Tokay, M.; Shrestha, M. S.; Bajracharya, S.

2014-01-01

Quantification of the contribution of the hydrologic components (snow, ice and rain) to river discharge in the Hindu Kush Himalayan (HKH) region is important for decision-making in water sensitive sectors, and for water resources management and flood risk reduction. In this area, access to and monitoring of the glaciers and their melt outflow is challenging due to difficult access, thus modeling based on remote sensing offers the potential for providing information to improve water resources management and decision making. This paper describes an integrated modeling system developed using downscaled NASA satellite based and earth system data products coupled with in-situ hydrologic data to assess the contribution of snow and glaciers to the flows of the rivers in the HKH region. Snow and glacier melt was estimated using the Utah Energy Balance (UEB) model, further enhanced to accommodate glacier ice melt over clean and debris-covered tongues, then meltwater was input into the USGS Geospatial Stream Flow Model (Geo- SFM). The two model components were integrated into Better Assessment Science Integrating point and Nonpoint Sources modeling framework (BASINS) as a user-friendly open source system and was made available to countries in high Asia. Here we present a case study from the Langtang Khola watershed in the monsoon-influenced Nepal Himalaya, used to validate our energy balance approach and to test the applicability of our modeling system. The snow and glacier melt model predicts that for the eight years used for model evaluation (October 2003-September 2010), the total surface water input over the basin was 9.43 m, originating as 62% from glacier melt, 30% from snowmelt and 8% from rainfall. Measured streamflow for those years were 5.02 m, reflecting a runoff coefficient of 0.53. GeoSFM simulated streamflow was 5.31 m indicating reasonable correspondence between measured and model confirming the capability of the integrated system to provide a quantification of water availability.

Managing Groundwater Recharge and Pumping for Late Summer Streamflow Increases: Quantifying Uncertainty Using Null Space Monte Carlo

NASA Astrophysics Data System (ADS)

Tolley, D. G., III; Foglia, L.; Harter, T.

2017-12-01

Late summer and early fall streamflow decreases caused by climate change and agricultural pumping contribute to increased water temperatures and result in large disconnected sections during dry years in many semi-arid regions with Mediterranean climate. This negatively impacts aquatic habitat of fish species such as coho and fall-run Chinook salmon. In collaboration with local stakeholders, the Scott Valley Integrated Hydrologic Model (SVIHMv3) was developed to assess future water management scenarios with the goal of improving aquatic species habitat while maintaining agricultural production in the valley. The Null Space Monte Carlo (NSMC) method available in PEST was used to quantify the range of predicted streamflow changes for three conjunctive use scenarios: 1) managed aquifer recharge (MAR), 2) in lieu recharge (ILR, substituting surface-water irrigation for irrigation with groundwater while flows are available), and 3) MAR + ILR. Random parameter sets were generated using the calibrated covariance matrix of the model, which were then recalibrated if the sum of squared residuals was greater than 10% of the original sum of squared weighted residuals. These calibration-constrained stochastic parameter sets were then used to obtain a distribution of streamflow changes resulting from implementing the conjunctive use scenarios. Preliminary results show that while the range of streamflow increases using managed aquifer recharge is much narrower (i.e., greater degree of certainty) than in lieu recharge, there are potentially much greater benefits to streamflow by implementing in lieu recharge (although also greater costs). Combining the two scenarios provides the greatest benefit for increasing late summer and early fall streamflow, as most of the MAR streamflow increases are during the spring and early summer which ILR is able to take advantage of. Incorporation of uncertainty into model predictions is critical for establishing and maintaining stakeholder trust, and can help identify management strategies that are most likely to produce desired outcomes.

Impact of LUCC on streamflow based on the SWAT model over the Wei River basin on the Loess Plateau in China

NASA Astrophysics Data System (ADS)

Wang, Hong; Sun, Fubao; Xia, Jun; Liu, Wenbin

2017-04-01

Under the Grain for Green Project in China, vegetation recovery construction has been widely implemented on the Loess Plateau for the purpose of soil and water conservation. Now it is becoming controversial whether the recovery construction involving vegetation, particularly forest, is reducing the streamflow in the rivers of the Yellow River basin. In this study, we chose the Wei River, the largest branch of the Yellow River, with revegetated construction area as the study area. To do that, we apply the widely used Soil and Water Assessment Tool (SWAT) model for the upper and middle reaches of the Wei River basin. The SWAT model was forced with daily observed meteorological forcings (1960-2009) calibrated against daily streamflow for 1960-1969, validated for the period of 1970-1979, and used for analysis for 1980-2009. To investigate the impact of LUCC (land use and land cover change) on the streamflow, we firstly use two observed land use maps from 1980 and 2005 that are based on national land survey statistics merged with satellite observations. We found that the mean streamflow generated by using the 2005 land use map decreased in comparison with that using the 1980 one, with the same meteorological forcings. Of particular interest here is that the streamflow decreased on agricultural land but increased in forest areas. More specifically, the surface runoff, soil flow, and baseflow all decreased on agricultural land, while the soil flow and baseflow of forest areas increased. To investigate that, we then designed five scenarios: (S1) the present land use (1980) and (S2) 10 %, (S3) 20 %, (S4) 40 %, and (S5) 100 % of agricultural land that was converted into mixed forest. We found that the streamflow consistently increased with agricultural land converted into forest by about 7.4 mm per 10 %. Our modeling results suggest that forest recovery construction has a positive impact on both soil flow and baseflow by compensating for reduced surface runoff, which leads to a slight increase in the streamflow in the Wei River with the mixed landscapes on the Loess Plateau that include earth-rock mountain area.

Evaluation of uncertainty in capturing the spatial variability and magnitudes of extreme hydrological events for the uMngeni catchment, South Africa

NASA Astrophysics Data System (ADS)

Kusangaya, Samuel; Warburton Toucher, Michele L.; van Garderen, Emma Archer

2018-02-01

Downscaled General Circulation Models (GCMs) output are used to forecast climate change and provide information used as input for hydrological modelling. Given that our understanding of climate change points towards an increasing frequency, timing and intensity of extreme hydrological events, there is therefore the need to assess the ability of downscaled GCMs to capture these extreme hydrological events. Extreme hydrological events play a significant role in regulating the structure and function of rivers and associated ecosystems. In this study, the Indicators of Hydrologic Alteration (IHA) method was adapted to assess the ability of simulated streamflow (using downscaled GCMs (dGCMs)) in capturing extreme river dynamics (high and low flows), as compared to streamflow simulated using historical climate data from 1960 to 2000. The ACRU hydrological model was used for simulating streamflow for the 13 water management units of the uMngeni Catchment, South Africa. Statistically downscaled climate models obtained from the Climate System Analysis Group at the University of Cape Town were used as input for the ACRU Model. Results indicated that, high flows and extreme high flows (one in ten year high flows/large flood events) were poorly represented both in terms of timing, frequency and magnitude. Simulated streamflow using dGCMs data also captures more low flows and extreme low flows (one in ten year lowest flows) than that captured in streamflow simulated using historical climate data. The overall conclusion was that although dGCMs output can reasonably be used to simulate overall streamflow, it performs poorly when simulating extreme high and low flows. Streamflow simulation from dGCMs must thus be used with caution in hydrological applications, particularly for design hydrology, as extreme high and low flows are still poorly represented. This, arguably calls for the further improvement of downscaling techniques in order to generate climate data more relevant and useful for hydrological applications such as in design hydrology. Nevertheless, the availability of downscaled climatic output provide the potential of exploring climate model uncertainties in different hydro climatic regions at local scales where forcing data is often less accessible but more accurate at finer spatial scales and with adequate spatial detail.

Assessment of De Facto Wastewater Reuse across the US: trends between 1980 and 2008.

PubMed

Rice, Jacelyn; Wutich, Amber; Westerhoff, Paul

2013-10-01

De facto wastewater reuse is the incidental presence of treated wastewater in a water supply source. In 1980 the EPA identified drinking water treatment plants (DWTPs) impacted by upstream wastewater treatment plant (WWTP) discharges and found the top 25 most impacted DWTPs contained between 2% and 16% wastewater discharges from upstream locations (i.e., de facto reuse) under average streamflow conditions. This study is the first to provide an update to the 1980 EPA analysis. An ArcGIS model of DWTPs and WWTPs across the U.S. was created to quantify de facto reuse for the top 25 cities in the 1980 EPA study. From 1980 to 2008, de facto reuse increased for 17 of the 25 DWTPs, as municipal flows upstream of the sites increased by 68%. Under low streamflow conditions, de facto reuse in DWTP supplies ranged from 7% to 100%, illustrating the importance of wastewater in sustainable water supplies. Case studies were performed on four cities to analyze the reasons for changes in de facto reuse over time. Three of the four sites have greater than 20% treated wastewater effluent within their drinking water source for streamflow less than the 25th percentile historic flow.

Reducing hydrologic model uncertainty in monthly streamflow predictions using multimodel combination

NASA Astrophysics Data System (ADS)

Li, Weihua; Sankarasubramanian, A.

2012-12-01

Model errors are inevitable in any prediction exercise. One approach that is currently gaining attention in reducing model errors is by combining multiple models to develop improved predictions. The rationale behind this approach primarily lies on the premise that optimal weights could be derived for each model so that the developed multimodel predictions will result in improved predictions. A new dynamic approach (MM-1) to combine multiple hydrological models by evaluating their performance/skill contingent on the predictor state is proposed. We combine two hydrological models, "abcd" model and variable infiltration capacity (VIC) model, to develop multimodel streamflow predictions. To quantify precisely under what conditions the multimodel combination results in improved predictions, we compare multimodel scheme MM-1 with optimal model combination scheme (MM-O) by employing them in predicting the streamflow generated from a known hydrologic model (abcd model orVICmodel) with heteroscedastic error variance as well as from a hydrologic model that exhibits different structure than that of the candidate models (i.e., "abcd" model or VIC model). Results from the study show that streamflow estimated from single models performed better than multimodels under almost no measurement error. However, under increased measurement errors and model structural misspecification, both multimodel schemes (MM-1 and MM-O) consistently performed better than the single model prediction. Overall, MM-1 performs better than MM-O in predicting the monthly flow values as well as in predicting extreme monthly flows. Comparison of the weights obtained from each candidate model reveals that as measurement errors increase, MM-1 assigns weights equally for all the models, whereas MM-O assigns higher weights for always the best-performing candidate model under the calibration period. Applying the multimodel algorithms for predicting streamflows over four different sites revealed that MM-1 performs better than all single models and optimal model combination scheme, MM-O, in predicting the monthly flows as well as the flows during wetter months.

Natural Reforestation Reclaims a Watershed: A Case History from West Virginia

Treesearch

W.P. Lima; J.H. Patric; N. Holowaychuk

1978-01-01

Thirteen years of hydrologic data from two contiguous small watersheds in West Virginia were analyzed to determine the effects on streamflow of natural reforestation on abandoned farmlands. During the study period (1958-1970), streamflow on the watersheds was unchanged. The history of land use on the study area helps explain the apparent lack of hydrologic effects of...

Error reduction and representation in stages (ERRIS) in hydrological modelling for ensemble streamflow forecasting

NASA Astrophysics Data System (ADS)

Li, Ming; Wang, Q. J.; Bennett, James C.; Robertson, David E.

2016-09-01

This study develops a new error modelling method for ensemble short-term and real-time streamflow forecasting, called error reduction and representation in stages (ERRIS). The novelty of ERRIS is that it does not rely on a single complex error model but runs a sequence of simple error models through four stages. At each stage, an error model attempts to incrementally improve over the previous stage. Stage 1 establishes parameters of a hydrological model and parameters of a transformation function for data normalization, Stage 2 applies a bias correction, Stage 3 applies autoregressive (AR) updating, and Stage 4 applies a Gaussian mixture distribution to represent model residuals. In a case study, we apply ERRIS for one-step-ahead forecasting at a range of catchments. The forecasts at the end of Stage 4 are shown to be much more accurate than at Stage 1 and to be highly reliable in representing forecast uncertainty. Specifically, the forecasts become more accurate by applying the AR updating at Stage 3, and more reliable in uncertainty spread by using a mixture of two Gaussian distributions to represent the residuals at Stage 4. ERRIS can be applied to any existing calibrated hydrological models, including those calibrated to deterministic (e.g. least-squares) objectives.

Streamflow and Nutrient Fluxes of the Mississippi-Atchafalaya River Basin and Subbasins for the Period of Record Through 2005

USGS Publications Warehouse

Aulenbach, Brent T.; Buxton, Herbert T.; Battaglin, William A.; Coupe, Richard H.

2007-01-01

U.S. Geological Survey has monitored streamflow and water quality systematically in the Mississippi-Atchafalaya River Basin (MARB) for more than five decades. This report provides streamflow and estimates of nutrient delivery (flux) to the Gulf of Mexico from both the Atchafalaya River and the main stem of the Mississippi River. This report provides streamflow and nutrient flux estimates for nine major subbasins of the Mississippi River. This report also provides streamflow and flux estimates for 21 selected subbasins of various sizes, hydrology, land use, and geographic location within the Basin. The information is provided at each station for the period for which sufficient water-quality data are available to make statistically based flux estimates (starting as early as water year1 1960 and going through water year 2005). Nutrient fluxes are estimated using the adjusted maximum likelihood estimate, a type of regression-model method; nutrient fluxes to the Gulf of Mexico also are estimated using the composite method. Regression models were calibrated using a 5-year moving calibration period; the model was used to estimate the last year of the calibration period. Nutrient flux estimates are provided for six water-quality constituents: dissolved nitrite plus nitrate, total organic nitrogen plus ammonia nitrogen (total Kjeldahl nitrogen), dissolved ammonia, total phosphorous, dissolved orthophosphate, and dissolved silica. Additionally, the contribution of streamflow and net nutrient flux for five large subbasins comprising the MARB were determined from streamflow and nutrient fluxes from seven of the aforementioned major subbasins. These five large subbasins are: 1. Lower Mississippi, 2. Upper Mississippi, 3. Ohio/Tennessee, 4. Missouri, and 5. Arkansas/Red.

Simulation of streamflow, evapotranspiration, and groundwater recharge in the lower San Antonio River Watershed, South-Central Texas, 2000-2007

USGS Publications Warehouse

Lizarraga, Joy S.; Ockerman, Darwin J.

2010-01-01

The U.S. Geological Survey (USGS), in cooperation with the San Antonio River Authority, the Evergreen Underground Water Conservation District, and the Goliad County Groundwater Conservation District, configured, calibrated, and tested a watershed model for a study area consisting of about 2,150 square miles of the lower San Antonio River watershed in Bexar, Guadalupe, Wilson, Karnes, DeWitt, Goliad, Victoria, and Refugio Counties in south-central Texas. The model simulates streamflow, evapotranspiration (ET), and groundwater recharge using rainfall, potential ET, and upstream discharge data obtained from National Weather Service meteorological stations and USGS streamflow-gaging stations. Additional time-series inputs to the model include wastewater treatment-plant discharges, withdrawals for cropland irrigation, and estimated inflows from springs. Model simulations of streamflow, ET, and groundwater recharge were done for 2000-2007. Because of the complexity of the study area, the lower San Antonio River watershed was divided into four subwatersheds; separate HSPF models were developed for each subwatershed. Simulation of the overall study area involved running simulations of the three upstream models, then running the downstream model. The surficial geology was simplified as nine contiguous water-budget zones to meet model computational limitations and also to define zones for which ET, recharge, and other water-budget information would be output by the model. The model was calibrated and tested using streamflow data from 10 streamflow-gaging stations; additionally, simulated ET was compared with measured ET from a meteorological station west of the study area. The model calibration is considered very good; streamflow volumes were calibrated to within 10 percent of measured streamflow volumes. During 2000-2007, the estimated annual mean rainfall for the water-budget zones ranged from 33.7 to 38.5 inches per year; the estimated annual mean rainfall for the entire watershed was 34.3 inches. Using the HSPF model it was estimated that for 2000-2007, less than 10 percent of the annual mean rainfall on the study watershed exited the watershed as streamflow, whereas about 82 percent, or an average of 28.2 inches per year, exited the watershed as ET. Estimated annual mean groundwater recharge for the entire study area was 3.0 inches, or about 9 percent of annual mean rainfall. Estimated annual mean recharge was largest in water-budget zone 3, the zone where the Carrizo Sand outcrops. In water-budget zone 3, the estimated annual mean recharge was 5.1 inches or about 15 percent of annual mean rainfall. Estimated annual mean recharge was smallest in water-budget zone 6, about 1.1 inches or about 3 percent of annual mean rainfall. The Cibolo Creek subwatershed and the subwatershed of the San Antonio River upstream from Cibolo Creek had the largest and smallest basin yields, about 4.8 inches and 1.2 inches, respectively. Estimated annual ET and annual recharge generally increased with increasing annual rainfall. Also, ET was larger in zones 8 and 9, the most downstream zones in the watershed. Model limitations include possible errors related to model conceptualization and parameter variability, lack of data to quantify certain model inputs, and measurement errors. Uncertainty regarding the degree to which available rainfall data represent actual rainfall is potentially the most serious source of measurement error.

Sensitivity and Uncertainty Analysis for Streamflow Prediction Using Different Objective Functions and Optimization Algorithms: San Joaquin California

NASA Astrophysics Data System (ADS)

Paul, M.; Negahban-Azar, M.

2017-12-01

The hydrologic models usually need to be calibrated against observed streamflow at the outlet of a particular drainage area through a careful model calibration. However, a large number of parameters are required to fit in the model due to their unavailability of the field measurement. Therefore, it is difficult to calibrate the model for a large number of potential uncertain model parameters. This even becomes more challenging if the model is for a large watershed with multiple land uses and various geophysical characteristics. Sensitivity analysis (SA) can be used as a tool to identify most sensitive model parameters which affect the calibrated model performance. There are many different calibration and uncertainty analysis algorithms which can be performed with different objective functions. By incorporating sensitive parameters in streamflow simulation, effects of the suitable algorithm in improving model performance can be demonstrated by the Soil and Water Assessment Tool (SWAT) modeling. In this study, the SWAT was applied in the San Joaquin Watershed in California covering 19704 km2 to calibrate the daily streamflow. Recently, sever water stress escalating due to intensified climate variability, prolonged drought and depleting groundwater for agricultural irrigation in this watershed. Therefore it is important to perform a proper uncertainty analysis given the uncertainties inherent in hydrologic modeling to predict the spatial and temporal variation of the hydrologic process to evaluate the impacts of different hydrologic variables. The purpose of this study was to evaluate the sensitivity and uncertainty of the calibrated parameters for predicting streamflow. To evaluate the sensitivity of the calibrated parameters three different optimization algorithms (Sequential Uncertainty Fitting- SUFI-2, Generalized Likelihood Uncertainty Estimation- GLUE and Parameter Solution- ParaSol) were used with four different objective functions (coefficient of determination- r2, Nash-Sutcliffe efficiency- NSE, percent bias- PBIAS, and Kling-Gupta efficiency- KGE). The preliminary results showed that using the SUFI-2 algorithm with the objective function NSE and KGE has improved significantly the calibration (e.g. R2 and NSE is found 0.52 and 0.47 respectively for daily streamflow calibration).

Watershed Modeling to Assess the Sensitivity of Streamflow, Nutrient, and Sediment Loads to Potential Climate Change and Urban Development in 20 U.S. Watersheds (Final Report)

EPA Science Inventory

In September 2013, EPA announced the release of the final report, Watershed Modeling to Assess the Sensitivity of Streamflow, Nutrient, and Sediment Loads to Potential Climate Change and Urban Development in 20 U.S. Watersheds.

Watershed modeling was conducted in ...

Simulated Effects of Year 2030 Water-Use and Land-Use Changes on Streamflow near the Interstate-495 Corridor, Assabet and Upper Charles River Basins, Eastern Massachusetts

USGS Publications Warehouse

Carlson, Carl S.; Desimone, Leslie A.; Weiskel, Peter K.

2008-01-01

Continued population growth and land development for commercial, industrial, and residential uses have created concerns regarding the future supply of potable water and the quantity of ground water discharging to streams in the area of Interstate 495 in eastern Massachusetts. Two ground-water models developed in 2002-2004 for the Assabet and Upper Charles River Basins were used to simulate water supply and land-use scenarios relevant for the entire Interstate-495 corridor. Future population growth, water demands, and commercial and residential growth were projected for year 2030 by the Metropolitan Area Planning Council. To assess the effects of future development on subbasin streamflows, seven scenarios were simulated by using existing computer-based ground-water-flow models with the data projected for year 2030. The scenarios incorporate three categories of projected 2030 water- and land-use data: (1) 2030 water use, (2) 2030 land use, and (3) a combination of 2030 water use and 2030 land use. Hydrologic, land-use, and water-use data from 1997 through 2001 for the Assabet River Basin study and 1989 through 1998 for the Upper Charles River Basin study were used to represent current conditions - referred to as 'basecase' conditions - in each basin to which each 2030 scenario was compared. The effects of projected 2030 land- and water-use change on streamflows in the Assabet River Basin depended upon the time of year, the hydrologic position of the subbasin in the larger basin, and the relative areas of new commercial and residential development projected for a subbasin. Effects of water use and land use on streamflow were evaluated by comparing average monthly nonstorm streamflow (base flow) for March and September simulated by using the models. The greatest decreases in streamflow (up to 76 percent in one subbasin), compared to the basecase, occurred in September, when streamflows are naturally at their lowest level. By contrast, simulated March streamflows decreased less than 6.5 percent from basecase streamflows in all subbasins for all scenarios. The simulations showed similar effects in the Upper Charles River Basin, but increased water use contributed to decreased simulated streamflow in most subbasins. Simulated changes in March streamflows for 2030 in the Upper Charles River Basin were within +- 6 percent of the basecase for all scenarios and subbasins. Percentage decreases in simulated September streamflows for 2030 were greater than in March but less than the September decreases that resulted for some subbasins in the Assabet River Basin. Only two subbasins of the Upper Charles River Basin had projected decreases greater than 5 percent. In the Mill River subbasin, the decrease was 11 percent, and in the Mine Brook subbasin, 6.6 percent. Changes in water use and wastewater return flow generally were found to have the greatest effect in the summer months when streamflow and aquifer recharge rates are low and water use is high. September increases in main-stem streamflow of both basins were due mainly to increased discharge of treated effluent from wastewater-treatment facilities on the main-stem rivers. In the Assabet River Basin, wastewater-treatment-facility discharge became a smaller proportion of total streamflow with distance downstream. In contrast, wastewater-treatment facility discharge in the Upper Charles River Basin became a greater proportion of streamflow with distance downstream. The effects of sewer-line extension and low-impact development on streamflows in two different subbasins of the Assabet River Basin also were simulated. The result of extending sewer lines with a corresponding decrease in septic-system return flow caused September streamflows to decrease as much as 15 percent in the Fort Pond Brook subbasin. The effect of low-impact development was simulated in the Hop Brook subbasin in areas projected for commercial development. In this simulation, the greater the area where low-i

Reducing streamflow forecast uncertainty: Application and qualitative assessment of the upper klamath river Basin, Oregon

USGS Publications Warehouse

Hay, L.E.; McCabe, G.J.; Clark, M.P.; Risley, J.C.

2009-01-01

The accuracy of streamflow forecasts depends on the uncertainty associated with future weather and the accuracy of the hydrologic model that is used to produce the forecasts. We present a method for streamflow forecasting where hydrologic model parameters are selected based on the climate state. Parameter sets for a hydrologic model are conditioned on an atmospheric pressure index defined using mean November through February (NDJF) 700-hectoPascal geopotential heights over northwestern North America [Pressure Index from Geopotential heights (PIG)]. The hydrologic model is applied in the Sprague River basin (SRB), a snowmelt-dominated basin located in the Upper Klamath basin in Oregon. In the SRB, the majority of streamflow occurs during March through May (MAM). Water years (WYs) 1980-2004 were divided into three groups based on their respective PIG values (high, medium, and low PIG). Low (high) PIG years tend to have higher (lower) than average MAM streamflow. Four parameter sets were calibrated for the SRB, each using a different set of WYs. The initial set used WYs 1995-2004 and the remaining three used WYs defined as high-, medium-, and low-PIG years. Two sets of March, April, and May streamflow volume forecasts were made using Ensemble Streamflow Prediction (ESP). The first set of ESP simulations used the initial parameter set. Because the PIG is defined using NDJF pressure heights, forecasts starting in March can be made using the PIG parameter set that corresponds with the year being forecasted. The second set of ESP simulations used the parameter set associated with the given PIG year. Comparison of the ESP sets indicates that more accuracy and less variability in volume forecasts may be possible when the ESP is conditioned using the PIG. This is especially true during the high-PIG years (low-flow years). ?? 2009 American Water Resources Association.

Integrated meteorological and hydrological drought model: A management tool for proactive water resources planning of semi-arid regions

NASA Astrophysics Data System (ADS)

Rad, Arash Modaresi; Ghahraman, Bijan; Khalili, Davar; Ghahremani, Zahra; Ardakani, Samira Ahmadi

2017-09-01

Conventionally, drought analysis has been limited to single drought category. Utilization of models incorporating multiple drought categories, can relax this limitation. A copula-based model is proposed, which uses meteorological and hydrological drought characteristics to assess drought events for ultimate management of water resources, at small scales, i.e., sub-watersheds. The chosen study area is a sub-basin located at Karkheh watershed (western Iran), with five raingauge stations and one hydrometric station, located upstream and at the outlet, respectively, which represent 41-year of data. Prior to drought analysis, time series of precipitation and streamflow records are investigated for possible dependency/significant trend. Considering semi-arid nature of the study area, boxplots are utilized to graphically capture the rainy months, which are used to evaluate the degree of correlation between streamflow and precipitation records via nonparametric correlations. Time scales of 3- and 12-month are considered, which are used to study vulnerability of early vegetation establishment and long-term ecosystem resilience, respectively. Among four common goodness of fit (GOF) tests, Anderson-Darling is found preferable for defining copula distribution functions through GOF measures, i.e., Akaike and Bayesian information criteria and normalized root mean square error. Furthermore, a GOF method is proposed to evaluate the uncertainty associated with different copula models using the concept of entropy. A new bivariate drought modeling approach is proposed through copulas. The proposed index named standardized precipitation-streamflow index (SPSI) unlike common indices which are used in conjunction with station data, can be applied on a regional basis. SPDI is compared with widely applied streamflow drought index (SDI) and standardized precipitation index (SPI). To assess the homogeneity of the dependence structure of SPSI regionally, Kendall-τ and upper tail coefficient relation is investigated for all stations located within the region. According to results, SPSI similar to nonparametric multivariate standardized drought index (NMSDI) was able to detect both onset of droughts dominated by precipitation as is similarly indicated by SPI and persistence of droughts dominated by streamflow as is similarly indicated by SDI. It also captures discordant case of normal period precipitation with dry period streamflow and vice versa. This makes SPSI a powerful tool for estimating a more practical and realistic drought condition. Finally, combination of severity-duration-frequency (SDF) of drought events through copulas resulted in SDF curves that can be used to obtain the recurrence of extreme droughts and assess drought related ecosystem failure or to aid in optimization of water resources allocation. Results indicated that the newly proposed index (SPSI) is able to represent two main characteristics of meteorological and hydrological drought (drought onset and persistency) and also providing an accurate estimation of the recurrence interval of extreme droughts. The procedures can be used to undertake proactive water resource management and planning to assure water security and sustainable agriculture and ecosystem survival for regions experiencing extreme droughts.

Evaluating climate change impacts on streamflow variability based on a multisite multivariate GCM downscaling method in the Jing River of China

NASA Astrophysics Data System (ADS)

Li, Zhi; Jin, Jiming

2017-11-01

Projected hydrological variability is important for future resource and hazard management of water supplies because changes in hydrological variability can cause more disasters than changes in the mean state. However, climate change scenarios downscaled from Earth System Models (ESMs) at single sites cannot meet the requirements of distributed hydrologic models for simulating hydrological variability. This study developed multisite multivariate climate change scenarios via three steps: (i) spatial downscaling of ESMs using a transfer function method, (ii) temporal downscaling of ESMs using a single-site weather generator, and (iii) reconstruction of spatiotemporal correlations using a distribution-free shuffle procedure. Multisite precipitation and temperature change scenarios for 2011-2040 were generated from five ESMs under four representative concentration pathways to project changes in streamflow variability using the Soil and Water Assessment Tool (SWAT) for the Jing River, China. The correlation reconstruction method performed realistically for intersite and intervariable correlation reproduction and hydrological modeling. The SWAT model was found to be well calibrated with monthly streamflow with a model efficiency coefficient of 0.78. It was projected that the annual mean precipitation would not change, while the mean maximum and minimum temperatures would increase significantly by 1.6 ± 0.3 and 1.3 ± 0.2 °C; the variance ratios of 2011-2040 to 1961-2005 were 1.15 ± 0.13 for precipitation, 1.15 ± 0.14 for mean maximum temperature, and 1.04 ± 0.10 for mean minimum temperature. A warmer climate was predicted for the flood season, while the dry season was projected to become wetter and warmer; the findings indicated that the intra-annual and interannual variations in the future climate would be greater than in the current climate. The total annual streamflow was found to change insignificantly but its variance ratios of 2011-2040 to 1961-2005 increased by 1.25 ± 0.55. Streamflow variability was predicted to become greater over most months on the seasonal scale because of the increased monthly maximum streamflow and decreased monthly minimum streamflow. The increase in streamflow variability was attributed mainly to larger positive contributions from increased precipitation variances rather than negative contributions from increased mean temperatures.

Estimating the Magnitude and Frequency of Peak Streamflows for Ungaged Sites on Streams in Alaska and Conterminous Basins in Canada

USGS Publications Warehouse

Curran, Janet H.; Meyer, David F.; Tasker, Gary D.

2003-01-01

Estimates of the magnitude and frequency of peak streamflow are needed across Alaska for floodplain management, cost-effective design of floodway structures such as bridges and culverts, and other water-resource management issues. Peak-streamflow magnitudes for the 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence-interval flows were computed for 301 streamflow-gaging and partial-record stations in Alaska and 60 stations in conterminous basins of Canada. Flows were analyzed from data through the 1999 water year using a log-Pearson Type III analysis. The State was divided into seven hydrologically distinct streamflow analysis regions for this analysis, in conjunction with a concurrent study of low and high flows. New generalized skew coefficients were developed for each region using station skew coefficients for stations with at least 25 years of systematic peak-streamflow data. Equations for estimating peak streamflows at ungaged locations were developed for Alaska and conterminous basins in Canada using a generalized least-squares regression model. A set of predictive equations for estimating the 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year peak streamflows was developed for each streamflow analysis region from peak-streamflow magnitudes and physical and climatic basin characteristics. These equations may be used for unregulated streams without flow diversions, dams, periodically releasing glacial impoundments, or other streamflow conditions not correlated to basin characteristics. Basin characteristics should be obtained using methods similar to those used in this report to preserve the statistical integrity of the equations.

Streamflow alteration at selected sites in Kansas

USGS Publications Warehouse

Juracek, Kyle E.; Eng, Ken

2017-06-26

An understanding of streamflow alteration in response to various disturbances is necessary for the effective management of stream habitat for a variety of species in Kansas. Streamflow alteration can have negative ecological effects. Using a modeling approach, streamflow alteration was assessed for 129 selected U.S. Geological Survey streamgages in the State for which requisite streamflow and basin-characteristic information was available. The assessment involved a comparison of the observed condition from 1980 to 2015 with the predicted expected (least-disturbed) condition for 29 streamflow metrics. The metrics represent various characteristics of streamflow including average flow (annual, monthly) and low and high flow (frequency, duration, magnitude).Streamflow alteration in Kansas was indicated locally, regionally, and statewide. Given the absence of a pronounced trend in annual precipitation in Kansas, a precipitation-related explanation for streamflow alteration was not supported. Thus, the likely explanation for streamflow alteration was human activity. Locally, a flashier flow regime (typified by shorter lag times and more frequent and higher peak discharges) was indicated for three streamgages with urbanized basins that had higher percentages of impervious surfaces than other basins in the State. The combination of localized reservoir effects and regional groundwater pumping from the High Plains aquifer likely was responsible, in part, for diminished conditions indicated for multiple streamflow metrics in western and central Kansas. Statewide, the implementation of agricultural land-management practices to reduce runoff may have been responsible, in part, for a diminished duration and magnitude of high flows. In central and eastern Kansas, implemented agricultural land-management practices may have been partly responsible for an inflated magnitude of low flows at several sites.

Balancing Ground-Water Withdrawals and Streamflow in the Hunt-Annaquatucket-Pettaquamscutt Basin, Rhode Island

USGS Publications Warehouse

Barlow, Paul M.; Dickerman, David C.

2001-01-01

Ground water withdrawn for water supply reduces streamflow in the Hunt-Annaquatucket-Pettaquamscutt Basin in Rhode Island. These reductions may adversely affect aquatic habitats. A hydrologic model was prepared by the U.S. Geological Survey in cooperation with the Rhode Island Water Resources Board, Town of North Kingstown, Rhode Island Department of Environmental Management, and Rhode Island Economic Development Corporation to aid water-resource planning in the basin. Results of the model provide information that helps water suppliers and natural-resource managers evaluate strategies for balancing ground-water development and streamflow reductions in the basin.

Simulating Streamflow Using Bias-corrected Multiple Satellite Rainfall Products in the Tekeze Basin, Ethiopia

NASA Astrophysics Data System (ADS)

Abitew, T. A.; Roy, T.; Serrat-Capdevila, A.; van Griensven, A.; Bauwens, W.; Valdes, J. B.

2016-12-01

The Tekeze Basin supports one of Africans largest Arch Dam located in northern Ethiopian has vital role in hydropower generation. However, little has been done on the hydrology of the basin due to limited in situ hydroclimatological data. Therefore, the main objective of this research is to simulate streamflow upstream of the Tekeze Dam using Soil and Water Assessment Tool (SWAT) forced by bias-corrected multiple satellite rainfall products (CMORPH, TMPA and PERSIANN-CCS). This talk will present the potential as well as skills of bias-corrected satellite rainfall products for streamflow prediction in in Tropical Africa. Additionally, the SWAT model results will also be compared with previous conceptual Hydrological models (HyMOD and HBV) from SERVIR Streamflow forecasting in African Basin project (http://www.swaat.arizona.edu/index.html).

Continuous Tidal Streamflow and Gage-Height Data for Bass and Cinder Creeks on Kiawah Island, South Carolina, September 2007

USGS Publications Warehouse

Conrads, Paul; Erbland, John W.

2009-01-01

A three-dimensional model of Bass and Cinder Creeks on Kiawah Island, South Carolina, was developed to evaluate methodologies for determining fecal coliform total maximum daily loads for shellfish waters. To calibrate the model, two index-velocity sites on the creeks were instrumented with continuous acoustic velocity meters and water-level sensors to compute a 21-day continuous record of tidal streamflows. In addition to monitoring tidal cycles, streamflow measurements were made at the index-velocity sites, and tidal-cycle streamflow measurements were made at the mouth of Bass Creek and on the Stono River to characterize the streamflow dynamics near the ocean boundary of the three-dimensional model at the beginning, September 6, 2007, and end, September 26, 2007, of the index-velocity meter deployment. The maximum floodtide and ebbtide measured on the Stono River by the mouth of Bass Creek for the two measurements were -155,000 and 170,000 cubic feet per second (ft3/s). At the mouth of Bass Creek, the maximum floodtide and ebbtide measurements during the 2 measurement days were +/-10,200 ft3/s. Tidal streamflows for the 21-day deployment on Bass Creek ranged from -2,510 ft3/s for an incoming tide to 4,360 ft3/s for an outgoing tide. On Cinder Creek, the incoming and outgoing tide varied from -2,180 to 2,400 ft3/s during the same period.

Impact of state updating and multi-parametric ensemble for streamflow hindcasting in European river basins

NASA Astrophysics Data System (ADS)

Noh, S. J.; Rakovec, O.; Kumar, R.; Samaniego, L. E.

2015-12-01

Accurate and reliable streamflow prediction is essential to mitigate social and economic damage coming from water-related disasters such as flood and drought. Sequential data assimilation (DA) may facilitate improved streamflow prediction using real-time observations to correct internal model states. In conventional DA methods such as state updating, parametric uncertainty is often ignored mainly due to practical limitations of methodology to specify modeling uncertainty with limited ensemble members. However, if parametric uncertainty related with routing and runoff components is not incorporated properly, predictive uncertainty by model ensemble may be insufficient to capture dynamics of observations, which may deteriorate predictability. Recently, a multi-scale parameter regionalization (MPR) method was proposed to make hydrologic predictions at different scales using a same set of model parameters without losing much of the model performance. The MPR method incorporated within the mesoscale hydrologic model (mHM, http://www.ufz.de/mhm) could effectively represent and control uncertainty of high-dimensional parameters in a distributed model using global parameters. In this study, we evaluate impacts of streamflow data assimilation over European river basins. Especially, a multi-parametric ensemble approach is tested to consider the effects of parametric uncertainty in DA. Because augmentation of parameters is not required within an assimilation window, the approach could be more stable with limited ensemble members and have potential for operational uses. To consider the response times and non-Gaussian characteristics of internal hydrologic processes, lagged particle filtering is utilized. The presentation will be focused on gains and limitations of streamflow data assimilation and multi-parametric ensemble method over large-scale basins.

Estimating rainfall time series and model parameter distributions using model data reduction and inversion techniques

NASA Astrophysics Data System (ADS)

Wright, Ashley J.; Walker, Jeffrey P.; Pauwels, Valentijn R. N.

2017-08-01

Floods are devastating natural hazards. To provide accurate, precise, and timely flood forecasts, there is a need to understand the uncertainties associated within an entire rainfall time series, even when rainfall was not observed. The estimation of an entire rainfall time series and model parameter distributions from streamflow observations in complex dynamic catchments adds skill to current areal rainfall estimation methods, allows for the uncertainty of entire rainfall input time series to be considered when estimating model parameters, and provides the ability to improve rainfall estimates from poorly gauged catchments. Current methods to estimate entire rainfall time series from streamflow records are unable to adequately invert complex nonlinear hydrologic systems. This study aims to explore the use of wavelets in the estimation of rainfall time series from streamflow records. Using the Discrete Wavelet Transform (DWT) to reduce rainfall dimensionality for the catchment of Warwick, Queensland, Australia, it is shown that model parameter distributions and an entire rainfall time series can be estimated. Including rainfall in the estimation process improves streamflow simulations by a factor of up to 1.78. This is achieved while estimating an entire rainfall time series, inclusive of days when none was observed. It is shown that the choice of wavelet can have a considerable impact on the robustness of the inversion. Combining the use of a likelihood function that considers rainfall and streamflow errors with the use of the DWT as a model data reduction technique allows the joint inference of hydrologic model parameters along with rainfall.

Verification of Advances in a Coupled Snow-runoff Modeling Framework for Operational Streamflow Forecasts

NASA Astrophysics Data System (ADS)

Barik, M. G.; Hogue, T. S.; Franz, K. J.; He, M.

2011-12-01

The National Oceanic and Atmospheric Administration's (NOAA's) River Forecast Centers (RFCs) issue hydrologic forecasts related to flood events, reservoir operations for water supply, streamflow regulation, and recreation on the nation's streams and rivers. The RFCs use the National Weather Service River Forecast System (NWSRFS) for streamflow forecasting which relies on a coupled snow model (i.e. SNOW17) and rainfall-runoff model (i.e. SAC-SMA) in snow-dominated regions of the US. Errors arise in various steps of the forecasting system from input data, model structure, model parameters, and initial states. The goal of the current study is to undertake verification of potential improvements in the SNOW17-SAC-SMA modeling framework developed for operational streamflow forecasts. We undertake verification for a range of parameters sets (i.e. RFC, DREAM (Differential Evolution Adaptive Metropolis)) as well as a data assimilation (DA) framework developed for the coupled models. Verification is also undertaken for various initial conditions to observe the influence of variability in initial conditions on the forecast. The study basin is the North Fork America River Basin (NFARB) located on the western side of the Sierra Nevada Mountains in northern California. Hindcasts are verified using both deterministic (i.e. Nash Sutcliffe efficiency, root mean square error, and joint distribution) and probabilistic (i.e. reliability diagram, discrimination diagram, containing ratio, and Quantile plots) statistics. Our presentation includes comparison of the performance of different optimized parameters and the DA framework as well as assessment of the impact associated with the initial conditions used for streamflow forecasts for the NFARB.

Simulation of streamflow, evapotranspiration, and groundwater recharge in the Lower Frio River watershed, south Texas, 1961-2008

USGS Publications Warehouse

Lizarraga, Joy S.; Ockerman, Darwin J.

2011-01-01

The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, Fort Worth District; the City of Corpus Christi; the Guadalupe-Blanco River Authority; the San Antonio River Authority; and the San Antonio Water System, configured, calibrated, and tested a watershed model for a study area consisting of about 5,490 mi2 of the Frio River watershed in south Texas. The purpose of the model is to contribute to the understanding of watershed processes and hydrologic conditions in the lower Frio River watershed. The model simulates streamflow, evapotranspiration (ET), and groundwater recharge by using a numerical representation of physical characteristics of the landscape, and meteorological and streamflow data. Additional time-series inputs to the model include wastewater-treatment-plant discharges, surface-water withdrawals, and estimated groundwater inflow from Leona Springs. Model simulations of streamflow, ET, and groundwater recharge were done for various periods of record depending upon available measured data for input and comparison, starting as early as 1961. Because of the large size of the study area, the lower Frio River watershed was divided into 12 subwatersheds; separate Hydrological Simulation Program-FORTRAN models were developed for each subwatershed. Simulation of the overall study area involved running simulations in downstream order. Output from the model was summarized by subwatershed, point locations, reservoir reaches, and the Carrizo-Wilcox aquifer outcrop. Four long-term U.S. Geological Survey streamflow-gaging stations and two short-term streamflow-gaging stations were used for streamflow model calibration and testing with data from 1991-2008. Calibration was based on data from 2000-08, and testing was based on data from 1991-99. Choke Canyon Reservoir stage data from 1992-2008 and monthly evaporation estimates from 1999-2008 also were used for model calibration. Additionally, 2006-08 ET data from a U.S. Geological Survey meteorological station in Medina County were used for calibration. Streamflow and ET calibration were considered good or very good. For the 2000-08 calibration period, total simulated flow volume and the flow volume of the highest 10 percent of simulated daily flows were calibrated to within about 10 percent of measured volumes at six U.S. Geological Survey streamflow-gaging stations. The flow volume of the lowest 50 percent of daily flows was not simulated as accurately but represented a small percent of the total flow volume. The model-fit efficiency for the weekly mean streamflow during the calibration periods ranged from 0.60 to 0.91, and the root mean square error ranged from 16 to 271 percent of the mean flow rate. The simulated total flow volumes during the testing periods at the long-term gaging stations exceeded the measured total flow volumes by approximately 22 to 50 percent at three stations and were within 7 percent of the measured total flow volumes at one station. For the longer 1961-2008 simulation period at the long-term stations, simulated total flow volumes were within about 3 to 18 percent of measured total flow volumes. The calibrations made by using Choke Canyon reservoir volume for 1992-2008, reservoir evaporation for 1999-2008, and ET in Medina County for 2006-08, are considered very good. Model limitations include possible errors related to model conceptualization and parameter variability, lack of data to better quantify certain model inputs, and measurement errors. Uncertainty regarding the degree to which available rainfall data represent actual rainfall is potentially the most serious source of measurement error. A sensitivity analysis was performed for the Upper San Miguel subwatershed model to show the effect of changes to model parameters on the estimated mean recharge, ET, and surface runoff from that part of the Carrizo-Wilcox aquifer outcrop. Simulated recharge was most sensitive to the changes in the lower-zone ET (LZ

Determination of baseline periods of record for selected streamflow-gaging stations in and near Oklahoma for use in modeling applications

USGS Publications Warehouse

Esralew, Rachel A.

2010-01-01

Use of historical streamflow data from a least-altered period of record can be used in calibration of various modeling applications that are used to characterize least-altered flow and predict the effects of proposed streamflow alteration. This information can be used to enhance water-resources planning. A baseline period of record was determined for selected streamflow-gaging stations that can be used as a calibration dataset for modeling applications. The baseline period of record was defined as a period that is least-altered by anthropogenic activity and has sufficient streamflow record length to represent extreme climate variability. Streamflow data from 171 stations in and near Oklahoma with a minimum of 10 complete water years of daily streamflow record through water year 2007 and drainage areas that were less than 2,500 square miles were considered for use in the baseline period analysis. The first step to determine the least-altered period of record was to evaluate station information by using previous publications, historical station record notes, and information gathered from oral and written communication with hydrographers familiar with selected stations. The second step was to indentify stations that had substantial effects from upstream regulation by evaluating the location and extent of dams in the drainage basin. The third step was (a) the analysis of annual hydrographs and included visual hydrograph analysis for selected stations with 20 or more years of streamflow record, (b) analysis of covariance of double-mass curves, and (c) Kendall's tau trend analysis to detect statistically significant trends in base flow, runoff, total flow, and base-flow index related to anthropogenic activity for selected stations with 15 or more years of streamflow record. A preliminary least-altered period of record for each stream was identified by removing the period of streamflow record when streams were substantially affected by anthropogenic activity. After streamflow record was removed from designation as a least-altered period, stations that did not have at least 10 years of remaining continuous streamflow record were considered to have an insufficient baseline period for modeling applications. An optimum minimum period of record was determined for each of the least-altered periods for each station to ensure a sufficient streamflow record length to provide a representative sample of annual climate variability. An optimum minimum period of 10 years or more was evaluated by analyzing the variability of annual precipitation for selected 5-, 10-, 15-, 25-, and 35-year periods for each of 20 climate divisions that contained stations used in the baseline period analysis. The distribution of annual precipitation was compared for each consecutive overlapping 5-year period to the period 1925-2007 by using a Wilcoxon rank-sum test. The least-altered period of record for stations was also compared to the period 1925-2007 by using a Wilcoxon rank-sum test. The results of this analysis were used to determine how many years of annual precipitation data were needed for the selected period to be statistically similar to the distribution of annual precipitation data for a long-term period, 1925-2007. Minimum optimum periods ranged from 10 to 35 years and varied by climate division. A final baseline period was determined for 111 stations that had a baseline period of at least 10 years of continuous streamflow record after the record-elimination process. A suitable baseline period of record for use in modeling applications could not be identified for 58 of the initial 171 stations because of substantial anthropogenic alteration of the stream or drainage basin and for 2 stations because the least-altered period of record was not representative of annual climate variability. The baseline period for each station was rated ?excellent?, ?good?, ?fair?, ?poor?, or ?no baseline period.? This rating was based on a qualitative evaluation of t

Impact of Climate Change on Water Resources in the Guadalquivir River Basin

NASA Astrophysics Data System (ADS)

Yeste Donaire, P.; García-Valdecasas-Ojeda, M.; Góngora García, T. M.; Gámiz-Fortis, S. R.; Castro-Diez, Y.; Esteban-Parra, M. J.

2017-12-01

Climate change has lead to a decrease of precipitation and an increase of temperature in the Mediterranean Basin during the last fifty years. These changes will be more intense over the course of the 21thcentury according to global climate projections. As a consequence, water resources are expected to decrease, particularly in the Guadalquivir River Basin. This study focuses on the hydrological response of the Guadalquivir River Basin to the climate change. For this end, firstly, the implementation of the Variable Infiltration Capacity (VIC) model in the Basin was carried out. The VIC model was calibrated with a dataset of daily precipitation, temperature and streamflow for the period 1990-2000. Precipitation and temperature data were extracted from SPAIN02, a dataset that covers the Peninsular Spain at 0.11º of spatial resolution. Streamflow data were gathered for a representative subset of gauging stations in the basin. These data were provided by the Spanish Center for Public Work Experimentation and Study (CEDEX). Subsequently, the VIC model was validated for the period 2000-2005 in order to verify that the model outputs fit well with the observational data. After the validation of the VIC model for present climate, secondly, the effect of climate change on the Guadalquivir River Basin will be analyzed by developing several simulations of the streamflow for future climate. Precipitation and temperature data will be obtained in this case from future projections coming from high resolution (at 0.088º) simulations carried out with the Weather Research and Forecasting (WRF) model for the Iberian Peninsula. These last simulations will be driven under two different Representative Concentration Pathway (RCP) scenarios, RCP 4.5 and RCP 8.5 for the periods 2021-50 and 2071-2100. The first results of this work show that the VIC model outputs are in good agreement with the observed streamflow for both the calibration and validation periods. In the context of climate change, a generalized decrease in surface and subsurface water resources is expected in the Guadalquivir River Basin. All these results will be of interest for water policy makers and practitioners in the next decades. ACKNOWLEDGEMENTS: This work has been financed by the projects P11-RNM-7941 (Junta de Andalucía) and CGL2013-48539-R (MINECO-Spain, FEDER).

Effects of uranium mining, Puerco River, New Mexico

USGS Publications Warehouse

Lopes, Thomas J.

1991-01-01

Effluent from uranium-mine dewatering and acidic water released by a tailings-pond dike failure increased radionuclide activities in streamflow in the Puerco River in New Mexico and Arizona. Median dissolved gross-alpha activity in the streamflow was 1,130 picocuries per liter from 1975 to 1986 when mine discharges ceased and 6.2 picocuries per liter from 1986 to 1989. From 1975 to July 1979, major ions in streamflow at the Puerco River at Gallup streamflow-gaging station were sodium, bicarbonate, and sulfate. On July 16, 1979, the day of the tailing spill, major ions in streamflow were magnesium, calcium, and sulfate. From 1979 to 1984, major ions in streamflow had a greater proportion of calcium and sulfate than prior to the spill, indicating flushing of residual tailings solution. Geochemical modeling of mine effluent indicates that uranium was unlikely to precipitate from effluent between the mines and Gallup or when mixed with wastewater downstream from Gallup. Geochemical modeling of acidic-tailings solution indicates that uranium was in solution as far downstream as Gallup. When the acidic-tailings solution mixed with 10- to 40-percent wastewater, uranium may have precipitated from solution as carnotite [K2(UO2)2(VO4)2] and tyuyamunite [Ca(UO2)2(VO4)2].

Using Temperature Forecasts to Improve Seasonal Streamflow Forecasts in the Colorado and Rio Grande Basins

NASA Astrophysics Data System (ADS)

Lehner, F.; Wood, A.; Llewellyn, D.; Blatchford, D. B.; Goodbody, A. G.; Pappenberger, F.

2017-12-01

Recent studies have documented the influence of increasing temperature on streamflow across the American West, including snow-melt driven rivers such as the Colorado or Rio Grande. At the same time, some basins are reporting decreasing skill in seasonal streamflow forecasts, termed water supply forecasts (WSFs), over the recent decade. While the skill in seasonal precipitation forecasts from dynamical models remains low, their skill in predicting seasonal temperature variations could potentially be harvested for WSFs to account for non-stationarity in regional temperatures. Here, we investigate whether WSF skill can be improved by incorporating seasonal temperature forecasts from dynamical forecasting models (from the North American Multi Model Ensemble and the European Centre for Medium-Range Weather Forecast System 4) into traditional statistical forecast models. We find improved streamflow forecast skill relative to traditional WSF approaches in a majority of headwater locations in the Colorado and Rio Grande basins. Incorporation of temperature into WSFs thus provides a promising avenue to increase the robustness of current forecasting techniques in the face of continued regional warming.

Determination of Baseline Periods of Record for Selected Streamflow-Gaging Stations in New Jersey for Determining Ecologically Relevant Hydrologic Indices (ERHI)

USGS Publications Warehouse

Esralew, Rachel A.; Baker, Ronald J.

2008-01-01

Hydrologic changes in New Jersey stream basins resulting from human activity can affect the flow and ecology of the streams. To assess future changes in streamflow resulting from human activity an understanding of the natural variability of streamflow is needed. The natural variability can be classified using Ecologically Relevant Hydrologic Indices (ERHIs). ERHIs are defined as selected streamflow statistics that characterize elements of the flow regime that substantially affect biological health and ecological sustainability. ERHIs are used to quantitatively characterize aspects of the streamflow regime, including magnitude, duration, frequency, timing, and rate of change. Changes in ERHI values can occur as a result of human activity, and changes in ERHIs over time at various stream locations can provide information about the degree of alteration in aquatic ecosystems at or near those locations. New Jersey streams can be divided into four classes (A, B, C, or D), where streams with similar ERHI values (determined from cluster analysis) are assigned the same stream class. In order to detect and quantify changes in ERHIs at selected streamflow-gaging stations, a 'baseline' period is needed. Ideally, a baseline period is a period of continuous daily streamflow record at a gaging station where human activity along the contributing stream reach or in the stream's basin is minimal. Because substantial urbanization and other development had already occurred before continuous streamflow-gaging stations were installed, it is not possible to identify baseline periods that meet this criterion for many reaches in New Jersey. Therefore, the baseline period for a considerably altered basin can be defined as a period prior to a substantial human-induced change in the drainage basin or stream reach (such as regulations or diversions), or a period during which development did not change substantially. Index stations (stations with minimal urbanization) were defined as streamflow-gaging stations in basins that contain less than 15 percent urban land use throughout the period of continuous streamflow record. A minimum baseline period of record for each stream class was determined by comparing the variability of selected ERHIs among consecutive 5-, 10-, 15-, and 20-year time increments for index stations. On the basis of this analysis, stream classes A and D were assigned a minimum of 20 years of continuous record as a baseline period and stream classes B and C, a minimum of 10 years. Baseline periods were calculated for 85 streamflow-gaging stations in New Jersey with 10 or more years of continuous daily streamflow data, and the values of 171 ERHIs also were calculated for these baseline periods for each station. Baseline periods were determined by using historical streamflow-gaging station data, estimated changes in impervious surface in the drainage basin, and statistically significant changes in annual base flow and runoff. Historical records were reviewed to identify years during which regulation, diversions, or withdrawals occurred in the drainage basins. Such years were not included in baseline periods of record. For some sites, the baseline period of record was shorter than the minimum period of record specified for the given stream class. In such cases, the baseline period was rated as 'poor'. Impervious surface was used as an indicator of urbanization and change in streamflow characteristics owing to increases in storm runoff and decreases in base flow. Percentages of impervious surface were estimated for 85 streamflow-gaging stations from available municipal population-density data by using a regression model. Where the period of record was sufficiently long, all years after the impervious surface exceeded 10 to 20 percent were excluded from the baseline period. The percentage of impervious surface also was used as a criterion in assigning qualitative ratings to baseline periods. Changes in trends of annual base fl

Flood-inundation and flood-mitigation modeling of the West Branch Wapsinonoc Creek Watershed in West Branch, Iowa

USGS Publications Warehouse

Cigrand, Charles V.

2018-03-26

The U.S. Geological Survey (USGS) in cooperation with the city of West Branch and the Herbert Hoover National Historic Site of the National Park Service assessed flood-mitigation scenarios within the West Branch Wapsinonoc Creek watershed. The scenarios are intended to demonstrate several means of decreasing peak streamflows and improving the conveyance of overbank flows from the West Branch Wapsinonoc Creek and its tributary Hoover Creek where they flow through the city and the Herbert Hoover National Historic Site located within the city.Hydrologic and hydraulic models of the watershed were constructed to assess the flood-mitigation scenarios. To accomplish this, the models used the U.S. Army Corps of Engineers Hydrologic Engineering Center-Hydrologic Modeling System (HEC–HMS) version 4.2 to simulate the amount of runoff and streamflow produced from single rain events. The Hydrologic Engineering Center-River Analysis System (HEC–RAS) version 5.0 was then used to construct an unsteady-state model that may be used for routing streamflows, mapping areas that may be inundated during floods, and simulating the effects of different measures taken to decrease the effects of floods on people and infrastructure.Both models were calibrated to three historic rainfall events that produced peak streamflows ranging between the 2-year and 10-year flood-frequency recurrence intervals at the USGS streamgage (05464942) on Hoover Creek. The historic rainfall events were calibrated by using data from two USGS streamgages along with surveyed high-water marks from one of the events. The calibrated HEC–HMS model was then used to simulate streamflows from design rainfall events of 24-hour duration ranging from a 20-percent to a 1-percent annual exceedance probability. These simulated streamflows were incorporated into the HEC–RAS model.The unsteady-state HEC–RAS model was calibrated to represent existing conditions within the watershed. HEC–RAS model simulations with the existing conditions and streamflows from the design rainfall events were then done to serve as a baseline for evaluating flood-mitigation scenarios. After these simulations were completed, three different flood-mitigation scenarios were developed with HEC–RAS: a detention-storage scenario, a conveyance improvement scenario, and a combination of both. In the detention-storage scenario, four in-channel detention structures were placed upstream from the city of West Branch to attenuate peak streamflows. To investigate possible improvements to conveying floodwaters through the city of West Branch, a section of abandoned railroad embankment and an old truss bridge were removed in the model, because these structures were producing backwater areas during flooding events. The third scenario combines the detention and conveyance scenarios so their joint efficiency could be evaluated. The scenarios with the design rainfall events were run in the HEC–RAS model so their flood-mitigation effects could be analyzed across a wide range of flood magnitudes.

Potential Impacts of Climate Change On Groundwater Recharge and Streamflow In A Central European Low Mountain Range

NASA Astrophysics Data System (ADS)

Eckhardt, K.; Ulbrich, U.

General Circulation Model simulations indicate a significant rise of temperature and changes in precipitation over Europe as part of the anthropogenic climate change. In this study, the impacts of climate change on groundwater recharge and streamflow in a central European low mountain range catchment are investigated using a concep- tual ecohydrologic model. Two climate change scenarios are considered, one with low and one with high climate sensitivity. The changes in temperature and precipitation associated with these projections are taken from multi-model estimates and enter the hydrologic model assuming a sinusodial annual cycle of temperature and precipitation changes. The resulting changes in annual mean groundwater recharge and streamflow are rather small, as increased atmospheric CO2 levels reduce stomatal conductance thus counteracting the increase of potential evapotranspiration induced by rising tem- peratures. There are, however, more pronounced changes associated with the mean annual cycle of groundwater recharge and streamflow. Snowmelt at the beginning of spring is reduced. Instead, runoff and hence flood risk in winter increase. In summer, groundwater recharge and streamflow are reduced by up to 50%. This could have neg- ative consequences for water quality, groundwater withdrawals and energy production by water power. Plant growth will be stimulated by the elevated atmospheric CO2 concentration. Due to the temperature rise, the growing season will begin earlier in the year. However, the risk of desiccation injuries increases as well.

Streamflow Impacts of Biofuel Policy-Driven Landscape Change

PubMed Central

Khanal, Sami; Anex, Robert P.; Anderson, Christopher J.; Herzmann, Daryl E.

2014-01-01

Likely changes in precipitation (P) and potential evapotranspiration (PET) resulting from policy-driven expansion of bioenergy crops in the United States are shown to create significant changes in streamflow volumes and increase water stress in the High Plains. Regional climate simulations for current and biofuel cropping system scenarios are evaluated using the same atmospheric forcing data over the period 1979–2004 using the Weather Research Forecast (WRF) model coupled to the NOAH land surface model. PET is projected to increase under the biofuel crop production scenario. The magnitude of the mean annual increase in PET is larger than the inter-annual variability of change in PET, indicating that PET increase is a forced response to the biofuel cropping system land use. Across the conterminous U.S., the change in mean streamflow volume under the biofuel scenario is estimated to range from negative 56% to positive 20% relative to a business-as-usual baseline scenario. In Kansas and Oklahoma, annual streamflow volume is reduced by an average of 20%, and this reduction in streamflow volume is due primarily to increased PET. Predicted increase in mean annual P under the biofuel crop production scenario is lower than its inter-annual variability, indicating that additional simulations would be necessary to determine conclusively whether predicted change in P is a response to biofuel crop production. Although estimated changes in streamflow volume include the influence of P change, sensitivity results show that PET change is the significantly dominant factor causing streamflow change. Higher PET and lower streamflow due to biofuel feedstock production are likely to increase water stress in the High Plains. When pursuing sustainable biofuels policy, decision-makers should consider the impacts of feedstock production on water scarcity. PMID:25289698

Post-processing ECMWF precipitation and temperature ensemble reforecasts for operational hydrologic forecasting at various spatial scales

NASA Astrophysics Data System (ADS)

Verkade, J. S.; Brown, J. D.; Reggiani, P.; Weerts, A. H.

2013-09-01

The ECMWF temperature and precipitation ensemble reforecasts are evaluated for biases in the mean, spread and forecast probabilities, and how these biases propagate to streamflow ensemble forecasts. The forcing ensembles are subsequently post-processed to reduce bias and increase skill, and to investigate whether this leads to improved streamflow ensemble forecasts. Multiple post-processing techniques are used: quantile-to-quantile transform, linear regression with an assumption of bivariate normality and logistic regression. Both the raw and post-processed ensembles are run through a hydrologic model of the river Rhine to create streamflow ensembles. The results are compared using multiple verification metrics and skill scores: relative mean error, Brier skill score and its decompositions, mean continuous ranked probability skill score and its decomposition, and the ROC score. Verification of the streamflow ensembles is performed at multiple spatial scales: relatively small headwater basins, large tributaries and the Rhine outlet at Lobith. The streamflow ensembles are verified against simulated streamflow, in order to isolate the effects of biases in the forcing ensembles and any improvements therein. The results indicate that the forcing ensembles contain significant biases, and that these cascade to the streamflow ensembles. Some of the bias in the forcing ensembles is unconditional in nature; this was resolved by a simple quantile-to-quantile transform. Improvements in conditional bias and skill of the forcing ensembles vary with forecast lead time, amount, and spatial scale, but are generally moderate. The translation to streamflow forecast skill is further muted, and several explanations are considered, including limitations in the modelling of the space-time covariability of the forcing ensembles and the presence of storages.

Decomposition of Sources of Errors in Seasonal Streamflow Forecasting over the U.S. Sunbelt

NASA Technical Reports Server (NTRS)

Mazrooei, Amirhossein; Sinah, Tusshar; Sankarasubramanian, A.; Kumar, Sujay V.; Peters-Lidard, Christa D.

2015-01-01

Seasonal streamflow forecasts, contingent on climate information, can be utilized to ensure water supply for multiple uses including municipal demands, hydroelectric power generation, and for planning agricultural operations. However, uncertainties in the streamflow forecasts pose significant challenges in their utilization in real-time operations. In this study, we systematically decompose various sources of errors in developing seasonal streamflow forecasts from two Land Surface Models (LSMs) (Noah3.2 and CLM2), which are forced with downscaled and disaggregated climate forecasts. In particular, the study quantifies the relative contributions of the sources of errors from LSMs, climate forecasts, and downscaling/disaggregation techniques in developing seasonal streamflow forecast. For this purpose, three month ahead seasonal precipitation forecasts from the ECHAM4.5 general circulation model (GCM) were statistically downscaled from 2.8deg to 1/8deg spatial resolution using principal component regression (PCR) and then temporally disaggregated from monthly to daily time step using kernel-nearest neighbor (K-NN) approach. For other climatic forcings, excluding precipitation, we considered the North American Land Data Assimilation System version 2 (NLDAS-2) hourly climatology over the years 1979 to 2010. Then the selected LSMs were forced with precipitation forecasts and NLDAS-2 hourly climatology to develop retrospective seasonal streamflow forecasts over a period of 20 years (1991-2010). Finally, the performance of LSMs in forecasting streamflow under different schemes was analyzed to quantify the relative contribution of various sources of errors in developing seasonal streamflow forecast. Our results indicate that the most dominant source of errors during winter and fall seasons is the errors due to ECHAM4.5 precipitation forecasts, while temporal disaggregation scheme contributes to maximum errors during summer season.

On the probability distribution of daily streamflow in the United States

USGS Publications Warehouse

Blum, Annalise G.; Archfield, Stacey A.; Vogel, Richard M.

2017-01-01

Daily streamflows are often represented by flow duration curves (FDCs), which illustrate the frequency with which flows are equaled or exceeded. FDCs have had broad applications across both operational and research hydrology for decades; however, modeling FDCs has proven elusive. Daily streamflow is a complex time series with flow values ranging over many orders of magnitude. The identification of a probability distribution that can approximate daily streamflow would improve understanding of the behavior of daily flows and the ability to estimate FDCs at ungaged river locations. Comparisons of modeled and empirical FDCs at nearly 400 unregulated, perennial streams illustrate that the four-parameter kappa distribution provides a very good representation of daily streamflow across the majority of physiographic regions in the conterminous United States (US). Further, for some regions of the US, the three-parameter generalized Pareto and lognormal distributions also provide a good approximation to FDCs. Similar results are found for the period of record FDCs, representing the long-term hydrologic regime at a site, and median annual FDCs, representing the behavior of flows in a typical year.

Assessing the viability of `over-the-loop' real-time short-to-medium range ensemble streamflow forecasts

NASA Astrophysics Data System (ADS)

Wood, A. W.; Clark, E.; Mendoza, P. A.; Nijssen, B.; Newman, A. J.; Clark, M. P.; Arnold, J.; Nowak, K. C.

2016-12-01

Many if not most national operational short-to-medium range streamflow prediction systems rely on a forecaster-in-the-loop approach in which some parts of the forecast workflow are automated, but others require the hands-on-effort of an experienced human forecaster. This approach evolved out of the need to correct for deficiencies in the models and datasets that were available for forecasting, and often leads to skillful predictions despite the use of relatively simple, conceptual models. On the other hand, the process is not reproducible, which limits opportunities to assess and incorporate process variations, and the effort required to make forecasts in this way is an obstacle to expanding forecast services - e.g., though adding new forecast locations or more frequent forecast updates, running more complex models, or producing forecast ensembles and hindcasts that can support verification. In the last decade, the hydrologic forecasting community has begun to develop more centralized, `over-the-loop' systems. The quality of these new forecast products will depend on their ability to leverage research in areas including earth system modeling, parameter estimation, data assimilation, statistical post-processing, weather and climate prediction, verification, and uncertainty estimation through the use of ensembles. Currently, the operational streamflow forecasting and water management communities have little experience with the strengths and weaknesses of over-the-loop approaches, even as the systems are being rolled out in major operational forecasting centers. There is thus a need both to evaluate these forecasting advances and to demonstrate their potential in a public arena, raising awareness in forecast user communities and development programs alike. To address this need, the National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the US Army Corps of Engineers, using the NCAR 'System for Hydromet Analysis, Research, and Prediction' (SHARP) to implement, assess and demonstrate real-time over-the-loop forecasts. We present early hindcast and verification results from SHARP for short to medium range streamflow forecasts in a number of US case study watersheds.

Using an Integrated Surface Water - Groundwater Flow Model for Evaluating the Hydrologic Impacts of Historic and Potential Future Dry Periods on Simulated Water Budgets in the Santa Rosa Plain Watershed, Northern California, USA

NASA Astrophysics Data System (ADS)

Hevesi, J. A.; Woolfenden, L. R.; Nishikawa, T.

2014-12-01

Communities in the Santa Rosa Plain watershed (SRPW), Sonoma County, CA, USA are experiencing increasing demand for limited water resources. Streamflow in the SRPW is runoff dominated; however, groundwater also is an important resource in the basin. The watershed has an area of 262 mi2 that includes natural, agricultural, and urban land uses. To evaluate the hydrologic system, an integrated hydrologic model was developed using the U.S. Geological Survey coupled groundwater and surface-water flow model, GSFLOW. The model uses a daily time step and a grid-based discretization of the SRPW consisting of 16,741 10-acre cells for 8 model layers to simulate all water budget components of the surface and subsurface hydrologic system. Simulation results indicate significant impacts on streamflow and recharge in response to the below average precipitation during the dry periods. The recharge and streamflow distributions simulated for historic dry periods were compared to future dry periods projected from 4 GCM realizations (two different GCMs and two different CO2 forcing scenarios) for the 21st century, with the dry periods defined as 3 consecutive years of below average precipitation. For many of the projected dry periods, the decreases in recharge and streamflow were greater than for the historic dry periods due to a combination of lower precipitation and increases in simulated evapotranspiration for the warmer 21st century projected by the GCM realizations. The greatest impact on streamflow for both historic and projected future dry periods is the diminished baseflow from late spring to early fall, with an increase in the percentage of intermittent and dry stream reaches. The results indicate that the coupled model is a useful tool for water managers to better understand the potential effects of future dry periods on spatially and temporally distributed streamflow and recharge, as well as other components of the water budget.

Determining effective forecast horizons for multi-purpose reservoirs with short- and long-term operating objectives

NASA Astrophysics Data System (ADS)

Luchner, Jakob; Anghileri, Daniela; Castelletti, Andrea

2017-04-01

Real-time control of multi-purpose reservoirs can benefit significantly from hydro-meteorological forecast products. Because of their reliability, the most used forecasts range on time scales from hours to few days and are suitable for short-term operation targets such as flood control. In recent years, hydro-meteorological forecasts have become more accurate and reliable on longer time scales, which are more relevant to long-term reservoir operation targets such as water supply. While the forecast quality of such products has been studied extensively, the forecast value, i.e. the operational effectiveness of using forecasts to support water management, has been only relatively explored. It is comparatively easy to identify the most effective forecasting information needed to design reservoir operation rules for flood control but it is not straightforward to identify which forecast variable and lead time is needed to define effective hedging rules for operational targets with slow dynamics such as water supply. The task is even more complex when multiple targets, with diverse slow and fast dynamics, are considered at the same time. In these cases, the relative importance of different pieces of information, e.g. magnitude and timing of peak flow rate and accumulated inflow on different time lags, may vary depending on the season or the hydrological conditions. In this work, we analyze the relationship between operational forecast value and streamflow forecast horizon for different multi-purpose reservoir trade-offs. We use the Information Selection and Assessment (ISA) framework to identify the most effective forecast variables and horizons for informing multi-objective reservoir operation over short- and long-term temporal scales. The ISA framework is an automatic iterative procedure to discriminate the information with the highest potential to improve multi-objective reservoir operating performance. Forecast variables and horizons are selected using a feature selection technique. The technique determines the most informative combination in a multi-variate regression model to the optimal reservoir releases based on perfect information at a fixed objective trade-off. The improved reservoir operation is evaluated against optimal reservoir operation conditioned upon perfect information on future disturbances and basic reservoir operation using only the day of the year and the reservoir level. Different objective trade-offs are selected for analyzing resulting differences in improved reservoir operation and selected forecast variables and horizons. For comparison, the effective streamflow forecast horizon determined by the ISA framework is benchmarked against the performances obtained with a deterministic model predictive control (MPC) optimization scheme. Both the ISA framework and the MPC optimization scheme are applied to the real-world case study of Lake Como, Italy, using perfect streamflow forecast information. The principal operation targets for Lake Como are flood control and downstream water supply which makes its operation a suitable case study. Results provide critical feedback to reservoir operators on the use of long-term streamflow forecasts and to the hydro-meteorological forecasting community with respect to the forecast horizon needed from reliable streamflow forecasts.

Effect of initial conditions of a catchment on seasonal streamflow prediction using ensemble streamflow prediction (ESP) technique for the Rangitata and Waitaki River basins on the South Island of New Zealand

NASA Astrophysics Data System (ADS)

Singh, Shailesh Kumar; Zammit, Christian; Hreinsson, Einar; Woods, Ross; Clark, Martyn; Hamlet, Alan

2013-04-01

Increased access to water is a key pillar of the New Zealand government plan for economic growths. Variable climatic conditions coupled with market drivers and increased demand on water resource result in critical decision made by water managers based on climate and streamflow forecast. Because many of these decisions have serious economic implications, accurate forecast of climate and streamflow are of paramount importance (eg irrigated agriculture and electricity generation). New Zealand currently does not have a centralized, comprehensive, and state-of-the-art system in place for providing operational seasonal to interannual streamflow forecasts to guide water resources management decisions. As a pilot effort, we implement and evaluate an experimental ensemble streamflow forecasting system for the Waitaki and Rangitata River basins on New Zealand's South Island using a hydrologic simulation model (TopNet) and the familiar ensemble streamflow prediction (ESP) paradigm for estimating forecast uncertainty. To provide a comprehensive database for evaluation of the forecasting system, first a set of retrospective model states simulated by the hydrologic model on the first day of each month were archived from 1972-2009. Then, using the hydrologic simulation model, each of these historical model states was paired with the retrospective temperature and precipitation time series from each historical water year to create a database of retrospective hindcasts. Using the resulting database, the relative importance of initial state variables (such as soil moisture and snowpack) as fundamental drivers of uncertainties in forecasts were evaluated for different seasons and lead times. The analysis indicate that the sensitivity of flow forecast to initial condition uncertainty is depend on the hydrological regime and season of forecast. However initial conditions do not have a large impact on seasonal flow uncertainties for snow dominated catchments. Further analysis indicates that this result is valid when the hindcast database is conditioned by ENSO classification. As a result hydrological forecasts based on ESP technique, where present initial conditions with histological forcing data are used may be plausible for New Zealand catchments.

Instream flow characterization of upper Salmon River basin streams, central Idaho, 2004

USGS Publications Warehouse

Maret, Terry R.; Hortness, Jon E.; Ott, Douglas S.

2005-01-01

Anadromous fish populations in the Columbia River Basin have plummeted in the last 100 years. This severe decline led to Federal listing of Chinook salmon (Oncorhynchus tshawytscha) and steelhead trout (Oncorhynchus mykiss) stocks as endangered or threatened under the Endangered Species Act (ESA) in the 1990s. Historically, the upper Salmon River Basin (upstream of the confluence with the Pahsimeroi River) in Idaho provided migration corridors and significant habitat for these ESA-listed species, in addition to the ESA-listed bull trout (Salvelinus confluentus). Human development has modified the original streamflow conditions in many streams in the upper Salmon River Basin. Summer streamflow modifications resulting from irrigation practices, have directly affected quantity and quality of fish habitat and also have affected migration and (or) access to suitable spawning and rearing habitat for these fish. As a result of these ESA listings and Action 149 of the Federal Columbia River Power System Biological Opinion of 2000, the Bureau of Reclamation was tasked to conduct streamflow characterization studies in the upper Salmon River Basin to clearly define habitat requirements for effective species management and habitat restoration. These studies include collection of habitat and streamflow information for the Physical Habitat Simulation System model, a widely applied method to determine relations between habitat and discharge requirements for various fish species and life stages. Model results can be used by resource managers to guide habitat restoration efforts by evaluating potential fish habitat and passage improvements by increasing streamflow. In 2004, instream flow characterization studies were completed on Salmon River and Beaver, Pole, Champion, Iron, Thompson, and Squaw Creeks. Continuous streamflow data were recorded upstream of all diversions on Salmon River and Pole, Iron, Thompson, and Squaw Creeks. In addition, natural summer streamflows were estimated for each study site using regional regression equations. This report describes Physical Habitat Simulation System modeling results for bull trout, Chinook salmon, and steelhead trout during summer streamflows. Habitat/discharge relations were summarized for adult and spawning life stages at each study site. Adult fish passage and discharge relations were evaluated at specific transects identified as a potential low-streamflow passage barrier at each study site. Continuous summer water temperature data for selected study sites were summarized and compared with Idaho Water Quality Standards and various water temperature requirements of targeted fish species. Continuous summer water temperature data recorded in 2003 and streamflow relations were evaluated for Fourth of July Creek using the Stream Segment Temperature model that simulates mean and maximum daily water temperatures with changes in streamflow. Results of these habitat studies can be used to prioritize and direct cost-effective actions to improve fish habitat for ESA-listed anadromous and native fish species in the basin. These actions may include acquiring water during critical low-flow periods by leasing or modifying irrigation delivery systems to minimize out-of-stream diversions.

Potential effects of climate change on streamflow, eastern and western slopes of the Sierra Nevada, California and Nevada

USGS Publications Warehouse

Jeton, A.E.; Dettinger, M.D.; Smith, J. LaRue

1996-01-01

Precipitation-runoff models of the East Fork Carson and North Fork American Rivers were developed and calibrated for use in evaluating the sensitivity of streamflow in the north-central Sierra Nevada to climate change. The East Fork Carson River drains part of the rain-shadowed, eastern slope of the Sierra Nevada and is generally higher than the North Fork American River, which drains the wetter, western slope. First, a geographic information system was developed to describe the spatial variability of basin characteristics and to help estimate model parameters. The result was a partitioning of each basin into noncontiguous, but hydrologically uniform, land units. Hydrologic descriptions of these units were developed and the Precipitation- Runoff Modeling System (PRMS) was used to simulate water and energy balances for each unit in response to daily weather conditions. The models were calibrated and verified using historical streamflows over 22-year (Carson River) and 42-year (American River) periods. Simulated annual streamflow errors average plus 10 percent of the observed flow for the East Fork Carson River basin and plus 15 percent for the North Fork American River basin. Interannual variability is well simulated overall, but, at daily scales, wet periods are simulated more accurately than drier periods. The simulated water budgets for the two basins are significantly different in seasonality of streamflow, sublimation, evapotranspiration, and snowmelt. The simulations indicate that differences in snowpack and snowmelt timing can play pervasive roles in determining the sensitivity of water resources to climate change, in terms of both resource availability and amount. The calibrated models were driven by more than 25 hypothetical climate-change scenarios, each 100 years long. The scenarios were synthesized and spatially disaggregated by methods designed to preserve realistic daily, monthly, annual, and spatial statistics. Simulated streamflow timing was not very sensitive to changes in mean precipitation, but was sensitive to changes in mean temperatures. Changes in annual streamflow amounts were amplified reflections of imposed mean precipitation changes, with especially large responses to wetter climates. In contrast, streamflow amount was surprisingly insensitive to mean temperature changes as a result of temporal links between peak snowmelt and the beginning of warm-season evapotranspiration. Comparisons of simulations driven by temporally detailed climate-model changes in which mean temperature changes vary from month to month and simulations in which uniform climate changes were imposed throughout the year indicate that the snowpack accumulates the influences of short-term conditions so that season average climate changes were more important than shorter term changes.

Applying A Multi-Objective Based Procedure to SWAT Modelling in Alpine Catchments

NASA Astrophysics Data System (ADS)

Tuo, Y.; Disse, M.; Chiogna, G.

2017-12-01

In alpine catchments, water management practices can lead to conflicts between upstream and downstream stakeholders, like in the Adige river basin (Italy). A correct prediction of available water resources plays an important part, for example, in defining how much water can be stored for hydropower production in upstream reservoirs without affecting agricultural activities downstream. Snow is a crucial hydrological component that highly affects seasonal behavior of streamflow. Therefore, a realistic representation of snow dynamics is fundamental for water management operations in alpine catchments. The Soil and Water Assessment Tool (SWAT) model has been applied in alpine catchments worldwide. However, during model calibration of catchment scale applications, snow parameters were generally estimated based on streamflow records rather than on snow measurements. This may lead to streamflow predictions with wrong snow melt contribution. This work highlights the importance of considering snow measurements in the calibration of the SWAT model for alpine hydrology and compares various calibration methodologies. In addition to discharge records, snow water equivalent time series of both subbasin scale and monitoring station were also utilized to evaluate the model performance by comparing with the SWAT subbasin and elevation band snow outputs. Comparing model results obtained calibrating the model using discharge data only and discharge data along with snow water equivalent data, we show that the latter approach allows us to improve the reliability of snow simulations while maintaining good estimations of streamflow. With a more reliable representation of snow dynamics, the hydrological model can provide more accurate references for proposing adequate water management solutions. This study offers to the wide SWAT user community an effective approach to improve streamflow predictions in alpine catchments and hence support decision makers in water allocation.

Analysis of the hydrological response of a distributed physically-based model using post-assimilation (EnKF) diagnostics of streamflow and in situ soil moisture observations

NASA Astrophysics Data System (ADS)

Trudel, Mélanie; Leconte, Robert; Paniconi, Claudio

2014-06-01

Data assimilation techniques not only enhance model simulations and forecast, they also provide the opportunity to obtain a diagnostic of both the model and observations used in the assimilation process. In this research, an ensemble Kalman filter was used to assimilate streamflow observations at a basin outlet and at interior locations, as well as soil moisture at two different depths (15 and 45 cm). The simulation model is the distributed physically-based hydrological model CATHY (CATchment HYdrology) and the study site is the Des Anglais watershed, a 690 km2 river basin located in southern Quebec, Canada. Use of Latin hypercube sampling instead of a conventional Monte Carlo method to generate the ensemble reduced the size of the ensemble, and therefore the calculation time. Different post-assimilation diagnostics, based on innovations (observation minus background), analysis residuals (observation minus analysis), and analysis increments (analysis minus background), were used to evaluate assimilation optimality. An important issue in data assimilation is the estimation of error covariance matrices. These diagnostics were also used in a calibration exercise to determine the standard deviation of model parameters, forcing data, and observations that led to optimal assimilations. The analysis of innovations showed a lag between the model forecast and the observation during rainfall events. Assimilation of streamflow observations corrected this discrepancy. Assimilation of outlet streamflow observations improved the Nash-Sutcliffe efficiencies (NSE) between the model forecast (one day) and the observation at both outlet and interior point locations, owing to the structure of the state vector used. However, assimilation of streamflow observations systematically increased the simulated soil moisture values.

Construction of estimated flow- and load-duration curves for Kentucky using the Water Availability Tool for Environmental Resources (WATER)

USGS Publications Warehouse

Unthank, Michael D.; Newson, Jeremy K.; Williamson, Tanja N.; Nelson, Hugh L.

2012-01-01

Flow- and load-duration curves were constructed from the model outputs of the U.S. Geological Survey's Water Availability Tool for Environmental Resources (WATER) application for streams in Kentucky. The WATER application was designed to access multiple geospatial datasets to generate more than 60 years of statistically based streamflow data for Kentucky. The WATER application enables a user to graphically select a site on a stream and generate an estimated hydrograph and flow-duration curve for the watershed upstream of that point. The flow-duration curves are constructed by calculating the exceedance probability of the modeled daily streamflows. User-defined water-quality criteria and (or) sampling results can be loaded into the WATER application to construct load-duration curves that are based on the modeled streamflow results. Estimates of flow and streamflow statistics were derived from TOPographically Based Hydrological MODEL (TOPMODEL) simulations in the WATER application. A modified TOPMODEL code, SDP-TOPMODEL (Sinkhole Drainage Process-TOPMODEL) was used to simulate daily mean discharges over the period of record for 5 karst and 5 non-karst watersheds in Kentucky in order to verify the calibrated model. A statistical evaluation of the model's verification simulations show that calibration criteria, established by previous WATER application reports, were met thus insuring the model's ability to provide acceptably accurate estimates of discharge at gaged and ungaged sites throughout Kentucky. Flow-duration curves are constructed in the WATER application by calculating the exceedence probability of the modeled daily flow values. The flow-duration intervals are expressed as a percentage, with zero corresponding to the highest stream discharge in the streamflow record. Load-duration curves are constructed by applying the loading equation (Load = Flow*Water-quality criterion) at each flow interval.

Streamflow simulation for continental-scale river basins

NASA Astrophysics Data System (ADS)

Nijssen, Bart; Lettenmaier, Dennis P.; Liang, Xu; Wetzel, Suzanne W.; Wood, Eric F.

1997-04-01

A grid network version of the two-layer variable infiltration capacity (VIC-2L) macroscale hydrologic model is described. VIC-2L is a hydrologically based soil- vegetation-atmosphere transfer scheme designed to represent the land surface in numerical weather prediction and climate models. The grid network scheme allows streamflow to be predicted for large continental rivers. Off-line (observed and estimated surface meteorological and radiative forcings) applications of the model to the Columbia River (1° latitude-longitude spatial resolution) and Delaware River (0.5° resolution) are described. The model performed quite well in both applications, reproducing the seasonal hydrograph and annual flow volumes to within a few percent. Difficulties in reproducing observed streamflow in the arid portion of the Snake River basin are attributed to groundwater-surface water interactions, which are not modeled by VIC-2L.

Modeling Streamflow and Water Temperature in the North Santiam and Santiam Rivers, Oregon, 2001-02

USGS Publications Warehouse

Sullivan, Annett B.; Roundsk, Stewart A.

2004-01-01

To support the development of a total maximum daily load (TMDL) for water temperature in the Willamette Basin, the laterally averaged, two-dimensional model CE-QUAL-W2 was used to construct a water temperature and streamflow model of the Santiam and North Santiam Rivers. The rivers were simulated from downstream of Detroit and Big Cliff dams to the confluence with the Willamette River. Inputs to the model included bathymetric data, flow and temperature from dam releases, tributary flow and temperature, and meteorologic data. The model was calibrated for the period July 1 through November 21, 2001, and confirmed with data from April 1 through October 31, 2002. Flow calibration made use of data from two streamflow gages and travel-time and river-width data. Temperature calibration used data from 16 temperature monitoring locations in 2001 and 5 locations in 2002. A sensitivity analysis was completed by independently varying input parameters, including point-source flow, air temperature, flow and water temperature from dam releases, and riparian shading. Scenario analyses considered hypothetical river conditions without anthropogenic heat inputs, with restored riparian vegetation, with minimum streamflow from the dams, and with a more-natural seasonal water temperature regime from dam releases.

Analysis on the adaptive countermeasures to ecological management under changing environment in the Tarim River Basin, China

NASA Astrophysics Data System (ADS)

Yang, Fan; Xue, Lianqing; Zhang, Luochen; Chen, Xinfang; Chi, Yixia

2017-12-01

This article aims to explore the adaptive utilization strategies of flow regime versus traditional practices in the context of climate change and human activities in the arid area. The study presents quantitative analysis of climatic and anthropogenic factors to streamflow alteration in the Tarim River Basin (TRB) using the Budyko method and adaptive utilization strategies to eco-hydrological regime by comparing the applicability between autoregressive moving average model (ARMA) model and combined regression model. Our results suggest that human activities played a dominant role in streamflow deduction in the mainstream with contribution of 120.7%~190.1%. While in the headstreams, climatic variables were the primary determinant of streamflow by 56.5~152.6% of the increase. The comparison revealed that combined regression model performed better than ARMA model with the qualified rate of 80.49~90.24%. Based on the forecasts of streamflow for different purposes, the adaptive utilization scheme of water flow is established from the perspective of time and space. Our study presents an effective water resources scheduling scheme for the ecological environment and provides references for ecological protection and water allocation in the arid area.

Short-term streamflow forecasting with global climate change implications A comparative study between genetic programming and neural network models

NASA Astrophysics Data System (ADS)

Makkeasorn, A.; Chang, N. B.; Zhou, X.

2008-05-01

SummarySustainable water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods may also result in property damages and the loss of life. To more efficiently use the limited amount of water under the changing world or to resourcefully provide adequate time for flood warning, the issues have led us to seek advanced techniques for improving streamflow forecasting on a short-term basis. This study emphasizes the inclusion of sea surface temperature (SST) in addition to the spatio-temporal rainfall distribution via the Next Generation Radar (NEXRAD), meteorological data via local weather stations, and historical stream data via USGS gage stations to collectively forecast discharges in a semi-arid watershed in south Texas. Two types of artificial intelligence models, including genetic programming (GP) and neural network (NN) models, were employed comparatively. Four numerical evaluators were used to evaluate the validity of a suite of forecasting models. Research findings indicate that GP-derived streamflow forecasting models were generally favored in the assessment in which both SST and meteorological data significantly improve the accuracy of forecasting. Among several scenarios, NEXRAD rainfall data were proven its most effectiveness for a 3-day forecast, and SST Gulf-to-Atlantic index shows larger impacts than the SST Gulf-to-Pacific index on the streamflow forecasts. The most forward looking GP-derived models can even perform a 30-day streamflow forecast ahead of time with an r-square of 0.84 and RMS error 5.4 in our study.

Computer Programs for Obtaining and Analyzing Daily Mean Steamflow Data from the U.S. Geological Survey National Water Information System Web Site

USGS Publications Warehouse

Granato, Gregory E.

2009-01-01

Streamflow information is important for many planning and design activities including water-supply analysis, habitat protection, bridge and culvert design, calibration of surface and ground-water models, and water-quality assessments. Streamflow information is especially critical for water-quality assessments (Warn and Brew, 1980; Di Toro, 1984; Driscoll and others, 1989; Driscoll and others, 1990, a,b). Calculation of streamflow statistics for receiving waters is necessary to estimate the potential effects of point sources such as wastewater-treatment plants and nonpoint sources such as highway and urban-runoff discharges on receiving water. Streamflow statistics indicate the amount of flow that may be available for dilution and transport of contaminants (U.S. Environmental Protection Agency, 1986; Driscoll and others, 1990, a,b). Streamflow statistics also may be used to indicate receiving-water quality because concentrations of water-quality constituents commonly vary naturally with streamflow. For example, concentrations of suspended sediment and sediment-associated constituents (such as nutrients, trace elements, and many organic compounds) commonly increase with increasing flows, and concentrations of many dissolved constituents commonly decrease with increasing flows in streams and rivers (O'Connor, 1976; Glysson, 1987; Vogel and others, 2003, 2005). Reliable, efficient and repeatable methods are needed to access and process streamflow information and data. For example, the Nation's highway infrastructure includes an innumerable number of stream crossings and stormwater-outfall points for which estimates of stream-discharge statistics may be needed. The U.S. Geological Survey (USGS) streamflow data-collection program is designed to provide streamflow data at gaged sites and to provide information that can be used to estimate streamflows at almost any point along any stream in the United States (Benson and Carter, 1973; Wahl and others, 1995; National Research Council, 2004). The USGS maintains the National Water Information System (NWIS), a distributed network of computers and file servers used to store and retrieve hydrologic data (Mathey, 1998; U.S. Geological Survey, 2008). NWISWeb is an online version of this database that includes water data from more than 24,000 streamflow-gaging stations throughout the United States (U.S. Geological Survey, 2002, 2008). Information from NWISWeb is commonly used to characterize streamflows at gaged sites and to help predict streamflows at ungaged sites. Five computer programs were developed for obtaining and analyzing streamflow from the National Water Information System (NWISWeb). The programs were developed as part of a study by the U.S. Geological Survey, in cooperation with the Federal Highway Administration, to develop a stochastic empirical loading and dilution model. The programs were developed because reliable, efficient, and repeatable methods are needed to access and process streamflow information and data. The first program is designed to facilitate the downloading and reformatting of NWISWeb streamflow data. The second program is designed to facilitate graphical analysis of streamflow data. The third program is designed to facilitate streamflow-record extension and augmentation to help develop long-term statistical estimates for sites with limited data. The fourth program is designed to facilitate statistical analysis of streamflow data. The fifth program is a preprocessor to create batch input files for the U.S. Environmental Protection Agency DFLOW3 program for calculating low-flow statistics. These computer programs were developed to facilitate the analysis of daily mean streamflow data for planning-level water-quality analyses but also are useful for many other applications pertaining to streamflow data and statistics. These programs and the associated documentation are included on the CD-ROM accompanying this report. This report and the appendixes on the

Urban hydrology—Science capabilities of the U.S. Geological Survey

USGS Publications Warehouse

Bell, Joseph M.; Simonson, Amy E.; Fisher, Irene J.

2016-04-29

Urbanization affects streamflow characteristics, coastal flooding, and groundwater recharge. Increasing impervious areas, streamflow diversions, and groundwater pumpage are some of the ways that the natural water cycle is affected by urbanization. Assessment of the relations among these factors and changes in land use helps water-resource managers with issues such as stormwater management and vulnerability to flood and drought. Scientists with the U.S. Geological Survey (USGS) have the expertise to monitor and model urban hydrologic systems. Streamflow and groundwater data are available in national databases, and analyses of these data, including identification of long-term streamflow trends and the efficacy of management practices, are published in USGS reports.

Methods for estimating the magnitude and frequency of peak streamflows for unregulated streams in Oklahoma

USGS Publications Warehouse

Lewis, Jason M.

2010-01-01

Peak-streamflow regression equations were determined for estimating flows with exceedance probabilities from 50 to 0.2 percent for the state of Oklahoma. These regression equations incorporate basin characteristics to estimate peak-streamflow magnitude and frequency throughout the state by use of a generalized least squares regression analysis. The most statistically significant independent variables required to estimate peak-streamflow magnitude and frequency for unregulated streams in Oklahoma are contributing drainage area, mean-annual precipitation, and main-channel slope. The regression equations are applicable for watershed basins with drainage areas less than 2,510 square miles that are not affected by regulation. The resulting regression equations had a standard model error ranging from 31 to 46 percent. Annual-maximum peak flows observed at 231 streamflow-gaging stations through water year 2008 were used for the regression analysis. Gage peak-streamflow estimates were used from previous work unless 2008 gaging-station data were available, in which new peak-streamflow estimates were calculated. The U.S. Geological Survey StreamStats web application was used to obtain the independent variables required for the peak-streamflow regression equations. Limitations on the use of the regression equations and the reliability of regression estimates for natural unregulated streams are described. Log-Pearson Type III analysis information, basin and climate characteristics, and the peak-streamflow frequency estimates for the 231 gaging stations in and near Oklahoma are listed. Methodologies are presented to estimate peak streamflows at ungaged sites by using estimates from gaging stations on unregulated streams. For ungaged sites on urban streams and streams regulated by small floodwater retarding structures, an adjustment of the statewide regression equations for natural unregulated streams can be used to estimate peak-streamflow magnitude and frequency.

Method for estimating spatially variable seepage loss and hydraulic conductivity in intermittent and ephemeral streams

USGS Publications Warehouse

Niswonger, R.G.; Prudic, David E.; Fogg, G.E.; Stonestrom, David A.; Buckland, E.M.

2008-01-01

A method is presented for estimating seepage loss and streambed hydraulic conductivity along intermittent and ephemeral streams using streamflow front velocities in initially dry channels. The method uses the kinematic wave equation for routing streamflow in channels coupled to Philip's equation for infiltration. The coupled model considers variations in seepage loss both across and along the channel. Water redistribution in the unsaturated zone is also represented in the model. Sensitivity of the streamflow front velocity to parameters used for calculating seepage loss and for routing streamflow shows that the streambed hydraulic conductivity has the greatest sensitivity for moderate to large seepage loss rates. Channel roughness, geometry, and slope are most important for low seepage loss rates; however, streambed hydraulic conductivity is still important for values greater than 0.008 m/d. Two example applications are presented to demonstrate the utility of the method.

Precipitation-runoff processes in the Feather River basin, northeastern California, and streamflow predictability, water years 1971-97

USGS Publications Warehouse

Koczot, Kathryn M.; Jeton, Anne E.; McGurk, Bruce; Dettinger, Michael D.

2005-01-01

Precipitation-runoff processes in the Feather River Basin of northern California determine short- and long-term streamflow variations that are of considerable local, State, and Federal concern. The river is an important source of water and power for the region. The basin forms the headwaters of the California State Water Project. Lake Oroville, at the outlet of the basin, plays an important role in flood management, water quality, and the health of fisheries as far downstream as the Sacramento-San Joaquin Delta. Existing models of the river simulate streamflow in hourly, daily, weekly, and seasonal time steps, but cannot adequately describe responses to climate and land-use variations in the basin. New spatially detailed precipitation-runoff models of the basin have been developed to simulate responses to climate and land-use variations at a higher spatial resolution than was available previously. This report characterizes daily rainfall, snowpack evolution, runoff, water and energy balances, and streamflow variations from, and within, the basin above Lake Oroville. The new model's ability to predict streamflow is assessed. The Feather River Basin sits astride geologic, topographic, and climatic divides that establish a hydrologic character that is relatively unusual among the basins of the Sierra Nevada. It straddles a north-south geologic transition in the Sierra Nevada between the granitic bedrock that underlies and forms most of the central and southern Sierra Nevada and volcanic bedrock that underlies the northernmost parts of the range (and basin). Because volcanic bedrock generally is more permeable than granitic, the northern, volcanic parts of the basin contribute larger fractions of ground-water flow to streams than do the southern, granitic parts of the basin. The Sierra Nevada topographic divide forms a high altitude ridgeline running northwest to southeast through the middle of the basin. The topography east of this ridgeline is more like the rain-shadowed basins of the northeastern Sierra Nevada than the uplands of most western Sierra Nevada river basins. The climate is mediterranean, with most of the annual precipitation occurring in winter. Because the basin includes large areas that are near the average snowline, rainfall and rain-snow mixtures are common during winter storms. Consequently, the overall timing and rates of runoff from the basin are highly sensitive to winter temperature fluctuations. The models were developed to simulate runoff-generating processes in eight drainages of the Feather River Basin. Together, these models simulate streamflow from 98 percent of the basin above Lake Oroville. The models simulate daily water and heat balances, snowpack evolution and snowmelt, evaporation and transpiration, subsurface water storage and outflows, and streamflow to key streamflow gage sites. The drainages are modeled as 324 hydrologic-response units, each of which is assumed homogeneous in physical characteristics and response to precipitation and runoff. The models were calibrated with emphasis on reproducing monthly streamflow rates, and model simulations were compared to the total natural inflows into Lake Oroville as reconstructed by the California Department of Water Resources for April-July snowmelt seasons from 1971 to 1997. The models are most sensitive to input values and patterns of precipitation and soil characteristics. The input precipitation values were allowed to vary on a daily basis to reflect available observations by making daily transformations to an existing map of long-term mean monthly precipitation rates that account for altitude and rain-shadow effects. The models effectively simulate streamflow into Lake Oroville during water years (October through September) 1971-97, which is demonstrated in hydrographs and statistical results presented in this report. The Butt Creek model yields the most accurate historical April-July simulations, whereas the West Branch

Simulated hydrologic response to climate change during the 21st century in New Hampshire

USGS Publications Warehouse

Bjerklie, David M.; Sturtevant, Luke P.

2018-01-24

The U.S. Geological Survey, in cooperation with the New Hampshire Department of Environmental Services and the Department of Health and Human Services, has developed a hydrologic model to assess the effects of short- and long-term climate change on hydrology in New Hampshire. This report documents the model and datasets developed by using the model to predict how climate change will affect the hydrologic cycle and provide data that can be used by State and local agencies to identify locations that are vulnerable to the effects of climate change in areas across New Hampshire. Future hydrologic projections were developed from the output of five general circulation models for two future climate scenarios. The scenarios are based on projected future greenhouse gas emissions and estimates of land-use and land-cover change within a projected global economic framework. An evaluation of the possible effect of projected future temperature on modeling of evapotranspiration is summarized to address concerns regarding the implications of the future climate on model parameters that are based on climate variables. The results of the model simulations are hydrologic projections indicating increasing streamflow across the State with large increases in streamflow during winter and early spring and general decreases during late spring and summer. Wide spatial variability in changes to groundwater recharge is projected, with general decreases in the Connecticut River Valley and at high elevations in the northern part of the State and general increases in coastal and lowland areas of the State. In general, total winter snowfall is projected to decrease across the State, but there is a possibility of increasing snow in some locations, particularly during November, February, and March. The simulated future changes in recharge and snowfall vary by watershed across the State. This means that each area of the State could experience very different changes, depending on topography or other factors. Therefore, planning for infrastructure and public safety needs to be flexible in order to address the range of possible outcomes indicated by the various model simulations. The absolute magnitude and timing of the daily streamflows, especially the larger floods, are not considered to be reliably simulated compared to changes in frequency and duration of daily streamflows and changes in accumulated monthly and seasonal streamflow volumes. Simulated current and future streamflow, groundwater recharge, and snowfall datasets include simulated data derived from the five general circulation models used in this study for a current reference time period and two future time periods. Average monthly streamflow time series datasets are provided for 27 streamgages in New Hampshire. Fourteen of the 27 streamgages associated with daily streamflow time series showed a good calibration. Average monthly groundwater recharge and snowfall time series for the same reference time period and two future time periods are also provided for each of the 467 hydrologic response units that compose the model.

Simulation of streamflow, evapotranspiration, and groundwater recharge in the middle Nueces River watershed, south Texas, 1961-2008

USGS Publications Warehouse

Dietsch, Benjamin J.; Wehmeyer, Loren L.

2012-01-01

Selected results of the model include streamflow yields for the subwatersheds and water-balance information for the Carrizo–Wilcox aquifer outcrop area. For the entire model domain, the area-weighted mean streamflow yield from 1961 to 2008 was 1.12 inches/year. The mean annual rainfall on the outcrop area during the 1961–2008 simulation period was 21.7 inches. Of this rainfall, an annual mean of 20.1 inches (about 93 percent) was simulated as evapotranspiration, 1.2 inches (about 6 percent) was simulated as groundwater recharge, and 0.5 inches (about 2 percent) was simulated as surface runoff.

Analytical flow duration curves for summer streamflow in Switzerland

NASA Astrophysics Data System (ADS)

Santos, Ana Clara; Portela, Maria Manuela; Rinaldo, Andrea; Schaefli, Bettina

2018-04-01

This paper proposes a systematic assessment of the performance of an analytical modeling framework for streamflow probability distributions for a set of 25 Swiss catchments. These catchments show a wide range of hydroclimatic regimes, including namely snow-influenced streamflows. The model parameters are calculated from a spatially averaged gridded daily precipitation data set and from observed daily discharge time series, both in a forward estimation mode (direct parameter calculation from observed data) and in an inverse estimation mode (maximum likelihood estimation). The performance of the linear and the nonlinear model versions is assessed in terms of reproducing observed flow duration curves and their natural variability. Overall, the nonlinear model version outperforms the linear model for all regimes, but the linear model shows a notable performance increase with catchment elevation. More importantly, the obtained results demonstrate that the analytical model performs well for summer discharge for all analyzed streamflow regimes, ranging from rainfall-driven regimes with summer low flow to snow and glacier regimes with summer high flow. These results suggest that the model's encoding of discharge-generating events based on stochastic soil moisture dynamics is more flexible than previously thought. As shown in this paper, the presence of snowmelt or ice melt is accommodated by a relative increase in the discharge-generating frequency, a key parameter of the model. Explicit quantification of this frequency increase as a function of mean catchment meteorological conditions is left for future research.

Evaluation of meteorological drought indices for streamflow modeling

NASA Astrophysics Data System (ADS)

Haslinger, Klaus; Koffler, Daniel; Blöschl, Günter; Parajka, Juraj; Schöner, Wolfgang; Laaha, Gregor

2013-04-01

In this paper we present a comprehensive analysis which aims to link various meteorological drought indices to streamflow data in Austria and Central Europe. The motivation arises from the fact that discharge time series are usually shorter (beginning in the middle of the 20th century) than meteorological time series. In the European Greater Alpine Region we are fortunate of having a gridded dataset for temperature and solid/liquid precipitation on a monthly time scale that spans from 1801 to 2003 - the HISTALP database. If there is a link between meteorological drought indices and streamflow, a reconstruction of streamflow, with emphasis on low flows, will be possible for the last 200 years. As meteorological drought indices the self-calibrating Palmer Drought Severity Index (scPDSI), the Standardized Precipitation Index (SPI) on various time scales as well as the moisture departure value d from the soil moisture modeling procedure of the scPDSI are used. The analysis focuses on three aspects, (i) temporal co-evolution of meteorological drought and streamflow indices, (ii) their at-site correlation at gauges, and (iii) their regional correlation structure depending on different climate and catchment conditions. The whole analysis is stratified by seasons, what allows us to explore the strength of the link for the dominant low flow generating process. In order to show a connection between these indices and streamflow data the drought event of 2003 serves as a reference. We will show the temporal evolution of the drought indices parallel to streamflow indices like MQ, Q95 and MAM(7) for the period from summer 2002, which encompasses a major flood event in the northern parts of Austria, to fall 2003 when the streamflow drought was most severe. This is carried out for different regions in Austria, representing different climatic and soil-specific characteristics. To quantify the link between drought indices and streamflow indices for the whole time series from 1801-2003, rank correlations are calculated, stratified by three different approaches. First, as mentioned above, a regional assessment is carried out. Second, the correlations are calculated separately for seasons (DJF, MAM, JJA, and SON). Third, different quantiles of the streamflow-data, ranging from Q50 to Q95, will be correlated with the drought indices. The results show that there is definitely a strong connection between the MQ and the scPDSI in one target region in the Northwest of Austria. The results are encouraging for further attempts to reconstruct extreme low flow events from meteorological data only. A statistical model for linking meteorological drought indices with streamflow under dry conditions is currently under development and results will be presented in the near future.

Estimation of average annual streamflows and power potentials for Alaska and Hawaii

DOE Office of Scientific and Technical Information (OSTI.GOV)

Verdin, Kristine L.

2004-05-01

This paper describes the work done to develop average annual streamflow estimates and power potential for the states of Alaska and Hawaii. The Elevation Derivatives for National Applications (EDNA) database was used, along with climatic datasets, to develop flow and power estimates for every stream reach in the EDNA database. Estimates of average annual streamflows were derived using state-specific regression equations, which were functions of average annual precipitation, precipitation intensity, drainage area, and other elevation-derived parameters. Power potential was calculated through the use of the average annual streamflow and the hydraulic head of each reach, which is calculated from themore » EDNA digital elevation model. In all, estimates of streamflow and power potential were calculated for over 170,000 stream segments in the Alaskan and Hawaiian datasets.« less

Drivers of annual to decadal streamflow variability in the lower Colorado River Basin

NASA Astrophysics Data System (ADS)

Lambeth-Beagles, R. S.; Troch, P. A.

2010-12-01

The Colorado River is the main water supply to the southwest region. As demand reaches the limit of supply in the southwest it becomes increasingly important to understand the dynamics of streamflow in the Colorado River and in particular the tributaries to the lower Colorado River. Climate change may pose an additional threat to the already-scarce water supply in the southwest. Due to the narrowing margin for error, water managers are keen on extending their ability to predict streamflow volumes on a mid-range to decadal scale. Before a predictive streamflow model can be developed, an understanding of the physical drivers of annual to decadal streamflow variability in the lower Colorado River Basin is needed. This research addresses this need by applying multiple statistical methods to identify trends, patterns and relationships present in streamflow, precipitation and temperature over the past century in four contributing watersheds to the lower Colorado River. The four watersheds selected were the Paria, Little Colorado, Virgin/Muddy, and Bill Williams. Time series data over a common period from 1906-2007 for streamflow, precipitation and temperature were used for the initial analysis. Through statistical analysis the following questions were addressed: 1) are there observable trends and patterns in these variables during the past century and 2) if there are trends or patterns, how are they related to each other? The Mann-Kendall test was used to identify trends in the three variables. Assumptions regarding autocorrelation and persistence in the data were taken into consideration. Kendall’s tau-b test was used to establish association between any found trends in the data. Initial results suggest there are two primary processes occurring. First, statistical analysis reveals significant upward trends in temperatures and downward trends in streamflow. However, there appears to be no trend in precipitation data. These trends in streamflow and temperature speak to increasing evaporation and transpiration processes. Second, annual variability in streamflow is not statistically correlated with annual temperature variability but appears to be highly correlated with annual precipitation variability. This implies that on a year-to-year basis, changes in streamflow volumes are directly affected by precipitation and not temperature. Future development of a predictive streamflow model will need to take into consideration these two processes to obtain accurate results. In order to extend predictive skill to the multi-year scale relationships between precipitation, temperature and persistent climate indices such as the Pacific Decadal Oscillation, Atlantic Multidecadal Oscillation and El Nino/Southern Oscillation will need to be examined.

A method for estimating peak and time of peak streamflow from excess rainfall for 10- to 640-acre watersheds in the Houston, Texas, metropolitan area

USGS Publications Warehouse

Asquith, William H.; Cleveland, Theodore G.; Roussel, Meghan C.

2011-01-01

Estimates of peak and time of peak streamflow for small watersheds (less than about 640 acres) in a suburban to urban, low-slope setting are needed for drainage design that is cost-effective and risk-mitigated. During 2007-10, the U.S. Geological Survey (USGS), in cooperation with the Harris County Flood Control District and the Texas Department of Transportation, developed a method to estimate peak and time of peak streamflow from excess rainfall for 10- to 640-acre watersheds in the Houston, Texas, metropolitan area. To develop the method, 24 watersheds in the study area with drainage areas less than about 3.5 square miles (2,240 acres) and with concomitant rainfall and runoff data were selected. The method is based on conjunctive analysis of rainfall and runoff data in the context of the unit hydrograph method and the rational method. For the unit hydrograph analysis, a gamma distribution model of unit hydrograph shape (a gamma unit hydrograph) was chosen and parameters estimated through matching of modeled peak and time of peak streamflow to observed values on a storm-by-storm basis. Watershed mean or watershed-specific values of peak and time to peak ("time to peak" is a parameter of the gamma unit hydrograph and is distinct from "time of peak") of the gamma unit hydrograph were computed. Two regression equations to estimate peak and time to peak of the gamma unit hydrograph that are based on watershed characteristics of drainage area and basin-development factor (BDF) were developed. For the rational method analysis, a lag time (time-R), volumetric runoff coefficient, and runoff coefficient were computed on a storm-by-storm basis. Watershed-specific values of these three metrics were computed. A regression equation to estimate time-R based on drainage area and BDF was developed. Overall arithmetic means of volumetric runoff coefficient (0.41 dimensionless) and runoff coefficient (0.25 dimensionless) for the 24 watersheds were used to express the rational method in terms of excess rainfall (the excess rational method). Both the unit hydrograph method and excess rational method are shown to provide similar estimates of peak and time of peak streamflow. The results from the two methods can be combined by using arithmetic means. A nomograph is provided that shows the respective relations between the arithmetic-mean peak and time of peak streamflow to drainage areas ranging from 10 to 640 acres. The nomograph also shows the respective relations for selected BDF ranging from undeveloped to fully developed conditions. The nomograph represents the peak streamflow for 1 inch of excess rainfall based on drainage area and BDF; the peak streamflow for design storms from the nomograph can be multiplied by the excess rainfall to estimate peak streamflow. Time of peak streamflow is readily obtained from the nomograph. Therefore, given excess rainfall values derived from watershed-loss models, which are beyond the scope of this report, the nomograph represents a method for estimating peak and time of peak streamflow for applicable watersheds in the Houston metropolitan area. Lastly, analysis of the relative influence of BDF on peak streamflow is provided, and the results indicate a 0:04log10 cubic feet per second change of peak streamflow per positive unit of change in BDF. This relative change can be used to adjust peak streamflow from the method or other hydrologic methods for a given BDF to other BDF values; example computations are provided.

August median streamflow on ungaged streams in Eastern Coastal Maine

USGS Publications Warehouse

Lombard, Pamela J.

2004-01-01

Methods for estimating August median streamflow were developed for ungaged, unregulated streams in eastern coastal Maine. The methods apply to streams with drainage areas ranging in size from 0.04 to 73.2 square miles and fraction of basin underlain by a sand and gravel aquifer ranging from 0 to 71 percent. The equations were developed with data from three long-term (greater than or equal to 10 years of record) continuous-record streamflow-gaging stations, 23 partial-record streamflow- gaging stations, and 5 short-term (less than 10 years of record) continuous-record streamflow-gaging stations. A mathematical technique for estimating a standard low-flow statistic, August median streamflow, at partial-record streamflow-gaging stations and short-term continuous-record streamflow-gaging stations was applied by relating base-flow measurements at these stations to concurrent daily streamflows at nearby long-term continuous-record streamflow-gaging stations (index stations). Generalized least-squares regression analysis (GLS) was used to relate estimates of August median streamflow at streamflow-gaging stations to basin characteristics at these same stations to develop equations that can be applied to estimate August median streamflow on ungaged streams. GLS accounts for different periods of record at the gaging stations and the cross correlation of concurrent streamflows among gaging stations. Thirty-one stations were used for the final regression equations. Two basin characteristics?drainage area and fraction of basin underlain by a sand and gravel aquifer?are used in the calculated regression equation to estimate August median streamflow for ungaged streams. The equation has an average standard error of prediction from -27 to 38 percent. A one-variable equation uses only drainage area to estimate August median streamflow when less accuracy is acceptable. This equation has an average standard error of prediction from -30 to 43 percent. Model error is larger than sampling error for both equations, indicating that additional or improved estimates of basin characteristics could be important to improved estimates of low-flow statistics. Weighted estimates of August median streamflow at partial- record or continuous-record gaging stations range from 0.003 to 31.0 cubic feet per second or from 0.1 to 0.6 cubic feet per second per square mile. Estimates of August median streamflow on ungaged streams in eastern coastal Maine, within the range of acceptable explanatory variables, range from 0.003 to 45 cubic feet per second or 0.1 to 0.6 cubic feet per second per square mile. Estimates of August median streamflow per square mile of drainage area generally increase as drainage area and fraction of basin underlain by a sand and gravel aquifer increase.

Implementation of local grid refinement (LGR) for the Lake Michigan Basin regional groundwater-flow model

USGS Publications Warehouse

Hoard, C.J.

2010-01-01

The U.S. Geological Survey is evaluating water availability and use within the Great Lakes Basin. This is a pilot effort to develop new techniques and methods to aid in the assessment of water availability. As part of the pilot program, a regional groundwater-flow model for the Lake Michigan Basin was developed using SEAWAT-2000. The regional model was used as a framework for assessing local-scale water availability through grid-refinement techniques. Two grid-refinement techniques, telescopic mesh refinement and local grid refinement, were used to illustrate the capability of the regional model to evaluate local-scale problems. An intermediate model was developed in central Michigan spanning an area of 454 square miles (mi2) using telescopic mesh refinement. Within the intermediate model, a smaller local model covering an area of 21.7 mi2 was developed and simulated using local grid refinement. Recharge was distributed in space and time using a daily output from a modified Thornthwaite-Mather soil-water-balance method. The soil-water-balance method derived recharge estimates from temperature and precipitation data output from an atmosphere-ocean coupled general-circulation model. The particular atmosphere-ocean coupled general-circulation model used, simulated climate change caused by high global greenhouse-gas emissions to the atmosphere. The surface-water network simulated in the regional model was refined and simulated using a streamflow-routing package for MODFLOW. The refined models were used to demonstrate streamflow depletion and potential climate change using five scenarios. The streamflow-depletion scenarios include (1) natural conditions (no pumping), (2) a pumping well near a stream; the well is screened in surficial glacial deposits, (3) a pumping well near a stream; the well is screened in deeper glacial deposits, and (4) a pumping well near a stream; the well is open to a deep bedrock aquifer. Results indicated that a range of 59 to 50 percent of the water pumped originated from the stream for the shallow glacial and deep bedrock pumping scenarios, respectively. The difference in streamflow reduction between the shallow and deep pumping scenarios was compensated for in the deep well by deriving more water from regional sources. The climate-change scenario only simulated natural conditions from 1991-2044, so there was no pumping stress simulated. Streamflows were calculated for the simulated period and indicated that recharge over the period generally increased from the start of the simulation until approximately 2017, and decreased from then to the end of the simulation. Streamflow was highly correlated with recharge so that the lowest streamflows occurred in the later stress periods of the model when recharge was lowest.

Simulation of streamflow and the effects of brush management on water yields in the upper Guadalupe River watershed, south-central Texas, 1995-2010

USGS Publications Warehouse

Bumgarner, Johnathan R.; Thompson, Florence E.

2012-01-01

The U.S. Geological Survey, in cooperation with the Texas State Soil and Water Conservation Board and the Upper Guadalupe River Authority, developed and calibrated a Soil and Water Assessment Tool watershed model of the upper Guadalupe River watershed in south-central Texas to simulate streamflow and the effects of brush management on water yields in the watershed and to Canyon Lake for 1995-2010. Model simulations were done to quantify the possible change in water yield of individual subbasins in the upper Guadalupe River watershed as a result of the replacement of ashe juniper (Juniperus ashei) with grasslands. The simulation results will serve as a tool for resource managers to guide their brush-management efforts. Model hydrology was calibrated with streamflow data collected at the U.S. Geological Survey streamflow-gaging station 08167500 Guadalupe River near Spring Branch, Tex., for 1995-2010. Simulated monthly streamflow showed very good agreement with measured monthly streamflow: a percent bias of -5, a coefficient of determination of 0.91, and a Nash-Sutcliffe coefficient of model efficiency of 0.85. Modified land-cover input datasets were generated for the model in order to simulate the replacement of ashe juniper with grasslands in 23 brush-management subbasins in the watershed. Each of the 23 simulations showed an increase in simulated water yields in the targeted subbasins and to Canyon Lake. The simulated increases in average annual water yields in the subbasins ranged from 6,370 to 119,000 gallons per acre of ashe juniper replaced with grasslands with an average of 38,900 gallons. The simulated increases in average annual water yields to Canyon Lake from upstream subbasins ranged from 6,640 to 72,700 gallons per acre of ashe juniper replaced with grasslands with an average of 34,700 gallons.

Estimation of streamflow response to wildfire and salvage logging in a snow-dominated catchment using a model-based change detection approach

NASA Astrophysics Data System (ADS)

Moore, R. D.; Mahrlein, M.; Chuang, Y. C. M.

2016-12-01

Forest cover changes associated with natural disturbance and forest management can have significant influences on the magnitude and timing of streamflow. This study quantified the effect of a wildfire that burned over 60% of the catchment of Fishtrap Creek in the southern interior of British Columbia in August 2003. Fishtrap Creek has been gauged from 1970 to present. The catchment drains 158 km2 at the gauging station and has a snow-dominated hydrologic regime. In 2006, about one-third of the burned area was salvage logged. A semi-distributed hydrologic model was calibrated and tested using the pre-fire streamflow data. Simulated daily streamflow based on the "best" parameter set, and assuming pre-fire forest cover, was used as a "virtual" control in a paired-catchment analysis. Each year was divided into 73 five-day periods (pentads), and separate pre-fire regressions were fit for each of the 73 pentad time series. This approach avoids issues with autocorrelation and can address seasonally varying model bias. Statistically significant increases in streamflow were detected in late winter and through the month of April, with no evidence for increased peak flows, which is inferred to reflect a de-synchronization of snowmelt between disturbed and undisturbed areas of the catchment. The results of the model-based change detection are consistent with statistical analyses using climatic variables as covariates, but have the advantage of providing more temporal detail. However, the power of the change detection can be limited by insufficiently long records of streamflow and driving weather variables for both the pre- and post-fire periods and model structural errors (e.g., an inability to reproduce winter baseflow). An interesting side result of the study was the identification of parameter uncertainty associated with uncertainty regarding forest cover during the calibration period.

Monthly streamflow forecasting in the Rhine basin

NASA Astrophysics Data System (ADS)

Schick, Simon; Rössler, Ole; Weingartner, Rolf

2017-04-01

Forecasting seasonal streamflow of the Rhine river is of societal relevance as the Rhine is an important water way and water resource in Western Europe. The present study investigates the predictability of monthly mean streamflow at lead times of zero, one, and two months with the focus on potential benefits by the integration of seasonal climate predictions. Specifically, we use seasonal predictions of precipitation and surface air temperature released by the European Centre for Medium-Range Weather Forecasts (ECMWF) for a regression analysis. In order to disentangle forecast uncertainty, the 'Reverse Ensemble Streamflow Prediction' framework is adapted here to the context of regression: By using appropriate subsets of predictors the regression model is constrained to either the initial conditions, the meteorological forcing, or both. An operational application is mimicked by equipping the model with the seasonal climate predictions provided by ECMWF. Finally, to mitigate the spatial aggregation of the meteorological fields the model is also applied at the subcatchment scale, and the resulting predictions are combined afterwards. The hindcast experiment is carried out for the period 1982-2011 in cross validation mode at two gauging stations, namely the Rhine at Lobith and Basel. The results show that monthly forecasts are skillful with respect to climatology only at zero lead time. In addition, at zero lead time the integration of seasonal climate predictions decreases the mean absolute error by 5 to 10 percentage compared to forecasts which are solely based on initial conditions. This reduction most likely is induced by the seasonal prediction of precipitation and not air temperature. The study is completed by bench marking the regression model with runoff simulations from ECMWFs seasonal forecast system. By simply using basin averages followed by a linear bias correction, these runoff simulations translate well to monthly streamflow. Though the regression model is only slightly outperformed, we argue that runoff out of the land surface component of seasonal climate forecasting systems is an interesting option when it comes to seasonal streamflow forecasting in large river basins.

The Global Streamflow Indices and Metadata archive (G-SIM): A compilation of global streamflow time series indices and meta-data

NASA Astrophysics Data System (ADS)

Do, Hong; Gudmundsson, Lukas; Leonard, Michael; Westra, Seth; Senerivatne, Sonia

2017-04-01

In-situ observations of daily streamflow with global coverage are a crucial asset for understanding large-scale freshwater resources which are an essential component of the Earth system and a prerequisite for societal development. Here we present the Global Streamflow Indices and Metadata archive (G-SIM), a collection indices derived from more than 20,000 daily streamflow time series across the globe. These indices are designed to support global assessments of change in wet and dry extremes, and have been compiled from 12 free-to-access online databases (seven national databases and five international collections). The G-SIM archive also includes significant metadata to help support detailed understanding of streamflow dynamics, with the inclusion of drainage area shapefile and many essential catchment properties such as land cover type, soil and topographic characteristics. The automated procedure in data handling and quality control of the project makes G-SIM a reproducible, extendible archive and can be utilised for many purposes in large-scale hydrology. Some potential applications include the identification of observational trends in hydrological extremes, the assessment of climate change impacts on streamflow regimes, and the validation of global hydrological models.

Short-term ensemble streamflow forecasting using operationally-produced single-valued streamflow forecasts - A Hydrologic Model Output Statistics (HMOS) approach

NASA Astrophysics Data System (ADS)

Regonda, Satish Kumar; Seo, Dong-Jun; Lawrence, Bill; Brown, James D.; Demargne, Julie

2013-08-01

We present a statistical procedure for generating short-term ensemble streamflow forecasts from single-valued, or deterministic, streamflow forecasts produced operationally by the U.S. National Weather Service (NWS) River Forecast Centers (RFCs). The resulting ensemble streamflow forecast provides an estimate of the predictive uncertainty associated with the single-valued forecast to support risk-based decision making by the forecasters and by the users of the forecast products, such as emergency managers. Forced by single-valued quantitative precipitation and temperature forecasts (QPF, QTF), the single-valued streamflow forecasts are produced at a 6-h time step nominally out to 5 days into the future. The single-valued streamflow forecasts reflect various run-time modifications, or "manual data assimilation", applied by the human forecasters in an attempt to reduce error from various sources in the end-to-end forecast process. The proposed procedure generates ensemble traces of streamflow from a parsimonious approximation of the conditional multivariate probability distribution of future streamflow given the single-valued streamflow forecast, QPF, and the most recent streamflow observation. For parameter estimation and evaluation, we used a multiyear archive of the single-valued river stage forecast produced operationally by the NWS Arkansas-Red River Basin River Forecast Center (ABRFC) in Tulsa, Oklahoma. As a by-product of parameter estimation, the procedure provides a categorical assessment of the effective lead time of the operational hydrologic forecasts for different QPF and forecast flow conditions. To evaluate the procedure, we carried out hindcasting experiments in dependent and cross-validation modes. The results indicate that the short-term streamflow ensemble hindcasts generated from the procedure are generally reliable within the effective lead time of the single-valued forecasts and well capture the skill of the single-valued forecasts. For smaller basins, however, the effective lead time is significantly reduced by short basin memory and reduced skill in the single-valued QPF.

Derivation of low flow frequency distributions under human activities and its implications

NASA Astrophysics Data System (ADS)

Gao, Shida; Liu, Pan; Pan, Zhengke; Ming, Bo; Guo, Shenglian; Xiong, Lihua

2017-06-01

Low flow, refers to a minimum streamflow in dry seasons, is crucial to water supply, agricultural irrigation and navigation. Human activities, such as groundwater pumping, influence low flow severely. In order to derive the low flow frequency distribution functions under human activities, this study incorporates groundwater pumping and return flow as variables in the recession process. Steps are as follows: (1) the original low flow without human activities is assumed to follow a Pearson type three distribution, (2) the probability distribution of climatic dry spell periods is derived based on a base flow recession model, (3) the base flow recession model is updated under human activities, and (4) the low flow distribution under human activities is obtained based on the derived probability distribution of dry spell periods and the updated base flow recession model. Linear and nonlinear reservoir models are used to describe the base flow recession, respectively. The Wudinghe basin is chosen for the case study, with daily streamflow observations during 1958-2000. Results show that human activities change the location parameter of the low flow frequency curve for the linear reservoir model, while alter the frequency distribution function for the nonlinear one. It is indicated that alter the parameters of the low flow frequency distribution is not always feasible to tackle the changing environment.

Linking river management to species conservation using dynamic landscape scale models

USGS Publications Warehouse

Freeman, Mary C.; Buell, Gary R.; Hay, Lauren E.; Hughes, W. Brian; Jacobson, Robert B.; Jones, John W.; Jones, S.A.; LaFontaine, Jacob H.; Odom, Kenneth R.; Peterson, James T.; Riley, Jeffrey W.; Schindler, J. Stephen; Shea, C.; Weaver, J.D.

2013-01-01

Efforts to conserve stream and river biota could benefit from tools that allow managers to evaluate landscape-scale changes in species distributions in response to water management decisions. We present a framework and methods for integrating hydrology, geographic context and metapopulation processes to simulate effects of changes in streamflow on fish occupancy dynamics across a landscape of interconnected stream segments. We illustrate this approach using a 482 km2 catchment in the southeastern US supporting 50 or more stream fish species. A spatially distributed, deterministic and physically based hydrologic model is used to simulate daily streamflow for sub-basins composing the catchment. We use geographic data to characterize stream segments with respect to channel size, confinement, position and connectedness within the stream network. Simulated streamflow dynamics are then applied to model fish metapopulation dynamics in stream segments, using hypothesized effects of streamflow magnitude and variability on population processes, conditioned by channel characteristics. The resulting time series simulate spatially explicit, annual changes in species occurrences or assemblage metrics (e.g. species richness) across the catchment as outcomes of management scenarios. Sensitivity analyses using alternative, plausible links between streamflow components and metapopulation processes, or allowing for alternative modes of fish dispersal, demonstrate large effects of ecological uncertainty on model outcomes and highlight needed research and monitoring. Nonetheless, with uncertainties explicitly acknowledged, dynamic, landscape-scale simulations may prove useful for quantitatively comparing river management alternatives with respect to species conservation.

Improving Alpine Streamflow Simulations by Incorporation of Evapotranspiration and Soil Moisture Data

NASA Astrophysics Data System (ADS)

Tobin, K. J.; Bennett, M. E.

2017-12-01

Over the last decade autocalibration routines have become commonplace in watershed modeling. This approach is most often used to simulate a streamflow at a basin's outlet. In alpine settings spring/early summer snowmelt is by far the dominant signal in this system. Therefore, there is great potential for a modeled watershed to underperform during other times of the year. This tendency has been noted in many prior studies. In this work, the Soil and Water Assessment Tool (SWAT) model was autocalibrated with the SUFI-2 routine. Two mountainous watersheds from Idaho and Utah were examined. In this study, the basins were calibrated on a monthly satellite based on the MODIS 16A2 product. The gridded MODIS product was ideally suited to derive an estimate of ET on a subbasin basis. Soil moisture data was derived from extrapolation of in situ sites from the SNOwpack TELemetry (SNOTEL) network. Previous work has indicated that in situ soil moisture can be applied to derive an estimate at a significant distance (>30 km) away from the in situ site. Optimized ET and soil moisture parameter values were then applied to streamflow simulations. Preliminary results indicate improved streamflow performance both during calibration (2005-2011) and validation (2012-2014) periods. Streamflow performance was monitored with not only standard objective metrics (bias and Nash Sutcliffe coefficients) but also improved baseflow accuracy, demonstrating the utility of this approach in improving watershed modeling fidelity outside the main snowmelt season.

State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application

NASA Astrophysics Data System (ADS)

Gibbs, Matthew S.; McInerney, David; Humphrey, Greer; Thyer, Mark A.; Maier, Holger R.; Dandy, Graeme C.; Kavetski, Dmitri

2018-02-01

Monthly to seasonal streamflow forecasts provide useful information for a range of water resource management and planning applications. This work focuses on improving such forecasts by considering the following two aspects: (1) state updating to force the models to match observations from the start of the forecast period, and (2) selection of a shorter calibration period that is more representative of the forecast period, compared to a longer calibration period traditionally used. The analysis is undertaken in the context of using streamflow forecasts for environmental flow water management of an open channel drainage network in southern Australia. Forecasts of monthly streamflow are obtained using a conceptual rainfall-runoff model combined with a post-processor error model for uncertainty analysis. This model set-up is applied to two catchments, one with stronger evidence of non-stationarity than the other. A range of metrics are used to assess different aspects of predictive performance, including reliability, sharpness, bias and accuracy. The results indicate that, for most scenarios and metrics, state updating improves predictive performance for both observed rainfall and forecast rainfall sources. Using the shorter calibration period also improves predictive performance, particularly for the catchment with stronger evidence of non-stationarity. The results highlight that a traditional approach of using a long calibration period can degrade predictive performance when there is evidence of non-stationarity. The techniques presented can form the basis for operational monthly streamflow forecasting systems and provide support for environmental decision-making.

Possible changes in ground-water flow to the Pecos River caused by Santa Rosa Lake, Guadalupe County, New Mexico

USGS Publications Warehouse

Risser, D.W.

1987-01-01

In 1980 Santa Rosa Dam began impounding water on the Pecos River about 7 miles north of Santa Rosa, New Mexico, to provide flood control, sediment control, and storage for irrigation. Santa Rosa Lake has caused changes in the groundwater flow system, which may cause changes in the streamflow of the Pecos River that cannot be detected at the present streamflow gaging stations. Data collected at these stations are used to measure the amount of water available for downstream users. A three-dimensional groundwater flow model for a 950 sq mi area between Anton Chico and Puerto de Luna was used to simulate the effects of Santa Rosa Lake on groundwater flow to a gaining reach of the Pecos River for lake levels of 4,675, 4,715, 4,725, 4,750, 4,776, and 4,797 feet above sea level and durations of impoundment of 30, 90, 182, and 365 days for all levels except 4 ,797 feet. These simulations indicated that streamflow in the Pecos River could increase by as much as 2 cu ft/sec between the dam and Puerto de Luna if the lake level were maintained at 4 ,797 feet for 90 days or 4,776 feet for 1 year. About 90% of this increased streamflow would occur < 0.5 mi downstream from the dam, some of which would be measured at the streamflow gaging station located 0.2 mile downstream from the dam. Simulations also indicated that the lake will affect groundwater flow such that inflow to the study area may be decreased by as much as 1.9 cu ft/sec. This water may leave the Pecos River drainage basin or be diverted back to the Pecos River downstream from the gaging station near Puerto de Luna. In either case, this quantity represents a net loss of water upstream from Puerto de Luna. Most simulations indicated that the decrease in groundwater flow into the study area would be of about the same quantity as the simulated increase in streamflow downstream from the dam. Therefore, the net effect of the lake on the flow of the Pecos River in the study area appears to be negligible. Model simulations indicated that effect of lake levels below 4 ,750 feet on water levels in observation wells completed in the San Andres Limestone could not be distinguished from the effects of other hydrologic stresses. (Author 's abstract)

Evaluation of Selected Model Constraints and Variables on Simulated Sustainable Yield from the Mississippi River Valley Alluvial Aquifer System in Arkansas

USGS Publications Warehouse

Czarnecki, John B.

2008-01-01

An existing conjunctive use optimization model of the Mississippi River Valley alluvial aquifer was used to evaluate the effect of selected constraints and model variables on ground-water sustainable yield. Modifications to the optimization model were made to evaluate the effects of varying (1) the upper limit of ground-water withdrawal rates, (2) the streamflow constraint associated with the White River, and (3) the specified stage of the White River. Upper limits of ground-water withdrawal rates were reduced to 75, 50, and 25 percent of the 1997 ground-water withdrawal rates. As the upper limit is reduced, the spatial distribution of sustainable pumping increases, although the total sustainable pumping from the entire model area decreases. In addition, the number of binding constraint points decreases. In a separate analysis, the streamflow constraint associated with the White River was optimized, resulting in an estimate of the maximum sustainable streamflow at DeValls Bluff, Arkansas, the site of potential surface-water withdrawals from the White River for the Grand Prairie Area Demonstration Project. The maximum sustainable streamflow, however, is less than the amount of streamflow allocated in the spring during the paddlefish spawning period. Finally, decreasing the specified stage of the White River was done to evaluate a hypothetical river stage that might result if the White River were to breach the Melinda Head Cut Structure, one of several manmade diversions that prevents the White River from permanently joining the Arkansas River. A reduction in the stage of the White River causes reductions in the sustainable yield of ground water.

Potential impact of climate change to the future streamflow of Yellow River Basin based on CMIP5 data

NASA Astrophysics Data System (ADS)

Yang, Xiaoli; Zheng, Weifei; Ren, Liliang; Zhang, Mengru; Wang, Yuqian; Liu, Yi; Yuan, Fei; Jiang, Shanhu

2018-02-01

The Yellow River Basin (YRB) is the largest river basin in northern China, which has suffering water scarcity and drought hazard for many years. Therefore, assessments the potential impacts of climate change on the future streamflow in this basin is very important for local policy and planning on food security. In this study, based on the observations of 101 meteorological stations in YRB, equidistant CDF matching (EDCDFm) statistical downscaling approach was applied to eight climate models under two emissions scenarios (RCP4.5 and RCP8.5) from phase five of the Coupled Model Intercomparison Project (CMIP5). Variable infiltration capacity (VIC) model with 0.25° × 0.25° spatial resolution was developed based on downscaled fields for simulating streamflow in the future period over YRB. The results show that with the global warming trend, the annual streamflow will reduced about 10 % during the period of 2021-2050, compared to the base period of 1961-1990 in YRB. There should be suitable water resources planning to meet the demands of growing populations and future climate changing in this region.

Digital model of the Arikaree Aquifer near Wheatland, southeastern Wyoming

USGS Publications Warehouse

Hoxie, Dwight T.

1977-01-01

A digital model that mathematically simulates the flow of ground water, approximating the flow system as two-dimensional, has been applied to predict the long-term effects of irrigation and proposed industrial pumping from the unconfined Arikaree aquifer in a 400 square-mile area in southeastern Wyoming. Three cases that represent projected maximum, mean, and minimum combined irrigation and industrial ground-water withdrawals at annual rates of 16,176, 11,168, and 6,749 acre-feet, respectively, were considered. Water-level declines of more than 5 feet over areas of 124, 120, and 98 square miles and depletions in streamflow of 14.4, 8.9, and 7.2 cfs from the Laramie and North Laramie Rivers were predicted to occur at the end of a 40-year simulation period for these maximum, mean, and minimum withdrawal rates, respectively. A tenfold incrase in the vertical hydraulic conductivity that was assumed for the streambeds results in smaller predicted drawdowns near the Laramie and North Laramie Rivers and a 36 percent increase in the predicted depletion in streamflow for the North Laramie River. (Woodard-USGS)

Modelling ecological flow regime: an example from the Tennessee and Cumberland River basins

USGS Publications Warehouse

Knight, Rodney R.; Gain, W. Scott; Wolfe, William J.

2012-01-01

Predictive equations were developed for 19 ecologically relevant streamflow characteristics within five major groups of flow variables (magnitude, ratio, frequency, variability, and date) for use in the Tennessee and Cumberland River basins using stepbackward regression. Basin characteristics explain 50% or more of the variation for 12 of the 19 equations. Independent variables identified through stepbackward regression were statistically significant in 78 of 304 cases (α > 0.0001) and represent four major groups: climate, physical landscape features, regional indicators, and land use. Of these groups, the regional and climate variables were the most influential for determining hydrologic response. Daily temperature range, geologic factor, and rock depth were major factors explaining the variability in 17, 15, and 13 equations, respectively. The equations and independent datasets were used to explore the broad relation between basin properties and streamflow and the implication of streamflow to the study of ecological flow requirements. Key results include a high degree of hydrologic variability among least disturbed Blue Ridge streams, similar hydrologic behaviour for watersheds with widely varying degrees of forest cover, and distinct hydrologic profiles for streams in different geographic regions. Published in 2011. This article is a US Government work and is in the public domain in the USA.

The impact of lake and reservoir parameterization on global streamflow simulation.

PubMed

Zajac, Zuzanna; Revilla-Romero, Beatriz; Salamon, Peter; Burek, Peter; Hirpa, Feyera A; Beck, Hylke

2017-05-01

Lakes and reservoirs affect the timing and magnitude of streamflow, and are therefore essential hydrological model components, especially in the context of global flood forecasting. However, the parameterization of lake and reservoir routines on a global scale is subject to considerable uncertainty due to lack of information on lake hydrographic characteristics and reservoir operating rules. In this study we estimated the effect of lakes and reservoirs on global daily streamflow simulations of a spatially-distributed LISFLOOD hydrological model. We applied state-of-the-art global sensitivity and uncertainty analyses for selected catchments to examine the effect of uncertain lake and reservoir parameterization on model performance. Streamflow observations from 390 catchments around the globe and multiple performance measures were used to assess model performance. Results indicate a considerable geographical variability in the lake and reservoir effects on the streamflow simulation. Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE) metrics improved for 65% and 38% of catchments respectively, with median skill score values of 0.16 and 0.2 while scores deteriorated for 28% and 52% of the catchments, with median values -0.09 and -0.16, respectively. The effect of reservoirs on extreme high flows was substantial and widespread in the global domain, while the effect of lakes was spatially limited to a few catchments. As indicated by global sensitivity analysis, parameter uncertainty substantially affected uncertainty of model performance. Reservoir parameters often contributed to this uncertainty, although the effect varied widely among catchments. The effect of reservoir parameters on model performance diminished with distance downstream of reservoirs in favor of other parameters, notably groundwater-related parameters and channel Manning's roughness coefficient. This study underscores the importance of accounting for lakes and, especially, reservoirs and using appropriate parameterization in large-scale hydrological simulations.

Impacts of Recent Climatic Wetting on Distributed Snow and Streamflow Responses in a Terminal Lake Basin.

NASA Astrophysics Data System (ADS)

Van Hoy, D.; Mahmood, T. H.; Jeannotte, T.; Todhunter, P. E.

2017-12-01

The recent shift in hydroclimatic conditions in the Northern Great Plains (NGP) has led to an increase in precipitation, rainfall rate, and wetland connectivity over the last few decades. These changes yield an integrated response resulting in high mean annual streamflow and subsequent flooding in many NGP basins such as the terminal Devils Lake Basin (DLB). In this study, we investigate the impacts of recent climatic wetting on distributed hydrologic responses such as snow processes and streamflow using a field-tested and physically-based cold region hydrologic model (CRHM). CHRM is designed for cold prairie regions and has modules to simulate major processes such as blowing snow transport, sublimation, interception, frozen soil infiltration, snowmelt and subsequent streamflow generation. Our modeling focuses on a tributary basin of the DLB known as the Mauvais Coulee Basin (MCB). Since there were no snow observations in the MCB, we conducted a detailed snow survey at distributed locations estimating snow depth, density, and snow water equivalent (SWE) using a prairie snow tube four times during winter of 2016-17. The MCB model was evaluated against distributed snow observations and streamflow measured at the basin outlet (USGS) for the year 2016-2017. Preliminary results indicate that the simulated SWEs at distributed locations and streamflow (NSE ≈ 0.70) are in good agreement with observations. The simulated SWE maps exhibit large spatiotemporal variation during 2016-17 winter due to spatial variability in precipitation, snow redistribution from stubble field to wooded areas, and snow accumulations in small depressions across the subbasins. The main source of snow appears to be the hills and ridges of the eastern and western edges of the basin, while the main sink is the large flat central valleys. The model will be used to examine the effect of recent changes to precipitation and temperature on snow processes and subsequent streamflow for 2004-2017 season. We will also investigate the hydrologic sensitivity to precipitation and temperature changes by altering input temperature and precipitation. Finally, our findings will point toward future process-based studies and simulated hydrologic responses that can be used to prepare flood hazard maps for cities around Devils Lake.

Distributed HUC-based modeling with SUMMA for ensemble streamflow forecasting over large regional domains.

NASA Astrophysics Data System (ADS)

Saharia, M.; Wood, A.; Clark, M. P.; Bennett, A.; Nijssen, B.; Clark, E.; Newman, A. J.

2017-12-01

Most operational streamflow forecasting systems rely on a forecaster-in-the-loop approach in which some parts of the forecast workflow require an experienced human forecaster. But this approach faces challenges surrounding process reproducibility, hindcasting capability, and extension to large domains. The operational hydrologic community is increasingly moving towards `over-the-loop' (completely automated) large-domain simulations yet recent developments indicate a widespread lack of community knowledge about the strengths and weaknesses of such systems for forecasting. A realistic representation of land surface hydrologic processes is a critical element for improving forecasts, but often comes at the substantial cost of forecast system agility and efficiency. While popular grid-based models support the distributed representation of land surface processes, intermediate-scale Hydrologic Unit Code (HUC)-based modeling could provide a more efficient and process-aligned spatial discretization, reducing the need for tradeoffs between model complexity and critical forecasting requirements such as ensemble methods and comprehensive model calibration. The National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the USACE to implement, assess, and demonstrate real-time, over-the-loop distributed streamflow forecasting for several large western US river basins and regions. In this presentation, we present early results from short to medium range hydrologic and streamflow forecasts for the Pacific Northwest (PNW). We employ a real-time 1/16th degree daily ensemble model forcings as well as downscaled Global Ensemble Forecasting System (GEFS) meteorological forecasts. These datasets drive an intermediate-scale configuration of the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model, which represents the PNW using over 11,700 HUCs. The system produces not only streamflow forecasts (using the MizuRoute channel routing tool) but also distributed model states such as soil moisture and snow water equivalent. We also describe challenges in distributed model-based forecasting, including the application and early results of real-time hydrologic data assimilation.

Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system

NASA Astrophysics Data System (ADS)

Sharma, Sanjib; Siddique, Ridwan; Reed, Seann; Ahnert, Peter; Mendoza, Pablo; Mejia, Alfonso

2018-03-01

The relative roles of statistical weather preprocessing and streamflow postprocessing in hydrological ensemble forecasting at short- to medium-range forecast lead times (day 1-7) are investigated. For this purpose, a regional hydrologic ensemble prediction system (RHEPS) is developed and implemented. The RHEPS is comprised of the following components: (i) hydrometeorological observations (multisensor precipitation estimates, gridded surface temperature, and gauged streamflow); (ii) weather ensemble forecasts (precipitation and near-surface temperature) from the National Centers for Environmental Prediction 11-member Global Ensemble Forecast System Reforecast version 2 (GEFSRv2); (iii) NOAA's Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM); (iv) heteroscedastic censored logistic regression (HCLR) as the statistical preprocessor; (v) two statistical postprocessors, an autoregressive model with a single exogenous variable (ARX(1,1)) and quantile regression (QR); and (vi) a comprehensive verification strategy. To implement the RHEPS, 1 to 7 days weather forecasts from the GEFSRv2 are used to force HL-RDHM and generate raw ensemble streamflow forecasts. Forecasting experiments are conducted in four nested basins in the US Middle Atlantic region, ranging in size from 381 to 12 362 km2. Results show that the HCLR preprocessed ensemble precipitation forecasts have greater skill than the raw forecasts. These improvements are more noticeable in the warm season at the longer lead times (> 3 days). Both postprocessors, ARX(1,1) and QR, show gains in skill relative to the raw ensemble streamflow forecasts, particularly in the cool season, but QR outperforms ARX(1,1). The scenarios that implement preprocessing and postprocessing separately tend to perform similarly, although the postprocessing-alone scenario is often more effective. The scenario involving both preprocessing and postprocessing consistently outperforms the other scenarios. In some cases, however, the differences between this scenario and the scenario with postprocessing alone are not as significant. We conclude that implementing both preprocessing and postprocessing ensures the most skill improvements, but postprocessing alone can often be a competitive alternative.

Comparison of Peak-Flow Estimation Methods for Small Drainage Basins in Maine

USGS Publications Warehouse

Hodgkins, Glenn A.; Hebson, Charles; Lombard, Pamela J.; Mann, Alexander

2007-01-01

Understanding the accuracy of commonly used methods for estimating peak streamflows is important because the designs of bridges, culverts, and other river structures are based on these flows. Different methods for estimating peak streamflows were analyzed for small drainage basins in Maine. For the smallest basins, with drainage areas of 0.2 to 1.0 square mile, nine peak streamflows from actual rainfall events at four crest-stage gaging stations were modeled by the Rational Method and the Natural Resource Conservation Service TR-20 method and compared to observed peak flows. The Rational Method had a root mean square error (RMSE) of -69.7 to 230 percent (which means that approximately two thirds of the modeled flows were within -69.7 to 230 percent of the observed flows). The TR-20 method had an RMSE of -98.0 to 5,010 percent. Both the Rational Method and TR-20 underestimated the observed flows in most cases. For small basins, with drainage areas of 1.0 to 10 square miles, modeled peak flows were compared to observed statistical peak flows with return periods of 2, 50, and 100 years for 17 streams in Maine and adjoining parts of New Hampshire. Peak flows were modeled by the Rational Method, the Natural Resources Conservation Service TR-20 method, U.S. Geological Survey regression equations, and the Probabilistic Rational Method. The regression equations were the most accurate method of computing peak flows in Maine for streams with drainage areas of 1.0 to 10 square miles with an RMSE of -34.3 to 52.2 percent for 50-year peak flows. The Probabilistic Rational Method was the next most accurate method (-38.5 to 62.6 percent). The Rational Method (-56.1 to 128 percent) and particularly the TR-20 method (-76.4 to 323 percent) had much larger errors. Both the TR-20 and regression methods had similar numbers of underpredictions and overpredictions. The Rational Method overpredicted most peak flows and the Probabilistic Rational Method tended to overpredict peak flows from the smaller (less than 5 square miles) drainage basins and underpredict peak flows from larger drainage basins. The results of this study are consistent with the most comprehensive analysis of observed and modeled peak streamflows in the United States, which analyzed statistical peak flows from 70 drainage basins in the Midwest and the Northwest.

WRF-Hydro Simulated Spatiotemporal Characteristics of Streamflow Extremes over the CONUS during 1993-2016 and Possible Connections with Climate Variability

NASA Astrophysics Data System (ADS)

Dugger, A. L.; Zhang, Y.; Gochis, D.; Yu, W.; McCreight, J. L.; Karsten, L.; Rafieeinasab, A.; Sampson, K. M.; Salas, F.; Read, L.; Pan, L.; Yates, D. N.; Cosgrove, B.; Clark, E. P.

2017-12-01

Streamflow extremes (lows and peaks) tend to have disproportionately higher impacts on the human and natural systems compared to mean streamflow. Examining and understanding the spatiotemporal distributions of streamflow extremes is of significant interests to both the research community and the water resources management. In this work, the output from the 24-year (1993 through 2016) retrospective runs of the National Water Model (NWM) version of WRF-Hydro will be analyzed for streamflow extremes over the CONUS domain. The CONUS domain was configured at 1-km resolution for land surface grid and 250-m resolution for terrain routing. The WRF-Hydro runs were forced by the regridded and downscaled NLDAS2 data. The analyses focus on daily mean streamflow values over the full water year and within the summer and winter seasons. Connections between NWM streamflow and other hydrologic variables (e.g. snowpack, soil moisture/saturation and ET) with variations in large-scale climate phenomena, e.g., El Niño - Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and North American monsoon are examined. The CONUS domain has a diverse environment and is characterized by complex terrain, heterogeneous land surfaces and ecosystems, and numerous hydrological basins. The potential dependence of streamflow extremes on regional terrain character, climatic conditions, and ecologic zones will also be investigated.

Simulated effects of proposed reservoir-development alternatives on streamflow quantity in the White River, Colorado and Utah

USGS Publications Warehouse

Kuhn, Gerhard; Ellis, S.R.

1984-01-01

Numerous reservoirs have been proposed for the White River basin in Colorado and Utah, primarily to provide water for oil-shale development. A multireservoir-flow model was used to simulate the effects of streamflow withdrawal at four of the proposed reservoirs using historical streamflow data from the 1932-81 water years. The proposed reservoirs considered in the study were Avery, Powell Park, Taylor Draw, and White River Reservoirs; construction of Taylor Draw Dam was completed during the study. Annual streamflow depletions from the White River ranging from about 93,000 to 226,000 acre-feet were simulated for the 50 year period. Simulated streamflow throughout the year generally became smaller and more constant as streamflow throughout the year generally became smaller and more constant as streamflow depletion increased. Minimum streamflow requirements would not have been met for a maximum of 13 years and water-use requirements associated with the proposed reservoirs would not have been met for a maximum of 3 years. The current water-use pattern, which depletes about 40,000 acre-feet per year and is dominated by irrigation of hay meadows and pastureland, was maintained in the simulation. Relations between reservoir active capacity and yield applicable to the White River also were developed. These relations show that reservoir storage of about 400,000 acre-feet is the maximum practicable for the White River. (USGS)

How far downstream do dams impact streamflow?

NASA Astrophysics Data System (ADS)

Troy, T.

2017-12-01

Water infrastructure can be a double-edged sword. For example, dams can provide significant flood protection and stable water supplies, but they negatively impact river ecosystems. As the United States enters an era of dam decommissioning instead of dam building, it raises the question of how far downstream dams provide protection against flood peaks and sustaining environmental flows. This study uses USGS streamflow observations, the National Inventory of Dams, and VIC-modeled streamflow as a proxy for naturalized streamflow to evaluate the scale at which dams impact a variety of hydrologic signatures such as flood return period flows, streamflow variability, and low flows. Results over the Delaware River show that the impact of dams quickly dissipates as one moves downstream, but this is due to the basin's characteristics. This analysis is performed over the contiguous United States, quantifying the length scale of impact as a function of dam capacity, position on the river network, and the hydroclimatology.

The contribution of glacier melt to streamflow

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schaner, Neil; Voisin, Nathalie; Nijssen, Bart

2012-09-13

Ongoing and projected future changes in glacier extent and water storage globally have lead to concerns about the implications for water supplies. However, the current magnitude of glacier contributions to river runoff is not well known, nor is the population at risk to future glacier changes. We estimate an upper bound on glacier melt contribution to seasonal streamflow by computing the energy balance of glaciers globally. Melt water quantities are computed as a fraction of total streamflow simulated using a hydrology model and the melt fraction is tracked down the stream network. In general, our estimates of the glacier meltmore » contribution to streamflow are lower than previously published values. Nonetheless, we find that globally an estimated 225 (36) million people live in river basins where maximum seasonal glacier melt contributes at least 10% (25%) of streamflow, mostly in the High Asia region.« less

Streamflow response to increasing precipitation extremes altered by forest management

NASA Astrophysics Data System (ADS)

Kelly, Charlene N.; McGuire, Kevin J.; Miniat, Chelcy Ford; Vose, James M.

2016-04-01

Increases in extreme precipitation events of floods and droughts are expected to occur worldwide. The increase in extreme events will result in changes in streamflow that are expected to affect water availability for human consumption and aquatic ecosystem function. We present an analysis that may greatly improve current streamflow models by quantifying the impact of the interaction between forest management and precipitation. We use daily long-term data from paired watersheds that have undergone forest harvest or species conversion. We find that interactive effects of climate change, represented by changes in observed precipitation trends, and forest management regime, significantly alter expected streamflow most often during extreme events, ranging from a decrease of 59% to an increase of 40% in streamflow, depending upon management. Our results suggest that vegetation might be managed to compensate for hydrologic responses due to climate change to help mitigate effects of extreme changes in precipitation.

An integrated uncertainty analysis and data assimilation approach for improved streamflow predictions

NASA Astrophysics Data System (ADS)

Hogue, T. S.; He, M.; Franz, K. J.; Margulis, S. A.; Vrugt, J. A.

2010-12-01

The current study presents an integrated uncertainty analysis and data assimilation approach to improve streamflow predictions while simultaneously providing meaningful estimates of the associated uncertainty. Study models include the National Weather Service (NWS) operational snow model (SNOW17) and rainfall-runoff model (SAC-SMA). The proposed approach uses the recently developed DiffeRential Evolution Adaptive Metropolis (DREAM) to simultaneously estimate uncertainties in model parameters, forcing, and observations. An ensemble Kalman filter (EnKF) is configured with the DREAM-identified uncertainty structure and applied to assimilating snow water equivalent data into the SNOW17 model for improved snowmelt simulations. Snowmelt estimates then serves as an input to the SAC-SMA model to provide streamflow predictions at the basin outlet. The robustness and usefulness of the approach is evaluated for a snow-dominated watershed in the northern Sierra Mountains. This presentation describes the implementation of DREAM and EnKF into the coupled SNOW17 and SAC-SMA models and summarizes study results and findings.

Simulated CONUS Flash Flood Climatologies from Distributed Hydrologic Models

NASA Astrophysics Data System (ADS)

Flamig, Z.; Gourley, J. J.; Vergara, H. J.; Kirstetter, P. E.; Hong, Y.

2016-12-01

This study will describe a CONUS flash flood climatology created over the period from 2002 through 2011. The MRMS reanalysis precipitation dataset was used as forcing into the Ensemble Framework For Flash Flood Forecasting (EF5). This high resolution 1-sq km 5-minute dataset is ideal for simulating flash floods with a distributed hydrologic model. EF5 features multiple water balance components including SAC-SMA, CREST, and a hydrophobic model all coupled with kinematic wave routing. The EF5/SAC-SMA and EF5/CREST water balance schemes were used for the creation of dual flash flood climatologies based on the differing water balance principles. For the period from 2002 through 2011 the daily maximum streamflow, unit streamflow, and time of peak streamflow was stored along with the minimum soil moisture. These variables are used to describe the states of the soils right before a flash flood event and the peak streamflow that was simulated during the flash flood event. The results will be shown, compared and contrasted. The resulting model simulations will be verified on basins less than 1,000-sq km with USGS gauges to ensure the distributed hydrologic models are reliable. The results will also be compared spatially to Storm Data flash flood event observations to judge the degree of agreement between the simulated climatologies and observations.

Use of hydrologic landscape classification to diagnose streamflow predictability in Oregon

EPA Science Inventory

We implement a spatially lumped rainfall-runoff model to predict daily streamflow at 88 catchments within Oregon, USA and analyze its performance within the context of Oregon Hydrologic Landscapes (OHL) classification. OHL classification is used to characterize the physio-climat...

The Massachusetts Sustainable-Yield Estimator: A decision-support tool to assess water availability at ungaged stream locations in Massachusetts

USGS Publications Warehouse

Archfield, Stacey A.; Vogel, Richard M.; Steeves, Peter A.; Brandt, Sara L.; Weiskel, Peter K.; Garabedian, Stephen P.

2010-01-01

Federal, State and local water-resource managers require a variety of data and modeling tools to better understand water resources. The U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection, has developed a statewide, interactive decision-support tool to meet this need. The decision-support tool, referred to as the Massachusetts Sustainable-Yield Estimator (MA SYE) provides screening-level estimates of the sustainable yield of a basin, defined as the difference between the unregulated streamflow and some user-specified quantity of water that must remain in the stream to support such functions as recreational activities or aquatic habitat. The MA SYE tool was designed, in part, because the quantity of surface water available in a basin is a time-varying quantity subject to competing demands for water. To compute sustainable yield, the MA SYE tool estimates a daily time series of unregulated, daily mean streamflow for a 44-year period of record spanning October 1, 1960, through September 30, 2004. Selected streamflow quantiles from an unregulated, daily flow-duration curve are estimated by solving six regression equations that are a function of physical and climate basin characteristics at an ungaged site on a stream of interest. Streamflow is then interpolated between the estimated quantiles to obtain a continuous daily flow-duration curve. A time series of unregulated daily streamflow subsequently is created by transferring the timing of the daily streamflow at a reference streamgage to the ungaged site by equating exceedence probabilities of contemporaneous flow at the two locations. One of 66 reference streamgages is selected by kriging, a geostatistical method, which is used to map the spatial relation among correlations between the time series of the logarithm of daily streamflows at each reference streamgage and the ungaged site. Estimated unregulated, daily mean streamflows show good agreement with observed unregulated, daily mean streamflow at 18 streamgages located across southern New England. Nash-Sutcliffe efficiency goodness-of-fit values are between 0.69 and 0.98, and percent root-mean-square-error values are between 19 and 283 percent. The MA SYE tool provides an estimate of streamflow adjusted for current (2000-04) water withdrawals and discharges using a spatially referenced database of permitted groundwater and surface-water withdrawal and discharge volumes. For a user-selected basin, the database is queried to obtain the locations of water withdrawal or discharge volumes within the basin. Groundwater and surface-water withdrawals and discharges are subtracted and added, respectively, from the unregulated, daily streamflow at an ungaged site to obtain a streamflow time series that includes the effects of these withdrawals and discharges. Users also have the option of applying an analytical solution to the time-varying, groundwater withdrawal and discharge volumes that take into account the effects of the aquifer properties on the timing and magnitude of streamflow alteration. For the MA SYE tool, it is assumed that groundwater and surface-water divides are coincident. For areas of southeastern Massachusetts and Cape Cod where this assumption is known to be violated, groundwater-flow models are used to estimate average monthly streamflows at fixed locations. There are several limitations to the quality and quantity of the spatially referenced database of groundwater and surface-water withdrawals and discharges. The adjusted streamflow values do not account for the effects on streamflow of climate change, septic-system discharge, impervious area, non-public water-supply withdrawals less than 100,000 gallons per day, and impounded surface-water bodies.

Evaluation on uncertainty sources in projecting hydrological changes over the Xijiang River basin in South China

NASA Astrophysics Data System (ADS)

Yuan, Fei; Zhao, Chongxu; Jiang, Yong; Ren, Liliang; Shan, Hongcui; Zhang, Limin; Zhu, Yonghua; Chen, Tao; Jiang, Shanhu; Yang, Xiaoli; Shen, Hongren

2017-11-01

Projections of hydrological changes are associated with large uncertainties from different sources, which should be quantified for an effective implementation of water management policies adaptive to future climate change. In this study, a modeling chain framework to project future hydrological changes and the associated uncertainties in the Xijiang River basin, South China, was established. The framework consists of three emission scenarios (ESs), four climate models (CMs), four statistical downscaling (SD) methods, four hydrological modeling (HM) schemes, and four probability distributions (PDs) for extreme flow frequency analyses. Direct variance method was adopted to analyze the manner by which uncertainty sources such as ES, CM, SD, and HM affect the estimates of future evapotranspiration (ET) and streamflow, and to quantify the uncertainties of PDs in future flood and drought risk assessment. Results show that ES is one of the least important uncertainty sources in most situations. CM, in general, is the dominant uncertainty source for the projections of monthly ET and monthly streamflow during most of the annual cycle, daily streamflow below the 99.6% quantile level, and extreme low flow. SD is the most predominant uncertainty source in the projections of extreme high flow, and has a considerable percentage of uncertainty contribution in monthly streamflow projections in July-September. The effects of SD in other cases are negligible. HM is a non-ignorable uncertainty source that has the potential to produce much larger uncertainties for the projections of low flow and ET in warm and wet seasons than for the projections of high flow. PD contributes a larger percentage of uncertainty in extreme flood projections than it does in extreme low flow estimates. Despite the large uncertainties in hydrological projections, this work found that future extreme low flow would undergo a considerable reduction, and a noticeable increase in drought risk in the Xijiang River basin would be expected. Thus, the necessity of employing effective water-saving techniques and adaptive water resources management strategies for drought disaster mitigation should be addressed.

Streamflow distribution maps for the Cannon River drainage basin, southeast Minnesota, and the St. Louis River drainage basin, northeast Minnesota

USGS Publications Warehouse

Smith, Erik A.; Sanocki, Chris A.; Lorenz, David L.; Jacobsen, Katrin E.

2017-12-27

Streamflow distribution maps for the Cannon River and St. Louis River drainage basins were developed by the U.S. Geological Survey, in cooperation with the Legislative-Citizen Commission on Minnesota Resources, to illustrate relative and cumulative streamflow distributions. The Cannon River was selected to provide baseline data to assess the effects of potential surficial sand mining, and the St. Louis River was selected to determine the effects of ongoing Mesabi Iron Range mining. Each drainage basin (Cannon, St. Louis) was subdivided into nested drainage basins: the Cannon River was subdivided into 152 nested drainage basins, and the St. Louis River was subdivided into 353 nested drainage basins. For each smaller drainage basin, the estimated volumes of groundwater discharge (as base flow) and surface runoff flowing into all surface-water features were displayed under the following conditions: (1) extreme low-flow conditions, comparable to an exceedance-probability quantile of 0.95; (2) low-flow conditions, comparable to an exceedance-probability quantile of 0.90; (3) a median condition, comparable to an exceedance-probability quantile of 0.50; and (4) a high-flow condition, comparable to an exceedance-probability quantile of 0.02.Streamflow distribution maps were developed using flow-duration curve exceedance-probability quantiles in conjunction with Soil-Water-Balance model outputs; both the flow-duration curve and Soil-Water-Balance models were built upon previously published U.S. Geological Survey reports. The selected streamflow distribution maps provide a proactive water management tool for State cooperators by illustrating flow rates during a range of hydraulic conditions. Furthermore, after the nested drainage basins are highlighted in terms of surface-water flows, the streamflows can be evaluated in the context of meeting specific ecological flows under different flow regimes and potentially assist with decisions regarding groundwater and surface-water appropriations. Presented streamflow distribution maps are foundational work intended to support the development of additional streamflow distribution maps that include statistical constraints on the selected flow conditions.

A comparison of methods to predict historical daily streamflow time series in the southeastern United States

USGS Publications Warehouse

Farmer, William H.; Archfield, Stacey A.; Over, Thomas M.; Hay, Lauren E.; LaFontaine, Jacob H.; Kiang, Julie E.

2015-01-01

Effective and responsible management of water resources relies on a thorough understanding of the quantity and quality of available water. Streamgages cannot be installed at every location where streamflow information is needed. As part of its National Water Census, the U.S. Geological Survey is planning to provide streamflow predictions for ungaged locations. In order to predict streamflow at a useful spatial and temporal resolution throughout the Nation, efficient methods need to be selected. This report examines several methods used for streamflow prediction in ungaged basins to determine the best methods for regional and national implementation. A pilot area in the southeastern United States was selected to apply 19 different streamflow prediction methods and evaluate each method by a wide set of performance metrics. Through these comparisons, two methods emerged as the most generally accurate streamflow prediction methods: the nearest-neighbor implementations of nonlinear spatial interpolation using flow duration curves (NN-QPPQ) and standardizing logarithms of streamflow by monthly means and standard deviations (NN-SMS12L). It was nearly impossible to distinguish between these two methods in terms of performance. Furthermore, neither of these methods requires significantly more parameterization in order to be applied: NN-SMS12L requires 24 regional regressions—12 for monthly means and 12 for monthly standard deviations. NN-QPPQ, in the application described in this study, required 27 regressions of particular quantiles along the flow duration curve. Despite this finding, the results suggest that an optimal streamflow prediction method depends on the intended application. Some methods are stronger overall, while some methods may be better at predicting particular statistics. The methods of analysis presented here reflect a possible framework for continued analysis and comprehensive multiple comparisons of methods of prediction in ungaged basins (PUB). Additional metrics of comparison can easily be incorporated into this type of analysis. By considering such a multifaceted approach, the top-performing models can easily be identified and considered for further research. The top-performing models can then provide a basis for future applications and explorations by scientists, engineers, managers, and practitioners to suit their own needs.

A Flexible Framework Hydrological Informatic Modeling System - HIMS

NASA Astrophysics Data System (ADS)

WANG, L.; Wang, Z.; Changming, L.; Li, J.; Bai, P.

2017-12-01

Simulating water cycling process temporally and spatially fitting for the characteristics of the study area was important for floods prediction and streamflow simulation with high accuracy, as soil properties, land scape, climate, and land managements were the critical factors influencing the non-linear relationship of rainfall-runoff at watershed scales. Most existing hydrological models cannot simulate water cycle process at different places with customized mechanisms with fixed single structure and mode. This study develops Hydro-Informatic Modeling System (HIMS) model with modular of each critical hydrological process with multiple choices for various scenarios to solve this problem. HIMS has the structure accounting for two runoff generation mechanisms of infiltration excess and saturation excess and estimated runoff with different methods including Time Variance Gain Model (TVGM), LCM which has good performance at ungauged areas, besides the widely used Soil Conservation Service-Curve Number (SCS-CN) method. Channel routing model contains the most widely used Muskingum, and kinematic wave equation with new solving method. HIMS model performance with its symbolic runoff generation model LCM was evaluated through comparison with the observed streamflow datasets of Lasha river watershed at hourly, daily, and monthly time steps. Comparisons between simulational and obervational streamflows were found with NSE higher than 0.87 and WE within ±20%. Water balance analysis about precipitation, streamflow, actual evapotranspiration (ET), and soil moisture change was conducted temporally at annual time step and it has been proved that HIMS model performance was reliable through comparison with literature results at the Lhasa River watershed.

On the performance of satellite precipitation products in riverine flood modeling: A review

NASA Astrophysics Data System (ADS)

Maggioni, Viviana; Massari, Christian

2018-03-01

This work is meant to summarize lessons learned on using satellite precipitation products for riverine flood modeling and to propose future directions in this field of research. Firstly, the most common satellite precipitation products (SPPs) during the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM) eras are reviewed. Secondly, we discuss the main errors and uncertainty sources in these datasets that have the potential to affect streamflow and runoff model simulations. Thirdly, past studies that focused on using SPPs for predicting streamflow and runoff are analyzed. As the impact of floods depends not only on the characteristics of the flood itself, but also on the characteristics of the region (population density, land use, geophysical and climatic factors), a regional analysis is required to assess the performance of hydrologic models in monitoring and predicting floods. The performance of SPP-forced hydrological models was shown to largely depend on several factors, including precipitation type, seasonality, hydrological model formulation, topography. Across several basins around the world, the bias in SPPs was recognized as a major issue and bias correction methods of different complexity were shown to significantly reduce streamflow errors. Model re-calibration was also raised as a viable option to improve SPP-forced streamflow simulations, but caution is necessary when recalibrating models with SPP, which may result in unrealistic parameter values. From a general standpoint, there is significant potential for using satellite observations in flood forecasting, but the performance of SPP in hydrological modeling is still inadequate for operational purposes.

Watershed Modeling to Assess the Sensitivity of Streamflow, Nutrient, and Sediment Loads to Potential Climate Change and Urban Development in 20 U.S. Watersheds (External Review Draft)

EPA Science Inventory

EPA has released for independent external peer review and public comment a draft report titled, Watershed Modeling to Assess the Sensitivity of Streamflow, Nutrient, and Sediment Loads to Potential Climate Change and Urban Development in 20 U.S. Watersheds. This is a draft...

Effects of sediment transport on survival of salmonid embryos in a natural stream: A simulation approach

Treesearch

Thomas E. Lisle; Jack Lewis

1992-01-01

A model is presented that simulates the effects of streamflow and sediment transport on survival of salmonid embryos incubating in spawning gravels in a natural channel. Components of the model include a 6-yr streamflow record, an empirical bed load-transport function, a relation between transport and infiltration of sandy bedload into a gravel bed, effects of fine-...

An analysis of alternative conceptual models relating hyporheic exchange flow to diel fluctuations in discharge during baseflow recession

Treesearch

Steven M. Wondzell; Michael N. Gooseff; Brian L. McGlynn

2009-01-01

Diel fluctuations in streamflow during base flow have been observed in many streams and are typically attributed to water losses from evapotranspiration (ET). However, there is no widely transferable conceptual model that explains how ET results in diel fluctuations in streamflow at the watershed outlet. For fluctuations to occur, two factors must be present: (1) some...

SWAT-based streamflow and embayment modeling of Karst-affected Chapel branch watershed, South Carolina

Treesearch

Devendra Amatya; M. Jha; A.E. Edwards; T.M. Williams; D.R. Hitchcock

2011-01-01

SWAT is a GIS-based basin-scale model widely used for the characterization of hydrology and water quality of large, complex watersheds; however, SWAT has not been fully tested in watersheds with karst geomorphology and downstream reservoir-like embayment. In this study, SWAT was applied to test its ability to predict monthly streamflow dynamics for a 1,555 ha karst...

Sensitivity of stream flow and water table depth to potential climatic variability in a coastal forested watershed

Treesearch

Zhaohua Dai; Carl Trettin; Changsheng Li; Devendra M. Amatya; Ge Sun; Harbin Li

2010-01-01

A physically based distributed hydrological model, MIKE SHE, was used to evaluate the effects of altered temperature and precipitation regimes on the streamflow and water table in a forested watershed on the southeastern Atlantic coastal plain. The model calibration and validation against both streamflow and water table depth showed that the MIKE SHE was applicable for...

Long-Term Historical Rainfall-Runoff Modeling Using High-Resolution Satellite-based Precipitation Products

NASA Astrophysics Data System (ADS)

Ashouri, H.; Nguyen, P.; Thorstensen, A. R.; Hsu, K. L.; Sorooshian, S.

2014-12-01

This study evaluates the performance of a newly developed long-term high-resolution satellite-based precipitation products, named Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network - Climate Data Record (PERSIANN-CDR), in hydrological modeling. PERSIANN-CDR estimations are biased corrected using GPCP monthly climatology data. PERSIANN-CDR provides daily rainfall estimates at 0.25° x 0.25° grid boxes for 1983-2014 (delayed present). This newly released product makes it feasible to model the streamflow over the past 30 years. Three test basins from the Distributed Hydrologic Model Intercomparison Project - Phase 2 (DMIP 2) are chosen. Comparing with other satellite products, the Version 7 TRMM Multi-satellite Precipitation Analysis (TMPA) product is used. Stage IV radar data is used as a reference data for evaluating the PERSIANN-CDR and TMPA precipitation data. All products are scaled to 0.25° and daily spatiotemporal resolution. The study is performed in two phases. In the first phase, the 2003-2011 period where all the products are available is chosen. Precipitation evaluation results, presented on Taylor Diagrams, show that TMPA and PERSIANN-CDR have close performances. The National Weather Service (NWS) Office of Hydrologic Development (OHD) Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) is then forced with the PERSIANN-CDR and the TMPA precipitation products, as well as the stage IV radar data. USGS Streamflow observations at the outlet of the basins are used as the reference streamflow data. The results show that in general, in all the three DMIP 2 basins the simulated hydrographs forced with PERSIANN-CDR and TMPA show good agreement, as the statistical measures such as root mean square error, bias, and correlation coefficient are close. In addition, with respect to the streamflow peaks, PERSIANN-CDR shows better performance than Stage IV radar data in capturing the extreme streamflow magnitudes. Based on the results from the first phase of the study and given the fact that PERSIANN-CDR covers the 1983-2014, in the second phase of the study we model the streamflow for the period of 1983-2014. The results will be presented in the meeting.

Analysis of trends in climate, streamflow, and stream temperature in north coastal California

USGS Publications Warehouse

Madej, Mary Ann; Medley, C. Nicholas; Patterson, Glenn; Parker, Melanie J.

2011-01-01

As part of a broader project analyzing trends in climate, streamflow, vegetation, salmon, and ocean conditions in northern California national park units, we compiled average monthly air temperature and precipitation data from 73 climate stations, streamflow data from 21 river gaging stations, and limited stream temperature data from salmon-bearing rivers in north coastal California. Many climate stations show a statistically significant increase in both average maximum and average minimum air temperature in early fall and midwinter during the last century. Concurrently, average September precipitation has decreased. In many coastal rivers, summer low flow has decreased and summer stream temperatures have increased, which affects summer rearing habitat for salmonids. Nevertheless, because vegetative cover has also changed during this time period, we cannot ascribe streamflow changes to climate change without first assessing water budgets. Although shifts in the timing of the centroid of runoff have been documented in snowmelt-dominated watersheds in the western United States, this was not the case in lower elevation coastal rivers analyzed in this study.

Identifying Hydrologic Processes in Agricultural Watersheds Using Precipitation-Runoff Models

USGS Publications Warehouse

Linard, Joshua I.; Wolock, David M.; Webb, Richard M.T.; Wieczorek, Michael

2009-01-01

Understanding the fate and transport of agricultural chemicals applied to agricultural fields will assist in designing the most effective strategies to prevent water-quality impairments. At a watershed scale, the processes controlling the fate and transport of agricultural chemicals are generally understood only conceptually. To examine the applicability of conceptual models to the processes actually occurring, two precipitation-runoff models - the Soil and Water Assessment Tool (SWAT) and the Water, Energy, and Biogeochemical Model (WEBMOD) - were applied in different agricultural settings of the contiguous United States. Each model, through different physical processes, simulated the transport of water to a stream from the surface, the unsaturated zone, and the saturated zone. Models were calibrated for watersheds in Maryland, Indiana, and Nebraska. The calibrated sets of input parameters for each model at each watershed are discussed, and the criteria used to validate the models are explained. The SWAT and WEBMOD model results at each watershed conformed to each other and to the processes identified in each watershed's conceptual hydrology. In Maryland the conceptual understanding of the hydrology indicated groundwater flow was the largest annual source of streamflow; the simulation results for the validation period confirm this. The dominant source of water to the Indiana watershed was thought to be tile drains. Although tile drains were not explicitly simulated in the SWAT model, a large component of streamflow was received from lateral flow, which could be attributed to tile drains. Being able to explicitly account for tile drains, WEBMOD indicated water from tile drains constituted most of the annual streamflow in the Indiana watershed. The Nebraska models indicated annual streamflow was composed primarily of perennial groundwater flow and infiltration-excess runoff, which conformed to the conceptual hydrology developed for that watershed. The hydrologic processes represented in the parameter sets resulting from each model were comparable at individual watersheds, but varied between watersheds. The models were unable to show, however, whether hydrologic processes other than those included in the original conceptual models were major contributors to streamflow. Supplemental simulations of agricultural chemical transport could improve the ability to assess conceptual models.

Prediction of Hydrological Drought: What Can We Learn From Continental-Scale Offline Simulations?

NASA Technical Reports Server (NTRS)

Koster, Randal; Mahanama, Sarith; Livneh, Ben; Lettenmaier, Dennis; Reichle, Rolf

2011-01-01

Land surface model experiments are used to quantify, across the coterminous United States, the contributions (isolated and combined) of soil moisture and snowpack initialization to the skill of seasonal streamflow forecasts at multiple leads and for different start dates. Forecasted streamflows are compared to naturalized streamflow observations where available and to synthetic (model-generated) streamflow data elsewhere. We find that snow initialization has a major impact on skill in the mountainous western U.S. and in a portion of the northern Great Plains; a mid-winter (January 1) initialization of snow in these areas leads to significant skill in the spring melting season. Soil moisture initialization also contributes to skill, and although the maximum contributions are not as large as those seen for snow initialization, the soil moisture contributions extend across a much broader geographical area. Soil moisture initialization can contribute to skill at long leads (up to 5 or 6 months), particularly for forecasts issued during winter.

Comparison of TOPMODEL streamflow simulations using NEXRAD-based and measured rainfall data, McTier Creek watershed, South Carolina

USGS Publications Warehouse

Feaster, Toby D.; Westcott, Nancy E.; Hudson, Robert J.M.; Conrads, Paul; Bradley, Paul M.

2012-01-01

Rainfall is an important forcing function in most watershed models. As part of a previous investigation to assess interactions among hydrologic, geochemical, and ecological processes that affect fish-tissue mercury concentrations in the Edisto River Basin, the topography-based hydrological model (TOPMODEL) was applied in the McTier Creek watershed in Aiken County, South Carolina. Measured rainfall data from six National Weather Service (NWS) Cooperative (COOP) stations surrounding the McTier Creek watershed were used to calibrate the McTier Creek TOPMODEL. Since the 1990s, the next generation weather radar (NEXRAD) has provided rainfall estimates at a finer spatial and temporal resolution than the NWS COOP network. For this investigation, NEXRAD-based rainfall data were generated at the NWS COOP stations and compared with measured rainfall data for the period June 13, 2007, to September 30, 2009. Likewise, these NEXRAD-based rainfall data were used with TOPMODEL to simulate streamflow in the McTier Creek watershed and then compared with the simulations made using measured rainfall data. NEXRAD-based rainfall data for non-zero rainfall days were lower than measured rainfall data at all six NWS COOP locations. The total number of concurrent days for which both measured and NEXRAD-based data were available at the COOP stations ranged from 501 to 833, the number of non-zero days ranged from 139 to 209, and the total difference in rainfall ranged from -1.3 to -21.6 inches. With the calibrated TOPMODEL, simulations using NEXRAD-based rainfall data and those using measured rainfall data produce similar results with respect to matching the timing and shape of the hydrographs. Comparison of the bias, which is the mean of the residuals between observed and simulated streamflow, however, reveals that simulations using NEXRAD-based rainfall tended to underpredict streamflow overall. Given that the total NEXRAD-based rainfall data for the simulation period is lower than the total measured rainfall at the NWS COOP locations, this bias would be expected. Therefore, to better assess the use of NEXRAD-based rainfall estimates as compared to NWS COOP rainfall data on the hydrologic simulations, TOPMODEL was recalibrated and updated simulations were made using the NEXRAD-based rainfall data. Comparisons of observed and simulated streamflow show that the TOPMODEL results using measured rainfall data and NEXRAD-based rainfall are comparable. Nonetheless, TOPMODEL simulations using NEXRAD-based rainfall still tended to underpredict total streamflow volume, although the magnitude of differences were similar to the simulations using measured rainfall. The McTier Creek watershed was subdivided into 12 subwatersheds and NEXRAD-based rainfall data were generated for each subwatershed. Simulations of streamflow were generated for each subwatershed using NEXRAD-based rainfall and compared with subwatershed simulations using measured rainfall data, which unlike the NEXRAD-based rainfall were the same data for all subwatersheds (derived from a weighted average of the six NWS COOP stations surrounding the basin). For the two simulations, subwatershed streamflow were summed and compared to streamflow simulations at two U.S. Geological Survey streamgages. The percentage differences at the gage near Monetta, South Carolina, were the same for simulations using measured rainfall data and NEXRAD-based rainfall. At the gage near New Holland, South Carolina, the percentage differences using the NEXRAD-based rainfall were twice as much as those using the measured rainfall. Single-mass curve comparisons showed an increase in the total volume of rainfall from north to south. Similar comparisons of the measured rainfall at the NWS COOP stations showed similar percentage differences, but the NEXRAD-based rainfall variations occurred over a much smaller distance than the measured rainfall. Nonetheless, it was concluded that in some cases, using NEXRAD-based rainfall data in TOPMODEL streamflow simulations may provide an effective alternative to using measured rainfall data. For this investigation, however, TOPMODEL streamflow simulations using NEXRAD-based rainfall data for both calibration and simulations did not show significant improvements with respect to matching observed streamflow over simulations generated using measured rainfall data.

Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling

NASA Astrophysics Data System (ADS)

Galelli, S.; Castelletti, A.

2013-02-01

Combining randomization methods with ensemble prediction is emerging as an effective option to balance accuracy and computational efficiency in data-driven modeling. In this paper we investigate the prediction capability of extremely randomized trees (Extra-Trees), in terms of accuracy, explanation ability and computational efficiency, in a streamflow modeling exercise. Extra-Trees are a totally randomized tree-based ensemble method that (i) alleviates the poor generalization property and tendency to overfitting of traditional standalone decision trees (e.g. CART); (ii) is computationally very efficient; and, (iii) allows to infer the relative importance of the input variables, which might help in the ex-post physical interpretation of the model. The Extra-Trees potential is analyzed on two real-world case studies (Marina catchment (Singapore) and Canning River (Western Australia)) representing two different morphoclimatic contexts comparatively with other tree-based methods (CART and M5) and parametric data-driven approaches (ANNs and multiple linear regression). Results show that Extra-Trees perform comparatively well to the best of the benchmarks (i.e. M5) in both the watersheds, while outperforming the other approaches in terms of computational requirement when adopted on large datasets. In addition, the ranking of the input variable provided can be given a physically meaningful interpretation.

Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling

NASA Astrophysics Data System (ADS)

Galelli, S.; Castelletti, A.

2013-07-01

Combining randomization methods with ensemble prediction is emerging as an effective option to balance accuracy and computational efficiency in data-driven modelling. In this paper, we investigate the prediction capability of extremely randomized trees (Extra-Trees), in terms of accuracy, explanation ability and computational efficiency, in a streamflow modelling exercise. Extra-Trees are a totally randomized tree-based ensemble method that (i) alleviates the poor generalisation property and tendency to overfitting of traditional standalone decision trees (e.g. CART); (ii) is computationally efficient; and, (iii) allows to infer the relative importance of the input variables, which might help in the ex-post physical interpretation of the model. The Extra-Trees potential is analysed on two real-world case studies - Marina catchment (Singapore) and Canning River (Western Australia) - representing two different morphoclimatic contexts. The evaluation is performed against other tree-based methods (CART and M5) and parametric data-driven approaches (ANNs and multiple linear regression). Results show that Extra-Trees perform comparatively well to the best of the benchmarks (i.e. M5) in both the watersheds, while outperforming the other approaches in terms of computational requirement when adopted on large datasets. In addition, the ranking of the input variable provided can be given a physically meaningful interpretation.

Instream flow characterization of Upper Salmon River basin streams, central Idaho, 2005

USGS Publications Warehouse

Maret, Terry R.; Hortness, Jon E.; Ott, Douglas S.

2006-01-01

Anadromous fish populations in the Columbia River Basin have plummeted in the last 100 years. This severe decline led to Federal listing of Chinook salmon (Oncorhynchus tshawytscha) and steelhead trout (Oncorhynchus mykiss) stocks as endangered or threatened under the Endangered Species Act (ESA) in the 1990s. Historically, the upper Salmon River Basin (upstream of the confluence with the Pahsimeroi River) in Idaho provided migration corridors and significant habitat for these ESA-listed species, in addition to the ESA-listed bull trout (Salvelinus confluentus). Human development has modified the original streamflow conditions in many streams in the upper Salmon River Basin. Summer streamflow modifications resulting from irrigation practices, have directly affected quantity and quality of fish habitat and also have affected migration and (or) access to suitable spawning and rearing habitat for these fish. As a result of these ESA listings and Action 149 of the Federal Columbia River Power System Biological Opinion of 2000, the Bureau of Reclamation was tasked to conduct streamflow characterization studies in the upper Salmon River Basin to clearly define habitat requirements for effective species management and habitat restoration. These studies include collection of habitat and streamflow information for the Physical Habitat Simulation System (PHABSIM) model, a widely applied method to determine relations between habitat and discharge requirements for various fish species and life stages. Model simulation results can be used by resource managers to guide habitat restoration efforts by evaluating potential fish habitat and passage improvements by increasing or decreasing streamflow. In 2005, instream flow characterization studies were completed on Big Boulder, Challis, Bear, Mill, and Morgan Creeks. Continuous streamflow data were recorded upstream of all diversions on Big Boulder. Instantaneous measurements of discharge were also made at selected sites. In addition, natural summer streamflows were estimated for each study site using regional regression equations. This report describes PHABSIM modeling results for bull trout, Chinook salmon, and steelhead trout during summer streamflows. Habitat/discharge relations were summarized for adult and spawning life stages at each study site. In addition, streamflow needs for riffle dwelling invertebrate taxa (Ephemeroptera, Plecoptera, and Trichoptera) are presented. Adult fish passage and discharge relations were evaluated at specific transects that were identified as potential low-streamflow passage barriers at each study site. Continuous summer water temperature data for selected study sites were summarized and compared with Idaho Water Quality Standards and various water temperature requirements of targeted fish species. Results of these habitat studies can be used to prioritize and direct cost-effective actions to improve fish habitat for ESA-listed anadromous and native fish species in the basin. These actions may include acquiring water during critical low-flow periods by leasing or modifying irrigation delivery systems to minimize out-of-stream diversions.

Quantifying the relative contribution of climate and human impacts on streamflow at seasonal scale

NASA Astrophysics Data System (ADS)

Xin, Z.; Zhang, L.; Li, Y.; Zhang, C.

2017-12-01

Both climate change and human activities have induced changes to hydrology. The quantification of their impacts on streamflow is a challenge, especially at the seasonal scale due to seasonality of climate and human impacts, i.e., water use for irrigation and water storage and release due to reservoir operation. In this study, the decomposition method based on the Budyko hypothesis is extended to the seasonal scale and is used to quantify the climate and human impacts on annual and seasonal streamflow changes. The results are further compared and verified with those simulated by the hydrological method of abcd model. Data are split into two periods (1953-1974 and 1975-2005) to quantify the change. Three seasons, including wet, dry and irrigation seasons are defined by introducing the monthly aridity index. In general, results showed a satisfactory agreement between the Budyko decomposition method and abcd model. Both climate change and human activities were found to induce a decrease in streamflow at the annual scale, with 67% of the change contributed by human activities. At the seasonal scale, the human-induced contribution to the reduced stream flow was 64% and 73% for dry and wet seasons, respectively; whereas in the irrigation season, the impact of human activities on reducing the streamflow was more pronounced (180%) since the climate contributes to increased streamflow. In addition, the quantification results were analyzed for each month in the wet season to reveal the effects of intense precipitation and reservoir operation rules during flood season.

Climate-driven disturbances in the San Juan River sub-basin of the Colorado River

NASA Astrophysics Data System (ADS)

Bennett, Katrina E.; Bohn, Theodore J.; Solander, Kurt; McDowell, Nathan G.; Xu, Chonggang; Vivoni, Enrique; Middleton, Richard S.

2018-01-01

Accelerated climate change and associated forest disturbances in the southwestern USA are anticipated to have substantial impacts on regional water resources. Few studies have quantified the impact of both climate change and land cover disturbances on water balances on the basin scale, and none on the regional scale. In this work, we evaluate the impacts of forest disturbances and climate change on a headwater basin to the Colorado River, the San Juan River watershed, using a robustly calibrated (Nash-Sutcliffe efficiency 0.76) hydrologic model run with updated formulations that improve estimates of evapotranspiration for semi-arid regions. Our results show that future disturbances will have a substantial impact on streamflow with implications for water resource management. Our findings are in contradiction with conventional thinking that forest disturbances reduce evapotranspiration and increase streamflow. In this study, annual average regional streamflow under the coupled climate-disturbance scenarios is at least 6-11 % lower than those scenarios accounting for climate change alone; for forested zones of the San Juan River basin, streamflow is 15-21 % lower. The monthly signals of altered streamflow point to an emergent streamflow pattern related to changes in forests of the disturbed systems. Exacerbated reductions of mean and low flows under disturbance scenarios indicate a high risk of low water availability for forested headwater systems of the Colorado River basin. These findings also indicate that explicit representation of land cover disturbances is required in modeling efforts that consider the impact of climate change on water resources.

Effects of land cover change on evapotranspiration and streamflow of small catchments in the Upper Xingu River Basin, Central Brazi

NASA Astrophysics Data System (ADS)

Costa, M. H.; Dias, L. C. P.; Macedo, M.; Coe, M. T.; Neill, C.

2014-12-01

This study assess the influence of land cover changes on evapotranspiration and streamflow in small catchments in the Upper Xingu River Basin (Mato Grosso state, Brazil). Streamflow was measured in catchments with uniform land use for September 1, 2008 to August 31, 2010. We used models to simulate evapotranspiration and streamflow for the four most common land cover types found in the Upper Xingu: tropical forest, cerrado (savanna), pasture, and soybean croplands. We used INLAND to perform single point simulations considering tropical rainforest, cerrado and pasturelands, and AgroIBIS for croplands. Converting natural vegetation to agriculture substantially modifies evapotranspiration and streamflow in small catchments. Measured mean streamflow in soy catchments was about three times greater than that of forest catchments, while the mean annual amplitude of flow in soy catchments was more than twice that of forest catchments. Simulated mean annual evapotranspiration was 39% lower in agricultural ecosystems (pasture and soybean cropland) than in natural ecosystems (tropical rainforest and cerrado). Observed and simulated mean annual streamflows in agricultural ecosystems were more than 100% higher than in natural ecosystems. The accuracy of the simulations is improved by using field-measured soil hydraulic properties. The inclusion of local measurements of key soil parameters is likely to improve hydrological simulations in other tropical regions.

Effects of land cover change on evapotranspiration and streamflow of small catchments in the Upper Xingu River Basin, Central Brazi

NASA Astrophysics Data System (ADS)

Costa, M. H.; Dias, L. C. P.; Macedo, M.; Coe, M. T.; Neill, C.

2015-12-01

This study assess the influence of land cover changes on evapotranspiration and streamflow in small catchments in the Upper Xingu River Basin (Mato Grosso state, Brazil). Streamflow was measured in catchments with uniform land use for September 1, 2008 to August 31, 2010. We used models to simulate evapotranspiration and streamflow for the four most common land cover types found in the Upper Xingu: tropical forest, cerrado (savanna), pasture, and soybean croplands. We used INLAND to perform single point simulations considering tropical rainforest, cerrado and pasturelands, and AgroIBIS for croplands. Converting natural vegetation to agriculture substantially modifies evapotranspiration and streamflow in small catchments. Measured mean streamflow in soy catchments was about three times greater than that of forest catchments, while the mean annual amplitude of flow in soy catchments was more than twice that of forest catchments. Simulated mean annual evapotranspiration was 39% lower in agricultural ecosystems (pasture and soybean cropland) than in natural ecosystems (tropical rainforest and cerrado). Observed and simulated mean annual streamflows in agricultural ecosystems were more than 100% higher than in natural ecosystems. The accuracy of the simulations is improved by using field-measured soil hydraulic properties. The inclusion of local measurements of key soil parameters is likely to improve hydrological simulations in other tropical regions.

Streamflow generation in humid West Africa: the role of Bas-fonds investigated with a physically based model of the Critical Zone

NASA Astrophysics Data System (ADS)

Hector, B.; Cohard, J. M.; Séguis, L.

2015-12-01

In West Africa, the drought initiated in the 70's-80's together with intense land-use change due to increasing food demand produced very contrasted responses on water budgets of the critical zone (CZ) depending on the lithological and pedological contexts. In Sahel, streamflow increased, mostly due to increasing hortonian runoff from soil crusting, and so did groundwater storage. On the contrary, in the more humid southern Sudanian area, streamflow decreased and no clear signal has been observed concerning water storage in this hard-rock basement area. There, Bas-fonds are fundamental landscape features. They are seasonally water-logged valley bottoms from which first order streams originate, mostly composed of baseflow. They are a key feature for understanding streamflow generation processes. They also carry an important agronomic potential due to their moisture and nutrient availability. The role of Bas-fond in streamflow generation processes is investigated using a physically-based coupled model of the CZ, ParFlow-CLM at catchment scale (10km²). The model is evaluated against classical hydrological measurements (water table, soil moisture, streamflow, fluxes), acquired in the AMMA-CATCH observing system for the West African monsoon, but also hybrid gravity data which measure integrated water storage changes. The bas-fond system is shown to be composed of two components with different time scales. The slow component is characterized by the seasonal and interannual amplitude of the permanent water table, which is disconnected from streams, fed by direct recharge and lowered by evapotranspiration, mostly from riparian areas. The fast component is characterized by thresholds in storage and perched and permanent water tables surrounding the bas-fond during the wet season, which are linked with baseflow generation. This is a first step toward integrating these features into larger scale modeling of the critical zone for evaluating the effect of precipitation intensification and land use changes scenarios in the area.

Simulation of salinity intrusion along the Georgia and South Carolina coasts using climate-change scenarios

USGS Publications Warehouse

Conrads, Paul; Roehl, Edwin A.; Daamen, Ruby C.; Cook, John B.

2013-01-01

Potential changes in climate could alter interactions between environmental and societal systems and adversely affect the availability of water resources in many coastal communities. Changes in streamflow patterns in conjunction with sea-level rise may change the salinity-intrusion dynamics of coastal rivers. Several municipal water-supply intakes are located along the Georgia and South Carolina coast that are proximal to the present day saltwater-freshwater interface of tidal rivers. Increases in the extent of salinity intrusion resulting from climate change could threaten the availability of freshwater supplies in the vicinity of these intakes. To effectively manage these supplies, water-resource managers need estimates of potential changes in the frequency, duration, and magnitude of salinity intrusion near their water-supply intakes that may occur as a result of climate change. This study examines potential effects of climate change, including altered streamflow and sea-level rise, on the dynamics of saltwater intrusion near municipal water-supply intakes in two coastal areas. One area consists of the Atlantic Intracoastal Waterway (AIW) and the Waccamaw River near Myrtle Beach along the Grand Strand of the South Carolina Coast, and the second area is on or near the lower Savannah River near Savannah, Georgia. The study evaluated how future sea-level rise and a reduction in streamflows can potentially affect salinity intrusion and threaten municipal water supplies and the biodiversity of freshwater tidal marshes in these two areas. Salinity intrusion occurs as a result of the interaction between three principal forces—streamflow, mean coastal water levels, and tidal range. To analyze and simulate salinity dynamics at critical coastal gaging stations near four municipal water-supply intakes, various data-mining techniques, including artificial neural network (ANN) models, were used to evaluate hourly streamflow, salinity, and coastal water-level data collected over a period exceeding 10 years. The ANN models were trained (calibrated) to learn the specific interactions that cause salinity intrusions, and resulting models were able to accurately simulate historical salinity dynamics in both study areas. Changes in sea level and streamflow quantity and timing can be simulated by the salinity intrusion models to evaluate various climate-change scenarios. The salinity intrusion models for the study areas are deployed in a decision support system to facilitate the use of the models for management decisions by coastal water-resource managers. The report describes the use of the salinity-intrusion models decision support system to evaluate salinity-intrusion dynamics for various climate-change scenarios, including incremental increases in sea level in combination with incremental decreases in streamflow. Operation of municipal water-treatment plants is problematic when the specific-conductance values for source water are greater than 1,000 to 2,000 microsiemens per centimeter (µS/cm). High specific-conductance values contribute to taste problems that require treatment. Data from a gage downstream from a municipal water intake indicate specific conductance exceeded 1,000 µS/cm about 5.4 percent of the time over the 14-year period from August 1995 to August 2008. Simulations of specific conductance at this gaging station that incorporates sea-level rises resulted in a doubling of the exceedances to 11.0 percent for a 1-foot increase and 17.6 percent for a 2-foot increase. The frequency of intrusion of water with specific conductance values of 1,000 µS/cm was less sensitive to incremental reductions in streamflow than to incremental increases in sea level. Simulations of conditions associated with a 10-percent reduction in streamflow, in combination with a 1-foot rise in sea level, increased the percentage of time specific conductance exceeded 1,000 µS/cm at this site from 11.0 to 13.3 percent, and a 20-percent reduction in streamflow increased the percentage of time to 16.6 percent. Precipitation and temperature data from a global circulation model were used, after scale adjustments, as input to a watershed model of the Yadkin-Pee Dee River basin, which flows into the Waccamaw River and Atlantic Intracoastal Waterway study area in South Carolina. The simulated streamflow for historical conditions and projected climate change in the future was used as input for the ANN model in decision support system. Results of simulations incorporating climate-change projections for alterations in streamflow indicate an increase in the frequency of salinity-intrusion events and a shift in the seasonal occurrence of the intrusion events from the summer to the fall.

Application of a stream-aquifer model to Monument Creek for development of a method to estimate transit losses for reusable water, El Paso County, Colorado

USGS Publications Warehouse

Kuhn, Gerhard; Arnold, L. Rick

2006-01-01

The U.S. Geological Survey, in cooperation with Colorado Springs Utilities, the Colorado Water Conservation Board, and the El Paso County Water Authority, began a study in 2004 to (1) apply a stream-aquifer model to Monument Creek, (2) use the results of the modeling to develop a transit-loss accounting program for Monument Creek, (3) revise the existing transit-loss accounting program for Fountain Creek to incorporate new water-management strategies and allow for incorporation of future changes in water-management strategies, and (4) integrate the two accounting programs into a single program with a Web-based user interface. The purpose of this report is to present the results of applying a stream-aquifer model to the Monument Creek study reach.Transit losses were estimated for reusable-water flows in Monument Creek that ranged from 1 to 200 cubic feet per second (ft3/s) and for native streamflows that ranged from 0 to 1,000 ft3/s. Transit losses were estimated for bank-storage, channel-storage, and evaporative losses. The same stream-aquifer model used in the previously completed (1988) Fountain Creek study was used in the Monument Creek study.Sixteen model nodes were established for the Monument Creek study reach, defining 15 subreaches. Channel length, aquifer length, and aquifer width for the subreaches were estimated from available topographic and geologic maps. Thickness of alluvial deposits and saturated thickness were estimated using lithologic and water-level data from about 100 wells and test holes in or near the Monument Creek study reach. Estimated average transmissivities for the subreaches ranged from 2,000 to 12,000 feet squared per day, and a uniform value of 0.20 was used for storage coefficient.Qualitative comparison of recorded and simulated streamflow at the downstream node for the calibration and verification simulations indicated that the two streamflows compared reasonably well. No adjustments were made to the model parameters. Differences between recorded and simulated streamflow volumes for all calibration and verification simulations ranged from about –8.8 to 7.5 percent; the total error for all simulations was about –0.7 percent.The model was used to estimate bank-storage losses for 10 to 15 native streamflows for each reusable-water flow of 1, 3, 5, 7, 10, 15, 20, 30, 40, 50, 100, and 200 ft3/s. Then the 10 to 15 bank-storage loss values were used in least-squares linear regression to estimate a relation between bank-storage loss and native streamflow for each of the 12 reusable-water flow rates. The 12 regression relations then were used to develop “look-up” tables of bank-storage loss for reusable-water flows ranging from 1 to 200 ft3/s (in 1-ft3/s increments). Additional model simulations indicated that (1) when the ratio of downstream native streamflow to upstream native streamflow was less than 1, bank-storage loss generally increased and (2) when the ratio of downstream native streamflow to upstream native streamflow was larger than 1, bank-storage loss generally decreased. These results were used to develop a bank-storage loss adjustment factor based on the ratio of native streamflow at the downstream node to native streamflow at the upstream node. The model also was used to estimate a recovery period, which is the length of time needed for the bank-storage loss to return to the stream. The recovery period was 1 day for six subreaches; 2 days for four subreaches; between 3 and 12 days for four subreaches; and 28 days for one subreach.Channel-storage losses are about 10 percent of the reusable-water flow for most of the subreaches, except for two subreaches, where the channel-storage losses are about 20 percent, and one subreach, where the losses are about 30 percent, owing to the greater channel lengths. Evaporative losses were estimated by the use of monthly pan-evaporation data and the incremental increase in stream width resulting from any reusable-water flows. Monthly pan-evaporation data were converted to a daily rate. The daily rate, when multiplied by the stream-width increase (in feet) that results from reusable-water flow and by the subreach length (in miles) gives the daily evaporative loss in cubic feet per second.

Identifying dominant controls on hydrologic parameter transfer from gauged to ungauged catchments: a comparative hydrology approach

USGS Publications Warehouse

Singh, R.; Archfield, S.A.; Wagener, T.

2014-01-01

Daily streamflow information is critical for solving various hydrologic problems, though observations of continuous streamflow for model calibration are available at only a small fraction of the world’s rivers. One approach to estimate daily streamflow at an ungauged location is to transfer rainfall–runoff model parameters calibrated at a gauged (donor) catchment to an ungauged (receiver) catchment of interest. Central to this approach is the selection of a hydrologically similar donor. No single metric or set of metrics of hydrologic similarity have been demonstrated to consistently select a suitable donor catchment. We design an experiment to diagnose the dominant controls on successful hydrologic model parameter transfer. We calibrate a lumped rainfall–runoff model to 83 stream gauges across the United States. All locations are USGS reference gauges with minimal human influence. Parameter sets from the calibrated models are then transferred to each of the other catchments and the performance of the transferred parameters is assessed. This transfer experiment is carried out both at the scale of the entire US and then for six geographic regions. We use classification and regression tree (CART) analysis to determine the relationship between catchment similarity and performance of transferred parameters. Similarity is defined using physical/climatic catchment characteristics, as well as streamflow response characteristics (signatures such as baseflow index and runoff ratio). Across the entire US, successful parameter transfer is governed by similarity in elevation and climate, and high similarity in streamflow signatures. Controls vary for different geographic regions though. Geology followed by drainage, topography and climate constitute the dominant similarity metrics in forested eastern mountains and plateaus, whereas agricultural land use relates most strongly with successful parameter transfer in the humid plains.

Effects of Cryospheric Change on Alpine Hydrology: Combining a Model With Observations in the Upper Reaches of the Hei River, China

NASA Astrophysics Data System (ADS)

Chen, R.; Wang, G.; Yang, Y.; Liu, J.; Han, C.; Song, Y.; Liu, Z.; Kang, E.

2018-04-01

Cryospheric changes have great effects on alpine hydrology, but these effects are still unclear owing to rare observations and suitable models in the western cold regions of China. Based on long-term field observations in the western cold regions of China, a cryospheric basin hydrological model was proposed to evaluate the cryospheric effects on streamflow in the upper Hei River basin (UHR), and the relationship between the cryosphere and streamflow was further discussed with measured data. The Norwegian Earth System Model outputs were chosen to project future streamflow under scenarios Representative Concentration Pathways (RCP)2.6, RCP4.5, and RCP8.5. The cryospheric basin hydrological model results were well validated by the measured precipitation, streamflow, evapotranspiration, soil temperature, glacier and snow cover area, and the water balance of land cover in the UHR. The moraine-talus region contributed most of the runoff (60%), even though it made up only about 20% of the area. On average, glacier and snow cover, respectively, contributed 3.5% and 25.4% of the fresh water to the streamflow in the UHR between 1960 and 2013. Because of the increased air temperature (2.9°C/54a) and precipitation (69.2 mm/54a) over the past 54 years, glacial and snowmelt runoff increased by 9.8% and 12.1%, respectively. The increase in air temperature brought forward the snowmelt flood peak and increased the winter flow due to permafrost degradation. Glaciers may disappear in the near future because of their small size, but snowmelt would increase due to increases in snowfall in the higher mountainous areas, and the basin runoff would increase slightly in the future.

Understanding uncertainties in future Colorado River streamflow

USGS Publications Warehouse

Julie A. Vano,; Bradley Udall,; Cayan, Daniel; Jonathan T Overpeck,; Brekke, Levi D.; Das, Tapash; Hartmann, Holly C.; Hidalgo, Hugo G.; Hoerling, Martin P; McCabe, Gregory J.; Morino, Kiyomi; Webb, Robert S.; Werner, Kevin; Lettenmaier, Dennis P.

2014-01-01

The Colorado River is the primary water source for more than 30 million people in the United States and Mexico. Recent studies that project streamf low changes in the Colorado River all project annual declines, but the magnitude of the projected decreases range from less than 10% to 45% by the mid-twenty-first century. To understand these differences, we address the questions the management community has raised: Why is there such a wide range of projections of impacts of future climate change on Colorado River streamflow, and how should this uncertainty be interpreted? We identify four major sources of disparities among studies that arise from both methodological and model differences. In order of importance, these are differences in 1) the global climate models (GCMs) and emission scenarios used; 2) the ability of land surface and atmospheric models to simulate properly the high-elevation runoff source areas; 3) the sensitivities of land surface hydrology models to precipitation and temperature changes; and 4) the methods used to statistically downscale GCM scenarios. In accounting for these differences, there is substantial evidence across studies that future Colorado River streamflow will be reduced under the current trajectories of anthropogenic greenhouse gas emissions because of a combination of strong temperature-induced runoff curtailment and reduced annual precipitation. Reconstructions of preinstrumental streamflows provide additional insights; the greatest risk to Colorado River streamf lows is a multidecadal drought, like that observed in paleoreconstructions, exacerbated by a steady reduction in flows due to climate change. This could result in decades of sustained streamflows much lower than have been observed in the ~100 years of instrumental record.

Summary of hydrologic modeling for the Delaware River Basin using the Water Availability Tool for Environmental Resources (WATER)

USGS Publications Warehouse

Williamson, Tanja N.; Lant, Jeremiah G.; Claggett, Peter; Nystrom, Elizabeth A.; Milly, Paul C.D.; Nelson, Hugh L.; Hoffman, Scott A.; Colarullo, Susan J.; Fischer, Jeffrey M.

2015-11-18

The Water Availability Tool for Environmental Resources (WATER) is a decision support system for the nontidal part of the Delaware River Basin that provides a consistent and objective method of simulating streamflow under historical, forecasted, and managed conditions. In order to quantify the uncertainty associated with these simulations, however, streamflow and the associated hydroclimatic variables of potential evapotranspiration, actual evapotranspiration, and snow accumulation and snowmelt must be simulated and compared to long-term, daily observations from sites. This report details model development and optimization, statistical evaluation of simulations for 57 basins ranging from 2 to 930 km2 and 11.0 to 99.5 percent forested cover, and how this statistical evaluation of daily streamflow relates to simulating environmental changes and management decisions that are best examined at monthly time steps normalized over multiple decades. The decision support system provides a database of historical spatial and climatic data for simulating streamflow for 2001–11, in addition to land-cover and general circulation model forecasts that focus on 2030 and 2060. WATER integrates geospatial sampling of landscape characteristics, including topographic and soil properties, with a regionally calibrated hillslope-hydrology model, an impervious-surface model, and hydroclimatic models that were parameterized by using three hydrologic response units: forested, agricultural, and developed land cover. This integration enables the regional hydrologic modeling approach used in WATER without requiring site-specific optimization or those stationary conditions inferred when using a statistical model.

Effects of groundwater levels and headwater wetlands on streamflow in the Charlie Creek basin, Peace River watershed, west-central Florida

USGS Publications Warehouse

Lee, T.M.; Sacks, L.A.; Hughes, J.D.

2010-01-01

The Charlie Creek basin was studied from April 2004 to December 2005 to better understand how groundwater levels in the underlying aquifers and storage and overflow of water from headwater wetlands preserve the streamflows exiting this least-developed tributary basin of the Peace River watershed. The hydrogeologic framework, physical characteristics, and streamflow were described and quantified for five subbasins of the 330-square mile Charlie Creek basin, allowing the contribution of its headwaters area and tributary subbasins to be separately quantified. A MIKE SHE model simulation of the integrated surface-water and groundwater flow processes in the basin was used to simulate daily streamflow observed over 21 months in 2004 and 2005 at five streamflow stations, and to quantify the monthly and annual water budgets for the five subbasins including the changing amount of water stored in wetlands. Groundwater heads were mapped in Zone 2 of the intermediate aquifer system and in the Upper Floridan aquifer, and were used to interpret the location of artesian head conditions in the Charlie Creek basin and its relation to streamflow. Artesian conditions in the intermediate aquifer system induce upward groundwater flow into the surficial aquifer and help sustain base flow which supplies about two-thirds of the streamflow from the Charlie Creek basin. Seepage measurements confirmed seepage inflow to Charlie Creek during the study period. The upper half of the basin, comprised largely of the Upper Charlie Creek subbasin, has lower runoff potential than the lower basin, more storage of runoff in wetlands, and periodically generates no streamflow. Artesian head conditions in the intermediate aquifer system were widespread in the upper half of the Charlie Creek basin, preventing downward leakage from expansive areas of wetlands and enabling them to act as headwaters to Charlie Creek once their storage requirements were met. Currently, the dynamic balance between wetland storage, rainfall-runoff processes, and groundwater-level differences in the upper basin allow it to generate approximately half of the streamflow from the Charlie Creek basin. Therefore, future development in the upper basin that would alter the hydraulic connectivity of wetlands during high flow conditions or expand recharging groundwater conditions could substantially affect streamflow in Charlie Creek. LIDAR (Light detection and ranging) based topographic maps and integrated modeling results were used to quantify the water stored in wetlands and other topographic depressions, and to describe the network of shallow stream channels connecting wetlands to Charlie Creek and its tributaries over distances of several thousand feet. Peak flows at all but one streamflow station were underpredicted in MIKE SHE simulations, possibly because the hydraulics of surface channels connecting wetlands to stream channels were not explicitly simulated in the model. Explicitly simulating the smaller channels connecting wetlands and stream channels should improve the ability of future watershed models to simulate peak flows in streams with headwater wetlands. The runoff potential was greater in the lower half of the Charlie Creek basin than in the upper half, and the streambed of Charlie Creek had greater potential to both directly gain streamflow from groundwater and lose streamflow to groundwater. Charlie Creek is more incised into the surficial aquifer in the lower basin than in the upper basin, and the streambed intersects the top of the intermediate aquifer system at two known locations. Groundwater levels in the intermediate aquifer system varied widely in the lower half of the basin from artesian conditions inducing upward flow toward the surficial aquifer and streams, to recharging conditions allowing downward flow and stream leakage. Recharge areas were greatest in May 2004 when rainfall was at a seasonal low and irrigation pumping was at a seasonal high. Recharge conditions

Reconstructing streamflow variation of the Baker River from tree-rings in Northern Patagonia since 1765

NASA Astrophysics Data System (ADS)

Lara, Antonio; Bahamondez, Alejandra; González-Reyes, Alvaro; Muñoz, Ariel A.; Cuq, Emilio; Ruiz-Gómez, Carolina

2015-10-01

The understanding of the long-term variation of large rivers streamflow with a high economic and social relevance is necessary in order to improve the planning and management of water resources in different regions of the world. The Baker River has the highest mean discharge of those draining both slopes of the Andes South of 20°S and it is among the six rivers with the highest mean streamflow in the Pacific domain of South America (1100 m3 s-1 at its outlet). It drains an international basin of 29,000 km2 shared by Chile and Argentina and has a high ecologic and economic value including conservation, tourism, recreational fishing, and projected hydropower. This study reconstructs the austral summer - early fall (January-April) streamflow for the Baker River from Nothofagus pumilio tree-rings for the period 1765-2004. Summer streamflow represents 45.2% of the annual discharge. The regression model for the period (1961-2004) explains 54% of the variance of the Baker River streamflow (R2adj = 0.54). The most significant temporal pattern in the record is the sustained decline since the 1980s (τ = -0.633, p = 1.0144 ∗ 10-5 for the 1985-2004 period), which is unprecedented since 1765. The Correlation of the Baker streamflow with the November-April observed Southern Annular Mode (SAM) is significant (1961-2004, r = -0.55, p < 0.001). The Baker record is also correlated with the available SAM tree-ring reconstruction based on other species when both series are filtered with a 25-year spline and detrended (1765-2004, r = -0.41, p < 0.01), emphasizing SAM as the main climatic forcing of the Baker streamflow. Three of the five summers with the highest streamflow in the entire reconstructed record occurred after the 1950s (1977, 1958 and 1959). The causes of this high streamflow events are not yet clear and cannot be associated with the reported recent increase in the frequency of glacial-lake outburst floods (GLOFs). The decreasing trend in the observed and reconstructed streamflow of the Baker River documented here for the 1980-2004 period is consistent with precipitation decrease associated with the SAM. Conversely, other studies have reported an increase of summer streamflow for a portion of the Baker River for the 1994-2008 period, explained by ice melt associated with temperature increase and glacier retreat and thinning. Future research should consider the development of new tree-ring reconstructions to increase the geographic range and to cover the last 1000 or more years using long-lived species (e.g. Fitzroya cupressoides and Pilgerodendron uviferum). Expanding the network and quality of instrumental weather, streamflow and other monitoring stations as well as the study and modeling of the complex hydrological processes in the Baker basin are necessary. This should be the basis for planning, policy design and decision making regarding water resources in the Baker basin.

Quantitative predictions of streamflow variability in the Susquehanna River Basin

NASA Astrophysics Data System (ADS)

Alexander, R.; Boyer, E. W.; Leonard, L. N.; Duffy, C.; Schwarz, G. E.; Smith, R. A.

2012-12-01

Hydrologic researchers and water managers have increasingly sought an improved understanding of the major processes that control fluxes of water and solutes across diverse environmental settings and large spatial scales. Regional analyses of observed streamflow data have led to advances in our knowledge of relations among land use, climate, and streamflow, with methodologies ranging from statistical assessments of multiple monitoring sites to the regionalization of the parameters of catchment-scale mechanistic simulation models. However, gaps remain in our understanding of the best ways to transfer the knowledge of hydrologic response and governing processes among locations, including methods for regionalizing streamflow measurements and model predictions. We developed an approach to predict variations in streamflow using the SPARROW (SPAtially Referenced Regression On Watershed attributes) modeling infrastructure, with mechanistic functions, mass conservation constraints, and statistical estimation of regional and sub-regional parameters. We used the model to predict discharge in the Susquehanna River Basin (SRB) under varying hydrological regimes that are representative of contemporary flow conditions. The resulting basin-scale water balance describes mean monthly flows in stream reaches throughout the entire SRB (represented at a 1:100,000 scale using the National Hydrologic Data network), with water supply and demand components that are inclusive of a range of hydrologic, climatic, and cultural properties (e.g., precipitation, evapotranspiration, soil and groundwater storage, runoff, baseflow, water use). We compare alternative models of varying complexity that reflect differences in the number and types of explanatory variables and functional expressions as well as spatial and temporal variability in the model parameters. Statistical estimation of the models reveals the levels of complexity that can be uniquely identified, subject to the information content and uncertainties of the hydrologic and climate measurements. Assessment of spatial variations in the model parameters and predictions provides an improved understanding of how much of the hydrologic response to land use, climate, and other properties is unique to specific locations versus more universally observed across catchments of the SRB. This approach advances understanding of water cycle variability at any location throughout the stream network, as a function of both landscape characteristics (e.g., soils, vegetation, land use) and external forcings (e.g., precipitation quantity and frequency). These improvements in predictions of streamflow dynamics will advance the ability to predict spatial and temporal variability in key solutes, such as nutrients, and their delivery to the Chesapeake Bay.

Ensemble Data Assimilation for Streamflow Forecasting: Experiments with Ensemble Kalman Filter and Particle Filter

NASA Astrophysics Data System (ADS)

Hirpa, F. A.; Gebremichael, M.; Hopson, T. M.; Wojick, R.

2011-12-01

We present results of data assimilation of ground discharge observation and remotely sensed soil moisture observations into Sacramento Soil Moisture Accounting (SACSMA) model in a small watershed (1593 km2) in Minnesota, the Unites States. Specifically, we perform assimilation experiments with Ensemble Kalman Filter (EnKF) and Particle Filter (PF) in order to improve streamflow forecast accuracy at six hourly time step. The EnKF updates the soil moisture states in the SACSMA from the relative errors of the model and observations, while the PF adjust the weights of the state ensemble members based on the likelihood of the forecast. Results of the improvements of each filter over the reference model (without data assimilation) will be presented. Finally, the EnKF and PF are coupled together to further improve the streamflow forecast accuracy.

Simulation of streamflow and sediment transport in two surface-coal-mined basins in Fayette County, Pennsylvania

USGS Publications Warehouse

Sams, J. I.; Witt, E. C.

1995-01-01

The Hydrological Simulation Program - Fortran (HSPF) was used to simulate streamflow and sediment transport in two surface-mined basins of Fayette County, Pa. Hydrologic data from the Stony Fork Basin (0.93 square miles) was used to calibrate HSPF parameters. The calibrated parameters were applied to an HSPF model of the Poplar Run Basin (8.83 square miles) to evaluate the transfer value of model parameters. The results of this investigation provide information to the Pennsylvania Department of Environmental Resources, Bureau of Mining and Reclamation, regarding the value of the simulated hydrologic data for use in cumulative hydrologic-impact assessments of surface-mined basins. The calibration period was October 1, 1985, through September 30, 1988 (water years 1986-88). The simulated data were representative of the observed data from the Stony Fork Basin. Mean simulated streamflow was 1.64 cubic feet per second compared to measured streamflow of 1.58 cubic feet per second for the 3-year period. The difference between the observed and simulated peak stormflow ranged from 4.0 to 59.7 percent for 12 storms. The simulated sediment load for the 1987 water year was 127.14 tons (0.21 ton per acre), which compares to a measured sediment load of 147.09 tons (0.25 ton per acre). The total simulated suspended-sediment load for the 3-year period was 538.2 tons (0.30 ton per acre per year), which compares to a measured sediment load of 467.61 tons (0.26 ton per acre per year). The model was verified by comparing observed and simulated data from October 1, 1988, through September 30, 1989. The results obtained were comparable to those from the calibration period. The simulated mean daily discharge was representative of the range of data observed from the basin and of the frequency with which specific discharges were equalled or exceeded. The calibrated and verified parameters from the Stony Fork model were applied to an HSPF model of the Poplar Run Basin. The two basins are in a similar physical setting. Data from October 1, 1987, through September 30, 1989, were used to evaluate the Poplar Run model. In general, the results from the Poplar Run model were comparable to those obtained from the Stony Fork model. The difference between observed and simulated total streamflow was 1.1 percent for the 2-year period. The mean annual streamflow simulated by the Poplar Run model was 18.3 cubic feet per second. This compares to an observed streamflow of 18.15 cubic feet per second. For the 2-year period, the simulated sediment load was 2,754 tons (0.24 ton per acre per year), which compares to a measured sediment load of 3,051.2 tons (0.27 ton per acre per year) for the Poplar Run Basin. Cumulative frequency-distribution curves of the observed and simulated streamflow compared well. The comparison between observed and simulated data improved as the time span increased. Simulated annual means and totals were more representative of the observed data than hourly data used in comparing storm events. The structure and organization of the HSPF model facilitated the simulation of a wide range of hydrologic processes. The simulation results from this investigation indicate that model parameters may be transferred to ungaged basins to generate representative hydrologic data through modeling techniques.

A Bayesian Alternative for Multi-objective Ecohydrological Model Specification

NASA Astrophysics Data System (ADS)

Tang, Y.; Marshall, L. A.; Sharma, A.; Ajami, H.

2015-12-01

Process-based ecohydrological models combine the study of hydrological, physical, biogeochemical and ecological processes of the catchments, which are usually more complex and parametric than conceptual hydrological models. Thus, appropriate calibration objectives and model uncertainty analysis are essential for ecohydrological modeling. In recent years, Bayesian inference has become one of the most popular tools for quantifying the uncertainties in hydrological modeling with the development of Markov Chain Monte Carlo (MCMC) techniques. Our study aims to develop appropriate prior distributions and likelihood functions that minimize the model uncertainties and bias within a Bayesian ecohydrological framework. In our study, a formal Bayesian approach is implemented in an ecohydrological model which combines a hydrological model (HyMOD) and a dynamic vegetation model (DVM). Simulations focused on one objective likelihood (Streamflow/LAI) and multi-objective likelihoods (Streamflow and LAI) with different weights are compared. Uniform, weakly informative and strongly informative prior distributions are used in different simulations. The Kullback-leibler divergence (KLD) is used to measure the dis(similarity) between different priors and corresponding posterior distributions to examine the parameter sensitivity. Results show that different prior distributions can strongly influence posterior distributions for parameters, especially when the available data is limited or parameters are insensitive to the available data. We demonstrate differences in optimized parameters and uncertainty limits in different cases based on multi-objective likelihoods vs. single objective likelihoods. We also demonstrate the importance of appropriately defining the weights of objectives in multi-objective calibration according to different data types.

Geohydrology of the French Creek basin and simulated effects of droughtand ground-water withdrawals, Chester County, Pennsylvania

USGS Publications Warehouse

Sloto, Ronald A.

2004-01-01

This report describes the results of a study by the U.S. Geological Survey, in cooperation with the Delaware River Basin Commission, to develop a regional ground-water-flow model of the French Creek Basin in Chester County, Pa. The model was used to assist water-resource managers by illustrating the interconnection between ground-water and surface-water systems. The 70.7-mi2 (square mile) French Creek Basin is in the Piedmont Physiographic Province and is underlain by crystalline and sedimentary fractured-rock aquifers. Annual water budgets were calculated for 1969-2001 for the French Creek Basin upstream of streamflow measurement station French Creek near Phoenixville (01472157). Average annual precipitation was 46.28 in. (inches), average annual streamflow was 20.29 in., average annual base flow determined by hydrograph separation was 12.42 in., and estimated average annual ET (evapotranspiration) was 26.10 in. Estimated average annual recharge was 14.32 in. and is equal to 31 percent of the average annual precipitation. Base flow made up an average of 61 percent of streamflow. Ground-water flow in the French Creek Basin was simulated using the finite-difference MODFLOW-96 computer program. The model structure is based on a simplified two-dimensional conceptualization of the ground-water-flow system. The modeled area was extended outside the French Creek Basin to natural hydrologic boundaries; the modeled area includes 40 mi2 of adjacent areas outside the basin. The hydraulic conductivity for each geologic unit was calculated from reported specific-capacity data determined from aquifer tests and was adjusted during model calibration. The model was calibrated for aboveaverage conditions by simulating base-flow and water-level measurements made on May 1, 2001, using a recharge rate of 20 in/yr (inches per year). The model was calibrated for below-average conditions by simulating base-flow and water-level measurements made on September 11 and 17, 2001, using a recharge rate of 6.2 in/yr. Average conditions were simulated by adjusting the recharge rate until simulated streamflow at streamflow-measurement station 01472157 matched the long-term (1968-2001) average base flow of 54.1 cubic feet per second. The recharge rate used for average conditions was 15.7 in/yr. The effect of drought in the French Creek Basin was simulated using a drought year recharge rate of 8 in/yr for 3 months. After 3 months of drought, the simulated streamflow of French Creek at streamflow-measurement station 01472157 decreased 34 percent. The simulations show that after 6 months of average recharge (15.7 in/yr) following drought, streamflow and water levels recovered almost to pre-drought conditions. The effect of increased ground-water withdrawals on stream base flow in the South Branch French Creek Subbasin was simulated under average and drought conditions with pumping rates equal to 50, 75, and 100 percent of the Delaware River Basin Commission Ground Water Protected Area (GWPA) withdrawal limit (1,393 million gallons per year) with all pumped water removed from the basin. For average recharge conditions, the simulated streamflow of South Branch French Creek at the mouth decreased 18, 28, and 37 percent at a withdrawal rate equal to 50, 75, and 100 percent of the GWPA limit, respectively. After 3 months of drought recharge conditions, the simulated streamflow of South Branch French Creek at the mouth decreased 27, 40, and 52 percent at a withdrawal rate equal to 50, 75, and 100 percent of the GWPA limit, respectively. The effect of well location on base flow, water levels, and the sources of water to the well was simulated by locating a hypothetical well pumping 200 gallons per minute in different places in the Beaver Run Subbasin with all pumped water removed from the basin. The smallest reduction in the base flow of Beaver Run was from a well on the drainage divide

Hydrological response to changing climate conditions: Spatial streamflow variability in the boreal region

NASA Astrophysics Data System (ADS)

Teutschbein, Claudia; Grabs, Thomas; Karlsen, Reinert H.; Laudon, Hjalmar; Bishop, Kevin

2016-04-01

It has long been recognized that streamflow-generating processes are not only dependent on climatic conditions, but also affected by physical catchment properties such as topography, geology, soils and land cover. We hypothesize that these landscape characteristics do not only lead to highly variable hydrologic behavior of rather similar catchments under the same stationary climate conditions (Karlsen et al., 2014), but that they also play a fundamental role for the sensitivity of a catchment to a changing climate (Teutschbein et al., 2015). A multi-model ensemble based on 15 regional climate models was combined with a multi-catchment approach to explore the hydrologic sensitivity of 14 partially nested and rather similar catchments in Northern Sweden to changing climate conditions and the importance of small-scale spatial variability. Current (1981-2010) and future (2061-2090) streamflow was simulated with the HBV model. As expected, projected increases in temperature and precipitation resulted in increased total available streamflow, with lower spring and summer flows, but substantially higher winter streamflow. Furthermore, significant changes in flow durations with lower chances of both high and low flows can be expected in boreal Sweden in the future. This overall trend in projected streamflow pattern changes was comparable among the analyzed catchments while the magnitude of change differed considerably. This suggests that catchments belonging to the same region can show distinctly different degrees of hydrological responses to the same external climate change signal. We reason that differences in spatially distributed physical catchment properties at smaller scales are not only of great importance for current streamflow behavior, but also play a major role as first-order control for the sensitivity of catchments to changing climate conditions. References Karlsen, R.H., T. Grabs, K. Bishop, H. Laudon, and J. Seibert (2014). Landscape controls on spatiotemporal variability of specific discharge in a boreal region, Abstract #H52B-07 presented at 2014 Fall Meeting, AGU, San Francisco, Calif., 15-19 Dec. [Available at http://adsabs.harvard.edu/abs/2014AGUFM.H52B..07K, last accessed 11 Jan 2016]. Teutschbein, C., T. Grabs, R.H. Karlsen, H. Laudon and K. Bishop (2015). Hydrological Response to Changing Climate Conditions: Spatial Streamflow Variability in the Boreal Region, Water Resour Res, doi: 10.1002/2015WR017337. [Available at http://onlinelibrary.wiley.com/doi/10.1002/2015WR017337/abstract, last accessed 11 Jan 2016].

Transit losses and traveltimes for water-supply releases Marion Lake during drought conditions, Cottonwood River, east-central Kansas

USGS Publications Warehouse

Jordan, P.R.; Hart, R.J.

1985-01-01

A streamflow routing model was used to calculate the transit losses and traveltimes. Channel and aquifer characteristics, and the model control parameters, were estimated from available data and then verified to the extent possible by comparing model simulated streamflow to observed streamflow at streamflow gaging stations. Transit losses and traveltimes for varying reservoir release rates and durations then were simulated for two different antecedent streamflow (drought) conditions. For the severe-drought antecedent-streamflow condition, it was assumed that only the downstream water use requirement would be released from the reservoir. For a less severe drought (LSD) antecedent streamflow condition, it was assumed than any releases from Marion Lake for water supply use downstream, would be in addition to a nominal dry weather release of 5 cu ft/sec. Water supply release rates of 10 and 25 cu ft/sec for the severe drought condition and 5, 10, and 25 cu ft/sec for the less severe drought condition were simulated for periods of 28 and 183 days commencing on July 1. Transit losses for the severe drought condition for all reservoir release rates and durations ranged from 12% to 78% of the maximum downstream flow rate and from 27% to 91% of the total volume of reservoir storage released. For the LSD condition, transit losses ranged from 7% to 29% of the maximum downstream flow rate and from 10% to 48% of the total volume of release. The 183-day releases had larger total transit losses, but losses on a percentage basis were less than the losses for the 28-day release period for both antecedent streamflow conditions. Traveltimes to full response (80% of the maximum downstream flow rate), however, showed considerable variation. For the release of 5 cu ft/sec during LSD conditions, base flow exceeded 80% of the maximum flow rate near the confluence; the traveltime to full response was undefined for those simulations. For the releases of 10 and 25 cu ft/sec during the same drought condition, traveltimes to full response ranged from 4.4 to 6.5 days. For releases of 10 and 25 cu ft/sec during severe drought conditions, traveltimes to full response near the confluence with the Neosho River ranged from 8.3 to 93 days. (Lantz-PTT)

Using water-quality profiles to characterize seasonal water quality and loading in the upper Animas River basin, southwestern Colorado

USGS Publications Warehouse

Leib, Kenneth J.; Mast, M. Alisa; Wright, Winfield G.

2003-01-01

One of the important types of information needed to characterize water quality in streams affected by historical mining is the seasonal pattern of toxic trace-metal concentrations and loads. Seasonal patterns in water quality are estimated in this report using a technique called water-quality profiling. Water-quality profiling allows land managers and scientists to assess priority areas to be targeted for characterization and(or) remediation by quantifying the timing and magnitude of contaminant occurrence. Streamflow and water-quality data collected at 15 sites in the upper Animas River Basin during water years 1991?99 were used to develop water-quality profiles. Data collected at each sampling site were used to develop ordinary least-squares regression models for streamflow and constituent concentrations. Streamflow was estimated by correlating instantaneous streamflow measured at ungaged sites with continuous streamflow records from streamflow-gaging stations in the subbasin. Water-quality regression models were developed to estimate hardness and dissolved cadmium, copper, and zinc concentrations based on streamflow and seasonal terms. Results from the regression models were used to calculate water-quality profiles for streamflow, constituent concentrations, and loads. Quantification of cadmium, copper, and zinc loads in a stream segment in Mineral Creek (sites M27 to M34) was presented as an example application of water-quality profiling. The application used a method of mass accounting to quantify the portion of metal loading in the segment derived from uncharacterized sources during different seasonal periods. During May, uncharacterized sources contributed nearly 95 percent of the cadmium load, 0 percent of the copper load (or uncharacterized sources also are attenuated), and about 85 percent of the zinc load at M34. During September, uncharacterized sources contributed about 86 percent of the cadmium load, 0 percent of the copper load (or uncharacterized sources also are attenuated), and about 52 percent of the zinc load at M34. Characterized sources accounted for more of the loading gains estimated in the example reach during September, possibly indicating the presence of diffuse inputs during snowmelt runoff. The results indicate that metal sources in the upper Animas River Basin may change substantially with season, regardless of the source.

Linear genetic programming application for successive-station monthly streamflow prediction

NASA Astrophysics Data System (ADS)

Danandeh Mehr, Ali; Kahya, Ercan; Yerdelen, Cahit

2014-09-01

In recent decades, artificial intelligence (AI) techniques have been pronounced as a branch of computer science to model wide range of hydrological phenomena. A number of researches have been still comparing these techniques in order to find more effective approaches in terms of accuracy and applicability. In this study, we examined the ability of linear genetic programming (LGP) technique to model successive-station monthly streamflow process, as an applied alternative for streamflow prediction. A comparative efficiency study between LGP and three different artificial neural network algorithms, namely feed forward back propagation (FFBP), generalized regression neural networks (GRNN), and radial basis function (RBF), has also been presented in this study. For this aim, firstly, we put forward six different successive-station monthly streamflow prediction scenarios subjected to training by LGP and FFBP using the field data recorded at two gauging stations on Çoruh River, Turkey. Based on Nash-Sutcliffe and root mean squared error measures, we then compared the efficiency of these techniques and selected the best prediction scenario. Eventually, GRNN and RBF algorithms were utilized to restructure the selected scenario and to compare with corresponding FFBP and LGP. Our results indicated the promising role of LGP for successive-station monthly streamflow prediction providing more accurate results than those of all the ANN algorithms. We found an explicit LGP-based expression evolved by only the basic arithmetic functions as the best prediction model for the river, which uses the records of the both target and upstream stations.

USGS tethered ACP platforms: New design means more safety and accuracy

USGS Publications Warehouse

Morlock, S.E.; Stewart, J.A.; Rehmel, M.S.

2004-01-01

The US Geological Survey has developed an innovative tethered platform that supports an Acoustic Current Profiler (ACP) in making stream-flow measurements (use of the term ACP in this article refers to a class of instruments and not a specific brand name or model). The tethered platform reduces the hazards involved in conventional methods of stream-flow measurement. The use of the platform reduces or eliminates time spent by personnel in streams and boats or on bridges and cableway and stream-flow measurement accuracy is increased.

Estimating Precipitation Input to a Watershed by Combining Gauge and Radar Derived Observations

NASA Astrophysics Data System (ADS)

Ercan, M. B.; Goodall, J. L.

2011-12-01

One challenge in creating an accurate watershed model is obtaining estimates of precipitation intensity over the watershed area. While precipitation measurements are generally available from gauging stations and radar instruments, both of these approaches for measuring precipitation have strengths and weakness. A typical way of addressing this challenge is to use gauged precipitation estimates to calibrate radar based estimates, however this study proposes a slightly different approach in which the optimal daily precipitation value is selected from either the gauged or the radar estimates based on the observed streamflow for that day. Our proposed approach is perhaps most relevant for cases of modeling watersheds that do not have a nearby precipitation gauge, or for regions that experience convective storms that are often highly spatially variable. Using the Eno River watershed located in Orange County, NC, three different precipitation datasets were created to predict streamflow at the watershed outlet for the time period 2005-2010 using the Soil and Water Assessment Tool (SWAT): (1) estimates based on only precipitation gauging stations, (2) estimates based only on gauged-corrected radar observations, and (3) the combination of precipitation estimates from the gauge and radar data determined using our proposed approach. The results show that the combined precipitation approach significantly improves streamflow predictions (Nash-Sutcliffe Coefficient, E = 0.66) when compared to the gauged estimates alone (E = 0.47) and the radar based estimates alone (E = 0.45). Our study was limited to one watershed, therefore additional studies are needed to control for factors such as climate, ecology, and hydrogeology that will likely influence the results of the analysis.

Initial sediment transport model of the mining-affected Aries River Basin, Romania

USGS Publications Warehouse

Friedel, Michael J.; Linard, Joshua I.

2008-01-01

The Romanian government is interested in understanding the effects of existing and future mining activities on long-term dispersal, storage, and remobilization of sediment-associated metals. An initial Soil and Water Assessment Tool (SWAT) model was prepared using available data to evaluate hypothetical failure of the Valea Sesei tailings dam at the Rosia Poieni mine in the Aries River basin. Using the available data, the initial Aries River Basin SWAT model could not be manually calibrated to accurately reproduce monthly streamflow values observed at the Turda gage station. The poor simulation of the monthly streamflow is attributed to spatially limited soil and precipitation data, limited constraint information due to spatially and temporally limited streamflow measurements, and in ability to obtain optimal parameter values when using a manual calibration process. Suggestions to improve the Aries River basin sediment transport model include accounting for heterogeneity in model input, a two-tier nonlinear calibration strategy, and analysis of uncertainty in predictions.

Climate Change Impacts for Conterminous USA: An Integrated Assessment Part 2. Models and Validation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thomson, Allison M.; Rosenberg, Norman J.; Izaurralde, R Cesar C.

As CO{sub 2} and other greenhouse gases accumulate in the atmosphere and contribute to rising global temperatures, it is important to examine how a changing climate may affect natural and managed ecosystems. In this series of papers, we study the impacts of climate change on agriculture, water resources and natural ecosystems in the conterminous United States using a suite of climate change predictions from General Circulation Models (GCMs) as described in Part 1. Here we describe the agriculture model EPIC and the HUMUS water model and validate them with historical crop yields and streamflow data. We compare EPIC simulated grainmore » and forage crop yields with historical crop yields from the US Department of Agriculture and find an acceptable level of agreement for this study. The validation of HUMUS simulated streamflow with estimates of natural streamflow from the US Geological Survey shows that the model is able to reproduce significant relationships and capture major trends.« less

Hydrological modeling as an evaluation tool of EURO-CORDEX climate projections and bias correction methods

NASA Astrophysics Data System (ADS)

Hakala, Kirsti; Addor, Nans; Seibert, Jan

2017-04-01

Streamflow stemming from Switzerland's mountainous landscape will be influenced by climate change, which will pose significant challenges to the water management and policy sector. In climate change impact research, the determination of future streamflow is impeded by different sources of uncertainty, which propagate through the model chain. In this research, we explicitly considered the following sources of uncertainty: (1) climate models, (2) downscaling of the climate projections to the catchment scale, (3) bias correction method and (4) parameterization of the hydrological model. We utilize climate projections at the 0.11 degree 12.5 km resolution from the EURO-CORDEX project, which are the most recent climate projections for the European domain. EURO-CORDEX is comprised of regional climate model (RCM) simulations, which have been downscaled from global climate models (GCMs) from the CMIP5 archive, using both dynamical and statistical techniques. Uncertainties are explored by applying a modeling chain involving 14 GCM-RCMs to ten Swiss catchments. We utilize the rainfall-runoff model HBV Light, which has been widely used in operational hydrological forecasting. The Lindström measure, a combination of model efficiency and volume error, was used as an objective function to calibrate HBV Light. Ten best sets of parameters are then achieved by calibrating using the genetic algorithm and Powell optimization (GAP) method. The GAP optimization method is based on the evolution of parameter sets, which works by selecting and recombining high performing parameter sets with each other. Once HBV is calibrated, we then perform a quantitative comparison of the influence of biases inherited from climate model simulations to the biases stemming from the hydrological model. The evaluation is conducted over two time periods: i) 1980-2009 to characterize the simulation realism under the current climate and ii) 2070-2099 to identify the magnitude of the projected change of streamflow under the climate scenarios RCP4.5 and RCP8.5. We utilize two techniques for correcting biases in the climate model output: quantile mapping and a new method, frequency bias correction. The FBC method matches the frequencies between observed and GCM-RCM data. In this way, it can be used to correct for all time scales, which is a known limitation of quantile mapping. A novel approach for the evaluation of the climate simulations and bias correction methods was then applied. Streamflow can be thought of as the "great integrator" of uncertainties. The ability, or the lack thereof, to correctly simulate streamflow is a way to assess the realism of the bias-corrected climate simulations. Long-term monthly mean as well as high and low flow metrics are used to evaluate the realism of the simulations under current climate and to gauge the impacts of climate change on streamflow. Preliminary results show that under present climate, calibration of the hydrological model comprises of a much smaller band of uncertainty in the modeling chain as compared to the bias correction of the GCM-RCMs. Therefore, for future time periods, we expect the bias correction of climate model data to have a greater influence on projected changes in streamflow than the calibration of the hydrological model.

Predicting future land cover change and its impact on streamflow and sediment load in a trans-boundary river basin

NASA Astrophysics Data System (ADS)

Wang, Jie; Wang, Hao; Ning, Shaowei; Hiroshi, Ishidaira

2018-06-01

Sediment load can provide very important perspective on erosion of river basin. The changes of human-induced vegetation cover, such as deforestation or afforestation, affect sediment yield process of a catchment. We have already evaluated that climate change and land cover change changed the historical streamflow and sediment yield, and land cover change is the main factor in Red river basin. But future streamflow and sediment yield changes under potential future land cover change scenario still have not been evaluated. For this purpose, future scenario of land cover change is developed based on historical land cover changes and land change model (LCM). In addition, future leaf area index (LAI) is simulated by ecological model (Biome-BGC) based on future land cover scenario. Then future scenarios of land cover change and LAI are used to drive hydrological model and new sediment rating curve. The results of this research provide information that decision-makers need in order to promote water resources planning efforts. Besides that, this study also contributes a basic framework for assessing climate change impacts on streamflow and sediment yield that can be applied in the other basins around the world.

Improved Assimilation of Streamflow and Satellite Soil Moisture with the Evolutionary Particle Filter and Geostatistical Modeling

NASA Astrophysics Data System (ADS)

Yan, Hongxiang; Moradkhani, Hamid; Abbaszadeh, Peyman

2017-04-01

Assimilation of satellite soil moisture and streamflow data into hydrologic models using has received increasing attention over the past few years. Currently, these observations are increasingly used to improve the model streamflow and soil moisture predictions. However, the performance of this land data assimilation (DA) system still suffers from two limitations: 1) satellite data scarcity and quality; and 2) particle weight degeneration. In order to overcome these two limitations, we propose two possible solutions in this study. First, the general Gaussian geostatistical approach is proposed to overcome the limitation in the space/time resolution of satellite soil moisture products thus improving their accuracy at uncovered/biased grid cells. Secondly, an evolutionary PF approach based on Genetic Algorithm (GA) and Markov Chain Monte Carlo (MCMC), the so-called EPF-MCMC, is developed to further reduce weight degeneration and improve the robustness of the land DA system. This study provides a detailed analysis of the joint and separate assimilation of streamflow and satellite soil moisture into a distributed Sacramento Soil Moisture Accounting (SAC-SMA) model, with the use of recently developed EPF-MCMC and the general Gaussian geostatistical approach. Performance is assessed over several basins in the USA selected from Model Parameter Estimation Experiment (MOPEX) and located in different climate regions. The results indicate that: 1) the general Gaussian approach can predict the soil moisture at uncovered grid cells within the expected satellite data quality threshold; 2) assimilation of satellite soil moisture inferred from the general Gaussian model can significantly improve the soil moisture predictions; and 3) in terms of both deterministic and probabilistic measures, the EPF-MCMC can achieve better streamflow predictions. These results recommend that the geostatistical model is a helpful tool to aid the remote sensing technique and the EPF-MCMC is a reliable and effective DA approach in hydrologic applications.

Diagnosis of streamflow prediction skills in Oregon using Hydrologic Landscape Classification

EPA Science Inventory

A complete understanding of why rainfall-runoff models provide good streamflow predictions at catchments in some regions, but fail to do so in other regions, has still not been achieved. Here, we argue that a hydrologic classification system is a robust conceptual tool that is w...

Hydrogeologic controls on streamflow sensitivity to climate variation

Treesearch

Anne Jefferson; Anne Nolin; Sarah Lewis; Christina Tague

2008-01-01

Climate models project warmer temperatures for the north-west USA, which will result in reduced snowpacks and decreased summer streamflow. This paper examines how groundwater, snowmelt, and regional climate patterns control discharge at multiple time scales, using historical records from two watersheds with contrasting geological properties and drainage efficiencies....

Streamflow profile classification using functional data analysis: A case study on the Kelantan River Basin

NASA Astrophysics Data System (ADS)

Jamaludin, Suhaila

2017-05-01

Extreme rainfall events such as floods and prolonged dry spells have become common phenomena in tropical countries like Malaysia. Floods are regular natural disasters in Malaysia, and happen nearly every year during the monsoon season. Recently, the magnitude of streamflow seems to have altered frequently, both spatially and temporally. Therefore, in order to have effective planning and an efficient water management system, it is advisable that streamflow data are analysed continuously over a period of time. If the data are treated as a set of functions rather than as a set of discrete values, then this ensures that they are not restricted by physical time. In addition, the derivatives of the functions may themselves be treated as functional data, which provides new information. The objective of this study is to develop a functional framework for hydrological applications using streamflow as the functional data. The daily flow series from the Kelantan River Basin were used as the main input in this study. Seven streamflow stations were employed in the analysis. Classification between the stations was done using the functional principal component, which was based on the results of the factor scores. The results indicated that two stations, namely the Kelantan River (Guillemard Bridge) and the Galas River, have a different flow pattern from the other streamflow stations. The flow curves of these two rivers are considered as the extreme curves because of their different magnitude and shape.

Streamflow characteristics and trends along Soldier Creek, Northeast Kansas

USGS Publications Warehouse

Juracek, Kyle E.

2017-08-16

Historical data for six selected U.S. Geological Survey streamgages along Soldier Creek in northeast Kansas were used in an assessment of streamflow characteristics and trends. This information is required by the Prairie Band Potawatomi Nation for the effective management of tribal water resources, including drought contingency planning. Streamflow data for the period of record at each streamgage were used to assess annual mean streamflow, annual mean base flow, mean monthly flow, annual peak flow, and annual minimum flow.Annual mean streamflows along Soldier Creek were characterized by substantial year-to-year variability with no pronounced long-term trends. On average, annual mean base flow accounted for about 20 percent of annual mean streamflow. Mean monthly flows followed a general seasonal pattern that included peak values in spring and low values in winter. Annual peak flows, which were characterized by considerable year-to-year variability, were most likely to occur in May and June and least likely to occur during November through February. With the exception of a weak yet statistically significant increasing trend at the Soldier Creek near Topeka, Kansas, streamgage, there were no pronounced long-term trends in annual peak flows. Annual 1-day, 30-day, and 90-day mean minimum flows were characterized by considerable year-to-year variability with no pronounced long-term trend. During an extreme drought, as was the case in the mid-1950s, there may be zero flow in Soldier Creek continuously for a period of one to several months.

The relationship between groundwater ages, streamflow ages, and storage selection functions under stationary conditions

NASA Astrophysics Data System (ADS)

Berghuijs, W.; Kirchner, J. W.

2017-12-01

Waters in aquifers are often much older than the streamwaters that drain them. Simple physically based reasoning suggests that these age contrasts should be expected wherever catchments are heterogeneous. However, a general quantitative catchment-scale explanation of these age contrasts remains elusive. We show that under stationary conditions conservation of mass and age dictate that the age distribution of water stored in a catchment can be directly estimated from the age distribution of its outflows, and vice versa. This in turn implies that the catchment's preference for the release or retention of waters of different ages can be estimated directly from the age distribution of outflow under stationary conditions. Using simple models of transit times, we show that the mean age of stored water can range from half as old as the mean age of streamflow (for plug flow conditions) to almost infinitely older (for strongly preferential flow). Streamflow age distributions reported in the literature often have long upper tails, consistent with preferential flow and implying that storage ages are substantially older than streamflow ages. Mean streamflow ages reported in the literature imply that most streamflow originates from a thin veneer of total groundwater storage. This preferential release of young streamflow implies that most groundwater is exchanged only slowly with the surface, and consequently must be very old. Where information is available for both storage ages and streamflow ages, our analysis establishes consistency relationships through which each could be used to better constrain the other. By quantifying the relationship between groundwater and streamflow ages, our analysis provides tools to jointly assess both of these important catchment properties.

Data Assimilation using observed streamflow and remotely-sensed soil moisture for improving sub-seasonal-to-seasonal forecasting

NASA Astrophysics Data System (ADS)

Arumugam, S.; Mazrooei, A.; Lakshmi, V.; Wood, A.

2017-12-01

Subseasonal-to-seasonal (S2S) forecasts of soil moisture and streamflow provides critical information for water and agricultural systems to support short-term planning and mangement. This study evaluates the role of observed streamflow and remotely-sensed soil moisture from SMAP (Soil Moisture Active Passive) mission in improving S2S streamflow and soil moisture forecasting using data assimilation (DA). We first show the ability to forecast soil moisture at monthly-to-seaasonal time scale by forcing climate forecasts with NASA's Land Information System and then compares the developed soil moisture forecast with the SMAP data over the Southeast US. Our analyses show significant skill in forecasting real-time soil moisture over 1-3 months using climate information. We also show that the developed soil moisture forecasts capture the observed severe drought conditions (2007-2008) over the Southeast US. Following that, we consider both SMAP data and observed streamflow for improving S2S streamflow and soil moisture forecasts for a pilot study area, Tar River basin, in NC. Towards this, we consider variational assimilation (VAR) of gauge-measured daily streamflow data in improving initial hydrologic conditions of Variable Infiltration Capacity (VIC) model. The utility of data assimilation is then assessed in improving S2S forecasts of streamflow and soil moisture through a retrospective analyses. Furthermore, the optimal frequency of data assimilation and optimal analysis window (number of past observations to use) are also assessed in order to achieve the maximum improvement in S2S forecasts of streamflow and soil moisture. Potential utility of updating initial conditions using DA and providing skillful forcings are also discussed.

Parameterization of the ACRU model for estimating biophysical and climatological change impacts, Beaver Creek, Alberta

NASA Astrophysics Data System (ADS)

Forbes, K. A.; Kienzle, S. W.; Coburn, C. A.; Byrne, J. M.

2006-12-01

Multiple threats, including intensification of agricultural production, non-renewable resource extraction and climate change, are threatening Southern Alberta's water supply. The objective of this research is to calibrate/evaluate the Agricultural Catchments Research Unit (ACRU) agrohydrological model; with the end goal of forecasting the impacts of a changing environment on water quantity. The strength of this model is the intensive multi-layered soil water budgeting routine that integrates water movement between the surface and atmosphere. The ACRU model was parameterized using data from Environment Canada's climate database for a twenty year period (1984-2004) and was used to simulate streamflow for Beaver Creek. The simulated streamflow was compared to Environment Canada's historical streamflow database to validate the model output. The Beaver Creek Watershed, located in the Porcupine Hills southwestern Alberta, Canada contains a heterogeneous cover of deciduous, coniferous, native prairie grasslands and forage crops. In a catchment with highly diversified land cover, canopy architecture cannot be overlooked in rainfall interception parameterization. Preliminary testing of ACRU suggests that streamflows were sensitive to varied levels of leaf area index (LAI), a representative fraction of canopy foliage. Further testing using remotely sensed LAI's will provide a more accurate representation of canopy foliage and ultimately best represent this important element of the hydrological cycle and the associated processes which govern the natural hydrology of the Beaver Creek watershed.

Estimates of ground-water recharge, base flow, and stream reach gains and losses in the Willamette River basin, Oregon

USGS Publications Warehouse

Lee, Karl K.; Risley, John C.

2002-03-19

Precipitation-runoff models, base-flow-separation techniques, and stream gain-loss measurements were used to study recharge and ground-water surface-water interaction as part of a study of the ground-water resources of the Willamette River Basin. The study was a cooperative effort between the U.S. Geological Survey and the State of Oregon Water Resources Department. Precipitation-runoff models were used to estimate the water budget of 216 subbasins in the Willamette River Basin. The models were also used to compute long-term average recharge and base flow. Recharge and base-flow estimates will be used as input to a regional ground-water flow model, within the same study. Recharge and base-flow estimates were made using daily streamflow records. Recharge estimates were made at 16 streamflow-gaging-station locations and were compared to recharge estimates from the precipitation-runoff models. Base-flow separation methods were used to identify the base-flow component of streamflow at 52 currently operated and discontinued streamflow-gaging-station locations. Stream gain-loss measurements were made on the Middle Fork Willamette, Willamette, South Yamhill, Pudding, and South Santiam Rivers, and were used to identify and quantify gaining and losing stream reaches both spatially and temporally. These measurements provide further understanding of ground-water/surface-water interactions.

Quantifying the Uncertainty in Streamflow Predictions Using Swat for Brazos-Colorado Coastal Watershed, Texas

NASA Astrophysics Data System (ADS)

Mandal, D.; Bhatia, N.; Srivastav, R. K.

2016-12-01

Soil Water Assessment Tool (SWAT) is one of the most comprehensive hydrologic models to simulate streamflow for a watershed. The two major inputs for a SWAT model are: (i) Digital Elevation Models (DEM), and (ii) Land Use and Land Cover Maps (LULC). This study aims to quantify the uncertainty in streamflow predictions using SWAT for San Bernard River in Brazos-Colorado coastal watershed, Texas, by incorporating the respective datasets from different sources: (i) DEM data will be obtained from ASTER GDEM V2, GMTED2010, NHD DEM, and SRTM DEM datasets with ranging resolution from 1/3 arc-second to 30 arc-second, and (ii) LULC data will be obtained from GLCC V2, MRLC NLCD2011, NOAA's C-CAP, USGS GAP, and TCEQ databases. Weather variables (Precipitation and Max-Min Temperature at daily scale) will be obtained from National Climatic Data Centre (NCDC) and SWAT in-built STASGO tool will be used to obtain the soil maps. The SWAT model will be calibrated using SWAT-CUP SUFI-2 approach and its performance will be evaluated using the statistical indices of Nash-Sutcliffe efficiency (NSE), ratio of Root-Mean-Square-Error to standard deviation of observed streamflow (RSR), and Percent-Bias Error (PBIAS). The study will help understand the performance of SWAT model with varying data sources and eventually aid the regional state water boards in planning, designing, and managing hydrologic systems.

Combined use of local and global hydrometeorological data with regional and global hydrological models in the Magdalena - Cauca river basin, Colombia

NASA Astrophysics Data System (ADS)

Rodriguez, Erasmo; Sanchez, Ines; Duque, Nicolas; Lopez, Patricia; Kaune, Alexander; Werner, Micha; Arboleda, Pedro

2017-04-01

The Magdalena Cauca Macrobasin (MCMB) in Colombia, with an area of about 257,000 km2, is the largest and most important water resources system in the country. With almost 80% of the Colombian population (46 million people) settled in the basin, it is the main source of water for demands including human consumption, agriculture, hydropower generation, industrial activities and ecosystems. Despite its importance, the basin has witnessed enormous changes in land-cover and extensive deforestation during the last three decades. To make things more complicated, the MCMB currently lacks a set of tools to support planning and decision making processes at scale of the whole watershed. Considering this, the MCMB has been selected as one of the six different regional case studies in the eartH2Observe research project, in which hydrological and meteorological reanalysis products are being validated for the period 1980-2012. The combined use of the hydrological and meteorological reanalysis data, with local hydrometeorological data (precipitation, temperature and streamflow) provided by the National Hydrometeorological Agency (IDEAM), has given us the opportunity to implement and test three hydrological models (VIC, WFLOW and a Water Balance Model based on the Budyko framework) at the basin scale. Additionally, results from the global models in the eartH2Observe hydrological reanalysis have been used to evaluate their performance against the observed streamflow data. This paper discusses the comparison between streamflow observations and simulations from the global hydrological models forced with the WFDEI data, and regional models forced with a combination of observed and meteorological reanalysis data, in the whole domain of the MCMB. For the three regional models analysed results show good performances for some sub-basins and poor performances for others. This can be due to the smoothing of the precipitation fields, interpolated from point daily rainfall data, the effect of horizontal precipitation (not included in the models) and weaknesses in the models structures; for example the poor performance of the VIC model in base flow dominated basins. In order to improve these simulations a strategy based on a hydrological model ensemble is currently being developed in the case study. Results from the global models, show that these consistently tend to overestimate runoff. This may be due to the coarse resolution used (50 km), biases in the ERA-Interim precipitation forcing, and the different partitioning within the global models of the precipitation into evapotranspiration and runoff. It is expected that within the Tier II hydrological reanalysis, where the models will produce outputs at 25 km resolution, some improvements may be identified.

Effects of Simulated Land-Use Changes on Water Quality of Lake Maumelle, Arkansas

USGS Publications Warehouse

Hart, Rheannon M.; Westerman, Drew A.; Petersen, James C.; Green, W. Reed; De Lanois, Jeanne L.

2011-01-01

Lake Maumelle is one of two principal drinking-water supplies for the Little Rock and North Little Rock metropolitan areas. Lake Maumelle and the Maumelle River (its primary tributary) are more pristine than most other reservoirs and streams in the region. However, as the Lake Maumelle watershed becomes increasingly more urbanized and timber harvesting becomes more frequent, concerns about the sustainability of the quality of the water supply also have increased. Two models were developed to partially address these concerns. A Hydrological Simulation Program-FORTRAN model was developed using input data collected from October 2004 through 2008. A CE-QUAL-W2 model was developed to simulate reservoir hydrodynamics and selected water quality using the simulated output from the Hydrological Simulation Program-FORTRAN model from January 2005 through 2008. The Hydrological Simulation Program-FORTRAN watershed model was calibrated to five streamflow-gaging stations, and in general, these stations characterize a range of subwatershed areas with varying land-use types. Continuous streamflow data, discrete sediment concentration data, and other discrete water-quality data were used to calibrate the Lake Maumelle Hydrological Simulation Program-FORTRAN model. The CE-QUAL-W2 reservoir model was calibrated to water-quality data and reservoir pool altitude collected during January 2005 through December 2008 at three lake stations. In general, the overall simulation for the Hydrological Simulation Program-FORTRAN and CE-UAL-W2 models matched reasonably well to the measured data. In general, simulated and measured suspended-sediment concentrations during periods of base flow (streamflows not substantially influenced by runoff) agree reasonably well for Williams Junction (with differences-simulated minus measured value-generally ranging from -14 to 19 mg/L, and percent difference-relative to the measured value-ranging from -87 to 642 percent) and Wye (differences generally ranging from -2 to 14 mg/L, -62 to 251 percent); however, the Hydrological Simulation Program-FORTRAN model generally does not match the suspended-sediment concentrations for all stations during periods of stormflow (streamflow substantially influenced by runoff). Generally, this is also the case for fecal coliform bacteria numbers and total organic carbon and nutrient concentrations. In general, water temperature and dissolved-oxygen concentration simulations followed measured seasonal trends for all stations with the largest differences occurring during periods of lowest water temperatures (for temperature) or during the periods of lowest measured dissolved-oxygen concentrations (for dissolved oxygen). For the CE-QUAL-W2 model, simulated vertical distributions of temperatures and dissolved-oxygen concentrations agreed with measured distributions even for complex temperature profiles. Considering the oligotrophic-mesotrophic (low to intermediate primary productivity and associated low nutrient concentrations) condition of Lake Maumelle, simulated algae, phosphorus, and ammonia concentrations compared well with generally low measured values.

Results of streamflow gain-loss studies in Texas, with emphasis on gains from and losses to major and minor aquifers, Texas, 2000

USGS Publications Warehouse

Slade, Raymond M.; Bentley, J. Taylor; Michaud, Dana

2002-01-01

Data for all 366 known streamflow gain-loss studies conducted by the U.S. Geological Survey in Texas were aggregated. A water-budget equation that includes discharges for main channels, tributaries, return flows, and withdrawals was used to document the channel gain or loss for each of 2,872 subreaches for the studies. The channel gain or loss represents discharge from or recharge to aquifers crossed by the streams. Where applicable, the major or minor aquifer outcrop traversed by each subreach was identified, as was the length and location for each subreach. These data will be used to estimate recharge or discharge for major and minor aquifers in Texas, as needed by the Ground-Water Availability Modeling Program being conducted by the Texas Water Development Board. The data also can be used, along with current flow rates for streamflow-gaging stations, to estimate streamflow at sites remote from gaging stations, including sites where streamflow availability is needed for permitted withdrawals.

Simulating the impacts of groundwater pumping on stream aquifer dynamics in semiarid northwestern Oklahoma, USA

NASA Astrophysics Data System (ADS)

Zume, Joseph; Tarhule, Aondover

2008-06-01

Visual MODFLOW, a numerical groundwater flow model, was used to evaluate the impacts of groundwater exploitation on streamflow depletion in the Alluvium and Terrace aquifer of the Beaver-North Canadian River (BNCR) in northwestern Oklahoma, USA. Water demand in semi-arid northwestern Oklahoma is projected to increase by 53% during the next five decades, driven primarily by irrigation, public water supply, and agricultural demand. Using MODFLOW’s streamflow routing package, pumping-induced changes in baseflow and stream leakage were analyzed to estimate streamflow depletion in the BNCR system. Simulation results indicate groundwater pumping has reduced baseflow to streams by approximately 29% and has also increased stream leakage into the aquifer by 18% for a net streamflow loss of 47%. The magnitude and intensity of streamflow depletion, however, varies for different stream segments, ranging from 0 to 20,804 m3/d. The method provides a framework for isolating and quantifying impacts of aquifer pumping on stream function in semiarid alluvial environments.

Ordinary kriging as a tool to estimate historical daily streamflow records

USGS Publications Warehouse

Farmer, William H.

2016-01-01

Efficient and responsible management of water resources relies on accurate streamflow records. However, many watersheds are ungaged, limiting the ability to assess and understand local hydrology. Several tools have been developed to alleviate this data scarcity, but few provide continuous daily streamflow records at individual streamgages within an entire region. Building on the history of hydrologic mapping, ordinary kriging was extended to predict daily streamflow time series on a regional basis. Pooling parameters to estimate a single, time-invariant characterization of spatial semivariance structure is shown to produce accurate reproduction of streamflow. This approach is contrasted with a time-varying series of variograms, representing the temporal evolution and behavior of the spatial semivariance structure. Furthermore, the ordinary kriging approach is shown to produce more accurate time series than more common, single-index hydrologic transfers. A comparison between topological kriging and ordinary kriging is less definitive, showing the ordinary kriging approach to be significantly inferior in terms of Nash–Sutcliffe model efficiencies while maintaining significantly superior performance measured by root mean squared errors. Given the similarity of performance and the computational efficiency of ordinary kriging, it is concluded that ordinary kriging is useful for first-order approximation of daily streamflow time series in ungaged watersheds.

Estimating the Exceedance Probability of the Reservoir Inflow Based on the Long-Term Weather Outlooks

NASA Astrophysics Data System (ADS)

Huang, Q. Z.; Hsu, S. Y.; Li, M. H.

2016-12-01

The long-term streamflow prediction is important not only to estimate water-storage of a reservoir but also to the surface water intakes, which supply people's livelihood, agriculture, and industry. Climatology forecasts of streamflow have been traditionally used for calculating the exceedance probability curve of streamflow and water resource management. In this study, we proposed a stochastic approach to predict the exceedance probability curve of long-term streamflow with the seasonal weather outlook from Central Weather Bureau (CWB), Taiwan. The approach incorporates a statistical downscale weather generator and a catchment-scale hydrological model to convert the monthly outlook into daily rainfall and temperature series and to simulate the streamflow based on the outlook information. Moreover, we applied Bayes' theorem to derive a method for calculating the exceedance probability curve of the reservoir inflow based on the seasonal weather outlook and its imperfection. The results show that our approach can give the exceedance probability curves reflecting the three-month weather outlook and its accuracy. We also show how the improvement of the weather outlook affects the predicted exceedance probability curves of the streamflow. Our approach should be useful for the seasonal planning and management of water resource and their risk assessment.

Relation of nitrate concentrations to baseflow in the Raccoon River, Iowa

USGS Publications Warehouse

Schilling, K.E.; Lutz, D.S.

2004-01-01

Excessive nitrate-nitrogen (nitrate) export from the Raccoon River in west central Iowa is an environmental concern to downstream receptors. The 1972 to 2000 record of daily streamflow and the results from 981 nitrate measurements were examined to describe the relation of nitrate to streamflow in the Raccoon River. No long term trends in streamflow and nitrate concentrations were noted in the 28-year record. Strong seasonal patterns were evident in nitrate concentrations, with higher concentrations occurring in spring and fall. Nitrate concentrations were linearly related to streamflow at daily, monthly, seasonal, and annual time scales. At all time scales evaluated, the relation was improved when baseflow was used as the discharge variable instead of total streamflow. Nitrate concentrations were found to be highly stratified according to flow, but there was little relation of nitrate to streamflow within each flow range. Simple linear regression models developed to predict monthly mean nitrate concentrations explained as much as 76 percent of the variability in the monthly nitrate concentration data for 2001. Extrapolation of current nitrate baseflow relations to historical conditions in the Raccoon River revealed that increasing baseflow over the 20th century could account for a measurable increase in nitrate concentrations.

Contribution of Soil Moisture Information to Streamflow Prediction in the Snowmelt Season: A Continental-Scale Analysis

NASA Technical Reports Server (NTRS)

Reichle, Rolf; Mahanama, Sarith; Koster, Randal; Lettenmaier, Dennis

2009-01-01

In areas dominated by winter snowcover, the prediction of streamflow during the snowmelt season may benefit from three pieces of information: (i) the accurate prediction of weather variability (precipitation, etc.) leading up to and during the snowmelt season, (ii) estimates of the amount of snow present during the winter season, and (iii) estimates of the amount of soil moisture underlying the snowpack during the winter season. The importance of accurate meteorological predictions and wintertime snow estimates is obvious. The contribution of soil moisture to streamflow prediction is more subtle yet potentially very important. If the soil is dry below the snowpack, a significant fraction of the snowmelt may be lost to streamflow and potential reservoir storage, since it may infiltrate the soil instead for later evaporation. Such evaporative losses are presumably smaller if the soil below the snowpack is wet. In this paper, we use a state-of-the-art land surface model to quantify the contribution of wintertime snow and soil moisture information -- both together and separately -- to skill in forecasting springtime streamflow. We find that soil moisture information indeed contributes significantly to streamflow prediction skill.

Reconstruction of missing daily streamflow data using dynamic regression models

NASA Astrophysics Data System (ADS)

Tencaliec, Patricia; Favre, Anne-Catherine; Prieur, Clémentine; Mathevet, Thibault

2015-12-01

River discharge is one of the most important quantities in hydrology. It provides fundamental records for water resources management and climate change monitoring. Even very short data-gaps in this information can cause extremely different analysis outputs. Therefore, reconstructing missing data of incomplete data sets is an important step regarding the performance of the environmental models, engineering, and research applications, thus it presents a great challenge. The objective of this paper is to introduce an effective technique for reconstructing missing daily discharge data when one has access to only daily streamflow data. The proposed procedure uses a combination of regression and autoregressive integrated moving average models (ARIMA) called dynamic regression model. This model uses the linear relationship between neighbor and correlated stations and then adjusts the residual term by fitting an ARIMA structure. Application of the model to eight daily streamflow data for the Durance river watershed showed that the model yields reliable estimates for the missing data in the time series. Simulation studies were also conducted to evaluate the performance of the procedure.

Documentation of a dissolved-solids model of the Tongue River, southeastern Montana

USGS Publications Warehouse

Woods, Paul F.

1981-01-01

A model has been developed for assessing potential increases in dissolved solids of the Tongue River as a result of leaching of overburden materials used to backfill pits in surface coal-mining operations. The model allows spatial and temporal simulation of streamflow and dissolved-solids loads and concentrations under user-defined scenarios of surface coal mining and agricultural development. The model routes an input quantity of streamflow and dissolved solids from the upstream end to the downstream end of a stream reach while algebraically accounting for gains and losses of streamflow and dissolved solids within the stream reach. Input data needed to operate the model include the following: simulation number, designation of hydrologic conditions for each simulated month, either user-defined or regression-defined concentrations of dissolved solids input by the Tongue River Reservoir, number of irrigated acres, number of mined acres, dissolved-solids concentration of mine leachates and quantity of other water losses. A listing of the Fortran computer program, definitions of all variables in the model, and an example output permit use of the model by interested persons. (USGS)

Analysis of snow-glacial historical and projected flows in Olivares river basin. Comparison between DHSVM and WEAP models.

NASA Astrophysics Data System (ADS)

Cepeda, Javier; Vargas, Ximena

2017-04-01

In the Andes Mountains, in central Chile, glaciers are a key element to both environment and economy, since they contribute highly to streamflow during the summer season. Many studies have been performed in order to understand the actual contribution of glacial-based streamflow and the expected response of glaciers to climatological alterations such as climate change. This work studies and analyses the historical and future streamflow on the Olivares river basin, located close to Chile's capital city, Santiago, under climatic change scenario RCP8.5. For this, we use two hydrological models with different topology, to have more consistency in the results, and analysing the differences because of the conceptualization of the processes and its spatial scale. DHSVM is a distributed, physically based model, while WEAP is a semi-distributed model that represents some processes conceptually and others physically based. Both models are calibrated considering streamflow and snow cover data from the period 2001-2012 at a daily scale. Additionally, comparisons between the modelled glacier area variations and LANDSAT images are performed to strengthen the calibration process. Climate change projections are obtained from five Global Circulation Models (GCM) under RCP8.5 scenario. Changes in glacier area, volume and glacial streamflow contribution to basin discharge are analysed, comparing two future time lapses, near-future period (2015-2044) and far-future (2045-2074), to a baseline period (1985-2004). The basin has an area of 543 km2, with elevations ranging from 1,528 to 6,024 m.a.s.l. and an important glacier presence. According to the National Glacier Cadastre developed by Chile Water Authority (DGA) in 2012, there are 80 uncovered glaciers within the basin, the most important being Juncal Sur, Olivares Alfa, Beta and Gamma. Glacier area represented 17% of the basin in 1985, while they made up only to 11% in 2015.The glaciers are located at altitudes ranging from 3,500 to 6,000 m.a.s.l., most on the vicinity of 4,500 m.a.s.l. Analysing variations in meteorological information between baseline, for the near and far future periods we obtain an increase of 1.3°C and 2.9°C respectively. Analogously, a decrease of 33.6 mm and 93.2 mm for the annual precipitation is projected for the same corresponding periods. Results from both models show that most of the glacial area will have melted away by the end of the far-future period, with only 1.2 km2 and 6.8 km2 remaining, according to DHSVM and WEAP models respectively. Also for the far future period, total streamflow decreases respect to baseline period between 15 and 46%, while glacier streamflow decreases between 53 and 85% in far future, depending of the GCM and hydrological model used.

Assessment of surface water resources availability using catchment modeling and the results of tracer studies in the meso-scale Migina Catchment, Rwanda

NASA Astrophysics Data System (ADS)

Munyaneza, O.; Mukubwa, A.; Maskey, S.; Wenninger, J.; Uhlenbrook, S.

2013-12-01

In the last couple of years, different hydrological research projects were undertaken in the Migina catchment (243.2 km2), a tributary of the Kagera river in Southern Rwanda. These projects were aimed to understand hydrological processes of the catchment using analytical and experimental approaches and to build a pilot case whose experience can be extended to other catchments in Rwanda. In the present study, we developed a hydrological model of the catchment, which can be used to inform water resources planning and decision making. The semi-distributed hydrological model HEC-HMS (version 3.5) was used with its soil moisture accounting, unit hydrograph, liner reservoir (for base flow) and Muskingum-Cunge (river routing) methods. We used rainfall data from 12 stations and streamflow data from 5 stations, which were collected as part of this study over a period of two years (May 2009 and June 2011). The catchment was divided into five sub-catchments each represented by one of the five observed streamflow gauges. The model parameters were calibrated separately for each sub-catchment using the observed streamflow data. Calibration results obtained were found acceptable at four stations with a Nash-Sutcliffe Model Efficiency of 0.65 on daily runoff at the catchment outlet. Due to the lack of sufficient and reliable data for longer periods, a model validation (split sample test) was not undertaken. However, we used results from tracer based hydrograph separation from a previous study to compare our model results in terms of the runoff components. It was shown that the model performed well in simulating the total flow volume, peak flow and timing as well as the portion of direct runoff and base flow. We observed considerable disparities in the parameters (e.g. groundwater storage) and runoff components across the five sub-catchments, that provided insights into the different hydrological processes at sub-catchment scale. We conclude that such disparities justify the need to consider catchment subdivisions, if such parameters and components of the water cycle are to form the base for decision making in water resources planning in the Migina catchment.

Hydrologic drought prediction under climate change: Uncertainty modeling with Dempster-Shafer and Bayesian approaches

NASA Astrophysics Data System (ADS)

Raje, Deepashree; Mujumdar, P. P.

2010-09-01

Representation and quantification of uncertainty in climate change impact studies are a difficult task. Several sources of uncertainty arise in studies of hydrologic impacts of climate change, such as those due to choice of general circulation models (GCMs), scenarios and downscaling methods. Recently, much work has focused on uncertainty quantification and modeling in regional climate change impacts. In this paper, an uncertainty modeling framework is evaluated, which uses a generalized uncertainty measure to combine GCM, scenario and downscaling uncertainties. The Dempster-Shafer (D-S) evidence theory is used for representing and combining uncertainty from various sources. A significant advantage of the D-S framework over the traditional probabilistic approach is that it allows for the allocation of a probability mass to sets or intervals, and can hence handle both aleatory or stochastic uncertainty, and epistemic or subjective uncertainty. This paper shows how the D-S theory can be used to represent beliefs in some hypotheses such as hydrologic drought or wet conditions, describe uncertainty and ignorance in the system, and give a quantitative measurement of belief and plausibility in results. The D-S approach has been used in this work for information synthesis using various evidence combination rules having different conflict modeling approaches. A case study is presented for hydrologic drought prediction using downscaled streamflow in the Mahanadi River at Hirakud in Orissa, India. Projections of n most likely monsoon streamflow sequences are obtained from a conditional random field (CRF) downscaling model, using an ensemble of three GCMs for three scenarios, which are converted to monsoon standardized streamflow index (SSFI-4) series. This range is used to specify the basic probability assignment (bpa) for a Dempster-Shafer structure, which represents uncertainty associated with each of the SSFI-4 classifications. These uncertainties are then combined across GCMs and scenarios using various evidence combination rules given by the D-S theory. A Bayesian approach is also presented for this case study, which models the uncertainty in projected frequencies of SSFI-4 classifications by deriving a posterior distribution for the frequency of each classification, using an ensemble of GCMs and scenarios. Results from the D-S and Bayesian approaches are compared, and relative merits of each approach are discussed. Both approaches show an increasing probability of extreme, severe and moderate droughts and decreasing probability of normal and wet conditions in Orissa as a result of climate change.

Climate-driven disturbances in the San Juan River sub-basin of the Colorado River

DOE PAGES

Bennett, Katrina E.; Bohn, Theodore J.; Solander, Kurt; ...

2018-01-26

Accelerated climate change and associated forest disturbances in the southwestern USA are anticipated to have substantial impacts on regional water resources. Few studies have quantified the impact of both climate change and land cover disturbances on water balances on the basin scale, and none on the regional scale. In this work, we evaluate the impacts of forest disturbances and climate change on a headwater basin to the Colorado River, the San Juan River watershed, using a robustly calibrated (Nash–Sutcliffe efficiency 0.76) hydrologic model run with updated formulations that improve estimates of evapotranspiration for semi-arid regions. Our results show that futuremore » disturbances will have a substantial impact on streamflow with implications for water resource management. Our findings are in contradiction with conventional thinking that forest disturbances reduce evapotranspiration and increase streamflow. In this study, annual average regional streamflow under the coupled climate–disturbance scenarios is at least 6–11 % lower than those scenarios accounting for climate change alone; for forested zones of the San Juan River basin, streamflow is 15–21 % lower. The monthly signals of altered streamflow point to an emergent streamflow pattern related to changes in forests of the disturbed systems. Exacerbated reductions of mean and low flows under disturbance scenarios indicate a high risk of low water availability for forested headwater systems of the Colorado River basin. Furthermore, these findings also indicate that explicit representation of land cover disturbances is required in modeling efforts that consider the impact of climate change on water resources.« less

Changes in the relation between snow station observations and basin scale snow water resources

NASA Astrophysics Data System (ADS)

Sexstone, G. A.; Penn, C. A.; Clow, D. W.; Moeser, D.; Liston, G. E.

2017-12-01

Snow monitoring stations that measure snow water equivalent or snow depth provide fundamental observations used for predicting water availability and flood risk in mountainous regions. In the western United States, snow station observations provided by the Natural Resources Conservation Service Snow Telemetry (SNOTEL) network are relied upon for forecasting spring and summer streamflow volume. Streamflow forecast accuracy has declined for many regions over the last several decades. Changes in snow accumulation and melt related to climate, land use, and forest cover are not accounted for in current forecasts, and are likely sources of error. Therefore, understanding and updating relations between snow station observations and basin scale snow water resources is crucial to improve accuracy of streamflow prediction. In this study, we investigated the representativeness of snow station observations when compared to simulated basin-wide snow water resources within the Rio Grande headwaters of Colorado. We used the combination of a process-based snow model (SnowModel), field-based measurements, and remote sensing observations to compare the spatiotemporal variability of simulated basin-wide snow accumulation and melt with that of SNOTEL station observations. Results indicated that observations are comparable to simulated basin-average winter precipitation but overestimate both the simulated basin-average snow water equivalent and snowmelt rate. Changes in the representation of snow station observations over time in the Rio Grande headwaters were also investigated and compared to observed streamflow and streamflow forecasting errors. Results from this study provide important insight in the context of non-stationarity for future water availability assessments and streamflow predictions.

Climate-driven disturbances in the San Juan River sub-basin of the Colorado River

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bennett, Katrina E.; Bohn, Theodore J.; Solander, Kurt

Accelerated climate change and associated forest disturbances in the southwestern USA are anticipated to have substantial impacts on regional water resources. Few studies have quantified the impact of both climate change and land cover disturbances on water balances on the basin scale, and none on the regional scale. In this work, we evaluate the impacts of forest disturbances and climate change on a headwater basin to the Colorado River, the San Juan River watershed, using a robustly calibrated (Nash–Sutcliffe efficiency 0.76) hydrologic model run with updated formulations that improve estimates of evapotranspiration for semi-arid regions. Our results show that futuremore » disturbances will have a substantial impact on streamflow with implications for water resource management. Our findings are in contradiction with conventional thinking that forest disturbances reduce evapotranspiration and increase streamflow. In this study, annual average regional streamflow under the coupled climate–disturbance scenarios is at least 6–11 % lower than those scenarios accounting for climate change alone; for forested zones of the San Juan River basin, streamflow is 15–21 % lower. The monthly signals of altered streamflow point to an emergent streamflow pattern related to changes in forests of the disturbed systems. Exacerbated reductions of mean and low flows under disturbance scenarios indicate a high risk of low water availability for forested headwater systems of the Colorado River basin. Furthermore, these findings also indicate that explicit representation of land cover disturbances is required in modeling efforts that consider the impact of climate change on water resources.« less

Analysis of streambed temperatures in ephemeral channels to determine streamflow frequency and duration

USGS Publications Warehouse

Constantz, James E.; Stonestrom, David A.; Stewart, Amy E.; Niswonger, Richard G.; Smith, Tyson R.

2001-01-01

Spatial and temporal patterns in streamflow are rarely monitored for ephemeral streams. Flashy, erosive streamflows common in ephemeral channels create a series of operational and maintenance problems, which makes it impractical to deploy a series of gaging stations along ephemeral channels. Streambed temperature is a robust and inexpensive parameter to monitor remotely, leading to the possibility of analyzing temperature patterns to estimate streamflow frequency and duration along ephemeral channels. A simulation model was utilized to examine various atmospheric and hydrological upper boundary conditions compared with a series of hypothetical temperature‐monitoring depths within the streambed. Simulation results indicate that streamflow events were distinguished from changing atmospheric conditions with greater certainty using temperatures at shallow depths (e.g., 10–20 cm) as opposed to the streambed surface. Three ephemeral streams in the American Southwest were instrumented to monitor streambed temperature for determining the accuracy of using this approach to ascertain the long‐term temporal and spatial extent of streamflow along each stream channel. Streambed temperature data were collected at the surface or at shallow depth along each stream channel, using thermistors encased in waterproof, single‐channel data loggers tethered to anchors in the channel. On the basis of comparisons with site information, such as direct field observations and upstream flow records, diurnal temperature variations successfully detected the presence and duration of streamflow for all sites.

Using satellite-based rainfall estimates for streamflow modelling: Bagmati Basin

USGS Publications Warehouse

Shrestha, M.S.; Artan, Guleid A.; Bajracharya, S.R.; Sharma, R. R.

2008-01-01

In this study, we have described a hydrologic modelling system that uses satellite-based rainfall estimates and weather forecast data for the Bagmati River Basin of Nepal. The hydrologic model described is the US Geological Survey (USGS) Geospatial Stream Flow Model (GeoSFM). The GeoSFM is a spatially semidistributed, physically based hydrologic model. We have used the GeoSFM to estimate the streamflow of the Bagmati Basin at Pandhera Dovan hydrometric station. To determine the hydrologic connectivity, we have used the USGS Hydro1k DEM dataset. The model was forced by daily estimates of rainfall and evapotranspiration derived from weather model data. The rainfall estimates used for the modelling are those produced by the National Oceanic and Atmospheric Administration Climate Prediction Centre and observed at ground rain gauge stations. The model parameters were estimated from globally available soil and land cover datasets – the Digital Soil Map of the World by FAO and the USGS Global Land Cover dataset. The model predicted the daily streamflow at Pandhera Dovan gauging station. The comparison of the simulated and observed flows at Pandhera Dovan showed that the GeoSFM model performed well in simulating the flows of the Bagmati Basin.

Testing the ability of a semidistributed hydrological model to simulate contributing area

NASA Astrophysics Data System (ADS)

Mengistu, S. G.; Spence, C.

2016-06-01

A dry climate, the prevalence of small depressions, and the lack of a well-developed drainage network are characteristics of environments with extremely variable contributing areas to runoff. These types of regions arguably present the greatest challenge to properly understanding catchment streamflow generation processes. Previous studies have shown that contributing area dynamics are important for streamflow response, but the nature of the relationship between the two is not typically understood. Furthermore, it is not often tested how well hydrological models simulate contributing area. In this study, the ability of a semidistributed hydrological model, the PDMROF configuration of Environment Canada's MESH model, was tested to determine if it could simulate contributing area. The study focused on the St. Denis Creek watershed in central Saskatchewan, Canada, which with its considerable topographic depressions, exhibits wide variation in contributing area, making it ideal for this type of investigation. MESH-PDMROF was able to replicate contributing area derived independently from satellite imagery. Daily model simulations revealed a hysteretic relationship between contributing area and streamflow not apparent from the less frequent remote sensing observations. This exercise revealed that contributing area extent can be simulated by a semi-distributed hydrological model with a scheme that assumes storage capacity distribution can be represented with a probability function. However, further investigation is needed to determine if it can adequately represent the complex relationship between streamflow and contributing area that is such a key signature of catchment behavior.

Modeling the Effects of Land Use and Climate Change on Streamflow in the Delaware River Basin

NASA Astrophysics Data System (ADS)

Kwon, P. Y. S.; Endreny, T. A.; Kroll, C. N.; Williamson, T. N.

2014-12-01

Forest-cover loss and drinking-water reservoirs in the upper Delaware River Basin of New York may alter summer low streamflows, which could degrade the in-stream habitat for the endangered dwarf wedgemussel. Our project analyzes how flow statistics change with land-cover change for 30-year increments of model-simulated streamflow hydrographs for three watersheds of concern to the National Park Service: the East Branch, West Branch, and main stem of the Delaware River. We use four treatments for land cover ranging from historical high to low forest cover. We subject each land cover to adjusted GCM climate scenarios for 1600, 1900, 1940, and 2040 to isolate land cover from potential climate-change effects. Hydrographs are simulated using the Water Availability Tool for Environmental Resources (WATER), a TOPMODEL-based United States Geological Survey hydrologic decision-support tool, which uses the variable-source-area concept and water budgets to generate streamflow. Model parameters for each watershed change with land-use, and capture differences in soil-physical properties that control how rainfall infiltrates, evaporates, transpires, is stored in the soil, and moves to the stream. Our results analyze flow statistics used as indicators of hydrologic alteration, and access streamflow events below the critical flow needed to provide sustainable habitat for dwarf wedgemussels. These metrics will demonstrate how changes in climate and land use might affect flow statistics. Initial results show that the 1940 WATER simulation outputs generally match observed unregulated low flows from that time period, while performance for regulated flow from the same time period and from 1600, 1900, and 2040 require model input adjustments. Our study will illustrate how increased forest cover could potentially restore in-stream habitat for the endangered dwarf wedgemussel for current and future climate conditions.

Role of surface-water and groundwater interactions on projected summertime streamflow in snow dominated regions : An integrated modeling approach

USGS Publications Warehouse

Huntington, Justin L.; Niswonger, Richard G.

2012-01-01

Previous studies indicate predominantly increasing trends in precipitation across the Western United States, while at the same time, historical streamflow records indicate decreasing summertime streamflow and 25th percentile annual flows. These opposing trends could be viewed as paradoxical, given that several studies suggest that increased annual precipitation will equate to increased annual groundwater recharge, and therefore increased summertime flow. To gain insight on mechanisms behind these potential changes, we rely on a calibrated, integrated surface and groundwater model to simulate climate impacts on surface water/groundwater interactions using 12 general circulation model projections of temperature and precipitation from 2010 to 2100, and evaluate the interplay between snowmelt timing and other hydrologic variables, including streamflow, groundwater recharge, storage, groundwater discharge, and evapotranspiration. Hydrologic simulations show that the timing of peak groundwater discharge to the stream is inversely correlated to snowmelt runoff and groundwater recharge due to the bank storage effect and reversal of hydraulic gradients between the stream and underlying groundwater. That is, groundwater flow to streams peaks following the decrease in stream depth caused by snowmelt recession, and the shift in snowmelt causes a corresponding shift in groundwater discharge to streams. Our results show that groundwater discharge to streams is depleted during the summer due to earlier drainage of shallow aquifers adjacent to streams even if projected annual precipitation and groundwater recharge increases. These projected changes in surface water/groundwater interactions result in more than a 30% decrease in the projected ensemble summertime streamflow. Our findings clarify causality of observed decreasing summertime flow, highlight important aspects of potential climate change impacts on groundwater resources, and underscore the need for integrated hydrologic models in climate change studies.

Assessment of groundwater/surface-water interaction and simulation of potential streamflow depletion induced by groundwater withdrawal, Uinta River near Roosevelt, Utah

USGS Publications Warehouse

Lambert, P.M.; Marston, T.; Kimball, B.A.; Stolp, B.J.

2011-01-01

Roosevelt City, Utah, asserts a need for an additional supply of water to meet municipal demands and has identified a potential location for additional groundwater development at the Sprouse well field near the West Channel of the Uinta River. Groundwater is commonly hydraulically linked to surface water and, under some conditions, the pumpage of groundwater can deplete water in streams and other water bodies. In 2008, the U.S. Geological Survey, in cooperation with Roosevelt City, the Utah Department of Natural Resources, and the Ute Indian Tribe, began a study to improve understanding of the local interconnection between groundwater and surface water and to assess the potential for streamflow depletion from future groundwater withdrawals at a potential Roosevelt City development location—the Sprouse well field near the West Channel of the Uinta River.In the study, streamflow gains and losses at the river/aquifer boundary near the well field and changes in those conditions over time were assessed through (1) synoptic measurement of discharge in the stream at multiple sites using tracer-dilution methods, (2) periodic measurement of the vertical hydraulic gradient across the streambed, and (3) continuous measurement of stream and streambed water temperature using heat as a tracer of flow across the streambed. Although some contradictions among the results of the three assessment methods were observed, results of the approaches generally indicated (1) losing streamflow conditions on the West Channel of the Uinta River north of and upstream from the Sprouse well field within the study area, (2) gaining streamflow conditions south of and downstream from the well field, and (3) some seasonal changes in those conditions that correspond with seasonal changes in stream stage and local water-table altitudes.A numerical groundwater flow model was developed on the basis of previously reported observations and observations made during this study, and was used to estimate potential streamflow depletion that might result from future groundwater withdrawals at the Sprouse well field. The model incorporates concepts of transient groundwater flow conditions including fluctuations in groundwater levels and storage, and the distribution of and temporal variations in gains to and losses from streamflow in the West Channel of the Uinta River near the Sprouse well field. Two predictive model simulations incorporated additional future discharge from the Sprouse well field totaling 325 acre-feet annually and biennially during summer months. Results of the predictive model simulations indicate that the water withdrawn by the additional pumping was derived initially from aquifer storage and then, with time, predominantly from streamflow depletion. By the 10th year of the predictive simulation incorporating annual summer pumping from an additional public-supply well in the Sprouse well field, the simulation results indicate that 89 percent of a future annual 325 acre-feet of discharge is derived from depletion of streamflow in the West Channel of the Uinta River. A similar result was observed in a predictive model simulating the same discharge rate but with the new well being pumped every other year.

Changes in the flood frequency in the Mahanadi basin under observed and projected future climate

NASA Astrophysics Data System (ADS)

Modi, P. A.; Lakshmi, V.; Mishra, V.

2017-12-01

The Mahanadi river basin is vulnerable to multiple types of extreme events due to its topography and river networks. These extreme events are not efficiently captured by the current LSMs partly due to lack of spatial hydrological data and uncertainty in the models. This study compares and evaluates the hydrologic simulations of the recently developed community Noah model with multi-parameterization options which is an upgradation of baseline Noah LSM. The model is calibrated and validated for the Mahanadi river basin and is driven by major atmospheric forcing from the Indian Meteorological Department (IMD), Global Precipitation Measurement (GPM), Tropical rainfall Measurement Mission (TRMM) and Multi-Source Weighted-Ensemble Precipitation (MSWEP designed for hydrological modeling) precipitation datasets along with some additional forcing derived from the VIC model at 0.25-degree spatial resolution. The Noah-MP LSM is calibrated using observed daily streamflow data from 1978-1989 (India-WRIS) at the gauge stations with least human interventions with a Nash Sutcliffe Efficiency higher than 0.60. Noah MP was calibrated using different schemes for runoff with variation in all parameters sensitive to surface and sub-surface runoff. Streamflow routing was performed using a stand-alone model (VIC model) to route daily model runoff at required gauge station. Surface runoff is mainly affected by the uncertainties in major atmospheric forcing and highly sensitive parameters pertaining to soil properties. Noah MP is validated using observed streamflow from 1975-2010 which indicates the consistency of streamflow with the historical observations (NSE>0.65) thus indicating an increase in probability of future flood events.

Fusing enhanced radar precipitation, in-situ hydrometeorological measurements and airborne LIDAR snowpack estimates in a hyper-resolution hydrologic model to improve seasonal water supply forecasts

NASA Astrophysics Data System (ADS)

Gochis, D. J.; Busto, J.; Howard, K.; Mickey, J.; Deems, J. S.; Painter, T. H.; Richardson, M.; Dugger, A. L.; Karsten, L. R.; Tang, L.

2015-12-01

Scarcity of spatially- and temporally-continuous observations of precipitation and snowpack conditions in remote mountain watersheds results in fundamental limitations in water supply forecasting. These limitationsin observational capabilities can result in strong biases in total snowmelt-driven runoff amount, the elevational distribution of runoff, river basin tributary contributions to total basin runoff and, equally important for water management, the timing of runoff. The Upper Rio Grande River basin in Colorado and New Mexico is one basin where observational deficiencies are hypothesized to have significant adverse impacts on estimates of snowpack melt-out rates and on water supply forecasts. We present findings from a coordinated observational-modeling study within Upper Rio Grande River basin whose aim was to quanitfy the impact enhanced precipitation, meteorological and snowpack measurements on the simulation and prediction of snowmelt driven streamflow. The Rio Grande SNOwpack and streamFLOW (RIO-SNO-FLOW) Prediction Project conducted enhanced observing activities during the 2014-2015 water year. Measurements from a gap-filling, polarimetric radar (NOXP) and in-situ meteorological and snowpack measurement stations were assimilated into the WRF-Hydro modeling framework to provide continuous analyses of snowpack and streamflow conditions. Airborne lidar estimates of snowpack conditions from the NASA Airborne Snow Observatory during mid-April and mid-May were used as additional independent validations against the various model simulations and forecasts of snowpack conditions during the melt-out season. Uncalibrated WRF-Hydro model performance from simulations and forecasts driven by enhanced observational analyses were compared against results driven by currently operational data inputs. Precipitation estimates from the NOXP research radar validate significantly better against independent in situ observations of precipitation and snow-pack increases. Correcting the operational NLDAS2 forcing data with the experimental observations led to significant improvements in the seasonal accumulation and ablation of mountain snowpack and ultimately led to marked improvement in model simulated streamflow as compared with streamflow observations.

Making climate change projections relevant to water management: opportunities and challenges in the Colorado River basin (Invited)

NASA Astrophysics Data System (ADS)

Vano, J. A.

2013-12-01

By 2007, motivated by the ongoing drought and release of new climate model projections associated with the IPCC AR4 report, multiple independent studies had made estimates of future Colorado River streamflow. Each study had a unique approach, and unique estimate for the magnitude for mid-21st century streamflow change ranging from declines of only 6% to declines of as much as 45%. The differences among studies provided for interesting scientific debates, but to many practitioners this appeared to be just a tangle of conflicting predictions, leading to the question 'why is there such a wide range of projections of impacts of future climate change on Colorado River streamflow, and how should this uncertainty be interpreted?' In response, a group of scientists from academic and federal agencies, brought together through a NOAA cross-RISA project, set forth to identify the major sources of disparities and provide actionable science and guidance for water managers and decision makers. Through this project, four major sources of disparities among modeling studies were identified that arise from both methodological and model differences. These differences, in order of importance, are: (1) the Global Climate Models (GCMs) and emission scenarios used; (2) the ability of land surface hydrology and atmospheric models to simulate properly the high elevation runoff source areas; (3) the sensitivities of land surface hydrology models to precipitation and temperature changes; and (4) the methods used to statistically downscale GCM scenarios. Additionally, reconstructions of pre-instrumental streamflows provided further insights about the greatest risk to Colorado River streamflow of a multi-decadal drought, like those observed in paleo reconstructions, exacerbated by a steady reduction in flows due to climate change. Within this talk I will provide an overview of these findings and insights into the opportunities and challenges encountered in the process of striving to make climate change projections more useful to water managers and decision makers.

Evaluation of Hydrologic Simulations Developed Using Multi-Model Synthesis and Remotely-Sensed Data within a Portfolio of Calibration Strategies

NASA Astrophysics Data System (ADS)

Lafontaine, J.; Hay, L.; Markstrom, S. L.

2016-12-01

The United States Geological Survey (USGS) has developed a National Hydrologic Model (NHM) to support coordinated, comprehensive and consistent hydrologic model development, and facilitate the application of hydrologic simulations within the conterminous United States (CONUS). As many stream reaches in the CONUS are either not gaged, or are substantially impacted by water use or flow regulation, ancillary information must be used to determine reasonable parameter estimations for streamflow simulations. Hydrologic models for 1,576 gaged watersheds across the CONUS were developed to test the feasibility of improving streamflow simulations linking physically-based hydrologic models with remotely-sensed data products (i.e. snow water equivalent). Initially, the physically-based models were calibrated to measured streamflow data to provide a baseline for comparison across multiple calibration strategy tests. In addition, not all ancillary datasets are appropriate for application to all parts of the CONUS (e.g. snow water equivalent in the southeastern U.S., where snow is a rarity). As it is not expected that any one data product or model simulation will be sufficient for representing hydrologic behavior across the entire CONUS, a systematic evaluation of which data products improve hydrologic simulations for various regions across the CONUS was performed. The resulting portfolio of calibration strategies can be used to guide selection of an appropriate combination of modeled and measured information for hydrologic model development and calibration. In addition, these calibration strategies have been developed to be flexible so that new data products can be assimilated. This analysis provides a foundation to understand how well models work when sufficient streamflow data are not available and could be used to further inform hydrologic model parameter development for ungaged areas.

Interaction between stream temperature, streamflow, and groundwater exchanges in alpine streams

USGS Publications Warehouse

Constantz, James E.

1998-01-01

Four alpine streams were monitored to continuously collect stream temperature and streamflow for periods ranging from a week to a year. In a small stream in the Colorado Rockies, diurnal variations in both stream temperature and streamflow were significantly greater in losing reaches than in gaining reaches, with minimum streamflow losses occurring early in the day and maximum losses occurring early in the evening. Using measured stream temperature changes, diurnal streambed infiltration rates were predicted to increase as much as 35% during the day (based on a heat and water transport groundwater model), while the measured increase in streamflow loss was 40%. For two large streams in the Sierra Nevada Mountains, annual stream temperature variations ranged from 0° to 25°C. In summer months, diurnal stream temperature variations were 30–40% of annual stream temperature variations, owing to reduced streamflows and increased atmospheric heating. Previous reports document that one Sierra stream site generally gains groundwater during low flows, while the second Sierra stream site may lose water during low flows. For August the diurnal streamflow variation was 11% at the gaining stream site and 30% at the losing stream site. On the basis of measured diurnal stream temperature variations, streambed infiltration rates were predicted to vary diurnally as much as 20% at the losing stream site. Analysis of results suggests that evapotranspiration losses determined diurnal streamflow variations in the gaining reaches, while in the losing reaches, evapotranspiration losses were compounded by diurnal variations in streambed infiltration. Diurnal variations in stream temperature were reduced in the gaining reaches as a result of discharging groundwater of relatively constant temperature. For the Sierra sites, comparison of results with those from a small tributary demonstrated that stream temperature patterns were useful in delineating discharges of bank storage following dam releases. Direct coupling may have occurred between streamflow and stream temperature for losing stream reaches, such that reduced streamflows facilitated increased afternoon stream temperatures and increased afternoon stream temperatures induced increased streambed losses, leading to even greater increases in both stream temperature and streamflow losses.

Assessing the performance of a plastic optical fibre turbidity sensor for measuring post-fire erosion from plot to catchment scale

NASA Astrophysics Data System (ADS)

Keizer, J. J.; Martins, M. A. S.; Prats, S. A.; Santos, L. F.; Vieira, D. C. S.; Nogueira, R.; Bilro, L.

2015-09-01

This study is the first comprehensive testing of a novel plastic optical fibre turbidity sensor with runoff samples collected in the field and, more specifically, with a total of 158 streamflow samples and 925 overland flow samples from a recently burnt forest area in north-central Portugal, collected mainly during the first year after the wildfire, as well as with 56 overland flow samples from a nearby long-unburnt study site. Sediment concentrations differed less between overland flow and streamflow samples than between study sites and, at one study site, between plots with and without effective erosion mitigation treatments. Maximum concentrations ranged from 0.91 to 8.19 g L-1 for the micro-plot overland flow samples from the six burnt sites, from 1.74 to 8.99 g L-1 for the slope-scale overland flow samples from these same sites, and amounted to 4.55 g L-1 for the streamflow samples. Power functions provided (reasonably) good fits to the - expected - relationships of increasing normalized light loss with increasing sediment concentrations for the different sample types from individual study sites. The corresponding adjusted R2 values ranged from 0.64 to 0.81 in the case of the micro-plot samples from the six burnt sites, from 0.72 to 0.89 in the case of the slope-scale samples from these same sites, and was 0.85 in the case of the streamflow samples. While the overall performance of the sensor was thus rather satisfactory, the results pointed to the need for scale of site-specific calibrations to maximize the reliability of the predictions of sediment concentration by the POF (plastic optical fibre) sensor. This especially applied to the cases in which sediment concentrations were comparatively low, for example following mulching with forest residues.

An initial abstraction and constant loss model, and methods for estimating unit hydrographs, peak streamflows, and flood volumes for urban basins in Missouri

USGS Publications Warehouse

Huizinga, Richard J.

2014-01-01

The rainfall-runoff pairs from the storm-specific GUH analysis were further analyzed against various basin and rainfall characteristics to develop equations to estimate the peak streamflow and flood volume based on a quantity of rainfall on the basin.

Modifying WEPP to improve streamflow simulation in a Pacific Northwest watershed

Treesearch

A. Srivastava; M. Dobre; J. Q. Wu; W. J. Elliot; E. A. Bruner; S. Dun; E. S. Brooks; I. S. Miller

2013-01-01

The assessment of water yield from hillslopes into streams is critical in managing water supply and aquatic habitat. Streamflow is typically composed of surface runoff, subsurface lateral flow, and groundwater baseflow; baseflow sustains the stream during the dry season. The Water Erosion Prediction Project (WEPP) model simulates surface runoff, subsurface lateral flow...

A precipitation-runoff model for analysis of the effects of water withdrawals on streamflow, Ipswich River basin, Massachusetts

USGS Publications Warehouse

Zarriello, Phillip J.; Ries, Kernell G.

2000-01-01

Water withdrawals from the 155-square-mile Ipswich River Basin in northeastern Massachusetts affect aquatic habitat, water quality, and recreational use of the river. To better understand the effects of these withdrawals on streamflow, particularly low flow, the Hydrological Simulation Program-FORTRAN (HSPF) was used to develop a watershed-scale precipitation-runoff model of the Ipswich River to simulate its hydrology and complex water-use patterns.An analytical solution was used to compute time series of streamflow depletions resulting from ground-water withdrawals at wells. The flow depletions caused by pumping from the wells were summed along with any surface-water withdrawals to calculate the total withdrawal along a stream reach. The water withdrawals, records of precipitation, and streamflow records on the Ipswich River at South Middleton and at Ipswich for the period 1989?93 were used to calibrate the model. Model-fit analysis indicates that the simulated flows matched observed flows over a wide range of conditions; at a minimum, the coefficient of model-fit efficiency indicates that the model explained 79 percent of the variance in the observed daily flow.Six alternative water-withdrawal and land-use scenarios were simulated with the model. Three scenarios were examined for the 1989?93 calibration period, and three scenarios were examined for the 1961?95 period to test alternative withdrawals and land use over a wider range of climatic conditions, and to compute 1-, 7-, and 30-day low-flow frequencies using a log-Pearson Type III analysis. Flow-duration curves computed from results of the 1989?93 simulations indicate that, at the South Middleton and Ipswich gaging stations, streamflows when no water withdrawals are being made are nearly identical to streamflows when no ground-water withdrawals are made. Streamflow under no water withdrawals at both stations are about an order of magnitude larger at the 99.8 percent exceedence probability than simulations with only ground-water withdrawals. Long-term simulations indicate that the differences between streamflow with no water withdrawals and average 1989?93 water withdrawals is similar to the difference between simulations for the same water-use conditions made for the 1989?93 period at both sites. The 7-day, 10-year low-flow (7Q10, a widely used regulatory statistic) at the South Middleton station was 4.1 cubic feet per second (ft3/s) with no water withdrawals and 1991 land use, 5.8 ft3/s no withdrawals and undeveloped land, and 0.54 ft3/s with average 1989?93 water withdrawals and 1991 land use. The 7Q10 at the Ipswich station was about 8.3 ft3/s for simulations with no water withdrawals for both the 1991 land use and the undeveloped land conditions, and 2.7 ft3/s for simulations with average 1989?93 water withdrawals and 1991 land use. Simulation results indicate that surface-water withdrawals have little effect on the duration and frequency of low flows, but the cumulative ground-water withdrawals substantially decrease low flows.

Responses of streamflow and sediment load to climate change and human activity in the Upper Yellow River, China: a case of the Ten Great Gullies Basin.

PubMed

Liu, Tong; Huang, He Qing; Shao, Mingan; Yao, Wenyi; Gu, Jing; Yu, Guoan

2015-01-01

Soil erosion and land desertification are the most serious environmental problems globally. This study investigated the changes in streamflow and sediment load from 1964 to 2012 in the Ten Great Gullies area of the Upper Yellow River. Tests for gradual trends (Mann-Kendall test) and abrupt changes (Pettitt test) identify that significant declines in streamflow and sediment load occurred in 1997-1998 in two typical gullies. A comparison of climatic variability before and after the change points shows no statistically significant trends in annual precipitation and potential evapotranspiration. Human activities have been very active in the region and during 1990-2010, 146.01 and 197.62 km2 of land were converted, respectively, to forests and grassland, with corresponding increases of 87.56 and 77.05%. In addition, a large number of check dams have been built up in the upper reaches of the ten gullies. These measures were likely responsible for the significant decline in the annual streamflow and sediment load over the last 49 years.

Rainfall, streamflow, and peak stage data collected at the Murfreesboro, Tennessee, gaging network, March 1989 through July 1992

USGS Publications Warehouse

Outlaw, G.S.; Butner, D.E.; Kemp, R.L.; Oaks, A.T.; Adams, G.S.

1992-01-01

Rainfall, stage, and streamflow data in the Murfreesboro area, Middle Tennessee, were collected from March 1989 through July 1992 from a network of 68 gaging stations. The network consists of 10 tipping-bucket rain gages, 2 continuous-record streamflow gages, 4 partial-record flood hydrograph gages, and 72 crest-stage gages. Data collected by the gages includes 5minute time-step rainfall hyetographs, 15-minute time-step flood hydrographs, and peak-stage elevations. Data are stored in a computer data base and are available for many computer modeling and engineering applications.

Bayesian inference of uncertainties in precipitation-streamflow modeling in a snow affected catchment

NASA Astrophysics Data System (ADS)

Koskela, J. J.; Croke, B. W. F.; Koivusalo, H.; Jakeman, A. J.; Kokkonen, T.

2012-11-01

Bayesian inference is used to study the effect of precipitation and model structural uncertainty on estimates of model parameters and confidence limits of predictive variables in a conceptual rainfall-runoff model in the snow-fed Rudbäck catchment (142 ha) in southern Finland. The IHACRES model is coupled with a simple degree day model to account for snow accumulation and melt. The posterior probability distribution of the model parameters is sampled by using the Differential Evolution Adaptive Metropolis (DREAM(ZS)) algorithm and the generalized likelihood function. Precipitation uncertainty is taken into account by introducing additional latent variables that were used as multipliers for individual storm events. Results suggest that occasional snow water equivalent (SWE) observations together with daily streamflow observations do not contain enough information to simultaneously identify model parameters, precipitation uncertainty and model structural uncertainty in the Rudbäck catchment. The addition of an autoregressive component to account for model structure error and latent variables having uniform priors to account for input uncertainty lead to dubious posterior distributions of model parameters. Thus our hypothesis that informative priors for latent variables could be replaced by additional SWE data could not be confirmed. The model was found to work adequately in 1-day-ahead simulation mode, but the results were poor in the simulation batch mode. This was caused by the interaction of parameters that were used to describe different sources of uncertainty. The findings may have lessons for other cases where parameterizations are similarly high in relation to available prior information.

Geo-social media as a proxy for hydrometeorological data for streamflow estimation and to improve flood monitoring

NASA Astrophysics Data System (ADS)

Restrepo-Estrada, Camilo; de Andrade, Sidgley Camargo; Abe, Narumi; Fava, Maria Clara; Mendiondo, Eduardo Mario; de Albuquerque, João Porto

2018-02-01

Floods are one of the most devastating types of worldwide disasters in terms of human, economic, and social losses. If authoritative data is scarce, or unavailable for some periods, other sources of information are required to improve streamflow estimation and early flood warnings. Georeferenced social media messages are increasingly being regarded as an alternative source of information for coping with flood risks. However, existing studies have mostly concentrated on the links between geo-social media activity and flooded areas. Thus, there is still a gap in research with regard to the use of social media as a proxy for rainfall-runoff estimations and flood forecasting. To address this, we propose using a transformation function that creates a proxy variable for rainfall by analysing geo-social media messages and rainfall measurements from authoritative sources, which are later incorporated within a hydrological model for streamflow estimation. We found that the combined use of official rainfall values with the social media proxy variable as input for the Probability Distributed Model (PDM), improved streamflow simulations for flood monitoring. The combination of authoritative sources and transformed geo-social media data during flood events achieved a 71% degree of accuracy and a 29% underestimation rate in a comparison made with real streamflow measurements. This is a significant improvement on the respective values of 39% and 58%, achieved when only authoritative data were used for the modelling. This result is clear evidence of the potential use of derived geo-social media data as a proxy for environmental variables for improving flood early-warning systems.

Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide

NASA Astrophysics Data System (ADS)

van Dijk, Albert I. J. M.; Peña-Arancibia, Jorge L.; Wood, Eric F.; Sheffield, Justin; Beck, Hylke E.

2013-05-01

Ideally, a seasonal streamflow forecasting system would ingest skilful climate forecasts and propagate these through calibrated hydrological models initialized with observed catchment conditions. At global scale, practical problems exist in each of these aspects. For the first time, we analyzed theoretical and actual skill in bimonthly streamflow forecasts from a global ensemble streamflow prediction (ESP) system. Forecasts were generated six times per year for 1979-2008 by an initialized hydrological model and an ensemble of 1° resolution daily climate estimates for the preceding 30 years. A post-ESP conditional sampling method was applied to 2.6% of forecasts, based on predictive relationships between precipitation and 1 of 21 climate indices prior to the forecast date. Theoretical skill was assessed against a reference run with historic forcing. Actual skill was assessed against streamflow records for 6192 small (<10,000 km2) catchments worldwide. The results show that initial catchment conditions provide the main source of skill. Post-ESP sampling enhanced skill in equatorial South America and Southeast Asia, particularly in terms of tercile probability skill, due to the persistence and influence of the El Niño Southern Oscillation. Actual skill was on average 54% of theoretical skill but considerably more for selected regions and times of year. The realized fraction of the theoretical skill probably depended primarily on the quality of precipitation estimates. Forecast skill could be predicted as the product of theoretical skill and historic model performance. Increases in seasonal forecast skill are likely to require improvement in the observation of precipitation and initial hydrological conditions.

Contradictory hydrological impacts of afforestation in the humid tropics evidenced by long-term field monitoring and simulation modelling

NASA Astrophysics Data System (ADS)

Lacombe, Guillaume; Ribolzi, Olivier; de Rouw, Anneke; Pierret, Alain; Latsachak, Keoudone; Silvera, Norbert; Pham Dinh, Rinh; Orange, Didier; Janeau, Jean-Louis; Soulileuth, Bounsamai; Robain, Henri; Taccoen, Adrien; Sengphaathith, Phouthamaly; Mouche, Emmanuel; Sengtaheuanghoung, Oloth; Tran Duc, Toan; Valentin, Christian

2016-07-01

The humid tropics are exposed to an unprecedented modernisation of agriculture involving rapid and mixed land-use changes with contrasted environmental impacts. Afforestation is often mentioned as an unambiguous solution for restoring ecosystem services and enhancing biodiversity. One consequence of afforestation is the alteration of streamflow variability which controls habitats, water resources, and flood risks. We demonstrate that afforestation by tree planting or by natural forest regeneration can induce opposite hydrological changes. An observatory including long-term field measurements of fine-scale land-use mosaics and of hydrometeorological variables has been operating in several headwater catchments in tropical southeast Asia since 2000. The GR2M water balance model, repeatedly calibrated over successive 1-year periods and used in simulation mode with the same year of rainfall input, allowed the hydrological effect of land-use change to be isolated from that of rainfall variability in two of these catchments in Laos and Vietnam. Visual inspection of hydrographs, correlation analyses, and trend detection tests allowed causality between land-use changes and changes in seasonal streamflow to be ascertained. In Laos, the combination of shifting cultivation system (alternation of rice and fallow) and the gradual increase of teak tree plantations replacing fallow led to intricate streamflow patterns: pluri-annual streamflow cycles induced by the shifting system, on top of a gradual streamflow increase over years caused by the spread of the plantations. In Vietnam, the abandonment of continuously cropped areas combined with patches of mix-trees plantations led to the natural re-growth of forest communities followed by a gradual drop in streamflow. Soil infiltrability controlled by surface crusting is the predominant process explaining why two modes of afforestation (natural regeneration vs. planting) led to opposite changes in streamflow regime. Given that commercial tree plantations will continue to expand in the humid tropics, careful consideration is needed before attributing to them positive effects on water and soil conservation.

How Hydroclimate Influences the Effectiveness of Particle Filter Data Assimilation of Streamflow in Initializing Short- to Medium-range Streamflow Forecasts

NASA Astrophysics Data System (ADS)

Clark, E.; Wood, A.; Nijssen, B.; Clark, M. P.

2017-12-01

Short- to medium-range (1- to 7-day) streamflow forecasts are important for flood control operations and in issuing potentially life-save flood warnings. In the U.S., the National Weather Service River Forecast Centers (RFCs) issue such forecasts in real time, depending heavily on a manual data assimilation (DA) approach. Forecasters adjust model inputs, states, parameters and outputs based on experience and consideration of a range of supporting real-time information. Achieving high-quality forecasts from new automated, centralized forecast systems will depend critically on the adequacy of automated DA approaches to make analogous corrections to the forecasting system. Such approaches would further enable systematic evaluation of real-time flood forecasting methods and strategies. Toward this goal, we have implemented a real-time Sequential Importance Resampling particle filter (SIR-PF) approach to assimilate observed streamflow into simulated initial hydrologic conditions (states) for initializing ensemble flood forecasts. Assimilating streamflow alone in SIR-PF improves simulated streamflow and soil moisture during the model spin up period prior to a forecast, with consequent benefits for forecasts. Nevertheless, it only consistently limits error in simulated snow water equivalent during the snowmelt season and in basins where precipitation falls primarily as snow. We examine how the simulated initial conditions with and without SIR-PF propagate into 1- to 7-day ensemble streamflow forecasts. Forecasts are evaluated in terms of reliability and skill over a 10-year period from 2005-2015. The focus of this analysis is on how interactions between hydroclimate and SIR-PF performance impact forecast skill. To this end, we examine forecasts for 5 hydroclimatically diverse basins in the western U.S. Some of these basins receive most of their precipitation as snow, others as rain. Some freeze throughout the mid-winter while others experience significant mid-winter melt events. We describe the methodology and present seasonal and inter-basin variations in DA-enhanced forecast skill.

Streamflow responses in Chile to megathrust earthquakes in the 20th and 21st centuries

NASA Astrophysics Data System (ADS)

Mohr, Christian; Manga, Michael; Wang, Chi-yuen; Korup, Oliver

2016-04-01

Both coseismic static stress and dynamic stresses associated with seismic waves may cause responses in hydrological systems. Such responses include changes in the water level, hydrochemistry and streamflow discharge. Earthquake effects on hydrological systems provide a means to study the interaction between stress changes and regional hydrology, which is otherwise rarely possible. Chile is a country of frequent and large earthquakes and thus provides abundant opportunities to study such interactions and processes. We analyze streamflow responses in Chile to several megathrust earthquakes, including the 1943 Mw 8.1 Coquimbo, 1950 Mw 8.2 Antofagasta, 1960 Mw 9.5 Valdivia, 1985 Mw 8.0 Valparaiso, 1995 Mw 8.0 Antofagasta, 2010 Mw 8.8 Maule, and the 2014 Mw 8.2 Iquique earthquakes. We use data from 716 stream gauges distributed from the Altiplano in the North to Tierra del Fuego in the South. This network covers the Andes mountain ranges, the central valley, the Coastal Mountain ranges and (mainly in the more southern parts) the Coastal flats. We combine empirical magnitude-distance relationships, machine learning tools, and process-based modeling to characterize responses. We first assess the streamflow anomalies and relate these to topographical, hydro-climatic, geological and earthquake-related (volumetric and dynamic strain) factors using various classifiers. We then apply 1D-groundwater flow modeling to selected catchments in order to test competing hypotheses for the origin of streamflow changes. We show that the co-seismic responses of streamflow mostly involved increasing discharges. We conclude that enhanced vertical permeability can explain most streamflow responses at the regional scale. The total excess water released by a single earthquake, i.e. the Maule earthquake, yielded up to 1 km3. Against the background of megathrust earthquakes frequently hitting Chile, the amount of water released by earthquakes is substantial, particularly for the arid northern areas that depend exclusively on groundwater resources.

Flood of April and May 2008 in Northern Maine

USGS Publications Warehouse

Lombard, Pamela J.

2010-01-01

Severe flooding occurred in Aroostook and Penobscot Counties in northern Maine between April 28 and May 1, 2008, and was most extreme in the town of Fort Kent. Peak streamflows in northern Aroostook County were the result of a persistent heavy snowpack that caused high streamflows when it quickly melted during the third week of April 2008. Snowmelt was followed by from two to four inches of rainfall over a 2-day period in northern Maine. Peak water-surface elevations resulting from the flood were obtained from 13 continuous-record streamgages and 63 surveyed high-water marks in Aroostook and Penobscot Counties. Peak streamflows were obtained from 20 sites on 15 streams through stage/discharge rating curves or hydraulic flow models. Peak water-surface elevations and streamflows were the highest ever recorded at seven continuous-record streamgages, which had between 25 and 84 years of record in northern Aroostook County. The annual exceedance probability (the percent chance of exceeding the streamflow recorded during the April/May 2008 flood during any given year) at six streamgages in northern Maine was equal to or less than 1 percent. Data from flood-insurance studies published by the Federal Emergency Management Agency were available for five of the locations analyzed for the April/May 2008 flood and were compared to streamflows and observed peak water-surface elevations from the 2008 flood. Water-surface elevations that would be expected given the observed flow as applied to the effective flood insurance studies ranged from between 1 and 4 feet from the water-surface elevations observed during the 2008 flood. Differences were likely the result of up to 30 years of additional data for the calculation of recurrence intervals and the fact that hydraulic models used for the models had not previously been calibrated to a flood of this magnitude.

A framework for improving a seasonal hydrological forecasting system using sensitivity analysis

NASA Astrophysics Data System (ADS)

Arnal, Louise; Pappenberger, Florian; Smith, Paul; Cloke, Hannah

2017-04-01

Seasonal streamflow forecasts are of great value for the socio-economic sector, for applications such as navigation, flood and drought mitigation and reservoir management for hydropower generation and water allocation to agriculture and drinking water. However, as we speak, the performance of dynamical seasonal hydrological forecasting systems (systems based on running seasonal meteorological forecasts through a hydrological model to produce seasonal hydrological forecasts) is still limited in space and time. In this context, the ESP (Ensemble Streamflow Prediction) remains an attractive forecasting method for seasonal streamflow forecasting as it relies on forcing a hydrological model (starting from the latest observed or simulated initial hydrological conditions) with historical meteorological observations. This makes it cheaper to run than a standard dynamical seasonal hydrological forecasting system, for which the seasonal meteorological forecasts will first have to be produced, while still producing skilful forecasts. There is thus the need to focus resources and time towards improvements in dynamical seasonal hydrological forecasting systems which will eventually lead to significant improvements in the skill of the streamflow forecasts generated. Sensitivity analyses are a powerful tool that can be used to disentangle the relative contributions of the two main sources of errors in seasonal streamflow forecasts, namely the initial hydrological conditions (IHC; e.g., soil moisture, snow cover, initial streamflow, among others) and the meteorological forcing (MF; i.e., seasonal meteorological forecasts of precipitation and temperature, input to the hydrological model). Sensitivity analyses are however most useful if they inform and change current operational practices. To this end, we propose a method to improve the design of a seasonal hydrological forecasting system. This method is based on sensitivity analyses, informing the forecasters as to which element of the forecasting chain (i.e., IHC or MF) could potentially lead to the highest increase in seasonal hydrological forecasting performance, after each forecast update.

Efficient multi-scenario Model Predictive Control for water resources management with ensemble streamflow forecasts

NASA Astrophysics Data System (ADS)

Tian, Xin; Negenborn, Rudy R.; van Overloop, Peter-Jules; María Maestre, José; Sadowska, Anna; van de Giesen, Nick

2017-11-01

Model Predictive Control (MPC) is one of the most advanced real-time control techniques that has been widely applied to Water Resources Management (WRM). MPC can manage the water system in a holistic manner and has a flexible structure to incorporate specific elements, such as setpoints and constraints. Therefore, MPC has shown its versatile performance in many branches of WRM. Nonetheless, with the in-depth understanding of stochastic hydrology in recent studies, MPC also faces the challenge of how to cope with hydrological uncertainty in its decision-making process. A possible way to embed the uncertainty is to generate an Ensemble Forecast (EF) of hydrological variables, rather than a deterministic one. The combination of MPC and EF results in a more comprehensive approach: Multi-scenario MPC (MS-MPC). In this study, we will first assess the model performance of MS-MPC, considering an ensemble streamflow forecast. Noticeably, the computational inefficiency may be a critical obstacle that hinders applicability of MS-MPC. In fact, with more scenarios taken into account, the computational burden of solving an optimization problem in MS-MPC accordingly increases. To deal with this challenge, we propose the Adaptive Control Resolution (ACR) approach as a computationally efficient scheme to practically reduce the number of control variables in MS-MPC. In brief, the ACR approach uses a mixed-resolution control time step from the near future to the distant future. The ACR-MPC approach is tested on a real-world case study: an integrated flood control and navigation problem in the North Sea Canal of the Netherlands. Such an approach reduces the computation time by 18% and up in our case study. At the same time, the model performance of ACR-MPC remains close to that of conventional MPC.

Watershed Scale Analysis of Groundwater Surface Water Interactions and Its Application to Conjunctive Management under Climatic and Anthropogenic Stresses over the US Sunbelt

NASA Astrophysics Data System (ADS)

Seo, Seung Beom

Although water is one of the most essential natural resources, human activities have been exerting pressure on water resources. In order to reduce these stresses on water resources, two key issues threatening water resources sustainability - interaction between surface water and groundwater resources and groundwater withdrawal impacts of streamflow depletion - were investigated in this study. First, a systematic decomposition procedure was proposed for quantifying the errors arising from various sources in the model chain in projecting the changes in hydrologic attributes using near-term climate change projections. Apart from the unexplained changes by GCMs, the process of customizing GCM projections to watershed scale through a model chain - spatial downscaling, temporal disaggregation and hydrologic model - also introduces errors, thereby limiting the ability to explain the observed changes in hydrologic variability. Towards this, we first propose metrics for quantifying the errors arising from different steps in the model chain in explaining the observed changes in hydrologic variables (streamflow, groundwater). The proposed metrics are then evaluated using a detailed retrospective analyses in projecting the changes in streamflow and groundwater attributes in four target basins that span across a diverse hydroclimatic regimes over the US Sunbelt. Our analyses focused on quantifying the dominant sources of errors in projecting the changes in eight hydrologic variables - mean and variability of seasonal streamflow, mean and variability of 3-day peak seasonal streamflow, mean and variability of 7-day low seasonal streamflow and mean and standard deviation of groundwater depth - over four target basins using an Penn state Integrated Hydrologic Model (PIHM) between the period 1956-1980 and 1981-2005. Retrospective analyses show that small/humid (large/arid) basins show increased (reduced) uncertainty in projecting the changes in hydrologic attributes. Further, changes in error due to GCMs primarily account for the unexplained changes in mean and variability of seasonal streamflow. On the other hand, the changes in error due to temporal disaggregation and hydrologic model account for the inability to explain the observed changes in mean and variability of seasonal extremes. Thus, the proposed metrics provide insights on how the error in explaining the observed changes being propagated through the model under different hydroclimatic regimes. To understand interaction between surface water and groundwater resources, transient pumping impacts on streamflow and groundwater level were analyzed by imposing shortterm pumping scenarios under historic drought conditions. Since surface water and groundwater systems are fully coupled and integrated systems, increased groundwater withdrawal during drought may reduce baseflow into the stream and prolong both systems' recovery from drought. Towards this, we proposed an uncertainty framework to understand the resiliency of groundwater and surface water systems using a fully-coupled hydrologic model under transient pumping. Using this framework, we quantified the restoration time of surface water and groundwater systems and also estimated the changes in the state variables after pumping. Groundwater pumping impacts over the watershed were also analyzed under different pumping volumes and different potential climate scenarios. Our analyses show that groundwater restoration time is more sensitive to changes in pumping volumes as opposed to changes in climate. After the cessation of pumping, streamflow recovers quickly in comparison to groundwater. Pumping impacts on other state variables are also discussed. Given that surface water and groundwater are inter-connected, optimal management of the both resources should be considered to improve the watershed resiliency under drought. Subsequently, conjunctive use of surface water and groundwater has been considered as an effective approach to mitigate water shortage problems that are primarily caused by a drought. It is found that appropriate use of groundwater withdrawal was able to reduce water scarcity in surface water resources in drought condition. Besides, recovery time constraint was embedded in the management model so that trade-off between minimizing water scarcity and maximizing sustainability on groundwater was successfully addressed.

Characterizing Macro Scale Patterns Of Uncertainty For Improved Operational Flood Forecasting Over The Conterminous United States

NASA Astrophysics Data System (ADS)

Vergara, H. J.; Kirstetter, P.; Gourley, J. J.; Flamig, Z.; Hong, Y.

2015-12-01

The macro scale patterns of simulated streamflow errors are studied in order to characterize uncertainty in a hydrologic modeling system forced with the Multi-Radar/Multi-Sensor (MRMS; http://mrms.ou.edu) quantitative precipitation estimates for flood forecasting over the Conterminous United States (CONUS). The hydrologic model is centerpiece of the Flooded Locations And Simulated Hydrograph (FLASH; http://flash.ou.edu) real-time system. The hydrologic model is implemented at 1-km/5-min resolution to generate estimates of streamflow. Data from the CONUS-wide stream gauge network of the United States' Geological Survey (USGS) were used as a reference to evaluate the discrepancies with the hydrological model predictions. Streamflow errors were studied at the event scale with particular focus on the peak flow magnitude and timing. A total of 2,680 catchments over CONUS and 75,496 events from a 10-year period are used for the simulation diagnostic analysis. Associations between streamflow errors and geophysical factors were explored and modeled. It is found that hydro-climatic factors and radar coverage could explain significant underestimation of peak flow in regions of complex terrain. Furthermore, the statistical modeling of peak flow errors shows that other geophysical factors such as basin geomorphometry, pedology, and land cover/use could also provide explanatory information. Results from this research demonstrate the utility of uncertainty characterization in providing guidance to improve model adequacy, parameter estimates, and input quality control. Likewise, the characterization of uncertainty enables probabilistic flood forecasting that can be extended to ungauged locations.

Characterizing the utility of the TMPA real-time product for hydrologic predictions over global river basins across scales

NASA Astrophysics Data System (ADS)

Gao, H.; Zhang, S.; Nijssen, B.; Zhou, T.; Voisin, N.; Sheffield, J.; Lee, K.; Shukla, S.; Lettenmaier, D. P.

2017-12-01

Despite its errors and uncertainties, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis real-time product (TMPA-RT) has been widely used for hydrological monitoring and forecasting due to its timely availability for real-time applications. To evaluate the utility of TMPA-RT in hydrologic predictions, many studies have compared modeled streamflows driven by TMPA-RT against gauge data. However, because of the limited availability of streamflow observations in data sparse regions, there is still a lack of comprehensive comparisons for TMPA-RT based hydrologic predictions at the global scale. Furthermore, it is expected that its skill is less optimal at the subbasin scale than the basin scale. In this study, we evaluate and characterize the utility of the TMPA-RT product over selected global river basins during the period of 1998 to 2015 using the TMPA research product (TMPA-RP) as a reference. The Variable Infiltration Capacity (VIC) model, which was calibrated and validated previously, is adopted to simulate streamflows driven by TMPA-RT and TMPA-RP, respectively. The objective of this study is to analyze the spatial and temporal characteristics of the hydrologic predictions by answering the following questions: (1) How do the precipitation errors associated with the TMPA-RT product transform into streamflow errors with respect to geographical and climatological characteristics? (2) How do streamflow errors vary across scales within a basin?

Watershed-scale modeling of streamflow change in incised montane meadows

USGS Publications Warehouse

Essaid, Hedeff I.; Hill, Barry R.

2014-01-01

Land use practices have caused stream channel incision and water table decline in many montane meadows of the Western United States. Incision changes the magnitude and timing of streamflow in water supply source watersheds, a concern to resource managers and downstream water users. The hydrology of montane meadows under natural and incised conditions was investigated using watershed simulation for a range of hydrologic conditions. The results illustrate the interdependence between: watershed and meadow hydrology; bedrock and meadow aquifers; and surface and groundwater flow through the meadow for the modeled scenarios. During the wet season, stream incision resulted in less overland flow and interflow and more meadow recharge causing a net decrease in streamflow and increase in groundwater storage relative to natural meadow conditions. During the dry season, incision resulted in less meadow evapotranspiration and more groundwater discharge to the stream causing a net increase in streamflow and a decrease in groundwater storage relative to natural meadow conditions. In general, for a given meadow setting, the magnitude of change in summer streamflow and long-term change in watershed groundwater storage due to incision will depend on the combined effect of: reduced evapotranspiration in the eroded meadow; induced groundwater recharge; replenishment of dry season groundwater storage depletion in meadow and bedrock aquifers by precipitation during wet years; and groundwater storage depletion that is not replenished by precipitation during wet years.

Retrospective evaluation of continental-scale streamflow nudging with WRF-Hydro National Water Model V1

NASA Astrophysics Data System (ADS)

McCreight, J. L.; Wu, Y.; Gochis, D.; Rafieeinasab, A.; Dugger, A. L.; Yu, W.; Cosgrove, B.; Cui, Z.; Oubeidillah, A.; Briar, D.

2016-12-01

The streamflow (discharge) data assimilation capability in version 1 of the National Water Model (NWM; a WRF-Hydro configuration) is applied and evaluated in a 5-year (2011-2015) retrospective study using NLDAS2 forcing data over CONUS. This talk will describe the NWM V1 operational nudging (continuous-time) streamflow data assimilation approach, its motivation, and its relationship to this retrospective evaluation. Results from this study will provide a an analysis-based (not forecast-based) benchmark for streamflow DA in the NWM. The goal of the assimilation is to reduce discharge bias and improve channel initial conditions for discharge forecasting (though forecasts are not considered here). The nudging method assimilates discharge observations at nearly 7,000 USGS gages (at frequency up to 1/15 minutes) to produce a (univariate) discharge reanalysis (i.e. this is the only variable affected by the assimilation). By withholding 14% nested gages throughout CONUS in a separate validation run, we evaluate the downstream impact of assimilation at upstream gages. Based on this sample, we estimate the skill of the streamflow reanalysis at ungaged locations and examine factors governing the skill of the assimilation. Comparison of assimilation and open-loop runs is presented. Performance of DA under both high and low flow regimes and selected flooding events is examined. Preliminary evaluation of nudging parameter sensitivity and its relationship to flow regime will be presented.

Applying a coupled hydrometeorological simulation system to flash flood forecasting over the Korean Peninsula

NASA Astrophysics Data System (ADS)

Ryu, Young; Lim, Yoon-Jin; Ji, Hee-Sook; Park, Hyun-Hee; Chang, Eun-Chul; Kim, Baek-Jo

2017-11-01

In flash flood forecasting, it is necessary to consider not only traditional meteorological variables such as precipitation, evapotranspiration, and soil moisture, but also hydrological components such as streamflow. To address this challenge, the application of high resolution coupled atmospheric-hydrological models is emerging as a promising alternative. This study demonstrates the feasibility of linking a coupled atmospheric-hydrological model (WRF/WRFHydro) with 150-m horizontal grid spacing for flash flood forecasting in Korea. The study area is the Namgang Dam basin in Southern Korea, a mountainous area located downstream of Jiri Mountain (1915 m in height). Under flash flood conditions, the simulated precipitation over the entire basin is comparable to the domain-averaged precipitation, but discharge data from WRF-Hydro shows some differences in the total available water and the temporal distribution of streamflow (given by the timing of the streamflow peak following precipitation), compared to observations. On the basis of sensitivity tests, the parameters controlling the infiltration of excess precipitation and channel roughness depending on stream order are refined and their influence on temporal distribution of streamflow is addressed with intent to apply WRF-Hydro to flash flood forecasting in the Namgang Dam basin. The simulation results from the WRF-Hydro model with optimized parameters demonstrate the potential utility of a coupled atmospheric-hydrological model for forecasting heavy rain-induced flash flooding over the Korean Peninsula.

Characterizing changes in streamflow and sediment supply in the Sacramento River Basin, California, using hydrological simulation program—FORTRAN (HSPF)

USGS Publications Warehouse

Stern, Michelle A.; Flint, Lorraine E.; Minear, Justin T.; Flint, Alan L.; Wright, Scott A.

2016-01-01

A daily watershed model of the Sacramento River Basin of northern California was developed to simulate streamflow and suspended sediment transport to the San Francisco Bay-Delta. To compensate for sparse data, a unique combination of model inputs was developed, including meteorological variables, potential evapotranspiration, and parameters defining hydraulic geometry. A slight decreasing trend of sediment loads and concentrations was statistically significant in the lowest 50% of flows, supporting the observed historical sediment decline. Historical changes in climate, including seasonality and decline of snowpack, contribute to changes in streamflow, and are a significant component describing the mechanisms responsible for the decline in sediment. Several wet and dry hypothetical climate change scenarios with temperature changes of 1.5 °C and 4.5 °C were applied to the base historical conditions to assess the model sensitivity of streamflow and sediment to changes in climate. Of the scenarios evaluated, sediment discharge for the Sacramento River Basin increased the most with increased storm magnitude and frequency and decreased the most with increases in air temperature, regardless of changes in precipitation. The model will be used to develop projections of potential hydrologic and sediment trends to the Bay-Delta in response to potential future climate scenarios, which will help assess the hydrological and ecological health of the Bay-Delta into the next century.

Citizen observatory of water as a data engine supporting the people-hydrology nexus: experience of the WeSenseIt project

NASA Astrophysics Data System (ADS)

Ferri, Michele; Baruffi, Francesco; Norbiato, Daniele; Monego, Martina; Tomei, Giovanni; Solomatine, Dimitri; Alfonso, Leonardo; Mazzoleni, Maurizio; Chacon, Juan Carlos; Wehn, Uta; Ciravegna, Fabio

2016-04-01

Citizen observatories (COs) present an interesting case of strong multi-facet feedback between the physical (water) system and humans. CO is a form of crowdsourcing ensuring a data flow from citizens observing environment (e.g. water level in a river) to a central data processing unit which is typically part of a more complex social arrangement (e.g. water authorities responsible for flood forecasting). The EU-funded project WeSenseIt (www.wesenseit.eu) aims at developing technologies and tools supporting creation of such COs [1,2,3,4]. Citizens which form a CO play the role of "social sensors" which however are very specific. The data streams from such sensors have varying temporal and spatial coverage and information value (uncertainty). The crowdsourced data can be of course simply visualized and presented to public, but it is much more interesting to consider cases when such data are assimilated into the existing forecasting systems, e.g. flood early warning systems based on hydrological and hydraulic models. COs may also affect water management and governance [4], and in fact can be seen as data engines supporting the people-hydrology nexus. In the framework of WeSenseIt project several approaches were developed allowing for optimal assimilation of intermittent data streams with varying spatial coverage into distributed hydrological models [1, 2]. The mentioned specific features of CO data required updates of the existing data assimilation algorithms (Ensemble Kalman Filter was used as the basic algorithm). The developed algorithms have been implemented in the operational flood forecasting systems of the Alto Adriatico Water Authority (AAWA), Venice. In this paper we analyse various scenarios of employing citizens data (COs) for flood forecasting. This study is partly supported by the FP7 European Project WeSenseIt Citizen Water Observatory (www.http://wesenseit.eu/). References [1] Mazzoleni, M., Alfonso, L., Chacon-Hurtado, J., Solomatine, D. (2015). Assimilating uncertain, dynamic and intermittent streamflow observations in hydrological models. Advances in Water Res., 83, 323-339 (Online on September 1, 2015). [2] Mazzoleni M., Verlaan M., Alfonso L., Monego M., Norbiato D., Ferri M., and Solomatine D.P. (2015) Can assimilation of crowdsourced streamflow observations in hydrological modelling improve flood prediction?, Hydrology and Earth System Sciences, under review. [3] Mazzoleni M., Alfonso L. and Solomatine D.P. (2015) Effect of spatial distribution and quality of sensors on the assimilation of distributed streamflow observations in hydrological modeling, Hydrological Sciences Journal, under review. [4] Wehn, U., McCarty, S., Lanfranchi, V. and Tapsell, S. (2015) Citizen observatories as facilitators of change in water governance? Experiences from three European cases, Special Issue on ICTs and Water, Journal of Environmental Engineering and Management, 2073-2086.

Climate Change Impacts to Hydro Power Reservoir Systems in British Columbia, Canada: Modelling, Validation and Projection of Historic and Future Streamflow and Snowpack

NASA Astrophysics Data System (ADS)

Bennett, K. E.; Schnorbus, M.; Werner, A. T.; Berland, A. J.

2010-12-01

The British Columbia Hydro Electric Corporation (BC Hydro) has a mandate to provide clean, renewable and reliable sources of hydro-electric power into the future, hence managing those resources in the context of climate change will be an important component of reservoir operational planning in British Columbia. The Pacific Climate Impacts Consortium (www.PacificClimate.org) has implemented the Variable Infiltration Capacity hydrologic model parameterized at 1/16th degree (~32 km2) to provide BC Hydro with future projections of changes to streamflow and snowpack to the 2050s. The headwaters of the Peace, Columbia, and Campbell River basins were selected for study; the Upper Peace River basin (101,000 km2) is a snowmelt-dominated watershed, and the Upper Columbia River Basin (104,000 km2) has a mixed snowmelt-glacier melt runoff regime, with glacier runoff contributing up to 15 to 20% of late summer discharge. The Upper Campbell River watershed (1,200 km2) has a mixed rainfall and snowmelt (hybrid) hydrologic regime. The model has been calibrated using historical streamflow observations and validated against these observations, as well as automated snow pillow measurements. Future streamflow changes are estimated based on eight Global Climate Models (GCMs) from the CMIP3 suite, downscaled using the Bias Correction Spatial Downscaling (BCSD) technique, run under three emissions scenarios (A2, A1B and B1; A1B is specifically reported on herein). Climate impacts by the 2050s in the three watersheds illustrate an increase in annual average temperature and precipitation ranging between +2.2°C to +2.8°C and +2% to +10% depending on basin, and an annual change in streamflow of -1% to +12% for the three watersheds. Changes are more profound on the seasonal time-scale and differ across basins. Summer streamflow in the Upper Campbell River watershed is projected to decline by -60%, where as the Upper Peace and Columbia systems are projected to decline by -25% and -22%, respectively. Streamflow is projected to increase during winter months for all basins, ranging from increases of +54% (Upper Campbell), +77% (Upper Peace) to +94% (Upper Columbia). These changes in streamflow illustrate a shift towards more rainfall dominated systems with lower snowpacks during the winter months, particularly in the Campbell system (shifting from 23% to 13% snow dominated by the 2050s), which is located at a relatively low elevation and proximal to the Pacific Ocean. Shifts in the distribution of water resources, and in particular snowpack reserves, may require BC Hydro to reconsider their operational planning framework for impacted systems.

Bathymetric and streamflow data for the Quillayute, Dickey, and Bogachiel Rivers, Clallam County, Washington, April-May 2010

USGS Publications Warehouse

Czuba, Jonathan A.; Barnas, Christiana R.; McKenna, Thomas E.; Justin, Gregory; Payne, Karen L.

2010-01-01

To facilitate the development of a two-dimensional hydrodynamic model of the Quillayute River estuary, the U.S. Geological Survey conducted a bathymetric survey of the Quillayute River and its tributaries, upstream of the La Push Harbor. Streamflow also was measured concurrent with the bathymetric survey. This report documents the bathymetric and streamflow data collected in the Quillayute (river mile 0.4-5.7), Dickey (river mile 0-0.4), and Bogachiel Rivers (river mile 0-0.8) on April 20-21 and May 4-6, 2010, including a longitudinal profile, about 7-miles long, of water-surface and riverbed elevations. In all, 173,800 bathymetric points were collected and streamflow measurements in the mainstem Quillayute River ranged from 3,630 to 7,800 cubic feet per second.

Effects of past and future groundwater development on the hydrologic system of Verde Valley, Arizona

USGS Publications Warehouse

Garner, Bradley D.; Pool, D.R.

2013-01-01

Communities in central Arizona’s Verde Valley must manage limited water supplies in the face of rapidly growing populations. Developing groundwater resources to meet human needs has raised questions about the effects of groundwater withdrawals by pumping on the area’s rivers and streams, particularly the Verde River. U.S. Geological Survey hydrologists used a regional groundwater flow model to simulate the effects of groundwater pumping on streamflow in the Verde River. The study found that streamflow in the Verde River between 1910 and 2005 had been reduced as the result of streamflow depletion by groundwater pumping, also known as capture. Additionally, using three hypothetical scenarios for a period from 2005 to 2110, the study’s findings suggest that streamflow reductions will continue and may increase in the future.

A real-time evaluation and demonstration of strategies for 'Over-The-Loop' ensemble streamflow forecasting in US watersheds

NASA Astrophysics Data System (ADS)

Wood, Andy; Clark, Elizabeth; Mendoza, Pablo; Nijssen, Bart; Newman, Andy; Clark, Martyn; Nowak, Kenneth; Arnold, Jeffrey

2017-04-01

Many if not most national operational streamflow prediction systems rely on a forecaster-in-the-loop approach that require the hands-on-effort of an experienced human forecaster. This approach evolved from the need to correct for long-standing deficiencies in the models and datasets used in forecasting, and the practice often leads to skillful flow predictions despite the use of relatively simple, conceptual models. Yet the 'in-the-loop' forecast process is not reproducible, which limits opportunities to assess and incorporate new techniques systematically, and the effort required to make forecasts in this way is an obstacle to expanding forecast services - e.g., though adding new forecast locations or more frequent forecast updates, running more complex models, or producing forecast and hindcasts that can support verification. In the last decade, the hydrologic forecasting community has begun develop more centralized, 'over-the-loop' systems. The quality of these new forecast products will depend on their ability to leverage research in areas including earth system modeling, parameter estimation, data assimilation, statistical post-processing, weather and climate prediction, verification, and uncertainty estimation through the use of ensembles. Currently, many national operational streamflow forecasting and water management communities have little experience with the strengths and weaknesses of over-the-loop approaches, even as such systems are beginning to be deployed operationally in centers such as ECMWF. There is thus a need both to evaluate these forecasting advances and to demonstrate their potential in a public arena, raising awareness in forecast user communities and development programs alike. To address this need, the US National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the US Army Corps of Engineers, using the NCAR 'System for Hydromet Analysis Research and Prediction Applications' (SHARP) to implement, assess and demonstrate real-time over-the-loop ensemble flow forecasts in a range of US watersheds. The system relies on fully ensemble techniques, including: an 100-member ensemble of meteorological model forcings and an ensemble particle filter data assimilation for initializing watershed states; analog/regression-based downscaling of ensemble weather forecasts from GEFS; and statistical post-processing of ensemble forecast outputs, all of which run in real-time within a workflow managed by ECWMF's ecFlow libraries over large US regional domains. We describe SHARP and present early hindcast and verification results for short to seasonal range streamflow forecasts in a number of US case study watersheds.

A statistical data assimilation method for seasonal streamflow forecasting to optimize hydropower reservoir management in data-scarce regions

NASA Astrophysics Data System (ADS)

Arsenault, R.; Mai, J.; Latraverse, M.; Tolson, B.

2017-12-01

Probabilistic ensemble forecasts generated by the ensemble streamflow prediction (ESP) methodology are subject to biases due to errors in the hydrological model's initial states. In day-to-day operations, hydrologists must compensate for discrepancies between observed and simulated states such as streamflow. However, in data-scarce regions, little to no information is available to guide the streamflow assimilation process. The manual assimilation process can then lead to more uncertainty due to the numerous options available to the forecaster. Furthermore, the model's mass balance may be compromised and could affect future forecasts. In this study we propose a data-driven approach in which specific variables that may be adjusted during assimilation are defined. The underlying principle was to identify key variables that would be the most appropriate to modify during streamflow assimilation depending on the initial conditions such as the time period of the assimilation, the snow water equivalent of the snowpack and meteorological conditions. The variables to adjust were determined by performing an automatic variational data assimilation on individual (or combinations of) model state variables and meteorological forcing. The assimilation aimed to simultaneously optimize: (1) the error between the observed and simulated streamflow at the timepoint where the forecasts starts and (2) the bias between medium to long-term observed and simulated flows, which were simulated by running the model with the observed meteorological data on a hindcast period. The optimal variables were then classified according to the initial conditions at the time period where the forecast is initiated. The proposed method was evaluated by measuring the average electricity generation of a hydropower complex in Québec, Canada driven by this method. A test-bed which simulates the real-world assimilation, forecasting, water release optimization and decision-making of a hydropower cascade was developed to assess the performance of each individual process in the reservoir management chain. Here the proposed method was compared to the PF algorithm while keeping all other elements intact. Preliminary results are encouraging in terms of power generation and robustness for the proposed approach.

Analysis of the U.S. geological survey streamgaging network

USGS Publications Warehouse

Scott, A.G.

1987-01-01

This paper summarizes the results from the first 3 years of a 5-year cost-effectiveness study of the U.S. Geological Survey streamgaging network. The objective of the study is to define and document the most cost-effective means of furnishing streamflow information. In the first step of this study, data uses were identified for 3,493 continuous-record stations currently being operated in 32 States. In the second step, evaluation of alternative methods of providing streamflow information, flow-routing models, and regression models were developed for estimating daily flows at 251 stations of the 3,493 stations analyzed. In the third step of the analysis, relationships were developed between the accuracy of the streamflow records and the operating budget. The weighted standard error for all stations, with current operating procedures, was 19.9 percent. By altering field activities, as determined by the analyses, this could be reduced to 17.8 percent. The existing streamgaging networks in four Districts were further analyzed to determine the impacts that satellite telemetry would have on the cost effectiveness. Satellite telemetry was not found to be cost effective on the basis of hydrologic data collection alone, given present cost of equipment and operation.This paper summarizes the results from the first 3 years of a 5-year cost-effectiveness study of the U. S. Geological Survey streamgaging network. The objective of the study is to define and document the most cost-effective means of furnishing streamflow information. In the first step of this study, data uses were identified for 3,493 continuous-record stations currently being operated in 32 States. In the second step, evaluation of alternative methods of providing streamflow information, flow-routing models, and regression models were developed for estimating daily flows at 251 stations of the 3, 493 stations analyzed. In the third step of the analysis, relationships were developed between the accuracy of the streamflow records and the operating budget. The weighted standard error for all stations, with current operating procedures, was 19. 9 percent. By altering field activities, as determined by the analyses, this could be reduced to 17. 8 percent. Additional study results are discussed.

Improving medium-range ensemble streamflow forecasts through statistical post-processing

NASA Astrophysics Data System (ADS)

Mendoza, Pablo; Wood, Andy; Clark, Elizabeth; Nijssen, Bart; Clark, Martyn; Ramos, Maria-Helena; Nowak, Kenneth; Arnold, Jeffrey

2017-04-01

Probabilistic hydrologic forecasts are a powerful source of information for decision-making in water resources operations. A common approach is the hydrologic model-based generation of streamflow forecast ensembles, which can be implemented to account for different sources of uncertainties - e.g., from initial hydrologic conditions (IHCs), weather forecasts, and hydrologic model structure and parameters. In practice, hydrologic ensemble forecasts typically have biases and spread errors stemming from errors in the aforementioned elements, resulting in a degradation of probabilistic properties. In this work, we compare several statistical post-processing techniques applied to medium-range ensemble streamflow forecasts obtained with the System for Hydromet Applications, Research and Prediction (SHARP). SHARP is a fully automated prediction system for the assessment and demonstration of short-term to seasonal streamflow forecasting applications, developed by the National Center for Atmospheric Research, University of Washington, U.S. Army Corps of Engineers, and U.S. Bureau of Reclamation. The suite of post-processing techniques includes linear blending, quantile mapping, extended logistic regression, quantile regression, ensemble analogs, and the generalized linear model post-processor (GLMPP). We assess and compare these techniques using multi-year hindcasts in several river basins in the western US. This presentation discusses preliminary findings about the effectiveness of the techniques for improving probabilistic skill, reliability, discrimination, sharpness and resolution.

An intercomparison of approaches for improving operational seasonal streamflow forecasts

NASA Astrophysics Data System (ADS)

Mendoza, Pablo A.; Wood, Andrew W.; Clark, Elizabeth; Rothwell, Eric; Clark, Martyn P.; Nijssen, Bart; Brekke, Levi D.; Arnold, Jeffrey R.

2017-07-01

For much of the last century, forecasting centers around the world have offered seasonal streamflow predictions to support water management. Recent work suggests that the two major avenues to advance seasonal predictability are improvements in the estimation of initial hydrologic conditions (IHCs) and the incorporation of climate information. This study investigates the marginal benefits of a variety of methods using IHCs and/or climate information, focusing on seasonal water supply forecasts (WSFs) in five case study watersheds located in the US Pacific Northwest region. We specify two benchmark methods that mimic standard operational approaches - statistical regression against IHCs and model-based ensemble streamflow prediction (ESP) - and then systematically intercompare WSFs across a range of lead times. Additional methods include (i) statistical techniques using climate information either from standard indices or from climate reanalysis variables and (ii) several hybrid/hierarchical approaches harnessing both land surface and climate predictability. In basins where atmospheric teleconnection signals are strong, and when watershed predictability is low, climate information alone provides considerable improvements. For those basins showing weak teleconnections, custom predictors from reanalysis fields were more effective in forecast skill than standard climate indices. ESP predictions tended to have high correlation skill but greater bias compared to other methods, and climate predictors failed to substantially improve these deficiencies within a trace weighting framework. Lower complexity techniques were competitive with more complex methods, and the hierarchical expert regression approach introduced here (hierarchical ensemble streamflow prediction - HESP) provided a robust alternative for skillful and reliable water supply forecasts at all initialization times. Three key findings from this effort are (1) objective approaches supporting methodologically consistent hindcasts open the door to a broad range of beneficial forecasting strategies; (2) the use of climate predictors can add to the seasonal forecast skill available from IHCs; and (3) sample size limitations must be handled rigorously to avoid over-trained forecast solutions. Overall, the results suggest that despite a rich, long heritage of operational use, there remain a number of compelling opportunities to improve the skill and value of seasonal streamflow predictions.

Multivariate Bias Correction Procedures for Improving Water Quality Predictions from the SWAT Model

NASA Astrophysics Data System (ADS)

Arumugam, S.; Libera, D.

2017-12-01

Water quality observations are usually not available on a continuous basis for longer than 1-2 years at a time over a decadal period given the labor requirements making calibrating and validating mechanistic models difficult. Further, any physical model predictions inherently have bias (i.e., under/over estimation) and require post-simulation techniques to preserve the long-term mean monthly attributes. This study suggests a multivariate bias-correction technique and compares to a common technique in improving the performance of the SWAT model in predicting daily streamflow and TN loads across the southeast based on split-sample validation. The approach is a dimension reduction technique, canonical correlation analysis (CCA) that regresses the observed multivariate attributes with the SWAT model simulated values. The common approach is a regression based technique that uses an ordinary least squares regression to adjust model values. The observed cross-correlation between loadings and streamflow is better preserved when using canonical correlation while simultaneously reducing individual biases. Additionally, canonical correlation analysis does a better job in preserving the observed joint likelihood of observed streamflow and loadings. These procedures were applied to 3 watersheds chosen from the Water Quality Network in the Southeast Region; specifically, watersheds with sufficiently large drainage areas and number of observed data points. The performance of these two approaches are compared for the observed period and over a multi-decadal period using loading estimates from the USGS LOADEST model. Lastly, the CCA technique is applied in a forecasting sense by using 1-month ahead forecasts of P & T from ECHAM4.5 as forcings in the SWAT model. Skill in using the SWAT model for forecasting loadings and streamflow at the monthly and seasonal timescale is also discussed.

Travel Times, Streamflow Velocities, and Dispersion Rates in the Yellowstone River, Montana

USGS Publications Warehouse

McCarthy, Peter M.

2009-01-01

The Yellowstone River is a vital natural resource to the residents of southeastern Montana and is a primary source of water for irrigation and recreation and the primary source of municipal water for several cities. The Yellowstone River valley is the primary east-west transportation corridor through southern Montana. This complex of infrastructure makes the Yellowstone River especially vulnerable to accidental spills from various sources such as tanker cars and trucks. In 2008, the U.S. Geological Survey (USGS), in cooperation with the Montana Department of Environmental Quality, initiated a dye-tracer study to determine instream travel times, streamflow velocities, and dispersion rates for the Yellowstone River from Lockwood to Glendive, Montana. The purpose of this report is to describe the results of this study and summarize data collected at each of the measurement sites between Lockwood and Glendive. This report also compares the results of this study to estimated travel times from a transport model developed by the USGS for a previous study. For this study, Rhodamine WT dye was injected at four locations in late September and early October 2008 during reasonably steady streamflow conditions. Streamflows ranged from 3,490 to 3,770 cubic feet per second upstream from the confluence of the Bighorn River and ranged from 6,520 to 7,570 cubic feet per second downstream from the confluence of the Bighorn River. Mean velocities were calculated for each subreach between measurement sites for the leading edge, peak concentration, centroid, and trailing edge at 10 percent of the peak concentration. Calculated velocities for the centroid of the dye plume for subreaches that were completely laterally mixed ranged from 1.83 to 3.18 ft/s within the study reach from Lockwood Bridge to Glendive Bridge. The mean of the completely mixed centroid velocity for the entire study reach, excluding the subreach between Forsyth Bridge and Cartersville Dam, was 2.80 ft/s. Longitudinal dispersion rates of the dye plume for this study ranged from 0.06 ft/s for the subreach upstream from Forsyth Bridge to 2.25 ft/s for the subreach upstream from Calyspo Bridge for subreaches where the dye was completely laterally mixed. A relation was determined between travel time of the peak concentration and time for the dye plume to pass a site (duration). This relation can be used to estimate when the receding concentration of a potential contaminant reaches 10 percent of its peak concentration for accidental spills into the Yellowstone River. Data from this dye-tracer study were used to evaluate velocity and concentration estimates from a transport model developed as part of an earlier USGS study. Comparison of the estimated and calculated velocities for the study reach indicate that the transport model estimates the velocities of the Yellowstone River between Huntley Bridge and Glendive Bridge with reasonable accuracy. Velocities of the peak concentration of the dye plume calculated for this study averaged 10 percent faster than the most probable velocities and averaged 12 percent slower than the maximum probable velocities estimated from the transport model. Peak Rhodamine WT dye concentrations were consistently lower than the transport model estimates except for the most upstream subreach of each dye injection. The most upstream subreach of each dye injection is expected to have a higher concentration because of incomplete lateral mixing. Lower measured peak concentrations for all other sites were expected because Rhodamine WT dye deteriorates when exposed to sunlight and will sorb onto the streambanks and stream bottom. Velocity-streamflow relations developed by using routine streamflow measurements at USGS gaging stations and the transport model can be used to estimate mean streamflow velocities throughout a range of streamflows. The variation in these velocity-streamflow relations emphasizes the uncertainty in estimating the mean streamflow veloc

Improvement of Operational Streamflow Prediction with MODIS-derived Fractional Snow Covered Area Observations

NASA Astrophysics Data System (ADS)

Bender, S.; Burgess, A.; Goodale, C. E.; Mattmann, C. A.; Miller, W. P.; Painter, T. H.; Rittger, K. E.; Stokes, M.; Werner, K.

2013-12-01

Water managers in the western United States depend heavily on the timing and magnitude of snowmelt-driven runoff for municipal supply, irrigation, maintenance of environmental flows, and power generation. The Colorado Basin River Forecast Center (CBRFC) of the National Weather Service issues operational forecasts of snowmelt-driven streamflow for watersheds within the Colorado River Basin (CRB) and eastern Great Basin (EGB), across a wide variety of scales. Therefore, the CBRFC and its stakeholders consider snowpack observations to be highly valuable. Observations of fractional snow covered area (fSCA) from satellite-borne instrumentation can better inform both forecasters and water users with respect to subsequent snowmelt runoff, particularly when combined with observations from ground-based station networks and/or airborne platforms. As part of a multi-year collaborative effort, CBRFC has partnered with the Jet Propulsion Laboratory (JPL) under funding from NASA to incorporate observations of fSCA from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) into the operational CBRFC hydrologic forecasting and modeling process. In the first year of the collaboration, CBRFC and NASA/JPL integrated snow products into the forecasting and decision making processes of the CBRFC and showed preliminary improvement in operational streamflow forecasts. In late 2012, CBRFC and NASA/JPL began retrospective analysis of relationships between the MODIS Snow Covered Area and Grain size (MODSCAG) fSCA and streamflow patterns for several watersheds within the CRB and the EGB. During the 2013 snowmelt runoff season, CBRFC forecasters used MODIS-derived fSCA semi-quantitatively as a binary indicator of the presence or lack of snow. Indication of the presence or lack of snow by MODIS assisted CBRFC forecasters in determining the cause of divergence between modeled and recently observed streamflow. Several examples of improved forecasts from across the CRB and EGB, informed by MODIS-derived fSCA, are described. Our analysis shows the value of MODIS fSCA to CBRFC and to users of CBRFC's streamflow forecasts. The relationships between the MODIS fSCA and the melt season streamflow vary with the magnitude of runoff, which is important to resource managers. The analysis also emphasizes the importance of the invaluable collaboration between an operational forecasting agency (CBRFC) and a research-oriented agency (NASA/JPL) specializing in remote sensing science. The collaboration is expected to continue over the next several years as CBRFC and JPL work to further improve modeling of snowmelt and prediction of snowmelt-driven streamflow in the CRB and EGB.

Analysis of Land Use and Land Cover Changes and Their Impacts on Future Runoff in the Luanhe River Basin in North China Using Markov and SWAT

NASA Astrophysics Data System (ADS)

Yang, W.; Long, D.

2017-12-01

Both land use/cover change (LUCC) and climate change exert significant impacts on runoff, which needs to be thoroughly examined in the context of urbanization, population growth, and climate change. The majority of studies focus on the impacts of either LUCC or climate on runoff in the upper reaches of the Panjiakou Reservoir in the Luanhe River basin, North China. In this study, first, two land use change matrices for periods 1970‒1980 and 1980‒2000 were constructed based on the theory of the Markov Chain which were used to predict the land use scenario of the basin in year 2020. Second, a distributed hydrological model, Soil Water Assessment Tools (SWAT), was set up and driven mainly by the China Gauge-based Daily Precipitation Analysis (CGDPA) product and outputs from three general circulation models (GCMs) of the Inter-Sectoral Impact Model Inter-comparison Project (ISI-MIP). Third, under the land use scenario in 2000, streamflow at the Chengde gauging station for the period 1998‒2014 was simulated with the CGDPA as input, and streamflow for the period 2015‒2025 under four representative concentration pathways (RCPs) was simulated using the outputs from GCMs and compared under the land use scenarios in 2000 and 2020. Results show that during 2015‒2025, the ensemble average precipitation in summer (i.e., from June to August) may increase up to 20% but decrease by -16% in fall (i.e., from September to November). The streamflow may increase in all the seasons, particularly in spring (i.e., from March to May) and summer reaching 150% and 142%, respectively. Furthermore, the streamflow may increase even more when the land use scenario for the period 1998‒2025 remains the same as that in 2000. The minimum (61mm) and maximum (77mm) mean annual runoff depth occur under the RCP4.5 and RCP6 scenarios, respectively, compared with the mean annual observed streamflow of 33 mm from 1998 to 2014. Finally, we analyzed the correlation among the main land use types (i.e., agricultural land, forest, and pasture) and evapotranspiration, surface runoff contribution to streamflow (SURQ), groundwater contribution to streamflow (GWQ), and the sum of the surface runoff and groundwater contributions to streamflow (SSGQ), respectively. It was found that the increase in agricultural land may induce the increase in SURQ but the decrease in GWQ.

Methods for estimating annual exceedance-probability streamflows for streams in Kansas based on data through water year 2015

USGS Publications Warehouse

Painter, Colin C.; Heimann, David C.; Lanning-Rush, Jennifer L.

2017-08-14

A study was done by the U.S. Geological Survey in cooperation with the Kansas Department of Transportation and the Federal Emergency Management Agency to develop regression models to estimate peak streamflows of annual exceedance probabilities of 50, 20, 10, 4, 2, 1, 0.5, and 0.2 percent at ungaged locations in Kansas. Peak streamflow frequency statistics from selected streamgages were related to contributing drainage area and average precipitation using generalized least-squares regression analysis. The peak streamflow statistics were derived from 151 streamgages with at least 25 years of streamflow data through 2015. The developed equations can be used to predict peak streamflow magnitude and frequency within two hydrologic regions that were defined based on the effects of irrigation. The equations developed in this report are applicable to streams in Kansas that are not substantially affected by regulation, surface-water diversions, or urbanization. The equations are intended for use for streams with contributing drainage areas ranging from 0.17 to 14,901 square miles in the nonirrigation effects region and, 1.02 to 3,555 square miles in the irrigation-affected region, corresponding to the range of drainage areas of the streamgages used in the development of the regional equations.

Evaluating the streamflow simulation capability of PERSIANN-CDR daily rainfall products in two river basins on the Tibetan Plateau

DOE PAGES

Liu, Xiaomang; Yang, Tiantian; Hsu, Koulin; ...

2017-01-10

On the Tibetan Plateau, the limited ground-based rainfall information owing to a harsh environment has brought great challenges to hydrological studies. Satellite-based rainfall products, which allow for a better coverage than both radar network and rain gauges on the Tibetan Plateau, can be suitable alternatives for studies on investigating the hydrological processes and climate change. In this study, a newly developed daily satellite-based precipitation product, termed Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks $-$ Climate Data Record (PERSIANN-CDR), is used as input for a hydrologic model to simulate streamflow in the upper Yellow and Yangtze River basinsmore » on the Tibetan Plateau. The results show that the simulated streamflows using PERSIANN-CDR precipitation and the Global Land Data Assimilation System (GLDAS) precipitation are closer to observation than that using limited gauge-based precipitation interpolation in the upper Yangtze River basin. The simulated streamflow using gauge-based precipitation are higher than the streamflow observation during the wet season. In the upper Yellow River basin, gauge-based precipitation, GLDAS precipitation, and PERSIANN-CDR precipitation have similar good performance in simulating streamflow. Finally, the evaluation of streamflow simulation capability in this study partly indicates that the PERSIANN-CDR rainfall product has good potential to be a reliable dataset and an alternative information source of a limited gauge network for conducting long-term hydrological and climate studies on the Tibetan Plateau.« less

Deducing Climatic Elasticity to Assess Projected Climate Change Impacts on Streamflow Change across China

NASA Astrophysics Data System (ADS)

Liu, Jianyu; Zhang, Qiang; Zhang, Yongqiang; Chen, Xi; Li, Jianfeng; Aryal, Santosh K.

2017-10-01

Climatic elasticity has been widely applied to assess streamflow responses to climate changes. To fully assess impacts of climate under global warming on streamflow and reduce the error and uncertainty from various control variables, we develop a four-parameter (precipitation, catchment characteristics n, and maximum and minimum temperatures) climatic elasticity method named PnT, based on the widely used Budyko framework and simplified Makkink equation. We use this method to carry out the first comprehensive evaluation of the streamflow response to potential climate change for 372 widely spread catchments in China. The PnT climatic elasticity was first evaluated for a period 1980-2000, and then used to evaluate streamflow change response to climate change based on 12 global climate models under Representative Concentration Pathway 2.6 (RCP2.6) and RCP 8.5 emission scenarios. The results show that (1) the PnT climatic elasticity method is reliable; (2) projected increasing streamflow takes place in more than 60% of the selected catchments, with mean increments of 9% and 15.4% under RCP2.6 and RCP8.5 respectively; and (3) uncertainties in the projected streamflow are considerable in several regions, such as the Pearl River and Yellow River, with more than 40% of the selected catchments showing inconsistent change directions. Our results can help Chinese policy makers to manage and plan water resources more effectively, and the PnT climatic elasticity should be applied to other parts of the world.

Evaluating the streamflow simulation capability of PERSIANN-CDR daily rainfall products in two river basins on the Tibetan Plateau

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Xiaomang; Yang, Tiantian; Hsu, Koulin

On the Tibetan Plateau, the limited ground-based rainfall information owing to a harsh environment has brought great challenges to hydrological studies. Satellite-based rainfall products, which allow for a better coverage than both radar network and rain gauges on the Tibetan Plateau, can be suitable alternatives for studies on investigating the hydrological processes and climate change. In this study, a newly developed daily satellite-based precipitation product, termed Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks $-$ Climate Data Record (PERSIANN-CDR), is used as input for a hydrologic model to simulate streamflow in the upper Yellow and Yangtze River basinsmore » on the Tibetan Plateau. The results show that the simulated streamflows using PERSIANN-CDR precipitation and the Global Land Data Assimilation System (GLDAS) precipitation are closer to observation than that using limited gauge-based precipitation interpolation in the upper Yangtze River basin. The simulated streamflow using gauge-based precipitation are higher than the streamflow observation during the wet season. In the upper Yellow River basin, gauge-based precipitation, GLDAS precipitation, and PERSIANN-CDR precipitation have similar good performance in simulating streamflow. Finally, the evaluation of streamflow simulation capability in this study partly indicates that the PERSIANN-CDR rainfall product has good potential to be a reliable dataset and an alternative information source of a limited gauge network for conducting long-term hydrological and climate studies on the Tibetan Plateau.« less