A Coupled Modeling Framework of the Co-evolution of Humans and Water: Case Study of Tarim River Basin, Western China

NASA Astrophysics Data System (ADS)

Liu, D.; Tian, F.; Lin, M.; Sivapalan, M.

2014-12-01

The complex interactions and feedbacks between humans and water are very essential issues but are poorly understood in the newly proposed discipline of socio-hydrology (Sivapalan et al., 2012). An exploratory model with the appropriate level of simplification can be valuable to improve our understanding of the co-evolution and self-organization of socio-hydrological systems driven by interactions and feedbacks operating at different scales. In this study, a simple coupled modeling framework for socio-hydrology co-evolution is developed for the Tarim River Basin in Western China, and is used to illustrate the explanatory power of such a model. The study area is the mainstream of the Tarim River, which is divided into two modeling units. The socio-hydrological system is composed of four parts, i.e., social sub-system, economic sub-system, ecological sub-system, and hydrological sub-system. In each modeling unit, four coupled ordinary differential equations are used to simulate the dynamics of the social sub-system represented by human population, the economic sub-system represented by irrigated crop area, the ecological sub-system represented by natural vegetation cover and the hydrological sub-system represented by stream discharge. The coupling and feedback processes of the four dominant sub-systems (and correspondingly four state variables) are integrated into several internal system characteristics interactively and jointly determined by themselves and by other coupled systems. For example, the stream discharge is coupled to the irrigated crop area by the colonization rate and mortality rate of the irrigated crop area in the upper reach and the irrigated area is coupled to stream discharge through irrigation water consumption. The co-evolution of the Tarim socio-hydrological system is then analyzed within this modeling framework to gain insights into the overall system dynamics and its sensitivity to the external drivers and internal system variables. In the modeling framework, the state of each subsystem is holistically described by one state variable and the framework is flexible enough to comprise more processes and constitutive relationships if they are needed to illustrate the interaction and feedback mechanisms of the human-water system.

OpenDA-WFLOW framework for improving hydrologic predictions using distributed hydrologic models

NASA Astrophysics Data System (ADS)

Weerts, Albrecht; Schellekens, Jaap; Kockx, Arno; Hummel, Stef

2017-04-01

Data assimilation (DA) holds considerable potential for improving hydrologic predictions (Liu et al., 2012) and increase the potential for early warning and/or smart water management. However, advances in hydrologic DA research have not yet been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. The objective of this work is to highlight the development of a generic linkage of the open source OpenDA package and the open source community hydrologic modeling framework Openstreams/WFLOW and its application in operational hydrological forecasting on various spatial scales. The coupling between OpenDA and Openstreams/wflow framework is based on the emerging standard Basic Model Interface (BMI) as advocated by CSDMS using cross-platform webservices (i.e. Apache Thrift) developed by Hut et al. (2016). The potential application of the OpenDA-WFLOW for operational hydrologic forecasting including its integration with Delft-FEWS (used by more than 40 operational forecast centers around the world (Werner et al., 2013)) is demonstrated by the presented case studies. We will also highlight the possibility to give real-time insight into the working of the DA methods applied for supporting the forecaster as mentioned as one of the burning issues by Liu et al., (2012).

GLOFRIM v1.0 - A globally applicable computational framework for integrated hydrological-hydrodynamic modelling

NASA Astrophysics Data System (ADS)

Hoch, Jannis M.; Neal, Jeffrey C.; Baart, Fedor; van Beek, Rens; Winsemius, Hessel C.; Bates, Paul D.; Bierkens, Marc F. P.

2017-10-01

We here present GLOFRIM, a globally applicable computational framework for integrated hydrological-hydrodynamic modelling. GLOFRIM facilitates spatially explicit coupling of hydrodynamic and hydrologic models and caters for an ensemble of models to be coupled. It currently encompasses the global hydrological model PCR-GLOBWB as well as the hydrodynamic models Delft3D Flexible Mesh (DFM; solving the full shallow-water equations and allowing for spatially flexible meshing) and LISFLOOD-FP (LFP; solving the local inertia equations and running on regular grids). The main advantages of the framework are its open and free access, its global applicability, its versatility, and its extensibility with other hydrological or hydrodynamic models. Before applying GLOFRIM to an actual test case, we benchmarked both DFM and LFP for a synthetic test case. Results show that for sub-critical flow conditions, discharge response to the same input signal is near-identical for both models, which agrees with previous studies. We subsequently applied the framework to the Amazon River basin to not only test the framework thoroughly, but also to perform a first-ever benchmark of flexible and regular grids on a large-scale. Both DFM and LFP produce comparable results in terms of simulated discharge with LFP exhibiting slightly higher accuracy as expressed by a Kling-Gupta efficiency of 0.82 compared to 0.76 for DFM. However, benchmarking inundation extent between DFM and LFP over the entire study area, a critical success index of 0.46 was obtained, indicating that the models disagree as often as they agree. Differences between models in both simulated discharge and inundation extent are to a large extent attributable to the gridding techniques employed. In fact, the results show that both the numerical scheme of the inundation model and the gridding technique can contribute to deviations in simulated inundation extent as we control for model forcing and boundary conditions. This study shows that the presented computational framework is robust and widely applicable. GLOFRIM is designed as open access and easily extendable, and thus we hope that other large-scale hydrological and hydrodynamic models will be added. Eventually, more locally relevant processes would be captured and more robust model inter-comparison, benchmarking, and ensemble simulations of flood hazard on a large scale would be allowed for.

An Ensemble-Based Forecasting Framework to Optimize Reservoir Releases

NASA Astrophysics Data System (ADS)

Ramaswamy, V.; Saleh, F.

2017-12-01

Increasing frequency of extreme precipitation events are stressing the need to manage water resources on shorter timescales. Short-term management of water resources becomes proactive when inflow forecasts are available and this information can be effectively used in the control strategy. This work investigates the utility of short term hydrological ensemble forecasts for operational decision making during extreme weather events. An advanced automated hydrologic prediction framework integrating a regional scale hydrologic model, GIS datasets and the meteorological ensemble predictions from the European Center for Medium Range Weather Forecasting (ECMWF) was coupled to an implicit multi-objective dynamic programming model to optimize releases from a water supply reservoir. The proposed methodology was evaluated by retrospectively forecasting the inflows to the Oradell reservoir in the Hackensack River basin in New Jersey during the extreme hydrologic event, Hurricane Irene. Additionally, the flexibility of the forecasting framework was investigated by forecasting the inflows from a moderate rainfall event to provide important perspectives on using the framework to assist reservoir operations during moderate events. The proposed forecasting framework seeks to provide a flexible, assistive tool to alleviate the complexity of operational decision-making.

Modeling framework for representing long-term effectiveness of best management practices in addressing hydrology and water quality problems: Framework development and demonstration using a Bayesian method

NASA Astrophysics Data System (ADS)

Liu, Yaoze; Engel, Bernard A.; Flanagan, Dennis C.; Gitau, Margaret W.; McMillan, Sara K.; Chaubey, Indrajeet; Singh, Shweta

2018-05-01

Best management practices (BMPs) are popular approaches used to improve hydrology and water quality. Uncertainties in BMP effectiveness over time may result in overestimating long-term efficiency in watershed planning strategies. To represent varying long-term BMP effectiveness in hydrologic/water quality models, a high level and forward-looking modeling framework was developed. The components in the framework consist of establishment period efficiency, starting efficiency, efficiency for each storm event, efficiency between maintenance, and efficiency over the life cycle. Combined, they represent long-term efficiency for a specific type of practice and specific environmental concern (runoff/pollutant). An approach for possible implementation of the framework was discussed. The long-term impacts of grass buffer strips (agricultural BMP) and bioretention systems (urban BMP) in reducing total phosphorus were simulated to demonstrate the framework. Data gaps were captured in estimating the long-term performance of the BMPs. A Bayesian method was used to match the simulated distribution of long-term BMP efficiencies with the observed distribution with the assumption that the observed data represented long-term BMP efficiencies. The simulated distribution matched the observed distribution well with only small total predictive uncertainties. With additional data, the same method can be used to further improve the simulation results. The modeling framework and results of this study, which can be adopted in hydrologic/water quality models to better represent long-term BMP effectiveness, can help improve decision support systems for creating long-term stormwater management strategies for watershed management projects.

Flexibility on storage-release based distributed hydrologic modeling with object-oriented approach

USDA-ARS?s Scientific Manuscript database

With the availability of advanced hydrologic data in the public domain such as remotely sensed and climate change scenario data, there is a need for a modeling framework that is capable of using these data to simulate and extend hydrologic processes with multidisciplinary approaches for sustainable ...

The Regional Hydrologic Extremes Assessment System: A software framework for hydrologic modeling and data assimilation

PubMed Central

Das, Narendra; Stampoulis, Dimitrios; Ines, Amor; Fisher, Joshua B.; Granger, Stephanie; Kawata, Jessie; Han, Eunjin; Behrangi, Ali

2017-01-01

The Regional Hydrologic Extremes Assessment System (RHEAS) is a prototype software framework for hydrologic modeling and data assimilation that automates the deployment of water resources nowcasting and forecasting applications. A spatially-enabled database is a key component of the software that can ingest a suite of satellite and model datasets while facilitating the interfacing with Geographic Information System (GIS) applications. The datasets ingested are obtained from numerous space-borne sensors and represent multiple components of the water cycle. The object-oriented design of the software allows for modularity and extensibility, showcased here with the coupling of the core hydrologic model with a crop growth model. RHEAS can exploit multi-threading to scale with increasing number of processors, while the database allows delivery of data products and associated uncertainty through a variety of GIS platforms. A set of three example implementations of RHEAS in the United States and Kenya are described to demonstrate the different features of the system in real-world applications. PMID:28545077

The Regional Hydrologic Extremes Assessment System: A software framework for hydrologic modeling and data assimilation.

PubMed

Andreadis, Konstantinos M; Das, Narendra; Stampoulis, Dimitrios; Ines, Amor; Fisher, Joshua B; Granger, Stephanie; Kawata, Jessie; Han, Eunjin; Behrangi, Ali

2017-01-01

The Regional Hydrologic Extremes Assessment System (RHEAS) is a prototype software framework for hydrologic modeling and data assimilation that automates the deployment of water resources nowcasting and forecasting applications. A spatially-enabled database is a key component of the software that can ingest a suite of satellite and model datasets while facilitating the interfacing with Geographic Information System (GIS) applications. The datasets ingested are obtained from numerous space-borne sensors and represent multiple components of the water cycle. The object-oriented design of the software allows for modularity and extensibility, showcased here with the coupling of the core hydrologic model with a crop growth model. RHEAS can exploit multi-threading to scale with increasing number of processors, while the database allows delivery of data products and associated uncertainty through a variety of GIS platforms. A set of three example implementations of RHEAS in the United States and Kenya are described to demonstrate the different features of the system in real-world applications.

JAMS - a software platform for modular hydrological modelling

NASA Astrophysics Data System (ADS)

Kralisch, Sven; Fischer, Christian

2015-04-01

Current challenges of understanding and assessing the impacts of climate and land use changes on environmental systems demand for an ever-increasing integration of data and process knowledge in corresponding simulation models. Software frameworks that allow for a seamless creation of integrated models based on less complex components (domain models, process simulation routines) have therefore gained increasing attention during the last decade. JAMS is an Open-Source software framework that has been especially designed to cope with the challenges of eco-hydrological modelling. This is reflected by (i) its flexible approach for representing time and space, (ii) a strong separation of process simulation components from the declarative description of more complex models using domain specific XML, (iii) powerful analysis and visualization functions for spatial and temporal input and output data, and (iv) parameter optimization and uncertainty analysis functions commonly used in environmental modelling. Based on JAMS, different hydrological and nutrient-transport simulation models were implemented and successfully applied during the last years. We will present the JAMS core concepts and give an overview of models, simulation components and support tools available for that framework. Sample applications will be used to underline the advantages of component-based model designs and to show how JAMS can be used to address the challenges of integrated hydrological modelling.

Design and Implementation of Hydrologic Process Knowledge-base Ontology: A case study for the Infiltration Process

NASA Astrophysics Data System (ADS)

Elag, M.; Goodall, J. L.

2013-12-01

Hydrologic modeling often requires the re-use and integration of models from different disciplines to simulate complex environmental systems. Component-based modeling introduces a flexible approach for integrating physical-based processes across disciplinary boundaries. Several hydrologic-related modeling communities have adopted the component-based approach for simulating complex physical systems by integrating model components across disciplinary boundaries in a workflow. However, it is not always straightforward to create these interdisciplinary models due to the lack of sufficient knowledge about a hydrologic process. This shortcoming is a result of using informal methods for organizing and sharing information about a hydrologic process. A knowledge-based ontology provides such standards and is considered the ideal approach for overcoming this challenge. The aims of this research are to present the methodology used in analyzing the basic hydrologic domain in order to identify hydrologic processes, the ontology itself, and how the proposed ontology is integrated with the Water Resources Component (WRC) ontology. The proposed ontology standardizes the definitions of a hydrologic process, the relationships between hydrologic processes, and their associated scientific equations. The objective of the proposed Hydrologic Process (HP) Ontology is to advance the idea of creating a unified knowledge framework for components' metadata by introducing a domain-level ontology for hydrologic processes. The HP ontology is a step toward an explicit and robust domain knowledge framework that can be evolved through the contribution of domain users. Analysis of the hydrologic domain is accomplished using the Formal Concept Approach (FCA), in which the infiltration process, an important hydrologic process, is examined. Two infiltration methods, the Green-Ampt and Philip's methods, were used to demonstrate the implementation of information in the HP ontology. Furthermore, a SPARQL service is provided for semantic-based querying of the ontology.

Comprehensive, Process-based Identification of Hydrologic Models using Satellite and In-situ Water Storage Data: A Multi-objective calibration Approach

NASA Astrophysics Data System (ADS)

Abdo Yassin, Fuad; Wheater, Howard; Razavi, Saman; Sapriza, Gonzalo; Davison, Bruce; Pietroniro, Alain

2015-04-01

The credible identification of vertical and horizontal hydrological components and their associated parameters is very challenging (if not impossible) by only constraining the model to streamflow data, especially in regions where the vertical processes significantly dominate the horizontal processes. The prairie areas of the Saskatchewan River basin, a major water system in Canada, demonstrate such behavior, where the hydrologic connectivity and vertical fluxes are mainly controlled by the amount of surface and sub-surface water storages. In this study, we develop a framework for distributed hydrologic model identification and calibration that jointly constrains the model response (i.e., streamflows) as well as a set of model state variables (i.e., water storages) to observations. This framework is set up in the form of multi-objective optimization, where multiple performance criteria are defined and used to simultaneously evaluate the fidelity of the model to streamflow observations and observed (estimated) changes of water storage in the gridded landscape over daily and monthly time scales. The time series of estimated changes in total water storage (including soil, canopy, snow and pond storages) used in this study were derived from an experimental study enhanced by the information obtained from the GRACE satellite. We test this framework on the calibration of a Land Surface Scheme-Hydrology model, called MESH (Modélisation Environmentale Communautaire - Surface and Hydrology), for the Saskatchewan River basin. Pareto Archived Dynamically Dimensioned Search (PA-DDS) is used as the multi-objective optimization engine. The significance of using the developed framework is demonstrated in comparison with the results obtained through a conventional calibration approach to streamflow observations. The approach of incorporating water storage data into the model identification process can more potentially constrain the posterior parameter space, more comprehensively evaluate the model fidelity, and yield more credible predictions.

An integrated modeling framework for exploring flow regime and water quality changes with increasing biofuel crop production in the U.S. Corn Belt

NASA Astrophysics Data System (ADS)

Yaeger, Mary A.; Housh, Mashor; Cai, Ximing; Sivapalan, Murugesu

2014-12-01

To better address the dynamic interactions between human and hydrologic systems, we develop an integrated modeling framework that employs a System of Systems optimization model to emulate human development decisions which are then incorporated into a watershed model to estimate the resulting hydrologic impacts. The two models are run interactively to simulate the coevolution of coupled human-nature systems, such that reciprocal feedbacks between hydrologic processes and human decisions (i.e., human impacts on critical low flows and hydrologic impacts on human decisions on land and water use) can be assessed. The framework is applied to a Midwestern U.S. agricultural watershed, in the context of proposed biofuels development. This operation is illustrated by projecting three possible future coevolution trajectories, two of which use dedicated biofuel crops to reduce annual watershed nitrate export while meeting ethanol production targets. Imposition of a primary external driver (biofuel mandate) combined with different secondary drivers (water quality targets) results in highly nonlinear and multiscale responses of both the human and hydrologic systems, including multiple tradeoffs, impacting the future coevolution of the system in complex, heterogeneous ways. The strength of the hydrologic response is sensitive to the magnitude of the secondary driver; 45% nitrate reduction target leads to noticeable impacts at the outlet, while a 30% reduction leads to noticeable impacts that are mainly local. The local responses are conditioned by previous human-hydrologic modifications and their spatial relationship to the new biofuel development, highlighting the importance of past coevolutionary history in predicting future trajectories of change.

Probabilistic flood inundation prediction within a coupled hydrodynamic, distributed hydrologic modeling framework

NASA Astrophysics Data System (ADS)

Adams, T. E.

2016-12-01

Accurate and timely predictions of the lateral exent of floodwaters and water level depth in floodplain areas are critical globally. This paper demonstrates the coupling of hydrologic ensembles, derived from the use of numerical weather prediction (NWP) model forcings as input to a fully distributed hydrologic model. Resulting ensemble output from the distributed hydrologic model are used as upstream flow boundaries and lateral inflows to a 1-D hydrodynamic model. An example is presented for the Potomac River in the vicinity of Washington, DC (USA). The approach taken falls within the broader goals of the Hydrologic Ensemble Prediction EXperiment (HEPEX).

A Flexible Modeling Framework For Hydraulic and Water Quality Performance Assessment of Stormwater Green Infrastructure

EPA Science Inventory

A flexible framework has been created for modeling multi-dimensional hydrological and water quality processes within stormwater green infrastructures (GIs). The framework models a GI system using a set of blocks (spatial features) and connectors (interfaces) representing differen...

A Local to National Scale Catchment Model Simulation Framework for Hydrological Predictions and Impact Assessments Under Uncertainty

NASA Astrophysics Data System (ADS)

Freer, Jim; Coxon, Gemma; Quinn, Niall; Dunne, Toby; Lane, Rosie; Bates, Paul; Wagener, Thorsten; Woods, Ross; Neal, Jeff; Howden, Nicholas; Musuuza, Jude

2017-04-01

There is a huge challenge in developing hydrological model structures that can be used for hypothesis testing, prediction, impact assessment and risk analyses over a wide range of spatial scales. There are many reasons why this is the case, from computational demands, to how we define and characterize different features and pathway connectivities in the landscape, that differ depending on the objectives of the study. However there is certainly a need more than ever to explore the trade-offs between the complexity of modelling applied (i.e. spatial discretization, levels of process representation, complexity of landscape representation) compared to the benefits realized in terms of predictive capability and robustness of these predictions during hydrological extremes and during change. Furthermore, there is a further balance, particularly associated with prediction uncertainties, in that it is not desirable to have modelling systems that are too complex compared to the observed data that would ever be available to apply them. This is particularly the case when models are applied to quantify national impact assessments, especially if these are based on validation assessments from smaller more detailed case studies. Therefore the hydrological community needs modelling tools and approaches that enable these trade-offs to be explored and to understand the level of representation needed in models to be 'fit-for-purpose' for a given application. This paper presents a catchment scale national modelling framework based on Dynamic-TOPMODEL specifically setup to fulfil these aims. A key component of the modelling framework is it's structural flexibility, as is the ability to assess model outputs using Monte Carlo simulation techniques. The model build has been automated to work at any spatial scale to the national scale, and within that to control the level of spatial discretisation and connectivity of locally accounted landscape elements in the form of hydrological response units (HRU's). This allows for the explicit consideration of spatial rainfall fields, landscape, soils and geological attributes and the spatial connectivity of hydrological flow pathways to explore what level of modelling complexity we need for different prediction problems. We shall present this framework and show how it can be used in flood and drought risk analyses as well as include attributes and features within the landscape to explore societal and climate impacts effectively within an uncertainty analyses framework.

Hydroclimatic regimes: a distributed water-balance framework for hydrologic assessment, classification, and management

USGS Publications Warehouse

Weiskel, Peter K.; Wolock, David M.; Zarriello, Phillip J.; Vogel, Richard M.; Levin, Sara B.; Lent, Robert M.

2014-01-01

Runoff-based indicators of terrestrial water availability are appropriate for humid regions, but have tended to limit our basic hydrologic understanding of drylands – the dry-subhumid, semiarid, and arid regions which presently cover nearly half of the global land surface. In response, we introduce an indicator framework that gives equal weight to humid and dryland regions, accounting fully for both vertical (precipitation + evapotranspiration) and horizontal (groundwater + surface-water) components of the hydrologic cycle in any given location – as well as fluxes into and out of landscape storage. We apply the framework to a diverse hydroclimatic region (the conterminous USA) using a distributed water-balance model consisting of 53 400 networked landscape hydrologic units. Our model simulations indicate that about 21% of the conterminous USA either generated no runoff or consumed runoff from upgradient sources on a mean-annual basis during the 20th century. Vertical fluxes exceeded horizontal fluxes across 76% of the conterminous area. Long-term-average total water availability (TWA) during the 20th century, defined here as the total influx to a landscape hydrologic unit from precipitation, groundwater, and surface water, varied spatially by about 400 000-fold, a range of variation ~100 times larger than that for mean-annual runoff across the same area. The framework includes but is not limited to classical, runoff-based approaches to water-resource assessment. It also incorporates and reinterprets the green- and blue-water perspective now gaining international acceptance. Implications of the new framework for several areas of contemporary hydrology are explored, and the data requirements of the approach are discussed in relation to the increasing availability of gridded global climate, land-surface, and hydrologic data sets.

Understanding the Dynamics of Socio-Hydrological Environment: a Conceptual Framework

NASA Astrophysics Data System (ADS)

Woyessa, Y.; Welderufael, W.; Edossa, D.

2011-12-01

Human actions affect ecological systems and the services they provide through various activities, such as land use, water use, pollution and climate change. Climate change is perhaps one of the most important sustainable development challenges that threaten to undo many of the development efforts being made to reach the targets set for the Millennium Development Goals. Understanding the change of ecosystems under different scenarios of climate and biophysical conditions could assist in bringing the issue of ecosystem services into decision making process. Similarly, the impacts of land use change on ecosystems and biodiversity have received considerable attention from ecologists and hydrologists alike. Land use change in a catchment can impact on water supply by altering hydrological processes, such as infiltration, groundwater recharge, base flow and direct runoff. In the past a variety of models were used for predicting land-use changes. Recently the focus has shifted away from using mathematically oriented models to agent-based modelling (ABM) approach to simulate land use scenarios. A conceptual framework is being developed which integrates climate change scenarios and the human dimension of land use decision into a hydrological model in order to assess its impacts on the socio-hydrological dynamics of a river basin. The following figures present the framework for the analysis and modelling of the socio-hydrological dynamics. Keywords: climate change, land use, river basin

A formal framework of scenario creation and analysis of extreme hydrological events

NASA Astrophysics Data System (ADS)

Lohmann, D.

2007-12-01

We are presenting a formal framework for a hydrological risk analysis. Different measures of risk will be introduced, such as average annual loss or occurrence exceedance probability. These are important measures for e.g. insurance companies to determine the cost of insurance. One key aspect of investigating the potential consequences of extreme hydrological events (floods and draughts) is the creation of meteorological scenarios that reflect realistic spatial and temporal patterns of precipitation that also have correct local statistics. 100,000 years of these meteorological scenarios are used in a calibrated rainfall-runoff-flood-loss-risk model to produce flood and draught events that have never been observed. The results of this hazard model are statistically analyzed and linked to socio-economic data and vulnerability functions to show the impact of severe flood events. We are showing results from the Risk Management Solutions (RMS) Europe Flood Model to introduce this formal framework.

Applicability of Hydrologic Landscapes for Model Calibration at the Watershed Scale in the Pacific Northwest

EPA Science Inventory

The Pacific Northwest Hydrologic Landscapes (PNW HL) at the assessment unit scale has provided a solid conceptual classification framework to relate and transfer hydrologically meaningful information between watersheds without access to streamflow time series. A collection of tec...

A framework for modelling the complexities of food and water security under globalisation

NASA Astrophysics Data System (ADS)

Dermody, Brian J.; Sivapalan, Murugesu; Stehfest, Elke; van Vuuren, Detlef P.; Wassen, Martin J.; Bierkens, Marc F. P.; Dekker, Stefan C.

2018-01-01

We present a new framework for modelling the complexities of food and water security under globalisation. The framework sets out a method to capture regional and sectoral interdependencies and cross-scale feedbacks within the global food system that contribute to emergent water use patterns. The framework integrates aspects of existing models and approaches in the fields of hydrology and integrated assessment modelling. The core of the framework is a multi-agent network of city agents connected by infrastructural trade networks. Agents receive socio-economic and environmental constraint information from integrated assessment models and hydrological models respectively and simulate complex, socio-environmental dynamics that operate within those constraints. The emergent changes in food and water resources are aggregated and fed back to the original models with minimal modification of the structure of those models. It is our conviction that the framework presented can form the basis for a new wave of decision tools that capture complex socio-environmental change within our globalised world. In doing so they will contribute to illuminating pathways towards a sustainable future for humans, ecosystems and the water they share.

Regional frameworks applied to hydrology: can landscape-based frameworks capture the hydrologic variability?

Treesearch

R. McManamay; D. Orth; C. Dolloff; E. Frimpong

2011-01-01

Regional frameworks have been used extensively in recent years to aid in broad-scale management. Widely used landscape-based regional frameworks, such as hydrologic landscape regions (HLRs) and physiographic provinces, may provide predictive tools of hydrologic variability. However, hydrologic-based regional frameworks, created using only streamflow data, are also...

Application of large-scale, multi-resolution watershed modeling framework using the Hydrologic and Water Quality System (HAWQS)

USDA-ARS?s Scientific Manuscript database

In recent years, large-scale watershed modeling has been implemented broadly in the field of water resources planning and management. Complex hydrological, sediment, and nutrient processes can be simulated by sophisticated watershed simulation models for important issues such as water resources all...

Improving National Water Modeling: An Intercomparison of two High-Resolution, Continental Scale Models, CONUS-ParFlow and the National Water Model

NASA Astrophysics Data System (ADS)

Tijerina, D.; Gochis, D.; Condon, L. E.; Maxwell, R. M.

2017-12-01

Development of integrated hydrology modeling systems that couple atmospheric, land surface, and subsurface flow is growing trend in hydrologic modeling. Using an integrated modeling framework, subsurface hydrologic processes, such as lateral flow and soil moisture redistribution, are represented in a single cohesive framework with surface processes like overland flow and evapotranspiration. There is a need for these more intricate models in comprehensive hydrologic forecasting and water management over large spatial areas, specifically the Continental US (CONUS). Currently, two high-resolution, coupled hydrologic modeling applications have been developed for this domain: CONUS-ParFlow built using the integrated hydrologic model ParFlow and the National Water Model that uses the NCAR Weather Research and Forecasting hydrological extension package (WRF-Hydro). Both ParFlow and WRF-Hydro include land surface models, overland flow, and take advantage of parallelization and high-performance computing (HPC) capabilities; however, they have different approaches to overland subsurface flow and groundwater-surface water interactions. Accurately representing large domains remains a challenge considering the difficult task of representing complex hydrologic processes, computational expense, and extensive data needs; both models have accomplished this, but have differences in approach and continue to be difficult to validate. A further exploration of effective methodology to accurately represent large-scale hydrology with integrated models is needed to advance this growing field. Here we compare the outputs of CONUS-ParFlow and the National Water Model to each other and with observations to study the performance of hyper-resolution models over large domains. Models were compared over a range of scales for major watersheds within the CONUS with a specific focus on the Mississippi, Ohio, and Colorado River basins. We use a novel set of approaches and analysis for this comparison to better understand differences in process and bias. This intercomparison is a step toward better understanding how much water we have and interactions between surface and subsurface. Our goal is to advance our understanding and simulation of the hydrologic system and ultimately improve hydrologic forecasts.

Vulnerability Assessment of Water Supply Systems: Status, Gaps and Opportunities

NASA Astrophysics Data System (ADS)

Wheater, H. S.

2015-12-01

Conventional frameworks for assessing the impacts of climate change on water resource systems use cascades of climate and hydrological models to provide 'top-down' projections of future water availability, but these are subject to high uncertainty and are model and scenario-specific. Hence there has been recent interest in 'bottom-up' frameworks, which aim to evaluate system vulnerability to change in the context of possible future climate and/or hydrological conditions. Such vulnerability assessments are generic, and can be combined with updated information from top-down assessments as they become available. While some vulnerability methods use hydrological models to estimate water availability, fully bottom-up schemes have recently been proposed that directly map system vulnerability as a function of feasible changes in water supply characteristics. These use stochastic algorithms, based on reconstruction or reshuffling methods, by which multiple water supply realizations can be generated under feasible ranges of change in water supply conditions. The paper reports recent successes, and points to areas of future improvement. Advances in stochastic modeling and optimization can address some technical limitations in flow reconstruction, while various data mining and system identification techniques can provide possibilities to better condition realizations for consistency with top-down scenarios. Finally, we show that probabilistic and Bayesian frameworks together can provide a potential basis to combine information obtained from fully bottom-up analyses with projections available from climate and/or hydrological models in a fully integrated risk assessment framework for deep uncertainty.

A framework for global river flood risk assessment

NASA Astrophysics Data System (ADS)

Winsemius, H. C.; Van Beek, L. P. H.; Bouwman, A.; Ward, P. J.; Jongman, B.

2012-04-01

There is an increasing need for strategic global assessments of flood risks. Such assessments may be required by: (a) International Financing Institutes and Disaster Management Agencies to evaluate where, when, and which investments in flood risk mitigation are most required; (b) (re-)insurers, who need to determine their required coverage capital; and (c) large companies to account for risks of regional investments. In this contribution, we propose a framework for global river flood risk assessment. The framework combines coarse scale resolution hazard probability distributions, derived from global hydrological model runs (typical scale about 0.5 degree resolution) with high resolution estimates of exposure indicators. The high resolution is required because floods typically occur at a much smaller scale than the typical resolution of global hydrological models, and exposure indicators such as population, land use and economic value generally are strongly variable in space and time. The framework therefore estimates hazard at a high resolution ( 1 km2) by using a) global forcing data sets of the current (or in scenario mode, future) climate; b) a global hydrological model; c) a global flood routing model, and d) importantly, a flood spatial downscaling routine. This results in probability distributions of annual flood extremes as an indicator of flood hazard, at the appropriate resolution. A second component of the framework combines the hazard probability distribution with classical flood impact models (e.g. damage, affected GDP, affected population) to establish indicators for flood risk. The framework can be applied with a large number of datasets and models and sensitivities of such choices can be evaluated by the user. The framework is applied using the global hydrological model PCR-GLOBWB, combined with a global flood routing model. Downscaling of the hazard probability distributions to 1 km2 resolution is performed with a new downscaling algorithm, applied on a number of target regions. We demonstrate the use of impact models in these regions based on global GDP, population, and land use maps. In this application, we show sensitivities of the estimated risks with regard to the use of different climate input datasets, decisions made in the downscaling algorithm, and different approaches to establish distributed estimates of GDP and asset exposure to flooding.

Advances in the spatially distributed ages-w model: parallel computation, java connection framework (JCF) integration, and streamflow/nitrogen dynamics assessment

USDA-ARS?s Scientific Manuscript database

AgroEcoSystem-Watershed (AgES-W) is a modular, Java-based spatially distributed model which implements hydrologic and water quality (H/WQ) simulation components under the Java Connection Framework (JCF) and the Object Modeling System (OMS) environmental modeling framework. AgES-W is implicitly scala...

The Canadian Hydrological Model (CHM): A multi-scale, variable-complexity hydrological model for cold regions

NASA Astrophysics Data System (ADS)

Marsh, C.; Pomeroy, J. W.; Wheater, H. S.

2016-12-01

There is a need for hydrological land surface schemes that can link to atmospheric models, provide hydrological prediction at multiple scales and guide the development of multiple objective water predictive systems. Distributed raster-based models suffer from an overrepresentation of topography, leading to wasted computational effort that increases uncertainty due to greater numbers of parameters and initial conditions. The Canadian Hydrological Model (CHM) is a modular, multiphysics, spatially distributed modelling framework designed for representing hydrological processes, including those that operate in cold-regions. Unstructured meshes permit variable spatial resolution, allowing coarse resolutions at low spatial variability and fine resolutions as required. Model uncertainty is reduced by lessening the necessary computational elements relative to high-resolution rasters. CHM uses a novel multi-objective approach for unstructured triangular mesh generation that fulfills hydrologically important constraints (e.g., basin boundaries, water bodies, soil classification, land cover, elevation, and slope/aspect). This provides an efficient spatial representation of parameters and initial conditions, as well as well-formed and well-graded triangles that are suitable for numerical discretization. CHM uses high-quality open source libraries and high performance computing paradigms to provide a framework that allows for integrating current state-of-the-art process algorithms. The impact of changes to model structure, including individual algorithms, parameters, initial conditions, driving meteorology, and spatial/temporal discretization can be easily tested. Initial testing of CHM compared spatial scales and model complexity for a spring melt period at a sub-arctic mountain basin. The meshing algorithm reduced the total number of computational elements and preserved the spatial heterogeneity of predictions.

Scaling, Similarity, and the Fourth Paradigm for Hydrology

NASA Technical Reports Server (NTRS)

Peters-Lidard, Christa D.; Clark, Martyn; Samaniego, Luis; Verhoest, Niko E. C.; van Emmerik, Tim; Uijlenhoet, Remko; Achieng, Kevin; Franz, Trenton E.; Woods, Ross

2017-01-01

In this synthesis paper addressing hydrologic scaling and similarity, we posit that roadblocks in the search for universal laws of hydrology are hindered by our focus on computational simulation (the third paradigm), and assert that it is time for hydrology to embrace a fourth paradigm of data-intensive science. Advances in information-based hydrologic science, coupled with an explosion of hydrologic data and advances in parameter estimation and modelling, have laid the foundation for a data-driven framework for scrutinizing hydrological scaling and similarity hypotheses. We summarize important scaling and similarity concepts (hypotheses) that require testing, describe a mutual information framework for testing these hypotheses, describe boundary condition, state flux, and parameter data requirements across scales to support testing these hypotheses, and discuss some challenges to overcome while pursuing the fourth hydrological paradigm. We call upon the hydrologic sciences community to develop a focused effort towards adopting the fourth paradigm and apply this to outstanding challenges in scaling and similarity.

The Spatially-Distributed Agroecosystem-Watershed (Ages-W) Hydrologic/Water Quality (H/WQ) model for assessment of conservation effects

USDA-ARS?s Scientific Manuscript database

AgroEcoSystem-Watershed (AgES-W) is a modular, Java-based spatially distributed model which implements hydrologic/water quality (H/WQ) simulation components under the Object Modeling System (OMS3) environmental modeling framework. AgES-W has recently been enhanced with the addition of nitrogen (N) a...

Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models

USGS Publications Warehouse

Clark, Martyn P.; Slater, Andrew G.; Rupp, David E.; Woods, Ross A.; Vrugt, Jasper A.; Gupta, Hoshin V.; Wagener, Thorsten; Hay, Lauren E.

2008-01-01

The problems of identifying the most appropriate model structure for a given problem and quantifying the uncertainty in model structure remain outstanding research challenges for the discipline of hydrology. Progress on these problems requires understanding of the nature of differences between models. This paper presents a methodology to diagnose differences in hydrological model structures: the Framework for Understanding Structural Errors (FUSE). FUSE was used to construct 79 unique model structures by combining components of 4 existing hydrological models. These new models were used to simulate streamflow in two of the basins used in the Model Parameter Estimation Experiment (MOPEX): the Guadalupe River (Texas) and the French Broad River (North Carolina). Results show that the new models produced simulations of streamflow that were at least as good as the simulations produced by the models that participated in the MOPEX experiment. Our initial application of the FUSE method for the Guadalupe River exposed relationships between model structure and model performance, suggesting that the choice of model structure is just as important as the choice of model parameters. However, further work is needed to evaluate model simulations using multiple criteria to diagnose the relative importance of model structural differences in various climate regimes and to assess the amount of independent information in each of the models. This work will be crucial to both identifying the most appropriate model structure for a given problem and quantifying the uncertainty in model structure. To facilitate research on these problems, the FORTRAN‐90 source code for FUSE is available upon request from the lead author.

A Web GIS Enabled Comprehensive Hydrologic Information System for Indian Water Resources Systems

NASA Astrophysics Data System (ADS)

Goyal, A.; Tyagi, H.; Gosain, A. K.; Khosa, R.

2017-12-01

Hydrological systems across the globe are getting increasingly water stressed with each passing season due to climate variability & snowballing water demand. Hence, to safeguard food, livelihood & economic security, it becomes imperative to employ scientific studies for holistic management of indispensable resource like water. However, hydrological study of any scale & purpose is heavily reliant on various spatio-temporal datasets which are not only difficult to discover/access but are also tough to use & manage. Besides, owing to diversity of water sector agencies & dearth of standard operating procedures, seamless information exchange is challenging for collaborators. Extensive research is being done worldwide to address these issues but regrettably not much has been done in developing countries like India. Therefore, the current study endeavours to develop a Hydrological Information System framework in a Web-GIS environment for empowering Indian water resources systems. The study attempts to harmonize the standards for metadata, terminology, symbology, versioning & archiving for effective generation, processing, dissemination & mining of data required for hydrological studies. Furthermore, modelers with humble computing resources at their disposal, can consume this standardized data in high performance simulation modelling using cloud computing within the developed Web-GIS framework. They can also integrate the inputs-outputs of different numerical models available on the platform and integrate their results for comprehensive analysis of the chosen hydrological system. Thus, the developed portal is an all-in-one framework that can facilitate decision makers, industry professionals & researchers in efficient water management.

On modeling complex interplay in small-scale self-organized socio-hydrological systems

NASA Astrophysics Data System (ADS)

Muneepeerakul, Rachata

2017-04-01

Successful and sustainable socio-hydrological systems, as in any coupled natural human-systems, require effective governance, which depends on the existence of proper infrastructure (both hard and soft). Recent work has addressed systems in which resource users and the organization responsible for maintaining the infrastructure are separate entities. However, many socio-hydrological systems, especially in developing countries, are small and without such formal division of labor; rather, such division of labor typically arises from self-organization within the population. In this work, we modify and mathematically operationalize a conceptual framework by developing a system of differential equations that capture the strategic behavior within such a self-organized population, its interplay with infrastructure characteristics and hydrological dynamics, and feedbacks between these elements. The model yields a number of insightful conditions related to long-term sustainability and collapse of the socio-hydrological system in the form of relationships between biophysical and social factors. These relationships encapsulate nonlinear interactions of these factors. The modeling framework is grounded in a solid conceptual foundation upon which additional modifications and realism can be built for potential reconciliation between socio-hydrology with other related fields and further applications.

The water-energy nexus at water supply and its implications on the integrated water and energy management.

PubMed

Khalkhali, Masoumeh; Westphal, Kirk; Mo, Weiwei

2018-09-15

Water and energy are highly interdependent in the modern world, and hence, it is important to understand their constantly changing and nonlinear interconnections to inform the integrated management of water and energy. In this study, a hydrologic model, a water systems model, and an energy model were developed and integrated into a system dynamics modeling framework. This framework was then applied to a water supply system in the northeast US to capture its water-energy interactions under a set of future population, climate, and system operation scenarios. A hydrologic model was first used to simulate the system's hydrologic inflows and outflows under temperature and precipitation changes on a weekly-basis. A water systems model that combines the hydrologic model and management rules (e.g., water release and transfer) was then developed to dynamically simulate the system's water storage and water head. Outputs from the water systems model were used in the energy model to estimate hydropower generation. It was found that critical water-energy synergies and tradeoffs exist, and there is a possibility for integrated water and energy management to achieve better outcomes. This analysis also shows the importance of a holistic understanding of the systems as a whole, which would allow utility managers to make proactive long-term management decisions. The modeling framework is generalizable to other water supply systems with hydropower generation capacities to inform the integrated management of water and energy resources. Copyright © 2018 Elsevier B.V. All rights reserved.

A hybrid framework for quantifying the influence of data in hydrological model calibration

NASA Astrophysics Data System (ADS)

Wright, David P.; Thyer, Mark; Westra, Seth; McInerney, David

2018-06-01

Influence diagnostics aim to identify a small number of influential data points that have a disproportionate impact on the model parameters and/or predictions. The key issues with current influence diagnostic techniques are that the regression-theory approaches do not provide hydrologically relevant influence metrics, while the case-deletion approaches are computationally expensive to calculate. The main objective of this study is to introduce a new two-stage hybrid framework that overcomes these challenges, by delivering hydrologically relevant influence metrics in a computationally efficient manner. Stage one uses computationally efficient regression-theory influence diagnostics to identify the most influential points based on Cook's distance. Stage two then uses case-deletion influence diagnostics to quantify the influence of points using hydrologically relevant metrics. To illustrate the application of the hybrid framework, we conducted three experiments on 11 hydro-climatologically diverse Australian catchments using the GR4J hydrological model. The first experiment investigated how many data points from stage one need to be retained in order to reliably identify those points that have the hightest influence on hydrologically relevant metrics. We found that a choice of 30-50 is suitable for hydrological applications similar to those explored in this study (30 points identified the most influential data 98% of the time and reduced the required recalibrations by 99% for a 10 year calibration period). The second experiment found little evidence of a change in the magnitude of influence with increasing calibration period length from 1, 2, 5 to 10 years. Even for 10 years the impact of influential points can still be high (>30% influence on maximum predicted flows). The third experiment compared the standard least squares (SLS) objective function with the weighted least squares (WLS) objective function on a 10 year calibration period. In two out of three flow metrics there was evidence that SLS, with the assumption of homoscedastic residual error, identified data points with higher influence (largest changes of 40%, 10%, and 44% for the maximum, mean, and low flows, respectively) than WLS, with the assumption of heteroscedastic residual errors (largest changes of 26%, 6%, and 6% for the maximum, mean, and low flows, respectively). The hybrid framework complements existing model diagnostic tools and can be applied to a wide range of hydrological modelling scenarios.

Design and experimentation of an empirical multistructure framework for accurate, sharp and reliable hydrological ensembles

NASA Astrophysics Data System (ADS)

Seiller, G.; Anctil, F.; Roy, R.

2017-09-01

This paper outlines the design and experimentation of an Empirical Multistructure Framework (EMF) for lumped conceptual hydrological modeling. This concept is inspired from modular frameworks, empirical model development, and multimodel applications, and encompasses the overproduce and select paradigm. The EMF concept aims to reduce subjectivity in conceptual hydrological modeling practice and includes model selection in the optimisation steps, reducing initial assumptions on the prior perception of the dominant rainfall-runoff transformation processes. EMF generates thousands of new modeling options from, for now, twelve parent models that share their functional components and parameters. Optimisation resorts to ensemble calibration, ranking and selection of individual child time series based on optimal bias and reliability trade-offs, as well as accuracy and sharpness improvement of the ensemble. Results on 37 snow-dominated Canadian catchments and 20 climatically-diversified American catchments reveal the excellent potential of the EMF in generating new individual model alternatives, with high respective performance values, that may be pooled efficiently into ensembles of seven to sixty constitutive members, with low bias and high accuracy, sharpness, and reliability. A group of 1446 new models is highlighted to offer good potential on other catchments or applications, based on their individual and collective interests. An analysis of the preferred functional components reveals the importance of the production and total flow elements. Overall, results from this research confirm the added value of ensemble and flexible approaches for hydrological applications, especially in uncertain contexts, and open up new modeling possibilities.

SWAT ungauged: Hydrological budget and crop yield predictions in the Upper Mississippi River Basin

USDA-ARS?s Scientific Manuscript database

Physically based, distributed hydrologic models are increasingly used in assessments of water resources, best management practices, and climate and land use changes. Model performance evaluation in ungauged basins is an important research topic. In this study, we propose a framework for developing S...

Probabilistic graphs as a conceptual and computational tool in hydrology and water management

NASA Astrophysics Data System (ADS)

Schoups, Gerrit

2014-05-01

Originally developed in the fields of machine learning and artificial intelligence, probabilistic graphs constitute a general framework for modeling complex systems in the presence of uncertainty. The framework consists of three components: 1. Representation of the model as a graph (or network), with nodes depicting random variables in the model (e.g. parameters, states, etc), which are joined together by factors. Factors are local probabilistic or deterministic relations between subsets of variables, which, when multiplied together, yield the joint distribution over all variables. 2. Consistent use of probability theory for quantifying uncertainty, relying on basic rules of probability for assimilating data into the model and expressing unknown variables as a function of observations (via the posterior distribution). 3. Efficient, distributed approximation of the posterior distribution using general-purpose algorithms that exploit model structure encoded in the graph. These attributes make probabilistic graphs potentially useful as a conceptual and computational tool in hydrology and water management (and beyond). Conceptually, they can provide a common framework for existing and new probabilistic modeling approaches (e.g. by drawing inspiration from other fields of application), while computationally they can make probabilistic inference feasible in larger hydrological models. The presentation explores, via examples, some of these benefits.

A new moving strategy for the sequential Monte Carlo approach in optimizing the hydrological model parameters

NASA Astrophysics Data System (ADS)

Zhu, Gaofeng; Li, Xin; Ma, Jinzhu; Wang, Yunquan; Liu, Shaomin; Huang, Chunlin; Zhang, Kun; Hu, Xiaoli

2018-04-01

Sequential Monte Carlo (SMC) samplers have become increasing popular for estimating the posterior parameter distribution with the non-linear dependency structures and multiple modes often present in hydrological models. However, the explorative capabilities and efficiency of the sampler depends strongly on the efficiency in the move step of SMC sampler. In this paper we presented a new SMC sampler entitled the Particle Evolution Metropolis Sequential Monte Carlo (PEM-SMC) algorithm, which is well suited to handle unknown static parameters of hydrologic model. The PEM-SMC sampler is inspired by the works of Liang and Wong (2001) and operates by incorporating the strengths of the genetic algorithm, differential evolution algorithm and Metropolis-Hasting algorithm into the framework of SMC. We also prove that the sampler admits the target distribution to be a stationary distribution. Two case studies including a multi-dimensional bimodal normal distribution and a conceptual rainfall-runoff hydrologic model by only considering parameter uncertainty and simultaneously considering parameter and input uncertainty show that PEM-SMC sampler is generally superior to other popular SMC algorithms in handling the high dimensional problems. The study also indicated that it may be important to account for model structural uncertainty by using multiplier different hydrological models in the SMC framework in future study.

The role of hydrological and water quality models in the application of the ecosystem services framework for the EU Water Framework Directive

NASA Astrophysics Data System (ADS)

Hallouin, Thibault; Bruen, Michael; Feeley, Hugh B.; Christie, Michael; Bullock, Craig; Kelly, Fiona; Kelly-Quinn, Mary

2017-04-01

The hydrological cycle is intimately linked with environmental processes that are essential for human welfare in many regards including, among others, the provision of safe water from surface and subsurface waterbodies, rain-fed agricultural production, or the provision of aquatic-sourced food. As well as being a receiver of these natural benefits, the human population is also a manager of the water and other natural resources and, as such, can affect their future sustainable provision. With global population growth and climate change, both the dependence of the human population on water resources and the threat they pose to these resources are likely to intensify so that the sustainability of the coupled natural and human system is threatened. In the European Union, the Water Framework Directive is driving policy and encouraging member states to manage their water resources wisely in order to maintain or restore ecological quality. To this end, the ecosystem services framework can be a useful tool to link the requirements in terms of ecological status into more tangible descriptors, that is the ecosystem services. In the ESManage Project, existing environmental system models such as hydrological models and water quality models are used as the basis to quantify the provision of many hydrological and aquatic ecosystem services by constructing indicators for the ecosystem services from the modelled environmental variables. By allowing different management options and policies to be compared, these models can be a valuable source of information for policy makers when they are used for climate and land use scenario analyses. Not all hydrological models developed for flood forecasting are suitable for this application and inappropriate models can lead to questionable conclusions. This paper demonstrates the readily available capabilities of a specially developed catchment hydrological model coupled with a water quality model to quantify a wide range of biophysically quantifiable water-related ecosystem services such as water provision (river flows, groundwater recharge and vegetation transpiration), flood regulation or nutrient and sediment retention. This combination of models will be used to carry out scenario analyses on IPCC climate change scenarios as well as various land use scenarios. Results will be presented for a test catchment in the Republic of Ireland.

Towards an integrated model of floodplain hydrology representing feedbacks and anthropogenic effects

NASA Astrophysics Data System (ADS)

Andreadis, K.; Schumann, G.; Voisin, N.; O'Loughlin, F.; Tesfa, T. K.; Bates, P.

2017-12-01

The exchange of water between hillslopes, river channels and floodplain can be quite complex and the difficulty in capturing the mechanisms behind it is exacerbated by the impact of human activities such as irrigation and reservoir operations. Although there has been a vast body of work on modeling hydrological processes, most of the resulting models have been limited with regards to aspects of the coupled human-natural system. For example, hydrologic models that represent processes such as evapotranspiration, infiltration, interception and groundwater dynamics often neglect anthropogenic effects or do not adequately represent the inherently two-dimensional floodplain flow. We present an integrated modeling framework that is comprised of the Variable Infiltration Capacity (VIC) hydrology model, the LISFLOOD-FP hydrodynamic model, and the Water resources Management (WM) model. The VIC model solves the energy and water balance over a gridded domain and simulates a number of hydrologic features such as snow, frozen soils, lakes and wetlands, while also representing irrigation demand from cropland areas. LISFLOOD-FP solves an approximation of the Saint-Venant equations to efficiently simulate flow in river channels and the floodplain. The implementation of WM accommodates a variety of operating rules in reservoirs and withdrawals due to consumptive demands, allowing the successful simulation of regulated flow. The models are coupled so as to allow feedbacks between their corresponding processes, therefore providing the ability to test different hypotheses about the floodplain hydrology of large-scale basins. We test this integrated framework over the Zambezi River basin by simulating its hydrology from 2000-2010, and evaluate the results against remotely sensed observations. Finally, we examine the sensitivity of streamflow and water inundation to changes in reservoir operations, precipitation and temperature.

Exploring uncertainty and model predictive performance concepts via a modular snowmelt-runoff modeling framework

Treesearch

Tyler Jon Smith; Lucy Amanda Marshall

2010-01-01

Model selection is an extremely important aspect of many hydrologic modeling studies because of the complexity, variability, and uncertainty that surrounds the current understanding of watershed-scale systems. However, development and implementation of a complete precipitation-runoff modeling framework, from model selection to calibration and uncertainty analysis, are...

Conceptual Modeling Framework for E-Area PA HELP Infiltration Model Simulations

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

Dyer, J. A.

A conceptual modeling framework based on the proposed E-Area Low-Level Waste Facility (LLWF) closure cap design is presented for conducting Hydrologic Evaluation of Landfill Performance (HELP) model simulations of intact and subsided cap infiltration scenarios for the next E-Area Performance Assessment (PA).

A coupled modeling framework of the co-evolution of humans and water: case study of Tarim River Basin, western China

NASA Astrophysics Data System (ADS)

Liu, D.; Tian, F.; Lin, M.; Sivapalan, M.

2014-04-01

The complex interactions and feedbacks between humans and water are very essential issues but are poorly understood in the newly proposed discipline of socio-hydrology (Sivapalan et al., 2012). An exploratory model with the appropriate level of simplification can be valuable to improve our understanding of the co-evolution and self-organization of socio-hydrological systems driven by interactions and feedbacks operating at different scales. In this study, a simple coupled modeling framework for socio-hydrology co-evolution is developed for the Tarim River Basin in Western China, and is used to illustrate the explanatory power of such a model. The study area is the mainstream of the Tarim River, which is divided into two modeling units. The socio-hydrological system is composed of four parts, i.e. social sub-system, economic sub-system, ecological sub-system, and hydrological sub-system. In each modeling unit, four coupled ordinary differential equations are used to simulate the dynamics of the social sub-system represented by human population, the economic sub-system represented by irrigated crop area, the ecological sub-system represented by natural vegetation cover and the hydrological sub-system represented by stream discharge. The coupling and feedback processes of the four dominant sub-systems (and correspondingly four state variables) are integrated into several internal system characteristics interactively and jointly determined by themselves and by other coupled systems. For example, the stream discharge is coupled to the irrigated crop area by the colonization rate and mortality rate of the irrigated crop area in the upper reach and the irrigated area is coupled to stream discharge through irrigation water consumption. In a similar way, the stream discharge and natural vegetation cover are coupled together. The irrigated crop area is coupled to human population by the colonization rate and mortality rate of the population. The inflow of the lower reach is determined by the outflow from the upper reach. The natural vegetation cover in the lower reach is coupled to the outflow from the upper reach and governed by regional water resources management policy. The co-evolution of the Tarim socio-hydrological system is then analyzed within this modeling framework to gain insights into the overall system dynamics and its sensitivity to the external drivers and internal system variables. In the modeling framework, the state of each subsystem is holistically described by one state variable and the framework is flexible enough to comprise more processes and constitutive relationships if they are needed to illustrate the interaction and feedback mechanisms of the human-water system.

A Bayesian alternative for multi-objective ecohydrological model specification

NASA Astrophysics Data System (ADS)

Tang, Yating; Marshall, Lucy; Sharma, Ashish; Ajami, Hoori

2018-01-01

Recent studies have identified the importance of vegetation processes in terrestrial hydrologic systems. Process-based ecohydrological models combine hydrological, physical, biochemical and ecological processes of the catchments, and as such are generally 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. The Bayesian approach offers an appealing alternative to traditional multi-objective hydrologic model calibrations by defining proper prior distributions that can be considered analogous to the ad-hoc weighting often prescribed in multi-objective calibration. Our study aims to develop appropriate prior distributions and likelihood functions that minimize the model uncertainties and bias within a Bayesian ecohydrological modeling framework based on a traditional Pareto-based model calibration technique. In our study, a Pareto-based multi-objective optimization and a formal Bayesian framework are implemented in a conceptual ecohydrological model that combines a hydrological model (HYMOD) and a modified Bucket Grassland Model (BGM). Simulations focused on one objective (streamflow/LAI) and multiple objectives (streamflow and LAI) with different emphasis defined via the prior distribution of the model error parameters. Results show more reliable outputs for both predicted streamflow and LAI using Bayesian multi-objective calibration with specified prior distributions for error parameters based on results from the Pareto front in the ecohydrological modeling. The methodology implemented here provides insight into the usefulness of multiobjective Bayesian calibration for ecohydrologic systems and the importance of appropriate prior distributions in such approaches.

Impact of model structure on flow simulation and hydrological realism: from a lumped to a semi-distributed approach

NASA Astrophysics Data System (ADS)

Garavaglia, Federico; Le Lay, Matthieu; Gottardi, Fréderic; Garçon, Rémy; Gailhard, Joël; Paquet, Emmanuel; Mathevet, Thibault

2017-08-01

Model intercomparison experiments are widely used to investigate and improve hydrological model performance. However, a study based only on runoff simulation is not sufficient to discriminate between different model structures. Hence, there is a need to improve hydrological models for specific streamflow signatures (e.g., low and high flow) and multi-variable predictions (e.g., soil moisture, snow and groundwater). This study assesses the impact of model structure on flow simulation and hydrological realism using three versions of a hydrological model called MORDOR: the historical lumped structure and a revisited formulation available in both lumped and semi-distributed structures. In particular, the main goal of this paper is to investigate the relative impact of model equations and spatial discretization on flow simulation, snowpack representation and evapotranspiration estimation. Comparison of the models is based on an extensive dataset composed of 50 catchments located in French mountainous regions. The evaluation framework is founded on a multi-criterion split-sample strategy. All models were calibrated using an automatic optimization method based on an efficient genetic algorithm. The evaluation framework is enriched by the assessment of snow and evapotranspiration modeling against in situ and satellite data. The results showed that the new model formulations perform significantly better than the initial one in terms of the various streamflow signatures, snow and evapotranspiration predictions. The semi-distributed approach provides better calibration-validation performance for the snow cover area, snow water equivalent and runoff simulation, especially for nival catchments.

A Bayesian Uncertainty Framework for Conceptual Snowmelt and Hydrologic Models Applied to the Tenderfoot Creek Experimental Forest

NASA Astrophysics Data System (ADS)

Smith, T.; Marshall, L.

2007-12-01

In many mountainous regions, the single most important parameter in forecasting the controls on regional water resources is snowpack (Williams et al., 1999). In an effort to bridge the gap between theoretical understanding and functional modeling of snow-driven watersheds, a flexible hydrologic modeling framework is being developed. The aim is to create a suite of models that move from parsimonious structures, concentrated on aggregated watershed response, to those focused on representing finer scale processes and distributed response. This framework will operate as a tool to investigate the link between hydrologic model predictive performance, uncertainty, model complexity, and observable hydrologic processes. Bayesian methods, and particularly Markov chain Monte Carlo (MCMC) techniques, are extremely useful in uncertainty assessment and parameter estimation of hydrologic models. However, these methods have some difficulties in implementation. In a traditional Bayesian setting, it can be difficult to reconcile multiple data types, particularly those offering different spatial and temporal coverage, depending on the model type. These difficulties are also exacerbated by sensitivity of MCMC algorithms to model initialization and complex parameter interdependencies. As a way of circumnavigating some of the computational complications, adaptive MCMC algorithms have been developed to take advantage of the information gained from each successive iteration. Two adaptive algorithms are compared is this study, the Adaptive Metropolis (AM) algorithm, developed by Haario et al (2001), and the Delayed Rejection Adaptive Metropolis (DRAM) algorithm, developed by Haario et al (2006). While neither algorithm is truly Markovian, it has been proven that each satisfies the desired ergodicity and stationarity properties of Markov chains. Both algorithms were implemented as the uncertainty and parameter estimation framework for a conceptual rainfall-runoff model based on the Probability Distributed Model (PDM), developed by Moore (1985). We implement the modeling framework in Stringer Creek watershed in the Tenderfoot Creek Experimental Forest (TCEF), Montana. The snowmelt-driven watershed offers that additional challenge of modeling snow accumulation and melt and current efforts are aimed at developing a temperature- and radiation-index snowmelt model. Auxiliary data available from within TCEF's watersheds are used to support in the understanding of information value as it relates to predictive performance. Because the model is based on lumped parameters, auxiliary data are hard to incorporate directly. However, these additional data offer benefits through the ability to inform prior distributions of the lumped, model parameters. By incorporating data offering different information into the uncertainty assessment process, a cross-validation technique is engaged to better ensure that modeled results reflect real process complexity.

Approaches to modelling hydrology and ecosystem interactions

NASA Astrophysics Data System (ADS)

Silberstein, Richard P.

2014-05-01

As the pressures of industry, agriculture and mining on groundwater resources increase there is a burgeoning un-met need to be able to capture these multiple, direct and indirect stresses in a formal framework that will enable better assessment of impact scenarios. While there are many catchment hydrological models and there are some models that represent ecological states and change (e.g. FLAMES, Liedloff and Cook, 2007), these have not been linked in any deterministic or substantive way. Without such coupled eco-hydrological models quantitative assessments of impacts from water use intensification on water dependent ecosystems under changing climate are difficult, if not impossible. The concept would include facility for direct and indirect water related stresses that may develop around mining and well operations, climate stresses, such as rainfall and temperature, biological stresses, such as diseases and invasive species, and competition such as encroachment from other competing land uses. Indirect water impacts could be, for example, a change in groundwater conditions has an impact on stream flow regime, and hence aquatic ecosystems. This paper reviews previous work examining models combining ecology and hydrology with a view to developing a conceptual framework linking a biophysically defensable model that combines ecosystem function with hydrology. The objective is to develop a model capable of representing the cumulative impact of multiple stresses on water resources and associated ecosystem function.

Using coupled hydrogeophysical models and data assimilation to enhance the information content in geoelectrical leak detection

NASA Astrophysics Data System (ADS)

Tso, C. H. M.; Johnson, T. C.; Song, X.; Chen, X.; Binley, A. M.

2017-12-01

Time-lapse electrical resistivity tomography (ERT) measurements provides indirect observation of hydrological processes in the Earth's shallow subsurface at high spatial and temporal resolutions. ERT has been used for a number of decades to detect leaks and monitor the evolution of associated contaminant plumes. However, this has been limited to a few hazardous environmental sites. Furthermore, an assessment of uncertainty in such applications has thus far been neglected, despite the clear need to provide site managers with appropriate information for decision making purposes. There is a need to establish a framework that allows leak detection with uncertainty assessment from geophysical observations. Ideally such a framework should allow the incorporation of additional data sources in order to reduce uncertainty in predictions. To tackle these issues, we propose an ensemble-based data assimilation framework that evaluates proposed hydrological models (i.e. different hydrogeological units, different leak locations and loads) against observed time-lapse ERT measurements. Each proposed hydrological model is run through the parallel coupled hydrogeophysical code PFLOTRAN-E4D (Johnson et al 2016) to obtain simulated ERT measurements. The ensemble of model proposals is then updated based on data misfit. Our approach does not focus on obtaining detailed images of hydraulic properties or plume movement. Rather, it seeks to estimate the contaminant mass discharge (CMD) across a user-defined plane in space probabilistically. The proposed approach avoids the ambiguity in interpreting detailed hydrological processes from geophysical images. The resultant distributions of CMD give a straightforward metric, with realistic uncertainty bounds, for decision making. The proposed framework is also computationally efficient so that it can exploit large, long-term ERT datasets, making it possible to track time-varying loadings of plume sources. In this presentation, we illustrate our framework on synthetic data and field data collected from an ERT trial simulating a leak at the Sellafield nuclear facility in the UK (Kuras et al 2016). We compare our results to interpretation from geophysical inversion and discuss the additional information that hydrological model proposals provide.

Including policy and management in socio-hydrology models: initial conceptualizations

NASA Astrophysics Data System (ADS)

Hermans, Leon; Korbee, Dorien

2017-04-01

Socio-hydrology studies the interactions in coupled human-water systems. So far, the use of dynamic models that capture the direct feedback between societal and hydrological systems has been dominant. What has not yet been included with any particular emphasis, is the policy or management layer, which is a central element in for instance integrated water resources management (IWRM) or adaptive delta management (ADM). Studying the direct interactions between human-water systems generates knowledges that eventually helps influence these interactions in ways that may ensure better outcomes - for society and for the health and sustainability of water systems. This influence sometimes occurs through spontaneous emergence, uncoordinated by societal agents - private sector, citizens, consumers, water users. However, the term 'management' in IWRM and ADM also implies an additional coordinated attempt through various public actors. This contribution is a call to include the policy and management dimension more prominently into the research focus of the socio-hydrology field, and offers first conceptual variables that should be considered in attempts to include this policy or management layer in socio-hydrology models. This is done by drawing on existing frameworks to study policy processes throughout both planning and implementation phases. These include frameworks such as the advocacy coalition framework, collective learning and policy arrangements, which all emphasis longer-term dynamics and feedbacks between actor coalitions in strategic planning and implementation processes. A case about longter-term dynamics in the management of the Haringvliet in the Netherlands is used to illustrate the paper.

Establishing a Framework for Community Modeling in Hydrologic Science: Recommendations from the CUAHSI CHyMP Initiative

NASA Astrophysics Data System (ADS)

Arrigo, J. S.; Famiglietti, J. S.; Murdoch, L. C.; Lakshmi, V.; Hooper, R. P.

2012-12-01

The Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) continues a major effort towards supporting Community Hydrologic Modeling. From 2009 - 2011, the Community Hydrologic Modeling Platform (CHyMP) initiative held three workshops, the ultimate goal of which was to produce recommendations and an implementation plan to establish a community modeling program that enables comprehensive simulation of water anywhere on the North American continent. Such an effort would include connections to and advances in global climate models, biogeochemistry, and efforts of other disciplines that require an understanding of water patterns and processes in the environment. To achieve such a vision will require substantial investment in human and cyber-infrastructure and significant advances in the science of hydrologic modeling and spatial scaling. CHyMP concluded with a final workshop, held March 2011, and produced several recommendations. CUAHSI and the university community continue to advance community modeling and implement these recommendations through several related and follow on efforts. Key results from the final 2011 workshop included agreement among participants that the community is ready to move forward with implementation. It is recognized that initial implementation of this larger effort can begin with simulation capabilities that currently exist, or that can be easily developed. CHyMP identified four key activities in support of community modeling: benchmarking, dataset evaluation and development, platform evaluation, and developing a national water model framework. Key findings included: 1) The community supported the idea of a National Water Model framework; a community effort is needed to explore what the ultimate implementation of a National Water Model is. A true community modeling effort would support the modeling of "water anywhere" and would include all relevant scales and processes. 2) Implementation of a community modeling program could initially focus on continental scale modeling of water quantity (rather than quality). The goal of this initial model is the comprehensive description of water stores and fluxes in such a way to permit linkage to GCM's, biogeochemical, ecological, and geomorphic models. This continental scale focus allows systematic evaluation of our current state of knowledge and data, leverages existing efforts done by large scale modelers, contributes to scientific discovery that informs globally and societal relevant questions, and provides an initial framework to evaluate hydrologic information relevant to other disciplines and a structure into which to incorporate other classes of hydrologic models. 3) Dataset development will be a key aspect of any successful national water model implementation. Our current knowledge of the subsurface is limiting our ability to truly integrate soil and groundwater into large scale models, and to answering critical science questions with societal relevance (i.e. groundwater's influence on climate). 4) The CHyMP workshops and efforts to date have achieved collaboration between university scientists, government agencies and the private sector that must be maintained. Follow on efforts in community modeling should aim at leveraging and maintaining this collaboration for maximum scientific and societal benefit.

The worth of data to reduce predictive uncertainty of an integrated catchment model by multi-constraint calibration

NASA Astrophysics Data System (ADS)

Koch, J.; Jensen, K. H.; Stisen, S.

2017-12-01

Hydrological models that integrate numerical process descriptions across compartments of the water cycle are typically required to undergo thorough model calibration in order to estimate suitable effective model parameters. In this study, we apply a spatially distributed hydrological model code which couples the saturated zone with the unsaturated zone and the energy portioning at the land surface. We conduct a comprehensive multi-constraint model calibration against nine independent observational datasets which reflect both the temporal and the spatial behavior of hydrological response of a 1000km2 large catchment in Denmark. The datasets are obtained from satellite remote sensing and in-situ measurements and cover five keystone hydrological variables: discharge, evapotranspiration, groundwater head, soil moisture and land surface temperature. Results indicate that a balanced optimization can be achieved where errors on objective functions for all nine observational datasets can be reduced simultaneously. The applied calibration framework was tailored with focus on improving the spatial pattern performance; however results suggest that the optimization is still more prone to improve the temporal dimension of model performance. This study features a post-calibration linear uncertainty analysis. This allows quantifying parameter identifiability which is the worth of a specific observational dataset to infer values to model parameters through calibration. Furthermore the ability of an observation to reduce predictive uncertainty is assessed as well. Such findings determine concrete implications on the design of model calibration frameworks and, in more general terms, the acquisition of data in hydrological observatories.

Hydrologic Predictions in the Anthropocene: A Research Framework Based on a Co-evolutionary Socio-hydrologic Perspective

NASA Astrophysics Data System (ADS)

Sivapalan, M.; Bloeschl, G.

2012-12-01

The world is facing a water management crisis, in the context of fast rising demand for water due to growth of human populations and changing lifestyles, and depletion of freshwater resources. In many parts of the world, poor distribution of freshwater in relation to demand is already the cause of serious water scarcity, exacerbated by climate change. Cumulatively, these result in increased human appropriation of water resources, significant modification of landscapes, and a strong human imprint on water cycle dynamics from local to global scales. Hydrologic predictions in such a fast changing environment face significant challenges. Traditional models for predictions treat the hydrologic system as a simple input-output system, and propagate variability of external inputs or disturbances through the various hydrologic subsystems, but assuming stationarity. However, in a fast changing world, none of the subsystems can be assumed to be stationary, but as co-evolving parts of a complex system. The role of humans takes on an important role, which can no longer be assumed to independent of the natural system. We need new socio-hydrologic frameworks to observe, monitor, understand and predict the co-evolution of coupled human-natural systems. In this talk, using examples from one or more real-world settings (from Australia and Europe) involving human interactions with hydrologic systems, we will present new theoretical frameworks that should be adopted to advance the emergent new sub-discipline of socio-hydrology. The proposed research agenda is organized under (i) process socio-hydrology, (ii) comparative socio-hydrology, and (iii) historical socio-hydrology.

Remote sensing, hydrological modeling and in situ observations in snow cover research: A review

NASA Astrophysics Data System (ADS)

Dong, Chunyu

2018-06-01

Snow is an important component of the hydrological cycle. As a major part of the cryosphere, snow cover also represents a valuable terrestrial water resource. In the context of climate change, the dynamics of snow cover play a crucial role in rebalancing the global energy and water budgets. Remote sensing, hydrological modeling and in situ observations are three techniques frequently utilized for snow cover investigations. However, the uncertainties caused by systematic errors, scale gaps, and complicated snow physics, among other factors, limit the usability of these three approaches in snow studies. In this paper, an overview of the advantages, limitations and recent progress of the three methods is presented, and more effective ways to estimate snow cover properties are evaluated. The possibility of improving remotely sensed snow information using ground-based observations is discussed. As a rapidly growing source of volunteered geographic information (VGI), web-based geotagged photos have great potential to provide ground truth data for remotely sensed products and hydrological models and thus contribute to procedures for cloud removal, correction, validation, forcing and assimilation. Finally, this review proposes a synergistic framework for the future of snow cover research. This framework highlights the cross-scale integration of in situ and remotely sensed snow measurements and the assimilation of improved remote sensing data into hydrological models.

Wildfire disturbance impacts on streamflow from western USA watersheds

NASA Astrophysics Data System (ADS)

Cadol, D.; Wine, M.; Makhnin, O.

2017-12-01

Worldwide rapid changes in climate overlaid on changing land management paradigms have dramatically altered ecological disturbance regimes worldwide including in western North America. Ecological disturbances impacted include woody encroachment, pest pathogen complexes, riparian forest changes, and wildfire. These disturbances impact the hydrologic cycle, though the nature of these impacts has been difficult to quantify. Perhaps the greatest challenge is that most basins worldwide are ungauged. Taking wildfire as a globally relevant example of a key ecological disturbance, even within gauged basins, post-wildfire hydrologic response is spatially and temporally variable, affected by a host of variables including fire frequency, area burned, and recovery trajectory. Hydrologic response to wildfire is further understood to be a non-linear function of watershed characteristics and climate. Here we provide a framework that utilizes remote sensing, statistical modeling, field measurements, and geospatial methods to provide first-order estimates of ecological disturbance hydrologic impacts. We apply this framework to compare ecological disturbance hydrologic impacts amongst selected watersheds in the western USA. Here we show that ecological disturbance impacts on hydrology are highly variable, and in many cases have an effect magnitude similar to that modeled for temperature and precipitation changes.

An Evaluation Tool for CONUS-Scale Estimates of Components of the Water Balance

NASA Astrophysics Data System (ADS)

Saxe, S.; Hay, L.; Farmer, W. H.; Markstrom, S. L.; Kiang, J. E.

2016-12-01

Numerous research groups are independently developing data products to represent various components of the water balance (e.g. runoff, evapotranspiration, recharge, snow water equivalent, soil moisture, and climate) at the scale of the conterminous United States. These data products are derived from a range of sources, including direct measurement, remotely-sensed measurement, and statistical and deterministic model simulations. An evaluation tool is needed to compare these data products and the components of the water balance they contain in order to identify the gaps in the understanding and representation of continental-scale hydrologic processes. An ideal tool will be an objective, universally agreed upon, framework to address questions related to closing the water balance. This type of generic, model agnostic evaluation tool would facilitate collaboration amongst different hydrologic research groups and improve modeling capabilities with respect to continental-scale water resources. By adopting a comprehensive framework to consider hydrologic modeling in the context of a complete water balance, it is possible to identify weaknesses in process modeling, data product representation and regional hydrologic variation. As part of its National Water Census initiative, the U.S. Geological survey is facilitating this dialogue to developing prototype evaluation tools.

Robust and Heterogeneous Hydrological Changes under Global Warming

NASA Astrophysics Data System (ADS)

Kumar, S.; Zwiers, F. W.; Dirmeyer, P.; Lawrence, D. M.; Shrestha, R. R.; Werner, A. T.

2015-12-01

The Intergovernmental Panel on Climate Change (IPCC) has continued to find it difficult to make clear assessments of streamflow changes [Assessment Report 5, Working Group II, Chapter 3] in large part because of the heterogeneity of observed and projected hydrological changes. While prior studies have found some evidence of human influence on precipitation changes, the detection of streamflow changes is not robust. Here, we show that the terrestrial branch of the hydrological cycle, namely the partitioning of precipitation into evapotranspiration and runoff, is an important piece of the puzzle that may explain the apparent disconnect between the detectability of precipitation and streamflow changes. We apply Budyko framework to quantify sensitivity of hydrological changes to climate driven changes in water balance regionally. We demonstrate that the hydrological sensitivity is 3 times greater in regions where the hydrological cycle is energy limited (wet regions) than water limited (dry regions), and therefore the detectability of streamflow changes is also greater by 30-40% in wet regions. Evidence from observations in western North America and an analysis of Coupled Model Intercomparison Project Phase 5 climate models at global scales indicate that use of the Budyko framework can help identify robust and spatially heterogeneous hydrological responses to external forcing on the climate system.

An Integrated Ensemble-Based Operational Framework to Predict Urban Flooding: A Case Study of Hurricane Sandy in the Passaic and Hackensack River Basins

NASA Astrophysics Data System (ADS)

Saleh, F.; Ramaswamy, V.; Georgas, N.; Blumberg, A. F.; Wang, Y.

2016-12-01

Advances in computational resources and modeling techniques are opening the path to effectively integrate existing complex models. In the context of flood prediction, recent extreme events have demonstrated the importance of integrating components of the hydrosystem to better represent the interactions amongst different physical processes and phenomena. As such, there is a pressing need to develop holistic and cross-disciplinary modeling frameworks that effectively integrate existing models and better represent the operative dynamics. This work presents a novel Hydrologic-Hydraulic-Hydrodynamic Ensemble (H3E) flood prediction framework that operationally integrates existing predictive models representing coastal (New York Harbor Observing and Prediction System, NYHOPS), hydrologic (US Army Corps of Engineers Hydrologic Modeling System, HEC-HMS) and hydraulic (2-dimensional River Analysis System, HEC-RAS) components. The state-of-the-art framework is forced with 125 ensemble meteorological inputs from numerical weather prediction models including the Global Ensemble Forecast System, the European Centre for Medium-Range Weather Forecasts (ECMWF), the Canadian Meteorological Centre (CMC), the Short Range Ensemble Forecast (SREF) and the North American Mesoscale Forecast System (NAM). The framework produces, within a 96-hour forecast horizon, on-the-fly Google Earth flood maps that provide critical information for decision makers and emergency preparedness managers. The utility of the framework was demonstrated by retrospectively forecasting an extreme flood event, hurricane Sandy in the Passaic and Hackensack watersheds (New Jersey, USA). Hurricane Sandy caused significant damage to a number of critical facilities in this area including the New Jersey Transit's main storage and maintenance facility. The results of this work demonstrate that ensemble based frameworks provide improved flood predictions and useful information about associated uncertainties, thus improving the assessment of risks as when compared to a deterministic forecast. The work offers perspectives for short-term flood forecasts, flood mitigation strategies and best management practices for climate change scenarios.

Toward improved calibration of watershed models: multisite many objective measures of information

USDA-ARS?s Scientific Manuscript database

This paper presents a computational framework for incorporation of disparate information from observed hydrologic responses at multiple locations into the calibration of watershed models. The framework consists of four components: (i) an a-priori characterization of system behavior; (ii) a formal an...

A Budyko-type Model for Human Water Consumption

NASA Astrophysics Data System (ADS)

Lei, X.; Zhao, J.; Wang, D.; Sivapalan, M.

2017-12-01

With the expansion of human water footprint, water crisis is no longer only a conflict or competition for water between different economic sectors, but also increasingly between human and the environment. In order to describe the emergent dynamics and patterns of the interaction, a theoretical framework that encapsulates the physical and societal controls impacting human water consumption is needed. In traditional hydrology, Budyko-type models are simple but efficient descriptions of vegetation-mediated hydrologic cycle in catchments, i.e., the partitioning of mean annual precipitation into runoff and evapotranspiration. Plant water consumption plays a crucial role in the process. Hypothesized similarities between human-water and vegetation-water interactions, including water demand, constraints and system functioning, give the idea of corresponding Budyko-type framework for human water consumption at the catchment scale. Analogous to variables of Budyko-type models for hydrologic cycle, water demand, water consumption, environmental water use and available water are corresponding to potential evaporation, actual evaporation, runoff and precipitation respectively. Human water consumption data, economic and hydro-meteorological data for 51 human-impacted catchments and 10 major river basins in China are assembled to look for the existence of a Budyko-type relationship for human water consumption, and to seek explanations for the spread in the observed relationship. Guided by this, a Budyko-type analytical model is derived based on application of an optimality principle, that of maximum water benefit. The model derived has the same functional form and mathematical features as those that apply for the original Budyko model. Parameters of the new Budyko-type model for human consumption are linked to economic and social factors. The results of this paper suggest that the functioning of both social and hydrologic subsystems within catchment systems can be explored within a common conceptual framework, thus providing a unified socio-hydrologic basis for the study of coupled human-water systems. The exploration of the theoretical connections between the two subsystems pushes the water system modeling from a problem-solving orientation to puzzle-solving orientation.

Water System Adaptation To Hydrological Changes: Module 8, Regulatory Framework Intersections: Past, Present, and Future

EPA Science Inventory

This course will introduce students to the fundamental principles of water system adaptation to hydrological changes, with emphasis on data analysis and interpretation, technical planning, and computational modeling. Starting with real-world scenarios and adaptation needs, the co...

Development of Hydro-Informatic Modelling System and its Application

NASA Astrophysics Data System (ADS)

Wang, Z.; Liu, C.; Zheng, H.; Zhang, L.; Wu, X.

2009-12-01

The understanding of hydrological cycle is the core of hydrology and the scientific base of water resources management. Meanwhile, simulation of hydrological cycle has long been regarded as an important tool for the assessment, utilization and protection of water resources. In this paper, a new tool named Hydro-Informatic Modelling System (HIMS) has been developed and introduced with case studies in the Yellow River Basin in China and 331 catchments in Australia. The case studies showed that HIMS can be employed as an integrated platform for hydrological simulation in different regions. HIMS is a modular based framework of hydrological model designed for different utilization such as flood forecasting, water resources planning and evaluating hydrological impacts of climate change and human activities. The unique of HIMS is its flexibility in providing alternative modules in the simulation of hydrological cycle, which successfully overcome the difficulties in the availability of input data, the uncertainty of parameters, and the difference of rainfall-runoff processes. The modular based structure of HIMS makes it possible for developing new hydrological models by the users.

Virtual hydrology observatory: an immersive visualization of hydrology modeling

NASA Astrophysics Data System (ADS)

Su, Simon; Cruz-Neira, Carolina; Habib, Emad; Gerndt, Andreas

2009-02-01

The Virtual Hydrology Observatory will provide students with the ability to observe the integrated hydrology simulation with an instructional interface by using a desktop based or immersive virtual reality setup. It is the goal of the virtual hydrology observatory application to facilitate the introduction of field experience and observational skills into hydrology courses through innovative virtual techniques that mimic activities during actual field visits. The simulation part of the application is developed from the integrated atmospheric forecast model: Weather Research and Forecasting (WRF), and the hydrology model: Gridded Surface/Subsurface Hydrologic Analysis (GSSHA). Both the output from WRF and GSSHA models are then used to generate the final visualization components of the Virtual Hydrology Observatory. The various visualization data processing techniques provided by VTK are 2D Delaunay triangulation and data optimization. Once all the visualization components are generated, they are integrated into the simulation data using VRFlowVis and VR Juggler software toolkit. VR Juggler is used primarily to provide the Virtual Hydrology Observatory application with fully immersive and real time 3D interaction experience; while VRFlowVis provides the integration framework for the hydrologic simulation data, graphical objects and user interaction. A six-sided CAVETM like system is used to run the Virtual Hydrology Observatory to provide the students with a fully immersive experience.

Comprehensive Representation of Hydrologic and Geomorphic Process Coupling in Numerical Models: Internal Dynamics and Basin Evolution

NASA Astrophysics Data System (ADS)

Istanbulluoglu, E.; Vivoni, E. R.; Ivanov, V. Y.; Bras, R. L.

2005-12-01

Landscape morphology has an important control on the spatial and temporal organization of basin hydrologic response to climate forcing, affecting soil moisture redistribution as well as vegetation function. On the other hand, erosion, driven by hydrology and modulated by vegetation, produces landforms over geologic time scales that reflect characteristic signatures of the dominant land forming process. Responding to extreme climate events or anthropogenic disturbances of the land surface, infrequent but rapid forms of erosion (e.g., arroyo development, landsliding) can modify topography such that basin hydrology is significantly influenced. Despite significant advances in both hydrologic and geomorphic modeling over the past two decades, the dynamic interactions between basin hydrology, geomorphology and terrestrial ecology are not adequately captured in current model frameworks. In order to investigate hydrologic-geomorphic-ecologic interactions at the basin scale we present initial efforts in integrating the CHILD landscape evolution model (Tucker et al. 2001) with the tRIBS hydrology model (Ivanov et al. 2004), both developed in a common software environment. In this talk, we present preliminary results of the numerical modeling of the coupled evolution of basin hydro-geomorphic response and resulting landscape morphology in two sets of examples. First, we discuss the long-term evolution of both the hydrologic response and the resulting basin morphology from an initially uplifted plateau. In the second set of modeling experiments, we implement changes in climate and land-use to an existing topography and compare basin hydrologic response to the model results when landscape form is fixed (e.g. no coupling between hydrology and geomorphology). Model results stress the importance of internal basin dynamics, including runoff generation mechanisms and hydrologic states, in shaping hydrologic response as well as the importance of employing comprehensive conceptualizations of hydrology in modeling landscape evolution.

Integrating the social sciences to understand human-water dynamics

NASA Astrophysics Data System (ADS)

Carr, G.; Kuil, L., Jr.

2017-12-01

Many interesting and exciting socio-hydrological models have been developed in recent years. Such models often aim to capture the dynamic interplay between people and water for a variety of hydrological settings. As such, peoples' behaviours and decisions are brought into the models as drivers of and/or respondents to the hydrological system. To develop and run such models over a sufficiently long time duration to observe how the water-human system evolves the human component is often simplified according to one or two key behaviours, characteristics or decisions (e.g. a decision to move away from a drought or flood area; a decision to pump groundwater, or a decision to plant a less water demanding crop). To simplify the social component, socio-hydrological modellers often pull knowledge and understanding from existing social science theories. This requires them to negotiate complex territory, where social theories may be underdeveloped, contested, dynamically evolving, or case specific and difficult to generalise or upscale. A key question is therefore, how can this process be supported so that the resulting socio-hydrological models adequately describe the system and lead to meaningful understanding of how and why it behaves as it does? Collaborative interdisciplinary research teams that bring together social and natural scientists are likely to be critical. Joint development of the model framework requires specific attention to clarification to expose all underlying assumptions, constructive discussion and negotiation to reach agreement on the modelled system and its boundaries. Mutual benefits to social scientists can be highlighted, i.e. socio-hydrological work can provide insights for further exploring and testing social theories. Collaborative work will also help ensure underlying social theory is made explicit, and may identify ways to include and compare multiple theories. As socio-hydrology progresses towards supporting policy development, approaches that brings in stakeholders and non-scientist participants to develop the conceptual modelling framework will become essential. They are also critical for fully understanding human-water dynamics.

Towards Optimal Operation of the Reservoir System in Upper Yellow River: Incorporating Long- and Short-term Operations and Using Rolling Updated Hydrologic Forecast Information

NASA Astrophysics Data System (ADS)

Si, Y.; Li, X.; Li, T.; Huang, Y.; Yin, D.

2016-12-01

The cascade reservoirs in Upper Yellow River (UYR), one of the largest hydropower bases in China, play a vital role in peak load and frequency regulation for Northwest China Power Grid. The joint operation of this system has been put forward for years whereas has not come into effect due to management difficulties and inflow uncertainties, and thus there is still considerable improvement room for hydropower production. This study presents a decision support framework incorporating long- and short-term operation of the reservoir system. For long-term operation, we maximize hydropower production of the reservoir system using historical hydrological data of multiple years, and derive operating rule curves for storage reservoirs. For short-term operation, we develop a program consisting of three modules, namely hydrologic forecast module, reservoir operation module and coordination module. The coordination module is responsible for calling the hydrologic forecast module to acquire predicted inflow within a short-term horizon, and transferring the information to the reservoir operation module to generate optimal release decision. With the hydrologic forecast information updated, the rolling short-term optimization is iterated until the end of operation period, where the long-term operating curves serve as the ending storage target. As an application, the Digital Yellow River Integrated Model (referred to as "DYRIM", which is specially designed for runoff-sediment simulation in the Yellow River basin by Tsinghua University) is used in the hydrologic forecast module, and the successive linear programming (SLP) in the reservoir operation module. The application in the reservoir system of UYR demonstrates that the framework can effectively support real-time decision making, and ensure both computational accuracy and speed. Furthermore, it is worth noting that the general framework can be extended to any other reservoir system with any or combination of hydrological model(s) to forecast and any solver to optimize the operation of reservoir system.

An object-oriented framework for distributed hydrologic and geomorphic modeling using triangulated irregular networks

NASA Astrophysics Data System (ADS)

Tucker, Gregory E.; Lancaster, Stephen T.; Gasparini, Nicole M.; Bras, Rafael L.; Rybarczyk, Scott M.

2001-10-01

We describe a new set of data structures and algorithms for dynamic terrain modeling using a triangulated irregular network (TINs). The framework provides an efficient method for storing, accessing, and updating a Delaunay triangulation and its associated Voronoi diagram. The basic data structure consists of three interconnected data objects: triangles, nodes, and directed edges. Encapsulating each of these geometric elements within a data object makes it possible to essentially decouple the TIN representation from the modeling applications that make use of it. Both the triangulation and its corresponding Voronoi diagram can be rapidly retrieved or updated, making these methods well suited to adaptive remeshing schemes. We develop a set of algorithms for defining drainage networks and identifying closed depressions (e.g., lakes) for hydrologic and geomorphic modeling applications. We also outline simple numerical algorithms for solving network routing and 2D transport equations within the TIN framework. The methods are illustrated with two example applications, a landscape evolution model and a distributed rainfall-runoff model.

Identification of the dominant hydrological process and appropriate model structure of a karst catchment through stepwise simplification of a complex conceptual model

NASA Astrophysics Data System (ADS)

Chang, Yong; Wu, Jichun; Jiang, Guanghui; Kang, Zhiqiang

2017-05-01

Conceptual models often suffer from the over-parameterization problem due to limited available data for the calibration. This leads to the problem of parameter nonuniqueness and equifinality, which may bring much uncertainty of the simulation result. How to find out the appropriate model structure supported by the available data to simulate the catchment is still a big challenge in the hydrological research. In this paper, we adopt a multi-model framework to identify the dominant hydrological process and appropriate model structure of a karst spring, located in Guilin city, China. For this catchment, the spring discharge is the only available data for the model calibration. This framework starts with a relative complex conceptual model according to the perception of the catchment and then this complex is simplified into several different models by gradually removing the model component. The multi-objective approach is used to compare the performance of these different models and the regional sensitivity analysis (RSA) is used to investigate the parameter identifiability. The results show this karst spring is mainly controlled by two different hydrological processes and one of the processes is threshold-driven which is consistent with the fieldwork investigation. However, the appropriate model structure to simulate the discharge of this spring is much simpler than the actual aquifer structure and hydrological processes understanding from the fieldwork investigation. A simple linear reservoir with two different outlets is enough to simulate this spring discharge. The detail runoff process in the catchment is not needed in the conceptual model to simulate the spring discharge. More complex model should need more other additional data to avoid serious deterioration of model predictions.

Synthesis of streamflow recession curves in dry environments

NASA Astrophysics Data System (ADS)

Arciniega, Saul; Breña-Naranjo, Agustín; Pedrozo-Acuña, Adrían

2015-04-01

The elucidation and predictability of hydrological systems can largely benefit by extracting observed patterns in processes, data and models. Such type of research framework in hydrology, also known as synthesis has gained significant attention over the last decade. For instance, hydrological synthesis implies that the identification of patterns in catchment behavior can enhance the extrapolation of hydrological signatures over large spatial and temporal scales. Hydrological signatures during dry periods such as streamflow recession curves (SRC) are of special interest in regions coping with water scarcity. Indeed, the study of SRCs from observed hydrographs allows to extract information about the storage-discharge relationship of a specific catchment and some of their groundwater hydraulic properties. This work aims at performing a synthesis work of SRCs in semi-arid & arid environments across Northern Mexico. Our dataset consisted in observed daily SRCs in 63 catchments with minima human interferences. Three streamflow recession extraction methods (Vogel, Brutsaert and Aksoy-Wittenberg) along with four recession models (Maillet, Boussinesq, Coutagne y Wittenberg) and three parameter estimation techniques (regressions, lower envelope y data binning) were used to determine the combination among different possible methods, processes and models that better describes SRCs in our study sites. Our results show that the extraction method proposed by Aksoy-Wittenberg along with Coutagne's nonlinear recession model provides a better approximation of SRCs across Northern Mexico, whereas regression was found to be the most adequate parameter estimation method. This study suggests that hydrological synthesis turned out to be an useful framework to identify similar patterns and model parameters during dry periods across Mexico's water-limited environments.

The HYPE Open Source Community

NASA Astrophysics Data System (ADS)

Strömbäck, Lena; Arheimer, Berit; Pers, Charlotta; Isberg, Kristina

2013-04-01

The Hydrological Predictions for the Environment (HYPE) model is a dynamic, semi-distributed, process-based, integrated catchment model (Lindström et al., 2010). It uses well-known hydrological and nutrient transport concepts and can be applied for both small and large scale assessments of water resources and status. In the model, the landscape is divided into classes according to soil type, vegetation and altitude. The soil representation is stratified and can be divided in up to three layers. Water and substances are routed through the same flow paths and storages (snow, soil, groundwater, streams, rivers, lakes) considering turn-over and transformation on the way towards the sea. In Sweden, the model is used by water authorities to fulfil the Water Framework Directive and the Marine Strategy Framework Directive. It is used for characterization, forecasts, and scenario analyses. Model data can be downloaded for free from three different HYPE applications: Europe (www.smhi.se/e-hype), Baltic Sea basin (www.smhi.se/balt-hype), and Sweden (vattenweb.smhi.se) The HYPE OSC (hype.sourceforge.net) is an open source initiative under the Lesser GNU Public License taken by SMHI to strengthen international collaboration in hydrological modelling and hydrological data production. The hypothesis is that more brains and more testing will result in better models and better code. The code is transparent and can be changed and learnt from. New versions of the main code will be delivered frequently. The main objective of the HYPE OSC is to provide public access to a state-of-the-art operational hydrological model and to encourage hydrologic expertise from different parts of the world to contribute to model improvement. HYPE OSC is open to everyone interested in hydrology, hydrological modelling and code development - e.g. scientists, authorities, and consultancies. The HYPE Open Source Community was initiated in November 2011 by a kick-off and workshop with 50 eager participants from twelve different countries. In beginning of 2013 we will release a new version of the code featuring new and better modularization, corresponding to hydrological processes which will make the code easier to understand and further develop. During 2013 we also plan a new workshop and HYPE course for everyone interested in the community. Lindström, G., Pers, C.P., Rosberg, R., Strömqvist, J., Arheimer, B. 2010. Development and test of the HYPE (Hydrological Predictions for the Environment) model - A water quality model for different spatial scales. Hydrology Research 41.3-4:295-319

Scale effect challenges in urban hydrology highlighted with a distributed hydrological model

NASA Astrophysics Data System (ADS)

Ichiba, Abdellah; Gires, Auguste; Tchiguirinskaia, Ioulia; Schertzer, Daniel; Bompard, Philippe; Ten Veldhuis, Marie-Claire

2018-01-01

Hydrological models are extensively used in urban water management, development and evaluation of future scenarios and research activities. There is a growing interest in the development of fully distributed and grid-based models. However, some complex questions related to scale effects are not yet fully understood and still remain open issues in urban hydrology. In this paper we propose a two-step investigation framework to illustrate the extent of scale effects in urban hydrology. First, fractal tools are used to highlight the scale dependence observed within distributed data input into urban hydrological models. Then an intensive multi-scale modelling work is carried out to understand scale effects on hydrological model performance. Investigations are conducted using a fully distributed and physically based model, Multi-Hydro, developed at Ecole des Ponts ParisTech. The model is implemented at 17 spatial resolutions ranging from 100 to 5 m. Results clearly exhibit scale effect challenges in urban hydrology modelling. The applicability of fractal concepts highlights the scale dependence observed within distributed data. Patterns of geophysical data change when the size of the observation pixel changes. The multi-scale modelling investigation confirms scale effects on hydrological model performance. Results are analysed over three ranges of scales identified in the fractal analysis and confirmed through modelling. This work also discusses some remaining issues in urban hydrology modelling related to the availability of high-quality data at high resolutions, and model numerical instabilities as well as the computation time requirements. The main findings of this paper enable a replacement of traditional methods of model calibration by innovative methods of model resolution alteration based on the spatial data variability and scaling of flows in urban hydrology.

National-Scale Hydrologic Classification & Agricultural Decision Support: A Multi-Scale Approach

NASA Astrophysics Data System (ADS)

Coopersmith, E. J.; Minsker, B.; Sivapalan, M.

2012-12-01

Classification frameworks can help organize catchments exhibiting similarity in hydrologic and climatic terms. Focusing this assessment of "similarity" upon specific hydrologic signatures, in this case the annual regime curve, can facilitate the prediction of hydrologic responses. Agricultural decision-support over a diverse set of catchments throughout the United States depends upon successful modeling of the wetting/drying process without necessitating separate model calibration at every site where such insights are required. To this end, a holistic classification framework is developed to describe both climatic variability (humid vs. arid, winter rainfall vs. summer rainfall) and the draining, storing, and filtering behavior of any catchment, including ungauged or minimally gauged basins. At the national scale, over 400 catchments from the MOPEX database are analyzed to construct the classification system, with over 77% of these catchments ultimately falling into only six clusters. At individual locations, soil moisture models, receiving only rainfall as input, produce correlation values in excess of 0.9 with respect to observed soil moisture measurements. By deploying physical models for predicting soil moisture exclusively from precipitation that are calibrated at gauged locations, overlaying machine learning techniques to improve these estimates, then generalizing the calibration parameters for catchments in a given class, agronomic decision-support becomes available where it is needed rather than only where sensing data are located.lassifications of 428 U.S. catchments on the basis of hydrologic regime data, Coopersmith et al, 2012.

A general Bayesian framework for calibrating and evaluating stochastic models of annual multi-site hydrological data

NASA Astrophysics Data System (ADS)

Frost, Andrew J.; Thyer, Mark A.; Srikanthan, R.; Kuczera, George

2007-07-01

SummaryMulti-site simulation of hydrological data are required for drought risk assessment of large multi-reservoir water supply systems. In this paper, a general Bayesian framework is presented for the calibration and evaluation of multi-site hydrological data at annual timescales. Models included within this framework are the hidden Markov model (HMM) and the widely used lag-1 autoregressive (AR(1)) model. These models are extended by the inclusion of a Box-Cox transformation and a spatial correlation function in a multi-site setting. Parameter uncertainty is evaluated using Markov chain Monte Carlo techniques. Models are evaluated by their ability to reproduce a range of important extreme statistics and compared using Bayesian model selection techniques which evaluate model probabilities. The case study, using multi-site annual rainfall data situated within catchments which contribute to Sydney's main water supply, provided the following results: Firstly, in terms of model probabilities and diagnostics, the inclusion of the Box-Cox transformation was preferred. Secondly the AR(1) and HMM performed similarly, while some other proposed AR(1)/HMM models with regionally pooled parameters had greater posterior probability than these two models. The practical significance of parameter and model uncertainty was illustrated using a case study involving drought security analysis for urban water supply. It was shown that ignoring parameter uncertainty resulted in a significant overestimate of reservoir yield and an underestimation of system vulnerability to severe drought.

Uncertainty Propagation of Non-Parametric-Derived Precipitation Estimates into Multi-Hydrologic Model Simulations

NASA Astrophysics Data System (ADS)

Bhuiyan, M. A. E.; Nikolopoulos, E. I.; Anagnostou, E. N.

2017-12-01

Quantifying the uncertainty of global precipitation datasets is beneficial when using these precipitation products in hydrological applications, because precipitation uncertainty propagation through hydrologic modeling can significantly affect the accuracy of the simulated hydrologic variables. In this research the Iberian Peninsula has been used as the study area with a study period spanning eleven years (2000-2010). This study evaluates the performance of multiple hydrologic models forced with combined global rainfall estimates derived based on a Quantile Regression Forests (QRF) technique. In QRF technique three satellite precipitation products (CMORPH, PERSIANN, and 3B42 (V7)); an atmospheric reanalysis precipitation and air temperature dataset; satellite-derived near-surface daily soil moisture data; and a terrain elevation dataset are being utilized in this study. A high-resolution, ground-based observations driven precipitation dataset (named SAFRAN) available at 5 km/1 h resolution is used as reference. Through the QRF blending framework the stochastic error model produces error-adjusted ensemble precipitation realizations, which are used to force four global hydrological models (JULES (Joint UK Land Environment Simulator), WaterGAP3 (Water-Global Assessment and Prognosis), ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) and SURFEX (Stands for Surface Externalisée) ) to simulate three hydrologic variables (surface runoff, subsurface runoff and evapotranspiration). The models are forced with the reference precipitation to generate reference-based hydrologic simulations. This study presents a comparative analysis of multiple hydrologic model simulations for different hydrologic variables and the impact of the blending algorithm on the simulated hydrologic variables. Results show how precipitation uncertainty propagates through the different hydrologic model structures to manifest in reduction of error in hydrologic variables.

A Web-based Distributed Voluntary Computing Platform for Large Scale Hydrological Computations

NASA Astrophysics Data System (ADS)

Demir, I.; Agliamzanov, R.

2014-12-01

Distributed volunteer computing can enable researchers and scientist to form large parallel computing environments to utilize the computing power of the millions of computers on the Internet, and use them towards running large scale environmental simulations and models to serve the common good of local communities and the world. Recent developments in web technologies and standards allow client-side scripting languages to run at speeds close to native application, and utilize the power of Graphics Processing Units (GPU). Using a client-side scripting language like JavaScript, we have developed an open distributed computing framework that makes it easy for researchers to write their own hydrologic models, and run them on volunteer computers. Users will easily enable their websites for visitors to volunteer sharing their computer resources to contribute running advanced hydrological models and simulations. Using a web-based system allows users to start volunteering their computational resources within seconds without installing any software. The framework distributes the model simulation to thousands of nodes in small spatial and computational sizes. A relational database system is utilized for managing data connections and queue management for the distributed computing nodes. In this paper, we present a web-based distributed volunteer computing platform to enable large scale hydrological simulations and model runs in an open and integrated environment.

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

Vrugt, Jasper A; Robinson, Bruce A; Ter Braak, Cajo J F

In recent years, a strong debate has emerged in the hydrologic literature regarding what constitutes an appropriate framework for uncertainty estimation. Particularly, there is strong disagreement whether an uncertainty framework should have its roots within a proper statistical (Bayesian) context, or whether such a framework should be based on a different philosophy and implement informal measures and weaker inference to summarize parameter and predictive distributions. In this paper, we compare a formal Bayesian approach using Markov Chain Monte Carlo (MCMC) with generalized likelihood uncertainty estimation (GLUE) for assessing uncertainty in conceptual watershed modeling. Our formal Bayesian approach is implemented usingmore » the recently developed differential evolution adaptive metropolis (DREAM) MCMC scheme with a likelihood function that explicitly considers model structural, input and parameter uncertainty. Our results demonstrate that DREAM and GLUE can generate very similar estimates of total streamflow uncertainty. This suggests that formal and informal Bayesian approaches have more common ground than the hydrologic literature and ongoing debate might suggest. The main advantage of formal approaches is, however, that they attempt to disentangle the effect of forcing, parameter and model structural error on total predictive uncertainty. This is key to improving hydrologic theory and to better understand and predict the flow of water through catchments.« less

A real-time control framework for urban water reservoirs operation

NASA Astrophysics Data System (ADS)

Galelli, S.; Goedbloed, A.; Schwanenberg, D.

2012-04-01

Drinking water demand in urban areas is growing parallel to the worldwide urban population, and it is acquiring an increasing part of the total water consumption. Since the delivery of sufficient water volumes in urban areas represents a difficult logistic and economical problem, different metropolitan areas are evaluating the opportunity of constructing relatively small reservoirs within urban areas. Singapore, for example, is developing the so-called 'Four National Taps Strategies', which detects the maximization of water yields from local, urban catchments as one of the most important water sources. However, the peculiar location of these reservoirs can provide a certain advantage from the logistical point of view, but it can pose serious difficulties in their daily management. Urban catchments are indeed characterized by large impervious areas: this results in a change of the hydrological cycle, with decreased infiltration and groundwater recharge, and increased patterns of surface and river discharges, with higher peak flows, volumes and concentration time. Moreover, the high concentrations of nutrients and sediments characterizing urban discharges can cause further water quality problems. In this critical hydrological context, the effective operation of urban water reservoirs must rely on real-time control techniques, which can exploit hydro-meteorological information available in real-time from hydrological and nowcasting models. This work proposes a novel framework for the real-time control of combined water quality and quantity objectives in urban reservoirs. The core of this framework is a non-linear Model Predictive Control (MPC) scheme, which employs the current state of the system, the future discharges furnished by a predictive model and a further model describing the internal dynamics of the controlled sub-system to determine an optimal control sequence over a finite prediction horizon. The main advantage of this scheme stands in its reduced computational requests and the capability of exploiting real-time hydro-meteorological information, which are crucial for an effective operation of these fast-varying hydrological systems. The framework is here demonstrated on the operation of Marina Reservoir (Singapore), whose recent construction in late 2008 increased the effective catchment area to about 50% of the total available. Its operation, which accounts for drinking water supply, flash floods control and water quality standards, is here designed by combining the MPC scheme with the process-based hydrological model SOBEK. Extensive simulation experiments show the validity of the proposed framework.

Research on classified real-time flood forecasting framework based on K-means cluster and rough set.

PubMed

Xu, Wei; Peng, Yong

2015-01-01

This research presents a new classified real-time flood forecasting framework. In this framework, historical floods are classified by a K-means cluster according to the spatial and temporal distribution of precipitation, the time variance of precipitation intensity and other hydrological factors. Based on the classified results, a rough set is used to extract the identification rules for real-time flood forecasting. Then, the parameters of different categories within the conceptual hydrological model are calibrated using a genetic algorithm. In real-time forecasting, the corresponding category of parameters is selected for flood forecasting according to the obtained flood information. This research tests the new classified framework on Guanyinge Reservoir and compares the framework with the traditional flood forecasting method. It finds that the performance of the new classified framework is significantly better in terms of accuracy. Furthermore, the framework can be considered in a catchment with fewer historical floods.

Quantifying and Generalizing Hydrologic Responses to Dam Regulation using a Statistical Modeling Approach

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

McManamay, Ryan A

2014-01-01

Despite the ubiquitous existence of dams within riverscapes, much of our knowledge about dams and their environmental effects remains context-specific. Hydrology, more than any other environmental variable, has been studied in great detail with regard to dam regulation. While much progress has been made in generalizing the hydrologic effects of regulation by large dams, many aspects of hydrology show site-specific fidelity to dam operations, small dams (including diversions), and regional hydrologic regimes. A statistical modeling framework is presented to quantify and generalize hydrologic responses to varying degrees of dam regulation. Specifically, the objectives were to 1) compare the effects ofmore » local versus cumulative dam regulation, 2) determine the importance of different regional hydrologic regimes in influencing hydrologic responses to dams, and 3) evaluate how different regulation contexts lead to error in predicting hydrologic responses to dams. Overall, model performance was poor in quantifying the magnitude of hydrologic responses, but performance was sufficient in classifying hydrologic responses as negative or positive. Responses of some hydrologic indices to dam regulation were highly dependent upon hydrologic class membership and the purpose of the dam. The opposing coefficients between local and cumulative-dam predictors suggested that hydrologic responses to cumulative dam regulation are complex, and predicting the hydrology downstream of individual dams, as opposed to multiple dams, may be more easy accomplished using statistical approaches. Results also suggested that particular contexts, including multipurpose dams, high cumulative regulation by multiple dams, diversions, close proximity to dams, and certain hydrologic classes are all sources of increased error when predicting hydrologic responses to dams. Statistical models, such as the ones presented herein, show promise in their ability to model the effects of dam regulation effects at large spatial scales as to generalize the directionality of hydrologic responses.« less

Explicitly integrating parameter, input, and structure uncertainties into Bayesian Neural Networks for probabilistic hydrologic forecasting

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

Zhang, Xuesong; Liang, Faming; Yu, Beibei

2011-11-09

Estimating uncertainty of hydrologic forecasting is valuable to water resources and other relevant decision making processes. Recently, Bayesian Neural Networks (BNNs) have been proved powerful tools for quantifying uncertainty of streamflow forecasting. In this study, we propose a Markov Chain Monte Carlo (MCMC) framework to incorporate the uncertainties associated with input, model structure, and parameter into BNNs. This framework allows the structure of the neural networks to change by removing or adding connections between neurons and enables scaling of input data by using rainfall multipliers. The results show that the new BNNs outperform the BNNs that only consider uncertainties associatedmore » with parameter and model structure. Critical evaluation of posterior distribution of neural network weights, number of effective connections, rainfall multipliers, and hyper-parameters show that the assumptions held in our BNNs are not well supported. Further understanding of characteristics of different uncertainty sources and including output error into the MCMC framework are expected to enhance the application of neural networks for uncertainty analysis of hydrologic forecasting.« less

Intercomparison of Streamflow Simulations between WRF-Hydro and Hydrology Laboratory-Research Distributed Hydrologic Model Frameworks

NASA Astrophysics Data System (ADS)

KIM, J.; Smith, M. B.; Koren, V.; Salas, F.; Cui, Z.; Johnson, D.

2017-12-01

The National Oceanic and Atmospheric Administration (NOAA)-National Weather Service (NWS) developed the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) framework as an initial step towards spatially distributed modeling at River Forecast Centers (RFCs). Recently, the NOAA/NWS worked with the National Center for Atmospheric Research (NCAR) to implement the National Water Model (NWM) for nationally-consistent water resources prediction. The NWM is based on the WRF-Hydro framework and is run at a 1km spatial resolution and 1-hour time step over the contiguous United States (CONUS) and contributing areas in Canada and Mexico. In this study, we compare streamflow simulations from HL-RDHM and WRF-Hydro to observations from 279 USGS stations. For streamflow simulations, HL-RDHM is run on 4km grids with the temporal resolution of 1 hour for a 5-year period (Water Years 2008-2012), using a priori parameters provided by NOAA-NWS. The WRF-Hydro streamflow simulations for the same time period are extracted from NCAR's 23 retrospective run of the NWM (version 1.0) over CONUS based on 1km grids. We choose 279 USGS stations which are relatively less affected by dams or reservoirs, in the domains of six different RFCs. We use the daily average values of simulations and observations for the convenience of comparison. The main purpose of this research is to evaluate how HL-RDHM and WRF-Hydro perform at USGS gauge stations. We compare daily time-series of observations and both simulations, and calculate the error values using a variety of error functions. Using these plots and error values, we evaluate the performances of HL-RDHM and WRF-Hydro models. Our results show a mix of model performance across geographic regions.

Ecosystem Services and Climate Change Considerations for ...

EPA Pesticide Factsheets

Freshwater habitats provide fishable, swimmable and drinkable resources and are a nexus of geophysical and biological processes. These processes in turn influence the persistence and sustainability of populations, communities and ecosystems. Climate change and landuse change encompass numerous stressors of potential exposure, including the introduction of toxic contaminants, invasive species, and disease in addition to physical drivers such as temperature and hydrologic regime. A systems approach that includes the scientific and technologic basis of assessing the health of ecosystems is needed to effectively protect human health and the environment. The Integrated Environmental Modeling Framework “iemWatersheds” has been developed as a consistent and coherent means of forecasting the cumulative impact of co-occurring stressors. The Framework consists of three facilitating technologies: Data for Environmental Modeling (D4EM) that automates the collection and standardization of input data; the Framework for Risk Assessment of Multimedia Environmental Systems (FRAMES) that manages the flow of information between linked models; and the Supercomputer for Model Uncertainty and Sensitivity Evaluation (SuperMUSE) that provides post-processing and analysis of model outputs, including uncertainty and sensitivity analysis. Five models are linked within the Framework to provide multimedia simulation capabilities for hydrology and water quality processes: the Soil Water

Parameter optimization of a hydrologic model in a snow-dominated basin using a modular Python framework

NASA Astrophysics Data System (ADS)

Volk, J. M.; Turner, M. A.; Huntington, J. L.; Gardner, M.; Tyler, S.; Sheneman, L.

2016-12-01

Many distributed models that simulate watershed hydrologic processes require a collection of multi-dimensional parameters as input, some of which need to be calibrated before the model can be applied. The Precipitation Runoff Modeling System (PRMS) is a physically-based and spatially distributed hydrologic model that contains a considerable number of parameters that often need to be calibrated. Modelers can also benefit from uncertainty analysis of these parameters. To meet these needs, we developed a modular framework in Python to conduct PRMS parameter optimization, uncertainty analysis, interactive visual inspection of parameters and outputs, and other common modeling tasks. Here we present results for multi-step calibration of sensitive parameters controlling solar radiation, potential evapo-transpiration, and streamflow in a PRMS model that we applied to the snow-dominated Dry Creek watershed in Idaho. We also demonstrate how our modular approach enables the user to use a variety of parameter optimization and uncertainty methods or easily define their own, such as Monte Carlo random sampling, uniform sampling, or even optimization methods such as the downhill simplex method or its commonly used, more robust counterpart, shuffled complex evolution.

Hydrology and ecology of pinyon-juniper woodlands: Conceptual framework and field studies

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

Wilcox, B.P.; Breshears, D.D.

1994-09-01

Pinyon-juniper woodlands represent an important ecosystem in the semiarid western United States. Concern over the sustainability of, and management approaches for, these woodlands is increasing. As in other semiarid environments, water dynamics and vegetation patterns in pinyon-juniper woodlands are highly interrelated. An understanding of these relationships can aid in evaluating various management strategies. In this paper we describe a conceptual framework designed to increase our understanding of water and vegetation in pinyon-juniper woodlands. The framework comprises five different scales, at each of which the landscape is divided into {open_quotes}functional units{close_quotes} on the basis of hydrologic characteristics. The hydrologic behavior ofmore » each unit and the connections between units are being evaluated using an extensive network of hydrological and ecological field studies on the Pajarito Plateau in northern New Mexico. Data from these studies, coupled with application of the conceptual model, have led to the development of a number of hypotheses concerning the interrelationships of water and vegetation in pinyon-juniper woodlands.« less

The use of remotely sensed soil moisture data in large-scale models of the hydrological cycle

NASA Technical Reports Server (NTRS)

Salomonson, V. V.; Gurney, R. J.; Schmugge, T. J.

1985-01-01

Manabe (1982) has reviewed numerical simulations of the atmosphere which provided a framework within which an examination of the dynamics of the hydrological cycle could be conducted. It was found that the climate is sensitive to soil moisture variability in space and time. The challenge arises now to improve the observations of soil moisture so as to provide up-dated boundary condition inputs to large scale models including the hydrological cycle. Attention is given to details regarding the significance of understanding soil moisture variations, soil moisture estimation using remote sensing, and energy and moisture balance modeling.

A multi-scale ensemble-based framework for forecasting compound coastal-riverine flooding: The Hackensack-Passaic watershed and Newark Bay

NASA Astrophysics Data System (ADS)

Saleh, F.; Ramaswamy, V.; Wang, Y.; Georgas, N.; Blumberg, A.; Pullen, J.

2017-12-01

Estuarine regions can experience compound impacts from coastal storm surge and riverine flooding. The challenges in forecasting flooding in such areas are multi-faceted due to uncertainties associated with meteorological drivers and interactions between hydrological and coastal processes. The objective of this work is to evaluate how uncertainties from meteorological predictions propagate through an ensemble-based flood prediction framework and translate into uncertainties in simulated inundation extents. A multi-scale framework, consisting of hydrologic, coastal and hydrodynamic models, was used to simulate two extreme flood events at the confluence of the Passaic and Hackensack rivers and Newark Bay. The events were Hurricane Irene (2011), a combination of inland flooding and coastal storm surge, and Hurricane Sandy (2012) where coastal storm surge was the dominant component. The hydrodynamic component of the framework was first forced with measured streamflow and ocean water level data to establish baseline inundation extents with the best available forcing data. The coastal and hydrologic models were then forced with meteorological predictions from 21 ensemble members of the Global Ensemble Forecast System (GEFS) to retrospectively represent potential future conditions up to 96 hours prior to the events. Inundation extents produced by the hydrodynamic model, forced with the 95th percentile of the ensemble-based coastal and hydrologic boundary conditions, were in good agreement with baseline conditions for both events. The USGS reanalysis of Hurricane Sandy inundation extents was encapsulated between the 50th and 95th percentile of the forecasted inundation extents, and that of Hurricane Irene was similar but with caveats associated with data availability and reliability. This work highlights the importance of accounting for meteorological uncertainty to represent a range of possible future inundation extents at high resolution (∼m).

Quantifying effects of hydrological and water quality disturbances on fish with food-web modeling

NASA Astrophysics Data System (ADS)

Zhao, Changsen; Zhang, Yuan; Yang, Shengtian; Xiang, Hua; Sun, Ying; Yang, Zengyuan; Yu, Qiang; Lim, Richard P.

2018-05-01

Accurately delineating the effects of hydrological and water quality habitat factors on the aquatic biota will significantly assist the management of water resources and restoration of river ecosystems. However, current models fail to comprehensively consider the effects of multiple habitat factors on the development of fish species. In this study, a dynamic framework for river ecosystems was set up to explore the effects of multiple habitat factors in terms of hydrology and water quality on the fish community in rivers. To achieve this the biomechanical forms of the relationships between hydrology, water quality, and aquatic organisms were determined. The developing processes of the food web without external disturbance were simulated by 208 models, constructed using Ecopath With Ecosim (EWE). These models were then used to analyze changes in biomass (ΔB) of two representative fish species, Opsariichthys bidens and Carassius auratus, which are widely distributed in Asia, and thus have attracted the attention of scholars and stakeholders, due to the consequence of habitat alteration. Results showed that the relationship between the changes in fish biomass and key habitat factors can be expressed in a unified form. T-tests for the unified form revealed that the means of the two data sets of simulated and observed ΔB for these two fish species (O. bidens and C. auratus) were equal at the significance level of 5%. Compared with other ecological dynamic models, our framework includes theories that are easy to understand and has modest requirements for assembly and scientific expertise. Moreover, this framework can objectively assess the influence of hydrological and water quality variance on aquatic biota with simpler theory and little expertise. Therefore, it is easy to be put into practice and can provide a scientific support for decisions in ecological restoration made by river administrators and stakeholders across the world.

Catchment Classification: Connecting Climate, Structure and Function

NASA Astrophysics Data System (ADS)

Sawicz, K. A.; Wagener, T.; Sivapalan, M.; Troch, P. A.; Carrillo, G. A.

2010-12-01

Hydrology does not yet possess a generally accepted catchment classification framework. Such a classification framework needs to: [1] give names to things, i.e. the main classification step, [2] permit transfer of information, i.e. regionalization of information, [3] permit development of generalizations, i.e. to develop new theory, and [4] provide a first order environmental change impact assessment, i.e., the hydrologic implications of climate, land use and land cover change. One strategy is to create a catchment classification framework based on the notion of catchment functions (partitioning, storage, and release). Results of an empirical study presented here connects climate and structure to catchment function (in the form of select hydrologic signatures), based on analyzing over 300 US catchments. Initial results indicate a wide assortment of signature relationships with properties of climate, geology, and vegetation. The uncertainty in the different regionalized signatures varies widely, and therefore there is variability in the robustness of classifying ungauged basins. This research provides insight into the controls of hydrologic behavior of a catchment, and enables a classification framework applicable to gauged and ungauged across the study domain. This study sheds light on what we can expect to achieve in mapping climate, structure and function in a top-down manner. Results of this study complement work done using a bottom-up physically-based modeling framework to generalize this approach (Carrillo et al., this session).

From terrestrial to aquatic fluxes: Integrating stream dynamics within a dynamic global vegetation modeling framework

NASA Astrophysics Data System (ADS)

Hoy, Jerad; Poulter, Benjamin; Emmett, Kristen; Cross, Molly; Al-Chokhachy, Robert; Maneta, Marco

2016-04-01

Integrated terrestrial ecosystem models simulate the dynamics and feedbacks between climate, vegetation, disturbance, and hydrology and are used to better understand biogeography and biogeochemical cycles. Extending dynamic vegetation models to the aquatic interface requires coupling surface and sub-surface runoff to catchment routing schemes and has the potential to enhance how researchers and managers investigate how changes in the environment might impact the availability of water resources for human and natural systems. In an effort towards creating such a coupled model, we developed catchment-based hydrologic routing and stream temperature model to pair with LPJ-GUESS, a dynamic global vegetation model. LPJ-GUESS simulates detailed stand-level vegetation dynamics such as growth, carbon allocation, and mortality, as well as various physical and hydrologic processes such as canopy interception and through-fall, and can be applied at small spatial scales, i.e., 1 km. We demonstrate how the coupled model can be used to investigate the effects of transient vegetation dynamics and CO2 on seasonal and annual stream discharge and temperature regimes. As a direct management application, we extend the modeling framework to predict habitat suitability for fish habitat within the Greater Yellowstone Ecosystem, a 200,000 km2 region that provides critical habitat for a range of aquatic species. The model is used to evaluate, quantitatively, the effects of management practices aimed to enhance hydrologic resilience to climate change, and benefits for water storage and fish habitat in the coming century.

Multi-state succession in wetlands: a novel use of state and transition models

USGS Publications Warehouse

Zweig, Christa L.; Kitchens, Wiley M.

2009-01-01

The complexity of ecosystems and mechanisms of succession are often simplified by linear and mathematical models used to understand and predict system behavior. Such models often do not incorporate multivariate, nonlinear feedbacks in pattern and process that include multiple scales of organization inherent within real-world systems. Wetlands are ecosystems with unique, nonlinear patterns of succession due to the regular, but often inconstant, presence of water on the landscape. We develop a general, nonspatial state and transition (S and T) succession conceptual model for wetlands and apply the general framework by creating annotated succession/management models and hypotheses for use in impact analysis on a portion of an imperiled wetland. The S and T models for our study area, Water Conservation Area 3A South (WCA3), Florida, USA, included hydrologic and peat depth values from multivariate analyses and classification and regression trees. We used the freeware Vegetation Dynamics Development Tool as an exploratory application to evaluate our S and T models with different management actions (equal chance [a control condition], deeper conditions, dry conditions, and increased hydrologic range) for three communities: slough, sawgrass (Cladium jamaicense), and wet prairie. Deeper conditions and increased hydrologic range behaved similarly, with the transition of community states to deeper states, particularly for sawgrass and slough. Hydrology is the primary mechanism for multi-state transitions within our study period, and we show both an immediate and lagged effect on vegetation, depending on community state. We consider these S and T succession models as a fraction of the framework for the Everglades. They are hypotheses for use in adaptive management, represent the community response to hydrology, and illustrate which aspects of hydrologic variability are important to community structure. We intend for these models to act as a foundation for further restoration management and experimentation which will refine transition and threshold concepts. 

A model for seasonal changes in GPS positions and seismic wave speeds due to thermoelastic and hydrologic variations

USGS Publications Warehouse

Tsai, V.C.

2011-01-01

It is known that GPS time series contain a seasonal variation that is not due to tectonic motions, and it has recently been shown that crustal seismic velocities may also vary seasonally. In order to explain these changes, a number of hypotheses have been given, among which thermoelastic and hydrology-induced stresses and strains are leading candidates. Unfortunately, though, since a general framework does not exist for understanding such seasonal variations, it is currently not possible to quickly evaluate the plausibility of these hypotheses. To fill this gap in the literature, I generalize a two-dimensional thermoelastic strain model to provide an analytic solution for the displacements and wave speed changes due to either thermoelastic stresses or hydrologic loading, which consists of poroelastic stresses and purely elastic stresses. The thermoelastic model assumes a periodic surface temperature, and the hydrologic models similarly assume a periodic near-surface water load. Since all three models are two-dimensional and periodic, they are expected to only approximate any realistic scenario; but the models nonetheless provide a quantitative framework for estimating the effects of thermoelastic and hydrologic variations. Quantitative comparison between the models and observations is further complicated by the large uncertainty in some of the relevant parameters. Despite this uncertainty, though, I find that maximum realistic thermoelastic effects are unlikely to explain a large fraction of the observed annual variation in a typical GPS displacement time series or of the observed annual variations in seismic wave speeds in southern California. Hydrologic loading, on the other hand, may be able to explain a larger fraction of both the annual variations in displacements and seismic wave speeds. Neither model is likely to explain all of the seismic wave speed variations inferred from observations. However, more definitive conclusions cannot be made until the model parameters are better constrained. Copyright ?? 2011 by the American Geophysical Union.

Modular GIS Framework for National Scale Hydrologic and Hydraulic Modeling Support

NASA Astrophysics Data System (ADS)

Djokic, D.; Noman, N.; Kopp, S.

2015-12-01

Geographic information systems (GIS) have been extensively used for pre- and post-processing of hydrologic and hydraulic models at multiple scales. An extensible GIS-based framework was developed for characterization of drainage systems (stream networks, catchments, floodplain characteristics) and model integration. The framework is implemented as a set of free, open source, Python tools and builds on core ArcGIS functionality and uses geoprocessing capabilities to ensure extensibility. Utilization of COTS GIS core capabilities allows immediate use of model results in a variety of existing online applications and integration with other data sources and applications.The poster presents the use of this framework to downscale global hydrologic models to local hydraulic scale and post process the hydraulic modeling results and generate floodplains at any local resolution. Flow forecasts from ECMWF or WRF-Hydro are downscaled and combined with other ancillary data for input into the RAPID flood routing model. RAPID model results (stream flow along each reach) are ingested into a GIS-based scale dependent stream network database for efficient flow utilization and visualization over space and time. Once the flows are known at localized reaches, the tools can be used to derive the floodplain depth and extent for each time step in the forecast at any available local resolution. If existing rating curves are available they can be used to relate the flow to the depth of flooding, or synthetic rating curves can be derived using the tools in the toolkit and some ancillary data/assumptions. The results can be published as time-enabled spatial services to be consumed by web applications that use floodplain information as an input. Some of the existing online presentation templates can be easily combined with available online demographic and infrastructure data to present the impact of the potential floods on the local community through simple, end user products. This framework has been successfully used in both the data rich environments as well as in locales with minimum available spatial and hydrographic data.

Combining Mechanistic Approaches for Studying Eco-Hydro-Geomorphic Coupling

NASA Astrophysics Data System (ADS)

Francipane, A.; Ivanov, V.; Akutina, Y.; Noto, V.; Istanbullouglu, E.

2008-12-01

Vegetation interacts with hydrology and geomorphic form and processes of a river basin in profound ways. Despite recent advances in hydrological modeling, the dynamic coupling between these processes is yet to be adequately captured at the basin scale to elucidate key features of process interaction and their role in the organization of vegetation and landscape morphology. In this study, we present a blueprint for integrating a geomorphic component into the physically-based, spatially distributed ecohydrological model, tRIBS- VEGGIE, which reproduces essential water and energy processes over the complex topography of a river basin and links them to the basic plant life regulatory processes. We present a preliminary design of the integrated modeling framework in which hillslope and channel erosion processes at the catchment scale, will be coupled with vegetation-hydrology dynamics. We evaluate the developed framework by applying the integrated model to Lucky Hills basin, a sub-catchment of the Walnut Gulch Experimental Watershed (Arizona). The evaluation is carried out by comparing sediment yields at the basin outlet, that follows a detailed verification of simulated land-surface energy partition, biomass dynamics, and soil moisture states.

Ensemble Analysis of Variational Assimilation of Hydrologic and Hydrometeorological Data into Distributed Hydrologic Model

NASA Astrophysics Data System (ADS)

Lee, H.; Seo, D.; Koren, V.

2008-12-01

A prototype 4DVAR (four-dimensional variational) data assimilator for gridded Sacramento soil-moisture accounting and kinematic-wave routing models in the Hydrology Laboratory's Research Distributed Hydrologic Model (HL-RDHM) has been developed. The prototype assimilates streamflow and in-situ soil moisture data and adjusts gridded precipitation and climatological potential evaporation data to reduce uncertainty in the model initial conditions for improved monitoring and prediction of streamflow and soil moisture at the outlet and interior locations within the catchment. Due to large degrees of freedom involved, data assimilation (DA) into distributed hydrologic models is complex. To understand and assess sensitivity of the performance of DA to uncertainties in the model initial conditions and in the data, two synthetic experiments have been carried out in an ensemble framework. Results from the synthetic experiments shed much light on the potential and limitations with DA into distributed models. For initial real-world assessment, the prototype DA has also been applied to the headwater basin at Eldon near the Oklahoma-Arkansas border. We present these results and describe the next steps.

Operational Research: Evaluating Multimodel Implementations for 24/7 Runtime Environments

NASA Astrophysics Data System (ADS)

Burkhart, J. F.; Helset, S.; Abdella, Y. S.; Lappegard, G.

2016-12-01

We present a new open source framework for operational hydrologic rainfall-runoff modeling. The Statkraft Hydrologic Forecasting Toolbox (Shyft) is unique from existing frameworks in that two primary goals are to provide: i) modern, professionally developed source code, and ii) a platform that is robust and ready for operational deployment. Developed jointly between Statkraft AS and The University of Oslo, the framework is currently in operation in both private and academic environments. The hydrology presently available in the distribution is simple and proven. Shyft provides a platform for distributed hydrologic modeling in a highly efficient manner. In it's current operational deployment at Statkraft, Shyft is used to provide daily 10-day forecasts for critical reservoirs. In a research setting, we have developed a novel implementation of the SNICAR model to assess the impact of aerosol deposition on snow packs. Several well known rainfall-runoff algorithms are available for use, allowing for intercomparing different approaches based on available data and the geographical environment. The well known HBV model is a default option, and other routines with more localized methods handling snow and evapotranspiration, or simplifications of catchment scale processes are included. For the latter, we have implemented the Kirchner response routine. Being developed in Norway, a variety snow-melt routines, including simplified degree day models or more advanced energy balance models, may be selected. Ensemble forecasts, multi-model implementations, and statistical post-processing routines enable a robust toolbox for investigating optimal model configurations in an operational setting. The Shyft core is written in modern templated C++ and has Python wrappers developed for easy access to module sub-routines. The code is developed such that the modules that make up a "method stack" are easy to modify and customize, allowing one to create new methods and test them rapidly. Due to the simple architecture and ease of access to the module routines, we see Shyft as an optimal choice to evaluate new hydrologic routines in an environment requiring robust, professionally developed software and welcome further community participation.

Socio-hydrologic modeling to understand and mediate the competition for water between agriculture development and environmental health: Murrumbidgee River Basin, Australia

NASA Astrophysics Data System (ADS)

van Emmerik, T. H. M.; Li, Z.; Sivapalan, M.; Pande, S.; Kandasamy, J.; Savenije, H. H. G.; Chanan, A.; Vigneswaran, S.

2014-03-01

Competition for water between humans and ecosystems is set to become a flash point in the coming decades in many parts of the world. An entirely new and comprehensive quantitative framework is needed to establish a holistic understanding of that competition, thereby enabling the development of effective mediation strategies. This paper presents a modeling study centered on the Murrumbidgee River Basin (MRB). The MRB has witnessed a unique system dynamics over the last 100 years as a result of interactions between patterns of water management and climate driven hydrological variability. Data analysis has revealed a pendulum swing between agricultural development and restoration of environmental health and ecosystem services over different stages of basin scale water resource development. A parsimonious, stylized, quasi-distributed coupled socio-hydrologic system model that simulates the two-way coupling between human and hydrological systems of the MRB is used to mimic dominant features of the pendulum swing. The model consists of coupled nonlinear ordinary differential equations that describe the interaction between five state variables that govern the co-evolution: reservoir storage, irrigated area, human population, ecosystem health, and a measure of environmental awareness. The model simulations track the propagation of the external climatic and socio-economic drivers through this coupled, complex system to the emergence of the pendulum swing. The model results point to a competition between human "productive" and environmental "restorative" forces that underpin the pendulum swing. Both the forces are endogenous, i.e., generated by the system dynamics in response to external drivers and mediated by humans through technology change and environmental awareness, respectively. We propose this as a generalizable modeling framework for coupled human hydrological systems that is potentially transferable to systems in different climatic and socio-economic settings.

A modular approach to addressing model design, scale, and parameter estimation issues in distributed hydrological modelling

USGS Publications Warehouse

Leavesley, G.H.; Markstrom, S.L.; Restrepo, Pedro J.; Viger, R.J.

2002-01-01

A modular approach to model design and construction provides a flexible framework in which to focus the multidisciplinary research and operational efforts needed to facilitate the development, selection, and application of the most robust distributed modelling methods. A variety of modular approaches have been developed, but with little consideration for compatibility among systems and concepts. Several systems are proprietary, limiting any user interaction. The US Geological Survey modular modelling system (MMS) is a modular modelling framework that uses an open source software approach to enable all members of the scientific community to address collaboratively the many complex issues associated with the design, development, and application of distributed hydrological and environmental models. Implementation of a common modular concept is not a trivial task. However, it brings the resources of a larger community to bear on the problems of distributed modelling, provides a framework in which to compare alternative modelling approaches objectively, and provides a means of sharing the latest modelling advances. The concepts and components of the MMS are described and an example application of the MMS, in a decision-support system context, is presented to demonstrate current system capabilities. Copyright ?? 2002 John Wiley and Sons, Ltd.

Detecting Human Hydrologic Alteration from Diversion Hydropower Requires Universal Flow Prediction Tools: A Proposed Framework for Flow Prediction in Poorly-gauged, Regulated Rivers

NASA Astrophysics Data System (ADS)

Kibler, K. M.; Alipour, M.

2016-12-01

Achieving the universal energy access Sustainable Development Goal will require great investment in renewable energy infrastructure in the developing world. Much growth in the renewable sector will come from new hydropower projects, including small and diversion hydropower in remote and mountainous regions. Yet, human impacts to hydrological systems from diversion hydropower are poorly described. Diversion hydropower is often implemented in ungauged rivers, thus detection of impact requires flow analysis tools suited to prediction in poorly-gauged and human-altered catchments. We conduct a comprehensive analysis of hydrologic alteration in 32 rivers developed with diversion hydropower in southwestern China. As flow data are sparse, we devise an approach for estimating streamflow during pre- and post-development periods, drawing upon a decade of research into prediction in ungauged basins. We apply a rainfall-runoff model, parameterized and forced exclusively with global-scale data, in hydrologically-similar gauged and ungauged catchments. Uncertain "soft" data are incorporated through fuzzy numbers and confidence-based weighting, and a multi-criteria objective function is applied to evaluate model performance. Testing indicates that the proposed framework returns superior performance (NSE = 0.77) as compared to models parameterized by rote calibration (NSE = 0.62). Confident that the models are providing `the right answer for the right reasons', our analysis of hydrologic alteration based on simulated flows indicates statistically significant hydrologic effects of diversion hydropower across many rivers. Mean annual flows, 7-day minimum and 7-day maximum flows decreased. Frequency and duration of flow exceeding Q25 decreased while duration of flows sustained below the Q75 increased substantially. Hydrograph rise and fall rates and flow constancy increased. The proposed methodology may be applied to improve diversion hydropower design in data-limited regions.

A Framework for Effective Use of Hydroclimate Models in Climate-Change Adaptation Planning for Managed Habitats with Limited Hydrologic Response Data.

PubMed

Esralew, Rachel A; Flint, Lorraine; Thorne, James H; Boynton, Ryan; Flint, Alan

2016-07-01

Climate-change adaptation planning for managed wetlands is challenging under uncertain futures when the impact of historic climate variability on wetland response is unquantified. We assessed vulnerability of Modoc National Wildlife Refuge (MNWR) through use of the Basin Characterization Model (BCM) landscape hydrology model, and six global climate models, representing projected wetter and drier conditions. We further developed a conceptual model that provides greater value for water managers by incorporating the BCM outputs into a conceptual framework that links modeled parameters to refuge management outcomes. This framework was used to identify landscape hydrology parameters that reflect refuge sensitivity to changes in (1) climatic water deficit (CWD) and recharge, and (2) the magnitude, timing, and frequency of water inputs. BCM outputs were developed for 1981-2100 to assess changes and forecast the probability of experiencing wet and dry water year types that have historically resulted in challenging conditions for refuge habitat management. We used a Yule's Q skill score to estimate the probability of modeled discharge that best represents historic water year types. CWD increased in all models across 72.3-100 % of the water supply basin by 2100. Earlier timing in discharge, greater cool season discharge, and lesser irrigation season water supply were predicted by most models. Under the worst-case scenario, moderately dry years increased from 10-20 to 40-60 % by 2100. MNWR could adapt by storing additional water during the cool season for later use and prioritizing irrigation of habitats during dry years.

Monitoring and forecasting the 2009-2010 severe drought in Southwest China

NASA Astrophysics Data System (ADS)

Zhang, X.; Tang, Q.; Liu, X.; Leng, G.; Li, Z.; Cui, H.

2015-12-01

From the fall of 2009 to the spring of 2010, an unprecedented drought swept across southwest China (SW) and led to a severe shortage in drinking water and a huge loss to regional economy. Monitoring and predicting the severe drought with several months in advance is of critical importance for such hydrological disaster assessment, preparation and mitigation. In this study, we attempted to carry out a model-based hydrological monitoring and seasonal forecasting framework, and assessed its skill in capturing the evolution of the SW drought in 2009-2010. Using the satellite-based meteorological forcings and the Variable Infiltration Capacity (VIC) hydrologic model, the drought conditions were assessed in a near-real-time manner based on a 62-year (1952-2013) retrospective simulation, wherein the satellite data was adjusted by a gauge-based forcing to remove systematic biases. Bias-corrected seasonal forecasting outputs from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2) was tentatively applied for a seasonal hydrologic prediction and its predictive skill was overall evaluated relative to a traditional Ensemble Streamflow Prediction (ESP) method with lead time varying from 1 to 6 months. The results show that the climate model-driven hydrologic predictability is generally limited to 1-month lead time and exhibits negligible skill improvement relative to ESP during this drought event, suggesting the initial hydrologic conditions (IHCs) play a dominant role in forecasting performance. The research highlights the value of the framework in providing accurate IHCs in a real-time manner which will greatly benefit drought early-warning.

Examination of Soil Moisture Retrieval Using SIR-C Radar Data and a Distributed Hydrological Model

NASA Technical Reports Server (NTRS)

Hsu, A. Y.; ONeill, P. E.; Wood, E. F.; Zion, M.

1997-01-01

A major objective of soil moisture-related hydrological-research during NASA's SIR-C/X-SAR mission was to determine and compare soil moisture patterns within humid watersheds using SAR data, ground-based measurements, and hydrologic modeling. Currently available soil moisture-inversion methods using active microwave data are only accurate when applied to bare and slightly vegetated surfaces. Moreover, as the surface dries down, the number of pixels that can provide estimated soil moisture by these radar inversion methods decreases, leading to less accuracy and, confidence in the retrieved soil moisture fields at the watershed scale. The impact of these errors in microwave- derived soil moisture on hydrological modeling of vegetated watersheds has yet to be addressed. In this study a coupled water and energy balance model operating within a topographic framework is used to predict surface soil moisture for both bare and vegetated areas. In the first model run, the hydrological model is initialized using a standard baseflow approach, while in the second model run, soil moisture values derived from SIR-C radar data are used for initialization. The results, which compare favorably with ground measurements, demonstrate the utility of combining radar-derived surface soil moisture information with basin-scale hydrological modeling.

Flood mapping in ungauged basins using fully continuous hydrologic-hydraulic modeling

NASA Astrophysics Data System (ADS)

Grimaldi, Salvatore; Petroselli, Andrea; Arcangeletti, Ettore; Nardi, Fernando

2013-04-01

SummaryIn this work, a fully-continuous hydrologic-hydraulic modeling framework for flood mapping is introduced and tested. It is characterized by a simulation of a long rainfall time series at sub-daily resolution that feeds a continuous rainfall-runoff model producing a discharge time series that is directly given as an input to a bi-dimensional hydraulic model. The main advantage of the proposed approach is to avoid the use of the design hyetograph and the design hydrograph that constitute the main source of subjective analysis and uncertainty for standard methods. The proposed procedure is optimized for small and ungauged watersheds where empirical models are commonly applied. Results of a simple real case study confirm that this experimental fully-continuous framework may pave the way for the implementation of a less subjective and potentially automated procedure for flood hazard mapping.

Generalized Likelihood Uncertainty Estimation (GLUE) Using Multi-Optimization Algorithm as Sampling Method

NASA Astrophysics Data System (ADS)

Wang, Z.

2015-12-01

For decades, distributed and lumped hydrological models have furthered our understanding of hydrological system. The development of hydrological simulation in large scale and high precision elaborated the spatial descriptions and hydrological behaviors. Meanwhile, the new trend is also followed by the increment of model complexity and number of parameters, which brings new challenges of uncertainty quantification. Generalized Likelihood Uncertainty Estimation (GLUE) has been widely used in uncertainty analysis for hydrological models referring to Monte Carlo method coupled with Bayesian estimation. However, the stochastic sampling method of prior parameters adopted by GLUE appears inefficient, especially in high dimensional parameter space. The heuristic optimization algorithms utilizing iterative evolution show better convergence speed and optimality-searching performance. In light of the features of heuristic optimization algorithms, this study adopted genetic algorithm, differential evolution, shuffled complex evolving algorithm to search the parameter space and obtain the parameter sets of large likelihoods. Based on the multi-algorithm sampling, hydrological model uncertainty analysis is conducted by the typical GLUE framework. To demonstrate the superiority of the new method, two hydrological models of different complexity are examined. The results shows the adaptive method tends to be efficient in sampling and effective in uncertainty analysis, providing an alternative path for uncertainty quantilization.

Framework for a hydrologic climate-response network in New England

USGS Publications Warehouse

Lent, Robert M.; Hodgkins, Glenn A.; Dudley, Robert W.; Schalk, Luther F.

2015-01-01

Many climate-related hydrologic variables in New England have changed in the past century, and many are expected to change during the next century. It is important to understand and monitor these changes because they can affect human water supply, hydroelectric power generation, transportation infrastructure, and stream and riparian ecology. This report describes a framework for hydrologic monitoring in New England by means of a climate-response network. The framework identifies specific inland hydrologic variables that are sensitive to climate variation; identifies geographic regions with similar hydrologic responses; proposes a fixed-station monitoring network composed of existing streamflow, groundwater, lake ice, snowpack, and meteorological data-collection stations for evaluation of hydrologic response to climate variation; and identifies streamflow basins for intensive, process-based studies and for estimates of future hydrologic conditions.

High-resolution Continental Scale Land Surface Model incorporating Land-water Management in United States

NASA Astrophysics Data System (ADS)

Shin, S.; Pokhrel, Y. N.

2016-12-01

Land surface models have been used to assess water resources sustainability under changing Earth environment and increasing human water needs. Overwhelming observational records indicate that human activities have ubiquitous and pertinent effects on the hydrologic cycle; however, they have been crudely represented in large scale land surface models. In this study, we enhance an integrated continental-scale land hydrology model named Leaf-Hydro-Flood to better represent land-water management. The model is implemented at high resolution (5km grids) over the continental US. Surface water and groundwater are withdrawn based on actual practices. Newly added irrigation, water diversion, and dam operation schemes allow better simulations of stream flows, evapotranspiration, and infiltration. Results of various hydrologic fluxes and stores from two sets of simulation (one with and the other without human activities) are compared over a range of river basin and aquifer scales. The improved simulations of land hydrology have potential to build consistent modeling framework for human-water-climate interactions.

Development of a coupled hydrological - hydrodynamic model for probabilistic catchment flood inundation modelling

NASA Astrophysics Data System (ADS)

Quinn, Niall; Freer, Jim; Coxon, Gemma; Dunne, Toby; Neal, Jeff; Bates, Paul; Sampson, Chris; Smith, Andy; Parkin, Geoff

2017-04-01

Computationally efficient flood inundation modelling systems capable of representing important hydrological and hydrodynamic flood generating processes over relatively large regions are vital for those interested in flood preparation, response, and real time forecasting. However, such systems are currently not readily available. This can be particularly important where flood predictions from intense rainfall are considered as the processes leading to flooding often involve localised, non-linear spatially connected hillslope-catchment responses. Therefore, this research introduces a novel hydrological-hydraulic modelling framework for the provision of probabilistic flood inundation predictions across catchment to regional scales that explicitly account for spatial variability in rainfall-runoff and routing processes. Approaches have been developed to automate the provision of required input datasets and estimate essential catchment characteristics from freely available, national datasets. This is an essential component of the framework as when making predictions over multiple catchments or at relatively large scales, and where data is often scarce, obtaining local information and manually incorporating it into the model quickly becomes infeasible. An extreme flooding event in the town of Morpeth, NE England, in 2008 was used as a first case study evaluation of the modelling framework introduced. The results demonstrated a high degree of prediction accuracy when comparing modelled and reconstructed event characteristics for the event, while the efficiency of the modelling approach used enabled the generation of relatively large ensembles of realisations from which uncertainty within the prediction may be represented. This research supports previous literature highlighting the importance of probabilistic forecasting, particularly during extreme events, which can be often be poorly characterised or even missed by deterministic predictions due to the inherent uncertainty in any model application. Future research will aim to further evaluate the robustness of the approaches introduced by applying the modelling framework to a variety of historical flood events across UK catchments. Furthermore, the flexibility and efficiency of the framework is ideally suited to the examination of the propagation of errors through the model which will help gain a better understanding of the dominant sources of uncertainty currently impacting flood inundation predictions.

Parameter estimation in physically-based integrated hydrological models with the ensemble Kalman filter: a practical application.

NASA Astrophysics Data System (ADS)

Botto, Anna; Camporese, Matteo

2017-04-01

Hydrological models allow scientists to predict the response of water systems under varying forcing conditions. In particular, many physically-based integrated models were recently developed in order to understand the fundamental hydrological processes occurring at the catchment scale. However, the use of this class of hydrological models is still relatively limited, as their prediction skills heavily depend on reliable parameter estimation, an operation that is never trivial, being normally affected by large uncertainty and requiring huge computational effort. The objective of this work is to test the potential of data assimilation to be used as an inverse modeling procedure for the broad class of integrated hydrological models. To pursue this goal, a Bayesian data assimilation (DA) algorithm based on a Monte Carlo approach, namely the ensemble Kalman filter (EnKF), is combined with the CATchment HYdrology (CATHY) model. In this approach, input variables (atmospheric forcing, soil parameters, initial conditions) are statistically perturbed providing an ensemble of realizations aimed at taking into account the uncertainty involved in the process. Each realization is propagated forward by the CATHY hydrological model within a parallel R framework, developed to reduce the computational effort. When measurements are available, the EnKF is used to update both the system state and soil parameters. In particular, four different assimilation scenarios are applied to test the capability of the modeling framework: first only pressure head or water content are assimilated, then, the combination of both, and finally both pressure head and water content together with the subsurface outflow. To demonstrate the effectiveness of the approach in a real-world scenario, an artificial hillslope was designed and built to provide real measurements for the DA analyses. The experimental facility, located in the Department of Civil, Environmental and Architectural Engineering of the University of Padova (Italy), consists of a reinforced concrete box containing a soil prism with maximum height of 3.5 m, length of 6 m and width of 2 m. The hillslope is equipped with six pairs of tensiometers and water content reflectometers, to monitor the pressure head and soil moisture content, respectively. Moreover, two tipping bucket flow gages were used to measure the surface and subsurface discharges at the outlet. A 12-day long experiment was carried out, during which a series of four rainfall events with constant rainfall rate were generated, interspersed with phases of drainage. During the experiment, measurements were collected at a relatively high resolution of 0.5 Hz. We report here on the capability of the data assimilation framework to estimate sets of plausible parameters that are consistent with the experimental setup.

How misapplication of the hydrologic unit framework diminishes the meaning of watersheds

USGS Publications Warehouse

Omernik, James M.; Griffith, Glenn E.; Hughes, Robert M.; Glover, James B.; Weber, Marc H.

2017-01-01

Hydrologic units provide a convenient but problematic nationwide set of geographic polygons based on subjectively determined subdivisions of land surface areas at several hierarchical levels. The problem is that it is impossible to map watersheds, basins, or catchments of relatively equal size and cover the whole country. The hydrologic unit framework is in fact composed mostly of watersheds and pieces of watersheds. The pieces include units that drain to segments of streams, remnant areas, noncontributing areas, and coastal or frontal units that can include multiple watersheds draining to an ocean or large lake. Hence, half or more of the hydrologic units are not watersheds as the name of the framework “Watershed Boundary Dataset” implies. Nonetheless, hydrologic units and watersheds are commonly treated as synonymous, and this misapplication and misunderstanding can have some serious scientific and management consequences. We discuss some of the strengths and limitations of watersheds and hydrologic units as spatial frameworks. Using examples from the Northwest and Southeast United States, we explain how the misapplication of the hydrologic unit framework has altered the meaning of watersheds and can impair understanding associations between spatial geographic characteristics and surface water conditions.

Towards robust quantification and reduction of uncertainty in hydrologic predictions: Integration of particle Markov chain Monte Carlo and factorial polynomial chaos expansion

NASA Astrophysics Data System (ADS)

Wang, S.; Huang, G. H.; Baetz, B. W.; Ancell, B. C.

2017-05-01

The particle filtering techniques have been receiving increasing attention from the hydrologic community due to its ability to properly estimate model parameters and states of nonlinear and non-Gaussian systems. To facilitate a robust quantification of uncertainty in hydrologic predictions, it is necessary to explicitly examine the forward propagation and evolution of parameter uncertainties and their interactions that affect the predictive performance. This paper presents a unified probabilistic framework that merges the strengths of particle Markov chain Monte Carlo (PMCMC) and factorial polynomial chaos expansion (FPCE) algorithms to robustly quantify and reduce uncertainties in hydrologic predictions. A Gaussian anamorphosis technique is used to establish a seamless bridge between the data assimilation using the PMCMC and the uncertainty propagation using the FPCE through a straightforward transformation of posterior distributions of model parameters. The unified probabilistic framework is applied to the Xiangxi River watershed of the Three Gorges Reservoir (TGR) region in China to demonstrate its validity and applicability. Results reveal that the degree of spatial variability of soil moisture capacity is the most identifiable model parameter with the fastest convergence through the streamflow assimilation process. The potential interaction between the spatial variability in soil moisture conditions and the maximum soil moisture capacity has the most significant effect on the performance of streamflow predictions. In addition, parameter sensitivities and interactions vary in magnitude and direction over time due to temporal and spatial dynamics of hydrologic processes.

Global optimum vegetation rain water use is determined by aridity

NASA Astrophysics Data System (ADS)

Good, S. P.; Wang, L.; Caylor, K. K.

2015-12-01

The amount of rainwater that vegetation is able to transpire directly determines the total productivity of ecosystems, yet broad-scale trends in this sub-component of total evapotranspiration remain unclear. Since development in the 1970's, the Budyko framework has provided a simple, first-order, approach to partitioning total rainfall into runoff and evapotranspiration across climates. However, this classic paradigm provides little insight into the strength of biological mediation (i.e. transpiration flux) of the hydrologic cycle. Through a minimalist stochastic hydrology model we analytically extend the classical Budyko framework to predict the magnitude of transpiration relative to total rainfall as a function of ecosystem aridity. Consistent with a synthesis of experimental partitioning studies across climates, this model suggests a peak in the biological contribution to the hydrologic cycle at intermediate moisture values, with both arid and wet climates seeing decreased transpiration:precipitation ratios. To best match observed transpiration:precipitation ratios requires incorporation of elevated evaporation at lower canopy covers due to greater energy availability at the soil surface and elevated evaporation at higher canopy covers due to greater interception amounts. This new approach provides a connection between current and future climate regimes, hydrologic flux partitioning, and macro-system ecology.

Exploring the Influence of Smallholders' Perceptions Regarding Water Availability on Crop Choice and Water Allocation Through Socio-Hydrological Modeling

NASA Astrophysics Data System (ADS)

Kuil, L.; Evans, T.; McCord, P. F.; Salinas, J. L.; Blöschl, G.

2018-04-01

While it is known that farmers adopt different decision-making behaviors to cope with stresses, it remains challenging to capture this diversity in formal model frameworks that are used to advance theory and inform policy. Guided by cognitive theory and the theory of bounded rationality, this research develops a novel, socio-hydrological model framework that can explore how a farmer's perception of water availability impacts crop choice and water allocation. The model is informed by a rich empirical data set at the household level collected during 2013 in Kenya's Upper Ewaso Ng'iro basin that shows that the crop type cultivated is correlated with water availability. The model is able to simulate this pattern and shows that near-optimal or "satisficing" crop patterns can emerge also when farmers were to make use of simple decision rules and have diverse perceptions on water availability. By focusing on farmer decision making it also captures the rebound effect, i.e., as additional water becomes available through the improvement of crop efficiencies it will be reallocated on the farm instead of flowing downstream, as a farmer will adjust his (her) water allocation and crop pattern to the new water conditions. This study is valuable as it is consistent with the theory of bounded rationality, and thus offers an alternative, descriptive model in addition to normative models. The framework can be used to understand the potential impact of climate change on the socio-hydrological system, to simulate and test various assumptions regarding farmer behavior and to evaluate policy interventions.

The integrated effects of future climate and hydrologic uncertainty on sustainable flood risk management

NASA Astrophysics Data System (ADS)

Steinschneider, S.; Wi, S.; Brown, C. M.

2013-12-01

Flood risk management performance is investigated within the context of integrated climate and hydrologic modeling uncertainty to explore system robustness. The research question investigated is whether structural and hydrologic parameterization uncertainties are significant relative to other uncertainties such as climate change when considering water resources system performance. Two hydrologic models are considered, a conceptual, lumped parameter model that preserves the water balance and a physically-based model that preserves both water and energy balances. In the conceptual model, parameter and structural uncertainties are quantified and propagated through the analysis using a Bayesian modeling framework with an innovative error model. Mean climate changes and internal climate variability are explored using an ensemble of simulations from a stochastic weather generator. The approach presented can be used to quantify the sensitivity of flood protection adequacy to different sources of uncertainty in the climate and hydrologic system, enabling the identification of robust projects that maintain adequate performance despite the uncertainties. The method is demonstrated in a case study for the Coralville Reservoir on the Iowa River, where increased flooding over the past several decades has raised questions about potential impacts of climate change on flood protection adequacy.

Spatial Modeling for Resources Framework (SMRF): A modular framework for developing spatial forcing data in mountainous terrain

NASA Astrophysics Data System (ADS)

Havens, S.; Marks, D. G.; Kormos, P.; Hedrick, A. R.; Johnson, M.; Robertson, M.; Sandusky, M.

2017-12-01

In the Western US, operational water supply managers rely on statistical techniques to forecast the volume of water left to enter the reservoirs. As the climate changes and the demand increases for stored water utilized for irrigation, flood control, power generation, and ecosystem services, water managers have begun to move from statistical techniques towards using physically based models. To assist with the transition, a new open source framework was developed, the Spatial Modeling for Resources Framework (SMRF), to automate and simplify the most common forcing data distribution methods. SMRF is computationally efficient and can be implemented for both research and operational applications. Currently, SMRF is able to generate all of the forcing data required to run physically based snow or hydrologic models at 50-100 m resolution over regions of 500-10,000 km2, and has been successfully applied in real time and historical applications for the Boise River Basin in Idaho, USA, the Tuolumne River Basin and San Joaquin in California, USA, and Reynolds Creek Experimental Watershed in Idaho, USA. These applications use meteorological station measurements and numerical weather prediction model outputs as input data. SMRF has significantly streamlined the modeling workflow, decreased model set up time from weeks to days, and made near real-time application of physics-based snow and hydrologic models possible.

Investigating the Capacity of Hydrological Models to Project Impacts of Climate Change in the Context of Water Allocation

NASA Astrophysics Data System (ADS)

Velez, Carlos; Maroy, Edith; Rocabado, Ivan; Pereira, Fernando

2017-04-01

To analyse the impacts of climate changes, hydrological models are used to project the hydrology responds under future conditions that normally differ from those for which they were calibrated. The challenge is to assess the validity of the projected effects when there is not data to validate it. A framework for testing the ability of models to project climate change was proposed by Refsgaard et al., (2014). The authors recommend the use of the differential-split sample test (DSST) in order to build confidence in the model projections. The method follow three steps: 1. A small number of sub-periods are selected according to one climate characteristics, 2. The calibration - validation test is applied on these periods, 3. The validation performances are compered to evaluate whether they vary significantly when climatic characteristics differ between calibration and validation. DSST rely on the existing records of climate and hydrological variables; and performances are estimated based on indicators of error between observed and simulated variables. Other authors suggest that, since climate models are not able to reproduce single events but rather statistical properties describing the climate, this should be reflected when testing hydrological models. Thus, performance criteria such as RMSE should be replaced by for instance flow duration curves or other distribution functions. Using this type of performance criteria, Van Steenbergen and Willems, (2012) proposed a method to test the validity of hydrological models in a climate changing context. The method is based on the evaluation of peak flow increases due to different levels of rainfall increases. In contrast to DSST, this method use the projected climate variability and it is especially useful to compare different modelling tools. In the framework of a water allocation project for the region of Flanders (Belgium) we calibrated three hydrological models: NAM, PDM and VHM; for 67 gauged sub-catchments with approx. 40 years of records. This paper investigates the capacity of the three hydrological models to project the impacts of climate change scenarios. It is proposed a general testing framework which combine the use of the existing information through an adapted form of DSST with the approach proposed by Van Steenbergen and Willems, (2012) adapted to assess statistical properties of flows useful in the context of water allocation. To assess the model we use robustness criteria based on a Log Nash-Sutcliffe, BIAS on cummulative volumes and relative changes based on Q50/Q90 estimated from the duration curve. The three conceptual rainfall-runoff models yielded different results per sub-catchments. A relation was found between robustness criteria and changes in mean rainfall and changes in mean potential evapotranspiration. Biases are greatly affected by changes in precipitation, especially when the climate scenarios involve changes in precipitation volume beyond the range used for calibration. Using the combine approach we were able to classify the modelling tools per sub-catchments and create an ensemble of best models to project the impacts of climate variability for the catchments of 10 main rivers in Flanders. Thus, managers could understand better the usability of the modelling tools and the credibility of its outputs for water allocation applications. References Refsgaard, J.C., Madsen, H., Andréassian, V., Arnbjerg-Nielsen, K., Davidson, T.A., Drews, M., Hamilton, D.P., Jeppesen, E., Kjellström, E., Olesen, J.E., Sonnenborg, T.O., Trolle, D., Willems, P., Christensen, J.H., 2014. A framework for testing the ability of models to project climate change and its impacts. Clim. Change. Van Steenbergen, N., Willems, P., 2012. Method for testing the accuracy of rainfall - runoff models in predicting peak flow changes due to rainfall changes , in a climate changing context. J. Hydrol. 415, 425-434.

Smart Frameworks and Self-Describing Models: Model Metadata for Automated Coupling of Hydrologic Process Components (Invited)

NASA Astrophysics Data System (ADS)

Peckham, S. D.

2013-12-01

Model coupling frameworks like CSDMS (Community Surface Dynamics Modeling System) and ESMF (Earth System Modeling Framework) have developed mechanisms that allow heterogeneous sets of process models to be assembled in a plug-and-play manner to create composite "system models". These mechanisms facilitate code reuse, but must simultaneously satisfy many different design criteria. They must be able to mediate or compensate for differences between the process models, such as their different programming languages, computational grids, time-stepping schemes, variable names and variable units. However, they must achieve this interoperability in a way that: (1) is noninvasive, requiring only relatively small and isolated changes to the original source code, (2) does not significantly reduce performance, (3) is not time-consuming or confusing for a model developer to implement, (4) can very easily be updated to accommodate new versions of a given process model and (5) does not shift the burden of providing model interoperability to the model developers, e.g. by requiring them to provide their output in specific forms that meet the input requirements of other models. In tackling these design challenges, model framework developers have learned that the best solution is to provide each model with a simple, standardized interface, i.e. a set of standardized functions that make the model: (1) fully-controllable by a caller (e.g. a model framework) and (2) self-describing. Model control functions are separate functions that allow a caller to initialize the model, advance the model's state variables in time and finalize the model. Model description functions allow a caller to retrieve detailed information on the model's input and output variables, its computational grid and its timestepping scheme. If the caller is a modeling framework, it can compare the answers to these queries with similar answers from other process models in a collection and then automatically call framework service components as necessary to mediate the differences between the coupled models. This talk will first review two key products of the CSDMS project, namely a standardized model interface called the Basic Model Interface (BMI) and the CSDMS Standard Names. The standard names are used in conjunction with BMI to provide a semantic matching mechanism that allows output variables from one process model to be reliably used as input variables to other process models in a collection. They include not just a standardized naming scheme for model variables, but also a standardized set of terms for describing the attributes and assumptions of a given model. To illustrate the power of standardized model interfaces and metadata, a smart, light-weight modeling framework written in Python will be introduced that can automatically (without user intervention) couple a set of BMI-enabled hydrologic process components together to create a spatial hydrologic model. The same mechanisms could also be used to provide seamless integration (import/export) of data and models.

A Smallholder Socio-hydrological Modelling Framework

NASA Astrophysics Data System (ADS)

Pande, S.; Savenije, H.; Rathore, P.

2014-12-01

Small holders are farmers who own less than 2 ha of farmland. They often have low productivity and thus remain at subsistence level. A fact that nearly 80% of Indian farmers are smallholders, who merely own a third of total farmlands and belong to the poorest quartile, but produce nearly 40% of countries foodgrains underlines the importance of understanding the socio-hydrology of a small holder. We present a framework to understand the socio-hydrological system dynamics of a small holder. It couples the dynamics of 6 main variables that are most relevant at the scale of a small holder: local storage (soil moisture and other water storage), capital, knowledge, livestock production, soil fertility and grass biomass production. The model incorporates rule-based adaptation mechanisms (for example: adjusting expenditures on food and fertilizers, selling livestocks etc.) of small holders when they face adverse socio-hydrological conditions, such as low annual rainfall, higher intra-annual variability in rainfall or variability in agricultural prices. It allows us to study sustainability of small holder farming systems under various settings. We apply the framework to understand the socio-hydrology of small holders in Aurangabad, Maharashtra, India. This district has witnessed suicides of many sugarcane farmers who could not extricate themselves out of the debt trap. These farmers lack irrigation and are susceptible to fluctuating sugar prices and intra-annual hydroclimatic variability. This presentation discusses two aspects in particular: whether government interventions to absolve the debt of farmers is enough and what is the value of investing in local storages that can buffer intra-annual variability in rainfall and strengthening the safety-nets either by creating opportunities for alternative sources of income or by crop diversification.

Quantifying water flow and retention in an unsaturated fracture-facial domain

USGS Publications Warehouse

Nimmo, John R.; Malek-Mohammadi, Siamak

2015-01-01

Hydrologically significant flow and storage of water occur in macropores and fractures that are only partially filled. To accommodate such processes in flow models, we propose a three-domain framework. Two of the domains correspond to water flow and water storage in a fracture-facial region, in addition to the third domain of matrix water. The fracture-facial region, typically within a fraction of a millimeter of the fracture wall, includes a flowing phase whose fullness is determined by the availability and flux of preferentially flowing water, and a static storage portion whose fullness is determined by the local matric potential. The flow domain can be modeled with the source-responsive preferential flow model, and the roughness-storage domain can be modeled with capillary relations applied on the fracture-facial area. The matrix domain is treated using traditional unsaturated flow theory. We tested the model with application to the hydrology of the Chalk formation in southern England, coherently linking hydrologic information including recharge estimates, streamflow, water table fluctuation, imaging by electron microscopy, and surface roughness. The quantitative consistency of the three-domain matrix-microcavity-film model with this body of diverse data supports the hypothesized distinctions and active mechanisms of the three domains and establishes the usefulness of this framework.

An Integrated Modeling Framework Forecasting Ecosystem Exposure-- A Systems Approach to the Cumulative Impacts of Multiple Stressors

NASA Astrophysics Data System (ADS)

Johnston, J. M.

2013-12-01

Freshwater habitats provide fishable, swimmable and drinkable resources and are a nexus of geophysical and biological processes. These processes in turn influence the persistence and sustainability of populations, communities and ecosystems. Climate change and landuse change encompass numerous stressors of potential exposure, including the introduction of toxic contaminants, invasive species, and disease in addition to physical drivers such as temperature and hydrologic regime. A systems approach that includes the scientific and technologic basis of assessing the health of ecosystems is needed to effectively protect human health and the environment. The Integrated Environmental Modeling Framework 'iemWatersheds' has been developed as a consistent and coherent means of forecasting the cumulative impact of co-occurring stressors. The Framework consists of three facilitating technologies: Data for Environmental Modeling (D4EM) that automates the collection and standardization of input data; the Framework for Risk Assessment of Multimedia Environmental Systems (FRAMES) that manages the flow of information between linked models; and the Supercomputer for Model Uncertainty and Sensitivity Evaluation (SuperMUSE) that provides post-processing and analysis of model outputs, including uncertainty and sensitivity analysis. Five models are linked within the Framework to provide multimedia simulation capabilities for hydrology and water quality processes: the Soil Water Assessment Tool (SWAT) predicts surface water and sediment runoff and associated contaminants; the Watershed Mercury Model (WMM) predicts mercury runoff and loading to streams; the Water quality Analysis and Simulation Program (WASP) predicts water quality within the stream channel; the Habitat Suitability Index (HSI) model scores physicochemical habitat quality for individual fish species; and the Bioaccumulation and Aquatic System Simulator (BASS) predicts fish growth, population dynamics and bioaccumulation of toxic substances. The capability of the Framework to address cumulative impacts will be demonstrated for freshwater ecosystem services and mountaintop mining.

An Efficient Approach to Modeling the Topographic Control of Surface Hydrology for Regional and Global Climate Modeling.

NASA Astrophysics Data System (ADS)

Stieglitz, Marc; Rind, David; Famiglietti, James; Rosenzweig, Cynthia

1997-01-01

The current generation of land-surface models used in GCMs view the soil column as the fundamental hydrologic unit. While this may be effective in simulating such processes as the evolution of ground temperatures and the growth/ablation of a snowpack at the soil plot scale, it effectively ignores the role topography plays in the development of soil moisture heterogeneity and the subsequent impacts of this soil moisture heterogeneity on watershed evapotranspiration and the partitioning of surface fluxes. This view also ignores the role topography plays in the timing of discharge and the partitioning of discharge into surface runoff and baseflow. In this paper an approach to land-surface modeling is presented that allows us to view the watershed as the fundamental hydrologic unit. The analytic form of TOPMODEL equations are incorporated into the soil column framework and the resulting model is used to predict the saturated fraction of the watershed and baseflow in a consistent fashion. Soil moisture heterogeneity represented by saturated lowlands subsequently impacts the partitioning of surface fluxes, including evapotranspiration and runoff. The approach is computationally efficient, allows for a greatly improved simulation of the hydrologic cycle, and is easily coupled into the existing framework of the current generation of single column land-surface models. Because this approach uses the statistics of the topography rather than the details of the topography, it is compatible with the large spatial scales of today's regional and global climate models. Five years of meteorological and hydrological data from the Sleepers River watershed located in the northeastern United States where winter snow cover is significant were used to drive the new model. Site validation data were sufficient to evaluate model performance with regard to various aspects of the watershed water balance, including snowpack growth/ablation, the spring snowmelt hydrograph, storm hydrographs, and the seasonal development of watershed evapotranspiration and soil moisture.

Scaling Stream Flow Response to Forest Disturbance: the SID Project

NASA Astrophysics Data System (ADS)

Buttle, J. M.; Beall, F. D.; Creed, I. F.; Gordon, A. M.; Mackereth, R.; McLaughlin, J. W.; Sibley, P. K.

2004-05-01

We do not have a good understanding of the hydrologic implications of forest harvesting in Ontario, either for current or alternative management approaches. Attempts to address these implications face a three-fold problem: data on hydrologic response to forest disturbance in Ontario are lacking; most studies of these responses have been in regions with forest cover and hydrologic conditions that differ from the Ontario context; and these studies have generally been conducted at relatively small scales (<1 km2). It is generally assumed that hydrologic changes induced by forest disturbance should diminish with increasing scale due to the buffering capacity of large drainage basins. Recent modeling exercises and reanalysis of paired-basin results call this widespread applicability of this assumption into question, with important implications for assessing the cumulative impacts of forest disturbance on basin stream flow. The SID (Scalable Indicators of Disturbance) project combines stream flow monitoring across basin scales with the RHESSys modeling framework to identify forest disturbance impacts on stream flow characteristics in Ontario's major forest ecozones. As a precursor to identifying stream flow response to forest disturbance, we are examining the relative control of basin geology, topography, typology and topology on stream flow characteristics under undisturbed conditions. This will assist in identifying the dominant hydrologic processes controlling basin stream flow that must be incorporated into the RHESSys model framework in order to emulate forest disturbance and its hydrologic impacts. We present preliminary results on stream flow characteristics in a low-relief boreal forest landscape, and explore how the dominant processes influencing these characteristics change with basin scale in this landscape under both reference and disturbance conditions.

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.

Improved Lower Mekong River Basin Hydrological Decision Making Using NASA Satellite-based Earth Observation Systems

NASA Astrophysics Data System (ADS)

Bolten, J. D.; Mohammed, I. N.; Srinivasan, R.; Lakshmi, V.

2017-12-01

Better understanding of the hydrological cycle of the Lower Mekong River Basin (LMRB) and addressing the value-added information of using remote sensing data on the spatial variability of soil moisture over the Mekong Basin is the objective of this work. In this work, we present the development and assessment of the LMRB (drainage area of 495,000 km2) Soil and Water Assessment Tool (SWAT). The coupled model framework presented is part of SERVIR, a joint capacity building venture between NASA and the U.S. Agency for International Development, providing state-of-the-art, satellite-based earth monitoring, imaging and mapping data, geospatial information, predictive models, and science applications to improve environmental decision-making among multiple developing nations. The developed LMRB SWAT model enables the integration of satellite-based daily gridded precipitation, air temperature, digital elevation model, soil texture, and land cover and land use data to drive SWAT model simulations over the Lower Mekong River Basin. The LMRB SWAT model driven by remote sensing climate data was calibrated and verified with observed runoff data at the watershed outlet as well as at multiple sites along the main river course. Another LMRB SWAT model set driven by in-situ climate observations was also calibrated and verified to streamflow data. Simulated soil moisture estimates from the two models were then examined and compared to a downscaled Soil Moisture Active Passive Sensor (SMAP) 36 km radiometer products. Results from this work present a framework for improving SWAT performance by utilizing a downscaled SMAP soil moisture products used for model calibration and validation. Index Terms: 1622: Earth system modeling; 1631: Land/atmosphere interactions; 1800: Hydrology; 1836 Hydrological cycles and budgets; 1840 Hydrometeorology; 1855: Remote sensing; 1866: Soil moisture; 6334: Regional Planning

Equifinality and process-based modelling

NASA Astrophysics Data System (ADS)

Khatami, S.; Peel, M. C.; Peterson, T. J.; Western, A. W.

2017-12-01

Equifinality is understood as one of the fundamental difficulties in the study of open complex systems, including catchment hydrology. A review of the hydrologic literature reveals that the term equifinality has been widely used, but in many cases inconsistently and without coherent recognition of the various facets of equifinality, which can lead to ambiguity but also methodological fallacies. Therefore, in this study we first characterise the term equifinality within the context of hydrological modelling by reviewing the genesis of the concept of equifinality and then presenting a theoretical framework. During past decades, equifinality has mainly been studied as a subset of aleatory (arising due to randomness) uncertainty and for the assessment of model parameter uncertainty. Although the connection between parameter uncertainty and equifinality is undeniable, we argue there is more to equifinality than just aleatory parameter uncertainty. That is, the importance of equifinality and epistemic uncertainty (arising due to lack of knowledge) and their implications is overlooked in our current practice of model evaluation. Equifinality and epistemic uncertainty in studying, modelling, and evaluating hydrologic processes are treated as if they can be simply discussed in (or often reduced to) probabilistic terms (as for aleatory uncertainty). The deficiencies of this approach to conceptual rainfall-runoff modelling are demonstrated for selected Australian catchments by examination of parameter and internal flux distributions and interactions within SIMHYD. On this basis, we present a new approach that expands equifinality concept beyond model parameters to inform epistemic uncertainty. The new approach potentially facilitates the identification and development of more physically plausible models and model evaluation schemes particularly within the multiple working hypotheses framework, and is generalisable to other fields of environmental modelling as well.

Strategies for Large Scale Implementation of a Multiscale, Multiprocess Integrated Hydrologic Model

NASA Astrophysics Data System (ADS)

Kumar, M.; Duffy, C.

2006-05-01

Distributed models simulate hydrologic state variables in space and time while taking into account the heterogeneities in terrain, surface, subsurface properties and meteorological forcings. Computational cost and complexity associated with these model increases with its tendency to accurately simulate the large number of interacting physical processes at fine spatio-temporal resolution in a large basin. A hydrologic model run on a coarse spatial discretization of the watershed with limited number of physical processes needs lesser computational load. But this negatively affects the accuracy of model results and restricts physical realization of the problem. So it is imperative to have an integrated modeling strategy (a) which can be universally applied at various scales in order to study the tradeoffs between computational complexity (determined by spatio- temporal resolution), accuracy and predictive uncertainty in relation to various approximations of physical processes (b) which can be applied at adaptively different spatial scales in the same domain by taking into account the local heterogeneity of topography and hydrogeologic variables c) which is flexible enough to incorporate different number and approximation of process equations depending on model purpose and computational constraint. An efficient implementation of this strategy becomes all the more important for Great Salt Lake river basin which is relatively large (~89000 sq. km) and complex in terms of hydrologic and geomorphic conditions. Also the types and the time scales of hydrologic processes which are dominant in different parts of basin are different. Part of snow melt runoff generated in the Uinta Mountains infiltrates and contributes as base flow to the Great Salt Lake over a time scale of decades to centuries. The adaptive strategy helps capture the steep topographic and climatic gradient along the Wasatch front. Here we present the aforesaid modeling strategy along with an associated hydrologic modeling framework which facilitates a seamless, computationally efficient and accurate integration of the process model with the data model. The flexibility of this framework leads to implementation of multiscale, multiresolution, adaptive refinement/de-refinement and nested modeling simulations with least computational burden. However, performing these simulations and related calibration of these models over a large basin at higher spatio- temporal resolutions is computationally intensive and requires use of increasing computing power. With the advent of parallel processing architectures, high computing performance can be achieved by parallelization of existing serial integrated-hydrologic-model code. This translates to running the same model simulation on a network of large number of processors thereby reducing the time needed to obtain solution. The paper also discusses the implementation of the integrated model on parallel processors. Also will be discussed the mapping of the problem on multi-processor environment, method to incorporate coupling between hydrologic processes using interprocessor communication models, model data structure and parallel numerical algorithms to obtain high performance.

Hydrologic Predictions in the Anthropocene: Exploration with Co-evolutionary Socio-hydrologic Models

NASA Astrophysics Data System (ADS)

Sivapalan, Murugesu; Tian, Fuqiang; Liu, Dengfeng

2013-04-01

Socio-hydrology studies the co-evolution and self-organization of humans in the hydrologic landscape, which requires a thorough understanding of the complex interactions between humans and water. On the one hand, the nature of water availability greatly impacts the development of society. On the other hand, humans can significantly alter the spatio-temporal distribution of water and in this way provide feedback to the society itself. The human-water system functions underlying such complex human-water interactions are not well understood. Exploratory models with the appropriate level of simplification in any given area can be valuable to understand these functions and the self-organization associated with socio-hydrology. In this study, a simple coupled modeling framework for socio-hydrology co-evolution is developed, and is used to illustrate the explanatory power of such models. In the Tarim River, humans depend heavily on agricultural production (other industries can be ignored for a start), and the social processes can be described principally by two variables, i.e., irrigated-area and human population. The eco-hydrological processes are expressed in terms of area under natural vegetation and stream discharge. The study area is the middle and the lower reaches of the Tarim River, which is divided into two modeling units, i.e. middle reach and lower reach. In each modeling unit, four ordinary differential equations are used to simulate the dynamics of the hydrological system represented by stream discharge, ecological system represented by area under natural vegetation, the economic system represented by irrigated area under agriculture and social system represented by human population. The four dominant variables are coupled together by several internal variables. For example, the stream discharge is coupled to irrigated area by the colonization rate and mortality rate of the irrigated area in the middle reach and the irrigated area is coupled to stream discharge by water used for irrigation. In a similar way, the stream discharge and natural vegetation are coupled together. The irrigated area is coupled to population by the colonization rate and mortality rate of the population. The discharge of the lower reach is determined by the discharge from the middle reach. The natural vegetation area in the lower reach is coupled to the discharge in the middle reach by water resources management policy. The co-evolution of the Tarim socio-hydrological system is then analyzed within this modeling framework to gain insights into the overall system dynamics and sensitivity to the external drivers and internal system variables.

Model output: fact or artefact?

NASA Astrophysics Data System (ADS)

Melsen, Lieke

2015-04-01

As a third-year PhD-student, I relatively recently entered the wonderful world of scientific Hydrology. A science that has many pillars that directly impact society, for example with the prediction of hydrological extremes (both floods and drought), climate change, applications in agriculture, nature conservation, drinking water supply, etcetera. Despite its demonstrable societal relevance, hydrology is often seen as a science between two stools. Like Klemeš (1986) stated: "By their academic background, hydrologists are foresters, geographers, electrical engineers, geologists, system analysts, physicists, mathematicians, botanists, and most often civil engineers." Sometimes it seems that the engineering genes are still present in current hydrological sciences, and this results in pragmatic rather than scientific approaches for some of the current problems and challenges we have in hydrology. Here, I refer to the uncertainty in hydrological modelling that is often neglected. For over thirty years, uncertainty in hydrological models has been extensively discussed and studied. But it is not difficult to find peer-reviewed articles in which it is implicitly assumed that model simulations represent the truth rather than a conceptualization of reality. For instance in trend studies, where data is extrapolated 100 years ahead. Of course one can use different forcing datasets to estimate the uncertainty of the input data, but how to prevent that the output is not a model artefact, caused by the model structure? Or how about impact studies, e.g. of a dam impacting river flow. Measurements are often available for the period after dam construction, so models are used to simulate river flow before dam construction. Both are compared in order to qualify the effect of the dam. But on what basis can we tell that the model tells us the truth? Model validation is common nowadays, but validation only (comparing observations with model output) is not sufficient to assume that a model reflects reality. E.g. due to nonuniqueness or so called equifinality; different model construction lead to same output (Oreskes et al., 1994, Beven, 2005). But also because validation only does not provide us information on whether we are 'right for the wrong reasons' (Kirchner, 2006; Oreskes et al., 1994). We can never know how right or wrong our models are, because we do not fully understand reality. But we can estimate the uncertainty from the model and the input data itself. Many techniques have been developed that help in estimating model uncertainty. E.g. model structural uncertainty, studied in the FUSE framework (Clark et al., 2008), parameter uncertainty with GLUE (Beven and Binley, 1992) and DREAM (Vrugt et al., 2008), input data uncertainty using BATEA (Kavetski et al., 2006). These are just some examples that pop-up in a first search. But somehow, these techniques are only used and applied in studies that focus on the model uncertainty itself, and hardly ever occur in studies that have a research question outside of the uncertainty-region. We know that models don't tell us the truth, but we have the tendency to claim they are, based on validation only. A model is always a simplification of reality, which by definition leads to uncertainty when model output and observations of reality are compared. The least we could do is estimate the uncertainty of the model and the data itself. My question therefore is: As a scientist, can we accept that we believe things of which we know they might not be true? And secondly: How to deal with this? How should model uncertainty change the way we communicate scientific results? References Beven, K., and A. Binley, The future of distributed models: Model calibration and uncertainty prediction, HP 6 (1992). Beven, K., A manifesto for the equifinality thesis, JoH 320 (2006). Clark, M.P., A.G. Slater, D.E. Rupp, R.A. Woods, J.A. Vrugt, H.V. Gupta, T. Wagener and L.E. Hay, Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models, WRR 44 (2008). Kavetski, D., G. Kuczera and S.W. Franks, Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory, WRR 42 (2006). Kirchner, J.W., Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology, WRR 42 (2006). Klemeš, V., Dilettantism in Hydrology: Transition or Destiny?, WRR 22-9 (1986). Oreskes, N., K. Shrader-Frechette, and K. Belitz, Verification, Validation and Confirmation of Numerical Models in Earth Sciences, SCIENCE 263 (1994). Vrugt, J.A., C.J.F. ter Braak, M.P. Clar, J.M. Hyman, and B.A. Robinson, Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation, WRR 44, (2008).

How Misapplication of the Hydrologic Unit Framework ...

EPA Pesticide Factsheets

Hydrologic units provide a convenient nationwide set of geographic polygons based on an arbitrary subdivision of the drainage of land surface areas at several hierarchical levels. Half or more of these units, however, are not true watersheds as the official name of the framework, Watershed Boundary Dataset (WBD), implies. Hydrologic units and watersheds are commonly treated as synonymous, and this misuse and misunderstanding can have some serious consequences. We discuss some of the strengths and limitations of watersheds and hydrologic units as spatial frameworks. Using examples from the Northwest and Southeast U.S., we explain how the misuse of the hydrologic unit framework has affected the meaning of watersheds and can impair the understanding of the associations of spatial geographic phenomena relative to a potentially infinite number of points on streams due to their linear nature. Watersheds are a fundamental geographic unit used to study the effects of natural and anthropogenic characteristics on the quality and quantity of water. Most scientists and resource managers historically have been in agreement on the spatial meaning of the term ‘watershed’ – that is, the topographic area within which water drains to a specific point on a stream, river, or particular waterbody. The Hydrologic Unit Code (HUC) framework, however, has changed this understanding. Hydrologic units provide a convenient nationwide set of geographic polygons based on an arbitra

Street Level Hydrology: An Urban Application of the WRF-Hydro Framework in Denver, Colorado

NASA Astrophysics Data System (ADS)

Read, L.; Hogue, T. S.; Salas, F. R.; Gochis, D.

2015-12-01

Urban flood modeling at the watershed scale carries unique challenges in routing complexity, data resolution, social and political issues, and land surface - infrastructure interactions. The ability to accurately trace and predict the flow of water through the urban landscape enables better emergency response management, floodplain mapping, and data for future urban infrastructure planning and development. These services are of growing importance as urban population is expected to continue increasing by 1.84% per year for the next 25 years, increasing the vulnerability of urban regions to damages and loss of life from floods. Although a range of watershed-scale models have been applied in specific urban areas to examine these issues, there is a trend towards national scale hydrologic modeling enabled by supercomputing resources to understand larger system-wide hydrologic impacts and feedbacks. As such it is important to address how urban landscapes can be represented in large scale modeling processes. The current project investigates how coupling terrain and infrastructure routing can improve flow prediction and flooding events over the urban landscape. We utilize the WRF-Hydro modeling framework and a high-resolution terrain routing grid with the goal of compiling standard data needs necessary for fine scale urban modeling and dynamic flood forecasting in the urban setting. The city of Denver is selected as a case study, as it has experienced several large flooding events in the last five years and has an urban annual population growth rate of 1.5%, one of the highest in the U.S. Our work highlights the hydro-informatic challenges associated with linking channel networks and drainage infrastructure in an urban area using the WRF-Hydro modeling framework and high resolution urban models for short-term flood prediction.

Three-dimensional hydrogeologic framework model of the Rio Grande transboundary region of New Mexico and Texas, USA, and northern Chihuahua, Mexico

USGS Publications Warehouse

Sweetkind, Donald S.

2017-09-08

As part of a U.S. Geological Survey study in cooperation with the Bureau of Reclamation, a digital three-dimensional hydrogeologic framework model was constructed for the Rio Grande transboundary region of New Mexico and Texas, USA, and northern Chihuahua, Mexico. This model was constructed to define the aquifer system geometry and subsurface lithologic characteristics and distribution for use in a regional numerical hydrologic model. The model includes five hydrostratigraphic units: river channel alluvium, three informal subdivisions of Santa Fe Group basin fill, and an undivided pre-Santa Fe Group bedrock unit. Model input data were compiled from published cross sections, well data, structure contour maps, selected geophysical data, and contiguous compilations of surficial geology and structural features in the study area. These data were used to construct faulted surfaces that represent the upper and lower subsurface hydrostratigraphic unit boundaries. The digital three-dimensional hydrogeologic framework model is constructed through combining faults, the elevation of the tops of each hydrostratigraphic unit, and boundary lines depicting the subsurface extent of each hydrostratigraphic unit. The framework also compiles a digital representation of the distribution of sedimentary facies within each hydrostratigraphic unit. The digital three-dimensional hydrogeologic model reproduces with reasonable accuracy the previously published subsurface hydrogeologic conceptualization of the aquifer system and represents the large-scale geometry of the subsurface aquifers. The model is at a scale and resolution appropriate for use as the foundation for a numerical hydrologic model of the study area.

Advancing Data Assimilation in Operational Hydrologic Forecasting: Progresses, Challenges, and Emerging Opportunities

NASA Technical Reports Server (NTRS)

Liu, Yuqiong; Weerts, A.; Clark, M.; Hendricks Franssen, H.-J; Kumar, S.; Moradkhani, H.; Seo, D.-J.; Schwanenberg, D.; Smith, P.; van Dijk, A. I. J. M.;

GLOFRIM - A globally applicable framework for integrated hydrologic-hydrodynamic inundation modelling

NASA Astrophysics Data System (ADS)

Hoch, J. M.; Neal, J. C.; Baart, F.; Van Beek, L. P.; Winsemius, H.; Bates, P. D.; Bierkens, M. F.

2017-12-01

Currently, many approaches to provide detailed flood hazard and risk estimates are built upon specific hydrologic or hydrodynamic model routines. By applying these routines in stand-alone mode important processes can however not accurately be described. For instance, global hydrologic models run at coarse spatial resolution, not supporting the detailed simulation of flood hazard. Hydrodynamic models excel in the computations of open water flow dynamics, but dependent on specific runoff or observed discharge as input. In most cases hydrodynamic models are forced at the boundaries and thus cannot account for water sources within the model domain, limiting the simulation of inundation dynamics to reaches fed by upstream boundaries. Recently, Hoch et al. (HESS, 2017) coupled PCR-GLOBWB (PCR) with the hydrodynamic model Delft3D Flexible Mesh (DFM). By means of the Basic Model Interface both models were connected on a cell-by-cell basis, allowing for spatially explicit coupling. Model results showed that discharge simulations can profit from model coupling compared to stand-alone runs. As model results of a coupled simulation depend on the quality of the models, it would be worthwhile to allow a suite of models to be coupled. To facilitate this, we present GLOFRIM, a globally applicable framework for integrated hydrologic-hydrodynamic inundation modelling. In the current version coupling between PCR and both DFM and LISFLOOD-FP (LFP) can be established (Hoch et al., GMDD, 2017). First results show that differences between both hydrodynamic models are present in the timing of peak discharge which is most likely due to differences in channel-floodplain interactions and bathymetry processing. Having benchmarked inundation extent, LFP and DFM agree for around half of the inundated area which is attributable to variations in grid size. Results also indicate that, despite using identical boundary conditions and forcing, the schematization itself as well as internal processes can still greatly influence results. In general, the application of GLOFRIM brings several advantages. For example, with PCR being a global model, it is possible to reduce the dependency of observation data for discharge boundaries, and benchmarking of hydrodynamic models is greatly facilitated by employing identical hydrologic forcing.

Integration of a three-dimensional process-based hydrological model into the Object Modeling System

USDA-ARS?s Scientific Manuscript database

The integration of a spatial process model into an environmental modelling framework can enhance the model’s capabilities. We present the integration of the GEOtop model into the Object Modeling System (OMS) version 3.0 and illustrate its application in a small watershed. GEOtop is a physically base...

Informing a hydrological model of the Ogooué with multi-mission remote sensing data

NASA Astrophysics Data System (ADS)

Kittel, Cecile; Bauer-Gottwein, Peter; Nielsen, Karina; Tøttrup, Christian

2017-04-01

Knowledge on hydrological regimes of river basins is crucial for water management. However, data requirements often limit the applicability of hydrological models in basins with scarce in-situ data. Remote sensing provides a unique possibility to acquire information on hydrological variables in these basins. This study explores how multi-mission remote sensing data can inform a hydrological model. The Ogooué basin in Gabon is used as study area. No previous modelling efforts have been conducted for the basin and only historical flow and precipitation observations are available. Publicly available remote sensing observations are used to parametrize, force, calibrate and validate a hydrological model of the Ogooué. The modelling framework used in the study, is a lumped conceptual rainfall-runoff model based on the Budyko framework coupled to a Muskingum routing scheme. Precipitation is a crucial driver of the land-surface water balance, therefore two satellite-based rainfall estimates, Tropical Rainfall Measuring Mission (TRMM) product 3B42 version 7 and Famine Early Warning System - Rainfall Estimate (FEWS-RFE), are compared. The comparison shows good seasonal and spatial agreement between the products; however, TRMM consistently predicts significantly more precipitation: 1726 mm on average per year against 1556 mm for FEWS-RFE. Best modeling results are obtained with the TRMM precipitation forcing. Model calibration combines historical in-situ flow observations and GRACE total water storage observations using the Jet Propulsion Laboratory (JPL) mascon solution in a multi-objective approach. The two models are calibrated using flow duration curves and climatology benchmarks to overcome the lack of simultaneity between simulated and observed discharge. The objectives are aggregated into a global objective function, and the models are calibrated using the Shuffled Complex Evolution Algorithm. Water height observations from drifting orbit altimetry missions are extracted along the river line, using a detailed water mask based on Sentinel-1 SAR imagery. 1399 single CryoSat-2 altimetry observations and 48 ICESat observations are acquired. Additionally, water heights have been measured by the repeat-orbit satellite missions Envisat and Jason-2 at 12 virtual stations along the river. The four missions show generally good agreement in terms of mean annual water height amplitudes. The altimetry observations are used to validate the hydrological model of the Ogooué River. By combining hydrological modelling and remote sensing, new information on an otherwise unstudied basin is obtained. The study shows the potential of using remote sensing observations to parameterize, force, calibrate and validate models of poorly gauged river basins. Specifically, the study shows how Sentinel-1 SAR imagery supports the extraction of satellite altimetry data over rivers. The model can be used to assess climate change scenarios, evaluate hydraulic infrastructure development projects and predict the impact of irrigation diversions.

A first computational framework for integrated hydrologic-hydrodynamic inundation modelling

NASA Astrophysics Data System (ADS)

Hoch, Jannis; Baart, Fedor; Neal, Jeffrey; van Beek, Rens; Winsemius, Hessel; Bates, Paul; Bierkens, Marc

2017-04-01

To provide detailed flood hazard and risk estimates for current and future conditions, advanced modelling approaches are required. Currently, many approaches are however built upon specific hydrologic or hydrodynamic model routines. By applying these routines in stand-alone mode important processes cannot accurately be described. For instance, global hydrologic models (GHM) run at coarse spatial resolution which does not identify locally relevant flood hazard information. Moreover, hydrologic models generally focus on correct computations of water balances, but employ less sophisticated routing schemes such as the kinematic wave approximation. Hydrodynamic models, on the other side, excel in the computations of open water flow dynamics, but are highly dependent on specific runoff or observed discharge for their input. In most cases hydrodynamic models are forced by applying discharge at the boundaries and thus cannot account for water sources within the model domain. Thus, discharge and inundation dynamics at reaches not fed by upstream boundaries cannot be modelled. In a recent study, Hoch et al. (HESS, 2017) coupled the GHM PCR-GLOBWB with the hydrodynamic model Delft3D Flexible Mesh. A core element of this study was that both models were connected on a cell-by-cell basis which allows for direct hydrologic forcing within the hydrodynamic model domain. The means for such model coupling is the Basic Model Interface (BMI) which provides a set of functions to directly access model variables. Model results showed that discharge simulations can profit from model coupling as their accuracy is higher compared to stand-alone runs. Model results of a coupled simulation clearly depend on the quality of the individual models. Depending on purpose, location or simply the models at hand, it would be worthwhile to allow a wider range of models to be coupled. As a first step, we present a framework which allows coupling of PCR-GLOBWB to both Delft3D Flexible Mesh and LISFLOOD-FP. The coupling framework consists of a main script and a set of functions performing the actual model coupling as well as data processing. All that is required therefore are model schematizations of the models involved for the domain of interest. It is noteworthy that no adaptions to already existing schematizations have to be made. Within the framework, it is possible to distribute input volume from PCR-GLOBWB over the 2D hydrodynamic grid ("2D option"), or if available, directly into the 1D channels ("1D option"). Besides, it is possible to input the water volumes into the hydrodynamic models either as fluxes or states. With PCR-GLOBWB being a global model, it is possible to apply the coupling scheme anywhere, which reduces the dependency of observation data for discharge boundaries. Reducing this dependency is of particular benefit for areas where only a limited number of accurate measurements are available. First results of applying the coupling framework show that differences between both hydrodynamic models are mainly apparent in the timing of peak discharge when using the 1D option. Regarding inundation extent, applying LISFLOOD-FP with a regular grid outperforms the flexible mesh of Delft3D for those areas where a coarser spatial resolution is used in the flexible mesh. When using the 2D option, however, using Delft3D Flexible Mesh is more robust than LISFLOOD-FP due to the differences in the solver used in the models. With Delft3D Flexible Mesh solving the full Saint-Vernant equations, and LISFLOOD-FP solving the local inertial wave approximation which lacks the convective acceleration term, the framework hence allows for choosing the hydrodynamic parts based on the local characteristics of a chosen study area.

Planning and design of studies for river-quality assessment in the Truckee and Carson River basins, California and Nevada

USGS Publications Warehouse

Nowlin, Jon O.; Brown, W.M.; Smith, L.H.; Hoffman, R.J.

1980-01-01

The objectives of the Geological Survey 's river-quality assessment in the Truckee and Carson River basins in California and Nevada are to identify the significant resource management problems; to develop techniques to assess the problems; and to effectively communicate results to responsible managers. Six major elements of the assessment to be completed by October 1981 are (1) a detailing of the legal, institutional, and structural development of water resources in the basins and the current problems and conflicts; (2) a compilation and synthesis of the physical hydrology of the basins; (3) development of a special workshop approach to involve local management in the direction and results of the study; (4) development of a comprehensive streamflow model emcompassing both basins to provide a quantitative hydrologic framework for water-quality analysis; (5) development of a water-quality transport model for selected constituents and characteristics on selected reaches of the Truckee River; and (6) a detailed examination of selected fish habitats for specified reaches of the Truckee River. Progress will be periodically reported in reports, maps, computer data files, mathematical models, a bibliography, and public presentations. In building a basic framework to develop techniques, the basins were viewed as a single hydrologic unit because of interconnecting diversion structures. The framework comprises 13 hydrographic subunits to facilitate modeling and sampling. Several significant issues beyond the scope of the assessment were considered as supplementary proposals; water-quality loadings in Truckee and Carson Rivers, urban runoff in Reno and management alternatives, and a model of limnological processes in Lahontan Reservoir. (USGS)

A framework for global river flood risk assessments

NASA Astrophysics Data System (ADS)

Winsemius, H. C.; Van Beek, L. P. H.; Jongman, B.; Ward, P. J.; Bouwman, A.

2012-08-01

There is an increasing need for strategic global assessments of flood risks in current and future conditions. In this paper, we propose a framework for global flood risk assessment for river floods, which can be applied in current conditions, as well as in future conditions due to climate and socio-economic changes. The framework's goal is to establish flood hazard and impact estimates at a high enough resolution to allow for their combination into a risk estimate. The framework estimates hazard at high resolution (~1 km2) using global forcing datasets of the current (or in scenario mode, future) climate, a global hydrological model, a global flood routing model, and importantly, a flood extent downscaling routine. The second component of the framework combines hazard with flood impact models at the same resolution (e.g. damage, affected GDP, and affected population) to establish indicators for flood risk (e.g. annual expected damage, affected GDP, and affected population). The framework has been applied using the global hydrological model PCR-GLOBWB, which includes an optional global flood routing model DynRout, combined with scenarios from the Integrated Model to Assess the Global Environment (IMAGE). We performed downscaling of the hazard probability distributions to 1 km2 resolution with a new downscaling algorithm, applied on Bangladesh as a first case-study application area. We demonstrate the risk assessment approach in Bangladesh based on GDP per capita data, population, and land use maps for 2010 and 2050. Validation of the hazard and damage estimates has been performed using the Dartmouth Flood Observatory database and damage estimates from the EM-DAT database and World Bank sources. We discuss and show sensitivities of the estimated risks with regard to the use of different climate input sets, decisions made in the downscaling algorithm, and different approaches to establish impact models.

A climate robust integrated modelling framework for regional impact assessment of climate change

NASA Astrophysics Data System (ADS)

Janssen, Gijs; Bakker, Alexander; van Ek, Remco; Groot, Annemarie; Kroes, Joop; Kuiper, Marijn; Schipper, Peter; van Walsum, Paul; Wamelink, Wieger; Mol, Janet

2013-04-01

Decision making towards climate proofing the water management of regional catchments can benefit greatly from the availability of a climate robust integrated modelling framework, capable of a consistent assessment of climate change impacts on the various interests present in the catchments. In the Netherlands, much effort has been devoted to developing state-of-the-art regional dynamic groundwater models with a very high spatial resolution (25x25 m2). Still, these models are not completely satisfactory to decision makers because the modelling concepts do not take into account feedbacks between meteorology, vegetation/crop growth, and hydrology. This introduces uncertainties in forecasting the effects of climate change on groundwater, surface water, agricultural yields, and development of groundwater dependent terrestrial ecosystems. These uncertainties add to the uncertainties about the predictions on climate change itself. In order to create an integrated, climate robust modelling framework, we coupled existing model codes on hydrology, agriculture and nature that are currently in use at the different research institutes in the Netherlands. The modelling framework consists of the model codes MODFLOW (groundwater flow), MetaSWAP (vadose zone), WOFOST (crop growth), SMART2-SUMO2 (soil-vegetation) and NTM3 (nature valuation). MODFLOW, MetaSWAP and WOFOST are coupled online (i.e. exchange information on time step basis). Thus, changes in meteorology and CO2-concentrations affect crop growth and feedbacks between crop growth, vadose zone water movement and groundwater recharge are accounted for. The model chain WOFOST-MetaSWAP-MODFLOW generates hydrological input for the ecological prediction model combination SMART2-SUMO2-NTM3. The modelling framework was used to support the regional water management decision making process in the 267 km2 Baakse Beek-Veengoot catchment in the east of the Netherlands. Computations were performed for regionalized 30-year climate change scenarios developed by KNMI for precipitation and reference evapotranspiration according to Penman-Monteith. Special focus in the project was on the role of uncertainty. How valid is the information that is generated by this modelling framework? What are the most important uncertainties of the input data, how do they affect the results of the model chain and how can the uncertainties of the data, results, and model concepts be quantified and communicated? Besides these technical issues, an important part of the study was devoted to the perception of stakeholders. Stakeholder analysis and additional working sessions yielded insight into how the models, their results and the uncertainties are perceived, how the modelling framework and results connect to the stakeholders' information demands and what kind of additional information is needed for adequate support on decision making.

Inspiring a Broader Socio-Hydrological Negotiation Approach With Interdisciplinary Field-Based Experience

NASA Astrophysics Data System (ADS)

Massuel, S.; Riaux, J.; Molle, F.; Kuper, M.; Ogilvie, A.; Collard, A.-L.; Leduc, C.; Barreteau, O.

2018-04-01

Socio-hydrology advanced the field of hydrology by considering humans and their activities as part of the water cycle, rather than as external drivers. Models are used to infer reproducible trends in human interactions with water resources. However, defining and handling water problems in this way may restrict the scope of such modeling approaches. We propose an interdisciplinary socio-hydrological approach to overcome this limit and complement modeling approaches. It starts from concrete field-based situations, combines disciplinary as well as local knowledge on water-society relationships, with the aim of broadening the hydrocentric analysis and modeling of water systems. The paper argues that an analysis of social dynamics linked to water is highly complementary to traditional hydrological tools but requires a negotiated and contextualized interdisciplinary approach to the representation and analysis of socio-hydro systems. This reflection emerged from experience gained in the field where a water-budget modeling framework failed to adequately incorporate the multiplicity of (nonhydrological) factors that determine the volumes of withdrawals for irrigation. The pathway subsequently explored was to move away from the hydrologic view of the phenomena and, in collaboration with social scientists, to produce a shared conceptualization of a coupled human-water system through a negotiated approach. This approach changed the way hydrological research issues were addressed and limited the number of strong assumptions needed for simplification in modeling. The proposed socio-hydrological approach led to a deeper understanding of the mechanisms behind local water-related problems and to debates on the interactions between social and political decisions and the dynamics of these problems.

VIC-CropSyst-v2: A regional-scale modeling platform to simulate the nexus of climate, hydrology, cropping systems, and human decisions

NASA Astrophysics Data System (ADS)

Malek, Keyvan; Stöckle, Claudio; Chinnayakanahalli, Kiran; Nelson, Roger; Liu, Mingliang; Rajagopalan, Kirti; Barik, Muhammad; Adam, Jennifer C.

2017-08-01

Food supply is affected by a complex nexus of land, atmosphere, and human processes, including short- and long-term stressors (e.g., drought and climate change, respectively). A simulation platform that captures these complex elements can be used to inform policy and best management practices to promote sustainable agriculture. We have developed a tightly coupled framework using the macroscale variable infiltration capacity (VIC) hydrologic model and the CropSyst agricultural model. A mechanistic irrigation module was also developed for inclusion in this framework. Because VIC-CropSyst combines two widely used and mechanistic models (for crop phenology, growth, management, and macroscale hydrology), it can provide realistic and hydrologically consistent simulations of water availability, crop water requirements for irrigation, and agricultural productivity for both irrigated and dryland systems. This allows VIC-CropSyst to provide managers and decision makers with reliable information on regional water stresses and their impacts on food production. Additionally, VIC-CropSyst is being used in conjunction with socioeconomic models, river system models, and atmospheric models to simulate feedback processes between regional water availability, agricultural water management decisions, and land-atmosphere interactions. The performance of VIC-CropSyst was evaluated on both regional (over the US Pacific Northwest) and point scales. Point-scale evaluation involved using two flux tower sites located in agricultural fields in the US (Nebraska and Illinois). The agreement between recorded and simulated evapotranspiration (ET), applied irrigation water, soil moisture, leaf area index (LAI), and yield indicated that, although the model is intended to work on regional scales, it also captures field-scale processes in agricultural areas.

Integrating an agent-based model into a large-scale hydrological model for evaluating drought management in California

NASA Astrophysics Data System (ADS)

Sheffield, J.; He, X.; Wada, Y.; Burek, P.; Kahil, M.; Wood, E. F.; Oppenheimer, M.

2017-12-01

California has endured record-breaking drought since winter 2011 and will likely experience more severe and persistent drought in the coming decades under changing climate. At the same time, human water management practices can also affect drought frequency and intensity, which underscores the importance of human behaviour in effective drought adaptation and mitigation. Currently, although a few large-scale hydrological and water resources models (e.g., PCR-GLOBWB) consider human water use and management practices (e.g., irrigation, reservoir operation, groundwater pumping), none of them includes the dynamic feedback between local human behaviors/decisions and the natural hydrological system. It is, therefore, vital to integrate social and behavioral dimensions into current hydrological modeling frameworks. This study applies the agent-based modeling (ABM) approach and couples it with a large-scale hydrological model (i.e., Community Water Model, CWatM) in order to have a balanced representation of social, environmental and economic factors and a more realistic representation of the bi-directional interactions and feedbacks in coupled human and natural systems. In this study, we focus on drought management in California and considers two types of agents, which are (groups of) farmers and state management authorities, and assumed that their corresponding objectives are to maximize the net crop profit and to maintain sufficient water supply, respectively. Farmers' behaviors are linked with local agricultural practices such as cropping patterns and deficit irrigation. More precisely, farmers' decisions are incorporated into CWatM across different time scales in terms of daily irrigation amount, seasonal/annual decisions on crop types and irrigated area as well as the long-term investment of irrigation infrastructure. This simulation-based optimization framework is further applied by performing different sets of scenarios to investigate and evaluate the effectiveness of different water management strategies and how policy interventions will facilitate drought adaptation in California.

Development and Implementation of the DTOPLATS-MP land surface model over the Continental US at 30 meters

NASA Astrophysics Data System (ADS)

Chaney, N.; Wood, E. F.

2014-12-01

The increasing accessibility of high-resolution land data (< 100 m) and high performance computing allows improved parameterizations of subgrid hydrologic processes in macroscale land surface models. Continental scale fully distributed modeling at these spatial scales is possible; however, its practicality for operational use is still unknown due to uncertainties in input data, model parameters, and storage requirements. To address these concerns, we propose a modeling framework that provides the spatial detail of a fully distributed model yet maintains the benefits of a semi-distributed model. In this presentation we will introduce DTOPLATS-MP, a coupling between the NOAH-MP land surface model and the Dynamic TOPMODEL hydrologic model. This new model captures a catchment's spatial heterogeneity by clustering high-resolution land datasets (soil, topography, and land cover) into hundreds of hydrologic similar units (HSUs). A prior DEM analysis defines the connections between each HSU. At each time step, the 1D land surface model updates each HSU; the HSUs then interact laterally via the subsurface and surface. When compared to the fully distributed form of the model, this framework allows a significant decrease in computation and storage while providing most of the same information and enabling parameter transferability. As a proof of concept, we will show how this new modeling framework can be run over CONUS at a 30-meter spatial resolution. For each catchment in the WBD HUC-12 dataset, the model is run between 2002 and 2012 using available high-resolution continental scale land and meteorological datasets over CONUS (dSSURGO, NLCD, NED, and NCEP Stage IV). For each catchment, the model is run with 1000 model parameter sets obtained from a Latin hypercube sample. This exercise will illustrate the feasibility of running the model operationally at continental scales while accounting for model parameter uncertainty.

The road to NHDPlus — Advancements in digital stream networks and associated catchments

USGS Publications Warehouse

Moore, Richard B.; Dewald, Thomas A.

2016-01-01

A progression of advancements in Geographic Information Systems techniques for hydrologic network and associated catchment delineation has led to the production of the National Hydrography Dataset Plus (NHDPlus). NHDPlus is a digital stream network for hydrologic modeling with catchments and a suite of related geospatial data. Digital stream networks with associated catchments provide a geospatial framework for linking and integrating water-related data. Advancements in the development of NHDPlus are expected to continue to improve the capabilities of this national geospatial hydrologic framework. NHDPlus is built upon the medium-resolution NHD and, like NHD, was developed by the U.S. Environmental Protection Agency and U.S. Geological Survey to support the estimation of streamflow and stream velocity used in fate-and-transport modeling. Catchments included with NHDPlus were created by integrating vector information from the NHD and from the Watershed Boundary Dataset with the gridded land surface elevation as represented by the National Elevation Dataset. NHDPlus is an actively used and continually improved dataset. Users recognize the importance of a reliable stream network and associated catchments. The NHDPlus spatial features and associated data tables will continue to be improved to support regional water quality and streamflow models and other user-defined applications.

Developing a Three Processes Framework to Analyze Hydrologic Performance of Urban Stormwater Management in a Watershed Scale

NASA Astrophysics Data System (ADS)

Lyu, H.; Ni, G.; Sun, T.

2016-12-01

Urban stormwater management contributes to recover water cycle to a nearly natural situation. It is a challenge for analyzing the hydrologic performance in a watershed scale, since the measures are various of sorts and scales and work in different processes. A three processes framework is developed to simplify the urban hydrologic process on the surface and evaluate the urban stormwater management. The three processes include source utilization, transfer regulation and terminal detention, by which the stormwater is controlled in order or discharged. Methods for analyzing performance are based on the water controlled proportions by each process, which are calculated using USEPA Stormwater Management Model. A case study form Beijing is used to illustrate how the performance varies under a set of designed events of different return periods. This framework provides a method to assess urban stormwater management as a whole system considering the interaction between measures, and to examine if there is any weak process of an urban watershed to be improved. The results help to make better solutions of urban water crisis.

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EPA Pesticide Factsheets

NHDPlus is a geospatial, hydrologic framework dataset that is intended for use by geospatial analysts and modelers to support water resources related applications. NHDPlus was developed by the USEPA in partnership with the US Geologic Survey

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EPA Pesticide Factsheets

NHDPlus is a geospatial, hydrologic framework dataset that is intended for use by geospatial analysts and modelers to support water resources related applications. NHDPlus was developed by the USEPA in partnership with the US Geologic Survey

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EPA Pesticide Factsheets

NHDPlus is a geospatial, hydrologic framework dataset that is intended for use by geospatial analysts and modelers to support water resources related applications. NHDPlus was developed by the USEPA in partnership with the US Geologic Survey

Assessment of the water balance over France using regionalized Turc-Pike formula

NASA Astrophysics Data System (ADS)

Le Lay, Matthieu; Garçon, Rémy; Gailhard, Joël; Garavaglia, Federico

2016-04-01

With extensive use of hydrological models over a wide range of hydro-climatic contexts, bias in hydro-climatic data may lead to unreliable models and thus hydrological forecasts and projections. This issue is particularly pregnant when considering mountainous areas with great uncertainties on precipitations, or when considering complex unconservative catchments (e.g. karstic systems). The Turc-Pike water balance formula, analogous to the classical Budyko formula, is a simple and efficient mathematical formulation relating long-term average streamflow to long-term average precipitation and potential evaporation. In this study, we propose to apply this framework to assess and eventually adjust the water-balance before calibrating an operational hydrologic model (MORDOR model). Considering a large set of 350 french catchments, the Turc-Pike formula is regionalized based on ecohydrologic criterions to handle various hydro-climatic contexts. This interannual regional model is then applied to assess the water-balance over numerous catchments and various conditions, such as karstic, snow-driven or glaciarized and even anthropized catchments. Results show that it is possible to obtain pretty realistic corrections of meteorological inputs (precipitations, temperature or potential evaporation) or hydrologic surface (or runoff). These corrections can often be confirmed a posteriori by exogenous information. Positive impacts on hydrologic model's calibration are also demonstrated. This methodology is now operational for hydrologic applications at EDF (Electricité de France, French electric utility company), and therefore applied on hundreds of catchments.

Quantifying the effects of climate and post-fire landscape change on hydrologic processes

NASA Astrophysics Data System (ADS)

Steimke, A.; Han, B.; Brandt, J.; Som Castellano, R.; Leonard, A.; Flores, A. N.

2016-12-01

Seasonally snow-dominated, forested mountain watersheds supply water to many human populations globally. However, the timing and magnitude of water delivery from these watersheds has already and will continue to change as the climate warms. Changes in vegetation also affect the runoff response of watersheds. The largest driver of vegetation change in many mountainous regions is wildfire, whose occurrence is affected by both climate and land management decisions. Here, we quantify how direct (i.e. changes in precipitation and temperature) and indirect (i.e. changing fire regimes) effects of climate change influence hydrologic parameters such as dates of peak streamflow, annual discharge, and snowpack levels. We used the Boise River Basin, ID as a model laboratory to calculate the relative magnitude of change stemming from direct and indirect effects of climate change. This basin is relevant to study as it is well-instrumented and major drainages have experienced burning at different spatial and temporal intervals, aiding in model calibration. We built a hydrology-based integrated model of the region using a multiagent simulation framework, Envision. We used a modified HBV (Hydrologiska Byråns Vattenbalansavdelning) rainfall-runoff model and calibrated it to historic streamflow and snowpack observations. We combined a diverse set of climate projections with wildfire scenarios (low vs. high) representing two distinct intervals in the regional historic fire record. In fire simulations, we altered land cover coefficients to reflect a burned state post-fire, which decreased overall evapotranspiration rates and increased water yields. However, direct climate effects had a larger signal on annual variations of hydrologic parameters. By comparing and analyzing scenario outputs, we identified links and sensitivities between land cover and regional hydrology in the context of a changing climate, with potential implications for local land and water managers. In future research, this framework will support investigations of climate-aware land management actions on basin hydrologic response.

Designing Green Stormwater Infrastructure for Hydrologic and Human Benefits: An Image Based Machine Learning Approach

NASA Astrophysics Data System (ADS)

Rai, A.; Minsker, B. S.

2014-12-01

Urbanization over the last century has degraded our natural water resources by increasing storm-water runoff, reducing nutrient retention, and creating poor ecosystem health downstream. The loss of tree canopy and expansion of impervious area and storm sewer systems have significantly decreased infiltration and evapotranspiration, increased stream-flow velocities, and increased flood risk. These problems have brought increasing attention to catchment-wide implementation of green infrastructure (e.g., decentralized green storm water management practices such as bioswales, rain gardens, permeable pavements, tree box filters, cisterns, urban wetlands, urban forests, stream buffers, and green roofs) to replace or supplement conventional storm water management practices and create more sustainable urban water systems. Current green infrastructure (GI) practice aims at mitigating the negative effects of urbanization by restoring pre-development hydrology and ultimately addressing water quality issues at an urban catchment scale. The benefits of green infrastructure extend well beyond local storm water management, as urban green spaces are also major contributors to human health. Considerable research in the psychological sciences have shown significant human health benefits from appropriately designed green spaces, yet impacts on human wellbeing have not yet been formally considered in GI design frameworks. This research is developing a novel computational green infrastructure (GI) design framework that integrates hydrologic requirements with criteria for human wellbeing. A supervised machine learning model is created to identify specific patterns in urban green spaces that promote human wellbeing; the model is linked to RHESSYS model to evaluate GI designs in terms of both hydrologic and human health benefits. An application of the models to Dead Run Watershed in Baltimore showed that image mining methods were able to capture key elements of human preferences that could improve tree-based GI design. Hydrologic benefits associated with these features were substantial, indicating that increased urban tree coverage and a more integrated GI design approach can significantly increase both human and hydrologic benefits.

Advancing Collaboration through Hydrologic Data and Model Sharing

NASA Astrophysics Data System (ADS)

Tarboton, D. G.; Idaszak, R.; Horsburgh, J. S.; Ames, D. P.; Goodall, J. L.; Band, L. E.; Merwade, V.; Couch, A.; Hooper, R. P.; Maidment, D. R.; Dash, P. K.; Stealey, M.; Yi, H.; Gan, T.; Castronova, A. M.; Miles, B.; Li, Z.; Morsy, M. M.

2015-12-01

HydroShare is an online, collaborative system for open sharing of hydrologic data, analytical tools, and models. It supports the sharing of and collaboration around "resources" which are defined primarily by standardized metadata, content data models for each resource type, and an overarching resource data model based on the Open Archives Initiative's Object Reuse and Exchange (OAI-ORE) standard and a hierarchical file packaging system called "BagIt". HydroShare expands the data sharing capability of the CUAHSI Hydrologic Information System by broadening the classes of data accommodated to include geospatial and multidimensional space-time datasets commonly used in hydrology. HydroShare also includes new capability for sharing models, model components, and analytical tools and will take advantage of emerging social media functionality to enhance information about and collaboration around hydrologic data and models. It also supports web services and server/cloud based computation operating on resources for the execution of hydrologic models and analysis and visualization of hydrologic data. HydroShare uses iRODS as a network file system for underlying storage of datasets and models. Collaboration is enabled by casting datasets and models as "social objects". Social functions include both private and public sharing, formation of collaborative groups of users, and value-added annotation of shared datasets and models. The HydroShare web interface and social media functions were developed using the Django web application framework coupled to iRODS. Data visualization and analysis is supported through the Tethys Platform web GIS software stack. Links to external systems are supported by RESTful web service interfaces to HydroShare's content. This presentation will introduce the HydroShare functionality developed to date and describe ongoing development of functionality to support collaboration and integration of data and models.

Debates - Stochastic subsurface hydrology from theory to practice: Introduction

NASA Astrophysics Data System (ADS)

Rajaram, Harihar

2016-12-01

This paper introduces the papers in the "Debates - Stochastic Subsurface Hydrology from Theory to Practice" series. Beginning in the 1970s, the field of stochastic subsurface hydrology has been an active field of research, with over 3500 journal publications, of which over 850 have appeared in Water Resources Research. We are fortunate to have insightful contributions from four groups of distinguished authors who discuss the reasons why the advanced research framework established in stochastic subsurface hydrology has not impacted the practice of groundwater flow and transport modeling and design significantly. There is reasonable consensus that a community effort aimed at developing "toolboxes" for applications of stochastic methods will make them more accessible and encourage practical applications.

A polynomial chaos ensemble hydrologic prediction system for efficient parameter inference and robust uncertainty assessment

NASA Astrophysics Data System (ADS)

Wang, S.; Huang, G. H.; Baetz, B. W.; Huang, W.

2015-11-01

This paper presents a polynomial chaos ensemble hydrologic prediction system (PCEHPS) for an efficient and robust uncertainty assessment of model parameters and predictions, in which possibilistic reasoning is infused into probabilistic parameter inference with simultaneous consideration of randomness and fuzziness. The PCEHPS is developed through a two-stage factorial polynomial chaos expansion (PCE) framework, which consists of an ensemble of PCEs to approximate the behavior of the hydrologic model, significantly speeding up the exhaustive sampling of the parameter space. Multiple hypothesis testing is then conducted to construct an ensemble of reduced-dimensionality PCEs with only the most influential terms, which is meaningful for achieving uncertainty reduction and further acceleration of parameter inference. The PCEHPS is applied to the Xiangxi River watershed in China to demonstrate its validity and applicability. A detailed comparison between the HYMOD hydrologic model, the ensemble of PCEs, and the ensemble of reduced PCEs is performed in terms of accuracy and efficiency. Results reveal temporal and spatial variations in parameter sensitivities due to the dynamic behavior of hydrologic systems, and the effects (magnitude and direction) of parametric interactions depending on different hydrological metrics. The case study demonstrates that the PCEHPS is capable not only of capturing both expert knowledge and probabilistic information in the calibration process, but also of implementing an acceleration of more than 10 times faster than the hydrologic model without compromising the predictive accuracy.

Digital hydrologic networks supporting applications related to spatially referenced regression modeling

USGS Publications Warehouse

Brakebill, John W.; Wolock, David M.; Terziotti, Silvia

2011-01-01

Digital hydrologic networks depicting surface-water pathways and their associated drainage catchments provide a key component to hydrologic analysis and modeling. Collectively, they form common spatial units that can be used to frame the descriptions of aquatic and watershed processes. In addition, they provide the ability to simulate and route the movement of water and associated constituents throughout the landscape. Digital hydrologic networks have evolved from derivatives of mapping products to detailed, interconnected, spatially referenced networks of water pathways, drainage areas, and stream and watershed characteristics. These properties are important because they enhance the ability to spatially evaluate factors that affect the sources and transport of water-quality constituents at various scales. SPAtially Referenced Regressions On Watershed attributes (SPARROW), a process-based ⁄ statistical model, relies on a digital hydrologic network in order to establish relations between quantities of monitored contaminant flux, contaminant sources, and the associated physical characteristics affecting contaminant transport. Digital hydrologic networks modified from the River Reach File (RF1) and National Hydrography Dataset (NHD) geospatial datasets provided frameworks for SPARROW in six regions of the conterminous United States. In addition, characteristics of the modified RF1 were used to update estimates of mean-annual streamflow. This produced more current flow estimates for use in SPARROW modeling.

Representing natural and manmade drainage systems in an earth system modeling framework

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

Li, Hongyi; Wu, Huan; Huang, Maoyi

Drainage systems can be categorized into natural or geomorphological drainage systems, agricultural drainage systems and urban drainage systems. They interact closely among themselves and with climate and human society, particularly under extreme climate and hydrological events such as floods. This editorial articulates the need to holistically understand and model drainage systems in the context of climate change and human influence, and discusses the requirements and examples of feasible approaches to representing natural and manmade drainage systems in an earth system modeling framework.

Modelling the interaction between flooding events and economic growth

NASA Astrophysics Data System (ADS)

Grames, Johanna; Fürnkranz-Prskawetz, Alexia; Grass, Dieter; Viglione, Alberto; Blöschl, Günter

2016-04-01

Recently socio-hydrology models have been proposed to analyze the interplay of community risk-coping culture, flooding damage and economic growth. These models descriptively explain the feedbacks between socio-economic development and natural disasters such as floods. Complementary to these descriptive models, we develop a dynamic optimization model, where the inter-temporal decision of an economic agent interacts with the hydrological system. This interdisciplinary approach matches with the goals of Panta Rhei i.e. to understand feedbacks between hydrology and society. It enables new perspectives but also shows limitations of each discipline. Young scientists need mentors from various scientific backgrounds to learn their different research approaches and how to best combine them such that interdisciplinary scientific work is also accepted by different science communities. In our socio-hydrology model we apply a macro-economic decision framework to a long-term flood-scenario. We assume a standard macro-economic growth model where agents derive utility from consumption and output depends on physical capital that can be accumulated through investment. To this framework we add the occurrence of flooding events which will destroy part of the capital. We identify two specific periodic long term solutions and denote them rich and poor economies. Whereas rich economies can afford to invest in flood defense and therefore avoid flood damage and develop high living standards, poor economies prefer consumption instead of investing in flood defense capital and end up facing flood damages every time the water level rises. Nevertheless, they manage to sustain at least a low level of physical capital. We identify optimal investment strategies and compare simulations with more frequent and more intense high water level events.

Application of the ELOHA Framework to Regulated Rivers in the Upper Tennessee River Basin: A Case Study

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

McManamay, Ryan A; Orth, Dr. Donald J; Dolloff, Dr. Charles A

2013-01-01

In order for habitat restoration in regulated rivers to be effective at large scales, broadly applicable frameworks are needed that provide measurable objectives and contexts for management. The Ecological Limits of Hydrologic Alteration (ELOHA) framework was created as a template to assess hydrologic alterations, develop relationships between altered streamflow and ecology, and establish environmental flow standards. We tested the utility of ELOHA in informing flow restoration applications for fish and riparian communities in regulated rivers in the Upper Tennessee River Basin (UTRB). We followed the steps of ELOHA to generate flow alteration-ecological response relationships and then determined whether those relationshipsmore » could predict fish and riparian responses to flow restoration in the Cheoah River, a regulated system within the UTRB. Although ELOHA provided a robust template to construct hydrologic information and predict hydrology for ungaged locations, our results do not support the assertion that over-generalized univariate relationships between flow and ecology can produce results sufficient to guide management in regulated rivers. After constructing multivariate models, we successfully developed predictive relationships between flow alterations and fish/riparian responses. In accordance with model predictions, riparian encroachment displayed consistent decreases with increases in flow magnitude in the Cheoah River; however, fish richness did not increase as predicted four years post- restoration. Our results suggest that altered temperature and substrate and the current disturbance regime may have reduced opportunities for fish species colonization. Our case study highlights the need for interdisciplinary science in defining environmental flows for regulated rivers and the need for adaptive management approaches once flows are restored.« less

Stream ecological condition modeling at the reach and the hydrologic unit (HUC) scale: A look at model performance and mapping

EPA Science Inventory

The National Hydrography and updated Watershed Boundary Datasets provide a ready-made framework for hydrographic modeling. Determining particular stream reaches or watersheds in poor ecological condition across large regions is an essential goal for monitoring and management. T...

Evaluation of global fine-resolution precipitation products and their uncertainty quantification in ensemble discharge simulations

NASA Astrophysics Data System (ADS)

Qi, W.; Zhang, C.; Fu, G.; Sweetapple, C.; Zhou, H.

2016-02-01

The applicability of six fine-resolution precipitation products, including precipitation radar, infrared, microwave and gauge-based products, using different precipitation computation recipes, is evaluated using statistical and hydrological methods in northeastern China. In addition, a framework quantifying uncertainty contributions of precipitation products, hydrological models, and their interactions to uncertainties in ensemble discharges is proposed. The investigated precipitation products are Tropical Rainfall Measuring Mission (TRMM) products (TRMM3B42 and TRMM3B42RT), Global Land Data Assimilation System (GLDAS)/Noah, Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and a Global Satellite Mapping of Precipitation (GSMAP-MVK+) product. Two hydrological models of different complexities, i.e. a water and energy budget-based distributed hydrological model and a physically based semi-distributed hydrological model, are employed to investigate the influence of hydrological models on simulated discharges. Results show APHRODITE has high accuracy at a monthly scale compared with other products, and GSMAP-MVK+ shows huge advantage and is better than TRMM3B42 in relative bias (RB), Nash-Sutcliffe coefficient of efficiency (NSE), root mean square error (RMSE), correlation coefficient (CC), false alarm ratio, and critical success index. These findings could be very useful for validation, refinement, and future development of satellite-based products (e.g. NASA Global Precipitation Measurement). Although large uncertainty exists in heavy precipitation, hydrological models contribute most of the uncertainty in extreme discharges. Interactions between precipitation products and hydrological models can have the similar magnitude of contribution to discharge uncertainty as the hydrological models. A better precipitation product does not guarantee a better discharge simulation because of interactions. It is also found that a good discharge simulation depends on a good coalition of a hydrological model and a precipitation product, suggesting that, although the satellite-based precipitation products are not as accurate as the gauge-based products, they could have better performance in discharge simulations when appropriately combined with hydrological models. This information is revealed for the first time and very beneficial for precipitation product applications.

A 3-step framework for understanding the added value of surface soil moisture measurements for large-scale runoff prediction via data assimilation - a synthetic study in the Arkansas-Red River basin

NASA Astrophysics Data System (ADS)

Mao, Y.; Crow, W. T.; Nijssen, B.

2017-12-01

Soil moisture (SM) plays an important role in runoff generation both by partitioning infiltration and surface runoff during rainfall events and by controlling the rate of subsurface flow during inter-storm periods. Therefore, more accurate SM state estimation in hydrologic models is potentially beneficial for streamflow prediction. Various previous studies have explored the potential of assimilating SM data into hydrologic models for streamflow improvement. These studies have drawn inconsistent conclusions, ranging from significantly improved runoff via SM data assimilation (DA) to limited or degraded runoff. These studies commonly treat the whole assimilation procedure as a black box without separating the contribution of each step in the procedure, making it difficult to attribute the underlying causes of runoff improvement (or the lack thereof). In this study, we decompose the overall DA process into three steps by answering the following questions (3-step framework): 1) how much can assimilation of surface SM measurements improve surface SM state in a hydrologic model? 2) how much does surface SM improvement propagate to deeper layers? 3) How much does (surface and deeper-layer) SM improvement propagate into runoff improvement? A synthetic twin experiment is carried out in the Arkansas-Red River basin ( 600,000 km2) where a synthetic "truth" run, an open-loop run (without DA) and a DA run (where synthetic surface SM measurements are assimilated) are generated. All model runs are performed at 1/8 degree resolution and over a 10-year period using the Variable Infiltration Capacity (VIC) hydrologic model at a 3-hourly time step. For the DA run, the ensemble Kalman filter (EnKF) method is applied. The updated surface and deeper-layer SM states with DA are compared to the open-loop SM to quantitatively evaluate the first two steps in the framework. To quantify the third step, a set of perfect-state runs are generated where the "true" SM states are directly inserted in the model to assess the maximum possible runoff improvement that can be achieved by improving SM states alone. Our results show that the 3-step framework is able to effectively identify the potential as well as bottleneck of runoff improvement and point out the cases where runoff improvement via assimilation of surface SM is prone to failure.

Tracer-aided modelling to explore non-linearities in flow paths, hydrological connectivity and faecal contamination risk

NASA Astrophysics Data System (ADS)

Neill, A. J.; Tetzlaff, D.; Strachan, N.; Soulsby, C.

2016-12-01

The non-linearities of runoff generation processes are strongly influenced by the connectivity of hillslopes and channel networks, particularly where overland flow is an important runoff mechanism. Despite major advances in understanding hydrological connectivity and runoff generation, the role of connectivity in the contamination of potable water supplies by faecal pathogens from grazing animals remains unclear. This is a water quality issue with serious implications for public health. Here, we sought to understand the dynamics of hydrological connectivity, flow paths and linked faecal pathogen transport in a montane catchment in Scotland with high deer populations. We firstly calibrated, within an uncertainty framework, a parsimonious tracer-aided hydrological model to daily discharge and stream isotope data. The model, developed on the basis of past empirical and tracer studies, conceptualises the catchment as three interacting hydrological source areas (dynamic saturation zone, dynamic hillslope, and groundwater) for which water fluxes, water ages and storage-based connectivity can be simulated. We next coupled several faecal indicator organism (FIO; a common indicator of faecal pathogen contamination) behaviour and transport schemes to the robust hydrological models. A further calibration was then undertaken based on the ability of each coupled model to simulate daily FIO concentrations. This gave us a final set of coupled behavioural models from which we explored how in-stream FIO dynamics could be related to the changing connectivity between the three hydrological source areas, flow paths, water ages and consequent dominant runoff generation processes. We found that high levels of FIOs were transient and episodic, and strongly correlated with periods of high connectivity through overland flow. This non-linearity in connectivity and FIO flux was successfully captured within our dynamic, tracer-aided hydrological model.

Assessing Uncertainties in Surface Water Security: A Probabilistic Multi-model Resampling approach

NASA Astrophysics Data System (ADS)

Rodrigues, D. B. B.

2015-12-01

Various uncertainties are involved in the representation of processes that characterize interactions between societal needs, ecosystem functioning, and hydrological conditions. Here, we develop an empirical uncertainty assessment of water security indicators that characterize scarcity and vulnerability, based on a multi-model and resampling framework. We consider several uncertainty sources including those related to: i) observed streamflow data; ii) hydrological model structure; iii) residual analysis; iv) the definition of Environmental Flow Requirement method; v) the definition of critical conditions for water provision; and vi) the critical demand imposed by human activities. We estimate the overall uncertainty coming from the hydrological model by means of a residual bootstrap resampling approach, and by uncertainty propagation through different methodological arrangements applied to a 291 km² agricultural basin within the Cantareira water supply system in Brazil. Together, the two-component hydrograph residual analysis and the block bootstrap resampling approach result in a more accurate and precise estimate of the uncertainty (95% confidence intervals) in the simulated time series. We then compare the uncertainty estimates associated with water security indicators using a multi-model framework and provided by each model uncertainty estimation approach. The method is general and can be easily extended forming the basis for meaningful support to end-users facing water resource challenges by enabling them to incorporate a viable uncertainty analysis into a robust decision making process.

NHDPlus (National Hydrography Dataset Plus)

EPA Pesticide Factsheets

NHDPlus is a geospatial, hydrologic framework dataset that is intended for use by geospatial analysts and modelers to support water resources related applications. NHDPlus was developed by the USEPA in partnership with the US Geologic Survey

Final Report: Phase II Nevada Water Resources Data, Modeling, and Visualization (DMV) Center

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

Jackman, Thomas; Minor, Timothy; Pohll, Gregory

2013-07-22

Water is unquestionably a critical resource throughout the United States. In the semi-arid west -- an area stressed by increase in human population and sprawl of the built environment -- water is the most important limiting resource. Crucially, science must understand factors that affect availability and distribution of water. To sustain growing consumptive demand, science needs to translate understanding into reliable and robust predictions of availability under weather conditions that could be average but might be extreme. These predictions are needed to support current and long-term planning. Similar to the role of weather forecast and climate prediction, water prediction overmore » short and long temporal scales can contribute to resource strategy, governmental policy and municipal infrastructure decisions, which are arguably tied to the natural variability and unnatural change to climate. Change in seasonal and annual temperature, precipitation, snowmelt, and runoff affect the distribution of water over large temporal and spatial scales, which impact the risk of flooding and the groundwater recharge. Anthropogenic influences and impacts increase the complexity and urgency of the challenge. The goal of this project has been to develop a decision support framework of data acquisition, digital modeling, and 3D visualization. This integrated framework consists of tools for compiling, discovering and projecting our understanding of processes that control the availability and distribution of water. The framework is intended to support the analysis of the complex interactions between processes that affect water supply, from controlled availability to either scarcity or deluge. The developed framework enables DRI to promote excellence in water resource management, particularly within the Lake Tahoe basin. In principle, this framework could be replicated for other watersheds throughout the United States. Phase II of this project builds upon the research conducted during Phase I, in which the hydrologic framework was investigated and the development initiated. Phase II concentrates on practical implementation of the earlier work but emphasizes applications to the hydrology of the Lake Tahoe basin. Phase 1 efforts have been refined and extended by creating a toolset for geographic information systems (GIS) that is usable for disparate types of geospatial and geo-referenced data. The toolset is intended to serve multiple users for a variety of applications. The web portal for internet access to hydrologic and remotely sensed product data, prototyped in Phase I, has been significantly enhanced. The portal provides high performance access to LANDSAT-derived data using techniques developed during the course of the project. The portal is interactive, and supports the geo-referenced display of hydrologic information derived from remotely sensed data, such as various vegetative indices used to calculate water consumption. The platform can serve both internal and external constituencies using inter-operating infrastructure that spans both sides of the DRI firewall. The platform is intended grow its supported data assets and to serve as a template for replication to other geographic areas. An unanticipated development during the project was the use of ArcGIS software on a new computer system, called the IBM PureSytems, and the parallel use of the systems for faster, more efficient image processing. Additional data, independent of the portal, was collected within the Sagehen basin and provides detailed information regarding the processes that control hydrologic responses within mountain watersheds. The newly collected data include elevation, evapotranspiration, energy balance and remotely sensed snow-pack data. A Lake Tahoe basin hydrologic model has been developed, in part to help predict the hydrologic impacts of climate change. The model couples both the surface and subsurface hydrology, with the two components having been independently calibrated. Results from the coupled simulations involving both surface water and groundwater processes show that it is possible to fairly accurately simulate lake effects and water budget variables over a wide range of dry and wet cycles in the historical record. The Lake Tahoe basin is representative of the hydrology, topography and climate throughout the Sierra Nevada Range, and the entire model development is prototypical of the efforts required to replicate the decision support framework to other locales. The Lake Tahoe model in particular, could allow water managers to evaluate more accurately components of the water budget (ET, runoff, groundwater, etc) and to answer important questions regarding water resources in northern Nevada. This report discusses the geographic scale and the hydrologic complexity of the calibrated model developed as part of this project, as well as simulation results for historical and future climate projects To enable human-driven data exploration and discovery, de novo software for a globalized rendering module that extends the capability of our evolving custom visualization engine from Phase I (called SMEngine) has been developed. The new rendering component, called Horizon, supports terrain rendering capable of displaying and interrogating both remotely sensed and modeled data. The development of Horizon necessitated adaptation of the visualization engine to allow extensible integration of components such as the global rendering module and support for associated features. The resulting software is general in its GIS capability, but a specific Lake Tahoe visualization application suitable for immersive decision support in the DRIVE6 virtual reality facility has been developed. During the development, various features to enhance the value of the visualization experience were explored, including the use of hyperspectral image overlays. An over-arching goal of the visualization aspect of the project has been to develop and demonstrate the CAVE (CAVE Automatic Virtual Environment) as a practical tool for hydrologic research.« less

Optimization of urban water supply portfolios combining infrastructure capacity expansion and water use decisions

NASA Astrophysics Data System (ADS)

Medellin-Azuara, J.; Fraga, C. C. S.; Marques, G.; Mendes, C. A.

2015-12-01

The expansion and operation of urban water supply systems under rapidly growing demands, hydrologic uncertainty, and scarce water supplies requires a strategic combination of various supply sources for added reliability, reduced costs and improved operational flexibility. The design and operation of such portfolio of water supply sources merits decisions of what and when to expand, and how much to use of each available sources accounting for interest rates, economies of scale and hydrologic variability. The present research provides a framework and an integrated methodology that optimizes the expansion of various water supply alternatives using dynamic programming and combining both short term and long term optimization of water use and simulation of water allocation. A case study in Bahia Do Rio Dos Sinos in Southern Brazil is presented. The framework couples an optimization model with quadratic programming model in GAMS with WEAP, a rain runoff simulation models that hosts the water supply infrastructure features and hydrologic conditions. Results allow (a) identification of trade offs between cost and reliability of different expansion paths and water use decisions and (b) evaluation of potential gains by reducing water system losses as a portfolio component. The latter is critical in several developing countries where water supply system losses are high and often neglected in favor of more system expansion. Results also highlight the potential of various water supply alternatives including, conservation, groundwater, and infrastructural enhancements over time. The framework proves its usefulness for planning its transferability to similarly urbanized systems.

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

Vernon, Christopher R.; Arntzen, Evan V.; Richmond, Marshall C.

Assessing the environmental benefits of proposed flow modification to large rivers provides invaluable insight into future hydropower project operations and relicensing activities. Providing a means to quantitatively define flow-ecology relationships is integral in establishing flow regimes that are mutually beneficial to power production and ecological needs. To compliment this effort an opportunity to create versatile tools that can be applied to broad geographic areas has been presented. In particular, integration with efforts standardized within the ecological limits of hydrologic alteration (ELOHA) is highly advantageous (Poff et al. 2010). This paper presents a geographic information system (GIS) framework for large rivermore » classification that houses a base geomorphic classification that is both flexible and accurate, allowing for full integration with other hydrologic models focused on addressing ELOHA efforts. A case study is also provided that integrates publically available National Hydrography Dataset Plus Version 2 (NHDPlusV2) data, Modular Aquatic Simulation System two-dimensional (MASS2) hydraulic data, and field collected data into the framework to produce a suite of flow-ecology related outputs. The case study objective was to establish areas of optimal juvenile salmonid rearing habitat under varying flow regimes throughout an impounded portion of the lower Snake River, USA (Figure 1) as an indicator to determine sites where the potential exists to create additional shallow water habitat. Additionally, an alternative hydrologic classification useable throughout the contiguous United States which can be coupled with the geomorphic aspect of this framework is also presented. This framework provides the user with the ability to integrate hydrologic and ecologic data into the base geomorphic aspect of this framework within a geographic information system (GIS) to output spatiotemporally variable flow-ecology relationship scenarios.« less

Model and Interoperability using Meta Data Annotations

NASA Astrophysics Data System (ADS)

David, O.

2011-12-01

Software frameworks and architectures are in need for meta data to efficiently support model integration. Modelers have to know the context of a model, often stepping into modeling semantics and auxiliary information usually not provided in a concise structure and universal format, consumable by a range of (modeling) tools. XML often seems the obvious solution for capturing meta data, but its wide adoption to facilitate model interoperability is limited by XML schema fragmentation, complexity, and verbosity outside of a data-automation process. Ontologies seem to overcome those shortcomings, however the practical significance of their use remains to be demonstrated. OMS version 3 took a different approach for meta data representation. The fundamental building block of a modular model in OMS is a software component representing a single physical process, calibration method, or data access approach. Here, programing language features known as Annotations or Attributes were adopted. Within other (non-modeling) frameworks it has been observed that annotations lead to cleaner and leaner application code. Framework-supported model integration, traditionally accomplished using Application Programming Interfaces (API) calls is now achieved using descriptive code annotations. Fully annotated components for various hydrological and Ag-system models now provide information directly for (i) model assembly and building, (ii) data flow analysis for implicit multi-threading or visualization, (iii) automated and comprehensive model documentation of component dependencies, physical data properties, (iv) automated model and component testing, calibration, and optimization, and (v) automated audit-traceability to account for all model resources leading to a particular simulation result. Such a non-invasive methodology leads to models and modeling components with only minimal dependencies on the modeling framework but a strong reference to its originating code. Since models and modeling components are not directly bound to framework by the use of specific APIs and/or data types they can more easily be reused both within the framework as well as outside. While providing all those capabilities, a significant reduction in the size of the model source code was achieved. To support the benefit of annotations for a modeler, studies were conducted to evaluate the effectiveness of an annotation based framework approach with other modeling frameworks and libraries, a framework-invasiveness study was conducted to evaluate the effects of framework design on model code quality. A typical hydrological model was implemented across several modeling frameworks and several software metrics were collected. The metrics selected were measures of non-invasive design methods for modeling frameworks from a software engineering perspective. It appears that the use of annotations positively impacts several software quality measures. Experience to date has demonstrated the multi-purpose value of using annotations. Annotations are also a feasible and practical method to enable interoperability among models and modeling frameworks.

Challenges in soil erosion research and prediction model development

USDA-ARS?s Scientific Manuscript database

Quantification of soil erosion has been traditionally considered as a surface hydrologic process with equations for soil detachment and sediment transport derived from the mechanics and hydraulics of the rainfall and surface flow. Under the current erosion modeling framework, the soil has a constant...

A novel framework to simulating non-stationary, non-linear, non-Normal hydrological time series using Markov Switching Autoregressive Models

NASA Astrophysics Data System (ADS)

Birkel, C.; Paroli, R.; Spezia, L.; Tetzlaff, D.; Soulsby, C.

2012-12-01

In this paper we present a novel model framework using the class of Markov Switching Autoregressive Models (MSARMs) to examine catchments as complex stochastic systems that exhibit non-stationary, non-linear and non-Normal rainfall-runoff and solute dynamics. Hereby, MSARMs are pairs of stochastic processes, one observed and one unobserved, or hidden. We model the unobserved process as a finite state Markov chain and assume that the observed process, given the hidden Markov chain, is conditionally autoregressive, which means that the current observation depends on its recent past (system memory). The model is fully embedded in a Bayesian analysis based on Markov Chain Monte Carlo (MCMC) algorithms for model selection and uncertainty assessment. Hereby, the autoregressive order and the dimension of the hidden Markov chain state-space are essentially self-selected. The hidden states of the Markov chain represent unobserved levels of variability in the observed process that may result from complex interactions of hydroclimatic variability on the one hand and catchment characteristics affecting water and solute storage on the other. To deal with non-stationarity, additional meteorological and hydrological time series along with a periodic component can be included in the MSARMs as covariates. This extension allows identification of potential underlying drivers of temporal rainfall-runoff and solute dynamics. We applied the MSAR model framework to streamflow and conservative tracer (deuterium and oxygen-18) time series from an intensively monitored 2.3 km2 experimental catchment in eastern Scotland. Statistical time series analysis, in the form of MSARMs, suggested that the streamflow and isotope tracer time series are not controlled by simple linear rules. MSARMs showed that the dependence of current observations on past inputs observed by transport models often in form of the long-tailing of travel time and residence time distributions can be efficiently explained by non-stationarity either of the system input (climatic variability) and/or the complexity of catchment storage characteristics. The statistical model is also capable of reproducing short (event) and longer-term (inter-event) and wet and dry dynamical "hydrological states". These reflect the non-linear transport mechanisms of flow pathways induced by transient climatic and hydrological variables and modified by catchment characteristics. We conclude that MSARMs are a powerful tool to analyze the temporal dynamics of hydrological data, allowing for explicit integration of non-stationary, non-linear and non-Normal characteristics.

Global Sensitivity Analysis for Large-scale Socio-hydrological Models using the Cloud

NASA Astrophysics Data System (ADS)

Hu, Y.; Garcia-Cabrejo, O.; Cai, X.; Valocchi, A. J.; Dupont, B.

2014-12-01

In the context of coupled human and natural system (CHNS), incorporating human factors into water resource management provides us with the opportunity to understand the interactions between human and environmental systems. A multi-agent system (MAS) model is designed to couple with the physically-based Republican River Compact Administration (RRCA) groundwater model, in an attempt to understand the declining water table and base flow in the heavily irrigated Republican River basin. For MAS modelling, we defined five behavioral parameters (κ_pr, ν_pr, κ_prep, ν_prep and λ) to characterize the agent's pumping behavior given the uncertainties of the future crop prices and precipitation. κ and ν describe agent's beliefs in their prior knowledge of the mean and variance of crop prices (κ_pr, ν_pr) and precipitation (κ_prep, ν_prep), and λ is used to describe the agent's attitude towards the fluctuation of crop profits. Notice that these human behavioral parameters as inputs to the MAS model are highly uncertain and even not measurable. Thus, we estimate the influences of these behavioral parameters on the coupled models using Global Sensitivity Analysis (GSA). In this paper, we address two main challenges arising from GSA with such a large-scale socio-hydrological model by using Hadoop-based Cloud Computing techniques and Polynomial Chaos Expansion (PCE) based variance decomposition approach. As a result, 1,000 scenarios of the coupled models are completed within two hours with the Hadoop framework, rather than about 28days if we run those scenarios sequentially. Based on the model results, GSA using PCE is able to measure the impacts of the spatial and temporal variations of these behavioral parameters on crop profits and water table, and thus identifies two influential parameters, κ_pr and λ. The major contribution of this work is a methodological framework for the application of GSA in large-scale socio-hydrological models. This framework attempts to find a balance between the heavy computational burden regarding model execution and the number of model evaluations required in the GSA analysis, particularly through an organic combination of Hadoop-based Cloud Computing to efficiently evaluate the socio-hydrological model and PCE where the sensitivity indices are efficiently estimated from its coefficients.

Characterizing the impact of spatiotemporal variations in stormwater infrastructure on hydrologic conditions

NASA Astrophysics Data System (ADS)

Jovanovic, T.; Mejia, A.; Hale, R. L.; Gironas, J. A.

2015-12-01

Urban stormwater infrastructure design has evolved in time, reflecting changes in stormwater policy and regulations, and in engineering design. This evolution makes urban basins heterogeneous socio-ecological-technological systems. We hypothesize that this heterogeneity creates unique impact trajectories in time and impact hotspots in space within and across cities. To explore this, we develop and implement a network hydro-engineering modeling framework based on high-resolution digital elevation and stormwater infrastructure data. The framework also accounts for climatic, soils, land use, and vegetation conditions in an urban basin, thus making it useful to study the impacts of stormwater infrastructure across cities. Here, to evaluate the framework, we apply it to urban basins in the metropolitan areas of Phoenix, Arizona. We use it to estimate different metrics to characterize the storm-event hydrologic response. We estimate both traditional metrics (e.g., peak flow, time to peak, and runoff volume) as well as new metrics (e.g., basin-scale dispersion mechanisms). We also use the dispersion mechanisms to assess the scaling characteristics of urban basins. Ultimately, we find that the proposed framework can be used to understand and characterize the impacts associated with stormwater infrastructure on hydrologic conditions within a basin. Additionally, we find that the scaling approach helps in synthesizing information but it requires further validation using additional urban basins.

Designing hydrologic monitoring networks to maximize predictability of hydrologic conditions in a data assimilation system: a case study from South Florida, U.S.A

NASA Astrophysics Data System (ADS)

Flores, A. N.; Pathak, C. S.; Senarath, S. U.; Bras, R. L.

2009-12-01

Robust hydrologic monitoring networks represent a critical element of decision support systems for effective water resource planning and management. Moreover, process representation within hydrologic simulation models is steadily improving, while at the same time computational costs are decreasing due to, for instance, readily available high performance computing resources. The ability to leverage these increasingly complex models together with the data from these monitoring networks to provide accurate and timely estimates of relevant hydrologic variables within a multiple-use, managed water resources system would substantially enhance the information available to resource decision makers. Numerical data assimilation techniques provide mathematical frameworks through which uncertain model predictions can be constrained to observational data to compensate for uncertainties in the model forcings and parameters. In ensemble-based data assimilation techniques such as the ensemble Kalman Filter (EnKF), information in observed variables such as canal, marsh and groundwater stages are propagated back to the model states in a manner related to: (1) the degree of certainty in the model state estimates and observations, and (2) the cross-correlation between the model states and the observable outputs of the model. However, the ultimate degree to which hydrologic conditions can be accurately predicted in an area of interest is controlled, in part, by the configuration of the monitoring network itself. In this proof-of-concept study we developed an approach by which the design of an existing hydrologic monitoring network is adapted to iteratively improve the predictions of hydrologic conditions within an area of the South Florida Water Management District (SFWMD). The objective of the network design is to minimize prediction errors of key hydrologic states and fluxes produced by the spatially distributed Regional Simulation Model (RSM), developed specifically to simulate the hydrologic conditions in several intensively managed and hydrologically complex watersheds within the SFWMD system. In a series of synthetic experiments RSM is used to generate the notionally true hydrologic state and the relevant observational data. The EnKF is then used as the mechanism to fuse RSM hydrologic estimates with data from the candidate network. The performance of the candidate network is measured by the prediction errors of the EnKF estimates of hydrologic states, relative to the notionally true scenario. The candidate network is then adapted by relocating existing observational sites to unobserved areas where predictions of local hydrologic conditions are most uncertain and the EnKF procedure repeated. Iteration of the monitoring network continues until further improvements in EnKF-based predictions of hydrologic conditions are negligible.

Modeling the Hydrological Regime of Turkana Lake (Kenya, Ethiopia) by Combining Spatially Distributed Hydrological Modeling and Remote Sensing Datasets

NASA Astrophysics Data System (ADS)

Anghileri, D.; Kaelin, A.; Peleg, N.; Fatichi, S.; Molnar, P.; Roques, C.; Longuevergne, L.; Burlando, P.

2017-12-01

Hydrological modeling in poorly gauged basins can benefit from the use of remote sensing datasets although there are challenges associated with the mismatch in spatial and temporal scales between catchment scale hydrological models and remote sensing products. We model the hydrological processes and long-term water budget of the Lake Turkana catchment, a transboundary basin between Kenya and Ethiopia, by integrating several remote sensing products into a spatially distributed and physically explicit model, Topkapi-ETH. Lake Turkana is the world largest desert lake draining a catchment of 145'500 km2. It has three main contributing rivers: the Omo river, which contributes most of the annual lake inflow, the Turkwel river, and the Kerio rivers, which contribute the remaining part. The lake levels have shown great variations in the last decades due to long-term climate fluctuations and the regulation of three reservoirs, Gibe I, II, and III, which significantly alter the hydrological seasonality. Another large reservoir is planned and may be built in the next decade, generating concerns about the fate of Lake Turkana in the long run because of this additional anthropogenic pressure and increasing evaporation driven by climate change. We consider different remote sensing datasets, i.e., TRMM-V7 for precipitation, MERRA-2 for temperature, as inputs to the spatially distributed hydrological model. We validate the simulation results with other remote sensing datasets, i.e., GRACE for total water storage anomalies, GLDAS-NOAH for soil moisture, ERA-Interim/Land for surface runoff, and TOPEX/Poseidon for satellite altimetry data. Results highlight how different remote sensing products can be integrated into a hydrological modeling framework accounting for their relative uncertainties. We also carried out simulations with the artificial reservoirs planned in the north part of the catchment and without any reservoirs, to assess their impacts on the catchment hydrological regime and the Lake Turkana level variability.

The PCR-GLOBWB global hydrological reanalysis product

NASA Astrophysics Data System (ADS)

Bierkens, M. F.; Wanders, N.; Sutanudjaja, E.; Van Beek, L. P.

2013-12-01

Accurate and long time series of hydrological data are important for understanding land surface water and energy budgets in many parts of the world, as well as for improving real-time hydrological monitoring and climate change anticipation. The ultimate goal of the present work is to produce a multi-decadal land surface hydrological reanalysis with retrospective and updated hydrological states and fluxes that are constrained to available in-situ river discharge measurements. Here we used PCR-GLOBWB (van Beek et al., 2011), which is a large-scale hydrological model intended for global to regional studies. PCR-GLOBWB provides a grid-based representation of terrestrial hydrology with a typical spatial resolution of approximately 50×50 km (currently 0.5° globally) on a daily basis. For each grid cell, PCR-GLOBWB is basically a leaky bucket type of water balance model with a process-based simulation of moisture storage in two vertically stacked soil layers as well as the water exchange between the soil and the atmosphere and the underlying groundwater reservoir. Exchange to the atmosphere comprises precipitation, evaporation and transpiration, as well as snow accumulation and melt, which are all simulated by considering vegetation phenology and sub-grid distributions of elevation, land cover and soil saturation distribution. The model thus includes detailed schemes for runoff-infiltration partitioning, interflow, groundwater recharge and baseflow, as well as river routing of discharge. . By embedding the PCR-GLOBWB model in an Ensemble Kalman Filter framework, we calibrated the model parameters based on the discharge observations from the Global Runoff Data Centre. The parameters calibrated are related to snow module, runoff-infiltration partitioning, groundwater recharge, channel discharge and baseflow processes, as well as pre-factors to correct forcing precipitation fields due to local topographic and orographic effects. Results show that the model parameters can be calibrated and forcing precipitation fields were successfully corrected. The calibrated model output was compared to the reference run of PCR-GLOBWB before calibration. Here we found significant improvement in simulation of the global terrestrial water cycle, specifically discharge simulation for major river basins in the world. The main outcome of this work is a 1960-2010 global reanalysis dataset that includes extensive daily hydrological components, such as precipitation, evaporation and transpiration, snow, soil moisture, groundwater storage and discharge. This reanalysis product may be used for understanding land surface memory processes, initializing regional studies and operational forecasts, as well as evaluating and improving our understanding of spatio-temporal variation of meteorological and hydrological processes. Moreover, The PCR-GLOBWB data assimilation framework developed in this work can also be extended by including more observational data, including remotely sensed data reflecting the distribution of energy and water (e.g., heat fluxes and soil moisture storage).

The ecological limits of hydrologic alteration (ELOHA): A new framework for developing regional environmental flow standards

USGS Publications Warehouse

Poff, N.L.; Richter, B.D.; Arthington, A.H.; Bunn, S.E.; Naiman, R.J.; Kendy, E.; Acreman, M.; Apse, C.; Bledsoe, B.P.; Freeman, Mary C.; Henriksen, J.; Jacobson, R.B.; Kennen, J.G.; Merritt, D.M.; O'Keeffe, J. H.; Olden, J.D.; Rogers, K.; Tharme, R.E.; Warner, A.

2010-01-01

The flow regime is a primary determinant of the structure and function of aquatic and riparian ecosystems for streams and rivers. Hydrologic alteration has impaired riverine ecosystems on a global scale, and the pace and intensity of human development greatly exceeds the ability of scientists to assess the effects on a river-by-river basis. Current scientific understanding of hydrologic controls on riverine ecosystems and experience gained from individual river studies support development of environmental flow standards at the regional scale. 2. This paper presents a consensus view from a group of international scientists on a new framework for assessing environmental flow needs for many streams and rivers simultaneously to foster development and implementation of environmental flow standards at the regional scale. This framework, the ecological limits of hydrologic alteration (ELOHA), is a synthesis of a number of existing hydrologic techniques and environmental flow methods that are currently being used to various degrees and that can support comprehensive regional flow management. The flexible approach allows scientists, water-resource managers and stakeholders to analyse and synthesise available scientific information into ecologically based and socially acceptable goals and standards for management of environmental flows. 3. The ELOHA framework includes the synthesis of existing hydrologic and ecological databases from many rivers within a user-defined region to develop scientifically defensible and empirically testable relationships between flow alteration and ecological responses. These relationships serve as the basis for the societally driven process of developing regional flow standards. This is to be achieved by first using hydrologic modelling to build a 'hydrologic foundation' of baseline and current hydrographs for stream and river segments throughout the region. Second, using a set of ecologically relevant flow variables, river segments within the region are classified into a few distinctive flow regime types that are expected to have different ecological characteristics. These river types can be further subclassified according to important geomorphic features that define hydraulic habitat features. Third, the deviation of current-condition flows from baseline-condition flow is determined. Fourth, flow alteration-ecological response relationships are developed for each river type, based on a combination of existing hydroecological literature, expert knowledge and field studies across gradients of hydrologic alteration. 4. Scientific uncertainty will exist in the flow alteration-ecological response relationships, in part because of the confounding of hydrologic alteration with other important environmental determinants of river ecosystem condition (e.g. temperature). Application of the ELOHA framework should therefore occur in a consensus context where stakeholders and decision-makers explicitly evaluate acceptable risk as a balance between the perceived value of the ecological goals, the economic costs involved and the scientific uncertainties in functional relationships between ecological responses and flow alteration. 5. The ELOHA framework also should proceed in an adaptive management context, where collection of monitoring data or targeted field sampling data allows for testing of the proposed flow alteration-ecological response relationships. This empirical validation process allows for a fine-tuning of environmental flow management targets. The ELOHA framework can be used both to guide basic research in hydroecology and to further implementation of more comprehensive environmental flow management of freshwater sustainability on a global scale. ?? 2009 Blackwell Publishing Ltd.

Brokering as a framework for hydrological model repeatability

NASA Astrophysics Data System (ADS)

Fuka, Daniel; Collick, Amy; MacAlister, Charlotte; Braeckel, Aaron; Wright, Dawn; Jodha Khalsa, Siri; Boldrini, Enrico; Easton, Zachary

2015-04-01

Data brokering aims to provide those in the the sciences with quick and repeatable access to data that represents physical, biological, and chemical characteristics; specifically to accelerate scientific discovery. Environmental models are useful tools to understand the behavior of hydrological systems. Unfortunately, parameterization of these hydrological models requires many different data, from different sources, and from different disciplines (e.g., atmospheric, geoscience, ecology). In basin scale hydrological modeling, the traditional procedure for model initialization starts with obtaining elevation models, land-use characterizations, soils maps, and weather data. It is often the researcher's past experience with these datasets that determines which datasets will be used in a study, and often newer, or more suitable data products will exist. An added complexity is that various science communities have differing data formats, storage protocols, and manipulation methods, which makes use by a non native user exceedingly difficult and time consuming. We demonstrate data brokering as a means to address several of these challenges. We present two test case scenarios in which researchers attempt to reproduce hydrological model results using 1) general internet based data gathering techniques, and 2) a scientific data brokering interface. We show that data brokering can increase the efficiency with which data are obtained, models are initialized, and results are analyzed. As an added benefit, it appears brokering can significantly increase the repeatability of a given study.

Revisiting an interdisciplinary hydrological modelling project. A socio-hydrology (?) example from the early 2000s

NASA Astrophysics Data System (ADS)

Seidl, Roman; Barthel, Roland

2016-04-01

Interdisciplinary scientific and societal knowledge plays an increasingly important role in global change research. Also, in the field of water resources interdisciplinarity as well as cooperation with stakeholders from outside academia have been recognized as important. In this contribution, we revisit an integrated regional modelling system (DANUBIA), which was developed by an interdisciplinary team of researchers and relied on stakeholder participation in the framework of the GLOWA-Danube project from 2001 to 2011 (Mauser and Prasch 2016). As the model was developed before the current increase in literature on participatory modelling and interdisciplinarity, we ask how a socio-hydrology approach would have helped and in what way it would have made the work different. The present contribution firstly presents the interdisciplinary concept of DANUBIA, mainly with focus on the integration of human behaviour in a spatially explicit, process-based numerical modelling system (Roland Barthel, Janisch, Schwarz, Trifkovic, Nickel, Schulz, and Mauser 2008; R. Barthel, Nickel, Meleg, Trifkovic, and Braun 2005). Secondly, we compare the approaches to interdisciplinarity in GLOWA-Danube with concepts and ideas presented by socio-hydrology. Thirdly, we frame DANUBIA and a review of key literature on socio-hydrology in the context of a survey among hydrologists (N = 184). This discussion is used to highlight gaps and opportunities of the socio-hydrology approach. We show that the interdisciplinary aspect of the project and the participatory process of stakeholder integration in DANUBIA were not entirely successful. However, important insights were gained and important lessons were learnt. Against the background of these experiences we feel that in its current state, socio-hydrology is still lacking a plan for knowledge integration. Moreover, we consider necessary that socio-hydrology takes into account the lessons learnt from these earlier examples of knowledge integration (see also, Hamilton, ElSawah, Guillaume, Jakeman, and Pierce 2015; Jakeman and Letcher 2003). Our contribution attempts to close a gap between previous concepts of integration of socio-economic aspects into hydrology (typically inspired by Integrated Water Resources Management) and the new socio-hydrology approach. We suppose that socio-hydrology could benefit from widening its scope and considering previous research at the boundaries between hydrology and social sciences. At the same time, concepts developed prior to socio-hydrology were seldom entirely successful. It might be beneficial to review these approaches developed earlier and those that are being developed in parallel from the perspective of socio-hydrology. References: Barthel, R., S. Janisch, N. Schwarz, A. Trifkovic, D. Nickel, C. Schulz, and W. Mauser. 2008. An integrated modelling framework for simulating regional-scale actor responses to global change in the water domain. Environmental Modelling & Software, 23: 1095-1121. Barthel, R., D. Nickel, A. Meleg, A. Trifkovic, and J. Braun. 2005. Linking the physical and the socio-economic compartments of an integrated water and land use management model on a river basin scale using an object-oriented water supply model. Physics and Chemistry of the Earth, 30: 389-397. doi: 10.1016/j.pce.2005.06.006 Hamilton, S. H., S. ElSawah, J. H. A. Guillaume, A. J. Jakeman, and S. A. Pierce. 2015. Integrated assessment and modelling: Overview and synthesis ofsalient dimensions. Environmental Modelling and Software, 64: 215-229. doi: 10.1016/j.envsoft.2014.12.005 Jakeman, A. J., and R. A. Letcher. 2003. Integrated assessment and modelling: features, principles and examples for catchment management. Environmental Modelling & Software, 18: 491-501. doi: http://dx.doi.org/10.1016/S1364-8152(03)00024-0 Mauser, W., and M. Prasch. 2016. Regional Assessment of Global Change Impacts - The Project GLOWA-Danube: Springer International Publishing.

Blueprint for a coupled model of sedimentology, hydrology, and hydrogeology in streambeds

NASA Astrophysics Data System (ADS)

Partington, Daniel; Therrien, Rene; Simmons, Craig T.; Brunner, Philip

2017-06-01

The streambed constitutes the physical interface between the surface and the subsurface of a stream. Across all spatial scales, the physical properties of the streambed control surface water-groundwater interactions. Continuous alteration of streambed properties such as topography or hydraulic conductivity occurs through erosion and sedimentation processes. Recent studies from the fields of ecology, hydrogeology, and sedimentology provide field evidence that sedimentological processes themselves can be heavily influenced by surface water-groundwater interactions, giving rise to complex feedback mechanisms between sedimentology, hydrology, and hydrogeology. More explicitly, surface water-groundwater exchanges play a significant role in the deposition of fine sediments, which in turn modify the hydraulic properties of the streambed. We explore these feedback mechanisms and critically review the extent of current interaction between the different disciplines. We identify opportunities to improve current modeling practices. For example, hydrogeological models treat the streambed as a static rather than a dynamic entity, while sedimentological models do not account for critical catchment processes such as surface water-groundwater exchange. We propose a blueprint for a new modeling framework that bridges the conceptual gaps between sedimentology, hydrogeology, and hydrology. Specifically, this blueprint (1) fully integrates surface-subsurface flows with erosion, transport, and deposition of sediments and (2) accounts for the dynamic changes in surface elevation and hydraulic conductivity of the streambed. Finally, we discuss the opportunities for new research within the coupled framework.

pyGIMLi: An open-source library for modelling and inversion in geophysics

NASA Astrophysics Data System (ADS)

Rücker, Carsten; Günther, Thomas; Wagner, Florian M.

2017-12-01

Many tasks in applied geosciences cannot be solved by single measurements, but require the integration of geophysical, geotechnical and hydrological methods. Numerical simulation techniques are essential both for planning and interpretation, as well as for the process understanding of modern geophysical methods. These trends encourage open, simple, and modern software architectures aiming at a uniform interface for interdisciplinary and flexible modelling and inversion approaches. We present pyGIMLi (Python Library for Inversion and Modelling in Geophysics), an open-source framework that provides tools for modelling and inversion of various geophysical but also hydrological methods. The modelling component supplies discretization management and the numerical basis for finite-element and finite-volume solvers in 1D, 2D and 3D on arbitrarily structured meshes. The generalized inversion framework solves the minimization problem with a Gauss-Newton algorithm for any physical forward operator and provides opportunities for uncertainty and resolution analyses. More general requirements, such as flexible regularization strategies, time-lapse processing and different sorts of coupling individual methods are provided independently of the actual methods used. The usage of pyGIMLi is first demonstrated by solving the steady-state heat equation, followed by a demonstration of more complex capabilities for the combination of different geophysical data sets. A fully coupled hydrogeophysical inversion of electrical resistivity tomography (ERT) data of a simulated tracer experiment is presented that allows to directly reconstruct the underlying hydraulic conductivity distribution of the aquifer. Another example demonstrates the improvement of jointly inverting ERT and ultrasonic data with respect to saturation by a new approach that incorporates petrophysical relations in the inversion. Potential applications of the presented framework are manifold and include time-lapse, constrained, joint, and coupled inversions of various geophysical and hydrological data sets.

Hydrological modelling in a drinking water catchment area as a means of evaluating pathogen risk reduction

NASA Astrophysics Data System (ADS)

Bergion, Viktor; Sokolova, Ekaterina; Åström, Johan; Lindhe, Andreas; Sörén, Kaisa; Rosén, Lars

2017-01-01

Waterborne outbreaks of gastrointestinal diseases are of great concern to drinking water producers and can give rise to substantial costs to the society. The World Health Organisation promotes an approach where the emphasis is on mitigating risks close to the contamination source. In order to handle microbial risks efficiently, there is a need for systematic risk management. In this paper we present a framework for microbial risk management of drinking water systems. The framework incorporates cost-benefit analysis as a decision support method. The hydrological Soil and Water Assessment Tool (SWAT) model, which was set up for the Stäket catchment area in Sweden, was used to simulate the effects of four different mitigation measures on microbial concentrations. The modelling results showed that the two mitigation measures that resulted in a significant (p < 0.05) reduction of Cryptosporidium spp. and Escherichia coli concentrations were a vegetative filter strip linked to cropland and improved treatment (by one Log10 unit) at the wastewater treatment plants. The mitigation measure with a vegetative filter strip linked to grazing areas resulted in a significant reduction of Cryptosporidium spp., but not of E. coli concentrations. The mitigation measure with enhancing the removal efficiency of all on-site wastewater treatment systems (total removal of 2 Log10 units) did not achieve any significant reduction of E. coli or Cryptosporidium spp. concentrations. The SWAT model was useful when characterising the effect of different mitigation measures on microbial concentrations. Hydrological modelling implemented within an appropriate risk management framework is a key decision support element as it identifies the most efficient alternative for microbial risk reduction.

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.

Delineating wetland catchments and modeling hydrologic connectivity using lidar data and aerial imagery

NASA Astrophysics Data System (ADS)

Wu, Qiusheng; Lane, Charles R.

2017-07-01

In traditional watershed delineation and topographic modeling, surface depressions are generally treated as spurious features and simply removed from a digital elevation model (DEM) to enforce flow continuity of water across the topographic surface to the watershed outlets. In reality, however, many depressions in the DEM are actual wetland landscape features with seasonal to permanent inundation patterning characterized by nested hierarchical structures and dynamic filling-spilling-merging surface-water hydrological processes. Differentiating and appropriately processing such ecohydrologically meaningful features remains a major technical terrain-processing challenge, particularly as high-resolution spatial data are increasingly used to support modeling and geographic analysis needs. The objectives of this study were to delineate hierarchical wetland catchments and model their hydrologic connectivity using high-resolution lidar data and aerial imagery. The graph-theory-based contour tree method was used to delineate the hierarchical wetland catchments and characterize their geometric and topological properties. Potential hydrologic connectivity between wetlands and streams were simulated using the least-cost-path algorithm. The resulting flow network delineated potential flow paths connecting wetland depressions to each other or to the river network on scales finer than those available through the National Hydrography Dataset. The results demonstrated that our proposed framework is promising for improving overland flow simulation and hydrologic connectivity analysis.

Insights about data assimilation frameworks for integrating GRACE with hydrological models

NASA Astrophysics Data System (ADS)

Schumacher, Maike; Kusche, Jürgen; Van Dijk, Albert I. J. M.; Döll, Petra; Schuh, Wolf-Dieter

2016-04-01

Improving the understanding of changes in the water cycle represents a challenging objective that requires merging information from various disciplines. Debates exist on selecting an appropriate assimilation technique to integrate GRACE-derived terrestrial water storage changes (TWSC) into hydrological models in order to downscale and disaggregate GRACE TWSC, overcome model limitations, and improve monitoring and forecast skills. Yet, the effect of the specific data assimilation technique in conjunction with ill-conditioning, colored noise, resolution mismatch between GRACE and model, and other complications is still unclear. Due to its simplicity, ensemble Kalman filters or smoothers (EnKF/S) are often applied. In this study, we show that modification of the filter approach might open new avenues to improve the integration process. Particularly, we discuss an improved calibration and data assimilation (C/DA) framework (Schumacher et al., 2016), which is based on the EnKF and was extended by the square root analysis scheme (SQRA) and the singular evolutive interpolated Kalman (SEIK) filter. In addition, we discuss an off-line data blending approach (Van Dijk et al., 2014) that offers the chance to merge multi-model ensembles with GRACE observations. The investigations include: (i) a theoretical comparison, focusing on similarities and differences of the conceptual formulation of the filter algorithms, (ii) a practical comparison, for which the approaches were applied to an ensemble of runs of the WaterGAP Global Hydrology Model (WGHM), as well as (iii) an impact assessment of the GRACE error structure on C/DA results. First, a synthetic experiment over the Mississippi River Basin (USA) was used to gain insights about the C/DA set-up before applying it to real data. The results indicated promising performances when considering alternative methods, e.g. applying the SEIK algorithm improved the correlation coefficient and root mean square error (RMSE) of TWSC by 0.1 and 6 mm, with respect to the EnKF. We successfully transferred our framework to the Murray-Darling Basin (Australia), one of the largest and driest river basins over the world. Finally, we provide recommendations on an optimal C/DA strategy for real GRACE data integrations. Schumacher M, Kusche J, Döll P (2016): A Systematic Impact Assessment of GRACE Error Correlation on Data Assimilation in Hydrological Models. J Geod Van Dijk AIJM, Renzullo LJ, Wada Y, Tregoning P (2014): A global water cycle reanalysis (2003-2012) merging satellite gravimetry and altimetry observations with a hydrological multi-model ensemble. Hydrol Earth Syst Sci

An integrated eco-hydrologic modeling framework for assessing the effects of interacting stressors on forest ecosystem services

EPA Science Inventory

The U.S. Environmental Protection Agency recently established the Ecosystem Services Research Program to help formulate methods and models for conducting comprehensive risk assessments that quantify how multiple ecosystem services interact and respond in concert to environmental ...

An integrated eco-hydrologic modeling framework for assessing the effects of interacting stressors on multiple ecosystem services

EPA Science Inventory

The U.S. Environmental Protection Agency recently established the Ecosystem Services Research Program to help formulate methods and models for conducting comprehensive risk assessments that quantify how multiple ecosystem services interact and respond in concert to environmental ...

An evaluation framework for participatory modelling

NASA Astrophysics Data System (ADS)

Krueger, T.; Inman, A.; Chilvers, J.

2012-04-01

Strong arguments for participatory modelling in hydrology can be made on substantive, instrumental and normative grounds. These arguments have led to increasingly diverse groups of stakeholders (here anyone affecting or affected by an issue) getting involved in hydrological research and the management of water resources. In fact, participation has become a requirement of many research grants, programs, plans and policies. However, evidence of beneficial outcomes of participation as suggested by the arguments is difficult to generate and therefore rare. This is because outcomes are diverse, distributed, often tacit, and take time to emerge. In this paper we develop an evaluation framework for participatory modelling focussed on learning outcomes. Learning encompasses many of the potential benefits of participation, such as better models through diversity of knowledge and scrutiny, stakeholder empowerment, greater trust in models and ownership of subsequent decisions, individual moral development, reflexivity, relationships, social capital, institutional change, resilience and sustainability. Based on the theories of experiential, transformative and social learning, complemented by practitioner experience our framework examines if, when and how learning has occurred. Special emphasis is placed on the role of models as learning catalysts. We map the distribution of learning between stakeholders, scientists (as a subgroup of stakeholders) and models. And we analyse what type of learning has occurred: instrumental learning (broadly cognitive enhancement) and/or communicative learning (change in interpreting meanings, intentions and values associated with actions and activities; group dynamics). We demonstrate how our framework can be translated into a questionnaire-based survey conducted with stakeholders and scientists at key stages of the participatory process, and show preliminary insights from applying the framework within a rural pollution management situation in the UK.

Socio-hydrologic modeling to understand and mediate the competition for water between agriculture development and environmental health: Murrumbidgee River basin, Australia

NASA Astrophysics Data System (ADS)

van Emmerik, T. H. M.; Li, Z.; Sivapalan, M.; Pande, S.; Kandasamy, J.; Savenije, H. H. G.; Chanan, A.; Vigneswaran, S.

2014-10-01

Competition for water between humans and ecosystems is set to become a flash point in the coming decades in many parts of the world. An entirely new and comprehensive quantitative framework is needed to establish a holistic understanding of that competition, thereby enabling the development of effective mediation strategies. This paper presents a modeling study centered on the Murrumbidgee River basin (MRB). The MRB has witnessed a unique system dynamics over the last 100 years as a result of interactions between patterns of water management and climate driven hydrological variability. Data analysis has revealed a pendulum swing between agricultural development and restoration of environmental health and ecosystem services over different stages of basin-scale water resource development. A parsimonious, stylized, quasi-distributed coupled socio-hydrologic system model that simulates the two-way coupling between human and hydrological systems of the MRB is used to mimic and explain dominant features of the pendulum swing. The model consists of coupled nonlinear ordinary differential equations that describe the interaction between five state variables that govern the co-evolution: reservoir storage, irrigated area, human population, ecosystem health, and environmental awareness. The model simulations track the propagation of the external climatic and socio-economic drivers through this coupled, complex system to the emergence of the pendulum swing. The model results point to a competition between human "productive" and environmental "restorative" forces that underpin the pendulum swing. Both the forces are endogenous, i.e., generated by the system dynamics in response to external drivers and mediated by humans through technology change and environmental awareness, respectively. Sensitivity analysis carried out with the model further reveals that socio-hydrologic modeling can be used as a tool to explain or gain insight into observed co-evolutionary dynamics of diverse human-water coupled systems. This paper therefore contributes to the ultimate development of a generic modeling framework that can be applied to human-water coupled systems in different climatic and socio-economic settings.

Use of decision support systems as a drought management tool

USGS Publications Warehouse

Frevert, D.; Lins, H.; ,

2005-01-01

Droughts present a unique challenge to water managers throughout the world and the current drought in the western United States is taxing facilities to the limit. Coping with this severe drought requires state of the art decision support systems including efficient and accurate hydrologic process models, detailed hydrologic data bases and effective river systems management modeling frameworks. This paper will outline a system of models developed by the Bureau of Reclamation, the US Geological Survey, the University of Colorado and a number of other governmental and university partners. The application of the technology to drought management in several key western river basins will be discussed.

Digital Hydrologic Networks Supporting Applications Related to Spatially Referenced Regression Modeling

USGS Publications Warehouse

Brakebill, J.W.; Wolock, D.M.; Terziotti, S.E.

2011-01-01

Digital hydrologic networks depicting surface-water pathways and their associated drainage catchments provide a key component to hydrologic analysis and modeling. Collectively, they form common spatial units that can be used to frame the descriptions of aquatic and watershed processes. In addition, they provide the ability to simulate and route the movement of water and associated constituents throughout the landscape. Digital hydrologic networks have evolved from derivatives of mapping products to detailed, interconnected, spatially referenced networks of water pathways, drainage areas, and stream and watershed characteristics. These properties are important because they enhance the ability to spatially evaluate factors that affect the sources and transport of water-quality constituents at various scales. SPAtially Referenced Regressions On Watershed attributes (SPARROW), a process-based/statistical model, relies on a digital hydrologic network in order to establish relations between quantities of monitored contaminant flux, contaminant sources, and the associated physical characteristics affecting contaminant transport. Digital hydrologic networks modified from the River Reach File (RF1) and National Hydrography Dataset (NHD) geospatial datasets provided frameworks for SPARROW in six regions of the conterminous United States. In addition, characteristics of the modified RF1 were used to update estimates of mean-annual streamflow. This produced more current flow estimates for use in SPARROW modeling. ?? 2011 American Water Resources Association. This article is a U.S. Government work and is in the public domain in the USA.

How do the methodological choices of your climate change study affect your results? A hydrologic case study across the Pacific Northwest

NASA Astrophysics Data System (ADS)

Chegwidden, O.; Nijssen, B.; Rupp, D. E.; Kao, S. C.; Clark, M. P.

2017-12-01

We describe results from a large hydrologic climate change dataset developed across the Pacific Northwestern United States and discuss how the analysis of those results can be seen as a framework for other large hydrologic ensemble investigations. This investigation will better inform future modeling efforts and large ensemble analyses across domains within and beyond the Pacific Northwest. Using outputs from the Coupled Model Intercomparison Project Phase 5 (CMIP5), we provide projections of hydrologic change for the domain through the end of the 21st century. The dataset is based upon permutations of four methodological choices: (1) ten global climate models (2) two representative concentration pathways (3) three meteorological downscaling methods and (4) four unique hydrologic model set-ups (three of which entail the same hydrologic model using independently calibrated parameter sets). All simulations were conducted across the Columbia River Basin and Pacific coastal drainages at a 1/16th ( 6 km) resolution and at a daily timestep. In total, the 172 distinct simulations offer an updated, comprehensive view of climate change projections through the end of the 21st century. The results consist of routed streamflow at 400 sites throughout the domain as well as distributed spatial fields of relevant hydrologic variables like snow water equivalent and soil moisture. In this presentation, we discuss the level of agreement with previous hydrologic projections for the study area and how these projections differ with specific methodological choices. By controlling for some methodological choices we can show how each choice affects key climatic change metrics. We discuss how the spread in results varies across hydroclimatic regimes. We will use this large dataset as a case study for distilling a wide range of hydroclimatological projections into useful climate change assessments.

A method for physically based model analysis of conjunctive use in response to potential climate changes

USGS Publications Warehouse

Hanson, R.T.; Flint, L.E.; Flint, A.L.; Dettinger, M.D.; Faunt, C.C.; Cayan, D.; Schmid, W.

2012-01-01

Potential climate change effects on aspects of conjunctive management of water resources can be evaluated by linking climate models with fully integrated groundwater-surface water models. The objective of this study is to develop a modeling system that links global climate models with regional hydrologic models, using the California Central Valley as a case study. The new method is a supply and demand modeling framework that can be used to simulate and analyze potential climate change and conjunctive use. Supply-constrained and demand-driven linkages in the water system in the Central Valley are represented with the linked climate models, precipitation-runoff models, agricultural and native vegetation water use, and hydrologic flow models to demonstrate the feasibility of this method. Simulated precipitation and temperature were used from the GFDL-A2 climate change scenario through the 21st century to drive a regional water balance mountain hydrologic watershed model (MHWM) for the surrounding watersheds in combination with a regional integrated hydrologic model of the Central Valley (CVHM). Application of this method demonstrates the potential transition from predominantly surface water to groundwater supply for agriculture with secondary effects that may limit this transition of conjunctive use. The particular scenario considered includes intermittent climatic droughts in the first half of the 21st century followed by severe persistent droughts in the second half of the 21st century. These climatic droughts do not yield a valley-wide operational drought but do cause reduced surface water deliveries and increased groundwater abstractions that may cause additional land subsidence, reduced water for riparian habitat, or changes in flows at the Sacramento-San Joaquin River Delta. The method developed here can be used to explore conjunctive use adaptation options and hydrologic risk assessments in regional hydrologic systems throughout the world.

Water Accounting Plus (WA+) - a water accounting procedure for complex river basins based on satellite measurements

NASA Astrophysics Data System (ADS)

Karimi, P.; Bastiaanssen, W. G. M.; Molden, D.

2012-11-01

Coping with the issue of water scarcity and growing competition for water among different sectors requires proper water management strategies and decision processes. A pre-requisite is a clear understanding of the basin hydrological processes, manageable and unmanageable water flows, the interaction with land use and opportunities to mitigate the negative effects and increase the benefits of water depletion on society. Currently, water professionals do not have a common framework that links hydrological flows to user groups of water and their benefits. The absence of a standard hydrological and water management summary is causing confusion and wrong decisions. The non-availability of water flow data is one of the underpinning reasons for not having operational water accounting systems for river basins in place. In this paper we introduce Water Accounting Plus (WA+), which is a new framework designed to provide explicit spatial information on water depletion and net withdrawal processes in complex river basins. The influence of land use on the water cycle is described explicitly by defining land use groups with common characteristics. Analogous to financial accounting, WA+ presents four sheets including (i) a resource base sheet, (ii) a consumption sheet, (iii) a productivity sheet, and (iv) a withdrawal sheet. Every sheet encompasses a set of indicators that summarize the overall water resources situation. The impact of external (e.g. climate change) and internal influences (e.g. infrastructure building) can be estimated by studying the changes in these WA+ indicators. Satellite measurements can be used for 3 out of the 4 sheets, but is not a precondition for implementing WA+ framework. Data from hydrological models and water allocation models can also be used as inputs to WA+.

Invited seminar, University of North Texas: An integrated eco-hydrologic modeling framework for assessing the effects of interacting stressors

EPA Science Inventory

The U.S. Environmental Protection Agency recently established the Ecosystem Services Research Program to help formulate methods and models for conducting comprehensive risk assessments that quantify how multiple ecosystem services interact and respond in concert to environmental ...

Invited OSU class lecture: An integrated eco-hydrologic modeling framework for assessing the effects of interacting stressors on multiple ecosystem services

EPA Science Inventory

The U.S. Environmental Protection Agency recently established the Ecosystem Services Research Program to help formulate methods and models for conducting comprehensive risk assessments that quantify how multiple ecosystem services interact and respond in concert to environmental ...

An integrated eco-hydrologic modeling framework for assessing the effects of interacting stressors on forest ecosystem services - ESRP mtg

EPA Science Inventory

The U.S. Environmental Protection Agency recently established the Ecosystem Services Research Program to help formulate methods and models for conducting comprehensive risk assessments that quantify how multiple ecosystem services interact and respond in concert to environmental ...

An integrated eco-hydrologic modeling framework for assessing the effects of interacting stressors on multiple ecosystem services - 4/27/10

EPA Science Inventory

The U.S. Environmental Protection Agency recently established the Ecosystem Services Research Program to help formulate methods and models for conducting comprehensive risk assessments that quantify how multiple ecosystem services interact and respond in concert to environmental ...

A new framework for modeling decentralized low impact developments using Soil and Water Assessment Tool

USDA-ARS?s Scientific Manuscript database

Assessing the performance of Low Impact Development (LID) practices at a catchment scale is important in managing urban watersheds. Few modeling tools exist that are capable of explicitly representing the hydrological mechanisms of LIDs while considering the diverse land uses of urban watersheds. ...

Impact of model relative accuracy in framework of rescaling observations in hydrological data assimilation studies

USDA-ARS?s Scientific Manuscript database

Soil moisture datasets (e.g. satellite-, model-, station-based) vary greatly with respect to their signal, noise, and/or combined time-series variability. Minimizing differences in signal variances is particularly important in data assimilation techniques to optimize the accuracy of the analysis obt...

A new framework for comprehensive, robust, and efficient global sensitivity analysis: 2. Application

NASA Astrophysics Data System (ADS)

Razavi, Saman; Gupta, Hoshin V.

2016-01-01

Based on the theoretical framework for sensitivity analysis called "Variogram Analysis of Response Surfaces" (VARS), developed in the companion paper, we develop and implement a practical "star-based" sampling strategy (called STAR-VARS), for the application of VARS to real-world problems. We also develop a bootstrap approach to provide confidence level estimates for the VARS sensitivity metrics and to evaluate the reliability of inferred factor rankings. The effectiveness, efficiency, and robustness of STAR-VARS are demonstrated via two real-data hydrological case studies (a 5-parameter conceptual rainfall-runoff model and a 45-parameter land surface scheme hydrology model), and a comparison with the "derivative-based" Morris and "variance-based" Sobol approaches are provided. Our results show that STAR-VARS provides reliable and stable assessments of "global" sensitivity across the full range of scales in the factor space, while being 1-2 orders of magnitude more efficient than the Morris or Sobol approaches.

From spatially variable streamflow to distributed hydrological models: Analysis of key modeling decisions

NASA Astrophysics Data System (ADS)

Fenicia, Fabrizio; Kavetski, Dmitri; Savenije, Hubert H. G.; Pfister, Laurent

2016-02-01

This paper explores the development and application of distributed hydrological models, focusing on the key decisions of how to discretize the landscape, which model structures to use in each landscape element, and how to link model parameters across multiple landscape elements. The case study considers the Attert catchment in Luxembourg—a 300 km2 mesoscale catchment with 10 nested subcatchments that exhibit clearly different streamflow dynamics. The research questions are investigated using conceptual models applied at hydrologic response unit (HRU) scales (1-4 HRUs) on 6 hourly time steps. Multiple model structures are hypothesized and implemented using the SUPERFLEX framework. Following calibration, space/time model transferability is tested using a split-sample approach, with evaluation criteria including streamflow prediction error metrics and hydrological signatures. Our results suggest that: (1) models using geology-based HRUs are more robust and capture the spatial variability of streamflow time series and signatures better than models using topography-based HRUs; this finding supports the hypothesis that, in the Attert, geology exerts a stronger control than topography on streamflow generation, (2) streamflow dynamics of different HRUs can be represented using distinct and remarkably simple model structures, which can be interpreted in terms of the perceived dominant hydrologic processes in each geology type, and (3) the same maximum root zone storage can be used across the three dominant geological units with no loss in model transferability; this finding suggests that the partitioning of water between streamflow and evaporation in the study area is largely independent of geology and can be used to improve model parsimony. The modeling methodology introduced in this study is general and can be used to advance our broader understanding and prediction of hydrological behavior, including the landscape characteristics that control hydrologic response, the dominant processes associated with different landscape types, and the spatial relations of catchment processes. This article was corrected on 14 MAR 2016. See the end of the full text for details.

How much can we trust a geological model underlying a subsurface hydrological investigation?

NASA Astrophysics Data System (ADS)

Wellmann, Florian; de la Varga, Miguel; Schaaf, Alexander; Burs, David

2017-04-01

Geological models often provide an important basis for subsequent hydrological investigations. As these models are generally built with a limited amount of information, they can contain significant uncertainties - and it is reasonable to assume that these uncertainties can potentially influence subsequent hydrological simulations. However, the investigation of uncertainties in geological models is not straightforward - and, even though recent advances have been made in the field, there is no out-of-the-box implementation to analyze uncertainties in a standard geological modeling package. We present here results of recent developments to address this problem with an efficient implementation of a geological modeling method for complex structural models, integrated in a Bayesian inference framework. The implemented geological modeling approach is based on a full 3-D implicit interpolation that directly respects interface positions and orientation measurements, as well as the influence of faults. In combination, the approach allows us to generate ensembles of geological model realizations, constrained by additional information in the form of likelihood functions to ensure consistency with additional geological aspects (e.g. sequence continuity, topology, fault network consistency), and we demonstrate the potential of the method in an exemplified case study. With this approach, we aim to contribute to a better understanding of the influence of geological uncertainties on subsurface hydrological investigations.

Emergent structures and understanding from a comparative uncertainty analysis of the FUSE rainfall-runoff modelling platform for >1,100 catchments

NASA Astrophysics Data System (ADS)

Freer, J. E.; Odoni, N. A.; Coxon, G.; Bloomfield, J.; Clark, M. P.; Greene, S.; Johnes, P.; Macleod, C.; Reaney, S. M.

2013-12-01

If we are to learn about catchments and their hydrological function then a range of analysis techniques can be proposed from analysing observations to building complex physically based models using detailed attributes of catchment characteristics. Decisions regarding which technique is fit for a specific purpose will depend on the data available, computing resources, and the underlying reasons for the study. Here we explore defining catchment function in a relatively general sense expressed via a comparison of multiple model structures within an uncertainty analysis framework. We use the FUSE (Framework for Understanding Structural Errors - Clark et al., 2008) rainfall-runoff modelling platform and the GLUE (Generalised Likelihood Uncertainty Estimation - Beven and Freer, 2001) uncertainty analysis framework. Using these techniques we assess two main outcomes: 1) Benchmarking our predictive capability using discharge performance metrics for a diverse range of catchments across the UK 2) evaluating emergent behaviour for each catchment and/or region expressed as ';best performing' model structures that may be equally plausible representations of catchment behaviour. We shall show how such comparative hydrological modelling studies show patterns of emergent behaviour linked both to seasonal responses and to different geoclimatic regions. These results have implications for the hydrological community regarding how models can help us learn about places as hypothesis testing tools. Furthermore we explore what the limits are to such an analysis when dealing with differing data quality and information content from ';pristine' to less well characterised and highly modified catchment domains. This research has been piloted in the UK as part of the Environmental Virtual Observatory programme (EVOp), funded by NERC to demonstrate the use of cyber-infrastructure and cloud computing resources to develop better methods of linking data and models and to support scenario analysis for research, policy and operational needs.

Land Surface Verification Toolkit (LVT) - A Generalized Framework for Land Surface Model Evaluation

NASA Technical Reports Server (NTRS)

Kumar, Sujay V.; Peters-Lidard, Christa D.; Santanello, Joseph; Harrison, Ken; Liu, Yuqiong; Shaw, Michael

2011-01-01

Model evaluation and verification are key in improving the usage and applicability of simulation models for real-world applications. In this article, the development and capabilities of a formal system for land surface model evaluation called the Land surface Verification Toolkit (LVT) is described. LVT is designed to provide an integrated environment for systematic land model evaluation and facilitates a range of verification approaches and analysis capabilities. LVT operates across multiple temporal and spatial scales and employs a large suite of in-situ, remotely sensed and other model and reanalysis datasets in their native formats. In addition to the traditional accuracy-based measures, LVT also includes uncertainty and ensemble diagnostics, information theory measures, spatial similarity metrics and scale decomposition techniques that provide novel ways for performing diagnostic model evaluations. Though LVT was originally designed to support the land surface modeling and data assimilation framework known as the Land Information System (LIS), it also supports hydrological data products from other, non-LIS environments. In addition, the analysis of diagnostics from various computational subsystems of LIS including data assimilation, optimization and uncertainty estimation are supported within LVT. Together, LIS and LVT provide a robust end-to-end environment for enabling the concepts of model data fusion for hydrological applications. The evolving capabilities of LVT framework are expected to facilitate rapid model evaluation efforts and aid the definition and refinement of formal evaluation procedures for the land surface modeling community.

Improving flood forecasting capability of physically based distributed hydrological model by parameter optimization

NASA Astrophysics Data System (ADS)

Chen, Y.; Li, J.; Xu, H.

2015-10-01

Physically based distributed hydrological models discrete the terrain of the whole catchment into a number of grid cells at fine resolution, and assimilate different terrain data and precipitation to different cells, and are regarded to have the potential to improve the catchment hydrological processes simulation and prediction capability. In the early stage, physically based distributed hydrological models are assumed to derive model parameters from the terrain properties directly, so there is no need to calibrate model parameters, but unfortunately, the uncertanties associated with this model parameter deriving is very high, which impacted their application in flood forecasting, so parameter optimization may also be necessary. There are two main purposes for this study, the first is to propose a parameter optimization method for physically based distributed hydrological models in catchment flood forecasting by using PSO algorithm and to test its competence and to improve its performances, the second is to explore the possibility of improving physically based distributed hydrological models capability in cathcment flood forecasting by parameter optimization. In this paper, based on the scalar concept, a general framework for parameter optimization of the PBDHMs for catchment flood forecasting is first proposed that could be used for all PBDHMs. Then, with Liuxihe model as the study model, which is a physically based distributed hydrological model proposed for catchment flood forecasting, the improverd Particle Swarm Optimization (PSO) algorithm is developed for the parameter optimization of Liuxihe model in catchment flood forecasting, the improvements include to adopt the linear decreasing inertia weight strategy to change the inertia weight, and the arccosine function strategy to adjust the acceleration coefficients. This method has been tested in two catchments in southern China with different sizes, and the results show that the improved PSO algorithm could be used for Liuxihe model parameter optimization effectively, and could improve the model capability largely in catchment flood forecasting, thus proven that parameter optimization is necessary to improve the flood forecasting capability of physically based distributed hydrological model. It also has been found that the appropriate particle number and the maximum evolution number of PSO algorithm used for Liuxihe model catchment flood forcasting is 20 and 30, respectively.

On how to avoid input and structural uncertainties corrupt the inference of hydrological parameters using a Bayesian framework

NASA Astrophysics Data System (ADS)

Hernández, Mario R.; Francés, Félix

2015-04-01

One phase of the hydrological models implementation process, significantly contributing to the hydrological predictions uncertainty, is the calibration phase in which values of the unknown model parameters are tuned by optimizing an objective function. An unsuitable error model (e.g. Standard Least Squares or SLS) introduces noise into the estimation of the parameters. The main sources of this noise are the input errors and the hydrological model structural deficiencies. Thus, the biased calibrated parameters cause the divergence model phenomenon, where the errors variance of the (spatially and temporally) forecasted flows far exceeds the errors variance in the fitting period, and provoke the loss of part or all of the physical meaning of the modeled processes. In other words, yielding a calibrated hydrological model which works well, but not for the right reasons. Besides, an unsuitable error model yields a non-reliable predictive uncertainty assessment. Hence, with the aim of prevent all these undesirable effects, this research focuses on the Bayesian joint inference (BJI) of both the hydrological and error model parameters, considering a general additive (GA) error model that allows for correlation, non-stationarity (in variance and bias) and non-normality of model residuals. As hydrological model, it has been used a conceptual distributed model called TETIS, with a particular split structure of the effective model parameters. Bayesian inference has been performed with the aid of a Markov Chain Monte Carlo (MCMC) algorithm called Dream-ZS. MCMC algorithm quantifies the uncertainty of the hydrological and error model parameters by getting the joint posterior probability distribution, conditioned on the observed flows. The BJI methodology is a very powerful and reliable tool, but it must be used correctly this is, if non-stationarity in errors variance and bias is modeled, the Total Laws must be taken into account. The results of this research show that the application of BJI with a GA error model outperforms the hydrological parameters robustness (diminishing the divergence model phenomenon) and improves the reliability of the streamflow predictive distribution, in respect of the results of a bad error model as SLS. Finally, the most likely prediction in a validation period, for both BJI+GA and SLS error models shows a similar performance.

Hydrologic controls on equilibrium soil depths

NASA Astrophysics Data System (ADS)

Nicótina, L.; Tarboton, D. G.; Tesfa, T. K.; Rinaldo, A.

2011-04-01

This paper deals with modeling the mutual feedbacks between runoff production and geomorphological processes and attributes that lead to patterns of equilibrium soil depth. Our primary goal is an attempt to describe spatial patterns of soil depth resulting from long-term interactions between hydrologic forcings and soil production, erosion, and sediment transport processes under the framework of landscape dynamic equilibrium. Another goal is to set the premises for exploiting the role of soil depths in shaping the hydrologic response of a catchment. The relevance of the study stems from the massive improvement in hydrologic predictions for ungauged basins that would be achieved by using directly soil depths derived from geomorphic features remotely measured and objectively manipulated. Hydrological processes are here described by explicitly accounting for local soil depths and detailed catchment topography. Geomorphological processes are described by means of well-studied geomorphic transport laws. The modeling approach is applied to the semiarid Dry Creek Experimental Watershed, located near Boise, Idaho. Modeled soil depths are compared with field data obtained from an extensive survey of the catchment. Our results show the ability of the model to describe properly the mean soil depth and the broad features of the distribution of measured data. However, local comparisons show significant scatter whose origins are discussed.

Development of a Simple Framework to Assess Hydrological Extremes using Solely Climate Data

NASA Astrophysics Data System (ADS)

Foulon, E.; Gagnon, P.; Rousseau, A. N.

2014-12-01

Extreme flow conditions such as droughts and floods are in general the direct consequences of short- to long-term weather/climate anomalies. For example, in southern Quebec, Canada, winter and summer 7-day low flows are due to summer and fall precipitations. Which prompts the question: is it possible to assess future extreme flow conditions from meteorological/climate indices or should we rely on the classical approach of using outputs of climate models as input to a hydrological model? The objective of this study is to assess six hydrological indices describing extreme flows at the watershed scale (Qmax, Qmin;7d, Qmin;30d for two seasons: winter and summer) using local climate indices without relying on the aforementioned classical approach. To establish the relationship between climate and hydrological indices, daily precipitations, minimum and maximum temperatures from 89 climate projections are used as inputs to a distributed hydrological model. River flows are simulated at the outlet of the Yamaska and Bécancour watersheds in Québec for the 1961-2100 periods. To identify the best predictors, hydrological indices are extracted from the flow series, and climate indices are computed for different time intervals (from a day up to four years). The difference between four-month, cumulative, climatic demand (P-ETP) explains 69% of the 7-day summer low flow during the calibration process. For both watersheds, preliminary findings indicate that the selected indices explain, on average, 38 and 60% of the variability of high- and low-flow indices, respectively. Overall, the results clearly illustrate that the change in the hydrological indices can be detected through the concurrent trends in the climate indices. The use of many climate projections ensures the relationships are not simulation-dependent and shows summer events are particularly at risk with increasing high flows and decreasing low flows. The development of a simple predictive tool to assess the impact of climate change on flows represents one of the major spin-off benefits of this study and may prooveto be useful to municipalities concerned with source water and flood management. Future work includes development of additional climate indices and application of the framework to more watersheds.

A Framework to Assess the Cumulative Hydrological Impacts of Dams on flow Regime

NASA Astrophysics Data System (ADS)

Wang, Y.; Wang, D.

2016-12-01

In this study we proposed a framework to assess the cumulative impact of dams on hydrological regime, and the impacts of the Three Gorges Dam on flow regime in Yangtze River were investigated with the framework. We reconstructed the unregulated flow series to compare with the regulated flow series in the same period. Eco-surplus and eco-deficit and the Indicators of Hydrologic Alteration parameters were used to examine the hydrological regime change. Among IHA parameters, Wilcoxon signed-rank test and Principal Components Analysis identified the representative indicators of hydrological alterations. Eco-surplus and eco-deficit showed that the reservoir also changed the seasonal regime of the flows in autumn and winter. Annual extreme flows and October flows changes lead to negative ecological implications downstream from the Three Gorges Dam. Ecological operation for the Three Gorges Dam is necessary to mitigate the negative effects on the river ecosystem in the middle reach of Yangtze River. The framework proposed here could be a robust method to assess the cumulative impacts of reservoir operation.

A framework for the case-specific assessment of Green Infrastructure in mitigating urban flood hazards

NASA Astrophysics Data System (ADS)

Schubert, Jochen E.; Burns, Matthew J.; Fletcher, Tim D.; Sanders, Brett F.

2017-10-01

This research outlines a framework for the case-specific assessment of Green Infrastructure (GI) performance in mitigating flood hazard in small urban catchments. The urban hydrologic modeling tool (MUSIC) is coupled with a fine resolution 2D hydrodynamic model (BreZo) to test to what extent retrofitting an urban watershed with GI, rainwater tanks and infiltration trenches in particular, can propagate flood management benefits downstream and support intuitive flood hazard maps useful for communicating and planning with communities. The hydrologic and hydraulic models are calibrated based on current catchment conditions, then modified to represent alternative GI scenarios including a complete lack of GI versus a full implementation of GI. Flow in the hydrologic/hydraulic models is forced using a range of synthetic rainfall events with annual exceedance probabilities (AEPs) between 1-63% and durations from 10 min to 24 h. Flood hazard benefits mapped by the framework include maximum flood depths and extents, flow intensity (m2/s), flood duration, and critical storm duration leading to maximum flood conditions. Application of the system to the Little Stringybark Creek (LSC) catchment shows that across the range of AEPs tested and for storm durations equal or less than 3 h, presently implemented GI reduces downstream flooded area on average by 29%, while a full implementation of GI would reduce downstream flooded area on average by 91%. A full implementation of GI could also lower maximum flow intensities by 83% on average, reducing the drowning hazard posed by urban streams and improving the potential for access by emergency responders. For storm durations longer than 3 h, a full implementation of GI lacks the capacity to retain the resulting rainfall depths and only reduces flooded area by 8% and flow intensity by 5.5%.

Evaluating the impact of farm scale innovation at catchment scale

NASA Astrophysics Data System (ADS)

van Breda, Phelia; De Clercq, Willem; Vlok, Pieter; Querner, Erik

2014-05-01

Hydrological modelling lends itself to other disciplines very well, normally as a process based system that acts as a catalogue of events taking place. These hydrological models are spatial-temporal in their design and are generally well suited for what-if situations in other disciplines. Scaling should therefore be a function of the purpose of the modelling. Process is always linked with scale or support but the temporal resolution can affect the results if the spatial scale is not suitable. The use of hydrological response units tends to lump area around physical features but disregards farm boundaries. Farm boundaries are often the more crucial uppermost resolution needed to gain more value from hydrological modelling. In the Letaba Catchment of South Africa, we find a generous portion of landuses, different models of ownership, different farming systems ranging from large commercial farms to small subsistence farming. All of these have the same basic right to water but water distribution in the catchment is somewhat of a problem. Since water quantity is also a problem, the water supply systems need to take into account that valuable production areas not be left without water. Clearly hydrological modelling should therefore be sensitive to specific landuse. As a measure of productivity, a system of small farmer production evaluation was designed. This activity presents a dynamic system outside hydrological modelling that is generally not being considered inside hydrological modelling but depends on hydrological modelling. For sustainable development, a number of important concepts needed to be aligned with activities in this region, and the regulatory actions also need to be adhered to. This study aimed at aligning the activities in a region to the vision and objectives of the regulatory authorities. South Africa's system of socio-economic development planning is complex and mostly ineffective. There are many regulatory authorities involved, often with unclear responsibilities and inadequate procedures of implementing objectives. Planning for development in South Africa needs to take various factors into account. Economic and green economic growth is pursued, while social imbalances are addressed and the environment is protected against unreasonable exploitation. The term Sustainable Development is a neutral concept in the vision of many of the regulating authorities; however, the implementation of sustainability is difficult. This study considers an approach which aligns activities in a specified region to the vision and objectives of the applicable regulatory authorities, as an alternative to achieving objectives strictly through enforcing regulations. It was determined whether objectives of development planning were realistic in terms of water availability. It was established that the position of a farm in the landscape is a determining factor of the impact it has on the catchment area's water supply. For this purpose, hydrological modelling (SWAT and SIMGRO) was done for the Letaba catchment of the Limpopo Province, on two scales to also accommodate small-scale farming communities more accurately. Parallel to the modelling, the National Development Plan (NDP), the National Framework for Sustainable Development (NFSD), the Integrated Sustainable Rural Development Strategy (ISRDS) and the principles of Water Allocation Reform (WAR) were regarded. For regional categorisation, the relevant municipal Integrated Development Plan (IDP), Spatial Development Framework (SDF), Local Economic Development (LED) plan and the applicable Catchment Management Strategy (CMS) were considered. The developed Integrated Evaluation Model combined all the visions and objectives of the mentioned strategic documents to specifically assess the contribution a small-scale farm makes. The evaluation results provided insight into the alignment of activities to the ideals of a region and can be useful when formulating actions to reach a common vision. Small-scale farms are well-aligned to the objectives of WAR, the CMS and ISRDS. The farms have a limited contribution to the ideals of the NDP and NFSD and results against the IDP, the SDF and the LED differ considerably for each farm. Furthermore, the results of the farms' alignment with regional objectives do not correspond to the hydrologically ideal locations. Therefore, the development of small-scale farming should take hydrological information into consideration. The Integrated Evaluation Model proves to be valuable, understandable and applicable to evaluate the alignment of small-scale farms to the visions of regulatory authorities. It is also foreseen that the Evaluation model be linked to the hydrological model. The work was also kindly supported and executed in the framework of the EU project EAU4Food.

Application of the ELOHA framework to regulated rivers in the upper Tennessee River Basin: A case study

Treesearch

Ryan A. McManamay; Donald J. Orth; Charles A. Dolloff; David C. Mathews

2013-01-01

In order for habitat restoration in regulated rivers to be effective at large scales, broadly applicable frameworks are needed that provide measurable objectives and contexts for management. The Ecological Limits of Hydrologic Alteration (ELOHA) framework was created as a template to assess hydrologic alterations, develop relationships between altered streamflow and...

How much expert knowledge is it worth to put in conceptual hydrological models?

NASA Astrophysics Data System (ADS)

Antonetti, Manuel; Zappa, Massimiliano

2017-04-01

Both modellers and experimentalists agree on using expert knowledge to improve our conceptual hydrological simulations on ungauged basins. However, they use expert knowledge differently for both hydrologically mapping the landscape and parameterising a given hydrological model. Modellers use generally very simplified (e.g. topography-based) mapping approaches and put most of the knowledge for constraining the model by defining parameter and process relational rules. In contrast, experimentalists tend to invest all their detailed and qualitative knowledge about processes to obtain a spatial distribution of areas with different dominant runoff generation processes (DRPs) as realistic as possible, and for defining plausible narrow value ranges for each model parameter. Since, most of the times, the modelling goal is exclusively to simulate runoff at a specific site, even strongly simplified hydrological classifications can lead to satisfying results due to equifinality of hydrological models, overfitting problems and the numerous uncertainty sources affecting runoff simulations. Therefore, to test to which extent expert knowledge can improve simulation results under uncertainty, we applied a typical modellers' modelling framework relying on parameter and process constraints defined based on expert knowledge to several catchments on the Swiss Plateau. To map the spatial distribution of the DRPs, mapping approaches with increasing involvement of expert knowledge were used. Simulation results highlighted the potential added value of using all the expert knowledge available on a catchment. Also, combinations of event types and landscapes, where even a simplified mapping approach can lead to satisfying results, were identified. Finally, the uncertainty originated by the different mapping approaches was compared with the one linked to meteorological input data and catchment initial conditions.

Modelling surface water-groundwater interaction with a conceptual approach: model development and application in New Zealand

NASA Astrophysics Data System (ADS)

Yang, J.; Zammit, C.; McMillan, H. K.

2016-12-01

As in most countries worldwide, water management in lowland areas is a big concern for New Zealand due to its economic importance for water related human activities. As a result, the estimation of available water resources in these areas (e.g., for irrigation and water supply purpose) is crucial and often requires an understanding of complex hydrological processes, which are often characterized by strong interactions between surface water and groundwater (usually expressed as losing and gaining rivers). These processes are often represented and simulated using integrated physically based hydrological models. However models with physically based groundwater modules typically require large amount of non-readily available geologic and aquifer information and are computationally intensive. Instead, this paper presents a conceptual groundwater model that is fully integrated into New Zealand's national hydrological model TopNet based on TopModel concepts (Beven, 1992). Within this conceptual framework, the integrated model can simulate not only surface processes, but also groundwater processes and surface water-groundwater interaction processes (including groundwater flow, river-groundwater interaction, and groundwater interaction with external watersheds). The developed model was applied to two New Zealand catchments with different hydro-geological and climate characteristics (Pareora catchment in the Canterbury Plains and Grey catchment on the West Coast). Previous studies have documented strong interactions between the river and groundwater, based on the analysis of a large number of concurrent flow measurements and associated information along the river main stem. Application of the integrated hydrological model indicates flow simulation (compared to the original hydrological model conceptualisation) during low flow conditions are significantly improved and further insights on local river dynamics are gained. Due to its conceptual characteristics and low level of data requirement, the integrated model could be used at local and national scales to improve the simulation of hydrological processes in non-topographically driven areas (where groundwater processes are important), and to assess impact of climate change on the integrated hydrological cycle in these areas.

Precipitation-runoff modeling system; user's manual

USGS Publications Warehouse

Leavesley, G.H.; Lichty, R.W.; Troutman, B.M.; Saindon, L.G.

1983-01-01

The concepts, structure, theoretical development, and data requirements of the precipitation-runoff modeling system (PRMS) are described. The precipitation-runoff modeling system is a modular-design, deterministic, distributed-parameter modeling system developed to evaluate the impacts of various combinations of precipitation, climate, and land use on streamflow, sediment yields, and general basin hydrology. Basin response to normal and extreme rainfall and snowmelt can be simulated to evaluate changes in water balance relationships, flow regimes, flood peaks and volumes, soil-water relationships, sediment yields, and groundwater recharge. Parameter-optimization and sensitivity analysis capabilites are provided to fit selected model parameters and evaluate their individual and joint effects on model output. The modular design provides a flexible framework for continued model system enhancement and hydrologic modeling research and development. (Author 's abstract)

Sequential data assimilation for a distributed hydrologic model considering different time scale of internal processes

NASA Astrophysics Data System (ADS)

Noh, S.; Tachikawa, Y.; Shiiba, M.; Kim, S.

2011-12-01

Applications of the sequential data assimilation methods have been increasing in hydrology to reduce uncertainty in the model prediction. In a distributed hydrologic model, there are many types of state variables and each variable interacts with each other based on different time scales. However, the framework to deal with the delayed response, which originates from different time scale of hydrologic processes, has not been thoroughly addressed in the hydrologic data assimilation. In this study, we propose the lagged filtering scheme to consider the lagged response of internal states in a distributed hydrologic model using two filtering schemes; particle filtering (PF) and ensemble Kalman filtering (EnKF). The EnKF is one of the widely used sub-optimal filters implementing an efficient computation with limited number of ensemble members, however, still based on Gaussian approximation. PF can be an alternative in which the propagation of all uncertainties is carried out by a suitable selection of randomly generated particles without any assumptions about the nature of the distributions involved. In case of PF, advanced particle regularization scheme is implemented together to preserve the diversity of the particle system. In case of EnKF, the ensemble square root filter (EnSRF) are implemented. Each filtering method is parallelized and implemented in the high performance computing system. A distributed hydrologic model, the water and energy transfer processes (WEP) model, is applied for the Katsura River catchment, Japan to demonstrate the applicability of proposed approaches. Forecasted results via PF and EnKF are compared and analyzed in terms of the prediction accuracy and the probabilistic adequacy. Discussions are focused on the prospects and limitations of each data assimilation method.

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.

Continuous hydrologic simulation of runoff for the Middle Fork and South Fork of the Beargrass Creek basin in Jefferson County, Kentucky

USGS Publications Warehouse

Jarrett, G. Lynn; Downs, Aimee C.; Grace-Jarrett, Patricia A.

1998-01-01

The Hydrological Simulation Pro-gram-FORTRAN (HSPF) was applied to an urban drainage basin in Jefferson County, Ky to integrate the large amounts of information being collected on water quantity and quality into an analytical framework that could be used as a management and planning tool. Hydrologic response units were developed using geographic data and a K-means analysis to characterize important hydrologic and physical factors in the basin. The Hydrological Simulation Program FORTRAN Expert System (HSPEXP) was used to calibrate the model parameters for the Middle Fork Beargrass Creek Basin for 3 years (June 1, 1991, to May 31, 1994) of 5-minute streamflow and precipitation time series, and 3 years of hourly pan-evaporation time series. The calibrated model parameters were applied to the South Fork Beargrass Creek Basin for confirmation. The model confirmation results indicated that the model simulated the system within acceptable tolerances. The coefficient of determination and coefficient of model-fit efficiency between simulated and observed daily flows were 0.91 and 0.82, respectively, for model calibration and 0.88 and 0.77, respectively, for model confirmation. The model is most sensitive to estimates of the area of effective impervious land in the basin; the spatial distribution of rain-fall; and the lower-zone evapotranspiration, lower-zone nominal storage, and infiltration-capacity parameters during recession and low-flow periods. The error contribution from these sources varies with season and antecedent conditions.

Monte Carlo-based calibration and uncertainty analysis of a coupled plant growth and hydrological model

NASA Astrophysics Data System (ADS)

Houska, T.; Multsch, S.; Kraft, P.; Frede, H.-G.; Breuer, L.

2014-04-01

Computer simulations are widely used to support decision making and planning in the agriculture sector. On the one hand, many plant growth models use simplified hydrological processes and structures - for example, by the use of a small number of soil layers or by the application of simple water flow approaches. On the other hand, in many hydrological models plant growth processes are poorly represented. Hence, fully coupled models with a high degree of process representation would allow for a more detailed analysis of the dynamic behaviour of the soil-plant interface. We coupled two of such high-process-oriented independent models and calibrated both models simultaneously. The catchment modelling framework (CMF) simulated soil hydrology based on the Richards equation and the van Genuchten-Mualem model of the soil hydraulic properties. CMF was coupled with the plant growth modelling framework (PMF), which predicts plant growth on the basis of radiation use efficiency, degree days, water shortage and dynamic root biomass allocation. The Monte Carlo-based generalized likelihood uncertainty estimation (GLUE) method was applied to parameterize the coupled model and to investigate the related uncertainty of model predictions. Overall, 19 model parameters (4 for CMF and 15 for PMF) were analysed through 2 × 106 model runs randomly drawn from a uniform distribution. The model was applied to three sites with different management in Müncheberg (Germany) for the simulation of winter wheat (Triticum aestivum L.) in a cross-validation experiment. Field observations for model evaluation included soil water content and the dry matter of roots, storages, stems and leaves. The shape parameter of the retention curve n was highly constrained, whereas other parameters of the retention curve showed a large equifinality. We attribute this slightly poorer model performance to missing leaf senescence, which is currently not implemented in PMF. The most constrained parameters for the plant growth model were the radiation-use efficiency and the base temperature. Cross validation helped to identify deficits in the model structure, pointing out the need for including agricultural management options in the coupled model.

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.

HIS Design: Big Data that Supports Hydrologic Modeling from Continental to Hillslope Scales

NASA Astrophysics Data System (ADS)

Rasmussen, T. C.; Deemy, J. B.; Younger, S. E.; Kirk, S. E.; Brockman, L. E.

2016-12-01

Analogous to Google Maps, hydrologic data, information, and knowledge resolve differently depending upon the spatial and temporal scales of interest. We show how a multi-scale hydrologic information system (HIS) can be designed and populated for a broad range of spatial (e.g., hillslope, local, regional, continental) and temporal (e.g., current, recent, historic, geologic) scales. Surface and subsurface hydrologic and transport processes are assumed to be scale-dependent, requiring unique governing equations and parameters at each scale. This robust and flexible framework is designed to meet the inventory, monitoring, and management needs of multiple federal agencies (i.e., Forest Service, National Park Service, Fish and Wildlife Service, National Wildlife Reserves). Multi-scale HIS examples are provided using Geographic Information Systems (GIS) for the Southeastern US.

A framework for identifying water management typologies for agent based modeling of water resources and its application in the Boise River Basin, USA.

NASA Astrophysics Data System (ADS)

Kaiser, K. E.; Flores, A. N.; Hillis, V.; Moroney, J.; Schneider, J.

2017-12-01

Modeling the management of water resources necessitates incorporation of complex social and hydrologic dynamics. Simulation of these socio-ecological systems requires characterization of the decision-making process of relevant actors, the mechanisms through which they exert control on the biophysical system, their ability to react and adapt to regional environmental conditions, and the plausible behaviors in response to changes in those conditions. Agent based models (ABMs) are a useful tool in simulating these complex adaptive systems because they can dynamically couple hydrological models and the behavior of decision making actors. ABMs can provide a flexible, integrated framework that can represent multi-scale interactions, and the heterogeneity of information networks and sources. However, the variability in behavior of water management actors across systems makes characterizing agent behaviors and relationships challenging. Agent typologies, or agent functional types (AFTs), group together individuals and/or agencies with similar functional roles, management objectives, and decision-making strategies. AFTs have been used to represent archetypal land managers in the agricultural and forestry sectors in large-scale socio-economic system models. A similar typology of water actors could simplify the representation of water management across river basins, and increase transferability and scaling of resulting ABMs. Here, we present a framework for identifying and classifying major water actors and show how we will link an ABM of water management to a regional hydrologic model in a western river basin. The Boise River Basin in southwest Idaho is an interesting setting to apply our AFT framework because of the diverse stakeholders and associated management objectives which include managing urban growth pressures and water supply in the face of climate change. Precipitation in the upper basin supplies 90% of the surface water used in the basin, thus managers of the reservoir system (located in the upper basin) must balance flood control for the metropolitan area with water supply for downstream agricultural and hydropower use. Identifying dominant water management typologies that include state and federal agencies will increase the transferability of water management ABMs in the western US.

Development of the Hydrological-Ecological Integrated watershed Flow Model (HEIFLOW): an application to the Heihe River Basin

NASA Astrophysics Data System (ADS)

Tian, Y.; Zheng, Y.; Zheng, C.; Han, F., Sr.

2017-12-01

Physically based and fully-distributed integrated hydrological models (IHMs) can quantitatively depict hydrological processes, both surface and subsurface, with sufficient spatial and temporal details. However, the complexity involved in pre-processing data and setting up models seriously hindered the wider application of IHMs in scientific research and management practice. This study introduces our design and development of Visual HEIFLOW, hereafter referred to as VHF, a comprehensive graphical data processing and modeling system for integrated hydrological simulation. The current version of VHF has been structured to accommodate an IHM named HEIFLOW (Hydrological-Ecological Integrated watershed-scale FLOW model). HEIFLOW is a model being developed by the authors, which has all typical elements of physically based and fully-distributed IHMs. It is based on GSFLOW, a representative integrated surface water-groundwater model developed by USGS. HEIFLOW provides several ecological modules that enable to simulate growth cycle of general vegetation and special plants (maize and populus euphratica). VHF incorporates and streamlines all key steps of the integrated modeling, and accommodates all types of GIS data necessary to hydrological simulation. It provides a GIS-based data processing framework to prepare an IHM for simulations, and has functionalities to flexibly display and modify model features (e.g., model grids, streams, boundary conditions, observational sites, etc.) and their associated data. It enables visualization and various spatio-temporal analyses of all model inputs and outputs at different scales (i.e., computing unit, sub-basin, basin, or user-defined spatial extent). The above system features, as well as many others, can significantly reduce the difficulty and time cost of building and using a complex IHM. The case study in the Heihe River Basin demonstrated the applicability of VHF for large scale integrated SW-GW modeling. Visualization and spatial-temporal analysis of the modeling results by HEIFLOW greatly facilitates our understanding on the complicated hydrologic cycle and relationship among the hydrological and ecological variables in the study area, and provides insights into the regional water resources management.

Using Unsupervised Learning to Unlock the Potential of Hydrologic Similarity

NASA Astrophysics Data System (ADS)

Chaney, N.; Newman, A. J.

2017-12-01

By clustering environmental data into representative hydrologic response units (HRUs), hydrologic similarity aims to harness the covariance between a system's physical environment and its hydrologic response to create reduced-order models. This is the primary approach through which sub-grid hydrologic processes are represented in large-scale models (e.g., Earth System Models). Although the possibilities of hydrologic similarity are extensive, its practical implementations have been limited to 1-d bins of oversimplistic metrics of hydrologic response (e.g., topographic index)—this is a missed opportunity. In this presentation we will show how unsupervised learning is unlocking the potential of hydrologic similarity; clustering methods enable generalized frameworks to effectively and efficiently harness the petabytes of global environmental data to robustly characterize sub-grid heterogeneity in large-scale models. To illustrate the potential that unsupervised learning has towards advancing hydrologic similarity, we introduce a hierarchical clustering algorithm (HCA) that clusters very high resolution (30-100 meters) elevation, soil, climate, and land cover data to assemble a domain's representative HRUs. These HRUs are then used to parameterize the sub-grid heterogeneity in land surface models; for this study we use the GFDL LM4 model—the land component of the GFDL Earth System Model. To explore HCA and its impacts on the hydrologic system we use a ¼ grid cell in southeastern California as a test site. HCA is used to construct an ensemble of 9 different HRU configurations—each configuration has a different number of HRUs; for each ensemble member LM4 is run between 2002 and 2014 with a 26 year spinup. The analysis of the ensemble of model simulations show that: 1) clustering the high-dimensional environmental data space leads to a robust representation of the role of the physical environment in the coupled water, energy, and carbon cycles at a relatively low number of HRUs; 2) the reduced-order model with around 300 HRUs effectively reproduces the fully distributed model simulation (30 meters) with less than 1/1000 of computational expense; 3) assigning each grid cell of the fully distributed grid to an HRU via HCA enables novel visualization methods for large-scale models—this has significant implications for how these models are applied and evaluated. We will conclude by outlining the potential that this work has within operational prediction systems including numerical weather prediction, Earth System models, and Early Warning systems.

A dynamic water-quality modeling framework for the Neuse River estuary, North Carolina

USGS Publications Warehouse

Bales, Jerad D.; Robbins, Jeanne C.

1999-01-01

As a result of fish kills in the Neuse River estuary in 1995, nutrient reduction strategies were developed for point and nonpoint sources in the basin. However, because of the interannual variability in the natural system and the resulting complex hydrologic-nutrient inter- actions, it is difficult to detect through a short-term observational program the effects of management activities on Neuse River estuary water quality and aquatic health. A properly constructed water-quality model can be used to evaluate some of the potential effects of manage- ment actions on estuarine water quality. Such a model can be used to predict estuarine response to present and proposed nutrient strategies under the same set of meteorological and hydrologic conditions, thus removing the vagaries of weather and streamflow from the analysis. A two-dimensional, laterally averaged hydrodynamic and water-quality modeling framework was developed for the Neuse River estuary by using previously collected data. Development of the modeling framework consisted of (1) computational grid development, (2) assembly of data for model boundary conditions and model testing, (3) selection of initial values of model parameters, and (4) limited model testing. The model domain extends from Streets Ferry to Oriental, N.C., includes seven lateral embayments that have continual exchange with the main- stem of the estuary, three point-source discharges, and three tributary streams. Thirty-five computational segments represent the mainstem of the estuary, and the entire framework contains a total of 60 computa- tional segments. Each computational cell is 0.5 meter thick; segment lengths range from 500 meters to 7,125 meters. Data that were used to develop the modeling framework were collected during March through October 1991 and represent the most comprehensive data set available prior to 1997. Most of the data were collected by the North Carolina Division of Water Quality, the University of North Carolina Institute of Marine Sciences, and the U.S. Geological Survey. Limitations in the modeling framework were clearly identified. These limitations formed the basis for a set of suggestions to refine the Neuse River estuary water-quality model.

Improved hydrological modeling using AGWA; incorporation of different management practices in hydrological modeling.

NASA Astrophysics Data System (ADS)

Vithanage, J.; Miller, S. N.; Paige, G. B.; Liu, T.

2017-12-01

We present a novel way to simulate the effects of rangeland management decisions in a GIS-based hydrologic modeling toolkit. We have implemented updates to the Automated Geospatial Watershed Assessment tool (AGWA) in which a landscape can be broken into management units (e.g., high intensity grazing, low intensity grazing, fire management, and unmanaged), each of which is assigned a different hydraulic conductivity (Ks) parameter in KINEmatic Runoff and EROSion model (KINEROS2). These updates are designed to provide modeling support to land managers tasked with rangeland watershed management planning and/or monitoring, and evaluation of water resources management. Changes to hydrologic processes and resulting hydrographs and sedigraphs are simulated within the AGWA framework. Case studies are presented in which a user selects various management scenarios and design storms, and the model identifies areas that become susceptible to change as a consequence of management decisions. The baseline (unmanaged) scenario is built using commonly available GIS data, after which the watershed is subdivided into management units. We used an array of design storms with various return periods and frequencies to evaluate the impact of management practices while changing the scale of watershed. Watershed parameters governing interception, infiltration, and surface runoff were determined with the aid of literature published on research studies carried out in the Walnut Gulch Experimental Watershed in southeast Arizona. We observed varied, but significant changes in hydrological responses (runoff) with different management practices as well with varied scales of watersheds. Results show that the toolkit can be used to quantify potential hydrologic change as a result of unitized land use decision-making.

Decision support system based on DPSIR framework for a low flow Mediterranean river basin

NASA Astrophysics Data System (ADS)

Bangash, Rubab Fatima; Kumar, Vikas; Schuhmacher, Marta

2013-04-01

The application of decision making practices are effectively enhanced by adopting a procedural approach setting out a general methodological framework within which specific methods, models and tools can be integrated. Integrated Catchment Management is a process that recognizes the river catchment as a basic organizing unit for understanding and managing ecosystem process. Decision support system becomes more complex by considering unavoidable human activities within a catchment that are motivated by multiple and often competing criteria and/or constraints. DPSIR is a causal framework for describing the interactions between society and the environment. This framework has been adopted by the European Environment Agency and the components of this model are: Driving forces, Pressures, States, Impacts and Responses. The proposed decision support system is a two step framework based on DPSIR. Considering first three component of DPSIR, Driving forces, Pressures and States, hydrological and ecosystem services models are developed. The last two components, Impact and Responses, helped to develop Bayesian Network to integrate the models. This decision support system also takes account of social, economic and environmental aspects. A small river of Catalonia (Northeastern Spain), Francoli River with a low flow (~2 m3/s) is selected for integration of catchment assessment models and to improve knowledge transfer from research to the stakeholders with a view to improve decision making process. DHI's MIKE BASIN software is used to evaluate the low-flow Francolí River with respect to the water bodies' characteristics and also to assess the impact of human activities aiming to achieve good water status for all waters to comply with the WFD's River Basin Management Plan. Based on ArcGIS, MIKE BASIN is a versatile decision support tool that provides a simple and powerful framework for managers and stakeholders to address multisectoral allocation and environmental issues in river basins. While InVEST is a spatially explicit tool, used to model and map a suite of ecosystem services caused by land cover changes or climate change impacts. Moreover, results obtained from low-flow hydrological simulation and ecosystem services models serves as useful tools to develop decision support system based on DPSIR framework by integrating models. Bayesian Networks is used as a knowledge integration and visualization tool to summarize the outcomes of hydrological and ecosystem services models at the "Response" stage of DPSIR. Bayesian Networks provide a framework for modelling the logical relationship between catchment variables and decision objectives by quantifying the strength of these relationships using conditional probabilities. Participatory nature of this framework can provide better communication of water research, particularly in the context of a perceived lack of future awareness-raising with the public that helps to develop more sustainable water management strategies. Acknowledgements The present study was financially supported by Spanish Ministry of Economy and Competitiveness for its financial support through the project SCARCE (Consolider-Ingenio 2010 CSD2009-00065). R. F. Bangash also received PhD fellowship from AGAUR (Commissioner for Universities and Research of the Department of Innovation, Universities and Enterprise of the "Generalitat de Catalunya" and the European Social Fund).

A Watershed Scale Life Cycle Assessment Framework for Hydrologic Design

NASA Astrophysics Data System (ADS)

Tavakol-Davani, H.; Tavakol-Davani, PhD, H.; Burian, S. J.

2017-12-01

Sustainable hydrologic design has received attention from researchers with different backgrounds, including hydrologists and sustainability experts, recently. On one hand, hydrologists have been analyzing ways to achieve hydrologic goals through implementation of recent environmentally-friendly approaches, e.g. Green Infrastructure (GI) - without quantifying the life cycle environmental impacts of the infrastructure through the ISO Life Cycle Assessment (LCA) method. On the other hand, sustainability experts have been applying the LCA to study the life cycle impacts of water infrastructure - without considering the important hydrologic aspects through hydrologic and hydraulic (H&H) analysis. In fact, defining proper system elements for a watershed scale urban water sustainability study requires both H&H and LCA specialties, which reveals the necessity of performing an integrated, interdisciplinary study. Therefore, the present study developed a watershed scale coupled H&H-LCA framework to bring the hydrology and sustainability expertise together to contribute moving the current wage definition of sustainable hydrologic design towards onto a globally standard concept. The proposed framework was employed to study GIs for an urban watershed in Toledo, OH. Lastly, uncertainties associated with the proposed method and parameters were analyzed through a robust Monte Carlo simulation using parallel processing. Results indicated the necessity of both hydrologic and LCA components in the design procedure in order to achieve sustainability.

Critical Hydrologic and Atmospheric Measurements in Complex Alpine Regions

NASA Astrophysics Data System (ADS)

Parlange, M. B.; Bou-Zeid, E.; Barrenetxea, G.; Krichane, M.; Ingelrest, F.; Couach, O.; Luyet, V.; Vetterli, M.; Lehning, M.; Duffy, C.; Tobin, C.; Selker, J.; Kumar, M.

2007-12-01

The Alps are often referred to as the « Water Towers of Europe » and as such play an essential role in European water resources. The impact of climatic change is expected to be particularly pronounced in the Alps and the lack of detailed hydrologic field observations is problematic for predictions of hydrologic and hazard assessment. Advances in information technology and communications provide important possibilities to improve the situation with relatively few measurements. We will present sensorscope technology (arrays of wireless weather stations including soil moisture, pressure, and temperature) that has now been deployed at the Le Genepi and Grand St. Bernard pass. In addition, a Distributed Temperature Sensor array on the stream beds has been deployed and stream discharge monitored. The high spatial resolution data collected in these previously "ungaged" regions are used in conjunction with new generation hydrologic models. The framework as to what is possible today with sensor arrays and modeling in extreme mountain environments is discussed.

Flood risk analysis for flood control and sediment transportation in sandy regions: A case study in the Loess Plateau, China

NASA Astrophysics Data System (ADS)

Guo, Aijun; Chang, Jianxia; Wang, Yimin; Huang, Qiang; Zhou, Shuai

2018-05-01

Traditional flood risk analysis focuses on the probability of flood events exceeding the design flood of downstream hydraulic structures while neglecting the influence of sedimentation in river channels on regional flood control systems. This work advances traditional flood risk analysis by proposing a univariate and copula-based bivariate hydrological risk framework which incorporates both flood control and sediment transport. In developing the framework, the conditional probabilities of different flood events under various extreme precipitation scenarios are estimated by exploiting the copula-based model. Moreover, a Monte Carlo-based algorithm is designed to quantify the sampling uncertainty associated with univariate and bivariate hydrological risk analyses. Two catchments located on the Loess plateau are selected as study regions: the upper catchments of the Xianyang and Huaxian stations (denoted as UCX and UCH, respectively). The univariate and bivariate return periods, risk and reliability in the context of uncertainty for the purposes of flood control and sediment transport are assessed for the study regions. The results indicate that sedimentation triggers higher risks of damaging the safety of local flood control systems compared with the event that AMF exceeds the design flood of downstream hydraulic structures in the UCX and UCH. Moreover, there is considerable sampling uncertainty affecting the univariate and bivariate hydrologic risk evaluation, which greatly challenges measures of future flood mitigation. In addition, results also confirm that the developed framework can estimate conditional probabilities associated with different flood events under various extreme precipitation scenarios aiming for flood control and sediment transport. The proposed hydrological risk framework offers a promising technical reference for flood risk analysis in sandy regions worldwide.

A Multi-Tiered Approach for Building Capacity in Hydrologic Modeling for Water Resource Management in Developing Regions

NASA Astrophysics Data System (ADS)

Markert, K. N.; Limaye, A. S.; Rushi, B. R.; Adams, E. C.; Anderson, E.; Ellenburg, W. L.; Mithieu, F.; Griffin, R.

2017-12-01

Water resource management is the process by which governments, businesses and/or individuals reach and implement decisions that are intended to address the future quantity and/or quality of water for societal benefit. The implementation of water resource management typically requires the understanding of the quantity and/or timing of a variety of hydrologic variables (e.g. discharge, soil moisture and evapotranspiration). Often times these variables for management are simulated using hydrologic models particularly in data sparse regions. However, there are several large barriers to entry in learning how to use models, applying best practices during the modeling process, and selecting and understanding the most appropriate model for diverse applications. This presentation focuses on a multi-tiered approach to bring the state-of-the-art hydrologic modeling capabilities and methods to developing regions through the SERVIR program, a joint NASA and USAID initiative that builds capacity of regional partners and their end users on the use of Earth observations for environmental decision making. The first tier is a series of trainings on the use of multiple hydrologic models, including the Variable Infiltration Capacity (VIC) and Ensemble Framework For Flash Flood Forecasting (EF5), which focus on model concepts and steps to successfully implement the models. We present a case study for this in a pilot area, the Nyando Basin in Kenya. The second tier is focused on building a community of practice on applied hydrology modeling aimed at creating a support network for hydrologists in SERVIR regions and promoting best practices. The third tier is a hydrologic inter-comparison project under development in the SERVIR regions. The objective of this step is to understand model performance under specific decision-making scenarios, and to share knowledge among hydrologists in SERVIR regions. The results of these efforts include computer programs, training materials, and new scientific understanding, all of which are shared in an open and collaborative environment for transparency and subsequent capacity building in SERVIR regions and beyond. The outcome of this work is increased awareness and capacity on the use of hydrologic models in developing regions to support water resource management and water security.

A Seamless Framework for Global Water Cycle Monitoring and Prediction

NASA Astrophysics Data System (ADS)

Sheffield, J.; Wood, E. F.; Chaney, N.; Fisher, C. K.; Caylor, K. K.

2013-12-01

The Global Earth Observation System of Systems (GEOSS) Water Strategy ('From Observations to Decisions') recognizes that 'water is essential for ensuring food and energy security, for facilitating poverty reduction and health security, and for the maintenance of ecosystems and biodiversity', and that water cycle data and observations are critical for improved water management and water security - especially in less developed regions. The GEOSS Water Strategy has articulated a number of goals for improved water management, including flood and drought preparedness, that include: (i) facilitating the use of Earth Observations for water cycle observations; (ii) facilitating the acquisition, processing, and distribution of data products needed for effective management; (iii) providing expertise, information systems, and datasets to the global, regional, and national water communities. There are several challenges that must be met to advance our capability to provide near real-time water cycle monitoring, early warning of hydrological hazards (floods and droughts) and risk assessment under climate change, regionally and globally. Current approaches to monitoring and predicting hydrological hazards are limited in many parts of the world, and especially in developing countries where national capacity is limited and monitoring networks are inadequate. This presentation describes the development of a seamless monitoring and prediction framework at all time scales that allows for consistent assessment of water variability from historic to current conditions, and from seasonal and decadal predictions to climate change projections. At the center of the framework is an experimental, global water cycle monitoring and seasonal forecast system that has evolved out of regional and continental systems for the US and Africa. The system is based on land surface hydrological modeling that is driven by satellite remote sensing precipitation to predict current hydrological conditions, flood potential and the state of drought. Seasonal climate model forecasts are downscaled and bias-corrected to drive the land surface model to provide hydrological forecasts and drought products out 6-9 months. The system relies on historic reconstructions of water variability over the 20th century, which forms the background climatology to which current conditions can be assessed. Future changes in water availability and drought risk are quantified based on bias-corrected and downscaled climate model projections that are used to drive the land surface models. For regions with lack of on-the-ground data we are field-testing low-cost environmental sensors and along with new satellite products for terrestrial hydrology and vegetation, integrating these into the system for improved monitoring and prediction. We provide an overview of the system and some examples of real-world applications to flood and drought events, with a focus on Africa.

Distributed simulation of long-term hydrological processes in a medium-sized periurban catchment under changing land use and rainwater management.

NASA Astrophysics Data System (ADS)

Labbas, Mériem; Braud, Isabelle; Branger, Flora; Kralisch, Sven

2013-04-01

Growing urbanization and related anthropogenic processes have a high potential to influence hydrological process dynamics. Typical consequences are an increase of surface imperviousness and modifications of water flow paths due to artificial channels and barriers (combined and separated system, sewer overflow device, roads, ditches, etc.). Periurban catchments, at the edge of large cities, are especially affected by fast anthropogenic modifications. They usually consist of a combination of natural areas, rural areas with dispersed settlements and urban areas mostly covered by built zones and spots of natural surfaces. In the context of the European Water Framework Directive (2000) and the Floods Directive (2007), integrated and sustainable solutions are needed to reduce flooding risks and river pollution at the scale of urban conglomerations or whole catchments. Their thorough management requires models able to assess the vulnerability of the territory and to compare the impact of different rainwater management options and planning issues. To address this question, we propose a methodology based on a multi-scale distributed hydrological modelling approach. It aims at quantifying the impact of ongoing urbanization and stormwater management on the long-term hydrological cycle in medium-sized periurban watershed. This method focuses on the understanding and formalization of dominant periurban hydrological processes from small scales (few ha to few km2) to larger scales (few hundred km2). The main objectives are to 1) simulate both urban and rural hydrological processes and 2) test the effects of different long-term land use and water management scenarios. The method relies on several tools and data: a distributed hydrological model adapted to the characteristics of periurban areas, land use and land cover maps from different dates (past, present, future) and information about rainwater management collected from local authorities. For the application of the method, the medium-scaled catchment of Yzeron (France) is chosen. It is subjected to a fast progression of urbanization since the eighties and has been monitored for a long time period. The fully-distributed hydrological model J2000, available through the JAMS modelling framework, was found appropriate to simulate the water balance of the Yzeron catchment at a daily time step. However, it was not designed especially for periurban areas, so its structure and parameters are under adaptation. Firstly, as hydrological responses in urban areas are quicker than in rural areas, a sub-daily time step is necessary to improve the simulation of periurban hydrological processes. Therefore, J2000 was adapted to be run at a hourly time step. Secondly, in order to better take into account rainwater management, an explicit representation of sewer networks is implemented in the J2000 model whose periurban version is called J2000P. It receives urban rainwater coming from impervious surfaces connected to a combined sewer system and delivers this water to the treatment plant or directly to the river in case of sewer overflow device outflows. We will present the impact of these modifications on the simulated hydrological regime.

A data-model integration approach toward improved understanding on wetland functions and hydrological benefits at the catchment scale

NASA Astrophysics Data System (ADS)

Yeo, I. Y.; Lang, M.; Lee, S.; Huang, C.; Jin, H.; McCarty, G.; Sadeghi, A.

2017-12-01

The wetland ecosystem plays crucial roles in improving hydrological function and ecological integrity for the downstream water and the surrounding landscape. However, changing behaviours and functioning of wetland ecosystems are poorly understood and extremely difficult to characterize. Improved understanding on hydrological behaviours of wetlands, considering their interaction with surrounding landscapes and impacts on downstream waters, is an essential first step toward closing the knowledge gap. We present an integrated wetland-catchment modelling study that capitalizes on recently developed inundation maps and other geospatial data. The aim of the data-model integration is to improve spatial prediction of wetland inundation and evaluate cumulative hydrological benefits at the catchment scale. In this paper, we highlight problems arising from data preparation, parameterization, and process representation in simulating wetlands within a distributed catchment model, and report the recent progress on mapping of wetland dynamics (i.e., inundation) using multiple remotely sensed data. We demonstrate the value of spatially explicit inundation information to develop site-specific wetland parameters and to evaluate model prediction at multi-spatial and temporal scales. This spatial data-model integrated framework is tested using Soil and Water Assessment Tool (SWAT) with improved wetland extension, and applied for an agricultural watershed in the Mid-Atlantic Coastal Plain, USA. This study illustrates necessity of spatially distributed information and a data integrated modelling approach to predict inundation of wetlands and hydrologic function at the local landscape scale, where monitoring and conservation decision making take place.

Climate Change Impacts on Freshwater Recreational Fishing in the United States

EPA Science Inventory

Using a geographic information system, a spatially explicit modeling framework was developed consisting grid cells organized into 2,099 eight-digit hydrologic unit code (HUC-8) polygons for the coterminous United States. Projected temperature and precipitation changes associated...

Linked Hydrologic-Hydrodynamic Model Framework to Forecast Impacts of Rivers on Beach Water Quality

NASA Astrophysics Data System (ADS)

Anderson, E. J.; Fry, L. M.; Kramer, E.; Ritzenthaler, A.

2014-12-01

The goal of NOAA's beach quality forecasting program is to use a multi-faceted approach to aid in detection and prediction of bacteria in recreational waters. In particular, our focus has been on the connection between tributary loads and bacteria concentrations at nearby beaches. While there is a clear link between stormwater runoff and beach water quality, quantifying the contribution of river loadings to nearshore bacterial concentrations is complicated due to multiple processes that drive bacterial concentrations in rivers as well as those processes affecting the fate and transport of bacteria upon exiting the rivers. In order to forecast potential impacts of rivers on beach water quality, we developed a linked hydrologic-hydrodynamic water quality framework that simulates accumulation and washoff of bacteria from the landscape, and then predicts the fate and transport of washed off bacteria from the watershed to the coastal zone. The framework includes a watershed model (IHACRES) to predict fecal indicator bacteria (FIB) loadings to the coastal environment (accumulation, wash-off, die-off) as a function of effective rainfall. These loadings are input into a coastal hydrodynamic model (FVCOM), including a bacteria transport model (Lagrangian particle), to simulate 3D bacteria transport within the coastal environment. This modeling system provides predictive tools to assist local managers in decision-making to reduce human health threats.

Oregon Hydrologic Landscapes: A Classification Framework

EPA Science Inventory

There is a growing need for hydrologic classification systems that can provide a basis for broad-scale assessments of the hydrologic functions of landscapes and watersheds and their responses to stressors such as climate change. We developed a hydrologic landscape (HL) classifica...

Assessment of variability in the hydrological cycle of the Loess Plateau, China: examining dependence structures of hydrological processes

NASA Astrophysics Data System (ADS)

Guo, A.; Wang, Y.

2017-12-01

Investigating variability in dependence structures of hydrological processes is of critical importance for developing an understanding of mechanisms of hydrological cycles in changing environments. In focusing on this topic, present work involves the following: (1) identifying and eliminating serial correlation and conditional heteroscedasticity in monthly streamflow (Q), precipitation (P) and potential evapotranspiration (PE) series using the ARMA-GARCH model (ARMA: autoregressive moving average; GARCH: generalized autoregressive conditional heteroscedasticity); (2) describing dependence structures of hydrological processes using partial copula coupled with the ARMA-GARCH model and identifying their variability via copula-based likelihood-ratio test method; and (3) determining conditional probability of annual Q under different climate scenarios on account of above results. This framework enables us to depict hydrological variables in the presence of conditional heteroscedasticity and to examine dependence structures of hydrological processes while excluding the influence of covariates by using partial copula-based ARMA-GARCH model. Eight major catchments across the Loess Plateau (LP) are used as study regions. Results indicate that (1) The occurrence of change points in dependence structures of Q and P (PE) varies across the LP. Change points of P-PE dependence structures in all regions almost fully correspond to the initiation of global warming, i.e., the early 1980s. (3) Conditional probabilities of annual Q under various P and PE scenarios are estimated from the 3-dimensional joint distribution of (Q, P and PE) based on the above change points. These findings shed light on mechanisms of the hydrological cycle and can guide water supply planning and management, particularly in changing environments.

Constraining Distributed Catchment Models by Incorporating Perceptual Understanding of Spatial Hydrologic Behaviour

NASA Astrophysics Data System (ADS)

Hutton, Christopher; Wagener, Thorsten; Freer, Jim; Han, Dawei

2016-04-01

Distributed models offer the potential to resolve catchment systems in more detail, and therefore simulate the hydrological impacts of spatial changes in catchment forcing (e.g. landscape change). Such models tend to contain a large number of poorly defined and spatially varying model parameters which are therefore computationally expensive to calibrate. Insufficient data can result in model parameter and structural equifinality, particularly when calibration is reliant on catchment outlet discharge behaviour alone. Evaluating spatial patterns of internal hydrological behaviour has the potential to reveal simulations that, whilst consistent with measured outlet discharge, are qualitatively dissimilar to our perceptual understanding of how the system should behave. We argue that such understanding, which may be derived from stakeholder knowledge across different catchments for certain process dynamics, is a valuable source of information to help reject non-behavioural models, and therefore identify feasible model structures and parameters. The challenge, however, is to convert different sources of often qualitative and/or semi-qualitative information into robust quantitative constraints of model states and fluxes, and combine these sources of information together to reject models within an efficient calibration framework. Here we present the development of a framework to incorporate different sources of data to efficiently calibrate distributed catchment models. For each source of information, an interval or inequality is used to define the behaviour of the catchment system. These intervals are then combined to produce a hyper-volume in state space, which is used to identify behavioural models. We apply the methodology to calibrate the Penn State Integrated Hydrological Model (PIHM) at the Wye catchment, Plynlimon, UK. Outlet discharge behaviour is successfully simulated when perceptual understanding of relative groundwater levels between lowland peat, upland peat and valley slopes within the catchment are used to identify behavioural models. The process of converting qualitative information into quantitative constraints forces us to evaluate the assumptions behind our perceptual understanding in order to derive robust constraints, and therefore fairly reject models and avoid type II errors. Likewise, consideration needs to be given to the commensurability problem when mapping perceptual understanding to constrain model states.

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.

Developing an Online Framework for Publication of Uncertainty Information in Hydrological Modeling

NASA Astrophysics Data System (ADS)

Etienne, E.; Piasecki, M.

2012-12-01

Inaccuracies in data collection and parameters estimation, and imperfection of models structures imply uncertain predictions of the hydrological models. Finding a way to communicate the uncertainty information in a model output is important in decision-making. This work aims to publish uncertainty information (computed by project partner at Penn State) associated with hydrological predictions on catchments. To this end we have developed a DB schema (derived from the CUAHSI ODM design) which is focused on storing uncertainty information and its associated metadata. The technologies used to build the system are: OGC's Sensor Observation Service (SOS) for publication, the uncertML markup language (also developed by the OGC) to describe uncertainty information, and use of the Interoperability and Automated Mapping (INTAMAP) Web Processing Service (WPS) that handles part of the statistics computations. We develop a service to provide users with the capability to exploit all the functionality of the system (based on DRUPAL). Users will be able to request and visualize uncertainty data, and also publish their data in the system.

Deriving flow directions for coarse-resolution (1-4 km) gridded hydrologic modeling

NASA Astrophysics Data System (ADS)

Reed, Seann M.

2003-09-01

The National Weather Service Hydrology Laboratory (NWS-HL) is currently testing a grid-based distributed hydrologic model at a resolution (4 km) commensurate with operational, radar-based precipitation products. To implement distributed routing algorithms in this framework, a flow direction must be assigned to each model cell. A new algorithm, referred to as cell outlet tracing with an area threshold (COTAT) has been developed to automatically, accurately, and efficiently assign flow directions to any coarse-resolution grid cells using information from any higher-resolution digital elevation model. Although similar to previously published algorithms, this approach offers some advantages. Use of an area threshold allows more control over the tendency for producing diagonal flow directions. Analyses of results at different output resolutions ranging from 300 m to 4000 m indicate that it is possible to choose an area threshold that will produce minimal differences in average network flow lengths across this range of scales. Flow direction grids at a 4 km resolution have been produced for the conterminous United States.

A Framework For Analysis Of Coastal Infrastructure Vunerabilty To Global Sea Level Rise

NASA Astrophysics Data System (ADS)

Obrien, P. S.; White, K. D.; Veatch, W.; Marzion, R.; Moritz, H.; Moritz, H. R.

2017-12-01

Recorded impacts of global sea rise on coastal water levels have been documented over the past 100 to 150 years. In the recent 40 years the assumption of hydrologic stationarity has been recognized as invalid. New coastal infrastructure designs must recognize the paradigm shift from hydrologic stationarity to non-stationarity in coastal hydrology. A framework for the evaluation of existing coastal infrastructure is proposed to effectively assess design vulnerability. Two data sets developed from existing structures are chosen to test a proposed framework for vunerabilty to global sea level rise, with the proposed name Climate Preparedness and Resilience Register (CPRR). The CPRR framework consists of four major elements; Datum Adjustment, Coastal Water Levels, Scenario Projections and Performance Thresholds.

Situating Green Infrastructure in Context: A Framework for Adaptive Socio-Hydrology in Cities.

PubMed

Schifman, L A; Herrmann, D L; Shuster, W D; Ossola, A; Garmestani, A; Hopton, M E

2017-12-01

Management of urban hydrologic processes using green infrastructure (GI) has largely focused on stormwater management. Thus, design and implementation of GI usually rely on physical site characteristics and local rainfall patterns, and do not typically account for human or social dimensions. This traditional approach leads to highly centralized stormwater management in a disconnected urban landscape, and can deemphasize additional benefits that GI offers, such as increased property value, greenspace aesthetics, heat island amelioration, carbon sequestration, and habitat for biodiversity. We propose a Framework for Adaptive Socio-Hydrology (FrASH) in which GI planning and implementation moves from a purely hydrology-driven perspective to an integrated socio-hydrological approach. This allows for an iterative, multifaceted decision-making process that would enable a network of stakeholders to collaboratively set a dynamic, context-guided project plan for the installation of GI, rather than a 'one-size-fits-all' installation. We explain how different sectors (e.g., governance, non-governmental organizations, academia, and industry) can create a connected network of organizations that work towards a common goal. Through a graphical Chambered Nautilus model, FrASH is experimentally applied to contrasting GI case studies and shows that this multi-stakeholder, connected, de-centralized network with a co-evolving decision-making project plan results in enhanced multi-functionality, potentially allowing for the management of resilience in urban systems at multiple scales.

A novel algorithm for delineating wetland depressions and ...

EPA Pesticide Factsheets

In traditional watershed delineation and topographic modeling, surface depressions are generally treated as spurious features and simply removed from a digital elevation model (DEM) to enforce flow continuity of water across the topographic surface to the watershed outlets. In reality, however, many depressions in the DEM are actual wetland landscape features that are seldom fully filled with water. For instance, wetland depressions in the Prairie Pothole Region (PPR) are seasonally to permanently flooded wetlands characterized by nested hierarchical structures with dynamic filling- spilling-merging surface-water hydrological processes. The objectives of this study were to delineate hierarchical wetland catchments and model their hydrologic connectivity using high-resolution LiDAR data and aerial imagery. We proposed a novel algorithm delineate the hierarchical wetland catchments and characterize their geometric and topological properties. Potential hydrologic connectivity between wetlands and streams were simulated using the least-cost path algorithm. The resulting flow network delineated putative temporary or seasonal flow paths connecting wetland depressions to each other or to the river network at scales finer than available through the National Hydrography Dataset. The results demonstrated that our proposed framework is promising for improving overland flow modeling and hydrologic connectivity analysis. Presentation at AWRA Spring Specialty Conference in Sn

A Reliability Estimation in Modeling Watershed Runoff With Uncertainties

NASA Astrophysics Data System (ADS)

Melching, Charles S.; Yen, Ben Chie; Wenzel, Harry G., Jr.

1990-10-01

The reliability of simulation results produced by watershed runoff models is a function of uncertainties in nature, data, model parameters, and model structure. A framework is presented here for using a reliability analysis method (such as first-order second-moment techniques or Monte Carlo simulation) to evaluate the combined effect of the uncertainties on the reliability of output hydrographs from hydrologic models. For a given event the prediction reliability can be expressed in terms of the probability distribution of the estimated hydrologic variable. The peak discharge probability for a watershed in Illinois using the HEC-1 watershed model is given as an example. The study of the reliability of predictions from watershed models provides useful information on the stochastic nature of output from deterministic models subject to uncertainties and identifies the relative contribution of the various uncertainties to unreliability of model predictions.

Quantitative and qualitative synthesis of socio-hydrological research

NASA Astrophysics Data System (ADS)

Xu, L.; Gober, P.; Wheater, H. S.; Kajikawa, Y.

2017-12-01

The challenge of climate change adaptation has raised awareness of the feedbacks and interconnections in complex human-natural coupled water systems. This has reinforced the call for a socio-hydrological approach to better understand, and represent in models, the associated system dynamics. Such models can potentially provide the tools to link knowledge about complex water systems to decision-making and policy frameworks. Socio-hydrology, as the subfield of human-natural coupled systems analysis, has been dramatically developed in the past few years. The purpose of this study is to empirically examine work that has been framed under the umbrella of socio-hydrology, to provide insights into the participants and their disciplinary perspectives, and to draw conclusions about where the field is headed. In doing so, we used a combined quantitative and qualitative approach to synthesise current knowledge of socio-hydrology and to propose some promising future directions in this subfield of water sciences. The general statistics of the existing literature showed that socio-hydrological research has become an emerging topic and is drawing more concern and engagement of hydrologists. However, the participation of social scientists is inadequate and greater cross-disciplinary integration is desirable. Current concerns in this subfield of water research centre on two basic challenges: (1) the need to embrace the social dimensions of water-related risks, and (2) the importance of interactions and feedbacks in dynamic socio-hydrological systems. A third challenge identified here relates to the large-scale implications of 1) and 2) above, i.e. virtual water flows as a mechanism to track the human use of water at the global scale. Accordingly, we propose five potential directions with regard to socio-hydrological models, interdisciplinary collaboration and transdisciplinary studies, the science-policy interface, resilience in socio-hydrological systems, and data sharing for human-water system studies.

Hydrological Modeling Reproducibility Through Data Management and Adaptors for Model Interoperability

NASA Astrophysics Data System (ADS)

Turner, M. A.

2015-12-01

Because of a lack of centralized planning and no widely-adopted standards among hydrological modeling research groups, research communities, and the data management teams meant to support research, there is chaos when it comes to data formats, spatio-temporal resolutions, ontologies, and data availability. All this makes true scientific reproducibility and collaborative integrated modeling impossible without some glue to piece it all together. Our Virtual Watershed Integrated Modeling System provides the tools and modeling framework hydrologists need to accelerate and fortify new scientific investigations by tracking provenance and providing adaptors for integrated, collaborative hydrologic modeling and data management. Under global warming trends where water resources are under increasing stress, reproducible hydrological modeling will be increasingly important to improve transparency and understanding of the scientific facts revealed through modeling. The Virtual Watershed Data Engine is capable of ingesting a wide variety of heterogeneous model inputs, outputs, model configurations, and metadata. We will demonstrate one example, starting from real-time raw weather station data packaged with station metadata. Our integrated modeling system will then create gridded input data via geostatistical methods along with error and uncertainty estimates. These gridded data are then used as input to hydrological models, all of which are available as web services wherever feasible. Models may be integrated in a data-centric way where the outputs too are tracked and used as inputs to "downstream" models. This work is part of an ongoing collaborative Tri-state (New Mexico, Nevada, Idaho) NSF EPSCoR Project, WC-WAVE, comprised of researchers from multiple universities in each of the three states. The tools produced and presented here have been developed collaboratively alongside watershed scientists to address specific modeling problems with an eye on the bigger picture of scientific reproducibility and transparency, and data publication and reuse.

Balancing the stochastic description of uncertainties as a function of hydrologic model complexity

NASA Astrophysics Data System (ADS)

Del Giudice, D.; Reichert, P.; Albert, C.; Kalcic, M.; Logsdon Muenich, R.; Scavia, D.; Bosch, N. S.; Michalak, A. M.

2016-12-01

Uncertainty analysis is becoming an important component of forecasting water and pollutant fluxes in urban and rural environments. Properly accounting for errors in the modeling process can help to robustly assess the uncertainties associated with the inputs (e.g. precipitation) and outputs (e.g. runoff) of hydrological models. In recent years we have investigated several Bayesian methods to infer the parameters of a mechanistic hydrological model along with those of the stochastic error component. The latter describes the uncertainties of model outputs and possibly inputs. We have adapted our framework to a variety of applications, ranging from predicting floods in small stormwater systems to nutrient loads in large agricultural watersheds. Given practical constraints, we discuss how in general the number of quantities to infer probabilistically varies inversely with the complexity of the mechanistic model. Most often, when evaluating a hydrological model of intermediate complexity, we can infer the parameters of the model as well as of the output error model. Describing the output errors as a first order autoregressive process can realistically capture the "downstream" effect of inaccurate inputs and structure. With simpler runoff models we can additionally quantify input uncertainty by using a stochastic rainfall process. For complex hydrologic transport models, instead, we show that keeping model parameters fixed and just estimating time-dependent output uncertainties could be a viable option. The common goal across all these applications is to create time-dependent prediction intervals which are both reliable (cover the nominal amount of validation data) and precise (are as narrow as possible). In conclusion, we recommend focusing both on the choice of the hydrological model and of the probabilistic error description. The latter can include output uncertainty only, if the model is computationally-expensive, or, with simpler models, it can separately account for different sources of errors like in the inputs and the structure of the model.

Socio-Hydrologic Modeling: Characterizing the Dynamics of Coupled Human-Water Systems Using Natural Science Methods

NASA Astrophysics Data System (ADS)

Sivapalan, M.; Elshafei, Y.; Srinivasan, V.

2014-12-01

A challenging research puzzle in the research on sustainable water management in the Anthropocene is why some societies successfully recover from "ecological destruction" to transition to "successful adaptation" over decadal timescales, while others fail. We present a conceptual modeling framework to understand and characterize these transitions. In this way, we aim to capture the potential drivers of the desired shift towards achieving sustainability of socio-hydrological systems. This is done through a synthesis of detailed socio-hydrological analyses of four river basins in three continents, carried out using different quantitative socio-hydrologic models: Murrumbidgee River Basin in eastern Australia, Lake Toolibin Catchment in Western Australia, Tarim River Basin in Western China and Kissimmee River Basin, in south-east United States. The case studies are analysed using either place-based models designed specifically to mimic observed long-term socio-hydrologic trends, or generic conceptual models with foundations in diverse strands of literature including sustainability science and resilience theory. A comparative analysis of the four case studies reveals a commonality in the building blocks employed to model these socio-hydrologic systems; including water balance, economic, environmental and human-feedback components. Each model reveals varying interpretations of a common organising principle that could explain the shift between productive (socio-economic) and restorative (environmental) forces that was evident in each of these systems observed over a long time frame. The emergent principle is related to the essential drivers of the human feedback component and rests with a general formulation of human well-being, as reflected by both their economic and environmental well-being. It is envisaged that the understanding of the system drivers gained from such a comparative study would enable more targeted water management strategies that can be administered in developing basins to achieve overall sustainability.

Hydrogeologic Framework in Three Drainage Basins in the New Jersey Pinelands, 2004-06

USGS Publications Warehouse

Walker, Richard L.; Reilly, Pamela A.; Watson, Kara M.

2008-01-01

The U.S. Geological Survey, in cooperation with the New Jersey Pinelands Commission, began a multi-phase hydrologic investigation in 2004 to characterize the hydrologic system supporting the aquatic and wetland communities of the New Jersey Pinelands area (Pinelands). The Pinelands is an ecologically diverse area in the southern New Jersey Coastal Plain underlain by the Kirkwood-Cohansey aquifer system. The demand for ground water from this aquifer system is increasing as local development increases. To assess the effects of ground-water withdrawals on Pinelands stream and wetland water levels, three drainage basins were selected for detailed hydrologic assessments, including the Albertson Brook, McDonalds Branch and the Morses Mill Stream basins. Study areas were defined surrounding the three drainage basins to provide sub-regional hydrogeologic data for the ground-water flow modeling phase of this study. In the first phase of the hydrologic assessments, a database of hydrogeologic information and a hydrogeologic framework model for each of the three study areas were produced. These framework models, which illustrate typical hydrogeologic variations among different geographic subregions of the Pinelands, are the structural foundation for predictive ground-water flow models to be used in assessing the hydrologic effects of increased ground-water withdrawals. During 2004-05, a hydrogeologic database was compiled using existing and new geophysical and lithologic data including suites of geophysical logs collected at 7 locations during the drilling of 21 wells and one deep boring within the three study areas. In addition, 27 miles of ground-penetrating radar (GPR) surface geophysical data were collected and analyzed to determine the depth and extent of shallow clays in the general vicinity of the streams. On the basis of these data, the Kirkwood-Cohansey aquifer system was divided into 7 layers to construct a hydrogeologic framework model for each study area. These layers are defined by their predominant sediment textures as aquifers and leaky confining layers. The confining layer at the base of the Kirkwood-Cohansey aquifer system, depending on location, is defined as one of two distinct clays of the Kirkwood Formation. The framework models are described using hydrogeologic sections, maps of structure tops of layers, and thickness maps showing variations of sediment textures of the various model layers. The three framework models are similar in structure but unique to their respective study areas. The hydraulic conductivity of the Kirkwood-Cohansey aquifer system in the vicinity of the three study areas was determined from analysis of 16 slug tests and 136 well-performance tests. The mean values for hydraulic conductivity in the three study areas ranged from about 84 feet per day to 130 feet per day. With the exception of the basal confining layers, the variable and discontinuous nature of clay layers within the Kirkwood-Cohansey aquifer system was confirmed by the geophysical and lithologic records. Leaky confining layers and discontinuous clays are generally more common in the upper part of the aquifer system. Although the Kirkwood-Cohansey aquifer system generally has been considered a water-table aquifer in most areas, localized clays in the aquifer layers and the effectiveness of the leaky confining layers may act to impede the flow of ground water in varying amounts depending on the degree of confinement and the location, duration, and magnitude of the hydraulic stresses applied. Considerable variability exists in the different sediment textures. The extent to which this hydrogeologic variability can be characterized is constrained by the extent of the available data. Thus, the hydraulic properties of the modeled layers were estimated on the basis of available horizontal hydraulic conductivity data and the range of sediment textures estimated from geophysical and lithologic data.

Large ensemble and large-domain hydrologic modeling: Insights from SUMMA applications in the Columbia River Basin

NASA Astrophysics Data System (ADS)

Ou, G.; Nijssen, B.; Nearing, G. S.; Newman, A. J.; Mizukami, N.; Clark, M. P.

2016-12-01

The Structure for Unifying Multiple Modeling Alternatives (SUMMA) provides a unifying modeling framework for process-based hydrologic modeling by defining a general set of conservation equations for mass and energy, with the capability to incorporate multiple choices for spatial discretizations and flux parameterizations. In this study, we provide a first demonstration of large-scale hydrologic simulations using SUMMA through an application to the Columbia River Basin (CRB) in the northwestern United States and Canada for a multi-decadal simulation period. The CRB is discretized into 11,723 hydrologic response units (HRUs) according to the United States Geologic Service Geospatial Fabric. The soil parameters are derived from the Natural Resources Conservation Service Soil Survey Geographic (SSURGO) Database. The land cover parameters are based on the National Land Cover Database from the year 2001 created by the Multi-Resolution Land Characteristics (MRLC) Consortium. The forcing data, including hourly air pressure, temperature, specific humidity, wind speed, precipitation, shortwave and longwave radiations, are based on Phase 2 of the North American Land Data Assimilation System (NLDAS-2) and averaged for each HRU. The simulation results are compared to simulations with the Variable Infiltration Capacity (VIC) model and the Precipitation Runoff Modeling System (PRMS). We are particularly interested in SUMMA's capability to mimic model behaviors of the other two models through the selection of appropriate model parameterizations in SUMMA.

The PCR-GLOBWB global hydrological reanalysis product

NASA Astrophysics Data System (ADS)

Wanders, Niko; Bierkens, Marc; Sutanudjaja, Edwin; van Beek, Rens

2014-05-01

Accurate and long time series of hydrological data are important for understanding land surface water and energy budgets in many parts of the world, as well as for improving real-time hydrological monitoring and climate change anticipation. The ultimate goal of the present work is to produce a multi-decadal "land surface hydrological reanalysis" dataset with retrospective and updated hydrological states and fluxes that are constrained to available in-situ river discharge measurements. Here we use PCR-GLOBWB (van Beek et al., 2011), which is a large-scale hydrological model intended for global to regional studies. PCR-GLOBWB provides a grid-based representation of terrestrial hydrology with a typical spatial resolution of approximately 50×50 km (currently 0.5° globally) on a daily basis. For each grid cell, PCR-GLOBWB simulates moisture storage in two vertically stacked soil layers as well as the water exchange between the soil and the atmosphere and the underlying groundwater reservoir. Exchange to the atmosphere comprises precipitation, evaporation and transpiration, as well as snow accumulation and melt, which are all simulated by considering vegetation phenology and sub-grid variations of elevation, land cover and soil saturation distribution. The model includes improved schemes for runoff-infiltration partitioning, interflow, groundwater recharge and baseflow, as well as river routing of discharge. It also dynamically simulates water storage in reservoirs, water demand and the withdrawal, allocation and consumptive use of surface water and groundwater resources. By embedding the PCR-GLOBWB model in an Ensemble Kalman Filter framework, we calibrate the model parameters based on the discharge observations from the Global Runoff Data Centre. The parameters calibrated are related to snow accumulation and melt, runoff-infiltration partitioning, groundwater recharge, channel discharge and baseflow processes, as well as pre-factors to correct forcing precipitation fields with consideration of local topographic and orographic effects. Results show that the model parameters can be successfully calibrated, while corrections to the forcing precipitation fields are substantial. Topography has the largest impact on the corrected precipitation and globally the precipitation is reduced by 3%. The calibrated model output is compared to the reference run of PCR-GLOBWB before calibration showing significant improvement in simulation of the global terrestrial water cycle. The RMSE is reduced by 10% on average, leading to improved discharge simulations, especially under base flow situations. The main outcome of this work is a 1960-2010 global reanalysis dataset that includes extensive daily hydrological components, such as precipitation, evaporation and transpiration, snow, soil moisture, groundwater storage and discharge. This reanalysis product may be used for understanding land surface memory processes, initializing regional studies and operational forecasts, as well as evaluating and improving our understanding of spatio-temporal variation of meteorological and hydrological processes. Moreover, The PCR-GLOBWB data assimilation framework developed in this work can also be extended by including more observational data, including remotely sensed data reflecting the distribution of energy and water (e.g., heat fluxes and soil moisture storage).

Transport of fluorobenzoate tracers in a vegetated hydrologic control volume: 2. Theoretical inferences and modeling

NASA Astrophysics Data System (ADS)

Queloz, Pierre; Carraro, Luca; Benettin, Paolo; Botter, Gianluca; Rinaldo, Andrea; Bertuzzo, Enrico

2015-04-01

A theoretical analysis of transport in a controlled hydrologic volume, inclusive of two willow trees and forced by erratic water inputs, is carried out contrasting the experimental data described in a companion paper. The data refer to the hydrologic transport in a large lysimeter of different fluorobenzoic acids seen as tracers. Export of solute is modeled through a recently developed framework which accounts for nonstationary travel time distributions where we parameterize how output fluxes (namely, discharge and evapotranspiration) sample the available water ages in storage. The relevance of this work lies in the study of hydrologic drivers of the nonstationary character of residence and travel time distributions, whose definition and computation shape this theoretical transport study. Our results show that a large fraction of the different behaviors exhibited by the tracers may be charged to the variability of the hydrologic forcings experienced after the injection. Moreover, the results highlight the crucial, and often overlooked, role of evapotranspiration and plant uptake in determining the transport of water and solutes. This application also suggests that the ways evapotranspiration selects water with different ages in storage can be inferred through model calibration contrasting only tracer concentrations in the discharge. A view on upscaled transport volumes like hillslopes or catchments is maintained throughout the paper.

Forecasting and Communicating Water-Related Disasters in Africa

NASA Astrophysics Data System (ADS)

Hong, Y.; Clark, R. A.; Mandl, D.; Gourley, J. J.; Flamig, Z.; Zhang, K.; Macharia, D.; Frye, S. W.; Cappelaere, P. G.; Handy, M.

2016-12-01

Accurate forecasting and communication of water and water-related hazards in developing regions could save untold lives and property. To this end, the CREST (Coupled Routing and Excess Storage) hydrologic model has been implemented over East Africa, and in dozens of other countries as a user-friendly, flexible, and highly extensible platform for monitoring water resources, floods, droughts, and landslides since 2009. We will present the updated CREST/EF5 hydrologic ensemble modeling framework with new model physics and better forecasts of streamflow, soil moisture, and other hydrologic states to RCMRD (the Regional Centre for Mapping of Resources for Development) and SERVIR global hub network. The central goal of this project is to develop an ensemble hydrologic prediction system, forced by weather and climate forecasts in a single continuum, to communicate forecasts on scales ranging from sub-daily to seasonal and in formats designed for better decision making about water and water-related disasters. The CREST/EF5 is a proven performer at getting researcher and officials in emerging regions excited about and confident in their ability to independently monitor, forecast, and understand water and water-related disasters, through a series of training workshops and capacity building activities in USA, Africa, Mesoamerica, and South Asia and is thus particularly well-suited for hydrologic capacity building in emerging countries.

The Saale-Project -A multidisciplinary approach towards sustainable integrative catchment management -

NASA Astrophysics Data System (ADS)

Bongartz, K.; Flügel, W. A.

2003-04-01

In the joint research project “Development of an integrated methodology for the sustainable management of river basins The Saale River Basin example”, coordinated by the Centre of Environmental Research (UFZ), concepts and tools for an integrated management of large river basins are developed and applied for the Saale river basin. The ultimate objective of the project is to contribute to the holistic assessment and benchmarking approaches in water resource planning, as required by the European Water Framework Directive. The study presented here deals (1) with the development of a river basin information and modelling system, (2) with the refinement of a regionalisation approach adapted for integrated basin modelling. The approach combines a user friendly basin disaggregation method preserving the catchment’s physiographic heterogeneity with a process oriented hydrological basin assessment for scale bridging integrated modelling. The well tested regional distribution concept of Response Units (RUs) will be enhanced by landscape metrics and decision support tools for objective, scale independent and problem oriented RU delineation to provide the spatial modelling entities for process oriented and distributed simulation of vertical and lateral hydrological transport processes. On basis of this RUs suitable hydrological modelling approaches will be further developed with strong respect to a more detailed simulation of the lateral surface and subsurface flows as well as the channel flow. This methodical enhancement of the well recognised RU-concept will be applied to the river basin of the Saale (Ac: 23 179 km2) and validated by a nested catchment approach, which allows multi-response-validation and estimation of uncertainties of the modelling results. Integrated modelling of such a complex basin strongly influenced by manifold human activities (reservoirs, agriculture, urban areas and industry) can only be achieved by coupling the various modelling approaches within a well defined model framework system. The latter is interactively linked with a sophisticated geo-relational database (DB) serving all research teams involved in the project. This interactive linkage is a core element comprising an object-oriented, internet-based modelling framework system (MFS) for building interdisciplinary modelling applications and offering different analysis and visualisation tools.

Strategy to Conduct Quantitative Ecohydrologic Analysis of a UNESCO World Heritage Site: the Peace-Athabasca Delta, Canada

NASA Astrophysics Data System (ADS)

Ward, E. M.; Gorelick, S.; Hadly, E. A.

2016-12-01

The 6000 km2 Peace-Athabasca Delta ("Delta") in northeastern Alberta, Canada, is a Ramsar Convention Wetland and UNESCO World Heritage Site ("in Danger" status pending) where hydropower development and climate change are creating ecological impacts through desiccation and reduction in Delta shoreline habitat. We focus on ecohydrologic changes and mitigation and adaptation options to advance the field of ecohydrology using interdisciplinary technology by combining, for the first time, satellite remote sensing and hydrologic simulation with individual-based population modeling of muskrat (Ondatra zibethicus), a species native to the Delta whose population dynamics are strongly controlled by the hydrology of floodplain lakes. We are building a conceptual and quantitative modeling framework linking climate change, upstream water demand, and hydrologic change in the floodplain to muskrat population dynamics with the objective of exploring the impacts of these stressors on this ecosystem. We explicitly account for cultural and humanistic influences and are committed to effective communication with the regional subsistence community that depends on muskrat for food and income. Our modeling framework can ultimately serve as the basis for improved stewardship and sustainable development upstream of stressed freshwater deltaic, coastal and lake systems worldwide affected by climate change, providing a predictive tool to quantify population changes of animals relevant to regional subsistence food security and commercial trapping.

Mapping model behaviour using Self-Organizing Maps

NASA Astrophysics Data System (ADS)

Herbst, M.; Gupta, H. V.; Casper, M. C.

2009-03-01

Hydrological model evaluation and identification essentially involves extracting and processing information from model time series. However, the type of information extracted by statistical measures has only very limited meaning because it does not relate to the hydrological context of the data. To overcome this inadequacy we exploit the diagnostic evaluation concept of Signature Indices, in which model performance is measured using theoretically relevant characteristics of system behaviour. In our study, a Self-Organizing Map (SOM) is used to process the Signatures extracted from Monte-Carlo simulations generated by the distributed conceptual watershed model NASIM. The SOM creates a hydrologically interpretable mapping of overall model behaviour, which immediately reveals deficits and trade-offs in the ability of the model to represent the different functional behaviours of the watershed. Further, it facilitates interpretation of the hydrological functions of the model parameters and provides preliminary information regarding their sensitivities. Most notably, we use this mapping to identify the set of model realizations (among the Monte-Carlo data) that most closely approximate the observed discharge time series in terms of the hydrologically relevant characteristics, and to confine the parameter space accordingly. Our results suggest that Signature Index based SOMs could potentially serve as tools for decision makers inasmuch as model realizations with specific Signature properties can be selected according to the purpose of the model application. Moreover, given that the approach helps to represent and analyze multi-dimensional distributions, it could be used to form the basis of an optimization framework that uses SOMs to characterize the model performance response surface. As such it provides a powerful and useful way to conduct model identification and model uncertainty analyses.

Mapping model behaviour using Self-Organizing Maps

NASA Astrophysics Data System (ADS)

Herbst, M.; Gupta, H. V.; Casper, M. C.

2008-12-01

Hydrological model evaluation and identification essentially depends on the extraction of information from model time series and its processing. However, the type of information extracted by statistical measures has only very limited meaning because it does not relate to the hydrological context of the data. To overcome this inadequacy we exploit the diagnostic evaluation concept of Signature Indices, in which model performance is measured using theoretically relevant characteristics of system behaviour. In our study, a Self-Organizing Map (SOM) is used to process the Signatures extracted from Monte-Carlo simulations generated by a distributed conceptual watershed model. The SOM creates a hydrologically interpretable mapping of overall model behaviour, which immediately reveals deficits and trade-offs in the ability of the model to represent the different functional behaviours of the watershed. Further, it facilitates interpretation of the hydrological functions of the model parameters and provides preliminary information regarding their sensitivities. Most notably, we use this mapping to identify the set of model realizations (among the Monte-Carlo data) that most closely approximate the observed discharge time series in terms of the hydrologically relevant characteristics, and to confine the parameter space accordingly. Our results suggest that Signature Index based SOMs could potentially serve as tools for decision makers inasmuch as model realizations with specific Signature properties can be selected according to the purpose of the model application. Moreover, given that the approach helps to represent and analyze multi-dimensional distributions, it could be used to form the basis of an optimization framework that uses SOMs to characterize the model performance response surface. As such it provides a powerful and useful way to conduct model identification and model uncertainty analyses.

Toward hydro-social modeling: Merging human variables and the social sciences with climate-glacier runoff models (Santa River, Peru)

NASA Astrophysics Data System (ADS)

Carey, Mark; Baraer, Michel; Mark, Bryan G.; French, Adam; Bury, Jeffrey; Young, Kenneth R.; McKenzie, Jeffrey M.

2014-10-01

Glacier shrinkage caused by climate change is likely to trigger diminished and less consistent stream flow in glacier-fed watersheds worldwide. To understand, model, and adapt to these climate-glacier-water changes, it is vital to integrate the analysis of both water availability (the domain of hydrologists) and water use (the focus for social scientists). Drawn from a case study of the Santa River watershed below Peru’s glaciated Cordillera Blanca mountain range, this paper provides a holistic hydro-social framework that identifies five major human variables critical to hydrological modeling because these forces have profoundly influenced water use over the last 60 years: (1) political agendas and economic development; (2) governance: laws and institutions; (3) technology and engineering; (4) land and resource use; and (5) societal responses. Notable shifts in Santa River water use-including major expansions in hydroelectricity generation, large-scale irrigation projects, and other land and resource-use practices-did not necessarily stem from changing glacier runoff or hydrologic shifts, but rather from these human variables. Ultimately, then, water usage is not predictable based on water availability alone. Glacier runoff conforms to certain expected trends predicted by models of progressively reduced glacier storage. However, societal forces establish the legal, economic, political, cultural, and social drivers that actually shape water usage patterns via human modification of watershed dynamics. This hydro-social framework has widespread implications for hydrological modeling in glaciated watersheds from the Andes and Alps to the Himalaya and Tien Shan, as well as for the development of climate change adaptation plans.

Integrated Modeling to Assess the Impacts of Changes in Climate and Socio Economics on Agriculture in the Columbia River Basin

NASA Astrophysics Data System (ADS)

Rajagopalan, K.; Chinnayakanahalli, K.; Adam, J. C.; Malek, K.; Nelson, R.; Stockle, C.; Brady, M.; Dinesh, S.; Barber, M. E.; Yorgey, G.; Kruger, C.

2012-12-01

The objective of this work is to assess the impacts of climate change and socio economics on agriculture in the Columbia River basin (CRB) in the Pacific Northwest region of the U.S. and a portion of Southwestern Canada. The water resources of the CRB are managed to satisfy multiple objectives including agricultural withdrawal, which is the largest consumptive user of CRB water with 14,000 square kilometers of irrigated area. Agriculture is an important component of the region's economy, with an annual value over 5 billion in Washington State alone. Therefore, the region is relevant for applying a modeling framework that can aid agriculture decision making in the context of a changing climate. To do this, we created an integrated biophysical and socio-economic regional modeling framework that includes human and natural systems. The modeling framework captures the interactions between climate, hydrology, crop growth dynamics, water management and socio economics. The biophysical framework includes a coupled macro-scale physically-based hydrology model (the Variable Infiltration Capacity, VIC model), and crop growth model (CropSyst), as well as a reservoir operations simulation model. Water rights data and instream flow target requirements are also incorporated in the model to simulate the process of curtailment during water shortage. The economics model informs the biophysical model of the short term agricultural producer response to water shortage as well as the long term agricultural producer response to domestic growth and international trade in terms of an altered cropping pattern. The modeling framework was applied over the CRB for the historical period 1976-2006 and compared to a future 30-year period centered on the 2030s. Impacts of climate change on irrigation water availability, crop irrigation demand, frequency of curtailment, and crop yields are quantified and presented. Sensitivity associated with estimates of water availability, irrigation demand, crop yields, unmet demand and available instream flows due to climate inputs, hydrology and crop model parameterization, water management assumptions, model integration assumptions, as well as multiple socio economic alternatives are also presented. Compared to historical conditions, for the 2030s time period, our results show an average additional irrigation water demand requirement of 370 million cubic meters in the CRB, an increased frequency of curtailment and a revenue impact between 70 and $150 million resulting from adverse crop yield impacts due to curtailment in the state of Washington. The impacts vary spatially and some of the CRB tributary watersheds are impacted more than others, e.g., unmet demand in the Yakima River basin is expected to increase by 50%. Increased irrigation demand, coupled with decreased seasonal supply poses difficult water resources management questions in the region.

A Catchment-Based Land Surface Model for GCMs and the Framework for its Evaluation

NASA Technical Reports Server (NTRS)

Ducharen, A.; Koster, R. D.; Suarez, M. J.; Kumar, P.

1998-01-01

A new GCM-scale land surface modeling strategy that explicitly accounts for subgrid soil moisture variability and its effects on evaporation and runoff is now being explored. In a break from traditional modeling strategies, the continental surface is disaggregated into a mosaic of hydrological catchments, with boundaries that are not dictated by a regular grid but by topography. Within each catchment, the variability of soil moisture is deduced from TOP-MODEL equations with a special treatment of the unsaturated zone. This paper gives an overview of this new approach and presents the general framework for its off-line evaluation over North-America.

HESS Opinions: A conceptual framework for assessing socio-hydrological resilience under change

NASA Astrophysics Data System (ADS)

Mao, Feng; Clark, Julian; Karpouzoglou, Timothy; Dewulf, Art; Buytaert, Wouter; Hannah, David

2017-07-01

Despite growing interest in resilience, there is still significant scope for increasing its conceptual clarity and practical relevance in socio-hydrological contexts: specifically, questions of how socio-hydrological systems respond to and cope with perturbations and how these connect to resilience remain unanswered. In this opinion paper, we propose a novel conceptual framework for understanding and assessing resilience in coupled socio-hydrological contexts, and encourage debate on the inter-connections between socio-hydrology and resilience. Taking a systems perspective, we argue that resilience is a set of systematic properties with three dimensions: absorptive, adaptive, and transformative, and contend that socio-hydrological systems can be viewed as various forms of human-water couplings, reflecting different aspects of these interactions. We propose a framework consisting of two parts. The first part addresses the identity of socio-hydrological resilience, answering questions such as resilience of what in relation to what. We identify three existing framings of resilience for different types of human-water systems and subsystems, which have been used in different fields: (1) the water subsystem, highlighting hydrological resilience to anthropogenic hazards; (2) the human subsystem, foregrounding social resilience to hydrological hazards; and (3) the coupled human-water system, exhibiting socio-hydrological resilience. We argue that these three system types and resiliences afford new insights into the clarification and evaluation of different water management challenges. The first two types address hydrological and social states, while the third type emphasises the feedbacks and interactions between human and water components within complex systems subject to internal or external disturbances. In the second part, we focus on resilience management and develop the notion of the resilience canvas, a novel heuristic device to identify possible pathways and to facilitate the design of bespoke strategies for enhancing resilience in the socio-hydrological context. The resilience canvas is constructed by combining absorptive and adaptive capacities as two axes. At the corners of the resulting two-dimensional space are four quadrants which we conceptualise as representing resilient, vulnerable, susceptible, and resistant system states. To address projected change-induced uncertainties, we recommend that efforts now be focused on shifting socio-hydrological systems from resistant towards resilient status. In sum, the novel framework proposed here clarifies the ambiguity inherent in socio-hydrological resilience, and provides a viable basis for further theoretical and practical development.

Integrating Sediment Connectivity into Water Resources Management Trough a Graph Theoretic, Stochastic Modeling Framework.

NASA Astrophysics Data System (ADS)

Schmitt, R. J. P.; Castelletti, A.; Bizzi, S.

2014-12-01

Understanding sediment transport processes at the river basin scale, their temporal spectra and spatial patterns is key to identify and minimize morphologic risks associated to channel adjustments processes. This work contributes a stochastic framework for modeling bed-load connectivity based on recent advances in the field (e.g., Bizzi & Lerner, 2013; Czubas & Foufoulas-Georgiu, 2014). It presents river managers with novel indicators from reach scale vulnerability to channel adjustment in large river networks with sparse hydrologic and sediment observations. The framework comprises three steps. First, based on a distributed hydrological model and remotely sensed information, the framework identifies a representative grain size class for each reach. Second, sediment residence time distributions are calculated for each reach in a Monte-Carlo approach applying standard sediment transport equations driven by local hydraulic conditions. Third, a network analysis defines the up- and downstream connectivity for various travel times resulting in characteristic up/downstream connectivity signatures for each reach. Channel vulnerability indicators quantify the imbalance between up/downstream connectivity for each travel time domain, representing process dependent latency of morphologic response. Last, based on the stochastic core of the model, a sensitivity analysis identifies drivers of change and major sources of uncertainty in order to target key detrimental processes and to guide effective gathering of additional data. The application, limitation and integration into a decision analytic framework is demonstrated for a major part of the Red River Basin in Northern Vietnam (179.000 km2). Here, a plethora of anthropic alterations ranging from large reservoir construction to land-use changes results in major downstream deterioration and calls for deriving concerted sediment management strategies to mitigate current and limit future morphologic alterations.

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.

Modeling Feedbacks Between Individual Human Decisions and Hydrology Using Interconnected Physical and Social Models

NASA Astrophysics Data System (ADS)

Murphy, J.; Lammers, R. B.; Proussevitch, A. A.; Ozik, J.; Altaweel, M.; Collier, N. T.; Alessa, L.; Kliskey, A. D.

2014-12-01

The global hydrological cycle intersects with human decision making at multiple scales, from dams and irrigation works to the taps in individuals' homes. Residential water consumers are commonly encouraged to conserve; these messages are heard against a background of individual values and conceptions about water quality, uses, and availability. The degree to which these values impact the larger-hydrological dynamics, the way that changes in those values have impacts on the hydrological cycle through time, and the feedbacks by which water availability and quality in turn shape those values, are not well explored. To investigate this domain we employ a global-scale water balance model (WBM) coupled with a social-science-grounded agent-based model (ABM). The integration of a hydrological model with an agent-based model allows us to explore driving factors in the dynamics in coupled human-natural systems. From the perspective of the physical hydrologist, the ABM offers a richer means of incorporating the human decisions that drive the hydrological system; from the view of the social scientist, a physically-based hydrological model allows the decisions of the agents to play out against constraints faithful to the real world. We apply the interconnected models to a study of Tucson, Arizona, USA, and its role in the larger Colorado River system. Our core concept is Technology-Induced Environmental Distancing (TIED), which posits that layers of technology can insulate consumers from direct knowledge of a resource. In Tucson, multiple infrastructure and institutional layers have arguably increased the conceptual distance between individuals and their water supply, offering a test case of the TIED framework. Our coupled simulation allows us to show how the larger system transforms a resource with high temporal and spatial variability into a consumer constant, and the effects of this transformation on the regional system. We use this to explore how pricing, messaging, and social dynamics impact demand, how changes in demand affect the regional water system, and under what system challenges the values of the individuals are likely to change. This study is a preamble to modeling multiple regionally connected cities and larger systems with impacts on hydrology at the continental and global scales.

Towards SWOT data assimilation for hydrology : automatic calibration of global flow routing model parameters in the Amazon basin

NASA Astrophysics Data System (ADS)

Mouffe, M.; Getirana, A.; Ricci, S. M.; Lion, C.; Biancamaria, S.; Boone, A.; Mognard, N. M.; Rogel, P.

2011-12-01

The Surface Water and Ocean Topography (SWOT) mission is a swath mapping radar interferometer that will provide global measurements of water surface elevation (WSE). The revisit time depends upon latitude and varies from two (low latitudes) to ten (high latitudes) per 22-day orbit repeat period. The high resolution and the global coverage of the SWOT data open the way for new hydrology studies. Here, the aim is to investigate the use of virtually generated SWOT data to improve discharge simulation using data assimilation techniques. In the framework of the SWOT virtual mission (VM), this study presents the first results of the automatic calibration of a global flow routing (GFR) scheme using SWOT VM measurements for the Amazon basin. The Hydrological Modeling and Analysis Platform (HyMAP) is used along with the MOCOM-UA multi-criteria global optimization algorithm. HyMAP has a 0.25-degree spatial resolution and runs at the daily time step to simulate discharge, water levels and floodplains. The surface runoff and baseflow drainage derived from the Interactions Sol-Biosphère-Atmosphère (ISBA) model are used as inputs for HyMAP. Previous works showed that the use of ENVISAT data enables the reduction of the uncertainty on some of the hydrological model parameters, such as river width and depth, Manning roughness coefficient and groundwater time delay. In the framework of the SWOT preparation work, the automatic calibration procedure was applied using SWOT VM measurements. For this Observing System Experiment (OSE), the synthetical data were obtained applying an instrument simulator (representing realistic SWOT errors) for one hydrological year to HYMAP simulated WSE using a "true" set of parameters. Only pixels representing rivers larger than 100 meters within the Amazon basin are considered to produce SWOT VM measurements. The automatic calibration procedure leads to the estimation of optimal parametersminimizing objective functions that formulate the difference between SWOT observations and modeled WSE using a perturbed set of parameters. Different formulations of the objective function were used, especially to account for SWOT observation errors, as well as various sets of calibration parameters.

Uncertainty in mixing models: a blessing in disguise?

NASA Astrophysics Data System (ADS)

Delsman, J. R.; Oude Essink, G. H. P.

2012-04-01

Despite the abundance of tracer-based studies in catchment hydrology over the past decades, relatively few studies have addressed the uncertainty associated with these studies in much detail. This uncertainty stems from analytical error, spatial and temporal variance in end-member composition, and from not incorporating all relevant processes in the necessarily simplistic mixing models. Instead of applying standard EMMA methodology, we used end-member mixing model analysis within a Monte Carlo framework to quantify the uncertainty surrounding our analysis. Borrowing from the well-known GLUE methodology, we discarded mixing models that could not satisfactorily explain sample concentrations and analyzed the posterior parameter set. This use of environmental tracers aided in disentangling hydrological pathways in a Dutch polder catchment. This 10 km2 agricultural catchment is situated in the coastal region of the Netherlands. Brackish groundwater seepage, originating from Holocene marine transgressions, adversely affects water quality in this catchment. Current water management practice is aimed at improving water quality by flushing the catchment with fresh water from the river Rhine. Climate change is projected to decrease future fresh water availability, signifying the need for a more sustainable water management practice and a better understanding of the functioning of the catchment. The end-member mixing analysis increased our understanding of the hydrology of the studied catchment. The use of a GLUE-like framework for applying the end-member mixing analysis not only quantified the uncertainty associated with the analysis, the analysis of the posterior parameter set also identified the existence of catchment processes otherwise overlooked.

Assessing uncertainties in surface water security: An empirical multimodel approach

NASA Astrophysics Data System (ADS)

Rodrigues, Dulce B. B.; Gupta, Hoshin V.; Mendiondo, Eduardo M.; Oliveira, Paulo Tarso S.

2015-11-01

Various uncertainties are involved in the representation of processes that characterize interactions among societal needs, ecosystem functioning, and hydrological conditions. Here we develop an empirical uncertainty assessment of water security indicators that characterize scarcity and vulnerability, based on a multimodel and resampling framework. We consider several uncertainty sources including those related to (i) observed streamflow data; (ii) hydrological model structure; (iii) residual analysis; (iv) the method for defining Environmental Flow Requirement; (v) the definition of critical conditions for water provision; and (vi) the critical demand imposed by human activities. We estimate the overall hydrological model uncertainty by means of a residual bootstrap resampling approach, and by uncertainty propagation through different methodological arrangements applied to a 291 km2 agricultural basin within the Cantareira water supply system in Brazil. Together, the two-component hydrograph residual analysis and the block bootstrap resampling approach result in a more accurate and precise estimate of the uncertainty (95% confidence intervals) in the simulated time series. We then compare the uncertainty estimates associated with water security indicators using a multimodel framework and the uncertainty estimates provided by each model uncertainty estimation approach. The range of values obtained for the water security indicators suggests that the models/methods are robust and performs well in a range of plausible situations. The method is general and can be easily extended, thereby forming the basis for meaningful support to end-users facing water resource challenges by enabling them to incorporate a viable uncertainty analysis into a robust decision-making process.

A pilot Virtual Observatory (pVO) for integrated catchment science - Demonstration of national scale modelling of hydrology and biogeochemistry (Invited)

NASA Astrophysics Data System (ADS)

Freer, J. E.; Bloomfield, J. P.; Johnes, P. J.; MacLeod, C.; Reaney, S.

2010-12-01

There are many challenges in developing effective and integrated catchment management solutions for hydrology and water quality issues. Such solutions should ideally build on current scientific evidence to inform policy makers and regulators and additionally allow stakeholders to take ownership of local and/or national issues, in effect bringing together ‘communities of practice’. A strategy being piloted in the UK as the Pilot Virtual Observatory (pVO), funded by NERC, is to demonstrate the use of cyber-infrastructure and cloud computing resources to investigate better methods of linking data and models and to demonstrate scenario analysis for research, policy and operational needs. The research will provide new ways the scientific and stakeholder communities come together to exploit current environmental information, knowledge and experience in an open framework. This poster presents the project scope and methodologies for the pVO work dealing with national modelling of hydrology and macro-nutrient biogeochemistry. We evaluate the strategies needed to robustly benchmark our current predictive capability of these resources through ensemble modelling. We explore the use of catchment similarity concepts to understand if national monitoring programs can inform us about the behaviour of catchments. We discuss the challenges to applying these strategies in an open access and integrated framework and finally we consider the future for such virtual observatory platforms for improving the way we iteratively improve our understanding of catchment science.

On the Fidelity of Semi-distributed Hydrologic Model Simulations for Large Scale Catchment Applications

NASA Astrophysics Data System (ADS)

Ajami, H.; Sharma, A.; Lakshmi, V.

2017-12-01

Application of semi-distributed hydrologic modeling frameworks is a viable alternative to fully distributed hyper-resolution hydrologic models due to computational efficiency and resolving fine-scale spatial structure of hydrologic fluxes and states. However, fidelity of semi-distributed model simulations is impacted by (1) formulation of hydrologic response units (HRUs), and (2) aggregation of catchment properties for formulating simulation elements. Here, we evaluate the performance of a recently developed Soil Moisture and Runoff simulation Toolkit (SMART) for large catchment scale simulations. In SMART, topologically connected HRUs are delineated using thresholds obtained from topographic and geomorphic analysis of a catchment, and simulation elements are equivalent cross sections (ECS) representative of a hillslope in first order sub-basins. Earlier investigations have shown that formulation of ECSs at the scale of a first order sub-basin reduces computational time significantly without compromising simulation accuracy. However, the implementation of this approach has not been fully explored for catchment scale simulations. To assess SMART performance, we set-up the model over the Little Washita watershed in Oklahoma. Model evaluations using in-situ soil moisture observations show satisfactory model performance. In addition, we evaluated the performance of a number of soil moisture disaggregation schemes recently developed to provide spatially explicit soil moisture outputs at fine scale resolution. Our results illustrate that the statistical disaggregation scheme performs significantly better than the methods based on topographic data. Future work is focused on assessing the performance of SMART using remotely sensed soil moisture observations using spatially based model evaluation metrics.

A Socio-Hydrological Model of the Voluntary Urban Water Conservation Behavior during Droughts

NASA Astrophysics Data System (ADS)

Sangwan, N.; Eisma, J. A.; Sung, K.; Yu, D. J.

2016-12-01

Several cities across the globe are increasingly struggling to meet the water demands of their population. By 2050, nearly 160 million urban dwellers are likely to face perennial water shortage due to ever rising population numbers and climate change. As observed once again during recent drought in California, voluntary water conservation is a key approach for managing urban water availability during periods of constrained supply. It relies on behavioral adaptation that is critical for long-term reductions in water use and building drought resilient communities. Strong interdependencies between human group behavior and regional hydrology in this context entail that the two components be coupled together in a socio-hydrology model to fully understand the dynamics of urban water systems. This work proposes a conceptual framework for one such model and simulates the dynamics of a voluntary conservation program in Marin Municipal Water District, California using dynamic systems modeling approach. Through this model, we plan to assess the effects of different social factors (such as social concern and conformist tendencies) and climato-hydrological conditions (viz. storage levels and weather forecast) on the trajectory of a voluntary conservation program. Our preliminary results have indicated several `tipping points' which can be capitalized on by policy makers to boost conservation at low social costs.

Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization

NASA Astrophysics Data System (ADS)

Chen, Y.; Li, J.; Xu, H.

2016-01-01

Physically based distributed hydrological models (hereafter referred to as PBDHMs) divide the terrain of the whole catchment into a number of grid cells at fine resolution and assimilate different terrain data and precipitation to different cells. They are regarded to have the potential to improve the catchment hydrological process simulation and prediction capability. In the early stage, physically based distributed hydrological models are assumed to derive model parameters from the terrain properties directly, so there is no need to calibrate model parameters. However, unfortunately the uncertainties associated with this model derivation are very high, which impacted their application in flood forecasting, so parameter optimization may also be necessary. There are two main purposes for this study: the first is to propose a parameter optimization method for physically based distributed hydrological models in catchment flood forecasting by using particle swarm optimization (PSO) algorithm and to test its competence and to improve its performances; the second is to explore the possibility of improving physically based distributed hydrological model capability in catchment flood forecasting by parameter optimization. In this paper, based on the scalar concept, a general framework for parameter optimization of the PBDHMs for catchment flood forecasting is first proposed that could be used for all PBDHMs. Then, with the Liuxihe model as the study model, which is a physically based distributed hydrological model proposed for catchment flood forecasting, the improved PSO algorithm is developed for the parameter optimization of the Liuxihe model in catchment flood forecasting. The improvements include adoption of the linearly decreasing inertia weight strategy to change the inertia weight and the arccosine function strategy to adjust the acceleration coefficients. This method has been tested in two catchments in southern China with different sizes, and the results show that the improved PSO algorithm could be used for the Liuxihe model parameter optimization effectively and could improve the model capability largely in catchment flood forecasting, thus proving that parameter optimization is necessary to improve the flood forecasting capability of physically based distributed hydrological models. It also has been found that the appropriate particle number and the maximum evolution number of PSO algorithm used for the Liuxihe model catchment flood forecasting are 20 and 30 respectively.

Hydrologic Model Selection using Markov chain Monte Carlo methods

NASA Astrophysics Data System (ADS)

Marshall, L.; Sharma, A.; Nott, D.

2002-12-01

Estimation of parameter uncertainty (and in turn model uncertainty) allows assessment of the risk in likely applications of hydrological models. Bayesian statistical inference provides an ideal means of assessing parameter uncertainty whereby prior knowledge about the parameter is combined with information from the available data to produce a probability distribution (the posterior distribution) that describes uncertainty about the parameter and serves as a basis for selecting appropriate values for use in modelling applications. Widespread use of Bayesian techniques in hydrology has been hindered by difficulties in summarizing and exploring the posterior distribution. These difficulties have been largely overcome by recent advances in Markov chain Monte Carlo (MCMC) methods that involve random sampling of the posterior distribution. This study presents an adaptive MCMC sampling algorithm which has characteristics that are well suited to model parameters with a high degree of correlation and interdependence, as is often evident in hydrological models. The MCMC sampling technique is used to compare six alternative configurations of a commonly used conceptual rainfall-runoff model, the Australian Water Balance Model (AWBM), using 11 years of daily rainfall runoff data from the Bass river catchment in Australia. The alternative configurations considered fall into two classes - those that consider model errors to be independent of prior values, and those that model the errors as an autoregressive process. Each such class consists of three formulations that represent increasing levels of complexity (and parameterisation) of the original model structure. The results from this study point both to the importance of using Bayesian approaches in evaluating model performance, as well as the simplicity of the MCMC sampling framework that has the ability to bring such approaches within the reach of the applied hydrological community.

The Impact of Climate Projection Method on the Analysis of Climate Change in Semi-arid Basins

NASA Astrophysics Data System (ADS)

Halper, E.; Shamir, E.

2016-12-01

In small basins with arid climates, rainfall characteristics are highly variable and stream flow is tightly coupled with the nuances of rainfall events (e.g. hourly precipitation patterns Climate change assessments in these basins typically employ CMIP5 projections downscaled with Bias Corrected Statistical Downscaling and Bias Correction/Constructed Analogs (BCSD-BCCA) methods, but these products have drawbacks. Specifically, BCSD-BCCA these projections do not explicitly account for localized physical precipitation mechanisms (e.g. monsoon and snowfall) that are essential to many hydrological systems in the U. S. Southwest. An investigation of the impact of different types of precipitation projections for two kinds of hydrologic studies is being conducted under the U.S. Bureau of Reclamation's Science and Technology Grant Program. An innovative modeling framework consisting of a weather generator of likely hourly precipitation scenarios, coupled with rainfall-runoff, river routing and groundwater models, has been developed in the Nogales, Arizona area. This framework can simulate the impact of future climate on municipal water operations. This framework allows the rigorous comparison of the BCSD-BCCA methods with alternative approaches including rainfall output from dynamical downscaled Regional Climate Models (RCM), a stochastic rainfall generator forced by either Global Climate Models (GCM) or RCM, and projections using historical records conditioned on either GCM or RCM. The results will provide guide for the use of climate change projections into hydrologic studies of semi-arid areas. The project extends this comparison to analyses of flood control. Large flows on the Bill Williams River are a concern for the operation of dams along the Lower Colorado River. After adapting the weather generator for this region, we will evaluate the model performance for rainfall and stream flow, with emphasis on statistical features important to the specific needs of flood management. The end product of the research is to develop a test to guide selection of a precipitation projection method (including downscaling procedure) for a given region and objective.

Geospatial Data Standards for Indian Water Resources Systems

NASA Astrophysics Data System (ADS)

Goyal, A.; Tyagi, H.; Gosain, A. K.; Khosa, R.

2016-12-01

Sustainable management of water resources is fundamental to the socio-economic development of any nation. There is an increasing degree of dependency on digital geographical data for monitoring, planning, managing and preserving the water resources and environmental quality. But the rising sophistication associated with the sharing of geospatial data among organizations or users, demands development of data standards for seamless information exchange among collaborators. Therefore, due to the realization that these datasets are vital for efficient use of Geographical Information Systems, there is a growing emphasis on data standards for modeling, encoding and communicating spatial data. Real world hydrologic interactions represented in a digital framework requires geospatial standards that may vary in contexts like: governance, resource inventory, cultural diversity, identifiers, role and scale. Though the prevalent standards for the hydrology data facilitate a particular need in a particular context but they lack a holistic approach. However, several worldwide initiatives such as Consortium for the Advancement of Hydrologic Sciences Inc. (USA), Infrastructure for Spatial Information in the European Community (Europe), Australian Water Resources Information System, etc., endeavour to address this issue of hydrology specific spatial data standards in a wholesome manner. But unfortunately there is no such provision for hydrology data exchange within the Indian community. Moreover, these standards somehow fail in providing powerful communication of the spatial hydrologic data. This study thus investigates the shortcomings of the existing industry standards for the hydrologic data models and then demonstrates a set of requirements for effective exchange of the hydrologic information in the Indian scenario.

Decent wage is more important than absolution of debts: A smallholder socio-hydrological modelling framework

NASA Astrophysics Data System (ADS)

Pande, Saket; Savenije, Hubert

2015-04-01

We present a framework to understand the socio-hydrological system dynamics of a small holder. Small holders are farmers who own less than 2 ha of farmland. It couples the dynamics of 6 main variables that are most relevant at the scale of a small holder: local storage (soil moisture and other water storage), capital, knowledge, livestock production, soil fertility and grass biomass production. The hydroclimatic variability is at sub-annual scale and influences the socio-hydrology at annual scale. The model incorporates rule-based adaptation mechanisms (for example: adjusting expenditures on food and fertilizers, selling livestocks etc.) of small holders when they face adverse socio-hydrological conditions, such as low annual rainfall, higher intra-annual variability in rainfall or variability in agricultural prices. We apply the framework to understand the socio-hydrology of a sugarcane small holder in Aurangabad, Maharashtra. This district has witnessed suicides of many sugarcane farmers who could not extricate themselves out of the debt trap. These farmers lack irrigation and are susceptible to fluctuating sugar prices and intra-annual hydro-climatic variability. We study the sensitivity of annual total capital averaged over 30 years, an indicator of small holder wellbeing, to initial capital that a small holder starts with and the prevalent wage rates. We find that a smallholder well being is low (below Rs 30000 per annum, a threshold above which a smallholder can afford a basic standard of living) and is rather insensitive to initial capital at low wage rates. Initial capital perhaps matters to small holder livelihoods at higher wage rates. Further, the small holder system appears to be resilient at around Rs 115/mandays in the sense that small perturbations in wage rates around this rate still sustains the smallholder above the basic standard of living. Our results thus indicate that government intervention to absolve the debt of farmers is not enough. It must invest in local storages that can buffer intra-annual variability in rainfall in tandem and good wages for alternative sources of income.

Enhancing hydrologic data assimilation by evolutionary Particle Filter and Markov Chain Monte Carlo

NASA Astrophysics Data System (ADS)

Abbaszadeh, Peyman; Moradkhani, Hamid; Yan, Hongxiang

2018-01-01

Particle Filters (PFs) have received increasing attention by researchers from different disciplines including the hydro-geosciences, as an effective tool to improve model predictions in nonlinear and non-Gaussian dynamical systems. The implication of dual state and parameter estimation using the PFs in hydrology has evolved since 2005 from the PF-SIR (sampling importance resampling) to PF-MCMC (Markov Chain Monte Carlo), and now to the most effective and robust framework through evolutionary PF approach based on Genetic Algorithm (GA) and MCMC, the so-called EPFM. In this framework, the prior distribution undergoes an evolutionary process based on the designed mutation and crossover operators of GA. The merit of this approach is that the particles move to an appropriate position by using the GA optimization and then the number of effective particles is increased by means of MCMC, whereby the particle degeneracy is avoided and the particle diversity is improved. In this study, the usefulness and effectiveness of the proposed EPFM is investigated by applying the technique on a conceptual and highly nonlinear hydrologic model over four river basins located in different climate and geographical regions of the United States. Both synthetic and real case studies demonstrate that the EPFM improves both the state and parameter estimation more effectively and reliably as compared with the PF-MCMC.

Hydrologic Modeling and Parameter Estimation under Data Scarcity for Java Island, Indonesia

NASA Astrophysics Data System (ADS)

Yanto, M.; Livneh, B.; Rajagopalan, B.; Kasprzyk, J. R.

2015-12-01

The Indonesian island of Java is routinely subjected to intense flooding, drought and related natural hazards, resulting in severe social and economic impacts. Although an improved understanding of the island's hydrology would help mitigate these risks, data scarcity issues make the modeling challenging. To this end, we developed a hydrological representation of Java using the Variable Infiltration Capacity (VIC) model, to simulate the hydrologic processes of several watersheds across the island. We measured the model performance using Nash-Sutcliffe Efficiency (NSE) at monthly time step. Data scarcity and quality issues for precipitation and streamflow warranted the application of a quality control procedure to data ensure consistency among watersheds resulting in 7 watersheds. To optimize the model performance, the calibration parameters were estimated using Borg Multi Objective Evolutionary Algorithm (Borg MOEA), which offers efficient searching of the parameter space, adaptive population sizing and local optima escape facility. The result shows that calibration performance is best (NSE ~ 0.6 - 0.9) in the eastern part of the domain and moderate (NSE ~ 0.3 - 0.5) in the western part of the island. The validation results are lower (NSE ~ 0.1 - 0.5) and (NSE ~ 0.1 - 0.4) in the east and west, respectively. We surmise that the presence of outliers and stark differences in the climate between calibration and validation periods in the western watersheds are responsible for low NSE in this region. In addition, we found that approximately 70% of total errors were contributed by less than 20% of total data. The spatial variability of model performance suggests the influence of both topographical and hydroclimatic controls on the hydrological processes. Most watersheds in eastern part perform better in wet season and vice versa for the western part. This modeling framework is one of the first attempts at comprehensively simulating the hydrology in this maritime, tropical continent and, offers insights for skillful hydrologic projections crucial for natural hazard mitigation.

How Misapplication of the Hydrologic Unit Framework Diminishes the Meaning of Watersheds

EPA Science Inventory

Hydrologic units provide a convenient nationwide set of geographic polygons based on an arbitrary subdivision of the drainage of land surface areas at several hierarchical levels. Half or more of these units, however, are not true watersheds as the official name of the framework,...

Monitoring Precipitation from Space: targeting Hydrology Community?

NASA Astrophysics Data System (ADS)

Hong, Y.; Turk, J.

2005-12-01

During the past decades, advances in space, sensor and computer technology have made it possible to estimate precipitation nearly globally from a variety of observations in a relatively direct manner. The success of Tropical Precipitation Measuring Mission (TRMM) has been a significant advance for modern precipitation estimation algorithms to move toward daily quarter degree measurements, while the need for precipitation data at temporal-spatial resolutions compatible with hydrologic modeling has been emphasized by the end user: hydrology community. Can the future deployment of Global Precipitation Measurement constellation of low-altitude orbiting satellites (covering 90% of the global with a sampling interval of less than 3-hours), in conjunction with the existing suite of geostationary satellites, results in significant improvements in scale and accuracy of precipitation estimates suitable for hydrology applications? This presentation will review the current state of satellite-derived precipitation estimation and demonstrate the early results and primary barriers to full global high-resolution precipitation coverage. An attempt to facilitate the communication between data producers and users will be discussed by developing an 'end-to-end' uncertainty propagation analysis framework to quantify both the precipitation estimation error structure and the error influence on hydrological modeling.

Assessment of Seasonal Water Balance Components over India Using Macroscale Hydrological Model

NASA Astrophysics Data System (ADS)

Joshi, S.; Raju, P. V.; Hakeem, K. A.; Rao, V. V.; Yadav, A.; Issac, A. M.; Diwakar, P. G.; Dadhwal, V. K.

2016-12-01

Hydrological models provide water balance components which are useful for water resources assessment and for capturing the seasonal changes and impact of anthropogenic interventions and climate change. The study under description is a national level modeling framework for country India using wide range of geo-spatial and hydro-meteorological data sets for estimating daily Water Balance Components (WBCs) at 0.15º grid resolution using Variable Infiltration Capacity model. The model parameters were optimized through calibration of model computed stream flow with field observed yielding Nash-Sutcliffe efficiency between 0.5 to 0.7. The state variables, evapotranspiration (ET) and soil moisture were also validated, obtaining R2 values of 0.57 and 0.69, respectively. Using long-term meteorological data sets, model computation were carried to capture hydrological extremities. During 2013, 2014 and 2015 monsoon seasons, WBCs were estimated and were published in web portal with 2-day time lag. In occurrence of disaster events, weather forecast was ingested, high surface runoff zones were identified for forewarning and disaster preparedness. Cumulative monsoon season rainfall of 2013, 2014 and 2015 were 105, 89 and 91% of long period average (LPA) respectively (Source: India Meteorological Department). Analysis of WBCs indicated that corresponding seasonal surface runoff was 116, 81 and 86% LPA and evapotranspiration was 109, 104 and 90% LPA. Using the grid-wise data, the spatial variation in WBCs among river basins/administrative regions was derived to capture the changes in surface runoff, ET between the years and in comparison with LPA. The model framework is operational and is providing periodic account of national level water balance fluxes which are useful for quantifying spatial and temporal variation in basin/sub-basin scale water resources, periodical water budgeting to form vital inputs for studies on water resources and climate change.

Improving Permafrost Hydrology Prediction Through Data-Model Integration

NASA Astrophysics Data System (ADS)

Wilson, C. J.; Andresen, C. G.; Atchley, A. L.; Bolton, W. R.; Busey, R.; Coon, E.; Charsley-Groffman, L.

2017-12-01

The CMIP5 Earth System Models were unable to adequately predict the fate of the 16GT of permafrost carbon in a warming climate due to poor representation of Arctic ecosystem processes. The DOE Office of Science Next Generation Ecosystem Experiment, NGEE-Arctic project aims to reduce uncertainty in the Arctic carbon cycle and its impact on the Earth's climate system by improved representation of the coupled physical, chemical and biological processes that drive how much buried carbon will be converted to CO2 and CH4, how fast this will happen, which form will dominate, and the degree to which increased plant productivity will offset increased soil carbon emissions. These processes fundamentally depend on permafrost thaw rate and its influence on surface and subsurface hydrology through thermal erosion, land subsidence and changes to groundwater flow pathways as soil, bedrock and alluvial pore ice and massive ground ice melts. LANL and its NGEE colleagues are co-developing data and models to better understand controls on permafrost degradation and improve prediction of the evolution of permafrost and its impact on Arctic hydrology. The LANL Advanced Terrestrial Simulator was built using a state of the art HPC software framework to enable the first fully coupled 3-dimensional surface-subsurface thermal-hydrology and land surface deformation simulations to simulate the evolution of the physical Arctic environment. Here we show how field data including hydrology, snow, vegetation, geochemistry and soil properties, are informing the development and application of the ATS to improve understanding of controls on permafrost stability and permafrost hydrology. The ATS is being used to inform parameterizations of complex coupled physical, ecological and biogeochemical processes for implementation in the DOE ACME land model, to better predict the role of changing Arctic hydrology on the global climate system. LA-UR-17-26566.

Hierarchical mixture of experts and diagnostic modeling approach to reduce hydrologic model structural uncertainty: STRUCTURAL UNCERTAINTY DIAGNOSTICS

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

Moges, Edom; Demissie, Yonas; Li, Hong-Yi

2016-04-01

In most water resources applications, a single model structure might be inadequate to capture the dynamic multi-scale interactions among different hydrological processes. Calibrating single models for dynamic catchments, where multiple dominant processes exist, can result in displacement of errors from structure to parameters, which in turn leads to over-correction and biased predictions. An alternative to a single model structure is to develop local expert structures that are effective in representing the dominant components of the hydrologic process and adaptively integrate them based on an indicator variable. In this study, the Hierarchical Mixture of Experts (HME) framework is applied to integratemore » expert model structures representing the different components of the hydrologic process. Various signature diagnostic analyses are used to assess the presence of multiple dominant processes and the adequacy of a single model, as well as to identify the structures of the expert models. The approaches are applied for two distinct catchments, the Guadalupe River (Texas) and the French Broad River (North Carolina) from the Model Parameter Estimation Experiment (MOPEX), using different structures of the HBV model. The results show that the HME approach has a better performance over the single model for the Guadalupe catchment, where multiple dominant processes are witnessed through diagnostic measures. Whereas, the diagnostics and aggregated performance measures prove that French Broad has a homogeneous catchment response, making the single model adequate to capture the response.« less

A framework for global river flood risk assessments

NASA Astrophysics Data System (ADS)

Winsemius, H. C.; Van Beek, L. P. H.; Jongman, B.; Ward, P. J.; Bouwman, A.

2013-05-01

There is an increasing need for strategic global assessments of flood risks in current and future conditions. In this paper, we propose a framework for global flood risk assessment for river floods, which can be applied in current conditions, as well as in future conditions due to climate and socio-economic changes. The framework's goal is to establish flood hazard and impact estimates at a high enough resolution to allow for their combination into a risk estimate, which can be used for strategic global flood risk assessments. The framework estimates hazard at a resolution of ~ 1 km2 using global forcing datasets of the current (or in scenario mode, future) climate, a global hydrological model, a global flood-routing model, and more importantly, an inundation downscaling routine. The second component of the framework combines hazard with flood impact models at the same resolution (e.g. damage, affected GDP, and affected population) to establish indicators for flood risk (e.g. annual expected damage, affected GDP, and affected population). The framework has been applied using the global hydrological model PCR-GLOBWB, which includes an optional global flood routing model DynRout, combined with scenarios from the Integrated Model to Assess the Global Environment (IMAGE). We performed downscaling of the hazard probability distributions to 1 km2 resolution with a new downscaling algorithm, applied on Bangladesh as a first case study application area. We demonstrate the risk assessment approach in Bangladesh based on GDP per capita data, population, and land use maps for 2010 and 2050. Validation of the hazard estimates has been performed using the Dartmouth Flood Observatory database. This was done by comparing a high return period flood with the maximum observed extent, as well as by comparing a time series of a single event with Dartmouth imagery of the event. Validation of modelled damage estimates was performed using observed damage estimates from the EM-DAT database and World Bank sources. We discuss and show sensitivities of the estimated risks with regard to the use of different climate input sets, decisions made in the downscaling algorithm, and different approaches to establish impact models.

Hydrology-oriented forest management trade-offs. A modeling framework coupling field data, simulation results and Bayesian Networks.

PubMed

Garcia-Prats, Alberto; González-Sanchis, María; Del Campo, Antonio D; Lull, Cristina

2018-10-15

Hydrology-oriented forest management sets water as key factor of the forest management for adaptation due to water is the most limiting factor in the Mediterranean forest ecosystems. The aim of this study was to apply Bayesian Network modeling to assess potential indirect effects and trade-offs when hydrology-oriented forest management is applied to a real Mediterranean forest ecosystem. Water, carbon and nitrogen cycles, and forest fire risk were included in the modeling framework. Field data from experimental plots were employed to calibrate and validate the mechanistic Biome-BGCMuSo model that simulates the storage and flux of water, carbon, and nitrogen between the ecosystem and the atmosphere. Many other 50-year long scenarios with different conditions to the ones measured in the field experiment were simulated and the outcomes employed to build the Bayesian Network in a linked chain of models. Hydrology-oriented forest management was very positive insofar as more water was made available to the stand because of an interception reduction. This resource was made available to the stand, which increased the evapotranspiration and its components, the soil water content and a slightly increase of deep percolation. Conversely, Stemflow was drastically reduced. No effect was observed on Runof due to the thinning treatment. The soil organic carbon content was also increased which in turn caused a greater respiration. The long-term effect of the thinning treatment on the LAI was very positive. This was undoubtedly due to the increased vigor generated by the greater availability of water and nutrients for the stand and the reduction of competence between trees. This greater activity resulted in an increase in GPP and vegetation carbon, and therefore, we would expect a higher carbon sequestration. It is worth emphasizing that this extra amount of water and nutrients was taken up by the stand and did not entail any loss of nutrients. Copyright © 2018 Elsevier B.V. All rights reserved.

DROUGHT IN THE ANTHROPOCENE: what/who causes abnormally dry conditions? (Invited)

NASA Astrophysics Data System (ADS)

Van Loon, A.; Van Lanen, H.

2013-12-01

Deforestation for agriculture, reservoir construction for hydropower, groundwater abstraction for irrigation, river diversion for navigation. These are only some examples of human interventions in river basins. The consequences of these interventions can be far-reaching, but are often difficult to distinguish from natural influences on the water system, such as meteorological droughts. River basin managers in water-stressed regions need to quantify both human and natural effects on the water system to adapt their water management accordingly. ';Drought' is a natural hazard, which is caused by climatic processes and their intrinsic variability, and cannot be prevented by short-term, local water management. ';Water scarcity' refers to the long-term unsustainable use of water resources and is a process that water managers and policy makers can influence. Water scarcity and drought are keywords for river basin managers in water-stressed regions, like Australia, California, China and the Mediterranean Basin. The interrelationship between drought and water scarcity, however, is complex. In regions with low water availability and high human pressures, water scarcity situations are common and can be exacerbated by drought events. The worst situation is a multi-year drought in a (semi )arid region with high demand for water. In monitoring the hydrological system for water management purposes, it is difficult (but essential) to determine which part of the temporal variation in a hydrological variable is caused by water scarcity (human induced) and which part by drought (natural). So the urgent question of many water managers is: how to distinguish between water scarcity and drought? Here, we present a new quantitative approach to distinguish, namely the observation-modelling framework proposed by Van Loon and Van Lanen (2013) to separate natural (drought) and human (water scarcity) effects on the hydrological system. The basis of the framework is simulation of the situation that would have occurred without human influence, i.e. the ';naturalised' situation, using a hydrological model. The resulting time series of naturalised state variables and fluxes can then be compared to observed time series. Additionally, anomalies (i.e. deviations from a threshold) are determined from both time series and compared. This analysis allows for quantification of the relative effect of drought and water scarcity. To show the general applicability of the framework, we investigated case study areas with contrasting climate and catchment properties in Spain, Czech Republic and the Netherlands. Using these case study areas we could analyse the effect of groundwater abstraction and water transfer on groundwater levels and streamflow. The proposed observation-modelling framework is rather generic. We demonstrate the range of methods that can be used and the range of human influences the framework can be applied to. The observation-modelling framework can help water managers, policy makers and stakeholders in water-stressed regions to combat water scarcity, and to better adapt to drought by decreasing their vulnerability. A clear distinction between drought and water scarcity is needed in the anthropocene.

HYDROSCAPE: A SCAlable and ParallelizablE Rainfall Runoff Model for Hydrological Applications

NASA Astrophysics Data System (ADS)

Piccolroaz, S.; Di Lazzaro, M.; Zarlenga, A.; Majone, B.; Bellin, A.; Fiori, A.

2015-12-01

In this work we present HYDROSCAPE, an innovative streamflow routing method based on the travel time approach, and modeled through a fine-scale geomorphological description of hydrological flow paths. The model is designed aimed at being easily coupled with weather forecast or climate models providing the hydrological forcing, and at the same time preserving the geomorphological dispersion of the river network, which is kept unchanged independently on the grid size of rainfall input. This makes HYDROSCAPE particularly suitable for multi-scale applications, ranging from medium size catchments up to the continental scale, and to investigate the effects of extreme rainfall events that require an accurate description of basin response timing. Key feature of the model is its computational efficiency, which allows performing a large number of simulations for sensitivity/uncertainty analyses in a Monte Carlo framework. Further, the model is highly parsimonious, involving the calibration of only three parameters: one defining the residence time of hillslope response, one for channel velocity, and a multiplicative factor accounting for uncertainties in the identification of the potential maximum soil moisture retention in the SCS-CN method. HYDROSCAPE is designed with a simple and flexible modular structure, which makes it particularly prone to massive parallelization, customization according to the specific user needs and preferences (e.g., rainfall-runoff model), and continuous development and improvement. Finally, the possibility to specify the desired computational time step and evaluate streamflow at any location in the domain, makes HYDROSCAPE an attractive tool for many hydrological applications, and a valuable alternative to more complex and highly parametrized large scale hydrological models. Together with model development and features, we present an application to the Upper Tiber River basin (Italy), providing a practical example of model performance and characteristics.

QUANTIFYING THE MICROMECHANICAL EFFECTS OF VARIABLE CEMENT IN GRANULAR POROUS MEDIA

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

Boutt, David F; Goodwin, Laurel B

2010-03-01

The mechanical and hydrologic behavior of clastic rocks and sediments is fundamentally controlled by variables such as grain size and shape, sorting, grain and cement mineralogy, porosity, and %cement - parameters that are not used directly in field-scale models of coupled flow and deformation. To improve our understanding of the relationship between these micromechanical properties and bulk behavior we focused on (1) relating detailed, quantitative characterization of the grain-pore systems to both hydrologic and mechanical properties of a suite of variably quartz-cemented quartz arenite samples and (2) the use of a combination of discrete element method (DEM) and poroelastic modelsmore » parameterized by data from the natural samples to isolate and compare the influence of changes in the mechanical and hydrologic properties of granular porous media due to changes in degree of cementation. Quartz overgrowths, the most common form of authigenic cements in sandstones, are responsible for significant porosity and permeability reduction. The distribution of quartz overgrowths is controlled by available pore space and the crystallographic orientations of individual quartz grains. Study of the St. Peter Sandstone allowed evaluation of the relative effects of quartz cementation and compaction on final grain and pore morphology, showing that progressive quartz cementation modifies the grain framework in consistent, predictable ways. Detailed microstructural characterization and multiple regression analyses show that with progressive diagenesis, the number and length of grain contacts increases as the number of pores increases, the number of large, well-connected pores decreases, and pores become rounder. These changes cause a decrease in pore size variability that leads to a decrease in bulk permeability and both stiffening and strengthening of the grain framework. The consistent nature of these changes allows us to predict variations in hydrologic and mechanical properties with progressive diagenesis, and explore the impact of these changes on aquifer behavior. Several examples of this predictive capability are offered. In one application, data from natural sandstones are used to calibrate the proportionality constant of the Kozeny- Carman relationship, improving the ability to predict permeability in quartz-cemented quartz arenites. In another, the bond-to-grain ratio (BGR) is used to parameterize a discrete element model with data acquired from sandstone samples. The DEM results provide input to poroelastic models used to explore the hydrologic, mechanical, and coupled hydrologic and mechanical response of the sandstone to pumping stresses. This modeling exercise shows that at the macroscale, changes in mechanical and hydrologic properties directly influence the magnitude and area of aquifer deformation. The significant difference in sensitivity of the system to the mechanical properties alone versus its sensitivity to coupled mechanical and hydrologic properties demonstrates the importance of including hydrologic properties that are adjusted for changes in cementation in fluid storage and deformation studies. The large magnitude of radial deformation compared to vertical deformation in these models emphasizes the importance of considering three dimensional deformation in fluid flow and deformation studies.« less

Towards a Dynamic Digital Observatory: Synthesizing Community Data and Model Development in the Susquehanna River Basin and Chesapeake Bay

NASA Astrophysics Data System (ADS)

Dressler, K. A.; Piasecki, M.; Bhatt, G.; Duffy, C. J.; Reed, P. M.

2007-12-01

Physically-based fully-distributed hydrologic models simulate hydrologic state variables spatiotemporally using information on forcing (climate) and landscape (topography, land use, hydrogeology) heterogeneities. Incorporating physical data layers in the hydrologic model requires intensive data development. Traditionally, GIS has been used for data management, data analysis and visualization; however, proprietary data structures, platform dependence, isolated data model and non-dynamic data-interaction with pluggable software components of existing GIS frameworks, makes it restrictive to perform sophisticated numerical modeling. In this effort we present a "tightly-coupled" GIS interface to Penn State Integrated Hydrologic Model (PIHM; www.pihm.psu.edu) called PIHMgis which is open source, platform independent and extensible. The tight coupling between GIS and the model is achieved by developing a shared data-model and hydrologic-model data structure. Domain discretization is fundamental to the approach and an unstructured triangular irregular network (e.g. Delaunay triangles) is generated with both geometric and parametric constraints. A local prismatic control volume is formed by vertical projection of the Delaunay triangles forming each layer of the model. Given a set of constraints (e.g. river network support, watershed boundary, altitude zones, ecological regions, hydraulic properties, climate zones, etc), an "optimal" mesh is generated. Time variant forcing for the model is typically derived from time series data available at points that are transferred onto a grid. Therefore, the modeling environment can use the Observations Database model developed by the Hydrologic Information Systems group of the Consortium of Universities for the Advancement of Hydrologic Sciences, Inc. (CUAHSI). As part of a initial testbed series the database has been implemented in support for the Susquehanna and Chesapeake Bay watersheds and is now being populated by national (USGS-NWIS; EPA- STORET), regional (Chesapeake Information Management System, CIMS; National Air Deposition Program, NADP), and local (RTH-Net, Burd Run) datasets. The data can be searched side by side in a one-stop-querying- center, www.hydroseek.org , another application developed as part of the CUAHSI HIS effort. The ultimate goal is to populate the observations database with as many catalogues (i.e. collections of information on what data sources contain) as possible including the build out of the local data sources, i.e. the Susquehanna River Basin Hydrologic Observatory System (SRBHOS) time series server.

Understanding of Coupled Terrestrial Carbon, Nitrogen and Water Dynamics—An Overview

PubMed Central

Chen, Baozhang; Coops, Nicholas C.

2009-01-01

Coupled terrestrial carbon (C), nitrogen (N) and hydrological processes play a crucial role in the climate system, providing both positive and negative feedbacks to climate change. In this review we summarize published research results to gain an increased understanding of the dynamics between vegetation and atmosphere processes. A variety of methods, including monitoring (e.g., eddy covariance flux tower, remote sensing, etc.) and modeling (i.e., ecosystem, hydrology and atmospheric inversion modeling) the terrestrial carbon and water budgeting, are evaluated and compared. We highlight two major research areas where additional research could be focused: (i) Conceptually, the hydrological and biogeochemical processes are closely linked, however, the coupling processes between terrestrial C, N and hydrological processes are far from well understood; and (ii) there are significant uncertainties in estimates of the components of the C balance, especially at landscape and regional scales. To address these two questions, a synthetic research framework is needed which includes both bottom-up and top-down approaches integrating scalable (footprint and ecosystem) models and a spatially nested hierarchy of observations which include multispectral remote sensing, inventories, existing regional clusters of eddy-covariance flux towers and CO2 mixing ratio towers and chambers. PMID:22291528

Understanding of coupled terrestrial carbon, nitrogen and water dynamics-an overview.

PubMed

Chen, Baozhang; Coops, Nicholas C

2009-01-01

Coupled terrestrial carbon (C), nitrogen (N) and hydrological processes play a crucial role in the climate system, providing both positive and negative feedbacks to climate change. In this review we summarize published research results to gain an increased understanding of the dynamics between vegetation and atmosphere processes. A variety of methods, including monitoring (e.g., eddy covariance flux tower, remote sensing, etc.) and modeling (i.e., ecosystem, hydrology and atmospheric inversion modeling) the terrestrial carbon and water budgeting, are evaluated and compared. We highlight two major research areas where additional research could be focused: (i) Conceptually, the hydrological and biogeochemical processes are closely linked, however, the coupling processes between terrestrial C, N and hydrological processes are far from well understood; and (ii) there are significant uncertainties in estimates of the components of the C balance, especially at landscape and regional scales. To address these two questions, a synthetic research framework is needed which includes both bottom-up and top-down approaches integrating scalable (footprint and ecosystem) models and a spatially nested hierarchy of observations which include multispectral remote sensing, inventories, existing regional clusters of eddy-covariance flux towers and CO(2) mixing ratio towers and chambers.

Simulating Complex, Cold-region Process Interactions Using a Multi-scale, Variable-complexity Hydrological Model

NASA Astrophysics Data System (ADS)

Marsh, C.; Pomeroy, J. W.; Wheater, H. S.

2017-12-01

Accurate management of water resources is necessary for social, economic, and environmental sustainability worldwide. In locations with seasonal snowcovers, the accurate prediction of these water resources is further complicated due to frozen soils, solid-phase precipitation, blowing snow transport, and snowcover-vegetation-atmosphere interactions. Complex process interactions and feedbacks are a key feature of hydrological systems and may result in emergent phenomena, i.e., the arising of novel and unexpected properties within a complex system. One example is the feedback associated with blowing snow redistribution, which can lead to drifts that cause locally-increased soil moisture, thus increasing plant growth that in turn subsequently impacts snow redistribution, creating larger drifts. Attempting to simulate these emergent behaviours is a significant challenge, however, and there is concern that process conceptualizations within current models are too incomplete to represent the needed interactions. An improved understanding of the role of emergence in hydrological systems often requires high resolution distributed numerical hydrological models that incorporate the relevant process dynamics. The Canadian Hydrological Model (CHM) provides a novel tool for examining cold region hydrological systems. Key features include efficient terrain representation, allowing simulations at various spatial scales, reduced computational overhead, and a modular process representation allowing for an alternative-hypothesis framework. Using both physics-based and conceptual process representations sourced from long term process studies and the current cold regions literature allows for comparison of process representations and importantly, their ability to produce emergent behaviours. Examining the system in a holistic, process-based manner can hopefully derive important insights and aid in development of improved process representations.

Dealing with equality and benefit for water allocation in a lake watershed: A Gini-coefficient based stochastic optimization approach

NASA Astrophysics Data System (ADS)

Dai, C.; Qin, X. S.; Chen, Y.; Guo, H. C.

2018-06-01

A Gini-coefficient based stochastic optimization (GBSO) model was developed by integrating the hydrological model, water balance model, Gini coefficient and chance-constrained programming (CCP) into a general multi-objective optimization modeling framework for supporting water resources allocation at a watershed scale. The framework was advantageous in reflecting the conflicting equity and benefit objectives for water allocation, maintaining the water balance of watershed, and dealing with system uncertainties. GBSO was solved by the non-dominated sorting Genetic Algorithms-II (NSGA-II), after the parameter uncertainties of the hydrological model have been quantified into the probability distribution of runoff as the inputs of CCP model, and the chance constraints were converted to the corresponding deterministic versions. The proposed model was applied to identify the Pareto optimal water allocation schemes in the Lake Dianchi watershed, China. The optimal Pareto-front results reflected the tradeoff between system benefit (αSB) and Gini coefficient (αG) under different significance levels (i.e. q) and different drought scenarios, which reveals the conflicting nature of equity and efficiency in water allocation problems. A lower q generally implies a lower risk of violating the system constraints and a worse drought intensity scenario corresponds to less available water resources, both of which would lead to a decreased system benefit and a less equitable water allocation scheme. Thus, the proposed modeling framework could help obtain the Pareto optimal schemes under complexity and ensure that the proposed water allocation solutions are effective for coping with drought conditions, with a proper tradeoff between system benefit and water allocation equity.

Accounting for inter-annual and seasonal variability in regionalization of hydrologic response in the Great Lakes basin

NASA Astrophysics Data System (ADS)

Kult, J. M.; Fry, L. M.; Gronewold, A. D.

2012-12-01

Methods for predicting streamflow in areas with limited or nonexistent measures of hydrologic response typically invoke the concept of regionalization, whereby knowledge pertaining to gauged catchments is transferred to ungauged catchments. In this study, we identify watershed physical characteristics acting as primary drivers of hydrologic response throughout the US portion of the Great Lakes basin. Relationships between watershed physical characteristics and hydrologic response are generated from 166 catchments spanning a variety of climate, soil, land cover, and land form regimes through regression tree analysis, leading to a grouping of watersheds exhibiting similar hydrologic response characteristics. These groupings are then used to predict response in ungauged watersheds in an uncertainty framework. Results from this method are assessed alongside one historical regionalization approach which, while simple, has served as a cornerstone of Great Lakes regional hydrologic research for several decades. Our approach expands upon previous research by considering multiple temporal characterizations of hydrologic response. Due to the substantial inter-annual and seasonal variability in hydrologic response observed over the Great Lakes basin, results from the regression tree analysis differ considerably depending on the level of temporal aggregation used to define the response. Specifically, higher levels of temporal aggregation for the response metric (for example, indices derived from long-term means of climate and streamflow observations) lead to improved watershed groupings with lower within-group variance. However, this perceived improvement in model skill occurs at the cost of understated uncertainty when applying the regression to time series simulations or as a basis for model calibration. In such cases, our results indicate that predictions based on long-term characterizations of hydrologic response can produce misleading conclusions when applied at shorter time steps. This study suggests that measures of hydrologic response quantified at these shorter time steps may provide a more robust basis for making predictions in applications of water resource management, model calibration and simulations, and human health and safety.

The framework of a UAS-aided flash flood modeling system for coastal regions

NASA Astrophysics Data System (ADS)

Zhang, H.; Xu, H.

2016-02-01

Flash floods cause severe economic damage and are one of the leading causes of fatalities connected with natural disasters in the Gulf Coast region. Current flash flood modeling systems rely on empirical hydrological models driven by precipitation estimates only. Although precipitation is the driving factor for flash floods, soil moisture, urban drainage system and impervious surface have been recognized to have significant impacts on the development of flash floods. We propose a new flash flooding modeling system that integrates 3-D hydrological simulation with satellite and multi-UAS observations. It will have three advantages over existing modeling systems. First, it will incorporate 1-km soil moisture data through integrating satellite images from European SMOS mission and NASA's SMAP mission. The utilization of high-resolution satellite images will provide essential information to determine antecedent soil moisture condition, which is an essential control on flood generation. Second, this system is able to adjust flood forecasting based on real-time inundation information collected by multi-UAS. A group of UAS will be deployed during storm events to capture the changing extent of flooded areas and water depth at multiple critical locations simultaneously. Such information will be transmitted to a hydrological model to validate and improve flood simulation. Third, the backbone of this system is a state-of-the-art 3-D hydrological model that assimilates the hydrological information from satellites and multi-UAS. The model is able to address surface water-groundwater interactions and reflect the effects of various infrastructures. Using Web-GIS technologies, the modeling results will be available online as interactive flood maps accessible to the public. To support the development and verification of this modeling system, surface and subsurface hydrological observations will be conducted in a number of small watersheds in the Coastal Bend region. We envision this system will provide an innovative means to benefit the forecasting, evaluation and mitigation of flash floods in costal regions.

Hydrologic connectivity as a framework for understanding biogeochemical flux through watersheds and along fluvial networks

NASA Astrophysics Data System (ADS)

Covino, Tim

2017-01-01

Hydrologic connections can link hillslopes to channel networks, streams to lakes, subsurface to surface, land to atmosphere, terrestrial to aquatic, and upstream to downstream. These connections can develop across vertical, lateral, and longitudinal dimensions and span spatial and temporal scales. Each of these dimensions and scales are interconnected, creating a mosaic of nested hydrologic connections and associated processes. In turn, these interacting and nested processes influence the transport, cycling, and transformation of organic material and inorganic nutrients through watersheds and along fluvial networks. Although hydrologic connections span dimensions and spatiotemporal scales, relationships between connectivity and carbon and nutrient dynamics are rarely evaluated within this framework. The purpose of this paper is to provide a cross-disciplinary view of hydrologic connectivity - highlighting the various forms of hydrologic connectivity that control fluxes of organic material and nutrients - and to help stimulate integration across scales and dimensions, and collaboration among disciplines.

Game Theoretic Modeling of Water Resources Allocation Under Hydro-Climatic Uncertainty

NASA Astrophysics Data System (ADS)

Brown, C.; Lall, U.; Siegfried, T.

2005-12-01

Typical hydrologic and economic modeling approaches rely on assumptions of climate stationarity and economic conditions of ideal markets and rational decision-makers. In this study, we incorporate hydroclimatic variability with a game theoretic approach to simulate and evaluate common water allocation paradigms. Game Theory may be particularly appropriate for modeling water allocation decisions. First, a game theoretic approach allows economic analysis in situations where price theory doesn't apply, which is typically the case in water resources where markets are thin, players are few, and rules of exchange are highly constrained by legal or cultural traditions. Previous studies confirm that game theory is applicable to water resources decision problems, yet applications and modeling based on these principles is only rarely observed in the literature. Second, there are numerous existing theoretical and empirical studies of specific games and human behavior that may be applied in the development of predictive water allocation models. With this framework, one can evaluate alternative orderings and rules regarding the fraction of available water that one is allowed to appropriate. Specific attributes of the players involved in water resources management complicate the determination of solutions to game theory models. While an analytical approach will be useful for providing general insights, the variety of preference structures of individual players in a realistic water scenario will likely require a simulation approach. We propose a simulation approach incorporating the rationality, self-interest and equilibrium concepts of game theory with an agent-based modeling framework that allows the distinct properties of each player to be expressed and allows the performance of the system to manifest the integrative effect of these factors. Underlying this framework, we apply a realistic representation of spatio-temporal hydrologic variability and incorporate the impact of decision-making a priori to hydrologic realizations and those made a posteriori on alternative allocation mechanisms. Outcomes are evaluated in terms of water productivity, net social benefit and equity. The performance of hydro-climate prediction modeling in each allocation mechanism will be assessed. Finally, year-to-year system performance and feedback pathways are explored. In this way, the system can be adaptively managed toward equitable and efficient water use.

Recent advances towards a theory of catchment hydrologic transport: age-ranked storage and the Ω-functions

NASA Astrophysics Data System (ADS)

Harman, C. J.

2014-12-01

Models that faithfully represent spatially-integrated hydrologic transport through the critical zone at sub-watershed scales are essential building blocks for large-scale models of land use and climate controls on non-point source contaminant delivery. A particular challenge facing these models is the need to represent the delay between inputs of soluble contaminants (such as nitrate) at the field scale, and the solute load that appears in streams. Recent advances in the theory of time-variable transit time distributions (e.g. Botter et al., GRL 38(L11403), 2011) have provided a rigorous framework for representing conservative solute transport and its coupling to hydrologic variability and partitioning. Here I will present a reformulation of this framework that offers several distinct advantages over existing formulations: 1) the derivation of the governing conservation equation is simple and intuitive, 2) the closure relations are expressed in a convenient and physically meaningful way as probability distributions Ω(ST)Omega(S_T) over the storage ranked by age STS_T, and 3) changes in transport behavior determined by storage-dependent dilution and flow-path dynamics (as distinct from those due only to changes in the rates and partitioning of water flux) are completely encapsulated by these probability distributions. The framework has been implemented to model to the rich dataset of long-term stream and precipitation chloride from the Plynlimon watershed in Wales, UK. With suitable choices for the functional form of the closure relationships, only a small number of free parameters are required to reproduce the observed chloride dynamics as well as previous models with many more parameters, including reproducing the observed fractal 1/f filtering of the streamflow chloride variability. The modeled transport dynamics are sensitive to the input precipitation variability and water balance partitioning to evapotranspiration. Apparent storage-dependent age-sampling suggests that the model can account for shifts in flow pathways across high and low flows. This approach suggests a path forward for catchment-scale coupled flow and transport modeling.

Integrated hydrologic and hydrodynamic modeling to assess water exchange in a data-scarce reservoir

NASA Astrophysics Data System (ADS)

Wu, Binbin; Wang, Guoqiang; Wang, Zhonggen; Liu, Changming; Ma, Jianming

2017-12-01

Integrated hydrologic and hydrodynamic modeling is useful in evaluating hydrodynamic characteristics (e.g. water exchange processes) in data-scarce water bodies, however, most studies lack verification of the hydrologic model. Here, water exchange (represented by water age) was investigated through integrated hydrologic and hydrodynamic modeling of the Hongfeng Reservoir, a poorly gauged reservoir in southwest China. The performance of the hydrologic model and parameter replacement among sub-basins with hydrological similarity was verified by historical data. Results showed that hydrological similarity based on the hierarchical cluster analysis and topographic index probability density distribution was reliable with satisfactory performance of parameter replacement. The hydrodynamic model was verified using daily water levels and water temperatures from 2009 and 2010. The water exchange processes in the Hongfeng Reservoir are very complex with temporal, vertical, and spatial variations. The temporal water age was primarily controlled by the variable inflow and outflow, and the maximum and minimum ages for the site near the dam were 406.10 d (15th June) and 90.74 d (3rd August), respectively, in 2010. Distinct vertical differences in water age showed that surface flow, interflow, and underflow appeared alternately, depending on the season and water depth. The worst water exchange situation was found in the central areas of the North Lake with the highest water ages in the bottom on both 15th June and 3rd August, in 2010. Comparison of the spatial water ages revealed that the more favorable hydraulic conditions on 3rd August mainly improved the water exchange in the dam areas and most areas of the South Lake, but had little effect on the bottom layers of the other deepest areas in the South and North Lakes. The presented framework can be applied in other data-scarce waterbodies worldwide to provide better understanding of water exchange processes.

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.

Towards Year-round Estimation of Terrestrial Water Storage over Snow-Covered Terrain via Multi-sensor Assimilation of GRACE/GRACE-FO and AMSR-E/AMSR-2.

NASA Astrophysics Data System (ADS)

Wang, J.; Xue, Y.; Forman, B. A.; Girotto, M.; Reichle, R. H.

2017-12-01

The Gravity and Recovery Climate Experiment (GRACE) has revolutionized large-scale remote sensing of the Earth's terrestrial hydrologic cycle and has provided an unprecedented observational constraint for global land surface models. However, the coarse-scale (in space and time), vertically-integrated measure of terrestrial water storage (TWS) limits GRACE's applicability to smaller scale hydrologic applications. In order to enhance model-based estimates of TWS while effectively adding resolution (in space and time) to the coarse-scale TWS retrievals, a multi-variate, multi-sensor data assimilation framework is presented here that simultaneously assimilates gravimetric retrievals of TWS in conjunction with passive microwave (PMW) brightness temperature (Tb) observations over snow-covered terrain. The framework uses the NASA Catchment Land Surface Model (Catchment) and an ensemble Kalman filter (EnKF). A synthetic assimilation experiment is presented for the Volga river basin in Russia. The skill of the output from the assimilation of synthetic observations is compared with that of model estimates generated without the benefit of assimilating the synthetic observations. It is shown that the EnKF framework improves modeled estimates of TWS, snow depth, and snow mass (a.k.a. snow water equivalent). The data assimilation routine produces a conditioned (updated) estimate that is more accurate and contains less uncertainty during both the snow accumulation phase of the snow season as well as during the snow ablation season.

Human impact parameterization in global hydrological models improves estimates of monthly discharges and hydrological extremes: a multi-model validation study

NASA Astrophysics Data System (ADS)

Veldkamp, Ted; Ward, Philip; de Moel, Hans; Aerts, Jeroen; Muller Schmied, Hannes; Portmann, Felix; Zhao, Fang; Gerten, Dieter; Masaki, Yoshimitsu; Pokhrel, Yadu; Satoh, Yusuke; Gosling, Simon; Zaherpour, Jamal; Wada, Yoshihide

2017-04-01

Human impacts on freshwater resources and hydrological features form the core of present-day water related hazards, like flooding, droughts, water scarcity, and water quality issues. Driven by the societal and scientific needs to correctly model such water related hazards a fair amount of resources has been invested over the past decades to represent human activities and their interactions with the hydrological cycle in global hydrological models (GHMs). Use of these GHMs - including the human dimension - is widespread, especially in water resources research. Evaluation or comparative assessments of the ability of such GHMs to represent real-world hydrological conditions are, unfortunately, however often limited to (near-)natural river basins. Such studies are, therefore, not able to test the model representation of human activities and its associated impact on estimates of freshwater resources or assessments of hydrological extremes. Studies that did perform a validation exercise - including the human dimension and looking into managed catchments - either focused only on one hydrological model, and/or incorporated only a few data points (i.e. river basins) for validation. To date, a comprehensive comparative analysis that evaluates whether and where incorporating the human dimension actually improves the performance of different GHMs with respect to their representation of real-world hydrological conditions and extremes is missing. The absence of such study limits the potential benchmarking of GHMs and their outcomes in hydrological hazard and risk assessments significantly, potentially hampering incorporation of GHMs and their modelling results in actual policy making and decision support with respect to water resources management. To address this issue, we evaluate in this study the performance of five state-of-the-art GHMs that include anthropogenic activities in their modelling scheme, with respect to their representation of monthly discharges and hydrological extremes. To this end, we compared their monthly discharge simulations under a naturalized and a time-dependent human impact simulation, with monthly GRDC river discharge observations of 2,412 stations over the period 1971-2010. Evaluation metrics that were used to assess the performance of the GHMs included the modified Kling-Gupta Efficiency index, and its individual parameters describing the linear correlation coefficient, the bias ratio, and the variability ratio, as well as indicators for hydrological extremes (Q90, Q10). Our results show that inclusion of anthropogenic activities in the modelling framework generally enhances the overall performance of the GHMs studied, mainly driven by bias-improvements, and to a lesser extent due to changes in modelled hydrological variability. Whilst the inclusion of anthropogenic activities takes mainly effect in the managed catchments, a significant share of the (near-)natural catchments is influenced as well. To get estimates of hydrological extremes right, especially when looking at low-flows, inclusion of human activities is paramount. Whilst high-flow estimates are mainly decreased, impact of human activities on low-flows is ambiguous, i.e. due to the relative importance of the timing of return flows and reservoir operations. Even with inclusion of the human dimension we find, nevertheless, a persistent overestimation of hydrological extremes across all models, which should be accounted for in future assessments.

Assessing Forest Carbon Response to Climate Change and Disturbances Using Long-term Hydro-climatic Observations and Simulations

NASA Astrophysics Data System (ADS)

Trettin, C.; Dai, Z.; Amatya, D. M.

2014-12-01

Long-term climatic and hydrologic observations on the Santee Experimental Forest in the lower coastal plain of South Carolina were used to estimate long-term changes in hydrology and forest carbon dynamics for a pair of first-order watersheds. Over 70 years of climate data indicated that warming in this forest area in the last decades was faster than the global mean; 35+ years of hydrologic records showed that forest ecosystem succession three years following Hurricane Hugo caused a substantial change in the ratio of runoff to precipitation. The change in this relationship between the paired watersheds was attributed to altered evapotranspiration processes caused by greater abundance of pine in the treatment watershed and regeneration of the mixed hardwood-pine forest on the reference watershed. The long-term records and anomalous observations are highly valuable for reliable calibration and validation of hydrological and biogeochemical models capturing the effects of climate variability. We applied the hydrological model MIKESHE that showed that runoff and water table level are sensitive to global warming, and that the sustained warming trends can be expected to decrease stream discharge and lower the mean water table depth. The spatially-explicit biogeochemical model Forest-DNDC, validated using biomass measurements from the watersheds, was used to assess carbon dynamics in response to high resolution hydrologic observation data and simulation results. The simulations showed that the long-term spatiotemporal carbon dynamics, including biomass and fluxes of soil carbon dioxide and methane were highly regulated by disturbance regimes, climatic conditions and water table depth. The utility of linked-modeling framework demonstrated here to assess biogeochemical responses at the watershed scale suggests applications for assessing the consequences of climate change within an urbanizing forested landscape. The approach may also be applicable for validating large-scale models.

Large scale modelling of catastrophic floods in Italy

NASA Astrophysics Data System (ADS)

Azemar, Frédéric; Nicótina, Ludovico; Sassi, Maximiliano; Savina, Maurizio; Hilberts, Arno

2017-04-01

The RMS European Flood HD model® is a suite of country scale flood catastrophe models covering 13 countries throughout continental Europe and the UK. The models are developed with the goal of supporting risk assessment analyses for the insurance industry. Within this framework RMS is developing a hydrologic and inundation model for Italy. The model aims at reproducing the hydrologic and hydraulic properties across the domain through a modeling chain. A semi-distributed hydrologic model that allows capturing the spatial variability of the runoff formation processes is coupled with a one-dimensional river routing algorithm and a two-dimensional (depth averaged) inundation model. This model setup allows capturing the flood risk from both pluvial (overland flow) and fluvial flooding. Here we describe the calibration and validation methodologies for this modelling suite applied to the Italian river basins. The variability that characterizes the domain (in terms of meteorology, topography and hydrologic regimes) requires a modeling approach able to represent a broad range of meteo-hydrologic regimes. The calibration of the rainfall-runoff and river routing models is performed by means of a genetic algorithm that identifies the set of best performing parameters within the search space over the last 50 years. We first establish the quality of the calibration parameters on the full hydrologic balance and on individual discharge peaks by comparing extreme statistics to observations over the calibration period on several stations. The model is then used to analyze the major floods in the country; we discuss the different meteorological setup leading to the historical events and the physical mechanisms that induced these floods. We can thus assess the performance of RMS' hydrological model in view of the physical mechanisms leading to flood and highlight the main controls on flood risk modelling throughout the country. The model's ability to accurately simulate antecedent conditions and discharge hydrographs over the affected area is also assessed, showing that spatio-temporal correlation is retained through the modelling chain. Results show that our modelling approach can capture a wide range of conditions leading to major floods in the Italian peninsula. Under the umbrella of the RMS European Flood HD models this constitutes, to our knowledge, the only operational flood risk model to be applied at continental scale with a coherent model methodology and a domain wide MonteCarlo stochastic set.

Regional scale flood modeling using NEXRAD rainfall, GIS, and HEC-HMS/RAS: a case study for the San Antonio River Basin Summer 2002 storm event.

PubMed

Knebl, M R; Yang, Z-L; Hutchison, K; Maidment, D R

2005-06-01

This paper develops a framework for regional scale flood modeling that integrates NEXRAD Level III rainfall, GIS, and a hydrological model (HEC-HMS/RAS). The San Antonio River Basin (about 4000 square miles, 10,000 km2) in Central Texas, USA, is the domain of the study because it is a region subject to frequent occurrences of severe flash flooding. A major flood in the summer of 2002 is chosen as a case to examine the modeling framework. The model consists of a rainfall-runoff model (HEC-HMS) that converts precipitation excess to overland flow and channel runoff, as well as a hydraulic model (HEC-RAS) that models unsteady state flow through the river channel network based on the HEC-HMS-derived hydrographs. HEC-HMS is run on a 4 x 4 km grid in the domain, a resolution consistent with the resolution of NEXRAD rainfall taken from the local river authority. Watershed parameters are calibrated manually to produce a good simulation of discharge at 12 subbasins. With the calibrated discharge, HEC-RAS is capable of producing floodplain polygons that are comparable to the satellite imagery. The modeling framework presented in this study incorporates a portion of the recently developed GIS tool named Map to Map that has been created on a local scale and extends it to a regional scale. The results of this research will benefit future modeling efforts by providing a tool for hydrological forecasts of flooding on a regional scale. While designed for the San Antonio River Basin, this regional scale model may be used as a prototype for model applications in other areas of the country.

The Challenges of Developing a Framework for Global Water Cycle Monitoring and Prediction (Alfred Wegener Medal Lecture)

NASA Astrophysics Data System (ADS)

Wood, Eric F.

2014-05-01

The Global Earth Observation System of Systems (GEOSS) Water Strategy ("From Observations to Decisions") recognizes that "water is essential for ensuring food and energy security, for facilitating poverty reduction and health security, and for the maintenance of ecosystems and biodiversity", and that water cycle data and observations are critical for improved water management and water security - especially in less developed regions. The GEOSS Water Strategy has articulated a number of goals for improved water management, including flood and drought preparedness, that include: (i) facilitating the use of Earth Observations for water cycle observations; (ii) facilitating the acquisition, processing, and distribution of data products needed for effective management; (iii) providing expertise, information systems, and datasets to the global, regional, and national water communities. There are several challenges that must be met to advance our capability to provide near real-time water cycle monitoring, early warning of hydrological hazards (floods and droughts) and risk assessment under climate change, regionally and globally. Current approaches to monitoring and predicting hydrological hazards are limited in many parts of the world, and especially in developing countries where national capacity is limited and monitoring networks are inadequate. This presentation describes the developments at Princeton University towards a seamless monitoring and prediction framework at all time scales that allows for consistent assessment of water variability from historic to current conditions, and from seasonal and decadal predictions to climate change projections. At the center of the framework is an experimental, global water cycle monitoring and seasonal forecast system that has evolved out of regional and continental systems for the US and Africa. The system is based on land surface hydrological modeling that is driven by satellite remote sensing precipitation to predict current hydrological conditions, flood potential and the state of drought. Seasonal climate model forecasts are downscaled and bias-corrected to drive the land surface model to provide hydrological forecasts and drought products out 6-9 months. The system relies on historic reconstructions of water variability over the 20th century, which forms the background climatology to which current conditions can be assessed. Future changes in water availability and drought risk are quantified based on bias-corrected and downscaled climate model projections that are used to drive the land surface models. For regions with lack of on-the-ground data we are field-testing low-cost environmental sensors and along with new satellite products for terrestrial hydrology and vegetation, integrating these into the system for improved monitoring and prediction. At every step there are scientific challenges whose solutions are only partially being solved. In addition there are challenges in delivering such systems as "climate services", especially to societies with low technical capacity such as rural agriculturalists in sub-Saharan Africa, but whose needs for such information are great. We provide an overview of the system and some examples of real-world applications to flood and drought events, with a focus on Africa.

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.

Physically-based extreme flood frequency with stochastic storm transposition and paleoflood data on large watersheds

NASA Astrophysics Data System (ADS)

England, John F.; Julien, Pierre Y.; Velleux, Mark L.

2014-03-01

Traditionally, deterministic flood procedures such as the Probable Maximum Flood have been used for critical infrastructure design. Some Federal agencies now use hydrologic risk analysis to assess potential impacts of extreme events on existing structures such as large dams. Extreme flood hazard estimates and distributions are needed for these efforts, with very low annual exceedance probabilities (⩽10-4) (return periods >10,000 years). An integrated data-modeling hydrologic hazard framework for physically-based extreme flood hazard estimation is presented. Key elements include: (1) a physically-based runoff model (TREX) coupled with a stochastic storm transposition technique; (2) hydrometeorological information from radar and an extreme storm catalog; and (3) streamflow and paleoflood data for independently testing and refining runoff model predictions at internal locations. This new approach requires full integration of collaborative work in hydrometeorology, flood hydrology and paleoflood hydrology. An application on the 12,000 km2 Arkansas River watershed in Colorado demonstrates that the size and location of extreme storms are critical factors in the analysis of basin-average rainfall frequency and flood peak distributions. Runoff model results are substantially improved by the availability and use of paleoflood nonexceedance data spanning the past 1000 years at critical watershed locations.

Towards a robust framework for catchment classification

NASA Astrophysics Data System (ADS)

Deshmukh, A.; Samal, A.; Singh, R.

2017-12-01

Classification of catchments based on various measures of similarity has emerged as an important technique to understand regional scale hydrologic behavior. Classification of catchment characteristics and/or streamflow response has been used reveal which characteristics are more likely to explain the observed variability of hydrologic response. However, numerous algorithms for supervised or unsupervised classification are available, making it hard to identify the algorithm most suitable for the dataset at hand. Consequently, existing catchment classification studies vary significantly in the classification algorithms employed with no previous attempt at understanding the degree of uncertainty in classification due to this algorithmic choice. This hinders the generalizability of interpretations related to hydrologic behavior. Our goal is to develop a protocol that can be followed while classifying hydrologic datasets. We focus on a classification framework for unsupervised classification and provide a step-by-step classification procedure. The steps include testing the clusterabiltiy of original dataset prior to classification, feature selection, validation of clustered data, and quantification of similarity of two clusterings. We test several commonly available methods within this framework to understand the level of similarity of classification results across algorithms. We apply the proposed framework on recently developed datasets for India to analyze to what extent catchment properties can explain observed catchment response. Our testing dataset includes watershed characteristics for over 200 watersheds which comprise of both natural (physio-climatic) characteristics and socio-economic characteristics. This framework allows us to understand the controls on observed hydrologic variability across India.

Hydrological effect of vegetation against rainfall-induced landslides

NASA Astrophysics Data System (ADS)

Gonzalez-Ollauri, Alejandro; Mickovski, Slobodan B.

2017-06-01

The hydrological effect of vegetation on rainfall-induced landslides has rarely been quantified and its integration into slope stability analysis methods remains a challenge. Our goal was to establish a reproducible, novel framework to evaluate the hydrological effect of vegetation on shallow landslides. This was achieved by accomplishing three objectives: (i) quantification in situ of the hydrological mechanisms by which woody vegetation (i.e. Salix sp.) might impact slope stability under wetting and drying conditions; (ii) to propose a new approach to predict plant-derived matric suctions under drying conditions; and (iii) to evaluate the suitability of the unified effective stress principle and framework (UES) to quantify the hydrological effect of vegetation against landslides. The results revealed that plant water uptake was the main hydrological mechanism contributing to slope stability, as the vegetated slope was, on average, 12.84% drier and had matric suctions three times higher than the fallow slope. The plant-related mechanisms under wetting conditions had a minimal effect on slope stability. The plant aerial parts intercepted up to 26.73% of the rainfall and concentrated a further 10.78% of it around the stem. Our approach successfully predicted the plant-derived matric suctions and UES proved to be adequate for evaluating the hydrological effect of vegetation on landslides. Although the UES framework presented here sets the basis for effectively evaluating the hydrological effect of vegetation on slope stability, it requires knowledge of the specific hydro-mechanical properties of plant-soil composites and this in itself needs further investigation.

Identifying and Evaluating the Relationships that Control a Land Surface Model's Hydrological Behavior

NASA Technical Reports Server (NTRS)

Koster, Randal D.; Mahanama, Sarith P.

2012-01-01

The inherent soil moisture-evaporation relationships used in today 's land surface models (LSMs) arguably reflect a lot of guesswork given the lack of contemporaneous evaporation and soil moisture observations at the spatial scales represented by regional and global models. The inherent soil moisture-runoff relationships used in the LSMs are also of uncertain accuracy. Evaluating these relationships is difficult but crucial given that they have a major impact on how the land component contributes to hydrological and meteorological variability within the climate system. The relationships, it turns out, can be examined efficiently and effectively with a simple water balance model framework. The simple water balance model, driven with multi-decadal observations covering the conterminous United States, shows how different prescribed relationships lead to different manifestations of hydrological variability, some of which can be compared directly to observations. Through the testing of a wide suite of relationships, the simple model provides estimates for the underlying relationships that operate in nature and that should be operating in LSMs. We examine the relationships currently used in a number of different LSMs in the context of the simple water balance model results and make recommendations for potential first-order improvements to these LSMs.

A coupled modeling framework for sustainable watershed management in transboundary river basins

NASA Astrophysics Data System (ADS)

Furqan Khan, Hassaan; Yang, Y. C. Ethan; Xie, Hua; Ringler, Claudia

2017-12-01

There is a growing recognition among water resource managers that sustainable watershed management needs to not only account for the diverse ways humans benefit from the environment, but also incorporate the impact of human actions on the natural system. Coupled natural-human system modeling through explicit modeling of both natural and human behavior can help reveal the reciprocal interactions and co-evolution of the natural and human systems. This study develops a spatially scalable, generalized agent-based modeling (ABM) framework consisting of a process-based semi-distributed hydrologic model (SWAT) and a decentralized water system model to simulate the impacts of water resource management decisions that affect the food-water-energy-environment (FWEE) nexus at a watershed scale. Agents within a river basin are geographically delineated based on both political and watershed boundaries and represent key stakeholders of ecosystem services. Agents decide about the priority across three primary water uses: food production, hydropower generation and ecosystem health within their geographical domains. Agents interact with the environment (streamflow) through the SWAT model and interact with other agents through a parameter representing willingness to cooperate. The innovative two-way coupling between the water system model and SWAT enables this framework to fully explore the feedback of human decisions on the environmental dynamics and vice versa. To support non-technical stakeholder interactions, a web-based user interface has been developed that allows for role-play and participatory modeling. The generalized ABM framework is also tested in two key transboundary river basins, the Mekong River basin in Southeast Asia and the Niger River basin in West Africa, where water uses for ecosystem health compete with growing human demands on food and energy resources. We present modeling results for crop production, energy generation and violation of eco-hydrological indicators at both the agent and basin-wide levels to shed light on holistic FWEE management policies in these two basins.

Development and Evaluation of an Integrated Hydrological Modeling Framework for Monitoring and Understanding Floods and Droughts

NASA Astrophysics Data System (ADS)

Yang, Z. L.; Wu, W. Y.; Lin, P.; Maidment, D. R.

2017-12-01

Extreme water events such as catastrophic floods and severe droughts have increased in recent decades. Mitigating the risk to lives, food security, infrastructure, energy supplies, as well as numerous other industries posed by these extreme events requires informed decision-making and planning based on sound science. We are developing a global water modeling capability by building models that will provide total operational water predictions (evapotranspiration, soil moisture, groundwater, channel flow, inundation, snow) at unprecedented spatial resolutions and updated frequencies. Toward this goal, this talk presents an integrated global hydrological modeling framework that takes advantage of gridded meteorological forcing, land surface modeling, channeled flow modeling, ground observations, and satellite remote sensing. Launched in August 2016, the National Water Model successfully incorporates weather forecasts to predict river flows for more than 2.7 million rivers across the continental United States, which transfers a "synoptic weather map" to a "synoptic river flow map" operationally. In this study, we apply a similar framework to a high-resolution global river network database, which is developed from a hierarchical Dominant River Tracing (DRT) algorithm, and runoff output from the Global Land Data Assimilation System (GLDAS) to a vector-based river routing model (The Routing Application for Parallel Computation of Discharge, RAPID) to produce river flows from 2001 to 2016 using Message Passing Interface (MPI) on Texas Advanced Computer Center's Stampede system. In this simulation, global river discharges for more than 177,000 rivers are computed every 30 minutes. The modeling framework's performance is evaluated with various observations including river flows at more than 400 gauge stations globally. Overall, the model exhibits a reasonably good performance in simulating the averaged patterns of terrestrial water storage, evapotranspiration and runoff. The system is appropriate for monitoring and studying floods and droughts. Directions for future research will be outlined and discussed.

Curtailing Agricultural Pumping in an Era of Extended Drought: Infusing Science and Leagality into a Common Hydrologic Framework

NASA Astrophysics Data System (ADS)

Carroll, R. W. H.; Pohll, G.; Benedict, J.; Felling, R.

2016-12-01

Many arid and semi-arid agricultural systems of the Great Basin in the western United States depend on supplemental groundwater pumping to augment diminished surface water flows during periods of drought. As droughts become longer and more severe in the region, unprecedented drawdown in these aquifer systems has occurred with legal and environmental implications on both surface and groundwater. The Walker River in the Great Basin supports extensive agriculture in the region and is the sole perennial stream to one of the few desert terminal lakes in North America. Continuous declines in the lake have spurred extensive research into management options to balance demands of agriculture and increase water deliveries to the lake. Smith and Mason Valleys are important agricultural centers within the Walker Basin. In 2015 the region entered its fifth year of drought and both valleys were the focus of curtailment orders to restrict the use of supplemental groundwater rights. To aid management decisions, hydrologic models were developed that simulate complex feedbacks between surface diversions, crop consumptive needs, groundwater recharge, return flow, and groundwater-surface water interactions. Demand-driven pumping that incorporates priority dates and maximum duty allocations are directly input to the hydrologic model to allow an assessment of groundwater curtailment options under a variety of drought scenarios to meet targeted water levels and downstream conveyance of surface water in a legally defensible framework. Hydrologic results using a sliding scale approach to priority based curtailment are presented in the arena of stakeholder participation and response.

Spatial analysis of instream nitrogen loads and factors controlling nitrogen delivery to streams in the southeastern United States using spatially referenced regression on watershed attributes (SPARROW) and regional classification frameworks

USGS Publications Warehouse

Hoos, A.B.; McMahon, G.

2009-01-01

Understanding how nitrogen transport across the landscape varies with landscape characteristics is important for developing sound nitrogen management policies. We used a spatially referenced regression analysis (SPARROW) to examine landscape characteristics influencing delivery of nitrogen from sources in a watershed to stream channels. Modelled landscape delivery ratio varies widely (by a factor of 4) among watersheds in the southeastern United States - higher in the western part (Tennessee, Alabama, and Mississippi) than in the eastern part, and the average value for the region is lower compared to other parts of the nation. When we model landscape delivery ratio as a continuous function of local-scale landscape characteristics, we estimate a spatial pattern that varies as a function of soil and climate characteristics but exhibits spatial structure in residuals (observed load minus predicted load). The spatial pattern of modelled landscape delivery ratio and the spatial pattern of residuals coincide spatially with Level III ecoregions and also with hydrologic landscape regions. Subsequent incorporation into the model of these frameworks as regional scale variables improves estimation of landscape delivery ratio, evidenced by reduced spatial bias in residuals, and suggests that cross-scale processes affect nitrogen attenuation on the landscape. The model-fitted coefficient values are logically consistent with the hypothesis that broad-scale classifications of hydrologic response help to explain differential rates of nitrogen attenuation, controlling for local-scale landscape characteristics. Negative model coefficients for hydrologic landscape regions where the primary flow path is shallow ground water suggest that a lower fraction of nitrogen mass will be delivered to streams; this relation is reversed for regions where the primary flow path is overland flow.

Spatial analysis of instream nitrogen loads and factors controlling nitrogen delivery to streams in the southeastern United States using spatially referenced regression on watershed attributes (SPARROW) and regional classification frameworks

USGS Publications Warehouse

Hoos, Anne B.; McMahon, Gerard

2009-01-01

Understanding how nitrogen transport across the landscape varies with landscape characteristics is important for developing sound nitrogen management policies. We used a spatially referenced regression analysis (SPARROW) to examine landscape characteristics influencing delivery of nitrogen from sources in a watershed to stream channels. Modelled landscape delivery ratio varies widely (by a factor of 4) among watersheds in the southeastern United States—higher in the western part (Tennessee, Alabama, and Mississippi) than in the eastern part, and the average value for the region is lower compared to other parts of the nation. When we model landscape delivery ratio as a continuous function of local-scale landscape characteristics, we estimate a spatial pattern that varies as a function of soil and climate characteristics but exhibits spatial structure in residuals (observed load minus predicted load). The spatial pattern of modelled landscape delivery ratio and the spatial pattern of residuals coincide spatially with Level III ecoregions and also with hydrologic landscape regions. Subsequent incorporation into the model of these frameworks as regional scale variables improves estimation of landscape delivery ratio, evidenced by reduced spatial bias in residuals, and suggests that cross-scale processes affect nitrogen attenuation on the landscape. The model-fitted coefficient values are logically consistent with the hypothesis that broad-scale classifications of hydrologic response help to explain differential rates of nitrogen attenuation, controlling for local-scale landscape characteristics. Negative model coefficients for hydrologic landscape regions where the primary flow path is shallow ground water suggest that a lower fraction of nitrogen mass will be delivered to streams; this relation is reversed for regions where the primary flow path is overland flow.

Towards large scale modelling of wetland water dynamics in northern basins.

NASA Astrophysics Data System (ADS)

Pedinotti, V.; Sapriza, G.; Stone, L.; Davison, B.; Pietroniro, A.; Quinton, W. L.; Spence, C.; Wheater, H. S.

2015-12-01

Understanding the hydrological behaviour of low topography, wetland-dominated sub-arctic areas is one major issue needed for the improvement of large scale hydrological models. These wet organic soils cover a large extent of Northern America and have a considerable impact on the rainfall-runoff response of a catchment. Moreover their strong interactions with the lower atmosphere and the carbon cycle make of these areas a noteworthy component of the regional climate system. In the framework of the Changing Cold Regions Network (CCRN), this study aims at providing a model for wetland water dynamics that can be used for large scale applications in cold regions. The modelling system has two main components : a) the simulation of surface runoff using the Modélisation Environmentale Communautaire - Surface and Hydrology (MESH) land surface model driven with several gridded atmospheric datasets and b) the routing of surface runoff using the WATROUTE channel scheme. As a preliminary study, we focus on two small representative study basins in Northern Canada : Scotty Creek in the lower Liard River valley of the Northwest Territories and Baker Creek, located a few kilometers north of Yellowknife. Both areas present characteristic landscapes dominated by a series of peat plateaus, channel fens, small lakes and bogs. Moreover, they constitute important fieldwork sites with detailed data to support our modelling study. The challenge of our new wetland model is to represent the hydrological functioning of the various landscape units encountered in those watersheds and their interactions using simple numerical formulations that can be later extended to larger basins such as the Mackenzie river basin. Using observed datasets, the performance of the model to simulate the temporal evolution of hydrological variables such as the water table depth, frost table depth and discharge is assessed.

An eco-hydrological modeling framework for assessing trade-offs among ecosystem services in response to alternative land use and climate

EPA Science Inventory

Scientists, policymakers, community planners and others have discussed ecosystem services for decades, however, society is still in the early stages of developing methodologies to quantify and value the services provided by ecosystems. For example, the U.S. Environmental Protect...

Coupling large scale hydrologic-reservoir-hydraulic models for impact studies in data sparse regions

NASA Astrophysics Data System (ADS)

O'Loughlin, Fiachra; Neal, Jeff; Wagener, Thorsten; Bates, Paul; Freer, Jim; Woods, Ross; Pianosi, Francesca; Sheffied, Justin

2017-04-01

As hydraulic modelling moves to increasingly large spatial domains it has become essential to take reservoirs and their operations into account. Large-scale hydrological models have been including reservoirs for at least the past two decades, yet they cannot explicitly model the variations in spatial extent of reservoirs, and many reservoirs operations in hydrological models are not undertaken during the run-time operation. This requires a hydraulic model, yet to-date no continental scale hydraulic model has directly simulated reservoirs and their operations. In addition to the need to include reservoirs and their operations in hydraulic models as they move to global coverage, there is also a need to link such models to large scale hydrology models or land surface schemes. This is especially true for Africa where the number of river gauges has consistently declined since the middle of the twentieth century. In this study we address these two major issues by developing: 1) a coupling methodology for the VIC large-scale hydrological model and the LISFLOOD-FP hydraulic model, and 2) a reservoir module for the LISFLOOD-FP model, which currently includes four sets of reservoir operating rules taken from the major large-scale hydrological models. The Volta Basin, West Africa, was chosen to demonstrate the capability of the modelling framework as it is a large river basin ( 400,000 km2) and contains the largest man-made lake in terms of area (8,482 km2), Lake Volta, created by the Akosombo dam. Lake Volta also experiences a seasonal variation in water levels of between two and six metres that creates a dynamic shoreline. In this study, we first run our coupled VIC and LISFLOOD-FP model without explicitly modelling Lake Volta and then compare these results with those from model runs where the dam operations and Lake Volta are included. The results show that we are able to obtain variation in the Lake Volta water levels and that including the dam operations and Lake Volta has significant impacts on the water levels across the domain.

Debates—Perspectives on socio-hydrology: Modeling flood risk as a public policy problem

NASA Astrophysics Data System (ADS)

Gober, Patricia; Wheater, Howard S.

2015-06-01

Socio-hydrology views human activities as endogenous to water system dynamics; it is the interaction between human and biophysical processes that threatens the viability of current water systems through positive feedbacks and unintended consequences. Di Baldassarre et al. implement socio-hydrology as a flood risk problem using the concept of social memory as a vehicle to link human perceptions to flood damage. Their mathematical model has heuristic value in comparing potential flood damages in green versus technological societies. It can also support communities in exploring the potential consequences of policy decisions and evaluating critical policy tradeoffs, for example, between flood protection and economic development. The concept of social memory does not, however, adequately capture the social processes whereby public perceptions are translated into policy action, including the pivotal role played by the media in intensifying or attenuating perceived flood risk, the success of policy entrepreneurs in keeping flood hazard on the public agenda during short windows of opportunity for policy action, and different societal approaches to managing flood risk that derive from cultural values and economic interests. We endorse the value of seeking to capture these dynamics in a simplified conceptual framework, but favor a broader conceptualization of socio-hydrology that includes a knowledge exchange component, including the way modeling insights and scientific results are communicated to floodplain managers. The social processes used to disseminate the products of socio-hydrological research are as important as the research results themselves in determining whether modeling is used for real-world decision making.

Multivariate missing data in hydrology - Review and applications

NASA Astrophysics Data System (ADS)

Ben Aissia, Mohamed-Aymen; Chebana, Fateh; Ouarda, Taha B. M. J.

2017-12-01

Water resources planning and management require complete data sets of a number of hydrological variables, such as flood peaks and volumes. However, hydrologists are often faced with the problem of missing data (MD) in hydrological databases. Several methods are used to deal with the imputation of MD. During the last decade, multivariate approaches have gained popularity in the field of hydrology, especially in hydrological frequency analysis (HFA). However, treating the MD remains neglected in the multivariate HFA literature whereas the focus has been mainly on the modeling component. For a complete analysis and in order to optimize the use of data, MD should also be treated in the multivariate setting prior to modeling and inference. Imputation of MD in the multivariate hydrological framework can have direct implications on the quality of the estimation. Indeed, the dependence between the series represents important additional information that can be included in the imputation process. The objective of the present paper is to highlight the importance of treating MD in multivariate hydrological frequency analysis by reviewing and applying multivariate imputation methods and by comparing univariate and multivariate imputation methods. An application is carried out for multiple flood attributes on three sites in order to evaluate the performance of the different methods based on the leave-one-out procedure. The results indicate that, the performance of imputation methods can be improved by adopting the multivariate setting, compared to mean substitution and interpolation methods, especially when using the copula-based approach.

Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre- and Post-Processing in Sequential Data Assimilation

NASA Astrophysics Data System (ADS)

Wang, S.; Ancell, B. C.; Huang, G. H.; Baetz, B. W.

2018-03-01

Data assimilation using the ensemble Kalman filter (EnKF) has been increasingly recognized as a promising tool for probabilistic hydrologic predictions. However, little effort has been made to conduct the pre- and post-processing of assimilation experiments, posing a significant challenge in achieving the best performance of hydrologic predictions. This paper presents a unified data assimilation framework for improving the robustness of hydrologic ensemble predictions. Statistical pre-processing of assimilation experiments is conducted through the factorial design and analysis to identify the best EnKF settings with maximized performance. After the data assimilation operation, statistical post-processing analysis is also performed through the factorial polynomial chaos expansion to efficiently address uncertainties in hydrologic predictions, as well as to explicitly reveal potential interactions among model parameters and their contributions to the predictive accuracy. In addition, the Gaussian anamorphosis is used to establish a seamless bridge between data assimilation and uncertainty quantification of hydrologic predictions. Both synthetic and real data assimilation experiments are carried out to demonstrate feasibility and applicability of the proposed methodology in the Guadalupe River basin, Texas. Results suggest that statistical pre- and post-processing of data assimilation experiments provide meaningful insights into the dynamic behavior of hydrologic systems and enhance robustness of hydrologic ensemble predictions.

IPA (v1): a framework for agent-based modelling of soil water movement

NASA Astrophysics Data System (ADS)

Mewes, Benjamin; Schumann, Andreas H.

2018-06-01

In the last decade, agent-based modelling (ABM) became a popular modelling technique in social sciences, medicine, biology, and ecology. ABM was designed to simulate systems that are highly dynamic and sensitive to small variations in their composition and their state. As hydrological systems, and natural systems in general, often show dynamic and non-linear behaviour, ABM can be an appropriate way to model these systems. Nevertheless, only a few studies have utilized the ABM method for process-based modelling in hydrology. The percolation of water through the unsaturated soil is highly responsive to the current state of the soil system; small variations in composition lead to major changes in the transport system. Hence, we present a new approach for modelling the movement of water through a soil column: autonomous water agents that transport water through the soil while interacting with their environment as well as with other agents under physical laws.

Understanding the relationship between audiomagnetotelluric data and models, and borehole data in a hydrological environment

USGS Publications Warehouse

McPhee, D.K.; Pellerin, L.

2008-01-01

Audiomagnetotelluric (AMT) data and resulting models are analyzed with respect to geophysical and geological borehole logs in order to clarify the relationship between the two methodologies of investigation of a hydrological environment. Several profiles of AMT data collected in basins in southwestern United States are being used for groundwater exploration and hydrogeological framework studies. In a systematic manner, the AMT data and models are compared to borehole data by computing the equivalent one-dimensional AMT model and comparing with the two-dimensional (2-D) inverse AMT model. The spatial length is used to determine if the well is near enough to the AMT profile to quantify the relationship between the two datasets, and determine the required resolution of the AMT data and models. The significance of the quality of the borehole data when compared to the AMT data is also examined.

Hydroclimatic Change in the Congo River Basin: Past, Present and Future169

NASA Astrophysics Data System (ADS)

Aloysius, N. R.

2016-12-01

Tropical regions provide habitat for the world's most diverse fauna and flora, sequester more atmospheric carbon and provide livelihood for millions of people. The hydrological cycle provides vital linkages for maintaining these ecosystem functions, yet, the understanding of its spatiotemporal variability is limited. Research on the hydrological cycle of the Congo River Basin (CRB), which encompasses the second largest rainforests, has been largely ignored. Global Climate Models (GCM) show limited skills in simulating CRB's climate and their future projections vary widely. Yet, GCMs provide the most plausible scenarios of future climate, based upon which changes in hydrologic fluxes can be predicted with the aid hydrological models. In order to address the gaps in knowledge and to highlight the research needs, we i) developed a spatially explicit hydrological model suitable for describing key hydrological processes, ii) evaluated the performance of GCMs in simulating precipitation and temperature in the region, iii) developed a set of climate change scenarios for the CRB and iv) developed a simplified modeling framework to quantify water management options for rain-fed agriculture with the objective of achieving the triple goals of sustainable development: food security, poverty alleviation and ecosystem conservation. The hydrology model, which was validated with observed stream flows at 50 locations, satisfactorily characterizes spatiotemporal variability of key fluxes. Our evaluation of 25 GCM outputs reveal that many GCMs poorly simulate regional precipitation. We implemented a statistical bias-correction method to develop precipitation and temperature projections for two future greenhouse gas emission scenarios. These climate forcings were, then, used to drive the hydrology model. Our results show that the near-term projections are not affected by emission scenarios. However, towards the mid-21st century, projections are emission scenario dependent. Available freshwater resources are projected to increase in the CRB, except in the semiarid southeast. Our findings have wider implications for climate change assessment and water resource management, because the region, with high population growth and limited capacity to adapt, are primary targets of land and water grabs. 155

The origin of high and low flows in the river Rhine: particle tracing and water quality calculations in a distributed hydrological model

NASA Astrophysics Data System (ADS)

Schellekens, Jaap; van Gils, Jos; Christophe, Christophe; Sperna-Weiland, Frederiek; Winsemius, Hessel

2013-04-01

The ability to quickly link a complete water quality model to any distributed hydrological model can be of great value. It provides the hydrological modeller with more information on the performance of the model by being able to add particle tracing and independent mass balance calculations to an existing distributed hydrological model. It also allows for full catchment water quality calculations forced by emissions to different hydrological compartments, taking into account the relevant processes in the different compartments of the hydrological model. A combined distributed hydrological model and hydrochemical model (Delwaq) have been combined within the modeling framework OpenStreams to model large scale hydrological processes in the Rhine basin upstream of the Dutch border at Lobith. Several models have been setup to evaluate (1) the origin of high and low flows in the Rhine basin based on subcatchment contribution and (2) the contribution of different land covers to the total flow with special reference to urban land cover. In addition (3) the relative share of fast and slow runoff components in the total river discharge has been quantified, as well as the age of these two fractions, both as a function of time. Finally (4) the transmission of a pollutant released in infiltrating water and undergoing sorption has been simulated, as a first test for implementing full water quality modelling. The results of a thirty-five year run using daily time steps for 1975 to 2010 were analysed for monthly average contribution to the total flow of each subcatchment and the different land cover types both for average flow conditions and for the top ten and bottom ten flow percentiles. Furthermore, a number of high and low flow events have been analysed in detail. They reveal the large contribution of the basin area upstream of Basel to the dry season flow, especially during the driest summers. Flood conditions in the basin have a more varied origin with the Moselle being the main contributor. The amount of urban land cover (6.7%) generated a fairly large amount of (quick) runoff. In times up to 21 % of the flow at Lobith is generated in urban areas. The location of urban areas (in general close to the river) in combination with the associated impermeable surfaces most probably cause the relatively large contribution of urban areas. The fast runoff fraction at Lobith has an average age between 5 and 25 days, depending on the hydrology within the year, while the slow runoff fraction shows an average age between 300 and 600 days, again depending on the hydrology within the year. The time needed to flush out 90% of the total volume of water from the basin is about 20 years.

Critical Watersheds: Climate Change, Tipping Points, and Energy-Water Impacts

NASA Astrophysics Data System (ADS)

Middleton, R. S.; Brown, M.; Coon, E.; Linn, R.; McDowell, N. G.; Painter, S. L.; Xu, C.

2014-12-01

Climate change, extreme climate events, and climate-induced disturbances will have a substantial and detrimental impact on terrestrial ecosystems. How ecosystems respond to these impacts will, in turn, have a significant effect on the quantity, quality, and timing of water supply for energy security, agriculture, industry, and municipal use. As a community, we lack sufficient quantitative and mechanistic understanding of the complex interplay between climate extremes (e.g., drought, floods), ecosystem dynamics (e.g., vegetation succession), and disruptive events (e.g., wildfire) to assess ecosystem vulnerabilities and to design mitigation strategies that minimize or prevent catastrophic ecosystem impacts. Through a combination of experimental and observational science and modeling, we are developing a unique multi-physics ecohydrologic framework for understanding and quantifying feedbacks between novel climate and extremes, surface and subsurface hydrology, ecosystem dynamics, and disruptive events in critical watersheds. The simulation capability integrates and advances coupled surface-subsurface hydrology from the Advanced Terrestrial Simulator (ATS), dynamic vegetation succession from the Ecosystem Demography (ED) model, and QUICFIRE, a novel wildfire behavior model developed from the FIRETEC platform. These advances are expected to make extensive contributions to the literature and to earth system modeling. The framework is designed to predict, quantify, and mitigate the impacts of climate change on vulnerable watersheds, with a focus on the US Mountain West and the energy-water nexus. This emerging capability is used to identify tipping points in watershed ecosystems, quantify impacts on downstream users, and formally evaluate mitigation efforts including forest (e.g., thinning, prescribed burns) and watershed (e.g., slope stabilization). The framework is being trained, validated, and demonstrated using field observations and remote data collections in the Valles Caldera National Preserve, including pre- and post-wildfire and infestation observations. Ultimately, the framework will be applied to the upper Colorado River basin. Here, we present an overview of the framework development strategy and latest field and modeling results.

Classification and prediction of river network ephemerality and its relevance for waterborne disease epidemiology

NASA Astrophysics Data System (ADS)

Perez-Saez, Javier; Mande, Theophile; Larsen, Joshua; Ceperley, Natalie; Rinaldo, Andrea

2017-12-01

The transmission of waterborne diseases hinges on the interactions between hydrology and ecology of hosts, vectors and parasites, with the long-term absence of water constituting a strict lower bound. However, the link between spatio-temporal patterns of hydrological ephemerality and waterborne disease transmission is poorly understood and difficult to account for. The use of limited biophysical and hydroclimate information from otherwise data scarce regions is therefore needed to characterize, classify, and predict river network ephemerality in a spatially explicit framework. Here, we develop a novel large-scale ephemerality classification and prediction methodology based on monthly discharge data, water and energy availability, and remote-sensing measures of vegetation, that is relevant to epidemiology, and maintains a mechanistic link to catchment hydrologic processes. Specifically, with reference to the context of Burkina Faso in sub-Saharan Africa, we extract a relevant set of catchment covariates that include the aridity index, annual runoff estimation using the Budyko framework, and hysteretical relations between precipitation and vegetation. Five ephemerality classes, from permanent to strongly ephemeral, are defined from the duration of 0-flow periods that also accounts for the sensitivity of river discharge to the long-lasting drought of the 70's-80's in West Africa. Using such classes, a gradient-boosted tree-based prediction yielded three distinct geographic regions of ephemerality. Importantly, we observe a strong epidemiological association between our predictions of hydrologic ephemerality and the known spatial patterns of schistosomiasis, an endemic parasitic waterborne disease in which infection occurs with human-water contact, and requires aquatic snails as an intermediate host. The general nature of our approach and its relevance for predicting the hydrologic controls on schistosomiasis occurrence provides a pathway for the explicit inclusion of hydrologic drivers within epidemiological models of waterborne disease transmission.

Hydrological classification of natural flow regimes to support environmental flow assessments in intensively regulated Mediterranean rivers, Segura River Basin (Spain).

PubMed

Belmar, Oscar; Velasco, Josefa; Martinez-Capel, Francisco

2011-05-01

Hydrological classification constitutes the first step of a new holistic framework for developing regional environmental flow criteria: the "Ecological Limits of Hydrologic Alteration (ELOHA)". The aim of this study was to develop a classification for 390 stream sections of the Segura River Basin based on 73 hydrological indices that characterize their natural flow regimes. The hydrological indices were calculated with 25 years of natural monthly flows (1980/81-2005/06) derived from a rainfall-runoff model developed by the Spanish Ministry of Environment and Public Works. These indices included, at a monthly or annual basis, measures of duration of droughts and central tendency and dispersion of flow magnitude (average, low and high flow conditions). Principal Component Analysis (PCA) indicated high redundancy among most hydrological indices, as well as two gradients: flow magnitude for mainstream rivers and temporal variability for tributary streams. A classification with eight flow-regime classes was chosen as the most easily interpretable in the Segura River Basin, which was supported by ANOSIM analyses. These classes can be simplified in 4 broader groups, with different seasonal discharge pattern: large rivers, perennial stable streams, perennial seasonal streams and intermittent and ephemeral streams. They showed a high degree of spatial cohesion, following a gradient associated with climatic aridity from NW to SE, and were well defined in terms of the fundamental variables in Mediterranean streams: magnitude and temporal variability of flows. Therefore, this classification is a fundamental tool to support water management and planning in the Segura River Basin. Future research will allow us to study the flow alteration-ecological response relationship for each river type, and set the basis to design scientifically credible environmental flows following the ELOHA framework.

Eco-hydrological Modeling in the Framework of Climate Change

NASA Astrophysics Data System (ADS)

Fatichi, Simone; Ivanov, Valeriy Y.; Caporali, Enrica

2010-05-01

A blueprint methodology for studying climate change impacts, as inferred from climate models, on eco-hydrological dynamics at the plot and small catchment scale is presented. Input hydro-meteorological variables for hydrological and eco-hydrological models for present and future climates are reproduced using a stochastic downscaling technique and a weather generator, "AWE-GEN". The generated time series of meteorological variables for the present climate and an ensemble of possible future climates serve as input to a newly developed physically-based eco-hydrological model "Tethys-Chloris". An application of the proposed methodology is realized reproducing the current (1961-2000) and multiple future (2081-2100) climates for the location of Tucson (Arizona). A general reduction of precipitation and a significant increase of air temperature are inferred. The eco-hydrological model is successively applied to detect changes in water recharge and vegetation dynamics for a desert shrub ecosystem, typical of the semi-arid climate of south Arizona. Results for the future climate account for uncertainties in the downscaling and are produced in terms of probability density functions. A comparison of control and future scenarios is discussed in terms of changes in the hydrological balance components, energy fluxes, and indices of vegetation productivity. An appreciable effect of climate change can be observed in metrics of vegetation performance. The negative impact on vegetation due to amplification of water stress in a warmer and dryer climate is offset by a positive effect of carbon dioxide augment. This implies a positive shift in plant capabilities to exploit water. Consequently, the plant water use efficiency and rain use efficiency are expected to increase. Interesting differences in the long-term vegetation productivity are also observed for the ensemble of future climates. The reduction of precipitation and the substantial maintenance of vegetation cover ultimately leads to the depletion of soil moisture and recharge to deeper layers. Such an outcome can affect the long-tem water availability in semi-arid systems and expose plants to more severe and frequent periods of stress.

Socio-hydrologic Modeling to Understand and Mediate the Competition for Water between Humans and Ecosystems: Murrumbidgee River Basin, Australia (Invited)

NASA Astrophysics Data System (ADS)

Sivapalan, M.

2013-12-01

Competition for water between humans and ecosystems is set to become a flash point in coming decades in all parts of the world. An entirely new and comprehensive quantitative framework is needed to establish a holistic understanding of that competition, thereby enabling development of effective mediation strategies. This paper presents a case study centered on the Murrumbidgee river basin in eastern Australia that illustrates the dynamics of the balance between water extraction and use for food production and efforts to mitigate and reverse consequent degradation of the riparian environment. Interactions between patterns of water management and climate driven hydrological variability within the prevailing socio-economic environment have contributed to the emergence of new whole system dynamics over the last 100 years. In particular, data analysis reveals a pendulum swing between an exclusive focus on agricultural development and food production in the initial stages of water resource development and its attendant socio-economic benefits, followed by the gradual realization of the adverse environmental impacts, efforts to mitigate these with the use of remedial measures, and ultimately concerted efforts and externally imposed solutions to restore environmental health and ecosystem services. A quasi-distributed coupled socio-hydrologic system model that explicitly includes the two-way coupling between human and hydrological systems, including evolution of human values/norms relating to water and the environment, is able to mimic broad features of this pendulum swing. The model consists of coupled nonlinear differential equations that include four state variables describing the co-evolution of storage capacity, irrigated area, human population, and ecosystem health. The model is used to generate insights into the dominant controls of the trajectory of co-evolution of the coupled human-water system, to serve as the theoretical framework for more detailed analysis of the system, and to generate organizing principles that may be transferable to other systems in different climatic and socio-economic settings.

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.

Legacy model integration for enhancing hydrologic interdisciplinary research

NASA Astrophysics Data System (ADS)

Dozier, A.; Arabi, M.; David, O.

2013-12-01

Many challenges are introduced to interdisciplinary research in and around the hydrologic science community due to advances in computing technology and modeling capabilities in different programming languages, across different platforms and frameworks by researchers in a variety of fields with a variety of experience in computer programming. Many new hydrologic models as well as optimization, parameter estimation, and uncertainty characterization techniques are developed in scripting languages such as Matlab, R, Python, or in newer languages such as Java and the .Net languages, whereas many legacy models have been written in FORTRAN and C, which complicates inter-model communication for two-way feedbacks. However, most hydrologic researchers and industry personnel have little knowledge of the computing technologies that are available to address the model integration process. Therefore, the goal of this study is to address these new challenges by utilizing a novel approach based on a publish-subscribe-type system to enhance modeling capabilities of legacy socio-economic, hydrologic, and ecologic software. Enhancements include massive parallelization of executions and access to legacy model variables at any point during the simulation process by another program without having to compile all the models together into an inseparable 'super-model'. Thus, this study provides two-way feedback mechanisms between multiple different process models that can be written in various programming languages and can run on different machines and operating systems. Additionally, a level of abstraction is given to the model integration process that allows researchers and other technical personnel to perform more detailed and interactive modeling, visualization, optimization, calibration, and uncertainty analysis without requiring deep understanding of inter-process communication. To be compatible, a program must be written in a programming language with bindings to a common implementation of the message passing interface (MPI), which includes FORTRAN, C, Java, the .NET languages, Python, R, Matlab, and many others. The system is tested on a longstanding legacy hydrologic model, the Soil and Water Assessment Tool (SWAT), to observe and enhance speed-up capabilities for various optimization, parameter estimation, and model uncertainty characterization techniques, which is particularly important for computationally intensive hydrologic simulations. Initial results indicate that the legacy extension system significantly decreases developer time, computation time, and the cost of purchasing commercial parallel processing licenses, while enhancing interdisciplinary research by providing detailed two-way feedback mechanisms between various process models with minimal changes to legacy code.

Waste IPSC : Thermal-Hydrologic-Chemical-Mechanical (THCM) modeling and simulation.

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

Freeze, Geoffrey A.; Wang, Yifeng; Arguello, Jose Guadalupe, Jr.

2010-10-01

Waste IPSC Objective is to develop an integrated suite of high performance computing capabilities to simulate radionuclide movement through the engineered components and geosphere of a radioactive waste storage or disposal system: (1) with robust thermal-hydrologic-chemical-mechanical (THCM) coupling; (2) for a range of disposal system alternatives (concepts, waste form types, engineered designs, geologic settings); (3) for long time scales and associated large uncertainties; (4) at multiple model fidelities (sub-continuum, high-fidelity continuum, PA); and (5) in accordance with V&V and software quality requirements. THCM Modeling collaborates with: (1) Other Waste IPSC activities: Sub-Continuum Processes (and FMM), Frameworks and Infrastructure (and VU,more » ECT, and CT); (2) Waste Form Campaign; (3) Used Fuel Disposition (UFD) Campaign; and (4) ASCEM.« less

An integrated modeling framework of socio-economic, biophysical, and hydrological processes in Midwest landscapes: Remote sensing data, agro-hydrological model, and agent-based model

NASA Astrophysics Data System (ADS)

Ding, Deng

Intensive human-environment interactions are taking place in Midwestern agricultural systems. An integrated modeling framework is suitable for predicting dynamics of key variables of the socio-economic, biophysical, hydrological processes as well as exploring the potential transitions of system states in response to changes of the driving factors. The purpose of this dissertation is to address issues concerning the interacting processes and consequent changes in land use, water balance, and water quality using an integrated modeling framework. This dissertation is composed of three studies in the same agricultural watershed, the Clear Creek watershed in East-Central Iowa. In the first study, a parsimonious hydrologic model, the Threshold-Exceedance-Lagrangian Model (TELM), is further developed into RS-TELM (Remote Sensing TELM) to integrate remote sensing vegetation data for estimating evapotranspiration. The goodness of fit of RS-TELM is comparable to a well-calibrated SWAT (Soil and Water Assessment Tool) and even slightly superior in capturing intra-seasonal variability of stream flow. The integration of RS LAI (Leaf Area Index) data improves the model's performance especially over the agriculture dominated landscapes. The input of rainfall datasets with spatially explicit information plays a critical role in increasing the model's goodness of fit. In the second study, an agent-based model is developed to simulate farmers' decisions on crop type and fertilizer application in response to commodity and biofuel crop prices. The comparison between simulated crop land percentage and crop rotations with satellite-based land cover data suggest that farmers may be underestimating the effects that continuous corn production has on yields (yield drag). The simulation results given alternative market scenarios based on a survey of agricultural land owners and operators in the Clear Creek Watershed show that, farmers see cellulosic biofuel feedstock production in the form of perennial grasses or corn stover as a more risky enterprise than their current crop production systems, likely because of market and production risks and lock in effects. As a result farmers do not follow a simple farm-profit maximization rule. In the third study, the consequent water quantity and quality change of the potential land use transitions given alternative biofuel crop market scenarios is explored in a case study in the Clear Creek watershed. A computer program is developed to implement the loose-coupling strategy to couple an agent-based land use model with SWAT. The simulation results show that watershed-scale water quantity (water yield and runoff) and quality variables (sediment and nutrient loads) decrease in values as switchgrass price increases. However, negligence of farmers risk aversions towards biofuel crop adoption would cause overestimation of the impacts of switchgrass price on water quantity and quality.

Ensemble-based flash-flood modelling: Taking into account hydrodynamic parameters and initial soil moisture uncertainties

NASA Astrophysics Data System (ADS)

Edouard, Simon; Vincendon, Béatrice; Ducrocq, Véronique

2018-05-01

Intense precipitation events in the Mediterranean often lead to devastating flash floods (FF). FF modelling is affected by several kinds of uncertainties and Hydrological Ensemble Prediction Systems (HEPS) are designed to take those uncertainties into account. The major source of uncertainty comes from rainfall forcing and convective-scale meteorological ensemble prediction systems can manage it for forecasting purpose. But other sources are related to the hydrological modelling part of the HEPS. This study focuses on the uncertainties arising from the hydrological model parameters and initial soil moisture with aim to design an ensemble-based version of an hydrological model dedicated to Mediterranean fast responding rivers simulations, the ISBA-TOP coupled system. The first step consists in identifying the parameters that have the strongest influence on FF simulations by assuming perfect precipitation. A sensitivity study is carried out first using a synthetic framework and then for several real events and several catchments. Perturbation methods varying the most sensitive parameters as well as initial soil moisture allow designing an ensemble-based version of ISBA-TOP. The first results of this system on some real events are presented. The direct perspective of this work will be to drive this ensemble-based version with the members of a convective-scale meteorological ensemble prediction system to design a complete HEPS for FF forecasting.

The concept of hydrologic landscapes

USGS Publications Warehouse

Winter, T.C.

2001-01-01

Hydrologic landscapes are multiples or variations of fundamental hydrologic landscape units. A fundamental hydrologic landscape unit is defined on the basis of land-surface form, geology, and climate. The basic land-surface form of a fundamental hydrologic landscape unit is an upland separated from a lowland by an intervening steeper slope. Fundamental hydrologic landscape units have a complete hydrologic system consisting of surface runoff, ground-water flow, and interaction with atmospheric water. By describing actual landscapes in terms of land-surface slope, hydraulic properties of soils and geologic framework, and the difference between precipitation and evapotranspiration, the hydrologic system of actual landscapes can be conceptualized in a uniform way. This conceptual framework can then be the foundation for design of studies and data networks, syntheses of information on local to national scales, and comparison of process research across small study units in a variety of settings. The Crow Wing River watershed in central Minnesota is used as an example of evaluating stream discharge in the context of hydrologic landscapes. Lake-research watersheds in Wisconsin, Minnesota, North Dakota, and Nebraska are used as an example of using the hydrologic-landscapes concept to evaluate the effect of ground water on the degree of mineralization and major-ion chemistry of lakes that lie within ground-water flow systems.

Distributed nitrate transport and reaction routines (NTR) inside the mesoscale Hydrological Model (mHM) framework: Development and Application in the Selke catchment

NASA Astrophysics Data System (ADS)

Sinha, Sumit; Rode, Michael; Kumar, Rohini; Yang, Xiaoqiang; Samaniego, Luis; Borchardt, Dietrich

2016-04-01

Precise measurements of where, when and how much denitrification occurs on the basis of measurements alone persist to be vexing and intractable research problem at all spatial and temporal scales. As a result, models have become essential and vital tools for furthering our current understanding of the processes that control denitrification on catchment scale. Emplacement of Water Framework Directive (WFD) and continued efforts in improving water treatment facilities has resulted in alleviating the problems associated with point sources of pollution. However, the problem of eutrophication still persists and is primarily associated with the diffused sources of pollution originating from agricultural area. In this study, the nitrate transport and reaction (NTR) routines are developed inside the distributed mesoscale Hydrological Model (mHM www.ufz.de/mhm) which is a fully distributed hydrological model with a novel parameter regionalization scheme (Samaniego et al. 2010; Kumar et al. 2013) and has been applied to whole Europe (Rakovec et al. 2016) and numerous catchments worldwide. The aforementioned NTR model is applied to a mesoscale river basin, Selke (463 km2) located in central Germany. The NTR model takes in account the critical and pertinent processes like transformation in vadose zone, atmospheric deposition, plant uptake, instream denitrification and also simulates the process of manure and fertilizer application. Both streamflow routines and the NTR model are run on daily time steps. The split-sample approach was used for model calibration (1994-1999) and validation (2000-2004). Flow dynamics at three gauging stations located inside this catchment are successfully captured by the model with consistently high Nash-Sutcliffe Efficiency (NSE) of at least 0.8. Regarding nitrate estimates, the NSE values are greater than 0.7 for both validation and calibration periods. Finally, the NTR model is used for identifying the critical source areas (CSAs) that contribute significantly to nutrient pollution due to different local hydrological and topographical conditions. Postulations for a comprehensive sensitivity analysis and further regionalization of key parameters of the NTR model are also investigated. References: Kumar, R., L. Samaniego, and S. Attinger (2013a), Implications of distributed hydrologic model parameterization on water fluxes at multiple scales and locations, Water Resour. Res., 49, 360-379, doi:10.1029/2012WR012195. Samaniego, L., R. Kumar, and S. Attinger (2010), Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale, Water Resour. Res., 46, W05523, doi:10.1029/2008WR007327. Rakovec, O., Kumar, R., Mai, J., Cuntz, M., Thober, S., Zink, M., Attinger, S., Schäfer, D., Schrön, M., Samaniego, L. (2016): Multiscale and multivariate evaluation of water fluxes and states over European river basins, J. Hydrometeorol., 17, 287-307, doi: 10.1175/JHM-D-15-0054.1.

Learning Physics-based Models in Hydrology under the Framework of Generative Adversarial Networks

NASA Astrophysics Data System (ADS)

Karpatne, A.; Kumar, V.

2017-12-01

Generative adversarial networks (GANs), that have been highly successful in a number of applications involving large volumes of labeled and unlabeled data such as computer vision, offer huge potential for modeling the dynamics of physical processes that have been traditionally studied using simulations of physics-based models. While conventional physics-based models use labeled samples of input/output variables for model calibration (estimating the right parametric forms of relationships between variables) or data assimilation (identifying the most likely sequence of system states in dynamical systems), there is a greater opportunity to explore the full power of machine learning (ML) methods (e.g, GANs) for studying physical processes currently suffering from large knowledge gaps, e.g. ground-water flow. However, success in this endeavor requires a principled way of combining the strengths of ML methods with physics-based numerical models that are founded on a wealth of scientific knowledge. This is especially important in scientific domains like hydrology where the number of data samples is small (relative to Internet-scale applications such as image recognition where machine learning methods has found great success), and the physical relationships are complex (high-dimensional) and non-stationary. We will present a series of methods for guiding the learning of GANs using physics-based models, e.g., by using the outputs of physics-based models as input data to the generator-learner framework, and by using physics-based models as generators trained using validation data in the adversarial learning framework. These methods are being developed under the broad paradigm of theory-guided data science that we are developing to integrate scientific knowledge with data science methods for accelerating scientific discovery.

Assessment of the Impacts of Climate Change on Stream Discharge and Water Quality in an Arid, Urbanized Watershed

NASA Astrophysics Data System (ADS)

Ranatunga, T.; Tong, S.; Yang, J.

2011-12-01

Hydrologic and water quality models can provide a general framework to conceptualize and investigate the relationships between climate and water resources. Under a hot and dry climate, highly urbanized watersheds are more vulnerable to changes in climate, such as excess heat and drought. In this study, a comprehensive watershed model, Hydrological Simulation Program FORTRAN (HSPF), is used to assess the impacts of future climate change on the stream discharge and water quality in Las Vegas Wash in Nevada, the only surface water body that drains from the Las Vegas Valley (an area with rapid population growth and urbanization) to Lake Mead. In this presentation, the process of model building, calibration and validation, the generation of climate change scenarios, and the assessment of future climate change effects on stream hydrology and quality are demonstrated. The hydrologic and water quality model is developed based on the data from current national databases and existing major land use categories of the watershed. The model is calibrated for stream discharge, nutrients (nitrogen and phosphorus) and sediment yield. The climate change scenarios are derived from the outputs of the Global Climate Models (GCM) and Regional Climate Models (RCM) simulations, and from the recent assessment reports from the Intergovernmental Panel on Climate Change (IPCC). The Climate Assessment Tool from US EPA's BASINS is used to assess the effects of likely future climate scenarios on the water quantity and quality in Las Vegas Wash. Also the presentation discusses the consequences of these hydrologic changes, including the deficit supplies of clean water during peak seasons of water demand, increased eutrophication potentials, wetland deterioration, and impacts on wild life habitats.

Can Earth System Model Provide Reasonable Natural Runoff Estimates to Support Water Management Studies?

NASA Astrophysics Data System (ADS)

Kao, S. C.; Shi, X.; Kumar, J.; Ricciuto, D. M.; Mao, J.; Thornton, P. E.

2017-12-01

With the concern of changing hydrologic regime, there is a crucial need to better understand how water availability may change and influence water management decisions in the projected future climate conditions. Despite that surface hydrology has long been simulated by land model within the Earth System modeling (ESM) framework, given the coarser horizontal resolution and lack of engineering-level calibration, raw runoff from ESM is generally discarded by water resource managers when conducting hydro-climate impact assessments. To identify a likely path to improve the credibility of ESM-simulated natural runoff, we conducted regional model simulation using the land component (ALM) of the Accelerated Climate Modeling for Energy (ACME) version 1 focusing on the conterminous United States (CONUS). Two very different forcing data sets, including (1) the conventional 0.5° CRUNCEP (v5, 1901-2013) and (2) the 1-km Daymet (v3, 1980-2013) aggregated to 0.5°, were used to conduct 20th century transient simulation with satellite phenology. Additional meteorologic and hydrologic observations, including PRISM precipitation and U.S. Geological Survey WaterWatch runoff, were used for model evaluation. For various CONUS hydrologic regions (such as Pacific Northwest), we found that Daymet can significantly improve the reasonableness of simulated ALM runoff even without intensive calibration. The large dry bias of CRUNCEP precipitation (evaluated by PRISM) in multiple CONUS hydrologic regions is believed to be the main reason causing runoff underestimation. The results suggest that when driving with skillful precipitation estimates, ESM has the ability to produce reasonable natural runoff estimates to support further water management studies. Nevertheless, model calibration will be required for regions (such as Upper Colorado) where ill performance is showed for multiple different forcings.

ENKI - An Open Source environmental modelling platfom

NASA Astrophysics Data System (ADS)

Kolberg, S.; Bruland, O.

2012-04-01

The ENKI software framework for implementing spatio-temporal models is now released under the LGPL license. Originally developed for evaluation and comparison of distributed hydrological model compositions, ENKI can be used for simulating any time-evolving process over a spatial domain. The core approach is to connect a set of user specified subroutines into a complete simulation model, and provide all administrative services needed to calibrate and run that model. This includes functionality for geographical region setup, all file I/O, calibration and uncertainty estimation etc. The approach makes it easy for students, researchers and other model developers to implement, exchange, and test single routines and various model compositions in a fixed framework. The open-source license and modular design of ENKI will also facilitate rapid dissemination of new methods to institutions engaged in operational water resource management. ENKI uses a plug-in structure to invoke separately compiled subroutines, separately built as dynamic-link libraries (dlls). The source code of an ENKI routine is highly compact, with a narrow framework-routine interface allowing the main program to recognise the number, types, and names of the routine's variables. The framework then exposes these variables to the user within the proper context, ensuring that distributed maps coincide spatially, time series exist for input variables, states are initialised, GIS data sets exist for static map data, manually or automatically calibrated values for parameters etc. By using function calls and memory data structures to invoke routines and facilitate information flow, ENKI provides good performance. For a typical distributed hydrological model setup in a spatial domain of 25000 grid cells, 3-4 time steps simulated per second should be expected. Future adaptation to parallel processing may further increase this speed. New modifications to ENKI include a full separation of API and user interface, making it possible to run ENKI from GIS programs and other software environments. ENKI currently compiles under Windows and Visual Studio only, but ambitions exist to remove the platform and compiler dependencies.

A multiobjective decision support/numerical modeling approach for design and evaluation of shallow landfill burial systems

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

Ascough, II, James Clifford

1992-05-01

The capability to objectively evaluate design performance of shallow landfill burial (SLB) systems is of great interest to diverse scientific disciplines, including hydrologists, engineers, environmental scientists, and SLB regulators. The goal of this work was to develop and validate a procedure for the nonsubjective evaluation of SLB designs under actual or simulated environmental conditions. A multiobjective decision module (MDM) based on scoring functions (Wymore, 1988) was implemented to evaluate SLB design performance. Input values to the MDM are provided by hydrologic models. The MDM assigns a total score to each SLB design alternative, thereby allowing for rapid and repeatable designmore » performance evaluation. The MDM was validated for a wide range of SLB designs under different climatic conditions. Rigorous assessment of SLB performance also requires incorporation of hydrologic probabilistic analysis and hydrologic risk into the overall design. This was accomplished through the development of a frequency analysis module. The frequency analysis module allows SLB design event magnitudes to be calculated based on the hydrologic return period. The multiobjective decision and freqeuncy anslysis modules were integrated in a decision support system (DSS) framework, SLEUTH (Shallow Landfill Evaluation Using Transport and Hydrology). SLEUTH is a Microsoft Windows {trademark} application, and is written in the Knowledge Pro Windows (Knowledge Garden, Inc., 1991) development language.« less

Ancient numerical daemons of conceptual hydrological modeling: 2. Impact of time stepping schemes on model analysis and prediction

NASA Astrophysics Data System (ADS)

Kavetski, Dmitri; Clark, Martyn P.

2010-10-01

Despite the widespread use of conceptual hydrological models in environmental research and operations, they remain frequently implemented using numerically unreliable methods. This paper considers the impact of the time stepping scheme on model analysis (sensitivity analysis, parameter optimization, and Markov chain Monte Carlo-based uncertainty estimation) and prediction. It builds on the companion paper (Clark and Kavetski, 2010), which focused on numerical accuracy, fidelity, and computational efficiency. Empirical and theoretical analysis of eight distinct time stepping schemes for six different hydrological models in 13 diverse basins demonstrates several critical conclusions. (1) Unreliable time stepping schemes, in particular, fixed-step explicit methods, suffer from troublesome numerical artifacts that severely deform the objective function of the model. These deformations are not rare isolated instances but can arise in any model structure, in any catchment, and under common hydroclimatic conditions. (2) Sensitivity analysis can be severely contaminated by numerical errors, often to the extent that it becomes dominated by the sensitivity of truncation errors rather than the model equations. (3) Robust time stepping schemes generally produce "better behaved" objective functions, free of spurious local optima, and with sufficient numerical continuity to permit parameter optimization using efficient quasi Newton methods. When implemented within a multistart framework, modern Newton-type optimizers are robust even when started far from the optima and provide valuable diagnostic insights not directly available from evolutionary global optimizers. (4) Unreliable time stepping schemes lead to inconsistent and biased inferences of the model parameters and internal states. (5) Even when interactions between hydrological parameters and numerical errors provide "the right result for the wrong reason" and the calibrated model performance appears adequate, unreliable time stepping schemes make the model unnecessarily fragile in predictive mode, undermining validation assessments and operational use. Erroneous or misleading conclusions of model analysis and prediction arising from numerical artifacts in hydrological models are intolerable, especially given that robust numerics are accepted as mainstream in other areas of science and engineering. We hope that the vivid empirical findings will encourage the conceptual hydrological community to close its Pandora's box of numerical problems, paving the way for more meaningful model application and interpretation.

Water age and stream solute dynamics at the Hubbard Brook Experimental Forest (US)

NASA Astrophysics Data System (ADS)

Botter, Gianluca; Benettin, Paolo; McGuire, Kevin; Rinaldo, Andrea

2016-04-01

The contribution discusses experimental and modeling results from a headwater catchment at the Hubbard Brook Experimental Forest (New Hampshire, USA) to explore the link between stream solute dynamics and water age. A theoretical framework based on water age dynamics, which represents a general basis for characterizing solute transport at the catchment scale, is used to model both conservative and weathering-derived solutes. Based on the available information about the hydrology of the site, an integrated transport model was developed and used to estimate the relevant hydrochemical fluxes. The model was designed to reproduce the deuterium content of streamflow and allowed for the estimate of catchment water storage and dynamic travel time distributions (TTDs). Within this framework, dissolved silicon and sodium concentration in streamflow were simulated by implementing first-order chemical kinetics based explicitly on dynamic TTD, thus upscaling local geochemical processes to catchment scale. Our results highlight the key role of water stored within the subsoil glacial material in both the short-term and long-term solute circulation at Hubbard Brook. The analysis of the results provided by the calibrated model allowed a robust estimate of the emerging concentration-discharge relationship, streamflow age distributions (including the fraction of event water) and storage size, and their evolution in time due to hydrologic variability.

A global framework to model spatial ecosystems exposure to home and personal care chemicals in Asia.

PubMed

Wannaz, Cedric; Franco, Antonio; Kilgallon, John; Hodges, Juliet; Jolliet, Olivier

2018-05-01

This paper analyzes spatially ecosystem exposure to home and personal care (HPC) chemicals, accounting for market data and environmental processes in hydrological water networks, including multi-media fate and transport. We present a global modeling framework built on ScenAT (spatial scenarios of emission), SimpleTreat (sludge treatment plants), and Pangea (spatial multi-scale multimedia fate and transport of chemicals), that we apply across Asia to four chemicals selected to cover a variety of applications, volumes of production and emission, and physico-chemical and environmental fate properties: the anionic surfactant linear alkylbenzene sulphonate (LAS), the antimicrobial triclosan (TCS), the personal care preservative methyl paraben (MeP), and the emollient decamethylcyclopentasiloxane (D5). We present maps of predicted environmental concentrations (PECs) and compare them with monitored values. LAS emission levels and PECs are two to three orders of magnitude greater than for other substances, yet the literature about monitored levels of LAS in Asia is very limited. We observe a good agreement for TCS in freshwater (Pearson r=0.82, for 253 monitored values covering 12 streams), a moderate agreement in general, and a significant model underestimation for MeP in sediments. While most differences could be explained by uncertainty in both chemical/hydrological parameters (DT50 water , DT50 sediments , K oc , f oc , TSS) and monitoring sites (e.g. spatial/temporal design), the underestimation of MeP concentrations in sediments may involve potential natural sources. We illustrate the relevance of local evaluations for short-lived substances in fresh water (LAS, MeP), and their inadequacy for substances with longer half-lives (TCS, D5). This framework constitutes a milestone towards higher tier exposure modeling approaches for identifying areas of higher chemical concentration, and linking large-scale fate modeling with (sub) catchment-scale ecological scenarios; a major limitation in model accuracy comes from the discrepancy between streams routed on a gridded, 0.5°×0.5° global hydrological network and actual locations of streams and monitoring sites. Copyright © 2017 Elsevier B.V. All rights reserved.

Influence of ecohydrologic feedbacks from simulated crop growth on integrated regional hydrologic simulations under climate scenarios

NASA Astrophysics Data System (ADS)

van Walsum, P. E. V.; Supit, I.

2012-06-01

Hydrologic climate change modelling is hampered by climate-dependent model parameterizations. To reduce this dependency, we extended the regional hydrologic modelling framework SIMGRO to host a two-way coupling between the soil moisture model MetaSWAP and the crop growth simulation model WOFOST, accounting for ecohydrologic feedbacks in terms of radiation fraction that reaches the soil, crop coefficient, interception fraction of rainfall, interception storage capacity, and root zone depth. Except for the last, these feedbacks are dependent on the leaf area index (LAI). The influence of regional groundwater on crop growth is included via a coupling to MODFLOW. Two versions of the MetaSWAP-WOFOST coupling were set up: one with exogenous vegetation parameters, the "static" model, and one with endogenous crop growth simulation, the "dynamic" model. Parameterization of the static and dynamic models ensured that for the current climate the simulated long-term averages of actual evapotranspiration are the same for both models. Simulations were made for two climate scenarios and two crops: grass and potato. In the dynamic model, higher temperatures in a warm year under the current climate resulted in accelerated crop development, and in the case of potato a shorter growing season, thus partly avoiding the late summer heat. The static model has a higher potential transpiration; depending on the available soil moisture, this translates to a higher actual transpiration. This difference between static and dynamic models is enlarged by climate change in combination with higher CO2 concentrations. Including the dynamic crop simulation gives for potato (and other annual arable land crops) systematically higher effects on the predicted recharge change due to climate change. Crop yields from soils with poor water retention capacities strongly depend on capillary rise if moisture supply from other sources is limited. Thus, including a crop simulation model in an integrated hydrologic simulation provides a valuable addition for hydrologic modelling as well as for crop modelling.

Uncertainty in flood forecasting: A distributed modeling approach in a sparse data catchment

NASA Astrophysics Data System (ADS)

Mendoza, Pablo A.; McPhee, James; Vargas, Ximena

2012-09-01

Data scarcity has traditionally precluded the application of advanced hydrologic techniques in developing countries. In this paper, we evaluate the performance of a flood forecasting scheme in a sparsely monitored catchment based on distributed hydrologic modeling, discharge assimilation, and numerical weather predictions with explicit validation uncertainty analysis. For the hydrologic component of our framework, we apply TopNet to the Cautin River basin, located in southern Chile, using a fully distributed a priori parameterization based on both literature-suggested values and data gathered during field campaigns. Results obtained from this step indicate that the incremental effort spent in measuring directly a set of model parameters was insufficient to represent adequately the most relevant hydrologic processes related to spatiotemporal runoff patterns. Subsequent uncertainty validation performed over a six month ensemble simulation shows that streamflow uncertainty is better represented during flood events, due to both the increase of state perturbation introduced by rainfall and the flood-oriented calibration strategy adopted here. Results from different assimilation configurations suggest that the upper part of the basin is the major source of uncertainty in hydrologic process representation and hint at the usefulness of interpreting assimilation results in terms of model input and parameterization inadequacy. Furthermore, in this case study the violation of Markovian state properties by the Ensemble Kalman filter did affect the numerical results, showing that an explicit treatment of the time delay between the generation of surface runoff and the arrival at the basin outlet is required in the assimilation scheme. Peak flow forecasting results demonstrate that there is a major problem with the Weather Research and Forecasting model outputs, which systematically overestimate precipitation over the catchment. A final analysis performed for a large flooding event that occurred in July 2006 shows that, in the absence of bias introduced by an incorrect model calibration, the updating of both model states and meteorological forecasts contributes to a better representation of streamflow uncertainty and to better hydrologic forecasts.

Water Quality Assessment in the Vouga Catchment (Portugal) through the Integration of Hydrologic Modeling, Economic Valuation, and Optimization Methods

NASA Astrophysics Data System (ADS)

Hawtree, Daniel; Julich, Stefan; Rocha, João; Roebeling, Peter; Feger, Karl-Heinz

2016-04-01

Hydrologic model assessments of the impacts of land-cover / use change (LCLUC) are fundamental for the development of catchment management plans, which are increasingly needed for meeting water quality standards (i.e. Water Framework Directive). These assessments can be difficult to conduct at the spatial scale required for such plans, due to data limitations and the challenge of up-scaling from field / small scale studies to larger regions. Furthermore, such hydrologic assessments are of limited practical use if the financial impacts of any potential land-cover / management changes on local stakeholders are adequately quantified and taken into planning consideration. To address these challenges, this study presents an approach that integrates hydrologic modeling, economic valuation, and landscape optimization methods. This approach is applied to the Vouga catchment, a large (2,298 km^2) mixed land-use catchment in north-central Portugal. The Vouga has high nutrient (nitrogen and phosphorus) impacts in a number of reaches, which have negative impacts on downstream wetlands and groundwater supplies. To examine potential improvements to water quality, the Soil and Water Assessment Tool (SWAT) was calibrated over a five period (2002 - 2007) to establish the baseline hydrologic and nutrient fluxes. This calibration relies upon the up-scaling of findings from previous field studies (on vegetation and soils), hydrologic assessments, and modeling studies. The agricultural income for local stakeholders was estimated from existing land-cover and management approaches is made, to establish the baseline financial conditions. An optimization algorithm is then applied to the baseline scenario using both the biophysical and financial information, which seeks to determine various (most) optimal states. The preliminary results from this work are presented, and the advantages and challenges of using such an approach for scenario analysis for catchment management are discussed

A trait-based framework for stream algal communities.

PubMed

Lange, Katharina; Townsend, Colin Richard; Matthaei, Christoph David

2016-01-01

The use of trait-based approaches to detect effects of land use and climate change on terrestrial plant and aquatic phytoplankton communities is increasing, but such a framework is still needed for benthic stream algae. Here we present a conceptual framework of morphological, physiological, behavioural and life-history traits relating to resource acquisition and resistance to disturbance. We tested this approach by assessing the relationships between multiple anthropogenic stressors and algal traits at 43 stream sites. Our "natural experiment" was conducted along gradients of agricultural land-use intensity (0-95% of the catchment in high-producing pasture) and hydrological alteration (0-92% streamflow reduction resulting from water abstraction for irrigation) as well as related physicochemical variables (total nitrogen concentration and deposited fine sediment). Strategic choice of study sites meant that agricultural intensity and hydrological alteration were uncorrelated. We studied the relationships of seven traits (with 23 trait categories) to our environmental predictor variables using general linear models and an information-theoretic model-selection approach. Life form, nitrogen fixation and spore formation were key traits that showed the strongest relationships with environmental stressors. Overall, FI (farming intensity) exerted stronger effects on algal communities than hydrological alteration. The large-bodied, non-attached, filamentous algae that dominated under high farming intensities have limited dispersal abilities but may cope with unfavourable conditions through the formation of spores. Antagonistic interactions between FI and flow reduction were observed for some trait variables, whereas no interactions occurred for nitrogen concentration and fine sediment. Our conceptual framework was well supported by tests of ten specific hypotheses predicting effects of resource supply and disturbance on algal traits. Our study also shows that investigating a fairly comprehensive set of traits can help shed light on the drivers of algal community composition in situations where multiple stressors are operating. Further, to understand non-linear and non-additive effects of such drivers, communities need to be studied along multiple gradients of natural variation or anthropogenic stressors.

Situating Green Infrastructure in Context: A Framework for Adaptive Socio-Hydrology in Cities

NASA Astrophysics Data System (ADS)

Schifman, L. A.; Herrmann, D. L.; Shuster, W. D.; Ossola, A.; Garmestani, A.; Hopton, M. E.

2017-12-01

Management of urban hydrologic processes using green infrastructure (GI) has largely focused on storm water management. Thus, design and implementation of GI usually rely on physical site characteristics and local rainfall patterns, and do not typically account for human or social dimensions. This traditional approach leads to highly centralized storm water management in a disconnected urban landscape and can deemphasize additional benefits that GI offers, such as increased property value, greenspace aesthetics, heat island amelioration, carbon sequestration, and habitat for biodiversity. We propose the Framework for Adaptive Socio-Hydrology (FrASH) in which GI planning and implementation moves from a purely hydrology-driven perspective to an integrated sociohydrological approach. This allows for an iterative, multifaceted decision-making process that would enable a network of stakeholders to collaboratively set a dynamic, context-guided project plan for the installation of GI, rather than a "one-size-fits-all" installation. We explain how different sectors (e.g., governance, nongovernmental organizations, communities, academia, and industry) can create a connected network of organizations that work toward a common goal. Through a graphical Chambered Nautilus model, FrASH is experimentally applied to contrasting GI case studies and shows that this multistakeholder, connected, decentralized network with a coevolving decision-making project plan results in enhanced multifunctionality, potentially allowing for the management of resilience in urban systems at multiple scales.

Cross-entropy clustering framework for catchment classification

NASA Astrophysics Data System (ADS)

Tongal, Hakan; Sivakumar, Bellie

2017-09-01

There is an increasing interest in catchment classification and regionalization in hydrology, as they are useful for identification of appropriate model complexity and transfer of information from gauged catchments to ungauged ones, among others. This study introduces a nonlinear cross-entropy clustering (CEC) method for classification of catchments. The method specifically considers embedding dimension (m), sample entropy (SampEn), and coefficient of variation (CV) to represent dimensionality, complexity, and variability of the time series, respectively. The method is applied to daily streamflow time series from 217 gauging stations across Australia. The results suggest that a combination of linear and nonlinear parameters (i.e. m, SampEn, and CV), representing different aspects of the underlying dynamics of streamflows, could be useful for determining distinct patterns of flow generation mechanisms within a nonlinear clustering framework. For the 217 streamflow time series, nine hydrologically homogeneous clusters that have distinct patterns of flow regime characteristics and specific dominant hydrological attributes with different climatic features are obtained. Comparison of the results with those obtained using the widely employed k-means clustering method (which results in five clusters, with the loss of some information about the features of the clusters) suggests the superiority of the cross-entropy clustering method. The outcomes from this study provide a useful guideline for employing the nonlinear dynamic approaches based on hydrologic signatures and for gaining an improved understanding of streamflow variability at a large scale.

From Sub-basin to Grid Scale Soil Moisture Disaggregation in SMART, A Semi-distributed Hydrologic Modeling Framework

NASA Astrophysics Data System (ADS)

Ajami, H.; Sharma, A.

2016-12-01

A computationally efficient, semi-distributed hydrologic modeling framework is developed to simulate water balance at a catchment scale. The Soil Moisture and Runoff simulation Toolkit (SMART) is based upon the delineation of contiguous and topologically connected Hydrologic Response Units (HRUs). In SMART, HRUs are delineated using thresholds obtained from topographic and geomorphic analysis of a catchment, and simulation elements are distributed cross sections or equivalent cross sections (ECS) delineated in first order sub-basins. ECSs are formulated by aggregating topographic and physiographic properties of the part or entire first order sub-basins to further reduce computational time in SMART. Previous investigations using SMART have shown that temporal dynamics of soil moisture are well captured at a HRU level using the ECS delineation approach. However, spatial variability of soil moisture within a given HRU is ignored. Here, we examined a number of disaggregation schemes for soil moisture distribution in each HRU. The disaggregation schemes are either based on topographic based indices or a covariance matrix obtained from distributed soil moisture simulations. To assess the performance of the disaggregation schemes, soil moisture simulations from an integrated land surface-groundwater model, ParFlow.CLM in Baldry sub-catchment, Australia are used. ParFlow is a variably saturated sub-surface flow model that is coupled to the Common Land Model (CLM). Our results illustrate that the statistical disaggregation scheme performs better than the methods based on topographic data in approximating soil moisture distribution at a 60m scale. Moreover, the statistical disaggregation scheme maintains temporal correlation of simulated daily soil moisture while preserves the mean sub-basin soil moisture. Future work is focused on assessing the performance of this scheme in catchments with various topographic and climate settings.

Informing a hydrological model of the Ogooué with multi-mission remote sensing data

NASA Astrophysics Data System (ADS)

Kittel, Cecile M. M.; Nielsen, Karina; Tøttrup, Christian; Bauer-Gottwein, Peter

2018-02-01

Remote sensing provides a unique opportunity to inform and constrain a hydrological model and to increase its value as a decision-support tool. In this study, we applied a multi-mission approach to force, calibrate and validate a hydrological model of the ungauged Ogooué river basin in Africa with publicly available and free remote sensing observations. We used a rainfall-runoff model based on the Budyko framework coupled with a Muskingum routing approach. We parametrized the model using the Shuttle Radar Topography Mission digital elevation model (SRTM DEM) and forced it using precipitation from two satellite-based rainfall estimates, FEWS-RFE (Famine Early Warning System rainfall estimate) and the Tropical Rainfall Measuring Mission (TRMM) 3B42 v.7, and temperature from ECMWF ERA-Interim. We combined three different datasets to calibrate the model using an aggregated objective function with contributions from (1) historical in situ discharge observations from the period 1953-1984 at six locations in the basin, (2) radar altimetry measurements of river stages by Envisat and Jason-2 at 12 locations in the basin and (3) GRACE (Gravity Recovery and Climate Experiment) total water storage change (TWSC). Additionally, we extracted CryoSat-2 observations throughout the basin using a Sentinel-1 SAR (synthetic aperture radar) imagery water mask and used the observations for validation of the model. The use of new satellite missions, including Sentinel-1 and CryoSat-2, increased the spatial characterization of river stage. Throughout the basin, we achieved good agreement between observed and simulated discharge and the river stage, with an RMSD between simulated and observed water amplitudes at virtual stations of 0.74 m for the TRMM-forced model and 0.87 m for the FEWS-RFE-forced model. The hydrological model also captures overall total water storage change patterns, although the amplitude of storage change is generally underestimated. By combining hydrological modeling with multi-mission remote sensing from 10 different satellite missions, we obtain new information on an otherwise unstudied basin. The proposed model is the best current baseline characterization of hydrological conditions in the Ogooué in light of the available observations.

A new process sensitivity index to identify important system processes under process model and parametric uncertainty

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

Dai, Heng; Ye, Ming; Walker, Anthony P.

Hydrological models are always composed of multiple components that represent processes key to intended model applications. When a process can be simulated by multiple conceptual-mathematical models (process models), model uncertainty in representing the process arises. While global sensitivity analysis methods have been widely used for identifying important processes in hydrologic modeling, the existing methods consider only parametric uncertainty but ignore the model uncertainty for process representation. To address this problem, this study develops a new method to probe multimodel process sensitivity by integrating the model averaging methods into the framework of variance-based global sensitivity analysis, given that the model averagingmore » methods quantify both parametric and model uncertainty. A new process sensitivity index is derived as a metric of relative process importance, and the index includes variance in model outputs caused by uncertainty in both process models and model parameters. For demonstration, the new index is used to evaluate the processes of recharge and geology in a synthetic study of groundwater reactive transport modeling. The recharge process is simulated by two models that converting precipitation to recharge, and the geology process is also simulated by two models of different parameterizations of hydraulic conductivity; each process model has its own random parameters. The new process sensitivity index is mathematically general, and can be applied to a wide range of problems in hydrology and beyond.« less

Coupled hydrological, ecological, decision and economic models for monetary valuation of riparian ecosystem services

NASA Astrophysics Data System (ADS)

Goodrich, D. C.; Brookshire, D.; Broadbent, C.; Dixon, M. D.; Brand, L. A.; Thacher, J.; Benedict, K. K.; Lansey, K. E.; Stromberg, J. C.; Stewart, S.; McIntosh, M.

2011-12-01

Water is a critical component for sustaining both natural and human systems. Yet the value of water for sustaining ecosystem services is not well quantified in monetary terms. Ideally decisions involving water resource management would include an apples-to-apples comparison of the costs and benefits in dollars of both market and non-market goods and services - human and ecosystem. To quantify the value of non-market ecosystem services, scientifically defensible relationships must be developed that link the effect of a decision (e.g. human growth) to the change in ecosystem attributes from current conditions. It is this linkage that requires the "poly-disciplinary" coupling of knowledge and models from the behavioral, physical, and ecological sciences. In our experience another key component of making this successful linkage is development of a strong poly-disciplinary scientific team that can readily communicate complex disciplinary knowledge to non-specialists outside their own discipline. The time to build such a team that communicates well and has a strong sense of trust should not be underestimated. The research described in the presentation incorporated hydrologic, vegetation, avian, economic, and decision models into an integrated framework to determine the value of changes in ecological systems that result from changes in human water use. We developed a hydro-bio-economic framework for the San Pedro River Region in Arizona that considers groundwater, stream flow, and riparian vegetation, as well as abundance, diversity, and distribution of birds. In addition, we developed a similar framework for the Middle Rio Grande of New Mexico. There are six research components for this project: (1) decision support and scenario specification, (2) regional groundwater model, (3) the riparian vegetation model, (4) the avian model, (5) methods for displaying the information gradients in the valuation survey instruments (Choice Modeling and Contingent Valuation), and (6) the economic framework. Our modeling framework began with the identification of factors that influence spatial and temporal changes in riparian vegetation on the two rivers. The linked modeling framework was then employed for making spatial predictions of the changes in presence of surface water, vegetation change, and avian populations in both river systems. An overview of the overall project will be provided, with lessons learned, and initial valuation survey results.

Synergetic Use of Sentinel-1 and 2 to Improve Agro-Hydrological Modeling

NASA Astrophysics Data System (ADS)

Ferrant, Sylvain; Kerr, Yann; Al-Bitar, Ahmad; Le Page, Michel; Selles, Adrien; Mermoz, Stephane; Bouvet, Alexandre; Marechal, Jean-Christophe; Tomer, Sat; Sekhar, Muddu; Dedieu, Gerard; Le Toan, Thuy; Bustillo, Vincent

2016-08-01

In the context of global changes and population growth, agricultural activities are a growing factor influencing water resources availability in term of quantity and quality. Water management strategies have to be analyzed at a regional catchment scales. Yet, agricultural practices, crop water and nutrient consumption that drive the main water and nutrient fluxes at the catchment scale have to be monitored at a high spatial (crop extension) and temporal resolution (crop growth period). This proceeding describes some advances in the framework of a co-funded ESA Living Planet Fellowship project, called ―agro-hydrology from space‖, which aims at demonstrating the improvement brought by synergetic observations of Sentinel-1 (S1) and Sentinel-2 (S2) satellite mission in agro- hydrological studies. Geo-information time-series of vegetation and water index with multi-spectral optical detection S2 together with surface roughness time series with C-band radar detection S1 are used to re-set soil water holding capacity parameters (depth, porosity) and agricultural practices (sowing date, irrigated area extent) of a crop model coupled with a hydrological model in two contrasted water management issues: stream water nitrate pollution in Gascogne region in south-west of France and groundwater depletion and shortages for irrigation in Deccan Plateau, in south-India.

Mitigating Agricultural Diffuse Pollution: Learning from The River Eden Demonstration Test Catchment Experiments

NASA Astrophysics Data System (ADS)

Reaney, S. M.; Barker, P. A.; Haygarth, P.; Quinn, P. F.; Aftab, A.; Barber, N.; Burke, S.; Cleasby, W.; Jonczyk, J. C.; Owen, G. J.; Perks, M. T.; Snell, M. A.; Surridge, B.

2016-12-01

Freshwater systems continue to fail to achieve their ecological potential and provide associated ecological services due to poor water quality. A key driver of the failure to achieve good status under the EU Water Framework Directive derives from non-point (diffuse) pollution of sediment, phosphorus and nitrogen from agricultural landscapes. While many mitigation options exist, a framework is lacking which provides a holistic understanding of the impact of mitigation scheme design on catchment function and agronomics. The River Eden Demonstration Test Catchment project (2009-2017) in NW England uses an interdisciplinary approach including catchment hydrology, sediment-nutrient fluxes and farmer attitudes, to understand ecological function and diffuse pollution mitigation feature performance. Water flow (both surface and groundwater) and quality monitoring focused on three ca. 10km2 catchments with N and P measurements every 30 minutes. Ecological status was determined by monthly diatom community analysis and supplemented by macrophyte, macroinvertebrate and fish surveys. Changes in erosion potential and hydrological connectivity were monitored using extensive Landsat images and detailed UAV monitoring. Simulation modelling work utilised hydrological simulation models (CRAFT, CRUM3 and HBV-Light) and SCIMAP based risk mapping. Farmer behaviour and attitudes have been assessed with surveys, interviews and diaries. A suite of mitigation features have been installed including changes to land management - e.g. aeriation, storage features within a `treatment train', riparian fencing and woodland creation. A detailed dataset of the integrated catchment hydrological, water quality and ecological behaviour over multiple years, including a drought period and an extreme rainfall event, highlights the interaction between ecology, hydrological and nutrient dynamics that are driven by sediment and nutrients exported within a small number of high magnitude storm events. Hence these high-resolution processes must be studied in conjunction, rather than in isolation, to understand system dynamics and critically to evaluate effective mitigation schemes.

Enhanced hydrological extremes in the western United States under global warming through the lens of water vapor wave activity

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

Lu, Jian; Xue, Daokai; Gao, Yang

Understanding how regional hydrological extremes would respond to warming is a grand challenge to the community of climate change research. To address this challenge, we construct an analysis framework based on column integrated water vapor (CWV) wave activity to diagnose the wave component of the hydrological cycle that contributes to hydrological extremes. By applying the analysis to the historical and future climate projections from the CMIP5 models, we found that the wet-versus-dry disparity of daily net precipitation along a zonal band can increase at a super Clausius-Clapeyron rate due to the enhanced stirring length of wave activity at the polewardmore » flank of the mean storm track. The local variant of CWV wave activity reveals the unique characteristics of atmospheric rivers (ARs) in terms of their transport function, enhanced mixing and hydrological cycling rate (HC). Under RCP8.5, the local moist wave activity increases by ~40% over the northeastern Pacific by the end of the 21st century, indicating more ARs hitting the west coast, giving rise to a ~20% increase in the related hydrological extremes - $ despite a weakening of the local HC.« less

Modeling the hydrological and mechanical effect of roots on shallow landslides

NASA Astrophysics Data System (ADS)

Arnone, E.; Caracciolo, D.; Noto, L. V.; Preti, F.; Bras, R. L.

2016-11-01

This study proposes a new methodology for estimating the additional shear strength (or cohesion) exerted by vegetation roots on slope stability analysis within a coupled hydrological-stability model. The mechanical root cohesion is estimated within a Fiber Bundle Model framework that allows for the evaluation of the root strength as a function of stress-strain relationships of populations of fibers. The use of such model requires the knowledge of the root architecture. A branching topology model based on Leonardo's rule is developed, providing an estimation of the amount of roots and the distribution of diameters with depth. The proposed methodology has been implemented into an existing distributed hydrological-stability model able to simulate the dynamics of factor of safety as a function of soil moisture dynamics. The model also accounts for the hydrological effects of vegetation, which reduces soil water content via root water uptake, thus increasing the stability. The entire methodology has been tested in a synthetic hillslope with two configurations of vegetation type, i.e., trees and shrubs, which have been compared to a configuration without vegetation. The vegetation has been characterized using roots data of two mediterranean plant species. The results demonstrate the capabilities of the topological model in accurately reproducing the observed root structure of the analyzed species. For the environmental setting modeled, the effects of root uptake might be more significant than the mechanical reinforcement; the additional resistance depends strictly on the vegetation root depth. Finally, for the simulated climatic environment, landslides are seasonal, in agreement with past observations.

Applicability of Hydrologic Landscapes for Model Calibration ...

EPA Pesticide Factsheets

The Pacific Northwest Hydrologic Landscapes (PNW HL) at the assessment unit scale has provided a solid conceptual classification framework to relate and transfer hydrologically meaningful information between watersheds without access to streamflow time series. A collection of techniques were applied to the HL assessment unit composition in watersheds across the Pacific Northwest to aggregate the hydrologic behavior of the Hydrologic Landscapes from the assessment unit scale to the watershed scale. This non-trivial solution both emphasizes HL classifications within the watershed that provide that majority of moisture surplus/deficit and considers the relative position (upstream vs. downstream) of these HL classifications. A clustering algorithm was applied to the HL-based characterization of assessment units within 185 watersheds to help organize watersheds into nine classes hypothesized to have similar hydrologic behavior. The HL-based classes were used to organize and describe hydrologic behavior information about watershed classes and both predictions and validations were independently performed with regard to the general magnitude of six hydroclimatic signature values. A second cluster analysis was then performed using the independently calculated signature values as similarity metrics, and it was found that the six signature clusters showed substantial overlap in watershed class membership to those in the HL-based classes. One hypothesis set forward from thi

A Physically-Based and Distributed Tool for Modeling the Hydrological and Mechanical Processes of Shallow Landslides

NASA Astrophysics Data System (ADS)

Arnone, E.; Noto, L. V.; Dialynas, Y. G.; Caracciolo, D.; Bras, R. L.

2015-12-01

This work presents the capabilities of a model, i.e. the tRIBS-VEGGIE-Landslide, in two different versions, i.e. developed within a probabilistic framework and coupled with a root cohesion module. The probabilistic model treats geotechnical and soil retention curve parameters as random variables across the basin and estimates theoretical probability distributions of slope stability and the associated "factor of safety" commonly used to describe the occurrence of shallow landslides. The derived distributions are used to obtain the spatio-temporal dynamics of probability of failure, conditioned on soil moisture dynamics at each watershed location. The framework has been tested in the Luquillo Experimental Forest (Puerto Rico) where shallow landslides are common. In particular, the methodology was used to evaluate how the spatial and temporal patterns of precipitation, whose variability is significant over the basin, affect the distribution of probability of failure. Another version of the model accounts for the additional cohesion exerted by vegetation roots. The approach is to use the Fiber Bundle Model (FBM) framework that allows for the evaluation of the root strength as a function of the stress-strain relationships of bundles of fibers. The model requires the knowledge of the root architecture to evaluate the additional reinforcement from each root diameter class. The root architecture is represented with a branching topology model based on Leonardo's rule. The methodology has been tested on a simple case study to explore the role of both hydrological and mechanical root effects. Results demonstrate that the effects of root water uptake can at times be more significant than the mechanical reinforcement; and that the additional resistance provided by roots depends heavily on the vegetation root structure and length.

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.

Development of a methodology to assess future trends in low flows at the watershed scale using solely climate data

NASA Astrophysics Data System (ADS)

Foulon, Étienne; Rousseau, Alain N.; Gagnon, Patrick

2018-02-01

Low flow conditions are governed by short-to-medium term weather conditions or long term climate conditions. This prompts the question: given climate scenarios, is it possible to assess future extreme low flow conditions from climate data indices (CDIs)? Or should we rely on the conventional approach of using outputs of climate models as inputs to a hydrological model? Several CDIs were computed using 42 climate scenarios over the years 1961-2100 for two watersheds located in Québec, Canada. The relationship between the CDIs and hydrological data indices (HDIs; 7- and 30-day low flows for two hydrological seasons) were examined through correlation analysis to identify the indices governing low flows. Results of the Mann-Kendall test, with a modification for autocorrelated data, clearly identified trends. A partial correlation analysis allowed attributing the observed trends in HDIs to trends in specific CDIs. Furthermore, results showed that, even during the spatial validation process, the methodological framework was able to assess trends in low flow series from: (i) trends in the effective drought index (EDI) computed from rainfall plus snowmelt minus PET amounts over ten to twelve months of the hydrological snow cover season or (ii) the cumulative difference between rainfall and potential evapotranspiration over five months of the snow free season. For 80% of the climate scenarios, trends in HDIs were successfully attributed to trends in CDIs. Overall, this paper introduces an efficient methodological framework to assess future trends in low flows given climate scenarios. The outcome may prove useful to municipalities concerned with source water management under changing climate conditions.

Living on the edge: Flood risks to societies Balázs M. Fekete, Shahabeddin Afshari Tork and Charles J. Vörösmarty

NASA Astrophysics Data System (ADS)

Fekete, B. M.; Afshari Tork, S.; Vorosmarty, C. J.

2015-12-01

Characterizing hydrological extreme events and assessing their societal impacts is perpetual challenge for hydrologists. Climate models predict that anticipated temperature rise leads to an intensification of the hydrological cycle and to a corresponding increase in the reoccurrence and the severity of extreme events. The societal impact of the hydrological extremes are interlinked with anthropogenic activities therefore the damages to manmade infrastructures are rarely a good measure of the extreme events' magnitudes. Extreme events are rare by definition therefore detecting change in their distributions requires long-term observational records. Currently, only in-situ monitoring time series has the temporal extent necessary for assessing the reoccurrence probabilities of extreme events, but they frequently lack the spatial coverage. Satellite remote sensing is often advocated to provide the required spatial coverage, but satellites have to compromise between spatial and temporal resolutions. Furthermore, the retrieval algorithms are often as complex as comparable hydrological models with similar degree of uncertainties in their parameterization and the validity of the final data products. In addition, anticipated changes over time in the reoccurrence frequencies of extreme events invalidates the stationarity assumption, which is the basis for using past observations to predict the probabilities future extreme events. Probably the best approach to provide more robust predictions of extreme events is the integration of the available data (in-situ and remote sensing) in a comprehensive data assimilation frameworks built on top of adequate hydrological modeling platforms. Our presentation will provide an overview of the current state of hydrological models to support data assimilations and the viable pathways to integrate in-situ and remote sensing observations for flood predictions. We will demonstrate the use of socio-economic data in combination with hydrological data assimilation to assess the resiliency to extreme flood events.

Hydrologic and water-quality rehabilitation of environments for suitable fish habitat

NASA Astrophysics Data System (ADS)

Zhao, C. S.; Yang, S. T.; Xiang, H.; Liu, C. M.; Zhang, H. T.; Yang, Z. L.; Zhang, Y.; Sun, Y.; Mitrovic, S. M.; Yu, Q.; Lim, R. P.

2015-11-01

Aquatic ecological rehabilitation is attracting increasing public and research attention, but without knowledge of the responses of aquatic species to their habitats the success of habitat restoration is uncertain. Thus efficient study of species response to habitat, through which to prioritize the habitat factors influencing aquatic ecosystems, is highly important. However many current models have too high requirement for assemblage information and have great bias in results due to consideration of only the species' attribute of presence/absence, abundance or biomass, thus hindering the wider utility of these models. This paper, using fish as a case, presents a framework for identification of high-priority habitat factors based on the responses of aquatic species to their habitats, using presence/absence, abundance and biomass data. This framework consists of four newly developed sub-models aiming to determine weightings for the evaluation of species' contributions to their communities, to quantitatively calculate an integrated habitat suitability index for multi-species based on habitat factors, to assess the suitable probability of habitat factors and to assess the rehabilitation priority of habitat factors. The framework closely links hydrologic, physical and chemical habitat factors to fish assemblage attributes drawn from monitoring datasets on hydrology, water quality and fish assemblages at a total of 144 sites, where 5084 fish were sampled and tested. Breakpoint identification techniques based on curvature in cumulated dominance along with a newly developed weighting calculation model based on theory of mass systems were used to help identify the dominant fish, based on which the presence and abundance of multiple fish were normalized to estimate the integrated habitat suitability index along gradients of various factors, based on their variation with principal habitat factors. Then, the appropriate probability of every principal habitat factor was estimated and graded, and the priority of habitat factors for rehabilitation was determined. Application of the model to Jinan City, a pilot city for the construction of a civilized and ecological city in China, proved effective, revealing that carbonate is the poorest habitat factor and has the highest priority for ecological rehabilitation. This was tested using two methods: alternative priority models and a dataset of all habitat factors in place of only the principal habitat factors. We also found that hydrological factors have higher priority than the water quality factors at the levels of both the whole city and its subordinate eco-regions and therefore that hydrological factors deserve special attention in the future ecosystem rehabilitation. Further, the current habitat state makes nearly half of the habitats in Jinan City undesirable for fish communities, largely due to long-term agricultural practices. Spatially, rivers in the mountainous region south of Jinan city and adjacent to the urban area and rivers in the agricultural region north of the city should be emphasized in future habitat rehabilitation. All of these findings have substantial ramifications for the rehabilitation of aquatic ecosystems in Jinan City as a reference for river ecological remediation in rivers with scarce ecological data worldwide.

Quantification of uncertainties related to the regional application of a conceptual hydrological model in Benin (West Africa)

NASA Astrophysics Data System (ADS)

Bormann, H.; Diekkrüger, B.

2003-04-01

A conceptual model is presented to simulate the water fluxes of regional catchments in Benin (West Africa). The model is applied in the framework of the IMPETUS project (an integrated approach to the efficient management of scarce water resources in West Africa) which aims to assess the effects of environmental and anthropogenic changes on the regional hydrological processes and on the water availability in Benin. In order to assess the effects of decreasing precipitation and increasing human activities on the hydrological processes in the upper Ouémé valley, a scenario analysis is performed to predict possible changes. Therefore a regional hydrological model is proposed which reproduces the recent hydrological processes, and which is able to consider the changes of landscape properties.The study presented aims to check the validity of the conceptual and lumped model under the conditions of the subhumid tree savannah and therefore analyses the importance of possible sources of uncertainty. Main focus is set on the uncertainties caused by input data, model parameters and model structure. As the model simulates the water fluxes at the catchment outlet of the Térou river (3133 km2) in a sufficient quality, first results of a scenario analysis are presented. Changes of interest are the expected future decrease in amount and temporal structure of the precipitation (e.g. minus X percent precipitation during the whole season versus minus X percent precipitation in the end of the rainy season, alternatively), the decrease in soil water storage capacity which is caused by erosion, and the increasing consumption of ground water for drinking water and agricultural purposes. Resuming from the results obtained, the perspectives of lumped and conceptual models are discussed with special regard to available management options of this kind of models. Advantages and disadvantages compared to alternative model approaches (process based, physics based) are discussed.

Achieving Sustainability in a Semi-Arid Basin in Northwest Mexico through an Integrated Hydrologic-Economic-Institutional Model

NASA Astrophysics Data System (ADS)

Munoz-Hernandez, A.; Mayer, A. S.

2008-12-01

The hydrologic systems in Northwest Mexico are at risk of over exploitation due to poor management of the water resources and adverse climatic conditions. The purpose of this work is to create and Integrated Hydrologic-Economic-Institutional Model to support future development in the Yaqui River basin, well known by its agricultural productivity, by directing the water management practices toward sustainability. The Yaqui River basin is a semi-arid basin with an area of 72,000 square kilometers and an average precipitation of 527 mm per year. The primary user of water is agriculture followed by domestic use and industry. The water to meet user demands comes from three reservoirs constructed, in series, along the river. The main objective of the integrated simulation-optimization model is to maximize the economic benefit within the basin, subject to physical and environmental constraints. Decision variables include the water allocation to major users and reservoirs as well as aquifer releases. Economic and hydrologic (including the interaction of the surface water and groundwater) simulation models were both included in the integrated model. The surface water model refers to a rainfall-runoff model created, calibrated, and incorporated into a MATLAB code that estimates the monthly storage in the main reservoirs by solving a water balance. The rainfall-runoff model was coupled with a groundwater model of the Yaqui Valley which was previously developed (Addams, 2004). This model includes flow in the main canals and infiltration to the aquifer. The economic benefit of water for some activities such as agricultural use, domestic use, hydropower generation, and environmental value was determined. Sensitivity analysis was explored for those parameters that are not certain such as price elasticities or population growth. Different water allocation schemes were created based on climate change, climate variability, and socio-economic scenarios. Addams L. 2004. Water resource policy evaluation using a combined hydrologic-economic-agronomic modeling framework: Yaqui Valley, Sonora, Mexico. Ph.D.dissertation, Stanford University.

Using snow data assimilation to improve ensemble streamflow forecasting for the Upper Colorado River Basin

NASA Astrophysics Data System (ADS)

Micheletty, P. D.; Perrot, D.; Day, G. N.; Lhotak, J.; Quebbeman, J.; Park, G. H.; Carney, S.

2017-12-01

Water supply forecasting in the western United States is inextricably linked to snowmelt processes, as approximately 70-85% of total annual runoff comes from water stored in seasonal mountain snowpacks. Snowmelt-generated streamflow is vital to a variety of downstream uses; the Upper Colorado River Basin (UCRB) alone provides water supply for 25 million people, irrigation water for 3.5 million acres, and drives hydropower generation at Lake Powell. April-July water supply forecasts produced by the National Weather Service (NWS) Colorado Basin River Forecast Center (CBRFC) are critical to basin water management. The primary objective of this project as part of the NASA Water Resources Applied Science Program, is to improve water supply forecasting for the UCRB by assimilating satellite and ground snowpack observations into a distributed hydrologic model at various times during the snow accumulation and melt seasons. To do this, we have built a framework that uses an Ensemble Kalman Filter (EnKF) to update modeled snow water equivalent (SWE) states in the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) with spatially interpolated SNOTEL snow water equivalent (SWE) observations and products from the MODIS Snow Covered-Area and Grain size retrieval algorithm (when available). We have generated April-July water supply reforecasts for a 20-year period (1991-2010) for several headwater catchments in the UCRB using HL-RDHM and snow data assimilation in the Ensemble Streamflow Prediction (ESP) framework. The existing CBRFC ESP reforecasts will provide a baseline for comparison to determine whether the data assimilation process adds skill to the water supply forecasts. Preliminary results from one headwater basin show improved skill in water supply forecasting when HL-RDHM is run with the data assimilation step compared to HL-RDHM run without the data assimilation step, particularly in years when MODSCAG data were available (2000-2010). The final forecasting framework developed during this project will be delivered to CBRFC and run operationally for a set of pilot basins.

Large-scale hydrological simulations using the soil water assessment tool, protocol development, and application in the danube basin.

PubMed

Pagliero, Liliana; Bouraoui, Fayçal; Willems, Patrick; Diels, Jan

2014-01-01

The Water Framework Directive of the European Union requires member states to achieve good ecological status of all water bodies. A harmonized pan-European assessment of water resources availability and quality, as affected by various management options, is necessary for a successful implementation of European environmental legislation. In this context, we developed a methodology to predict surface water flow at the pan-European scale using available datasets. Among the hydrological models available, the Soil Water Assessment Tool was selected because its characteristics make it suitable for large-scale applications with limited data requirements. This paper presents the results for the Danube pilot basin. The Danube Basin is one of the largest European watersheds, covering approximately 803,000 km and portions of 14 countries. The modeling data used included land use and management information, a detailed soil parameters map, and high-resolution climate data. The Danube Basin was divided into 4663 subwatersheds of an average size of 179 km. A modeling protocol is proposed to cope with the problems of hydrological regionalization from gauged to ungauged watersheds and overparameterization and identifiability, which are usually present during calibration. The protocol involves a cluster analysis for the determination of hydrological regions and multiobjective calibration using a combination of manual and automated calibration. The proposed protocol was successfully implemented, with the modeled discharges capturing well the overall hydrological behavior of the basin. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

Assimilation of satellite altimetry data in hydrological models for improved inland surface water information: Case studies from the "Sentinel-3 Hydrologic Altimetry Processor prototypE" project (SHAPE)

NASA Astrophysics Data System (ADS)

Gustafsson, David; Pimentel, Rafael; Fabry, Pierre; Bercher, Nicolas; Roca, Mónica; Garcia-Mondejar, Albert; Fernandes, Joana; Lázaro, Clara; Ambrózio, Américo; Restano, Marco; Benveniste, Jérôme

2017-04-01

This communication is about the Sentinel-3 Hydrologic Altimetry Processor prototypE (SHAPE) project, with a focus on the components dealing with assimilation of satellite altimetry data into hydrological models. The SHAPE research and development project started in September 2015, within the Scientific Exploitation of Operational Missions (SEOM) programme of the European Space Agency. The objectives of the project are to further develop and assess recent improvement in altimetry data, processing algorithms and methods for assimilation in hydrological models, with the overarching goal to support improved scientific use of altimetry data and improved inland water information. The objective is also to take scientific steps towards a future Inland Water dedicated processor on the Sentinel-3 ground segment. The study focuses on three main variables of interest in hydrology: river stage, river discharge and lake level. The improved altimetry data from the project is used to estimate river stage, river discharge and lake level information in a data assimilation framework using the hydrological dynamic and semi-distributed model HYPE (Hydrological Predictions for the Environment). This model has been developed by SMHI and includes data assimilation module based on the Ensemble Kalman filter method. The method will be developed and assessed for a number of case studies with available in situ reference data and satellite altimetry data based on mainly the CryoSat-2 mission on which the new processor will be run; Results will be presented from case studies on the Amazon and Danube rivers and Lake Vänern (Sweden). The production of alti-hydro products (water level time series) are improved thanks to the use of water masks. This eases the geo-selection of the CryoSat-2 altimetric measurements since there are acquired from a geodetic orbit and are thus spread along the river course in space and and time. The specific processing of data from this geodetic orbit space-time pattern will be discussed as well as the subsequent possible strategies for data assimilation into models (and eventually highlight a generalized approach toward multi-mission data processing). Notably, in case of data assimilation along the course of rivers, the river slope might be estimated and compensated for, in order to produce local water level "pseudo time series" at arbitrary locations, and specifically at model's inlets.

Building an ensemble of climate scenarios for decision-making in hydrology: benefits, pitfalls and uncertainties

NASA Astrophysics Data System (ADS)

Braun, Marco; Chaumont, Diane

2013-04-01

Using climate model output to explore climate change impacts on hydrology requires several considerations, choices and methods in the post treatment of the datasets. In the effort of producing a comprehensive data base of climate change scenarios for over 300 watersheds in the Canadian province of Québec, a selection of state of the art procedures were applied to an ensemble comprising 87 climate simulations. The climate data ensemble is based on global climate simulations from the Coupled Model Intercomparison Project - Phase 3 (CMIP3) and regional climate simulations from the North American Regional Climate Change Assessment Program (NARCCAP) and operational simulations produced at Ouranos. Information on the response of hydrological systems to changing climate conditions can be derived by linking climate simulations with hydrological models. However, the direct use of raw climate model output variables as drivers for hydrological models is limited by issues such as spatial resolution and the calibration of hydro models with observations. Methods for downscaling and bias correcting the data are required to achieve seamless integration of climate simulations with hydro models. The effects on the results of four different approaches to data post processing were explored and compared. We present the lessons learned from building the largest data base yet for multiple stakeholders in the hydro power and water management sector in Québec putting an emphasis on the benefits and pitfalls in choosing simulations, extracting the data, performing bias corrections and documenting the results. A discussion of the sources and significance of uncertainties in the data will also be included. The climatological data base was subsequently used by the state owned hydro power company Hydro-Québec and the Centre d'expertise hydrique du Québec (CEHQ), the provincial water authority, to simulate future stream flows and analyse the impacts on hydrological indicators. While this submission focuses on the production of climatic scenarios for application in hydrology, the submission « The (cQ)2 project: assessing watershed scale hydrological changes for the province of Québec at the 2050 horizon, a collaborative framework » by Catherine Guay describes how Hydro-Québec and CEHQ put the data into use.

Framework for a U.S. Geological Survey Hydrologic Climate-Response Program in Maine

USGS Publications Warehouse

Hodgkins, Glenn A.; Lent, Robert M.; Dudley, Robert W.; Schalk, Charles W.

2009-01-01

This report presents a framework for a U.S. Geological Survey (USGS) hydrologic climate-response program designed to provide early warning of changes in the seasonal water cycle of Maine. Climate-related hydrologic changes on Maine's rivers and lakes in the winter and spring during the last century are well documented, and several river and lake variables have been shown to be sensitive to air-temperature changes. Monitoring of relevant hydrologic data would provide important baseline information against which future climate change can be measured. The framework of the hydrologic climate-response program presented here consists of four major parts: (1) identifying homogeneous climate-response regions; (2) identifying hydrologic components and key variables of those components that would be included in a hydrologic climate-response data network - as an example, streamflow has been identified as a primary component, with a key variable of streamflow being winter-spring streamflow timing; the data network would be created by maintaining existing USGS data-collection stations and establishing new ones to fill data gaps; (3) regularly updating historical trends of hydrologic data network variables; and (4) establishing basins for process-based studies. Components proposed for inclusion in the hydrologic climate-response data network have at least one key variable for which substantial historical data are available. The proposed components are streamflow, lake ice, river ice, snowpack, and groundwater. The proposed key variables of each component have extensive historical data at multiple sites and are expected to be responsive to climate change in the next few decades. These variables are also important for human water use and (or) ecosystem function. Maine would be divided into seven climate-response regions that follow major river-basin boundaries (basins subdivided to hydrologic units with 8-digit codes or larger) and have relatively homogeneous climates. Key hydrologic variables within each climate-response region would be analyzed regularly to maintain up-to-date analyses of year-to-year variability, decadal variability, and longer term trends. Finally, one basin in each climate-response region would be identified for process-based hydrologic and ecological studies.

Evapotranspiration and runoff from large land areas: Land surface hydrology for atmospheric general circulation models

NASA Technical Reports Server (NTRS)

Famiglietti, J. S.; Wood, Eric F.

1993-01-01

A land surface hydrology parameterization for use in atmospheric GCM's is presented. The parameterization incorporates subgrid scale variability in topography, soils, soil moisture and precipitation. The framework of the model is the statistical distribution of a topography-soils index, which controls the local water balance fluxes, and is therefore taken to represent the large land area. Spatially variable water balance fluxes are integrated with respect to the topography-soils index to yield our large topography-soils distribution, and interval responses are weighted by the probability of occurrence of the interval. Grid square averaged land surface fluxes result. The model functions independently as a macroscale water balance model. Runoff ratio and evapotranspiration efficiency parameterizations are derived and are shown to depend on the spatial variability of the above mentioned properties and processes, as well as the dynamics of land surface-atmosphere interactions.

An eco-hydrological modeling framework for assessing trade-offs among ecosystem services in response to alternative land use scenarios

NASA Astrophysics Data System (ADS)

Mckane, R.; Abdelnour, A. G.; Brookes, A.; Djang, K.; Stieglitz, M.; Pan, F.; Bolte, J.; Papenfus, M.; Burdick, C.

2012-12-01

Scientists, policymakers, community planners and others have discussed ecosystem services for decades, however, society is still in the early stages of developing methodologies to quantify and value the services that ecosystems provide. For example, the U.S. Environmental Protection Agency recently established the Sustainable and Healthy Communities Research Program to develop such methodologies, so that natural capital can be better accounted for in decisions that affect the supply of the ecosystem goods and services upon which human well-being depends. Essential to this goal are highly integrated models that can be used to define policy and management strategies for entire ecosystems, not simply individual components of the ecosystem. We developed the VELMA (Visualizing Ecosystems for Land Management Assessments) eco-hydrologic modeling framework to help address this emerging risk assessment objective. Here we describe a proof-of-concept application of VELMA to the H.J. Andrews Experimental Forest, a forested 64 km2 basin and Long Term Ecological Research site in the western Cascade Range of Oregon, USA. VELMA is a spatially-distributed eco-hydrologic model that links a land surface hydrologic model with a terrestrial biogeochemistry model for simulating the integrated responses of vegetation, soil, and water resources to interacting stressors. We used the model to simulate the effects of three different land use scenarios (100% old-growth, 100% clearcut harvest, and present-day land cover consisting of 45% old-growth and 55% harvested) on trade-offs among five ecosystem services: timber production, carbon sequestration, greenhouse gas regulation, water quantity, and water quality. Compared to the old-growth simulation, over a 60-yr period the clearcut simulation reduced total ecosystem carbon stocks (-40%), and initially increased total stream discharge (+28%), stream nitrogen export (>300%), and total CO2 and N2O radiative forcing (>200%). The simulation for present-day land cover resulted in intermediate values, albeit substantially closer to old growth than to clearcut values. Ongoing work is focused on incorporating VELMA within a flexible decision support platform (Envision) that integrates a wide variety of models, decision tools, and datasets for evaluating economic, social and environmental trade-offs associated with alternative decision scenarios. This framework will be used to address questions about the sustainability of natural capital vital to local and regional economies, initially in the PNW and Great Plains. For example, can those factors that have the greatest potential to improve future trajectories of ecosystem services and human well-being be identified? What green and grey infrastructure improvements, carbon and nitrogen management practices, and growth and development policies can most effectively be managed to attain a sustainable and desirable future?

A national framework for flood forecasting model assessment for use in operations and investment planning over England and Wales

NASA Astrophysics Data System (ADS)

Moore, Robert J.; Wells, Steven C.; Cole, Steven J.

2016-04-01

It has been common for flood forecasting systems to be commissioned at a catchment or regional level in response to local priorities and hydrological conditions, leading to variety in system design and model choice. As systems mature and efficiencies of national management are sought, there can be a drive towards system rationalisation, gaining an overview of model performance and consideration of simplification through model-type convergence. Flood forecasting model assessments, whilst overseen at a national level, may be commissioned and managed at a catchment and regional level, take a variety of forms and be large in number. This presents a challenge when an integrated national assessment is required to guide operational use of flood forecasts and plan future investment in flood forecasting models and supporting hydrometric monitoring. This contribution reports on how a nationally consistent framework for flood forecasting model performance has been developed to embrace many past, ongoing and future assessments for local river systems by engineering consultants across England & Wales. The outcome is a Performance Summary for every site model assessed which, on a single page, contains relevant catchment information for context, a selection of overlain forecast and observed hydrographs and a set of performance statistics with associated displays of novel condensed form. One display provides performance comparison with other models that may exist for the site. The performance statistics include skill scores for forecasting events (flow/level threshold crossings) of differing severity/rarity, indicating their probability and likely timing, which have real value in an operational setting. The local models assessed can be of any type and span rainfall-runoff (conceptual and transfer function) and flow routing (hydrological and hydrodynamic) forms. Also accommodated by the framework is the national G2G (Grid-to-Grid) distributed hydrological model, providing area-wide coverage across the fluvial rivers of England and Wales, which can be assessed at gauged sites. Thus the performance of the national G2G model forecasts can be directly compared with that from the local models. The Performance Summary for each site model is complemented by a national spatial analysis of model performance stratified by model-type, geographical region and forecast lead-time. The map displays provide an extensive evidence-base that can be interrogated, through a Flood Forecasting Model Performance web portal, to reveal fresh insights into comparative performance across locations, lead-times and models. This work was commissioned by the Environment Agency in partnership with Natural Resources Wales and the Flood Forecasting Centre for England and Wales.

Effects of human water management on California drought risk

NASA Astrophysics Data System (ADS)

He, Xiaogang; Wada, Yoshihide; Wanders, Niko; Sheffield, Justin

2017-04-01

Contribution of human water management to the intensification or mitigation of hydrological drought over California is investigated using the PCR-GLOBWB hydrological model at 0.5˚ resolution for the period 1979-2014. We demonstrate that including water management in the modeling framework results in more accurate discharge representation. During the severe 2014 drought, water management alleviated the drought deficit by ˜50% in Southern California through reservoir operation during low flow periods. However, human water consumption (mostly irrigation) in the Central Valley increased drought duration and deficit by 50% and 50-100%, respectively. Return level analysis indicates that there is more than 50% chance that the probability of occurrence of an extreme 2014-magnitude drought event was at least doubled under the influence of human activities compared to natural variability. This impact is most significant over the San Joaquin Drainage basin with a 50% and 75% likelihood that the return period is more than 3.5 and 1.5 times larger, respectively, because of the human impact on drought. A detailed study of the relative attribution of different types of human activities (e.g., groundwater pumping, reservoir operation and irrigation) on changes in drought risk over California is conducted through a higher 10 km resolution simulation. This hydrological modeling, attribution and risk assessment framework is further extended to other drought-prone areas and major drought events in the contiguous U.S., including the 2006/2007 southeastern U.S. drought, the 2011 Texas-northern Mexico drought over the southern plains and the 2012 drought over the central Great Plains.

Continental hydrosystem modelling: the concept of nested stream-aquifer interfaces

NASA Astrophysics Data System (ADS)

Flipo, N.; Mouhri, A.; Labarthe, B.; Biancamaria, S.; Rivière, A.; Weill, P.

2014-08-01

Coupled hydrological-hydrogeological models, emphasising the importance of the stream-aquifer interface, are more and more used in hydrological sciences for pluri-disciplinary studies aiming at investigating environmental issues. Based on an extensive literature review, stream-aquifer interfaces are described at five different scales: local [10 cm-~10 m], intermediate [~10 m-~1 km], watershed [10 km2-~1000 km2], regional [10 000 km2-~1 M km2] and continental scales [>10 M km2]. This led us to develop the concept of nested stream-aquifer interfaces, which extends the well-known vision of nested groundwater pathways towards the surface, where the mixing of low frequency processes and high frequency processes coupled with the complexity of geomorphological features and heterogeneities creates hydrological spiralling. This conceptual framework allows the identification of a hierarchical order of the multi-scale control factors of stream-aquifer hydrological exchanges, from the larger scale to the finer scale. The hyporheic corridor, which couples the river to its 3-D hyporheic zone, is then identified as the key component for scaling hydrological processes occurring at the interface. The identification of the hyporheic corridor as the support of the hydrological processes scaling is an important step for the development of regional studies, which is one of the main concerns for water practitioners and resources managers. In a second part, the modelling of the stream-aquifer interface at various scales is investigated with the help of the conductance model. Although the usage of the temperature as a tracer of the flow is a robust method for the assessment of stream-aquifer exchanges at the local scale, there is a crucial need to develop innovative methodologies for assessing stream-aquifer exchanges at the regional scale. After formulating the conductance model at the regional and intermediate scales, we address this challenging issue with the development of an iterative modelling methodology, which ensures the consistency of stream-aquifer exchanges between the intermediate and regional scales. Finally, practical recommendations are provided for the study of the interface using the innovative methodology MIM (Measurements-Interpolation-Modelling), which is graphically developed, scaling in space the three pools of methods needed to fully understand stream-aquifer interfaces at various scales. In the MIM space, stream-aquifer interfaces that can be studied by a given approach are localised. The efficiency of the method is demonstrated with two examples. The first one proposes an upscaling framework, structured around river reaches of ~10-100 m, from the local to the watershed scale. The second example highlights the usefulness of space borne data to improve the assessment of stream-aquifer exchanges at the regional and continental scales. We conclude that further developments in modelling and field measurements have to be undertaken at the regional scale to enable a proper modelling of stream-aquifer exchanges from the local to the continental scale.

Representation of riverine DOC within a GCM: First framework for coupling soil carbon and lateral hydrology in MPI-ESM

NASA Astrophysics Data System (ADS)

Brovkin, V.; Gehlot, S.; Hagemann, S.

2017-12-01

The current state of the art General Circulation Models (GCMs) do not consider the lateral transport of dissolved organic carbon (DOC) from land to ocean via rivers/streams and the global carbon budget is primarily evaluated based only on vertical gas exchange processes between land or ocean carbon reservoirs. In high latitudes, the permafrost plays an important role in contributing to riverine organic carbon. Moreover, the vertical gas exchange processes are active during the lateral riverine carbon transport but are not considered in the impact of thawing permafrost on global climate. The interplay between permafrost and lateral hydrology is a substantial factor impacting the organic carbon inflow to the Arctic and its associated atmospheric exchange. In this research, we propose a framework of coupling the soil carbon transport via rivers using the hydrological discharge scheme (HD-Model) of MPI-ESM (Max-Planck Institute for Meteorology Earth System Model). The soil carbon classification is based on the solubility (YASSO soil carbon pools) and their subsequent attribution to the dissolved organic carbon via runoff (fast carbon pool) and baseflow (slow carbon pool). The HD-model, which simulates the river discharge for all land areas at a resolution of 0.5 degree, will be modified with inclusion of the DOC as tracer over test areas. Evaluation of DOC transport scheme is intended at reservoir level via available site measurements. The analysis will include global river networks for organic carbon transport with focus on permafrost and high latitude areas. Decomposition of DOC en-route land to ocean via vertical gas exchange processes will be included.

Producing an integrated climate-land-energy-water (CLEW) model for glaciated regions in the developing world

NASA Astrophysics Data System (ADS)

Delman, E. M.; Thomas, B. F.; Famiglietti, J. S.

2013-12-01

Growing concern over the impact of climate change on global freshwater resources has spurred a demand for practical, basin-specific adaptation tools. The potential for water stress is particularly inflated in the glaciated watersheds of the developing world; widespread and rapid glacial retreat has forced regional resource managers to reconcile the reality of a diminishing supply with an overall increase in demand, while accounting for the underlying geopolitical and cultural context. An integrated approach, such as the development of a Climate-Land-Energy-Water (CLEW) model that examines relationships among climate, land-use, and the energy and water sectors, can be used to assess the impact of different climate change scenarios on basin sustainability and vulnerability. This study will first constrain the hydrologic budget in the Río Santa Watershed of Peru using satellite imagery, historical and contemporary stream discharge data, hydrologic modeling, climatic data analysis, and isotopic and chemical tracers. Ultimately, glacier retreat will be examined at the watershed scale and be used as an input in the CLEW model framework to assess hydrologic budget scenarios and the subsequent impact on regional economic and environmental sustainability.

Groundwater Controls on Vegetation Composition and Patterning in Mountain Meadows

NASA Astrophysics Data System (ADS)

Loheide, S. P.; Lowry, C.; Moore, C. E.; Lundquist, J. D.

2010-12-01

Mountain meadows are groundwater dependent ecosystems that are hotspots of biodiversity and productivity in the Sierra Nevada of California. Meadow vegetation relies on shallow groundwater during the region’s dry summer growing season. Vegetation composition in this environment is influenced both by 1) oxygen stress that occurs when portions of the root zone are saturated and anaerobic conditions are created that limit root respiration and 2) water stress that occurs when the water table drops and water-limited conditions are created in the root zone. A watershed model that explicitly accounts for snowmelt processes was linked to a fine resolution groundwater flow model of Tuolumne Meadows in Yosemite National Park, CA to simulated spatially distributed water table dynamics. This linked hydrologic model was calibrated to observations from a well observation network for 2006-2008, and validated using data from 2009. A vegetation survey was also conducted at the site in which the three dominant species were identified at more than 200 plots distributed across the meadow. Nonparametric multiplicative regression was performed to create and select the best models for predicting vegetation dominance based on simulated hydrologic regime. The hydrologic niche of three vegetation types representing wet, moist, and dry meadow vegetation communities was best described using both 1) a sum exceedance value calculated as the integral of water table position above a threshold of oxygen stress and 2) a sum deceedance value calculated as the integral of water table position below a threshold of water stress. This linked hydrologic and vegetative modeling framework advances our ability to predict the propagation of human-induced climatic and land-use/-cover changes through the hydrologic system to the ecosystem.

GEOMORPHIC AND HYDROLOGIC INTERACTIONS IN THE DETERMINATION OF EQUILIBRIUM SOIL DEPTH

NASA Astrophysics Data System (ADS)

Nicotina, L.; Rinaldo, A.; Tarboton, D. G.

2009-12-01

In this work we propose numerical studies of the interactions between hydrology and geomorphology in the formation of the actual soil depth that drives ecologic and hydrologic processes. Sediment transport and geomorphic landscape evolution processes (i.e. erosion/deposition vs. soil production) strongly influence hydrology, carbon sequestration, soil formation and stream water chemistry. The process of rock conversion into soil originates a strong hydrologic control through the formation of the soil depth that participates to hydrologic processes, influence vegetation type and patterns and actively participate in the co-evolution mechanisms that shape the landscape. The description of spatial patterns in hydrology is usually constrained by the availability of field data, especially when dealing with quantities that are not easily measurable. In these circumstances it is deemed fundamental the capability of deriving hydrologic boundary conditions from physically based approaches. Here we aim, in a general framework, at the formulation of an integrated approach for the prediction of soil depth by mean of i) soil production models and ii) geomorphic transport laws. The processes that take place in the critical zone are driven by the extension of it and have foundamental importance over short time scales as well as on geologic time scales (i.e. as biota affects climate that drives hydrology and thus contributes on shaping the landscape). Our study aims at the investigation of the relationships between soil depth, topography and runoff production, we also address the mechanisms that bring to the development of actual patterns of soil depths which at the same time influence runoff. We use a schematic representation of the hydrologic processes that relies on the description of the topography (throuh a topographic wetness index) and the spatially variable soil depths. Such a model is applied in order to investigate the development of equilibrium soil depth patterns under different hydrologic regimes and under two different hypothesis for the dynamic equilibrium (local or topographic dynamic equilibrium) of soils as well as the temporal scales associated to them. The obtained results are tested against a field survey of soil depths carried out in the Dry Creek catchment located in southern Idaho, near Boise (USA). The develped approach results to be suitable for the problem at hand as the hydrologic model results to be sensitive to the soil depths distribution.

A new and integrated hydro-economic accounting and analytical framework for water resources: a case study for North China.

PubMed

Guan, Dabo; Hubacek, Klaus

2008-09-01

Water is a critical issue in China for a variety of reasons. China is poor of water resources with 2,300 m(3) of per capita availability, which is less than 13 of the world average. This is exacerbated by regional differences; e.g. North China's water availability is only about 271 m(3) of per capita value, which is only 125 of the world's average. Furthermore, pollution contributes to water scarcity and is a major source for diseases, particularly for the poor. The Ministry of Hydrology [1997. China's Regional Water Bullets. Water Resource and Hydro-power Publishing House, Beijing, China] reports that about 65-80% of rivers in North China no longer support any economic activities. Previous studies have emphasized the amount of water withdrawn but rarely take water quality into consideration. The quality of the return flows usually changes; the water quality being lower than the water flows that entered the production process initially. It is especially important to measure the impacts of wastewater to the hydro-ecosystem. Thus, water consumption should not only account for the amount of water inputs but also the amount of water contaminated in the hydro-ecosystem by the discharged wastewater. In this paper we present a new accounting and analytical approach based on economic input-output modelling combined with a mass balanced hydrological model that links interactions in the economic system with interactions in the hydrological system. We thus follow the tradition of integrated economic-ecologic input-output modelling. Our hydro-economic accounting framework and analysis tool allows tracking water consumption on the input side, water pollution leaving the economic system and water flows passing through the hydrological system thus enabling us to deal with water resources of different qualities. Following this method, the results illustrate that North China requires 96% of its annual available water, including both water inputs for the economy and contaminated water that is ineligible for any uses.

Effect of antecedent-hydrological conditions on rainfall triggering of debris flows in ash-fall pyroclastic mantled slopes of Campania (southern Italy)

USGS Publications Warehouse

Napolitano, E.; Fusco, F; Baum, Rex L.; Godt, Jonathan W.; De Vita, P.

2016-01-01

Mountainous areas surrounding the Campanian Plain and the Somma-Vesuvius volcano (southern Italy) are among the most risky areas of Italy due to the repeated occurrence of rainfallinduced debris flows along ash-fall pyroclastic soil-mantled slopes. In this geomorphological framework, rainfall patterns, hydrological processes taking place within multi-layered ash-fall pyroclastic deposits and soil antecedent moisture status are the principal factors to be taken into account to assess triggering rainfall conditions and the related hazard. This paper presents the outcomes of an experimental study based on integrated analyses consisting of the reconstruction of physical models of landslides, in situ hydrological monitoring, and hydrological and slope stability modeling, carried out on four representative source areas of debris flows that occurred in May 1998 in the Sarno Mountain Range. The hydrological monitoring was carried out during 2011 using nests of tensiometers and Watermark pressure head sensors and also through a rainfall and air temperature recording station. Time series of measured pressure head were used to calibrate a hydrological numerical model of the pyroclastic soil mantle for 2011, which was re-run for a 12-year period beginning in 2000, given the availability of rainfall and air temperature monitoring data. Such an approach allowed us to reconstruct the regime of pressure head at a daily time scale for a long period, which is representative of about 11 hydrologic years with different meteorological conditions. Based on this simulated time series, average winter and summer hydrological conditions were chosen to carry out hydrological and stability modeling of sample slopes and to identify Intensity- Duration rainfall thresholds by a deterministic approach. Among principal results, the opposing winter and summer antecedent pressure head (soil moisture) conditions were found to exert a significant control on intensity and duration of rainfall triggering events. Going from winter to summer conditions requires a strong increase of intensity and/or duration to induce landslides. The results identify an approach to account for different hazard conditions related to seasonality of hydrological processes inside the ash-fall pyroclastic soil mantle. Moreover, they highlight another important factor of uncertainty that potentially affects rainfall thresholds triggering shallow landslides reconstructed by empirical approaches.

Data Management System for the National Energy-Water System (NEWS) Assessment Framework

NASA Astrophysics Data System (ADS)

Corsi, F.; Prousevitch, A.; Glidden, S.; Piasecki, M.; Celicourt, P.; Miara, A.; Fekete, B. M.; Vorosmarty, C. J.; Macknick, J.; Cohen, S. M.

2015-12-01

Aiming at providing a comprehensive assessment of the water-energy nexus, the National Energy-Water System (NEWS) project requires the integration of data to support a modeling framework that links climate, hydrological, power production, transmission, and economical models. Large amounts of Georeferenced data has to be streamed to the components of the inter-disciplinary model to explore future challenges and tradeoffs in the US power production, based on climate scenarios, power plant locations and technologies, available water resources, ecosystem sustainability, and economic demand. We used open source and in-house build software components to build a system that addresses two major data challenges: On-the-fly re-projection, re-gridding, interpolation, extrapolation, nodata patching, merging, temporal and spatial aggregation, of static and time series datasets in virtually any file formats and file structures, and any geographic extent for the models I/O, directly at run time; Comprehensive data management based on metadata cataloguing and discovery in repositories utilizing the MAGIC Table (Manipulation and Geographic Inquiry Control database). This innovative concept allows models to access data on-the-fly by data ID, irrespective of file path, file structure, file format and regardless its GIS specifications. In addition, a web-based information and computational system is being developed to control the I/O of spatially distributed Earth system, climate, and hydrological, power grid, and economical data flow within the NEWS framework. The system allows scenario building, data exploration, visualization, querying, and manipulation any loaded gridded, point, and vector polygon dataset. The system has demonstrated its potential for applications in other fields of Earth science modeling, education, and outreach. Over time, this implementation of the system will provide near real-time assessment of various current and future scenarios of the water-energy nexus.

visCOS: An R-package to evaluate model performance of hydrological models

NASA Astrophysics Data System (ADS)

Klotz, Daniel; Herrnegger, Mathew; Wesemann, Johannes; Schulz, Karsten

2016-04-01

The evaluation of model performance is a central part of (hydrological) modelling. Much attention has been given to the development of evaluation criteria and diagnostic frameworks. (Klemeš, 1986; Gupta et al., 2008; among many others). Nevertheless, many applications exist for which objective functions do not yet provide satisfying summaries. Thus, the necessity to visualize results arises in order to explore a wider range of model capacities, be it strengths or deficiencies. Visualizations are usually devised for specific projects and these efforts are often not distributed to a broader community (e.g. via open source software packages). Hence, the opportunity to explicitly discuss a state-of-the-art presentation technique is often missed. We therefore present a comprehensive R-package for evaluating model performance by visualizing and exploring different aspects of hydrological time-series. The presented package comprises a set of useful plots and visualization methods, which complement existing packages, such as hydroGOF (Zambrano-Bigiarini et al., 2012). It is derived from practical applications of the hydrological models COSERO and COSEROreg (Kling et al., 2014). visCOS, providing an interface in R, represents an easy-to-use software package for visualizing and assessing model performance and can be implemented in the process of model calibration or model development. The package provides functions to load hydrological data into R, clean the data, process, visualize, explore and finally save the results in a consistent way. Together with an interactive zoom function of the time series, an online calculation of the objective functions for variable time-windows is included. Common hydrological objective functions, such as the Nash-Sutcliffe Efficiency and the Kling-Gupta Efficiency, can also be evaluated and visualized in different ways for defined sub-periods like hydrological years or seasonal sections. Many hydrologists use long-term water-balances as a pivotal tool in model evaluation. They allow inferences about different systematic model-shortcomings and are an efficient way for communicating these in practice (Schulz et al., 2015). The evaluation and construction of such water balances is implemented with the presented package. During the (manual) calibration of a model or in the scope of model development, many model runs and iterations are necessary. Thus, users are often interested in comparing different model results in a visual way in order to learn about the model and to analyse parameter-changes on the output. A method to illuminate these differences and the evolution of changes is also included. References: • Gupta, H.V.; Wagener, T.; Liu, Y. (2008): Reconciling theory with observations: elements of a diagnostic approach to model evaluation, Hydrol. Process. 22, doi: 10.1002/hyp.6989. • Klemeš, V. (1986): Operational testing of hydrological simulation models, Hydrolog. Sci. J., doi: 10.1080/02626668609491024. • Kling, H.; Stanzel, P.; Fuchs, M.; and Nachtnebel, H. P. (2014): Performance of the COSERO precipitation-runoff model under non-stationary conditions in basins with different climates, Hydrolog. Sci. J., doi: 10.1080/02626667.2014.959956. • Schulz, K., Herrnegger, M., Wesemann, J., Klotz, D. Senoner, T. (2015): Kalibrierung COSERO - Mur für Pro Vis, Verbund Trading GmbH (Abteilung STG), final report, Institute of Water Management, Hydrology and Hydraulic Engineering, University of Natural Resources and Applied Life Sciences, Vienna, Austria, 217pp. • Zambrano-Bigiarini, M; Bellin, A. (2010): Comparing Goodness-of-fit Measures for Calibration of Models Focused on Extreme Events. European Geosciences Union (EGU), Geophysical Research Abstracts 14, EGU2012-11549-1.

netherland hydrological modeling instrument

NASA Astrophysics Data System (ADS)

Hoogewoud, J. C.; de Lange, W. J.; Veldhuizen, A.; Prinsen, G.

2012-04-01

Netherlands Hydrological Modeling Instrument A decision support system for water basin management. J.C. Hoogewoud , W.J. de Lange ,A. Veldhuizen , G. Prinsen , The Netherlands Hydrological modeling Instrument (NHI) is the center point of a framework of models, to coherently model the hydrological system and the multitude of functions it supports. Dutch hydrological institutes Deltares, Alterra, Netherlands Environmental Assessment Agency, RWS Waterdienst, STOWA and Vewin are cooperating in enhancing the NHI for adequate decision support. The instrument is used by three different ministries involved in national water policy matters, for instance the WFD, drought management, manure policy and climate change issues. The basis of the modeling instrument is a state-of-the-art on-line coupling of the groundwater system (MODFLOW), the unsaturated zone (metaSWAP) and the surface water system (MOZART-DM). It brings together hydro(geo)logical processes from the column to the basin scale, ranging from 250x250m plots to the river Rhine and includes salt water flow. The NHI is validated with an eight year run (1998-2006) with dry and wet periods. For this run different parts of the hydrology have been compared with measurements. For instance, water demands in dry periods (e.g. for irrigation), discharges at outlets, groundwater levels and evaporation. A validation alone is not enough to get support from stakeholders. Involvement from stakeholders in the modeling process is needed. There fore to gain sufficient support and trust in the instrument on different (policy) levels a couple of actions have been taken: 1. a transparent evaluation of modeling-results has been set up 2. an extensive program is running to cooperate with regional waterboards and suppliers of drinking water in improving the NHI 3. sharing (hydrological) data via newly setup Modeling Database for local and national models 4. Enhancing the NHI with "local" information. The NHI is and has been used for many decision supports and evaluations. The main focus of the instrument is operational drought management and evaluating adaptive measures for different climate scenario's. It has also been used though as a basis to evaluate water quality of WFD-water bodies and measures, nutrient-leaching and describing WFD groundwater bodies. There is a toolkit to translate the hydrological NHI results to values for different water users. For instance with the NHI results agricultural yields can be calculated, effects on ground water dependant ecosystems, subsidence, shipping, drinking water supply. This makes NHI a valuable decision support system in Dutch water management.

Quantifying Stream-Aquifer Exchanges Over Scales: the Concept of Nested Interfaces

NASA Astrophysics Data System (ADS)

Flipo, N.; Mouhri, A.; Labarthe, B.; Saleh, F. S.

2013-12-01

Recent developments in hydrological modelling are based on a view of the interface being a single continuum through which water flows. These coupled hydrological-hydrogeological models, emphasizing the importance of the stream-aquifer interface (SAI), are more and more used in hydrological sciences for pluri-disciplinary studies aiming at questioning environmental issues. This notion of a single continuum comes from the historical modelling of hydrosystems based on the hypothesis of a homogeneous media that led to the Darcy law. Nowadays, there is a need to first bridge the gap between hydrological and eco-hydrological views of the SAIs, and, second, to rationalize the modelling of SAI within a consistent framework that fully takes into account the multi-dimensionality of the SAIs. We first define the concept of nested SAIs as a key transitional component of continental hydrosystem. We then demonstrate the usefulness of the concept for the multi-dimensional study of the SAI, with a special emphasis on the stream network which is identified as the key component for scaling hydrological processes occurring at the interface. Finally we focus on SAI modelling at various scales with up-to-date methodologies and give some guidance for the multi-dimensional modelling of the interface using the innovative methodology MIM (Measurements-Interpolation-Modelling), which is graphically developed. MIM scales in space three pools of methods needed to fully understand SAIs. The outcome of MIM is the localization in space of the type of SAI that can be studied by a given approach. The efficiency of the method is illustrated from the local (approx. 1m) to the regional scale (> 10 000 km2) with two examples from the Paris basin (France). The first one consists in the implementation of a sampling system of stream-aquifer exchanges, which is coupled with local 2D thermo-hydro models and a pseudo 3D hydro(geo)logical model at the watershed scale (40 km2). The quantification of monthly stream-aquifer exchanges over 14 000 km of river network in the Paris basin (74 000 km2) corresponds to a unique regional scale example.

Spatially Explicit Simulation of Mesotopographic Controls on Peatland Hydrology and Carbon Fluxes

NASA Astrophysics Data System (ADS)

Sonnentag, O.; Chen, J. M.; Roulet, N. T.

2006-12-01

A number of field carbon flux measurements, paleoecological records, and model simulations have acknowledged the importance of northern peatlands in terrestrial carbon cycling and methane emissions. An important parameter in peatlands that influences both net primary productivity, the net gain of carbon through photosynthesis, and decomposition under aerobic and anaerobic conditions, is the position of the water table. Biological and physical processes involved in peatland carbon dynamics and their hydrological controls operate at different spatial scales. The highly variable hydraulic characteristics of the peat profile and the overall shape of the peat body as defined by its surface topography at the mesoscale (104 m2) are of major importance for peatland water table dynamics. Common types of peatlands include bogs with a slightly domed centre. As a result of the convex profile, their water supply is restricted to atmospheric inputs, and water is mainly shed by shallow subsurface flow. From a modelling perspective the influence of mesotopographic controls on peatland hydrology and thus carbon balance requires that process-oriented models that examine the links between peatland hydrology, ecosystem functioning, and climate must incorporate some form of lateral subsurface flow consideration. Most hydrological and ecological modelling studies in complex terrain explicitly account for the topographic controls on lateral subsurface flow through digital elevation models. However, modelling studies in peatlands often employ simple empirical parameterizations of lateral subsurface flow, neglecting the influence of peatlands low relief mesoscale topography. Our objective is to explicitly simulate the mesotopographic controls on peatland hydrology and carbon fluxes using the Boreal Ecosystem Productivity Simulator (BEPS) adapted to northern peatlands. BEPS is a process-oriented ecosystem model in a remote sensing framework that takes into account peatlands multi-layer canopy through vertically stratified mapped leaf area index. Model outputs are validated against multi-year measurements taken at an eddy-covariance flux tower located within Mer Bleue bog, a typical raised bog near Ottawa, Ontario, Canada. Model results for seasonal water table dynamics and evapotranspiration at daily time steps in 2003 are in good agreement with measurements with R2=0.74 and R2=0.79, respectively, and indicate the suitability of our pursued approach.

How can a modular Master Program in Hydrology provide a framework for future education challenges?

NASA Astrophysics Data System (ADS)

Weiler, Markus; Lange, Jens

2010-05-01

A new Master program in Hydrology started at the University of Freiburg in 2008 as a continuation of the Diploma program in Hydrology due to the proposed changes according to the Bologna ac-cord. This imposed formation provided a perfect opportunity to develop a new program that is able to meet the challenges of future hydrology students to work in a nonstationary world due to climate and land use change. A modular program with individual three week hydrological courses was es-tablished, which builds on a general bachelor knowledge in natural sciences. Besides broad theory, students are taught in all relevant methods of hydrological field data collection and laboratory analy-sis. Recurrently, practical data analysis is carried out using freeware software tools. Examples in-clude time series analysis, (geo-)statistics and independently programmed water balance models including uncertainty assessments. Students work on data sets of different climatic zones and are made aware of hydrological problem areas around the globe. Hence, graduates know how to collect, analyse and evaluate hydrological information and may prepare their own, independent tools to pre-dict future changes. In addition, the new modular program includes instructors from the industry and public authorities to provide the students a broad perspective of their future profession. Finally, the new program allows directly to teach university students and practicing hydrologists together to provide evolving methods in hydrology to the practitioners and to allow contacts to professional for the university students.

Parsing multiple processes of high temperature impacts on corn/soybean yield using a newly developed CLM-APSIM modeling framework

NASA Astrophysics Data System (ADS)

Peng, B.; Guan, K.; Chen, M.

2016-12-01

Future agricultural production faces a grand challenge of higher temperature under climate change. There are multiple physiological or metabolic processes of how high temperature affects crop yield. Specifically, we consider the following major processes: (1) direct temperature effects on photosynthesis and respiration; (2) speed-up growth rate and the shortening of growing season; (3) heat stress during reproductive stage (flowering and grain-filling); (4) high-temperature induced increase of atmospheric water demands. In this work, we use a newly developed modeling framework (CLM-APSIM) to simulate the corn and soybean growth and explicitly parse the above four processes. By combining the strength of CLM in modeling surface biophysical (e.g., hydrology and energy balance) and biogeochemical (e.g., photosynthesis and carbon-nitrogen interactions), as well as that of APSIM in modeling crop phenology and reproductive stress, the newly developed CLM-APSIM modeling framework enables us to diagnose the impacts of high temperature stress through different processes at various crop phenology stages. Ground measurements from the advanced SoyFACE facility at University of Illinois is used here to calibrate, validate, and improve the CLM-APSIM modeling framework at the site level. We finally use the CLM-APSIM modeling framework to project crop yield for the whole US Corn Belt under different climate scenarios.

Future Visions of the Brahmaputra - Establishing Hydrologic Baseline and Water Resources Context

NASA Astrophysics Data System (ADS)

Ray, P. A.; Yang, Y. E.; Wi, S.; Brown, C. M.

2013-12-01

The Brahmaputra River Basin (China-India-Bhutan-Bangladesh) is on the verge of a transition from a largely free flowing and highly variable river to a basin of rapid investment and infrastructure development. This work demonstrates a knowledge platform for the basin that compiles available data, and develops hydrologic and water resources system models of the basin. A Variable Infiltration Capacity (VIC) model of the Brahmaputra basin supplies hydrologic information of major tributaries to a water resources system model, which routes runoff generated via the VIC model through water infrastructure, and accounts for water withdrawals for agriculture, hydropower generation, municipal demand, return flows and others human activities. The system model also simulates agricultural production and the economic value of water in its various uses, including municipal, agricultural, and hydropower. Furthermore, the modeling framework incorporates plausible climate change scenarios based on the latest projections of changes to contributing glaciers (upstream), as well as changes to monsoon behavior (downstream). Water resources projects proposed in the Brahmaputra basin are evaluated based on their distribution of benefits and costs in the absence of well-defined water entitlements, and relative to a complex regional water-energy-food nexus. Results of this project will provide a basis for water sharing negotiation among the four countries and inform trans-national water-energy policy making.

Constructing an everywhere and locally relevant predictive model of the West-African critical zone

NASA Astrophysics Data System (ADS)

Hector, B.; Cohard, J. M.; Pellarin, T.; Maxwell, R. M.; Cappelaere, B.; Demarty, J.; Grippa, M.; Kergoat, L.; Lebel, T.; Mamadou, O.; Mougin, E.; Panthou, G.; Peugeot, C.; Vandervaere, J. P.; Vischel, T.; Vouillamoz, J. M.

2017-12-01

Considering water resources and hydrologic hazards, West Africa is among the most vulnerable regions to face both climatic (e.g. with the observed intensification of precipitation) and anthropogenic changes. With +3% of demographic rate, the region experiences rapid land use changes and increased pressure on surface and groundwater resources with observed consequences on the hydrological cycle (water table rise result of the sahelian paradox, increase in flood occurrence, etc.) Managing large hydrosystems (such as transboundary aquifers or rivers basins as the Niger river) requires anticipation of such changes. However, the region significantly lacks observations, for constructing and validating critical zone (CZ) models able to predict future hydrologic regime, but also comprises hydrosystems which encompass strong environmental gradients (e.g. geological, climatic, ecological) with highly different dominating hydrological processes. We address these issues by constructing a high resolution (1 km²) regional scale physically-based model using ParFlow-CLM which allows modeling a wide range of processes without prior knowledge on their relative dominance. Our approach combines multiple scale modeling from local to meso and regional scales within the same theoretical framework. Local and meso-scale models are evaluated thanks to the rich AMMA-CATCH CZ observation database which covers 3 supersites with contrasted environments in Benin (Lat.: 9.8°N), Niger (Lat.: 13.3°N) and Mali (Lat.: 15.3°N). At the regional scale the lack of relevant map of soil hydrodynamic parameters is addressed using remote sensing data assimilation. Our first results show the model's ability to reproduce the known dominant hydrological processes (runoff generation, ET, groundwater recharge…) across the major West-African regions and allow us to conduct virtual experiments to explore the impact of global changes on the hydrosystems. This approach is a first step toward the construction of a reference model to study regional CZ sensitivity to global changes and will help to identify prior parameters required and to construct meta-models for deeper investigations of interactions within the CZ.

Groundwater controls on vegetation composition and patterning in mountain meadows

NASA Astrophysics Data System (ADS)

Lowry, Christopher S.; Loheide, Steven P., II; Moore, Courtney E.; Lundquist, Jessica D.

2011-10-01

Mountain meadows are groundwater-dependent ecosystems that are hot spots of biodiversity and productivity. In the Sierra Nevada mountains of California, these ecosystems rely on shallow groundwater to support their vegetation communities during the dry summer growing season in the region's Mediterranean montane climate. Vegetation composition in this environment is influenced by both (1) oxygen stress that occurs when portions of the root zone are saturated and anaerobic conditions limit root respiration and (2) water stress that occurs when the water table drops and the root zone becomes water limited. A spatially distributed watershed model that explicitly accounts for snowmelt processes was linked to a fine-resolution groundwater flow model of Tuolumne Meadows in Yosemite National Park, California, to simulate water table dynamics. This linked hydrologic model was calibrated to observations from a well observation network for 2006-2009. A vegetation survey was also conducted at the site in which the three dominant species were identified at more than 200 plots distributed across the meadow. Nonparametric multiplicative regression was performed to create and select the best models for predicting vegetation dominance on the basis of the simulated hydrologic regime. The hydrologic niches of three vegetation types representing wet, moist, and dry meadow vegetation communities were found to be best described using both (1) a sum exceedance value calculated as the integral of water table position above a depth threshold of oxygen stress and (2) a sum exceedance value calculated as the integral of water table position below a depth threshold of water stress. This linked hydrologic and vegetative modeling framework advances our ability to predict the propagation of human-induced climatic and land use or land cover changes through the hydrologic system to the ecosystem. The hydroecologic functioning of meadows provides an example of the extent to which cascading hydrologic processes at watershed, hillslope, and riparian zones and within channels are reflected in the composition and distribution of riparian vegetation.

Using a new high resolution regional model for malaria that accounts for population density and surface hydrology to determine sensitivity of malaria risk to climate drivers

NASA Astrophysics Data System (ADS)

Tompkins, Adrian; Ermert, Volker; Di Giuseppe, Francesca

2013-04-01

In order to better address the role of population dynamics and surface hydrology in the assessment of malaria risk, a new dynamical disease model been developed at ICTP, known as VECTRI: VECtor borne disease community model of ICTP, TRIeste (VECTRI). The model accounts for the temperature impact on the larvae, parasite and adult vector populations. Local host population density affects the transmission intensity, and the model thus reproduces the differences between peri-urban and rural transmission noted in Africa. A new simple pond model framework represents surface hydrology. The model can be used on with spatial resolutions finer than 10km to resolve individual health districts and thus can be used as a planning tool. Results of the models representation of interannual variability and longer term projections of malaria transmission will be shown for Africa. These will show that the model represents the seasonality and spatial variations of malaria transmission well matching a wide range of survey data of parasite rate and entomological inoculation rate (EIR) from across West and East Africa taken in the period prior to large-scale interventions. The model is used to determine the sensitivity of malaria risk to climate variations, both in rainfall and temperature, and then its use in a prototype forecasting system coupled with ECMWF forecasts will be demonstrated.

Vegetation of Upper Coastal Plain Depression Wetlands: Environmental Templates and Wetland Dynamics Within A Landscape Framework

Treesearch

Diane De Steven; Maureen M. Toner

2004-01-01

Reference wetlands play an important role in efforts to protect wetlands and assess wetland condition. Because wetland vegetation integrates the influence of many ecological factors, a useful reference system would identify natural vegetation types and include models relating vegetation to important regional geomorphic, hydrologic, and geochemical properties. Across...

Effects of spatially distributed sectoral water management on the redistribution of water resources in an integrated water model: SECTORAL WATER MANAGEMENT IN IA-ESM

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

Voisin, Nathalie; Hejazi, Mohamad I.; Leung, L. Ruby

To advance understanding of the interactions between human activities and the water cycle, an integrated terrestrial water cycle component has been developed for Earth system models. This includes a land surface model fully coupled to a river routing model and a generic water management model to simulate natural and regulated flows. A global integrated assessment model and its regionalized version for the U.S. are used to simulate water demand consistent with the energy technology and socio-economics scenarios. Human influence on the hydrologic cycle includes regulation and storage from reservoirs, consumptive use and withdrawal from multiple sectors ( irrigation and non-irrigation)more » and overall redistribution of water resources in space and time. As groundwater provides an important source of water supply for irrigation and other uses, the integrated modeling framework has been extended with a simplified representation of groundwater as an additional supply source, and return flow generated from differences between withdrawals and consumptive uses from both groundwater and surface water systems. The groundwater supply and return flow modules are evaluated by analyzing the simulated regulated flow, reservoir storage and supply deficit for irrigation and non-irrigation sectors over major hydrologic regions of the conterminous U.S. The modeling framework is then used to provide insights on the reliability of water resources by isolating the reliability due to return flow and/or groundwater sources of water. Our results show that high sectoral ratio of withdrawals over consumptive demand adds significant stress on the water resources management that can be alleviated by reservoir storage capacity. The return flow representation therefore exhibits a clear east-west contrast in its hydrologic signature, as well as in its ability to help meet water demand. Groundwater use has a limited hydrologic signature but the most pronounced signature is in terms of decreasing water supply deficit. The combined return flow and groundwater use signature conserves the east-west constrast with overall uncertainties due to the groundwater-return flow representation, varying ratios combined with different hydroclimate conditions, storage infrastructures, sectoral water uses and dependence on groundwater. The redistribution of surface and groundwater by human activities, and the uncertainties in their representation have important implications to the water and energy balances in the Earth system and land-atmosphere interactions.« less

Integrating a distributed hydrological model and SEEA-Water for improving water account and water allocation management under a climate change context.

NASA Astrophysics Data System (ADS)

Jauch, Eduardo; Almeida, Carina; Simionesei, Lucian; Ramos, Tiago; Neves, Ramiro

2015-04-01

The crescent demand and situations of water scarcity and droughts are a difficult problem to solve by water managers, with big repercussions in the entire society. The complexity of this question is increased by trans-boundary river issues and the environmental impacts of the usual adopted solutions to store water, like reservoirs. To be able to answer to the society requirements regarding water allocation in a sustainable way, the managers must have a complete and clear picture of the present situation, as well as being able to understand the changes in the water dynamics both in the short and long time period. One of the available tools for the managers is the System of Environmental-Economic Accounts for Water (SEEA-Water), a subsystem of SEEA with focus on water accounts, developed by the United Nations Statistical Division (UNSD) in collaboration with the London Group on Environmental Accounting, This system provides, between other things, with a set of tables and accounts for water and water related emissions, organizing statistical data making possible the derivation of indicators that can be used to assess the relations between economy and environment. One of the main issues with the SEEA-Water framework seems to be the requirement of large amounts of data, including field measurements of water availability in rivers/lakes/reservoirs, soil and groundwater, as also precipitation, irrigation and other water sources and uses. While this is an incentive to collecting and using data, it diminishes the usefulness of the system on countries where this data is not yet available or is incomplete, as it can lead to a poor understanding of the water availability and uses. Distributed hydrological models can be used to fill missing data required by the SEEA-Water framework. They also make it easier to assess different scenarios (usually soil use, water demand and climate changes) for a better planning of water allocation. In the context of the DURERO project (www.durero.eu), the hydrological model MOHID LAND (www.mohid.com) was used to model the Douro river basin providing information to the SEEA-Water system for the Portuguese side of the basin. The model was also used to model the Tâmega river watershed, a sub-basin of the Douro basin, with different climate change scenarios, using the results to build the SEEA-Water accounts for this pilot river basin. The aim of the present work was to understand the potential of the integration of a distributed hydrological model with the SEEA-Water framework and how this can help improving water allocation management and water account under a climate change context.

An index-based robust decision making framework for watershed management in a changing climate.

PubMed

Kim, Yeonjoo; Chung, Eun-Sung

2014-03-01

This study developed an index-based robust decision making framework for watershed management dealing with water quantity and quality issues in a changing climate. It consists of two parts of management alternative development and analysis. The first part for alternative development consists of six steps: 1) to understand the watershed components and process using HSPF model, 2) to identify the spatial vulnerability ranking using two indices: potential streamflow depletion (PSD) and potential water quality deterioration (PWQD), 3) to quantify the residents' preferences on water management demands and calculate the watershed evaluation index which is the weighted combinations of PSD and PWQD, 4) to set the quantitative targets for water quantity and quality, 5) to develop a list of feasible alternatives and 6) to eliminate the unacceptable alternatives. The second part for alternative analysis has three steps: 7) to analyze all selected alternatives with a hydrologic simulation model considering various climate change scenarios, 8) to quantify the alternative evaluation index including social and hydrologic criteria with utilizing multi-criteria decision analysis methods and 9) to prioritize all options based on a minimax regret strategy for robust decision. This framework considers the uncertainty inherent in climate models and climate change scenarios with utilizing the minimax regret strategy, a decision making strategy under deep uncertainty and thus this procedure derives the robust prioritization based on the multiple utilities of alternatives from various scenarios. In this study, the proposed procedure was applied to the Korean urban watershed, which has suffered from streamflow depletion and water quality deterioration. Our application shows that the framework provides a useful watershed management tool for incorporating quantitative and qualitative information into the evaluation of various policies with regard to water resource planning and management. Copyright © 2013 Elsevier B.V. All rights reserved.

The urban watershed continuum: evolving spatial and temporal dimensions

Treesearch

Sujay S. Kaushal; Kenneth T. Belt

2012-01-01

Urban ecosystems are constantly evolving, and they are expected to change in both space and time with active management or degradation. An urban watershed continuum framework recognizes a continuum of engineered and natural hydrologic flowpaths that expands hydrologic networks in ways that are seldom considered. It recognizes that the nature of hydrologic connectivity...

First-order exchange coefficient coupling for simulating surface water-groundwater interactions: Parameter sensitivity and consistency with a physics-based approach

USGS Publications Warehouse

Ebel, B.A.; Mirus, B.B.; Heppner, C.S.; VanderKwaak, J.E.; Loague, K.

2009-01-01

Distributed hydrologic models capable of simulating fully-coupled surface water and groundwater flow are increasingly used to examine problems in the hydrologic sciences. Several techniques are currently available to couple the surface and subsurface; the two most frequently employed approaches are first-order exchange coefficients (a.k.a., the surface conductance method) and enforced continuity of pressure and flux at the surface-subsurface boundary condition. The effort reported here examines the parameter sensitivity of simulated hydrologic response for the first-order exchange coefficients at a well-characterized field site using the fully coupled Integrated Hydrology Model (InHM). This investigation demonstrates that the first-order exchange coefficients can be selected such that the simulated hydrologic response is insensitive to the parameter choice, while simulation time is considerably reduced. Alternatively, the ability to choose a first-order exchange coefficient that intentionally decouples the surface and subsurface facilitates concept-development simulations to examine real-world situations where the surface-subsurface exchange is impaired. While the parameters comprising the first-order exchange coefficient cannot be directly estimated or measured, the insensitivity of the simulated flow system to these parameters (when chosen appropriately) combined with the ability to mimic actual physical processes suggests that the first-order exchange coefficient approach can be consistent with a physics-based framework. Copyright ?? 2009 John Wiley & Sons, Ltd.

Operational validation of a multi-period and multi-criteria model conditioning approach for the prediction of rainfall-runoff processes in small forest catchments

NASA Astrophysics Data System (ADS)

Choi, H.; Kim, S.

2012-12-01

Most of hydrologic models have generally been used to describe and represent the spatio-temporal variability of hydrological processes in the watershed scale. Though it is an obvious fact that hydrological responses have the time varying nature, optimal values of model parameters were normally considered as time invariants or constants in most cases. The recent paper of Choi and Beven (2007) presents a multi-period and multi-criteria model conditioning approach. The approach is based on the equifinality thesis within the Generalised Likelihood Uncertainty Estimation (GLUE) framework. In their application, the behavioural TOPMODEL parameter sets are determined by several performance measures for global (annual) and short (30-days) periods, clustered using a Fuzzy C-means algorithm, into 15 types representing different hydrological conditions. Their study shows a good performance on the calibration of a rainfall-runoff model in a forest catchment, and also gives strong indications that it is uncommon to find model realizations that were behavioural over all multi-periods and all performance measures, and multi-period model conditioning approach may become new effective tool for predictions of hydrological processes in ungauged catchments. This study is a follow-up study on the Choi and Beven's (2007) model conditioning approach to test how the approach is effective for the prediction of rainfall-runoff responses in ungauged catchments. To achieve this purpose, 6 small forest catchments are selected among the several hydrological experimental catchments operated by Korea Forest Research Institute. In each catchment, long-term hydrological time series data varying from 10 to 30 years were available. The areas of the selected catchments range from 13.6 to 37.8 ha, and all areas are covered by coniferous or broad-leaves forests. The selected catchments locate in the southern coastal area to the northern part of South Korea. The bed rocks are Granite gneiss, Granite or Limestone. The study is progressed based on the followings. Firstly, hydrological time series of each catchment are sampled and clustered into multi-period having distinctly different temporal characteristics, and secondly, behavioural parameter distributions are determined in each multi-period based on the specification of multi-criteria model performance measures. Finally, behavioural parameter sets of each multi-period of single catchment are applied on the corresponding period of other catchments, and the cross-validations are conducted in this manner for all catchments The multi-period model conditioning approach is clearly effective to reduce the width of prediction limits, giving better model performance against the temporal variability of hydrological characteristics, and has enough potential to be the effective prediction tool for ungauged catchments. However, more advanced and continuous studies are needed to expand the application of this approach in prediction of hydrological responses in ungauged catchments,

Design and Applications of a GeoSemantic Framework for Integration of Data and Model Resources in Hydrologic Systems

NASA Astrophysics Data System (ADS)

Elag, M.; Kumar, P.

2016-12-01

Hydrologists today have to integrate resources such as data and models, which originate and reside in multiple autonomous and heterogeneous repositories over the Web. Several resource management systems have emerged within geoscience communities for sharing long-tail data, which are collected by individual or small research groups, and long-tail models, which are developed by scientists or small modeling communities. While these systems have increased the availability of resources within geoscience domains, deficiencies remain due to the heterogeneity in the methods, which are used to describe, encode, and publish information about resources over the Web. This heterogeneity limits our ability to access the right information in the right context so that it can be efficiently retrieved and understood without the Hydrologist's mediation. A primary challenge of the Web today is the lack of the semantic interoperability among the massive number of resources, which already exist and are continually being generated at rapid rates. To address this challenge, we have developed a decentralized GeoSemantic (GS) framework, which provides three sets of micro-web services to support (i) semantic annotation of resources, (ii) semantic alignment between the metadata of two resources, and (iii) semantic mediation among Standard Names. Here we present the design of the framework and demonstrate its application for semantic integration between data and models used in the IML-CZO. First we show how the IML-CZO data are annotated using the Semantic Annotation Services. Then we illustrate how the Resource Alignment Services and Knowledge Integration Services are used to create a semantic workflow among TopoFlow model, which is a spatially-distributed hydrologic model and the annotated data. Results of this work are (i) a demonstration of how the GS framework advances the integration of heterogeneous data and models of water-related disciplines by seamless handling of their semantic heterogeneity, (ii) an introduction of new paradigm for reusing existing and new standards as well as tools and models without the need of their implementation in the Cyberinfrastructures of water-related disciplines, and (iii) an investigation of a methodology by which distributed models can be coupled in a workflow using the GS services.

Distributed Hydrologic Modeling Apps for Decision Support in the Cloud

NASA Astrophysics Data System (ADS)

Swain, N. R.; Latu, K.; Christiensen, S.; Jones, N.; Nelson, J.

2013-12-01

Advances in computation resources and greater availability of water resources data represent an untapped resource for addressing hydrologic uncertainties in water resources decision-making. The current practice of water authorities relies on empirical, lumped hydrologic models to estimate watershed response. These models are not capable of taking advantage of many of the spatial datasets that are now available. Physically-based, distributed hydrologic models are capable of using these data resources and providing better predictions through stochastic analysis. However, there exists a digital divide that discourages many science-minded decision makers from using distributed models. This divide can be spanned using a combination of existing web technologies. The purpose of this presentation is to present a cloud-based environment that will offer hydrologic modeling tools or 'apps' for decision support and the web technologies that have been selected to aid in its implementation. Compared to the more commonly used lumped-parameter models, distributed models, while being more intuitive, are still data intensive, computationally expensive, and difficult to modify for scenario exploration. However, web technologies such as web GIS, web services, and cloud computing have made the data more accessible, provided an inexpensive means of high-performance computing, and created an environment for developing user-friendly apps for distributed modeling. Since many water authorities are primarily interested in the scenario exploration exercises with hydrologic models, we are creating a toolkit that facilitates the development of a series of apps for manipulating existing distributed models. There are a number of hurdles that cloud-based hydrologic modeling developers face. One of these is how to work with the geospatial data inherent with this class of models in a web environment. Supporting geospatial data in a website is beyond the capabilities of standard web frameworks and it requires the use of additional software. In particular, there are at least three elements that are needed: a geospatially enabled database, a map server, and geoprocessing toolbox. We recommend a software stack for geospatial web application development comprising: MapServer, PostGIS, and 52 North with Python as the scripting language to tie them together. Another hurdle that must be cleared is managing the cloud-computing load. We are using HTCondor as a solution to this end. Finally, we are creating a scripting environment wherein developers will be able to create apps that use existing hydrologic models in our system with minimal effort. This capability will be accomplished by creating a plugin for a Python content management system called CKAN. We are currently developing cyberinfrastructure that utilizes this stack and greatly lowers the investment required to deploy cloud-based modeling apps. This material is based upon work supported by the National Science Foundation under Grant No. 1135482

Snow multivariable data assimilation for hydrological predictions in Alpine sites

NASA Astrophysics Data System (ADS)

Piazzi, Gaia; Thirel, Guillaume; Campo, Lorenzo; Gabellani, Simone; Stevenin, Hervè

2017-04-01

Snowpack dynamics (snow accumulation and ablation) strongly impacts on hydrological processes in Alpine areas. During the winter season the presence of snow cover (snow accumulation) reduces the drainage in the basin with a resulting lower watershed time of concentration in case of possible rainfall events. Moreover, the release of the significant water volume stored in winter (snowmelt) considerably contributes to the total discharge during the melting period. Therefore when modeling hydrological processes in snow-dominated catchments the quality of predictions deeply depends on how the model succeeds in catching snowpack dynamics. The integration of a hydrological model with a snow module allows improving predictions of river discharges. Besides the well-known modeling limitations (uncertainty in parameterizations; possible errors affecting both meteorological forcing data and initial conditions; approximations in boundary conditions), there are physical factors that make an exhaustive reconstruction of snow dynamics complicated: snow intermittence in space and time, stratification and slow phenomena like metamorphism processes, uncertainty in snowfall evaluation, wind transportation, etc. Data Assimilation (DA) techniques provide an objective methodology to combine several independent snow-related data sources (model simulations, ground-based measurements and remote sensed observations) in order to obtain the most likely estimate of snowpack state. This study presents SMASH (Snow Multidata Assimilation System for Hydrology), a multi-layer snow dynamic model strengthened by a multivariable DA framework for hydrological purposes. The model is physically based on mass and energy balances and can be used to reproduce the main physical processes occurring within the snowpack: accumulation, density dynamics, melting, sublimation, radiative balance, heat and mass exchanges. The model is driven by observed forcing meteorological data (air temperature, wind velocity, relative air humidity, precipitation and incident solar radiation) to provide a complete estimate of snowpack state. The implementation of a DA scheme enables to assimilate simultaneously ground-based observations of different snow-related variables (snow depth, snow density, surface temperature and albedo). SMASH performances are evaluated by using observed data supplied by meteorological stations located in three experimental Alpine sites: Col de Porte (1325 m, France); Torgnon (2160 m, Italy); Weissfluhjoch (2540 m, Switzerland). A comparison analysis between the resulting performaces of Particle Filter and Ensemble Kalman Filter schemes is shown.

Minimum Flows and Levels Method of the St. Johns River Water Management District, Florida, USA

NASA Astrophysics Data System (ADS)

Neubauer, Clifford P.; Hall, Greeneville B.; Lowe, Edgar F.; Robison, C. Price; Hupalo, Richard B.; Keenan, Lawrence W.

2008-12-01

The St. Johns River Water Management District (SJRWMD) has developed a minimum flows and levels (MFLs) method that has been applied to rivers, lakes, wetlands, and springs. The method is primarily focused on ecological protection to ensure systems meet or exceed minimum eco-hydrologic requirements. MFLs are not calculated from past hydrology. Information from elevation transects is typically used to determine MFLs. Multiple MFLs define a minimum hydrologic regime to ensure that high, intermediate, and low hydrologic conditions are protected. MFLs are often expressed as statistics of long-term hydrology incorporating magnitude (flow and/or level), duration (days), and return interval (years). Timing and rates of change, the two other critical hydrologic components, should be sufficiently natural. The method is an event-based, non-equilibrium approach. The method is used in a regulatory water management framework to ensure that surface and groundwater withdrawals do not cause significant harm to the water resources and ecology of the above referenced system types. MFLs are implemented with hydrologic water budget models that simulate long-term system hydrology. The method enables a priori hydrologic assessments that include the cumulative effects of water withdrawals. Additionally, the method can be used to evaluate management options for systems that may be over-allocated or for eco-hydrologic restoration projects. The method can be used outside of the SJRWMD. However, the goals, criteria, and indicators of protection used to establish MFLs are system-dependent. Development of regionally important criteria and indicators of protection may be required prior to use elsewhere.

Geochemistry and the Understanding of Groundwater Systems

NASA Astrophysics Data System (ADS)

Glynn, P. D.; Plummer, L. N.; Weissmann, G. S.; Stute, M.

2009-12-01

Geochemical techniques and concepts have made major contributions to the understanding of groundwater systems. Advances continue to be made through (1) development of measurement and characterization techniques, (2) improvements in computer technology, networks and numerical modeling, (3) investigation of coupled geologic, hydrologic, geochemical and biologic processes, and (4) scaling of individual observations, processes or subsystem models into larger coherent model frameworks. Many applications benefit from progress in these areas, such as: (1) understanding paleoenvironments, in particular paleoclimate, through the use of groundwater archives, (2) assessing the sustainability (recharge and depletion) of groundwater resources, and (3) their vulnerability to contamination, (4) evaluating the capacity and consequences of subsurface waste isolation (e.g. geologic carbon sequestration, nuclear and chemical waste disposal), (5) assessing the potential for mitigation/transformation of anthropogenic contaminants in groundwater systems, and (6) understanding the effect of groundwater lag times in ecosystem-scale responses to natural events, land-use changes, human impacts, and remediation efforts. Obtaining “representative” groundwater samples is difficult and progress in obtaining “representative” samples, or interpreting them, requires new techniques in characterizing groundwater system heterogeneity. Better characterization and simulation of groundwater system heterogeneity (both physical and geochemical) is critical to interpreting the meaning of groundwater “ages”; to understanding and predicting groundwater flow, solute transport, and geochemical evolution; and to quantifying groundwater recharge and discharge processes. Research advances will also come from greater use and progress (1) in the application of environmental tracers to ground water dating and in the analysis of new geochemical tracers (e.g. compound specific isotopic analyses, noble gas isotopes, analyses of natural organic tracers), (2) in inverse geochemical and hydrological modeling, (3) in the understanding and simulation of coupled biological, geological, geochemical and hydrological processes, and (4) in the description and quantification of processes occurring at the boundaries of groundwater systems (e.g. unsaturated zone processes, groundwater/surface water interactions, impacts of changing geomorphology and vegetation). Improvements are needed in the integration of widely diverse information. Better techniques are needed to construct coherent conceptual frameworks from individual observations, simulated or reconstructed information, process models, and intermediate scale models. Iterating between data collection, interpretation, and the application of forward, inverse, and statistical modeling tools is likely to provide progress in this area. Quantifying groundwater system processes by using an open-system thermodynamic approach in a common mass- and energy-flow framework will also facilitate comparison and understanding of diverse processes.

A process-oriented hydro-biogeochemical model enabling simulation of gaseous carbon and nitrogen emissions and hydrologic nitrogen losses from a subtropical catchment.

PubMed

Zhang, Wei; Li, Yong; Zhu, Bo; Zheng, Xunhua; Liu, Chunyan; Tang, Jialiang; Su, Fang; Zhang, Chong; Ju, Xiaotang; Deng, Jia

2018-03-01

Quantification of nitrogen losses and net greenhouse gas (GHG) emissions from catchments is essential for evaluating the sustainability of ecosystems. However, the hydrologic processes without lateral flows hinder the application of biogeochemical models to this challenging task. To solve this issue, we developed a coupled hydrological and biogeochemical model, Catchment Nutrients Management Model - DeNitrification-DeComposition Model (CNMM-DNDC), to include both vertical and lateral mass flows. By incorporating the core biogeochemical processes (including decomposition, nitrification, denitrification and fermentation) of the DNDC into the spatially distributed hydrologic framework of the CNMM, the simulation of lateral water flows and their influences on nitrogen transportation can be realized. The CNMM-DNDC was then calibrated and validated in a small subtropical catchment belonged to Yanting station with comprehensive field observations. Except for the calibration of water flows (surface runoff and leaching water) in 2005, stream discharges of water and nitrate in 2007, the model validations of soil temperature, soil moisture, crop yield, water flows in 2006 and associated nitrate loss, fluxes of methane, ammonia, nitric oxide and nitrous oxide, and stream discharges of water and nitrate in 2008 were statistically in good agreement with the observations. Meanwhile, our initial simulation of the catchment showed scientific predictions. For instance, it revealed the following: (i) dominant ammonia volatilization among the losses of nitrogenous gases (accounting for 11-21% of the applied annual fertilizer nitrogen in croplands); (ii) hotspots of nitrate leaching near the main stream; and (iii) a net GHG sink function of the catchment. These results implicate the model's promising capability of predicting ecosystem productivity, hydrologic nitrogen loads, losses of gaseous nitrogen and emissions of GHGs, which could be used to provide strategies for establishing sustainable catchments. In addition, the model's capability would be further proved by applying in other catchments with different backgrounds. Copyright © 2017. Published by Elsevier B.V.

Factors Affecting P Loads to Surface Waters: Comparing the Roles of Precipitation and Land Management Practices

NASA Astrophysics Data System (ADS)

Motew, M.; Booth, E.; Carpenter, S. R.; Kucharik, C. J.

2014-12-01

Surface water quality is a major concern in the Yahara watershed (YW) of southern Wisconsin, home to a thriving dairy industry, the city of Madison, and five highly valued lakes that are eutrophic. Despite management interventions to mitigate runoff, there has been no significant trend in P loading to the lakes since 1975. Increases in manure production and heavy rainfall events over this time period may have offset any effects of management. We developed a comprehensive, integrated modeling framework that can simulate the effects of multiple drivers on ecosystem services, including surface water quality. The framework includes process-based representation of terrestrial ecosystems (Agro-IBIS) and groundwater flow (MODFLOW), hydrologic routing of water and nutrients across the landscape (THMB), and assessment of lake water quality (YWQM). Biogeochemical cycling and hydrologic transport of P have been added to the framework to enable detailed simulation of P dynamics within the watershed, including interactions with climate and management. The P module features in-soil cycling of organic, inorganic, and labile forms of P; manure application, decomposition, and subsequent loss of dissolved P in runoff; loss of particulate-bound P with erosion; and transport of dissolved and particulate P within waterways. Model results will compare the effects of increased heavy rainfall events, increased manure production, and implementation of best management practices on P loads to the Yahara lakes.

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.

An approach to predict water quality in data-sparse catchments using hydrological catchment similarity

NASA Astrophysics Data System (ADS)

Pohle, Ina; Glendell, Miriam; Stutter, Marc I.; Helliwell, Rachel C.

2017-04-01

An understanding of catchment response to climate and land use change at a regional scale is necessary for the assessment of mitigation and adaptation options addressing diffuse nutrient pollution. It is well documented that the physicochemical properties of a river ecosystem respond to change in a non-linear fashion. This is particularly important when threshold water concentrations, relevant to national and EU legislation, are exceeded. Large scale (regional) model assessments required for regulatory purposes must represent the key processes and mechanisms that are more readily understood in catchments with water quantity and water quality data monitored at high spatial and temporal resolution. While daily discharge data are available for most catchments in Scotland, nitrate and phosphorus are mostly available on a monthly basis only, as typified by regulatory monitoring. However, high resolution (hourly to daily) water quantity and water quality data exist for a limited number of research catchments. To successfully implement adaptation measures across Scotland, an upscaling from data-rich to data-sparse catchments is required. In addition, the widespread availability of spatial datasets affecting hydrological and biogeochemical responses (e.g. soils, topography/geomorphology, land use, vegetation etc.) provide an opportunity to transfer predictions between data-rich and data-sparse areas by linking processes and responses to catchment attributes. Here, we develop a framework of catchment typologies as a prerequisite for transferring information from data-rich to data-sparse catchments by focusing on how hydrological catchment similarity can be used as an indicator of grouped behaviours in water quality response. As indicators of hydrological catchment similarity we use flow indices derived from observed discharge data across Scotland as well as hydrological model parameters. For the latter, we calibrated the lumped rainfall-runoff model TUWModel using multiple objective functions. The relationships between indicators of hydrological catchment similarity, physical catchment characteristics and nitrate and phosphorus concentrations in rivers are then investigated using multivariate statistics. This understanding of the relationship between catchment characteristics, hydrological processes and water quality will allow us to implement more efficient regulatory water quality monitoring strategies, to improve existing water quality models and to model mitigation and adaptation scenarios to global change in data-sparse catchments.

An ontology for component-based models of water resource systems

NASA Astrophysics Data System (ADS)

Elag, Mostafa; Goodall, Jonathan L.

2013-08-01

Component-based modeling is an approach for simulating water resource systems where a model is composed of a set of components, each with a defined modeling objective, interlinked through data exchanges. Component-based modeling frameworks are used within the hydrologic, atmospheric, and earth surface dynamics modeling communities. While these efforts have been advancing, it has become clear that the water resources modeling community in particular, and arguably the larger earth science modeling community as well, faces a challenge of fully and precisely defining the metadata for model components. The lack of a unified framework for model component metadata limits interoperability between modeling communities and the reuse of models across modeling frameworks due to ambiguity about the model and its capabilities. To address this need, we propose an ontology for water resources model components that describes core concepts and relationships using the Web Ontology Language (OWL). The ontology that we present, which is termed the Water Resources Component (WRC) ontology, is meant to serve as a starting point that can be refined over time through engagement by the larger community until a robust knowledge framework for water resource model components is achieved. This paper presents the methodology used to arrive at the WRC ontology, the WRC ontology itself, and examples of how the ontology can aid in component-based water resources modeling by (i) assisting in identifying relevant models, (ii) encouraging proper model coupling, and (iii) facilitating interoperability across earth science modeling frameworks.

Improved regional-scale groundwater representation by the coupling of the mesoscale Hydrologic Model (mHM v5.7) to the groundwater model OpenGeoSys (OGS)

NASA Astrophysics Data System (ADS)

Jing, Miao; Heße, Falk; Kumar, Rohini; Wang, Wenqing; Fischer, Thomas; Walther, Marc; Zink, Matthias; Zech, Alraune; Samaniego, Luis; Kolditz, Olaf; Attinger, Sabine

2018-06-01

Most large-scale hydrologic models fall short in reproducing groundwater head dynamics and simulating transport process due to their oversimplified representation of groundwater flow. In this study, we aim to extend the applicability of the mesoscale Hydrologic Model (mHM v5.7) to subsurface hydrology by coupling it with the porous media simulator OpenGeoSys (OGS). The two models are one-way coupled through model interfaces GIS2FEM and RIV2FEM, by which the grid-based fluxes of groundwater recharge and the river-groundwater exchange generated by mHM are converted to fixed-flux boundary conditions of the groundwater model OGS. Specifically, the grid-based vertical reservoirs in mHM are completely preserved for the estimation of land-surface fluxes, while OGS acts as a plug-in to the original mHM modeling framework for groundwater flow and transport modeling. The applicability of the coupled model (mHM-OGS v1.0) is evaluated by a case study in the central European mesoscale river basin - Nägelstedt. Different time steps, i.e., daily in mHM and monthly in OGS, are used to account for fast surface flow and slow groundwater flow. Model calibration is conducted following a two-step procedure using discharge for mHM and long-term mean of groundwater head measurements for OGS. Based on the model summary statistics, namely the Nash-Sutcliffe model efficiency (NSE), the mean absolute error (MAE), and the interquartile range error (QRE), the coupled model is able to satisfactorily represent the dynamics of discharge and groundwater heads at several locations across the study basin. Our exemplary calculations show that the one-way coupled model can take advantage of the spatially explicit modeling capabilities of surface and groundwater hydrologic models and provide an adequate representation of the spatiotemporal behaviors of groundwater storage and heads, thus making it a valuable tool for addressing water resources and management problems.

Distributed Application of the Unified Noah LSM with Hydrologic Flow Routing on an Appalachian Headwater Basin

NASA Astrophysics Data System (ADS)

Garcia, M.; Kumar, S.; Gochis, D.; Yates, D.; McHenry, J.; Burnet, T.; Coats, C.; Condrey, J.

2006-05-01

Collaboration between scientists at UMBC-GEST and NASA-GSFC, the NCAR Research Applications Laboratory (RAL), and Baron Advanced Meteorological Services (BAMS), has produced a modeling framework for the application of traditional land surface models (LSMs) in a distributed hydrologic system which can be used for diagnosis and prediction of routed stream discharge hydrographs. This collaboration is oriented on near-term system implementation across Romania for flood and flash-flood analyses and forecasting as part of the World Bank-funded Destructive Waters Abatement (DESWAT) program. Meteorological forcing from surface observations, model analyses and numerical forecasts are employed in the NASA-GSFC Land Information System (LIS) to drive the Unified Noah LSM with Noah-Distributed components, stream network delineation and routing schemes original to this work. The Unified Noah LSM is the outgrowth of a joint modeling effort between several research partners including NCAR, the NOAA National Center for Environmental Prediction (NCEP), and the Air Force Weather Agency (AFWA). At NCAR, hydrologically-oriented extensions to the Noah LSM have been developed for LSM applications in a distributed domain in order to address the lateral redistribution of soil moisture by surface and subsurface flow processes. These advancements have been integrated into the NASA-GSFC Land Information System (LIS) and coupled with an original framework for hydraulic channel network definition and specification, linkages with the Noah-Distributed overland and subsurface flow framework, and distributed cell- to-cell (or link-node) hydraulic routing. This poster presents an overview of the system components and their organization, as well as results of the first U.S. case study performed with this system under various configurations. The case study simulated precipitation events over a headwater basin in the southern Appalachian Mountains in October 2005 following the landfall of Tropical Storm Tammy in South Carolina. These events followed on a long dry period in the region, lending to the demonstration of watershed response to strong precipitation forcing under nearly ideal and easily-specified initial conditions. The results presented here will compare simulated versus observed streamflow conditions at various locations in the test watershed using a selection of routing methods.

A Methodology for Forecasting Damage & Economic Consequences to Floods: Building on the National Flood Interoperability Experiment (NFIE)

NASA Astrophysics Data System (ADS)

Tootle, G. A.; Gutenson, J. L.; Zhu, L.; Ernest, A. N. S.; Oubeidillah, A.; Zhang, X.

2015-12-01

The National Flood Interoperability Experiment (NFIE) held June 3-July 17, 2015 at the National Water Center (NWC) in Tuscaloosa, Alabama sought to demonstrate an increase in flood predictive capacity for the coterminous United States (CONUS). Accordingly, NFIE-derived technologies and workflows offer the ability to forecast flood damage and economic consequence estimates that coincide with the hydrologic and hydraulic estimations these physics-based models generate. A model providing an accurate prediction of damage and economic consequences is a valuable asset when allocating funding for disaster response, recovery, and relief. Damage prediction and economic consequence assessment also offer an adaptation planning mechanism for defending particularly valuable or vulnerable structures. The NFIE, held at the NWC on The University of Alabama (UA) campus led to the development of this large scale flow and inundation forecasting framework. Currently, the system can produce 15-hour lead-time forecasts for the entire coterminous United States (CONUS). A concept which is anticipated to become operational as of May 2016 within the NWC. The processing of such a large-scale, fine resolution model is accomplished in a parallel computing environment using large supercomputing clusters. Traditionally, flood damage and economic consequence assessment is calculated in a desktop computing environment with a ménage of meteorology, hydrology, hydraulic, and damage assessment tools. In the United States, there are a range of these flood damage/ economic consequence assessment software's available to local, state, and federal emergency management agencies. Among the more commonly used and freely accessible models are the Hydrologic Engineering Center's Flood Damage Reduction Analysis (HEC-FDA), Flood Impact Assessment (HEC-FIA), and Federal Emergency Management Agency's (FEMA's) United States Multi-Hazard (Hazus-MH). All of which exist only in a desktop environment. With this, authors submit an initial framework for estimating damage and economic consequences to floods using flow and inundation products from the NFIE framework. This adaptive system utilizes existing nationwide datasets describing location and use of structures and can take assimilate a range of data resolutions.

Improved vegetation parameterization for hydrological model and assessment of land cover change impacts on flow regime of the Upper Bhima basin, India

NASA Astrophysics Data System (ADS)

Mohaideen, M. M. Diwan; Varija, K.

2018-05-01

This study investigates the potential and applicability of variable infiltration capacity (VIC) hydrological model to simulate different hydrological components of the Upper Bhima basin under two different Land Use Land Cover (LULC) (the year 2000 and 2010) conditions. The total drainage area of the basin was discretized into 1694 grids of about 5.5 km by 5.5 km: accordingly the model parameters were calibrated at each grid level. Vegetation parameters for the model were prepared using temporal profile of Leaf Area Index (LAI) from Moderate-Resolution Imaging Spectroradiometer and LULC. This practice provides a methodological framework for the improved vegetation parameterization along with region-specific condition for the model simulation. The calibrated and validated model was run using the two LULC conditions separately with the same observed meteorological forcing (1996-2001) and soil data. The change in LULC has resulted to an increase in the average annual evapotranspiration over the basin by 7.8%, while the average annual surface runoff and baseflow decreased by 18.86 and 5.83%, respectively. The variability in hydrological components and the spatial variation of each component attributed to LULC were assessed at the basin grid level. It was observed that 80% of the basin grids showed an increase in evapotranspiration (ET) (maximum of 292 mm). While the majority of the grids showed a decrease in surface runoff and baseflow, some of the grids showed an increase (i.e. 21 and 15% of total grids—surface runoff and baseflow, respectively).

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.

Establishing a Numerical Modeling Framework for Hydrologic Engineering Analyses of Extreme Storm Events

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

Chen, Xiaodong; Hossain, Faisal; Leung, L. Ruby

In this study a numerical modeling framework for simulating extreme storm events was established using the Weather Research and Forecasting (WRF) model. Such a framework is necessary for the derivation of engineering parameters such as probable maximum precipitation that are the cornerstone of large water management infrastructure design. Here this framework was built based on a heavy storm that occurred in Nashville (USA) in 2010, and verified using two other extreme storms. To achieve the optimal setup, several combinations of model resolutions, initial/boundary conditions (IC/BC), cloud microphysics and cumulus parameterization schemes were evaluated using multiple metrics of precipitation characteristics. Themore » evaluation suggests that WRF is most sensitive to IC/BC option. Simulation generally benefits from finer resolutions up to 5 km. At the 15km level, NCEP2 IC/BC produces better results, while NAM IC/BC performs best at the 5km level. Recommended model configuration from this study is: NAM or NCEP2 IC/BC (depending on data availability), 15km or 15km-5km nested grids, Morrison microphysics and Kain-Fritsch cumulus schemes. Validation of the optimal framework suggests that these options are good starting choices for modeling extreme events similar to the test cases. This optimal framework is proposed in response to emerging engineering demands of extreme storm events forecasting and analyses for design, operations and risk assessment of large water infrastructures.« less

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.

Hydrologic Model Development and Calibration: Contrasting a Single- and Multi-Objective Approach for Comparing Model Performance

NASA Astrophysics Data System (ADS)

Asadzadeh, M.; Maclean, A.; Tolson, B. A.; Burn, D. H.

2009-05-01

Hydrologic model calibration aims to find a set of parameters that adequately simulates observations of watershed behavior, such as streamflow, or a state variable, such as snow water equivalent (SWE). There are different metrics for evaluating calibration effectiveness that involve quantifying prediction errors, such as the Nash-Sutcliffe (NS) coefficient and bias evaluated for the entire calibration period, on a seasonal basis, for low flows, or for high flows. Many of these metrics are conflicting such that the set of parameters that maximizes the high flow NS differs from the set of parameters that maximizes the low flow NS. Conflicting objectives are very likely when different calibration objectives are based on different fluxes and/or state variables (e.g., NS based on streamflow versus SWE). One of the most popular ways to balance different metrics is to aggregate them based on their importance and find the set of parameters that optimizes a weighted sum of the efficiency metrics. Comparing alternative hydrologic models (e.g., assessing model improvement when a process or more detail is added to the model) based on the aggregated objective might be misleading since it represents one point on the tradeoff of desired error metrics. To derive a more comprehensive model comparison, we solved a bi-objective calibration problem to estimate the tradeoff between two error metrics for each model. Although this approach is computationally more expensive than the aggregation approach, it results in a better understanding of the effectiveness of selected models at each level of every error metric and therefore provides a better rationale for judging relative model quality. The two alternative models used in this study are two MESH hydrologic models (version 1.2) of the Wolf Creek Research basin that differ in their watershed spatial discretization (a single Grouped Response Unit, GRU, versus multiple GRUs). The MESH model, currently under development by Environment Canada, is a coupled land-surface and hydrologic model. Results will demonstrate the conclusions a modeller might make regarding the value of additional watershed spatial discretization under both an aggregated (single-objective) and multi-objective model comparison framework.

Geologic Framework Model Analysis Model Report

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

R. Clayton

2000-12-19

The purpose of this report is to document the Geologic Framework Model (GFM), Version 3.1 (GFM3.1) with regard to data input, modeling methods, assumptions, uncertainties, limitations, and validation of the model results, qualification status of the model, and the differences between Version 3.1 and previous versions. The GFM represents a three-dimensional interpretation of the stratigraphy and structural features of the location of the potential Yucca Mountain radioactive waste repository. The GFM encompasses an area of 65 square miles (170 square kilometers) and a volume of 185 cubic miles (771 cubic kilometers). The boundaries of the GFM were chosen to encompassmore » the most widely distributed set of exploratory boreholes (the Water Table or WT series) and to provide a geologic framework over the area of interest for hydrologic flow and radionuclide transport modeling through the unsaturated zone (UZ). The depth of the model is constrained by the inferred depth of the Tertiary-Paleozoic unconformity. The GFM was constructed from geologic map and borehole data. Additional information from measured stratigraphy sections, gravity profiles, and seismic profiles was also considered. This interim change notice (ICN) was prepared in accordance with the Technical Work Plan for the Integrated Site Model Process Model Report Revision 01 (CRWMS M&O 2000). The constraints, caveats, and limitations associated with this model are discussed in the appropriate text sections that follow. The GFM is one component of the Integrated Site Model (ISM) (Figure l), which has been developed to provide a consistent volumetric portrayal of the rock layers, rock properties, and mineralogy of the Yucca Mountain site. The ISM consists of three components: (1) Geologic Framework Model (GFM); (2) Rock Properties Model (RPM); and (3) Mineralogic Model (MM). The ISM merges the detailed project stratigraphy into model stratigraphic units that are most useful for the primary downstream models and the repository design. These downstream models include the hydrologic flow models and the radionuclide transport models. All the models and the repository design, in turn, will be incorporated into the Total System Performance Assessment (TSPA) of the potential radioactive waste repository block and vicinity to determine the suitability of Yucca Mountain as a host for the repository. The interrelationship of the three components of the ISM and their interface with downstream uses are illustrated in Figure 2.« less

Evaluating hydrological response to forecasted land-use change—scenario testing with the automated geospatial watershed assessment (AGWA) tool

USGS Publications Warehouse

Kepner, William G.; Semmens, Darius J.; Hernandez, Mariano; Goodrich, David C.

2009-01-01

Envisioning and evaluating future scenarios has emerged as a critical component of both science and social decision-making. The ability to assess, report, map, and forecast the life support functions of ecosystems is absolutely critical to our capacity to make informed decisions to maintain the sustainable nature of our ecosystem services now and into the future. During the past two decades, important advances in the integration of remote imagery, computer processing, and spatial-analysis technologies have been used to develop landscape information that can be integrated with hydrologic models to determine long-term change and make predictive inferences about the future. Two diverse case studies in northwest Oregon (Willamette River basin) and southeastern Arizona (San Pedro River) were examined in regard to future land use scenarios relative to their impact on surface water conditions (e.g., sediment yield and surface runoff) using hydrologic models associated with the Automated Geospatial Watershed Assessment (AGWA) tool. The base reference grid for land cover was modified in both study locations to reflect stakeholder preferences 20 to 60 yrs into the future, and the consequences of landscape change were evaluated relative to the selected future scenarios. The two studies provide examples of integrating hydrologic modeling with a scenario analysis framework to evaluate plausible future forecasts and to understand the potential impact of landscape change on ecosystem services.

A Cloud Based Framework For Monitoring And Predicting Subsurface System Behaviour

NASA Astrophysics Data System (ADS)

Versteeg, R. J.; Rodzianko, A.; Johnson, D. V.; Soltanian, M. R.; Dwivedi, D.; Dafflon, B.; Tran, A. P.; Versteeg, O. J.

2015-12-01

Subsurface system behavior is driven and controlled by the interplay of physical, chemical, and biological processes which occur at multiple temporal and spatial scales. Capabilities to monitor, understand and predict this behavior in an effective and timely manner are needed for both scientific purposes and for effective subsurface system management. Such capabilities require three elements: Models, Data and an enabling cyberinfrastructure, which allow users to use these models and data in an effective manner. Under a DOE Office of Science funded STTR award Subsurface Insights and LBNL have designed and implemented a cloud based predictive assimilation framework (PAF) which automatically ingests, controls quality and stores heterogeneous physical and chemical subsurface data and processes these data using different inversion and modeling codes to provide information on the current state and evolution of subsurface systems. PAF is implemented as a modular cloud based software application with five components: (1) data acquisition, (2) data management, (3) data assimilation and processing, (4) visualization and result delivery and (5) orchestration. Serverside PAF uses ZF2 (a PHP web application framework) and Python and both open source (ODM2) and in house developed data models. Clientside PAF uses CSS and JS to allow for interactive data visualization and analysis. Client side modularity (which allows for a responsive interface) of the system is achieved by implementing each core capability of PAF (such as data visualization, user configuration and control, electrical geophysical monitoring and email/SMS alerts on data streams) as a SPA (Single Page Application). One of the recent enhancements is the full integration of a number of flow and mass transport and parameter estimation codes (e.g., MODFLOW, MT3DMS, PHT3D, TOUGH, PFLOTRAN) in this framework. This integration allows for autonomous and user controlled modeling of hydrological and geochemical processes. In our presentation we will discuss our software architecture and present the results of using these codes and the overall developed performance of our framework using hydrological, geochemical and geophysical data from the LBNL SFA2 Rifle field site.

Preliminary three-dimensional geohydrologic framework of the San Antonio Creek Groundwater Basin, Santa Barbara County, California

NASA Astrophysics Data System (ADS)

Cromwell, G.; Sweetkind, D. S.; O'leary, D. R.

2017-12-01

The San Antonio Creek Groundwater Basin is a rural agricultural area that is heavily dependent on groundwater to meet local water demands. The U.S. Geological Survey (USGS) is working cooperatively with Santa Barbara County and Vandenberg Air Force Base to assess the quantity and quality of the groundwater resources within the basin. As part of this assessment, an integrated hydrologic model that will help stakeholders to effectively manage the water resources in the basin is being developed. The integrated hydrologic model includes a conceptual model of the subsurface geology consisting of stratigraphy and variations in lithology throughout the basin. The San Antonio Creek Groundwater Basin is a relatively narrow, east-west oriented valley that is structurally controlled by an eastward-plunging syncline. Basin-fill material beneath the valley floor consists of relatively coarse-grained, permeable, marine and non-marine sedimentary deposits, which are underlain by fine-grained, low-permeability, marine sedimentary rocks. To characterize the system, surficial and subsurface geohydrologic data were compiled from geologic maps, existing regional geologic models, and lithology and geophysical logs from boreholes, including two USGS multiple-well sites drilled as part of this study. Geohydrologic unit picks and lithologic variations are incorporated into a three-dimensional framework model of the basin. This basin (model) includes six geohydrologic units that follow the structure and stratigraphy of the area: 1) Bedrock - low-permeability marine sedimentary rocks; 2) Careaga Formation - fine to coarse grained near-shore sandstone; 3) Paso Robles Formation, lower portion - sandy-gravely deposits with clay and limestone; 4) Paso Robles Formation, middle portion - clayey-silty deposits; 5) Paso Robles Formation, upper portion - sandy-gravely deposits; and 6) recent Quaternary deposits. Hydrologic data show that the upper and lower portions of the Paso Robles Formation are the primary grou­ndwater-bearing units within the basin, and that the fine-grained layer within this Formation locally restricts vertical groundwater flow.

Hydrological resiliency in the Western Boreal Plains: classification of hydrological responses using wavelet analysis to assess landscape resilience

NASA Astrophysics Data System (ADS)

Probert, Samantha; Kettridge, Nicholas; Devito, Kevin; Hannah, David; Parkin, Geoff

2017-04-01

The Boreal represents a system of substantial resilience to climate change, with minimal ecological change over the past 6000 years. However, unprecedented climatic warming, coupled with catchment disturbances could exceed thresholds of hydrological function in the Western Boreal Plains. Knowledge of ecohydrological and climatic feedbacks that shape the resilience of boreal forests has advanced significantly in recent years, but this knowledge is yet to be applied and understood at landscape scales. Hydrological modelling at the landscape scale is challenging in the WBP due to diverse, non-topographically driven hydrology across the mosaic of terrestrial and aquatic ecosystems. This study functionally divides the geologic and ecological components of the landscape into Hydrologic Response Areas (HRAs) and wetland, forestland, interface and pond Hydrologic Units (HUs) to accurately characterise water storage and infer transmission at multiple spatial and temporal scales. Wavelet analysis is applied to pond and groundwater levels to describe the patterns of water storage in response to climate signals; to isolate dominant controls on hydrological responses and to assess the relative importance of physical controls between wet and dry climates. This identifies which components of the landscape exhibit greater magnitude and frequency of variability to wetting and drying trends, further to testing the hierarchical framework for hydrological storage controls of: climate, bedrock geology, surficial geology, soil, vegetation, and topography. Classifying HRA and HU hydrological function is essential to understand and predict water storage and redistribution through drought cycles and wet periods. This work recognises which landscape components are most sensitive under climate change and disturbance and also creates scope for hydrological resiliency research in Boreal systems by recognising critical landscape components and their role in landscape collapse or catastrophic shift in ecosystem function under future climatic scenarios.

Distributed watershed modeling of design storms to identify nonpoint source loading areas

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

Endreny, T.A.; Wood, E.F.

1999-03-01

Watershed areas that generate nonpoint source (NPS) polluted runoff need to be identified prior to the design of basin-wide water quality projects. Current watershed-scale NPS models lack a variable source area (VSA) hydrology routine, and are therefore unable to identify spatially dynamic runoff zones. The TOPLATS model used a watertable-driven VSA hydrology routine to identify runoff zones in a 17.5 km{sup 2} agricultural watershed in central Oklahoma. Runoff areas were identified in a static modeling framework as a function of prestorm watertable depth and also in a dynamic modeling framework by simulating basin response to 2, 10, and 25 yrmore » return period 6 h design storms. Variable source area expansion occurred throughout the duration of each 6 h storm and total runoff area increased with design storm intensity. Basin-average runoff rates of 1 mm h{sup {minus}1} provided little insight into runoff extremes while the spatially distributed analysis identified saturation excess zones with runoff rates equaling effective precipitation. The intersection of agricultural landcover areas with these saturation excess runoff zones targeted the priority potential NPS runoff zones that should be validated with field visits. These intersected areas, labeled as potential NPS runoff zones, were mapped within the watershed to demonstrate spatial analysis options available in TOPLATS for managing complex distributions of watershed runoff. TOPLATS concepts in spatial saturation excess runoff modelling should be incorporated into NPS management models.« less

The One-Water Hydrologic Flow Model - The next generation in fully integrated hydrologic simulation software

NASA Astrophysics Data System (ADS)

Boyce, S. E.; Hanson, R. T.

2015-12-01

The One-Water Hydrologic Flow Model (MF-OWHM) is a MODFLOW-based integrated hydrologic flow model that is the most complete version, to date, of the MODFLOW family of hydrologic simulators needed for the analysis of a broad range of conjunctive-use issues. MF-OWHM fully links the movement and use of groundwater, surface water, and imported water for consumption by agriculture and natural vegetation on the landscape, and for potable and other uses within a supply-and-demand framework. MF-OWHM is based on the Farm Process for MODFLOW-2005 combined with Local Grid Refinement, Streamflow Routing, Surface-water Routing Process, Seawater Intrusion, Riparian Evapotranspiration, and the Newton-Raphson solver. MF-OWHM also includes linkages for deformation-, flow-, and head-dependent flows; additional observation and parameter options for higher-order calibrations; and redesigned code for facilitation of self-updating models and faster simulation run times. The next version of MF-OWHM, currently under development, will include a new surface-water operations module that simulates dynamic reservoir operations, the conduit flow process for karst aquifers and leaky pipe networks, a new subsidence and aquifer compaction package, and additional features and enhancements to enable more integration and cross communication between traditional MODFLOW packages. By retaining and tracking the water within the hydrosphere, MF-OWHM accounts for "all of the water everywhere and all of the time." This philosophy provides more confidence in the water accounting by the scientific community and provides the public a foundation needed to address wider classes of problems such as evaluation of conjunctive-use alternatives and sustainability analysis, including potential adaptation and mitigation strategies, and best management practices. By Scott E. Boyce and Randall T. Hanson

A toolkit for determining historical eco-hydrological interactions

NASA Astrophysics Data System (ADS)

Singer, M. B.; Sargeant, C. I.; Evans, C. M.; Vallet-Coulomb, C.

2016-12-01

Contemporary climate change is predicted to result in perturbations to hydroclimatic regimes across the globe, with some regions forecast to become warmer and drier. Given that water is a primary determinant of vegetative health and productivity, we can expect shifts in the availability of this critical resource to have significant impacts on forested ecosystems. The subject is particularly complex in environments where multiple sources of water are potentially available to vegetation and which may also exhibit spatial and temporal variability. To anticipate how subsurface hydrological partitioning may evolve in the future and impact overlying vegetation, we require well constrained, historical data and a modelling framework for assessing the dynamics of subsurface hydrology. We outline a toolkit to retrospectively investigate dynamic water use by trees. We describe a synergistic approach, which combines isotope dendrochronology of tree ring cellulose with a biomechanical model, detailed climatic and isotopic data in endmember waters to assess the mean isotopic composition of source water used in annual tree rings. We identify the data requirements and suggest three versions of the toolkit based on data availability. We present sensitivity analyses in order to identify the key variables required to constrain model predictions and then develop empirical relationships for constraining these parameters based on climate records. We demonstrate our methodology within a Mediterranean riparian forest site and show how it can be used along with subsurface hydrological modelling to validate source water determinations, which are fundamental to understanding climatic fluctuations and trends in subsurface hydrology. We suggest that the utility of our toolkit is applicable in riparian zones and in a range of forest environments where distinct isotopic endmembers are present.

An operational hydrological ensemble prediction system for the city of Zurich (Switzerland): assessing the added value of probabilistic forecasts

NASA Astrophysics Data System (ADS)

Addor, N.; Jaun, S.; Fundel, F.; Zappa, M.

2012-04-01

The Sihl River flows through Zurich, Switzerland's most populated city, for which it represents the largest flood threat. To anticipate extreme discharge events and provide decision support in case of flood risk, a hydrometeorological ensemble prediction system (HEPS) was launched operationally in 2008. This model chain relies on deterministic (COSMO-7) and probabilistic (COSMO-LEPS) atmospheric forecasts, which are used to force a semi-distributed hydrological model (PREVAH) coupled to a hydraulic model (FLORIS). The resulting hydrological forecasts are eventually communicated to the stakeholders involved in the Sihl discharge management. This fully operational setting provides a real framework with which we assessed the potential of deterministic and probabilistic discharge forecasts for flood mitigation. To study the suitability of HEPS for small-scale basins and to quantify the added value conveyed by the probability information, a 31-month reforecast was produced for the Sihl catchment (336 km2). Several metrics support the conclusion that the performance gain is of up to 2 days lead time for the catchment considered. Brier skill scores show that probabilistic hydrological forecasts outperform their deterministic counterparts for all the lead times and event intensities considered. The small size of the Sihl catchment does not prevent skillful discharge forecasts, but makes them particularly dependent on correct precipitation forecasts. Our evaluation stresses that the capacity of the model to provide confident and reliable mid-term probability forecasts for high discharges is limited. We finally highlight challenges for making decisions on the basis of hydrological predictions, and discuss the need for a tool to be used in addition to forecasts to compare the different mitigation actions possible in the Sihl catchment.

Boise Hydrogeophysical Research Site: Control Volume/Test Cell and Community Research Asset

NASA Astrophysics Data System (ADS)

Barrash, W.; Bradford, J.; Malama, B.

2008-12-01

The Boise Hydrogeophysical Research Site (BHRS) is a research wellfield or field-scale test facility developed in a shallow, coarse, fluvial aquifer with the objectives of supporting: (a) development of cost- effective, non- or minimally-invasive quantitative characterization and imaging methods in heterogeneous aquifers using hydrologic and geophysical techniques; (b) examination of fundamental relationships and processes at multiple scales; (c) testing theories and models for groundwater flow and solute transport; and (d) educating and training of students in multidisciplinary subsurface science and engineering. The design of the wells and the wellfield support modular use and reoccupation of wells for a wide range of single-well, cross-hole, multiwell and multilevel hydrologic, geophysical, and combined hydrologic-geophysical experiments. Efforts to date by Boise State researchers and collaborators have been largely focused on: (a) establishing the 3D distributions of geologic, hydrologic, and geophysical parameters which can then be used as the basis for jointly inverting hard and soft data to return the 3D K distribution and (b) developing subsurface measurement and imaging methods including tomographic characterization and imaging methods. At this point the hydrostratigraphic framework of the BHRS is known to be a hierarchical multi-scale system which includes layers and lenses that are recognized with geologic, hydrologic, radar, seismic, and EM methods; details are now emerging which may allow 3D deterministic characterization of zones and/or material variations at the meter scale in the central wellfield. Also the site design and subsurface framework have supported a variety of testing configurations for joint hydrologic and geophysical experiments. Going forward we recognize the opportunity to increase the R&D returns from use of the BHRS with additional infrastructure (especially for monitoring the vadose zone and surface water-groundwater interactions), more collaborative activity, and greater access to site data. Our broader goal of becoming more available as a research asset for the scientific community also supports the long-term business plan of increasing funding opportunities to maintain and operate the site.

Forests growing under dry conditions have higher hydrological resilience to drought than do more humid forests.

PubMed

Helman, David; Lensky, Itamar M; Yakir, Dan; Osem, Yagil

2017-07-01

More frequent and intense droughts are projected during the next century, potentially changing the hydrological balances in many forested catchments. Although the impacts of droughts on forest functionality have been vastly studied, little attention has been given to studying the effect of droughts on forest hydrology. Here, we use the Budyko framework and two recently introduced Budyko metrics (deviation and elasticity) to study the changes in the water yields (rainfall minus evapotranspiration) of forested catchments following a climatic drought (2006-2010) in pine forests distributed along a rainfall gradient (P = 280-820 mm yr -1 ) in the Eastern Mediterranean (aridity factor = 0.17-0.56). We use a satellite-based model and meteorological information to calculate the Budyko metrics. The relative water yield ranged from 48% to 8% (from the rainfall) in humid to dry forests and was mainly associated with rainfall amount (increasing with increased rainfall amount) and bedrock type (higher on hard bedrocks). Forest elasticity was larger in forests growing under drier conditions, implying that drier forests have more predictable responses to drought, according to the Budyko framework, compared to forests growing under more humid conditions. In this context, younger forests were shown more elastic than older forests. Dynamic deviation, which is defined as the water yield departure from the Budyko curve, was positive in all forests (i.e., less-than-expected water yields according to Budyko's curve), increasing with drought severity, suggesting lower hydrological resistance to drought in forests suffering from larger rainfall reductions. However, the dynamic deviation significantly decreased in forests that experienced relatively cooler conditions during the drought period. Our results suggest that forests growing under permanent dry conditions might develop a range of hydrological and eco-physiological adjustments to drought leading to higher hydrological resilience. In the context of predicted climate change, such adjustments are key factors in sustaining forested catchments in water-limited regions. © 2016 John Wiley & Sons Ltd.

Planning Framework for Mesolevel Optimization of Urban Runoff Control Schemes

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

Zhou, Qianqian; Blohm, Andrew; Liu, Bo

A planning framework is developed to optimize runoff control schemes at scales relevant for regional planning at an early stage. The framework employs less sophisticated modeling approaches to allow a practical application in developing regions with limited data sources and computing capability. The methodology contains three interrelated modules: (1)the geographic information system (GIS)-based hydrological module, which aims at assessing local hydrological constraints and potential for runoff control according to regional land-use descriptions; (2)the grading module, which is built upon the method of fuzzy comprehensive evaluation. It is used to establish a priority ranking system to assist the allocation of runoffmore » control targets at the subdivision level; and (3)the genetic algorithm-based optimization module, which is included to derive Pareto-based optimal solutions for mesolevel allocation with multiple competing objectives. The optimization approach describes the trade-off between different allocation plans and simultaneously ensures that all allocation schemes satisfy the minimum requirement on runoff control. Our results highlight the importance of considering the mesolevel allocation strategy in addition to measures at macrolevels and microlevels in urban runoff management. (C) 2016 American Society of Civil Engineers.« less

Vegetation function and non-uniqueness of the hydrological response

NASA Astrophysics Data System (ADS)

Ivanov, V. Y.; Fatichi, S.; Kampf, S. K.; Caporali, E.

2012-04-01

Through local moisture uptake vegetation exerts seasonal and longer-term impacts on the watershed hydrological response. However, the role of vegetation may go beyond the conventionally implied and well-understood "sink" function in the basin soil moisture storage equation. We argue that vegetation function imposes a "homogenizing" effect on pre-event soil moisture spatial storage, decreasing the likelihood that a rainfall event will result in a topographically-driven redistribution of soil water and the consequent formation of variable source areas. In combination with vegetation temporal dynamics, this may lead to the non-uniqueness of the hydrological response with respect to the mean basin wetness. This study designs a set of relevant numerical experiments carried out with two physically-based models; one of the models, HYDRUS, resolves variably saturated subsurface flow using a fully three-dimensional formulation, while the other model, tRIBS+VEGGIE, uses a one-dimensional formulation applied in a quasi-three-dimensional framework in combination with the model of vegetation dynamics. We demonstrate that (1) vegetation function modifies spatial heterogeneity in moisture spatial storage by imposing different degrees of subsurface flow connectivity; explore mechanistically (2) how and why a basin with the same mean soil moisture can have distinctly different spatial soil moisture distributions; and demonstrate (2) how these distinct moisture distributions result in a hysteretic runoff response to precipitation. Furthermore, the study argues that near-surface soil moisture is an insufficient indicator of the initial moisture state of a catchment with the implication of its limited effect on hydrological predictability.

Interoperability challenges in river discharge modelling: A cross domain application scenario

NASA Astrophysics Data System (ADS)

Santoro, Mattia; Andres, Volker; Jirka, Simon; Koike, Toshio; Looser, Ulrich; Nativi, Stefano; Pappenberger, Florian; Schlummer, Manuela; Strauch, Adrian; Utech, Michael; Zsoter, Ervin

2018-06-01

River discharge is a critical water cycle variable, as it integrates all the processes (e.g. runoff and evapotranspiration) occurring within a river basin and provides a hydrological output variable that can be readily measured. Its prediction is of invaluable help for many water-related tasks including water resources assessment and management, flood protection, and disaster mitigation. Observations of river discharge are important to calibrate and validate hydrological or coupled land, atmosphere and ocean models. This requires using datasets from different scientific domains (Water, Weather, etc.). Typically, such datasets are provided using different technological solutions. This complicates the integration of new hydrological data sources into application systems. Therefore, a considerable effort is often spent on data access issues instead of the actual scientific question. This paper describes the work performed to address multidisciplinary interoperability challenges related to river discharge modeling and validation. This includes definition and standardization of domain specific interoperability standards for hydrological data sharing and their support in global frameworks such as the Global Earth Observation System of Systems (GEOSS). The research was developed in the context of the EU FP7-funded project GEOWOW (GEOSS Interoperability for Weather, Ocean and Water), which implemented a "River Discharge" application scenario. This scenario demonstrates the combination of river discharge observations data from the Global Runoff Data Centre (GRDC) database and model outputs produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) predicting river discharge based on weather forecast information in the context of the GEOSS.

Towards improved hydrologic predictions using data assimilation techniques for water resource management at the continental scale

NASA Astrophysics Data System (ADS)

Naz, Bibi; Kurtz, Wolfgang; Kollet, Stefan; Hendricks Franssen, Harrie-Jan; Sharples, Wendy; Görgen, Klaus; Keune, Jessica; Kulkarni, Ketan

2017-04-01

More accurate and reliable hydrologic simulations are important for many applications such as water resource management, future water availability projections and predictions of extreme events. However, simulation of spatial and temporal variations in the critical water budget components such as precipitation, snow, evaporation and runoff is highly uncertain, due to errors in e.g. model structure and inputs (hydrologic parameters and forcings). In this study, we use data assimilation techniques to improve the predictability of continental-scale water fluxes using in-situ measurements along with remotely sensed information to improve hydrologic predications for water resource systems. The Community Land Model, version 3.5 (CLM) integrated with the Parallel Data Assimilation Framework (PDAF) was implemented at spatial resolution of 1/36 degree (3 km) over the European CORDEX domain. The modeling system was forced with a high-resolution reanalysis system COSMO-REA6 from Hans-Ertel Centre for Weather Research (HErZ) and ERA-Interim datasets for time period of 1994-2014. A series of data assimilation experiments were conducted to assess the efficiency of assimilation of various observations, such as river discharge data, remotely sensed soil moisture, terrestrial water storage and snow measurements into the CLM-PDAF at regional to continental scales. This setup not only allows to quantify uncertainties, but also improves streamflow predictions by updating simultaneously model states and parameters utilizing observational information. The results from different regions, watershed sizes, spatial resolutions and timescales are compared and discussed in this study.

Emergent Archetype Hydrological-Biogeochemical Response Patterns in Heterogeneous Catchments

NASA Astrophysics Data System (ADS)

Jawitz, J. W.; Gall, H. E.; Rao, P.

2013-12-01

What can spatiotemporally integrated patterns observed in stream hydrologic and biogeochemical signals generated in response to transient hydro-climatic and anthropogenic forcing tell us about the interactions between spatially heterogeneous soil-mediated hydrological and biogeochemical processes? We seek to understand how the spatial structure of solute sources coupled with hydrologic responses affect observed concentration-discharge (C-Q) patterns. These patterns are expressions of the spatiotemporal structure of solute loads exported from managed catchments, and their likely ecological consequences manifested in receiving water bodies (e.g., wetlands, rivers, lakes, and coastal waters). We investigated the following broad questions: (1) How does the correlation between flow-generating areas and biogeochemical source areas across a catchment evolve under stochastic hydro-climatic forcing? (2) What are the feasible hydrologic and biogeochemical responses that lead to the emergence of the observed archetype C-Q patterns? and; (3) What implications do these coupled dynamics have for catchment monitoring and implementation of management practices? We categorize the observed temporal signals into three archetypical C-Q patterns: dilution; accretion, and constant concentration. We introduce a parsimonious stochastic model of heterogeneous catchments, which act as hydrologic and biogeochemical filters, to examine the relationship between spatial heterogeneity and temporal history of solute export signals. The core concept of the modeling framework is considering the types and degree of spatial correlation between solute source zones and flow generating zones, and activation of different portions of the catchments during rainfall events. Our overarching hypothesis is that each of the archetype C-Q patterns can be generated by explicitly linking landscape-scale hydrologic responses and spatial distributions of solute source properties within a catchment. The model simulations reproduce the three major C-Q patterns observed in published data, offering valuable insight into coupled catchment processes. The findings have important implications for effective catchment management for water quality improvement, and stream monitoring strategies.

Socio-hydrologic Modeling to Understand and Mediate the Competition for Water between Humans and Ecosystems: Murrumbidgee River Basin, Australia

NASA Astrophysics Data System (ADS)

van Emmerik, Tim; Sivapalan, Murugesu; Li, Zheng; Pande, Saket; Savenije, Hubert

2014-05-01

Around the world the demand for water resources is growing in order to satisfy rapidly increasing human populations, leading to competition for water between humans and ecosystems. An entirely new and comprehensive quantitative framework is needed to establish a holistic understanding of that competition, thereby enabling development and evaluation of effective mediation strategies. We present a case study centered on the Murrumbidgee river basin in eastern Australia that illustrates the dynamics of the balance between water extraction and use for food production and efforts to mitigate and reverse consequent degradation of the riparian environment. Interactions between patterns of water resources management and climate driven hydrological variability within the prevailing socio-economic environment have contributed to the emergence of new whole system dynamics over the last 100 years. In particular, data analysis reveals a pendulum swing between an exclusive focus on agricultural development and food production in the initial stages of water resources development and its attendant socio-economic benefits, followed by the gradual realization of the adverse environmental impacts, efforts to mitigate these with the use of remedial measures, and ultimately concerted efforts and externally imposed solutions to restore environmental health and ecosystem services. A quasi-distributed coupled socio-hydrologic system model that explicitly includes the two-way coupling between human and hydrological systems, including evolution of human values/norms relating to water and the environment, is able to mimic broad features of this pendulum swing. The model consists of coupled nonlinear differential equations that include four state variables describing the co-evolution of storage capacity, irrigated area, human population, and ecosystem health, which are all connected by feedback mechanisms. The model is used to generate insights into the dominant controls of the trajectory of co-evolution of the coupled human-water system, to serve as the theoretical framework for more detailed analysis of the system, and to generate organizing principles that may be transferable to other systems in different climatic and socio-economic settings.

Watershed-based survey designs

USGS Publications Warehouse

Detenbeck, N.E.; Cincotta, D.; Denver, J.M.; Greenlee, S.K.; Olsen, A.R.; Pitchford, A.M.

2005-01-01

Watershed-based sampling design and assessment tools help serve the multiple goals for water quality monitoring required under the Clean Water Act, including assessment of regional conditions to meet Section 305(b), identification of impaired water bodies or watersheds to meet Section 303(d), and development of empirical relationships between causes or sources of impairment and biological responses. Creation of GIS databases for hydrography, hydrologically corrected digital elevation models, and hydrologic derivatives such as watershed boundaries and upstream–downstream topology of subcatchments would provide a consistent seamless nationwide framework for these designs. The elements of a watershed-based sample framework can be represented either as a continuous infinite set defined by points along a linear stream network, or as a discrete set of watershed polygons. Watershed-based designs can be developed with existing probabilistic survey methods, including the use of unequal probability weighting, stratification, and two-stage frames for sampling. Case studies for monitoring of Atlantic Coastal Plain streams, West Virginia wadeable streams, and coastal Oregon streams illustrate three different approaches for selecting sites for watershed-based survey designs.

Hydrologic and geochemical data assimilation at the Hanford 300 Area

NASA Astrophysics Data System (ADS)

Chen, X.; Hammond, G. E.; Murray, C. J.; Zachara, J. M.

2012-12-01

In modeling the uranium migration within the Integrated Field Research Challenge (IFRC) site at the Hanford 300 Area, uncertainties arise from both hydrologic and geochemical sources. The hydrologic uncertainty includes the transient flow boundary conditions induced by dynamic variations in Columbia River stage and the underlying heterogeneous hydraulic conductivity field, while the geochemical uncertainty is a result of limited knowledge of the geochemical reaction processes and parameters, as well as heterogeneity in uranium source terms. In this work, multiple types of data, including the results from constant-injection tests, borehole flowmeter profiling, and conservative tracer tests, are sequentially assimilated across scales within a Bayesian framework to reduce the hydrologic uncertainty. The hydrologic data assimilation is then followed by geochemical data assimilation, where the goal is to infer the heterogeneous distribution of uranium sources using uranium breakthrough curves from a desorption test that took place at high spring water table. We demonstrate in our study that Ensemble-based data assimilation techniques (Ensemble Kalman filter and smoother) are efficient in integrating multiple types of data sequentially for uncertainty reduction. The computational demand is managed by using the multi-realization capability within the parallel PFLOTRAN simulator.

Pitfalls and Limitations in the Interpretation of Geophysical Images for Hydrologic Properties and Processes

NASA Astrophysics Data System (ADS)

Day-Lewis, F. D.

2014-12-01

Geophysical imaging (e.g., electrical, radar, seismic) can provide valuable information for the characterization of hydrologic properties and monitoring of hydrologic processes, as evidenced in the rapid growth of literature on the subject. Geophysical imaging has been used for monitoring tracer migration and infiltration, mapping zones of focused groundwater/surface-water exchange, and verifying emplacement of amendments for bioremediation. Despite the enormous potential for extraction of hydrologic information from geophysical images, there also is potential for misinterpretation and over-interpretation. These concerns are particularly relevant when geophysical results are used within quantitative frameworks, e.g., conversion to hydrologic properties through petrophysical relations, geostatistical estimation and simulation conditioned to geophysical inversions, and joint inversion. We review pitfalls to interpretation associated with limited image resolution, spatially variable image resolution, incorrect data weighting, errors in the timing of measurements, temporal smearing resulting from changes during data acquisition, support-volume/scale effects, and incorrect assumptions or approximations involved in modeling geophysical or other jointly inverted data. A series of numerical and field-based examples illustrate these potential problems. Our goal in this talk is to raise awareness of common pitfalls and present strategies for recognizing and avoiding them.

An open source hydroeconomic model for California's water supply system: PyVIN

NASA Astrophysics Data System (ADS)

Dogan, M. S.; White, E.; Herman, J. D.; Hart, Q.; Merz, J.; Medellin-Azuara, J.; Lund, J. R.

2016-12-01

Models help operators and decision makers explore and compare different management and policy alternatives, better allocate scarce resources, and predict the future behavior of existing or proposed water systems. Hydroeconomic models are useful tools to increase benefits or decrease costs of managing water. Bringing hydrology and economics together, these models provide a framework for different disciplines that share similar objectives. This work proposes a new model to evaluate operation and adaptation strategies under existing and future hydrologic conditions for California's interconnected water system. This model combines the network structure of CALVIN, a statewide optimization model for California's water infrastructure, along with an open source solver written in the Python programming language. With the flexibilities of the model, reservoir operations, including water supply and hydropower, groundwater pumping, and the Delta water operations and requirements can now be better represented. Given time series of hydrologic inputs to the model, typical outputs include urban, agricultural and wildlife refuge water deliveries and shortage costs, conjunctive use of surface and groundwater systems, and insights into policy and management decisions, such as capacity expansion and groundwater management policies. Water market operations also represented in the model, allocating water from lower-valued users to higher-valued users. PyVIN serves as a cross-platform, extensible model to evaluate systemwide water operations. PyVIN separates data from the model structure, enabling model to be easily applied to other parts of the world where water is a scarce resource.

An approach for modelling snowcover ablation and snowmelt runoff in cold region environments

NASA Astrophysics Data System (ADS)

Dornes, Pablo Fernando

Reliable hydrological model simulations are the result of numerous complex interactions among hydrological inputs, landscape properties, and initial conditions. Determination of the effects of these factors is one of the main challenges in hydrological modelling. This situation becomes even more difficult in cold regions due to the ungauged nature of subarctic and arctic environments. This research work is an attempt to apply a new approach for modelling snowcover ablation and snowmelt runoff in complex subarctic environments with limited data while retaining integrity in the process representations. The modelling strategy is based on the incorporation of both detailed process understanding and inputs along with information gained from observations of basin-wide streamflow phenomenon; essentially a combination of deductive and inductive approaches. The study was conducted in the Wolf Creek Research Basin, Yukon Territory, using three models, a small-scale physically based hydrological model, a land surface scheme, and a land surface hydrological model. The spatial representation was based on previous research studies and observations, and was accomplished by incorporating landscape units, defined according to topography and vegetation, as the spatial model elements. Comparisons between distributed and aggregated modelling approaches showed that simulations incorporating distributed initial snowcover and corrected solar radiation were able to properly simulate snowcover ablation and snowmelt runoff whereas the aggregated modelling approaches were unable to represent the differential snowmelt rates and complex snowmelt runoff dynamics. Similarly, the inclusion of spatially distributed information in a land surface scheme clearly improved simulations of snowcover ablation. Application of the same modelling approach at a larger scale using the same landscape based parameterisation showed satisfactory results in simulating snowcover ablation and snowmelt runoff with minimal calibration. Verification of this approach in an arctic basin illustrated that landscape based parameters are a feasible regionalisation framework for distributed and physically based models. In summary, the proposed modelling philosophy, based on the combination of an inductive and deductive reasoning, is a suitable strategy for reliable predictions of snowcover ablation and snowmelt runoff in cold regions and complex environments.

GIS embedded hydrological modeling: the SID&GRID project

NASA Astrophysics Data System (ADS)

Borsi, I.; Rossetto, R.; Schifani, C.

2012-04-01

The SID&GRID research project, started April 2010 and funded by Regione Toscana (Italy) under the POR FSE 2007-2013, aims to develop a Decision Support System (DSS) for water resource management and planning based on open source and public domain solutions. In order to quantitatively assess water availability in space and time and to support the planning decision processes, the SID&GRID solution consists of hydrological models (coupling 3D existing and newly developed surface- and ground-water and unsaturated zone modeling codes) embedded in a GIS interface, applications and library, where all the input and output data are managed by means of DataBase Management System (DBMS). A graphical user interface (GUI) to manage, analyze and run the SID&GRID hydrological models based on open source gvSIG GIS framework (Asociación gvSIG, 2011) and a Spatial Data Infrastructure to share and interoperate with distributed geographical data is being developed. Such a GUI is thought as a "master control panel" able to guide the user from pre-processing spatial and temporal data, running the hydrological models, and analyzing the outputs. To achieve the above-mentioned goals, the following codes have been selected and are being integrated: 1. Postgresql/PostGIS (PostGIS, 2011) for the Geo Data base Management System; 2. gvSIG with Sextante (Olaya, 2011) geo-algorithm library capabilities and Grass tools (GRASS Development Team, 2011) for the desktop GIS; 3. Geoserver and Geonetwork to share and discover spatial data on the web according to Open Geospatial Consortium; 4. new tools based on the Sextante GeoAlgorithm framework; 5. MODFLOW-2005 (Harbaugh, 2005) groundwater modeling code; 6. MODFLOW-LGR (Mehl and Hill 2005) for local grid refinement; 7. VSF (Thoms et al., 2006) for the variable saturated flow component; 8. new developed routines for overland flow; 9. new algorithms in Jython integrated in gvSIG to compute the net rainfall rate reaching the soil surface, as input for the unsaturated/saturated flow model. At this stage of the research (which will end April 2013), two primary components of the master control panel are being developed: i. a SID&GRID toolbar integrated into gvSIG map context; ii. a new Sextante set of geo-algorithm to pre- and post-process the spatial data to run the hydrological models. The groundwater part of the code has been fully integrated and tested and 3D visualization tools are being developed. The LGR capability has been extended to the 3D solution of the Richards' equation in order to solve in detail the unsaturated zone where required. To be updated about the project, please follow us at the website: http://ut11.isti.cnr.it/SIDGRID/

An Integrated Scenario Ensemble-Based Framework for Hurricane Evacuation Modeling: Part 2-Hazard Modeling.

PubMed

Blanton, Brian; Dresback, Kendra; Colle, Brian; Kolar, Randy; Vergara, Humberto; Hong, Yang; Leonardo, Nicholas; Davidson, Rachel; Nozick, Linda; Wachtendorf, Tricia

2018-04-25

Hurricane track and intensity can change rapidly in unexpected ways, thus making predictions of hurricanes and related hazards uncertain. This inherent uncertainty often translates into suboptimal decision-making outcomes, such as unnecessary evacuation. Representing this uncertainty is thus critical in evacuation planning and related activities. We describe a physics-based hazard modeling approach that (1) dynamically accounts for the physical interactions among hazard components and (2) captures hurricane evolution uncertainty using an ensemble method. This loosely coupled model system provides a framework for probabilistic water inundation and wind speed levels for a new, risk-based approach to evacuation modeling, described in a companion article in this issue. It combines the Weather Research and Forecasting (WRF) meteorological model, the Coupled Routing and Excess STorage (CREST) hydrologic model, and the ADvanced CIRCulation (ADCIRC) storm surge, tide, and wind-wave model to compute inundation levels and wind speeds for an ensemble of hurricane predictions. Perturbations to WRF's initial and boundary conditions and different model physics/parameterizations generate an ensemble of storm solutions, which are then used to drive the coupled hydrologic + hydrodynamic models. Hurricane Isabel (2003) is used as a case study to illustrate the ensemble-based approach. The inundation, river runoff, and wind hazard results are strongly dependent on the accuracy of the mesoscale meteorological simulations, which improves with decreasing lead time to hurricane landfall. The ensemble envelope brackets the observed behavior while providing "best-case" and "worst-case" scenarios for the subsequent risk-based evacuation model. © 2018 Society for Risk Analysis.

Geomorphically based predictive mapping of soil thickness in upland watersheds

NASA Astrophysics Data System (ADS)

Pelletier, Jon D.; Rasmussen, Craig

2009-09-01

The hydrologic response of upland watersheds is strongly controlled by soil (regolith) thickness. Despite the need to quantify soil thickness for input into hydrologic models, there is currently no widely used, geomorphically based method for doing so. In this paper we describe and illustrate a new method for predictive mapping of soil thicknesses using high-resolution topographic data, numerical modeling, and field-based calibration. The model framework works directly with input digital elevation model data to predict soil thicknesses assuming a long-term balance between soil production and erosion. Erosion rates in the model are quantified using one of three geomorphically based sediment transport models: nonlinear slope-dependent transport, nonlinear area- and slope-dependent transport, and nonlinear depth- and slope-dependent transport. The model balances soil production and erosion locally to predict a family of solutions corresponding to a range of values of two unconstrained model parameters. A small number of field-based soil thickness measurements can then be used to calibrate the local value of those unconstrained parameters, thereby constraining which solution is applicable at a particular study site. As an illustration, the model is used to predictively map soil thicknesses in two small, ˜0.1 km2, drainage basins in the Marshall Gulch watershed, a semiarid drainage basin in the Santa Catalina Mountains of Pima County, Arizona. Field observations and calibration data indicate that the nonlinear depth- and slope-dependent sediment transport model is the most appropriate transport model for this site. The resulting framework provides a generally applicable, geomorphically based tool for predictive mapping of soil thickness using high-resolution topographic data sets.

The impact of changing climate conditions on the hydrological behavior of several Mediterranean sub-catchments in Crete

NASA Astrophysics Data System (ADS)

Eirini Vozinaki, Anthi; Tapoglou, Evdokia; Tsanis, Ioannis

2017-04-01

Climate change, although is already happening, consists of a big threat capable of causing lots of inconveniences in future societies and their economies. In this work, the climate change impact on the hydrological behavior of several Mediterranean sub-catchments, in Crete, is presented. The sensitivity of these hydrological systems to several climate change scenarios is also provided. The HBV hydrological model has been used, calibrated and validated for the study sub-catchments against measured weather and streamflow data and inputs. The impact of climate change on several hydro-meteorological parameters (i.e. precipitation, streamflow etc.) and hydrological signatures (i.e. spring flood peak, length and volume, base flow, flow duration curves, seasonality etc.) have been statistically elaborated and analyzed, defining areas of increased probability risk associated additionally to flooding or drought. The potential impacts of climate change on current and future water resources have been quantified by driving HBV model with current and future scenarios, respectively, for specific climate periods. This work aims to present an integrated methodology for the definition of future climate and hydrological risks and the prediction of future water resources behavior. Future water resources management could be rationally effectuated, in Mediterranean sub-catchments prone to drought or flooding, using the proposed methodology. The research reported in this paper was fully supported by the Project "Innovative solutions to climate change adaptation and governance in the water management of the Region of Crete - AQUAMAN" funded within the framework of the EEA Financial Mechanism 2009-2014.

Enhancing Data Assimilation by Evolutionary Particle Filter and Markov Chain Monte Carlo

NASA Astrophysics Data System (ADS)

Moradkhani, H.; Abbaszadeh, P.; Yan, H.

2016-12-01

Particle Filters (PFs) have received increasing attention by the researchers from different disciplines in hydro-geosciences as an effective method to improve model predictions in nonlinear and non-Gaussian dynamical systems. The implication of dual state and parameter estimation by means of data assimilation in hydrology and geoscience has evolved since 2005 from SIR-PF to PF-MCMC and now to the most effective and robust framework through evolutionary PF approach based on Genetic Algorithm (GA) and Markov Chain Monte Carlo (MCMC), the so-called EPF-MCMC. In this framework, the posterior distribution undergoes an evolutionary process to update an ensemble of prior states that more closely resemble realistic posterior probability distribution. The premise of this approach is that the particles move to optimal position using the GA optimization coupled with MCMC increasing the number of effective particles, hence the particle degeneracy is avoided while the particle diversity is improved. The proposed algorithm is applied on a conceptual and highly nonlinear hydrologic model and the effectiveness, robustness and reliability of the method in jointly estimating the states and parameters and also reducing the uncertainty is demonstrated for few river basins across the United States.

Modelling food-web mediated effects of hydrological variability and environmental flows.

PubMed

Robson, Barbara J; Lester, Rebecca E; Baldwin, Darren S; Bond, Nicholas R; Drouart, Romain; Rolls, Robert J; Ryder, Darren S; Thompson, Ross M

2017-11-01

Environmental flows are designed to enhance aquatic ecosystems through a variety of mechanisms; however, to date most attention has been paid to the effects on habitat quality and life-history triggers, especially for fish and vegetation. The effects of environmental flows on food webs have so far received little attention, despite food-web thinking being fundamental to understanding of river ecosystems. Understanding environmental flows in a food-web context can help scientists and policy-makers better understand and manage outcomes of flow alteration and restoration. In this paper, we consider mechanisms by which flow variability can influence and alter food webs, and place these within a conceptual and numerical modelling framework. We also review the strengths and weaknesses of various approaches to modelling the effects of hydrological management on food webs. Although classic bioenergetic models such as Ecopath with Ecosim capture many of the key features required, other approaches, such as biogeochemical ecosystem modelling, end-to-end modelling, population dynamic models, individual-based models, graph theory models, and stock assessment models are also relevant. In many cases, a combination of approaches will be useful. We identify current challenges and new directions in modelling food-web responses to hydrological variability and environmental flow management. These include better integration of food-web and hydraulic models, taking physiologically-based approaches to food quality effects, and better representation of variations in space and time that may create ecosystem control points. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

Repurposing of open data through large scale hydrological modelling - hypeweb.smhi.se

NASA Astrophysics Data System (ADS)

Strömbäck, Lena; Andersson, Jafet; Donnelly, Chantal; Gustafsson, David; Isberg, Kristina; Pechlivanidis, Ilias; Strömqvist, Johan; Arheimer, Berit

2015-04-01

Hydrological modelling demands large amounts of spatial data, such as soil properties, land use, topography, lakes and reservoirs, ice and snow coverage, water management (e.g. irrigation patterns and regulations), meteorological data and observed water discharge in rivers. By using such data, the hydrological model will in turn provide new data that can be used for new purposes (i.e. re-purposing). This presentation will give an example of how readily available open data from public portals have been re-purposed by using the Hydrological Predictions for the Environment (HYPE) model in a number of large-scale model applications covering numerous subbasins and rivers. HYPE is a dynamic, semi-distributed, process-based, and integrated catchment model. The model output is launched as new Open Data at the web site www.hypeweb.smhi.se to be used for (i) Climate change impact assessments on water resources and dynamics; (ii) The European Water Framework Directive (WFD) for characterization and development of measure programs to improve the ecological status of water bodies; (iii) Design variables for infrastructure constructions; (iv) Spatial water-resource mapping; (v) Operational forecasts (1-10 days and seasonal) on floods and droughts; (vi) Input to oceanographic models for operational forecasts and marine status assessments; (vii) Research. The following regional domains have been modelled so far with different resolutions (number of subbasins within brackets): Sweden (37 000), Europe (35 000), Arctic basin (30 000), La Plata River (6 000), Niger River (800), Middle-East North-Africa (31 000), and the Indian subcontinent (6 000). The Hype web site provides several interactive web applications for exploring results from the models. The user can explore an overview of various water variables for historical and future conditions. Moreover the user can explore and download historical time series of discharge for each basin and explore the performance of the model towards observed river flow. The presentation will describe the Open Data sources used, show the functionality of the web site and discuss model performance and experience from this world-wide hydrological modelling of multi-basins using open data.

Quantitative modeling of soil genesis processes

NASA Technical Reports Server (NTRS)

Levine, E. R.; Knox, R. G.; Kerber, A. G.

1992-01-01

For fine spatial scale simulation, a model is being developed to predict changes in properties over short-, meso-, and long-term time scales within horizons of a given soil profile. Processes that control these changes can be grouped into five major process clusters: (1) abiotic chemical reactions; (2) activities of organisms; (3) energy balance and water phase transitions; (4) hydrologic flows; and (5) particle redistribution. Landscape modeling of soil development is possible using digitized soil maps associated with quantitative soil attribute data in a geographic information system (GIS) framework to which simulation models are applied.

Operational flash flood forecasting platform based on grid technology

NASA Astrophysics Data System (ADS)

Thierion, V.; Ayral, P.-A.; Angelini, V.; Sauvagnargues-Lesage, S.; Nativi, S.; Payrastre, O.

2009-04-01

Flash flood events of south of France such as the 8th and 9th September 2002 in the Grand Delta territory caused important economic and human damages. Further to this catastrophic hydrological situation, a reform of flood warning services have been initiated (set in 2006). Thus, this political reform has transformed the 52 existing flood warning services (SAC) in 22 flood forecasting services (SPC), in assigning them territories more hydrological consistent and new effective hydrological forecasting mission. Furthermore, national central service (SCHAPI) has been created to ease this transformation and support local services in their new objectives. New functioning requirements have been identified: - SPC and SCHAPI carry the responsibility to clearly disseminate to public organisms, civil protection actors and population, crucial hydrologic information to better anticipate potential dramatic flood event, - a new effective hydrological forecasting mission to these flood forecasting services seems essential particularly for the flash floods phenomenon. Thus, models improvement and optimization was one of the most critical requirements. Initially dedicated to support forecaster in their monitoring mission, thanks to measuring stations and rainfall radar images analysis, hydrological models have to become more efficient in their capacity to anticipate hydrological situation. Understanding natural phenomenon occuring during flash floods mainly leads present hydrological research. Rather than trying to explain such complex processes, the presented research try to manage the well-known need of computational power and data storage capacities of these services. Since few years, Grid technology appears as a technological revolution in high performance computing (HPC) allowing large-scale resource sharing, computational power using and supporting collaboration across networks. Nowadays, EGEE (Enabling Grids for E-science in Europe) project represents the most important effort in term of grid technology development. This paper presents an operational flash flood forecasting platform which have been developed in the framework of CYCLOPS European project providing one of virtual organizations of EGEE project. This platform has been designed to enable multi-simulations processes to ease forecasting operations of several supervised watersheds on Grand Delta (SPC-GD) territory. Grid technology infrastructure, in providing multiple remote computing elements enables the processing of multiple rainfall scenarios, derived to the original meteorological forecasting transmitted by Meteo-France, and their respective hydrological simulations. First results show that from one forecasting scenario, this new presented approach can permit simulations of more than 200 different scenarios to support forecasters in their aforesaid mission and appears as an efficient hydrological decision-making tool. Although, this system seems operational, model validity has to be confirmed. So, further researches are necessary to improve models core to be more efficient in term of hydrological aspects. Finally, this platform could be an efficient tool for developing others modelling aspects as calibration or data assimilation in real time processing.

An integrated system for rainfall induced shallow landslides modeling

NASA Astrophysics Data System (ADS)

Formetta, Giuseppe; Capparelli, Giovanna; Rigon, Riccardo; Versace, Pasquale

2014-05-01

Rainfall induced shallow landslides (RISL) cause significant damages involving loss of life and properties. Predict susceptible locations for RISL is a complex task that involves many disciplines: hydrology, geotechnical science, geomorphology, statistic. Usually to accomplish this task two main approaches are used: statistical or physically based model. In this work an open source (OS), 3-D, fully distributed hydrological model was integrated in an OS modeling framework (Object Modeling System). The chain is closed by linking the system to a component for safety factor computation with infinite slope approximation able to take into account layered soils and suction contribution to hillslope stability. The model composition was tested for a case study in Calabria (Italy) in order to simulate the triggering of a landslide happened in the Cosenza Province. The integration in OMS allows the use of other components such as a GIS to manage inputs-output processes, and automatic calibration algorithms to estimate model parameters. Finally, model performances were quantified by comparing modelled and simulated trigger time. This research is supported by Ambito/Settore AMBIENTE E SICUREZZA (PON01_01503) project.

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.

Development and Application of a Process-based River System Model at a Continental Scale

NASA Astrophysics Data System (ADS)

Kim, S. S. H.; Dutta, D.; Vaze, J.; Hughes, J. D.; Yang, A.; Teng, J.

2014-12-01

Existing global and continental scale river models, mainly designed for integrating with global climate model, are of very course spatial resolutions and they lack many important hydrological processes, such as overbank flow, irrigation diversion, groundwater seepage/recharge, which operate at a much finer resolution. Thus, these models are not suitable for producing streamflow forecast at fine spatial resolution and water accounts at sub-catchment levels, which are important for water resources planning and management at regional and national scale. A large-scale river system model has been developed and implemented for water accounting in Australia as part of the Water Information Research and Development Alliance between Australia's Bureau of Meteorology (BoM) and CSIRO. The model, developed using node-link architecture, includes all major hydrological processes, anthropogenic water utilisation and storage routing that influence the streamflow in both regulated and unregulated river systems. It includes an irrigation model to compute water diversion for irrigation use and associated fluxes and stores and a storage-based floodplain inundation model to compute overbank flow from river to floodplain and associated floodplain fluxes and stores. An auto-calibration tool has been built within the modelling system to automatically calibrate the model in large river systems using Shuffled Complex Evolution optimiser and user-defined objective functions. The auto-calibration tool makes the model computationally efficient and practical for large basin applications. The model has been implemented in several large basins in Australia including the Murray-Darling Basin, covering more than 2 million km2. The results of calibration and validation of the model shows highly satisfactory performance. The model has been operalisationalised in BoM for producing various fluxes and stores for national water accounting. This paper introduces this newly developed river system model describing the conceptual hydrological framework, methods used for representing different hydrological processes in the model and the results and evaluation of the model performance. The operational implementation of the model for water accounting is discussed.

Uncertainty Analysis of Runoff Simulations and Parameter Identifiability in the Community Land Model – Evidence from MOPEX Basins

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

Huang, Maoyi; Hou, Zhangshuan; Leung, Lai-Yung R.

2013-12-01

With the emergence of earth system models as important tools for understanding and predicting climate change and implications to mitigation and adaptation, it has become increasingly important to assess the fidelity of the land component within earth system models to capture realistic hydrological processes and their response to the changing climate and quantify the associated uncertainties. This study investigates the sensitivity of runoff simulations to major hydrologic parameters in version 4 of the Community Land Model (CLM4) by integrating CLM4 with a stochastic exploratory sensitivity analysis framework at 20 selected watersheds from the Model Parameter Estimation Experiment (MOPEX) spanning amore » wide range of climate and site conditions. We found that for runoff simulations, the most significant parameters are those related to the subsurface runoff parameterizations. Soil texture related parameters and surface runoff parameters are of secondary significance. Moreover, climate and soil conditions play important roles in the parameter sensitivity. In general, site conditions within water-limited hydrologic regimes and with finer soil texture result in stronger sensitivity of output variables, such as runoff and its surface and subsurface components, to the input parameters in CLM4. This study demonstrated the feasibility of parameter inversion for CLM4 using streamflow observations to improve runoff simulations. By ranking the significance of the input parameters, we showed that the parameter set dimensionality could be reduced for CLM4 parameter calibration under different hydrologic and climatic regimes so that the inverse problem is less ill posed.« less

Helicopter Electromagnetic Surveys for Hydrological Framework Studies in Nebraska

NASA Astrophysics Data System (ADS)

Smith, B. D.; Abraham, J. A.; Cannia, J. C.; Steele, G. V.; Peterson, S. M.

2008-12-01

Management and allocation of water resources in Nebraska is based in part on understanding the relation between surface-water and ground-water systems. To help understand these complex relations, the U.S. Geological Survey (USGS) conducted airborne resistivity and magnetic (frequency domain helicopter electromagnetic, HEM) surveys in Eastern (2007) and Western (2008) Nebraska. These surveys were integrated with hydrologic studies (aquifer characteristics and modeling), and ground and borehole geophysical surveys to characterize and map the hydrogeologic framework in three-dimensions. The three study areas selected in Eastern Nebraska (Ashland, Firth, and Oakland) have glacial terrains and bedrock that typify different hydrogeologic settings for surface and ground water. The Eastern Nebraska Water Resources Assessment is a joint State of Nebraska and USGS study including the Conservation and Survey Division (University of Nebraska) and the following Natural Resources Districts (NRD): Lower Platte South, Lower Platte North, Lower Elkhorn, Lewis and Clark, Nemaha, and Papio-Missouri River. Approximately 600 line km were flown with HEM in each of the three glacial terrains with a line spacing of approximately 270 m and samples every three meters. One dimensional imaging was done along the flight lines for the HEM in each area. Models were compared to ground resistivity and time domain electromagnetic soundings and to borehole lithologic and geophysical logs. The map of the subsurface hydrogeologic properties inferred from the HEM modeling significantly improves the resolution of hydrologic models and understanding of ground-water resources. Surveys in western Nebraska panhandle, were done along the North Platte River and Lodgepole Creek Valleys. The geology consists of Quaternary alluvium, and interbeded Tertiary sandstones and siltstones above Cretaceous shale. The Quaternary alluvium comprises the primary aquifer in the North Platte River Valley, whereas thin alluvial sediments and Tertiary sandstone channels comprise the primary aquifers in Lodgepole Creek Valley. Locally, Tertiary Siltstone and Cretaceous shale is weathered and incised. A prominent factor in the hydrologic setting of the North Platte River Valley is recharge through un-lined irrigation canals. Surveys in western Nebraska were funded by the North Platte and South Platte NRDs. These NRDS have employed the best in science-based integrated water resources management. The ground-water flow modeling study in western Nebraska will use the HEM data as part of model datasets, to create a tool used to evaluate implications of water management options over most of the surface-water irrigated area.

Soil Moisture Drought Monitoring and Forecasting Using Satellite and Climate Model Data over Southwestern China

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

Zhang, Xuejun; Tang, Qiuhong; Liu, Xingcai

Real-time monitoring and predicting drought development with several months in advance is of critical importance for drought risk adaptation and mitigation. In this paper, we present a drought monitoring and seasonal forecasting framework based on the Variable Infiltration Capacity (VIC) hydrologic model over Southwest China (SW). The satellite precipitation data are used to force VIC model for near real-time estimate of land surface hydrologic conditions. As initialized with satellite-aided monitoring, the climate model-based forecast (CFSv2_VIC) and ensemble streamflow prediction (ESP)-based forecast (ESP_VIC) are both performed and evaluated through their ability in reproducing the evolution of the 2009/2010 severe drought overmore » SW. The results show that the satellite-aided monitoring is able to provide reasonable estimate of forecast initial conditions (ICs) in a real-time manner. Both of CFSv2_VIC and ESP_VIC exhibit comparable performance against the observation-based estimates for the first month, whereas the predictive skill largely drops beyond 1-month. Compared to ESP_VIC, CFSv2_VIC shows better performance as indicated by the smaller ensemble range. This study highlights the value of this operational framework in generating near real-time ICs and giving a reliable prediction with 1-month ahead, which has great implications for drought risk assessment, preparation and relief.« less

An Open Source modular platform for hydrological model implementation

NASA Astrophysics Data System (ADS)

Kolberg, Sjur; Bruland, Oddbjørn

2010-05-01

An implementation framework for setup and evaluation of spatio-temporal models is developed, forming a highly modularized distributed model system. The ENKI framework allows building space-time models for hydrological or other environmental purposes, from a suite of separately compiled subroutine modules. The approach makes it easy for students, researchers and other model developers to implement, exchange, and test single routines in a fixed framework. The open-source license and modular design of ENKI will also facilitate rapid dissemination of new methods to institutions engaged in operational hydropower forecasting or other water resource management. Written in C++, ENKI uses a plug-in structure to build a complete model from separately compiled subroutine implementations. These modules contain very little code apart from the core process simulation, and are compiled as dynamic-link libraries (dll). A narrow interface allows the main executable to recognise the number and type of the different variables in each routine. The framework then exposes these variables to the user within the proper context, ensuring that time series exist for input variables, initialisation for states, GIS data sets for static map data, manually or automatically calibrated values for parameters etc. ENKI is designed to meet three different levels of involvement in model construction: • Model application: Running and evaluating a given model. Regional calibration against arbitrary data using a rich suite of objective functions, including likelihood and Bayesian estimation. Uncertainty analysis directed towards input or parameter uncertainty. o Need not: Know the model's composition of subroutines, or the internal variables in the model, or the creation of method modules. • Model analysis: Link together different process methods, including parallel setup of alternative methods for solving the same task. Investigate the effect of different spatial discretization schemes. o Need not: Write or compile computer code, handle file IO for each modules, • Routine implementation and testing. Implementation of new process-simulating methods/equations, specialised objective functions or quality control routines, testing of these in an existing framework. o Need not: Implement user or model interface for the new routine, IO handling, administration of model setup and run, calibration and validation routines etc. From being developed for Norway's largest hydropower producer Statkraft, ENKI is now being turned into an Open Source project. At the time of writing, the licence and the project administration is not established. Also, it remains to port the application to other compilers and computer platforms. However, we hope that ENKI will prove useful for both academic and operational users.

airGRteaching: an R-package designed for teaching hydrology with lumped hydrological models

NASA Astrophysics Data System (ADS)

Thirel, Guillaume; Delaigue, Olivier; Coron, Laurent; Andréassian, Vazken; Brigode, Pierre

2017-04-01

Lumped hydrological models are useful and convenient tools for research, engineering and educational purposes. They propose catchment-scale representations of the precipitation-discharge relationship. Thanks to their limited data requirements, they can be easily implemented and run. With such models, it is possible to simulate a number of hydrological key processes over the catchment with limited structural and parametric complexity, typically evapotranspiration, runoff, underground losses, etc. The Hydrology Group at Irstea (Antony) has been developing a suite of rainfall-runoff models over the past 30 years. This resulted in a suite of models running at different time steps (from hourly to annual) applicable for various issues including water balance estimation, forecasting, simulation of impacts and scenario testing. Recently, Irstea has developed an easy-to-use R-package (R Core Team, 2016), called airGR (Coron et al., 2016, 2017), to make these models widely available. Although its initial target public was hydrological modellers, the package is already used for educational purposes. Indeed, simple models allow for rapidly visualising the effects of parameterizations and model components on flows hydrographs. In order to avoid the difficulties that students may have when manipulating R and datasets, we developed (Delaigue and Coron, 2016): - Three simplified functions to prepare data, calibrate a model and run a simulation - Simplified and dynamic plot functions - A shiny (Chang et al., 2016) interface that connects this R-package to a browser-based visualisation tool. On this interface, the students can use different hydrological models (including the possibility to use a snow-accounting model), manually modify their parameters and automatically calibrate their parameters with diverse objective functions. One of the visualisation tabs of the interface includes observed precipitation and temperature, simulated snowpack (if any), observed and simulated discharges, which are updated immediately (a calibration only needs a couple of seconds or less, a simulation is almost immediate). In addition, time series of internal variables, live-visualisation of internal variables evolution and performance statistics are provided. This interface allows for hands-on exercises that can include for instance the analysis by students of: - The effects of each parameter and model components on simulated discharge - The effects of objective functions based on high flows- or low flows-focused criteria on simulated discharge - The seasonality of the model components. References Winston Chang, Joe Cheng, JJ Allaire, Yihui Xie and Jonathan McPherson (2016). shiny: Web Application Framework for R. R package version 0.13.2. https://CRAN.R-project.org/package=shiny Coron L., Thirel G., Perrin C., Delaigue O., Andréassian V., airGR: a suite of lumped hydrological models in an R-package, Environmental Modelling and software, 2017, submitted. Coron, L., Perrin, C. and Michel, C. (2016). airGR: Suite of GR hydrological models for precipitation-runoff modelling. R package version 1.0.3. https://webgr.irstea.fr/airGR/?lang=en. Olivier Delaigue and Laurent Coron (2016). airGRteaching: Tools to simplify the use of the airGR hydrological package by students. R package version 0.0.1. https://webgr.irstea.fr/airGR/?lang=en R Core Team (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Mountain-Scale Coupled Processes (TH/THC/THM)

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

P. Dixon

The purpose of this Model Report is to document the development of the Mountain-Scale Thermal-Hydrological (TH), Thermal-Hydrological-Chemical (THC), and Thermal-Hydrological-Mechanical (THM) Models and evaluate the effects of coupled TH/THC/THM processes on mountain-scale UZ flow at Yucca Mountain, Nevada. This Model Report was planned in ''Technical Work Plan (TWP) for: Performance Assessment Unsaturated Zone'' (BSC 2002 [160819], Section 1.12.7), and was developed in accordance with AP-SIII.10Q, Models. In this Model Report, any reference to ''repository'' means the nuclear waste repository at Yucca Mountain, and any reference to ''drifts'' means the emplacement drifts at the repository horizon. This Model Report provides themore » necessary framework to test conceptual hypotheses for analyzing mountain-scale hydrological/chemical/mechanical changes and predict flow behavior in response to heat release by radioactive decay from the nuclear waste repository at the Yucca Mountain site. The mountain-scale coupled TH/THC/THM processes models numerically simulate the impact of nuclear waste heat release on the natural hydrogeological system, including a representation of heat-driven processes occurring in the far field. The TH simulations provide predictions for thermally affected liquid saturation, gas- and liquid-phase fluxes, and water and rock temperature (together called the flow fields). The main focus of the TH Model is to predict the changes in water flux driven by evaporation/condensation processes, and drainage between drifts. The TH Model captures mountain-scale three dimensional (3-D) flow effects, including lateral diversion at the PTn/TSw interface and mountain-scale flow patterns. The Mountain-Scale THC Model evaluates TH effects on water and gas chemistry, mineral dissolution/precipitation, and the resulting impact to UZ hydrological properties, flow and transport. The THM Model addresses changes in permeability due to mechanical and thermal disturbances in stratigraphic units above and below the repository host rock. The Mountain-Scale THM Model focuses on evaluating the changes in 3-D UZ flow fields arising out of thermal stress and rock deformation during and after the thermal periods.« less

Effects of land use on lake nutrients: The importance of scale, hydrologic connectivity, and region

USGS Publications Warehouse

Soranno, Patricia A.; Cheruvelil, Kendra Spence; Wagner, Tyler; Webster, Katherine E.; Bremigan, Mary Tate

2015-01-01

Catchment land uses, particularly agriculture and urban uses, have long been recognized as major drivers of nutrient concentrations in surface waters. However, few simple models have been developed that relate the amount of catchment land use to downstream freshwater nutrients. Nor are existing models applicable to large numbers of freshwaters across broad spatial extents such as regions or continents. This research aims to increase model performance by exploring three factors that affect the relationship between land use and downstream nutrients in freshwater: the spatial extent for measuring land use, hydrologic connectivity, and the regional differences in both the amount of nutrients and effects of land use on them. We quantified the effects of these three factors that relate land use to lake total phosphorus (TP) and total nitrogen (TN) in 346 north temperate lakes in 7 regions in Michigan, USA. We used a linear mixed modeling framework to examine the importance of spatial extent, lake hydrologic class, and region on models with individual lake nutrients as the response variable, and individual land use types as the predictor variables. Our modeling approach was chosen to avoid problems of multi-collinearity among predictor variables and a lack of independence of lakes within regions, both of which are common problems in broad-scale analyses of freshwaters. We found that all three factors influence land use-lake nutrient relationships. The strongest evidence was for the effect of lake hydrologic connectivity, followed by region, and finally, the spatial extent of land use measurements. Incorporating these three factors into relatively simple models of land use effects on lake nutrients should help to improve predictions and understanding of land use-lake nutrient interactions at broad scales.

Effects of Land Use on Lake Nutrients: The Importance of Scale, Hydrologic Connectivity, and Region

PubMed Central

Soranno, Patricia A.; Cheruvelil, Kendra Spence; Wagner, Tyler; Webster, Katherine E.; Bremigan, Mary Tate

2015-01-01

Catchment land uses, particularly agriculture and urban uses, have long been recognized as major drivers of nutrient concentrations in surface waters. However, few simple models have been developed that relate the amount of catchment land use to downstream freshwater nutrients. Nor are existing models applicable to large numbers of freshwaters across broad spatial extents such as regions or continents. This research aims to increase model performance by exploring three factors that affect the relationship between land use and downstream nutrients in freshwater: the spatial extent for measuring land use, hydrologic connectivity, and the regional differences in both the amount of nutrients and effects of land use on them. We quantified the effects of these three factors that relate land use to lake total phosphorus (TP) and total nitrogen (TN) in 346 north temperate lakes in 7 regions in Michigan, USA. We used a linear mixed modeling framework to examine the importance of spatial extent, lake hydrologic class, and region on models with individual lake nutrients as the response variable, and individual land use types as the predictor variables. Our modeling approach was chosen to avoid problems of multi-collinearity among predictor variables and a lack of independence of lakes within regions, both of which are common problems in broad-scale analyses of freshwaters. We found that all three factors influence land use-lake nutrient relationships. The strongest evidence was for the effect of lake hydrologic connectivity, followed by region, and finally, the spatial extent of land use measurements. Incorporating these three factors into relatively simple models of land use effects on lake nutrients should help to improve predictions and understanding of land use-lake nutrient interactions at broad scales. PMID:26267813

Hydrologic and water quality monitoring on Turkey Creek watershed, Francis Marion National Forest, SC

Treesearch

D.M. Amatya; T.J. Callahan; A. Radecki-Pawlik; P. Drewes; C. Trettin; W.F. Hansen

2008-01-01

The re-initiation of a 7,260 ha forested watershed study on Turkey Creek, a 3rd order stream, within the Francis Marion National forest in South Carolina, completes the development of a multi-scale hydrology and ecosystem monitoring framework in the Atlantic Coastal Plain. Hydrology and water quality monitoring began on the Santee Experimental...

An integrated framework to assess adaptation options to climate change impacts in an irrigated basin in Central North Chile

NASA Astrophysics Data System (ADS)

Vicuna, S.; Melo, O.; Meza, F. J.; Alvarez, P.; Maureira, F.; Sanchez, A.; Tapia, A.; Cortes, M.; Dale, L. L.

2013-12-01

Future climate conditions could potentially affect water supply and demand on water basins throughout the world but especially on snowmelt-driven agriculture oriented basins that can be found throughout central Chile. Increasing temperature and reducing precipitation will affect both the magnitude and timing of water supply this part of the world. Different adaptation strategies could be implemented to reduce the impacts of such scenarios. Some could be incorporated as planned policies decided at the basin or Water Use Organization levels. Examples include changing large scale irrigation infrastructure (reservoirs and main channels) either physically or its operation. Complementing these strategies it is reasonable to think that at a disaggregated level, farmers would also react (adapt) to these new conditions using a mix of options to either modify their patterns of consumption (irrigation efficiency, crop mix, crop area reduction), increase their ability to access new sources of water (groundwater, water markets) or finally compensate their expected losses (insurance). We present a modeling framework developed to represent these issues using as a case study the Limarí basin located in Central Chile. This basin is a renowned example of how the development of reservoirs and irrigation infrastructure can reduce climate vulnerabilities allowing the economic development of a basin. Farmers in this basin tackle climate variability by adopting different strategies that depend first on the reservoir water volume allocation rule, on the type and size of investment they have at their farms and finally their potential access to water markets and other water supplies options. The framework developed can be used to study these strategies under current and future climate scenarios. The cornerstone of the framework is an hydrology and water resources model developed on the WEAP platform. This model is able to reproduce the large scale hydrologic features of the basin such as snowmelt hydrology, reservoir operation and groundwater dynamics. Crop yield under different water irrigation patterns have been inferred using a calibrated Cropsyst model. These crop yields together with user association irrigation constraints are used in a GAMS optimization model embedded dynamically in WEAP in order to obtain every year decisions on crop mix (including fallow land), irrigation patterns and participation in the spot water market. The GAMS optimization model has been calibrated using annual crop mix time series derived using a combination of sources of information ranging from different type of census plus satellite images. The resulting modeling platform is able to simulate under historic and future climate scenarios water availability in different locations of the basin with associated crop yield and economic consequences. The platform also allows the implementation of autonomous and planned adaptation strategies that could reduce the impacts of climate variability and climate change.

Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model

NASA Astrophysics Data System (ADS)

Demirel, Mehmet C.; Mai, Juliane; Mendiguren, Gorka; Koch, Julian; Samaniego, Luis; Stisen, Simon

2018-02-01

Satellite-based earth observations offer great opportunities to improve spatial model predictions by means of spatial-pattern-oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET) are utilised for spatial model calibration tailored to target the pattern performance of the model. The proposed calibration framework combines temporally aggregated observed spatial patterns with a new spatial performance metric and a flexible spatial parameterisation scheme. The mesoscale hydrologic model (mHM) is used to simulate streamflow and AET and has been selected due to its soil parameter distribution approach based on pedo-transfer functions and the build in multi-scale parameter regionalisation. In addition two new spatial parameter distribution options have been incorporated in the model in order to increase the flexibility of root fraction coefficient and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parameterisations are utilised as they are most relevant for simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance using standard metrics, we developed a simple but highly discriminative spatial metric, i.e. one comprised of three easily interpretable components measuring co-location, variation and distribution of the spatial data. The study shows that with flexible spatial model parameterisation used in combination with the appropriate objective functions, the simulated spatial patterns of actual evapotranspiration become substantially more similar to the satellite-based estimates. Overall 26 parameters are identified for calibration through a sequential screening approach based on a combination of streamflow and spatial pattern metrics. The robustness of the calibrations is tested using an ensemble of nine calibrations based on different seed numbers using the shuffled complex evolution optimiser. The calibration results reveal a limited trade-off between streamflow dynamics and spatial patterns illustrating the benefit of combining separate observation types and objective functions. At the same time, the simulated spatial patterns of AET significantly improved when an objective function based on observed AET patterns and a novel spatial performance metric compared to traditional streamflow-only calibration were included. Since the overall water balance is usually a crucial goal in hydrologic modelling, spatial-pattern-oriented optimisation should always be accompanied by traditional discharge measurements. In such a multi-objective framework, the current study promotes the use of a novel bias-insensitive spatial pattern metric, which exploits the key information contained in the observed patterns while allowing the water balance to be informed by discharge observations.

Multi-criteria decision analysis using hydrological indicators for decision support - a conceptual framework.

NASA Astrophysics Data System (ADS)

Butchart-Kuhlmann, Daniel; Kralisch, Sven; Meinhardt, Markus; Fleischer, Melanie

2017-04-01

Assessing the quantity and quality of water available in water stressed environments under various potential climate and land-use changes is necessary for good water and environmental resources management and governance. Within the region covered by the Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) project, such areas are common. One goal of the SASSCAL project is to develop and provide an integrated decision support system (DSS) with which decision makers (DMs) within a given catchment can obtain objective information regarding potential changes in water flow quantity and timing. The SASSCAL DSS builds upon existing data storage and distribution capability, through the SASSCAL Information System (IS), as well as the J2000 hydrological model. Using output from validated J2000 models, the SASSCAL DSS incorporates the calculation of a range of hydrological indicators based upon Indicators of Hydrological Alteration/Environmental Flow Components (IHA/EFC) calculated for a historic time series (pre-impact) and a set of model simulations based upon a selection of possible climate and land-use change scenarios (post-impact). These indicators, obtained using the IHA software package, are then used as input for a multi-criteria decision analysis (MCDA) undertaken using the open source diviz software package. The results of these analyses will provide DMs with an indication as to how various hydrological indicators within a catchment may be altered under different future scenarios, as well providing a ranking of how each scenario is preferred according to different DM preferences. Scenarios are represented through a combination of model input data and parameter settings in J2000, and preferences are represented through criteria weighting in the MCDA. Here, the methodology is presented and applied to the J2000 Luanginga model results using a set of hypothetical decision maker preference values as input for an MCDA based on the PROMETHEE II outranking method. Future work on the SASSCAL DSS will entail automation of this process, as well as its application to other hydrological models and land-use and/or climate change scenarios.

Construction of a Distributed-network Digital Watershed Management System with B/S Techniques

NASA Astrophysics Data System (ADS)

Zhang, W. C.; Liu, Y. M.; Fang, J.

2017-07-01

Integrated watershed assessment tools for supporting land management and hydrologic research are becoming established tools in both basic and applied research. The core of these tools are mainly spatially distributed hydrologic models as they can provide a mechanism for investigating interactions among climate, topography, vegetation, and soil. However, the extensive data requirements and the difficult task of building input parameter files for driving these distributed models, have long been an obstacle to the timely and cost-effective use of such complex models by watershed managers and policy-makers. Recently, a web based geographic information system (GIS) tool to facilitate this process has been developed for a large watersheds of Jinghe and Weihe catchments located in the loess plateau of the Huanghe River basin in north-western China. A web-based GIS provides the framework within which spatially distributed data are collected and used to prepare model input files of these two watersheds and evaluate model results as well as to provide the various clients for watershed information inquiring, visualizing and assessment analysis. This Web-based Automated Geospatial Watershed Assessment GIS (WAGWA-GIS) tool uses widely available standardized spatial datasets that can be obtained via the internet oracle databank designed with association of Map Guide platform to develop input parameter files for online simulation at different spatial and temporal scales with Xing’anjiang and TOPMODEL that integrated with web-based digital watershed. WAGWA-GIS automates the process of transforming both digital data including remote sensing data, DEM, Land use/cover, soil digital maps and meteorological and hydrological station geo-location digital maps and text files containing meteorological and hydrological data obtained from stations of the watershed into hydrological models for online simulation and geo-spatial analysis and provides a visualization tool to help the user interpret results. The utility of WAGWA-GIS in jointing hydrologic and ecological investigations has been demonstrated on such diverse landscapes as Jinhe and Weihe watersheds, and will be extended to be utilized in the other watersheds in China step by step in coming years

Monte Carlo Based Calibration and Uncertainty Analysis of a Coupled Plant Growth and Hydrological Model

NASA Astrophysics Data System (ADS)

Houska, Tobias; Multsch, Sebastian; Kraft, Philipp; Frede, Hans-Georg; Breuer, Lutz

2014-05-01

Computer simulations are widely used to support decision making and planning in the agriculture sector. On the one hand, many plant growth models use simplified hydrological processes and structures, e.g. by the use of a small number of soil layers or by the application of simple water flow approaches. On the other hand, in many hydrological models plant growth processes are poorly represented. Hence, fully coupled models with a high degree of process representation would allow a more detailed analysis of the dynamic behaviour of the soil-plant interface. We used the Python programming language to couple two of such high process oriented independent models and to calibrate both models simultaneously. The Catchment Modelling Framework (CMF) simulated soil hydrology based on the Richards equation and the Van-Genuchten-Mualem retention curve. CMF was coupled with the Plant growth Modelling Framework (PMF), which predicts plant growth on the basis of radiation use efficiency, degree days, water shortage and dynamic root biomass allocation. The Monte Carlo based Generalised Likelihood Uncertainty Estimation (GLUE) method was applied to parameterize the coupled model and to investigate the related uncertainty of model predictions to it. Overall, 19 model parameters (4 for CMF and 15 for PMF) were analysed through 2 x 106 model runs randomly drawn from an equally distributed parameter space. Three objective functions were used to evaluate the model performance, i.e. coefficient of determination (R2), bias and model efficiency according to Nash Sutcliffe (NSE). The model was applied to three sites with different management in Muencheberg (Germany) for the simulation of winter wheat (Triticum aestivum L.) in a cross-validation experiment. Field observations for model evaluation included soil water content and the dry matters of roots, storages, stems and leaves. Best parameter sets resulted in NSE of 0.57 for the simulation of soil moisture across all three sites. The shape parameter of the retention curve n was highly constrained whilst other parameters of the retention curve showed a large equifinality. The root and storage dry matter observations were predicted with a NSE of 0.94, a low bias of 58.2 kg ha-1 and a high R2 of 0.98. Dry matters of stem and leaves were predicted with less, but still high accuracy (NSE=0.79, bias=221.7 kg ha-1, R2=0.87). We attribute this slightly poorer model performance to missing leaf senescence which is currently not implemented in PMF. The most constrained parameters for the plant growth model were the radiation-use-efficiency and the base temperature. Cross validation helped to identify deficits in the model structure, pointing out the need of including agricultural management options in the coupled model.

Monte Carlo based calibration and uncertainty analysis of a coupled plant growth and hydrological model

NASA Astrophysics Data System (ADS)

Houska, T.; Multsch, S.; Kraft, P.; Frede, H.-G.; Breuer, L.

2013-12-01

Computer simulations are widely used to support decision making and planning in the agriculture sector. On the one hand, many plant growth models use simplified hydrological processes and structures, e.g. by the use of a small number of soil layers or by the application of simple water flow approaches. On the other hand, in many hydrological models plant growth processes are poorly represented. Hence, fully coupled models with a high degree of process representation would allow a more detailed analysis of the dynamic behaviour of the soil-plant interface. We used the Python programming language to couple two of such high process oriented independent models and to calibrate both models simultaneously. The Catchment Modelling Framework (CMF) simulated soil hydrology based on the Richards equation and the van-Genuchten-Mualem retention curve. CMF was coupled with the Plant growth Modelling Framework (PMF), which predicts plant growth on the basis of radiation use efficiency, degree days, water shortage and dynamic root biomass allocation. The Monte Carlo based Generalised Likelihood Uncertainty Estimation (GLUE) method was applied to parameterize the coupled model and to investigate the related uncertainty of model predictions to it. Overall, 19 model parameters (4 for CMF and 15 for PMF) were analysed through 2 × 106 model runs randomly drawn from an equally distributed parameter space. Three objective functions were used to evaluate the model performance, i.e. coefficient of determination (R2), bias and model efficiency according to Nash Sutcliffe (NSE). The model was applied to three sites with different management in Muencheberg (Germany) for the simulation of winter wheat (Triticum aestivum L.) in a cross-validation experiment. Field observations for model evaluation included soil water content and the dry matters of roots, storages, stems and leaves. Best parameter sets resulted in NSE of 0.57 for the simulation of soil moisture across all three sites. The shape parameter of the retention curve n was highly constrained whilst other parameters of the retention curve showed a large equifinality. The root and storage dry matter observations were predicted with a NSE of 0.94, a low bias of -58.2 kg ha-1 and a high R2 of 0.98. Dry matters of stem and leaves were predicted with less, but still high accuracy (NSE = 0.79, bias = 221.7 kg ha-1, R2 = 0.87). We attribute this slightly poorer model performance to missing leaf senescence which is currently not implemented in PMF. The most constrained parameters for the plant growth model were the radiation-use-efficiency and the base temperature. Cross validation helped to identify deficits in the model structure, pointing out the need of including agricultural management options in the coupled model.

A modeling approach to establish environmental flow threshold in ungauged semidiurnal tidal river

NASA Astrophysics Data System (ADS)

Akter, A.; Tanim, A. H.

2018-03-01

Due to shortage of flow monitoring data in ungauged semidiurnal river, 'environmental flow' (EF) determination based on its key component 'minimum low flow' is always difficult. For EF assessment this study selected a reach immediately after the Halda-Karnafuli confluence, a unique breeding ground for Indian Carp fishes of Bangladesh. As part of an ungauged tidal river, EF threshold establishment faces challenges in changing ecological paradigms with periodic change of tides and hydrologic alterations. This study describes a novel approach through modeling framework comprising hydrological, hydrodynamic and habitat simulation model. The EF establishment was conceptualized according to the hydrologic process of an ungauged semi-diurnal tidal regime in four steps. Initially, a hydrologic model coupled with a hydrodynamic model to simulate flow considering land use changes effect on streamflow, seepage loss of channel, friction dominated tidal decay as well as lack of long term flow characteristics. Secondly, to define hydraulic habitat feature, a statistical analysis on derived flow data was performed to identify 'habitat suitability'. Thirdly, to observe the ecological habitat behavior based on the identified hydrologic alteration, hydraulic habitat features were investigated. Finally, based on the combined habitat suitability index flow alteration and ecological response relationship was established. Then, the obtained EF provides a set of low flow indices of desired regime and thus the obtained discharge against maximum Weighted Usable Area (WUA) was defined as EF threshold for the selected reach. A suitable EF regime condition was obtained within flow range 25-30.1 m3/s i.e., around 10-12% of the mean annual runoff of 245 m3/s and these findings are within researchers' recommendation of minimum flow requirement. Additionally it was observed that tidal characteristics are dominant process in semi-diurnal regime. However, during the study period (2010-2015) the validated model with those reported observations can provide guidance for the decision support system (DSS) to maintain EF range in an ungauged tidal river.

Monitoring Soil Infiltration In Semi-Arid Regions With Meteosat And A Coupled Model Approach Using PROMET And SLC

NASA Astrophysics Data System (ADS)

Klug, P.; Bach, H.; Migdall, S.

2013-12-01

In arid regions the infiltration of sparse rainfalls and resulting ground water recharge is a critical quantity for the water cycle. With the PROMET model the infiltration process can be simulated in detail, since 4 soil layers together with the hourly calculation time step allow simulating the vertical water transport. Wet soils are darker than dry soils. Using the SLC reflectance model this effect can be simulated and compared to temporal high resolution time series of measured reflectances from Meteosat in order to monitor the drying process. This study demonstrates how MSG can be used to better parameterize the simulation of the infiltration process and reduce uncertainties in ground water recharge estimation. The study is carried out in the frame of the EU FP7 project CLIMB (Climate Induced Changes on the Hydrology of Mediterranean Basins). According to climate projections, Mediterranean countries are at risk of changes in the hydrological budget, the agricultural productivity and drinking water supply in the future. The CLIMB FP-7 project coordinated by the University of Munich (LMU) aims at employing integrated hydrological modelling in a new framework to reduce existing uncertainties in climate change impact analysis of the Mediterranean region [1, 2].

Understanding the Spatial and Temporal Variations in Hormone Transport within the Stream Ecosystem

NASA Astrophysics Data System (ADS)

Mallakpour, I.; Ward, A. S.; Basu, N. B.

2012-12-01

Agricultural, urban, and industrial activities, including land application of manures and discharge of municipal and industrial wastewater, act as point and nonpoint sources for steroid hormones in soils, water, and sediments. Hormones are endocrine disruptors, and their occurrence in stream ecosystems has been implicated in the decline of certain species and change of sex in fish. Laboratory studies indicate that steroid hormones tend to have moderately large sorption coefficients and relatively short half-lives, from a few hours to a few days, suggesting that their persistence and subsequent leaching from soils will be limited. However, these chemicals continue to be detected in streams, indicating that laboratory studies may not capture the coupled hydrologic and biogeochemical dynamics occurring at the field or stream-reach scale. Understanding the spatial and temporal persistence of these chemicals downstream of a confined animal feeding operation (CAFO) or wastewater treatment plant (WWTP) requires a coupled hydrologic and biogeochemical model that takes into account multiple interacting species, sediment processes, and different aerobic and anaerobic reaction pathways and rates. In this study, we focus on two hormones, estrone (E1) and 17β-estradiol (E2), with redox dynamics controlling the conversion between E1 and E2. A 1D stream-reach model with a main-channel and a hyporheic zone was developed similar to the commonly used OTIS model. Processes such as photolysis, decay, and sorption to sediments were included in the model framework. The inclusion of coupled reactions, with specific reaction rates and pathways driven by different reaction pathway, that in turn can be dynamic during a storm event (for example, increasing discharge might lead to more aerobic conditions), was the novelty of the approach. The modeling framework was then used to quantify the relative importance of the different reaction pathways under varying flow conditions, and evaluate the persistence of these chemicals as a function of hydrologic and biogeochemical controls.

Integrating SMOS brightness temperatures with a new conceptual spatially distributed hydrological model for improving flood and drought predictions at large scale.

NASA Astrophysics Data System (ADS)

Hostache, Renaud; Rains, Dominik; Chini, Marco; Lievens, Hans; Verhoest, Niko E. C.; Matgen, Patrick

2017-04-01

Motivated by climate change and its impact on the scarcity or excess of water in many parts of the world, several agencies and research institutions have taken initiatives in monitoring and predicting the hydrologic cycle at a global scale. Such a monitoring/prediction effort is important for understanding the vulnerability to extreme hydrological events and for providing early warnings. This can be based on an optimal combination of hydro-meteorological models and remote sensing, in which satellite measurements can be used as forcing or calibration data or for regularly updating the model states or parameters. Many advances have been made in these domains and the near future will bring new opportunities with respect to remote sensing as a result of the increasing number of spaceborn sensors enabling the large scale monitoring of water resources. Besides of these advances, there is currently a tendency to refine and further complicate physically-based hydrologic models to better capture the hydrologic processes at hand. However, this may not necessarily be beneficial for large-scale hydrology, as computational efforts are therefore increasing significantly. As a matter of fact, a novel thematic science question that is to be investigated is whether a flexible conceptual model can match the performance of a complex physically-based model for hydrologic simulations at large scale. In this context, the main objective of this study is to investigate how innovative techniques that allow for the estimation of soil moisture from satellite data can help in reducing errors and uncertainties in large scale conceptual hydro-meteorological modelling. A spatially distributed conceptual hydrologic model has been set up based on recent developments of the SUPERFLEX modelling framework. As it requires limited computational efforts, this model enables early warnings for large areas. Using as forcings the ERA-Interim public dataset and coupled with the CMEM radiative transfer model, SUPERFLEX is capable of predicting runoff, soil moisture, and SMOS-like brightness temperature time series. Such a model is traditionally calibrated using only discharge measurements. In this study we designed a multi-objective calibration procedure based on both discharge measurements and SMOS-derived brightness temperature observations in order to evaluate the added value of remotely sensed soil moisture data in the calibration process. As a test case we set up the SUPERFLEX model for the large scale Murray-Darling catchment in Australia ( 1 Million km2). When compared to in situ soil moisture time series, model predictions show good agreement resulting in correlation coefficients exceeding 70 % and Root Mean Squared Errors below 1 %. When benchmarked with the physically based land surface model CLM, SUPERFLEX exhibits similar performance levels. By adapting the runoff routing function within the SUPERFLEX model, the predicted discharge results in a Nash Sutcliff Efficiency exceeding 0.7 over both the calibration and the validation periods.

Use of distributed snow cover information to update snow storages of a lumped rainfall-runoff model operationally

NASA Astrophysics Data System (ADS)

Lisniak, D.; Meissner, D.; Klein, B.; Pinzinger, R.

2013-12-01

The German Federal Institute of Hydrology (BfG) offers navigational water-level forecasting services on the Federal Waterways, like the rivers Rhine and Danube. In cooperation with the Federal States this mandate also includes the forecasting of flood events. For the River Rhine, the most frequented inland waterway in Central Europe, the BfG employs a hydrological model (HBV) coupled to a hydraulic model (SOBEK) by the FEWS-framework to perform daily forecasts of water-levels operationally. Sensitivity studies have shown that the state of soil water storage in the hydrological model is a major factor of uncertainty when performing short- to medium-range forecasts some days ahead. Taking into account the various additional sources of uncertainty associated with hydrological modeling, including measurement uncertainties, it is essential to estimate an optimal initial state of the soil water storage before propagating it in time, forced by meteorological forecasts, and transforming it into discharge. We show, that using the Ensemble Kalman Filter these initial states can be updated straightforward under certain hydrologic conditions. However, this approach is not sufficient if the runoff is mainly generated by snow melt. Since the snow cover evolution is modeled rather poorly by the HBV-model in our operational setting, flood events caused by snow melt are consistently underestimated by the HBV-model, which has long term effects in basins characterized by a nival runoff regime. Thus, it appears beneficial to update the snow storage of the HBV-model with information derived from regionalized snow cover observations. We present a method to incorporate spatially distributed snow cover observations into the lumped HBV-model. We show the plausibility of this approach and asses the benefits of a coupled snow cover and soil water storage updating, which combine a direct insertion with an Ensemble Kalman Filter. The Ensemble Kalman Filter used here takes into account the internal routing mechanism of the HBV-model, which causes a delayed response of the simulated discharge at the catchment outlet to changes in internal states.

Impacts of climate change and internal climate variability on french rivers streamflows

NASA Astrophysics Data System (ADS)

Dayon, Gildas; Boé, Julien; Martin, Eric

2016-04-01

The assessment of the impacts of climate change often requires to set up long chains of modeling, from the model to estimate the future concentration of greenhouse gases to the impact model. Throughout the modeling chain, sources of uncertainty accumulate making the exploitation of results for the development of adaptation strategies difficult. It is proposed here to assess the impacts of climate change on the hydrological cycle over France and the associated uncertainties. The contribution of the uncertainties from greenhouse gases emission scenario, climate models and internal variability are addressed in this work. To have a large ensemble of climate simulations, the study is based on Global Climate Models (GCM) simulations from the Coupled Model Intercomparison Phase 5 (CMIP5), including several simulations from the same GCM to properly assess uncertainties from internal climate variability. Simulations from the four Radiative Concentration Pathway (RCP) are downscaled with a statistical method developed in a previous study (Dayon et al. 2015). The hydrological system Isba-Modcou is then driven by the downscaling results on a 8 km grid over France. Isba is a land surface model that calculates the energy and water balance and Modcou a hydrogeological model that routes the surface runoff given by Isba. Based on that framework, uncertainties uncertainties from greenhouse gases emission scenario, climate models and climate internal variability are evaluated. Their relative importance is described for the next decades and the end of this century. In a last part, uncertainties due to internal climate variability on streamflows simulated with downscaled GCM and Isba-Modcou are evaluated against observations and hydrological reconstructions on the whole 20th century. Hydrological reconstructions are based on the downscaling of recent atmospheric reanalyses of the 20th century and observations of temperature and precipitation. We show that the multi-decadal variability of streamflows observed in the 20th century is generally weaker in the hydrological simulations done with the historical simulations from climate models. References: Dayon et al. (2015), Transferability in the future climate of a statistical downscaling mehtod for precipitation in France, J. Geophys. Res. Atmos., 120, 1023-1043, doi:10.1002/2014JD022236

High-Resolution Assimilation of GRACE Terrestrial Water Storage Observations to Represent Local-Scale Water Table Depths

NASA Astrophysics Data System (ADS)

Stampoulis, D.; Reager, J. T., II; David, C. H.; Famiglietti, J. S.; Andreadis, K.

2017-12-01

Despite the numerous advances in hydrologic modeling and improvements in Land Surface Models, an accurate representation of the water table depth (WTD) still does not exist. Data assimilation of observations of the joint NASA and DLR mission, Gravity Recovery and Climate Experiment (GRACE) leads to statistically significant improvements in the accuracy of hydrologic models, ultimately resulting in more reliable estimates of water storage. However, the usually shallow groundwater compartment of the models presents a problem with GRACE assimilation techniques, as these satellite observations account for much deeper aquifers. To improve the accuracy of groundwater estimates and allow the representation of the WTD at fine spatial scales we implemented a novel approach that enables a large-scale data integration system to assimilate GRACE data. This was achieved by augmenting the Variable Infiltration Capacity (VIC) hydrologic model, which is the core component of the Regional Hydrologic Extremes Assessment System (RHEAS), a high-resolution modeling framework developed at the Jet Propulsion Laboratory (JPL) for hydrologic modeling and data assimilation. The model has insufficient subsurface characterization and therefore, to reproduce groundwater variability not only in shallow depths but also in deep aquifers, as well as to allow GRACE assimilation, a fourth soil layer of varying depth ( 1000 meters) was added in VIC as the bottom layer. To initialize a water table in the model we used gridded global WTD data at 1 km resolution which were spatially aggregated to match the model's resolution. Simulations were then performed to test the augmented model's ability to capture seasonal and inter-annual trends of groundwater. The 4-layer version of VIC was run with and without assimilating GRACE Total Water Storage anomalies (TWSA) over the Central Valley in California. This is the first-ever assimilation of GRACE TWSA for the determination of realistic water table depths, at fine scales that are required for local water management. In addition, Open Loop and GRACE-assimilation simulations of water table depth were compared to in-situ data over the state of California, derived from observation wells operated/maintained by the U.S. Geological Service.

Representing Water Scarcity in Future Agricultural Assessments

NASA Technical Reports Server (NTRS)

Winter, Jonathan M.; Lopez, Jose R.; Ruane, Alexander C.; Young, Charles A.; Scanlon, Bridget R.; Rosenzweig, Cynthia

2017-01-01

Globally, irrigated agriculture is both essential for food production and the largest user of water. A major challenge for hydrologic and agricultural research communities is assessing the sustainability of irrigated croplands under climate variability and change. Simulations of irrigated croplands generally lack key interactions between water supply, water distribution, and agricultural water demand. In this article, we explore the critical interface between water resources and agriculture by motivating, developing, and illustrating the application of an integrated modeling framework to advance simulations of irrigated croplands. We motivate the framework by examining historical dynamics of irrigation water withdrawals in the United States and quantitatively reviewing previous modeling studies of irrigated croplands with a focus on representations of water supply, agricultural water demand, and impacts on crop yields when water demand exceeds water supply. We then describe the integrated modeling framework for simulating irrigated croplands, which links trends and scenarios with water supply, water allocation, and agricultural water demand. Finally, we provide examples of efforts that leverage the framework to improve simulations of irrigated croplands as well as identify opportunities for interventions that increase agricultural productivity, resiliency, and sustainability.

The ecohydrology of water limited landscapes

NASA Astrophysics Data System (ADS)

Huxman, T. E.

2011-12-01

Developing a mechanistic understanding of the coupling of ecological and hydrological systems is crucial for understanding the land-surface response of large areas of the globe to changes in climate. The distribution of biodiversity, the quantity and quality of streamflow, the biogeochemistry that constrains vegetation cover and production, and the stability of soil systems in watersheds are all functions of water-life coupling. Many key ecosystem services are governed by the dynamics of near-surface hydrology and biological feedbacks on the landscape occur through plant influence over available soil moisture. Thus, ecohydrology has tremendous potential to contribute to a predictive framework for understanding earth system dynamics. Despite the importance of such couplings and water as a major limiting resource in ecosystems throughout the globe, ecology still struggles with a mechanistic understanding of how changes in rainfall affect the biology of plants and microbes, or how changes in plant communities affect hydrological dynamics in watersheds. Part of the problem comes from our lack of understanding of how plants effectively partition available water among individuals in communities and how that modifies the physical environment, affecting additional resource availability and the passage of water along other hydrological pathways. The partitioning of evapotranspiration between transpiration by plants and evaporation from the soil surface is key to interrelated ecological, hydrological, and atmospheric processes and likely varies with vegetation structure and atmospheric dynamics. In addition, the vertical stratification of autotrophic and heterotrophic components in the soil profile, and the speed at which each respond to increased water, exert strong control over the carbon cycle. The magnitude of biosphere-atmosphere carbon exchange depends on the time-depth-distribution of soil moisture, a fundamental consequence of local precipitation pulse characteristics, soil texture and plant functional type. The transport of metabolic products within plants and their differential activation result in non-intuitive patterns of exchange associated with the major drivers creating problems with the scaling of physiological processes of individual plants to ecosystems. Such dynamics, along with hysteretic behavior creates challenges for measurement, evaluation, modeling and predicting ecosystem behavior. New frameworks and conceptual approaches to modeling ecosystem metabolism and the role of water are helping to describe the consequences of precipitation variability and change.

Development of river flood model in lower reach of urbanized river basin

NASA Astrophysics Data System (ADS)

Yoshimura, Kouhei; Tajima, Yoshimitsu; Sanuki, Hiroshi; Shibuo, Yoshihiro; Sato, Shinji; Lee, SungAe; Furumai, Hiroaki; Koike, Toshio

2014-05-01

Japan, with its natural mountainous landscape, has demographic feature that population is concentrated in lower reach of elevation close to the coast, and therefore flood damage with large socio-economic value tends to occur in low-lying region. Modeling of river flood in such low-lying urbanized river basin is complex due to the following reasons. In upstream it has been experienced urbanization, which changed land covers from natural forest or agricultural fields to residential or industrial area. Hence rate of infiltration and runoff are quite different from natural hydrological settings. In downstream, paved covers and construct of sewerage system in urbanized areas affect direct discharges and it enhances higher and faster flood peak arrival. Also tidal effect from river mouth strongly affects water levels in rivers, which must be taken into account. We develop an integrated river flood model in lower reach of urbanized areas to be able to address above described complex feature, by integrating model components: LSM coupled distributed hydrological model that models anthropogenic influence on river discharges to downstream; urban hydrological model that simulates run off response in urbanized areas; Saint Venant's equation approximated river model that integrates upstream and urban hydrological models with considering tidal effect from downstream. These features are integrated in a common modeling framework so that model interaction can be directly performed. The model is applied to the Tsurumi river basin, urbanized low-lying river basin in Yokohama and model results show that it can simulate water levels in rivers with acceptable model errors. Furthermore the model is able to install miscellaneous water planning constructs, such as runoff reduction pond in urbanized area, flood control field along the river channel, levee, etc. This can be a useful tool to investigate cost performance of hypothetical water management plan against impact of climate change in the region.

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.

Towards a Seamless Framework for Drought Analysis and Prediction from Seasonal to Climate Change Time Scales (Plinius Medal Lecture)

NASA Astrophysics Data System (ADS)

Sheffield, Justin

2013-04-01

Droughts arguably cause the most impacts of all natural hazards in terms of the number of people affected and the long-term economic costs and ecosystem stresses. Recent droughts worldwide have caused humanitarian and economic problems such as food insecurity across the Horn of Africa, agricultural economic losses across the central US and loss of livelihoods in rural western India. The prospect of future increases in drought severity and duration driven by projected changes in precipitation patterns and increasing temperatures is worrisome. Some evidence for climate change impacts on drought is already being seen for some regions, such as the Mediterranean and east Africa. Mitigation of the impacts of drought requires advance warning of developing conditions and enactment of drought plans to reduce vulnerability. A key element of this is a drought early warning system that at its heart is the capability to monitor evolving hydrological conditions and water resources storage, and provide reliable and robust predictions out to several months, as well as the capacity to act on this information. At longer time scales, planning and policy-making need to consider the potential impacts of climate change and its impact on drought risk, and do this within the context of natural climate variability, which is likely to dominate any climate change signal over the next few decades. There are several challenges that need to be met to advance our capability to provide both early warning at seasonal time scales and risk assessment under climate change, regionally and globally. Advancing our understanding of drought predictability and risk requires knowledge of drought at all time scales. This includes understanding of past drought occurrence, from the paleoclimate record to the recent past, and understanding of drought mechanisms, from initiation, through persistence to recovery and translation of this understanding to predictive models. Current approaches to monitoring and predicting drought are limited in many parts of the world, and especially in developing countries where national capacity is limited. Evaluation of past droughts and their mechanisms is limited by data availability and especially before the instrumental period of the last 50-100 years, for which there is reliance on incomplete spatial proxy data, such as tree rings. Seasonal predictability is currently mainly limited to tropical and sub-tropical regions through connections with sea surface temperature variations such as ENSO. Predictability in mid-latitudes is low and especially for precipitation, although dynamical model predictions appear to be edging statistical models in many aspects of seasonal prediction. This presentation describes ongoing research on evaluation of drought risk and drought mechanisms at regional to global scales with the eventual goal of developing a seamless monitoring and prediction framework at all time scales. Such a framework would allow consistent assessment of drought from historic to current conditions, and from seasonal and decadal predictions to climate change projections. At the center of the framework is an experimental global drought monitoring and seasonal forecast system that has evolved out of regional and continental systems for the US and Africa. The system is based on land surface hydrological modeling that is driven by satellite remote sensing precipitation to predict current hydrological conditions and the state of drought. Seasonal climate model forecasts are downscaled and bias-corrected to drive the land surface model to provide hydrological forecasts and drought products out 6-9 months. The system relies on historic reconstructions of drought variability over the 20th century, which forms the background climatology to which current conditions can be assessed and drought mechanisms can be diagnosed. Future drought risk is quantified based on bias-corrected and downscaled climate model projections that are used to drive the land surface models. Current research is focused on several aspects, including: 1) quantifying the uncertainties in historic drought reconstructions; 2) analysis of drought propagation through the coupled hydrological/vegetation system; 3) the utility of new data sources such as on the ground sensors and new satellite products for terrestrial hydrology and vegetation, for improved monitoring and prediction, especially in poorly observed regions; 4) advancing predictive skill for all aspects of drought occurrence through diagnosis of the driving mechanisms and feedbacks of historic droughts; and 5) quantification and reduction of uncertainties in future projections of drought under climate change. The steps towards the development of a seamless framework for analysis and prediction in the context of this research are discussed.

Regional scale hydrology with a new land surface processes model

NASA Technical Reports Server (NTRS)

Laymon, Charles; Crosson, William

1995-01-01

Through the CaPE Hydrometeorology Project, we have developed an understanding of some of the unique data quality issues involved in assimilating data of disparate types for regional-scale hydrologic modeling within a GIS framework. Among others, the issues addressed here include the development of adequate validation of the surface water budget, implementation of the STATSGO soil data set, and implementation of a remote sensing-derived landcover data set to account for surface heterogeneity. A model of land surface processes has been developed and used in studies of the sensitivity of surface fluxes and runoff to soil and landcover characterization. Results of these experiments have raised many questions about how to treat the scale-dependence of land surface-atmosphere interactions on spatial and temporal variability. In light of these questions, additional modifications are being considered for the Marshall Land Surface Processes Model. It is anticipated that these techniques can be tested and applied in conjunction with GCIP activities over regional scales.