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Sample records for real-time flood forecasting

  1. Real-time flood forecasting

    USGS Publications Warehouse

    Lai, C.; Tsay, T.-K.; Chien, C.-H.; Wu, I.-L.

    2009-01-01

    Researchers at the Hydroinformatic Research and Development Team (HIRDT) of the National Taiwan University undertook a project to create a real time flood forecasting model, with an aim to predict the current in the Tamsui River Basin. The model was designed based on deterministic approach with mathematic modeling of complex phenomenon, and specific parameter values operated to produce a discrete result. The project also devised a rainfall-stage model that relates the rate of rainfall upland directly to the change of the state of river, and is further related to another typhoon-rainfall model. The geographic information system (GIS) data, based on precise contour model of the terrain, estimate the regions that were perilous to flooding. The HIRDT, in response to the project's progress, also devoted their application of a deterministic model to unsteady flow of thermodynamics to help predict river authorities issue timely warnings and take other emergency measures.

  2. Support vector regression for real-time flood stage forecasting

    NASA Astrophysics Data System (ADS)

    Yu, Pao-Shan; Chen, Shien-Tsung; Chang, I.-Fan

    2006-09-01

    SummaryFlood forecasting is an important non-structural approach for flood mitigation. The flood stage is chosen as the variable to be forecasted because it is practically useful in flood forecasting. The support vector machine, a novel artificial intelligence-based method developed from statistical learning theory, is adopted herein to establish a real-time stage forecasting model. The lags associated with the input variables are determined by applying the hydrological concept of the time of response, and a two-step grid search method is applied to find the optimal parameters, and thus overcome the difficulties in constructing the learning machine. Two structures of models used to perform multiple-hour-ahead stage forecasts are developed. Validation results from flood events in Lan-Yang River, Taiwan, revealed that the proposed models can effectively predict the flood stage forecasts one-to-six-hours ahead. Moreover, a sensitivity analysis was conducted on the lags associated with the input variables.

  3. Rainfall-based real-time flood forecasting

    NASA Astrophysics Data System (ADS)

    Bertoni, Juan Carlos; Tucci, Carlos Eduardo; Clarke, Robin Thomas

    1992-02-01

    The use of conceptual rainfall-runoff models in real-time flood forecasting still presents problems, some of which relate to the updating of the mathematical model and to uncertainties associated with future rainfall. Both topics are approached in this study, in which a conceptual rainfall-runoff model (IPH-II) for real-time flood forecasting and a simplified stochastic model to determine the value of including quantitative rainfall forecasts were used. The methods were tested using data from a small watershed (the River Ray at Grendon Underwood, UK), for which 17 years of records were available. The results show that a simple method used to forecast rain falling during the next few hours, may help to improve real-time discharge estimates.

  4. Real time flood forecasting in the Upper Danube basin

    NASA Astrophysics Data System (ADS)

    Nester, Thomas; Komma, Jürgen; Blöschl, Günter

    2016-04-01

    In this contribution, we report on experiences with developing the flood forecasting model for the Upper Danube basin and its operational use since 2006. The model system consists of a hydrological model for the catchments and a hydrodynamic model for the Danube and uses meteorological forecasts for the next 48 hours. The parameters of the hydrological model were estimated based on the Dominant Processes Concept. Runoff data are assimilated in real time to update modelled soil moisture. An analysis of the performance of the hydrological model indicates 88% of the snow cover in the basin to be modelled correctly on more than 80% of the days. Runoff forecasting errors decrease with catchment area and increase with forecast lead time. The forecast ensemble spread is shown to be a meaningful indicator of the forecast uncertainty. We also show forecasts from the 2013 flood in the Upper Danube basin. There was a tendency for the precipitation forecasts to underestimate event precipitation and for the runoff model to overestimate runoff generation which resulted in, overall, rather accurate runoff forecasts.

  5. A channel dynamics model for real-time flood forecasting

    USGS Publications Warehouse

    Hoos, A.B.; Koussis, A.D.; Beale, G.O.

    1989-01-01

    A new channel dynamics scheme ASPIRE (alternative system predictor in real time), designed specifically for real-time river flow forecasting, is introduced to reduce uncertainty in the forecast. ASPIRE is a storage routing model that limits the influence of catchment model forecast errors to the downstream station closest to the catchment. Comparisons with the Muskingum routing scheme in field tests suggest that the ASPIRE scheme can provide more accurate forecasts, probably because discharge observations are used to a maximum advantage and routing reaches (and model errors in each reach) are uncoupled. Using ASPIRE in conjunction with the Kalman filter did not improve forecast accuracy relative to a deterministic updating procedure. Theoretical analysis suggests that this is due to a large process noise to measurement noise ratio. -Authors

  6. Real-time flood forecasting with high-resolution NWP rainfall and dual data assimilation

    NASA Astrophysics Data System (ADS)

    Liu, Jia; Bray, Michaela; Han, Dawei

    2014-05-01

    Mesoscale Numerical Weather Prediction (NWP) models are nowadays gaining more and more attention in providing high-resolution rainfall forecasts for real-time flood forecasting. In this study, the newest generation NWP model, Weather Research & Forecasting (WRF) model, is integrated with the rainfall-runoff model in real-time to generate accurate flow forecasts at the catchment scale. The rainfall-runoff model is chosen as the Probability Distribution Model (PDM), which has widely been used for flood forecasting. Dual data assimilation is carried out for real-time updating of the flood forecasting system. The 3-Dimensional Variational (3DVar) data assimilation scheme is incorporated with WRF to assimilate meteorological observations and weather radar reflectivity data in order to improve the WRF rainfall forecasts; meanwhile real-time flow observations are assimilated by the Auto-Regressive Moving Average (ARMA) model to update the forecasted flow transformed by PDM. The Brue catchment located in Southwest England with a drainage area of 135.2 km2 is chosen to be the study area. A dense rain gauge network was set up during a project named HYREX (Hydrological radar experiment), which contains 49 rain gauges and a C-band weather radar, providing with sufficient hydrological and radar data for WRF model verification and data assimilation. Besides the radar reflectivity data, two types of NCAR archived data (SYNOP and SOUND, http://dss.ucar.edu) are also assimilated by 3DVar, which provide real-time surface and upper-level observations of pressure, temperature, humidity and wind from fixed and mobile stations. Four 24 hour storm events are selected from the HYREX project with different characteristics regarding storm formation and rainfall-runoff responses. Real-time flood forecasting is then carried out by the constructed forecasting system for the four storm events with a forecast lead time of 12 hours. The forecasting accuracy of the whole system is found to be

  7. Real-time flood forecasting by employing artificial neural network based model with zoning matching approach

    NASA Astrophysics Data System (ADS)

    Sulaiman, M.; El-Shafie, A.; Karim, O.; Basri, H.

    2011-10-01

    Flood forecasting models are a necessity, as they help in planning for flood events, and thus help prevent loss of lives and minimize damage. At present, artificial neural networks (ANN) have been successfully applied in river flow and water level forecasting studies. ANN requires historical data to develop a forecasting model. However, long-term historical water level data, such as hourly data, poses two crucial problems in data training. First is that the high volume of data slows the computation process. Second is that data training reaches its optimal performance within a few cycles of data training, due to there being a high volume of normal water level data in the data training, while the forecasting performance for high water level events is still poor. In this study, the zoning matching approach (ZMA) is used in ANN to accurately monitor flood events in real time by focusing the development of the forecasting model on high water level zones. ZMA is a trial and error approach, where several training datasets using high water level data are tested to find the best training dataset for forecasting high water level events. The advantage of ZMA is that relevant knowledge of water level patterns in historical records is used. Importantly, the forecasting model developed based on ZMA successfully achieves high accuracy forecasting results at 1 to 3 h ahead and satisfactory performance results at 6 h. Seven performance measures are adopted in this study to describe the accuracy and reliability of the forecasting model developed.

  8. 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. PMID:26442493

  9. 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.

  10. An operational real-time flood forecasting system in Southern Italy

    NASA Astrophysics Data System (ADS)

    Ortiz, Enrique; Coccia, Gabriele; Todini, Ezio

    2015-04-01

    A real-time flood forecasting system has been operating since year 2012 as a non-structural measure for mitigating the flood risk in Campania Region (Southern Italy), within the Sele river basin (3.240 km2). The Sele Flood Forecasting System (SFFS) has been built within the FEWS (Flood Early Warning System) platform developed by Deltares and it assimilates the numerical weather predictions of the COSMO LAM family: the deterministic COSMO-LAMI I2, the deterministic COSMO-LAMI I7 and the ensemble numerical weather predictions COSMO-LEPS (16 members). Sele FFS is composed by a cascade of three main models. The first model is a fully continuous physically based distributed hydrological model, named TOPKAPI-eXtended (Idrologia&Ambiente s.r.l., Naples, Italy), simulating the dominant processes controlling the soil water dynamics, runoff generation and discharge with a spatial resolution of 250 m. The second module is a set of Neural-Networks (ANN) built for forecasting the river stages at a set of monitored cross-sections. The third component is a Model Conditional Processor (MCP), which provides the predictive uncertainty (i.e., the probability of occurrence of a future flood event) within the framework of a multi-temporal forecast, according to the most recent advancements on this topic (Coccia and Todini, HESS, 2011). The MCP provides information about the probability of exceedance of a maximum river stage within the forecast lead time, by means of a discrete time function representing the variation of cumulative probability of exceeding a river stage during the forecast lead time and the distribution of the time occurrence of the flood peak, starting from one or more model forecasts. This work shows the Sele FFS performance after two years of operation, evidencing the added-values that can provide to a flood early warning and emergency management system.

  11. An extended real-time flood impact forecasting system for the Chapare watershed in Bolivia

    NASA Astrophysics Data System (ADS)

    Rossi, Lauro; Gabellani, Simone; Masoero, Alessandro; Dolia, Daniele; Rudari, Roberto

    2016-04-01

    All over the world a lot of cities are located in flood-prone areas and million of people are exposed to inundation risk. To cope with that the social safety demands efficient civil protection structures able to reduce flood risk by issuing warnings. This task requires civil protection organisms to adopt systems able to support their activities in predicting floods and rainfall impacts. For this reason flood early warning systems, based on rainfall observations and predictions, has become very useful because they are able to provide in advance a quantitative evaluation of possible effects in term of discharge and peak flow. Traditionally those forecasting systems use hydrologic models coupled with meteorological models to forecast discharge in relevant river sections and are called hydro-meteorological chains. In order to have a better representation of the flood dynamics, these hydro-meteorological chains can be expanded to include bi-dimensional hydraulic models where the level exposure is high or flow singularities (e.g. junctions, deltas, etc.) require more accurate investigation. That information allows the generation of real-time inundation scenarios that can be used by civil protection and authorities to estimate impact on population and take counter-measures. The new real-time flood impact forecasting chain consists of a suite of hydrometeorological tools that combines meteorological models, a disaggregation tool and a fully distributed hydrological model and a bidimensional hydraulic model that produces inundation scenarios in the most exposed river segments of the flood plain and a scenario tool that allows the assessment of assets involved. The complete modelling chain has been implemented in the Chapare watershed in Bolivia and it is managed by the Dewetra platform, which since 2013 is used by the Civil Defense and National Meteorological service as the main national Early Warning supporting tool.

  12. Forecasting surface water flooding hazard and impact in real-time

    NASA Astrophysics Data System (ADS)

    Cole, Steven J.; Moore, Robert J.; Wells, Steven C.

    2016-04-01

    Across the world, there is increasing demand for more robust and timely forecast and alert information on Surface Water Flooding (SWF). Within a UK context, the government Pitt Review into the Summer 2007 floods provided recommendations and impetus to improve the understanding of SWF risk for both off-line design and real-time forecasting and warning. Ongoing development and trial of an end-to-end real-time SWF system is being progressed through the recently formed Natural Hazards Partnership (NHP) with delivery to the Flood Forecasting Centre (FFC) providing coverage over England & Wales. The NHP is a unique forum that aims to deliver coordinated assessments, research and advice on natural hazards for governments and resilience communities across the UK. Within the NHP, a real-time Hazard Impact Model (HIM) framework has been developed that includes SWF as one of three hazards chosen for initial trialling. The trial SWF HIM system uses dynamic gridded surface-runoff estimates from the Grid-to-Grid (G2G) hydrological model to estimate the SWF hazard. National datasets on population, infrastructure, property and transport are available to assess impact severity for a given rarity of SWF hazard. Whilst the SWF hazard footprint is calculated in real-time using 1, 3 and 6 hour accumulations of G2G surface runoff on a 1 km grid, it has been possible to associate these with the effective rainfall design profiles (at 250m resolution) used as input to a detailed flood inundation model (JFlow+) run offline to produce hazard information resolved to 2m resolution. This information is contained in the updated Flood Map for Surface Water (uFMfSW) held by the Environment Agency. The national impact datasets can then be used with the uFMfSW SWF hazard dataset to assess impacts at this scale and severity levels of potential impact assigned at 1km and for aggregated county areas in real-time. The impact component is being led by the Health and Safety Laboratory (HSL) within the NHP

  13. The POLIMI forecasting chain for real time flood and drought predictions

    NASA Astrophysics Data System (ADS)

    Ceppi, Alessandro; Ravazzani, Giovanni; Corbari, Chiara; Mancini, Marco

    2016-04-01

    Nowadays coupling meteorological and hydrological models is recognized by scientific community as a necessary way to forecast extreme hydrological phenomena, in order to activate useful mitigation measurements and alert systems in advance. The development and implementation of a real-time forecasting chain with a hydro-meteorological operational alert procedure for flood and drought events is presented in this study. Different weather models are used to build the POLIMI operative chain: the probabilistic COSMO-LEPS model with 16 ensembles developed by ARPA-Emilia Romagna, the deterministic Bolam and Moloch models, developed by the Italian ISAC-CNR, and nine further simulations obtained by different runs of the WRF-ARW (3), WRF-NMM (2), ETA2012 (1) and the GFS (3), provided by the private Epson Meteo Center and Terraria companies. All the meteorological runs are then implemented with the rainfall-runoff physically-based distributed FEST-WB model, developed at Politecnico di Milano to obtain a multi-model approach system with hydrological ensemble forecasts in different areas of study over the Italian country. As far as concerning drought predictions, three test-beds are monitored: two in maize fields, one in the Puglia region (South of Italy), and another in the Po Valley area, (northern Italy), and one in a golf course in Milan city. The hydrological model was here calibrated and validated against measurements of latent heat flux and soil moisture acquired by an eddy-covariance station, TDR probes and remote sensing images. Regarding flood forecasts, two test-sites are chosen: the first one is the urban area northern Milan where three catchments (the Seveso, Olona, and Lambro River basins) are used to show how early warning systems are an effective complement to structural measures for flood control in Milan city which flooded frequently in the last 25 years, while the second test-site is the Idro Lake, located between the Lombardy and Trentino region where the

  14. A search for model parsimony in a real time flood forecasting system

    NASA Astrophysics Data System (ADS)

    Grossi, G.; Balistrocchi, M.

    2009-04-01

    As regards the hydrological simulation of flood events, a physically based distributed approach is the most appealing one, especially in those areas where the spatial variability of the soil hydraulic properties as well as of the meteorological forcing cannot be left apart, such as in mountainous regions. On the other hand, dealing with real time flood forecasting systems, less detailed models requiring a minor number of parameters may be more convenient, reducing both the computational costs and the calibration uncertainty. In fact in this case a precise quantification of the entire hydrograph pattern is not necessary, while the expected output of a real time flood forecasting system is just an estimate of the peak discharge, the time to peak and in some cases the flood volume. In this perspective a parsimonious model has to be found in order to increase the efficiency of the system. A suitable case study was identified in the northern Apennines: the Taro river is a right tributary to the Po river and drains about 2000 km2 of mountains, hills and floodplain, equally distributed . The hydrometeorological monitoring of this medium sized watershed is managed by ARPA Emilia Romagna through a dense network of uptodate gauges (about 30 rain gauges and 10 hydrometers). Detailed maps of the surface elevation, land use and soil texture characteristics are also available. Five flood events were recorded by the new monitoring network in the years 2003-2007: during these events the peak discharge was higher than 1000 m3/s, which is actually quite a high value when compared to the mean discharge rate of about 30 m3/s. The rainfall spatial patterns of such storms were analyzed in previous works by means of geostatistical tools and a typical semivariogram was defined, with the aim of establishing a typical storm structure leading to flood events in the Taro river. The available information was implemented into a distributed flood event model with a spatial resolution of 90m

  15. Model Integration for Real-Time Flood Forecasting Inundation Mapping for Nashville Tributaries

    NASA Astrophysics Data System (ADS)

    Charley, W.; Moran, B.; LaRosa, J.

    2012-12-01

    In May of 2010, between 14 and 19 inches of rain fell on the Nashville metro area in two days, quickly overwhelming tributaries to the Cumberland River and causing wide-spread, serious flooding. Tractor-trailers and houses were seen floating down Mill Creek, a primary tributary in the south eastern area of Nashville. Twenty-six people died and over 2 billion dollars in damage occurred as a result of the flood. Since that time, several other significant rainfall events have occurred in the area. Emergency responders were unable to deliver aid or preventive measures to areas under threat of flooding (or under water) in time to reduce damages because they could not identify those areas far enough in advance of the floods. Nashville Metro Water, the National Weather Service, the US Geological Survey and the US Army Corps of Engineers established a joint venture to seek ways to better forecast short-term flood events in the region. One component of this effort was a pilot project to compute and display real time inundation maps for Mill Creek, a 108 square-mile basin to the south east of Nashville. HEC-RTS (Real-Time Simulation) was used to assimilate and integrate the hydrologic model HEC-HMS with the hydraulics model HEC-RAS and the inundation mapping program HEC-RAS Mapper. The USGS, along with the other agencies, installed additional precipitation and flow/stage gages in the area. Measurements are recorded every 5-30 minutes and are posted on the USGS NWIS database, which are downloaded by HEC-RTS. Using this data in combination with QPFs (Quantitative Precipitation Forecasts) from the NWS, HEC-RTS applies HEC-HMS and HEC-RAS to estimate current and forecast stage hydrographs. The peak stages are read by HEC-RAS Mapper to compute inundation depths for 6 by 6 foot grid cells. HEC-RTS displays the inundation on a high resolution MrSid aerial photo, along with subbasin boundary, street and various other layers. When a user zooms in and "mouses" over a cell, the

  16. Real-time forecast of the 2005 and 2007 summer severe floods in the Huaihe River Basin of China

    NASA Astrophysics Data System (ADS)

    Lin, Charles A.; Wen, Lei; Lu, Guihua; Wu, Zhiyong; Zhang, Jianyun; Yang, Yang; Zhu, Yufei; Tong, Linying

    2010-02-01

    SummaryWe have developed a one-way coupled hydro-meteorological modeling system consisting of the mesoscale atmospheric model MC2 (Canadian Mesoscale Compressible Community), the Chinese Xinanjiang hydrological model for runoff generation, a flow routing model, and a module for acquiring real-time gauge precipitation. The system had been successfully tested in a hindcast mode using a total of 18 meteorological cases from 1998 and 2003 in the Huaihe River Basin (HRB; 270,000 km 2) of China, and has been used to generate daily precipitation and flood forecasts in real-time for the 2005, 2006 and 2007 flooding season over the Wangjiaba sub-basin (30,500 km 2), part of the HRB. We run MC2 daily to produce a 96-h precipitation forecast, and then use the combined gauge-model precipitation to drive the hydrological model off-line to forecast the hydrograph at the Wangjiaba Station that is at the outlet of the Wangjiaba sub-basin. We examine the daily forecasts for the two most severe flood events encountered in the past three flooding seasons. The two events occurred in July 4-15, 2005 and June 30-July 25, 2007, which necessitated the use of several flood spillway and flood detention areas along the mainstream of the Huaihe River. A total of 19 daily 96-h precipitation forecasts from the two events are examined. The 19 daily forecasts with different lead times compare reasonably well with observations, although the skill as measured by the MC2 relative error and the MC2 forecast success rate is uneven over a 4-day forecast period. MC2 can better forecast the 96-h accumulation compared to 24-h amounts. We also analyze 10 daily hydrograph forecasts from the two events. The flood peak of the two events at the Wangjiaba Station is predicted well in both timing and intensity with a lead time beyond four days, although the quality of our daily hydrograph forecasts as measured by the relative percentage error of the forecast peak discharge and the Nash-Sutcliffe coefficient is

  17. Efficiency of a real time flood forecasting system in the Alps and in the Apennines: deterministic versus ensemble predictions

    NASA Astrophysics Data System (ADS)

    Grossi, G.

    2009-04-01

    Real time hydrological forecasting is still a challenging task for most of the Italian territory, especially in mountain areas where both the topography and the meteorological forcing are affected by a strong spatial variability. Nevertheless there is an increasing request to provide some clues for the development of efficient real time flood forecasting systems, for warning population as well as for water management purposes. In this perspective the efficiency of a real time forecasting system needs to be investigated, with particular care to the uncertainty of the provided prediction and to how this prediction will be handled by water resources managers and land protection services. To this aim a real time flood forecasting system using both deterministic and ensemble meteorological predictions has been implemented at University of Brescia and applied to an Alpine area (the Toce River - Piemonte Region) and to an Apennine area (the Taro River - Emilia Romagna Region). The Map D- Phase experiment (autumn 2007) was a good test for the implemented system: daily rainfall fields provided by high resolution deterministic limited area meteorological models and esemble rainfall predictions provided by coarser resolution meteorological models could be used to force a hydrological model and produce either a single deterministic or an esemble of flood forecats. Namely only minor flood events occurred in the Alpine area in autumn 2007, while one major flood event affected the Taro river at the end of November 2007. Focusing on this major event the potentials of the forecasting system was tested and evaluated with reference also to the geographical and climatic characteristics of the investigated area.

  18. Key Techniques and Challenges of Developing Real-Time Flood Forecasting Systems based on the Xin'anjiang Model

    NASA Astrophysics Data System (ADS)

    Zhang, X.; Fang, Y.; Qu, B.

    2015-12-01

    The Xin'anjiang (XAJ) model is a semi-distributed conceptual model for use in humid or semi humid regions. The XAJ model is capable of modeling the major components of hydrological cycle including evapotranspiration, runoff and soil moisture. It has been applied to catchments of different scales to study the hydrological processes. In China many real-time flood forecasting systems are based on XAJ model. Although these systems have been successful in terms of flood control and disaster deduction, challenges still exist. Based on the real-time flood forecasting systems we developed for Yangtze River Basin and Pearl River Basin, We will present the challenges and key techniques of application of XAJ model for flood forecasting, including catchment delineation, model parameters estimation for ungauged catchment, leading time precipitation and evapotranspiration estimation as well as real time foresting influenced by hydraulic engineering. We will also present some results of improving the XAJ model by coupling energy balance scheme to better simulate the actual evapotranspiration.

  19. FUNDAMENTAL STUDY ON REAL-TIME FLOOD FORECASTING METHOD FOR LOCALLY HEAVY RAINFALL IN URBAN DRAINAGE AREAS

    NASA Astrophysics Data System (ADS)

    Kimura, Makoto; Kido, Yoshinobu; Nakakita, Eiichi

    Recently, locally heavy rainfall occurs frequently at highly urbanized area, and causes serious personal accidents, so importance of flood forecasting system is growing in order to reduce damage of inundation. However, flood forecasting that secured lead-time for evacuation is extremely difficult, because the rainfall flows out rapidly. In this study, the numerical simulation model that can finely express inundation mechanism of urban drainage areas was applied with the most recent available data and analysis tool. The influence of the factor (i.e. sewer system, overland and rainfall information) which affected inundation mechanism was evaluated through the sensibility analysis with this model, and evaluation results show some requirements of model condition and information on time and space resolution of real-time flood forecasting.

  20. Definition of Pluviometric Thresholds For A Real Time Flood Forecasting System In The Arno Watershed

    NASA Astrophysics Data System (ADS)

    Amadio, P.; Mancini, M.; Mazzetti, P.; Menduni, G.; Nativi, S.; Rabuffetti, D.; Ravazzani, G.; Rosso, R.

    The pluviometric flood forecasting thresholds are an easy method that helps river flood emergency management collecting data from limited area meteorologic model or telemetric raingauges. The thresholds represent the cumulated rainfall depth which generate critic discharge for a particular section. The thresholds were calculated for different sections of Arno river and for different antecedent moisture condition using the flood event distributed hydrologic model FEST. The model inputs were syntethic hietographs with different shape and duration. The system realibility has been verified by generating 500 year syntethic rainfall for 3 important subwatersheds of the studied area. A new technique to consider spatial variability of rainfall and soil properties effects on hydrograph has been investigated. The "Geomorphologic Weights" were so calculated. The alarm system has been implemented in a dedicated software (MIMI) that gets measured and forecast rainfall data from Autorità di Bacino and defines the state of the alert of the river sections.

  1. Application of satellite-based rainfall and medium range meteorological forecast in real-time flood forecasting in the Mahanadi River basin

    NASA Astrophysics Data System (ADS)

    Nanda, Trushnamayee; Beria, Harsh; Sahoo, Bhabagrahi; Chatterjee, Chandranath

    2016-04-01

    Increasing frequency of hydrologic extremes in a warming climate call for the development of reliable flood forecasting systems. The unavailability of meteorological parameters in real-time, especially in the developing parts of the world, makes it a challenging task to accurately predict flood, even at short lead times. The satellite-based Tropical Rainfall Measuring Mission (TRMM) provides an alternative to the real-time precipitation data scarcity. Moreover, rainfall forecasts by the numerical weather prediction models such as the medium term forecasts issued by the European Center for Medium range Weather Forecasts (ECMWF) are promising for multistep-ahead flow forecasts. We systematically evaluate these rainfall products over a large catchment in Eastern India (Mahanadi River basin). We found spatially coherent trends, with both the real-time TRMM rainfall and ECMWF rainfall forecast products overestimating low rainfall events and underestimating high rainfall events. However, no significant bias was found for the medium rainfall events. Another key finding was that these rainfall products captured the phase of the storms pretty well, but suffered from consistent under-prediction. The utility of the real-time TRMM and ECMWF forecast products are evaluated by rainfall-runoff modeling using different artificial neural network (ANN)-based models up to 3-days ahead. Keywords: TRMM; ECMWF; forecast; ANN; rainfall-runoff modeling

  2. An integrated error parameter estimation and lag-aware data assimilation scheme for real-time flood forecasting

    NASA Astrophysics Data System (ADS)

    Li, Yuan; Ryu, Dongryeol; Western, Andrew W.; Wang, Q. J.; Robertson, David E.; Crow, Wade T.

    2014-11-01

    For operational flood forecasting, discharge observations may be assimilated into a hydrologic model to improve forecasts. However, the performance of conventional filtering schemes can be degraded by ignoring the time lag between soil moisture and discharge responses. This has led to ongoing development of more appropriate ways to implement sequential data assimilation. In this paper, an ensemble Kalman smoother (EnKS) with fixed time window is implemented for the GR4H hydrologic model (modèle du Génie Rural à 4 paramètres Horaire) to update current and antecedent model states. Model and observation error parameters are estimated through the maximum a posteriori method constrained by prior information drawn from flow gauging data. When evaluated in a hypothetical forecasting mode using observed rainfall, the EnKS is found to be more stable and produce more accurate discharge forecasts than a standard ensemble Kalman filter (EnKF) by reducing the mean of the ensemble root mean squared error (MRMSE) by 13-17%. The latter tends to over-correct current model states and leads to spurious peaks and oscillations in discharge forecasts. When evaluated in a real-time forecasting mode using rainfall forecasts from a numerical weather prediction model, the benefit of the EnKS is reduced as uncertainty in rainfall forecasts becomes dominant, especially at large forecast lead time.

  3. Distributed precipitation corrections in Alpine areas for a real-time flood forecasting system

    NASA Astrophysics Data System (ADS)

    Herrnegger, Mathew; Senoner, Tobias; Nachtnebel, Hans-Peter

    2014-05-01

    This contribution presents a method for estimating spatial and temporal distributed precipitation correction factors. The approach is applied for a flood forecasting model in the Upper Enns and Upper Mur catchments in the Central Austrian Alps. Precipitation exhibits a large spatio-temporal variability in Alpine areas. Additionally the density of the monitoring network is low and measurements are subjected to major errors. This can lead to significant deficits in stream flow simulations, e.g. for flood forecasting models. Therefore precipitation correction factors are frequently applied. These correction factors are however mostly applied for whole catchments in a lumped manor, neglecting, that the magnitude of precipitation errors are spatially distributed. For the presented study a multiplicative linear correction model is therefore implemented, which enables a distribution of the correction factors as a function of elevation. The applied rainfall-runoff model COSERO is set up with a spatial resolution of 1x1km2. The correction of the rainfall pattern is thereby applied for every grid cell. To account for the local meteorological conditions, the correction model is derived for two elevation zones: (1) Valley floors to 2000 m a.s.l. and (2) above 2000 m a.s.l. to mountain peaks. Measurement errors also depend on the precipitation type, with higher magnitudes in winter months during snow fall. Therefore additionally separate correction factors for winter and summer months are estimated. The parameters for the correction model are estimated for every catchment based on independent station observations and observed and simulated runoff of the conceptual rainfall-runoff model. As driving input the INCA-precipitation fields of the Austrian Central Institute for Meteorology and Geodynamics (ZAMG) are used. Due to the mentioned errors, these precipitation fields are corrected according to the described method. The results show a significant improvement of the simulated

  4. Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control

    NASA Astrophysics Data System (ADS)

    Chang, Fi-John; Chen, Pin-An; Lu, Ying-Ray; Huang, Eric; Chang, Kai-Yao

    2014-09-01

    Urban flood control is a crucial task, which commonly faces fast rising peak flows resulting from urbanization. To mitigate future flood damages, it is imperative to construct an on-line accurate model to forecast inundation levels during flood periods. The Yu-Cheng Pumping Station located in Taipei City of Taiwan is selected as the study area. Firstly, historical hydrologic data are fully explored by statistical techniques to identify the time span of rainfall affecting the rise of the water level in the floodwater storage pond (FSP) at the pumping station. Secondly, effective factors (rainfall stations) that significantly affect the FSP water level are extracted by the Gamma test (GT). Thirdly, one static artificial neural network (ANN) (backpropagation neural network-BPNN) and two dynamic ANNs (Elman neural network-Elman NN; nonlinear autoregressive network with exogenous inputs-NARX network) are used to construct multi-step-ahead FSP water level forecast models through two scenarios, in which scenario I adopts rainfall and FSP water level data as model inputs while scenario II adopts only rainfall data as model inputs. The results demonstrate that the GT can efficiently identify the effective rainfall stations as important inputs to the three ANNs; the recurrent connections from the output layer (NARX network) impose more effects on the output than those of the hidden layer (Elman NN) do; and the NARX network performs the best in real-time forecasting. The NARX network produces coefficients of efficiency within 0.9-0.7 (scenario I) and 0.7-0.5 (scenario II) in the testing stages for 10-60-min-ahead forecasts accordingly. This study suggests that the proposed NARX models can be valuable and beneficial to the government authority for urban flood control.

  5. Status and Future of a Real-time Global Flood Detection and Forecasting System Using Satellite Rainfall Information

    NASA Astrophysics Data System (ADS)

    Adler, R. F.; Wu, H.; Hong, Y.; Policelli, F.; Pierce, H.

    2011-12-01

    Over the last several years a Global Flood Monitoring System (GFMS) has been running in real-time to detect the occurrence of floods (see trmm.gsfc.nasa.gov and click on "Floods and Landslides"). The system uses 3-hr resolution composite rainfall analyses (TRMM Multi-satellite Precipitation Analysis [TMPA]) as input into a hydrological model that calculates water depth at each grid (at 0.25 degree latitude-longitude) over the tropics and mid-latitudes. These calculations can provide information useful to national and international agencies in understanding the location, intensity, timeline and impact on populations of these significant hazard events. The status of these flood calculations will be shown by case study examples and a statistical comparison against a global flood event database. The validation study indicates that results improve with longer duration (> 3 days) floods and that the statistics are impacted by the presence of dams, which are not accounted for in the model calculations. Limitations in the flood calculations that are related to the satellite rainfall estimates include space and time resolution limitations and underestimation of shallow orographic and monsoon system rainfall. The current quality of these flood estimations is at the level of being useful, but there is a potential for significant improvement, mainly through improved and more timely satellite precipitation information and improvement in the hydrological models being used. NASA's Global Precipitation Measurement (GPM) program should lead to better precipitation analyses utilizing space-time interpolations that maintain accurate intensity distributions along with methods to disaggregate the rain information research should lead to improved rain estimation for shallow, orographic rainfall systems and some types of monsoon rainfall, a current problem area for satellite rainfall. Higher resolution flood models with accurate routing and regional calibration, and the use of satellite

  6. All-season flash flood forecasting system for real-time operations

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Flash floods can cause extensive damage to both life and property, especially because they are difficult to predict. Flash flood prediction requires high-resolution meteorologic observations and predictions, as well as calibrated hydrologic models in addition to extensive data handling. We have de...

  7. A wavelet-based non-linear autoregressive with exogenous inputs (WNARX) dynamic neural network model for real-time flood forecasting using satellite-based rainfall products

    NASA Astrophysics Data System (ADS)

    Nanda, Trushnamayee; Sahoo, Bhabagrahi; Beria, Harsh; Chatterjee, Chandranath

    2016-08-01

    Although flood forecasting and warning system is a very important non-structural measure in flood-prone river basins, poor raingauge network as well as unavailability of rainfall data in real-time could hinder its accuracy at different lead times. Conversely, since the real-time satellite-based rainfall products are now becoming available for the data-scarce regions, their integration with the data-driven models could be effectively used for real-time flood forecasting. To address these issues in operational streamflow forecasting, a new data-driven model, namely, the wavelet-based non-linear autoregressive with exogenous inputs (WNARX) is proposed and evaluated in comparison with four other data-driven models, viz., the linear autoregressive moving average with exogenous inputs (ARMAX), static artificial neural network (ANN), wavelet-based ANN (WANN), and dynamic nonlinear autoregressive with exogenous inputs (NARX) models. First, the quality of input rainfall products of Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA), viz., TRMM and TRMM-real-time (RT) rainfall products is assessed through statistical evaluation. The results reveal that the satellite rainfall products moderately correlate with the observed rainfall, with the gauge-adjusted TRMM product outperforming the real-time TRMM-RT product. The TRMM rainfall product better captures the ground observations up to 95 percentile range (30.11 mm/day), although the hit rate decreases for high rainfall intensity. The effect of antecedent rainfall (AR) and climate forecast system reanalysis (CFSR) temperature product on the catchment response is tested in all the developed models. The results reveal that, during real-time flow simulation, the satellite-based rainfall products generally perform worse than the gauge-based rainfall. Moreover, as compared to the existing models, the flow forecasting by the WNARX model is way better than the other four models studied herein with the

  8. A two-stage method of quantitative flood risk analysis for reservoir real-time operation using ensemble-based hydrologic forecasts

    NASA Astrophysics Data System (ADS)

    Liu, P.

    2013-12-01

    Quantitative analysis of the risk for reservoir real-time operation is a hard task owing to the difficulty of accurate description of inflow uncertainties. The ensemble-based hydrologic forecasts directly depict the inflows not only the marginal distributions but also their persistence via scenarios. This motivates us to analyze the reservoir real-time operating risk with ensemble-based hydrologic forecasts as inputs. A method is developed by using the forecast horizon point to divide the future time into two stages, the forecast lead-time and the unpredicted time. The risk within the forecast lead-time is computed based on counting the failure number of forecast scenarios, and the risk in the unpredicted time is estimated using reservoir routing with the design floods and the reservoir water levels of forecast horizon point. As a result, a two-stage risk analysis method is set up to quantify the entire flood risks by defining the ratio of the number of scenarios that excessive the critical value to the total number of scenarios. The China's Three Gorges Reservoir (TGR) is selected as a case study, where the parameter and precipitation uncertainties are implemented to produce ensemble-based hydrologic forecasts. The Bayesian inference, Markov Chain Monte Carlo, is used to account for the parameter uncertainty. Two reservoir operation schemes, the real operated and scenario optimization, are evaluated for the flood risks and hydropower profits analysis. With the 2010 flood, it is found that the improvement of the hydrologic forecast accuracy is unnecessary to decrease the reservoir real-time operation risk, and most risks are from the forecast lead-time. It is therefore valuable to decrease the avarice of ensemble-based hydrologic forecasts with less bias for a reservoir operational purpose.

  9. Calibrating the FloodMap Model to Improve the Integrated HydroProg-FloodMap Real-Time Multimodel Ensemble System for Forecasting Inundation

    NASA Astrophysics Data System (ADS)

    Świerczyńska, M. G.; Yu, D.; Miziński, B.; Niedzielski, T.; Latocha, A.; Parzóch, K.

    2015-12-01

    HydroProg is a novel system (research project no. 2011/01/D/ST10/04171 of the National Science Centre of Poland) which produces early warnings against peak flows. It works in real time and uses outputs from multiple hydrologic models to compute the multimodel ensemble prediction of riverflow, i.e. the hydrograph. The system has been experimentally implemented for the upper Nysa Kłodzka river basin (SW Poland). We also integrated the system with the well-established hydrodynamic model, known as FloodMap, to forecast flood inundation (HydroProg computes hydrograph prediction and FloodMap maps the hydrograph prognosis into terrain). The HydroProg-FloodMap solution works at five sites. The real-time experimental forecasts are available at http://www.klodzko.hydroprog.uni.wroc.pl/. The FloodMap model is calibrated at each site on a basis of the available Digital Elevation Model (DEM) or Digital Surface Model (DSM) and hydrograph data. However, since the launch of the HydroProg-FloodMap solution no true data on inundation has been available to check the model outputs against observation, and hence to redo the calibration if necessary. If we consider past events, which occurred before the launch of the system, there exists the observed inundation map for the Żelazno site. It was produced by geomorphological mapping of consequences of the flood in June 2009. The aim of the study is therefore to use this specific data set for a single site, calibrate the FloodMap model using inundation data, and identify the physical-geographical characteristics of terrain under which we are allowed to extrapolate the parameters to the other four sites. We conducted a spatial analysis of land use (based on Polish national database of topographical objects) and topography (based on DEM/DSM from the Light Detection and Ranging (LiDAR)) in order to identify similarities of the studied areas and hence to improve the estimates of the Manning's roughness coefficient.

  10. Advancing the cyberinfrastructure for sustaining high resolution, real-time streamflow and flood forecasts at a national scale

    NASA Astrophysics Data System (ADS)

    Arctur, D. K.; Maidment, D. R.; Clark, E. P.; Gochis, D. J.; Somos-Valenzuela, M. A.; Salas, F. R.; Nelson, J.

    2015-12-01

    In just the last year, it has become feasible to generate and refresh national 15-hour forecasts of streamflow and flood inundation, every hour at high resolution (average 3km stream segments), based on a workflow integrating US National Weather Service forecasts, the WRF-Hydro land surface model, the RAPID streamflow routing model, and other models. This capability has come about through a collaboration of numerous agencies, academic research and data centers, and commercial software vendors. This presentation provides insights and lessons learned for the development and evolution of a scalable architecture for water observations and forecasts that should be sustained operationally.

  11. A hydro-meteorological ensemble prediction system for real-time flood forecasting purposes in the Milano area

    NASA Astrophysics Data System (ADS)

    Ravazzani, Giovanni; Amengual, Arnau; Ceppi, Alessandro; Romero, Romualdo; Homar, Victor; Mancini, Marco

    2015-04-01

    Analysis of forecasting strategies that can provide a tangible basis for flood early warning procedures and mitigation measures over the Western Mediterranean region is one of the fundamental motivations of the European HyMeX programme. Here, we examine a set of hydro-meteorological episodes that affected the Milano urban area for which the complex flood protection system of the city did not completely succeed before the occurred flash-floods. Indeed, flood damages have exponentially increased in the area during the last 60 years, due to industrial and urban developments. Thus, the improvement of the Milano flood control system needs a synergism between structural and non-structural approaches. The flood forecasting system tested in this work comprises the Flash-flood Event-based Spatially distributed rainfall-runoff Transformation, including Water Balance (FEST-WB) and the Weather Research and Forecasting (WRF) models, in order to provide a hydrological ensemble prediction system (HEPS). Deterministic and probabilistic quantitative precipitation forecasts (QPFs) have been provided by WRF model in a set of 48-hours experiments. HEPS has been generated by combining different physical parameterizations (i.e. cloud microphysics, moist convection and boundary-layer schemes) of the WRF model in order to better encompass the atmospheric processes leading to high precipitation amounts. We have been able to test the value of a probabilistic versus a deterministic framework when driving Quantitative Discharge Forecasts (QDFs). Results highlight (i) the benefits of using a high-resolution HEPS in conveying uncertainties for this complex orographic area and (ii) a better simulation of the most of extreme precipitation events, potentially enabling valuable probabilistic QDFs. Hence, the HEPS copes with the significant deficiencies found in the deterministic QPFs. These shortcomings would prevent to correctly forecast the location and timing of high precipitation rates and

  12. Going to space: Implementation of spatial input data processing in real-time flood forecasting in the Czech Republic

    NASA Astrophysics Data System (ADS)

    Vlasak, T.; Krejci, J.; Danhelka, J.

    2009-04-01

    Real-time forecasting systems were developed and used in the Czech Republic since late 90's. AquaLog forecasting system is used for forecasting in the Czech part of the Elbe river basin. AquaLog uses SAC-SMA for rainfall-runoff modeling and SNOW17 for modeling of snow cover accumulation and melting. There are about 150 forecasting profiles (computed sub-basins). Data input pre-processing module AquaBase for data quality check and correction. Previous version of AquaBase and AquaLog (ver.5) operated in the scale of basins using time-series of observed precipitation and temperature to compute MAP and MAT based on Thiessen polygons method for basins of typical size of 200-500 km2 (basins were internally divided into 2-4 computation units based on elevation to simulate more precisely the snow cover). New version of AquaBase (ver.6) process data into regular 1 km grid offering to choose between several interpolating techniques: - Inverse distance weighted (IDW) with optional value of power parameter, number of stations and diameter in km to be taken into account - IDW quadrant (IDWq) using only the nearest station from every quadrant of space (optional power parameter and diameter applicable) - IDW and temperature gradient (for temperature only) correcting the interpolated value according to optional vertical temperature gradient and elevation difference among used stations (optional power parameter and diameter applicable) - Krigging with optional parameters - Co-krigging with optional parameters - Use of radar-raingauge dynamically combined field product AquaBase ver.6 enables editing of precipitation and temperature field by implementing pseudo-gauges anywhere in the space and editing its values. AquaLog computation unit were changed to small sub-basins of typical size of 10 to 15 km2 and specific unit hydrographs were derived for every sub-basin. MAP and MAT based on grid analysis in AquaBase environment input every particular sub-basin. Long time runoff simulation

  13. Assessing the impact of updating approaches of the performances on a real-time flood forecasting model: a study on 178 French catchments

    NASA Astrophysics Data System (ADS)

    Berthet, L.; Andréassian, V.; Perrin, C.

    2009-04-01

    We present the comparison of performances obtained by a real-time operational rainfall-runoff model running with different updating methods based on the assimilation of past observed streamflow data. The tested updating techniques are: (i) direct state updating, (ii) parameter updating and (iii) output updating (various methods ranging from simple regressions to ARIMA models and artificial neural network (ANN) approach). The comparison is drawn over a large sample of 178 French catchments encompassing the hydroclimatic variability of the country. The model is a continuous one, specifically designed to be run in 'forecasting' mode. We study specifically the impact of the updating method on model performance. Characteristic times of the updating techniques are defined and then compared to characteristic times of the model, the catchment and to the desired lead times. This approach helps to understand when and where a given updating technique is appropriate. The comparison gives results we believe useful for operational forecaster to choose their real-time flood forecasting system.

  14. The flood event of 10-12 November 2013 on the Tiber River basin (central Italy): real-time flood forecasting with uncertainty supporting risk management and decision-making

    NASA Astrophysics Data System (ADS)

    Berni, Nicola; Brocca, Luca; Barbetta, Silvia; Pandolfo, Claudia; Stelluti, Marco; Moramarco, Tommaso

    2014-05-01

    The Italian national hydro-meteorological early warning system is composed by 21 regional offices (Functional Centres, CF). Umbria Region (central Italy) CF provides early warning for floods and landslides, real-time monitoring and decision support systems (DSS) for the Civil Defence Authorities when significant events occur. The alert system is based on hydrometric and rainfall thresholds with detailed procedures for the management of critical events in which different roles of authorities and institutions involved are defined. The real-time flood forecasting system is based also on different hydrological and hydraulic forecasting models. Among these, the MISDc rainfall-runoff model ("Modello Idrologico SemiDistribuito in continuo"; Brocca et al., 2011) and the flood routing model named STAFOM-RCM (STAge Forecasting Model-Rating Curve Model; Barbetta et al., 2014) are continuously operative in real-time providing discharge and stage forecasts, respectively, with lead-times up to 24 hours (when quantitative precipitation forecasts are used) in several gauged river sections in the Upper-Middle Tiber River basin. Models results are published in real-time in the open source CF web platform: www.cfumbria.it. MISDc provides discharge and soil moisture forecasts for different sub-basins while STAFOM-RCM provides stage forecasts at hydrometric sections. Moreover, through STAFOM-RCM the uncertainty of the forecast stage hydrograph is provided in terms of 95% Confidence Interval (CI) assessed by analyzing the statistical properties of model output in terms of lateral. In the period 10th-12th November 2013, a severe flood event occurred in Umbria mainly affecting the north-eastern area and causing significant economic damages, but fortunately no casualties. The territory was interested by intense and persistent rainfall; the hydro-meteorological monitoring network recorded locally rainfall depth over 400 mm in 72 hours. In the most affected area, the recorded rainfall depths

  15. A global, real-time flood monitoring model

    NASA Astrophysics Data System (ADS)

    Balcerak, Ernie

    2014-07-01

    Floods kill thousands of people and cause billions of dollars in damage each year, and many floods occur in areas of the world that lack resources for flood monitoring and forecasting systems. Wu et al. report on an experimental real-time global flood monitoring system that employs a widely used land surface model coupled with a hierarchical dominant river tracing-based runoff routing model and satellite-based precipitation data to provide streamflow and flood detection/estimation information over most of the globe every 3 hours.

  16. Development of a Distributed Hydrologic Model Using Triangulated Irregular Networks for Continuous, Real-Time Flood Forecasting

    NASA Astrophysics Data System (ADS)

    Ivanov, V. Y.; Vivoni, E. R.; Bras, R. L.; Entekhabi, D.

    2001-05-01

    The Triangulated Irregular Networks (TINs) are widespread in many finite-element modeling applications stressing high spatial non-uniformity while describing the domain of interest in an optimized fashion that results in superior computational efficiency. TINs, being adaptive to the complexity of any terrain, are capable of maintaining topological relations between critical surface features and therefore afford higher flexibility in data manipulation. The TIN-based Real-time Integrated Basin Simulator (tRIBS) is a distributed hydrologic model that utilizes the mesh architecture and the software environment developed for the CHILD landscape evolution model and employs the hydrologic routines of its raster-oriented version, RIBS. As a totally independent software unit, the tRIBS consolidates the strengths of the distributed approach and efficient computational data platform. The current version couples the unsaturated and the saturated zones and accounts for the interaction of moving infiltration fronts with a variable groundwater surface, allowing the model to handle both storm and interstorm periods in a continuous fashion. Recent model enhancements have included the development of interstorm hydrologic fluxes through an evapotranspiration scheme as well as incorporation of a rainfall interception module. Overall, the tRIBS model has proven to properly mimic successive phases of the distributed catchment response by reproducing various runoff production mechanisms and handling their meteorological constraints. Important improvements in modeling options, robustness to data availability and overall design flexibility have also been accomplished. The current efforts are focused on further model developments as well as the application of the tRIBS to various watersheds.

  17. Real-time forecasts of dengue epidemics

    NASA Astrophysics Data System (ADS)

    Yamana, T. K.; Shaman, J. L.

    2015-12-01

    Dengue is a mosquito-borne viral disease prevalent in the tropics and subtropics, with an estimated 2.5 billion people at risk of transmission. In many areas with endemic dengue, disease transmission is seasonal but prone to high inter-annual variability with occasional severe epidemics. Predicting and preparing for periods of higher than average transmission is a significant public health challenge. Here we present a model of dengue transmission and a framework for optimizing model simulations with real-time observational data of dengue cases and environmental variables in order to generate ensemble-based forecasts of the timing and severity of disease outbreaks. The model-inference system is validated using synthetic data and dengue outbreak records. Retrospective forecasts are generated for a number of locations and the accuracy of these forecasts is quantified.

  18. Towards real-time flood forecasting in hydraulics: merits of in situ discharge and water level data assimilation for the modeling of the Marne catchment in France

    NASA Astrophysics Data System (ADS)

    Ricci, S. M.; Habert, J.; Le Pape, E.; Piacentini, A.; Jonville, G.; Thual, O.; Zaoui, F.

    2011-12-01

    The present study describes the assimilation of river flow and water level observations and the resulting improvement in flood forecasting. The Kalman Filter algorithm was built on top of the one-dimensional hydraulic model, MASCARET, [1] which describes the Saint-Venant equations. The assimilation algorithm folds in two steps: the first one was based on the assumption that the upstream flow can be adjusted using a three-parameter correction; the second one consisted of directly correcting the hydraulic state. This procedure was previously applied on the Adour Maritime Catchment using water level observations [2]. On average, it was shown that the data assimilation procedure enables an improvement of 80% in the simulated water level over the reanalysis period, 60 % in the forecast water level at a one-hour lead time, and 25% at a twelve-hour lead time. The procedure was then applied on the Marne Catchment, which includes karstic tributaries, located East of the Paris basin, characterized by long flooding periods and strong sensitivity to local precipitations. The objective was to geographically extend and improve the existing model used by the flood forecasting service located in Chalons-en-Champagne. A hydrological study first enabled the specification of boundary conditions (upstream flow or lateral inflow), then the hydraulic model was calibrated using in situ discharge data (adjustment of Strickler coefficients or cross sectional geometry). The assimilation of water level data enabled the reduction of the uncertainty in the hydrological boundary conditions and led to significant improvement of the simulated water level in re-analysis and forecast modes. Still, because of errors in the Strickler coefficients or cross section geometry, the improvement of the simulated water level sometimes resulted in a degradation of discharge values. This problem was overcome by controlling the correction of the hydrological boundary conditions by directly assimilating

  19. Near-real-time simulation and internet-based delivery of forecast-flood inundation maps using two-dimensional hydraulic modeling--A pilot study for the Snoqualmie River, Washington

    USGS Publications Warehouse

    Jones, Joseph L.; Fulford, Janice M.; Voss, Frank D.

    2002-01-01

    A system of numerical hydraulic modeling, geographic information system processing, and Internet map serving, supported by new data sources and application automation, was developed that generates inundation maps for forecast floods in near real time and makes them available through the Internet. Forecasts for flooding are generated by the National Weather Service (NWS) River Forecast Center (RFC); these forecasts are retrieved automatically by the system and prepared for input to a hydraulic model. The model, TrimR2D, is a new, robust, two-dimensional model capable of simulating wide varieties of discharge hydrographs and relatively long stream reaches. TrimR2D was calibrated for a 28-kilometer reach of the Snoqualmie River in Washington State, and is used to estimate flood extent, depth, arrival time, and peak time for the RFC forecast. The results of the model are processed automatically by a Geographic Information System (GIS) into maps of flood extent, depth, and arrival and peak times. These maps subsequently are processed into formats acceptable by an Internet map server (IMS). The IMS application is a user-friendly interface to access the maps over the Internet; it allows users to select what information they wish to see presented and allows the authors to define scale-dependent availability of map layers and their symbology (appearance of map features). For example, the IMS presents a background of a digital USGS 1:100,000-scale quadrangle at smaller scales, and automatically switches to an ortho-rectified aerial photograph (a digital photograph that has camera angle and tilt distortions removed) at larger scales so viewers can see ground features that help them identify their area of interest more effectively. For the user, the option exists to select either background at any scale. Similar options are provided for both the map creator and the viewer for the various flood maps. This combination of a robust model, emerging IMS software, and application

  20. Streamflow forecast uncertainty evolution and its effect on real-time reservoir operation

    NASA Astrophysics Data System (ADS)

    Chen, Lu; Singh, Vijay P.; Lu, Weiwei; Zhang, Junhong; Zhou, Jianzhong; Guo, Shenglian

    2016-09-01

    When employing streamflow forecasting in practical applications, such as reservoir operation, one important issue is to deal with the uncertainty involved in forecasting. Traditional studies dealing with the uncertainty in streamflow forecasting have been limited in describing the evolution of forecast uncertainty. This paper proposes a copula-based uncertainty evolution (CUE) model to describe the evolution of streamflow forecast uncertainty. The generated forecast uncertainty series fits the observed series well in terms of observed mean, standard deviation and skewness. Daily flow with forecast uncertainty are simulated and used to determine the effect of forecast uncertainty on real-time reservoir operation of the Three Gorges Reservoir (TGR), China. Results show that using the forecast inflow coupled with the pre-release module for reservoir operation of TGR in flood season cannot increase the flood risk.

  1. Incorporate Hydrologic Forecast for Real-Time Reservoir Operations

    NASA Astrophysics Data System (ADS)

    Zhao, T.; Cai, X.; Zhao, J.

    2011-12-01

    Advances in weather forecasting, hydrologic modeling, and hydro-climatic teleconnection relationships have significantly improved streamflow forecast precision and lead-time. The advances provide great opportunities to improve the operation rules of water resources systems, for example, updating reservoir operation curves using long-term forecast, or even replacing operation rules by real-time optimization and simulation models utilizing various streamflow forecast products. However, incorporation of forecast for real-time optimization of reservoir operation needs more understanding of the forecast uncertainty (FU) evolution with forecast horizon (FH, the advance time of a forecast) and the complicating effect of FU and FH. Increasing horizon may provide more information for decision making in a long time framework but with increasing error and less reliable information. This presentation addresses the challenges on the use of hydrologic forecast for real-time reservoir operations through the following two particular studies: 1) Evaluating the effectiveness of the various hydrological forecast products for reservoir operation with an explicit simulation of dynamic evolution of uncertainties involved in those products. A hypothetical example shows that optimal reservoir operation varies with the hydrologic forecast products. The utility of the reservoir operation with ensemble or probabilistic streamflow forecast (with a probabilistic uncertainty distribution) is the highest compared to deterministic streamflow forecast (DSF) with the forecast uncertainty represented in the form of deterministic forecast errors and DSF-based probabilistic streamflow forecast with the forecast uncertainty represented by a conditional distribution of forecast uncertainty for a given DSF. 2) Identifying an effective forecast horizon (EFH) under a limited inflow forecast considering the complicating effect of FH and FU, as well as streamflow variability and reservoir characteristics

  2. Real-time simulation of large-scale floods

    NASA Astrophysics Data System (ADS)

    Liu, Q.; Qin, Y.; Li, G. D.; Liu, Z.; Cheng, D. J.; Zhao, Y. H.

    2016-08-01

    According to the complex real-time water situation, the real-time simulation of large-scale floods is very important for flood prevention practice. Model robustness and running efficiency are two critical factors in successful real-time flood simulation. This paper proposed a robust, two-dimensional, shallow water model based on the unstructured Godunov- type finite volume method. A robust wet/dry front method is used to enhance the numerical stability. An adaptive method is proposed to improve the running efficiency. The proposed model is used for large-scale flood simulation on real topography. Results compared to those of MIKE21 show the strong performance of the proposed model.

  3. Real-time forecasting following a damaging earthquake

    NASA Astrophysics Data System (ADS)

    Marzocchi, Warner; Lombardi, Anna Maria

    2009-11-01

    We describe the results of a prospective, real-time earthquake forecast experiment made during a seismic emergency. A Mw 6.3 earthquake struck the city of L'Aquila, Italy on April 6, 2009, causing hundreds of deaths and vast damage. Immediately following this event, we began producing daily earthquake forecasts for the region, and we provided these forecasts to Civil Protection - the agency responsible for managing the emergency. The forecasts are based on a stochastic model that combines the Gutenberg-Richter distribution of earthquake magnitudes and power-law decay in space and time of triggered earthquakes. The results from the first month following the L'Aquila earthquake exhibit a good fit between forecasts and observations, indicating that accurate earthquake forecasting is now a realistic goal. Our experience with this experiment demonstrates an urgent need for a connection between probabilistic forecasts and decision-making in order to establish - before crises - quantitative and transparent protocols for decision support.

  4. A simplified real time method to forecast semi-enclosed basins storm surge

    NASA Astrophysics Data System (ADS)

    Pasquali, D.; Di Risio, M.; De Girolamo, P.

    2015-11-01

    Semi-enclosed basins are often prone to storm surge events. Indeed, their meteorological exposition, the presence of large continental shelf and their shape can lead to strong sea level set-up. A real time system aimed at forecasting storm surge may be of great help to protect human activities (i.e. to forecast flooding due to storm surge events), to manage ports and to safeguard coasts safety. This paper aims at illustrating a simple method able to forecast storm surge events in semi-enclosed basins in real time. The method is based on a mixed approach in which the results obtained by means of a simplified physics based model with low computational costs are corrected by means of statistical techniques. The proposed method is applied to a point of interest located in the Northern part of the Adriatic Sea. The comparison of forecasted levels against observed values shows the satisfactory reliability of the forecasts.

  5. Real-time flood extent maps based on social media

    NASA Astrophysics Data System (ADS)

    Eilander, Dirk; van Loenen, Arnejan; Roskam, Ruud; Wagemaker, Jurjen

    2015-04-01

    During a flood event it is often difficult to get accurate information about the flood extent and the people affected. This information is very important for disaster risk reduction management and crisis relief organizations. In the post flood phase, information about the flood extent is needed for damage estimation and calibrating hydrodynamic models. Currently, flood extent maps are derived from a few sources such as satellite images, areal images and post-flooding flood marks. However, getting accurate real-time or maximum flood extent maps remains difficult. With the rise of social media, we now have a new source of information with large numbers of observations. In the city of Jakarta, Indonesia, the intensity of unique flood related tweets during a flood event, peaked at 8 tweets per second during floods in early 2014. A fair amount of these tweets also contains observations of water depth and location. Our hypothesis is that based on the large numbers of tweets it is possible to generate real-time flood extent maps. In this study we use tweets from the city of Jakarta, Indonesia, to generate these flood extent maps. The data-mining procedure looks for tweets with a mention of 'banjir', the Bahasa Indonesia word for flood. It then removes modified and retweeted messages in order to keep unique tweets only. Since tweets are not always sent directly from the location of observation, the geotag in the tweets is unreliable. We therefore extract location information using mentions of names of neighborhoods and points of interest. Finally, where encountered, a mention of a length measure is extracted as water depth. These tweets containing a location reference and a water level are considered to be flood observations. The strength of this method is that it can easily be extended to other regions and languages. Based on the intensity of tweets in Jakarta during a flood event we can provide a rough estimate of the flood extent. To provide more accurate flood extend

  6. Multireservoir real-time operations for flood control using balanced water level index method.

    PubMed

    Wei, Chih-Chiang; Hsu, Nien-Sheng

    2008-09-01

    This paper presents a real-time simulation-optimization operation procedure for determining the reservoir releases at each time step during a flood. The proposed procedure involves two models, i.e., a hydrological forecasting model and a reservoir operation model. In the reservoir operation model, this paper compares two flood-control operation strategies for a multipurpose multireservoir system. While Strategy 1 is the real-time joint reservoir operations without using the balanced water level index (BWLI) method, Strategy 2 involves real-time joint reservoir operations using the BWLI method. The two strategies presented are formulated as mixed-integer linear programming (MILP) problems. The idea of using the BWLI method is derived from the HEC-5 program developed by the US Army Corps of Engineers. The proposed procedure has been applied to the Tanshui River Basin system in Taiwan using the 6h ahead forecast data of six typhoons. A comparison of the results obtained from the two strategies reveals that Strategy 2 performs much better than Strategy 1 in determining the reservoir real-time releases throughout the system during flood emergencies in order to minimize flooding, while maintaining all reservoirs in the system in balance if possible. Consequently, the proposed model using the BWLI method demonstrates its effectiveness in estimating real-time releases.

  7. Multireservoir real-time operations for flood control using balanced water level index method.

    PubMed

    Wei, Chih-Chiang; Hsu, Nien-Sheng

    2008-09-01

    This paper presents a real-time simulation-optimization operation procedure for determining the reservoir releases at each time step during a flood. The proposed procedure involves two models, i.e., a hydrological forecasting model and a reservoir operation model. In the reservoir operation model, this paper compares two flood-control operation strategies for a multipurpose multireservoir system. While Strategy 1 is the real-time joint reservoir operations without using the balanced water level index (BWLI) method, Strategy 2 involves real-time joint reservoir operations using the BWLI method. The two strategies presented are formulated as mixed-integer linear programming (MILP) problems. The idea of using the BWLI method is derived from the HEC-5 program developed by the US Army Corps of Engineers. The proposed procedure has been applied to the Tanshui River Basin system in Taiwan using the 6h ahead forecast data of six typhoons. A comparison of the results obtained from the two strategies reveals that Strategy 2 performs much better than Strategy 1 in determining the reservoir real-time releases throughout the system during flood emergencies in order to minimize flooding, while maintaining all reservoirs in the system in balance if possible. Consequently, the proposed model using the BWLI method demonstrates its effectiveness in estimating real-time releases. PMID:17923249

  8. Intelligent Real-Time Reservoir Operation for Flood Control

    NASA Astrophysics Data System (ADS)

    Chang, L.; Hsu, H.

    2008-12-01

    Real-time flood control of a multi-purpose reservoir should consider decreasing the flood peak stage downstream and storing floodwaters for future usage during typhoon seasons. It is a continuous and instant decision-making process based on relevant operating rules, policy and water laws, in addition the immediate rainfall and the hydrology information; however, it is difficult to learn the intelligent experience from the elder operators. The main purpose of this study is to establish the automatic reservoir flood control model to achieve the goal of a reservoir operation during flood periods. In this study, we propose an intelligent reservoir operating methodology for real-time flood control. First, the genetic algorithm is used to search the optimal solutions, which can be considered as extracting the knowledge of reservoir operation strategies. Then, the adaptive network-based fuzzy inference system (ANFIS), which uses a hybrid learning procedure for extracting knowledge in the form of fuzzy if-then rules, is used to learn the input-output patterns and then to estimate the optimal flood operation. The Shihmen reservoir in Northern Taiwan was used as a case study, where its 26 typhoon events are investigated by the proposed method. The results demonstrate that the proposed control model can perform much better than the original reservoir operator in 26 flood events and effectively achieve decreasing peak flood stage downstream and storing floodwaters for future usage.

  9. Real Time Monitoring of Flooding from Microwave Satellite Observations

    NASA Technical Reports Server (NTRS)

    Galantowicz, John F.; Frey, Herb (Technical Monitor)

    2002-01-01

    We have developed a new method for making high-resolution flood extent maps (e.g., at the 30-100 m scale of digital elevation models) in real-time from low-resolution (20-70 km) passive microwave observations. The method builds a "flood-potential" database from elevations and historic flood imagery and uses it to create a flood-extent map consistent with the observed open water fraction. Microwave radiometric measurements are useful for flood monitoring because they sense surface water in clear-or-cloudy conditions and can provide more timely data (e.g., compared to radars) from relatively wide swath widths and an increasing number of available platforms (DMSP, ADEOS-II, Terra, NPOESS, GPM). The chief disadvantages for flood mapping are the radiometers' low resolution and the need for local calibration of the relationship between radiances and open-water fraction. We present our method for transforming microwave sensor-scale open water fraction estimates into high-resolution flood extent maps and describe 30-day flood map sequences generated during a retrospective study of the 1993 Great Midwest Flood. We discuss the method's potential improvement through as yet unimplemented algorithm enhancements and expected advancements in microwave radiometry (e.g., improved resolution and atmospheric correction).

  10. A Real-Time Web Services Hub to Improve Situation Awareness during Flash Flood Events

    NASA Astrophysics Data System (ADS)

    Salas, F. R.; Liu, F.; Maidment, D. R.; Hodges, B. R.

    2011-12-01

    The central Texas corridor is one of the most flash flood-prone regions in the United States. Over the years, flash floods have resulted in hundreds of flood fatalities and billions of dollars in property damage. In order to mitigate risk to residents and infrastructure during flood events, both citizens and emergency responders need to exhibit proactive behavior instead of reactive. Real-time and forecasted flood information is fairly limited and hard to come by at varying spatial scales. The University of Texas at Austin has collaborated with IBM Research-Austin and ESRI to build a distributed real-time flood information system through a framework that leverages large scale data management and distribution, Open Geospatial Consortium standardized web services, and smart map applications. Within this paradigm, observed precipitation data encoded in WaterML is ingested into HEC-HMS and then delivered to a high performance hydraulic routing software package developed by IBM that utilizes the latest advancements in VLSI design, numerical linear algebra and numerical integration techniques on contemporary multicore architecture to solve fully dynamic Saint Venant equations at both small and large scales. In this paper we present a real-time flood inundation map application that in conjunction with a web services Hub, seamlessly integrates hydrologic information available through both public and private data services, model services and mapping services. As a case study for this project, we demonstrate how this system has been implemented in the City of Austin, Texas.

  11. Real-time Monitoring and Simulating of Urban Flood, a Case Study in Guangzhou

    NASA Astrophysics Data System (ADS)

    Huang, H.; Wang, X.; Zhang, S.; Liu, Y.

    2014-12-01

    In recent years urban flood frequently occurred and seriously impacted city's normal operation, particular on transportation. The increase of urban flood could be attributed to many factors, such as the increase of impervious land surface and extreme precipitation, the decrease of surface storage capacity, poor maintenance of drainage utilities, and so on. In order to provide accurate and leading prediction on urban flooding, this study acquires precise urban topographic data via air-borne Lidar system, collects detailed underground drainage pipes, and installs in-situ monitoring networks on precipitation, water level, video record and traffic speed in the downtown area of Panyu District, Guangzhou, China. Based on the above data acquired, a urban flood model with EPA SWMM5 is established to simulate the flooding and inundation processes in the study area of 20 km2. The model is driven by the real-time precipitation data and calibrated by the water level data, which are converted to flooding volume with precise topographic data. After calibration, the model could be employed to conduct sensitivity analysis for investigating primary factors of urban flooding, and to simulate the flooding processes in different scenarios, which are beneficial to assessment of flooding risk and drainage capacity. This model is expected to provide real-time forecasting in emergency management.

  12. Real Time Flood Alert System (RTFAS) for Puerto Rico

    USGS Publications Warehouse

    Lopez-Trujillo, Dianne

    2010-01-01

    The Real Time Flood Alert System is a web-based computer program, developed as a data integration tool, and designed to increase the ability of emergency managers to rapidly and accurately predict flooding conditions of streams in Puerto Rico. The system includes software and a relational database to determine the spatial and temporal distribution of rainfall, water levels in streams and reservoirs, and associated storms to determine hazardous and potential flood conditions. The computer program was developed as part of a cooperative agreement between the U.S. Geological Survey Caribbean Water Science Center and the Puerto Rico Emergency Management Agency, and integrates information collected and processed by these two agencies and the National Weather Service.

  13. A Real-time Operational Global Ocean Forecast System

    NASA Astrophysics Data System (ADS)

    Mehra, A.; Rivin, I.

    2010-12-01

    Efforts are ongoing to implement a real-time operational global ocean forecast system at NCEP/NWS/NOAA. This system will be based on an eddy resolving 1/12 degree global HYCOM model (Chassignet et al., 2009) and is part of a larger national backbone capability of ocean modeling at NWS in a strong partnership with US Navy. Long term plans include coupling it to Hurricane prediction models (eg. HWRF) and for providing ocean component for seasonal to interannual climate forecast systems (eg. CFS). The forecast system will run once a day and produce a week long forecast using the daily initialization fields produced at NAVOCEANO using NCODA, a 3D multi-variate data assimilation methodology (Cummings, 2005). The operational ocean model configuration has 32 hybrid layers and a horizontal grid size of (4500 x 3298). It is forced with surface fluxes from the operational Global Forecast System (GFS) fields. References: Chassignet, E.P., H.E. Hurlburt, E.J. Metzger, O.M. Smedstad, J. Cummings, G.R. Halliwell, R. Bleck, R. Baraille, A.J. Wallcraft, C. Lozano, H.L. Tolman, A. Srinivasan, S. Hankin, P. Cornillon, R. Weisberg, A. Barth, R. He, F. Werner, and J. Wilkin, 2009. U.S. GODAE: Global Ocean Prediction with the HYbrid Coordinate Ocean Model (HYCOM). Oceanography, 22(2), 64-75. Cummings, J.A., 2005: Operational multivariate ocean data assimilation. Quart. J. Royal Met. Soc., Part C, 131(613), 3583-3604.

  14. Real-time drought forecasting system for irrigation management

    NASA Astrophysics Data System (ADS)

    Ceppi, A.; Ravazzani, G.; Corbari, C.; Salerno, R.; Meucci, S.; Mancini, M.

    2014-09-01

    In recent years frequent periods of water scarcity have enhanced the need to use water more carefully, even in European areas which traditionally have an abundant supply of water, such as the Po Valley in northern Italy. In dry periods, water shortage problems can be enhanced by conflicting uses of water, such as irrigation, industry and power production (hydroelectric and thermoelectric). Furthermore, in the last decade the social perspective in relation to this issue has been increasing due to the possible impact of climate change and global warming scenarios which emerge from the IPCC Fifth Assessment Report (IPCC, 2013). Hence, the increased frequency of drought periods has stimulated the improvement of irrigation and water management. In this study we show the development and implementation of the PREGI real-time drought forecasting system; PREGI is an Italian acronym that means "hydro-meteorological forecast for irrigation management". The system, planned as a tool for irrigation optimization, is based on meteorological ensemble forecasts (20 members) at medium range (30 days) coupled with hydrological simulations of water balance to forecast the soil water content on a maize field in the Muzza Bassa Lodigiana (MBL) consortium in northern Italy. The hydrological model was validated against measurements of latent heat flux acquired by an eddy-covariance station, and soil moisture measured by TDR (time domain reflectivity) probes; the reliability of this forecasting system and its benefits were assessed in the 2012 growing season. The results obtained show how the proposed drought forecasting system is able to have a high reliability of forecast at least for 7-10 days ahead of time.

  15. Preparing for floods: flood forecasting and early warning

    NASA Astrophysics Data System (ADS)

    Cloke, Hannah

    2016-04-01

    Flood forecasting and early warning has continued to stride ahead in strengthening the preparedness phases of disaster risk management, saving lives and property and reducing the overall impact of severe flood events. For example, continental and global scale flood forecasting systems such as the European Flood Awareness System and the Global Flood Awareness System provide early information about upcoming floods in real time to various decisionmakers. Studies have found that there are monetary benefits to implementing these early flood warning systems, and with the science also in place to provide evidence of benefit and hydrometeorological institutional outlooks warming to the use of probabilistic forecasts, the uptake over the last decade has been rapid and sustained. However, there are many further challenges that lie ahead to improve the science supporting flood early warning and to ensure that appropriate decisions are made to maximise flood preparedness.

  16. Using Weekly Weather Forecast for Real-Time Irrigation Scheduling

    NASA Astrophysics Data System (ADS)

    Cai, X.; Wang, D.; Hejazi, M.

    2008-12-01

    Irrigation scheduling determines the timing and amount of water applied to an irrigated cropland during the crop growing season. Through a case study with Havana Lowlands region, Illinois, this study addresses two questions: 1) how can we consider the uncertainty of the weekly forecast for real-time irrigation scheduling? 2) What is the economic value of the forecast for irrigation farmers? We propose a coupled simulation and optimization modeling approach. The simulation part adopts an existing soil-water-atmosphere-plant (SWAP) simulation model. The SWAP model simulates soil moisture, the key state variable for irrigation decision, at the current time (day) using observed weather data; meanwhile the model assimilates the daily updated weekly forecast to predict the soil moisture in the future week. The weekly time window moves forward from the beginning to the end of the crop season. The optimization model is formulated as a multiple-stage stochastic optimization model that is based on weather forecast scenarios characterized by probabilities. The multiple-stage decisions are the deterministic water application at the current day ("what to do now") and the application scenarios associated with probabilities in each of the 7 days within one week time horizon ("wait- and-see"). Based on the soil moisture provided by the simulation model and empirical irrigation rules, irrigation water application is determined by the optimization model implemented with a genetic algorithm (GA). The modeling approach is tested with five years (2002-2006), which include two dry years, two normal years and one wet year, with the rainfall in the crop growth season ranging from 230mm to 410mm. The value of forecast is shown to be different across dry, normal, and wet years. In the year of 2002, observed irrigation scheduling is available. Thus the optimized irrigation scheduling is compared to the observed regarding the net crop profit and irrigation water saving.

  17. Real-time forecasts of tomorrow's earthquakes in California

    USGS Publications Warehouse

    Gerstenberger, M.C.; Wiemer, S.; Jones, L.M.; Reasenberg, P.A.

    2005-01-01

    Despite a lack of reliable deterministic earthquake precursors, seismologists have significant predictive information about earthquake activity from an increasingly accurate understanding of the clustering properties of earthquakes. In the past 15 years, time-dependent earthquake probabilities based on a generic short-term clustering model have been made publicly available in near-real time during major earthquake sequences. These forecasts describe the probability and number of events that are, on average, likely to occur following a mainshock of a given magnitude, but are not tailored to the particular sequence at hand and contain no information about the likely locations of the aftershocks. Our model builds upon the basic principles of this generic forecast model in two ways: it recasts the forecast in terms of the probability of strong ground shaking, and it combines an existing time-independent earthquake occurrence model based on fault data and historical earthquakes with increasingly complex models describing the local time-dependent earthquake clustering. The result is a time-dependent map showing the probability of strong shaking anywhere in California within the next 24 hours. The seismic hazard modelling approach we describe provides a better understanding of time-dependent earthquake hazard, and increases its usefulness for the public, emergency planners and the media.

  18. Multi-phase intelligent decision model for reservoir real-time flood control during typhoons

    NASA Astrophysics Data System (ADS)

    Hsu, Nien-Sheng; Huang, Chien-Lin; Wei, Chih-Chiang

    2015-03-01

    This study applies an Adaptive Network-based Fuzzy Inference System (ANFIS) and a Real-Time Recurrent Learning Neural Network (RTRLNN) with an optimized reservoir release hydrograph using Mixed Integer Linear Programming (MILP) from historical typhoon events to develop a multi-phase intelligent real-time reservoir operation model for flood control. The flood control process is divided into three stages: (1) before flood (Stage I); (2) before peak flow (Stage II); and (3) after peak flow (Stage III). The models are then constructed with either three phase modules (ANFIS-3P and RTRLNN-3P) or two phase (Stage I + II and Stage III) modules (ANFIS-2P and RTRLNN-2P). The multi-phase modules are developed with consideration of the difference in operational decision mechanisms, decision information, release functions, and targets between each flood control stage to solve the problem of time-consuming computation and difficult system integration of MILP. In addition, the model inputs include the coupled short lead time and total reservoir inflow forecast information that are developed using radar- and satellite-based meteorological monitoring techniques, forecasted typhoon tracks, meteorological image similarity analysis, ANFIS and RTRLNN. This study uses the Tseng-Wen Reservoir basin as the study area, and the model results showed that RTRLNN outperformed ANFIS in the simulated outcomes from the optimized hydrographs. This study also applies the models to Typhoons Kalmaegi and Morakot to compare the simulations to historical operations. From the operation results, the RTRLNN-3P model is better than RTRLNN-2P and historical operations. Further, because the RTRLNN-3P model combines the innovative multi-phase module with monitored and forecasted decision information, the operation can simultaneously, effectively and automatically achieve the dual goals of flood detention at peak flow periods and water supply at the end of a typhoon event.

  19. Real-time Global Flood and Landslide Prediction Using Satellite Observations (Invited)

    NASA Astrophysics Data System (ADS)

    Adler, R. F.; Yilmaz, K. K.; Kirschbaum, D.; Hong, Y.; Pierce, H.; Policelli, F.

    2009-12-01

    A global flood and landslide detection/prediction system is now running in real-time using multi-satellite rainfall analysis in combination with hydrological models and algorithms to estimate flood and landslide locations (http://trmm.gsfc.nasa.gov/publications_dir/potential_flood_hydro.html). The system also uses satellite-based land surface information such as digital elevation information from the NASA SRTM (Shuttle Radar Terrain Mission) and vegetation information from MODIS in the model and algorithm calculations. Progress in using the TRMM Multi-satellite Precipitation Analysis (TMPA) as input to these flood and landslide forecasts is outlined, with case studies as well as validation in terms of flood/landslide events. Examples shown include the August 2009 landslide events in Taiwan and the major flood in Burma in spring of 2008. The flood determination algorithm consists of three major components: 1) multi-satellite precipitation estimation; 2) characterization of land surface including digital elevation information and other surface information, topography-derived hydrologic parameters such as flow direction, flow accumulation, river network and basin boundaries, etc.; 3) a hydrological model to infiltrate rainfall and route overland runoff. Results of calculated water depth over a threshold are then displayed about six hours after real-time. Time-history of inundations are also calculated and displayed. Validation analysis indicates good results for flood detection and evolution, but with limitations in the current routing calculations. Occasional flood events are missed due to limitations in the satellite rain estimations. An improved global hydrological model is being tested and initial improved results will be shown. Global numerical weather prediction rainfall forecasts are also being used experimentally to extend the period of utility of the flood information. In terms of landslides, the satellite rainfall information is combined with a global

  20. Benchmarking flood models from space in near real-time: accommodating SRTM height measurement errors with low resolution flood imagery

    NASA Astrophysics Data System (ADS)

    Schumann, G.; di Baldassarre, G.; Alsdorf, D.; Bates, P. D.

    2009-04-01

    In February 2000, the Shuttle Radar Topography Mission (SRTM) measured the elevation of most of the Earth's surface with spatially continuous sampling and an absolute vertical accuracy greater than 9 m. The vertical error has been shown to change with topographic complexity, being less important over flat terrain. This allows water surface slopes to be measured and associated discharge volumes to be estimated for open channels in large basins, such as the Amazon. Building on these capabilities, this paper demonstrates that near real-time coarse resolution radar imagery of a recent flood event on a 98 km reach of the River Po (Northern Italy) combined with SRTM terrain height data leads to a water slope remarkably similar to that derived by combining the radar image with highly accurate airborne laser altimetry. Moreover, it is shown that this space-borne flood wave approximation compares well to a hydraulic model and thus allows the performance of the latter, calibrated on a previous event, to be assessed when applied to an event of different magnitude in near real-time. These results are not only of great importance to real-time flood management and flood forecasting but also support the upcoming Surface Water and Ocean Topography (SWOT) mission that will routinely provide water levels and slopes with higher precision around the globe.

  1. Building Cyberinfrastructure to Support a Real-time National Flood Model

    NASA Astrophysics Data System (ADS)

    Salas, F. R.; Maidment, D. R.; Tolle, K.; Navarro, C.; David, C. H.; Corby, R.

    2014-12-01

    The National Weather Service (NWS) is divided into 13 regional forecast centers across the country where the Sacramento Soil Moisture Accounting (SAC-SMA) model is run on average over a 10 day period, 5 days in the past and 5 days in the future. Model inputs and outputs such as precipitation and surface runoff are spatially aggregated over approximately 6,600 forecast basins with an average area of 1,200 square kilometers. In contrast, the NHDPlus dataset, which represents the geospatial fabric of the country, defines over 3 million catchments with an average area of 3 square kilometers. Downscaling the NWS land surface model outputs to the NHDPlus catchment scale in real-time requires the development of cyberinfrastructure to manage, share, compute and visualize large quantities of hydrologic data; streamflow computations through time for over 3 million river reaches. Between September 2014 and May 2015, the National Flood Interoperability Experiment (NFIE), coordinated through the Integrated Water Resource Science and Services (IWRSS) partners, will focus on building a national flood model for the country. This experiment will work to seamlessly integrate data and model services available on local and cloud servers (e.g. Azure) through disparate data sources operating at various spatial and temporal scales. As such, this paper will present a scalable information model that leverages the Routing Application for Parallel Computation of Discharge (RAPID) model to produce real-time flow estimates for approximately 67,000 NHDPlus river reaches in the NWS West Gulf River Forecast Center region.

  2. Real-time forecasting urban drainage models: full or simplified networks?

    PubMed

    Leitão, J P; Simões, N E; Maksimović, C; Ferreira, F; Prodanović, D; Matos, J S; Sá Marques, A

    2010-01-01

    Lead time between rainfall prediction results and flood prediction results obtained by hydraulic simulations is one of the crucial factors in the implementation of real-time flood forecasting systems. Therefore, hydraulic simulation times must be as short as possible, with sufficient spatial and temporal flood distribution modelling accuracy. One of the ways to reduce the time required to run hydraulic model simulations is increasing computational speed by simplifying the model networks. This simplification can be conducted by removing and changing some secondary elements using network simplification techniques. The emphasis of this paper is to assess how the level of urban drainage network simplification influences the computational time and overall simulation results' accuracy. The models used in this paper comprise a sewer network and an overland flow drainage system in both 1D/1D and 1D/2D approaches. The 1D/1D model is used as the reference model to generate several models with different levels of simplifications. The results presented in this paper suggest that the 1D/2D models are not yet suitable to be used in real-time flood prediction applications due to long simulation time, while on the other hand, the simplified 1D/1D models show that considerable reductions in simulation time can be achieved without compromising simulation results (flow and water depth) accuracy. PMID:21045338

  3. Real-Time Application of Multi-Satellite Precipitation Analysis for Floods and Landslides

    NASA Technical Reports Server (NTRS)

    Adler, Robert; Hong, Yang; Huffman, George

    2007-01-01

    Satellite data acquired and processed in real time now have the potential to provide the spacetime information on rainfall needed to monitor flood and landslide events around the world. This can be achieved by integrating the satellite-derived forcing data with hydrological models and landslide algorithms. Progress in using the TRMM Multi-satellite Precipitation Analysis (TMPA) as input to flood and landslide forecasts is outlined, with a focus on understanding limitations of the rainfall data and impacts of those limitations on flood/landslide analyses. Case studies of both successes and failures will be shown, as well as comparison with ground comparison data sets-- both in terms of rainfall and in terms of flood/landslide events. In addition to potential uses in real-time, the nearly ten years of TMPA data allow retrospective running of the models to examine variations in extreme events. The flood determination algorithm consists of four major components: 1) multi-satellite precipitation estimation; 2) characterization of land surface including digital elevation from NASA SRTM (Shuttle Radar Terrain Mission), topography-derived hydrologic parameters such as flow direction, flow accumulation, basin, and river network etc.; 3) a hydrological model to infiltrate rainfall and route overland runoff; and 4) an implementation interface to relay the input data to the models and display the flood inundation results to potential users and decision-makers, In terms of landslides, the satellite rainfall information is combined with a global landslide susceptibility map, derived from a combination of global surface characteristics (digital elevation topography, slope, soil types, soil texture, and land cover classification etc.) using a weighted linear combination approach. In those areas identified as "susceptible" (based on the surface characteristics), landslides are forecast where and when a rainfall intensity/duration threshold is exceeded. Results are described

  4. Best Practice for Rainfall Measurement, Torrential Flood Monitoring and Real Time Alerting System in Serbia

    NASA Astrophysics Data System (ADS)

    Stefanovic, Milutin; Milojevic, Mileta; Zlatanovic, Nikola

    2014-05-01

    Serbia occupies 88.000 km2 and its confined zone menaced with torrent flood occupies 50.000km2. Floods on large rivers and torrents are the most frequent natural disasters in Serbia. This is the result of a geographic position and relief of Serbia. Therefore, defense from these natural disasters has been institutionalized since the 19th century. Through its specialized bodies and public companies, the State organized defense from floods on large rivers and protection of international and other main roads. The Topčiderska River is one of a number of rivers in Serbia that is a threat to both urban and rural environments. In this text, general characteristics of this river will be illustrated, as well as the historical natural hazards that have occurred in the part of Belgrade near Topčiderska River. Belgrade is the capital of Serbia, its political, administrative and financial center, which means that there are significant financial capacities and human resources for investments in all sectors, and specially in the water resources sector. Along the Topčiderska catchment there are many industrial, traffic and residential structures that are in danger of floods and flood protection is more difficult with rapid high flows. The goal is to use monitoring on the Topčiderska River basin to set up a modern system for monitoring in real time and forecast of torrential floods. This paper represents a system of remote detection and monitoring of torrential floods and rain measurements in real time on Topciderka river and ready for a quick response.

  5. Real-time eruption forecasting using the material Failure Forecast Method with a Bayesian approach

    NASA Astrophysics Data System (ADS)

    Boué, A.; Lesage, P.; Cortés, G.; Valette, B.; Reyes-Dávila, G.

    2015-04-01

    Many attempts for deterministic forecasting of eruptions and landslides have been performed using the material Failure Forecast Method (FFM). This method consists in adjusting an empirical power law on precursory patterns of seismicity or deformation. Until now, most of the studies have presented hindsight forecasts based on complete time series of precursors and do not evaluate the ability of the method for carrying out real-time forecasting with partial precursory sequences. In this study, we present a rigorous approach of the FFM designed for real-time applications on volcano-seismic precursors. We use a Bayesian approach based on the FFM theory and an automatic classification of seismic events. The probability distributions of the data deduced from the performance of this classification are used as input. As output, it provides the probability of the forecast time at each observation time before the eruption. The spread of the a posteriori probability density function of the prediction time and its stability with respect to the observation time are used as criteria to evaluate the reliability of the forecast. We test the method on precursory accelerations of long-period seismicity prior to vulcanian explosions at Volcán de Colima (Mexico). For explosions preceded by a single phase of seismic acceleration, we obtain accurate and reliable forecasts using approximately 80% of the whole precursory sequence. It is, however, more difficult to apply the method to multiple acceleration patterns.

  6. Flood Forecasting in Wales: Challenges and Solutions

    NASA Astrophysics Data System (ADS)

    How, Andrew; Williams, Christopher

    2015-04-01

    With steep, fast-responding river catchments, exposed coastal reaches with large tidal ranges and large population densities in some of the most at-risk areas; flood forecasting in Wales presents many varied challenges. Utilising advances in computing power and learning from best practice within the United Kingdom and abroad have seen significant improvements in recent years - however, many challenges still remain. Developments in computing and increased processing power comes with a significant price tag; greater numbers of data sources and ensemble feeds brings a better understanding of uncertainty but the wealth of data needs careful management to ensure a clear message of risk is disseminated; new modelling techniques utilise better and faster computation, but lack the history of record and experience gained from the continued use of more established forecasting models. As a flood forecasting team we work to develop coastal and fluvial forecasting models, set them up for operational use and manage the duty role that runs the models in real time. An overview of our current operational flood forecasting system will be presented, along with a discussion on some of the solutions we have in place to address the challenges we face. These include: • real-time updating of fluvial models • rainfall forecasting verification • ensemble forecast data • longer range forecast data • contingency models • offshore to nearshore wave transformation • calculation of wave overtopping

  7. Real-time extreme weather event attribution with forecast seasonal SSTs

    NASA Astrophysics Data System (ADS)

    Haustein, K.; Otto, F. E. L.; Uhe, P.; Schaller, N.; Allen, M. R.; Hermanson, L.; Christidis, N.; McLean, P.; Cullen, H.

    2016-06-01

    Within the last decade, extreme weather event attribution has emerged as a new field of science and garnered increasing attention from the wider scientific community and the public. Numerous methods have been put forward to determine the contribution of anthropogenic climate change to individual extreme weather events. So far nearly all such analyses were done months after an event has happened. Here we present a new method which can assess the fraction of attributable risk of a severe weather event due to an external driver in real-time. The method builds on a large ensemble of atmosphere-only general circulation model simulations forced by seasonal forecast sea surface temperatures (SSTs). Taking the England 2013/14 winter floods as an example, we demonstrate that the change in risk for heavy rainfall during the England floods due to anthropogenic climate change, is of similar magnitude using either observed or seasonal forecast SSTs. Testing the dynamic response of the model to the anomalous ocean state for January 2014, we find that observed SSTs are required to establish a discernible link between a particular SST pattern and an atmospheric response such as a shift in the jetstream in the model. For extreme events occurring under strongly anomalous SST patterns associated with known low-frequency climate modes, however, forecast SSTs can provide sufficient guidance to determine the dynamic contribution to the event.

  8. Further Evaluation of a Satellite-based Real-time Global Flood Monitoring System

    NASA Astrophysics Data System (ADS)

    Wu, H.; Adler, R. F.; Tian, Y.; Hong, Y.; Policelli, F.

    2011-12-01

    A real-time global flood monitoring system (GFMS) driven by Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) rainfall was further developed with a relatively more physically based hydrological model. The performance in flood detection of this new version of the GFMS was evaluated against available flood event archives (Wu et al, 2011). This new GFMS is quantitatively evaluated in terms of flood event detection during the TRMM era (1998-2010) using a global retrospective simulation (3-hourly and 1/8 degree spatial resolution) with the TMPA 3B42V6 rainfall. Four methods were explored to define flood events from the model results, including three percentile-based statistic methods and a Log Pearson-III flood frequency curve method. The evaluation showed the GFMS detection performance improves with longer flood durations and larger affected areas. The impact of dams was detected in the validation statistics. The presence of dams tends to result in more false alarms and false alarm duration. The GFMS statistics for flood durations > 3 days and for areas without dams vary across the four identification methods, but center around a POD of ~ 0.70 and a FAR of ~ 0.65. When both flood events-based categorical verification metrics and flood duration metrics are considered, a method using the 95th percentile runoff depth plus two parameters related to variability and basin size (method 3) may be more suitable for application to our routine, real-time flood calculations. The evaluation showed the GFMS detection performance improves with longer flood durations and larger affected areas. The new GFMS (operationally available at http://trmm.gsfc.nasa.gov/) improved not only the flood detection performance, but also in the presentation of flood evolution (start, development and recession) in the drainage network. The new GFMS is further evaluated with more quantitative flood properties including flood peak timing, peak stage, peak volumes

  9. Real-time demand forecasting in the emergency department.

    PubMed

    Jones, Spencer S

    2007-10-11

    Shifts in the supply of and demand for emergency department (ED) services have led to ED overcrowding and make the efficient allocation of ED resources increasingly important. Reliable means of modeling and forecasting the demand for resources are critical to any ED resource planning strategy. Vector Autoregression (VAR) is a flexible multivariate time-series forecasting methodology that is well suited to modeling demand for resources in the ED.

  10. Real Time Monitoring of Flooding from Microwave Satellite Observations

    NASA Technical Reports Server (NTRS)

    Galantowicz, John F.; Frey, H. (Technical Monitor)

    2001-01-01

    In this report, we review the progress to date including results from data analyses and present a schedule of milestones for the remainder of the project. We discuss the processing of flood extent data and SSM/I brightness temperature data for the 1993 Midwest Flood. We present preliminary results from the derivation of open water fraction from brightness temperatures.

  11. Real time forecasting of near-future evolution.

    PubMed

    Gerrish, Philip J; Sniegowski, Paul D

    2012-09-01

    A metaphor for adaptation that informs much evolutionary thinking today is that of mountain climbing, where horizontal displacement represents change in genotype, and vertical displacement represents change in fitness. If it were known a priori what the 'fitness landscape' looked like, that is, how the myriad possible genotypes mapped onto fitness, then the possible paths up the fitness mountain could each be assigned a probability, thus providing a dynamical theory with long-term predictive power. Such detailed genotype-fitness data, however, are rarely available and are subject to change with each change in the organism or in the environment. Here, we take a very different approach that depends only on fitness or phenotype-fitness data obtained in real time and requires no a priori information about the fitness landscape. Our general statistical model of adaptive evolution builds on classical theory and gives reasonable predictions of fitness and phenotype evolution many generations into the future.

  12. Flood Warning and Forecasting System in Slovakia

    NASA Astrophysics Data System (ADS)

    Leskova, Danica

    2016-04-01

    In 2015, it finished project Flood Warning and Forecasting System (POVAPSYS) as part of the flood protection in Slovakia till 2010. The aim was to build POVAPSYS integrated computerized flood forecasting and warning system. It took a qualitatively higher level of output meteorological and hydrological services in case of floods affecting large territorial units, as well as local flood events. It is further unfolding demands on performance and coordination of meteorological and hydrological services, troubleshooting observation, evaluation of data, fast communication, modeling and forecasting of meteorological and hydrological processes. Integration of all information entering and exiting to and from the project POVAPSYS provides Hydrological Flood Forecasting System (HYPOS). The system provides information on the current hydrometeorological situation and its evolution with the generation of alerts and notifications in case of exceeding predefined thresholds. HYPOS's functioning of the system requires flawless operability in critical situations while minimizing the loss of its key parts. HYPOS is a core part of the project POVAPSYS, it is a comprehensive software solutions based on a modular principle, providing data and processed information including alarms, in real time. In order to achieve full functionality of the system, in proposal, we have put emphasis on reliability, robustness, availability and security.

  13. New Techniques for Real-Time Stage Forecasting for Tributaries in the Nashville Area

    NASA Astrophysics Data System (ADS)

    Charley, W.; Moran, B.; LaRosa, J.

    2011-12-01

    On Saturday, May 1, 2010, heavy rain began falling in the Cumberland River Valley, Tennessee, and continued through the following day. 13.5 inches was measured at Nashville, an unprecedented amount that doubled the previous 2-day record, and exceeded the May monthly total record of 11 inches. Elsewhere in the valley, amounts of over 19 inches were measured. This intensity of rainfall quickly overwhelmed tributaries to the Cumberland in the Nashville area, causing wide-spread and serious flooding. Tractor-trailers and houses were seen floating down Mill Creek, a primary tributary in the south eastern area of Nashville. Twenty-six people died and over 2 billion dollars in damage occurred as a result of the flood. Since that time, several other significant rainfall events have occurred in the area. As a result of the flood, agencies in the Nashville area want better capabilities to forecast stages for the local tributaries. Better stage forecasting will help local agencies close roads, evacuate homes and businesses and similar actions. An interagency group, consisting of Metro Nashville Water Services and Office of Emergency Management, the National Weather Service, the US Geological Survey and the US Army Corps of Engineers, has been established to seek ways to better forecast short-term events in the region. It should be noted that the National Weather Service has the official responsibility of forecasting stages. This paper examines techniques and algorithms that are being developed to meet this need and the practical aspects of integrating them into a usable product that can quickly and accurately forecast stages in the short-time frame of the tributaries. This includes not only the forecasting procedure, but also the procedure to acquire the latest precipitation and stage data to make the forecasts. These procedures are integrated into the program HEC-RTS, the US Army Corps of Engineers Real-Time Simulation program. HEC-RTS is a Java-based integration tool that

  14. Real time probabilistic precipitation forecasts in the Milano urban area: comparison between a physics and pragmatic approach

    NASA Astrophysics Data System (ADS)

    Ceppi, Alessandro; Ravazzani, Giovanni; Lombardi, Gabriele; Amengual, Arnau; Homar, Victor; Romero, Romu; Mancini, Marco

    2016-04-01

    Precipitation forecasts from mesoscale numerical weather prediction (NWP) models often contain features that are not deterministically predictable. In particular, accurate forecasts of deep moist convection and extreme rainfall are arduous to be predicted in terms of amount, time and target over small hydrological basins due to uncertainties arising from the numerical weather prediction (NWP), physical parameterizations and high sensitivity to misrepresentation of the atmospheric state, therefore they require a probabilistic forecast approach. Here, we examine some hydro-meteorological episodes that affected the Milano urban watersheds using a flood forecasting system which comprises the Flash-flood Event-based Spatially distributed rainfall-runoff Transformation, including Water Balance (FEST-WB) and the Weather Research and Forecasting (WRF) models. The first approach is based on a hydrological ensemble prediction system (HEPS) designed to explicitly cope with uncertainties in the initial and lateral boundary conditions (IC/LBCs) and physical parameterizations of the NWP model. The second involves a pragmatic post-processing procedure by randomly shifting in space the precipitation field provided by the deterministic WRF model run in order to get a cluster of different simulations. Although the physics-based approach needs a high computational cost, it outperforms the pragmatic set of configurations, which, however, turns out to be an acceptable low-budget alternative for real time flood forecasts over small urban basins when a single deterministic run is available.

  15. Near Real Time Data for Operational Space Weather Forecasting

    NASA Astrophysics Data System (ADS)

    Berger, T. E.

    2014-12-01

    Space weather operations presents unique challenges for data systems and providers. Space weather events evolve more quickly than terrestrial weather events. While terrestrial weather occurs on timescales of minutes to hours, space weather storms evolve on timescales of seconds to minutes. For example, the degradation of the High Frequency Radio communications between the ground and commercial airlines is nearly instantaneous when a solar flare occurs. Thus the customer is observing impacts at the same time that the operational forecast center is seeing the event unfold. The diversity and spatial scale of the space weather system is such that no single observation can capture the salient features. The vast space that encompasses space weather and the scarcity of observations further exacerbates the situation and make each observation even more valuable. The physics of interplanetary space, through which many major storms propagate, is very different from the physics of the ionosphere where most of the impacts are felt. And while some observations can be made from ground-based observatories, many of the most critical data comes from satellites, often in unique orbits far from Earth. In this presentation, I will describe some of the more important sources and types of data that feed into the operational alerts, watches, and warnings of space weather storms. Included will be a discussion of some of the new space weather forecast models and the data challenges that they bring forward.

  16. [Real-time forecasting model for monitoring pollutant with differential optical absorption spectroscopy].

    PubMed

    Li, Su-Wen; Liu, Wen-Qing; Xie, Pin-Hua; Wang, Feng-Sui; Yang, Yi-Jun

    2009-11-01

    For real-time and on-line monitoring DOAS (differential optical absorption spectroscopy) system, a model based on an improved Elman network for monitoring pollutant concentrations was proposed. In order to reduce the systematical complexity, the forecasting factors have been obtained based on the step-wise regression method. The forecasting factors were current concentrations, temperature and relative humidity, and wind speed and wind direction. The dynamic back propagation (BP) algorithm was used for creating training set. The experiment results show that the predicted value follows the real well. So the modified Elman network can meet the demand of DOAS system's real time forecasting.

  17. [Real-time forecasting model for monitoring pollutant with differential optical absorption spectroscopy].

    PubMed

    Li, Su-Wen; Liu, Wen-Qing; Xie, Pin-Hua; Wang, Feng-Sui; Yang, Yi-Jun

    2009-11-01

    For real-time and on-line monitoring DOAS (differential optical absorption spectroscopy) system, a model based on an improved Elman network for monitoring pollutant concentrations was proposed. In order to reduce the systematical complexity, the forecasting factors have been obtained based on the step-wise regression method. The forecasting factors were current concentrations, temperature and relative humidity, and wind speed and wind direction. The dynamic back propagation (BP) algorithm was used for creating training set. The experiment results show that the predicted value follows the real well. So the modified Elman network can meet the demand of DOAS system's real time forecasting. PMID:20101985

  18. Development of a real-time geospace simulator for space weather forecast

    NASA Astrophysics Data System (ADS)

    Shinagawa, H.; Shimazu, H.; Fujita, S.; Tanaka, T.; Terada, N.; Nakamura, M.; Obara, T.

    Space weather forecast requires real-time numerical models along with various real-time observational data on the ground and in space Real-time numerical models not only give present information on the space environment but also predict upcoming space weather disturbances Recently a real-time global MHD model of the solar wind interaction with the earth s magnetosphere has been developed by National Institute of Information and Communications Technology NICT in collaboration with Kyushu University The model is now operated at the NICT s space weather forecast center to understand present state of the magnetospheric environment and to predict magnetic disturbances However the present real-time model has not satisfactorily included particle effects on the space environment and realistic ionosphere and thermosphere High-energy particles could damage satellite instruments and human bodies in space while ionospheric storms could affect various communication and broadcasting systems Therefore it is important to develop a numerical model which is able to predict such disturbances We have started to improve our real-time magnetospheric model by including the particle effects as well as the ionosphere-thermosphere system We will describe the current status and future prospect of the real-time geospace model

  19. Earthquake and failure forecasting in real-time: A Forecasting Model Testing Centre

    NASA Astrophysics Data System (ADS)

    Filgueira, Rosa; Atkinson, Malcolm; Bell, Andrew; Main, Ian; Boon, Steven; Meredith, Philip

    2013-04-01

    Across Europe there are a large number of rock deformation laboratories, each of which runs many experiments. Similarly there are a large number of theoretical rock physicists who develop constitutive and computational models both for rock deformation and changes in geophysical properties. Here we consider how to open up opportunities for sharing experimental data in a way that is integrated with multiple hypothesis testing. We present a prototype for a new forecasting model testing centre based on e-infrastructures for capturing and sharing data and models to accelerate the Rock Physicist (RP) research. This proposal is triggered by our work on data assimilation in the NERC EFFORT (Earthquake and Failure Forecasting in Real Time) project, using data provided by the NERC CREEP 2 experimental project as a test case. EFFORT is a multi-disciplinary collaboration between Geoscientists, Rock Physicists and Computer Scientist. Brittle failure of the crust is likely to play a key role in controlling the timing of a range of geophysical hazards, such as volcanic eruptions, yet the predictability of brittle failure is unknown. Our aim is to provide a facility for developing and testing models to forecast brittle failure in experimental and natural data. Model testing is performed in real-time, verifiably prospective mode, in order to avoid selection biases that are possible in retrospective analyses. The project will ultimately quantify the predictability of brittle failure, and how this predictability scales from simple, controlled laboratory conditions to the complex, uncontrolled real world. Experimental data are collected from controlled laboratory experiments which includes data from the UCL Laboratory and from Creep2 project which will undertake experiments in a deep-sea laboratory. We illustrate the properties of the prototype testing centre by streaming and analysing realistically noisy synthetic data, as an aid to generating and improving testing methodologies in

  20. Value of Probabilistic Weather Forecasts: Assessment by Real-Time Optimization of Irrigation Scheduling

    SciTech Connect

    Cai, Ximing; Hejazi, Mohamad I.; Wang, Dingbao

    2011-09-29

    This paper presents a modeling framework for real-time decision support for irrigation scheduling using the National Oceanic and Atmospheric Administration's (NOAA's) probabilistic rainfall forecasts. The forecasts and their probability distributions are incorporated into a simulation-optimization modeling framework. In this study, modeling irrigation is determined by a stochastic optimization program based on the simulated soil moisture and crop water-stress status and the forecasted rainfall for the next 1-7 days. The modeling framework is applied to irrigated corn in Mason County, Illinois. It is found that there is ample potential to improve current farmers practices by simply using the proposed simulation-optimization framework, which uses the present soil moisture and crop evapotranspiration information even without any forecasts. It is found that the values of the forecasts vary across dry, normal, and wet years. More significant economic gains are found in normal and wet years than in dry years under the various forecast horizons. To mitigate drought effect on crop yield through irrigation, medium- or long-term climate predictions likely play a more important role than short-term forecasts. NOAA's imperfect 1-week forecast is still valuable in terms of both profit gain and water saving. Compared with the no-rain forecast case, the short-term imperfect forecasts could lead to additional 2.4-8.5% gain in profit and 11.0-26.9% water saving. However, the performance of the imperfect forecast is only slightly better than the ensemble weather forecast based on historical data and slightly inferior to the perfect forecast. It seems that the 1-week forecast horizon is too limited to evaluate the role of the various forecast scenarios for irrigation scheduling, which is actually a seasonal decision issue. For irrigation scheduling, both the forecast quality and the length of forecast time horizon matter. Thus, longer forecasts might be necessary to evaluate the role

  1. Accounting for uncertainty in distributed flood forecasting models

    NASA Astrophysics Data System (ADS)

    Cole, Steven J.; Robson, Alice J.; Bell, Victoria A.; Moore, Robert J.; Pierce, Clive E.; Roberts, Nigel

    2010-05-01

    Recent research investigating the uncertainty of distributed hydrological flood forecasting models will be presented. These findings utilise the latest advances in rainfall estimation, ensemble nowcasting and Numerical Weather Prediction (NWP). The hydrological flood model that forms the central focus of the study is the Grid-to-Grid Model or G2G: this is a distributed grid-based model that produces area-wide flood forecasts across the modelled domain. Results from applying the G2G Model across the whole of England and Wales on a 1 km grid will be shown along with detailed regional case studies of major floods, such as those of summer 2007. Accounting for uncertainty will be illustrated using ensemble rainfall forecasts from both the Met Office's STEPS nowcasting and high-resolution (~1.5 km) NWP systems. When these rainfall forecasts are used as input to the G2G Model, risk maps of flood exceedance can be produced in animated form that allow the evolving flood risk to be visualised in space and time. Risk maps for a given forecast horizon (e.g. the next 6 hours) concisely summarise a wealth of spatio-temporal flood forecast information and provide an efficient means to identify ‘hot spots' of flood risk. These novel risk maps can be used to support flood warning in real-time and are being trialled operationally across England and Wales by the new joint Environment Agency and Met Office Flood Forecasting Centre.

  2. Evaluation of a Satellite-based Near Real-time Global Flood Prediction System

    NASA Astrophysics Data System (ADS)

    Yilmaz, K. K.; Adler, R. F.; Hong, Y.; Pierce, H. F.

    2008-12-01

    Satellite-based rainfall and geospatial datasets are potentially useful for cost effective detection and early warning of natural hazards, such as floods, specifically for regions of the world where local data are sparse or non-existent. An initial satellite-based near real-time global flood prediction system is operationally available on our website (http://trmm.gsfc.nasa.gov/publications_dir/potential_flood_hydro.html). The key input to the current system is the near real-time rainfall estimates from the NASA-based Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA; 3 hourly, 0.258 x 0.258 degree). A relatively simple hydrologic model, based on the runoff curve number (CN) and antecedent precipitation index (API) methods, transforms rainfall into runoff. In this study we will present an in-depth testing/evaluation of this current flood prediction system, discuss its strengths and limitations and point toward potential improvements necessary for increasing its near real-time global flood prediction reliability and accuracy. This evaluation study will focus on the severe flooding events and will include comparison of the current product with observed runoff/inundation data at global and watershed scale as well as with other available remotely sensed products (e.g., MODIS-based inundation maps from Dartmouth Flood Observatory).

  3. Portals for Real-Time Earthquake Data and Forecasting: Challenge and Promise (Invited)

    NASA Astrophysics Data System (ADS)

    Rundle, J. B.; Holliday, J. R.; Graves, W. R.; Feltstykket, R.; Donnellan, A.; Glasscoe, M. T.

    2013-12-01

    Earthquake forecasts have been computed by a variety of countries world-wide for over two decades. For the most part, forecasts have been computed for insurance, reinsurance and underwriters of catastrophe bonds. However, recent events clearly demonstrate that mitigating personal risk is becoming the responsibility of individual members of the public. Open access to a variety of web-based forecasts, tools, utilities and information is therefore required. Portals for data and forecasts present particular challenges, and require the development of both apps and the client/server architecture to deliver the basic information in real time. The basic forecast model we consider is the Natural Time Weibull (NTW) method (JBR et al., Phys. Rev. E, 86, 021106, 2012). This model uses small earthquakes (';seismicity-based models') to forecast the occurrence of large earthquakes, via data-mining algorithms combined with the ANSS earthquake catalog. This method computes large earthquake probabilities using the number of small earthquakes that have occurred in a region since the last large earthquake. Localizing these forecasts in space so that global forecasts can be computed in real time presents special algorithmic challenges, which we describe in this talk. Using 25 years of data from the ANSS California-Nevada catalog of earthquakes, we compute real-time global forecasts at a grid scale of 0.1o. We analyze and monitor the performance of these models using the standard tests, which include the Reliability/Attributes and Receiver Operating Characteristic (ROC) tests. It is clear from much of the analysis that data quality is a major limitation on the accurate computation of earthquake probabilities. We discuss the challenges of serving up these datasets over the web on web-based platforms such as those at www.quakesim.org , www.e-decider.org , and www.openhazards.com.

  4. Real-time forecasting of the April 11, 2012 Sumatra tsunami

    USGS Publications Warehouse

    Wang, Dailin; Becker, Nathan C.; Walsh, David; Fryer, Gerard J.; Weinstein, Stuart A.; McCreery, Charles S.; ,

    2012-01-01

    The April 11, 2012, magnitude 8.6 earthquake off the northern coast of Sumatra generated a tsunami that was recorded at sea-level stations as far as 4800 km from the epicenter and at four ocean bottom pressure sensors (DARTs) in the Indian Ocean. The governments of India, Indonesia, Sri Lanka, Thailand, and Maldives issued tsunami warnings for their coastlines. The United States' Pacific Tsunami Warning Center (PTWC) issued an Indian Ocean-wide Tsunami Watch Bulletin in its role as an Interim Service Provider for the region. Using an experimental real-time tsunami forecast model (RIFT), PTWC produced a series of tsunami forecasts during the event that were based on rapidly derived earthquake parameters, including initial location and Mwp magnitude estimates and the W-phase centroid moment tensor solutions (W-phase CMTs) obtained at PTWC and at the U. S. Geological Survey (USGS). We discuss the real-time forecast methodology and how successive, real-time tsunami forecasts using the latest W-phase CMT solutions improved the accuracy of the forecast.

  5. Real time tests for long lead-time forecasting of the magnetic field vectors within CMEs

    NASA Astrophysics Data System (ADS)

    Savani, Neel; Vourlidas, Angelos; Pulkkinen, Antti; Wold, Alexandra M.

    2016-07-01

    The direction of magnetic vectors within coronal mass ejections, CMEs, has significant importance for forecasting terrestrial behavior. We have developed a technique to estimate the time-varying magnetic field at Earth for periods within CMEs (Savani et al 2015, 2016). This technique reduces the complex dynamics in order to create a reliable prediction methodology to operate everyday under robust conditions. In this presentation, we focus on the results and skill scores of the forecasting technique calculated from 40 historical CME events from the pre-STEREO mission. Since these results provided substantial improvements in the long lead-time Kp index forecasts, we have now begun testing under real-time conditions. We will also show the preliminary results of our methodology under these real-time conditions within the CCMC hosted at NASA Goddard Space Flight Center.

  6. Evaluation of a Satellite-based Near Real-time Global Flood Prediction System

    NASA Astrophysics Data System (ADS)

    Yilmaz, K. K.; Adler, R.; Pierce, H.

    2009-04-01

    Satellite-based rainfall and geospatial datasets are potentially useful for cost effective detection and early warning of natural hazards, such as floods, specifically for regions of the world where local data are sparse or non-existent. Recently, our group has implemented an initial satellite-based near real-time global flood prediction system that is operationally available. In this system, a relatively simple hydrologic model, based on the runoff curve number (CN) and antecedent precipitation index (API) methods, transforms rainfall into runoff. Runoff is then routed grid-to-grid to estimate flow. The key input to the current system is the near real-time rainfall estimates from the NASA-based Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA; 3 hourly, 0.25x0.25 degree). In this study we will present an in-depth testing/evaluation of this current flood prediction system, discuss its strengths and limitations and point toward potential improvements necessary for increasing its near real-time global flood prediction reliability and accuracy. This evaluation study focuses on the severe flooding events and will include comparison of the current product with observed runoff and inundation data at global and watershed scale as well as with other available remotely sensed products, such as those from Dartmouth Flood Observatory. Initial evaluation suggests that current global near-real time flood predictions provide valuable information related to spatial extent and onset time of extreme flooding events. However the accuracy diminishes in tracking the later stages of the flood event. This behavior suggests that one way to improve the current system is a new (possibly finer scale) routing component. Of course, flood predictions are intimately tied to the accuracy of the satellite-based rainfall estimates. Our presentation will also compare the performance of the flood prediction system when the current version of the NASA TMPA real

  7. Real-Time Assessment of the 16 September 2015 Chile Tsunami and Implications for Near-Field Forecast

    NASA Astrophysics Data System (ADS)

    Tang, Liujuan; Titov, Vasily V.; Moore, Christopher; Wei, Yong

    2016-02-01

    The magnitude 8.3 earthquake in central Chile on 16 September 2015 and the resulting tsunami severely affected the region, with 15 deaths ( Onemi in Monitoreo por sismo de mayor intensidad. (In Spanish) [Available at: http://www.onemi.cl/alerta/se-declara-alerta-roja-por-sismo-de-mayor-intensidad-y-alarma-de-tsunami/], 2015), over one million evacuated, and flooding in nearby coastal cities. We present our real-time assessment of the 2015 Chile tsunami using the Short-term Inundation Forecasting for Tsunamis system, and post-event analyses with local community models in Chile. We evaluate three real-time tsunami sources, which were inverted at the time that the first quarter-, half-, and full-wave passed the first tsunameter (DART 32402, located approximately 580 km north-northwest of the epicenter), respectively. Measurement comparisons from 26 deep-ocean tsunameters and 38 coastal tide stations show that good model accuracies are achieved for all three sources, particularly for the local sites that recorded the most destructive waves. The study highlights the forecast speed, time and accuracy dependence, and their implications for the local forecast capability. Our analyses suggest that the tsunami's main origination area is about 100-200 km long and 100 km wide, to the north of the earthquake epicenter along the trench and the total estimated tsunami wave energy is 7.9 × 1013 J (with 13 % uncertainty). The study provides important guidelines for the earliest reliable estimate of tsunami energy and local forecasts. They can be obtained with the first quarter-wave of tsunameter recording. These results are also confirmed by a forecast analysis of the 2011 Japan tsunami. Furthermore, we find that the first half-wave tsunameter data are sufficient to accurately forecast the 2015 Chile tsunami, due to the specific orientation between the nearest tsunameter and the source. The study also suggests expanding the operational use of the local community models in real

  8. Use of Real-time Satellite Rainfall Information in a Global Flood Estimation System

    NASA Astrophysics Data System (ADS)

    Adler, R. F.; Wu, H.; Tian, Y.

    2012-12-01

    The TRMM Multi-satellite Precipitation Analysis (TMPA) is a merger of precipitation information from mainly passive microwave sensors on polar orbiting satellites. This information is cross-calibrated in terms of rainrate using data from the Tropical Rainfall Measuring Mission (TRMM) flying in an inclined orbit at 35°. A research quality analysis is produced a few months after observation time, but a real-time product is also generated within a few hours of observation. This real-time, or RT, product can be used to quickly diagnose heavy rain events over most of the globe. This rainfall information is also used as the key input into an experimental system, the Global Flood Monitoring System (GFMS), which produces real-time, quasi-global flood estimates. Images and output data are available for use by the community (http://oas.gsfc.nasa.gov/globalflood/). The method uses the 3-hr resolution composite rainfall analyses as input into a hydrological model that calculates water depth and streamflow at each grid (at 0.125 ° latitude-longitude) over the tropics and mid-latitudes. Flood detection and intensity estimates are based on water depth thresholds calculated from a 13-year retrospective run using the satellite rainfall and model. Examination of individual cases in real-time or retrospectively often indicates skill in detecting the occurrence of a flood event and a reasonable evolution of water depth (at the scale of the calculation) and downstream movement of high water levels. A recently published study evaluating calculated flood occurrence from the GFMS against a global flood event database is reviewed. The statistics indicate that flood detection results improve with longer duration (> 3 days) floods and that the statistics are impacted by the presence of large dams, which are not accounted for in the model calculations. Overall, for longer floods in basins without large dams, the Probability of Detection (POD) of floods is ~ 0.7, while the False Alarm Rate

  9. On selection of the optimal data time interval for real-time hydrological forecasting

    NASA Astrophysics Data System (ADS)

    Liu, J.; Han, D.

    2013-09-01

    With the advancement in modern telemetry and communication technologies, hydrological data can be collected with an increasingly higher sampling rate. An important issue deserving attention from the hydrological community is which suitable time interval of the model input data should be chosen in hydrological forecasting. Such a problem has long been recognised in the control engineering community but is a largely ignored topic in operational applications of hydrological forecasting. In this study, the intrinsic properties of rainfall-runoff data with different time intervals are first investigated from the perspectives of the sampling theorem and the information loss using the discrete wavelet transform tool. It is found that rainfall signals with very high sampling rates may not always improve the accuracy of rainfall-runoff modelling due to the catchment low-pass-filtering effect. To further investigate the impact of a data time interval in real-time forecasting, a real-time forecasting system is constructed by incorporating the probability distributed model (PDM) with a real-time updating scheme, the autoregressive moving-average (ARMA) model. Case studies are then carried out on four UK catchments with different concentration times for real-time flow forecasting using data with different time intervals of 15, 30, 45, 60, 90 and 120 min. A positive relation is found between the forecast lead time and the optimal choice of the data time interval, which is also highly dependent on the catchment concentration time. Finally, based on the conclusions from the case studies, a hypothetical pattern is proposed in three-dimensional coordinates to describe the general impact of the data time interval and to provide implications of the selection of the optimal time interval in real-time hydrological forecasting. Although nowadays most operational hydrological systems still have low data sampling rates (daily or hourly), the future is that higher sampling rates will become

  10. On selection of the optimal data time interval for real-time hydrological forecasting

    NASA Astrophysics Data System (ADS)

    Liu, J.; Han, D.

    2012-09-01

    With the advancement in modern telemetry and communication technologies, hydrological data can be collected with an increasingly higher sampling rate. An important issue deserving attention from the hydrological community is what suitable time interval of the model input data should be chosen in hydrological forecasting. Such a problem has long been recognised in the control engineering community but is a largely ignored topic in operational applications of hydrological forecasting. In this study, the intrinsic properties of rainfall-runoff data with different time intervals are first investigated from the perspectives of the sampling theorem and the information loss using the discrete wavelet decomposition tool. It is found that rainfall signals with very high sampling rates may not always improve the accuracy of rainfall-runoff modelling due to the catchment low-pass filtering effect. To further investigate the impact of data time interval in real-time forecasting, a real-time forecasting system is constructed by incorporating the Probability Distributed Model (PDM) with a real-time updating scheme, the autoregressive-moving average (ARMA) model. Case studies are then carried out on four UK catchments with different concentration times for real-time flow forecasting using data with different time intervals of 15 min, 30 min, 45 min, 60 min, 90 min and 120 min. A positive relation is found between the forecast lead time and the optimal choice of the data time interval, which is also highly dependent on the catchment concentration time. Finally, based on the conclusions from the case studies, a hypothetical pattern is proposed in three-dimensional coordinates to describe the general impact of the data time interval and to provide implications on the selection of the optimal time interval in real-time hydrological forecasting. Although nowadays most operational hydrological systems still have low data sampling rates (daily or hourly), the trend in the future is that

  11. Real-time forecasting of sample failure in laboratory rock deformation experiments

    NASA Astrophysics Data System (ADS)

    Bell, Andrew; Main, Ian; Atkinson, Malcolm; Filgueira, Rosa; Meredith, Philip; Boon, Steve

    2013-04-01

    The ability to accurately forecast catastrophic failure in rocks is likely to be a key component in reliable eruption forecasting models. The processes controlling the approach to failure produce highly non-linear behaviour, with a large stochastic component due to material heterogeneity. In the laboratory, mechanical, hydraulic, and rock physical properties are known to change in systematic ways prior to catastrophic failure. The effectiveness of such signals in real-time forecasting has never been tested before in a controlled laboratory setting; previous work has often been qualitative in nature, and subject to retrospective selection bias. Here we describe a collaborative experiment in real-time data assimilation to explore the limits of predictability of rock failure in a best-case scenario. Data are streamed from a remote rock deformation laboratory to a user-friendly portal, where several proposed physical/stochastic models can be analyzed in parallel in real time, using a variety of statistical fitting techniques, including least squares regression, maximum likelihood fitting, Markov-chain Monte-Carlo and Bayesian analysis. The results are posted and regularly updated on the web site prior to catastrophic failure, to ensure a true and verifiable prospective test of forecasting power.

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

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

  13. Real-time operative earthquake forecasting: the case of L'Aquila sequence

    NASA Astrophysics Data System (ADS)

    Marzocchi, W.; Lombardi, A.

    2009-12-01

    A reliable earthquake forecast is one of the fundamental components required for reducing seismic risk. Despite very recent efforts devoted to test the validity of available models, the present skill at forecasting the evolution of seismicity is still largely unknown. The recent Mw 6.3 earthquake - that struck near the city of L'Aquila, Italy on April 6, 2009, causing hundreds of deaths and vast damages - offered to scientists a unique opportunity to test for the first time the forecasting capability in a real-time application. Here, we describe the results of this first prospective experiment. Immediately following the large event, we began producing daily one-day earthquake forecasts for the region, and we provided these forecasts to Civil Protection - the agency responsible for managing the emergency. The forecasts are based on a stochastic model that combines the Gutenberg-Richter distribution of earthquake magnitudes and power-law decay in space and time of triggered earthquakes. The results from the first month following the L'Aquila earthquake exhibit a good fit between forecasts and observations, indicating that accurate earthquake forecasting is now a realistic goal. Our experience with this experiment demonstrates an urgent need for a connection between probabilistic forecasts and decision-making in order to establish - before crises - quantitative and transparent protocols for decision support.

  14. Real-Time CME Forecasting Using HMI Active-Region Magnetograms and Flare History

    NASA Technical Reports Server (NTRS)

    Falconer, David; Moore, Ron; Barghouty, Abdulnasser F.; Khazanov, Igor

    2011-01-01

    We have recently developed a method of predicting an active region s probability of producing a CME, an X-class Flare, an M-class Flare, or a Solar Energetic Particle Event from a free-energy proxy measured from SOHO/MDI line-of-sight magnetograms. This year we have added three major improvements to our forecast tool: 1) Transition from MDI magnetogram to SDO/HMI magnetogram allowing us near-real-time forecasts, 2) Automation of acquisition and measurement of HMI magnetograms giving us near-real-time forecasts (no older than 2 hours), and 3) Determination of how to improve forecast by using the active region s previous flare history in combination with its free-energy proxy. HMI was turned on in May 2010 and MDI was turned off in April 2011. Using the overlap period, we have calibrated HMI to yield what MDI would measure. This is important since the value of the free-energy proxy used for our forecast is resolution dependent, and the forecasts are made from results of a 1996-2004 database of MDI observations. With near-real-time magnetograms from HMI, near-real-time forecasts are now possible. We have augmented the code so that it continually acquires and measures new magnetograms as they become available online, and updates the whole-sun forecast from the coming day. The next planned improvement is to use an active region s previous flare history, in conjunction with its free-energy proxy, to forecast the active region s event rate. It has long been known that active regions that have produced flares in the past are likely to produce flares in the future, and that active regions that are nonpotential (have large free-energy) are more likely to produce flares in the future. This year we have determined that persistence of flaring is not just a reflection of an active region s free energy. In other words, after controlling for free energy, we have found that active regions that have flared recently are more likely to flare in the future.

  15. Global system for hydrological monitoring and forecasting in real time at high resolution

    NASA Astrophysics Data System (ADS)

    Ortiz, Enrique; De Michele, Carlo; Todini, Ezio; Cifres, Enrique

    2016-04-01

    This project presented at the EGU 2016 born of solidarity and the need to dignify the most disadvantaged people living in the poorest countries (Africa, South America and Asia, which are continually exposed to changes in the hydrologic cycle suffering events of large floods and/or long periods of droughts. It is also a special year this 2016, Year of Mercy, in which we must engage with the most disadvantaged of our Planet (Gaia) making available to them what we do professionally and scientifically. The project called "Global system for hydrological monitoring and forecasting in real time at high resolution" is Non-Profit and aims to provide at global high resolution (1km2) hydrological monitoring and forecasting in real time and continuously coupling Weather Forecast of Global Circulation Models, such us GFS-0.25° (Deterministic and Ensembles Run) forcing a physically based distributed hydrological model computationally efficient, such as the latest version extended of TOPKAPI model, named TOPKAPI-eXtended. Finally using the MCP approach for the proper use of ensembles for Predictive Uncertainty assessment essentially based on a multiple regression in the Normal space, can be easily extended to use ensembles to represent the local (in time) smaller or larger conditional predictive uncertainty, as a function of the ensemble spread. In this way, each prediction in time accounts for both the predictive uncertainty of the ensemble mean and that of the ensemble spread. To perform a continuous hydrological modeling with TOPKAPI-X model and have hot start of hydrological status of watersheds, the system assimilated products of rainfall and temperature derived from remote sensing, such as product 3B42RT of TRMM NASA and others.The system will be integrated into a Decision Support System (DSS) platform, based on geographical data. The DSS is a web application (For Pc, Tablet/Mobile phone): It does not need installation (all you need is a web browser and an internet

  16. Global system for hydrological monitoring and forecasting in real time at high resolution

    NASA Astrophysics Data System (ADS)

    Ortiz, Enrique; De Michele, Carlo; Todini, Ezio; Cifres, Enrique

    2016-04-01

    This project presented at the EGU 2016 born of solidarity and the need to dignify the most disadvantaged people living in the poorest countries (Africa, South America and Asia, which are continually exposed to changes in the hydrologic cycle suffering events of large floods and/or long periods of droughts. It is also a special year this 2016, Year of Mercy, in which we must engage with the most disadvantaged of our Planet (Gaia) making available to them what we do professionally and scientifically. The project called "Global system for hydrological monitoring and forecasting in real time at high resolution" is Non-Profit and aims to provide at global high resolution (1km2) hydrological monitoring and forecasting in real time and continuously coupling Weather Forecast of Global Circulation Models, such us GFS-0.25° (Deterministic and Ensembles Run) forcing a physically based distributed hydrological model computationally efficient, such as the latest version extended of TOPKAPI model, named TOPKAPI-eXtended. Finally using the MCP approach for the proper use of ensembles for Predictive Uncertainty assessment essentially based on a multiple regression in the Normal space, can be easily extended to use ensembles to represent the local (in time) smaller or larger conditional predictive uncertainty, as a function of the ensemble spread. In this way, each prediction in time accounts for both the predictive uncertainty of the ensemble mean and that of the ensemble spread. To perform a continuous hydrological modeling with TOPKAPI-X model and have hot start of hydrological status of watersheds, the system assimilated products of rainfall and temperature derived from remote sensing, such as product 3B42RT of TRMM NASA and others.The system will be integrated into a Decision Support System (DSS) platform, based on geographical data. The DSS is a web application (For Pc, Tablet/Mobile phone): It does not need installation (all you need is a web browser and an internet

  17. Forecaster priorities for improving probabilistic flood forecasts

    NASA Astrophysics Data System (ADS)

    Wetterhall, Fredrik; Pappenberger, Florian; Alfieri, Lorenzo; Cloke, Hannah; Thielen, Jutta

    2014-05-01

    Hydrological ensemble prediction systems (HEPS) have in recent years been increasingly used for the operational forecasting of floods by European hydrometeorological agencies. The most obvious advantage of HEPS is that more of the uncertainty in the modelling system can be assessed. In addition, ensemble prediction systems generally have better skill than deterministic systems both in the terms of the mean forecast performance and the potential forecasting of extreme events. Research efforts have so far mostly been devoted to the improvement of the physical and technical aspects of the model systems, such as increased resolution in time and space and better description of physical processes. Developments like these are certainly needed; however, in this paper we argue that there are other areas of HEPS that need urgent attention. This was also the result from a group exercise and a survey conducted to operational forecasters within the European Flood Awareness System (EFAS) to identify the top priorities of improvement regarding their own system. They turned out to span a range of areas, the most popular being to include verification of an assessment of past forecast performance, a multi-model approach for hydrological modelling, to increase the forecast skill on the medium range (>3 days) and more focus on education and training on the interpretation of forecasts. In light of limited resources, we suggest a simple model to classify the identified priorities in terms of their cost and complexity to decide in which order to tackle them. This model is then used to create an action plan of short-, medium- and long-term research priorities with the ultimate goal of an optimal improvement of EFAS in particular and to spur the development of operational HEPS in general.

  18. Flood forecasting in Niger-Benue basin using satellite and quantitative precipitation forecast data

    NASA Astrophysics Data System (ADS)

    Haile, Alemseged Tamiru; Tefera, Fekadu Teshome; Rientjes, Tom

    2016-10-01

    Availability of reliable, timely and accurate rainfall data is constraining the establishment of flood forecasting and early warning systems in many parts of Africa. We evaluated the potential of satellite and weather forecast data as input to a parsimonious flood forecasting model to provide information for flood early warning in the central part of Nigeria. We calibrated the HEC-HMS rainfall-runoff model using rainfall data from post real time Tropical Rainfall Measuring Mission (TRMM) Multi satellite Precipitation Analysis product (TMPA). Real time TMPA satellite rainfall estimates and European Centre for Medium-Range Weather Forecasts (ECMWF) rainfall products were tested for flood forecasting. The implication of removing the systematic errors of the satellite rainfall estimates (SREs) was explored. Performance of the rainfall-runoff model was assessed using visual inspection of simulated and observed hydrographs and a set of performance indicators. The forecast skill was assessed for 1-6 days lead time using categorical verification statistics such as Probability Of Detection (POD), Frequency Of Hit (FOH) and Frequency Of Miss (FOM). The model performance satisfactorily reproduced the pattern and volume of the observed stream flow hydrograph of Benue River. Overall, our results show that SREs and rainfall forecasts from weather models have great potential to serve as model inputs for real-time flood forecasting in data scarce areas. For these data to receive application in African transboundary basins, we suggest (i) removing their systematic error to further improve flood forecast skill; (ii) improving rainfall forecasts; and (iii) improving data sharing between riparian countries.

  19. A practical MGA-ARIMA model for forecasting real-time dynamic rain-induced attenuation

    NASA Astrophysics Data System (ADS)

    Gong, Shuhong; Gao, Yifeng; Shi, Houbao; Zhao, Ge

    2013-05-01

    novel and practical modified genetic algorithm (MGA)-autoregressive integrated moving average (ARIMA) model for forecasting real-time dynamic rain-induced attenuation has been established by combining genetic algorithm ideas with the ARIMA model. It is proved that due to the introduction of MGA into the ARIMA(1,1,7) model, the MGA-ARIMA model has the potential to be conveniently applied in every country or area by creating a parameter database used by the ARIMA(1,1,7) model. The parameter database is given in this paper based on attenuation data measured in Xi'an, China. The methods to create the parameter databases in other countries or areas are offered, too. Based on the experimental results, the MGA-ARIMA model has been proved practical for forecasting dynamic rain-induced attenuation in real time. The novel model given in this paper is significant for developing adaptive fade mitigation technologies at millimeter wave bands.

  20. Optimization Based Data Mining Approah for Forecasting Real-Time Energy Demand

    SciTech Connect

    Omitaomu, Olufemi A; Li, Xueping; Zhou, Shengchao

    2015-01-01

    The worldwide concern over environmental degradation, increasing pressure on electric utility companies to meet peak energy demand, and the requirement to avoid purchasing power from the real-time energy market are motivating the utility companies to explore new approaches for forecasting energy demand. Until now, most approaches for forecasting energy demand rely on monthly electrical consumption data. The emergence of smart meters data is changing the data space for electric utility companies, and creating opportunities for utility companies to collect and analyze energy consumption data at a much finer temporal resolution of at least 15-minutes interval. While the data granularity provided by smart meters is important, there are still other challenges in forecasting energy demand; these challenges include lack of information about appliances usage and occupants behavior. Consequently, in this paper, we develop an optimization based data mining approach for forecasting real-time energy demand using smart meters data. The objective of our approach is to develop a robust estimation of energy demand without access to these other building and behavior data. Specifically, the forecasting problem is formulated as a quadratic programming problem and solved using the so-called support vector machine (SVM) technique in an online setting. The parameters of the SVM technique are optimized using simulated annealing approach. The proposed approach is applied to hourly smart meters data for several residential customers over several days.

  1. Global Near Real-Time Satellite-based Flood Monitoring and Product Dissemination

    NASA Astrophysics Data System (ADS)

    Smith, M.; Slayback, D. A.; Policelli, F.; Brakenridge, G. R.; Tokay, M.

    2012-12-01

    Flooding is among the most destructive, frequent, and costly natural disasters faced by modern society, with several major events occurring each year. In the past few years, major floods have devastated parts of China, Thailand, Pakistan, Australia, and the Philippines, among others. The toll of these events, in financial costs, displacement of individuals, and deaths, is substantial and continues to rise as climate change generates more extreme weather events. When these events do occur, the disaster management community requires frequently updated and easily accessible information to better understand the extent of flooding and better coordinate response efforts. With funding from NASA's Applied Sciences program, we have developed, and are now operating, a near real-time global flood mapping system to help provide critical flood extent information within 24-48 hours after flooding events. The system applies a water detection algorithm to MODIS imagery received from the LANCE (Land Atmosphere Near real-time Capability for EOS) system at NASA Goddard. The LANCE system typically processes imagery in less than 3 hours after satellite overpass, and our flood mapping system can output flood products within ½ hour of acquiring the LANCE products. Using imagery from both the Terra (10:30 AM local time overpass) and Aqua (1:30 PM) platforms allows an initial assessment of flooding extent by late afternoon, every day, and more robust assessments after accumulating imagery over a longer period; the MODIS sensors are optical, so cloud cover remains an issue, which is partly overcome by using multiple looks over one or more days. Other issues include the relatively coarse scale of the MODIS imagery (250 meters), the difficulty of detecting flood waters in areas with continuous canopy cover, confusion of shadow (cloud or terrain) with water, and accurately identifying detected water as flood as opposed to normal water extents. We have made progress on some of these issues

  2. Application of Global Real-Time Landslide Forecasting System for International use

    NASA Astrophysics Data System (ADS)

    Kirschbaum, D. B.; Lerner-Lam, A.; Hong, Y.; Adler, R.

    2008-12-01

    The variability of natural hazard events by category significantly vary in their spatial and temporal extents and onsets, requiring a catered, and focused approach to appropriately address the risk and vulnerability of the specific hazard event. The advent of satellite data products has helped to monitor tropical cyclones, droughts, and flooding conditions and consequent impacts. Geophysical events such as earthquake are continually monitored on a global seismic network. However, a warning or monitoring system has not been established at larger scales for landslides, a hazard with the smallest spatial extent but highest frequency and arguably largest impacts globally. One of the major challenges in landslide hazard research is the field's focus on site specific investigations, drawing on high resolution surface data as well as detailed landslide inventories and rainfall information to provide an estimate of static landslide hazard susceptibility. Few studies have approached the issue of landslide risk and susceptibility from a dynamic standpoint to estimate the potential for landslide susceptibility conditions in a time frame that allows for a better understanding of the physical processes both scientifically and as it relates to societal response. To present a more dynamic representation of landslide hazard risk at larger spatial scales new research has developed an algorithm which couples a landslide hazard susceptibility map with real-time satellite derived rainfall to forecast areas with high landslide potential at the global scale. The algorithm draws on near-real time Tropical Rainfall Measuring Mission (TRMM) data as well as other satellite products to obtain a 3-hourly picture of locations across the world where the surface susceptibility conditions are high and the rainfall accumulation exceeds a defined threshold. The resulting forecasts are updated every 3 hours on a website, highlighting pixels satisfying these conditions on a 0.25º grid. The spatial

  3. Integrating satellite and tower phenology: a case-study in real-time ecological forecasting

    NASA Astrophysics Data System (ADS)

    Dietze, M.

    2014-12-01

    Phenological transitions have large impacts on ecosystem processes, species interactions, and climate. However, phenology is a critical source of uncertainty in projections of climate change on terrestrial ecosystems and the current generation of ecosystem models are highly variable and biased in their phenology predictions. Most phenological modeling has focused on diagnosing phenological variability and predicting long term responses to climate scenarios. Phenological predictions for the current season, on the other hand, are being made based on long-term means or expert opinion rather than real data. To our knowledge previous research has not applied operational data assimilation approaches to produce operational, real-time forecasts of phenology. We present a phenology forecast data product that is automatically updated every day using current observations and weather forecasts. Specifically we fuse MODIS NDVI and PhenoCam based GCC with a threshold logistic process model at five sites across eastern forests, from North Carolina to New Hampshire. Prior to application, models were calibrated (2000-2012) using a Bayesian state space model. Forecasts for fall 2013, spring 2014, and fall 2014 were then generated on a daily basis using a particle filter. The system successfully tracked seasonal phenology but forecasts showed high uncertainty and sensitivity to alternative model structures. Furthermore, we found that current phenological models in the literature are not formulated in a way that allows for dynamic forecasts. Work remains to be done to extend this work to a fully spatial context. In particular there is a need to determine the spatial range of influence of the tower PhenoCam data and to account for both land cover and random effects. More broadly, this work demonstrates the possibilities for the development of real-time ecological forecasting in other areas.

  4. Global Near Real-Time MODIS and Landsat Flood Mapping and Product Delivery

    NASA Astrophysics Data System (ADS)

    Policelli, F. S.; Slayback, D. A.; Tokay, M. M.; Brakenridge, G. R.

    2014-12-01

    Flooding is the most destructive, frequent, and costly natural disaster faced by modern society, and is increasing in frequency and damage (deaths, displacements, and financial costs) as populations increase and climate change generates more extreme weather events. When major flooding events occur, the disaster management community needs frequently updated and easily accessible information to better understand the extent of flooding and coordinate response efforts. With funding from NASA's Applied Sciences program, we developed and are now operating a near real-time global flood mapping system to help provide flood extent information within 24-48 hours of events. The principal element of the system applies a water detection algorithm to MODIS imagery, which is processed by the LANCE (Land Atmosphere Near real-time Capability for EOS) system at NASA Goddard within a few hours of satellite overpass. Using imagery from both the Terra (10:30 AM local time overpass) and Aqua (1:30 PM) platforms allows the system to deliver an initial daily assessment of flood extent by late afternoon, and more robust assessments after accumulating cloud-free imagery over several days. Cloud cover is the primary limitation in detecting surface water from MODIS imagery. Other issues include the relatively coarse scale of the MODIS imagery (250 meters) for some events, the difficulty of detecting flood waters in areas with continuous canopy cover, confusion of shadow (cloud or terrain) with water, and accurately identifying detected water as flood as opposed to normal water extent. We are working on improvements to address these limitations. We have also begun delivery of near real time water maps at 30 m resolution from Landsat imagery. Although Landsat is not available daily globally, but only every 8 days if imagery from both operating platforms (Landsat 7 and 8) is accessed, it can provide useful higher resolution data on water extent when a clear acquisition coincides with an active

  5. Improving real-time inflow forecasting into hydropower reservoirs through a complementary modelling framework

    NASA Astrophysics Data System (ADS)

    Gragne, A. S.; Sharma, A.; Mehrotra, R.; Alfredsen, K.

    2015-08-01

    Accuracy of reservoir inflow forecasts is instrumental for maximizing the value of water resources and benefits gained through hydropower generation. Improving hourly reservoir inflow forecasts over a 24 h lead time is considered within the day-ahead (Elspot) market of the Nordic exchange market. A complementary modelling framework presents an approach for improving real-time forecasting without needing to modify the pre-existing forecasting model, but instead formulating an independent additive or complementary model that captures the structure the existing operational model may be missing. We present here the application of this principle for issuing improved hourly inflow forecasts into hydropower reservoirs over extended lead times, and the parameter estimation procedure reformulated to deal with bias, persistence and heteroscedasticity. The procedure presented comprises an error model added on top of an unalterable constant parameter conceptual model. This procedure is applied in the 207 km2 Krinsvatn catchment in central Norway. The structure of the error model is established based on attributes of the residual time series from the conceptual model. Besides improving forecast skills of operational models, the approach estimates the uncertainty in the complementary model structure and produces probabilistic inflow forecasts that entrain suitable information for reducing uncertainty in the decision-making processes in hydropower systems operation. Deterministic and probabilistic evaluations revealed an overall significant improvement in forecast accuracy for lead times up to 17 h. Evaluation of the percentage of observations bracketed in the forecasted 95 % confidence interval indicated that the degree of success in containing 95 % of the observations varies across seasons and hydrologic years.

  6. Application of Snpp/viirs Data in Near Real-Time Supra-Snow Flood Detection

    NASA Astrophysics Data System (ADS)

    Li, S.; Sun, D.; Goldberg, M.; Sjoberg, B.; Plumb, E. W.; Holloway, E.; Lindsey, S.; Kreller, M.

    2015-12-01

    Supra-snow/ice flood is very common in high latitude areas from winter to spring break-up seasons along rivers flowing to even higher latitude areas, but this flood type doesn't draw much attention due to poor ground conditions for river watch and ground observations. Satellite data from SNPP/VIIRS (Suomi-National Polar-orbit Partnership/Visible/Infrared Imager Radiometer Suite) instead have shown great advantages in supra-snow/ice flood detection due to its large swath coverage, multiple daily observations in high latitude areas and moderate spatial resolution. Thus, methods for supra-snow/ice water detection were developed to detect near real-time supra-snow/ice floods automatically using SNPP/VIIRS imagery. The methods were mainly based on spectral features of supra-snow/ice floodwater, assisting by geometry-based algorithm and object-based algorithm to remove cloud shadows and terrain shadows over snow/ice surface. The detected supra-snow/ice floodwater was further applied in water fraction retrieval for better representation of flood extent using a modified histogram method based on linear combination model. The developed methods were successfully applied in dynamic monitoring of 2015's supra-snow/ice flood along Sag River in Alaska, which was claimed as a state disaster by Alaska state government, and further tested with more than 1000 VIIRS granules year around. Analyses through visual inspection with VIIRS false-color composite images and quantitative comparison with Landsat-8 OLI images show promising and robust performance in detection of supra-snow/ice floodwater, indicating a high feasibility for the method to be applied in operations for near real-time supra-snow/ice flood detection.

  7. A centralized real-time controller for the reservoir's management on the Seine River using ensemble weather forecasting

    NASA Astrophysics Data System (ADS)

    Ficchi, Andrea; Raso, Luciano; Jay-Allemand, Maxime; Dorchies, David; Malaterre, Pierre-Olivier; Pianosi, Francesca; Van Overloop, Peter-Jules

    2013-04-01

    The reservoirs on the Seine River, upstream of Paris, are regulated with the objective of reducing floods and supporting low flows. The current management of these reservoirs is empirical, reactive, and decentralized, mainly based on filling curves, constructed from an analysis of historical floods and low flows. When inflows are significantly different from their seasonal average, this management strategy proves inefficient. Climate change is also a challenge, for the possible modification of future hydrologic conditions. To improve such management strategy, in this study we investigate the use of Tree-Based Model Predictive Control (TB-MPC), a proactive and centralized method that uses all the information available in real-time, including ensemble weather forecasting. In TB-MPC, a tree is generated from an ensemble of weather forecast. The tree structure summarizes the information contained in the ensemble, specifying the time, along the optimization horizon, when forecast trajectories diverge and thus uncertainty is expected to be resolved. This information is then used in the model predictive control framework. The TB-MPC controller is implemented in combination with the integrated model of the water system, including a semi-distributed hydrologic model of the watershed, a simplified hydraulic model of the river network, and the four reservoir models. Optimization takes into account the cost associated to floods and low-flows, and a penalty cost based on the final reservoir storages. The performances of the TB-MPC controller will be simulated and compared with those of deterministic MPC and with the actual management performances. This work is part of the Climaware European project (2010-2013) set up to develop and to assess measures for sustainable water resources management regarding adaptation to climate change.

  8. A Real-Time Measurement System for Long-Life Flood Monitoring and Warning Applications

    PubMed Central

    Marin-Perez, Rafael; García-Pintado, Javier; Gómez, Antonio Skarmeta

    2012-01-01

    A flood warning system incorporates telemetered rainfall and flow/water level data measured at various locations in the catchment area. Real-time accurate data collection is required for this use, and sensor networks improve the system capabilities. However, existing sensor nodes struggle to satisfy the hydrological requirements in terms of autonomy, sensor hardware compatibility, reliability and long-range communication. We describe the design and development of a real-time measurement system for flood monitoring, and its deployment in a flash-flood prone 650 km2 semiarid watershed in Southern Spain. A developed low-power and long-range communication device, so-called DatalogV1, provides automatic data gathering and reliable transmission. DatalogV1 incorporates self-monitoring for adapting measurement schedules for consumption management and to capture events of interest. Two tests are used to assess the success of the development. The results show an autonomous and robust monitoring system for long-term collection of water level data in many sparse locations during flood events. PMID:22666028

  9. Global, Daily, Near Real-Time Satellite-based Flood Monitoring and Product Dissemination

    NASA Astrophysics Data System (ADS)

    Slayback, D. A.; Policelli, F. S.; Brakenridge, G. R.; Tokay, M. M.; Smith, M. M.; Kettner, A. J.

    2013-12-01

    Flooding is the most destructive, frequent, and costly natural disaster faced by modern society, and is expected to increase in frequency and damage with climate change and population growth. Some of 2013's major floods have impacted the New York City region, the Midwest, Alberta, Australia, various parts of China, Thailand, Pakistan, and central Europe. The toll of these events, in financial costs, displacement of individuals, and deaths, is substantial and continues to rise as climate change generates more extreme weather events. When these events do occur, the disaster management community requires frequently updated and easily accessible information to better understand the extent of flooding and better coordinate response efforts. With funding from NASA's Applied Sciences program, we developed and are now operating a near real-time global flood mapping system to help provide critical flood extent information within 24-48 hours of events. The system applies a water detection algorithm to MODIS imagery received from the LANCE (Land Atmosphere Near real-time Capability for EOS) system at NASA Goddard within a few hours of satellite overpass. Using imagery from both the Terra (10:30 AM local time overpass) and Aqua (1:30 PM) platforms allows an initial daily assessment of flooding extent by late afternoon, and more robust assessments after accumulating cloud-free imagery over several days. Cloud cover is the primary limitation in detecting surface water from MODIS imagery. Other issues include the relatively coarse scale of the MODIS imagery (250 meters), the difficulty of detecting flood waters in areas with continuous canopy cover, confusion of shadow (cloud or terrain) with water, and accurately identifying detected water as flood as opposed to normal water extents. We have made progress on many of these issues, and are working to develop higher resolution flood detection using alternate sensors, including Landsat and various radar sensors. Although these

  10. Flood and Landslide Applications of Near Real-time Satellite Rainfall Products

    NASA Technical Reports Server (NTRS)

    Hong, Yang; Adler, Robert F.; Negri, Andrew; Huffman, George J.

    2007-01-01

    Floods and associated landslides are one of the most widespread natural hazards on Earth, responsible for tens of thousands of deaths and billions of dollars in property damage every year. During 1993-2002, over 1000 of the more than 2,900 natural disasters reported were due to floods. These floods and associated landslides claimed over 90,000 lives, affected over 1.4 billion people and cost about $210 billion. The impact of these disasters is often felt most acutely in less developed regions. In many countries around the world, satellite-based precipitation estimation may be the best source of rainfall data due to lack of surface observing networks. Satellite observations can be of essential value in improving our understanding of the occurrence of hazardous events and possibly in lessening their impact on local economies and in reducing injuries, if they can be used to create reliable warning systems in cost-effective ways. This article addressed these opportunities and challenges by describing a combination of satellite-based real-time precipitation estimation with land surface characteristics as input, with empirical and numerical models to map potential of landslides and floods. In this article, a framework to detect floods and landslides related to heavy rain events in near-real-time is proposed. Key components of the framework are: a fine resolution precipitation acquisition system; a comprehensive land surface database; a hydrological modeling component; and landslide and debris flow model components. A key precipitation input dataset for the integrated applications is the NASA TRMM-based multi-satellite precipitation estimates. This dataset provides near real-time precipitation at a spatial-temporal resolution of 3 hours and 0.25deg x 0.25deg. By careful integration of remote sensing and in-situ observations, and assimilation of these observations into hydrological and landslide/debris flow models with surface topographic information, prediction of useful

  11. Real-time monitoring and short-term forecasting of drought in Norway

    NASA Astrophysics Data System (ADS)

    Kwok Wong, Wai; Hisdal, Hege

    2013-04-01

    Drought is considered to be one of the most costly natural disasters. Drought monitoring and forecasting are thus important for sound water management. In this study hydrological drought characteristics applicable for real-time monitoring and short-term forecasting of drought in Norway were developed. A spatially distributed hydrological model (HBV) implemented in a Web-based GIS framework provides a platform for drought analyses and visualizations. A number of national drought maps can be produced, which is a simple and effective way to communicate drought conditions to decision makers and the public. The HBV model is driven by precipitation and air temperature data. On a daily time step it calculates the water balance for 1 x 1 km2 grid cells characterized by their elevation and land use. Drought duration and areal drought coverage for runoff and subsurface storage (sum of soil moisture and groundwater) were derived. The threshold level method was used to specify drought conditions on a grid cell basis. The daily 10th percentile thresholds were derived from seven-day windows centered on that calendar day from the reference period 1981-2010 (threshold not exceeded 10% of the time). Each individual grid cell was examined to determine if it was below its respective threshold level. Daily drought-stricken areas can then be easily identified when visualized on a map. The drought duration can also be tracked and calculated by a retrospective analysis. Real-time observations from synoptic stations interpolated to a regular grid of 1 km resolution constituted the forcing data for the current situation. 9-day meteorological forecasts were used as input to the HBV model to obtain short-term hydrological drought forecasts. Downscaled precipitation and temperature fields from two different atmospheric models were applied. The first two days of the forecast period adopted the forecasts from Unified Model (UM4) while the following seven days were based on the 9-day forecasts

  12. A Real-time Irrigation Forecasting System in Jiefangzha Irrigation District, China

    NASA Astrophysics Data System (ADS)

    Cong, Z.

    2015-12-01

    In order to improve the irrigation efficiency, we need to know when and how much to irrigate in real time. If we know the soil moisture content at this time, we can forecast the soil moisture content in the next days based on the rainfall forecasting and the crop evapotranspiration forecasting. Then the irrigation should be considered when the forecasting soil moisture content reaches to a threshold. Jiefangzha Irrigation District, a part of Hetao Irrigation District, is located in Inner Mongolia, China. The irrigated area of this irrigation district is about 140,000 ha mainly planting wheat, maize and sunflower. The annual precipitation is below 200mm, so the irrigation is necessary and the irrigation water comes from the Yellow river. We set up 10 sites with 4 TDR sensors at each site (20cm, 40cm, 60cm and 80cm depth) to monitor the soil moisture content. The weather forecasting data are downloaded from the website of European Centre for Medium-Range Weather Forecasts (ECMWF). The reference evapotranspiration is estimated based on FAO-Blaney-Criddle equation with only the air temperature from ECMWF. Then the crop water requirement is forecasted by the crop coefficient multiplying the reference evapotranspiration. Finally, the soil moisture content is forecasted based on soil water balance with the initial condition is set as the monitoring soil moisture content. When the soil moisture content reaches to a threshold, the irrigation warning will be announced. The irrigation mount can be estimated through three ways: (1) making the soil moisture content be equal to the field capacity; (2) making the soil moisture saturated; or (3) according to the irrigation quota. The forecasting period is 10 days. The system is developed according to B2C model with Java language. All the databases and the data analysis are carried out in the server. The customers can log in the website with their own username and password then get the information about the irrigation forecasting

  13. Adapting CALIPSO Climate Measurements for Near Real Time Analyses and Forecasting

    NASA Technical Reports Server (NTRS)

    Vaughan, Mark A.; Trepte, Charles R.; Winker, David M.; Avery, Melody A.; Campbell, James; Hoff, Ray; Young, Stuart; Getzewich, Brian J.; Tackett, Jason L.; Kar, Jayanta

    2011-01-01

    The Cloud-Aerosol Lidar and Infrared Pathfinder satellite Observations (CALIPSO) mission was originally conceived and designed as a climate measurements mission, with considerable latency between data acquisition and the release of the level 1 and level 2 data products. However, the unique nature of the CALIPSO lidar backscatter profiles quickly led to the qualitative use of CALIPSO?s near real time (i.e., ? expedited?) lidar data imagery in several different forecasting applications. To enable quantitative use of their near real time analyses, the CALIPSO project recently expanded their expedited data catalog to include all of the standard level 1 and level 2 lidar data products. Also included is a new cloud cleared level 1.5 profile product developed for use by operational forecast centers for verification of aerosol predictions. This paper describes the architecture and content of the CALIPSO expedited data products. The fidelity and accuracy of the expedited products are assessed via comparisons to the standard CALIPSO data products.

  14. Coupling flood forecasting and social media crowdsourcing

    NASA Astrophysics Data System (ADS)

    Kalas, Milan; Kliment, Tomas; Salamon, Peter

    2016-04-01

    Social and mainstream media monitoring is being more and more recognized as valuable source of information in disaster management and response. The information on ongoing disasters could be detected in very short time and the social media can bring additional information to traditional data feeds (ground, remote observation schemes). Probably the biggest attempt to use the social media in the crisis management was the activation of the Digital Humanitarian Network by the United Nations Office for the Coordination of Humanitarian Affairs in response to Typhoon Yolanda. The network of volunteers performing rapid needs & damage assessment by tagging reports posted to social media which were then used by machine learning classifiers as a training set to automatically identify tweets referring to both urgent needs and offers of help. In this work we will present the potential of coupling a social media streaming and news monitoring application ( GlobalFloodNews - www.globalfloodsystem.com) with a flood forecasting system (www.globalfloods.eu) and the geo-catalogue of the OGC services discovered in the Google Search Engine (WMS, WFS, WCS, etc.) to provide a full suite of information available to crisis management centers as fast as possible. In GlobalFloodNews we use advanced filtering of the real-time Twitter stream, where the relevant information is automatically extracted using natural language and signal processing techniques. The keyword filters are adjusted and optimized automatically using machine learning algorithms as new reports are added to the system. In order to refine the search results the forecasting system will be triggering an event-based search on the social media and OGC services relevant for crisis response (population distribution, critical infrastructure, hospitals etc.). The current version of the system makes use of USHAHIDI Crowdmap platform, which is designed to easily crowdsource information using multiple channels, including SMS, email

  15. Real-time Ensemble Forecasting of Coronal Mass Ejections using the WSA-ENLIL+Cone Model

    NASA Astrophysics Data System (ADS)

    Mays, M. L.; Taktakishvili, A.; Pulkkinen, A. A.; MacNeice, P. J.; Rastaetter, L.; Kuznetsova, M. M.; Odstrcil, D.

    2013-12-01

    Ensemble forecasting of coronal mass ejections (CMEs) provides significant information in that it provides an estimation of the spread or uncertainty in CME arrival time predictions due to uncertainties in determining CME input parameters. Ensemble modeling of CME propagation in the heliosphere is performed by forecasters at the Space Weather Research Center (SWRC) using the WSA-ENLIL cone model available at the Community Coordinated Modeling Center (CCMC). SWRC is an in-house research-based operations team at the CCMC which provides interplanetary space weather forecasting for NASA's robotic missions and performs real-time model validation. A distribution of n (routinely n=48) CME input parameters are generated using the CCMC Stereo CME Analysis Tool (StereoCAT) which employs geometrical triangulation techniques. These input parameters are used to perform n different simulations yielding an ensemble of solar wind parameters at various locations of interest (satellites or planets), including a probability distribution of CME shock arrival times (for hits), and geomagnetic storm strength (for Earth-directed hits). Ensemble simulations have been performed experimentally in real-time at the CCMC since January 2013. We present the results of ensemble simulations for a total of 15 CME events, 10 of which were performed in real-time. The observed CME arrival was within the range of ensemble arrival time predictions for 5 out of the 12 ensemble runs containing hits. The average arrival time prediction was computed for each of the twelve ensembles predicting hits and using the actual arrival time an average absolute error of 8.20 hours was found for all twelve ensembles, which is comparable to current forecasting errors. Some considerations for the accuracy of ensemble CME arrival time predictions include the importance of the initial distribution of CME input parameters, particularly the mean and spread. When the observed arrivals are not within the predicted range, this

  16. Application of Cabled Offshore Ocean Bottom Tsunami Gauge Data for a Real-Time Tsunami Forecasting

    NASA Astrophysics Data System (ADS)

    Tsushima, H.; Hino, R.; Fujimoto, H.; Tanioka, Y.

    2006-12-01

    Tsunami forecasting is one of the most effective methods to mitigate tsunami hazards. Now, in Japan, tsunami warning is announced within three minutes after the occurrence of an earthquake. This warning is based only on the seismic data and therefore has some problems; no one can assess the validity of the forecasting until tsunami reaches coasts and, in principle, tsunami amplitudes may be underestimated when "tsunami earthquakes", which generate tsunamis much larger than those expected from its magnitude estimated by seismic data, occur. To avoid these problems, tsunami data observed at offshore should be effectively used for the forecasting because the offshore stations can detect tsunami earlier than it reaches to the coastal area. In this study, we develop an algorithm for real-time tsunami forecasting system using offshore ocean bottom tsunami gauge data. Our targets are near-field tsunamis generated by earthquakes occurring along the Kuril and the Japan Trench subduction zones. To forecast coastal tsunami in real-time, we plan to use pressure data measured by cabled ocean bottom tsunami gauges. In this region, four cabled underwater tsunami sensors are deployed. The offshore tsunami waveforms are inverted for a coseismic sea-floor vertical displacement distribution, which is used to estimate coastal tsunami waveforms before actual tsunami arrives. Green's functions are calculated in advance to reduce the time to accomplish the inversion and successive waveform synthesis. We performed numerical test of this methodology assuming the case of the 1896 Sanriku tsunami earthquake (Ms=7.2, Mt=8.2). Tsunami waveforms were calculated using the source parameters by Tanioka and Satake (1996), and regarded as the observed waveforms. Then, we compare the "observed" tsunami waveforms and the synthetics for coastal tide stations. The sea-floor displacements, used for the waveform synthesis, were inverted from the "observations" at four offshore stations. The calculated

  17. Rainfall estimation for real time flood monitoring using geostationary meteorological satellite data

    NASA Astrophysics Data System (ADS)

    Veerakachen, Watcharee; Raksapatcharawong, Mongkol

    2015-09-01

    Rainfall estimation by geostationary meteorological satellite data provides good spatial and temporal resolutions. This is advantageous for real time flood monitoring and warning systems. However, a rainfall estimation algorithm developed in one region needs to be adjusted for another climatic region. This work proposes computationally-efficient rainfall estimation algorithms based on an Infrared Threshold Rainfall (ITR) method calibrated with regional ground truth. Hourly rain gauge data collected from 70 stations around the Chao-Phraya river basin were used for calibration and validation of the algorithms. The algorithm inputs were derived from FY-2E satellite observations consisting of infrared and water vapor imagery. The results were compared with the Global Satellite Mapping of Precipitation (GSMaP) near real time product (GSMaP_NRT) using the probability of detection (POD), root mean square error (RMSE) and linear correlation coefficient (CC) as performance indices. Comparison with the GSMaP_NRT product for real time monitoring purpose shows that hourly rain estimates from the proposed algorithm with the error adjustment technique (ITR_EA) offers higher POD and approximately the same RMSE and CC with less data latency.

  18. Automating Flood Hazard Mapping Methods for Near Real-time Storm Surge Inundation and Vulnerability Assessment

    NASA Astrophysics Data System (ADS)

    Weigel, A. M.; Griffin, R.; Gallagher, D.

    2015-12-01

    Storm surge has enough destructive power to damage buildings and infrastructure, erode beaches, and threaten human life across large geographic areas, hence posing the greatest threat of all the hurricane hazards. The United States Gulf of Mexico has proven vulnerable to hurricanes as it has been hit by some of the most destructive hurricanes on record. With projected rises in sea level and increases in hurricane activity, there is a need to better understand the associated risks for disaster mitigation, preparedness, and response. GIS has become a critical tool in enhancing disaster planning, risk assessment, and emergency response by communicating spatial information through a multi-layer approach. However, there is a need for a near real-time method of identifying areas with a high risk of being impacted by storm surge. Research was conducted alongside Baron, a private industry weather enterprise, to facilitate automated modeling and visualization of storm surge inundation and vulnerability on a near real-time basis. This research successfully automated current flood hazard mapping techniques using a GIS framework written in a Python programming environment, and displayed resulting data through an Application Program Interface (API). Data used for this methodology included high resolution topography, NOAA Probabilistic Surge model outputs parsed from Rich Site Summary (RSS) feeds, and the NOAA Census tract level Social Vulnerability Index (SoVI). The development process required extensive data processing and management to provide high resolution visualizations of potential flooding and population vulnerability in a timely manner. The accuracy of the developed methodology was assessed using Hurricane Isaac as a case study, which through a USGS and NOAA partnership, contained ample data for statistical analysis. This research successfully created a fully automated, near real-time method for mapping high resolution storm surge inundation and vulnerability for the

  19. The policy and science supporting flash flood forecasting in Scotland

    NASA Astrophysics Data System (ADS)

    Cranston, Michael; Maxey, Richard; Speight, Linda; Tavendale, Amy; Cole, Steven; Robson, Alice; Moore, Robert

    2013-04-01

    In 2012, the Scottish Environment Protection Agency (SEPA) published its Flood Warning Strategy. The strategy aims to ensure that emerging science is at the heart of supporting its strategic aim of reducing the impact of river flooding through the provision of reliable and timely flood warnings and allowing Scotland's flood warning authority to develop forecasting approaches in areas not previously considered. One specific area of agreed commitment is in the development of methods for forecasting in rapid response or flashy catchments. Previous policies have stated that flood warning provision would not be possible without adequate hydrological response time (greater than three hours). The particular challenge with meeting this new aim is on the reliance of increasingly uncertain flooding predictions at the shorter timescale against a more cautious and traditional approach to flood warning which relies on hydrological observations and real time verification of forecasts. This therefore places increasing demands on developing hydrometeorological forecasting capabilities. This paper will present on some scientific developments supporting the latest policy. In particular on Grid-2-Grid, a distributed hydrological model, which has been in operation across Scotland for over a year (Cranston, et al., 2012) and on a specific assessment of its capabilities using high resolution and ensemble rainfall forecasts. The paper will focus on Comrie, a community in Scotland that has been devastated twice during 2012 by flash flooding and considers the various challenges in meeting this strategic aim. References Cranston, M., Maxey, R., Tavendale, A., Buchanan, P., Motion, A., Moore, R. M., Cole, S., Robson, A. and Minett, A. (2012) Countrywide flood forecasting in Scotland: challenges for hydrometeorological uncertainty and prediction. Weather Radar and Hydrology (Proceedings of a symposium held in Exeter, UK, April 2011), IAHS Publ. 351, 2012)

  20. ASSESSMENT OF AN ENSEMBLE OF SEVEN REAL-TIME OZONE FORECASTS OVER EASTERN NORTH AMERICA DURING THE SUMMER OF 2004

    EPA Science Inventory

    The real-time forecasts of ozone (O3) from seven air quality forecast models (AQFMs) are statistically evaluated against observations collected during July and August of 2004 (53 days) through the Aerometric Information Retrieval Now (AIRNow) network at roughly 340 mon...

  1. Real-time forecast of aftershocks from a single seismic station signal

    NASA Astrophysics Data System (ADS)

    Lippiello, E.; Cirillo, A.; Godano, G.; Papadimitriou, E.; Karakostas, V.

    2016-06-01

    The evaluation of seismic hazard in the hours following large earthquakes is strongly affected by biases due to difficulties in determining earthquake location. This leads to the huge incompleteness of instrumental catalogs. Here we show that if, on the one hand, the overlap of aftershock coda waves hides many small events, on the other hand, it leads to a well-determined empirical law controlling the decay of the amplitude of the seismic signal at a given site. The fitting parameters of this law can be related to those controlling the temporal decay of the aftershock number, and it is then possible to obtain short-term postseismic occurrence probability from a single recorded seismic signal. We therefore present a novel procedure which, without requiring earthquake location, produces more accurate and almost real-time forecast, in a site of interest, directly from the signal of a seismic station installed at that site.

  2. Forecasting Daily Patient Outflow From a Ward Having No Real-Time Clinical Data

    PubMed Central

    Tran, Truyen; Luo, Wei; Phung, Dinh; Venkatesh, Svetha

    2016-01-01

    Background: Modeling patient flow is crucial in understanding resource demand and prioritization. We study patient outflow from an open ward in an Australian hospital, where currently bed allocation is carried out by a manager relying on past experiences and looking at demand. Automatic methods that provide a reasonable estimate of total next-day discharges can aid in efficient bed management. The challenges in building such methods lie in dealing with large amounts of discharge noise introduced by the nonlinear nature of hospital procedures, and the nonavailability of real-time clinical information in wards. Objective Our study investigates different models to forecast the total number of next-day discharges from an open ward having no real-time clinical data. Methods We compared 5 popular regression algorithms to model total next-day discharges: (1) autoregressive integrated moving average (ARIMA), (2) the autoregressive moving average with exogenous variables (ARMAX), (3) k-nearest neighbor regression, (4) random forest regression, and (5) support vector regression. Although the autoregressive integrated moving average model relied on past 3-month discharges, nearest neighbor forecasting used median of similar discharges in the past in estimating next-day discharge. In addition, the ARMAX model used the day of the week and number of patients currently in ward as exogenous variables. For the random forest and support vector regression models, we designed a predictor set of 20 patient features and 88 ward-level features. Results Our data consisted of 12,141 patient visits over 1826 days. Forecasting quality was measured using mean forecast error, mean absolute error, symmetric mean absolute percentage error, and root mean square error. When compared with a moving average prediction model, all 5 models demonstrated superior performance with the random forests achieving 22.7% improvement in mean absolute error, for all days in the year 2014. Conclusions In the

  3. Real time forecasts through physical and stochastic models of earthquake clustering

    NASA Astrophysics Data System (ADS)

    Murru, M.; Console, R.; Catalli, F.; Falcone, G.

    2005-12-01

    The phenomenon of earthquake interaction has become a popular subject of study because it can shed light on the physical processes leading to earthquakes, and because it has a potential value for short-term earthquake forecast and hazard mitigation. In this study we start from a purely stochastic approach known as the so-called epidemic model (ETAS) introduced by Ogata in 1988 and its variations. Then we build up an approach by which this model and the rate-and-state constitutive law introduced by Dieterich in the `90s have been merged in a single algorithm and statistically tested. Tests on real seismicity and comparison with a plain time-independent Poissonian model through likelihood-based methods have reliably proved their validity. The models are suitable for real-time forecast of the seismic activity. In the context of the low-magnitude Italian seismicity recorded from 1987 to 2005, the new model incorporating the physical concept of the rate-and-state theory performs not better than the purely stochastic model. Nevertheless, it has the advantage of needing a smaller number of free parameters and providing new interesting insights on the physics of the seismogenic process.

  4. A Multi-Model Real Time Forecasting Prototype System in the Mara Basin (Kenya/Tanzania)

    NASA Astrophysics Data System (ADS)

    Serrat-Capdevila, A.; Valdes, J. B.; Valdes, R.; Demaria, E. M.; Durcik, M.; Maitaria, K.; Roy, T.

    2013-12-01

    Remote sensing data and hydrologic models can respond to monitoring and forecasting needs in Africa and other poorly gauged regions. We present here the progress to date in developing a multi-model platform to provide hydrologic monitoring and forecasting using real time remote sensing observations. Satellite precipitation products such as CMORPH, TMPA (at 0.25° resolution) and PERSIANN-CCS (at 4km resolution) are used to force two models of different structure. One model is physically based and distributed, and the other is conceptual and lumped at the sub-basin level. The performance of both models is evaluated using different metrics, and the uncertainty in their predictions based on the errors incurred during the historical simulations period is computed. The models were compared and the potential increase in performance from using both models versus a single one will be assessed. This work provides insights into the advantages of a multi-model platform over a single model, with respect to different management and decision-making purposes. The methods were applied to the Mara Basin (Kenya/Tanzania), where growing human demands on water and land use are likely to alter significantly the hydrologic balance of the basin and the ecosystems that depend on it. These efforts are part of the Applied Sciences Team of the NASA SERVIR Program in collaboration with its East Africa Hub at the Regional Center for Mapping of Resources for Development (Nairobi,Kenya).

  5. The JPL GRIP Portal - Serving Near Real-time Observation and Model Forecast for Hurricane Study

    NASA Astrophysics Data System (ADS)

    Li, P.; Hristova-Veleva, S. M.; Turk, F. J.; Vu, Q.; Knosp, B. W.; Lambrigtsen, B.; Poulsen, W. L.; Shen, T. J.; Licata, S. J.

    2010-12-01

    NASA conducted a field experiment, the Genesis and Rapid Intensification Processes (GRIP), in the summer of 2010 to better understand how tropical storms form and develop into major hurricanes. The DC-8 aircraft and the Global Hawk Unmanned Airborne System (UAS) were deployed loaded with instruments for measurements including lightning, temperature, 3D wind, precipitation, liquid and ice water contents, aerosol and cloud profiles. JPL created a web portal to collect, process and display both the satellite and the airborne observations in near real-time (NRT) and integrated then with the hurricane forecast models. The objective of the JPL GRIP portal is to provide environmental context and temporal continuity for the field campaign observations to help: (1) mission planning, (2) understanding of the physical processes, and (3) improving models through validation and data assimilation. Built on top of the JPL Tropical Cyclone Information System (TCIS) infrastructure, we developed a GRIP portal presenting a near-real time (NRT) basin-scale view of the atmospheric and surface conditions over the Atlantic, characterizing large-scale and storm-scale processes, as depicted by satellites and models. Using Google Earth embedded in the web browser and two independent calendars, we provide 3D visualization of a comprehensive collection of observations and model results as overlapping image overlays, wind vectors, curtain plots, or clickable tracks. We also provide Google Earth time animations of multiple data and model variables. In the portal, we offer more than two dozen NRT satellite products from a wide variety of instruments, model forecasts from four large-scale models (i.e., NOGAPS, GFS, ECMWF, and UKMET), and the best tracks and the forecast tracks from National Hurricane Center’s ATCF models. As they become available, we also display the airborne observations from HAMSR, APR2 and Dropsonde. It is a great challenge to set up a reliable infrastructure to collect data

  6. Understanding uncertainty in distributed flash flood forecasting for semiarid regions 1909

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Semi-arid flash floods pose a significant danger for life and property in the US. One effective way to mitigate flood risk is by implementing a rainfall-runoff model in a real-time forecast and warning system. This study used a physically based, distributed semi-arid rainfall-runoff model driven by ...

  7. Global and Regional Real-time Systems for Flood and Drought Monitoring and Prediction

    NASA Astrophysics Data System (ADS)

    Hong, Y.; Gourley, J. J.; Xue, X.; Flamig, Z.

    2015-12-01

    A Hydrometeorological Extreme Mapping and Prediction System (HyXtreme-MaP), initially built upon the Coupled Routing and Excess STorage (CREST) distributed hydrological model, is driven by real-time quasi-global TRMM/GPM satellites and by the US Multi-Radar Multi-Sensor (MRMS) radar network with dual-polarimetric upgrade to simulate streamflow, actual ET, soil moisture and other hydrologic variables at 1/8th degree resolution quasi-globally (http://eos.ou.edu) and at 250-meter 2.5-mintue resolution over the Continental United States (CONUS: http://flash.ou.edu).­ Multifaceted and collaborative by-design, this end-to-end research framework aims to not only integrate data, models, and applications but also brings people together (i.e., NOAA, NASA, University researchers, and end-users). This presentation will review the progresses, challenges and opportunities of such HyXTREME-MaP System used to monitor global floods and droughts, and also to predict flash floods over the CONUS.

  8. Real-time tsunami inundation forecasting and damage mapping towards enhancing tsunami disaster resiliency

    NASA Astrophysics Data System (ADS)

    Koshimura, S.; Hino, R.; Ohta, Y.; Kobayashi, H.; Musa, A.; Murashima, Y.

    2014-12-01

    With use of modern computing power and advanced sensor networks, a project is underway to establish a new system of real-time tsunami inundation forecasting, damage estimation and mapping to enhance society's resilience in the aftermath of major tsunami disaster. The system consists of fusion of real-time crustal deformation monitoring/fault model estimation by Ohta et al. (2012), high-performance real-time tsunami propagation/inundation modeling with NEC's vector supercomputer SX-ACE, damage/loss estimation models (Koshimura et al., 2013), and geo-informatics. After a major (near field) earthquake is triggered, the first response of the system is to identify the tsunami source model by applying RAPiD Algorithm (Ohta et al., 2012) to observed RTK-GPS time series at GEONET sites in Japan. As performed in the data obtained during the 2011 Tohoku event, we assume less than 10 minutes as the acquisition time of the source model. Given the tsunami source, the system moves on to running tsunami propagation and inundation model which was optimized on the vector supercomputer SX-ACE to acquire the estimation of time series of tsunami at offshore/coastal tide gauges to determine tsunami travel and arrival time, extent of inundation zone, maximum flow depth distribution. The implemented tsunami numerical model is based on the non-linear shallow-water equations discretized by finite difference method. The merged bathymetry and topography grids are prepared with 10 m resolution to better estimate the tsunami inland penetration. Given the maximum flow depth distribution, the system performs GIS analysis to determine the numbers of exposed population and structures using census data, then estimates the numbers of potential death and damaged structures by applying tsunami fragility curve (Koshimura et al., 2013). Since the tsunami source model is determined, the model is supposed to complete the estimation within 10 minutes. The results are disseminated as mapping products to

  9. A Real-Time MODIS Vegetation Composite for Land Surface Models and Short-Term Forecasting

    NASA Technical Reports Server (NTRS)

    Case, Jonathan L.; LaFontaine, Frank J.; Kumar, Sujay V.; Jedlovec, Gary J.

    2011-01-01

    The NASA Short-term Prediction Research and Transition (SPoRT) Center is producing real-time, 1- km resolution Normalized Difference Vegetation Index (NDVI) gridded composites over a Continental U.S. domain. These composites are updated daily based on swath data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the polar orbiting NASA Aqua and Terra satellites, with a product time lag of about one day. A simple time-weighting algorithm is applied to the NDVI swath data that queries the previous 20 days of data to ensure a continuous grid of data populated at all pixels. The daily composites exhibited good continuity both spatially and temporally during June and July 2010. The composites also nicely depicted high greenness anomalies that resulted from significant rainfall over southwestern Texas, Mexico, and New Mexico during July due to early-season tropical cyclone activity. The SPoRT Center is in the process of computing greenness vegetation fraction (GVF) composites from the MODIS NDVI data at the same spatial and temporal resolution for use in the NASA Land Information System (LIS). The new daily GVF dataset would replace the monthly climatological GVF database (based on Advanced Very High Resolution Radiometer [AVHRR] observations from 1992-93) currently available to the Noah land surface model (LSM) in both LIS and the public version of the Weather Research and Forecasting (WRF) model. The much higher spatial resolution (1 km versus 0.15 degree) and daily updates based on real-time satellite observations have the capability to greatly improve the simulation of the surface energy budget in the Noah LSM within LIS and WRF. Once code is developed in LIS to incorporate the daily updated GVFs, the SPoRT Center will conduct simulation sensitivity experiments to quantify the impacts and improvements realized by the MODIS real-time GVF data. This presentation will describe the methodology used to develop the 1-km MODIS NDVI composites and

  10. Cloud-Based Numerical Weather Prediction for Near Real-Time Forecasting and Disaster Response

    NASA Technical Reports Server (NTRS)

    Molthan, Andrew; Case, Jonathan; Venners, Jason; Schroeder, Richard; Checchi, Milton; Zavodsky, Bradley; Limaye, Ashutosh; O'Brien, Raymond

    2015-01-01

    The use of cloud computing resources continues to grow within the public and private sector components of the weather enterprise as users become more familiar with cloud-computing concepts, and competition among service providers continues to reduce costs and other barriers to entry. Cloud resources can also provide capabilities similar to high-performance computing environments, supporting multi-node systems required for near real-time, regional weather predictions. Referred to as "Infrastructure as a Service", or IaaS, the use of cloud-based computing hardware in an on-demand payment system allows for rapid deployment of a modeling system in environments lacking access to a large, supercomputing infrastructure. Use of IaaS capabilities to support regional weather prediction may be of particular interest to developing countries that have not yet established large supercomputing resources, but would otherwise benefit from a regional weather forecasting capability. Recently, collaborators from NASA Marshall Space Flight Center and Ames Research Center have developed a scripted, on-demand capability for launching the NOAA/NWS Science and Training Resource Center (STRC) Environmental Modeling System (EMS), which includes pre-compiled binaries of the latest version of the Weather Research and Forecasting (WRF) model. The WRF-EMS provides scripting for downloading appropriate initial and boundary conditions from global models, along with higher-resolution vegetation, land surface, and sea surface temperature data sets provided by the NASA Short-term Prediction Research and Transition (SPoRT) Center. This presentation will provide an overview of the modeling system capabilities and benchmarks performed on the Amazon Elastic Compute Cloud (EC2) environment. In addition, the presentation will discuss future opportunities to deploy the system in support of weather prediction in developing countries supported by NASA's SERVIR Project, which provides capacity building

  11. Improvement in cloud predictions using satellite data assimilation for real-time forecasting

    NASA Astrophysics Data System (ADS)

    Vellore, R.; Koracin, D.; Wetzel, M.

    2006-12-01

    adiabatic lapse rate; (b) the second step is to compute the cloud top height using cloud base temperature, and the satellite- derived cloud top temperature following the wet adiabatic lapse rate in the cloud layer; (c) the third step is to obtain a representative lapse rate for the computing domain; (d) the fourth step is to compute the cloud top heights for the individual satellite pixels in the entire domain. The information on cloud top height and cloud top temperature obtained from the cloudy pixels is then dynamically assimilated into the model analysis using Cressman's objective analysis. Using the improved model analyses, a deterministic forecast will be carried out with an option of four-dimensional data assimilation of model winds and thermodynamic variables for a pre- forecast period of one complete diurnal cycle. Verification will be carried out using the hourly surface observations and cloud base measurements, and also using the satellite cloud imagery against the simulated cloud imagery and associated cloud products. The data assimilation of the derived cloud products is being tested in modeling systems such as the Mesoscale Model 5 (MM5) and the Weather Research Forecasting Model (WRF). The data assimilation of cloud products and verification is intended for the pre-processing module in a real-time forecasting system using various objective analysis procedures such as the Cressman-type, multi-quadric and 3DVAR. This study is to develop an efficient forecasting system to support naval aircraft and rotorcraft operations at the Fallon Naval Air Station, Fallon, Nevada.

  12. [Real-time irrigation forecast of cotton mulched with plastic film under drip irrigation based on meteorological date].

    PubMed

    Shen, Xiao-jun; Sun, Jing-sheng; Li, Ming-si; Zhang, Ji-yang; Wang, Jing-lei; Li, Dong-wei

    2015-02-01

    It is important to improve the real-time irrigation forecasting precision by predicting real-time water consumption of cotton mulched with plastic film under drip irrigation based on meteorological data and cotton growth status. The model parameters for calculating ET0 based on Hargreaves formula were determined using historical meteorological data from 1953 to 2008 in Shihezi reclamation area. According to the field experimental data of growing season in 2009-2010, the model of computing crop coefficient Kc was established based on accumulated temperature. On the basis of crop water requirement (ET0) and Kc, a real-time irrigation forecast model was finally constructed, and it was verified by the field experimental data in 2011. The results showed that the forecast model had high forecasting precision, and the average absolute values of relative error between the predicted value and measured value were about 3.7%, 2.4% and 1.6% during seedling, squaring and blossom-boll forming stages, respectively. The forecast model could be used to modify the predicted values in time according to the real-time meteorological data and to guide the water management in local film-mulched cotton field under drip irrigation.

  13. [Real-time irrigation forecast of cotton mulched with plastic film under drip irrigation based on meteorological date].

    PubMed

    Shen, Xiao-jun; Sun, Jing-sheng; Li, Ming-si; Zhang, Ji-yang; Wang, Jing-lei; Li, Dong-wei

    2015-02-01

    It is important to improve the real-time irrigation forecasting precision by predicting real-time water consumption of cotton mulched with plastic film under drip irrigation based on meteorological data and cotton growth status. The model parameters for calculating ET0 based on Hargreaves formula were determined using historical meteorological data from 1953 to 2008 in Shihezi reclamation area. According to the field experimental data of growing season in 2009-2010, the model of computing crop coefficient Kc was established based on accumulated temperature. On the basis of crop water requirement (ET0) and Kc, a real-time irrigation forecast model was finally constructed, and it was verified by the field experimental data in 2011. The results showed that the forecast model had high forecasting precision, and the average absolute values of relative error between the predicted value and measured value were about 3.7%, 2.4% and 1.6% during seedling, squaring and blossom-boll forming stages, respectively. The forecast model could be used to modify the predicted values in time according to the real-time meteorological data and to guide the water management in local film-mulched cotton field under drip irrigation. PMID:26094459

  14. Detiding DART® Buoy Data for Real-Time Extraction of Source Coefficients for Operational Tsunami Forecasting

    NASA Astrophysics Data System (ADS)

    Percival, Donald B.; Denbo, Donald W.; Eblé, Marie C.; Gica, Edison; Huang, Paul Y.; Mofjeld, Harold O.; Spillane, Michael C.; Titov, Vasily V.; Tolkova, Elena I.

    2015-06-01

    US Tsunami Warning Centers use real-time bottom pressure (BP) data transmitted from a network of buoys deployed in the Pacific and Atlantic Oceans to tune source coefficients of tsunami forecast models. For accurate coefficients and therefore forecasts, tides and background noise at the buoys must be accounted for through detiding. In this study, five methods for coefficient estimation are compared, each of which handles detiding differently. The first three subtract off a tidal prediction based on (1) a localized harmonic analysis involving 29 days of data immediately preceding the tsunami event, (2) 68 preexisting harmonic constituents specific to each buoy, and (3) an empirical orthogonal function fit to the previous 25 h of data. Method (4) is a Kalman smoother that uses method (1) as its input. These four methods estimate source coefficients after detiding. Method (5) estimates the coefficients simultaneously with a two-component harmonic model that accounts for the tides. The five methods are evaluated using archived data from 11 DART® buoys, to which selected artificial tsunami signals are superimposed. These buoys represent a full range of observed tidal conditions and background BP noise in the Pacific and Atlantic, and the artificial signals have a variety of patterns and induce varying signal-to-noise ratios. The root-mean-square errors (RMSEs) of least squares estimates of source coefficients using varying amounts of data are used to compare the five detiding methods. The RMSE varies over two orders of magnitude among detiding methods, generally decreasing in the order listed, with method (5) yielding the most accurate estimate of the source coefficient. The RMSE is substantially reduced by waiting for the first full wave of the tsunami signal to arrive. As a case study, the five methods are compared using data recorded from the devastating 2011 Japan tsunami.

  15. Application of hydrological models for flood forecasting and flood control in India and Bangladesh

    NASA Astrophysics Data System (ADS)

    Refsgaard, J. C.; Havnø, K.; Ammentorp, H. C.; Verwey, A.

    A general mathematical modelling system for real-time flood forecasting and flood control planning is described. The system comprises a lumped conceptual rainfall-runoff model, a hydrodynamic model for river routing, reservoir and flood plain simulation, an updating procedure for real-time operation and a comprehensive data management system. The system is presently applied for real-time forecasting of the two 20 000 km 2 (Yamuna and Damodar) catchments in India as well as for flood control modelling at the same two catchments in India. In another project the system is being established for the entire Bangladesh with a coarse discretization and for the South East Region of Bangladesh with a fine model discretization. The objectives of the modelling application in Bangladesh are to enable predictions of the effects of alternative river regulation structures in terms of changes in water levels, inundations, siltration and salinity. The modelling system has been transferred to the Central Water Commission of India and the Master Plan Organization of Bangladesh in connection with comprehensive training programmes. The models are presently being operated by Indian and Bangladeshi engineers in the two countries.

  16. Ensemble Data Assimilation with HSPF for Improved Real-Time Water Quality Forecasting

    NASA Astrophysics Data System (ADS)

    Kim, S.; Riazi, H.; rafieei nasab, A.; Shin, C.; Seo, D.

    2013-05-01

    An ensemble data assimilation (DA) procedure for the Hydrologic Simulation Program - Fortran (HSPF) model has been developed, tested and evaluated for implementation in real-time water quality forecasting. The procedure, referred to herein as MLEF-HSPF, uses maximum likelihood ensemble filter (MLEF) which combines strengths of variational assimilation (VAR) and ensemble Kalman filter (EnKF). To evaluate the procedure, MLEF-HSPF was run daily for a 2-yr period for the Kumho River Subbasin of the Nakdong River Basin in Korea. A set of performance measures was used to assess the marginal value of DA-aided predictions of stream flow and water quality variables such as water temperature, dissolved oxygen (DO), biochemical oxygen demand (BOD), ammonium (NH4), nitrate (NO3), phosphate (PO4) and chlorophyll a. Due to large dimensionality of the state vector and complexity of the biochemical processes involved, DA with HSPF poses additional challenges. In this presentation, we describe MLEF-HSPF, summarize the evaluation results and identify the challenges.

  17. A pan-African Flood Forecasting System

    NASA Astrophysics Data System (ADS)

    Thiemig, V.; Bisselink, B.; Pappenberger, F.; Thielen, J.

    2014-05-01

    The African Flood Forecasting System (AFFS) is a probabilistic flood forecast system for medium- to large-scale African river basins, with lead times of up to 15 days. The key components are the hydrological model LISFLOOD, the African GIS database, the meteorological ensemble predictions of the ECMWF and critical hydrological thresholds. In this paper the predictive capability is investigated in a hindcast mode, by reproducing hydrological predictions for the year 2003 where important floods were observed. Results were verified with ground measurements of 36 subcatchments as well as with reports of various flood archives. Results showed that AFFS detected around 70% of the reported flood events correctly. In particular, the system showed good performance in predicting riverine flood events of long duration (>1 week) and large affected areas (>10 000 km2) well in advance, whereas AFFS showed limitations for small-scale and short duration flood events. The case study for "Save flooding" illustrated the good performance of AFFS in forecasting timing and severity of the floods, gave an example of the clear and concise output products, and showed that the system is capable of producing flood warnings even in ungauged river basins. Hence, from a technical perspective, AFFS shows a large potential as an operational pan-African flood forecasting system, although issues related to the practical implication will still need to be investigated.

  18. Multiple indices method for real-time tsunami inundation forecast using a dense offshore observation network

    NASA Astrophysics Data System (ADS)

    Yamamoto, N.; Aoi, S.; Hirata, K.; Suzuki, W.; Kunugi, T.; Nakamura, H.

    2015-12-01

    We started to develop a new methodology for real-time tsunami inundation forecast system (Aoi et al., 2015, this meeting) using densely offshore tsunami observations of the Seafloor Observation Network for Earthquakes and Tsunamis (S-net), which is under construction along the Japan Trench (Kanazawa et al., 2012, JpGU; Uehira et al., 2015, IUGG). In our method, the most important concept is involving any type and/or form uncertainties in the tsunami forecast, which cannot be dealt with any of standard linear/nonlinear least square approaches. We first prepare a Tsunami Scenario Bank (TSB), which contains offshore tsunami waveforms at the S-net stations and tsunami inundation information calculated from any possible tsunami source. We then quickly select several acceptable tsunami scenarios that can explain offshore observations by using multiple indices and appropriate thresholds, after a tsunami occurrence. At that time, possible tsunami inundations coupled with selected scenarios are forecasted (Yamamoto et al., 2014, AGU). Currently, we define three indices: correlation coefficient and two variance reductions, whose L2-norm part is normalized either by observations or calculations (Suzuki et al., 2015, JpGU; Yamamoto et al., 2015, IUGG). In this study, we construct the TSB, which contains various tsunami source models prepared for the probabilistic tsunami hazard assessment in the Japan Trench region (Hirata et al., 2014, AGU). To evaluate the propriety of our method, we adopt the fault model based on the 2011 Tohoku earthquake as a pseudo "observation". We also calculate three indices using coastal maximum tsunami height distributions between observation and calculation. We then obtain the correlation between coastal and offshore indices. We notice that the index value of coastal maximum tsunami heights is closer to 1 than the index value of offshore waveforms, i.e., the coastal maximum tsunami height may be predictable within appropriate thresholds defined for

  19. Flood forecasting for Tucurui Hydroelectrical Plant, Brazil

    SciTech Connect

    Solomon, S.I.; Basso, E.; Osorio, C.; Melo de Moraes, H.; Serrano, A.

    1986-04-01

    The construction of the Tucurui Hydroelectric Plant on the Tocantins River basin in Brazil requires flood forecasting to ensure the safety of the cofferdam. The latter has been initially designed for a flood with a return frequency of one in 25 years. Lack of adequate forecasting facilities during the earlier stages of construction has resulted in significant damages and construction delays. Statistical forecasting models were developed by Projeto de Hidrologia y Climatologie da Amazonia (PHCA) for the purpose of preventing further damage at the site. The application of these models during the 1980 flood season, when the highest flood on record occurred at the Tucurui site, proved of great assistance in preventing the flooding of the cofferdam. In conjunction with the development of these models a number of data collection platforms using data transmission through the GOES system were installed to provide the data required for forecasting.

  20. Solar forecast and real-time monitoring needs of the Study of Energy Release in Flares (SERF)

    NASA Technical Reports Server (NTRS)

    Rust, D. M.

    1979-01-01

    Complementary, simultaneous observations of flares from as many observatories, both ground based and orbiting, as possible planned for the Solar Maximum Year are considered. The need for forecasts of solar activity on long term, one week, and two day intervals is described. Real time reporting is not needed, but daily summaries of activity and permanent records are important.

  1. Evaluation of Real-time Hurricane Forecasts Using the Advanced Hurricane WRF Model for the 2007 Atlantic Hurricane Season.

    NASA Astrophysics Data System (ADS)

    Done, J. M.

    2007-12-01

    Real-time forecasts have been conducted with the Advanced Hurricane WRF Model (AHW) for named storms of the 2007 Atlantic hurricane season. Taking advantage of increased computational power over previous years, 5- day forecasts are conducted daily using three domains; two nests of 4km and 1.3km grid-spacing track the vortex within a fixed parent domain of 12km grid-spacing. In this presentation, forecast accuracy in terms of track and intensity will be presented. The quality of the forecast storm intensity can vary dramatically between storms, and sometimes between successive forecasts of a given storm. This variability in model performance is explored by analyzing the statistics of the observed and model storm intensities for the 2007 hurricane season. Conditions under which the model performs poorly are identified and a series of sensitivity simulations highlight aspects of the modeling system to which the forecast intensity is most sensitive.

  2. Effects of Real-Time NASA Vegetation Data on Model Forecasts of Severe Weather

    NASA Technical Reports Server (NTRS)

    Case, Jonathan L.; Bell, Jordan R.; LaFontaine, Frank J.; Peters-Lidard, Christa D.

    2012-01-01

    The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Greenness Vegetation Fraction (GVF) dataset, which is updated daily using swaths of Normalized Difference Vegetation Index data from the Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA-EOS Aqua and Terra satellites. NASA SPoRT started generating daily real-time GVF composites at 1-km resolution over the Continental United States beginning 1 June 2010. A companion poster presentation (Bell et al.) primarily focuses on impact results in an offline configuration of the Noah land surface model (LSM) for the 2010 warm season, comparing the SPoRT/MODIS GVF dataset to the current operational monthly climatology GVF available within the National Centers for Environmental Prediction (NCEP) and Weather Research and Forecasting (WRF) models. This paper/presentation primarily focuses on individual case studies of severe weather events to determine the impacts and possible improvements by using the real-time, high-resolution SPoRT-MODIS GVFs in place of the coarser-resolution NCEP climatological GVFs in model simulations. The NASA-Unified WRF (NU-WRF) modeling system is employed to conduct the sensitivity simulations of individual events. The NU-WRF is an integrated modeling system based on the Advanced Research WRF dynamical core that is designed to represents aerosol, cloud, precipitation, and land processes at satellite-resolved scales in a coupled simulation environment. For this experiment, the coupling between the NASA Land Information System (LIS) and the WRF model is utilized to measure the impacts of the daily SPoRT/MODIS versus the monthly NCEP climatology GVFs. First, a spin-up run of the LIS is integrated for two years using the Noah LSM to ensure that the land surface fields reach an equilibrium state on the 4-km grid mesh used. Next, the spin-up LIS is run in two separate modes beginning on 1 June 2010, one continuing with the climatology GVFs while the

  3. A global flash flood forecasting system

    NASA Astrophysics Data System (ADS)

    Baugh, Calum; Pappenberger, Florian; Wetterhall, Fredrik; Hewson, Tim; Zsoter, Ervin

    2016-04-01

    The sudden and devastating nature of flash flood events means it is imperative to provide early warnings such as those derived from Numerical Weather Prediction (NWP) forecasts. Currently such systems exist on basin, national and continental scales in Europe, North America and Australia but rely on high resolution NWP forecasts or rainfall-radar nowcasting, neither of which have global coverage. To produce global flash flood forecasts this work investigates the possibility of using forecasts from a global NWP system. In particular we: (i) discuss how global NWP can be used for flash flood forecasting and discuss strengths and weaknesses; (ii) demonstrate how a robust evaluation can be performed given the rarity of the event; (iii) highlight the challenges and opportunities in communicating flash flood uncertainty to decision makers; and (iv) explore future developments which would significantly improve global flash flood forecasting. The proposed forecast system uses ensemble surface runoff forecasts from the ECMWF H-TESSEL land surface scheme. A flash flood index is generated using the ERIC (Enhanced Runoff Index based on Climatology) methodology [Raynaud et al., 2014]. This global methodology is applied to a series of flash floods across southern Europe. Results from the system are compared against warnings produced using the higher resolution COSMO-LEPS limited area model. The global system is evaluated by comparing forecasted warning locations against a flash flood database of media reports created in partnership with floodlist.com. To deal with the lack of objectivity in media reports we carefully assess the suitability of different skill scores and apply spatial uncertainty thresholds to the observations. To communicate the uncertainties of the flash flood system output we experiment with a dynamic region-growing algorithm. This automatically clusters regions of similar return period exceedence probabilities, thus presenting the at-risk areas at a spatial

  4. Development of Real-time Tsunami Inundation Forecast Using Ocean Bottom Tsunami Networks along the Japan Trench

    NASA Astrophysics Data System (ADS)

    Aoi, S.; Yamamoto, N.; Suzuki, W.; Hirata, K.; Nakamura, H.; Kunugi, T.; Kubo, T.; Maeda, T.

    2015-12-01

    In the 2011 Tohoku earthquake, in which huge tsunami claimed a great deal of lives, the initial tsunami forecast based on hypocenter information estimated using seismic data on land were greatly underestimated. From this lesson, NIED is now constructing S-net (Seafloor Observation Network for Earthquakes and Tsunamis along the Japan Trench) which consists of 150 ocean bottom observatories with seismometers and pressure gauges (tsunamimeters) linked by fiber optic cables. To take full advantage of S-net, we develop a new methodology of real-time tsunami inundation forecast using ocean bottom observation data and construct a prototype system that implements the developed forecasting method for the Pacific coast of Chiba prefecture (Sotobo area). We employ a database-based approach because inundation is a strongly non-linear phenomenon and its calculation costs are rather heavy. We prepare tsunami scenario bank in advance, by constructing the possible tsunami sources, and calculating the tsunami waveforms at S-net stations, coastal tsunami heights and tsunami inundation on land. To calculate the inundation for target Sotobo area, we construct the 10-m-mesh precise elevation model with coastal structures. Based on the sensitivities analyses, we construct the tsunami scenario bank that efficiently covers possible tsunami scenarios affecting the Sotobo area. A real-time forecast is carried out by selecting several possible scenarios which can well explain real-time tsunami data observed at S-net from tsunami scenario bank. An advantage of our method is that tsunami inundations are estimated directly from the actual tsunami data without any source information, which may have large estimation errors. In addition to the forecast system, we develop Web services, APIs, and smartphone applications and brush them up through social experiments to provide the real-time tsunami observation and forecast information in easy way to understand toward urging people to evacuate.

  5. Forecasting Extreme Flooding in South Asia (Invited)

    NASA Astrophysics Data System (ADS)

    Webster, P. J.

    2010-12-01

    In most years there is extensive flooding across India, Pakistan and Bangladesh. On average, 40 million people are displaced by floods in India and half that many again in Bangladesh. Occasionally, even more extensive and severe flooding occurs across South Asia. In 2007 and 2008 the Brahmaputra flooded three times causing severe disruption of commerce, agriculture and life in general. Systems set up by an international collaboration predicted these Bangladesh floods with an operational system at the 10 and 15-day horizon. These forecasts determined the risk of flooding and allowed the Bangladeshis in peril to prepare, harvesting crops and storing of household and agricultural assets. Savings in increments of annual income resulted form the forecasts. In July and August 2010, severe flooding occurred in Pakistan causing horrendous damage and loss of life. But these floods were also predictable at the 10-day time scale if the same forecasting system developed for Bangladesh had been implemented. Similar systems could be implemented in India but would require local cooperation. We describe the manner in which quantified probabilistic precipitation forecasts, coupled with hydrological models can provide useful and timely extended warnings of flooding.

  6. On the integration of HydroProg and FloodMap: towards real-time inundation predictions in the upper Nysa Klodzka basin (SW Poland)

    NASA Astrophysics Data System (ADS)

    Yu, Dapeng; Mizinski, Bartlomiej; Latocha, Agnieszka; Parzoch, Krzysztof; Niedzielski, Tomasz

    2015-04-01

    The paper summarizes attempts to link the HydroProg system for issuing the real-time warnings against hydrologic hazards (research project no. 2011/01/D/ST10/04171 of the National Science Centre of Poland) with the hydrodynamic FloodMap model. HydroProg itself integrates hydrometeorological gauging networks with dissimilar hydrologic models in order to deliver multiple river stage prognoses and their multimodel ensemble prediction. FloodMap uses HydroProg-delivered forecasts and, along with data on topography and bed profiles, produces short-term (3-hour) prognoses of inundation. The research is carried out in five test sites located in the upper Nysa Klodzka basin (SW Poland), where the HydroProg-Klodzko prototype is experimentally implemented, and is steadily providing the users with experimental prognoses of river stages (www.klodzko.hydroprog.uni.wroc.pl). The successful implementation of HydroProg in Klodzko County is due to the partnership with its authorities who developed and maintain the Local System for Flood Monitoring (Lokalny System Oslony Przeciwpowodziowej - LSOP). For the purpose of HydroProg-FloodMap integration we selected: (1) three hydrograph prediction approaches offered by HydroProg-Klodzko, (2) five specific peak flow events, and (3) five test sites along four mountainous rivers of the study area, focusing on 3-hour hydrograph predictions. The calibration of the FloodMap model is based on an overbank flow reconstruction, produced as a result of mapping geomorphological consequences of the flood that occurred in the Zelazno site on 26-28 June 2009. The discussion as to whether the calibrated model can be extrapolated and used in the remaining test sites is also provided. By utilizing FloodMap with observed water depth data we produce simulated inundation, which we verify against the orthophoto images acquired by the Unmanned Aerial Vehicle (UAV). Subsequently, we again run the FloodMap model with the HydroProg-delivered prognoses of

  7. Verification of a probabilistic flood forecasting system for an Alpine Region of northern Italy

    NASA Astrophysics Data System (ADS)

    Laiolo, P.; Gabellani, S.; Rebora, N.; Rudari, R.; Ferraris, L.; Ratto, S.; Stevenin, H.

    2012-04-01

    Probabilistic hydrometeorological forecasting chains are increasingly becoming an operational tool used by civil protection centres for issuing flood alerts. One of the most important requests of decision makers is to have reliable systems, for this reason an accurate verification of their predictive performances become essential. The aim of this work is to validate a probabilistic flood forecasting system: Flood-PROOFS. The system works in real time, since 2008, in an alpine Region of northern Italy, Valle d'Aosta. It is used by the Civil Protection regional service to issue warnings and by the local water company to protect its facilities. Flood-PROOFS uses as input Quantitative Precipitation Forecast (QPF) derived from the Italian limited area model meteorological forecast (COSMO-I7) and forecasts issued by regional expert meteorologists. Furthermore the system manages and uses both real time meteorological and satellite data and real time data on the maneuvers performed by the water company on dams and river devices. The main outputs produced by the computational chain are deterministic and probabilistic discharge forecasts in different cross sections of the considered river network. The validation of the flood prediction system has been conducted on a 25 months period considering different statistical methods such as Brier score, Rank histograms and verification scores. The results highlight good performances of the system as support system for emitting warnings but there is a lack of statistics especially for huge discharge events.

  8. Numerical modelling for real-time forecasting of marine oil pollution and hazard assessment

    NASA Astrophysics Data System (ADS)

    De Dominicis, Michela; Pinardi, Nadia; Bruciaferri, Diego; Liubartseva, Svitlana

    2015-04-01

    (MEDESS4MS) system, which is an integrated operational multi-model oil spill prediction service, that can be used by different users to run simulations of oil spills at sea, even in real time, through a web portal. The MEDESS4MS system gathers different oil spill modelling systems and data from meteorological and ocean forecasting systems, as well as operational information on response equipment, together with environmental and socio-economic sensitivity maps. MEDSLIK-II has been also used to provide an assessment of hazard stemming from operational oil ship discharges in the Southern Adriatic and Northern Ionian (SANI) Seas. Operational pollution resulting from ships consists of a movable hazard with a magnitude that changes dynamically as a result of a number of external parameters varying in space and time (temperature, wind, sea currents). Simulations of oil releases have been performed with realistic oceanographic currents and the results show that the oil pollution hazard distribution has an inherent spatial and temporal variability related to the specific flow field variability.

  9. Real-Time Ocean Forecasting System in support of U.S. Coast Guard's Search and Rescue Operations

    NASA Astrophysics Data System (ADS)

    Schoch, C.; Chao, Y.; Howlett, E.; Allen, A. A.

    2012-12-01

    This talk will describe a real-time ocean forecasting system off the U.S. west coast developed to enhance U.S. Coast Guard (USCG) decision support tools for search and rescue operations. The forecasting model is based on the Regional Ocean Modeling System (ROMS) with multi-domain nested configurations. A multi-scale 3-dimensional variational (3DVAR) data assimilation scheme is used to assimilate both in situ (e.g., gliders) and remotely sensed data from both satellite and land-based platforms (e.g., high-frequency (HF) radars). The performance of this real-time ocean forecasting system was evaluated during a two-week field experiment during July-August 2009 in Prince William Sound, Alaska. The 72-hour ocean forecast fields in Alaska's Prince William Sound and California coastal ocean are now produced in real-time and accessible by the USCG's decision support tool during search and rescue operations. Recent test results using the independent data collected by the USCG will be discussed.

  10. Operational Precipitation prediction in Support of Real-Time Flash Flood Prediction and Reservoir Management

    NASA Astrophysics Data System (ADS)

    Georgakakos, K. P.

    2006-05-01

    The presentation will outline the implementation and performance evaluation of a number of national and international projects pertaining to operational precipitation estimation and prediction in the context of hydrologic warning systems and reservoir management support. In all cases, uncertainty measures of the estimates and predictions are an integral part of the precipitation models. Outstanding research issues whose resolution is likely to lead to improvements in the operational environment are presented. The presentation draws from the experience of the Hydrologic Research Center (http://www.hrc-lab.org) prototype implementation projects at the Panama Canal, Central America, Northern California, and South-Central US. References: Carpenter, T.M, and K.P. Georgakakos, "Discretization Scale Dependencies of the Ensemble Flow Range versus Catchment Area Relationship in Distributed Hydrologic Modeling," Journal of Hydrology, 2006, in press. Carpenter, T.M., and K.P. Georgakakos, "Impacts of Parametric and Radar Rainfall Uncertainty on the Ensemble Streamflow Simulations of a Distributed Hydrologic Model," Journal of Hydrology, 298, 202-221, 2004. Georgakakos, K.P., Graham, N.E., Carpenter, T.M., Georgakakos, A.P., and H. Yao, "Integrating Climate- Hydrology Forecasts and Multi-Objective Reservoir Management in Northern California," EOS, 86(12), 122,127, 2005. Georgakakos, K.P., and J.A. Sperfslage, "Operational Rainfall and Flow Forecasting for the Panama Canal Watershed," in The Rio Chagres: A Multidisciplinary Profile of a Tropical Watershed, R.S. Harmon, ed., Kluwer Academic Publishers, The Netherlands, Chapter 16, 323-334, 2005. Georgakakos, K. P., "Analytical results for operational flash flood guidance," Journal of Hydrology, doi:10.1016/j.jhydrol.2005.05.009, 2005.

  11. Optimized Flood Forecasts Using a Statistical Enemble

    NASA Astrophysics Data System (ADS)

    Silver, Micha; Fredj, Erick

    2016-04-01

    The method presented here assembles an optimized flood forecast from a set of consecutive WRF-Hydro simulations by applying coefficients which we derive from straightforward statistical procedures. Several government and research institutions that produce climate data offer ensemble forecasts, which merge predictions from different models to gain a more accurate fit to observed data. Existing ensemble forecasts present climate and weather predictions only. In this research we propose a novel approach to constructing hydrological ensembles for flood forecasting. The ensemble flood forecast is created by combining predictions from the same model, but initiated at different times. An operative flood forecasting system, run by the Israeli Hydrological Service, produces flood forecasts twice daily with a 72 hour forecast period. By collating the output from consecutive simulation runs we have access to multiple overlapping forecasts. We then apply two statistical procedures to blend these consecutive forecasts, resulting in a very close fit to observed flood runoff. We first employ cross-correlation with a time lag to determine a time shift for each of the original, consecutive forecasts. This shift corrects for two possible sources of error: slow or fast moving weather fronts in the base climate data; and mis-calibrations of the WRF-Hydro model in determining the rate of flow of surface runoff and in channels. We apply this time shift to all consecutive forecasts, then run a linear regression with the observed runoff data as the dependent variable and all shifted forecasts as the predictor variables. The solution to the linear regression equation is a set of coefficients that corrects the amplitude errors in the forecasts. These resulting regression coefficients are then applied to the consecutive forecasts producing a statistical ensemble which, by design, closely matches the observed runoff. After performing this procedure over many storm events in the Negev region

  12. Ensemble flood forecasting on the Tocantins River - Brazil

    NASA Astrophysics Data System (ADS)

    Fan, Fernando; Collischonn, Walter; Jiménez, Karena; Sorribas, Mino; Buarque, Diogo; Siqueira, Vinicius

    2014-05-01

    The Tocantins River basin is located in the northern region of Brazil and has about 300.000 km2 of drainage area upstream of its confluence with river Araguaia, its major tributary. The Tocantins River is intensely used for hydropower production, with seven major dams, including Tucuruí, world's fourth largest in terms of installed capacity. In this context, the use of hydrological streamflow forecasts at this basin is very useful to support the decision making process for reservoir operation, and can produce benefits by reducing damages from floods, increasing dam safety and upgrading efficiency in power generation. The occurrence of floods along the Tocantins River is a relatively frequent event, where one recent example is the year of 2012, when a large flood occurred in the Tocantins River with discharge peaks exceeding 16.000m³/s, and causing damages to cities located along the river. After this flooding event, a hydrological forecasting system was developed and is operationally in use since mid-2012 in order to assist the decision making of dam operation along the river basin. The forecasting system is based on the MGB-IPH model, a large scale distributed hydrological model, and initially used only telemetric data as observed information and deterministic rainfall forecasts from the Brazilian Meteorological Forecasting Centre (CPTEC) with 7-days lead time as input. Since August-2013 the system has been updated and now works with two new features: (i) a technique for merging satellite TRMM real-time precipitation estimative with gauged information is applied to reduce the uncertainty due to the lack of observed information over a portion of the basin, since the total number of rain gages available is scarce compared to the total basin area; (ii) rainfall ensemble forecasts with 16-days lead time provided by the Global Ensemble Forecasting System (GEFs), from the 2nd Generation of NOAA Global Ensemble Reforecast Data Set, maintained by the National Center for

  13. Integrated Flood Forecast and Virtual Dam Operation System for Water Resources and Flood Risk Management

    NASA Astrophysics Data System (ADS)

    Shibuo, Yoshihiro; Ikoma, Eiji; Lawford, Peter; Oyanagi, Misa; Kanauchi, Shizu; Koudelova, Petra; Kitsuregawa, Masaru; Koike, Toshio

    2014-05-01

    While availability of hydrological- and hydrometeorological data shows growing tendency and advanced modeling techniques are emerging, such newly available data and advanced models may not always be applied in the field of decision-making. In this study we present an integrated system of ensemble streamflow forecast (ESP) and virtual dam simulator, which is designed to support river and dam manager's decision making. The system consists of three main functions: real time hydrological model, ESP model, and dam simulator model. In the real time model, the system simulates current condition of river basins, such as soil moisture and river discharges, using LSM coupled distributed hydrological model. The ESP model takes initial condition from the real time model's output and generates ESP, based on numerical weather prediction. The dam simulator model provides virtual dam operation and users can experience impact of dam control on remaining reservoir volume and downstream flood under the anticipated flood forecast. Thus the river and dam managers shall be able to evaluate benefit of priori dam release and flood risk reduction at the same time, on real time basis. Furthermore the system has been developed under the concept of data and models integration, and it is coupled with Data Integration and Analysis System (DIAS) - a Japanese national project for integrating and analyzing massive amount of observational and model data. Therefore it has advantage in direct use of miscellaneous data from point/radar-derived observation, numerical weather prediction output, to satellite imagery stored in data archive. Output of the system is accessible over the web interface, making information available with relative ease, e.g. from ordinary PC to mobile devices. We have been applying the system to the Upper Tone region, located northwest from Tokyo metropolitan area, and we show application example of the system in recent flood events caused by typhoons.

  14. Decision making based on global flood forecasts and satellite-derived inundation maps in data-sparse regions

    NASA Astrophysics Data System (ADS)

    Revilla-Romero, Beatriz; Hirpa, Feyera A.; Thielen-del Pozo, Jutta; Salamon, Peter; Brakenridge, G. Robert; Pappenberger, Florian; De Groeve, Tom

    2016-04-01

    Early flood warning and real-time monitoring systems play a key role in flood risk reduction and disaster response decisions. Global-scale flood forecasting and satellite-based flood detection systems are currently operating, however their reliability for decision making applications needs to be assessed. In this study, we performed comparative evaluations of several operational global flood forecasting and flood detection systems, using major flood events recorded over 2012-2014. Specifically, we evaluated the spatial extent and temporal characteristics of flood detections from the Global Flood Detection System (GFDS) and the Global Flood Awareness System (GloFAS). Furthermore, we compared the GFDS flood maps with those from NASA's two Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results reveal that: 1) general agreement was found between the GFDS and MODIS flood detection systems, 2) large differences exist in the spatio-temporal characteristics of the GFDS detections and GloFAS forecasts, and 3) the quantitative validation of global flood disasters in data-sparse regions is highly challenging. Overall, the satellite remote sensing provides useful near real-time flood information that can be useful for risk management. We highlight the known limitations of global flood detection and forecasting systems, and propose ways forward to improve the reliability of large scale flood monitoring tools.

  15. Real-time global flood estimation using satellite-based precipitation and a coupled land surface and routing model

    SciTech Connect

    Wu, Huan; Adler, Robert F.; Tian, Yudong; Huffman, George J.; Li, Hongyi; Wang, JianJian

    2014-03-01

    A widely used land surface model, the Variable Infiltration Capacity (VIC) model, is coupled with a newly developed hierarchical dominant river tracing-based runoff-routing model to form the Dominant river tracing-Routing Integrated with VIC Environment (DRIVE) model, which serves as the new core of the real-time Global Flood Monitoring System (GFMS). The GFMS uses real-time satellite-based precipitation to derive flood monitoring parameters for the latitude band 50°N–50°S at relatively high spatial (~12 km) and temporal (3 hourly) resolution. Examples of model results for recent flood events are computed using the real-time GFMS (http://flood.umd.edu). To evaluate the accuracy of the new GFMS, the DRIVE model is run retrospectively for 15 years using both research-quality and real-time satellite precipitation products. Evaluation results are slightly better for the research-quality input and significantly better for longer duration events (3 day events versus 1 day events). Basins with fewer dams tend to provide lower false alarm ratios. For events longer than three days in areas with few dams, the probability of detection is ~0.9 and the false alarm ratio is ~0.6. In general, these statistical results are better than those of the previous system. Streamflow was evaluated at 1121 river gauges across the quasi-global domain. Validation using real-time precipitation across the tropics (30°S–30°N) gives positive daily Nash-Sutcliffe Coefficients for 107 out of 375 (28%) stations with a mean of 0.19 and 51% of the same gauges at monthly scale with a mean of 0.33. Finally, there were poorer results in higher latitudes, probably due to larger errors in the satellite precipitation input.

  16. Real-Time Global Flood Estimation Using Satellite-Based Precipitation and a Coupled Land Surface and Routing Model

    NASA Technical Reports Server (NTRS)

    Wu, Huan; Adler, Robert F.; Tian, Yudong; Huffman, George J.; Li, Hongyi; Wang, JianJian

    2014-01-01

    A widely used land surface model, the Variable Infiltration Capacity (VIC) model, is coupled with a newly developed hierarchical dominant river tracing-based runoff-routing model to form the Dominant river tracing-Routing Integrated with VIC Environment (DRIVE) model, which serves as the new core of the real-time Global Flood Monitoring System (GFMS). The GFMS uses real-time satellite-based precipitation to derive flood monitoring parameters for the latitude band 50 deg. N - 50 deg. S at relatively high spatial (approximately 12 km) and temporal (3 hourly) resolution. Examples of model results for recent flood events are computed using the real-time GFMS (http://flood.umd.edu). To evaluate the accuracy of the new GFMS, the DRIVE model is run retrospectively for 15 years using both research-quality and real-time satellite precipitation products. Evaluation results are slightly better for the research-quality input and significantly better for longer duration events (3 day events versus 1 day events). Basins with fewer dams tend to provide lower false alarm ratios. For events longer than three days in areas with few dams, the probability of detection is approximately 0.9 and the false alarm ratio is approximately 0.6. In general, these statistical results are better than those of the previous system. Streamflow was evaluated at 1121 river gauges across the quasi-global domain. Validation using real-time precipitation across the tropics (30 deg. S - 30 deg. N) gives positive daily Nash-Sutcliffe Coefficients for 107 out of 375 (28%) stations with a mean of 0.19 and 51% of the same gauges at monthly scale with a mean of 0.33. There were poorer results in higher latitudes, probably due to larger errors in the satellite precipitation input.

  17. An Analytical Framework for Flood Water Conservation Considering Forecast Uncertainty and Acceptable Risk

    NASA Astrophysics Data System (ADS)

    Ding, W.; Zhang, C.

    2015-12-01

    Reservoir water levels are usually not allowed to exceed the flood limited water level (FLWL) during flood season, which neglects the meteorological and real-time forecast information and leads to the great waste of water resources. With the development of weather forecasting, hydrologic modeling, and hydro-climatic teleconnection, the streamflow forecast precision have improved a lot, which provides the technical support for the flood water utilization. This paper addresses how much flood water can be conserved for use after the flood season through the operation of reservoir based on uncertain forecast information by taking into account the residual flood control capacity (the difference between flood conveyance capacity and the expected inflow in a lead time). A two-stage model for dynamic control of the flood limited water level (the maximum allowed water level during the flood season, DC-FLWL) is established considering forecast uncertainty and acceptable flood risk. It is found that DC-FLWL is applicable when the reservoir inflow ranges from small to medium levels of the historical records, while both forecast uncertainty and acceptable risk in the downstream affect the feasible space of DC-FLWL. As forecast uncertainty increases (under a given risk level) or as acceptable risk level decreases (under a given forecast uncertainty level), the minimum required safety margin for flood control increases, and the chance for DC-FLWL decreases. The derived hedging rules from the modeling framework illustrate either the dominant role of water conservation or flood control or the tradeoff between the two objectives under different levels of forecast uncertainty and acceptable risk. These rules may provide useful guidelines for conserving water from flood, especially in the area with heavy water stress.

  18. Developing Real-Time Emissions Estimates for Enhanced Air Quality Forecasting

    EPA Science Inventory

    Exploring the relationship between ambient temperature, energy demand, and electric generating unit point source emissions and potential techniques for incorporating real-time information on the modulating effects of these variables using the Mid-Atlantic/Northeast Visibility Uni...

  19. Flood Forecasting in River System Using ANFIS

    SciTech Connect

    Ullah, Nazrin; Choudhury, P.

    2010-10-26

    The aim of the present study is to investigate applicability of artificial intelligence techniques such as ANFIS (Adaptive Neuro-Fuzzy Inference System) in forecasting flood flow in a river system. The proposed technique combines the learning ability of neural network with the transparent linguistic representation of fuzzy system. The technique is applied to forecast discharge at a downstream station using flow information at various upstream stations. A total of three years data has been selected for the implementation of this model. ANFIS models with various input structures and membership functions are constructed, trained and tested to evaluate efficiency of the models. Statistical indices such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and Coefficient of Efficiency (CE) are used to evaluate performance of the ANFIS models in forecasting river flood. The values of the indices show that ANFIS model can accurately and reliably be used to forecast flood in a river system.

  20. A Real-Time Eulerian Photochemical Model Forecast System: Overview and Initial Ozone Forecast Performance in the Northeast U.S. Corridor.

    NASA Astrophysics Data System (ADS)

    McHenry, John N.; Ryan, William F.; Seaman, Nelson L.; Coats, Carlie J., Jr.; Pudykiewicz, Janusz; Arunachalam, Sarav; Vukovich, Jeffery M.

    2004-04-01

    This article reports on the first implementation of a real-time Eulerian photochemical model forecast system in the United States. The forecast system consists of a tripartite set of one-way coupled models that run routinely on a parallel microprocessor supercomputer. The component models are the fifth-generation Pennsylvania State University (PSU) NCAR Mesoscale Model (MM5), the Sparse-Matrix Operator Kernel for Emissions (SMOKE) model, and the Multiscale Air Quality Simulation Platform—Real Time (MAQSIP-RT) photochemical model. Though the system has been run in real time since the summer of 1998, forecast results obtained during August of 2001 at 15-km grid spacing over New England and the northern mid-Atlantic—conducted as part of an “early start” NOAA air quality forecasting initiative—are described in this article.The development and deployment of a real-time numerical air quality prediction (NAQP) system is technically challenging. MAQSIP-RT contains a full pho-tochemical oxidant gas-phase chemical mechanism together with transport, dry deposition, and sophisticated cloud treatment. To enable the NAQP system to run fast enough to meet operational forecast deadlines, significant work was devoted to data flow design and software engineering of the models and control codes. The result is a turnkey system now in use by a number of agencies concerned with operational ozone forecasting.Results of the chosen episode are compared against three other models/modeling techniques: a traditional statistical model used routinely in the metropolitan Philadelphia, Pennsylvania, area, a set of publicly issued forecasts in the northeastern United States, and the operational Canadian Hemispheric and Regional Ozone and NOx System (CHRONOS) model. For the test period it is shown that the NAQP system performs as well or better than all of these operational approaches. Implications for the impending development of an operational U.S. ozone forecasting capability are

  1. A Satellite Driven Real-time Forecasting Platform in the Upper Zambezi Basin: A Multi-model Comparison

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

    In large basins such as the Upper Zambezi where concentration times are of many days or even weeks, satellite precipitation products available in real-time become a key component enabling - with the use of hydrologic models - streamflow forecasts for downstream locations with enough lead time to inform decision-making. We present a real-time streamflow forecasting application based on this concept, using the TMPA and CMORPH rainfall products (which we bias-correct using the CHIRPS product) to force four distributed hydrologic models (VIC, HyMod, HBV, Sacramento) covering a variety of levels of model complexity. This study aims at establishing a multi-model satellite-based streamflow forecasting platform as a tool that can inform water management in real-time. This work is part of the efforts of the SERVIR Applied Sciences Team to bring NASA Earth Observation Applications into decision support tools for managing water resources in the Upper Zambezi, in collaboration with the Southern African Development Community Climate Services Center and the Zambezi Watercourse Commission.

  2. Recent development for near-field tsunami forecasting based on real time GNSS and offshore tsunami data

    NASA Astrophysics Data System (ADS)

    Ohta, Y.; Tsushima, H.; Kawamoto, S.; Miyagawa, K.; Yahagi, T.; Sato, Y.; Hino, R.; Demachi, T.; Iinuma, T.; Miura, S.

    2014-12-01

    The 2011 Tohoku earthquake and its associated tsunami clearly showed the need for an accurate tsunami early warning system. In a short time between the occurrence of earthquakes and associating tsunamis and the tsunami arrivals to near-field coastal inhabited regions, we can use many different kinds of observations for real-time tsunami forecasting. Since individual type of the observations has its advantages and disadvantages, it is strongly required to make use of multiple kinds of data for improving estimated size and arrival timing of imminent tsunamis by reinforcing one another. For example, the rapid analysis of short-period seismic wave data, such as earthquake early warning system in Japan will provide the first information on the size and location of an earthquake, helping issuing tsunami information immediately after earthquakes. Real-time GNSS data have an advantage over the short-time seismograms because robust estimations of location and dimension of coseismic faults can be derived from spatial patterns of permanent coseismic displacement measured by real-time GNSS data. It is one of the important lessons learnt from the 2011 Tohoku earthquake that estimation of reliable finite source fault models is indispensable in tsunami forecasting after massive earthquakes. Offshore measurements of coming tsunamis must be data most relevant to the arrival times and sizes of tsunamis along shorelines. However, it takes more time to obtain credible spatial distribution of tsunami wave height from the observations due to much slower propagation of tsunamis than seismic waves and deformations. In the presentation, we will introduce the current status of the real-time crustal deformation monitoring system based on the GNSS data developed by Geospatial Information Authority of Japan and Tohoku University. We also briefly introduce the real-time tsunami forecasting based on the offshore tsunami data, developed by the Meteorological Research Institute of Japan

  3. Real-Time Foreshock Probability Forecasting Experiments in Japan, Southern California and Whole Globe

    NASA Astrophysics Data System (ADS)

    Ogata, Y.

    2014-12-01

    I am concerned with whether currently occurring earthquakes will be "foreshocks" of a significantly larger earthquake or not. When plural earthquakes occur in a region, I attempt to statistically discriminate foreshocks from a swarm or the mainshock-aftershock sequence. The forecast needs identification of an earthquake cluster using the single-link algorithm; and then the probability is calculated based on the clustering strength and magnitude correlations. The probability forecast model were estimated from the JMA hypocenter data of earthquakes of M≧4 in the period 1926-1993 (Ogata et al., 1996). Then we presented the performance and validation of the forecasts during 1994 - 2010 by using the same model (Ogata and Katsura, 2012). The forecasts perform significantly better than the unconditional (average) foreshock probability throughout Japan region. The frequency of the actual foreshocks is consistent with the forecasted probabilities. In my poster, I would like to discuss details of the outcomes in the forecasting and evaluations. Furthermore, I would like to apply the forecasting in California and global catalogs to show some universality in the forecasting procedure. Reference: [1] Ogata, Y., Utsu, T. and Katsura, K. (1996). Statistical discrimination of foreshocks from other earthquake clusters, Geophys. J. Int. 127, 17-30. [2]Ogata, Y. and Katsura, K. (2012). Prospective foreshock forecast experiment during the last 17 years, Geophys. J. Int., 191, 1237-1244.

  4. A Real-time, Coupled, Refined Forecasting System for Coastal Prediction

    NASA Astrophysics Data System (ADS)

    Armstrong, B. N.; Warner, J. C.; Signell, R. P.

    2010-12-01

    In the coastal zone, storms are one of the primary environmental forces causing coastal change. These discrete events often produce large waves, storm surges, and flooding, resulting in coastal erosion. In addition, strong storm-generated currents may pose threats to life, property, and navigation. The ability to predict these events, their location, duration, and magnitude allows resource managers to better prepare for the storm impacts as well as guide post-storm survey assessments and recovery efforts. As a step towards increasing our capability for prediction of these events and to help us study the physical processes that occur we have developed an automated system to run components of the Coupled Ocean - Atmosphere - Wave - Sediment Transport (COAWST) Modeling System as a daily forecast. The current daily system couples Regional Ocean Model System (ROMS) and Simulation Waves Nearshore (SWAN) models to predict currents, salinity, temperature, wave height and direction, and sediment transport for the US East Coast and Gulf of Mexico on a 5 km scale. As part of the system a refined grid for the area of Cape Hatteras, NC at a resolution of 1 km is included. Management of the system is controlled by the Windows Scheduler to start Matlab® and run scripts and functions. Data required by the modeling system include daily modeled wave, wind, atmospheric surface inputs, and climatology fields. The Unidata Internet Data Distribution/Local Data Manager (http://www.unidata.ucar.edu/software/ldm/) is used to download National Centers for Environmental Prediction (NCEP) GFS global 5 degree data and NCEP NAM Conus 12km data to a local server. The Matlab “structs” tool and NJ-Toolbox (http://njtbx.sourceforge.net/njdocs/njtbxhelp/njtbxhelp.html) are used to access these large data sets on the local server as well as Wave Watch 3 (WW3) and NCEP model data sets available remotely on the Nomads http://nomads.ncep.noaa.gov site and Hybrid Coordinate Ocean Model (HYCOM) data

  5. Real-time demonstration and evaluation of over-the-loop short to medium-range ensemble streamflow forecasting

    NASA Astrophysics Data System (ADS)

    Wood, A. W.; Clark, E.; Newman, A. J.; Nijssen, B.; Clark, M. P.; Gangopadhyay, S.; Arnold, J. R.

    2015-12-01

    The US National Weather Service River Forecasting Centers are beginning to operationalize short range to medium range ensemble predictions that have been in development for several years. This practice contrasts with the traditional single-value forecast practice at these lead times not only because the ensemble forecasts offer a basis for quantifying forecast uncertainty, but also because the use of ensembles requires a greater degree of automation in the forecast workflow than is currently used. For instance, individual ensemble member forcings cannot (practically) be manually adjusted, a step not uncommon with the current single-value paradigm, thus the forecaster is required to adopt a more 'over-the-loop' role than before. The relative lack of experience among operational forecasters and forecast users (eg, water managers) in the US with over-the-loop approaches motivates the creation of a real-time demonstration and evaluation platform for exploring the potential of over-the-loop workflows to produce usable ensemble short-to-medium range forecasts, as well as long range predictions. We describe the development and early results of such an effort by a collaboration between NCAR and the two water agencies, the US Army Corps of Engineers and the US Bureau of Reclamation. Focusing on small to medium sized headwater basins around the US, and using multi-decade series of ensemble streamflow hindcasts, we also describe early results, assessing the skill of daily-updating, over-the-loop forecasts driven by a set of ensemble atmospheric outputs from the NCEP GEFS for lead times from 1-15 days.

  6. The Central European Flood in June 2013: Experiences from a Near-Real Time Disaster Analysis in Germany

    NASA Astrophysics Data System (ADS)

    Schröter, Kai; Khazai, Bijan; Mühr, Bernhard; Elmer, Florian; Bessel, Tina; Möhrle, Stella; Dittrich, André; Kreibich, Heidi; Fohringer, Joachim; Kunz-Plapp, Tina; Trieselmann, Werner; Kunz, Michael; Merz, Bruno

    2014-05-01

    The central European flood in June 2013 once again revealed that complete flood protection is not possible. Inundations caused severe damage to buildings, infrastructure and agricultural lands. Official estimates of total damage in Germany amount to approx. 8bn € which is lower than the damage caused by the August 2002 flood - the most expensive natural hazard experienced so far in Germany. Repeated and long lasting precipitation in combination with extremely adverse preconditions induced a large scale flood event. In Germany, particularly the catchment areas of the Danube and Elbe were affected. The June 2013 flood has been the most severe flood event in terms of spatial extent and magnitude of flood peaks in Germany during the last 60 years. Large scale inundation occurred as a consequence of levee breaches near Deggendorf (Danube), Groß Rosenau and Fischbeck (Elbe). The flood has had a great impact on people, transportation and the economy. In many areas more than 50,000 thousand people were evacuated. Electrical grid and local water supply utilities failed during the floods. Furthermore, traffic was disrupted in the interregional transportation network including federal highways and long distance railways. CEDIM analysed and assessed the flood event within its current research activity on near real time forensic disaster analysis (CEDIM FDA: www.cedim.de). This contribution gives an overview about the CEDIM FDA analyses' results. It describes the key hydro-meteorological factors that triggered this extraordinary event and draws comparisons to major flood events in August 2002 and July 1954. Further, it shows the outcomes of a rapid initial impact assessment on the district level using social, economic and institutional indicators which are supplemented with information on the number of people evacuated and transportation disruptions and combined with the magnitude of the event.

  7. Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm

    PubMed Central

    2012-01-01

    Background Mathematical and computational models for infectious diseases are increasingly used to support public-health decisions; however, their reliability is currently under debate. Real-time forecasts of epidemic spread using data-driven models have been hindered by the technical challenges posed by parameter estimation and validation. Data gathered for the 2009 H1N1 influenza crisis represent an unprecedented opportunity to validate real-time model predictions and define the main success criteria for different approaches. Methods We used the Global Epidemic and Mobility Model to generate stochastic simulations of epidemic spread worldwide, yielding (among other measures) the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. Using a Monte Carlo Maximum Likelihood analysis, the model provided an estimate of the seasonal transmission potential during the early phase of the H1N1 pandemic and generated ensemble forecasts for the activity peaks in the northern hemisphere in the fall/winter wave. These results were validated against the real-life surveillance data collected in 48 countries, and their robustness assessed by focusing on 1) the peak timing of the pandemic; 2) the level of spatial resolution allowed by the model; and 3) the clinical attack rate and the effectiveness of the vaccine. In addition, we studied the effect of data incompleteness on the prediction reliability. Results Real-time predictions of the peak timing are found to be in good agreement with the empirical data, showing strong robustness to data that may not be accessible in real time (such as pre-exposure immunity and adherence to vaccination campaigns), but that affect the predictions for the attack rates. The timing and spatial unfolding of the pandemic are critically sensitive to the level of mobility data integrated into the model. Conclusions Our results show that large-scale models can be used to provide valuable real-time forecasts of

  8. Ensemble forecasting of short-term system scale irrigation demands using real-time flow data and numerical weather predictions

    NASA Astrophysics Data System (ADS)

    Perera, Kushan C.; Western, Andrew W.; Robertson, David E.; George, Biju; Nawarathna, Bandara

    2016-06-01

    Irrigation demands fluctuate in response to weather variations and a range of irrigation management decisions, which creates challenges for water supply system operators. This paper develops a method for real-time ensemble forecasting of irrigation demand and applies it to irrigation command areas of various sizes for lead times of 1 to 5 days. The ensemble forecasts are based on a deterministic time series model coupled with ensemble representations of the various inputs to that model. Forecast inputs include past flow, precipitation, and potential evapotranspiration. These inputs are variously derived from flow observations from a modernized irrigation delivery system; short-term weather forecasts derived from numerical weather prediction models and observed weather data available from automatic weather stations. The predictive performance for the ensemble spread of irrigation demand was quantified using rank histograms, the mean continuous rank probability score (CRPS), the mean CRPS reliability and the temporal mean of the ensemble root mean squared error (MRMSE). The mean forecast was evaluated using root mean squared error (RMSE), Nash-Sutcliffe model efficiency (NSE) and bias. The NSE values for evaluation periods ranged between 0.96 (1 day lead time, whole study area) and 0.42 (5 days lead time, smallest command area). Rank histograms and comparison of MRMSE, mean CRPS, mean CRPS reliability and RMSE indicated that the ensemble spread is generally a reliable representation of the forecast uncertainty for short lead times but underestimates the uncertainty for long lead times.

  9. Tools and Products of Real-Time Modeling: Opportunities for Space Weather Forecasting

    NASA Technical Reports Server (NTRS)

    Hesse, Michael

    2009-01-01

    The Community Coordinated Modeling Center (CCMC) is a US inter-agency activity aiming at research in support of the generation of advanced space weather models. As one of its main functions, the CCMC provides to researchers the use of space science models, even if they are not model owners themselves. The second CCMC activity is to support Space Weather forecasting at national Space Weather Forecasting Centers. This second activity involves model evaluations, model transitions to operations, and the development of draft Space Weather forecasting tools. This presentation will focus on the last element. Specifically, we will discuss present capabilities, and the potential to derive further tools. These capabilities will be interpreted in the context of a broad-based, bootstrapping activity for modern Space Weather forecasting.

  10. Real-time forecasting of near-field tsunamis based on source estimation from offshore tsunami data (Invited)

    NASA Astrophysics Data System (ADS)

    Tsushima, H.; Hayashi, Y.; Maeda, K.; Yokota, T.

    2013-12-01

    Near-field tsunamis in areas close to subduction zones can reach the coast in a few tens of minutes or less, and cause loss of life as well as severe damage to houses and infrastructures in coastal communities. Real-time tsunami forecasting is one of the effective ways to mitigate tsunami disasters. Transmission of a tsunami warning based on rapid and accurate tsunami forecasting to coastal communities helps the residents to make the decisions about their evacuation behaviors. Offshore tsunami data take an important role in tsunami forecasting. Tsunamis can be detected at offshore stations earlier than at coastal sites, and the data provide direct information about the impending tsunamis. In this paper, we present a method to forecast near-field tsunamis from offshore tsunami data using inversion and tsunami amplification factor techniques. We also introduce a prototype of tsunami forecasting system in which our forecasting method is installed. Our tsunami forecasting algorithm is based on a source estimation. For the algorithm, offshore tsunami waveform data are inverted for spatial distribution of an initial sea-surface displacement, and then tsunami waveforms are synthesized from the estimated source and pre-computed Green's functions by a linear superposition to forecast tsunamis at an offshore point near a coastal site. The predicted tsunami heights at the offshore points are amplified to obtain those at coastal sites using the amplification factors derived from actual tsunami observations empirically. No assumptions concerning the fault geometry and the size of an earthquake are required in the algorithm. An empirical amplification factor includes the effect of actual topography on tsunami heights that should be difficult to be modeled by the linear combination of the Green's functions. The predictions are repeated by progressively updating the offshore tsunami waveform data. Because individual predictions can be calculated within a few minutes, tsunami

  11. Real-time Aerosol Forecasting over North America using RAP-Chem and the GSI.

    NASA Astrophysics Data System (ADS)

    Pagowski, M.

    2015-12-01

    RAP-Chem is an implementation of WRF-Chem meteorology-chemistry model that is run daily at NOAA/ESRL over continental domain for air-quality forecasting. The chemical forecasts are combined with observations of species using three-dimensional variational data assimilation procedure implemented in the Gridpoint Statistical Interpolation (GSI). In the presentation we detail the method of the assimilation and show verification statistics of the model performance.

  12. Using Landslide Failure Forecast Models in Near Real Time: the Mt. de La Saxe case-study

    NASA Astrophysics Data System (ADS)

    Manconi, Andrea; Giordan, Daniele

    2014-05-01

    Forecasting the occurrence of landslide phenomena in space and time is a major scientific challenge. The approaches used to forecast landslides mainly depend on the spatial scale analyzed (regional vs. local), the temporal range of forecast (long- vs. short-term), as well as the triggering factor and the landslide typology considered. By focusing on short-term forecast methods for large, deep seated slope instabilities, the potential time of failure (ToF) can be estimated by studying the evolution of the landslide deformation over time (i.e., strain rate) provided that, under constant stress conditions, landslide materials follow creep mechanism before reaching rupture. In the last decades, different procedures have been proposed to estimate ToF by considering simplified empirical and/or graphical methods applied to time series of deformation data. Fukuzono, 1985 proposed a failure forecast method based on the experience performed during large scale laboratory experiments, which were aimed at observing the kinematic evolution of a landslide induced by rain. This approach, known also as the inverse-velocity method, considers the evolution over time of the inverse value of the surface velocity (v) as an indicator of the ToF, by assuming that failure approaches while 1/v tends to zero. Here we present an innovative method to aimed at achieving failure forecast of landslide phenomena by considering near-real-time monitoring data. Starting from the inverse velocity theory, we analyze landslide surface displacements on different temporal windows, and then apply straightforward statistical methods to obtain confidence intervals on the time of failure. Our results can be relevant to support the management of early warning systems during landslide emergency conditions, also when the predefined displacement and/or velocity thresholds are exceeded. In addition, our statistical approach for the definition of confidence interval and forecast reliability can be applied also to

  13. Cooperative satellite-based flood detection, mapping, and river monitoring in near real time

    NASA Technical Reports Server (NTRS)

    Brakenridge, Robert G.; Nghiem, Son V.

    2004-01-01

    The North Atlantic Oscillation (NAO), the Pacific-North American (PNA) teleconnection pattern, and the El Nino-Southern Oscillation (ENSO) combine to influence the planetary wave structure over the northern hemisphere. Floods and droughts are associated around the world with ENSO through such teleconnections, and improved flood prediction relies on understanding them better. The scientific study of floods, and consistent measurements thereof, are needed in order to allow 'Greenhouse warming' predictions about flooding to be tested, and the hydrologic effects of other phenomena such as ENSO to be evaluated. The needed tasks are: 1) detection/warning of flooding, 2) flood magnitude assessment, 3) flood inundation mapping, and 4) preservation of the record of flooding. Accomplishing these same tasks provides direct local societal benefits as well: they can save lives and reduce economic loss. We emphasize that the basic science observations need not be divorced from the immediate practical applications: both can occur together, and just as is the case for meteorological remote sensing.

  14. Medium range flood forecasts at global scale

    NASA Astrophysics Data System (ADS)

    Voisin, N.; Wood, A. W.; Lettenmaier, D. P.; Wood, E. F.

    2006-12-01

    While weather and climate forecast methods have advanced greatly over the last two decades, this capability has yet to be evidenced in mitigation of water-related natural hazards (primarily floods and droughts), especially in the developing world. Examples abound of extreme property damage and loss of life due to floods in the underdeveloped world. For instance, more than 4.5 million people were affected by the July 2000 flooding of the Mekong River and its tributaries in Cambodia, Vietnam, Laos and Thailand. The February- March 2000 floods in the Limpopo River of Mozambique caused extreme disruption to that country's fledgling economy. Mitigation of these events through advance warning has typically been modest at best. Despite the above noted improvement in weather and climate forecasts, there is at present no system for forecasting of floods globally, notwithstanding that the potential clearly exists. We describe a methodology that is eventually intended to generate global flood predictions routinely. It draws heavily from the experimental North American Land Data Assimilation System (NLDAS) and the companion Global Land Data Assimilation System (GLDAS) for development of nowcasts, and the University of Washington Experimental Hydrologic Prediction System to develop ensemble hydrologic forecasts based on Numerical Weather Prediction (NWP) models which serve both as nowcasts (and hence reduce the need for in situ precipitation and other observations in parts of the world where surface networks are critically deficient) and provide forecasts for lead times as long as fifteen days. The heart of the hydrologic modeling system is the University of Washington/Princeton University Variable Infiltration Capacity (VIC) macroscale hydrology model. In the prototype (tested using retrospective data), VIC is driven globally up to the time of forecast with daily ERA40 precipitation (rescaled on a monthly basis to a station-based global climatology), ERA40 wind, and ERA40

  15. Probabilistic flood forecast: Exact and approximate predictive distributions

    NASA Astrophysics Data System (ADS)

    Krzysztofowicz, Roman

    2014-09-01

    For quantification of predictive uncertainty at the forecast time t0, the future hydrograph is viewed as a discrete-time continuous-state stochastic process {Hn: n=1,…,N}, where Hn is the river stage at time instance tn>t0. The probabilistic flood forecast (PFF) should specify a sequence of exceedance functions {F‾n: n=1,…,N} such that F‾n(h)=P(Zn>h), where P stands for probability, and Zn is the maximum river stage within time interval (t0,tn], practically Zn=max{H1,…,Hn}. This article presents a method for deriving the exact PFF from a probabilistic stage transition forecast (PSTF) produced by the Bayesian forecasting system (BFS). It then recalls (i) the bounds on F‾n, which can be derived cheaply from a probabilistic river stage forecast (PRSF) produced by a simpler version of the BFS, and (ii) an approximation to F‾n, which can be constructed from the bounds via a recursive linear interpolator (RLI) without information about the stochastic dependence in the process {H1,…,Hn}, as this information is not provided by the PRSF. The RLI is substantiated by comparing the approximate PFF against the exact PFF. Being reasonably accurate and very simple, the RLI may be attractive for real-time flood forecasting in systems of lesser complexity. All methods are illustrated with a case study for a 1430 km headwater basin wherein the PFF is produced for a 72-h interval discretized into 6-h steps.

  16. Real-time forecasts of tomorrow's earthquakes in California: a new mapping tool

    USGS Publications Warehouse

    Gerstenberger, Matt; Wiemer, Stefan; Jones, Lucy

    2004-01-01

    We have derived a multi-model approach to calculate time-dependent earthquake hazard resulting from earthquake clustering. This file report explains the theoretical background behind the approach, the specific details that are used in applying the method to California, as well as the statistical testing to validate the technique. We have implemented our algorithm as a real-time tool that has been automatically generating short-term hazard maps for California since May of 2002, at http://step.wr.usgs.gov

  17. Construction of Real-time Forecast System on the Boreal Summer Intraseasonal Oscillation

    NASA Astrophysics Data System (ADS)

    Kim, H.; Wheeler, M. C.; Lee, J.; Gottschalck, J.

    2013-12-01

    Hae-Jeong Kim1, Matthew C. Wheeler2, June-Yi Lee3 and Jon C. Gottschalck4 1APEC Climate Center, 12 Centum 7-ro, Haeundae-gu, Busan, 612-020, South Korea 2Centre for Australian Weather and Climate Research Bureau of Meteorology, Melbourne, Australia 3Global Monsoon Climate Laboratory, Pusan National University, Busan, Korea 4Climate Prediction Center, NOAA/National Weather Service, Washington D. C., USA *E-mail : shout@apcc21.org The boreal summer intraseasonal oscillation (BSISO) is one of the dominant mode of variability in the Asian summer monsoon and global monsoon (e.g. Webster et al., 1998; Lee et al., 2013). The BSISO influences summer monsoon onsets (e.g. Wang and Xie, 1997) and interacts with a wide range of atmospheric circulation and associated weather (e.g. Lee et al., 2011; Wang et al., 2012). In addition, the wet and dry spells of the BSISO strongly can influence extreme hydro-meteorological events, major driving forces of natural disasters (Lau and Waliser 2005). Thus, it is important to monitor and predict the BSISO. As the occurrence of and concern over extreme climate events rises, moreover, the provision of high-quality BSISO forecasts will become increasingly relevant. APCC has recently begun to provide the BSISO forecast information service at http://www.apcc21.org/eng/service/bsiso/fore/japcc030601.jsp. The forecast is contributed by the Australian Bureau of Meteorology, the US National Centers for Environmental Prediction, the European Center for Medium Range Weather Forecasts and UK Meteorology Office in cooperation with the CAS/WCRP Working Group on Numerical Experimentation (WGNE) Madden Julian Oscillation (MJO) Task Force. The APCC BSISO forecasts are displayed by newly developed indices proposed by Lee at al. (2013) that are able to overcome the limitation of the RMM index (Wheeler and Hendon, 2004) in terms of representing BSISO activity with northward propagation over off-equatorial monsoon domain. The BSISO forecast information can be

  18. JPSS application in a near real time regional numerical forecast system at CIMSS

    NASA Astrophysics Data System (ADS)

    Li, J.; Wang, P.; Han, H.; Zhu, F.; Schmit, T. J.; Goldberg, M.

    2015-12-01

    Observations from next generation of environmental sensors onboard the Suomi National Polar-Orbiting Parnership (S-NPP) and its successor, the Joint Polar Satellite System (JPSS), provide us the critical information for numerical weather forecast (NWP). How to better represent these satellite observations and how to get value added information into NWP system still need more studies. Recently scientists from Cooperative Institute of Meteorological Satellite Studies (CIMSS) at University of Wisconsin-Madison have developed a near realtime regional Satellite Data Assimilation system for Tropical storm forecasts (SDAT) (http://cimss.ssec.wisc.edu/sdat). The system is built with the community Gridpoint Statistical Interpolation (GSI) assimilation and advanced Weather Research Forecast (WRF) model. With GSI, SDAT can assimilate all operational available satellite data including GOES, AMSUA/AMSUB, HIRS, MHS, ATMS, AIRS and IASI radiances and some satellite derived products. In addition, some research products, such as hyperspectral IR retrieved temperature and moisture profiles, GOES imager atmospheric motion vector (AMV) and GOES sounder layer precipitable water (LPW), are also added into the system. Using SDAT as a research testbed, studies have been conducted to show how to improve high impact weather forecast by better handling cloud information in satellite data. Previously by collocating high spatial resolution MODIS data with hyperspectral resolution AIRS data, precise clear pixels of AIRS can be identified and some partially or thin cloud contamination from pixels can be removed by taking advantage of high spatial resolution and high accurate MODIS cloud information. The results have demonstrated that both of these strategies have greatly improved the hurricane track and intensity forecast. We recently have extended these methodologies into processing CrIS/VIIRS data. We also tested similar ideas in microwave sounders by the collocation of AMSU/MODIS and ATMS

  19. Real-time forecasting and predictability of catastrophic failure events: from rock failure to volcanoes and earthquakes

    NASA Astrophysics Data System (ADS)

    Main, I. G.; Bell, A. F.; Naylor, M.; Atkinson, M.; Filguera, R.; Meredith, P. G.; Brantut, N.

    2012-12-01

    Accurate prediction of catastrophic brittle failure in rocks and in the Earth presents a significant challenge on theoretical and practical grounds. The governing equations are not known precisely, but are known to produce highly non-linear behavior similar to those of near-critical dynamical systems, with a large and irreducible stochastic component due to material heterogeneity. In a laboratory setting mechanical, hydraulic and rock physical properties are known to change in systematic ways prior to catastrophic failure, often with significant non-Gaussian fluctuations about the mean signal at a given time, for example in the rate of remotely-sensed acoustic emissions. The effectiveness of such signals in real-time forecasting has never been tested before in a controlled laboratory setting, and previous work has often been qualitative in nature, and subject to retrospective selection bias, though it has often been invoked as a basis in forecasting natural hazard events such as volcanoes and earthquakes. Here we describe a collaborative experiment in real-time data assimilation to explore the limits of predictability of rock failure in a best-case scenario. Data are streamed from a remote rock deformation laboratory to a user-friendly portal, where several proposed physical/stochastic models can be analysed in parallel in real time, using a variety of statistical fitting techniques, including least squares regression, maximum likelihood fitting, Markov-chain Monte-Carlo and Bayesian analysis. The results are posted and regularly updated on the web site prior to catastrophic failure, to ensure a true and and verifiable prospective test of forecasting power. Preliminary tests on synthetic data with known non-Gaussian statistics shows how forecasting power is likely to evolve in the live experiments. In general the predicted failure time does converge on the real failure time, illustrating the bias associated with the 'benefit of hindsight' in retrospective analyses

  20. A New Algorithm for Real-Time Tsunami Forecast Using a Dense Network of Cabled Ocean-Bottom Pressure Gauges

    NASA Astrophysics Data System (ADS)

    Yamamoto, N.; Aoi, S.; Hirata, K.; Kunugi, T.; Nakamura, H.; Suzuki, W.

    2014-12-01

    We started to develop a new algorithm for real-time tsunami forecast based on offshore tsunami observations with 150 cabled ocean-bottom pressure gauges of the Seafloor Observation Network for Earthquakes and Tsunamis (S-net), under construction along the Japan Trench (Kanazawa et al., 2012, JpGU; Uehira et al., 2012, AGU). The most important concept on the new algorithm is involving any type and/or form uncertainties in the tsunami forecast, which cannot be dealt with any of standard linear/nonlinear least square approaches. We first construct a tsunami scenario bank (TSB). It contains offshore tsunami waveforms at the 150 stations and maximum coastal tsunami heights, calculated using nonlinear long-wave theory with runup boundary condition from any possible tsunami sources (fault models) that affect target coastal regions. From TSB, then we quickly explore a range of several suitable tsunami scenarios, that can explain offshore observations. At the same time, maximum possible tsunami heights along the target coastlines, coupled with selected scenarios, are predicted. In the near future, it is possible to forecast real-time tsunami inundation by adding its component in TSB under the same strategy. In this study, we focus on near-field tsunami occurring off the Pacific coast of Tohoku and Hokkaido. Provisionally, we generate 1848 tsunami scenarios, prepared for a research project of nationwide Probabilistic Tsunami Hazard Assessment for Japan (Hirata et al., 2014, AOGS), to construct TSB. For a given pseudo "observed waveforms", the developing algorithm rapidly picks up an allowable range of tsunami scenarios from TSB. In this procedure, we use multiple indexes such as correlation coefficient, sum of squared residual, as well as geometric mean and geometric standard deviation in ratios of scenarios to observations. Use of multiple indexes rather than any single index as linear inversion does reinforce to obtain robust tsunami forecast.

  1. A Review of Real-Time Markov Model ENSO Forecast in 1996-2015: Why did it Forecast a Strong El Nino since March 2015?

    NASA Astrophysics Data System (ADS)

    Xue, Y.

    2015-12-01

    The Markov model for real time ENSO forecast at Climate Prediction Center of National Centers for Environmental Prediction (NCEP) is based on observed sea surface temperature, sea level from the NCEP ocean reanalysis, and pseudo wind stress from the Florida State University in 1980-1995. The Markov model is constructed in a reduced multivariate EOF (MEOF) space with 3 MEOFs. The cross-validated hindcast skill of NINO3.4 in 1980-1995 is competitive among dynamical and statistical models. The model was implemented into operation at CPC in early 2000s since it successfully forecasted the El Nino in winter 1997/98 starting from November 1996 initial conditions (I.C.). In this study, we assessed the real time forecast skill of ENSO by the Markov model in 1996-2015 and compared it with that of other operational forecast models. It is found that the Markov model has lower forecast skill of ENSO in the 2000s than that in the 1980s and 1990s, which is common among ENSO forecast models. The lower forecast skill of the Markov model in the 2000s can be attributed to weak precursor of positive heat content anomaly in the equatorial Pacific and a shorter lead time of the precursor relative to NINO3.4, both of which is related to the decadal change of ENSO. However, out of surprise, the Markov model successfully forecasted the El Nino in winter 2014/15 starting from February 2014 I.C.. In addition, the Markov model forecasted the continuation of the El Nino into the spring/summer/fall of 2015. Starting from March 2015 I.C., the Markov model forecasted a strong El Nino in winter 2015/16. This surprising long-lead forecast skill can be attributed to the positive second principal component (PC) of MEOF that leads NINO3.4 by 6-9 months, a precursor commonly seen in the 1980s and 1990s. This provided us confidence in the model forecast of a strong El Nino in winter 2015/16 that is highly consistent with the ensemble forecast of dynamical models.

  2. Integration of Remote Sensing derived Actual Evapotranspiration with Meteorological Data for Real Time Demand Forecasting in Semi-arid Regions

    NASA Astrophysics Data System (ADS)

    Ullah, M. K.; Hafeez, M. M.; Chemin, Y.; Faux, R.; Sixsmith, J.

    2010-12-01

    Irrigated agriculture is major consumer of fresh water, but a large part of the water devour for irrigation is wasted due to poor management of irrigation systems. Improving water management in irrigated areas require the analysis of real time water demand in order to determine the possibilities in which it may be modified and rationalised. Real time water demand information in irrigated areas is a key for planning about sustainable use of irrigation water. These activities are needed not only to improve water productivity, but also to increase the sustainability of irrigated agriculture by saving irrigation water. Demand forecasting entail the complete understanding of spatial and expected temporal variability of metrological parameters and evapotranspiration (ET). ET is the overriding aspect for irrigation demand forecasting at farm to catchment scale. Many models have been used to measure the ET rate, either empirical or functional. The major disadvantage of this approach is that most methods generate only point values, resulting in estimates that are not representative of large areas. These methods are based on crop factors under ideal conditions and cannot therefore represent actual crop ET. Satellite remote sensing is a powerful mean to estimate ET over various spatial and temporal scales. For improved irrigation system management and operation, a holistic approach of integrating remote sensing derived ET from SAM-ET (spatial algorithm for mapping ET) algorithm, for Australian agro-ecosystem, with forecasted meteorological data and field application loss functions for major crops were used to forecast actual water demand in Coleambally Irrigation Area (CIA), New South Wales, Australia. It covers approximately 79,000 ha of intensive irrigation and comprise of number of secondary and tertiary canals. In order to capture the spatial variability, CIA has been divided into 22 nodes based on direction of flow and connectivity. All hydrological data of inflow (i

  3. An automatic system for on-line flash flood forecasting

    NASA Astrophysics Data System (ADS)

    Makin, I.; Rumyantsev, D.; Shemanayev, K.; Shkarbanov, R.

    2012-04-01

    The research group at Russian State Hydrometeorological University continues developing hydrologic software, called SLS+, which might be useful for background flash flood forecasting in poorly gauged regions. Now the SLS+ software has a user-friendly web interface for on-line background flash flood forecasting in training and operational (real time or near real time) modes, and allows issuing stream flow forecasts based on precipitation and evaporation data obtained either from archives, or from field sensors, respectively. The system currently includes two hydrological models, the Sacramento Soil Moisture Accounting model (USA) and Multi-Layer Conceptual Model (Russia). These models can be calibrated either manually, or automatically based on four calibration algorithms: Shuffled Complex Evolution algorithm (SCE), which is quite useful if (1) a number of calibrated parameters does not exceed 6-7 and boundaries of the parameter space are well defined and (2) the parameter space is not too wide; Basic Stepwise Line Search (SLS) algorithm, which is efficient and computationally "inexpensive", if an initial point for pattern optimization is well defined; SLS-2L algorithm (where 2L is an abbreviation for "two loops" or "two cycles"), which is used in regions with scarce soil data and allows first to predetermine the soil hydraulic parameters, and then use these parameters for the refined model parameterization; SLS-E algorithm (where E stands for "Ensemble generation"), which implies the generation of ensembles of one or several forcing processes (for instance, effective precipitation and evaporation) and model calibration for each of those ensembles. This method is primarily designed for models with undistracted parameters at a relatively low density of ground-based meteorological observation network. Currently the trial version of the system is available for testing upon request.

  4. A Pro-active Real-time Forecasting and Decision Support System for Daily Management of Marine Works

    NASA Astrophysics Data System (ADS)

    Bollen, Mark; Leyssen, Gert; Smets, Steven; De Wachter, Tom

    2016-04-01

    Marine Works involving turbidity generating activities (eg. dredging, dredge spoil placement) can generate environmental stress in and around a project area in the form of sediment plumes causing light reduction and sedimentation. If these works are situated near sensitive habitats like sea-grass beds, coral reefs or sensitive human activities eg. aquaculture farms or water intakes, or if contaminants are present in the water soil environmental scrutiny is advised. Environmental Regulations can impose limitations to these activities in the form of turbidity thresholds, spill budgets, contaminant levels. Breaching environmental regulations can result in increased monitoring, adaptation of the works planning and production rates and ultimately in a (temporary) stop of activities all of which entail time and cost impacts for a contractor and/or client. Sediment plume behaviour is governed by the dredging process, soil properties and ambient conditions (currents, water depth) and can be modelled. Usually this is done during the preparatory EIA phase of a project, for estimation of environmental impact based on climatic scenarios. An operational forecasting tool is developed to adapt marine work schedules to the real-time circumstances and thus evade exceedance of critical threshold levels at sensitive areas. The forecasting system is based on a Python-based workflow manager with a MySQL database and a Django frontend web tool for user interaction and visualisation of the model results. The core consists of a numerical hydrodynamic model with sediment transport module (Mike21 from DHI). This model is driven by space and time varying wind fields and wave boundary conditions, and turbidity inputs (suspended sediment source terms) based on marine works production rates and soil properties. The resulting threshold analysis allows the operator to indicate potential impact at the sensitive areas and instigate an adaption of the marine work schedule if needed. In order to use

  5. Factors controlling storm impacts on coastal barriers and beaches - A preliminary basis for near real-time forecasting

    USGS Publications Warehouse

    Morton, R.A.

    2002-01-01

    Analysis of ground conditions and meteorological and oceanographic parameters for some of the most severe Atlantic and Gulf Coast storms in the U.S. reveals the primary factors affecting morphological storm responses of beaches and barrier islands. The principal controlling factors are storm characteristics, geographic position relative to storm path, timing of storm events, duration of wave exposure, wind stress, degree of flow confinement, antecedent topography and geologic framework, sediment textures, vegetative cover, and type and density of coastal development. A classification of commonly observed storm responses demonstrates the sequential interrelations among (1) land elevations, (2) water elevations in the ocean and adjacent lagoon (if present), and (3) stages of rising water during the storm. The predictable coastal responses, in relative order from high frequency beach erosion to low frequency barrier inundation, include: beach erosion, berm migration, dune erosion, washover terrace construction, perched fan deposition, sheetwash, washover channel incision, washout formation, and forced and unforced ebb flow. Near real-time forecasting of expected storm impacts is possible if the following information is available for the coast: a detailed morphological and topographic characterization, accurate storm-surge and wave-runup models, the real-time reporting of storm parameters, accurate forecasts of the storm position relative to a particular coastal segment, and a conceptual model of geological processes that encompasses observed morphological changes caused by extreme storms.

  6. Puget Sound Operational Forecast System - A Real-time Predictive Tool for Marine Resource Management and Emergency Responses

    SciTech Connect

    Yang, Zhaoqing; Khangaonkar, Tarang; Chase, Jared M.; Wang, Taiping

    2009-12-01

    To support marine ecological resource management and emergency response and to enhance scientific understanding of physical and biogeochemical processes in Puget Sound, a real-time Puget Sound Operational Forecast System (PS-OFS) was developed by the Coastal Ocean Dynamics & Ecosystem Modeling group (CODEM) of Pacific Northwest National Laboratory (PNNL). PS-OFS employs the state-of-the-art three-dimensional coastal ocean model and closely follows the standards and procedures established by National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS). PS-OFS consists of four key components supporting the Puget Sound Circulation and Transport Model (PS-CTM): data acquisition, model execution and product archive, model skill assessment, and model results dissemination. This paper provides an overview of PS-OFS and its ability to provide vital real-time oceanographic information to the Puget Sound community. PS-OFS supports pacific northwest region’s growing need for a predictive tool to assist water quality management, fish stock recovery efforts, maritime emergency response, nearshore land-use planning, and the challenge of climate change and sea level rise impacts. The structure of PS-OFS and examples of the system inputs and outputs, forecast results are presented in details.

  7. Real-time weather forecasting in the Western Mediterranean Basin: An application of the RAMS model

    NASA Astrophysics Data System (ADS)

    Gómez, I.; Caselles, V.; Estrela, M. J.

    2014-03-01

    A regional forecasting system based on the Regional Atmospheric Modeling System (RAMS) is being run at the CEAM Foundation. The model is started twice daily with a forecast range of 72 h. For the period June 2007 to August 2010 the verification of the model has been done using a series of automatic meteorological stations from the CEAM network and located within the Valencia Region (Western Mediterranean Basin). Air temperature, relative humidity and wind speed and direction of the output of the model have been compared with observations. For these variables, an operational verification has been performed by computing different statistical scores for 18 weather stations. This verification process has been carried out for each season of the year separately. As a result, it has been revealed that the model presents significant differences in the forecast of the meteorological variables analysed throughout the year. Moreover, due to the physical complexity of the area of study, the model presents different degree of accuracy between coastal and inland stations. Precipitation has also been verified by means of yes/no contingency tables as well as scatter plots. These tables have been built using 4 specific thresholds that have permitted to compute some categorical statistics. From the results found, it is shown that the precipitation forecast in the area of study is in general over-predicted, but with marked differences between the seasons of the year. Finally, dividing the available data by season of the year, has permitted us to analyze differences in the observed patterns for the magnitudes mentioned above. These results have been used to better understand the behavior of the RAMS model within the Valencia Region.

  8. Probabilistic Flash Flood Forecasting using Stormscale Ensembles

    NASA Astrophysics Data System (ADS)

    Hardy, J.; Gourley, J. J.; Kain, J. S.; Clark, A.; Novak, D.; Hong, Y.

    2013-12-01

    Flash flooding is one of the most costly and deadly natural hazards in the US and across the globe. The loss of life and property from flash floods could be mitigated with better guidance from hydrological models, but these models have limitations. For example, they are commonly initialized using rainfall estimates derived from weather radars, but the time interval between observations of heavy rainfall and a flash flood can be on the order of minutes, particularly for small basins in urban settings. Increasing the lead time for these events is critical for protecting life and property. Therefore, this study advances the use of quantitative precipitation forecasts (QPFs) from a stormscale NWP ensemble system into a distributed hydrological model setting to yield basin-specific, probabilistic flash flood forecasts (PFFFs). Rainfall error characteristics of the individual members are first diagnosed and quantified in terms of structure, amplitude, and location (SAL; Wernli et al., 2008). Amplitude and structure errors are readily correctable due to their diurnal nature, and the fine scales represented by the CAPS QPF members are consistent with radar-observed rainfall, mainly showing larger errors with afternoon convection. To account for the spatial uncertainty of the QPFs, we use an elliptic smoother, as in Marsh et al. (2012), to produce probabilistic QPFs (PQPFs). The elliptic smoother takes into consideration underdispersion, which is notoriously associated with stormscale ensembles, and thus, is good for targeting the approximate regions that may receive heavy rainfall. However, stormscale details contained in individual members are still needed to yield reasonable flash flood simulations. Therefore, on a case study basis, QPFs from individual members are then run through the hydrological model with their predicted structure and corrected amplitudes, but the locations of individual rainfall elements are perturbed within the PQPF elliptical regions using Monte

  9. Near real time Forensic Disaster Analysis of the central European flood in June 2013 in Germany: Impact and management

    NASA Astrophysics Data System (ADS)

    Khazai, Bijan; Bessel, Tina; Möhrle, Stella; Dittrich, André; Schröter, Kai; Mühr, Bernhard; Elmer, Florian; Kunz-Plapp, Tina; Trieselmann, Werner; Kunz, Michael

    2014-05-01

    Within its current research activity on near real time Forensic Disaster Analysis (FDA), researchers from the Center for Disaster Management and Risk Reduction Technology (CEDIM) aim to identify major risk drivers and to understand the root causes of disaster and infer the implications for disaster mitigation. A key component of this activity is the development of rapid assessment tools which allow for a science based estimate of disaster impacts. The central European flood in June 2013 caused in Germany severe damage to buildings, infrastructure and agricultural lands and has had a great impact on people, transportation and the economy. In many areas thousands of people were evacuated. Electrical grid and local water supply utilities failed during the floods. Furthermore, traffic was disrupted in the interregional transportation network including federal highways and long distance railways. CEDIM analysed the impact and management of the flood event within an FDA activity. An analysis on the amount and spatial distribution of flood-related Twitter messages in Germany revealed a high interest in the flood in the social media. Furthermore, an analysis of the resilience of selected affected areas in Germany has been carried out to assess the impact of the flood on the district level. The resilience indicator is based on social, economic and institutional indicators which are supplemented with information on the number of people evacuated and transportation disruptions. Combined with the magnitude of the event, an index is calculated that allows for a rapid initial but preliminary estimate of the flood impact. Results show high resilience of the administrative districts along the Danube while heavy impacts are seen along the Mulde and Elbe.

  10. HESS Opinions "Forecaster priorities for improving probabilistic flood forecasts"

    NASA Astrophysics Data System (ADS)

    Wetterhall, F.; Pappenberger, F.; Alfieri, L.; Cloke, H. L.; Thielen-del Pozo, J.; Balabanova, S.; Daňhelka, J.; Vogelbacher, A.; Salamon, P.; Carrasco, I.; Cabrera-Tordera, A. J.; Corzo-Toscano, M.; Garcia-Padilla, M.; Garcia-Sanchez, R. J.; Ardilouze, C.; Jurela, S.; Terek, B.; Csik, A.; Casey, J.; Stankūnavičius, G.; Ceres, V.; Sprokkereef, E.; Stam, J.; Anghel, E.; Vladikovic, D.; Alionte Eklund, C.; Hjerdt, N.; Djerv, H.; Holmberg, F.; Nilsson, J.; Nyström, K.; Sušnik, M.; Hazlinger, M.; Holubecka, M.

    2013-11-01

    Hydrological ensemble prediction systems (HEPS) have in recent years been increasingly used for the operational forecasting of floods by European hydrometeorological agencies. The most obvious advantage of HEPS is that more of the uncertainty in the modelling system can be assessed. In addition, ensemble prediction systems generally have better skill than deterministic systems both in the terms of the mean forecast performance and the potential forecasting of extreme events. Research efforts have so far mostly been devoted to the improvement of the physical and technical aspects of the model systems, such as increased resolution in time and space and better description of physical processes. Developments like these are certainly needed; however, in this paper we argue that there are other areas of HEPS that need urgent attention. This was also the result from a group exercise and a survey conducted to operational forecasters within the European Flood Awareness System (EFAS) to identify the top priorities of improvement regarding their own system. They turned out to span a range of areas, the most popular being to include verification of an assessment of past forecast performance, a multi-model approach for hydrological modelling, to increase the forecast skill on the medium range (>3 days) and more focus on education and training on the interpretation of forecasts. In light of limited resources, we suggest a simple model to classify the identified priorities in terms of their cost and complexity to decide in which order to tackle them. This model is then used to create an action plan of short-, medium- and long-term research priorities with the ultimate goal of an optimal improvement of EFAS in particular and to spur the development of operational HEPS in general.

  11. Skill assessment of a real-time forecast system utilizing a coupled hydrologic and coastal hydrodynamic model during Hurricane Irene (2011)

    NASA Astrophysics Data System (ADS)

    Dresback, Kendra M.; Fleming, Jason G.; Blanton, Brian O.; Kaiser, Carola; Gourley, Jonathan J.; Tromble, Evan M.; Luettich, Richard A.; Kolar, Randall L.; Hong, Yang; Van Cooten, Suzanne; Vergara, Humberto J.; Flamig, Zac L.; Lander, Howard M.; Kelleher, Kevin E.; Nemunaitis-Monroe, Kodi L.

    2013-12-01

    Due to the devastating effects of recent hurricanes in the Gulf of Mexico (e.g., Katrina, Rita, Ike and Gustav), the development of a high-resolution, real-time, total water level prototype system has been accelerated. The fully coupled model system that includes hydrology is an extension of the ADCIRC Surge Guidance System (ASGS), and will henceforth be referred to as ASGS-STORM (Scalable, Terrestrial, Ocean, River, Meteorological) to emphasize the major processes that are represented by the system.The ASGS-STORM system incorporates tides, waves, winds, rivers and surge to produce a total water level, which provides a holistic representation of coastal flooding. ASGS-STORM was rigorously tested during Hurricane Irene, which made landfall in late August 2011 in North Carolina. All results from ASGS-STORM for the advisories were produced in real-time, forced by forecast wind and pressure fields computed using a parametric tropical cyclone model, and made available via the web. Herein, a skill assessment, analyzing wind speed and direction, significant wave heights, and total water levels, is used to evaluate ASGS-STORM's performance during Irene for three advisories and the best track from the National Hurricane Center (NHC). ASGS-STORM showed slight over-prediction for two advisories (Advisory 23 and 25) due to the over-estimation of the storm intensity. However, ASGS-STORM shows notable skill in capturing total water levels, wind speed and direction, and significant wave heights in North Carolina when utilizing Advisory 28, which had a slight shift in the track but provided a more accurate estimation of the storm intensity, along with the best track from the NHC. Results from ASGS-STORM have shown that as the forecast of the advisories improves, so does the accuracy of the models used in the study; therefore, accurate input from the weather forecast is a necessary, but not sufficient, condition to ensure the accuracy of the guidance provided by the system. While

  12. Multi-index method using offshore ocean-bottom pressure data for real-time tsunami forecast

    NASA Astrophysics Data System (ADS)

    Yamamoto, Naotaka; Aoi, Shin; Hirata, Kenji; Suzuki, Wataru; Kunugi, Takashi; Nakamura, Hiromitsu

    2016-07-01

    We developed a real-time tsunami forecast method using only pressure data collected from the bottom of the ocean via a dense offshore observation network. The key feature of the method is rapid matching between offshore tsunami observations and pre-calculated offshore tsunami spatial distributions. We first calculate the tsunami waveforms at offshore stations and the maximum coastal tsunami heights from any possible tsunami source model and register them in the proposed Tsunami Scenario Bank (TSB). When a tsunami occurs, we use multiple indices to quickly select dozens of appropriate tsunami scenarios that can explain the offshore observations. At the same time, the maximum coastal tsunami heights coupled with the selected tsunami scenarios are forecast. We apply three indices, which are the correlation coefficient and two kinds of variance reductions normalized by the L2-norm of either the observation or calculation, to match the observed spatial distributions with the pre-calculated spatial distributions in the TSB. We examine the ability of our method to select appropriate tsunami scenarios by conducting synthetic tests using a scenario based on "pseudo-observations." For these tests, we construct a tentative TSB, which contains tsunami waveforms at locations in the Seafloor Observation Network for Earthquakes and Tsunamis along the Japan Trench and maximum coastal tsunami heights, using about 2000 tsunami source models along the Japan Trench. Based on the test results, we confirm that the method can select appropriate tsunami scenarios within a certain precision by using the two kinds of variance reductions, which are sensitive to the tsunami size, and the correlation coefficient, which is sensitive to the tsunami source location. In this paper, we present the results and discuss the characteristics and behavior of the multi-index method. The addition of tsunami inundation components to the TSB is expected to enable the application of this method to real-time

  13. Real time wave forecasting using wind time history and numerical model

    NASA Astrophysics Data System (ADS)

    Jain, Pooja; Deo, M. C.; Latha, G.; Rajendran, V.

    Operational activities in the ocean like planning for structural repairs or fishing expeditions require real time prediction of waves over typical time duration of say a few hours. Such predictions can be made by using a numerical model or a time series model employing continuously recorded waves. This paper presents another option to do so and it is based on a different time series approach in which the input is in the form of preceding wind speed and wind direction observations. This would be useful for those stations where the costly wave buoys are not deployed and instead only meteorological buoys measuring wind are moored. The technique employs alternative artificial intelligence approaches of an artificial neural network (ANN), genetic programming (GP) and model tree (MT) to carry out the time series modeling of wind to obtain waves. Wind observations at four offshore sites along the east coast of India were used. For calibration purpose the wave data was generated using a numerical model. The predicted waves obtained using the proposed time series models when compared with the numerically generated waves showed good resemblance in terms of the selected error criteria. Large differences across the chosen techniques of ANN, GP, MT were not noticed. Wave hindcasting at the same time step and the predictions over shorter lead times were better than the predictions over longer lead times. The proposed method is a cost effective and convenient option when a site-specific information is desired.

  14. Precipitation and floodiness: forecasts of flood hazard at the regional scale

    NASA Astrophysics Data System (ADS)

    Stephens, Liz; Day, Jonny; Pappenberger, Florian; Cloke, Hannah

    2016-04-01

    In 2008, a seasonal forecast of an increased likelihood of above-normal rainfall in West Africa led the Red Cross to take early humanitarian action (such as prepositioning of relief items) on the basis that this forecast implied heightened flood risk. However, there are a number of factors that lead to non-linearity between precipitation anomalies and flood hazard, so in this presentation we use a recently developed global-scale hydrological model driven by the ERA-Interim/Land precipitation reanalysis (1980-2010) to quantify this non-linearity. Using these data, we introduce the concept of floodiness to measure the incidence of floods over a large area, and quantify the link between monthly precipitation, river discharge and floodiness anomalies. Our analysis shows that floodiness is not well correlated with precipitation, demonstrating the problem of using seasonal precipitation forecasts as a proxy for forecasting flood hazard. This analysis demonstrates the value of developing hydrometeorological forecasts of floodiness for decision-makers. As a result, we are now working with the European Centre for Medium-Range Weather Forecasts and the Joint Research Centre, as partners of the operational Global Flood Awareness System (GloFAS), to implement floodiness forecasts in real-time.

  15. Operational flood forecasting system of Umbria Region "Functional Centre

    NASA Astrophysics Data System (ADS)

    Berni, N.; Pandolfo, C.; Stelluti, M.; Ponziani, F.; Viterbo, A.

    2009-04-01

    The hydrometeorological alert office (called "Decentrate Functional Centre" - CFD) of Umbria Region, in central Italy, is the office that provides technical tools able to support decisions when significant flood/landslide events occur, furnishing 24h support for the whole duration of the emergency period, according to the national directive DPCM 27 February 2004 concerning the "Operating concepts for functional management of national and regional alert system during flooding and landslide events for civil protection activities purposes" that designs, within the Italian Civil Defence Emergency Management System, a network of 21 regional Functional Centres coordinated by a central office at the National Civil Protection Department in Rome. Due to its "linking" role between Civil Protection "real time" activities and environmental/planning "deferred time" ones, the Centre is in charge to acquire and collect both real time and quasi-static data: quantitative data from monitoring networks (hydrometeorological stations, meteo radar, ...), meteorological forecasting models output, Earth Observation data, hydraulic and hydrological simulation models, cartographic and thematic GIS data (vectorial and raster type), planning studies related to flooding areas mapping, dam managing plans during flood events, non instrumental information from direct control of "territorial presidium". A detailed procedure for the management of critical events was planned, also in order to define the different role of various authorities and institutions involved. Tiber River catchment, of which Umbria region represents the main upper-medium portion, includes also regional trans-boundary issues very important to cope with, especially for what concerns large dam behavior and management during heavy rainfall. The alert system is referred to 6 different warning areas in which the territory has been divided into and based on a threshold system of three different increasing critical levels according

  16. Timetable of an operational flood forecasting system

    NASA Astrophysics Data System (ADS)

    Liechti, Katharina; Jaun, Simon; Zappa, Massimiliano

    2010-05-01

    At present a new underground part of Zurich main station is under construction. For this purpose the runoff capacity of river Sihl, which is passing beneath the main station, is reduced by 40%. If a flood is to occur the construction site is evacuated and gates can be opened for full runoff capacity to prevent bigger damages. However, flooding the construction site, even if it is controlled, is coupled with costs and retardation. The evacuation of the construction site at Zurich main station takes about 2 to 4 hours and opening the gates takes another 1 to 2 hours each. In the upper part of the 336 km2 Sihl catchment the Sihl lake, a reservoir lake, is situated. It belongs and is used by the Swiss Railway Company for hydropower production. This lake can act as a retention basin for about 46% of the Sihl catchment. Lowering the lake level to gain retention capacity, and therewith safety, is coupled with direct loss for the Railway Company. To calculate the needed retention volume and the water to be released facing unfavourable weather conditions, forecasts with a minimum lead time of 2 to 3 days are needed. Since the catchment is rather small, this can only be realised by the use of meteorological forecast data. Thus the management of the construction site depends on accurate forecasts to base their decisions on. Therefore an operational hydrological ensemble prediction system (HEPS) was introduced in September 2008 by the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). It delivers daily discharge forecasts with a time horizon of 5 days. The meteorological forecasts are provided by MeteoSwiss and stem from the operational limited-area COSMO-LEPS which downscales the ECMWF ensemble prediction system to a spatial resolution of 7 km. Additional meteorological data for model calibration and initialisation (air temperature, precipitation, water vapour pressure, global radiation, wind speed and sunshine duration) and radar data are also provided by

  17. LiDAR-Derived Flood-Inundation Maps for Real-Time Flood-Mapping Applications, Tar River Basin, North Carolina

    USGS Publications Warehouse

    Bales, Jerad D.; Wagner, Chad R.; Tighe, Kirsten C.; Terziotti, Silvia

    2007-01-01

    reaches at 0.305-meter increments for water levels ranging from bankfull to approximately the highest recorded water level at the downstream-most gage in each modeled reach. Inundated areas were identified by subtracting the water-surface elevation in each 1.5-meter by 1.5-meter grid cell from the land-surface elevation in the cell through an automated routine that was developed to identify all inundated cells hydraulically connected to the cell at the downstream-most gage in the model domain. Inundation maps showing transportation networks and orthoimagery were prepared for display on the Internet. These maps also are linked to the U.S. Geological Survey North Carolina Water Science Center real-time streamflow website. Hence, a user can determine the near real-time stage and water-surface elevation at a U.S. Geological Survey streamgage site in the Tar River basin and link directly to the flood-inundation maps for a depiction of the estimated inundated area at the current water level. Although the flood-inundation maps represent distinct boundaries of inundated areas, some uncertainties are associated with these maps. These are uncertainties in the topographic data for the hydraulic model computational grid and inundation maps, effective friction values (Manning's n), model-validation data, and forecast hydrographs, if used. The Tar River flood-inundation maps were developed by using a steady-flow hydraulic model. This assumption clearly has less of an effect on inundation maps produced for low flows than for high flows when it typically takes more time to inundate areas. A flood in which water levels peak and fall slowly most likely will result in more inundation than a similar flood in which water levels peak and fall quickly. Limitations associated with the steady-flow assumption for hydraulic modeling vary from site to site. The one-dimensional modeling approach used in this study resulted in good agreement between measurements and simulations. T

  18. FEWS Vecht, a crossing boundaries flood forecasting system

    NASA Astrophysics Data System (ADS)

    van Heeringen, Klaas-Jan; Filius, Pieter; Tromp, Gerben; Renner, Tobias

    2013-04-01

    The river Vecht is a cross boundary river, starting in Germany and flowing to the Netherlands. The river is completely dependant on rainfall in the catchment. Being one of the smaller big rivers in the Netherlands, there was still no operational forecasting system avaible because of the hugh number of involved organisations (2 in Germany, 5 in the Netherlands) and many other stake holders. In 2011 a first operational forecasting system has been build by using the Delft-FEWS software. It collects the real time fluvial and meteorological observations from all the organisations, in that sense being a portal where all the collected information is available and can be consistantly interpreted as a whole. In 2012 an HBV rainfall runoff model and a Sobek 1D hydraulic model has been build. These models have been integrated into the FEWS system and are operationally running since the 2012 autumn. The system forecasts 5 days ahead using a 5 days ECMWF rainfall ensemble forecast. It enables making scenarios, especially useful for the operation of storage reservoirs. During the 2012 Christmas days a (relatively small) T=2 flood occurred (Q=175-200 m3/s) and proved the system to run succesfully. Dissemination of the forecasts is performed by using the FEWS system in all organisations, connected to the central system through internet. There is also a (password protected) website available that provides the current forecast to all stake holders in the catchment. The challenge of the project was not to make the models and to build the fews, but to connect all data and all operators together into one system, even cross boundary. Also in that sense the FEWS Vecht system has proved to be very succesful.

  19. A real time data acquisition system by satellite relay. [hydrology and flood measurement in New England

    NASA Technical Reports Server (NTRS)

    Cooper, S.

    1974-01-01

    The overall aim was to evaluate the future usefulness of satellites in the performance of coordination and management functions related to the operation of flood control and other multipurpose projects in New England. Results on the data collection portion of the work are presented. The principal task was to develop statistics that demonstrate the relationship between conventional means of acquiring hydrologic data and the contribution made by using the satellite and its data collection platforms. Main interest was in determining the availability, reliability and usability of the data. Significant results on DCS show that the DCP's are reliable and useful and satellite data collection appears feasible on a nationwide basis.

  20. Hourly runoff forecasting for flood risk management: Application of various computational intelligence models

    NASA Astrophysics Data System (ADS)

    Badrzadeh, Honey; Sarukkalige, Ranjan; Jayawardena, A. W.

    2015-10-01

    Reliable river flow forecasts play a key role in flood risk mitigation. Among different approaches of river flow forecasting, data driven approaches have become increasingly popular in recent years due to their minimum information requirements and ability to simulate nonlinear and non-stationary characteristics of hydrological processes. In this study, attempts are made to apply four different types of data driven approaches, namely traditional artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), wavelet neural networks (WNN), and, hybrid ANFIS with multi resolution analysis using wavelets (WNF). Developed models applied for real time flood forecasting at Casino station on Richmond River, Australia which is highly prone to flooding. Hourly rainfall and runoff data were used to drive the models which have been used for forecasting with 1, 6, 12, 24, 36 and 48 h lead-time. The performance of models further improved by adding an upstream river flow data (Wiangaree station), as another effective input. All models perform satisfactorily up to 12 h lead-time. However, the hybrid wavelet-based models significantly outperforming the ANFIS and ANN models in the longer lead-time forecasting. The results confirm the robustness of the proposed structure of the hybrid models for real time runoff forecasting in the study area.

  1. The state of the art of flood forecasting - Hydrological Ensemble Prediction Systems

    NASA Astrophysics Data System (ADS)

    Thielen-Del Pozo, J.; Pappenberger, F.; Salamon, P.; Bogner, K.; Burek, P.; de Roo, A.

    2010-09-01

    , has become evident. However, despite the demonstrated advantages, worldwide the incorporation of HEPS in operational flood forecasting is still limited. The applicability of HEPS for smaller river basins was tested in MAP D-Phase, an acronym for "Demonstration of Probabilistic Hydrological and Atmospheric Simulation of flood Events in the Alpine region" which was launched in 2005 as a Forecast Demonstration Project of World Weather Research Programme of WMO, and entered a pre-operational and still active testing phase in 2007. In Europe, a comparatively high number of EPS driven systems for medium-large rivers exist. National flood forecasting centres of Sweden, Finland and the Netherlands, have already implemented HEPS in their operational forecasting chain, while in other countries including France, Germany, Czech Republic and Hungary, hybrids or experimental chains have been installed. As an example of HEPS, the European Flood Alert System (EFAS) is being presented. EFAS provides medium-range probabilistic flood forecasting information for large trans-national river basins. It incorporates multiple sets of weather forecast including different types of EPS and deterministic forecasts from different providers. EFAS products are evaluated and visualised as exceedance of critical levels only - both in forms of maps and time series. Different sources of uncertainty and its impact on the flood forecasting performance for every grid cell has been tested offline but not yet incorporated operationally into the forecasting chain for computational reasons. However, at stations where real-time discharges are available, a hydrological uncertainty processor is being applied to estimate the total predictive uncertainty from the hydrological and input uncertainties. Research on long-term EFAS results has shown the need for complementing statistical analysis with case studies for which examples will be shown.

  2. Development of a System to Generate Near Real Time Tropospheric Delay and Precipitable Water Vapor in situ at Geodetic GPS Stations, to Improve Forecasting of Severe Weather Events

    NASA Astrophysics Data System (ADS)

    Moore, A. W.; Bock, Y.; Geng, J.; Gutman, S. I.; Laber, J. L.; Morris, T.; Offield, D. G.; Small, I.; Squibb, M. B.

    2012-12-01

    We describe a system under development for generating ultra-low latency tropospheric delay and precipitable water vapor (PWV) estimates in situ at a prototype network of geodetic GPS sites in southern California, and demonstrating their utility in forecasting severe storms commonly associated with flooding and debris flow events along the west coast of North America through infusion of this meteorological data at NOAA National Weather Service (NWS) Forecast Offices and the NOAA Earth System Research Laboratory (ESRL). The first continuous geodetic GPS network was established in southern California in the early 1990s and much of it was converted to real-time (latency <1s) high-rate (1Hz) mode over the following decades. GPS stations are multi-purpose and can also provide estimates of tropospheric zenith delays, which can be converted into mm-accuracy PWV using collocated pressure and temperature measurements, the basis for GPS meteorology (Bevis et al. 1992, 1994; Duan et al. 1996) as implemented by NOAA with a nationwide distribution of about 300 GPS-Met stations providing PW estimates at subhourly resolution currently used in operational weather forecasting in the U.S. We improve upon the current paradigm of transmitting large quantities of raw data back to a central facility for processing into higher-order products. By operating semi-autonomously, each station will provide low-latency, high-fidelity and compact data products within the constraints of the narrow communications bandwidth that often occurs in the aftermath of natural disasters. The onsite ambiguity-resolved precise point positioning solutions are enabled by a power-efficient, low-cost, plug-in Geodetic Module for fusion of data from in situ sensors including GPS and a low-cost MEMS meteorological sensor package. The decreased latency (~5 minutes) PW estimates will provide the detailed knowledge of the distribution and magnitude of PW that NWS forecasters require to monitor and predict severe winter

  3. Surface Temperature Variation Prediction Model Using Real-Time Weather Forecasts

    NASA Astrophysics Data System (ADS)

    Karimi, M.; Vant-Hull, B.; Nazari, R.; Khanbilvardi, R.

    2015-12-01

    Combination of climate change and urbanization are heating up cities and putting the lives of millions of people in danger. More than half of the world's total population resides in cities and urban centers. Cities are experiencing urban Heat Island (UHI) effect. Hotter days are associated with serious health impacts, heart attaches and respiratory and cardiovascular diseases. Densely populated cities like Manhattan, New York can be affected by UHI impact much more than less populated cities. Even though many studies have been focused on the impact of UHI and temperature changes between urban and rural air temperature, not many look at the temperature variations within a city. These studies mostly use remote sensing data or typical measurements collected by local meteorological station networks. Local meteorological measurements only have local coverage and cannot be used to study the impact of UHI in a city and remote sensing data such as MODIS, LANDSAT and ASTER have with very low resolution which cannot be used for the purpose of this study. Therefore, predicting surface temperature in urban cities using weather data can be useful.Three months of Field campaign in Manhattan were used to measure spatial and temporal temperature variations within an urban setting by placing 10 fixed sensors deployed to measure temperature, relative humidity and sunlight. Fixed instrument shelters containing relative humidity, temperature and illumination sensors were mounted on lampposts in ten different locations in Manhattan (Vant-Hull et al, 2014). The shelters were fixed 3-4 meters above the ground for the period of three months from June 23 to September 20th of 2013 making measurements with the interval of 3 minutes. These high resolution temperature measurements and three months of weather data were used to predict temperature variability from weather forecasts. This study shows that the amplitude of spatial and temporal variation in temperature for each day can be predicted

  4. Real-time variational assimilation of streamflow and radar-based precipitation data into operational hydrologic forecasting

    NASA Astrophysics Data System (ADS)

    Seo, D.; Koren, V.; Cajina, L.; Corby, R.; Finn, B.; Bell, F.

    2003-04-01

    To deal with various sources of error on the initial and boundary conditions, and in model parameters and structure, some form of state updating is necessary in operational forecasting that makes use of real-time streamflow observations. Here we analyze the benefit of variational assimilation as an automatic updating technique in an operational setting. Compared to state space-based techniques (e.g. Kalman filtering), variational assimilation (VAR)-based techniques offer at least two important advantages in state updating of operational hydrologic models that are, in particular, driven by radar-based precipitation input. 1) Because VAR does not require the hydrologic model to be rendered into a state-space form, no modifications are necessary to the model. Hence, the model parameters are completely transferable between calibration and state updating/assimilation. 2) Because VAR is a smoother, as opposed to a filter, VAR is very effective in assimilating data that are subject to significant biases, such as radar-based precipitation estimates. Following long-term off-line evaluation, a prototype VAR algorithm has been implemented recently at the National Weather Service West Gulf River Forecast Center (NWS/WGRFC). In this presentation, we describe the operational experience to date and the issues identified, and offer directions for further improvement.

  5. Flood member detection for real-time structural health monitoring of sub-sea structures of offshore steel oilrigs

    NASA Astrophysics Data System (ADS)

    Mijarez, Rito; Gaydecki, Patrick; Burdekin, Michael

    2007-10-01

    A structural flood detection system for real-time health monitoring in the hollow sub-sea members of new offshore steel oilrigs is presented. Field-proven flood member detection techniques, integrated within the concept of health monitoring, offer an alternative to underwater nondestructive testing methods based on ultrasound and x-rays, which have been used to detect the presence of seawater in these applications, often with diverse or remote operating vehicles. The system employs a single piezoelectric transducer which can be permanently attached to the inner wall of every sub-sea structure and which is powered by a normally inert seawater battery. Upon activation, the sensor transmits ultrasonic chirp or tone encoded pulses, in the range of 21-42 kHz, to a monitoring receiver system at deck level for decoding and identifying flooded members. Experiments have been carried out using a jointed steel pipe structure, 7 m in length, 0.5 m in diameter and 16 mm in thickness. This structure was flooded and completely immersed in seawater. Two approaches to the system were considered during the investigation, depending on the communication channel exploited; the former utilized guided waves, on the basis of exploiting the steel structure as a wave-guide; the latter employed underwater ultrasound, based on using the seawater as a propagation medium. Although severe losses were encountered in both approaches, the system effectively identified the signals above the background noise. This work forms the foundation for the future development of a system that can be used with large, commercial offshore platforms.

  6. Integral assessment of floodplains as a basis for spatially-explicit flood loss forecasts

    NASA Astrophysics Data System (ADS)

    Zischg, Andreas Paul; Mosimann, Markus; Weingartner, Rolf

    2016-04-01

    A key aspect of disaster prevention is flood discharge forecasting which is used for early warning and therefore as a decision support for intervention forces. Hereby, the phase between the issued forecast and the time when the expected flood occurs is crucial for an optimal planning of the intervention. Typically, river discharge forecasts cover the regional level only, i.e. larger catchments. However, it is important to note that these forecasts are not useable directly for specific target groups on local level because these forecasts say nothing about the consequences of the predicted flood in terms of affected areas, number of exposed residents and houses. For this, on one hand simulations of the flooding processes and on the other hand data of vulnerable objects are needed. Furthermore, flood modelling in a high spatial and temporal resolution is required for robust flood loss estimation. This is a resource-intensive task from a computing time point of view. Therefore, in real-time applications flood modelling in 2D is not suited. Thus, forecasting flood losses in the short-term (6h-24h in advance) requires a different approach. Here, we propose a method to downscale the river discharge forecast to a spatially-explicit flood loss forecast. The principal procedure is to generate as many flood scenarios as needed in advance to represent the flooded areas for all possible flood hydrographs, e.g. very high peak discharges of short duration vs. high peak discharges with high volumes. For this, synthetic flood hydrographs were derived from the hydrologic time series. Then, the flooded areas of each scenario were modelled with a 2D flood simulation model. All scenarios were intersected with the dataset of vulnerable objects, in our case residential, agricultural and industrial buildings with information about the number of residents, the object-specific vulnerability, and the monetary value of the objects. This dataset was prepared by a data-mining approach. For each

  7. Advances in Global Flood Forecasting Systems

    NASA Astrophysics Data System (ADS)

    Thielen-del Pozo, J.; Pappenberger, F.; Burek, P.; Alfieri, L.; Kreminski, B.; Muraro, D.

    2012-12-01

    A trend of increasing number of heavy precipitation events over many regions in the world during the past century has been observed (IPCC, 2007), but conclusive results on a changing frequency or intensity of floods have not yet been established. However, the socio-economic impact particularly of floods is increasing at an alarming trend. Thus anticipation of severe events is becoming a key element of society to react timely to effectively reduce socio-economic damage. Anticipation is essential on local as well as on national or trans-national level since management of response and aid for major disasters requires a substantial amount of planning and information on different levels. Continental and trans-national flood forecasting systems already exist. The European Flood Awareness System (EFAS) has been developed in close collaboration with the National services and is going operational in 2012, enhancing the national forecasting centres with medium-range probabilistic added value information while at the same time providing the European Civil Protection with harmonised information on ongoing and upcoming floods for improved aid management. Building on experiences and methodologies from EFAS, a Global Flood Awareness System (GloFAS) has now been developed jointly between researchers from the European Commission Joint Research Centre (JRC) and the European Centre for Medium-Range Weather Forecast (ECWMF). The prototype couples HTESSEL, the land-surface scheme of the ECMWF NWP model with the LISFLOOD hydrodynamic model for the flow routing in the river network. GloFAS is set-up on global scale with horizontal grid spacing of 0.1 degree. The system is driven with 51 ensemble members from VAREPS with a time horizon of 15 days. In order to allow for the routing in the large rivers, the coupled model is run for 45 days assuming zero rainfall after day 15. Comparison with observations have shown that in some rivers the system performs quite well while in others the hydro

  8. Development of Hydrometeorological Monitoring and Forecasting as AN Essential Component of the Early Flood Warning System:

    NASA Astrophysics Data System (ADS)

    Manukalo, V.

    2012-12-01

    Defining issue The river inundations are the most common and destructive natural hazards in Ukraine. Among non-structural flood management and protection measures a creation of the Early Flood Warning System is extremely important to be able to timely recognize dangerous situations in the flood-prone areas. Hydrometeorological information and forecasts are a core importance in this system. The primary factors affecting reliability and a lead - time of forecasts include: accuracy, speed and reliability with which real - time data are collected. The existing individual conception of monitoring and forecasting resulted in a need in reconsideration of the concept of integrated monitoring and forecasting approach - from "sensors to database and forecasters". Result presentation The Project: "Development of Flood Monitoring and Forecasting in the Ukrainian part of the Dniester River Basin" is presented. The project is developed by the Ukrainian Hydrometeorological Service in a conjunction with the Water Management Agency and the Energy Company "Ukrhydroenergo". The implementation of the Project is funded by the Ukrainian Government and the World Bank. The author is nominated as the responsible person for coordination of activity of organizations involved in the Project. The term of the Project implementation: 2012 - 2014. The principal objectives of the Project are: a) designing integrated automatic hydrometeorological measurement network (including using remote sensing technologies); b) hydrometeorological GIS database construction and coupling with electronic maps for flood risk assessment; c) interface-construction classic numerical database -GIS and with satellite images, and radar data collection; d) providing the real-time data dissemination from observation points to forecasting centers; e) developing hydrometeoroogical forecasting methods; f) providing a flood hazards risk assessment for different temporal and spatial scales; g) providing a dissemination of

  9. LAV@HAZARD: a Web-GIS Framework for Real-Time Forecasting of Lava Flow Hazards

    NASA Astrophysics Data System (ADS)

    Del Negro, C.; Bilotta, G.; Cappello, A.; Ganci, G.; Herault, A.

    2014-12-01

    Crucial to lava flow hazard assessment is the development of tools for real-time prediction of flow paths, flow advance rates, and final flow lengths. Accurate prediction of flow paths and advance rates requires not only rapid assessment of eruption conditions (especially effusion rate) but also improved models of lava flow emplacement. Here we present the LAV@HAZARD web-GIS framework, which combines spaceborne remote sensing techniques and numerical simulations for real-time forecasting of lava flow hazards. By using satellite-derived discharge rates to drive a lava flow emplacement model, LAV@HAZARD allows timely definition of parameters and maps essential for hazard assessment, including the propagation time of lava flows and the maximum run-out distance. We take advantage of the flexibility of the HOTSAT thermal monitoring system to process satellite images coming from sensors with different spatial, temporal and spectral resolutions. HOTSAT was designed to ingest infrared satellite data acquired by the MODIS and SEVIRI sensors to output hot spot location, lava thermal flux and discharge rate. We use LAV@HAZARD to merge this output with the MAGFLOW physics-based model to simulate lava flow paths and to update, in a timely manner, flow simulations. Thus, any significant changes in lava discharge rate are included in the predictions. A significant benefit in terms of computational speed was obtained thanks to the parallel implementation of MAGFLOW on graphic processing units (GPUs). All this useful information has been gathered into the LAV@HAZARD platform which, due to the high degree of interactivity, allows generation of easily readable maps and a fast way to explore alternative scenarios. We will describe and demonstrate the operation of this framework using a variety of case studies pertaining to Mt Etna, Sicily. Although this study was conducted on Mt Etna, the approach used is designed to be applicable to other volcanic areas around the world.

  10. A Real-Time Nowcast/Forecast System for Radar Electrojet Clutter Driven by Global Assimilative Models of the Ionosphere

    NASA Astrophysics Data System (ADS)

    Carrano, C. S.; Alcala, C. M.; Liang, P.; Groves, K. M.; Donatelli, D. E.; Daniell, R. E.

    2006-12-01

    -region. This deficiency is particularly pronounced during geomagnetic storm activity, when the ionospheric response deviates most from climatological behavior. The latest version of the SBR-IES tool can accept, as input, real-time specifications of the ionosphere provided by global assimilative models (e.g. PRISM or GAIM) that are currently or soon to be in operational use at AFWA. Forecasts of radar clutter can be generated using forecasts of the ionospheric state provided by the Ionospheric Forecast Model (IFM), for example. In the near future we plan to include the high resolution specification of the electric field provided by the real-time incoherent scatter radars of the Super Dual Auroral Radar Network (SuperDARN). It is expected that the use of data assimilative models to provide the background ionospheric densities, temperatures, and electric field will lead to substantially more accurate and high resolution predictions of radar electrojet clutter. Moreover, these improvements can be made without sacrificing real-time impact assessment requirements.

  11. Forecasting skills of the ensemble hydro-meteorological system for the Po river floods

    NASA Astrophysics Data System (ADS)

    Ricciardi, Giuseppe; Montani, Andrea; Paccagnella, Tiziana; Pecora, Silvano; Tonelli, Fabrizio

    2013-04-01

    The Po basin is the largest and most economically important river-basin in Italy. Extreme hydrological events, including floods, flash floods and droughts, are expected to become more severe in the next future due to climate change, and related ground effects are linked both with environmental and social resilience. A Warning Operational Center (WOC) for hydrological event management was created in Emilia Romagna region. In the last years, the WOC faced challenges in legislation, organization, technology and economics, achieving improvements in forecasting skill and information dissemination. Since 2005, an operational forecasting and modelling system for flood modelling and forecasting has been implemented, aimed at supporting and coordinating flood control and emergency management on the whole Po basin. This system, referred to as FEWSPo, has also taken care of environmental aspects of flood forecast. The FEWSPo system has reached a very high level of complexity, due to the combination of three different hydrological-hydraulic chains (HEC-HMS/RAS - MIKE11 NAM/HD, Topkapi/Sobek), with several meteorological inputs (forecasted - COSMOI2, COSMOI7, COSMO-LEPS among others - and observed). In this hydrological and meteorological ensemble the management of the relative predictive uncertainties, which have to be established and communicated to decision makers, is a debated scientific and social challenge. Real time activities face professional, modelling and technological aspects but are also strongly interrelated with organization and human aspects. The authors will report a case study using the operational flood forecast hydro-meteorological ensemble, provided by the MIKE11 chain fed by COSMO_LEPS EQPF. The basic aim of the proposed approach is to analyse limits and opportunities of the long term forecast (with a lead time ranging from 3 to 5 days), for the implementation of low cost actions, also looking for a well informed decision making and the improvement of

  12. Enhancing flood forecasting with the help of processed based calibration

    NASA Astrophysics Data System (ADS)

    Cullmann, Johannes; Krauße, Thomas; Philipp, Andy

    -234; Cullmann, J., Schmitz, G.H., Görner, W., 2006. A new strategy for online flood forecasting in mountainous catchments. in: IAHS Red Book, vol. 303]. Merging of the singular parameter class models is done with the help of a sigmoidal weighting procedure. The new approach thus integrates all available information from the specially calibrated WaSiM-ETH class models, accounting for the different processes and dynamics governing the various event classes. For example it portrays the flood formation process with parameters accounting for the characteristics of the event class models. Implications arising from this study are demonstrated for a catchment in the Erzgebirge (Ore-mountains) in East Germany (1700 km). The computational efficiency, together with the convincing agreement between the predicted and observed flood peaks underlines the potential of the new parameterisation strategy in the context of operational real time forecasting.

  13. An integrated modeling framework for real-time irrigation scheduling: the benefit of spectroscopy and weather forecasts

    NASA Astrophysics Data System (ADS)

    Brook, Anna; Polinova, Maria; Housh, Mashor

    2016-04-01

    ). These studies have only incorporated short-term (weekly) forecasts, missing the potential benefit of the mid-term (seasonal) climate forecasts The latest progress in new data acquisition technologies (mainly in the field of Earth observation by remote sensing and imaging spectroscopy systems) as well as the state-of-the-art achievements in the fields of geographical information systems (GIS), computer science and climate and climate impact modelling enable to develop both integrated modelling and realistic spatial simulations. The present method is the use of field spectroscopy technology to keep constant monitoring of the field. The majority of previously developed decision support systems use satellite remote sensing data that provide very limited capabilities (conventional and basic parameters). The alternative is to use a more progressive technology of hyperspectral airborne or ground-based imagery data that provide an exhaustive description of the field. Nevertheless, this alternative is known to be very costly and complex. As such, we will present a low-cost imaging spectroscopy technology supported by detailed and fine-resolution field spectroscopy as a cost effective option for near field real-time monitoring tool. In order to solve the soil water balance and to predict the water irrigation volume a pedological survey is realized in the evaluation study areas.The remote sensing and field spectroscopy were applied to integrate continuous feedbacks from the field (e.g. soil moisture, organic/inorganic carbon, nitrogen, salinity, fertilizers, sulphur acid, texture; crop water-stress, plant stage, LAI , chlorophyll, biomass, yield prediction applying PROSPECT+SILT ; Fraction of Absorbed Photosynthetically Active Radiation FAPAR) estimated based on remote sensing information to minimize the errors associated with crop simulation process. A stochastic optimization model will be formulated that take into account both mid-term seasonal probabilistic climate prediction

  14. SOM-based Hybrid Neural Network Model for Flood Inundation Extent Forecasting

    NASA Astrophysics Data System (ADS)

    Chang, Li-Chiu; Shen, Hung-Yu; Chang, Fi-John

    2014-05-01

    In recent years, the increasing frequency and severity of floods caused by climate change and/or land overuse has been reported both nationally and globally. Therefore, estimation of flood depths and extents may provide disaster information for alleviating risk and loss of life and property. The conventional inundation models commonly need a huge amount of computational time to carry out a high resolution spatial inundation map. Moreover, for implementing appropriate mitigation strategies of various flood conditions, different flood scenarios and the corresponding mitigation alternatives are required. Consequently, it is difficult to reach real-time forecast of the inundation extent by conventional inundation models. This study proposed a SOM-RNARX model, for on-line forecasting regional flood inundation depths and extents. The SOM-RNARX model is composed of SOM (Self-Organizing Map) and RNARX (recurrent configuration of nonlinear autoregressive with exogenous inputs). The SOM network categorizes various flood inundation maps of the study area to produce a meaningful regional flood topological map. The RNARX model is built to forecast the total flooded volume of the study area. To find the neuron with the closest total inundated volume to the forecasted total inundated volumes, the forecasted value is used to adjust the weights (inundated depths) of the closest neuron and obtain a regional flood inundation map. The proposed methodology was trained and tested based on a large number of inundation data generated by a well validated two-dimensional simulation model in Yilan County, Taiwan. For comparison, the CHIM (clustering-based hybrid inundation model) model which was issued by Chang et al. (2010) was performed. The major difference between these two models is that CHIM classify flooding characteristics, and SOM-RNARX extracts the relationship between rainfall pattern and flooding spatial distribution. The results show that (1)two models can adequately provide on

  15. An evaluation of the real-time tropical cyclone forecast skill of the Navy Operational Global Atmospheric Prediction System in the western North Pacific

    SciTech Connect

    Fiorino, M.; Goerss, J.S.; Jensen, J.J.; Harrison, E.J. Jr. Naval Research Lab., Monterey, CA Fleet Numerical Oceanography Center, Monterey, CA ARC Professional Services Group, Inc., Landover, MD )

    1993-03-01

    The paper evaluates the meteorological quality and operational utility of the Navy Operational Global Atmospheric Prediction System (NOGAPS) in forecasting tropical cyclones. It is shown that the model can provide useful predictions of motion and formation on a real-time basis in the western North Pacific. The meterological characteristics of the NOGAPS tropical cyclone predictions are evaluated by examining the formation of low-level cyclone systems in the tropics and vortex structure in the NOGAPS analysis and verifying 72-h forecasts. The adjusted NOGAPS track forecasts showed equitable skill to the baseline aid and the dynamical model. NOGAPS successfully predicted unusual equatorward turns for several straight-running cyclones. 35 refs.

  16. An integrated modeling framework for real-time irrigation scheduling: the benefit of spectroscopy and weather forecasts

    NASA Astrophysics Data System (ADS)

    Brook, Anna; Polinova, Maria; Housh, Mashor

    2016-04-01

    ). These studies have only incorporated short-term (weekly) forecasts, missing the potential benefit of the mid-term (seasonal) climate forecasts The latest progress in new data acquisition technologies (mainly in the field of Earth observation by remote sensing and imaging spectroscopy systems) as well as the state-of-the-art achievements in the fields of geographical information systems (GIS), computer science and climate and climate impact modelling enable to develop both integrated modelling and realistic spatial simulations. The present method is the use of field spectroscopy technology to keep constant monitoring of the field. The majority of previously developed decision support systems use satellite remote sensing data that provide very limited capabilities (conventional and basic parameters). The alternative is to use a more progressive technology of hyperspectral airborne or ground-based imagery data that provide an exhaustive description of the field. Nevertheless, this alternative is known to be very costly and complex. As such, we will present a low-cost imaging spectroscopy technology supported by detailed and fine-resolution field spectroscopy as a cost effective option for near field real-time monitoring tool. In order to solve the soil water balance and to predict the water irrigation volume a pedological survey is realized in the evaluation study areas.The remote sensing and field spectroscopy were applied to integrate continuous feedbacks from the field (e.g. soil moisture, organic/inorganic carbon, nitrogen, salinity, fertilizers, sulphur acid, texture; crop water-stress, plant stage, LAI , chlorophyll, biomass, yield prediction applying PROSPECT+SILT ; Fraction of Absorbed Photosynthetically Active Radiation FAPAR) estimated based on remote sensing information to minimize the errors associated with crop simulation process. A stochastic optimization model will be formulated that take into account both mid-term seasonal probabilistic climate prediction

  17. Real time electromagnetic monitoring system used for short-term earthquakes forecast related to the seismic-active Vrancea zone

    NASA Astrophysics Data System (ADS)

    Stanica, Dumitru; Armand Stanica, Dragos

    2016-04-01

    The existence of the pre-seismic electromagnetic signals related to the earthquakes is still under scientific debate and requires new reliable information about their possible inter-relationship. In this paper, to obtain new insights into the seismic active Vrancea zone (Romania), a 3-D magnetotelluric imaging has been used to strengthen the connection between the geodynamic model and a possible generation mechanism of the intermediate depth earthquakes. Consequently, it is considered that before an earthquake initiation, due to the torsion effect, a high stress reached inside the seismogenic volume that may generates dehydration and rupture processes of the rocks, associated with the fluid migration through the lithospheric faults system, what leads to the resistivity changes. These changes have been investigated by using ULF electromagnetic data recorded in real time at the Geodynamic Observatory Provita de Sus (GOPS), placed on the Carpathian Electrical Conductivity Anomaly (CECA) at about 100km far from the seismic active Vrancea zone. The daily mean distribution of the normalized function Bzn(f) = Bz(f)/Bperp(f) (where: Bz is vertical component of the geomagnetic field; Bperp is geomagnetic component perpendicular to strike; f is frequency in Hz) and its standard deviation are performed by using a FFT band-pass filter analysis in the ULF range 0.001Hz to 0.0083Hz, for which a 2-D geoelectrical structure under GOPS has been identified. To provide reliable information in anticipating the likelihood occurrence of an earthquake of Mw higher than 4, a statistical analysis based on standardized random variable equation has been used to identify the anomalous intervals on the new time series (Bzn*) carried out in a span of three years (2013-2015). The final conclusion is that the Bzn* shows a significant anomalous effect some days (weeks) before an impending earthquake and it should be used for short-term earthquakes forecast.

  18. Characterizing 13 Years of Surface Water Variability from MODIS-based Near Real-Time Flood Mapping Products in the Indus River, Tonle Sap Lake, and Lake Chad.

    NASA Astrophysics Data System (ADS)

    Slayback, D. A.; Brakenridge, G. R.; Policelli, F. S.

    2015-12-01

    Driven by an increase in extreme weather events in a warming world, flooding appears to be increasing in many regions. Since 2012, we have been using the twice-daily near-global observations of the two MODIS instruments to operate a near real-time flood mapping capability. Primarily intended to support disaster response efforts, our system generates daily near-global maps of flood water extent, at 250 m resolution. Although cloud cover is a challenge, the twice-daily coverage from the Terra and Aqua satellites helps to capture most major events. We use the MOD44W product (the "MODIS 250-m land-water mask") to differentiate "normal" water from flood water. Products from the system are freely available, and used by disaster response agencies and academic and industry researchers. An open question, however, is: how "normal" are recently observed floods? Destructive and — as reported by the press — record floods seem to be occurring more and more frequently. With the MODIS archive going back to 1999 (Terra satellite) and 2002 (Aqua satellite), we now have more than a decade of twice-daily near-global observations to begin answering this question. Although the 13 years of available twice-daily data (2002-2015) are not sufficient to fully characterize surface water normals (e.g., 100-year floods), we can start examining recent trends in surface water extent and flood frequency. To do so, we have back-processed our surface water product through mid-2002 (Aqua launch) for a few regions, and have used this to evaluate the variability in surface water extent and flood frequency. These results will eventually feed back into an improved characterization of flood water in our near real-time flood product. Here we will present results on trends in surface water extent and flood frequency for a few regions, including the Indus in Pakistan, the Tonle Sap lake in Cambodia, and lake Chad in Africa.

  19. Evaluation of Flood Forecast and Warning in Elbe river basin - Impact of Forecaster's Strategy

    NASA Astrophysics Data System (ADS)

    Danhelka, Jan; Vlasak, Tomas

    2010-05-01

    Czech Hydrometeorological Institute (CHMI) is responsible for flood forecasting and warning in the Czech Republic. To meet that issue CHMI operates hydrological forecasting systems and publish flow forecast in selected profiles. Flood forecast and warning is an output of system that links observation (flow and atmosphere), data processing, weather forecast (especially NWP's QPF), hydrological modeling and modeled outputs evaluation and interpretation by forecaster. Forecast users are interested in final output without separating uncertainties of separate steps of described process. Therefore an evaluation of final operational forecasts was done for profiles within Elbe river basin produced by AquaLog forecasting system during period 2002 to 2008. Effects of uncertainties of observation, data processing and especially meteorological forecasts were not accounted separately. Forecast of flood levels exceedance (peak over the threshold) during forecasting period was the main criterion as flow increase forecast is of the highest importance. Other evaluation criteria included peak flow and volume difference. In addition Nash-Sutcliffe was computed separately for each time step (1 to 48 h) of forecasting period to identify its change with the lead time. Textual flood warnings are issued for administrative regions to initiate flood protection actions in danger of flood. Flood warning hit rate was evaluated at regions level and national level. Evaluation found significant differences of model forecast skill between forecasting profiles, particularly less skill was evaluated at small headwater basins due to domination of QPF uncertainty in these basins. The average hit rate was 0.34 (miss rate = 0.33, false alarm rate = 0.32). However its explored spatial difference is likely to be influenced also by different fit of parameters sets (due to different basin characteristics) and importantly by different impact of human factor. Results suggest that the practice of interactive

  20. Improving the accuracy of flood forecasting with transpositions of ensemble NWP rainfall fields considering orographic effects

    NASA Astrophysics Data System (ADS)

    Yu, Wansik; Nakakita, Eiichi; Kim, Sunmin; Yamaguchi, Kosei

    2016-08-01

    The use of meteorological ensembles to produce sets of hydrological predictions increased the capability to issue flood warnings. However, space scale of the hydrological domain is still much finer than meteorological model, and NWP models have challenges with displacement. The main objective of this study to enhance the transposition method proposed in Yu et al. (2014) and to suggest the post-processing ensemble flood forecasting method for the real-time updating and the accuracy improvement of flood forecasts that considers the separation of the orographic rainfall and the correction of misplaced rain distributions using additional ensemble information through the transposition of rain distributions. In the first step of the proposed method, ensemble forecast rainfalls from a numerical weather prediction (NWP) model are separated into orographic and non-orographic rainfall fields using atmospheric variables and the extraction of topographic effect. Then the non-orographic rainfall fields are examined by the transposition scheme to produce additional ensemble information and new ensemble NWP rainfall fields are calculated by recombining the transposition results of non-orographic rain fields with separated orographic rainfall fields for a generation of place-corrected ensemble information. Then, the additional ensemble information is applied into a hydrologic model for post-flood forecasting with a 6-h interval. The newly proposed method has a clear advantage to improve the accuracy of mean value of ensemble flood forecasting. Our study is carried out and verified using the largest flood event by typhoon 'Talas' of 2011 over the two catchments, which are Futatsuno (356.1 km2) and Nanairo (182.1 km2) dam catchments of Shingu river basin (2360 km2), which is located in the Kii peninsula, Japan.

  1. The FAST-T approach for operational, real time, short term hydrological forecasting: Results from the Betania Hydropower Reservoir case study

    NASA Astrophysics Data System (ADS)

    Domínguez, Efraín; Angarita, Hector; Rosmann, Thomas; Mendez, Zulma; Angulo, Gustavo

    2013-04-01

    A viable quantitative hydrological forecasting service is a combination of technological elements, personnel and knowledge, working together to establish a stable operational cycle of forecasts emission, dissemination and assimilation; hence, the process for establishing such system usually requires significant resources and time to reach an adequate development and integration in order to produce forecasts with acceptable levels of performance. Here are presented the results of this process for the recently implemented Operational Forecast Service for the Betania's Hydropower Reservoir - or SPHEB, located at the Upper-Magdalena River Basin (Colombia). The current scope of the SPHEB includes forecasting of water levels and discharge for the three main streams affluent to the reservoir, for lead times between +1 to +57 hours, and +1 to +10 days. The core of the SPHEB is the Flexible, Adaptive, Simple and Transient Time forecasting approach, namely FAST-T. This comprises of a set of data structures, mathematical kernel, distributed computing and network infrastructure designed to provide seamless real-time operational forecast and automatic model adjustment in case of failures in data transmission or assimilation. Among FAST-T main features are: an autonomous evaluation and detection of the most relevant information for the later configuration of forecasting models; an adaptively linearized mathematical kernel, the optimal adaptive linear combination or OALC, which provides a computationally simple and efficient algorithm for real-time applications; and finally, a meta-model catalog, containing prioritized forecast models at given stream conditions. The SPHEB is at present feed by the fraction of hydrological monitoring network installed at the basin that has telemetric capabilities via NOAA-GOES satellites (8 stages, approximately 47%) with data availability of about a 90% at one hour intervals. However, there is a dense network of 'conventional' hydro

  2. Application of WRF - SWAT OpenMI 2.0 based models integration for real time hydrological modelling and forecasting

    NASA Astrophysics Data System (ADS)

    Bugaets, Andrey; Gonchukov, Leonid

    2014-05-01

    Intake of deterministic distributed hydrological models into operational water management requires intensive collection and inputting of spatial distributed climatic information in a timely manner that is both time consuming and laborious. The lead time of the data pre-processing stage could be essentially reduced by coupling of hydrological and numerical weather prediction models. This is especially important for the regions such as the South of the Russian Far East where its geographical position combined with a monsoon climate affected by typhoons and extreme heavy rains caused rapid rising of the mountain rivers water level and led to the flash flooding and enormous damage. The objective of this study is development of end-to-end workflow that executes, in a loosely coupled mode, an integrated modeling system comprised of Weather Research and Forecast (WRF) atmospheric model and Soil and Water Assessment Tool (SWAT 2012) hydrological model using OpenMI 2.0 and web-service technologies. Migration SWAT into OpenMI compliant involves reorganization of the model into a separate initialization, performing timestep and finalization functions that can be accessed from outside. To save SWAT normal behavior, the source code was separated from OpenMI-specific implementation into the static library. Modified code was assembled into dynamic library and wrapped into C# class implemented the OpenMI ILinkableComponent interface. Development of WRF OpenMI-compliant component based on the idea of the wrapping web-service clients into a linkable component and seamlessly access to output netCDF files without actual models connection. The weather state variables (precipitation, wind, solar radiation, air temperature and relative humidity) are processed by automatic input selection algorithm to single out the most relevant values used by SWAT model to yield climatic data at the subbasin scale. Spatial interpolation between the WRF regular grid and SWAT subbasins centroid (which are

  3. Action-based flood forecasting for triggering humanitarian action

    NASA Astrophysics Data System (ADS)

    Coughlan de Perez, Erin; van den Hurk, Bart; van Aalst, Maarten K.; Amuron, Irene; Bamanya, Deus; Hauser, Tristan; Jongma, Brenden; Lopez, Ana; Mason, Simon; Mendler de Suarez, Janot; Pappenberger, Florian; Rueth, Alexandra; Stephens, Elisabeth; Suarez, Pablo; Wagemaker, Jurjen; Zsoter, Ervin

    2016-09-01

    Too often, credible scientific early warning information of increased disaster risk does not result in humanitarian action. With financial resources tilted heavily towards response after a disaster, disaster managers have limited incentive and ability to process complex scientific data, including uncertainties. These incentives are beginning to change, with the advent of several new forecast-based financing systems that provide funding based on a forecast of an extreme event. Given the changing landscape, here we demonstrate a method to select and use appropriate forecasts for specific humanitarian disaster prevention actions, even in a data-scarce location. This action-based forecasting methodology takes into account the parameters of each action, such as action lifetime, when verifying a forecast. Forecasts are linked with action based on an understanding of (1) the magnitude of previous flooding events and (2) the willingness to act "in vain" for specific actions. This is applied in the context of the Uganda Red Cross Society forecast-based financing pilot project, with forecasts from the Global Flood Awareness System (GloFAS). Using this method, we define the "danger level" of flooding, and we select the probabilistic forecast triggers that are appropriate for specific actions. Results from this methodology can be applied globally across hazards and fed into a financing system that ensures that automatic, pre-funded early action will be triggered by forecasts.

  4. Prediction and uncertainty of Hurricane Sandy (2012) explored through a real-time cloud-permitting ensemble analysis and forecast system assimilating airborne Doppler radar observations

    NASA Astrophysics Data System (ADS)

    Munsell, Erin B.; Zhang, Fuqing

    2014-03-01

    the Pennsylvania State University (PSU) real-time convection-permitting hurricane analysis and forecasting system (WRF-EnKF) that assimilates airborne Doppler radar observations, the sensitivity and uncertainty of forecasts initialized several days prior to landfall of Hurricane Sandy (2012) are assessed. The performance of the track and intensity forecasts of both the deterministic and ensemble forecasts by the PSU WRF-EnKF system show significant skill and are comparable to or better than forecasts produced by operational dynamical models, even at lead times of 4-5 days prior to landfall. Many of the ensemble members correctly capture the interaction of Sandy with an approaching midlatitude trough, which precedes Sandy's forecasted landfall in the Mid-Atlantic region of the United States. However, the ensemble reveals considerable forecast uncertainties in the prediction of Sandy. For example, in the ensemble forecast initialized at 0000 UTC 26 October 2012, 10 of the 60 members do not predict a United States landfall. Using ensemble composite and sensitivity analyses, the essential dynamics and initial condition uncertainties that lead to forecast divergence among the members in tracks and precipitation are examined. It is observed that uncertainties in the environmental steering flow are the most impactful factor on the divergence of Sandy's track forecasts, and its subsequent interaction with the approaching midlatitude trough. Though the midlatitude system does not strongly influence the final position of Sandy, differences in the timing and location of its interactions with Sandy lead to considerable differences in rainfall forecasts, especially with respect to heavy precipitation over land.

  5. Improving flash flood forecasting with distributed hydrological model by parameter optimization

    NASA Astrophysics Data System (ADS)

    Chen, Yangbo

    2016-04-01

    In China, flash food is usually regarded as flood occured in small and medium sized watersheds with drainage area less than 200 km2, and is mainly induced by heavy rains, and occurs in where hydrological observation is lacked. Flash flood is widely observed in China, and is the flood causing the most casualties nowadays in China. Due to hydrological data scarcity, lumped hydrological model is difficult to be employed for flash flood forecasting which requires lots of observed hydrological data to calibrate model parameters. Physically based distributed hydrological model discrete the terrain of the whole watershed into a number of grid cells at fine resolution, assimilate different terrain data and precipitation to different cells, and derive model parameteris from the terrain properties, thus having the potential to be used in flash flood forecasting and improving flash flood prediction capability. In this study, the Liuxihe Model, a physically based distributed hydrological model mainly proposed for watershed flood forecasting is employed to simulate flash floods in the Ganzhou area in southeast China, and models have been set up in 5 watersheds. Model parameters have been derived from the terrain properties including the DEM, the soil type and land use type, but the result shows that the flood simulation uncertainty is high, which may be caused by parameter uncertainty, and some kind of uncertainty control is needed before the model could be used in real-time flash flood forecastin. Considering currently many Chinese small and medium sized watersheds has set up hydrological observation network, and a few flood events could be collected, it may be used for model parameter optimization. For this reason, an automatic model parameter optimization algorithm using Particle Swam Optimization(PSO) is developed to optimize the model parameters, and it has been found that model parameters optimized even only with one observed flood events could largely reduce the flood

  6. Study of Beijiang catchment flash-flood forecasting model

    NASA Astrophysics Data System (ADS)

    Chen, Y.; Li, J.; Huang, S.; Dong, Y.

    2015-05-01

    Beijiang catchment is a small catchment in southern China locating in the centre of the storm areas of the Pearl River Basin. Flash flooding in Beijiang catchment is a frequently observed disaster that caused direct damages to human beings and their properties. Flood forecasting is the most effective method for mitigating flash floods, the goal of this paper is to develop the flash flood forecasting model for Beijiang catchment. The catchment property data, including DEM, land cover types and soil types, which will be used for model construction and parameter determination, are downloaded from the website freely. Based on the Liuxihe Model, a physically based distributed hydrological model, a model for flash flood forecasting of Beijiang catchment is set up. The model derives the model parameters from the terrain properties, and further optimized with the observed flooding process, which improves the model performance. The model is validated with a few observed floods occurred in recent years, and the results show that the model is reliable and is promising for flash flood forecasting.

  7. The RHYTMME system: an operational real-time warning and mapping system for flash floods, debris flows, landslide and rock falls in Southeastern France.

    NASA Astrophysics Data System (ADS)

    Fouchier, Catherine; Mériaux, Patrice; Atger, Frédéric; Ecrepont, Stéphane; Liébault, Frédéric; Bertrand, Mélanie; Bel, Coraline; Batista, Dominique; Azemard, Pierre; Saint-Martin, Clotilde; Javelle, Pierre

    2016-04-01

    Almost all municipalities of Southeastern France are concerned by natural hazards triggered by heavy rainfalls such as floods, debris flows, landslides and rock falls. Although some tools exist to forecast and monitor heavy rains and floods in France, their spatial resolution sometimes does not meet the needs of local risk managers who have to monitor events at a small spatial scale. In order to improve the risk management in the mountainous and Mediterranean areas of Southeastern France, Irstea and Météo-France have led the RHYTMME project. The goal of this project is to improve the ability to forecast and localize high-risk rainfall-induced hazards in the Provence-Alpes-Côte d'Azur administrative area. This goal is currently under achievement thanks to the implementation of a real-time warning and mapping system for rainfall induced natural hazards, fed by radar data and whose outputs are made available via the Internet to operators in charge of risk management (local and regional authorities, emergency and rescue services, road and rail networks managers, ...). This system provides maps which display in real-time: - the radar estimations of rainfall for different rain durations and at the spatial resolution of 1 km² (Westrelin et al., 2013), - the estimation of the scarcity of these rainfall estimations, also at the spatial resolution of 1 km², thanks to a comparison with threshold values provided by a regionalized stochastic hourly point rainfall generator (Arnaud et al., 2007), - an anticipation of the rivers discharges, computed at the outlet of 1700 watersheds of Southeastern France thanks to the AIGA warning system which combines a rainfall runoff model and an estimation of the scarcity of the discharges thanks to a comparison with threshold values (Javelle et al., 2014). Maps of susceptibility to debris flow, landslide and rock falls can also be displayed in the RHYTMME warning system along with the real time maps of rainfall hazard (Batista, 2013a

  8. Proper estimation of hydrological parameters from flood forecasting aspects

    NASA Astrophysics Data System (ADS)

    Miyamoto, Mamoru; Matsumoto, Kazuhiro; Tsuda, Morimasa; Yamakage, Yuzuru; Iwami, Yoichi; Yanami, Hitoshi; Anai, Hirokazu

    2016-04-01

    The hydrological parameters of a flood forecasting model are normally calibrated based on an entire hydrograph of past flood events by means of an error assessment function such as mean square error and relative error. However, the specific parts of a hydrograph, i.e., maximum discharge and rising parts, are particularly important for practical flood forecasting in the sense that underestimation may lead to a more dangerous situation due to delay in flood prevention and evacuation activities. We conducted numerical experiments to find the most proper parameter set for practical flood forecasting without underestimation in order to develop an error assessment method for calibration appropriate for flood forecasting. A distributed hydrological model developed in Public Works Research Institute (PWRI) in Japan was applied to fifteen past floods in the Gokase River basin of 1,820km2 in Japan. The model with gridded two-layer tanks for the entire target river basin included hydrological parameters, such as hydraulic conductivity, surface roughness and runoff coefficient, which were set according to land-use and soil-type distributions. Global data sets, e.g., Global Map and Digital Soil Map of the World (DSMW), were employed as input data for elevation, land use and soil type. The values of fourteen types of parameters were evenly sampled with 10,001 patterns of parameter sets determined by the Latin Hypercube Sampling within the search range of each parameter. Although the best reproduced case showed a high Nash-Sutcliffe Efficiency of 0.9 for all flood events, the maximum discharge was underestimated in many flood cases. Therefore, two conditions, which were non-underestimation in the maximum discharge and rising parts of a hydrograph, were added in calibration as the flood forecasting aptitudes. The cases with non-underestimation in the maximum discharge and rising parts of the hydrograph also showed a high Nash-Sutcliffe Efficiency of 0.9 except two flood cases

  9. The suitability of remotely sensed soil moisture for improving operational flood forecasting

    NASA Astrophysics Data System (ADS)

    Wanders, N.; Karssenberg, D.; de Roo, A.; de Jong, S. M.; Bierkens, M. F. P.

    2014-06-01

    the system. The added values of the satellite data is largest when these observations are assimilated in combination with distributed discharge observations. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.

  10. Flash flood warnings for ungauged basins based on high-resolution precipitation forecasts

    NASA Astrophysics Data System (ADS)

    Demargne, Julie; Javelle, Pierre; Organde, Didier; de Saint Aubin, Céline; Janet, Bruno

    2016-04-01

    Early detection of flash floods, which are typically triggered by severe rainfall events, is still challenging due to large meteorological and hydrologic uncertainties at the spatial and temporal scales of interest. Also the rapid rising of waters necessarily limits the lead time of warnings to alert communities and activate effective emergency procedures. To better anticipate such events and mitigate their impacts, the French national service in charge of flood forecasting (SCHAPI) is implementing a national flash flood warning system for small-to-medium (up to 1000 km²) ungauged basins based on a discharge-threshold flood warning method called AIGA (Javelle et al. 2014). The current deterministic AIGA system has been run in real-time in the South of France since 2005 and has been tested in the RHYTMME project (rhytmme.irstea.fr/). It ingests the operational radar-gauge QPE grids from Météo-France to run a simplified hourly distributed hydrologic model at a 1-km² resolution every 15 minutes. This produces real-time peak discharge estimates along the river network, which are subsequently compared to regionalized flood frequency estimates to provide warnings according to the AIGA-estimated return period of the ongoing event. The calibration and regionalization of the hydrologic model has been recently enhanced for implementing the national flash flood warning system for the entire French territory by 2016. To further extend the effective warning lead time, the flash flood warning system is being enhanced to ingest Météo-France's AROME-NWC high-resolution precipitation nowcasts. The AROME-NWC system combines the most recent available observations with forecasts from the nowcasting version of the AROME convection-permitting model (Auger et al. 2015). AROME-NWC pre-operational deterministic precipitation forecasts, produced every hour at a 2.5-km resolution for a 6-hr forecast horizon, were provided for 3 significant rain events in September and November 2014 and

  11. Perturbation of convection-permitting NWP forecasts for flash-flood ensemble forecasting

    NASA Astrophysics Data System (ADS)

    Vincendon, B.; Ducrocq, V.; Nuissier, O.; Vié, B.

    2011-05-01

    Mediterranean intense weather events often lead to devastating flash-floods. Extending the forecasting lead times further than the watershed response times, implies the use of numerical weather prediction (NWP) to drive hydrological models. However, the nature of the precipitating events and the temporal and spatial scales of the watershed response make them difficult to forecast, even using a high-resolution convection-permitting NWP deterministic forecasting. This study proposes a new method to sample the uncertainties of high-resolution NWP precipitation forecasts in order to quantify the predictability of the streamflow forecasts. We have developed a perturbation method based on convection-permitting NWP-model error statistics. It produces short-term precipitation ensemble forecasts from single-value meteorological forecasts. These rainfall ensemble forecasts are then fed into a hydrological model dedicated to flash-flood forecasting to produce ensemble streamflow forecasts. The verification on two flash-flood events shows that this forecasting ensemble performs better than the deterministic forecast. The performance of the precipitation perturbation method has also been found to be broadly as good as that obtained using a state-of-the-art research convection-permitting NWP ensemble, while requiring less computing time.

  12. Real-time prediction of atmospheric Lagrangian coherent structures based on forecast data: An application and error analysis

    NASA Astrophysics Data System (ADS)

    BozorgMagham, Amir E.; Ross, Shane D.; Schmale, David G.

    2013-09-01

    The language of Lagrangian coherent structures (LCSs) provides a new means for studying transport and mixing of passive particles advected by an atmospheric flow field. Recent observations suggest that LCSs govern the large-scale atmospheric motion of airborne microorganisms, paving the way for more efficient models and management strategies for the spread of infectious diseases affecting plants, domestic animals, and humans. In addition, having reliable predictions of the timing of hyperbolic LCSs may contribute to improved aerobiological sampling of microorganisms with unmanned aerial vehicles and LCS-based early warning systems. Chaotic atmospheric dynamics lead to unavoidable forecasting errors in the wind velocity field, which compounds errors in LCS forecasting. In this study, we reveal the cumulative effects of errors of (short-term) wind field forecasts on the finite-time Lyapunov exponent (FTLE) fields and the associated LCSs when realistic forecast plans impose certain limits on the forecasting parameters. Objectives of this paper are to (a) quantify the accuracy of prediction of FTLE-LCS features and (b) determine the sensitivity of such predictions to forecasting parameters. Results indicate that forecasts of attracting LCSs exhibit less divergence from the archive-based LCSs than the repelling features. This result is important since attracting LCSs are the backbone of long-lived features in moving fluids. We also show under what circumstances one can trust the forecast results if one merely wants to know if an LCS passed over a region and does not need to precisely know the passage time.

  13. Hydrological model calibration for enhancing global flood forecast skill

    NASA Astrophysics Data System (ADS)

    Hirpa, Feyera A.; Beck, Hylke E.; Salamon, Peter; Thielen-del Pozo, Jutta

    2016-04-01

    Early warning systems play a key role in flood risk reduction, and their effectiveness is directly linked to streamflow forecast skill. The skill of a streamflow forecast is affected by several factors; among them are (i) model errors due to incomplete representation of physical processes and inaccurate parameterization, (ii) uncertainty in the model initial conditions, and (iii) errors in the meteorological forcing. In macro scale (continental or global) modeling, it is a common practice to use a priori parameter estimates over large river basins or wider regions, resulting in suboptimal streamflow estimations. The aim of this work is to improve flood forecast skill of the Global Flood Awareness System (GloFAS; www.globalfloods.eu), a grid-based forecasting system that produces flood forecast unto 30 days lead, through calibration of the distributed hydrological model parameters. We use a combination of in-situ and satellite-based streamflow data for automatic calibration using a multi-objective genetic algorithm. We will present the calibrated global parameter maps and report the forecast skill improvements achieved. Furthermore, we discuss current challenges and future opportunities with regard to global-scale early flood warning systems.

  14. Short-term Ensemble Flood Forecasting Experiments in Brazil

    NASA Astrophysics Data System (ADS)

    Collischonn, Walter; Meller, Adalberto; Fan, Fernando; Moreira, Demerval; Dias, Pedro; Buarque, Diogo; Bravo, Juan

    2013-04-01

    Flood Forecasting and issuing early warnings to communities under risk can help reduce the impacts of those events. However, to be effective, warnings should be given several hours in advance. The best solution to extend the lead time is possibly the use of rainfall-runoff models with input given by rainfall and streamflow observations and by forecasts of future precipitation derived from numerical weather prediction (NWP) models. Recent studies showed that probabilistic or ensemble flood forecasts produced using ensemble precipitation forecasts as input data outperform deterministic flood forecasts in several cases in Europe and the United States, and ensemble flood forecasting systems are increasingly becoming operational in these regions. In Brazil, on the other hand, operational flood warning systems are rare, and often based on simplified river routing or linear transfer function models. However, a large number of global and regional meteorological models is operationally run covering most of the country, and forecasts of those models are available for recent years. We used this available data to conduct experiments of short term ensemble flood forecasting in the Paraopeba River basin (12 thousand km2), located in Southeastern Brazil. Streamflow forecasts were produced using the MGB-IPH hydrological model, using a simple empirical state updating method and using an ensemble of precipitation forecasts generated by several models, with different initial conditions and parameterizations, from several weather forecasting centers. A single deterministic streamflow forecast, based on a quantitative precipitation forecast derived from the optimal combination of several outputs of NWP models was used as a reference to assess the performance of the ensemble streamflow forecasts. Flood forecasts experiments were performed for three rainy seasons (austral summer) between 2008-2011. The results for predictions of dichotomous events, which mean exceeding or not flood

  15. The Flood Forecasting Centre (FFC) in the UK

    NASA Astrophysics Data System (ADS)

    Davies, P.

    2009-09-01

    The Met Office and the Environment Agency in the UK have set up a joint Flood Forecasting Centre (FFC), based at the London offices of the Met Office. This partnership will improve the UK's ability to respond to flooding events by providing an earlier national forecasting and alert service to central and local government departments so as to give them more time to prepare for floods and reduce the risk of loss of life and damage to property. The creation of the centre is in response to a key recommendation of Sir Michael Pitt's Review following the summer 2007 floods over the UK. For the first time, the FFC combines the Environment Agency's expertise in flood risk management and the Met Office's expertise in weather forecasting under one roof. My presentation will describe the benefits it will bring to the emergency responder community. It will also cover the tools available to the centre such as the new generation of high resolution weather models now coming on line. As a result, flood forecasting and warning systems, (which historically have been based on the lack of sufficiently fine scale rainfall information), need to be revisited in the light of the new meteorological modelling capabilities. This is particularly true for surface water flooding, where these new capabilities offer, for the first time, the possibility of providing credible alerts.

  16. Tsunami forecast by joint inversion of real-time tsunami waveforms and seismic of GPS data: application to the Tohoku 2011 tsunami

    USGS Publications Warehouse

    Yong, Wei; Newman, Andrew V.; Hayes, Gavin P.; Titov, Vasily V.; Tang, Liujuan

    2014-01-01

    Correctly characterizing tsunami source generation is the most critical component of modern tsunami forecasting. Although difficult to quantify directly, a tsunami source can be modeled via different methods using a variety of measurements from deep-ocean tsunameters, seismometers, GPS, and other advanced instruments, some of which in or near real time. Here we assess the performance of different source models for the destructive 11 March 2011 Japan tsunami using model–data comparison for the generation, propagation, and inundation in the near field of Japan. This comparative study of tsunami source models addresses the advantages and limitations of different real-time measurements with potential use in early tsunami warning in the near and far field. The study highlights the critical role of deep-ocean tsunami measurements and rapid validation of the approximate tsunami source for high-quality forecasting. We show that these tsunami measurements are compatible with other real-time geodetic data, and may provide more insightful understanding of tsunami generation from earthquakes, as well as from nonseismic processes such as submarine landslide failures.

  17. Operational water management of Rijnland water system and pilot of ensemble forecasting system for flood control

    NASA Astrophysics Data System (ADS)

    van der Zwan, Rene

    2013-04-01

    The Rijnland water system is situated in the western part of the Netherlands, and is a low-lying area of which 90% is below sea-level. The area covers 1,100 square kilometres, where 1.3 million people live, work, travel and enjoy leisure. The District Water Control Board of Rijnland is responsible for flood defence, water quantity and quality management. This includes design and maintenance of flood defence structures, control of regulating structures for an adequate water level management, and waste water treatment. For water quantity management Rijnland uses, besides an online monitoring network for collecting water level and precipitation data, a real time control decision support system. This decision support system consists of deterministic hydro-meteorological forecasts with a 24-hr forecast horizon, coupled with a control module that provides optimal operation schedules for the storage basin pumping stations. The uncertainty of the rainfall forecast is not forwarded in the hydrological prediction. At this moment 65% of the pumping capacity of the storage basin pumping stations can be automatically controlled by the decision control system. Within 5 years, after renovation of two other pumping stations, the total capacity of 200 m3/s will be automatically controlled. In critical conditions there is a need of both a longer forecast horizon and a probabilistic forecast. Therefore ensemble precipitation forecasts of the ECMWF are already consulted off-line during dry-spells, and Rijnland is running a pilot operational system providing 10-day water level ensemble forecasts. The use of EPS during dry-spells and the findings of the pilot will be presented. Challenges and next steps towards on-line implementation of ensemble forecasts for risk-based operational management of the Rijnland water system will be discussed. An important element in that discussion is the question: will policy and decision makers, operator and citizens adapt this Anticipatory Water

  18. The near real time Forensic Disaster Analysis of the central European flood in June 2013 - A graphical representation of the main results

    NASA Astrophysics Data System (ADS)

    Schröter, Kai; Elmer, Florian; Trieselmann, Werner; Kreibich, Heidi; Kunz, Michael; Khazai, Bijan; Dransch, Doris; Wenzel, Friedemann; Zschau, Jochen; Merz, Bruno; Mühr, Bernhard; Kunz-Plapp, Tina; Möhrle, Stella; Bessel, Tina; Fohringer, Joachim

    2014-05-01

    The Central European flood of June 2013 is one of the most severe flood events that have occurred in Central Europe in the past decades. All major German river basins were affected (Rhine, Danube, and Elbe as well as the smaller Weser catchment).In terms of spatial extent and event magnitude, it was the most severe event at least since 1950. Within the current research focus on near real time forensic disaster analysis, the Center for Disaster Management and Risk Reduction Technology (CEDIM) assessed and analysed the multiple facets of the flood event from the beginning. The aim is to describe the on-going event, analyse the event sources, link the physical characteristics to the impact and consequences of the event and to understand the root causes that turn the physical event into a disaster (or prevent it from becoming disastrous). For the near real time component of this research, tools for rapid assessment and concise presentation of analysis results are essential. This contribution provides a graphical summary of the results of the CEDIM-FDA analyses on the June 2013 flood. It demonstrates the potential of visual representations for improving the communication and hence usability of findings in a rapid, intelligible and expressive way as a valuable supplement to usual event reporting. It is based on analyses of the hydrometeorological sources, the flood pathways (from satellite imagery, data extraction from social media), the resilience of the affected regions, and causal loss analysis. The prototypical representation of the FDA-results for the June 2013 flood provides an important step in the development of graphical event templates for the visualisation of forensic disaster analyses. These are intended to become a standard component of future CEDIM-FDA event activities.

  19. Use of weather radar for flood forecasting in the Sieve River Basin: A sensitivity analysis

    SciTech Connect

    Pessoa, M.L.; Bras, R.L.; Williams, E.R. )

    1993-03-01

    Weather radar, in combination with a distributed rainfall-runoff model, promises to significantly improve real-time flood forecasting. This paper investigates the value of radar-derived precipitation in forecasting streamflow in the Sieve River basin, near Florence, Italy. The basin is modeled with a distributed rainfall-runoff model that exploits topographic information available from digital elevation maps. The sensitivity of the flood forecast to various properties of the radar-derived rainfall is studied. It is found that use of the proper radar reflectivity-rainfall intensity (Z-R) relationship is the most crucial factor in obtaining correct flood hydrographs. Errors resulting from spatially averaging radar rainfall are acceptable, but the use of discrete point information (i.e. raingage) can lead to serious problems. Reducing the resolution of the 5-min radar signal by temporally averaging over 15 and 30 min does not lead to major errors. Using 3-bit radar data (rather than the usual 8-bit data) to represent intensities results in significant operational savings without serious problems in hydrograph accuracy. 24 refs., 28 figs., 2 tabs.

  20. Probabilistic flood warning using grand ensemble weather forecasts

    NASA Astrophysics Data System (ADS)

    He, Y.; Wetterhall, F.; Cloke, H.; Pappenberger, F.; Wilson, M.; Freer, J.; McGregor, G.

    2009-04-01

    As the severity of floods increases, possibly due to climate and landuse change, there is urgent need for more effective and reliable warning systems. The incorporation of numerical weather predictions (NWP) into a flood warning system can increase forecast lead times from a few hours to a few days. A single NWP forecast from a single forecast centre, however, is insufficient as it involves considerable non-predictable uncertainties and can lead to a high number of false or missed warnings. An ensemble of weather forecasts from one Ensemble Prediction System (EPS), when used on catchment hydrology, can provide improved early flood warning as some of the uncertainties can be quantified. EPS forecasts from a single weather centre only account for part of the uncertainties originating from initial conditions and stochastic physics. Other sources of uncertainties, including numerical implementations and/or data assimilation, can only be assessed if a grand ensemble of EPSs from different weather centres is used. When various models that produce EPS from different weather centres are aggregated, the probabilistic nature of the ensemble precipitation forecasts can be better retained and accounted for. The availability of twelve global EPSs through the 'THORPEX Interactive Grand Global Ensemble' (TIGGE) offers a new opportunity for the design of an improved probabilistic flood forecasting framework. This work presents a case study using the TIGGE database for flood warning on a meso-scale catchment. The upper reach of the River Severn catchment located in the Midlands Region of England is selected due to its abundant data for investigation and its relatively small size (4062 km2) (compared to the resolution of the NWPs). This choice was deliberate as we hypothesize that the uncertainty in the forcing of smaller catchments cannot be represented by a single EPS with a very limited number of ensemble members, but only through the variance given by a large number ensembles

  1. Design and development of a Java-based graphical user interface to monitor/control a meteorological real-time forecasting system

    NASA Astrophysics Data System (ADS)

    Gómez, Igor; José Estrela, María

    2010-10-01

    A regional forecasting system based on the Regional Atmospheric Modeling System (RAMS) is being run at the CEAM Foundation. The operational model involves several processes running in the background at specified times and executing a set of systematic steps. This system is being used as a support for a heat-wave warning system, a wind forecasting system for fire warning and prevention, and for general forecasting tasks. However, it is relatively difficult to use by researchers and forecasters without sophisticated information technology (IT) skill. In this paper, we report an effort to develop a tool to facilitate the monitoring of the system. This tool is based on the client-server architecture and enables those with little IT skill to monitor/control the state of the different processes involved in the real-time simulation. This tool has been successfully used in controlling the RAMS-based applications developed at CEAM since 2006. The design and the functionality and utilities of the tool reviewed in this paper could be exported and customized to be used by other research centres and institutions who offer services based on operational atmospheric models as routine jobs (MM5, WRF, etc.), as e.g. air pollution forecasting systems, other prevention and emergency response systems, etc.

  2. Probabilistic flood forecasting for Rapid Response Catchments using a countrywide distributed hydrological model: experience from the UK

    NASA Astrophysics Data System (ADS)

    Cole, Steven J.; Moore, Robert J.; Robson, Alice J.; Mattingley, Paul S.

    2014-05-01

    prediction (NWP) model can provide realistic looking rainfall forecasts, significant uncertainties remain in timing, location and whether a particular feature develops or not. Generally the smaller the scale of the rainfall feature, the shorter the lead-time at which these uncertainties become important. Therefore ensembles are needed to provide uncertainty context for longer lead-time G2G flow forecasts, particularly for small-scale RRCs. A systematic assessment framework has been developed for exploring and understanding the utility of G2G flood forecasts for RRCs. Firstly perfect knowledge of rainfall observations is assumed for past and future times, so as not to confound the hydrological model analysis with errors from rainfall forecasts. Secondly an assessment is made of using deterministic rainfall forecasts (from NWP UKV) in a full emulation of real-time G2G forecasts, and using foreknowledge of rainfall observations as a reference baseline. Finally use of rainfall forecast ensembles with G2G to produce probabilistic flood forecasts is considered, empploying a combination of case-study and longer-term analyses. Blended Ensemble rainfall forecasts (combining radar ensemble nowcast and NWP rainfalls) are assessed in two forms: forecasts out to 24 hours updated 4 times a day, and nowcasts out to 7 hours updated every 15 minutes. Results from the assessment will be presented along with candidates for new operational products and tools that can support flood warning for RRCs, taking account of the inherent uncertainty in the forecasts.

  3. Comparison between genetic programming and an ensemble Kalman filter as data assimilation techniques for probabilistic flood forecasting

    NASA Astrophysics Data System (ADS)

    Mediero, L.; Garrote, L.; Requena, A.; Chávez, A.

    2012-04-01

    Flood events are among the natural disasters that cause most economic and social damages in Europe. Information and Communication Technology (ICT) developments in last years have enabled hydrometeorological observations available in real-time. High performance computing promises the improvement of real-time flood forecasting systems and makes the use of post processing techniques easier. This is the case of data assimilation techniques, which are used to develop an adaptive forecast model. In this paper, a real-time framework for probabilistic flood forecasting is presented and two data assimilation techniques are compared. The first data assimilation technique uses genetic programming to adapt the model to the observations as new information is available, updating the estimation of the probability distribution of the model parameters. The second data assimilation technique uses an ensemble Kalman filter to quantify errors in both hydrologic model and observations, updating estimates of system states. Both forecast models take the result of the hydrologic model calibration as a starting point and adapts the individuals of this first population to the new observations in each operation time step. Data assimilation techniques have great potential when are used in hydrological distributed models. The distributed RIBS (Real-time Interactive Basin Simulator) rainfall-runoff model was selected to simulate the hydrological process in the basin. The RIBS model is deterministic, but it is run in a probabilistic way through Monte Carlo simulations over the probability distribution functions that best characterise the most relevant model parameters, which were identified by a probabilistic multi-objective calibration developed in a previous work. The Manzanares River basin was selected as a case study. Data assimilation processes are computationally intensive. Therefore, they are well suited to test the applicability of the potential of the Grid technology to

  4. Flood forecasting using medium-range probabilistic weather prediction

    NASA Astrophysics Data System (ADS)

    Gouweleeuw, B. T.; Thielen, J.; Franchello, G.; de de Roo, A. P. J.; Buizza, R.

    2005-10-01

    Following the developments in short- and medium-range weather forecasting over the last decade, operational flood forecasting also appears to show a shift from a so-called single solution or 'best guess' deterministic approach towards a probabilistic approach based on ensemble techniques. While this probabilistic approach is now more or less common practice and well established in the meteorological community, operational flood forecasters have only started to look for ways to interpret and mitigate for end-users the prediction products obtained by combining so-called Ensemble Prediction Systems (EPS) of Numerical Weather Prediction (NWP) models with rainfall-runoff models. This paper presents initial results obtained by combining deterministic and EPS hindcasts of the global NWP model of the European Centre for Medium-Range Weather Forecasts (ECMWF) with the large-scale hydrological model LISFLOOD for two historic flood events: the river Meuse flood in January 1995 and the river Odra flood in July 1997. In addition, a possible way to interpret the obtained ensemble based stream flow prediction is proposed.

  5. Looking at the big scale - Global Flood Forecasting

    NASA Astrophysics Data System (ADS)

    Burek, P.; Alfieri, L.; Thielen-del Pozo, J.; Muraro, D.; Pappenberger, F.; Krzeminsk, B.

    2012-04-01

    Reacting to the increasing need for better preparedness to worldwide hydrological extremes, the Joint Research Centre has joined forces with the European Centre for Medium-Range Weather Forecast (ECMWF), to couple state-of-the art weather forecasts with a hydrological model on global scale. On a pre-operationally basis a fully hydro-meteorological flood forecasting model is running since July 2011 and producing daily probabilistic discharge forecast with worldwide coverage and forecast horizon of about 1 month. An important aspect of this global system is that it is set-up on continental scale and therefore independent of administrative and political boundaries - providing downstream countries with information on upstream river conditions as well as continental and global overviews. The prototype of a Global Flood Alert System consists of HTESSEL land surface scheme coupled with LISFLOOD hydrodynamic model for the flow routing in the river network. Both hydrological models are set up on global coverage with horizontal grid resolution of 0.1° and daily time step for input and output data. To estimate corresponding discharge warning thresholds for selected return periods, the coupled HTESSEL-LISFLOOD hydrological model is driven with ERA-Interim input meteorological data for a 21 year period from 1989 onward. For daily forecasts the ensemble stream flow predictions are run by feeding Variable Resolution Ensemble Prediction System (VarEPS) weather forecasts into the coupled model. VarEPS consist of 51-member ensemble global forecasts for 15 days. The hydrological simulations are computed for a 45-day time horizon, to account the routing of flood waves through large river basins with time of concentration of the order of one month. Both results, the discharge thresholds from the long term run and the multiple hydrographs of the daily ensemble stream flow prediction are joined together to produce probabilistic information of critical threshold exceedance. Probabilistic

  6. An Improved Global Flood Forecasting System Using Satellite Rainfall Information and a Hydrological Model (Invited)

    NASA Astrophysics Data System (ADS)

    Adler, R. F.; Wu, H.; Tian, Y.

    2013-12-01

    A real-time experimental system to estimate and forecast floods over the globe, the Global Flood Monitoring System (GFMS), has been significantly improved to provide flood detection, streamflow and inundation mapping information at higher resolution (as fine as 1 km) and nowcasts and forecasts (out to five days). Images and output data are available for use by the community with updates available every three hours (http://flood.umd.edu). The system uses satellite-based rainfall information, currently the TRMM Multi-satellite Precipitation Analysis [TMPA]), other satellite and conventional information and a newly-developed hydrological and routing combination model. The improved combined model, the Dominant river Routing Integrated with VIC Environment (DRIVE) system, is based on the VIC (Variable Infiltration Capacity) land surface model (U. of Washington) and the Dominant River Tracing Routing (DRTR) method. Within the DRIVE system the surface hydrological calculations are carried out at 0.125° latitude-longitude resolution with routing, streamflow and other calculations done at that resolution and at 1km resolution. Flood detection and intensity estimates are based on water depth and streamflow thresholds calculated from a 15-year retrospective run using the satellite rainfall and model. This period is also used for testing and evaluation with results indicating improved streamflow estimation and flood detection statistics. The satellite rainfall data are integrated with global model NASA GEOS-5 Numerical Weather Prediction (NWP) rainfall predictions (adjusted to the satellite data) to extend the flood calculations out to five days. Examples of results for recent flood events are presented along with validation statistics and comparison with other flood observations (e.g., inundation calculations vs. MODIS and/or SAR flood maps). The outlook for further development in this area in terms of increased utility for national and international disaster management

  7. Status and Future of Global Flood and Landslide Nowcasts and Forecasts Using Satellite Precipitation Observations (Invited)

    NASA Astrophysics Data System (ADS)

    Adler, R. F.; Wu, H.; Kirschbaum, D. B.; Policelli, F.; Hong, Y.; Tian, Y.; Pierce, H.

    2010-12-01

    The advent of quasi-global, real-time precipitation analyses has lead to the reality of running global hydrological models and algorithms for the estimation of the occurrence of floods and rain-induced landslides. These calculations provide information useful to national and international agencies in understanding the intensity, timeline and impact on populations of these significant hazard events. The quality of such applied hydrological estimations should improve with time due to continuation and improvement of multi-satellite precipitation observations through the Global Precipitation Measurement (GPM) program and the further development of the models and algorithms. This talk will summarize the results from the NASA-based, real-time flood and landslide nowcasts and forecasts and describe directions for improving results going into the GPM era. Global flood and landslide estimation systems have been running in real-time at 0.25° latitude/longitude resolution using multi-satellite rainfall analyses for several years, with results available through the TRMM website (trmm.gsfc.nasa.gov). Published evaluations of the current system indicate useful skill in comparison with global event inventories. The evaluations indicate higher skill for larger rainfall systems (e.g., tropical cyclone landfall vs. flash flood). This result is reasonable considering the resolution of the rainfall information (0.25° and 3-hr) and the resolution of the current models/algorithms (0.25°). Improvements over the next few years will include 1) better precipitation analyses utilizing space-time interpolations that maintain accurate intensity distributions, 2) improved rain estimation for shallow, orographic rainfall systems and some types of monsoon rainfall, 3) higher resolution landslide algorithms with combined physical/empirical approaches, 4) higher resolution flood models with accurate routing and regional calibration, and 5) use of satellite soil moisture for more accurate pre

  8. Integrating Flooding Control with Sediment Reduction in the Real-Time Operation Model for Tseng-Wen Reservoir

    NASA Astrophysics Data System (ADS)

    chou, Y.; Chang, L.; Hsu, C.

    2012-12-01

    Typhoons are kind of natural hazards happened most frequently during summer in Taiwan. Typhoons induce the risk of instant damages such as dam break or floods caused by the overflow in downstream area. Besides, high turbidity inflow of reservoirs caused by erosions and mudslides in upstream area during typhoons brings a huge volume of sediments which highly decreases the storage volume of reservoir. Therefore, applying flooding management of reservoirs to increase the release quantities of sediments and to maintain the storage volumes of reservoirs becomes an important issue today. In this study, an optimal flooding operation model with considering sediment reduction which integrates the genetic algorithm (GA), HEC-RAS simulation, artificial neural network (ANN) and reservoir watershed sediment modeling is proposed. The objective function of the proposed model deals with four sub-objects includes water resource, flooding hazard reduction, peak release flow reduction and sediment reduction. The operation results are applied on Tseng-Wen Reservoir during five typhoon events include Typhoon TALIM (2005), SEPAT (2007), KORSA (2007), KALMAEGI (2008), SINLAKU (2008) and JANGMI (2008). Comparison between the results of models with and without sediment reduction, the increase amounts of sediment release for the model with sediment reduction respectively are 27 and 39 tons during Typhoon JANGMI and SINLAKU. Based on the comparison, the proposed model has ability to increase the release quantity of sediment.

  9. Impact of precipitation forecast uncertainties and initial soil moisture conditions on a probabilistic flood forecasting chain

    NASA Astrophysics Data System (ADS)

    Silvestro, Francesco; Rebora, Nicola

    2014-11-01

    One of the main difficulties that flood forecasters are faced with is evaluating how errors and uncertainties in forecasted precipitation propagate into streamflow forecast. These errors, must be combined with the effects of different initial soil moisture conditions that generally have a significant impact on the final results of a flood forecast. This is further complicated by the fact that a probabilistic approach is needed, especially when small and medium size basins are considered (the variability of the streamflow scenarios is in fact strongly influenced by the aforementioned factors). Moreover, the ensemble size is a degree of freedom when a precipitation downscaling algorithm is part of the forecast chain. In fact, a change of ensemble size could lead to different final results once the other inputs and parameters are fixed. In this work, a series of synthetic experiments have been designed and implemented to test an operational probabilistic flood forecast system in order to augment the knowledge of how streamflow forecasts can be affected by errors and uncertainties associated with the three aforementioned elements: forecasted rainfall, soil moisture initial conditions, and ensemble size.

  10. Three-Month Real-Time Dengue Forecast Models: An Early Warning System for Outbreak Alerts and Policy Decision Support in Singapore

    PubMed Central

    Shi, Yuan; Liu, Xu; Kok, Suet-Yheng; Rajarethinam, Jayanthi; Liang, Shaohong; Yap, Grace; Chong, Chee-Seng; Lee, Kim-Sung; Tan, Sharon S.Y.; Chin, Christopher Kuan Yew; Lo, Andrew; Kong, Waiming; Ng, Lee Ching; Cook, Alex R.

    2015-01-01

    Background: With its tropical rainforest climate, rapid urbanization, and changing demography and ecology, Singapore experiences endemic dengue; the last large outbreak in 2013 culminated in 22,170 cases. In the absence of a vaccine on the market, vector control is the key approach for prevention. Objectives: We sought to forecast the evolution of dengue epidemics in Singapore to provide early warning of outbreaks and to facilitate the public health response to moderate an impending outbreak. Methods: We developed a set of statistical models using least absolute shrinkage and selection operator (LASSO) methods to forecast the weekly incidence of dengue notifications over a 3-month time horizon. This forecasting tool used a variety of data streams and was updated weekly, including recent case data, meteorological data, vector surveillance data, and population-based national statistics. The forecasting methodology was compared with alternative approaches that have been proposed to model dengue case data (seasonal autoregressive integrated moving average and step-down linear regression) by fielding them on the 2013 dengue epidemic, the largest on record in Singapore. Results: Operationally useful forecasts were obtained at a 3-month lag using the LASSO-derived models. Based on the mean average percentage error, the LASSO approach provided more accurate forecasts than the other methods we assessed. We demonstrate its utility in Singapore’s dengue control program by providing a forecast of the 2013 outbreak for advance preparation of outbreak response. Conclusions: Statistical models built using machine learning methods such as LASSO have the potential to markedly improve forecasting techniques for recurrent infectious disease outbreaks such as dengue. Citation: Shi Y, Liu X, Kok SY, Rajarethinam J, Liang S, Yap G, Chong CS, Lee KS, Tan SS, Chin CK, Lo A, Kong W, Ng LC, Cook AR. 2016. Three-month real-time dengue forecast models: an early warning system for outbreak

  11. Assimilation of stream discharge for flood forecasting: Updating a semidistributed model with an integrated data assimilation scheme

    NASA Astrophysics Data System (ADS)

    Li, Yuan; Ryu, Dongryeol; Western, Andrew W.; Wang, Q. J.

    2015-05-01

    Real-time discharge observations can be assimilated into flood models to improve forecast accuracy; however, the presence of time lags in the routing process and a lack of methods to quantitatively represent different sources of uncertainties challenge the implementation of data assimilation techniques for operational flood forecasting. To address these issues, an integrated error parameter estimation and lag-aware data assimilation (IEELA) scheme was recently developed for a lumped model. The scheme combines an ensemble-based maximum a posteriori (MAP) error estimation approach with a lag-aware ensemble Kalman smoother (EnKS). In this study, the IEELA scheme is extended to a semidistributed model to provide for more general application in flood forecasting by including spatial and temporal correlations in model uncertainties between subcatchments. The result reveals that using a semidistributed model leads to more accurate forecasts than a lumped model in an open-loop scenario. The IEELA scheme improves the forecast accuracy significantly in both lumped and semidistributed models, and the superiority of the semidistributed model remains in the data assimilation scenario. However, the improvements resulting from IEELA are confined to the outlet of the catchment where the discharge observations are assimilated. Forecasts at "ungauged" internal locations are not improved, and in some instances, even become less accurate.

  12. DEVELOPMENT OF A REAL-TIME FORECASTING SYSTEM FOR ROAD FACILITIES IN YAMAGUCHI PREFECTURE AND ITS PRACTICAL APPLICATIONS

    NASA Astrophysics Data System (ADS)

    Yamane, Satoru; Yoshimura, Takashi; Miyamoto, Ayaho

    Maintenance strategy for road facilities on the road network which is an essential lifeline supporting our life is becoming a major social concern in safety and securer civil societies for not only Yamaguchi prefecture but also other prefectures in recently. This paper describes a road facilities maintenance man-agement support system combined with the latest information and communication technologies, such as the information function at the position of the GPS cellular phone with web GIS, etc. By using the system, because information can be shared by using location information function, photograph function, e-mail functionality, and web GIS of the GPS cellular phone to its maximum, an efficient, effective maintenance management can be done. From the comparison of the results of applying the system to an actual road network in Yamaguchi prefecture area, the road administrator can in real time confirm the position and the situation of the facilities damage of the road from the Internet, and a quick mending can be done.

  13. Medium Range Ensembles Flood Forecasts for Community Level Applications

    NASA Astrophysics Data System (ADS)

    Fakhruddin, S.; Kawasaki, A.; Babel, M. S.; AIT

    2013-05-01

    Early warning is a key element for disaster risk reduction. In recent decades, there has been a major advancement in medium range and seasonal forecasting. These could provide a great opportunity to improve early warning systems and advisories for early action for strategic and long term planning. This could result in increasing emphasis on proactive rather than reactive management of adverse consequences of flood events. This can be also very helpful for the agricultural sector by providing a diversity of options to farmers (e.g. changing cropping pattern, planting timing, etc.). An experimental medium range (1-10 days) flood forecasting model has been developed for Bangladesh which provides 51 set of discharge ensembles forecasts of one to ten days with significant persistence and high certainty. This could help communities (i.e. farmer) for gain/lost estimation as well as crop savings. This paper describe the application of ensembles probabilistic flood forecast at the community level for differential decision making focused on agriculture. The framework allows users to interactively specify the objectives and criteria that are germane to a particular situation, and obtain the management options that are possible, and the exogenous influences that should be taken into account before planning and decision making. risk and vulnerability assessment was conducted through community consultation. The forecast lead time requirement, users' needs, impact and management options for crops, livestock and fisheries sectors were identified through focus group discussions, informal interviews and questionnaire survey.

  14. Development and application of an atmospheric-hydrologic-hydraulic flood forecasting model driven by TIGGE ensemble forecasts

    NASA Astrophysics Data System (ADS)

    Bao, Hongjun; Zhao, Linna

    2012-02-01

    A coupled atmospheric-hydrologic-hydraulic ensemble flood forecasting model, driven by The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data, has been developed for flood forecasting over the Huaihe River. The incorporation of numerical weather prediction (NWP) information into flood forecasting systems may increase forecast lead time from a few hours to a few days. A single NWP model forecast from a single forecast center, however, is insufficient as it involves considerable non-predictable uncertainties and leads to a high number of false alarms. The availability of global ensemble NWP systems through TIGGE offers a new opportunity for flood forecast. The Xinanjiang model used for hydrological rainfall-runoff modeling and the one-dimensional unsteady flow model applied to channel flood routing are coupled with ensemble weather predictions based on the TIGGE data from the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the UK Met Office (UKMO), and the US National Centers for Environmental Prediction (NCEP). The developed ensemble flood forecasting model is applied to flood forecasting of the 2007 flood season as a test case. The test case is chosen over the upper reaches of the Huaihe River above Lutaizi station with flood diversion and retarding areas. The input flood discharge hydrograph from the main channel to the flood diversion area is estimated with the fixed split ratio of the main channel discharge. The flood flow inside the flood retarding area is calculated as a reservoir with the water balance method. The Muskingum method is used for flood routing in the flood diversion area. A probabilistic discharge and flood inundation forecast is provided as the end product to study the potential benefits of using the TIGGE ensemble forecasts. The results demonstrate satisfactory flood forecasting with clear signals of probability of floods up to a

  15. Exploring the Limits of Flood Forecasting in Mountain Basins by using QPE and QPF Products in a Physically-based, Distributed Hydrologic Model during Summer Convection

    NASA Astrophysics Data System (ADS)

    Moreno, H. A.; Vivoni, E. R.; Gochis, D. J.

    2012-12-01

    reduced predictability at intermediate-size basins. Overall, the fusion of high-resolution radar nowcasts and the convenient parallel capabilities of the distributed hydrologic model provide an efficient framework for generating real-time flood forecasts suitable for operational environments.

  16. Development of web-based services for a novel ensemble flood forecasting and risk assessment system

    NASA Astrophysics Data System (ADS)

    He, Y.; Manful, D. Y.; Cloke, H. L.; Wetterhall, F.; Li, Z.; Bao, H.; Pappenberger, F.; Wesner, S.; Schubert, L.; Yang, L.; Hu, Y.

    2009-12-01

    interoperability through strong security and workflow capabilities. A physical network diagram and a work flow scheme of all the models, codes and databases used to achieve the NEWS algorithm are presented. They constitute a first step in the development of a platform for providing real time flood forecasting services on the web to mitigate 21st century weather phenomena.

  17. Development of web-based services for an ensemble flood forecasting and risk assessment system

    NASA Astrophysics Data System (ADS)

    Yaw Manful, Desmond; He, Yi; Cloke, Hannah; Pappenberger, Florian; Li, Zhijia; Wetterhall, Fredrik; Huang, Yingchun; Hu, Yuzhong

    2010-05-01

    through strong security and workflow capabilities. A physical network diagram and a work flow scheme of all the models, codes and databases used to achieve the NEWS algorithm are presented. They constitute a first step in the development of a platform for providing real time flood forecasting services on the web to mitigate 21st century weather phenomena.

  18. Real-time forecasting at weekly timescales of the SST and SLA of the Ligurian Sea with a satellite-based ocean forecasting (SOFT) system

    NASA Astrophysics Data System (ADS)

    ÁLvarez, A.; Orfila, A.; Tintoré, J.

    2004-03-01

    Satellites are the only systems able to provide continuous information on the spatiotemporal variability of vast areas of the ocean. Relatively long-term time series of satellite data are nowadays available. These spatiotemporal time series of satellite observations can be employed to build empirical models, called satellite-based ocean forecasting (SOFT) systems, to forecast certain aspects of future ocean states. SOFT systems can predict satellite-observed fields at different timescales. The forecast skill of SOFT systems forecasting the sea surface temperature (SST) at monthly timescales has been extensively explored in previous works. In this work we study the performance of two SOFT systems forecasting, respectively, the SST and sea level anomaly (SLA) at weekly timescales, that is, providing forecasts of the weekly averaged SST and SLA fields with 1 week in advance. The SOFT systems were implemented in the Ligurian Sea (Western Mediterranean Sea). Predictions from the SOFT systems are compared with observations and with the predictions obtained from persistence models. Results indicate that the SOFT system forecasting the SST field is always superior in terms of predictability to persistence. Minimum prediction errors in the SST are obtained during winter and spring seasons. On the other hand, the biggest differences between the performance of SOFT and persistence models are found during summer and autumn. These changes in the predictability are explained on the basis of the particular variability of the SST field in the Ligurian Sea. Concerning the SLA field, no improvements with respect to persistence have been found for the SOFT system forecasting the SLA field.

  19. Net-zero Building Cluster Simulations and On-line Energy Forecasting for Adaptive and Real-Time Control and Decisions

    NASA Astrophysics Data System (ADS)

    Li, Xiwang

    Buildings consume about 41.1% of primary energy and 74% of the electricity in the U.S. Moreover, it is estimated by the National Energy Technology Laboratory that more than 1/4 of the 713 GW of U.S. electricity demand in 2010 could be dispatchable if only buildings could respond to that dispatch through advanced building energy control and operation strategies and smart grid infrastructure. In this study, it is envisioned that neighboring buildings will have the tendency to form a cluster, an open cyber-physical system to exploit the economic opportunities provided by a smart grid, distributed power generation, and storage devices. Through optimized demand management, these building clusters will then reduce overall primary energy consumption and peak time electricity consumption, and be more resilient to power disruptions. Therefore, this project seeks to develop a Net-zero building cluster simulation testbed and high fidelity energy forecasting models for adaptive and real-time control and decision making strategy development that can be used in a Net-zero building cluster. The following research activities are summarized in this thesis: 1) Development of a building cluster emulator for building cluster control and operation strategy assessment. 2) Development of a novel building energy forecasting methodology using active system identification and data fusion techniques. In this methodology, a systematic approach for building energy system characteristic evaluation, system excitation and model adaptation is included. The developed methodology is compared with other literature-reported building energy forecasting methods; 3) Development of the high fidelity on-line building cluster energy forecasting models, which includes energy forecasting models for buildings, PV panels, batteries and ice tank thermal storage systems 4) Small scale real building validation study to verify the performance of the developed building energy forecasting methodology. The outcomes of

  20. Flood forecasting for the Ukrainian part of the Tisza Basin: linking with the numerical weather forecasts, comparative testing of distributed and lumped models

    NASA Astrophysics Data System (ADS)

    Belov, S.; Donchytz, G.; Kivva, S.; Kuschan, A.; Zheleznyak, M.

    2003-04-01

    The implementation of new flood forecasting systems for the Ukrainian part of the Tisza basin has started last years by the customisation of Mike-11 model for the Uzh River and Latoritsa River (part of the Bodrog Catchment) in the frame of the joint project with the 'DHI Water&Environment'. The calibration and testing of the lumped parameter model NAM was provided in collaboration with the Ukrainian Hydrometcenter and the Uzhgorod Hydrometcenter for the period 1998-2000, which includes two hazardous floods of years 1998 and 2000. The tuning of hydrodynamical module of Mike-11 is provided in collaboration with the Transcarpathian Branch of State Committee of Water Management (SCWM), Uzhgorod. The information about existing and designed hydraulic structures in the river channels, -bridges, polders, dikes, pump stations is used for the model tuning. The flood forecasting system for Uzh River and Latoritsa River based on Mike -11 is in pre-operational use in Uzhgorod Hydromet and SCUWM offices. The advance time of the flood forecasts can be increased by the real-time assimilation of the precipitation forecasts of a Numerical Weather Predictions (NWP) model. The Penn State University /UCAR NWP model MM5 was customized for the Ukrainian territory in resolution 30*30 km on the basis of the rare gridded forecasting data from the German meteorological center Offenbach, assimilating the data from the Ukrainian meteorological stations, processed by the Ukrainian Hydrometcenter. The region of the Uzh and Latoritsa watersheds was simulated by MM5 in the resolution 10*10 km for the linking with the Mike -11 (NAM). The preliminary results of flood forecasting on the basis of the meteorological forecasts are analyzed. For further improvement of the flood forecasting systems the implementations of GIS based distributed models are planned. Two types of distributed models based upon physically meaningful parameters are comparatively studied- 2-D finite- difference model RUNTOX (Kivva

  1. Remote Sensing-Derived Water Extent and Level to Constrain Hydraulic Flood Forecasting Models: Opportunities and Challenges

    NASA Astrophysics Data System (ADS)

    Grimaldi, Stefania; Li, Yuan; Pauwels, Valentijn R. N.; Walker, Jeffrey P.

    2016-09-01

    Accurate, precise and timely forecasts of flood wave arrival time, depth and velocity at each point of the floodplain are essential to reduce damage and save lives. Current computational capabilities support hydraulic models of increasing complexity over extended catchments. Yet a number of sources of uncertainty (e.g., input and boundary conditions, implementation data) may hinder the delivery of accurate predictions. Field gauging data of water levels and discharge have traditionally been used for hydraulic model calibration, validation and real-time constraint. However, the discrete spatial distribution of field data impedes the testing of the model skill at the two-dimensional scale. The increasing availability of spatially distributed remote sensing (RS) observations of flood extent and water level offers the opportunity for a comprehensive analysis of the predictive capability of hydraulic models. The adequate use of the large amount of information offered by RS observations triggers a series of challenging questions on the resolution, accuracy and frequency of acquisition of RS observations; on RS data processing algorithms; and on calibration, validation and data assimilation protocols. This paper presents a review of the availability of RS observations of flood extent and levels, and their use for calibration, validation and real-time constraint of hydraulic flood forecasting models. A number of conclusions and recommendations for future research are drawn with the aim of harmonising the pace of technological developments and their applications.

  2. Basic atmospheric measurements via Arduino Uno microcontroller with commercially available sensors towards simple real-time weather forecasting for increased classroom engagement

    NASA Astrophysics Data System (ADS)

    Eckel, Ryan; Tanner, Meghan; Senevirathne, Indrajith

    Makers, engineers and the applied physics community have adapted Arduino microcontrollers due to their versatility, robustness and cost effectiveness. Arduino microcontroller environment coupled with commercially available sensors have been used to systematically measure, record and analyze temperature, humidity and barometric pressure for building a simplified weather station for subsequent educational purposes. This data will become available in classroom settings for real-time analysis towards simple weather forecasting. Setup was assembled via breadboard, wire and simple soldering with an Arduino Uno ATmega328P microcontroller connected to a PC. The microcontroller was programmed with Arduino Software while the bootloader was used to upload the code. Commercial DHT22 humidity and temperature sensor, and BMP180 barometric pressure sensor were used to obtain relative humidity, temperature and the barometric pressure. A weather resistant enclosure protected the system while stable real-time data measurements were obtained, and uploaded onto the PC. The data was used to predict atmospheric conditions and lifting condensation level (LCL). Discussion will focus on capabilities and limitations of these systems and corresponding teaching aspects. Lock Haven University Nanotechnology Program.

  3. Impact of rainfall spatial variability on Flash Flood Forecasting

    NASA Astrophysics Data System (ADS)

    Douinot, Audrey; Roux, Hélène; Garambois, Pierre-André; Larnier, Kevin

    2014-05-01

    According to the United States National Hazard Statistics database, flooding and flash flooding have caused the largest number of deaths of any weather-related phenomenon over the last 30 years (Flash Flood Guidance Improvement Team, 2003). Like the storms that cause them, flash floods are very variable and non-linear phenomena in time and space, with the result that understanding and anticipating flash flood genesis is far from straightforward. In the U.S., the Flash Flood Guidance (FFG) estimates the average number of inches of rainfall for given durations required to produce flash flooding in the indicated county. In Europe, flash flood often occurred on small catchments (approximately 100 km2) and it has been shown that the spatial variability of rainfall has a great impact on the catchment response (Le Lay and Saulnier, 2007). Therefore, in this study, based on the Flash flood Guidance method, rainfall spatial variability information is introduced in the threshold estimation. As for FFG, the threshold is the number of millimeters of rainfall required to produce a discharge higher than the discharge corresponding to the first level (yellow) warning of the French flood warning service (SCHAPI: Service Central d'Hydrométéorologie et d'Appui à la Prévision des Inondations). The indexes δ1 and δ2 of Zoccatelli et al. (2010), based on the spatial moments of catchment rainfall, are used to characterize the rainfall spatial distribution. Rainfall spatial variability impacts on warning threshold and on hydrological processes are then studied. The spatially distributed hydrological model MARINE (Roux et al., 2011), dedicated to flash flood prediction is forced with synthetic rainfall patterns of different spatial distributions. This allows the determination of a warning threshold diagram: knowing the spatial distribution of the rainfall forecast and therefore the 2 indexes δ1 and δ2, the threshold value is read on the diagram. A warning threshold diagram is

  4. Reinforced recurrent neural networks for multi-step-ahead flood forecasts

    NASA Astrophysics Data System (ADS)

    Chen, Pin-An; Chang, Li-Chiu; Chang, Fi-John

    2013-08-01

    Considering true values cannot be available at every time step in an online learning algorithm for multi-step-ahead (MSA) forecasts, a MSA reinforced real-time recurrent learning algorithm for recurrent neural networks (R-RTRL NN) is proposed. The main merit of the proposed method is to repeatedly adjust model parameters with the current information including the latest observed values and model's outputs to enhance the reliability and the forecast accuracy of the proposed method. The sequential formulation of the R-RTRL NN is derived. To demonstrate its reliability and effectiveness, the proposed R-RTRL NN is implemented to make 2-, 4- and 6-step-ahead forecasts in a famous benchmark chaotic time series and a reservoir flood inflow series in North Taiwan. For comparison purpose, three comparative neural networks (two dynamic and one static neural networks) were performed. Numerical and experimental results indicate that the R-RTRL NN not only achieves superior performance to comparative networks but significantly improves the precision of MSA forecasts for both chaotic time series and reservoir inflow case during typhoon events with effective mitigation in the time-lag problem.

  5. A first large-scale flood inundation forecasting model

    SciTech Connect

    Schumann, Guy J-P; Neal, Jeffrey C.; Voisin, Nathalie; Andreadis, Konstantinos M.; Pappenberger, Florian; Phanthuwongpakdee, Kay; Hall, Amanda C.; Bates, Paul D.

    2013-11-04

    At present continental to global scale flood forecasting focusses on predicting at a point discharge, with little attention to the detail and accuracy of local scale inundation predictions. Yet, inundation is actually the variable of interest and all flood impacts are inherently local in nature. This paper proposes a first large scale flood inundation ensemble forecasting model that uses best available data and modeling approaches in data scarce areas and at continental scales. The model was built for the Lower Zambezi River in southeast Africa to demonstrate current flood inundation forecasting capabilities in large data-scarce regions. The inundation model domain has a surface area of approximately 170k km2. ECMWF meteorological data were used to force the VIC (Variable Infiltration Capacity) macro-scale hydrological model which simulated and routed daily flows to the input boundary locations of the 2-D hydrodynamic model. Efficient hydrodynamic modeling over large areas still requires model grid resolutions that are typically larger than the width of many river channels that play a key a role in flood wave propagation. We therefore employed a novel sub-grid channel scheme to describe the river network in detail whilst at the same time representing the floodplain at an appropriate and efficient scale. The modeling system was first calibrated using water levels on the main channel from the ICESat (Ice, Cloud, and land Elevation Satellite) laser altimeter and then applied to predict the February 2007 Mozambique floods. Model evaluation showed that simulated flood edge cells were within a distance of about 1 km (one model resolution) compared to an observed flood edge of the event. Our study highlights that physically plausible parameter values and satisfactory performance can be achieved at spatial scales ranging from tens to several hundreds of thousands of km2 and at model grid resolutions up to several km2. However, initial model test runs in forecast mode

  6. Forecast skill of a high-resolution real-time mesoscale model designed for weather support of operations at Kennedy Space Center and Cape Canaveral Air Station

    NASA Technical Reports Server (NTRS)

    Taylor, Gregory E.; Zack, John W.; Manobianco, John

    1994-01-01

    NASA funded Mesoscale Environmental Simulations and Operations (MESO), Inc. to develop a version of the Mesoscale Atmospheric Simulation System (MASS). The model has been modified specifically for short-range forecasting in the vicinity of KSC/CCAS. To accomplish this, the model domain has been limited to increase the number of horizontal grid points (and therefore grid resolution) and the model' s treatment of precipitation, radiation, and surface hydrology physics has been enhanced to predict convection forced by local variations in surface heat, moisture fluxes, and cloud shading. The objective of this paper is to (1) provide an overview of MASS including the real-time initialization and configuration for running the data pre-processor and model, and (2) to summarize the preliminary evaluation of the model's forecasts of temperature, moisture, and wind at selected rawinsonde station locations during February 1994 and July 1994. MASS is a hydrostatic, three-dimensional modeling system which includes schemes to represent planetary boundary layer processes, surface energy and moisture budgets, free atmospheric long and short wave radiation, cloud microphysics, and sub-grid scale moist convection.

  7. Development Of An Open System For Integration Of Heterogeneous Models For Flood Forecasting And Hazard Mitigation

    NASA Astrophysics Data System (ADS)

    Chang, W.; Tsai, W.; Lin, F.; Lin, S.; Lien, H.; Chung, T.; Huang, L.; Lee, K.; Chang, C.

    2008-12-01

    makes the tighter collaboration work among these hydrological models. In addition, in order to make communication between system users and decision makers efficient and effective, a real-time and multi-user communication platform, designated as Co-life, is incorporated in the present study. Through its application sharing function, the flood forecasting results can be displayed for all attendees situated at different locations to help the processes of decision making for hazard mitigation. Fig. 2 shows the cyber-conference of WRA officials with the Co-life system for hazard mitigation during the typhoon event.

  8. Long-range forecast of all India summer monsoon rainfall using adaptive neuro-fuzzy inference system: skill comparison with CFSv2 model simulation and real-time forecast for the year 2015

    NASA Astrophysics Data System (ADS)

    Chaudhuri, S.; Das, D.; Goswami, S.; Das, S. K.

    2016-02-01

    All India summer monsoon rainfall (AISMR) characteristics play a vital role for the policy planning and national economy of the country. In view of the significant impact of monsoon system on regional as well as global climate systems, accurate prediction of summer monsoon rainfall has become a challenge. The objective of this study is to develop an adaptive neuro-fuzzy inference system (ANFIS) for long range forecast of AISMR. The NCEP/NCAR reanalysis data of temperature, zonal and meridional wind at different pressure levels have been taken to construct the input matrix of ANFIS. The membership of the input parameters for AISMR as high, medium or low is estimated with trapezoidal membership function. The fuzzified standardized input parameters and the de-fuzzified target output are trained with artificial neural network models. The forecast of AISMR with ANFIS is compared with non-hybrid multi-layer perceptron model (MLP), radial basis functions network (RBFN) and multiple linear regression (MLR) models. The forecast error analyses of the models reveal that ANFIS provides the best forecast of AISMR with minimum prediction error of 0.076, whereas the errors with MLP, RBFN and MLR models are 0.22, 0.18 and 0.73 respectively. During validation with observations, ANFIS shows its potency over the said comparative models. Performance of the ANFIS model is verified through different statistical skill scores, which also confirms the aptitude of ANFIS in forecasting AISMR. The forecast skill of ANFIS is also observed to be better than Climate Forecast System version 2. The real-time forecast with ANFIS shows possibility of deficit (65-75 cm) AISMR in the year 2015.

  9. Evaluation of NWP Precipitation Forecasts for Global Flood Warning

    NASA Astrophysics Data System (ADS)

    Tian, Y.; Adler, R. F.; Peters-Lidard, C. D.

    2008-12-01

    Precipitation forecasts from numerical weather prediction (NWP) models can potentially improve our ability for global flood and landslide warning. In this study, the skills and errors of three NWP precipitation forecast products were analyzed. These forecast products include GEOS5, GDAS and ECMWF, with lead time ranging from 12 hours to 5 days. They were evaluated against the satellite-based, gauge-corrected precipitation estimates, TMPA 3B42, over the land surface as well as the globe. To gain a better perspective, we also evaluated several other satellite-based precipitation products, including GPCP, TMPA 3B42RT, CMORPH and PERSIANN, against TMPA 3B42. Our analysis shows the three NWP forecasts tend to systematically over-estimate global precipitation by approximately 50%. This positive bias does not change much with lead time. In contrast, the satellite-based estimates (GPCP, TMPA, 3B42RT, CMORPH and PERSIANN) have biases mostly less than 20%. In addition, the RMS errors increase with the lead time in NWP forecasts, and in particular for GEOS5, the most increase in RMS errors takes place when the lead time goes from 1 day to 2 days. The RMS errors in the NWP products are also about twice as much as those of the satellite-based products. Further analysis indicates false alarms dominate the errors in the NWP forecasts. Among the NWP products, GEOS5 has slightly better performance than the other two. The implication of these error characteristics on global flood and landslide warning will be discussed.

  10. Towards real-time eruption forecasting in the Auckland Volcanic Field: application of BET_EF during the New Zealand National Disaster Exercise `Ruaumoko'

    NASA Astrophysics Data System (ADS)

    Lindsay, Jan; Marzocchi, Warner; Jolly, Gill; Constantinescu, Robert; Selva, Jacopo; Sandri, Laura

    2010-03-01

    The Auckland Volcanic Field (AVF) is a young basaltic field that lies beneath the urban area of Auckland, New Zealand’s largest city. Over the past 250,000 years the AVF has produced at least 49 basaltic centers; the last eruption was only 600 years ago. In recognition of the high risk associated with a possible future eruption in Auckland, the New Zealand government ran Exercise Ruaumoko in March 2008, a test of New Zealand’s nation-wide preparedness for responding to a major disaster resulting from a volcanic eruption in Auckland City. The exercise scenario was developed in secret, and covered the period of precursory activity up until the eruption. During Exercise Ruaumoko we adapted a recently developed statistical code for eruption forecasting, namely BET_EF (Bayesian Event Tree for Eruption Forecasting), to independently track the unrest evolution and to forecast the most likely onset time, location and style of the initial phase of the simulated eruption. The code was set up before the start of the exercise by entering reliable information on the past history of the AVF as well as the monitoring signals expected in the event of magmatic unrest and an impending eruption. The average probabilities calculated by BET_EF during Exercise Ruaumoko corresponded well to the probabilities subjectively (and independently) estimated by the advising scientists (differences of few percentage units), and provided a sound forecast of the timing (before the event, the eruption probability reached 90%) and location of the eruption. This application of BET_EF to a volcanic field that has experienced no historical activity and for which otherwise limited prior information is available shows its versatility and potential usefulness as a tool to aid decision-making for a wide range of volcano types. Our near real-time application of BET_EF during Exercise Ruaumoko highlighted its potential to clarify and possibly optimize decision-making procedures in a future AVF eruption

  11. Assimilation of soil moisture observations from remote sensing in operational flood forecasting

    NASA Astrophysics Data System (ADS)

    Mazzoleni, Maurizio; Alfonso, Leonardo; Ferri, Michele; Monego, Martina; Norbiato, Daniele; Solomatine, Dimitri P.

    2014-05-01

    Flooding and the resulting damages occurred in Europe in recent decades showed that the need of a preparation to critical events can be considered as a key factor in reducing their impact on society. It has been shown that early warning systems may reduce significantly the direct and indirect damages and costs of a flood impact. In order to improve the forecasting systems, data assimilation methods were proposed in the last years to integrate real-time observations into hydrological and hydrodynamic models. The aim of this work is to assimilate observations of soil moisture into an operational flood forecasting system in Italy in order to evaluate the effect on the water level along the main river channel. The methodology is applied in the Bacchiglione catchment, located in the North of Italy, having a drainage area of about 1400 km2, length of main reach of 118km and average discharge of 30m3/s at Padova. In order to represent this system, the Bacchiglione basin was considered as a set of different sub-basins characterized by its own hydrologic response and connected each other mainly by propagation phenomena. A 1D hydrodynamic model was then used to estimate water level along the main channel. The assimilation of the soil moisture observations was carried out using a variant of the Kalman filter-based technique. The main idea of this study was to update the model state (the soil water capacity) as response of the distributed information of soil moisture, and then estimate the flow hydrograph at the basin outlet. As a basis we used the approach by Brocca et al.(2012), using a different model structure and with adaption allowing for real-time use. The results of this work show how the added value of soil moisture into the hydrological model can improve the forecast of the flow hydrograph and the consequent water level in the main channel. This study is part of the FP7 European Project WeSenseIt. [1] Brocca, L., Moramarco, T., Melone, F., Wagner, W., Hasenauer, S

  12. Superposition of three sources of uncertainties in operational flood forecasting chains

    NASA Astrophysics Data System (ADS)

    Zappa, Massimiliano; Jaun, Simon; Germann, Urs; Walser, André; Fundel, Felix

    2011-05-01

    One of the less known aspects of operational flood forecasting systems in complex topographic areas is the way how the uncertainties of its components propagate and superpose when they are fed into a hydrological model. This paper describes an experimental framework for investigating the relative contribution of meteorological forcing uncertainties, initial conditions uncertainties and hydrological model parameter uncertainties in the realization of hydrological ensemble forecasts. Simulations were done for a representative small-scale basin of the Swiss Alps, the Verzasca river basin (186 km 2). For seven events in the time frame from June 2007 to November 2008 it was possible to quantify the uncertainty for a five-day forecast range yielded by inputs of an ensemble numerical weather prediction (NWP) model (COSMO-LEPS, 16 members), the uncertainty in real-time assimilation of weather radar precipitation fields expressed using an ensemble approach (REAL, 25 members), and the equifinal parameter realizations of the hydrological model adopted (PREVAH, 26 members). Combining the three kinds of uncertainty results in a hydrological ensemble of 10,400 members. Analyses of sub-samples from the ensemble provide insight in the contribution of each kind of uncertainty to the total uncertainty. The results confirm our expectations and show that for the operational simulation of peak-runoff events the hydrological model uncertainty is less pronounced than the uncertainty obtained by propagating radar precipitation fields (by a factor larger than 4 in our specific setup) and NWP forecasts through the hydrological model (by a factor larger than 10). The use of precipitation radar ensembles for generating ensembles of initial conditions shows that the uncertainty in initial conditions decays within the first 48 hours of the forecast. We also show that the total spread obtained when superposing two or more sources of uncertainty is larger than the cumulated spread of experiments

  13. Observed and forecast flood-inundation mapping application-A pilot study of an eleven-mile reach of the White River, Indianapolis, Indiana

    USGS Publications Warehouse

    Kim, Moon H.; Morlock, Scott E.; Arihood, Leslie D.; Kiesler, James L.

    2011-01-01

    Near-real-time and forecast flood-inundation mapping products resulted from a pilot study for an 11-mile reach of the White River in Indianapolis. The study was done by the U.S. Geological Survey (USGS), Indiana Silver Jackets hazard mitigation taskforce members, the National Weather Service (NWS), the Polis Center, and Indiana University, in cooperation with the City of Indianapolis, the Indianapolis Museum of Art, the Indiana Department of Homeland Security, and the Indiana Department of Natural Resources, Division of Water. The pilot project showed that it is technically feasible to create a flood-inundation map library by means of a two-dimensional hydraulic model, use a map from the library to quickly complete a moderately detailed local flood-loss estimate, and automatically run the hydraulic model during a flood event to provide the maps and flood-damage information through a Web graphical user interface. A library of static digital flood-inundation maps was created by means of a calibrated two-dimensional hydraulic model. Estimated water-surface elevations were developed for a range of river stages referenced to a USGS streamgage and NWS flood forecast point colocated within the study reach. These maps were made available through the Internet in several formats, including geographic information system, Keyhole Markup Language, and Portable Document Format. A flood-loss estimate was completed for part of the study reach by using one of the flood-inundation maps from the static library. The Federal Emergency Management Agency natural disaster-loss estimation program HAZUS-MH, in conjunction with local building information, was used to complete a level 2 analysis of flood-loss estimation. A Service-Oriented Architecture-based dynamic flood-inundation application was developed and was designed to start automatically during a flood, obtain near real-time and forecast data (from the colocated USGS streamgage and NWS flood forecast point within the study reach

  14. Real-time Reservoir Operation Based on a Combination of Long-term and Short-term Optimization and Hydrological Ensemble Forecasts

    NASA Astrophysics Data System (ADS)

    Meier, P.; Tilmant, A.; Boucher, M.; Anctil, F.

    2012-12-01

    features two large and three smaller reservoirs. Electricity is produced at four of the five dams. Besides the production of hydropower, the reservoirs are used to mitigate floods and for recreational purposes. The hydrological ensemble forecasts are generated with the HYDROTEL model, using meteorological ensemble forecasts issued by Environment Canada as forcing data. The framework described above is applied on a rolling horizon optimization mode on a period of almost two years from March 2002 to the end of 2003. The autumn of 2003 is characterized by a period of strong rainfall events which eventually led to flooding in parts of the river basin. Results show, that this coupled optimization is able to maximize the power production without neglecting the multiple purposes of the reservoir. While the optimization process itself is relatively straight-forward, taking a decision form the set of optimal policies is not. For each ensemble member of the hydrological forecast an optimal operation policy is obtained. Therefore, a strategy of decision taking has to be developed, which allows the incorporation of the information provided by the ensemble forecast as a whole. Different decision making strategies are presented and assessed.

  15. Radar-driven High-resolution Hydrometeorological Forecasts of the 26 September 2007 Venice flash flood

    NASA Astrophysics Data System (ADS)

    Massimo Rossa, Andrea; Laudanna Del Guerra, Franco; Borga, Marco; Zanon, Francesco; Settin, Tommaso; Leuenberger, Daniel

    2010-05-01

    Space and time scales of flash floods are such that flash flood forecasting and warning systems depend upon the accurate real-time provision of rainfall information, high-resolution numerical weather prediction (NWP) forecasts and the use of hydrological models. Currently available high-resolution NWP model models can potentially provide warning forecasters information on the future evolution of storms and their internal structure, thereby increasing convective-scale warning lead times. However, it is essential that the model be started with a very accurate representation of on-going convection, which calls for assimilation of high-resolution rainfall data. This study aims to assess the feasibility of using carefully checked radar-derived quantitative precipitation estimates (QPE) for assimilation into NWP and hydrological models. The hydrometeorological modeling chain includes the convection-permitting NWP model COSMO-2 and a hydrologic-hydraulic models built upon the concept of geomorphological transport. Radar rainfall observations are assimilated into the NWP model via the latent heat nudging method. The study is focused on 26 September 2007 extreme flash flood event which impacted the coastal area of north-eastern Italy around Venice. The hydro-meteorological modeling system is implemented over the Dese river, a 90 km2 catchment flowing to the Venice lagoon. The radar rainfall observations are carefully checked for artifacts, including beam attenuation, by means of physics-based correction procedures and comparison with a dense network of raingauges. The impact of the radar QPE in the assimilation cycle of the NWP model is very significant, in that the main individual organized convective systems were successfully introduced into the model state, both in terms of timing and localization. Also, incorrectly localized precipitation in the model reference run without rainfall assimilation was correctly reduced to about the observed levels. On the other hand, the

  16. Improvements in NOAA SURFRAD and ISIS sites for near real-time solar irradiance for verification of NWP solar forecasts for the DOE NOAA Solar Forecast Improvement Project (SFIP)

    NASA Astrophysics Data System (ADS)

    Lantz, K. O.; McComiskey, A. C.; Long, C. N.; Marquis, M.; Olson, J. B.; James, E.; Benjamin, S.; Clack, C.

    2015-12-01

    The DOE-NOAA Solar Forecasting Improvement Project's (SFIP) main goal is to improve solar forecasting and thereby increase penetration of solar renewable energy on the electric grid. NOAA's ISIS and SURFRAD network is part of this initiative by providing high quality solar irradiance measurements for verification of improvements in solar forecasting for the short-term, day ahead, and ramp events. There are 14 ISIS and SURFRAD stations across the continental United States. We will give an overview of recent improvements in the networks for this project. The NOAA SURFRAD team has three main components: 1) In addition to the existing stations, two mobile SURFRAD stations have been built and deployed for 1 year each at two separate solar utility plants. 2) NOAA SURFRAD/ISIS will update the communications at their sites to provide near real-time data for verification activities at the 14 sites. 3) Global horizontal irradiance (GHI), direct normal solar irradiance (DNI), and aerosol optical depth at various spatial and temporal averaging will be compared to forecasts from the 3-km High-Resolution Rapid Refresh (HRRR) and an advanced version of the 13-km Rapid Refresh (RAP) models. We will explore statistical correlations between in-coming and out-going shortwave radiation and longwave radiation at the surface for specific meteorological regimes and how well these are captured by NWP models.

  17. PAI-OFF: A new proposal for online flood forecasting in flash flood prone catchments

    NASA Astrophysics Data System (ADS)

    Schmitz, G. H.; Cullmann, J.

    2008-10-01

    SummaryThe Process Modelling and Artificial Intelligence for Online Flood Forecasting (PAI-OFF) methodology combines the reliability of physically based, hydrologic/hydraulic modelling with the operational advantages of artificial intelligence. These operational advantages are extremely low computation times and straightforward operation. The basic principle of the methodology is to portray process models by means of ANN. We propose to train ANN flood forecasting models with synthetic data that reflects the possible range of storm events. To this end, establishing PAI-OFF requires first setting up a physically based hydrologic model of the considered catchment and - optionally, if backwater effects have a significant impact on the flow regime - a hydrodynamic flood routing model of the river reach in question. Both models are subsequently used for simulating all meaningful and flood relevant storm scenarios which are obtained from a catchment specific meteorological data analysis. This provides a database of corresponding input/output vectors which is then completed by generally available hydrological and meteorological data for characterizing the catchment state prior to each storm event. This database subsequently serves for training both a polynomial neural network (PoNN) - portraying the rainfall-runoff process - and a multilayer neural network (MLFN), which mirrors the hydrodynamic flood wave propagation in the river. These two ANN models replace the hydrological and hydrodynamic model in the operational mode. After presenting the theory, we apply PAI-OFF - essentially consisting of the coupled "hydrologic" PoNN and "hydrodynamic" MLFN - to the Freiberger Mulde catchment in the Erzgebirge (Ore-mountains) in East Germany (3000 km 2). Both the demonstrated computational efficiency and the prediction reliability underline the potential of the new PAI-OFF methodology for online flood forecasting.

  18. Comprehensive evaluation of multi-year real-time air quality forecasting using an online-coupled meteorology-chemistry model over southeastern United States

    NASA Astrophysics Data System (ADS)

    Zhang, Yang; Hong, Chaopeng; Yahya, Khairunnisa; Li, Qi; Zhang, Qiang; He, Kebin

    2016-08-01

    An online-coupled meteorology-chemistry model, WRF/Chem-MADRID, has been deployed for real time air quality forecast (RT-AQF) in southeastern U.S. since 2009. A comprehensive evaluation of multi-year RT-AQF shows overall good performance for temperature and relative humidity at 2-m (T2, RH2), downward surface shortwave radiation (SWDOWN) and longwave radiation (LWDOWN), and cloud fraction (CF), ozone (O3) and fine particles (PM2.5) at surface, tropospheric ozone residuals (TOR) in O3 seasons (May-September), and column NO2 in winters (December-February). Moderate-to-large biases exist in wind speed at 10-m (WS10), precipitation (Precip), cloud optical depth (COT), ammonium (NH4+), sulfate (SO42-), and nitrate (NO3-) from the IMPROVE and SEARCH networks, organic carbon (OC) at IMPROVE, and elemental carbon (EC) and OC at SEARCH, aerosol optical depth (AOD) and column carbon monoxide (CO), sulfur dioxide (SO2), and formaldehyde (HCHO) in both O3 and winter seasons, column nitrogen dioxide (NO2) in O3 seasons, and TOR in winters. These biases indicate uncertainties in the boundary layer and cloud process treatments (e.g., surface roughness, microphysics cumulus parameterization), emissions (e.g., O3 and PM precursors, biogenic, mobile, and wildfire emissions), upper boundary conditions for all major gases and PM2.5 species, and chemistry and aerosol treatments (e.g., winter photochemistry, aerosol thermodynamics). The model shows overall good skills in reproducing the observed multi-year trends and inter-seasonal variability in meteorological and radiative variables such as T2, WS10, Precip, SWDOWN, and LWDOWN, and relatively well in reproducing the observed trends in surface O3 and PM2.5, but relatively poor in reproducing the observed column abundances of CO, NO2, SO2, HCHO, TOR, and AOD. The sensitivity simulations using satellite-constrained boundary conditions for O3 and CO show substantial improvement for both spatial distribution and domain-mean performance

  19. Regional hydrological models for distributed flash-floods forecasting: towards an estimation of potential impacts and damages

    NASA Astrophysics Data System (ADS)

    Le Bihan, Guillaume; Payrastre, Olivier; Gaume, Eric; Pons, Frederic; Moncoulon, David

    2016-04-01

    Hydrometeorological forecasting is an essential component of real-time flood management. The information it provides is of great help for crisis managers to anticipate the inundations and the associated risks. In the particular case of flash-floods, which may affect a large amount of small watersheds spread over the territory (up to 300 000 km of waterways considering a drained area of 5 km² minimum in France), appropriate flood forecasting systems are still under development. In France, highly distributed hydrological models have been implemented, enabling a real-time assessment of the potential intensity of flash-floods from the records of weather radars: AIGA-hydro system (Lavabre et al., 2005; Javelle et al., 2014), PreDiFlood project (Naulin et al., 2013). The approach presented here aims to go one step further by offering a direct assessment of the potential impacts of the simulated floods on inhabited areas. This approach is based on an a priori analysis of the study area in order (1) to evaluate with a simplified hydraulic approach (DTM treatment) the potentially flooded areas for different discharge levels, and (2) to identify the associated buildings and/or population at risk from geographic databases. This preliminary analysis enables to build an impact model (discharge-impact curve) on each river reach, which is then used to directly estimate the potentially affected assets based on a distributed rainfall runoff model. The overall principle of this approach was already presented at the 8th Hymex workshop. Therefore, the presentation will be here focused on the first validation results in terms of (1) accuracy of flooded areas simulated from DTM treatments, and (2) relevance of estimated impacts. The inundated areas simulated were compared to the European Directive cartography results (where available), showing an overall good correspondence in a large majority of cases, but also very significant errors for approximatively 10% of the river reaches

  20. Shyska environment: taking decisions in real-time

    NASA Astrophysics Data System (ADS)

    García, S. G.

    2003-04-01

    The flash floods constitute one of the main problems of the Mediterranean semiarid zones. The convective storms of high intensity, typical of these zones, produce hydrological events that can turn out to be catastrophic. Due to the scanty existing time between the runoff hydrograph peak and the associated rainfall, the warning of flooding must be based on meteorological and hydrological forecasting. The coordination among the responsible authorities in the operational flood management, needs real-time operative decision support system. The use of Geographical Information Systems (GIS) in the development of these support tools, it constitutes a foreseeable direction. The development and use of new methodologies and tools that allows the operative integration of spatiotemporal information from different sources as input to distributed spatially rainfall-runoff models, are proposed. In this work, a real-time operative decision support computer system named Shyska is applied. This environment integrates the potentiality of GIS to the processing of information to different space-time scales, and spatially distributed hydrologic models oriented to runoff simulation and prediction, using topographical attributes extracted from DEM. Its final aim is to assist Hydrological Information Automatic Systems (SAIH systems in Spain), facilitating information management and use in real-time when alert and flash flood situations occur. Shyska has been applied to semiarid basins in the southeast of Spain, using pluviometric information from telemetric networks and products of remote sensing (rainfall fields), presenting satisfactory results. Finally, it is to be underlined that this work incorporates the use of new technologies (GIS, remote sensing, Digital Elevation Models) and recent developments in aspects of estimation and treatment of the spatial distribution of soil moisture and the flow in the unsaturated zone, in semiarid zones. The main objective is to improve the forecasts

  1. General characteristics of causes of urban flood damage and flood forecasting/warning system in Seoul, Korea Young-Il Moon1, 2, Jong-Suk Kim1, 2 1 Department of Civil Engineering, University of Seoul, Seoul 130-743, South Korea 2 Urban Flood Research Inst

    NASA Astrophysics Data System (ADS)

    Moon, Young-Il; Kim, Jong-Suk

    2015-04-01

    Due to rapid urbanization and climate change, the frequency of concentrated heavy rainfall has increased, causing urban floods that result in casualties and property damage. As a consequence of natural disasters that occur annually, the cost of damage in Korea is estimated to be over two billion US dollars per year. As interest in natural disasters increase, demands for a safe national territory and efficient emergency plans are on the rise. In addition to this, as a part of the measures to cope with the increase of inland flood damage, it is necessary to build a systematic city flood prevention system that uses technology to quantify flood risk as well as flood forecast based on both rivers and inland water bodies. Despite the investment and efforts to prevent landside flood damage, research and studies of landside-river combined hydro-system is at its initial stage in Korea. Therefore, the purpose of this research introduces the causes of flood damage in Seoul and shows a flood forecasting and warning system in urban streams of Seoul. This urban flood forecasting and warning system conducts prediction on flash rain or short-term rainfall by using radar and satellite information and performs prompt and accurate prediction on the inland flooded area and also supports synthetic decision-making for prevention through real-time monitoring. Although we cannot prevent damage from typhoons or localized heavy rain, we can minimize that damage with accurate and timely forecast and a prevention system. To this end, we developed a flood forecasting and warning system, so in case of an emergency there is enough time for evacuation and disaster control. Keywords: urban flooding, flood risk, inland-river system, Korea Acknowledgments This research was supported by a grant (13AWMP-B066744-01) from Advanced Water Management Research Program (AWMP) funded by Ministry of Land, Infrastructure and Transport of Korean government.

  2. Meteo-marine parameters from the TS-X satellite on a near-real time basis and wave model forecast validation

    NASA Astrophysics Data System (ADS)

    Gebhardt, Claus; Susanne, Lehner; Pleskachevsky, Andrey; Jacobsen, Sven; Rosenthal, Wolfgang; Hoffmann, Peter; Kieser, Jens; Bruns, Thomas

    2015-04-01

    The TerraSAR-X satellite (TS-X), which was launched in 2007, hosts a state-of-the-art X-band Synthetic Aperture Radar (SAR). As a result of its relatively low orbit (ca. 500 km), it has improved properties against SAR-specific image distortion of the ocean surface. Being a SAR sensor, its observations are independent off sun light and cloud conditions. Meteo-marine parameters are provided on a near real time basis. Both sea state and wind conditions are derived from the radar images. This is accomplished by empirical retrieval algorithms. For the wind retrieval, the backscatter coefficients of radar scatterometers for different wind speeds and directions are utilised (XMOD2). For the wave retrieval, the empirical algorithm XWAVE has been developed as transformation of the radar images into two dimensional wave spectra. Spectral properties like the peak wavelength, peak period, and significant wave height are derived from the wave spectra. In our presentation, we give an overview on the TS-X wave and wind retrieval algorithms (XWAVE and XMOD2). The quality of the sea state results from TS-X is demonstrated by comparisons to collocated buoy measurements. In the German Bight, the forecast quality of the high-resolution coastal wave model CWAM of the German Meteorological Service (DWD) is validated using TS-X results. The comparison of the numerical model hindcasts with TS-X satellite data complements and extends the conventional validation by in-situ data. This is because of the large areal coverage and in-situ sensors cannot be easily deployed in certain regions, e.g. in locations with strong ship traffic.

  3. Forecasting of Storm Surge Floods Using ADCIRC and Optimized DEMs

    NASA Technical Reports Server (NTRS)

    Valenti, Elizabeth; Fitzpatrick, Patrick

    2005-01-01

    Increasing the accuracy of storm surge flood forecasts is essential for improving preparedness for hurricanes and other severe storms and, in particular, for optimizing evacuation scenarios. An interactive database, developed by WorldWinds, Inc., contains atlases of storm surge flood levels for the Louisiana/Mississippi gulf coast region. These atlases were developed to improve forecasting of flooding along the coastline and estuaries and in adjacent inland areas. Storm surge heights depend on a complex interaction of several factors, including: storm size, central minimum pressure, forward speed of motion, bottom topography near the point of landfall, astronomical tides, and most importantly, maximum wind speed. The information in the atlases was generated in over 100 computational simulations, partly by use of a parallel-processing version of the ADvanced CIRCulation (ADCIRC) model. ADCIRC is a nonlinear computational model of hydrodynamics, developed by the U.S. Army Corps of Engineers and the US Navy, as a family of two- and three-dimensional finite element based codes. It affords a capability for simulating tidal circulation and storm surge propagation over very large computational domains, while simultaneously providing high-resolution output in areas of complex shoreline and bathymetry. The ADCIRC finite-element grid for this project covered the Gulf of Mexico and contiguous basins, extending into the deep Atlantic Ocean with progressively higher resolution approaching the study area. The advantage of using ADCIRC over other storm surge models, such as SLOSH, is that input conditions can include all or part of wind stress, tides, wave stress, and river discharge, which serve to make the model output more accurate.

  4. A pan-African medium-range ensemble flood forecast system

    NASA Astrophysics Data System (ADS)

    Thiemig, V.; Bisselink, B.; Pappenberger, F.; Thielen, J.

    2015-08-01

    The African Flood Forecasting System (AFFS) is a probabilistic flood forecast system for medium- to large-scale African river basins, with lead times of up to 15 days. The key components are the hydrological model LISFLOOD, the African GIS database, the meteorological ensemble predictions by the ECMWF (European Centre for Medium-Ranged Weather Forecasts) and critical hydrological thresholds. In this paper, the predictive capability is investigated in a hindcast mode, by reproducing hydrological predictions for the year 2003 when important floods were observed. Results were verified by ground measurements of 36 sub-catchments as well as by reports of various flood archives. Results showed that AFFS detected around 70 % of the reported flood events correctly. In particular, the system showed good performance in predicting riverine flood events of long duration (> 1 week) and large affected areas (> 10 000 km2) well in advance, whereas AFFS showed limitations for small-scale and short duration flood events. The case study for the flood event in March 2003 in the Sabi Basin (Zimbabwe) illustrated the good performance of AFFS in forecasting timing and severity of the floods, gave an example of the clear and concise output products, and showed that the system is capable of producing flood warnings even in ungauged river basins. Hence, from a technical perspective, AFFS shows a large potential as an operational pan-African flood forecasting system, although issues related to the practical implication will still need to be investigated.

  5. Automatic removal of outliers in hydrologic time series and quality control of rainfall data: processing a real-time database of the Local System for Flood Monitoring in Klodzko County, Poland

    NASA Astrophysics Data System (ADS)

    Mizinski, Bartlomiej; Niedzielski, Tomasz; Kryza, Maciej; Szymanowski, Mariusz

    2013-04-01

    Real-time hydrological forecasting requires the highest quality of both hydrologic and meteorological data collected in a given river basin. Large outliers may lead to inaccurate predictions, with substantial departures between observations and prognoses considered even in short term. Although we need the correctness of both riverflow and rainfall data, they cannot be processed in the same way to produce a filtered output. Indeed, hydrologic time series at a given gauge can be interpolated in time domain after having detected suspicious values, however if no outlier has been detected at the upstream sites. In the case of rainfall data, interpolation is not suitable as we cannot verify the potential outliers at a given site against data from other sites especially in the complex terrain. This is due to the fact that very local convective events may occur, leading to large rainfall peaks at a limited space. Hence, instead of interpolating data, we rather perform a flagging procedure that only ranks outliers according to the likelihood of occurrence. Following the aforementioned assumptions, we have developed a few modules that serve a purpose of a fully automated correction of a database that is updated in real-time every 15 minutes, and the main objective of the work was to produce a high-quality database for a purpose of hydrologic rainfall-runoff modeling and ensemble prediction. The database in question is available courtesy of the County Office in Kłodzko (SW Poland), the institution which owns and maintains the Local System for Flood Monitoring in Kłodzko County. The dedicated prediction system, known as HydroProg, is now being built at the University of Wrocław (Poland). As the entire prediction system, the correction modules work automatically in real time and are developed in R language. They are plugged in to a larger IT infrastructure. Hydrologic time series, which are water levels recorded every 15 minutes at 22 gauges located in Kłodzko County, are

  6. Evaluating the Performance of Wavelet-based Data-driven Models for Multistep-ahead Flood Forecasting in an Urbanized Watershed

    NASA Astrophysics Data System (ADS)

    Kasaee Roodsari, B.; Chandler, D. G.

    2015-12-01

    A real-time flood forecast system is presented to provide emergency management authorities sufficient lead time to execute plans for evacuation and asset protection in urban watersheds. This study investigates the performance of two hybrid models for real-time flood forecasting at different subcatchments of Ley Creek watershed, a heavily urbanized watershed in the vicinity of Syracuse, New York. Hybrid models include Wavelet-Based Artificial Neural Network (WANN) and Wavelet-Based Adaptive Neuro-Fuzzy Inference System (WANFIS). Both models are developed on the basis of real time stream network sensing. The wavelet approach is applied to decompose the collected water depth timeseries to Approximation and Detail components. The Approximation component is then used as an input to ANN and ANFIS models to forecast water level at lead times of 1 to 10 hours. The performance of WANN and WANFIS models are compared to ANN and ANFIS models for different lead times. Initial results demonstrated greater predictive power of hybrid models.

  7. Operational flood forecasting: further lessons learned form a recent inundation in Tuscany, Italy

    NASA Astrophysics Data System (ADS)

    Caparrini, F.; Castelli, F.; di Carlo, E.

    2010-09-01

    After a few years of experimental setup, model refinement and parameters calibration, a distributed flood forecasting system for the Tuscany region was promoted to operational use in early 2008. The hydrologic core of the system, MOBIDIC, is a fully distributed soil moisture accounting model, with sequential assimilation of hydrometric data. The model is forced by the real-time dense hydrometeorological network of the Regional Hydrologic Service as well from the QPF products of a number of different limited area meteorological models (LAMI, WRF+ECMWF, WRF+GFS). Given the relatively short response time of the Tuscany basins, the river flow forecasts based on ground measured precipitation are operationally used mainly as a monitoring tool, while the true usable predictions are necessarily based on the QPF input. The first severe flooding event the system had to face occurred in late December 2009, when a failure of the right levee of the Serchio river caused an extensive inundation (on December 25th). In the days following the levee breaking, intensive monitoring and forecast was needed (another flood peak occurred on the night between December 29th and January 1st 2010) as a support for decisions regarding the management of the increased vulnerability of the area and the planning of emergency reparation works at the river banks. The operational use of the system during such a complex event, when both the meteorological and the hydrological components may be said to have performed well form a strict modeling point of view, brought to attention a number of additional issues about the system as a whole. The main of these issues may be phrased in terms of additional system requirements, namely: the ranking of different QPF products in terms of some likelihood measure; the rapid redefinition of alarm thresholds due to sudden changes in the river flow capacity; the supervised prediction for evaluating the consequences of different management scenarios for reservoirs

  8. An integrated error estimation and lag-aware data assimilation scheme for real-time flood forecasting

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The performance of conventional filtering methods can be degraded by ignoring the time lag between soil moisture and discharge response when discharge observations are assimilated into streamflow modelling. This has led to the ongoing development of more optimal ways to implement sequential data ass...

  9. Using Passive Microwaves for Open Water Monitoring and Flood Forecasting

    NASA Astrophysics Data System (ADS)

    Parinussa, R.; Johnson, F.; Sharma, A.; Lakshmi, V.

    2015-12-01

    One of the biggest and severest natural disasters that society faces is floods. An important component that can help in reducing the impact of floods is satellite remote sensing as it allows for consistent monitoring and obtaining catchment information in absence of physical contact. Nowadays, passive microwave remote sensing observations are available in near real time (NRT) with a couple of hours delay from the actual sensing. The Advanced Microwave Scanning Radiometer 2 (AMSR2) is a multi-frequency passive microwave sensor onboard the Global Change Observation Mission 1 - Water that was launched in May 2012. Several of these frequencies have a high sensitivity to the land surface and they also have the capacity to penetrate clouds. These advantages come at the cost of the relatively coarse spatial resolution (footprints range from ~5 to ~50 km) which in turn allows for global monitoring. A relatively simple methodology to monitor the fraction of open water from AMSR2 observations is presented here. Low frequency passive microwave observations have sensitivity to the land surface but are modulated by overlying signals from physical temperature and vegetation cover. We developed a completely microwave based artificial neural network supported by physically based components to monitor the fraction of open water. Three different areas, located in China, Southeast Asia and Australia, were selected for testing purposes and several different characteristics were examined. First, the overall performance of the methodology was evaluated against the NASA NRT Global Flood Mapping system. Second, the skills of the various different AMSR2 frequencies were tested and revealed that artificial contamination is a factor to consider. The different skills of the tested frequencies are of interest to apply the methodology to alternative passive microwave sensors. This will be of benefit in using the numerous multi-frequency passive microwaves sensors currently observing our Earth

  10. An analytical framework for flood water conservation considering forecast uncertainty and acceptable risk

    NASA Astrophysics Data System (ADS)

    Ding, Wei; Zhang, Chi; Peng, Yong; Zeng, Ruijie; Zhou, Huicheng; Cai, Ximing

    2015-06-01

    This paper addresses how much flood water can be conserved for use after the flood season through the operation of reservoir by taking into account the residual flood control capacity (the difference between flood conveyance capacity and the expected inflow in a lead time). A two-stage model for dynamic control of the flood-limited water level (the maximum allowed water level during the flood season, DC-FLWL) is established considering forecast uncertainty and acceptable flood risk. It is found that DC-FLWL is applicable when the reservoir inflow ranges from small to medium levels of the historical records, while both forecast uncertainty and acceptable risk in the downstream affect the feasible space of DC-FLWL. As forecast uncertainty increases (under a given risk level) or as acceptable risk level decreases (under a given forecast uncertainty level), the minimum required safety margin for flood control increases, and the chance for DC-FLWL decreases. The derived hedging rules from the modeling framework illustrate either the dominant role of water conservation or flood control or the trade-off between the two objectives under different levels of forecast uncertainty and acceptable risk. These rules may provide useful guidelines for conserving water from flood, especially in the area with heavy water stress. The analysis is illustrated via a case study with a real-world reservoir in northeastern China.

  11. Urban flood early warning systems: approaches to hydrometeorological forecasting and communicating risk

    NASA Astrophysics Data System (ADS)

    Cranston, Michael; Speight, Linda; Maxey, Richard; Tavendale, Amy; Buchanan, Peter

    2015-04-01

    One of the main challenges for the flood forecasting community remains the provision of reliable early warnings of surface (or pluvial) flooding. The Scottish Flood Forecasting Service has been developing approaches for forecasting the risk of surface water flooding including capitalising on the latest developments in quantitative precipitation forecasting from the Met Office. A probabilistic Heavy Rainfall Alert decision support tool helps operational forecasters assess the likelihood of surface water flooding against regional rainfall depth-duration estimates from MOGREPS-UK linked to historical short-duration flooding in Scotland. The surface water flood risk is communicated through the daily Flood Guidance Statement to emergency responders. A more recent development is an innovative risk-based hydrometeorological approach that links 24-hour ensemble rainfall forecasts through a hydrological model (Grid-to-Grid) to a library of impact assessments (Speight et al., 2015). The early warning tool - FEWS Glasgow - presents the risk of flooding to people, property and transport across a 1km grid over the city of Glasgow with a lead time of 24 hours. Communication of the risk was presented in a bespoke surface water flood forecast product designed based on emergency responder requirements and trialled during the 2014 Commonwealth Games in Glasgow. The development of new approaches to surface water flood forecasting are leading to improved methods of communicating the risk and better performance in early warning with a reduction in false alarm rates with summer flood guidance in 2014 (67%) compared to 2013 (81%) - although verification of instances of surface water flooding remains difficult. However the introduction of more demanding hydrometeorological capabilities with associated greater levels of uncertainty does lead to an increased demand on operational flood forecasting skills and resources. Speight, L., Cole, S.J., Moore, R.J., Pierce, C., Wright, B., Golding, B

  12. Sensitivity analysis of surface runoff generation in urban flood forecasting.

    PubMed

    Simões, N E; Leitão, J P; Maksimović, C; Sá Marques, A; Pina, R

    2010-01-01

    Reliable flood forecasting requires hydraulic models capable to estimate pluvial flooding fast enough in order to enable successful operational responses. Increased computational speed can be achieved by using a 1D/1D model, since 2D models are too computationally demanding. Further changes can be made by simplifying 1D network models, removing and by changing some secondary elements. The Urban Water Research Group (UWRG) of Imperial College London developed a tool that automatically analyses, quantifies and generates 1D overland flow network. The overland flow network features (ponds and flow pathways) generated by this methodology are dependent on the number of sewer network manholes and sewer inlets, as some of the overland flow pathways start at manholes (or sewer inlets) locations. Thus, if a simplified version of the sewer network has less manholes (or sewer inlets) than the original one, the overland flow network will be consequently different. This paper compares different overland flow networks generated with different levels of sewer network skeletonisation. Sensitivity analysis is carried out in one catchment area in Coimbra, Portugal, in order to evaluate overland flow network characteristics. PMID:20453333

  13. Coupling ensemble weather predictions based on TIGGE database with Grid-Xinanjiang model for flood forecast

    NASA Astrophysics Data System (ADS)

    Bao, H.-J.; Zhao, L.-N.; He, Y.; Li, Z.-J.; Wetterhall, F.; Cloke, H. L.; Pappenberger, F.; Manful, D.

    2011-02-01

    The incorporation of numerical weather predictions (NWP) into a flood forecasting system can increase forecast lead times from a few hours to a few days. A single NWP forecast from a single forecast centre, however, is insufficient as it involves considerable non-predictable uncertainties and lead to a high number of false alarms. The availability of global ensemble numerical weather prediction systems through the THORPEX Interactive Grand Global Ensemble' (TIGGE) offers a new opportunity for flood forecast. The Grid-Xinanjiang distributed hydrological model, which is based on the Xinanjiang model theory and the topographical information of each grid cell extracted from the Digital Elevation Model (DEM), is coupled with ensemble weather predictions based on the TIGGE database (CMC, CMA, ECWMF, UKMO, NCEP) for flood forecast. This paper presents a case study using the coupled flood forecasting model on the Xixian catchment (a drainage area of 8826 km2) located in Henan province, China. A probabilistic discharge is provided as the end product of flood forecast. Results show that the association of the Grid-Xinanjiang model and the TIGGE database gives a promising tool for an early warning of flood events several days ahead.

  14. Model initialisation, data assimilation and probabilistic flood forecasting for distributed hydrological models

    NASA Astrophysics Data System (ADS)

    Cole, S. J.; Robson, A. J.; Bell, V. A.; Moore, R. J.

    2009-04-01

    The hydrological forecasting component of the Natural Environment Research Council's FREE (Flood Risk from Extreme Events) project "Exploitation of new data sources, data assimilation and ensemble techniques for storm and flood forecasting" addresses the initialisation, data assimilation and uncertainty of hydrological flood models utilising advances in rainfall estimation and forecasting. Progress will be reported on the development and assessment of simple model-initialisation and state-correction methods for a distributed grid-based hydrological model, the G2G Model. The potential of the G2G Model for area-wide flood forecasting is demonstrated through a nationwide application across England and Wales. Probabilistic flood forecasting in spatial form is illustrated through the use of high-resolution NWP rainfalls, and pseudo-ensemble forms of these, as input to the G2G Model. The G2G Model is configured over a large area of South West England and the Boscastle storm of 16 August 2004 is used as a convective case study. Visualisation of probabilistic flood forecasts is achieved through risk maps of flood threshold exceedence that indicate the space-time evolution of flood risk during the event.

  15. Fews-Risk: A step towards risk-based flood forecasting

    NASA Astrophysics Data System (ADS)

    Bachmann, Daniel; Eilander, Dirk; de Leeuw, Annemargreet; Diermanse, Ferdinand; Weerts, Albrecht; de Bruijn, Karin; Beckers, Joost; Boelee, Leonore; Brown, Emma; Hazlewood, Caroline

    2015-04-01

    Operational flood prediction and the assessment of flood risk are important components of flood management. Currently, the model-based prediction of discharge and/or water level in a river is common practice for operational flood forecasting. Based on the prediction of these values decisions about specific emergency measures are made within operational flood management. However, the information provided for decision support is restricted to pure hydrological or hydraulic aspects of a flood. Information about weak sections within the flood defences, flood prone areas and assets at risk in the protected areas are rarely used in a model-based flood forecasting system. This information is often available for strategic planning, but is not in an appropriate format for operational purposes. The idea of FEWS-Risk is the extension of existing flood forecasting systems with elements of strategic flood risk analysis, such as probabilistic failure analysis, two dimensional flood spreading simulation and the analysis of flood impacts and consequences. Thus, additional information is provided to the decision makers, such as: • Location, timing and probability of failure of defined sections of the flood defence line; • Flood spreading, extent and hydraulic values in the hinterland caused by an overflow or a breach flow • Impacts and consequences in case of flooding in the protected areas, such as injuries or casualties and/or damages to critical infrastructure or economy. In contrast with purely hydraulic-based operational information, these additional data focus upon decision support for answering crucial questions within an operational flood forecasting framework, such as: • Where should I reinforce my flood defence system? • What type of action can I take to mend a weak spot in my flood defences? • What are the consequences of a breach? • Which areas should I evacuate first? This presentation outlines the additional required workflows towards risk-based flood

  16. Taking into account hydrological modelling uncertainty in Mediterranean flash-floods forecasting

    NASA Astrophysics Data System (ADS)

    Edouard, Simon; Béatrice, Vincendon; Véronique, Ducrocq

    2015-04-01

    Title : Taking into account hydrological modelling uncertainty in Mediterranean flash-floods forecasting Authors : Simon EDOUARD*, Béatrice VINCENDON*, Véronique Ducrocq* * : GAME/CNRM(Météo-France, CNRS)Toulouse,France Mediterranean intense weather events often lead to devastating flash-floods (FF). Increasing the lead time of FF forecasts would permit to better anticipate their catastrophic consequences. These events are one part of Mediterranean hydrological cycle. HyMeX (HYdrological cycle in the Mediterranean EXperiment) aims at a better understanding and quantification of the hydrological cycle and related processes in the Mediterranean. In order to get a lot of data, measurement campaigns were conducted. The first special observing period (SOP1) of these campaigns, served as a test-bed for a real-time hydrological ensemble prediction system (HEPS) dedicated to FF forecasting. It produced an ensemble of quantitative discharge forecasts (QDF) using the ISBA-TOP system. ISBATOP is a coupling between the surface scheme ISBA and a version of TOPMODEL dedicated to Mediterranean fast responding rivers. ISBA-TOP was driven with several quantitative precipitation forecasts (QPF) ensembles based on AROME atmospheric convection-permitting model. This permitted to take into account the uncertainty that affects QPF and that propagates up to the QDF. This uncertainty is major for discharge forecasting especially in the case of Mediterranean flash-floods. But other sources of uncertainty need to be sampled in HEPS systems. One of them is inherent to the hydrological modelling. The ISBA-TOP coupled system has been improved since the initial version, that was used for instance during Hymex SOP1. The initial ISBA-TOP consisted into coupling a TOPMODEL approach with ISBA-3L, which represented the soil stratification with 3 layers. The new version consists into coupling the same TOPMODEL approach with a version of ISBA where more than ten layers describe the soil vertical

  17. The use of real-time off-site observations as a methodology for increasing forecast skill in prediction of large wind power ramps one or more hours ahead of their impact on a wind plant.

    SciTech Connect

    Martin Wilde, Principal Investigator

    2012-12-31

    ABSTRACT Application of Real-Time Offsite Measurements in Improved Short-Term Wind Ramp Prediction Skill Improved forecasting performance immediately preceding wind ramp events is of preeminent concern to most wind energy companies, system operators, and balancing authorities. The value of near real-time hub height-level wind data and more general meteorological measurements to short-term wind power forecasting is well understood. For some sites, access to onsite measured wind data - even historical - can reduce forecast error in the short-range to medium-range horizons by as much as 50%. Unfortunately, valuable free-stream wind measurements at tall tower are not typically available at most wind plants, thereby forcing wind forecasters to rely upon wind measurements below hub height and/or turbine nacelle anemometry. Free-stream measurements can be appropriately scaled to hub-height levels, using existing empirically-derived relationships that account for surface roughness and turbulence. But there is large uncertainty in these relationships for a given time of day and state of the boundary layer. Alternatively, forecasts can rely entirely on turbine anemometry measurements, though such measurements are themselves subject to wake effects that are not stationary. The void in free-stream hub-height level measurements of wind can be filled by remote sensing (e.g., sodar, lidar, and radar). However, the expense of such equipment may not be sustainable. There is a growing market for traditional anemometry on tall tower networks, maintained by third parties to the forecasting process (i.e., independent of forecasters and the forecast users). This study examines the value of offsite tall-tower data from the WINDataNOW Technology network for short-horizon wind power predictions at a wind farm in northern Montana. The presentation shall describe successful physical and statistical techniques for its application and the practicality of its application in an operational

  18. A Coastal Flood Decision Support Tool for Forecast Operations in Alaska

    NASA Astrophysics Data System (ADS)

    van Breukelen, C. M.; Moore, A.; Plumb, E. W.

    2015-12-01

    ABSTRACT Coastal flooding and erosion poses a serious threat to infrastructure, livelihood, and property for communities along Alaska's northern and western coastline. While the National Weather Service Alaska Region (NWS-AR) forecasts conditions favorable for coastal flooding, an improvement can be made in communicating event impacts between NWS-AR and local residents. Scientific jargon used by NWS-AR to indicate the severity of flooding potential is often misconstrued by residents. Additionally, the coastal flood forecasting process is cumbersome and time consuming due to scattered sources of flood guidance. To alleviate these problems, a single coastal flooding decision support tool was created for the Fairbanks Weather Forecast Office to help bridge the communication gap, streamline the forecast and warning process, and take into account both the meteorological and socioeconomic systems at work during a flood event. This tool builds on previous research and data collected by the Alaska Division of Geological and Geophysical Surveys (DGGS) and the NWS-AR, using high resolution elevation data to model the impacts of storm tide rise above the mean lower low water level on five of the most at-risk communities along the Alaskan coast. Important local buildings and infrastructure are highlighted, allowing forecasters to relate the severity of the storm tide in terms of local landmarks that are familiar to residents. In this way, this decision support tool allows for a conversion from model output storm tide levels into real world impacts that are easily understood by forecasters, emergency managers, and other stakeholders, helping to build a Weather-Ready Nation. An overview of the new coastal flood decision support tool in NWS-AR forecast operations will be discussed. KEYWORDS Forecasting; coastal flooding; coastal hazards; decision support

  19. Spatially distributed flood forecasting in flash flood prone areas: Application to road network supervision in Southern France

    NASA Astrophysics Data System (ADS)

    Naulin, J.-P.; Payrastre, O.; Gaume, E.

    2013-04-01

    SummaryAccurate flood forecasts are critical to an efficient flood event management strategy. Until now, hydro-meteorological forecasts have mainly been used to establish early-warnings in France (meteorological and flood vigilance maps) or over the world (flash-flood guidances). These forecasts are typically limited either to the main streams covered by the flood forecasting services or to watersheds with specific assets like check dams, which in most cases are well gauged river sections, thus leaving aside large parts of the territory. This paper presents a distributed hydro-meteorological forecasting approach, which makes use of the high spatial and temporal resolution rainfall estimates that are now available, to provide information at ungauged sites. The proposed system intended to detect road inundation risks had initially been developed and tested in areas of limited size. This paper presents the extension of such a system to an entire region (i.e. the Gard region in Southern France), including over 2000 crossing points between rivers and roads and its validation with respect to a large data set of actual reported road inundations observed during recent flash flood events. These initial validation results appear to be most promising. The eventual proposed tool would provide the necessary information for flood event management services to identify the areas at risk and adopt appropriate safety and rescue measures: i.e. pre-positioning of rescue equipment, interruption of the traffic on the exposed roads and determination of safe access or evacuation routes. Moreover, beyond the specific application to the supervision of a road network, the research undertaken herein also provides results for the performance of hydro-meteorological forecasts on ungauged headwaters.

  20. An Evaluation of Real-time Air Quality Forecasts and their Urban Emissions over Eastern Texas During the Summer of 2006 Second Texas Air Quality Study Field Study

    EPA Science Inventory

    Forecasts of ozone (O3) and particulate matter (diameter less than 2.5 µm, PM2.5) from seven air quality forecast models (AQFMs) are statistically evaluated against observations collected during August and September of 2006 (49 days) through the AIRNow netwo...

  1. The FireWork air quality forecast system with near-real-time biomass burning emissions: Recent developments and evaluation of performance for the 2015 North American wildfire season

    PubMed Central

    Pavlovic, Radenko; Chen, Jack; Anderson, Kerry; Moran, Michael D.; Beaulieu, Paul-André; Davignon, Didier; Cousineau, Sophie

    2016-01-01

    ABSTRACT Environment and Climate Change Canada’s FireWork air quality (AQ) forecast system for North America with near-real-time biomass burning emissions has been running experimentally during the Canadian wildfire season since 2013. The system runs twice per day with model initializations at 00 UTC and 12 UTC, and produces numerical AQ forecast guidance with 48-hr lead time. In this work we describe the FireWork system, which incorporates near-real-time biomass burning emissions based on the Canadian Wildland Fire Information System (CWFIS) as an input to the operational Regional Air Quality Deterministic Prediction System (RAQDPS). To demonstrate the capability of the system we analyzed two forecast periods in 2015 (June 2–July 15, and August 15–31) when fire activity was high, and observed fire-smoke-impacted areas in western Canada and the western United States. Modeled PM2.5 surface concentrations were compared with surface measurements and benchmarked with results from the operational RAQDPS, which did not consider near-real-time biomass burning emissions. Model performance statistics showed that FireWork outperformed RAQDPS with improvements in forecast hourly PM2.5 across the region; the results were especially significant for stations near the path of fire plume trajectories. Although the hourly PM2.5 concentrations predicted by FireWork still displayed bias for areas with active fires for these two periods (mean bias [MB] of –7.3 µg m−3 and 3.1 µg m−3), it showed better forecast skill than the RAQDPS (MB of –11.7 µg m−3 and –5.8 µg m−3) and demonstrated a greater ability to capture temporal variability of episodic PM2.5 events (correlation coefficient values of 0.50 and 0.69 for FireWork compared to 0.03 and 0.11 for RAQDPS). A categorical forecast comparison based on an hourly PM2.5 threshold of 30 µg m−3 also showed improved scores for probability of detection (POD), critical success index (CSI), and false alarm rate (FAR

  2. A Multitemporal Remote Sensing Approach to Streamflow Prediction and Flood Vulnerability Forecasting

    NASA Astrophysics Data System (ADS)

    Weissling, B. P.; Xie, H.

    2006-12-01

    precipitation, land surface temperature, and select vegetation indices accounted for 78% (R2adj = 0.78) of the variance of gage station observed streamflow for calendar year 2004. Efforts are underway to calibrate and validate this model for other time periods within the data availability window of MODIS imagery products, and for other watersheds of varying size and similar climatic regime within the Guadalupe River and neighboring basins. The success of this remote sensing approach will have implications for developing near real-time flood risk and vulnerability forecasting models for both gaged and ungaged watersheds, as well as water supply management in regions of the world with limited resources to undertake conventional ground-based hydrologic studies.

  3. California climate change, hydrologic response, and flood forecasting

    SciTech Connect

    Miller, Norman L.

    2003-11-11

    There is strong evidence that the lower atmosphere has been warming at an unprecedented rate during the last 50 years, and it is expected to further increase at least for the next 100 years. Warmer air mass implies a higher capacity to hold water vapor and an increased likelihood of an acceleration of the global water cycle. This acceleration is not validated and considerable new research has gone into understanding aspects of the water cycle (e.g. Miller et al. 2003). Several significant findings on the hydrologic response to climate change can be reported. It is well understood that the observed and expected warming is related to sea level rise. In a recent seminar at Lawrence Berkeley National Laboratory, James Hansen (Director of the Institute for Space Studies, National Aeronautics and Space Administration) stressed that a 1.25 Wm{sup -2} increase in radiative forcing will lead to an increase in the near surface air temperature by 1 C. This small increase in temperature from 2000 levels is enough to cause very significant impacts to coasts. Maury Roos (Chief Hydrologist, California Department of Water Resources) has shown that a 0.3 m rise in sea level shifts the San Francisco Bay 100-year storm surge flood event to a 10-year event. Related coastal protection costs for California based on sea level rise are shown. In addition to rising sea level, snowmelt-related streamflow represents a particular problem in California. Model studies have indicated that there will be approximately a 50% decrease in snow pack by 2100. This potential deficit must be fully recognized and plans need to be put in place well in advance. In addition, the warmer atmosphere can hold more water vapor and result in more intense warm winter-time precipitation events that result in flooding. During anticipated high flow, reservoirs need to release water to maintain their structural integrity. California is at risk of water shortages, floods, and related ecosystem stresses. More research

  4. Operational aspects of asynchronous filtering for improved flood forecasting

    NASA Astrophysics Data System (ADS)

    Rakovec, Oldrich; Weerts, Albrecht; Sumihar, Julius; Uijlenhoet, Remko

    2014-05-01

    and assimilation and is suitable to be connected to any kind of environmental model. This setup is embedded in the Delft Flood Early Warning System (Delft-FEWS, Werner et al., 2013) for making all simulations and forecast runs and handling of all hydrological and meteorological data. References: Evensen, G. (2009), Data Assimilation: The Ensemble Kalman Filter, Springer, doi:10.1007/978-3-642-03711-5. OpenDA (2013), The OpenDA data-assimilation toolbox, www.openda.org, (last access: 1 November 2013). OpenStreams (2013), OpenStreams, www.openstreams.nl, (last access: 1 November 2013). Sakov, P., G. Evensen, and L. Bertino (2010), Asynchronous data assimilation with the EnKF, Tellus, Series A: Dynamic Meteorology and Oceanography, 62(1), 24-29, doi:10.1111/j.1600-0870.2009.00417.x. Werner, M., J. Schellekens, P. Gijsbers, M. van Dijk, O. van den Akker, and K. Heynert (2013), The Delft-FEWS flow forecasting system, Environ. Mod. & Soft., 40(0), 65-77, doi: http://dx.doi.org/10.1016/j.envsoft.2012.07.010.

  5. Modeling flash flood events in an ungaged semi-arid basin using a real-time distributed model: Fish Creek near Anza Borrego, California

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Fast responding headwater basins and canyons pose a significant threat to life and property throughout the semi-arid western United States. This paper presents the results from the application of the real-time distributed model KINematic runoff and EROsin model (KINEROS2) to the complex terrain of t...

  6. The potential of satellite radar altimetry in flood forecasting: concept and implementation for the Niger-Benue river basin

    NASA Astrophysics Data System (ADS)

    Pandey, R.; Amarnath, G.

    2015-06-01

    Flood forecasting in the downstream part of any hydrological basin is extremely difficult due to the lack of basin-wide hydrological information in near real-time and the absence of a data-sharing treaty among the transboundary nations. The accuracy of forecasts emerging from a hydrological model could be compromised without prior knowledge of the day-to-day flow regulation at different locations upstream of the Niger and Benue rivers. Only satellite altimeter monitoring allows us to identify the actual river levels upstream that reflect the human intervention at that location. This is critical for making accurate downstream forecasts. This present study aims to demonstrate the capability of altimeter-based flood forecasting along the Niger-Benue River in Nigeria. The study includes the comparison of decadal (at every 10 days from Jason-2) or monthly (at every 35 days from Envisat/AltiKa) observations from 2002 to 2014, with historical in situ measurements from 1990 to 2012. The water level obtained from these sources shows a good correlation (0.7-0.9). After validation of hydrological parameters obtained from two sources, a quantitative relation (rating curve) of upstream water level and downstream discharge is derived. This relation is then adopted for calculation of discharge at observation points, which is used to propagate the flow downstream at a desired location using a hydraulic river model. Results from this study from Jason-2 shows a promising correlation (R2 ≥ 90% with a Nash-Sutcliffe coefficient of more than 0.70) with 5~days ahead of downstream flow prediction over the Benue stream.

  7. Near-field tsunami forecast system based on near real-time seismic moment tensor estimation in the regions of Indonesia, the Philippines, and Chile

    NASA Astrophysics Data System (ADS)

    Inazu, Daisuke; Pulido, Nelson; Fukuyama, Eiichi; Saito, Tatsuhiko; Senda, Jouji; Kumagai, Hiroyuki

    2016-05-01

    We have developed a near-field tsunami forecast system based on an automatic centroid moment tensor (CMT) estimation using regional broadband seismic observation networks in the regions of Indonesia, the Philippines, and Chile. The automatic procedure of the CMT estimation has been implemented to estimate tsunamigenic earthquakes. A tsunami propagation simulation model is used for the forecast and hindcast. A rectangular fault model based on the estimated CMT is employed to represent the initial condition of tsunami height. The forecast system considers uncertainties due to two possible fault planes and two possible scaling laws and thus shows four possible scenarios with these associated uncertainties for each estimated CMT. The system requires approximately 15 min to estimate the CMT after the occurrence of an earthquake and approximately another 15 min to make the tsunami forecast results including the maximum tsunami height and its arrival time at the epicentral region and near-field coasts available. The retrospectively forecasted tsunamis were evaluated by the deep-sea pressure and tide gauge observations, for the past eight tsunamis ( M w 7.5-8.6) that occurred throughout the regional seismic networks. The forecasts ranged from half to double the amplitudes of the deep-sea pressure observations and ranged mostly within the same order of magnitude as the maximum heights of the tide gauge observations. It was found that the forecast uncertainties increased for greater earthquakes (e.g., M w > 8) because the tsunami source was no longer approximated as a point source for such earthquakes. The forecast results for the coasts nearest to the epicenter should be carefully used because the coasts often experience the highest tsunamis with the shortest arrival time (e.g., <30 min).

  8. Using High Resolution Numerical Weather Prediction Models to Reduce and Estimate Uncertainty in Flood Forecasting

    NASA Astrophysics Data System (ADS)

    Cole, S. J.; Moore, R. J.; Roberts, N.

    2007-12-01

    Forecast rainfall from Numerical Weather Prediction (NWP) and/or nowcasting systems is a major source of uncertainty for short-term flood forecasting. One approach for reducing and estimating this uncertainty is to use high resolution NWP models that should provide better rainfall predictions. The potential benefit of running the Met Office Unified Model (UM) with a grid spacing of 4 and 1 km compared to the current operational resolution of 12 km is assessed using the January 2005 Carlisle flood in northwest England. These NWP rainfall forecasts, and forecasts from the Nimrod nowcasting system, were fed into the lumped Probability Distributed Model (PDM) and the distributed Grid-to-Grid model to predict river flow at the outlets of two catchments important for flood warning. The results show the benefit of increased resolution in the UM, the benefit of coupling the high- resolution rainfall forecasts to hydrological models and the improvement in timeliness of flood warning that might have been possible. Ongoing work aims to employ these NWP rainfall forecasts in ensemble form as part of a procedure for estimating the uncertainty of flood forecasts.

  9. Reduction of the uncertainties in the water level-discharge relation of a 1D hydraulic model in the context of operational flood forecasting

    NASA Astrophysics Data System (ADS)

    Habert, J.; Ricci, S.; Le Pape, E.; Thual, O.; Piacentini, A.; Goutal, N.; Jonville, G.; Rochoux, M.

    2016-01-01

    This paper presents a data-driven hydrodynamic simulator based on the 1-D hydraulic solver dedicated to flood forecasting with lead time of an hour up to 24 h. The goal of the study is to reduce uncertainties in the hydraulic model and thus provide more reliable simulations and forecasts in real time for operational use by the national hydrometeorological flood forecasting center in France. Previous studies have shown that sequential assimilation of water level or discharge data allows to adjust the inflows to the hydraulic network resulting in a significant improvement of the discharge while leaving the water level state imperfect. Two strategies are proposed here to improve the water level-discharge relation in the model. At first, a modeling strategy consists in improving the description of the river bed geometry using topographic and bathymetric measurements. Secondly, an inverse modeling strategy proposes to locally correct friction coefficients in the river bed and the flood plain through the assimilation of in situ water level measurements. This approach is based on an Extended Kalman filter algorithm that sequentially assimilates data to infer the upstream and lateral inflows at first and then the friction coefficients. It provides a time varying correction of the hydrological boundary conditions and hydraulic parameters. The merits of both strategies are demonstrated on the Marne catchment in France for eight validation flood events and the January 2004 flood event is used as an illustrative example throughout the paper. The Nash-Sutcliffe criterion for water level is improved from 0.135 to 0.832 for a 12-h forecast lead time with the data assimilation strategy. These developments have been implemented at the SAMA SPC (local flood forecasting service in the Haute-Marne French department) and used for operational forecast since 2013. They were shown to provide an efficient tool for evaluating flood risk and to improve the flood early warning system

  10. Using subseasonal-to-seasonal (S2S) extreme rainfall forecasts for extended-range flood prediction in Australia

    NASA Astrophysics Data System (ADS)

    White, C. J.; Franks, S. W.; McEvoy, D.

    2015-06-01

    Meteorological and hydrological centres around the world are looking at ways to improve their capacity to be able to produce and deliver skilful and reliable forecasts of high-impact extreme rainfall and flooding events on a range of prediction timescales (e.g. sub-daily, daily, multi-week, seasonal). Making improvements to extended-range rainfall and flood forecast models, assessing forecast skill and uncertainty, and exploring how to apply flood forecasts and communicate their benefits to decision-makers are significant challenges facing the forecasting and water resources management communities. This paper presents some of the latest science and initiatives from Australia on the development, application and communication of extreme rainfall and flood forecasts on the extended-range "subseasonal-to-seasonal" (S2S) forecasting timescale, with a focus on risk-based decision-making, increasing flood risk awareness and preparedness, capturing uncertainty, understanding human responses to flood forecasts and warnings, and the growing adoption of "climate services". The paper also demonstrates how forecasts of flood events across a range of prediction timescales could be beneficial to a range of sectors and society, most notably for disaster risk reduction (DRR) activities, emergency management and response, and strengthening community resilience. Extended-range S2S extreme flood forecasts, if presented as easily accessible, timely and relevant information are a valuable resource to help society better prepare for, and subsequently cope with, extreme flood events.

  11. Evaluation of radar-based precipitation estimates for flash flood forecasting in the Three Gorges Region

    NASA Astrophysics Data System (ADS)

    Li, Z.; Yang, D.; Hong, Y.; Qi, Y.; Cao, Q.

    2015-05-01

    Spatial rainfall pattern plays a critical role in determining hydrological responses in mountainous areas, especially for natural disasters such as flash floods. In this study, to improve the skills of flood forecasting in the mountainous Three Gorges Region (TGR) of the Yangtze River, we developed a first version of a high-resolution (1 km) radar-based quantitative precipitation estimation (QPE) consideration of many critical procedures, such as beam blockage analysis, ground-clutter filter, rain type identification and adaptive Z-R relations. A physically-based distributed hydrological model (GBHM) was established and further applied to evaluate the performance of radar-based QPE for regional flood forecasting, relative to the gauge-driven simulations. With two sets of input data (gauge and radar) collected during summer 2010, the applicability of the current radar-based QPE to rainstorm monitoring and flash flood forecasting in the TGR is quantitatively analysed and discussed.

  12. ADAPTATION AND APPLICATION OF THE COMMUNITY MULTISCALE AIR QUALITY (CMAQ) MODELING SYSTEM FOR REAL-TIME AIR QUALITY FORECASTING DURING THE SUMMER OF 2004

    EPA Science Inventory

    The ability to forecast local and regional air pollution events is challenging since the processes governing the production and sustenance of atmospheric pollutants are complex and often non-linear. Comprehensive atmospheric models, by representing in as much detail as possible t...

  13. Real-Time Bias-Adjusted O3 and PM2.5 Air Quality Index Forecasts and their Performance Evaluations over the Continental United States

    EPA Science Inventory

    The National Air Quality Forecast Capacity (NAQFC) system, which links NOAA's North American Mesoscale (NAM) meteorological model with EPA's Community Multiscale Air Quality (CMAQ) model, provided operational ozone (O3) and experimental fine particular matter (PM2...

  14. Development of a real time streamflow monitoring system for the Indian sub-continental basins

    NASA Astrophysics Data System (ADS)

    Shah, H. L.; Mishra, V.

    2015-12-01

    Real-time streamflow monitoring is essential in the Indian sub-continental river basins as a large population is affected by floods. Moreover, streamflow monitoring may help in managing the water resources in the agriculture dominated region. In the Indian sub-continental basins, it is challenging to obtain the real time information of streamflow, which is valuable for reservoir operations, water management, and flood forecasts. We setup the Variable Infiltration Capacity (VIC) hydrological model at daily temporal resolution and 0.25◦ spatial resolution using the bias corrected satellite precipitation product from the Tropical rainfall Measurement Mission Real Time (TRMM-3B42RTV7) and bias corrected temperature product from the Global Ensemble Forecast System (GEFS), version 2. Near-real-time precipitation and temperatures are bias corrected using the historic precipitation and temperature data from the India Meteorological Department (IMD). Moreover, we evaluated data assimilation approaches to improve the real-time monitoring of streamflow in the sub-continental basins.

  15. A systematic review of sensitivities in the Swedish flood-forecasting system

    NASA Astrophysics Data System (ADS)

    Arheimer, Berit; Lindström, Göran; Olsson, Jonas

    2011-05-01

    Since the early 1970s operational flood forecasts in Sweden have been based on the hydrological HBV model. However, the model is only one component in a chain of processes for production of hydrological forecasts. During the last 35 years there has been considerable work on improving different parts of the forecast procedure and results from specific studies have been reported frequently. Yet, the results have not been compared in any overall assessment of potential for improvements. Therefore we formulated and applied a method for translating results from different studies to a common criterion of error reduction. The aim was to quantify potential improvements in a systems perspective and to identify in which part of the production chain efforts would result in significantly better forecasts. The most sensitive (> 20% error reduction) components were identified for three different operational-forecast types. From the analyses of historical efforts to minimise the errors in the Swedish flood-forecasting system, it was concluded that 1) general runoff simulations and predictions could be significantly improved by model structure and calibration, model equations (e.g. evapotranspiration expression), and new precipitation input using radar data as a complement to station gauges; 2) annual spring-flood forecasts could be significantly improved by better seasonal meteorological forecast, fresh re-calibration of the hydrological model based on long time-series, and data assimilation of snow-pack measurements using georadar or gamma-ray technique; 3) short-term (2 days) forecasts could be significantly improved by up-dating using an auto-regressive method for discharge, and by ensembles of meteorological forecasts using the median at occasions when the deterministic forecast is out of the ensemble range. The study emphasises the importance of continuously evaluating the entire production chain to search for potential improvements of hydrological forecasts in the

  16. A high-resolution real-time forecasting system for predicting the fate of oil spills in the Strait of Bonifacio (western Mediterranean Sea).

    PubMed

    Cucco, A; Sinerchia, M; Ribotti, A; Olita, A; Fazioli, L; Perilli, A; Sorgente, B; Borghini, M; Schroeder, K; Sorgente, R

    2012-06-01

    The Strait of Bonifacio is a long and narrow area between Corsica and Sardinia. To manage environmental emergencies related to the spill of oil from vessels, an innovative forecasting system was developed. This tool is capable of operationally predicting the dispersion of hydrocarbon spills in the coastal area of the Bonifacio Strait, either from an instantaneous or continuous spill and either in forward or backward mode. Experimental datasets, including ADCP water current measurements and the trajectories of drifter buoys released in the area, were used to evaluate the accuracy of this system. A comparison between the simulation results and experimental data revealed that both the water circulation and the surface transport processes are accurately reproduced by the model. The overall accuracy of the system in reproducing the transport of an oil spill at sea was estimated for both forward and backward prediction mode and in relation to different forecasting time lags.

  17. Evaluation of real-time hydrometeorological ensemble prediction on hydrologic scales in Northern California

    NASA Astrophysics Data System (ADS)

    Georgakakos, Konstantine P.; Graham, Nicholas E.; Modrick, Theresa M.; Murphy, Michael J.; Shamir, Eylon; Spencer, Cristopher R.; Sperfslage, Jason A.

    2014-11-01

    The paper presents an evaluation of real time ensemble forecasts produced during 2010-2012 by the demonstration project INFORM (Integrated Forecast and Reservoir Management) in Northern California. In addition, the innovative elements of the forecast component of the INFORM project are highlighted. The forecast component is designed to dynamically downscale operational multi-lead ensemble forecasts from the Global Ensemble Forecast System (GEFS) and the Climate Forecast system (CFS) of the National Centers of Environmental Prediction (NCEP), and to use adaptations of the operational hydrologic models of the US National Weather Service California Nevada River Forecast Center to provide ensemble reservoir inflow forecasts in real time. A full-physics 10-km resolution (10 km on the side) mesoscale model was implemented for the ensemble prediction of surface precipitation and temperature over the domain of Northern California with lead times out to 16 days with 6-hourly temporal resolution. An intermediate complexity regional model with a 10 km resolution was implemented to downscale the NCEP CFS ensemble forecasts for lead times out to 41.5 days. Methodologies for precipitation and temperature model forecast adjustment to comply with the corresponding observations were formulated and tested as regards their effectiveness for improving the ensemble predictions of these two variables and also for improving reservoir inflow forecasts. The evaluation is done using the real time databases of INFORM and concerns the snow accumulation and melt seasons. Performance is measured by metrics that range from those that use forecast means to those that use the entire forecast ensemble. The results show very good skill in forecasting precipitation and temperature over the subcatchments of the INFORM domain out to a week in advance for all basins, models and seasons. For temperature, in some cases, non-negligible skill has been obtained out to four weeks for the melt season

  18. Evaluation of Mekong River commission operational flood forecasts, 2000-2012

    NASA Astrophysics Data System (ADS)

    Pagano, T. C.

    2014-07-01

    This study created a 13-year historical archive of operational flood forecasts issued by the Regional Flood Management and Mitigation Center (RFMMC) of the Mekong River Commission. The RFMMC issues 1- to 5-day daily deterministic river height forecasts for 22 locations throughout the wet season (June-October). When these forecasts reach near flood level, government agencies and the public are encouraged to take protective action against damages. When measured by standard skill scores, the forecasts perform exceptionally well (e.g., 1 day-ahead Nash-Sutcliffe > 0.99) although much of this apparent skill is due to the strong seasonal cycle and the narrow natural range of variability at certain locations. Five-day forecasts upstream of Phnom Penh typically have 0.8 m error standard deviation, whereas below Phnom Penh the error is typically 0.3 m. The coefficients of persistence for 1-day forecasts are typically 0.4-0.8 and 5-day forecasts are typically 0.1-0.7. RFMMC uses a series of benchmarks to define a metric of percentage satisfactory forecasts. As the benchmarks were derived based on the average error, certain locations and lead times consistently appear less satisfactory than others. Instead, different benchmarks were proposed and derived based on the 70th percentile of absolute error over the 13-year period. There are no obvious trends in the percentage of satisfactory forecasts from 2002 to 2012, regardless of the benchmark chosen. Finally, when evaluated from a categorical "crossing above/not-crossing above flood level" perspective, the forecasts have a moderate probability of detection (48% at 1 day ahead, 31% at 5 days ahead) and false alarm rate (13% at 1 day ahead, 74% at 5 days ahead).

  19. Evaluation of Mekong River Commission operational flood forecasts, 2000-2012

    NASA Astrophysics Data System (ADS)

    Pagano, T. C.

    2013-11-01

    This study created a 13 yr historical archive of operational flood forecasts issued by the Regional Flood Management and Mitigation Center (RFMMC) of the Mekong River Commission. The RFMMC issues 1 to 5 day-ahead daily deterministic river height forecasts for 22 locations throughout the wet season (June-October). When these forecasts reach near Flood Level, government agencies and the public are encouraged to take protective action against damages. When measured by standard skill scores, the forecasts perform exceptionally well (e.g. 1 day-ahead Nash-Sutcliffe > 0.99) although much of this apparent skill is due to the strong seasonal cycle and the narrow natural range of variability at certain locations. 5 day-ahead forecasts upstream of Phnom Penh typically have 0.8 m error standard deviation, whereas below Phnom Penh the error is typically 0.3 m. The Coefficients of Persistence for 1 day-ahead forecasts are typically 0.4-0.8 and 5 day-ahead forecasts are typically 0.1-0.7. RFMMC uses a series of benchmarks to define a metric of Percentage Satisfactory forecasts. As the benchmarks were derived based on the average error, certain locations and lead-times consistently appear less satisfactory than others. Instead, different benchmarks were proposed and derived based on the 70th percentile of absolute error over the 13 yr period. There are no obvious trends in the Percentage of Satisfactory forecasts from 2002-2012, regardless of the benchmark chosen. Finally, when evaluated from a categorical "crossing above/not-crossing above flood level" perspective, the forecasts have a moderate probability of detection (48% at 1 day-ahead, 31% at 5 day-ahead) and false alarm rate (13% at 1 day-ahead, 74% at 5 days-ahead).

  20. 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

  1. Forecast-based Integrated Flood Detection System for Emergency Response and Disaster Risk Reduction (Flood-FINDER)

    NASA Astrophysics Data System (ADS)

    Arcorace, Mauro; Silvestro, Francesco; Rudari, Roberto; Boni, Giorgio; Dell'Oro, Luca; Bjorgo, Einar

    2016-04-01

    Most flood prone areas in the globe are mainly located in developing countries where making communities more flood resilient is a priority. Despite different flood forecasting initiatives are now available from academia and research centers, what is often missing is the connection between the timely hazard detection and the community response to warnings. In order to bridge the gap between science and decision makers, UN agencies play a key role on the dissemination of information in the field and on capacity-building to local governments. In this context, having a reliable global early warning system in the UN would concretely improve existing in house capacities for Humanitarian Response and the Disaster Risk Reduction. For those reasons, UNITAR-UNOSAT has developed together with USGS and CIMA Foundation a Global Flood EWS called "Flood-FINDER". The Flood-FINDER system is a modelling chain which includes meteorological, hydrological and hydraulic models that are accurately linked to enable the production of warnings and forecast inundation scenarios up to three weeks in advance. The system is forced with global satellite derived precipitation products and Numerical Weather Prediction outputs. The modelling chain is based on the "Continuum" hydrological model and risk assessments produced for GAR2015. In combination with existing hydraulically reconditioned SRTM data and 1D hydraulic models, flood scenarios are derived at multiple scales and resolutions. Climate and flood data are shared through a Web GIS integrated platform. First validation of the modelling chain has been conducted through a flood hindcasting test case, over the Chao Phraya river basin in Thailand, using multi temporal satellite-based analysis derived for the exceptional flood event of 2011. In terms of humanitarian relief operations, the EO-based services of flood mapping in rush mode generally suffer from delays caused by the time required for their activation, programming, acquisitions and

  2. Dynamic Critical Rainfall-Based Flash Flood Early Warning and Forecasting for Medium-Small Rivers

    NASA Astrophysics Data System (ADS)

    Liu, Z.; Yang, D.; Hu, J.

    2012-04-01

    China is extremely frequent food disasters hit countries, annual flood season flash floods triggered by rainfall, mudslides, landslides have caused heavy casualties and property losses, not only serious threaten the lives of the masses, but the majority of seriously restricting the mountain hill areas of economic and social development and the people become rich, of building a moderately prosperous society goals. In the next few years, China will focus on prevention and control area in the flash flood disasters initially built "for the surveillance, communications, forecasting, early warning and other non-engineering measure based, non-engineering measures and the combinations of engineering measures," the mitigation system. The latest progresses on global torrential flood early warning and forecasting techniques are reviewed in this paper, and then an early warning and forecasting approach is proposed on the basis of a distributed hydrological model according to dynamic critical rainfall index. This approach has been applied in Suichuanjiang River basin in Jiangxi province, which is expected to provide valuable reference for building a national flash flood early warning and forecasting system as well as control of such flooding.

  3. An evaluation of real-time air quality forecasts and their urban emissions over eastern Texas during the summer of 2006 Second Texas Air Quality Study field study

    NASA Astrophysics Data System (ADS)

    McKeen, S.; Grell, G.; Peckham, S.; Wilczak, J.; Djalalova, I.; Hsie, E.-Y.; Frost, G.; Peischl, J.; Schwarz, J.; Spackman, R.; Holloway, J.; de Gouw, J.; Warneke, C.; Gong, W.; Bouchet, V.; Gaudreault, S.; Racine, J.; McHenry, J.; McQueen, J.; Lee, P.; Tang, Y.; Carmichael, G. R.; Mathur, R.

    2009-04-01

    Forecasts of ozone (O3) and particulate matter (diameter less than 2.5 μm, PM2.5) from seven air quality forecast models (AQFMs) are statistically evaluated against observations collected during August and September of 2006 (49 days) through the Aerometric Information Retrieval Now (AIRNow) network throughout eastern Texas and adjoining states. Ensemble O3 and PM2.5 forecasts created by combining the seven separate forecasts with equal weighting, and simple bias-corrected forecasts, are also evaluated in terms of standard statistical measures, threshold statistics, and variance analysis. For O3 the models and ensemble generally show statistical skill relative to persistence for the entire region, but fail to predict high-O3 events in the Houston region. For PM2.5, none of the models, or ensemble, shows statistical skill, and all but one model have significant low bias. Comprehensive comparisons with the full suite of chemical and aerosol measurements collected aboard the NOAA WP-3 aircraft during the summer 2006 Second Texas Air Quality Study and the Gulf of Mexico Atmospheric Composition and Climate Study (TexAQS II/GoMACCS) field study are performed to help diagnose sources of model bias at the surface. Aircraft flights specifically designed for sampling of Houston and Dallas urban plumes are used to determine model and observed upwind or background biases, and downwind excess concentrations that are used to infer relative emission rates. Relative emissions from the U.S. Environmental Protection Agency 1999 National Emission Inventory (NEI-99) version 3 emissions inventory (used in two of the model forecasts) are evaluated on the basis of comparisons between observed and model concentration difference ratios. Model comparisons demonstrate that concentration difference ratios yield a reasonably accurate measure (within 25%) of relative input emissions. Boundary layer height and wind data are combined with the observed up-wind and downwind concentration

  4. Real-time radiography

    SciTech Connect

    Bossi, R.H.; Oien, C.T.

    1981-02-26

    Real-time radiography is used for imaging both dynamic events and static objects. Fluorescent screens play an important role in converting radiation to light, which is then observed directly or intensified and detected. The radiographic parameters for real-time radiography are similar to conventional film radiography with special emphasis on statistics and magnification. Direct-viewing fluoroscopy uses the human eye as a detector of fluorescent screen light or the light from an intensifier. Remote-viewing systems replace the human observer with a television camera. The remote-viewing systems have many advantages over the direct-viewing conditions such as safety, image enhancement, and the capability to produce permanent records. This report reviews real-time imaging system parameters and components.

  5. Evaluating the co-production of a near real time Earthquake Aftershock forecasting tool for humanitarian risk assessment and emergency planning

    NASA Astrophysics Data System (ADS)

    Quinn, Keira; Hope, Max; McCloskey, John; NicBhloscaidh, Mairead; Jimenez, Abigail; Dunlop, Paul

    2015-04-01

    Concern Worldwide and the University of Ulster Geophysics Research Group are engaged in a project to co-produce a suite of software and mapping tools to assess aftershock hazard in near real-time during the emergency response phase of earthquake disaster, and inform humanitarian emergency planning and response activities. This paper uses a social learning approach to evaluate this co-production process. Following Wenger (1999) we differentiate between the earthquake science and humanitarian communities of practice (CoP) along three dimensions: enterprise (the purpose of CoPs and the problems participants are working to address), repertoire (knowledge, skills, language), and identity (values and boundaries). We examine the effectiveness of learning between CoP, focusing on boundary work and objects, and various organisational structures and aspects of the wider political economy of learning that enable and hinder the co-production process. We conclude by identifying a number of ways to more effectively integrate earthquake science into humanitarian decision-making, policy development and programme design.

  6. Evaluation of flash-flood discharge forecasts in complex terrain using precipitation

    USGS Publications Warehouse

    Yates, D.; Warner, T.T.; Brandes, E.A.; Leavesley, G.H.; Sun, Jielun; Mueller, C.K.

    2001-01-01

    Operational prediction of flash floods produced by thunderstorm (convective) precipitation in mountainous areas requires accurate estimates or predictions of the precipitation distribution in space and time. The details of the spatial distribution are especially critical in complex terrain because the watersheds are generally small in size, and small position errors in the forecast or observed placement of the precipitation can distribute the rain over the wrong watershed. In addition to the need for good precipitation estimates and predictions, accurate flood prediction requires a surface-hydrologic model that is capable of predicting stream or river discharge based on the precipitation-rate input data. Different techniques for the estimation and prediction of convective precipitation will be applied to the Buffalo Creek, Colorado flash flood of July 1996, where over 75 mm of rain from a thunderstorm fell on the watershed in less than 1 h. The hydrologic impact of the precipitation was exacerbated by the fact that a significant fraction of the watershed experienced a wildfire approximately two months prior to the rain event. Precipitation estimates from the National Weather Service's operational Weather Surveillance Radar-Doppler 1988 and the National Center for Atmospheric Research S-band, research, dual-polarization radar, colocated to the east of Denver, are compared. In addition, very short range forecasts from a convection-resolving dynamic model, which is initialized variationally using the radar reflectivity and Doppler winds, are compared with forecasts from an automated-algorithmic forecast system that also employs the radar data. The radar estimates of rain rate, and the two forecasting systems that employ the radar data, have degraded accuracy by virtue of the fact that they are applied in complex terrain. Nevertheless, the radar data and forecasts from the dynamic model and the automated algorithm could be operationally useful for input to surface

  7. On the reliable use of satellite-derived surface water products for global flood monitoring

    NASA Astrophysics Data System (ADS)

    Hirpa, F. A.; Revilla-Romero, B.; Thielen, J.; Salamon, P.; Brakenridge, R.; Pappenberger, F.; de Groeve, T.

    2015-12-01

    Early flood warning and real-time monitoring systems play a key role in flood risk reduction and disaster response management. To this end, real-time flood forecasting and satellite-based detection systems have been developed at global scale. However, due to the limited availability of up-to-date ground observations, the reliability of these systems for real-time applications have not been assessed in large parts of the globe. In this study, we performed comparative evaluations of the commonly used satellite-based global flood detections and operational flood forecasting system using 10 major flood cases reported over three years (2012-2014). Specially, we assessed the flood detection capabilities of the near real-time global flood maps from the Global Flood Detection System (GFDS), and from the Moderate Resolution Imaging Spectroradiometer (MODIS), and the operational forecasts from the Global Flood Awareness System (GloFAS) for the major flood events recorded in global flood databases. We present the evaluation results of the global flood detection and forecasting systems in terms of correctly indicating the reported flood events and highlight the exiting limitations of each system. Finally, we propose possible ways forward to improve the reliability of large scale flood monitoring tools.

  8. Initial assessment of a multi-model approach to spring flood forecasting in Sweden

    NASA Astrophysics Data System (ADS)

    Olsson, J.; Uvo, C. B.; Foster, K.; Yang, W.

    2015-06-01

    Hydropower is a major energy source in Sweden and proper reservoir management prior to the spring flood onset is crucial for optimal production. This requires useful forecasts of the accumulated discharge in the spring flood period (i.e. the spring-flood volume, SFV). Today's SFV forecasts are generated using a model-based climatological ensemble approach, where time series of precipitation and temperature from historical years are used to force a calibrated and initialised set-up of the HBV model. In this study, a number of new approaches to spring flood forecasting, that reflect the latest developments with respect to analysis and modelling on seasonal time scales, are presented and evaluated. Three main approaches, represented by specific methods, are evaluated in SFV hindcasts for three main Swedish rivers over a 10-year period with lead times between 0 and 4 months. In the first approach, historically analogue years with respect to the climate in the period preceding the spring flood are identified and used to compose a reduced ensemble. In the second, seasonal meteorological ensemble forecasts are used to drive the HBV model over the spring flood period. In the third approach, statistical relationships between SFV and the large-sale atmospheric circulation are used to build forecast models. None of the new approaches consistently outperform the climatological ensemble approach, but for specific locations and lead times improvements of 20-30 % are found. When combining all forecasts in a weighted multi-model approach, a mean improvement over all locations and lead times of nearly 10 % was indicated. This demonstrates the potential of the approach and further development and optimisation into an operational system is ongoing.

  9. Decision-relevant early-warning thresholds for ensemble flood forecasting systems

    NASA Astrophysics Data System (ADS)

    Stephens, Liz; Pappenberger, Florian; Cloke, Hannah; Alfieri, Lorenzo

    2014-05-01

    Over and under warning of potential future floods is problematic for decision-making, and could ultimately lead to trust being lost in the forecasts. The use of ensemble flood forecasting systems for early warning therefore requires a consideration of how to determine and implement decision-relevant thresholds for flood magnitude and probability. This study uses a year's worth of hindcasts from the Global Flood Awareness System (GloFAS) to explore the sensitivity of the warning system to the choice of threshold. We use a number of different methods for choosing these thresholds, building on current approaches that use model climatologies to determine the critical flow magnitudes, to those that can provide 'first guesses' of potential impacts (through integration with global-scale inundation mapping), as well as methods that could incorporate resource limitations.

  10. Real-time sonography

    SciTech Connect

    Fleischey, A.C.; James, A.E. Jr.

    1984-01-01

    This textbook acquaints the reader with normal and pathologic anatomy as depicted on dynamic or real-time scanning. Chapters are organized by specialty, such as abdominal, urologic, or pediatric. The text is illustrated with still-frame images and line drawings. The drawings show important areas of interest and provide graphic notation as to where and in what orientation the scan was obtained.

  11. A pan-African medium-range ensemble flood forecast system

    NASA Astrophysics Data System (ADS)

    Thiemig, Vera; Bisselink, Bernard; Pappenberger, Florian; Thielen, Jutta

    2015-04-01

    The African Flood Forecasting System (AFFS) is a probabilistic flood forecast system for medium- to large-scale African river basins, with lead times of up to 15 days. The key components are the hydrological model LISFLOOD, the African GIS database, the meteorological ensemble predictions of the ECMWF and critical hydrological thresholds. In this study the predictive capability is investigated, to estimate AFFS' potential as an operational flood forecasting system for the whole of Africa. This is done in a hindcast mode, by reproducing pan-African hydrological predictions for the whole year of 2003 where important flood events were observed. Results were analysed in two ways, each with its individual objective. The first part of the analysis is of paramount importance for the assessment of AFFS as a flood forecasting system, as it focuses on the detection and prediction of flood events. Here, results were verified with reports of various flood archives such as Dartmouth Flood Observatory, the Emergency Event Database, the NASA Earth Observatory and Reliefweb. The number of hits, false alerts and missed alerts as well as the Probability of Detection, False Alarm Rate and Critical Success Index were determined for various conditions (different regions, flood durations, average amount of annual precipitations, size of affected areas and mean annual discharge). The second part of the analysis complements the first by giving a basic insight into the prediction skill of the general streamflow. For this, hydrological predictions were compared against observations at 36 key locations across Africa and the Continuous Rank Probability Skill Score (CRPSS), the limit of predictability and reliability were calculated. Results showed that AFFS detected around 70 % of the reported flood events correctly. In particular, the system showed good performance in predicting riverine flood events of long duration (> 1 week) and large affected areas (> 10 000 km2) well in advance, whereas

  12. Integrating Fluvial and Oceanic Drivers in Operational Flooding Forecasts for San Francisco Bay

    NASA Astrophysics Data System (ADS)

    Herdman, Liv; Erikson, Li; Barnard, Patrick; Kim, Jungho; Cifelli, Rob; Johnson, Lynn

    2016-04-01

    The nine counties that make up the San Francisco Bay area are home to 7.5 million people and these communties are susceptible to flooding along the bay shoreline and inland creeks that drain to the bay. A forecast model that integrates fluvial and oceanic drivers is necessary for predicting flooding in this complex urban environment. The U.S. Geological Survey ( USGS) and National Weather Service (NWS) are developing a state-of-the-art flooding forecast model for the San Francisco Bay area that will predict watershed and ocean-based flooding up to 72 hours in advance of an approaching storm. The model framework for flood forecasts is based on the USGS-developed Coastal Storm Modeling System (CoSMoS) that was applied to San Francisco Bay under the Our Coast Our Future project. For this application, we utilize Delft3D-FM, a hydrodynamic model based on a flexible mesh grid, to calculate water levels that account for tidal forcing, seasonal water level anomalies, surge and in-Bay generated wind waves from the wind and pressure fields of a NWS forecast model, and tributary discharges from the Research Distributed Hydrologic Model (RDHM), developed by the NWS Office of Hydrologic Development. The flooding extent is determined by overlaying the resulting water levels onto a recently completed 2-m digital elevation model of the study area which best resolves the extensive levee and tidal marsh systems in the region. Here we present initial pilot results of hindcast winter storms in January 2010 and December 2012, where the flooding is driven by oceanic and fluvial factors respectively. We also demonstrate the feasibility of predicting flooding on an operational time scale that incorporates both atmospheric and hydrologic forcings.

  13. Validating quantitative precipitation forecast for the Flood Meteorological Office, Patna region during 2011-2014

    NASA Astrophysics Data System (ADS)

    Giri, R. K.; Panda, Jagabandhu; Rath, Sudhansu S.; Kumar, Ravindra

    2016-06-01

    In order to issue an accurate warning for flood, a better or appropriate quantitative forecasting of precipitation is required. In view of this, the present study intends to validate the quantitative precipitation forecast (QPF) issued during southwest monsoon season for six river catchments (basin) under the flood meteorological office, Patna region. The forecast is analysed statistically by computing various skill scores of six different precipitation ranges during the years 2011-2014. The analysis of QPF validation indicates that the multi-model ensemble (MME) based forecasting is more reliable in the precipitation ranges of 1-10 and 11-25 mm. However, the reliability decreases for higher ranges of rainfall and also for the lowest range, i.e., below 1 mm. In order to testify synoptic analogue method based MME forecasting for QPF during an extreme weather event, a case study of tropical cyclone Phailin is performed. It is realized that in case of extreme events like cyclonic storms, the MME forecasting is qualitatively useful for issue of warning for the occurrence of floods, though it may not be reliable for the QPF. However, QPF may be improved using satellite and radar products.

  14. A New Method for Near Real Time Precipitation Estimates Using a Derived Statistical Relationship between Precipitable Water Vapor and Precipitation

    NASA Astrophysics Data System (ADS)

    Roman, J.

    2015-12-01

    The IPCC 5th Assessment found that the predicted warming of 1oC would increase the risk of extreme events such as heat waves, droughts, and floods. Weather extremes, like floods, have shown the vulnerability and susceptibility society has to these extreme weather events, through impacts such as disruption of food production, water supply, health, and damage of infrastructure. This paper examines a new way of near-real time forecasting of precipitation. A 10-year statistical climatological relationship was derived between precipitable water vapor (PWV) and precipitation by using the NASA Atmospheric Infrared Sounder daily gridded PWV product and the NASA Tropical Rainfall Measuring Mission daily gridded precipitation total. Forecasting precipitation estimates in real time is dire for flood monitoring and disaster management. Near real time PWV observations from AIRS on Aqua are available through the Goddard Earth Sciences Data and Information Service Center. In addition, PWV observations are available through direct broadcast from the NASA Suomi-NPP ATMS/CrIS instrument, the operational follow on to AIRS. The derived climatological relationship can be applied to create precipitation estimates in near real time by utilizing the direct broadcasting capabilities currently available in the CONUS region. The application of this relationship will be characterized through case-studies by using near real-time NASA AIRS Science Team v6 PWV products and ground-based SuomiNet GPS to estimate the current precipitation potential; the max amount of precipitation that can occur based on the moisture availability. Furthermore, the potential contribution of using the direct broadcasting of the NUCAPS ATMS/CrIS PWV products will be demonstrated. The analysis will highlight the advantages of applying this relationship in near-real time for flash flood monitoring and risk management. Relevance to the NWS River Forecast Centers will be discussed.

  15. Operational retrieval of Asian sand and dust storm from FY-2C geostationary meteorological satellite and its application to real time forecast in Asia

    NASA Astrophysics Data System (ADS)

    Hu, X. Q.; Lu, N. M.; Niu, T.; Zhang, P.

    2007-06-01

    This paper describes an operational retrieval algorithm for the sand/dust storm (SDS) from FY-2C/S-VISSR (Stretched - Visible and Infrared Spin-Scan Radiometer) developed at the National Satellite Meteorological Center (NSMC) of China. This algorithm, called Dust Retrieval Algorithm based on Geostationary Imager (DRAGI), is based on the optical and radiative physical properties of SDS in mid-infrared and thermal infrared spectral regions as well as the observation of all bands in the geostationary imager, which include the Brightness Temperature Difference (BTD) in split window channels, Infrared Difference Dust Index (IDDI) and the ratio of middle infrared reflectance to visible reflectance. It also combines the visible and water vapor bands observation of the geostationary imager to identify the dust clouds from the surface targets and meteorological clouds. The output product is validated by and related to other dust aerosol observations such as the synoptic weather reports, surface visibility, aerosol optical depth (AOD) and ground-based PM10 observations. Using the SDS-IDDI data and a data assimilation scheme, the dust forecast model CUACE/Dust achieved a substantial improvement to the SDS predictions in spring 2006.

  16. Operational retrieval of Asian sand and dust storm from FY-2C geostationary meteorological satellite and its application to real time forecast in Asia

    NASA Astrophysics Data System (ADS)

    Hu, X. Q.; Lu, N. M.; Niu, T.; Zhang, P.

    2008-03-01

    This paper describes an operational retrieval algorithm for the sand/dust storm (SDS) from FY-2C/S-VISSR (Stretched-Visible and Infrared Spin-Scan Radiometer) developed at the National Satellite Meteorological Center (NSMC) of China. This algorithm, called Dust Retrieval Algorithm based on Geostationary Imager (DRAGI), is based on the optical and radiative physical properties of SDS in mid-infrared and thermal infrared spectral regions as well as the observation of all bands in the geostationary imager, which include the Brightness Temperature Difference (BTD) in split window channels, Infrared Difference Dust Index (IDDI) and the ratio of middle infrared reflectance to visible reflectance. It also combines the visible and water vapor bands observation of the geostationary imager to identify the dust clouds from the surface targets and meteorological clouds. The output product is validated by and related to other dust aerosol observations such as the synoptic weather reports, surface visibility, aerosol optical depth (AOD) and ground-based PM10 observations. Using the SDS-IDD product and a data assimilation scheme, the dust forecast model CUACE/Dust achieved a substantial improvement to the SDS predictions in spring 2006.

  17. A Prototype Visualization of Real-time River Drainage Network Response to Rainfall

    NASA Astrophysics Data System (ADS)

    Demir, I.; Krajewski, W. F.

    2011-12-01

    The Iowa Flood Information System (IFIS) is a web-based platform developed by the Iowa Flood Center (IFC) to provide access to and visualization of flood inundation maps, real-time flood conditions, flood forecasts both short-term and seasonal, and other flood-related data for communities in Iowa. The key element of the system's architecture is the notion of community. Locations of the communities, those near streams and rivers, define basin boundaries. The IFIS streams rainfall data from NEXRAD radar, and provides three interfaces including animation for rainfall intensity, daily rainfall totals and rainfall accumulations for past 14 days for Iowa. A real-time interactive visualization interface is developed using past rainfall intensity data. The interface creates community-based rainfall products on-demand using watershed boundaries of each community as a mask. Each individual rainfall pixel is tracked in the interface along the drainage network, and the ones drains to same pixel location are accumulated. The interface loads recent rainfall data in five minute intervals that are combined with current values. Latest web technologies are utilized for the development of the interface including HTML 5 Canvas, and JavaScript. The performance of the interface is optimized to run smoothly on modern web browsers. The interface controls allow users to change internal parameters of the system, and operation conditions of the animation. The interface will help communities understand the effects of rainfall on water transport in stream and river networks and make better-informed decisions regarding the threat of floods. This presentation provides an overview of a unique visualization interface and discusses future plans for real-time dynamic presentations of streamflow forecasting.

  18. Development of roughness updating based on artificial neural network in a river hydraulic model for flash flood forecasting

    NASA Astrophysics Data System (ADS)

    Fu, J. C.; Hsu, M. H.; Duann, Y.

    2016-02-01

    Flood is the worst weather-related hazard in Taiwan because of steep terrain and storm. The tropical storm often results in disastrous flash flood. To provide reliable forecast of water stages in rivers is indispensable for proper actions in the emergency response during flood. The river hydraulic model based on dynamic wave theory using an implicit finite-difference method is developed with river roughness updating for flash flood forecast. The artificial neural network (ANN) is employed to update the roughness of rivers in accordance with the observed river stages at each time-step of the flood routing process. Several typhoon events at Tamsui River are utilized to evaluate the accuracy of flood forecasting. The results present the adaptive n-values of roughness for river hydraulic model that can provide a better flow state for subsequent forecasting at significant locations and longitudinal profiles along rivers.

  19. Satellite-supported flood forecasting in river networks: A real case study

    NASA Astrophysics Data System (ADS)

    García-Pintado, Javier; Mason, David C.; Dance, Sarah L.; Cloke, Hannah L.; Neal, Jeff C.; Freer, Jim; Bates, Paul D.

    2015-04-01

    Satellite-based (e.g., Synthetic Aperture Radar [SAR]) water level observations (WLOs) of the floodplain can be sequentially assimilated into a hydrodynamic model to decrease forecast uncertainty. This has the potential to keep the forecast on track, so providing an Earth Observation (EO) based flood forecast system. However, the operational applicability of such a system for floods developed over river networks requires further testing. One of the promising techniques for assimilation in this field is the family of ensemble Kalman (EnKF) filters. These filters use a limited-size ensemble representation of the forecast error covariance matrix. This representation tends to develop spurious correlations as the forecast-assimilation cycle proceeds, which is a further complication for dealing with floods in either urban areas or river junctions in rural environments. Here we evaluate the assimilation of WLOs obtained from a sequence of real SAR overpasses (the X-band COSMO-Skymed constellation) in a case study. We show that a direct application of a global Ensemble Transform Kalman Filter (ETKF) suffers from filter divergence caused by spurious correlations. However, a spatially-based filter localization provides a substantial moderation in the development of the forecast error covariance matrix, directly improving the forecast and also making it possible to further benefit from a simultaneous online inflow error estimation and correction. Additionally, we propose and evaluate a novel along-network metric for filter localization, which is physically-meaningful for the flood over a network problem. Using this metric, we further evaluate the simultaneous estimation of channel friction and spatially-variable channel bathymetry, for which the filter seems able to converge simultaneously to sensible values. Results also indicate that friction is a second order effect in flood inundation models applied to gradually varied flow in large rivers. The study is not conclusive

  20. Real-Time PCR

    NASA Astrophysics Data System (ADS)

    Evrard, A.; Boulle, N.; Lutfalla, G. S.

    Over the past few years there has been a considerable development of DNA amplification by polymerase chain reaction (PCR), and real-time PCR has now superseded conventional PCR techniques in many areas, e.g., the quantification of nucleic acids and genotyping. This new approach is based on the detection and quantification of a fluorescent signal proportional to the amount of amplicons generated by PCR. Real-time detection is achieved by coupling a thermocycler with a fluorimeter. This chapter discusses the general principles of quantitative real-time PCR, the different steps involved in implementing the technique, and some examples of applications in medicine. The polymerase chain reaction (PCR) provides a way of obtaining a large number of copies of a double-stranded DNA fragment of known sequence. This DNA amplification technique, developed in 1985 by K. Mullis (Cetus Corporation), saw a spectacular development over the space of a few years, revolutionising the methods used up to then in molecular biology. Indeed, PCR has many applications, such as the detection of small amounts of DNA, cloning, and quantitative analysis (assaying), each of which will be discussed further below.

  1. 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

  2. The Ensemble Framework for Flash Flood Forecasting: Global and CONUS Applications

    NASA Astrophysics Data System (ADS)

    Flamig, Z.; Vergara, H. J.; Clark, R. A.; Gourley, J. J.; Kirstetter, P. E.; Hong, Y.

    2015-12-01

    The Ensemble Framework for Flash Flood Forecasting (EF5) is a distributed hydrologic modeling framework combining water balance components such as the Variable Infiltration Curve (VIC) and Sacramento Soil Moisture Accounting (SAC-SMA) with kinematic wave channel routing. The Snow-17 snow pack model is included as an optional component in EF5 for basins where snow impacts are important. EF5 also contains the Differential Evolution Adaptive Metropolis (DREAM) parameter estimation scheme for model calibration. EF5 is made to be user friendly and as such training has been developed into a weeklong course. This course has been tested in modeling workshops held in Namibia and Mexico. EF5 has also been applied to specialized applications including the Flooded Locations and Simulated Hydrographs (FLASH) project. FLASH aims to provide flash flood monitoring and forecasting over the CONUS using Multi-Radar Multi-Sensor precipitation forcing. Using the extensive field measurements database from the 10,000 USGS measurement locations across the CONUS, parameters were developed for the kinematic wave routing in FLASH. This presentation will highlight FLASH performance over the CONUS on basins less than 1,000 km2 and discuss the development of simulated streamflow climatology over the CONUS for data mining applications. A global application of EF5 has also been developed using satellite based precipitation measurements combined with numerical weather prediction forecasts to produce flood and impact forecasts. The performance of this global system will be assessed and future plans detailed.

  3. Impact of Different Data Assimilation Strategies for SMOS Observations on Flood Forecasting Accuracy

    NASA Astrophysics Data System (ADS)

    Pauwels, V. R. N.; Verhoest, N.; Lievens, H.; Martens, B.; van Den Berg, M. J.; Al-Bitar, A.; Merlin, O.; Kumar Tomer, S.; Cabot, F.; Kerr, Y. H.; Pan, M.; Wood, E. F.; Drusch, M.; Hendricks Franssen, H. J.; Vereecken, H.; De Lannoy, G. J. M.; Dumedah, G.; Walker, J. P.

    2014-12-01

    During the last decade, significant efforts have been directed towards establishing and improving flood forecasting systems for large river basins. Examples include the European Flood Alert System, and the Bureau of Meteorology Flood Warning Systems in Australia. A number of attempts have also been made to increase the accuracy of the forecasted flood volumes from these systems. One attractive way in which this can be achieved is to use remotely sensed surface soil moisture contents to constrain the hydrologic model predictions. Satellite missions such as SMOS can provide very useful information on the wetness conditions of these basins, which in many cases is an important initial condition for discharge generation. Assimilation of these satellite data is thus a logical way to proceed. We will present results from two different assimilation strategies for the Murray-Darling basin in Australia using the Variable Infiltration Capacity (VIC) model. Firstly, the SMOS soil moisture data are assimilated into the hydrologic model at their original spatial resolution. As the spatial resolution of the remote sensing data (25 km) is coarser than the spatial resolution of the model (10 km), a multiscale data assimilation algorithm needs to be implemented. Secondly, the SMOS data are downscaled to the model resolution, prior to their assimilation. In this presentation, the impact of the assimilation of both products on the accuracy of the forecasted flood volumes is assessed.

  4. Improvement of operational flood forecasting through the assimilation of satellite observations and multiple river flow data

    NASA Astrophysics Data System (ADS)

    Castelli, Fabio; Ercolani, Giulia

    2016-05-01

    Data assimilation has the potential to improve flood forecasting. However, it is rarely employed in distributed hydrologic models for operational predictions. In this study, we present variational assimilation of river flow data at multiple locations and of land surface temperature (LST) from satellite in a distributed hydrologic model that is part of the operational forecasting chain for the Arno river, in central Italy. LST is used to estimate initial condition of soil moisture through a coupled surface energy/water balance scheme. We present here several hindcast experiments to assess the performances of the assimilation system. The results show that assimilation can significantly improve flood forecasting, although in the limit of data error and model structure.

  5. Enhancing the quality of hydrologic model calibrations and their transfer to operational flood forecasters

    NASA Astrophysics Data System (ADS)

    Aggett, Graeme; Spies, Ryan; Szfranski, Bill; Hahn, Claudia; Weil, Page

    2016-04-01

    An adequate forecasting model may not perform well if it is inadequately calibrated. Model calibration is often constrained by the lack of adequate calibration data, especially for small river basins with high spatial rainfall variability. Rainfall/snow station networks may not be dense enough to accurately estimate the catchment rainfall/SWE. High discharges during flood events are subject to significant error due to flow gauging difficulty. Dynamic changes in catchment conditions (e.g., urbanization; losses in karstic systems) invariably introduce non-homogeneity in the water level and flow data. This presentation will highlight some of the challenges in reliable calibration of National Weather Service (i.e. US) operational flood forecast models, emphasizing the various challenges in different physiographic/climatic domains. It will also highlight the benefit of using various data visualization techniques to transfer information about model calibration to operational forecasters so they may understand the influence of the calibration on model performance under various conditions.

  6. Natural Uncertainty Measure for Forecasting Floods in Ungauged Basins

    NASA Astrophysics Data System (ADS)

    Mantilla, Ricardo; Krajewski, Witold F.; Gupta, Vijay K.; Ayalew, Tibebu B.

    2015-04-01

    Recent data analysis have shown that peak flows for individual Rainfall-Runoff (RF-RO) events exhibit power law scaling with respect to drainage area, but the scaling slopes and intercepts change from one event to the next. We test this feature in the 32,400 km2 Iowa River basin, and give supporting evidence for our hypothesis that scaling slope and intercept incorporates all the pertinent physical processes that produce floods. These developments serve as the foundations for the key question that is addressed here: How to define uncertainty bounds for flood prediction for each event? We theoretically introduce the concept of Natural Uncertainty Measure for peak discharge (NUMPD) and test it using data from the Iowa River basin. We conjecture that NUMPD puts a limit to predictive uncertainty using measurements and modeling. In other words, the best any amount of data collection combined with any model can do is to come close to predicting NUMPD, but it cannot match or reduce it any further. For the applications of flood predictions, the concepts of Type-I and Type-II uncertainties in flood prediction are explained. We demonstrate Type-I uncertainty using the concept of NUMPD. Our results offer a context for Type-II uncertainty. Our results make a unique contribution to International Association of Hydrologic Sciences (IAHS) decade-long initiative on Predictions in Unaguged Basins (PUB) (2003-2012).

  7. Real time Faraday spectrometer

    DOEpatents

    Smith, Jr., Tommy E.; Struve, Kenneth W.; Colella, Nicholas J.

    1991-01-01

    This invention uses a dipole magnet to bend the path of a charged particle beam. As the deflected particles exit the magnet, they are spatially dispersed in the bend-plane of the magnet according to their respective momenta and pass to a plurality of chambers having Faraday probes positioned therein. Both the current and energy distribution of the particles is then determined by the non-intersecting Faraday probes located along the chambers. The Faraday probes are magnetically isolated from each other by thin metal walls of the chambers, effectively providing real time current-versus-energy particle measurements.

  8. Real time SAR processing

    NASA Technical Reports Server (NTRS)

    Premkumar, A. B.; Purviance, J. E.

    1990-01-01

    A simplified model for the SAR imaging problem is presented. The model is based on the geometry of the SAR system. Using this model an expression for the entire phase history of the received SAR signal is formulated. From the phase history, it is shown that the range and the azimuth coordinates for a point target image can be obtained by processing the phase information during the intrapulse and interpulse periods respectively. An architecture for a VLSI implementation for the SAR signal processor is presented which generates images in real time. The architecture uses a small number of chips, a new correlation processor, and an efficient azimuth correlation process.

  9. Real-Time Revolution?

    PubMed

    Berlin, Joey

    2016-03-01

    Austin Regional Clinic (ARC) physicians and officials know patient feedback is important, but getting patients to provide it can be a challenge. A pilot program of a new, real-time feedback system provided ARC patients a high-tech convenience previous attempts lacked and produced participation numbers dwarfing those past efforts. ARC's initial results with the system, in which patients answer five to seven questions on a computer tablet and can leave free-text comments, were so successful the clinic is already planning to expand it to all of its locations by the end of June.

  10. Potentialities of ensemble strategies for flood forecasting over the Milano urban area

    NASA Astrophysics Data System (ADS)

    Ravazzani, Giovanni; Amengual, Arnau; Ceppi, Alessandro; Homar, Víctor; Romero, Romu; Lombardi, Gabriele; Mancini, Marco

    2016-08-01

    Analysis of ensemble forecasting strategies, which can provide a tangible backing for flood early warning procedures and mitigation measures over the Mediterranean region, is one of the fundamental motivations of the international HyMeX programme. Here, we examine two severe hydrometeorological episodes that affected the Milano urban area and for which the complex flood protection system of the city did not completely succeed. Indeed, flood damage have exponentially increased during the last 60 years, due to industrial and urban developments. Thus, the improvement of the Milano flood control system needs a synergism between structural and non-structural approaches. First, we examine how land-use changes due to urban development have altered the hydrological response to intense rainfalls. Second, we test a flood forecasting system which comprises the Flash-flood Event-based Spatially distributed rainfall-runoff Transformation, including Water Balance (FEST-WB) and the Weather Research and Forecasting (WRF) models. Accurate forecasts of deep moist convection and extreme precipitation are difficult to be predicted due to uncertainties arising from the numeric weather prediction (NWP) physical parameterizations and high sensitivity to misrepresentation of the atmospheric state; however, two hydrological ensemble prediction systems (HEPS) have been designed to explicitly cope with uncertainties in the initial and lateral boundary conditions (IC/LBCs) and physical parameterizations of the NWP model. No substantial differences in skill have been found between both ensemble strategies when considering an enhanced diversity of IC/LBCs for the perturbed initial conditions ensemble. Furthermore, no additional benefits have been found by considering more frequent LBCs in a mixed physics ensemble, as ensemble spread seems to be reduced. These findings could help to design the most appropriate ensemble strategies before these hydrometeorological extremes, given the computational

  11. Forecasting future water levels to aid in flash flood risk management

    NASA Astrophysics Data System (ADS)

    Smith, P. J.; Beven, K.; Marchandise, A.; Pappenberger, F.

    2012-04-01

    Flash floods are typically triggered by local intense rainfall in small catchments with short response times. The work presented explores the use of ensemble numerical weather prediction products coupled with a simplified hydrological model to forecast such floods, with up to 2 days lead time, when water level observations can be regularly assimilated. The techniques outlined are presented with reference to a case study, the Gardon d'Anduze basin in France. A Data Based Mechanistic time series model of the rainfall run-off dynamics of the catchment is constructed. The model formulated to address two common sources of observational errors, shifting baselines in the water level observation and incorrect characterisation of the magnitude of the precipitation. It is cast in a state space form shown to be an effective forecaster when driven by observed precipitation data. Substitution of ensemble precipitation forecasts the observed data is used to generate forecasts with with longer lead times. A simple adaptation of the hydrological model is used to represent the uncertainty in the forecasts that may result from incorrect characterisation of the magnitude of the forecast precipitation. Observed water levels are assimilated condition the model forecasts. They can be used both to condition the initial states to the hydrological model prior to being run with the ensemble NWP input but also to condition the hydrological forecasts generated by running the ensemble NWP inputs after they have been generated. The balance between using the hydrological forecasts of most recent NWP ensemble, or those of an older generated by an older ensemble which have undergone more data assimilation is considered.

  12. Flood monitoring for ungauged rivers: the power of combining space-based monitoring and global forecasting models

    NASA Astrophysics Data System (ADS)

    Revilla-Romero, Beatriz; Netgeka, Victor; Raynaud, Damien; Thielen, Jutta

    2013-04-01

    Flood warning systems typically rely on forecasts from national meteorological services and in-situ observations from hydrological gauging stations. This capacity is not equally developed in flood-prone developing countries. Low-cost satellite monitoring systems and global flood forecasting systems can be an alternative source of information for national flood authorities. The Global Flood Awareness System (GloFAS) has been develop jointly with the European Centre for Medium-Range Weather Forecast (ECMWF) and the Joint Research Centre, and it is running quasi operational now since June 2011. The system couples state-of-the art weather forecasts with a hydrological model driven at a continental scale. The system provides downstream countries with information on upstream river conditions as well as continental and global overviews. In its test phase, this global forecast system provides probabilities for large transnational river flooding at the global scale up to 30 days in advance. It has shown its real-life potential for the first time during the flood in Southeast Asia in 2011, and more recently during the floods in Australia in March 2012, India (Assam, September-October 2012) and Chad Floods (August-October 2012).The Joint Research Centre is working on further research and development, rigorous testing and adaptations of the system to create an operational tool for decision makers, including national and regional water authorities, water resource managers, hydropower companies, civil protection and first line responders, and international humanitarian aid organizations. Currently efforts are being made to link GloFAS to the Global Flood Detection System (GFDS). GFDS is a Space-based river gauging and flood monitoring system using passive microwave remote sensing which was developed by a collaboration between the JRC and Dartmouth Flood Observatory. GFDS provides flood alerts based on daily water surface change measurements from space. Alerts are shown on a

  13. Interpolation of observed rainfall fields for flood forecasting in data poor areas

    NASA Astrophysics Data System (ADS)

    Rogelis Prada, M. C.; Werner, M. G. F.

    2010-09-01

    Observed rainfall fields constitute a crucial input for operational flood forecasting, providing boundary conditions to hydrological models for prediction of flows and levels in relevant forecast points. Such observed fields are derived through interpolation from available observed data from rain gauges. The reliability of the derived rainfall field depends on the density of the gauge network within the basin, as well as on the variability of the rainfall itself, and the interpolation method. In this paper interpolation methods to estimate rainfall fields under data- poor environments are researched, with the derived rainfall fields being used in operational flood warnings. Methods are applied in a small catchment in Bogotá, Colombia. This catchment has a complex climatology, which is strongly influenced by the inter-tropical convergence zone and orographic enhancement. As is common in such catchments in developing countries, the rainfall gauging network is sparse, while the need for reliable rainfall in flood forecasting is high. The extensive high flood risk zones in the lower areas of the catchment, where urbanization processes are characterized by unplanned occupation of areas close to rivers, is common in developing countries. Results show the sensitivity of interpolated rainfall fields to the interpolation methods chosen, and the importance of the use of indicator variables for improving the spatial distribution of interpolated rainfall. The value of these methods in establishing optimal new gauging sites for augmenting the sparse gauge network is demonstrated.

  14. Real time automated inspection

    DOEpatents

    Fant, K.M.; Fundakowski, R.A.; Levitt, T.S.; Overland, J.E.; Suresh, B.R.; Ulrich, F.W.

    1985-05-21

    A method and apparatus are described relating to the real time automatic detection and classification of characteristic type surface imperfections occurring on the surfaces of material of interest such as moving hot metal slabs produced by a continuous steel caster. A data camera transversely scans continuous lines of such a surface to sense light intensities of scanned pixels and generates corresponding voltage values. The voltage values are converted to corresponding digital values to form a digital image of the surface which is subsequently processed to form an edge-enhanced image having scan lines characterized by intervals corresponding to the edges of the image. The edge-enhanced image is thresholded to segment out the edges and objects formed by the edges by interval matching and bin tracking. Features of the objects are derived and such features are utilized to classify the objects into characteristic type surface imperfections. 43 figs.

  15. Real time automated inspection

    DOEpatents

    Fant, Karl M.; Fundakowski, Richard A.; Levitt, Tod S.; Overland, John E.; Suresh, Bindinganavle R.; Ulrich, Franz W.

    1985-01-01

    A method and apparatus relating to the real time automatic detection and classification of characteristic type surface imperfections occurring on the surfaces of material of interest such as moving hot metal slabs produced by a continuous steel caster. A data camera transversely scans continuous lines of such a surface to sense light intensities of scanned pixels and generates corresponding voltage values. The voltage values are converted to corresponding digital values to form a digital image of the surface which is subsequently processed to form an edge-enhanced image having scan lines characterized by intervals corresponding to the edges of the image. The edge-enhanced image is thresholded to segment out the edges and objects formed by the edges are segmented out by interval matching and bin tracking. Features of the objects are derived and such features are utilized to classify the objects into characteristic type surface imperfections.

  16. Remodeling and Flood Forecasting due to Climate Change and Land Used:

    NASA Astrophysics Data System (ADS)

    Mohammad, Munira; Bárdossy, András.

    2010-05-01

    This study is to review the impact of climate change and land used on flooding through the SMART Project. It also simulate the Flood Forecasting in Klang River Basin in order to compare the changes in the existing river system in Klang River Basin with the Storm water Management and Road Tunnel (SMART) which is now already operating in the city center of Kuala Lumpur.The catchment area of the Klang River basin is 1,288 square kilometers (km2), and it is the most urbanized region in Malaysia, encompassing the Federal Territory of Kuala Lumpur and part of the state of Selangor. The basin spreads over nine local government authorities and faces serious environmental degradation and flooding problems from urbanization, industrialization, and population growth. More than half of the basin has been urbanized, and much of this continuing urban development has taken place on land that is prone to flooding. Flooding problem in Klang River Basin is still exist even measures and numerous flood mitigation projects and programs has been carried out by many parties. Even though that the new drainage guideline has been proposed since year 2000, flood reduction for Klang River basins is not successful enough. This problem contributed to the needs of this research to enhance the existing flood forecasting and mitigation project. This study analyzed and quantified the spatial patterns and time-variability of daily, monthly and yearly rainfall in Kuala Lumpur. An overview of rainfall patterns will be obtained through the analysis of 12 point data sources. Statistical properties of annual, monthly, and daily rainfall were derived. Spatial correlation fields for the annual and monthly rainfalls were studied.

  17. Development of an Operational Typhoon Swell Forecasting and Coastal Flooding Early Warning System

    NASA Astrophysics Data System (ADS)

    Fan, Y. M.; Wu, L. C.; Doong, D. J.; Kao, C. C.; Wang, J. H.

    2012-04-01

    Coastal floods and typhoon swells are a consistent threat to oceanfront countries, causing major human suffering and substantial economic losses, such as wrecks, ship capsized, and marine construction failure, etc. Climate change is exacerbating the problem. An early warning system is essential to mitigate the loss of life and property from coastal flooding and typhoon swells. The purpose of this study is to develop a typhoon swell forecasting and coastal flooding early warning system by integrating existing sea-state monitoring technology, numerical ocean forecasting models, historical database and experiences, as well as computer science. The proposed system has capability offering data for the past, information for the present, and for the future. The system was developed for Taiwanese coast due to its frequent threat by typhoons. An operational system without any manual work is the basic requirement of the system. Integration of various data source is the system kernel. Numerical ocean models play the important role within the system because they provide data for assessment of possible typhoon swell and flooding. The system includes regional wave model (SWAN) which nested with the large domain wave model (NWW III), is operationally set up for coastal waves forecasting, especially typhoon swell forecasting before typhoon coming, and the storm surge predicted by a POM model. Data assimilation technology is incorporated for enhanced accuracy. A warning signal is presented when the storm water level that accumulated from astronomical tide, storm surge, and wave-induced run-up exceeds the alarm sea level. This warning system has been in practical use for coastal flooding damage mitigation in Taiwan for years. Example of the system operation during Typhoon Haitung struck Taiwan in 2005 is illustrated in this study.

  18. Advanced flood forecasting in Alpine watersheds by coupling meteorological observations and forecasts with a distributed hydrological model

    NASA Astrophysics Data System (ADS)

    Jasper, Karsten; Gurtz, Joachim; Lang, Herbert

    2002-10-01

    Flood forecasting may be improved by coupling atmospheric and hydrological models. To investigate the current potential of such an approach in complex mountain watersheds, the authors carried out a number of combined high-resolution one-way driven model experiments to generate runoff hydrographs for seven extreme flood events which occurred in the Lago Maggiore basin between 1993 and 2000. The Alpine Ticino-Verzasca-Maggia basin (2627 km 2) is located directly to the south of the main Alpine ridge embracing a great part of the drainage area of Lago Maggiore. For this basin, the grid-based hydrological catchment model WaSiM-ETH was employed to determine the continuous runoff hydrographs. In the model experiments, two different sets of meteorological input data were used: (1) surface observation data from station measurements and from weather radar, and (2) forecast data from five different high-resolution numerical weather prediction (NWP) models with grid cell sizes between 2 and 14 km. This paper presents and compares selected results of these flood runoff simulations with particular attention to the experimental design of the model coupling. The configuration and initialization of the hydrological model runs are outlined as well as the down-scale techniques which proved to provide an adequate spatial interpolation of the meteorological variables onto the 500 m×500 m grid of the hydrological model. In order to evaluate the various hydrological model results as generated from the different outputs from the five NWP models, some coupled experiments with 'non-standard' NWP model outputs have been carried out. In particular, the results of these sensitivity studies point to inherent limits of high-resolution flood runoff predictions in complex mountain terrain.

  19. Long-Lead Quantitative Flood Forecasts in Ungauged Basins Using Bayesian Neural Networks

    NASA Astrophysics Data System (ADS)

    Barros, A. P.; Yoo, J.

    2004-05-01

    Previously, Kim and Barros (2001) demonstrated the use of a hierarchy of neural network models to forecast flood peaks in four small and medium size ungauged basins (750 to about 9,000 km-sq) in the Northern Appalachian Mountains in Pennsylvania. Using regional rainfall, radiosonde and mesoscale infrared (IR) satellite imagery, their approach consisted of identifying the presence and type of convective activity from the IR imagery, information which was subsequently used to characterize the dominant synoptic scale weather patters and predict storm path and evolution using rainfall and radiosonde data far away from the forecast location. In this regard, the organizational skeleton of the inputs is built to mimic our understanding of physical processes associated with rainstorms. The approach was very successful with skill scores on the order of 80-90 per cent for 18-hour lead-time forecasts of winter and spring floods in response to heavy rainfall (i.e. not associated with snowmelt alone). One weakness of this work was however the lack of a measure of forecast uncertainty, or alternatively a measure of forecast reliability that could be used in hydrometeorological operations. To address this question, we have modified and adapted the existing neural network models according to the principles of Bayesian statistics. In this context, forecasts are issued along with an error bar and are associated with a known probability distribution. One additional advantage of this methodology is that it provides an objective basis for selecting the best model during learning based on the posterior distribution of the parameters. In this context, forecasts are issued along with an error bar and are associated with a known probability distribution. An intercomparison study against Kim and Barros (2001) shows that the 18- and 24-hour lead time BNN forecasts are statistically more robust than those generated by the standard backward-learning NNs. We submit that given the consistently

  20. Real-Time Assimilation of Goes-Derived Products into A Mesoscale Model and It's Impact on Short-Term (06-36hr) Forecasts from 17 October 1998 through the Present

    NASA Technical Reports Server (NTRS)

    Lapenta, William M.; Suggs, Ron; Jedlovec, Gary; McNider, Richard T.

    1999-01-01

    As the parameterizations of surface energy budgets in regional models have become more complete physically, models have the potential to be much more realistic in simulations of coupling between surface radiation, hydrology, and surface energy transfer. Realizing the importance of properly specifying the surface energy budget, many institutions are using land-surface models to represent the lower boundary forcing associated with biophysical processes and soil hydrology. However, the added degrees of freedom due to inclusion of such land-surface schemes require the specification of additional parameters within the model system such as vegetative resistances, green vegetation fraction, leaf area index, soil physical and hydraulic characteristics, stream flow, runoff, and the vertical distribution of soil moisture. Spatial heterogeneity of these parameters makes correct specification problematic since measurements are not routinely available. A technique has been developed for assimilating GOES-IR skin temperature tendencies, solar insolation, and surface albedo into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. The technique has been successfully employed in a number of mesoscale models in case-study mode. We have taken the next step and developed a study to determine if assimilating these types of data into mesoscale models in real-time can improve short-term (648h) forecasts of temperature, relative humidity, and QPF on a daily basis over relatively large regions. Therefore, an operational modeling/assimilation system has been developed at the GHCC during the past summer that allows us to produce simulations out to 48 hours in a timely manor. The PSU/NCAR MM5 is used in a nested configuration with a 25 km grid covering the southeastern third of the US. The model has been on-line since 1 July 1998 and forecast products are posted on our web site. The satellite

  1. Development of a model-based flood emergency management system in Yujiang River Basin, South China

    NASA Astrophysics Data System (ADS)

    Zeng, Yong; Cai, Yanpeng; Jia, Peng; Mao, Jiansu

    2014-06-01

    Flooding is the most frequent disaster in China. It affects people's lives and properties, causing considerable economic loss. Flood forecast and operation of reservoirs are important in flood emergency management. Although great progress has been achieved in flood forecast and reservoir operation through using computer, network technology, and geographic information system technology in China, the prediction accuracy of models are not satisfactory due to the unavailability of real-time monitoring data. Also, real-time flood control scenario analysis is not effective in many regions and can seldom provide online decision support function. In this research, a decision support system for real-time flood forecasting in Yujiang River Basin, South China (DSS-YRB) is introduced in this paper. This system is based on hydrological and hydraulic mathematical models. The conceptual framework and detailed components of the proposed DSS-YRB is illustrated, which employs real-time rainfall data conversion, model-driven hydrologic forecasting, model calibration, data assimilation methods, and reservoir operational scenario analysis. Multi-tiered architecture offers great flexibility, portability, reusability, and reliability. The applied case study results show the development and application of a decision support system for real-time flood forecasting and operation is beneficial for flood control.

  2. Real time polarimetric dehazing.

    PubMed

    Mudge, Jason; Virgen, Miguel

    2013-03-20

    Remote sensing is a rich topic due to its utility in gathering detailed accurate information from locations that are not economically feasible traveling destinations or are physically inaccessible. However, poor visibility over long path lengths is problematic for a variety of reasons. Haze induced by light scatter is one cause for poor visibility and is the focus of this article. Image haze comes about as a result of light scattering off particles and into the imaging path causing a haziness to appear on the image. Image processing using polarimetric information of light scatter can be used to mitigate image haze. An imaging polarimeter which provides the Stokes values in real time combined with a "dehazing" algorithm can automate image haze removal for instant applications. Example uses are to improve visual display providing on-the-spot detection or imbedding in an active control loop to improve viewing and tracking while on a moving platform. In addition, removing haze in this manner allows the trade space for a system operational waveband to be opened up to bands which are object matched and not necessarily restricted by scatter effects. PMID:23518739

  3. Development of Flood Forecasting Using Statistical Method in Four River Basins in Terengganu, Malaysia

    NASA Astrophysics Data System (ADS)

    Noor, M. S. F. M.; Sidek, L. M.; Basri, H.; Husni, M. M. M.; Jaafar, A. S.; Kamaluddin, M. H.; Majid, W. H. A. W. A.; Mohammad, A. H.; Osman, S.

    2016-03-01

    One of the critical regions in Malaysia is Terengganu which is located at east coast of Peninsular Malaysia. In Terengganu, flood is experienced regularly because of attributed topography and climate including northeast monsoon. Moreover, rainfall is with high intensity during the November to February in Terengganu as forcing factor to produce of flood. In this study, main objectives are water stage forecasting and deriving the related equations based on least squared method. For this study, it is used two methods which called inclusion of residual (Method A) and non-inclusion residual (Method B) respectively. Result depicts that Method B outperformed to forecast the water stage at selected case studies (Besut, Dungun, Kemaman, Terengganu).

  4. Bias correction of satellite precipitation products for flood forecasting application at the Upper Mahanadi River Basin in Eastern India

    NASA Astrophysics Data System (ADS)

    Beria, H.; Nanda, T., Sr.; Chatterjee, C.

    2015-12-01

    High resolution satellite precipitation products such as Tropical Rainfall Measuring Mission (TRMM), Climate Forecast System Reanalysis (CFSR), European Centre for Medium-Range Weather Forecasts (ECMWF), etc., offer a promising alternative to flood forecasting in data scarce regions. At the current state-of-art, these products cannot be used in the raw form for flood forecasting, even at smaller lead times. In the current study, these precipitation products are bias corrected using statistical techniques, such as additive and multiplicative bias corrections, and wavelet multi-resolution analysis (MRA) with India Meteorological Department (IMD) gridded precipitation product,obtained from gauge-based rainfall estimates. Neural network based rainfall-runoff modeling using these bias corrected products provide encouraging results for flood forecasting upto 48 hours lead time. We will present various statistical and graphical interpretations of catchment response to high rainfall events using both the raw and bias corrected precipitation products at different lead times.

  5. Potential application of wavelet neural network ensemble to forecast streamflow for flood management

    NASA Astrophysics Data System (ADS)

    Kasiviswanathan, K. S.; He, Jianxun; Sudheer, K. P.; Tay, Joo-Hwa

    2016-05-01

    Streamflow forecasting, especially the long lead-time forecasting, is still a very challenging task in hydrologic modeling. This could be due to the fact that the forecast accuracy measured in terms of both the amplitude and phase or temporal errors and the forecast precision/reliability quantified in terms of the uncertainty significantly deteriorate with the increase of the lead-time. In the model performance evaluation, the conventional error metrics, which primarily quantify the amplitude error and do not explicitly account for the phase error, have been commonly adopted. For the long lead-time forecasting, the wavelet based neural network (WNN) among a variety of advanced soft computing methods has been shown to be promising in the literature. This paper presented and compared WNN and artificial neural network (ANN), both of which were combined with the ensemble method using block bootstrap sampling (BB), in terms of the forecast accuracy and precision at various lead-times on the Bow River, Alberta, Canada. Apart from conventional model performance metrics, a new index, called percent volumetric error, was proposed, especially for quantifying the phase error. The uncertainty metrics including percentage of coverage and average width were used to evaluate the precision of the modeling approaches. The results obtained demonstrate that the WNN-BB consistently outperforms the ANN-BB in both the categories of the forecast accuracy and precision, especially in the long lead-time forecasting. The findings strongly suggest that the WNN-BB is a robust modeling approach for streamflow forecasting and thus would aid in flood management.

  6. Simulating and Forecasting Flooding Events in the City of Jeddah, Saudi Arabia

    NASA Astrophysics Data System (ADS)

    Ghostine, Rabih; Viswanadhapalli, Yesubabu; Hoteit, Ibrahim

    2014-05-01

    Metropolitan cities in the Kingdom of Saudi Arabia, as Jeddah and Riyadh, are more frequently experiencing flooding events caused by strong convective storms that produce intense precipitation over a short span of time. The flooding in the city of Jeddah in November 2009 was described by civil defense officials as the worst in 27 years. As of January 2010, 150 people were reported killed and more than 350 were missing. Another flooding event, less damaging but comparably spectacular, occurred one year later (Jan 2011) in Jeddah. Anticipating floods before they occur could minimize human and economic losses through the implementation of appropriate protection, provision and rescue plans. We have developed a coupled hydro-meteorological model for simulating and predicting flooding events in the city of Jeddah. We use the Weather Research Forecasting (WRF) model assimilating all available data in the Jeddah region for simulating the storm events in Jeddah. The resulting rain is then used on 10 minutes intervals to feed up an advanced numerical shallow water model that has been discretized on an unstructured grid using different numerical schemes based on the finite elements or finite volume techniques. The model was integrated on a high-resolution grid size varying between 0.5m within the streets of Jeddah and 500m outside the city. This contribution will present the flooding simulation system and the simulation results, focusing on the comparison of the different numerical schemes on the system performances in terms of accuracy and computational efficiency.

  7. First steps in incorporating data-driven modelling to flood early warning in Norway's Flood Forecasting Service

    NASA Astrophysics Data System (ADS)

    Borsányi, Péter; Hamududu, Byman; Wong Kwok, Wai; Magnusson, Jan; Shi, Min

    2016-04-01

    The national Flood Early Warning Services (FEWS) in Norway use time-series of precipitation and temperature data as input to conceptual physically based rainfall-runoff models for forecasts. Runoff is forecasted in selected catchments and the warnings are based on regionalization of these. This concept proved useful in many catchments, however there are some exceptions, where forecasts are of worse quality. To improve this, data-driven modelling (DDM) techniques are sought applied. The first objective of the study is to identify those DDM methods, which are feasible for application and can easily be fit in the present, well-developed procedures of the operational FEWS. Therefore an experiment is conducted, where about thirty years of daily accumulated precipitation and daily mean temperature as input and observed runoff as output data are used. This was repeated from five, regionally and physically different catchments. In each case different DDMs were developed and their simulation results compared to those generated by the operational (conceptual based) models and to the observations. The methods of Artificial Neural Networks, Genetic Programming, Evolutionary Polynomial Regression and Support Vector Machines were used in the experiment. Various combinations of the last, the last two and the last three timesteps (in this case: days) of the data was tested as possible inputs. Forecast quality was described by Absolute Accumulated Error, Root Mean Square Error, Nash-Sutcliff Efficiency, the Ideal Point Error (combination of the previous) as well as by Taylor-diagrams. The first comparisons show promising results, which need to be further examined. The follow-up study will first focus on standardizing and automating the tests on forecast quality to be able to perform the studies on a larger number of datasets, as well as for other forecast periods. We expect the DDM to perform better in cases where conceptual models don't perform well. In these cases the quality

  8. Assimilation of multiple river flow data for enhanced operational flood forecasts

    NASA Astrophysics Data System (ADS)

    Ercolani, Giulia; Castelli, Fabio

    2016-04-01

    Data assimilation (DA) is widely recognized as a powerful tool to improve flood forecasts, and the need for an effective transition of research advances into operational forecasting systems has been increasingly claimed in recent years. Nevertheless, the majority of studies investigates DA capabilities through synthetic experiments, while applications conducted from an operational perspective are rare. In this work we present variational assimilation of discharge data at multiple locations in a distributed hydrologic model (Mobidic) that is part of the operational forecasting chain for the Arno river, in central Italy.The variational approach needs the derivation of an adjoint model, that is challenging for hydrologic models, but it requires less restrictive hypothesis than Kalman and Monte Carlo filters and smoothers. The developed assimilation system adjusts on a distributed basis initial condition of discharge, initial condition of soil moisture and a parameter representing the frequency of no-rainfall in a time step. The correction evaluated at discharge measurement stations spreads upstream thanks to the coupling between equations of flow channel routing, that results into the coupling between equations of the adjoint model. Sequential assimilations are realized on windows of 6 hours. We extensively examine the performances of the DA system through several hindcast experiments that mimic operational conditions. The case studies include both flood events and false alarms that occurred in the period 2009-2010 in the Arno river basin (about 8230 km2).The hydrologic model is run with the spatial and temporal resolutions that are employed operationally, i.e. 500 m and 15 minutes.The enhancement in discharge forecasts is evaluated through classical performance indexes as error on peak flow and Nash-Sutcliffe efficiency, with strong emphasis on the dependence on lead time. In addition, uncertainty of the estimations is assessed using the Hessian of the cost function

  9. Towards spatially distributed flood forecasts in flash flood prone areas: application to the supervision of a road network in the South of France

    NASA Astrophysics Data System (ADS)

    Naulin, Jean-Philippe; Payrastre, Olivier; Gaume, Eric; Delrieu, Guy

    2013-04-01

    Accurate flood forecasts are crucial for an efficient flood event management. Until now, hydro-meteorological forecasts have been mainly used for early-warnings in France (Meteorological and flood vigilance maps) or over the world (Flash-flood guidances). These forecasts are generally limited to the main streams covered by the flood forecasting services or to specific watersheds with particular assets like check dams which are in most cases well gauged river sections, leaving aside large parts of the territory. A distributed hydro-meteorological forecasting approach will be presented, able to take advantage of the high spatial and temporal resolution rainfall estimates that are now available to provide information at ungauged sites. The proposed system aiming at detecting road inundation risks had been initially developed and tested in areas of limited size. Its extension to a whole region (the Gard region in the South of France) will be presented, including over 2000 crossing points between rivers and roads and its validation against a large data set of actually reported road inundations observed during recent flash-flood events. These first validation results appear promising. Such a tool would provide the necessary information for flood event management services to identify the areas at risk and to take the appropriate safety and rescue measures: pre-positioning of rescue means, stopping of the traffic on exposed roads, determination of safe accesses or evacuation routes. Moreover, beyond the specific application to the supervision of a road network, this work provides also results concerning the performances of hydro-meteorological forecasts for ungauged headwaters.

  10. The total probabilities from high-resolution ensemble forecasting of floods

    NASA Astrophysics Data System (ADS)

    Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian

    2015-04-01

    Ensemble forecasting has for a long time been used in meteorological modelling, to give an indication of the uncertainty of the forecasts. As meteorological ensemble forecasts often show some bias and dispersion errors, there is a need for calibration and post-processing of the ensembles. Typical methods for this are Bayesian Model Averaging (Raftery et al., 2005) and Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). There are also methods for regionalizing these methods (Berrocal et al., 2007) and for incorporating the correlation between lead times (Hemri et al., 2013). To make optimal predictions of floods along the stream network in hydrology, we can easily use the ensemble members as input to the hydrological models. However, some of the post-processing methods will need modifications when regionalizing the forecasts outside the calibration locations, as done by Hemri et al. (2013). We present a method for spatial regionalization of the post-processed forecasts based on EMOS and top-kriging (Skøien et al., 2006). We will also look into different methods for handling the non-normality of runoff and the effect on forecasts skills in general and for floods in particular. Berrocal, V. J., Raftery, A. E. and Gneiting, T.: Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts, Mon. Weather Rev., 135(4), 1386-1402, doi:10.1175/MWR3341.1, 2007. Gneiting, T., Raftery, A. E., Westveld, A. H. and Goldman, T.: Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation, Mon. Weather Rev., 133(5), 1098-1118, doi:10.1175/MWR2904.1, 2005. Hemri, S., Fundel, F. and Zappa, M.: Simultaneous calibration of ensemble river flow predictions over an entire range of lead times, Water Resour. Res., 49(10), 6744-6755, doi:10.1002/wrcr.20542, 2013. Raftery, A. E., Gneiting, T., Balabdaoui, F. and Polakowski, M.: Using Bayesian Model Averaging to Calibrate Forecast Ensembles, Mon. Weather Rev

  11. Willingness-to-pay for a probabilistic flood forecast: a risk-based decision-making game

    NASA Astrophysics Data System (ADS)

    Arnal, Louise; Ramos, Maria-Helena; Coughlan de Perez, Erin; Cloke, Hannah Louise; Stephens, Elisabeth; Wetterhall, Fredrik; van Andel, Schalk Jan; Pappenberger, Florian

    2016-08-01

    Probabilistic hydro-meteorological forecasts have over the last decades been used more frequently to communicate forecast uncertainty. This uncertainty is twofold, as it constitutes both an added value and a challenge for the forecaster and the user of the forecasts. Many authors have demonstrated the added (economic) value of probabilistic over deterministic forecasts across the water sector (e.g. flood protection, hydroelectric power management and navigation). However, the richness of the information is also a source of challenges for operational uses, due partially to the difficulty in transforming the probability of occurrence of an event into a binary decision. This paper presents the results of a risk-based decision-making game on the topic of flood protection mitigation, called "How much are you prepared to pay for a forecast?". The game was played at several workshops in 2015, which were attended by operational forecasters and academics working in the field of hydro-meteorology. The aim of this game was to better understand the role of probabilistic forecasts in decision-making processes and their perceived value by decision-makers. Based on the participants' willingness-to-pay for a forecast, the results of the game show that the value (or the usefulness) of a forecast depends on several factors, including the way users perceive the quality of their forecasts and link it to the perception of their own performances as decision-makers.

  12. Non-parametric data-based approach for the quantification and communication of uncertainties in river flood forecasts

    NASA Astrophysics Data System (ADS)

    Van Steenbergen, N.; Willems, P.

    2012-04-01

    Reliable flood forecasts are the most important non-structural measures to reduce the impact of floods. However flood forecasting systems are subject to uncertainty originating from the input data, model structure and model parameters of the different hydraulic and hydrological submodels. To quantify this uncertainty a non-parametric data-based approach has been developed. This approach analyses the historical forecast residuals (differences between the predictions and the observations at river gauging stations) without using a predefined statistical error distribution. Because the residuals are correlated with the value of the forecasted water level and the lead time, the residuals are split up into discrete classes of simulated water levels and lead times. For each class, percentile values are calculated of the model residuals and stored in a 'three dimensional error' matrix. By 3D interpolation in this error matrix, the uncertainty in new forecasted water levels can be quantified. In addition to the quantification of the uncertainty, the communication of this uncertainty is equally important. The communication has to be done in a consistent way, reducing the chance of misinterpretation. Also, the communication needs to be adapted to the audience; the majority of the larger public is not interested in in-depth information on the uncertainty on the predicted water levels, but only is interested in information on the likelihood of exceedance of certain alarm levels. Water managers need more information, e.g. time dependent uncertainty information, because they rely on this information to undertake the appropriate flood mitigation action. There are various ways in presenting uncertainty information (numerical, linguistic, graphical, time (in)dependent, etc.) each with their advantages and disadvantages for a specific audience. A useful method to communicate uncertainty of flood forecasts is by probabilistic flood mapping. These maps give a representation of the

  13. A Web-based Data Intensive Visualization of Real-time River Drainage Network Response to Rainfall

    NASA Astrophysics Data System (ADS)

    Demir, I.; Krajewski, W. F.

    2012-04-01

    The Iowa Flood Information System (IFIS) is a web-based platform developed by the Iowa Flood Center (IFC) to provide access to and visualization of flood inundation maps, real-time flood conditions, flood forecasts both short-term and seasonal, and other flood-related data for communities in Iowa. The key element of the system's architecture is the notion of community. Locations of the communities, those near streams and rivers, define basin boundaries. The IFIS streams rainfall data from NEXRAD radar, and provides three interfaces including animation for rainfall intensity, daily rainfall totals and rainfall accumulations for past 14 days for Iowa. A real-time interactive visualization interface is developed using past rainfall intensity data. The interface creates community-based rainfall products on-demand using watershed boundaries of each community as a mask. Each individual rainfall pixel is tracked in the interface along the drainage network, and the ones drains to same pixel location are accumulated. The interface loads recent rainfall data in five minute intervals that are combined with current values. Latest web technologies are utilized for the development of the interface including HTML 5 Canvas, and JavaScript. The performance of the interface is optimized to run smoothly on modern web browsers. The interface controls allow users to change internal parameters of the system, and operation conditions of the animation. The interface will help communities understand the effects of rainfall on water transport in stream and river networks and make better-informed decisions regarding the threat of floods. This presentation provides an overview of a unique visualization interface and discusses future plans for real-time dynamic presentations of streamflow forecasting.

  14. Semi-distributed flood forecasting system for the Middle Vistula reach

    NASA Astrophysics Data System (ADS)

    Romanowicz, Renata; Karamuz, Emilia; Osuch, Marzena

    2014-05-01

    project "Stochastic flood forecasting system (The River Vistula reach from Zawichost to Warsaw)" carried by the Institute of Geophysics, Polish Academy of Sciences on the order of the National Science Centre (contract No. 2011/01/B/ST10/06866). The water level data were provided by the Institute of Meteorology and Water Management (IMGW), Poland.

  15. Visualising probabilistic flood forecast information: expert preferences and perceptions of best practice in uncertainty communication

    NASA Astrophysics Data System (ADS)

    Pappenberger, F.; Stephens, E. M.; Thielen, J.; Salomon, P.; Demeritt, D.; van Andel, S.; Wetterhall, F.; Alfieri, L.

    2011-12-01

    The aim of this paper is to understand and to contribute to improved communication of the probabilistic flood forecasts generated by Hydrological Ensemble Prediction Systems (HEPS) with particular focus on the inter expert communication. Different users are likely to require different kinds of information from HEPS and thus different visualizations. The perceptions of this expert group are important both because they are the designers and primary users of existing HEPS. Nevertheless, they have sometimes resisted the release of uncertainty information to the general public because of doubts about whether it can be successfully communicated in ways that would be readily understood to non-experts. In this paper we explore the strengths and weaknesses of existing HEPS visualization methods and thereby formulate some wider recommendations about best practice for HEPS visualization and communication. We suggest that specific training on probabilistic forecasting would foster use of probabilistic forecasts with a wider range of applications. The result of a case study exercise showed that there is no overarching agreement between experts on how to display probabilistic forecasts and what they consider essential information that should accompany plots and diagrams. In this paper we propose a list of minimum properties that, if consistently displayed with probabilistic forecasts, would make the products more easily understandable.

  16. Rapid setup of the high resolution interactive hydrological / hydraulic model for flood forecasting at global scale

    NASA Astrophysics Data System (ADS)

    Donchyts, G.; Haag, A.; Winsemius, H.; Baart, F.; Hut, R.; Drost, N.; Van De Giesen, N.

    2014-12-01

    Rapid predictions of flood using high resolution process-based numerical models applied at global scale is a useful tool for flood forecasting. Usually it requires days or even months to create such a model for a specific area and in most cases the process assumes a lot of manual work. Our goal is to significantly decrease the time required for this process by means of software integration of data processing tools, numerical models and global data sets. The methodology is based on the use of the global hydrological model PCR-GLOBWB to identify the potential flood areas. An automated set of tools will be applied to generate a coupled hydrological / hydraulic model using a high resolution input data based on free global data sets such as SRTM, HydroSHEDS, CORINE, and OpenStreetMaps. This information should be sufficient to generate high resolution input for distributed rainfall-runoff and shallow water flow models. For the detection of potential flood areas, and generation of the unstructured model grid required by the D-Flow FM hydrodynamic model, we will use Height Above the Nearest Drainage (HAND) dataset derived from SRTM. For coupling the distributed hydrological and shallow water models we will use the Basic Model Interface (BMI). BMI is a lightweight API that enables communication with numerical models at runtime. We will validate benefits of the algorithm by applying it to the San Francisco bay area. The models and data processing tools will be integrated into an interactive user interface that will enable data exploration and will allow generation of new models based on user request or automatic rules. Using our approach we expect to make significant steps towards realizing our goal of global availability of flood forecasting models.

  17. Development of Hydrological Model of Klang River Valley for flood forecasting

    NASA Astrophysics Data System (ADS)

    Mohammad, M.; Andras, B.

    2012-12-01

    This study is to review the impact of climate change and land used on flooding through the Klang River and to compare the changes in the existing river system in Klang River Basin with the Storm water Management and Road Tunnel (SMART) which is now already operating in the city centre of Kuala Lumpur. Klang River Basin is the most urbanized region in Malaysia. More than half of the basin has been urbanized on the land that is prone to flooding. Numerous flood mitigation projects and studies have been carried out to enhance the existing flood forecasting and mitigation project. The objective of this study is to develop a hydrological model for flood forecasting in Klang Basin Malaysia. Hydrological modelling generally requires large set of input data and this is more often a challenge for a developing country. Due to this limitation, the Tropical Rainfall Measuring Mission (TRMM) rainfall measurement, initiated by the US space agency NASA and Japanese space agency JAXA was used in this study. TRMM data was transformed and corrected by quantile to quantile transformation. However, transforming the data based on ground measurement doesn't make any significant improvement and the statistical comparison shows only 10% difference. The conceptual HYMOD model was used in this study and calibrated using ROPE algorithm. But, using the whole time series of the observation period in this area resulted in insufficient performance. The depth function which used in ROPE algorithm are then used to identified and calibrated using only unusual event to observed the improvement and efficiency of the model.

  18. Pre- and post-processing of hydro-meteorological ensembles for the Norwegian flood forecasting system in 145 basins.

    NASA Astrophysics Data System (ADS)

    Jahr Hegdahl, Trine; Steinsland, Ingelin; Merete Tallaksen, Lena; Engeland, Kolbjørn

    2016-04-01

    Probabilistic flood forecasting has an added value for decision making. The Norwegian flood forecasting service is based on a flood forecasting model that run for 145 basins. Covering all of Norway the basins differ in both size and hydrological regime. Currently the flood forecasting is based on deterministic meteorological forecasts, and an auto-regressive procedure is used to achieve probabilistic forecasts. An alternative approach is to use meteorological and hydrological ensemble forecasts to quantify the uncertainty in forecasted streamflow. The hydrological ensembles are based on forcing a hydrological model with meteorological ensemble forecasts of precipitation and temperature. However, the ensembles of precipitation are often biased and the spread is too small, especially for the shortest lead times, i.e. they are not calibrated. These properties will, to some extent, propagate to hydrological ensembles, that most likely will be uncalibrated as well. Pre- and post-processing methods are commonly used to obtain calibrated meteorological and hydrological ensembles respectively. Quantitative studies showing the effect of the combined processing of the meteorological (pre-processing) and the hydrological (post-processing) ensembles are however few. The aim of this study is to evaluate the influence of pre- and post-processing on the skill of streamflow predictions, and we will especially investigate if the forecasting skill depends on lead-time, basin size and hydrological regime. This aim is achieved by applying the 51 medium-range ensemble forecast of precipitation and temperature provided by the European Center of Medium-Range Weather Forecast (ECMWF). These ensembles are used as input to the operational Norwegian flood forecasting model, both raw and pre-processed. Precipitation ensembles are calibrated using a zero-adjusted gamma distribution. Temperature ensembles are calibrated using a Gaussian distribution and altitude corrected by a constant gradient

  19. Comparing One-way and Two-way Coupled Hydrometeorological Forecasting Systems for Flood Forecasting in the Mediterranean Region

    NASA Astrophysics Data System (ADS)

    Givati, Amir; Gochis, David; Rummler, Thomas; Kunstmann, Harald; Yu, Wei

    2016-04-01

    A pair of hydro-meteorological modeling systems were calibrated and evaluated for the Ayalon basin in central Israel to assess the advantages and limitations of one-way versus two-way coupled modeling systems for flood prediction. The models used included the Hydrological Engineering Center-Hydrological Modeling System (HEC-HMS) model and the Weather Research and Forecasting (WRF) Hydro modeling system. The models were forced by observed, interpolated precipitation from rain-gauges within the basin, and with modeled precipitation from the WRF atmospheric model. Detailed calibration and evaluation was carried out for two major winter storms in January and December 2013. Then both modeling systems were executed and evaluated in an operational mode for the full 2014/2015 rainy season. Outputs from these simulations were compared to observed measurements from hydrometric stations at the Ayalon basin outlet. Various statistical metrics were employed to quantify and analyze the results: correlation, Root Mean Square Error (RMSE) and the Nash-Sutcliffe (NS) efficiency coefficient. Foremost, the results presented in this study highlight the sensitivity of hydrological responses to different sources of precipitation data, and less so, to hydrologic model formulation. With observed precipitation data both calibrated models closely simulated the observed hydrographs. The two-way coupled WRF/WRF-Hydro modeling system produced improved both the precipitation and hydrological simulations as compared to the one-way WRF simulations. Findings from this study suggest that the use of two-way atmospheric-hydrological coupling has the potential to improve precipitation and, therefore, hydrological forecasts for early flood warning applications. However more research needed in order to better understand the land-atmosphere coupling mechanisms driving hydrometeorological processes on a wider variety precipitation and terrestrial hydrologic systems.

  20. River Ice and Flood Detection Products Derived from Suomi NPP VIIRS Satellite Data to Support Hydrologic Forecast Operations in Alaska

    NASA Astrophysics Data System (ADS)

    van Breukelen, C. M.; Plumb, E. W.; Li, S.; Holloway, E.; Stevens, E.

    2015-12-01

    A lack of river ice data during spring break-up in Alaska creates many forecast challenges for National Weather Service (NWS) forecasters. Limited and infrequent ice conditions and flood observations are provided by river observers, community officials, and pilots. Although these observations are invaluable, there are extensive spatial and temporal data gaps across Alaska during spring break-up. The Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) satellite imagery has proved to be an extremely beneficial situational awareness and decision support tool for NWS forecast operations. In particular, the VIIRS satellite imagery became highly effective in identifying extensive flooding of many Alaskan rivers due to ice jams during the 2013 spring breakup season. A devastating ice jam flood in the Yukon River community of Galena prompted the development of river ice and flood detection products derived from the VIIRS satellite imagery with the support of the Joint Polar Satellite System/Proving Ground and Risk Reduction (JPSS/PGRR) Program. The two new products from S-NPP/VIIRS imagery provided critical decision making information to NWS forecasters responsible for issuing flood warnings for the region. Since 2013, the NWS continues to evaluate the use of these products in an operational forecast setting, and has expanded the evaluation period to include summertime flooding. There are limitations of these products due to cloud cover, sun zenith angles, product validation, and other issues unique to Alaska. The NWS will continue to provide feedback to the JPSS/PGRR Program in order to further refine and improve the algorithms used to create the river ice and flood detection products. This presentation will demonstrate how these products have been integrated into the NWS forecast process for several types of flood events in Alaska.

  1. Data assimilation (4D-VAR) to forecast flood in shallow-waters with sediment erosion

    NASA Astrophysics Data System (ADS)

    Bélanger, Eric; Vincent, Alain

    2005-01-01

    In this paper, the four-dimensional variational data assimilation technique (4D-VAR) is presented as a tool to forecast floods. Our study is limited to purely hydrological flows and supposes that the weather, here a big rain, has been already forecasted by meteorological services. The technique consists in minimizing, in the sense of Lagrange, the cost function: a measure of the difference between calculated data and available observations, here the water level. This is done under constraints that are the equations of the physical model. In our case, we modified the shallow-water equations to include a simplified sediment transport model. The steepest descent algorithm is then used to find the minimum. This is made possible because we can compute analytically the gradient of the cost function by using the adjoint equations of the model. As an application of the 4D-VAR technique, the overflowing of the Chicoutimi River at the Chute-Garneau dam, during the 1996 flood, is investigated. It is found that the 4D-VAR method reduces the error in the water height forecast even when the erosion model is not activated. In terms of Lyapunov exponents, we estimate the predictability horizon of such an event to be about half-an-hour after a big rain. However, this limit of predictability can be increased by using more observations or by using a finer computational grid.

  2. Benchmarking an operational procedure for rapid flood mapping and risk assessment in Europe

    NASA Astrophysics Data System (ADS)

    Dottori, Francesco; Salamon, Peter; Kalas, Milan; Bianchi, Alessandra; Feyen, Luc

    2016-04-01

    The development of real-time methods for rapid flood mapping and risk assessment is crucial to improve emergency response and mitigate flood impacts. This work describes the benchmarking of an operational procedure for rapid flood risk assessment based on the flood predictions issued by the European Flood Awareness System (EFAS). The daily forecasts produced for the major European river networks are translated into event-based flood hazard maps using a large map catalogue derived from high-resolution hydrodynamic simulations, based on the hydro-meteorological dataset of EFAS. Flood hazard maps are then combined with exposure and vulnerability information, and the impacts of the forecasted flood events are evaluated in near real-time in terms of flood prone areas, potential economic damage, affected population, infrastructures and cities. An extensive testing of the operational procedure is carried out using the catastrophic floods of May 2014 in Bosnia-Herzegovina, Croatia and Serbia. The reliability of the flood mapping methodology is tested against satellite-derived flood footprints, while ground-based estimations of economic damage and affected population is compared against modelled estimates. We evaluated the skill of flood hazard and risk estimations derived from EFAS flood forecasts with different lead times and combinations. The assessment includes a comparison of several alternative approaches to produce and present the information content, in order to meet the requests of EFAS users. The tests provided good results and showed the potential of the developed real-time operational procedure in helping emergency response and management.

  3. Real-Time Benchmark Suite

    1992-01-17

    This software provides a portable benchmark suite for real time kernels. It tests the performance of many of the system calls, as well as the interrupt response time and task response time to interrupts. These numbers provide a baseline for comparing various real-time kernels and hardware platforms.

  4. Technical Note: The Normal Quantile Transformation and its application in a flood forecasting system

    NASA Astrophysics Data System (ADS)

    Bogner, K.; Pappenberger, F.; Cloke, H. L.

    2011-10-01

    The Normal Quantile Transform (NQT) has been used in many hydrological and meteorological applications in order to make the Cumulated Density Function (CDF) of the observed, simulated and forecast river discharge, water level or precipitation data Gaussian. It is also the heart of the meta-Gaussian model for assessing the total predictive uncertainty of the Hydrological Uncertainty Processor (HUP) developed by Krzysztofowicz. In the field of geo-statistics this transformation is better known as Normal-Score Transform. In this paper some possible problems caused by small sample sizes for the applicability in flood forecasting systems will be discussed and illustrated by examples. For the practical implementation commands and examples from the freely available and widely used statistical computing language R (R Development Core Team, 2011) will be given (represented in Courier font) and possible solutions are suggested by combining extreme value analysis and non-parametric regression methods.

  5. 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

  6. Technical Note: Initial assessment of a multi-method approach to spring-flood forecasting in Sweden

    NASA Astrophysics Data System (ADS)

    Olsson, J.; Uvo, C. B.; Foster, K.; Yang, W.

    2016-02-01

    Hydropower is a major energy source in Sweden, and proper reservoir management prior to the spring-flood onset is crucial for optimal production. This requires accurate forecasts of the accumulated discharge in the spring-flood period (i.e. the spring-flood volume, SFV). Today's SFV forecasts are generated using a model-based climatological ensemble approach, where time series of precipitation and temperature from historical years are used to force a calibrated and initialized set-up of the HBV model. In this study, a number of new approaches to spring-flood forecasting that reflect the latest developments with respect to analysis and modelling on seasonal timescales are presented and evaluated. Three main approaches, represented by specific methods, are evaluated in SFV hindcasts for the Swedish river Vindelälven over a 10-year period with lead times between 0 and 4 months. In the first approach, historically analogue years with respect to the climate in the period preceding the spring flood are identified and used to compose a reduced ensemble. In the second, seasonal meteorological ensemble forecasts are used to drive the HBV model over the spring-flood period. In the third approach, statistical relationships between SFV and the large-sale atmospheric circulation are used to build forecast models. None of the new approaches consistently outperform the climatological ensemble approach, but for early forecasts improvements of up to 25 % are found. This potential is reasonably well realized in a multi-method system, which over all forecast dates reduced the error in SFV by ˜ 4 %. This improvement is limited but potentially significant for e.g. energy trading.

  7. Flood forecasting; An alternate response for PMF at the Saluda Dam

    SciTech Connect

    Colon, R.; Wallace, J.R.; Olson, R.W. ); Massey, K.L. )

    1989-01-01

    This paper reports on the Saluda hydroelectric project, a peaking power project located bout 11 miles upstream from Columbia, South Carolina on the Saluda River. The Saluda Dam creates Lake Murray reservoir with a drainage area of about 2,420 square miles and a surface area of approximately 48,000 acres at maximum pool elevation. Storage for this reservoir is about 1.3 million acre-feet between elevations 330 and 360 ft-NGVD. Flood forecasting at the dam is discussed in this paper.

  8. A grid-based distributed flood forecasting model for use with weather radar data: Part 1. Formulation

    NASA Astrophysics Data System (ADS)

    Bell, V. A.; Moore, R. J.

    A practical methodology for distributed rainfall-runoff modelling using grid square weather radar data is developed for use in real-time flood forecasting. The model, called the Grid Model, is configured so as to share the same grid as used by the weather radar, thereby exploiting the distributed rainfall estimates to the full. Each grid square in the catchment is conceptualised as a storage which receives water as precipitation and generates water by overflow and drainage. This water is routed across the catchment using isochrone pathways. These are derived from a digital terrain model assuming two fixed velocities of travel for land and river pathways which are regarded as model parameters to be optimised. Translation of water between isochrones is achieved using a discrete kinematic routing procedure, parameterised through a single dimensionless wave speed parameter, which advects the water and incorporates diffusion effects through the discrete space-time formulation. The basic model routes overflow and drainage separately through a parallel system of kinematic routing reaches, characterised by different wave speeds but using the same isochrone-based space discretisation; these represent fast and slow pathways to the basin outlet, respectively. A variant allows the slow pathway to have separate isochrones calculated using Darcy velocities controlled by the hydraulic gradient as estimated by the local gradient of the terrain. Runoff production within a grid square is controlled by its absorption capacity which is parameterised through a simple linkage function to the mean gradient in the square, as calculated from digital terrain data. This allows absorption capacity to be specified differently for every grid square in the catchment through the use of only two regional parameters and a DTM measurement of mean gradient for each square. An extension of this basic idea to consider the distribution of gradient within the square leads analytically to a Pareto

  9. Monitoring and near-real time forecasting of landslide surface displacements using continuous GNSS observations and a combined statistical-mechanical model: the service of the French Landslide Observatory - OMIV. (Invited)

    NASA Astrophysics Data System (ADS)

    Malet, J.; Deprez, A.; Bernardie, S.; Desramaut, N.; Ulrich, P.; Masson, F.; Grandjean, G.

    2013-12-01

    The objective of this work is to present a methodology developed by the French Landslide Observatory (e.g. OMIV), a collaborative structure aiming at collecting the same type of kinematic, hydrologic and seismic observations on landslides and at disseminating the data to the scientific community (e.g. http://omiv.unistra.fr), for the near-real time characterization of surface displacements using permanent GNSS stations. In France, several GNSS receivers have been installed on active landslides (e.g. La Clapière rockslide, Avignonet and Villerville rotational slides, Super-Sauze and La Valette mudslides) since a few years. These landslides show very different displacement rates (ranging from a few centimetres to several meters per year) and different kinematic regimes (e.g. continuous displacement of nearly constant rate or succession of periods of acceleration/deceleration). For the monitoring of landslides where the required degree of accuracy is millimetric, GNSS has been mainly used through campaigns measurements as a complement to conventional geodetic methods. Permanent monitoring is still not usually performed operationally, mostly because of the cost of the stations compared to conventional deformation monitoring techniques. In addition, if GNSS measurements can reach a millimetre-level of accuracy for long observation sessions (typically 12h), their accuracy decreases with the duration of the observation sessions, and attaining a one millimetre-level accuracy requires sophisticated a posteriori data processing techniques. To acquire a very precise 3D coordinate that can be used as a reference for other monitoring techniques and to develop early-warning systems, permanent GNSS with automatic data transmission is certainly the only solution that can be used on very constraining sites with either difficult accessibility, absence of long-term stability of the slopes around the landslide, or absence of direct visibility. The objectives of this work are (1) to

  10. Real-time flutter identification

    NASA Technical Reports Server (NTRS)

    Roy, R.; Walker, R.

    1985-01-01

    The techniques and a FORTRAN 77 MOdal Parameter IDentification (MOPID) computer program developed for identification of the frequencies and damping ratios of multiple flutter modes in real time are documented. Physically meaningful model parameterization was combined with state of the art recursive identification techniques and applied to the problem of real time flutter mode monitoring. The performance of the algorithm in terms of convergence speed and parameter estimation error is demonstrated for several simulated data cases, and the results of actual flight data analysis from two different vehicles are presented. It is indicated that the algorithm is capable of real time monitoring of aircraft flutter characteristics with a high degree of reliability.

  11. Willingness-to-pay for a probabilistic flood forecast: a risk-based decision-making game

    NASA Astrophysics Data System (ADS)

    Arnal, Louise; Ramos, Maria-Helena; Coughlan, Erin; Cloke, Hannah L.; Stephens, Elisabeth; Wetterhall, Fredrik; van Andel, Schalk-Jan; Pappenberger, Florian

    2016-04-01

    Forecast uncertainty is a twofold issue, as it constitutes both an added value and a challenge for the forecaster and the user of the forecasts. Many authors have demonstrated the added (economic) value of probabilistic forecasts over deterministic forecasts for a diversity of activities in the water sector (e.g. flood protection, hydroelectric power management and navigation). However, the richness of the information is also a source of challenges for operational uses, due partially to the difficulty to transform the probability of occurrence of an event into a binary decision. The setup and the results of a risk-based decision-making experiment, designed as a game on the topic of flood protection mitigation, called ``How much are you prepared to pay for a forecast?'', will be presented. The game was played at several workshops in 2015, including during this session at the EGU conference in 2015, and a total of 129 worksheets were collected and analysed. The aim of this experiment was to contribute to the understanding of the role of probabilistic forecasts in decision-making processes and their perceived value by decision-makers. Based on the participants' willingness-to-pay for a forecast, the results of the game showed that the value (or the usefulness) of a forecast depends on several factors, including the way users perceive the quality of their forecasts and link it to the perception of their own performances as decision-makers. Balancing avoided costs and the cost (or the benefit) of having forecasts available for making decisions is not straightforward, even in a simplified game situation, and is a topic that deserves more attention from the hydrological forecasting community in the future.

  12. Time Series Models Adoptable for Forecasting Nile Floods and Ethiopian Rainfalls.

    NASA Astrophysics Data System (ADS)

    El-Fandy, M. G.; Taiel, S. M. M.; Ashour, Z. H.

    1994-01-01

    Long-term rainfall forecasting is used in making economic and agricultural decisions in many countries. It may also be a tool in minimizing the devastation resulting from recurrent droughts. To be able to forecast the total annual rainfall or the levels of seasonal floods, a class of models has first been chosen. The model parameters have then been estimated with an appropriate parameter estimation algorithm. Finally, diagnostic tests have been performed to verify the adequacy of the model. These are the general principles of system identification, which is the most crucial part of the forecasting procedure. In this paper several sets of data have been studied using different statistical procedures. The examined data include a historical 835-year record representing the levels of the seasonal Nile floods in Cairo, Egypt, during the period A.D. 622-1457. These readings were originally carried out by the Arabsto a great degree of accuracy in order to be used in estimating yearly taxes or Zacat (islamic duties). The observations also comprise recent total annual rainfall data over Addis Ababa (Ethiopia) (1907-1984), the total annual discharges of Ethiopian rivers (including the river Sobat discharges at Hillet Doleib, Blue Nile discharge at Roseris, river Dinder, river Rahar, and river Atbara), equatorial lake plateau supply as contributed at Aswan during the period 1912-1982, and the total annual discharges at Aswan during the period 1871-1982. Periodograms have been used to uncover possible peridodicities. Trends of rainfall and discharges of some rivers of east and central Africa have been also estimated.Using the first half of the available record, two autoregressive integrated moving average (ARIMA) time series models have been identified, one for the levels of the seasonal Nile floods in Cairo, the second to model the annual rainfall over Ethiopia. The time series models have been applied in 1-year-ahead forecasting to the other hall of the available record and

  13. Real-time software receiver

    NASA Technical Reports Server (NTRS)

    Ledvina, Brent M. (Inventor); Psiaki, Mark L. (Inventor); Powell, Steven P. (Inventor); Kintner, Jr., Paul M. (Inventor)

    2007-01-01

    A real-time software receiver that executes on a general purpose processor. The software receiver includes data acquisition and correlator modules that perform, in place of hardware correlation, baseband mixing and PRN code correlation using bit-wise parallelism.

  14. Real-time software receiver

    NASA Technical Reports Server (NTRS)

    Ledvina, Brent M. (Inventor); Psiaki, Mark L. (Inventor); Powell, Steven P. (Inventor); Kintner, Jr., Paul M. (Inventor)

    2006-01-01

    A real-time software receiver that executes on a general purpose processor. The software receiver includes data acquisition and correlator modules that perform, in place of hardware correlation, baseband mixing and PRN code correlation using bit-wise parallelism.

  15. Real-time vision systems

    SciTech Connect

    Johnson, R.; Hernandez, J.E.; Lu, Shin-yee

    1994-11-15

    Many industrial and defence applications require an ability to make instantaneous decisions based on sensor input of a time varying process. Such systems are referred to as `real-time systems` because they process and act on data as it occurs in time. When a vision sensor is used in a real-time system, the processing demands can be quite substantial, with typical data rates of 10-20 million samples per second. A real-time Machine Vision Laboratory (MVL) was established in FY94 to extend our years of experience in developing computer vision algorithms to include the development and implementation of real-time vision systems. The laboratory is equipped with a variety of hardware components, including Datacube image acquisition and processing boards, a Sun workstation, and several different types of CCD cameras, including monochrome and color area cameras and analog and digital line-scan cameras. The equipment is reconfigurable for prototyping different applications. This facility has been used to support several programs at LLNL, including O Division`s Peacemaker and Deadeye Projects as well as the CRADA with the U.S. Textile Industry, CAFE (Computer Aided Fabric Inspection). To date, we have successfully demonstrated several real-time applications: bullet tracking, stereo tracking and ranging, and web inspection. This work has been documented in the ongoing development of a real-time software library.

  16. Petascale Diagnostic Assessment of the Global Portfolio Rainfall Space Missions' Ability to Support Flood Forecasting

    NASA Astrophysics Data System (ADS)

    Reed, P. M.; Chaney, N.; Herman, J. D.; Wood, E. F.; Ferringer, M. P.

    2015-12-01

    This research represents a multi-institutional collaboration between Cornell University, The Aerospace Corporation, and Princeton University that has completed a Petascale diagnostic assessment of the current 10 satellite missions providing rainfall observations. Our diagnostic assessment has required four core tasks: (1) formally linking high-resolution astrodynamics design and coordination of space assets with their global hydrological impacts within a Petascale "many-objective" global optimization framework, (2) developing a baseline diagnostic evaluation of a 1-degree resolution global implementation of the Variable Infiltration Capacity (VIC) model to establish the required satellite observation frequencies and coverage to maintain acceptable global flood forecasts, (3) evaluating the limitations and vulnerabilities of the full suite of current satellite precipitation missions including the recently approved Global Precipitation Measurement (GPM) mission, and (4) conceptualizing the next generation spaced-based platforms for water cycle observation. Our team exploited over 100 Million hours of computing access on the 700,000+ core Blue Waters machine to radically advance our ability to discover and visualize key system tradeoffs and sensitivities. This project represents to our knowledge the first attempt to develop a 10,000 member Monte Carlo global hydrologic simulation at one degree resolution that characterizes the uncertain effects of changing the available frequencies of satellite precipitation on drought and flood forecasts. The simulation—optimization components of the work have set a theoretical baseline for the best possible frequencies and coverages for global precipitation given unlimited investment, broad international coordination in reconfiguring existing assets, and new satellite constellation design objectives informed directly by key global hydrologic forecasting requirements. Our research poses a step towards realizing the integrated

  17. Quantifying Uncertainty in Distributed Flash Flood Forecasting for a Semiarid Region

    NASA Astrophysics Data System (ADS)

    Samadi, S.; Pourreza Bilondi, M.; Ghahraman, B.; Akhoond-Ali, A. M.

    2015-12-01

    Reliability of semiarid flood forecasting is affected by several factors, including rainfall forcing, the system input-state-output behavior, initial soil moisture conditions and model parameters and structure. This study employed Bayesian frameworks to enable the explicit description and assessment of parameter and predictive uncertainty for convective rainfall-runoff modeling of a semiarid watershed system in Iran. We examined the performance and uncertainty analysis of a mixed conceptual and physical based rainfall-runoff model (AFFDEF) linked with three Markov chain Monte Carlo (MCMC) samplers: the DiffeRential Evolution Adaptive Metropolis (DREAM), the Shuffled Complex Evolution Metropolis (SCEM-UA), and DREAM- ZS, to forecast four potential semiarid convective events with varying rainfall duration (<24 hrs) and amount (>20 mm). Calibration results demonstrated that model predictive uncertainty was heavily dominated by error and bias in the soil water storage capacity which reflect inadequate representation of the upper soil zone processes by hydrological model. Furthermore, parameters associated with infiltration and interception capacity along with contributing area threshold for digital river network were identified the key model parameters and more influential on the modeled flood hydrograph. In addition, parameter inference in the DREAM model showed a consistent behavior with the priori assumption by closely matching the inferred error distribution to the empirical distribution of the model residual, indicating that model parameters are well identified. DREAM result further revealed that the uncertainty associated with rainfall of lower magnitudes was higher than rainfall of higher magnitudes. Uncertainty quantification of semiarid convective events provided significant insights into the mathematical relationship and characteristics of short-term forecast error and may be applicable to other semiarid watershed systems with the similar rainfall

  18. Long lead-time flood forecasting using data-driven modeling approaches

    NASA Astrophysics Data System (ADS)

    Bhatia, N.; He, J.; Srivastav, R. K.

    2014-12-01

    In spite of numerous structure measures being taken for floods, accurate flood forecasting is essential to condense the damages in hazardous areas considerably. The need of producing more accurate flow forecasts motivates the researchers to develop advanced innovative methods. In this study, it is proposed to develop a hybrid neural network model to exploit the strengths of artificial neural networks (ANNs). The proposed model has two components: i.) Dual - ANN model developed using river flows; and ii.) Multiple Linear Regression (MLR) model trained on meteorological data (Rainfall and Snow on ground). Potential model inputs that best represent the process of river basin were selected in stepwise manner by identifying input-output relationship using a linear approach, Partial Correlation Input Selection (PCIS) combined with Akaike Information Criterion (AIC) technique. The presented hybrid model was compared with three conventional methods: i) Feed-forward artificial neural network (FF-ANN) using daily river flows; ii) FF-ANN applied on decomposed river flows (low flow, rising limb and falling limb of hydrograph); and iii) Recursive method for daily river flows with lead-time of 7 days. The applicability of the presented model is illustrated through daily river flow data of Bow River, Canada. Data from 1912 to 1976 were used to train the models while data from 1977 to 2006 were used to validate the models. The results of the study indicate that the proposed model is robust enough to capture the non-linear nature of hydrograph and proves to be highly promising to forecast peak flows (extreme values) well in advance (higher lead time).

  19. A flood routing Muskingum type simulation and forecasting model based on level data alone

    NASA Astrophysics Data System (ADS)

    Franchini, Marco; Lamberti, Paolo

    1994-07-01

    While the use of remote hydrometers for measuring the level in water courses is both economical and widespread, the same cannot be said for cross section and channel profile measurements and, even less, for rating curves at the measuring cross sections, all of which are more often than not incomplete, out of date, and unreliable. The mass of data involved in level measurements alone induces a degree of perplexity in those who try to use them, for example, for flood event simulations or the construction of forecasting models which are not purely statistical. This paper proposes a method which uses recorded level data alone to construct a simulation model and a forecasting model, both of them characterized by an extremely simple structure that can be used on any pocket calculator. These models, referring to a river reach bounded by two measuring sections, furnish the downstream levels, where the upstream levels are known, and the downstream level at time t + Δt*, where the upstream and downstream levels are known at time t, respectively. The numerical applications performed show that while the simulation model is somewhat penalized by the simplifications adopted, giving not consistently satisfactory results on validation, the forecasting model generated good results in all the cases examined and seems reliable.

  20. Multiscale error analysis, correction, and predictive uncertainty estimation in a flood forecasting system

    NASA Astrophysics Data System (ADS)

    Bogner, K.; Pappenberger, F.

    2011-07-01

    River discharge predictions often show errors that degrade the quality of forecasts. Three different methods of error correction are compared, namely, an autoregressive model with and without exogenous input (ARX and AR, respectively), and a method based on wavelet transforms. For the wavelet method, a Vector-Autoregressive model with exogenous input (VARX) is simultaneously fitted for the different levels of wavelet decomposition; after predicting the next time steps for each scale, a reconstruction formula is applied to transform the predictions in the wavelet domain back to the original time domain. The error correction methods are combined with the Hydrological Uncertainty Processor (HUP) in order to estimate the predictive conditional distribution. For three stations along the Danube catchment, and using output from the European Flood Alert System (EFAS), we demonstrate that the method based on wavelets outperforms simpler methods and uncorrected predictions with respect to mean absolute error, Nash-Sutcliffe efficiency coefficient (and its decomposed performance criteria), informativeness score, and in particular forecast reliability. The wavelet approach efficiently accounts for forecast errors with scale properties of unknown source and statistical structure.

  1. Flood-inundation maps for the Schoharie Creek at Prattsville, New York, 2014

    USGS Publications Warehouse

    Nystrom, Elizabeth A.

    2016-02-18

    These flood-inundation maps, along with near-real-time stage data from USGS streamgages and forecasted stage data from the National Weather Service, can provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures as well as for postflood recovery efforts.

  2. Near Real-time Derived Products from MODIS

    NASA Technical Reports Server (NTRS)

    Suggs, Ron; Jedlovec, Gary; Haines, Stephanie

    2004-01-01

    As part of the Short-term Prediction Research Transition (SPoRT) program at NASA/MSFC, near real-time total precipitable water and land/sea surface temperature products from MODIS, on NASA's Terra and Aqua satellites, are being derived from a subset of MODIS channels with spectral characteristics similar to those planned for the GOES-R ABI. Under the SPoRT program these products are made available to several NWS Forecast Offices to assist in the preparation of short-term forecasts. This transition activity, from research to operations, serves to prepare forecasters for the next generation of satellite observing capabilities through real-time, hands-on applications to their forecast problems. The derived products are produced from a physical retrieval algorithm which can be applied to polar or geostationary measurements. The algorithm is a perturbation solution of the radiation transfer equation for a nonscattering atmosphere requiring first-guess temperature and moisture profiles. For this application first-guess information is obtained from the latest model forecasts. The utility of this retrieval approach is that it provides a near real-time high resolution update of a model's forecasted parameter allowing forecasters to validate and monitor the performance of the model's forecast. The poster will provide a description of the retrieval methodology and examples of the derived products. Case studies will also be presented comparing these products with those obtained from the Earth Observing System (EOS) MODIS science team algorithms.

  3. Turning movement estimation in real time

    SciTech Connect

    Martin, P.T.

    1997-08-01

    Fast processors offer exciting opportunities for real-time traffic monitoring. Conventional transportation planning models that assume stable and predictable travel patterns do not lend themselves to on-line traffic forecasting. This paper describes how a new traffic flow inference model has the potential to determine comprehensive flow information in real time. Its philosophical basis is borrowed from the field of operational research, where it has been used for optimizing water and electricity flows. This paper shows how road traffic turning movement flows can be estimated from link detected flows at small recurrent intervals, in real time. The paper details the formulation of the problem, outlines the structure of the data set that provides the detector data for the model input and observed turning flows for the model evaluation. The theoretical principles that define the model are described briefly. Turning movement flow estimates, at 5-min intervals, from two independent surveys are presented and analyzed. The results show an overall mean coefficient of determination (r{sup 2}) of 79--82% between observed and modeled turning movement flows.

  4. Usefulness of satellite water vapour imagery in forecasting strong convection: A flash-flood case study

    NASA Astrophysics Data System (ADS)

    Georgiev, Christo G.; Kozinarova, Gergana

    Using a case study of a severe convective event as an example, a framework for interpreting 6.2 µm channel satellite imagery that enables to indicate upper-level conditioning of the convective environment is presented and discussed. In order to illustrate the approach, all convective cells during the summer of 2007 that produced precipitations over Bulgaria are considered. They are classified regarding the observed moisture pattern in mid-upper levels as well as the low-level conditions of air humidity and convergence of the flow. Water vapour (WV) images are used to study the evolution of the upper-level moist and dry structures. The proposed interpretation is that the role of the upper-level dry boundaries identified in the WV imagery as favoured areas for the initiation of deep moist convection cannot be understood (and hence cannot be forecasted accurately) by considering them in isolation from the dynamic rate at which they are maintained. The paper examines the 23 June 2006 flash flood in Sofia city as a case, in which the operational forecast of the National Institute of Meteorology and Hydrology of Bulgaria based on the mesoscale NWP model ALADIN underestimated the severity of the convective process. A comparison between the satellite water vapour imagery and the corresponding geopotential field of the dynamical tropopause, expressed in terms of potential vorticity (PV), shows an error in the performance of the ARPEGE operational numerical model. There is an obvious mismatch between the PV anomaly structure and the dry zone of the imagery. The forecast field shows underestimation of the tropopause height gradient and displacement of the PV anomaly to the southwest of the real position seen in the satellite image. It is concluded that the observed poor forecast is a result of the ARPEGE failure to treat correctly the interaction between the PV anomaly and the low-level warm anomaly.

  5. Forecasting of Storm-Surge Floods Using ADCIRC and Optimized DEMs

    NASA Technical Reports Server (NTRS)

    Valenti, Elizabeth; Fitzpatrick, Patrick

    2006-01-01

    Increasing the accuracy of storm-surge flood forecasts is essential for improving preparedness for hurricanes and other severe storms and, in particular, for optimizing evacuation scenarios. An interactive database, developed by WorldWinds, Inc., contains atlases of storm-surge flood levels for the Louisiana/Mississippi gulf coast region. These atlases were developed to improve forecasting of flooding along the coastline and estuaries and in adjacent inland areas. Storm-surge heights depend on a complex interaction of several factors, including: storm size, central minimum pressure, forward speed of motion, bottom topography near the point of landfall, astronomical tides, and, most importantly, maximum wind speed. The information in the atlases was generated in over 100 computational simulations, partly by use of a parallel-processing version of the ADvanced CIRCulation (ADCIRC) model. ADCIRC is a nonlinear computational model of hydrodynamics, developed by the U.S. Army Corps of Engineers and the US Navy, as a family of two- and three-dimensional finite-element-based codes. It affords a capability for simulating tidal circulation and storm-surge propagation over very large computational domains, while simultaneously providing high-resolution output in areas of complex shoreline and bathymetry. The ADCIRC finite-element grid for this project covered the Gulf of Mexico and contiguous basins, extending into the deep Atlantic Ocean with progressively higher resolution approaching the study area. The advantage of using ADCIRC over other storm-surge models, such as SLOSH, is that input conditions can include all or part of wind stress, tides, wave stress, and river discharge, which serve to make the model output more accurate. To keep the computational load manageable, this work was conducted using only the wind stress, calculated by using historical data from Hurricane Camille, as the input condition for the model. Hurricane storm-surge simulations were performed on an

  6. Real-Time Moire Holography

    NASA Astrophysics Data System (ADS)

    Soares, O. D. D.; Lage, A. I. V. S.

    1986-08-01

    Interferometric techniques including hologrametry, both classical and electronic, present high sensitivity making difficult its practical use in real-time. The introduction of the differencial concept as moire evaluation techniques permits to use with advantage an arbitrary reference pattern within the correlation range. The carrier spatial spectrum can be directly the interferogram fringe pattern instead of the original interference pattern of wavelength dimensional scale. A moire techniques is in itself an optical processing method reducing evaluation time which is advantageous when real-time response is desired from hybrid metrological systems. The moire evaluation is performed via a dynamical digital memory that executes arithmetic operations on two frames temporally in sequence, at TV rate. These characteristics of the moire evaluation techniques can be implemented on a real-time holographic (or speckle based) hybrid system with great practical advantage for dynamical studies.

  7. Probabilistic flood forecasting tool for Andalusia (Spain). Application to September 2012 disaster event in Vera Playa.

    NASA Astrophysics Data System (ADS)

    García, Darío; Baquerizo, Asunción; Ortega, Miguel; Herrero, Javier; Ángel Losada, Miguel

    2013-04-01

    Torrential and heavy rains are frequent in Andalusia (Southern Spain) due to the characteristic Mediterranean climate (semi-arid areas). This, in combination with a massive occupation of floodable (river sides) and coastal areas, produces severe problems of management and damage to the population and social and economical activities when extreme events occur. Some of the most important problems are being produced during last years in Almería (Southeastern Andalusia). Between 27 and 28 September 2012 rainstorms characterized by 240mm in 24h (exceeding precipitation for a return period of 500 years) occurred. Antas River and Jático creek, that are normally dry, became raging torrents. The massive flooding of occupied areas resulted in eleven deaths and two missing in Andalucía, with a total estimated cost of all claims for compensation on the order of 197 million euros. This study presents a probabilistic flood forecasting tool including the effect of river and marine forcings. It is based on a distributed, physically-based hydrological model (WiMMed). For Almería the model has been calibrated with the largest event recorded in Cantoria gauging station (data since 1965) on 19 October 1973. It was then validated with the second strongest event (26 October 1977). Among the different results of the model, it can provide probability floods scenarios in Andalusia with up 10 days weather forecasts. The tool has been applied to Vera, a 15.000 inhabitants town located in the east of Almería along the Antas River at an altitude of 95 meters. Its main economic resource is the "beach and sun" based-tourism, which has experienced an enormous growth during last decades. Its coastal stretch has been completely built in these years, occupying floodable areas and constricting the channel and rivers mouths. Simulations of the model in this area for the 1973 event and published in March 2011 on the internet event already announced that the floods of September 2012 may occur.

  8. Ensemble hydro-meteorological forecasting for early warning of floods and scheduling of hydropower production

    NASA Astrophysics Data System (ADS)

    Solvang Johansen, Stian; Steinsland, Ingelin; Engeland, Kolbjørn

    2016-04-01

    Running hydrological models with precipitation and temperature ensemble forcing to generate ensembles of streamflow is a commonly used method in operational hydrology. Evaluations of streamflow ensembles have however revealed that the ensembles are biased with respect to both mean and spread. Thus postprocessing of the ensembles is needed in order to improve the forecast skill. The aims of this study is (i) to to evaluate how postprocessing of streamflow ensembles works for Norwegian catchments within different hydrological regimes and to (ii) demonstrate how post processed streamflow ensembles are used operationally by a hydropower producer. These aims were achieved by postprocessing forecasted daily discharge for 10 lead-times for 20 catchments in Norway by using EPS forcing from ECMWF applied the semi-distributed HBV-model dividing each catchment into 10 elevation zones. Statkraft Energi uses forecasts from these catchments for scheduling hydropower production. The catchments represent different hydrological regimes. Some catchments have stable winter condition with winter low flow and a major flood event during spring or early summer caused by snow melting. Others has a more mixed snow-rain regime, often with a secondary flood season during autumn, and in the coastal areas, the stream flow is dominated by rain, and the main flood season is autumn and winter. For post processing, a Bayesian model averaging model (BMA) close to (Kleiber et al 2011) is used. The model creates a predictive PDF that is a weighted average of PDFs centered on the individual bias corrected forecasts. The weights are here equal since all ensemble members come from the same model, and thus have the same probability. For modeling streamflow, the gamma distribution is chosen as a predictive PDF. The bias correction parameters and the PDF parameters are estimated using a 30-day sliding window training period. Preliminary results show that the improvement varies between catchments depending

  9. Real time hydro-metereological hazards monitoring system for the Ravenna municipality

    NASA Astrophysics Data System (ADS)

    Bertoni, W.; Cattarossi, A.; Gonella, M.

    2003-04-01

    The Ravenna municipality (Italy, Emilia Romagna region), through a cooperative agreement with ENI S.p.A’s., AGIP division, is carrying out a research study for the development of a real time monitoring system of hydro-meteorological conditions. The system aims to support the city Crisis Response Unit to provide more efficient support all over the municipal territory that is the largest in Italy with more than 700 km2. The support unit, a GIS computer based application, directly links to a broad range of sources, gathering real time information from a Local Area Model (meteorological data), a Wave Model (sea hydrodynamic circulation), monitoring stations, located partially on the Adriatic sea (AGIP offshore platform, SIMN) and partially over the Ravenna inland (SPDS, SIN). In the first phase, now completed and undergoing testing, this vast and diversified collection of data feeds a number of statistical models with up to 72 hours of forecast capabilities. The GIS application displays actual and forecast sea conditions offshore of Ravenna littorals in addition to actual and forecast flood conditions along the Ravenna Province inland. Model generated data are used for the forecast, which is then calibrated using the measured data. When the predefined warning limits are exceeded, end users are alerted via prerecorded phone messages, SMS, or visually through the direct or remote interaction with the GIS system (remotely accessible via portable computers). In the second stage, the statistical approach will be substituted by a more deterministic approach. A coupled hydrologic-hydraulic model will be used to forecast water stages along rivers and runoff volume along major watersheds. Moreover, already functioning capabilities allows direct control of remote monitoring points (stream and rain gages, etc.) The entire Real Time Monitoring System was developed on a GIS platform. The GEOdatabase, a relational database based on MSDE technology, is the core of the application

  10. Real Time Sonic Boom Display

    NASA Technical Reports Server (NTRS)

    Haering, Ed

    2014-01-01

    This presentation will provide general information about sonic boom mitigation technology to the public in order to supply information to potential partners and licensees. The technology is a combination of flight data, atmospheric data and terrain information implemented into a control room real time display for flight planning. This research is currently being performed and as such, any results and conclusions are ongoing.

  11. The Hurricane-Flood-Landslide Continuum: Forecasting Hurricane Effects at Landfall

    NASA Technical Reports Server (NTRS)

    Negri, A.; Golden, J. H.; Updike, R.

    2004-01-01

    Hurricanes, typhoons, and cyclones strike Central American, Caribbean, Southeast Asian and Pacific Island nations even more frequently than the U.S. The global losses of life and property from the floods, landslides and debris flows caused by cyclonic storms are staggering. One of the keys to reducing these losses, both in the U.S. and internationally, is to have better forecasts of what is about to happen from several hours to days before the event. Particularly in developing nations where science, technology and communication are limited, advance-warning systems can have great impact. In developing countries, warnings of even a few hours or days can mitigate or reduce catastrophic losses of life. With the foregoing needs in mind, we propose an initial project of three years total duration that will aim to develop and transfer a warning system for a prototype region in the Central Caribbean, specifically the islands of Puerto Rico and Hispanola. The Hurricane-Flood-Landslide Continuum will include satellite observations to track and nowcast dangerous levels of precipitation, atmospheric and hydrological models to predict near-future runoff, and streamflow changes in affected regions, and landslide models to warn when and where landslides and debris flows are imminent. Since surface communications are likely to be interrupted during these crises, the project also includes the capability to communicate disaster information via satellite to vital government officials in Puerto Rico, Haiti, and Dominican Republic.

  12. Uncertainties due to soil data in Flood Risk Forecasts with the Water Balance Model LARSIM

    NASA Astrophysics Data System (ADS)

    Mitterer, Johannes

    2016-04-01

    Reliable flood forecasts with quantitative statements about contained uncertainties are essential for far reaching decisions in disaster management. In this paper uncertainties resulting from soil data are analysed for the in the German-speaking world widely used water balance model LARSIM and quantified as far as possible. At the beginning a structural and statistical analysis about the wittingly simple designed soil module is performed. It consists of a storage volume with four separate runoff components only defined by the storage size. Additionally, the model structure is examined with regard to effects of uncertain soil data using a soil map from the Bavarian State Institute for Forestry which already contains estimated minimum and maximum values for important soil parameters. For further analysis, two German catchments in Upper Franconia located at the White Main with a size of 250 km² each, covering a huge variety of soil types are used as case examples. Skeleton is identified as an important source of uncertainty in soil data comparing the quantifiable information of available soil maps and using field and laboratory analysis. Furthermore, surface runoff and fast interflow fluxes show up to be sensitive for peaks of flood events, whereas slow interflow and base flow fluxes have smaller and more long term effects on discharges and the water balance. A reduction of the soil storage basically leads to a more intensified reaction of discharges than an enlargement. The calculation of two extreme scenarios within the statistical analysis result in simulated gage measurements varying from -42 % till +218 % compared to the scenario with the main value of the map. A percental variation of the soil storage shows a doubling of the flood discharges, if the storage size is halved and a reduction up to 20% using a doubled one. Finally, a Monte Carlo Simulation is performed using the statistical data of the soil map combined with a normal distribution, whereby the

  13. Evaluating the one-way coupling of WRF-Hydro for flood forecasting

    NASA Astrophysics Data System (ADS)

    Yucel, Ismail; Onen, Alper; Yilmaz, Koray; Gochis, David

    2016-04-01

    A fully-distributed, multi-physics, multi-scale hydrologic and hydraulic modeling system, WRF-Hydro, is used to assess the potential for skillful flood forecasting based on precipitation inputs derived from the Weather Research and Forecasting (WRF) model and the EUMETSAT Multi-sensor Precipitation Estimates (MPEs). Similar to past studies it was found that WRF model precipitation forecast errors related to model initial conditions are reduced when the three dimensional atmospheric data assimilation (3DVAR) scheme in the WRF model simulations is used. A comparative evaluation of the impact of MPE versus WRF precipitation estimates, both with and without data assimilation, in driving WRF-Hydro simulated streamflow is then made. The ten rainfall-runoff events that occurred in the Black Sea Region were used for testing and evaluation. With the availability of streamflow data across rainfall-runoff events, the cal- ibration is only performed on the Bartin sub-basin using two events and the calibrated parameters are then transferred to other neighboring three ungauged sub-basins in the study area. The rest of the events from all sub-basins are then used to evaluate the performance of the WRF-Hydro system with the cali- brated parameters. Following model calibration, the WRF-Hydro system was capable of skillfully repro- ducing observed flood hydrographs in terms of the volume of the runoff produced and the overall shape of the hydrograph. Streamflow simulation skill was significantly improved for those WRF model simula- tions where storm precipitation was accurately depicted with respect to timing, location and amount. Accurate streamflow simulations were more evident in WRF model simulations where the 3DVAR scheme was used compared to when it was not used. Because of substantial dry bias feature of MPE, as compared with surface rain gauges, streamflow derived using this precipitation product is in general very poor. Overall, root mean squared errors for runoff were

  14. A Research on Development of The Multi-mode Flood Forecasting System Version Management

    NASA Astrophysics Data System (ADS)

    Shen, J.-C.; Chang, C. H.; Lien, H. C.; Wu, S. J.; Horng, M. J.

    2009-04-01

    paper proposed the feasible avenues and solutions to smoothly integrate different configurations from different teams. In the current system has been completed by 20 of Taiwan's main rivers in the building of the basic structure of the flood forecasting. And regular updating of the relevant parameters, using the new survey results, in order to have a better flood forecasting results.

  15. Implementation of Real-Time Bias-Adjusted O3 and PM2.5 Air Quality Forecasts and their Performance Evaluations during 2008 over the Continental United States

    EPA Science Inventory

    The National Oceanic and Atmospheric Administration (NOAA), in partnership with the United States Environmental Protection Agency (EPA), is operationally implementing an Air Quality Forecast (AQF) system. This program, which couples NOAA's North American Mesoscale (NAM) weather p...

  16. Scientific developments within the Global Flood Partnership

    NASA Astrophysics Data System (ADS)

    de Groeve, Tom; Alfieri, Lorenzo; Thielen, Jutta

    2015-04-01

    More than 90 scientists, end users, and decision makers in the field of flood forecasting, remote sensing, hazard and risk assessment and emergency management collaborate in the Global Flood Partnership (GFP). The Partnership, launched in 2014, aims at the development of flood observational and modelling infrastructure, leveraging on existing initiatives for better predicting and managing flood disaster impacts and flood risk globally. Scientists collaborate in the GFP in different pillars, respectively focused on (1) development of tools and systems for global flood monitoring (Flood Toolbox), (2) applying the tools for publishing near real-time impact-based flood awareness information (Flood Observatory), and (3) collecting flood maps and impact information in a distributed database (Flood Record). The talk will focus on concrete collaboration results in 2014 and 2015, showing the added value of collaborating under a partnership. These include an overview of 10 services, 5 tools (algorithms or software) and 4 datasets related to global flood forecasting and observation. Through the various results (on interopera