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Streamflow Forecasts (streamflow + forecast)

Distribution by Scientific Domains
Earth and Environmental Science36%

Selected Abstracts

Evaluation of Short-to-Medium Range Streamflow Forecasts Obtained Using an Enhanced Version of SRM,

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 3 2010
Brian J. Harshburger
Harshburger, Brian J., Karen S. Humes, Von P. Walden, Brandon C. Moore, Troy R. Blandford, and Albert Rango, 2010. Evaluation of Short-to-Medium Range Streamflow Forecasts Obtained Using an Enhanced Version of SRM. Journal of the American Water Resources Association (JAWRA) 46(3):603-617. DOI: 10.1111/j.1752-1688.2010.00437.x Abstract:, As demand for water continues to escalate in the western United States, so does the need for accurate streamflow forecasts. Here, we describe a methodology for generating short-to-medium range (1 to 15 days) streamflow forecasts using an enhanced version of the Snowmelt Runoff Model (SRM), snow-covered area data derived from MODIS products, data from Snow Telemetry stations, and meteorological forecasts. The methodology was tested on three mid-elevation, snowmelt-dominated basins ranging in size from 1,600 to 3,500 km2. To optimize the model performance and aid in its operational implementation, two enhancements have been made to SRM: (1) the use of an antecedent temperature index method to track snowpack cold content, and (2) the use of both maximum and minimum critical temperatures to partition precipitation into rain, snow, or a mixture of rain and snow. The comparison of retrospective model simulations with observed streamflow shows that the enhancements significantly improve the model performance. Streamflow forecasts generated using the enhanced version of the model compare well with the observed streamflow for the earlier leadtimes; forecast performance diminishes with leadtime due to errors in the meteorological forecasts. The three basins modeled in this research are typical of many mid-elevation basins throughout the American West, thus there is potential for this methodology to be applied successfully to other mountainous basins. [source]

A Streamflow Forecasting Framework using Multiple Climate and Hydrological Models,

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 4 2009
Paul J. Block
Abstract:, Water resources planning and management efficacy is subject to capturing inherent uncertainties stemming from climatic and hydrological inputs and models. Streamflow forecasts, critical in reservoir operation and water allocation decision making, fundamentally contain uncertainties arising from assumed initial conditions, model structure, and modeled processes. Accounting for these propagating uncertainties remains a formidable challenge. Recent enhancements in climate forecasting skill and hydrological modeling serve as an impetus for further pursuing models and model combinations capable of delivering improved streamflow forecasts. However, little consideration has been given to methodologies that include coupling both multiple climate and multiple hydrological models, increasing the pool of streamflow forecast ensemble members and accounting for cumulative sources of uncertainty. The framework presented here proposes integration and offline coupling of global climate models (GCMs), multiple regional climate models, and numerous water balance models to improve streamflow forecasting through generation of ensemble forecasts. For demonstration purposes, the framework is imposed on the Jaguaribe basin in northeastern Brazil for a hindcast of 1974-1996 monthly streamflow. The ECHAM 4.5 and the NCEP/MRF9 GCMs and regional models, including dynamical and statistical models, are integrated with the ABCD and Soil Moisture Accounting Procedure water balance models. Precipitation hindcasts from the GCMs are downscaled via the regional models and fed into the water balance models, producing streamflow hindcasts. Multi-model ensemble combination techniques include pooling, linear regression weighting, and a kernel density estimator to evaluate streamflow hindcasts; the latter technique exhibits superior skill compared with any single coupled model ensemble hindcast. [source]

Hydro-meteorological variability in the greater Ganges,Brahmaputra,Meghna basins

INTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 12 2004
MD. Rashed Chowdhury
Abstract The flows of the Ganges, Brahmaputra and Meghna (GBM) are highly seasonal, and heavily influenced by monsoon rainfall. As a result, these rivers swell to their banks and often overflow during the monsoon months. This is most pronounced in the downstream regions, particularly in Bangladesh, which is the lowest riparian country. The objective of this paper is to study this hydro-meteorological variability in the greater GBM regions, including the headwater regions in India and their role in streamflows in Bangladesh, and explore the large-scale oceanic factors affecting this hydro-meteorological variability. Global precipitation data, Bangladesh rainfall and streamflow records have been analysed and related to large-scale climate patterns, including upstream rainfall, regional atmospheric circulation and patterns of sea-surface temperature. The findings have quantified how the streamflows of these rivers in Bangladesh are highly correlated with the rainfall in the upper catchments with typically a lag of about 1 month. Therefore, streamflows in Bangladesh could be reasonably estimated for 1 to 3 months in advance (especially for the Ganges and Brahmaputra rivers) by employing simple correlation, if rainfall data from countries further up are available on a real-time and continuous basis. In the absence of rainfall data, streamflow forecasts are still possible from unusually warm or cold sea-surface temperatures in the tropics. The study concludes that hydro-meteorological information flow between Bangladesh and other neighbouring countries is essential for developing a knowledge base for evaluating the potential implications of seasonal streamflow forecast in the GBM basins in Bangladesh. Copyright © 2004 Royal Meteorological Society [source]

Evaluation of Short-to-Medium Range Streamflow Forecasts Obtained Using an Enhanced Version of SRM,

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 3 2010
Brian J. Harshburger
Harshburger, Brian J., Karen S. Humes, Von P. Walden, Brandon C. Moore, Troy R. Blandford, and Albert Rango, 2010. Evaluation of Short-to-Medium Range Streamflow Forecasts Obtained Using an Enhanced Version of SRM. Journal of the American Water Resources Association (JAWRA) 46(3):603-617. DOI: 10.1111/j.1752-1688.2010.00437.x Abstract:, As demand for water continues to escalate in the western United States, so does the need for accurate streamflow forecasts. Here, we describe a methodology for generating short-to-medium range (1 to 15 days) streamflow forecasts using an enhanced version of the Snowmelt Runoff Model (SRM), snow-covered area data derived from MODIS products, data from Snow Telemetry stations, and meteorological forecasts. The methodology was tested on three mid-elevation, snowmelt-dominated basins ranging in size from 1,600 to 3,500 km2. To optimize the model performance and aid in its operational implementation, two enhancements have been made to SRM: (1) the use of an antecedent temperature index method to track snowpack cold content, and (2) the use of both maximum and minimum critical temperatures to partition precipitation into rain, snow, or a mixture of rain and snow. The comparison of retrospective model simulations with observed streamflow shows that the enhancements significantly improve the model performance. Streamflow forecasts generated using the enhanced version of the model compare well with the observed streamflow for the earlier leadtimes; forecast performance diminishes with leadtime due to errors in the meteorological forecasts. The three basins modeled in this research are typical of many mid-elevation basins throughout the American West, thus there is potential for this methodology to be applied successfully to other mountainous basins. [source]

A Streamflow Forecasting Framework using Multiple Climate and Hydrological Models,

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 4 2009
Paul J. Block
Abstract:, Water resources planning and management efficacy is subject to capturing inherent uncertainties stemming from climatic and hydrological inputs and models. Streamflow forecasts, critical in reservoir operation and water allocation decision making, fundamentally contain uncertainties arising from assumed initial conditions, model structure, and modeled processes. Accounting for these propagating uncertainties remains a formidable challenge. Recent enhancements in climate forecasting skill and hydrological modeling serve as an impetus for further pursuing models and model combinations capable of delivering improved streamflow forecasts. However, little consideration has been given to methodologies that include coupling both multiple climate and multiple hydrological models, increasing the pool of streamflow forecast ensemble members and accounting for cumulative sources of uncertainty. The framework presented here proposes integration and offline coupling of global climate models (GCMs), multiple regional climate models, and numerous water balance models to improve streamflow forecasting through generation of ensemble forecasts. For demonstration purposes, the framework is imposed on the Jaguaribe basin in northeastern Brazil for a hindcast of 1974-1996 monthly streamflow. The ECHAM 4.5 and the NCEP/MRF9 GCMs and regional models, including dynamical and statistical models, are integrated with the ABCD and Soil Moisture Accounting Procedure water balance models. Precipitation hindcasts from the GCMs are downscaled via the regional models and fed into the water balance models, producing streamflow hindcasts. Multi-model ensemble combination techniques include pooling, linear regression weighting, and a kernel density estimator to evaluate streamflow hindcasts; the latter technique exhibits superior skill compared with any single coupled model ensemble hindcast. [source]

Comparing the scores of hydrological ensemble forecasts issued by two different hydrological models

ATMOSPHERIC SCIENCE LETTERS, Issue 2 2010
A. Randrianasolo
Abstract A comparative analysis is conducted to assess the quality of streamflow forecasts issued by two different modeling conceptualizations of catchment response, both driven by the same weather ensemble prediction system (PEARP Météo-France). The two hydrological modeling approaches are the physically based and distributed hydrometeorological model SIM (Météo-France) and the lumped soil-moisture-accounting type rainfall-runoff model GRP (Cemagref). Discharges are simulated at 211 catchments in France over 17 months. Skill scores are computed for the first 2 days of forecast range. The results suggest good performance of both hydrological models and illustrate the benefit of streamflow data assimilation for ensemble short-term forecasting. Copyright © 2010 Royal Meteorological Society [source]

Hydrological ensemble prediction and verification for the Meuse and Scheldt basins

ATMOSPHERIC SCIENCE LETTERS, Issue 2 2010
Joris Van den Bergh
Abstract We present the hydrological ensemble prediction system developed at the Royal Meteorological Institute (RMI) of Belgium to study the Meuse and Scheldt basins. An overview is presented of the hydrological model and the operational setup of the forecasting system. We present some results of a 3-year hindcast that was performed to verify the quality of the probabilistic forecasting system. The raw precipitation forecasts and streamflow forecasts are considered: we provide skill scores and relative economic value for various subcatchments of the Meuse and Scheldt basins. Copyright © 2010 Royal Meteorological Society [source]