Coupling the short-term global forecast system weather data with a variable source area hydrologic model. (December 2016)

• Record Type: Journal Article
• Authors: Sommerlot, Andrew R.; Wagena, Moges B.; Fuka, Daniel R.; Easton, Zachary M.
• Is Part Of: Environmental modelling & software. Volume 86(2016)
• Page Start: 68
• Page End: 80
• Publication Date: 2016-12
• Subjects: Real-time forecast -- SWAT -- Soil and Water Assessment Tool-Variable Source Area Model -- Short-term forecast -- Distributed hydrology -- Non-point source pollution -- Runoff probability -- Soil moisture -- High performance computing; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.09.008
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Few current modeling tools are designed to predict short-term, high-risk runoff from hydrologically sensitive areas (HSAs) in watersheds. This study couples the Soil and Water Assessment Tool-Variable Source Area model with the Climate Forecast System Reanalysis model and the Global Forecast System-Model Output Statistics model short term weather forecast, to develop a HSA prediction tool designed to assist producers, landowners, and planners in identifying high-risk areas generating storm runoff and pollution. Short-term predictions for stream flow and soil moisture level were estimated in the South Fork of the Shenandoah river watershed. Daily volumetric flow forecasts were found to be satisfactory four days into the future, and distributed model predictions accurately captured sub-field scale HSAs. The model has the potential to provide valuable forecasts that can be used to improve the effectiveness of agricultural management practices and reduce the risk of non-point source pollution. Highlights: A watershed model-Global Forecast System model are coupled to develop a critical source area prediction tool. All software is open source and the application applies globally. The tool predicts watershed discharge and spatially distributed soil moisture conditions 1–4 days into the future. The tool is designed to assist land managers in identifying high-risk areas generating storm runoff and pollution.