Use of daily precipitation uncertainties in streamflow simulation and forecast |
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Authors: | Yeonsang Hwang Martyn P Clark Balaji Rajagopalan |
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Affiliation: | (1) College of Engineering, Arkansas State University, P.O. Box 1740, State University, AR 72467, USA;(2) Research Applications Laboratory, National Center for Atmospheric Research, PO Box 3000, Boulder, CO 80307-3000, USA;(3) Department of Civil, Environmental and Architectural Engineering, University of Colorado, Campus Box 428, Boulder, CO 80309, USA |
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Abstract: | Among other sources of uncertainties in hydrologic modeling, input uncertainty due to a sparse station network was tested.
The authors tested impact of uncertainty in daily precipitation on streamflow forecasts. In order to test the impact, a distributed
hydrologic model (PRMS, Precipitation Runoff Modeling System) was used in two hydrologically different basins (Animas basin
at Durango, Colorado and Alapaha basin at Statenville, Georgia) to generate ensemble streamflows. The uncertainty in model
inputs was characterized using ensembles of daily precipitation, which were designed to preserve spatial and temporal correlations
in the precipitation observations. Generated ensemble flows in the two test basins clearly showed fundamental differences
in the impact of input uncertainty. The flow ensemble showed wider range in Alapaha basin than the Animas basin. The wider
range of streamflow ensembles in Alapaha basin was caused by both greater spatial variance in precipitation and shorter time
lags between rainfall and runoff in this rainfall dominated basin. This ensemble streamflow generation framework was also
applied to demonstrate example forecasts that could improve traditional ESP (Ensemble Streamflow Prediction) method. |
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