Mid‐Range Streamflow Forecasts Based on Climate Modeling – Statistical Correction and Evaluation1 |
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| Authors: | Jae H. Ryu Richard N. Palmer Matthew W. Wiley Sangman Jeong |
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| Affiliation: | 1. Respectively, Hydrologist, School of Natural Resources, University of Nebraska, Lincoln, Nebraska 68588‐0988;2. Professor and Head, Department of Civil and Environmental Engineering, University of Massachusetts, Amherst, Massachusetts 01003‐9293;3. Hydro Forecasting Engineer, 3TIER, 2001 Sixth Avenue, Suite 2100, Seattle, Washington 98121;4. Professor, Department of Civil and Environmental Engineering, Kongju National University, Kongju, Republic of Korea. |
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| Abstract: | Abstract: Mid‐range streamflow predictions are extremely important for managing water resources. The ability to provide mid‐range (three to six months) streamflow forecasts enables considerable improvements in water resources system operations. The skill and economic value of such forecasts are of great interest. In this research, output from a general circulation model (GCM) is used to generate hydrologic input for mid‐range streamflow forecasts. Statistical procedures including: (1) transformation, (2) correction, (3) observation of ensemble average, (4) improvement of forecast, and (5) forecast skill test are conducted to minimize the error associated with different spatial resolution between the large‐scale GCM and the finer‐scale hydrologic model and to improve forecast skills. The accuracy of a streamflow forecast generated using a hydrologic model forced with GCM output for the basin was evaluated by forecast skill scores associated with the set of streamflow forecast values in a categorical forecast. Despite the generally low forecast skill score exhibited by the climate forecasting approach, precipitation forecast skill clearly improves when a conditional forecast is performed during the East Asia summer monsoon, June through August. |
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| Keywords: | streamflow forecast climate variability hydrologic models monsoon |
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