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Abstract ▼ AI-Helper

In water resources management, streamflow prediction is one of the essential components that effects on the operators" decision making. For effective water use, e.g. water allocation or hydropower generation, long-term (more than monthly unit) streamflow prediction based on precipitation forecast is...

In water resources management, streamflow prediction is one of the essential components that effects on the operators" decision making. For effective water use, e.g. water allocation or hydropower generation, long-term (more than monthly unit) streamflow prediction based on precipitation forecast is necessary. One of the typical methods for mid-and long-term streamflow prediction is Ensemble Streamflow Prediction (ESP). ESP predicts the streamflow through the combination of historic meteorological ensemble members, future meteorological forecast. Because the existing ESP theory is based on the categorical forecasts provided by U.S. National Weather Service, direct application for the domestic forecast system which differs from the categorical forecast give rise to the use of evenly distributed ensemble weights. In this research, the new ESP system for the integrated real-time water management system that uses quantitatively weather outlook information provided by the Korean Meteorological Administration was suggested. The suggested method showed better probabilistic forecast of streamflow specially in flood season.

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