Coupled hydrological and atmospheric model- ing is an effective tool for providing advanced flood forecasting. However, the uncertainties in precipitation forecasts are still considerable. To address uncertainties, a ...Coupled hydrological and atmospheric model- ing is an effective tool for providing advanced flood forecasting. However, the uncertainties in precipitation forecasts are still considerable. To address uncertainties, a one-way coupled atmospheric-hydrological modeling sys- tem, with a combination of high-resolution and ensemble precipitation forecasting, has been developed. It consists of three high-resolution single models and four sets of ensemble forecasts from the THORPEX Interactive Grande Global Ensemble database. The former provides higher forecasting accuracy, while the latter provides the range of forecasts. The combined precipitation forecasting was then implemented to drive the Chinese National Flood Forecasting System in the 2007 and 2008 Huai River flood hindcast analysis. The encouraging results demonstrated that the system can clearly give a set of forecasting hydrographs for a flood event and has a promising relative stability in discharge peaks and timing for warning purposes. It not only gives a deterministic prediction, but also generates probability forecasts. Even though the signal was not persistent until four days before the peak discharge was observed in the 2007 flood event, the visualization based on threshold exceedance provided clear and concise essential warning information at an early stage. Forecasters could better prepare for the possibility of a flood at an early stage, and then issue an actual warning if the signal strengthened. This process may provide decision support for civil protection authorities. In future studies, different weather forecasts will be assigned various weight coefficients to represent the covariance of predictors and the extremes of distributions.展开更多
基金This work is supported by the Foundation for the Author of National Excellent Doctoral Dissertation of PR China （Grant No. 201161）, the Program for New Century Excellent Talents in University （Grant No. NCET-12-0842）, the Special Public Sector Research Program of Ministry of Water Resources （Grant Nos. 201301040, 201401008, and 201301070）, the Natural Science Foundation of Jiangsu Province of China （Grant No. BK20131368）, and the National Water Pollution Control and Management Technology Project of China （Grant No. 2012ZX07101-010）.
文摘Coupled hydrological and atmospheric model- ing is an effective tool for providing advanced flood forecasting. However, the uncertainties in precipitation forecasts are still considerable. To address uncertainties, a one-way coupled atmospheric-hydrological modeling sys- tem, with a combination of high-resolution and ensemble precipitation forecasting, has been developed. It consists of three high-resolution single models and four sets of ensemble forecasts from the THORPEX Interactive Grande Global Ensemble database. The former provides higher forecasting accuracy, while the latter provides the range of forecasts. The combined precipitation forecasting was then implemented to drive the Chinese National Flood Forecasting System in the 2007 and 2008 Huai River flood hindcast analysis. The encouraging results demonstrated that the system can clearly give a set of forecasting hydrographs for a flood event and has a promising relative stability in discharge peaks and timing for warning purposes. It not only gives a deterministic prediction, but also generates probability forecasts. Even though the signal was not persistent until four days before the peak discharge was observed in the 2007 flood event, the visualization based on threshold exceedance provided clear and concise essential warning information at an early stage. Forecasters could better prepare for the possibility of a flood at an early stage, and then issue an actual warning if the signal strengthened. This process may provide decision support for civil protection authorities. In future studies, different weather forecasts will be assigned various weight coefficients to represent the covariance of predictors and the extremes of distributions.
