Evaluation of Quantitative Precipitation Predictions by ECMWF, CMA, and UKMO for Flood Forecasting: Application to Two Basins in China

Abstract

Numerical weather predictions (NWPs) are very useful in hydrological modeling, including for river flow forecasting and flood warning in river basins. However, uncertainties in NWPs also significantly impact the accuracy of streamflow forecasting. Therefore, evaluating the accuracy of NWPs is crucial to achieve reliable streamflow forecasts. In this study, rainfall prediction skills of three NWP models [developed by the European Centre for Medium-Range Weather Forecasts (ECMWF); the U.K. Meteorological Office (UKMO); and the China Meteorological Administration (CMA)] are evaluated in two basins (Linxian and Jiuzhaigou) in China, which have different hydroclimatic, topographic, and other characteristics. The evaluation is made by comparing the model predictions with measurements of ground-based rain gauges during the flood seasons (May to October) during 211–213. Four different evaluation measures are used: the confusion matrix, correlation coefficient, Nash–Sutcliffe efficiency, and root-mean square error. The influence of rainfall station representativeness (i.e., location and density of rain gauges in the basin) is also analyzed in detail. The results show that ECMWF has the highest skill in precipitation forecast over the two studied basins, followed by UKMO and CMA. The performance of UKMO is also found to be very close to that of ECMWF. The results also indicate that the precipitation prediction of each of the three models is better for the Linxian Basin when compared to that for the Jiuzhaigou Basin. The present results have important implications for the use of NWP data in hydrological modeling, especially for flood forecasting.