221 / 2024-02-28 10:52:20

Evaluation and bias-correction of S2S hydrological prediction in the source area of the Yangtze River

Sub-seasonal to seasonal prediction,Tibetan Plateau,The source area of the Yangtze River,Bias correction strategies

High-precision hydrological prediction is the key to reducing the water-related hazard risk. Sub-seasonal to seasonal (S2S) prediction can provide more helpful information due to its longer lead time. As the upper-stream boundary of the basin, the source area of the Yangtze River plays an important role in the hydrological prediction of the whole basin. S2S prediction in this area can further extend the timeliness of prediction. However, the existing S2S meteorological predictions often have large errors in this area, and have coarse spatial resolutions, which cannot be directly used to force hydrological models with high requirements for data quality. Therefore, how to improve the effect and accuracy of S2S meteorological and hydrological prediction in the source area of the Yangtze River is worthy of attention. Based on the international S2S prediction program, the effect of bias correction on S2S prediction and the impacts of two correction strategies (pre- and post-processing) on S2S hydrological prediction for the source area of the Yangtze River are analyzed. S2S precipitation and temperature products from four centers are evaluated in the TP. Then, the S2S products are bias corrected and the changes in prediction effects after correction are analyzed. The Xin'an River model established in the source area of the Yangtze River is driven by the S2S products, and the impacts of pre- and post-processing on hydrological prediction are compared. The results show that S2S product from the European Centre for Medium-Range Weather Forecasts (ECMWF) has the best performance in predicting precipitation. When predicting temperature, S2S product from the United Kingdom's Met Office (UKMO) performs best. S2S product from ECMWF performs well in predicting minimum temperature, but has large error in predicting maximum temperature. Bias correction can improve the ability of S2S products from all centers to predict precipitation and temperature significantly. In the source area of the Yangtze River, the correlation coefficients between sub-seasonal hydrological predictions and observation improve and the error decreases after bias correction. Meanwhile, the correction effect is more obvious with the increase of lead time. Taking both the correction effect and calculation steps into account, correcting runoff is more suitable for the sub-seasonal hydrological prediction correction of the source area of the Yangtze River.