A stochastic weather generator based framework for generating ensemble sub-monthly precipitation for streamflow prediction

Abstract

Study region: Xiangjiang and Hanjiang River basins in the Yangtze River basin, a humid subtropical inland region of central-southern China. Study focus: The general precipitation predictive skills of the mainstay numerical models are still rather limited beyond 10 days, further deteriorating the performance of sub-monthly streamflow prediction. This study proposes a sub-monthly streamflow prediction framework for organically combining stochastic weather generator (SWG) and monthly precipitation prediction to generate ensemble sub-monthly precipitation for streamflow prediction. The SWG-based schemes are then compared with the common numerical hydrometeorology ensemble streamflow prediction over two river basins in China. New hydrological insights for the region: Results show that the numerical streamflow predictions exhibit a less accurate deterministic performance than the SWG-based framework with the climatology scheme over sub-monthly horizon, with leadtime-averaged mean absolute relative error dropping from 19.9 % to 8.3 %, and 21.8–11.1 % for Xiangjiang and Hanjiang river basins, respectively. In addition, the more restrictive parametric adjustment procedure with adjusted precipitation amounts can bring added values and further improve the accuracy of SWG-based daily streamflow prediction for sub-monthly leadtimes. In terms of the probabilistic prediction performance, the SWG-based methods yield approximately equivalent results with the numerical hydrometeorology streamflow prediction, with leadtime-averaged Continuous Ranked Probability Skill Score of the scheme with modified parameters of precipitation amounts being 0.51 and 0.56 for Xiangjiang and Hanjiang River basins, respectively. Furthermore, the SWG-based schemes are more suitable for predicting high-flow events during the flood season, with a significantly smaller Brier Score. However, there are little improvements in probabilistic prediction performance when transition probabilities of precipitation occurrence are progressively modified.

Keywords

• Stochastic weather generator
• Sub-monthly precipitation prediction
• Ensemble streamflow prediction
• Prediction framework