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

This is the authors' abstract. We don't add key points for this paper.

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.