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

Authors: LU, YANG - Wuhan University; CHEN, JIE - Wuhan University; Zhang, Xunchang; XIONG, LIHUA - Wuhan University

Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/7/2025
Publication Date: 1/17/2025
Citation: Lu, Y., Chen, J., Zhang, X.J., Xiong, L. 2025. A stochastic weather generator-based framework for generating ensemble sub-monthly precipitation for streamflow prediction. Journal of Hydrology. 58. Article 102186. https://doi.org/10.1016/j.ejrh.2025.102186.
DOI: https://doi.org/10.1016/j.ejrh.2025.102186

Interpretive Summary: Reliable sub-monthly streamflow prediction provides valuable information for disaster warning and water resources management. However, 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 disaggregation framework for organically combining stochastic weather generator (SWG) and monthly precipitation prediction to generate ensemble sub-monthly precipitation for streamflow prediction. To perform the framework, parameters that characterize precipitation occurrence and amounts are optionally adjusted based on the monthly precipitation prediction through three alternative schemes for illustration. The SWG-based schemes are then compared with the common numerical hydrometeorology ensemble streamflow prediction over two river basins in China (Xiangjiang and Hanjiang river basins). 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% to 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. Furthermore, the SWG-based schemes are more suitable for predicting high-flow events during the flood season. Overall, the proposed SWG-based framework offers a comparatively inexpensive way to achieve promising predictive skills for sub-monthly streamflow. The results would be useful to hydrologists for developing sub-monthly stream flow forecasts which are critical for flood warning and water resources management.

Technical Abstract: Reliable sub-monthly streamflow prediction provides valuable information for disaster warning and water resources management. However, 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 disaggregation framework for organically combining stochastic weather generator (SWG) and monthly precipitation prediction to generate ensemble sub-monthly precipitation for streamflow prediction. To perform the framework, parameters that characterize precipitation occurrence and amounts are optionally adjusted based on the monthly precipitation prediction through three alternative schemes for illustration. The SWG-based schemes are then compared with the common numerical hydrometeorology ensemble streamflow prediction over two river basins in China (Xiangjiang and Hanjiang river basins). 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% to 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 when transition probabilities of precipitation occurrence are progressively modified. Overall, the proposed SWG-based framework offers a comparatively inexpensive way to achieve promising predictive skills for sub-monthly streamflow.