Streamflow information is of great significance for flood control, water resources utilization and management, ecological services, etc. Continuous streamflow prediction in ungauged basins remains a challenge, mainly due to data paucity and environmental changes. This study focuses on the modification of a nonlinear hydrological system approach known as the time variant gain model and the development of a regressive method based on the modified approach. This method directly correlates rainfall to runoff through physically based mathematical transformations without requiring additional information of evaporation or soil moisture. Also, it contains parsimonious parameters that can be derived from watershed properties. Both characteristics make this method suitable for practical uses in ungauged basins. The Huai River Basin of China was selected as the study area to test the regressive method. The results show that the proposed methodology provides an effective way to predict streamflow of ungauged basins with reasonable accuracy by incorporating regional watershed information (soil, land use, topography, etc.). This study provides a useful predictive tool for future water resources utilization and management for data-sparse areas or watersheds with environmental changes.
Streamflow prediction in ungauged basins by regressive regionalization: a case study in Huai River Basin, China
Jiyun Song, Jun Xia, Liping Zhang, Zhi-Hua Wang, Hui Wan, Dunxian She; Streamflow prediction in ungauged basins by regressive regionalization: a case study in Huai River Basin, China. Hydrology Research 1 October 2016; 47 (5): 1053–1068. doi: https://doi.org/10.2166/nh.2015.155
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