The impact of precipitation spatiotemporal feature extraction based on K-means clustering on hydrological model predictions
Fengming LIANG , Chao TAN , Fenghua HUANG , Bikui ZHAO , Zhimin LIU , Tao CHENG , Zejun LI , Binbin ZUO
Water Resources and Hydropower Engineering ›› 2026, Vol. 57 ›› Issue (1) : 192 -204.
[Objective] Rainfall, as an important input variable in hydrological models, significantly affects the prediction capabilities of these models. Studying the temporal and spatial variation characteristics of rainfall at different scales is crucial for enhancing the stability and prediction accuracy of hydrological models. [Methods] The Xiagushan watershed in Henan Province was selected as the study area. Historical flood events were simulated using the SCS-CN and Xin'anjiang models. The temporal and spatial characteristics of rainfall and the simulation accuracy of the models were statistically analyzed to explore the impact of rainfall variability on the modeling capabilities of these hydrological models and hydrological response. [Results] The average coefficient of determination for the Xin'anjiang model and the SCS-CN model were 0. 83 and 0. 85, respectively, with the SCSCN model slightly outperforming the Xin'anjiang model. Both models showed the best performance in simulating floods with short durations and concentrated rainfall centers, whereas the simulation accuracy was lower when the rainfall center was closer to the watershed outlet. [Conclusion] Under low flood conditions, the accuracy of both hydrological models is significantly influenced by the average rainfall intensity. Under moderate flood conditions, the Xin'anjiang model is most affected by the average rainfall intensity and the rainfall spatial variability coefficient, while the SCS-CN model is mainly influenced by the rainfall location index. Under high flood conditions, the rainfall location index becomes the main factor affecting the simulation performance of both models, with contribution rates of 30. 23% and 33. 97%, respectively. Therefore, model parameters can be optimized based on the temporal and spatial characteristics of rainfall to improve the simulation accuracy and reliability of the models.
SCS-CN hydrological model / Xin'anjiang model / rainfall pattern / temporal and spatial distribution characteristics of rainfall / spatiotemporal changes / climate change / flood forecasting / global hydrological cycle
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