Research on precipitation-runoff relationship and runoff prediction in Jinghe River Basin

Abstract

[Objective] The Jinghe River Basin in a semi-arid region is selected as the study area to analyze the spatiotemporal variation characteristics of runoff and to investigate the response relationship of runoff to precipitation changes in the river basin.[Methods] Spatiotemporal variations in runoff were analyzed using runoff data from hydrological stations and precipitation data within the river basin from 2022 to 2024. The river basin was divided into four regions using the K-Means clustering algorithm based on the contribution of station precipitation to runoff, and the precipitation-runoff lag relationship in different regions was further analyzed. Finally, short-term runoff prediction models for the Jinghe River with lead times of 1 h, 3 h, and 6 h were developed based on a long short-term memory(LSTM) neural network.[Results] The result showed that:(1) the daily average runoff of the Jinghe River exhibited pronounced seasonal variation, with the flood season being the high-discharge period. Extreme peaks were concentrated in mid-to-late July, with three consecutive years showing a single extreme peak with multiple fluctuations.(2) Analysis of three typical flood peaks in the Jinghe River showed that when antecedent soil moisture content was similar, the maximum hourly precipitation and precipitation duration within the river basin were the key factors influencing the flood peaks. Specifically, longer precipitation duration, higher intensity, and larger total precipitation led to longer flood duration and higher peak discharges. When the antecedent soil moisture content in the 0~28 cm soil layer was below 0.2 m³/m³, the basin-wide average lag time reached 30 h, nearly twice that when the soil moisture content exceeded 0.4 m³/m³. Regional differences were observed in the precipitation-runoff transformation within the river basin. Under higher antecedent soil moisture content conditions, the optimal average lag times for regions 1~4 were 11 h, 12 h, 14 h, and 28 h, respectively. Under lower soil moisture content conditions, the lag times were 23 h, 30 h, 34 h, and 35 h, respectively.(3) A short-term runoff prediction model for the Jinghe River was established using the LSTM machine learning algorithm. The effects of different influencing factors on the model were tested, revealing that antecedent discharge at Jingcun and Yuluoping stations and basin average areal precipitation were identified as key factors. For lead times of 1 h, 3 h, and 6 h, the optimal models achieved Nash-Sutcliffe efficiency coefficients of 0.902, 0.774, and 0.676, respectively, and root mean square errors of 2.788 m³/s, 4.230 m³/s, and 5.070 m³/s, respectively. Prediction accuracy decreased with increasing lead time. The model performed well in simulating runoff during large floods and stable-discharge periods.[Conclusion] The identified lag effect of flood peaks on basin precipitation and the developed runoff prediction models can provide technical support for water resource regulation and flood prediction in the Jinghe River Basin and offer new insights for studying the precipitation-runoff relationships in small and medium-sized river basins in semi-arid regions.

Keywords

Jinghe River Basin / runoff variation / precipitation-runoff relationship / lag effect / long short-term memory neural network(LSTM) / spatiotemporal variation / climate change / semi-arid region