Abstract:

[Objective] Most existing studies on the response relationship between runoff variations and meteorological drought and flood characteristics focus on annual, seasonal, monthly, or weekly scales. This study aims to clarify the quantitative response relationship between meteorological drought and flood characteristics at the daily scale and runoff in the Jialing River Basin, and to effectively evaluate the impact of extreme meteorological drought and flood events on the flow process. [Methods] Based on long-term daily precipitation and flow data from 1989 to 2022, this study employed the SWAP index method and run theory to identify meteorological drought and flood events at the daily scale in the Jialing River Basin. Traditional multiple regression and emerging machine learning models were compared to simulate the internal relationship between meteorological drought and flood characteristics and flow change, revealing the response of runoff variation to drought and flood characteristics. [Results] The results showed that from 1989 to 2022, a total of 68 meteorological drought events occurred in the Jialing River Basin, leading to an average reduction of 48.25% in flow at the Beibei station. Compared to drought duration and intensity, the timing of drought events had a more significant impact on runoff variation and was the primary controlling factor influencing runoff variation. The support vector regression model considering only this factor could more accurately evaluate the change rate of flow caused by drought. During the same period, 40 meteorological flood events occurred in the Jialing River Basin, leading to an average increase of 130.46% in flow at the Beibei station. The accumulated precipitation before the flood peak had the greatest impact on the change rate of flow, and the timing of maximum precipitation before the flood peak had the greatest impact on the timing of flood peak. Multiple regression models were recommended to evaluate the response relationships between flood characteristic factors and the change rate of flow, as well as the timing of flood peak. To evaluate the impact of flood events on peak flow, the random forest model was recommended. The accumulated precipitation before the flood peak was the primary controlling factor influencing peak flow variation. [Conclusion] This study innovatively explores the response relationship between meteorological drought and flood characteristics at the daily scale and runoff variation in the river basin. The findings indicate that emerging machine learning models, such as support vector machines and random forests, can effectively simulate the complex mechanisms through which meteorological drought and flood events affect runoff in the river basin. This has significant implications for the scientific evaluation and prediction of the impact of extreme climate events on runoff characteristics.

Key words: meteorological drought and flood, daily scale, multiple regression model, runoff, response relationship, Jialing River Basin