Variation Characteristics and Attribution Analysis of Seasonal Hydrological Drought in the Basin Above the Ankang Station of the Hanjiang River Based on the Coupling of Machine Learning and a Hydrological Model

Under complex and changing environmental conditions, hydrological drought in the upper Hanjiang River (UHR) is becoming increasingly severe, so investigating the variation characteristics and influencing factors of hydrological drought in this basin can provide favorable support for drought prevention and water resources management. In this study, based on monthly runoff data from the Ankang Hydrological Station of the UHR, the mutation change year at the Ankang Station was first identified using the Pettitt mutation test and the B-G segmentation algorithm. Subsequently, the ABCD hydrological model coupled with eight machine learning algorithms was employed to simulate the runoff variation process in the Ankang Station. Finally, we used the Standardized Runoff Index to describe the hydrological drought conditions and quantitatively analyzed the impacts of human activities and climate change on the seasonal hydrological drought in the UHR. The results showed that (1) the coupled machine learning–hydrological model can effectively improve the simulation accuracy of the runoff change process. (2) The coupled ABCD–Random Forest model has the highest accuracy. (3) Hydrological drought exhibits a significant increasing trend in spring and autumn, a significant decreasing trend in winter, and a non-significant increasing trend in summer. (4) Climate change serves as the primary driver of hydrological drought variations across four seasons in the UHR.