## 1 Introduction  
The occurrence of flooding stands as a substantial natural catastrophe, posing a severe peril to human communities and profoundly affecting both lives and property safety. Concurrently, as global temperatures have continued to rise in recent decades, an upsurge in anomalous climatic events, including floods triggered by unprecedented rainstorms, has been observed [1; 2; 3]. The global natural disasters recorded by the Emergency Events Database (EM-DAT) include a total of 14,581 natural disaster events from 1900 to 2018 worldwide, within which flood events account for the highest proportion of 33% [4]. The Soil and Water Assessment Tool (SWAT) model, developed by the US Department of Agriculture in 1995, was constructed on the basis of the Simulator for Water Resources in Rural Basins (SWRRB) model [5]. During nearly three decades of development, eight distinct versions of the model have been introduced [6; 7]. What initially began as the SWAT/GRASS interface has since evolved into the AVSWAT and ArcSWAT interfaces [8]. The initial SWAT model began as computer code and was later evolved into a version integrated with a Geographic Information System (GIS) interface. This integration facilitated easy data processing and model visualization. Furthermore, it simplified the linkage to sensitivity, calibration, and uncertainty analysis tools, such as SWAT-CUP. Throughout the development of the interface, continuous improvements were made to enhance its functionality, resulting in its widespread recognition and application. Initially designed for evaluating hydrological, sediment, and water quality conditions in watersheds, the SWAT model is now primarily employed for hydrology, sedimentation, crop growth, nutrient cycling, and management of pest hazards. [PERSON] et al. [9] adjusted crop module parameters for corn and sorghum within the SWAT framework and obtained improvements in modelling results. [PERSON] et al. [10] incorporated seasonally varying soil erodibility into the SWAT model, and they effectively simulated daily nutrient export in a cold prairie watershed in Canada. Additionally, [PERSON] et al. [11] examined the effects of almond pest management practices on water quality in California, USA, and their study revealed that almond pesticide usage had a significant impact on pesticide loads within the San Joaquin River watershed. The capability of adapting to different infiltration procedures on varying time scales enables the SWAT model to simulate on various time scales, including annual, monthly, daily, hourly, and even minute-long intervals [12]. [PERSON] and colleagues [13] adeptly utilized the SWAT model to replicate runoff at both daily and monthly scales for the Tungabhadra watershed, illuminating a robust correlation between observed and simulated values. Additionally, [PERSON] et al. [14; 15] leveraged the China Meteorological Assimilation Driving Datasets (CMADS) meteorological dataset in conjunction with the SWAT model to scrutinize significant watersheds in China across diverse temporal contexts, successfully meeting model evaluation criteria through correlation coefficient (R\({}^{2}\)) and Nash-Sutcliffe model efficiency coefficient (NSE) assessments. Nonetheless, the heightened temporal granularity intrinsic to hourly simulations might lead to a relatively reduced accuracy when contrasted with daily simulations. In-depth analysis of hourly scale simulation outcomes by [PERSON] and colleagues involved the application of the SWAT model's Green-Ampt methodology to scrutinize the impact of hourly precipitation resolution on model performance and hydrological attributes [16]. Furthermore, [PERSON] and co-authors [17] harnessed the SWAT model at an hourly scale for the Pawtuxet Watershed. However, the NSE results, falling below anticipated levels, underscored important insights for both the calibration and validation phases. [PERSON] and colleagues [18] introduced the innovative hourly simulation module of the SWAT model in 2010.