## 2 Materials and Methods
### Data and Preprocessing  
Uncrewed aerial vehicle (UAV) remote sensing and field surveys mainly obtain topographic data, and UAVs are used for high-resolution topographic mapping. The UAV (DJI Phantom 4 RTK, Shenzhen, China) provided mapping accuracy of \(\pm\)2 cm horizontally and \(\pm\)5 cm vertically, covering a total area of 500 km\({}^{2}\) divided into 50 zones. Topographic data from UAV remote sensing is used for coordinate correction and stitching. GPS data is used for coordinate correction. A DEM with 0.5 m resolution was created for detailed analysis, supplemented by publicly available SRTM data (30 m resolution) to ensure full coverage. Geostatistical methods smoothed and filtered the data, removing noise and outliers.  
Land use data are mainly derived from remote sensing satellite imagery. Remote sensing image data from 2018 to 2023 were selected, and different types of land use (grassland, cropland, construction land) were identified through classification and analysis using image processing software (ArcGIS Pro 3.1). ENVI 5.6 software performed geometric corrections to remove distortions and misalignments in the images. Image classification was also done using supervised and unsupervised classification methods to identify different land feature types in the images. Machine learning algorithms such as Support Vector Machines (SVMs) and Random Forests (RFs) were introduced to improve the classification accuracy, and field survey data was used to verify and adjust the results, ultimately producing an accurate land use map.  
Meteorological data (rainfall, temperature, humidity, wind speed) were mainly obtained from automatic weather stations. A total of 10 weather stations were deployed within the study area, each recording data every 15 min. They record meteorological data regularly daily and transmit them via wireless networks to the research center for aggregation and analysis. The data is converted and processed uniformly to ensure data integrity and consistency. Historical meteorological records are extracted from the China Meteorological Administration database, including monthly average temperature, rainfall, and evaporation data over the past 30 years, for time series analysis to fill in missing values and eliminate outliers. Specifically, missing values accounted for less than 2% of the dataset and were inputted using linear interpolation techniques, ensuring minimal distortion in trend analysis. Outlier detection was performed using the IQR method, and less than 1% of data points were removed or adjusted.  
Hydrological and water quality data are mainly obtained through long-term observations at monitoring stations distributed at significant rivers, lakes, and groundwater sampling sites. Water samples are collected and their physico-chemical parameters (flow, water level, pH, dissolved oxygen, suspended solids, nitrogen and phosphorus concentrations) are measured regularly. Real-time data are also collected using automated water quality monitoring equipment to ensure the timeliness and accuracy of the data. Meanwhile, the hydrological and water quality data collected at the monitoring stations must first be processed and verified. Water samples are analyzed in the laboratory to measure their physico-chemical parameters, and the results are entered into the database. Data cleansing techniques remove outliers and noise from the real-time data the automatedmonitoring equipment collects. The final complete hydrological and water quality dataset supports the parameterization and calibration of the SWMM. To ensure consistency, laboratory measurements were cross-checked against automated readings, with deviations of less than 5% observed for key parameters such as pH and dissolved oxygen. The final complete hydrological and water quality dataset supports the parameterization and calibration of the SWMM.  
The data used for the study were summarized in Table 1, including topographic, land use, meteorological, and hydrological data sources.