A Novel Weather Generator and Soil Attribute Database for SWAT to Improve the Simulation Accuracy in the Heilongjiang Region of China

This study addresses the issue of missing basic data and insufficient accuracy in predicting runoff and non-point-source pollution in the Heilongjiang region of China using the Soil and Water Assessment Tool (SWAT) model. Based on the China Ground Climate Data Daily Dataset (V3.0) and SPAW soil characteristic calculation formula, and assisted by the Python V3.0 language for data processing and computation, new high-precision weather generators and soil attribute databases suitable for the Heilongjiang region of China were established. The weather generator is based on daily data and contains detailed meteorological parameters such as temperature, humidity, wind speed, rainfall, etc., used to characterize the periodic changes in meteorological elements. And the differences and fluctuations outside this change curve were also retained in the basic construction of the weather generator. The soil database covers various parameters, such as soil type, texture, structure, nutrient content, organic matter content, etc., enabling the SWAT model to better simulate hydrological and pollutant transport processes in the soil. Additionally, point-source input data, including various industrial and domestic wastewater discharge situations, were collected and organized to improve data quality. Furthermore, a series of agricultural management measures were developed based on the use of fertilizers and pesticides for simulation, providing an important basis for analyzing non-point-source pollution using the SWAT model. By comparing the different results of the simulation using optimized databases, it is shown that the above work improved the simulation accuracy of the SWAT model in predicting runoff and pollution load in Heilongjiang, China. The NSE of runoff simulation increased from 0.923 to 0.988, and the NSE of ammonia nitrogen and CBOD simulation increased from 0.852 and 0.758 to 0.930 and 0.902, respectively. It is expected that these efforts will provide strong data support for subsequent research and provide a theoretical basis for government decision-makers to build scientifically rigorous and effective pollution control strategies.