## 1 Introduction  
Agricultural non-point source pollution (NPSP) results from domestic and agricultural pollutants. It has had serious consequences, such as population decrease, agricultural land loss, and water quality degradation [1]. The combination of point source pollution (PSP) [2; 3] and NPSP influences water resources negatively. According to the China Environmental Pollution Bulletin, concerning the pollution statistics of more than 131 basins in China, more than one-quarter of these basins are severely eutrophic [1; 4]. Compared to PSP, which includes industry and domestic pollution, NPSP results from pollution in farmland and urban settlements. With rapid urbanization, more and more urban domestic waste has contaminated groundwater and rivers [5; 6]. Moreover, agricultural NPSP tends to increase as the agriculture and use of farmland intensifies [7; 8]. NPSP has a great influence on human health and the ecological environment. As a form of pollution distributed regionally, NPSP not only exists in groundwater but also poses a threat to various animals and plants living in the region [7]. The concentration of nitrogen and phosphorus elements in farmland reflects their accumulation in the soil as well as the degree of pollution and absorbed pollution. Research on NPSP has typically focused on understanding the processes behind pollution stemming from nitrogen and phosphorus elements discharged into rivers, as well as on how to better determine the concentration of pollutants in water resources such as rivers. Such data can support decisions regarding the prevention and control of water pollution. Agricultural NPSP and urban domestic NPSP are different types of NPSP. For agricultural NPSP, the pollution source includes agricultural soil pollution and livestock excretion. On the other hand, urban domestic NPSP derives from urban erosion. The surface uncovered by rainfall erosion is a key issue to consider for NPSP [9]. Therefore, land use change is a key factor affecting NPSP. Urbanization converts natural land into an impermeable surface. Such an impermeable surface affects hydrological processes, thereby reducing throttling and infiltration while increasing surface runoff. Surface runoff is the main factor of NPSP due to it carrying garbage into downstream areas [10]. To build water resource pollution models, we must consider the changes related to underlying surfaces, such as hydrological processes, sediment erosion, soil runoff, land use, and topography [1; 11; 12]. NPSP monitoring models can be divided into two types: on-site monitoring models and predictive models. On-site monitoring is achieved by collecting data on site to monitor pollution sources. Since the 1990s, there has been impressive literature [11] reviewing NPSP and its corresponding models. The complexity of the mathematical principles used in modeling varies, and thus there are both empirical models and physical models that we can employ. An empirical model, also commonly known as a black-box model, refers to the unclear relationship between hydrological factors and amounts of pollution. Such a model does not consider complex physical processes, and thus it is simpler and usually more user-friendly than physical models. Empirical models were thus adopted in the present paper. There are numerous empirical models, such as ECM [13], General Soil Loss Equation, SWAT [14], AnAGNPS [15], output coefficient [16], and HSPF [17]. Their applications are limited by the characteristics of the watersheds they are applied to and the characteristics of input data. An empirical model named DPeRS, which is a distributed water resource NPSP model, was proposed by [PERSON] et al. in 2012 [18]. This model combines statistical modeling and remote sensing to form a black-box hydrological model. It has been successfully applied in numerous watersheds in China, such as the Haihe River [18; 19], Chaohe River [20], Huaihe River [21], and Liaohe River [18; 19; 20; 21; 22].