Integrated Modeling and Management of Non-Point Source Pollution in the Bailin River Basin: Best Practices for Reducing Nutrient Loads

Integrated Modeling and Management of Non-Point Source Pollution in the Bailin River Basin: Best Practices for Reducing Nutrient Loads | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrated Modeling and Management of Non-Point Source Pollution in the Bailin River Basin: Best Practices for Reducing Nutrient Loads Hao Wang, Yifeng Liu, Shijiang Zhu, Yang Liu, Wen Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5961509/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The Bailin River, a key tributary of the Yangtze River, faces significant water quality challenges due to agricultural non-point source (NPS) pollution exacerbated by industrial discharge and urban runoff. This study employs the Soil and Water Assessment Tool (SWAT) to analyze the temporal and spatial dynamics of runoff as well as total nitrogen (TN) and total phosphorus (TP) loads in the Bailin River basin from 2020 to 2023. A critical source area analysis was performed to identify regions disproportionately contributing to pollutant loads. Through various simulations, including different Best Management Practices (BMPs) scenarios, the study explores their effectiveness in reducing nutrient loads. The findings reveal that nutrient losses are significantly concentrated during the flood season, with TN and TP losses accounting for 58.61% and 58.92% of annual totals, respectively. Specific BMP scenarios, combining optimized fertilization, vegetation buffer strips, and grass ditches, demonstrated substantial pollutant reduction, with the best combinations exceeding 58% reductions for both TN and TP. The study emphasizes the necessity of targeted interventions in critical source areas to optimize management strategies and achieve better water quality outcomes. Continuous monitoring and adaptive management practices will be crucial to addressing ongoing challenges of non-point source pollution in this basin. Ultimately, this research contributes to a deeper understanding of NPS pollution in mountainous watersheds and highlights effective management pathways for improved ecological health and water quality. Non-point Source Pollution Soil and Water Assessment Tool (SWAT) Best Management Practices (BMPs) Nutrient Load Reduction Bailin River Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Water is one of the indispensable basic materials for the development of human society. However, global water security is facing increasing severe challenges due to the rapid population growth and economic development, as well as the pollution of water resources(Basu et al., 2022 ). with the effective governance of the point source pollution in domestic and industry, the non-point source pollution becomes the biggest pollution source directly affects water quality and aquatic ecosystems, especially the agricultural non-point source pollution(Chen et al., 2021 ; Gu et al., 2023 ). Around China and American-European developed countries, more than 60% lakes are suffering various extents of eutrophication, and about 50–70% of the problem is caused by the non-point source pollution (Hoehn et al., 2021 ; Fang et al., 2022). Generally, the non-point source pollutants enter the river from large and scattered areas. It is hardly to assess and control the non-point source pollution mainly due to the large randomness and temporal-spatial variations of the pollution transmission and dispersal processes (Huang et al., 2021 ). Models of non-point source pollution are vital tools for understanding the complex interactions within watersheds and developing effective management strategies. These models typically fall into two main categories: the empirical model and the physical model. Empirical models, including the output coefficient method, the inventory analysis method and the pollutant discharge coefficient method, are applied to analyze the characteristics of pollutant loss by establishing the relationship between pollution load and runoff, topography or land use through causal analysis or statistical analysis. However, the empirical models are based on observed data and statistical relationships rather than deriving from first principles. These models can become outdated as conditions change over time if not regularly validated with new data (Zhou et al., 2023 ). On the contrary, physical models, also known as mechanistic models, are based on the fundamental principles of physics and chemistry that govern pollutant behavior in the environment. These models can quantitatively describe the occurrence, migration and transformation of non-point source pollutants, thus being frequently used in regulatory assessments and for designing pollution control measures (Zhang et al., 2024 ). To date, researches on the physical models of the non-point source pollution have stepped into the era of a hundred flowers in bloom. The most widely used physical models are the Soil and Water Assessment Tool (SWAT), the Hydrological Simulation Program-FORTRAN (HSPF), the Water Erosion Prediction Project (WEPP), and the Storm Water Management Model (SWMM) (Xie et al., 2024 ). Among them, the SWAT model has proven to be an invaluable tool in in understanding and managing the non-point source pollution (Zuo et al., 2023 ; Fang et al., 2024 ). Recent studies have demonstrated its versatility in various applications, including agricultural assessments, urban planning, climate change impacts, and BMP evaluations (Liu et al., 2023 ; Yan et al., 2024 ). For instance, Chen et al. ( 2021 ) focused on multi-site calibration techniques that optimize the performance of SWAT in simulating hydrological processes at various sub-basins. These advancements help reduce uncertainties in pollutant load estimations and improve the model's predictive capacity across diverse landscapes. Yan et al. ( 2020 ) utilized SWAT to simulate the effects of LULC changes on nutrient runoff in the Yangtze River Basin, finding that urban expansion significantly elevated nitrogen and phosphorus levels in the water bodies. Such findings highlight the importance of sustainable land-use planning to mitigate NPS pollution. Niraula et al. (2019) explored how future climate scenarios could impact nitrogen and phosphorus loading in a watershed, using SWAT to simulate various precipitation and temperature changes. Their findings suggest that more intense rainfall events will likely increase nutrient and sediment loads, thus exacerbating water quality issues. Liu et al. (2022) demonstrate the effectiveness of buffer strips, cover crops, and other BMPs in controlling nutrient runoff. Their simulations showed that BMPs could reduce nitrogen and phosphorus loads by 25–50%, indicating the importance of integrating BMPs into watershed management strategies. Zhang et al. (2020) showed how SWAT can simulate the effect of soil conservation measures, such as terracing and reforestation, on reducing sediment yield in highly erodible watersheds. The ability to model both large-scale and small-scale interventions provides insights into effective strategies for controlling sediment-based NPS pollution. As challenges related to NPS pollution persist, the integration of advanced technologies such as remote sensing and machine learning with SWAT presents exciting prospects for future research and management practices. Xu et al. ( 2022 ) successfully integrated remote sensing data with SWAT to enhance the accuracy of land use and land cover changes in assessing NPS pollution in a temperate watershed. This approach allowed for real-time monitoring and improved management decisions. Krishnan et al. (2022) integrated ML techniques with SWAT for predicting streamflow and sediment transport in a watershed. Their results showed that machine learning models could effectively capture nonlinear relationships and enhance the predictive capability of the SWAT model. The non-point source (NPS) pollution is particularly challenging to manage due to its widespread nature and dependence on varying factors such as land use, climate, and topography (Gao et al., 2023 ). Effective management strategies often involve the implementation of Best Management Practices (BMPs) to mitigate the effects of NPS by controlling runoff and reducing the input of pollutants into water bodies (Jeong et al., 2024 ; Mohebzadeh et al., 2024 ). The critical source areas of nonpoint source pollution (NPS) are specific regions within a watershed that disproportionately contribute to overall pollutant loading (Fang et al., 2024 ; Li et al., 2024 ). By focusing conservation and remediation efforts on these critical areas, land managers and policymakers can optimize resource allocation and achieve greater reductions in water pollution (Wen et al., 2024 ). As a primary tributary of the Yangtze River, the Bailin River plays a crucial role in the river network of the middle reaches of the Yangtze River. Over the past few decades, the river's water quality has been negatively affected by factors such as industrial wastewater discharge, livestock pollution, and excessive agricultural fertilization. Prior to 2020, the water quality of the Bailin River was not optimistic, with most sections showing an increasing trend in pollutant concentration levels, often falling into class III to V water quality. Therefore, our study focuses on reducing the non-point source pollution load of the Bailin River basin. For this purpose, the distributed hydrological model SWAT was adopted to analysis the spatial and temporal dynamic changes of runoff and total nitrogen pollution load in the study area. And then the critical source areas for the non-point source pollution prevention and control were defined. Furthermore, different BMPs (Best Management Practices) scenario simulations were carried out, and the best management measures to treat the non-point source pollution in the Bailin River Basin were proposed. 2. Materials and Methods 2.1 The study area The Bailin River is a primary tributary of the left bank of the Yangtze River. It is 59.3 km long and flows an area of about 478 km 2 , which is fan-shaped, and the landform is semi-alpine mountain and hilly terrain (Fig. 1 ). The average annual rainfall is 1100 mm, and the evaporation is 1280 mm. The rainy season in the basin is from June to September. In terms of rainfall distribution, it has the characteristics of spatial-temporal concentration, uneven intensity, and often forms a local rainstorm center. The Bailin River has long dry season with small flow, and short flood season with rapid flood fluctuation. Monitoring data indicate that the water quality in various sections of the river has deteriorated due to elevated levels of pollutants such as heavy metals, nutrients (nitrogen and phosphorus), and organic matter. The water pollution in the Bailin River is a multifaceted issue driven by urbanization, industrial activities, agricultural runoff, and waste management challenges. Addressing these influencing factors is crucial to improving the water quality and ecological health of this important tributary of the Yangtze River. 2.2 The SWAT model The SWAT (Soil and Water Assessment Tool) model is a comprehensive, semi-distributed hydrological model designed for predicting the impact of land management practices on water, sediment, and agricultural chemical yields in large watersheds (Cai et al., 2023 ). In this study, climate data, soil data, fertilization data, and water resource utilization information of the Bailin River basin from 2020 to 2023 were collected as the input parameters of the SWAT model. After calibration and validation using the SWAT-CUP software, the model was used to analyze the temporal and spatial variations of runoff, total nitrogen, and total phosphorus output loads in the study area. Additionally, the study explored the reduction effects of different management measures on pollutant loads, proposed targeted approaches for non-point source pollution in the Bailin River basin. 2.3 The land use and soil types The digital elevation model (DEM) was derived from the geospatial data cloud ( https://www.gscloud.cn ) with a resolution of 30 m, and the land use data were obtained through visual remote sensing image interpretation (Fig. 2 ). Specifically, the samples of citrus orchards and other types of ground objects are collected by visual recognition on Google Earth platform. Afterwards, the pre-classified image containing citrus orchard information was extracted via the random forest classification of the remote sensing data. Moreover, the citrus orchards were distinguished from other woodlands by the catastrophe feature of NDVI as it decreased significantly in October and gradually increased in November. Referring to the re-normalized vegetation index RNVI (Re-Normalization of Vegetation Indices) proposed by Liang et al ( 2021 ), the pre-classified images containing citrus orchard information were discriminated. If the RNVI value of the pixel is negative, the pixel is determined as a citrus orchard. Otherwise, the pixel is determined as a woodland. The NDVI and RNVI were calculated as follows: $$\:\text{N}\text{D}\text{V}\text{I}=\frac{{\rho\:}_{\text{N}\text{I}\text{R}}-{\rho\:}_{\text{R}\text{E}\text{D}}}{{\rho\:}_{\text{N}\text{I}\text{R}}+{\rho\:}_{\text{R}\text{E}\text{D}}}$$ $$\:\text{R}\text{N}\text{V}\text{I}=\frac{{\text{V}\text{I}}_{a}-{\text{V}\text{I}}_{b}}{{\text{V}\text{I}}_{a}+{\text{V}\text{I}}_{b}}$$ where \(\:{\rho\:}_{\text{N}\text{I}\text{R}}\) is the reflectance in near-infrared wavelength; \(\:{\rho\:}_{\text{R}\text{E}\text{D}}\) is the red spectral band; \(\:{\text{V}\text{I}}_{a}\) is the maximum vegetation index at the last month of fruit expanding stage; \(\:{\text{V}\text{I}}_{b}\) is the maximum vegetation index at the first month after fruit expanding stage. The soil types in the study area were extracted from the Harmonized World Soil Data-base (HWSD) with a resolution of 1km (Fig. 2 ). Soil parameters such as soil mechanical composition and organic carbon content could directly use the HWSD data. Whereas, some other soil parameters were modified according to local situation, including soil humidity, effective soil water holding capacity, saturated hydraulic conductivity, and soil erosion factor (Valencia et al., 2024 ). 2.4 Scenario design The non-point source pollution in the critical source areas might be significantly sever than in the other areas. In this study, the non-point source pollution reduction strategies in such areas were addressed by setting various management scenarios, of which the impact on water quality was predicted by the SWAT model. Details about the non-point source pollution management scenarios were shown in Table 1 . Concretely, scenario A 0 was a benchmark scenario built on the current situation of the Bailin River basin, it provided a reference point against which other scenarios with different management measures can be compared. Scenarios B 1 to B 3 were non-point source pollution management measures from the viewpoint of optimized fertilization. Scenarios C 1 to C 3 were different vegetation buffer strips set up in the drainage area around citrus orchard and cultivated land. Scenarios D 1 and D 2 were grass ditches planted within the canals nearby citrus orchard and cultivated land. While scenarios E 1 to E 9 were management measures under the combination of optimized fertilization and vegetation buffer strips. All above scenarios were incorporated into the SWAT model for further evaluating of the non-point source pollution load reduction efficiency, and the calculated equation is described by(Avci et al., 2023b ): $$\:E=\frac{{P}_{b}-{P}_{B}}{{P}_{b}}$$ where \(\:E\) is the non-point source pollution load reduction efficiency; \(\:{P}_{b}\) is the non-point source pollution load under scenario A 0 ; \(\:{P}_{B}\) is the non-point source pollution load from other scenarios with different management measures. Table 1 Scenario settings NO. Management measures Parameter setting A 0 - - B 1 10% Fertilizer reduction Reduced by 10 % in .mgt FRT＿KGA B 2 20% Fertilizer reduction Reduced by 20 % in .mgt FRT＿KGA B 3 Local recommended fertilization Adjust the date and amount of fertilization in .mgt C 1 2 m Vegetation buffer strips Set FILTER＿ RATIO to 60 in .ops C 2 5 m Vegetation buffer strips Set FILTER＿ RATIO to 30 in .ops C 3 10 m Vegetation buffer strips Set FILTER＿ RATIO to 15 in .ops D 1 1km Grass ditch Set GWATL to 1 in .ops D 2 2km Grass ditch Set GWATL to 2 in .ops E 1 Combination of B 1 and C 1 Reduced by 10 % in .mgt FRT＿KGA & Set FILTER＿ RATIO to 60 in .ops E 2 Combination of B 2 and C 1 Reduced by 20 % in .mgt FRT＿KGA & Set FILTER＿ RATIO to 60 in .ops E 3 Combination of B 3 and C 1 Adjust the date and amount of fertilization in .mgt & Set FILTER＿ RATIO to 60 in .ops E 4 Combination of B 1 and C 2 Reduced by 10 % in .mgt FRT＿KGA & Set FILTER＿ RATIO to 30 in .ops E 5 Combination of B 2 and C 2 Reduced by 20 % in .mgt FRT＿KGA & Set FILTER＿ RATIO to 30 in .ops E 6 Combination of B 3 and C 2 Adjust the date and amount of fertilization in .mgt & Set FILTER＿ RATIO to 30 in .ops E 7 Combination of B 1 and C 3 Reduced by 10 % in .mgt FRT＿KGA & Set FILTER＿ RATIO to 15 in .ops E 8 Combination of B 2 and C 3 Reduced by 20 % in .mgt FRT＿KGA & Set FILTER＿ RATIO to 15 in .ops E 9 Combination of B 3 and C 3 Adjust the date and amount of fertilization in .mgt & Set FILTER＿ RATIO to 15 in .ops 3. Results 3.1 Sensitivity analysis Sensitivity analysis is a crucial step in identifying parameters that have the most significant impact on model outputs such as runoff and nutrient loads, modelers can prioritize these parameters during calibration and validation to achieve better model performance and accuracy (Dai et al., 2024 ). In this research, the SWAT-CUP was run for 20 iterations comprising 500 simulations each, and the global sensitivity analysis method was utilized to examine the sensitivity of various parameters within the SWAT model. The analysis utilized T-statistics and P-values to evaluate parameter sensitivity, revealing that higher absolute T-stat values indicate greater sensitivity (Fig. 3 ), while P-values closer to zero denote increased importance. The findings highlighted that parameters such as RCHRG_DP, SMTMP, SFTMP, and CN2 significantly influenced runoff, while RS4, CMN, BC3, and N_UPDIS considerably impacted TN levels. Additionally, ERORGP and PHOSKD were found to be critical for TP levels (Table 2 ). Table 2 Parameter calibration results of SWAT model Number Parameter Scope of values Best value Sensitivity priority Parameter definition FLOW CN2.mgt -0.5-0.5 -0.104 6 SCS runoff curve number .(Hernández-Marín et al., 2024 ) SOL_AWC().sol -0.5-0.5 0.083 23 Available water capacity of the soil layer.(Hernández-Marín et al., 2024 ) SOL_BD().sol -0.5-0.5 0.241 29 Wet bulk density of soil .(Hernández-Marín et al., 2024 ) REVAPMN.gw 0-500 200.165 20 Threshold depth of water in the shallow aquifer for "revap" to occur (mm).(Khadka, 2022 ) CH_K2.rte -0.01-500 16.586 18 Efective hydraulic conductivity in the alluvium of the main channel.(Hernández-Marín et al., 2024 ) EPCO.hru 0–1 0.291 8 Plant uptake compensation factor. (Khadka, 2022 ) RCHRG_DP.gw 0–1 0.597 3 Deep aquifer percolation fraction.(Khadka, 2022 ) SFTMP.bsn -20-20 -10.133 4 Snowfall temperature.(Rautela et al., 2023 ) SMTMP.bsn -20-20 2.805 5 Snow melt base temperature.(Rautela et al., 2023 ) SMFMX.bsn 0–20 0.454 14 Maximum melt rate for snow during the year (occurs on summer solstice).(Rautela et al., 2023 ) TN RSDCO.bsn 0.02–0.1 0.070 15 Residue decomposition coefficient.(Zhai et al., 2014 ) SDNCO.bsn 0–1 0.401 25 Denitrification threshold water content.(Yuan and Chiang, 2015 ) SOL_ORGN().chm 0-100 66.331 12 Initial organic nitrogen concentration in soil layer(Yan et al., 2019 ) BC1.swq 0.1-1 0.832 16 Rate constant for biological oxidation of NH 4 to NO 2 in the reach at 20℃. BC2.swq 0.2-2 1.318 17 Rate constant for biological oxidation of NO 2 to NO 3 in the reach at 20℃. BC3.swq 0.2–0.4 0.274 2 Rate constant for hydrolysis of organic N to NH4 in the reach at 20 ℃. RS4.swq 0.001-0.1 0.033 10 Rate coefficient for organic N settling in the reach at 20 ℃. N_UPDIS.bsn 0-100 69.395 11 Nitrogen uptake distribution parameter AI1.wwq 0.07–0.09 0.078 22 Fraction of algal biomass that is nigrogen. CDN.bsn 0–3 1.340 26 Denitrification exponential rate coefficient. CMN.bsn 0.001–0.003 0.00127 9 Rate factor for humus mineralization of active organic nitrogen. SOL_NO3().chm 0-1000 72.420 13 Initial NO 3 concentration in the soil layer. NPERCO.bsn 0–1 0.015 28 Nitrogen percolation coefficient. TP PHOSKD.bsn 100–200 124.440 7 Phosphorus soil partitioning coefficient. SOL_ORGP().chm 0-100 55.217 19 Initial organic P concentration in surface soil layer. AI2.wwq 0.01–0.02 0.016 27 Fraction of algal biomass that is phosphorus. BC4.swq 0.01–0.7 0.441 24 Rate constant for mineralization of organic P to dissolved P in the reach at 20 ℃. ERORGP.hru 0–5 0.003 1 Organic P enrichment ratio. RS5.swq 0.001-0.1 0.080 21 Organic phosphorus settling rate in the reach at 20 ℃. 3.2 Calibration and validation of the SWAT model In this research, the SWAT model was operated from 2020 to 2023, with a calibration period from 2020 to 2022 and a validation period from 2022 to 2023. The model’s performance was evaluated using several indexes, including the Nash-Sutcliffe Efficiency coefficient (NSE), the coefficient of determination (R 2 ), and the Percent Bias (PBIAS), with acceptable simulation results defined as R 2 > 0.6, NSE > 0.5, and PBIAS ≤ ± 35%. Parameter adjustments were made using SWAT-CUP to ensure compliance with these criteria, resulting in favorable outcomes: during the calibration period, runoff showed R 2 = 0.84, NSE = 0.82, and PBIAS = -4.4%, while for the validation period, R 2 = 0.90, NSE = 0.77, and PBIAS = -2.7%. Additionally, Total Nitrogen (TN) and Total Phosphorus (TP) also indicated strong performance, the R 2 , NSE, and PBIAS during the calibration period were 0.81, 0.77, 7.4%, and 0.81, 0.77, -8.3%, respectively. And those of the validation period were 0.92, 0.70, 1.4% and 0.87, 0.80, 16.6%, respectively. Therefore, it demonstrated that the SWAT model could effectively reflect the hydrological and water quality characteristics of the Bailin River basin. 3.3 Temporal characteristics of the pollution loads From 2020 to 2023, the simulated monthly runoff in the Bailin River basin ranged from 0.29 to 19.25 m 3 /s, while the simulated monthly total nitrogen (TN) and total phosphorus (TP) were from 1.48 to 93.98 tons and 0.04 to 4.01 tons, respectively. As shown in Fig. 4 and Fig. 5 , the trends of TN and TP in the Bailin River basin were similar to that of runoff flow with time. As the runoff flow increased, both TN and TP also increased, exhibiting a “V” shaped fluctuation. Notably, in July 2020, an extreme rainstorm occurred in the Bailin River basin, resulting in peaks for monthly runoff, TN, and TP at the beginning of July, reaching 19.25 m 3 /s, 93.98 tons, and 4.012 tons, respectively. In the subsequent two years, the monthly runoff reached a small peak in April, measuring 7.76 m 3 /s and 10.78 m 3 /s, respectively, at which point monthly TN and TP reached their annual peaks. During the flood season (April to September), the TN and TP losses could account for 58.61% and 58.92% of the annual totals, respectively. The process of nitrogen and phosphorus loss through runoff is quite complex, influenced by various factors such as precipitation characteristics, agricultural activities, underlying surface conditions, land utilization, and soil physicochemical properties. An investigation into the management conditions of citrus orchards in the study area revealed that, due to declining economic returns from citrus cultivation in recent years, many farmers opted to seek work elsewhere. This labor loss resulted in the reduction of fertilization from three applications (in spring for flower and fruit conservation, in summer for fruit swelling, and in autumn and winter for replenishment) to only one application of replenishment fertilizer in November. With lower winter temperatures leading to decreased microbial activity in the soil, chemical fertilizers were not fully decomposed for plant absorption, causing nutrients to primarily concentrate in the soil surface layer. When the first significant rainfall occurred in the spring of the following year, initial scouring effects caused a substantial loss of TN, which accounted for 42% of the total TN loss during the flood season in April 2022. Additionally, most citrus orchards in the Bailin River basin are planted on slopes, accelerating the surface runoff processes and the migration of particulate nitrogen and phosphorus. From 2020 to 2022, rainfall showed a positive correlation with total nitrogen and total phosphorus, with R 2 values of 0.55 and 0.52, respectively. However, during the period from 2022 to 2023, the R 2 values increased to 0.74 and 0.91. Before 2020, pollutant loads not only originated from rainfall scouring but also included significant pollution sources from nearby industrial wastewater, domestic sewage, and livestock farming. After 2020, as a series of water ecological restoration efforts were implemented in the Bailin River basin, many coastal factories and farms gradually ceased operations, leading to an increased proportion of nitrogen and phosphorus loss attributed to rainfall scouring. In terms of interannual variations of the pollutant output (Fig. 6 ), the total annual TN output in 2021 decreased by 31.00% compared to the previous year, and the total annual TP output decreased by about 28.58%. In 2022, the total annual TN output again decreased by 11.81%, and the total annual TP output decreased by 26.40%. It is worth noting that in 2023, the total annual TN output dropped by 64.60%, and the total annual TP output decreased by 69.31%. The most significant reduction was observed in 2023. It is speculated that the Longquan Sewage Treatment Plant put into operation in 2023 collects industrial wastewater and residential sewage in Longquan Town, which greatly reduces the amount of pollutants entering the Bailin River. 3.4 Spatial characteristics of the pollution loads Based on the long-term average pollutant output loads in the Bailin River basin, the degree of pollution in its sub-basins has been classified into the following four levels: TN < 8.19 kg/(hm 2 ·a) and TP < 1.54 kg/(hm 2 ·a); TN < 15.99 kg/(hm 2 ·a) and TP < 2.10 kg/(hm 2 ·a); TN < 36.10 kg/(hm 2 ·a) and TP 36.10 kg/(hm 2 ·a) and TP > 3.33 kg/(hm 2 ·a). The research results indicate that the TN and TP loads exhibit considerable spatial variations, specifically characterized by lower levels in the upstream areas, higher levels in the middle and lower reaches, and elevated concentrations near the riverbanks. In the southwestern part of the basin, land use is primarily for residential and agricultural activities. The discharge of industrial wastewater along the river, the direct emissions of urban and rural domestic sewage, as well as the increasingly intensive agricultural practices have led to the accumulation of non-point source pollution, resulting in excessive pollutant output loads in this area. In contrast, the northern part of the basin is mainly mountainous with a low population density and predominant land uses consisting of forested areas, thus generating relatively low levels of pollutants. Figure 7 illustrates the spatial distribution of TN and TP loads in the Bailin River basin from 2020 to 2023. In 2020, sub-basins 12, 14, 16, 17, 18, 20, and 21 fell into the fourth category of the TN and TP output loads. Among the severely polluted sub-basins, 12 and 14 are situated in the middle reach of the main stream of the Bailin River in Longquan Town, where the primary land uses include residential land, citrus orchards, and cultivated land. Prior to 2020, Longquan Town, with a resident population exceeding 110,000, lacked adequate sewage treatment facilities, resulting in significant amounts of untreated industrial and municipal sewage being discharged directly. Following the decreased profitability of citrus farming and the outmigration of rural labor, local farmers primarily used easily accessible chemical fertilizers, with minimal application of organic fertilizers; many farmers even avoided using organic fertilizers altogether, leading to significant non-point source pollution issues. Sub-basins 16, 17, and 20 are located near the high-tech industrial area and the main urban area, where direct discharge of industrial wastewater and urban/rural domestic sewage contributes to excessive pollutant output loads in these areas. Sub-basins 18 and 21 are located downstream of the tributaries of the Bailin River, which features numerous citrus industrial parks along its banks. The terrain is characterized by a higher left side and a lower right side, with a broad, gently flowing water body that promotes sedimentation of silt and sand. Phosphorus output from the river mainly consists of particulate phosphorus adsorbed onto sediments, with a considerable amount of dissolved phosphorus being re-absorbed by sediment during its migration, resulting in high total phosphorus output loads. However, starting in 2021, the government has been focusing on promoting water ecological environment restoration efforts in the Bailin River basin, which includes the construction of the Longquan Sewage Treatment Plant, expansion of the Huayan Sewage Treatment Plant, relocating large-scale livestock farms within a one-kilometer radius of the main Bailin River, and the establishment of Bailin River Ecological Wetland Park. By 2023, except for sub-basins 18 and 21, the TN and TP loads of other previously severely polluted sub-basins were all less than Level Four, indicating initial successes in the water environment management efforts within the Bailin River basin. 3.5 The best management practices (BMPs) for the critical source areas Sub-basins 14, 16, 18, and 21 have a total area of about 27.4 km 2 , accounting for only 2.1% of the total area of the Bailin River basin. However, the pollutant output load from this area is 176.10 kg/(hm 2 ·a), which constitutes 45.3% of the total output load in the study area, significantly higher than that of other sub-basins. This aligns with the characteristic of critical source areas, where a smaller area contributes disproportionately larger pollutant loads. Therefore, sub-basins 14, 16, 18, and 21 have been chosen as the critical source areas, for which the best management practices to reduce the non-point source pollution were screened out. The primary land uses in these critical source areas are farmland and citrus orchards, with cultivated land making up 12.1% of the basin area and citrus orchards comprising 43.9%. Series of management scenarios for these areas have been simulated, with the reduction efficiencies of various Best Management Practices (BMPs) for TN and TP shown in Fig. 8 . Scenarios B 1 to B 3 were non-point source pollution management measures from the viewpoint of optimized fertilization. The reduction of TN loads for scenarios B 1 to B 3 were 7,488.0 kg/a, 12,179.2 kg/a, and 21,832.4 kg/a, and the reduction rates were 8.3%, 13.5%, and 24.2%, respectively. The reductions in TP loads were 1,420.2 kg/a, 2,367.0 kg/a, and 4,102.8 kg/a, with reduction rates of 6.3%, 10.5%, and 18.2%, respectively. The TN reduction rates were slightly higher than those for TP loads, primarily due to the higher nitrogen content in chemical fertilizers compared to phosphorus. The optimized fertilization strategy not only reduces the amount of fertilizers applied but also increases the frequency of application. This helps decrease the proportion of fertilization that occurs before April and mitigates nutrient loss caused by initial runoff, thus yielding significant effects on TN reduction. Vegetation buffer strips (VBS) are defined as buffer areas composed of trees (arboraceous plants) and other vegetation located adjacent to receiving water bodies. Their functions include intercepting sediment particles and adsorbed pollutants, promoting runoff infiltration, and stabilizing soil particles (Liu et al., 2023 ). Researchers indicated that vegetated swales and Vegetation buffer strips are more effective in reducing nitrogen and phosphorus pollutants compared to retention ponds and constructed wetlands (Ahsan et al., 2023 ; Sharma et al., 2023 ). In this study, three vegetation buffer strips (C 1 , C 2 , C 3 ) of different widths were set, the reduction rates for TN were 20.6%, 37.6%, and 43.3%, while for TP were 25.6%, 43.3%, and 50.6%, respectively. The Bailin River basin represents a typical mountainous watershed with significant elevation changes in the river network and concentrated rainfall throughout the year. Vegetation buffer strips help slow down surface runoff and trap particulate phosphorus adsorbed to sediments. Therefore, the reduction rate for TP exceeds that for TN. Furthermore, the reduction rate for C 3 compared to C 2 was only improved by 5.7%. This is mainly because when a Vegetation buffer strip is too wide, most pollutants are already filtered out at the front of the strip, and increasing width does not significantly enhance pollutant removal rates. Analyzing from a cost-effectiveness perspective, C 2 is more suitable than C 3 as a priority choice for managing the critical source areas. Grass ditches refer to channels in which grass is planted to convey runoff. The vegetation not only slows down the flow and intercepts sediment particles but also allows the roots to absorb nitrogen and phosphorus from the water flow (Oduor et al., 2023 ). Studies found that grass ditches had the greatest reduction rates for sediment, TN, and TP at the watershed outlet (Rohith et al., 2024 ; Wu et al., 2024 ; Xie et al., 2024 ). In this research, we designed two lengths of grass ditches (D 1 and D 2 ), the TN reductions were 21,922.6 kg/a and 30,583.4 kg/a, with reduction rates of 24.3% and 33.9%, respectively. The reductions in TP were 6,740.3 kg/a and 8,679 kg/a, with reduction rates of 29.9% and 38.5%, respectively. In terms of the reduction efficiency of individual measures, C3 and C2 showed the best reduction effects, followed by D2, D1, B3, C1, while B1 and B2 yielded the poorest results. Moreover, this study also simulated the reduction effects of different combinations of measure, with the reduction rates of TN/TP were as follows: E 9 (60.3%/65.3%) > E 6 (58.4%/63.1%) > E 8 (53.1%/60.3%) > E 5 (50.6%/58.3%) > E 7 (47.3%/54.7%) > E 4 (45.5%/52.3%) > E 3 (42.4%/46.3%) > E 2 (38.6%/41.4%) > E 1 (32.4%/37.6%). It can be concluded that E 9 and E 6 performed the best results, achieving TN and TP reduction rates exceeding 58%. Additionally, the results also revealed that the TN and TP reduction rates for each combination of BMPs are greater than those for individual BMPs, yet the overall reductions remain lower than the sum of reductions from two individual BMPs. 4. Discussions 4.1 The accuracy of the SWAT model Addressing agricultural pollution in rural areas is an important measure for implementing the rural revitalization strategy. Due to the extensive and widespread nature of agricultural production activities in China, and the relatively low level of agricultural intensification in some regions, the management and control of non-point source pollution are particularly challenging (Luo et al., 2023 ). Therefore, monitoring pollution sources and identifying critical source areas are crucial. However, the hydrological and water quality monitoring efforts in small and medium-sized rural basins in China are insufficient. There are not enough river monitoring sites, and the frequency of measurements is low (Kumwimba et al., 2024 ). Although this study calibrated the SWAT model using monthly runoff and TN load data, achieving a high degree of correlation between simulated and observed values, there is still a need to strengthen flow and water quality monitoring in the Bailin River basin in the future to more thoroughly validate the SWAT simulation results (Gao et al., 2023 ). 4.2 The TN and TP outputs The output loads of pollutants in the Bailin River basin are primarily concentrated during the flood season, with TN and TP losses from April to September accounting for 58.61% and 58.92% of the annual total, respectively. As to the TN losses, nitrate nitrogen, nitrite nitrogen, and ammonium nitrogen are predominant, representing dissolved nitrogen that has a strong correlation with runoff (Wang et al., 2024a ). Nevertheless, the form of phosphorus loss is mainly particulate phosphorus (Nie et al., 2024 ). Research indicates that rainfall, as the primary external force for erosion and sediment production in the basin, contributes significantly to the increase in river sediment transport (Chen et al. , 2022; Guan et al., 2024 ). During the flood season, the high rainfall leads to substantial river sediment transport, resulting in intensified loss of adsorbed particulate phosphorus (Avci et al., 2023a ). The area of cultivated land and citrus orchards in the basin accounts for approximately 64.3%, with nitrogen and phosphorus primarily concentrated in the upper layers of the soil. When the first concentrated rainfall occurs in April of the following year in the Three Gorges Reservoir area, initial erosion effects are likely to take place (Zheng et al., 2024 ). The southwestern part of the basin is densely populated and features numerous factories and cultivated orchards along the riverbanks, leading to excessive pollutant output loads, making it a critical source area. In contrast, the northern part of the basin is primarily mountainous, with a low population density and land use predominantly consisting of forested areas, resulting in lower pollutant generation. 4.3 Treatment effects of different management scenarios The best management practices (BMPs) primarily reduce agricultural non-point source pollution through three approaches: source control, interception along transport pathways, and end-of-pipe treatment (Sharma et al., 2023 ; Tarabih et al., 2024 ). In this study, the reduction of chemical fertilizer application (B 1 to B 3 ) belongs to the source control. However, the effectiveness of fertilizer reduction in reducing TN load exhibits significant regional specificity, influenced by factors such as meteorological conditions, agricultural practices, underlying surface characteristics, and land cover (Aggarwal et al., 2024 ). In this research, the individual application of reduced fertilizer use resulted in relatively low TN and TP reduction rates. This may be attributed to the concentration of rainfall in the Bailin River basin occurring mainly from April to August, which does not align with the fertilization periods of major crops. Additionally, long-term excessive fertilization in the area has led to nutrient accumulation in the soil, making it difficult to significantly lower soil nitrogen and phosphorus levels through short-term reductions in fertilizer use, thereby limiting its effectiveness in reducing non-point source pollution loads. Similar patterns were observed in the Fengle River basin, where reductions of 10%, 20%, and 30% in fertilizer usage resulted in total phosphorus (TP) reduction rates of only 3.52%~5.83%, 7.05%~11.65%, and 10.60%~17.46% (Wang et al., 2024b ). In contrast to non-engineering measures like reduced fertilizer application and optimized fertilization, engineering measures such as vegetation buffer strips and grass ditches demonstrate more substantial and efficient TN and TP reduction effects. This aligns with previous research findings (Plunge et al., 2022 ). The study found that establishing a 10-meter vegetation buffer strip yielded the highest reduction rates for TN and TP loads. However, vegetation buffer strips as engineering measures require land use and involve significant construction and maintenance costs. Consequently, suitable buffer strip areas should be selected based on local economic, hydrological, geological, and other conditions (Shrestha et al., 2021 ). The downstream part of the Bailin River basin faces prominent land-use conflicts, and given that the reduction rate of a 10-meter buffer strip is only 5.7% higher than that of a 5-meter strip, a cost-benefit analysis suggests that the 5-meter option (C 2 ) may be a more suitable choice for prioritizing critical source area management compared to the 10-meter option (C 3 ). Grass ditches, by slowing down river flow, can serve as a pre-treatment measure to mitigate non-point source pollution caused by rainfall runoff, filtering and intercepting pollutants in the runoff (Mohebzadeh et al., 2024 ). In the Bailin River basin, grassed ditches effectively reduce TN and TP loads without occupying significant construction land, making them a viable secondary option for addressing the critical pollution areas. On the whole, the combined BMPs have a more pronounced impact on TN and TP reduction, with the integration of local recommended fertilization and the 5/10-meter buffer strips (E 6 and E 9 ) achieving reduction rates exceeding 58%. The reduction rate of combined BMPs is not merely a straightforward accumulation of the individual measures' rates but rather a reflection of the interactions between different measures. For instance, both vegetation filter strips and grass ditches share similarities in their mechanisms for pollutant reduction, primarily through the interception and adsorption of pollutants by vegetation, thus decreasing the amount entering the river (Horvath et al., 2023 ; Zhou et al., 2024 ). To a certain extent, the combined implementation yields effects better than the additive efficiency of the individual measures. 5. Conclusions The Bailin River Basin, as a critical tributary of the Yangtze River, faces profound challenges relating to non-point source (NPS) pollution primarily from agricultural runoff, urbanization, and industrial discharges. This study utilized the Soil and Water Assessment Tool (SWAT) model to simulate and assess the dynamics of runoff, total nitrogen (TN), and total phosphorus (TP) loads over a three-year period. The research provided critical insights into the effectiveness of various best management practices (BMPs) aimed at mitigating NPS pollution in the basin. One of the key findings is the significant concentration of nutrient losses during the flood season, with TN and TP losses from April to September representing over 58% of the annual totals. This highlights the critical need for management strategies that target this specific period to curtail the influx of pollutants into the river. The findings underscore the complex interactions between land use, rainfall patterns, and pollutant transport mechanisms in the basin, which can result in significant temporal fluctuations in water quality parameters. The study also emphasized the importance of continuous monitoring and adaptive management practices tailored to local conditions. Given the ongoing changes in land use and climate variability, the effectiveness of the proposed BMPs must be regularly assessed and refined. The data suggests a positive trend over time concerning nutrient load reductions, significant improvements in water quality can be attributed to recent ecological restoration efforts, including the construction of sewage treatment facilities and improved agricultural practices. In conclusion, this research presents a comprehensive understanding of non-point source pollution dynamics in the Bailin River Basin and affirms the critical role of targeted, integrated management strategies. The results can assist policymakers, land managers, and researchers in developing effective frameworks for sustainable river basin management that enhance water quality and ecological integrity. The success of these approaches in the Bailin River may serve as a model for similar mountainous watersheds facing the challenges of agricultural NPS pollution on a global scale. Future research should continue to explore innovative solutions, harnessing advancements in technology, including remote sensing and machine learning, to improve the prediction and management of water resources under the pressures of environmental change. Declarations CRediT authorship and contribution statement Hao Wang: Writing - original draft, Methodology, Software, Investigation; Yifeng Liu: Data curation, Writing - original Draft; Shijiang Zhu: Writing – original draft, Funding acquisition; Yang Liu: Investigation, Methodology; Wen Xu: Conceptualization, Supervision, Writing - review & editing; Funding acquisition. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data Availability The data that has been used is confidential, and will be made available on request. Acknowledgements We would like to thank the National Natural Science Foundation of China and the Department of Water Resources of Hubei Province for providing financial support to complete the research. We also extend our gratitude to the Bureau of Ecological Environment of Yichang City for supplying the water quality monitoring data. Funding Declaration This work was supported by the Youth Fund from the National Natural Science Foundation of China (grant number: 52000120); the Key Scientific Research Projects of Water Conservancy in Hubei Province (grant numbers: HBSLKY201919 and HBSLKY202322); and the 111 project of Hubei Province. References Aggarwal, S., Sharma, V., Rallapalli, S., Lenhart, C., Magner, J., 2024. Farmer adoption-based prompt networking and modeling for targeting optimal agro-conservation practices. Environ. Modell. Softw. 177. Ahsan, A., Das, S.K., Khan, M., Ng, A.W.M., Al-Ansari, N., Ahmed, S., Imteaz, M., Tariq, M., Shafiquzzaman, M., 2023. Modeling the impacts of best management practices (BMPs) on pollution reduction in the Yarra River catchment, Australia. Appl. Water Sci 13. Avci, B.C., Kesgin, E., Atam, M., Tan, R.I., 2023a. Modeling agricultural practice impacts on surface water quality: case of Northern Aegean watershed, Turkey. Int. J. Environ. Sci. Technol 20, 5265-5280. Avci, B.C., Kesgin, E., Atam, M., Tan, R.I., 2023b. Spatial-Temporal Response of Sediment Loads to Climate Change and Soil Conservation Practices in the Northern Aegean Watershed, Turkiye. WATER 15. Basu, N.B., Van Meter, K.J., Byrnes, D.K., Van Cappellen, P., Brouwer, R., Jacobsen, B.H., Jarsjö, J., Rudolph, D.L., Cunha, M.C., Nelson, N., Bhattacharya, R., Destouni, G., Olsen, S.B., 2022. Managing nitrogen legacies to accelerate water quality improvement. Nat. Geosci 15, 97-105. Cai, Y.F., Zhang, F., Shi, J.C., Johnson, V.C., Ahmed, Z., Wang, J.G., Wang, W.W., 2023. Enhancing SWAT model with modified method to improve Eco-hydrological simulation in arid region. J. Clean Prod 403. Chen, Y.F., Miao, J.F., Zhu, Z.T., 2021. Measuring green total factor productivity of China's agricultural sector: A three-stage SBM-DEA model with non-point source pollution and CO2 emissions. J. Clean Prod 318. Chen, X., Rudolph, D.L., Cunha, M.C., Nelson, N., Bhattacharya, R., Destouni, G., Olsen, S.B., 2021. Multi-site calibration of SWAT for improved hydrological and NPS pollution simulations. Water Resour. Manag 10, 97-205. Dai, C., Zhang, X.L., Tan, X.Z., Hu, M.C., Sun, W., 2024. A stochastic simulation-based chance-constrained programming model for optimizing watershed best management practices for nonpoint source pollution control under uncertainty. J. Hydrol 632. Fang, S.B., Deitch, M.J., Gebremicael, T.G., Angelini, C., Ortals, C.J., 2024. Identifying critical source areas of non-point source pollution to enhance water quality: Integrated SWAT modeling and multi-variable statistical analysis to reveal key variables and thresholds. Water Res 253. Fang, S., Wang, J., Huang, Yang, H., 2023. Eutrophication trends and nutrient loading sources in urban lakes: A comparative study between China and Europe. Environ. Pollut, 298, 118870. Gao, W.M., Dai, A.Q., Wu, J.L., Li, Y.B., Hou, J., Wang, X.L., Li, K.Q., 2023. Hydrological status of the Dagu River Basin and management suggestions based on soil and water assessment tool multi-station calibration. Appl. Water Sci 13 Gu, B.J., Zhang, X.M., Lam, S.K., Yu, Y.L., van Grinsven, H.J.M., Zhang, S.H., Wang, X.X., Bodirsky, B.L., Wang, S.T., Duan, J.K., Ren, C.C., Bouwman, L., de Vries, W., Xu, J.M., Sutton, M.A., Chen, D.L., 2023. Cost-effective mitigation of nitrogen pollution from global croplands. Nature 613. Guan, R.H., Wu, L.X., Li, Y., Ma, B.G., Liu, Y., Zhao, C., Wang, Z.W., Zhao, Y., 2024. Evaluating the Impacts of Fertilization and Rainfall on Multi-Form Phosphorus Losses from Agricultural Fields: A Case Study on the North China Plain. Agronomy-Basel 14. Hernández-Marín, M.A., Ortiz-Gómez, R., Zavala, M., Rodríguez-Rodríguez, J.A., Medellin, P.A., Ortiz-Robles, F.A., 2024. Hydrological simulation using the SWAT model in a semi-arid region in the southern part of Zacatecas, Mexico. Environ. Earth Sci 83. Horvath, I.R., Zhang, K., Mayer, B.K., Parolari, A.J., 2023. Effects of Regional Climate and BMP Type on Stormwater Nutrient Concentrations in BMPs: A Meta-Analysis. Environ. Sci. Technol 57, 5079-5088. Huang, J.C., Zhang, Y.J., Bing, H.J., Peng, J., Dong, F.F., Gao, J.F., Arhonditsis, G.B., 2021. Characterizing the river water quality in China: Recent progress and on-going challenges. Water Res 201. Hoehn, K., Jacob, M., Schmid, D. W., 2021. Assessing the effectiveness of policies and practices in mitigating non-point source pollution in lakes. Water Research, 191, 116805.（Water Res.） Jeong, D.S., Kim, J.H., Kim, J.H., Park, Y., 2024. Evaluation of the Best Management Practices for Reducing Phosphorus Load in a Watershed in Terms of Cost and Greenhouse Gas Emissions. Agronomy-Basel 14. Khadka, A., 2022. Runoff Modeling Using SWAT Model in Little Wabash River Watershed, Illinois. Kumwimba, M.N., Ajibade, F.O., Dzakpasu, M., Soana, E., 2024. Editorial: Advances in ecotechnologies for the control of non-point source pollution in agricultural and urban watersheds. Front. Environ. Sci 12. Krishnan,Oduor, B.O., Campo-Bescós, M.A., Lana-Renault, N., Kyllmar, K., Mårtensson, K., Casalí, J., 2022. Improving streamflow predictions in SWAT using machine learning techniques. Agronomy-Basel, 29(12), 17321-17334. Li, X., Du, L., Zhang, S.H., Shi, K., Yang, Q.C., Li, L.P., Jiang, J.K., Ren, Z., Liu, X.H., 2024. Improving the identification of pollution source areas with catchment-resolution sensitivity analysis. Environ. Pollut 360. Liang, C.X., Huang, Q., Wang, S., Wang C., Yu Q.Y., Wu W.B., 2021. Citrus orchard identification based on multi-temporal remote sensing vegetation index. Transactions of the CSAE 37, 168-176.（in Chinese） Liu, W., Zhang, L., Wu, H.L., Wang, Y.F., Zhang, Y.L., Xu, J.Y., Wei, D.Y., Zhang, R., Yu, Y., Wu, D.S., Xie, X.C., 2023. Strategy for cost-effective BMPs of non-point source pollution in the small agricultural watershed of Poyang Lake: A case study of the Zhuxi River. Chemosphere 333. Liu, X., Shang, J., 2022. Effectiveness of BMPs in reducing agricultural nutrient runoff using SWAT. Agric. Water Manage 30, 51607-51623. Luo, M., Liu, X.X., Legesse, N., Liu, Y., Wu, S., Han, F.X., Ma, Y.H., 2023. Evaluation of Agricultural Non-point Source Pollution: a Review. Water Air Soil Pollut 234. Mohebzadeh, H., Biswas, A., DeVries, B., Rudra, R., Shukla, R., Daggupati, P., 2024. Evaluation of the impact of best management practices on ephemeral gully and sheet/rill erosion using the AnnAGNPS model. Catena 246. Nie, Y.X., Huang, G.X., Tong, S.C., Chen, W., Song, Y.H., Wang, X., Zhang, Q.H., Jiang, P.F., 2024. Assessing the impacts of rice terraces and ponds on the sediment and phosphorus loads in a typical hilly watershed of three gorge reservoir, China. J. Clean Prod 459. Niraula, Zhang, Z.S., Montas, H., Shirmohammadi, A., Leisnham, P., Negahban-Azar, M., 2019. Evaluating climate change impacts on watershed-scale nitrogen and phosphorus loads. J. Hydrol 742. Oduor, B.O., Campo-Bescós, M.A., Lana-Renault, N., Kyllmar, K., Mårtensson, K., Casalí, J., 2023. Quantification of agricultural best management practices impacts on sediment and phosphorous export in a small catchment in southeastern Sweden. Agric. Water Manage 290. Plunge, S., Gudas, M., Povilaitis, A., 2022. Effectiveness of best management practices for non-point source agricultural water pollution control with changing climate - Lithuania's case. Agric. Water Manage 267. Rautela, K.S., Kumar, D., Gandhi, B.G.R., Kumar, A., Dubey, A.K., 2023. Long-term hydrological simulation for the estimation of snowmelt contribution of Alaknanda River Basin, Uttarakhand using SWAT. AQUA 72, 139-159. Rohith, A.N., Karki, R., Veith, T.L., Preisendanz, H.E., Duncan, J.M., Kleinman, P.J.A., Cibin, R., 2024. Prioritizing conservation practice locations for effective water quality improvement using the Agricultural Conservation Planning Framework (ACPF) and the Soil and Water Assessment Tool (SWAT). J. Environ. Manage.349. Sharma, S., Bijukshe, S., Puppala, S.S., 2023. Monitoring, Modeling and Planning Best Management Practices (BMPs) in the Atwood and Tappan Lake Watersheds with Stakeholders Engagements. WATER 15. Shrestha, N.K., Rudra, R.P., Daggupati, P., Goel, P.K., Shukla, R., 2021. A comparative evaluation of the continuous and event-based modelling approaches for identifying critical source areas for sediment and phosphorus losses. J. Environ. Manage. 277. Tarabih, O.M., Arias, M.E., Santos, A.L., Hua, J.Y., Cooper, R.Z., Khanal, A., Dang, T.D., Khare, Y.P., Charkhgard, H., Rains, M.C., Zhang, Q., 2024. Effects of the spatial distribution of best management practices for watershed wide nutrient load reduction. Ecol. Eng 201. Valencia, S., Villegas, J.C., Hoyos, N., Duque-Villegas, M., Salazar, J.F., 2024. Streamflow response to land use/land cover change in the tropical Andes using multiple SWAT model variants. J. Hydrol.-Reg. Stud 54. Wang, N., Wang, L., Jin, L., Wu, J.J., Pang, M., Wei, D., Li, Y., Wang, J.Q., Xu, T., Yang, Z.X., Xie, J.Z., 2024a. Rainfall Runoff and Nitrogen Loss Characteristics on the Miyun Reservoir Slope. WATER 16. Wang, X.F., Xiao, J.J., Wu, H.Q., Luo, M., Zhang, C., CHU, Q., 2024b. Output characteristics and optimal management measures of phosphorus pollution in Fengle River Basin. Journal of Agricultural Resources and Environment 41, 891-900.（in Chinese） Wen, W.J., Zhuang, Y.H., Jiang, T.Y., Li, W.D., Li, H.X., Cai, W., Xu, D., Zhang, L., 2024. "Period-area-source" hierarchical management for agricultural non-point source pollution in typical watershed with integrated planting and breeding. J. Hydrol 635. Wu, Z.J., Liang, H., Wu, H.Q., Xie, Z.Y., Liu, H.Y., Zhang, J.H., Zhu, J.Q., Zheng, B.F., Wan, W., 2024. Application of improved risk assessment methods to best management practices research for non-point source pollution: From a hilly mountainous-dominated region of Southern China. Ecol. Indic 165. Xie, C., Zhang, L.Y., Wang, M.L., Jiang, Q.N., 2024. Scenario analysis on the management practices and optimization of rural non-point source pollution with the coupling of source-process-end: A case study of the Guishui river basin. Ecol. Indic 159. Xu, L.Y., Jiang, J., Lu, M.Y., Du, J.G., 2022. Integrating remote sensing data with SWAT for enhanced assessment of non-point source pollution. Remote Sens. Environ 267, 112710. Yan, W., Wang, J.H., Zou, H., Min, M., Duan, X.J., 2024. Modeling economic-environmental-ecological trade-offs for non-point source control strategies: A case study of Dianchi lake watershed, China. Ecol. Indic 158. Yan, X., Lu, W., An, Y., Chang, Z., 2019. Uncertainty analysis of parameters in non-point source pollution simulation: case study of the application of the Soil and Water Assessment Tool model to Yitong River watershed in northeast China. Agric. Water Manage 447. Yan, N.K., Rudra, R.P., Daggupati, P., Goel, P.K., Shukla, R., 2020. Assessing the impacts of land-use changes on non-point source pollution in the Yangtze River Basin using SWAT. Environ. Sci. Policy 321-1733. Yuan, Y., Chiang, L.-C., 2015. Sensitivity analysis of SWAT nitrogen simulations with and without in-stream processes. Arch. Agron. Soil Sci 61, 969-987. Zhai, X.Y., Zhang, Y.Y., Wang, X.L., Xia, J., Liang, T., 2014. Non-point source pollution modelling using Soil and Water Assessment Tool and its parameter sensitivity analysis in Xin'anjiang catchment, China. Hydrol. Process 28, 1627-1640. Zhang, S., Zhang, L.L., Meng, Q.Y., Wang, C.C., Ma, J.J., Li, H., Ma, K., 2024. Evaluating agricultural non-point source pollution with high-resolution remote sensing technology and SWAT model: A case study in Ningxia Yellow River Irrigation District, China. Ecol. Indic 166. Zheng, Y.X., Li, S.L., Keenlyside, N., He, S.P., Suo, L.L., 2024. Projecting Spring Consecutive Rainfall Events in the Three Gorges Reservoir Based on Triple-Nested Dynamical Downscaling. Adv. Atmos. Sci 41, 1539-1558. Zhou, B.W., Parsons, C., Van Cappellen, P., 2024. Urban Stormwater Phosphorus Export Control: Comparing Traditional and Low-impact Development Best Management Practices. Environ. Sci. Technol 58, 11376-11385. Zhou, J.H., Liu, X.H., Liu, X.J., Wang, W.L., Wang, L.Q., 2023. Assessing agricultural non-point source pollution loads in typical basins of upper Yellow River by incorporating critical impacting factors. Process Saf. Environ. Protect 177, 17-28. Zuo, D.P., Bi, Y.Q., Song, Y.H., Xu, Z.X., Wang, G.Q., Ma, G.W., Abbaspour, K.C., Yang, H., 2023. The response of non-point source pollution to land use change and risk assessment based on model simulation and grey water footprint theory in an agricultural river basin of Yangtze River, China. Ecol. Indic 154. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5961509","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":412111057,"identity":"50cf0a6e-e138-47fb-8a37-a2bcca70b798","order_by":0,"name":"Hao Wang","email":"","orcid":"","institution":"China Three Gorges University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Wang","suffix":""},{"id":412111058,"identity":"a74313b1-f0c0-48b5-b129-ef1e37aeec7d","order_by":1,"name":"Yifeng Liu","email":"","orcid":"","institution":"China Three Gorges University","correspondingAuthor":false,"prefix":"","firstName":"Yifeng","middleName":"","lastName":"Liu","suffix":""},{"id":412111060,"identity":"bc15f534-99e2-4d5f-9d19-ff5ac0f24158","order_by":2,"name":"Shijiang Zhu","email":"","orcid":"","institution":"China Three Gorges University","correspondingAuthor":false,"prefix":"","firstName":"Shijiang","middleName":"","lastName":"Zhu","suffix":""},{"id":412111061,"identity":"87e1bba5-2258-470c-8b87-0143879c02ab","order_by":3,"name":"Yang Liu","email":"","orcid":"","institution":"Chinese Hydraulic Engineering Society","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Liu","suffix":""},{"id":412111062,"identity":"cb045489-82fc-4fe5-a5b3-7832c1f82595","order_by":4,"name":"Wen Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYNCCCijNQ7yWMwwSJGphbCNFi8GNHMPHhfPs6uQjEhgfvG1jkDcnpEVyRo6x8cxtyRKGNxKYDee2MRjubCCghV8id5s077YDEoYzEtikedsYEgwOENDCJpG7/TfvHLAW9t9EaQHZwszbcEBCXiKBjZkoLZI97z9L8xxLltzA87BZcs45CcMNhLQYHE9L/MxTY8cv35588MObMht5grYg9B5gbABSEsSqBwL5BhIUj4JRMApGwcgCAHnmOPpCoGAAAAAAAElFTkSuQmCC","orcid":"","institution":"China Three Gorges University","correspondingAuthor":true,"prefix":"","firstName":"Wen","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2025-02-05 02:08:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5961509/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5961509/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75889249,"identity":"70c312c8-f5ef-48f4-8e3f-6eb295b2225f","added_by":"auto","created_at":"2025-02-10 09:38:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1020791,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical location of the study area\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5961509/v1/fc5136ab69dc6a6616ee25f3.png"},{"id":75889254,"identity":"51a6d2f3-77d5-44e2-b4d4-aabce5a1a6fd","added_by":"auto","created_at":"2025-02-10 09:38:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1416066,"visible":true,"origin":"","legend":"\u003cp\u003eLand use and soil types of the study area\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5961509/v1/af76b615f3a9eab676030d2e.png"},{"id":75889143,"identity":"64ee7f6a-b447-4459-bcb3-7662e176245a","added_by":"auto","created_at":"2025-02-10 09:38:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":273307,"visible":true,"origin":"","legend":"\u003cp\u003eThe absolute T-stat values to examine parameter sensitivity\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5961509/v1/2544eda5495760e4b8b6001c.png"},{"id":75889191,"identity":"2a70703b-d4c6-4641-8b4c-8fd3a1aa51c1","added_by":"auto","created_at":"2025-02-10 09:38:39","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":161513,"visible":true,"origin":"","legend":"\u003cp\u003eThe SWAT simulated runoff and the correlation of runoff and precipitation\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5961509/v1/8655c61cecd64c1e2ddf843d.jpeg"},{"id":75889246,"identity":"18abce5e-6669-4ccd-b773-b094836f5157","added_by":"auto","created_at":"2025-02-10 09:38:42","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":485127,"visible":true,"origin":"","legend":"\u003cp\u003eThe SWAT simulated TN/TP and correlation of TN/TP and precipitation\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5961509/v1/6f4a581fc5803edb8ce2b6ce.jpeg"},{"id":75889223,"identity":"35182afc-ddd0-4f14-ab5f-e3552e40ac59","added_by":"auto","created_at":"2025-02-10 09:38:41","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":476264,"visible":true,"origin":"","legend":"\u003cp\u003eInterannual variation of TN(TP) in Bailin River Basin\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5961509/v1/9be59b3b5651d4d5d0d9844b.jpeg"},{"id":75889217,"identity":"c220f070-00e0-4aac-b3fd-6129af514646","added_by":"auto","created_at":"2025-02-10 09:38:41","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":191677,"visible":true,"origin":"","legend":"\u003cp\u003eThe spatial variations of TN and TP load from 2020 to 2023\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5961509/v1/e107aa62f6549cafd01f0123.jpeg"},{"id":75890250,"identity":"24c1a64a-a153-4c22-b004-d5eb2371902c","added_by":"auto","created_at":"2025-02-10 09:46:39","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":761070,"visible":true,"origin":"","legend":"\u003cp\u003eThe reduction effect of different BMPs on TN and TP loads\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-5961509/v1/1d504c1f6b1b8f524c3d5aa3.png"},{"id":76425585,"identity":"36e6b942-d961-4025-91d8-bef76891fc5b","added_by":"auto","created_at":"2025-02-17 05:34:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7683748,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5961509/v1/d46bb827-db6a-4dd0-9080-d0116e9dcf51.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated Modeling and Management of Non-Point Source Pollution in the Bailin River Basin: Best Practices for Reducing Nutrient Loads","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWater is one of the indispensable basic materials for the development of human society. However, global water security is facing increasing severe challenges due to the rapid population growth and economic development, as well as the pollution of water resources(Basu et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). with the effective governance of the point source pollution in domestic and industry, the non-point source pollution becomes the biggest pollution source directly affects water quality and aquatic ecosystems, especially the agricultural non-point source pollution(Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gu et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Around China and American-European developed countries, more than 60% lakes are suffering various extents of eutrophication, and about 50\u0026ndash;70% of the problem is caused by the non-point source pollution (Hoehn et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Fang et al., 2022).\u003c/p\u003e \u003cp\u003eGenerally, the non-point source pollutants enter the river from large and scattered areas. It is hardly to assess and control the non-point source pollution mainly due to the large randomness and temporal-spatial variations of the pollution transmission and dispersal processes (Huang et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Models of non-point source pollution are vital tools for understanding the complex interactions within watersheds and developing effective management strategies. These models typically fall into two main categories: the empirical model and the physical model. Empirical models, including the output coefficient method, the inventory analysis method and the pollutant discharge coefficient method, are applied to analyze the characteristics of pollutant loss by establishing the relationship between pollution load and runoff, topography or land use through causal analysis or statistical analysis. However, the empirical models are based on observed data and statistical relationships rather than deriving from first principles. These models can become outdated as conditions change over time if not regularly validated with new data (Zhou et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). On the contrary, physical models, also known as mechanistic models, are based on the fundamental principles of physics and chemistry that govern pollutant behavior in the environment. These models can quantitatively describe the occurrence, migration and transformation of non-point source pollutants, thus being frequently used in regulatory assessments and for designing pollution control measures (Zhang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo date, researches on the physical models of the non-point source pollution have stepped into the era of a hundred flowers in bloom. The most widely used physical models are the Soil and Water Assessment Tool (SWAT), the Hydrological Simulation Program-FORTRAN (HSPF), the Water Erosion Prediction Project (WEPP), and the Storm Water Management Model (SWMM) (Xie et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Among them, the SWAT model has proven to be an invaluable tool in in understanding and managing the non-point source pollution (Zuo et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Fang et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Recent studies have demonstrated its versatility in various applications, including agricultural assessments, urban planning, climate change impacts, and BMP evaluations (Liu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yan et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For instance, Chen et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) focused on multi-site calibration techniques that optimize the performance of SWAT in simulating hydrological processes at various sub-basins. These advancements help reduce uncertainties in pollutant load estimations and improve the model's predictive capacity across diverse landscapes. Yan et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) utilized SWAT to simulate the effects of LULC changes on nutrient runoff in the Yangtze River Basin, finding that urban expansion significantly elevated nitrogen and phosphorus levels in the water bodies. Such findings highlight the importance of sustainable land-use planning to mitigate NPS pollution. Niraula et al. (2019) explored how future climate scenarios could impact nitrogen and phosphorus loading in a watershed, using SWAT to simulate various precipitation and temperature changes. Their findings suggest that more intense rainfall events will likely increase nutrient and sediment loads, thus exacerbating water quality issues. Liu et al. (2022) demonstrate the effectiveness of buffer strips, cover crops, and other BMPs in controlling nutrient runoff. Their simulations showed that BMPs could reduce nitrogen and phosphorus loads by 25\u0026ndash;50%, indicating the importance of integrating BMPs into watershed management strategies. Zhang et al. (2020) showed how SWAT can simulate the effect of soil conservation measures, such as terracing and reforestation, on reducing sediment yield in highly erodible watersheds. The ability to model both large-scale and small-scale interventions provides insights into effective strategies for controlling sediment-based NPS pollution. As challenges related to NPS pollution persist, the integration of advanced technologies such as remote sensing and machine learning with SWAT presents exciting prospects for future research and management practices. Xu et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) successfully integrated remote sensing data with SWAT to enhance the accuracy of land use and land cover changes in assessing NPS pollution in a temperate watershed. This approach allowed for real-time monitoring and improved management decisions. Krishnan et al. (2022) integrated ML techniques with SWAT for predicting streamflow and sediment transport in a watershed. Their results showed that machine learning models could effectively capture nonlinear relationships and enhance the predictive capability of the SWAT model.\u003c/p\u003e \u003cp\u003eThe non-point source (NPS) pollution is particularly challenging to manage due to its widespread nature and dependence on varying factors such as land use, climate, and topography (Gao et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Effective management strategies often involve the implementation of Best Management Practices (BMPs) to mitigate the effects of NPS by controlling runoff and reducing the input of pollutants into water bodies (Jeong et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mohebzadeh et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The critical source areas of nonpoint source pollution (NPS) are specific regions within a watershed that disproportionately contribute to overall pollutant loading (Fang et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). By focusing conservation and remediation efforts on these critical areas, land managers and policymakers can optimize resource allocation and achieve greater reductions in water pollution (Wen et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As a primary tributary of the Yangtze River, the Bailin River plays a crucial role in the river network of the middle reaches of the Yangtze River. Over the past few decades, the river's water quality has been negatively affected by factors such as industrial wastewater discharge, livestock pollution, and excessive agricultural fertilization. Prior to 2020, the water quality of the Bailin River was not optimistic, with most sections showing an increasing trend in pollutant concentration levels, often falling into class III to V water quality. Therefore, our study focuses on reducing the non-point source pollution load of the Bailin River basin. For this purpose, the distributed hydrological model SWAT was adopted to analysis the spatial and temporal dynamic changes of runoff and total nitrogen pollution load in the study area. And then the critical source areas for the non-point source pollution prevention and control were defined. Furthermore, different BMPs (Best Management Practices) scenario simulations were carried out, and the best management measures to treat the non-point source pollution in the Bailin River Basin were proposed.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 The study area\u003c/h2\u003e \u003cp\u003eThe Bailin River is a primary tributary of the left bank of the Yangtze River. It is 59.3 km long and flows an area of about 478 km\u003csup\u003e2\u003c/sup\u003e, which is fan-shaped, and the landform is semi-alpine mountain and hilly terrain (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The average annual rainfall is 1100 mm, and the evaporation is 1280 mm.\u003c/p\u003e \u003cp\u003eThe rainy season in the basin is from June to September. In terms of rainfall distribution, it has the characteristics of spatial-temporal concentration, uneven intensity, and often forms a local rainstorm center. The Bailin River has long dry season with small flow, and short flood season with rapid flood fluctuation. Monitoring data indicate that the water quality in various sections of the river has deteriorated due to elevated levels of pollutants such as heavy metals, nutrients (nitrogen and phosphorus), and organic matter. The water pollution in the Bailin River is a multifaceted issue driven by urbanization, industrial activities, agricultural runoff, and waste management challenges. Addressing these influencing factors is crucial to improving the water quality and ecological health of this important tributary of the Yangtze River.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 The SWAT model\u003c/h2\u003e \u003cp\u003eThe SWAT (Soil and Water Assessment Tool) model is a comprehensive, semi-distributed hydrological model designed for predicting the impact of land management practices on water, sediment, and agricultural chemical yields in large watersheds (Cai et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this study, climate data, soil data, fertilization data, and water resource utilization information of the Bailin River basin from 2020 to 2023 were collected as the input parameters of the SWAT model. After calibration and validation using the SWAT-CUP software, the model was used to analyze the temporal and spatial variations of runoff, total nitrogen, and total phosphorus output loads in the study area. Additionally, the study explored the reduction effects of different management measures on pollutant loads, proposed targeted approaches for non-point source pollution in the Bailin River basin.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 The land use and soil types\u003c/h2\u003e \u003cp\u003eThe digital elevation model (DEM) was derived from the geospatial data cloud (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gscloud.cn\u003c/span\u003e\u003cspan address=\"https://www.gscloud.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with a resolution of 30 m, and the land use data were obtained through visual remote sensing image interpretation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Specifically, the samples of citrus orchards and other types of ground objects are collected by visual recognition on Google Earth platform. Afterwards, the pre-classified image containing citrus orchard information was extracted via the random forest classification of the remote sensing data. Moreover, the citrus orchards were distinguished from other woodlands by the catastrophe feature of NDVI as it decreased significantly in October and gradually increased in November. Referring to the re-normalized vegetation index RNVI (Re-Normalization of Vegetation Indices) proposed by Liang et al (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the pre-classified images containing citrus orchard information were discriminated. If the RNVI value of the pixel is negative, the pixel is determined as a citrus orchard. Otherwise, the pixel is determined as a woodland. The NDVI and RNVI were calculated as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{N}\\text{D}\\text{V}\\text{I}=\\frac{{\\rho\\:}_{\\text{N}\\text{I}\\text{R}}-{\\rho\\:}_{\\text{R}\\text{E}\\text{D}}}{{\\rho\\:}_{\\text{N}\\text{I}\\text{R}}+{\\rho\\:}_{\\text{R}\\text{E}\\text{D}}}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\text{R}\\text{N}\\text{V}\\text{I}=\\frac{{\\text{V}\\text{I}}_{a}-{\\text{V}\\text{I}}_{b}}{{\\text{V}\\text{I}}_{a}+{\\text{V}\\text{I}}_{b}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\rho\\:}_{\\text{N}\\text{I}\\text{R}}\\)\u003c/span\u003e\u003c/span\u003e is the reflectance in near-infrared wavelength; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\rho\\:}_{\\text{R}\\text{E}\\text{D}}\\)\u003c/span\u003e\u003c/span\u003e is the red spectral band; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{V}\\text{I}}_{a}\\)\u003c/span\u003e\u003c/span\u003e is the maximum vegetation index at the last month of fruit expanding stage; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{V}\\text{I}}_{b}\\)\u003c/span\u003e\u003c/span\u003e is the maximum vegetation index at the first month after fruit expanding stage.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe soil types in the study area were extracted from the Harmonized World Soil Data-base (HWSD) with a resolution of 1km (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Soil parameters such as soil mechanical composition and organic carbon content could directly use the HWSD data. Whereas, some other soil parameters were modified according to local situation, including soil humidity, effective soil water holding capacity, saturated hydraulic conductivity, and soil erosion factor (Valencia et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Scenario design\u003c/h2\u003e \u003cp\u003eThe non-point source pollution in the critical source areas might be significantly sever than in the other areas. In this study, the non-point source pollution reduction strategies in such areas were addressed by setting various management scenarios, of which the impact on water quality was predicted by the SWAT model. Details about the non-point source pollution management scenarios were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Concretely, scenario A\u003csub\u003e0\u003c/sub\u003e was a benchmark scenario built on the current situation of the Bailin River basin, it provided a reference point against which other scenarios with different management measures can be compared. Scenarios B\u003csub\u003e1\u003c/sub\u003e to B\u003csub\u003e3\u003c/sub\u003e were non-point source pollution management measures from the viewpoint of optimized fertilization. Scenarios C\u003csub\u003e1\u003c/sub\u003e to C\u003csub\u003e3\u003c/sub\u003e were different vegetation buffer strips set up in the drainage area around citrus orchard and cultivated land. Scenarios D\u003csub\u003e1\u003c/sub\u003e and D\u003csub\u003e2\u003c/sub\u003e were grass ditches planted within the canals nearby citrus orchard and cultivated land. While scenarios E\u003csub\u003e1\u003c/sub\u003e to E\u003csub\u003e9\u003c/sub\u003e were management measures under the combination of optimized fertilization and vegetation buffer strips. All above scenarios were incorporated into the SWAT model for further evaluating of the non-point source pollution load reduction efficiency, and the calculated equation is described by(Avci et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e):\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:E=\\frac{{P}_{b}-{P}_{B}}{{P}_{b}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E\\)\u003c/span\u003e\u003c/span\u003e is the non-point source pollution load reduction efficiency; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{b}\\)\u003c/span\u003e\u003c/span\u003e is the non-point source pollution load under scenario A\u003csub\u003e0\u003c/sub\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{B}\\)\u003c/span\u003e\u003c/span\u003e is the non-point source pollution load from other scenarios with different management measures.\u003c/p\u003e \u003cp\u003eTable 1 Scenario settings\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"662\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4.2962%;\"\u003e\n \u003cp\u003eNO.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2269%;\"\u003e\n \u003cp\u003eManagement measures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2104%;\"\u003e\n \u003cp\u003eParameter setting\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4.2962%;\"\u003e\n \u003cp\u003eA\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2269%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2104%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4.2962%;\"\u003e\n \u003cp\u003eB\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2269%;\"\u003e\n \u003cp\u003e10% Fertilizer reduction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2104%;\"\u003e\n \u003cp\u003eReduced by 10 % in .mgt FRT＿KGA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4.2962%;\"\u003e\n \u003cp\u003eB\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2269%;\"\u003e\n \u003cp\u003e20% Fertilizer reduction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2104%;\"\u003e\n \u003cp\u003eReduced by 20 % in .mgt FRT＿KGA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4.2962%;\"\u003e\n \u003cp\u003eB\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2269%;\"\u003e\n \u003cp\u003eLocal recommended fertilization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2104%;\"\u003e\n \u003cp\u003eAdjust the date and amount of fertilization in .mgt\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4.2962%;\"\u003e\n \u003cp\u003eC\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2269%;\"\u003e\n \u003cp\u003e2 m Vegetation buffer strips\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2104%;\"\u003e\n \u003cp\u003eSet FILTER＿\u0026nbsp;RATIO to 60 in .ops\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4.2962%;\"\u003e\n \u003cp\u003eC\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2269%;\"\u003e\n \u003cp\u003e5 m Vegetation buffer strips\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2104%;\"\u003e\n \u003cp\u003eSet FILTER＿\u0026nbsp;RATIO to 30 in .ops\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4.2962%;\"\u003e\n \u003cp\u003eC\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2269%;\"\u003e\n \u003cp\u003e10 m Vegetation buffer strips\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2104%;\"\u003e\n \u003cp\u003eSet FILTER＿\u0026nbsp;RATIO to 15 in .ops\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4.2962%;\"\u003e\n \u003cp\u003eD\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2269%;\"\u003e\n \u003cp\u003e1km Grass ditch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2104%;\"\u003e\n \u003cp\u003eSet GWATL to 1 in .ops\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4.2962%;\"\u003e\n \u003cp\u003eD\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2269%;\"\u003e\n \u003cp\u003e2km Grass ditch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2104%;\"\u003e\n \u003cp\u003eSet GWATL to 2 in .ops\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4.2962%;\"\u003e\n \u003cp\u003eE\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2269%;\"\u003e\n \u003cp\u003eCombination of B\u003csub\u003e1\u003c/sub\u003e and C\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2104%;\"\u003e\n \u003cp\u003eReduced by 10 % in .mgt FRT＿KGA \u0026amp; Set FILTER＿\u0026nbsp;RATIO to 60 in .ops\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4.2962%;\"\u003e\n \u003cp\u003eE\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2269%;\"\u003e\n \u003cp\u003eCombination of B\u003csub\u003e2\u003c/sub\u003e and C\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2104%;\"\u003e\n \u003cp\u003eReduced by 20 % in .mgt FRT＿KGA \u0026amp; Set FILTER＿ RATIO to 60 in .ops\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4.2962%;\"\u003e\n \u003cp\u003eE\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2269%;\"\u003e\n \u003cp\u003eCombination of B\u003csub\u003e3\u003c/sub\u003e and C\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2104%;\"\u003e\n \u003cp\u003eAdjust the date and amount of fertilization in .mgt \u0026amp; Set FILTER＿\u0026nbsp;RATIO to 60 in .ops\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4.2962%;\"\u003e\n \u003cp\u003eE\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2269%;\"\u003e\n \u003cp\u003eCombination of B\u003csub\u003e1\u003c/sub\u003e and C\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2104%;\"\u003e\n \u003cp\u003eReduced by 10 % in .mgt FRT＿KGA \u0026amp; Set FILTER＿\u0026nbsp;RATIO to 30 in .ops\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4.2962%;\"\u003e\n \u003cp\u003eE\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2269%;\"\u003e\n \u003cp\u003eCombination of B\u003csub\u003e2\u003c/sub\u003e and C\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2104%;\"\u003e\n \u003cp\u003eReduced by 20 % in .mgt FRT＿KGA \u0026amp; Set FILTER＿\u0026nbsp;RATIO to 30 in .ops\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4.2962%;\"\u003e\n \u003cp\u003eE\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2269%;\"\u003e\n \u003cp\u003eCombination of B\u003csub\u003e3\u003c/sub\u003e and C\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.2104%;\"\u003e\n \u003cp\u003eAdjust the date and amount of fertilization in .mgt \u0026amp; Set FILTER＿\u0026nbsp;RATIO to 30 in .ops\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4.2962%;\"\u003e\n \u003cp\u003eE\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2269%;\"\u003e\n \u003cp\u003eCombination of B\u003csub\u003e1\u003c/sub\u003e and C\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66.8982%;\"\u003e\n \u003cp\u003eReduced by 10 % in .mgt FRT＿KGA \u0026amp; Set FILTER＿\u0026nbsp;RATIO to 15 in .ops\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4.2962%;\"\u003e\n \u003cp\u003eE\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2269%;\"\u003e\n \u003cp\u003eCombination of B\u003csub\u003e2\u003c/sub\u003e and C\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66.8982%;\"\u003e\n \u003cp\u003eReduced by 20 % in .mgt FRT＿KGA \u0026amp; Set FILTER＿\u0026nbsp;RATIO to 15 in .ops\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 4.2962%;\"\u003e\n \u003cp\u003eE\u003csub\u003e9\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2269%;\"\u003e\n \u003cp\u003eCombination of B\u003csub\u003e3\u003c/sub\u003e and C\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66.8982%;\"\u003e\n \u003cp\u003eAdjust the date and amount of fertilization in .mgt \u0026amp; Set FILTER＿\u0026nbsp;RATIO to 15 in .ops\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sensitivity analysis\u003c/h2\u003e \u003cp\u003eSensitivity analysis is a crucial step in identifying parameters that have the most significant impact on model outputs such as runoff and nutrient loads, modelers can prioritize these parameters during calibration and validation to achieve better model performance and accuracy (Dai et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this research, the SWAT-CUP was run for 20 iterations comprising 500 simulations each, and the global sensitivity analysis method was utilized to examine the sensitivity of various parameters within the SWAT model. The analysis utilized T-statistics and P-values to evaluate parameter sensitivity, revealing that higher absolute T-stat values indicate greater sensitivity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), while P-values closer to zero denote increased importance. The findings highlighted that parameters such as RCHRG_DP, SMTMP, SFTMP, and CN2 significantly influenced runoff, while RS4, CMN, BC3, and N_UPDIS considerably impacted TN levels. Additionally, ERORGP and PHOSKD were found to be critical for TP levels (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameter calibration results of SWAT model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScope of values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBest value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSensitivity priority\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eParameter definition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLOW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCN2.mgt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.5-0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSCS runoff curve number .(Hern\u0026aacute;ndez-Mar\u0026iacute;n et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSOL_AWC().sol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.5-0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAvailable water capacity of the soil layer.(Hern\u0026aacute;ndez-Mar\u0026iacute;n et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSOL_BD().sol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.5-0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWet bulk density of soil .(Hern\u0026aacute;ndez-Mar\u0026iacute;n et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eREVAPMN.gw\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0-500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e200.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThreshold depth of water in the shallow aquifer for \"revap\" to occur (mm).(Khadka, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCH_K2.rte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01-500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEfective hydraulic conductivity in the alluvium of the main channel.(Hern\u0026aacute;ndez-Mar\u0026iacute;n et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEPCO.hru\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePlant uptake compensation\u003c/p\u003e \u003cp\u003efactor. (Khadka, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRCHRG_DP.gw\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDeep aquifer percolation fraction.(Khadka, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSFTMP.bsn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-20-20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-10.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSnowfall temperature.(Rautela et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSMTMP.bsn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-20-20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSnow melt base temperature.(Rautela et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSMFMX.bsn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMaximum melt rate for snow during the year (occurs on summer solstice).(Rautela et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRSDCO.bsn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u0026ndash;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eResidue decomposition coefficient.(Zhai et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSDNCO.bsn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDenitrification threshold water content.(Yuan and Chiang, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSOL_ORGN().chm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInitial organic nitrogen concentration in soil layer(Yan et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC1.swq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRate constant for biological oxidation of NH\u003csub\u003e4\u003c/sub\u003e to NO\u003csub\u003e2\u003c/sub\u003e in the reach at 20℃.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC2.swq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRate constant for biological oxidation of NO\u003csub\u003e2\u003c/sub\u003e to NO\u003csub\u003e3\u003c/sub\u003e in the reach at 20℃.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC3.swq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u0026ndash;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRate constant for hydrolysis of organic N to NH4 in the reach at 20 ℃.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRS4.swq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001-0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRate coefficient for organic N settling in the reach at 20 ℃.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN_UPDIS.bsn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNitrogen uptake distribution parameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI1.wwq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u0026ndash;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFraction of algal biomass that is nigrogen.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDN.bsn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDenitrification exponential rate coefficient.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMN.bsn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u0026ndash;0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRate factor for humus mineralization of active organic nitrogen.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSOL_NO3().chm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0-1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInitial NO\u003csub\u003e3\u003c/sub\u003e concentration in the soil layer.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNPERCO.bsn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNitrogen percolation coefficient.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePHOSKD.bsn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u0026ndash;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePhosphorus soil partitioning coefficient.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSOL_ORGP().chm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInitial organic P concentration in surface soil layer.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI2.wwq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u0026ndash;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFraction of algal biomass that is phosphorus.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC4.swq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u0026ndash;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRate constant for mineralization of organic P to dissolved P in the reach at 20 ℃.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eERORGP.hru\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOrganic P enrichment ratio.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRS5.swq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001-0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOrganic phosphorus settling rate in the reach at 20 ℃.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Calibration and validation of the SWAT model\u003c/h2\u003e \u003cp\u003eIn this research, the SWAT model was operated from 2020 to 2023, with a calibration period from 2020 to 2022 and a validation period from 2022 to 2023. The model\u0026rsquo;s performance was evaluated using several indexes, including the Nash-Sutcliffe Efficiency coefficient (NSE), the coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e), and the Percent Bias (PBIAS), with acceptable simulation results defined as R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.6, NSE\u0026thinsp;\u0026gt;\u0026thinsp;0.5, and PBIAS\u0026thinsp;\u0026le;\u0026thinsp;\u0026plusmn;\u0026thinsp;35%. Parameter adjustments were made using SWAT-CUP to ensure compliance with these criteria, resulting in favorable outcomes: during the calibration period, runoff showed R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.84, NSE\u0026thinsp;=\u0026thinsp;0.82, and PBIAS = -4.4%, while for the validation period, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.90, NSE\u0026thinsp;=\u0026thinsp;0.77, and PBIAS = -2.7%. Additionally, Total Nitrogen (TN) and Total Phosphorus (TP) also indicated strong performance, the R\u003csup\u003e2\u003c/sup\u003e, NSE, and PBIAS during the calibration period were 0.81, 0.77, 7.4%, and 0.81, 0.77, -8.3%, respectively. And those of the validation period were 0.92, 0.70, 1.4% and 0.87, 0.80, 16.6%, respectively. Therefore, it demonstrated that the SWAT model could effectively reflect the hydrological and water quality characteristics of the Bailin River basin.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Temporal characteristics of the pollution loads\u003c/h2\u003e \u003cp\u003eFrom 2020 to 2023, the simulated monthly runoff in the Bailin River basin ranged from 0.29 to 19.25 m\u003csup\u003e3\u003c/sup\u003e/s, while the simulated monthly total nitrogen (TN) and total phosphorus (TP) were from 1.48 to 93.98 tons and 0.04 to 4.01 tons, respectively. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the trends of TN and TP in the Bailin River basin were similar to that of runoff flow with time. As the runoff flow increased, both TN and TP also increased, exhibiting a \u0026ldquo;V\u0026rdquo; shaped fluctuation. Notably, in July 2020, an extreme rainstorm occurred in the Bailin River basin, resulting in peaks for monthly runoff, TN, and TP at the beginning of July, reaching 19.25 m\u003csup\u003e3\u003c/sup\u003e/s, 93.98 tons, and 4.012 tons, respectively. In the subsequent two years, the monthly runoff reached a small peak in April, measuring 7.76 m\u003csup\u003e3\u003c/sup\u003e/s and 10.78 m\u003csup\u003e3\u003c/sup\u003e/s, respectively, at which point monthly TN and TP reached their annual peaks. During the flood season (April to September), the TN and TP losses could account for 58.61% and 58.92% of the annual totals, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe process of nitrogen and phosphorus loss through runoff is quite complex, influenced by various factors such as precipitation characteristics, agricultural activities, underlying surface conditions, land utilization, and soil physicochemical properties. An investigation into the management conditions of citrus orchards in the study area revealed that, due to declining economic returns from citrus cultivation in recent years, many farmers opted to seek work elsewhere. This labor loss resulted in the reduction of fertilization from three applications (in spring for flower and fruit conservation, in summer for fruit swelling, and in autumn and winter for replenishment) to only one application of replenishment fertilizer in November. With lower winter temperatures leading to decreased microbial activity in the soil, chemical fertilizers were not fully decomposed for plant absorption, causing nutrients to primarily concentrate in the soil surface layer. When the first significant rainfall occurred in the spring of the following year, initial scouring effects caused a substantial loss of TN, which accounted for 42% of the total TN loss during the flood season in April 2022. Additionally, most citrus orchards in the Bailin River basin are planted on slopes, accelerating the surface runoff processes and the migration of particulate nitrogen and phosphorus.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFrom 2020 to 2022, rainfall showed a positive correlation with total nitrogen and total phosphorus, with R\u003csup\u003e2\u003c/sup\u003e values of 0.55 and 0.52, respectively. However, during the period from 2022 to 2023, the R\u003csup\u003e2\u003c/sup\u003e values increased to 0.74 and 0.91. Before 2020, pollutant loads not only originated from rainfall scouring but also included significant pollution sources from nearby industrial wastewater, domestic sewage, and livestock farming. After 2020, as a series of water ecological restoration efforts were implemented in the Bailin River basin, many coastal factories and farms gradually ceased operations, leading to an increased proportion of nitrogen and phosphorus loss attributed to rainfall scouring.\u003c/p\u003e \u003cp\u003eIn terms of interannual variations of the pollutant output (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), the total annual TN output in 2021 decreased by 31.00% compared to the previous year, and the total annual TP output decreased by about 28.58%. In 2022, the total annual TN output again decreased by 11.81%, and the total annual TP output decreased by 26.40%. It is worth noting that in 2023, the total annual TN output dropped by 64.60%, and the total annual TP output decreased by 69.31%. The most significant reduction was observed in 2023. It is speculated that the Longquan Sewage Treatment Plant put into operation in 2023 collects industrial wastewater and residential sewage in Longquan Town, which greatly reduces the amount of pollutants entering the Bailin River.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Spatial characteristics of the pollution loads\u003c/h2\u003e \u003cp\u003eBased on the long-term average pollutant output loads in the Bailin River basin, the degree of pollution in its sub-basins has been classified into the following four levels: TN\u0026thinsp;\u0026lt;\u0026thinsp;8.19 kg/(hm\u003csup\u003e2\u003c/sup\u003e\u0026middot;a) and TP\u0026thinsp;\u0026lt;\u0026thinsp;1.54 kg/(hm\u003csup\u003e2\u003c/sup\u003e\u0026middot;a); TN\u0026thinsp;\u0026lt;\u0026thinsp;15.99 kg/(hm\u003csup\u003e2\u003c/sup\u003e\u0026middot;a) and TP\u0026thinsp;\u0026lt;\u0026thinsp;2.10 kg/(hm\u003csup\u003e2\u003c/sup\u003e\u0026middot;a); TN\u0026thinsp;\u0026lt;\u0026thinsp;36.10 kg/(hm\u003csup\u003e2\u003c/sup\u003e\u0026middot;a) and TP\u0026thinsp;\u0026lt;\u0026thinsp;3.33 kg/(hm\u003csup\u003e2\u003c/sup\u003e\u0026middot;a); TN\u0026thinsp;\u0026gt;\u0026thinsp;36.10 kg/(hm\u003csup\u003e2\u003c/sup\u003e\u0026middot;a) and TP\u0026thinsp;\u0026gt;\u0026thinsp;3.33 kg/(hm\u003csup\u003e2\u003c/sup\u003e\u0026middot;a). The research results indicate that the TN and TP loads exhibit considerable spatial variations, specifically characterized by lower levels in the upstream areas, higher levels in the middle and lower reaches, and elevated concentrations near the riverbanks. In the southwestern part of the basin, land use is primarily for residential and agricultural activities. The discharge of industrial wastewater along the river, the direct emissions of urban and rural domestic sewage, as well as the increasingly intensive agricultural practices have led to the accumulation of non-point source pollution, resulting in excessive pollutant output loads in this area. In contrast, the northern part of the basin is mainly mountainous with a low population density and predominant land uses consisting of forested areas, thus generating relatively low levels of pollutants.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e illustrates the spatial distribution of TN and TP loads in the Bailin River basin from 2020 to 2023. In 2020, sub-basins 12, 14, 16, 17, 18, 20, and 21 fell into the fourth category of the TN and TP output loads. Among the severely polluted sub-basins, 12 and 14 are situated in the middle reach of the main stream of the Bailin River in Longquan Town, where the primary land uses include residential land, citrus orchards, and cultivated land. Prior to 2020, Longquan Town, with a resident population exceeding 110,000, lacked adequate sewage treatment facilities, resulting in significant amounts of untreated industrial and municipal sewage being discharged directly. Following the decreased profitability of citrus farming and the outmigration of rural labor, local farmers primarily used easily accessible chemical fertilizers, with minimal application of organic fertilizers; many farmers even avoided using organic fertilizers altogether, leading to significant non-point source pollution issues. Sub-basins 16, 17, and 20 are located near the high-tech industrial area and the main urban area, where direct discharge of industrial wastewater and urban/rural domestic sewage contributes to excessive pollutant output loads in these areas. Sub-basins 18 and 21 are located downstream of the tributaries of the Bailin River, which features numerous citrus industrial parks along its banks. The terrain is characterized by a higher left side and a lower right side, with a broad, gently flowing water body that promotes sedimentation of silt and sand. Phosphorus output from the river mainly consists of particulate phosphorus adsorbed onto sediments, with a considerable amount of dissolved phosphorus being re-absorbed by sediment during its migration, resulting in high total phosphorus output loads.\u003c/p\u003e \u003cp\u003eHowever, starting in 2021, the government has been focusing on promoting water ecological environment restoration efforts in the Bailin River basin, which includes the construction of the Longquan Sewage Treatment Plant, expansion of the Huayan Sewage Treatment Plant, relocating large-scale livestock farms within a one-kilometer radius of the main Bailin River, and the establishment of Bailin River Ecological Wetland Park. By 2023, except for sub-basins 18 and 21, the TN and TP loads of other previously severely polluted sub-basins were all less than Level Four, indicating initial successes in the water environment management efforts within the Bailin River basin.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 The best management practices (BMPs) for the critical source areas\u003c/h2\u003e \u003cp\u003eSub-basins 14, 16, 18, and 21 have a total area of about 27.4 km\u003csup\u003e2\u003c/sup\u003e, accounting for only 2.1% of the total area of the Bailin River basin. However, the pollutant output load from this area is 176.10 kg/(hm\u003csup\u003e2\u003c/sup\u003e\u0026middot;a), which constitutes 45.3% of the total output load in the study area, significantly higher than that of other sub-basins. This aligns with the characteristic of critical source areas, where a smaller area contributes disproportionately larger pollutant loads. Therefore, sub-basins 14, 16, 18, and 21 have been chosen as the critical source areas, for which the best management practices to reduce the non-point source pollution were screened out. The primary land uses in these critical source areas are farmland and citrus orchards, with cultivated land making up 12.1% of the basin area and citrus orchards comprising 43.9%. Series of management scenarios for these areas have been simulated, with the reduction efficiencies of various Best Management Practices (BMPs) for TN and TP shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eScenarios B\u003csub\u003e1\u003c/sub\u003e to B\u003csub\u003e3\u003c/sub\u003e were non-point source pollution management measures from the viewpoint of optimized fertilization. The reduction of TN loads for scenarios B\u003csub\u003e1\u003c/sub\u003e to B\u003csub\u003e3\u003c/sub\u003e were 7,488.0 kg/a, 12,179.2 kg/a, and 21,832.4 kg/a, and the reduction rates were 8.3%, 13.5%, and 24.2%, respectively. The reductions in TP loads were 1,420.2 kg/a, 2,367.0 kg/a, and 4,102.8 kg/a, with reduction rates of 6.3%, 10.5%, and 18.2%, respectively. The TN reduction rates were slightly higher than those for TP loads, primarily due to the higher nitrogen content in chemical fertilizers compared to phosphorus. The optimized fertilization strategy not only reduces the amount of fertilizers applied but also increases the frequency of application. This helps decrease the proportion of fertilization that occurs before April and mitigates nutrient loss caused by initial runoff, thus yielding significant effects on TN reduction.\u003c/p\u003e \u003cp\u003eVegetation buffer strips (VBS) are defined as buffer areas composed of trees (arboraceous plants) and other vegetation located adjacent to receiving water bodies. Their functions include intercepting sediment particles and adsorbed pollutants, promoting runoff infiltration, and stabilizing soil particles (Liu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Researchers indicated that vegetated swales and Vegetation buffer strips are more effective in reducing nitrogen and phosphorus pollutants compared to retention ponds and constructed wetlands (Ahsan et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this study, three vegetation buffer strips (C\u003csub\u003e1\u003c/sub\u003e, C\u003csub\u003e2\u003c/sub\u003e, C\u003csub\u003e3\u003c/sub\u003e) of different widths were set, the reduction rates for TN were 20.6%, 37.6%, and 43.3%, while for TP were 25.6%, 43.3%, and 50.6%, respectively.\u003c/p\u003e \u003cp\u003eThe Bailin River basin represents a typical mountainous watershed with significant elevation changes in the river network and concentrated rainfall throughout the year. Vegetation buffer strips help slow down surface runoff and trap particulate phosphorus adsorbed to sediments. Therefore, the reduction rate for TP exceeds that for TN. Furthermore, the reduction rate for C\u003csub\u003e3\u003c/sub\u003e compared to C\u003csub\u003e2\u003c/sub\u003e was only improved by 5.7%. This is mainly because when a Vegetation buffer strip is too wide, most pollutants are already filtered out at the front of the strip, and increasing width does not significantly enhance pollutant removal rates. Analyzing from a cost-effectiveness perspective, C\u003csub\u003e2\u003c/sub\u003e is more suitable than C\u003csub\u003e3\u003c/sub\u003e as a priority choice for managing the critical source areas.\u003c/p\u003e \u003cp\u003eGrass ditches refer to channels in which grass is planted to convey runoff. The vegetation not only slows down the flow and intercepts sediment particles but also allows the roots to absorb nitrogen and phosphorus from the water flow (Oduor et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Studies found that grass ditches had the greatest reduction rates for sediment, TN, and TP at the watershed outlet (Rohith et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xie et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this research, we designed two lengths of grass ditches (D\u003csub\u003e1\u003c/sub\u003e and D\u003csub\u003e2\u003c/sub\u003e), the TN reductions were 21,922.6 kg/a and 30,583.4 kg/a, with reduction rates of 24.3% and 33.9%, respectively. The reductions in TP were 6,740.3 kg/a and 8,679 kg/a, with reduction rates of 29.9% and 38.5%, respectively.\u003c/p\u003e \u003cp\u003eIn terms of the reduction efficiency of individual measures, C3 and C2 showed the best reduction effects, followed by D2, D1, B3, C1, while B1 and B2 yielded the poorest results. Moreover, this study also simulated the reduction effects of different combinations of measure, with the reduction rates of TN/TP were as follows: E\u003csub\u003e9\u003c/sub\u003e (60.3%/65.3%)\u0026thinsp;\u0026gt;\u0026thinsp;E\u003csub\u003e6\u003c/sub\u003e (58.4%/63.1%)\u0026thinsp;\u0026gt;\u0026thinsp;E\u003csub\u003e8\u003c/sub\u003e (53.1%/60.3%)\u0026thinsp;\u0026gt;\u0026thinsp;E\u003csub\u003e5\u003c/sub\u003e (50.6%/58.3%)\u0026thinsp;\u0026gt;\u0026thinsp;E\u003csub\u003e7\u003c/sub\u003e (47.3%/54.7%)\u0026thinsp;\u0026gt;\u0026thinsp;E\u003csub\u003e4\u003c/sub\u003e (45.5%/52.3%)\u0026thinsp;\u0026gt;\u0026thinsp;E\u003csub\u003e3\u003c/sub\u003e (42.4%/46.3%)\u0026thinsp;\u0026gt;\u0026thinsp;E\u003csub\u003e2\u003c/sub\u003e (38.6%/41.4%)\u0026thinsp;\u0026gt;\u0026thinsp;E\u003csub\u003e1\u003c/sub\u003e (32.4%/37.6%). It can be concluded that E\u003csub\u003e9\u003c/sub\u003e and E\u003csub\u003e6\u003c/sub\u003e performed the best results, achieving TN and TP reduction rates exceeding 58%. Additionally, the results also revealed that the TN and TP reduction rates for each combination of BMPs are greater than those for individual BMPs, yet the overall reductions remain lower than the sum of reductions from two individual BMPs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussions","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1 The accuracy of the SWAT model\u003c/h2\u003e \u003cp\u003eAddressing agricultural pollution in rural areas is an important measure for implementing the rural revitalization strategy. Due to the extensive and widespread nature of agricultural production activities in China, and the relatively low level of agricultural intensification in some regions, the management and control of non-point source pollution are particularly challenging (Luo et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, monitoring pollution sources and identifying critical source areas are crucial. However, the hydrological and water quality monitoring efforts in small and medium-sized rural basins in China are insufficient. There are not enough river monitoring sites, and the frequency of measurements is low (Kumwimba et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although this study calibrated the SWAT model using monthly runoff and TN load data, achieving a high degree of correlation between simulated and observed values, there is still a need to strengthen flow and water quality monitoring in the Bailin River basin in the future to more thoroughly validate the SWAT simulation results (Gao et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.2 The TN and TP outputs\u003c/h2\u003e \u003cp\u003eThe output loads of pollutants in the Bailin River basin are primarily concentrated during the flood season, with TN and TP losses from April to September accounting for 58.61% and 58.92% of the annual total, respectively. As to the TN losses, nitrate nitrogen, nitrite nitrogen, and ammonium nitrogen are predominant, representing dissolved nitrogen that has a strong correlation with runoff (Wang et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). Nevertheless, the form of phosphorus loss is mainly particulate phosphorus (Nie et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Research indicates that rainfall, as the primary external force for erosion and sediment production in the basin, contributes significantly to the increase in river sediment transport (Chen \u003cem\u003eet al.\u003c/em\u003e, 2022; Guan et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). During the flood season, the high rainfall leads to substantial river sediment transport, resulting in intensified loss of adsorbed particulate phosphorus (Avci et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe area of cultivated land and citrus orchards in the basin accounts for approximately 64.3%, with nitrogen and phosphorus primarily concentrated in the upper layers of the soil. When the first concentrated rainfall occurs in April of the following year in the Three Gorges Reservoir area, initial erosion effects are likely to take place (Zheng et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The southwestern part of the basin is densely populated and features numerous factories and cultivated orchards along the riverbanks, leading to excessive pollutant output loads, making it a critical source area. In contrast, the northern part of the basin is primarily mountainous, with a low population density and land use predominantly consisting of forested areas, resulting in lower pollutant generation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Treatment effects of different management scenarios\u003c/h2\u003e \u003cp\u003eThe best management practices (BMPs) primarily reduce agricultural non-point source pollution through three approaches: source control, interception along transport pathways, and end-of-pipe treatment (Sharma et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tarabih et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this study, the reduction of chemical fertilizer application (B\u003csub\u003e1\u003c/sub\u003e to B\u003csub\u003e3\u003c/sub\u003e) belongs to the source control. However, the effectiveness of fertilizer reduction in reducing TN load exhibits significant regional specificity, influenced by factors such as meteorological conditions, agricultural practices, underlying surface characteristics, and land cover (Aggarwal et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this research, the individual application of reduced fertilizer use resulted in relatively low TN and TP reduction rates. This may be attributed to the concentration of rainfall in the Bailin River basin occurring mainly from April to August, which does not align with the fertilization periods of major crops. Additionally, long-term excessive fertilization in the area has led to nutrient accumulation in the soil, making it difficult to significantly lower soil nitrogen and phosphorus levels through short-term reductions in fertilizer use, thereby limiting its effectiveness in reducing non-point source pollution loads. Similar patterns were observed in the Fengle River basin, where reductions of 10%, 20%, and 30% in fertilizer usage resulted in total phosphorus (TP) reduction rates of only 3.52%~5.83%, 7.05%~11.65%, and 10.60%~17.46% (Wang et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast to non-engineering measures like reduced fertilizer application and optimized fertilization, engineering measures such as vegetation buffer strips and grass ditches demonstrate more substantial and efficient TN and TP reduction effects. This aligns with previous research findings (Plunge et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The study found that establishing a 10-meter vegetation buffer strip yielded the highest reduction rates for TN and TP loads. However, vegetation buffer strips as engineering measures require land use and involve significant construction and maintenance costs. Consequently, suitable buffer strip areas should be selected based on local economic, hydrological, geological, and other conditions (Shrestha et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The downstream part of the Bailin River basin faces prominent land-use conflicts, and given that the reduction rate of a 10-meter buffer strip is only 5.7% higher than that of a 5-meter strip, a cost-benefit analysis suggests that the 5-meter option (C\u003csub\u003e2\u003c/sub\u003e) may be a more suitable choice for prioritizing critical source area management compared to the 10-meter option (C\u003csub\u003e3\u003c/sub\u003e). Grass ditches, by slowing down river flow, can serve as a pre-treatment measure to mitigate non-point source pollution caused by rainfall runoff, filtering and intercepting pollutants in the runoff (Mohebzadeh et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the Bailin River basin, grassed ditches effectively reduce TN and TP loads without occupying significant construction land, making them a viable secondary option for addressing the critical pollution areas.\u003c/p\u003e \u003cp\u003eOn the whole, the combined BMPs have a more pronounced impact on TN and TP reduction, with the integration of local recommended fertilization and the 5/10-meter buffer strips (E\u003csub\u003e6\u003c/sub\u003e and E\u003csub\u003e9\u003c/sub\u003e) achieving reduction rates exceeding 58%. The reduction rate of combined BMPs is not merely a straightforward accumulation of the individual measures' rates but rather a reflection of the interactions between different measures. For instance, both vegetation filter strips and grass ditches share similarities in their mechanisms for pollutant reduction, primarily through the interception and adsorption of pollutants by vegetation, thus decreasing the amount entering the river (Horvath et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To a certain extent, the combined implementation yields effects better than the additive efficiency of the individual measures.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe Bailin River Basin, as a critical tributary of the Yangtze River, faces profound challenges relating to non-point source (NPS) pollution primarily from agricultural runoff, urbanization, and industrial discharges. This study utilized the Soil and Water Assessment Tool (SWAT) model to simulate and assess the dynamics of runoff, total nitrogen (TN), and total phosphorus (TP) loads over a three-year period. The research provided critical insights into the effectiveness of various best management practices (BMPs) aimed at mitigating NPS pollution in the basin.\u003c/p\u003e \u003cp\u003eOne of the key findings is the significant concentration of nutrient losses during the flood season, with TN and TP losses from April to September representing over 58% of the annual totals. This highlights the critical need for management strategies that target this specific period to curtail the influx of pollutants into the river. The findings underscore the complex interactions between land use, rainfall patterns, and pollutant transport mechanisms in the basin, which can result in significant temporal fluctuations in water quality parameters.\u003c/p\u003e \u003cp\u003eThe study also emphasized the importance of continuous monitoring and adaptive management practices tailored to local conditions. Given the ongoing changes in land use and climate variability, the effectiveness of the proposed BMPs must be regularly assessed and refined. The data suggests a positive trend over time concerning nutrient load reductions, significant improvements in water quality can be attributed to recent ecological restoration efforts, including the construction of sewage treatment facilities and improved agricultural practices.\u003c/p\u003e \u003cp\u003eIn conclusion, this research presents a comprehensive understanding of non-point source pollution dynamics in the Bailin River Basin and affirms the critical role of targeted, integrated management strategies. The results can assist policymakers, land managers, and researchers in developing effective frameworks for sustainable river basin management that enhance water quality and ecological integrity. The success of these approaches in the Bailin River may serve as a model for similar mountainous watersheds facing the challenges of agricultural NPS pollution on a global scale. Future research should continue to explore innovative solutions, harnessing advancements in technology, including remote sensing and machine learning, to improve the prediction and management of water resources under the pressures of environmental change.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCRediT authorship and contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHao Wang:\u0026nbsp;\u003c/strong\u003eWriting - original draft, Methodology, Software, Investigation; \u003cstrong\u003eYifeng Liu:\u0026nbsp;\u003c/strong\u003eData curation, Writing - original Draft;\u003cstrong\u003e\u0026nbsp;Shijiang Zhu:\u0026nbsp;\u003c/strong\u003eWriting – original draft, Funding acquisition; \u003cstrong\u003eYang Liu:\u0026nbsp;\u003c/strong\u003eInvestigation, Methodology; \u003cstrong\u003eWen Xu:\u0026nbsp;\u003c/strong\u003eConceptualization, Supervision, Writing - review \u0026amp; editing; Funding acquisition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that has been used is confidential, and will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the National Natural Science Foundation of China and the Department of Water Resources of Hubei Province for providing financial support to complete the research. We also extend our gratitude to the Bureau of Ecological Environment of Yichang City for supplying the water quality monitoring data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Youth Fund from the National Natural Science Foundation of China (grant number: 52000120); the Key Scientific Research Projects of Water Conservancy in Hubei Province (grant numbers: HBSLKY201919 and HBSLKY202322); and the 111 project of Hubei Province.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAggarwal, S., Sharma, V., Rallapalli, S., Lenhart, C., Magner, J., 2024. Farmer adoption-based prompt networking and modeling for targeting optimal agro-conservation practices. Environ. Modell. Softw. 177. \u003c/li\u003e\n\u003cli\u003eAhsan, A., Das, S.K., Khan, M., Ng, A.W.M., Al-Ansari, N., Ahmed, S., Imteaz, M., Tariq, M., Shafiquzzaman, M., 2023. Modeling the impacts of best management practices (BMPs) on pollution reduction in the Yarra River catchment, Australia. Appl. Water Sci 13.\u003c/li\u003e\n\u003cli\u003eAvci, B.C., Kesgin, E., Atam, M., Tan, R.I., 2023a. Modeling agricultural practice impacts on surface water quality: case of Northern Aegean watershed, Turkey. Int. J. Environ. Sci. Technol 20, 5265-5280.\u003c/li\u003e\n\u003cli\u003eAvci, B.C., Kesgin, E., Atam, M., Tan, R.I., 2023b. Spatial-Temporal Response of Sediment Loads to Climate Change and Soil Conservation Practices in the Northern Aegean Watershed, Turkiye. WATER 15.\u003c/li\u003e\n\u003cli\u003eBasu, N.B., Van Meter, K.J., Byrnes, D.K., Van Cappellen, P., Brouwer, R., Jacobsen, B.H., Jarsj\u0026ouml;, J., Rudolph, D.L., Cunha, M.C., Nelson, N., Bhattacharya, R., Destouni, G., Olsen, S.B., 2022. Managing nitrogen legacies to accelerate water quality improvement. Nat. Geosci 15, 97-105.\u003c/li\u003e\n\u003cli\u003eCai, Y.F., Zhang, F., Shi, J.C., Johnson, V.C., Ahmed, Z., Wang, J.G., Wang, W.W., 2023. Enhancing SWAT model with modified method to improve Eco-hydrological simulation in arid region. J. Clean Prod 403.\u003c/li\u003e\n\u003cli\u003eChen, Y.F., Miao, J.F., Zhu, Z.T., 2021. Measuring green total factor productivity of China\u0026apos;s agricultural sector: A three-stage SBM-DEA model with non-point source pollution and CO\u0026lt;sub\u0026gt;2\u0026lt;/sub\u0026gt; emissions. J. Clean Prod 318.\u003c/li\u003e\n\u003cli\u003eChen, X., Rudolph, D.L., Cunha, M.C., Nelson, N., Bhattacharya, R., Destouni, G., Olsen, S.B., 2021. Multi-site calibration of SWAT for improved hydrological and NPS pollution simulations. Water Resour. Manag 10, 97-205.\u003c/li\u003e\n\u003cli\u003eDai, C., Zhang, X.L., Tan, X.Z., Hu, M.C., Sun, W., 2024. A stochastic simulation-based chance-constrained programming model for optimizing watershed best management practices for nonpoint source pollution control under uncertainty. J. Hydrol 632.\u003c/li\u003e\n\u003cli\u003eFang, S.B., Deitch, M.J., Gebremicael, T.G., Angelini, C., Ortals, C.J., 2024. Identifying critical source areas of non-point source pollution to enhance water quality: Integrated SWAT modeling and multi-variable statistical analysis to reveal key variables and thresholds. Water Res 253.\u003c/li\u003e\n\u003cli\u003eFang, S., Wang, J., Huang, Yang, H., 2023. Eutrophication trends and nutrient loading sources in urban lakes: A comparative study between China and Europe. Environ. Pollut, 298, 118870.\u003c/li\u003e\n\u003cli\u003eGao, W.M., Dai, A.Q., Wu, J.L., Li, Y.B., Hou, J., Wang, X.L., Li, K.Q., 2023. Hydrological status of the Dagu River Basin and management suggestions based on soil and water assessment tool multi-station calibration. Appl. Water Sci 13\u003c/li\u003e\n\u003cli\u003eGu, B.J., Zhang, X.M., Lam, S.K., Yu, Y.L., van Grinsven, H.J.M., Zhang, S.H., Wang, X.X., Bodirsky, B.L., Wang, S.T., Duan, J.K., Ren, C.C., Bouwman, L., de Vries, W., Xu, J.M., Sutton, M.A., Chen, D.L., 2023. Cost-effective mitigation of nitrogen pollution from global croplands. Nature 613.\u003c/li\u003e\n\u003cli\u003eGuan, R.H., Wu, L.X., Li, Y., Ma, B.G., Liu, Y., Zhao, C., Wang, Z.W., Zhao, Y., 2024. Evaluating the Impacts of Fertilization and Rainfall on Multi-Form Phosphorus Losses from Agricultural Fields: A Case Study on the North China Plain. Agronomy-Basel 14.\u003c/li\u003e\n\u003cli\u003eHern\u0026aacute;ndez-Mar\u0026iacute;n, M.A., Ortiz-G\u0026oacute;mez, R., Zavala, M., Rodr\u0026iacute;guez-Rodr\u0026iacute;guez, J.A., Medellin, P.A., Ortiz-Robles, F.A., 2024. Hydrological simulation using the SWAT model in a semi-arid region in the southern part of Zacatecas, Mexico. Environ. Earth Sci 83.\u003c/li\u003e\n\u003cli\u003eHorvath, I.R., Zhang, K., Mayer, B.K., Parolari, A.J., 2023. Effects of Regional Climate and BMP Type on Stormwater Nutrient Concentrations in BMPs: A Meta-Analysis. Environ. Sci. Technol 57, 5079-5088.\u003c/li\u003e\n\u003cli\u003eHuang, J.C., Zhang, Y.J., Bing, H.J., Peng, J., Dong, F.F., Gao, J.F., Arhonditsis, G.B., 2021. Characterizing the river water quality in China: Recent progress and on-going challenges. Water Res 201.\u003c/li\u003e\n\u003cli\u003eHoehn, K., Jacob, M., Schmid, D. W., 2021. Assessing the effectiveness of policies and practices in mitigating non-point source pollution in lakes. Water Research, 191, 116805.（Water Res.）\u003c/li\u003e\n\u003cli\u003eJeong, D.S., Kim, J.H., Kim, J.H., Park, Y., 2024. Evaluation of the Best Management Practices for Reducing Phosphorus Load in a Watershed in Terms of Cost and Greenhouse Gas Emissions. Agronomy-Basel 14.\u003c/li\u003e\n\u003cli\u003eKhadka, A., 2022. Runoff Modeling Using SWAT Model in Little Wabash River Watershed, Illinois.\u003c/li\u003e\n\u003cli\u003eKumwimba, M.N., Ajibade, F.O., Dzakpasu, M., Soana, E., 2024. Editorial: Advances in ecotechnologies for the control of non-point source pollution in agricultural and urban watersheds. Front. Environ. Sci 12.\u003c/li\u003e\n\u003cli\u003eKrishnan,Oduor, B.O., Campo-Besc\u0026oacute;s, M.A., Lana-Renault, N., Kyllmar, K., M\u0026aring;rtensson, K., Casal\u0026iacute;, J., 2022. Improving streamflow predictions in SWAT using machine learning techniques. Agronomy-Basel, 29(12), 17321-17334.\u003c/li\u003e\n\u003cli\u003eLi, X., Du, L., Zhang, S.H., Shi, K., Yang, Q.C., Li, L.P., Jiang, J.K., Ren, Z., Liu, X.H., 2024. Improving the identification of pollution source areas with catchment-resolution sensitivity analysis. Environ. Pollut 360. \u003c/li\u003e\n\u003cli\u003eLiang, C.X., Huang, Q., Wang, S., Wang C., Yu Q.Y., Wu W.B., 2021. Citrus orchard identification based on multi-temporal remote sensing vegetation index. Transactions of the CSAE 37, 168-176.（in Chinese）\u003c/li\u003e\n\u003cli\u003eLiu, W., Zhang, L., Wu, H.L., Wang, Y.F., Zhang, Y.L., Xu, J.Y., Wei, D.Y., Zhang, R., Yu, Y., Wu, D.S., Xie, X.C., 2023. Strategy for cost-effective BMPs of non-point source pollution in the small agricultural watershed of Poyang Lake: A case study of the Zhuxi River. Chemosphere 333.\u003c/li\u003e\n\u003cli\u003eLiu, X., Shang, J., 2022. Effectiveness of BMPs in reducing agricultural nutrient runoff using SWAT. Agric. Water Manage 30, 51607-51623.\u003c/li\u003e\n\u003cli\u003eLuo, M., Liu, X.X., Legesse, N., Liu, Y., Wu, S., Han, F.X., Ma, Y.H., 2023. Evaluation of Agricultural Non-point Source Pollution: a Review. Water Air Soil Pollut 234.\u003c/li\u003e\n\u003cli\u003eMohebzadeh, H., Biswas, A., DeVries, B., Rudra, R., Shukla, R., Daggupati, P., 2024. Evaluation of the impact of best management practices on ephemeral gully and sheet/rill erosion using the AnnAGNPS model. Catena 246.\u003c/li\u003e\n\u003cli\u003eNie, Y.X., Huang, G.X., Tong, S.C., Chen, W., Song, Y.H., Wang, X., Zhang, Q.H., Jiang, P.F., 2024. Assessing the impacts of rice terraces and ponds on the sediment and phosphorus loads in a typical hilly watershed of three gorge reservoir, China. J. Clean Prod 459. \u003c/li\u003e\n\u003cli\u003eNiraula, Zhang, Z.S., Montas, H., Shirmohammadi, A., Leisnham, P., Negahban-Azar, M., 2019. Evaluating climate change impacts on watershed-scale nitrogen and phosphorus loads. J. Hydrol 742.\u003c/li\u003e\n\u003cli\u003eOduor, B.O., Campo-Besc\u0026oacute;s, M.A., Lana-Renault, N., Kyllmar, K., M\u0026aring;rtensson, K., Casal\u0026iacute;, J., 2023. Quantification of agricultural best management practices impacts on sediment and phosphorous export in a small catchment in southeastern Sweden. Agric. Water Manage 290. \u003c/li\u003e\n\u003cli\u003ePlunge, S., Gudas, M., Povilaitis, A., 2022. Effectiveness of best management practices for non-point source agricultural water pollution control with changing climate - Lithuania\u0026apos;s case. Agric. Water Manage 267. \u003c/li\u003e\n\u003cli\u003eRautela, K.S., Kumar, D., Gandhi, B.G.R., Kumar, A., Dubey, A.K., 2023. Long-term hydrological simulation for the estimation of snowmelt contribution of Alaknanda River Basin, Uttarakhand using SWAT. AQUA 72, 139-159.\u003c/li\u003e\n\u003cli\u003eRohith, A.N., Karki, R., Veith, T.L., Preisendanz, H.E., Duncan, J.M., Kleinman, P.J.A., Cibin, R., 2024. Prioritizing conservation practice locations for effective water quality improvement using the Agricultural Conservation Planning Framework (ACPF) and the Soil and Water Assessment Tool (SWAT). J. Environ. Manage.349.\u003c/li\u003e\n\u003cli\u003eSharma, S., Bijukshe, S., Puppala, S.S., 2023. Monitoring, Modeling and Planning Best Management Practices (BMPs) in the Atwood and Tappan Lake Watersheds with Stakeholders Engagements. WATER 15.\u003c/li\u003e\n\u003cli\u003eShrestha, N.K., Rudra, R.P., Daggupati, P., Goel, P.K., Shukla, R., 2021. A comparative evaluation of the continuous and event-based modelling approaches for identifying critical source areas for sediment and phosphorus losses. J. Environ. Manage. 277.\u003c/li\u003e\n\u003cli\u003eTarabih, O.M., Arias, M.E., Santos, A.L., Hua, J.Y., Cooper, R.Z., Khanal, A., Dang, T.D., Khare, Y.P., Charkhgard, H., Rains, M.C., Zhang, Q., 2024. Effects of the spatial distribution of best management practices for watershed wide nutrient load reduction. Ecol. Eng 201.\u003c/li\u003e\n\u003cli\u003eValencia, S., Villegas, J.C., Hoyos, N., Duque-Villegas, M., Salazar, J.F., 2024. Streamflow response to land use/land cover change in the tropical Andes using multiple SWAT model variants. J. Hydrol.-Reg. Stud 54.\u003c/li\u003e\n\u003cli\u003eWang, N., Wang, L., Jin, L., Wu, J.J., Pang, M., Wei, D., Li, Y., Wang, J.Q., Xu, T., Yang, Z.X., Xie, J.Z., 2024a. Rainfall Runoff and Nitrogen Loss Characteristics on the Miyun Reservoir Slope. WATER 16.\u003c/li\u003e\n\u003cli\u003eWang, X.F., Xiao, J.J., Wu, H.Q., Luo, M., Zhang, C., CHU, Q., 2024b. Output characteristics and optimal management measures of phosphorus pollution in Fengle River Basin. Journal of Agricultural Resources and Environment 41, 891-900.（in Chinese）\u003c/li\u003e\n\u003cli\u003eWen, W.J., Zhuang, Y.H., Jiang, T.Y., Li, W.D., Li, H.X., Cai, W., Xu, D., Zhang, L., 2024. \u0026quot;Period-area-source\u0026quot; hierarchical management for agricultural non-point source pollution in typical watershed with integrated planting and breeding. J. Hydrol 635.\u003c/li\u003e\n\u003cli\u003eWu, Z.J., Liang, H., Wu, H.Q., Xie, Z.Y., Liu, H.Y., Zhang, J.H., Zhu, J.Q., Zheng, B.F., Wan, W., 2024. Application of improved risk assessment methods to best management practices research for non-point source pollution: From a hilly mountainous-dominated region of Southern China. Ecol. Indic 165.\u003c/li\u003e\n\u003cli\u003eXie, C., Zhang, L.Y., Wang, M.L., Jiang, Q.N., 2024. Scenario analysis on the management practices and optimization of rural non-point source pollution with the coupling of source-process-end: A case study of the Guishui river basin. Ecol. Indic 159.\u003c/li\u003e\n\u003cli\u003eXu, L.Y., Jiang, J., Lu, M.Y., Du, J.G., 2022. Integrating remote sensing data with SWAT for enhanced assessment of non-point source pollution. Remote Sens. Environ 267, 112710.\u003c/li\u003e\n\u003cli\u003eYan, W., Wang, J.H., Zou, H., Min, M., Duan, X.J., 2024. Modeling economic-environmental-ecological trade-offs for non-point source control strategies: A case study of Dianchi lake watershed, China. Ecol. Indic 158.\u003c/li\u003e\n\u003cli\u003eYan, X., Lu, W., An, Y., Chang, Z., 2019. Uncertainty analysis of parameters in non-point source pollution simulation: case study of the application of the Soil and Water Assessment Tool model to Yitong River watershed in northeast China. Agric. Water Manage 447. \u003c/li\u003e\n\u003cli\u003eYan, N.K., Rudra, R.P., Daggupati, P., Goel, P.K., Shukla, R., 2020. Assessing the impacts of land-use changes on non-point source pollution in the Yangtze River Basin using SWAT. Environ. Sci. Policy 321-1733.\u003c/li\u003e\n\u003cli\u003eYuan, Y., Chiang, L.-C., 2015. Sensitivity analysis of SWAT nitrogen simulations with and without in-stream processes. Arch. Agron. Soil Sci 61, 969-987.\u003c/li\u003e\n\u003cli\u003eZhai, X.Y., Zhang, Y.Y., Wang, X.L., Xia, J., Liang, T., 2014. Non-point source pollution modelling using Soil and Water Assessment Tool and its parameter sensitivity analysis in Xin\u0026apos;anjiang catchment, China. Hydrol. Process 28, 1627-1640.\u003c/li\u003e\n\u003cli\u003eZhang, S., Zhang, L.L., Meng, Q.Y., Wang, C.C., Ma, J.J., Li, H., Ma, K., 2024. Evaluating agricultural non-point source pollution with high-resolution remote sensing technology and SWAT model: A case study in Ningxia Yellow River Irrigation District, China. Ecol. Indic 166.\u003c/li\u003e\n\u003cli\u003eZheng, Y.X., Li, S.L., Keenlyside, N., He, S.P., Suo, L.L., 2024. Projecting Spring Consecutive Rainfall Events in the Three Gorges Reservoir Based on Triple-Nested Dynamical Downscaling. Adv. Atmos. Sci 41, 1539-1558.\u003c/li\u003e\n\u003cli\u003eZhou, B.W., Parsons, C., Van Cappellen, P., 2024. Urban Stormwater Phosphorus Export Control: Comparing Traditional and Low-impact Development Best Management Practices. Environ. Sci. Technol 58, 11376-11385.\u003c/li\u003e\n\u003cli\u003eZhou, J.H., Liu, X.H., Liu, X.J., Wang, W.L., Wang, L.Q., 2023. Assessing agricultural non-point source pollution loads in typical basins of upper Yellow River by incorporating critical impacting factors. Process Saf. Environ. Protect 177, 17-28.\u003c/li\u003e\n\u003cli\u003eZuo, D.P., Bi, Y.Q., Song, Y.H., Xu, Z.X., Wang, G.Q., Ma, G.W., Abbaspour, K.C., Yang, H., 2023. The response of non-point source pollution to land use change and risk assessment based on model simulation and grey water footprint theory in an agricultural river basin of Yangtze River, China. Ecol. Indic 154.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Non-point Source Pollution, Soil and Water Assessment Tool (SWAT), Best Management Practices (BMPs), Nutrient Load Reduction, Bailin River","lastPublishedDoi":"10.21203/rs.3.rs-5961509/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5961509/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Bailin River, a key tributary of the Yangtze River, faces significant water quality challenges due to agricultural non-point source (NPS) pollution exacerbated by industrial discharge and urban runoff. This study employs the Soil and Water Assessment Tool (SWAT) to analyze the temporal and spatial dynamics of runoff as well as total nitrogen (TN) and total phosphorus (TP) loads in the Bailin River basin from 2020 to 2023. A critical source area analysis was performed to identify regions disproportionately contributing to pollutant loads. Through various simulations, including different Best Management Practices (BMPs) scenarios, the study explores their effectiveness in reducing nutrient loads. The findings reveal that nutrient losses are significantly concentrated during the flood season, with TN and TP losses accounting for 58.61% and 58.92% of annual totals, respectively. Specific BMP scenarios, combining optimized fertilization, vegetation buffer strips, and grass ditches, demonstrated substantial pollutant reduction, with the best combinations exceeding 58% reductions for both TN and TP. The study emphasizes the necessity of targeted interventions in critical source areas to optimize management strategies and achieve better water quality outcomes. Continuous monitoring and adaptive management practices will be crucial to addressing ongoing challenges of non-point source pollution in this basin. Ultimately, this research contributes to a deeper understanding of NPS pollution in mountainous watersheds and highlights effective management pathways for improved ecological health and water quality.\u003c/p\u003e","manuscriptTitle":"Integrated Modeling and Management of Non-Point Source Pollution in the Bailin River Basin: Best Practices for Reducing Nutrient Loads","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-10 09:38:13","doi":"10.21203/rs.3.rs-5961509/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f8068709-fca2-4033-bd38-a3283e89c355","owner":[],"postedDate":"February 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-17T05:24:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-10 09:38:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5961509","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5961509","identity":"rs-5961509","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}