Evaluating the effects of different pollution reduction scenarios on the total phosphorus concentration of a mountainous river basin in southwest China using SWAT model: a case study of the Donghe River in Baoshan, Yunnan

Water is not only a fundamental requirement for human life and health (Fath et al. 2017), but also a crucial resource for agriculture, industry, and energy production (Liu et al. 2015). Water quality prediction and management are key aspects of maintaining the health of water bodies and ensuring their sustainable utilization. Accurately predicting changes in water quality trends and identifying factors that influence water quality are essential for effective water resource management and environmental protection strategies (Vamvakeridou-Lyroudia & Karatzas 2017; Wang et al. 2020c). Numerous scholars and researchers worldwide have explored and applied various water quality models and prediction methods to assess and predict the pollution status of water bodies (Zhang et al. 2017b, 2019b; Li et al. 2020a). As a result, a variety of hydrological and water quality models such as water quality modeling network (WQMN) (Peng et al. 2018; Fu et al. 2019; Li et al. 2020a), corps of engineers water quality and eutrophication two-dimensional model (CE-QUAL-W2) (Reckendorfer et al. 2019; Safi et al. 2020), soil and water assessment tool (SWAT) (Zhang et al. 2016; Chen et al. 2020), hydrological simulation program-Fortran (HSPF) (Shoemaker & Brua 2018; Shrestha & Chen 2019), water quality analysis simulation program (QUAL2K) (Yin et al. 2017; Zhang et al. 2020), and aquatic toxicology model (AQUATOX) (Tsai et al. 2018; Wang et al. 2019a) have been successfully applied in various watershed contexts. The speed of updating optimization models using statistical analysis methods (Chen & Huang 2016; Zhang et al. 2018b), artificial intelligence methods (Wu et al. 2019b; Qu & Xia 2020), machine learning methods (Hadjisolomou et al. 2021; Kouadri et al. 2021; AlDahoul et al. 2022), and hybrid methods (Bai et al. 2018; Zeng et al. 2019) has been accelerated. The SWAT model is a widely used tool for watershed hydrological and water quality simulation and management strategy evaluation (Neitsch et al. 2011; Arnold et al. 2012). Initially developed with support from the United States Department of Agriculture-Agricultural Research Service (USDA-ARS), the SWAT model has a development history of over 30 years (Arnold et al. 1998). It is capable of integrating factors such as soil, hydrology, meteorology, and human activities to accurately simulate watershed hydrological and water quality processes, demonstrating high accuracy and reliability in simulating and predicting watershed hydrological processes (Neitsch et al. 2011; Fu et al. 2016). Particularly for mountainous watershed areas, characterized by significant terrain variations, unstable rainfall intensities, diverse land-use types, and complex water cycling processes, numerous studies have shown that the SWAT model performs well in simulating mountainous watershed areas (Gassman et al. 2007; Park et al. 2013; Wu et al. 2017).

As one of the most populous countries in the world, China has a tremendous demand for water resources (Huang & Wang 2019). However, with the accelerated population growth, economic development, and urbanization process, China is facing severe pressure and challenges in water pollution (Zhang et al. 2018a, 2018b, 2018c, Wang et al. 2019a, 2019b). In the Donghe River Basin of Baoshan City, Yunnan Province, water resources are generally insufficient. In recent years, due to rapid urbanization, rapid industrial development, increased agricultural activities, and inadequate matching of pollution control measures, domestic sewage and industrial and agricultural wastewater have been continuously discharged into the river, resulting in the exceeding of water environmental quality standards in the basin. The monitoring results in July 2021 showed that the main and tributary streams in the Donghe River Basin of Baoshan City, Yunnan Province, generally do not meet the Class IV surface water standards, and the main stem is even below Class V standards. The main pollutants in the basin are total phosphorus (TP), ammonia nitrogen (NH₃-N), and chemical oxygen demand (COD), with TP being the major pollutant. Therefore, it is urgent to propose a comprehensive and integrated management plan for water environmental pollution in the Donghe River Basin. Preventing and controlling water environmental pollution in the Donghe River Basin is of great significance for local environmental improvement, promoting economic development, and improving people's well-being.

The main objectives of this study are: (1) to establish a highly applicable hydrological and water quality model based on the SWAT model in the Donghe River Basin, and to establish the response relationship between different pollutant emissions and TP concentration using SWAT model technology; (2) to construct three comprehensive pollution reduction scenarios with increasing emission reduction intensities based on the SWAT model, and to predict whether these scenarios can achieve the expected TP concentration standards in the Donghe River Basin; (3) to analyze the driving factors of TP concentration variations in the basin by combining the predictive results of the SWAT model with the actual situation of the basin. The results of this study will contribute to a better understanding of the current water resources status and water quality issues in the Donghe River Basin of Baoshan City, Yunnan Province, and provide references for the development of scientifically sound water resources management and protection strategies. Additionally, this study can provide valuable insights for water resources management and protection in similar basins. The application of the SWAT model will enhance the awareness of the importance of water resources management and support accurate and reliable decision-making for the protection and management of water resources.

There are two sewage treatment plants operating at full capacity in the urban area. The first sewage treatment plant uses the Carrousel oxidation ditch process, and the effluent is discharged into the Donghe River after undergoing ultraviolet disinfection. The second sewage treatment plant uses the oxidation ditch activated sludge process. The effluent from both treatment plants complies with Grade B of the ‘Pollutant Discharge Standards for Urban Sewage Treatment Plants’ (GB18918-2002). However, the current centralized treatment capacity of the domestic sewage treatment plants is small, and the design standards for effluent are relatively low, making it difficult to ensure the complete treatment of sewage in the urban area.

Among various pollutant indicators such as TP, COD, and NH₃-N, we selected TP as the focus of our study based on several considerations: (1) Our previous sampling and testing of the water and sediments in various river sections of the study area revealed that TP was the most severe pollutant, significantly impacting water quality deterioration. (2) TP is widely recognized as an important indicator of eutrophication and water quality pollution and is extensively used in water environmental research and management. Exploring its concentration variation can provide further insights into the extent of agricultural, industrial, domestic, and livestock and poultry emissions' impact on water quality (Zhang et al. 2019a; Liu et al. 2020a). (3) TP is closely related to the stability and health of aquatic ecosystems, making its study crucial for understanding water health conditions and environmental management (Huang et al. 2021).

Based on the reference year of 2020, the point source and non-point source pollution emissions of TP in the Donghe River Basin were calculated (refer to the information on TP pollution source analysis in Table 6). Point source pollution mainly includes industrial point sources, urban sewage treatment plants, and livestock and poultry farming. A point source database was compiled based on the emission characteristics and TP discharge of point sources, and it was incorporated into the SWAT model. Non-point sources were calculated at the sub-basin level and then coupled with HRUs in the model. Non-point source loads of HRUs were obtained through weight allocation (Zhang et al. 2021a). However, from Table 6, we can observe that the largest contributor to TP emissions is non-point source pollution from domestic sources, accounting for 50.6% of the TP emissions. Agricultural non-point source pollution is the next important contributor, accounting for 27.7%.

SWAT-CUP is a tool specifically designed for calibration and validation of the SWAT model (Abbaspour et al. 2007a, 2015). It provides a comprehensive set of statistical methods and algorithms to help researchers optimize model parameters and improve model performance and accuracy. We used the SWAT-CUP software to calibrate and validate the constructed SWAT model. The SUFI-2 algorithm (Arnold et al. 2012) has the advantage of a global search for the optimal solution, the CE-UA algorithm (Duan et al. 1994) has the advantages of global search and adaptive adjustment, and the trial-and-error method has the advantage of flexible adjustment based on specific problems and actual conditions. We comprehensively used the SUFI-2 algorithm (a global optimization algorithm for uncertainty analysis of model parameters), the SCE-UA algorithm, and the trial-and-error method, following the principle of adjusting runoff parameters before adjusting nutrient parameters (Moriasi et al. 2007), to select sensitive parameters for runoff and TP(The specific calibrated hydrological parameters and water quality parameters can be found in Tables 7 and 8, respectively). The period from 2017 to 2018 was selected as the warm-up period for parameter calibration, and the period from 2019 to 2020 was used for validation. The calibration and validation of runoff and TP simulation results were conducted at a monthly time scale. The specific calibration and validation process is as follows: firstly, the runoff parameters were calibrated and validated using the hydrological stations in Baoshan, Hanzhuang, and Xinjie. Then, the monthly average TP concentration data from the Sandaba and Shilongping national control sections were multiplied by the simulated water quantity data to obtain TP loads as the observed values for calibration and validation of nutrient parameters in the watershed.

In Equations (1) and (2), represents the observed values, represents the simulated values, represents the multi-year average observed values, n represents the number of samples.

The objectives that need to be met in setting up the scenarios for this study are as follows: (1) the implemented emission reduction scenarios should achieve a significant reduction in emissions. (2) The selected emission reduction scenarios should have low-cost investment and high operational feasibility (Liu et al. 2020c). (3) The selected emission reduction scenarios should comply with local policies for the ecological environment and water resource protection (Jin et al. 2019).

To achieve this, a baseline scenario was established as a reference along with three gradually increasing reduction scenarios. In the three scenarios, pollution from domestic non-point sources will be reduced by 25, 50, and 75%, respectively, while point source pollution will be reduced by 30, 60, and 90%, respectively. In scenarios 2 and 3, water diversion will be implemented in the urban river section. (See Tables 10 and 11 for the scenario settings in the Donghe River Basin).

To better reflect the interannual variation of TP concentration in the Donghe River Basin, the cumulative departure percentage of annual flow was calculated based on the natural flow data from the Baoshan hydrological station for the years 2007–2020. The year 2018 was determined as a wet year, while 2019 was identified as a dry year (Zhang et al. 2009; Yang et al. 2013; Sun et al. 2020b). Using the meteorological and runoff conditions of 2018 and 2019, the TP load under the baseline scenario and the three reduction scenarios were simulated, and the resulting concentration changes were inferred.

The changes in TP annual average concentration in the four monitoring sections during wet and dry years are shown in Figure 12. According to Figure 12, the Shuangqiao (Dashahe) section has a TP annual average concentration that falls within Class V in the baseline scenario. In the three reduction scenarios, the TP annual average concentration in this section meets Class II standards, and it is observed that there is minimal variation in TP concentration across the three scenarios. The Shuangqiao (Donghe), Shilongping, and Dieshuiheqiao sections have TP annual average concentrations in the baseline scenario that are classified as Class V. In the three reduction scenarios, the TP annual average concentrations in these sections meet Class IV to Class III standards. Among them, for the section of the Donghe River, the Shuangqiao (Donghe) section has higher TP concentrations compared to the Shilongping and Dieshuiheqiao sections in the baseline scenario, indicating higher phosphorus loading and more severe TP pollution in the urban river section.

The Shuangqiao (Donghe) section shows a greater reduction in TP concentration compared to the Shilongping and Dieshuiheqiao sections, indicating that the primary measures of reducing pollution from non-point sources and dilution through water diversion in the urban river section have a more significant impact on improving TP concentration in the middle reaches of the Donghe River. This demonstrates the effectiveness of scaling up sewage treatment plants and improving discharge standards in improving water quality. Under Scenario 1 and Scenario 2 conditions, there is little change in TP concentration in the Shilongping and Dieshuiheqiao sections. Only when the sewage collection rate and water quality are significantly improved at the industrial and commercial park sewage treatment plant in Scenario 3, do these sections show some improvement in TP concentration. This indicates that TP concentration in these two monitoring sections is significantly influenced by centralized sewage treatment in the industrial and commercial parks.

In terms of interannual variations, during dry years, the flow rate in the Donghe River basin is lower, resulting in slower water velocity and reduced exchange with the surroundings. This limited exchange makes TP less likely to diffuse, leading to higher TP concentrations in the basin during dry years. Compared to wet years, it is more challenging to meet the TP concentration standards during dry years.

From a spatial distribution perspective, the order of TP concentrations in the monitored sections, including Shuangqiao (Dashahe), Shuangqiao (Donghe), Shilongping, and Dieshuiheqiao, under the emission reduction scenarios can be roughly summarized as follows: Dieshuiheqiao > Shilongping > Shuangqiao (Donghe) > Shuangqiao (Dashahe). Along the mainstream of the Donghe River, these sections are sequentially distributed from the middle to the lower reaches. The following reasons contribute to this pattern: (1) As the river flows from the upper to middle reaches, the water volume gradually increases, while the lower reaches may experience reduced flow velocity, decreased water volume, and insufficient hydraulic energy. This can result in slow water flow and lower velocity in the downstream section, weakening the transport and dilution capacity of TP, and increasing the risk of TP accumulation in the water. (2) The downstream section of the river is more susceptible to other pollution sources, such as non-point source pollution from agricultural fields, livestock farming, industrial sources, and rural domestic sewage. (3) In the scenario plan, the reduction in TP emissions from non-point source pollution is not uniformly distributed across the entire basin but rather varies based on the establishment of wastewater treatment plants in specific areas and the improvement of discharge standards. Since the coverage of wastewater treatment plants is limited to certain regions, the establishment of a treatment plant in a specific area may contribute significantly to reducing TP concentrations in nearby river sections, while the improvement in TP conditions in the entire basin may not be as pronounced. Therefore, the order in which wastewater treatment plants are established may also have some influence on the results.

In general, the annual average TP concentrations in the four monitored sections show an overall decreasing trend in both wet and dry years under the three emission reduction scenarios. Furthermore, the TP concentrations under the emission reduction scenarios are significantly lower than those under the baseline scenario. All three emission reduction scenarios can ensure that the annual average TP concentrations in the tributaries of the Dashariver and the main stream of the Donghe River, which flows into the Nu River, meet the water quality standards. This indicates that by implementing different emission reduction scenarios, it is possible to effectively reduce TP concentrations and improve water quality in the Donghe River Basin. Moreover, these improvements in TP concentrations are stable and consistent under different annual conditions.

The monthly average TP concentration variations in the four monitored sections during wet and dry years are shown in Figure 13. According to Figure 13, under the baseline scenario, the tributary section of Shuangqiao (Dashariver) meets the Class III standard for only 4 months throughout the year, while in the emission reduction scenarios, it fails to meet the Class III standard in May during wet years and in April during dry years. Shuangqiao (Donghe), Shilongping, and Dieshuihe Bridge sections under the baseline scenario only meet the Class IV standard in July or August and fail to meet the standard in the remaining months. Under scenario 1, TP concentrations do not meet the standard from January to April, and under scenario 2, TP concentrations do not meet the standard in January, March, and April. In scenario 3, TP concentrations in April during dry years do not meet the standard. It can be observed that the number of non-compliant months decreases significantly under the emission reduction scenarios. The non-compliant months in the four monitoring sections are mostly concentrated in January to April, and among these months, TP concentrations in dry years are generally higher compared to wet years.

In terms of seasonal variations, the TP concentrations in the four monitoring sections of the Donghe River Basin generally exhibit an increase during winter and spring, followed by a decrease during summer and autumn. They rise from January to April, reaching the peak TP concentration during March and April, and gradually decrease in a fluctuating pattern from May to October. There is a slow increase again in TP concentrations from November to December. Based on Table 12, it can be observed that there is a certain degree of negative correlation between changes in TP concentration and rainfall in the watershed. The Donghe River Basin experiences distinct wet and dry seasons, with a long and abundant rainy season during summer and autumn. During this period, TP concentrations in the river are reduced due to dilution from rainfall. However, during the dry season of winter and spring, with less rainfall and reduced groundwater replenishment, decreased flow and insufficient hydraulic energy, TP accumulates in the water, leading to an increase in TP concentrations. From a seasonal perspective, the dry season extends from early November to late April of the following year, indicating that TP concentrations in the Donghe River Basin reach a peak in March and April each year. During the TP concentration peak, the order of TP concentrations from high to low in the monitoring sections is roughly as follows: Dieshuihe Bridge > Shilongping > Shuangqiao (Donghe) > Shuangqiao (Dashahe).

Table 12

The correlation and significance test for the relationship between the monthly average TP concentrations and monthly rainfall of each monitoring section under the three emission reduction scenarios

Monitoring sections .		Pearson correlation coefficient .
		Wet year .		Dry year .
		r .	P-value .	r .	P-value .
Shuangqiao (Dashahe)	Scenario 1	−0.63	0.0293	−0.63	0.0296
	Scenario 2	−0.54	0.0727	−0.60	0.0399
	Scenario 3	−0.51	0.0899	−0.59	0.0456
Shuangqiao (Donghe)	Scenario 1	−0.57	0.0507	−0.60	0.0405
	Scenario 2	−0.67	0.2421	−0.48	0.1180
	Scenario 3	−0.48	0.0842	−0.41	0.1845
Shilongping	Scenario 1	−0.54	0.0706	−0.56	0.0596
	Scenario 2	−0.44	0.2818	−0.46	0.1330
	Scenario 3	−0.54	0.1472	−0.40	0.0013
Dieshuiheqiao	Scenario 1	−0.55	0.1949	−0.44	0.1542
	Scenario 2	−0.49	0.2923	−0.34	0.1764
	Scenario 3	−0.54	0.0559	−0.49	0.1073
			−0.52		0.1126

Monitoring sections .		Pearson correlation coefficient .
		Wet year .		Dry year .
		r .	P-value .	r .	P-value .
Shuangqiao (Dashahe)	Scenario 1	−0.63	0.0293	−0.63	0.0296
	Scenario 2	−0.54	0.0727	−0.60	0.0399
	Scenario 3	−0.51	0.0899	−0.59	0.0456
Shuangqiao (Donghe)	Scenario 1	−0.57	0.0507	−0.60	0.0405
	Scenario 2	−0.67	0.2421	−0.48	0.1180
	Scenario 3	−0.48	0.0842	−0.41	0.1845
Shilongping	Scenario 1	−0.54	0.0706	−0.56	0.0596
	Scenario 2	−0.44	0.2818	−0.46	0.1330
	Scenario 3	−0.54	0.1472	−0.40	0.0013
Dieshuiheqiao	Scenario 1	−0.55	0.1949	−0.44	0.1542
	Scenario 2	−0.49	0.2923	−0.34	0.1764
	Scenario 3	−0.54	0.0559	−0.49	0.1073
			−0.52		0.1126

Note: ⁠, Comprehensive correlation coefficient; ⁠, Comprehensive p-value.

View Large

From the monthly average concentration variations in wet and dry years, it can be observed that the TP concentrations in all sections show a fluctuating decreasing trend under the three reduction scenarios. None of the three reduction scenarios were able to achieve full-year compliance with TP concentration standards in the Dashariver section of the Donghe River. Only Scenario 3 during wet years was able to achieve full-year compliance with TP concentrations in the main stream of the Donghe River entering the Nujiang River, while the rest of the scenarios did not meet the standards. The months of non-compliance are mostly concentrated in the dry season, from January to April and in December. Compared to wet years, there is an increase in non-compliance months during dry years.

This study investigated the variations in TP concentrations at four monitoring sections, namely Dashariver (Shuangqiao), Donghe (Shuangqiao), Shilongping, and Dieshuiheqiao, in the Donghe River basin under three integrated reduction scenarios. Based on the simulation results using the SWAT model, it can be observed that the reduction scenarios, involving the reduction of pollution from domestic sources, dilution through water diversion in urban river sections, and other measures, have shown positive effects on reducing TP concentrations and achieving water quality compliance in the Dashahe tributary and the main stream of the Donghe River. However, during the dry season months of January to April and December, when rainfall is reduced, the decreased river flow and limited dilution and exchange processes result in restricted TP diffusion and increased accumulation (Zhu et al. 2017; Xie et al. 2019; Xu et al. 2021). This indicates that there is still a possibility of TP concentration exceeding the standards in the Donghe River basin despite the improvement achieved by the reduction scenarios. It emphasizes the need for further exploration and refinement of the reduction strategies.

According to the simulation results of the SWAT model in the Donghe River basin, further optimization of the reduction strategies is required to achieve TP concentration compliance throughout the entire basin. The following aspects should be considered: (1) wastewater treatment plants play a crucial role in reducing domestic pollution and improving water quality. It is necessary to continue expanding the operational scale and pollution reduction capacity of urban wastewater treatment plants. (2) Water replenishment is an important direction. Emphasis should be placed on supplying water from December to April of the following year to increase river flow and hydraulic energy, promoting self-purification and dilution effects. Furthermore, considering the specific conditions of the Donghe River basin, the following issues should be addressed in future research: (1) Rural domestic pollution, agricultural non-point source pollution, industrial sources, and livestock and poultry farming continue to be significant sources of TP in the Donghe River basin. Macroscopic and microscopic improvements and enhancements should be gradually implemented. Macroscopically, this involves considering the planning and optimal siting of pollution sources, as well as methods and mechanisms for coordinated pollution control. Microscopically, effective and rational reduction and control measures should be implemented for each pollution source (Chen et al. 2018b, 2019; Xiong et al. 2020), such as improving agricultural management practices (Chinnasamy et al. 2015), implementing appropriate fertilization techniques, and constructing wetlands and retention facilities (Wang et al. 2020b; Chen et al. 2021a). (2) Pollutants such as BOD₅, COD, NH₃-N, TN, and SS remain significant concerns for water pollution in the basin. It is essential to establish a comprehensive monitoring system for the entire basin and conduct regular monitoring and water environmental safety assessments (Tan et al. 2017; Liu et al. 2020d).

According to the analysis of the results, it is found that there is a certain negative correlation between TP concentration in the tributaries of the Da Sha River and the main stem of the East River and rainfall. It is concluded that seasonal rainfall may be a driving factor for TP concentration variations in the East River basin. Further investigation should be conducted to explore the relationship between characteristics of rainfall such as spatial and temporal distribution, event size, and frequency (Zhang et al. 2017a), and TP concentration. Combining rainfall characteristics (Li et al. 2018; Sun et al. 2019) with water quality management measures, more detailed management strategies can be developed to reduce TP input. In addition to rainfall, the TP concentration dynamics in the Donghe River basin may involve various hydrological, physical, and biological processes. Hydrological processes (Wang et al. 2020a), such as evapotranspiration, streamflow velocity (Alizadeh et al. 2018), water level, hydraulic energy, and groundwater recharge, play a role. Physical processes include the transport and deposition of pollutants in rivers and the interception and removal of sediments by lakes and reservoirs (Deng et al. 2019). Biological processes, such as the degradation of pollutants by phytoplankton and algae (Chen et al. 2018a; Shamshirband et al. 2019), are also relevant. Furthermore, climate change can have a significant impact on rainfall patterns and hydrological processes (Liu et al. 2020b). Investigating the relationships between other factors and pollutant concentrations can provide a more comprehensive and in-depth understanding of the underlying mechanisms driving the concentration, transport, transformation, and cycling of pollutants in the Donghe River basin.

During the modeling process of the SWAT model, we identified the limitations and shortcomings of the model and proposed directions for improvement. Firstly, reducing the uncertainty of SWAT model parameters is crucial. Improvement lies in enhancing parameter sensitivity analysis and optimizing parameter fitting (Abbaspour et al. 2007b). For parameter sensitivity analysis, multiple sensitivity indices, such as impact coefficient, variance analysis, total effect indices, and local sensitivity indices can be utilized to obtain more comprehensive and accurate information about parameter sensitivity. Non-parametric sensitivity analysis methods, such as machine learning algorithms and neural networks, can also be employed to uncover complex parameter relationships and nonlinear responses. Additionally, sensitivity analysis can be conducted at both global and local levels. Global sensitivity analysis reveals the model's overall response to parameter variations, while local sensitivity analysis delves into specific parameter impacts. For parameter optimization, a combination of different optimization algorithms such as genetic algorithms, particle swarm optimization algorithms, and simulated annealing algorithms can be considered (Bekele & Nicklow 2005) to form hybrid optimization algorithms. This approach leverages the strengths of various algorithms to improve parameter optimization outcomes. Constraints can also be introduced during the optimization process, such as physical constraints, statistical constraints, or constraints based on observational data. Expert knowledge (Panday & Huyck 2004) can be utilized to limit parameter ranges or provide initial parameter values, narrowing down the parameter search space and accelerating the optimization process. This ensures that the parameter estimation results align with reality and feasibility. Secondly, accounting for the basin characteristics and spatial heterogeneity of the actual watershed is essential. No model can perfectly replicate the mechanisms operating in an actual watershed. To improve the model's performance in capturing basin characteristics and spatial heterogeneity, algorithmic improvements (Li et al. 2019a, 2019b), the introduction of new parameter estimation methods from a watershed perspective (Abbaspour & Rouholahnejad 2015), the inclusion of new hydrological process modules (Du et al. 2018), or multi-model fusion can be considered.

This study investigated the effectiveness of three emission reduction scenarios in reducing the concentration of TP, the primary pollutant, in the Dong River Basin in Longyang District, Yunnan Province, China, using the SWAT model. The findings are as follows:

• (1)

  The SWAT model, calibrated and validated at Bao Shan, Han Zhuang, and Xin Jie stations located in sub-basins of the study area, demonstrated good applicability in simulating both runoff and TP concentration, with determination coefficients (R2) and Nash–Sutcliffe efficiency (NSE) values exceeding 0.75 for runoff and R² and NSE values exceeding 0.70 for TP concentration at Sha Ba and Shi Long Ping monitoring sections. However, limitations still exist when applying the model to the actual basin, suggesting the need for employing multiple analytical methods for parameter sensitivity analysis and optimization to enhance simulation accuracy.

• (2)

  The simulation results of the three TP reduction scenarios using the SWAT model revealed notable improvements in TP concentrations in the target river sections, where the tributaries of Da Sha River join the Dong River and where the Dong River flows into the Nu River. These findings demonstrate the effectiveness of emission reduction scenarios in mitigating TP pollution in the Dong River Basin. Nevertheless, during the dry season months of January to April and December, particularly the peak TP concentration in April, the reduced dilution effect of rainfall leads to TP accumulation. As a result, there is still a risk of TP concentrations exceeding the standards in the Dong River Basin during this period, emphasizing the importance of implementing preventive measures to ensure year-round water quality compliance.

• (3)

  The pollution control and management of the Dong River Basin exhibit complex and comprehensive characteristics, requiring long-term, multifaceted prevention, treatment, and adjustment strategies. In addition to pollution sources from urban areas and wastewater treatment plants, rural domestic sources, agricultural sources, industrial sources, and livestock and poultry farming sources are also significant contributors to TP in the Dong River Basin. Furthermore, the basin faces challenges related to other pollutants such as biochemical oxygen demand (BOD5), chemical oxygen demand (COD), ammonia nitrogen (NH₃-N), total nitrogen (TN), and suspended solids (SS). Therefore, future research should focus on continuous improvement and refinement of pollution control strategies to address these issues effectively.

This work was supported by the National Key R&D Program of China (2016YFC0401701), the Belt and Road Special Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering (2019490911), the Belt and Road Special Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering (2020491111) and the Natural Science Foundation of Hebei Province of China (Grant No. E2020402057).

C.Z. conceptualized the whole article, developed the methodology, brought the resources, validated and wrote the review and edited the article. Y. W. conducted formal analysis, visualized the article, validated and wrote the original draft. C. H. investigated the process and arranged the resources. W. H. validated the article.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.