A high-precision nitrogen and phosphorus simulation and prediction method based on quantile mapping and coupling with a distributed hydrological model SWAT

Abstract

The application provides a high-precision nitrogen and phosphorus simulation and prediction method based on quantile mapping and coupling of a distributed hydrological model SWAT, and belongs to the technical field of environmental science and engineering, and comprises the following steps: firstly, obtaining multi-source data of a research basin and preprocessing, then obtaining CMIP6 future climate data and performing downscaling processing, then constructing a multi-factor joint scenario system, simulating and calculating the load by using the SWAT, quantifying uncertainty and identifying risk, and finally comprehensively expressing to form nitrogen and phosphorus flux and risk distribution results. The high-precision nitrogen and phosphorus simulation and prediction method based on quantile mapping and coupling of the distributed hydrological model SWAT solves the problems of insufficient precision of existing climate data, low coupling degree of water quality models and difficulty in quantifying uncertainty of prediction results.

Description

Technical Field

[0001] This invention relates to the fields of environmental science and engineering technology, and in particular to a high-precision nitrogen and phosphorus simulation and prediction method based on quantile mapping and the SWAT distributed hydrological model. Background Technology

[0002] With the continuous intensification of agricultural production activities and the ongoing transformation of land use patterns, the input pathways, migration routes, and output modes of nutrients such as nitrogen (N) and phosphorus (P) within watershed systems have become increasingly complex and diverse. Factors such as increased fertilization in farmland, expansion of livestock and poultry farming, rural domestic emissions, and soil disturbance caused by land reclamation have significantly increased the intensity of nitrogen and phosphorus cycling within the soil-vegetation-water system. When large amounts of nitrogen and phosphorus enter rivers, lakes, and reservoirs through surface runoff, interflow, or groundwater infiltration, they dramatically increase the concentration of available nutrients in the water, leading to abnormal algal blooms and a series of serious ecological and environmental problems, including eutrophication, frequent algal blooms, decreased dissolved oxygen, and degradation of aquatic habitats.

[0003] The Beijing-Tianjin-Hebei region is characterized by high agricultural intensity and a high concentration of population and industry. The region has extensive arable land with high fertilizer input levels, along with a dense river network and numerous reservoirs and water diversion projects. This makes the migration and accumulation of nutrients between different water bodies extremely complex. Furthermore, the region's significant topographic relief, diverse underlying surface types, uneven spatial and temporal distribution of precipitation, and the coexistence of extreme rainfall and seasonal drought all contribute to the significant spatial and temporal heterogeneity of nitrogen and phosphorus migration via runoff and infiltration.

[0004] The SWAT model is increasingly widely used in watershed hydrology and nitrogen and phosphorus load simulation. However, current research still has many limitations and shortcomings. Specifically, meteorological stations are sparsely distributed and have limited coverage, while climate model data generally exhibit systematic biases in precipitation and temperature, making it difficult for the model's driving inputs to accurately capture the true spatiotemporal changes. Bias correction methods such as quantile mapping are often treated as independent preprocessing tools, lacking dynamic coupling and feedback mechanisms with the SWAT model, resulting in difficulties in targeted optimization of correction effects for water quality processes. SWAT models have a high degree of parameterization approximation for key processes, making it difficult to accurately characterize spatiotemporal heterogeneity, and the model is extremely sensitive to input disturbances, easily generating accumulated errors. More importantly, the uncertainty of the prediction results lacks systematic quantification and risk expression, which greatly weakens the credibility and application value of the research conclusions. Currently, most methods still treat quantile mapping and other correction methods as independent preprocessing steps, making it difficult to achieve dynamic coupling and model optimization with the SWAT model, thus limiting the credibility of the prediction results. Summary of the Invention

[0005] The purpose of this invention is to provide a high-precision nitrogen and phosphorus simulation and prediction method based on quantile mapping and the SWAT distributed hydrological model, which solves the problems of insufficient accuracy of existing climate data, low coupling degree of water quality models, and difficulty in quantifying the uncertainty of prediction results.

[0006] To achieve the above objectives, this invention provides a high-precision nitrogen and phosphorus simulation and prediction method based on quantile mapping coupled with the distributed hydrological model SWAT, comprising the following steps: S1. Obtain multi-source basic data of the research watershed, and perform data preprocessing on the multi-source basic data to form a standardized dataset; S2. Obtain future climate data under CMIP6 multi-model multi-emission pathways, and use the quantile change mapping spatial downscaling method to perform statistical downscaling and systematic bias correction on the future climate data to construct a high-resolution meteorological driving sequence at the regional scale. S3. Based on S2, land use and agricultural activity changes are introduced to construct a multi-factor joint scenario system; S4. Based on the SWAT model that has been calibrated and validated for historical periods, future climate data and multi-factor joint scenario parameters are input into the model to simulate future scenarios, and the load is calculated using the concentration-flow coupling method. S5. Introduce Monte Carlo stochastic simulation and Bayesian uncertainty analysis methods to quantify the uncertainty of simulation results and identify water quality risks; S6. The simulation results of multiple scenarios are comprehensively expressed at the spatiotemporal scale to form the trend of nitrogen and phosphorus flux changes and the distribution of water quality risks in different future periods.

[0007] Preferably, the multi-source basic data includes digital elevation model (DEM), land use / cover data, soil property data, historical meteorological observation data, hydrological monitoring data, and water quality monitoring data; the digital elevation model is used to construct the watershed spatial structure, determine the runoff confluence path, river network structure, and sub-watershed boundaries, and provide topographic constraints for the construction of hydrological response units.

[0008] Preferably, data preprocessing includes unifying the coordinate system and resampling the spatial resolution of spatial data, unifying the time series data to a daily time scale, identifying outliers using statistical thresholding or box plot methods, and filling in missing data using linear interpolation or regression interpolation methods.

[0009] Preferably, the quantile change mapping spatial downscaling method constructs the cumulative distribution function of historical observed meteorological variables and model simulated meteorological variables, and uses the quantile change mapping relationship to correct future climate change signals, thereby reducing the impact of climate model bias on hydrological and water quality simulation results, while maintaining the consistency of future climate change signals. The expression for the mapping relationship of quantile changes is: ; in, For the original model meteorological variables under future scenarios, For the corrected meteorological variables, The cumulative distribution function of historically observed meteorological variables. This is the cumulative distribution function of the meteorological variables simulated by the model.

[0010] Preferably, the multi-factor joint scenario system incorporates human activity factors such as land use change and agricultural activity adjustments, and combines them with climate change scenarios to form a set of joint scenarios covering multiple development patterns and uncertainties, which are used to characterize the evolutionary features of future watersheds under the combined effects of natural processes and human activities.

[0011] Preferably, the expression for calculating the load using the concentration-flow coupling method is: ; in, No. sub-basin at time Nitrogen or phosphorus load, For the corresponding traffic, This corresponds to the nitrogen or phosphorus concentration.

[0012] Preferably, in S5, the Monte Carlo random sampling method is used to perturb the key parameters and scenario variables of the model multiple times to construct multiple sets of simulation samples. The confidence interval and parameter sensitivity of the prediction results are evaluated by combining the Bayesian uncertainty analysis method to identify sensitive areas and key periods of significant changes in nitrogen and phosphorus fluxes.

[0013] Therefore, the present invention employs the above-mentioned high-precision nitrogen and phosphorus simulation and prediction method based on quantile mapping and the distributed hydrological model SWAT, and the technical effects are as follows: 1. Significantly improved simulation accuracy: By adopting the quantile change mapping spatial downscaling method, the systematic errors in hydrological and water quality simulation caused by the bias of traditional meteorological data are eliminated. By optimizing the accuracy of meteorological driving data and combining it with the SWAT model, the simulation capability of key hydrological processes such as runoff, evapotranspiration, and soil moisture content is improved. High-precision simulation of total nitrogen and total phosphorus fluxes on the spatiotemporal scale is achieved, accurately depicting the detailed characteristics of nitrogen and phosphorus migration and transformation.

[0014] 2. Achieve deep module integration: The quantile mapping bias correction is deeply coupled with the hydrological-water quality simulation process of SWAT, ensuring that the meteorological correction results can meet SWAT's requirements for data resolution, time series structure and statistical characteristics. The bias correction parameters are optimized in conjunction with the SWAT model calibration process, making the simulation of water quantity and pollutant migration more coordinated and consistent as a whole, and effectively improving the overall stability and applicability of the simulation system.

[0015] 3. Applicable to future scenario forecasting, improving the reliability of water quality prediction: By applying the quantile mapping method to future scenario meteorological data, the impact of model bias on water quality prediction is effectively reduced, making the statistical characteristics of future driving data more consistent with current meteorological observations; with the help of corrected high-reliability meteorological series, this technology can reliably assess the impact of future climate change on nitrogen and phosphorus migration patterns, providing reliable prediction support for water environment change trends under different emission scenarios.

[0016] 4. Possesses good universality and scalability: The coupling framework constructed by this technology has both universality and flexibility, and can be applied to watershed simulation tasks of different scales, different climate zones and different land use types; the bias correction module is compatible with meteorological data from different sources, and the process coupling structure of the SWAT model can also be extended to the simulation of sediment, other nutrients or pollutants as needed, which has strong application prospects and promotion value. Attached Figure Description

[0017] Figure 1 This is a technical roadmap for the high-precision nitrogen and phosphorus simulation and prediction method of the present invention; Figure 2 This is a flowchart illustrating the application of the SWAT model in this invention. Figure 3 This is a flowchart of the uncertainty analysis for this invention; Figure 4 This is a land use change map of the Beijing-Tianjin-Hebei region from 2000 to 2020, as shown in this embodiment of the invention. Figure 5 This is a schematic diagram showing the ranking of the importance of land use change in the Beijing-Tianjin-Hebei region for TN prediction in an embodiment of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0020] Example 1 like Figures 1-3 As shown, this invention provides a high-precision nitrogen and phosphorus simulation and prediction method based on quantile mapping coupled with the distributed hydrological model SWAT. A typical watershed in the Beijing-Tianjin-Hebei region is selected as a case study area. This region has intensive agricultural activities, diverse land use types, and is significantly affected by climate change, resulting in prominent nitrogen and phosphorus loss problems.

[0021] First, multi-source baseline data were collected for the study watershed and its upstream control area, specifically including Digital Elevation Model (DEM), land use / cover data, soil property data, historical meteorological observation data, hydrological monitoring data, and water quality monitoring data. The DEM, using 30-meter resolution Shuttle Radar Topography Mission (SRTM) data, was used to construct the watershed spatial structure and determine runoff confluence paths, river network structure, and sub-watershed boundaries. The high accuracy of the DEM data ensured a true reflection of topographic features, providing a reliable foundation for subsequent hydrological response unit delineation.

[0022] Land use / cover data were selected from three periods of Landsat satellite remote sensing imagery from 2000, 2010, and 2020. Land use types were extracted using supervised classification methods, including five major categories: cultivated land, forest land, grassland, construction land, and water bodies. For example... Figure 4 As shown, the time series changes in land use data provide important evidence for analyzing the impact of human activities on nitrogen and phosphorus migration.

[0023] Soil property data were obtained from the World Soil Database (HWSD) at a scale of 1:1,000,000, including soil type maps and physicochemical property data such as soil texture, organic matter content, and pH value. These data are crucial for accurately simulating the adsorption, desorption, and migration processes of nitrogen and phosphorus in the soil.

[0024] Historical meteorological observation data, including daily precipitation, maximum temperature, and minimum temperature from meteorological stations within and around the basin over the past 30 years, are collected to construct historical meteorological sequences, serving as a reference benchmark for statistical downscaling.

[0025] Hydrological monitoring data is used to obtain water level and flow rate observation data of the watershed outlet section and major tributaries, which are used for model calibration and verification to ensure that the model accurately simulates the runoff process.

[0026] Water quality monitoring data is collected from the total nitrogen (TN) and total phosphorus (TP) concentrations or fluxes of major water bodies (rivers, lakes, and reservoirs) within the watershed to evaluate the model's simulation performance on nitrogen and phosphorus loads.

[0027] All spatial data were unified to the same coordinate system and resampled to a 30-meter resolution to ensure spatial consistency among the data. Meteorological, hydrological, and water quality time series data were unified to a daily scale, outliers were identified using statistical thresholding or box plot methods, and missing data were filled in using linear interpolation or regression interpolation methods to construct a standardized dataset that meets the requirements of subsequent model-driven and parameter setting.

[0028] This invention utilizes future climate scenario data from multiple climate models and emission pathways provided by the Sixth Coupled Model Intercomparison Project (CMIP6), including meteorological variables such as precipitation and temperature under three representative pathways: SSP1-2.6 (low emission scenario), SSP2-4.5 (medium emission scenario), and SSP5-8.5 (high emission scenario). Because climate model output data contains systematic biases and has low spatial resolution, directly using it to drive the SWAT model would lead to distorted simulation results. Therefore, we employ the Quantile Delta-Mapped Spatial Disaggregation (QDMSD) method to statistically downscale and correct for systematic biases in the CMIP6 model output.

[0029] The core idea of ​​the QDMSD method is to correct future climate change signals by constructing a quantile mapping relationship between historically observed meteorological variables and model-simulated meteorological variables. The specific steps are as follows: The cumulative distribution functions of historical meteorological variables (such as precipitation) and model-simulated meteorological variables are constructed respectively.

[0030] By comparing the quantiles of historical observations and model simulations, a mapping relationship between the two is established; that is, for any given model simulation quantile, the corresponding historical observation quantile is found.

[0031] By using the established quantile mapping relationship, the meteorological variables simulated by the model under future climate scenarios are corrected to obtain the corrected meteorological variables.

[0032] The expression for the mapping relationship of quantile changes is: ; in, For the original model meteorological variables under future scenarios, For the corrected meteorological variables, The cumulative distribution function of historically observed meteorological variables. This is the cumulative distribution function of the meteorological variables simulated by the model.

[0033] By combining spatial interpolation techniques (such as Kriging interpolation) and scale transformation methods, the corrected meteorological data are converted into high spatial resolution daily meteorological driving sequences that meet the requirements of watershed scale and model input.

[0034] The QDMSD method effectively reduced the impact of climate model bias on hydrological and water quality simulation results, while maintaining the consistency of future climate change signals, providing reliable meteorological driving data for subsequent simulations.

[0035] Based on climate change scenarios, we further incorporate human activity factors such as land use change and agricultural activity adjustments to construct a multi-factor joint future scenario system of "climate-land use-agricultural structure".

[0036] Based on historical land use change trends and future planning assumptions, different land use change scenarios are set, such as farmland expansion, forest degradation, and accelerated urbanization. Agricultural planting structure and management parameters are adjusted, including fertilization intensity, crop type, and planting ratio. For example, a high fertilization intensity scenario is set to simulate the impact of agricultural intensification on nitrogen and phosphorus loss. Different climate scenarios are combined with human activity scenarios to form a set of joint scenarios covering multi-path uncertainties. For example, the SSP1-2.6 climate scenario is combined with farmland expansion and high fertilization intensity human activity scenarios to form a possible future development path. This scenario system can comprehensively characterize the combined impact of watershed human activities and climate change under different future development paths, providing a rich selection of scenarios for subsequent simulations.

[0037] Based on the SWAT model, which has been calibrated and validated for historical periods, we will use the high-resolution meteorological driving data constructed in Phase 1 and the land use and agricultural management parameters set in Phase 2 as model inputs to conduct continuous simulations for different future time periods (such as the 2030s, 2050s, and 2080s). Through distributed hydrological and water quality coupled simulation, the SWAT model can obtain the spatiotemporal variation characteristics of nitrogen and phosphorus fluxes at the watershed and sub-watershed scales.

[0038] Based on DEM data, sub-basins and hydrological response units (HRUs) were delineated. Meteorological data such as precipitation and temperature were used to drive the water balance equation, simulating runoff components including surface runoff, interflow, and groundwater. Building upon the water balance, processes such as soil erosion, nutrient adsorption and desorption, plant uptake, and microbial transformation were considered to simulate the migration and transformation of nitrogen and phosphorus in the soil-vegetation-water system. A concentration-discharge coupling method was used to calculate nitrogen and phosphorus loads; that is, for each sub-basin and each time step, nitrogen and phosphorus loads were calculated based on the simulated flow rate and nitrogen and phosphorus concentrations.

[0039] The expression for calculating the load using the concentration-flow coupling method is as follows: ; in, No. sub-basin at time Nitrogen or phosphorus load, For the corresponding traffic, This corresponds to the nitrogen or phosphorus concentration.

[0040] Through continuous simulation, we obtained runoff processes and total nitrogen and total phosphorus output fluxes at the watershed and sub-watershed scales under different joint scenarios, providing basic data for subsequent uncertainty quantification and water quality risk identification.

[0041] To address the uncertainties introduced by multi-scenario simulations, Monte Carlo stochastic simulation and Bayesian uncertainty analysis were employed to comprehensively quantify the uncertainties in model parameters, climate drivers, and scenario pathways. Multiple perturbations were applied to key model parameters (such as soil erosion factors and nutrient cycling rates) and scenario variables (such as precipitation and fertilization intensity) to construct multiple sets of simulation samples. Each sampling randomly selected parameter values ​​from a pre-defined distribution to ensure sample diversity and representativeness. Combining prior information and simulation sample data, Bayes' theorem was used to update the posterior distribution of parameters, assessing the confidence interval and parameter sensitivity of the prediction results. The impact of parameter uncertainty on the prediction results was quantified by calculating the posterior mean, standard deviation, and other statistical measures of the parameters. Statistical analysis of the distribution range of total nitrogen and total phosphorus fluxes under different simulation samples quantified the combined impact of climate-driven changes, land-use changes, and model parameter uncertainties on the prediction results.

[0042] Based on the quantification of uncertainty, this study further identifies spatial regions and key periods where total nitrogen and total phosphorus loads will significantly increase under conditions of rising temperatures, increased extreme rainfall events, or prolonged drought. Using GIS technology, the simulated nitrogen and phosphorus load data are overlaid with watershed spatial information to identify high-load areas (hotspots) and sensitive areas. Spatial distribution maps of nitrogen and phosphorus loads are then drawn to visually demonstrate spatial differences in load. Figure 5 The indirect reflection of the importance ranking of features in the random forest model shows that high-load areas are often closely related to specific land use types or agricultural activity intensities. Analyzing the changing trends of nitrogen and phosphorus loads across different time periods (e.g., seasons, years) helps identify critical periods. For example, comparing the differences in nitrogen and phosphorus loads between the rainy and dry seasons assesses the contribution of extreme rainfall events to the load. Integrating spatial and temporal analysis results, a water quality risk zoning map is generated, providing a scientific basis for watershed non-point source pollution control and water environment management. For example, the watershed can be divided into high-risk, medium-risk, and low-risk areas, allowing for differentiated governance measures to be developed for different risk levels.

[0043] like Figure 4As shown, significant changes occurred in land use patterns in the Beijing-Tianjin-Hebei region between 2000 and 2020, with an increase in cultivated land and construction land area and a decrease in forest and grassland area. These changes significantly impacted nitrogen and phosphorus loads. Simulations of nitrogen and phosphorus loads under different land use scenarios revealed that cultivated land expansion led to a significant increase in nitrogen and phosphorus loss, while the reduction in forest and grassland areas decreased their capacity for nitrogen and phosphorus interception and absorption. Therefore, rationally adjusting land use structure and protecting the ecological environment are effective ways to reduce nitrogen and phosphorus loss. Agricultural activities are one of the main sources of nitrogen and phosphorus loss in the Beijing-Tianjin-Hebei region. Simulations of nitrogen and phosphorus loads under different fertilization intensities showed that high fertilization intensity significantly increased nitrogen and phosphorus loss. Therefore, optimizing fertilization programs, reducing fertilizer application, and improving fertilizer utilization are key measures to reduce agricultural non-point source pollution. Furthermore, adjusting the agricultural planting structure and increasing the proportion of nitrogen-fixing crops such as legumes also helps reduce nitrogen and phosphorus loss.

[0044] Under the backdrop of future climate change, precipitation and temperature in the Beijing-Tianjin-Hebei region will undergo significant changes. Simulations of nitrogen and phosphorus loads under different climate scenarios reveal that high-emission scenarios involve frequent extreme rainfall events, leading to a substantial increase in nitrogen and phosphorus losses. Therefore, strengthening the basin's flood control and disaster reduction capabilities and improving its ability to respond to extreme climate events are crucial measures to ensure the safety of the basin's water environment. Meanwhile, the increase in nitrogen and phosphorus loads is relatively small under low-emission scenarios, indicating that reducing greenhouse gas emissions plays a positive role in mitigating the impacts of climate change on the water environment.

[0045] Therefore, this invention employs a high-precision nitrogen and phosphorus simulation and prediction method based on quantile mapping coupled with the distributed hydrological model SWAT. By constructing a complete coupled simulation framework and uncertainty quantification system, the accuracy and reliability of nitrogen and phosphorus simulation are significantly improved. A case study of the Beijing-Tianjin-Hebei watershed demonstrates that this method can effectively reveal the impact mechanisms of land use change, agricultural activities, and climate change on nitrogen and phosphorus loads, providing a scientific basis and technical support for watershed non-point source pollution control, water environment management, and climate change adaptation decision-making.

[0046] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A high-precision nitrogen and phosphorus simulation and prediction method based on quantile mapping coupled with the distributed hydrological model SWAT, characterized in that, Includes the following steps: S1. Obtain multi-source basic data of the research watershed, and perform data preprocessing on the multi-source basic data to form a standardized dataset; S2. Obtain future climate data under CMIP6 multi-model multi-emission pathways, and use the quantile change mapping spatial downscaling method to perform statistical downscaling and systematic bias correction on the future climate data to construct a high-resolution meteorological driving sequence at the regional scale. S3. Based on S2, land use and agricultural activity changes are introduced to construct a multi-factor joint scenario system; S4. Based on the SWAT model that has been calibrated and validated for historical periods, input future climate data and multi-factor joint scenario parameters into the model to simulate future scenarios, and use the concentration-flow coupling method to calculate the load. S5. Introduce Monte Carlo stochastic simulation and Bayesian uncertainty analysis methods to quantify the uncertainty of simulation results and identify water quality risks; S6. The simulation results of multiple scenarios are comprehensively expressed at the spatiotemporal scale to form the trend of nitrogen and phosphorus flux changes and the distribution of water quality risks in different future periods.

2. The high-precision nitrogen and phosphorus simulation and prediction method based on quantile mapping and the SWAT distributed hydrological model according to claim 1, characterized in that, Multi-source basic data includes digital elevation model (DEM), land use / cover data, soil property data, historical meteorological observation data, hydrological monitoring data, and water quality monitoring data; Digital elevation models are used to construct the spatial structure of watersheds, determine runoff confluence paths, river network structures, and sub-watershed boundaries, and provide topographic constraints for the construction of hydrological response units.

3. The high-precision nitrogen and phosphorus simulation and prediction method based on quantile mapping and the SWAT distributed hydrological model according to claim 1, characterized in that, Data preprocessing includes unifying the coordinate system and resampling the spatial resolution of spatial data, unifying the time series data to a daily time scale, identifying outliers using statistical thresholding or box plot methods, and filling in missing data using linear interpolation or regression interpolation methods.

4. The high-precision nitrogen and phosphorus simulation and prediction method based on quantile mapping and the SWAT distributed hydrological model according to claim 1, characterized in that, The quantile change mapping spatial downscaling method constructs the cumulative distribution function of historical observed meteorological variables and model-simulated meteorological variables, and uses the quantile change mapping relationship to correct future climate change signals, thereby reducing the impact of climate model bias on hydrological and water quality simulation results, while maintaining the consistency of future climate change signals. The expression for the mapping relationship of quantile changes is: ； in, For the original model meteorological variables under future scenarios, For the corrected meteorological variables, The cumulative distribution function of historically observed meteorological variables. This is the cumulative distribution function of the meteorological variables simulated by the model.

5. The high-precision nitrogen and phosphorus simulation and prediction method based on quantile mapping and the SWAT distributed hydrological model according to claim 1, characterized in that, The multi-factor joint scenario system incorporates human activity factors such as land use change and agricultural activity adjustments, and combines them with climate change scenarios to form a set of joint scenarios covering multiple development patterns and uncertainties. This system is used to characterize the evolutionary features of future watersheds under the combined effects of natural processes and human activities.

6. The high-precision nitrogen and phosphorus simulation and prediction method based on quantile mapping and the SWAT distributed hydrological model according to claim 1, characterized in that, The expression for calculating the load using the concentration-flow coupling method is as follows: ； in, No. sub-basin at time Nitrogen or phosphorus load, For the corresponding traffic, This corresponds to the nitrogen or phosphorus concentration.

7. The high-precision nitrogen and phosphorus simulation and prediction method based on quantile mapping and the SWAT distributed hydrological model according to claim 1, characterized in that, In S5, the Monte Carlo random sampling method is used to perturb the key parameters and scenario variables of the model multiple times to construct multiple sets of simulation samples. The Bayesian uncertainty analysis method is combined to evaluate the confidence interval and parameter sensitivity of the prediction results and identify sensitive areas and key periods of significant changes in nitrogen and phosphorus fluxes.

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