Analysis method and system for watershed nitrate loss based on prediction results of climate and land use type changes

Abstract

The present invention provides a method and system for analyzing nitrate loss in a basin based on the prediction results of climate and land use type changes, which relates to the field of determination and analysis of basin water resources. Based on the observed data of meteorological stations and meteorological factors of GCMs, the present invention predicts future precipitation and temperature under different SSP scenarios; based on historical land use data and LUH2 land use data, spatio-temporal conversion is performed to obtain future land use types under different SSP scenarios; the two sets of output results are used as input conditions for the surface water-groundwater coupling model, and the nitrate loss data in the basin under climate and land use changes are analyzed. The present invention can predict future climate and land use type changes, analyze the evolution laws of future water resources volume and surface water-groundwater interaction volume in the study area, and give more accurate quantitative results on the impact of climate and land use type changes on nitrate loss in the basin.

Description

Technical Field

[0001] The present invention relates to the field of determination and analysis of basin water resources, and particularly to a method and system for analyzing nitrate loss in a basin based on the prediction results of climate and land use type changes. Background Art

[0002] Water pollution caused by nitrates is mainly affected by two driving factors: climate and land use. Climate change affects the regional hydrological cycle through the seasonal and annual variability of precipitation, and further affects the migration and transformation process of pollutants in the water environment. Land use change affects the infiltration capacity of the soil and surface runoff, and further changes the basin hydrological cycle process and the spatio-temporal variation of pollutant input to affect the migration and transformation process of pollutants.

[0003] Therefore, technical means are needed to quantify the impact of climate and land use changes on nitrate loss in the basin.

[0004] When the prior art quantifies the impact of climate and land use changes on nitrate loss in the basin, the interaction between climate, land use and vegetation in hydrological simulation in the future scenario is rarely considered, resulting in distorted prediction results. Summary of the Invention

[0005] Object of the Invention: To propose a method for analyzing nitrate loss in a basin based on the prediction results of climate and land use type changes, and further to propose a system for implementing the above method to solve the above problems existing in the prior art.

[0006] In the first aspect, a method for analyzing nitrate loss in a basin based on the prediction results of climate and land use type changes is proposed. The method includes the following steps:

[0007] Based on the meteorological station observation data and GCMs meteorological factors, use the statistical downscaling model to predict the future precipitation and temperature under different SSP scenarios;

[0008] Based on the historical land use data and LUH2 land use data, perform spatio-temporal conversion to obtain the future land use types under different SSP scenarios;

[0009] Construct a surface water-groundwater coupling model;

[0010] Use the future precipitation and temperature, and the future land use types as the input conditions of the surface water-groundwater coupling model, and use the surface water-groundwater coupling model to analyze and obtain the future cross-sectional runoff, cross-sectional nitrate load, and surface water-groundwater nitrate interaction volume;

[0011] Based on the future cross-sectional runoff, cross-sectional nitrate load, and surface water-groundwater nitrate interaction volume, the basin nitrate loss data under climate and land use changes are analyzed and obtained.

[0012] In a further embodiment of the first aspect, based on the meteorological station observation data and GCMs meteorological factors, the future precipitation and temperature under different SSP scenarios are predicted using a statistical downscaling model, specifically including:

[0013] Taking the meteorological station observation data and GCMs meteorological factors as predictors, a statistical relationship between the predictors and the predicted variables is established according to multiple linear regression and ridge regression to generate a statistical downscaling model;

[0014] Inputting the atmospheric circulation model output data into the constructed statistical downscaling model to generate the future precipitation and temperature under different SSPs:

[0015]

[0016]

[0017]

[0018] In the formula, is the daily precipitation occurrence probability; is the daily precipitation amount; is the average daily temperature; are the predictors; n is the number of predictors; α, β, γ are the parameters of the model; is the error of the statistical downscaling model.

[0019] In a further embodiment of the first aspect, based on the historical land use data and LUH2 land use data, a spatio-temporal transformation is performed to obtain the future land use types under different SSP scenarios, specifically including:

[0020] Obtain the two matrix files of States and Transitions in the LUH2 dataset to get the historical land use data and LUH2 land use data;

[0021] Perform time transformation and space transformation on the historical land use data and LUH2 land use data in sequence to obtain the future land use types under different SSP scenarios;

[0022] The expression of the time transformation is as follows:

[0023]

[0024] In the formula, and are the land use states at times t and t + 1 respectively; is the state transition probability, and its expression is:

[0025]

[0026] The expression of the spatial transformation is as follows:

[0027]

[0028] In the formula, LU represents the set of finite and discrete states of the cells; t and t + 1 represent different times; N represents the neighborhood of the cells; f(*) represents the conversion rule of partial spatial cells.

[0029] In a further embodiment of the first aspect, the surface water - groundwater coupling model is obtained by coupling a basic water balance model, a three - dimensional groundwater flow model in porous media, a surface water - groundwater interaction model, and a convective - dispersion chemical reaction model of nitrate in saturated porous media;

[0030] The future runoff is output through the basic water balance model and the three - dimensional groundwater flow model in porous media;

[0031] The surface water - groundwater nitrate interaction amount is output through the nitrate interaction model;

[0032] The hydrochloride load is output through the convective - dispersion chemical reaction model of nitrate in saturated porous media.

[0033] In a further embodiment of the first aspect, the control equation of the basic water balance model is as follows:

[0034]

[0035] In the formula, represents the final soil water content; represents the initial soil water content; represents the precipitation on the i - th day; represents the surface runoff; represents the evapotranspiration; represents the infiltration recharge amount of surface water to groundwater; represents the groundwater runoff;

[0036] The control equation of the three - dimensional groundwater flow model in porous media is as follows:

[0037]

[0038] In the formula, 、 、 represent the hydraulic conductivities along the x, y, and z directions respectively; h is the groundwater level; W is the source - sink term; where \(S\) is the water storage rate; \(t\) is time.

[0039] In a further embodiment of the first aspect, the expression of the surface water - groundwater interaction model is as follows:

[0040]

[0041] where \(I\) is the exchange amount of surface water and groundwater; \(K\) is the permeability coefficient of the riverbed; \(L\) is the length of the river channel; \(P\) is the wetted perimeter of the river channel; is the water level of the river; is the groundwater level; \(Z\) is the thickness of the riverbed.

[0042] In a further embodiment of the first aspect, the expression of the convective - dispersion chemical reaction model of nitrate in a saturated porous medium is as follows:

[0043]

[0044] where \(k\) is the number of reactants, ; is the concentration of the reactant; is the hydrodynamic dispersion coefficient; is the seepage velocity; is the soil porosity; is the volume flux of water in the source - sink term of the aquifer per unit volume; is the reaction rate of the reactant in the aqueous phase.

[0045] In a further embodiment of the first aspect, the basin nitrate loss data under climate and land - use change is calculated according to the following formula:

[0046]

[0047] According to the basin nitrate loss data, calculate the contribution ratio of climate and land - use change to nitrate loss :

[0048]

[0049] where is the annual average nitrate loss of nitrate relative to the baseline period under different future - predicted scenarios; represents different scenarios; \(i = 1,2,3\) represent three different climate scenarios of SSP126, SSP245, and SSP585; \(j = 1,2\) represents whether to use the land - use type corresponding to the climate scenario, \(j = 1\) represents not using the future - predicted land - use type, and \(j = 2\) represents using the future - predicted land - use type; represents the nitrate load at the outlet section in the corresponding scenario; represents the nitrate load at the outlet section under the reference period conditions; and represent the lengths of the prediction period and the reference period respectively.

[0050] In the second aspect of the present invention, a system for analyzing nitrate loss in a basin is proposed. This system is used to execute the method for analyzing nitrate loss in a basin based on the prediction results of climate and land use type changes disclosed in the first aspect. The system for analyzing nitrate loss in a basin includes a first execution module, a second execution module, a third execution module, a loop calculation module, and an analysis module.

[0051] The first execution module predicts future precipitation and temperature under different SSP scenarios by using a statistical downscaling model based on meteorological station observation data and GCMs meteorological factors.

[0052] The second execution module performs spatio-temporal conversion based on historical land use data and LUH2 land use data to obtain future land use types under different SSP scenarios.

[0053] The third execution module is used to construct a surface water - groundwater coupling model.

[0054] The loop calculation module is used to input the future precipitation and temperature, and the future land use types into the surface water - groundwater coupling model constructed by the third execution module, and analyze and obtain future runoff, nitrate load, surface water - groundwater nitrate interaction amount, etc. by using the surface water - groundwater coupling model.

[0055] The analysis module is used to analyze and obtain the basin nitrate loss data under climate and land use changes based on the future runoff, nitrate load, and surface water - groundwater nitrate interaction amount.

[0056] Beneficial effects: By using the method for analyzing nitrate loss in a basin based on the prediction results of climate and land use type changes proposed in the present invention, future climate and land use type changes can be predicted, and the evolution laws of future water resource quantity and surface water - groundwater interaction amount in the study area can be analyzed, and more accurate quantitative results can be given for the impact of climate and land use type changes on basin nitrate loss. The present invention can provide a theoretical basis and technical support for the comprehensive management of basin water resources. Description of the Drawings

[0057] Figure 1 is a flowchart of the method for analyzing nitrate loss in a basin based on the prediction results of climate and land use type changes in the embodiment.

[0058] Figure 2 is the time series of annual precipitation in the simulation period and the prediction periods of different climate scenarios in a certain basin in the embodiment.

[0059] Figure 3 is the cross-section NO of a river basin under different climate scenarios in the embodiment 3 -N Load forecast results.

[0060] Figure 4 is the weight of the impact of climate and land use in the study area on nitrate pollution in the example. DETAILED DESCRIPTION

[0061] In the following description, a large number of specific details are provided to provide a more thorough understanding of the present invention. However, it is apparent to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present invention, some technical features known in the art are not described.

[0062] Embodiment 1:

[0063] This embodiment discloses a method for analyzing nitrate loss in a watershed based on the prediction results of climate and land use type changes. Figure 1 , the specific steps are as follows:

[0064] Step 1: Based on the meteorological station observation data and GCMs meteorological factors, the statistical downscaling model is used to predict the future precipitation and temperature under different SSP scenarios.

[0065] The current climate model may have large deviations if directly applied to regional scale research due to resolution issues. Statistical downscaling is widely used in the simulation and prediction of climate variables such as precipitation and temperature due to its diverse and simple and flexible methods, small amount of calculation, and easy model construction. Commonly used statistical downscaling methods include regression analysis-based and weather generator-based methods.

[0066] The construction of a statistical downscaling model mainly involves three key steps: selecting prediction factors, calibrating and validating the model, and generating future climate scenarios. In this process, selecting appropriate prediction factors is crucial for accurately calculating future climate scenarios. The CanESM5 model provides 26 atmospheric circulation factors for this purpose. In this embodiment, the selection of prediction factors is based on a stepwise regression method. In the statistical downscaling process, in order to obtain a more accurate relationship between atmospheric circulation and ground observation data, this embodiment first uses reanalysis data to replace the atmospheric circulation model output data to establish a statistical downscaling model. The atmospheric circulation model output data is then input into the constructed downscaling model for future climate scenario prediction.

[0067]

[0068]

[0069]

[0070] In the formula, is the probability of daily precipitation occurrence; is the daily precipitation; is the average daily temperature; are the predictor variables; n is the number of predictor variables; α, β, and γ are the parameters of the model; is the error of the statistical downscaling model.

[0071] Based on the measured meteorological data of the meteorological station from 1979 to 2014, as well as the historical simulation data provided by CanESM5 and the NCEP reanalysis data, the model is constructed. The simulation period from 1979 to 2014 is divided into two segments, which are used for the calibration and verification of the ASD model respectively. Among them, the period from 1979 to 2000 is used for model calibration, and the period from 2001 to 2014 is used for model verification.

[0072] Step 2: Based on the historical land use data and LUH2 land use data, perform spatio-temporal conversion to obtain the future land use types under different SSP scenarios.

[0073] The prediction of future land use types is based on a statistical model, which is used to describe the state transition process of future land use types in time and space. Based on LUH2 and the statistical model to predict future land use types, first, based on the two matrix files of States and Transitions in the LUH2 dataset, calculate the transition probability matrix from the base year 2020 to the prediction year 2050 to achieve the prediction of land use types under different SSP scenarios.

[0074] The mathematical equation for time conversion is as follows:

[0075]

[0076] In the formula, and are the land use states at times t and t + 1 respectively; is the state transition probability, and its expression is:

[0077]

[0078] The expression for the spatial conversion is as follows:

[0079]

[0080] In the formula, LU represents the set of finite and discrete states of the cells; t and t + 1 represent different times; N represents the neighborhood of the cells; f(*) represents the partial spatial cell conversion rule.

[0081] Step 3: Construct a surface water - groundwater coupling model. This surface water - groundwater coupling model combines surface runoff and confluence, river hydrological processes, the migration and transformation of nutrients, and groundwater hydrological processes to provide an integrated coupled hydrological model for the basin.

[0082] This surface water - groundwater coupling model is obtained by coupling a basic water balance model, a three - dimensional groundwater flow model in porous media, a surface water - groundwater interaction model, and a convective - dispersion chemical reaction model of nitrate in saturated porous media.

[0083] The governing equation of the basic water balance model is as follows:

[0084]

[0085] In the formula, represents the final soil water content; represents the initial soil water content; represents the precipitation on the i - th day; represents the surface runoff; represents the evapotranspiration; represents the infiltration recharge from surface water to groundwater; represents the groundwater runoff;

[0086] The governing equation of the three - dimensional groundwater flow model in porous media is as follows:

[0087]

[0088] In the formula, , , represent the hydraulic conductivities along the x, y, and z directions respectively; h is the groundwater level; W is the source - sink term; is the storage coefficient; t is the time.

[0089] The surface water - groundwater interaction model is calculated based on formula (9):

[0090]

[0091] In the formula, I is the exchange amount between surface water and groundwater; K is the hydraulic conductivity of the riverbed; L is the length of the river channel; P is the wetted perimeter of the river channel; is the river water level; is the groundwater level; Z is the thickness of the riverbed.

[0092] The convective - dispersion chemical reaction model of nitrate in saturated porous media is calculated based on equation (10):

[0093]

[0094] where k is the number of reactants, ; is the concentration of the reactant; is the hydrodynamic dispersion coefficient; is the seepage velocity; is the soil porosity; is the volume flux of water in the source-sink term of the aquifer per unit volume; is the reaction rate of the reactant in the aqueous phase.

[0095] When constructing the surface water-groundwater coupling model, it is first necessary to construct the surface water model based on DEM, land use type, soil type, and meteorological data, etc. Then, construct the groundwater model based on formation information, hydrogeological parameters, source-sink terms and other data. The data transfer between the surface water and groundwater models mainly lies in the in formula (7) as the infiltration recharge amount of surface water to groundwater. In the groundwater model, the surface water-groundwater interaction amount is calculated through formula (9) and transferred to formula (7) to update the parameters of the surface water model. In this process, nitrate will migrate and transform along with the water volume, and the specific reaction process is calculated based on formula (10).

[0096] The coupling model needs to calibrate data such as surface water runoff, surface water nitrate load, groundwater level, and groundwater nitrate concentration. The method of combining automatic calibration and manual calibration can be adopted. First, calibrate a single model, and then calibrate the coupling model; first calibrate the water volume part, and then calibrate the water quality part to complete the parameter calibration of the model.

[0097] Step 4: Use the daily precipitation , daily average temperature predicted in Step 1 under different SSPs and the land use types in different periods predicted in Step 2 as the input conditions for the surface water-groundwater coupling model constructed in Step 3 ( corresponding to the daily precipitation in Equation 7), and run the surface water-groundwater coupling model. Combine the six different meteorological and land use data in Table 1, run six groups of surface water-groundwater coupling models, and analyze the evolution trends of runoff and nitrate load in the future study area under different SSPs.

[0098] Table 1 Scenario settings of the scenario comparison method

[0099] Serial number Meteorological data Land use data Scenario 1 SSP126 SSP126 Scenario 2 SSP245 SSP245 Scenario 3 SSP585 SSP585 Scenario 4 SSP126 Year 2020 Scenario 5 SSP245 Year 2020 Scenario 6 SSP585 Year 2020

[0100] Step 5: Based on the future runoff, nitrate load, and surface water-groundwater nitrate interaction volume, analyze and obtain the basin nitrate loss data under climate and land use changes. To quantify the impacts of climate and land use changes on basin nitrate loss, based on the results obtained from running the model in Step 4, use Equations (11) and (12) for calculation to quantify the impacts of climate and land use changes. The specific calculation method is as follows:

[0101]

[0102]

[0103] In the formula, is the annual average nitrate loss of nitrate relative to the baseline period under different future prediction scenarios; represents different scenarios; i = 1, 2, 3 represent three different climate scenarios of SSP126, SSP245, and SSP585; j = 1, 2 represent whether to use the land use type corresponding to the climate scenario, j = 1 represents not using the future predicted land use type, and j = 2 represents using the future predicted land use type; represents the nitrate load at the outlet section in the corresponding scenario; represents the nitrate load at the outlet section under baseline period conditions; and represent the lengths of the prediction period and the baseline period, respectively.

[0104] Example 2:

[0105] Combined with the specific steps disclosed in Example 1, in this example, an experimental basin in a certain area (hereinafter referred to as Basin A) is used as a typical study area. The study area is surrounded by mountains on three sides, has good basin closure, and intense surface water-groundwater interaction. The area of Basin A is 80.13 km 2 , and currently, the monitoring data is relatively complete, which can provide the data required for model construction.

[0106] Regarding the future precipitation and temperature predictions under different SSPs in Step 1, we select the ASD (Automated Statistical Downscaling) model and the CanESM5 meteorological dataset provided by the Canadian Climate Center to predict the future precipitation and temperature in the study area. This time, based on the measured meteorological data of the local meteorological station from 1979 to 2014, as well as the historical simulation data and NCEP reanalysis data provided by CanESM5, model construction is carried out. The simulation period from 1979 - 2014 is divided into two segments, which are respectively used for the calibration and verification of the ASD model. Among them, 1979 - 2000 is used for model calibration, and 2001 - 2014 is used for model verification. See Figure 2, during the simulation period (1979 - 2014), the average annual precipitation was 1074 mm. The ASD model predicted that the precipitation from 2015 to 2050 under three different climate scenarios was as follows: the annual precipitation in the SSP126 scenario was 1086 mm, which was relatively similar to the current precipitation; the annual precipitation in the SSP245 scenario was 1210 mm, an increase of 12.7% compared to the current precipitation; the average annual precipitation in the SSP585 scenario was 1308 mm, an increase of 21.8% compared to the current precipitation. The prediction of temperature was consistent with the precipitation method.

[0107] In the future land use prediction under different SSPs in step 2, based on the LUH2 dataset and the CA - Markov model, the forest land area remained the highest in the SSP126 scenario, which was in line with the sustainable development path of low - carbon emissions. In the SSP245 scenario, it decreased to 27.95 km 2 ; while in the SSP585 scenario, it decreased significantly to 18.16 km 2 . By 2050, in the SSP126 scenario, the cultivated land area decreased to 38.27 km 2 ; in the SSP245 scenario, it increased to 45.57 km 2 ; while in the SSP585 scenario, it reached 55.75 km 2 . The built - up land area was 2.79 km in 2020 2 , and by 2050, it remained basically unchanged in the SSP126 scenario, at 2.82 km 2 , increased to 3.34 km in the SSP245 scenario 2 , and increased to 6.50 km in the SSP585 scenario 2 . For the remaining two land use types, the area changes of grassland and water area were relatively small.

[0108] Input the climate and land use data under different SSP scenarios predicted in step 2 and step 3 into the coupled SWAT - MODFLOW - RT3D model to drive the coupled model, and obtain the monthly average nitrate loads at the outlet section of Basin A from 2023 to 2050 under the three scenarios of SSP126, SSP245, and SSP585 as Figure 3As shown in the figure. The nitrate loads at the outlet section under the three scenarios show a relatively stable changing trend. Among them, the monthly average nitrate load under the SSP126 scenario is 1,711.4 kg, while that under the SSP245 scenario is 2,318.4 kg, an increase of 35% compared with the SSP126 scenario; the SSP585 scenario is 2,528.7 kg, an increase of 47.7% compared with the SSP126 scenario. The large change in nitrate load is directly related to the increase in precipitation and the runoff at the outlet section. The movement of water drives the movement of nitrate in the river. Specifically for the monthly changes, with the occurrence of extreme precipitation, the nitrate flow in the A Basin during the flood season becomes larger. Under the SSP126 scenario, during the entire 28-year prediction period, only the nitrate load in 2 months exceeds 10,000 kg. While under the SSP245 scenario, it increases to 4 times, and all occur in June and July of each year; for the SSP585 scenario with the most drastic climate change, it increases to 5 times, and the maximum value is in July 2047, with the nitrate load at the outlet section reaching as high as 24,040 kg. It can be seen that under the future changing environment, the nitrate load will increase with the increase in runoff, and the flood peak will carry away more nitrates.

[0109] Based on the results after running the 6 scenarios in Table 1 in Step 4 and combined with the method in Step 5, the influence results of climate change and land use change on the nitrate loss at the outlet section of the A Basin are calculated Figure 4 . It can be found that under the SSP126 scenario, the driving weights of climate change and land use type change on the nitrate load in the A Basin are 87% and 13% respectively; under the SSP245 scenario, the driving weights of climate change and land use type change on the nitrate load in the A Basin are 94% and 6% respectively; under the SSP585 scenario, the driving weights of climate change and land use type change on the nitrate load in the A Basin are 97% and 3% respectively. We find that for the A Basin, the influence of climate change on the nitrate load is dominant in all cases. This indicates that climate factors (such as precipitation, temperature change, etc.) play a decisive role in the change of nitrate inflow into the river. As the SSP scenario changes from 126 to 585, that is, from a scenario with lower greenhouse gas emissions to a scenario with higher emissions, the influence of climate change on the nitrate load becomes more significant. This reflects that under a higher emission scenario, the impact of climate change is more intense and has a greater impact on the water cycle and related chemical processes. Under all scenarios, the influence of land use change on the nitrate load is relatively small, especially in the SSP585 scenario. Generally speaking, under different climate change scenarios, the influence of climate change on the nitrate load in the A Basin is much greater than that of land use change.

[0110] As described above, although the present invention has been shown and described with reference to specific preferred embodiments, it should not be construed as a limitation on the present invention itself. Various changes in form and detail may be made thereto without departing from the spirit and scope of the present invention as defined by the appended claims.

Claims

1. A method for analyzing nitrate loss in a watershed based on the prediction results of climate and land use type changes, characterized in that: The steps include: Based on the meteorological station observation data and GCMs meteorological factors, the statistical downscaling model is used to predict the future precipitation and temperature under different SSP scenarios, including: The meteorological station observation data and GCMs meteorological factors are used as forecast factors, and the statistical relationship between the forecast factors and the forecast quantity is established based on multiple linear regression and ridge regression to generate a statistical downscaling model. The output data of the atmospheric circulation model are input into the constructed statistical downscaling model to generate future precipitation and temperature under different SSPs: ； ； ； In the formula, is the probability of daily precipitation; is the daily precipitation; is the average daily temperature; is the prediction factor; n is the number of prediction factors; α, β, γ are the parameters of the model; is the error of the statistical downscaling model; Based on historical land use data and LUH2 land use data, spatiotemporal conversion was performed to obtain future land use types under different SSP scenarios; Constructing surface water-groundwater coupling model; The future precipitation, temperature and future land use type are used as input conditions of the surface water-groundwater coupling model, and the future cross-sectional runoff, cross-sectional nitrate load and surface water-groundwater nitrate interaction are obtained by analyzing the surface water-groundwater coupling model; Based on the future cross-sectional runoff, cross-sectional nitrate load, and surface water-groundwater nitrate interaction, the basin nitrate loss data under climate and land use changes are analyzed and obtained.

2. The method for analyzing nitrate loss in a watershed based on the prediction results of climate and land use type changes according to claim 1, characterized in that: Based on the historical land use data and LUH2 land use data, the spatiotemporal conversion is performed to obtain the future land use types under different SSP scenarios, including: Obtain the two matrix files of LUH2 dataset States and Transitions, and obtain the characteristic matrix of historical land use data and LUH2 land use data; Performing time conversion and space conversion on the characteristic matrices of the historical land use data and the LUH2 land use data in sequence to obtain future land use types under different SSP scenarios; The expression of the time conversion is as follows: ； In the formula, and are the land use status at time t and t+1 respectively; is the state transition probability, which is expressed as: ； The expression of the space conversion is as follows: ； Where LU represents the finite, discrete state set of the cell; t and t+1 represent different moments; N represents the neighborhood of the cell; and f(*) represents the spatial cell transformation rule.

3. The method for analyzing nitrate loss in a watershed based on the prediction results of climate and land use type changes according to claim 1 or 2, characterized in that: The surface water-groundwater coupling model is obtained by coupling a basic water balance model, a three-dimensional groundwater flow model in porous media, a surface water-groundwater interaction model, and a convective diffusion chemical reaction model of nitrate in saturated porous media. Outputting future runoff through the basic water balance model, the three-dimensional groundwater flow model in porous media, and the surface water-groundwater interaction model; The surface water-groundwater interaction model is used to output the surface water-groundwater water volume interaction, and the nitrate interaction is performed simultaneously with the water volume interaction; The hydrochloride load is output through the basic water balance model, the three-dimensional groundwater flow model in porous media, the surface water-groundwater interaction model and the advection-dispersion chemical reaction model of nitrate in saturated porous media.

4. The method for analyzing nitrate loss in a watershed based on the prediction results of climate and land use type changes according to claim 3, characterized in that: The control equation of the basic water balance model is as follows: ； In the formula, Indicates the final moisture content of the soil; represents the initial soil moisture content; represents the precipitation on the i-th day; It represents the surface runoff; It represents evapotranspiration; It indicates the infiltration recharge of surface water to groundwater; represents underground runoff; The governing equations of the three-dimensional groundwater flow model in porous media are as follows: ； In the formula, , , denote the permeability coefficients along the x, y, and z directions, respectively; h is the groundwater level; W is the source-sink term; is the water storage rate; t is the time.

5. The method for analyzing nitrate loss in a watershed based on the prediction results of climate and land use type changes according to claim 3, characterized in that: The expression of the surface water-groundwater interaction model is as follows: ； In the formula, I is the exchange volume between surface water and groundwater; K is the permeability coefficient of the riverbed; L is the length of the river channel; P is the wetted perimeter of the river channel; for the water level of the river; is the groundwater level; Z is the thickness of the riverbed.

6. The method for analyzing nitrate loss in a watershed based on the prediction results of climate and land use type changes according to claim 3, characterized in that: The expression of the convective diffusion chemical reaction model of nitrate in the saturated porous medium is as follows: ； In the formula, k is the number of reactants, ; is the concentration of the reactant; is the hydrodynamic dispersion coefficient; is the seepage velocity; is the soil porosity; is the volume flux of water in the source and sink terms per unit volume of the aquifer; is the reaction rate of the reactants in the aqueous phase.

7. The method for analyzing nitrate loss in a watershed based on the prediction results of climate and land use type changes according to claim 4, 5 or 6, characterized in that: The nitrate loss data of the watershed under the climate and land use changes are calculated according to the following formula: ； In the formula, is the annual average nitrate loss under different scenarios predicted in the future relative to the base period; Indicates different scenarios; i=1, 2, 3, representing three different climate scenarios: SSP126, SSP245 and SSP585; j=1, 2, representing whether to use the land use type of the corresponding climate scenario, j=1 means not to use the land use type predicted in the future, j=2 means to use the land use type predicted in the future; represents the nitrate load at the outlet section in the corresponding scenario; It represents the nitrate load at the outlet section under the base period conditions; and Represent the lengths of the forecast period and the base period, respectively.

8. The method for analyzing nitrate loss in a watershed based on the prediction results of climate and land use type changes according to claim 7, characterized in that: Also includes: Based on the nitrate loss data of the watershed, the contribution of climate and land use change to nitrate loss was calculated. : ； In the formula, It is the average annual nitrate loss under different scenarios predicted in the future relative to the base period.

9. A watershed nitrate loss analysis system, used to execute the watershed nitrate loss analysis method based on climate and land use type change prediction results as described in any one of claims 1 to 8; characterized in that: The watershed nitrate loss analysis system includes: The first execution module predicts the future precipitation and temperature under different SSP scenarios using a statistical downscaling model based on meteorological station observation data and GCMs meteorological factors; The second execution module: The second execution module performs spatiotemporal conversion based on the historical land use data and the LUH2 land use data to obtain the future land use types under different SSP scenarios; A third execution module; the third execution module is used to construct a surface water-groundwater coupling model; A circulation calculation module; the circulation calculation module is used to input the future precipitation and temperature, and the future land use type into the surface water-groundwater coupling model constructed by the third execution module, and use the surface water-groundwater coupling model to analyze and obtain the future runoff, nitrate load, and surface water-groundwater nitrate interaction; Analysis module: The analysis module is used to analyze the basin nitrate loss data under climate and land use changes based on the future runoff, nitrate load, and surface water-groundwater nitrate interaction.

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