Basin nitrate loss analysis method and system based on climate and land utilization type change prediction result

Abstract

The invention provides a drainage basin nitrate loss analysis method and system based on climate and land utilization type change prediction results, and relates to the field of drainage basin water resource determination and analysis. According to the method, on the basis of meteorological station observation data and GCMs meteorological factors, future precipitation and air temperature under different SSP scenes are predicted; based on the historical land utilization data and the LUH2 land utilization data, space-time conversion is executed to obtain future land utilization types under different SSP scenes; and taking the two sets of output results as input conditions of a surface water-underground water coupling model, and analyzing to obtain drainage basin nitrate loss data under climate and land utilization changes. According to the method, future climate and land utilization type changes can be predicted, the evolution law of the water resource amount and the surface water-underground water interaction amount of a future research area is analyzed, and a more accurate quantitative result is provided for the influence of the climate and land utilization type changes on drainage basin nitrate loss.

Description

Technical Field

[0001] The present invention relates to the field of water resources measurement and analysis in a watershed, and in particular to a method and system for analyzing nitrate loss in a watershed based on 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 seasonal and annual variability of precipitation, which in turn affects the migration and transformation of pollutants in the water environment. Land use changes will affect the infiltration capacity of the soil and surface runoff, thereby changing the hydrological cycle of the basin and the temporal and spatial changes of pollutant input to affect the migration and transformation of pollutants.

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

[0004] When quantifying the impact of climate and land use changes on nitrate loss in watersheds, existing technologies rarely consider the interactions among climate, land use, and vegetation in hydrological simulations under future scenarios, resulting in distorted prediction results. Summary of the invention

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

[0006] First, a method for analyzing nitrate loss in a watershed based on the predicted 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, the statistical downscaling model is used to predict the future precipitation and temperature under different SSP scenarios;

[0008] 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;

[0009] Constructing surface water-groundwater coupling model;

[0010] 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;

[0011] 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.

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

[0013] 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.

[0014] 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:

[0015]

[0016]

[0017]

[0018] 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.

[0019] In a further embodiment of the first aspect, based on the historical land use data and the LUH2 land use data, performing spatiotemporal conversion to obtain future land use types under different SSP scenarios specifically includes:

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

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

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

[0023]

[0024] 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:

[0025]

[0026] The expression of the space conversion is as follows:

[0027]

[0028] 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 transformation rule of the partial space cell.

[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 a porous medium, a surface water-groundwater interaction model, and a convection-diffusion chemical reaction model of nitrate in a saturated porous medium;

[0030] outputting future runoff through the basic water balance model and the three-dimensional groundwater flow model in the porous medium;

[0031] Outputting the surface water-groundwater nitrate interaction amount through the nitrate interaction model;

[0032] The hydrochloride loading is output by the advection-diffusion chemical reaction model of nitrate in the saturated porous medium.

[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, 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;

[0036] The governing equations of the three-dimensional groundwater flow model in porous media are as follows:

[0037]

[0038] 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.

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

[0040]

[0041] 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.

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

[0043]

[0044] 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.

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

[0046]

[0047] Based on the nitrate loss data of the watershed, the contribution of climate and land use change to nitrate loss was calculated. :

[0048]

[0049] 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.

[0050] In a second aspect of the present invention, a watershed nitrate loss analysis system is provided, which is used to execute the watershed nitrate loss analysis method based on the climate and land use type change prediction results disclosed in the first aspect. The watershed nitrate loss analysis system includes a first execution module, a second execution module, a third execution module, a cyclic calculation module, and an analysis module.

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

[0052] The second execution module performs spatiotemporal 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 cyclic calculation module is used to input the future precipitation, temperature and 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 future runoff, nitrate load, surface water-groundwater nitrate interaction, etc.

[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.

[0056] Beneficial effects: The proposed method for analyzing nitrate loss in a watershed based on the prediction results of climate and land use type changes can be used to predict future climate and land use type changes, analyze the evolution of water resources and surface water-groundwater interaction in the future study area, and provide more accurate quantitative results for the impact of climate and land use type changes on nitrate loss in the watershed. The present invention can provide a theoretical basis and technical support for the comprehensive management of water resources in the watershed. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0059] Figure 3 It is the prediction result of NO3-N load of a section under different climate scenarios in a certain river basin in the embodiment.

[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; 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.

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

[0072] Step 2: Based on the historical land use data and LUH2 land use data, spatiotemporal transformation is performed 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 that describes the state transition process of future land use types in time and space. Based on LUH2 and the statistical model, the transition probability matrix from the base year 2020 to the forecast year 2050 is first calculated based on the two matrix files of LUH2 dataset States and Transitions. , 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 status at time t and t+1 respectively; is the state transition probability, which is expressed as:

[0077]

[0078] The expression of the space conversion is as follows:

[0079]

[0080] 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 transformation rule of the partial space cell.

[0081] Step 3: Construct a surface water-groundwater coupling model. This surface water-groundwater coupling model combines surface runoff, river hydrological processes, nutrient migration and transformation with groundwater hydrological processes to provide a comprehensive coupled hydrological model for the basin.

[0082] The surface water-groundwater coupling model is obtained by coupling the basic water balance model, the three-dimensional groundwater flow model in porous media, the surface water-groundwater interaction model, and the convection-diffusion chemical reaction model of nitrate in saturated porous media.

[0083] The governing equations of the basic water balance model are as follows:

[0084]

[0085] 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;

[0086] The governing equations for the three-dimensional groundwater flow model in porous media are as follows:

[0087]

[0088] 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.

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

[0090]

[0091] 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.

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

[0093]

[0094] 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.

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

[0096] The coupled model needs to calibrate data such as surface water runoff, surface water nitrate load, groundwater level and groundwater nitrate concentration. A combination of automatic calibration and manual calibration can be used. First calibrate the single model and then calibrate the coupled model. First calibrate the water volume part and then complete the model parameter calibration based on the idea of ​​calibrating the water quality part.

[0097] Step 4: The daily precipitation under different SSPs predicted in step 1 is , daily average temperature The land use types predicted in step 2 for different periods are used as input conditions for the surface water-groundwater coupling model constructed in step 3 ( The surface water-groundwater coupling model was run by combining the six different meteorological and land use data in Table 1 to analyze the evolution trend of runoff and nitrate load under different SSPs in the study area in the future.

[0098] Table 1 Scenario settings for scenario comparison method

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

[0100] Step 5: Based on the future runoff, nitrate load, and surface water-groundwater nitrate interaction, the basin nitrate loss data under climate and land use changes are analyzed. In order to quantify the impact of climate and land use changes on basin nitrate loss, based on the results of running the model in step 4, formulas (11) and (12) are used to calculate and quantify the impact 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 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.

[0104] Embodiment 2:

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

[0106] In terms of the prediction of future precipitation and temperature under different SSPs in step 1, we selected the ASD (Automated Statistical Downscaling) model and the CanESM5 meteorological dataset provided by the Canadian Climate Centre to predict future precipitation and temperature in the study area. This model was constructed based on the meteorological data measured by the local meteorological station from 1979 to 2014, as well as the historical simulation data provided by CanESM5 and the NCEP reanalysis data. The simulation period 1979-2014 was divided into two sections, which were used for the calibration and verification of the ASD model respectively. 1979-2000 was used for model calibration, and 2001-2014 was used for model verification. See Figure 2The average annual precipitation in the simulation period (1979-2014) is 1074 mm. The ASD model predicts that the precipitation in 2015-2050 under three different climate scenarios is 1086 mm under SSP126, which is similar to the current precipitation, 1210 mm under SSP245, an increase of 12.7% over the current precipitation, and 1308 mm under SSP585, an increase of 21.8% over the current precipitation. The temperature prediction is consistent with the precipitation method.

[0107] In step 2, the future land use prediction under different SSPs is based on the LUH2 dataset and the CA-Markov model. The forest area remains the highest under the SSP126 scenario, which is consistent with the sustainable development path of low carbon emissions. Under the SSP245 scenario, it decreases to 27.95 km 2 Under the SSP585 scenario, it is greatly reduced to 18.16 km 2 By 2050, under the SSP126 scenario, the cultivated land area will be reduced to 38.27 km 2 ; Under SSP245 scenario, it increases to 45.57 km 2 Under the SSP585 scenario, it reaches 55.75 km 2 The construction land in 2020 is 2.79 km 2 By 2050, the SSP126 scenario will remain basically unchanged at 2.82 km. 2 , increases to 3.34 km under SSP245 scenario 2 , increases to 6.50 km under SSP585 scenario 2 The remaining two land use types, grassland and water, showed smaller changes in area.

[0108] The climate and land use data under different SSP scenarios predicted in step 2 and step 3 are input into the coupled SWAT-MODFLOW-RT3D model to drive the coupled model and obtain the monthly average nitrate loads from 2023 to 2050 under the three scenarios of SSP126, SSP245 and SSP585 at the outlet section of basin A. Figure 3As shown. The nitrate load at the outlet section under the three scenarios showed a relatively stable trend of change. Among them, the monthly average nitrate load under the SSP126 scenario was 1711.4 kg, while the SSP245 scenario was 2318.4 kg, an increase of 35% over the SSP126 scenario; the SSP585 scenario was 2528.7 kg, an increase of 47.7% over the SSP126 scenario. The large changes in nitrate load are directly related to the increase in precipitation and runoff at the outlet section. The movement of water drives the movement of nitrate in the river. Specifically, in terms of monthly changes, with the occurrence of extreme precipitation, the nitrate flow in Basin A during the flood season increases. Under the SSP126 scenario, only 2 months of nitrate load exceeded 10,000 kg during the entire 28-year forecast period. In the SSP245 scenario, the number increased to 4 times, and all occurred in June and July every year; as for the SSP585 scenario with the most drastic climate change, the number increased to 5 times, with the maximum value in July 2047, when the nitrate load at the outlet section was as high as 24,040 kg. It can be seen that in the future changing environment, the nitrate load will increase with the increase of runoff, and the flood peaks will carry away more nitrate.

[0109] Based on the results of the six scenarios in Table 1 in step 4, combined with the method in step 5, the calculated results of the impact of climate change and land use change on nitrate loss at the outlet section of Basin A were Figure 4 . It can be found that under the SSP126 scenario, the driving weights of climate change and land use type change on nitrate load in Basin A are 87% and 13% respectively; under the SSP245 scenario, the driving weights of climate change and land use type change on nitrate load in Basin A are 94% and 6% respectively; under the SSP585 scenario, the driving weights of climate change and land use type change on nitrate load in Basin A are 97% and 3% respectively. We found that for Basin A, the impact of climate change on nitrate load is dominant. This shows that climate factors (such as precipitation, temperature change, etc.) play a decisive role in the change of nitrate inflow into the river. With the change of SSP scenarios from 126 to 585, that is, from low greenhouse gas emissions to high emissions scenarios, the impact of climate change on nitrate load becomes more and more significant. This reflects that under higher emission scenarios, the impact of climate change is more drastic and has a greater impact on water cycle and related chemical processes. Under all scenarios, the impact of land use change on nitrate load is relatively small, especially under the SSP585 scenario. In general, under different climate change scenarios, the impact of climate change on nitrate load in Basin A is much greater than the impact 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 limiting the present invention itself. Various changes in form and details may be made without departing from the spirit and scope of the present invention as defined in 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; 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 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.

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, 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.

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 2 or 3, 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.

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 4, 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.

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 4, 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.

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, 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.

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 5, 6 or 7, 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.

9. The method for analyzing nitrate loss in a watershed based on the prediction results of climate and land use type changes according to claim 8, 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.

10. 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 9; 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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