Water environment-economy coupling simulation model and application thereof in non-point source pollution simulation

Abstract

The invention discloses a water environment-economy coupling simulation model and an application thereof in non-point source pollution simulation, and the method comprises the steps: building a social economic module and an ecological hydrological model SWAT based on an empirical mathematical programming method, and carrying out the calibration of the built social economic module and ecological hydrological model; according to the water environment-economy coupling simulation method based on the social economy module and the ecological hydrological model, the influence of different water resources and environment management strategies on the farmer income, the water consumption, the nitrogen and phosphorus non-point source pollution time and space distribution and the like by changing the farmer planting behavior can be simulated and predicted; therefore, adaptive water resources and environment management strategies such as adaptive water prices, water resource distribution strategies and organic fertilizer subsidy strategies are provided for decision makers for different regions, multiple targets of farmer income increase, water saving and emission reduction, water quality improvement and the like are achieved, and the method has important economic and social values.

Description

Technical Field

[0001] The invention relates to the fields of agricultural water resources and water environment, and in particular to a water environment-economy coupling simulation model and its application in non-point source pollution simulation. Background Art

[0002] On the basis of analyzing the relationship between economic, social and natural systems, revealing the impact of water resources and environmental management strategies on irrigation water use, hydrological cycle, farmers' income, non-point source pollution, etc. is the key to formulating reasonable and sound agricultural water resources and environmental management strategies, which is of great significance to the comprehensive management of river basins and the green and efficient development of agriculture.

[0003] Traditional policy simulation models based on microeconomic theory simulate the impact of water resources and environmental management strategies on planted area and irrigation and fertilization behavior, but often fail to reflect the impact of water resources and environmental management strategies on hydrology and water environment. For example, empirical mathematical programming (PMP) can be used to simulate changes in crop planting structure and agricultural management measures under different water resources and environmental management strategies, but it cannot reflect the impact of strategies on the spatial and temporal distribution of pollutants and hydrological factors. Traditional distributed watershed hydrological models can simulate the environmental effects of different agricultural management measures, but take the implementation of agricultural management measures as an external scenario, and do not include the impact mechanism of policies on planting structure and agricultural management measures decision-making in the socio-economic system as an endogenous variable into the model. For example, the SWAT (Soil and Water Assessment Tools) model can directly simulate the source pollution load under the set irrigation and fertilization management agricultural management measures scenario, but ignores the impact of different water management strategies on planting, irrigation, and fertilization behavior in the socio-economic system.

[0004] The hybrid hydrological-economic model that couples the microeconomic model and the watershed model can simultaneously reflect the dynamics of the socio-economic system and the complex hydrological processes, and has application prospects in the study of water resources and environmental simulation driven by water resources and environmental management strategies. However, the coupling and information transmission of sub-models of different scales in the hybrid model are the difficulties in model construction. Therefore, coupling the PMP method and the distributed hydrological model SWAT to establish a water environment-economy coupling simulation model, overcome the scale mismatch problem between the policy simulation model and the watershed hydrological model, and realize the simulation of the spatiotemporal distribution of farmers' planting behavior responses, hydrological factors and non-point source pollution driven by water resources and environmental management strategies are key issues that need to be solved urgently.

[0005] In order to solve the above technical problems, the present invention couples the empirical mathematical programming method and the distributed hydrological model SWAT to establish a water environment-economy coupling simulation model, solves the scale mismatch problem of the empirical mathematical programming model and the distributed hydrological model SWAT by dividing the hydrological-economic decision unit (HEDU), simulates the complex linkage process in which water resources and environmental management strategies change the planting structure and the implementation of agricultural management measures, thereby affecting the spatiotemporal distribution characteristics of the hydrological cycle and non-point source pollution, and overcomes the technical bottleneck of non-point source pollution simulation driven by water resources and water environment management strategies. Summary of the invention

[0006] The purpose of the present invention is to provide a water environment-economy coupling simulation model. This method can establish a water environment-economy coupling simulation model, overcome the scale mismatch problem between the policy simulation model and the basin distributed hydrological model, and realize the simulation of the spatiotemporal distribution laws of hydrological factors and non-point source pollution driven by water resources and environmental management strategies.

[0007] The technical solution adopted by the present invention is as follows: a water environment-economy coupling simulation model, including two modules, a social economic module and an eco-hydrological module, characterized in that the method steps are as follows:

[0008] Step S1: Establish a socio-economic module, specifically a water resources and environmental management policy effect simulation model based on empirical mathematical programming methods;

[0009] Step S2: calibrate and solve the socio-economic module to obtain a calibrated water resources and environmental management policy effect simulation model based on empirical mathematical programming methods;

[0010] Step S3: Establishing an eco-hydrological module, and selecting the SWAT model to establish the eco-hydrological module;

[0011] Step S4: Establish a water environment-economy coupling simulation model, a coupled calibrated water resources and environment management policy effect simulation model based on empirical mathematical programming method and a SWAT model;

[0012] Step S5: Establish a solution method for the water environment-economy coupling simulation model, test and run the iterative water environment-economy coupling simulation model until convergence.

[0013] Furthermore, in step S1, a socio-economic module is established, i.e., a simulation model of water resources and environmental management policy effects based on empirical mathematical programming methods. The specific steps are as follows:

[0014] Step S11, constructing a socio-economic module using a water resources and environmental management policy effect simulation model based on an empirical mathematical programming method. The objective function in the socio-economic module is specifically shown in formulas (1) to (3):

[0015]

[0016] SUB i,j,k =ρ i,j,k +B i,j,k (2);

[0017] AC i,j,k =α i,j,k +0.5γ i,j,k X i,j,k (3);

[0018] Among them, maxZ represents the maximum value of farmer income, i represents crop type, j represents measure type, k represents region, l represents the number of crop types, m represents the number of measure types, n represents the total number of regions, and PR i is the price per unit of crop i, YI i,j,k is the yield per unit area of ​​crop i measure j in region k, SUB i,j,k is the unit area subsidy for crop i and measure j in region k, X i,j,k is the planting area of ​​crop i and measure j in region k, AC i,j,k is the average cost per unit area of ​​measure j for crop i in region k, w i,j,k is the water price for crop i and measure j in region k, CR i,j,k is the irrigation quota for crop i and measure j in region k, ρ i,j,k is the additional subsidy for crop i in region k due to implementation of measure j, B i,j,k is the basic arable land fertility subsidy for crop i and measure j in region k, α i,j,k and γ i,j,k is the parameter to be calibrated;

[0019] Step S12, the constraints of the water resources and environmental management policy effect simulation model of the empirical mathematical programming method, the constraints include six types of constraints, namely: water resources availability constraint, nitrogen allowable emission constraint, phosphorus allowable emission constraint, cultivated land resources availability constraint, grain output constraint and non-negative constraint;

[0020] Step S121, water resource availability constraint, as shown in formula (4):

[0021]

[0022] Among them, Q k The amount of water available for allocation to regional agricultural irrigation;

[0023] Step S122, the nitrogen allowable emission limit, the non-point source total nitrogen TN load cannot exceed the allowable emission limit; as shown in formula (5) and formula (6):

[0024]

[0025] Among them, LN i,j,k is the nitrogen fertilizer consumption of crop i and measure j in region k, η N is the TN loss coefficient in crop production, I i is the straw production coefficient of crop i, PN i is the TN emission coefficient of crop straw i, t represents the livestock type, o represents the number of livestock species, F k,t is the number of humans, large livestock, and aquatic animals in region k, N,t TN is the TN emission coefficient of human or livestock t, TN k is the permissible emission of non-point sources TN in region k, δ N is the TN loss coefficient of straw, r represents the straw utilization mode, u represents the number of straw utilization modes, EN i,r is the TN pollution coefficient of straw under crop i utilization mode r, θ i,r is the amount of crop straw under crop i utilization mode r;

[0026] Step S123, the phosphorus emission limit is set, and the non-point source total phosphorus TP load cannot exceed the allowable emission limit; as shown in formula (7) and formula (8):

[0027]

[0028] Among them, LP i,j,k is the phosphorus fertilizer consumption of crop i and measure j in region k, η P is the TP loss coefficient in crop production, PP i is the TP emission coefficient of crop straw i, ζ P,t TP emission coefficient for humans or livestock, TP k is the permissible emission of non-point sources TP in region k, EP i,r is the TP pollution coefficient of crop straw under crop i utilization mode r, δ P is the TP loss coefficient of straw;

[0029] Step S124, the available amount of cultivated land resources is constrained, as shown in formula (9) and formula (10):

[0030]

[0031] Among them, Z k The area of ​​cultivated land reserved for region k, P k is the available amount of arable land in region k;

[0032] Step S125, food production constraint, as shown in formula (11):

[0033]

[0034] Among them, D i is the basic social demand of crop i, TPE is the total number of people in the study area;

[0035] Step S126, non-negative constraint, as shown in formula (12):

[0036]

[0037] Furthermore, in step S2, the social economic module is calibrated and solved, and the specific steps are as follows:

[0038] Step S21, calibrate the unknown parameter α i,j,k and γ i,j,k , establish an auxiliary model to calibrate the parameter α i,j,k and γ i,j,k In the auxiliary model, the calibration constraints are added to the water resources and environmental management policy effect simulation model based on the empirical mathematical programming method. In the objective function determined in step S11, the accounting cost BC observed in the base period is used. i,j,k The average cost per unit area of ​​alternative AC i,j,k , the other parameters and structures in the water resources and environmental management policy effect simulation model based on the empirical mathematical programming method remain unchanged; as shown in formula (13):

[0039]

[0040] Among them, BC i,j,k is the accounting cost per unit area of ​​observation for crop i and measure j in region k;

[0041] Step S22, the calibration constraint of the auxiliary model makes the planting area of ​​crop i under measure j in region k smaller than the actual observation value of the reference year in the study area, as shown in formula (14):

[0042]

[0043] in is the observed planting area of ​​crop i under measure j in region k, ε i,j,k is a small positive coefficient, and the value in [] corresponds to the dual value of the constraint, λ i,j,k is the calibration constraint;

[0044] Step S23, other constraints in the auxiliary model remain unchanged, and the auxiliary model and the water resources and environmental management policy effect simulation model based on empirical mathematical programming methods are solved in the reference period. i,j,kAll must satisfy the Kuhn-Tucker optimization condition, Get parameter α i,j,k and γ i,j,k The linear relationship is as shown in formula (15):

[0045]

[0046] Among them, λ i,j,k is the dual value of the calibration constraint;

[0047] Step S24, linking the observed unit area cost excluding irrigation water fee with the auxiliary model, so that the average cost of different crops under different management methods is equal to the observed cost in the base period, as shown in formula (16):

[0048]

[0049] Step S25, α i,j,k and γ i,j,k The final value of is calculated as shown in formula (17) and formula (18):

[0050] α i,j,k =BC i,j,k -λ i,j,k (17);

[0051]

[0052] Step S26: i,j,k and γ i,j,k Substituting the number into the formula (1) of step S11, the calibrated water resources and environmental management policy effect simulation model based on the empirical mathematical programming method can be obtained, and X i,j,k And the solution to the maximum farmer income maxZ.

[0053] Furthermore, in step S4, a water environment-economy coupling simulation model is established, and a water resources and environment management policy effect simulation model based on an empirical mathematical programming method and a SWAT model after coupling calibration are established; the specific steps are as follows:

[0054] Step S31, coupling the water resources and environmental management policy effect simulation model based on the empirical mathematical programming method and the distributed hydrological model SWAT to propose a water environment-economy coupling simulation model;

[0055] Step S32, dividing the hydrological-economic decision-making unit HEDU, linking the water resources and environmental management policy effect simulation model based on the empirical mathematical programming method and the distributed hydrological model SWAT;

[0056] Step S33, after the multiple hydrological-economic decision-making units HEDUs are divided, the number of the multiple hydrological-economic decision-making units HEDUs in each administrative area and sub-basin is an integer, and the sub-basin and administrative area to which each hydrological-economic decision-making unit HEDU belongs is unique;

[0057] Step S34, the water resources and environmental management policy effect simulation model based on the empirical mathematical programming method obtains the combination of crops and agricultural management measures such as irrigation and fertilization in each administrative area, and the obtained combination of agricultural management measures will be input into the distributed hydrological model SWAT.

[0058] Further, step S32, dividing the hydrological-economic decision-making unit HEDU, linking the water resources and environmental management policy effect simulation model based on the empirical mathematical programming method and the distributed hydrological model SWAT; the steps are as follows:

[0059] Step S321, superimposing the land use type and the administrative area spatial distribution map to form a new land use type map;

[0060] Step S322, naming and coding each unique combination of land use type and administrative unit;

[0061] Step S323, adding a new crop code to the distributed hydrological model SWAT plant database, and supplementing plant growth parameters according to the crop type;

[0062] Step S324, adding the updated code and land use type to the distributed hydrological model SWAT to perform hydrological-economic decision-making unit HEDU division.

[0063] Furthermore, in step S5, the water environment-economy coupling simulation model is solved and run iteratively until convergence is reached; the specific steps are as follows:

[0064] Step S41, running the socio-economic module to obtain crop planting structure and irrigation and fertilization management decisions under different policy scenarios; inputting the latest information on land use and agricultural management measures into the distributed hydrological model SWAT to analyze the spatiotemporal distribution of non-point source pollution loads and changes in water production, water level and evaporation;

[0065] Step S42, the distributed hydrological model SWAT provides information for the constraint conditions of the water resources and environmental management policy effect simulation model based on the empirical mathematical programming method;

[0066] Step S421, when the pollution load obtained by the distributed hydrological model SWAT is greater than the allowable pollutant discharge, the pollutant loss coefficient or discharge coefficient in the water resources and environmental management policy effect simulation model is adjusted until the convergence condition is reached, that is, the pollution load of the distributed hydrological model SWAT meets the allowable nitrogen and phosphorus pollution discharge constraints in steps S122 and S123;

[0067] Step S422, when the pollution load obtained by the distributed hydrological model SWAT is less than the allowable pollutant discharge, the pollutant loss coefficient or discharge coefficient in the water resources and environmental management policy effect simulation model is adjusted until the land use and management measures area remain basically unchanged, or the pollution load of the distributed hydrological model SWAT meets the allowable nitrogen and phosphorus pollution discharge constraints in steps S122 and S123;

[0068] Step S43, for the available water volume, the available water volume provided by the distributed hydrological model SWAT is applied to the water resource availability constraint in step S121, and the water resource and environmental management policy effect simulation model based on the empirical mathematical programming method is run to obtain a new combination of land use and management measures, and the combined measures are input into the distributed hydrological model SWAT to obtain new eco-hydrological information, so as to continuously iterate; the iteration end condition is that the water volume in step S121 meets the water resource availability constraint or the land use and management measure area combination obtained by the water resource and environmental management policy effect simulation model remains basically unchanged; the water environment-economy coupling simulation model can be used to study the spatiotemporal distribution of hydrological factors and non-point source pollution loads under various water resource and environmental policies such as water resource management policies, climate change policies, and agricultural subsidy policies.

[0069] Furthermore, a water environment-economy coupling simulation model is applied in non-point source pollution simulation, that is, the economic module is used to simulate the effects of different water resources and environmental management policies; the ecohydrological module is used to simulate hydrological and ecological processes such as sediment and nutrient transport, vegetation and crop growth;

[0070] The water environment-economy coupling simulation model analyzes the feedback relationship between the socio-economic system and the natural system, and reveals the complex linkage process in which agricultural water resources and environmental management policies affect the spatiotemporal distribution characteristics of non-point source pollution by changing crop planting and the implementation of agricultural management measures. It realizes the simulation of farmers' planting behavior response and the spatiotemporal distribution law of non-point source pollution driven by water resources and environmental management strategies.

[0071] The beneficial effect of the present invention is that the method of the present invention can simulate and predict the impact of different water resources and environmental management strategies on water consumption, farmers' income, planting area and the temporal and spatial distribution of nitrogen and phosphorus non-point source pollution loads, so as to provide decision makers with adaptive water resources and environmental management strategies such as adaptive water prices, water resources allocation strategies, organic fertilizer subsidy strategies, etc. for different regions, so as to achieve multiple goals such as increasing farmers' income, saving water and reducing emissions, and improving water quality, and has important economic and social value. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments;

[0073] Figure 1 It is the overall framework diagram of the water environment-economy coupling simulation model of the present invention coupling empirical mathematical programming and distributed hydrological model SWAT;

[0074] Figure 2 It is a calibration result diagram of the water resources and environmental management policy effect simulation model based on the empirical mathematical programming method of the present invention;

[0075] Figure 3 It is a calibration verification result diagram of runoff and sediment of the SWAT model of the present invention;

[0076] Figure 4 It is a calibration verification result diagram of total nitrogen and total phosphorus of the SWAT model of the present invention. DETAILED DESCRIPTION

[0077] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below through specific implementation methods in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0078] The water environment-economy coupling simulation model includes two modules. The socio-economic module is a simulation model of the effects of water resources and environmental management policies based on empirical mathematical programming methods, and the eco-hydrological module is a distributed hydrological model SWAT. The planting structure and irrigation and fertilization management decisions driven by water resources and environmental strategies obtained by the socio-economic module can be reflected in the distributed hydrological model SWAT; at the same time, the eco-hydrological information such as runoff and pollution emissions obtained by the distributed hydrological model SWAT can also be fed back to the constraints of the socio-economic module. The specific method framework is as follows: Figure 1 shown.

[0079] A water environment-economy coupling simulation model, the model construction method steps are as follows:

[0080] Step S1: Establish a socio-economic module, specifically a water resources and environmental management policy effect simulation model based on empirical mathematical programming methods;

[0081] Step S2: calibrate and solve the socio-economic module to obtain a calibrated water resources and environmental management policy effect simulation model based on empirical mathematical programming methods;

[0082] Step S3: Establishing an eco-hydrological module, and selecting the SWAT model to establish the eco-hydrological module;

[0083] Step S4: Establish a water environment-economy coupling simulation model, a coupled calibrated water resources and environment management policy effect simulation model based on empirical mathematical programming method and a SWAT model;

[0084] Step S5: Establish a solution method for the water environment-economy coupling simulation model, test and run the iterative water environment-economy coupling simulation model until convergence.

[0085] Furthermore, in step S1, a social economic module is established, and the specific steps are as follows:

[0086] Step S11, constructing a socio-economic module using a water resources and environmental management policy effect simulation model based on an empirical mathematical programming method. The objective function in the socio-economic module is specifically shown in formulas (1) to (3):

[0087]

[0088] SUB i,j,k =ρ i,j,k +B i,j,k (2);

[0089] AC i,j,k =α i,j,k +0.5γ i,j,k X i,j,k (3);

[0090] Among them, maxZ represents the maximum value of farmer income, i represents crop type, j represents measure type, k represents region, l represents the number of crop types, m represents the number of measure types, n represents the total number of regions, and PR i is the price per unit of crop i, YI i,j,k is the yield per unit area of ​​crop i measure j in region k, SUB i,j,k is the unit area subsidy for crop i and measure j in region k, X i,j,k is the planting area of ​​crop i and measure j in region k, AC i,j,k is the average cost per unit area of ​​measure j for crop i in region k, w i,j,k is the water price for crop i and measure j in region k, CR i,j,kis the irrigation quota for crop i and measure j in region k, ρ i,j,k is the additional subsidy for crop i in region k due to implementation of measure j, B i,j,k is the basic arable land fertility subsidy for crop i and measure j in region k, α i,j,k and γ i,j,k is the parameter to be calibrated;

[0091] Step S12, constraints, which include six types of constraints: water resource availability constraint, nitrogen allowable emission constraint, phosphorus allowable emission constraint, arable land resource availability constraint, grain yield constraint and non-negative constraint;

[0092] Step S121, water resource availability constraint, as shown in formula (4):

[0093]

[0094] Among them, Q k The amount of water available for allocation to regional agricultural irrigation;

[0095] Step S122, the nitrogen allowable emission limit, the non-point source total nitrogen TN load cannot exceed the allowable emission limit; as shown in formula (5) and formula (6):

[0096]

[0097] Among them, LN i,j,k is the nitrogen fertilizer consumption of crop i and measure j in region k, η N is the TN loss coefficient in crop production, I i is the straw production coefficient of crop i, PN i is the TN emission coefficient of crop straw i, t represents the livestock type, o represents the number of livestock species, F k,t is the number of humans, large livestock, and aquatic animals in region k, N,t TN is the TN emission coefficient of human or livestock t, TN k is the permissible emission of non-point sources TN in region k, δ N is the TN loss coefficient of straw, r represents the straw utilization mode, u represents the number of straw utilization modes, EN i,r is the TN pollution coefficient of straw under crop i utilization mode r, θ i,r is the amount of crop straw under crop i utilization mode r;

[0098] Step S123, the phosphorus emission limit is set, and the non-point source total phosphorus TP load cannot exceed the allowable emission limit; as shown in formula (7) and formula (8):

[0099]

[0100] Among them, LPi,j,k is the phosphorus fertilizer consumption of crop i and measure j in region k, η P is the TP loss coefficient in crop production, PP i is the TP emission coefficient of crop straw i, ζ P,t TP emission coefficient for humans or livestock, TP k is the permissible emission of non-point sources TP in region k, EP i,r is the TP pollution coefficient of crop straw under crop i utilization mode r, δ P is the TP loss coefficient of straw;

[0101] Step S124, the available amount of cultivated land resources is constrained, as shown in formula (9) and formula (10):

[0102]

[0103] Among them, Z k The area of ​​cultivated land reserved for region k, P k is the available amount of arable land in region k;

[0104] Step S125, food production constraint, as shown in formula (11):

[0105]

[0106] Among them, D i is the basic social demand of crop i, TPE is the total number of people in the study area;

[0107] Step S126, non-negative constraint, as shown in formula (12):

[0108]

[0109] Furthermore, in step S2, the social economic module is calibrated and solved, and the specific steps are as follows:

[0110] Step S21, calibrate the unknown parameter α i,j,k and γ i,j,k , establish an auxiliary model to calibrate the parameter α i,j,k and γ i,j,k In the auxiliary model, the calibration constraints are added to the water resources and environmental management policy effect simulation model based on the empirical mathematical programming method. In the objective function determined in step S11, the accounting cost BC observed in the base period is used. i,j,k The average cost per unit area of ​​alternative AC i,j,k , the other parameters and structures in the water resources and environmental management policy effect simulation model based on the empirical mathematical programming method remain unchanged; as shown in formula (13):

[0111]

[0112] Among them, BC i,j,k is the accounting cost per unit area of ​​observation for crop i and measure j in region k;

[0113] Step S22, the calibration constraint of the auxiliary model makes the planting area of ​​crop i under measure j in region k smaller than the actual observation value of the reference year in the study area, as shown in formula (14):

[0114]

[0115] in is the observed planting area of ​​crop i under measure j in region k, ε i,j,k is a small positive coefficient, and the value in [] corresponds to the dual value of the constraint, λ i,j,k is the calibration constraint;

[0116] Step S23, other constraints in the auxiliary model remain unchanged, and the auxiliary model and the water resources and environmental management policy effect simulation model based on empirical mathematical programming methods are solved in the reference period. i,j,k All must satisfy the Kuhn-Tucker optimization condition, Get parameter α i,j,k and γ i,j,k The linear relationship is as shown in formula (15):

[0117]

[0118] Among them, λ i,j,k is the dual value of the calibration constraint;

[0119] Step S24, linking the observed unit area cost excluding irrigation water fee with the auxiliary model, so that the average cost of different crops under different management methods is equal to the observed cost in the base period, as shown in formula (16):

[0120]

[0121] Step S25, α i,j,k and γ i,j,k The final value of is calculated as shown in formula (17) and formula (18):

[0122] α i,j,k =BC i,j,k -λ i,j,k (17);

[0123]

[0124] Step S26: i,j,k and γ i,j,kSubstituting the number into the formula (1) of step S11, the calibrated water resources and environmental management policy effect simulation model based on the empirical mathematical programming method can be obtained, and X i,j,k The corresponding calibration results of the water resources and environmental management policy effect simulation model based on empirical mathematical programming methods are as follows: Figure 2 shown.

[0125] Furthermore, in step S4, a water environment-economy simulation model coupling method is established, and the specific steps are as follows:

[0126] Step S31, coupling the water resources and environmental management policy effect simulation model based on the empirical mathematical programming method and the distributed hydrological model SWAT to propose a water environment-economy coupling simulation model;

[0127] Step S32, the steps of dividing the hydrological-economic decision-making unit HEDU and linking the water resources and environmental management policy effect simulation model based on the empirical mathematical programming method and the distributed hydrological model SWAT are as follows:

[0128] Step S321, superimposing the land use type and the administrative area spatial distribution map to form a new land use type map;

[0129] Step S322, naming and coding each unique combination of land use type and administrative unit;

[0130] Step S323, adding a new crop code to the distributed hydrological model SWAT plant database, and supplementing plant growth parameters according to the crop type;

[0131] Step S324, adding the updated code and land use type to the distributed hydrological model SWAT to perform hydrological-economic decision-making unit HEDU division;

[0132] Step S33, after the multiple hydrological-economic decision-making units HEDUs are divided, the number of the multiple hydrological-economic decision-making units HEDUs in each administrative area and sub-basin is an integer, and the sub-basin and administrative area to which each hydrological-economic decision-making unit HEDU belongs is unique;

[0133] Step S34, the water resources and environmental management policy effect simulation model based on the empirical mathematical programming method obtains the combination of crops and agricultural management measures such as irrigation and fertilization in each administrative area, and the obtained combination of agricultural management measures will be input into the distributed hydrological model SWAT.

[0134] Further, in step S5, the iterative water environment-economy simulation model is solved and run, and iteratively runs until convergence is reached;

[0135] Step S41, running the socio-economic module to obtain crop planting structure and irrigation and fertilization management decisions under different policy scenarios; inputting the latest information on land use and agricultural management measures into the distributed hydrological model SWAT to analyze the spatiotemporal distribution of non-point source pollution loads and changes in water production, water level and evaporation;

[0136] Step S42, the distributed hydrological model SWAT provides information for the constraint conditions of the water resources and environmental management policy effect simulation model based on the empirical mathematical programming method,

[0137] Step S421, when the pollution load obtained by the distributed hydrological model SWAT is greater than the allowable pollutant discharge, the pollutant loss coefficient or discharge coefficient in the water resources and environmental management policy effect simulation model is adjusted until the convergence condition is reached, that is, the pollution load of the distributed hydrological model SWAT meets the allowable nitrogen and phosphorus pollution discharge constraints in steps S122 and S123;

[0138] Step S422, when the pollution load obtained by the distributed hydrological model SWAT is less than the allowable pollutant discharge, the pollutant loss coefficient or discharge coefficient in the water resources and environmental management policy effect simulation model is adjusted until the land use and management measures area remain basically unchanged, or the pollution load of the distributed hydrological model SWAT meets the allowable nitrogen and phosphorus pollution discharge constraints in steps S122 and S123;

[0139] Step S43, for the available water volume, the available water volume provided by the distributed hydrological model SWAT is applied to the water resource availability constraint in step S121, and the water resource and environmental management policy effect simulation model based on the empirical mathematical programming method is run to obtain a new combination of land use and management measures, and the combined measures are input into the distributed hydrological model SWAT to obtain new eco-hydrological information, so as to continuously iterate; the iteration end condition is that the water volume in step S121 meets the water resource availability constraint or the land use and management measure area combination obtained by the water resource and environmental management policy effect simulation model remains basically unchanged; the water environment-economy coupling simulation model can be used to study the spatiotemporal distribution of hydrological factors and non-point source pollution loads under various water resource and environmental policies such as water resource management policies, climate change policies, and agricultural subsidy policies.

[0140] Furthermore, in step S11, i,j,k is the additional subsidy for crop i in region k due to implementation of measure j, which in this study is the subsidy for partial replacement of chemical fertilizers with organic fertilizers;

[0141] Furthermore, the Kuhn-Tucker optimization condition in step S23 is a necessary condition for determining a point as an extreme point in a nonlinear programming model. For the planning problem of finding the maximum value, the solution of the water resources and environmental management policy effect simulation model based on the empirical mathematical programming method in the baseline period isi,j,k The formula (19) must be satisfied:

[0142] PR i YI i,j,k +SUB i,j,k -w i,j,k CR i,j,k -BC i,j,k =λ i,j,k h′(X i,j,k )+u i,j,k g′(X i,j,k ) (19);

[0143] The auxiliary model satisfies formula (20):

[0144] PR i YI i,j,k +SUB i,j,k -w i,j,k CR i,j,k -α i,j,k -γ i,j,k X i,j,k =u i,j,k g′(X i,j,k ) (20);

[0145] Among them, u i,j,k represents the dual value corresponding to the resource constraint in step S12, h′(X i,j,k ) is in X i,j,k The first derivative of the calibration constraint vector acting at point , g′(X i,j,k ) is in X i,j,k The first derivative of the resource constraint vector acting at point .

[0146] Furthermore, the optimal solution of the water resources and environmental management policy effect simulation model calibrated in step S26 in the baseline period can be consistent with the actual production situation; by adjusting the parameter values ​​of subsidies, water resources availability, allowable pollution emissions and water prices, the impact of various water resources and environmental management strategies on farmers' income, planting area, irrigation water consumption and non-point source pollutant emissions can be simulated and analyzed.

[0147] Furthermore, in the coupling process of the water environment-economy coupling simulation model in step S31, the minimum spatial calculation unit of the water resources and environmental management policy effect simulation model based on the empirical mathematical programming method is an administrative unit or a representative group, and the time resolution is one year;

[0148] Furthermore, the SUF12 algorithm in the SWAT-CUP software was used to calibrate and verify the distributed hydrological model SWAT through iterative calculations. The calibration and verification order was runoff, sediment, non-point source TN, and TP loads. The consistency between the model simulation results and the measured values ​​reflects the applicability of the model in the study area. The present invention uses the Nash-Sutcliffe efficiency coefficient NSE and the certainty coefficient R 2 To judge the applicability of the distributed hydrological model SWAT, if R 2 If and NSE are greater than 0.5, the SWAT model meets the accuracy requirements and can simulate runoff, sediment, total nitrogen and total phosphorus well. The corresponding calibration verification results are as follows: Figure 3 and Figure 4 As shown;

[0149] Furthermore, the distributed hydrological model SWAT can be used to reveal the impact of crop and agricultural management scenarios on non-point source pollution at the HRU scale; however, the distributed hydrological model SWAT only considers farmers' irrigation and fertilization decisions as external scenarios, and fails to incorporate economic and social processes such as the impact of policies on crop and agricultural management decisions into the model, making it difficult to reflect the dynamic feedback relationship between the socio-economic system and the natural system. Coupling the distributed hydrological model with the policy simulation model, clarifying the feedback relationship between the socio-economic system and the natural system, and revealing the law of water quantity and water quality changes under the influence of policies are important prerequisites for making reliable water resources and environmental management decisions.

[0150] Furthermore, the large amount of spatial data and attribute data required for the construction of the distributed hydrological model SWAT mainly include digital elevation models (DEM), land use type maps, soil type maps, meteorological data and site information, hydrological and water quality data and site information, agricultural management measures data, point source and non-point source pollution data; the distributed hydrological model SWAT divides the study area into sub-basins according to the land surface hydrological flow paths driven by gravity and elevation; it can further divide the sub-basins into hydrological response units HRUs according to land use, slope and soil properties; the distributed hydrological model SWAT can generate HRUs, daily, monthly and annual simulation outputs at the sub-basin and full-basin scales, which can reflect the impact of different crops and management measures on the spatiotemporal distribution of different factors in the hydrological cycle;

[0151] Furthermore, the distributed hydrological model SWAT is run with hydrological response units HRUs as the spatial scale and a finer daily-scale resolution;

[0152] Furthermore, in order to solve the problem of spatial scale mismatch between the policy simulation model and the distributed hydrological model, a hydrological-economic decision-making unit (HEDU) was proposed, which is composed of a hydrological response unit and an administrative district decision-making unit. The HEDU is the smallest decision-making unit that can simultaneously obtain ecohydrological and socioeconomic information.

[0153] Furthermore, as a typical distributed hydrological model, the SWAT distributed hydrological model can be used to simulate hydrological and ecological processes such as sediment and nutrient transport, vegetation and crop growth, and has been widely used in academic research, engineering construction, and government departments;

[0154] Furthermore, this method uses the SUFl2 algorithm in the SWAT-CUP software to calibrate and verify the distributed hydrological model SWAT through iterative calculations. The calibration and verification order is runoff, sediment, non-point source TN, and TP loads; the consistency between the model simulation results and the measured values ​​reflects the applicability of the model in the study area; the present invention uses the Nash-Sutcliffe efficiency coefficient NSE and the certainty coefficient R 2 To judge the applicability of the distributed hydrological model SWAT; if R 2 and NSE is greater than 0.5, the SWAT model meets the accuracy requirements and can simulate runoff, sediment, total nitrogen, and total phosphorus well;

[0155] Furthermore, this study couples the water resources and environmental management policy effect simulation model based on empirical mathematical programming methods and the distributed hydrological model SWAT to propose a water environment-economy coupling simulation model; the water environment-economy coupling simulation model can reflect the dynamics of the social and economic system and natural physical processes such as the hydrological cycle and pollutant migration in detail, analyze the feedback relationship between the social and economic system and the natural system, and analyze the complex linkage process in which agricultural water resources and environmental management policies affect the spatiotemporal distribution characteristics of non-point source pollution by changing crop planting and agricultural management measures.

[0156] Furthermore, the distributed hydrological model SWAT in step S42 provides information for the constraints of the policy simulation model, such as the TN and TP outputs of the pollution load allowable emission constraint and the available water volume Q in the water resource availability constraint. k .

[0157] Furthermore, in step S45, the combination of agricultural management measures is input into the distributed hydrological model SWAT. The specific steps are as follows: assuming that n kinds of crops and m kinds of management measure combinations are obtained in the water resources and environmental management policy effect simulation model based on the empirical mathematical programming method, assuming that k = n × m, the water resources and environmental management policy effect simulation model based on the empirical mathematical programming method can obtain k kinds of crops and management measure combinations; according to the planting area data of each crop using each measure in each administrative area obtained by the water resources and environmental management policy effect simulation model based on the empirical mathematical programming method, the proportion of the planting area of ​​each crop using a certain measure to the total area is calculated to obtain the sequence A 1 , A 2 , …, A k Similarly, for the t minimum decision-making units HERUs that can simultaneously obtain eco-hydrological and socio-economic information in each administrative area, the proportion of each minimum decision-making unit HERU that can simultaneously obtain eco-hydrological and socio-economic information to the cultivated land area is calculated to obtain the sequence B 1 , B 2 , …, B t ; Data B 1 , B 2 , …, B t As a plug-in, insert sequence A 1 , A 2 , …, A k In this process, find the area ratio of the corresponding points, and set the area matching error within 10%; Assume B 1 A 1 Search the plug-in board position backwards from the starting point, assuming 110% × A i >B 1 >90% A i , then B 1 Plug plate position A i If B 1 Less than sequence A 1 , A 2 , …, A k If all values ​​in B1 are equal, then 1 , B 2 , …, B t The other values ​​in are added together for matching; for example, 110%×A j >B 1 +B 2 >90% A j , then B 1 and B 2 The plug position is A j ; From B 1 Value B t-1Search for the board positions that meet the matching error interval in sequence; finally, check the last item B in the B sequence t Whether it is possible to find a plug-in position that satisfies the matching error interval from the remaining items in sequence A; if it can be found, the plug-in ends; a suitable match can be found in sequence A; thus, each minimum decision unit HERU can match the corresponding crop and agricultural management measures; the average values ​​of runoff, sediment, total nitrogen and total phosphorus of the distributed hydrological model SWAT obtained by repeating the plug-in process are used as the final output value of the distributed hydrological model SWAT.

Claims

1. A water environment-economy coupling simulation model, comprising two modules, a socio-economic module and an eco-hydrological module, characterized in that: The steps are as follows: Step S1: Establish a socio-economic module, specifically a water resources and environmental management policy effect simulation model based on empirical mathematical programming methods; Step S2: calibrate and solve the socio-economic module to obtain a calibrated water resources and environmental management policy effect simulation model based on empirical mathematical programming methods; Step S3: Establishing an eco-hydrological module, and selecting the SWAT model to establish the eco-hydrological module; Step S4: Establish a water environment-economy coupling simulation model, a coupled calibrated water resources and environment management policy effect simulation model based on empirical mathematical programming method and a SWAT model; Step S5: Establish a solution method for the water environment-economy coupling simulation model, test and run the iterative water environment-economy coupling simulation model until convergence.

2. A water environment-economy coupling simulation model according to claim 1, characterized in that: In step S1, a socio-economic module is established, i.e., a simulation model of water resources and environmental management policy effects based on empirical mathematical programming methods. The specific steps are as follows: Step S11, constructing a socio-economic module using a water resources and environmental management policy effect simulation model based on an empirical mathematical programming method. The objective function in the socio-economic module is specifically shown in formulas (1) to (3): SUB i,j,k =ρ i,j,k +B i,j,k (2); AC i,j,k =a i,j,k +0.5g i,j,k X i,j,k (3); Among them, maxZ represents the maximum value of farmer income, i represents crop type, j represents measure type, k represents region, l represents the number of crop types, m represents the number of measure types, n represents the total number of regions, and PR i is the price per unit of crop i, YI i,j,k is the yield per unit area of ​​crop i measure j in region k, SUB i,j,k is the unit area subsidy for crop i and measure j in region k, X i,j,k is the planting area of ​​crop i and measure j in region k, AC i,j,k is the average cost per unit area of ​​measure j for crop i in region k, w i,j,k is the water price for crop i and measure j in region k, CR i,j,k is the irrigation quota for crop i and measure j in region k, ρ i,j,k is the additional subsidy for crop i in region k due to implementation of measure j, B i,j,k is the basic arable land fertility subsidy for crop i and measure j in region k, α i,j,k and γ i,j,k is the parameter to be calibrated; Step S12, the constraints of the water resources and environmental management policy effect simulation model of the empirical mathematical programming method, the constraints include six types of constraints, namely: water resources availability constraint, nitrogen allowable emission constraint, phosphorus allowable emission constraint, cultivated land resources availability constraint, grain output constraint and non-negative constraint; Step S121, water resource availability constraint, as shown in formula (4): Among them, Q k The amount of water available for allocation to regional agricultural irrigation; Step S122, the nitrogen allowable emission limit, the non-point source total nitrogen TN load cannot exceed the allowable emission limit; as shown in formula (5) and formula (6): Among them, LN i,j,k is the nitrogen fertilizer consumption of crop i and measure j in region k, η N is the TN loss coefficient in crop production, I i is the straw production coefficient of crop i, PN i is the TN emission coefficient of crop straw i, t represents the livestock type, o represents the number of livestock species, F k,t is the number of humans, large livestock, and aquatic animals in region k, N,t TN is the TN emission coefficient of human or livestock t, TN k is the permissible emission of non-point sources TN in region k, δ N is the TN loss coefficient of straw, r represents the straw utilization mode, u represents the number of straw utilization modes, EN i,r is the TN pollution coefficient of straw under crop i utilization mode r, θ i,r is the amount of crop straw under crop i utilization mode r; Step S123, the phosphorus emission limit is set, and the non-point source total phosphorus TP load cannot exceed the allowable emission limit; as shown in formula (7) and formula (8): Among them, LP i,j,k is the phosphorus fertilizer consumption of crop i and measure j in region k, η P is the TP loss coefficient in crop production, PP i is the TP emission coefficient of crop straw i, ζ P,t TP emission coefficient for humans or livestock, TP k is the permissible emission of non-point sources TP in region k, EP i,r is the TP pollution coefficient of crop straw under crop i utilization mode r, δ P is the TP loss coefficient of straw; Step S124, the available amount of cultivated land resources is constrained, as shown in formula (9) and formula (10): Among them, Z k The area of ​​cultivated land reserved for region k, P k is the available amount of arable land in region k; Step S125, food production constraint, as shown in formula (11): Among them, D i is the basic social demand of crop i, TPE is the total number of people in the study area; Step S126, non-negative constraint, as shown in formula (12):

3. A water environment-economy coupling simulation model according to claim 2, characterized in that: In step S2, the socio-economic module is calibrated and solved. The specific steps are as follows: Step S21, calibrate the unknown parameter α i,j,k and γ i,j,k , establish an auxiliary model to calibrate the parameter α i,j,k and γ i,j,k In the auxiliary model, the calibration constraints are added to the water resources and environmental management policy effect simulation model based on the empirical mathematical programming method. In the objective function determined in step S11, the accounting cost BC observed in the base period is used. i,j,k The average cost per unit area of ​​alternative AC i,j,k , the other parameters and structures in the water resources and environmental management policy effect simulation model based on the empirical mathematical programming method remain unchanged; as shown in formula (13): Among them, BC i,j,k is the accounting cost per unit area of ​​observation for crop i and measure j in region k; Step S22, the calibration constraint of the auxiliary model makes the planting area of ​​crop i under measure j in region k smaller than the actual observation value of the reference year in the study area, as shown in formula (14): in is the observed planting area of ​​crop i under measure j in region k, ε i,j,k is a small positive coefficient, and the value in [] corresponds to the dual value of the constraint, λ i,j,k is the calibration constraint; Step S23: other constraints in the auxiliary model remain unchanged, and the auxiliary model and the water resources and environment management policy effect simulation model based on empirical mathematical programming methods are solved in the reference period. i,j,k All must satisfy the Kuhn-Tucker optimization condition, Get parameter α i,j,k and γ i,j,k The linear relationship is as shown in formula (15): Among them, λ i,j,k is the dual value of the calibration constraint; Step S24, linking the observed unit area cost excluding irrigation water fee with the auxiliary model, so that the average cost of different crops under different management methods is equal to the observed cost in the base period, as shown in formula (16): Step S25, α i,j,k and γ i,j,k The final value of is calculated as shown in formula (17) and formula (18): a i,j,k =BC i,j,k -l i,j,k (17); Step S26: i,j,k and γ i,j,k Substituting the number into the formula (1) of step S11, the calibrated water resources and environmental management policy effect simulation model based on the empirical mathematical programming method can be obtained, and X i,j,k And the solution to the maximum farmer income maxZ.

4. A water environment-economy coupling simulation model according to claim 3, characterized in that: In step S4, a water environment-economy coupling simulation model is established, and a water resources and environment management policy effect simulation model based on an empirical mathematical programming method and a SWAT model are coupled and calibrated; the specific steps are as follows: Step S31, coupling the water resources and environmental management policy effect simulation model based on the empirical mathematical programming method and the distributed hydrological model SWAT to propose a water environment-economy coupling simulation model; Step S32, dividing the hydrological-economic decision-making unit HEDU, linking the water resources and environmental management policy effect simulation model based on the empirical mathematical programming method and the distributed hydrological model SWAT; Step S33, after the multiple hydrological-economic decision-making units HEDUs are divided, the number of the multiple hydrological-economic decision-making units HEDUs in each administrative area and sub-basin is an integer, and the sub-basin and administrative area to which each hydrological-economic decision-making unit HEDU belongs is unique; Step S34, the water resources and environmental management policy effect simulation model based on the empirical mathematical programming method obtains the combination of crops and agricultural management measures such as irrigation and fertilization in each administrative area, and the obtained combination of agricultural management measures will be input into the distributed hydrological model SWAT.

5. A water environment-economy coupling simulation model according to claim 4, characterized in that: Step S32, dividing the hydrological-economic decision-making unit HEDU, linking the water resources and environmental management policy effect simulation model based on the empirical mathematical programming method and the distributed hydrological model SWAT; the steps are as follows: Step S321, superimposing the land use type and the administrative area spatial distribution map to form a new land use type map; Step S322, naming and coding each unique combination of land use type and administrative unit; Step S323, adding a new crop code to the distributed hydrological model SWAT plant database, and supplementing plant growth parameters according to the crop type; Step S324, adding the updated code and land use type to the distributed hydrological model SWAT to perform hydrological-economic decision-making unit HEDU division.

6. A water environment-economy coupling simulation model according to claim 5, characterized in that: In step S5, the water environment-economy simulation model is solved and run iteratively until convergence is reached. The specific steps are as follows: Step S41, running the socio-economic module to obtain crop planting structure and irrigation and fertilization management decisions under different policy scenarios; inputting the latest information on land use and agricultural management measures into the distributed hydrological model SWAT to analyze the spatiotemporal distribution of non-point source pollution loads and changes in water production, water level and evaporation; Step S42, the distributed hydrological model SWAT provides information for the constraint conditions of the water resources and environmental management policy effect simulation model based on the empirical mathematical programming method; Step S421, when the pollution load obtained by the distributed hydrological model SWAT is greater than the allowable pollutant discharge, the pollutant loss coefficient or discharge coefficient in the water resources and environmental management policy effect simulation model is adjusted until the convergence condition is reached, that is, the pollution load of the distributed hydrological model SWAT meets the allowable nitrogen and phosphorus pollution discharge constraints in steps S122 and S123; Step S422, when the pollution load obtained by the distributed hydrological model SWAT is less than the allowable pollutant discharge, the pollutant loss coefficient or discharge coefficient in the water resources and environmental management policy effect simulation model is adjusted until the land use and management measures area remain basically unchanged, or the pollution load of the distributed hydrological model SWAT meets the allowable nitrogen and phosphorus pollution discharge constraints in steps S122 and S123; Step S43, for the available water volume, the available water volume provided by the distributed hydrological model SWAT is applied to the water resource availability constraint in step S121, and the water resource and environmental management policy effect simulation model based on the empirical mathematical programming method is run to obtain a new combination of land use and management measures, and the combined measures are input into the distributed hydrological model SWAT to obtain new eco-hydrological information, so as to continuously iterate; the iteration end condition is that the water volume in step S121 meets the water resource availability constraint or the land use and management measure area combination obtained by the water resource and environmental management policy effect simulation model remains basically unchanged; the water environment-economy coupling simulation model can be used to study the spatiotemporal distribution of hydrological factors and non-point source pollution loads under various water resource and environmental policies such as water resource management policies, climate change policies, and agricultural subsidy policies.

7. The application of the water environment-economy coupling simulation model according to claim 6 is characterized by: The socio-economic module is used to simulate the effects of different water resources and environmental management policies; the eco-hydrology module is used to simulate hydrological and ecological processes such as sediment and nutrient transport, vegetation and crop growth; The water environment-economy coupling simulation model analyzes the feedback relationship between the socio-economic system and the natural system, and reveals the complex linkage process in which agricultural water resources and environmental policies affect the spatiotemporal distribution characteristics of non-point source pollution by changing crop planting and the implementation of agricultural management measures. It also simulates the response of farmers' planting behavior and the spatiotemporal distribution of non-point source pollution driven by water resources and environmental management strategies.

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