A regional water and soil resource joint optimization allocation method considering water and soil mutual feedback relationship

Abstract

The present application belongs to the field of regional water and soil resources optimal allocation, and discloses a kind of regional water and soil resources joint optimal allocation method considering water-soil mutual feedback relationship.The model established includes water production module, land simulation module and water-soil joint allocation module.According to the characteristics of joint allocation model multi-objective function, multi-decision variable and multi-module coupling, a three-layer nested method is proposed.The outer layer uses constraint method to convert multi-objective problem into a series of single objective problems.The middle layer uses successive approximation method to realize dynamic mutual feedback among the three modules.The inner layer mainly optimizes the distribution of land area and water supply through NLP algorithm optimization by LINGO software, and obtains Pareto solution set through iterative solution.Aiming at the problem of multiple scheme selection, a regional water and soil resources joint optimal allocation scheme evaluation system is proposed, and an approximate ideal solution ordering comprehensive evaluation model based on objective weighting method CRITIC weighting is constructed.The optimal scheme is selected from the Pareto solution set after evaluating multiple schemes.

Description

Technical Field

[0001] This invention belongs to the field of regional water and soil resource optimization and allocation, specifically involving a regional water and soil resource joint optimization and allocation method that considers the mutual feedback relationship between water and soil. It is a regional water and soil resource joint optimization and allocation method based on the dual water cycle theory, considering the interaction between water and soil resources, and coupling the natural water cycle with the artificial water intake process. Background Technology

[0002] In recent years, with the continuous acceleration of urbanization, the rapid increase in urban population, and the sustained expansion of infrastructure construction, human demand for water and soil resources has also been increasing daily. The joint optimization and allocation of regional water and soil resources can comprehensively optimize water use structure, optimize water resource and land use patterns, and achieve coordination and unity of economic, social, and ecological benefits, thereby alleviating the contradictions between socio-economic development, resource utilization, and ecological environmental protection. Therefore, it has become a very popular research topic.

[0003] This invention focuses on the feedback relationship between water and soil resources, coupling the natural water cycle with artificial water extraction processes. It views water, soil, ecological environment, and socio-economic system as a whole, and rationally allocates limited water and land resources within the system in terms of time and region based on the characteristics and feedback mechanisms of water and soil resources. While existing research in this area exists both domestically and internationally, it primarily focuses on the optimal allocation of resource quantities, neglecting the impact of land use pattern changes on dynamic water yield changes and failing to delve into the feedback relationship between water and soil resources. Therefore, establishing a regional joint optimization allocation method for water and soil resources that considers the feedback relationship is particularly necessary. Summary of the Invention

[0004] The purpose of this invention is to address the problems of current water and soil optimization methods that neglect the interaction between water and soil resources, artificially separate the relationship between the natural water cycle and the social water cycle, and fail to consider the impact of land use pattern changes on the dynamic changes in water yield. This invention provides a regional water and soil resource joint optimization allocation method that considers the mutual feedback relationship between water and soil. The key is to couple the water yield module, land simulation module, and water and soil joint allocation module to establish a regional water and soil resource joint optimization allocation model that considers the mutual feedback relationship between water and soil. To address the characteristics of this model—multiple objective functions, multiple decision variables, and multiple coupled modules—a coupled CSM-SA-NLP three-layer nested algorithm is proposed for solving the model. The optimal solution is then selected from the Pareto solution set based on the TOPSIS comprehensive evaluation model with CRITIC weighting. This invention's method can realize functions such as dynamic mutual feedback between water and soil resources, land use pattern simulation, and water resource allocation, providing new ideas and technical references for traditional regional water and soil resource allocation research.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A method for joint optimization allocation of regional water and soil resources considering the mutual feedback relationship between water and soil, characterized by the following steps:

[0007] Step (1) Construct the water production module. The main function of the water production module in the overall model is to use the SWAT model to simulate the amount of surface water resources, groundwater resources and soil water in the region, and calculate the amount of usable water in the region based on the amount of surface water, groundwater and external water transfer. Soil water is used for water demand analysis. The SWAT model calculates water production by inputting DEM data, land use pattern, soil data and meteorological data, and then performs parameter calibration and verification based on runoff data, thereby simulating the amount of usable water in the study area.

[0008] While the SWAT model's natural sub-basin division method is suitable for watershed runoff simulation, water and soil resource allocation is often based on county-level administrative regions. The mismatch between hydrological and administrative units can significantly impact socio-economic data distribution and water and soil resource allocation. Therefore, this paper proposes a nested three-level unit division method based on sub-basin and administrative region to meet the needs of combining water resource basin and administrative region management. First, natural sub-basins are extracted from DEM digital elevation data as basic units. Considering that socio-economic information is statistically analyzed at the administrative region level, administrative divisions are then superimposed on the natural sub-basins to form computational units with both natural and social attributes. Finally, land use type and soil type are superimposed on the computational units to complete the hydrological response unit (HRU) division.

[0009] After adopting the above three-level unit division method, the surface and groundwater resources of each administrative region are simulated by the SWAT model. After superimposing the external water transfer, the available water volume of each administrative region can be calculated.

[0010]

[0011] In the formula: j is the administrative division number; aw j For the available water volume of administrative zone j, SW n GW n The surface and groundwater resources of the nth sub-basin, respectively, in billions of m³. 3 γ is the groundwater extraction coefficient; TW j The amount of water transferred out of administrative district j, in billions of m³ 3 k is a proportionality coefficient. If the sub-basin n is completely within the administrative division j, then k = 1; if it is not at all, then k = 0; if it is partially within the administrative division j, then k is the area ratio.

[0012] Step (2) involves constructing a water and soil co-configuration module. The purpose is to achieve the optimal matching between water and soil resources based on the interaction between them. The optimization criterion of this water and soil co-configuration module is to maximize economic and ecological benefits while meeting social needs. Based on a comprehensive consideration of economic, social, and ecological benefits, the objective functions are to maximize regional GDP and the highest vegetation coverage rate (EGE-RVC) based on ecological green equivalent. The water and soil resources are then optimized and configured in conjunction with the water resources system configuration map. The objective function is as follows:

[0013] 1) Economic benefit target: Maximize GDP

[0014]

[0015] In the formula: i represents the land use type code. According to the national standard "Classification of Current Land Use", the land use types in the study area are reclassified into 6 categories, namely cultivated land, forest land, grassland, water area, construction land and unused land; k j,i For the i-th land use type in administrative division, the GDP per unit area is 100 million yuan / km². 2 ;x j,i For administrative division j, the area of ​​the i-th land type is , km². 2 ;

[0016] 2) Ecological Objective: Maximize the regional vegetation coverage rate (EGE-RVC) based on ecological green equivalent.

[0017]

[0018] Where: g i Let i be the average green equivalent of the i-th type of land;

[0019] Step (3): For the multi-objective problem, the constraint method is used to transform the multi-objective problem into a series of single-objective problems. The constraint method selects the maximum regional GDP as the objective function and transforms EGE-RVC into inequality constraints, as follows:

[0020]

[0021] In the formula: q m (x,y) represents the constraints; L is the lower bound of EGE-RVC, obtained by limiting GDP to minimize EGE-RVC; U is the upper bound of EGE-RVC, obtained by limiting GDP to maximize EGE-RVC; M represents the number of constraints.

[0022] The constraints are as follows:

[0023] 1) Water supply capacity constraints: The sum of water supply for each land use type in each administrative district shall not exceed its total available water volume; the water supply capacity constraint is as follows:

[0024]

[0025] In the formula: y j,i For the water supply of the i-th type of land in administrative division j, in billions of m³ 3 ;aw j The available water volume for administrative division j, in billions of m³ 3 ;

[0026] 2) Water demand constraints: The water supply for each administrative division and land use type should fall between its upper and lower limits. The lower limit of water demand is determined based on the minimum water supply guarantee rate for different land use types. Since the SWAT model can simulate soil water, soil water for cultivated land, forest land, and grassland is considered to rationally allocate available water. Soil water is deducted when determining the upper and lower limits of water demand for cultivated land, forest land, and grassland. The water demand constraints are as follows:

[0027] wd j,i -W j,i,soil ≤y j,i ≤wu j,i -W j,i,soil (6)

[0028] In the formula: wu j,i wd j,i The upper and lower limits of water demand for the i-th type of land in administrative division j, in billions of m³ 3 W j,i,soil Let m be the soil water content of the i-th land type in administrative division j, where i = 1, 2, 3, and m. 3 ;

[0029] 3) Water supply guarantee constraints: The water supply-demand ratio for each administrative division and each land use type shall not be less than its water supply guarantee rate setting value; the water supply guarantee constraints are as follows:

[0030] λ i ≤y i / S i ≤1 (7)

[0031] In the formula: λ i Set the water supply guarantee value for the i-th type of land; S i S represents the water requirement for the i-th type of land, obtained by multiplying the water requirement per unit area of ​​each type of land by its area. i =c i x i 100 million m 3 ;y i For the water supply of the i-th land use type, in billions of m³ 3 ;

[0032] 4) Area constraints for each administrative division and land use type are determined based on the national requirements for strict protection of arable land, ecological land, and construction land, as well as the requirements of the overall land use plan for the study area. The area constraints for each administrative division and land use type are as follows:

[0033]

[0034] A d,j,i ≤x j,i ≤A u,j,i (9)

[0035] 5) The non-negativity constraint of the variable is as follows:

[0036] x j,i ≥0, y j,i ≥0 (10)

[0037] In the formula: D j Let j be the area of ​​administrative division j, in km² 2 A u,j,i A d,j,i These represent the upper and lower limits of the area of ​​the i-th type of land in zone j, in km². 2 ;

[0038] Step (4) involves constructing a land simulation module. This module converts the optimized land use patterns of each administrative region into land use patterns, which can then be input into the water production module for new water volume simulations. The land simulation module is based on the GeoSOS-FLUS model, and the specific steps are as follows:

[0039] 1) Select DEM, slope, aspect, population, GDP, and distance as driving factors;

[0040] 2) The land use adaptability probability is calculated using the ANN algorithm;

[0041] 3) Based on the land use pattern and land use adaptability probability data obtained from the water and soil joint configuration module, a cellular automata based on an adaptive mechanism is used to simulate the spatial optimization of land use.

[0042] Step (5) involves solving the overall model using the proposed coupled CSM-SA-NLP three-layer nested algorithm to obtain multiple sets of water and soil resource optimization allocation schemes and plot the Pareto front curves of GDP and EGE-RVC. The specific steps of the CSM-SA-NLP three-layer nested method are as follows:

[0043] 1) Input the initial land type area Water supply Land use pattern S 0 θ1, θ2 and related parameters, set the iteration number k = 1, θ1 = 10km2 θ2 = 0.0010 billion m 2 ;

[0044] 2) Select the maximum regional GDP as the objective function, and transform EGE-RVC into inequality constraints, thus obtaining N single-objective problems;

[0045] 3) Simulate the available water volume in each county / district using the SWAT model and input it into the water and soil joint configuration module;

[0046] 4) Use the built-in NLP algorithm of LINGO software to solve the water and soil joint allocation module, output the area allocation and water supply allocation results for each land type, and divide the area of ​​each land type into... Inputting the GeoSOS-FLUS model yields the new land use pattern S k ;

[0047] 5) Area of ​​each land type and water supply Perform conditional judgment, if and Then proceed to the next step; otherwise, set k = k + 1 and change the land use pattern S. k Input the SWAT model and repeat steps 3)-4);

[0048] 6) Determine whether all single-objective problems have been traversed. If so, output the area and corresponding water supply scheme for each land use type. If not, proceed to solve the next single-objective problem.

[0049] 7) Output the area and water supply of the land use type that meets the conditions, the corresponding GDP, and the EGE-RVC scheme, and finally depict the Pareto front of GDP and EGE-RVC;

[0050] Step (6): For the problem of selecting multiple options, the optimal option is selected from the Pareto solution set using the TOPSIS comprehensive evaluation model based on CRITIC weighting. To eliminate the influence of different units on the weights of evaluation indicators, the CRITIC method normalizes the data before calculation. This time, the minimum and maximum methods are used for data normalization. Based on the normalization, the following indicators are calculated:

[0051] 1) Calculate the correlation coefficient r of the indicators xy :

[0052]

[0053] In the formula, varx and vary are the variances of indicators x and y, respectively; Cov(x,y) is the covariance of indicators x and y.

[0054] 2) Calculate the quantitative result f of the conflict between the j-th indicator and the other n evaluation indicators. j :

[0055]

[0056] 3) Calculate the amount of information c contained in the j-th indicator. j :

[0057]

[0058] In the formula, δ j Let be the standard deviation of the j-th indicator.

[0059] 4) Determine the weight w of the j-th indicator. j :

[0060]

[0061] The specific steps of the TOPSIS comprehensive evaluation model based on CRITIC weighting are as follows:

[0062] Assume there are n options to be evaluated and m evaluation indicators, forming an original indicator data matrix X = (x ij ) n×m , where x ij This represents the value of the x-th scheme in the j-th evaluation index i.

[0063] 1) Determine if there are negative numbers in the input matrix, and calculate the proportion P of the i-th sample under the j-th indicator. ij .

[0064]

[0065] 2) The weight of each indicator is calculated using the CRITIC method from formulas (11) to (14).

[0066] 3) After standardizing the data matrix X, normalize it to obtain matrix Z. ij .

[0067]

[0068] 4) Construct the weighting matrix Z ij * .

[0069]

[0070] 5) Find the maximum and minimum values ​​of each indicator.

[0071]

[0072] 6) Calculate the optimal distance D i + The worst distance D i - .

[0073]

[0074]

[0075] 7) Calculate the relative proximity C i .

[0076]

[0077] 8) Optimization scheme selection.

[0078] The beneficial effects of this invention are as follows: This invention considers the mutual feedback relationship between water and soil, that is, changes in land use patterns will affect the water cycle process, thereby affecting the distribution and composition of water resources, while the quantity and distribution of water resources will also affect socio-economic development, thereby changing land structure and layout. The two interact and restrict each other. By coupling the water production module, land simulation module and water and soil joint configuration module, the functions of dynamic mutual feedback between water and soil resources, land use pattern simulation, and water resource allocation are realized. The impact of changes in land use patterns on the dynamic changes in water production is considered, and the coordination and unity of economic, social and ecological benefits are achieved. The final water and soil resource allocation scheme is presented on small-scale spatial units such as counties. The method of this invention not only reflects the dynamic mutual feedback relationship between water and soil resources, but also realizes the refined management of optimized allocation. Attached Figure Description

[0079] Figure 1 This is a diagram showing the mutual feedback relationship between water and soil resources;

[0080] Figure 2 It is a module coupling diagram of the regional water and soil resources joint optimization allocation model;

[0081] Figure 3 This is a coupling diagram of the water production module;

[0082] Figure 4 This is a schematic diagram of the combined water and soil configuration module;

[0083] Figure 5 This is a schematic diagram of the operation mechanism of the land simulation module;

[0084] Figure 6 This is a map showing the water resources system configuration in Luoyang City;

[0085] Figure 7 This is a flowchart of the computation process for the coupled CSM-SA-NLP three-layer nested algorithm;

[0086] Figure 8This is the curve showing the relationship between GDP and EGE-RVC;

[0087] Figure 9 It is a comprehensive evaluation result;

[0088] Figure 10 It refers to the water supply structure before and after optimization;

[0089] Figure 11 The following are comparison maps of land use patterns: (a) Comparison map of cultivated land use patterns; (b) Comparison map of forest land use patterns; (c) Comparison map of grassland land use patterns; (d) Comparison map of water area land use patterns; (e) Comparison map of construction land use patterns; (f) Comparison map of unused land use patterns. Detailed Implementation

[0090] The present invention will now be described in further detail with reference to the embodiments.

[0091] Those skilled in the art will understand that the following embodiments are for illustrative purposes only and should not be construed as limiting the scope of the invention. Where specific techniques or conditions are not specified in the embodiments, they are performed in accordance with the techniques or conditions described in the literature in the field or according to the product manual. Materials or equipment whose manufacturers are not specified are all conventional products that can be obtained by purchase.

[0092] Taking Luoyang City as an example, the effectiveness of the joint optimization allocation method proposed in this invention is verified. Luoyang City has a total area of ​​15229 km². 2 Luoyang City administers 1 city (Yanshi City), 8 counties (Mengjin County, Xin'an County, Yiyang County, Yichuan County, Ruyang County, Song County, Luanchuan County, and Luoning County), and 6 districts (Jianxi District, Xigong District, Laocheng District, Chanhe District, Luolong District, and Jili District). Due to the small size of some administrative districts in Luoyang City, for the convenience of calculation and statistics, Jianxi District, Xigong District, Laocheng District, Chanhe District, and Luolong District were merged into the urban area, and Jili District was merged into Mengjin County, thus dividing Luoyang City into 10 administrative districts. The meteorological data input to the SWAT model consisted of daily precipitation, wind speed, temperature, relative humidity, and solar radiation from 16 meteorological stations in and around Luoyang. Runoff data were selected from monthly runoff data from six stations, including Baimasi Station, Longmen Town Station, and Luhun Station, spanning from 2001 to 2014. Socioeconomic data for Luoyang were collected from the *Luoyang Statistical Yearbook 2016*, annual *Water Resources Bulletin*, the *Luoyang Land Use Master Plan (2006-2020)*, national road network data, and population distribution data. All data were projected in the WGS_1984_UTM_Zone_49N coordinate system.

[0093] Depend on Figure 1It is evident that the impact of land on water resources is primarily reflected in the different land use types and areas under the same rainfall scenario, resulting in different land runoff. The interaction between water and soil resources essentially involves changes in land use patterns affecting the water cycle, thereby influencing the distribution and composition of water resources. Conversely, the quantity and distribution of water resources also affect socio-economic development, further altering land structure and layout. These two aspects interact and constrain each other. Optimal allocation of regional water and soil resources is a complex feedback system involving resources, society, economy, population, ecology, and environment. Therefore, it is necessary to consider the interaction relationships among various elements within the water-soil-socio-economic-ecological environment system.

[0094] Depend on Figure 2 As can be seen, the regional water and soil resources joint optimization allocation model consists of three parts: a water production module, a land simulation module, and a water and soil joint allocation module. Each module has a clear hierarchy and function. The water production module, based on the SWAT model, simulates the available water volume in each county within the region by inputting information such as rainfall and land use patterns, and then transmits this simulation to the water and soil joint allocation module. The water and soil joint allocation module optimizes the area and water supply of each land use type in each county. The land simulation module, based on the GeoSOS-FLUS model, converts the optimized area of ​​each land type into land use pattern information recognizable by the water production module. Through the coupling of these three modules, dynamic feedback simulation and joint optimization allocation of water and soil resources can be achieved.

[0095] Depend on Figure 3 It can be seen that the main function of the water production module in the model is to use the SWAT model to simulate the amount of surface water resources, groundwater resources and soil water in the region, and calculate the amount of usable water in the region based on the amount of surface water, groundwater and external water transfer. Soil water is used for water demand analysis.

[0096] Depend on Figure 4 It is evident that the purpose of joint optimization of regional water and soil resources is to achieve optimal matching among resources based on the interaction between water and soil resources. This module takes maximizing economic and ecological benefits under the premise of meeting social needs as the optimization criterion. Based on a comprehensive consideration of economic, social, and ecological benefits, it uses the maximum regional GDP and the highest EGE-RVC as the objective functions, and uses constraints such as arable land red line, ecological red line, water supply capacity, and water use guarantee as constraint thresholds. It also combines the water resources system configuration map to optimize the allocation of water and soil resources.

[0097] Depend on Figure 5It is known that the land simulation module is based on the GeoSOS-FLUS model. The more driving factors input into the model, the more accurate the results. By considering multiple driving factors of human activities and natural effects, the suitability probability of the distribution of each land use type in the study area is calculated using a neural network algorithm. Then, based on the cellular automata principle of adaptive inertial mechanism, a new land use pattern is simulated.

[0098] Depend on Figure 6 It can be seen that the water resource system in Luoyang City is configured with routes that can accurately deliver water volume.

[0099] Depend on Figure 7 It is evident that the proposed three-layer nested algorithm, coupled with CSM-SA-NLP, can solve the problems of multi-objective functions, multiple decision variables, and multi-module coupling in joint configuration models. Specifically, the outer layer primarily utilizes constraint methods to transform the multi-objective problem into a series of single-objective problems; the middle layer mainly employs successive approximation methods to achieve dynamic feedback among the three modules; and the inner layer primarily determines the area allocation and water allocation for each land use type. This enables functions such as dynamic feedback of water and soil resources, land use pattern simulation, and water resource allocation.

[0100] Depend on Figure 8 It can be seen that, through dynamic feedback among the three modules and iterative optimization of the water and soil joint configuration module, 10 non-dominated solutions were finally obtained. The model results show that as EGE-RVC increases, GDP gradually decreases. As EGE-RVC increases from 0.65 to 0.74, GDP decreases from 375.2868 billion yuan to 335.494 billion yuan.

[0101] Depend on Figure 9 As can be seen, Scheme 6 has the highest overall score, therefore Scheme 6 is selected as the optimal configuration scheme, with an economic benefit of 367.664 billion yuan, an EGE-RVC of 0.7, and a usable water volume of 1.57086 billion cubic meters. 3 The water supply volume was 1.50373 billion cubic meters. 3 In Scheme 6, through dynamic feedback between modules, the simulated available water volume for Luoyang City is 1.51414 billion m³. 3 1.54718 billion m 3 1.57086 billion m 3 After optimization using the method proposed in this invention, GDP increased by 67.664 billion yuan, a rise of 22.55%, and ecological benefits increased by 2%.

[0102] Depend on Figure 10It can be seen that, from the supply side, the optimized water supply structure has undergone significant changes. The proportion of surface water supply has increased significantly in all counties except Luanchuan County, while the proportion of groundwater supply has increased in the opposite direction. The proportion of water supplied by external diversion has remained largely unchanged. In terms of changes in land type and area, the cultivated land area in Luanchuan County has decreased significantly, while the forest area has increased significantly. Furthermore, Luanchuan County and Song County have no external water supply. The proportion of groundwater supply in Luanchuan County, Ruyang County, Yiyang County, and Luoning County is relatively high. The external water supply from the urban area and Yanshi City is also relatively high. The proportion of water supply from the three sources is relatively balanced in the remaining counties.

[0103] Depend on Figure 11 It can be seen that the optimized cultivated land area decreased, mainly in the northeastern region, while the eastern region has more rainfall and its water resources are basically sufficient; the forest area decreased, and the areas with better forest suitability are mainly located in the south, where there is more water, which is suitable for forest growth; the grassland area increased, and the areas with better grassland suitability are mainly concentrated in the central and northeastern regions; the construction land area increased, and the areas with better construction land suitability are located in the northeast, mainly because the region has a higher economy.

[0104] As shown in Table 1, the constructed regional water and soil resource joint optimization allocation model, by considering the interaction between water and soil resources and coupling the natural water cycle with artificial water use processes, optimizes and adjusts land use patterns and water supply allocation, ultimately increasing the available water volume by 56.72 million m³. 3 The total water supply < total water demand < total available water volume. This is because, during the water allocation process, the available water volume in most counties exceeds the demand, while in a few counties the available water volume is less than the demand. A small amount of water shortage is allowed while ensuring water demand. When water resources are scarce, land use types with high water supply guarantee rates are prioritized. Since the first iteration simulated the current situation, the new land use pattern has not yet been optimized; therefore, it is considered as an allocation result without considering the water-soil interaction. Compared with the allocation without considering the water-soil interaction, after optimization, the water shortage rate of each land use type gradually decreases, and water resources in the region are rationally planned and coordinated. The water shortage rate of forest land decreased from 4.9% to 0 because the soil water of forest land was considered, reducing water demand and resulting in supply exceeding demand. The grassland area increased significantly after optimization, resulting in a lower reduction in the grassland water shortage rate. This is because the model construction assumed that the water supply-demand ratio of each land use type was not less than its water supply guarantee. When water resources are scarce, land use types with high water supply guarantee rates are prioritized. Compared with configurations that did not consider the water-soil interaction, the water shortage rate of each land use type gradually decreased after optimization. The reason why the water shortage rate of forest land decreased from 4.9% to 0 is that the soil water of forest land was taken into account, which reduced the water demand and resulted in a supply exceeding demand; the reason for the low reduction in the water shortage rate of grassland is mainly due to the significant increase in grassland area after optimization. Comprehensive analysis shows that the water resources in the region have been rationally planned and coordinated.

[0105] Table 1 shows the water resource allocation results in Luoyang City.

[0106]

[0107]

[0108] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for joint optimization allocation of regional water and soil resources considering the mutual feedback relationship between water and soil, characterized in that, Includes the following steps: Step (1) Construct the water production module. The main function of the water production module in the overall model is to use the SWAT model to simulate the amount of surface water resources, groundwater resources and soil water in the region, and calculate the amount of usable water in the region based on the amount of surface water, groundwater and external water transfer. Soil water is used for water demand analysis. The SWAT model calculates water production by inputting DEM data, land use pattern, soil data and meteorological data, and then performs parameter calibration and verification based on runoff data, thereby simulating the amount of usable water in the study area. A nested three-level unit division method of sub-basin-administrative region is proposed to meet the needs of combining water resource basin and administrative region management. First, natural sub-basins are extracted as basic units using DEM digital elevation data. Considering that socio-economic information is statistically analyzed based on administrative regions, administrative divisions are then superimposed on the natural sub-basins to form calculation units with both natural and social attributes. Finally, land use type and soil type are superimposed on the calculation units to complete the division of hydrological response units. After adopting the above three-level unit division method, the surface and groundwater resources of each administrative region are simulated by the SWAT model. After superimposing the external water transfer, the available water volume of each administrative region is calculated. In the formula: j is the administrative division number; aw j For the available water volume of administrative zone j, SW n GW n The surface and groundwater resources of the nth sub-basin, respectively, in billions of m³. 3 γ is the groundwater extraction coefficient; TW j The amount of water transferred out of administrative district j, in billions of m³ 3 k is a proportionality coefficient. If the sub-basin n is completely within the partition j, then k = 1; if it is not at all, then k = 0; if it is partially within the partition j, then k is the area ratio. Step (2) involves constructing a water and soil co-configuration module. The purpose is to achieve the optimal matching between water and soil resources based on the interaction between them. The optimization criterion of this water and soil co-configuration module is to maximize economic and ecological benefits while meeting social needs. Based on a comprehensive consideration of economic, social, and ecological benefits, the objective functions are to maximize regional GDP and the highest vegetation coverage rate (EGE-RVC) based on ecological green equivalent. The water and soil resources are then optimized and configured in conjunction with the water resources system configuration map. The objective function is as follows: 1) Economic benefit target: Maximize GDP In the formula: i represents the land use type code. According to the national standard "Classification of Current Land Use", the land use types in the study area are reclassified into 6 categories, namely cultivated land, forest land, grassland, water area, construction land and unused land; k j,i For the i-th land use type in administrative division, the GDP per unit area is 100 million yuan / km². 2 ;x j,i For administrative division j, the area of ​​the i-th land type is , in km². 2 ; 2) Ecological Objective: Maximize the regional vegetation coverage rate (EGE-RVC) based on ecological green equivalent. Where: g i Let i be the average green equivalent of the i-th type of land; Step (3): For the multi-objective problem, the constraint method is used to transform the multi-objective problem into a series of single-objective problems. The constraint method selects the maximum regional GDP as the objective function and transforms EGE-RVC into inequality constraints, as follows: In the formula: q m (x,y) represents the constraints; L is the lower bound of EGE-RVC, obtained by limiting GDP to minimize EGE-RVC; U is the upper bound of EGE-RVC, obtained by limiting GDP to maximize EGE-RVC; M represents the number of constraints. The constraints are as follows: 1) Water supply capacity constraints: The sum of water supply for each land use type in each administrative district shall not exceed its total available water volume; the water supply capacity constraint is as follows: In the formula: y j,i For the water supply of the i-th type of land in administrative division j, in billions of m³ 3 ;aw j The available water volume for administrative division j, in billions of m³ 3 ; 2) Water demand constraints: The water supply for each administrative division and land use type should be between its upper and lower limits. The lower limit of water demand is determined based on the minimum water supply guarantee rate for different land use types. Since the SWAT model can simulate soil water, soil water for cultivated land, forest land, and grassland is considered to rationally allocate available water. When determining the upper and lower limits of water demand for cultivated land, forest land, and grassland, their soil water is deducted. The water demand constraints are as follows: wd j,i -W j,i,soil ≤y j,i ≤wu j,i -W j,i,soil (6) Where: wu j,i wd j,i The upper and lower limits of water demand for the i-th type of land in administrative division j, in billions of m³ 3 ; W j,i,soil Let m be the soil water content of the i-th land type in administrative division j, where i = 1, 2, 3, and m. 3 ; 3) Water supply guarantee constraints: The water supply-demand ratio for each administrative division and each land use type shall not be less than its water supply guarantee rate setting value; the water supply guarantee constraints are as follows: l i ≤y i / S i ≤1 (7) In the formula: λ i Set the water supply guarantee value for the i-th type of land; S i S represents the water requirement for the i-th type of land, obtained by multiplying the water requirement per unit area of ​​each type of land by its area. i =c i x i 100 million m 3 ;y i For the water supply of the i-th land use type, in billions of m³ 3 ; 4) Area constraints for each administrative division and land use type are determined based on the national requirements for strict protection of arable land, ecological land, and construction land, as well as the requirements of the overall land use plan for the study area. The area constraints for each administrative division and land use type are as follows: A d,j,i ≤x j,i ≤A u,j,i (9) 5) The non-negativity constraint of the variable is as follows: x j,i ≥0, and j,i ≥0 (10) In the formula: D j Let j be the area of ​​administrative division j, in km² 2 A u,j,i A d,j,i These represent the upper and lower limits of the area of ​​the i-th type of land in zone j, in km². 2 ; Step (4) involves constructing a land simulation module. This module converts the optimized land use patterns of each administrative region into land use patterns, which can then be input into the water production module for new water volume simulations. The land simulation module is based on the GeoSOS-FLUS model, and the specific steps are as follows: 1) Select DEM, slope, aspect, population, GDP, and distance as driving factors; 2) The land use adaptability probability is calculated using the ANN algorithm; 3) Based on the land use pattern and land use adaptability probability data obtained from the water and soil joint configuration module, a cellular automata based on an adaptive mechanism is used to simulate the spatial optimization of land use. Step (5) uses the proposed coupled CSM-SA-NLP three-layer nested method to solve the overall model, obtains multiple sets of water and soil resource optimization allocation schemes, and plots the Pareto front curves of GDP and EGE-RVC; Step (6): For the problem of multiple solutions, the optimal solution is selected from the Pareto solution set using the TOPSIS comprehensive evaluation model based on CRITIC weighting.

2. The regional water and soil resources joint optimization allocation method considering the water-soil mutual feedback relationship according to claim 1, characterized in that, The specific steps of the CSM-SA-NLP three-layer nested method are as follows: 1) Input the initial land type area Water supply Land use pattern S 0 θ1, θ2 and related parameters, set the iteration number k = 1, θ1 = 10km 2 θ2 = 0.0010 billion m 2 ; 2) Select the maximum regional GDP as the objective function, and transform EGE-RVC into inequality constraints, thus obtaining N single-objective problems; 3) Simulate the available water volume in each county / district using the SWAT model and input it into the water and soil joint configuration module; 4) Use the built-in NLP algorithm of LINGO software to solve the water and soil joint allocation module, output the area allocation and water supply allocation results for each land type, and divide the area of ​​each land type into... Inputting the GeoSOS-FLUS model yields the new land use pattern S k ; 5) Area of ​​each land type and water supply Perform conditional judgment, if and Then proceed to the next step; otherwise, set k = k + 1 and set the new land use pattern S. k Input the SWAT model and repeat steps 3)-4); 6) Determine whether all single-objective problems have been traversed. If so, output the area and corresponding water supply scheme for each land use type. If not, proceed to solve the next single-objective problem.

Images