Water resource optimization scheduling method based on water and soil resource space coupling

Abstract

The invention discloses a water resource optimization scheduling method based on water and soil resource space coupling, and the method comprises the steps: dividing an agricultural land of a target region into grid units, collecting the basic data of each grid unit, calculating a water and soil resource space coupling evaluation index value based on the grid units and the basic data, and carrying out the water and soil resource space coupling evaluation index value. The water and soil resource coupling degree of each grid unit is calculated, and a hot spot area and a cold spot area of water and soil resource space coupling are identified by adopting a spatial autocorrelation analysis method; on the basis of hot spot region and cold spot region division distribution, determining a water resource optimization scheduling region and a regulation and control method by combining a water land resource space coupling evaluation index value; fine management and efficient utilization of water and soil resources are achieved, and the method has important significance in improving the utilization efficiency of agricultural water resources and promoting agricultural sustainable development.

Description

Technical Field

[0001] The present invention belongs to the technical field of optimal water resource allocation, and particularly relates to an optimal water resource allocation method based on the spatial coupling of water and soil resources. Background Art

[0002] The rational allocation and efficient utilization of water resources and land resources are important foundations for ensuring the sustainable development of agriculture. Currently, the water resource allocation methods for agricultural land often consider water resources and land resources separately, ignoring the spatial coupling relationship between the two, resulting in low resource allocation efficiency.

[0003] Most existing methods are analyzed based on administrative divisions, with a low spatial resolution, making it difficult to reflect the spatial heterogeneity of water and soil resource utilization within the region. There is a lack of a systematic spatial coupling evaluation index system for water and soil resources, and it is unable to accurately identify the hotspots and problem areas of resource utilization, resulting in insufficient practicality and operability of the allocation scheme; CN117689169A discloses an optimal water resource allocation method that comprehensively considers carbon storage and water demand. Based on the construction of an optimal water resource allocation model, according to the obtained response relationship, an objective function is created with the maximum regional carbon storage as the goal, and two objectives, namely the minimum water shortage in the water receiving area and the minimum system pumping volume, are comprehensively considered to establish constraint conditions, and an optimal allocation model is established. Through repeated iterative tests on the constructed objective function and constraint conditions, a multi-objective allocation optimization method for the water volume and carbon storage of the inter-basin water transfer project in the study area is obtained. Although this method considers the land property classification, it lacks the consideration of the spatial coupling of water and soil resources. Summary of the Invention

[0004] The purpose of the present invention is to provide an optimal water resource allocation method based on the spatial coupling of water and soil resources. By obtaining the dominant limiting factors in different regions based on the spatial coupling of water and soil resources, engineering measures or land improvement measures are taken targeted to achieve the purpose of improving the accuracy of regulation measures.

[0005] To achieve the above-mentioned invention purpose, the specific technical solutions are as follows:

[0006] An optimal water resource allocation method based on the spatial coupling of water and soil resources, applied to the optimal water resource allocation of agricultural land, the method comprising the following steps:

[0007] Step S1, dividing the agricultural land in the target area into l×l grid cells, and collecting the basic data of each grid cell, the basic data including: terrain slope data, soil physical and chemical property data, and hydrometeorological data.

[0008] Further, the agricultural land in the target area is divided into l×l grid cells, where the value range of l is 0.1 km to 2 km.

[0009] Step S2: Based on the grid cells and the basic data, calculate the spatial coupling evaluation index values of water and land resources. The evaluation index values include the agricultural cultivation condition index LSI, the agricultural water supply condition index WRS, and the light and heat condition index CRI.

[0010] Step S3: Calculate the coupling degree of water and soil resources for each grid cell, and use the Moran's index spatial autocorrelation analysis method to identify the hot spots and cold spots of the spatial coupling of water and soil resources.

[0011] Furthermore, the mathematical expression of the coupling degree x of water and soil resources is: where U 1 is the comprehensive index of water resources, U 2 is the comprehensive index of land resources,

[0012] Using the Moran's index spatial autocorrelation analysis method to identify hot spots and cold spots, the Moran's index I of the i-th grid cell i is expressed as: x i and x j are the coupling degrees of water and soil resources of grid cells i and j, n is the number of grid cells in the target area, and x is the average value of the coupling degree of water and soil resources; S 2 is the variance of the observed values, w ij is the element of the spatial weight matrix, calculated using the inverse distance weighting method, d ij is the distance between the center points of grid cells i and j, and d 0 is the distance threshold, set to d 0 = 5l.

[0013] The test value of the i-th grid cell where E(I i ) is the expected value of the test value, VAR(I i ) is the variance value, where,

[0014] When I i > 0 and the test value Z i > 1.96, it is determined that the i-th grid cell is a hot spot area;

[0015] When I i < 0 and the test value Z i < -1.96, it is determined that the i-th grid cell is a cold spot area;

[0016] Step S4: Based on the distribution of hot spots and cold spots and combined with the spatial coupling evaluation index values of water and land resources, determine the regions for optimized water resource allocation and the regulation methods.

[0017] Further, based on the grid cells and the basic data, the calculation of the spatial coupling evaluation index values of water and land resources is specifically as follows:

[0018] The mathematical expression of the agricultural cultivation condition index LSI is: LSI = S × T × O, where the value range of LSI is [0, 1]; S is the slope classification index, which is divided into 5 grades of flat land, gentle slope land, gentle steep slope land, and steep slope land according to the slope ≤ 2°, 2 - 6°, 6 - 15°, 15 - 25°, and > 25°, and the corresponding classification indexes are: 1.0, 0.8, 0.6, 0.4, and 0.2; T is the soil texture index, taking 1.0 when the clay content > 25%, 0.8 when the clay content is 15 - 25%, and 0.6 when the clay content < 15%; O is the soil organic matter content index, taking 1.0 when the organic matter > 2%, 0.8 when the organic matter is 1 - 2%, and 0.6 when the organic matter < 1%.

[0019] The agricultural water supply condition index WRS adopts a comprehensive evaluation model of natural precipitation conditions and agricultural irrigation water supply conditions:

[0020] where P is the annual average precipitation, in the unit of mm; P c is the crop water requirement, in the unit of mm; AIC is the irrigation water supply condition index, and the calculation method is as follows:

[0021] AIC = RI i × C i , where RI i is the reliability degree of the irrigation water source, which refers to the ratio of the number of days when the irrigation water source can supply normally to the crop growth cycle; C i is the convenience degree of irrigation water supply, D a is the distance between the grid cell and the water supply source. When the distance exceeds 40 km, take D a = 40 km; H a is the lift height difference between the grid cell and the water supply source. When the height difference is negative, take H a = 0 m. When the height difference exceeds 30 m, take H a = 30 m; D amax and H amax are the preset maximum water transfer distance and maximum lift height, D amax = 40 km, H amax = 30 m;

[0022] The calculation method of the light and heat condition index CRI is: Among them, GDD is the growing degree days, which refers to the total accumulated temperature when the daily average temperature is higher than 10 °C during the crop growth season. GDD c is the optimal growing degree days required by the crop; SR is the solar radiation, and SR c is the optimal solar radiation required for crop growth.

[0023] Furthermore, the regions and regulation methods for optimizing water resource allocation are as follows:

[0024] For hot spots, maintain the existing pattern of water and soil resource utilization; for cold spots, determine the regulation direction according to the limiting factors of each grid cell:

[0025] When WRS < LSI, that is, water resources are the dominant limiting factor, increase the water supply capacity through engineering measures;

[0026] When LSI < WRS, that is, land resources are the dominant limiting factor, improve the land quality through land improvement;

[0027] When both WRS and LSI do not exceed half of the average value of the target area, promote the improvement of water and soil resources through the coordinated advancement of engineering measures and land improvement.

[0028] Furthermore, the mathematical expression of the model for optimizing water resource allocation is:

[0029] The constraint conditions of the model include: total water resource constraint Food security constraint Y i ≥Y min ; ecological water use constraint W eco ≥0.1W t ; water and soil resource coupling degree constraint x i ≥x min ; where E is the water resource utilization efficiency; Y i is the crop yield (kg / ha) of the i-th grid cell; M i is the water resource allocation of the i-th grid cell; ET ai 、ET mi are the actual evapotranspiration and the maximum water demand (mm) of the i-th grid cell respectively; M t is the total available water resources; W eco is the ecological water consumption.

[0030] Furthermore, the mathematical expression of the evaluation index EI for the effect of optimizing water resource allocation is:

[0031] Among them, Δx is the increase in the water and soil resource coupling degree; ΔW is the newly added water resource input; Y actis the optimized yield per unit area; Y 0 is the yield per unit area before optimization.

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0033] By means of the agricultural cultivation condition index, the agricultural water supply condition index, and the light and heat condition index, the present invention comprehensively evaluates the utilization status of water and soil resources in the target area, effectively identifies the hot spots and cold spots of the coupling of water and soil resources based on spatial autocorrelation analysis, and provides a scientific basis for differential regulation; through the method provided by the present invention, the refined management and efficient utilization of water and soil resources can be realized, which is of great significance for improving the utilization efficiency of agricultural water resources, ensuring food security, and promoting the sustainable development of agriculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is a flowchart of a water resource optimal scheduling method based on the spatial coupling of water and soil resources according to the present invention; DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without making creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0036] As Figure 1 shown, a water resource optimal scheduling method based on the spatial coupling of water and soil resources according to the present invention is applied to the optimal scheduling of water resources for agricultural land, and the method includes the following steps:

[0037] Step S1: Divide the agricultural land in the target area into l×l grid cells, and collect the basic data of each grid cell. The basic data includes: terrain slope data, soil physical and chemical property data, and hydrometeorological data.

[0038] The agricultural land in the target area is divided into l×l grid cells, where the value range of l is 0.1 km to 2 km; in practical applications, the size selection of the grid cells needs to consider the area of the research area and the feasibility of data collection. For example, for an agricultural area of 1000 square kilometers, a grid division of 1 km×1 km can be selected, which can ensure both accuracy and convenience for management. The collection of basic data can be combined with remote sensing data (such as MODIS, Landsat, etc.) and ground observation station data. The terrain slope data can be obtained through DEM data, the soil physical and chemical properties can be obtained through soil sampling and analysis, and the hydrometeorological data can be obtained through automatic weather stations and hydrological stations.

[0039] The selection of the grid scale should be flexibly determined according to the characteristics of the study area. For example, in mountainous and hilly areas, due to the drastic terrain changes, a smaller grid scale (such as 0.1 - 0.5 km) should be selected to reflect the micro-topographic changes; in plain areas, where the terrain changes gently, a larger grid scale (such as 1 - 2 km) can be chosen; at the same time, the limitation of computing resources also needs to be considered. Although a smaller grid has higher accuracy, it will significantly increase the computational workload.

[0040] Step S2: Based on the grid cells and the basic data, calculate the spatial coupling evaluation index values of water and land resources. The evaluation index values include the agricultural cultivation condition index LSI, the agricultural water supply condition index WRS, and the light and heat condition index CRI.

[0041] Step S3: Calculate the coupling degree of water and soil resources for each grid cell, and use the Moran's I index spatial autocorrelation analysis method to identify the hot spots and cold spots of the spatial coupling of water and soil resources.

[0042] The mathematical expression of the coupling degree x of water and soil resources is: where U 1 is the comprehensive index of water resources, U 2 is the comprehensive index of land resources,

[0043] This coupling degree calculation formula is actually a normalized coupling coordination degree model. The value range of the coupling degree x is [0, 0.5], where 0 represents complete non-coupling and 0.5 represents complete coupling; the coupling degree can be divided into different levels: x < 0.1 is severe disorder, 0.1 ≤ x < 0.2 is moderate disorder, 0.2 ≤ x < 0.3 is mild disorder, 0.3 ≤ x < 0.4 is primary coordination, and x ≥ 0.4 is good coordination. This grading can help managers more intuitively judge the utilization status of regional water and soil resources. It can also be improved by considering introducing a weight coefficient into the comprehensive index of water resources, that is, modified to where α is the weight coefficient, which can be adjusted according to the regional characteristics.

[0044] Use the Moran's I index spatial autocorrelation analysis method to identify hot spots and cold spots. The Moran's I index I of the i-th grid cell i is expressed as: x i and x j are the coupling degrees of water and soil resources of grid cells i and j, n is the number of grid cells in the target area, is the average value of the coupling degree of water and soil resources; S 2 is the variance of the observed values, w ij is the element of the spatial weight matrix, which is calculated using the inverse distance weighting method, d ijis the distance between the center points of grid cells i and j, d 0 is the distance threshold, set to d 0 = 5l;

[0045] The test value of the i-th grid cell where E(I i ) is the expected value of the test value, VAR(I i ) is the variance value, where,

[0046] When I i > 0 and the test value Z i > 1.96, it is determined that the i-th grid cell is a hot spot area;

[0047] When I i < 0 and the test value Z i < -1.96, it is determined that the i-th grid cell is a cold spot area.

[0048] It should be noted that the weight setting in this embodiment is only one way. When performing Moran's index analysis, the construction of the spatial weight matrix is crucial. In addition to the inverse distance weighting method used in this scheme, other weight calculation methods can also be considered:

[0049] (1) Adjacency weighting method: Assign a weight of 1 to adjacent grid cells and 0 to non-adjacent ones;

[0050] (2) Exponential decay weighting method: The weight decays exponentially with distance;

[0051] (3) K-nearest neighbor weighting method: Only consider the nearest K grid cells.

[0052] In addition, the determination thresholds for hot and cold spots (1.96) can be adjusted according to the confidence level requirements. For example, 1.65 can be used at a 90% confidence level and 2.58 at a 99% confidence level; during the calculation of Moran's index, it should be noted that when the sample size is large (n > 200), the Monte Carlo simulation method is used to obtain the significance level of Moran's index to avoid errors caused by the normal distribution assumption; the selection of the distance threshold d 0 can also use the incremental method, that is, start from the minimum distance and gradually increase until at least one neighbor exists for all grid cells.

[0053] Step S4, based on the distribution of hot spot areas and cold spot areas, combined with the spatial coupling evaluation index values of water and land resources, determine the areas and regulation methods for water resource optimal scheduling.

[0054] Based on the grid cells and the basic data, calculating the spatial coupling evaluation index values of water and land resources specifically is:

[0055] The mathematical expression of the agricultural cultivation condition index LSI is: LSI = S × T × O, where the value range of LSI is [0, 1]; S is the slope classification index. According to the slope ≤ 2°, 2 - 6°, 6 - 15°, 15 - 25° and > 25°, it is divided into 5 grades: flat land, gentle land, gentle slope land, gentle steep slope land and steep slope land, and the corresponding classification indexes are: 1.0, 0.8, 0.6, 0.4 and 0.2; T is the soil texture index. When the clay content > 25%, take 1.0, when the clay content is 15 - 25%, take 0.8, and when the clay content < 15%, take 0.6; O is the soil organic matter content index. When the organic matter > 2%, take 1.0, when the organic matter is 1 - 2%, take 0.8, and when the organic matter < 1%, take 0.6;

[0056] The agricultural water supply condition index WRS adopts a comprehensive evaluation model of natural precipitation conditions and agricultural irrigation water supply conditions:

[0057] Among them, P is the annual average precipitation, unit mm; P c is the crop water requirement, unit mm; AIC is the irrigation water supply condition index, and the calculation method is as follows:

[0058] Among them, RI i is the reliability degree of the irrigation water source, which refers to the ratio of the number of days when the irrigation water source can supply normally to the crop growth cycle; C i is the convenience degree of the irrigation water supply, D a is the distance between the grid unit and the water supply source. When the distance exceeds 40 km, take D a = 40 km; H a is the lift height difference between the grid unit and the water supply source. When the height difference is negative, take H a = 0 m. When the height difference exceeds 30 m, take H a = 30 m; D amax and H amax are the preset maximum water transfer distance and maximum lift height, D amax = 40 km, H amax = 30 m;

[0059] The calculation method of the light and heat condition index CRI is: Among them, GDD is the growing degree days, which refers to the total temperature accumulated when the daily average temperature is higher than 10°C during the crop growth season. GDD c is the optimal growing degree days required by the crop; SR is the solar radiation amount, SR c is the optimal solar radiation amount required for crop growth.

[0060] The GDD calculation method is as follows: for areas with small temperature differences, the single triangle method is adopted; for areas with large temperature differences, the double triangle method is adopted; for areas with large temperature fluctuations, the sine curve method is adopted; the GDD c value varies according to different crops, for rice: 1500 - 2000, for wheat: 1800 - 2200, for corn: 2000 - 2500.

[0061] The methods for determining the regions and regulation of optimal water resource allocation include:

[0062] For hotspots, maintain the existing pattern of water and soil resource utilization; for cold spots, determine the regulation direction according to the limiting factors of each grid cell:

[0063] When WRS < LSI, that is, water resources are the dominant limiting factor, increase the water supply capacity through engineering measures; the engineering measures in water resource restricted areas include: canal system transformation, anti-seepage treatment, cross-section optimization, automation transformation; water-saving irrigation, construction of sprinkler irrigation, micro-irrigation, and drip irrigation systems; water source projects, construction of reservoirs, diversion canals, and pumping stations; emergency backup, construction of emergency backup water sources and connection projects.

[0064] When LSI < WRS, that is, land resources are the dominant limiting factor, improve land quality through land improvement; the improvement measures in land resource restricted areas include: soil improvement, straw returning to the field, deep plowing and loosening of the soil, soil conditioner; terrain transformation, construction of terraced fields, leveling of the field surface, construction of farm roads; soil fertility improvement, application of organic fertilizers, planting of green manure, crop rotation; protection measures, windbreak forest network, soil and water conservation, ecological agriculture.

[0065] When both WRS and LSI do not exceed half of the average value of the target area, promote the improvement of water and soil resources through the coordinated implementation of engineering measures and land improvement; the coordinated measures in double restricted areas include: synchronous transformation of land rearrangement and irrigation and drainage systems, coordinated promotion of high-efficiency water conservation and soil improvement, and overall construction of ecological governance and agricultural infrastructure.

[0066] The mathematical expression of the optimal water resource allocation model is:

[0067] The constraint conditions of the model include: total water resource constraint Food security constraint Y i ≥Y min ; ecological water use constraint W eco ≥0.1W t ; water and soil resource coupling degree constraint x i ≥x min ; where E is the water resource utilization efficiency; Y i is the crop yield (kg / ha) of the i-th grid cell; M i is the water resource allocation of the i-th grid cell; ETai , ET mi are the actual evapotranspiration and the maximum water demand (mm) of the i-th grid cell respectively; M t is the total amount of available water resources; W eco is the ecological water consumption.

[0068] The solution method of the model selects different methods according to the number of grids. For small-scale problems (<100 grids), linear programming can be used. For medium-scale problems (100 - 1000 grids), genetic algorithms are adopted. For large-scale problems (>1000 grids), distributed computing or neural networks are used.

[0069] The mathematical expression of the evaluation index EI for the optimal water resources allocation effect is:

[0070] Among them, Δx is the increase in the coupling degree of water and soil resources; ΔW is the newly added water resources input; Y act is the yield per unit area after optimization; Y 0 is the yield per unit area before optimization; The evaluation period includes: short-term evaluation, with the crop growth period as the unit; medium-term evaluation, with the year as the unit; long-term evaluation, with a 3 - 5-year evaluation period.

[0071] The supporting facilities for implementing the method in this embodiment include: establishing a GIS spatial data management system, developing water and soil resources coupling analysis software, and constructing an optimal allocation decision support system; The monitoring system includes: an automatic meteorological monitoring network, installing intelligent irrigation control equipment, and establishing a soil moisture monitoring network.

[0072] Taking the application of optimal water resources allocation in a certain area as an example, the total area of the research area is about 1000 square kilometers. The main crops are winter wheat - summer corn rotation. The current problems are: uneven utilization efficiency of water and soil resources within the region, insufficient water supply in some areas, and land degradation in some areas.

[0073] Using a 1km×1km grid division, a total of 1000 grid cells are divided. The terrain altitude is 80 - 150m, and the slope is 0 - 15°; The soil is mainly loam, with a clay content of 15 - 30% and an organic matter content of 0.8 - 2.5%. The average annual precipitation is 650mm, and the average annual temperature is 14.5℃; The surface water resources volume is 250 million m 3 / year, and the groundwater resources volume is 180 million m 3 / year. Part of the grid data of the research area is shown in Table 1.

[0074] Table 1: Grid data of the research area

[0075] Mesh number LSI WRS CRI Coupling degree x A126 0.85 0.72 0.92 0.42 B274 0.65 0.45 0.88 0.28 C332 0.45 0.82 0.90 0.31

[0076] Three hot spots (a total of 180 grids) are identified: Hot spot 1: The core plain area, with superior water and soil resources conditions; Hot spot 2: The area along the irrigation canals, with good water supply conditions; Hot spot 3: The area with fertile soil, with good tillage conditions. Two cold spots (a total of 220 grids) are identified: Cold spot 1: The hilly area, with relatively poor land quality; Cold spot 2: The end of the irrigation area, with insufficient water supply.

[0077] Implement land improvement on Cold spot 1, build 2,000 mu of terraced fields; conduct soil improvement, return straw to the field, and apply more organic fertilizers; the LSI is increased by 0.15, and the coupling degree is increased by 0.08. Implement canal seepage prevention on Cold spot 2, transform 15 kilometers of irrigation canals; build new water storage ponds with a water storage capacity of 500,000 m 3 , the WRS is increased by 0.25, and the coupling degree is increased by 0.12.

[0078] Through optimized scheduling, the irrigation water use coefficient is increased from 0.52 to 0.65, and the water yield per cubic meter is increased from 1.2 kg / m 3 to 1.5 kg / m 3 , the average mu yield of grain is increased by 12%, and the water productivity is increased by 23%. The average coupling degree of the region is increased from 0.32 to 0.41. It can be seen from this example that the method of this embodiment can effectively improve the utilization efficiency of water and soil resources, improve agricultural production conditions, and has significant practical value in practical applications. Among them, spatial coupling analysis helps to identify problem areas, and differentiated optimization measures improve the accuracy of regulation, and finally achieve the expected improvement effect.

[0079] The specific implementation manners described above further elaborate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above are only specific implementation manners of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A water resources optimization scheduling method based on spatial coupling of water and soil resources is applied to the optimization scheduling of water resources for agricultural land, characterized in that: The method includes the following steps: Step S1: Divide the agricultural land in the target area into l×l grid cells, and collect the basic data of each grid cell. The basic data includes: terrain slope data, soil physical and chemical property data, and hydro-meteorological data; Step S2: Based on the grid cells and the basic data, calculate the spatial coupling evaluation index values of water and land resources. The evaluation index values include the agricultural cultivation condition index LSI, the agricultural water supply condition index WRS, and the light and heat condition index CRI; Step S3: Calculate the coupling degree of water and soil resources in each grid cell, and use the Moran index spatial autocorrelation analysis method to identify the hot spots and cold spots of the spatial coupling of water and soil resources; The mathematical expression of the coupling degree x of water and soil resources is: Among them, U1 is the comprehensive water resources index, U2 is the comprehensive index of land resources, The Moran index spatial autocorrelation analysis method is used to identify hot and cold spots. The Moran index I of the i-th grid cell is i It is expressed as: x i 、x j is the coupling degree of water and soil resources between grid cells i and j, n is the number of grid cells in the target area, is the average coupling degree of water and soil resources; S 2 is the observed value variance, w ij is the spatial weight matrix element, calculated using the inverse distance weight method. d ij is the distance between the center points of grid cells i and j, d0 is the distance threshold, which is set to d0 = 5l; The test value of the i-th grid cell Among them E(I i ) is the expected test value, VAR(I i ) is the variance value, in, When I i >0 and test value Z i When >1.96, the i-th grid cell is determined to be a hot spot area; when i i <0 and test value Z i When <-1.96, the i-th grid cell is determined to be a cold spot area; Step S4: Based on the distribution of hot spots and cold spots, and combined with the spatial coupling evaluation index values of water, land and resources, determine the areas and regulation methods for the optimal allocation of water resources.

2. The water resources optimization scheduling method based on spatial coupling of water and land resources according to claim 1 is characterized in that: Based on the grid cells and the basic data, calculating the spatial coupling evaluation index values of water and land resources specifically includes: The mathematical expression of the agricultural cultivation condition index LSI is: LSI = S×T×O, where the value range of LSI is [0, 1]; S is the slope grading index, which is divided into 5 grades of flat land, flat land, gentle slope land, gentle steep slope land and steep slope land according to the slope ≤2°, 2-6°, 6-15°, 15-25° and >25°, and the corresponding grading indexes are: 1.0, 0.8, 0.6, 0.4 and 0.2; T is the soil texture index, taking 1.0 when the clay content >25%, taking 0.8 when the clay content is 15-25%, and taking 0.6 when the clay content <15%; O is the soil organic matter content index, taking 1.0 when the organic matter >2%, taking 0.8 when the organic matter is 1-2%, and taking 0.6 when the organic matter <1%; The agricultural water supply condition index WRS adopts a comprehensive evaluation model of natural precipitation conditions and agricultural irrigation water supply conditions: Where P is the average annual precipitation, in mm; P c is the crop water requirement, in mm; AIC is the irrigation water supply condition index, calculated as follows: AIC=RI i ×C i , Among them, RI i C is the reliability of irrigation water source, which refers to the ratio of the number of days that irrigation water source can be normally supplied to the crop growth cycle; i The convenience of irrigation water supply, D a is the distance between the grid unit and the water source. When the distance exceeds 40 km, take D a =40km; H a is the water lifting height difference between the grid unit and the water supply source. When the height difference is negative, take H a = 0m, when the height difference exceeds 30m, take H a =30m; D amax and H amax is the preset maximum water transfer distance and maximum water lifting height, D amax =40km, H amax =30m; The calculation method of the light and heat condition index CRI is: GDD is growing degree days, which refers to the total accumulated temperature when the daily average temperature is higher than 10℃ during the crop growing season. c is the optimal growing degree days required by crops; SR is the solar radiation, SR c The optimal amount of solar radiation required for crop growth.

3. The water resources optimization scheduling method based on spatial coupling of water and land resources according to claim 2 is characterized in that: Determining the areas and regulation methods for the optimal allocation of water resources includes: For hot spots, maintain the existing pattern of water and soil resource utilization; for cold spots, determine the regulation direction according to the limiting factors of each grid cell: When WRS < LSI, that is, water resources are the dominant limiting factor, increase the water supply capacity through engineering measures; When LSI < WRS, that is, land resources are the dominant limiting factor, improve the land quality through land improvement; When both WRS and LSI do not exceed half of the average value of the target area, promote the improvement of water and soil resources through engineering measures and land improvement in a coordinated manner.

4. The water resources optimization scheduling method based on spatial coupling of water and land resources according to claim 3 is characterized in that: The mathematical expression of the model for optimal water resource scheduling is: The constraints of the model include: water resources total amount constraint Food security constraints i ≥Y min ; Ecological water use constraints W eco ≥0.1W t ; Water and soil resources coupling degree constraint x i ≥x min ; Where E is the water resource utilization efficiency; Y i is the crop yield of the ith grid unit (kg / ha); M i is the water resource allocation of the i-th grid unit; ET ai , E.T. mi are the actual evapotranspiration and maximum water requirement of the ith grid unit (mm); M t is the total amount of available water resources; W eco Ecological water consumption.

5. The water resources optimization scheduling method based on spatial coupling of water and land resources according to claim 4 is characterized in that: The mathematical expression of the evaluation index E for the effect of optimal allocation of water resources is: Among them, Δx is the increase in the coupling degree of water and soil resources; ΔW is the additional water resources input; Y act is the yield per unit area after optimization; Y0 is the yield per unit area before optimization.

6. The water resources optimization scheduling method based on spatial coupling of water and land resources according to any one of claims 1 to 5, characterized in that: Divide the agricultural land in the target area into l×l grid cells, where the value range of l is 0.1 km to 2 km.

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