Water resources optimization scheduling method based on spatial coupling of water and land resources

Abstract

The present invention discloses a water resource optimization scheduling method based on spatial coupling of water and land resources. The method divides agricultural land in a target area into grid units, collects basic data of each grid unit, calculates water-land resource spatial coupling evaluation index values ​​based on the grid units and the basic data, calculates the water and land resource coupling degree of each grid unit, and uses a spatial autocorrelation analysis method to identify hot spots and cold spots of water and land resource spatial coupling. Based on the distribution of hot spots and cold spots, combined with the water and land resource spatial coupling evaluation index values, the method determines the areas and control methods for water resource optimization scheduling. The method realizes the refined management and efficient utilization of water and land resources, which is of great significance to improving the efficiency of agricultural water resource utilization and promoting the sustainable development of agriculture.

Description

Technical Field

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

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

[0003] Most existing methods are based on administrative divisions for analysis, with 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 impossible to accurately identify hot spots and problem areas of resource utilization, resulting in insufficient practicality and operability of the scheduling scheme. CN117689169A discloses a water resource optimization scheduling method that comprehensively considers carbon storage and water demand. It is based on the construction of a water resource optimization scheduling model. According to the obtained response relationship, an objective function is created with the maximum regional carbon storage as the goal, and the two goals of minimizing water shortage in the receiving area and minimizing system pumping volume are comprehensively considered. Constraints are set, an optimization scheduling model is established, and the constructed objective function and constraints are repeatedly iteratively tested to obtain a multi-objective scheduling optimization method for water and carbon storage of inter-basin water diversion projects in the study area. Although this method takes into account the nature classification of land, it lacks consideration of the spatial coupling of water and soil resources. Summary of the Invention

[0004] The purpose of the present invention is to provide a water resource optimization scheduling method based on the spatial coupling of water and soil resources. Based on the dominant limiting factors of different regions obtained according to the spatial coupling of water and soil resources, targeted engineering measures or land improvement measures are taken to achieve the purpose of improving the accuracy of control measures.

[0005] In order to achieve the above-mentioned invention object, the specific technical solution is as follows:

[0006] A water resource optimization scheduling method based on spatial coupling of water and soil resources is applied to optimize water resource scheduling for agricultural land. The method comprises the following steps:

[0007] Step S1: Divide the agricultural land in the target area into l×l grid units, and collect basic data of each grid unit. The basic data includes: terrain slope data, soil physical and chemical property data, and hydrological and meteorological data.

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

[0009] Step S2, based on the grid cells and the basic data, calculate the water-land resource spatial coupling evaluation index value, the evaluation index value includes the agricultural farming condition index LSI, the agricultural water supply condition index WRS and the light and heat condition index CRI.

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

[0011] Furthermore, the mathematical expression of the water and soil resource coupling degree x is: Among them, U1 is the comprehensive index of water resources, U2 is the comprehensive index of land resources,

[0012] 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 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, and x 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, and is set to d0 = 5l.

[0013] 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,

[0014] When I i >0 and test value Z i When >1.96, the i-th grid cell is determined to be a hotspot area;

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

[0016] Step S4: Based on the distribution of hotspot areas and coldspot areas and in combination with the water-land resource spatial coupling evaluation index value, determine the area and control method for water resource optimization scheduling.

[0017] Furthermore, based on the grid cells and the basic data, the water-land resource spatial coupling evaluation index value is calculated as follows:

[0018] The mathematical expression of the agricultural farming 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 five levels according to slopes ≤2°, 2-6°, 6-15°, 15-25°, and >25°: flat land, flat land, gentle slope, gently steep slope, and steep slope, with corresponding classification indices of 1.0, 0.8, 0.6, 0.4, and 0.2, respectively; T is the soil texture index, which is 1.0 when the clay content is >25%, 0.8 when the clay content is 15-25%, and 0.6 when the clay content is <15%; O is the soil organic matter content index, which is 1.0 when the organic matter content is >2%, 0.8 when the organic matter content is 1-2%, and 0.6 when the organic matter content is <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 average annual precipitation, in mm; P c is the crop water requirement, in mm; AIC is the irrigation water supply condition index, which is calculated as follows:

[0021] 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 height difference between the grid unit and the water 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;

[0022] 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 average daily temperature is above 10℃ during the crop growing season. cis the optimal growing degree days required by crops; SR is the solar radiation, and SR c is the optimal solar radiation required for crop growth.

[0023] Furthermore, the determination of the region and regulation method for optimal water resource allocation includes:

[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 optimal water resource allocation model 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 optimal water resource allocation effect is:

[0031] where Δx is the increase in the water and soil resource coupling degree; ΔW is the newly added water resource input; Y act is the yield per unit area after optimization; Y0 is the yield per unit area before optimization.

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

[0033] The present invention comprehensively evaluates the utilization status of water and soil resources in the target area through agricultural farming condition indicators, agricultural water supply condition indicators and light and heat condition indicators, and effectively identifies hot and cold spots of water and soil resource coupling based on spatial autocorrelation analysis, providing a scientific basis for differentiated regulation; the method provided by the present invention can achieve refined management and efficient utilization of water and soil resources, which is of great significance to improving the efficiency of agricultural water resource utilization, ensuring food security and promoting sustainable agricultural development. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a flow chart of the water resources optimization scheduling method based on spatial coupling of water and land resources of the present invention; DETAILED DESCRIPTION

[0035] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention are described clearly and completely below. Obviously, the embodiments described are only part of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0036] like Figure 1 As shown in FIG, the water resources optimization scheduling method based on spatial coupling of water and soil resources of the present invention is applied to the water resources optimization scheduling of agricultural land. The method includes the following steps:

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

[0038] Agricultural land in the target area is divided into l×l grid cells, where l ranges from 0.1 km to 2 km. In practical applications, the size of the grid cells should be determined based on the area of ​​the study and the feasibility of data collection. For example, for an agricultural area of ​​1000 square kilometers, a 1 km×1 km grid can be selected to ensure both accuracy and ease of management. Basic data collection can be combined with remote sensing data (such as MODIS and Landsat) and ground observation station data. Topographic slope data can be obtained from DEM data, soil physical and chemical properties can be obtained through soil sampling and analysis, and hydrometeorological data can be obtained from automatic weather stations and hydrological stations.

[0039] The choice of grid size should be flexibly determined based on the characteristics of the study area. For example, in mountainous or hilly areas, where the terrain changes dramatically, a smaller grid size (e.g., 0.1-0.5 km) should be selected to reflect microtopographic variations. In plain areas, where the terrain changes more gently, a larger grid size (e.g., 1-2 km) can be selected. Computational resource limitations must also be considered; smaller grids, while offering higher accuracy, significantly increase the computational effort.

[0040] Step S2, calculating water-land resource spatial coupling evaluation index values ​​based on the grid cells and the basic data, the evaluation index values ​​including the agricultural farming condition index LSI, the agricultural water supply condition index WRS, and the light and heat condition index CRI;

[0041] Step S3, calculating the water and soil resource coupling degree of each grid cell, and using the Moran index spatial autocorrelation analysis method to identify hot spots and cold spots of water and soil resource spatial coupling;

[0042] The mathematical expression of the water and soil resource coupling degree x is: Among them, U1 is the comprehensive index of water resources, U2 is the comprehensive index of land resources,

[0043] The coupling degree calculation formula is actually a normalized coupling coordination model. The value range of coupling degree x is [0, 0.5], where 0 indicates no coupling at all and 0.5 indicates complete coupling. The coupling degree can be divided into different levels: x < 0.1 is severe imbalance, 0.1 ≤ x < 0.2 is moderate imbalance, 0.2 ≤ x < 0.3 is mild imbalance, 0.3 ≤ x < 0.4 is primary coordination, and x ≥ 0.4 is good coordination. This classification can help managers more intuitively judge the utilization status of regional water and soil resources. It can also improve the comprehensive water resource index by introducing a weight coefficient, that is, it is modified to Where α is the weight coefficient, which can be adjusted according to regional characteristics.

[0044] 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 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 value of the 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, set to d0 = 5l;

[0045] 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,

[0046] When I i >0 and test value Z i When >1.96, the i-th grid cell is determined to be a hotspot area;

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

[0048] It should be noted that the weight setting in this embodiment is only one method. 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 solution, other weight calculation methods can also be considered:

[0049] (1) Adjacency weight method: adjacent grids are weighted as 1, and non-adjacent grids are weighted as 0;

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

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

[0052] In addition, the threshold for determining hot spots and cold spots (1.96) can be adjusted according to the confidence level requirements, such as 1.65 for 90% confidence and 2.58 for 99% confidence. It should be noted that when calculating the Moran index, when the sample size is large (n>200), the Monte Carlo simulation method should be used to obtain the significance level of the Moran index to avoid errors caused by the normal distribution assumption. The distance threshold d0 can also be selected using an incremental method, that is, starting from the minimum distance and gradually increasing until all grids have at least one neighbor.

[0053] Step S4: Based on the distribution of hotspot areas and coldspot areas and in combination with the water-land resource spatial coupling evaluation index value, determine the area and control method for water resource optimization scheduling.

[0054] Based on the grid cells and the basic data, the water-land resource spatial coupling evaluation index value is calculated as follows:

[0055] The mathematical expression of the agricultural farming 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 five levels according to slopes ≤2°, 2-6°, 6-15°, 15-25°, and >25°: flat land, flat land, gentle slope, gently steep slope, and steep slope, with corresponding classification indices of 1.0, 0.8, 0.6, 0.4, and 0.2, respectively; T is the soil texture index, which is 1.0 when the clay content is >25%, 0.8 when the clay content is 15-25%, and 0.6 when the clay content is <15%; O is the soil organic matter content index, which is 1.0 when the organic matter content is >2%, 0.8 when the organic matter content is 1-2%, and 0.6 when the organic matter content is <1%;

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

[0057] 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, which is calculated as follows:

[0058] 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 height difference between the grid unit and the water 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;

[0059] 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 average daily temperature is above 10℃ during the crop growing season. c is the optimal growing degree days required for crops; SR is the solar radiation, SR c The optimal amount of solar radiation required for crop growth.

[0060] The GDD calculation method uses the single triangle method for areas with small temperature differences, the double triangle method for areas with large temperature differences, and the sine curve method for areas with large temperature fluctuations; 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 methods of optimal water resource allocation include:

[0062] 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:

[0063] When WRS < LSI, that is, water resources are the dominant limiting factor, increase water supply capacity through engineering measures; the engineering measures in water resource restricted areas include: canal system renovation, anti-seepage treatment, cross-section optimization, automation renovation; 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, and construction of farm roads; soil fertility improvement, application of organic fertilizers, planting of green manure, and crop rotation; protection measures, windbreak forest network, soil and water conservation, and 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 renovation 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 volume of the i-th grid cell; ETai ET mi are the actual evapotranspiration and maximum water requirement of the i-th grid unit (mm); M t is the total amount of available water resources; W eco Ecological water consumption.

[0068] The model is solved using different methods depending on the number of grids. Linear programming can be used for small-scale problems (<100 grids), genetic algorithms can be used for medium-scale problems (100-1000 grids), and distributed computing or neural networks can be used for large-scale problems (>1000 grids).

[0069] The mathematical expression of the water resources optimization scheduling effect evaluation index EI is:

[0070] Among them, Δx is the improvement of water and soil resource coupling; ΔW is the new water resource input; Y act is the yield per unit area after optimization; Y0 is the yield per unit area before optimization; the evaluation cycle includes: short-term evaluation, based on the crop growth period; mid-term evaluation, based on the year; long-term evaluation, based on 3-5 years.

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

[0072] Taking the application of water resource optimization and scheduling in a certain region as an example, the total area of ​​the study area is about 1,000 square kilometers, and the main crops are winter wheat-summer corn rotation. The current problems are: uneven water and soil resource utilization efficiency in the region, insufficient water supply in some areas, and land degradation in some areas.

[0073] The area is divided into 1000 grid cells using a 1km×1km grid. The terrain has an altitude of 80-150m and a slope of 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 are 250 million m3. 3 / year, groundwater resources are 180 million m 3 / year, some grid data of the study area are shown in Table 1.

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

[0075] Grid 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 hotspot areas (a total of 180 grids) were identified: Hotspot area 1: the core area of ​​the plain, with superior water and soil resources; Hotspot area 2: the area along the irrigation canal, with good water supply conditions; Hotspot area 3: the area with fertile soil and good farming conditions; Two coldspot areas (a total of 220 grids) were identified: Coldspot area 1: the hilly area, with poor land quality; Coldspot area 2: the end of the irrigation area, with insufficient water supply.

[0077] Land reclamation was implemented in cold spot area 1, with 2,000 mu of terraces built; soil improvement was carried out, straw was returned to the fields, and organic fertilizer was applied; LSI increased by 0.15 and coupling increased by 0.08; channel anti-seepage was implemented in cold spot area 2, and 15 kilometers of irrigation channels were renovated; a new reservoir was built with a water storage capacity of 500,000 m 3 , WRS increased by 0.25 and coupling degree increased by 0.12.

[0078] Through optimized scheduling, the irrigation water utilization coefficient increased from 0.52 to 0.65, and the single cubic water yield increased from 1.2 kg / m 3 Increased to 1.5kg / m 3 , average grain yield per mu increased by 12%, water productivity increased by 23%, and the average regional coupling degree increased from 0.32 to 0.41. This example shows that the method of this embodiment can effectively improve the efficiency of water and soil resource utilization and agricultural production conditions in practical applications, with significant practical value. Spatial coupling analysis helps identify problem areas, and differentiated optimization measures improve the accuracy of regulation, ultimately achieving the expected improvement results.

[0079] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection 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 by: 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 tillage condition index LSI, the agricultural water supply condition index WRS, and the light and heat condition index CRI; The mathematical expression of the agricultural tillage 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: flat land, gentle slope land, gentle steep slope land, and steep slope land according to the slope; T is the soil texture index; O is the soil organic matter content index; 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, which is 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; H a D is the water lifting height difference between the grid unit and the water source; amax and H amax The preset maximum water transfer distance and maximum water lifting height; 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 average daily temperature is above 10℃ during the crop growing season. c is the optimal growing degree days required for crops; SR is the solar radiation, SR c The optimal amount of solar radiation required for crop growth; 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 of water and soil resources x is: Among them, U1 is the comprehensive index of water resources, 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 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 value of the 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, 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 hotspot area; When I i <0 and the 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, combined with the spatial coupling evaluation index values of water and land 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: 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 the coordinated advancement of engineering measures and land improvement.

3. The water resources optimization scheduling method based on spatial coupling of water and land resources according to claim 2 is characterized in that: The mathematical expression of the water resources optimal scheduling model is: The constraints of the model include: total water resources constraint Food security constraint Y i ≥Y min ; Ecological water use constraints W eco ≥0.1W t ; Water and soil resources coupling constraint x i ≥x min ; Among them, E is the efficiency of water resource utilization; Y i is the crop yield of the i-th grid unit; M i is the water resource allocation of the i-th grid unit; ET ai ET mi are the actual evapotranspiration and maximum water demand of the i-th grid unit respectively; M t is the total amount of available water resources; W eco Ecological water consumption.

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 evaluation index EI for the effect of the optimal allocation of water resources is: Among them, Δx is the improvement of water and soil resource coupling; ΔW is the new water resource input; Y act is the yield per unit area after optimization; Y0 is the yield per unit area before optimization.

5. The water resources optimization scheduling method based on spatial coupling of water and land resources according to any one of claims 1 to 4, 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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