Optimized regulation and control method for land utilization of multi-outlet watershed

Abstract

The invention provides an optimal regulation and control method for land utilization of a multi-outlet watershed, and the method comprises the steps: 1, constructing a spatial attribute regression model, and carrying out the quantitative analysis of the influence of each land utilization type in the watershed of a research region on a water ecological load; step 2, obtaining corresponding regulation potential in combination with the rigid boundary of the research area in the step 1; optimizing the spatial attribute regression model, and evaluating the land utilization regulation potential and the ecological restoration regulation potential of the corresponding drainage basin by using the optimized spatial attribute regression model; 3, setting a land utilization regulation and control scene and an ecological restoration regulation and control scene, simulating an environment effect related to the land utilization regulation and control scene and the ecological restoration regulation and control scene by utilizing the optimized space attribute regression model, and formulating an optimized restoration measure scheme; according to the method, multiple drainage basin outlets can be simulated at the same time, the relation between drainage basin water bodies and land areas is comprehensively considered, the pollution degree of pollutants can be quantified, and the influence of land utilization on pollution loads is visually reflected.

Description

Technical Field

[0001] The present application relates to the field of land utilization technology, and in particular to a method for optimizing and regulating land utilization in a multi-outlet watershed. Background Art

[0002] In the field of land use, the problem of non-point source pollution has become increasingly prominent, making it gradually become the dominant factor in the deterioration of water quality and eutrophication of surface water bodies such as rivers, lakes and reservoirs; since the generation and transmission process of non-point source pollution is random and intermittent, and the uncertainty is complex, how to carry out comprehensive management of non-point source pollution at the basin scale has become the focus and difficulty in the current field of basin water pollution control.

[0003] Land use change is one of the main factors affecting the water ecosystem of a river basin. It changes the nutrient accumulation, particle deposition, hydrological conditions and other processes of the water body, thereby affecting the water ecosystem. Its impact is huge and difficult to control and manage. However, land use is one of the most controllable factors in a river basin. The national land space planning clearly defines the control scope and boundaries of urban space, agricultural space, and ecological space in each region. Scientific and reasonable land planning can greatly improve and restore the hydrology and habitat of rivers, reduce pollution output, and intercept and transform land pollutants. Therefore, how to optimize and regulate land use in the river basin has become a hot issue.

[0004] Numerical models are widely used in watershed management practices because they can simulate pollutant migration and transformation processes based on the mechanisms of pollution formation and clarify the temporal and spatial distribution patterns of pollutant migration. Models can be used to establish relationships between pollution sources and water quality responses, providing a better basis for decision-making in the refined management of watershed water environments. Currently, a large number of models have been applied to watershed management, achieving good results. Among them, the spatial attribute regression model is a water quality model that combines mechanistic and statistical models. Using hybrid statistical and process-based methods, it links monitoring data with watershed characteristics and pollutant source information to estimate pollutant migration within watersheds and water bodies, thereby exploring the relationship between human activities, natural processes, and pollutant migration. This model has the advantages of low data requirements, transparent structure, and strong universality. It overcomes the problems of high data requirements, weak interpretability, and poor applicability in constructing some complex watershed models. Furthermore, the model can simultaneously simulate multiple watershed outlets, comprehensively consider the relationship between water bodies and land areas within the watershed, analyze the water environment status of typical functional zones, quantify the degree of pollution, and reflect the impact of land use on pollution loads.

[0005] However, the model cannot evaluate the regulatory potential of different measures, and therefore cannot formulate an effective and practical spatial regulation plan for the water environment of the control unit. This application intends to overcome the above-mentioned shortcomings, but after a detailed search, no relevant technical solutions were found.

[0006] Therefore, it is necessary to provide a new technical solution to solve the above technical problems. Summary of the Invention

[0007] This application provides a method for optimizing and regulating land use in a multi-outlet watershed, comprising the following steps: Step 1: Obtain basic watershed data of the study area, construct a spatial attribute regression model, and quantitatively analyze the impact of various land use types in the study area on water ecological loads through the spatial attribute regression model; Step 2: Based on the impact of each land use type on the water ecological load in step 1 and the rigid boundaries in the national land space planning of the study area, a land use plan and an ecological restoration plan are established; then, the spatial attribute regression model and the annealing algorithm are coupled to obtain an optimized spatial attribute regression model, and the optimized spatial attribute regression model is used to evaluate the land use regulation potential and ecological restoration regulation potential of the corresponding watershed; the rigid boundaries in the national land space planning of the above study area are the three-zone and three-line rigid boundaries of the study area, where the three zones are urban space, agricultural space, and ecological space; and the three lines are the ecological protection red line, the permanent basic farmland protection red line, and the urban open boundary; Step 3: Based on step 2, set the land use regulation scenario and ecological restoration regulation scenario, use the optimized spatial attribute regression model to simulate the environmental effects related to the land use regulation scenario and ecological restoration regulation scenario, and formulate an optimized restoration measure plan.

[0008] As a preferred solution, the step 1 of obtaining basic watershed data of the study area includes: S11: Determine the flow direction and cumulative flow volume; Calculate the flow direction of each grid based on the DEM, count the number of all water flow paths entering the upstream of each grid, obtain the cumulative flow of the grid, and then determine the cumulative flow threshold; S12: Refinement and delimitation of river networks; According to the runoff accumulation threshold, the river network is extracted from the runoff accumulation data, and the nodes of the river network are determined based on the extracted river network. The nodes include the starting point of the river, the confluence point of the tributaries, and the river mouth. S13: extract sub-basin boundaries; A water outlet is set at each node in the river network. Based on the water flow direction data, all grids upstream of each water outlet that flow to the outlet are traced in reverse. The collection of these grids is the sub-basin corresponding to the water outlet. S14: Obtain continuously monitored hydrological data and water quality data, meteorological data, soil condition data, land use data, and statistical data of various pollution sources in each sub-basin.

[0009] As a preferred solution, in S12, the river network is extracted from the runoff accumulation data and then calibrated with the existing river network data.

[0010] As a preferred solution, the calculation formula of the spatial attribute regression model in step 1 is: in, Indicates flow through subbasin The pollution flux, in kg / year, Represents river section Adjacent upstream river reaches, river sections Sub-basin One of the river sections divided; represents the index of the upstream river section, represents the pollution flux in the upstream river section; Represents the upstream flux transported to the river reach The proportion of It represents the transmission attenuation function of pollutants along the river. There are different equation forms for rivers and lakes. If the water body is a river, then the corresponding and If the water body is a reservoir or a lake, then and ,in, represents the parameter matrix related to river transmission attenuation, represents the parameter matrix related to reservoir / lake transmission attenuation; represents the coefficient to be fitted for river transmission attenuation, represents the coefficient to be fitted for the transmission attenuation of the reservoir or lake; NS represents the sub-basin The total number of pollution sources contained in n is the index of the pollution source, and NS represents the sub-basin The total number of pollution sources included in Represents the amount of pollution generated by each source, represents the emission coefficient of the pollution source, represents the soil-water transfer factor, represents the parameter matrix, represents the coefficient corresponding to the parameter D, It represents the attenuation function of locally generated pollutants in this section of the river. There are different equation forms for rivers and lakes. If the water body is a river, then the corresponding and If the water body is a reservoir or a lake, then and ,in Represents the coefficients to be fitted.

[0011] As a preferred solution, the spatial attribute regression model optimized in step 2 is obtained by setting the constraints of the model based on the initially established spatial attribute regression model as the objective function. The constraints are as follows: ; ; Where N represents the number of sub-basins. is the index of the subbasin, Indicates the amount of pollution generated by each pollution source, Sopt represents the control target value of the pollution source, and are the upper and lower boundaries of the pollution source respectively.

[0012] As a preferred solution, the step three includes: S31: The amount of land types and the current land use within the existing rigid boundaries are used as the control boundaries for each sub-basin, and the areas of control measures are set to achieve the short-term target or the long-term target; S32: Using the established spatial attribute regression model as the objective function and taking the minimum load into the lake from each sub-basin as the goal, the optimized spatial attribute regression model is used to calculate the spatial priority restoration areas for restoration measures; S33: Based on the spatial priority restoration area, a spatial optimization restoration plan for each restoration measure is obtained.

[0013] The present invention has the following beneficial effects: First, the construction of spatial attribute regression models requires relatively little data and has strong interpretability, making it suitable for use in most regions; Second, the spatial attribute regression model can simulate multiple basin outlets simultaneously, comprehensively consider the relationship between the basin water body and land area, quantify the pollution degree of pollutants, and intuitively reflect the impact of land use on pollution load; 3. Based on the spatial attribute regression model, this application uses a mature annealing algorithm to optimize the spatial attribute regression model, which can evaluate different restoration measures and formulate effective and practical restoration plans for the control unit water environment space, thereby guiding local governments to carry out targeted measures and countermeasures. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is the logic block diagram of this application; DETAILED DESCRIPTION

[0015] The following combination Figure 1 The specific embodiments of the present invention are described in detail. It should be noted that the specific embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention. Example 1

[0016] This embodiment provides a method for optimizing and controlling land use in a multi-outlet watershed, including the following steps: Step 1: Obtain basic watershed data of the study area, construct a spatial attribute regression model, and quantitatively analyze the impact of various land use types in the study area on water ecological loads through the spatial attribute regression model; The acquisition of basic watershed data of the study area specifically includes: S11: Determine the flow direction and cumulative flow volume; The flow direction of each grid is calculated based on the digital elevation model (DEM) data, which is downloaded from the scientific database of the Chinese Academy of Sciences. Based on the flow direction data, the number of all flow paths entering the upstream of each grid is counted to obtain the cumulative runoff of the grid, and then the cumulative runoff threshold is determined. The cumulative runoff of the grid can be calculated by, for example, using the flow tool in the hydrological analysis in ArcGIS (Arc Geographic Information System), inputting the flow direction data, and running it to generate the cumulative runoff raster data. The more flow paths entering the upstream of each grid, the more potential river channels the area has, i.e., river networks. The prototype of the method; preferably, before determining the flow direction of each grid, the DEM data is first processed by filling depressions, such as using the depression filling tool in the GIS software to process the DEM data; in ArcGIS, the depression filling tool in the hydrological analysis tool set can be used to remove depressions in the DEM data and generate a depression-free DEM to ensure the accuracy of the water flow direction. After processing the DEM data, the water flow direction of each grid is calculated based on the depression-free DEM data using algorithms such as the maximum slope drop method. In ArcGIS, this can be achieved through the flow direction tool in the hydrological analysis module to obtain water flow direction raster data.

[0017] Methods for calculating the runoff accumulation threshold include but are not limited to: 1. Empirical judgment method: The threshold can be determined based on the topographic and geomorphological characteristics of the study area and with reference to relevant research or industry experience in similar areas. For example, the terrain in mountainous areas is steep and the runoff converges quickly. A smaller runoff accumulation may form a water flow path, and the threshold can be set to a smaller value, such as 500-2000 grids; the terrain in plain areas is flat, and a larger runoff accumulation is required to form an obvious water flow, and the threshold can be set to a larger value, such as 5000-10000 grids or even larger; 2. Trial and error method: By setting a series of different runoff accumulation thresholds, the river network is extracted separately, and then the extracted results are compared with the actual river network obtained through field surveys, high-resolution remote sensing image interpretation, etc., and the threshold that best matches the actual river network is selected; 3. Statistical analysis method: Use the statistical function of GIS software to analyze the relationship between runoff accumulation and parameters such as river length and number of tributaries, draw a scatter plot and perform function fitting. , find the mutation point in the curve, and use the cumulative confluence value corresponding to the mutation point as the threshold; for example, as the threshold increases, the river length and the number of tributaries initially increase slowly, and then increase rapidly after reaching a certain value. This value can be used as a preliminary threshold reference and then adjusted based on actual conditions; 4. Based on the river network density method: determine the target river network density according to research needs, that is, the river length per unit area, first set different thresholds to extract the river network and calculate its river network density, and then select the threshold with the river network density closest to the target value as the final threshold; for example, if the study requires a medium-density river network, find the threshold value that meets the river network density requirement through experiments.

[0018] S12: Refinement and delimitation of river networks; Based on the runoff accumulation threshold, the river network is extracted from the runoff accumulation data. The nodes of the river network are determined based on the extracted river network. The nodes include the starting point of the river, the confluence point of the tributaries, and the river mouth. Furthermore, if there is existing river network data, it can be calibrated with the existing river network data. Based on the topographic and geomorphological characteristics of the study area and actual needs, an appropriate runoff accumulation threshold is set, and the grids with runoff accumulation greater than or equal to the threshold are regarded as potential water flow paths. The network composed of these grids is the raster river network. For example, in ArcGIS, map algebra tools can be used to extract grids that meet the threshold conditions through "greater than or equal to" operations to generate raster river network data, and the extracted raster river network is converted into vector format for subsequent editing, analysis and mapping. In ArcGIS, the raster to vector tool in ArcToolbox can be used to convert raster river network data into vector river network data, and finally a river network distribution map is obtained.

[0019] S13: extract sub-basin boundaries; A drainage outlet is set at each node in the river network, such as the node where a tributary joins the main stream. Runoff from each sub-basin ultimately flows into the river network from this point. Based on flow direction data, all grid cells upstream of each drainage outlet that flow toward that outlet are traced backwards. The collection of these grid cells is the sub-basin corresponding to the drainage outlet. The rasterized sub-basin boundaries are converted into vector polygons and modified based on actual terrain, such as mountains and artificial dams, to ensure consistency with the watershed. The above S11-S13 can be implemented using ArcGIS, specifically: using the Hydrology Tools set to sequentially perform "filling depressions, water flow direction, runoff accumulation, river network extraction, identification of river network nodes, and sub-basin division"; it can also be implemented through QGIS (Quantum Geographic Information System), specifically: through the GRASS plug-in (Geographic Resource Analysis Support System plug-in) or Whitebox Tools in the Processing toolbox to achieve similar terrain analysis and sub-basin extraction.

[0020] S14: Based on the above S11-S13, continuously monitored hydrological data and water quality data, meteorological data, soil condition data, land use type data, and various pollution source data in each sub-basin are obtained.

[0021] The above-mentioned continuously monitored hydrological data and water quality data are obtained based on the local environmental protection department or website in the study sub-basin area; the meteorological data are preferably daily meteorological data, which are obtained through the China Meteorological Science Data Sharing Service Network; the soil condition data can be obtained through the World Harmonious Soil Database, the Soil Branch of the National Earth System Science Data Center, the website of the natural resources department, the website of the ecological environment department, etc.; the land use type data are mainly obtained based on remote sensing interpretation or environmental protection departments; the data on various pollution sources are mainly obtained based on statistical yearbooks or local environmental protection departments.

[0022] The model structure of the above spatial attribute regression model is as follows: Figure 1 As shown in Figure 2, the calculation formula of the spatial attribute regression model is: ; in, Indicates flow through subbasin The pollution flux, in kg / year, Represents river section Adjacent upstream river reaches, river sections Sub-basin One of the river sections divided; Indicates the index of the upstream river section, traversal The upstream river section in the collection, represents the pollution flux in the upstream river section; Represents the upstream flux transported to the river reach The proportion of It represents the transmission attenuation function of pollutants along the river. There are different equation forms for rivers and lakes. If the water body is a river, then the corresponding and If the water body is a reservoir or a lake, then and ,in, represents the parameter matrix related to river transmission attenuation, represents the parameter matrix related to reservoir / lake transmission attenuation; represents the coefficient to be fitted for river transmission attenuation, represents the coefficient to be fitted for the transmission attenuation of the reservoir or lake; NS represents the sub-basin The total number of pollution sources contained in , n is the index of the pollution source; Represent the amount of pollution generated by each source, which can represent both the actual emission of the pollution source and the area of ​​different types of land; represents the emission coefficient of the pollution source, represents the soil-water transfer factor, represents the parameter matrix, represents the coefficient corresponding to parameter D (the transmission coefficient of pollution sources such as point sources that directly discharge into the river is 1), It represents the attenuation function of locally generated pollutants in this section of the river. There are different equation forms for rivers and lakes. If the water body is a river, then the corresponding and If the water body is a reservoir or a lake, then and ,in Represents the coefficients to be fitted; although the function and They are similar in form, but they consider different river retention times. The former assumes that pollutants are attenuated by the entire river section, while the latter assumes that pollutants enter the river section halfway and are only attenuated by halfway.

[0023] The continuously monitored hydrological data and water quality data, meteorological data, soil conditions, land use type data, and various pollution source data in each sub-basin are used to establish a set of simultaneous equations. The nonlinear least squares method is applied for parameter fitting, a spatial attribute regression model is established, and the relevant model parameters are calibrated. The established spatial attribute regression model is used to quantitatively analyze the impact of different pollution sources and land use types in the basin on the water ecological load.

[0024] There are significant differences in the impacts of different pollution sources and land use types on water ecological loads. The following analysis is conducted from the two dimensions of pollution source type and land use type. Water ecological loads include but are not limited to pollution pressure, structural damage and other loads borne by aquatic ecosystems.

[0025] The impact of different pollution sources on water ecological load. Pollution sources directly increase the load on water ecosystems by inputting pollutants into water bodies or changing hydrological processes. The main impacts are as follows: pollutants enter water bodies through dispersed pathways such as surface runoff, farmland drainage, and atmospheric deposition. Pollutants mainly include agricultural fertilizers and pesticides, urban surface runoff, and rural domestic sewage. They are difficult to control and need to be controlled through comprehensive measures such as land use adjustment and vegetation buffer zone construction, rather than being solved by a single project.

[0026] The impact of different land use types on water ecological load. Land use types indirectly affect water ecological load by changing surface cover, hydrological processes and pollution source distribution. The details are as follows: Natural ecological land (forests, grasslands, wetlands, etc.). Forests and grasslands reduce soil erosion through vegetation interception and root consolidation, absorb pollutants (such as nitrogen and phosphorus), and reduce non-point source pollution loads; wetlands can purify water bodies through sedimentation and microbial decomposition, buffer flood peaks, and maintain the diversity of aquatic ecosystems; however, excessive development of natural ecological land, such as deforestation and land reclamation, will lose ecological functions and increase pollution loads.

[0027] Excessive use of fertilizers and pesticides in agricultural land (arable land, gardens, etc.) leads to the loss of nitrogen and phosphorus, which is the main source of non-point source pollution; exposed arable land can easily cause soil erosion, carry sediment and pollutants into water bodies, aggravate river siltation and water turbidity; its pollution load can be reduced through measures such as returning farmland to forests and building ecological ditches.

[0028] Urban construction land (cities, towns, industrial land, etc.) has a high proportion of impervious surfaces and a large surface runoff coefficient, which can easily carry urban garbage, oil, heavy metals, etc. into water bodies, causing initial rainwater pollution; occupying natural waters, such as filling lakes to create land, will destroy the integrity of the aquatic ecosystem and reduce the self-purification capacity of the water body.

[0029] Unused land (bare land, wasteland, etc.), if it is original bare land, such as deserts and Gobi, the ecology is fragile but the pollution contribution is low; if it is degraded land, such as mining wasteland, it may release heavy metals or saline-alkali substances due to soil erosion, increasing the water pollution load.

[0030] Step 2: Based on the impact of each land use type on the water ecological load in step 1 and the rigid boundaries in the national land space planning of the study area, a land use plan and an ecological restoration plan were established; then, the spatial attribute regression model and the annealing algorithm were coupled to obtain an optimized spatial attribute regression model, and the optimized spatial attribute regression model was used to evaluate the land use regulation potential and ecological restoration regulation potential of the corresponding watershed; the rigid boundaries in the national land space planning of the above study area are the three-zone and three-line rigid boundaries of the study area, where the three zones are urban space, agricultural space, and ecological space; and the three lines are the ecological protection red line, the permanent basic farmland protection red line, and the urban open boundary; In this step, the land use regulation potential and ecological restoration regulation potential of the corresponding sub-basin are evaluated through the optimized spatial attribute regression model, so as to ensure that limited funds can be allocated more accurately in the future, and give priority to the treatment of areas with "low investment and quick results" to achieve efficient improvement of water quality at the outlet section of the basin; because the land use methods in different spaces, such as farmland, towns, woodland, etc., have very different effects on pollution reduction and emission reduction, for example, farmland with reduced fertilizer use can reduce nitrogen and phosphorus pollution more quickly than scattered villages, which directly affects the water quality of the outlet section of the basin, and the treatment funds in the treatment process are limited, and it is impossible to apply equal efforts to all areas; this embodiment uses the optimized spatial attribute regression model to evaluate the two types of potential of each area in the sub-basin, and gives priority to investing funds in areas with high regulation potential to achieve "spending the least money to achieve the best water quality improvement effect."

[0031] The land-use regulation potential refers to the maximum possibility of pollution reduction or water quality improvement that can be achieved by adjusting land-use patterns (such as changing land use types, optimizing layout, etc.). For example, if a certain area is high-density farmland with large fertilizer and pesticide usage and serious non-point source pollution, if part of it is converted into an ecological buffer zone, such as wetlands and grasslands, it can significantly intercept pollutants. The emission reduction brought about by the conversion from farmland to buffer zone is the land-use regulation potential of the area. The focus is on whether there is room for optimization of the land-use pattern in a certain area and how much pollutants can be reduced after the adjustment.

[0032] The ecological restoration and regulation potential refers to the maximum possibility of pollution reduction or water quality improvement that can be achieved through ecological restoration measures (such as repairing damaged ecosystems, enhancing natural purification capacity, etc.); for example, a certain area is a degraded river bank with sparse vegetation, which cannot intercept pollutants in surface runoff. By planting aquatic plants and restoring riparian forests, the adsorption and degradation capacity of pollutants can be enhanced. The emission reduction brought about by this improvement in ecosystem function is the ecological restoration and regulation potential of the area; for example, a certain area is a dried-up wetland that has lost its original purification function. By replenishing water and restoring wetland hydrology, it can play the role of a natural sewage treatment plant again. The purification space restored by this ecosystem also belongs to this type of potential; the focus is on the extent of damage to the ecosystems in a certain area, such as wetlands, woodlands, rivers, etc.; and how much purification capacity can be enhanced after restoration, such as how much pollutants can be intercepted and how much water quality indicators can be improved.

[0033] The optimized spatial attribute regression model is used to evaluate the land use regulation potential and ecological restoration regulation potential of the corresponding sub-basin, identify the "most cost-effective" governance area, and concentrate limited funds on areas that can improve the water quality of the outlet section as quickly and as much as possible, thus avoiding waste of resources. These two types of regulation potential are essentially an assessment of how much water quality improvement space can be brought about by human intervention in nature and land, and are the key basis for scientific decision-making.

[0034] More specifically: the assessment of land use regulation potential is mainly based on the planning of the three zones and three lines in the national land planning, and the corresponding land use regulation potential is formulated and brought into the spatial attribute regression model constructed based on the current land use status, and compared with the model simulation results calculated using the original land use type (utilization rate), so as to identify the controllable space and obtain the land use regulation potential; the assessment of ecological restoration regulation potential mainly uses the optimized spatial attribute regression model, and substitutes the regulation threshold of the relevant regulation potential into the optimized spatial attribute regression model to find the relevant ecological restoration regulation potential.

[0035] Among them, the annealing algorithm is a random optimization algorithm based on the Monte-Carlo iterative solution strategy. Its starting point is based on the similarity between the annealing process of solid materials in physics and general combinatorial optimization problems. The simulated annealing algorithm starts from a certain higher initial temperature, and as the temperature parameter continues to decrease, combined with the probabilistic jump characteristics, it randomly searches for the global optimal solution of the objective function in the solution space. That is, the local optimal solution can probabilistically jump out and eventually tend to the global optimal solution.

[0036] The above optimized spatial attribute regression model is based on the initially established spatial attribute regression model as the objective function and the constraints of the model are set as follows: ; ; Where N represents the number of sub-basins. is the index of the subbasin, It is used to traverse the sub-basins from the 1st sub-basin to the Nth sub-basin, indicating the summation and constraint of the pollution source generation in each sub-basin; It represents the amount of pollution generated by each pollution source (such as cultivated land area or cultivated land load), and Sopt represents the control target value of the pollution source. and are the upper and lower boundaries of the pollution source respectively.

[0037] Step 3: Based on step 2, set the land use regulation scenario and ecological restoration regulation scenario, use the optimized spatial attribute regression model to simulate and evaluate the relevant environmental effects, and formulate an optimized restoration measures plan.

[0038] In step 2, the land use regulation potential and ecological restoration regulation potential of different areas in the sub-basin have been evaluated. That is, we know which areas are more effective in adjusting land use and which areas are more effective in restoring the ecology. According to the land use regulation potential and ecological restoration regulation potential, corresponding land use regulation scenarios and ecological restoration regulation scenarios are set. The optimized spatial attribute regression model is used to simulate the environmental effects that can be brought about by the above regulation scenarios, such as how much pollutant emissions are reduced and how much water quality is improved. Finally, based on the simulation results, the "best effect and lowest cost" plan is selected to form an optimized restoration measure plan.

[0039] The land use regulation scenario and ecological restoration regulation scenario are specifically designed to design specific and feasible governance plans based on the regulation potential in step 2. More specifically: Land use regulation scenario: A specific plan designed to adjust land use patterns, such as converting 10% of highly polluted farmland (high fertilizer application) in the basin into ecological wetlands; returning farmland to forests, which can reduce the amount of fertilizer applied by reducing the area of ​​arable land, thereby having a positive impact on water quality.

[0040] Ecological restoration and regulation scenario: A specific plan designed to restore the ecosystem, such as: restoring riparian vegetation (planting reeds, willows, etc.) within 100 meters on both sides of the river, covering 50% of the river section in the basin; replenishing water and revegetating three degraded wetlands in the basin to restore their purification function.

[0041] Using the optimized spatial attribute regression model to simulate relevant environmental effects means using the model to quantify the environmental effects under different scenarios and determine which scenario is more effective. The optimized spatial attribute regression model combines geographic spatial characteristics (such as soil, topography, and pollution sources in different regions) to calculate after the implementation of each scenario: how much pollutant emissions can be reduced (such as specific indicators such as nitrogen, phosphorus, and COD); how much the water quality of the basin outlet section can be improved (such as from Class IV water to Class III water); the difference in contribution between different regions (such as under the same scenario, how much higher is the emission reduction in Region A than in Region B); use data to illustrate which scenario is more cost-effective, such as Scenario A can reduce nitrogen emissions by 20 tons with an investment of 1 million, and Scenario B can reduce nitrogen emissions by 25 tons with an investment of 1.2 million. It is necessary to compare and select the appropriate plan.

[0042] Formulate an optimized restoration measure plan, and based on the results of model simulation, screen out the scenario combination with the best environmental effect, lowest cost and highest feasibility to form a final governance plan; if the simulation finds that the combination of converting 20% ​​of highly polluted farmland into wetlands (land use scenario) and repairing 30% of the coastal zone (ecological restoration scenario) can improve the water quality of the outlet section by one level at the lowest cost, then this combination will be determined as the optimized restoration measure plan to ensure that limited capital investment can maximize water quality improvement.

[0043] Specifically: S31: The existing land type reserves and current land use conditions within the existing rigid boundaries are used as the control boundaries for each sub-basin, and the area of ​​restoration measures is set to achieve the short-term goal or the long-term goal; specifically, the existing rigid boundaries are the rigid boundaries specified in the existing three zones and three lines; When formulating plans for watershed pollution control or ecological restoration, the land type restrictions defined by the three zones and three lines and the existing land use status must be used as hard boundaries. At the same time, different phased implementation scenarios must be designed based on the restoration goals and actual feasibility. Restoration measures cannot be achieved overnight, and short-term and long-term goals must be achieved in stages. Therefore, specific plans for different stages must be designed.

[0044] Land type retention: Based on the three zones and three lines, the minimum / maximum area of ​​a certain type of land must be maintained. For example, the permanent basic farmland retention in a certain river basin must not be less than 500,000 mu, and the proportion of ecological land must not be less than 30%; Current land use: refers to the actual distribution of various types of land in the current basin, such as 600,000 mu of cultivated land, 200,000 mu of forest land, and 100,000 mu of urban construction land; Control boundaries: The above-mentioned land types and existing land use conditions are used as restrictions, and restoration measures cannot exceed these boundaries. For example, if the permanent basic farmland control line requires that cultivated land must not be less than 500,000 mu, and the existing cultivated land is 600,000 mu, then the maximum area of ​​returning farmland to forest cannot exceed 100,000 mu, which is the control boundary. If an area falls within the ecological protection red line, urban construction cannot be carried out in that area, and only ecological restoration can be carried out, such as planting trees, replenishing water, etc.

[0045] The area of ​​restoration measures set to achieve the short-term goal or the long-term goal is to divide the restoration tasks into stages according to time and clearly define the areas to be completed at different stages. For example, the long-term goal of a certain watershed is to convert farmland into forest with a total target of 100,000 mu. However, considering factors such as the transition of farmers' livelihoods and the annual arrival of funds, the short-term goal is to complete 30,000 mu within 3 years, the medium-term goal is to complete another 50,000 mu within 5 years, and the long-term goal is to complete the remaining 20,000 mu within 10 years. The goals of each stage must meet the regulatory boundaries. For example, after converting 30,000 mu of farmland in the near future, the remaining farmland must still meet the requirements of permanent basic farmland, which is no less than 500,000 mu. The area of ​​returning farmland to forest needs to be carried out gradually, so different scenarios are set here, as follows: Scenario for the near-term goal: From 2025 to 2028, 30,000 mu of easily eroded arable land with a slope of more than 25 degrees will be converted from farmland to planted with trees and small water conservancy facilities; Medium-term target scenario: From 2029 to 2033, another 50,000 mu of arable land with a slope of 15 to 25 degrees will be converted to mixed forests of trees and shrubs, and forest-based economies, such as medicinal herb cultivation, will be developed to ensure farmers' income. Scenario for long-term goals: From 2034 to 2040, the remaining 20,000 mu of land will be retired from farmland. At the same time, vegetation will be optimized in the first two phases of retired land areas to enhance ecosystem stability.

[0046] This step can not only ensure that the plan is legal and compliant, but also ensure that the repair process is smooth and effective, avoiding problems caused by one-size-fits-all or aggressive governance.

[0047] S32: Using the established spatial attribute regression model as the objective function and taking the minimum load into the lake from each sub-basin as the goal, the optimized spatial attribute regression model is used to calculate the spatial priority restoration areas for restoration measures; S33: Based on the spatial priority restoration area, a spatial optimization restoration plan for each restoration measure is obtained. Example 2

[0048] This embodiment provides a specific application scenario. This embodiment selects a lake basin in the Yangtze River Basin as a case analysis: Step 1: Based on the basic data of the watershed in the study area, a spatial attribute regression model is constructed. In this embodiment, a 30m resolution DEM of the five rivers and seven outlets of a certain lake is downloaded from the scientific database of the Chinese Academy of Sciences, and the river network and sub-watershed divisions in the study area are generated based on the DEM data; the data in the sub-watershed are obtained through the following methods: meteorological data obtained from the China Meteorological Science Data Sharing Service Network, continuously monitored hydrological data, water quality data, land use type data, and various pollution source data obtained by the environmental protection department of a certain province corresponding to a certain lake; soil condition data obtained from the website of the ecological environment department of a certain province corresponding to a certain lake.

[0049] The data unified into the sub-basin are used to establish a set of simultaneous equations, and the nonlinear least squares method is applied to perform parameter fitting. The relationship between the water body and the land area in the basin is comprehensively considered to construct a spatial attribute regression model.

[0050] Based on the 2017 total phosphorus water quality monitoring data, land use type data, and total phosphorus pollution source statistics of the Wuhe Qikou Basin, the relevant model parameters were calibrated; among them, the determination coefficient R of the established spatial attribute regression model was 2 =0.8639, the Nash efficiency coefficient is 0.8558, and the root mean square error is 0.22. The simulation effect of the spatial attribute regression model is good.

[0051] After completing the modeling of the spatial attribute regression model, the spatial attribute regression model was used to quantitatively analyze the impact of different pollution sources and land use types in the basin on the water ecological load; the results showed that in 2017, the pollution source with the highest proportion of total phosphorus entering the river in a certain lake basin was cultivated land, accounting for 25.59%; followed by urban living sources, accounting for 23.21%, which was the main source of total phosphorus pollution in a certain lake basin in 2017 and should become the main direction of pollution source control.

[0052] Step 2: Based on the rigid boundaries of the three zones and three lines in the national land space planning of the study area, the control potential (land use plan and ecological restoration plan) was established. The spatial attribute regression model and annealing algorithm were then used to optimize the spatial attribute regression model. The optimized spatial attribute regression model was used to evaluate the land use control potential and ecological restoration control potential of the corresponding watershed. According to the three-zone and three-line plan in a certain province's land and space planning, the urban development boundary covers an area of ​​5,101 square kilometers. Urban pollution will further intensify in the future. Therefore, it is difficult to optimize and regulate urban land use. This embodiment mainly evaluates the land use regulation potential of cultivated land and the ecological restoration regulation potential of cultivated land.

[0053] The plan points out that the designated area of ​​cultivated land reserves in the agricultural space is not less than 29,273 square kilometers. Based on the proportion of the basin of a certain lake, five rivers and seven outlets in the entire basin, combined with the district and county records in the three-zone and three-line plan, relevant conversion calculations are carried out, and finally the basic farmland reserves area of ​​each sub-basin are obtained. The above data are brought into the spatial attribute regression model for relevant simulation, and the reduction in the load transmitted to the downstream export due to cultivated land regulation can be obtained, which is the land use regulation potential.

[0054] The difference between the area of ​​land use regulation and the minimum area of ​​cultivated land reserves is added to the area of ​​ecological space, that is, the maximum area of ​​ecological space. The minimum area of ​​cultivated land reserves and the maximum area of ​​ecological space are brought into the above-mentioned established regulation potential assessment model. On the basis of the completion rate of relevant parameters, the reduction of total phosphorus pollution after returning farmland to forest is simulated, and this is used as the regulation potential of returning farmland to forest, that is, the ecological restoration regulation potential assessment.

[0055] Step 3: Based on step 2, set the land use regulation scenario and ecological restoration regulation scenario, use the optimized spatial attribute regression model to simulate and evaluate the relevant environmental effects, and formulate an optimized restoration measures plan.

[0056] Land use optimization and regulation are mainly based on the above-mentioned spatial attribute regression model and combined with the linear programming method for evaluation; first, returning farmland to forest is to use the amount of cultivated land and the existing cultivated land in the three zones and three lines as the control boundary of each sub-basin, set the area of ​​returning farmland to forest to achieve the short-term goal (or to achieve the long-term goal), and then use the established spatial attribute regression model as the objective function, with the goal of minimizing the load entering the lake in each sub-basin, and use the optimized spatial attribute regression model to calculate the spatial priority control area of ​​returning farmland to forest, and then derive the spatial optimization control plan of returning farmland to forest.

[0057] In summary, due to the adoption of the above technical solution, this application has the following advantages: First, the construction of spatial attribute regression models requires relatively little data and has strong interpretability, making it suitable for use in most regions; Second, the spatial attribute regression model can simulate multiple basin outlets simultaneously, comprehensively consider the relationship between the basin water body and land area, quantify the pollution degree of pollutants, and intuitively reflect the impact of land use on pollution load; 3. Based on the spatial attribute regression model, this application uses a mature annealing algorithm to optimize the spatial attribute regression model, which can evaluate different restoration measures and formulate effective and practical restoration plans for the control unit water environment space, thereby guiding local governments to carry out targeted measures and countermeasures.

[0058] The preferred embodiment of the present application is described in detail above in conjunction with the accompanying drawings. However, the present application is not limited to the specific details in the above-mentioned embodiments. Within the technical concept of the present application, various simple modifications can be made to the technical solution of the present application, and these simple modifications all fall within the scope of protection of the present application.

[0059] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any appropriate manner unless there is any contradiction. In order to avoid unnecessary repetition, the various possible combinations of this application will not be described separately.

[0060] In addition, the various implementation methods of the present application can be arbitrarily combined, as long as they do not violate the concept of the present application, and they should also be regarded as the contents disclosed in the present application.

Claims

1. A method for optimizing and controlling land use in a multi-outlet watershed, characterized in that: The steps include: Step 1: Obtain basic watershed data of the study area, construct a spatial attribute regression model, and quantitatively analyze the impact of various land use types in the study area on water ecological loads through the spatial attribute regression model; Step 2: Based on the impact of each land use type on water ecological load in step 1 and the rigid boundaries in the national land space planning of the study area, establish a land use plan and an ecological restoration plan; Then, the spatial attribute regression model and the annealing algorithm are coupled to obtain an optimized spatial attribute regression model, which is then used to evaluate the land use regulation potential and ecological restoration regulation potential of the corresponding watershed. Step 3: Based on step 2, set the land use regulation scenario and ecological restoration regulation scenario, use the optimized spatial attribute regression model to simulate the environmental effects related to the land use regulation scenario and ecological restoration regulation scenario, and formulate an optimized restoration measure plan.

2. The method for optimizing and controlling land use in a multi-outlet watershed according to claim 1, characterized in that: The acquisition of basin basic data of the study area in step 1 includes: S11: Determine the flow direction and cumulative flow volume; Calculate the flow direction of each grid based on the DEM, count the number of all water flow paths entering the upstream of each grid, obtain the cumulative flow of the grid, and then determine the cumulative flow threshold; S12: Refinement and delimitation of river networks; According to the runoff accumulation threshold, the river network is extracted from the runoff accumulation data, and the nodes of the river network are determined based on the extracted river network. The nodes include the starting point of the river, the confluence point of the tributaries, and the river mouth. S13: extract sub-basin boundaries; A water outlet is set at each node of the river network. Based on the water flow direction data, all grids upstream of each water outlet that flow to the outlet are traced in reverse. The collection of these grids is the sub-basin corresponding to the water outlet. S14: Obtain continuously monitored hydrological data and water quality data, meteorological data, soil condition data, land use data, and statistical data of various pollution sources in each sub-basin.

3. The method for optimizing and controlling land use in a multi-outlet watershed according to claim 2, characterized in that: In the S12, the river network is extracted from the runoff accumulation data and then calibrated with the existing river network data.

4. The method for optimizing and controlling land use in a multi-outlet watershed according to claim 1, characterized in that: The calculation formula of the spatial attribute regression model in step 1 is: in, represents the pollution flux flowing through sub-basin i, in kg / year, represents the upstream river section adjacent to river section in, where river section in is one of the river sections divided by sub-basin i; represents the index of the upstream river section, represents the pollution flux in the upstream river section; represents the proportion of upstream flux transmitted to the river reach in, It represents the transmission attenuation function of pollutants along the river. There are different equation forms for rivers and lakes. If the water body is a river, then the corresponding and , if the water body is a reservoir or a lake, then and ,in, represents the parameter matrix related to river transmission attenuation, represents the parameter matrix related to the transmission attenuation of the reservoir or lake; represents the coefficient to be fitted for river transmission attenuation, represents the coefficient to be fitted for the transmission attenuation of the reservoir or lake; NS represents the total number of pollution sources contained in sub-basin i, and n is the index of the pollution source. Represents the amount of pollution generated by each source, represents the emission coefficient of the pollution source, represents the soil-water transfer factor, represents the parameter matrix, represents the coefficient corresponding to the parameter D, It represents the attenuation function of locally generated pollutants in this section of the river. There are different equation forms for rivers and lakes. If the water body is a river, then the corresponding and , if the water body is a reservoir or a lake, then and .

5. The method for optimizing and controlling land use in a multi-outlet watershed according to claim 1, characterized in that: The optimized spatial attribute regression model in step 2 is obtained by setting the constraints of the model based on the initially established spatial attribute regression model as the objective function. The constraints are as follows: ； Where N represents the number of sub-basins, i is the index of the sub-basin, Represents the amount of pollution generated by each pollution source, Sopt represents the control target value of the pollution source, Si and Sj are the upper and lower boundaries of the pollution source respectively.

6. The method for optimizing and controlling land use in a multi-outlet watershed according to claim 1, characterized in that: The step three includes: S31: The amount of land types and the current land use within the existing rigid boundaries are used as the control boundaries for each sub-basin, and the areas of control measures are set to achieve the short-term target or the long-term target; S32: Using the established spatial attribute regression model as the objective function and taking the minimum load into the lake from each sub-basin as the goal, the optimized spatial attribute regression model is used to calculate the spatial priority restoration areas for restoration measures; S33: Based on the spatial priority restoration area, a spatial optimization restoration plan for each restoration measure is obtained.

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