Stable and suitable coordination method for watershed mountain, water, forest, lake, grass and sand

Abstract

The invention discloses a stable and suitable coordination method for watershed mountain water, forest field, lake grass and sand, and relates to the technical field of ecological environment protection and governance, and the method comprises the following steps: 1, collecting watershed ecological background data, and constructing an ecological hydrological database; 2, constructing a landscape pattern-ecological system coupling model according to the ecological hydrological database, and carrying out ecological function partitioning; 3, constructing a comprehensive coupling relation framework in the drainage basin according to the ecological function partition, and identifying a key ecological hydrological problem; 4, constructing a nonlinear-multi-target dynamic planning model according to the key ecological hydrological problem; 5, solving the nonlinear-multi-target dynamic programming model to obtain a parameter optimal solution; and 6, constructing a coordination strategy according to the optimal solution of the parameters. The method comprises the following steps: establishing a drainage basin ecosystem collaborative regulation model by taking collaborative optimization of a drainage basin ecological hydrological function as a core target, determining optimal parameters by optimizing the model, and formulating a coordination strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological environment protection and governance, and more specifically to a method for stabilizing and appropriately coordinating mountains, waters, forests, fields, lakes, grasslands and sand in a watershed. Background Art

[0002] River basin ecosystems are composed of a complex interplay of elements, including mountains, rivers, forests, farmlands, lakes, grasslands, and deserts. However, affected by climate change and human activities, many river basins face ecological challenges such as soil erosion, vegetation degradation, water shortages, wetland shrinkage, and desertification. These challenges not only reduce the stability and service functions of river basin ecosystems but also increase the risk of ecological degradation. Therefore, a systematic approach is urgently needed to optimize the spatial configuration and ecological functions of these elements at the river basin scale, coordinate the use of water and soil resources, and improve the adaptability and sustainability of river basin ecosystems.

[0003] Current watershed ecological restoration technologies mostly focus on single elements, such as water resource regulation or vegetation restoration, and fail to fully consider the coordinated management of the overall watershed ecosystem. For example, while certain engineering measures may improve water supply locally, if water flows between mountains, rivers, wetlands, farmland, and grasslands are not properly coordinated, conflicts between ecological water demands may be exacerbated, leading to unstable restoration results. Furthermore, traditional restoration technologies lack systematic analysis of water resource carrying capacity, soil moisture dynamics, and vegetation structure optimization, making it difficult to achieve long-term, stable ecosystem restoration.

[0004] Complex interactions exist among the watershed's elements of mountains, rivers, forests, farmlands, lakes, grasslands, and sand. Optimizing their configuration is crucial for maintaining key ecological functions, such as the hydrological cycle, soil moisture conservation, and biodiversity conservation. Therefore, a comprehensive technology is needed to coordinate water supply and demand, optimize vegetation configuration, enhance soil conservation capacity, and strengthen ecosystem stability and adaptability.

[0005] Therefore, how to achieve the coordinated and optimized configuration of elements of the watershed ecosystem and improve the stability and adaptability of the system is an urgent problem that technicians in this field need to solve. Summary of the Invention

[0006] In light of this, the present invention provides a method for the stable and optimal coordination of mountains, rivers, forests, farmlands, lakes, grasslands, and sand in a watershed. With the coordinated optimization of the watershed's eco-hydrological functions as its core goal, this paper establishes a stable and optimal coordination model for the coordinated regulation of watershed ecosystems. By optimizing this model, the watershed's water cycle, material circulation, and energy flow are maintained in a healthy manner, thereby enhancing the basin's overall ecological stability and providing theoretical and technical support for watershed ecological and environmental protection and sustainable socioeconomic development.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] The method for stabilizing and optimizing the distribution of mountains, rivers, forests, farmlands, lakes, grasslands and sand in a watershed includes the following steps:

[0009] Step 1: Collect basin ecological background data and build an eco-hydrological database;

[0010] Step 2: Construct a landscape pattern-ecosystem coupling model based on the eco-hydrological database to obtain the coupling mechanism of the mountain-water-forest-field-lake-grass-desert life community and the ecological function zoning of the watershed;

[0011] Step 3: Based on the coupling mechanism of the mountain-water-forest-farm-lake-grass-desert life community and the ecological function zoning, a comprehensive coupling relationship framework between different ecosystems, ecological functions, and socio-economics within the basin was constructed to obtain the coupling degree of different elements in the basin and key eco-hydrological issues;

[0012] Step 4: Construct a nonlinear multi-objective dynamic programming model based on the coupling degree of watershed elements and key eco-hydrological issues;

[0013] Step 5: Solve the nonlinear-multi-objective dynamic programming model to obtain the optimal solution for the parameters;

[0014] Step 6: Construct a coordination strategy based on the optimal parameter solution.

[0015] Preferably, the basin ecological background data include long-term observation data, remote sensing image data, and field survey data from meteorological stations, rain gauges, hydrological stations, groundwater monitoring stations, and ecosystem flux stations around the basin. After multi-source data integration, 10-meter resolution sentinel satellite images and 30-meter resolution digital elevation models (DEMs) are generated; based on the basin ecological background data, sentinel satellite images, and digital elevation models, hydrological and meteorological data, soil and vegetation ecological data, and topographic and geomorphological data are extracted to construct an eco-hydrological database to provide data support for subsequent ecosystem coupling modeling.

[0016] Preferably, the specific steps of step 2 include:

[0017] Step 21: Use variable screening method to screen out key ecological factors affecting a single ecosystem in the eco-hydrological database;

[0018] Step 22: Statistically analyze the interaction pathways between key ecological factors, conduct landscape pattern-ecological process interaction analysis and ecological service changes and their driving forces analysis, and obtain key ecological processes corresponding to landscape patterns;

[0019] Step 23: Use the land change model and land surface process model to simulate the topographic and geomorphic characteristics, output the land change and land surface processes, construct a landscape pattern-ecological process coupling model based on the land change and land surface processes, and determine the interaction between the distribution pattern of different ecosystems and ecological processes based on the landscape pattern-ecological process coupling model;

[0020] Step 24: Based on the interaction between distribution patterns and ecological processes, an agent-based modeling approach is used to simulate the impact of social systems on natural ecosystems by setting different factors in the social system. Combining the coupling coordination model and the association model method, an integrated model of the natural-social system bidirectional coupling is constructed.

[0021] Step 25: Run the natural-social system bidirectional coupling integrated model, conduct a rationality analysis on the output simulation results, obtain the coupling mechanism of the mountain, water, forest, farmland, lake, grassland and desert life community, and divide the watershed to obtain ecological functional zoning.

[0022] Preferably, the specific steps of step 3 include:

[0023] S31: Based on the coupling mechanism of the mountain-water-forest-field-lake-grass-desert life community and the ecological function zoning, and combining the state-stress and pressure-stress interactions between the social and economic systems, a comprehensive coupling relationship framework between different ecosystems, ecological functions, social systems, and economic systems within the basin is constructed to obtain the coupling degree of each element of the basin and identify key eco-hydrological issues.

[0024] S32: Construct a nonlinear-multi-objective dynamic programming model based on the coupling degree of watershed elements and key eco-hydrological issues.

[0025] Preferably, the objective function of the nonlinear-multi-objective dynamic programming model includes the optimal target of balancing the watershed ecosystem and hydrological functions, the maximum target of livestock breeding, the maximum target of grain production, the minimum target of environmental restoration costs, the minimum target of social changes, and the optimal target of environmental quality.

[0026] Preferably, the constraints of the nonlinear-multi-objective dynamic programming model include water resource constraints, area constraints, social constraints, irrigation water volume constraints, canal water delivery efficiency constraints, surface and groundwater availability constraints, water balance constraints, hydrological function target constraints, ecological function target constraints, and non-negative constraints.

[0027] Preferably, a multi-objective optimization algorithm is used to solve the nonlinear-multi-objective dynamic programming model, including a gradient algorithm, a genetic algorithm, an annealing algorithm or a particle swarm algorithm.

[0028] Preferably, a nonlinear-multi-objective dynamic programming model is solved to obtain the landscape type j that matches the watershed plot i when all constraints are met, so that the objective function is optimized.

[0029] Through the above technical solutions, it can be seen that compared with the existing technology, the present invention discloses a method for stable and suitable coordination of mountains, waters, forests, fields, lakes, grasses and sands in a watershed, which is applicable to the fields of watershed ecological protection, ecological restoration, sustainable utilization and climate change adaptive regulation, etc., taking into account the characteristics of watershed diversity, relationship complexity, structural heterogeneity, functional diversity and system integrity, according to the appropriate water-soil resource carrying capacity and human-land relationship of the watershed, and scientific principles such as water resource balance theory, biodiversity maintenance theory, landscape ecology theory, based on the changing laws of ecological elements such as mountains, waters, forests, fields, lakes, grasses, and sands and the research results of eco-hydrological processes, a stable and suitable coordination model based on the maintenance of benign eco-hydrological relationships is constructed (a nonlinear -Multi-objective dynamic programming model), covering constraints such as water resources, area, society, hydrological function objectives, and ecological function objectives, with the multi-objective optimization goals of optimizing eco-hydrological functions, optimizing economic and social benefits, and maximizing resources and environment. Combining the high correlation between natural and human factors, it stabilizes and appropriately coordinates ecological elements such as mountains, water, forests, fields, lakes, grass, and sand, further coordinating the supply, regulation, culture, and support service functions of the basin. Starting from the trade-off and synergistic relationship between "protection and restoration goals-social and economic costs-ecological and environmental benefits", it establishes a three-in-one protection and governance model and countermeasures for the basin, and jointly integrates ecological construction, protection, and governance technologies with the characteristics of a life community of mountains, waters, forests, fields, lakes, grasses, and sands. The present invention aims to improve the overall stability and sustainable development capacity of the basin by systematically optimizing the spatial pattern of ecological elements in the basin, enhancing its ecological functions such as hydrological regulation, soil conservation, vegetation restoration, and biodiversity maintenance, and solving the problems of existing technologies in regulating and protecting the basin system. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0031] Figure 1 A schematic flow chart of a method for stabilizing and appropriately coordinating the watershed's mountains, waters, forests, farmlands, lakes, grasslands, and sands provided by the present invention;

[0032] Figure 2 This is a schematic diagram of the coordination results of the embodiments provided by the present invention. DETAILED DESCRIPTION

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments 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.

[0034] The embodiment of the present invention discloses a method for stabilizing and appropriately coordinating the mountain, water, forest, farmland, lake, grass and sand in a watershed. Figure 1 As shown, the following steps are included:

[0035] S1: Collect basin ecological background data and build an eco-hydrological database;

[0036] S2: Based on the eco-hydrological database, a landscape pattern-ecosystem coupling model was constructed to obtain the coupling mechanism of the mountain-water-forest-field-lake-grass-desert life community and the ecological function zoning of the watershed;

[0037] S3: Based on the coupling mechanism of the mountain-water-forest-field-lake-grass-desert life community and the ecological function zoning, a comprehensive coupling relationship framework among different ecosystems, ecological functions, social systems, and economic systems within the basin is constructed. The coupling degree of different elements in the basin is analyzed to identify key eco-hydrological issues.

[0038] S4: Construct a nonlinear-multi-objective dynamic programming model based on the coupling degree of watershed elements and key eco-hydrological issues; construct the objective function of the nonlinear-multi-objective dynamic programming model with the optimal balance between watershed ecological and hydrological functions, maximum livestock breeding, maximum grain production, minimum social change, and optimal environmental quality as the objective function, and establish the constraints of the nonlinear-multi-objective dynamic programming model;

[0039] S5: Solve the objective function using a multi-objective optimization algorithm based on the constraints to obtain the optimal solution for the parameters of the objective function;

[0040] S6: Construct a coordination strategy based on the optimal solution of parameters.

[0041] The present invention comprehensively considers the interactions among mountains, rivers, forests, farmlands, lakes, grasslands, and sands to achieve overall optimization. It improves the scientific nature and accuracy of the scheme by integrating basin ecological background data and ecological modeling technologies such as GIS, remote sensing, and big data. It has broad application prospects and can be customized according to the geographical environment, climatic conditions, and socioeconomic needs of different basins. Through ecological regulation and restoration measures, it can achieve long-term and stable ecological and economic benefits.

[0042] Furthermore, the basin's ecological background data includes long-term observation data, remote sensing image data, and field survey data from meteorological stations, rainfall stations, hydrological stations, groundwater monitoring stations, and ecosystem flux stations around the basin. After multi-source data integration, 10-meter-resolution sentinel satellite images and 30-meter-resolution digital elevation models (DEMs) are generated; the eco-hydrological database includes hydrological and meteorological data, soil and vegetation ecological data, and topographic data, providing data support for subsequent ecosystem coupling modeling.

[0043] Furthermore, hydrometeorological data were extracted based on long-term observation data and remote sensing data, including historical precipitation data of the basin (including monthly, quarterly and annual precipitation), evaporation, transpiration, groundwater level, runoff, lake and wetland hydrology, air temperature and other data, as well as water, heat, carbon and nitrogen fluxes calculated based on the above data.

[0044] Furthermore, soil and vegetation ecological data were extracted through field survey data and 10-meter resolution Sentinel satellite images, including soil structure, water and heat conditions, and dominant species, height, and coverage of vegetation communities. Key ecological indicators such as the NDVI, leaf area index (LAI), and net primary productivity (NPP) were also calculated using 10-meter resolution Sentinel satellite images.

[0045] Furthermore, the topographic and geomorphic data, including the watershed boundaries and topographic and geomorphic features, were extracted based on the 30-meter resolution digital elevation model (DEM) combined with the 10-meter resolution sentinel satellite imagery.

[0046] Furthermore, the landscape pattern of the watershed includes mountains, water bodies, woodlands, farmlands, lakes, grasslands and sandy lands. Each landscape pattern includes multiple ecosystems, and each ecosystem includes multiple ecological processes.

[0047] Furthermore, the specific steps of S2 include:

[0048] S21: Use variable screening methods to identify key ecological factors affecting a single ecosystem in the eco-hydrological database; variable screening methods include principal component analysis or feature engineering;

[0049] S22: Statistically analyze the interaction pathways between key ecological factors, conduct landscape pattern-ecological process interaction analysis, and analyze changes in ecological services and their driving forces to obtain key ecological processes corresponding to landscape patterns. Statistical analysis methods include structural equation modeling (SEM) or path analysis. Through statistical analysis, quantitatively evaluate the interaction pathways between key ecological factors and identify key ecological processes that form specific landscape patterns.

[0050] S23: Use land change models and land surface process models to simulate land change and land surface processes based on topographic and geomorphological characteristics, and construct a landscape pattern-ecological process coupling model based on land change and land surface processes. Use the landscape pattern-ecological process coupling model to determine the interaction between the distribution pattern of different ecosystems and ecological processes.

[0051] S24: Based on the interaction between distribution patterns and ecological processes, we use agent-based modeling (ABM) to simulate the impact of these factors on natural ecosystems by setting different scenarios such as policy changes, human behavior changes, and technological advances in the social system. We then analyze the two-way feedback mechanism between social and natural ecosystems using coupling coordination models and correlation models, and construct an integrated model that reflects the two-way coupling between natural and social systems.

[0052] S25: Run the integrated model, conduct rationality analysis on the output simulation results, implement application verification, reveal the coupling mechanism of the mountain, water, forest, farmland, lake, grassland and desert life community, and carry out ecological function zoning of the watershed.

[0053] Furthermore, the specific steps of S3 include:

[0054] S31: Based on the coupling mechanism of the mountain-water-forest-farm-lake-grass-desert life community and the ecological function zoning, combined with the known "state-stress" and "pressure-stress" interactions between social and economic systems, the comprehensive coupling relationship between different ecosystems, ecological functions, social systems, and economic systems within the basin is derived. Based on the comprehensive coupling relationship framework, the coupling degree of each basin element is obtained and key ecohydrological issues are identified.

[0055] S32: Construct an objective function based on the coupling degree of watershed sub-factors and key eco-hydrological issues. The objective function includes the optimal trade-off between watershed ecosystem and hydrological functions, maximizing livestock population, maximizing grain production, minimizing environmental remediation costs, minimizing social change, and optimizing environmental quality. Using key eco-hydrological issues as parameters of the objective function, relationships are established around the coupling degree of watershed sub-factors to obtain the objective function.

[0056] Furthermore, the objective function OptF is expressed as:

[0057] OptF=[MaxOb,MaxSC,MaxGR,MinCOST,MinCH,MinEN]

[0058]

[0059] MinEN=ω1·I wq +ω2·I aq +ω3·Ibio

[0060] in, represents the optimal target of the trade-off between the watershed ecosystem and hydrological functions; α k represents the weight coefficient of k hydrological function indicators; α l Represents the weight coefficient of l ecological function indicators; Hy k represents k hydrological function indicators, including groundwater level, runoff, precipitation, etc.; Ec l represents an ecological function indicator, including vegetation coverage, biodiversity, primary productivity, etc.; X ij is a binary variable indicating whether the i-th watershed plot is allocated to the j-th landscape type use, X ij =1 means allocation, X ij =0 means no allocation; n means the total number of plots in the watershed; m means the total number of landscape types;

[0061] Indicates the maximum target for livestock breeding; sc ij is the amount of livestock that can be raised in the jth landscape type, sc jmax represents the maximum number of livestock that can be raised in the jth landscape type;

[0062] Indicates the maximum grain production target; gr ij is the grain yield of j landscape type;

[0063] represents the minimum cost of environmental restoration; iw is the unit cost of the wth environmental restoration in the ith watershed plot, q iw is the scale or quantity of the wth environmental restoration in the i-th watershed plot, where Costsum represents the total cost; q min ≤q iw ≤q max , where qmin represents the feasibility scale of ecological restoration, and qmax represents the maximum feasible scale;

[0064] represents the minimum target of social change; β1, β2, and β3 are the coefficients of economic change, environmental change, and social change respectively; P i It is the weight of each economic factor, indicating its contribution to the total economic change; GDP i is the GDP of the ith basin plot or industry; Q j is the weight of each environmental factor, indicating its contribution to the total environmental change; Q j is the environmental ecological index of the jth landscape type; R cis the weight of each social factor, indicating its contribution to the total social change; c is the cth social indicator;

[0065] MinEN=ω1·I wq +ω2·I aq +ω3·I bio Represents the optimal goal of environmental quality; I wq , I aq , I bio They represent water quality index, air quality index, and biodiversity index respectively. ω1, ω2, and ω3 correspond to weight coefficients of indicators, which are used to balance the importance of different factors.

[0066] Preferably, the constraints include water resource constraints, area constraints, social constraints, irrigation water quantity constraints, canal water delivery efficiency constraints, surface and groundwater availability constraints, water balance constraints, hydrological function target constraints, ecological function target constraints, and non-negativity constraints;

[0067] The water resource constraint MaxQHS is expressed as: Q(ij,t)≥MaxQ HS , Q(ij,t) represents the amount of water resources in different basins, is the weight of the i-th watershed plot as the j-th landscape type unit; according to the ecological function zoning, under the current configuration conditions of mountains, rivers, forests, farmlands, lakes, grasslands and sands, the hydrological-economic integration (WEAP) model is used to simulate and obtain the river runoff simulation result Q(t). The river runoff simulation result Q(t) is discretized to the research unit of the economic model through the geographic spatial interpolation method, and the water resources in different watersheds are calculated;

[0068] The area constraint is expressed as:

[0069]

[0070] Among them, A ij A is the land use area of the jth landscape type in the i-th watershed plot, total is the total land use area; A sczj A is the area of the suitable climate zone for land use of the jth landscape type; ic is the grassland area of the ith basin plot, A itc is the natural grassland area of the ith basin plot, A irc is the area of artificial grassland in the i-th watershed plot; A il is the forest area of the ith watershed plot, A itl is the natural forest area of the i-th watershed plot, A ijl is the area of artificial forest in the i-th watershed plot; A is is the mountain area of the ith basin block, A ixsis the current mountain area of the i-th basin plot;

[0071] Social constraints are expressed as:

[0072]

[0073] Indicates that the planned area of the land use type of the i-th watershed plot must be greater than or equal to the area protected by national regulations;

[0074] The irrigation water constraint is expressed as:

[0075]

[0076] Among them, A z is the area occupied by irrigation measure z, Ir z is the irrigation water volume per unit area, Ir permit To allow for irrigation water volume;

[0077] The canal water delivery efficiency constraint is expressed as:

[0078]

[0079] Among them, η z The water delivery efficiency of the canal system;

[0080] The surface and groundwater availability constraints are expressed as:

[0081]

[0082] in, and is the surface water and groundwater allocated for groundwater and surface water in month t under the i-th basin plot (mm / hm2); is the precipitation in month t under the i-th watershed plot (mm); represents the evapotranspiration of the ith basin plot in month t (mm);

[0083] The water balance constraint is expressed as:

[0084]

[0085] Among them, I i is the water input to the system, including precipitation, river input, etc. j The water output in the system, including evaporation, runoff, etc.

[0086] For the formulated configuration results of mountains, rivers, forests, farmlands, lakes, grasslands and sands, the basic hydrological service functions such as river runoff, sediment, flow velocity and lake ecological water level must be met at the watershed scale. Therefore, the hydrological function target constraint is set, which is expressed as:

[0087]

[0088] Among them, H k' , k = 1, 2, 3, 4, 5, 6, 7, respectively representing the runoff regulation capacity, sediment content, and river flow velocity under the current vegetation configuration; K k' K represents the balance threshold or ecological capacity of the k'th type of hydrological service function in the basin, that is, the target achievement value or target expectation value, with priority level l, l = 1, 2, 3, 4; 1' Indicates the regulating capacity of runoff, which is the annual runoff variability; K 2' is the suitable sediment content of the river, indicating the sediment content of the river during period t; K 3' is the river flow velocity, V d <V<V s , the river flow velocity should be between the river sedimentation velocity V d and river scouring velocity V s Between; K 4' The continuous flow state of the river channel in the runoff area, V(j,t)≠0, the value range of j is the grid unit in the runoff area of the basin, and t is the non-freezing period; K 5' The continuous flow state of the river channel in the runoff area: V(j,t)≠0, the value range of j is the grid unit in the runoff area of the basin, and t is the non-freezing period; K 6' The continuous flow state of the river channel in the runoff area, V(j,t)≠0, the value range of j is the grid unit in the runoff area of the basin, and t is the non-freezing period; K 7' is the lake water level, H≥H s , the lake water level must be greater than the ecological water level H s ;

[0089] The optimal configuration of mountains, rivers, forests, farmlands, lakes, grasslands, and sands must meet the basic ecological functions of different types of ecosystems. Therefore, the ecological function target constraint is set, which can be expressed as:

[0090]

[0091] Among them, V k , k = 1, 2, 3, 4, respectively representing the lower limit of biodiversity of healthy ecosystems, the minimum ecological flow of rivers, the lowest ecological water level of lakes, and the minimum ecological water demand of wetlands under the current optimal configuration; K klIt represents the balance threshold or ecological capacity of the kth ecological service function in the basin, that is, the target achievement value or target expectation value, with priority level l, l = 1, 2, 3, 4; K1 is the basin biodiversity index, with the first-level target value of 20, the second-level target value of 30, and the third-level target value of 60; K2 is the minimum basic flow of the river: the first-level target value is 10% of the multi-year average runoff, the second-level target value is 20% of the multi-year average runoff, and the third-level target value is 30% of the multi-year average runoff; K3 is the degree of satisfaction of the minimum ecological water level of the lake: the first-level target value is the 7-day sliding average water level below the minimum ecological level. Low ecological water level: The second-level target value is that the 3-day sliding average water level is lower than the lowest ecological water level, but the 7-day sliding average water level is not lower than the lowest ecological water level; the third-level target value is that the daily average water level is lower than the lowest ecological water level, but the 3-day sliding average water level is lower than the lowest ecological water level; the fourth-level target value is that the daily average water level is higher than the lowest ecological water level throughout the year; K4 is the minimum ecological water requirement of the wetland: the first-level target value is that the wetland vegetation and water surface account for 60% to 70% of the total area; the second-level target value is that the wetland vegetation and water surface account for 70% to 80% of the total area; and the third-level target value is that the wetland vegetation and water surface account for more than 80% of the total area;

[0092] A non-negative constraint is expressed as follows: all variables in all constraints are ≥ 0.

[0093] Furthermore, the multi-objective optimization algorithms in S5 include gradient algorithms, genetic algorithms, annealing algorithms, or particle swarm algorithms. The multi-objective optimization algorithm is used to solve the model, and the optimal parameter solution obtained can guide the specified coordination strategy to maintain a benign relationship between ecology and hydrology. The model is solved to obtain the landscape type j that matches the watershed plot i when all constraints are met, so that the objective function is optimized. The process includes:

[0094] S51: Initialize the parameters of the nonlinear-multi-objective dynamic programming model; the parameters include weight coefficients, ecosystem dynamics parameters, etc., and assign initial values to the parameters according to actual needs;

[0095] S52: Use a multi-objective optimization algorithm to find the optimal solution of the parameters that meets the constraints.

[0096] Furthermore, in S6, a coordination strategy is formulated according to actual needs based on the meaning and constraints of each parameter in the optimal solution.

[0097] In a specific embodiment, a basin mountain, water, forest, farmland, lake, grassland, and sand stability suitability coordination method is applied to the middle and lower reaches of a river in the dune-meadow alternating area in the eastern part of a certain region. The results of the mountain, water, forest, farmland, lake, grassland, and sand stability suitability coordination are as follows: grassland accounts for 18.64% of the total area of the basin, farmland accounts for 10.34%, water accounts for 1.09%, forest land accounts for 2.82%, sand accounts for 66.38%, and others account for 0.53%. Figure 2 shown.

[0098] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0099] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for stabilizing and appropriately coordinating the distribution of mountains, waters, forests, farmlands, lakes, grasslands, and sand in a watershed, characterized in that: The following steps are involved: Step 1: Collect basin ecological background data and build an eco-hydrological database; Step 2: Construct a landscape pattern-ecosystem coupling model based on the eco-hydrological database to obtain the coupling mechanism of the mountain-water-forest-field-lake-grass-desert life community and the ecological function zoning of the watershed; Step 3: Based on the coupling mechanism of the mountain-water-forest-farm-lake-grass-desert life community and the ecological function zoning, a comprehensive coupling relationship framework between different ecosystems, ecological functions, and socio-economics within the basin was constructed to obtain the coupling degree of different elements in the basin and key eco-hydrological issues; Step 4: Construct a nonlinear multi-objective dynamic programming model based on the coupling degree of watershed elements and key eco-hydrological issues; Step 5: Solve the nonlinear-multi-objective dynamic programming model to obtain the optimal solution for the parameters; Step 6: Construct a coordination strategy based on the optimal parameter solution.

2. The method for stabilizing and appropriately coordinating the watershed mountains, waters, forests, farmlands, lakes, grasslands and sand according to claim 1 is characterized in that: The basin's ecological background data includes long-term observation data, remote sensing image data, and field survey data from meteorological stations, rainfall stations, hydrological stations, groundwater monitoring stations, and ecosystem flux stations around the basin. After multi-source data integration, 10-meter-resolution Sentinel satellite images and 30-meter-resolution digital elevation models are generated. Based on the basin's ecological background data, Sentinel satellite images, and digital elevation models, hydrological and meteorological data, soil and vegetation ecological data, and topographic and geomorphological data are extracted to construct an eco-hydrological database.

3. The method for stabilizing and appropriately coordinating the watershed mountains, waters, forests, farmlands, lakes, grasslands and sand according to claim 1 is characterized in that: The specific steps of step 2 include: Step 21: Use variable screening method to screen out key ecological factors affecting a single ecosystem in the eco-hydrological database; Step 22: Statistically analyze the interaction pathways between key ecological factors, conduct landscape pattern-ecological process interaction analysis and ecological service changes and their driving forces analysis, and obtain key ecological processes corresponding to landscape patterns; Step 23: Use the land change model and land surface process model to simulate the topographic and geomorphic characteristics, output the land change and land surface processes, construct a landscape pattern-ecological process coupling model based on the land change and land surface processes, and determine the interaction between the distribution pattern of different ecosystems and ecological processes based on the landscape pattern-ecological process coupling model; Step 24: Based on the interaction between distribution patterns and ecological processes, an agent-based modeling approach is used to simulate the impact of social systems on natural ecosystems by setting different factors in the social system. Combining the coupling coordination model and the association model method, an integrated model of the natural-social system bidirectional coupling is constructed. Step 25: Run the natural-social system bidirectional coupling integrated model, conduct a rationality analysis on the output simulation results, obtain the coupling mechanism of the mountain, water, forest, farmland, lake, grassland and desert life community, and divide the watershed to obtain ecological functional zoning.

4. The method for stabilizing and appropriately coordinating the watershed mountains, waters, forests, farmlands, lakes, grasslands and sand according to claim 3 is characterized in that: The specific steps of step 3 include: S31: Based on the coupling mechanism of the mountain-water-forest-field-lake-grass-desert life community and the ecological function zoning, and combining the state-stress and pressure-stress interactions between the social and economic systems, a comprehensive coupling relationship framework between different ecosystems, ecological functions, social systems, and economic systems within the basin is constructed to obtain the coupling degree of each element of the basin and identify key eco-hydrological issues. S32: Construct a nonlinear-multi-objective dynamic programming model based on the coupling degree of watershed elements and key eco-hydrological issues.

5. The method for stabilizing and appropriately coordinating the watershed mountains, waters, forests, farmlands, lakes, grasslands and sand according to claim 1 is characterized in that: The objective functions of the nonlinear-multi-objective dynamic programming model include the optimal trade-off between the watershed ecosystem and hydrological functions, the maximum livestock breeding target, the maximum grain yield target, the minimum environmental restoration cost target, the minimum social change target, and the optimal environmental quality target.

6. The method for stabilizing and appropriately coordinating the watershed mountains, waters, forests, farmlands, lakes, grasslands and sand according to claim 5, characterized in that: The constraints of the nonlinear-multi-objective dynamic programming model include water resource constraints, area constraints, social constraints, irrigation water volume constraints, canal water delivery efficiency constraints, surface and groundwater availability constraints, water balance constraints, hydrological function target constraints, ecological function target constraints, and non-negative constraints.

7. The method for stabilizing and appropriately coordinating the watershed mountains, waters, forests, farmlands, lakes, grasslands and sand according to claim 5, characterized in that: Multi-objective optimization algorithms are used to solve nonlinear multi-objective dynamic programming models, including gradient algorithms, genetic algorithms, annealing algorithms or particle swarm algorithms.

8. The method for stabilizing and appropriately coordinating the watershed mountains, waters, forests, farmlands, lakes, grasslands and sand according to claim 6, characterized in that: Solve the nonlinear multi-objective dynamic programming model to obtain the landscape type j that matches the watershed plot i when all constraints are met, so that the objective function reaches the optimal value.

Images