A multi-system research method for watershed ecosystem services and water and soil resources

Abstract

A kind of multi-system research method for watershed ecosystem service and water and soil resources, it relates to the technical field of watershed ecosystem service, including method one, watershed scale ecosystem service evaluation based on discrete wavelet transform model;Method two, watershed water and soil resource matching calculation combined with information entropy theory;Method three, establish the multi-system coupling coordination model of watershed ecosystem service and water and soil resources.The present application realizes the quantitative expression of the correlation and response relationship of the multi-system of watershed ecosystem service and water and soil resources and the accurate evaluation of watershed scale ecosystem service, and provides a more easy-to-understand and simple operation mode for watershed decision makers.

Description

Technical Field

[0001] This invention relates to the field of watershed ecosystem services technology, specifically to a multi-system research method for watershed ecosystem services and water and soil resources. Background Technology

[0002] How to effectively enhance watershed ecosystem services, accurately identify the inherent mechanisms and laws of the "mountain-river-forest-farmland-lake-grassland community of life," and carry out systematic protection and restoration are urgent problems to be solved in my country's current watershed ecological protection work. Watershed ecosystem services and water and soil resources have a responsive relationship; that is, water and land resources, as driving factors, influence changes in ecosystem services. The dynamic changes and trend predictions of ecosystem service value will prompt experts, scholars, and watershed managers to make new adjustments to the water and soil resource pattern. Ecosystem services refer to the environmental conditions and natural benefits that ecosystems provide for the survival of humans and various organisms, based on beneficiary considerations. In other words, it refers to the benefits that humans themselves and their activities can obtain from the ecosystem. Water and land resources, as part of the natural system in a complex ecosystem, are also affected by the social and economic systems. They are important production and living factors in the watershed and the material basis for human survival and development. The rational allocation and matching of water and soil resources refers to the temporal and spatial arrangement, design, combination, and layout of water and land resources to improve the efficiency of water and soil resource utilization and achieve sustainable use of water and soil resources.

[0003] Among these, precise assessment of watershed ecosystem services can identify the distribution of spatial ecological resources within the watershed; the determination of the matching status of water and soil resources has a profound impact on the sustainable development of the watershed's economy, politics, society, and ecological environment, driving changes in ecosystem services. In-depth research on the coupling and coordination of watershed ecosystem services and water and soil resources is a crucial foundation for promoting the sustainable development of the watershed's ecological environment, providing theoretical guidance and decision-making support for subsequent research on new patterns of watershed land space development and protection.

[0004] Current state of technology:

[0005] (1) Large-scale ecosystem service assessment based on wavelet transform model method

[0006] Taking the research of Li Xiaomei and Sun et al. in ecosystem assessment as an example, the research approach of wavelet transform is to treat the original signal of large-scale data (i.e., the watershed) as a global function, that is, the original function to be analyzed. Based on the approximate decomposition of this function (dividing the watershed into different spatiotemporal scales), ecosystem service assessment is carried out. Then, by superimposing spatiotemporal sequence signals, scale extension and scale reconstruction are performed, ultimately deriving the transformation law of ecosystem services at the watershed scale. Wavelet functions such as Harr, Daubechies (DB), Morlet, and Mexican Hat are widely used. Based on the scale of the research factors, wavelet transform is divided into discrete transform and continuous transform. For example, Sun et al. assessed the ecosystem services of coastal wetlands at the Liaoning provincial scale using spatial continuous sampling, so they selected a continuous wavelet transform model. However, the distribution of main streams, tributaries, and reservoirs in my country's watersheds is relatively discrete and spatially heterogeneous. To achieve more accurate results, the wavelet transform model needs to consider the characteristics of discrete distribution and strong spatial heterogeneity of ecological resources, and verify the feasibility of the method.

[0007] (2) Research Methods on Regional Water and Soil Resource Matching Patterns

[0008] Existing research often employs the Gini coefficient method or agricultural water and soil resource matching coefficient calculation models to characterize the spatiotemporal matching degree of water and soil resources. These studies include calculations of water and soil resource matching in Central Asian countries, the Yellow River Delta, the Naoli River Basin, various cities in Shandong Province, the Gharehsoo River Basin, and the Oder River Basin. Both of these methods calculate the water and soil resource matching coefficient based on the ratio of agricultural water resources to cultivated land area, but do not incorporate ecological water and soil resource matching. However, the information entropy theory introduced in recent years can simultaneously consider water and land use for both agriculture and ecology. The concept of information entropy was first proposed and used by the American mathematician Shannon, and has since been widely applied in ecology, landscape architecture, land resource science, and many other fields. "Entropy" originates from thermodynamics and in information theory is mainly used to measure the disorder of information and characterize the evolutionary characteristics of a system, that is, to measure the amount of information, stability, and uncertainty of a system.

[0009] (3) Precise quantitative research on multi-system coupling and coordination

[0010] Research on integrated ecosystem service assessment and spatial pattern optimization of watershed water and soil resources shows that current studies are mostly focused on one subsystem (ecosystem, water resource system, or land resource system) and two subsystems (water resource and land resource system, water resource and ecosystem, land resource and ecosystem), lacking simultaneous research on watershed ecosystem services with the three subsystems of water resources and land resources.

[0011] In addition, most domestic and foreign scholars have studied the coupling relationship between water resources or land resources and the ecological environment. For example, Rost et al., Gao Qi et al., Li Chenyang et al., and others have studied the coupling relationship between water resources and ecosystems. Studies by authors such as Ouyang Zhiyun, Barros, Bussi, and Li Shengpeng have investigated the coupling relationship between land resources and ecosystems. However, there is a lack of in-depth research on linking watershed ecosystem services with the spatial distribution patterns of water and soil resources based on the theory of "system coupling and coordination." This results in the limited effectiveness of existing multi-objective optimization and regulation of watersheds aimed at "maximizing ecosystem service benefits, maximizing land use economic and ecological benefits, and minimizing total water shortage."

[0012] Existing studies on coupling relationships are merely simple correlation studies. For example, Rost et al. assessed the impact of water resources on agricultural and non-agricultural terrestrial ecosystems by quantifying surface water and groundwater; Polasky et al. studied the impact of declining water quality on ecosystem service value; Wang Jie et al. studied the coupling relationship between land use patterns and ecosystem service value of important ecological corridors by simulating the structure of these corridors. Qiao Bin et al. conducted spatial autocorrelation analysis on land use and ecosystem service value in Maduo County, Qinghai Province, based on 3km×3km grid units; Li Shengpeng et al. simulated the spatiotemporal evolution of the coordination degree of the ecological and economic system in Fujian Province from 2000 to 2026 based on land use scenarios. However, there is still a lack of studies on watershed ecosystem services and water and soil resources using precise quantitative models of system coupling coordination degree.

[0013] In summary, the existing technology has the following drawbacks:

[0014] (1) At present, the multi-system coupling and coordination model lacks simultaneous research on the three subsystems of watershed ecosystem services, water resources, and land resources, resulting in the insignificant effect of the existing multi-objective optimization and regulation of watersheds with the goals of "maximizing ecosystem service benefits, maximizing land use economic and ecological benefits, and minimizing total water shortage".

[0015] (2) Existing large-scale ecosystem service assessments lack consideration of the discrete distribution and strong spatial heterogeneity of watershed ecological resources, thus failing to scientifically identify the distribution of ecological spatial resources, diagnose ecologically sensitive and vulnerable areas, and carry out ecological protection and restoration.

[0016] (3) The existing water and soil resource matching pattern calculation lacks the inclusion of ecological water use and ecological land use, which makes the current method not widely applicable. Summary of the Invention

[0017] To address the problems of existing technologies, this invention provides a multi-system research method for watershed ecosystem services and water and soil resources. This method studies watershed ecosystem services, water resources, and land resources simultaneously, solving the problem that the existing multi-objective optimization and regulation of watersheds, which aims to maximize ecosystem service benefits, land use economic and ecological benefits, and minimize total water shortage, is not effective. Considering the discrete distribution and strong spatial heterogeneity of watershed ecological resources, this method can scientifically identify the distribution of ecological spatial resources, diagnose ecologically sensitive and vulnerable areas, and carry out ecological protection and restoration. When calculating the matching pattern of water and soil resources, ecological water use and ecological land use are included, which significantly enhances the applicability of the research method.

[0018] To solve the above problems, the technical solution of the present invention is as follows:

[0019] A multi-system research method for watershed ecosystem services and water and soil resources includes: Method 1, watershed-scale ecosystem service assessment based on discrete wavelet transform model; Method 2, watershed water and soil resource matching calculation combined with information entropy theory; and Method 3, construction of a multi-system coupling and coordination model for watershed ecosystem services and water and soil resources.

[0020] Preferably, method one includes:

[0021] (1) Optimization and applicability of scale transformation model: In view of the geographical spatial characteristics of the discrete distribution of ecological resources of the main stream, tributaries and reservoirs in the basin, a discrete scale transformation model, namely a discrete wavelet transform model, is adopted to discretize the scale and displacement of the basic wavelet; in the process of constructing the scale transformation model, binary discretization is adopted, that is, the scale and displacement of the continuous wavelet transform are discretized according to the power of 2, so as to achieve the goal of optimizing the applicability of the discrete wavelet transform model in the assessment of basin ecosystem services;

[0022] (2) Scale-up assessment of watershed ecosystem services: The watershed ecological space is deconstructed, expanded and reconstructed, including three steps: quantifying the feature space (scale deconstruction), applying wavelet transform (scale expansion), and cluster identification (scale reconstruction). Scale reconstruction is based on the real part, modulus and variance of wavelet coefficients. The real part of wavelet coefficients reflects the trend of ecosystem service change. The modulus of wavelet coefficients reflects the intensity and spatial range of the evolution process. The variance of wavelet coefficients confirms the spatiotemporal scale at which the evolution of watershed ecosystem services occurs most significantly. Finally, the watershed ecosystem services are assessed by simplifying the dataset of watershed ecosystem services and evaluating watershed ecosystem services with the same wavelet cluster. Through layer-by-layer scaling, the assessment of the entire watershed ecosystem services is finally achieved, and the accurate identification of ecologically sensitive and vulnerable areas at the watershed scale is realized.

[0023] (3) Verification of the accuracy of the assessment results: Typical case points at a small scale in the watershed were selected, and the simulated values ​​of the wavelet transform model and the measured values ​​were compared for the overall watershed and individual ecosystem service assessment data; the coefficient of determination R was used. 2 The accuracy of the method is verified by measuring the root mean square error (RMSE) and relative mean deviation (RMD). Simultaneously, the results of the watershed ecosystem service assessment achieved through discrete wavelet transform are compared with the results of the Costanza value coefficient method and meta-analysis to further verify the accuracy and feasibility of the wavelet transform method.

[0024] Preferably, in method one, the construction process of the discrete wavelet transform model is as follows:

[0025]

[0026]

[0027] The scale of the decomposition is reconstructed, and the corresponding reconstruction formula, i.e., the formula for the inverse wavelet transform, is as follows:

[0028]

[0029] In formulas (1), (2), and (3), f(t) represents the ecosystem service value of the sampling points at different scales in the watershed, a represents the different scale factors, τ represents the translation factor—that is, the corresponding spatial distance, C represents the wavelet coefficients, and ψ(t) is a square-integrable function, i.e., ψ(t)∈L. 2 (R) After performing an inner product between the basic wavelet and the function to be analyzed, the wavelet coefficients at different scales can be obtained.

[0030] Preferably, in method one, based on the calculated wavelet coefficients C, code representing the real part, modulus, squared modulus, and wavelet variance of the wavelet coefficients is written using Matlab software: shibu = real(coefs); mo = abs(coefs); mofang = (mo).^2; fangcha = sum(abs(coefs).^2,2) to calculate the real part, modulus, and wavelet variance of the wavelet coefficients. The data in Matlab is then converted into the "Kriging grid method" recognized by Surfer 8.0, and the contour maps are drawn in Surfer 8.0 software. Scale reconstruction is achieved based on the real part, modulus, and variance of the wavelet coefficients, and then scale overshoot is achieved by combining wavelet clustering technology.

[0031] Preferably, in the first method, the real part of the wavelet coefficients reflects the scale variation pattern of watershed ecosystem services and their distribution in different patterns, and is thus used to assess the changing trend of ecosystem services at a larger scale; the modulus of the wavelet coefficients reflects the intensity of the evolution of watershed ecosystem services, and the larger the modulus, the stronger the change, which is used to assess the range of the strong change; the wavelet variance value confirms the spatiotemporal scale at which the evolution of watershed ecosystem services occurs most significantly.

[0032] Preferably, the second method includes:

[0033] (1) Combining water resources bulletin data and remote sensing interpretation of land resources data, the balance between agricultural water use and ecological water use is used as the matching prototype, and the balance between cultivated land and ecological land use is used as the matching object. The spatial matching pattern measurement model of water and soil resources is constructed by combining information entropy theory with spatial Lorenz curve and Gini coefficient principle. The matching degree of the two systems of "water resources and land resources" is comprehensively evaluated and classified into: Level I with relatively good matching degree, Level II with good matching degree, Level III with poor matching degree, and Level IV with extremely poor matching degree.

[0034] (2) Construct a spatial matching pattern measurement model for water and soil resources. The calculation process is as follows:

[0035]

[0036] In equation (4), G is the water and soil resource matching coefficient. x represents the Gini coefficient for water and soil resource matching. i To determine the balance between agricultural water consumption and ecological water consumption in different administrative regions of the basin J x The cumulative percentage, y i To achieve a balance between cultivated land and ecological land use in different administrative regions of the basin J y The cumulative percentage; when i = 1, (x i-1 y i-1 (0, 0) is considered as (0, 0); when m = 1, the water use structure type is agricultural water use, and the land use type is cultivated land; when m = 2, the water use structure type is ecological water use, and the land use type is ecological land use; where x i Data is on the X-axis and y-axis. i The data is on the Y-axis, forming a spatial Lorenz curve;

[0037]

[0038] In equation (5), the balance degree J is the sum of the information entropy H and the maximum entropy H. maxThe ratio between them describes the differences in area size between land types and the differences in water consumption between water use types; H is the information entropy of a single type of water resource use structure or land resource use structure, measured in nanoseconds, used to reflect the quantity, spatial uniformity, and orderliness of a certain water use type and land resource use type in the watershed; p i The proportion of a certain type of water consumption and land use area within each unit to the total water consumption and land use area of ​​that type in the watershed; when the water consumption and land use area S within each unit i When they are equal, that is When E = 0, the entropy value is the maximum; the equilibrium degree J takes the range of E∈[0,1]. When E = 0, the water resource utilization structure or land resource utilization structure is in the most uneven state, and when E = 1, the water resource utilization structure or land resource utilization structure reaches the ideal equilibrium state.

[0039] Preferably, method three includes:

[0040] (1) Based on the concept of capacity coupling and the capacity coupling coefficient model in physics, a coupling model of the interaction of three subsystems, namely "ecosystem services, water resources, and land resources", is established based on the efficacy function, order parameter contribution value, and coupling degree analysis.

[0041]

[0042] In equation (6), C represents the system coupling degree, C∈(0,1), m represents the number of subsystems, and takes a value of 3. i (i = 1, 2, 3) are the order parameters of the three subsystems "ecosystem services, water resources, and land resources", representing the contribution rate of each order parameter (each subsystem) to the overall system. The calculation formula is as follows:

[0043] U i =∑λ ij ×λx' ij (7)

[0044] In equation (7), λ ij Let x be the weight of the j-th influence factor in the i-th order parameter. ij (j=1,2,...,n) represents the j-th indicator or influence factor of the i-th order parameter, x' ij The ordered efficiency coefficient of the coupled system of "ecosystem services, water resources, and land resources" represents the variable x. ij The contribution value to the system's effectiveness reflects the satisfaction level of each influencing factor in achieving its objectives; where U1, U2, and U3 are the contribution values ​​of the water resources subsystem, land resources subsystem, and ecosystem service subsystem to the overall system's orderliness, respectively; x' ij The calculation formula is as follows:

[0045]

[0046]

[0047] In formula (8), x' ij These are positive indicators, meaning they represent indicators within the "ecosystem services, water resources, and land resources" subsystems with a positive contribution rate. The higher these indicator values ​​are, the better the system evaluation.

[0048] In formula (9), x' ij These are negative indicators, meaning they represent indicators within the "ecosystem services, water resources, and land resources" subsystems with a negative contribution rate. The smaller these indicator values ​​are, the better the system evaluation.

[0049] (2) To represent the degree of benign coupling in the interaction of the three subsystems, a coordination degree is introduced, and a coordination degree function for the coupling of watershed ecosystem services and water and soil resources is constructed. The calculation formula is as follows:

[0050] D = (C × T) 1 / 2 (10)

[0051] In equation (10), D represents the system coordination degree; T represents the comprehensive coordination index of "ecosystem services, water resources, and land resources" in the watershed, reflecting the overall coordination effect of the watershed's water and soil resources ecosystem. The formula for calculating T is as follows:

[0052] T=α×U1+β×U2+γ×U3 (11)

[0053] In Equation (11), α, β, and γ are the undetermined coefficients of the subsystem contribution. The undetermined coefficients of the contribution of the three subsystems of water resources, land resources, and ecosystem services are selected as follows: α = 0.35, β = 0.35, and γ = 0.30. At the same time, according to the system coupling coordination degree (D), the coupling coordination degree of water resources ecosystem services and water resources in the basin is divided into four categories: coordination degree greater than 0.5 is the coordination category, including highly coordinated and basically coordinated categories; coordination degree less than 0.5 is the imbalance category, including the imbalanced and imbalanced decline categories.

[0054] Preferably, in method three, the highly coordinated category is divided into high-quality coordinated development type and good coordinated development type; the basic coordinated category includes intermediate coordinated development type, primary coordinated development type, and barely coordinated development type; the near-disorder category includes near-disorder and decline type and mildly disordered and decline type; the disordered and declining category includes moderately disordered and decline type, severely disordered and decline type, and extremely disordered and decline type.

[0055] This invention provides a multi-system research method for watershed ecosystem services and water and soil resources, which has the following characteristics:

[0056] Beneficial effects:

[0057] (1) This invention provides a precise quantitative expression method for determining the coupling and coordination effect of "ecosystem services-water resources-soil resources" in a watershed. It focuses on how to identify the correlation and response relationship of "ecosystem services-water resources-soil resources" in a watershed, and clarifies the internal evolution relationship and response mechanism of "ecosystem services-water resources-soil resources" in a watershed from the perspective of multi-system coupling and coordination.

[0058] (2) For the assessment of large-scale ecosystem services in watersheds, a wavelet transform method suitable for the discrete distribution of ecological resources in watersheds is constructed. The key is to solve how to perform spatial “decomposition-expansion-reconstruction” scale transformation to achieve accurate assessment of watershed ecosystem services, and then scientifically identify and diagnose ecologically sensitive and vulnerable areas at the watershed scale.

[0059] (3) When calculating the water and soil resources matching pattern in a watershed, this invention uses the information entropy theory to simultaneously consider the water use and land use of agriculture and ecology in the watershed, making the principle of the water and soil resources matching pattern calculation method universal and providing watershed decision-makers with a more understandable and simpler operating method.

[0060] (4) When studying the synergistic effect between watershed ecosystem services and water and soil resource sequence parameters, a system coupling coordination model was constructed to realize the quantitative expression of the multi-system coupling coordination effect of watershed ecosystem services and water and soil resources. Attached Figure Description

[0061] Figure 1 A diagram illustrating the relationship between watershed ecosystem services and water and soil resources.

[0062] Figure 2 A schematic diagram of the research method based on the "evaluation-matching-coupling" technical path provided by this invention;

[0063] Figure 3 A schematic diagram of the process of deconstructing, expanding, and reconstructing the spatial scale of the watershed using the discrete wavelet transform model of this invention;

[0064] Figure 4 This invention incorporates information entropy theory to consider the optimal allocation of water and soil resources for ecological water use and ecological land use, as illustrated in the diagram. Detailed Implementation

[0065] The following description provides a detailed explanation of the embodiments of the present invention in a step-by-step manner. This description is only a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0066] In the description of this invention, it should be noted that the terms "upper," "lower," "left," "right," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or a specific orientational structure and operation. Therefore, they should not be construed as limiting this invention.

[0067] In its most basic embodiment, the present invention provides a multi-system research method for watershed ecosystem services and water and soil resources, such as... Figure 1-4 As shown, the methods include: Method 1, watershed-scale ecosystem service assessment based on discrete wavelet transform model; Method 2, watershed water and soil resource matching calculation combined with information entropy theory; and Method 3, constructing a multi-system coupling and coordination model of watershed ecosystem services and water and soil resources.

[0068] In a further embodiment, such as Figure 2 , 3 As shown, method one includes:

[0069] (1) Optimization and applicability of scale transformation model: In view of the geographical spatial characteristics of the discrete distribution of ecological resources in the main stream, tributaries and reservoirs of the basin, the discrete scale transformation model, namely the discrete wavelet transform model (DWT), is adopted to discretize the scale and displacement of the basic wavelet; in the process of constructing the scale transformation model, binary discretization is adopted, that is, the scale and displacement of the continuous wavelet transform are discretized according to the power of 2, so as to achieve the goal of optimizing the applicability of the discrete wavelet transform model in the assessment of basin ecosystem services;

[0070] (2) Scale-up assessment of watershed ecosystem services: The watershed ecological space is deconstructed, expanded and reconstructed, including three steps: quantifying the feature space (scale deconstruction), applying wavelet transform (scale expansion), and cluster identification (scale reconstruction). Scale reconstruction is based on the real part, modulus and variance of wavelet coefficients. The real part of wavelet coefficients reflects the trend of ecosystem service change. The modulus of wavelet coefficients reflects the intensity and spatial range of the evolution process. The variance of wavelet coefficients confirms the spatiotemporal scale at which the evolution of watershed ecosystem services occurs most significantly. Finally, the watershed ecosystem services are assessed by simplifying the dataset of watershed ecosystem services and evaluating watershed ecosystem services with the same wavelet cluster. Through layer-by-layer scaling, the assessment of the entire watershed ecosystem services is finally achieved, and the accurate identification of ecologically sensitive and vulnerable areas at the watershed scale is realized.

[0071] (3) Verification of the accuracy of the assessment results: Typical case points at a small scale in the watershed were selected, and the simulated values ​​of the wavelet transform model and the measured values ​​were compared for the overall watershed and individual ecosystem service assessment data; the coefficient of determination R was used. 2The accuracy of the method is verified by measuring the root mean square error (RMSE) and relative mean deviation (RMD). Simultaneously, the results of the watershed ecosystem service assessment achieved through discrete wavelet transform are compared with the results of the Costanza value coefficient method and meta-analysis to further verify the accuracy and feasibility of the wavelet transform method.

[0072] In a further embodiment, such as Figure 2 , 3 As shown, in method one, the construction process of the discrete wavelet transform model is as follows:

[0073]

[0074]

[0075] The scale of the decomposition is reconstructed, and the corresponding reconstruction formula, i.e., the formula for the inverse wavelet transform, is as follows:

[0076]

[0077] In formulas (1), (2) and (3), f(t) represents the ecosystem service value of the sample points at different scales in the watershed, a represents different scale factors, τ represents the translation factor - i.e. the corresponding spatial distance, and C represents the wavelet coefficient.

[0078] In a further embodiment, such as Figure 2 , 3 As shown, in method one, based on the calculated wavelet coefficients C, Matlab software is used to program and write codes representing the real part, modulus, squared modulus, and wavelet variance of the wavelet coefficients: shibu=real(coefs); mo=abs(coefs); mofang=(mo).^2; fangcha=sum(abs(coefs).^2,2) to calculate the real part, modulus, and wavelet variance of the wavelet coefficients. The data in Matlab is then organized and transformed into the "Kriging grid method" recognized by Surfer 8.0, and the contour maps are drawn in Surfer 8.0 software. Scale reconstruction is achieved based on the real part, modulus, and variance of the wavelet coefficients, and then scale overshoot is achieved by combining wavelet clustering technology.

[0079] In a further embodiment, such as Figure 2 , 3As shown, in the first method, the real part of the wavelet coefficients reflects the scale variation pattern of watershed ecosystem services and their distribution in different patterns, which is then used to assess the changing trend of ecosystem services on a larger scale; the modulus of the wavelet coefficients reflects the intensity of the evolution of watershed ecosystem services, and the larger the modulus, the stronger the change, which is used to assess the range of the strong change; the wavelet variance value confirms the spatiotemporal scale at which the evolution of watershed ecosystem services occurs most significantly.

[0080] In a further embodiment, such as Figure 2 , 4 As shown, the second method includes:

[0081] (1) Combining water resources bulletin data and remote sensing interpretation of land resources data, the balance between agricultural water use and ecological water use is used as the matching archetype, and the balance between cultivated land and ecological land use is used as the matching object. The spatial matching pattern measurement model of water and soil resources is constructed by combining information entropy theory with the spatial Lorenz curve and Gini coefficient principle. The matching degree of the two systems of "water resources and land resources" is comprehensively evaluated and classified into four levels: Level I is relatively good, Level II is good, Level III is poor, and Level IV is extremely poor; see Table 1:

[0082] Table 1. Classification of Water and Soil Resources Matching Degree in the Basin

[0083]

[0084] (2) Construct a spatial matching pattern measurement model for water and soil resources. The calculation process is as follows:

[0085]

[0086] In equation (4), G is the water and soil resource matching coefficient. x represents the Gini coefficient for water and soil resource matching. i To determine the balance between agricultural water consumption and ecological water consumption in different administrative regions of the basin J x (See Formula 5) Cumulative percentage, y i To achieve a balance between cultivated land and ecological land use in different administrative regions of the basin J y (See Formula 5) cumulative percentage; when i = 1, (x i-1 y i-1 (0, 0) is considered as (0, 0); when m = 1, the water use structure type is agricultural water use, and the land use type is cultivated land; when m = 2, the water use structure type is ecological water use, and the land use type is ecological land use; where x i Data is on the X-axis and y-axis. i The data is on the Y-axis, forming a spatial Lorenz curve;

[0087]

[0088] In equation (5), the balance degree J is the sum of the information entropy H and the maximum entropy H. max The ratio between them describes the differences in area size between land types and the differences in water consumption between water use types; H is the information entropy of a single type of water resource use structure or land resource use structure, measured in nats, used to reflect the quantity, spatial uniformity, and orderliness of a certain water use type and land resource use type in the watershed; p i The proportion of a certain type of water consumption and land use area within each unit to the total water consumption and land use area of ​​that type in the watershed; when the water consumption and land use area S within each unit i When they are equal, that is When E = 0, the entropy value is the maximum; the equilibrium degree J takes the range of E∈[0,1]. When E = 0, the water resource utilization structure or land resource utilization structure is in the most uneven state, and when E = 1, the water resource utilization structure or land resource utilization structure reaches the ideal equilibrium state.

[0089] In a further embodiment, such as Figure 2 , 4 As shown, the third method includes:

[0090] (1) Based on the concept of capacity coupling and the capacity coupling coefficient model in physics, a coupling model of the interaction of three subsystems, namely "ecosystem services, water resources, and land resources", is established based on the efficacy function, order parameter contribution value, and coupling degree analysis.

[0091]

[0092] In equation (6), C represents the system coupling degree, C∈(0,1), m represents the number of subsystems, and takes a value of 3. i (i = 1, 2, 3) are the order parameters of the three subsystems "ecosystem services, water resources, and land resources", representing the contribution rate of each order parameter (each subsystem) to the overall system. The calculation formula is as follows:

[0093] U i =∑λ ij ×λx' ij (7)

[0094] In equation (7), λ ij Let x be the weight of the j-th influence factor in the i-th order parameter. ij (j=1,2,...,n) represents the j-th indicator or influence factor of the i-th order parameter, x' ij The ordered efficiency coefficient of the coupled system of "ecosystem services, water resources, and land resources" represents the variable x. ijThe contribution value to the system's effectiveness reflects the satisfaction level of each influencing factor in achieving its objectives; where U1, U2, and U3 are the contribution values ​​of the water resources subsystem, land resources subsystem, and ecosystem service subsystem to the overall system's orderliness, respectively; x' ij The calculation formula is as follows:

[0095]

[0096]

[0097] In formula (8), x' ij These are positive indicators, meaning they represent indicators within the "ecosystem services, water resources, and land resources" subsystems with a positive contribution rate. The higher these indicator values ​​are, the better the system evaluation.

[0098] In formula (9), x' ij These are negative indicators, meaning they represent indicators within the "ecosystem services, water resources, and land resources" subsystems with a negative contribution rate. The smaller these indicator values ​​are, the better the system evaluation.

[0099] (2) To represent the degree of benign coupling in the interaction of the three subsystems, a coordination degree is introduced, and a coordination degree function for the coupling of watershed ecosystem services and water and soil resources is constructed. The calculation formula is as follows:

[0100] D = (C × T) 1 / 2 (10)

[0101] In equation (10), D represents the system coordination degree; T represents the comprehensive coordination index of "ecosystem services, water resources, and land resources" in the watershed, reflecting the overall coordination effect of the watershed's water and soil resources ecosystem. The formula for calculating T is as follows:

[0102] T=α×U1+β×U2+γ×U3 (11)

[0103] In Equation (11), α, β, and γ are the undetermined coefficients of the contribution of the subsystems. Considering that the sustainable development of the watershed's water and soil resources ecosystem is based on the watershed's water and land resources, and that the watershed's water resources have an important constraint on the development and utilization of land resources, the undetermined coefficients of the contribution of the three systems of watershed water resources, land resources, and ecosystem services are selected as follows: α = 0.35, β = 0.35, and γ = 0.30. At the same time, according to the system coupling coordination degree (D), the coupling coordination degree of watershed ecosystem services and water and soil resources is divided into four categories: coordination degree greater than 0.5 is the coordination category, including highly coordinated and basically coordinated categories; coordination degree less than 0.5 is the imbalance category, including the imbalanced and imbalanced decline categories.

[0104] In a further embodiment, such as Figure 2 ,4 As shown in Table 2, in the method 3 described above, the highly coordinated category is divided into high-quality coordinated development type and good coordinated development type; the basic coordinated category includes intermediate coordinated development type, primary coordinated development type and barely coordinated development type; the near-disorder category includes near-disorder and decline type and mild disorder and decline type; the disorder and decline category includes moderate disorder and decline type, severe disorder and decline type and extreme disorder and decline type.

[0105] Table 2 Classification of Coordination Degree between Watershed Ecosystem Services and Water and Soil Resources

[0106]

Claims

1. A multi-system research method for watershed ecosystem services and water and soil resources, characterized by: The methods include: Method 1, watershed-scale ecosystem service assessment based on discrete wavelet transform model; Method 2, watershed water and soil resource matching calculation combined with information entropy theory; and Method 3, constructing a multi-system coupling and coordination model of watershed ecosystem services and water and soil resources. Method 2 includes: (1) Combining water resources bulletin data and remote sensing interpretation of land resources data, the balance between agricultural water use and ecological water use is used as the matching prototype, and the balance between cultivated land and ecological land use is used as the matching object. The spatial matching pattern measurement model of water and soil resources is constructed by combining information entropy theory with spatial Lorenz curve and Gini coefficient principle. The matching degree of the two systems of "water resources and land resources" is comprehensively evaluated and classified into: Level I with better matching degree, Level II with good matching degree, Level III with poor matching degree, and Level IV with extremely poor matching degree. (2) Construct a spatial matching pattern measurement model for water and soil resources. The calculation process is as follows: G= （4）； In equation (4), G is the water and soil resource matching coefficient. This represents the Gini coefficient for water and soil resource matching. To determine the balance between agricultural water consumption and ecological water consumption in different administrative regions of the basin J x The cumulative percentage, To achieve a balance between cultivated land and ecological land use in different administrative regions of the basin J y The cumulative percentage; when i=1, ( ) is considered ( When m=1, the water use structure type is agricultural water use, and the land use type is arable land; when m=2, the water use structure type is ecological water use, and the land use type is ecological land use; among them, with The data is on the X-axis. The data is used as the Y-axis to form a spatial Lorenz curve; J= = =﹣ p i ·lnp i ) / ln·n (5) In equation (5), the balance degree J is the sum of the information entropy H and the maximum entropy H. max The ratio between them describes the differences in area size between land types and the differences in water consumption between water use types; H is the information entropy of a single type of water resource use structure or land resource use structure, measured in nanoseconds, used to reflect the quantity, spatial uniformity, and orderliness of a certain water use type and land resource use type in the watershed; p i The proportion of a certain type of water consumption and land use area within each unit to the total water consumption and land use area of ​​that type in the watershed; when the water consumption and land use area S within each unit i When they are equal, that is, S1=S2=... ...=S n = When E=0, the entropy value is the maximum; the equilibrium degree J takes the range of E∈[0, 1]. When E=0, the water resource utilization structure or land resource utilization structure is in the most uneven state, and when E=1, the water resource utilization structure or land resource utilization structure reaches the ideal equilibrium state.

2. The multi-system research method for watershed ecosystem services and water and soil resources as described in claim 1, characterized in that: Method 1 includes: (1) Optimization and applicability of scale transformation model: In combination with the geographical spatial characteristics of the discrete distribution of ecological resources in the main stream, tributaries and reservoirs of the basin, a discrete scale transformation model, namely a discrete wavelet transform model, is adopted to discretize the scale and displacement of the basic wavelet; in the process of constructing the scale transformation model, binary discretization is adopted, that is, the scale and displacement of the continuous wavelet transform are discretized according to the power of 2, so as to achieve the goal of optimizing the applicability of the discrete wavelet transform model in the assessment of basin ecosystem services; (2) Scale-up assessment of watershed ecosystem services: The watershed ecological space is deconstructed, expanded and reconstructed, including three steps: quantifying the feature space (scale deconstruction), applying wavelet transform (scale expansion), and cluster identification (scale reconstruction). Scale reconstruction is based on the real part, modulus and variance of wavelet coefficients. The real part of wavelet coefficients reflects the trend of ecosystem service change. The modulus of wavelet coefficients reflects the intensity and spatial range of the evolution process. The variance of wavelet coefficients confirms the spatiotemporal scale at which the evolution of watershed ecosystem services occurs most obviously. Finally, the watershed ecosystem services are assessed by simplifying the dataset of watershed ecosystem services and assessing the watershed ecosystem services of the same wavelet cluster. Through layer-by-layer scaling, the assessment of the entire watershed ecosystem services is finally achieved, and the accurate identification of ecologically sensitive and vulnerable areas at the watershed scale is realized. (3) Verification of the accuracy of the assessment results: Select typical case points at the small scale of the watershed, and compare the simulated values ​​and measured values ​​of the wavelet transform model for the overall watershed and individual ecosystem service assessment data; use the coefficient of determination R 2 The accuracy of the method is verified by measuring the root mean square error (RMSE) and relative mean deviation (RMD). Simultaneously, the results of the watershed ecosystem service assessment achieved through discrete wavelet transform are compared with the results of the Costanza value coefficient method and meta-analysis to further verify the accuracy and feasibility of the wavelet transform method.

3. The multi-system research method for watershed ecosystem services and water and soil resources as described in claim 2, characterized in that: In method one, the construction process of the discrete wavelet transform model is as follows: （1）； a＞0 （2）； The scale of the decomposition is reconstructed, and the corresponding reconstruction formula, i.e., the formula for the inverse wavelet transform, is as follows: （3）； In formulas (1), (2), and (3), f(t) represents the ecosystem service value of the sampling points at different scales within the watershed, and a represents different scale factors. Let C be the translation factor—that is, the corresponding spatial distance—and C be the wavelet coefficients. Let ψ(t) be a square-integrable function, i.e., ψ(t)∈L. 2 (R) After performing an inner product between the basic wavelet and the function to be analyzed, the wavelet coefficients at different scales can be obtained.

4. The multi-system research method for watershed ecosystem services and water and soil resources as described in claim 3, characterized in that, In method one, based on the calculated wavelet coefficients C, code is written using Matlab software to represent the real part, modulus, squared modulus, and variance of the wavelet coefficients: shibu = real (coefs); mo = abs(coefs); mofang = (mo).^2; fangcha = sum (abs (coefs).^2,2) to calculate the real part, modulus, and variance of the wavelet coefficients. The data in Matlab is then processed and converted into the "Kriging grid method" recognized by Surfer 8.0, and the contour maps are drawn in Surfer 8.0 software. Scale reconstruction is then achieved based on the real part, modulus, and variance of the wavelet coefficients, and scale overshoot is achieved by combining wavelet clustering technology.

5. A multi-system research method for watershed ecosystem services and water and soil resources as described in claim 4, characterized in that, In the first method, the real part of the wavelet coefficients reflects the scale variation pattern of watershed ecosystem services and their distribution in different patterns, which is then used to assess the changing trend of ecosystem services on a larger scale; the modulus of the wavelet coefficients reflects the intensity of the evolution of watershed ecosystem services, and the larger the modulus, the stronger the change, which is used to assess the range of the strong change; the wavelet variance value confirms the spatiotemporal scale at which the evolution of watershed ecosystem services occurs most significantly.

6. The multi-system research method for watershed ecosystem services and water and soil resources as described in claim 5, characterized in that, The third method includes: (1) Based on the concept of capacity coupling and the capacity coupling coefficient model in physics, a coupling model of the interaction of the three subsystems "ecosystem services, water resources, and land resources" is established based on the efficacy function, order parameter contribution value, and coupling degree analysis, namely: C=m{ } 1 / m i=1, 2, ..., m (6); In equation (6), C represents the system coupling degree, C∈(0, 1), m represents the number of subsystems, and takes a value of 3. i (i=1,2,3) are the order parameters of the three subsystems "ecosystem services, water resources, and land resources", representing the contribution rate of each order parameter of each subsystem to the overall system. The calculation formula is as follows: IN i =∑λ ij ×λx' ij (7); In equation (7), λ ij Let x be the weight of the j-th influence factor in the i-th order parameter. ij (j=1,2,...,n) represents the j-th index or influence factor of the i-th order parameter, x' ij The ordered efficiency coefficient of the coupled system of "ecosystem services, water resources, and land resources" represents the variable x. ij The contribution value to the system's effectiveness reflects the satisfaction level of each influencing factor in achieving its objectives; where U1, U2, and U3 are the contribution values ​​of the water resources subsystem, land resources subsystem, and ecosystem service subsystem to the overall system's orderliness, respectively; x' ij The calculation formula is as follows: x’ ij = （8）； x’ ij = （9）； In formula (8), x' ij These are positive indicators, meaning they represent indicators within the "ecosystem services, water resources, and land resources" subsystems with a positive contribution rate. The higher these indicator values ​​are, the better the system evaluation. In formula (9), x' ij These are negative indicators, meaning they represent indicators with a negative contribution rate within the "ecosystem services, water resources, and land resources" subsystems. The smaller these indicator values ​​are, the better the system evaluation. (2) To represent the degree of benign coupling in the interaction of the three subsystems, a coordination degree is introduced, and a coordination degree function for the coupling of watershed ecosystem services and water and soil resources is constructed. The calculation formula is as follows: D=(C×T) 1 / 2 (10); In equation (10), D represents the system coordination degree; T represents the comprehensive coordination index of "ecosystem services, water resources, and land resources" in the watershed, reflecting the overall coordination effect of the watershed's water and soil resources ecosystem. The formula for calculating T is as follows: T= ×U1+ ×U2+ ×U3 (11); In equation (11), , , The undetermined contribution coefficients for the three subsystems—watershed water resources, land resources, and ecosystem services—are as follows: =0.35, =0.35, =0.30; Meanwhile, based on the system coupling coordination degree D, the degree of coupling coordination between watershed ecosystem services and water and soil resources is divided into four categories: coordination degree greater than 0.5 is the coordination category, including highly coordinated and basically coordinated categories; coordination degree less than 0.5 is the imbalance category, including critical imbalance and imbalanced decline categories.

7. A multi-system research method for watershed ecosystem services and water and soil resources as described in claim 6, characterized in that, In the method three, the highly coordinated category is divided into high-quality coordinated development type and good coordinated development type; the basic coordinated category includes intermediate coordinated development type, primary coordinated development type and barely coordinated development type; the near-disorder category includes near-disorder and decline type and mildly disordered and decline type; the disordered and decline category includes moderately disordered and decline type, severely disordered and decline type and extremely disordered and decline type.

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