A lakeside zone landscape configuration method based on aquatic animal diversity

Abstract

The present application relates to the technical field of water environment treatment and biodiversity protection technology, and particularly relates to a lakeside zone landscape configuration method based on aquatic animal diversity. The present application takes the actual response of aquatic animal diversity to water quality as the core, solves the concentration threshold of each water quality index when the diversity index reaches the balanced optimum through curve fitting and multi-objective optimization, and takes the concentration threshold as the water quality target range. The water quality target determination method based on ecological response realizes the collaborative improvement of water quality improvement and biodiversity, and is more scientific and economical. The method breaks through the traditional idea of setting water quality targets according to the environmental function and protection target of surface water area, makes the establishment of water quality targets more in line with the actual state of the ecological system, effectively reduces the subjective deviation of human experience determination, improves the scientificity and economy of target setting, and at the same time, maximizes the protection of the structural and functional integrity of the water ecological system.

Description

Technical Field

[0001] This invention relates to the technical field of water environment management and biodiversity conservation technologies, specifically to a method for configuring lakeside landscapes based on aquatic animal diversity. Background Technology

[0002] Improving water quality is of great significance for ensuring the health of aquatic ecosystems, maintaining their ecosystem service functions, and promoting the construction of aquatic ecological civilization. Currently, when formulating surface water environmental quality standards, my country mainly relies on the environmental functions and protection objectives of surface water bodies, with less consideration given to the spatiotemporal heterogeneity of water bodies and their potential impact on aquatic biodiversity. This situation may lead to two contradictory consequences: on the one hand, some water quality indicators may be set too strictly, potentially resulting in unnecessary water environment governance costs; on the other hand, the standards fail to fully reflect the true requirements of the ecosystem, meaning that even if some water bodies "meet the standards," they may still experience aquatic biodiversity degradation.

[0003] At the technical practice level, current water environment management and ecological restoration technologies mostly focus on reducing external pollution (such as point source pollution control) and restoring submerged vegetation inside lakes. However, the systematic exploration and coordinated regulation of the ecological functions of the lake shore zone, which is located in the key area where water and land meet, is still insufficient. The existing technical system has the following obvious limitations: (1) In terms of pollution control strategies, linear models or single threshold management strategies are generally used to regulate the input of nutrients such as nitrogen and phosphorus, ignoring the complex nonlinear response relationship between aquatic animal diversity and nutrient concentration, resulting in a disconnect between the governance target and the actual ecological process, and the governance effect is often difficult to maintain in the long term. (2) In terms of lake shore landscape design and functional layout, most schemes still focus on visual beautification or single pollutant interception, failing to systematically and mechanistically design the lake shore zone as a key ecological hub for regulating nutrient cycle flux and balance, and its ecological purification potential and biodiversity support function have not been fully released. While existing research has made some progress in systems thinking and technology integration, a universally applicable solution remains lacking for establishing a method for determining water quality targets centered on aquatic animal diversity, and using this method to guide the coordinated regulation and optimization of lakeshore landscape patterns. Therefore, there is an urgent need to propose an innovative method that can systematically analyze the nonlinear coupling relationship between nitrogen and phosphorus nutrients and aquatic animal diversity, aiming to achieve precise control of nitrogen and phosphorus pollution and scientifically guide lakeshore landscape design and optimization. Summary of the Invention

[0004] This invention aims to provide a method for lakeside landscape configuration based on aquatic animal diversity. The method collects datasets of aquatic animal diversity indicators and corresponding water quality indicators. Using water quality data as independent variables and aquatic animal diversity indicators as dependent variables, curve fitting is performed on each dependent variable and all independent variables to solve for the optimal fitting equation. Based on this, a multi-objective optimization function is further constructed, and an optimization algorithm is used to find the threshold range of each water quality indicator concentration when the aquatic animal diversity indicators reach equilibrium optimality, which serves as the target range for water quality. Based on these threshold ranges, the optimization of the lakeside landscape pattern is scientifically guided to effectively reduce the nitrogen and phosphorus load entering the lake.

[0005] The solution adopted by this invention to achieve its objective is: a method for configuring lakeside landscapes based on aquatic animal diversity, characterized by comprising the following steps: Step 1: Construct an index system for aquatic animal diversity. Aquatic animal diversity assessment indicators include the number of species, population size, and biomass; Step 2: Match water quality data with aquatic animal diversity data: Long-term water quality data and survey data on the number of aquatic animal species, population size, and biomass were collected. The independent and dependent variable datasets were matched by the monitoring and survey dates. Water quality data was recorded as the independent variable dataset, and the survey data on the number of aquatic animal species, population size, and biomass were recorded as the dependent variable dataset. Step 3: Establish response curves for each dependent and independent variable, and determine the coefficient of determination for each curve. R 2 Compare and select the one with the largest R 2 The equation is taken as the optimal curve equation; Step 4: Determine the water quality targets for the water body: Based on the optimal curve equation in step 3, a multi-objective optimization function is further constructed. The optimization algorithm is used to find the water quality concentration threshold range when the number of species, population size and biomass of fish, benthic animals and filter-feeding zooplankton reach equilibrium and optimality. This threshold range is the target range of water quality. Step 5: Using the target range of water quality output in Step 4 as a hard constraint, construct the response function of different lakeside landscape patterns and actual water quality, and determine the lakeside landscape configuration scheme through iterative optimization.

[0006] Preferably, in step 1, the aquatic animals include fish, benthic animals, and filter-feeding zooplankton.

[0007] Preferably, in step 2, the water quality data includes the total nitrogen concentration and total phosphorus concentration of the water body.

[0008] Preferably, in step 2, the long-term series refers to the past 8 to 10 years.

[0009] Preferably, in step 2, for water bodies lacking long-term water quality monitoring and aquatic animal diversity survey data, water samples are collected at monitoring points that are uniformly and randomly selected in the water body, and the water quality data is tested. At the same time, the number of aquatic animal species, population size, and biomass are investigated, with the number of monitoring points being 10 to 30.

[0010] Preferably, in step 3, R language data analysis and visualization software is used to establish the relationship curves between each dependent variable and independent variable.

[0011] Preferably, step 3 includes the following steps: 3.1 Input the datasets of independent and dependent variables obtained in step 2 into statistical analysis software to construct response relationship curves for each dependent and independent variable; 3.2. For each relationship curve R 2 Compare and select the one with the largest R 2 The equations of these equations are taken as the optimal curve equations, and are denoted as follows: Z 1i = f 1i ( X 1, X 2) Z 2i = f 2i ( X 1, X 2) Z 3i = f 3i ( X 1, X 2), of which X 1, X 2 represents the measured values ​​of total nitrogen and total phosphorus concentrations, respectively. i =1, 2, 3, representing fish, benthic animals, and filter-feeding zooplankton, respectively. Z 1i , Z 2i , Z 3i These represent the number of species, population size, and biomass, respectively.

[0012] Preferably, step 4 includes the following steps: 4.1 Based on the established curve equation, construct a multi-objective optimization function (weighted sum method) and calculate the... Z 1i , Z2i , Z 3i When the equilibrium is optimal, the corresponding X 1, X 2 intervals, Max ( Z 1i (This is denoted as the optimal balance between the number of fish, benthic animals, and filter-feeding zooplankton.) X 1 and X 2 intervals, respectively denoted as ( X 1nmin， X 1nmax )and( X 2nmin， X 2nmax ); Max ( Z 2i (This is denoted as the optimal balance between the populations of fish, benthic animals, and filter-feeding zooplankton.) X 1 and X 2 intervals, respectively denoted as ( X 1pmin， X 1pmax )and( X 2pmin， X 2pmax ); Max ( Z 3i (This is denoted as the optimal balance between the biomass of fish, benthic animals, and filter-feeding zooplankton.) X 1 and X 2 intervals, respectively denoted as ( X 1bmin， X 1bmax )and( X 2bmin， X 2bmax ); 4.2 If it exists X 1, X The interval of 2 makes Z 1i , Z 2i , Z 3i If both reach their respective maximum values, then this interval is taken as the target range for water quality, denoted as ( ). X 1tmin， X 1tmax ), ( X 2tmin， X 2tmaxIf no such interval exists, a multi-objective optimization method (weighted sum method) is used to obtain the equilibrium optimal solution, which is then used as the recommended value for the target water quality concentration.

[0013] Preferably, step 5 includes the following steps: 5.1 Data Acquisition and Classification: Based on remote sensing image data and spatial analysis tools, the area proportion data of each land use type in the lakeside area were obtained; 5.2 Construction of Functional Relationship: Construct a response relationship curve between water quality data and area proportion data of each land use type; wherein, the area proportion of each land use type is used as the independent variable, and the total nitrogen and total phosphorus concentrations in the actual water body are used as the dependent variables; 5.3 Determination of Influence Relationships: Based on the function curve determined in step 5.2, determine the impact of the area proportion of different land use types on the reduction and output of total nitrogen and total phosphorus in water bodies; 5.4 Determination of Control Thresholds: Under cost constraints, and in conjunction with the reduction or output impact relationship determined in step 5.3, set reasonable control thresholds for the area proportion of each land use type; among them, the area proportion of land use types that have an output impact on total phosphorus should be reduced; and the area proportion of land use types that have a reduction impact on total nitrogen and total phosphorus should be increased. 5.5 Spatial Configuration Scheme Generation: Based on the control threshold set in step 5.4, the spatial layout and configuration scheme of the lakeside land use structure is optimized by iterative optimization algorithm, and the optimal spatial configuration scheme that meets the water quality target constraint is output.

[0014] Reduction refers to lowering the content of total phosphorus and total nitrogen.

[0015] Output refers to: increasing the content of total phosphorus and total nitrogen.

[0016] Preferably, in step 5.1, the remote sensing images of the lakeside zone are interpreted and classified using ArcGIS spatial analysis tools to obtain land use / cover data, including: construction land, forest land, paddy fields, emergent plant zones (such as reeds and cattails), mudflats (vegetation cover <5%), and grasslands (wetland herbs).

[0017] Preferably, land types that have a net reduction effect on total nitrogen and total phosphorus are typically woodlands, grasslands, emergent vegetation zones, and mudflats; land types that have a net output effect include construction land and paddy fields (especially during the fertilization period). Preferably, in step 5.2, a redundancy analysis method is used to establish a response relationship curve between water quality indicators and the area proportion of each land use type, and to identify key land use types that have a significant impact on water quality and their direction and intensity of influence.

[0018] Preferably, the land use structure spatial configuration scheme in step 5.5 specifically involves: for land use types whose area proportion needs to be reduced, instead of directly changing their land use nature, engineering measures are used to transform their ecological functions into types that reduce the impact of total nitrogen and total phosphorus. By strengthening or restoring their interception and purification capabilities, the equivalent area proportion of the type with reduced impact is systematically increased.

[0019] Preferably, the specific steps include: 5.5.1 Based on the control threshold set in step 5.4, the spatial layout and configuration scheme of each land use type in the lakeside area is determined by an iterative optimization algorithm. The spatial configuration scheme includes areas of the reduction impact type that need to increase their area proportion and areas of the output impact type that need to decrease their area proportion. 5.5.2 For areas with output impact types that need to have their area proportion reduced in the spatial configuration scheme, engineering measures should be adopted to convert their ecological functions into impact reduction types, rather than directly changing their land use types, in order to increase the area proportion of land use types that have a reducing impact on total nitrogen and total phosphorus. 5.5.3 For areas in the spatial configuration scheme that require an increased area ratio to mitigate the impact, engineering measures should be adopted to strengthen or restore their interception and purification functions for total nitrogen and total phosphorus.

[0020] Preferably, in step 5.5.1, the spatial configuration scheme is based on the vertical distance from each grid cell to the shoreline, and sets allowable configuration rules for each land use type within different distance intervals; the allowable configuration rules are as follows: in areas closer to the shoreline, land use types that have a reducing effect on total nitrogen and total phosphorus are given priority; in areas farther from the shoreline, land use types that have an output effect on total nitrogen and total phosphorus are allowed.

[0021] Preferably, the allowed configuration rule is as follows: when the vertical distance from the grid cell to the shoreline is less than or equal to the first distance threshold, the cell is allowed to be configured with a land use type with interception and purification function; when the vertical distance from the grid cell to the shoreline is greater than the first distance threshold, the cell is allowed to be configured with a land use type with output and emission function; the first distance threshold is determined according to the terrain conditions and the current land use status, and the value range is 50 to 200 meters.

[0022] Preferably, the spatial configuration scheme is automatically generated by an iterative optimization algorithm without pre-setting fixed functional partition boundaries; the optimization algorithm uses the water quality target in step 4 as a hard constraint, so that the configuration probability of land use types with interception and purification functions on the water-side decreases with increasing distance from the lake shoreline, while the configuration probability of land use types with output and discharge functions on the water-side increases with increasing distance from the lake shoreline.

[0023] Interception and purification functions refer to types that have the effect of reducing, absorbing, and intercepting total nitrogen and total phosphorus, such as woodlands, grasslands, emergent vegetation zones, and mudflats.

[0024] Output and emission functions refer to types of land that have the function of releasing, outputting, or emitting total nitrogen and total phosphorus, such as construction land and paddy fields.

[0025] Preferably, in step 5.5.2, the ecological function of the output impact type is converted into the engineering measures of the impact reduction type, including: transforming the water-side construction land into a permeable pavement-rain garden-vegetation buffer zone system, which adds runoff purification function while retaining the construction function; and transforming the paddy field into an ecological ditch-wetland composite system, which adds pollutant interception function while retaining the rice planting function.

[0026] Preferably, in step 5.5.3, the engineering measures to strengthen or restore the interception and purification functions of the type of impact reduction include: in forest areas, adding grass filter belts and runoff dispersion facilities to extend the water flow residence time; in mudflats and grassland areas, restoring native wetland plant communities to construct a stepped three-dimensional vegetation structure; in emergent plant belt areas, restoring native emergent plant communities to prevent disturbance and invasion; and in nearshore water areas, restoring submerged plant communities to form a continuous vegetation belt and enhance self-purification capacity.

[0027] Preferably, the restoration of native wetland plant communities, emergent plant communities, and submerged plant communities to construct a stepped, three-dimensional vegetation structure includes the following sub-steps: 5.5.3.1 Species screening: Based on the physiological and ecological characteristics of plants, screen native dominant wetland plants and / or emergent plants and / or submerged plants with nitrogen and phosphorus removal efficiencies higher than the preset threshold and biomass greater than the preset threshold. 5.5.3.2 Experimental Design: Multiple combinations of plant species and multiple planting density gradients were set up; 5.5.3.3 Transect layout: Transects are laid out along the ecological buffer zone of the target lake shoreline; the total number of transects is set as N=a×b, where a is the number of plant species combination levels and b is the number of planting density levels; under each plant species combination condition, different transects are planted at different planting densities. 5.5.3.4 Effectiveness Monitoring: Regularly monitor and calculate the water purification rate of each sample zone; 5.5.3.5 Model Construction: Using plant species combination and planting density as independent variables and water purification rate as dependent variable, a function response model was constructed using R language data analysis and visualization software to quantitatively analyze the influence of plant species composition and planting density on water purification rate. 5.5.3.6 Scheme Determination: Based on the functional relationship constructed in 5.5.3.5, the combination of plant species with the highest water purification rate and their corresponding planting density are selected, the native wetland plant community, emergent plant community and submerged plant community are restored, and a stepped three-dimensional vegetation structure is constructed.

[0028] By optimizing spatial layout, controlling planting density, and designing community structure, a lakeside plant community with efficient nitrogen and phosphorus removal capabilities is constructed.

[0029] The present invention has the following advantages and beneficial effects: The lakeside landscape configuration method based on aquatic animal diversity and the water quality target determination method based on ecological response of the present invention are more scientific and economical.

[0030] This invention breaks away from the traditional approach of setting water quality targets based on the environmental functions and protection objectives of surface water bodies. Instead, it focuses on the actual response of aquatic animal diversity to water quality. Through curve fitting and multi-objective optimization, it solves for the concentration thresholds of each water quality indicator when diversity indicators reach equilibrium, thus defining the target range for the water body. This method makes the establishment of water quality targets more closely aligned with the actual state of the ecosystem, effectively reducing subjective biases from human experience-based judgments, improving the scientific rigor and economic efficiency of target setting, and maximizing the protection of the structural and functional integrity of the aquatic ecosystem.

[0031] The lakeshore landscape configuration method based on aquatic animal diversity of this invention can reshape the ecological function of the lakeshore zone and achieve synergistic improvement of water quality and biodiversity. This invention elevates the lakeshore zone from a traditional "passive interception zone" to an ecological space with active regulation capabilities. Through landscape pattern optimization based on water quality targets, it simultaneously achieves the two major goals of improving water quality and enhancing aquatic animal diversity, significantly enhancing the comprehensive benefits and cost-effectiveness of lakeshore ecological restoration.

[0032] The method of this invention takes aquatic animal diversity as the ultimate goal, rather than simply pursuing water quality indicators, and constructs a complete causal chain of "aquatic animals → water quality → landscape". Attached Figure Description

[0033] Figure 1 The diagram shown is a flowchart illustrating the method of the present invention. Figure 2 The diagram shown is a flowchart of step 5 of the present invention. Detailed Implementation

[0034] To better understand the present invention, the following embodiments are further illustrations of the present invention, but the content of the present invention is not limited to the following embodiments.

[0035] In this invention, biomass refers to the dry weight of organic matter (including the weight of food stored in the organism) actually existing in a unit area at a specific time, and is mainly used to characterize weight.

[0036] The number of species refers to the total number of species of a particular biological category in a specific area, specifically how many species are in that biological category, and is mainly used to characterize richness.

[0037] Population size refers to the number of individuals of all species of a particular biological category in a specific area, and is mainly used to characterize abundance.

[0038] Example 1 like Figure 1 As shown, a lakeside landscape configuration method based on aquatic animal diversity includes the following steps: Step 1: Construct an index system for aquatic animal diversity. Specific aquatic animals include fish, benthic animals, and filter-feeding zooplankton. Specific indicators for evaluating aquatic animal diversity include the number of species, population size, and biomass.

[0039] Step 2: Match water quality data with aquatic animal diversity data: Long-term water quality data and survey data on the number of aquatic animal species, population size, and biomass were collected. The independent and dependent variable datasets were matched by the monitoring and survey dates. Water quality data was designated as the independent variable dataset, and the survey data on the number of aquatic animal species, population size, and biomass were designated as the dependent variable dataset. The water quality data included the total nitrogen concentration and total phosphorus concentration in the water body.

[0040] Step 3: Use R language data analysis and visualization software to establish the relationship curves between each dependent and independent variable, and analyze each relationship curve. R 2 Compare and select the one with the largest R 2 The equation is taken as the optimal curve equation.

[0041] The specific steps are as follows: 3.1 Input the datasets of independent and dependent variables obtained in step 2 into R language data analysis and visualization software to construct the relationship curves between each dependent and independent variable; 3.2. For each relationship curve R 2 Compare and select the one with the largest R 2 The equations of these equations are taken as the optimal curve equations, and are denoted as follows: Z 1i = f 1i ( X 1,X 2) Z 2i = f 2i ( X 1, X 2) Z 3i = f 3i ( X 1, X 2), of which X 1, X 2 represents the measured values ​​of total nitrogen and total phosphorus concentrations, respectively. i =1, 2, 3, representing fish, benthic animals, and filter-feeding zooplankton, respectively. Z 1i , Z 2i , Z 3i These represent the number of species, population size, and biomass, respectively.

[0042] Step 4: Determine water quality improvement targets: Based on the curve equation in step 3, construct a multi-objective optimization function and calculate the water quality concentration threshold range that achieves the optimal balance between the number of species, population size, and biomass of fish, benthic animals, and filter-feeding zooplankton. This threshold range is the target range for water quality.

[0043] The specific steps are as follows: 4.1 Based on the established curve equation, construct a multi-objective optimization function (weighted sum method) and calculate the... Z 1i , Z 2i , Z 3i When the equilibrium is optimal, the corresponding X 1, X 2 intervals, Max ( Z 1i (This is denoted as the optimal balance between the number of fish, benthic animals, and filter-feeding zooplankton.) X 1 and X 2 intervals, respectively denoted as ( X 1nmin， X 1nmax )and( X 2nmin， X 2nmax ); Max ( Z 2i (This is denoted as the optimal balance between the populations of fish, benthic animals, and filter-feeding zooplankton.)X 1 and X 2 intervals, respectively denoted as ( X 1pmin， X 1pmax )and( X 2pmin， X 2pmax ); Max ( Z 3i (This is denoted as the optimal balance between the biomass of fish, benthic animals, and filter-feeding zooplankton.) X 1 and X 2 intervals, respectively denoted as ( X 1bmin， X 1bmax )and( X 2bmin， X 2bmax ); 4.2 If it exists X 1, X The interval of 2 makes Z 1i , Z 2i , Z 3i If both reach their respective maximum values, then this interval is taken as the target range for water quality, denoted as ( ). X 1tmin， X 1tmax ), ( X 2tmin， X 2tmax If no such interval exists, a multi-objective optimization method (weighted sum method) is used to obtain the equilibrium optimal solution, which is then used as the recommended value for the target water quality concentration.

[0044] Step 5: Determine the optimal landscape layout scheme; We construct response functions of water quality indicators to different landscape patterns, and determine the landscape spatial layout and configuration scheme through iterative optimization.

[0045] like Figure 2 As shown, the specific steps are as follows: 5.1 Data Acquisition and Classification: Based on remote sensing image data and combined with ArcGIS spatial analysis tools, land use type data of the lakeside area are interpreted and acquired; the land use types include at least construction land, forest land, paddy fields, emergent vegetation zones (such as reeds and cattails), mudflats (vegetation cover <5%), and grasslands (wetland herbs). 5.2 Construction of Functional Relationship: The redundancy analysis method was used to construct the response relationship curve between water quality index data and land use data; where the area proportion of each land use type was used as the independent variable and the total nitrogen and total phosphorus concentrations in the water body were used as the dependent variables. 5.3 Determination of Impact Relationships: Based on the function curves determined in step 5.2, determine the impact of different land use types on the reduction and output of total nitrogen and total phosphorus; 5.4 Determination of Control Thresholds: Under cost constraints, and in conjunction with the reduction and output impact relationship determined in step 5.3, reasonable control thresholds are set for each land use type; among them, the area proportion of land use types that have an output impact on total phosphorus should be reduced; and the area proportion of land use types that have a reduction impact on total nitrogen and total phosphorus should be increased. 5.5 Spatial Configuration Scheme Generation: Based on the control threshold set in step 5.4, the spatial layout and configuration scheme of land use structure is determined through iterative optimization algorithm.

[0046] In step 5.5, the land use structure spatial configuration scheme specifically involves: for land use types whose area proportion needs to be reduced, instead of directly changing their land use nature, engineering measures are used to transform their ecological functions into types that reduce the impact of total nitrogen and total phosphorus. By strengthening or restoring their interception and purification capabilities, the equivalent area proportion of the impact-reducing types is systematically increased.

[0047] Specifically, the following steps are included: 5.5.1 Based on the control threshold set in step 5.4, the spatial layout and configuration scheme of each land use type in the lakeside area is determined by an iterative optimization algorithm. The spatial configuration scheme includes areas of the type that need to increase the area proportion to reduce the impact and areas of the type that need to reduce the area proportion to reduce the impact. 5.5.2 For areas with output impact types that need to have their area proportion reduced in the spatial configuration scheme, engineering measures should be adopted to convert their ecological functions into impact reduction types, rather than directly changing their land use types, in order to increase the area proportion of land use types that have a reducing impact on total nitrogen and total phosphorus. 5.5.3 For areas in the spatial configuration scheme that require an increased area ratio to mitigate the impact, engineering measures should be adopted to strengthen or restore their interception and purification functions for total nitrogen and total phosphorus.

[0048] In step 5.5.1, the spatial configuration scheme is based on the vertical distance from each grid cell to the shoreline, and sets the allowable configuration rules for each land use type within different distance intervals. The allowable configuration rules are as follows: in areas closer to the shoreline, land use types that have a reducing effect on total nitrogen and total phosphorus are given priority; in areas farther from the shoreline, land use types that have an output effect on total nitrogen and total phosphorus are allowed.

[0049] The specific configuration rules are as follows: when the vertical distance from the grid cell to the shoreline is less than or equal to the first distance threshold, the cell is allowed to be configured with a land use type that has an interception and purification function; when the vertical distance from the grid cell to the shoreline is greater than the first distance threshold, the cell is allowed to be configured with a land use type that has an output and emission function; the first distance threshold is determined based on the terrain conditions and the current land use status, and the value range is 50 to 200 meters.

[0050] The spatial configuration scheme is automatically generated through an iterative optimization algorithm without pre-setting fixed functional zoning boundaries. The optimization algorithm uses the water quality target in step four as a hard constraint, so that the probability of land use types with interception and purification functions being configured on the water-side decreases with increasing distance from the lake shoreline, while the probability of land use types with output and discharge functions being configured on the water-side increases with increasing distance from the lake shoreline.

[0051] Interception and purification functions refer to types that have the effect of reducing, absorbing, and intercepting total nitrogen and total phosphorus, such as woodlands, emergent vegetation zones, mudflats, and grasslands.

[0052] Output and emission functions refer to types of land that have the function of releasing, outputting, or emitting total nitrogen and total phosphorus, such as construction land and paddy fields.

[0053] In step 5.5.2, the ecological functions of the output impact type are converted into engineering measures of the impact reduction type, including: transforming the water-side construction land into a permeable pavement-rain garden-vegetation buffer zone system, which adds runoff purification function while retaining the construction function; and transforming paddy fields into an ecological ditch-wetland composite system, which adds pollutant interception function while retaining the rice planting function.

[0054] In step 5.5.3, engineering measures to strengthen or restore the interception and purification functions of the impact reduction type include: in forest areas, adding grass filter belts and runoff dispersion facilities to extend the water residence time; in mudflats and grasslands, restoring native wetland plant communities and constructing a stepped three-dimensional vegetation structure; in emergent plant belt areas, restoring the integrity of native emergent plant communities to prevent disturbance and invasion; and in nearshore water areas, restoring submerged plant communities to form a continuous vegetation belt and enhance self-purification capacity.

[0055] Restoring native wetland plant communities, emergent plant communities, and submerged plant communities to construct a stepped, three-dimensional vegetation structure includes the following sub-steps: 5.5.3.1 Species screening: Based on the physiological and ecological characteristics of plants, screen native dominant wetland plants and / or emergent plants and / or submerged plants with nitrogen and phosphorus removal efficiencies higher than the preset threshold and biomass greater than the preset threshold. 5.5.3.2 Experimental Design: Multiple combinations of plant species and multiple planting density gradients were set up; 5.5.3.3 Transect layout: Transects are laid out along the ecological buffer zone of the target lake shoreline; the total number of transects is set as N=a×b, where a is the number of plant species combination levels and b is the number of planting density levels; under each plant species combination condition, different transects are planted at different planting densities. 5.5.3.4 Effectiveness Monitoring: Regularly monitor and calculate the water purification rate of each sample zone; 5.5.3.5 Model Construction: Using plant species combination and planting density as independent variables and water purification rate as dependent variable, a function response model was constructed using R language data analysis and visualization software to quantitatively analyze the influence of plant species composition and planting density on water purification rate. 5.5.3.6 Scheme Determination: Based on the functional relationship constructed in 5.5.3.5, the combination of plant species with the highest water purification rate and their corresponding planting density are selected, the native wetland plant community, emergent plant community and submerged plant community are restored, and a stepped three-dimensional vegetation structure is constructed.

[0056] By optimizing spatial layout, controlling planting density, and designing community structure, a lakeside plant community with efficient nitrogen and phosphorus removal capabilities is constructed.

[0057] Example 2 A method for configuring lakeside landscapes based on aquatic animal diversity includes the following steps: Step 1: Construct an index system for aquatic animal diversity. The aquatic animals selected in this embodiment include three categories: fish, benthic animals, and filter-feeding zooplankton.

[0058] The constructed aquatic animal diversity evaluation indicators include the number of species, population size, and biomass, forming a total of 9 evaluation indicators.

[0059] Step 2: Match water quality data with aquatic animal diversity data: In this embodiment, since long-term water quality monitoring and aquatic animal diversity survey data were lacking in the implementation area, data were collected using the following methods: Representative monitoring points were selected along the lake shoreline, with a total of 10 to 30 monitoring points set up to cover different water quality conditions and lake shoreline landscape types.

[0060] Water samples were collected at each monitoring point, and the following water quality indicators were tested: total nitrogen concentration was determined by alkaline potassium persulfate digestion ultraviolet spectrophotometry (HJ 636–2012), total phosphorus concentration was determined by ammonium molybdate spectrophotometry (GB / T 11893–1989), and ammonia nitrogen concentration was determined by Nessler's reagent spectrophotometry (HJ 535–2009).

[0061] Aquatic animal surveys were conducted simultaneously at each monitoring point: fish were sampled using multi-mesh composite gillnets, benthic animals were sampled using Petersen mud collectors, and filter-feeding zooplankton were sampled using planktonic nets. The number of species, population size, and biomass of each group were counted.

[0062] The water quality test data from each monitoring point are matched one-to-one with the aquatic animal survey data to form a dataset for step 3.

[0063] Step 3: Input the independent variable dataset (total nitrogen concentration X1, total phosphorus concentration X2) and dependent variable dataset (9 aquatic animal diversity indicators) obtained in Step 2 into the R language data analysis and visualization software.

[0064] In R software, various forms of relationship curves are constructed for each set of independent and dependent variables, and the coefficient of determination of each relationship curve is calculated. R 2 Compare and select the one with the largest R 2 The equation is taken as the optimal curve equation.

[0065] The specific steps are as follows: 3.1 Input the datasets of independent and dependent variables obtained in step 2 into R language data analysis and visualization software to construct the relationship curves between each dependent and independent variable; 3.2. Coefficients of determination for each relationship curve R 2 Compare and select the one with the largest R 2 The equations of these equations are taken as the optimal curve equations, and are denoted as follows: Z 1i = f 1i ( X 1, X 2) Z 2i = f 2i ( X 1, X 2) Z 3i = f 3i ( X 1, X 2), of which X 1, X 2 represents the measured values ​​of total nitrogen and total phosphorus concentrations, respectively. i =1, 2, 3, representing fish, benthic animals, and filter-feeding zooplankton, respectively. Z 1i , Z 2i ,Z 3i These represent the number of species, population size, and biomass, respectively.

[0066] In this embodiment, the optimal curve equation for the number of fish species is the logistic model ( R 2 =0.82), the optimal curve equation for fish population size is the exponential model ( R 2 =0.78), the optimal curve equation for fish biomass is a polynomial model ( R 2 =0.75); the optimal curve equation for the number of benthic animal species is the logistic model ( R 2 =0.79), the optimal curve equation for benthic animal population size is a linear model ( R 2 =0.71), the optimal curve equation for benthic animal biomass is the exponential model ( R 2 =0.73); the optimal curve equation for the number of filter-feeding zooplankton species is the logistic model ( R 2 =0.81), the optimal curve equation for the population size of filter-feeding zooplankton is a polynomial model ( R 2 =0.76), the optimal curve equation for filter-feeding zooplankton biomass is the logistic model ( R 2 =0.80).

[0067] Step 4: Determine water quality improvement targets: Using the curve equation established in step 3, a multi-objective optimization function is constructed to calculate the water quality concentration threshold range that achieves the optimal balance between the number of species, population size, and biomass of fish, benthic animals, and filter-feeding zooplankton. This threshold range is the target range for water quality.

[0068] The specific steps are as follows: 4.1. Use equations to obtain water quality data when the number of species, population size, and biomass of fish, benthic animals, and filter-feeding zooplankton reach equilibrium. Construct a multi-objective optimization function using the established curve equation, and calculate the optimal water quality data. Z 1i , Z 2i , Z 3i When the equilibrium is optimal X 1, X The concentration threshold range of 2, Max ( Z 1i(This is denoted as the optimal balance between the number of fish, benthic animals, and filter-feeding zooplankton.) X 1 and X 2. The ranges are respectively denoted as ( X 1nmin， X 1nmax )and( X 2nmin， X 2nmax In this embodiment, the solution results are: total nitrogen 0.50~1.20 mg / L, total phosphorus 0.04~0.09 mg / L; Max ( Z 2i (This is denoted as the optimal balance between the populations of fish, benthic animals, and filter-feeding zooplankton.) X 1 and X 2. The ranges are respectively denoted as ( X 1pmin， X 1pmax )and( X 2pmin， X 2pmax In this embodiment, the solution results are: total nitrogen 0.55~1.15 mg / L, total phosphorus 0.045~0.085 mg / L; Max ( Z 3i (This is denoted as the optimal balance between the biomass of fish, benthic animals, and filter-feeding zooplankton.) X 1 and X 2. The ranges are respectively denoted as ( X 1bmin， X 1bmax )and( X 2bmin， X 2bmax In this embodiment, the solution results are 0.60~1.10 mg / L for total nitrogen and 0.05~0.08 mg / L for total phosphorus.

[0069] 4.2. Further solve for the simultaneous satisfaction based on the curve equation. Z 1i , Z 2i , Z 3i Simultaneously taking the maximum value X 1, X The concentration threshold range of 2 is denoted as ( X 1tmin， X 1tmax ), ( X 2tmin， X2tmax The threshold range is the target value for improving water quality. This embodiment calculates that there exists an optimal water quality range that simultaneously meets all three indicators: total nitrogen threshold range is 0.60~1.10 mg / L, and total phosphorus threshold range is 0.05~0.08 mg / L. This threshold range is the target value for improving water quality.

[0070] This embodiment simultaneously satisfies Z 1i Z 2i Z 3i The water concentration threshold range at which the maximum value is obtained does not require multi-objective optimization.

[0071] This threshold range serves as a hard constraint for subsequent steps.

[0072] Step 5: Determine the optimal landscape layout scheme; We construct response functions of water quality indicators to different landscape patterns, and determine the landscape spatial layout and configuration scheme through iterative optimization.

[0073] The specific steps are as follows: 5.1 Data Acquisition and Classification: Based on remote sensing image data and combined with ArcGIS spatial analysis tools, interpret and obtain the land use type area percentage data of the target watershed (construction land 10%, forest land 20%, paddy fields 40%, emergent vegetation zone (such as reeds and cattails) 10%, mudflats (vegetation cover <5%) 7%, and grassland (wetland herbaceous, non-emergent) 13%). 5.2 Construction of Functional Relationship: The redundancy analysis method was used to construct the response relationship curve between water quality index data and land use data; where the area proportion of each land use type was used as the independent variable and the total nitrogen and total phosphorus concentrations in the water body were used as the dependent variables. 5.3 Determination of Impact Relationships: Based on the function curves determined in step 5.2, determine the impact of different land use types on the reduction and output of total nitrogen and total phosphorus; For example, construction land and paddy fields have an output effect (i.e., increasing the area proportion of these land types will lead to an increase in the total nitrogen and total phosphorus concentrations in the actual water body), so their area proportion needs to be reduced; forest land, emergent vegetation belts, mudflats, and grasslands have a mitigation effect (i.e., increasing the area proportion of these land types will lead to a decrease in the total nitrogen and total phosphorus concentrations in the actual water body), so their area proportion needs to be increased.

[0074] 5.4 Determination of Control Thresholds: Under cost constraints, and in conjunction with the reduction or output impact relationship determined in step 5.3, set reasonable control thresholds for each land use type; among them, the area proportion of land use types that have an output impact on total phosphorus should be reduced; and the area proportion of land use types that have a reduction impact on total nitrogen and total phosphorus should be increased.

[0075] The total budget for this embodiment is 15 million yuan. Under the budget constraint of 15 million yuan, the control thresholds for the area ratio of each land type are as follows: construction land is reduced from the current 10% to 6%~8%, paddy fields are reduced from the current 40% to 25%~27%, forest land is increased from the current 20% to 28%~30%, emergent vegetation zone is maintained at 10%~15%, mudflats are increased from the current 7% to 8%~10%, and grassland is increased from the current 13% to 16%~17%.

[0076] 5.5 Spatial Configuration Scheme Generation: Based on the control threshold set in step 5.4, the spatial layout and configuration scheme of land use structure is determined through iterative optimization algorithm.

[0077] This embodiment does not predefine fixed functional zone boundaries. Based on the vertical distance of each grid unit to the shoreline, permissible configuration rules are set: in areas closer to the shoreline, land use types with interception and purification functions are prioritized; in areas farther from the shoreline, land use types with emission output functions are permitted. Specifically, the permissible configuration rules are: when the vertical distance of a grid unit to the shoreline is less than or equal to 100 meters, that unit is allowed to be configured with a type having interception and purification functions; when the vertical distance of a grid unit to the shoreline is greater than 100 meters, that unit is allowed to be configured with a type having emission output functions.

[0078] Using a defined total phosphorus concentration of 0.05–0.08 mg / L and total nitrogen concentration of 0.60–1.10 mg / L as hard constraints, and minimizing total cost as the objective function, an iterative optimization algorithm is employed to optimize the spatial layout of land use. The optimization algorithm uses each grid cell as a decision variable, the water quality target from step four as a hard constraint, and the control direction from step five as a guide, automatically optimizing the land use type configuration of each grid cell.

[0079] During the optimization process, each candidate solution needs to call the response function in step 5.2 to predict the corresponding actual total nitrogen and total phosphorus concentrations in the water body based on the landscape pattern indicators of that solution. Then, it is determined whether the predicted concentrations meet the hard constraints, and at the same time, it is checked whether they meet the allowable configuration rules and area proportion constraints. The solution that meets all hard constraints is a feasible solution, and feasible solutions are better than infeasible solutions; among the feasible solutions, the solution with the lowest total cost is selected.

[0080] Through iterative optimization, an optimal spatial configuration scheme that meets the actual water quality targets and cost constraints was obtained. In this scheme, land use types with interception and purification functions are clustered on the water-adjacent side, while land use types with discharge functions are clustered on the land side, but continuous and regular zoning boundaries are not required.

[0081] For areas with emission output functions that need to have their area proportion reduced in the spatial configuration plan (originally construction land and paddy fields), engineering measures are adopted to transform their ecological functions into those with interception and purification functions, rather than directly changing their land use type.

[0082] For waterfront construction areas, the project will be transformed into a permeable paving-rain garden-vegetated buffer zone system. Specific measures include: converting impermeable hardened surfaces to permeable concrete or grass pavers; creating rain gardens at storm drains, filled with improved soil and absorbent media, and planted with hygrophytes such as irises and cannas; and establishing a vegetated buffer zone between buildings and the lakeshore, planted with a multi-layered native tree, shrub, and grass vegetation. This will enhance runoff purification while preserving the building's functionality.

[0083] For paddy fields, the transformation into an ecological ditch-wetland composite system includes the following measures: setting up ecological ditches inside the fields, laying zeolite and ceramsite as adsorption media at the bottom of the ditches, and using interlocking block structures with pre-reserved planting holes for the ditches; constructing small surface flow wetlands at the end of the fields and planting emergent plants such as reeds and cattails; and extending the hydraulic retention time to increase the pollutant interception function while retaining the function of rice cultivation.

[0084] For areas with interception and purification functions that require an increased area proportion in the spatial configuration plan (originally woodland, emergent vegetation belt, mudflats, and grasslands), engineering measures are adopted to enhance or restore their interception and purification functions for total nitrogen and total phosphorus: For forest areas, additional grass-planted filter belts and runoff dispersion facilities will be added. Specific measures include: planting shade-tolerant herbaceous plants in forest clearings to form filter belts; setting up runoff dispersion ditches along contour lines to guide surface runoff to flow evenly across the forest, increasing water infiltration and pollutant retention time.

[0085] For mudflats and grasslands, the restoration of native wetland plant communities and the construction of a stepped, three-dimensional vegetation structure include: constructing a three-tiered terrain structure with alternating terraces and depressions along the lakeshore from water to land, with emergent plant zones, wetland plant zones, and tree-shrub-grass composite zones arranged sequentially from low to high. The wetland plant zones will be planted with wetland shrubs such as *Cornus hainanensis*, *Spiraea japonica*, and *Lespedeza bicolor*, using a dense, triangular planting pattern; the tree-shrub-grass composite zones will have trees such as *Metasequoia glyptostroboides* planted on the slope tops to form a three-dimensional restoration structure. Climbing plants such as *Lonicera japonica* and *Trachelospermum jasminoides* will be planted at the base of the ecological retention walls.

[0086] For emergent vegetation zones, plant reeds, cattails, and calamus, with a planting density of 30-45 plants per square meter.

[0087] For the nearshore zone of water bodies, the restoration of submerged plant communities to create an "underwater forest" includes specific measures such as: planting native submerged plants such as Vallisneria natans, Hydrilla verticillata, and Ceratophyllum demersum using artificial cuttings, with a planting density of 15 cuttings per square meter; and scientifically regulating water levels through dikes to ensure that the growth needs of submerged plants can be met during both the wet and dry seasons.

[0088] In addition, ecological restoration of the shoreline was carried out simultaneously: natural stone slope protection or "hexagonal chain" ecological slope protection technology was adopted, and hexagonal vegetation blocks with a porosity of more than 30% were reserved to construct micro-habitats for benthic organisms; pond embankments were broken up and water systems were connected to improve hydrodynamic conditions; environmentally friendly cutter suction dredgers were used to remove eutrophic bottom sediment, and the construction area was sealed with anti-fouling curtains before construction to prevent disturbance from spreading to the lake area; dead aquatic plants and invasive species were salvaged to restore habitat space for native plants.

[0089] This embodiment achieves the following technical effects through the above steps: First, the ultimate goal is to achieve aquatic biodiversity. Through multi-objective optimization, we ensure that the water quality threshold range can support a balanced and optimal balance among fish, benthic animals, and filter-feeding zooplankton.

[0090] Second, regulation should be based on ecological functions. By identifying the interception and purification functions or the output and emission functions of each land use type for total nitrogen and total phosphorus, the direction of regulation can be determined, without being limited to specific land type names.

[0091] Third, fixed zoning boundaries are not preset. Spatial configuration schemes are automatically generated through iterative optimization algorithms, allowing configuration rules to be set based on distance thresholds, but without requiring the formation of continuous and regular strip-shaped zones, which is more in line with the reality of scattered land use distribution in the lakeside area.

[0092] Fourth, determine the configuration plan before implementing engineering measures. First, use optimization algorithms to determine which areas require functional adjustments, and then determine specific engineering measures for these areas. This logical sequence is scientific and reasonable.

[0093] Fifth, engineering measures transform the function rather than directly changing the land type. For emission-producing areas that need to reduce their area proportion, engineering measures are used to transform their ecological function into an interception and purification function, while preserving their original land use function, thus reducing the difficulty and cost of implementation.

[0094] Sixth, costs are controllable. Within a budget constraint of 15 million yuan, regulatory priorities are determined by ranking marginal benefits.

[0095] Seventh, scientific plant configuration. By constructing a tiered, three-dimensional vegetation structure, the restoration of "wet-emergent-submerged" plant communities is systematically achieved, effectively enhancing the ability to intercept and purify nitrogen and phosphorus.

[0096] The above description is merely a preferred embodiment of the present invention, and should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for configuring lakeside landscapes based on aquatic animal diversity, characterized in that, Includes the following steps: Step 1: Construct an index system for aquatic animal diversity. Aquatic animal diversity assessment indicators include the number of species, population size, and biomass; Step 2: Match water quality data with aquatic animal diversity data: Long-term water quality data and survey data on the number of aquatic animal species, population size, and biomass were collected. The independent and dependent variable datasets were matched by the monitoring and survey dates. Water quality data was recorded as the independent variable dataset, and the survey data on the number of aquatic animal species, population size, and biomass were recorded as the dependent variable dataset. Step 3: Establish response curves for each dependent and independent variable, and determine the coefficient of determination for each curve. R 2 Compare and select the one with the largest R 2 The equation is taken as the optimal curve equation; Step 4: Determine the water quality targets for the water body: Based on the optimal curve equation in step 3, a multi-objective optimization function is further constructed. The optimization algorithm is used to find the water quality concentration threshold range when the number of species, population size and biomass of fish, benthic animals and filter-feeding zooplankton reach equilibrium and optimality. This threshold range is the target range of water quality. Step 5: Using the target range of water quality output in Step 4 as a hard constraint, construct the response function of different lakeside landscape patterns and actual water quality, and determine the lakeside landscape configuration scheme through iterative optimization.

2. The lakeside landscape configuration method based on aquatic animal diversity according to claim 1, characterized in that: In step 1, the aquatic animals include fish, benthic animals, and filter-feeding zooplankton.

3. The lakeside landscape configuration method based on aquatic animal diversity according to claim 1, characterized in that: In step 2, the water quality data includes the total nitrogen concentration and total phosphorus concentration of the water body.

4. The lakeside landscape configuration method based on aquatic animal diversity according to claim 1, characterized in that: In step 2, the long-term series refers to the past 8 to 10 years.

5. The lakeside landscape configuration method based on aquatic animal diversity according to claim 1, characterized in that: In step 2, for water bodies lacking long-term water quality monitoring and aquatic animal diversity survey data, water samples are collected at monitoring points that are uniformly and randomly selected in the water body, and the water quality data is tested. At the same time, the number of aquatic animal species, population size, and biomass are investigated. The number of monitoring points is 10 to 30.

6. The lakeside landscape configuration method based on aquatic animal diversity according to claim 1, characterized in that: In step 3, the relationship curves between each dependent variable and independent variable are established using R language data analysis and visualization software.

7. The lakeside landscape configuration method based on aquatic animal diversity according to claim 1, characterized in that: Step 3 includes the following steps: 3.1 Input the datasets of independent and dependent variables obtained in step 2 into statistical analysis software to construct response relationship curves for each dependent and independent variable; 3.

2. For each relationship curve R 2 Compare and select the one with the largest R 2 The equations of these equations are taken as the optimal curve equations, and are denoted as follows: Z 1i = f 1i ( X 1, X 2) Z 2i = f 2i ( X 1, X 2) Z 3i = f 3i ( X 1, X 2), of which X 1, X 2 represents the measured values ​​of total nitrogen and total phosphorus concentrations, respectively. i =1, 2, 3, representing fish, benthic animals, and filter-feeding zooplankton, respectively. Z 1i , Z 2i , Z 3i These represent the number of species, population size, and biomass, respectively.

8. The lakeside landscape configuration method based on aquatic animal diversity according to claim 1, characterized in that: In step 4 Includes the following steps: 4.1 Based on the established curve equation, construct a multi-objective optimization function and calculate the optimal function. Z 1i , Z 2i , Z 3i When the equilibrium is optimal, the corresponding X 1, X 2 intervals, Max ( Z 1i (This is denoted as the optimal balance between the number of fish, benthic animals, and filter-feeding zooplankton.) X 1 and X 2 intervals, respectively denoted as ( X 1nmin， X 1nmax )and( X 2nmin， X 2nmax ); Max ( Z 2i (This is denoted as the optimal balance between the populations of fish, benthic animals, and filter-feeding zooplankton.) X 1 and X 2 intervals, respectively denoted as ( X 1pmin， X 1pmax )and( X 2pmin， X 2pmax ); Max ( Z 3i (This is denoted as the optimal balance between the biomass of fish, benthic animals, and filter-feeding zooplankton.) X 1 and X 2 intervals, respectively denoted as ( X 1bmin， X 1bmax )and( X 2bmin， X 2bmax ); 4.2 If it exists X 1, X The interval of 2 makes Z 1i , Z 2i , Z 3i If both reach their respective maximum values, then this interval is taken as the target range for water quality, denoted as ( ). X 1tmin， X 1tmax ), ( X 2tmin， X 2tmax If no such interval exists, a multi-objective optimization method is used to obtain the equilibrium optimal solution, which is then used as the recommended value for the target water quality concentration.

9. The lakeside landscape configuration method based on aquatic animal diversity according to claim 1, characterized in that: In step 5 Includes the following steps: 5.1 Data Acquisition and Classification: Based on remote sensing image data and spatial analysis tools, the area proportion data of each land use type in the lakeside area were obtained; 5.2 Construction of Functional Relationship: Construct a response relationship curve between water quality data and area proportion data of each land use type; wherein, the area proportion of each land use type is used as the independent variable, and the total nitrogen and total phosphorus concentrations in the actual water body are used as the dependent variables; 5.3 Determination of Influence Relationships: Based on the function curve determined in step 5.2, determine the impact of the area proportion of different land use types on the reduction and output of total nitrogen and total phosphorus in water bodies; 5.4 Determination of Control Thresholds: Under cost constraints, and in conjunction with the reduction or output impact relationship determined in step 5.3, set reasonable control thresholds for the area proportion of each land use type; among them, the area proportion of land use types that have an output impact on total phosphorus should be reduced; and the area proportion of land use types that have a reduction impact on total nitrogen and total phosphorus should be increased. 5.5 Spatial Configuration Scheme Generation: Based on the control threshold set in step 5.4, the spatial layout and configuration scheme of the lakeside land use structure is optimized by iterative optimization algorithm, and the optimal spatial configuration scheme that meets the water quality target constraint is output.

10. The lakeside landscape configuration method based on aquatic animal diversity according to claim 1, as described in claim 9, is characterized in that: In step 5.5, the spatial layout and configuration scheme of land use structure specifically includes: for land use types that need to reduce their area proportion, their ecological functions are converted into types that have a reducing effect on total nitrogen and total phosphorus, and engineering measures are adopted to strengthen or restore their interception and purification functions on total nitrogen and total phosphorus, so as to increase the area proportion of land use types that have a reducing effect on total nitrogen and total phosphorus.

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