A simulation method for the distribution of suitable spawning habitats of sedentary fish in a river-connected lake

Abstract

The present invention provides a method for simulating the distribution of suitable spawning habitats for sedentary fish in lakes connected to rivers, belonging to the technical field of ecological protection, and comprising the following steps: Step S1, selecting typical sedentary fish species in lakes connected to rivers as the research object; Step S2, investigating the habitat factors affecting their spawning habitats, clarifying the suitable ranges of each habitat factor, and drawing the suitability curves of each habitat factor; Step S3, constructing a hydrodynamic model for the area of lakes connected to rivers; Step S4, calculating the normalized difference vegetation index; Step S5, according to the fuzzy logic method, taking each habitat factor and the normalized difference vegetation index as inputs, constructing a suitability simulation model for the suitable spawning habitats of sedentary fish in lakes connected to rivers, and calculating the comprehensive suitability index value of the spawning habitats. The present invention comprehensively considers factors such as water depth, flow velocity, and vegetation cover required for fish spawning, and can simulate and quantify the changes in the comprehensive suitability of spawning habitats for sedentary fish in lakes connected to rivers.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological protection, and in particular to a method for simulating the distribution of suitable spawning habitats for resident fish in lakes connected to rivers. Background Art

[0002] Resident fish in lakes connected to rivers reproduce naturally in the lake area, which is the main body of the lake fishery and plays a huge role in the natural fishery function. In addition to suitable temperature, water quality and water depth conditions, vegetation medium is also a necessary condition for the spawning of resident fish. Therefore, the simulation technology for the distribution of suitable spawning habitats is relatively complex. Most of the existing studies focus on simulating and analyzing the suitable hydrodynamic conditions for migratory fish, and there is still a lack of simulation technology for the distribution of suitable spawning habitats for resident fish that includes hydrological, hydrodynamic and vegetation medium factors. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for simulating the distribution of suitable spawning habitats for resident fish in lakes connected to rivers, which comprehensively considers factors such as water depth, flow velocity, and vegetation cover required for fish spawning, and can simulate and quantify the change of the comprehensive suitability of spawning habitats for resident fish in lakes connected to rivers.

[0004] To achieve the above purpose, the present invention provides a method for simulating the distribution of suitable spawning habitats for resident fish in lakes connected to rivers, including the following steps:

[0005] Step S1: Select typical species of resident fish in lakes connected to rivers as the research object;

[0006] Step S2: According to the typical species of resident fish in lakes connected to rivers selected in Step S1, investigate the habitat factors affecting their spawning habitats, clarify the suitable ranges of each habitat factor, and draw the suitability curves of each habitat factor;

[0007] Step S3: Construct a hydrodynamic model for the area of lakes connected to rivers, and perform calibration and verification;

[0008] Step S4: Use remote sensing technology to extract the vegetation spectral information of the area of lakes connected to rivers, and then obtain the vegetation distribution map and calculate the normalized difference vegetation index;

[0009] Step S5: According to the fuzzy logic method, take each habitat factor and the normalized difference vegetation index as inputs, construct a suitability simulation model for the suitable spawning habitats of resident fish in lakes connected to rivers, and then calculate the comprehensive suitability index value of the spawning habitats;

[0010] Step S51: Establish a membership function;

[0011] Step S52: Fuzzify the clear values through the membership function to obtain the input fuzzy set;

[0012] Step S53: Formulate fuzzy rules to relate habitat factors, normalized difference vegetation index (NDVI) and the comprehensive suitability index;

[0013] Step S54: Conduct fuzzy inference to obtain the output fuzzy set, and use the centroid method to defuzzify the output fuzzy set, thereby calculating the comprehensive suitability index values of the spawning habitats corresponding to typical fish species in each grid cell of the hydrodynamic model;

[0014] Step S55: According to the calculated comprehensive suitability index values of the spawning habitats, classify the habitat quality of the river-connected lake resident fish during the spawning period into different suitability levels, and draw a map of the comprehensive suitability evaluation results of the spawning habitats of the river-connected lake resident fish.

[0015] Preferably, in step S2, the habitat factors include water depth, flow velocity, and vegetation coverage.

[0016] Preferably, in step S4, the formula for calculating the normalized difference vegetation index (NDVI) is as follows:

[0017] ;

[0018] where, is the reflectance in the near-infrared band; is the reflectance in the red band.

[0019] Preferably, in step S52, the input fuzzy set is represented as follows:

[0020] ;

[0021] where, is the input fuzzy set; are the respective habitat factors, .

[0022] Preferably, in step S54, the output fuzzy set is represented as follows:

[0023] ;

[0024] where, is the output fuzzy set, i.e., the set of habitat suitability , ; is the number of habitat suitability levels.

[0025] Preferably, in step S54, the formula for calculating the comprehensive suitability index value is as follows:

[0026] ;

[0027] where, is the comprehensive suitability index value of the calculation unit; is the output membership curve; are the elements in the habitat suitability set.

[0028] Therefore, the present invention adopts the above-mentioned simulation method for the distribution of suitable spawning habitats of sedentary fish in connected lakes, and the beneficial technical effects are as follows:

[0029] (1) Aiming at the complex hydrological-vegetation mechanism required for sedentary fish to spawn, the present invention comprehensively considers factors such as water depth, flow velocity, and vegetation cover required for fish spawning, and can simulate and quantify the change of the comprehensive suitability of spawning habitats of sedentary fish in connected lakes.

[0030] (2) The simulation method for the distribution of suitable spawning habitats of sedentary fish in connected lakes constructed by the present invention can analyze the quantitative relationship between the area of suitable spawning habitats for fish and the water level, and obtain the lake water level most suitable for the spawning habitats of sedentary fish in connected lakes, which is of great significance for improving the natural proliferation level of sedentary fish, protecting lake fishery resources and biodiversity. Description of the Drawings

[0031] Figure 1 is the flowchart of a simulation method for the distribution of suitable spawning habitats of sedentary fish in connected lakes according to the present invention;

[0032] Figure 2 is the flowchart of the calculation by the fuzzy logic method;

[0033] Figure 3 is the water depth membership function;

[0034] Figure 4 is the habitat suitability curve of sedentary fish spawning period in Poyang Lake; among them, Figure 4 (a) in is the water depth suitability curve; Figure 4 (b) in is the flow velocity suitability curve; Figure 4 (c) in is the vegetation cover suitability curve;

[0035] Figure 5 is the process flow of the construction and calculation of the hydrodynamic model of Poyang Lake;

[0036] Figure 6 is the NDVI spatial distribution of Poyang Lake during the rising water period from 2016 to 2018; among them, Figure 6 (a) in is the NDVI spatial distribution of Poyang Lake during the rising water period in 2016; Figure 6 (b) in is the NDVI spatial distribution of Poyang Lake during the rising water period in 2017; Figure 6 (c) in is the NDVI spatial distribution of Poyang Lake during the rising water period in 2018;

[0037] Figure 7It is the comprehensive suitability distribution map of the spawning habitats of resident fish in Poyang Lake during the rising water period in 2016. Among them, Figure 7 in (a) is the distribution of the comprehensive suitability when the lake water level is 12.8 m; Figure 7 in (b) is the distribution of the comprehensive suitability when the lake water level is 15.2 m; Figure 7 in (c) is the distribution of the comprehensive suitability when the lake water level is 17.8 m. Specific implementation manners

[0038] The technical solutions of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0039] Unless otherwise defined, the technical terms or scientific terms used in the present invention shall have the ordinary meanings understood by those of ordinary skill in the field to which the present invention belongs.

[0040] Embodiment 1

[0041] As Figure 1 shown, the present invention provides a simulation method for the distribution of suitable spawning habitats of resident fish in a connected lake, including the following steps:

[0042] S1. Select typical resident fish species in a connected lake as the research object.

[0043] In this embodiment, Poyang Lake is taken as an example, and the typical resident fish species in the connected lake are carp and crucian carp. The spawning habits of the two fish are the same, so they are collectively referred to as "carp and crucian carp" when studying the suitable spawning and inhabiting habitats.

[0044] S2. According to the carp and crucian carp selected in step S1, investigate the habitat factors affecting their spawning habitats, clarify the suitable ranges of each habitat factor, and draw the suitability curves of each habitat factor.

[0045] According to data analysis and research, water depth, flow velocity, and vegetation cover are selected as the main habitat factors for the spawning habitats of carp and crucian carp in Poyang Lake. The value range of habitat suitability is usually between 0 and 1. 0 means no preference for special habitat conditions, and 1 means the highest preference for specific habitat conditions. The larger the value, the better the suitable habitat conditions. It is analyzed and determined that the suitable water depth range for the spawning and reproduction period of carp and crucian carp in Poyang Lake is 0 - 1.8 m, and the optimal water depth range is 0.5 - 1.6 m; the suitable flow velocity range is 0 - 0.6 m / s, and the optimal flow velocity range is 0.1 - 0.4 m / s; the suitable vegetation cover range is 20% - 100%, and the optimal vegetation cover range is 30% - 80%. The suitability curves of the three main habitat factors are as Figure 4 shown.

[0046] S3. Build a hydrodynamic model for the connected lake area, and calibrate and verify it.

[0047] The established hydrodynamic mathematical model of Poyang Lake is based on the three-dimensional incompressible Navier-Stokes equations with a uniform Reynolds value and obeys the Boussinesq and hydrostatic pressure assumptions. The governing equations are as follows:

[0048] ;

[0049] ;

[0050] ;

[0051] where, denotes the partial derivative; is the calculation time of the model; , are the coordinates in different directions in the Cartesian coordinate system respectively; is the corresponding water level; is the total water depth during the calculation; is the component of the velocity in the is the component of the velocity in the is the average velocity in the is the average velocity in the denotes and the product of; is the air pressure term; is the component of the bottom friction stress in the direction; is the component of the bottom friction stress in the direction; is the component of the horizontal viscous stress in the direction; is the component of the horizontal viscous stress in the direction; is the water flow velocity of the source term in the direction; is the water flow velocity of the source term in the direction; ; is the magnitude of the angular velocity; is the magnitude of the latitude; is the acceleration of the earth; is the density of water; is the density of seawater under normal conditions; , , , are the radiation stresses along the The normal stress in the axial direction, perpendicular to the shear stress on the axial section, along the normal stress in the axial direction, perpendicular to the shear stress on the axial section; is the source term; is the horizontal viscous stress term, including viscous force , turbulent stress and horizontal convection , and these quantities are obtained from the eddy viscosity equation based on the velocity gradient averaged along the water depth:

[0052] ;

[0053] where is the area of the cross-section of the unit through which water flows.

[0054] As Figure 5 shown, constructing the hydrodynamic model of Poyang Lake mainly includes several steps such as grid division, boundary condition setting, initial condition setting, model calibration and verification:

[0055] (1) When modeling, first perform spatial grid meshing based on the lake basin topographic data, and generate grid-based topographic data through spatial interpolation.

[0056] (2) In terms of boundary condition setting, along the closed boundary (land boundary), all variables flowing perpendicular to the boundary must be 0. For the momentum equation, it can be known that it is completely steady along the land boundary; the open boundary condition can be specified as a flow process or a water level process. Considering the actual situation of the connection between Poyang Lake and the Yangtze River, water level data can be used as the downstream boundary condition, and the flow process can be used as the upstream boundary condition; on the basis of the downstream water level and upstream flow boundaries, meteorological and hydrological conditions such as precipitation and evaporation can also be considered to jointly drive the mathematical model for simulation calculation. The water level, flow, precipitation, and evaporation data required for boundary setting are from the actual monitoring data of hydrological monitoring stations and meteorological monitoring stations in the region.

[0057] (3) In terms of initial conditions, since there are measured data from existing hydrological stations, the spatially varying lake water level can be used as the initial water level condition, that is, first obtain the dfs2 or dfsu warm start file through a certain period of preheating, and then input the results generated during the simulation process into the model.

[0058] (4) Through the calibration and verification of the hydrodynamic model, the model outputs hydrodynamic indicators such as water level, water depth, and water area in typical hydrological years, providing high-resolution simulated water depth and flow velocity conditions for determining the suitable spawning habitat for fish.

[0059] S4. Use remote sensing technology to extract the vegetation spectral information of the lake-connected area, and then obtain the vegetation distribution map and calculate the normalized difference vegetation index.

[0060] The Normalized Difference Vegetation Index (NDVI) is an important vegetation index with a value range of [-1, 1], which can represent the vegetation coverage. The larger the value, the better the vegetation condition. Generally, the NDVI range of green vegetation is [-0.2, 0.8], and its calculation formula is:

[0061] ;

[0062] Among them, represents the reflectance in the near-infrared band; represents the reflectance in the red light band.

[0063] In this embodiment, the ENVI software is used to perform data preprocessing such as radiometric calibration, atmospheric correction, and cropping on the downloaded original remote sensing images, and then the NDVI is calculated using the raster calculator in the Spatial Analysis tool on the GIS platform. The specific results are as Figure 6 shown.

[0064] S5. According to the fuzzy logic method, taking each habitat factor and the normalized difference vegetation index as inputs, a suitability simulation model for the suitable spawning habitats of river-connected lake resident fish is constructed, and then the comprehensive suitability index value of the spawning habitats is calculated, mainly including the fuzzification process, fuzzy inference, and defuzzification process, as Figure 2 shown.

[0065] S51. Establish a membership function;

[0066] The definition of the membership function is as follows:

[0067] For any value x of a certain environmental factor, there is a number A(x) ∈ [0, 1] corresponding to it. Then the value of A(x) is called the membership degree of x to A. When x varies within the value range of the environmental factor, A(x) is a function, called the membership function of A. The closer the membership degree A(x) is to 1, the higher the degree that x belongs to A; the closer A(x) is to 0, the lower the degree that x belongs to A. The membership function A(x) taking values in the interval (0, 1) characterizes the degree of x belonging to A.

[0068] Taking the construction of the membership function A(x) of the water depth x of carp and crucian carp as an example, the most suitable water depth range is 0.5 - 1.6 m. When the water depth exceeds 1.8 m, the spawning of carp and crucian carp is affected, and when it reaches 2.4 m, the spawning environment will be severely damaged. Taking 0.5 m and 1.6 m as two important nodes, the membership function is constructed, and the water depth levels for the spawning of carp and crucian carp are divided into three levels: "deep", "medium", and "shallow". The three suitability function curves of x belonging to "deep", "medium", and "shallow" are as Figure 3 shown.

[0069] S52. Fuzzify the clear value input through the membership function to obtain the input fuzzy set;

[0070] The input fuzzy set is represented as follows:

[0071] ;

[0072] where is the input fuzzy set; are the respective habitat factors, .

[0073] S53. Formulate fuzzy rules to relate the habitat factors, normalized difference vegetation index, and comprehensive suitability index.

[0074] Fuzzy inference maps the input fuzzy sets to specific output fuzzy sets through fuzzy rules. Fuzzy rules are used to establish the connection between evaluation factors and evaluation results and are the key to reflecting the correlation between input elements, output results, and multiple variables. The formulation of fuzzy rules is as follows:

[0075] Based on the ecological habits of species and the ecological relationships between species, formulate a set of rules covering all objects in the form of IF-AND-THEN, that is, "IF X belongs to a, AND Y belongs to b, THEN Z belongs to c". All variables are converted into linguistic variables, and finally c is the output value of fuzzy inference. The number of rules stored in the fuzzy rule base under the initial conditions is where is the number of input variables, is the th fuzzy classification number of the input variable. For example, "If the flow velocity is moderate and the water depth is moderate, then the habitat suitability is relatively high", "flow velocity" and "water depth" are the objects of the input fuzzy set, and "habitat suitability" is the object of the output fuzzy set. The input corresponding to each rule will generate a strength representing the membership degree in the output fuzzy set for the conclusion of this fuzzy rule. The fuzzy rules need to cover all objects in the fuzzy set, and the correspondence between input conditions and output results can be represented using a fuzzy matrix.

[0076] After the fuzzy sets are established, it is necessary to formulate fuzzy rules to relate each sub-index to the comprehensive suitability index. In the fuzzy set system, the fuzzy inference rules corresponding to each process include a conditional part and a conclusion part. The 27 fuzzy rules formulated by the present invention based on the combination of existing research results and expert experience knowledge are shown in Table 1.

[0077] Table 1 Fuzzy inference rules for habitat suitability index

[0078] ;

[0079] S54. Perform fuzzy reasoning to obtain the output fuzzy set, and use the centroid method to defuzzify the output fuzzy set, so as to calculate the comprehensive suitability index value of the spawning habitat corresponding to the typical fish species in each grid cell of the hydrodynamic model.

[0080] The output fuzzy set is expressed as follows:

[0081] ;

[0082] where, is the output fuzzy set, that is, the set of habitat suitability ; is the number of habitat suitability.

[0083] The calculation formula of the comprehensive suitability index value is as follows:

[0084] ;

[0085] In the formula: is the comprehensive suitability index value of the calculation unit; is the output membership curve.

[0086] S55. According to the calculated comprehensive suitability index value of the spawning habitat, divide the habitat quality of the sedentary fish in the lake connected to the river during the spawning period into different suitability levels, and draw the evaluation result map of the comprehensive suitability of the spawning habitat of the sedentary fish in the lake connected to the river.

[0087] Compile a spawning habitat model of sedentary fish in Poyang Lake based on fuzzy logic through the built-in Python module of GIS, perform layer overlay analysis on the water depth, flow velocity, and vegetation cover factors of each unit during the rising water period of three typical years, namely high water year, normal water year, and low water year, and determine the comprehensive suitability level HSI according to the CSF value. Finally, obtain the distribution status of the spawning habitat at different water levels represented by Xingzi Station. As Figure 7 shown, divide the habitat quality of sedentary fish during the spawning period into five levels: extremely low suitability area (0 < CSF < 0.2), low suitability area (0.2 < CSF < 0.4), medium suitability area (0.4 < CSF < 0.6), high suitability area (0.6 < CSF < 0.8), and extremely high suitability area (0.8 < CSF < 1). Finally, obtain the evaluation result map of the comprehensive suitability of the spawning habitat of sedentary fish in Poyang Lake. The closer the color is to red, the lower the comprehensive suitability value, and the closer the color is to green, the higher the comprehensive suitability value.

[0088] It should be noted that the content not elaborated in detail in the present invention is all prior art and is well known to those skilled in the art.

[0089] Therefore, the present invention adopts the above-mentioned simulation method for the distribution of suitable spawning habitats of sedentary fish in connected lakes, comprehensively considering factors such as water depth, flow velocity, and vegetation cover required for fish spawning, and can simulate and quantify the changes in the comprehensive suitability of spawning habitats of sedentary fish in connected lakes.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify or equivalently replace the technical solutions of the present invention, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for simulating the distribution of suitable spawning habitats for resident fish in lakes and rivers, characterized in that: The following steps are involved: Step S1, selecting typical resident fish species in lakes connected to rivers as research objects; Step S2: According to the typical resident fish species in lakes connected to rivers selected in step S1, investigate the habitat factors that affect their spawning habitats, clarify the suitable range of each habitat factor, and draw the suitability curve of each habitat factor; Step S3, constructing a hydrodynamic model of the Tongjiang Lake area, and performing calibration and verification; When modeling, we first divide the lake basin into spatial grids according to the lake basin terrain data, and generate grid-based terrain data through spatial interpolation. In terms of boundary condition setting, along the closed boundary, all variables that flow perpendicular to the boundary are set to 0. In terms of initial conditions, the spatially varying lake water level is used as the initial water level condition. The water level, water depth, and water area hydrodynamic indicators of a typical hydrological year are output to provide high-resolution simulated water depth and flow velocity conditions for determining suitable habitats for fish spawning. Step S4, using remote sensing technology to extract vegetation spectral information in the Tongjiang Lake area, thereby obtaining a vegetation distribution map and calculating a normalized vegetation index; Step S5, according to the fuzzy logic method, taking various habitat factors and normalized vegetation index as input, constructing a suitability simulation model of suitable spawning habitats for sedentary fish in Tongjiang lakes, and then calculating the comprehensive suitability index value of the spawning habitat; Step S51, establishing a membership function; Step S52, fuzzifying the explicit value input through the membership function to obtain an input fuzzy set; Step S53, formulating fuzzy rules to link habitat factors, normalized difference vegetation index and comprehensive suitability index; Step S54, performing fuzzy reasoning to obtain an output fuzzy set, and defuzzifying the output fuzzy set using the centroid method, thereby calculating the comprehensive suitability index value of the spawning habitat corresponding to the typical fish species in each grid cell of the hydrodynamic model; Step S55, according to the calculated comprehensive suitability index value of spawning habitat, the habitat quality of resident fish in Tongjiang lakes during the spawning period is divided into different suitability levels, and a comprehensive suitability evaluation result diagram of spawning habitat of resident fish in Tongjiang lakes is drawn; A fuzzy logic-based suitability simulation model for spawning habitats of sedentary fish was compiled, and the water depth, flow velocity, and vegetation coverage factors of each unit in three typical annual flood seasons (wet year, normal year, and dry year) were analyzed by layer overlay, and the comprehensive suitability level was determined based on the calculated comprehensive suitability index value. In step S2, the habitat factors include water depth, flow velocity and vegetation coverage; In step S53, the fuzzy inference rules are as follows: When the water depth is shallow, the vegetation cover is low, and the flow rate is slow, the habitat suitability is medium; When water depth is shallow, vegetation cover is low, and flow velocity is medium, habitat suitability is low; When the water depth is shallow, the vegetation cover is low, and the flow velocity is fast, the habitat suitability is medium; When the water depth is shallow, the vegetation cover is medium, and the flow rate is slow, the habitat suitability is high; When the water depth is shallow, the vegetation cover is medium, and the flow rate is medium, the habitat suitability is medium; When the water depth is shallow, the vegetation cover is medium, and the flow rate is fast, the habitat suitability is medium; When the water depth is shallow, the vegetation cover is high, and the flow velocity is slow, the habitat suitability is high; When water depth is shallow, vegetation cover is high, and flow velocity is medium, habitat suitability is high; When the water depth is shallow, the vegetation cover is high, and the flow velocity is fast, the habitat suitability is medium; When water depth is medium, vegetation cover is low, and flow velocity is slow, habitat suitability is low; When water depth is medium, vegetation cover is low, and flow velocity is medium, habitat suitability is slow; When water depth is medium, vegetation cover is low, and flow velocity is fast, habitat suitability is low; When the water depth is medium, the vegetation cover is medium, and the flow rate is slow, the habitat suitability is medium; When the water depth is medium, the vegetation cover is medium, and the flow rate is medium, the habitat suitability is fast; When the water depth is medium, the vegetation cover is medium, and the flow velocity is fast, the habitat suitability is high; When water depth is medium, vegetation cover is high, and flow velocity is slow, habitat suitability is medium; When water depth is medium, vegetation cover is high, and flow velocity is medium, habitat suitability is low; When the water depth is medium, the vegetation cover is high, and the flow velocity is fast, the habitat suitability is medium; When water depth is deep, vegetation cover is low, and flow velocity is slow, habitat suitability is low; When water depth is deep, vegetation cover is low, and flow velocity is medium, habitat suitability is medium; When the water depth is deep, the vegetation cover is low, and the flow velocity is fast, the habitat suitability is very low; When the water depth is deep, the vegetation cover is medium, and the flow rate is slow, the habitat suitability is medium; When the water depth is deep, the vegetation cover is medium, and the flow velocity is medium, the habitat suitability is low; When the water depth is deep, the vegetation cover is medium, and the flow rate is fast, the habitat suitability is medium; When the water depth is deep, the vegetation cover is high, and the flow velocity is slow, the habitat suitability is medium; When water depth is deep, vegetation cover is high, and flow velocity is medium, habitat suitability is fast; When water depth is deep, vegetation cover is high, and flow velocity is fast, habitat suitability is low.

2. The method for simulating the distribution of suitable spawning habitats for resident fish in Tongjiang lakes according to claim 1, characterized in that: In step S4, the normalized vegetation index calculation formula is as follows: Among them, ρ NIR is the reflectivity in the near-infrared band; ρ RED is the reflectivity in the red light band.

3. The method for simulating the distribution of suitable spawning habitats for resident fish in Tongjiang lakes according to claim 2, characterized in that: In step S52, the input fuzzy set is expressed as follows: U = {u1,u2,u3}; Among them, U is the input fuzzy set; u i are habitat factors, i=1,2,3.

4. The method for simulating the distribution of suitable spawning habitats for resident fish in Tongjiang lakes according to claim 3, characterized in that: In step S54, the output fuzzy set is expressed as follows: V={v1,v2,v3,...,v m }; Among them, V is the output fuzzy set, that is, the habitat suitability v j A set of, j = 1, 2, 3, …, m; m is the number of habitat suitability.

5. The method for simulating the distribution of suitable spawning habitats for resident fish in rivers and lakes according to claim 4, characterized in that: In step S54, the calculation formula of the comprehensive suitability index value is as follows: Among them, CSF is the comprehensive suitability index value of the calculation unit; μ v (v) is the output membership curve; v is each element in the habitat suitability set.

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