Simulation method for proper spawning habitat distribution of settled fishes in Tongjiang lake

Abstract

The invention provides a method for simulating proper spawning habitat distribution of settled fishes in a Tongjiang lake, and belongs to the technical field of ecological protection, and the method comprises the following steps: S1, selecting typical settled fish species in the Tongjiang lake as a research object; s2, investigating habitat factors influencing the spawning habitat, determining a suitable range of each habitat factor, and drawing a suitable degree curve of each habitat factor; s3, constructing a hydrodynamic model of the river-through lake area; s4, calculating a normalized vegetation index; and S5, according to a fuzzy logic method, taking each habitat factor and the normalized vegetation index as input, constructing a suitability simulation model of the suitable spawning habitat of the settled fishes in the Tongjiang lake, and calculating a comprehensive suitability index value of the spawning habitat. According to the method, factors such as water depth, flow velocity and vegetation coverage required by fish spawning are comprehensively considered, and the change condition of the comprehensive suitability of the settling fish spawning habitat in the Tongjiang lake can be simulated and quantified.

Description

Technical Field

[0001] The 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 Tongjiang lakes. Background Art

[0002] Sedentary fish in Tongjiang Lake reproduce naturally in the lake area and are the main body of the lake fishery, playing a huge role in the natural fishery function. In addition to suitable temperature, water quality and water depth, the spawning of sedentary fish also requires vegetation media, so the simulation technology of the distribution of suitable habitats for their spawning is relatively complex. Most of the existing studies simulate and analyze the suitable hydrodynamic conditions for migratory fish, and there is still a lack of simulation technology for the distribution of suitable habitats for spawning of sedentary fish, including hydrological, hydrodynamic and vegetation media factors. Summary of the invention

[0003] The purpose of the present invention is to provide a simulation method for the distribution of suitable spawning habitats for resident fish in lakes along the Yangtze River, which comprehensively considers factors such as water depth, flow velocity, vegetation coverage, etc. required for fish spawning, and can simulate and quantify the changes in the comprehensive suitability of the spawning habitats for resident fish in lakes along the Yangtze River.

[0004] To achieve the above object, the present invention provides a method for simulating the distribution of suitable spawning habitats for resident fish in Tongjiang lakes, comprising the following steps: 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; 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 map of the spawning habitat of resident fish in Tongjiang lakes is drawn.

[0005] Preferably, in step S2, the habitat factors include water depth, flow velocity and vegetation coverage.

[0006] Preferably, in step S4, the normalized vegetation index calculation formula is as follows: ; in, is the reflectivity in the near-infrared band; is the reflectivity in the red light band.

[0007] Preferably, in step S52, the input fuzzy set is represented as follows: ; in, is the input fuzzy set; are the habitat factors, .

[0008] Preferably, in step S54, the output fuzzy set is expressed as follows: ; in, is the output fuzzy set, namely, habitat suitability A collection of ; is the number of habitat suitability.

[0009] Preferably, in step S54, the calculation formula of the comprehensive suitability index value is as follows: ; in, is the comprehensive suitability index value of the calculation unit; is the output membership curve; are the elements in the habitat suitability set.

[0010] Therefore, the present invention adopts the above-mentioned simulation method for the distribution of suitable spawning habitats for resident fish in Tongjiang lakes, and the beneficial technical effects are as follows: (1) The present invention targets the complex hydrological-vegetation mechanism required for spawning of sedentary fish, comprehensively considers factors such as water depth, flow velocity, and vegetation coverage required for fish spawning, and can simulate and quantify the changes in the comprehensive suitability of the spawning habitats of sedentary fish in Tongjiang Lake.

[0011] (2) The simulation method for the distribution of suitable spawning habitats for sedentary fish in Tongjiang Lakes constructed by the present invention can analyze the quantitative relationship between the area of ​​suitable habitat for fish spawning and the water level, and obtain the lake water level that is most suitable for the spawning habitat of sedentary fish in Tongjiang Lakes. This is of great significance for improving the natural proliferation level of sedentary fish and protecting lake fishery resources and biodiversity. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a flow chart of a method for simulating the distribution of suitable spawning habitats for resident fish in Tongjiang lakes according to the present invention; Figure 2 Calculate the flow chart for the fuzzy logic method; Figure 3 is the water depth membership function; Figure 4 is the habitat suitability curve for spawning period of sedentary fish in Poyang Lake; Figure 4 (a) is the water depth suitability curve; Figure 4 (b) is the velocity suitability curve; Figure 4 (c) in the figure is the vegetation cover suitability curve; Figure 5 The construction and calculation steps of the hydrodynamic model of Poyang Lake; Figure 6 The spatial distribution of NDVI in Poyang Lake during the flood season from 2016 to 2018. Figure 6 (a) shows the spatial distribution of NDVI in Poyang Lake during the flood season in 2016; Figure 6 (b) shows the spatial distribution of NDVI in Poyang Lake during the flood season in 2017; Figure 6 (c) shows the spatial distribution of NDVI in Poyang Lake during the flood season in 2018; Figure 7 This is the distribution map of the comprehensive suitability of spawning habitats for sedentary fish in Poyang Lake during the flood season in 2016; Figure 7 (a) is the distribution of comprehensive suitability when the lake water level is 12.8 m; Figure 7 (b) in the figure is the distribution of comprehensive suitability when the lake water level is 15.2 m; Figure 7 (c) in the figure is the distribution of comprehensive suitability when the lake water level is 17.8m. DETAILED DESCRIPTION

[0013] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0014] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0015] Embodiment 1 like Figure 1 As shown, the present invention provides a method for simulating the distribution of suitable spawning habitats for resident fish in Tongjiang lakes, comprising the following steps: S1. Select typical sedentary fish species in lakes connected to rivers as research objects.

[0016] This embodiment takes Poyang Lake as an example, in which typical resident fish species in lakes connected to the river are carp and crucian carp. The two fish have the same spawning habits, so they are collectively referred to as "carp and crucian carp" when studying suitable spawning habitats.

[0017] S2. According to the carp and crucian carp 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.

[0018] Based on data analysis and research, water depth, flow rate and vegetation coverage were selected as the main habitat factors for the spawning of carp and crucian carp in Poyang Lake. The value range of habitat suitability is usually between 0 and 1, where 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. The analysis determined that the suitable water depth range for the spawning and breeding period of carp and crucian carp in Poyang Lake is 0~1.8m, and the optimal water depth range is 0.5~1.6m; the suitable flow rate range is 0~0.6m / s, and the optimal flow rate range is 0.1~0.4m / s; the suitable vegetation coverage range is 20%~100%, and the optimal vegetation coverage range is 30%~80%. The suitability curves of the three main habitat factors are shown below. Figure 4 shown.

[0019] S3. Construct a hydrodynamic model for the Tongjiang lake area and calibrate and verify it.

[0020] The constructed Poyang Lake hydrodynamic mathematical model is based on the three-dimensional incompressible and Reynolds value uniformly distributed Navier-Stokes equations, and is subject to the Boussinesq and hydrostatic pressure assumptions. The control equations are as follows: ; ; ; in, Refers to partial derivatives; is the calculation time of the model; , are the coordinates of different directions in the Cartesian coordinate system; is the corresponding water level; is the total water depth during calculation; for The component of velocity in a direction; for The velocity component in the direction; for Average speed in the direction; for Average speed in the direction; refer to and The product of is the air pressure term; is the friction stress of the bottom bed Directional component; is the friction stress of the bottom bed Directional component; is the horizontal viscous stress at Directional weight; is the horizontal viscous stress at Directional weight; For the source term Water flow velocity in direction; For the source term Water flow velocity in direction; ; is the magnitude of angular velocity; is the latitude size; is the acceleration of the Earth; is the density of water; is the density of seawater under normal conditions; , , , The radiation stress along Normal stress in the axial direction, perpendicular to Shear stress on the axial section, along Normal stress in the axial direction, perpendicular to Shear stress on the axial section; is the source term; is the horizontal viscous stress term, including the viscous force , turbulent stress and horizontal convection , these quantities are derived from the eddy viscosity equation based on the velocity gradient averaged along the water depth: ; in, It is the area of ​​the water-passing cross section of the unit body.

[0021] like Figure 5As shown in the figure, the construction of the Poyang Lake hydrodynamic model is mainly divided into several steps: grid division, boundary condition setting, initial condition setting, model calibration and verification: (1) When modeling, we first divide the lake basin terrain data into spatial grids, and then generate grid-based terrain data through spatial interpolation.

[0022] (2) In terms of boundary condition setting, along the closed boundary (land boundary), all variables that flow perpendicular to the boundary must be 0. For the momentum equation, it can be seen that it is completely stable along the land boundary; the open boundary condition can be specified as a flow process or a water level process. In view of the actual situation that Poyang Lake is connected to the Yangtze River, water level data can be used as the downstream boundary condition and the flow process as the upper 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 calculations. The water level, flow, precipitation, and evaporation data required for boundary setting are derived from the actual monitoring data of the hydrological monitoring stations and meteorological monitoring stations in the region.

[0023] (3) In terms of initial conditions, since there are measured data from hydrological stations, the spatially varying lake water level can be used as the initial water level condition. That is, the dfs2 or dfsu hot start file is first obtained through a certain period of preheating, and then the results generated by the simulation process are input into the model.

[0024] (4) Through calibration and verification of the hydrodynamic model, the model outputs hydrodynamic indicators such as water level, water depth, and water area in a typical hydrological year, providing high-resolution simulated water depth and flow velocity conditions for determining suitable habitats for fish spawning.

[0025] S4. Use remote sensing technology to extract vegetation spectral information in the Tongjiang Lake area, obtain vegetation distribution map, and calculate the normalized vegetation index.

[0026] Normalized Difference Vegetation Index (NDVI) is an important vegetation index with a value range of [-1,1]. It can represent the coverage of vegetation. The larger the value, the better the vegetation condition. Generally, the NDVI range of green vegetation is [-0.2,0.8]. Its calculation formula is: ; in, Represents the reflectivity in the near-infrared band; Represents the reflectivity of the red light band.

[0027] This example uses ENVI software to perform data preprocessing such as radiation calibration, atmospheric correction, and cropping on the downloaded original remote sensing images, and then uses the raster calculator in the Spatial Analysis tool on the GIS platform to calculate NDVI. The specific results are as follows: Figure 6 shown.

[0028] S5. According to the fuzzy logic method, various habitat factors and normalized vegetation index were used as input to construct a suitability simulation model for spawning habitats of sedentary fish in Tongjiang Lakes, and then the comprehensive suitability index value of spawning habitats was calculated, which mainly included fuzzification process, fuzzy reasoning and defuzzification process, such as Figure 2 shown.

[0029] S51, establishing a membership function; The membership function is defined as follows: For any value x of an environmental factor, there is a number A(x)∈[0,1] corresponding to it. The value of A(x) is called the membership of x to A. When x changes within the range of environmental factors, A(x) is a function called the membership function of A. The closer the membership A(x) is to 1, the higher the degree to which x belongs to A. The closer A(x) is to 0, the lower the degree to which x belongs to A. The membership function A(x) with a value in the interval (0,1) is used to represent the degree to which x belongs to A.

[0030] Taking the construction of the membership function A(x) of carp and crucian carp water depth x as an example, the most suitable water depth range is 0.5~1.6m. When the water depth exceeds 1.8m, the spawning of carp and crucian carp is affected, and when it reaches 2.4m, the spawning environment will be seriously damaged. The membership function is constructed with 0.5m and 1.6m as two important nodes, and the water depth for carp and crucian carp spawning is divided into three levels: "deep", "medium" and "shallow". The three suitability function curves of x belonging to "deep", "medium" and "shallow" are shown as follows: Figure 3 shown.

[0031] S52, fuzzifying the explicit value input through the membership function to obtain an input fuzzy set; The input fuzzy set is expressed as follows: ; in, is the input fuzzy set; are the habitat factors, .

[0032] S53. Formulate fuzzy rules to link habitat factors, normalized difference vegetation index and comprehensive suitability index.

[0033] Fuzzy reasoning uses fuzzy rules to map input fuzzy sets to specific output fuzzy sets. Fuzzy rules are used to establish the connection between evaluation factors and evaluation results, and are the key to reflecting the correlation between input factors and output results and multiple variables. The formulation of fuzzy rules is as follows: According to the ecological habits of species and the ecological relationship between species, a set of rules in the form of IF-AND-THEN covering all objects is formulated, that is, "IF X belongs to a, AND Y belongs to b, THEN Z belongs to c", all variables are converted into language variables, and finally c is the output value of fuzzy reasoning. The number of rules stored in the fuzzy rule base under the initial conditions is ,in is the number of input variables, For the The number of fuzzy levels of the input variables. For example, "If the flow rate is moderate and the water depth is moderate, then the habitat suitability is high", "flow rate" and "water depth" are input fuzzy set objects, and "habitat suitability" is the output fuzzy set object. The corresponding input under each rule will produce a strength of the conclusion of the fuzzy rule, which represents the membership degree in the output fuzzy set. Fuzzy rules need to cover all objects in the fuzzy set, and the fuzzy matrix can be used to represent the correspondence between input conditions and output results.

[0034] After the fuzzy set is established, fuzzy rules need to be formulated to link each sub-index with the comprehensive suitability index. In the fuzzy set system, the fuzzy reasoning rules corresponding to each process include a conditional part and a conclusion part. The 27 fuzzy rules formulated by this invention based on the existing research results and expert experience knowledge are shown in Table 1.

[0035] Table 1 Fuzzy inference rules for habitat suitability index ;

[0036] 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 unit of the hydrodynamic model.

[0037] The output fuzzy set is expressed as follows: ; in, is the output fuzzy set, namely, habitat suitability A collection of; is the number of habitat suitability.

[0038] The calculation formula of comprehensive suitability index is as follows: ; Where: is the comprehensive suitability index value of the calculation unit; is the output membership curve.

[0039] S55. Based on the calculated comprehensive suitability index value of spawning habitats, the habitat quality of resident fish species in Tongjiang lakes during the spawning period is divided into different suitability levels, and a comprehensive suitability evaluation result map of the spawning habitats of resident fish species in Tongjiang lakes is drawn.

[0040] The spawning habitat model of sedentary fish in Poyang Lake based on fuzzy logic was compiled by using the built-in Python module of GIS. The water depth, flow velocity and vegetation coverage factors of each unit in three typical annual flood seasons, namely, high water year, normal water year and low water year, were analyzed by layer overlay. CSF The value of determines the comprehensive suitability level HSI, and finally obtains the distribution of spawning habitats at different water levels represented by Xingzi Station. Figure 7 As shown in the figure, the habitat quality of spawning sedentary fish is divided into very low suitability areas (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), extremely high suitability area (0.8﹤ CSF ﹤1) 5 levels, and finally the comprehensive suitability evaluation result map of the spawning habitat of sedentary fish in Poyang Lake was obtained. The closer the color is to red, the lower the comprehensive suitability value is, and the closer to green, the higher the comprehensive suitability value is.

[0041] It is worth noting that the contents not elaborated in detail in the present invention are all prior art and are well known to those skilled in the art.

[0042] Therefore, the present invention adopts the above-mentioned simulation method of the distribution of suitable spawning habitats for resident fish in Tongjiang lakes, comprehensively considering factors such as water depth, flow velocity, vegetation coverage, etc. required for fish spawning, and can simulate and quantify the changes in the comprehensive suitability of the spawning habitats of resident fish in Tongjiang lakes.

[0043] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for simulating the distribution of suitable spawning habitats for resident fish in Tongjiang lakes, 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; 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 map of the spawning habitat of resident fish in Tongjiang lakes is drawn.

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 S2, the habitat factors include water depth, flow velocity and vegetation coverage.

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 S4, the normalized vegetation index calculation formula is as follows: ； in, is the reflectivity in the near-infrared band; is the reflectivity in the red light band.

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 S52, the input fuzzy set is expressed as follows: ； in, is the input fuzzy set; are the habitat factors, .

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 output fuzzy set is expressed as follows: ； in, is the output fuzzy set, namely, habitat suitability A collection of ; is the number of habitat suitability.

6. The method for simulating the distribution of suitable spawning habitats for resident fish in rivers and lakes according to claim 5, characterized in that: In step S54, the calculation formula of the comprehensive suitability index value is as follows: ； in, is the comprehensive suitability index value of the calculation unit; is the output membership curve; are the elements in the habitat suitability set.

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