Mangrove forest community succession prediction method under influence of sea level rise

Abstract

The invention discloses a mangrove forest community succession prediction method under the influence of sea level rise, and relates to the field of environment simulation and ecological protection, and the method comprises the following steps: obtaining the beach face elevation, water depth, mangrove forest distribution, coastline, tide harmonic constants and historical typhoon path data of a prediction region; constructing a self-adaptive triangular mesh, and interpolating mangrove forest distribution data space to a mesh unit; defining a closed boundary by utilizing coastline data, driving a three-dimensional finite element model in combination with a tide harmonic constant and typhoon path data, and solving a grid point water level time sequence under the current and sea level rising scene; the flooding duration of each grid unit in the current and sea level rising scenes is calculated; according to the flooding time change and the mangrove forest species tolerance threshold value, community space distribution is updated, and a prediction map is output. According to the method, the space influence of flooding time change caused by sea level rise on mangrove forest community succession is accurately quantified, and reliable technical support is provided for coastal zone ecological protection and restoration.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of environmental simulation and ecological protection, more particularly to a mangrove community succession prediction method under the influence of sea level rise. BACKGROUND

[0002] Mangrove is one of the most typical ecosystems in coastal areas, and sea level rise poses a great threat to mangrove plants. The most direct impact is to prolong the flooding time of the plants, change the optimal water level and optimal salt tolerance for plant growth, and cause the succession or even extinction of mangrove communities. Precise prediction of the spatial distribution changes of mangrove species can provide scientific decision-making basis for coastal wetland protection and restoration, biodiversity maintenance and coastal resilience enhancement.

[0003] Currently, the traditional evaluation method directly compares the tidal level change with the beach elevation through GIS spatial superposition, ignoring the nonlinear effect of the tidal zone hydrodynamic. For example, the coupling effect of storm surge and astronomical tide, the local flow field changes caused by topographic curvature and other key processes are not quantified, resulting in significant deviation in the calculation of flooding time, especially in the steep terrain area. The existing grid system cannot adaptively encrypt the complex terrain area, which restricts the simulation accuracy of water level field. Such simplified model cannot reflect the actual tidal flat dynamic process, which further affects the accuracy of water level time series.

[0004] At the same time, the existing scheme only predicts the total area change of mangrove, and does not establish a quantitative correlation rule between flooding time and species tolerance. Mangrove plants respond differently to the length of flooding time, but the traditional method lacks a competitive succession mechanism design, and the succession rule does not consider the threshold value of flooding change, which cannot accurately obtain the spatial distribution results of species succession.

[0005] Therefore, how to design a mangrove community succession prediction method under the influence of sea level rise, which can integrate high-resolution hydrodynamic simulation, species response mechanism and dynamic scenario adaptation, to realize the accurate prediction of mangrove community succession is a problem that needs to be solved by those skilled in the art. SUMMARY

[0006] Therefore, the present application provides a mangrove community succession prediction method under the influence of sea level rise, which aims to solve the technical defects of traditional methods that ignore the nonlinear dynamic process of tides and cannot accurately predict species succession. By simulating the water level time series in detail, quantifying the change of flooding time and its dynamic relationship with the species tolerance threshold, the influence of sea level rise on the spatial distribution of mangrove communities can be better evaluated, and the ecological restoration planning can be supported.

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

[0008] A mangrove community succession prediction method under sea level rise influence, comprising the following steps:

[0009] S1, obtaining the beach elevation, water depth, mangrove distribution, coastline, tidal harmonic constant and historical typhoon path data of the prediction area;

[0010] S2, constructing an adaptive triangular mesh based on the beach elevation and water depth data, and spatially interpolating the mangrove distribution data to the mesh elements;

[0011] S3, defining a closed boundary using the coastline data, combining the tidal harmonic constant and typhoon path data to drive a three-dimensional finite element model, and solving the grid point water level time series under the current and sea level rise scenarios;

[0012] S4, combining the beach elevation data and the water level time series, respectively calculating the duration of each grid element under the current and sea level rise scenarios;

[0013] S5, updating the community spatial distribution according to the change of the duration of flooding and the tolerance threshold of mangrove species, and outputting the prediction map.

[0014] Preferably, in S2, the adaptive triangular mesh is constructed based on the beach elevation and water depth data, comprising:

[0015] initially Delaunay triangulates the prediction area to generate a base mesh with a maximum edge length ≤50m;

[0016] calculates the elevation curvature of each triangular element, and if the curvature value >0.05m -1 , marks it as a unit that needs to be encrypted;

[0017] recursively performs binary encryption on the unit that needs to be encrypted until the curvature ≤0.05m -1 or the edge length ≤10m;

[0018] merges adjacent triangular elements to form a final mesh with an area ≥100m 2 .

[0019] Preferably, in S2, the mangrove distribution data is spatially interpolated to the mesh elements, comprising:

[0020] extracting the spatial coordinates and species coding attributes of the mangrove distribution vector data;

[0021] calculating the center of gravity coordinates of the mesh elements as the interpolation target points;

[0022] searching for mangrove sample points within a radius of 50m of the target point, and calculating the species type probability P k based on the inverse distance weighting method;

[0023] outputting the maximum species type probability max(Pk ) corresponding species is assigned to the grid cell, if max(P k ) is less than a preset threshold, it is marked as mixed community.

[0024] Preferably, the S3 comprises:

[0025] Extract the closed polygon boundary based on the coastline vector data, and convert it into the Dirichlet boundary condition in the finite element model;

[0026] Input the tidal harmonic constants into the open boundary of the model to generate the astronomic tide water level time series η tide ;

[0027] Parse the historical typhoon path data, and generate the typhoon storm surge field η storm by the Holland typhoon model;

[0028] Superimpose the astronomic tide water level time series η tide and the typhoon storm surge field η storm as the model forcing source term η total ;

[0029] Solve the three-dimensional hydrodynamic equation based on the Boussinesq approximation by using the semi-implicit Euler format, and output the current scenario grid point water level time series η

[0030] In the sea level rise scenario, superimpose the absolute rise amount ΔSLR to η to obtain the sea level rise scenario water level time series η

[0031] Preferably, in the semi-implicit Euler format, the time step Δt satisfies:

[0032]

[0033] Where Δx is the grid size, g is the gravity acceleration, and H is the water depth.

[0034] Preferably, the S4 comprises:

[0035] Extract the beach elevation z bed of the center of gravity of each cell in the adaptive triangular mesh;

[0036] Interpolate the grid point water level time series η t to the center of gravity of each cell to generate the cell water level time series η t,cell ;

[0037] For each time step t, calculate the cell submergence state function in the current and sea level rise scenarios, respectively:

[0038] I(t) = H(η t,cell -zbed )

[0039] where H(·) is the Heaviside step function;

[0040] accumulating the current scenario flooding duration within the full simulation period T and the sea level rise scenario flooding duration

[0041] Preferably, the S5 comprises:

[0042] comparing the current and the sea level rise scenario flooding duration variation

[0043] For each grid cell, according to ΔT flood symbols and species tolerance threshold T c performing a competitive succession rule: if ΔT flood > 0 and then species k is extinct; if ΔT flood < 0 and then species k is invasive; otherwise, species k remains unchanged;

[0044] updating the cell species code based on the extinction and invasion status, and preferentially filling the pioneer species;

[0045] mapping the updated species code to the grid cell to generate a GeoTIFF format community distribution prediction map.

[0046] Preferably, the competitive succession rule further comprises:

[0047] when ΔT flood > 0, the extinction species' empty spot is occupied by the adjacent species with the first ranked flooding tolerance, and the flooding tolerance sequence is in turn Kandang, Kandelph, Kaseb, and Kalm; if there is no adjacent species, it is marked as a bare beach;

[0048] when ΔT flood < 0, the landward species invasion needs to satisfy |ΔT flood | ≥ 0.3T c , and the invasion priority is that the existing mangrove species expansion is prior to the new species colonization.

[0049] According to the technical solution described above, compared with the prior art, the technical solution of the present application has the following beneficial effects:

[0050] 1. The method realizes high-resolution depiction of complex coastal terrain by adaptive triangular meshing and three-dimensional finite element model, and significantly improves the accuracy of water level time series simulation by synthesizing multiple source dynamic elements such as astronomical tide and storm surge, which overcomes the defects of traditional GIS linear superposition ignoring nonlinear tidal effect, and provides a reliable hydrodynamic basis for flood duration calculation.

[0051] 2. Based on the competitive succession rules of the change amount of flood duration and the tolerance threshold of species, a spatial response model of mangrove community is constructed; by setting the species extinction / invasion conditions and priority, quantitative prediction from hydrological change to community succession is realized, and the problem that the existing technology cannot predict species replacement is solved.

[0052] 3. The module design is used to separate the current and sea level rise scenario calculation process, the water level time series is corrected by superimposing the absolute sea level rise, and the spatial distribution map in GeoTIFF format is output, the framework can be flexibly adapted to different sea level rise rates and extreme climate scenarios, and is more suitable for long-term planning of mangrove protection and restoration. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings.

[0054] Figure 1 A flow chart of a mangrove community succession prediction method under the influence of sea level rise is provided for the embodiments of the present application.

[0055] Figure 2 An implementation process schematic diagram of mangrove community succession prediction for a certain protected area is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0057] As shown in Figure 1 The present embodiment provides a mangrove community succession prediction method under the influence of sea level rise, comprising the following steps:

[0058] S1, obtain the beach elevation, water depth, mangrove distribution, coastline, tidal harmonic constant and historical typhoon path data of the prediction area;

[0059] S2, construct an adaptive triangular mesh based on the beach elevation and water depth data, and spatially interpolate the mangrove distribution data to the mesh cells;

[0060] S3, define a closed boundary using the coastline data, drive a three-dimensional finite element model combining the tidal harmonic constant and typhoon path data, and solve the water level time series of the grid points under the current and sea level rise scenarios;

[0061] S4, combine the beach elevation data and water level time series to calculate the duration of each grid cell under the current and sea level rise scenarios;

[0062] S5, update the community spatial distribution according to the change of flooding time and the species tolerance threshold, and output the prediction map.

[0063] This method finely depicts the intertidal zone terrain by constructing an adaptive triangular mesh, simulates the water level time series change by coupling the tidal and storm surge dynamic process, realizes high-precision dynamic prediction of mangrove community spatial distribution based on the competitive succession rules of flooding time change and species tolerance threshold, and significantly improves the quantitative evaluation ability of species succession rules under the background of sea level rise.

[0064] The following further details each step in the above method:

[0065] In this embodiment S1, the beach elevation, water depth, mangrove distribution, coastline, tidal harmonic constant and historical typhoon path data of the prediction area are obtained;

[0066] Specifically, the beach elevation data is obtained by airborne LiDAR scanning, the point cloud density is ≥5 points / m 2 , and the complete underwater topography is generated by fusing multi-beam sounding data; the mangrove distribution data is obtained based on remote sensing image interpretation, the object-oriented classification method is used to extract species patches, and a vector layer containing species codes is output; the coastline data is extracted from the nautical chart and corrected to the mean high water level datum; the tidal harmonic constant data extracts 8 main tidal harmonic constants M2, S2, N2, K2, O1, K1, P1 and Q1 within the boundary of the prediction area; the historical typhoon path data is integrated from the typhoon best path dataset, and the historical typhoon events with a center distance of ≤300km from the coastline are selected;

[0067] Finally, all the obtained data are projected to a unified coordinate system, a spatio-temporal index is constructed and stored as a netCDF format data package.

[0068] In this embodiment S2, an adaptive triangular mesh is constructed based on the beach elevation and water depth data, and the mangrove distribution data is spatially interpolated to the mesh cells;

[0069] The adaptive triangular mesh construction based on beach elevation and water depth data includes:

[0070] The predicted region is initially triangulated using Delaunay triangulation to generate a base mesh with a maximum side length ≤ 50m;

[0071] Calculate the elevation curvature of each triangular element. If the curvature value is > 0.05m... -1 Then it is marked as a unit that needs to be encrypted;

[0072] Recursively perform binary search encryption on the units that need encryption until the curvature is ≤0.05m. -1 Or the side length is ≤10m;

[0073] Merging adjacent triangular units to form an area ≥100m² 2 The final grid.

[0074] In addition, the grid resolution can be dynamically adjusted based on the topographic curvature of the predicted area, automatically densifying the grid in areas with severe topographic undulations and using a sparse grid in open boundaries and areas with gentle topography. By dynamically adapting to changes in topographic undulations, it achieves local high-resolution subdivision in areas with dense mangrove root systems and tidal creek development, ensuring that the model can accurately capture the control effect of micro-topography on the flooding process. Compared with traditional uniform grids, it improves the accuracy of tidal flat hydrological connectivity simulation.

[0075] Furthermore, spatial interpolation of mangrove distribution data to grid cells includes:

[0076] Extracting spatial coordinates and species coding attributes from mangrove distribution vector data;

[0077] Calculate the centroid coordinates of the grid cells as the interpolation target point;

[0078] Search for mangrove sample points within a 50m radius of the target point, and calculate the species type probability P based on the inverse distance weighting method. k ;

[0079]

[0080] Where, d i δ represents the distance from sample point i to the target location. ik As an indicator variable, δ represents the value of sample point i belonging to species k. ik =1, otherwise, δ ik =0, meaning that the sample point will only contribute to the probability calculation of the target species when it matches the target species.

[0081] assign the grid cell with the species corresponding to the maximum species type probability max(P k ), and mark the grid cell as a mixed community if max(P k ) is less than a preset threshold.

[0082] In the implementation process, adaptive triangular mesh construction first generates an initial mesh based on the Delaunay algorithm, and then dynamically identifies the terrain mutation area through curvature analysis, and recursively encrypts the grid cells in these areas, and keeps larger grid size in the terrain gentle area. The mangrove distribution data is converted to the grid cell through spatial interpolation, and the unit center is taken as the reference, the adjacent mangrove sample points are searched, and the species probability weight is calculated, and finally the unit attribute is determined according to the dominant species probability threshold, and the mixed community is marked in the fuzzy area.

[0083] In this step, the adaptive mesh significantly reduces the calculation redundancy while ensuring the accuracy of the terrain detail description; the spatial interpolation combined with the probability weight effectively solves the problem of fuzzy boundary of mangrove patches, and the mapping of species distribution and grid cells is more in line with the ecological continuity law.

[0084] In this embodiment S3, the coastline data is used to define the closed boundary, and a three-dimensional finite element model is driven combined with the tidal harmonic constant and the typhoon path data to solve the grid point water level time series under the current and sea level rise scenarios; specifically including:

[0085] Based on the coastline vector data, the closed polygon boundary is extracted and converted into the Dirichlet boundary condition in the finite element model;

[0086] The tidal harmonic constant is input into the open boundary of the model to generate the astronomical tide water level time series η tide ;

[0087] The historical typhoon path data is analyzed, and the typhoon storm surge field η storm is generated by the Holland typhoon model;

[0088] The astronomical tide water level time series η tide and the typhoon storm surge field η storm are superimposed as the model forcing term η total ;

[0089] The three-dimensional hydrodynamic equation based on the Boussinesq approximation is solved by using the semi-implicit Euler format, and the grid point water level time series η

[0090] In the sea level rise scenario, the absolute rise amount ΔSLR is superimposed on η to obtain the sea level rise scenario water level time series η

[0091] In the semi-implicit Euler format, the time step Δt satisfies:

[0092]

[0093] Where Δx is the grid size, g is the gravitational acceleration, and H is the water depth.

[0094] In this step, the construction of the three-dimensional finite element model satisfies the following requirements: the Boussinesq approximation is used to handle the vertical momentum equation of the fluid, and the switching between Cartesian coordinate system and spherical coordinate system is supported to adapt to the prediction area of ​​different spatial scales; the three-dimensional model couples the nonlinear interaction between astronomical tides and storm surges, characterizes the complex hydrodynamic process in the shallow water area of ​​the intertidal zone, and further combines a semi-implicit scheme to balance computational efficiency and stability, providing a reliable water level time series input for flooding time calculation.

[0095] In this embodiment S4, combining beach elevation data and water level time series, the duration of flooding for each grid cell in the current and sea-level rise scenarios is calculated, including:

[0096] Extracting the beach elevation z of the centroid of each element in the adaptive triangular mesh. bed ;

[0097] The grid point water level time series η t Interpolate to the centroid of each cell to generate the cell water level time series η. t,cell ;

[0098] For each time step t, calculate the cell inundation state function in the current scenario and the sea-level rise scenario respectively:

[0099] I(t)=H(η t,cell -z bed )

[0100] Where H(·) is the Heaviside step function;

[0101] Accumulate the current scenario flood duration within the complete simulation period T. and the duration of flooding under sea level rise scenarios The simulation period T must cover at least one complete astronomical tide cycle and include the period of typical typhoon events;

[0102] Its flooding state determination based on the unit centroid avoids grid scale effect errors, and the complete tidal cycle simulation ensures the representativeness of flooding duration statistics, providing key environmental stress indicators for community succession.

[0103] In this embodiment, S5, the spatial distribution of the mangrove community is updated and a prediction map is output based on changes in flooding time and the tolerance threshold of mangrove species; including:

[0104] Compare the changes in flood duration between the current scenario and the sea-level rise scenario.

[0105] For each grid cell, according to ΔT flood Symbol and species tolerance threshold T c Perform competitive succession rules: if ΔT flood > 0 and then species k is extinct; if ΔT flood < 0 and then species k can invade; otherwise, species k remains unchanged;

[0106] Update the species code of the unit based on the extinction and invasion state, and preferentially fill the pioneer species;

[0107] Map the updated species code to the grid cell to generate a community distribution prediction map in GeoTIFF format.

[0108] Further, the competitive succession rules also include:

[0109] ΔT flood > 0, the empty spot of the extinct species is occupied by the adjacent species with the first ranked flood tolerance, and the flood tolerance sequence is in turn white bone soil, kandelia candel, red sea olive, and wood olive; if there is no adjacent species, it is marked as a bare beach;

[0110] ΔT flood < 0, the invasion of landward species needs to satisfy | ΔT flood | ≥ 0.3T c , and the invasion priority is that the expansion of existing mangrove species is prior to the planting of new species.

[0111] In this step, the unit compares the change in flooding time under the two scenarios, and performs succession rules according to the species tolerance threshold: the species is extinct if the flooding time is prolonged and exceeds the tolerance threshold, and the species can invade if the flooding time is shortened and is below the threshold; the extinct area is occupied by the adjacent species with the highest flood tolerance, and the invasion needs to satisfy the change threshold and follow the priority principle of existing species expansion. Finally, the grid species attribute is updated and the spatial distribution map is output;

[0112] This competitive succession rule integrates the species tolerance physiological mechanism and the diffusion priority, so that the prediction result is more in line with the ecological law of mangrove community succession; the pioneer species filling strategy enhances the application value of the model to the restoration of degraded areas.

[0113] As shown in Figure 2 To evaluate the impact of sea level rise on a certain mangrove protection area, the mangrove community succession prediction method in this embodiment is used to predict the core area of the protection area, and the specific implementation process is as follows:

[0114] 1) Data acquisition and preprocessing;

[0115] The beach elevation data is obtained by airborne LiDAR scanning, and the complete terrain is formed by fusing the nearshore multi-beam sounding data; Based on high-resolution satellite remote sensing images, the distribution patches of three main mangrove plants, Avicennia marina, Kandelia candel and Rhizophora stylosa, are interpreted, and the vector layer is output; The coastline data is extracted from the latest nautical chart and corrected to the mean high water level; The tidal harmonic constants of 8 main constituent tides such as M2 and S2 are obtained; The typhoon path data with a center distance of ≤300km from the coastline in history is screened.

[0116] All data is converted to the same coordinate system and stored as a data package for easy calling.

[0117] 2) Grid construction and species interpolation;

[0118] Based on the beach elevation and water depth data, an adaptive triangular mesh is constructed: first, the region is initially triangulated to generate a base mesh with a maximum edge length of 50m; In the area with dense tidal ditches and developed mangrove roots (due to large terrain curvature), the mesh is encrypted to an edge length of 10m, and the flat area remains the original size;

[0119] Then the mangrove distribution data is interpolated to the mesh, taking the center of each mesh as the target point, searching for the mangrove sample points within a 50m range, calculating the probability of each species in the mesh by inverse distance weighting method, assigning the species with the highest probability to the mesh, and marking the boundary fuzzy area as mixed community.

[0120] 3) Water level time series simulation;

[0121] The extracted coastline data is used to define the closed boundary of the model, and the tidal harmonic constants are input into the open boundary of the model to generate the astronomical tide water level time series; Combined with the screened historical typhoon data, the storm surge water level caused by the typhoon is simulated by the Holland model, and the total water level forcing source is obtained by superimposing the astronomical tide;

[0122] A three-dimensional finite element model is used to simulate the water level changes under the current scenario and the sea level rise of 0.5m scenario respectively, and the water level time series data of each grid point in the two scenarios is output.

[0123] 4) Calculation of waterlogging duration;

[0124] The beach elevation of the center of each grid cell is extracted, and the simulated water level time series is interpolated to the center position to obtain the cell water level time series. The inundation state of each time step is determined by the step function, and the annual waterlogging duration of each grid in the current scenario and the sea level rise scenario is calculated cumulatively to obtain the waterlogging time difference of the two scenarios.

[0125] 5) Community succession prediction and result output;

[0126] The change amount of flooding time of the two scenarios is compared, and the succession rule is executed combined with the species tolerance threshold: if the flooding time of a certain grid increases and exceeds the current species tolerance threshold, the species disappears, and the empty space is occupied by the adjacent species with the strongest flooding tolerance; if the flooding time decreases and is lower than the tolerance threshold of a certain species, the species is allowed to invade the land, and the existing species is preferred to expand.

[0127] Finally, the species information of each grid is updated, and a prediction map of the mangrove community distribution in the protected area is generated, clearly showing the succession trend of the expansion of the white bone soil to the land and the contraction of the Kandelia obovata in the middle tidal zone after the sea level rises by 0.5 m.

[0128] The embodiment fuses multi-source geospatial data and a three-dimensional water dynamic model to construct a dynamic prediction framework for mangrove community succession under the background of sea level rise; the method not only realizes high-precision simulation of intertidal microtopography and hydrological processes, but also introduces competitive succession rules based on physiological tolerance mechanism and diffusion priority, significantly improving the ecological rationality and spatial accuracy of ecosystem response prediction. The actual application results show that the method can effectively identify the migration path of key species and the change trend of community structure, providing scientific basis and technical support for adaptive management and protection planning of mangrove ecosystems.

[0129] The embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts of each embodiment can be referred to each other. For the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the related parts can be referred to the method part.

[0130] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting the succession of a mangrove community under the influence of sea level rise, characterized by, The method comprises the following steps: S1, obtaining the beach elevation, water depth, mangrove distribution, coastline, tidal harmonic constant and historical typhoon path data of the prediction area; S2, constructing an adaptive triangular mesh based on the beach elevation and water depth data, and spatially interpolating the mangrove distribution data to the mesh cells; S3, defining a closed boundary using the coastline data, driving a three-dimensional finite element model combined with the tidal harmonic constant and typhoon path data, and solving the water level time series of the grid points under the current and sea level rise scenarios; S4, combining the beach elevation data and water level time series, respectively calculating the duration of each mesh cell being flooded under the current and sea level rise scenarios; S5, updating the community spatial distribution and outputting the prediction map according to the change of the flooding time and the tolerance threshold of the mangrove species.

2. The method of claim 1, wherein, In the S2, the adaptive triangular mesh is constructed based on the beach elevation and water depth data, which comprises: Performing initial Delaunay triangulation on the prediction area to generate a base mesh with a maximum edge length ≤ 50m; Calculate the elevation curvature of each triangular element, if the curvature value > 0.05m -1 then mark as need to encrypt element; Recursive binary encryption is performed on the units to be encrypted until the curvature is <0.05 m -1 or the edge length is <10 m; Merge adjacent triangular cells to form a final mesh with area > 100 m 2 . 3.The method of claim 1, wherein, In the S2, the mangrove distribution data is spatially interpolated to the mesh cells, which comprises: Extracting the spatial coordinates and species coding attributes of the mangrove distribution vector data; Calculating the barycentric coordinates of the mesh cells as the interpolation target points; Search for mangrove sample points within a radius of 50 m of the target point, and calculate the species type probability P based on the inverse distance weighting method k ; assigning the grid cell with the species corresponding to the maximum species type probability max(P k ), and marking the grid cell as a mixed community if max(P k ) is less than a predetermined threshold. 4.The method of claim 1, wherein, The S3 comprises: Extracting the closed polygon boundary based on the coastline vector data and converting it to the Dirichlet boundary condition in the finite element model; The tidal harmonic constants are input into the model open boundary to generate the astronomical tide water level time series η tide ; Analyzing historical typhoon path data, generating typhoon storm surge field η by Holland typhoon model storm ; superimposed astronomic tide water level time series η tide with typhoon storm surge field η storm as model forcing source term η total ; The semi-implicit Euler format is used to solve the three-dimensional hydrodynamic equation based on the Boussinesq approximation, and the water level time series of the current scenario grid point is output In sea level rise scenarios, the absolute rise amount ΔSLR is added to to obtain sea level rise scenario water levels 5. The method of claim 1, wherein, In the semi-implicit Euler scheme, the time step Δt satisfies: Where Δx is the grid size, g is the gravitational acceleration, and H is the water depth.

6. The method of claim 1, wherein, The S4 comprises: extracting the beach elevation z from the center of gravity of each cell in the adaptive triangulation bed ; Grid point water level time series η t Interpolated to each cell center, generating cell water level time series η t,cell ; For each time step t, the cell flooding state function under the current and sea level rise scenarios is calculated respectively: I(t) = H(η t,cell -z bed ) Where H(·) is the Heaviside step function; accumulating the current scenario flooding duration over a full simulation period T and the sea level rise scenario flooding duration 7. The method of claim 1, wherein the method is characterized by: The S5 comprises: Comparing the change in duration of inundation between current and sea level rise scenarios For each grid cell, according to ΔT flood Symbol and species tolerance threshold T c Execute the competitive succession rule: if ΔT flood > 0 and then species k goes extinct; if ΔT flood < 0 and then species k can invade; otherwise, species k remains unchanged; Updating the species coding of the cells based on the extinction and invasion state, and preferentially filling the pioneer species; Mapping the updated species coding to the mesh cells to generate a GeoTIFF format community distribution prediction map. 8.The method of claim 1, wherein, The competitive succession rule further comprises: ΔT flood When ΔT > 0, the dead species' patch was occupied by the adjacent species with the first order of flood tolerance, and the order of flood tolerance was as follows: Avicennia marina, Kandelia candel, Sonneratia alba, Rhizophora stylosa. If there was no adjacent species, the patch was marked as bare beach. ΔT flood At t = 0, the invasion of landward species needs to satisfy |ΔT flood |≥ 0.3T c , and the invasion priority is that the expansion of existing mangrove species is prior to the colonization of new species.

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