Ecological regulation and control method for tidal creek form based on suaeda salsa growth suitability

Abstract

The invention discloses a suaeda salsa growth adaptability-based tidal creek form ecological regulation and control method, which comprises the following steps of: acquiring and analyzing a primary data source in a research area, setting a computational grid according to the range of the research area and a research purpose, and preprocessing the analyzed data; taking the preprocessed data as an input quantity of a MaxEnt model, and outputting an environment variable result; according to an environment variable result, key influence factors are identified, and an ecological suitability interval is determined; constructing a Delft3D model, carrying out simulation, and outputting a scene simulation result; and analyzing a scene simulation result and an ecological suitability interval, determining a regulation and control direction, and performing regulation and control and implementation of the tidal creek network. According to the method, the problem that in the prior art, a single threshold lacks coupling and is difficult to reach the standard at a time is solved, the influence mechanism of the tidal creek form on the wetland pioneer plant habitat is scientifically quantified, the optimized tidal creek form index is determined, and a tidal creek regulation and control scheme considering the hydrological process and ecological requirements is provided and used for improving the habitat conditions of the estuary wetland suaeda salsa.

Description

Technical Field

[0001] This invention belongs to the field of estuarine wetland ecological restoration and hydrological regulation technology, specifically involving an ecological regulation method based on the adaptability of Suaeda salsa to saline-alkali soil morphology of tidal channels. Background Technology

[0002] Estuarine wetlands are important ecosystems where land and sea meet, playing a vital role in hydrological regulation, biodiversity maintenance, carbon sequestration and emission reduction, and defense against coastal erosion. Tidal channels are key hydrogeomorphic units within wetlands, their morphology directly influencing water and salt distribution, sediment transport, and vegetation succession. *Suaeda salsa* (a type of salt-tolerant plant) Suaeda salsa As a typical pioneer halophyte, it is a key species in the formation and succession of northern salt marsh communities. Its well-developed above-ground and underground structures can reduce flow velocity, slow down wave erosion, and improve the stability of estuaries. At the same time, it provides important habitats and feeding grounds for coastal birds and benthic animals, and plays an important role in maintaining the ecological function of wetlands.

[0003] In estuarine ecosystems, there is a significant coupling feedback between tidal channel networks and vegetation patterns: tidal channel morphology controls near-surface water salinity and hydrodynamics, thereby affecting the spatial distribution and succession of salt marsh plants; while vegetation alters the bed roughness and sediment-erosion balance, which in turn reshapes tidal channel connectivity. This correlation and process coupling between tidal channels and vegetation has been confirmed by multiple studies, but existing research mostly remains at the level of statistical correlation or empirical thresholds, or only discusses the geometric stability of the inlet / channel from the perspective of water and sediment dynamics. For example, existing literature 1 (zheng zongsheng, zhou yunxuan, tian bo, ding xianwen. The spatial relationship between salt marsh vegetation patterns, soil elevation and tidal channels using remote sensing at Chongming Dongtan Nature Reserve, China [J]. Acta Oceanologica Sinica, 2016, 35(4): 26-34. DOI: 10.1007 / s13131-016-0831-z) has not yet directly mapped the species habitat suitability range into implementable engineering control quantities and conducted multi-scenario comparative analysis before implementation.

[0004] In recent years, the concept of "nature-based solutions" (NbS) has been widely applied in wetland restoration. By appropriately adjusting the scale and spatial layout of tidal channels and combining them with ecological engineering measures such as vegetation restoration, the hydrological structure of wetlands can be effectively improved and their ability to resist risks can be enhanced. For example, Reference 2 (Liu Luyu, Qu Fanzhu, Li Yunzhao, et al. Relationship between distribution of tidal channels and vegetation cover in coastal wetlands of the Yellow River Delta [J]. Journal of Ecology, 2020, 39(06):1830-1837) analyzed the distribution pattern and dynamic changes of tidal channels and vegetation cover in coastal wetlands of the Yellow River Delta using LandsatTM / OLI images from 2005, 2010 and 2017, and used remote sensing and geographic information technology. The grid search method was used to conduct correlation analysis between tidal channels and vegetation cover in the study area. The results showed that from 2005 to 2017, the vegetation cover of the coastal wetlands in the Yellow River Delta continuously increased, and the area with low vegetation cover decreased by 233.73 km². 2 The area with high vegetation cover increased by 165.85 km². 2 From 2005 to 2017, the length and area of ​​tidal channels in the coastal wetlands of the Yellow River Delta increased continuously, as did their frequency. In 2017, the length of tidal channels in the southeastern part of the coastal wetlands reached 216.13 km, and the area was 22.23 km². 2 The length and area increased by 36.91% and 49% respectively compared to 2005. From 2005 to 2017, the distribution of tidal channels in the Yellow River Delta was negatively correlated with vegetation cover, with significant correlations between tidal channels and vegetation cover in 2010 and 2017 (P < 0.05). This indicates that the spatial distribution of tidal channels in the Yellow River Delta is closely related to the growth of regional vegetation. However, Reference 2 only provides a static "critical density" without coupling plane-section synergistic parameters such as width-depth ratio and curvature, making it impossible to reverse-engineer the channel shape required for the "target coverage of 60%"; it lacks a quantitative response of vegetation-hydraulic dynamics, resulting in an extrapolation error of >20%; it lacks the earthwork conversion for a 0.8 m wide articulated suction dredger, forcing construction to modify the shape, with a density deviation of 10-15%; and it lacks two-way multi-scenario comparison, allowing only post-event verification, making it difficult to guarantee that the scheme will meet the target on the first attempt. Therefore, existing methods are based on static single thresholds and lack the coupling mechanism of tidal morphology-hydraulic dynamics-vegetation, making it difficult to achieve the target in one go. They mainly rely on empirical zoning or single-factor analysis and have not formed a quantitative control scheme. Summary of the Invention

[0005] The purpose of this invention is to provide an ecological regulation method for tidal creek morphology based on the adaptability of Suaeda salsa to saline-alkali land. This method solves the problem of existing technologies lacking coupling and difficulty in achieving the target in one go. It can scientifically quantify the impact mechanism of tidal creek morphology on the habitat of wetland pioneer plants, determine the optimized tidal creek morphology index, and propose a tidal creek regulation scheme that takes into account both hydrological processes and ecological needs. This improves the habitat conditions of Suaeda salsa in estuarine wetlands and promotes the restoration and enhancement of wetland ecosystem functions.

[0006] To achieve the above objectives, this invention provides an ecological regulation method for tidal creek morphology based on the adaptability of Suaeda salsa to saline-alkali soil, the method comprising: Step 1: Obtain and analyze primary data sources within the study area; the primary data sources include remote sensing image data, measured sediment data, hydrological data, and topographic and nautical chart bathymetry data used to construct a regional topographic elevation model. Step 2: Set up a computational grid according to the scope of the study area and the research objectives, and preprocess the analyzed data, that is, process the environmental variables and species presence points into input data that can be directly called by the adaptability model and has a consistent spatial benchmark. Step 3: Use the preprocessed data as input for subsequent species adaptability modeling and analyze the output environmental variable results; Step 4: Based on the environmental variable results, identify key influencing factors and determine the ecological suitability range; Step 5: Establish a Delft 3D model, conduct scenario simulation based on the ecological suitability range and the width of the main tidal channel inlet section, and obtain the scenario simulation results; Step Six: Analyze the scenario simulation results and ecological suitability ranges to determine the direction of regulation; Step 7: Implement tidal channel network regulation and control according to the regulation direction.

[0007] Preferably, in step one, the hydrological data is used to characterize the tidal channel morphology, Suaeda salsa distribution, and tidal hydrological processes in the study area, acquiring tidal hydrological and boundary condition data, as well as topographic and upstream runoff processes, for boundary setting and verification of the model; the topographic and nautical chart data, including DEM and nautical chart bathymetry data, are used as topographic input data for the Delft3D model to characterize the seabed elevation of the study area and complete boundary setting and verification. The analysis includes extracting tidal channel network distribution information and Suaeda salsa distribution information from the remote sensing image data, and conducting field surveys to verify the distribution of Suaeda salsa to characterize its annual growth status; the study area is an estuarine wetland.

[0008] Preferably, in step two, the preprocessing is based on the distribution information of the tidal channel network, establishing a computational grid in the study area, statistically analyzing the tidal channel morphology indicators within each grid, and calculating several tidal channel morphology indicators; based on the distribution information of Suaeda salsa, establishing a computational grid in the study area, and taking the center point of the grid where the Suaeda salsa coverage area is greater than 50% of the grid area as the suitable distribution point of Suaeda salsa.

[0009] Preferably, the tidal channel morphology indicators include tidal channel area, tidal channel curvature, number of tidal channels, tidal channel density, tidal channel frequency, tidal channel length, and tidal channel straight length, etc.; in order to analyze the relationship between tidal channels and Suaeda salsa, the size of the tidal channel grid and the size of the Suaeda salsa grid are different, wherein the characteristic scale of the grid used to statistically analyze the tidal channel morphology indicators is at least one order of magnitude larger than the characteristic scale of the grid used to determine the suitable distribution points of Suaeda salsa.

[0010] More preferably, the grid used for statistical analysis of tidal channel morphology indicators is kept at a size difference of one order of magnitude from the grid used for determining suitable distribution points of Suaeda salsa in saline-alkali land.

[0011] Preferably, in step three, the tidal creek morphology index is used as an environmental variable, and the suitable distribution point of the Suaeda salsa is used as the species existence point; the environmental variable and the species existence point are used as inputs to establish a Suaeda salsa suitability model, namely the MaxEnt model (MaxEnt 3.4.4 software, maximum entropy model); through several repeated runs and training set / validation set partitioning, the environmental variable results are output.

[0012] Preferably, in step four, the method includes determining the ecological suitability range of key tidal creek morphology indicators affecting the distribution of Suaeda salsa using a curve fitting method based on the Suaeda salsa MaxEnt model, for use in regulating subsequent scenario simulations.

[0013] Preferably, in step five, the method includes constructing numerical scenarios and conducting scenario simulations in the Delft3D model Flow module; establishing a numerical model covering the estuary section and tidal flat area, setting upstream runoff processes and downstream tidal level boundary conditions, using a nested grid strategy to describe the local hydrodynamic processes of the tidal channel, and verifying the results through measured water levels; setting several scenario simulation control schemes based on the ecological suitability range of the tidal channel morphology and the width of the main tidal channel inlet section, constructing a multi-scale nested model based on the Delft3D model Flow module for simulation, and outputting the results of changes in scenario tidal channel morphology indicators, which are used to quantitatively predict and compare different control schemes before project implementation.

[0014] More preferably, after determining the direction of ecological regulation, by changing the width of the main tidal channel inflow section, the key influencing factors in the tidal channel morphology indicators are maintained within the ecological suitability range, or evolve towards the ecological suitability range, while maintaining good network connectivity and suppressing excessive expansion. Among these factors, the width of the main tidal channel inflow section is the main influencing factor affecting the evolution of the tidal channel network and the habitat quality of Suaeda salsa. Its size directly determines whether the tidal channel morphology indicators converge towards the ecological suitability range, controls the intensity of tidal water and salinity entering the study area, determines the expansion scale, development degree, and water and salinity conditions of the tidal channels, and thus determines the effect of vegetation restoration.

[0015] Preferably, in step six, the results of the changes in the tidal channel morphology index are analyzed and evaluated, and compared with the ecological suitability range. The scheme that causes the tidal channel morphology index to evolve towards the ecological suitability range is selected as the control scheme.

[0016] Preferably, in step seven, the tidal channel network in the study area is subjected to engineering regulation according to the regulation scheme. After the engineering regulation, the step further includes periodically monitoring the wetland vegetation and soil water and salt conditions, and adjusting the tidal channel morphology based on the monitoring results.

[0017] The present invention provides an ecological regulation method based on the tidal creek morphology of Suaeda salsa in saline-alkali land, which solves the problem of lack of coupling and difficulty in achieving the target in one step in the existing technology, and has the following advantages: 1. The core of the tidal channel morphology regulation method proposed in this invention lies in establishing a relationship between species ecological suitability ranges and engineering variables. Based on the ecological suitability range obtained from the MaxEnt model, suitable tidal channel morphological conditions for *Suaeda salsa* are determined through quantitative thresholds. These are then transformed into designable engineering variables (such as cross-sectional width: when the cross-section is reduced to 80% (condition B), key indicators such as tidal channel area and curvature are closest to the ecological suitability range, network connectivity is maintained well, and overexpansion is suppressed) – multi-scenario screening, and the tidal channel network pattern is optimized accordingly. In practical applications, this method can significantly improve estuarine wetland vegetation habitats, promote the recovery and expansion of pioneer species such as *Suaeda salsa*, and simultaneously enhance tidal channel hydrological connectivity and nutrient supply efficiency, thereby strengthening the overall ecosystem function of wetlands and showing broad application prospects.

[0018] 2. This invention combines the distribution of Suaeda salsa in saline-alkali land with the hydrodynamic process of tidal channels. Different control measures can be predicted and compared before the implementation of the optimization scheme, ensuring that the final tidal channel control scheme can effectively improve the wetland water and salt environment and promote the improvement of Suaeda salsa habitat conditions in practical applications. It has good feasibility and stability.

[0019] 3. This invention can use similar models such as MaxEnt and Delft3D, whose output variables and evaluation rules are consistent and follow the ecological suitability interval-parameter mapping and matching evaluation rules of this invention, thus achieving the same technical effect. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the ecological regulation method of tidal gully morphology based on the adaptation of Suaeda salsa to saline-alkali soil, as described in Embodiment 1 of the present invention.

[0021] Figure 2 This is a map of the Liaohe River Estuary research area for this invention.

[0022] Figure 3The visual interpretation effect and mesh of the tidal channel network of the present invention in 2020.

[0023] Figure 4 This invention provides the effective calculation grid distribution for Suaeda salsa in 2020.

[0024] Figure 5 This invention is based on a field survey of the distribution of Suaeda salsa in saline-alkali land.

[0025] Figure 6 The relative positions of the large area of ​​Liaohe Estuary and the typical study area in the Delft 3D model of this invention are shown.

[0026] Figure 7 This is a simulation of the situation where the width of the main tidal channel inlet cross-section is not changed within the typical research area of ​​this invention.

[0027] Figure 8 This is an analysis diagram of the ecological suitability range of the area of ​​the key morphological factors in this invention.

[0028] Figure 9 This is an analysis diagram of the ecological suitability range of the curvature of the key morphological factor in this invention. Detailed Implementation

[0029] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Example 1 An ecological regulation method based on the adaptation of Suaeda salsa to saline-alkali soil tidal creek morphology, such as Figure 1 As shown, this is a flowchart illustrating the ecological regulation method for tidal creek morphology based on the adaptation of Suaeda salsa to saline-alkali soil in Embodiment 1 of the present invention. The method includes: Step 1: Obtain and analyze primary data sources within the study area. The primary data sources include remote sensing imagery, measured sediment data, hydrological data, and topographic data used to construct a regional topographic elevation model. The hydrological data consists of upstream runoff data released by the Panshan Sluice Hydrological Station (taken from the measured flow data of the Panshan Sluice section in May-June 2020 in the "2020 Liaoning Provincial Hydrological Yearbook") and downstream tidal boundary time series reconstructed from the main tidal modulus and constants of the TPXO7.2 tidal model. The topographic data includes a 2m resolution DEM generated from Gaofen-7 image pairs, a 0.1m resolution DEM obtained from UAV RTK low-altitude photogrammetry (DJI Phantom 4 RTK, 100m relative to ground altitude), and nautical chart bathymetry data. The topographic data are fused to form a regional DEM, used to characterize the seabed elevation of the study area and as topographic input for the Delft 3D model.

[0031] (1) Study area This invention takes the Liaohe Estuary Wetland, a typical coastal wetland in northern China, as the research area to explore the relationship between its tidal channel morphology indicators and the distribution of Suaeda salsa in saline-alkali land.

[0032] like Figure 2 The image shows a map of the Liaohe River Estuary research area of ​​this invention. The Liaohe River Estuary Wetland is located in Panjin City, Liaoning Province, and is one of the most typical estuarine wetland systems in China's warm temperate zone, as well as an internationally important wetland.

[0033] (2) Data source Remote sensing image data: Sentinel-2 L2A image, resolution 10 m, taken in June 2020, growing season, cloud cover ≤10%, sourced from Copernicus Data Space Ecosystem (EU Copernicus Data Open Platform, publicly available); the image interpretation was calibrated in conjunction with field plot surveys, and based on this, the NDVI range of Suaeda salsa was determined to be 0.032-0.253.

[0034] The topographic data was obtained by fusing a 2m resolution DEM (captured in June 2020) generated from Gaofen-7 image pairs, a 0.1m resolution DEM acquired through UAV RTK low-altitude photogrammetry, and nautical chart bathymetry data. The UAV RTK aerial survey results, a high-precision digital elevation model with a spatial resolution of 0.1m, were obtained using a multi-rotor UAV equipped with an RTK positioning system. This model was primarily used to supplement the micro-topographic features of tidal channels and their adjacent terrain within the typical study area. The UAV aerial survey results were not directly used as model input but rather participated in the regional DEM fusion process as high-precision local topographic data. The drone used was a DJI Phantom 4 RTK, with a relative altitude of 100 m. The subject of the photographs was topography, and the final results were expressed as orthophotos and DEMs with an accuracy of 0.1 m. The aerial survey covered a typical study area within the research region, with a coverage area of ​​5.701 km², and was used to refine the representation of tidal channels and adjacent micro-topography. In addition, the water depth in shallow water areas was retrieved by combining nautical chart bathymetry data to form the bottom elevation of the Delft 3D model.

[0035] The hydrological and boundary conditions in the hydrological data are as follows: the upstream runoff process is taken from the flow data of the Panshan Sluice section in the "2020 Liaoning Provincial Hydrological Yearbook", and the downstream tidal boundary adopts the main tidal harmonic constants of the TPXO7.2 tidal model, which are superimposed to form the time series water level. The measured sediment data were obtained from monthly measurements of sediment content, particle size, and dry density at the estuary of the study area over a period of 12 months, with the average values ​​from May to June used as the simulation input. Parameters such as roughness and shear stress were initially set based on literature references and then iteratively verified by water level comparisons. Reference cited (Li Qingxian. Research and Application Based on Two-Dimensional Integrated Land WebGIS [D]. China University of Geosciences (Beijing), DOI: 2014.DOI:10.27493 / d.cnki.gzdzy.2014.000080): Species suitability modeling was performed using MaxEnt 3.4.4. All the above platforms are publicly available. The specific simulation settings and measured sediment data are shown in Table 1 for the average values ​​measured in May and June. The measured NDVI values ​​ranged from 0.032 to 0.253.

[0036] Table 1 Measured Sediment Data (3) Information extraction Target information is extracted from remote sensing image data, mainly including tidal channel networks and the distribution of Suaeda salsa in saline land.

[0037] ① Tidal channel network extraction Tidal channel networks can be obtained through visual interpretation, combined with on-site reconnaissance and empirical judgment for correction to ensure accuracy. Besides visual interpretation, the Normalized Difference Water Index (NDWI) can be used to extract water body extents during spring tides and neap tides, and the tidal channel distribution can be obtained by inverting the intersection of the data. However, this method has limitations, such as lower identification accuracy and the difficulty in perfectly matching the image acquisition time with the actual tidal process, which may affect the accuracy of tidal channel extraction.

[0038] like Figure 3 The image shows the visual interpretation results and mesh of the tidal channel network of the present invention in 2020. (By...) Figure 3 As can be seen, the tidal channel network (yellow area) is relatively well-developed, with clear hierarchical distinctions between primary and secondary tidal channels (light blue midline), naturally curved shapes, and a relatively uniform overall distribution. A 1000 m × 1000 m computational grid covers the entire area, facilitating gridded quantitative statistics. The tidal flat area (red) is extensive and interwoven with the tidal channel system. This indicates that significant tidal dynamics occurred in 2020, forming a well-connected natural tidal channel network, providing an important spatial structural foundation for wetland hydrological exchange and the ecosystem at that time.

[0039] ② Distribution and extraction of Suaeda salsa in saline-alkali soil The determination can be made based on the Normalized Difference Vegetation Index (NDVI) combined with field surveys (e.g.) Figure 5 As shown, this invention presents a field survey of the distribution of Suaeda salsa in saline-alkali soil. (The information is derived from...) Figure 5 It can be seen that the combination of visual resolution and NDVI discrimination supports the reliability of the "NDVI interval - effective grid - existence point" determination rule. First, the overall vegetation distribution is determined. The determination of vegetation distribution is based on the calculation of NDVI, and the NDVI calculation formula is:

[0040] NDVI = (B8 - B4) / (B8 + B4) (1) In equation (1), B8 represents the near-infrared band and B4 represents the red band.

[0041] By calculating NDVI, it is possible to effectively distinguish Suaeda salsa from bare land or water bodies, thereby obtaining vegetation distribution information. After completing the NDVI calculation, the NDVI value range of Suaeda salsa is determined by combining field surveys and experience. After determining the range, the potential distribution area of ​​Suaeda salsa is extracted from remote sensing image data.

[0042] Step 2: Set up a computational grid according to the scope of the study area and the research objectives, and process the environmental variables and species presence points into input data that can be directly called by the adaptability model and has a consistent spatial benchmark.

[0043] After extracting the information on tidal channels and vegetation distribution, the data needs further processing to provide a unified input for subsequent modeling and analysis. This further processing includes layering, grid generation, and coordinate unification.

[0044] (1) Layered processing In the analysis, tidal channels, tidal channel networks, and tidal channel morphological characteristics were processed in layers. Tidal channels are the basic units, tidal channel networks reflect their overall distribution pattern, and tidal channel morphological indicators are the core parameters used for quantitative description and model analysis. First, the vector results of the tidal channel network were converted into linear data for morphological statistics. Based on this, a computational grid was established according to the tidal flat area of ​​the tidal channel distribution zone, with a grid size of 1000 m × 1000 m to cover the entire area and facilitate gridded quantitative statistics. The tidal channel network vector data was overlaid and segmented with the grid, so that each tidal channel segment corresponds to a corresponding grid cell. Through this process, the morphological characteristics of the tidal channels can be statistically analyzed within each grid. Tidal channel morphological indicators include total tidal channel length, tidal channel area, tidal channel density, tidal channel curvature, tidal channel frequency, number of tidal channels, and average tidal channel length, etc., as detailed in Table 2.

[0045] To analyze the relationship between tidal channel morphology and the suitable distribution of Suaeda salsa in saline-alkali land, the grid scale used to statistically analyze tidal channel morphology indicators should be at least one order of magnitude larger than the grid scale used to determine the suitable distribution points of Suaeda salsa. For example, the distribution of Suaeda salsa is processed using a 100 m × 100 m grid, and the center point of the grid with a coverage area greater than 50% of the grid area is taken as the suitable distribution point of Suaeda salsa.

[0046] Table 2 Specific Calculation of Tidal Channel Morphology Indicators After completing the statistics, the tidal channel morphology indicators of each grid are organized and exported as ASC format files to ensure that they can be directly called by the MaxEnt model to realize subsequent adaptation modeling.

[0047] (2) Grid division Simultaneously, the distribution data of *Suaeda salsa* also needs to be gridded accordingly. A higher-precision grid (e.g., 100 m × 100 m) is established within the mudflats of the tidal channel distribution area and overlaid with the *Suaeda salsa* distribution data. It is important to note that, depending on the scale of the restoration area, the tidal channel calculation grid should generally be several times larger than the vegetation calculation grid to better reflect the spatial relationship between *Suaeda salsa* and the tidal channel. The *Suaeda salsa* distribution data is overlaid and segmented with the grid, and connected to the corresponding grid cells. Then, the area of ​​*Suaeda salsa* patches within each grid is accurately calculated. When the *Suaeda salsa* coverage area within a grid exceeds 50% of the total area of ​​the calculation grid, that grid is considered a valid calculation grid. The coordinates of the center points of these valid calculation grids are extracted as the distribution points of *Suaeda salsa*. These distribution points are exported as a species presence point data file (CSV file) that can be directly accessed by the MaxEnt model for subsequent suitability modeling.

[0048] like Figure 4 As shown, this invention provides the effective computational grid distribution for Suaeda salsa in 2020. (The text appears to be incomplete and requires further context.) Figure 4 It can be seen that the effective computational grid (green area) of *Suaeda salsa* in 2020 was widely distributed. The vegetation was not only concentrated in the central part of the study area, but also extended significantly northeastward (upper right) along a distinct narrow band, forming a large area of ​​cover. This indicates that in 2020, the habitat conditions in the region were generally suitable for the growth of *Suaeda salsa*, especially in the northeastern corridor area, where vegetation development was good, the connectivity of the effective grid was high, and it showed a strong expansion trend.

[0049] (3) Coordinate unification: In ArcGIS, the tidal morphology index and the distribution points of Suaeda salsa in salt land are processed to unify the coordinate system, and the tidal morphology index of each grid is exported as an ASC format file.

[0050] Step 3: Use the data prepared for adaptability modeling as input to the MaxEnt model, and output the environment variable results: (1) Input data and parameters for modeling This invention imports both the ASC format file and the CSV file of the distribution points of Suaeda salsa in salt-tolerant areas into the MaxEnt model (MaxEnt3.4.4) for modeling.

[0051] In modeling, the distribution points of *Suaeda salsa* in saline-alkali land were used as species samples, and tidal creek morphology indicators were used as environmental variables. The model running parameters were set as follows: feature types: Linear, Quadratic, Product, Hinge; 10 iterations; Bootstrap sampling method, with 75% of samples used for training and 25% for validation; regularization multiplier set to 1; maximum number of iterations: 2000; output format: Cloglog. Model accuracy was evaluated using AUC values.

[0052] (2) Output results of modeling The model results show that AUC=0.845, indicating that the model has high prediction accuracy and stability. The ranking of the contribution rates of the output environmental variables is detailed in Table 3.

[0053] Table 3. Ranking of relative contribution rates to the distribution of Suaeda salsa in saline-alkali land Step 4: Based on the environmental variable results, identify key influencing factors and determine the ecological suitability range: (1) Determine comprehensively based on the response curve Based on the contribution rate of environmental variables output by the model, the area and curvature of the tidal ditch are regarded as key influencing factors. By comprehensively analyzing the response curves of the two, the ecological suitability range can be determined, that is, the range of tidal morphology indicators where the probability of Suaeda salsa in saline land is greater than 0.5, as determined by the response curve of the MaxEnt model.

[0054] like Figure 8 As shown, this is a diagram illustrating the ecological suitability range of the key morphological factors in this invention. Figure 8 The response curves for the tidal channel area index are shown. The horizontal axis represents the tidal channel area per unit area (km² / km²), and the vertical axis represents the probability of Suaeda salsa presence under these conditions (Cloglog output). The probability of presence peaks in the range of approximately 0.035 km² / km² to 0.245 km² / km², and drops below 0.5 when it exceeds this range, indicating that both excessively large and small tidal channel areas are detrimental to the stable distribution of Suaeda salsa.

[0055] like Figure 9 As shown, this is a diagram illustrating the ecological suitability range of the curvature of the key morphological factor in this invention. Figure 9 The response curve of the tidal ditch curvature index is shown. The horizontal axis is the tidal ditch curvature and the vertical axis is the probability of Suaeda salsa presence. The probability of Suaeda salsa is highest in the curvature range of about 1.0 to 1.5. The probability decreases beyond this range, indicating that tidal ditches that are too straight or too curved are not conducive to the formation of stable vegetation zones.

[0056] (2) Determine vegetation habitat conditions The probability of Suaeda salsa presence is highest when the tidal channel area per unit area is between 0.035 km² / km² and 0.245 km² / km² within the aforementioned ecological suitability range, and the tidal channel curvature is between 1.0 and 1.5. When the tidal channel area and curvature output by the scenario simulation both fall within the ecological suitability range (area 0.035–0.245 km² / km², curvature 1.0–1.5) or show a continuous convergence trend towards this range, it is determined that the tidal channel morphology is evolving in a direction favorable to Suaeda salsa.

[0057] Step 5: Construct a Delft3D model, perform scenario simulations based on the ecological suitability range and the width of the main tidal channel inlet cross-section, and output the scenario simulation results: (1) Data preparation To conduct scenario simulations of tidal channel morphology control schemes, it is necessary to obtain the hydrological, topographic, and sediment parameters required for the Delft3D model Flow module to run. These parameters are obtained based on the primary data sources in step one (measured sediment data, hydrological data, and topographic data used to construct the regional topographic elevation model) combined with existing research and model specifications, as detailed in Tables 1 and 4.

[0058] Table 4 Delft3D Parameters The primary data sources acquired in step one of this invention include: remote sensing image data, measured sediment data, hydrological data, and topographic data. The hydrological data consists of the upstream runoff process published by the Panshan Sluice Hydrological Station (taken from the measured flow process of the Panshan Sluice section in May-June 2020 in the "2020 Liaoning Provincial Hydrological Yearbook") and the downstream tidal boundary time series reconstructed from the main tidal modulus and constants of the TPXO7.2 tidal model. The topographic data includes a 2-m resolution DEM generated based on Gaofen-7 imagery, a 0.1-m resolution DEM obtained through UAV RTK low-altitude photogrammetry, and nautical chart bathymetry data. The remote sensing image data, measured sediment data, hydrological data, and topographic data undergo coordinate and benchmark unification, topographic data fusion and gridding, boundary and initial field construction, roughness and vegetation resistance setting, sediment parameter assembly, time series shaping, numerical control parameter setting, calibration, and verification to make the data into a model-recognizable format. The specific operations are as follows:

[0059] ① Coordinates and datum are unified: Spatial data used for model boundary and terrain construction were unified to the same coordinate datum: including the cross-section location of the Panshanzha hydrological station, the location of the Red Beach tide level observation station, information on the tidal channel network and the distribution of Suaeda salsa in salt flats, remote sensing image data, as well as Gaofen-7 DEM, UAV RTK low-altitude photogrammetric DEM and nautical chart bathymetry data used to construct the regional DEM. Among them, the Red Beach tide level observation station is a local tide level observation station near the study area used for model verification, and its unified coordinates are (121.76921490, 40.88435001). This coordinate information was obtained together with the hourly tide level measurement data from May to June 2020.

[0060] The aforementioned vector and raster spatial data underwent coordinate system transformation, unified projection, and study area clipping within the ArcGIS platform. DEM elevation data were unified to the same vertical datum, and elevation differences between the Gaofen-7 DEM, UAV aerial survey DEM, and nautical chart bathymetry data were adjusted and standardized to ensure strict spatial correspondence of the terrain, providing a unified spatial benchmark for subsequent computational grid construction and boundary condition settings.

[0061] ② Terrain data fusion and gridding: The terrain data used in this invention consists of a DEM generated from Gaofen-7 imagery, a high-precision DEM obtained from UAV low-altitude photogrammetry, and nautical chart bathymetry data. The aforementioned terrain and nautical chart bathymetry data are processed for consistency under a unified coordinate and elevation datum, and then fused to generate a regional DEM; the regional DEM serves as the sole terrain input data for the Delft3D model.

[0062] The construction process of the regional DEM includes: using the Gaofen-7 DEM as the overall topographic framework of the study area, local replacement and densification of the basic topography are performed using the UAV aerial survey DEM within the coverage area, and nautical chart bathymetry data are fused in the river and water areas to supplement water depth information, thus forming a fused regional DEM that takes into account both the continuity of the overall regional topography and the detailed expression of tidal channel micro-topography; subsequently, discrete outlier removal and local missing survey areas are filled in the fused results to obtain continuous and smooth seabed elevation data. On this basis, a computational grid is constructed according to the geometry and tidal channel distribution characteristics of the study area: the large-scale computational grid is used to describe the overall hydrodynamic processes of the study area, and the densified computational grid is used to finely characterize the tidal channel morphology in typical study areas; the densified computational grid and the large-scale computational grid are connected by nested boundaries to achieve continuous transfer of water level and flow rate.

[0063] ③ Boundary and initial field construction: The upstream runoff boundary of the large-scale model adopts the upstream runoff process published by the Panshanzha hydrological station, with the time series being the measured cross-sectional flow process from May to June 2020. Interpolation and shaping are performed according to the time step calculated by the Delft3D model. The downstream open boundary adopts the downstream tidal level boundary condition, which is reconstructed from the main tidal harmonic constants extracted by the TPXO7.2 tidal model to obtain the tidal level time series. The reconstructed series is then fine-tuned for phase and amplitude using hourly measured tidal level values ​​from the Honghai Beach tidal observation station from May to June 2020. The typical study area model is coupled with the large-scale model through nested boundaries, with the water level and flow time series output by the large-scale model providing boundary driving conditions for the sub-model.

[0064] ④ Roughness and vegetation resistance settings: To maintain a simple model structure and highlight the influence of the main tidal channel inflow section as a control variable on tidal channel morphology, this invention uses the same Manning roughness coefficient for the entire computational domain of both the large-scale model and the typical study area sub-model. Instead of assigning values ​​for different zones in Delft3D (main channel, tidal channel bottom, bare beach, and vegetation zone), a separate vegetation resistance term was not set. The study area's substrate is mainly composed of silty fine sand, with relatively gentle overall undulations. The submergence thickness of the Suaeda salsa community is relatively small compared to the water depth, and its influence on large-scale water level processes can be equivalently characterized by a unified roughness coefficient. In estuary and tidal flat hydrodynamic simulations, when the research objective focuses on comparing the relative trends of hydrodynamic and geomorphological responses under different conditions rather than accurately reproducing the instantaneous velocity distribution of a specific local cross-section, using a unified roughness coefficient is a common and reasonable generalization method. This reduces uncertainties in substrate and vegetation parameters and ensures comparability between different scenarios.

[0065] The unified roughness coefficient of this invention references existing hydrodynamic research and engineering experience in estuarine wetlands. During model calibration, fine-tuning was performed using measured tidal levels from the Red Beach observation station, resulting in good consistency between the calculated and observed water levels in terms of tidal range, phase, and process morphology (Pearson correlation coefficient reaches 0.69). Therefore, it can be considered that the comprehensive impact of vegetation and substrate differences on overall hydrodynamics is equivalently reflected in this unified roughness parameter. Based on this, comparisons of different main tidal channel inflow sections are sufficient to determine whether tidal channel morphology indicators are evolving towards the ecological suitability range of Suaeda salsa, meeting the accuracy requirements for scenario simulation and scheme selection in this invention. For other application scenarios requiring more detailed characterization of local flow velocities or vegetation resistance effects, zonal roughness and vegetation resistance parameters can be further introduced within the framework of this method; this invention does not constitute a limitation in this regard.

[0066] ⑤ Sediment parameter assembly: Based on the statistical results of suspended sediment concentration and particle size in the measured sediment data, the representative median particle size, dry density, and initial sediment concentration range of the sediment in the study area were determined. Combining the characteristics of fine-grained sediment in the estuary with relevant literature, initial values ​​for key sediment parameters such as settling velocity, critical initiation shear stress, and critical deposition shear stress were selected. For parameters that are difficult to measure directly, initial values ​​were given within the recommended range in the literature. During the model calibration phase, sensitivity analysis and fine-tuning were conducted based on water level, flow velocity, and topographic evolution characteristics to ensure that sediment transport and bed evolution are physically reasonable.

[0067] ⑥ Time series shaping: The upstream runoff process, downstream tidal boundary, and data from observed tidal stations were unified into the model's calculation period, and interpolation was performed according to the model's time step to ensure that all time series are continuous, non-overlapping, and without gaps on the same time axis. Significantly abnormal peaks or missing data segments were smoothed or interpolated to ensure that the model fully reflects typical hydrodynamic processes.

[0068] ⑦ Numerical control parameter setting and stability check: Select an appropriate time step based on the computational grid size and water depth range, and set water depth thresholds for wet and dry nodes to ensure the rationality of tidal flat inundation. Choose a suitable convection scheme and iterative convergence criterion, and adopt Delft3D recommended values ​​for control parameters if necessary. Check the stability of the water level and velocity fields through short-term trial calculations. If abnormal simulation results occur, adjust the parameters within the allowable range until the model runs stably throughout the entire computation period.

[0069] ⑧ Calibration and Verification: Measured tidal data from the Red Beach observation station (coordinates: 121.76921490, 40.88435001) located near the study area were selected as calibration data. The simulation results were compared with the measured results. By adjusting parameters such as roughness and boundary conditions within a reasonable range, the calculated results achieved good consistency with the observed results in terms of water level and topographic change trends in the Yuanyang Island area. The correlation coefficient and error index met the preset accuracy requirements. After calibration, the model was validated using data from independent time periods or independent stations. Only after confirming that the model had sufficient generalization ability was it used for subsequent scenario library calculations.

[0070] The sources of each parameter can be summarized as follows: Upstream runoff processes, local tidal level observations, typical study area DEMs, nautical chart bathymetry data, and measured sediment data form the basis for model parameterization and calibration. Literature and standard recommended values ​​are used to determine parameters that are difficult to measure comprehensively, such as settling velocity, critical shear stress, sediment dry density, and initial roughness. These are used as initial values ​​within a reasonable range and fine-tuned during calibration. Model default and empirical parameters are some numerical control parameters and minor parameters that have a small impact on the results, provided that stability and accuracy are allowed. The default settings of the Delft3D platform or manual recommendations are adopted. Tidal harmonics and reconstruction results are for downstream boundary conditions driven by astronomical tides. Harmonic analysis is performed using public tidal models or long-term tidal level observations, and the tidal level time series is reconstructed. After comparison and verification with observation station data, these results are used as model boundary inputs.

[0071] Through the above steps, the remote sensing image data, measured sediment data, hydrological data and topographic data obtained in step one are all transformed into input parameters that meet the requirements of the Delft3D model, thereby ensuring the reliability of subsequent scenario simulation results. Based on this, those skilled in the art can accurately reproduce the hydrodynamic-morphological modeling process of this application.

[0072] (2) Model The terrain conditions used in the Delft3D model are uniformly derived from the fused regional DEM, instead of using Gaofen-7 DEM or UAV aerial survey DEM as separate model inputs. To reduce the computational burden of large-scale simulations, a multi-scale nested model can be constructed based on the Delft3D model's Flow module. First, a comprehensive simulation is completed and validated on a large scale, and then control schemes are set in typical study areas.

[0073] like Figure 6 As shown, the relative positions of the large-area and typical study area Delft 3D models of the Liaohe River Estuary in this invention are illustrated. The large-scale hydrodynamic simulation of the outer sea boundary is driven by the harmonic constant of the TPXO7.2 tidal model, while the upstream boundary is applied with the upstream runoff process (cross-sectional flow process) published by the Panshan Gate hydrological station. The coarse grid resolution is approximately 80 m to 100 m, and the subdomain resolution is 10 m. The open boundary of the typical study area is provided by large-scale time-series results, ensuring energy consistency and flux conservation, and is used to characterize the hydrodynamic field of the main tidal channel mouth-tributary channel.

[0074] (3) Experimental design After completing the hydrodynamic modeling and verifying the model's accuracy using measured data, scenario simulation experiments can be conducted in a typical study area. While keeping other conditions constant, a single variable—the width of the main tidal channel inlet cross-section, i.e., the width of the water passage at the estuary of the main tidal channel in a typical area—is selected as the sole adjustment variable for the scenario simulation. Five sets of operating conditions are set, as follows: Operating Condition A: The width of the main tidal channel inlet section is set to 50% of the current width; Condition B: The width of the main tidal channel inlet section is set to 80% of the current width; Operating Condition C: Maintain the width of the main tidal channel inlet section unchanged; Operating Condition D: The width of the main tidal channel inlet section is set to 120% of the current width; Condition E: The width of the main tidal channel inlet section is set to 150% of the current width.

[0075] The same tidal cycle was used to simulate the tidal channel morphology index, thereby assessing the impact of this variable on the suitable environment for Suaeda salsa growth. The model accuracy was verified by observing the tidal process at the Red Beach observation station. The Pearson correlation coefficient between the simulated and observed tidal levels was 0.69 (p<0.05), indicating that the model can reasonably reproduce the tidal process during the study period.

[0076] Step Six: Analyze the scenario simulation results and ecological suitability ranges to determine the direction of regulation: The results of the simulations for each scenario were compared and analyzed to identify the impact trends of different cross-sectional adjustments on the tidal channel pattern and the adaptability of Suaeda salsa in saline-alkali land. The results are shown in Table 5.

[0077] Table 5. Influence trends on tidal ditch patterns and the adaptability of Suaeda salsa in saline-alkali land Comparative analysis of Table 5 reveals that when the cross-sectional width continuously increases (120%, 150%), the tidal channel area remains at a high level, exceeding the ecological suitability range, indicating an overdevelopment trend. This easily leads to erosion of the marginal mudflats, making it difficult for vegetation to establish itself stably. The increased number of nodes indicates a highly disturbed state. When the cross-sectional width remains at the current level (100% operating condition), the tidal channel area and curvature are still in the high range, showing that the tidal channel network is still expanding outwards and has not returned to the optimal ecological suitability range for Suaeda salsa. This indicates that simply maintaining the status quo cannot reverse the trend of vegetation habitat degradation. Conversely, when the cross-sectional width is moderately reduced within a controllable range (80% operating condition), the tidal channel area decreases and converges towards the ecological suitability range, and the tidal channel curvature remains stable within the range of approximately 1.1 to 1.2. At the same time, the number of nodes remains at a high level, indicating that hydrological connectivity has not been damaged. In this state, tidal energy is reduced but not completely cut off, and local hydrodynamics transition from "erosion-dominated" to "deposition-stability," which is conducive to the formation of continuous growth zones of Suaeda salsa along the edges of tidal channels and in shallow waters. When the cross-section is excessively reduced (50% condition), although the area of ​​the tidal channel shrinks further, it will lead to insufficient hydraulic supply to some secondary tidal channels, reduced connectivity, and the risk of local water stagnation and excessive siltation. This may cause local salt accumulation, hypoxia, or insufficient water exchange, which is not conducive to subsequent stable succession.

[0078] In summary, it can be seen that the width of the main tidal channel inflow section is not necessarily "the larger the better" or "the smaller the better," but rather there exists an ecologically suitable window within a certain range. Simulation results show that approximately 80% of the section width can simultaneously satisfy three conditions: ① While the tidal channel area converges towards the suitable ecological suitability range for Suaeda salsa, the curvature remains within the ecological suitability range corresponding to the high suitability probability of Suaeda salsa; ② The tidal channel network maintains good hydrological connectivity, without large-scale blockage; ③ Tidal energy entering the area is effectively suppressed, and the mudflat edges possess stable depositional conditions, facilitating the establishment and expansion of Suaeda salsa. Therefore, when the section width is reduced to 80% (condition B), key indicators such as tidal channel area and curvature are closest to the ecological suitability range, and network connectivity remains good, suppressing excessive expansion. An evaluation of the impact of univariate adjustment on the tidal channel morphological characteristics and the convergence trend towards the ecological suitability range reveals that when the section width is reduced to 80%, the tidal channel morphological indicators are closest to the ecological suitability range, representing the optimal control direction.

[0079] Step 7: Implement tidal channel network regulation according to the regulation direction: After identifying the favorable direction for variable adjustment, it can be applied to practical engineering operations in conjunction with nature-based solutions (NbS). The recommended approach is to moderately narrow the main tidal channel inlet cross-section to 80% of its current size, supplemented by lateral branch dredging, to maintain tidal dynamics and sedimentation balance; specific measures include ecological dredging and natural scouring guidance, seasonal water level regulation to promote the establishment of Suaeda salsa seeds, etc.

[0080] After implementation, remote sensing and ground surveys will be used to monitor wetland vegetation distribution and soil water and salinity for 1, 3, and 5 years. Based on the feedback results, the tidal channel pattern will be adjusted appropriately to ultimately achieve the sustainable restoration of Suaeda salsa and the enhancement of wetland ecological functions. This approach of "variable determination + NbS application" can scientifically guide the evolution of tidal channel morphology towards a suitable range and achieve an organic combination of ecological restoration and engineering measures, providing a quantifiable and replicable demonstration path for nature-based ecological restoration of degraded coastal wetlands.

[0081] like Figure 7 The figure shows a simulation of the situation where the width of the main tidal channel inlet cross-section remains unchanged within the typical research area of ​​this invention. (From...) Figure 7 As can be seen, this not only visually presents the hydrodynamic spatial differentiation characteristics of the tidal channel system in a typical study area under natural conditions (without engineering intervention), but more importantly, it serves as a benchmark reference system, providing crucial comparative evidence for subsequent analysis of the potential impact of different intervention schemes (changing the width of the inflow cross-section) on the hydrodynamics and morphology of the tidal channel. Through this comparison, it is possible to scientifically assess which intervention scheme is most conducive to the evolution of tidal channel morphological indicators towards the suitable habitat range of Suaeda salsa (as determined in step four), thereby guiding ecological restoration practices.

[0082] Comparative Analysis of Experiment Example 1 To compare the common passive evolution and empirical widening (cross-section widening) scenarios in existing practices, this embodiment sets up three sets of comparative examples: Condition C is to maintain the status quo (corresponding to a large number of "post-monitoring" or "passive evolution" scenarios in the literature: under the existing hydrodynamic conditions, the vegetation zone is suppressed by erosion - which is consistent with the consensus in statistical correlation studies that the vegetation in the adjacent area of ​​the ditch is sparse and recovers with distance / energy decay, but no engineering control is set to "pull the morphology back to the ecologically suitable range". (https: / / doi.org / 10.1007 / s13131-016-0831-z; https: / / doi.org / 10.1016 / S0304-3800(01)00253-8Get rights and content);

[0083] Conditions D and E represent the scenario of expanding the main tidal channel inlet cross-section based on the existing conditions (compared to many traditional dredging / widening or channel widening practices: short-term enhancement of tidal channels and connectivity, but prone to overdevelopment of tidal channels and edge erosion, and difficulty in vegetation settlement - consistent with the conclusion of "excessive energy inhibits salt marsh recovery" in tidal mouth stability simulation and restoration review). The difference is that traditional practices do not use the species ecological suitability range as the objective function, nor do they conduct multi-scenario matching evaluation with ecological suitability ranges, https: / / doi.org / 10.1016 / j.coastaleng.2011.08.005), used to simulate traditional dredging / widening and other practices to enhance tidal energy. The above scenario settings correspond to the typical practices of passive evolution or widening the cross-section for dredging in existing research and practice.

[0084] The width of the main tidal channel inlet cross-section is the main controllable engineering variable of this invention, governing the intensity of tidal energy and water inflow, and thus determining the comprehensive response of the tidal channel area, curvature, and connectivity to the suitability of Suaeda salsa in saline-alkali land. To verify whether the tidal channel regulation method based on ecological suitability intervals proposed in this invention is superior to the existing methods based on experience or single-index adjustments, scenarios with different main tidal channel inlet cross-section widths were selected as controls for comparison. Among them, scenario C is the conventional situation without regulation using the method of Example 1 of this invention, scenarios D and E correspond to the situation where the tidal channel continues to develop, and scenario B is the target scenario set under the guidance of the method of Example 1 of this invention.

[0085] In conditions C through E, as the cross-section remained constant or expanded, the tidal channel area fluctuated between 1.750 and 1.768 km², with the total length remaining above 40 km and the number of nodes between 97 and 102 (see Table 5 above), indicating a continuous expansion trend in the tidal channel network. The curvature remained between approximately 1.160 and 1.162, but the overall tidal channel area was too high, exceeding the ecological suitability range, posing risks of overdeveloped channels, excessive tidal energy, and vegetation erosion suppression. In contrast, under condition B, where only the main tidal channel inflow cross-section was reduced to 80%, the tidal channel area was 1.725 km², significantly lower than conditions C and E, and the tidal channel curvature remained at 1.158, still within the suitable range. This result indicates that under condition B, key tidal channel morphological indicators such as tidal channel area and curvature converged towards the ecological suitability range determined in step four, and the tidal channel network structure remained intact.

[0086] Compared to uncontrolled or simply widened tidal channel cross-sections (conditions C, D, and E), the target cross-section control scheme based on ecological suitability range guidance proposed in this invention (condition B) can bring the tidal channel morphology indicators closer to the suitable range for Suaeda salsa, thereby providing a more stable water-salt environment and deposition conditions for Suaeda salsa, and is expected to increase the actual distribution probability of Suaeda salsa. The above comparative results demonstrate that the method of this invention not only proposes directions on "how to control," but also quantifies specific engineering variables, significantly different from existing schemes that rely on experience-based settings or only undergo ex-post verification.

[0087] Existing technologies typically propose empirical thresholds based on the statistical correlation between tidal channels and vegetation. These thresholds cannot be directly converted into implementable engineering adjustments, and there is a lack of a predictive process based on multi-scenario hydrodynamic-morphological simulation. Therefore, it is difficult to promptly select control schemes that both maintain hydrological connectivity and promote vegetation restoration. This invention addresses this gap through a process of "ecological suitability interval identification—engineering variable mapping—multi-scenario screening."

[0088] Although the present invention has been described in detail through the preferred embodiments above, it should be understood that the above description should not be considered as a limitation of the present invention. Various modifications and substitutions to the present invention will be apparent to those skilled in the art after reading the above description. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. A method for ecological regulation of tidal creek morphology based on the adaptation of Suaeda salsa to saline-alkali soil, characterized in that, The method includes: Step 1: Obtain and analyze primary data sources within the study area; The primary data sources include remote sensing image data, measured sediment data, hydrological data, and topographic data and nautical chart bathymetry data used to construct regional topographic elevation models. Step 2: Set up the computational grid according to the research area and research objectives, and preprocess the analyzed data; Step 3: Use the preprocessed data as input for subsequent species adaptability modeling and analyze the output environmental variable results; Step 4: Based on the environmental variable results, identify key influencing factors and determine the ecological suitability range; Step 5: Establish a Delft 3D model, conduct scenario simulation based on the ecological suitability range and the width of the main tidal channel inlet section, and obtain the scenario simulation results; Step Six: Analyze the scenario simulation results and ecological suitability ranges to determine the direction of regulation; Step 7: Implement tidal channel network regulation and control according to the regulation direction.

2. The ecological regulation method for tidal channel morphology according to claim 1, characterized in that, In step one, the analysis includes extracting tidal channel network distribution information and Suaeda salsa distribution information from the remote sensing image data, and conducting field surveys to verify the distribution of Suaeda salsa. The hydrological data is used to characterize the tidal channel morphology, Suaeda salsa distribution, and tidal hydrological processes in the study area, and to obtain tidal hydrological and boundary condition data, as well as topography and upstream runoff processes, for boundary setting and verification of the model. The topographic data and nautical chart bathymetry data are used as topographic input data for the Delft3D model to characterize the seabed elevation of the study area and complete boundary setting and verification.

3. The ecological regulation method for tidal channel morphology according to claim 2, characterized in that, In step two, the preprocessing is based on the distribution information of the tidal channel network, establishing a computational grid in the study area, statistically analyzing the tidal channel morphology indicators within each grid, and calculating several tidal channel morphology indicators; based on the distribution information of Suaeda salsa, establishing a computational grid in the study area, and taking the center point of the grid where the Suaeda salsa coverage area is greater than 50% of the grid area as the suitable distribution point of Suaeda salsa.

4. The ecological regulation method for tidal channel morphology according to claim 3, characterized in that, The tidal creek morphology indicators include tidal creek area, tidal creek curvature, number of tidal creeks, tidal creek density, tidal creek frequency, tidal creek length, and tidal creek straight length; the feature scale of the grid used to statistically analyze the tidal creek morphology indicators is at least an order of magnitude larger than the feature scale of the grid used to determine the suitable distribution points of Suaeda salsa in saline-alkali land.

5. The ecological regulation method for tidal channel morphology according to claim 4, characterized in that, In step three, the tidal creek morphology index is used as an environmental variable, and the suitable distribution point of Suaeda salsa is used as the species existence point. The environmental variable and the species existence point are used as inputs to establish the Suaeda salsa MaxEnt model for species suitability analysis. Through several repeated runs and training / validation set partitioning, the environmental variable results are output.

6. The ecological regulation method for tidal channel morphology according to claim 1, characterized in that, In step four, the method includes determining the ecological suitability range of key tidal creek morphology indicators affecting the distribution of Suaeda salsa using a curve fitting method based on the MaxEnt model of Suaeda salsa, for use in regulating subsequent scenario simulations.

7. The ecological regulation method for tidal channel morphology according to claim 1, characterized in that, In step five, the method includes constructing numerical scenarios and conducting scenario simulations in the Delft3D model Flow module; establishing a numerical model covering the estuary and tidal flat areas, setting upstream runoff processes and downstream tidal level boundary conditions, using a nested grid strategy to describe the local hydrodynamic processes of the tidal channel, and verifying the results through measured water levels; setting several scenario simulation control schemes based on the ecological suitability range of the tidal channel morphology and the width of the main tidal channel inlet section, constructing a multi-scale nested model based on the Delft3D model Flow module for simulation, and outputting the results of changes in scenario tidal channel morphology indicators, which are used to quantitatively predict and compare different control schemes before project implementation.

8. The ecological regulation method for tidal channel morphology according to claim 7, characterized in that, After determining the direction of ecological regulation, by changing the width of the main tidal channel inlet section, the key influencing factors in the tidal channel morphology index are maintained within the ecological suitability range, or evolve towards the ecological suitability range.

9. The ecological regulation method for tidal channel morphology according to claim 1, characterized in that, In step six, the results of the changes in the tidal channel morphology index are analyzed and evaluated, and compared with the ecological suitability range. The scheme that causes the tidal channel morphology index to evolve towards the ecological suitability range is selected as the control scheme.

10. The method for ecological regulation of tidal channel morphology according to claim 9, characterized in that, In step seven, the tidal channel network in the study area is engineered according to the control scheme. After the engineering control, the process also includes periodically monitoring the wetland vegetation and soil water and salt conditions, and adjusting the tidal channel morphology based on the monitoring results.

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