Beach wetland near-natural ecological restoration method based on hydrological connectivity threshold value

Abstract

The invention relates to the technical field of wetland ecological restoration, in particular to a beach wetland near-natural ecological restoration method based on a hydrological connectivity threshold value, which comprises the following steps of: firstly, collecting multi-source data such as river network boundary vector data and wetland topographic data of a drainage basin, and selecting a representative remote sensing image; analyzing a hydrological sequence change trend and water body information, and calculating a hydrological situation change degree; then constructing a hydrological and hydrodynamic model, and cooperatively observing space-time evolution of the wetland; the hydrological connectivity is quantified, and the evolution relation between the hydrological connectivity and the wetland is analyzed; and finally, according to the response of wetland evolution to hydrological communication, identifying a hydrological communication threshold value, and proposing water system communication and ecological channel restoration suggestions. The method is scientific, reasonable and high in practicability, can effectively reveal the wetland evolution mechanism, provides an important reference basis for basin hydrological regulation and control and wetland ecological restoration under the influence of human activities, and has important application value in the field of wetland ecological restoration.

Description

Technical Field

[0001] The present invention relates to the technical field of wetland ecological restoration, and particularly relates to a near-natural ecological restoration method for beach wetlands based on a hydrological connectivity threshold. Background Art

[0002] The construction of water conservancy projects is a major measure for humans to transform nature, which has important economic functions such as flood control, power generation, irrigation, water supply, and shipping. At the same time, it also affects the ecological environment of river wetlands through various channels, mainly including changes in hydrological regime, channel blockage, wetland degradation, etc. The hydrological regime determines and affects the material cycle, energy process, habitat conditions, and biological interactions of river ecosystems. Changes in the hydrological regime change habitat conditions at different spatial and temporal scales, thus affecting the distribution and abundance of species, and further affecting the composition and diversity of biological communities.

[0003] Since the 1940s, the impact of river hydrological processes on the evolution of wetland ecosystems has gradually received attention. A large number of studies have shown that the changes in river hydrological regime caused by the construction of water conservancy projects have become the main threat factors to the integrity and stability of wetland ecosystems. In response to the large number of dams, China has actively carried out a large number of studies on the impact of water conservancy projects on wetland ecosystems, indicating that the operation and control of water conservancy projects have changed the original hydrological characteristics of rivers, and indicators such as the high and low flows, flood pulses, and durations of rivers have changed, which has led to the degradation of river wetlands downstream of water conservancy projects to a certain extent. The change in the water-sediment relationship affects the wetland geomorphic form, reduces the hydrological connectivity, changes the wetland inundation pattern, and further affects the wetland evolution process, resulting in changes in the structure and function of the wetland ecosystem. Therefore, under the background of human activities and climate change, it is very important to reveal the evolution mechanism of river wetlands and quantify the changes in hydrological connectivity for carrying out wetland ecological restoration.

[0004] Hydrological connectivity refers to the processes of material exchange, energy flow, organic matter transportation, and movement mediated by water, which are affected by the hydrological continuity among the composition, structure, and pattern of geographical units. The dynamic changes and conversions of hydrological connectivity can lead to alterations in the hydrodynamic conditions of rivers and lakes, exerting a cascading impact on water quality, aquatic ecology, and water environment conditions and triggering feedback effects, thereby directly influencing water environment quality, wetland ecological security, and ecosystem stability. Hydrological connectivity can directly characterize and reflect the dynamic changes in wetland structure and function, possessing prominent advantages in wetland ecological research. The changes in river hydrological regime affect the hydrological connectivity process of river-lake wetlands, while hydrological connectivity establishes the interconnection between the material and energy flows of landscape units during the surface water circulation process of wetlands. Overseas research on hydrological connectivity started earlier, mainly conducting in-depth studies on hydrological connectivity, landscape connectivity, and sediment connectivity at different scales in different types of ecosystems. Currently, the quantification methods of hydrological connectivity mainly include field monitoring based on hydrological monitoring indicators, hydrological models based on key hydrological processes, landscape pattern indices reflecting the impact of landscape types and dynamic changes on hydrological connectivity, and graph theory methods combining the point and line topological relationships in digital river networks. Domestic related research started later, with a greater focus on the evolution of river-lake ecosystem water system connectivity and its driving factors. There is a relative lack of research on wetland ecological restoration and the impact of hydrological connectivity changes on basin wetland ecosystems. How to explore the evolution law of river-lake wetland water systems based on the relevant quantification theory of hydrological connectivity and propose effective ecological restoration strategies based on hydrological connectivity thresholds is a key aspect that needs to be considered in current basin comprehensive management.

[0005] Domestic related research has mostly focused on large river-connected lakes such as Poyang Lake and Dongting Lake, with less attention paid to river beach wetlands; moreover, in terms of the ecological protection and restoration of beach wetlands, engineering measures are mostly adopted, lacking the theory or method of carrying out near-natural ecological restoration of beach wetlands by utilizing the hydrological connectivity between rivers and lakes based on the impact of hydrological regime changes on basin wetland ecosystems. Summary of the Invention

[0006] To address the deficiencies in the existing technology, the objective of the present invention is to propose a near-natural ecological restoration method for floodplain wetlands based on hydrological connectivity thresholds. The present invention first extracts multi-temporal water body ranges from multi-source remote sensing data, inversely calculates the dynamic changes of hydrological elements such as water area, water level, water depth, and water storage capacity, analyzes the spatio-temporal variation characteristics of wetland area, morphology, and elevation, and reveals the spatio-temporal variation law of floodplain wetland inundation; couples the hydrological and hydrodynamic model with the wetland topographic evolution model, optimizes the hydrodynamic model parameters using remote sensing information, and reveals the co-evolution mechanism of river channel hydrological processes and wetland patterns; combines the remote sensing extraction information with the output results of the hydrological model to quantify the hydrological connectivity of the river channel and wetland in different periods, and identify the hydrological connectivity thresholds based on the regional wetland ecological restoration objectives; thus, taking the wetland restoration area and the optimal inundation threshold as the guidance, specific suggestions for river system connectivity and wetland ecological corridor restoration are proposed.

[0007] To solve the above technical problems, the present invention is realized through the following technical solutions:

[0008] A near-natural ecological restoration method for floodplain wetlands based on hydrological connectivity thresholds, comprising

[0009] Step 1, collect and organize the vector data of the river network boundary in the basin, wetland topographic data, digital elevation model data of floodplains, long-term sequential daily-scale hydrological data measured at hydrological stations in historical periods, and wetland survey data. Based on the hydrological data, identify the high and low water levels and flows during the flood season, normal season, and dry season of each year in historical periods, and select representative multi-temporal remote sensing images according to the time range of occurrence;

[0010] Step 2, based on the long-term sequential hydrological data, analyze the inter-annual change trends of flow and water level, and calculate the degree of change in the basin hydrological situation in different historical periods; at the same time, extract the water surface coverage information of rivers and floodplain wetlands based on multi-temporal remote sensing data, establish a water body component dataset under the time series, and analyze the inter-annual and intra-annual change laws of the water body range and area of floodplain wetlands in historical periods and the evolution of river channel morphology indices;

[0011] Step 3, overlay the results of water body ranges in different time phases, extract the inner and outer boundaries of the wetland, and combine the measured hydrological data at hydrological stations to analyze the wetland inundation area and frequency characteristics; construct a wetland regional hydrological and hydrodynamic model based on the wetland topographic data, hydrological data, and multi-temporal remote sensing image data, and analyze the wetland inundation duration and water depth characteristics; through the correlation analysis of the wetland inundation characteristics and water level, thus reveal the response relationship between the wetland inundation characteristics and water level and the wetland evolution law under different hydrological situations, and identify the key periods and typical regions corresponding to the water levels that cause the evolution of floodplain wetlands;

[0012] Step 4: Based on the hydrological data and the water body component dataset, use the index method to quantify the wetland hydrological connectivity in different historical periods; according to the wetland evolution law under different hydrological regimes, analyze the response relationship between the wetland evolution law and the change of hydrological connectivity.

[0013] Step 5: Based on the response relationship between the wetland evolution law and hydrological connectivity in different historical periods, analyze the change trends of the regional surface hydrological connection process, connection path and connection area, use the cluster distribution characteristics of the geostatistical connectivity curve to analyze the threshold effect of hydrological connectivity, identify the hydrological connectivity threshold based on the regional wetland ecological restoration goal, and propose suggestions for river system connectivity and wetland ecological corridor restoration.

[0014] As a further optimized solution of the present invention, in the above step 1, the length of the historical hydrological data is not less than 20 years and spans the time node of human activity influence, and the number of multi-temporal remote sensing images is not less than 5 and the year intervals are roughly the same.

[0015] As a further optimized solution of the present invention, in the above step 2, the Mann-Kendall trend test is used to analyze the inter-annual change trend of the flow and water level sequences of the hydrological stations. When the significance level α = 0.05, the standard normal distribution statistic is greater than 1.96, indicating that the basin hydrological sequence changes significantly over time.

[0016] As a further optimized solution of the present invention, in the above step 2, the range of variation approach (RVA) is used to calculate the degree of change in hydrological regime. By comparing the hydrological indices for the pre- and post-human-impact periods, the overall degree of change in the hydrological regime is evaluated.

[0017] As a further optimized solution of the present invention, the 32 hydrological indices of the IHA index system are divided into the following five groups:

[0018] The first group is the monthly mean indices, including 12 indices, which are the monthly average values for each month.

[0019] The second group is the magnitude and duration indices of annual extreme hydrological conditions, including 11 indices, which are the magnitudes of the maximum and minimum 1-day, 3-day, 7-day, 30-day and 90-day periods within a year, and the base flow index of the river.

[0020] The third group is the occurrence time of annual extreme hydrological conditions, including 2 indices, which are the Julian days on which the maximum and minimum 1-day hydrological events occur in a year.

[0021] The fourth group is the frequency and duration indices of high and low pulses, including 4 indices, which are the occurrence frequencies and durations of high and low pulses per year.

[0022] The fifth group is the speed and frequency of hydrological condition changes, including three indicators, namely the average increase rate, decrease rate of the flow between two adjacent days, and the number of conversions of the flow process;

[0023] Setting of the RVA target range: After sorting each IHA indicator, frequency calculation is carried out, and the values with occurrence frequencies of 75% and 25% are used as the upper and lower limits of the parameters of each IHA indicator.

[0024] As a further optimization scheme of the present invention, the degree of change of each IHA indicator before and after the impact node of human activities is evaluated and quantified through the hydrological alteration degree Di, as shown in the following formula:

[0025]

[0026] In the formula: Di is the hydrological alteration degree of the i-th IHA; N o is the number of observations, referring to the number of years that the i-th IHA falls within the RVA target after being affected by human activities; N e is the expected number of years, referring to the expected number of years that the i-th IHA falls within the RVA target after being affected by human activities, which can be expressed as r×NT, where r is the proportion of the IHA falling within the RVA target before being affected by human activities. If 75% and 25% of each IHA are used as the RVA target range, then r = 50%, and NT is the total number of years of flow records after being affected by human activities; a positive value indicates that the number of years when the IHA indicator value falls within the RVA target after the impact is greater than the expected number of years, and a negative value indicates that the number of years when the IHA indicator value falls within the RVA target after the impact is less than the expected number of years;

[0027] When the value of |Di| is between 0 and 33%, it belongs to no or low change; when the value of |Di| is between 33% and 67%, it belongs to moderate change; when the value of |Di| is between 67% and 100%, it belongs to high change;

[0028] Based on the hydrological change situations of 32 IHA indicators, the overall hydrological alteration degree D0 is used as the basis for evaluation, as shown in the following formula:

[0029]

[0030] When the value of D0 is between 0 and 33%, it belongs to no or low change; when the value of D0 is between 33% and 67%, it belongs to moderate change; when the value of D0 is between 67% and 100%, it belongs to high change.

[0031] As a further optimization scheme of the present invention, in step 2, when establishing the water body component dataset, the normalized difference water index NDWI and the improved normalized difference water index MNDWI are used to extract the old channel water body information; for image data without the mid-infrared band, the NDWI index is used to extract the water body information; otherwise, the MNDWI index is used for extraction;

[0032] NDWI = (Green - NIR) / (Green + NIR)

[0033] MNDWI = (Green - MIR) / (Green + MIR)

[0034] In the formula, Green is the green light band, NIR is the near-infrared band, and MIR is the mid-infrared band.

[0035] As a further optimization scheme of the present invention, in step 3, based on the calculation results of MNDWI or NDWI, the inner and outer boundaries of the wetland are extracted by the threshold method, that is, combined with the gray histogram of the MNDWI or NDWI water body index, the entire value range is divided into intervals of the same size, and the gray value with a slope close to 0 between the water body and the non-water body is found, so as to determine the optimal threshold of MNDWI or NDWI. According to the determined threshold, the water body index image is subjected to dry-wet binary processing, so as to obtain the wetland boundary of the remote sensing image; based on the MIKE21C river hydrodynamic terrain evolution model, a regional hydrological hydrodynamic model of the wetland is constructed, and the model inputs are measured hydrological data, wetland terrain data, and multi-temporal remote sensing image data.

[0036] As a further optimization scheme of the present invention, in step 4, based on the geostatistical connectivity function GCF, the maximum connectivity distance, and the seasonal connected water body area of the dry-wet binary raster dataset, the surface hydrological connectivity of the beach wetland in different hydrological periods, different hydrological years, and different directions is quantitatively evaluated; the calculation formula of the GCF function is as follows:

[0037]

[0038] In the formula, ∏ is the product operator, CF(n; z c ) is the probability that the values of n unit distances along a specific direction are all greater than the threshold z c , I(u j ; z c ) represents the judgment that z(u j ) at location u j is greater than the threshold z c . When u j is greater than z c , I(u j ; z c ) = 1, otherwise I(u j ; z c ) = 0;

[0039] Combined with the long-term water component dataset extracted by remote sensing in step 2, a dry-wet binary map of the study area was generated based on the Arcgis platform. Based on the geostatistical connectivity function Matlab program, the dry-wet binary map was used as input data to calculate the GCF function values along the west-east and north-south directions of the study area at different historical periods. The GCF values and their corresponding connectivity distances were output to approximate the changes in the horizontal and vertical hydrological connectivity of the wetland.

[0040] Based on the output GCF function value and its corresponding connectivity distance, the GCF curve of the study area was drawn using Origin software. The connectivity distance when the GCF value dropped to 0 in each period was identified, and all water areas in the raster image were numbered. The water areas in each period were sorted and reclassified according to their area size. The reclassified data were input into ArcGIS, and a spatiotemporal pattern map of surface hydrological connectivity in the study area was exported.

[0041] As a further optimization scheme of the present invention, in step 5, based on the analysis of suitable habitat characteristics of representative animals and plants in the study area, the habitat index requirements that are conducive to ecological restoration in the area under the changing hydrological situation are obtained, including wetland restoration area and optimal inundation threshold, which are the regional wetland ecological restoration targets; based on the cluster distribution characteristics of the GCF curve in step 4, the GCF value under the regional wetland ecological restoration target is calculated.

[0042] The near-natural ecological restoration method for beach wetlands based on hydrological connectivity thresholds proposed in this invention achieves multiple beneficial effects:

[0043] (1) Scientific and reasonable, close to actual engineering application:

[0044] The present invention conducts collaborative observations on the historical evolution of island wetlands based on multi-source remote sensing data and hydrological models. By coupling the terrain evolution model with the hydrodynamic model, the collaborative evolution mechanism of hydrological processes and wetland patterns is identified. The remote sensing extracted information and the output results of the hydrological model are combined to quantify the hydrological connectivity in different periods. Based on the regional wetland ecological restoration goals, the hydrological connectivity threshold is identified, and suggestions for the restoration of water system connectivity and ecological channels in island wetlands are proposed. The method has a strong statistical basis and physical properties, can objectively reflect the response characteristics of the hydrological connectivity pattern of island wetlands and the ecosystem function to changes in hydrological conditions, and fully consider the near-natural characteristics of the ecological restoration method. The method is flexible, repeatable and practical.

[0045] (2) Provide important and highly operational reference for watershed hydrological regulation and wetland ecological restoration under the influence of human activities:

[0046] The present invention makes full use of the respective advantages of multi-source remote sensing data and hydrological models to identify the wetland evolution mechanism under the change of hydrological regime. By quantitatively calculating the hydrological connectivity in different historical periods, the hydrological connectivity threshold is identified based on the regional wetland ecological restoration goal, and suggestions for wetland water system connectivity and ecological channel restoration strategies are proposed with the wetland restoration area and the optimal inundation threshold as the guidance, providing an important and highly operable reference basis for basin hydrological regulation and wetland ecological restoration under the influence of human activities. Brief Description of the Drawings

[0047] Figure 1 It is a schematic diagram of the near-natural ecological restoration method for beach wetlands based on the hydrological connectivity threshold of the present invention. Detailed Embodiment

[0048] Next, the embodiments in the present application will be clearly and completely described in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present application.

[0049] Without departing from the spirit or scope of the present application, various modifications and changes can be made in the present application, which are obvious to those skilled in the art. Therefore, the present application is intended to cover the modifications and changes of the present application that fall within the scope of the corresponding claims (the claimed technical solutions) and their equivalents. It should be noted that the embodiments provided in the embodiments of the present application can be combined with each other without conflict.

[0050] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the drawings and specific embodiments.

[0051] As described in the background art section, it is of great research significance to design and manufacture a near-natural ecological restoration method for beach wetlands based on the hydrological connectivity threshold.

[0052] In view of this, please refer to Figure 1 , the embodiments of the present application provide a near-natural ecological restoration method for beach wetlands based on the hydrological connectivity threshold, including the following steps:

[0053] Step 1, collect and collate the vector data of the river network boundary in the basin, wetland terrain data, beach DEM data, long-term time-series daily-scale hydrological data measured at hydrological stations in historical periods (including flow and water level, etc.), wetland survey data, etc. Based on the hydrological data, identify the high and low water levels and flows in the wet season, normal season, and dry season of each year in historical periods, and select representative multi-temporal remote sensing images according to the time range of occurrence.

[0054] Step 1 further includes the following steps:

[0055] (1.1) To ensure that the duration of the historical hydrological data selected for the basin is of practical significance, the data length should be no less than 20 years and span the time nodes of human activities (such as river damming, etc.).

[0056] (1.2) To ensure that the duration and time interval of the selected multi-temporal remote sensing images are representative, in the image data of the representative water level section, multiple image data with approximately the same year interval should be selected, and the number of images should be no less than 5.

[0057] In Step 2, on the one hand, based on the long-time series hydrological data in Step 1, analyze the inter-annual variation trends of flow and water level, and calculate the change degree of the basin hydrological situation in different historical periods; on the other hand, based on the multi-temporal remote sensing data in Step 1, extract the water surface coverage information of rivers and floodplain wetlands, establish a water body component data set under the time series, so as to analyze the inter-annual and intra-annual variation laws of the water body range and area of floodplain wetlands in historical periods, and calculate the evolution of morphological indices such as river channel length, width and bending coefficient.

[0058] Step 2 further includes the following steps:

[0059] (2.1) To ensure that the basin hydrological sequence has change characteristics in practical significance, the Mann-Kendall (MK) trend test is used to analyze the inter-annual variation trends of the flow and water level sequences of hydrological stations in the selection of historical data. When the significance level is selected as α = 0.05, the standard normal distribution statistic is greater than 1.96, which indicates that the basin hydrological sequence changes significantly over time.

[0060] (2.2) This method calculates the change degree of the hydrological situation in different historical periods according to the Range of Variability Approach (RVA). This method is based on the Indicators of Hydrological Alteration (IHA). The main idea is to describe the water body according to the characteristic values of five aspects: magnitude, occurrence time, frequency, duration and change rate from the daily hydrological data of the river, convert the hydrological data into a series of hydrological indicators with ecological significance, and then set the upper and lower limits of each indicator parameter according to the hydrological statistical data before being affected by human activities, that is, the RVA target range. By comparing the IHA indicators before and after being affected by human activities, the change degree of the river affected by human activities can be evaluated, and the impact on the ecosystem can be further analyzed accordingly.

[0061] Among them, according to the five basic characteristics of hydrological regime (magnitude, occurrence time, frequency, duration, and change rate), the 32 hydrological indicators of the IHA index system are divided into the following five groups:

[0062] The first group is the monthly mean indicators, including the monthly average values of each month, with a total of 12 indicators;

[0063] The second group is the magnitude and duration indicators of annual extreme hydrological conditions, including the magnitudes of the maximum and minimum 1-day, 3-day, 7-day, 30-day, and 90-day within a year, and the base flow index of the river, with a total of 11 indicators;

[0064] The third group is the occurrence time of annual extreme hydrological conditions, including 2 indicators, namely the Julian days (the number of days counted from January 1 of each year) when the maximum and minimum 1-day hydrological events occur;

[0065] The fourth group is the frequency and duration indicators of high and low pulses, including the occurrence frequencies and durations of high pulses (daily flows with a pre-impact frequency of 75%) and low pulses (corresponding daily flows with a frequency of 25%) each year, with a total of 4 indicators;

[0066] The fifth group is the speed and frequency of hydrological condition changes, including 3 indicators, namely the average increase rate, decrease rate of adjacent daily flows, and the number of conversions of the flow process (i.e., the number of times the daily flow changes from increasing to decreasing or from decreasing to increasing).

[0067] Among them, for the setting of the RVA target range, after sorting the IHA indicators, frequency calculations are carried out, and the values with occurrence frequencies of 75% and 25% are used as the upper and lower limits of the parameters of each IHA indicator.

[0068] Among them, the degree of hydrological alteration is used to evaluate and quantify the changes in each IHA indicator before and after the node of human activities, and its definition is as follows. Generally, the hydrological alteration degree Di can be simply divided into three different severity levels. Generally, it is defined that if the value of |Di| is between 0 and 33%, it belongs to no or low alteration; between 33% and 67%, it belongs to moderate alteration; between 67% and 100%, it belongs to high alteration.

[0069]

[0070] In the formula: Di is the hydrological alteration degree of the i-th IHA; N o is the number of observations, referring to the number of years that the i-th IHA falls within the RVA target after being affected by human activities; N eis the expected number of years, which refers to the number of years that the i-th IHA is expected to fall within the RVA target after being affected by human activities, and can be expressed as r×NT, where r is the proportion of IHA falling within the RVA target before being affected by human activities. If the 75% and 25% of each IHA are used as the RVA target range, then r = 50%, and NT is the total number of years of flow records after being affected by human activities; a positive value indicates that the number of years when the IHA index value after the impact falls within the RVA target is greater than the expected number of years, and a negative value indicates that the number of years when the IHA index value after the impact falls within the RVA target is less than the expected number of years.

[0071] Among them, in order to reasonably judge the overall change degree of the river hydrological regime after being disturbed, it is necessary to comprehensively consider the hydrological changes of 32 IHA indicators and use the overall hydrological alteration degree D0 as the basis for evaluation. Its definition is as follows.

[0072]

[0073] When the D0 value is between 0 and 33%, it belongs to no or low alteration; when the D0 value is between 33% and 67%, it belongs to moderate alteration; when the D0 value is between 67% and 100%, it belongs to high alteration.

[0074] (2.3) In the establishment of the water body component dataset by this method, the normalized difference water index (NDWI) and the modified normalized difference water index (MNDWI) are used to extract the water body information of the old channel. Their calculation formulas are as follows. Among them, although the NDWI index can be used for the extraction of water body information, this index ignores the characteristics that the spectral characteristics of land and buildings are similar to those of water bodies in the green and near-infrared bands, and noise will be formed during the extraction of water body information; after replacing the near-infrared band in the NDWI formula with the mid-infrared band, the MNDWI method increases the index, differentiates from land and buildings, reduces noise, and the extracted water body information will be more accurate. It can not only effectively remove the mountain shadow information, but also has better effects on water bodies within the urban area. Therefore, for image data without the mid-infrared band, the NDWI index is used to extract the water body information; otherwise, the MNDWI index is used for extraction.

[0075] NDWI = (Green - NIR) / (Green + NIR)

[0076] MNDWI = (Green - MIR) / (Green + MIR)

[0077] In the formula, Green is the green band, NIR is the near-infrared band, and MIR is the mid-infrared band.

[0078] Step 3: On the one hand, superimpose the water body range results at different time phases in Step 2, extract the inner and outer boundaries of the wetland, and combine the measured hydrological data of the hydrological station in Step 1 to analyze the characteristics such as the inundated area and frequency of the wetland. On the other hand, based on the regional topography, hydrology, and remote sensing data in Step 1, construct a hydrological and hydrodynamic model for the wetland area. Analyze the characteristics such as the inundation duration and water depth of the wetland according to the hydrological output results. Through the correlation analysis of the wetland inundation characteristics (including inundated area, frequency, duration) and water level, reveal the response relationship between the wetland inundation characteristics and water level and the wetland evolution law under different hydrological situations, and identify the critical periods and typical areas corresponding to the water levels that cause the evolution of the beach wetland.

[0079] Step 3 further includes the following steps:

[0080] (3.1) Based on the calculation results of MNDWI or NDWI, this method extracts the inner and outer boundaries of the wetland through the threshold method, that is, combines the gray histogram of the MNDWI or NDWI water body index, divides the entire value range into intervals of the same size, and finds the gray value with a slope close to 0 between the water body and the non-water body, so as to determine the best threshold of MNDWI or NDWI. Perform dry-wet binary processing on the water body index image according to the determined threshold to obtain the wetland boundary of the remote sensing image.

[0081] (3.2) This method constructs a hydrological and hydrodynamic model for the wetland area based on the MIKE21C river hydrodynamic terrain evolution model. The model inputs are measured hydrological data, wetland terrain data, and multi-temporal remote sensing image data.

[0082] Step 4: Based on the hydrological data in Step 1 and the water body component dataset in Step 2, comprehensively use the index method to quantitatively calculate the wetland hydrological connectivity in different historical periods. According to the wetland evolution characteristics under different hydrological situations in Step 3, analyze the response relationship between wetland evolution and hydrological connectivity changes.

[0083] Step 4 further includes the following steps:

[0084] (4.1) This method uses indicators such as the Geostatistical Connectivity Function (GCF) based on the dry-wet binary raster dataset, the maximum connectivity distance, and the seasonal connected water body area to quantitatively evaluate the surface hydrological connectivity of the beach wetland in different hydrological periods, different hydrological years, and different directions. The GCF function calculates the possibility of hydraulic connection between two wet pixels under a given distance condition, which is represented by the ratio of the total number of connected wet pixels to the total set of wet pixel pairs. The formula is as follows. The slower the function curve drops with the increase of distance, the higher the connectivity inside the wetland. Among them, high connectivity at short distances mostly represents fragmented water bodies, while high connectivity at long distances mostly represents large continuous water bodies.

[0085]

[0086] In the formula, ∏ is the product operator, and CF(n; z c ) is the probability that the values at n unit distances along a specific direction are all greater than the threshold z c , and I(u j ; z c ) represents the judgment that z(u j ) at location u j is greater than the threshold z c . When u j is greater than z c , I(u j ; z c ) = 1, otherwise I(u j ; z c ) = 0.

[0087] Combined with the long - time - series water body component dataset extracted by remote sensing in step 2, a dry - wet binary map of the study area is generated based on the Arcgis platform; based on the geostatistical connectivity function Matlab program, taking the dry - wet binary map as input data, calculate the GCF function values in the west - east and north - south directions in different historical periods of the study area, and output the GCF values and their corresponding connectivity distances, which are used to approximately characterize the changes in the lateral and longitudinal hydrological connectivity of the wetland.

[0088] (4.2) Based on the output GCF function values and their corresponding connectivity distances, use Origin software to draw the GCF curve graph of the study area, identify the connectivity distances when the GCF values drop to 0 in each period, number all water areas in the raster image; sort and re - classify the water areas in each period according to the area size, input the re - classified data into Arcgis, and export the spatio - temporal pattern map of the surface hydrological connectivity of the study area.

[0089] Step 5: Based on the response relationship between the wetland evolution law and hydrological connectivity in different historical periods in step 4, analyze the changing trends of the regional surface hydrological connection process, connection path, and connection area, use the cluster distribution characteristics of the geostatistical connectivity curve to analyze the threshold effect of hydrological connectivity, identify the hydrological connectivity threshold based on the regional wetland ecological restoration goals (including the wetland restoration area and the optimal inundation threshold), and thus propose specific suggestions for water system connection and wetland ecological corridor restoration.

[0090] Step 5 further includes the following steps:

[0091] (5.1) Based on the analysis of the suitable habitat characteristics of representative animals and plants in the study area, obtain the habitat index requirements (including the wetland restoration area and the optimal inundation threshold) that are beneficial to the ecological restoration of the area under changing hydrological conditions, which are the regional wetland ecological restoration goals.

[0092] (5.2) Based on the cluster distribution characteristics of the GCF curve in step 4, calculate the GCF value under the regional wetland ecological restoration objectives (including wetland restoration area and optimal inundation threshold).

[0093] The above are only the preferred implementation schemes of the present invention, but the present invention is not limited to the above specific implementation schemes. Without departing from the principle of the present invention, those of ordinary skill in the art can also make several modifications, supplements, or use similar methods for substitution, which should also be regarded as the protection scope of the present invention. However, the possibility of using other terms cannot be excluded. Using these terms is only for more conveniently describing and explaining the essence of the present invention, and interpreting them as any additional limitation is contrary to the spirit of the present invention.

Claims

1. A near-natural ecological restoration method for floodplain wetlands based on hydrological connectivity thresholds, characterized in that, including Step 1: Collect and organize the vector data of the river network boundary in the basin, wetland terrain data, digital elevation model data of sandbanks, long-term sequential daily-scale hydrological data measured by hydrological stations in historical periods, and wetland survey data. Based on the hydrological data, identify the high and low water levels and flow rates during the flood season, normal water season, and dry season in each year of the historical period, and select representative multi-temporal remote sensing images according to the time range of occurrence; Step 2: Analyze the inter-annual variation trends of flow rate and water level based on the long-term sequential hydrological data, and calculate the degree of change in the hydrological regime of the basin in different historical periods; at the same time, extract the water surface coverage information of rivers and sandbank wetlands based on the multi-temporal remote sensing data, establish a water body component data set under the time series, and analyze the inter-annual and intra-annual variation laws of the water body range and area of sandbank wetlands in the historical period and the evolution of the river channel morphology index; Step 3: Overlay the water body range results of different time phases, extract the inner and outer boundaries of the wetland, and analyze the wetland inundation area and frequency characteristics in combination with the measured hydrological data of hydrological stations; construct a hydrological-hydrodynamic model of the wetland area based on the wetland terrain data, hydrological data, and multi-temporal remote sensing image data, and analyze the wetland inundation duration and water depth characteristics; through the correlation analysis of the wetland inundation characteristics and water level, reveal the response relationship between the wetland inundation characteristics and water level and the wetland evolution law under different hydrological regimes, and identify the critical periods and typical areas corresponding to the water levels that cause the evolution of sandbank wetlands; Step 4: Quantify the wetland hydrological connectivity in different historical periods based on the hydrological data and the water body component data set; according to the wetland evolution law under different hydrological regimes, analyze the response relationship between the wetland evolution law and the change of hydrological connectivity; Step 5: Based on the response relationship between the wetland evolution law and hydrological connectivity in different historical periods, analyze the change trends of the regional surface hydrological connection process, connection path, and connection area, use the cluster distribution characteristics of the geostatistical connectivity curve to analyze the threshold effect of hydrological connectivity, identify the hydrological connectivity threshold based on the regional wetland ecological restoration goal, and put forward suggestions for water system connection and wetland ecological channel restoration.

2. The near-natural ecological restoration method for floodplain wetlands based on the hydrological connectivity threshold according to claim 1, wherein: In the above Step 1, the length of the historical hydrological data is not less than 20 years and spans the time node of human activity influence, and the number of multi-temporal remote sensing images is not less than 5 and the year intervals are roughly the same.

3. The near-natural ecological restoration method of continental beach wetlands based on the hydrological connectivity threshold according to claim 1, characterized in that: In the above Step 2, the Mann-Kendall trend test is used to analyze the inter-annual variation trends of the flow rate and water level sequences of hydrological stations. When the significance level α = 0.05, the standard normal distribution statistic is greater than 1.96, indicating that the basin hydrological sequence changes significantly over time.

4. The near-natural ecological restoration method for continental shoal wetlands based on the hydrological connectivity threshold according to claim 1, wherein: In the above Step 2, the range of variation approach (RVA) is used to calculate the degree of change in the hydrological regime. By comparing the hydrological indicators (IHA) before and after the influence of human activities, the overall degree of change in the hydrological regime is evaluated.

5. The near-natural ecological restoration method for floodplain wetlands based on the hydrological connectivity threshold according to claim 4, characterized in that: The 32 hydrological indicators of the IHA index system are divided into the following five groups: The first group is the monthly mean indicators, including 12 indicators, which are the monthly averages of each month respectively; The second group is the magnitude and duration indicators of annual extreme hydrological conditions, including 11 indicators, which are the magnitudes of the maximum and minimum 1d, 3d, 7d, 30d, and 90d within the year, and the base flow index of the river; The third group is the time when annual extreme hydrological conditions occur, including two indicators, namely the Julian day when the maximum and minimum 1-day hydrological events occur in a year; The fourth group is the frequency and duration indicators of high and low pulses, including four indicators, namely the occurrence frequency and duration of high pulses and low pulses each year; The fifth group is the speed and frequency of hydrological condition changes, including three indicators, namely the average increase rate, decrease rate of flow between adjacent two days, and the number of conversions of the flow process; Setting of the RVA target range: After sorting each IHA indicator, frequency calculation is carried out, and the values with occurrence frequencies of 75% and 25% are used as the upper and lower limits of the parameters of each IHA indicator.

6. The near-natural ecological restoration method for floodplain wetlands based on the hydrological connectivity threshold according to claim 4 or 5, characterized in that: The degree of hydrological alteration Di is used to evaluate and quantify the change degree of each IHA indicator before and after the node affected by human activities, as shown in the following formula: Where: Di is the hydrological alteration degree of the i-th IHA; N o is the number of observations, referring to the number of years that the i-th IHA falls within the RVA target after being affected by human activities; N e is the expected number of years, referring to the number of years that the i-th IHA is expected to fall within the RVA target after being affected by human activities, which can be expressed as r×NT, where r is the proportion of the IHA falling within the RVA target before being affected by human activities. If the 75% and 25% of each IHA are used as the RVA target range, then r = 50%, and NT is the total number of years of flow records after being affected by human activities; a positive value indicates that the number of years when the IHA index value falls within the RVA target after the impact is greater than the expected number of years, and a negative value indicates that the number of years when the IHA index value falls within the RVA target after the impact is less than the expected number of years; When the value of |Di| is between 0 and 33%, it belongs to no or low alteration; when the value of |Di| is between 33% and 67%, it belongs to moderate alteration; when the value of |Di| is between 67% and 100%, it belongs to high alteration; Based on the hydrological change conditions of 32 IHA indicators, the overall hydrological alteration degree D0 is used as the basis for judgment, as shown in the following formula: When the value of D0 is between 0 and 33%, it belongs to no or low alteration; when the value of D0 is between 33% and 67%, it belongs to moderate alteration; when the value of D0 is between 67% and 100%, it belongs to high alteration.

7. The near-natural ecological restoration method for floodplain wetlands based on the hydrological connectivity threshold according to claim 1, wherein: In the step 2, in establishing the water body component dataset, the Normalized Difference Water Index (NDWI) and the Modified Normalized Difference Water Index (MNDWI) are used to extract the old channel water body information; for the image data without the mid-infrared band, the NDWI index is used to extract the water body information; otherwise, the MNDWI index is used for extraction; NDWI = (Green - NIR) / (Green + NIR) MNDWI = (Green - MIR) / (Green + MIR) In the formula, Green is the green light band, NIR is the near-infrared band, and MIR is the mid-infrared band.

8. The near-natural ecological restoration method for floodplain wetlands based on the hydrological connectivity threshold according to claim 7, characterized in that: In the step 3, based on the calculation results of MNDWI or NDWI, the inner and outer boundaries of the wetland are extracted by the threshold method, that is, combining the gray histogram of the MNDWI or NDWI water body index, dividing the entire value range into intervals of the same size, finding the gray value with a slope close to 0 between the water body and the non-water body, so as to determine the optimal threshold of MNDWI or NDWI, and performing dry-wet binary processing on the water body index image according to the determined threshold, so as to obtain the wetland boundary of the remote sensing image; based on the MIKE21C river hydrodynamic terrain evolution model, a wetland area hydrodynamics model is constructed, and the model inputs are measured hydrological data, wetland terrain data and multi-temporal remote sensing image data.

9. The near-natural ecological restoration method for continental beach wetlands based on the hydrological connectivity threshold according to claim 1, wherein: In the step 4, based on the geostatistical connectivity function GCF, the maximum connectivity distance and the seasonal connected water body area of the dry-wet binary raster dataset, the surface hydrological connectivity of the beach wetland in different hydrological periods, different hydrological years and different directions is quantitatively evaluated; the calculation formula of the GCF function is as follows: Wherein, ∏ is a product operator, and CF(n; z c ) is the probability that the values at n unit distances along a specific direction are all greater than the threshold z c , and I(u j ; z c ) represents the judgment that z(u j ) at location u j is greater than the threshold z c . When u j is greater than z c , I(u j ; z c ) = 1, otherwise I(u j ; z c ) = 0; Combined with the long-term water component dataset extracted by remote sensing in step 2, a dry-wet binary map of the study area was generated based on the Arcgis platform. Based on the geostatistical connectivity function Matlab program, the dry-wet binary map was used as input data to calculate the GCF function values along the west-east and north-south directions of the study area at different historical periods. The GCF values and their corresponding connectivity distances were output to approximate the changes in the horizontal and vertical hydrological connectivity of the wetland. Based on the output GCF function value and its corresponding connectivity distance, the GCF curve of the study area was drawn using Origin software. The connectivity distance when the GCF value dropped to 0 in each period was identified, and all water areas in the raster image were numbered. The water areas in each period were sorted and reclassified according to their area size. The reclassified data were input into ArcGIS, and a spatiotemporal pattern map of surface hydrological connectivity in the study area was exported.

10. The near-natural ecological restoration method of beach wetlands based on the hydrological connectivity threshold according to claim 1, characterized in that: In step 5, based on the analysis of suitable habitat characteristics of representative animals and plants in the study area, the habitat index requirements that are conducive to ecological restoration in the area under the changing hydrological situation are obtained, including wetland restoration area and optimal inundation threshold, which are the regional wetland ecological restoration targets; based on the cluster distribution characteristics of the GCF curve in step 4, the GCF value under the regional wetland ecological restoration target is estimated.

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