Methods for managing benthic animal diversity in urban rain-fed rivers based on multi-source water replenishment

Abstract

This invention relates to the field of ecological restoration and water environment management technology, specifically a method for managing benthic animal diversity in urban rain-fed rivers based on multi-source water replenishment. The method includes acquiring basic river channel data and water replenishment source data for the target river section; identifying the source water body and the target water body; determining potential diffusion paths through hydraulic simulation and analyzing connectivity reachability, thereby activating river connectivity devices to optimize potential diffusion paths; analyzing the minimum sustainable community size required for the target benthic animals and predicting the theoretical source replenishment amount that can be transported through water replenishment, comparing the replenishment status assessment results to determine water replenishment control parameters, and generating and implementing a water replenishment regulation scheme. This invention promotes the diffusion and colonization of sensitive benthic animal populations by quantitatively assessing connectivity, population size, and replenishment needs, utilizing multi-source water replenishment as a controllable hydraulic means, thus providing decision support for river ecological restoration.

Description

Technical Field

[0001] This invention relates to the field of ecological restoration and water environment management technology, specifically a method for managing benthic animal diversity in urban rain-fed rivers based on multi-source water replenishment. Background Technology

[0002] Urban rain-fed rivers typically face the dual pressures of insufficient natural water inflow and urban surface hardening. Their hydrological processes are highly dependent on rainfall, resulting in intermittent water flow and a lack of ecological base flow. To maintain basic water volume and improve the water environment during the dry season, multi-source water replenishment methods are commonly used, utilizing reclaimed water, rainwater, and other water sources to supplement the river channel.

[0003] However, even after water replenishment, when water quality meets standards and riverbed structure is restored, benthic animal communities do not improve accordingly. Benthic animal diversity remains at a low level, with communities dominated by pollution-tolerant species, and sensitive groups showing slow recovery or even persistent absence. Current technology generally assumes that biological communities will naturally recover once environmental conditions are restored. However, in highly fragmented urban rain-fed rivers with severely degraded upstream seed sources, benthic animals often lack the ability to spontaneously rebuild. On the one hand, migration and dispersal pathways are blocked; on the other hand, the number of seed sources available for replenishment within the region is limited.

[0004] Therefore, relying solely on traditional water replenishment methods that improve water quantity and quality is insufficient to overcome the current bottleneck in ecological restoration. There is an urgent need for a method for managing benthic animal diversity in urban rain-fed rivers based on multi-source water replenishment, in order to further promote seed source replenishment and enhance the sustainable colonization capacity of benthic animals in the river channel. Summary of the Invention

[0005] (1) Technical problems to be solved

[0006] The purpose of this invention is to provide a method for managing benthic animal diversity in urban rain-fed rivers based on multi-source water replenishment, in order to solve the technical problem that existing multi-source water replenishment only focuses on water quantity and water quality regulation, without considering the diffusion and migration process of benthic animals and the population maintenance needs, which makes it difficult for sensitive benthic animals to form stable communities.

[0007] (2) Technical solution

[0008] To achieve the above objectives, on the one hand, the present invention provides a method for managing benthic animal diversity in urban rain-fed rivers based on multi-source water replenishment, the method comprising:

[0009] Step S1: Obtain basic river channel data and water replenishment source data for the target river section; the basic river channel data includes river channel range data, river channel structure data, water body distribution information, and river channel hydraulic conditions data; the water replenishment source data includes water intake location data, available water replenishment flow data, and maximum sustainable water replenishment duration data for various water replenishment sources.

[0010] Step S2: Delineate the boundary of the target river section based on the river range data; identify the source water bodies within the target river section that serve as potential benthic animal seed sources, as well as the target water bodies where sensitive benthic animal populations need to be restored; determine at least one potential diffusion path from the source water body to the target water body through hydraulic simulation based on the river hydraulic condition data; analyze the potential diffusion path to obtain the connectivity reachability value of the seed source transport channel; when the connectivity reachability value is lower than a preset connectivity threshold, activate the corresponding river connectivity device to form an optimized potential diffusion path.

[0011] Step S3: Based on the population density, hydraulic connectivity index, and river structure data of the source water body, analyze and obtain the minimum sustainable community size required for the target benthic animals to maintain population stability in the target river section; based on the available water replenishment flow data, river hydraulic condition data, and optimized potential diffusion paths, predict the theoretical source replenishment amount that can be transported to the target water body through water replenishment; compare the theoretical source replenishment amount with the minimum sustainable community size to obtain the replenishment status determination result.

[0012] Step S4: Determine the water replenishment control parameters based on the replenishment status determination result. The water replenishment control parameters include the pump station start-up and shutdown sequence, water replenishment flow rate, and water source switching conditions. Based on the water replenishment control parameters, generate a water replenishment regulation scheme and control the water replenishment device to execute the water replenishment regulation scheme.

[0013] Preferably, the method for identifying the source water bodies within the target river section that serve as potential sources of benthic animals, and the target water bodies from which the sensitive benthic animal populations need to be restored, includes:

[0014] The historical and current monitoring data of each water body within the target river section are obtained, and the monitoring data includes at least benthic animal community structure data and hydrodynamic characteristic data.

[0015] Based on the benthic animal community structure data, the biological integrity index and the absolute abundance of the target sensitive groups are calculated for each water body; water bodies with biological integrity indices higher than a preset biological integrity threshold and absolute abundance higher than a preset abundance threshold are identified as seed source water bodies.

[0016] Based on the hydrodynamic characteristic data, the average flow velocity, water depth variation coefficient, and flow rate variation coefficient of each water body are calculated; water bodies that are not currently identified as seed source water bodies, whose average flow velocity is within the suitable flow velocity range for the preset target group, whose water depth variation coefficient is lower than the first stability threshold, and whose flow rate variation coefficient is lower than the second stability threshold are identified as target water bodies.

[0017] Preferably, the method for determining at least one potential diffusion path from the seed source water body to the target water body through hydraulic simulation based on the river hydraulic condition data includes:

[0018] A two-dimensional hydrodynamic model is constructed to cover the riverbed between the source water body and the target water body; wherein, each computational grid of the two-dimensional hydrodynamic model is associated with an initial biological diffusion resistance coefficient, which is pre-determined based on the substrate type data and aquatic vegetation coverage data obtained at the corresponding grid.

[0019] In the two-dimensional hydrodynamic model, the seed source water body is used as the initial water release source to simulate the evolution of water flow under the basic hydrological scenario represented by the river hydraulic conditions data, and the flow velocity data of each grid is obtained; based on the flow velocity data of each grid, the initial bio-diffusion resistance coefficient of the corresponding grid is dynamically corrected to obtain the corrected bio-diffusion resistance coefficient field.

[0020] Based on the modified biodiffusion resistance coefficient field, the path with the minimum cumulative biodiffusion resistance coefficient from the source water body to the target water body is searched and determined as a potential diffusion path.

[0021] Preferably, the method for dynamically correcting the initial biodiffusion resistance coefficient of the corresponding grid based on the flow velocity data of each grid includes:

[0022] Obtain a first correction coefficient and a second correction coefficient; when the flow velocity data is lower than a preset drift initiation flow velocity threshold, multiply the initial biological diffusion resistance coefficient of the corresponding grid with the first correction coefficient to obtain the corrected biological diffusion resistance coefficient; when the grid flow velocity is not lower than the preset drift initiation flow velocity threshold but lower than the preset active diffusion upper limit flow velocity threshold, multiply the initial biological diffusion resistance coefficient of the corresponding grid with the second correction coefficient to obtain the corrected biological diffusion resistance coefficient.

[0023] Preferably, the method for analyzing the potential diffusion paths to obtain the connectivity reachability value of the seed source transport channels includes:

[0024] The cumulative biodiffusion resistance coefficient and the modified biodiffusion resistance coefficient field are calculated based on the potential diffusion pathways. Extract the path feature parameters of the potential diffusion path, the path feature parameters including the total path length. and the longest duration for which the path can maintain continuous hydraulic connectivity under the hydrological scenario characterized by the river channel hydraulic conditions data. .

[0025] The total length of the path , cumulative biodiffusion resistance coefficient and and longest duration Perform normalization to obtain the normalized length. Normalization resistance and normalized time According to the normalized length Normalization resistance and normalized time Calculate the connectivity reachability value The connectivity reachability value The calculation formula is:

[0026] .

[0027] in, , and The preset weighting coefficients are used, and they satisfy the following conditions: , and This is the preset attenuation coefficient.

[0028] Preferably, the method for analyzing the minimum sustainable community size required for the target benthic animals to maintain population stability in the target river section based on the population density, hydraulic connectivity index, and channel structure data of the source water body includes:

[0029] Habitat factor data for the target water body, including water area, are obtained based on river structure data. Average water depth Substrate type index and water flow velocity The habitat factor data were normalized to obtain standardized scores. , , , According to the standardized score , , , Calculate the habitat suitability index of the target water body ;in, , , , These are preset preference weighting coefficients based on the target species' habitat.

[0030] Obtain the population carrying capacity parameters per unit area of ​​the target benthic animals. According to the habitat suitability index Parameters of population carrying capacity per unit area The theoretical carrying capacity of the target water body was calculated. .

[0031] Obtain the population statistics parameters of the target benthic animals, including the intrinsic growth rate. Environmental randomness variance estimated by combining historical hydrological fluctuation data According to the intrinsic growth rate Environmental randomness variance and theoretical carrying capacity The minimum sustainable community size was calculated. The minimum sustainable community size The calculation formula is:

[0032] .

[0033] in, To set a target for population survival probability, The preset interannual population decline rate is used.

[0034] Preferably, the method for predicting the theoretical seed source replenishment that can be transported to the target water body through water replenishment, based on the available replenishment flow data, river hydraulic condition data, and optimized potential diffusion paths, includes:

[0035] The optimized potential diffusion path is discretized into a continuous sequence of cell grids. Using available water replenishment flow data as upstream input boundary conditions and river hydraulic condition data as basic parameters, a one-dimensional unsteady flow dynamics simulation is performed along the cell grid sequence to obtain the time-averaged flow velocity of each grid during the water replenishment period. With water flow rate And optimize duration .

[0036] Based on the average flow rate of each grid And the pre-set target benthic animal individual drift initiation flow rate With critical damage flow rate Through The individual's survival probability is obtained by linear interpolation within the interval. Based on the water quality data associated with the grid and the tolerance parameters of the target benthic animals, an individual survival rate correction factor is calculated. .

[0037] Determine the effective population output density data over time based on the output process of the seed source water body. Output density data based on the effective population. Individual survival rate correction factor Flow rate of the grid and survival probability Calculate the effective diffusion flux of individual cells through the corresponding grid per unit time by water replenishment. .

[0038] According to the optimized duration Effective diffusion individual flux over time The theoretical seed source replenishment amount that can be transported to the target water body in a single water replenishment event was calculated. ;in, For seed source water bodies to the first Path transport efficiency of each grid The effective retention rate of the target water body. This represents the total effective individual transport volume per unit time, after traversing the path and experiencing efficiency decay. This indicates integration over time.

[0039] Preferably, the method for calculating the individual survival rate correction factor includes:

[0040] The grid was obtained during the water replenishment period. Key water quality parameter vector The key water quality parameters include dissolved oxygen concentration and specific pollutant concentration; the tolerance threshold vector of the target benthic animals to the key water quality parameters is obtained. Based on the aforementioned key water quality parameter vector and tolerance threshold vector Calculate the individual survival probability correction factor under current water quality conditions. The individual survival probability correction factor The calculation formula is:

[0041] .

[0042] in, For the first The preset sensitivity coefficient of each water quality parameter The key water quality parameter vector The Middle Water quality parameters in the grid The monitored concentration value at the location, The tolerance threshold vector The target benthic animals to the first The tolerance threshold concentration of the water quality parameter, Indicates all The calculation results of key water quality parameters are multiplied together.

[0043] (3) Beneficial effects

[0044] Compared with existing technologies, the beneficial effect of this invention is that it transforms multi-source water replenishment from a conventional means of improving water quantity and quality into a precise ecological regulation method. By coupling hydraulic simulation and ecological analysis, it sequentially achieves the identification and optimization of potential diffusion pathways, the calculation of the minimum sustainable community size, and the prediction of theoretical seed source replenishment, thereby generating specific water replenishment control parameters. Through proactive hydraulic regulation, it reconstructs temporary effective diffusion channels, directly promoting the migration and colonization of sensitive benthic animals from seed source water bodies to target water bodies. This solves the problem of difficult natural recovery in urban rain-fed rivers due to obstructed diffusion, providing operable technical support for ecological restoration projects. Attached Figure Description

[0045] Figure 1 This is a flowchart of the method for managing benthic animal diversity in urban rain-fed rivers based on multi-source water replenishment, as described in Embodiment 1 of the present invention. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0047] Before providing examples, it is necessary to describe the application scenarios of this invention. In river regulation and ecological restoration engineering practice, this invention is particularly applicable to key sections of urban rainwater-generating rivers that are undergoing systematic planning or have already entered the implementation phase. These projects typically have or plan to construct necessary control facilities (such as rubber dams and control gates), and incorporate topographic mapping, hydrological monitoring, and ecological baseline surveys as part of the project's foundational work, thereby providing the necessary equipment and data support for the method. Addressing the reality that a single water source often cannot simultaneously meet all the requirements for ecological water quantity, suitable water quality, and sustainability, this invention provides a quantitative and operable decision support based on multi-source water replenishment to achieve the restoration goal of targeted recovery of sensitive species.

[0048] Example 1: As Figure 1 As shown, this embodiment provides a method for managing benthic animal diversity in urban rain-fed rivers based on multi-source water replenishment. The method includes:

[0049] Step S1: Obtain basic river channel data and water replenishment source data for the target river section; the basic river channel data includes river channel range data, river channel structure data, water body distribution information, and river channel hydraulic conditions data; the water replenishment source data includes water intake location data, available water replenishment flow data, and maximum sustainable water replenishment duration data for various water replenishment sources.

[0050] Step S2: Delineate the target river section boundary based on the river channel range data; identify the source water bodies within the target river section that serve as potential benthic animal populations, and the target water bodies where sensitive benthic animal populations need to be restored; determine at least one potential diffusion path from the source water body to the target water body through hydraulic simulation based on the river channel hydraulic condition data; analyze the potential diffusion path to obtain the connectivity reachability value of the source transport channel; when the connectivity reachability value is lower than a preset connectivity threshold, activate the corresponding river channel connectivity device to form an optimized potential diffusion path; if the connectivity reachability value is not lower than the preset connectivity threshold, it is not necessary to activate the corresponding river channel connectivity device, and the potential diffusion path can be used as the optimized potential diffusion path.

[0051] Step S3: Based on the population density, hydraulic connectivity index, and river structure data of the source water body, analyze and obtain the minimum sustainable community size required for the target benthic animals to maintain population stability in the target river section; based on the available water replenishment flow data, river hydraulic condition data, and optimized potential diffusion paths, predict the theoretical source replenishment amount that can be transported to the target water body through water replenishment; compare the theoretical source replenishment amount with the minimum sustainable community size to obtain the replenishment status determination result.

[0052] Step S4: Determine the water replenishment control parameters based on the replenishment status determination result. The water replenishment control parameters include the pump station start-up and shutdown sequence, water replenishment flow rate, and water source switching conditions. Based on the water replenishment control parameters, generate a water replenishment regulation scheme and control the water replenishment device to execute the water replenishment regulation scheme.

[0053] For example, consider the ecological restoration of a rain-fed river in a city at the end of the dry season. The target river section is approximately 2 kilometers long and has become intermittently puddled due to scarce rainfall during the dry season. Basic river channel data and water source data for the target river section were obtained. The basic river channel data was acquired through on-site surveying, drone aerial photography, and historical monitoring reports. The water source data came from the municipal water affairs department's dispatch system. Based on the river channel data, the target river section for this ecological restoration was defined as a 1.5-kilometer area encompassing both upstream and downstream sections.

[0054] An investigation revealed that an upstream pond (S1) with an area of ​​approximately 200 square meters and relatively stable depth still harbors a small number of amphipods with low pollution tolerance (representing a sensitive group), and was therefore identified as a seed source water body. A downstream pond (T1) with an area of ​​approximately 500 square meters, a gravel bottom, but currently exhibiting extremely low amphipod density, was identified as a target water body requiring restoration. When no suitable natural seed source water body can be identified within the target river section, artificial wetlands or upstream protected area water bodies that have undergone ecological cultivation and possess stable populations of sensitive groups can be designated as seed source water bodies.

[0055] Based on river hydraulic condition data (such as riverbed elevation and roughness), a potential diffusion path along the main channel from S1 to T1 was determined using the hydraulic simulation software HEC-RAS. Analysis of this potential diffusion path yielded a connectivity reachability value of 0.37, lower than the preset connectivity threshold of 0.6. Therefore, a river connectivity device located in the middle of the path was activated. This device includes, but is not limited to, adjustable rubber dams, control gates, and flap gates. By adjusting its opening or height, local flow conditions were altered, thereby reducing diffusion resistance and forming an optimized potential diffusion path.

[0056] Analysis yielded the minimum sustainable community size required to maintain a stable amphipod population in the target water body T1. The estimated number of individuals is approximately 324. Meanwhile, under the planned water replenishment conditions, the theoretical seed stock N of amphipods that can be transported from S1 to T1 via water flow is estimated to be approximately 2345 individuals.

[0057] Comparing the theoretical seed source replenishment amount of 2345 with the minimum sustainable community size of 324, the replenishment status was determined to be "sufficient." Based on this result, to achieve the goal of restoring the overall benthic animal diversity of the river section, the following water replenishment control parameters were determined: During periods of high amphipod activity, priority was given to using higher-quality reclaimed water for pulsed replenishment at a flow rate of 30 L / s to promote effective seed source dispersal. Rainwater was used as a supplementary water source, replenishing the river during reclaimed water intermittent periods or when the flow was insufficient, to maintain water connectivity. This precise water replenishment control scheme, integrating multi-source scheduling and ecological timing, was generated and implemented.

[0058] The method for identifying source water bodies within the target river section that serve as potential sources of benthic animals, and target water bodies from which sensitive benthic animal populations need to be restored, includes:

[0059] The historical and current monitoring data of each water body within the target river section are obtained, and the monitoring data includes at least benthic animal community structure data and hydrodynamic characteristic data.

[0060] Based on the benthic animal community structure data, the biological integrity index and the absolute abundance of the target sensitive groups are calculated for each water body; water bodies with biological integrity indices higher than a preset biological integrity threshold and absolute abundance higher than a preset abundance threshold are identified as seed source water bodies.

[0061] Based on the hydrodynamic characteristic data, the average flow velocity, water depth variation coefficient, and flow rate variation coefficient of each water body are calculated; water bodies that are not currently identified as seed source water bodies, whose average flow velocity is within the suitable flow velocity range for the preset target group, whose water depth variation coefficient is lower than the first stability threshold, and whose flow rate variation coefficient is lower than the second stability threshold are identified as target water bodies.

[0062] For example, five main isolated waterholes were investigated. Benthic community structure data and hydrodynamic characteristics data for each waterhole were obtained. Community structure data were obtained by collecting benthic animal samples and identifying and analyzing them in the laboratory, while hydrodynamic characteristics data were obtained through on-site hydrological monitoring equipment.

[0063] Calculations showed that only waterhole S1 had a biological integrity index of 72, exceeding the preset biological integrity threshold of 60, and an amphipod absolute abundance of 65 individuals per square meter, exceeding the preset abundance threshold of 50. Therefore, waterhole S1 was identified as a source water body. Based on hydrodynamic characteristic data, the average flow velocity, water depth coefficient of variation, and flow rate coefficient of variation for the remaining waterholes were calculated. The preset suitable flow velocity range for the target amphipod group was set to 0.1 to 0.3 meters per second, the first stability threshold for the water depth coefficient of variation was 0.4, and the second stability threshold for the flow rate coefficient of variation was 0.6. Waterhole T1 had an average flow velocity of 0.15 meters per second, within the preset suitable flow velocity range; a water depth coefficient of variation of 0.25, below the first stability threshold; and a flow rate coefficient of variation of 0.45, below the second stability threshold. Since waterhole T1 was not identified as a source water body, it was identified as the target water body. Specifically, the first and second stability thresholds were determined based on the habitat stability requirements in industry standards, combined with statistical values ​​from surveys of similar healthy aquatic ecosystems in the local area; the preset suitable flow velocity range was determined based on the flow velocity preference range described in the amphipod habitat. Specifically, 3-5 reference points within the watershed that are minimally disturbed by human activity and well-preserved were selected, and the average Benthic Biodiversity Integrity Index (B-IBI) was calculated. The 75th percentile of this average was set as the biodiversity integrity threshold of 60 for identifying healthy stock sources. Simultaneously, the average abundance of amphipods at these reference points was statistically analyzed, and this average abundance was set as the abundance threshold of 50 individuals / m² for identifying stock sources.

[0064] The method for determining at least one potential diffusion path from the seed source water body to the target water body through hydraulic simulation based on the river hydraulic condition data includes:

[0065] A two-dimensional hydrodynamic model is constructed to cover the riverbed between the source water body and the target water body; wherein, each computational grid of the two-dimensional hydrodynamic model is associated with an initial biological diffusion resistance coefficient, which is pre-determined based on the substrate type data and aquatic vegetation coverage data obtained at the corresponding grid.

[0066] In the two-dimensional hydrodynamic model, the seed source water body is used as the initial water release source to simulate the evolution of water flow under the basic hydrological scenario represented by the river hydraulic conditions data, and the flow velocity data of each grid is obtained; based on the flow velocity data of each grid, the initial bio-diffusion resistance coefficient of the corresponding grid is dynamically corrected to obtain the corrected bio-diffusion resistance coefficient field.

[0067] Based on the modified biodiffusion resistance coefficient field, the path with the minimum cumulative biodiffusion resistance coefficient from the source water body to the target water body is searched and determined as a potential diffusion path.

[0068] The method for dynamically correcting the initial biodiffusion resistance coefficient of each grid based on the flow velocity data of each grid includes:

[0069] Obtain a first correction coefficient and a second correction coefficient, wherein the first correction coefficient is greater than 1 and the second correction coefficient is greater than 0 and less than 1; when the flow velocity data is lower than a preset drift initiation flow velocity threshold, multiply the initial biological diffusion resistance coefficient of the corresponding grid with the first correction coefficient to obtain the corrected biological diffusion resistance coefficient; when the grid flow velocity is not lower than the preset drift initiation flow velocity threshold but lower than the preset active diffusion upper limit flow velocity threshold, multiply the initial biological diffusion resistance coefficient of the corresponding grid with the second correction coefficient to obtain the corrected biological diffusion resistance coefficient.

[0070] For example, the source water body S1 and the target water body T1 are connected by a dry riverbed approximately 400 meters long, partially silted up and with scattered emergent vegetation. To determine the potential diffusion path from S1 to T1, a two-dimensional hydrodynamic model covering this riverbed is first constructed based on field-mapped river topography and substrate data. Each computational grid in the two-dimensional hydrodynamic model is assigned an initial biodiffusion resistance coefficient between 1 and 10, based on the corresponding substrate type (e.g., hard clay, gravel, or silt) and aquatic vegetation cover (in percentage). For example, a lower resistance coefficient of 2 is assigned to hard, bare riverbeds, while a higher resistance coefficient of 8 is assigned to areas with thick silt or dense vegetation.

[0071] In a two-dimensional hydrodynamic model, the water source S1 is used as the starting point for water release. The water flow evolution process under conditions of a small-scale rainfall runoff or initial water replenishment is simulated, and flow velocity data for each grid is obtained. The initial resistance coefficient is dynamically corrected based on this flow velocity data. The numerical range recorded in the observational study report of the amphipod flume experiment sets the preset drift initiation velocity threshold for amphipods at 0.05 m / s and the preset active diffusion upper limit velocity threshold at 0.25 m / s. After calibration with reference to historical case data, the first correction coefficient is determined to be 1.5, and the second correction coefficient is 0.6. For grids with simulated flow velocities below 0.05 m / s, diffusion resistance is considered to increase, and the initial resistance coefficient is multiplied by 1.5; for grids with flow velocities between 0.05 m / s and 0.25 m / s, diffusion conditions are considered favorable, and the initial resistance coefficient is multiplied by 0.6; for grids with flow velocities exceeding 0.25 m / s, the resistance coefficient remains unchanged. Through the above corrections, the corrected biodiffusion resistance coefficient field is obtained. In the modified biodiffusion resistance coefficient field, the Dijkstra algorithm, the shortest path search algorithm, is applied to find the path from S1 to T1 with the minimum cumulative biodiffusion resistance coefficient, and this path is identified as the potential diffusion path.

[0072] The method for analyzing the potential diffusion paths to obtain the connectivity reachability values ​​of the seed source transport channels includes:

[0073] The cumulative biodiffusion resistance coefficient and the modified biodiffusion resistance coefficient field are calculated based on the potential diffusion pathways. Extract the path feature parameters of the potential diffusion path, the path feature parameters including the total path length. and the longest duration for which the path can maintain continuous hydraulic connectivity under the hydrological scenario characterized by the river channel hydraulic conditions data. .

[0074] The total length of the path , cumulative biodiffusion resistance coefficient and and longest duration Perform normalization to obtain the normalized length. Normalization resistance and normalized time According to the normalized length Normalization resistance and normalized time Calculate the connectivity reachability value The connectivity reachability value The calculation formula is:

[0075] .

[0076] in, , and The preset weighting coefficients are used, and they satisfy the following conditions: , and This is the preset attenuation coefficient.

[0077] For example, based on the modified biodiffusion resistance coefficient field, the biodiffusion resistance coefficients of all grids are accumulated along the potential diffusion path to obtain the cumulative biodiffusion resistance coefficient and... The value is 285. The geometric length of the potential diffusion path is extracted to obtain the total path length. The length is 420 meters. Based on the typical dry season hydrological conditions characterized by the aforementioned river channel hydraulic conditions data, hydraulic analysis reveals the longest duration during which continuous flow can be maintained along this path after it is established. It lasts for 36 hours.

[0078] Total path length , cumulative biodiffusion resistance coefficient and and longest duration Deviation standardization ( The value ranges from 300 to 600 meters. The value range is from 200 to 500. The value ranges from 12 to 72 hours. Normalization is performed to obtain the normalized length. The normalized resistance is 0.4. The normalized time is 0.28. The connectivity reachability value is 0.4. Using the analytic hierarchy process (AHP), a judgment system was established with the successful dispersal of benthic animals as the objective. The importance of three criteria—path length, cumulative dispersal resistance, and connectivity time—was compared pairwise to construct a judgment matrix. The eigenvectors of the matrix were calculated using the square root method. After passing a consistency test, the eigenvectors were normalized to obtain the preset weight coefficients. It is 0.3. It is 0.4. The preset attenuation coefficient is 0.3. for , for This study collected at least five case studies of urban rain-fed rivers with known good ecological restoration results. It obtained typical diffusion path characteristics from these cases and assessed the actual connectivity effects through marker species diffusion success rates. Using the formula for calculating the connectivity reachability value C as a model, a pre-set attenuation coefficient was adjusted using an algorithm such as particle swarm optimization. for , for This maximizes the correlation coefficient between the calculated C-value sequences and the actual connectivity evaluation sequences.

[0079] The method for analyzing the minimum sustainable community size required for the target benthic animals to maintain population stability in the target river section based on the population density, hydraulic connectivity index, and channel structure data of the source water body includes:

[0080] Habitat factor data for the target water body, including water area, are obtained based on river structure data. Average water depth Substrate type index and water flow velocity The habitat factor data were normalized to obtain standardized scores. , , , According to the standardized score , , , Calculate the habitat suitability index of the target water body ;in, , , , These are preset preference weighting coefficients based on the target species' habitat.

[0081] Obtain the population carrying capacity parameters per unit area of ​​the target benthic animals. According to the habitat suitability index Parameters of population carrying capacity per unit area The theoretical carrying capacity of the target water body was calculated. .

[0082] Obtain the population statistics parameters of the target benthic animals, including the intrinsic growth rate. Environmental randomness variance estimated by combining historical hydrological fluctuation data According to the intrinsic growth rate Environmental randomness variance and theoretical carrying capacity The minimum sustainable community size was calculated. The minimum sustainable community size The calculation formula is:

[0083] .

[0084] in, To set a target for population survival probability, The preset interannual population decline rate is used.

[0085] For example, the water surface area A is 500 square meters, and the average water depth D is 0.6 meters. The sediment type index S_b is determined based on the riverbed composition; in this example, gravel accounts for a high proportion, so it is assigned a value of 0.7 (range 0-1). Monitoring shows that the average water flow velocity V under normal conditions is 0.15 meters per second. Normalization is performed using the maximum-minimum normalization method, resulting in a standardized score between 0 and 1. It is 0.8. It is 0.5. It is 0.7. It is 0.6.

[0086] Based on the frequency of amphipod habitat selection, the significance of each habitat factor (area, water depth, substrate, and current velocity) was extracted and ranked. An expert scoring method was then used to assign scores, and a weighted average was calculated to obtain the final score. It is 0.2. It is 0.3. It is 0.3. The value is 0.2. Therefore, the habitat suitability index is... .

[0087] carrying capacity parameters per unit area of ​​amphipods Based on long-term observation reports of amphipod population density in similar ecological areas, the upper quartile of the reported density range was selected as a reference benchmark. After downward adjustment based on the specific productivity level of the target river section, the density was determined to be 20 individuals per square meter. Theoretical carrying capacity. The intrinsic growth rate of amphipods. Referring to internationally accepted ecological parameter databases, the reported growth rate parameters for freshwater amphipod species of the genus *Amphioxus* were analyzed, and the median of their common values ​​was determined to be 1.2 / year. Environmental randomness variance. The variance of the random impact of environmental fluctuations on population growth was estimated to be 0.25 by analyzing the monthly average flow data of local hydrological stations.

[0088] Based on the general goals for species sustainable survival in ecological restoration projects (such as a 95% confidence level), and referring to commonly used parameters in population viability analysis (PVA), the preset population survival probability is set. The preset interannual population decline rate is 0.95. The minimum sustainable community size of the amphipod population in target water body T1 is 0.1. .

[0089] The method for predicting the theoretical seed source replenishment amount that can be transported to the target water body through water replenishment, based on the available water replenishment flow data, river hydraulic condition data, and optimized potential diffusion paths, includes:

[0090] The optimized potential diffusion path is discretized into a continuous sequence of cell grids. Using available water replenishment flow data as upstream input boundary conditions and river hydraulic condition data as basic parameters, a one-dimensional unsteady flow dynamics simulation is performed along the cell grid sequence to obtain the time-averaged flow velocity of each grid during the water replenishment period. With water flow rate And optimize duration .

[0091] Based on the average flow rate of each grid And the pre-set target benthic animal individual drift initiation flow rate With critical damage flow rate Through The individual's survival probability is obtained by linear interpolation within the interval. Based on the water quality data associated with the grid and the tolerance parameters of the target benthic animals, an individual survival rate correction factor is calculated. .

[0092] Determine the effective population output density data over time based on the output process of the seed source water body. Output density data based on the effective population. Individual survival rate correction factor Flow rate of the grid and survival probability Calculate the effective diffusion flux of individual cells through the corresponding grid per unit time by water replenishment. .

[0093] According to the optimized duration Effective diffusion individual flux over time The theoretical seed source replenishment amount that can be transported to the target water body in a single water replenishment event was calculated. ;in, For seed source water bodies to the first Path transport efficiency of each grid The effective retention rate of the target water body. This represents the total effective individual transport volume per unit time, after traversing the path and experiencing efficiency decay. This indicates integration over time.

[0094] The calculation method for the individual survival rate correction factor includes:

[0095] The grid was obtained during the water replenishment period. Key water quality parameter vector The key water quality parameters include dissolved oxygen concentration and specific pollutant concentration; the tolerance threshold vector of the target benthic animals to the key water quality parameters is obtained. Based on the aforementioned key water quality parameter vector and tolerance threshold vector Calculate the individual survival probability correction factor under current water quality conditions. The individual survival probability correction factor The calculation formula is:

[0096] .

[0097] in, For the first The preset sensitivity coefficient of each water quality parameter The key water quality parameter vector The Middle Water quality parameters in the grid The monitored concentration value at the location, The tolerance threshold vector The target benthic animals to the first The tolerance threshold concentration of the water quality parameter, Indicates all The calculation results of key water quality parameters are multiplied together.

[0098] For example, a potential diffusion path of approximately 420 meters in length is discretized into 42 consecutive cell grids, each representing a 10-meter-long river segment. The planned available reclaimed water replenishment flow rate, 50 liters per second, is used as the upstream input boundary condition. The replenishment process is simulated using a one-dimensional unsteady flow model along the optimized potential diffusion path, yielding the time-averaged flow velocity for each grid during the replenishment period. With water flow rate For example, in a grid in the middle of the path. The time-average flow velocity was obtained through simulation. The flow rate is 0.18 meters per second. The rate is 100 liters per second. Based on this simulation, the optimal duration for maintaining effective connectivity of the path under these optimized water replenishment conditions was determined. The time was 60 hours. Based on the hydrodynamic tolerance test data of amphipods, the median or conservative value was used to determine the individual drift initiation current velocity of amphipods. The critical damage velocity is 0.05 meters per second. It is 0.30 meters per second. For With a grid of 0.18 m / s, its values ​​are in the interval Within. The probability of individual hydraulic survival is calculated using linear interpolation. .

[0099] Obtain key water quality parameters of the water body replenishing the grid. Dissolved oxygen concentration The concentration of ammonia nitrogen for a specific pollutant is 5 mg / L. The value was 0.5 mg / L. Based on the water quality standards for protecting freshwater aquatic organisms in the "Surface Water Environmental Quality Standards" and referring to the results of acute / chronic toxicity tests for amphipods, the tolerance threshold for these two key water quality parameters for amphipods was determined to be dissolved oxygen. The ammonia nitrogen level was 4 mg / L. The concentration was 1 mg / L. After fitting the concentration-response curve near the tolerance threshold using a logistic model, the preset sensitivity coefficient corresponding to the dissolved oxygen concentration was determined. Preset sensitivity coefficient corresponding to ammonia nitrogen concentration Both are 2. For dissolved oxygen For ammonia nitrogen: Therefore, the individual survival probability correction factor Population density of amphipods in water body S1, the source of the seed culture. The density is 20 individuals per cubic meter (calculated by converting the absolute abundance of amphipods from 65 individuals per square meter to the average water depth, and taking into account the actual distribution ratio of organisms in the water, after converting the area density to volume density), which is approximately 0.02 individuals per liter. This represents the effective population output density that can be transported by the water flow during water replenishment and output. With output time The biomass decline during the seed source water output process is influenced by multiple factors, including local water flow mixing conditions and biological behavior, making it difficult to characterize with a precise function. Therefore, the equivalent effective output duration based on the principle of mass conservation is used to quantify the comprehensive effect of this decline process. It is estimated that under the current water replenishment conditions, the equivalent effective output duration is 3 hours. Due to incomplete mixing, organism escape, and sedimentation losses during the seed source water output process, the effective output density declines to approximately 40% of the initial population density. The average effective output density during this time is... per liter, and when calculating the theoretical seed supply, The value is assigned to 3 hours, or 10800 seconds. Water flow rate. The flow rate is 100 liters per second. The seed source water body reaches the... Path transport efficiency per grid Based on the optimized cumulative biodiffusion resistance coefficient from the seed source water body to the grid and Determine (optimized cumulative biodiffusion resistance coefficient and) The biodiffusion resistance coefficient of the optimized potential diffusion pathway is then calculated using the following formula: ,in A preset drag attenuation constant, reflecting the weakening effect of accumulated drag on transport efficiency, was obtained by fitting historical data and has a value of 0.001. Calculations were performed on all grids along the path. The weighted average is approximately 0.65. Effective retention rate of the target water body per second. Based on the hydraulic characteristics of the target water body T1, it is determined that T1 is a slow-flowing pond with good interception capacity for individuals arriving with the water flow. This represents the time integral of the effective instantaneous flux of individuals output from the seed source water body and ultimately reaching the target water body at each moment during the water replenishment period, to calculate the theoretical seed source replenishment amount. .

[0100] Finally, it should be noted that although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for managing benthic animal diversity in urban rain-fed rivers based on multi-source water replenishment, characterized in that, The method includes: Acquire basic river channel data and water replenishment source data for the target river section; the basic river channel data includes river channel extent data, river channel structure data, water body distribution information, and river channel hydraulic conditions data; the water replenishment source data includes water intake location data, available water replenishment flow data, and maximum sustainable water replenishment duration data for various water replenishment sources. The target river section boundary is delineated based on the river range data; the source water bodies within the target river section that serve as potential benthic animal seed sources, and the target water bodies where sensitive benthic animal populations need to be restored, are identified; based on the river hydraulic condition data, at least one potential diffusion path from the source water body to the target water body is determined through hydraulic simulation; the potential diffusion path is analyzed to obtain the connectivity reachability value of the seed source transport channel; when the connectivity reachability value is lower than a preset connectivity threshold, the corresponding river connectivity device is activated to form an optimized potential diffusion path; Based on the population density, hydraulic connectivity index, and river structure data of the source water body, the minimum sustainable community size required for the target benthic animals to maintain population stability in the target river section is analyzed; based on the available water replenishment flow data, river hydraulic condition data, and optimized potential diffusion paths, the theoretical source replenishment amount that can be transported to the target water body through water replenishment is predicted; the theoretical source replenishment amount is compared with the minimum sustainable community size to obtain the replenishment status determination result; Based on the replenishment status determination result, water replenishment control parameters are determined, including pump station start-up and shutdown sequence, water replenishment flow rate, and water source switching conditions; based on the water replenishment control parameters, a water replenishment regulation scheme is generated, and the water replenishment device is controlled to execute the water replenishment regulation scheme; The method for determining at least one potential diffusion path from the seed source water body to the target water body through hydraulic simulation based on the river hydraulic condition data includes: A two-dimensional hydrodynamic model is constructed to cover the riverbed between the source water body and the target water body; wherein, each computational grid of the two-dimensional hydrodynamic model is associated with an initial biological diffusion resistance coefficient, which is pre-determined based on the substrate type data and aquatic vegetation coverage data obtained at the corresponding grid. In the two-dimensional hydrodynamic model, the seed source water body is used as the initial water release source to simulate the evolution process of water flow under the basic hydrological scenario represented by the river hydraulic conditions data, and the flow velocity data of each grid is obtained; based on the flow velocity data of each grid, the initial bio-diffusion resistance coefficient of the corresponding grid is dynamically corrected to obtain the corrected bio-diffusion resistance coefficient field. Based on the modified biodiffusion resistance coefficient field, search and determine the path from the source water body to the target water body with the minimum cumulative biodiffusion resistance coefficient, as a potential diffusion path; The method for analyzing the potential diffusion paths to obtain the connectivity reachability values ​​of the seed source transport channels includes: The cumulative biodiffusion resistance coefficient and the modified biodiffusion resistance coefficient field are calculated based on the potential diffusion pathways. Extract the path feature parameters of the potential diffusion path, the path feature parameters including the total path length. and the longest duration for which the path can maintain continuous hydraulic connectivity under the hydrological scenario characterized by the river channel hydraulic conditions data. ; The total length of the path , cumulative biodiffusion resistance coefficient and and longest duration Perform normalization to obtain the normalized length. Normalization resistance and normalized time According to the normalized length Normalization resistance and normalized time Calculate the connectivity reachability value The connectivity reachability value The calculation formula is: ； in, , and The preset weighting coefficients are used, and they satisfy the following conditions: , and This is the preset attenuation coefficient.

2. The method for managing benthic animal diversity in urban rain-fed rivers based on multi-source water replenishment as described in claim 1, characterized in that, The method for identifying source water bodies within the target river section that serve as potential sources of benthic animals, and target water bodies from which sensitive benthic animal populations need to be restored, includes: Acquire historical and current monitoring data of each water body within the target river section, including at least benthic animal community structure data and hydrodynamic characteristic data; Based on the benthic animal community structure data, the biological integrity index and the absolute abundance of the target sensitive groups in each water body are calculated; water bodies with biological integrity indices higher than a preset biological integrity threshold and absolute abundance higher than a preset abundance threshold are identified as seed source water bodies. Based on the hydrodynamic characteristic data, the average flow velocity, water depth variation coefficient, and flow rate variation coefficient of each water body are calculated; water bodies that are not currently identified as seed source water bodies, whose average flow velocity is within the suitable flow velocity range for the preset target group, whose water depth variation coefficient is lower than the first stability threshold, and whose flow rate variation coefficient is lower than the second stability threshold are identified as target water bodies.

3. The method for managing benthic animal diversity in urban rain-fed rivers based on multi-source water replenishment as described in claim 2, characterized in that, The method for dynamically correcting the initial biodiffusion resistance coefficient of each grid based on the flow velocity data of each grid includes: Obtain a first correction coefficient and a second correction coefficient; when the flow velocity data is lower than a preset drift initiation flow velocity threshold, multiply the initial biological diffusion resistance coefficient of the corresponding grid with the first correction coefficient to obtain the corrected biological diffusion resistance coefficient; when the grid flow velocity is not lower than the preset drift initiation flow velocity threshold but lower than the preset active diffusion upper limit flow velocity threshold, multiply the initial biological diffusion resistance coefficient of the corresponding grid with the second correction coefficient to obtain the corrected biological diffusion resistance coefficient.

4. The method for managing benthic animal diversity in urban rain-fed rivers based on multi-source water replenishment as described in claim 3, characterized in that, The method for analyzing the minimum sustainable community size required for the target benthic animals to maintain population stability in the target river section based on the population density, hydraulic connectivity index, and channel structure data of the source water body includes: Habitat factor data for the target water body, including water area, are obtained based on river structure data. Average water depth Substrate type index and water flow velocity The habitat factor data were normalized to obtain standardized scores. , , , According to the standardized score , , , Calculate the habitat suitability index of the target water body ;in, , , , These are preset preference weighting coefficients based on the target species' habitat; Obtain the population carrying capacity parameters per unit area of ​​the target benthic animals. According to the habitat suitability index Parameters of population carrying capacity per unit area The theoretical carrying capacity of the target water body was calculated. ; Obtain the population statistics parameters of the target benthic animals, including the intrinsic growth rate. Environmental randomness variance estimated by combining historical hydrological fluctuation data According to the intrinsic growth rate Environmental randomness variance and theoretical carrying capacity The minimum sustainable community size was calculated. The minimum sustainable community size The calculation formula is: ； in, To set a target for population survival probability, The preset interannual population decline rate is used.

5. The method for managing benthic animal diversity in urban rain-fed rivers based on multi-source water replenishment according to claim 4, characterized in that, The method for predicting the theoretical seed source replenishment amount that can be transported to the target water body through water replenishment, based on the available water replenishment flow data, river hydraulic condition data, and optimized potential diffusion paths, includes: The optimized potential diffusion path is discretized into a continuous sequence of cell grids. Using available water replenishment flow data as upstream input boundary conditions and river hydraulic condition data as basic parameters, a one-dimensional unsteady flow dynamics simulation is performed along the cell grid sequence to obtain the time-averaged flow velocity of each grid during the water replenishment period. With water flow rate And optimize duration ; Based on the average flow rate of each grid And the pre-set target benthic animal individual drift initiation flow rate With critical damage flow rate Through The individual's survival probability is obtained by linear interpolation within the interval. Based on the water quality data associated with the grid and the tolerance parameters of the target benthic animals, an individual survival rate correction factor is calculated. ; Determine the effective population output density data over time based on the output process of the seed source water body. Output density data based on the effective population. Individual survival rate correction factor Flow rate of the grid and survival probability Calculate the effective diffusion flux of individual cells through the corresponding grid per unit time by water replenishment. ; According to the optimized duration Effective diffusion individual flux over time The theoretical seed source replenishment amount that can be transported to the target water body in a single water replenishment event was calculated. ;in, For seed source water bodies to the first Path transport efficiency of each grid The effective retention rate of the target water body. This represents the total effective individual transport volume per unit time, after traversing the path and experiencing efficiency decay. This indicates integration over time.

6. The method for managing benthic animal diversity in urban rain-fed rivers based on multi-source water replenishment as described in claim 5, characterized in that, The calculation method for the individual survival rate correction factor includes: The grid was obtained during water replenishment. Key water quality parameter vector The key water quality parameters include dissolved oxygen concentration and specific pollutant concentration; the tolerance threshold vector of the target benthic animals to the key water quality parameters is obtained. Based on the aforementioned key water quality parameter vector and tolerance threshold vector Calculate the individual survival probability correction factor under current water quality conditions. The individual survival probability correction factor The calculation formula is: ； in, For the first The preset sensitivity coefficient of each water quality parameter The key water quality parameter vector The Middle Water quality parameters in the grid The monitored concentration value at the location, The tolerance threshold vector The target benthic animals to the first The tolerance threshold concentration of the water quality parameter, Indicates all The calculation results of the water quality parameters are multiplied together.

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