Urban river network water habitat quality driving factor identification method

Abstract

The invention discloses an urban river network water habitat quality driving factor identification method. The method comprises the following steps: constructing a hydrological model and a hydrodynamic model according to acquired rainfall data, air temperature data, an elevation data set, land utilization data, soil data, runoff actual measurement data, tide level data, riverbed data, water level actual measurement data and river network communication node data; obtaining a hydrological and hydrodynamic coupling model and hydrodynamic factors according to the hydrological model and the hydrodynamic model; according to the area of each grid unit of the to-be-identified region, the suitability index of the hydrodynamic factor and a preset factor weight, carrying out orthogonal test calculation under a plurality of influence factors to obtain a response index value; and according to the response index value and the change numerical value corresponding to the influence factor, calculating a variance ratio statistic, and taking the influence factor of which the variance ratio statistic is greater than a preset significance level as a water habitat quality driving factor of the to-be-identified area. According to the invention, the identification efficiency is improved while the cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of habitat quality assessment, and particularly to a method for identifying driving factors of aquatic habitat quality in urban river networks. Background Art

[0002] A habitat is the physical, chemical, and biological environment that provides survival for organisms within a region, and the quality of the habitat determines whether the ecosystem can ensure the survival and reproduction of individual organisms or communities. At present, various methods have been developed for aquatic habitat quality assessment, covering a wide range of fields from physical-chemical environment assessment to ecological model application and different spatial scales. Traditional methods for aquatic habitat quality assessment usually rely on field survey data, with site layout and sampling analysis carried out for the study area, mainly including monitoring of hydrological elements, water quality monitoring, intensity of human activities within the river channel, riverbank stability, riverbank vegetation coverage, ecological community survey, and analysis of biological indicator species. The data obtained by field survey methods is intuitive and easy to understand, but restricted by space and time, field surveys require high costs and are inefficient. Summary of the Invention

[0003] An embodiment of the present invention provides a method for identifying driving factors of aquatic habitat quality in urban river networks, which can effectively solve the problems of high cost and low efficiency in identifying driving factors of aquatic habitat quality in the prior art due to spatial and temporal limitations.

[0004] An embodiment of the present invention provides a method for identifying driving factors of aquatic habitat quality in urban river networks, including:

[0005] Obtaining rainfall data, temperature data, elevation data sets, land use data, soil data, measured runoff data, tide level data, riverbed data, measured water level data, and river network connection node data of the area to be identified;

[0006] Constructing a hydrological model based on the rainfall data, temperature data, elevation data sets, land use data, soil data, and measured runoff data;

[0007] Constructing a hydrodynamic model based on the measured runoff data, tide level data, riverbed data, measured water level data, and river network connection node data;

[0008] Coupling the hydrological model and the hydrodynamic model to obtain a coupled hydrological-hydrodynamic model and hydrodynamic factors output by the coupled hydrological-hydrodynamic model;

[0009] Dividing the area to be identified into several grid cells according to the coupled hydrological-hydrodynamic model;

[0010] Perform an orthogonal experiment under several preset influencing factors according to the grid area corresponding to each grid cell, the suitability index corresponding to the hydrodynamic factor, and the preset factor weight, and calculate the response index value of the area to be identified;

[0011] According to the response index value and the change value corresponding to each influencing factor, calculate the variance ratio statistic of each influencing factor, and use the influencing factor with the variance ratio statistic greater than the preset significance level as the driving factor for the aquatic habitat quality of the area to be identified.

[0012] Furthermore, construct a hydrological model based on the rainfall data, temperature data, elevation dataset, land use data, soil data, and measured runoff data, including:

[0013] Construct an initial hydrological model according to the rainfall data, temperature data, elevation dataset, land use data, and soil data;

[0014] Simulate according to the current hydrological model to determine the runoff simulation data at different time series; among them, the current hydrological model at the first simulation is the initial hydrological model;

[0015] Calculate the first Nash coefficient and the first determination coefficient for characterizing the current hydrological model according to the measured runoff data and the runoff simulation data;

[0016] When the first Nash coefficient is greater than the preset Nash coefficient threshold and the first determination coefficient is greater than the preset determination coefficient threshold, obtain the final hydrological model;

[0017] Otherwise, perform parameter calibration according to the measured runoff data and the runoff simulation data to obtain calibrated parameters; and update the current hydrological model according to the calibrated parameters.

[0018] Furthermore, construct a hydrodynamic model according to the measured runoff data, tide level data, riverbed data, measured water level data, and river network connection node data, including:

[0019] Determine the river network boundary of the area to be identified according to the river network connection node data;

[0020] Set the downstream outflow boundary of the river network according to the tide level data within the river network boundary;

[0021] Set the upstream inflow boundary of the river network according to the measured runoff data within the river network boundary;

[0022] Construct an initial hydrodynamic model according to the downstream outflow boundary of the river network, the upstream inflow boundary of the river network, and the riverbed data;

[0023] Perform simulations based on the current hydrodynamic model to determine water level simulation data; among them, the current hydrodynamic model during the first simulation is the initial hydrodynamic model;

[0024] Calculate the second Nash coefficient and the second coefficient of determination used to characterize the current hydrodynamic model based on the water level simulation data and the measured water level data;

[0025] Calibrate the parameters of the current hydrodynamic model according to the measured water level data and the water level simulation data until the second Nash coefficient is greater than the preset Nash coefficient threshold and the second coefficient of determination is greater than the preset coefficient of determination threshold to obtain the final hydrodynamic model.

[0026] Further, couple the hydrological model and the hydrodynamic model to obtain a coupled hydrological-hydrodynamic model and the hydrodynamic factors output by the coupled hydrological-hydrodynamic model, including:

[0027] Use the runoff simulation data output by the hydrological model as the boundary condition of the hydrodynamic model and input it into the hydrodynamic model for coupling to obtain a coupled hydrological-hydrodynamic model;

[0028] Predict the hydrodynamic factors based on the coupled hydrological-hydrodynamic model.

[0029] Further, conduct orthogonal experiments under several preset influencing factors according to the grid area corresponding to each grid cell, the suitability index corresponding to the hydrodynamic factors, and the preset factor weights, and calculate the response index value of the area to be identified, including:

[0030] Calculate the habitat suitability of each grid cell according to the suitability index corresponding to the hydrodynamic factors and the preset factor weights;

[0031] Calculate the weighted suitable habitat area according to the number of grids corresponding to the grid cell, the grid area corresponding to each grid cell, and the habitat suitability;

[0032] Construct an orthogonal experiment table according to the preset influencing factors;

[0033] Conduct orthogonal experiments according to the orthogonal experiment table, and use the mean value of the weighted suitable habitat area as the response index value of the area to be identified.

[0034] Further, calculate the variance ratio statistic of each influencing factor according to the response index value and the change value corresponding to each influencing factor, including:

[0035] Under the condition that the change values corresponding to each influencing factor are the same, calculate the sum of squares of deviations and the sum of squares of errors of each influencing factor according to the response index value;

[0036] Calculate the factor degrees of freedom for each influencing factor according to the corresponding change values of each influencing factor;

[0037] Calculate the total degrees of freedom of the experiment according to the corresponding number of orthogonal experiments, and determine the error degrees of freedom according to the total degrees of freedom of the experiment and the factor degrees of freedom;

[0038] Calculate the mean sum of squared deviations according to the sum of squared deviations of each influencing factor and the factor degrees of freedom; and calculate the mean error sum of squared deviations according to the sum of squared errors and the error degrees of freedom;

[0039] Obtain the variance ratio statistic of each influencing factor according to the ratio of the mean sum of squared deviations and the mean error sum of squared deviations.

[0040] Further, it also includes: evaluating the change in the aquatic habitat quality of the river network in the area to be identified according to the aquatic habitat quality driving factors.

[0041] As an improvement of the above solution, another embodiment of the present invention correspondingly provides a device for identifying the driving factors of the aquatic habitat quality of the river network, including:

[0042] A data acquisition module, configured to acquire rainfall data, temperature data, elevation data set, land use data, soil data, measured runoff data, tide level data, riverbed data, measured water level data, and river network connection node data of the area to be identified;

[0043] A hydrological model construction module, configured to construct a hydrological model according to the rainfall data, temperature data, elevation data set, land use data, soil data, and measured runoff data;

[0044] A hydrodynamic model construction module, configured to construct a hydrodynamic model according to the measured runoff data, tide level data, riverbed data, measured water level data, and river network connection node data;

[0045] A model coupling module, configured to couple the hydrological model and the hydrodynamic model to obtain a coupled hydrological and hydrodynamic model and the hydrodynamic factors output by the coupled hydrological and hydrodynamic model;

[0046] A regional grid division module, configured to divide the area to be identified into several grid units according to the coupled hydrological and hydrodynamic model;

[0047] An influencing factor orthogonal experiment module, configured to perform an orthogonal experiment under several preset influencing factors according to the grid area corresponding to each grid unit, the suitability index corresponding to the hydrodynamic factors, and a preset factor weight, and calculate the response index value of the area to be identified;

[0048] A quality driving factor identification module, configured to calculate the variance ratio statistic of each influencing factor according to the response index value and the change value corresponding to each influencing factor, and use the influencing factors with the variance ratio statistic greater than a preset significance level as the aquatic habitat quality driving factors in the area to be identified.

[0049] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a method for identifying the aquatic habitat quality driving factors of an urban river network as described in the above embodiment.

[0050] Another embodiment of the present invention provides a computer-readable storage medium. The computer-readable storage medium includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute a method for identifying the aquatic habitat quality driving factors of an urban river network as described in the above embodiment.

[0051] By implementing the present invention, at least the following beneficial effects are achieved:

[0052] The present invention provides a method for identifying driving factors of the aquatic habitat quality in an urban river network. The method can obtain rainfall data, temperature data, elevation data sets, land use data, soil data, measured runoff data, tide level data, riverbed data, measured water level data, and river network connection node data of the area to be identified; construct a hydrological model based on the rainfall data, temperature data, elevation data sets, land use data, soil data, and measured runoff data; construct a hydrodynamic model based on the measured runoff data, tide level data, riverbed data, measured water level data, and river network connection node data; couple the hydrological model and the hydrodynamic model to obtain a coupled hydrological-hydrodynamic model and hydrodynamic factors output by the coupled hydrological-hydrodynamic model; divide the area to be identified into several grid cells according to the coupled hydrological-hydrodynamic model; conduct an orthogonal experiment under several preset influencing factors according to the grid area corresponding to each grid cell, the suitability index corresponding to the hydrodynamic factors, and a preset factor weight, and calculate the response index value of the area to be identified; calculate the variance ratio statistic of each influencing factor according to the response index value and the change value corresponding to each influencing factor, and use the influencing factors with the variance ratio statistic greater than the preset significance level as the driving factors of the aquatic habitat quality in the area to be identified. By coupling the hydrological model and the hydrodynamic model, the response of the river network aquatic habitat quality in the area to be identified under the change of influencing factors is simulated to obtain hydrodynamic factors, and the effect of the influencing factors in the area to be identified on the hydrodynamic factors is simulated to obtain the driving factors of the aquatic habitat quality, and the influence of multiple influencing factors on the area to be identified is proposed to comprehensively evaluate the aquatic habitat quality. Only by constructing a hydrological model, a hydrodynamic model, and a coupled hydrological-hydrodynamic model based on the measured data of the area to be identified, the driving factors of the aquatic habitat quality can be obtained, greatly reducing the dependence on field sampling, not requiring long-term on-site monitoring, being unrestricted by space and time, being able to identify the driving factors of the aquatic habitat quality at any location and any time, reducing the cost of long-term on-site monitoring, and at the same time being able to obtain more comprehensive driving factors of the aquatic habitat quality in a shorter time, improving the efficiency of identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a schematic flowchart of a method for identifying driving factors of the aquatic habitat quality in an urban river network provided by an embodiment of the present invention;

[0054] Figure 2 is a technical route diagram of a method for identifying driving factors of the aquatic habitat quality in an urban river network provided by an embodiment of the present invention;

[0055] Figure 3 is a schematic structural diagram of a device for identifying driving factors of the aquatic habitat quality in an urban river network provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0057] See Figure 1 , which is a schematic flowchart of a method for identifying driving factors of the aquatic habitat quality of an urban river network provided by an embodiment of the present invention, including:

[0058] S1. Obtain rainfall data, temperature data, elevation data set, land use data, soil data, measured runoff data, tide level data, riverbed data, measured water level data, and river network connection node data of the area to be identified;

[0059] Specifically, the elevation data set is the data of DEM (Digital Elevation Model), from the original elevation data set of SRTM DEM (Space Shuttle Radar Topography Mission Digital Elevation Model) with a resolution of 90 m (the side length of the ground area represented by each data point is 90 meters); the land use data is the land use type, from the CGLS LC100 land use product (Copernicus Global Land Service 100-meter resolution land cover product); the soil data is the soil type, from the HWSD (Harmonized World Soil Database); the river network connection node data is obtained according to high-resolution remote sensing images. The time series of the remaining meteorological and hydrological data is the same.

[0060] S2. Construct a hydrological model according to the rainfall data, temperature data, elevation data set, land use data, soil data, and measured runoff data;

[0061] Specifically, constructing a hydrological model according to the rainfall data, temperature data, elevation data set, land use data, soil data, and measured runoff data includes:

[0062] Construct an initial hydrological model according to the rainfall data, temperature data, elevation data set, land use data, and soil data;

[0063] Simulate according to the current hydrological model to determine the runoff simulation data under different time series; among them, the current hydrological model at the first simulation is the initial hydrological model;

[0064] Calculate the first Nash coefficient and the first determination coefficient for characterizing the current hydrological model according to the measured runoff data and the runoff simulation data;

[0065] When the first Nash coefficient is greater than a preset Nash coefficient threshold and the first determination coefficient is greater than a preset determination coefficient threshold, a final hydrological model is obtained.

[0066] Otherwise, parameter calibration is performed based on the measured runoff data and the simulated runoff data to obtain calibrated parameters; and the current hydrological model is updated according to the calibrated parameters.

[0067] In a preferred embodiment of the present invention, the measured runoff data is obtained by monitoring at a hydrological station; the first Nash coefficient represents the Nash coefficient (NSE) of the current hydrological model; the first determination coefficient represents the determination coefficient (R2) of the current hydrological model. Using the ArcSWAT tool, an initial hydrological model is constructed based on the rainfall data, temperature data, elevation dataset, land use data, and soil data; then the rainfall data and temperature data are used for model driving, and simulations are performed according to the current hydrological model to determine the simulated runoff data at different time series; based on the measured runoff data and the simulated runoff data, the first Nash coefficient and the first determination coefficient for characterizing the current hydrological model are calculated; when the first Nash coefficient is greater than a preset Nash coefficient threshold (0.6) and the first determination coefficient is greater than a preset determination coefficient threshold (0.5), a final hydrological model is obtained, otherwise, parameter calibration is performed based on the measured runoff data and the simulated runoff data to obtain calibrated parameters; and the current hydrological model is updated according to the calibrated parameters. The hydrological model is a SWAT hydrological model, and the SWAT hydrological model can well simulate processes such as the hydrological cycle, runoff generation and concentration, and reservoir operation, and has good applicability in the Dongjiang River Basin. In this embodiment, for the Dongjiang River Basin, the driving factors of the aquatic environment quality are identified, the sub-basins are divided using the ArcSWAT tool, and the reservoir operation data is adjusted, mainly including the starting time of reservoir operation, the reservoir storage capacity and water surface area parameters corresponding to the normal spillway and the emergency spillway, the measured outflow data of the reservoir, etc., and the reservoir simulation is realized based on the measured daily outflow method, and the model is driven using rainfall and temperature data. Using the runoff data monitored at the hydrological station, the SUFI-2 algorithm of the SWAT-CUP software is used for hydrological parameter calibration, and the calibrated parameters are returned and input into SWAT for verification. At the same time, the calibration and verification performance of the SWAT hydrological model is evaluated using the time series comparison chart and the Nash coefficient (NSE) and determination coefficient (R2). In this embodiment, the measured runoff data of the Longchuan Station and the Boluo Station are used for calibration and verification. It is considered that when NSE > 0.6 and R2 > 0.5, the simulation effect of the hydrological model is good, and the coincidence degree between the peak flow and the low flow during the dry season of the simulated runoff data and the measured runoff data is compared. If the deviation degree is small, it is considered that the hydrological model can basically reflect the fluctuations of the high and low values of the flow.

[0068] S3. Construct a hydrodynamic model based on the measured runoff data, tidal level data, riverbed data, measured water level data, and river network connection node data;

[0069] Specifically, constructing a hydrodynamic model based on the measured runoff data, tidal level data, riverbed data, measured water level data, and river network connection node data includes:

[0070] Determine the river network boundary of the area to be identified according to the river network connection node data;

[0071] Set the downstream outflow boundary of the river network within the river network boundary according to the tidal level data;

[0072] Set the upstream inflow boundary of the river network within the river network boundary according to the measured runoff data;

[0073] Construct an initial hydrodynamic model according to the downstream outflow boundary of the river network, the upstream inflow boundary of the river network, and the riverbed data;

[0074] Conduct simulations based on the current hydrodynamic model to determine the simulated water level data; among them, the current hydrodynamic model at the first simulation is the initial hydrodynamic model;

[0075] Calculate the second Nash coefficient and the second coefficient of determination used to characterize the current hydrodynamic model according to the simulated water level data and the measured water level data;

[0076] Calibrate the parameters of the current hydrodynamic model according to the measured water level data and the simulated water level data until the second Nash coefficient is greater than the preset Nash coefficient threshold and the second coefficient of determination is greater than the preset coefficient of determination threshold to obtain the final hydrodynamic model.

[0077] In a preferred embodiment of the present invention, the second Nash coefficient represents the Nash coefficient of the hydrodynamic model; the second coefficient of determination represents the coefficient of determination of the hydrodynamic model. The hydrodynamic model is the Delft3D-Flow hydrodynamic model. Delft3D is a three-dimensional water environment numerical simulation software developed by Deltares in the Netherlands, and Delft3D-Flow is a part of it. Orthogonal curvilinear grids are used to solve the shallow water equations in the horizontal direction, and the alternating direction implicit method (ADI) is used to effectively solve the shallow water equations in the horizontal direction (2D). Delft3D-Flow runs by discretizing the area to be identified into a grid system, dividing the water body into smaller units, and calculating the water flow movement of each grid cell using spatial and temporal grids. The model is easy to operate and has good simulation effects. Therefore, in this embodiment, river network connection node data is obtained through high-resolution remote sensing images, the GRID module in Delft3D is used to create structured grids for the river network in the lower reaches of the Dongjiang River, and bathymetric data (sounding data) is used for interpolation of the riverbed elevation. The data comes from the ETOPO1 dataset (Global Topographic Elevation Dataset Version 1) and field measurement data of the delta channels. The outflow boundary of the river network is set based on tidal level data, and the inflow boundary of the upper reaches of the river network is set based on the measured daily runoff data of the Boluo Station. For the inflow ports lacking measured flow data, their inflow boundaries are set as constants. Finally, the measured water level data is used to calibrate the model parameters, and the Nash coefficient (NSE) and coefficient of determination (R2) are also used to evaluate the accuracy of the model. When NSE > 0.6 and R2 > 0.5, the simulation effect of the hydrodynamic model is considered good.

[0078] S4. Couple the hydrological model and the hydrodynamic model to obtain a coupled hydrological-hydrodynamic model and the hydrodynamic factors output by the coupled hydrological-hydrodynamic model;

[0079] Specifically, coupling the hydrological model and the hydrodynamic model to obtain a coupled hydrological-hydrodynamic model and the hydrodynamic factors output by the coupled hydrological-hydrodynamic model includes:

[0080] Taking the runoff simulation data output by the hydrological model as the boundary condition of the hydrodynamic model and inputting it into the hydrodynamic model for coupling to obtain a coupled hydrological-hydrodynamic model;

[0081] Predicting the hydrodynamic factors according to the coupled hydrological-hydrodynamic model.

[0082] In a preferred embodiment of the present invention, in order to evaluate the hydrological and hydrodynamic responses of a river network basin under different scenarios, the river network connection node data is interpreted based on high spatio-temporal resolution remote sensing images. In a loosely coupled manner, the runoff output results (runoff simulation data) of the sub-basins of the SWAT hydrological model are used as the boundary conditions for input into the Delft3D-Flow hydrodynamic model to predict hydrodynamic factors such as hydrology and hydrodynamics of the river network under the influence of multi-scenario combinations.

[0083] S5. Divide the area to be identified into a number of grid cells according to the hydrological and hydrodynamic coupling model;

[0084] In a preferred embodiment of the present invention, based on the hydrological and hydrodynamic coupling model, the area to be identified can be divided into a number of grid cells by using a hydrodynamic model.

[0085] S6. Conduct an orthogonal experiment under a number of preset influencing factors according to the grid area corresponding to each grid cell, the suitability index corresponding to the hydrodynamic factor, and the preset factor weight, and calculate the response index value of the area to be identified;

[0086] Specifically, conducting an orthogonal experiment under a number of preset influencing factors according to the grid area corresponding to each grid cell, the suitability index corresponding to the hydrodynamic factor, and the preset factor weight, and calculating the response index value of the area to be identified includes:

[0087] Calculate the habitat suitability of each grid cell according to the suitability index corresponding to the hydrodynamic factor and the preset factor weight;

[0088] Calculate the weighted suitable habitat area according to the number of grids corresponding to the grid cell, the grid area corresponding to each grid cell, and the habitat suitability;

[0089] Construct an orthogonal experiment table according to the preset influencing factors;

[0090] Conduct an orthogonal experiment according to the orthogonal experiment table, and use the mean value of the weighted suitable habitat area as the response index value of the area to be identified.

[0091] In a preferred embodiment of the present invention, the hydrodynamic factors include flow velocity and water depth; the Habitat Suitability Index (HSI) is an index used to evaluate the suitability of the habitat (area to be evaluated) of a species for the survival and reproduction of the species; the Weighted Suitable Habitat Area (WUA) is used to characterize the river network water ecological factors; the suitability index value corresponding to the hydrodynamic factor is 0 or 1; the preset factor weight represents the weight value corresponding to the hydrodynamic factor, and the greater the weight, the higher the influence degree of the factor, and the weight value is determined by the biological statistical method. The orthogonal experiment is to scientifically and reasonably match each level of each factor in the experiment with multiple factors and multiple levels, and while not affecting the experimental effect, minimize the number of experiments as much as possible to achieve the purpose of reducing the workload.

[0092] In a preferred embodiment of the present invention, in the orthogonal experimental design for analyzing the driving factors of the water ecological environment quality of the river network, first, reasonable influencing factors need to be selected according to the specific situation of the area to be identified. The river network in the lower reaches of the Dongjiang River is near the Pearl River Estuary. The sea level rise will greatly affect the hydrology and hydrodynamic conditions of the river network in the lower reaches of the Dongjiang River and trigger serious saltwater intrusion events, etc. At the same time, climate change has always been one of the hot issues of concern. The significant characteristics of climate change are, first, the increase in temperature, and second, the increase in extreme rainfall. The urban construction scale in the river network area in the lower reaches of the Dongjiang River is relatively large, further exacerbating the climate variability in this area. The intensity of water resources development and utilization in the Dongjiang River Basin is relatively large. There are three major reservoirs, namely Xinfengjiang Reservoir, Fengshuba Reservoir, and Baipenzhu Reservoir, in the basin, and the runoff is greatly affected by reservoir regulation. Therefore, rainfall, temperature, sea level rise, and reservoir construction are selected as the key factors of concern for the response analysis of the water ecological environment quality of the river network in the lower reaches of the Dongjiang River, that is, the preset influencing factors.

[0093] Schematically, the HSI is calculated as follows: Where: SI i is the suitability index of the i-th hydrodynamic factor with a value ranging from 0 to 1, 0 indicates unsuitable, and 1 indicates very suitable; ω i is the factor weight corresponding to the i-th hydrodynamic factor. The greater the weight, the higher the influence degree of the hydrodynamic factor. m is the number of hydrodynamic factors considered. In this embodiment, the suitability of the water depth and flow velocity changes for the target species is mainly considered, so m = 2. The Weighted Suitable Habitat Area (WUA) can be calculated by the following formula: In the formula, ΔA j is the grid area corresponding to the j-th grid cell; HSI j$HSI_j$ is the suitability index corresponding to the $j$-th grid cell, and $n$ is the number of divided grids. For each scenario, the simulated runoff is used to drive the Delft3D-Flow hydrodynamic model to calculate the WUA of the corresponding river suitable habitat quality. Based on the assumption that the larger the suitable area, the higher the species biomass, the potential impact of the basin runoff change process on the habitat of the target species in the river network can be evaluated.

[0094] Preferably, the hydrodynamic factors (flow velocity, water depth) output of the Delft3D-Flow hydrodynamic model in the downstream river network of the Dongliu Basin are used to couple and analyze the spatial distribution of the suitable habitat quality of the river network with a preset physical habitat model. The physical habitat model uses the weighted suitable habitat area (WUA) to characterize the hydro-ecological factors of the river network. Taking the hydrodynamic factors such as water level and flow velocity simulated by each grid cell of the hydrodynamic model as inputs to drive the physical habitat model, the habitat suitability (Habitat Suitability Index, HSI) of each grid cell can be obtained. Multiplying the HSI of each grid cell by the grid area can obtain the WUA of the suitable habitat quality of the river. Therefore, in this embodiment, an orthogonal test table is constructed according to the preset influencing factors; then an orthogonal test is carried out according to the orthogonal test table, and the mean value of the weighted suitable habitat area is used as the response index value of the area to be identified. The orthogonal test table is shown in Table 1:

[0095] Table 1

[0096]

[0097]

[0098] S7. According to the response index value and the change value corresponding to each influencing factor, calculate the variance ratio statistic of each influencing factor, and use the influencing factor with the variance ratio statistic greater than the preset significance level as the driving factor for the aquatic habitat quality of the area to be identified.

[0099] Specifically, calculating the variance ratio statistic of each influencing factor according to the response index value and the change value corresponding to each influencing factor includes:

[0100] When the change values corresponding to each influencing factor are the same, calculate the sum of squares of deviations and the sum of squares of errors of each influencing factor according to the response index value;

[0101] Calculate the factor degrees of freedom of each influencing factor according to the change value corresponding to each influencing factor;

[0102] Calculate the total degrees of freedom of the test according to the corresponding number of orthogonal tests, and determine the error degrees of freedom according to the total degrees of freedom of the test and the factor degrees of freedom;

[0103] Calculate the mean sum of squared deviations based on the sum of squared deviations of each influencing factor and the degrees of freedom of the factor; and calculate the mean sum of squared errors based on the sum of squared errors and the degrees of freedom of the error.

[0104] Obtain the variance ratio statistic of each influencing factor according to the ratio of the mean sum of squared deviations and the mean sum of squared errors.

[0105] Preferably, it further includes: evaluating the change in the aquatic habitat quality of the river network in the area to be identified according to the aquatic habitat quality driving factors.

[0106] By implementing this embodiment, according to the evaluation results, analyze the current situation of the aquatic habitat quality of the river network in the area to be identified, determine which driving factors have a greater impact on the aquatic habitat quality, and which areas have better or worse aquatic habitat quality. Compare the current evaluation results with historical data to analyze the change trend of the aquatic habitat quality of the river network over time, and judge whether it is improving, deteriorating or remaining stable. Identify the main driving factors and reasons for the change in the aquatic habitat quality, such as the change in hydrological characteristics due to the construction of water conservancy projects. Through technical means such as GIS, draw a spatial distribution map of the evaluation results of the aquatic habitat quality, analyze the spatial differences in the aquatic habitat quality in different areas of the river network, identify high-risk areas and key protection areas, and provide a basis for targeted management and protection measures. According to the evaluation results and analysis conclusions, put forward corresponding measures for the protection and restoration of the aquatic habitat quality of the river network for the existing problems. For example, for the problem of water pollution, put forward suggestions to strengthen sewage treatment and control the discharge of pollution sources; for the problem of damage to the riparian zone, suggest carrying out ecological restoration projects in the riparian zone to increase vegetation coverage. Develop a reasonable management strategy for the aquatic habitat of the river network, establish a long-term monitoring system, regularly monitor and evaluate the driving factors of the aquatic habitat quality, timely grasp the change dynamics of the aquatic habitat quality, provide a scientific basis for management decisions, and ensure the continuous improvement and stability of the aquatic habitat quality of the river network.

[0107] Specifically, the change value corresponding to each influencing factor is the change level of the preset influencing factor. As shown in Table 1, the change levels of rainfall are determined to be -25%, -10%, 0%, 10%, 25%; the change levels of temperature are -1.5°C, -0.5°C, 0°C, 0.5°C, 1.5°C, and the change levels of sea level are 0m, 0.2m, 0.4m, 0.6m and 1m; the change level of the reservoir is 0 (no reservoir) and 1 (with reservoir). Conduct a statistical analysis on the annual change rate of the historical measured data of rainfall and temperature stations, and at the same time consider the extreme development situation to determine the change levels of each factor. Based on this, construct an experimental plan according to the standard orthogonal experimental table, and use the method of pseudo-levels to arrange factors with fewer levels (such as reservoirs) to complete the orthogonal experimental design. In Table 1, E and F are empty columns used to calculate the error value caused by the experimental error; the response index value is the mean value of WUA corresponding to the change level of the factor simulated by the coupling model.

[0108] In a preferred embodiment of the present invention, first, the sum of squares of deviations and the sum of squares of errors of each influencing factor are calculated according to the response index values, and then the total sum of squares of deviations is calculated. The total sum of squares of deviations reflects the overall difference of the response index values, including the differences caused by different change values of the influencing factors and the differences caused by experimental errors. The larger it is, the greater the difference between the response index values, and the selected influencing factors are significant influencing factors for the aquatic habitat quality in this area. Among them, y i is the response index value of the i-th orthogonal experiment, and n is the corresponding number of orthogonal experiments. Taking rainfall as an example, if rainfall is arranged in the j-th column of the orthogonal experiment table, the sum of squares of deviations caused by rainfall is as follows: Among them, K i is the sum of the response index values of the experiments with the same level of rainfall in the j-th column, and r is the number of levels (change values) of rainfall. To calculate the sum of squares of errors of the experimental error, the design table generally leaves empty columns, called error columns. The sum of the sum of squares of deviations of all error columns is the sum of squares of errors of the error: is the mean square error. To calculate the mean sum of squares of deviations, the degrees of freedom of the influencing factors need to be calculated. The total degrees of freedom of the experiment is: f T = n - 1, the degrees of freedom of each influencing factor is: f j = r - 1, the degrees of freedom of the error is: f e = ∑f 空列 , then the mean sum of squares of deviations is calculated as follows:

[0109] , S 2 can be the sum of squares of deviations of the influencing factors, that is, the above f can be the degrees of freedom of each influencing factor, that is, the above f j , according to the ratio of the mean sum of squares of deviations and the mean square error, the variance ratio statistic of each influencing factor is obtained: When F > F 0.05 (n1, n2), it indicates that for the preset significance level α = 0.05, rainfall has a significant impact on the experimental results. The larger the F value, the greater the impact of the factor on the results. Therefore, by comparing the F values of all influencing factors, the influencing factor corresponding to the largest F value is the key driving factor of the aquatic habitat quality of the river network, that is, the key factor among the aquatic habitat quality driving factors. Among them, n1 is the degrees of freedom of rainfall, and n2 is the degrees of freedom of the error.

[0110] In a preferred embodiment of the present invention, methods such as hydrological-hydrodynamic coupling simulation, physical habitat model, and orthogonal experimental design are adopted to construct a coupled scenario of climate disturbance - water conservancy project construction under changing environments, drive the distributed hydrological model of the basin and predict the hydrological response of the basin under changing environments, establish a hydrological-hydrodynamic coupling model and a physical habitat model, realize the process simulation of "runoff - hydrodynamic - aquatic habitat quality", identify the driving factors of the aquatic habitat quality of urban river networks based on the orthogonal experimental design and variance analysis methods, and the research technical route is as Figure 2As shown in the figure. With the development of remote sensing technology, remote sensing-based aquatic habitat assessment methods have gradually been applied. Remote sensing can provide large-scale and high-frequency data acquisition, especially for dynamically monitoring the health status of water bodies in large-scale regions. Remote sensing data such as the Normalized Difference Vegetation Index (NDVI), surface heat, etc. can be used to evaluate the quality of habitats. For example, the Remote Sensing Ecological Index is constructed using the Normalized Difference Vegetation Index, humidity, surface heat, bare soil index, and built-up index to evaluate the ecological environment quality of the Poyang Lake Basin. However, due to limitations in resolution and revisit cycle, remote sensing technology is mostly used to evaluate the habitat quality at the macroscopic scale of the basin, and it is difficult to obtain continuous habitat quality assessment results. High-resolution images also have problems such as difficulty in data acquisition. In recent years, ecological models have gradually become effective tools for evaluating aquatic habitat quality. The models can not only handle complex ecological processes but also simulate the impacts of different environmental factors (such as hydrology, water quality, land use, etc.) on aquatic habitat quality. For example, the Physical Habitat Model calculates the adaptability of target species to habitat flow velocity, water depth, etc., links river channel flow with changes in habitat ecological processes, reflects the relationship between flow changes and river habitat quality responses, and combined with the hydro-hydraulic coupling model, can predict and evaluate the aquatic habitat quality under different climate change and human activity scenarios. The advantage of these methods is that they can comprehensively consider multiple factors and complex ecological interactions and provide more comprehensive assessment results. However, in the selection of hydro-hydraulic and ecological models, the applicability of the models in the study area and the simplicity of operation need to be considered. Most of the above methods' model frameworks and methods focus on the analysis of habitat quality driving factors at the basin scale. Therefore, the selected habitat quality evaluation indicators are based on the basin scale, and less attention is paid to the analysis of habitat quality driving factors at the meso-scale of river networks. Most habitat quality driving factor identification methods ignore the error terms that inevitably exist in habitat quality simulation and assessment. The events triggered by current climate change (such as floods, droughts, etc.) and human behaviors (such as water conservancy projects, etc.) have led to changes in the hydrological cycle of the river network system in the Dongjiang River Basin, causing changes in water volume and hydrological regime, etc., having a profound impact on the river network aquatic ecosystem, and may accumulate and deteriorate at certain times and locations, leading to the degradation and even extinction of the aquatic ecosystem function and a decline in ecosystem diversity. The essence of this series of problems is the cascading effect of climate change and human activities on the hydrological cycle and hydrodynamic processes on the aquatic ecosystem. The changing environment first leads to changes in hydrological and hydrodynamic parameters, imposing stress on biological individuals and populations, affecting their life history and ecological behaviors, and inducing a certain degree of adaptability. Similarly, at the ecosystem level, it is reflected as a certain degree of feedback of the ecosystem structure and function to external changing conditions.In this embodiment, a coupled hydrological and hydrodynamic model is constructed, combined with a physical habitat model. Based on orthogonal experimental design and coupled scenario schemes, the coupled impacts of climate change and human activities on the hydrological cycle and hydrodynamic processes of the basin-river network system, as well as the response process mechanism of the aquatic habitat quality, are analyzed. The error analysis method is applied to identify the driving factors of the aquatic habitat quality in the river network, providing a scientific basis for formulating effective water ecological security guarantee policies and adapting to and mitigating the impacts of climate change.

[0111] By implementing the present invention, for the first time, the methods of coupled hydrological and hydrodynamic simulation, physical habitat model, orthogonal experimental design and variance analysis are combined. By establishing the coupled scenarios of climate change and water conservancy project construction, the response of the aquatic habitat quality in the river network under changing environments is accurately simulated, and the driving factors of the river network habitat quality are identified. Traditional studies often focus on the impacts of single factors, while this embodiment comprehensively considers multiple influencing factors such as rainfall, temperature, sea-level rise and reservoir construction and their interactions, and comprehensively evaluates the changes in the aquatic habitat quality of urban river networks and the analysis of their driving factors. Through the combination of accurate coupled hydrological and hydrodynamic simulation and physical habitat model, it can provide a scientific basis for the evaluation of the aquatic habitat quality of urban river networks. At the same time, this embodiment has the characteristics of low cost and easy implementation. With the acceleration of the urbanization process, the aquatic habitat quality faces great challenges. Especially under the impacts of climate change and human activities (such as reservoir construction and urban expansion), the ecological security of urban river networks becomes more vulnerable. Therefore, identifying these key driving factors and revealing their potential impacts on the aquatic habitat quality in this embodiment has important practical significance for formulating precise ecological governance strategies, improving the water ecological environment quality and achieving sustainable development. With the intensification of global climate change, sea-level rise and urbanization process, the problem of the change in the aquatic habitat quality of current basins and river networks has become an urgent problem to be solved. This embodiment conducts long-term multi-scale continuous simulation analysis in combination with the coupled scenarios of factors such as climate change, sea-level rise and reservoir construction, which can provide timely decision-making support and strong data support for the management and optimization of the current aquatic habitat quality of urban river networks. This timely research can help relevant decision-makers cope with current and potential future ecological crises. The multi-level and comprehensive analysis method of the coupled hydrological and hydrodynamic model and physical habitat model adopted in this embodiment can effectively describe the interaction relationship between hydrology, climate and aquatic habitats. The orthogonal experimental design method helps to systematically screen out the key driving factors that have the greatest impact on the aquatic habitat quality in the river network from multiple factors and multiple levels, and conduct scientific evaluation through variance analysis, ensuring the comprehensive consideration of different influencing factors, forming a complete and tight systematic method framework for identifying the driving factors of the aquatic habitat quality of urban river networks, and also providing a reference method framework for the research on the aquatic habitat quality of other basins and urban areas.

[0112] By implementing this embodiment, the hydrological model and the hydrodynamic model are coupled to simulate the response of the river network aquatic habitat quality in the area to be identified under the change of influencing factors, obtain hydrodynamic factors, and simulate the effect of the influencing factors in the area to be identified on the hydrodynamic factors to obtain aquatic habitat quality driving factors, and propose the influence of multiple influencing factors on the area to be identified, comprehensively evaluate the aquatic habitat quality. Only by using the hydrological model, hydrodynamic model and hydro - dynamic coupling model constructed according to the measured data of the area to be identified, the aquatic habitat quality driving factors can be obtained, greatly reducing the dependence on field sampling, eliminating the need for long - term on - site monitoring, being unrestricted by space and time, enabling the identification of aquatic habitat quality driving factors at any location and any time, reducing the cost of long - term on - site monitoring, and at the same time being able to obtain more comprehensive aquatic habitat quality driving factors in a shorter time, improving the efficiency of identification.

[0113] See Figure 3 , which is a schematic structural diagram of a device for identifying aquatic habitat quality driving factors in an urban river network provided by an embodiment of the present invention, includes:

[0114] A data acquisition module, configured to acquire rainfall data, temperature data, elevation data set, land use data, soil data, measured runoff data, tide level data, riverbed data, measured water level data, and river network connection node data of the area to be identified;

[0115] A hydrological model construction module, configured to construct a hydrological model according to the rainfall data, temperature data, elevation data set, land use data, soil data, and measured runoff data;

[0116] A hydrodynamic model construction module, configured to construct a hydrodynamic model according to the measured runoff data, tide level data, riverbed data, measured water level data, and river network connection node data;

[0117] A model coupling module, configured to couple the hydrological model and the hydrodynamic model to obtain a hydro - dynamic coupling model and the hydrodynamic factors output by the hydro - dynamic coupling model;

[0118] A regional grid division module, configured to divide the area to be identified into several grid units according to the hydro - dynamic coupling model;

[0119] An influencing factor orthogonal test module, configured to perform an orthogonal test under several preset influencing factors according to the grid area corresponding to each grid unit, the suitability index corresponding to the hydrodynamic factors, and a preset factor weight, and calculate the response index value of the area to be identified;

[0120] A quality driving factor identification module, configured to calculate the variance ratio statistic of each influencing factor according to the response index value and the change value corresponding to each influencing factor, and use the influencing factor with the variance ratio statistic greater than the preset significance level as the aquatic habitat quality driving factor of the area to be identified.

[0121] The present invention provides an identification device for aquatic habitat quality driving factors in an urban river network. According to the data acquisition module, rainfall data, temperature data, elevation data set, land use data, soil data, measured runoff data, tide level data, riverbed data, measured water level data, and river network connection node data of the area to be identified are acquired; in the hydrological model construction module, a hydrological model is constructed according to the rainfall data, temperature data, elevation data set, land use data, soil data, and measured runoff data; in the hydrodynamic model construction module, a hydrodynamic model is constructed according to the measured runoff data, tide level data, riverbed data, measured water level data, and river network connection node data; in the model coupling module, the hydrological model and the hydrodynamic model are coupled to obtain a coupled hydrological-hydrodynamic model and the hydrodynamic factors output by the coupled hydrological-hydrodynamic model; then in the regional grid division module, the area to be identified is divided into several grid cells according to the coupled hydrological-hydrodynamic model; then in the influencing factor orthogonal experiment module, orthogonal experiments are carried out under several preset influencing factors according to the grid area corresponding to each grid cell, the suitability index corresponding to the hydrodynamic factors, and the preset factor weights, and the response index value of the area to be identified is calculated; finally, in the quality driving factor identification module, the variance ratio statistic of each influencing factor is calculated according to the response index value and the change value corresponding to each influencing factor, and the influencing factor with the variance ratio statistic greater than the preset significance level is used as the aquatic habitat quality driving factor of the area to be identified. By coupling the hydrological model and the hydrodynamic model, the response of the river network aquatic habitat quality in the area to be identified under the change of influencing factors is simulated to obtain hydrodynamic factors, and the effect of the influencing factors in the area to be identified on the hydrodynamic factors is simulated to obtain the aquatic habitat quality driving factors, and the influence of multiple influencing factors on the area to be identified is proposed to comprehensively evaluate the aquatic habitat quality. Only by constructing a hydrological model, a hydrodynamic model, and a coupled hydrological-hydrodynamic model based on the measured data of the area to be identified can the aquatic habitat quality driving factors be obtained, greatly reducing the dependence on field sampling, not requiring long-term on-site monitoring, not being restricted by space and time, being able to identify the aquatic habitat quality driving factors at any location and at any time, reducing the cost of long-term on-site monitoring, and at the same time being able to obtain more comprehensive aquatic habitat quality driving factors in a shorter time, improving the efficiency of identification.

[0122] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationships between the modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement without creative efforts.

[0123] Those skilled in the art can clearly understand that for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the foregoing method embodiment, and will not be elaborated here.

[0124] Another embodiment of the present invention further provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a method for identifying driving factors of the aquatic habitat quality of an urban river network as described in the above embodiment. The terminal device can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device may include, but is not limited to, a processor and a memory.

[0125] The processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the terminal device, and connects various parts of the entire terminal device through various interfaces and lines.

[0126] The memory can be used to store the computer program. By running or executing the computer program stored in the memory and invoking the data stored in the memory, the processor realizes various functions of the terminal device. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include high-speed random access memory, and can also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one magnetic disk storage device, flash memory device or other volatile solid-state storage devices.

[0127] Another embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the method for identifying driving factors of the aquatic habitat quality of an urban river network described in the above embodiment.

[0128] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above various method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0129] The above is the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements are also regarded as the protection scope of the present invention.

Claims

1. A method for identifying the driving factors of the aquatic habitat quality of an urban river network, characterized in that, Including: Obtaining rainfall data, temperature data, elevation dataset, land use data, soil data, measured runoff data, tide level data, riverbed data, measured water level data, and river network connection node data of the area to be identified; Constructing a hydrological model based on the rainfall data, temperature data, elevation dataset, land use data, soil data, and measured runoff data; Constructing a hydrodynamic model based on the measured runoff data, tide level data, riverbed data, measured water level data, and river network connection node data; Coupling the hydrological model and the hydrodynamic model to obtain a coupled hydrological-hydrodynamic model and hydrodynamic factors output by the coupled hydrological-hydrodynamic model; Dividing the area to be identified into several grid cells according to the coupled hydrological-hydrodynamic model; Conducting an orthogonal experiment under several preset influencing factors according to the grid area corresponding to each grid cell, the suitability index corresponding to the hydrodynamic factors, and a preset factor weight, and calculating the response index value of the area to be identified; Calculating the variance ratio statistic of each influencing factor according to the response index value and the change value corresponding to each influencing factor, and taking the influencing factors with the variance ratio statistic greater than the preset significance level as the driving factors for the aquatic habitat quality of the area to be identified.

2. The method for identifying the driving factors of the aquatic habitat quality of an urban river network according to claim 1, wherein, Constructing a hydrological model based on the rainfall data, temperature data, elevation dataset, land use data, soil data, and measured runoff data, including: Constructing an initial hydrological model according to the rainfall data, temperature data, elevation dataset, land use data, and soil data; Simulating according to the current hydrological model to determine the runoff simulation data at different time series; where the current hydrological model at the first simulation is the initial hydrological model; Calculating a first Nash coefficient and a first determination coefficient for characterizing the current hydrological model according to the measured runoff data and the runoff simulation data; Obtaining the final hydrological model when the first Nash coefficient is greater than the preset Nash coefficient threshold and the first determination coefficient is greater than the preset determination coefficient threshold; Otherwise, parameter calibration is performed according to the measured runoff data and the runoff simulation data to obtain calibrated parameters; and the current hydrological model is updated according to the calibrated parameters.

3. The method for identifying the driving factors of the aquatic habitat quality of an urban river network according to claim 2, wherein, Constructing a hydrodynamic model according to the measured runoff data, tide level data, riverbed data, measured water level data, and river network connection node data, including: Determining the river network boundary of the area to be identified according to the river network connection node data; Setting the downstream outflow boundary of the river network according to the tide level data within the river network boundary; Setting the upstream inflow boundary of the river network according to the measured runoff data within the river network boundary; Constructing an initial hydrodynamic model according to the downstream outflow boundary of the river network, the upstream inflow boundary of the river network, and the riverbed data; Simulating according to the current hydrodynamic model to determine the water level simulation data; where the current hydrodynamic model at the first simulation is the initial hydrodynamic model; Calculating a second Nash coefficient and a second determination coefficient for characterizing the current hydrodynamic model according to the water level simulation data and the measured water level data; Calibrate the parameters of the current hydrodynamic model according to the measured water level data and the simulated water level data until the second Nash coefficient is greater than the preset Nash coefficient threshold and the second coefficient of determination is greater than the preset coefficient of determination threshold, to obtain the final hydrodynamic model.

4. The method for identifying the driving factors of the aquatic habitat quality of an urban river network according to claim 3, characterized in that, Couple the hydrological model and the hydrodynamic model to obtain a hydrological-hydrodynamic coupling model and the hydrodynamic factors output by the hydrological-hydrodynamic coupling model, including: Use the runoff simulation data output by the hydrological model as the boundary condition of the hydrodynamic model, and input it into the hydrodynamic model for coupling to obtain a hydrological-hydrodynamic coupling model; Predict the hydrodynamic factors according to the hydrological-hydrodynamic coupling model.

5. The method for identifying driving factors of the aquatic habitat quality of an urban river network according to claim 1, characterized in that, Conduct an orthogonal experiment under several preset influencing factors according to the grid area corresponding to each grid cell, the suitability index corresponding to the hydrodynamic factor, and the preset factor weight, and calculate the response index value of the area to be identified, including: Calculate the habitat suitability of each grid cell according to the suitability index corresponding to the hydrodynamic factor and the preset factor weight; Calculate the weighted suitable habitat area according to the number of grids corresponding to the grid cell, the grid area corresponding to each grid cell, and the habitat suitability; Construct an orthogonal experiment table according to the preset influencing factors; Conduct an orthogonal experiment according to the orthogonal experiment table, and use the mean value of the weighted suitable habitat area as the response index value of the area to be identified.

6. The method for identifying the driving factors of the aquatic habitat quality of an urban river network according to claim 1, wherein, Calculate the variance ratio statistic of each influencing factor according to the response index value and the change value corresponding to each influencing factor, including: Under the condition that the change values corresponding to each influencing factor are the same, calculate the sum of squared deviations and the sum of squared errors of each influencing factor according to the response index value; Calculate the factor degrees of freedom of each influencing factor according to the change value corresponding to each influencing factor; Calculate the total degrees of freedom of the experiment according to the corresponding number of orthogonal experiments, and determine the error degrees of freedom according to the total degrees of freedom of the experiment and the factor degrees of freedom; Calculate the mean sum of squared deviations according to the sum of squared deviations of each influencing factor and the factor degrees of freedom; and calculate the mean sum of squared errors according to the sum of squared errors and the error degrees of freedom; Obtain the variance ratio statistic of each influencing factor according to the ratio of the mean sum of squared deviations and the mean sum of squared errors.

7. A method for identifying driving factors of the aquatic habitat quality of an urban river network according to claim 1, characterized in that, It also includes: Evaluate the change in the aquatic habitat quality of the river network in the area to be identified according to the aquatic habitat quality driving factor.

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