A method for identifying driving factors of water habitat quality of urban river network

Abstract

The application discloses a kind of urban river network aquatic habitat quality driving factor identification methods, method includes: according to the rainfall data, air temperature data, elevation data set, land use data, soil data, runoff measured data, tidal data, riverbed data, water level measured data and river network connection node data are constructed hydrological model and hydrodynamic model;According to hydrological model and hydrodynamic model obtain hydrological hydrodynamic coupling model and hydrodynamic factor;According to the area of each grid unit of the region to be identified, the suitability index of hydrodynamic factor and the preset factor weight, the response index value is obtained by orthogonal test under several influence factors;According to the variance ratio statistics corresponding to the change value of response index value and influence factor, the influence factor with variance ratio statistics greater than the preset significance level is used as the aquatic habitat quality driving factor of the region to be identified. By implementing the application, the identification efficiency is improved while the cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of habitat quality assessment, and particularly relates to a method for identifying driving factors of water habitat quality of urban river network. BACKGROUND

[0002] Habitat is the physical, chemical and biological environment for the survival of organisms in a region, and the quality of the habitat determines whether the ecosystem can guarantee the survival and reproduction of individual organisms or communities. At present, water habitat quality assessment techniques have developed a variety of methods, covering a wide range of fields from physical-chemical environment assessment to ecological model application and different spatial scales. Traditional water habitat quality assessment methods usually rely on field survey data, and site layout and sampling analysis are carried out for the study area, mainly including hydrological factor monitoring, water quality monitoring, human activity intensity in the river, river bank stability, river bank vegetation coverage, ecological community investigation and biological indicator species analysis. The field survey method obtains intuitive and easy-to-understand data, but is constrained by space and time, and the field survey needs to spend a high cost and is low in efficiency. SUMMARY

[0003] The embodiment of the present application provides a method for identifying driving factors of water habitat quality of urban river network, which can effectively solve the problem of high cost and low efficiency of identifying driving factors of water habitat quality due to the space and time constraints of the prior art.

[0004] An embodiment of the present application provides a method for identifying driving factors of water habitat quality of urban river network, comprising:

[0005] obtaining rainfall data, air temperature data, elevation data set, land use data, soil data, runoff measured data, tidal level data, riverbed data, water level measured data and river network connected node data of a to-be-identified region;

[0006] constructing a hydrological model according to the rainfall data, the air temperature data, the elevation data set, the land use data, the soil data and the runoff measured data;

[0007] constructing a hydrodynamic model according to the runoff measured data, the tidal level data, the riverbed data, the water level measured data and the river network connected node data;

[0008] coupling the hydrological model and the hydrodynamic model to obtain a hydrological-hydrodynamic coupling model and a hydrodynamic factor output by the hydrological-hydrodynamic coupling model;

[0009] dividing the to-be-identified region into a plurality of grid units according to the hydrological-hydrodynamic coupling model;

[0010] According to the grid area corresponding to each grid unit, the suitability index corresponding to the water dynamic factor, and a preset factor weight, orthogonal tests are performed under several preset influencing factors to calculate a response index value of the to-be-identified region.

[0011] According to the response index value and a change value corresponding to each influencing factor, a variance ratio statistic of each influencing factor is calculated, and an influencing factor with a variance ratio statistic greater than a preset significance level is taken as a water habitat quality driving factor of the to-be-identified region.

[0012] Further, a hydrological model is constructed according to the rainfall data, the air temperature data, the elevation data set, the land use data, the soil data, and the runoff measured data, including:

[0013] An initial hydrological model is constructed according to the rainfall data, the air temperature data, the elevation data set, the land use data, and the soil data.

[0014] Simulation is performed according to the current hydrological model to determine runoff simulation data at different time sequences; the current hydrological model at the first simulation is the initial hydrological model.

[0015] A first Nash coefficient and a first determination coefficient for characterizing the current hydrological model are calculated according to the runoff measured data and the runoff simulation data.

[0016] In a case where 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.

[0017] Otherwise, parameter calibration is performed according to the runoff measured data and the runoff simulation data to obtain a calibrated parameter, and the current hydrological model is updated according to the calibrated parameter.

[0018] Further, a hydrodynamic model is constructed according to the runoff measured data, the tidal level data, the riverbed data, the water level measured data, and the river network connected node data, including:

[0019] A river network boundary of the to-be-identified region is determined according to the river network connected node data.

[0020] A river network downstream outflow boundary is set according to the tidal level data within the river network boundary.

[0021] A river network upstream inflow boundary is set according to the runoff measured data within the river network boundary.

[0022] An initial hydrodynamic model is constructed according to the river network downstream outflow boundary, the river network upstream inflow boundary, and the riverbed data.

[0023] According to the current water dynamic model, water level simulation data is determined; wherein, the current water dynamic model in the first simulation is an initial water dynamic model;

[0024] According to the water level simulation data and the water level measured data, a second Nash coefficient and a second determination coefficient for representing the current water dynamic model are calculated;

[0025] According to the water level measured data and the water level simulation data, the current water dynamic model is parameterized until the second Nash coefficient is greater than a preset Nash coefficient threshold and the second determination coefficient is greater than a preset determination coefficient threshold, and a final water dynamic model is obtained.

[0026] Further, the hydrological model and the water dynamic model are coupled to obtain a hydrological water dynamic coupling model and a water dynamic factor output by the hydrological water dynamic coupling model, including:

[0027] The runoff simulation data output by the hydrological model is taken as a boundary condition of the water dynamic model and is input into the water dynamic model for coupling to obtain a hydrological water dynamic coupling model;

[0028] The water dynamic factor is predicted according to the hydrological water dynamic coupling model.

[0029] Further, according to the grid area corresponding to each grid cell, the suitability index corresponding to the water dynamic factor and a preset factor weight, an orthogonal test is performed under a plurality of preset influencing factors to calculate a response index value of the to-be-identified region, including:

[0030] According to the suitability index corresponding to the water dynamic factor and a preset factor weight, the habitat suitability of each grid cell is calculated;

[0031] According to the grid number corresponding to the grid cell, the grid area corresponding to each grid cell and the habitat suitability, a weighted suitable habitat area is calculated;

[0032] An orthogonal test table is constructed according to the preset influencing factors;

[0033] According to the orthogonal test table, an orthogonal test is performed, and the mean value of the weighted suitable habitat area is taken as the response index value of the to-be-identified region.

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

[0035] In the case that the change value corresponding to each influencing factor is the same, the squared deviation sum and the error sum of squares of each influencing factor are calculated according to the response index value;

[0036] According to the change value corresponding to each influence factor, the factor freedom degree of each influence factor is calculated;

[0037] According to the corresponding orthogonal test number, the total freedom degree of the test is calculated, and according to the total freedom degree of the test and the factor freedom degree, the error freedom degree is determined;

[0038] According to the dispersion square of each influence factor and the factor freedom degree, the average dispersion square sum is calculated, and according to the error square sum and the error freedom degree, the average error square sum is calculated;

[0039] According to the ratio of the average dispersion square sum and the average error square sum, the variance ratio statistic of each influence factor is obtained.

[0040] Further, it also includes: according to the water habitat quality driving factor, the river network water habitat quality change of the to-be-identified region is evaluated.

[0041] As an improvement of the above-mentioned scheme, another embodiment of the present application correspondingly provides a river network water habitat quality driving factor identification device, comprising:

[0042] A data acquisition module is configured to acquire rainfall data, air temperature data, elevation data set, land use data, soil data, runoff measured data, tidal level data, riverbed data, water level measured data, and river network connected node data of a to-be-identified region.

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

[0044] A hydrodynamic model construction module is configured to construct a hydrodynamic model according to the runoff measured data, tidal level data, riverbed data, water level measured data, and river network connected node data.

[0045] A model coupling module is configured to couple the hydrological model and the hydrodynamic model to obtain a hydrological hydrodynamic coupling model and a hydrodynamic factor output by the hydrological hydrodynamic coupling model.

[0046] A regional grid division module is configured to divide the to-be-identified region into a plurality of grid units according to the hydrological hydrodynamic coupling model.

[0047] An influence factor orthogonal test module is configured to perform orthogonal test under a plurality of preset influence factors according to the grid area corresponding to each grid unit, the suitability index corresponding to the hydrodynamic factor, and a preset factor weight, to calculate a response index value of the to-be-identified region.

[0048] A quality driving factor identification module is configured to calculate a variance ratio statistic of each influencing factor according to the response index value and the change value corresponding to each influencing factor, and identify the influencing factor with a variance ratio statistic greater than a preset significance level as a water environment quality driving factor of the to-be-identified region.

[0049] Another embodiment of the present application provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the method for identifying a water environment quality driving factor of an urban river network as described in the above embodiments when executing the computer program.

[0050] Another embodiment of the present application provides a computer readable storage medium comprising a stored computer program, wherein the computer readable storage medium controls a device in which the computer readable storage medium is located to execute the method for identifying a water environment quality driving factor of an urban river network as described in the above embodiments when the computer program is running.

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

[0052] The application provides a kind of urban river network aquatic habitat quality driving factor identification method, its method can obtain rainfall data, air temperature data, elevation data set, land use data, soil data, runoff measured data, tidal data, riverbed data, water level measured data and river network connected node data of the region to be identified;According to the rainfall data, air temperature data, elevation data set, land use data, soil data and runoff measured data, a hydrological model is constructed;According to the runoff measured data, tidal data, riverbed data, water level measured data and river network connected node data, a hydrodynamic model is constructed;Coupling is carried out according to the hydrological model and the hydrodynamic model, and a hydrological hydrodynamic coupling model and a hydrodynamic factor output by the hydrological hydrodynamic coupling model are obtained;The hydrological hydrodynamic coupling model is used to divide the region to be identified into several grid units;According to the grid area corresponding to each grid unit, the suitability index corresponding to the hydrodynamic factor and the preset factor weight, orthogonal test is carried out under several preset influencing factors, and the response index value of the region to be identified is calculated;According to the response index value and the change value corresponding to each influencing factor, the variance ratio statistic of each influencing factor is calculated, and the influencing factor with the variance ratio statistic greater than the preset significance level is taken as the aquatic habitat quality driving factor of the region to be identified.Coupling is carried out between the hydrological model and the hydrodynamic model, the river network aquatic habitat quality response of the region to be identified under the influence of the influencing factor is simulated, the hydrodynamic factor is obtained, and the influence of the influencing factor of the region to be identified on the hydrodynamic factor is simulated, the aquatic habitat quality driving factor is obtained, the influence of multiple influencing factors on the region to be identified is proposed, and the aquatic habitat quality is comprehensively evaluated.Only the hydrological model, the hydrodynamic model and the hydrological hydrodynamic coupling model constructed according to the measured data of the region to be identified can obtain the aquatic habitat quality driving factor, greatly reducing the dependence on field sampling, without long-term on-site monitoring, without space and time constraints, and the aquatic habitat quality driving factor can be identified at any place and at any time, reducing the cost of long-term on-site monitoring, and the more comprehensive aquatic habitat quality driving factor can be obtained in a shorter time, improving the identification efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 is a flowchart of a kind of urban river network aquatic habitat quality driving factor identification method provided by an embodiment of the application;

[0054] Figure 2 is a technical line diagram of a kind of urban river network aquatic habitat quality driving factor identification method provided by an embodiment of the application;

[0055] Figure 3 is a structural schematic diagram of a kind of urban river network aquatic habitat quality driving factor identification device provided by an embodiment of the application. DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0057] Referring to Figure 1 is a flowchart of a method for identifying a driving factor of water habitat quality of an urban river network according to an embodiment of the present application, comprising:

[0058] S1, obtaining rainfall data, air temperature data, an elevation data set, land use data, soil data, runoff measured data, tidal level data, riverbed data, water level measured data, and river network connected node data of a region to be identified;

[0059] Specifically, the elevation data set is DEM (Digital Elevation Model) data, which is obtained from an SRTM DEM (Shuttle Radar Topography Mission Digital Elevation Model) 90m resolution (the length of the ground area represented by each data point is 90 meters) original elevation data set; the land use data is a land use type, which is obtained from a CGLSLC100 land use product (Coburn Global Land Service 100-meter resolution land cover product); the soil data is a soil type, which is obtained from a HWSD (World Soil Database); and the river network connected node data is obtained according to high-resolution remote sensing images. The time series of the remaining meteorological and hydrological data are the same.

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

[0061] Specifically, the hydrological model is constructed according to the rainfall data, the air temperature data, the elevation data set, the land use data, the soil data, and the runoff measured data, comprising:

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

[0063] determining runoff simulation data at different time series according to simulation of the current hydrological model; wherein the current hydrological model at the first simulation is the initial hydrological model;

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

[0065] if 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 according to the runoff measured data and the runoff simulation data to obtain a calibrated parameter, and the current hydrological model is updated according to the calibrated parameter.

[0067] In a preferred embodiment of the present application, the runoff measured data is obtained by monitoring a hydrological station; the first Nash coefficient represents a Nash coefficient (NSE) of the current hydrological model; and the first determination coefficient represents a determination coefficient (R2) of the current hydrological model. An initial hydrological model is constructed by using an ArcSWAT tool according to the rainfall data, the temperature data, an elevation data set, land use data and soil data; then the model is driven by using the rainfall data and the temperature data, and the runoff simulation data in different time sequences is determined by simulation according to the current hydrological model; the first Nash coefficient and the first determination coefficient for representing the current hydrological model are calculated according to the runoff measured data and the runoff simulation data; if 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 according to the runoff measured data and the runoff simulation data to obtain a calibrated parameter, and the current hydrological model is updated according to the calibrated parameter. The hydrological model is a SWAT hydrological model, which can well simulate hydrological cycle, runoff generation and confluence, reservoir scheduling and other processes, and has good applicability in the Dongjiang River Basin. In this embodiment, the driving factors for water environment quality in the Dongjiang River Basin are identified, the sub-basins are divided by using the ArcSWAT tool, and the reservoir operation data is adjusted, mainly including the reservoir operation start time, the reservoir capacity and water surface area parameters corresponding to the normal spillway and the very spillway, and the measured outflow data of the reservoir, etc. The reservoir simulation is realized based on the measured daily outflow method, and the model is driven by using the rainfall and temperature data. The runoff data monitored by the hydrological station is used to calibrate the hydrological parameters by using the SUFI-2 algorithm of the SWAT-CUP software, and the calibrated parameters are returned to the input of the SWAT for verification. At the same time, the performance of the calibration and verification of the SWAT hydrological model is evaluated by using the time sequence comparison chart and the Nash coefficient (NSE) and the determination coefficient (R2). In this embodiment, the runoff measured data of the Longchuan Station and the Boluo Station is used for calibration and verification, and it is considered that the simulation effect of the hydrological model is good when NSE>0.6 and R2>0.5. The coincidence degree of the flood peak flow and the low flow in the dry season between the runoff simulation data and the runoff measured data is compared, and if the deviation is small, it is considered that the hydrological model can basically reflect the fluctuation of the high value and the low value of the flow.

[0068] S3, constructing a hydrodynamic model according to the runoff measured data, the tidal level data, the riverbed data, the water level measured data, and the river network connected node data;

[0069] Specifically, constructing a hydrodynamic model according to the runoff measured data, the tidal level data, the riverbed data, the water level measured data, and the river network connected node data comprises:

[0070] determining a river network boundary of a to-be-identified area according to the river network connected node data;

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

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

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

[0074] performing simulation according to a current hydrodynamic model to determine water level simulation data; wherein the current hydrodynamic model at the first simulation is the initial hydrodynamic model;

[0075] calculating a second Nash coefficient and a second determination coefficient for representing the current hydrodynamic model according to the water level simulation data and the water level measured data;

[0076] performing parameter calibration on the current hydrodynamic model according to the water level measured data and the water level simulation data until the second Nash coefficient is greater than a preset Nash coefficient threshold and the second determination coefficient is greater than a preset determination coefficient threshold, to obtain a final hydrodynamic model.

[0077] In a preferred embodiment of the present application, the second Nash coefficient represents a Nash coefficient of a hydrodynamic model; and the second determination coefficient represents a determination coefficient of the hydrodynamic model. The hydrodynamic model is a Delft3D-Flow hydrodynamic model, Delft3D is a three-dimensional water environment numerical simulation software developed by Deltares Company in the Netherlands, and Delft3D-Flow is a part of Delft3D. The shallow water equation in the horizontal direction is solved by using the orthogonal curvilinear grid, and the alternating direction implicit method (ADI) is used to effectively solve the shallow water equation in the horizontal direction (2D). Delft3D-Flow runs by discretizing the to-be-identified area into a grid system, dividing the water body into smaller units, and calculating the flow movement of each grid unit by using spatial and temporal grids. The model is easy to operate and has good simulation effect, so in this embodiment, the river network connected node data is obtained by using high-resolution remote sensing images, the GRID module in Delft3D is used to create a structured grid of the river network in the lower reaches of the Dongjiang River, and the riverbed elevation is interpolated based on bathymetric data (depth measurement data), which is obtained from the ETOPO1 dataset (global terrain elevation dataset version 1) and the field measurement data of the delta river channel. The outflow boundary of the river network downstream is set based on the tidal level data, the inflow boundary of the river network upstream is set based on the daily runoff measurement data of the Boluo station, and the inflow boundary of the inflow port lacking measured flow data is set as a constant. Finally, the water level measurement data is used to calibrate the model parameters, and the Nash coefficient (NSE) and the determination coefficient (R2) are also used to evaluate the accuracy of the model. When NSE>0.6 and R2>0.5, it is considered that the simulation effect of the hydrodynamic model is good.

[0078] S4, coupling according to the hydrological model and the hydrodynamic model to obtain a hydrological hydrodynamic coupling model and a hydrodynamic factor output by the hydrological hydrodynamic coupling model;

[0079] Specifically, coupling according to the hydrological model and the hydrodynamic model to obtain a hydrological hydrodynamic coupling model and a hydrodynamic factor output by the hydrological hydrodynamic coupling model, comprising:

[0080] inputting the runoff simulation data output by the hydrological model as a boundary condition of the hydrodynamic model into the hydrodynamic model to obtain a hydrological hydrodynamic coupling model;

[0081] predicting a hydrodynamic factor according to the hydrological hydrodynamic coupling model.

[0082] In a preferred embodiment of the present application, in order to evaluate the hydrological and hydrodynamic responses of river network basins under different scenarios, the river network connection node data is interpreted based on high spatiotemporal resolution remote sensing images, the runoff output results (runoff simulation data) of the sub-basins of the SWAT hydrological model are input as the boundary conditions of the Delft3D-Flow hydrodynamic model in a loose coupling manner, and the hydrodynamic factors such as river network hydrology and hydrodynamics under the influence of multiple scenario combinations are predicted.

[0083] S5, dividing the to-be-identified region into a plurality of grid cells according to the hydrological and hydrodynamic coupling model;

[0084] In a preferred embodiment of the present application, the to-be-identified region can be divided into a plurality of grid cells by using the hydrodynamic model on the basis of the hydrological and hydrodynamic coupling model.

[0085] S6, calculating the response index value of the to-be-identified region under a plurality 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 performing orthogonal test.

[0086] Specifically, the response index value of the to-be-identified region is calculated by performing orthogonal test under a plurality 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, including:

[0087] According to the suitability index corresponding to the hydrodynamic factor and the preset factor weight, the habitat suitability of each grid cell is calculated.

[0088] According to the grid number corresponding to the grid cell, the grid area corresponding to each grid cell, and the habitat suitability, the weighted suitable habitat area is calculated.

[0089] An orthogonal test table is constructed according to the preset influencing factors.

[0090] According to the orthogonal test table, orthogonal test is performed, and the mean value of the weighted suitable habitat area is taken as the response index value of the to-be-identified region.

[0091] In a preferred embodiment of the present application, the hydrodynamic factor includes flow velocity and water depth; the habitat suitability index (HSI) is an index for evaluating the degree of suitability of a habitat (the region to be evaluated) for a species to survive and reproduce; the weighted suitable habitat area (WUA) is used to represent the river network water ecological factor; the suitability index corresponding to the hydrodynamic factor is valued as 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 a biological statistical method. The orthogonal experiment is to scientifically and reasonably match each level of each factor in the multi-factor and multi-level test, to reduce the test times as much as possible while not affecting the test effect, so as to reduce the workload.

[0092] In a preferred embodiment of the present application, in the orthogonal test design of the river network water habitat quality driving factor analysis, first, reasonable influencing factors are selected according to the specific circumstances of the region to be identified. The river network in the lower reaches of the Dongjiang River is near the Pearl River estuary, and the sea level rise will greatly affect the hydrological and hydrodynamic conditions of the river network in the lower reaches of the Dongjiang River, and will cause serious salt tide intrusion events. At the same time, climate change has always been one of the hot issues of concern, and the significant features of climate change are temperature rise and extreme rainfall increase. The urban construction scale in the river network area of the lower reaches of the Dongjiang River is large, which further aggravates the climate variability in the area. The water resources development and utilization intensity in the Dongjiang River basin is large, and there are three large reservoirs, Xinfengjiang Reservoir, Fengshuba Reservoir and Baipenzhu Reservoir, in the basin, and the runoff is greatly affected by reservoir regulation. Therefore, rainfall, air temperature, sea level rise and reservoir construction are selected as the key factors for response analysis of the water habitat quality of the river network in the lower reaches of the Dongjiang River, that is, the preset influencing factors.

[0093] Illustratively, the HSI is calculated as follows: wherein: SI i is the suitability index of the ith hydrodynamic factor with a value of 0-1, 0 indicating unsuitability and 1 indicating very suitability; ω i is the factor weight corresponding to the ith hydrodynamic factor, and the greater the weight, the higher the influence degree of the hydrodynamic factor, and m is the number of considered hydrodynamic factors, in this embodiment, the influence of water depth and flow velocity change on the suitability of the target species is mainly considered, so m=2. The weighted suitable habitat area (WUA) can be calculated by the following formula: wherein, ΔA j is the grid area corresponding to the jth grid unit; HSI jLet HSI be the suitability index corresponding to the j-th grid cell, and n be the number of grid cells. For each scenario, the WUA of the corresponding river suitable habitat is calculated using the simulated runoff-driven Delft3D-Flow hydrodynamic model. Based on the assumption that the larger the suitable area, the higher the species biomass, the potential impact of watershed runoff changes on the habitats of target species in the river network can be assessed.

[0094] Preferably, the hydrodynamic factors (flow velocity, water depth) output of the Delft3D-Flow hydrodynamic model of the downstream river network in the Dongliu River Basin are coupled with a preset physical habitat model to analyze the spatial distribution of suitable habitat quality in the river network. The physical habitat model uses the weighted suitable habitat area (WUA) to characterize the river network's aquatic ecological factors. The water level, flow velocity, and other hydrodynamic factors simulated in each grid cell of the hydrodynamic model are used as inputs to drive the physical habitat model, thus obtaining the Habitat Suitability Index (HSI) for each grid cell. Multiplying the HSI of each grid cell by the grid area yields the WUA of the river's suitable habitat quality. Therefore, in this embodiment, an orthogonal experimental table is constructed based on preset influencing factors; then, orthogonal experiments are conducted according to the orthogonal experimental table, and the mean of the weighted suitable habitat area is used as the response index value for the area to be identified. The orthogonal experimental table is shown in Table 1.

[0095] Table 1

[0096]

[0097]

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

[0099] Specifically, based on the response index value and the corresponding change value of each influencing factor, the variance ratio statistic for each influencing factor is calculated, including:

[0100] When the change value corresponding to each influencing factor is the same, calculate the sum of squared deviations and the sum of squared errors for each influencing factor based on the response index value;

[0101] Calculate the factor freedom of each influencing factor based on the change value corresponding to each influencing factor;

[0102] Calculate the total degrees of freedom of the experiment based on the corresponding orthogonal experiment number, and determine the error degrees of freedom based on the total degrees of freedom of the experiment and the degrees of freedom of the factors;

[0103] According to the squared deviation of each influencing factor and the degree of freedom of the factor, an average squared deviation sum is calculated; and according to the error sum and the error degree of freedom, an average error sum is calculated;

[0104] According to the ratio of the average squared deviation sum and the average error sum, a variance ratio statistic of each influencing factor is obtained.

[0105] Preferably, it further comprises: evaluating the river network aquatic habitat quality change of the to-be-identified region according to the aquatic habitat quality driving factor.

[0106] By implementing the embodiment, according to the evaluation result, the current status of the river network aquatic habitat quality of the to-be-identified region is analyzed, it is determined which driving factors have greater influence on the aquatic habitat quality, and which regions have better or worse aquatic habitat quality. The current evaluation result is compared with historical data, the change trend of the river network aquatic habitat quality over time is analyzed, and it is judged whether it is improved, deteriorated or kept stable. The main driving factors and reasons causing the change of the aquatic habitat quality are found out, for example, the hydrological characteristics are changed due to the construction of water conservancy projects. Through technical means such as GIS, a spatial distribution map of the aquatic habitat quality evaluation result is drawn, the spatial difference of the aquatic habitat quality of different regions of the river network is analyzed, and high-risk regions and protection key regions are identified, which provides basis for targeted management and protection measures. According to the evaluation result and analysis conclusion, corresponding river network aquatic habitat quality protection and restoration measures are proposed for existing problems. For example, for water pollution problems, suggestions such as strengthening sewage treatment and controlling pollutant discharge are proposed; for river bank destruction problems, suggestions such as carrying out river bank ecological restoration engineering and increasing vegetation coverage are proposed. Reasonable river network aquatic habitat management strategies are developed, a long-term monitoring system is established, and the driving factors of the aquatic habitat quality are monitored and evaluated regularly, so that the change dynamics of the aquatic habitat quality is grasped in time, scientific basis is provided for management decision-making, and the continuous improvement and stability of the river network aquatic habitat quality are ensured.

[0107] Specifically, the change value corresponding to each influencing factor is a preset change level of the influencing factor, such as the change levels of rainfall in Table 1 are -25%, -10%, 0%, 10%, and 25%; the change levels of air temperature are -1.5℃, -0.5℃, 0℃, 0.5℃, 1.5℃, the change levels of sea level are 0m, 0.2m, 0.4m, 0.6m, and 1m; and the change levels of reservoirs are 0 (no reservoir) and 1 (with reservoir). The annual change rate of the historical measured data of the stations of rainfall and air temperature is statistically analyzed, and the extreme development situation is considered to determine the change levels of various factors, and based on this, a test scheme is constructed according to a standard orthogonal test table, and a quasi-level method is used to arrange factors with fewer levels (such as reservoirs) to complete the orthogonal test design. In Table 1, E and F are empty columns for calculating error values caused by test errors; and the response index value is the WUA average value corresponding to the change level of the factor obtained through the coupling model simulation.

[0108] In a preferred embodiment of the present application, 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, and includes the difference caused by different change values of the influencing factors and the difference caused by the test errors. The greater the value is, the greater the difference between the response index values is, and the selected influencing factor is a significant influencing factor of the water habitat quality in the region. wherein y i is the response index value of the orthogonal test i, and n is the corresponding number of orthogonal tests. Taking rainfall as an example, if the rainfall is arranged in the jth column of the orthogonal test table, the sum of squares of deviations caused by the rainfall is wherein K i is the sum of the response index values of the same level test of the jth column of rainfall, and r is the level number (change value) of the rainfall. In order to calculate the sum of squares of errors of the test errors, the design table generally has an empty column, which is called the error column. The sum of the sum of squares of deviations of all error columns is the sum of squares of errors of the errors: is the average sum of squares of errors. In order to calculate the average sum of squares of deviations, the factor degrees of freedom of the influencing factors need to be calculated. The total degrees of freedom of the test is: f T =n-1, the degrees of freedom of each influencing factor is: f j =r-1, and the degrees of freedom of the error is: f e =∑f 空列 Then the average sum of squares of deviations is calculated as follows:

[0109] , S 2 may be the sum of squares of deviations of the influencing factors, i.e. the above f may be the factor degrees of freedom of each influencing factor, i.e. the above f j According to the ratio of the average sum of squares of deviations and the average sum of squares of errors, the variance ratio statistic of each influencing factor is obtained: When F>F 0.05 (n1,n2), it is indicated that, for the preset significance level a=0.05, the rainfall has a significant influence on the test results. The greater the F value is, the greater the influence of the factor on the results is. Therefore, by comparing the F values of all influencing factors, the influencing factor corresponding to the maximum F value is the key driving factor of the river network water habitat quality, i.e. the key factor in the water habitat quality driving factor. Wherein n1 is the degrees of freedom of the rainfall, and n2 is the degrees of freedom of the error.

[0110] ​In a preferred embodiment of the present application, hydrological and hydrodynamic coupling simulation, physical habitat model, orthogonal test design and other methods are used to construct climate disturbance-water conservancy construction coupling scenarios under changing environment, drive the distributed hydrological model of the basin and predict the hydrological response of the basin under changing environment, establish the hydrological and hydrodynamic coupling model and the physical habitat model, realize the process simulation of "runoff-hydrodynamic force-water habitat quality", identify the driving factors of urban river network water habitat quality based on the orthogonal test design and variance analysis method, and the research technical route is as follows Figure 2The water habitat quality assessment methods based on remote sensing are gradually applied with the development of remote sensing technology. Remote sensing can provide large-scale and high-frequency data acquisition, especially in large-scale areas, to dynamically monitor the health status of water bodies. Remote sensing data such as normalized difference vegetation index (NDVI) and land surface temperature can be used to assess habitat quality. For example, the remote sensing ecological index constructed by normalized difference vegetation index, humidity, land surface temperature, bare soil index, and application building index is used to evaluate the ecological environment quality of Poyang Lake Basin. However, remote sensing technology is limited by resolution and revisit period, and is mainly used to evaluate the habitat quality at the macro scale of the basin, and it is difficult to obtain continuous habitat quality assessment results. High-resolution images have the problem of difficult data acquisition. In recent years, ecological models have gradually become an effective tool for assessing water habitat quality. Models not only can handle complex ecological processes, but also can simulate the impact of different environmental factors (such as hydrology, water quality, land use, etc.) on water habitat quality. For example, the physical habitat model calculates the adaptability of target species to habitat flow velocity and water depth, links river flow to changes in habitat ecological processes, reflects the response relationship between flow changes and river habitat quality, and combines with hydrological and hydrodynamic coupling models to predict and evaluate water habitat quality under different climate change and human activity scenarios. The advantages of these methods are that they can consider multiple factors and complex ecological interactions, and can provide more comprehensive assessment results. However, in the selection of hydrological and hydrodynamic models, the applicability of the model in the study area and the simplicity of the operation need to be considered. Most of the model frameworks and methods of the above methods are based on the analysis of habitat quality driving factors at the basin scale, so 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 river network scale. Most habitat quality driving factor identification methods ignore the inevitable error term in habitat quality simulation and evaluation. Today's climate change events (such as floods, droughts, etc.) and human behavior (such as water conservancy projects) have caused changes in the hydrological cycle of the river network in the East River Basin, leading to changes in water quantity and hydrological regime, which have a profound impact on the river network water ecosystem and may accumulate and worsen at certain times and locations, leading to degradation and even extinction of the water ecosystem function and decline in biodiversity. The essence of these problems is the cascading effects of climate change and human activities on the hydrological cycle and hydrodynamic processes on the water ecosystem. Changing environment first leads to changes in hydrology and hydrodynamic parameters, which affect biological individuals and populations, influence their life history and ecological behavior, and induce a certain degree of adaptability. Similarly, at the ecosystem level, it reflects a certain degree of feedback of ecosystem structure and function to external changes.The embodiment analyzes the coupling influence of climate change and human activities on the hydrological cycle and hydrodynamic process of the basin-river network system and the response process mechanism of the water habitat quality by constructing a hydrological and hydrodynamic coupling model, combining a physical habitat model, based on orthogonal test design and coupling scenario, and identifies the driving factors of river network water habitat quality by error analysis method, so as to provide a scientific basis for formulating effective water ecological security policy and adapting and mitigating the influence of climate change.

[0111] By implementing the present application, the hydrological and hydrodynamic coupling simulation, the physical habitat model, the orthogonal test design and the variance analysis method are combined, the coupling scenario of climate change and water conservancy construction is established, the response of river network water habitat quality under the changing environment is accurately simulated, and the driving factors of river network habitat quality are identified. Traditional researches often focus on the influence of a single factor, while the embodiment comprehensively considers multiple influencing factors such as rainfall, air temperature, sea level rise and reservoir construction and their interactions, and comprehensively evaluates the changes of urban river network water habitat quality and the driving factor analysis. Through the combination of accurate hydrological and hydrodynamic coupling simulation and physical habitat model, scientific basis can be provided for the evaluation of urban river network water habitat quality, and the embodiment has the characteristics of low cost and easy implementation. With the acceleration of urbanization, water habitat quality is facing great challenges, especially under the influence of climate change and human activities (such as reservoir construction and urban expansion), the ecological safety of urban river network is increasingly vulnerable. Therefore, the embodiment identifies these key driving factors and reveals their potential influence on water habitat quality, which has important practical significance for formulating accurate ecological management strategies, improving water ecological environment quality and realizing sustainable development. With the intensification of global climate change, sea level rise and urbanization process, the current changes of water habitat quality in the basin and river network have become an urgent problem to be solved. The embodiment can provide timely decision support for the management and optimization of current urban river network water habitat quality by combining the coupling scenario of factors such as climate change, sea level rise and reservoir construction for long-sequence multi-scale continuous simulation analysis. This time-effective research can help relevant decision-makers cope with the current and future ecological crisis. The multi-level and comprehensive analysis method of hydrological and hydrodynamic coupling model and physical habitat model can effectively describe the interaction between hydrology, climate and water habitat. The orthogonal test design method helps to systematically select the key driving factors that have the greatest influence on river network water habitat quality from multiple factors and levels, and the scientific evaluation is carried out through variance analysis, which ensures the comprehensive consideration of different influencing factors, forms a systematic method framework for identifying the driving factors of urban river network water habitat quality, and also provides a referenceable method framework for the water habitat quality research of other basins and urban areas.

[0112] By implementing the embodiment, the hydrological model and the hydrodynamic model are coupled, the response of the river network water habitat quality of the to-be-identified region under the change of the influencing factor is simulated, the hydrodynamic factor is obtained, the effect of the influencing factor of the to-be-identified region on the hydrodynamic factor is simulated, the influence of the multiple influencing factors on the to-be-identified region is proposed, and the water habitat quality is comprehensively evaluated. Only the hydrological model, the hydrodynamic model and the hydrological and hydrodynamic coupling model constructed according to the measured data of the to-be-identified region are needed, the water habitat quality driving factor can be obtained, the dependence on field sampling is greatly reduced, long-term field monitoring is not needed, and the water habitat quality driving factor can be identified at any place and at any time, the cost of long-term field monitoring is reduced, the water habitat quality driving factor can be obtained in a shorter time, and the identification efficiency is improved.

[0113] Referring to Figure 3 is a structural schematic diagram of a city river network water habitat quality driving factor identification device provided by an embodiment of the present application, comprising:

[0114] The data acquisition module is configured to acquire rainfall data, air temperature data, an elevation data set, land use data, soil data, runoff measured data, tidal level data, riverbed data, water level measured data and river network connected node data of the to-be-identified region.

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

[0116] The hydrodynamic model construction module is configured to construct a hydrodynamic model according to the runoff measured data, the tidal level data, the riverbed data, the water level measured data and the river network connected node data.

[0117] The model coupling module is configured to couple the hydrological model and the hydrodynamic model to obtain a hydrological and hydrodynamic coupling model and a hydrodynamic factor output by the hydrological and hydrodynamic coupling model.

[0118] The region grid division module is configured to divide the to-be-identified region into a plurality of grid units according to the hydrological and hydrodynamic coupling model.

[0119] The influencing factor orthogonal test module is configured to perform orthogonal test under a plurality of preset influencing factors according to a grid area corresponding to each grid unit, a suitability index corresponding to the hydrodynamic factor and a preset factor weight, and calculate a response index value of the to-be-identified region.

[0120] The quality driving factor identification module is configured to calculate a variance ratio statistic of each influencing factor according to the response index value and a change value corresponding to each influencing factor, and take the influencing factor with the variance ratio statistic greater than a preset significance level as a water habitat quality driving factor of the to-be-identified region.

[0121] The application provides a device for identifying water habitat quality driving factors of an urban river network, which comprises a data acquisition module, a hydrological model construction module, a hydrodynamic model construction module, a model coupling module, a regional grid division module, and a quality driving factor identification module. The application couples the hydrological model and the hydrodynamic model, simulates the response of the river network water habitat quality of the to-be-identified region to the change of the influencing factors, obtains the hydrodynamic factors, simulates the effect of the influencing factors of the to-be-identified region on the hydrodynamic factors, obtains the water habitat quality driving factors, proposes the influence of the multiple influencing factors on the to-be-identified region, comprehensively evaluates the water habitat quality, and only needs to construct the hydrological model, the hydrodynamic model and the hydrological-hydrodynamic coupled model according to the measured data of the to-be-identified region to obtain the water habitat quality driving factors, greatly reduces the dependence on field sampling, does not need long-term field monitoring, is not restricted by space and time, and can identify the water habitat quality driving factors at any place and at any time, reduces the cost of long-term field monitoring, and obtains more comprehensive water habitat quality driving factors in a shorter time, thereby improving the identification efficiency.

[0122] It should be noted that the apparatus embodiments described above are merely illustrative, and the units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., they can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment according to actual needs. In addition, the connection relationship between the modules in the apparatus embodiments provided by the present application indicates that there is a communication connection between them, which can be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

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

[0124] Another embodiment of the present application also 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, and the processor implements a city river network water habitat quality driving factor identification method as described in the above embodiments when executing the computer program. The terminal device can be a desktop computer, a notebook computer, a palm computer, a cloud server, and other computing devices. The terminal device can include, but is not limited to, a processor and a memory.

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

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

[0127] Another embodiment of the present application provides a computer readable storage medium, comprising a stored computer program, wherein the computer readable storage medium controls a device where the computer readable storage medium is located to perform the urban river network water habitat quality driving factor identification method according to the above embodiment when the computer program runs.

[0128] The storage medium is a computer readable storage medium, and the computer program is stored in the computer readable storage medium. The computer program can realize the steps of each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a U disk, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0129] The above is the preferred embodiment of the present application. It should be noted that, for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which are also considered to be within the scope of protection of the present application.

Claims

1. A method for identifying driving factors of aquatic habitat quality in urban river networks, characterized in that, include: Acquire rainfall data, temperature data, elevation dataset, land use data, soil data, measured runoff data, tidal data, riverbed data, measured water level data, and river network connectivity node data for the area to be identified; A hydrological model is constructed based on the rainfall data, temperature data, elevation dataset, land use data, soil data, and measured runoff data. A hydrodynamic model is constructed based on the measured runoff data, tidal data, riverbed data, measured water level data, and river network connectivity node data. By coupling the hydrological model and the hydrodynamic model, a hydro-hydrodynamic coupled model and the hydrodynamic factors output by the hydro-hydrodynamic coupled model are obtained. The area to be identified is divided into several grid units according to the hydrological and hydrodynamic coupling model. Based on the grid area corresponding to each grid cell, the suitability index corresponding to the hydrodynamic factor, and the preset factor weight, an orthogonal experiment is conducted under several preset influencing factors to calculate the response index value of the area to be identified. Based on the response index value and the change value corresponding to each influencing factor, the variance ratio statistic of each influencing factor is calculated, and the influencing factors whose variance ratio statistic is greater than the preset significance level are used as the aquatic habitat quality driving factors of the area to be identified. Specifically, based on the response index values ​​and the corresponding changes in each influencing factor, the variance ratio statistic for each influencing factor is calculated, including: When the change value corresponding to each influencing factor is the same, calculate the sum of squared deviations and the sum of squared errors for each influencing factor based on the response index value; Calculate the factor freedom of each influencing factor based on the change value corresponding to each influencing factor; Calculate the total degrees of freedom of the experiment based on the corresponding orthogonal experiment number, and determine the error degrees of freedom based on the total degrees of freedom of the experiment and the degrees of freedom of the factors; The average sum of squared deviations is calculated based on the squared deviations of each influencing factor and the degrees of freedom of the factor; and the average sum of squared errors is calculated based on the sum of squared errors and the degrees of freedom of the errors. The variance ratio statistic for each influencing factor is obtained based on the ratio of the mean sum of squared deviations to the mean sum of squared errors.

2. The method for identifying driving factors of urban river network aquatic habitat quality as described in claim 1, characterized in that, Based on the aforementioned rainfall data, temperature data, elevation dataset, land use data, soil data, and measured runoff data, a hydrological model is constructed, including: Based on the rainfall data, temperature data, elevation dataset, land use data, and soil data, an initial hydrological model is constructed. Simulations are performed based on the current hydrological model to determine runoff simulation data for different time series; the current hydrological model used in the first simulation is the initial hydrological model. Based on the measured runoff data and the simulated runoff data, calculate the first Nash coefficient and the first determination coefficient to characterize the current hydrological model; 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, the final hydrological model is obtained. Otherwise, parameter calibration is performed based on the measured runoff data and the simulated runoff data to obtain calibration parameters; and the current hydrological model is updated based on the calibration parameters.

3. The method for identifying driving factors of urban river network aquatic habitat quality as described in claim 2, characterized in that, Based on the measured runoff data, tidal data, riverbed data, measured water level data, and river network connectivity data, a hydrodynamic model is constructed, including: The river network boundary of the area to be identified is determined based on the river network connectivity node data. Within the boundary of the river network, a downstream outflow boundary of the river network is set according to the tidal data; Within the river network boundary, an upstream inflow boundary is set based on the measured runoff data; An initial hydrodynamic model is constructed based on the downstream outflow boundary of the river network, the upstream inflow boundary of the river network, and the riverbed data. Simulations are performed based on the current hydrodynamic model to determine the simulated water level data; the current hydrodynamic model used in the first simulation is the initial hydrodynamic model. Based on the simulated water level data and the measured water level data, calculate the second Nash coefficient and the second determination coefficient to characterize the current hydrodynamic model; Based on the measured water level data and the simulated water level data, the parameters of the current hydrodynamic model are calibrated until the second Nash coefficient is greater than the preset Nash coefficient threshold and the second determination coefficient is greater than the preset determination coefficient threshold, thus obtaining the final hydrodynamic model.

4. The method for identifying driving factors of urban river network aquatic habitat quality as described in claim 3, characterized in that, By coupling the hydrological model and the hydrodynamic model, a hydro-hydrodynamic coupled model and the hydrodynamic factors output by the hydro-hydrodynamic coupled model are obtained, including: The runoff simulation data output by the hydrological model is used as the boundary condition of the hydrodynamic model and input into the hydrodynamic model for coupling to obtain a hydro-hydrodynamic coupled model. Hydrodynamic factors are predicted based on the hydrodynamic coupling model.

5. The method for identifying driving factors of urban river network aquatic habitat quality as described in claim 1, characterized in that, Based on the grid area corresponding to each grid cell, the suitability index corresponding to the hydrodynamic factor, and the preset factor weights, an orthogonal experiment is conducted under several preset influencing factors to calculate the response index value of the region to be identified, including: The habitat suitability of each grid cell is calculated based on the suitability index corresponding to the hydrodynamic factors and the preset factor weights. The weighted suitable habitat area is calculated based on the number of grids corresponding to the grid unit, the area of ​​each grid unit, and the habitat suitability. Construct an orthogonal experimental table based on the preset influencing factors; An orthogonal experiment was conducted based on the orthogonal experimental table, and the mean value of the area of ​​suitable habitat for the weights was used as the response index value of the area to be identified.

6. The method for identifying driving factors of urban river network aquatic habitat quality as described in claim 1, characterized in that, Also includes: The changes in the quality of the river network aquatic habitat in the area to be identified are assessed based on the aforementioned aquatic habitat quality driving factors.

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