Method for predicting fish density in river habitat based on eco-hydraulics

Abstract

The application discloses a river habitat and fish density prediction method based on ecological hydraulics, relates to the cross technical field of water conservancy projects, ecological environment protection, river ecological hydraulics and ecological water conservancy, and comprises the following steps: step one, selecting a river region for key research; step two, carrying out grid division on the river region; step three, simulating a sediment change mode of the river; step four, selecting a predicted fish; and step five, combining ecological fitness, effective ecological utilization area and overall fitness. The application fuses river dynamics, sediment kinetics and ecological evaluation theory, takes flow velocity, water depth and sediment change as core environmental factors, combines a fish habitat fitness evaluation system and a logistics population model, and constructs a complete technical chain of "environment simulation-fitness evaluation-quantity density prediction", so that the limitation of a single discipline perspective of a traditional method is effectively solved, and the scientificity and accuracy of river ecological habitat and fish density simulation and prediction are greatly improved.

Description

Technical Field

[0001] This invention relates to the fields of water conservancy engineering, ecological environment protection, river ecohydraulics and ecohydraulic interdisciplinary technologies, specifically to a method for predicting river habitats and fish density based on ecohydraulics. Background Technology

[0002] River ecosystems are an important link between terrestrial and aquatic ecosystems. The quality of river habitats directly determines the survival, reproduction, and distribution of fish populations. Fish density, as a key indicator reflecting the ecological health of rivers, has important guiding significance for river ecological protection, rational utilization of water resources, and water conservancy project planning.

[0003] In recent years, with the large-scale construction of water conservancy projects such as sluice gates, dams, pumping stations, and hydropower stations, coupled with river changes caused by natural factors, the flow characteristics and sediment transport patterns of rivers have undergone significant alterations. This has disrupted the stability of river ecological habitats and profoundly impacted the number and density of fish populations. However, current technologies lack a complete and systematic approach that can comprehensively consider factors such as river dynamics, sediment kinematics, and ecological adaptability to accurately simulate the impact of river changes on fish populations. Consequently, river ecological protection and related engineering planning lack scientifically sound and effective data support.

[0004] Therefore, developing a multidisciplinary, widely applicable, and accurate method for predicting river habitats and fish density is of great practical significance and high engineering research value for improving the level of river ecological assessment, guiding river ecological restoration, and protecting fish resources. Summary of the Invention

[0005] The purpose of this invention is to provide a method for predicting river habitats and fish density based on ecohydraulics, in order to solve the problems mentioned in the background art.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0007] A method for predicting river habitat and fish density based on ecohydraulics, step one: select key river areas for study and determine evaluation elements and indicators. The evaluation elements include river geomorphology, water flow characteristics, riverbank width and length, river sediment characteristics, and fish population and quantity survey data.

[0008] Step 2: Divide the selected river region into grids, set the region boundary conditions and initial conditions, and use the shallow water equation in river dynamics to dynamically simulate the flow velocity and water depth in the river;

[0009] Step 3: Use sediment kinematics to simulate the sediment change pattern of the river, and update the flow velocity and water depth data in real time. After comparing and verifying the simulation results with the data from the river monitoring points, save the flow velocity, water depth and sediment information in .dat format.

[0010] Step 4: Select the target fish population to be predicted, establish a habitat fitness evaluation system with a value range of 0-1, calculate the dynamic ecological fitness of each grid through weight allocation and combination and save it in dat format, and at the same time calculate the effective ecological utilization area and overall fitness.

[0011] Step 5: Using the logistic model function, combined with ecological fitness, effective ecological utilization area and overall fitness, simulate and predict the fish population. Save the results in .dat format and display the changes in fish population and density distribution through visualization software.

[0012] A further improvement of the technical solution of the present invention is that: the river geomorphological indicators in step 1 include river channel morphology and riverbed slope; the water flow characteristic indicators include flow velocity distribution and water flow stability; and the river sediment characteristic indicators include sediment particle size distribution and sediment content.

[0013] A further improvement of the technical solution of the present invention is that: the shallow water equation in step 2 includes a continuity equation and a momentum equation, and the parameters involved include water depth h, flow velocity u / v in the x / y direction, river surface height, plane coordinates x / y, water density, water shear stress components and riverbed shear stress. The riverbed shear stress is calculated by a formula, where n is the riverbed coefficient and n is the Manning coefficient.

[0014] A further improvement of the technical solution of the present invention is that: in step 2, the grid division adopts a rectangular grid, and the grid size is set to 50m-200m according to the accuracy requirements of the research area. The boundary conditions include upstream and downstream water level boundaries and flow boundaries, and the initial conditions include initial flow velocity and initial water depth.

[0015] A further improvement to the technical solution of this invention lies in that: the sediment change model in step 3 is achieved by calculating the sediment flux, where the sediment flux Q is... b The calculation formula is Where g is the acceleration due to gravity, and D is the diameter of the sediment particles. The Scheide coefficient; riverbed changes Z f Calculated using formula , where p is the porosity of the riverbed medium.

[0016] A further improvement of the technical solution of the present invention is that: the error threshold for the simulation result verification in step 3 is: flow velocity simulation error ≤8%, water depth simulation error ≤10%, sediment content simulation error ≤12%. If the verification fails, the model parameters are readjusted and the simulation is repeated.

[0017] A further improvement of the technical solution of the present invention is that: the evaluation indicators of the habitat adaptability evaluation system in step 4 include flow velocity adaptability, water depth adaptability and riverbed medium adaptability, and the weight of each indicator is determined according to the habitat habits of the target fish, and the total weight is 1.

[0018] A further improvement of the technical solution of the present invention is that: in step 4, the ecological fitness HSI is calculated by a formula, where SI is the fitness value of each individual parameter, the effective ecological utilization area WUA is the sum of the areas of grid cells with ecological fitness greater than 0, and the overall fitness OSI is the weighted average of the ecological fitness of all grid cells.

[0019] A further improvement to the technical solution of the present invention is that: in step 5, the fish density is obtained by predicting the number of fish, the total area of ​​the river study area, and empirical parameters and corrections, and the empirical parameters are calibrated by actual monitoring data.

[0020] A further improvement of the technical solution of the present invention is that the viewing software in step 5 includes Surfer and ArcGIS, which can intuitively present the changing trend of fish numbers over time and the spatial density distribution of fish in the river area.

[0021] Due to the adoption of the above technical solution, the technical progress achieved by this invention compared to the prior art is as follows:

[0022] This invention provides a method for predicting river habitats and fish density based on ecohydraulics. By integrating river dynamics, sediment kinematics, and ecological assessment theories, and taking flow velocity, water depth, and sediment changes as core environmental factors, combined with a fish habitat fitness assessment system and a logistic population model, a complete technical chain of "environmental simulation - fitness assessment - fish density prediction" is constructed. This effectively solves the limitations of traditional methods with a single disciplinary perspective and significantly improves the scientificity and accuracy of river ecological habitat and fish density simulation and prediction.

[0023] This invention provides a method for predicting river habitats and fish density based on ecohydraulics. Through dynamic simulation and parameter coupling calculation, this invention overcomes the excessive reliance of traditional simulation methods on measured river data. Even in river areas where measured data is scarce, effective simulation can be achieved through model parameter calibration and reasonable setting of boundary conditions. At the same time, this method is adaptable to river areas with different landform types, water flow characteristics, and fish populations, and has a wide range of applications, providing flexible and reliable technical support for various river ecological protection, management, and restoration projects.

[0024] This invention provides a method for predicting river habitat and fish density based on ecohydraulics. By simultaneously conducting habitat sensitivity analysis, it provides quantitative data support for assessing the degree of river cross-section degradation, optimizing ecological restoration plans, and predicting the ecological impact of water conservancy projects. Its application can effectively guide river ecological protection and fish resource management, help reduce the negative impact of water conservancy projects on river ecology, and promote the synergistic improvement of ecological and engineering benefits, demonstrating significant engineering application value and enormous economic and social benefits. Attached Figure Description

[0025] Figure 1 The various modules and flowcharts of this invention are shown below;

[0026] Figure 2 This is the ecological fitness diagram of the present invention;

[0027] Figure 3 This is a diagram showing the effective utilization area and overall adaptability of the river in this invention;

[0028] Figure 4 This is a graph showing the change in the number of fish species according to the present invention;

[0029] Figure 5 This is a density distribution diagram of the fish species in the river according to the present invention. Detailed Implementation

[0030] The present invention will be further described in detail below with reference to embodiments:

[0031] Example 1

[0032] Depend on Figure 1 The diagram illustrates the various modules of this invention and the flowcharts used in this method. The main modules used in this invention are four: a river dynamics module, a sediment kinematics module, a habitat model, and a fish population and density module.

[0033] River dynamics models mainly rely on the continuity equation and momentum equation in shallow water equations to simulate the flow velocity and water depth of rivers.

[0034]

[0035]

[0036]

[0037] In the shallow water equation, h represents the water depth; u and v represent the flow velocities. The elevation is the river surface height; x and y are the coordinates. The density of water; , , , Shear stress in water; , This represents the shear stress in the riverbed. The riverbed shear stress is caused by...

[0038]

[0039]

[0040] The parameter is calculated by the following formula. .in Riverbed coefficient, where n is the Manning coefficient.

[0041] Sediment dynamics models primarily calculate sediment flux, and then use this flux to determine riverbed changes and sediment transport patterns. The formula for calculating sediment flux is as follows:

[0042]

[0043] in

[0044]

[0045] Where Qb is the sediment flux, g is gravity, and D is the sediment particle size. This is the Shedd coefficient. Changes in river sediment are calculated using the following formula.

[0046]

[0047] Where Zf represents river changes and p represents voids.

[0048] Using river dynamics and sediment dynamics models, three parameters can be calculated: flow velocity, water depth, and sediment variation. These three parameters are obtained through... Figure 2 The following formula is used to calculate the dynamic ecological fitness of each grid in the river.

[0049]

[0050] HSI represents ecological fitness, and SI represents parameter fitness. The effective utilization area and overall fitness of the river are calculated using the following method, and the results are as follows: Figure 3 As shown.

[0051]

[0052]

[0053] Where WUA represents the effective utilization area, OSI represents the overall fitness, and A represents the river grid area. The number and density distribution of fish in the river are calculated using the following formula.

[0054]

[0055] Fish density distribution.

[0056]

[0057] in and These are empirical parameters used to adjust the data at the monitoring points.

[0058] Example 2

[0059] Taking a section of a small to medium-sized river as the study area, and the target fish species to be predicted as the dominant species in the river, crucian carp, the method of this invention is used to predict river habitat adaptability and crucian carp density. The specific implementation steps are as follows:

[0060] S1: Determine the study area and evaluation elements: Select a 10km section of the river as the study area, investigate the river geomorphology (meandering channel, riverbed slope of 0.5‰), water flow characteristics (historical average flow of 5m³ / s), average riverbank width of 80m, length of 10km, and river sediment characteristics (sediment particle size D mainly concentrated in 0.05-0.5mm). Through field sampling survey, the current number of crucian carp is about 3000, and their habitat habits are suitable for a flow velocity of 0.2-0.8m / s, a water depth of 0.5-2.0m, and a riverbed medium of silt and sand.

[0061] S2: River Flow Velocity and Depth Simulation: The study river section was divided into 1000 grid cells using a rectangular grid of 100m × 100m. The upstream boundary was set as the flow boundary (5m³ / s), and the downstream boundary as the water level boundary (25.0m). The initial flow velocity was set to 0.3m / s, and the initial water depth was set to 1.0m. Based on the shallow water equation, the continuity and momentum equations were solved numerically to obtain the spatiotemporal distribution data of flow velocity and water depth for each grid cell. For example, the flow velocity of a certain grid cell was 0.4m / s and the water depth was 0.8m during the dry season, and the flow velocity was 0.7m / s and the water depth was 1.5m during the wet season.

[0062] S3: Sediment Change Simulation and Data Validation: Based on the sediment particle size D=0.2mm, Scheide coefficient=0.03, and gravitational acceleration g=9.8m / s² in this river section, the sediment flux Qb=0.002m³ / (m・s) was calculated; the riverbed porosity p=0.3, and the annual variation of riverbed change Zf was calculated to be 0.02m. The simulated flow velocity, water depth, and sediment data were compared with the measured data from three monitoring points in this river section. The simulation error for flow velocity was within 5%, the simulation error for water depth was within 8%, and the simulation error for sediment content was within 10%. After successful validation, the relevant data were saved in .dat format.

[0063] S4: Ecological Adaptability Calculation and Key Parameter Acquisition: Based on the habitat habits of crucian carp, an adaptation evaluation system was established: Flow velocity adaptability SIu: SIu=1 when the flow velocity is 0.5m / s, and SIu=0 when the flow velocity is less than 0.2m / s or greater than 0.8m / s; Water depth adaptability SIh: SIh=1 when the water depth is 1.2m, and SIh=0 when the water depth is less than 0.5m or greater than 2.0m; Riverbed medium adaptability SIs: SIs=1 for silty riverbeds, and SIs=0.3 for other textures. Weights were assigned according to the importance of each parameter: flow velocity weight 0.4, water depth weight 0.4, and riverbed medium weight 0.2.

[0064] The ecological fitness index (HSI) of each grid cell is calculated using a formula. For example, in one grid cell with a flow velocity of 0.5 m / s (SIu=1), a water depth of 1.2 m (SIh=1), and a sedimentary riverbed (SIs=1), the HSI is calculated as 1 × 0.4 + 1 × 0.4 + 1 × 0.2 = 1.0. In another grid cell with a flow velocity of 0.1 m / s (SIu=0), a water depth of 0.4 m (SIh=0), and a pebble riverbed (SIs=0.3), the HSI is calculated as 0 × 0.4 + 0 × 0.4 + 0.3 × 0.2 = 0.06. The calculated effective ecological utilization area (WUA) for this river section is 6.2 km², and the overall fitness index (OSI) is 0.75.

[0065] S5: Fish Population and Density Prediction: A logistic model was used, with the following parameters: carrying capacity K = 10,000 fish (determined based on the effective ecological utilization area and crucian carp habitat density threshold), and intrinsic growth rate r = 0.2. Substituting OSI = 0.75 into the model, the predicted crucian carp population was 4,800 fish after 1 year, 6,500 fish after 2 years, and 8,200 fish after 3 years. Combining empirical parameters = 1.05 and = 0.98, the average crucian carp density in this river section was corrected to 820 fish / km², showing a higher density in the upper and middle reaches of the river and a lower density in the lower reaches, which is basically consistent with the actual monitoring results.

[0066] The results of this embodiment show that the method of the present invention can accurately simulate the adaptability of river habitats and fish density, providing a scientific basis for the ecological protection and water conservancy project planning of this river section.

[0067] The present invention has been described in detail above. However, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, any modifications or improvements that do not depart from the spirit of the present invention are within the scope of protection of the present invention.

Claims

1. A method for predicting river habitat and fish density based on ecohydraulics, characterized by: Step 1: Select key river areas for research and determine evaluation elements and indicators. The evaluation elements include river geomorphology, water flow characteristics, riverbank width and length, river sediment characteristics, and fish population and quantity survey data. Step 2: Divide the selected river region into grids, set the region boundary conditions and initial conditions, and use the shallow water equation in river dynamics to dynamically simulate the flow velocity and water depth in the river; Step 3: Use sediment kinematics to simulate the sediment change pattern of the river, and update the flow velocity and water depth data in real time. After comparing and verifying the simulation results with the data from the river monitoring points, save the flow velocity, water depth and sediment information in .dat format. Step 4: Select the target fish population to be predicted, establish a habitat fitness evaluation system with a value range of 0-1, calculate the dynamic ecological fitness of each grid through weight allocation and combination and save it in dat format, and at the same time calculate the effective ecological utilization area and overall fitness. Step 5: Using the logistic model function, combined with ecological fitness, effective ecological utilization area and overall fitness, simulate and predict the fish population. Save the results in .dat format and display the changes in fish population and density distribution through visualization software.

2. The method for predicting river habitat and fish density based on ecohydraulics according to claim 1, characterized in that: The river geomorphological indicators mentioned in step 1 include river channel morphology and riverbed slope; the water flow characteristic indicators include flow velocity distribution and water flow stability; and the river sediment characteristic indicators include sediment particle size distribution and sediment content.

3. The method for predicting river habitat and fish density based on ecohydraulics according to claim 1, characterized in that: The shallow water equation in step 2 includes the continuity equation and the momentum equation. The parameters involved include water depth h, flow velocity u / v in the x / y direction, river surface height, plane coordinates x / y, water density, water shear stress components, and riverbed shear stress. The riverbed shear stress is calculated by the formula, where is the riverbed coefficient and n is the Manning coefficient.

4. The method for predicting river habitat and fish density based on ecohydraulics according to claim 1, characterized in that: In step 2, the grid division adopts a rectangular grid, and the grid size is set to 50m-200m according to the accuracy requirements of the study area. The boundary conditions include upstream and downstream water level boundaries and flow boundaries, and the initial conditions include initial flow velocity and initial water depth.

5. The method for predicting river habitat and fish density based on ecohydraulics according to claim 1, characterized in that: The sediment change model described in step 3 is achieved by calculating sediment flux, where Q is the sediment flux. b The calculation formula is Where g is the acceleration due to gravity, and D is the diameter of the sediment particles. The Scheide coefficient; riverbed changes Z f Calculated using formula , where p is the porosity of the riverbed medium.

6. The method for predicting river habitat and fish density based on ecohydraulics according to claim 1, characterized in that: The error thresholds for verifying the simulation results in step 3 are: flow velocity simulation error ≤ 8%, water depth simulation error ≤ 10%, and sediment content simulation error ≤ 12%. If the verification fails, the model parameters are readjusted and the simulation is repeated.

7. The method for predicting river habitat and fish density based on ecohydraulics according to claim 1, characterized in that: The evaluation indicators of the habitat adaptability evaluation system in step 4 include flow velocity adaptability, water depth adaptability, and riverbed medium adaptability. The weights of each indicator are determined according to the habitat habits of the target fish species, and the total weight is 1.

8. The method for predicting river habitat and fish density based on ecohydraulics according to claim 1, characterized in that: In step 4, the ecological fitness HSI is calculated using a formula, where SI is the fitness value of each individual parameter, WUA is the sum of the areas of grid cells with ecological fitness greater than 0, and OSI is the weighted average of the ecological fitness of all grid cells.

9. The method for predicting river habitat and fish density based on ecohydraulics according to claim 1, characterized in that: In step 5, the fish density is obtained by predicting the number of fish, the total area of ​​the river study area, and empirical parameters and corrections. The empirical parameters are calibrated using actual monitoring data.

10. The method for predicting river habitat and fish density based on ecohydraulics according to claim 1, characterized in that: The viewing software mentioned in step 5 includes Surfer and ArcGIS, which can intuitively present the changing trend of fish numbers over time and the spatial density distribution of fish in river areas.

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