Method for obtaining water flow propagation time in dry period, medium and electronic device

Abstract

This invention relates to the field of water conservancy technology, and discloses a method, medium, and electronic equipment for obtaining water flow propagation time during the dry season. The method includes acquiring initial data for the study area; the initial data includes: watershed topographic data, river network boundary data, inflow location data, and inflow data; the initial data is input into a low-to-medium water level runoff evolution model for simulation calculation to obtain the water flow propagation time during the dry season. This invention accurately characterizes the hydrodynamic processes during the dry season in the study area, predicts the available water resources in each river section within the watershed, provides a quantitative basis for water use allocation, and facilitates the optimization of dry season scheduling schemes for water conservancy projects such as reservoirs and sluices within the watershed, predicts the effectiveness of water replenishment scheduling schemes, and specifically addresses key issues in watershed management during the dry season.

Description

Technical Field

[0001] This invention relates to the field of water conservancy technology, specifically to a method, medium, and electronic device for obtaining water flow propagation time during the dry season. Background Technology

[0002] In existing technologies, research on runoff evolution mainly focuses on flood evolution, with limited research on the characteristics of runoff evolution during the dry season. Existing studies on lake runoff evolution under dry conditions are primarily based on measured data, examining changes in cross-sections along the course, trends in the relationship between water level and flow during low dry periods, and changes in riverbed scouring and deposition. This provides technical support for water replenishment scheduling during the dry season, but lacks the development of relevant evolution models. Some studies utilize hydrological and hydrodynamic methods to construct models to study lake water balance; however, these traditional hydrodynamic models have extremely high requirements for basic data, resulting in high computational costs and relatively low efficiency when simulating large and complex water systems like lakes. Other studies employ artificial intelligence methods such as artificial neural networks, using Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs) to construct lake water level change prediction models, which can predict water level changes to some extent. However, these models still have limitations when simulating the complex hydrodynamic processes and complex nonlinear response relationships between water level and inflow in lakes at low and medium water levels, and cannot comprehensively and accurately depict the true state of lakes at low and medium water levels.

[0003] Therefore, there is an urgent need to develop a method, medium, and electronic equipment for obtaining water flow propagation time during the dry season to solve the problems existing in the current technology. Summary of the Invention

[0004] The purpose of this invention is to provide a method, medium, and electronic device for obtaining water flow propagation time during the dry season, in order to solve the problem that existing technologies cannot comprehensively and accurately depict the true condition of lakes at low to medium water levels. The specific technical solution is as follows:

[0005] A method for obtaining water flow propagation time during the dry season includes the following steps:

[0006] S1. Obtain initial data for the study area; initial data includes: watershed topographic data, river network boundary data, inflow location data, and inflow data;

[0007] S2. Input the initial data obtained in S1 into the low-water-level runoff evolution model for simulation calculation to obtain the water propagation time during the dry season.

[0008] The shallow water wave equation in the runoff evolution model for medium and low water levels is as follows:

[0009] ;

[0010] ;

[0011] in: U Represents a vector of conserved variables; E express x The flux vector in the direction; G express y The flux vector in the direction; S The source term vector is represented by t; the time variable is represented by t. Indicates water level height; Indicates water in x Velocity in the direction; Indicates water in y Velocity in the direction; Represents gravitational acceleration; for x Momentum in the direction of; for y Momentum in the direction of; for xy Directional momentum coupled flux; This is a transpose.

[0012] Preferably, in S1, the river network boundary data includes the outer boundary of the study area and the river channel boundary, which are used to divide the data into triangular grids, and the triangular grids are used as the computing units for simulation calculations.

[0013] Preferably, the inflow location data in S1 includes the inflow location coordinates, inlet radius, and inflow flow rate.

[0014] Preferably, the inflow location coordinates are the inlets of each river, and the inlet radius is approximately 70% to 80% of the river channel radius at that location. The river channel radius is half the width of the river channel.

[0015] Preferably, the inflow data in S1 consists of flow data at daily, hourly, minute, and second intervals from each boundary hydrological station.

[0016] The present invention also discloses a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method for obtaining water flow propagation time during the dry season as described above.

[0017] The present invention also discloses an electronic device, comprising: a processor and a memory communicatively connected to the processor;

[0018] The memory stores computer-executed instructions;

[0019] The processor executes computer execution instructions stored in the memory to implement the method for obtaining water flow propagation time during the dry season as described above.

[0020] The effect of applying the technical solution of this invention is:

[0021] This invention provides a method for obtaining water propagation time during the dry season. The method includes acquiring initial data for the study area, including: watershed topographic data, river network boundary data, inflow location data, and inflow data. The initial data is then input into a low-to-medium water level runoff evolution model for simulation calculation to obtain the water propagation time during the dry season. This method accurately characterizes the hydrodynamic processes during the dry season in the study area, predicts the available water resources in each river section within the watershed, provides a quantitative basis for water use allocation, and facilitates the optimization of dry season scheduling schemes for water conservancy projects such as reservoirs and sluices within the watershed. It also predicts the effectiveness of water replenishment scheduling schemes and addresses key issues in watershed management during the dry season.

[0022] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the figures. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the method for obtaining water flow propagation time during the dry season in this embodiment;

[0025] Figure 2 This is a schematic diagram of the grid division in the embodiment;

[0026] Figure 3 This is the 90m resolution DEM elevation data for the Dongting Lake area in the example;

[0027] Figure 4 This is a vector diagram of the water system extracted in the embodiment;

[0028] Figure 5 This is a simplified diagram of the water system in the embodiment;

[0029] Figure 6(a) is a schematic diagram of the outer boundary of the simulated region in the embodiment;

[0030] Figure 6(b) shows the coordinates of the outer boundary of the simulated region;

[0031] Figure 7(a) is a schematic diagram of the river boundary in the simulated area in the embodiment;

[0032] Figure 7(b) is a schematic diagram of the river coordinates in the simulated area in the embodiment;

[0033] Figure 8(a) is a schematic diagram of the inflow location in the embodiment;

[0034] Figure 8(b) is a schematic diagram of the inflow position coordinates in the embodiment;

[0035] Figure 9 This is a computational grid partitioning diagram of the study area in the embodiment;

[0036] Figure 10 This is a schematic diagram illustrating the flow rate changes in a certain section of the river in the embodiment.

[0037] Figure 11 This is a schematic diagram showing the changes in water depth, water level, and flow velocity at a certain point in the river in the embodiment. Detailed Implementation

[0038] To enable those skilled in the art to better understand the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] Example:

[0040] A method for obtaining water flow propagation time during the dry season, the flowchart of which is detailed below. Figure 1 The process involves: defining the simulation area and acquiring regional watershed topographic data (DEM); extracting the regional river system; simplifying the river system using a visual translation method; extracting the overall regional boundary coordinates and river channel boundary coordinates; obtaining the inflow location coordinates and inlet radius; inputting the inflow flow rate into the low-to-medium water level runoff evolution model; running the model and outputting the point flow velocity, water depth, water level, and cross-sectional flow process. Specifically, the steps include:

[0041] S1. Obtain initial data for the study area; initial data includes: watershed topographic data, river network boundary data, inflow location data, and inflow data;

[0042] S2. Input the initial data obtained in S1 into the low-water-level runoff evolution model for simulation calculation to obtain the water propagation time during the dry season.

[0043] The model code is written in Python. In principle, it involves specifying a study area represented by a triangular mesh, with mesh attributes including ground elevation and frictional resistance. Inflow, watershed boundary conditions, and simulation time must also be set. During model execution, water depth and horizontal momentum are tracked over time by solving the governing equations within each triangular mesh cell. By querying the model's output, flow velocity, water depth, discharge, and regional inundation map information over time can be extracted for specific locations. The flow process can also be displayed through animation.

[0044] The core of the low-to-medium water level runoff evolution model is the shallow water wave equation, which is a set of differential conservation equations describing fluid flow on the ground. Its form is as follows:

[0045] ;

[0046] ;

[0047] in: U The vector of conserved variables, which includes mass and momentum in two directions, is the core variable for solving the equation; E express x The flux vector in the direction describes the physical quantity in x The rate of transfer in the direction reflects the conservation of momentum. x Directional constraints; G Indicate y The flux vector in the direction describes the physical quantity in y The rate of transfer in the direction reflects the conservation of momentum. y Directional constraints; S This represents the source term vector, which represents the physical effects in the equation other than the transmission of fluxes E and G. x , y represents a spatial variable; t represents a time variable, with the unit being seconds (s). u Indicates water in x Velocity in a certain direction, measured in m / s; v Indicates water in y Velocity in a certain direction, measured in m / s; This indicates the water level, or water depth, and is expressed in meters (m). Represents gravitational acceleration; for x Momentum in the direction of motion, expressed in kg / (m s); for y Momentum in the direction of motion, expressed in kg / (m s); for xy Directional momentum coupling flux, in kg / (m s2 ); This is a transpose.

[0048] Running the Dongting Lake low-water-level runoff evolution model requires four types of input data: watershed topographic data (DEM), river network boundary data, inflow location data, and inflow data. The river network boundary data includes the overall outer boundary of the simulation area and the river channel boundary, used to divide the area into triangular meshes, which are then used as the computational units for the simulation. Inflow location data includes inflow location coordinates, inlet radius, and inflow discharge. In the model, each river inlet is treated as a pipe opening, typically located in the middle of the river channel, with an inlet radius approximately 70%–80% of the river channel radius (half the river channel width). Inflow data can be discharge data from various boundary hydrological stations at daily, hourly, minute, and second intervals.

[0049] The main parameters set for the model simulation in this embodiment are as follows:

[0050] First step: Select elevation data, and select the previously prepared topographic data of the study watershed;

[0051] The second step: Selecting a water flow algorithm. This embodiment uses several algorithms and their corresponding characteristics, as listed in Table 1 below:

[0052]

[0053] Table 1. Water Flow Algorithm and Attribute Description

[0054] The third and fourth items are for setting the external and internal resolutions. After setting them, the model will generate triangular meshes of different sizes for the inside and outside of the river channel based on the river channel boundary points. See details. Figure 2 A lower resolution value results in more triangular mesh elements and slower model computation. When setting the resolution, the internal resolution should be higher than the external resolution. The resolution can be adjusted by adding or subtracting values. The default external resolution is 1,000,000, the internal resolution is 500,000, and the maximum resolution is 10,000. When setting these values, you should appropriately decrease them to increase the resolution.

[0055] Fifth item: River friction factor, set to a uniform value for the entire basin;

[0056] Items 6 and 7: Watershed markers, which can be chosen arbitrarily as long as they are within the designated study area;

[0057] Item 8: Model evolution time step;

[0058] Item 9: Total Evolution Duration, also known as Total Simulation Duration, includes the model warm-up period and the simulation period. For example, if the total simulation duration is 51 days, the warm-up period is 20 days, and the simulation period is 31 days, the total simulation duration should be consistent with the duration of the input inflow flow rate data. For example, if there is 31 days of daily average inflow flow rate data, the total simulation duration should be less than or equal to 51 days; otherwise, the model calculation will stop after 51 days due to a lack of flow rate data.

[0059] Item 10: Other data includes river network boundary data (regional outer boundary coordinates, river channel boundary coordinates), inflow location data, and inflow data. After the data is prepared, place them in the xlsx workbook you set up, corresponding to the boundary, riverwall, inflow, and flood work columns respectively. You can directly upload this workbook in the model's "Upload Data File" interface.

[0060] After uploading the workbook, a computational grid diagram will be automatically generated. Click "Start" to begin the simulation. Returning to the system homepage, you will see the newly created simulation task. Clicking "Details" will show the calculation progress. Once the calculation is complete, click the triangle to enter the visualization interface. Click the triangle at the bottom to demonstrate the water flow process.

[0061] Clicking on a location in the river channel will display a marker. Clicking this marker will show a graph of the changes in water level, flow velocity, and water depth. To view the flow rate, you first need to create a cross-section. Using the pen tool in the upper right corner, draw two points on a cross-section of the river channel. A marker will appear. Clicking this marker will show a graph of the changes in flow rate at that cross-section.

[0062] Taking Dongting Lake as an example, the water propagation time during the dry season of Dongting Lake is calculated using a runoff evolution model at medium and low water levels. Details are as follows:

[0063] I. Extraction and simplification of river network systems.

[0064] Based on 90m resolution DEM elevation data of the Dongting Lake area, see details. Figure 3 The river network system was extracted using digital geographic elevation data through ArcGIS software. See details. Figure 4 .

[0065] Because the river network extracted from the DEM contains numerous tributaries, and considering the accuracy issues of the DEM, the river channels in some locations may be disordered, thus requiring adjustment and simplification. The main modification is based on the DEM image, using a visual translation method to edit the river network vector data.

[0066] This simulation of the low and medium water levels in Dongting Lake is simplified to "four rivers and three outlets." The "four rivers" are Xiangjiang (Xiangtan), Zijiang (Taojiang), Yuanjiang (Taoyuan), and Lishui (Shimen). The "three outlets" are Songzihe (Xinjiangkou), Huduhe (Mituosi), and Ouchihe (Guanjiapu). The lower boundary, the outlet of Dongting Lake, is Chenglingji. The generalized water system is as follows: Figure 5 As shown.

[0067] II. Boundary conditions.

[0068] The simulation grid boundary conditions include the overall outer boundary of the simulation region and the river channel boundaries, used to divide the region into triangular meshes, which are then used as the computational units for the simulation. The outer boundary of the region is the enclosing area that includes all river channels, as detailed in Figures 6(a) and 6(b). The river channel boundaries are simplified and corrected river network boundary lines, as detailed in Figures 7(a) and 7(b).

[0069] The input conditions also include the coordinates of the inflow location, the inlet radius, and the inflow discharge. The inflow location coordinates refer to the inlets of each river in the simulation area, with the inlet radius being approximately 70% to 80% of the river channel radius at that location. The river channel radius is half the width of the river channel. The inflow locations and coordinates for this simulation are shown in Figures 8(a) and 8(b). The inflow discharge data used in this simulation are the daily average discharge data from seven hydrological stations at the "four rivers and three inlets" locations.

[0070] III. Basic Model Parameters.

[0071] 1. Water flow algorithm: The default algorithm DE0 was selected for this calculation.

[0072] 2. External and Internal Resolution. The input values ​​are the maximum size of the triangular mesh inside and outside the river channel. After setting these values, the model will generate triangular meshes of varying sizes based on the river channel boundary points. The smaller the resolution value, the more triangular mesh elements there are, and the slower the model's computation speed. In this calculation, an external resolution of 1,000,000 and an internal resolution of 500,000 are selected.

[0073] 3. The model evolution time step is set to 3600 seconds in this case;

[0074] 4. The total evolution time is the total simulation time, including the model warm-up period and the simulation period. The total simulation time is 35 days, the warm-up period is 20 days, and the simulation period is 15 days from October 13th to October 27th.

[0075] IV. Simulation Results.

[0076] Using a low-water-level model of Dongting Lake, the evolution of low water levels was calculated during a typical dry year in the study area. In 2022, Dongting Lake experienced a historically rare hydrological drought. After June, monthly precipitation in the Dongting Lake basin was more than 20% below the multi-year average, especially in August when the average precipitation was only 19.70 mm, a decrease of 85.40%, resulting in a drought exceeding a 200-year return period. This study selected the dry season of October 13th to October 27th, 2022, as the simulation period to calculate the evolution of low water levels during this time.

[0077] Input the DEM data of the study area and the daily average inflow data of the three inlets and four rivers to form a computational grid. See details. Figure 9 .

[0078] After simulation calculation, it can be seen that the water flow propagation time from Xiangtan, Taojiang, Taoyuan, Shimen to Chenglingji during the dry season is approximately 2~3 days, 2~3 days, 3~4 days, and 6~7 days, respectively.

[0079] For details on the flow rate changes at a certain section of the river in the project area, please refer to [link / reference]. Figure 10 For details regarding the changes in water depth, water level, and flow velocity at a specific point in the river within the project area, please refer to [link / reference needed]. Figure 11 .

[0080] The model in this invention relies on the constructed river network system of Dongting Lake, clearly defining the basin boundary conditions, inflow conditions, and simulation time. Based on regional topographic data, it simulates the changes in water level, flow velocity, and water depth at any river point during the dry season, and the water flow process can be displayed in animation. This accurately depicts the hydrodynamic processes of Dongting Lake during the dry season, predicts the available water resources in each river section within the basin, provides a quantitative basis for water allocation, and facilitates the optimization of dry season scheduling plans for reservoirs, sluices, and other water conservancy projects within the basin. It also predicts the effectiveness of water replenishment scheduling plans and addresses key issues in basin management during the dry season.

[0081] This embodiment also provides an electronic device, including: a processor and a memory communicatively connected to the processor;

[0082] The memory stores computer-executed instructions;

[0083] The processor executes computer execution instructions stored in the memory to implement the method for obtaining water flow propagation time during the dry season as described above.

[0084] In addition, this embodiment also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method for obtaining water flow propagation time during the dry season as described above.

[0085] It should be noted that the division of the various modules in the above system is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. These modules can be implemented entirely in software via processing element calls; they can be fully implemented in hardware; or some modules can be implemented by processing element calls to software, while others are implemented in hardware. Each module can be a separate processing element, or it can be integrated into a chip in the aforementioned device. Alternatively, it can be stored as program code in the memory of the aforementioned device, and its functions can be called and executed by a processing element of the device. Furthermore, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed through the integrated logic circuits in the hardware of the processor element or through software instructions.

[0086] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0087] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0088] When an integrated unit / module is implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor can be any suitable hardware processor, such as a Central Processing Unit (CPU), Graphics Processing Unit (GPU), Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component. Unless otherwise specified, memory can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as a USB flash drive, Random-Access Memory (RAM), Static Random-Access Memory (SRAM), Dynamic Random-Access Memory (DRAM), Enhanced Dynamic Random-Access Memory (EDRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), Resistive Random Access Memory (RRAM), High-Bandwidth Memory (HBM), and Hybrid Memory Cube (HMC). Cube, magnetic storage, flash memory, disk, optical disk, portable hard drive or magnetic disk, and other media that can store program code.

[0089] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application.

[0090] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for obtaining water flow propagation time during the dry season, characterized in that, Includes the following steps: S1. Obtain initial data for the study area; initial data includes: watershed topographic data, river network boundary data, inflow location data, and inflow data; In S1, the inflow location data includes the inflow location coordinates, inlet radius, and inflow flow rate; the inflow data in S1 consists of flow rate data at daily, hourly, minute, and second intervals for each boundary hydrological station; S2. Input the initial data obtained in S1 into the low-water-level runoff evolution model for simulation calculation to obtain the water propagation time during the dry season. The shallow water wave equation in the runoff evolution model for medium and low water levels is as follows: ； ； ； ； in: U Represents a vector of conserved variables; E express x The flux vector in the direction; G express y The flux vector in the direction; S The source term vector is represented by t; the time variable is represented by t. Indicates water level height; Indicates water in x Velocity in the direction; Indicates water in y Velocity in the direction; Represents gravitational acceleration; for x Momentum in the direction of; for y Momentum in the direction of; for xy Directional momentum coupled flux; This is a transpose.

2. The method for obtaining water flow propagation time during the dry season according to claim 1, characterized in that, In S1, the river network boundary data includes the outer boundary of the study area and the river channel boundary, which are used to divide the data into triangular grids. The triangular grids are used as the computing units for simulation calculations.

3. The method for obtaining water flow propagation time during the dry season according to claim 1, characterized in that, The inflow location coordinates are the inlets of each river, and the inlet radius is approximately 70% to 80% of the river channel radius at that location. The river channel radius is half the width of the river channel.

4. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method for obtaining water flow propagation time during the dry season as described in any one of claims 1-3.

5. An electronic device, characterized in that, include: A processor and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes the computer execution instructions stored in the memory to implement the method for obtaining water flow propagation time during the dry season as described in any one of claims 1-3.

Images