A river flood deduction method based on water energy complementary mechanism

Abstract

This application belongs to the field of hydrological forecasting technology, specifically disclosing a method for river flood projection based on a hydro-energy complementarity mechanism. This application first determines the potential maximum storage capacity of the target river at the current moment under the constraint of water flow energy only, and simultaneously determines the potential maximum water supply of the target river at the current moment under the constraint of water supply only. Then, it nonlinearly couples the potential maximum storage capacity and water supply at the current moment using parameters of spatial heterogeneity along the river's course to obtain the projected storage capacity of the target river under the constraints of water supply and water flow energy. Finally, it substitutes the projected storage capacity into the water balance equation to derive the downstream outflow process of the target river. This application's scheme can characterize the coupling features of water volume and energy during flood evolution, making up for the shortcomings of traditional models in expressing related processes, and is more suitable for forecasting complex nonlinear floods under conditions of insufficient basic river data.

Description

Technical Field

[0001] This application belongs to the field of hydrological forecasting technology and relates to a method for predicting river floods based on a hydropower complementarity mechanism. Background Technology

[0002] River flood simulation is a core link connecting the runoff generation process in a watershed with downstream flood control scheduling. Given that the evolution of floods in natural rivers is essentially a complex, unsteady flow process, exhibiting strong suddenness and nonlinearity, achieving high-precision flood simulation is crucial for forecasting downstream peak flow, evolution duration, and inundation extent.

[0003] Currently, the mainstream flood calculation methods in academia mainly fall into two categories: hydrodynamic models and hydrological conceptual models. The former focuses on a more detailed description of the flow process, but usually relies on relatively complete topographic data and boundary conditions, and involves significant computational load and complex parameter calibration, making its application difficult in areas with limited data. The latter, by generalizing the process of a watershed or river channel, has the advantages of relatively simple structure and convenient application, but its ability to characterize complex nonlinear flood processes remains limited, and the physical orientation of some parameters is also relatively limited. To address the practical needs of river flood calculation under conditions of insufficient basic data, it is urgent to explore a calculation method that balances process representation capabilities with engineering applicability, in order to better meet the requirements of combining theoretical analysis with practical applications. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of this application is to provide a river flood estimation method based on the hydropower complementarity mechanism, which aims to solve the technical problem that the current river flood estimation method under the condition of insufficient basic data is unable to cope with complex nonlinear flood processes.

[0005] The first aspect of this application relates to a method for estimating river floods based on a hydropower complementarity mechanism, comprising: The effective duration of the current inflow in the target river channel is defined as the effective response parameter. Based on the effective response parameter, the potential maximum storage capacity of the target river channel at the current moment under the constraint of water flow energy is determined, and the potential maximum water supply of the target river channel at the current moment under the constraint of water supply is also determined. By nonlinearly coupling the potential maximum storage capacity and the potential maximum water supply at the current moment using the spatial heterogeneity parameters along the river, the projected storage capacity of the target river under the constraints of water supply and water flow energy is obtained. The downstream outflow process of the target river channel is obtained based on the calculated storage capacity.

[0006] Preferably, the historical upstream inflow process and historical downstream outflow process of the target river channel are substituted into the river flood estimation method to deduce the downstream outflow process. With the goal of minimizing the fitting error between the derived downstream outflow process and the historical downstream outflow process, the effective response parameters and the spatial heterogeneity parameters along the river are obtained through inversion.

[0007] Preferably, the objective function is to maximize the Nash efficiency coefficients of the derived downstream outflow process and the historical downstream outflow process, and the effective response parameters and the spatial heterogeneity parameters along the flow path are calibrated using a shuffled complex evolutionary algorithm.

[0008] Preferably, the potential maximum storage capacity at the current moment is specifically: ; in, This represents the maximum potential storage capacity at the current moment. For valid response parameters; for The storage capacity is calculated over time. for The upstream inflow process at all times for The upstream inflow process at any given moment.

[0009] Preferably, the spatial heterogeneity parameter along the river is related to the channel storage state along the target river.

[0010] Preferably, the projected storage capacity is specifically: ; in, for The projected storage capacity of the target river channel at all times. For parameters of spatial heterogeneity along the path, This represents the maximum potential water supply at the current moment. This represents the maximum potential storage capacity at the current moment.

[0011] Preferably, the downstream outflow process of the target river channel is obtained based on the estimated storage capacity, specifically by substituting the estimated storage capacity into the water balance equation to obtain the downstream outflow process of the target river channel.

[0012] In a second aspect, this application provides an electronic device, comprising: at least one memory for storing a program; and at least one processor for executing the program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to execute the method described in the first aspect or any possible implementation thereof.

[0013] Thirdly, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to perform the method described in the first aspect or any possible implementation thereof.

[0014] Overall, the technical solutions conceived in this application have the following beneficial effects compared with the prior art: (1) This application constructs the potential maximum storage capacity at the current moment under the constraint of water flow energy and the potential maximum water supply at the current moment under the constraint of water supply, and integrates the two into the projected storage capacity of the river channel through the spatial heterogeneity parameter along the channel. This storage capacity projection method incorporates the physical constraints in the flood evolution process to a certain extent, and can reflect the mutual constraint and compensation relationship between water volume and energy. It makes up for the shortcomings of traditional black box or semi-empirical models in the expression of physical mechanisms to a certain extent, and is more suitable for the forecasting of complex nonlinear floods under the condition of insufficient basic river channel data.

[0015] (2) Existing flood projection techniques focus on the detailed characterization of fluid dynamics mechanisms, which are highly dependent on detailed parameters, such as various flow parameters, topographic parameters, and boundary parameters. These techniques suffer from complex parameter calibration, high computational cost, and low efficiency. The flood projection method proposed in this application requires only two core parameters in its projection model: the effective response parameter and the spatial heterogeneity parameter along the river, to characterize the nonlinear regulation and storage characteristics of flood waves in the river channel. Compared to hydrodynamic models that rely on multiple empirical parameters or complex hydraulic geometry elements, the parameter system in this application is relatively simple. Flood prediction can be achieved by obtaining only two core parameters in the projection model through inversion. This method features low computational cost and high computational efficiency, providing a relatively simple implementation method for real-time flood control scheduling or large-scale basin flood projection.

[0016] (3) This application describes the flattening and hysteresis effects of floods in complex river channel evolution by nonlinearly coupling the current potential maximum storage capacity and the current potential maximum water supply in a norm form. The Shuffle Complex Evolutionary Algorithm (SCE-UA), which aims to maximize the Nash efficiency coefficient (NSE), ensures the global optimality of the core parameters. Implementation examples show that in highly nonlinear river sections, the average NSE of this application during the test period can reach 0.92, which is significantly better than the 0.88 of the traditional Muskingum model, and the root mean square error (RMSE) is also lower, thus proving that the method of this application has a strong nonlinear simulation capability.

[0017] (4) This application only requires upstream inflow and downstream outflow processes to complete parameter calibration and flood projection, without relying on detailed hydraulic data such as river cross-section geometry and roughness that are difficult to obtain. Combined with regional parameter transfer or physical geographic feature regression methods, this application can be effectively applied to river flood forecasting in areas with no or scarce data, significantly expanding the applicability of the model.

[0018] (5) The effective response parameters in this application comprehensively reflect the superimposed contribution of multiple hydraulic effects (such as friction, inertia, and regulation) to the change in river channel storage capacity during the inflow process; the spatial heterogeneity parameters along the course quantitatively characterize the coupling strength and balance relationship between water supply constraints and energy constraints. The two parameters correspond to the relevant role characteristics in the river flood projection process and can provide a reference for parameter analysis and regional pattern research. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating a method for extrapolating river floods based on a hydropower complementarity mechanism, as provided in an embodiment of this application.

[0020] Figure 2 A comparative schematic diagram of the measured-deduced flow process lines of the present application method and the Muskingen method provided for embodiments of this application.

[0021] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0023] In this application, the term "and / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A existing alone, A and B existing simultaneously, and B existing alone. In this application, the symbol " / " indicates that the related objects are in an "or" relationship, for example, A / B means A or B.

[0024] In this application, the terms “first” and “second” are used to distinguish different objects, rather than to describe a specific order of objects.

[0025] In this application, the term "electrical connection" can refer to a direct circuit connection or a signal transmission via a communication protocol.

[0026] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0027] In the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more, for example, multiple moments refer to two or more moments, and multiple parameters refer to two or more parameters, etc.

[0028] The embodiments of this application are described below with reference to the accompanying drawings.

[0029] like Figure 1 As shown, the embodiments of this application specifically include the following steps: S1. Determine the maximum potential storage capacity of the target river channel at the current moment, only under the constraint of water flow energy.

[0030] Specifically, the energy constraint of water flow is characterized by the effective response parameter, which represents the contribution of the upstream inflow process to the change in storage capacity. The effective response parameter is the effective duration of the current inflow in the target channel, understood as the time required for the target channel's water storage to return to dynamic equilibrium under the current inflow conditions, also known as the relaxation time. In this embodiment, it is specifically manifested as follows: ; in, The potential maximum storage capacity at the current moment; The effective response parameters; for The storage capacity is calculated over time. For the upstream inflow process, subscript and Indicates different times.

[0031] This application uses effective response parameters to comprehensively reflect the superimposed contribution of multiple hydraulic effects (such as friction, inertia, and regulation) on the changes in river storage capacity during the inflow process.

[0032] S2. Determine the maximum potential water supply of the target river channel at the current moment, under water supply constraints only, specifically as follows: ; in, The potential maximum water supply at the current moment. For time step.

[0033] S3. By nonlinearly coupling the potential maximum storage capacity and the potential maximum water supply at the current moment using the spatial heterogeneity parameters along the river, the projected storage capacity of the target river channel under the constraints of water supply and water flow energy is obtained.

[0034] The mathematical form of the deduced storage capacity in this embodiment is specifically expressed as follows: ; in, for The projected storage capacity of the target river channel at all times. For parameters of spatial heterogeneity along the path, The potential maximum water supply at the current moment. This represents the maximum potential storage capacity at the current moment.

[0035] This application integrates the current potential maximum storage capacity and the current potential maximum water supply into the projected storage capacity of the river channel by using spatial heterogeneity parameters along the channel. This storage capacity calculation formula incorporates the physical constraints of the flood evolution process to a certain extent, and can reflect the mutual constraints and compensation relationship between water and energy, thus making up for the shortcomings of traditional black box or semi-empirical models in expressing physical mechanisms.

[0036] S4. Substitute the estimated storage capacity into the water balance equation to deduce the downstream outflow process of the target river channel. In this embodiment, the water balance equation is specifically: ; in, This refers to the downstream outflow process derived from the deduction, with the subscript indicating the time.

[0037] The flood simulation method proposed in this application requires only two core parameters: the effective response parameter and the spatial heterogeneity parameter along the river course, to characterize the nonlinear storage and regulation features of flood waves in the river channel. Compared with hydrodynamic models that rely on multiple empirical parameters or complex hydraulic geometry elements, the parameter system of this application is relatively simple, which helps to reduce the complexity of the parameter calibration process and can provide a simplified implementation method for real-time flood control scheduling or large-scale basin flood simulation.

[0038] Both core parameters correspond to the relevant characteristics of the river flood evolution process, and can provide a reference for parameter analysis and regional pattern research.

[0039] Parameter calibration and simulation accuracy evaluation: The historical upstream inflow and historical downstream outflow processes of the target river channel are divided into a rate-setting period and a testing period.

[0040] Utilization rates are determined periodically by analyzing historical upstream inflow and downstream outflow processes to determine the values ​​of effective response parameters and friction space heterogeneity parameters. Substituting the historical upstream inflow and downstream outflow processes at regular intervals into the aforementioned river flood projection method, specifically into the following model: ; ; The downstream outflow process is derived through deduction. With the goal of minimizing the fitting error between the derived downstream outflow process and the historical downstream outflow process at the rate period, the effective response parameters and the spatial heterogeneity parameters along the flow path are obtained through inversion.

[0041] In this embodiment, the objective function is to maximize the Nash efficiency coefficient (NSE) of the derived downstream outflow process and the historical downstream outflow process. The Shuffle Complex Evolutionary Algorithm (SCE-UA) is used to calibrate the effective response parameters and the spatial heterogeneity parameters along the flow path.

[0042] The Nash efficiency coefficient is as follows: ; in, This represents the Nash efficiency coefficient. This is the historical downstream outflow process. To extrapolate the downstream outflow process, To extrapolate the duration of the period, Indicates the time sequence number.

[0043] Based on the physical meaning of the effective response parameters and the parameters of spatial heterogeneity along the path, a general range of values ​​is determined. The ranges are shown in Table 1. Table 1

[0044] by With the goal of maximizing, the Shuffle Complex Evolutionary Algorithm (SCE-UA) is used to optimize the core parameters of the model within the aforementioned range. and Perform calibration to obtain the globally optimal parameter combination.

[0045] In this embodiment, the calibration results of the effective response parameters and the along-path spatial heterogeneity parameters are shown in Table 2: Table 2

[0046] Accuracy evaluation: Historical upstream inflow and downstream outflow processes during the test period were selected to verify the model accuracy. The downstream outflow process of a certain river channel was simulated using both the method proposed in this application and the traditional Muskingen model. The simulated process parameters were used for simulation, and the simulated and measured flow process curves were compared to evaluate the accuracy. The simulation results are attached. Figure 2 .

[0047] The average accuracy comparison results are shown in Table 3: Table 3

[0048] In summary, the method of this application achieves an average Nash efficiency coefficient of 0.92 during the test period in strongly nonlinear river sections, which is significantly better than the 0.88 of the traditional Muskingen model. The accuracy indicators of the simulation by the model of this application are significantly higher than those of the Muskingen model, indicating that the model of this application has strong physical adaptability to complex river flow patterns and has strong applicability and application value.

[0049] The method presented in this application requires only upstream inflow and downstream outflow processes to complete parameter calibration and flood projection, without relying on difficult-to-obtain hydraulic data such as detailed river cross-section geometry and roughness. Combined with regional parameter transfer or physical geographic feature regression methods, this application can be effectively applied to river flood forecasting in areas with no or scarce data, significantly expanding the model's applicability.

[0050] The above description is merely a preferred embodiment of the model in this application and is not intended to limit the invention. Parameters and The value of adapts to different river channel physical properties. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

[0051] Based on the methods in the above embodiments, this application provides an electronic device, such as... Figure 3 As shown, the electronic device may include a processor, a communications interface, a memory, and a communication bus, wherein the processor, communications interface, and memory communicate with each other via the communication bus. The processor can invoke logical instructions stored in the memory to execute the methods described in the above embodiments.

[0052] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium 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 described in the various embodiments of this application.

[0053] Based on the methods in the above embodiments, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0054] Based on the methods in the above embodiments, this application provides a computer program product that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0055] It is understood that the processor in the embodiments of this application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.

[0056] The method steps in this application embodiment can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an ASIC.

[0057] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0058] It is understood that the various numerical designations used in the embodiments of this application are merely for the convenience of description and are not intended to limit the scope of the embodiments of this application.

[0059] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for estimating river floods based on a hydropower complementarity mechanism, characterized in that, include: The effective duration of the current inflow in the target river channel is defined as the effective response parameter. Based on the effective response parameter, the potential maximum storage capacity of the target river channel at the current moment is determined only under the constraint of water flow energy. The specific potential maximum storage capacity at the current moment is: ； in, This represents the maximum potential storage capacity at the current moment. For valid response parameters; for The storage capacity is calculated over time. for The upstream inflow process at all times for The upstream inflow process at any given moment; Simultaneously, determine the maximum potential water supply of the target river channel at the current moment, under water supply constraints; specifically: ； in, The potential maximum water supply at the current moment. For time step; By nonlinearly coupling the potential maximum storage capacity and the potential maximum water supply at the current moment using parameters of spatial heterogeneity along the river, the projected storage capacity of the target river channel under the constraints of water supply and water flow energy is obtained; the projected storage capacity is specifically: ； in, for The projected storage capacity of the target river channel at all times. For parameters of spatial heterogeneity along the path, This represents the maximum potential water supply at the current moment. This represents the maximum potential storage capacity at the current moment. The downstream outflow process of the target river channel is obtained based on the calculated storage capacity; The spatial heterogeneity parameter along the river is related to the channel storage state along the target river.

2. The river flood projection method according to claim 1, characterized in that, The historical upstream inflow and downstream outflow processes of the target river are substituted into the river flood estimation method to deduce the downstream outflow process. With the goal of minimizing the fitting error between the derived downstream outflow process and the historical downstream outflow process, the effective response parameters and the spatial heterogeneity parameters along the river are obtained through inversion.

3. The river flood projection method according to claim 2, characterized in that, Specifically, the objective function is to maximize the Nash efficiency coefficients of the derived downstream outflow process and the historical downstream outflow process. The effective response parameters and the spatial heterogeneity parameters along the flow path are calibrated using a shuffled complex evolutionary algorithm.

4. The river flood projection method according to claim 1, characterized in that, The downstream outflow process of the target river channel is obtained based on the estimated storage capacity, specifically by substituting the estimated storage capacity into the water balance equation to obtain the downstream outflow process of the target river channel.

5. An electronic device, characterized in that, Includes memory and one or more processors; The memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code including computer instructions; The one or more processors invoke the computer instructions to cause the electronic device to execute the river flood simulation method as described in any one of claims 1-4.

6. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on an electronic device, the electronic device performs the river flood projection method as described in any one of claims 1-4.

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