Groundwater runoff deduction method and system based on upper boundary replenishment of full polarization radar

Abstract

The present application relates to the field of groundwater flow numerical simulation, and provides a groundwater runoff deduction method and system based on full polarization radar upper boundary recharge to solve the problem of overall deduction deviation caused by distorted recharge boundary. The groundwater runoff deduction method based on full polarization radar upper boundary recharge comprises traversing according to a grid to obtain a surface and shallow hydrological parameter field, combining a distributed surface hydrological model and topographic and geomorphic data to generate upper boundary recharge data sets and embed them into the upper boundary of a three-dimensional groundwater numerical model, starting remote sensing inversion-hydrological simulation coupling iteration deduction, adaptively calculating coupling correction weights in each time step and dynamically adjusting the upper boundary recharge data of the three-dimensional groundwater numerical model according to the coupling correction weights, generating an initial runoff path field and performing verification, realizing high-precision inversion of shallow near-surface hydrological parameters, focusing on improving the accuracy of the upper boundary recharge conditions of the groundwater model, reducing the data acquisition cost and period, and improving the spatial continuity and timeliness of parameter inversion.

Description

Technical Field

[0001] This invention relates to the field of numerical simulation of groundwater flow, and in particular to a method and system for extrapolating groundwater runoff based on the upper boundary recharge of a fully polarized radar. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Groundwater runoff pathways are core foundational data for groundwater resource assessment, pollution source tracing, rational development and utilization, and ecological environmental protection. The accuracy of these projections directly determines the scientific validity and reliability of related engineering decisions. Currently, groundwater runoff projections based on the upper boundary recharge of fully polarimetric radar mainly employ either single-model projections or simple data coupling, which have significant technical limitations. Traditional single-layer groundwater numerical models rely on a large amount of measured hydrogeological data. However, acquiring this data requires the deployment of numerous monitoring wells and boreholes, which is not only costly and time-consuming but also lacks accuracy in obtaining upper boundary recharge conditions, failing to reflect the dynamic changes in regional groundwater runoff. Existing remote sensing and hydrological model coupling methods are mostly loosely coupled, using remote sensing data only as the initial input to the model without establishing a real-time dynamic mapping relationship between remote sensing inversion parameters and upper boundary recharge parameters. This makes it impossible to synchronously correct upper boundary recharge errors, resulting in low coupling accuracy and difficulty in adapting to the runoff path extrapolation requirements under complex hydrogeological conditions. Fully polarimetric radar can only detect shallow near-surface information. Existing technologies mistakenly use it to correct deep groundwater runoff parameters, violating radar detection mechanisms and further reducing the reliability of extrapolation results. Fully polarimetric radar can only invert shallow near-surface hydrological parameters and cannot directly characterize deep groundwater runoff characteristics. Existing coupling methods do not focus on the core advantages of radar to improve upper boundary accuracy, leading to distortion of the recharge boundary and causing overall extrapolation deviations. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a groundwater runoff estimation method and system based on the upper boundary recharge of fully polarized radar. This method can provide precise technical support for groundwater resource assessment, pollution source tracing, ecological protection, and other related engineering projects, and has a wide range of applications.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: The first aspect of the present invention provides a method for extrapolating groundwater runoff based on the upper boundary recharge of a fully polarized radar.

[0006] In one or more embodiments, a method for extrapolating groundwater runoff based on the upper boundary recharge of a fully polarimetric radar is provided, including: Based on multi-time series fully polarimetric radar remote sensing data and hydrogeological measured data of a preset area, the surface and shallow hydrological parameter fields are obtained by traversing the grid using a polarimetric scattering and hydrological parameter coupling inversion method. Based on surface and shallow hydrological parameter fields and distributed surface hydrological models, coupled with topographic data, the surface runoff generation and confluence process is simulated to obtain the vertical infiltration flux and lateral recharge for each time period, and to generate the upper boundary recharge dataset. The upper boundary replenishment dataset is embedded into the upper boundary of the three-dimensional groundwater numerical model of the preset area to establish the parameter mapping relationship between the remote sensing inversion parameters and the upper boundary replenishment parameters of the three-dimensional groundwater numerical model. Based on the parameter mapping relationship, the remote sensing inversion-hydrological simulation coupled iterative deduction is initiated. According to the weighted sum of the remote sensing inversion parameter deviation and the model upper boundary recharge simulation deviation with the corresponding confidence coefficient, the coupling correction weight is adaptively calculated in each time step and the upper boundary recharge data of the three-dimensional groundwater numerical model is dynamically adjusted accordingly to generate the initial runoff path field. The initial runoff path field is verified. If the verification fails, the coupling correction weights and upper boundary supply parameters are adjusted, and the remote sensing inversion-hydrological simulation coupled iterative deduction is restarted until the verification passes, and the runoff path vector and hydrological parameter set are obtained.

[0007] As one implementation method, the process of adaptively calculating the coupling correction weights within each time step is as follows: ; In the formula, The coupling correction weights are for the t-th time step; For the confidence coefficient of remote sensing parameters; This represents the deviation value of the remote sensing inversion parameters at the t-th time step. Calculate the confidence coefficient for the three-dimensional groundwater numerical model; Let be the simulation deviation value of the upper boundary replenishment of the three-dimensional groundwater numerical model at the t-th time step.

[0008] As one implementation method, based on the upper boundary recharge data of the dynamically adjusted three-dimensional groundwater numerical model, the groundwater motion equation is solved by the finite difference method, the groundwater head and velocity vector of each grid cell are calculated, and a velocity vector field is formed. Then, the initial groundwater runoff path field is generated by the particle tracking method, and the trajectory, velocity and arrival time of each particle are recorded.

[0009] As one implementation method, the inversion formula corresponding to the polarization scattering and hydrological parameter coupling inversion method is: ; In the formula, These are single-grid hydrological parameter values; The horizontal transmission and horizontal reception polarization scattering coefficient; The horizontal transmission and vertical reception polarization scattering coefficient; The vertical transmission and vertical reception polarization scattering coefficient; These are the weighting coefficients for the horizontal polarization components; These are the weighting coefficients for the cross-polarization components; These are the weighting coefficients for the vertical polarization components.

[0010] As one implementation method, verifying the initial runoff path field includes: spatial topology consistency verification, flow conservation verification, and spatiotemporal continuity verification.

[0011] As one implementation method, if the verification fails, the coupling correction weight and the upper boundary supply parameter are adjusted according to the location and deviation level of the abnormal area, and the remote sensing inversion-hydrological simulation coupling iterative deduction is restarted.

[0012] As one implementation method, a three-dimensional groundwater numerical model is constructed based on the aquifer structure and geological stratification of the preset area. The preset area is discretized using a structured grid, and the upper boundary replenishment dataset is assigned to the upper boundary of the three-dimensional groundwater numerical model.

[0013] A second aspect of the present invention provides a groundwater runoff estimation system based on the upper boundary recharge of a fully polarized radar.

[0014] In one or more embodiments, a groundwater runoff estimation system based on the upper boundary recharge of a fully polarized radar includes: The surface and shallow hydrological parameter field generation module is used to obtain the surface and shallow hydrological parameter field by traversing the grid based on multi-time series fully polarimetric radar remote sensing data and hydrogeological measured data of a preset area and using the polarimetric scattering and hydrological parameter coupling inversion method. The upper boundary recharge dataset generation module is used to simulate the surface runoff generation and confluence process based on the surface and shallow hydrological parameter field and the distributed surface hydrological model, coupled with topographic data, to obtain the vertical infiltration flux and lateral recharge for each time period, and generate the upper boundary recharge dataset. The parameter mapping relationship construction module is used to embed the upper boundary replenishment dataset into the upper boundary of the three-dimensional groundwater numerical model of the preset area, and establish the parameter mapping relationship between the remote sensing inversion parameters and the upper boundary replenishment parameters of the three-dimensional groundwater numerical model. The initial runoff path field generation module is used to initiate a coupled iterative deduction of remote sensing inversion and hydrological simulation based on parameter mapping relationship. According to the weighted sum of remote sensing inversion parameter deviation and model upper boundary recharge simulation deviation with the corresponding confidence coefficient, the coupling correction weight is adaptively calculated in each time step and the upper boundary recharge data of the three-dimensional groundwater numerical model is dynamically adjusted accordingly to generate the initial runoff path field. The initial runoff path field verification module is used to verify the initial runoff path field. If the verification fails, the coupling correction weight and the upper boundary supply parameters are adjusted to restart the remote sensing inversion-hydrological simulation coupled iterative deduction until the verification passes, and the runoff path vector and hydrological parameter set are obtained.

[0015] A third aspect of the present invention provides a computer-readable storage medium.

[0016] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method for extrapolating groundwater runoff based on the upper boundary recharge of fully polarized radar.

[0017] A fourth aspect of the present invention provides an electronic device.

[0018] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the groundwater runoff estimation method based on the upper boundary recharge of fully polarized radar as described above.

[0019] Compared with the prior art, the beneficial effects of the present invention are: This invention achieves high-precision inversion of shallow near-surface hydrological parameters by fusing multi-time-series fully polarimetric radar remote sensing data and hydrogeological field measurement data, combined with a polarimetric scattering-hydrological parameter coupling inversion algorithm. It focuses on improving the accuracy of the upper boundary recharge conditions of groundwater models, eliminating the need for dense monitoring well deployment, reducing data acquisition costs and timelines, and simultaneously improving the spatial continuity and timeliness of parameter inversion. Based on remote sensing-hydrological coupling, a real-time dynamic mapping relationship is established between remote sensing inversion parameters and groundwater numerical upper boundary recharge parameters. Combined with a remote sensing-hydrological coupling correction weight adaptive iterative algorithm, dynamic calibration of the upper boundary recharge flux is achieved, solving the problems of low accuracy and inability to correct upper boundary recharge errors in existing loose coupling methods, thus improving the accuracy of runoff path extrapolation. Attached Figure Description

[0020] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0021] Figure 1This is a flowchart of the groundwater runoff extrapolation method based on the upper boundary recharge of fully polarized radar according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the groundwater runoff estimation system based on the upper boundary recharge of fully polarized radar according to an embodiment of the present invention. Figure 3 This is a schematic diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0022] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0023] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0024] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0025] Figure 1 A schematic diagram of the groundwater runoff extrapolation method based on the upper boundary recharge of fully polarimetric radar, according to an embodiment of the present invention, is provided. Figure 1 The groundwater runoff estimation method based on the upper boundary recharge of fully polarized radar in this embodiment may include the following steps S1 to S5.

[0026] The specific implementation process of steps S1 to S5 is as follows: Step S1: Based on multi-time series fully polarimetric radar remote sensing data and hydrogeological measured data of the preset area, the surface and shallow hydrological parameter fields are obtained by traversing the grid using the polarimetric scattering and hydrological parameter coupling inversion method.

[0027] The predefined area is used as the study area. Multi-time series fully polarimetric radar remote sensing data, hydrogeological measured data and topographic data of the study area are acquired. Preprocessing steps such as spatiotemporal benchmark unification, radiometric calibration, geometric fine correction and noise suppression are performed to generate a multi-source fusion dataset.

[0028] The spatiotemporal standardization, radiometric calibration, geometric precision correction, and noise suppression here can all be achieved using existing technologies, and will not be described in detail here.

[0029] Unifying all data onto the same coordinate system (such as WGS84) and time series using a unified spatiotemporal reference is fundamental to multi-source fusion, enabling precise pixel-level overlay of data from different sources. Radiometric calibration converts the digitally quantized values ​​(DN values) recorded by sensors into physically meaningful backscattering coefficients or radiance values, allowing for quantitative comparison of data acquired at different times and from different sensors. Geometric precision correction eliminates positional shifts and distortions caused by topographic relief, Earth curvature, and sensor attitude, ensuring precise matching of imagery to actual geographic coordinates with errors controlled at the sub-pixel level. Noise suppression eliminates speckle noise unique to fully polarimetric radar, resulting in smoother images while preserving edge and detail information as much as possible, improving the accuracy of subsequent ground feature extraction and hydrological parameter inversion.

[0030] In some specific embodiments, a unified geodetic coordinate system is used to complete the spatiotemporal registration of multi-source data, the data is sorted according to the radar remote sensing imaging time series, coherent noise and geometric distortion of radar data are removed, outliers in the measured data are eliminated, and a spatiotemporally continuous multi-source fusion dataset is generated.

[0031] For example, Sentinel-1A / B fully polarimetric synthetic aperture radar data were acquired from January to December 2024, with a spatial resolution of 10m and a temporal resolution of 6 days, resulting in 122 valid data scenes. Simultaneously, measured data on groundwater level, surface runoff, and rainfall from 15 hydrological monitoring stations in the study area, hydrogeological parameter data from 32 boreholes, and SRTMDEM topographic data at a 30m resolution were also acquired.

[0032] All data were spatiotemporally registered using the WGS84 geodetic coordinate system and the UTC time base, unifying radar and measured data to the same spatial grid and time series. Radiometric calibration was performed on the radar data, converting digital quantization values ​​into backscattering coefficients using the official Sentinel-1 calibration coefficients. Geometric fine correction was performed using 20 uniformly distributed ground control points, resulting in a geometric error of less than 0.5 pixels after correction. Lee filtering was used to remove coherent noise from the radar data, with a filter window size of 5×5. Outliers exceeding three standard deviations in the measured data were removed, and linear interpolation was used to complete a small number of missing data points, generating a spatiotemporally continuous multi-source fusion dataset.

[0033] Load multi-time series fully polarimetric radar remote sensing data and hydrogeological measured data from the multi-source fusion dataset, start polarimetric scattering and hydrological parameter coupling inversion operation, complete hydrological parameter calculation by raster traversal, and output surface and shallow hydrological parameter fields with no missing values, which are only used for upper boundary replenishment calculation.

[0034] In practical implementation, the inversion formula corresponding to the polarization scattering and hydrological parameter coupling inversion method is as follows: ; In the formula, These are single-grid hydrological parameter values; The horizontal transmission and horizontal reception polarization scattering coefficient; The horizontal transmission and vertical reception polarization scattering coefficient; The vertical transmission and vertical reception polarization scattering coefficient; These are the weighting coefficients for the horizontal polarization components; These are the weighting coefficients for the cross-polarization components; These are the weighting coefficients for the vertical polarization components.

[0035] The calculation results are assigned grid-by-grid according to the above formula to generate a continuous and complete field of surface and shallow hydrological parameters, supporting the calculation of the upper boundary recharge. For example, The value is 0.32; The value is 0.45; The value is 0.23. The above weighting coefficients were calibrated using soil moisture content and permeability data from 20 measured points in the study area.

[0036] Soil moisture content at three depths (0-20cm, 20-50cm, and 50-100cm) was retrieved with an accuracy controlled within ±5%. The vertical permeability coefficient of the land surface was also retrieved, ranging from 1×10⁻⁶. -6 m / s to 1×10 -3 m / s; The upper boundary recharge potential is calculated based on shallow infiltration characteristics. Values ​​are assigned grid by grid at a resolution of 10m×10m to generate a continuous and complete surface-shallow hydrological parameter field, which is used only for upper boundary recharge calculation.

[0037] Step S2: Based on the surface and shallow hydrological parameter field and the distributed surface hydrological model, coupled with topographic data, simulate the surface runoff generation and confluence process to obtain the vertical infiltration flux and lateral recharge for each time period, and generate the upper boundary recharge dataset.

[0038] The surface and shallow hydrological parameter fields are assigned to the input layer of the distributed surface hydrological model. The runoff resistance coefficient is calculated by combining topographic slope and geomorphological type zoning, the surface runoff generation and confluence process is simulated, and the vertical infiltration flux and lateral recharge are output for each time period to generate a high-precision upper boundary recharge dataset.

[0039] The distributed surface hydrological model here can be implemented using the SWAT (Soil and Water Assessment Tool) distributed surface hydrological model. The entire model is jointly determined by topographic, soil, land use, and meteorological driving data.

[0040] For example, a SWAT distributed surface hydrological model was constructed for the study area, dividing it into 23 sub-basins and 126 hydrological response units. The generated surface-shallow hydrological parameter field was assigned to the soil property module of the model, and the topographic slope was calculated using DEM (Digital Elevation Model) data. The study area was then divided into three geomorphic zones: plain, gentle slope, and hilly, with runoff resistance coefficients assigned to 0.03, 0.06, and 0.12, respectively.

[0041] The surface runoff generation and confluence process for the entire year of 2024 was simulated with a time step of 1 day. The Green-Ampt infiltration model was used to calculate the vertical infiltration flux of different hydrological response units, and Darcy's law was used to calculate the lateral recharge between sub-basins. The vertical infiltration flux and lateral recharge of each grid were output for each time period to generate a high-precision upper boundary recharge dataset with a time resolution of 1 day and a spatial resolution of 10m×10m.

[0042] The Green-Ampt infiltration model is a classic theoretical model used to simulate the infiltration process of initially dry soil under shallow water conditions. It is widely used in hydrology, agriculture, geology, and environmental engineering. Based on Darcy's law, the Green-Ampt infiltration model assumes that the soil is homogeneous and forms a clear horizontal wetting front during infiltration. The soil above the wetting front is fully saturated, while the soil below remains initially dry. Water movement is driven by gravity and matrix potential, moving unidirectionally downwards.

[0043] Step S3: Embed the upper boundary replenishment dataset into the upper boundary of the three-dimensional groundwater numerical model of the preset area, and establish the parameter mapping relationship between the remote sensing inversion parameters and the upper boundary replenishment parameters of the three-dimensional groundwater numerical model.

[0044] In this embodiment, a three-dimensional groundwater numerical model is constructed based on the aquifer structure and geological stratification of the preset area. The preset area is discretized using a structured grid, and the upper boundary replenishment dataset is assigned to the upper boundary of the three-dimensional groundwater numerical model.

[0045] In the specific implementation process, MODFLOW-2005 was used to construct a three-dimensional groundwater numerical model for the study area. The study area was vertically divided into three layers, corresponding to the shallow unconfined aquifer, the weakly permeable layer, and the intermediate aquifer, respectively. The thickness of each layer was determined based on borehole data. A structured grid was used to discretize the study area, with a horizontal grid size of 100m × 100m, generating a total of 5000 horizontal grids and 15000 three-dimensional grid elements. MODFLOW-2005 is a modular three-dimensional finite difference groundwater flow model, widely used to simulate steady and unsteady flow problems in the saturated zone, supporting the analysis of groundwater systems under complex hydrogeological conditions.

[0046] The generated upper boundary recharge dataset is assigned to the upper boundary recharge term of the model, establishing a one-to-one mapping relationship between remote sensing inversion parameters and model upper boundary recharge parameters: soil profile moisture content corresponds to the initial moisture content of the model, surface vertical permeability coefficient corresponds to the hydraulic conductivity coefficient of the model, and upper boundary recharge intensity corresponds to the model recharge flux. Model initialization is completed, and the initial hydraulic head distribution of the model is set to the measured groundwater level on January 1, 2024.

[0047] Step S4: Based on the parameter mapping relationship, initiate the remote sensing inversion-hydrological simulation coupled iterative deduction. According to the weighted sum of the remote sensing inversion parameter deviation and the model upper boundary recharge simulation deviation with the corresponding confidence coefficient, the coupling correction weight is adaptively calculated in each time step and the upper boundary recharge data of the three-dimensional groundwater numerical model is dynamically adjusted accordingly to generate the initial runoff path field.

[0048] In the specific implementation process, the adaptive calculation of the coupling correction weights within each time step is as follows: ; In the formula, The coupling correction weights are for the t-th time step; For the confidence coefficient of remote sensing parameters; This represents the deviation value of the remote sensing inversion parameters at the t-th time step. Calculate the confidence coefficient for the three-dimensional groundwater numerical model; Let be the simulation deviation value of the upper boundary replenishment of the three-dimensional groundwater numerical model at the t-th time step.

[0049] Combined adjustment weight Its function is to determine the proportion of remote sensing inversion results and model simulation results in the final upper boundary replenishment flux.

[0050] Final upper boundary supply flux = × Radar calculated value + (1- ) × Model simulated value; Parameter updates and model iterations are triggered according to a preset time step. The upper boundary supply parameters of the model are calibrated based on a remote sensing and hydrological coupled weighted adaptive iterative algorithm. Runoff paths are generated by continuous tracking of the velocity vector field. After completing the single-step extrapolation, intermediate results are stored.

[0051] For example, the simulation time step is set to 1 day, and the total simulation duration is 365 days. Within each time step, the latest remote sensing inversion parameters are first read, and the coupling correction weights are adaptively and iteratively calculated, for example... The value is 0.6; The value is set to 0.4. Based on the upper boundary recharge data of the dynamically adjusted three-dimensional groundwater numerical model, the groundwater motion equation is solved using the finite difference method. The groundwater head and velocity vector of each grid cell are calculated to form a velocity vector field. Then, the particle tracking method is used to generate the initial groundwater runoff path field, recording the trajectory, velocity, and arrival time of each particle. For example, when the remote sensing inversion deviation exceeds the preset remote sensing inversion deviation threshold (such as signal distortion after heavy rain): Reduce; when the model simulation deviation exceeds the preset simulation deviation threshold (e.g., model drift): Increase. When both deviations are less than the corresponding preset threshold: =0.5.

[0052] It should be noted that those skilled in the art can set the preset remote sensing inversion deviation threshold and the preset simulation deviation threshold according to the actual situation.

[0053] Step S5: Verify the initial runoff path field. If the verification fails, adjust the coupling correction weights and upper boundary supply parameters and restart the remote sensing inversion-hydrological simulation coupled iterative deduction until the verification passes, and obtain the runoff path vector and hydrological parameter set.

[0054] Specifically, the verification of the initial runoff path field includes: spatial topology consistency verification, flow conservation verification, and spatiotemporal continuity verification.

[0055] Spatial topology consistency verification: Check the continuity of runoff paths, identify path breaks and intersection anomalies, requiring path break lengths to be less than 100m and the number of intersections to be less than 5% of the total number of paths; Flow conservation verification: Calculate the inflow and outflow of each sub-basin, requiring the flow conservation error to be less than 5%; Spatiotemporal continuity check: Check the changes in runoff paths between adjacent time steps, requiring the path offset to be less than 50m / day.

[0056] If the verification fails, adjust the coupling correction weights and upper boundary recharge parameters according to the location and deviation level of the abnormal area, and restart the remote sensing inversion-hydrological simulation coupled iterative deduction. If the verification passes, output the final groundwater runoff path vector file, which includes parameters such as the spatial coordinates, direction, velocity, and flow rate of the runoff path, and also output the corresponding set of hydrological parameters such as groundwater head distribution and recharge distribution.

[0057] Through the above steps, this invention achieves deep coupling between fully polarized radar remote sensing and a three-dimensional groundwater numerical model, strictly adheres to the radar detection mechanism, avoids misuse of deep parameters, and the simulation results have a good fit with the measured groundwater level data of more than 0.92. This provides technical support for groundwater resource assessment, pollution prevention and control, and rational development and utilization.

[0058] The embodiments of this invention strictly adhere to the principle that fully polarimetric radar only detects shallow, near-surface groundwater and does not interfere with deep groundwater runoff parameters, thus avoiding inference deviations caused by parameter misuse from the source. The entire inference process is automated, the time step can be flexibly adjusted, and it is adaptable to study areas of different scales and hydrogeological conditions. The inference is highly efficient and repeatable, and can provide accurate technical support for related projects such as groundwater resource assessment, pollution source tracing, and ecological protection. It has a wide range of applications.

[0059] like Figure 2 As shown, the groundwater runoff estimation system based on the upper boundary recharge of a fully polarimetric radar provided in this embodiment of the invention can be implemented in software. The groundwater runoff estimation system based on the upper boundary recharge of a fully polarimetric radar includes the following software modules: The surface and shallow hydrological parameter field generation module 201 is used to obtain the surface and shallow hydrological parameter field by traversing the grid based on multi-time series fully polarimetric radar remote sensing data and hydrogeological measured data of a preset area and using the polarimetric scattering and hydrological parameter coupling inversion method. The upper boundary recharge dataset generation module 202 is used to simulate the surface runoff generation and confluence process based on the surface and shallow hydrological parameter field and the distributed surface hydrological model, coupled with topographic data, to obtain the vertical infiltration flux and lateral recharge for each time period, and generate the upper boundary recharge dataset. The parameter mapping relationship construction module 203 is used to embed the upper boundary replenishment dataset into the upper boundary of the three-dimensional groundwater numerical model of the preset area, and establish the parameter mapping relationship between the remote sensing inversion parameters and the upper boundary replenishment parameters of the three-dimensional groundwater numerical model. The initial runoff path field generation module 204 is used to initiate a coupled iterative deduction of remote sensing inversion and hydrological simulation based on parameter mapping relationship. According to the weighted sum of remote sensing inversion parameter deviation and model upper boundary recharge simulation deviation with the corresponding confidence coefficient, the coupling correction weight is adaptively calculated in each time step and the upper boundary recharge data of the three-dimensional groundwater numerical model is dynamically adjusted accordingly to generate the initial runoff path field. The initial runoff path field verification module 205 is used to verify the initial runoff path field. If the verification fails, the coupling correction weight and the upper boundary supply parameters are adjusted to restart the remote sensing inversion-hydrological simulation coupled iterative deduction until the verification passes, and the runoff path vector and hydrological parameter set are obtained.

[0060] It should be noted that each module in the groundwater runoff estimation system based on the upper boundary supply of fully polarized radar in this embodiment corresponds one-to-one with each step in the groundwater runoff estimation method based on the upper boundary supply of fully polarized radar in the above embodiment, and their specific implementation processes are the same, so they will not be repeated here.

[0061] The structure of the electronic device according to an embodiment of the present invention will be described in detail below. Figure 3 This is a schematic diagram of the composition structure of an electronic device provided in an embodiment of the present invention. It can be understood that... Figure 3 The diagram shows only an exemplary structure of the electronic device, not the entire structure. Some or all of the structures shown may be implemented as needed.

[0062] The electronic device provided in this embodiment of the invention includes: at least one processor 301, a memory 302, a user interface 303, and at least one network interface 304. The various components in the groundwater runoff projection system based on the upper boundary recharge of a fully polarized radar are coupled together via a bus system 305. It can be understood that the bus system 305 is used to realize the connection and communication between these components. In addition to a data bus, the bus system 305 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 3 The general designated all buses as Bus System 305.

[0063] The user interface 303 may include a monitor, keyboard, mouse, trackball, click wheel, buttons, touchpad, or touch screen.

[0064] It is understood that memory 302 can be volatile memory or non-volatile memory, or both. In this embodiment of the invention, memory 302 is capable of storing data to support the operation of the terminal. Examples of this data include any computer programs used to operate on the terminal, such as operating systems and applications. The operating system includes various system programs, such as framework layers, core library layers, driver layers, etc., used to implement various basic services and handle hardware-based tasks. Applications can include various applications.

[0065] In some embodiments, the groundwater runoff estimation system based on the upper boundary recharge of a fully polarimetric radar provided in this invention can be implemented using a combination of hardware and software. For example, the groundwater runoff estimation system based on the upper boundary recharge of a fully polarimetric radar provided in this invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the groundwater runoff estimation method based on the upper boundary recharge of a fully polarimetric radar provided in this invention. For example, the processor in the form of a hardware decoding processor can employ one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0066] As an example, processor 301 can be an integrated circuit chip with signal processing capabilities, such as a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., wherein the general-purpose processor can be a microprocessor or any conventional processor, etc.

[0067] As an example of the hardware implementation of the groundwater runoff estimation system based on the upper boundary recharge of fully polarized radar provided in this embodiment of the invention, the device provided in this embodiment of the invention can be directly executed by a processor 301 in the form of a hardware decoding processor. For example, it can be executed by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components to implement the groundwater runoff estimation method based on the upper boundary recharge of fully polarized radar provided in this embodiment of the invention.

[0068] The memory 302 in this embodiment of the invention is used to store various types of data to support the operation of the groundwater runoff estimation system based on the upper boundary recharge of fully polarimetric radar, or to store data for execution. Figure 1 The program code for the method shown. Examples of this data include: any executable instructions for operating on a groundwater runoff estimation system based on the upper boundary recharge of a fully polarimetric radar, such as executable instructions, and the program for implementing the groundwater runoff estimation method based on the upper boundary recharge of a fully polarimetric radar according to embodiments of the present invention can be contained in the executable instructions.

[0069] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including functions for executing... Figure 1 The program code for the method shown. In such an embodiment, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by the central processing unit, it performs the various functions defined in the apparatus of this application.

[0070] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0071] The above are merely preferred embodiments of the present invention and are not intended to limit the present 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 extrapolating groundwater runoff based on the upper boundary recharge of a fully polarimetric radar, characterized in that, include: Based on multi-time series fully polarimetric radar remote sensing data and hydrogeological measured data of a preset area, the surface and shallow hydrological parameter fields are obtained by traversing the grid using a polarimetric scattering and hydrological parameter coupling inversion method. Based on surface and shallow hydrological parameter fields and distributed surface hydrological models, coupled with topographic data, the surface runoff generation and confluence process is simulated to obtain the vertical infiltration flux and lateral recharge for each time period, and to generate the upper boundary recharge dataset. The upper boundary replenishment dataset is embedded into the upper boundary of the three-dimensional groundwater numerical model of the preset area to establish the parameter mapping relationship between the remote sensing inversion parameters and the upper boundary replenishment parameters of the three-dimensional groundwater numerical model. Based on the parameter mapping relationship, the remote sensing inversion-hydrological simulation coupled iterative deduction is initiated. According to the weighted sum of the remote sensing inversion parameter deviation and the model upper boundary recharge simulation deviation with the corresponding confidence coefficient, the coupling correction weight is adaptively calculated in each time step and the upper boundary recharge data of the three-dimensional groundwater numerical model is dynamically adjusted accordingly to generate the initial runoff path field. The initial runoff path field is verified. If the verification fails, the coupling correction weights and upper boundary supply parameters are adjusted, and the remote sensing inversion-hydrological simulation coupled iterative deduction is restarted until the verification passes, and the runoff path vector and hydrological parameter set are obtained.

2. The groundwater runoff extrapolation method based on the upper boundary recharge of fully polarimetric radar as described in claim 1, characterized in that, The process of adaptively calculating the coupling correction weights within each time step is as follows: ； In the formula, The coupling correction weights are for the t-th time step; For the confidence coefficient of remote sensing parameters; This represents the deviation value of the remote sensing inversion parameters at the t-th time step. Calculate the confidence coefficient for the three-dimensional groundwater numerical model; Let be the simulation deviation value of the upper boundary replenishment of the three-dimensional groundwater numerical model at the t-th time step.

3. The groundwater runoff extrapolation method based on the upper boundary recharge of fully polarimetric radar as described in claim 1, characterized in that, Based on the upper boundary recharge data of the dynamically adjusted three-dimensional groundwater numerical model, the groundwater motion equation is solved using the finite difference method. The groundwater head and velocity vector of each grid cell are calculated to form a velocity vector field. Then, the initial groundwater runoff path field is generated using the particle tracking method, and the trajectory, velocity and arrival time of each particle are recorded.

4. The groundwater runoff extrapolation method based on the upper boundary recharge of fully polarimetric radar as described in claim 1, characterized in that, The inversion formula corresponding to the polarization scattering and hydrological parameter coupling inversion method is: ； In the formula, These are single-grid hydrological parameter values; The horizontal transmission and horizontal reception polarization scattering coefficient; The horizontal transmission and vertical reception polarization scattering coefficient; The vertical transmission and vertical reception polarization scattering coefficient; These are the weighting coefficients for the horizontal polarization components; These are the weighting coefficients for the cross-polarization components; These are the weighting coefficients for the vertical polarization components.

5. The groundwater runoff extrapolation method based on the upper boundary recharge of fully polarimetric radar as described in claim 1, characterized in that, Verification of the initial runoff path field includes: spatial topology consistency verification, flow conservation verification, and spatiotemporal continuity verification.

6. The groundwater runoff extrapolation method based on the upper boundary recharge of fully polarimetric radar as described in claim 1, characterized in that, If the verification fails, adjust the coupling correction weights and upper boundary supply parameters according to the location and deviation level of the abnormal area, and restart the remote sensing inversion-hydrological simulation coupled iterative deduction.

7. The groundwater runoff extrapolation method based on the upper boundary recharge of fully polarimetric radar as described in claim 1, characterized in that, A three-dimensional groundwater numerical model is constructed based on the aquifer structure and geological stratification of the preset area. The preset area is discretized using a structured grid, and the upper boundary replenishment dataset is assigned to the upper boundary of the three-dimensional groundwater numerical model.

8. A groundwater runoff estimation system based on the upper boundary recharge of a fully polarimetric radar, characterized in that, The groundwater runoff extrapolation method based on the upper boundary recharge of fully polarimetric radar as described in any one of claims 1-7 includes: The surface and shallow hydrological parameter field generation module is used to obtain the surface and shallow hydrological parameter field by traversing the grid based on multi-time series fully polarimetric radar remote sensing data and hydrogeological measured data of a preset area and using the polarimetric scattering and hydrological parameter coupling inversion method. The upper boundary recharge dataset generation module is used to simulate the surface runoff generation and confluence process based on the surface and shallow hydrological parameter field and the distributed surface hydrological model, coupled with topographic data, to obtain the vertical infiltration flux and lateral recharge for each time period, and generate the upper boundary recharge dataset. The parameter mapping relationship construction module is used to embed the upper boundary replenishment dataset into the upper boundary of the three-dimensional groundwater numerical model of the preset area, and establish the parameter mapping relationship between the remote sensing inversion parameters and the upper boundary replenishment parameters of the three-dimensional groundwater numerical model. The initial runoff path field generation module is used to initiate a coupled iterative deduction of remote sensing inversion and hydrological simulation based on parameter mapping relationship. According to the weighted sum of remote sensing inversion parameter deviation and model upper boundary recharge simulation deviation with the corresponding confidence coefficient, the coupling correction weight is adaptively calculated in each time step and the upper boundary recharge data of the three-dimensional groundwater numerical model is dynamically adjusted accordingly to generate the initial runoff path field. The initial runoff path field verification module is used to verify the initial runoff path field. If the verification fails, the coupling correction weight and the upper boundary supply parameters are adjusted to restart the remote sensing inversion-hydrological simulation coupled iterative deduction until the verification passes, and the runoff path vector and hydrological parameter set are obtained.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the groundwater runoff extrapolation method based on the upper boundary supply of fully polarized radar as described in any one of claims 1-7.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the groundwater runoff extrapolation method based on the upper boundary recharge of fully polarized radar as described in any one of claims 1-7.

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