Method and device for optimizing and checking precipitation infiltration coefficient, electronic equipment and storage medium

Abstract

The application provides a method and device for optimizing and checking precipitation infiltration coefficients, electronic equipment and a storage medium, and belongs to the technical field of hydrogeology and groundwater resource evaluation. In the method, a series coupling framework of a calibrated Hydrus model (point scale physical model) -> a precipitation infiltration coefficient initial field in space (plane scale) -> a regional groundwater flow numerical model (volume scale verification) is constructed. Distributed groundwater level monitoring data is used as a global constraint, and the precipitation infiltration coefficient initial field is optimized through an inversion algorithm, so that a scientific leap of the precipitation infiltration coefficient from discrete point values to a continuous high-precision field is realized, and the global verification is completed by using the regional response, so that the spatial representativeness and accuracy problem of the precipitation infiltration coefficient is fundamentally solved.

Description

Technical Field

[0001] This invention relates to the field of hydrogeology and groundwater resource assessment technology, and in particular to a method, apparatus, electronic device and storage medium for optimizing and verifying precipitation infiltration coefficient. Background Technology

[0002] In groundwater resource assessment and calculation, the acquisition and verification of hydrogeological parameters are indispensable and crucial steps. The precipitation infiltration coefficient, as a core parameter reflecting the amount of precipitation infiltration recharge, directly affects the accuracy of groundwater resource assessment results.

[0003] Currently, methods such as precipitation infiltration tests and ground permeameters are commonly used to obtain and verify this coefficient. However, due to the limited number of test sites and insufficient representativeness, when conducting groundwater resource calculations and evaluations on a national scale (provinces or first-level river basins), the empirical coefficient zoning method is usually still required. Therefore, parameter verification is particularly necessary when using empirical coefficients or existing test data.

[0004] With the increasing demands for accuracy in groundwater resource assessments and the continuous advancement of hydrogeological monitoring technologies, the precision and coverage density of data monitoring have significantly improved. Against this backdrop, further enhancing the accuracy of hydrogeological parameters has become increasingly important, with the verification of precipitation infiltration coefficients being particularly crucial.

[0005] The traditional process for obtaining precipitation infiltration coefficient has the following drawbacks: 1) On the one hand, although the field measurement method based on limited test points is relatively direct, it is difficult to deploy densely in large areas (such as provincial or watershed scales) due to cost and cycle constraints, resulting in a serious lack of spatial representativeness of the parameters obtained. On the other hand, although the empirical coefficient method currently widely used solves the problem of "existence", it is essentially a rough generalization and fails to accurately reflect the spatial heterogeneity of precipitation infiltration, making it difficult to meet the increasingly demanding needs for refined evaluation and management of groundwater resources.

[0006] 2) Surface methods such as precipitation infiltration tests are difficult to capture the spatial variation of gas lithology and structure, leading to "point-to-surface" errors; while subsurface permeameters can represent processes at a "point," their high construction costs prevent large-scale deployment. Empirical coefficient methods rely on historical data and expert experience, and their extrapolation applicability is often challenged under complex underlying surface conditions or climate change contexts, introducing systematic errors that are difficult to quantify.

[0007] 3) Limited, discrete test point data are insufficient to accurately characterize regional spatial heterogeneity, making the upgrading of parameters from "point" to "area" scales lack rigorous scientific basis. Secondly, empirical coefficients are often generalized values ​​with unclear physical mechanisms, and are difficult to update in a timely manner with dynamic changes in surface conditions and climate models, leading to lags and uncertainties in evaluation results. Furthermore, existing methods cannot fully utilize the increasingly abundant monitoring data to constrain and optimize the parameter field as a whole, thus becoming a major bottleneck in improving the accuracy of regional groundwater resource assessment.

[0008] In summary, traditional methods for obtaining regional-scale precipitation infiltration coefficients suffer from technical problems such as insufficient spatial representativeness, reliance on experience, and a lack of effective regional verification methods. Summary of the Invention

[0009] In view of this, the purpose of the present invention is to provide a method, apparatus, electronic device and storage medium for optimizing and verifying precipitation infiltration coefficient, so as to alleviate the technical problems of insufficient spatial representativeness, reliance on experience and lack of effective regional verification means in traditional methods when obtaining regional-scale precipitation infiltration coefficient.

[0010] In a first aspect, the present invention provides a method for optimizing and verifying the precipitation infiltration coefficient, comprising: Acquire basic geographic data, hydro-meteorological data, hydrogeological parameters, and distributed groundwater level monitoring data for the study area; Based on the basic geographic data, the hydro-meteorological data, and the hydrogeological parameters, a target profile is selected in the study area, and a one-dimensional Hydrus model is established for the target profile. Then, the Hydrus model is calibrated using local precipitation infiltration test data to obtain a calibrated Hydrus model. The calibrated Hydrus model can accurately simulate the precipitation infiltration process at the point scale. The study area is divided into multiple computational units. The calibrated Hydrus model is used to simulate the precipitation infiltration process under a standard precipitation scenario for each computational unit. The initial value of the precipitation infiltration coefficient for each computational unit is calculated based on the simulation results. Based on spatial auxiliary data related to precipitation infiltration and the initial value of the precipitation infiltration coefficient for each computational unit, an initial field of precipitation infiltration coefficient covering the study area is generated by spatial interpolation method. The initial field of precipitation infiltration coefficient is used as a recharge source and input into the constructed regional groundwater flow numerical model. The regional groundwater flow numerical model is run to simulate the groundwater level process line at each monitoring point. The groundwater level process lines at each monitoring point are compared with the distributed groundwater level monitoring data, and the fitting error is calculated. Based on the fitting error, an objective function is generated, and an optimization algorithm is used to automatically adjust the initial field of the precipitation infiltration coefficient until the objective function meets the preset standard. The initial field of precipitation infiltration coefficients corresponding to the objective function satisfying the preset standard is used as the optimized precipitation infiltration coefficient field.

[0011] Furthermore, the groundwater level process curve at each monitoring point represents the change in water level at each monitoring point over time. Based on the fitting error, an objective function is generated, and an optimization algorithm is used to automatically adjust the initial field of the precipitation infiltration coefficient until the objective function meets a preset standard. This includes: Minimizing the fitting error is taken as the objective function; Using a parameter estimation optimization algorithm, the initial field of precipitation infiltration coefficient is automatically and iteratively adjusted until the objective function meets the preset standard.

[0012] Furthermore, the standard precipitation scenario includes: a uniform precipitation event of preset intensity; the simulation results include: cumulative infiltration and precipitation; the calibrated Hydrus model is used to simulate the precipitation infiltration process under the standard precipitation scenario for each calculation unit; and the initial value of the precipitation infiltration coefficient for each calculation unit is calculated based on the simulation results, including: A uniform, preset-intensity precipitation event is used as the input to the calibrated Hydrus model to simulate the precipitation infiltration process under the precipitation event, and the initial value of the precipitation infiltration coefficient of each calculation unit is calculated based on the ratio of the cumulative infiltration amount to the precipitation amount.

[0013] Furthermore, the parameter estimation optimization algorithm is the PEST parameter estimation software or its core algorithm.

[0014] Furthermore, the basic geographic data includes at least one of: digital elevation model, land use map, soil type map and geological map; The hydrometeorological data includes at least one of precipitation, evaporation, and temperature data; The hydrogeological parameters include at least one of the following: vadose zone lithology and saturated aquifer parameters.

[0015] Furthermore, the regional groundwater flow numerical model is a three-dimensional groundwater flow numerical model constructed based on Modflow, FEFLOW, or GMS.

[0016] Furthermore, the uniform, preset intensity precipitation event is a precipitation event with a daily precipitation of 50 mm.

[0017] Secondly, the present invention also provides an apparatus for optimizing and verifying the precipitation infiltration coefficient, comprising: The acquisition unit is used to acquire basic geographic data, hydro-meteorological data, hydrogeological parameters, and distributed groundwater level monitoring data for the study area. A construction and calibration unit is used to select a target profile in the study area based on the basic geographic data, the hydro-meteorological data, and the hydrogeological parameters, and to establish a one-dimensional Hydrus model for the target profile. Then, the Hydrus model is calibrated using local precipitation infiltration test data to obtain a calibrated Hydrus model. The calibrated Hydrus model can accurately simulate the precipitation infiltration process at the point scale. The simulation and calculation unit is used to divide the study area into multiple calculation units, simulate the precipitation infiltration process under the standard precipitation scenario for each calculation unit using the calibrated Hydrus model, and calculate the initial value of the precipitation infiltration coefficient for each calculation unit based on the simulation results. A generation unit is used to generate an initial field of precipitation infiltration coefficients covering the study area using a spatial interpolation method, based on spatial auxiliary data related to precipitation infiltration and the initial value of the precipitation infiltration coefficient of each calculation unit. The input running unit is used to input the initial field of precipitation infiltration coefficient as a recharge source into the constructed regional groundwater flow numerical model, run the regional groundwater flow numerical model, and simulate the groundwater level process line at each monitoring point; The comparison calculation unit is used to compare the groundwater level process line of each monitoring point with the distributed groundwater level monitoring data and calculate the fitting error; The adjustment unit is used to generate an objective function based on the fitting error and automatically adjust the initial field of the precipitation infiltration coefficient using an optimization algorithm until the objective function meets a preset standard. The setting unit is used to take the initial field of precipitation infiltration coefficient corresponding to the objective function satisfying the preset standard as the optimized precipitation infiltration coefficient field.

[0018] Thirdly, the present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the method described in the first aspect.

[0019] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the method described in the first aspect.

[0020] This invention provides a method for optimizing and verifying precipitation infiltration coefficients, comprising: acquiring basic geographic data, hydro-meteorological data, hydrogeological parameters, and distributed groundwater level monitoring data of the study area; selecting a target profile within the study area based on the basic geographic data, hydro-meteorological data, and hydrogeological parameters, and establishing a one-dimensional Hydrus model for the target profile; then calibrating the Hydrus model using local precipitation infiltration test data to obtain a calibrated Hydrus model, wherein the calibrated Hydrus model can accurately simulate the precipitation infiltration process at a point scale; dividing the study area into multiple computational units, simulating the precipitation infiltration process under a standard precipitation scenario for each computational unit using the calibrated Hydrus model, and calculating the infiltration coefficient for each unit based on the simulation results. The method calculates the initial value of precipitation infiltration coefficient for each computational unit; based on spatial auxiliary data related to precipitation infiltration and the initial value of precipitation infiltration coefficient for each computational unit, an initial field of precipitation infiltration coefficient covering the study area is generated using spatial interpolation; the initial field of precipitation infiltration coefficient is used as a recharge source and input into the constructed regional groundwater flow numerical model, and the regional groundwater flow numerical model is run to simulate the groundwater level process line at each monitoring point; the groundwater level process line at each monitoring point is compared with the distributed groundwater level monitoring data, and the fitting error is calculated; an objective function is generated based on the fitting error, and an optimization algorithm is used to automatically adjust the initial field of precipitation infiltration coefficient until the objective function meets the preset standard; the initial field of precipitation infiltration coefficient corresponding to the objective function meeting the preset standard is used as the optimized precipitation infiltration coefficient field. As can be seen from the above description, the method for optimizing and verifying precipitation infiltration coefficient of the present invention constructs a series coupling framework of calibrated Hydrus model (point-scale physical model) → initial field of precipitation infiltration coefficient in space (area-scale) → regional groundwater flow numerical model (volume-scale verification). By using distributed groundwater level monitoring data as a global constraint, the initial field of precipitation infiltration coefficient is optimized through an inversion algorithm. This achieves a scientific leap from discrete point values ​​to a continuous, high-precision field for precipitation infiltration coefficient. Furthermore, global verification is completed using regional response (i.e., the fitting error between the groundwater level process line at each monitoring point and the distributed groundwater level monitoring data). This fundamentally solves the problem of spatial representativeness and accuracy of precipitation infiltration coefficient. In other words, the optimized precipitation infiltration coefficient field obtained by this invention is continuous, covers the entire study area, and has been optimized and verified using regional response, resulting in good accuracy. It is achieved through physical models and data-driven methods, without relying on experience, making it more scientific. This alleviates the technical problems of insufficient spatial representativeness, reliance on experience, and lack of effective regional verification methods in traditional methods for obtaining regional-scale precipitation infiltration coefficients. Attached Figure Description

[0021] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 A flowchart illustrating a method for optimizing and verifying the precipitation infiltration coefficient, provided by an embodiment of the present invention; Figure 2 This invention provides a technical roadmap for optimizing and verifying the precipitation infiltration coefficient. Figure 3 A schematic diagram of an apparatus for optimizing and verifying precipitation infiltration coefficient provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0023] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only 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.

[0024] Traditional methods for obtaining regional-scale precipitation infiltration coefficients suffer from insufficient spatial representativeness, reliance on experience, and a lack of effective regional verification methods.

[0025] Based on this, the method for optimizing and verifying the precipitation infiltration coefficient in this invention constructs a cascaded coupling framework of a calibrated Hydrus model (point-scale physical model) → an initial spatial field of precipitation infiltration coefficient (surface scale) → a regional groundwater flow numerical model (volume-scale verification). Utilizing distributed groundwater level monitoring data as a global constraint, the initial field of precipitation infiltration coefficient is optimized through an inversion algorithm, achieving a scientific leap from discrete point values ​​to a continuous, high-precision field. Furthermore, global verification is completed using regional response (i.e., the fitting error between the groundwater level process line at each monitoring point and the distributed groundwater level monitoring data), fundamentally solving the problem of spatial representativeness and accuracy of the precipitation infiltration coefficient. In other words, the optimized precipitation infiltration coefficient field obtained by this invention is continuous, covers the entire study area, and is optimized and verified using regional response, resulting in good accuracy. It is achieved through a physical model and data-driven approach, without relying on experience, making it more scientific.

[0026] To facilitate understanding of this embodiment, a method for optimizing and verifying the precipitation infiltration coefficient disclosed in this embodiment of the invention will first be described in detail.

[0027] Example 1: According to an embodiment of the present invention, an embodiment of a method for optimizing and verifying precipitation infiltration coefficient is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0028] Figure 1 This is a flowchart of a method for optimizing and verifying the precipitation infiltration coefficient according to an embodiment of the present invention, as shown below. Figure 1 As shown, the method includes the following steps: Step S102: Obtain basic geographic data, hydro-meteorological data, hydrogeological parameters, and distributed groundwater level monitoring data for the study area; Specifically, the first step is data preparation and preprocessing. The prepared data is as follows: Basic geographic data: collect digital elevation models (DEMs), land use maps, soil type maps, and geological maps of the study area.

[0029] Hydrometeorological data: Collect long-term precipitation, evaporation, and temperature data to calculate the upper boundary conditions of the Hydrus model.

[0030] Hydrogeological parameters: Collect initial data such as vadose zone lithology and saturated aquifer parameters (permeability coefficient, specific yield, etc.).

[0031] Distributed groundwater level monitoring data: Long-term data from distributed groundwater level monitoring points within the study area are obtained as key target values ​​for model validation.

[0032] Step S104: Select a target profile in the study area based on basic geographic data, hydro-meteorological data and hydrogeological parameters, and establish a one-dimensional Hydrus model for the target profile. Then, use local precipitation infiltration test data to calibrate the parameters of the Hydrus model to obtain the calibrated Hydrus model. The calibrated Hydrus model can accurately simulate the precipitation infiltration process at the point scale. Specifically, "point" scale physical modeling: Select representative typical profiles (i.e. target profiles, such as different vadose zone lithology and land use types) and establish a one-dimensional Hydrus model for the target profiles.

[0033] Parameter localization: Using local precipitation infiltration test or ground permeameter data (i.e. precipitation infiltration test data), the key parameters (such as soil hydraulic parameters) in the Hydrus model are calibrated (i.e. adjusted) to obtain the calibrated Hydrus model, ensuring the accuracy of the calibrated Hydrus model in simulating the precipitation infiltration process at the "point" scale.

[0034] Applications of a one-dimensional Hydrus model: (1) Accurate simulation of physical processes at the "point scale": Based on physical laws such as the Richards equation, it dynamically simulates the migration process of water in a selected typical profile (vadose zone), including: Rainwater infiltration: How rainwater enters the soil surface.

[0035] Redistribution: The downward movement and redistribution of water under the influence of gravitational potential, pressure potential, and matrix potential.

[0036] Evaporation and transpiration: How water returns to the atmosphere through soil surface and plant transpiration.

[0037] Ultimately, the model can output the change over time in the amount of recharge reaching the water table (i.e., the actual effective infiltration, which is the simulated cumulative infiltration).

[0038] (2) Providing a physical basis for upscaling calculations at the “regional scale”: This is its most crucial role. The output of the calibrated Hydrus model is the direct input for generating the initial field of precipitation infiltration coefficients for the study area.

[0039] (3) Provide a standardized calculation benchmark: When running the calibrated Hydrus model for each calculation unit, a uniform standard precipitation scenario (such as 50 mm / d) is used. This is like using the same "ruler" to measure the "water absorption" (i.e. precipitation infiltration coefficient) of all different "materials" (i.e. calculation units with different vadose zone lithology and land use).

[0040] Calculation formula: In a calculation unit, the precipitation infiltration coefficient = simulated cumulative infiltration (recharge reaching the groundwater level) / standard precipitation. This coefficient reflects the precipitation infiltration capacity determined purely by the physical characteristics of the underlying surface (vadose zone) of the calculation unit, eliminating the influence of actual precipitation intensity fluctuations, and making the calculation results between different units comparable.

[0041] In summary, the purpose of establishing the Hydrus model is not merely to simulate a few points, but to encapsulate physical mechanisms into a reusable tool. By applying it to computational units representing different spatial attributes, physical laws can be applied to the entire study area, thereby generating a physically meaningful initial field of precipitation infiltration coefficients, rather than purely empirical speculation. This provides a high-quality scientific starting point for subsequent verification and optimization using regional groundwater flow numerical models, and is one of the fundamental reasons why this method is superior to traditional empirical coefficient methods.

[0042] The "customized" modeling process: Building a one-dimensional Hydrus model is a specific technical process that can be broken down into the following steps: Model geometry definition: In the software, the model is defined as a one-dimensional vertical cylinder, and its depth is determined. This depth typically reaches the water table or is sufficient to cover the main vadose zone thickness for the study.

[0043] Model layering and parameter initialization: Based on the geological exploration or soil survey data of the selected target profile, this one-dimensional column is divided into several layers, and each layer is assigned initial soil hydraulic parameters. These core parameters include: Saturated water content (θs): The water content of soil when the pores are completely filled with water.

[0044] Residual moisture content (θr): The amount of water in the soil that cannot be expelled by gravity.

[0045] Saturated permeability coefficient (Ks): The hydraulic conductivity of soil when it is saturated, which directly affects the infiltration rate.

[0046] van Genuchten-Mualem model parameters (α,n): shape parameters used to describe the soil moisture characteristic curve (the relationship between soil water potential and water content) and hydraulic conductivity.

[0047] The initial values ​​for these parameters can be estimated from built-in soil databases (such as ROSETTA) based on soil texture (ratio of sand, silt, and clay).

[0048] Set boundary conditions: Upper boundary: Usually set as the atmospheric boundary, allowing simulation of precipitation infiltration, surface runoff, and soil evaporation. Meteorological data such as precipitation and evaporation are required as input as the driving force.

[0049] Lower boundary: Depending on the actual situation, it can be set as a free drainage boundary (assuming that the bottom flux is determined only by the internal gradient), a constant head boundary (such as a shallow groundwater area), or a variable head boundary.

[0050] Parameter localization calibration (key step): Using measured precipitation infiltration test or ground permeameter data (such as changes in soil moisture content after a rainfall event or observed recharge values) at the target profile location as a "benchmark," the established soil hydraulic parameters are fine-tuned (calibrated). By adjusting the parameters, the model's simulation output is made to match the measured data as closely as possible. This process ensures the accuracy of the calibrated Hydrus model's simulation at this specific location.

[0051] Step S106: Divide the study area into multiple calculation units, use the calibrated Hydrus model to simulate the precipitation infiltration process under the standard precipitation scenario for each calculation unit, and calculate the initial value of the precipitation infiltration coefficient for each calculation unit based on the simulation results. Specifically, spatial discretization and attribute assignment: the study area is divided into several computational units (such as grids or sub-basins).

[0052] Upscaling: Based on the calibrated Hydrus model, a standard precipitation scenario (such as heavy rainfall of 50 mm / d) is simulated for each computational unit to simulate the precipitation infiltration process, and the initial value of the precipitation infiltration coefficient of each computational unit is calculated based on the simulation results.

[0053] The initial value of the precipitation infiltration coefficient is a numerical value calculated for a single computational unit (such as a grid). It represents the ability of precipitation to convert into groundwater recharge under the specific vadose zone and land use conditions represented by that unit under the "standard precipitation scenario". It is discrete, point-based data.

[0054] Step S108: Based on spatial auxiliary data related to precipitation infiltration and the initial value of precipitation infiltration coefficient for each computational unit, an initial field of precipitation infiltration coefficient covering the study area is generated by spatial interpolation method. Specifically, coefficient calculation and mapping: Based on the simulation results (cumulative infiltration), the precipitation infiltration coefficient of each unit is calculated, and combined with spatial data such as land use and vadose zone thickness (i.e., spatial auxiliary data related to precipitation infiltration), a high-resolution initial field of precipitation infiltration coefficients for the study area is generated through geostatistical methods (such as Kriging interpolation).

[0055] The initial field of precipitation infiltration coefficients is a continuous, spatialized data layer (usually a raster map) covering the entire study area. Each pixel (corresponding to a location) on the map has a precipitation infiltration coefficient value. It is continuous, regional data.

[0056] Discrete "initial values ​​of precipitation infiltration coefficient for each computational unit" are the core raw materials and known sample points for creating a continuous "initial field of precipitation infiltration coefficient for the study area". The process of generating the initial field of precipitation infiltration coefficient is to use these limited but physically representative sample points to "predict" the coefficient value at every location in the entire area through spatial interpolation techniques.

[0057] The generation process of the initial field of precipitation infiltration coefficient: co-kriging - introducing auxiliary variables This is where the advancement of this scheme lies: instead of simply performing direct interpolation, it cleverly utilizes known spatial data covering the entire region, such as "land use" and "vadose zone thickness," as auxiliary information to guide the interpolation process. This method is called "co-kriging."

[0058] The reason for introducing these auxiliary variables: Because the precipitation infiltration coefficient is mainly controlled by two major factors: Surface conditions: Land use (such as forest land, grassland, cultivated land, and building land) directly affects the interception, infiltration, and runoff of precipitation.

[0059] Subsurface conditions: The thickness of the vadose zone and the lithology determine the path and resistance of water infiltration.

[0060] Therefore, the spatial distribution of land use and vadose zone thickness essentially reveals the spatial differentiation pattern of precipitation infiltration coefficient. Co-kriging utilizes this statistical correlation to improve prediction accuracy.

[0061] The specific steps of co-kriging: Establish statistical relationships: The statistical relationship between the known precipitation infiltration coefficient values ​​of the "computation unit" and the corresponding "land use type" and "vadose zone thickness" was analyzed. For example, it was found that the average infiltration coefficient of "forest land" was significantly higher than that of "construction land"; the thinner the vadose zone, the higher the infiltration coefficient was generally.

[0062] Joint interpolation: Co-kriging algorithms consider the following simultaneously: a. The spatial location and value of the coefficient point of the calculation unit are known.

[0063] b. Values ​​of auxiliary variables (land use, vadose zone thickness) at all points in the entire region.

[0064] When predicting the coefficients of an unsampled point P, the algorithm considers not only the values ​​and distances of nearby known computational unit coefficient points, but also the land use type and vadose zone thickness of point P itself. If point P is woodland, the algorithm will refer more to the coefficient values ​​of other "woodland" computational units for estimation.

[0065] Generate the initial field: The algorithm iterates through every grid point in the study area, calculating the estimated precipitation infiltration coefficient for that point using the rules described above. Finally, a complete, high-resolution raster map is generated, representing the initial field of precipitation infiltration coefficients.

[0066] Step S110: Input the initial field of precipitation infiltration coefficient as the recharge source into the constructed regional groundwater flow numerical model, run the regional groundwater flow numerical model, and simulate the groundwater level process line at each monitoring point. Specifically, model coupling involves inputting the initial field of precipitation infiltration coefficient as a source term into the constructed regional groundwater flow numerical model.

[0067] Forward simulation: Run the coupled model to simulate the groundwater level process lines at each monitoring point during the study period.

[0068] The aforementioned numerical model of groundwater flow is essentially a regional-scale validator and inversion engine: The core purpose of this model is not to directly simulate the precipitation infiltration process, but rather to act as a "judge" and "optimizer" to verify and optimize the accuracy of the initial field of precipitation infiltration coefficients generated by the calibrated Hydrus model. Its specific uses can be broken down into the following three points: As a "physical response simulator", it transforms infiltration recharge into water level response.

[0069] Function: This model can simulate the flow of groundwater in three-dimensional space. When the initial field of precipitation infiltration coefficient is input into the model as the "recharge source", the model will calculate how these recharges will cause the groundwater level in the study area to change with time and space, i.e., simulate the groundwater level process line, based on the characteristics of the underground medium (permeability coefficient, water storage coefficient, etc.) and boundary conditions.

[0070] It serves as a benchmark for fitting comparison, providing a verification target.

[0071] Function: The water level changes simulated by the model (i.e., groundwater level process lines at each monitoring point) are compared with the actual, distributed, long-term groundwater level data monitored in the study area (i.e., distributed groundwater level monitoring data). By calculating the fitting error between the two (such as root mean square error RMSE and Nash efficiency coefficient NSE), the accuracy of the current precipitation infiltration coefficient field can be quantitatively assessed.

[0072] Logic: If the simulated water level matches the measured water level closely, it indicates that the input precipitation infiltration coefficient field is reasonable; if the error is large, it indicates that there is a deviation in the initial field, which needs to be adjusted. The measured water level data plays the role of "truth" or "benchmark" here.

[0073] As a "platform for inversion optimization", it drives the automatic correction of precipitation infiltration coefficient field.

[0074] Function: This is the core application of this invention. Using the aforementioned fitting error as the objective function, an automatic parameter optimization algorithm (such as PEST) drives the model to run iteratively multiple times. The algorithm automatically and intelligently adjusts the precipitation infiltration coefficient value of each calculation unit in the entire area, then reruns the model to calculate the water level, compares the error, and repeats this process until a set of precipitation infiltration coefficient fields that best fits the simulated water level with the measured water level is found.

[0075] Essentially, this is an inverse problem-solving process. Given the "result" (regional water level dynamics), the "cause" (regional precipitation infiltration coefficient) is derived in reverse. This model provides the computational framework and platform for this inverse process.

[0076] Step S112: Compare the groundwater level process lines at each monitoring point with the distributed groundwater level monitoring data, and calculate the fitting error; Specifically, the groundwater level process lines at each monitoring point are compared with the measured water level data (i.e., distributed groundwater level monitoring data), and the fitting error (such as root mean square error RMSE and Nash efficiency coefficient NSE) is calculated.

[0077] Step S114: Generate an objective function based on the fitting error, and use an optimization algorithm to automatically adjust the initial field of precipitation infiltration coefficient until the objective function meets the preset standard. Specifically, intelligent feedback and optimization: using the aforementioned fitting error as the objective function, an optimization algorithm (such as PEST parameter estimation software) is employed to automatically and repeatedly adjust the precipitation infiltration coefficient field (initial precipitation infiltration coefficient field) to achieve the best fit between the simulated water level (i.e., the groundwater level process line at each monitoring point) and the measured water level (i.e., distributed groundwater level monitoring data). It utilizes the "group" constraint of regional water levels to inversely correct the infiltration coefficient, solving the problem of traditional methods lacking effective regional verification methods.

[0078] Step S116: The initial field of precipitation infiltration coefficient corresponding to the objective function satisfying the preset standard is used as the optimized precipitation infiltration coefficient field.

[0079] Specifically, when the fitting accuracy between the simulated water level and the measured water level meets the preset standard, the final optimized precipitation infiltration coefficient field is output. This coefficient field can be directly applied to the next round of groundwater resource assessment calculations, and its accuracy and reliability are far superior to the traditional empirical coefficient method.

[0080] This invention provides a method for optimizing and verifying precipitation infiltration coefficients, comprising: acquiring basic geographic data, hydro-meteorological data, hydrogeological parameters, and distributed groundwater level monitoring data of the study area; selecting a target profile within the study area based on the basic geographic data, hydro-meteorological data, and hydrogeological parameters, and establishing a one-dimensional Hydrus model for the target profile; then calibrating the Hydrus model using local precipitation infiltration test data to obtain a calibrated Hydrus model, wherein the calibrated Hydrus model can accurately simulate the precipitation infiltration process at a point scale; dividing the study area into multiple computational units, simulating the precipitation infiltration process under a standard precipitation scenario for each computational unit using the calibrated Hydrus model, and calculating the infiltration coefficient for each unit based on the simulation results. The method calculates the initial value of precipitation infiltration coefficient for each computational unit; based on spatial auxiliary data related to precipitation infiltration and the initial value of precipitation infiltration coefficient for each computational unit, an initial field of precipitation infiltration coefficient covering the study area is generated using spatial interpolation; the initial field of precipitation infiltration coefficient is used as a recharge source and input into the constructed regional groundwater flow numerical model, and the regional groundwater flow numerical model is run to simulate the groundwater level process line at each monitoring point; the groundwater level process line at each monitoring point is compared with the distributed groundwater level monitoring data, and the fitting error is calculated; an objective function is generated based on the fitting error, and an optimization algorithm is used to automatically adjust the initial field of precipitation infiltration coefficient until the objective function meets the preset standard; the initial field of precipitation infiltration coefficient corresponding to the objective function meeting the preset standard is used as the optimized precipitation infiltration coefficient field. As can be seen from the above description, the method for optimizing and verifying precipitation infiltration coefficient of the present invention constructs a series coupling framework of calibrated Hydrus model (point-scale physical model) → initial field of precipitation infiltration coefficient in space (area-scale) → regional groundwater flow numerical model (volume-scale verification). By using distributed groundwater level monitoring data as a global constraint, the initial field of precipitation infiltration coefficient is optimized through an inversion algorithm. This achieves a scientific leap from discrete point values ​​to a continuous, high-precision field for precipitation infiltration coefficient. Furthermore, global verification is completed using regional response (i.e., the fitting error between the groundwater level process line at each monitoring point and the distributed groundwater level monitoring data). This fundamentally solves the problem of spatial representativeness and accuracy of precipitation infiltration coefficient. In other words, the optimized precipitation infiltration coefficient field obtained by this invention is continuous, covers the entire study area, and has been optimized and verified using regional response, resulting in good accuracy. It is achieved through physical models and data-driven methods, without relying on experience, making it more scientific. This alleviates the technical problems of insufficient spatial representativeness, reliance on experience, and lack of effective regional verification methods in traditional methods for obtaining regional-scale precipitation infiltration coefficients.

[0081] The above provides a brief overview of the method for optimizing and verifying the precipitation infiltration coefficient of the present invention. The specific details involved are described in detail below.

[0082] In an optional embodiment of the present invention, the standard precipitation scenario includes: a uniform precipitation event of preset intensity; the simulation results include: cumulative infiltration and precipitation; the calibrated Hydrus model is used to simulate the precipitation infiltration process under the standard precipitation scenario for each computational unit; and the initial value of the precipitation infiltration coefficient for each computational unit is calculated based on the simulation results, specifically including the following steps: A uniform, preset-intensity precipitation event is used as the input to the calibrated Hydrus model to simulate the precipitation infiltration process under this precipitation event, and the initial value of the precipitation infiltration coefficient of each calculation unit is calculated based on the ratio of cumulative infiltration to precipitation.

[0083] In an optional embodiment of the present invention, the groundwater level process line at each monitoring point represents the change of the water level at each monitoring point over time. An objective function is generated based on the fitting error, and an optimization algorithm is used to automatically adjust the initial field of the precipitation infiltration coefficient until the objective function meets a preset standard. Specifically, the following steps are included: (1) Minimize the fitting error as the objective function; (2) Using parameter estimation optimization algorithms, the initial field of precipitation infiltration coefficient is automatically and iteratively adjusted until the objective function meets the preset standard.

[0084] In an optional embodiment of the present invention, the parameter estimation optimization algorithm is the PEST parameter estimation software or its core algorithm.

[0085] In an optional embodiment of the present invention, the basic geographic data includes at least one of: digital elevation model, land use map, soil type map and geological map; Hydrometeorological data includes at least one of precipitation, evaporation, and temperature data; Hydrogeological parameters include at least one of the following: vadose zone lithology and saturated aquifer parameters.

[0086] In an optional embodiment of the present invention, the regional groundwater flow numerical model is a three-dimensional groundwater flow numerical model constructed based on Modflow, FEFLOW, or GMS.

[0087] In an optional embodiment of the present invention, a uniform, preset intensity precipitation event is a precipitation event with a daily precipitation of 50 mm.

[0088] The method of the present invention has the following effects: 1. This invention represents a scientific leap from "point" to "area," fundamentally improving the spatial representativeness of the parameter (precipitation infiltration coefficient). Traditional methods yield precipitation infiltration coefficients that are either discrete point values ​​or roughly regional empirical values. This invention generates a continuous, high-resolution initial field of precipitation infiltration coefficients through Hydrus-1D model simulation and Geographic Information System (GIS) spatial analysis technology. Furthermore, by coupling a regional groundwater model (i.e., a regional groundwater flow numerical model) and using distributed monitoring data (distributed groundwater level monitoring data) for reverse verification, a parameter field that is spatially accurate and physically reasonable is finally obtained. This completely solves the core defect of traditional methods—insufficient spatial representativeness due to "representing the entire area with points"—...

[0089] 2. By integrating physical mechanisms and data-driven approaches, the accuracy and reliability of parameters are significantly improved. This invention is not simply data fitting. It first uses a Hydrus model based on physical equations to ensure that the infiltration and transport of precipitation in the vadose zone conforms to physical laws. Then, it uses regional groundwater level monitoring data (i.e., distributed groundwater level monitoring data) to constrain and optimize the results of this physical process. This dual guarantee of "physical mechanism + data verification" ensures that the final precipitation infiltration coefficient avoids the arbitrariness of purely empirical methods and overcomes the local distortion problems that may exist in single models. Its accuracy and reliability are far superior to traditional methods.

[0090] 3. It provides dynamic updating and adaptability to hydrogeological parameters. Traditional empirical coefficients are difficult to change once determined. This invention establishes a dynamic and updatable technical process. When the underlying surface conditions in the study area change significantly (such as land use change) or a large amount of new monitoring data is added, this technical process can be quickly restarted to recalibrate and optimize the parameter field. This allows the precipitation infiltration coefficient to dynamically adapt to environmental changes, providing continuous and accurate data support for water resource management, and has long-term application value that traditional static methods cannot match.

[0091] 4. This invention automates and intelligentizes the calibration process, significantly improving work efficiency. It deeply integrates numerical simulation technology with automated parameter estimation techniques (such as PEST), changing the traditional parameter calibration model that relies on manual trial and error. By establishing an optimization process with water level fitting error as the objective function, the computer automatically, iteratively, and intelligently finds the optimal parameter field. This greatly reduces manual intervention, freeing professionals from tedious calculations, while also improving the scientific rigor and efficiency of the calibration process, making it particularly suitable for groundwater resource assessment projects under large-area and complex conditions.

[0092] 5. This method provides solid and reliable technical support for regional groundwater resource assessment. Ultimately, the high-precision, high-spatial-resolution precipitation infiltration coefficient field generated by this method can be directly applied to regional groundwater flow numerical simulation and resource calculation, significantly improving the accuracy and reliability of groundwater resource assessment results from the source. This provides a more scientific data foundation for decision-making in water resource planning, management, rational development and utilization, and groundwater environmental protection, and has significant social, economic, and environmental benefits.

[0093] The key technical points of this invention are as follows: 1. A dual-model coupling mechanism between the Hydrox and regional groundwater models. This invention does not use the Hydrox model alone, but creatively couples it with a regional-scale groundwater flow model (such as Modflow). The physical processes accurately simulated by the Hydrox model at the "point" scale are used as source terms input into the regional model, and then the inversion capability of the regional model is used to constrain and optimize the initial field.

[0094] In traditional methods, the Hydrus model is only used for point studies, while regional models lack detailed physical processes in the vadose zone. This coupling mechanism achieves a seamless connection between "physical mechanisms" and "regional responses," ensuring the scientific rigor of parameter upgrades from point to area.

[0095] 2. Upscaling techniques based on "standard scenario simulation" and geostatistics. A method is proposed to batch calculate the precipitation infiltration coefficient of each computational unit by running the Hydrus model under a "standard precipitation scenario," and then integrating geospatial attributes to generate a high-resolution initial field. This replaces the traditional method of manually assigning partitions based on expert experience.

[0096] This solves the bottleneck of scaling from limited experimental data to a continuous spatial parameter field, enabling the generated initial field to have both a physical basis and reflect the spatial heterogeneity of the underlying surface, providing a high-quality starting point for subsequent optimization.

[0097] 3. Inversion optimization process with "distributed groundwater level monitoring data" as constraints. The actual groundwater level monitoring data distributed in various locations is treated as an "overall constraint field". The precipitation infiltration coefficient field of the whole area is automatically and intelligently adjusted through optimization algorithms (such as PEST) to force the simulated water level to fit the measured water level best.

[0098] This invention overcomes the limitations of traditional methods that lack effective regional verification tools. By utilizing the most readily available regional water level data, it reverse-corrects the most difficult-to-determine regional infiltration parameters, achieving a fundamental shift from "correcting regional parameters with regional results"—a qualitative leap from traditional point-to-point verification.

[0099] 4. An integrated parameter verification paradigm combining physical mechanisms and data-driven approaches. This invention deeply integrates numerical simulation based on physical equations (white box) with inversion optimization based on monitoring data (black box), forming a novel "gray box" verification paradigm. It is neither purely physical simulation nor purely data fitting, but rather combines the strengths of both.

[0100] This approach overcomes the parameter uncertainties inherent in purely physical models for regional applications, as well as the shortcomings of purely data-driven models, such as a lack of physical meaning and poor extrapolation. While ensuring the rationality of the process, this paradigm maximizes the alignment between the results and reality, significantly improving the robustness and universality of the solution.

[0101] The main contents of this invention are as follows: A novel, systematic, automated method for optimizing and verifying regional hydrogeological parameters, deeply integrating physical mechanisms and data-driven approaches, is proposed. Through a multi-level coupling architecture of "point-surface-volume" and a closed-loop optimization process of "forward-inverse modeling," it cleverly addresses the inherent shortcomings of traditional methods in terms of spatial representativeness and verification effectiveness.

[0102] 1. Core Architecture: Five-Step Closed-Loop Optimization Process refer to Figure 2 Step 1: Data Preparation and Preprocessing Inputs: Collect basic geographic data (DEM, land use, soil type and geological map), hydrometeorological data (precipitation, evaporation sequence), hydrogeological parameters (vadose zone lithology, aquifer parameters) and monitoring data (distributed groundwater level).

[0103] Function: It provides comprehensive and standardized input data for subsequent modeling and is the data foundation of the entire method.

[0104] Step 2: "Point" scale physical mechanism modeling (Hydrus model) Core objective: To establish a one-dimensional Hydrus model on selected typical profiles (representing different lithologies and land uses).

[0105] Function: Leveraging its well-defined physical mechanisms, it precisely simulates the migration (infiltration, redistribution, etc.) of moisture within the vadose zone. Local infiltration test data is used to calibrate the model parameters, ensuring accuracy at the "point" scale. This forms the basis for the physical meaning of the parameters.

[0106] Step 3: Initial field generation at the "surface" scale (upscaling and spatialization) Core approach: Discretize the study area into numerous computational cells (grids). Run a calibrated Hydrus model on each computational cell (with a standard precipitation scenario as input, such as 50 mm / d) to calculate the "standard infiltration" of that cell, thereby obtaining the initial value of the precipitation infiltration coefficient for that cell.

[0107] Function: By using "standard scenario simulation" and "geostatistical interpolation," the results of a limited "point" scale physical model are upscaled to generate a continuous, high-resolution initial field of precipitation infiltration coefficients for the entire region. This initial field combines physical foundations and spatial heterogeneity, providing a high-quality starting point for subsequent optimization.

[0108] Step 4: Volume-scale region verification and optimization (dual model coupling and inversion) Core innovation: The initial field obtained in the third step is used as a "precipitation recharge source" and input into the constructed regional three-dimensional groundwater flow model (such as Modflow).

[0109] Forward simulation: Run the regional model to simulate the water level process lines at each monitoring point.

[0110] Comparison and feedback: Compare the simulated water level with the measured water level and calculate the fitting error (e.g., RMSE).

[0111] Intelligent optimization: Using the fitting error as the objective function, optimization algorithms (such as PEST) are used to automatically and iteratively adjust the precipitation infiltration coefficient field of the entire region until the simulation results and the measured data are optimally matched.

[0112] Function: By using the most readily available regional water level monitoring data as an "overall constraint," the most difficult-to-determine regional infiltration parameters are corrected in reverse. This achieves a fundamental shift in "correcting regional parameters with regional results," overcoming the bottleneck of traditional methods lacking regional verification.

[0113] Step 5: Determination and application of the final parameter field Output: Once the fitting accuracy meets the requirements, the final optimized precipitation infiltration coefficient field is output for groundwater resource assessment.

[0114] 2. Key Mechanism: Integration of Physical Mechanisms and Data-Driven Approaches This approach is not a simple data fitting, but rather constructs a "grey box" model: White box (physical mechanism): The Hydrus model ensures that the water transport process conforms to physical laws, avoiding the problems of pure data models lacking physical meaning and having poor extrapolation.

[0115] Black box (data-driven): Regional model inversion optimization uses monitoring data to force the model output to approximate reality, overcoming the parameter uncertainty of pure physical models in regional applications.

[0116] Integration advantages: It ensures the rationality of the process and pursues the consistency between the results and reality, significantly improving the reliability and robustness of the parameters.

[0117] The inventive points are as follows: 1. Invention Point 1: A regional parameter field optimization architecture based on multi-level coupling of "point-surface-volume". Technical problems to be solved: Traditional methods cannot achieve the scientific upgrade of parameters from "point" to "area" and lack effective regional verification methods.

[0118] Technical approach: A cascaded coupling framework was constructed, consisting of a Hydrus (point-scale physical model) → spatial initial field (surface scale) → regional groundwater flow model (volume-scale verification). Regional water level monitoring data was used as a global constraint, and the initial field was optimized through an inversion algorithm.

[0119] Technical results: It has achieved a scientific leap from discrete point values ​​of parameters to continuous high-precision fields, and completed global verification by utilizing regional response, fundamentally solving the problem of spatial representativeness and accuracy of parameters.

[0120] 2. Invention Point Two: Upscaling Technology Based on Physical Mechanisms of Standard Scenario Simulation and Geostatistics Technical problem to be solved: When generating a continuous spatial parameter field from limited point data, traditional methods rely on subjective experience and lack physical mechanisms.

[0121] Technical approach: By running the Hydrus model under a unified and comparable "standard precipitation scenario", the initial values ​​of the infiltration coefficient of each calculation unit are calculated in batches, and then interpolation is performed in combination with geospatial attributes.

[0122] Technical effect: The generated initial field has a clear physical meaning and reasonable spatial heterogeneity, providing a high-quality starting point for subsequent optimization and replacing subjective experience-based assignment.

[0123] 3. Invention Point Three: Regional Parameter Field Inversion Optimization Mechanism Constrained by Distributed Water Level Technical problems to be solved: Traditional parameter calibration relies on manual trial and error, which is inefficient, highly subjective, and difficult to handle multi-parameter optimization problems at the regional scale.

[0124] Technical means: Treat distributed water level monitoring data as a "group" constraint, establish a function with water level fitting error as the objective, and use automated parameter estimation algorithms (such as PEST) to intelligently adjust the parameter field of the whole area.

[0125] Technical benefits: It automates and automates the parameter calibration process, freeing professionals from tedious calculations, improving calibration efficiency and scientific rigor, and enabling the handling of large-scale and complex optimization problems.

[0126] 4. Invention Point Four: A Gray-Box Verification Paradigm Integrating "Physical Mechanism + Data-Driven" Technical problem to be solved: Pure physical models or pure data-driven models each have their limitations in regional parameter verification.

[0127] Technical approach: Deeply integrate the white-box model (Hydrus) based on physical equations with the black-box optimization based on monitoring data inversion to form a gray-box verification paradigm.

[0128] Technical benefits: It balances the rationality of the process with the consistency of the results, improves the robustness and universality of the scheme, and provides a new paradigm for the verification of hydrogeological parameters.

[0129] Example 2: This invention also provides an apparatus for optimizing and verifying the precipitation infiltration coefficient. This apparatus is mainly used to execute the method for optimizing and verifying the precipitation infiltration coefficient provided in Embodiment 1 of this invention. The apparatus for optimizing and verifying the precipitation infiltration coefficient provided in this invention will be described in detail below.

[0130] Figure 3 This is a schematic diagram of an apparatus for optimizing and verifying the precipitation infiltration coefficient according to an embodiment of the present invention, as shown below. Figure 3 As shown, the device mainly includes: an acquisition unit 10, a construction and calibration unit 20, a simulation and calculation unit 30, a generation unit 40, an input and operation unit 50, a comparison and calculation unit 60, an adjustment unit 70, and a setting unit 80, wherein: The acquisition unit is used to acquire basic geographic data, hydro-meteorological data, hydrogeological parameters, and distributed groundwater level monitoring data for the study area. A construction and calibration unit is used to select target profiles in the study area based on basic geographic data, hydro-meteorological data and hydrogeological parameters, and to establish a one-dimensional Hydrus model for the target profiles. Then, the Hydrus model is calibrated using local precipitation infiltration test data to obtain a calibrated Hydrus model. The calibrated Hydrus model can accurately simulate the precipitation infiltration process at the point scale. Simulation and computation units are used to divide the study area into multiple computation units. The calibrated Hydrus model is used to simulate the precipitation infiltration process under the standard precipitation scenario for each computation unit, and the initial value of the precipitation infiltration coefficient for each computation unit is calculated based on the simulation results. The generation unit is used to generate an initial field of precipitation infiltration coefficient covering the study area based on spatial auxiliary data related to precipitation infiltration and the initial value of precipitation infiltration coefficient for each computational unit, using a spatial interpolation method. The input running unit is used to input the initial field of precipitation infiltration coefficient as a recharge source into the constructed regional groundwater flow numerical model, run the regional groundwater flow numerical model, and simulate the groundwater level process line at each monitoring point; The comparison calculation unit is used to compare the groundwater level process line of each monitoring point with the distributed groundwater level monitoring data and calculate the fitting error; The adjustment unit is used to generate an objective function based on the fitting error and automatically adjust the initial field of precipitation infiltration coefficient using an optimization algorithm until the objective function meets the preset standard. The setting unit is used to take the initial field of precipitation infiltration coefficient corresponding to the objective function satisfying the preset standard as the optimized precipitation infiltration coefficient field.

[0131] This invention provides an apparatus for optimizing and verifying precipitation infiltration coefficients, comprising: acquiring basic geographic data, hydro-meteorological data, hydrogeological parameters, and distributed groundwater level monitoring data of the study area; selecting a target profile within the study area based on the basic geographic data, hydro-meteorological data, and hydrogeological parameters, and establishing a one-dimensional Hydrus model for the target profile; then calibrating the Hydrus model using local precipitation infiltration test data to obtain a calibrated Hydrus model, wherein the calibrated Hydrus model can accurately simulate the precipitation infiltration process at a point scale; dividing the study area into multiple computational units, simulating the precipitation infiltration process under a standard precipitation scenario for each computational unit using the calibrated Hydrus model, and calculating the infiltration coefficient of each unit based on the simulation results. The calculation process involves several steps: First, the initial value of the precipitation infiltration coefficient for each calculation unit is calculated. Then, based on spatial auxiliary data related to precipitation infiltration and the initial value of the precipitation infiltration coefficient for each calculation unit, an initial field of precipitation infiltration coefficient covering the study area is generated using spatial interpolation. This initial field is then input as a recharge source into the constructed regional groundwater flow numerical model. The model is run to simulate the groundwater level process lines at each monitoring point. The groundwater level process lines at each monitoring point are compared with distributed groundwater level monitoring data to calculate the fitting error. Based on the fitting error, an objective function is generated, and an optimization algorithm is used to automatically adjust the initial field of precipitation infiltration coefficient until the objective function meets a preset standard. The initial field of precipitation infiltration coefficient corresponding to the objective function meeting the preset standard is then used as the optimized precipitation infiltration coefficient field. As described above, the device for optimizing and verifying precipitation infiltration coefficients in this invention constructs a cascaded coupling framework: a calibrated Hydrus model (point-scale physical model) → an initial field of spatial precipitation infiltration coefficient (surface-scale) → a regional groundwater flow numerical model (volume-scale verification). By using distributed groundwater level monitoring data as a global constraint, the initial field of precipitation infiltration coefficient is optimized through an inversion algorithm. This achieves a scientific leap from discrete point values ​​to a continuous, high-precision field for precipitation infiltration coefficient. Furthermore, global verification is completed using regional response (i.e., the fitting error between the groundwater level process line at each monitoring point and the distributed groundwater level monitoring data). This fundamentally solves the problem of spatial representativeness and accuracy of precipitation infiltration coefficient. In other words, the optimized precipitation infiltration coefficient field obtained by this invention is continuous, covers the entire study area, and has been optimized and verified using regional response, resulting in good accuracy. It is achieved through physical models and data-driven methods, without relying on experience, making it more scientific. This alleviates the technical problems of insufficient spatial representativeness, reliance on experience, and lack of effective regional verification methods in traditional methods for obtaining regional-scale precipitation infiltration coefficients.

[0132] Optionally, the groundwater level process line at each monitoring point represents the change in water level at each monitoring point over time. The adjustment unit is also used to: take minimizing the fitting error as the objective function; and automatically and iteratively adjust the initial field of precipitation infiltration coefficient using a parameter estimation optimization algorithm until the objective function meets the preset standard.

[0133] Optionally, the standard precipitation scenario includes: a uniform precipitation event of preset intensity, and the simulation results include: cumulative infiltration and precipitation. The simulation and calculation unit is also used to: use the uniform precipitation event of preset intensity as input to the calibrated Hydrus model, simulate the precipitation infiltration process under the precipitation event, and calculate the initial value of the precipitation infiltration coefficient of each calculation unit based on the ratio of cumulative infiltration to precipitation.

[0134] Optionally, the parameter estimation optimization algorithm is the PEST parameter estimation software or its core algorithm.

[0135] Optionally, the basic geographic data includes at least one of the following: digital elevation model, land use map, soil type map and geological map; the hydrometeorological data includes at least one of precipitation, evaporation and temperature data; and the hydrogeological parameters include at least one of vadose zone lithology and saturated aquifer parameters.

[0136] Optionally, the regional groundwater flow numerical model is a three-dimensional groundwater flow numerical model built based on Modflow, FEFLOW, or GMS.

[0137] Optionally, a uniform, preset intensity precipitation event is defined as a precipitation event with a daily precipitation of 50 mm.

[0138] The device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0139] like Figure 4 As shown in the embodiment of this application, an electronic device 600 includes a processor 601, a memory 602, and a bus. The memory 602 stores machine-readable instructions that can be executed by the processor 601. When the electronic device is running, the processor 601 communicates with the memory 602 via the bus. The processor 601 executes the machine-readable instructions to perform the steps of the method described above for optimizing and verifying the precipitation infiltration coefficient.

[0140] Specifically, the memory 602 and processor 601 mentioned above can be general-purpose memory and processor, without any specific limitations. When the processor 601 runs the computer program stored in the memory 602, it can execute the above-mentioned method for optimizing and verifying the precipitation infiltration coefficient.

[0141] The processor 601 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 601 or by instructions in software form. The processor 601 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 602, and processor 601 reads the information from memory 602 and, in conjunction with its hardware, completes the steps of the above method.

[0142] Corresponding to the above-described method for optimizing and verifying precipitation infiltration coefficient, this application embodiment also provides a computer-readable storage medium storing machine-executable instructions. When the machine-executable instructions are called and run by a processor, the machine-executable instructions cause the processor to perform the steps of the above-described method for optimizing and verifying precipitation infiltration coefficient.

[0143] The device for optimizing and verifying the precipitation infiltration coefficient provided in this application embodiment can be specific hardware on the equipment or software or firmware installed on the equipment. The implementation principle and technical effects of the device provided in this application embodiment are the same as those in the foregoing method embodiments. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the foregoing method embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can all be referred to the corresponding processes in the above method embodiments, and will not be repeated here.

[0144] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0145] For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0146] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0147] In addition, the functional units in the embodiments provided in this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0148] If the aforementioned function is implemented as a software functional unit and sold or used as an independent product, it 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 an electronic device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method for optimizing and verifying the precipitation infiltration coefficient described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0149] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0150] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A method for optimizing and verifying the precipitation infiltration coefficient, characterized in that, include: Acquire basic geographic data, hydro-meteorological data, hydrogeological parameters, and distributed groundwater level monitoring data for the study area; Based on the basic geographic data, the hydro-meteorological data, and the hydrogeological parameters, a target profile is selected in the study area, and a one-dimensional Hydrus model is established for the target profile. Then, the Hydrus model is calibrated using local precipitation infiltration test data to obtain a calibrated Hydrus model. The calibrated Hydrus model can accurately simulate the precipitation infiltration process at the point scale. The study area is divided into multiple computational units. The calibrated Hydrus model is used to simulate the precipitation infiltration process under a standard precipitation scenario for each computational unit. The initial value of the precipitation infiltration coefficient for each computational unit is calculated based on the simulation results. Based on spatial auxiliary data related to precipitation infiltration and the initial value of the precipitation infiltration coefficient for each computational unit, an initial field of precipitation infiltration coefficient covering the study area is generated by spatial interpolation method. The initial field of precipitation infiltration coefficient is used as a recharge source and input into the constructed regional groundwater flow numerical model. The regional groundwater flow numerical model is run to simulate the groundwater level process line at each monitoring point. The groundwater level process lines at each monitoring point are compared with the distributed groundwater level monitoring data, and the fitting error is calculated. Based on the fitting error, an objective function is generated, and an optimization algorithm is used to automatically adjust the initial field of the precipitation infiltration coefficient until the objective function meets the preset standard. The initial field of precipitation infiltration coefficients corresponding to the objective function satisfying the preset standard is used as the optimized precipitation infiltration coefficient field.

2. The method according to claim 1, characterized in that, The groundwater level hydrographs at each monitoring point represent the change in water level over time. An objective function is generated based on the fitting error. An optimization algorithm is then used to automatically adjust the initial field of the precipitation infiltration coefficient until the objective function meets a preset standard, including: Minimizing the fitting error is taken as the objective function; Using a parameter estimation optimization algorithm, the initial field of precipitation infiltration coefficient is automatically and iteratively adjusted until the objective function meets the preset standard.

3. The method according to claim 1, characterized in that, The standard precipitation scenario includes a uniform precipitation event of preset intensity. The simulation results include cumulative infiltration and precipitation. The calibrated Hydrus model is used to simulate the precipitation infiltration process under the standard precipitation scenario for each calculation unit, and the initial value of the precipitation infiltration coefficient for each calculation unit is calculated based on the simulation results, including: A uniform, preset-intensity precipitation event is used as the input to the calibrated Hydrus model to simulate the precipitation infiltration process under the precipitation event, and the initial value of the precipitation infiltration coefficient of each calculation unit is calculated based on the ratio of the cumulative infiltration amount to the precipitation amount.

4. The method according to claim 2, characterized in that, The parameter estimation optimization algorithm is the PEST parameter estimation software or its core algorithm.

5. The method according to claim 1, characterized in that, The basic geographic data includes at least one of the following: digital elevation model, land use map, soil type map and geological map; The hydrometeorological data includes at least one of precipitation, evaporation, and temperature data; The hydrogeological parameters include at least one of the following: vadose zone lithology and saturated aquifer parameters.

6. The method according to claim 1, characterized in that, The numerical model of groundwater flow in the region is a three-dimensional numerical model of groundwater flow constructed based on Modflow, FEFLOW, or GMS.

7. The method according to claim 3, characterized in that, The uniform, preset intensity precipitation event is a precipitation event with a daily precipitation of 50 mm.

8. A device for optimizing and verifying the precipitation infiltration coefficient, characterized in that, include: The acquisition unit is used to acquire basic geographic data, hydro-meteorological data, hydrogeological parameters, and distributed groundwater level monitoring data for the study area. A construction and calibration unit is used to select a target profile in the study area based on the basic geographic data, the hydro-meteorological data, and the hydrogeological parameters, and to establish a one-dimensional Hydrus model for the target profile. Then, the Hydrus model is calibrated using local precipitation infiltration test data to obtain a calibrated Hydrus model. The calibrated Hydrus model can accurately simulate the precipitation infiltration process at the point scale. The simulation and calculation unit is used to divide the study area into multiple calculation units, simulate the precipitation infiltration process under the standard precipitation scenario for each calculation unit using the calibrated Hydrus model, and calculate the initial value of the precipitation infiltration coefficient for each calculation unit based on the simulation results. A generation unit is used to generate an initial field of precipitation infiltration coefficients covering the study area using a spatial interpolation method, based on spatial auxiliary data related to precipitation infiltration and the initial value of the precipitation infiltration coefficient of each calculation unit. The input running unit is used to input the initial field of precipitation infiltration coefficient as a recharge source into the constructed regional groundwater flow numerical model, run the regional groundwater flow numerical model, and simulate the groundwater level process line at each monitoring point; The comparison calculation unit is used to compare the groundwater level process line of each monitoring point with the distributed groundwater level monitoring data and calculate the fitting error; The adjustment unit is used to generate an objective function based on the fitting error and automatically adjust the initial field of the precipitation infiltration coefficient using an optimization algorithm until the objective function meets a preset standard. The setting unit is used to take the initial field of precipitation infiltration coefficient corresponding to the objective function satisfying the preset standard as the optimized precipitation infiltration coefficient field.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program thereon, characterized in that, The computer program is executed by the processor to perform the method of any one of claims 1 to 7.

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