An aquifer analysis-based surface and groundwater interaction simulation method and system

Abstract

The application discloses a kind of based on aquifer analysis surface and groundwater interactive simulation method and system, it is related to surface and groundwater interactive simulation technical field.A kind of based on aquifer analysis surface and groundwater interactive simulation system, including data acquisition module, data processing module, aquifer analysis module, model simplification module, interactive simulation module and resource management module.The present application is by to the high-resolution data in preprocessed surface data and underground data is carried out data sampling, not only can in the key information in data, to ensure that the precision of model is not significantly influenced with significantly reducing the data amount needing to be handled, to reduce calculation complexity and amount of calculation and improve the calculation efficiency and operating efficiency of model, also can help model better handle noise and abnormal value and reduce the overfitting risk of model, to improve the robustness and generalization ability of model and performance and reliability in practical application.

Description

Technical Field

[0001] This invention relates to the field of surface and groundwater interaction simulation technology, and specifically to a method and system for simulating surface and groundwater interaction based on aquifer analysis. Background Technology

[0002] Surface water refers to water bodies existing on the Earth's surface, including rivers, lakes, reservoirs, and wetlands. Groundwater refers to water stored in soil pores and rock fissures below the Earth's surface. Surface water can replenish groundwater through seepage, especially during periods of low water levels in rivers and lakes. Groundwater can also replenish surface water through springs and other springs to maintain the baseflow of rivers and other water bodies. Surface water and groundwater jointly participate in the Earth's water cycle, and their exchange and flow constitute a complete hydrological system. Surface-groundwater interaction simulation is a scientific method used to study and predict the interactions between surface water bodies and groundwater. This simulation is of great significance in water resource management, pollution control, and ecological restoration.

[0003] Existing methods and systems for simulating surface-groundwater interaction are typically based on models such as the SWAT-MODFLOW coupled model, GSFLOW model, HydroGeoSphere (HGS) model, and CATHY. However, to accurately simulate the interaction process between surface and groundwater, these models require a large amount of input data, including topography, soil properties, meteorological data, and hydrogeological parameters. This results in high computational complexity and poor computational efficiency. Furthermore, existing interaction simulation methods usually only estimate the permeability coefficient based on the type and lithology of the aquifer when analyzing it, without considering the influence of soil depth and environmental factors on the permeability coefficient of the aquifer. Consequently, these models have poor simulation effects on surface-groundwater interaction.

[0004] Based on the above, this invention proposes a method and system for simulating the interaction between surface and groundwater based on aquifer analysis, which has high computational efficiency and good simulation effect. Summary of the Invention

[0005] To overcome the shortcomings of existing surface and groundwater interaction simulation methods and systems, which require a large amount of input data to ensure simulation results, leading to high computational complexity and poor efficiency, and which only estimate the permeability coefficient based on the aquifer type and lithology without considering the influence of soil depth and environmental factors on the aquifer's permeability coefficient, resulting in poor simulation effects of surface and groundwater interaction, this invention proposes a surface and groundwater interaction simulation method and system based on aquifer analysis that is computationally efficient and has good simulation results.

[0006] A method for simulating surface and groundwater interaction based on aquifer analysis includes the following steps:

[0007] Acquire surface and subsurface data of the area to be simulated. Surface data includes meteorological data, topographic data, and land use data, while subsurface data includes groundwater data and geological structure data.

[0008] Surface and subsurface data are preprocessed to obtain preprocessed surface and subsurface data. High-resolution data in the preprocessed surface and subsurface data are sampled to obtain simplified surface and subsurface data.

[0009] The location and type of aquifers are classified based on simplified geological structure data, and the permeability coefficient of the corresponding aquifers is calculated based on their location and type.

[0010] Based on the HGS model, a conceptual model and a numerical model of surface water-groundwater interaction are constructed. The numerical model is parameterized and simplified based on data characteristics or theoretical assumptions. The permeability coefficient of the aquifer and the simplified surface and groundwater data are input into the simplified numerical model. The surface water-groundwater interaction is simulated by running the simplified numerical model and the simulation results are obtained.

[0011] Based on the simulation results, the water resource status of the simulated area is assessed, and a reasonable development and management plan for the water resources of the simulated area is set according to the assessment results.

[0012] As a preferred aspect of the invention, the meteorological data includes precipitation, temperature and humidity; the topographic data includes elevation, slope and surface water level and evaporation; the land use data includes land use type and vegetation cover; the groundwater data includes groundwater level, flow velocity and storage rate; and the geological structure data includes stratum type, distribution and lithology.

[0013] As a preferred aspect of the invention, the specific steps for sampling high-resolution data are as follows:

[0014] Based on the characteristics of high-resolution data, select variables for stratification;

[0015] The data is divided into different levels according to the hierarchical variables;

[0016] The number of samples to be drawn from each stratum is determined based on the proportion of each stratum in the population.

[0017] Samples are drawn randomly within each level;

[0018] The sample data extracted from each layer are merged to obtain a simplified dataset.

[0019] As a preferred aspect of the invention, the specific formula for calculating the permeability coefficient of the corresponding aquifer is as follows: ;in It is the permeability coefficient. It is the reference permeability coefficient. It is the burial depth of the aquifer. It is the flow rate of groundwater. It is a reference flow velocity. It is the soil temperature. It refers to the soil temperature, while , and These are the influence coefficients of the aquifer burial depth, groundwater flow velocity, and soil temperature on the permeability coefficient.

[0020] As a preferred aspect of the invention, the specific method for simulating the interaction between surface water and groundwater through the numerical model of surface water-groundwater interaction is as follows:

[0021] The flow of surface water is simulated using a two-dimensional diffusion wave equation, the mathematical expression of which is: ;in It's the water depth. It is time. It is the acceleration due to gravity, and It refers to the slope of the terrain;

[0022] The flow of groundwater is simulated using the three-dimensional Richards equations, and its mathematical expression is as follows: ;in It is the volumetric water content. It is time. It is the hydraulic conductivity coefficient and a function of water content, while It is the water head; it couples the flow equations of surface water and groundwater to achieve interactive simulation of surface water and groundwater.

[0023] As a preferred aspect of the invention, the specific steps for parameterizing and simplifying the numerical model are as follows:

[0024] Based on data characteristics or theoretical assumptions, choose an appropriate function form to express the relationship between the input variables or between one output variable and another;

[0025] Based on historical data, the parameters of the function are determined or estimated using the method of minimizing the mean square error or the least squares method.

[0026] The constructed function model is applied to the numerical model, and the interaction between surface water and groundwater is simulated.

[0027] As a preferred aspect of the invention, the specific steps for verifying and optimizing the parameters of the simplified numerical model are as follows:

[0028] Historical surface and subsurface data of the area to be simulated are obtained. The obtained data are preprocessed and sampled. The simplified data and the permeability coefficient of the aquifer are then input into the simplified numerical model to obtain the simulation results.

[0029] The simulation results were compared with the measured data to evaluate the performance of the simplified numerical model, and the model parameters were optimized using a genetic algorithm.

[0030] After parameter optimization, the simplified numerical model is rerun and compared until the model output matches the measured data to the preset standard.

[0031] A surface and groundwater interaction simulation system based on aquifer analysis includes a data acquisition module, a data processing module, an aquifer analysis module, a model simplification module, an interactive simulation module, and a resource management module.

[0032] Data acquisition module: used to acquire surface and subsurface data of the area to be simulated;

[0033] Data processing module: used to preprocess surface and underground data, and to sample high-resolution data from the preprocessed surface and underground data;

[0034] Aquifer Analysis Module: This module is used to classify the location and type of aquifers based on simplified geological structure data, and to calculate the permeability coefficient of the corresponding aquifer based on its location and type.

[0035] Model simplification module: used to construct conceptual and numerical models of surface water-groundwater interaction based on the HGS model, and to perform parameter simplification of the numerical model based on data characteristics or theoretical assumptions;

[0036] Interactive simulation module: This module is used to input the permeability coefficient of the aquifer, as well as simplified surface and groundwater data, into a simplified numerical model. By running the simplified numerical model, it performs an interactive simulation of surface water and groundwater and obtains the simulation results.

[0037] Resource Management Module: Used to assess the water resource status of the simulated area based on the simulation results, and to set reasonable development and management plans for the water resources of the simulated area based on the assessment results.

[0038] The present invention has the following advantages:

[0039] 1. This invention samples high-resolution data from preprocessed surface and subsurface data, which not only significantly reduces the amount of data to be processed while preserving key information and ensuring that the model's accuracy is not significantly affected, thereby reducing computational complexity and workload and improving the model's computational and operational efficiency, but also helps the model better handle noise and outliers and reduce the risk of overfitting. This improves the model's robustness and generalization ability, as well as its performance and reliability in practical applications, thus enhancing the computational efficiency and simulation effect of this surface and groundwater interaction simulation method and system.

[0040] 2. This invention simplifies the numerical model by parameterizing it based on data characteristics or theoretical assumptions. This not only effectively simplifies the structure and input data of the numerical model, thereby reducing the number of parameters required and lowering computational complexity and cost, thus improving the model's operating efficiency and interpretability, and enhancing its performance and reliability, but also allows for rapid adjustment of the model's design to adapt to new design requirements by simply changing parameter values. This eliminates the need to build the model from scratch, enabling it to flexibly cope with different simulation scenarios and improving the computational efficiency and simulation effect of this surface and groundwater interaction simulation method and system.

[0041] 3. This invention calculates the permeability coefficient of the corresponding aquifer based on simplified groundwater hydrological data and the location and type of the aquifer. It can comprehensively consider the influence of the aquifer's burial depth, groundwater flow velocity, and soil temperature on the permeability coefficient, and obtain an accurate permeability coefficient. This allows for a more precise simulation of the groundwater flow path, velocity, and volume, thereby improving the model's simulation effect and accuracy of groundwater flow. Furthermore, an accurate permeability coefficient can reduce the accumulation of errors in the model's calculations, effectively improving the overall simulation accuracy of the model and enhancing the simulation effect of this surface-groundwater interaction simulation method and system. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of a surface and groundwater interaction simulation system based on aquifer analysis used in an embodiment of the present invention. Detailed Implementation

[0043] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this invention.

[0044] Example 1: A method for simulating the interaction between surface and groundwater based on aquifer analysis, comprising the following steps:

[0045] Acquire surface and subsurface data of the area to be simulated. Surface data includes meteorological data, topographic data, and land use data, while subsurface data includes groundwater data and geological structure data.

[0046] Surface and subsurface data are preprocessed to obtain preprocessed surface and subsurface data. High-resolution data in the preprocessed surface and subsurface data are sampled to obtain simplified surface and subsurface data.

[0047] The location and type of aquifers are classified based on simplified geological structure data. The permeability coefficient of the corresponding aquifers is obtained by calculation based on simplified groundwater hydrological data and the location and type of aquifers.

[0048] A conceptual and numerical model of surface water-groundwater interaction is constructed based on the HGS model. The numerical model is parameterized and simplified based on data characteristics or theoretical assumptions. The permeability coefficient of the aquifer, as well as the simplified surface and groundwater data, are input into the simplified numerical model. Based on the input data, the parameters, boundary conditions, and initial conditions of the simplified numerical model are established. The surface water-groundwater interaction is simulated by running the simplified numerical model, and the simulation results are obtained. The simulation results include changes in groundwater level and the exchange flux between surface water and groundwater.

[0049] Based on the simulation results, the water resource status of the simulated area is assessed, and a reasonable development and management plan for the water resources of the simulated area is set according to the assessment results.

[0050] The meteorological data includes precipitation, temperature, humidity, and wind speed, used to simulate hydrological processes such as evaporation and runoff. The topographic data includes elevation, slope, and surface water level, flow rate, and evaporation, used to determine surface water flow direction and confluence paths. The land use data includes land use type and vegetation cover. The groundwater hydrological data includes groundwater level, flow velocity, storage rate, and specific yield, used to determine the initial groundwater level and boundary conditions and to construct a groundwater model. The geological structure data includes stratum type, porosity, distribution, thickness, and lithology.

[0051] The specific steps for preprocessing surface and subsurface data are as follows:

[0052] Missing value handling: Handling null or missing values ​​in the dataset, using linear interpolation or polynomial interpolation to fill missing values, or directly using the mean or median of the dataset to fill missing values, to ensure data integrity and model stability;

[0053] Outlier handling involves identifying outliers in the dataset that do not conform to the expected pattern using Z-Score or IQR methods. Outliers are then removed and replaced with the mean or median of the dataset, or interpolation methods are used to correct them, in order to avoid these outliers negatively impacting the model's simulation.

[0054] Data standardization involves using min-max standardization to standardize data of different dimensions. The formula for min-max standardization is as follows:

[0055] ;in Represents the original data value. This represents the minimum value in the dataset. This represents the maximum value in the dataset. This represents the standardized data value.

[0056] The specific steps for sampling high-resolution data from the preprocessed surface and subsurface data are as follows:

[0057] Determine the stratification variables. Based on the characteristics of the high-resolution data, select the variables used for stratification. For example, the data can be stratified according to geological structure, hydrological characteristics, and pollutant concentration.

[0058] Divide the data into different levels according to the stratified variables. For example, divide areas with different permeability coefficients into different levels, or divide the data into different levels according to soil type.

[0059] Determine the sample size for each stratum, and determine the number of samples to be drawn from each stratum based on the proportion of each stratum in the population;

[0060] Select samples and draw samples within each level using uniform sampling, random sampling, or importance-based sampling methods;

[0061] The sample data is merged by combining the sample data extracted from each layer to obtain the final sample dataset, which is the simplified dataset.

[0062] The above steps, by sampling high-resolution data from preprocessed surface and subsurface data, not only significantly reduce the amount of data to be processed while preserving key information and ensuring that the model's accuracy is not significantly affected, thereby reducing computational complexity and workload and improving the model's computational and operational efficiency, but also help the model better handle noise and outliers and reduce the risk of overfitting. This improves the model's robustness and generalization ability, as well as its performance and reliability in practical applications, thus enhancing the computational efficiency and simulation effect of this surface and groundwater interaction simulation method and system.

[0063] For example, suppose we have a set of high-resolution soil permeability coefficient data covering a large watershed with very high spatial resolution. To simplify the data and improve model efficiency, we can perform stratified sampling as follows:

[0064] The stratification criteria were determined by dividing the soil into three levels: high permeability, medium permeability, and low permeability, based on the distribution of soil permeability coefficients.

[0065] The soil permeability coefficient data is divided into three levels, each corresponding to a different range of permeability coefficients.

[0066] The sampling strategy employs a uniform sampling strategy at each level, extracting a sample point at regular intervals (e.g., 100 meters).

[0067] Data simplification involves processing the sampled data using interpolation methods (such as bilinear interpolation) to generate simplified soil permeability coefficient data.

[0068] Verification and adjustment involve comparing the simplified data with the original data to ensure that the simplified data accurately reflects the characteristics of the original data. If significant differences are found between the simplified data and the original data in certain areas, the sampling density can be increased or the stratification strategy can be adjusted accordingly.

[0069] Experiment 1:

[0070]

[0071] The measured groundwater level was obtained by actual measurement of groundwater level data over seven days; the simulated groundwater level data for Experiment 1 was obtained by simulation based on input data with a resolution of 30 meters; the simulated groundwater level data for Experiment 2 was obtained by simulation based on input data with a resolution of 100 meters. It should be noted that the input data with a resolution of 100 meters was obtained by sampling the input data with a resolution of 30 meters; RMSE and R² are the goodness-of-fit evaluation indicators between the simulated groundwater level data and the measured groundwater level data for Experiment 1 and Experiment 2.

[0072] Results analysis: Due to the presence of more noise and errors in the data, the simulation results of Experiment 1 (30-meter resolution) showed a decrease in the goodness of fit between the simulation results and the measured data, despite the higher resolution. That is, the RMSE was higher and the R² was lower. Although the data resolution of Experiment 2 (100-meter resolution) was lower, the data quality was better, and the simulation results showed a higher goodness of fit between the simulation results and the measured data, that is, the RMSE was lower and the R² was higher.

[0073] Conclusion: Data quality has a significant impact on simulation results. Even with high resolution, the presence of noise and errors in the data can lead to a decrease in the goodness of fit of the simulation results. In practical applications, a trade-off between data resolution and data quality is necessary to obtain the best simulation results.

[0074] The specific formula for calculating the permeability coefficient of the corresponding aquifer based on simplified groundwater hydrological data and the location and type of the aquifer is as follows: ;in It is the permeability coefficient. It is the reference permeability coefficient. It is the burial depth of the aquifer. It is the flow rate of groundwater. It is a reference flow velocity. It is the soil temperature. It refers to the soil temperature, while , and These are the influence coefficients of the aquifer burial depth, groundwater flow velocity, and soil temperature on the permeability coefficient, and the values ​​of each influence coefficient can be obtained through laboratory measurement or empirical formula method.

[0075] It should be noted that the reference flow velocity and reference soil temperature are obtained based on the normal data of the aquifer, while the reference permeability coefficient is estimated based on the type and lithology of the aquifer. The types of aquifers include loose rock porous aquifers, general clastic rock fissure porous aquifers, permeable (waterless) layers or water bodies, carbonate rock karst aquifers, red bed fissure porous aquifers, and bedrock fissure aquifers. Bedrock fissure aquifers can be further divided into flexible rock fissure aquifers, mudstone fissure aquifers, shale fissure aquifers, granite fissure aquifers, and siliceous rock fissure aquifers according to lithology.

[0076] The above steps calculate the permeability coefficient of the corresponding aquifer based on simplified groundwater hydrological data and the location and type of the aquifer. This comprehensively considers the influence of the aquifer's burial depth, groundwater flow velocity, and soil temperature on the permeability coefficient, resulting in an accurate permeability coefficient. This allows for a more precise simulation of the groundwater flow path, velocity, and volume, improving the model's simulation effect and accuracy. Furthermore, an accurate permeability coefficient reduces the accumulation of errors in the model's calculations, effectively improving the overall simulation accuracy and enhancing the simulation effect of this surface-groundwater interaction simulation method and system.

[0077] The specific method for simulating the interaction between surface water and groundwater using the numerical model of surface water-groundwater interaction is as follows:

[0078] The flow of surface water is simulated using a two-dimensional diffusion wave equation. This equation is a diffusion wave approximation of the shallow water equation and is suitable for simulating processes such as surface runoff. Its mathematical expression is as follows: ;in It's the water depth. It is time. It is the acceleration due to gravity, and It refers to the slope of the terrain;

[0079] The flow of groundwater is simulated using the three-dimensional Richards equations, which describe the flow motion of water in saturated-unsaturated porous media. The mathematical expression is as follows: ;in It is the volumetric water content. It is time. It is the hydraulic conductivity coefficient and a function of water content, while It's the water source;

[0080] The flow equations of surface water and groundwater are coupled to achieve interactive simulation of surface water and groundwater.

[0081] It should be noted that the aforementioned close coupling between surface water and groundwater is achieved by the numerical model of surface water-groundwater interaction, which simultaneously solves the two-dimensional diffusion wave equation of surface water and the three-dimensional Richards equation of groundwater using a global implicit method. Furthermore, this numerical model of surface water-groundwater interaction is not merely a simple coupling of surface water and groundwater flow equations, but a complex system that comprehensively considers multiple hydrological processes and physical mechanisms. The integration of other hydrological processes includes: solute transport (the numerical model can simulate the transport of solutes in surface water and groundwater, including diffusion, convection, and molecular diffusion); heat transport (the numerical model can also simulate heat transport processes between the surface and the ground, which is crucial for understanding heat exchange and temperature changes in hydrological systems); ecological processes (the numerical model can simulate the flow of matter and energy in ecosystems, including soil moisture and plant growth); and geochemical processes (the numerical model can simulate chemical reactions and material transport processes in groundwater and soil, including the migration and transformation of inorganic matter).

[0082] The specific steps for parameterizing and simplifying the numerical model based on data characteristics or theoretical assumptions are as follows:

[0083] Hypothetical Function Form: Based on data characteristics or theoretical assumptions, choose an appropriate function form to express the relationship between input and output variables, or the relationship between one output variable and another.

[0084] Parameter estimation: Using historical or experimental data, statistical methods or optimization algorithms are used to determine or estimate the parameters of the function so that it fits the data as closely as possible. For statistical methods, the minimum mean square error method is used to determine the parameters of the function, while for optimization algorithms, the least squares method is used to estimate the parameters of the function.

[0085] Application Model: The constructed function model is applied to the numerical model, and the interaction between surface water and groundwater is simulated.

[0086] The above steps simplify the numerical model by parameterizing it based on data characteristics or theoretical assumptions. This not only effectively simplifies the structure and input data of the numerical model, thereby reducing the number of parameters required and lowering computational complexity and cost, thus improving the model's operational efficiency and interpretability, and enhancing its performance and reliability, but also allows for rapid adjustment of the model's design to adapt to new design requirements by simply changing parameter values. This eliminates the need to build the model from scratch, enabling it to flexibly cope with different simulation scenarios and improving the computational efficiency and simulation effect of this surface and groundwater interaction simulation method and system.

[0087] For example, if we want to simulate the exchange flux between surface water and groundwater, we typically need to input complex hydrogeological data and boundary conditions. To simplify this process, parametric modeling methods can be used, which include the following steps:

[0088] Assuming a functional form, we choose a linear function based on water level difference to describe the exchange flux between surface water and groundwater, with the following functional form: ;in Represents exchange flux. This represents the commutation coefficient, while and These represent the water levels of surface water and groundwater, respectively.

[0089] Estimate parameters by using historical or experimental data and optimization algorithms (such as least squares method) to estimate the commutation coefficients. Alternatively, the commutation coefficients can be determined using the method of minimizing the mean square error. This allows it to fit the exchange flux data between surface water and groundwater;

[0090] Validate the model by evaluating its goodness of fit through statistical tests and comparing it with actual data to ensure the model's predictive ability.

[0091] The parameterized exchange flux model is applied to the numerical model to simulate the interaction between surface water and groundwater. By adjusting the parameters, the changes in exchange flux under different hydrological conditions can be quickly assessed.

[0092] Experiment 2:

[0093]

[0094] The measured exchange flux was obtained through actual measurements of the seven-day exchange flux between groundwater and surface water; the exchange flux in Experiment 1 (model) was obtained through model simulation of the seven-day exchange flux; and the exchange flux in Experiment 2 (function) was obtained through formula... The calculated seven-day exchange flux data, of which The exchange coefficients are determined based on historical data and using the method of minimizing mean square error; RMSE and R² are the goodness-of-fit evaluation indicators of the exchange flux of experimental group 1 and experimental group 2 to the measured data.

[0095] Conclusion: The goodness of fit of experimental group 2 (function) (RMSE=0.08m³ / s, R²=0.97) is better than that of experimental group 1 (model) (RMSE=0.12m³ / s, R²=0.95), indicating that combining the formula to calculate the exchange flux can improve the goodness of fit and computational efficiency of the model.

[0096] The specific steps for verifying and optimizing the parameters of the simplified numerical model are as follows:

[0097] Historical surface and subsurface data of the area to be simulated are obtained. The obtained data are preprocessed and sampled. The simplified data and the permeability coefficient of the aquifer are then input into the simplified numerical model to obtain the simulation results.

[0098] The simulation results are compared with the measured data to evaluate the accuracy and reliability of the simplified numerical model, and the model parameters are optimized using genetic algorithms, dynamic dimension search, or particle swarm optimization algorithms.

[0099] After parameter optimization, the simplified numerical model is rerun and compared until the model output matches the measured data to the preset standard.

[0100] Example 2: A surface and groundwater interaction simulation system based on aquifer analysis, such as... Figure 1 As shown, it includes a data acquisition module, a data processing module, an aquifer analysis module, a model simplification module, an interactive simulation module, and a resource management module.

[0101] Data acquisition module: used to acquire surface and subsurface data of the area to be simulated;

[0102] Data processing module: used to preprocess surface data and subsurface data to obtain preprocessed surface data and subsurface data, and to sample high-resolution data in the preprocessed surface data and subsurface data to obtain simplified surface data and subsurface data;

[0103] Aquifer Analysis Module: This module is used to classify the location and type of aquifers based on simplified geological structure data, and to calculate the permeability coefficient of the corresponding aquifer based on groundwater hydrological data and the location and type of the aquifer.

[0104] Model simplification module: used to construct conceptual and numerical models of surface water-groundwater interaction based on the HGS model, and to perform parameter simplification of the numerical model based on data characteristics or theoretical assumptions;

[0105] Interactive simulation module: This module is used to input the permeability coefficient of the aquifer, as well as simplified surface and groundwater data, into the simplified numerical model. Based on the input data, it establishes the parameters, boundary conditions, and initial conditions of the simplified numerical model. The module then runs the simplified numerical model to perform interactive simulation of surface water and groundwater and obtains the simulation results.

[0106] Resource Management Module: Used to assess the water resource status of the simulated area based on the simulation results, and to set reasonable development and management plans for the water resources of the simulated area based on the assessment results.

[0107] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims. Parts not described in detail in this specification are prior art known to those skilled in the art.

Claims

1. A method for simulating interaction between surface water and groundwater based on aquifer analysis, characterized by, The method comprises the following steps: obtaining surface data and underground data of a region to be simulated, wherein the surface data comprises meteorological data, terrain data and land use data, and the underground data comprises underground hydrological data and geological structure data; preprocessing the surface data and the underground data to obtain preprocessed surface data and underground data, and performing data sampling on high-resolution data in the preprocessed surface data and the preprocessed underground data to obtain simplified surface data and simplified underground data; dividing the position and type of an aquifer based on the simplified geological structure data, and calculating the permeability coefficient of the corresponding aquifer based on the position and type of the aquifer; constructing a conceptual model and a numerical model of surface water-groundwater interaction based on the HGS model, parameterizing and simplifying the numerical model based on data characteristics or theoretical assumptions, inputting the permeability coefficient of the aquifer and the simplified surface data and underground data into the simplified numerical model, and performing simulation of surface water-groundwater interaction by running the simplified numerical model to obtain simulation results; evaluating the water resource status of the simulated region based on the simulation results, and setting a reasonable development and management scheme for the water resources of the simulated region according to the evaluation results.

2. The method of claim 1, wherein, The meteorological data comprises precipitation, temperature and humidity, the terrain data comprises elevation, slope, water level of surface water bodies and evaporation, the land use data comprises land use type and vegetation coverage, the underground hydrological data comprises underground water level, flow rate and water storage rate, and the geological structure data comprises stratum type, distribution and lithology.

3. The method of claim 1, wherein, The specific steps of data sampling on the high-resolution data are as follows: selecting a variable for stratification according to the characteristics of the high-resolution data; dividing the data into different levels according to the stratification variable; determining the number of samples to be extracted from each level according to the proportion of each level in the whole; extracting samples in each level by random sampling; merging the sample data extracted from each level to obtain a simplified data set.

4. The method of claim 3, wherein, The specific formula for calculating the permeability coefficient of the corresponding aquifer is as follows: ; wherein is the permeability coefficient, is the reference permeability coefficient, is the aquifer burial depth, is the groundwater flow velocity, is the reference flow velocity, is the soil temperature, is the reference soil temperature, and , and are the influence coefficients of the aquifer burial depth, the groundwater flow velocity and the soil temperature on the permeability coefficient, respectively.

5. The method of claim 1, wherein, The specific way of realizing the interaction simulation of surface water and groundwater by the numerical model of surface water-groundwater interaction is as follows: the flow of surface water is simulated by a two-dimensional diffusion wave equation, and the mathematical expression is as follows: ; where is the water depth, is time, is the acceleration of gravity, and is the terrain slope; the flow of groundwater is simulated by a three-dimensional Richards equation, and the mathematical expression is as follows: ; wherein is the volumetric water content, is time, is the hydraulic conductivity, which is a function of the water content, and is the water head; the flow equations for surface water and groundwater are coupled, enabling interactive simulation of surface and groundwater.

6. The method of claim 5, wherein, The specific steps of parameterizing and simplifying the numerical model are as follows: selecting a suitable function form to express the relationship between an input variable or one output variable and another output variable according to data characteristics or theoretical assumptions; determining or estimating the parameters of the function by using the least mean square error method or the least square method based on historical data; applying the constructed function model to the numerical model, and performing the interaction simulation of surface water and groundwater.

7. The method of claim 6, wherein, The specific steps of verifying and optimizing the parameters of the simplified numerical model are as follows: obtaining historical surface data and historical underground data of the region to be simulated, preprocessing and sampling the obtained data, inputting the simplified data and the permeability coefficient of the aquifer into the simplified numerical model, and obtaining simulation results; The simulation results are compared with the measured data to evaluate the performance of the simplified numerical model, and the genetic algorithm is used to optimize the model parameters; After parameter optimization, the simplified numerical model is re-run and comparative analysis is performed until the model output and the measured data reach the preset standard.

8. A surface and groundwater interaction simulation system based on aquifer analysis, applied to the surface and groundwater interaction simulation method based on aquifer analysis according to any one of claims 1-7, characterized in that, It comprises a data acquisition module, a data processing module, an aquifer analysis module, a model simplification module, an interactive simulation module, and a resource management module, The data acquisition module is used to acquire surface data and underground data of the region to be simulated; The data processing module is used to preprocess the surface data and underground data, and to sample the high-resolution data in the preprocessed surface data and underground data; The aquifer analysis module is used to divide the location and type of the aquifer according to the simplified geological structure data, and to calculate the permeability coefficient of the corresponding aquifer based on the location and type of the aquifer; The model simplification module is used to construct a conceptual model and a numerical model of surface water-groundwater interaction based on the HGS model, and to parameterize and simplify the numerical model based on data characteristics or theoretical assumptions; The interactive simulation module is used to input the permeability coefficient of the aquifer and the simplified surface data and underground data into the simplified numerical model, to perform interactive simulation of surface water-groundwater through running the simplified numerical model, and to obtain simulation results; The resource management module is used to evaluate the water resource situation of the simulated region based on the simulation results, and to set reasonable development and management schemes for the water resources of the simulated region according to the evaluation results.

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