An evaluation method for surface water-groundwater conversion relationship coupled with a hydrological model

Abstract

The application discloses a kind of surface water-groundwater conversion relationship evaluation methods coupled with hydrological model, belong to hydrological evaluation technical field.The method includes: constructing surface water-groundwater coupling hydrological model containing surface water module, groundwater module and coupling interface, after calibration and verification to surface water-groundwater coupling hydrological model, input the basic data of target area, and time sequence simulation is simulated in each grid to the surface water-groundwater conversion process of target area, obtain the grid unit data set containing grid calculation unit position information and simulation data sequence;Based on ST-DBSCAN, the grid unit data set is carried out spatial and temporal clustering partition, and the total data of region is obtained by combining mathematical summary analysis;According to the total data of region, the overall conversion direction and intensity grade of surface water-groundwater in target area are determined.The method realizes the accurate quantification of surface water and groundwater conversion relationship, and provides technical support for hydrological resource management.

Description

Technical Field

[0001] This invention relates to the field of hydrological assessment technology, specifically to a method for evaluating the surface water-groundwater transformation relationship using a coupled hydrological model. Background Technology

[0002] The transformation relationship between surface water and groundwater is a core component of the hydrological cycle system, and its accurate assessment is crucial for the optimal allocation of regional water resources, ecological environmental protection, and water conservancy project planning. Currently, hydrological models are the primary technical means to simulate this transformation process. Among them, the SWAT model is widely used due to its advantages in simulating surface water runoff and evapotranspiration, while the MODFLOW model excels in simulating groundwater levels and seepage. To achieve coordinated simulation of surface and groundwater processes, existing technologies have attempted to construct coupled models. However, most coupling schemes only achieve simple data transfer between modules and do not fully consider the dynamic interaction mechanism between surface water and groundwater at the grid scale, resulting in simulation results that fail to reflect the spatial heterogeneity of transformation characteristics within the region.

[0003] Existing methods for evaluating transformation relationships have significant limitations: on the one hand, clustering and partitioning are mostly based on a single indicator in the spatial or temporal dimension, lacking dual consideration of both spatial and temporal dimensions, and thus failing to accurately delineate regions with similar transformation characteristics; on the other hand, mathematical summary analysis relies heavily on simple statistical calculations, lacking standardized flux accumulation and net flux accounting processes, and the adjustable parameters are vaguely defined and the accuracy judgment criteria are unclear during calibration and verification, resulting in insufficient reliability and applicability of the evaluation results, making it difficult to meet the refined needs of water resource management in complex areas.

[0004] Therefore, a method for evaluating the transformation relationship between surface water and groundwater is proposed to address the above-mentioned problems. Summary of the Invention

[0005] The purpose of this invention is to provide a method for evaluating the surface water-groundwater transformation relationship using a coupled hydrological model, in order to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for evaluating the surface water-groundwater transformation relationship using a coupled hydrological model includes the following steps:

[0008] S1. Construct a surface water-groundwater coupled hydrological model for the target area, and calibrate and verify the surface water-groundwater coupled hydrological model to obtain a calibrated and verified surface water-groundwater coupled hydrological model;

[0009] S2. The surface water-groundwater coupling hydrological model was calibrated and validated to simulate the surface water-groundwater transformation process in the target area, and a raster cell dataset was obtained.

[0010] S3. Perform spatiotemporal clustering partitioning and mathematical summary analysis on the raster cell dataset to obtain the total regional data of the target area;

[0011] S4. Determine the overall transformation direction and intensity level of surface water-groundwater in the target area based on the total regional data of the target area.

[0012] Preferably, the surface water-groundwater coupled hydrological model includes a surface water module, a groundwater module, and a coupling interface. The surface water module is constructed using the SWAT (Soil and Water Assessment Tool) model to simulate surface water runoff, surface water level, surface water evapotranspiration, and surface water infiltration in the target area. The digital elevation model (DEM), soil type map, and land use type map of the target area serve as the spatial basis data for the SWAT model. Combined with rainfall and daily average temperature in the basis data, HRUs (watershed hydrological response units) are constructed, and the HRUs are spatially matched with the divided grid computing units to ensure that each grid computing unit corresponds to at least one HRU. At the same time, the surface water runoff calculation method in the SWAT model is set to the SCS curve number method, the infiltration model adopts the Green-Ampt infiltration model, and the evapotranspiration calculation module adopts the Penman-Monteith formula.

[0013] The SCS curve number method is as follows: ;

[0014] Where Q is surface water runoff, P is rainfall, and S is the potential maximum retention capacity. (CN represents the curve number);

[0015] The Green-Ampt infiltration model is as follows: ;

[0016] Where f is the amount of surface water infiltration. For soil saturated hydraulic conductivity, For moistening and suction power, The difference between the soil saturated moisture content and the initial moisture content is given by , and F is the cumulative surface water infiltration.

[0017] The Penman-Monteith formula is as follows: ;in, Evapotranspiration (or surface water evapotranspiration) The slope of the saturated water vapor pressure curve. G represents net surface radiation, and G represents soil heat flux. Where is the hygrometer constant, and T is the daily average temperature. The wind speed at a height of 2m above the ground is... The saturated vapor pressure, This is the actual water vapor pressure;

[0018] The groundwater module is constructed using the MODFLOW (Modular Three-Dimensional Finite-Difference Ground-Water Flow Model) model to simulate the groundwater level and discharge in the target area. Based on the hydrogeological survey data of the target area, a three-dimensional mesh aligned with the grid computing unit space is constructed. The aquifer structure parameters corresponding to each grid computing unit are defined, and the calculation method for groundwater discharge in the groundwater model adopts the groundwater flow control equation in finite difference form. The groundwater flow control equation is as follows:

[0019] ;

[0020] in, , , The permeability coefficients are in the x, y, and z directions. Groundwater level, This includes groundwater recharge and discharge items (including groundwater discharge and surface water recharge flux to groundwater fed back from the coupling interface). For the specific water storage coefficient, For time;

[0021] The coupling interface is used to connect the surface water module and the groundwater module, and the coupling interface interacts with the surface water level output by the surface water module. The surface water recharge flux to groundwater and the groundwater level output by the groundwater module. And the flux of groundwater to replenish surface water, based on the difference between surface water level and groundwater level within each grid computing cell. Combined with the aquifer permeability coefficient corresponding to the grid calculation unit Darcy's law is used to calculate the bidirectional exchange flux between surface water and groundwater. The formula for Darcy's law is: When... hour, ,in, To replenish groundwater flux from surface water, The length of the surface water flow path. For the area of ​​a raster cell, when hour, ,in, To replenish surface water flux from groundwater, the coupling interface will , Feedback is sent to the surface water module and the groundwater module respectively, forming a dynamic closed loop of interaction between surface water and groundwater, and the data interaction frequency of the coupling interface is consistent with the simulation time scale (day, month or year).

[0022] Preferably, the operation flow of the surface water-groundwater coupled hydrological model is as follows:

[0023] The basic data of the target area are spatially interpolated and temporally normalized according to the raster computing unit. Among them, the rainfall and daily average temperature are split into time series according to the time scale and input into the surface water module. The initial surface water level is used as the initial boundary condition of the surface water module, and the initial groundwater level is used as the initial hydraulic head value of the groundwater module.

[0024] The surface water module and the groundwater module are started simultaneously. In the first time step (t=1), the surface water module simulates the surface water runoff based on rainfall, daily average temperature, initial surface water level, and adjustable parameters. Surface water infiltration Surface water level ; and surface water evapotranspiration The groundwater module calculates the groundwater level based on the initial groundwater level and adjustable parameters using the groundwater flow control equation. and groundwater discharge The surface water module and the groundwater module output their results to the coupling interface, respectively.

[0025] The coupling interface receives the output results from the surface water module and the groundwater module, and calculates the surface water recharge flux to groundwater for each grid computing unit using Darcy's law. Or groundwater replenishment of surface water flux and will and Feedback is sent to the groundwater module and the surface water module respectively. The surface water module and the groundwater module adjust the input of the next time step (t=2) through the water balance equation. The interaction process is repeated step by step according to the time span to realize the iterative simulation of the surface water-groundwater conversion process in the target area.

[0026] The water balance equation is as follows:

[0027] ;

[0028] ;

[0029] in, Timing step size The surface water module replenishment volume, Previous time step The groundwater flux that replenishes surface water. Timing step size Surface water evapotranspiration Timing step size The amount of surface water infiltration. Timing step Rainfall, Timing step size The amount of groundwater module recharge, Previous time step The surface water replenishment flux to groundwater, Timing step size Groundwater discharge;

[0030] Within the entire simulation time step [1, n] (n is the total number of time steps, determined by the time scale, i.e., n = LT / xt, where LT is the time span and xt is the time scale), the surface water-groundwater coupled hydrological model records the simulation data of each grid computing unit in the order of the time step. The simulation data includes the flux of surface water to groundwater and the flux of groundwater to surface water. The simulation data is sorted by time to form a simulation data sequence.

[0031] Preferably, the calibration and verification process is as follows: using the measured groundwater level and surface water runoff in the target area as calibration indicators, and simultaneously running the surface water-groundwater coupled hydrological model to be calibrated and verified to obtain the simulation data corresponding to the calibration indicators, calculating the Nash efficiency coefficient and the mean relative error based on the calibration indicators and their corresponding simulation data. If either the Nash efficiency coefficient is <0.85 or the mean relative error is greater than 7%, then adjusting the adjustable parameters of the surface water-groundwater coupled hydrological model to be calibrated and verified to obtain an optimized surface water-groundwater coupled hydrological model. The adjustable parameters include the number of curves in the surface water module, the soil saturated hydraulic conductivity, and the permeability coefficient of the groundwater module. , , The optimized surface water-groundwater coupled hydrological model is run again until the Nash efficiency coefficient is ≥0.85 and the mean relative error is ≤7%. At this point, the optimized surface water-groundwater coupled hydrological model is used as the calibration and verification surface water-groundwater coupled hydrological model.

[0032] The Nash efficiency coefficient The calculation formula is:

[0033] ;

[0034] in, Let i be the measured groundwater level or surface water runoff. For the i-th measured groundwater level or surface water runoff, the simulated data is... The mean of the measured groundwater level or surface water runoff is n, where n is the number of data samples.

[0035] The relative error mean The calculation formula is:

[0036] .

[0037] Preferably, the simulation method for the surface water-groundwater transformation process in the target area is as follows:

[0038] S21. Determine the time scale, time span, and raster resolution for simulating the surface water-groundwater conversion process in the target area, and divide the target area into several raster computing units according to the raster resolution. The time scale is selected from one of day, month, and year, and the raster resolution is set according to the spatial area of ​​the target area and the simulation accuracy requirements, usually 10m-1000m.

[0039] S22. After spatial matching of the basic data of the target area by grid computing unit, the data is input into the calibrated and verified surface water-groundwater coupled hydrological model and then the model is run grid-by-grid and time-series. The grid-by-grid means that each grid computing unit independently carries the simulation operation, and the time-series means that the simulation is carried out sequentially according to the set time scale. Within each time series step, the surface water-groundwater exchange state of the current time series step is updated based on the simulation result of the grid computing unit in the previous time series step, which is used to simulate the entire process of surface water-groundwater transformation. The basic data includes rainfall, daily average temperature, initial surface water level and initial groundwater level.

[0040] S23. Extract the simulated data of each raster computing unit within the time span and integrate them to form a raster unit dataset.

[0041] Preferably, the raster cell dataset consists of several raster cell data items, and each raster cell data item consists of the location information of its corresponding raster computing unit and a simulated data sequence. The simulated data sequence is formed by sorting the simulated data by time, including a surface water recharge to groundwater flux sequence and a groundwater recharge to surface water flux sequence. The simulated data includes surface water recharge to groundwater flux and groundwater recharge to surface water flux.

[0042] Preferably, the spatiotemporal clustering partitioning process is as follows: based on the location information and simulated data sequence in the raster unit data items, ST-DBSCAN (spatial-density clustering algorithm) is used to merge several raster computing units that make up the target area into several raster clusters based on spatial proximity and temporal series consistency. Here, spatial proximity refers to the Euclidean distance based on the location information of the raster computing units to measure the spatial proximity between the raster computing units, and temporal series consistency refers to the temporal similarity between the raster computing units based on the mean and variance of the simulated data sequence of the raster computing units. When ST-DBSCAN clusters the raster computing units, it automatically selects raster computing units that simultaneously satisfy the Euclidean distance being less than the spatial proximity threshold and the temporal similarity being greater than the temporal similarity threshold, based on a pre-set spatial neighborhood distance threshold, a temporal similarity threshold, and the minimum number of raster units in the raster cluster. These are then aggregated to form raster clusters whose number of raster computing units is greater than or equal to the minimum number of raster units in the raster cluster.

[0043] Preferably, the process of the mathematical summary analysis is as follows:

[0044] The simulated data sequences corresponding to each raster computing unit within the raster cluster are extracted, and the simulated data sequences corresponding to each raster computing unit are substituted into the time series flux accumulation formula to calculate the total flux of surface water recharged to groundwater and the total flux of groundwater recharged to surface water for each raster computing unit. The total flux of surface water recharged to groundwater and the total flux of groundwater recharged to surface water for all raster computing units within the raster cluster are substituted into the regional net flux summation formula to calculate the regional net conversion total flux of the raster cluster.

[0045] The formula for accumulating time-series flux is:

[0046] , ;

[0047] in, The total flux of surface water to groundwater replenishment for the grid cluster. The total flux of groundwater to surface water replenishment for the grid cluster. , These are the surface water recharge to groundwater flux and the groundwater recharge to surface water flux of the i-th grid computing unit within the grid cluster at the t-th time step, respectively, where n is the total number of time steps within the time span;

[0048] The formula for summing the net flux in the region is: ,

[0049] in denoted as the net total flux of the raster cluster, and m is the total number of raster computational cells within the raster cluster.

[0050] Preferably, the total regional data consists of the total net transformation flux of each raster cluster, the spatial area of ​​each raster cluster, and the dominant transformation type, wherein the dominant transformation type is based on the individual raster cluster's... determination, Type that replenishes groundwater with surface water. For the groundwater-to-surface-water recharge type; the process for determining the overall transformation direction is as follows: calculate the overall net transformation flux of the target area, which is the sum of the regional net transformation fluxes of all grid clusters. When the overall net transformation flux > 0, the overall transformation direction is determined to be surface water recharged to groundwater; when the overall net transformation flux < 0, the overall transformation direction is determined to be groundwater recharged to surface water; when the overall net transformation flux = 0, the overall transformation direction is determined to be dynamic equilibrium.

[0051] The formula for calculating the total net conversion flux is:

[0052] ;

[0053] in, The total net conversion flux of the target area. Let M be the net total flux of the k-th raster cluster, and M be the total number of raster clusters.

[0054] Preferably, the determination process for the intensity level is as follows: The net conversion intensity per unit area is calculated based on the total net conversion flux and spatial area of ​​the target region; and the net conversion intensity per unit area is then used to determine the intensity level. Intensity levels are divided when When, the strength level is weak conversion, when When, the intensity level is medium conversion, when When, the strength level is strong conversion, when When this occurs, the intensity level is determined to be extremely high conversion, where... In this context, T refers to the time span.

[0055] The formula for calculating the net conversion intensity per unit area is:

[0056] ;

[0057] in, The spatial area of ​​the target region. This represents the simulated time span.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1. This invention optimizes the surface water-groundwater coupling architecture. Through a coupling interface with bidirectional feedback of water level and flux, it realizes dynamic interaction between surface and underground modules at the scale of grid computing units, breaking the traditional simple data transmission mode and accurately reflecting the spatial heterogeneity of transformation characteristics.

[0060] 2. This invention employs the ST-DBSCAN spatiotemporal density clustering algorithm, which divides grid clusters based on both spatial proximity and temporal series consistency, thus solving the problem that traditional single-dimensional clustering cannot accurately define similar transformation regions.

[0061] 3. Establish a standardized mathematical summary and analysis process, clarify the formulas for time-series flux accumulation and regional net flux calculation, and define the adjustable parameter range and accuracy judgment criteria of the model to solve the limitations of traditional methods such as non-standard process and ambiguous parameters, thereby improving the reliability of evaluation results. Attached Figure Description

[0062] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1 This is a flowchart of the method steps of the present invention. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0065] Examples, such as Figure 1 As shown, a method for evaluating the surface water-groundwater transformation relationship using a coupled hydrological model includes the following steps:

[0066] S1. Construct a surface water-groundwater coupled hydrological model for the target area, and calibrate and verify the surface water-groundwater coupled hydrological model to obtain a calibrated and verified surface water-groundwater coupled hydrological model;

[0067] S2. The surface water-groundwater coupling hydrological model was calibrated and validated to simulate the surface water-groundwater transformation process in the target area, and a raster cell dataset was obtained.

[0068] S3. Perform spatiotemporal clustering partitioning and mathematical summary analysis on the raster cell dataset to obtain the total regional data of the target area;

[0069] S4. Determine the overall transformation direction and intensity level of surface water-groundwater in the target area based on the total regional data of the target area.

[0070] Furthermore, the working principle of the present invention will be illustrated below using a small mountain watershed as an example:

[0071] The target area has a spatial area of ​​50 The terrain is mainly low mountains and hills, with an elevation range of 200-800m, and the terrain is high in the northwest and low in the southeast. The soil types are mainly yellow soil and red soil. The land use types include forest land, cultivated land, residential areas and others. The hydrogeological conditions are a single unconfined aquifer with sandstone as the main lithology, a permeability coefficient of 10-30m / d, and a groundwater depth of 3-15m. There is a perennial stream in the watershed, no large-scale water conservancy projects, and frequent interaction between surface water and groundwater.

[0072] A surface water-groundwater coupled hydrological model was constructed, comprising a surface water module, a groundwater module, and a coupling interface. The surface water module adopted the SWAT model, using a 1:50,000 digital elevation model (DEM), a 1:10,000 soil type map, and a land use type map of the target area as spatial basis data. This was combined with rainfall data from three rain gauge stations within the watershed from 2018 to 2020 (annual average rainfall of 1200 mm) and daily average temperature data from one meteorological station (annual average temperature of 16°C). The watershed hydrological response units (HRUs) were constructed, and spatial matching was performed between the HRUs and the subsequently divided grid computing units to ensure that each grid computing unit corresponds to at least one HRU. Simultaneously, the SWAT model was configured to use the SCS curve number method for surface water runoff calculation, the Green-Ampt infiltration model for infiltration model, and the Penman-Monteith formula for evapotranspiration calculation. The groundwater module adopted the MODFLOW model, constructing a three-dimensional grid (500 grids in the x-direction, 100 grids in the y-direction, and 2 grids in the z-direction) spatially aligned with the grid computing units based on hydrogeological survey data of the target area. The aquifer structural parameters corresponding to each grid computing unit were defined (aquifer thickness 5-20m, specific storage coefficient 1×...). The groundwater discharge is calculated using the finite difference form of the groundwater flow control equation. The coupling interface is used to connect the surface water module and the groundwater module, and exchange the surface water level, surface water recharge to groundwater flux and groundwater level, groundwater recharge to surface water output by the two modules. Based on the surface water level and groundwater level difference in each grid computing unit, the bidirectional exchange flux is calculated using Darcy's law in combination with the aquifer permeability coefficient. The data exchange frequency is consistent with the simulation time scale (month).

[0073] The constructed surface water-groundwater coupled hydrological model was calibrated and validated. The measured groundwater level (3 monitoring wells, monitored monthly) and surface water runoff (watershed outlet hydrological station, monitored monthly) in the target area from 2018 to 2020 were used as calibration indicators. The surface water-groundwater coupled hydrological model was run to obtain simulation data, and the Nash efficiency coefficient and mean relative error were calculated. Initial simulation results showed that the groundwater level NSE = 0.78, the surface water runoff NSE = 0.82, and the surface water runoff MRE = 8.5%, which did not meet the calibration standards. Therefore, the adjustable parameters (the number of curves in the surface water module, CN) were adjusted. The adjustable parameters were adjusted within the ranges of ±5 for the value, ±20% for the soil saturated hydraulic conductivity, ±15% for the x, y, and z direction permeability coefficients of the groundwater module, and ±10% for the specific storage coefficient. After optimizing the adjustable parameters, the surface water-groundwater coupled hydrological model was run again. Finally, the groundwater level NSE=0.88, surface water runoff NSE=0.86, groundwater level MRE=5.2%, and surface water runoff MRE=6.3%, all of which met the requirement of "Nash efficiency coefficient ≥0.85 and mean relative error ≤7%". At this time, the surface water-groundwater coupled hydrological model is the calibrated and verified surface water-groundwater coupled hydrological model.

[0074] The time scale was chosen as "month," with a simulation spanning from January 2021 to December 2023 (a total of 36 time steps). The raster resolution was set to 100m × 100m, and the target area was divided into 5000 raster computing units (each raster computing unit has a spatial area of ​​0.01). Subsequently, basic data (rainfall, daily average temperature, initial surface water level of 1.2m, and initial groundwater level of 8.5m) for the target area from 2021 to 2023 were collected. Spatial interpolation and temporal normalization were performed by grid computing units. Rainfall and daily average temperature were split into monthly time series. The initial surface water level was used as the initial boundary condition for the surface water module, and the initial groundwater level was used as the initial head value for the groundwater module. The basic data were input into the calibrated and verified surface water-groundwater coupled hydrological model, and the calibrated and verified surface water-groundwater coupled hydrological model was run grid by grid and time series. Finally, the simulation data of each grid computing unit within 36 time series steps were extracted and integrated to form a grid unit dataset. This grid unit dataset contains 5,000 grid unit data items. Each grid unit data item consists of the location information (latitude and longitude coordinates) of the corresponding grid computing unit and the simulation data sequence (surface water recharge to groundwater flux and groundwater recharge to surface water flux for 36 time series steps).

[0075] The ST-DBSCAN algorithm was used to perform spatiotemporal clustering partitioning of the raster cell dataset, with spatial proximity and temporal series consistency as the clustering criteria. Spatial proximity was measured by the Euclidean distance between raster computational units, with a spatial neighborhood distance threshold set at 500m. Temporal series consistency was measured by the mean and variance of the simulated data series, with a temporal similarity threshold set at 0.8. The minimum number of raster cells per cluster was set to 5. After clustering, the 5000 raster computational units of the target region were merged into 8 raster clusters, each with a spatial area of ​​6.2 square meters. 5.8 7.5 6.0 8.3 5.2 7.1 3.9 The data covers the entire target area. Then, a mathematical summary analysis is performed. First, the simulated data sequence of each grid computing unit in each grid cluster is extracted and accumulated to obtain the total flux of surface water recharge to groundwater and the total flux of groundwater recharge to surface water for each grid computing unit. Then, the two types of total fluxes of all grid computing units in the grid cluster are summarized to calculate the regional net transformation total flux of each grid cluster. At the same time, the dominant transformation type is determined according to the positive or negative value of the regional net transformation total flux. Finally, the regional total data of the target area, including the spatial area of ​​each grid cluster, the regional net transformation total flux, and the dominant transformation type, are formed. The regional total data of the target area is shown in Table 1 below.

[0076] Table 1;

[0077] Calculate the overall net conversion flux of the target area, which is the sum of the net conversion fluxes of the 8 raster clusters. The overall net conversion flux is calculated as: 2.6 + (-0.6) + 2.7 + 1.9 + 2.8 + (-0.4) + 2.4 + 0.4 = 11.8 × Since the overall net conversion flux is greater than 0, the overall conversion direction of surface water to groundwater in the target area is determined to be surface water recharges groundwater. Then, based on the overall net conversion flux and the spatial area of ​​the target area, the net conversion intensity per unit area is calculated. The spatial area of ​​the target area is 50. The simulation spans 3 years (T=3 years), and the net conversion intensity per unit area is I = |total net conversion flux| / (target area × T) = 11.8 × / (50 (×3 years) = 7.87× According to the preset strength grade classification standard, 1× ≤I<5× For moderate conversion, the net conversion intensity per unit area in this embodiment is close to 1× The intensity level was determined to be moderate. This embodiment, through the method of the present invention, shows that the overall transformation direction of surface water to groundwater in the target area is surface water recharges groundwater, with an intensity level of moderate transformation. This result is consistent with the conclusions of the on-site hydrological survey of the watershed, verifying the accuracy and reliability of the method of the present invention.

[0078] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them; modifications to the technical solutions described in the foregoing embodiments, or equivalent substitutions of some of the technical features, 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, and should all be included within the protection scope of this application.

Claims

1. A method for evaluating the surface water-groundwater transformation relationship using a coupled hydrological model, characterized in that, Includes the following steps: S1. Construct a surface water-groundwater coupled hydrological model for the target area, and calibrate and verify the surface water-groundwater coupled hydrological model to obtain a calibrated and verified surface water-groundwater coupled hydrological model; S2. The surface water-groundwater coupling hydrological model was calibrated and validated to simulate the surface water-groundwater transformation process in the target area, and a raster cell dataset was obtained. S3. Perform spatiotemporal clustering partitioning and mathematical summary analysis on the raster cell dataset to obtain the total regional data of the target area; S4. Determine the overall transformation direction and intensity level of surface water-groundwater in the target area based on the total regional data of the target area.

2. The method for evaluating the surface water-groundwater transformation relationship using a coupled hydrological model according to claim 1, characterized in that, The surface water-groundwater coupled hydrological model includes a surface water module, a groundwater module, and a coupling interface. The surface water module is constructed using the SWAT model to simulate surface water runoff, surface water level, surface water evapotranspiration, and surface water infiltration in the target area. The groundwater module is constructed using the MODFLOW model to simulate groundwater level and groundwater discharge in the target area. The coupling interface is used to connect the surface water module and the groundwater module.

3. The method for evaluating the surface water-groundwater transformation relationship using a coupled hydrological model according to claim 2, characterized in that, The calibration and verification process is as follows: Using the measured groundwater level and surface water runoff in the target area as calibration indicators, the surface water-groundwater coupled hydrological model to be calibrated and verified is run simultaneously to obtain the simulation data corresponding to the calibration indicators. Based on the calibration indicators and their corresponding simulation data, the Nash efficiency coefficient and the mean relative error are calculated. If either the Nash efficiency coefficient is <0.85 or the mean relative error is greater than 7%, the adjustable parameters of the surface water-groundwater coupled hydrological model to be calibrated and verified are adjusted to obtain an optimized surface water-groundwater coupled hydrological model. The optimized surface water-groundwater coupled hydrological model is then... Run the model again until the Nash efficiency coefficient is ≥0.85 and the mean relative error is ≤7%. The optimized surface water-groundwater coupled hydrological model at this point is used as the calibration and verification surface water-groundwater coupled hydrological model.

4. The method for evaluating the surface water-groundwater transformation relationship using a coupled hydrological model according to claim 3, characterized in that, The simulation method for the surface water-groundwater transformation process in the target area is as follows: S21. Determine the time scale, time span, and raster resolution for simulating the surface water-groundwater conversion process in the target area, and divide the target area into several raster computing units according to the raster resolution. The time scale can be any one of day, month, and year. S22. After inputting the basic data of the target area into the calibrated and verified surface water-groundwater coupled hydrological model, the model is run grid-by-grid in a time-series manner to simulate the entire process of surface water-groundwater transformation. The basic data includes rainfall, daily average temperature, initial surface water level and initial groundwater level. S23. Extract the simulated data of each raster computing unit within the time span and integrate them to form a raster unit dataset.

5. The method for evaluating the surface water-groundwater transformation relationship using a coupled hydrological model according to claim 4, characterized in that, The raster cell dataset consists of several raster cell data items, and each raster cell data item consists of the location information of its corresponding raster computing unit and a simulated data sequence. The simulated data sequence is formed by sorting the simulated data by time, and the simulated data includes the flux of surface water to groundwater and the flux of groundwater to surface water.

6. The method for evaluating the surface water-groundwater transformation relationship using a coupled hydrological model according to claim 5, characterized in that, The spatiotemporal clustering partitioning process is as follows: based on the location information and simulated data sequence in the raster unit data items, ST-DBSCAN is used to merge several raster computing units that make up the target area into several raster clusters based on spatial proximity and temporal series consistency.

7. The method for evaluating the surface water-groundwater transformation relationship using a coupled hydrological model according to claim 6, characterized in that, The process of summarizing and analyzing mathematical time is as follows: The simulated data sequences corresponding to each raster computing unit within the raster cluster are extracted, and the simulated data sequences corresponding to each raster computing unit are substituted into the time series flux accumulation formula to calculate the total flux of surface water recharged to groundwater and the total flux of groundwater recharged to surface water for each raster computing unit. The total flux of surface water recharged to groundwater and the total flux of groundwater recharged to surface water for all raster computing units within the raster cluster are substituted into the regional net flux summation formula to calculate the regional net conversion total flux of the raster cluster.

8. The method for evaluating the surface water-groundwater transformation relationship using a coupled hydrological model according to claim 7, characterized in that, The total regional data consists of the total net conversion flux of each grid cluster, the spatial area of ​​each grid cluster, and the dominant conversion type. The process for determining the overall conversion direction is as follows: calculate the total net conversion flux of the target region, which is the sum of the total net conversion flux of all grid clusters. When the total net conversion flux is >0, the overall conversion direction is determined to be surface water recharge to groundwater. When the total net conversion flux is <0, the overall conversion direction is determined to be groundwater recharge to surface water. When the total net conversion flux is =0, the overall conversion direction is determined to be dynamic equilibrium.

9. The method for evaluating the surface water-groundwater transformation relationship using a coupled hydrological model according to claim 8, characterized in that, The determination process for the intensity level is as follows: The net conversion intensity per unit area is calculated based on the total net conversion flux and spatial area of ​​the target region; then, based on the net conversion intensity per unit area… Intensity levels are divided when When, the strength level is weak conversion, when When, the intensity level is medium conversion, when When, the strength level is strong conversion, when If so, the intensity level is determined to be extremely high.

Images