A method for quickly identifying a potential enrichment area of groundwater in a hilly area

Abstract

The application discloses a method for quickly identifying a groundwater enrichment potential area in a hilly area, and aims at quickly identifying the groundwater enrichment potential area in the hilly area. First, by defining and quantifying key factors such as water-bearing rock group, terrain slope, vegetation coverage and annual rainfall, a mathematical relationship model of groundwater runoff modulus and the factors is established. Then, according to an optimal catchment area threshold, a catchment area is demarcated, and based on water demand, catchment area and groundwater runoff modulus, an identification principle is set to determine the enrichment potential area. Finally, through multi-stage screening and sorting, the catchment area meeting the water demand requirement is optimized, so as to scientifically guide groundwater resource exploration and development and utilization in the hilly area, and realize optimization and sustainable development of water resource management. The method not only improves the efficiency of identifying the groundwater enrichment potential area, but also improves the accuracy of detecting the groundwater, and provides important scientific basis and operation guide for decision makers.

Description

Technical Field

[0001] This invention relates to the fields of hydrogeology and groundwater resources, specifically to a method for rapidly identifying groundwater enrichment potential areas in hilly regions. Background Technology

[0002] Groundwater is a crucial emergency water source buried beneath the surface, playing a vital role in addressing extreme meteorological droughts. Groundwater includes unconfined aquifers and deep confined aquifers, with deep confined aquifers exhibiting greater stability, safety, and reliability. Deep confined aquifers are mostly distributed in mountainous and hilly areas (referred to as hilly areas). Previously, groundwater enrichment potential zones in hilly areas were identified through comprehensive analysis of previous hydrogeological data (hydrological parameters and borehole data, etc.). However, for large-scale identification in large watersheds, this approach suffers from data gaps, low accuracy, and inefficiency. To address these bottlenecks, this study developed a method for rapidly identifying groundwater enrichment potential zones in hilly areas. This method primarily involves defining and quantifying key factors influencing groundwater runoff modulus, establishing a multivariate linear relationship between groundwater runoff modulus and aquifer composition, topographic slope, vegetation cover, and annual rainfall. This systematically delineates catchment areas, enabling rapid identification of groundwater enrichment potential zones under the dual constraints of catchment area and groundwater runoff modulus under different water demand conditions. Summary of the Invention

[0003] A method for rapidly identifying groundwater enrichment potential zones in hilly areas includes the following steps;

[0004] S1. Determine the key factors affecting the groundwater runoff modulus and quantify them. The groundwater runoff modulus refers to the runoff volume of groundwater over a 1 square kilometer aquifer distribution area, representing the amount of groundwater existing in the form of groundwater runoff in a region. Analysis suggests that the groundwater runoff modulus is related to aquifer group, topographic slope, vegetation cover, and annual rainfall. To establish the mathematical relationship between the groundwater runoff modulus and the four key influencing factors, the four key influencing factors need to be quantified.

[0005] S2. Establish a multivariate linear relationship between groundwater runoff modulus and aquifer group, topographic slope, vegetation cover and annual rainfall. Select a certain number of representative blocks to obtain the groundwater runoff modulus. Calculate the quantitative indicators of aquifer group X1, topographic slope X2, vegetation cover X3 and annual rainfall X4 according to step S1. The representative blocks should be evenly distributed and contain different aquifer groups.

[0006] S3. Delineate the catchment area based on the optimal catchment area threshold;

[0007] S4. Establish identification principles for groundwater enrichment potential areas. Based on water demand analysis, and combined with the catchment area and groundwater runoff modulus, establish identification principles to identify groundwater enrichment potential areas.

[0008] S5. Select groundwater enrichment potential areas in hilly areas. For large-scale identification of groundwater enrichment potential areas in large watersheds, adopt the principle of proceeding from the known to the unknown. Based on the watershed distribution map obtained in S3, quantitative maps of aquifer groups, topographic slope, vegetation cover, and annual rainfall are obtained through S1. Based on the multivariate linear equations of groundwater runoff modulus and aquifer groups, topographic slope, vegetation cover, and annual rainfall obtained in S2, a groundwater runoff modulus distribution map of each watershed is obtained. The distribution map of natural groundwater recharge resources of each watershed is calculated using the formula Q=M×F, and sorted by size and displayed in a hierarchical manner. According to water demand and guarantee coefficient, watersheds with sufficient natural groundwater recharge resources are selected first, and then the corresponding groundwater enrichment potential areas are selected according to the identification principle.

[0009] Furthermore, a method for rapidly identifying groundwater enrichment potential areas in hilly regions,

[0010] In step S1, it is necessary to establish the mathematical relationship between the groundwater runoff modulus and the four key influencing factors. The four key influencing factors need to be quantified. The specific steps for quantifying the four key influencing factors are as follows.

[0011] Aquifer Group X1: Based on four types—carbonate rock, igneous rock, metamorphic rock, and clastic rock—values ​​of 4, 3, 2, and 1 are assigned respectively. The quantitative value of an aquifer group in a block is calculated using an area-weighted average. ;

[0012] Terrain slope X2: Calculate terrain slope based on ArcToolbox. Input DEM data, and the coordinates must be in the projected coordinate system. In the ArcToolbox tool, run the Spatial Analyst tool, surface analysis, and slope calculation in sequence. Calculate the average terrain slope of a block X2 as its quantified value, with the unit being radians.

[0013] Vegetation Cover X3: First, the Normalized Difference Vegetation Index (NDVI) is calculated using the near-infrared (NIR) and infrared (R) bands of remote sensing data. Where NIR and R are the reflectance data of the near-infrared and infrared bands in the remote sensing image, respectively, -1≤NDVI≤1, negative values ​​indicate ground cover such as clouds, water, snow, etc.; 0 indicates the presence of rocks or bare soil, etc.; positive values ​​indicate vegetation cover, which increases with increasing cover. Then, the vegetation cover FVC is calculated using the pixel dichotomy method, and the calculation formula is as follows:

[0014] NDVI min The lowest NDVI value within the study area; NDVI maxTo determine the maximum NDVI value within the study area, the NDVI values ​​for the study were statistically analyzed, and the NDVI value corresponding to a cumulative probability of 5% was taken as the NDVI. min The NDVI value corresponding to a cumulative probability of 95% is taken as the NDVI. max Note that when NDVI is less than NDVI min When FVC is all 0; when NDVI is greater than NDVI max When FVC is set to 1, vegetation coverage can be calculated using remote sensing data processing software such as ENVI, and the average vegetation coverage multiplied by 3 is taken as its quantified value.

[0015] Rainfall X4: The annual rainfall (X4) is calculated by collecting rainfall data from surrounding meteorological stations and is expressed in millimeters.

[0016] Furthermore, a method for rapidly identifying groundwater enrichment potential areas in hilly regions,

[0017] In step S2, a multivariate linear relationship is established between the groundwater runoff modulus and the aquifer group, topographic slope, vegetation cover, and annual rainfall. The specific steps are as follows:

[0018] The groundwater runoff modulus is obtained using the following three methods:

[0019] Based on existing hydrogeological data, the average value M was taken. 平 ;

[0020] Flow measurement at cross-section during dry season, M=Q 枯 / F;

[0021] Baseflow segmentation is performed using long-series river flow monitoring data from hydrological stations, M = Q 基 / F, where F is the catchment area;

[0022] Using the groundwater runoff modulus as the independent variable, and the aquifer group X1, topographic slope X2, vegetation cover X3, and annual rainfall X4 as the dependent variables, a multiple linear regression model was used to establish a multiple linear relationship equation between the groundwater runoff modulus and the aquifer group, topographic slope, vegetation cover, and annual rainfall. The equation is as follows: M = a × X1 + b × X2 + c × X3 + d × X4 + e, where M is the groundwater runoff modulus, X1 is the quantified value of the aquifer group, X2 is the quantified value of the topographic slope, X3 is the quantified value of the vegetation cover, X4 is the quantified value of the annual rainfall, a, b, c, and d are coefficients, and e is the error value.

[0023] Furthermore, a method for rapidly identifying groundwater enrichment potential areas in hilly regions,

[0024] In step S3, the system delineates the catchment area based on the optimal catchment area threshold. The specific steps are as follows:

[0025] S31. Input the DEM digital elevation data of the study area. The coordinate system must be projected. Determine whether there are depressions in the DEM data. If so, fill the depressions. If not, directly calculate the flow direction.

[0026] S32. The D8 method is selected for flow direction calculation. The elevation difference between each grid cell in the DEM and its 8 adjacent grid cells is used to determine the flow direction according to the steepest slope principle, resulting in a flow direction grid map. The flow volume is summarized according to the flow direction grid data to form the confluence grid layer data.

[0027] S33. Define the river, calculate the flow grid. Before defining the river, you need to input the initial threshold of the catchment area. The smaller the threshold, the denser the flow; the larger the threshold, the sparser the flow. Divide the river into segments, create river segment grids with unique identifiers, generate confluence lines, and convert the river grids into vector features.

[0028] S34. River network accuracy analysis: Compare and analyze the existing water system with the confluence lines, and determine whether the river network accuracy meets the requirements from two aspects: river network density and river network overlap.

[0029] River network density D Where D is the river network density, km / km 2 L represents the total river length under the corresponding catchment area threshold, in km; S represents the study area, in km². 2 ;

[0030] River network integration Where N is the degree of river network integration; A i S represents the area of ​​the fragmented polygon formed by the superposition of the existing water system and the confluence lines; S is the area of ​​the study area (km²). 2 ;

[0031] According to the arithmetic progression method, input the catchment area threshold and analyze the river network accuracy. When the calculated river network density is close to the current river network density and the river network overlap is less than 2%, it is considered that the optimal river network accuracy requirement has been met, that is, the optimal catchment area threshold. Input the optimal catchment area threshold and run step S33 again.

[0032] S35. Catchment Raster Delineation: Based on the preceding river grid, a catchment raster is generated, and the catchment area is vectorized. The catchment area processing is adjusted. Based on the catchment area vector file and the watershed vector file, the catchment area is readjusted. ArcGIS software is used to smooth the adjusted catchment area vector file, and finally, a catchment area distribution map is obtained.

[0033] Furthermore, a method for rapidly identifying groundwater enrichment potential areas in hilly regions,

[0034] In step S4, based on the water demand analysis, the identification principle is established by combining the catchment area and the groundwater runoff modulus, as follows;

[0035] The recognition principles are established under three scenarios, as follows:

[0036] When 500m 3 / d≤Q 需求 ≤1000m 3 When / d, press Identify areas with potential for groundwater accumulation;

[0037] When 1000m 3 / d<Q 需求 ≤3000m 3 When / d, press Identify areas with potential for groundwater accumulation;

[0038] When Q 需求 >3000m 3 When / d, press Identify areas with potential for groundwater accumulation;

[0039] Among them, Q 需求 The actual amount of groundwater resources needed, in meters. 3 / d;Q 补给 Natural groundwater recharge resources, m 3 / d; η: Guarantee coefficient, Q 补给 With Q 需求 The ratio is taken as 1.8 to 2.2; M: groundwater runoff modulus corresponding to each catchment area, L / (s·km) 2 F: Area of ​​each catchment area, km² 2 .

[0040] The beneficial effects of this invention are as follows: Through quantification and modeling, potential groundwater enrichment areas within hilly regions can be rapidly identified. A multivariate linear relationship was established between groundwater runoff modulus and aquifer composition, topographic slope, vegetation cover, and annual rainfall, significantly improving the accuracy and efficiency of identification in large-scale, large-area watersheds. Prioritizing areas with abundant groundwater resources helps optimize water resource utilization and management strategies to the greatest extent possible. Accurate identification and utilization of enrichment potential areas not only solves the problem of water discovery and drought relief under extreme meteorological droughts but also helps protect and maintain the water resource ecosystem in hilly areas. Attached Figure Description

[0041] Figure 1 A flowchart of a method for rapidly identifying groundwater enrichment potential zones in hilly areas;

[0042] Figure 2 Curves showing the relationship between catchment area and groundwater runoff modulus under different water demand conditions; Detailed Implementation

[0043] A method for rapidly identifying groundwater enrichment potential areas in hilly regions, the process is as follows: Figure 1 As shown, it includes the following steps;

[0044] S1. Determine the key factors affecting the groundwater runoff modulus and quantify them. The groundwater runoff modulus refers to the runoff volume of groundwater over a 1 square kilometer aquifer distribution area, representing the amount of groundwater existing in the form of groundwater runoff in a region. Analysis suggests that the groundwater runoff modulus is related to aquifer group, topographic slope, vegetation cover, and annual rainfall. To establish the mathematical relationship between the groundwater runoff modulus and the four key influencing factors, the four key influencing factors need to be quantified.

[0045] S2. Establish a multivariate linear relationship between groundwater runoff modulus and aquifer group, topographic slope, vegetation cover and annual rainfall. Select a certain number of representative blocks to obtain the groundwater runoff modulus. Calculate the quantitative indicators of aquifer group X1, topographic slope X2, vegetation cover X3 and annual rainfall X4 according to step S1. The representative blocks should be evenly distributed and contain different aquifer groups.

[0046] S3. Delineate the catchment area based on the optimal catchment area threshold;

[0047] S4. Establish identification principles for groundwater enrichment potential areas. Based on water demand analysis, and combined with the catchment area and groundwater runoff modulus, establish identification principles to identify groundwater enrichment potential areas.

[0048] S5. Select groundwater enrichment potential areas in hilly areas. For large-scale identification of groundwater enrichment potential areas in large watersheds, adopt the principle of proceeding from the known to the unknown. Based on the watershed distribution map obtained in S3, quantitative maps of aquifer groups, topographic slope, vegetation cover, and annual rainfall are obtained through S1. Based on the multivariate linear equations of groundwater runoff modulus and aquifer groups, topographic slope, vegetation cover, and annual rainfall obtained in S2, a groundwater runoff modulus distribution map of each watershed is obtained. The distribution map of natural groundwater recharge resources of each watershed is calculated using the formula Q=M×F, and sorted by size and displayed in a hierarchical manner. According to water demand and guarantee coefficient, watersheds with sufficient natural groundwater recharge resources are selected first, and then the corresponding groundwater enrichment potential areas are selected according to the identification principle.

[0049] Furthermore, a method for rapidly identifying groundwater enrichment potential areas in hilly regions,

[0050] In step S1, it is necessary to establish the mathematical relationship between the groundwater runoff modulus and the four key influencing factors. The four key influencing factors need to be quantified. The specific steps for quantifying the four key influencing factors are as follows.

[0051] Aquifer Group X1: Based on four types—carbonate rock, igneous rock, metamorphic rock, and clastic rock—values ​​of 4, 3, 2, and 1 are assigned respectively. The quantitative value of an aquifer group in a block is calculated using an area-weighted average. ;

[0052] Terrain slope X2: Calculate terrain slope based on ArcToolbox. Input DEM data, and the coordinates must be in the projected coordinate system. In the ArcToolbox tool, run the Spatial Analyst tool, surface analysis, and slope calculation in sequence. Calculate the average terrain slope of a block X2 as its quantified value, with the unit being radians.

[0053] Vegetation Cover X3: First, the Normalized Difference Vegetation Index (NDVI) is calculated using the near-infrared (NIR) and infrared (R) bands of remote sensing data. Where NIR and R are the reflectance data of the near-infrared and infrared bands in the remote sensing image, respectively, -1≤NDVI≤1, negative values ​​indicate ground cover such as clouds, water, snow, etc.; 0 indicates the presence of rocks or bare soil, etc.; positive values ​​indicate vegetation cover, which increases with increasing cover. Then, the vegetation cover FVC is calculated using the pixel dichotomy method, and the calculation formula is as follows:

[0054] NDVI min The lowest NDVI value within the study area; NDVI max To determine the maximum NDVI value within the study area, the NDVI values ​​for the study were statistically analyzed, and the NDVI value corresponding to a cumulative probability of 5% was taken as the NDVI. min The NDVI value corresponding to a cumulative probability of 95% is taken as the NDVI. max Note that when NDVI is less than NDVI min When FVC is all 0; when NDVI is greater than NDVI max When FVC is set to 1, vegetation coverage can be calculated using remote sensing data processing software such as ENVI, and the average vegetation coverage multiplied by 3 is taken as its quantified value.

[0055] Rainfall X4: The annual rainfall (X4) is calculated by collecting rainfall data from surrounding meteorological stations and is expressed in millimeters.

[0056] Furthermore, a method for rapidly identifying groundwater enrichment potential areas in hilly regions,

[0057] In step S2, a multivariate linear relationship is established between the groundwater runoff modulus and the aquifer group, topographic slope, vegetation cover, and annual rainfall. The specific steps are as follows:

[0058] The groundwater runoff modulus is obtained using the following three methods:

[0059] Based on existing hydrogeological data, the average value M was taken. 平 ;

[0060] Flow measurement at cross-section during dry season, M=Q 枯 / F;

[0061] Baseflow segmentation is performed using long-series river flow monitoring data from hydrological stations, M = Q 基 / F, where F is the catchment area;

[0062] Using the groundwater runoff modulus as the independent variable, and the aquifer group X1, topographic slope X2, vegetation cover X3, and annual rainfall X4 as the dependent variables, a multiple linear regression model was used to establish a multiple linear relationship equation between the groundwater runoff modulus and the aquifer group, topographic slope, vegetation cover, and annual rainfall. The equation is as follows: M = a × X1 + b × X2 + c × X3 + d × X4 + e, where M is the groundwater runoff modulus, X1 is the quantified value of the aquifer group, X2 is the quantified value of the topographic slope, X3 is the quantified value of the vegetation cover, X4 is the quantified value of the annual rainfall, a, b, c, and d are coefficients, and e is the error value.

[0063] Furthermore, a method for rapidly identifying groundwater enrichment potential areas in hilly regions,

[0064] In step S3, the system delineates the catchment area based on the optimal catchment area threshold. The specific steps are as follows:

[0065] S31. Input the DEM digital elevation data of the study area. The coordinate system must be projected. Determine whether there are depressions in the DEM data. If so, fill the depressions. If not, directly calculate the flow direction.

[0066] S32. The D8 method is selected for flow direction calculation. The elevation difference between each grid cell in the DEM and its 8 adjacent grid cells is used to determine the flow direction according to the steepest slope principle, resulting in a flow direction grid map. The flow volume is summarized according to the flow direction grid data to form the confluence grid layer data.

[0067] S33. Define the river, calculate the flow grid. Before defining the river, you need to input the initial threshold of the catchment area. The smaller the threshold, the denser the flow; the larger the threshold, the sparser the flow. Divide the river into segments, create river segment grids with unique identifiers, generate confluence lines, and convert the river grids into vector features.

[0068] S34. River network accuracy analysis: Compare and analyze the existing water system with the confluence lines, and determine whether the river network accuracy meets the requirements from two aspects: river network density and river network overlap.

[0069] River network density D Where D is the river network density, km / km 2L represents the total river length under the corresponding catchment area threshold, in km; S represents the study area, in km². 2 ;

[0070] River network integration Where N is the degree of river network integration; A i S represents the area of ​​the fragmented polygon formed by the superposition of the existing water system and the confluence lines; S is the area of ​​the study area (km²). 2 ;

[0071] According to the arithmetic progression method, input the catchment area threshold and analyze the river network accuracy. When the calculated river network density is close to the current river network density and the river network overlap is less than 2%, it is considered that the optimal river network accuracy requirement has been met, that is, the optimal catchment area threshold. Input the optimal catchment area threshold and run step S33 again.

[0072] S35. Catchment Raster Delineation: Based on the preceding river grid, a catchment raster is generated, and the catchment area is vectorized. The catchment area processing is adjusted. Based on the catchment area vector file and the watershed vector file, the catchment area is readjusted. ArcGIS software is used to smooth the adjusted catchment area vector file, and finally, a catchment area distribution map is obtained.

[0073] Furthermore, a method for rapidly identifying groundwater enrichment potential areas in hilly regions,

[0074] In step S4, based on the water demand analysis, the identification principle is established by combining the catchment area and the groundwater runoff modulus, as follows;

[0075] The recognition principles are established under three scenarios, as follows:

[0076] When 500m 3 / d≤Q 需求 ≤1000m 3 When / d, press Identify areas with potential for groundwater accumulation;

[0077] When 1000m 3 / d<Q 需求 ≤3000m 3 When / d, press Identify areas with potential for groundwater accumulation;

[0078] When Q 需求 >3000m 3 When / d, press Identify areas with potential for groundwater accumulation;

[0079] Among them, Q 需求 The actual amount of groundwater resources needed, in meters. 3 / d;Q 补给 Natural groundwater recharge resources, m3 / d; η: Guarantee coefficient, Q 补给 With Q 需求 The ratio is taken as 1.8 to 2.2; M: groundwater runoff modulus corresponding to each catchment area, L / (s·km) 2 F: Area of ​​each catchment area, km² 2 .

[0080] The following figures show groundwater resource requirements of 500, 1000, 1500, 2000, 3000, and 5000 m³ respectively, with guarantee coefficients of 1.8, 2.0, and 2.2. 3 / d lists the commonly used requirements for catchment area and groundwater runoff modulus, such as Figure 2 As shown in Table 1.

[0081] Table 1. Principles for Identifying Groundwater Accumulation Potential Zones under Different Water Demand Levels

[0082] .

Claims

1. A method for rapidly identifying groundwater enrichment potential zones in hilly areas, characterized in that, Includes the following steps; S1. Determine the key factors affecting the groundwater runoff modulus and quantify them. The groundwater runoff modulus refers to the runoff volume of groundwater over a 1 square kilometer aquifer distribution area, representing the amount of groundwater existing in the form of groundwater runoff in a region. Analysis suggests that the groundwater runoff modulus is related to aquifer group, topographic slope, vegetation cover, and annual rainfall. To establish the mathematical relationship between the groundwater runoff modulus and the four key influencing factors, the four key influencing factors need to be quantified. S2. Establish a multivariate linear relationship between groundwater runoff modulus and aquifer group, topographic slope, vegetation cover and annual rainfall. Select a certain number of representative blocks to obtain the groundwater runoff modulus. Calculate the quantitative indicators of aquifer group X1, topographic slope X2, vegetation cover X3 and annual rainfall X4 according to step S1. The representative blocks should be evenly distributed and contain different aquifer groups. S3. Delineate the catchment area based on the optimal catchment area threshold; S4. Based on the water demand analysis, and combined with the catchment area and groundwater runoff modulus, identification principles are established, and these principles are established under three scenarios, as follows: When 500m 3 / d≤Q 需求 ≤1000m 3 When / d, press Identify areas with potential for groundwater accumulation; When 1000m 3 / d<Q 需求 ≤3000m 3 When / d, press Identify areas with potential for groundwater accumulation; When Q 需求 >3000m 3 When / d, press Identify areas with potential for groundwater accumulation; Among them, Q 需求 The actual amount of groundwater resources needed, in meters. 3 / d;Q 补给 Natural groundwater recharge resources, m 3 / d; η: Guarantee coefficient, Q 补给 With Q 需求 The ratio is taken as 1.8 to 2.2; M: groundwater runoff modulus corresponding to each catchment area, L / (s·km) 2 F: Area of ​​each catchment area, km² 2 This allows for the identification of areas with potential for groundwater enrichment. S5. Select groundwater enrichment potential areas in hilly areas. For large-scale identification of groundwater enrichment potential areas in large watersheds, adopt the principle of proceeding from the known to the unknown. Based on the watershed distribution map obtained in S3, quantitative maps of aquifer groups, topographic slope, vegetation cover, and annual rainfall are obtained through S1. Based on the multivariate linear equations of groundwater runoff modulus and aquifer groups, topographic slope, vegetation cover, and annual rainfall obtained in S2, a groundwater runoff modulus distribution map of each watershed is obtained. The distribution map of natural groundwater recharge resources of each watershed is calculated using the formula Q=M×F, and sorted by size and displayed in a hierarchical manner. According to water demand and guarantee coefficient, watersheds with sufficient natural groundwater recharge resources are selected first, and then the corresponding groundwater enrichment potential areas are selected according to the identification principle.

2. The method for rapidly identifying groundwater enrichment potential areas in hilly regions as described in claim 1, characterized in that, In step S1, it is necessary to establish the mathematical relationship between the groundwater runoff modulus and the four key influencing factors. The four key influencing factors need to be quantified. The specific steps for quantifying the four key influencing factors are as follows. Aquifer Group X1: Based on four types—carbonate rock, igneous rock, metamorphic rock, and clastic rock—values ​​of 4, 3, 2, and 1 are assigned respectively. The quantitative value of an aquifer group in a block is calculated using an area-weighted average. ; Terrain slope X2: Calculate terrain slope based on ArcToolbox. Input DEM data, and the coordinates must be in the projected coordinate system. In the ArcToolbox tool, run the Spatial Analyst tool, surface analysis, and slope calculation in sequence. Calculate the average terrain slope of a block X2 as its quantified value, with the unit being radians. Vegetation Cover X3: First, the Normalized Difference Vegetation Index (NDVI) is calculated using the near-infrared (NIR) and infrared (R) bands of remote sensing data. Where NIR and R are the reflectance data of the near-infrared and infrared bands in the remote sensing image, respectively, -1≤NDVI≤1, negative values ​​indicate that the ground cover is cloud, water, or snow; 0 indicates the presence of rock or bare soil; positive values ​​indicate the presence of vegetation cover, which increases with increasing cover. Then, the vegetation cover FVC is calculated using the pixel dichotomy method, and the calculation formula is as follows: NDVI min The lowest NDVI value within the study area; NDVI max To determine the maximum NDVI value within the study area, the NDVI values ​​for the study were statistically analyzed, and the NDVI value corresponding to a cumulative probability of 5% was taken as the NDVI. min The NDVI value corresponding to a cumulative probability of 95% is taken as the NDVI. max Note that when NDVI is less than NDVI min When FVC is all 0; when NDVI is greater than NDVI max At that time, all FVC values ​​were set to 1. The vegetation coverage was calculated using ENVI remote sensing data processing software, and the average vegetation coverage value was multiplied by 3 as its quantification value. Rainfall X4: This is a statistical calculation of annual rainfall multiplied by 4, based on rainfall data from surrounding meteorological stations, in millimeters.

3. The method for rapidly identifying groundwater enrichment potential areas in hilly regions as described in claim 1, characterized in that, In step S2, a multivariate linear relationship is established between the groundwater runoff modulus and the aquifer group, topographic slope, vegetation cover, and annual rainfall. The specific steps are as follows: The groundwater runoff modulus is obtained using the following three methods: Based on existing hydrogeological data, the average value M was taken. 平 ; Flow measurement at cross-section during dry season, M=Q 枯 / F; Baseflow segmentation is performed using long-series river flow monitoring data from hydrological stations, M = Q 基 / F, where F is the catchment area; Using the groundwater runoff modulus as the independent variable, and the aquifer group X1, topographic slope X2, vegetation cover X3, and annual rainfall X4 as the dependent variables, a multiple linear regression model was used to establish a multiple linear relationship equation between the groundwater runoff modulus and the aquifer group, topographic slope, vegetation cover, and annual rainfall. The equation is as follows: M = a × X1 + b × X2 + c × X3 + d × X4 + e, where M is the groundwater runoff modulus, X1 is the quantified value of the aquifer group, X2 is the quantified value of the topographic slope, X3 is the quantified value of the vegetation cover, X4 is the quantified value of the annual rainfall, a, b, c, and d are coefficients, and e is the error value.

4. The method for rapidly identifying groundwater enrichment potential areas in hilly regions as described in claim 1, characterized in that, In step S3, the system delineates the catchment area based on the optimal catchment area threshold. The specific steps are as follows: S31. Input the DEM digital elevation data of the study area. The coordinate system must be projected. Determine whether there are depressions in the DEM data. If so, fill the depressions. If not, directly calculate the flow direction. S32. The D8 method is selected for flow direction calculation. The elevation difference between each grid cell in the DEM and its 8 adjacent grid cells is used to determine the flow direction according to the steepest slope principle, resulting in a flow direction grid map. The flow volume is summarized according to the flow direction grid data to form the confluence grid layer data. S33. Define the river, calculate the flow grid. Before defining the river, you need to input the initial threshold of the catchment area. The smaller the threshold, the denser the flow; the larger the threshold, the sparser the flow. Divide the river into segments, create river segment grids with unique identifiers, generate confluence lines, and convert the river grids into vector features. S34. River network accuracy analysis: Compare and analyze the existing water system with the confluence lines, and determine whether the river network accuracy meets the requirements from two aspects: river network density and river network overlap. River network density D Where D is the river network density, km / km 2 L represents the total river length under the corresponding catchment area threshold, in km; S represents the study area, in km². 2 ; River network integration Where N is the degree of river network integration; A i S represents the area of ​​the fragmented polygon formed by the superposition of the existing water system and the confluence lines; S is the area of ​​the study area (km²). 2 ; According to the arithmetic progression method, input the catchment area threshold and analyze the river network accuracy. When the calculated river network density is close to the current river network density and the river network overlap is less than 2%, it is considered that the optimal river network accuracy requirement has been met, that is, the optimal catchment area threshold. Input the optimal catchment area threshold and run step S33 again. S35. Catchment Raster Delineation: Based on the preceding river grid, a catchment raster is generated, and the catchment area is vectorized. The catchment area processing is adjusted. Based on the catchment area vector file and the watershed vector file, the catchment area is readjusted. ArcGIS software is used to smooth the adjusted catchment area vector file, and finally, a catchment area distribution map is obtained.

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