Method for rapidly identifying groundwater enrichment potential area in hilly area

Abstract

The invention discloses a method for rapidly identifying a groundwater enrichment potential area in a hilly area, and aims to rapidly identify the groundwater enrichment potential area in the hilly area. The method comprises the following steps: firstly, establishing a mathematical relationship model between the underground water runoff modulus and key factors such as a water-bearing rock group, topographic gradient, vegetation coverage and annual rainfall by determining and quantifying the key factors; then, a water collecting area is defined according to the optimal water collecting area threshold value, and an enrichment potential area is determined based on the water quantity requirement, the water collecting area and the groundwater runoff modulus setting recognition principle; and finally, through multi-stage screening and sorting, preferably selecting a water collection area meeting the water demand requirement, and guiding groundwater resource exploration, development and utilization in the hilly region by a scientific basis, so as to realize optimization and sustainable development of water resource management. The method not only improves the efficiency of identifying the underground water enrichment potential area, but also improves the accuracy of detecting underground water, and provides important scientific basis and operation guidance for decision makers.

Description

Technical Field

[0001] The invention relates to the technical field of hydrogeology and groundwater resources, and in particular to a method for rapidly identifying groundwater enrichment potential areas in hilly areas. Background Art

[0002] Groundwater is an important emergency water supply source buried below the ground. It plays an active role in responding to extreme meteorological droughts. Groundwater includes groundwater and deep confined water. Deep confined water is more stable, safe and secure. Most deep confined water is distributed in mountainous and hilly areas (referred to as hilly areas). In the past, the potential areas for groundwater enrichment in hilly areas were obtained by comprehensive analysis of previous hydrogeological data (hydrogeological parameters and drilling data, etc.). However, for large-scale identification of large basins, there are problems such as data blank areas, low identification accuracy and efficiency. In order to solve the above bottleneck problems, a method for rapid identification of groundwater enrichment potential areas in hilly areas was developed this time. It mainly determines the key factors affecting the groundwater runoff modulus, performs quantitative processing, establishes the multivariate linear relationship between the groundwater runoff modulus and the water-bearing rock group, terrain slope, vegetation coverage and annual rainfall, systematically delineates the catchment area, and realizes the rapid identification of groundwater enrichment potential areas under the dual constraints of the catchment area area and groundwater runoff modulus under different water demand conditions. Summary of the invention

[0003] A method for rapidly identifying groundwater enrichment potential areas in hilly areas comprises the following steps; S1. Determine the key factors affecting groundwater runoff modulus and conduct quantitative processing. Groundwater runoff modulus refers to the amount of groundwater runoff on an aquifer distribution area of ​​1 square kilometer, indicating the amount of groundwater in a region in the form of groundwater runoff. Analysis shows that groundwater runoff modulus is related to water-bearing rock groups, terrain slope, vegetation coverage and annual rainfall. To establish the mathematical relationship between groundwater runoff modulus and the four key factors, the four key factors need to be quantified; S2. Establish a multivariate linear relationship between groundwater runoff modulus and water-bearing rock group, terrain slope, vegetation coverage and annual rainfall, select a certain number of representative blocks, obtain groundwater runoff modulus, and calculate the quantitative indicators of water-bearing rock group X1, terrain slope X2, vegetation coverage X3 and annual rainfall X4 according to step S1. The locations of representative blocks should be evenly distributed with different water-bearing rock groups; S3. Systematic delineation of catchment areas based on optimal catchment area thresholds; S4. Establish the identification principle of groundwater enrichment potential areas. According to the water demand analysis, the identification principle is established in combination with the catchment area and the groundwater runoff modulus to identify the groundwater enrichment potential areas; S5. Optimize the potential areas for groundwater enrichment in hilly areas, identify the potential areas for groundwater enrichment in large areas of large river basins, adopt the principle of from known to unknown, based on the watershed distribution map obtained in S3, obtain the quantitative map of water-bearing rock group, terrain slope, vegetation coverage and annual rainfall indicators through S1, and use the multivariate linear equation of groundwater runoff modulus and water-bearing rock group, terrain slope, vegetation coverage and annual rainfall in S2 as the basis to obtain the groundwater runoff modulus distribution map of each watershed, and calculate the distribution map of groundwater natural recharge resources in each watershed through the formula Q=M×F, and sort them by size and display them in grades. According to the water demand and security coefficient, first select the watershed where the natural groundwater recharge resources meet the requirements, and then select the corresponding groundwater enrichment potential areas based on the identification principle.

[0004] Furthermore, a method for quickly identifying groundwater enrichment potential areas in hilly areas is proposed. In step S1, a mathematical relationship between the groundwater runoff modulus and four key influencing factors needs to be established, and the four key influencing factors need to be quantified. The specific steps for quantifying the four key influencing factors are as follows; Water-bearing rock group X1: The four types of carbonate rock, igneous rock, metamorphic rock and clastic rock are assigned values ​​of 4, 3, 2 and 1 respectively. The quantitative value of the water-bearing rock group in a block is weighted averaged by area, that is, ; Terrain slope X2: Calculate terrain slope based on ArcToolbox. Input DEM data. The coordinates must be in the projection coordinate system. In ArcToolbox, run Spatial Analyst, Surface Analysis, and Slope Calculation in sequence. Calculate the average terrain slope X2 of a block as its quantitative value in radians. Vegetation coverage X3: First, use the near infrared band NIR and infrared band R of remote sensing data to calculate the normalized vegetation index NDVI. , where NIR and R are the reflectance data of the near-infrared band and infrared band in the remote sensing image, respectively, -1≤NDVI≤1, negative values ​​indicate that the ground is covered with clouds, water, snow, etc.; 0 indicates that there are rocks or bare soil, etc.; positive values ​​indicate that there is vegetation coverage, and it increases with the increase of coverage. Then the pixel dichotomy method is used to calculate the vegetation coverage FVC, and the calculation formula is as follows: , where NDVI min is the minimum NDVI value in the study area; NDVI max The maximum NDVI value in the study area was used to calculate the NDVI value of the study, and the NDVI value corresponding to the cumulative probability of 5% was taken as the NDVI min ; Take the NDVI value corresponding to the cumulative probability of 95% as the NDVI max , note that when NDVI is less than NDVImin When NDVI is greater than NDVI, FVC is all 0; max When all FVCs are 1, remote sensing data processing software such as ENVI can be used to calculate vegetation coverage, and finally the mean vegetation coverage value X3 is taken as its quantitative value; Precipitation X4: Collects rainfall data from surrounding meteorological stations and calculates annual rainfall (X4) in millimeters.

[0005] Furthermore, a method for quickly identifying groundwater enrichment potential areas in hilly areas is proposed. In step S2, a multivariate linear relationship between groundwater runoff modulus and water-bearing rock group, terrain slope, vegetation coverage and annual rainfall is established, and the specific steps are as follows; The groundwater runoff modulus is obtained using the following three methods: Consult the existing hydrogeological data and take the average value M 平 ; Flow measurement at cross section during dry season, M=Q 枯 / F; Using the long-sequence river flow monitoring data from hydrological stations, base flow segmentation is performed, M = Q 基 / F, F is the catchment area; Taking the groundwater runoff modulus as the independent variable, the water-bearing rock group X1, terrain slope X2, vegetation coverage X3 and annual rainfall X4 as the dependent variables, the multivariate linear regression model was used to establish the multivariate linear relationship equation between the groundwater runoff modulus and the water-bearing rock group, terrain slope, vegetation coverage 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 water-bearing rock group, X2 is the quantified value of the terrain slope, X3 is the quantified value of the vegetation coverage, X4 is the quantified value of the annual rainfall, a, b, c, d are all coefficients, and e is the error value.

[0006] Furthermore, a method for quickly identifying groundwater enrichment potential areas in hilly areas is proposed. In step S3, the system delineates the catchment area based on the optimal catchment area threshold, and the specific steps are as follows; S31. Input the DEM digital elevation data of the study area. The coordinates must be in the projection coordinate system. Determine whether there is a depression in the DEM data. If so, fill the depression. If not, directly calculate the flow direction. S32. The D8 method is selected for flow direction calculation. The elevation difference between each grid in the DEM and its eight adjacent grids is used to determine the flow direction according to the principle of the steepest slope. A flow direction grid map is obtained. The amount of water flowing through is summarized according to the flow direction grid data to form the confluence grid layer data. S33. Define the river, mainly to calculate the water flow grid. Before defining the river, you need to input the initial threshold of the catchment area. The smaller the threshold, the denser the water flow, and the larger the threshold, the thinner the water flow. Divide the river into sections, create a river section grid with a unique identifier, generate confluence lines, and convert the river grid into a vector element. S34. River network accuracy analysis: compare and analyze the current water system with the confluence line, and judge whether the river network accuracy meets the requirements from the two aspects of river network density and river network fit: River network density D , where D is the river network density, km / km 2 ; L is the total length of the river under the corresponding catchment area threshold, km; S is the area of ​​the study area, km 2 ; River network fit , where N is the river network fit; A i is the area of ​​the patch polygon generated by the superposition of the existing water system and the confluence line; S is the area of ​​the study area, km 2 ; According to the arithmetic increasing method, the catchment area threshold is input, and the river network accuracy is analyzed. When the calculated river network density is close to the current river network density, and the river network fit is less than 2%, it is considered that the optimal river network accuracy requirement, that is, the optimal catchment area threshold, is met. The optimal catchment area threshold is input, and step S33 is re-run; S35. Delineate the watershed grid. Generate the watershed grid based on the previous river grid. Vectorize the watershed. Adjust the watershed processing. Re-adjust the watershed based on the watershed vector file and the watershed vector file. Use ArcGIS software to smooth the adjusted watershed vector file. Finally, get the watershed distribution map.

[0007] Furthermore, a method for quickly identifying groundwater enrichment potential areas in hilly areas is proposed. In step S4, according to the water demand analysis, the identification principle is established in combination with the catchment area and the groundwater runoff modulus, as follows; Identification principles are established in three scenarios, as follows: When 500m 3 / d≤Q 需求 ≤1000m 3 / d, press Identify areas of groundwater enrichment potential; When 1000m 3 / d<Q 需求 ≤3000m 3 / d, press Identify areas of groundwater enrichment potential; When Q 需求 >3000m 3 / d, press Identify areas of groundwater enrichment potential; Among them, Q 需求 : The actual amount of groundwater resources required, m 3 / d;Q 补给 : Natural groundwater recharge resources, m 3 / d; η: security factor, Q 补给 With Q 需求 The ratio is 1.8 to 2.2; M: groundwater runoff modulus corresponding to each catchment area, L / (s ▪km 2 ); F: area of ​​each catchment area, km 2 .

[0008] The beneficial effects of the present invention are as follows: Through quantification and modeling, potential groundwater enrichment potential areas in hilly areas can be quickly identified. A multivariate linear relationship between groundwater runoff modulus and water-bearing rock groups, terrain slope, vegetation coverage and annual rainfall has been established, which greatly improves the identification accuracy and efficiency in large-scale identification of large river basins. Preferring areas with abundant groundwater resources will help to optimize water resource utilization and management strategies to the greatest extent. By accurately identifying and utilizing enrichment potential areas, not only can the problem of finding water and resisting drought under extreme meteorological droughts be solved, but it can also help protect and maintain the water resource ecosystem in hilly areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A flowchart of a method for rapidly identifying groundwater enrichment potential areas in hilly areas; Figure 2 It is the relationship curve between the catchment area and groundwater runoff modulus under different water demand conditions; DETAILED DESCRIPTION

[0010] A method for quickly identifying groundwater enrichment potential areas in hilly areas, the process is as follows Figure 1 As shown, the following steps are included; S1. Determine the key factors affecting groundwater runoff modulus and conduct quantitative processing. Groundwater runoff modulus refers to the amount of groundwater runoff on an aquifer distribution area of ​​1 square kilometer, indicating the amount of groundwater in a region in the form of groundwater runoff. Analysis shows that groundwater runoff modulus is related to water-bearing rock groups, terrain slope, vegetation coverage and annual rainfall. To establish the mathematical relationship between groundwater runoff modulus and the four key factors, the four key factors need to be quantified; S2. Establish a multivariate linear relationship between groundwater runoff modulus and water-bearing rock group, terrain slope, vegetation coverage and annual rainfall, select a certain number of representative blocks, obtain groundwater runoff modulus, and calculate the quantitative indicators of water-bearing rock group X1, terrain slope X2, vegetation coverage X3 and annual rainfall X4 according to step S1. The locations of representative blocks should be evenly distributed with different water-bearing rock groups; S3. Systematic delineation of catchment areas based on optimal catchment area thresholds; S4. Establish the identification principle of groundwater enrichment potential areas. According to the water demand analysis, the identification principle is established in combination with the catchment area and the groundwater runoff modulus to identify the groundwater enrichment potential areas; S5. Optimize the potential areas for groundwater enrichment in hilly areas, identify the potential areas for groundwater enrichment in large areas of large river basins, adopt the principle of from known to unknown, based on the watershed distribution map obtained in S3, obtain the quantitative map of water-bearing rock group, terrain slope, vegetation coverage and annual rainfall indicators through S1, and use the multivariate linear equation of groundwater runoff modulus and water-bearing rock group, terrain slope, vegetation coverage and annual rainfall in S2 as the basis to obtain the groundwater runoff modulus distribution map of each watershed, and calculate the distribution map of groundwater natural recharge resources in each watershed through the formula Q=M×F, and sort them by size and display them in grades. According to the water demand and security coefficient, first select the watershed where the natural groundwater recharge resources meet the requirements, and then select the corresponding groundwater enrichment potential areas based on the identification principle.

[0011] Furthermore, a method for quickly identifying groundwater enrichment potential areas in hilly areas is proposed. In step S1, a mathematical relationship between the groundwater runoff modulus and four key influencing factors needs to be established, and the four key influencing factors need to be quantified. The specific steps for quantifying the four key influencing factors are as follows; Water-bearing rock group X1: The four types of carbonate rock, igneous rock, metamorphic rock and clastic rock are assigned values ​​of 4, 3, 2 and 1 respectively. The quantitative value of the water-bearing rock group in a block is weighted averaged by area, that is, ; Terrain slope X2: Calculate terrain slope based on ArcToolbox. Input DEM data. The coordinates must be in the projection coordinate system. In ArcToolbox, run Spatial Analyst, Surface Analysis, and Slope Calculation in sequence. Calculate the average terrain slope X2 of a block as its quantitative value in radians. Vegetation coverage X3: First, use the near infrared band NIR and infrared band R of remote sensing data to calculate the normalized vegetation index NDVI. , where NIR and R are the reflectance data of the near-infrared band and infrared band in the remote sensing image, respectively, -1≤NDVI≤1, negative values ​​indicate that the ground is covered with clouds, water, snow, etc.; 0 indicates that there are rocks or bare soil, etc.; positive values ​​indicate that there is vegetation coverage, and it increases with the increase of coverage. Then the pixel dichotomy method is used to calculate the vegetation coverage FVC, and the calculation formula is as follows: , where NDVI min is the minimum NDVI value in the study area; NDVI max The maximum NDVI value in the study area was used to calculate the NDVI value of the study, and the NDVI value corresponding to the cumulative probability of 5% was taken as the NDVI min ; Take the NDVI value corresponding to the cumulative probability of 95% as the NDVI max , note that when NDVI is less than NDVI min When NDVI is greater than NDVI, FVC is all 0; max When all FVCs are 1, remote sensing data processing software such as ENVI can be used to calculate vegetation coverage, and finally the mean vegetation coverage value X3 is taken as its quantitative value; Precipitation X4: Collects rainfall data from surrounding meteorological stations and calculates annual rainfall (X4) in millimeters.

[0012] Furthermore, a method for quickly identifying groundwater enrichment potential areas in hilly areas is proposed. In step S2, a multivariate linear relationship between groundwater runoff modulus and water-bearing rock group, terrain slope, vegetation coverage and annual rainfall is established, and the specific steps are as follows; The groundwater runoff modulus is obtained using the following three methods: Consult the existing hydrogeological data and take the average value M 平 ; Flow measurement at cross section during dry season, M=Q 枯 / F; Using the long-sequence river flow monitoring data from hydrological stations, base flow segmentation is performed, M = Q 基 / F, F is the catchment area; Taking the groundwater runoff modulus as the independent variable, the water-bearing rock group X1, terrain slope X2, vegetation coverage X3 and annual rainfall X4 as the dependent variables, the multivariate linear regression model was used to establish the multivariate linear relationship equation between the groundwater runoff modulus and the water-bearing rock group, terrain slope, vegetation coverage 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 water-bearing rock group, X2 is the quantified value of the terrain slope, X3 is the quantified value of the vegetation coverage, X4 is the quantified value of the annual rainfall, a, b, c, d are all coefficients, and e is the error value.

[0013] Furthermore, a method for quickly identifying groundwater enrichment potential areas in hilly areas is proposed. In step S3, the system delineates the catchment area based on the optimal catchment area threshold, and the specific steps are as follows; S31. Input the DEM digital elevation data of the study area. The coordinates must be in the projection coordinate system. Determine whether there is a depression in the DEM data. If so, fill the depression. If not, directly calculate the flow direction. S32. The D8 method is selected for flow direction calculation. The elevation difference between each grid in the DEM and its eight adjacent grids is used to determine the flow direction according to the principle of the steepest slope. A flow direction grid map is obtained. The amount of water flowing through is summarized according to the flow direction grid data to form the confluence grid layer data. S33. Define the river, mainly to calculate the water flow grid. Before defining the river, you need to input the initial threshold of the catchment area. The smaller the threshold, the denser the water flow, and the larger the threshold, the thinner the water flow. Divide the river into sections, create a river section grid with a unique identifier, generate confluence lines, and convert the river grid into a vector element. S34. River network accuracy analysis: compare and analyze the current water system with the confluence line, and judge whether the river network accuracy meets the requirements from the two aspects of river network density and river network fit: River network density D , where D is the river network density, km / km 2 ; L is the total length of the river under the corresponding catchment area threshold, km; S is the area of ​​the study area, km 2 ; River network fit , where N is the river network fit; A i is the area of ​​the patch polygon generated by the superposition of the existing water system and the confluence line; S is the area of ​​the study area, km 2 ; According to the arithmetic increasing method, the catchment area threshold is input, and the river network accuracy is analyzed. When the calculated river network density is close to the current river network density, and the river network fit is less than 2%, it is considered that the optimal river network accuracy requirement, that is, the optimal catchment area threshold, is met. The optimal catchment area threshold is input, and step S33 is re-run; S35. Delineate the watershed grid. Generate the watershed grid based on the previous river grid. Vectorize the watershed. Adjust the watershed processing. Re-adjust the watershed based on the watershed vector file and the watershed vector file. Use ArcGIS software to smooth the adjusted watershed vector file. Finally, get the watershed distribution map.

[0014] Furthermore, a method for quickly identifying groundwater enrichment potential areas in hilly areas is proposed. In step S4, according to the water demand analysis, the identification principle is established in combination with the catchment area and the groundwater runoff modulus, as follows; Identification principles are established in three scenarios, as follows: When 500m 3 / d≤Q 需求 ≤1000m 3 / d, press Identify areas of groundwater enrichment potential; When 1000m 3 / d<Q 需求 ≤3000m 3 / d, press Identify areas of groundwater enrichment potential; When Q 需求 >3000m 3 / d, press Identify areas of groundwater enrichment potential; Among them, Q 需求 : The actual amount of groundwater resources required, m 3 / d;Q 补给 : Natural groundwater recharge resources, m 3 / d; η: security factor, Q 补给 With Q 需求 The ratio is 1.8 to 2.2; M: groundwater runoff modulus corresponding to each catchment area, L / (s ▪km 2 ); F: area of ​​each catchment area, km 2 .

[0015] The following are the groundwater resource requirements of 500, 1000, 1500, 2000, 3000 and 5000m with guarantee coefficients of 1.8, 2.0 and 2.2 respectively. 3 / d, lists the commonly used requirements for catchment area and groundwater runoff modulus, such as Figure 2 And as shown in Table 1.

[0016] .

Claims

1. A method for quickly identifying groundwater enrichment potential areas in hilly areas, characterized in that: The steps include: S1. Determine the key factors affecting groundwater runoff modulus and conduct quantitative processing. Groundwater runoff modulus refers to the amount of groundwater runoff on an aquifer distribution area of ​​1 square kilometer, indicating the amount of groundwater in a region in the form of groundwater runoff. Analysis shows that groundwater runoff modulus is related to water-bearing rock groups, terrain slope, vegetation coverage and annual rainfall. To establish the mathematical relationship between groundwater runoff modulus and the four key factors, the four key factors need to be quantified; S2. Establish a multivariate linear relationship between groundwater runoff modulus and water-bearing rock group, terrain slope, vegetation coverage and annual rainfall, select a certain number of representative blocks, obtain groundwater runoff modulus, and calculate the quantitative indicators of water-bearing rock group X1, terrain slope X2, vegetation coverage X3 and annual rainfall X4 according to step S1. The locations of representative blocks should be evenly distributed with different water-bearing rock groups; S3. Systematic delineation of catchment areas based on optimal catchment area thresholds; S4. Based on the water demand analysis, establish identification principles in combination with the catchment area and groundwater runoff modulus to identify the groundwater enrichment potential areas; S5. Optimize the potential areas for groundwater enrichment in hilly areas, identify the potential areas for groundwater enrichment in large areas of large river basins, adopt the principle of from known to unknown, based on the watershed distribution map obtained in S3, obtain the quantitative map of water-bearing rock group, terrain slope, vegetation coverage and annual rainfall indicators through S1, and use the multivariate linear equation of groundwater runoff modulus and water-bearing rock group, terrain slope, vegetation coverage and annual rainfall in S2 as the basis to obtain the groundwater runoff modulus distribution map of each watershed, and calculate the distribution map of groundwater natural recharge resources in each watershed through the formula Q=M×F, and sort them by size and display them in grades. According to the water demand and security coefficient, first select the watershed where the natural groundwater recharge resources meet the requirements, and then select the corresponding groundwater enrichment potential areas based on the identification principle.

2. A method for rapidly identifying groundwater enrichment potential areas in hilly areas as claimed in claim 1, characterized in that: In step S1, a mathematical relationship between the groundwater runoff modulus and four key influencing factors needs to be established, and the four key influencing factors need to be quantified. The specific steps for quantifying the four key influencing factors are as follows; Water-bearing rock group X1: The four types of carbonate rock, igneous rock, metamorphic rock and clastic rock are assigned values ​​of 4, 3, 2 and 1 respectively. The quantitative value of the water-bearing rock group in a block is weighted averaged by area, that is, ; Terrain slope X2: Calculate terrain slope based on ArcToolbox. Input DEM data. The coordinates must be in the projection coordinate system. In ArcToolbox, run Spatial Analyst, Surface Analysis, and Slope Calculation in sequence. Calculate the average terrain slope X2 of a block as its quantitative value in radians. Vegetation coverage X3: First, use the near infrared band NIR and infrared band R of remote sensing data to calculate the normalized vegetation index NDVI. , where NIR and R are the reflectance data of the near-infrared band and infrared band in the remote sensing image, respectively, -1≤NDVI≤1, negative values ​​indicate that the ground is covered with clouds, water, and snow; 0 indicates that there are rocks and bare soil; positive values ​​indicate that there is vegetation coverage, and it increases with the increase of coverage. Then the pixel dichotomy method is used to calculate the vegetation coverage FVC, and the calculation formula is as follows: , where NDVI min is the minimum NDVI value in the study area; NDVI max The maximum NDVI value in the study area was used to calculate the NDVI value of the study, and the NDVI value corresponding to the cumulative probability of 5% was taken as the NDVI min ; Take the NDVI value corresponding to the cumulative probability of 95% as the NDVI max , note that when NDVI is less than NDVI min When NDVI is greater than NDVI, FVC is all 0; max When all FVCs are 1, the vegetation coverage calculation is performed using ENVI remote sensing data processing software, and finally the mean vegetation coverage value X3 is taken as its quantitative value; Precipitation X4: Collects rainfall data from surrounding meteorological stations and calculates annual rainfall X4 in millimeters.

3. A method for rapidly identifying groundwater enrichment potential areas in hilly areas as claimed in claim 1, characterized in that: In step S2, a multivariate linear relationship between groundwater runoff modulus and water-bearing rock group, terrain slope, vegetation coverage and annual rainfall is established, and the specific steps are as follows; The groundwater runoff modulus is obtained using the following three methods: Consult the existing hydrogeological data and take the average value M 平 ; Flow measurement at cross section during dry season, M=Q 枯 / F; Using the long-sequence river flow monitoring data from hydrological stations, base flow segmentation is performed, M = Q 基 / F, F is the catchment area; Taking the groundwater runoff modulus as the independent variable, the water-bearing rock group X1, terrain slope X2, vegetation coverage X3 and annual rainfall X4 as the dependent variables, the multivariate linear regression model was used to establish the multivariate linear relationship equation between the groundwater runoff modulus and the water-bearing rock group, terrain slope, vegetation coverage 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 water-bearing rock group, X2 is the quantified value of the terrain slope, X3 is the quantified value of the vegetation coverage, X4 is the quantified value of the annual rainfall, a, b, c, d are all coefficients, and e is the error value.

4. A method for rapidly identifying groundwater enrichment potential areas in hilly areas as claimed in claim 1, characterized in that: In step S3, the system delineates the catchment area based on the optimal catchment area threshold, and the specific steps are as follows; S31. Input the DEM digital elevation data of the study area. The coordinates must be in the projection coordinate system. Determine whether there is a depression in the DEM data. If so, fill the depression. If not, directly calculate the flow direction. S32. The D8 method is selected for flow direction calculation. The elevation difference between each grid in the DEM and its eight adjacent grids is used to determine the flow direction according to the principle of the steepest slope. A flow direction grid map is obtained. The amount of water flowing through is summarized according to the flow direction grid data to form the confluence grid layer data. S33. Define the river, mainly to calculate the water flow grid. Before defining the river, you need to input the initial threshold of the catchment area. The smaller the threshold, the denser the water flow, and the larger the threshold, the thinner the water flow. Divide the river into sections, create a river section grid with a unique identifier, generate confluence lines, and convert the river grid into a vector element. S34. River network accuracy analysis: compare and analyze the current water system with the confluence line, and judge whether the river network accuracy meets the requirements from the two aspects of river network density and river network fit: River network density D , where D is the river network density, km / km 2 ; L is the total length of the river under the corresponding catchment area threshold, km; S is the area of ​​the study area, km 2 ; River network fit , where N is the river network fit; A i is the area of ​​the patch polygon generated by the superposition of the existing water system and the confluence line; S is the area of ​​the study area, km 2 ; According to the arithmetic increasing method, the catchment area threshold is input, and the river network accuracy is analyzed. When the calculated river network density is close to the current river network density, and the river network fit is less than 2%, it is considered that the optimal river network accuracy requirement, that is, the optimal catchment area threshold, is met. The optimal catchment area threshold is input, and step S33 is re-run; S35. Delineate the watershed grid. Generate the watershed grid based on the previous river grid. Vectorize the watershed. Adjust the watershed processing. Re-adjust the watershed based on the watershed vector file and the watershed vector file. Use ArcGIS software to smooth the adjusted watershed vector file. Finally, get the watershed distribution map.

5. A method for rapidly identifying groundwater enrichment potential areas in hilly areas as claimed in claim 1, characterized in that: In step S4, according to the water demand analysis, the identification principle is established in combination with the catchment area and the groundwater runoff modulus, as follows; Identification principles are established in three scenarios, as follows: When 500m 3 / d≤Q 需求 ≤1000m 3 / d, press Identify areas of groundwater enrichment potential; When 1000m 3 / d<Q 需求 ≤3000m 3 / d, press Identify areas of groundwater enrichment potential; When Q 需求 >3000m 3 / d, press Identify areas of groundwater enrichment potential; Among them, Q 需求 : The actual amount of groundwater resources required, m 3 / d;Q 补给 : Natural groundwater recharge resources, m 3 / d; η: security factor, Q 补给 With Q 需求 The ratio is 1.8 to 2.2; M: groundwater runoff modulus corresponding to each catchment area, L / (s ▪km 2 ); F: area of ​​each catchment area, km 2 .

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