A water resource vulnerability quantification and regulation method suitable for karst slopes

Abstract

This invention relates to the field of water supply management technology, specifically to a method for quantitative regulation of water resource vulnerability in karst slopes. The method includes the following steps: acquiring the structure of a gravel cushion layer, guiding the slope flow path, extracting stratified moisture data, analyzing vertical migration changes, establishing a particle size and porosity matching relationship, correlating moisture distribution with material sequence, constructing an interlayer linkage mechanism, tracking runoff and evaporation dynamics, and forming a water resource vulnerability regulation pattern. In this invention, the slope flow path is guided by the thickness and arrangement sequence of the cushion layer, enhancing the retention and diversion of moisture on the slope. Combined with the migration characteristics of shallow and mid-layer moisture, the stratified identification ability of vertical moisture distribution is strengthened. Through the matching analysis of particle size parameters and pore structure, the regulatory matching of modified materials between different strata is improved. A synergistic relationship between moisture migration path and particle size sequence is constructed, forming an interlayer linkage moisture regulation pattern, enhancing the spatial retention and accumulation of moisture on the slope.

Description

Technical Field

[0001] This invention relates to the field of water supply management technology, and in particular to a method for quantitatively controlling the vulnerability of water resources in karst slopes. Background Technology

[0002] Water supply management technology falls under the category of public utility management informatization. Its core aspects include water metering, water resource allocation, water quality monitoring, dynamic soil moisture collection, surface runoff control, and the application of vegetation cover and soil structure improvement measures. This technology systematically encompasses water supply network operation, water resource utilization efficiency improvement, and regional hydrological process regulation, aiming to manage and control water supply and demand through information collection, monitoring feedback, and quantitative analysis. Traditional methods for quantitative regulation of water resource vulnerability, particularly in karst slope environments where shallow soil layers and karst fissures lead to easy water loss, typically involve methods such as pasture cover, water balance calculations, construction of arc-shaped intercepting ditches, and laying of gravel cushion layers to conserve water and regulate hydrological parameters. These methods primarily rely on atmospheric-soil interface infiltration, evaporation inhibition, and runoff retention, combined with measured data collected by soil moisture sensors and slope runoff monitors, to form a quantitative evaluation and management tool for the vulnerability of water resources in karst slopes.

[0003] Existing water resource regulation methods lack effective identification of the layer division in crushed stone cushion structures. In slope flow control, they rely heavily on uniform structural configurations, failing to adjust the flow path distribution according to specific spatial structures. In terms of vertical water distribution, they rely on single-point data analysis, lacking a systematic capture of the differences in water behavior between shallow and middle layers, resulting in insufficient regulation precision. In terms of material usage, the response relationship between particle size and pore structure has not been established, and improper particle size configuration can easily lead to weakened water regulation function. In the application of structural layers, there is a lack of linkage design between various materials. For example, under heterogeneous slope conditions, the water transport rhythm cannot be effectively regulated, which can easily lead to rapid runoff concentration or short-term water imbalance, affecting the continuity of regulation and the adaptability of the structure. Summary of the Invention

[0004] To address the technical problems existing in the prior art, this invention provides a method for quantitatively controlling the vulnerability of water resources in karst slopes.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for quantitatively controlling the vulnerability of water resources in karst slopes, comprising the following steps:

[0006] S1: Obtain the stratigraphic structure of the gravel cushion layer in the shallow area of ​​the karst slope, compare the differences between the bottom and upper layers, introduce runoff reduction parameters, guide the change of slope water flow path according to the thickness ratio and arrangement direction, and obtain the slope runoff delay structure morphology.

[0007] S2: Based on the slope runoff delay structure, the shallow porosity and middle layer water capacity are extracted, the movement process of water in the vertical path is analyzed, the changes of water in the vertical path are tracked, and the root zone stratified water distribution status is obtained.

[0008] S3: Based on the layered moisture distribution state of the root zone, extract the porosity and capillary porosity ratio, analyze the correspondence between particle size parameters and pore characteristics, analyze the performance of differentiated particle size materials in pore structure matching, and obtain the matching relationship between modifier particle size and porosity.

[0009] S4: Based on the matching relationship between the particle size and porosity of the improver, the moisture distribution is substituted into the correlation with the particle size parameter to analyze the application sequence of the material and the moisture regulation path, and to advance the response process of the particle size parameter in the structural layer, so as to obtain the multi-linkage regulation and linkage state.

[0010] S5: Based on the aforementioned multi-stage regulation and linkage status, analyze water storage data and evaporation loss information, compare and track runoff, water volume and material path, analyze the application sequence of particle size materials and the action path of water behavior, and obtain a quantitative regulation pattern for water resource vulnerability.

[0011] As a further aspect of the present invention, the slope runoff delay structure includes the thickness ratio of the cushion layer, the layer arrangement order, and the change mode of the water flow path; the root zone stratified water distribution state includes shallow layer porosity, middle layer water capacity, and evaporation loss intensity; the matching relationship between the amendment particle size and porosity includes particle size range matching, pore structure adaptability, and permeability regulation capacity; the multi-link regulation linkage state includes the stratified application mode of particle size materials, water delay path, and runoff control mode; and the quantitative regulation pattern of water resource vulnerability includes water retention time, water distribution characteristics, and the relationship between surface cover and particle size response.

[0012] As a further aspect of the present invention, the runoff reduction parameter refers to establishing a correspondence between thickness and water flow delay based on the thickness ratio and arrangement of the crushed stone cushion layer, and measuring the degree to which slope runoff is reduced or delayed.

[0013] The vertical path refers to the trajectory of water moving along the depth direction between the shallow and middle soil layers, describing the vertical dynamic process of rainfall infiltration, water conduction, and distribution.

[0014] As a further aspect of the present invention, the pore characteristics refer to the porosity, capillary-void ratio and spatial distribution structural properties of soil, reflecting the matching relationship and permeability between materials with different particle sizes and soil structure.

[0015] The evaporation loss information refers to establishing a quantitative relationship between vegetation cover thickness and evaporation intensity by observing vegetation cover thickness and evaporation intensity, revealing the dynamic connection between shallow and middle layer water.

[0016] The distribution direction of the amendment refers to the order and direction of the distribution of fine and coarse amendments in the soil pore structure, based on the measured soil porosity and capillary porosity ratio, combined with the particle size characteristics of the amendment.

[0017] As a further aspect of the present invention, the specific steps of S1 are as follows:

[0018] S101: Obtain stratigraphic data of the gravel cushion layer in the shallow area of ​​karst slope, extract the location coordinates of the thickness observation values ​​of the bottom, middle and upper layers, compare and analyze the thickness data of the measuring points, correspond the thickness change sequence with the slope runoff data, and obtain the thickness comparison result set.

[0019] S102: Based on the thickness comparison result set, extract the spatial arrangement direction parameters of the crushed stone cushion layer, analyze the arrangement angle data and thickness comparison results, compare and analyze the direction data of each point according to the sequential extraction method, remove sample points with continuous directional fluctuations, and obtain the arrangement direction sequence set.

[0020] S103: Call the corresponding parameters in the thickness comparison result set and the arrangement direction sequence set, analyze the connection relationship between water flow residence time and flow direction in the path change, and obtain the slope runoff delay structure morphology.

[0021] As a further aspect of the present invention, the specific steps of S2 are as follows:

[0022] S201: Based on the measured data of shallow porosity and middle water capacity in the root zone of the slope runoff delay structure, the seepage velocity parameters of the shallow and middle layers are extracted, and the data of each layer are compared according to the location coordinates to identify the differences in water-bearing response at different depths, thus obtaining a layered seepage response dataset.

[0023] S202: Based on the layered infiltration response dataset, collect time series data of rainfall infiltration rate, superimpose infiltration rate change segments and infiltration data, retrieve water movement segments between shallow and middle layers, and obtain vertical water movement trajectory sequence.

[0024] S203: Call the vertical water movement trajectory sequence, simultaneously extract the observed values ​​of pasture cover thickness and evaporation loss, compare the water volume parameters and evaporation data in the depth direction point by point, track the distribution trend of water in the vertical direction, and obtain the root zone stratified water distribution status.

[0025] As a further aspect of the present invention, the specific steps of S3 are as follows:

[0026] S301: Based on the stratified water distribution state of the root zone, collect the measured porosity and capillary porosity ratio in the observation section, match the data of each measuring point, arrange each group of data in spatial order, identify the different pore structures, and obtain the pore structure distribution sequence.

[0027] S302: Based on the pore structure distribution sequence, extract the particle size range parameters of fine and coarse modifiers, analyze the pore size values ​​and particle size range data in the pore structure, compare the offset between particle size values ​​and pore size, and obtain the particle size corresponding offset matrix.

[0028] S303: Call the particle size corresponding offset matrix, extract the correspondence between the matching position and the matching particle size in each type of pore structure, classify according to particle size stability, filter the combination data of structural differences, and obtain the matching relationship between the modifier particle size and porosity.

[0029] As a further aspect of the present invention, the specific steps of S4 are as follows:

[0030] S401: Based on the matching relationship between the particle size and porosity of the modifier, the data of each type of particle size material and the layered moisture distribution are matched, and the distribution area of ​​each type of material in the differentiated layer is extracted accordingly. The particle size type and depth parameters are aggregated to obtain the particle size distribution association dataset.

[0031] S402: Call the particle size distribution association dataset, retrieve the application of each type of material in the differentiated layer, sort the permeation range and residence time corresponding to each type of particle size, identify the particle size type that changes in the water movement path, and obtain the particle size behavior performance sequence.

[0032] S403: Based on the particle size behavior sequence, compare the role of each type of material in controlling water movement and runoff delay layer by layer, extract the corresponding path between particle size type and behavior, and split the material action sequence according to the depth level to obtain the multi-linkage regulation and linkage state.

[0033] As a further aspect of the present invention, in the distribution process of each type of particle size material in the particle size distribution association dataset at different layers, the relationship between porosity and depth is used as the control basis. By comparing the correspondence of different particle size materials in each layer of moisture distribution area, a dynamic correspondence between particle size materials and distribution layers is formed.

[0034] In the process of obtaining the particle size behavior sequence, the infiltration performance and retention characteristics of each type of particle size material in the water movement path are used as a reference. By comparing the order of the differential particle size materials in the interlayer movement process, the particle size type with performance characteristics is identified and maintained in the sequence.

[0035] In the process of determining the multi-stage regulation and linkage state, the particle size type of the performance characteristics is compared with its role in the differentiated depth level. The role order of each type of particle size material in the process of water migration and runoff delay is analyzed by the correspondence of continuous layers, so as to form the regulation and linkage relationship for the differentiated level.

[0036] As a further aspect of the present invention, the specific steps of S5 are as follows:

[0037] S501: Based on the multi-link control linkage state, obtain the measured moisture data of the shallow water storage area and the evaporation loss area, extract the surface cover thickness value of the differentiated area, pair the moisture state of each location with the corresponding cover thickness, and compare them according to the location index to obtain the cover thickness moisture association sequence.

[0038] S502: Based on the moisture correlation sequence of the coverage thickness, extract the water residence time and material particle size parameters of the region, compare the sequence differences between moisture residence characteristics and particle size distribution, divide the range of action of particle size parameters according to the region location, and obtain the particle size moisture response relationship set.

[0039] S503: Call the particle size moisture response relationship set, extract the delay segments and change points in the moisture propagation path, serialize the moisture state corresponding to each type of particle size material in the path, merge the structural performance according to the path change order, and obtain the quantitative regulation pattern of water resource vulnerability.

[0040] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0041] In this invention, the thickness and arrangement sequence of the subbase layer guide the slope water flow path, enhancing the retention and diversion of water on the slope. Combined with the migration characteristics of shallow and middle layers, the ability to identify the stratification of vertical water distribution is strengthened. Through the matching analysis of particle size parameters and pore structure, the regulatory matching of the modified material between different layers is improved, and a synergistic relationship between water migration path and particle size sequence is constructed to form a water regulation pattern with interlayer linkage, thereby enhancing the spatial retention and accumulation of water on the slope. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0043] Figure 1 This is a schematic diagram of the steps of the present invention;

[0044] Figure 2 This is a detailed schematic diagram of S1 of the present invention;

[0045] Figure 3 This is a detailed schematic diagram of S2 of the present invention;

[0046] Figure 4 This is a detailed schematic diagram of S3 of the present invention;

[0047] Figure 5 This is a detailed schematic diagram of S4 of the present invention;

[0048] Figure 6 This is a detailed schematic diagram of S5 of the present invention. Detailed Implementation

[0049] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0050] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0051] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0052] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0053] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0054] Please see Figure 1 This invention provides a method for quantitatively regulating water resource vulnerability in karst slopes, comprising the following steps:

[0055] S1: Obtain the stratigraphic structure of the gravel cushion layer in the shallow area of ​​karst slope, including the thickness data and spatial arrangement of the bottom, middle and upper layers. Based on the characteristics of exposed karst landforms, shallow soil layers and easy water loss, compare the differences in cushion layer thickness and connect it with surface runoff reduction parameters. Gradually guide the change of slope water flow path according to the thickness ratio and arrangement direction to obtain the slope runoff delay structure morphology.

[0056] S2: Based on the slope runoff retardation structure, the measured data of shallow porosity and middle water holding capacity in the root zone are extracted. The differences in infiltration rate and water holding capacity of each soil layer are compared. The changes in rainfall infiltration rate are introduced to track the movement of water between the shallow and middle layers. The thickness parameters of natural cover of pasture and the corresponding evaporation loss values ​​are obtained simultaneously. By comparing the depth direction, the changes of water in the vertical path are tracked to obtain the stratified water distribution status in the root zone.

[0057] S3: Based on the stratified water distribution in the root zone, and by comparing the measured porosity and capillary porosity ratio of the soil, measurement data were extracted point by point in the observation section. The pore structures at different locations were compared, and the pore parameters of each group were checked against the particle size range of fine and coarse amendments. The matching performance of materials with different particle sizes in each type of pore structure was analyzed, and the matching relationship between amendment particle size and porosity was obtained.

[0058] S4: Based on the matching relationship between the particle size and porosity of the modifier, the layered moisture distribution is substituted and correlated with the particle size parameter. The application of each type of material in the differentiated layers is compared according to the depth direction. The performance differences of the modifier in controlling moisture movement and delaying runoff are analyzed. The application sequence and action path of each type of particle size material on moisture behavior are analyzed by advancing layer by layer to obtain the multi-linked regulation and linkage state.

[0059] S5: Based on the multi-stage regulation and linkage, the measured data of shallow water storage area and evaporation loss area are extracted. The numerical relationship between the water status and surface cover thickness at different locations is compared. The performance of water flow residence time, water distribution characteristics and particle size parameters in different areas is gradually tracked. According to the change sequence of water behavior and structural conditions, the slope runoff delay process, water distribution changes and material application path are unfolded in sequence to promote the response process between each layer of information and obtain a quantitative regulation pattern of water resource vulnerability.

[0060] The structural morphology of slope runoff delay includes the thickness ratio of the subgrade, the order of layer arrangement, and the change mode of water flow path. The stratified water distribution state in the root zone includes shallow porosity, middle layer water capacity, and evaporation loss intensity. The matching relationship between amendment particle size and porosity includes particle size range matching, pore structure adaptability, and permeability regulation capacity. The multi-linked regulation and linkage state includes the stratified application mode of particle size materials, water delay path, and runoff control mode. The quantitative regulation pattern of water resource vulnerability includes water retention time, water distribution characteristics, and the relationship between surface cover and particle size response.

[0061] Please see Figure 2 The specific steps of S1 are as follows:

[0062] S101: Obtain stratigraphic data of the gravel cushion layer in the shallow area of ​​karst slope, extract the location coordinates of the thickness observation values ​​of the bottom, middle and upper layers, compare and analyze the thickness data of the measuring points, correspond the thickness change sequence with the slope runoff data, and obtain the thickness comparison result set.

[0063] To obtain stratigraphic data of the gravel cushion layer in shallow areas of karst slopes, the interface features of the bottom, middle, and upper layers can be extracted from field profile surveys. The depth distance between each layer and the surface is recorded using a vertical stratified survey method. Measurement rope fixing points are set at different measurement points. The main particles and sedimentary surfaces of the gravel at each vertical depth are visually determined. The thickness of each layer is measured vertically using a standard ruler, and the coordinate information of each measurement point is obtained using a handheld terminal device. The thickness observation value and spatial location are recorded point by point. For the difference range between layers in the thickness data, an analysis range with a lower limit of 0.1 meters and an upper limit of 1.5 meters is defined. Data exceeding the limit is discarded, and valid thickness values ​​are retained for subsequent comparison operations. Based on the thickness value corresponding to each set of coordinates, the thickness ratio between the bottom and upper layers is calculated. The thickness value of each point is classified according to the hierarchical relationship through a segmented calculation method. For example, the thickness value of each measurement point is classified according to the hierarchical relationship. A layer with a thickness of 0.6 meters in the upper layer, 0.3 meters in the middle layer, and 0.9 meters in the bottom layer has a thickness ratio of 0.9:0.3:0.6, which is converted into a ratio sequence of 3:1:2. The thickness ratio relationship of all sampling points is calculated in this way. Then, the runoff observation data corresponding to each measuring point is obtained. The instantaneous flow rate value per unit time is collected by burying a miniature water level sensor and compared with the thickness ratio sequence. Data pairs with corresponding relationships are extracted, and data points with missing values ​​or measurement errors exceeding the set deviation range are removed. The remaining valid data are normalized. The thickness ratio and runoff of all measuring points are uniformly numbered according to the comparison sequence. The change in runoff distribution trend under different thickness ratios is judged by cross-validation. A scatter plot is drawn with the thickness ratio as the horizontal axis and the runoff as the vertical axis, and the mapping characteristics between the two are analyzed. Finally, the thickness comparison result set is obtained.

[0064] S102: Based on the thickness comparison result set, extract the spatial arrangement direction parameters of the crushed stone cushion layer, analyze the arrangement angle data and thickness comparison results, compare and analyze the direction data of each point according to the sequential extraction method, remove sample points with continuous directional fluctuations, and obtain the arrangement direction sequence set.

[0065] First, the thickness ratio sequence and corresponding coordinate values ​​are retrieved to extract the spatial arrangement direction parameters of the crushed stone cushion layer at each measuring point. By setting directional measuring rods and benchmark points in the field, the angle value of the crushed stone arrangement orientation within the cushion layer at each measuring point is obtained. The angle values ​​are recorded and assigned corresponding coordinate labels. Then, the coordinate labels are correlated with the original thickness comparison data. The angle and thickness ratio values ​​are matched simultaneously in the data of each measuring point. The arrangement angle data and thickness comparison results are analyzed. By setting a comparison range, the angle values ​​are divided into multiple segments; for example, 0°~60° is classified as a primary segment, and 60°~120° as a secondary segment. The thickness ratio change sequence within each segment is indexed by the measuring point number. The corresponding angle segments are aggregated, and the standard deviation of the angle values ​​in each aggregated result is calculated and compared with the overall mean difference. If the difference exceeds the set range of the angle comparison offset value, the measuring point is marked as a direction fluctuation sample point. The offset setting range is set to 10° as the limit. Any angle difference between adjacent measuring points exceeding this value is judged as a continuous direction fluctuation area. All sample point data in such areas are removed. At the same time, sample points with fluctuation difference within 10° and azimuth variation between continuous measuring points less than the set value are retained. Finally, the direction angle values ​​of all measuring points in the retained sample points are arranged in coordinate index order and converted into a direction sequence. All sequence data are output by numbering to obtain the arrangement direction sequence set.

[0066] S103: Call the corresponding parameters in the thickness comparison result set and the arrangement direction sequence set to analyze the connection between water flow residence time and flow direction in the path change, and obtain the slope runoff delay structure morphology.

[0067] First, for each measuring point, the corresponding thickness comparison value and crushed stone arrangement angle data are retrieved to extract the interaction characteristics of numerical distribution and directional angle in the thickness variation section. Specifically, the measuring point data are arranged in coordinate order, and the thickness value and arrangement direction value of the cushion layer are extracted point by point for bivariate cross-analysis. During the cross-analysis, the angle between the thickness value of each point and the arrangement angle values ​​of its two adjacent measuring points is judged. The judgment criterion is whether the angle is between 30° and 60°. If it is not in this range, it is marked as a directional transition section, and a mark is added to the transition section for subsequent comparison. Then, for the marked transition section, the upstream water flow data sequence is retrieved, and the difference between the upstream water input and the downstream flow output of the transition section is compared to calculate the flow velocity change in this area and indirectly calculate the water residence time in this section. The water residence time is obtained by the ratio of the measuring point spacing to the flow velocity. If the value is greater than a certain reference time, it is defined as a delay section. Here, the reference time is selected as 2 seconds. Combined with the measured data, the normal flow velocity is in the range of 0.3 m / s to 0.5 m / s. The corresponding residence time at a 1-meter interval should be within 2 seconds. If it exceeds this time, it indicates that there is a water flow delay phenomenon in this area. At the same time, multiple sections with this characteristic are connected and processed into continuous delay paths. During the path processing, the path flow direction is adjusted according to the arrangement of measuring points to ensure that the formed path has the logic of actual water flow direction. Finally, the flow direction adjustment results in each delay path are superimposed and the trend is depicted. The water flow path graphic is extended according to the coordinate system. By comparing the curve direction, angle variation and thickness concentration points, the main runoff inflection point positions and their connection methods are extracted to form a delay path network connected by multiple flow direction connection segments, thus obtaining the slope runoff delay structure morphology.

[0068] Please see Figure 3 The specific steps of S2 are as follows:

[0069] S201: Based on the measured data of shallow porosity and middle water capacity in the root zone of the slope runoff delay structure, the seepage velocity parameters of the shallow and middle layers are extracted. The data of each layer are compared according to the location coordinates to identify the differences in water-bearing response at different depths, and a layered seepage response dataset is obtained.

[0070] First, raw data samples of the shallow and intermediate layers were extracted from the infiltration observation points set up on-site. The shallow observation points were generally buried at a depth of 0.2 to 0.4 meters, and the intermediate observation points at a depth of 0.6 to 1.0 meters. The porosity of each measuring point was determined using the ring sampler method. The water content was calculated by comparing the mass of the sampled wet soil with the mass of the dried soil, and then the porosity parameter was obtained through volume conversion. The water volume data was obtained through irrigation infiltration experiments, measuring the amount of water absorbed by the soil per unit time and recording the corresponding absorption depth. Both sets of data were entered with the same number. Subsequently, the data of each group of shallow and intermediate layers were extracted accordingly. The infiltration velocity was used as the core control parameter. By setting a constant water level at the same location, the infiltration time at a certain water depth was recorded, and the infiltration depth per unit time was calculated and measured in centimeters per hour. For example, the shallow infiltration velocity at measuring point A was 2.3 cm per hour, and the intermediate velocity was 1.6 cm per hour. The difference between the two values ​​yielded a depth infiltration difference of 0.7 cm per hour. To determine the distribution pattern of seepage velocity differences, the judgment intervals were defined as follows: 0-1 cm / h as the low-difference zone, 1-2 cm / h as the medium-difference zone, and greater than 2 cm / h as the high-difference zone. All measurement points were categorized and labeled. Next, the porosity, water capacity, and seepage velocity values ​​measured at each point were organized according to spatial coordinates. Lateral comparisons of seepage velocities at adjacent points on the same vertical profile were performed to identify numerical differences in water response between the shallow and middle layers. A continuous decrease in seepage velocity within the same profile indicates a lag in water conduction in the middle soil layer, while a reverse change indicates enhanced water permeability in localized areas. By comparing the seepage change sequences of each profile, typical distribution segments of rapid seepage in the shallow layer and slow conduction in the middle layer were identified. The water-bearing response values ​​corresponding to these differential depths were extracted and grouped into continuous seepage response data units, which were then integrated to form a seepage difference distribution map for each observation area. Finally, the seepage difference data from all profiles were uniformly organized according to location index and compiled into a dataset that can be used for subsequent stratified water migration analysis, resulting in a stratified seepage response dataset.

[0071] S202: Based on the stratified infiltration response dataset, time series data of rainfall infiltration rate are collected, and infiltration rate change segments and infiltration data are superimposed. Water movement segments between shallow and middle layers are retrieved to obtain vertical water movement trajectory sequences.

[0072] First, rain gauges and infiltration observation wells were deployed in the field monitoring area to collect time-series data on rainfall infiltration rates. During continuous rainfall, infiltration depth values ​​were recorded at regular intervals, along with the corresponding rainfall intensity. The measured time-series data were sorted by monitoring point number and grouped into a continuous sequence. For each monitoring point, the infiltration rate variation segments were superimposed with the shallow and middle layer infiltration velocity parameters in the stratified infiltration response dataset. The trends of infiltration rate and permeability at different stages were compared by comparing time-slice data. If the infiltration rate was greater than the shallow layer infiltration velocity and close to the middle layer infiltration velocity, it was determined to be the initial stage of water infiltration into the middle layer. When the infiltration rate was less than the shallow layer infiltration velocity, it indicated that water mainly remained in the shallow layer. To improve the accuracy of the comparison, the infiltration rate range was divided into three segments: 0 to 5 mm / min was defined as the slow infiltration stage, 5 to 15 mm / min as the medium infiltration stage, and greater than 15 mm / min as the rapid infiltration stage. The response differences between the shallow and middle layers infiltration velocities in each stage were classified and compared. Taking measuring point B as an example, the peak infiltration rate under continuous rainfall reached 12 mm / min, corresponding to a shallow infiltration rate of 6 mm / min and a mid-layer infiltration rate of 9 mm / min, indicating that water had partially entered the mid-layer during the mid-infiltration stage at this measuring point. Subsequently, the infiltration rate segments and infiltration response curves of all measuring points were compared laterally to extract the time periods when the infiltration rate and infiltration response increased synchronously, and the difference in water cut ratio between the shallow and mid-layers was calculated. If this difference was within the range of 2% to 5%, it was defined as a water movement segment. The measuring point sequence within this range was arranged in depth order to form a continuous vertical movement record. The water movement segments of all measuring points were indexed and compared according to spatial coordinates to identify the starting and ending depth positions of each segment. Then, by comparing their sequence order on different profiles, the direction of water transfer and the continuity of flow along the vertical path were identified. Through continuous comparison and serialization, the depth position and rate of change during the water movement process from the shallow to the mid-layer were integrated, and the trajectory of water change along the vertical path was summarized, resulting in a vertical water movement trajectory sequence.

[0073] S203: Call the vertical water movement trajectory sequence, simultaneously extract the observed values ​​of pasture cover thickness and evaporation loss, compare the water volume parameters and evaporation data in the depth direction point by point, track the distribution trend of water in the vertical direction, and obtain the root zone stratified water distribution status.

[0074] First, the depth ranges of moisture movement at each monitoring point were matched point-by-point. The corresponding pasture cover thickness and evaporation loss observations at the same location were extracted simultaneously and arranged with depth as a reference, forming a basic data sequence coupling moisture movement and land cover. Then, the water quantity parameters on the vertical profile of each monitoring point were processed. The water content of the shallow, middle, and deep layers was recorded separately, and the percentage of water content per unit volume of soil was calculated using the readings from the volumetric water content sensor. For example, the water content was 18% at 0.3 meters in the shallow layer, 23% at 0.7 meters in the middle layer, and 21% at 1.0 meter in the deep layer. The values ​​of the three layers were arranged in depth order, and their corresponding cover thicknesses were recorded. Cover thickness data was obtained from the thickness of the turf layer at the sampling points, with the thickness difference between monitoring points controlled within the range of 1 cm to 4 cm. Next, the moisture content and cover thickness of each layer were compared to calculate the variation in water volume between layers. If the moisture content in the shallow layer decreased by less than 2% compared to the middle layer, it was identified as a moisture-shifting equilibrium zone; if it exceeded 5%, it was identified as an evaporation-dominant zone. Simultaneously, the evaporation loss data of each measuring point were synchronously overlaid with the water volume change curve according to a time series, comparing the temporal correspondence between evaporation intensity and the decrease in shallow layer moisture content. For example, at measuring point C, the average evaporation rate was 0.35 mm / hour, corresponding to a 4% decrease in shallow layer moisture content over 6 hours, indicating a rapid shallow layer evaporation response. Subsequently, the data from the middle layer was compared; if the water content in the middle layer decreased by no more than 1% in the same time period, it indicated that water evaporation in this area was mainly concentrated in the surface layer. The ratios of shallow and middle layer evaporation to moisture content changes at all measuring points were aggregated and sorted according to spatial coordinates, comparing the vertical relationship between evaporation and water volume at different locations to track the migration trend of water at different depths. By comparing and identifying the above points one by one, the change sequences of evaporation loss, cover thickness and moisture content are integrated to form a continuous vertical change chain of moisture. The complete path of the moisture distribution gradient is depicted by the spatial correlation of each measuring point, and finally the stratified moisture distribution state of the root zone is obtained.

[0075] Please see Figure 4 The specific steps of S3 are as follows:

[0076] S301: Based on the stratified water distribution in the root zone, the measured porosity and capillary porosity ratio in the observation section are collected, the data of each measuring point are matched, and each group of data is arranged in spatial order to identify the different pore structures and obtain the pore structure distribution sequence.

[0077] First, soil sampling points were set up within the observation section, and samples were taken from the shallow and middle layers. The ring sampler method and capillary water rise test were used to obtain the measured porosity and capillary porosity ratio at each depth. The measured wet and dry soil masses after sampling were converted into volumetric water content, and then the porosity and capillary porosity ratio were calculated based on the pore volume and capillary water height. Subsequently, the measured data of each measuring point were numbered and spatially located, and the coordinates of the sampling point and the corresponding soil layer depth were recorded to ensure a one-to-one correspondence between the shallow, middle, and deep layer data. Taking observation point D as an example, the measured porosity was 0.42 in the shallow layer and 0.36 in the middle layer, with capillary porosity ratios of 0.78 and 0.65, respectively. After the four parameters were entered into the system with the same coordinate positions, a basic porosity data reference set was formed. Next, the data from each measuring point were horizontally matched. The difference between the porosity and capillary porosity ratio between adjacent measuring points was compared. If the porosity difference was within 0.05 and the capillary porosity ratio difference was within 0.1, it was determined to be a structurally continuous segment. If the difference exceeded the above range, it was identified as a structurally abrupt change zone. To quantify the differences in pore structure, the data were arranged spatially along each profile line, and the porosity change trend was recorded and its variation in the depth direction was calculated. By comparing the differences in porosity and capillary porosity ratio between the shallow and middle layers, transitional segments from coarse-grained to fine-grained pore morphology were identified. For example, in profile E, the shallow layer porosity was 0.45 while the middle layer was 0.32, a difference of 0.13, indicating that the soil pores in this segment were relatively fine. Subsequently, the pore data at each point within the profile were aggregated according to spatial coordinates to form a continuous sequence of multiple measurement points. The porosity difference and the rate of change of capillary porosity ratio between adjacent points were compared, and the extension pattern of the pore structure along the slope direction was traced based on spatial sorting. Finally, all profile data were summarized according to spatial index to form an arrangement sequence of porosity and capillary porosity ratio in different sections, identifying similar structural regions and different sections, thus obtaining the pore structure distribution sequence.

[0078] S302: Based on the pore structure distribution sequence, extract the particle size range parameters of fine and coarse-grained modifiers, analyze the pore size values ​​and particle size range data in the pore structure, compare the offset between particle size values ​​and pore size, and obtain the particle size corresponding offset matrix.

[0079] First, particle size range data were extracted from the basic parameter sets of fine and coarse-grained modifiers. The particle size range for fine-grained modifiers was set to 0.1 to 1.0 mm, and for coarse-grained modifiers, it was set to 1.0 to 5.0 mm. The median particle size was used as a representative value for comparison. Then, pore size data for each spatial location in the pore structure distribution sequence was extracted. The pore size was calibrated in millimeters and matched with the corresponding modifier particle size range. Taking measuring point E as an example, the average pore size is 0.85 mm, corresponding to a median of 0.55 mm for fine-grained modifiers and 3.2 mm for coarse-grained modifiers. The numerical differences between these two values ​​and the pore size were calculated, yielding offsets of 0.3 mm and 2.35 mm, respectively. To ensure the continuity of the analysis, the pore size values ​​of each measuring point were sorted by spatial coordinates and compared one-to-one with the particle size parameters, and the offset sequence was recorded. This comparison method clarifies the degree of similarity between pore sizes in different regions and particle size ranges, forming a data table consisting of measurement point numbers, pore size, fine particle offset values, and coarse particle offset values. Further, the offset values ​​of each measurement point are categorized into three intervals: an offset less than 0.5 mm is defined as the matching interval, 0.5 to 1.5 mm as the transition interval, and greater than 1.5 mm as the deviation interval. Based on this interval division, the distribution trends of different types of modifiers in various pore structures are compared. For example, the offset values ​​between measurement points A and F are mostly less than 0.5 mm, indicating that the fine particle modifier is similar to the pore size in this region; while between measurement points G and K, the offset values ​​of coarse particle modifiers are mostly above 1.8 mm, showing that the pore size in this region is relatively fine. Next, the offset values ​​of all measurement points within the entire profile are matrixed, with rows representing spatial location and columns representing particle size type, and the offset values ​​are filled into the corresponding cells, forming a human-readable offset data matrix. Finally, based on the variation pattern of the offset values ​​of each segment in the matrix, the priority area for particle size matching and the particle size deviation area are identified, and the particle size corresponding offset matrix is ​​obtained.

[0080] S303: Call the particle size corresponding offset matrix, extract the correspondence between the matching position and the matching particle size in each type of pore structure, classify according to particle size stability, filter the combination data of structural differences, and obtain the matching relationship between the modifier particle size and porosity.

[0081] First, matching data points with an offset of less than 0.5 mm in each pore structure were extracted and considered as valid matching positions corresponding to particle size and pore size. The corresponding porosity and particle size values ​​were recorded. Taking profiles A to E as an example, the porosity range in the shallow region is 0.38 to 0.46, corresponding to fine-grained modifier particle sizes of 0.3 to 0.8 mm. The offsets are all less than 0.5 mm, confirming their validity. Next, the matching data in each type of pore structure were spatially classified. Points with similar porosity and particle size differences not exceeding 0.2 mm were grouped into the same category, forming multiple matching combination units. To clarify the distribution pattern of different types of modifiers in the pore structure, all matching points were sorted according to porosity from smallest to largest, and particle size stability was judged by the fluctuation range of particle size values. The particle size fluctuation range was calculated by taking the absolute value of the particle size difference between adjacent matching points and comparing it with the average particle size. When the fluctuation range did not exceed 15%, it was defined as a stable range; when it exceeded 25%, it was defined as an unstable range. For example, in the shallow, fine-grained region, the average particle size is 0.55 mm, and the difference in particle size between adjacent points is within 0.07 mm, classifying it as a stable region. In contrast, the average particle size in the middle-layer, coarse-grained region is 3.1 mm, with a difference of 0.9 mm between adjacent points, classifying it as an unstable region. Subsequently, the matching and unstable regions for each type of pore structure were statistically analyzed, recording their corresponding porosity ranges and particle size characteristics. These data were then reordered by category, and the stable particle size distribution intervals corresponding to different pore types were extracted, forming structural difference combination data. Finally, by comparing the particle size distribution and porosity change trends in each combination data, it was identified that fine-grained modifiers mainly act on regions with porosity greater than 0.40, while coarse-grained modifiers are concentrated in segments with porosity less than 0.35, with cross-matching between the two particle size types in the transitional segment. Through the above comparison and screening process, a quantitative correspondence between porosity and particle size was summarized, yielding the matching relationship between modifier particle size and porosity.

[0082] Please see Figure 5 The specific steps of S4 are as follows:

[0083] S401: Based on the matching relationship between the particle size and porosity of the modifier, the data of each type of particle size material is matched with the layered moisture distribution. The distribution area of ​​each type of material in the differentiated layer is extracted, and the particle size type and depth parameters are aggregated to obtain the particle size distribution association dataset.

[0084] First, the basic parameter sets for various particle size materials were extracted. The particle size range for fine-grained materials was set to 0.2 to 1.0 mm, and for coarse-grained materials, it was set to 1.0 to 5.0 mm. The porosity range for each material in the preceding data was recorded. Then, the stratified moisture distribution dataset was used to extract the moisture content values ​​and depth coordinates of the shallow, middle, and deep layers, and the moisture data of each layer was paired with the corresponding particle size materials. Taking profile A as an example, the moisture content of the shallow layer was 22%, the middle layer was 18%, and the deep layer was 14%. After comparing with the particle size parameters, the shallow layer was matched with fine-grained materials, and the middle and deep layers were matched with medium-coarse and coarse-grained materials, respectively. Next, the distribution positions of each type of material were arranged according to the depth direction, and the vertical distribution range of different particle size types was recorded. To identify the adaptability of particle size materials in the stratified moisture state, the ratio of porosity to moisture content within the same depth range was compared. If the ratio was less than 1.5, it was marked as a fine-grained preferential region; between 1.5 and 3.0 was a medium-grained distribution region; and greater than 3.0 was a coarse-grained dominant region. For example, in observation area B, the shallow layer ratio is 1.2, corresponding to fine-grained materials; the middle layer is 2.4, corresponding to medium-grained materials; and the deep layer is 3.6, corresponding to coarse-grained materials. Subsequently, the particle size type and depth parameters of each measuring point are aggregated to form a four-dimensional dataset containing location coordinates, porosity, moisture content, and particle size type. To ensure spatial continuity, the particle size types of adjacent measuring points are compared. If three consecutive measuring points have the same particle size type, it is defined as a stable distribution zone, and its start and end depth ranges are recorded. After processing in this way, multiple vertical particle size distribution zones can be formed in the profile, each corresponding to a specific moisture content range and porosity range. Finally, the particle size type, moisture characteristics, and spatial depth information of each distribution zone are integrated to establish a matching index between particle size type and stratification location, resulting in a particle size distribution association dataset.

[0085] S402: Call the particle size distribution association dataset, retrieve the application of each type of material in the differentiated layers, sort the permeation range and residence time corresponding to each type of particle size, identify the particle size type that changes in the water movement path, and obtain the particle size behavior performance sequence.

[0086] First, the distribution parameters of each type of particle size material at different depths were read and matched with the water permeability characteristic data to extract comprehensive information on particle size, porosity, moisture content, and flow rate in each layer. Taking shallow fine-grained materials as an example, their particle size ranges from 0.3 to 0.8 mm, corresponding to a permeability range of 2.5 to 4.2 cm. Medium-grained materials in the middle layer have a particle size of 1.2 to 2.0 mm and a permeability range of 4.8 to 7.3 cm. Deep coarse-grained materials have a particle size of 3.0 to 4.5 mm and a permeability range exceeding 8.0 cm. Then, based on the relationship between permeability depth and water retention time, the materials were sorted. Retention time was determined using the rate of change in water content per unit volume as a reference; a change in water content of less than 0.5% was considered water retention, and a change exceeding 2% was considered rapid permeation. For example, in slope profile A, the average residence time of fine-grained materials in the shallow layer is 1.8 hours, that of medium-grained materials in the middle layer is 1.2 hours, and that of coarse-grained materials in the deep layer is 0.6 hours, thus determining the lag characteristics of different particle size types during water movement. Next, the paired data of particle size and infiltration time for each layer are categorized, with samples having similar residence times grouped together, and the difference in infiltration velocity between adjacent layers is compared. When the difference in infiltration velocity between adjacent layers exceeds 30%, it is recorded as a point of change, indicating the trend of particle size type change during water migration. Taking profile B as an example, the infiltration velocity from the middle layer to the deep layer increases from 3.6 mm / h to 5.1 mm / h, corresponding to a jump in particle size from 1.6 mm to 3.3 mm, defined as the coarsening change zone. To clearly reflect the dynamic performance of particle size in the water pathway, each point of change is arranged according to spatial depth and temporal order, forming a human-readable sequence record table. The table lists fields such as measurement point number, stratigraphic depth, particle size range, permeation range, and residence time to enable point-by-point comparison at the data level. Finally, the performance change records of each measurement point are continuously compared, and sample segments with consistent particle size change directions are extracted and rearranged according to stratigraphic order to form a particle size change trajectory from shallow to deep, thus obtaining a sequence of particle size behavior performance.

[0087] S403: Based on the particle size behavior sequence, the role of each type of material in controlling water movement and runoff delay is compared layer by layer. The corresponding path between particle size type and behavior is extracted. The material action sequence is split according to the depth level to obtain the multi-link regulation linkage state.

[0088] First, measured data on the spatial location, permeation rate, water retention time, and runoff delay of materials with different particle sizes in the sequence were obtained in their corresponding layers. The layers were divided into three levels according to depth: shallow, medium, and deep, corresponding to fine (0.3–0.8 mm), medium (1.2–2.0 mm), and coarse (3.0–4.5 mm) particle sizes, respectively. The effects of each particle size on controlling water migration rate and adjusting retention time were compared layer by layer. Taking the shallow layer as an example, in sampling points A01 to A10, the average permeation rate of fine-sized materials was 3.2 mm / h, and the retention time per unit volume of water was 1.9 hours. Compared with medium-sized materials with a particle size of 1.5 mm in the adjacent layer, the permeation rate was 4.8 mm / h, and the retention time was 1.1 hours. Based on the percentage change in rate and the difference in retention time, the delay control effect of the material on the runoff path was determined. If the rate increase was greater than 30% and the retention time difference was less than 1 hour, it was recorded as a behavioral turning point. Secondly, based on the continuity of each particle size behavior transition point in spatial depth, the transition segments between fine and medium particle sizes, and between medium and coarse particle sizes, are connected to generate corresponding material behavior paths. Each connecting segment in the path is identified by key fields such as material type, layer depth, permeation rate, and residence time. Next, based on the number and order of path segments, the frequency of material type occurrence is statistically analyzed. If a certain particle size type appears more than three times consecutively in multiple path segments, its behavioral stability is recorded as high-level; otherwise, it is considered medium or low-level. Correspondingly, the stability level is paired with the moisture control behavior segment by segment in the sequence. Taking profile sample point B03 as an example, 1.6 mm and 3.5 mm particle size materials are used from the middle layer to the deep layer, respectively. With a 20% increase in permeation rate and a 0.7-hour reduction in residence time, the path segment is recorded as an effective connection, and a behavioral level of 2 is assigned to this segment. Finally, the material types within the path segment are broken down layer by layer according to depth level, and the order of materials in each layer is numbered and marked. Starting from the top layer, the material is defined as the first stage of particle size control, the second stage of particle size control, and the control paths of each sample point are summarized in sequence. The connection performance between particle sizes is integrated to obtain the multi-stage control linkage state.

[0089] Please see Figure 6 The specific steps of S5 are as follows:

[0090] S501: Based on the multi-link control linkage state, the measured moisture data of the shallow water storage area and the evaporation loss area are obtained, the surface cover thickness value of the differentiated area is extracted, the moisture status of each location is paired with the corresponding cover thickness, and the comparison is performed according to the location index to obtain the cover thickness moisture association sequence.

[0091] First, observational data were extracted from each shallow water retention area and evaporation concentration area. The moisture values ​​of each measuring point were extracted and the area type was classified according to the area location number. Points numbered T01 to T15 were assigned to water retention areas, and T16 to T30 to evaporation areas. Then, surface cover thickness data were collected for the corresponding area points, and the thickness values ​​were recorded in centimeters in the corresponding index entries. At point T03, the observed moisture content was 21% and the surface cover thickness was 2.5 cm; at point T18, the moisture content was 11% and the thickness was 0.8 cm. A one-to-one paired data entry was established for each measuring point. Next, all points were indexed according to their spatial arrangement to form a unified numbering order for subsequent comparison operations. Adjacent locations within the same area were grouped into consecutive segments. Points T01 to T05 were arranged from west to east, and points T16 to T20 were arranged from north to south. Subsequently, a longitudinal comparison was performed on the paired data, using the ratio of moisture value to cover thickness as the correlation indicator. This ratio was 8.4 in sample point T03 and 13.75 in T18, revealing spatial differences. These differences were then categorized into three levels: 0-10, 10-15, and above 15. Sample points T01 to T05 were mostly in the first level, while samples T16 to T20 mostly fell into the second level. Further comparisons were performed, comparing the cover thickness difference between adjacent points. Points exceeding 1.2 cm were marked as a transition point, which could serve as a reference for subsequent boundary delineation. Finally, all paired items were categorized into different level segments and stored according to their location index. The output data sequence, obtained as a cover thickness-moisture correlation sequence, used sample point number, region type, moisture value, cover thickness, and corresponding difference level as fields.

[0092] S502: Based on the moisture correlation sequence of the cover thickness, the water residence time and material particle size parameters of the region are extracted. The sequence differences between moisture residence characteristics and particle size distribution are compared. The range of effect of particle size parameters is divided according to the region location to obtain the particle size moisture response relationship set.

[0093] First, water residence time data were extracted from each observation location within the shallow water storage and evaporation zones. The sample point number and corresponding residence time were recorded in minutes; for example, the residence time for sample point T03 was 18 minutes, and for sample point T18 it was 6 minutes. Then, the particle size parameters of the materials used at each sample point were extracted, and the median particle size value for each type of material was recorded in millimeters. Sample point T03 used 1.2 mm coarse particles, and T18 used 0.3 mm fine particles. Next, a pairing table of particle size and residence time was established. All paired data were sequentially arranged according to observation location to form a linear sequence list and assigned serial numbers. Subsequently, the trend of change between water residence time and particle size was compared point by point. The particle size difference and time difference were compared side-by-side for adjacent samples to determine whether the changes had a consistent direction. If the particle size increased and the corresponding time increased, it was recorded as a positive response; otherwise, it was recorded as a negative response. Taking T03 and T04 as examples, if T03 is 1.2 mm for 18 minutes and T04 is 0.9 mm for 12 minutes, it is judged as a positive response; conversely, if T04 is 1.5 mm for 10 minutes, it is marked as a negative response. The comparison segments are then statistically analyzed, and the number of consecutive positive or negative segments is counted according to the sequence number. The comparison results are further categorized into regional location indexes, divided into multiple regional blocks according to the actual geographical order of the sample point distribution. The particle size parameter variation range and residence characteristics within each block are combined and summarized. For example, in block A, the particle size distribution range is 0.8 to 1.4 mm, corresponding to a residence time of 9 to 20 minutes; in block B, the particle size is 0.2 to 0.6 mm, corresponding to a residence time of 4 to 11 minutes. Next, the response range of particle size parameters was analyzed within each block, and the concentrated intervals of moisture retention time for different particle size values ​​were statistically analyzed. If the proportion of sample points with a retention time concentrated between 16 and 20 minutes for particle sizes of 1.0 to 1.2 mm exceeded 60%, it was determined that the particle size had a strong correlation within that block. This process was repeated for all blocks, creating a table of particle size and moisture response relationships. Finally, the results for all blocks were archived, identifying the particle size type, corresponding retention time interval, and spatial location number, and arranged in regional order to generate an integrated list, resulting in a set of particle size and moisture response relationships.

[0094] S503: Call the particle size moisture response relationship set, extract the delay segment and change point in the moisture propagation path, serialize the moisture state of each type of particle size material in the path, merge the structural performance according to the path change order, and obtain the quantitative regulation pattern of water resource vulnerability.

[0095] First, key node information in the moisture propagation path is extracted from the relation set. The path segments corresponding to each particle size material are sorted in temporal and spatial order, and the location of abrupt changes in moisture retention time is identified as the starting point of the lag segment. If the moisture change between two adjacent points exceeds 0.25 times the average fluctuation value of the previous segment, the point is marked as a change point. Taking samples T05 to T09 as an example, the particle size increases from 0.8 mm to 1.3 mm, and the moisture content decreases from 17% to 10%, a change of 7 percentage points, exceeding the set standard value by 1.5 times. Therefore, T07 is marked as a path change point. Subsequently, for each change point, the corresponding three core parameters—particle size, retention time, and penetration depth—are extracted and matched sequentially according to spatial sequence number to form a particle size-moisture-depth three-dimensional data set. After serializing all data sets, a sequence list from the upper to the lower edge of the slope is established using the path number as an index. This list records the moisture state evolution trend of each path segment and the corresponding change in material particle size. For example, in the upper slope sampling points A02 to A06, the particle size is concentrated in the range of 1.0 to 1.4 mm, corresponding to a moisture content decrease range of 23% to 15%; in the middle slope sampling points A07 to A12, the particle size is 1.5 to 2.2 mm, with a moisture content change range of 15% to 9%. These continuous path segments are then classified and grouped. The number of path segments with the same direction of particle size and moisture change determines whether they belong to the same structural unit. When the particle size increases and the moisture content decreases simultaneously in a continuous path segment, it is classified into a "decreasing path group," and vice versa, it is classified into a "retention path group." Furthermore, similar path groups are aggregated according to spatial location, and the path sequences in the shallow, middle, and deep layers are statistically analyzed layer by layer, with the number of delayed segments within each layer's path being cumulatively calculated. For example, 5 delayed segments were identified in the shallow layer, 4 in the middle layer, and 3 in the deep layer. Finally, based on the aggregation results, the sequence number, particle size type, moisture variation range, number of delay segments, and spatial distribution depth of different path groups are uniformly correlated to form an overall multi-level response matrix, which describes the comprehensive performance of materials of different particle sizes in the vertical and horizontal flow directions, and obtains a quantitative regulation pattern of water resource vulnerability.

[0096] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for quantitatively controlling water resource vulnerability in karst slopes, characterized in that, Includes the following steps: S1: Obtain the stratigraphic structure of the gravel cushion layer in the shallow area of ​​the karst slope, compare the differences between the bottom and upper layers, introduce runoff reduction parameters, guide the change of slope water flow path according to the thickness ratio and arrangement direction, and obtain the slope runoff delay structure morphology. S2: Based on the slope runoff delay structure, the shallow porosity and middle layer water capacity are extracted, the movement process of water in the vertical path is analyzed, the changes of water in the vertical path are tracked, and the root zone stratified water distribution status is obtained. S3: Based on the layered moisture distribution state of the root zone, extract the porosity and capillary porosity ratio, analyze the correspondence between particle size parameters and pore characteristics, analyze the performance of differentiated particle size materials in pore structure matching, and obtain the matching relationship between modifier particle size and porosity. S4: Based on the matching relationship between the particle size and porosity of the improver, the moisture distribution is substituted into the correlation with the particle size parameter to analyze the application sequence of the material and the moisture regulation path, and to advance the response process of the particle size parameter in the structural layer, so as to obtain the multi-linkage regulation and linkage state. S5: Based on the aforementioned multi-stage regulation and linkage status, analyze water storage data and evaporation loss information, compare and track runoff, water volume and material path, analyze the application sequence of particle size materials and the action path of water behavior, and obtain a quantitative regulation pattern for water resource vulnerability.

2. The method for quantitative regulation of water resource vulnerability in karst slopes according to claim 1, characterized in that, The slope runoff delay structure includes the thickness ratio of the cushion layer, the order of layer arrangement, and the change mode of water flow path. The root zone stratified water distribution includes shallow porosity, middle layer water capacity, and evaporation loss intensity. The matching relationship between the amendment particle size and porosity includes particle size range matching, pore structure adaptability, and permeability regulation capacity. The multi-link regulation linkage includes the stratified application mode of particle size materials, water delay path, and runoff control mode. The quantitative regulation pattern of water resource vulnerability includes water retention time, water distribution characteristics, and the relationship between surface cover and particle size response.

3. The method for quantitative regulation of water resource vulnerability in karst slopes according to claim 1, characterized in that, The runoff reduction parameter refers to establishing a correspondence between thickness and flow delay based on the thickness ratio and arrangement of the crushed stone cushion layer, and measuring the degree to which slope runoff is reduced or delayed. The vertical path refers to the trajectory of water moving along the depth direction between the shallow and middle soil layers, describing the vertical dynamic process of rainfall infiltration, water conduction, and distribution.

4. The method for quantitative regulation of water resource vulnerability in karst slopes according to claim 1, characterized in that, The pore characteristics refer to the porosity, capillary-void ratio and spatial distribution structural attributes in the soil, reflecting the matching relationship and permeability between materials with different particle sizes and soil structure. The evaporation loss information refers to establishing a quantitative relationship between vegetation cover thickness and evaporation intensity by observing vegetation cover thickness and evaporation intensity, revealing the dynamic connection between shallow and middle layer moisture.

5. The method for quantitative regulation of water resource vulnerability in karst slopes according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Obtain stratigraphic data of the gravel cushion layer in the shallow area of ​​karst slope, extract the location coordinates of the thickness observation values ​​of the bottom, middle and upper layers, compare and analyze the thickness data of the measuring points, correspond the thickness change sequence with the slope runoff data, and obtain the thickness comparison result set. S102: Based on the thickness comparison result set, extract the spatial arrangement direction parameters of the crushed stone cushion layer, analyze the arrangement angle data and thickness comparison results, compare and analyze the direction data of each point according to the sequential extraction method, remove sample points with continuous directional fluctuations, and obtain the arrangement direction sequence set. S103: Call the corresponding parameters in the thickness comparison result set and the arrangement direction sequence set, analyze the connection relationship between water flow residence time and flow direction in the path change, and obtain the slope runoff delay structure morphology.

6. The method for quantitative regulation of water resource vulnerability in karst slopes according to claim 1, characterized in that, The specific steps of S2 are as follows: S201: Based on the measured data of shallow porosity and middle water capacity in the root zone of the slope runoff delay structure, the seepage velocity parameters of the shallow and middle layers are extracted, and the data of each layer are compared according to the location coordinates to identify the differences in water-bearing response at different depths, thus obtaining a layered seepage response dataset. S202: Based on the layered infiltration response dataset, collect time series data of rainfall infiltration rate, superimpose infiltration rate change segments and infiltration data, retrieve water movement segments between shallow and middle layers, and obtain vertical water movement trajectory sequence. S203: Call the vertical water movement trajectory sequence, simultaneously extract the observed values ​​of pasture cover thickness and evaporation loss, compare the water volume parameters and evaporation data in the depth direction point by point, track the distribution trend of water in the vertical direction, and obtain the root zone stratified water distribution status.

7. The method for quantitative regulation of water resource vulnerability in karst slopes according to claim 1, characterized in that, The specific steps for S3 are as follows: S301: Based on the stratified water distribution state of the root zone, collect the measured porosity and capillary porosity ratio in the observation section, match the data of each measuring point, arrange each group of data in spatial order, identify the different pore structures, and obtain the pore structure distribution sequence. S302: Based on the pore structure distribution sequence, extract the particle size range parameters of fine and coarse modifiers, analyze the pore size values ​​and particle size range data in the pore structure, compare the offset between particle size values ​​and pore size, and obtain the particle size corresponding offset matrix. S303: Call the particle size corresponding offset matrix, extract the correspondence between the matching position and the matching particle size in each type of pore structure, classify according to particle size stability, filter the combination data of structural differences, and obtain the matching relationship between the modifier particle size and porosity.

8. The method for quantitative regulation of water resource vulnerability in karst slopes according to claim 1, characterized in that, The specific steps of S4 are as follows: S401: Based on the matching relationship between the particle size and porosity of the modifier, the data of each type of particle size material and the layered moisture distribution are matched, and the distribution area of ​​each type of material in the differentiated layer is extracted accordingly. The particle size type and depth parameters are aggregated to obtain the particle size distribution association dataset. S402: Call the particle size distribution association dataset, retrieve the application of each type of material in the differentiated layer, sort the permeation range and residence time corresponding to each type of particle size, identify the particle size type that changes in the water movement path, and obtain the particle size behavior performance sequence. S403: Based on the particle size behavior sequence, compare the role of each type of material in controlling water movement and runoff delay layer by layer, extract the corresponding path between particle size type and behavior, and split the material action sequence according to the depth level to obtain the multi-linkage regulation and linkage state.

9. The method for quantitative regulation of water resource vulnerability in karst slopes according to claim 8, characterized in that, In the distribution process of each type of particle size material in the particle size distribution association dataset, the relationship between porosity and depth is used as the control basis. By comparing the correspondence of the differentiated particle size materials in each layer of moisture distribution area, a dynamic correspondence between particle size materials and distribution layers is formed. In the process of obtaining the particle size behavior sequence, the infiltration performance and retention characteristics of each type of particle size material in the water movement path are used as a reference. By comparing the order of the differential particle size materials in the interlayer movement process, the particle size type with performance characteristics is identified and maintained in the sequence. In the process of determining the multi-stage regulation and linkage state, the particle size type of the performance characteristics is compared with its role in the differentiated depth level. The role order of each type of particle size material in the process of water migration and runoff delay is analyzed by the correspondence of continuous layers, so as to form the regulation and linkage relationship for the differentiated level.

10. The method for quantitative regulation of water resource vulnerability in karst slopes according to claim 1, characterized in that, The specific steps of S5 are as follows: S501: Based on the multi-link control linkage state, obtain the measured moisture data of the shallow water storage area and the evaporation loss area, extract the surface cover thickness value of the differentiated area, pair the moisture state of each location with the corresponding cover thickness, and compare them according to the location index to obtain the cover thickness moisture association sequence. S502: Based on the moisture correlation sequence of the coverage thickness, extract the water residence time and material particle size parameters of the region, compare the sequence differences between moisture residence characteristics and particle size distribution, divide the range of action of particle size parameters according to the region location, and obtain the particle size moisture response relationship set. S503: Call the particle size moisture response relationship set, extract the delay segments and change points in the moisture propagation path, serialize the moisture state corresponding to each type of particle size material in the path, merge the structural performance according to the path change order, and obtain the quantitative regulation pattern of water resource vulnerability.

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