Mountainous small watershed rainwater conversion law monitoring method and system

Abstract

The application discloses a mountain small watershed rainwater conversion rule monitoring method and system, relates to the hydrogeological monitoring technical field, and discloses the mountain small watershed rainwater conversion rule monitoring method and system. Through integration of multi-source data, layered monitoring of groundwater dynamics, construction of a three-dimensional geological model and parameter calibration, the system systematically captures the conversion process of rainwater from the ground to the ground, solves the problems of incomplete monitoring and low model precision of the traditional method, can systematically monitor the whole process of rainwater conversion, improves the analysis precision of groundwater migration rules, and supports water resource prediction and geological disaster early warning.

Description

Technical Field

[0001] This application relates to the field of hydrogeological monitoring technology, and in particular to methods and systems for monitoring the transformation patterns of rainwater in small watersheds in mountainous areas. Background Technology

[0002] Traditional monitoring of small watersheds in mountainous areas faces numerous challenges in monitoring rainwater transformation patterns due to complex terrain and variable geological conditions. Existing monitoring methods often only obtain localized and partial data, failing to comprehensively and accurately reflect the hydrological processes of rainwater in underground and surface watersheds within mountainous areas. For example, previous methods for monitoring groundwater migration paths and velocities were limited, making it impossible to accurately grasp the flow of groundwater in different geological layers; the determination of the development height of aquifer fracture zones relied heavily on empirical estimations or simple field observations, lacking scientific and accurate quantitative methods. Moreover, in applying monitoring data to water resource simulation and prediction and geological disaster early warning, the reliability of prediction and early warning results is low due to insufficient data completeness and accuracy.

[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of this application is to provide a method and system for monitoring the transformation patterns of rainwater in small watersheds in mountainous areas, aiming to improve the accuracy of groundwater transport pattern analysis and support water resource prediction and geological disaster early warning.

[0005] To achieve the above objectives, this application proposes a method for monitoring the rainwater transformation patterns in small watersheds in mountainous areas. The method includes: Collect and integrate meteorological data, hydrological data, land use data, topographic and geological data, water system data, and hydrogeological map data of the target area, delineate the boundaries of hydrological units in small watersheds in mountainous areas, and generate boundary data; Based on the boundary data, a hydrogeological survey is conducted within the defined mountainous small watershed hydrogeological unit area to divide secondary hydrogeological units, measure the watershed catchment area, underlying surface parameter characteristics, permeability coefficient and hydrogeological parameters, and generate hydrogeological parameter data. Based on the hydrological parameter data, monitoring devices are deployed within the secondary hydrogeological units to acquire precipitation data, flow data, water level data, and soil moisture content data, and then integrate them to generate comprehensive monitoring data. Based on the flow direction data, flow velocity data, water temperature data, and water level data in the comprehensive monitoring data, a flow field map of the underground horizontal surface is generated. Based on the underground horizontal flow field map, the location data of at least one precipitation funnel center is analyzed and determined, and the location data of the exploration borehole is generated according to the location data. Drilling is performed using the location data of the exploration hole to generate an observation hole; a tracer is introduced from a first predetermined position of the observation hole, and a vertical monitoring sensor is deployed at a second predetermined position of the observation hole to obtain groundwater vertical flow velocity data and tracer concentration data as vertical monitoring data; Based on the underground horizontal flow field diagram and the vertical monitoring data, the vertical migration path and migration velocity of groundwater are analyzed to generate groundwater vertical migration law data. Preliminary data on the development height of the water-conducting fracture zone is generated by direct observation through the observation hole; combined with the underground horizontal flow field map and the vertical monitoring data, the preliminary data on the development height of the water-conducting fracture zone is corrected by geostatistical methods to generate data on the development height of the water-conducting fracture zone. Based on the hydrological parameter data, the comprehensive monitoring data, the groundwater vertical transport data, and the water-conducting fracture zone development height data, a three-dimensional geological model of a small watershed in a mountainous area is constructed; precipitation data, flow data, and water level data are extracted from the comprehensive monitoring data and input into the three-dimensional geological model as measured parameters for model calibration, generating transformation law data; Based on the transformation law data, the impact of water on slope stability is analyzed, and slope stability impact data is generated. The transformation law data and the slope stability impact data can then be applied to water resource simulation and prediction and geological disaster early warning.

[0006] In one embodiment, based on the hydrological parameter data, a monitoring device is deployed within a secondary hydrogeological unit to acquire precipitation data, flow data, water level data, and soil moisture content data through the monitoring device, and the steps of integrating these data to generate comprehensive monitoring data include: Rain gauges were deployed within the secondary hydrogeological units to obtain precipitation data; Deploy flow monitoring stations at control sections to acquire flow data; Water level monitoring stations were deployed at the borehole locations to obtain water level data; Soil moisture content monitoring devices are deployed in the soil layer to obtain soil moisture content data; The precipitation data, flow data, water level data, and soil moisture content data are integrated to generate comprehensive monitoring data.

[0007] In one embodiment, the step of deploying a water level monitoring station at the borehole location to acquire water level data includes: Monitoring points were set up at the borehole locations, and flow direction data were continuously collected using groundwater monitoring instruments. Flow velocity data were collected at the same monitoring point using a flow meter. Temperature sensors are deployed at monitoring points to collect water temperature data, and water level data is collected using water level gauges. The flow direction data, the flow velocity data, the water temperature data, and the water level data are combined to form the water level data.

[0008] In one embodiment, the step of generating a groundwater horizontal flow field map based on the flow direction data, flow velocity data, water temperature data, and water level data in the comprehensive monitoring data includes: Extract water level data from the comprehensive monitoring data and generate a two-dimensional isobaric map through spatial interpolation; Based on the flow direction and velocity data, the groundwater vector intensity and direction at each monitoring point are calculated. The two-dimensional isostatic map and the groundwater vector distribution map are superimposed to form an initial flow field map; Based on the spatial distribution characteristics of the water temperature data, the hydraulic gradient anomaly area of ​​the initial flow field diagram is corrected; Output the corrected flow field diagram at the underground horizontal surface.

[0009] In one embodiment, the steps of drilling to generate an observation well based on the location data of the exploratory well; injecting a tracer from a first predetermined location in the observation well; and deploying a vertical monitoring sensor at a second predetermined location in the observation well to obtain groundwater vertical flow velocity data and tracer concentration data as vertical monitoring data include: Based on the analysis results of the underground horizontal flow field map, the location data of the tracer release point is determined as the first predetermined location, and the location data of the vertical monitoring point is determined as the second predetermined location. A tracer is introduced at a first predetermined position of the observation hole; A vertical velocity sensor is deployed at a second predetermined location along the vertical migration direction of groundwater to collect vertical velocity data; A concentration sensor is simultaneously deployed at a second predetermined location to monitor the arrival time and concentration change data of the tracer. The vertical flow velocity data and tracer concentration change data are combined to generate vertical monitoring data.

[0010] In one embodiment, the step of analyzing the vertical migration path and velocity of groundwater based on the underground horizontal flow field map and the vertical monitoring data to generate groundwater vertical migration law data includes: Extract the vertical flow velocity data and tracer concentration data from the vertical monitoring data; By combining the horizontal movement trend data in the underground horizontal flow field map, spatial overlay analysis is performed to generate transport path data; Based on the transport path data and vertical flow velocity data, the transport velocity data is calculated; By integrating the migration path data and migration velocity data, groundwater vertical migration pattern data are generated.

[0011] In one embodiment, the steps of generating preliminary data on the development height of the water-conducting fracture zone by directly observing through the observation hole, and then refining the preliminary data on the development height of the water-conducting fracture zone using geostatistical methods in conjunction with the underground horizontal flow field map and the vertical monitoring data, to generate data on the development height of the water-conducting fracture zone, include: Obtain the water level data and flow direction data from the underground horizontal flow field diagram; Combining the vertical monitoring data, three-dimensional spatial interpolation is performed using the Kriging interpolation algorithm to generate spatial distribution data of the development height of the water-conducting fracture zone; Based on the spatial distribution data, the maximum value is selected to generate the development height data of the water-conducting fracture zone.

[0012] In one embodiment, a three-dimensional geological model of a small mountain watershed is constructed based on the hydrological parameter data, the comprehensive monitoring data, the groundwater vertical transport data, and the development height data of the water-conducting fracture zone; the steps of extracting precipitation data, flow data, and water level data from the comprehensive monitoring data and inputting them into the three-dimensional geological model as measured parameters for model calibration, and generating transformation law data include: Input the precipitation data, flow data, and water level data into the three-dimensional geological model; Adjust the permeability coefficient and hydraulic head parameters in the hydrological parameter data to match the simulated flow data output by the three-dimensional geological model with the measured flow data; A calibrated permeability coefficient dataset and a calibrated head parameter dataset are generated based on the matching results of simulated flow data and measured flow data; By integrating the calibrated permeability coefficient dataset, the calibrated hydraulic head parameter dataset, and the model structure parameters, a calibrated three-dimensional geological model is output. Based on the calibrated three-dimensional geological model, the rainwater transformation process is simulated to generate transformation law data.

[0013] In one embodiment, the step of analyzing the impact of water on slope stability and generating slope stability impact data based on the transformation law data includes: Extract the dynamic change data of groundwater from the transformation pattern data; Calculate the change in slope seepage pressure by combining topographic and geological data; The slope stability is assessed based on the changes in seepage pressure, and slope stability impact data is generated.

[0014] Furthermore, to achieve the above objectives, this application also proposes a monitoring system for rainwater transformation patterns in small watersheds in mountainous areas. The monitoring system includes: a memory, a processor, and a monitoring program for rainwater transformation patterns in small watersheds in mountainous areas stored in the memory and executable on the processor. The monitoring program for rainwater transformation patterns in small watersheds in mountainous areas is configured to implement the steps of the monitoring method for rainwater transformation patterns in small watersheds in mountainous areas as described in any one of claims 1 to 9.

[0015] The method and system for monitoring rainwater transformation patterns in small watersheds in mountainous areas proposed in this application systematically capture the transformation process of rainwater from the surface to the ground by integrating multi-source data, monitoring groundwater dynamics in layers, constructing a three-dimensional geological model and calibrating parameters. This solves the problems of incomplete monitoring and low model accuracy of traditional methods. It can systematically monitor the entire process of rainwater transformation, improve the accuracy of groundwater transport pattern analysis, and support water resource prediction and geological disaster early warning. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating an embodiment of the method for monitoring the transformation patterns of rainwater in small watersheds in mountainous areas, as described in this application; Figure 2 This is a schematic diagram of the structure of an embodiment of the monitoring system for rainwater transformation patterns in small watersheds in mountainous areas, as provided in this application.

[0019] Explanation of icon numbers: 10. Memory; 20. Processor.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of this application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0022] It should be understood that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0023] In existing technologies, monitoring the rainwater transformation patterns in small watersheds in mountainous areas faces challenges due to complex terrain and variable geological conditions. Traditional methods rely on localized data collection, making it difficult to comprehensively reflect hydrological processes. For example, monitoring methods for groundwater transport paths and velocities are limited, and the development height of aquifer fracture zones is often estimated empirically, lacking scientific quantitative methods. Insufficient data completeness and accuracy lead to low reliability in water resource simulation and prediction, as well as geological disaster early warning. In typical application scenarios, complex geological structures make it difficult to accurately characterize the interactions between precipitation infiltration, groundwater transport, and surface runoff. Existing monitoring systems cannot effectively integrate multi-source data, making it difficult to support the accuracy calibration of three-dimensional geological models.

[0024] To address the aforementioned issues, this study focuses on three aspects of existing technologies: incomplete dynamic groundwater monitoring, lack of methods for quantifying water-conducting fracture zones, and insufficient model calibration accuracy. These aspects include multi-source data integration, construction of a three-dimensional monitoring network, and dynamic calibration of model parameters. First, a refined monitoring grid is achieved through secondary hydrogeological unit division, and a three-dimensional monitoring system is established by combining planar flow field and vertical tracer data. Second, geostatistical methods are used to combine field observation data with flow field characteristics, improving the scientific accuracy of calculating the height of water-conducting fracture zones. Finally, a dynamic simulation mechanism for rainwater transformation processes is established through iterative calibration of measured parameters and a three-dimensional geological model.

[0025] Based on this, this application provides a method for monitoring the rainwater transformation pattern in small watersheds in mountainous areas, referring to... Figure 1 The method for monitoring the rainwater transformation pattern in small watersheds in mountainous areas includes steps S100 to S1000, wherein: Step S100: Collect and integrate meteorological data, hydrological data, land use data, topographic and geological data, water system data and hydrogeological map data of the target area, delineate the boundaries of small watershed hydrological units in mountainous areas, and generate boundary data; Step S200: Based on the boundary data, conduct a hydrogeological survey within the defined mountain small watershed hydrogeological unit area, divide secondary hydrogeological units, measure the watershed catchment area, underlying surface parameter characteristics, permeability coefficient and hydrogeological head parameters, and generate hydrogeological parameter data. Step S300: Based on the hydrological parameter data, a monitoring device is deployed in the secondary hydrogeological unit to acquire precipitation data, flow data, water level data and soil moisture content data through the monitoring device, and integrate them to generate comprehensive monitoring data; Step S400: Based on the flow direction data, flow velocity data, water temperature data, and water level data in the comprehensive monitoring data, a groundwater horizontal flow field map is generated. Step S500: Based on the underground horizontal flow field map, analyze and determine the location data of at least one precipitation funnel center, and generate the location data of the exploration borehole according to the location data; Step S600: Drilling operation is performed on the location data of the exploration hole to generate an observation hole; tracer is introduced from the first predetermined position of the observation hole, and a vertical monitoring sensor is deployed at the second predetermined position of the observation hole to obtain the vertical flow velocity data of groundwater and the tracer concentration data as vertical monitoring data; Step S700: Based on the underground horizontal flow field diagram and the vertical monitoring data, analyze the vertical migration path and migration velocity of groundwater to generate groundwater vertical migration law data; Step S800: Preliminary data on the development height of the water-conducting fracture zone is generated by direct observation through the observation hole; combined with the underground horizontal flow field map and the vertical monitoring data, the preliminary data on the development height of the water-conducting fracture zone is corrected by geostatistical methods to generate data on the development height of the water-conducting fracture zone. Step S900: Based on the hydrological parameter data, the comprehensive monitoring data, the groundwater vertical transport law data, and the water-conducting fracture zone development height data, a three-dimensional geological model of a small watershed in the mountainous area is constructed; precipitation data, flow data, and water level data are extracted from the comprehensive monitoring data and input into the three-dimensional geological model as measured parameters for model calibration, generating transformation law data; Step S1000: Based on the transformation law data, analyze the impact of water on slope stability and generate slope stability impact data, so as to apply the transformation law data and the slope stability impact data to water resource simulation prediction and geological disaster early warning.

[0026] In this embodiment, the subdivision of secondary hydrogeological units refers to the spatial subdivision of the watershed based on geological structures and hydrological characteristics. This can be achieved using geological profile analysis and permeability coefficient zoning methods to improve the spatial resolution of monitoring data. The groundwater horizontal flow field map is a vector distribution map reflecting the horizontal movement of groundwater, generated through spatial interpolation algorithms and vector overlay techniques to identify areas of abnormal groundwater flow. Vertical monitoring sensors are devices used to measure the vertical movement parameters of groundwater. This can be achieved by combining fiber optic gyroscope current meters and fluorescence tracer detection systems to capture the vertical transport characteristics of groundwater. The correction of the development height of water-conducting fracture zones refers to optimizing the spatial distribution model of fracture zones based on monitoring data. This is achieved by applying the Kriging interpolation algorithm for three-dimensional interpolation calculations to improve the accuracy of geological structure characterization. Three-dimensional geological model calibration refers to the process of optimizing model parameters using measured data, specifically achieved using the Monte Carlo parameter inversion method to improve the accuracy of rainwater transformation process simulation.

[0027] In this embodiment, the method first integrates multi-source data from meteorology, hydrology, and geology to determine the boundary of the monitoring area. Secondary units are divided through hydrogeological surveys, and key parameters such as permeability coefficients are obtained to establish the basic framework of the monitoring network. Rain gauges, flow stations, and water level gauges are deployed within the secondary units to form a surface-to-groundwater collaborative monitoring system. Planar flow field maps are constructed based on the collected flow direction and velocity data to identify areas of abnormal groundwater movement. Observation wells are arranged at the center of the determined precipitation funnel, and the vertical movement characteristics of groundwater are acquired through tracer deployment and vertical sensors. Combining the planar flow field and vertical transport data, geostatistical methods are used to correct the development height of the water-conducting fracture zone. Multi-dimensional monitoring data are input into a three-dimensional geological model, and key parameters such as permeability coefficients are dynamically calibrated using measured parameters to ultimately establish an accurate model of rainwater transformation patterns.

[0028] In this embodiment, spatially refined acquisition of hydrological parameters is achieved through secondary unit division and the construction of a three-dimensional monitoring network. The combination of planar flow field maps and vertical tracer data overcomes the limitations of traditional single-dimensional monitoring, and the introduction of geostatistical methods enhances the scientific rigor of quantifying water-conducting fracture zones. The dynamic calibration mechanism between the three-dimensional geological model and measured data effectively addresses the shortcomings of traditional model parameter fixation, significantly improving the simulation accuracy of rainwater transformation processes. Through the above technical solutions, this application achieves three-dimensional visualization tracking of groundwater transport paths in small mountain watersheds, accurately depicting the transformation process of precipitation infiltration-groundwater transport-surface runoff. The scientific calculation of the development height of water-conducting fracture zones provides a reliable basis for slope stability analysis, and the dynamic calibration mechanism of model parameters ensures the accuracy of water resource prediction, providing data support for geological disaster early warning.

[0029] In one feasible implementation, based on the hydrological parameter data, the step of deploying monitoring devices within a secondary hydrogeological unit to acquire precipitation data, flow data, water level data, and soil moisture content data through the monitoring devices, and integrating them to generate comprehensive monitoring data includes: deploying rain gauges within the secondary hydrogeological unit to acquire precipitation data; deploying flow monitoring stations at control sections to acquire flow data; deploying water level monitoring stations at borehole locations to acquire water level data; deploying soil moisture content monitoring devices in the soil layer to acquire soil moisture content data; and integrating the precipitation data, flow data, water level data, and soil moisture content data to generate comprehensive monitoring data.

[0030] In this embodiment, a secondary hydrogeological unit refers to a geological region with similar permeability and recharge / drainage conditions, delineated through hydrogeological surveys. Specifically, topographic slope, stratum attitude, and fracture development can be used as the basis for delineation to optimize the spatial layout of monitoring points. A rain gauge is an instrument for measuring precipitation, specifically a tipping bucket or weighing rain sensor, used to collect spatiotemporal distribution data of precipitation within the secondary unit. A control section refers to a cross-section in a river channel or valley with stable hydraulic conditions, specifically selected in areas with regular riverbed morphology, used to accurately obtain surface runoff data. A flow monitoring station is a facility for measuring water flow, specifically a combination of a Doppler current meter and a water level gauge, used to continuously record changes in surface water flow. A borehole location refers to a borehole location with groundwater outcrops or aquifer characteristics determined based on hydrogeological surveys, specifically determined through ground-penetrating radar detection combined with core sampling, used to obtain dynamic groundwater level data. A water level monitoring station is a device for monitoring groundwater levels, specifically using pressure-type or float-type water level gauges, to record fluctuations in aquifer water levels. The soil layer refers to the medium layer between the surface and the weathered bedrock layer; monitoring points can be set up according to soil texture to reflect the precipitation infiltration process. A soil moisture content monitoring device is an instrument for measuring soil moisture content, specifically using a time-domain reflectometry (TDAR) or capacitive sensors, to acquire data on changes in soil moisture content at different depths.

[0031] In this embodiment, rain gauges are systematically deployed within the secondary hydrogeological unit to achieve spatially uniform sampling of precipitation. For example, 1-2 rain gauges are deployed per square kilometer within the unit. Flow monitoring stations are set up at the control section on a straight section of the river channel. Real-time flow is calculated by measuring flow velocity at the cross-section and combining the water level-flow curve. Water level monitoring stations are installed at suitable locations in the boreholes based on the aquifer depth, and automated data acquisition equipment records water level data every 15 minutes. Moisture content sensors are deployed in the soil layer at three depths: 0-30cm, 30-60cm, and 60-100cm, to simultaneously monitor the precipitation infiltration process. All monitoring data are collected to the data center via a wireless transmission module and fused using a time-series alignment method to form a comprehensive monitoring dataset containing four-dimensional spatiotemporal information.

[0032] In this embodiment, the proposed solution establishes a three-dimensional monitoring network within secondary units, enabling simultaneous observation of precipitation, surface water, groundwater, and soil water. Specifically, this solution reveals the vertical transport patterns of water through stratified monitoring and achieves high-frequency continuous monitoring using automated equipment, effectively solving the problem of insufficient spatial coverage in traditional monitoring data. Furthermore, the multi-element collaborative observation within secondary units significantly improves the spatiotemporal resolution of hydrological process data. Specifically, it allows for the simultaneous acquisition of correlated data on precipitation infiltration, surface runoff formation, groundwater level fluctuations, and soil moisture transport, providing comprehensive data support for accurate analysis of rainwater transformation mechanisms. For example, during heavy rainfall events, it can simultaneously capture the temporal correlation characteristics of peak precipitation, runoff lag response, abrupt changes in soil moisture content, and water level rise, thereby accurately calculating the rainwater transformation rate in different media.

[0033] In one feasible implementation, the steps of deploying a water level monitoring station at the borehole location to obtain water level data include: setting up a monitoring point at the borehole location and continuously collecting flow direction data using a groundwater monitoring instrument; collecting flow velocity data using a flow meter at the same monitoring point; deploying a temperature sensor at the monitoring point to collect water temperature data and collecting water level data using a water level gauge; and combining the flow direction data, the flow velocity data, the water temperature data, and the water level data as the water level data.

[0034] In this embodiment, flow direction data refers to the horizontal flow direction information of groundwater at the borehole location continuously recorded by a groundwater monitoring instrument, specifically implemented using an electromagnetic flow direction probe, to reflect the planar movement trend of groundwater. Flow velocity data refers to the horizontal flow rate of groundwater collected by a flow meter, specifically implemented using a rotor-type flow meter or an acoustic Doppler sensor, to quantify the intensity of groundwater movement. Water temperature data refers to the groundwater temperature parameters obtained by a temperature sensor, specifically implemented using a platinum resistance temperature probe, to assist in determining the source of groundwater recharge and flow state. Water level data refers to the groundwater depth or pressure head value measured by a water level gauge, specifically implemented using a pressure-type water level sensor, to characterize the hydraulic state of the aquifer.

[0035] In this embodiment, when setting monitoring points at the borehole location, a groundwater monitoring instrument is installed to continuously monitor the flow direction, and a flow velocity meter is deployed at the same spatial location for synchronous measurement, eliminating measurement errors caused by equipment spacing. A temperature sensor and a water level gauge are integrated into the monitoring well to achieve spatiotemporal consistency in the acquisition of water temperature and water level data. The four types of data are aligned and merged using a unified timestamp to form a water level dataset containing multi-dimensional features. This technical solution overcomes the deficiency of traditional single-level water level monitoring in reflecting the dynamic characteristics of water flow through multi-parameter synchronous acquisition and fusion.

[0036] In this embodiment, the solution achieves multi-dimensional synchronous monitoring of water flow status through the integrated deployment and data fusion of multiple sensors, providing more complete basic data support for subsequent flow field analysis. This enables three-dimensional acquisition of groundwater dynamic parameters. Through the collaborative analysis of flow direction, velocity, water temperature and water level data, the recharge and discharge relationship of groundwater can be accurately identified, providing high-precision input parameters for groundwater flow field modeling and effectively improving the reliability of rainwater transformation law analysis.

[0037] In one feasible implementation, the step of generating a groundwater flow field map based on the flow direction data, flow velocity data, water temperature data, and water level data in the comprehensive monitoring data includes: extracting the water level data from the comprehensive monitoring data and generating a two-dimensional isostatic map through spatial interpolation; calculating the groundwater vector intensity and direction at each monitoring point based on the flow direction data and flow velocity data; superimposing the two-dimensional isostatic map and the groundwater vector distribution map to form an initial flow field map; correcting the hydraulic gradient anomaly zone of the initial flow field map by combining the spatial distribution characteristics of the water temperature data; and outputting the corrected groundwater flow field map.

[0038] In this embodiment, spatial interpolation refers to the method of converting water level data from discrete monitoring points into a continuous two-dimensional planar distribution. Specifically, it can be implemented using Kriging interpolation or inverse distance weighted interpolation algorithms to construct contour maps reflecting regional water level change trends. Groundwater vector intensity refers to the horizontal movement rate of groundwater per unit time, which can be calculated from measured data from a flow velocity meter and data from a flow direction sensor, and is used to characterize the dynamic characteristics of water flow at different locations. Hydraulic gradient anomaly zones refer to areas on the contour map that do not conform to the normal groundwater flow patterns. These can be identified by comparing water temperature distribution with changes in water level gradient; for example, abrupt changes in water temperature may occur at geological interfaces with significant permeability differences.

[0039] In this embodiment, when drawing the groundwater flow field map, water level data collected by the monitoring network is first extracted, and a two-dimensional isohyetal map covering the entire study area is generated using an interpolation algorithm. Subsequently, based on the flow direction and velocity data synchronously acquired from each monitoring point, vector data sets with direction and magnitude are calculated. After superimposing the vector data onto the isohyetal map, the overall trend of horizontal groundwater flow can be visually displayed. During this process, by analyzing the spatial distribution characteristics of water temperature data, such as water temperature anomalies in fault zones or lithological contact zones, areas in the initial flow field map that do not conform to the hydraulic gradient theory are locally corrected, ultimately forming a groundwater flow field distribution map that conforms to actual geological conditions.

[0040] In this embodiment, by integrating vector data with water temperature distribution characteristics, flow field anomalies caused by geological structures can be effectively identified and corrected. For example, in areas with developed fractures or abrupt changes in rock permeability, local deviations in the flow direction can be corrected using water temperature anomaly data. Thus, this application overcomes the limitation of traditional flow field maps in accurately representing the dynamic characteristics of groundwater, achieving a precise visualization of horizontal flow fields under complex geological conditions. Through vector overlay and multi-source data fusion, the influence of geological structures on water flow paths can be effectively identified, providing reliable spatial data support for subsequently determining the center location of precipitation funnels and analyzing vertical transport patterns.

[0041] In one feasible implementation, the steps of drilling to generate an observation well based on the location data of the exploratory well; injecting tracer from a first predetermined location in the observation well and deploying a vertical monitoring sensor at a second predetermined location in the observation well to obtain groundwater vertical velocity data and tracer concentration data as vertical monitoring data include: determining the tracer injection point location data as the first predetermined location and the vertical monitoring point location data as the second predetermined location based on the analysis results of the groundwater flow field map; injecting tracer at the first predetermined location in the observation well; deploying a vertical velocity sensor at the second predetermined location along the vertical migration direction of groundwater to collect vertical velocity data; simultaneously deploying a concentration sensor at the second predetermined location to monitor the tracer arrival time and concentration change data; and merging the vertical velocity data and tracer concentration change data to generate vertical monitoring data.

[0042] In this embodiment, the first predetermined location refers to the specific coordinates of the tracer release point, which can be determined through hydraulic gradient analysis of the groundwater flow field map, ensuring the tracer diffuses along the vertical migration path of groundwater. The second predetermined location refers to the spatial positioning of the vertical monitoring point, which can be determined based on the vector direction of the flow field map and the permeability differences of geological layers, used to capture dynamic changes during vertical migration. The vertical velocity sensor is a device used to measure the vertical velocity of groundwater, specifically a thermal pulse or electromagnetic sensor, to achieve continuous acquisition of vertical velocity. The concentration sensor is a device used to detect the tracer concentration, specifically a fluorescence spectrometer or an electrochemical sensor, to monitor the tracer arrival time and peak concentration in real time.

[0043] In this embodiment, after identifying the main migration trends of groundwater through a groundwater flow field map, areas with significant changes in hydraulic gradient are selected as tracer release points. After the tracer is released at a first predetermined location in the observation well, it diffuses with the vertical movement of groundwater to a second predetermined location. A vertical velocity sensor is deployed at the second predetermined location to continuously record the temporal changes in vertical velocity; a concentration sensor simultaneously collects tracer concentration data, and the tracer migration time and diffusion range are determined by analyzing the concentration change curve. The vertical velocity data and concentration data are merged to form a complete vertical migration monitoring dataset.

[0044] In this embodiment, the proposed method directly acquires the physical parameters of vertical transport through the coordinated monitoring of tracer deployment and vertical sensors, avoiding the bias of empirical estimation. Simultaneously, the synchronous acquisition of tracer concentration data and flow velocity data allows for the accurate calculation of vertical transport rate and diffusion range, overcoming the shortcomings of traditional methods that rely solely on flow velocity estimation. This achieves precise monitoring of the vertical transport path and velocity of groundwater, solving the problem of incomplete vertical data acquisition under complex geological conditions using traditional monitoring methods. Through the synergistic effect of tracer deployment and sensor deployment, changes in flow velocity and concentration can be captured simultaneously, providing a reliable data foundation for analyzing the vertical transport patterns of groundwater, thereby improving the simulation accuracy of three-dimensional geological models and the accuracy of geological disaster early warning.

[0045] In one feasible implementation, the steps of analyzing the vertical migration path and velocity of groundwater based on the underground horizontal flow field map and the vertical monitoring data to generate groundwater vertical migration law data include: extracting vertical velocity data and tracer concentration data from the vertical monitoring data; performing spatial overlay analysis by combining the horizontal movement trend data from the underground horizontal flow field map to generate migration path data; calculating migration velocity data based on the migration path data and vertical velocity data; and integrating the migration path data and migration velocity data to generate groundwater vertical migration law data.

[0046] In this embodiment, vertical velocity data refers to the velocity of groundwater moving in the vertical direction, collected by a vertical velocity sensor. Specifically, this can be achieved using an electromagnetic flowmeter or a thermal diffusion sensor, used to directly quantify the intensity of vertical groundwater movement. Tracer concentration data is the curve showing the concentration change of the tracer at the monitoring point over time. Specifically, this can be achieved using a fluorescent tracer in conjunction with an optical sensor, used to track the vertical migration trajectory of groundwater. Spatial overlay analysis refers to the fusion of horizontal flow field vector data and vertical migration data in a three-dimensional coordinate system. Specifically, this can be achieved using a geographic information system spatial analysis module, used to reveal the coupling relationship between the vertical and horizontal movements of groundwater. Migration path data refers to the three-dimensional spatial trajectory of groundwater during vertical flow. Specifically, this can be generated by inversely extrapolating from the tracer migration time series and velocity data, used to characterize the distribution characteristics of vertical infiltration channels in groundwater.

[0047] In this embodiment, a vertical velocity sensor is deployed at the second predetermined position of the observation well to collect continuous time-series data of the vertical movement velocity of groundwater in real time. A tracer concentration sensor synchronously records the arrival time of the tracer at the monitoring point and the change in peak concentration, forming a concentration-time curve. Horizontal movement trend data is extracted from the groundwater flow field map, including the flow direction vector and velocity scalar for each monitoring point. During spatial analysis, the vertical velocity data and horizontal velocity vector are vector-synthesized using a three-dimensional coordinate system to form a three-dimensional groundwater motion vector field. The tracer concentration diffusion path is calculated by inverting the peak concentration occurrence time and velocity data to generate the tracer's vertical migration trajectory. Finally, the three-dimensional motion vector field and the tracer migration trajectory are spatially matched to determine the dominant path of groundwater vertical migration and its corresponding velocity distribution.

[0048] In this embodiment, the proposed solution uses a spatial overlay analysis method to perform three-dimensional fusion of horizontal flow field vector data and vertical transport monitoring data. This method can accurately identify the vertical transport path of groundwater under complex geological conditions, and quantify the spatial influence of the horizontal flow field on the vertical transport velocity. It achieves three-dimensional dynamic analysis of the vertical transport process of groundwater, accurately reveals the collaborative transport mechanism of groundwater in the vertical and horizontal directions, provides key parameter support for the construction of a three-dimensional geological model, and effectively solves the technical defects of traditional monitoring methods that cannot accurately depict the vertical transport path and velocity distribution.

[0049] In one feasible implementation, the steps of generating preliminary data on the development height of the water-conducting fracture zone through direct observation via the observation hole, and then refining the preliminary data using geostatistical methods by combining the underground horizontal flow field map and the vertical monitoring data to generate the final water-conducting fracture zone development height data include: acquiring water level and flow direction data from the underground horizontal flow field map; performing three-dimensional spatial interpolation using the Kriging interpolation algorithm based on the vertical monitoring data to generate spatial distribution data of the water-conducting fracture zone development height; and selecting the maximum value from the spatial distribution data to generate the final water-conducting fracture zone development height data.

[0050] In this embodiment, the Kriging interpolation algorithm refers to a spatial interpolation method based on geostatistics. It constructs a variogram model by analyzing the spatial correlation between data points, thereby predicting the attribute values ​​of unsampled points. Specifically, it can be implemented using ordinary Kriging or pan-Kriging algorithms. For example, it can use flow velocity and tracer concentration variation data from vertical monitoring data as input parameters, combined with the spatial distribution characteristics of water level and flow direction data, to establish a three-dimensional interpolation model. This algorithm can effectively handle non-uniformly distributed data and improve the accuracy of predicting the height of water-conducting fracture zones. The development height of the water-conducting fracture zone refers to the maximum vertical extension range of the water-conducting fracture network in the rock mass. Specifically, the initial height can be determined by comprehensively considering the characteristics of the fracture filling material in the observation borehole, permeability test data, and vertical flow velocity variation curves, and then corrected for local anomalies using spatial interpolation methods. This parameter directly affects the simulation accuracy of the vertical migration path of groundwater and is a key indicator for assessing slope stability.

[0051] In this embodiment, fracture distribution characteristics are obtained within the observation well through core logging and downhole television observation. The initial development height of the water-conducting fracture zone is then determined using permeability tests. Subsequently, water level data from the horizontal flow field map is spatially correlated with flow velocity and tracer concentration data from vertical monitoring data. A three-dimensional spatial distribution model is constructed using the Kriging interpolation algorithm. For example, using the observation well location as a control point, the water-conducting fracture height at each point is taken as a known value, and a spatial autocorrelation function is calculated to generate an interpolation surface. Finally, the maximum value is extracted from the interpolation results as the final data for the development height of the water-conducting fracture zone, eliminating the influence of local observation errors on the overall assessment.

[0052] Understandably, existing technologies typically rely on core analysis from a single observation well or empirical formulas to estimate the height of water-conducting fracture zones, failing to reflect their spatial heterogeneity. For example, existing technologies using linear interpolation to process observation data neglect the nonlinear characteristics of fracture development, leading to significant deviations in height values ​​at fault intersections. This proposed solution, by fusing multi-source monitoring data and applying the Kriging interpolation algorithm, accurately captures the spatial distribution patterns of water-conducting fracture zones, particularly in areas with complex geological structures, where the interpolation results better match the three-dimensional distribution characteristics of the actual fracture network. Through this technical solution, this application addresses the problems of low accuracy and insufficient spatial coverage in traditional methods for determining the height of water-conducting fracture zones. By integrating planar flow field data with vertical monitoring data and combining geostatistical interpolation methods, a three-dimensional spatial quantitative characterization of the development height of water-conducting fracture zones is achieved. This method can accurately identify the maximum vertical extension range of fracture zones, providing reliable structural parameters for subsequent construction of three-dimensional geological models, thereby improving the accuracy of rainwater transformation simulation and providing a scientific basis for slope stability assessment and geological disaster early warning.

[0053] In one feasible implementation, a three-dimensional geological model of a small mountain watershed is constructed based on the hydrological parameter data, the comprehensive monitoring data, the groundwater vertical transport data, and the development height data of the water-conducting fracture zone. The steps for extracting precipitation data, flow data, and water level data from the comprehensive monitoring data and inputting them into the three-dimensional geological model as measured parameters for model calibration, and generating transformation law data, include: inputting the precipitation data, flow data, and water level data into the three-dimensional geological model; adjusting the permeability coefficient and hydraulic head parameters in the hydrological parameter data to match the simulated flow data output by the three-dimensional geological model with the measured flow data; generating a calibrated permeability coefficient dataset and a calibrated hydraulic head parameter dataset based on the matching results of the simulated flow data and the measured flow data; integrating the calibrated permeability coefficient dataset, the calibrated hydraulic head parameter dataset, and the model structure parameters to output the calibrated three-dimensional geological model; and simulating the rainwater transformation process based on the calibrated three-dimensional geological model to generate transformation law data.

[0054] In this embodiment, the three-dimensional geological model refers to a numerical model of a groundwater system with spatial distribution characteristics, constructed by integrating hydrogeological parameters, monitoring data, and geological structure information. Specifically, it can be implemented using geological modeling software combined with finite element analysis methods to simulate rainwater infiltration, runoff, and groundwater migration processes. Model calibration refers to the process of adjusting the model input parameters to achieve the best fit between the simulation results and measured data. This can be achieved using parameter optimization algorithms such as genetic algorithms or least squares methods to eliminate initial parameter errors. The permeability coefficient is a parameter characterizing the ability of soil and rock to allow water flow. It can be determined through field pumping tests or laboratory permeability tests, and its numerical adjustment directly affects the model's accuracy in simulating groundwater migration rates. The hydraulic head parameter reflects the energy state of groundwater and can be calculated using water level monitoring data combined with Darcy's law. Its calibration helps correct the model's ability to characterize hydraulic gradients.

[0055] In this embodiment, precipitation data, flow data, and water level data are input into the three-dimensional geological model as measured parameters. The permeability coefficient and hydraulic head parameters are iteratively adjusted to control the error between the simulated flow data and the measured flow data within a preset threshold range. When the simulation results match the measured data, the current parameter combination is recorded as the calibrated permeability coefficient dataset and hydraulic head parameter dataset. The calibrated parameter sets are integrated with the model's original structural parameters to form a calibrated three-dimensional geological model. Based on this model, dynamic simulations of rainwater infiltration, surface runoff, and groundwater recharge processes are performed, outputting transformation data containing spatiotemporal distribution characteristics.

[0056] In this embodiment, the proposed solution automates and standardizes the parameter optimization process through a dynamic matching mechanism between measured and simulated data, effectively reducing human error. Furthermore, the collaborative calibration of permeability coefficient and hydraulic head parameters more comprehensively reflects the spatial heterogeneity of hydrogeological conditions. Thus, this application can significantly improve the simulation accuracy of three-dimensional geological models for rainwater transformation processes in small mountain watersheds, providing a reliable data foundation for subsequent water resource forecasting and geological disaster early warning. The calibrated model parameter set can accurately characterize the permeability characteristics and hydraulic gradients of different geological units, enabling the transformation law data to reflect the complex interactions in actual hydrogeological conditions, such as the dynamic response relationship between rainfall infiltration and the water conductivity of fracture zones.

[0057] In one feasible implementation, the steps of analyzing the impact of water on slope stability and generating slope stability impact data based on the transformation law data include: extracting groundwater dynamic change data from the transformation law data; calculating the slope seepage pressure change value by combining topographic and geological data; assessing slope stability based on the seepage pressure change value, and generating slope stability impact data.

[0058] In this embodiment, groundwater dynamic change data refers to a dataset reflecting the changes in groundwater level, flow velocity, and flow direction over time, acquired through monitoring devices. Specifically, this can be achieved through continuous data collection using level gauges, flow meters, and temperature sensors, and is used to characterize the dynamic effects of groundwater on the slope's soil and rock mass. Slope seepage pressure change refers to the change in pore water pressure generated during the movement of groundwater within the slope. This can be calculated by establishing a groundwater seepage model and combining it with topographic slope and soil permeability coefficient parameters, and is used to quantify the degree of water's influence on slope stability. Slope stability assessment refers to judging the balance between the slope's anti-sliding force and sliding force based on seepage pressure change values. This can be achieved using the limit equilibrium method or numerical simulation methods, and is used to predict potential landslide risks.

[0059] In this embodiment, groundwater dynamic change data is extracted from transformation law data and combined with slope and rock layer distribution information from topographic and geological data. A seepage mechanics model is used to calculate the pore water pressure distribution at different time points. For example, under rainfall infiltration conditions, the rise in groundwater level leads to an increase in seepage pressure within the slope. Darcy's law combined with the permeability coefficient of the soil and rock mass can then be used to calculate the seepage pressure gradient. Based on the calculation results, the slope stability coefficient formula is further employed to dynamically evaluate seepage pressure as a component of the sliding force, ultimately outputting quantitative data reflecting the slope's safety status.

[0060] In some specific implementations, topographic and geological data may include digital elevation models and geological profiles, and permeability coefficients may be obtained through field pumping tests or laboratory permeability tests. For example, a slope seepage-stress coupling model can be established using finite element software, with groundwater dynamic data as the boundary condition input, to simulate the spatiotemporal evolution of seepage pressure inside the slope under different rainfall intensities, thereby assessing the trend of stability changes.

[0061] In this embodiment, the proposed method integrates dynamic hydrological parameters and topographic geological data from the transformation data to achieve quantitative calculation of seepage pressure changes. This calculation is directly linked to the slope stability assessment model, significantly improving the timeliness and accuracy of the analysis. It can precisely quantify the impact of dynamic groundwater changes on slope stability, solving the assessment bias problem caused by insufficient data timeliness in traditional methods. By incorporating seepage pressure changes into stability calculations, high-risk landslide areas can be identified more scientifically, providing reliable data support for geological disaster early warning.

[0062] In the embodiments of this application, the method for monitoring the transformation law of rainwater in small watersheds in mountainous areas systematically captures the transformation process of rainwater from the surface to the ground by integrating multi-source data, monitoring groundwater dynamics in layers, constructing a three-dimensional geological model and calibrating parameters. This solves the problems of incomplete monitoring and low model accuracy of traditional methods, and can systematically monitor the entire process of rainwater transformation, improve the accuracy of groundwater transport law analysis, and support water resource prediction and geological disaster early warning.

[0063] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the monitoring method for rainwater transformation patterns in small watersheds in mountainous areas. Any simple modifications based on this technical concept are within the scope of protection of this application.

[0064] This application also provides a monitoring system for rainwater transformation patterns in small watersheds in mountainous areas, referencing... Figure 2 The mountain small watershed rainwater transformation law monitoring system includes: a memory 10, a processor 20, and a mountain small watershed rainwater transformation law monitoring program stored on the memory 10 and executable on the processor 20. The mountain small watershed rainwater transformation law monitoring program is configured to implement the steps of the mountain small watershed rainwater transformation law monitoring method.

[0065] The mountainous small watershed rainwater transformation law monitoring system provided in this application, employing the mountainous small watershed rainwater transformation law monitoring method in the above embodiments, can improve the accuracy of groundwater transport law analysis and support water resource forecasting and geological disaster early warning. Compared with the prior art, the beneficial effects of the mountainous small watershed rainwater transformation law monitoring system provided in this application are the same as those of the mountainous small watershed rainwater transformation law monitoring method provided in the above embodiments, and other technical features of the mountainous small watershed rainwater transformation law monitoring system are the same as those disclosed in the above embodiments, and will not be repeated here.

[0066] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0067] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. All equivalent structural transformations made under the technical concept of this application using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included within the scope of patent protection of this application.

Claims

1. A method for monitoring the transformation patterns of rainwater in small watersheds in mountainous areas, characterized in that, The method includes: Collect and integrate meteorological data, hydrological data, land use data, topographic and geological data, water system data, and hydrogeological map data of the target area, delineate the boundaries of hydrological units in small watersheds in mountainous areas, and generate boundary data; Based on the boundary data, a hydrogeological survey is conducted within the defined mountainous small watershed hydrogeological unit area to divide secondary hydrogeological units, measure the watershed catchment area, underlying surface parameter characteristics, permeability coefficient and hydrogeological parameters, and generate hydrogeological parameter data. Based on the hydrological parameter data, monitoring devices are deployed within the secondary hydrogeological units to acquire precipitation data, flow data, water level data, and soil moisture content data, and then integrate them to generate comprehensive monitoring data. Based on the flow direction data, flow velocity data, water temperature data, and water level data in the comprehensive monitoring data, a flow field map of the underground horizontal surface is generated. Based on the underground horizontal flow field map, the location data of at least one precipitation funnel center is analyzed and determined, and the location data of the exploration borehole is generated according to the location data. Drilling is performed using the location data of the exploration hole to generate an observation hole; a tracer is introduced from a first predetermined position of the observation hole, and a vertical monitoring sensor is deployed at a second predetermined position of the observation hole to obtain groundwater vertical flow velocity data and tracer concentration data as vertical monitoring data; Based on the above-ground horizontal flow field diagram and the above-ground vertical monitoring data, the vertical migration path and migration velocity of groundwater are analyzed to generate groundwater vertical migration law data. Preliminary data on the development height of the water-conducting fracture zone is generated by direct observation through the observation hole; combined with the underground horizontal flow field map and the vertical monitoring data, the preliminary data on the development height of the water-conducting fracture zone is corrected by geostatistical methods to generate data on the development height of the water-conducting fracture zone. Based on the hydrological parameter data, the comprehensive monitoring data, the groundwater vertical transport data, and the water-conducting fracture zone development height data, a three-dimensional geological model of a small watershed in a mountainous area is constructed; precipitation data, flow data, and water level data are extracted from the comprehensive monitoring data and input into the three-dimensional geological model as measured parameters for model calibration, generating transformation law data; Based on the transformation law data, the impact of water on slope stability is analyzed, and slope stability impact data is generated. The transformation law data and the slope stability impact data can then be applied to water resource simulation and prediction and geological disaster early warning.

2. The method for monitoring the rainwater transformation pattern in small watersheds in mountainous areas as described in claim 1, characterized in that, Based on the aforementioned hydrological parameter data, the steps of deploying monitoring devices within secondary hydrogeological units, acquiring precipitation data, flow data, water level data, and soil moisture content data through these monitoring devices, and integrating them to generate comprehensive monitoring data include: Rain gauges were deployed within the secondary hydrogeological units to obtain precipitation data; Deploy flow monitoring stations at control sections to acquire flow data; Water level monitoring stations were deployed at the borehole locations to obtain water level data; Soil moisture content monitoring devices are deployed in the soil layer to obtain soil moisture content data; The precipitation data, flow data, water level data, and soil moisture content data are integrated to generate comprehensive monitoring data.

3. The method for monitoring the rainwater transformation pattern in small watersheds in mountainous areas as described in claim 2, characterized in that, The steps for deploying a water level monitoring station at the borehole location to obtain water level data include: Monitoring points were set up at the borehole locations, and flow direction data were continuously collected using groundwater monitoring instruments. Flow velocity data were collected at the same monitoring point using a flow meter. Temperature sensors are deployed at monitoring points to collect water temperature data, and water level data is collected using water level gauges. The flow direction data, the flow velocity data, the water temperature data, and the water level data are combined to form the water level data.

4. The method for monitoring the rainwater transformation pattern in small watersheds in mountainous areas as described in claim 1, characterized in that, The steps for generating a groundwater horizontal flow field map based on the flow direction data, flow velocity data, water temperature data, and water level data from the comprehensive monitoring data include: Extract water level data from the comprehensive monitoring data and generate a two-dimensional isobaric map through spatial interpolation; Based on the flow direction and velocity data, the groundwater vector intensity and direction at each monitoring point are calculated. The two-dimensional isostatic map and the groundwater vector distribution map are superimposed to form an initial flow field map; Based on the spatial distribution characteristics of the water temperature data, the hydraulic gradient anomaly area of ​​the initial flow field diagram is corrected; Output the corrected flow field diagram at the underground horizontal surface.

5. The method for monitoring the rainwater transformation pattern in small watersheds in mountainous areas as described in claim 1, characterized in that, The steps of drilling to generate an observation well based on the location data of the exploratory well; introducing a tracer from a first predetermined location in the observation well; and deploying a vertical monitoring sensor at a second predetermined location in the observation well to obtain groundwater vertical flow velocity data and tracer concentration data as vertical monitoring data include: Based on the analysis results of the underground horizontal flow field map, the location data of the tracer release point is determined as the first predetermined location, and the location data of the vertical monitoring point is determined as the second predetermined location. A tracer is introduced at a first predetermined position of the observation hole; A vertical velocity sensor is deployed at a second predetermined location along the vertical migration direction of groundwater to collect vertical velocity data; A concentration sensor is simultaneously deployed at a second predetermined location to monitor the arrival time and concentration change data of the tracer. The vertical flow velocity data and tracer concentration change data are combined to generate vertical monitoring data.

6. The method for monitoring the rainwater transformation pattern in small watersheds in mountainous areas as described in claim 1, characterized in that, Based on the aforementioned horizontal flow field map and the aforementioned vertical monitoring data, the steps for analyzing the vertical migration path and velocity of groundwater and generating groundwater vertical migration pattern data include: Extract the vertical flow velocity data and tracer concentration data from the vertical monitoring data; By combining the horizontal movement trend data in the underground horizontal flow field map, spatial overlay analysis is performed to generate transport path data; Based on the transport path data and vertical flow velocity data, the transport velocity data is calculated; By integrating the migration path data and migration velocity data, groundwater vertical migration pattern data are generated.

7. The method for monitoring the rainwater transformation pattern in small watersheds in mountainous areas as described in claim 1, characterized in that, The steps for generating preliminary data on the development height of the water-conducting fracture zone through direct observation via the observation well, and then refining the preliminary data using geostatistical methods based on the underground horizontal flow field map and the vertical monitoring data, include: Obtain the water level data and flow direction data from the underground horizontal flow field diagram; Combining the vertical monitoring data, three-dimensional spatial interpolation is performed using the Kriging interpolation algorithm to generate spatial distribution data of the development height of the water-conducting fracture zone; Based on the spatial distribution data, the maximum value is selected to generate the development height data of the water-conducting fracture zone.

8. The method for monitoring the rainwater transformation pattern in small watersheds in mountainous areas as described in claim 1, characterized in that, Based on the hydrological parameter data, the comprehensive monitoring data, the groundwater vertical transport data, and the water-conducting fracture zone development height data, a three-dimensional geological model of a small mountain watershed is constructed. The steps of extracting precipitation data, flow data, and water level data from the comprehensive monitoring data and inputting them into the three-dimensional geological model as measured parameters for model calibration, and generating transformation law data, include: Input the precipitation data, flow data, and water level data into the three-dimensional geological model; Adjust the permeability coefficient and hydraulic head parameters in the hydrological parameter data to match the simulated flow data output by the three-dimensional geological model with the measured flow data; A calibrated permeability coefficient dataset and a calibrated head parameter dataset are generated based on the matching results of simulated flow data and measured flow data; By integrating the calibrated permeability coefficient dataset, the calibrated hydraulic head parameter dataset, and the model structure parameters, a calibrated three-dimensional geological model is output. Based on the calibrated three-dimensional geological model, the rainwater transformation process is simulated to generate transformation law data.

9. The method for monitoring the rainwater transformation pattern in small watersheds in mountainous areas as described in claim 1, characterized in that, Based on the aforementioned transformation law data, the steps for analyzing the impact of water on slope stability and generating slope stability impact data include: Extract the dynamic change data of groundwater from the transformation pattern data; Calculate the change in slope seepage pressure by combining topographic and geological data; The slope stability is assessed based on the changes in seepage pressure, and slope stability impact data is generated.

10. A monitoring system for rainwater transformation patterns in small watersheds in mountainous areas, characterized in that, The mountainous small watershed rainwater transformation law monitoring system includes: a memory, a processor, and a mountainous small watershed rainwater transformation law monitoring program stored in the memory and executable on the processor. The mountainous small watershed rainwater transformation law monitoring program is configured to implement the steps of the mountainous small watershed rainwater transformation law monitoring method as described in any one of claims 1 to 9.

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