Method for monitoring seepage quantity of lake bottom of hot karst lake in permafrost region

Abstract

The application discloses a kind of permafrost area hot karst lake bottom leakage quantity monitoring method, belong to permafrost monitoring technical field, the method of the present application constructs temperature, pore water pressure and the synchronous monitoring system of lake level.Temperature data is used to identify the spatial range of seepage, and water level and pressure data are used to accurately determine the hydraulic gradient driving seepage.The method can effectively distinguish between lake water infiltration and groundwater recharge by analyzing the fine time response relationship between lake water level and pore water pressure changes.In addition, based on automatic monitoring and remote transmission, the system can continuously operate and dynamically update the key parameters such as thawed zone thickness and effective leakage area, and naturally output the time series results of leakage quantity.

Description

Technical Field

[0001] This invention relates to the field of permafrost monitoring technology, and in particular to a method for monitoring the seepage of the bottom of thermokarst lakes in permafrost regions. Background Technology

[0002] The Qinghai-Tibet Plateau, with its ecosystem forming a core component of the "mountains, rivers, forests, fields, lakes, grasslands, sand, and ice" community of life, holds a vast amount of organic carbon and plays a vital role in regulating global climate. Within this region, the widespread development of thermokarst lakes and their dynamic changes profoundly impact the plateau's water conservation capacity, carbon cycle processes, and high-altitude ecological security. Lakebed seepage, as a crucial link connecting surface water and groundwater and regulating permafrost thermal stability, is of particular research significance. Accurately quantifying this seepage process is not only fundamental to maintaining the sustainable use of water resources and ecological stability on the plateau, but also an urgent scientific need to assess the stability of the permafrost carbon pool and predict its climate feedback effects.

[0003] The seepage of water at the bottom of thermokarst lakes is essentially a hydraulic connection between the lake water and the groundwater beneath the underlying frozen layer. This process is highly dependent on unfrozen channels, or thaw zones, formed in the lakebed sediments due to freeze-thaw cycles. These channels are not static but expand and contract significantly with the seasons. In summer, rising temperatures cause the channels to expand, providing more pathways for water flow; in winter, the cold causes the channels to contract or even freeze, greatly restricting seepage. This dynamic change in channels with the seasons is central to understanding the hydrological processes in this region.

[0004] A complete calculation of groundwater seepage requires consideration of three fundamental elements: water source conditions, seepage channels, and seepage intensity. Most conventional hydrological calculations typically address only one or two of these elements. However, for thermokarst lakes, these three elements are subject to dramatic and interconnected changes in both time and space. Regarding water sources, lake levels fluctuate due to recharge; regarding channels, the thawing zone expands and contracts seasonally; and regarding intensity, the hydraulic gradient constantly adjusts due to frost heave and thaw settlement. This means that any reliable seepage calculation must dynamically capture the coordinated evolution of these three elements across both time and space. Existing mainstream methods for estimating lakebed seepage are typically based on the assumptions of stable seepage channels and invariant boundary conditions. When applied to thermokarst lakes, these methods face fundamental challenges. They cannot effectively characterize the seasonal evolution of thawing channels, distinguish false seepage signals caused by soil freeze-thaw deformation, or accurately reflect the true process of seepage intensity changes with channel variations. Therefore, traditional methods often exhibit significant errors in such highly dynamic environments, failing to meet the demands for precise and dynamic assessments.

[0005] To address the aforementioned challenges, a novel inversion calculation approach is urgently needed. This method should overcome the limitations of traditional static assumptions by integrating multi-source time-series monitoring data such as lakebed temperature, pore water pressure, and water level to dynamically track the spatiotemporal evolution of seepage channels, identify active areas where seepage actually occurs in real time, and simultaneously update hydraulic driving conditions. This allows for a synchronized understanding of the interconnected changes in water source, channel, and intensity, ultimately yielding more realistic and reliable estimates of lakebed seepage. Summary of the Invention

[0006] The purpose of this invention is to address the deficiencies in existing technologies by providing a method for monitoring the seepage of the bottom of thermokarst lakes in permafrost regions.

[0007] The technical solution adopted to achieve the purpose of this invention is: A method for monitoring the seepage of the bottom of a thermokarst lake in a permafrost region includes the following steps: Step 1: Obtain the environmental and geographical characteristics of thermokarst lakes in permafrost regions, the characteristics of thermokarst lakes, and the characteristics of permafrost surrounding the lakes. Step 2: Based on the characteristics of Step 1, the lake bottom sedimentary layer is divided into multiple layers at uniform intervals from top to bottom. A water pressure sensor and a temperature sensor are installed in each layer to form a monitoring matrix of water pressure sensor and temperature sensor. The pore water pressure and lake bottom temperature of each layer are monitored in real time. A water level sensor is installed above the lake surface to monitor the lake water level in real time. The thermal parameters and freezing threshold required for seepage inversion are obtained by testing the lake bottom soil samples. Step 3: Throughout the year, the layers where the lake bottom temperature is higher than the freezing threshold are considered stable melting zones. Within the stable melting zones, it is further determined whether each layer is an effective infiltration zone for lake water.

[0008] Step 4: Within the effective infiltration zone of the lake water, the dominant region of lake bottom seepage is identified by decomposing the lake bottom geothermal temperature. Within this dominant region, a lake bottom heat and mass transfer model is established. After calibration, the lake bottom temperature field of the thermokarst lake is obtained by using the environmental geographical features, lake bottom geothermal temperature, pore water pressure, and thermal parameters from relevant data as inputs. A pure heat conduction background temperature field of the lake bottom sediments is also constructed. If there is a deviation between the temperature of the thermokarst lake bottom temperature field and the temperature of the pure heat conduction background temperature field, the stratum corresponding to the temperature of the thermokarst lake bottom temperature field is the high permeability melting zone channel of the lake bottom. The total lake bottom seepage of the high permeability melting zone channel is then calculated.

[0009] In the above technical solution, in step 1, the environmental geographical features include information such as the location, landform, topography, vegetation, and meteorology of the thermokarst lake; the features of the thermokarst lake include the lake surface elevation, lake bottom topography, sediment seepage characteristics, and water level change data; the features of the permafrost around the lake include permafrost temperature, active layer thickness, upper limit of permafrost, and range of permafrost thawing channels.

[0010] In the above technical solution, step 4 involves decomposing the lakebed geothermal temperature using the following formula: ; in, For temperature, t For time, The rate of change of lakebed temperature per unit time; The effective thermal diffusivity of lakebed sediments; For the Laplace operator of the temperature field; The Darcy flow velocity vector in the lakebed sediments; For temperature gradient; The effective porosity of lakebed sediments; This is the term for heat conduction; This is the percolation heat transfer term.

[0011] In the above technical solution, when The time indicates that the temperature change of the lake bottom sediments in the region is caused by the convective heat transfer effect caused by seepage, and the region is judged to be a seepage-dominated area.

[0012] In the above technical solution, λ≥1.5.

[0013] In the above technical solution, the heat and mass transfer model at the bottom of the lake in step 4 is as follows: ; ; In the formula, For divergence operators, This refers to the seepage flow rate per unit area of ​​lakebed sediments. The average flow velocity in the pores is... For effective thermal conductivity, The density of water, The specific heat capacity of water, For effective volumetric heat capacity, convection term It reflects the disturbance characteristics of the temperature profile caused by the lake bottom seepage process.

[0014] In the above technical solution, the effective volumetric heat capacity is: ; in, For the volumetric heat capacity of the matrix, For latent heat of solidification, This is a function of ice content as a function of temperature.

[0015] In the above technical solution, the formula for calculating the seepage flow per unit area of ​​lakebed sediments in step 4 is as follows: ; ; ; In the formula, In depth z place t The function for determining temperature change over time. For depth z Place t Temperature at any moment K eq ( t This represents the combined equivalent permeability under freezing-thawing conditions and the influence of the development of non-freezing channels. This represents the head difference corresponding to the pore water pressure. To stabilize the change in effective infiltration thickness of the melting zone over time, Z1 and Z2 are the upper and lower bounds of the vertical analysis range of lakebed sediments. T f This is the freeze threshold.

[0016] In the above technical solution, based on the seepage rate per unit area of ​​lakebed sediments, the total seepage volume at the lakebed is calculated as follows: ; In the formula, Indicates at time t Total seepage from the bottom of thermokarst lakes in permafrost regions; The effective permeability area of ​​the lake bottom is obtained by spatial interpolation of monitoring data on the lake bottom temperature.

[0017] Compared with the prior art, the beneficial effects of the present invention are: 1. The method of this invention overcomes the fundamental difficulty in identifying seepage channels under severe freeze-thaw conditions. Existing technologies typically assume stable seepage conditions at the lake bottom, which does not match the actual situation of dramatic seasonal changes in the thawing zone of thermokarst lake bottoms, leading to distorted calculation results. This method, by deploying a temperature sensor matrix, directly monitors the freezing and thawing states of sediments, achieving dynamic delineation of unfrozen areas. Furthermore, by utilizing the convective heat transfer signals caused by water seepage, active seepage paths can be accurately distinguished from the background temperature field. This principle avoids misjudging seasonally frozen soil layers or strata deformation as seepage channels, ensuring that the inversion calculation is based on reflecting real physical processes, significantly improving the feasibility and reliability of its application in permafrost regions. 2. The method of this invention systematically improves estimation accuracy through the synergistic constraint of multi-source information. Traditional methods often rely on single hydrological parameters, such as calculations based solely on water level changes or the permeability coefficient at individual points, resulting in high uncertainty and susceptibility to interference. This method constructs a synchronous monitoring system for temperature, pore water pressure, and lake water level. Temperature data is used to identify the spatial range of seepage occurrence, while water level and pressure data together accurately determine the hydraulic gradient driving seepage. These different types of monitoring data mutually verify and constrain each other in time and space, effectively reducing calculation errors caused by local data anomalies or parameter assumption deviations, making the final estimated leakage volume more stable and accurate. 3. The method of this invention enables the identification of the directionality of hydrological processes and the monitoring of long-term dynamics. Most existing technologies can only provide the total amount of seepage, failing to clarify the actual direction of water flow and reflecting continuous changes. This method, by analyzing the precise time-response relationship between lake water level and pore water pressure changes, can effectively distinguish between two different water exchange processes: lake water infiltration and groundwater recharge. Furthermore, based on automated monitoring and remote transmission, the system can continuously operate and dynamically update key parameters such as melt zone thickness and effective seepage area, naturally outputting time-series results of seepage. This allows research to move beyond static assessments and reveal seasonal patterns and long-term trends, serving the construction of hydrological models and the study of the effects of climate change. 4. The method of this invention provides a complete technical solution from data acquisition to mechanism research, possessing clear scientific value and practical prospects. This solution is not an improvement on a single algorithm, but a systematic solution encompassing optimized deployment, reliable transmission, intelligent processing, and dynamic inversion. It specifically addresses the challenge of quantifying lakebed seepage in permafrost regions, and the high spatiotemporal resolution data it produces can provide crucial support for regional water resource management, wetland ecological assessment, and permafrost carbon cycle research. In practice, this method is easily integrated with engineering monitoring, and its modular design facilitates deployment. It can directly serve ecological barrier construction and national park management, and its core dynamic inversion concept also offers valuable insights for seepage monitoring in other fields. 5. The method of this invention improves the scientific rigor of channel boundary delineation and the accuracy of area calculation by using experimentally determined localized freezing thresholds as the criterion for thaw zone identification. Traditional methods often use zero degrees Celsius as a universal freeze-thaw boundary temperature. This simplification does not consider the specific influences of solutes in actual lake water, sediment pore water salinity, and pressure conditions on the phase transition point, potentially introducing systematic errors. This method, based on laboratory experiments on actual water and sediment samples from the study lake area, determines specific freezing temperature thresholds. In data processing, these measured values ​​are used as the standard for isotherm plotting and spatial delineation of thaw zone boundaries. This makes the identification of thaw zone channels more closely reflect the on-site geochemical and physical conditions, reducing boundary misjudgments caused by discrepancies between theoretical and actual phase transition temperatures. Consequently, the final calculated dynamic seepage area is more accurate and reliable, enhancing the physical authenticity of the entire inversion calculation model and the credibility of the results. Attached Figure Description

[0018] Figure 1 This is a schematic diagram showing the distribution of thermokarst lakes, permafrost, and thaw zones.

[0019] Figure 2 This is a seasonal variation map of the channel in the hot karst lake melting zone, where a represents the cold season and b represents the warm season.

[0020] Figure 3 This is a schematic diagram illustrating the evolution of the area of ​​the through-regional melting zone from the cold season to the warm season, where a is... Figure 2 In the diagram, a represents section AA, b represents the area of ​​the through-flow thawing channel from the cold season to the warm season, c represents the area of ​​the through-flow thawing channel from the cold season to the warm season, and d represents... Figure 2 AA section of b in the diagram.

[0021] Figure 4 A cross-sectional view of the monitoring hole layout.

[0022] Figure 5 This is a vertical distribution diagram of the temperature sensor and the water pressure sensor.

[0023] Figure 6 A schematic diagram depicting the boundary of the through-flow melting zone for isotherms.

[0024] Figure 7 This is a schematic diagram of the process of the present invention.

[0025] Figure 8 This is a schematic diagram of the data acquisition and transmission framework of the present invention. Detailed Implementation

[0026] The present invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention.

[0027] like Figure 7 As shown, a method for monitoring the seepage of the bottom of a thermokarst lake in a permafrost region includes the following steps: Step 1: Obtain relevant data on thermokarst lakes in permafrost regions, including environmental and geographical features, thermokarst lake characteristics, and permafrost characteristics surrounding the lakes. The environmental and geographical features include information on the location, landforms, topography, vegetation, and meteorology of the thermokarst lakes. The thermokarst lake characteristics include lake surface elevation, lakebed topography, sediment seepage characteristics, and water level change data. The permafrost characteristics surrounding the lakes include permafrost temperature, active layer thickness, upper limit of permafrost, and the range of permafrost thawing channels.

[0028] like Figures 4-5 As shown, in step 2, based on the morphology of the thermokarst lake and the distribution of freeze-thaw sensitive areas, a monitoring plan is formulated (lake bottom water-heat joint monitoring system, lake bottom temperature and seepage, and water level measurement point layout). A lake bottom seepage monitoring profile and sensor layout at observation points are established to cover potential thawing channels and key seepage areas. Based on environmental and geographical characteristics, multiple temperature measurement holes and one pore water pressure monitoring hole are drilled vertically downwards at the most stable core location of the lake bottom thawing zone. The depth of both the temperature measurement hole and the pore water pressure monitoring hole extends from the lake bottom down to the lower limit of permafrost. (This ensures that the deep monitoring holes can completely cover the maximum possible distribution range of the thermokarst lake bottom thawing zone in the warm season (usually summer and autumn) throughout the year, ensuring that no matter how the thawing zone expands with the seasons, the hole location remains within the continuously unfrozen thawing channel, providing a comprehensive data foundation for subsequent delineation of the thawing zone boundary. This data is used for calculation...) The vertical hydraulic gradient is an indispensable basis for calculating and determining the direction of seepage, and its continuity is key to ensuring the reliability of the inversion calculation. The temperature measurement well and pore water pressure monitoring well are divided into multiple layers at uniform intervals from top to bottom. A temperature sensor is deployed at each layer of the temperature measurement well, forming a temperature sensor monitoring matrix to monitor the lake bottom temperature in real time. Similarly, a water pressure sensor is deployed at each layer of the pore water pressure monitoring well, forming a row of water pressure sensors to monitor the pore water pressure at each layer in real time. A water level sensor is installed above the lake surface to monitor the lake water level in real time, forming a coordinated "temperature-pressure-water level" observation system. Finally, continuous monitoring is achieved through an automatic acquisition system, and the data is transmitted in real time to a data platform, providing high temporal resolution input for dynamic inversion. Furthermore, by testing lake bottom soil samples, the hydrophysical and thermal parameters and freezing threshold of the lake bottom soil samples required for seepage inversion are obtained. T f The thermal parameters include sediment porosity, permeability coefficient, specific heat, and thermal conductivity, providing input for the calculation of lake bottom heat and mass transfer models. Freezing threshold. T fThis refers to the temperature threshold at which pore water in lakebed sediments undergoes a phase transition from liquid to solid. It is used to determine whether the sediments are in a frozen or unfrozen state and is a key criterion for identifying unfrozen channels and effective permeable layers in thermokarst lakes. It is determined by analyzing the inflection points in temperature changes during freezing or thawing processes in the lakebed sedimentary layer over time. These inflection points correspond to the latent heat release or absorption stage during the phase transition of pore water, reflecting the critical temperature at which the sediments transition from an unfrozen to a frozen state or vice versa. In the presence of solutes, the freezing threshold can be adjusted based on the solute content of the pore water.

[0029] Step 3: Throughout the year, the layers where the lake bottom temperature is higher than the freezing threshold are considered stable melting zones. Within the stable melting zones, it is further determined whether each layer is an effective lake water infiltration zone. If the time lag between the lake water level change monitored in Step 2 and the pore water pressure change, the layer corresponding to the pore water pressure change is determined to be an effective lake water infiltration zone.

[0030] Step 4: Within the effective infiltration zone of the lake water, the dominant region of lake bottom seepage is identified by decomposing the lake bottom geothermal temperature. Within this dominant region, a lake bottom heat and mass transfer model is established. The model is calibrated using the monitored lake bottom geothermal temperature. After calibration, the lake bottom temperature field of the thermokarst lake is obtained using environmental geographical features, lake bottom geothermal temperature, pore water pressure, and thermal parameters as inputs. A pure heat conduction background temperature field of lake bottom sediments is constructed under the condition of ignoring the seepage convection term. If there is a deviation between the temperature of the thermokarst lake bottom temperature field and the temperature of the pure heat conduction background temperature field, the stratum corresponding to the temperature of the thermokarst lake bottom temperature field is the high permeability melting zone channel of the lake bottom. The total lake bottom seepage of the high permeability melting zone channel is calculated.

[0031] Furthermore, the lakebed geothermal temperature is decomposed using the following formula: ; in, For temperature, t For time, The rate of change of lakebed temperature per unit time. The effective thermal diffusivity of lakebed sediments reflects their thermal conductivity under frozen or thawed conditions. Its value depends on the sediment type, water content, and freezing state. is the Laplace operator for the temperature field, used to describe the spatial variation characteristics of temperature caused by heat conduction; This is the Darcy velocity vector in lakebed sediments, used to characterize the direction and intensity of water seepage in sediment pores; The temperature gradient is used to describe the direction and rate of temperature change in space. For percolation heat transfer; The effective porosity of lakebed sediments is used to characterize the proportion of pore space involved in seepage and heat exchange.

[0032] When the following conditions are met: ; This indicates that the temperature change of the lake bottom sediments in the region is caused by the convective heat transfer effect caused by seepage. Therefore, it can be determined that the region is a seepage-dominated area. Here, λ is a dimensionless discrimination threshold coefficient used to adjust the sensitivity of the judgment between the convective heat transfer effect and the heat conduction effect. Preferably, λ≥1.5.

[0033] Furthermore, the heat and mass transfer model at the lake bottom: The governing equations for the lakebed seepage field are established as follows: The sedimentary layer at the bottom of the thermokarst lake is considered as a saturated porous medium. The seepage process at the bottom of the lake satisfies Darcy's law, and the seepage flux is expressed as: ; The formula for calculating the effective penetration thickness of the stable melting zone is as follows: ; ; In the formula, In depth z place t The function for determining temperature change over time. For depth z Place t The temperature of the moment, when > Freeze threshold T f At that time, If it is 1, ≤Frozen Threshold T f At that time, =0, This refers to the seepage flow rate per unit area of ​​lakebed sediments. To account for the dynamic permeability coefficient caused by freeze-thaw cycles, Due to head difference, Z ( t Z1 and Z2 represent the changes in the effective permeation thickness of the temperature melting zone over time, and the upper and lower bounds of the vertical analysis range of lakebed sediments.

[0034] The average flow velocity in the pores is: ; In the formula, for t The average pore velocity at time t. The effective porosity of the lakebed sediments is represented by this seepage field, which serves as the dynamic basis for subsequent hydrothermal coupling calculations.

[0035] Construction of the lakebed heat transfer equation coupled with convection heat transport term: The temperature field of the lakebed sedimentary layer is jointly controlled by heat conduction and seepage heat transport, and its energy conservation equation is as follows: ; in, It is a divergence operator used to calculate the total flux change (heat diffusion) of heat flux density in space. Temperature gradient, For effective thermal conductivity, and These are the density and specific heat capacity of water, respectively. For effective volumetric heat capacity, convection term It reflects the disturbance characteristics of the temperature profile caused by the lake bottom seepage process.

[0036] Introduction of latent heat effect of freeze-thaw phase change and expression of effective heat capacity: Considering the buffering effect of water-ice phase change at the freeze-thaw interface of the lake bottom on heat transfer, the effective volumetric heat capacity is expressed by the apparent heat capacity method: ; in, For the volumetric heat capacity of the matrix, For latent heat of solidification, This is a function of ice content as a function of temperature. Introducing this term can characterize the regulatory effect of latent heat release at the freeze-thaw interface on the seepage heat-carrying process.

[0037] Furthermore, the total seepage at different depths of the lakebed is defined as: ;

[0038] In the formula, Indicates at time Total seepage from the bottom of a thermokarst lake in the sub-permafrost region, expressed as volumetric flow rate, is used to characterize the overall intensity of lake water seeping downwards through the lake bottom or groundwater replenishing the lake. To show the change in effective leakage area over time, The effective permeability area of ​​the lake bottom is obtained by spatial interpolation of monitoring data on the lake bottom temperature. This represents the area of ​​a small element on the lake bottom, used for spatial integration of the local seepage flux within the effective seepage area of ​​the lake bottom.

[0039] like Figure 6 As shown, by spatial interpolating the matrix data of lakebed geothermal temperature (using Kriging interpolation as an option), layers at different times and depths equal to the freezing threshold are dynamically plotted. The isotherms are used to determine the real-time boundary and area of ​​the stable melting zone; and by combining pore water pressure with the water level of the thermokarst lake, the real-time hydraulic gradient driving the seepage can be calculated. This deployment scheme takes into account both the comprehensiveness of spatial coverage and the continuity and economy of key parameter measurement, laying a solid data foundation for the subsequent dynamic and accurate inversion of leakage.

[0040] Furthermore, such as Figure 8 As shown, data is collected from temperature sensors, water pressure sensors, and water level sensors via a data acquisition module. Specifically, the data acquisition module includes a power supply unit, an acquisition unit, and a storage unit. This embodiment is a complete system integrating on-site sensing, automatic acquisition, remote transmission, and cloud computing. The power supply unit provides continuous power to the equipment throughout the field site. The acquisition unit synchronously acquires and converts the signals from each sensor at a high frequency (e.g., once per minute). The storage unit acts as a buffer to ensure data is not lost during communication interruptions. This design ensures the complete acquisition of continuous sequences of key parameters during seasonal freeze-thaw cycles.

[0041] After being collected and digitized, the data enters the data transmission layer and is transmitted to the cloud. This layer, centered on the DTU module, is responsible for remotely and transparently transmitting the collected data to a remote server center via a widely covered 4G or 5G public wireless network. This step enables real-time connection between the monitoring site and the data processing center, allowing researchers to remotely monitor the dynamics of the lake area thousands of miles away.

[0042] All data transmitted to the cloud is ultimately received, processed, and further applied by the server. The server first writes massive amounts of raw time-series data (such as minute-by-minute temperature, pressure, and water level records) into a dedicated time-series database. This database efficiently stores and manages time-series data. Subsequently, the server extracts the necessary data from the database and automatically performs a series of core calculations, including leakage channel identification, hydraulic gradient calculation, and seepage flow inversion. Finally, the system outputs the calculation results through a visual interface or reports, completing the automated production process from raw data to scientific conclusions.

[0043] The above description is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for monitoring the seepage volume of a thermokarst lakebed in a permafrost region, characterized in that, Includes the following steps: Step 1: Obtain the environmental and geographical characteristics of thermokarst lakes in permafrost regions, the characteristics of thermokarst lakes, and the characteristics of permafrost surrounding the lakes. Step 2: Based on the characteristics of Step 1, the lake bottom sedimentary layer is divided into multiple layers at uniform intervals from top to bottom. A water pressure sensor and a temperature sensor are installed in each layer to form a monitoring matrix of water pressure sensor and temperature sensor. The pore water pressure and lake bottom temperature of each layer are monitored in real time. A water level sensor is installed above the lake surface to monitor the lake water level in real time. The thermal parameters and freezing threshold required for seepage inversion are obtained by testing the lake bottom soil samples. Step 3: Throughout the year, the layers where the lake bottom temperature is higher than the freezing threshold are considered stable melting zones. Within the stable melting zones, it is further determined whether each layer is an effective infiltration zone for lake water. Step 4: Within the effective infiltration zone of the lake water, the dominant region of lake bottom seepage is identified by decomposing the lake bottom geothermal temperature. Within this dominant region, a lake bottom heat and mass transfer model is established. After calibration, the lake bottom temperature field of the thermokarst lake is obtained using environmental geographical features, lake bottom geothermal temperature, pore water pressure, and thermal parameters as inputs. A pure heat conduction background temperature field of the lake bottom sediments is also constructed. If there is a deviation between the temperature of the thermokarst lake bottom temperature field and the temperature of the pure heat conduction background temperature field, the stratum corresponding to the temperature of the thermokarst lake bottom temperature field is the high permeability melting zone channel of the lake bottom. The total lake bottom seepage of the high permeability melting zone channel is then calculated.

2. The monitoring method according to claim 1, characterized in that, In step 1, the environmental geographical features include the location, landform, topography, vegetation, and meteorology of the thermokarst lake; the features of the thermokarst lake include the lake surface elevation, lake bottom topography, sediment seepage characteristics, and water level change data; and the features of the permafrost around the lake include permafrost temperature, active layer thickness, upper limit of permafrost, and range of permafrost thawing channels.

3. The monitoring method according to claim 1, characterized in that, In step 4, the lakebed temperature is decomposed using the following formula: ； in, T The temperature of the lakebed t For time, The rate of change of lakebed temperature per unit time; The effective thermal diffusivity of lakebed sediments; For the Laplace operator of the temperature field; The Darcy flow velocity vector in the lakebed sediments; For temperature gradient; The effective porosity of lakebed sediments; This is the term for heat conduction; This is the percolation heat transfer term.

4. The monitoring method according to claim 3, characterized in that, when The time indicates that the temperature change of the lake bottom sediments in the region is caused by the convective heat transfer effect caused by seepage, and the region is judged to be a seepage-dominated area.

5. The monitoring method according to claim 4, characterized in that, λ≥1.

5.

6. The monitoring method according to claim 1, characterized in that, In step 4, the heat and mass transfer model at the bottom of the lake is as follows: ； ； In the formula, For divergence operators, This refers to the seepage flow rate per unit area of ​​lakebed sediments. The average flow velocity in the pores is... For effective thermal conductivity, The density of water, The specific heat capacity of water, For effective volumetric heat capacity, convection term It reflects the disturbance characteristics of the temperature profile caused by the lake bottom seepage process.

7. The monitoring method according to claim 6, characterized in that, Effective volumetric heat capacity: ； in, For the volumetric heat capacity of the matrix, For latent heat of solidification, This is a function of ice content as a function of temperature.

8. The monitoring method according to claim 1, characterized in that, In step 4, a pure thermal conduction background temperature field of lakebed sediments is constructed under the condition of ignoring seepage convection terms.

9. The monitoring method according to claim 6, characterized in that, In step 4, the formula for calculating the seepage flow per unit area of ​​lakebed sediments is as follows: ； ； ； In the formula, In depth z place t The function for determining temperature change over time. For depth z Place t Temperature at any moment K eq ( t This represents the combined equivalent permeability under freezing-thawing conditions and the influence of the development of non-freezing channels. This represents the head difference corresponding to the pore water pressure. To stabilize the change in the effective penetration thickness of the melting zone over time, Z 1, Z 2 represents the upper and lower boundaries of the vertical analysis range for lakebed sediments. T f This is the freeze threshold.

10. The monitoring method according to claim 9, characterized in that, Based on the seepage rate per unit area of ​​lakebed sediments, the total seepage rate at the lakebed is calculated as follows: ； In the formula, Indicates at time Total seepage from the bottom of thermokarst lakes in permafrost regions; To show the change in effective leakage area over time, The effective permeability area of ​​the lake bottom is obtained by spatial interpolation of monitoring data on the lake bottom temperature.

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