An evaluation method for regulating and storage function of thermokarst lake in alpine permafrost region

Abstract

The present application relates to the technical field of hydrology, and discloses a kind of high-cold frozen soil area hot melt lake pond's regulating function evaluation method, comprising: obtaining the multiple monitoring data of target permafrost region basin, after correction and uniform time processing, effective rainfall event and its event window division are carried out;In event window, calculate the change amount of soil storage, the change amount of groundwater storage, hot melt lake pond storage change amount;Calculate the normalized water storage efficiency index;Get hysteresis curve, peak time lag, hysteresis loop area, dynamic connectivity index, statistical connectivity index;According to hysteresis curve, judge the function mode of hot melt lake pond, divide the rainfall threshold of hot melt lake pond function conversion;Calculate comprehensive regulating index, carry out regulating capacity analysis, realize the regulating function evaluation of hot melt lake pond;The present application can not only finely identify the water storage, water supply and regulation behavior of hot melt lake pond under the event scale, but also accurately evaluate the dynamic hydrological function of hot melt lake pond.

Description

Technical Field

[0001] This invention relates to the field of hydrology, specifically to a method for assessing the regulation and storage function of thermocrystallized lakes and ponds in high-altitude permafrost regions. Background Technology

[0002] The Qinghai-Tibet Plateau's permafrost region is widely characterized by thawed lakes and ponds, which are rapidly developing and undergoing morphological adjustments driven by climate warming. These lakes and ponds significantly impact regional hydrological cycles, hydrothermal processes in the active permafrost layer, and watershed runoff generation and distribution mechanisms. Existing technologies related to thawed lakes and ponds mainly include: automatic identification methods for thawed lake and pond areas based on remote sensing imagery; lake basin evolution simulation methods based on thermodynamics and topographic evolution; water balance calculation techniques; and lake ecological effect assessment methods. These methods are primarily used to characterize the long-term changes or static attributes of thawed lakes and ponds, enabling macroscopic analysis of lake surface area, lake basin evolution, and lake water recharge sources. However, due to the unique conditions of permafrost regions, such as significant freeze-thaw seasonality, well-developed permafrost groundwater, and high soil moisture content, the hydrological connectivity between thawed lakes and ponds, soil, groundwater, and river water bodies exhibits significant temporal variability and spatial heterogeneity. This results in complex and dynamic runoff generation and distribution paths, response times, and water storage and drainage behaviors during rainfall events. From the perspective of watershed hydrological process analysis, clarifying the roles of thermokeratomes in flood peak reduction, runoff replenishment, and short-term water storage during rainfall events is crucial for understanding the hydrological cycle changes and regulatory capacity in permafrost regions. However, existing technologies mainly focus on lake morphology changes, thermodynamic processes, or long-term water budget analysis, failing to accurately obtain the response characteristics and storage changes of various hydrological components during watershed runoff generation and confluence processes. They also cannot accurately identify and classify the water storage, supply, and regulation functions of thermokeratomes, ultimately resulting in an inability to accurately quantify and assess the dynamic regulation function of thermokeratomes in high-altitude permafrost regions at the event scale. Summary of the Invention

[0003] To address the aforementioned shortcomings in existing technologies, this invention provides a method for evaluating the storage capacity of thermocrystallized lakes in high-altitude permafrost regions. This method solves the problem that existing technologies cannot accurately quantify and evaluate the dynamic storage capacity of thermocrystallized lakes in high-altitude permafrost regions at an event scale.

[0004] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows:

[0005] A method for assessing the water storage capacity of thermocrystallized lakes and ponds in high-altitude permafrost regions includes the following steps:

[0006] S1. Select a target permafrost watershed that includes thermally melted lakes and ponds, and construct a comprehensive monitoring system for the target permafrost watershed by deploying monitoring equipment in order to obtain various types of monitoring data for the target permafrost watershed.

[0007] S2. After correcting and unifying the time of multiple types of monitoring data, effective rainfall events and their event windows are divided.

[0008] S3. Within the event window of each effective rainfall event, calculate the changes in soil storage, groundwater storage, and thermal melting lake and pond storage.

[0009] S4. Calculate the normalized water storage efficiency index based on the changes in soil storage, groundwater storage, and thermal melting lake storage.

[0010] S5. Within the event window of each effective rainfall event, construct a hysteresis diagnosis system for the thermal melting lake and pond regulation function to obtain hysteresis curves, peak time delay, hysteresis loop area, dynamic connectivity index, and statistical connectivity index.

[0011] S6. Based on the hysteresis curve, determine the functional mode of the thermal melting lake and pond, and classify the rainfall threshold for the functional transformation of the thermal melting lake and pond, so as to determine the functional event mode to which the effective rainfall event belongs.

[0012] S7. Calculate the comprehensive regulation index based on the normalized water storage efficiency index, hysteresis loop area, and peak time delay to conduct regulation capacity analysis and ultimately assess the regulation function of the thermocline lakes in the target permafrost watershed.

[0013] The present invention has the following beneficial effects:

[0014] 1. The present invention proposes a method for evaluating the regulation and storage function of thermo-melt lakes and ponds in high-altitude permafrost regions. This method enables precise identification of the water storage, supply, and regulation and storage behavior of thermo-melt lakes and ponds at the event scale. By calculating the changes in soil storage, groundwater storage, and thermo-melt lake storage, it accurately describes the water storage or recharge behavior of different hydrological components during effective rainfall events. This method can accurately evaluate and finely characterize the dynamic hydrological function of thermo-melt lakes and ponds.

[0015] 2. This invention also provides a quantifiable diagnostic index system for the regulation and storage of thermal fusion lakes and ponds. By calculating the normalized water storage efficiency, hysteresis loop area, peak time delay and river-lake connectivity index, a stable and repeatable diagnostic index system for regulation and storage is constructed. This system can be used to determine the peak-shaving capacity of thermal fusion lakes and ponds and their dominant role in effective rainfall events, thereby improving the physical clarity and quantitative reliability of the identification of thermal fusion lake and pond regulation and storage.

[0016] 3. This invention is also applicable to thermal thaw lakes and ponds of different sizes and shapes, and has good scalability. The method proposed in this invention has a clear structure and clear data requirements. It can be directly applied in permafrost areas with basic monitoring conditions, or simplified by using alternative data. It has good engineering applicability and regional portability. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a method for evaluating the regulation and storage function of thermocrystallized lakes and ponds in high-altitude permafrost regions, as proposed in this invention. Detailed Implementation

[0018] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0019] like Figure 1 As shown, a method for evaluating the water storage capacity of thermocrystallized lakes and ponds in high-altitude permafrost regions includes the following steps S1-S7:

[0020] S1. Select a target permafrost watershed that includes thermally melted lakes and ponds, and construct a comprehensive monitoring system for the target permafrost watershed by deploying monitoring equipment, so as to obtain various types of monitoring data of the target permafrost watershed.

[0021] Specifically, step S1 includes S11-S16:

[0022] S11. Select a target permafrost watershed that includes thermally melted lakes and ponds, and set up meteorological monitoring units, soil heat monitoring units, groundwater monitoring units, thermally melted lake and pond water level monitoring units, and river monitoring units to construct a comprehensive observation system for the target watershed.

[0023] In this embodiment, firstly, in permafrost regions, watersheds with clearly defined hydrological boundaries and a continuous distribution of thawed lakes, slopes, and rivers are selected as the target permafrost watershed. This watershed should simultaneously include thawed lakes, hydrological control sections, and soil and groundwater observation points to cover the main hydrological processes within the influence range of the thawed lakes. The target permafrost watershed is typically an alpine meadow or alpine grassland covered area, with a watershed area generally around 1 km². 2 -20km 2 The time scale can be adjusted according to actual needs; in terms of time scale, this invention is based on continuous monitoring data of 15min-1h, and is applicable to single rainfall events (hours-several days) and annual scale water balance analysis; for areas with only daily scale data, it can be simplified without changing the basic principle, but the accuracy of rainfall event identification and response time calculation will be reduced accordingly; in addition, this invention assumes that the lake surface area changes little within a single rainfall event, and is suitable for shallow thermomelt lakes with a water depth of generally less than 2m and a relatively stable planar morphology.

[0024] Secondly, observation equipment will be deployed, including meteorological monitoring units, soil thermal monitoring units, groundwater monitoring units, thermal melting lake and pond water level monitoring units, and river monitoring units, to construct a comprehensive observation system for the permafrost watershed. This system will be used to collect various types of monitoring data from the target permafrost watershed, as detailed below:

[0025] S12. Using meteorological monitoring units, obtain meteorological data of the target permafrost basin, including precipitation, air temperature, wind speed, relative humidity, and radiation.

[0026] In this embodiment, the meteorological monitoring unit is deployed as follows: Automatic weather stations are set up in locations within the watershed that are open, have a surface type consistent with the main body of the watershed, and are far from local obstructions. These locations must be able to reflect the average meteorological conditions of the watershed. The area covered by this invention is typically 1 km². 2 -20km 2 For small watersheds analyzing rainfall events on timescales ranging from hours to several days, high spatiotemporal resolution and watershed representative meteorological data are required. However, existing public station data in high-altitude permafrost regions are insufficient in terms of resolution and station density to meet this requirement. Therefore, this meteorological monitoring unit should continuously record precipitation. (mm), air temperature (°C), wind speed Basic meteorological elements such as (m / s) are included; in addition, to improve the accuracy of water balance calculations, observations of relative humidity (or water vapor pressure) and radiation (shortwave, longwave, or net radiation) can be added to support the estimation of potential evapotranspiration using the FAO's Penman-Monteith method. ; while actual evaporation Then through the formula It can be obtained through calculation, where The crop coefficient, which characterizes the comprehensive impedance effect of the underlying surface in high-altitude permafrost regions, was determined based on long-term observations and regression analysis of lysimeter data.

[0027] S13. Using a soil thermal monitoring unit, obtain the soil volumetric water content at different depths in the target permafrost watershed.

[0028] In this embodiment, the soil thermal monitoring unit is deployed as follows: soil profile monitoring points are set up on the slope or shore near the thermomelting lake, and soil temperature, humidity, and salinity sensors are buried at different depths. These sensors simultaneously measure parameters such as soil volumetric water content and temperature, and this invention primarily uses the output soil volumetric water content data. Preferably, the sensors are buried at depths of 20cm, 40cm, 80cm, and 120cm (if the active permafrost layer is thick, depths can be increased to 160cm, 200cm, etc.). The soil volumetric water content at different depths is denoted as... (%),in These are soil layers (such as layers 1-4). For a moment.

[0029] S14. Using the groundwater monitoring unit, obtain the groundwater level in the groundwater monitoring well of the target permafrost watershed.

[0030] In this embodiment, the groundwater monitoring unit is deployed as follows: groundwater monitoring wells are installed in the upstream recharge area of ​​the thermomelt lake, the potential interaction area near the lake shore, and the watershed outlet or downstream discharge area. The locations should meet the conditions of relatively flat terrain, convenient drilling construction, and the ability to reflect the three main hydrological processes of upstream recharge, lake shore exchange, and downstream discharge of the thermomelt lake. A water level recorder is installed in the groundwater monitoring well to continuously record the groundwater depth or elevation, i.e., the groundwater level. (m).

[0031] S15. Use the thermal melting lake and pond water level monitoring unit to obtain the lake water level in the target permafrost area.

[0032] In this embodiment, the thermal melting lake / pond water level monitoring unit is configured as follows: automatic water level gauges are installed in the thermal melting lake / pond to continuously record the lake water level elevation. (m), and determine the lake area through drone operations or remote sensing. (m) 2 ).

[0033] S16. Use river monitoring units to obtain river water levels in the target permafrost basin.

[0034] In this embodiment, the river monitoring unit is deployed as follows: water level gauges are installed at the main channel or outlet section of the watershed to continuously record the river water level. (m), and the sampling interval is preferably 15 min.

[0035] In addition, to convert the water level process into a flow rate process, multiple measured flow rates were conducted at this cross-section under different inflow conditions to obtain a series of corresponding water level-flow rate discrete points. , , They represent the first The measured river water level and cross-sectional flow rate were used as the basis for obtaining an empirical water level-flow rate curve by fitting the curve using the least squares method, such as by using a power function.

[0036]

[0037] Or in polynomial form:

[0038]

[0039] in, sectional flow rate (m³)3 / s), This is the measured river water level. To calculate the starting water level, , , , , All represent fitting coefficients, determined by fitting measured river water level-discharge data;

[0040] Therefore, by utilizing the aforementioned water level-discharge relationship, a continuous water level sequence can be obtained. Convert to continuous flow process This can provide input for subsequent event-scale water balance calculations and hysteresis analysis.

[0041] S2. After correcting and unifying the time of multiple types of monitoring data, effective rainfall events and their event windows are divided.

[0042] Specifically, step S2 includes S21-S23:

[0043] S21. Perform unified time base correction, outlier removal, linear interpolation, and noise smoothing on multiple types of monitoring data to generate a continuous time series.

[0044] S22. Unify the continuous time series to the same time step to generate time series monitoring data.

[0045] In this embodiment, steps S21-S22 involve performing unified time base correction on all monitoring data and generating a continuous and physically reasonable time series through methods such as anomaly removal, linear interpolation, and noise smoothing. Subsequently, all data are unified to the same time step, such as 15 min or 1 h, to ensure comparability and consistency between different data sources during the calculation process.

[0046] S23. Screen the daily rainfall in the time series monitoring data, and determine the rainfall that is greater than or equal to the set threshold as a valid rainfall event. At the same time, obtain the occurrence time of each valid rainfall event and the time when each hydrological component recovers to the pre-rain background level. Use the occurrence time as the start time of the event window of the valid rainfall event, and use the time when each hydrological component recovers to the pre-rain background level as the end time of the event window of the valid rainfall event. Finally, obtain each valid rainfall event and its event window.

[0047] In this embodiment, based on daily precipitation, precipitation ≥1mm is defined as an effective rainfall event, and each effective rainfall event and the subsequent dry spell are considered as a complete event window, with the start of the event window being taken as the rainfall occurrence time. The endpoint is the time it takes for each hydrological component to recover to the pre-rain background level. The event window includes both the water storage phase (rainfall period) and the release phase (post-rainfall period), ensuring that the entire watershed runoff generation and confluence process is expressed within a single window.

[0048] S3. Within the event window of each effective rainfall event, calculate the changes in soil storage, groundwater storage, and thermal melting lake / pond storage.

[0049] In this embodiment, this step calculates the storage changes of the three types of water storage components using a uniform volume or water depth scale. ,in The specific process of collecting soil, groundwater, and thermally melted lakes and ponds is as follows:

[0050] 1. Changes in soil storage

[0051] The soil profile was divided into several layers, each with a thickness of [missing information]. (mm), volumetric water content is Within the event window, the change in soil storage is calculated using the difference in moisture content between the start and end of the event:

[0052]

[0053] in, This represents the change in soil storage (mm), reflecting the increase or loss of soil water during the event. Indicates the number of soil layers. , They represent the first The soil layer at the start of the event window With the finish line Volumetric water content (%) Indicates the first The thickness of the soil layer (mm).

[0054] 2. Changes in groundwater reserves

[0055] Changes in groundwater storage are calculated by multiplying the change in water level by the specific yield (specific output); assuming the change in groundwater level in a certain monitoring well is... water supply degree is ,but:

[0056]

[0057]

[0058] in, This indicates the change in groundwater storage (mm). This indicates the change in groundwater level (mm) at a certain groundwater monitoring well. The water supply specificity is a dimensionless value determined based on experiments or experience. , These represent the start and end times of a groundwater monitoring well within an event window. With the finish line The groundwater level.

[0059] Furthermore, in the presence of multiple groundwater monitoring points, to obtain the change in groundwater storage at the watershed scale, a weighted average is calculated based on the area of ​​the Thiessen polygon corresponding to the groundwater monitoring well. Methods such as tributary area, spatial distribution representativeness, or inverse distance weighting can also be used depending on the actual situation. The final result is a weighted average change in groundwater storage, i.e.:

[0060]

[0061] in, This represents the weighted average change in groundwater storage, which is the overall change in groundwater storage in the target permafrost region's watershed. Indicates the number of groundwater monitoring wells. Indicates the first The weight of each groundwater monitoring well, Indicates the first Changes in groundwater storage (mm) of each groundwater monitoring well.

[0062] 3. Changes in the reserves of thermally melted lakes and ponds

[0063]

[0064]

[0065] in, This represents the change in lake water volume, specifically the change in the volume of thermally melted lakes and ponds (m³). 2 ), This indicates the change in lake water level (m). The lake's surface area, , These represent the starting time of the event window, respectively. With the finish line Lake water level (m) 2 ).

[0066] Furthermore, if it is necessary to convert the changes in thermal meltwater reservoir storage into water depth at the watershed scale, the following formula can be used for conversion:

[0067]

[0068] in, The change in thermal lacustrine storage (in water depth form, mm) after being converted to the entire watershed area is used to maintain comparability with changes in soil and groundwater storage. The watershed area is (m²).

[0069] In this step, the changes in soil storage and groundwater storage already represent the entire watershed. However, since the thermal melting lakes are only a part of the watershed, their storage needs to be calculated by subtracting the watershed area to arrive at the average water depth of the watershed. This represents the change in thermal lacustrine reserves after being converted to the entire watershed area.

[0070] S4. Calculate the normalized water storage efficiency index based on the changes in soil storage, groundwater storage, and thermal melting lake storage.

[0071] In this embodiment, to compare water storage capacity under different effective rainfall events, the present invention constructs a normalized water storage efficiency index to measure the change in storage of each hydrological component formed by a unit of rainfall; for any hydrological component (Soil, groundwater, and thermally melted lakes and ponds), their normalized water storage efficiency index is defined as:

[0072]

[0073] in, This indicates hydrological components, including soil, groundwater, and thermocline lakes and ponds. This indicates the change in the reserves of hydrological components. Indicates hydrological components The normalized water storage efficiency index (which can be positive or negative) reflects the proportion of rainfall per unit event that is temporarily stored by that component. This indicates the rainfall (mm) of a valid rainfall event within the event window, which can be the total rainfall of the valid rainfall event or the sum of the rainfall of the valid rainfall events accumulated over the days.

[0074] And when This indicates that the hydrological component has a net water storage capacity during the event, when This indicates the net water loss (water supply) of this hydrological component during the event; it is achieved by comparing the normalized water storage efficiency index of the soil. Normalized water storage efficiency index of groundwater Normalized water storage efficiency index of thermal melting lakes and ponds It can determine the main hydrological roles played by soil, groundwater, and thermomelt lakes and ponds in effective rainfall events.

[0075] S5. Within the event window of each effective rainfall event, construct a hysteresis diagnosis system for the thermal melting lake and pond regulation function to obtain hysteresis curves, peak time delay, hysteresis loop area, dynamic connectivity index, and statistical connectivity index.

[0076] In this embodiment, this step involves constructing a hysteresis diagnostic system for the thermal melting lake's water storage function, and its operation process is as follows:

[0077] 1. Response time and peak delay calculation

[0078] For each effective rainfall event, the response time parameters were statistically analyzed, including the time when the lake water level reached its peak. Time when groundwater level reaches its peak And the time when the river water level or flow reaches its peak. ;

[0079] Calculate the peak time delay of rivers and thermomelt lakes Peak time lag of rivers and groundwater ,Right now:

[0080] .

[0081] .

[0082] Among them, peak time delay Used to reflect the degree to which thermally melted lakes and ponds delay or advance river flood peaks; peak lag. It is used to reflect the lag of groundwater response to river flood peaks and its buffering effect on river runoff during the event.

[0083] 2. Hysteresis Curve Construction

[0084] Using river water level (or standard river water level) as the x-axis and the change in lake water level or groundwater level as the y-axis, and connecting the data points at each moment within each effective rainfall event in chronological order, hysteresis curves of rivers and thermomelt lakes or groundwater are generated; and these hysteresis curves are closed or semi-closed curves; secondly, the direction (clockwise or counterclockwise) and area (hysteresis loop area) of the hysteresis curves can serve as important bases for subsequent determination of the functional type of thermomelt lakes.

[0085] 3. Calculation of hysteresis loop area

[0086] The area enclosed by the hysteresis curve is calculated using the polygonal shoelace formula; assuming the hysteresis curve is composed of curves arranged in chronological order... Points Composition, and The formula for calculating the area of ​​the hysteresis loop is:

[0087]

[0088] in, This represents the hysteresis loop area, reflecting the strength of the coupling process between the thermal melting lake / pond and the river, or between groundwater and the river. This represents the total number of moments within the event window that constitute a valid rainfall event. This indicates the river water level at time 1. Indicates time The river water level (m). Indicates time The river water level, Indicates time The river water level, This indicates the lake water level or groundwater level at time 1. Indicates time The lake water level or groundwater level (m). Indicates time The lake water level or groundwater level, Indicates time The water level of the lake or the groundwater level.

[0089] 4. Constructing a river and lake connectivity index

[0090] Based on the calculation of peak time delay and hysteresis relationship, this invention further introduces the Connectivity Index (CI) to characterize the degree of synchronization between the changes in water level (or flow) of thermomelt lakes and rivers, which is an important supplementary indicator for determining the degree of participation of thermomelt lakes in the runoff generation and confluence process.

[0091] The river and lake connectivity index is defined in two ways depending on the purpose of the analysis:

[0092] (1) Dynamic connectivity index based on water level change ratio

[0093] Used to describe the relative relationship between the increase in lake water level and the increase in river water level during an event, it is defined as follows:

[0094]

[0095] in, Represents the dynamic connectivity index. This indicates the change in lake water level (in meters) within the event window. Indicates the change in river water level (m) within the event window;

[0096] And when When the water level of a thermomelt lake or pond responds to rainfall events more strongly than that of a river, it usually indicates that the thermomelt lake or pond plays a significant role in water storage or energy buffering during such events; when This indicates that rivers are more sensitive to direct responses to rainfall events, while thermomelting lakes and ponds are more in a state of passive replenishment or weak regulation.

[0097] (2) Statistical connectivity index based on correlation

[0098] The linear co-response used to characterize the time series of lake water levels and river water levels is defined as:

[0099]

[0100] in, This represents the statistical connectivity index, also known as the correlation coefficient. Represents the covariance function. Indicates the time within the event window The lake water level, Indicates the time within the event window The river water level, , represent the standard deviations of the lake water level and the river water level, respectively, with values ​​ranging from [-1, 1];

[0101] and This indicates that the responses are almost synchronous and the connectivity is strong; This indicates that the response is random or weakly correlated; This indicates a situation where there may be a significant delayed response in thermally melted lakes and ponds, and usually indicates storage regulation behavior.

[0102] In addition, the two connectivity indices mentioned above can be used individually or in combination as needed, serving as key technical indicators for supplementing hysteresis information, enhancing storage capacity, and determining stability.

[0103] S6. Based on the hysteresis curve, determine the functional mode of the thermal melting lake and pond, and classify the rainfall threshold for the functional transformation of the thermal melting lake and pond, so as to determine the functional event mode to which the effective rainfall event belongs.

[0104] In this embodiment, the process of determining the storage-supply conversion threshold of a thermomelt lake involves classifying and identifying the hydrological function of the thermomelt lake after calculating the changes in storage volume, normalized water storage efficiency, and hysteresis parameters across multiple effective rainfall events. The operation process is as follows:

[0105] 1. Functional Pattern Recognition

[0106] Based on the hysteresis curve morphology of rivers and thermokergoes, the functional modes are divided into:

[0107] When the hysteresis curve closes counterclockwise and the thermal melting lake continuously replenishes the river after rainfall, it is determined that the thermal melting lake mainly operates in a water supply mode, releasing water primarily to downstream river channels and groundwater systems.

[0108] When the hysteresis curve closes clockwise and the lake level rises and weakens the peak change of the river in the short term, it is determined that the thermomelting lake is mainly in the regulation and storage mode and plays a role in peak shaving and temporary storage in the event.

[0109] In addition, the functional mode also involves a hybrid mode, characterized by a partially closed hysteresis curve or an indistinct direction, and the lake water both significantly increases in storage during the event and releases water into rivers or groundwater in the later stages, so that the change in lake water storage at the end of the event is close to zero or only slightly increases or decreases, indicating that the thermomelting lake has a certain function of water storage and supply during the event.

[0110] 2. Rainfall threshold identification

[0111] Statistical analysis of rainfall amount in each effective rainfall event and the corresponding thermal melting lake and pond functional modes, based on rainfall The x-axis represents the area of ​​the hysteresis loop along the direction of the hysteresis curve. Plot a scatter plot with the vertical axis as the ordinate; where the storage and regulation mode is dominant, A negative value indicates that the hysteresis curve is clockwise; when the supply-side model is dominant, A positive value indicates that the hysteresis curve is counterclockwise;

[0112] Based on the scatter plot, linear regression is performed using the least squares method to generate the area of ​​the hysteresis loop in the direction of the hysteresis curve. With rainfall The fitting relationship is as follows:

[0113]

[0114] in, All are fitting coefficients;

[0115] Based on the fitting coefficients, the area of ​​the hysteresis loop in the direction of the hysteresis curve is defined. The rainfall threshold corresponding to the functional mode conversion of the thermal melting lake pond when the value is 0 is expressed as:

[0116]

[0117] in, The rainfall threshold for the functional mode transformation of thermal fusion lakes and ponds is generally represented by the rainfall threshold for the transformation of thermal fusion lakes and ponds from water supply-oriented to water storage-oriented.

[0118] Meanwhile, to characterize the sample dispersion, the rainfall threshold can be calculated based on the regression residuals and parameter standard errors. The confidence interval is typically set at a 90% or 95% confidence level; and the rainfall threshold... The dimension is mm, used to characterize the range of event-scale rainfall that triggers the dominant behavior of lakes and ponds in typical watersheds of permafrost regions.

[0119] Furthermore, this invention can also perform automatic functional identification of thermal fusion lakes and ponds based on multi-event clustering. Specifically, to further improve the objectivity and universality of thermal fusion lake and pond functional identification, this invention introduces a multi-event clustering method on top of the aforementioned functional mode determination step, automatically classifying the feature vectors of multiple valid rainfall events. Through clustering, the event types of thermal fusion lakes and ponds can be automatically identified without the need for manual threshold setting, achieving semi-automation and standardization of the functional diagnosis process. The operation process is as follows:

[0120] (1) Constructing the event feature vector matrix

[0121] For each effective rainfall event The key indicators are combined into a feature vector:

[0122]

[0123]

[0124] in, Indicates the first The event feature vector matrix of a single effective rainfall event. The normalized water storage efficiency index represents the thermal melting lake pond. The normalized water storage efficiency index represents the efficiency of groundwater storage. This indicates the change in the lake's water storage. This represents the change in thermal lacustrine reserves after being factored into the entire watershed area. This represents the area of ​​the hysteresis loop in the direction of the hysteresis curve, when When the value is positive, it indicates a counter-clockwise direction; when... A negative value indicates a clockwise direction. Indicates the peak time lag of rivers and thermomelt lakes / ponds. Represents the dynamic connectivity index. This represents the statistical connectivity index.

[0125] In this step, the thermally melted lake is considered part of the watershed, and its storage capacity needs to be calculated by subtracting the watershed area from the watershed area to convert it to the average watershed depth. However, in practice, only a vector is needed to represent the changes in the lake water itself, so either volume or equivalent water depth can be used. The only difference between the two is a constant conversion between the watershed area and the unit, i.e., using the formula... The parameters can be set. Convert to parameters Furthermore, considering the numerical magnitude, due to the parameters Numerical values ​​are relatively small, similar in magnitude to other vectors, making standardization easier and allowing for parameter selection. .

[0126] Furthermore, the feature vector can have its dimensions increased or decreased according to the monitoring conditions, but its basic structure remains consistent.

[0127] (2) Use clustering method to automatically identify the functional type of thermal melting lakes and ponds

[0128] To improve the objectivity of identifying the functions of thermally melted lakes and ponds, this invention uses an unsupervised learning method based on an event feature matrix to automatically identify the hydrological function type of each valid rainfall event. Clustering methods can include K-means, hierarchical clustering, DBSCAN, and other unsupervised learning methods. In this embodiment, K-means clustering is used for automatic event classification.

[0129] 1) Construction of feature matrix

[0130] The above has arranged the feature vectors of all events to form an event feature matrix. To avoid weight bias caused by different units of measurement, the feature matrix needs to be standardized to make each dimension comparable.

[0131] 2) Number of clusters Setting

[0132] The K-means method requires pre-setting the number of clusters. Theoretically, the thermal melting lake and pond event functions of this invention are divided into three categories: water supply type, water storage type, and hybrid type. Therefore, in this embodiment, The value is 3.

[0133] 3) Clustering Iterative Process

[0134] The K-means clustering method classifies events through iterative steps; the first step is the initialization step, which randomly selects events from all events. The event is used as the initial cluster center; then the Euclidean distance from each event vector to each cluster center is calculated, and the event is assigned to the nearest center. For each cluster, the mean of all event feature vectors is calculated, and the mean is used as the new cluster center. Finally, the process is iteratively updated and the previous step is repeated until the cluster centers converge or the maximum number of iterations is reached, and finally three stable event clusters are obtained.

[0135] 4) Interpretation and functional determination of clustering results

[0136] For the three types of event clusters obtained, the mean vector of each cluster in the feature space is calculated, and the hydrological function type of the cluster is determined based on its index characteristics:

[0137] If the event cluster as a whole behaves as , If the hysteresis is counterclockwise, or the CI is low or negative, it is considered a water supply event.

[0138] If the event cluster as a whole behaves as , hysteresis clockwise, If CI is close to 1, it is determined to be a storage-type event;

[0139] If the indicator falls between the two, showing neither significant water storage nor obvious water supply characteristics, it is judged as a mixed event.

[0140] 5) Output Results

[0141] The clustering results can also output the percentage of events, which can be used to evaluate the dominant functional characteristics of thermomelting lakes and ponds throughout the observation period; if necessary, indicators such as the silhouette coefficient can also be calculated to evaluate the clustering effect.

[0142] (3) Use the clustering results to back-calculate the threshold conversion function

[0143] To reduce the uncertainty in single-event discrimination, this invention further uses the results of cluster analysis for stabilization processing of threshold identification; after completing event clustering, the rainfall amount corresponding to each type of valid rainfall event is... Rainfall sequences for water supply, water storage, and mixed events were extracted separately. The rainfall for each category was then sorted from smallest to largest and verified against the functional mode hierarchy structure defined earlier. As the event category transitioned from water supply to water storage, the rainfall in the transition interval was compared with the data obtained in the previous steps. The results should be basically consistent; if there is overlap in the distribution of events, the difference in rainfall corresponding to the cluster center should be used as the basis for correction, so as to obtain a smoother and more noise-resistant threshold result than the simple statistical method.

[0144] It should be noted that the clustering back-inference threshold here is essentially a calculation of the statistical threshold. The enhancements and validations were performed without defining new thresholds. Associating event functional categories with rainfall distribution helps improve the stability, applicability, and robustness to anomalous events in threshold identification.

[0145] S7. Calculate the comprehensive regulation index based on the normalized water storage efficiency index, hysteresis loop area, and peak time delay to conduct regulation capacity analysis and ultimately assess the regulation function of the thermocline lakes in the target permafrost watershed.

[0146] In this embodiment, the process of outputting the evaluation index system for the regulation and storage capacity of thermally melted lakes and ponds, in order to evaluate the regulation and storage function of thermally melted lakes and ponds, is as follows:

[0147] 1. Event Scale Indicators

[0148] Normalized water storage efficiency of thermal melting lakes Normalized efficiency of groundwater Normalized water storage efficiency of soil Peak time delay Peak time delay Hysteresis loop area Its relationship with rainfall and the frequency of occurrence of each functional mode (water supply / storage).

[0149] 2. Annual or seasonal scale indicators

[0150] Components of annual or seasonal water balance: Total annual precipitation Annual runoff depth Annual actual evapotranspiration Annual change in reserves It is used to determine the overall water balance of the watershed where the thermal fusion lake is located; the proportion of water storage and supply in the thermal fusion lake in different seasons reflects the regulation and storage characteristics of the thermal fusion lake in the warm / cold season.

[0151] 3. Classification of storage capacity

[0152] It should be noted that the event function classification (water supply type, storage type, and mixed type) in this invention is for identifying the response process of a single effective rainfall event, while the storage capacity classification here is a comprehensive long-term evaluation of the overall capacity of thermogravimetric lakes and ponds based on the results of multiple events. The two have different functions and complement each other.

[0153] To facilitate comparisons between different thermal meltwater lakes, a tiered system for water storage capacity is constructed based on the statistical distribution of relevant indicators; the specific process is as follows:

[0154] First, the indicators are standardized to obtain dimensionless indicators; based on this, a comprehensive water storage index is constructed, namely:

[0155]

[0156]

[0157] in, Indicates the comprehensive water storage index. , , All represent weighting coefficients, used to reflect the relative importance of each indicator in the comprehensive evaluation. This invention adopts equal weights, that is... , , , These represent the normalized water storage efficiency index of the thermomelt pond, the normalized hysteresis loop area of ​​the hysteresis trajectory curves of the river and the thermomelt pond, and the normalized peak time lag of the river and the thermomelt pond, respectively.

[0158] Furthermore, the comprehensive regulation and storage index reflects the overall regulation and storage capacity of the thermomelting lakes and ponds during multiple effective rainfall events.

[0159] Secondly, statistical analysis was conducted on the comprehensive water storage index values ​​obtained from all effective rainfall events or the entire year's monitoring period. The threshold for water storage capacity levels was set using the quantile method. Specifically, the first quantile of 33% and the second quantile of 66% were taken as the classification points: when the comprehensive water storage index exceeds the second quantile (i.e., >66%), it is judged as a lake or pond with high water storage capacity; when the comprehensive water storage index is between the two quantiles (i.e., ≥33% and ≤66%), it is judged as a lake or pond with medium water storage capacity; when the comprehensive water storage index is lower than the lower quantile (i.e., <33%), it is judged as a lake or pond with weak water storage capacity or mainly used for water supply.

[0160] Furthermore, the grading results can also be physically interpreted in conjunction with the characteristics of the indicators, specifically: when Larger Positive and When the capacity is relatively large, corresponding to a significant thermogravimetric regulation process and strong peak-shaving capacity, it is judged as having high regulation capacity; when all indicators are at a medium level, it is judged as having medium regulation capacity; when... When the value is small or negative, the hysteresis area is small, and the water supply mode dominates in multiple events, it indicates that the thermomelting lake is mainly for replenishment and has a weak regulation and storage function.

[0161] Therefore, through the above steps, this invention forms a complete method for assessing the regulation and storage function of thermocline lakes and ponds in permafrost regions. It realizes the whole-process assessment from multi-source monitoring data collection, event classification, storage calculation, hysteresis diagnosis to functional pattern recognition and hierarchical evaluation of regulation and storage capacity, and can provide quantitative basis for hydrological process simulation, ecological protection and engineering planning in permafrost regions.

[0162] To verify the effectiveness of the proposed method for evaluating the storage capacity of thermocrystallized lakes in high-altitude permafrost regions, the following experiments were conducted:

[0163] Taking a single effective rainfall event of 12.6 mm in the Hela Village watershed in the permafrost region of the Qinghai-Tibet Plateau as an example, including:

[0164] 1. Construct a comprehensive observation system and acquire raw data.

[0165] Automatic weather stations, lake level monitoring points, groundwater monitoring wells, soil profile monitoring points, and river control sections were deployed within the target permafrost region.

[0166] Meteorological data such as rainfall, temperature, and wind speed were collected at 15-minute intervals; lake water level... River water level and groundwater level Record soil volumetric water content at 15-minute intervals. The lake surface area was simultaneously acquired using sensors buried at depths of 20, 40, 80, and 120 cm respectively. River flow was determined using drone imagery. It was calculated based on the water level-discharge relationship curve.

[0167] 2. Perform data preprocessing and divide the event window for effective rainfall events.

[0168] According to meteorological monitoring data, rainfall began at 14:00 on July 18, 2025, with a total precipitation of 12.6 mm; the event division method of this invention was used to determine the event start time. The event ended at 14:00 on July 18th; all hydrological elements basically returned to pre-rain levels by 14:00 on July 20th, therefore the event ended at [time missing]. At this point, the event window length is approximately 48 hours; all monitoring data undergoes outlier removal, time synchronization, interpolation, and unified time step processing before calculation.

[0169] 3. Calculate the changes in soil, groundwater, and thermal lacustrine reserves.

[0170] Based on the hydrological response within the event window, the following changes in storage volume were obtained:

[0171] Changes in soil water storage Based on the depth defined above, the event start time The soil volumetric water content was 24.0%, 29.5%, 30.6%, and 39.1%, respectively, and the event ended at [time missing]. The soil volumetric water content was 25.3%, 30.4%, 31.1%, and 39.2%, respectively. The calculated values ​​were... mm, the calculation process is as follows:

[0172] The depths of the sensors in each layer are 20cm, 40cm, 80cm, and 120cm, corresponding to the soil layer thicknesses:

[0173] First layer (0cm-20cm):

[0174] Second layer (20cm-40cm):

[0175] Third layer (40cm-80cm):

[0176] Fourth layer (120cm-80cm):

[0177] Changes in volumetric water content of each layer:

[0178] First layer: 25.3% - 24.0% = 1.3%

[0179] Second layer: 30.4% - 29.5% = 0.9%

[0180] Third layer: 31.1% - 30.6% = 0.5%

[0181] Fourth layer: 39.2% - 39.1% = 0.1%

[0182] Substituting into the formula for calculating changes in soil storage, we get:

[0183]

[0184] Changes in groundwater storage The groundwater level rose from 0.66m to 0.78m, therefore the change in groundwater level... The value is taken as 0.12m, and the feedwater specificity is taken as... Calculations yielded mm.

[0185] Changes in thermal melting pond reserves The lake water level rose from 0.34m to 0.52m, therefore... m, lake surface area m 2 drainage area m 2 , converted to The calculation process is as follows:

[0186]

[0187] If the unit is mm, it will be rounded to one decimal place; if the unit is m, it will be rounded to two decimal places.

[0188] 4. Calculate the normalized water storage efficiency index

[0189] The effective rainfall event is the amount of rainfall. =12.6mm, then: the normalized water storage efficiency index of the soil: =0.54 ( (To two decimal places); Normalized water storage efficiency index of groundwater: =0.67 ( (Rounded to two decimal places); Normalized water storage efficiency index of thermally melted lakes and ponds: =0.19 ( (To two decimal places); the results indicate that groundwater storage contributed the most to this event.

[0190] 5. Hysteresis diagnosis and connectivity index calculation

[0191] (1) Peak delay

[0192] River water levels peaked approximately 5 hours after the event began; lake water levels peaked at 8.5 hours; therefore: This indicates that the river responded before the thermal melting of the lakes and ponds.

[0193] (2) Hysteresis curve and hysteresis loop area

[0194] According to the lake water level With river water level , For a moment The lake water level, For a moment The river water levels are shown in Table 1. A hysteresis curve is constructed, which closes clockwise. The hysteresis loop area is calculated using the shoelace formula. (Keep three decimal places).

[0195] Table 1. Lake and River Water Levels During the Event

[0196]

[0197] (3) River and lake connectivity index

[0198] If the river water level rises from 0.77m to 1.21m, then... m, the dynamic connectivity index: Based on the data in Table 1, using The statistical connectivity index (correlation coefficient) can then be calculated: This indicates that the thermomelting lake has a synchronous yet lagging response to changes in river water levels, which is a type of regulation behavior.

[0199] 6. Functional pattern recognition and threshold determination of thermally melted lakes and ponds

[0200] Considering the hysteresis direction, water storage efficiency, and connectivity indicators, this effective rainfall event was characterized by significant water storage in thermomelted lakes and ponds. ), and has a peak flow reduction and delay effect on the river ( The hysteresis curve is clockwise, therefore it is classified as a storage-type event. In this embodiment, there are 60 valid rainfall event samples, and the hysteresis loop area in the direction of the hysteresis curve is calculated for each event. The amount of rainfall in effective rainfall events The x-axis represents the area of ​​the hysteresis loop along the direction of the hysteresis curve. A scatter plot was created for the ordinate, and linear regression was performed using the least squares method to obtain... The relation, in which , .make The rainfall threshold for the functional transformation of lakes and ponds can then be calculated. .

[0201] To characterize the statistical uncertainty of this threshold, the covariance matrix of the regression parameters and the error propagation method were calculated. standard error At a 90% confidence level, The confidence interval is 5.3 mm–8.9 mm; at a 95% confidence level, the confidence interval is approximately 5.0 mm–9.2 mm. Therefore, it can be considered that the typical rainfall threshold for the transformation of the thermogravimetric lake function in this watershed from water supply-oriented to regulation-oriented is approximately 7.09 mm, with a reasonable variation range of approximately 5 mm–9 mm.

[0202] 7. Output the assessment results of the storage capacity of this effective rainfall event.

[0203] Based on the rating system of this invention, the calculation result of the effective rainfall event here is: the water storage efficiency of the thermal melting lake is moderate ( The peak time lag between rivers and thermomelt lakes / ponds is significant. ), with a large hysteresis area ( ), strong connectivity ( Therefore, the thermo-melting lakes in this effective rainfall event can be rated as thermo-melting lakes with medium to high regulation and storage capacity, exhibiting obvious peak shaving, delay and water storage characteristics.

[0204] In summary, the method for evaluating the storage capacity of thermocrystallized lakes and ponds in high-altitude permafrost regions proposed in this invention achieves the following technical effects:

[0205] 1. It achieved precise identification of the water storage, supply, and regulation behavior of thermally melted lakes and ponds at the event scale, namely:

[0206] For the first time, an integrated observation and analysis framework for thermo-melted lakes, soil, groundwater, and rivers was constructed in a permafrost region, using changes in soil storage as a key indicator. Changes in groundwater storage Changes in the reserves of thermally melted lakes and ponds The quantitative calculation can accurately describe the water storage or replenishment behavior of different hydrological components during rainfall events, and achieve precise characterization of the dynamic hydrological function of thermomelt lakes and ponds.

[0207] 2. A quantifiable diagnostic index system for the regulation and storage of thermal meltwater lakes and ponds is provided.

[0208] By normalizing water storage efficiency, hysteresis loop area, peak time delay, and river-lake connectivity index, a stable and repeatable regulation and storage diagnostic index system was constructed. This system can be used to determine the peak-shaving capacity of thermogravimetric lakes and ponds and their dominant role in events, thereby improving the physical clarity and quantitative reliability of regulation and storage identification.

[0209] 3. Clustering methods are also introduced to achieve automated identification of the functions of thermal melting lakes and ponds.

[0210] By performing cluster analysis on the feature vectors of multiple events, the function of thermomelt lakes and ponds can be classified into water supply type, storage type or mixed type. This method does not rely on artificial threshold settings and can adapt to different climates, permafrost environments and lake basin conditions, significantly improving the generalization ability and stability of the method.

[0211] 4. Capable of identifying the rainfall threshold at which thermal thawing ponds switch from water supply mode to water storage mode.

[0212] Through multi-event statistics, this invention automatically extracts the rainfall threshold for the storage-supply conversion of thermally melted lakes and ponds. It can serve as an important criterion for rainfall response in permafrost regions, runoff generation and confluence characteristics of watersheds, and the regulation capacity of thermomelting lakes and ponds, providing a scientific basis for hydrological simulation, ecological protection, and engineering layout.

[0213] 5. Applicable to thermal fusion ponds of different sizes and shapes, with good scalability.

[0214] The method proposed in this invention has a clear structure and well-defined data requirements. It can be directly applied in permafrost regions with basic monitoring conditions, or its application can be simplified by using alternative data (such as reanalysis products or remote sensing). It has good engineering applicability and regional portability.

[0215] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

[0216] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A method for evaluating the water storage capacity of thermocrystallized lakes and ponds in high-altitude permafrost regions, characterized in that, Includes the following steps: S1. Select a target permafrost watershed that includes thermally melted lakes and ponds, and construct a comprehensive monitoring system for the target permafrost watershed by deploying monitoring equipment in order to obtain various types of monitoring data for the target permafrost watershed. S2. After correcting and unifying the time of multiple types of monitoring data, effective rainfall events and their event windows are divided. S3. Within the event window of each effective rainfall event, calculate the changes in soil storage, groundwater storage, and thermal melting lake and pond storage. S4. Calculate the normalized water storage efficiency index based on the changes in soil storage, groundwater storage, and thermal melting lake storage. S5. Within the event window of each effective rainfall event, construct a hysteresis diagnostic system for the thermal melting lake and pond regulation function to obtain hysteresis curves, peak time lags, hysteresis loop areas, dynamic connectivity indices, and statistical connectivity indices, specifically: S51. Statistically analyze the response time parameters of each effective rainfall event, including the time when the lake water level reaches its peak. Time when groundwater level reaches its peak And the time when the river water level or flow reaches its peak. Calculate the peak time delay of rivers and thermomelt lakes. Peak time lag of rivers and groundwater ,Right now: ； ； S52. Using river water level as the horizontal axis and the change in lake water level or groundwater level as the vertical axis, and connecting the data points at each moment within each effective rainfall event according to the time sequence, generate hysteresis curves for rivers and thermomelt lakes or groundwater. S53. Based on the hysteresis curve, calculate the area of ​​the hysteresis loop, i.e.: in, Indicates the area of ​​the hysteresis loop. This represents the total number of moments within the event window that constitute a valid rainfall event. This indicates the river water level at time 1. Indicates time The river water level, Indicates time The river water level, Indicates time The river water level, This indicates the lake water level or groundwater level at time 1. Indicates time The lake water level or groundwater level, Indicates time The lake water level or groundwater level, Indicates time The water level of the lake or the groundwater level; S54. Calculate the dynamic connectivity index based on the changes in lake and river water levels, i.e.: in, Represents the dynamic connectivity index. This indicates the change in lake water level. This indicates the change in river water level within the event window; S55. Conduct a correlation analysis between lake water levels and river water levels, and calculate the statistical connectivity index, i.e.: in, Represents the statistical connectivity index. Represents the covariance function. Indicates the time within the event window The lake water level, Indicates the time within the event window The river water level, , These represent the standard deviations of lake water levels and river water levels, respectively. S6. Based on the hysteresis curve, determine the functional mode of the thermal melting lake and pond, and classify the rainfall threshold for the functional transformation of the thermal melting lake and pond, so as to determine the functional event mode to which the effective rainfall event belongs. S7. Calculate the comprehensive regulation index based on the normalized water storage efficiency index, hysteresis loop area, and peak time delay to conduct regulation capacity analysis and ultimately assess the regulation function of the thermocline lakes in the target permafrost watershed.

2. The method for evaluating the regulation and storage function of thermocrystallized lakes and ponds in high-altitude permafrost regions according to claim 1, characterized in that, Step S1 specifically includes: S11. Select a target permafrost watershed that includes thermally melted lakes and ponds, and set up meteorological monitoring units, soil heat monitoring units, groundwater monitoring units, thermally melted lake and pond water level monitoring units, and river monitoring units to construct a comprehensive observation system for the target watershed. S12. Using meteorological monitoring units, obtain meteorological data of the target permafrost basin, including precipitation, air temperature, wind speed, relative humidity, and radiation. S13. Using a soil thermal monitoring unit, obtain the soil volumetric water content at different depths in the target permafrost watershed. S14. Using groundwater monitoring units, obtain the groundwater level in the groundwater monitoring wells of the target permafrost watershed; S15. Use the thermal melting lake and pond water level monitoring unit to obtain the lake water level in the target permafrost area. S16. Use river monitoring units to obtain river water levels in the target permafrost basin.

3. The method for evaluating the regulation and storage function of thermocrystallized lakes and ponds in high-altitude permafrost regions according to claim 1, characterized in that, Step S2 specifically includes: S21. Perform unified time base correction, outlier removal, linear interpolation, and noise smoothing on multiple types of monitoring data to generate a continuous time series. S22. Unify continuous time series to the same time step to generate time series monitoring data; S23. Screen the daily rainfall in the time series monitoring data, and determine the rainfall that is greater than or equal to the set threshold as a valid rainfall event. At the same time, obtain the occurrence time of each valid rainfall event and the time when each hydrological component recovers to the pre-rain background level. Use the occurrence time as the start time of the event window of the valid rainfall event, and use the time when each hydrological component recovers to the pre-rain background level as the end time of the event window of the valid rainfall event. Finally, obtain each valid rainfall event and its event window.

4. The method for evaluating the regulation and storage function of thermocrystallized lakes and ponds in high-altitude permafrost regions according to claim 1, characterized in that, The formulas for calculating the changes in soil storage, groundwater storage, and thermal thaw lake / pond storage are as follows: in, This indicates the change in soil storage. Indicates the number of soil layers. , They represent the first The soil layer at the start of the event window With the finish line Volumetric water content; Indicates the first The thickness of the soil layer, This indicates the change in groundwater storage. This indicates the change in groundwater level at a certain groundwater monitoring well. Indicates water supply degree. , These represent the start and end times of a groundwater monitoring well within an event window. With the finish line groundwater level This indicates the change in lake water volume, specifically the change in the volume of thermally melted lakes and ponds. This indicates the change in lake water level. The lake's surface area, , These represent the starting time of the event window, respectively. With the finish line The lake's water level.

5. The method for evaluating the regulation and storage function of thermocrystallized lakes and ponds in high-altitude permafrost regions according to claim 1, characterized in that, The formula for calculating the normalized water storage efficiency index is: in, This indicates hydrological components, including soil, groundwater, and thermocline lakes and ponds. This indicates the change in the reserves of hydrological components. Indicates hydrological components The normalized water storage efficiency index, This indicates the amount of rainfall during the effective rainfall event within the event window.

6. The method for evaluating the regulation and storage function of thermocrystallized lakes and ponds in high-altitude permafrost regions according to claim 1, characterized in that, Step S6 specifically includes: S61. Based on the hysteresis curve, determine the functional mode of the thermal melting pond, specifically: Determine whether the hysteresis curves of the river and the hot melt lake are counterclockwise closed and whether the hot melt lake continuously replenishes the river after rainfall. If so, the hot melt lake is in water supply mode; otherwise, proceed with the second determination. Perform the second judgment to determine whether the hysteresis curves of the river and the thermomelt pond are clockwise closed and whether the lake water level rises and weakens the peak change of the river in the short term. If so, the thermomelt pond is in the regulation and storage mode; otherwise, the thermomelt pond is in the mixed mode. S62. Calculate the rainfall amount for each effective rainfall event. and the corresponding thermal melting lake and pond functional modes, based on rainfall The x-axis represents the area of ​​the hysteresis loop along the direction of the hysteresis curve. Plot a scatter plot with the vertical axis as the ordinate; where the storage and regulation mode is dominant, A negative value indicates that the hysteresis curve is closed clockwise; when the supply-side model is dominant, A positive value indicates that the hysteresis curve is closed counterclockwise; Based on the scatter plot, linear regression is performed using the least squares method to generate the area of ​​the hysteresis loop in the direction of the hysteresis curve. With rainfall The fitting relationship is as follows: in, , All are fitting coefficients; Based on the fitting coefficients, the area of ​​the hysteresis loop in the direction of the hysteresis curve is defined. The rainfall threshold corresponding to the functional mode conversion of the thermal melting lake pond when the value is 0 is expressed as: in, This is the rainfall threshold.

7. The method for evaluating the regulation and storage function of thermocrystallized lakes and ponds in high-altitude permafrost regions according to claim 1, characterized in that, Step S7 specifically includes: S71. After standardizing the hysteresis loop area and peak time delay, and combining them with the normalized water storage efficiency index, calculate the comprehensive regulation and storage index, i.e.: in, Indicates the comprehensive water storage index. , , All represent weighting coefficients. , , These represent the normalized water storage efficiency index of the thermomelt lake, the normalized hysteresis loop area of ​​the hysteresis trajectory curves of the river and the thermomelt lake, and the normalized peak time lag of the river and the thermomelt lake, respectively. S72. The threshold for the storage capacity level is set using the quantile method, including the first quantile and the second quantile; S73. Determine whether the comprehensive storage index is greater than the second quantile. If so, the thermal melting lake is a lake with high storage capacity. Otherwise, determine whether the comprehensive storage index is greater than or equal to the first quantile and less than or equal to the second quantile. If so, the thermal melting lake is a lake with medium storage capacity. Otherwise, the thermal melting lake has weak storage capacity or is in water supply mode.

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