Water and soil loss early warning method and device based on climate change

Abstract

The invention provides a water and soil loss early warning method and device based on climate change, and relates to the technical field of machine learning, and the method comprises the steps: collecting the surface temperature data and freeze-thaw state data of a target region in real time, and determining the freeze-thaw cycle state of a surface active layer based on a set freeze-thaw state criterion; calculating the melt water runoff generation amount of the surface active layer based on the freeze-thaw cycle state, and calculating the comprehensive runoff yield by combining rainfall forecast data; dynamically calculating the unit surface scouring force based on the comprehensive runoff runoff yield, and judging whether the unit surface scouring force reaches an erosion starting state or not based on a soil instability limit shear threshold value under the freezing-thawing interface; and triggering water and soil loss early warning when the superposing effective melt water runoff volume of the earth surface freeze-thaw conversion state reaches a preset percentile and the earth surface unit scouring force exceeds a soil instability limit shear threshold value. According to the invention, early warning can be carried out according to climate change for water and soil loss in alpine regions.

Description

Technical Field

[0001] The present application relates to the technical field of machine learning, and specifically to a soil and water loss early warning method and device based on climate change. Background Art

[0002] Soil and water loss early warning refers to a dynamic management activity that uses physical models, statistical inference or machine learning methods to assess the potential risk of soil erosion in the region in real time based on continuous monitoring and dynamic analysis of the surface environmental status, hydrological and meteorological changes, and soil erosion mechanisms. When it is predicted that soil structure damage and sediment loss have reached or are about to reach the set threshold, risk alerts are issued in advance and response measures are linked to prevent or reduce the degradation of soil and water resources, loss of ecological functions, and the occurrence of secondary disasters.

[0003] Soil and water loss early warning based on climate change in alpine areas is of great significance for the deep loosening of soil structure and thermal thaw collapse caused by permafrost degradation due to climate warming. By real-time monitoring of the surface freeze-thaw cycle dynamics, the evolution of effective meltwater runoff and changes in surface mechanical stability, it can identify in advance the high-risk state of large-scale erosion and debris flow activities in the surface soil of the freeze-thaw zone under the influence of extreme rainfall events in summer, effectively prevent ecological damage, infrastructure damage and the spread of secondary disasters, and provide scientific, efficient and forward-looking technical support for ecological protection, engineering safety and climate adaptability management in alpine areas. Summary of the Invention

[0004] The present application provides a method and device for early warning of soil and water loss based on climate change, which can provide early warning of soil and water loss in alpine areas according to climate change.

[0005] In a first aspect of the present application, a method for early warning of soil and water loss based on climate change is provided, the method comprising:

[0006] Real-time collection of surface temperature data and freeze-thaw status data in the target area, and determination of the freeze-thaw cycle status of the active surface layer based on the set freeze-thaw status criteria;

[0007] Based on the freeze-thaw cycle state and geothermal gradient data, permafrost thickness data, surface roughness data and initial pore water saturation data, the meltwater runoff generation of the active surface layer is estimated, and the comprehensive runoff flow is estimated in combination with rainfall forecast data;

[0008] Based on the comprehensive runoff flow, dynamically calculating the surface unit scouring force, and determining whether the surface unit scouring force reaches an erosion initiation state based on the soil instability limit shear threshold at the freeze-thaw interface;

[0009] When the surface freeze-thaw conversion state superimposed on the effective meltwater runoff reaches a preset percentile, and the surface unit scouring force exceeds the soil instability limit shear threshold, a soil erosion warning is triggered.

[0010] Based on the above technical solution, preferably, the real-time collection of surface temperature data and freeze-thaw status data of the target area and the determination of the freeze-thaw cycle status of the active surface layer based on the set freeze-thaw status criterion specifically include:

[0011] By deploying a surface temperature sensor array and a geothermal depth gradient probe in the target area, a multi-source surface temperature data and freeze-thaw status data joint collection system is formed in combination with satellite remote sensing observation data, wherein the surface temperature data is collected at the same frequency as the freeze-thaw status data;

[0012] When, according to the surface temperature data, the surface temperature is continuously lower than a preset first temperature within a preset time period and the rate of change of the geothermal gradient is in a downward trend, determining that the surface active layer is in a frozen state;

[0013] When the surface temperature is continuously higher than the preset second temperature and the geothermal gradient change rate changes from negative to positive, and the daily average surface temperature rise reaches a preset threshold, and the surface freeze-thaw interface position is detected to move upward by more than a preset thickness change value according to the freeze-thaw state data, the surface active layer is determined to be in a melting state;

[0014] When the surface temperature is between the preset second temperature and the preset first temperature, and the daily amplitude of the geothermal gradient exceeds a preset amplitude, it is determined that the surface active layer is in a critical transition state;

[0015] Alternatively, when the upward movement rate of the surface freeze-thaw interface presents an unstable fluctuating state and there are high-frequency alternating changes between the frozen state and the melted state in the freeze-thaw state data, the surface active layer is determined to be in a critical transition state.

[0016] On the basis of the above technical solution, preferably, the method of estimating the meltwater runoff generation of the surface active layer based on the freeze-thaw cycle state and geothermal gradient data, frozen soil thickness data, surface roughness data and initial pore water saturation data specifically includes:

[0017] Determining whether the active surface layer is currently in the frozen state, the thawed state, or the critical transition state based on the freeze-thaw cycle state, wherein the frozen state corresponds to zero meltwater runoff, and the thawed state and the critical transition state trigger meltwater runoff generation estimation;

[0018] The heat flux variation trend from the ground surface to the frozen interface is calculated based on the geothermal gradient data, the daily movement rate of the freeze-thaw interface is dynamically calculated using the heat diffusion formula, and the effective thawing thickness of the surface active layer is determined in combination with the permafrost thickness data;

[0019] Based on the effective melt thickness, combined with the surface roughness data and the initial pore water saturation data, an active layer meltwater runoff model is constructed, wherein the initial effective porosity, capillary suction and permeability coefficient within the surface active layer are dynamically adjusted according to the initial pore water saturation data and the surface roughness data;

[0020] At each time step, the potential meltwater generation per unit area caused by the movement of the freeze-thaw interface is first calculated based on the active layer meltwater runoff model. The soil water replenishment state is determined based on the initial pore water saturation. If the soil pores are not saturated, part of the meltwater is converted into soil water storage. If the soil pores are saturated, the remaining meltwater is converted into surface runoff.

[0021] The surface resistance coefficient is calculated based on the surface roughness data, the runoff rate per unit area is corrected, and the active layer meltwater runoff generation per unit time is accumulated, and the meltwater runoff generation is continuously updated in real time based on the latest collected data.

[0022] On the basis of the above technical solution, preferably, the method of estimating the meltwater runoff generation of the active surface layer based on the freeze-thaw cycle state and geothermal gradient data, frozen soil thickness data, surface roughness data and initial pore water saturation data, and estimating the comprehensive runoff flow in combination with rainfall forecast data, specifically includes:

[0023] Obtaining a rainfall intensity time series corresponding to the target area based on the rainfall forecast data;

[0024] Dynamically correcting the initial runoff curve number of the target area based on the surface roughness data and the initial pore water saturation data to obtain a corrected runoff curve number;

[0025] Calculating the initial rainfall loss according to the modified runoff curve number, wherein the initial rainfall loss is a preset ratio of the parameter corresponding to the modified runoff curve number;

[0026] Determining whether the rainfall intensity generates effective runoff based on the rainfall intensity time series and the initial rainfall loss; and calculating the rainfall runoff volume when the rainfall intensity is greater than the initial rainfall loss. The rainfall runoff volume is the square of the remainder after deducting the initial rainfall loss from the rainfall intensity, divided by the rainfall intensity and the initial rainfall loss, and then adding the difference between the parameters corresponding to the modified runoff curve number.

[0027] The meltwater runoff generation amount is used as the basic meltwater runoff input, and linearly superimposed with the rainfall runoff amount to obtain the comprehensive runoff flow.

[0028] Based on the above technical solution, preferably, the method of dynamically calculating the surface unit scouring force based on the comprehensive runoff flow and determining whether the surface unit scouring force reaches the erosion initiation state based on the soil instability limit shear threshold at the freeze-thaw interface includes:

[0029] Calculating the thickness of the surface free water flow based on the comprehensive runoff flow, wherein the thickness of the surface free water flow is determined according to a functional relationship between the comprehensive runoff flow and the local surface slope;

[0030] Based on the surface free water flow thickness, combined with surface slope data and surface roughness data, the surface flow velocity is calculated using the Manning formula, and the surface flow velocity is dynamically updated as the surface free water flow thickness changes;

[0031] Calculating the surface hydrodynamic shear force based on the surface free water flow thickness and the surface flow velocity, combined with the water density, gravitational acceleration, and the tangent value of the surface slope;

[0032] deriving a surface unit scour force according to the surface hydrodynamic shear force and the surface area of the surface active layer;

[0033] Comparing the surface unit scouring force with the soil instability limit shear threshold in real time, wherein the soil instability limit shear threshold is preset or dynamically adjusted based on soil structural characteristics, moisture content, temperature state and porosity;

[0034] When the surface unit scouring force is greater than or equal to the soil instability limit shear threshold, it is determined that the surface active layer has reached the erosion initiation state;

[0035] When the surface unit scouring force is less than the soil instability limit shear threshold, it is determined that the surface active layer has not reached the erosion initiation state.

[0036] Based on the above technical solution, preferably, when the surface freeze-thaw conversion state superimposed on the effective meltwater runoff reaches a preset percentile, and the surface unit scouring force exceeds the soil instability limit shear threshold, a soil erosion warning is triggered, specifically including:

[0037] monitoring the surface freeze-thaw conversion state in real time, and determining that the surface freeze-thaw conversion state is complete when the surface active layer changes from the frozen state to the melted state or the critical transition state and maintains the state for the preset time continuously, and recording the starting time point of the completion of the surface freeze-thaw conversion state;

[0038] Starting from the starting time point, the meltwater runoff generated by the active surface layer is continuously accumulated and superimposed with the rainfall runoff in the corresponding time step to obtain the effective meltwater runoff, which is the linear superposition result of the meltwater runoff generated by the active layer and the rainfall runoff;

[0039] Based on historical statistical samples, the percentile distribution of the effective meltwater runoff in the target area is set, and a preset percentile corresponding to a specific risk level is selected. When the effective meltwater runoff reaches the flow value corresponding to the preset percentile, it is determined that the first trigger condition is met;

[0040] In each time step, when the surface unit scouring force is greater than the soil instability limit shear threshold, it is determined that the second trigger condition is met;

[0041] When the first trigger condition and the second trigger condition are met at the same time, a soil and water loss warning is triggered.

[0042] Based on the above technical solution, preferably, after the soil erosion warning is triggered when the surface freeze-thaw conversion state superimposed on the effective meltwater runoff reaches a preset percentile and the surface unit scouring force exceeds the soil instability limit shear threshold, the method further includes:

[0043] Establishing an uncertainty management mechanism, the uncertainty management mechanism is used to receive various monitoring data of the target area in real time and uniformly quantify the measurement error, inference error and environmental disturbance error of the monitoring data;

[0044] Based on the uncertainty quantification results, a particle filter method or sequential Monte Carlo deduction method is used to generate multiple sets of erosion process scenario samples. The comparison results of the surface unit scour force and the soil instability limit shear threshold under each scenario and the probability of the erosion initiation state are calculated respectively, forming a dynamic distribution curve of erosion probability.

[0045] Real-time calculation of the soil erosion risk probability at the current moment, which indicates the likelihood of the active surface layer entering an erosion state under current environmental and data uncertainty conditions;

[0046] Set multiple levels of soil and water loss warning thresholds, including normal state, level 1 warning state, level 2 warning state, and level 3 warning state. Each warning state corresponds to an erosion probability interval.

[0047] When the soil and water loss risk probability enters different erosion probability intervals, dynamically adjusting the corresponding soil and water loss warning level;

[0048] The soil and water loss risk probability is continuously and dynamically updated, and the soil and water loss warning level and response measures are adjusted until the environmental data stabilizes and falls back to a safe range and the erosion probability is lower than the warning cancellation threshold, thereby canceling the current soil and water loss warning status.

[0049] In a second aspect of the present application, a soil and water loss early warning device based on climate change is provided, wherein the device is used to execute any one of the soil and water loss early warning methods based on climate change described above, and the device includes an acquisition module, a processing module, and an output module, wherein:

[0050] The acquisition module is used to collect surface temperature data and freeze-thaw status data of the target area in real time, and determine the freeze-thaw cycle status of the active surface layer based on the set freeze-thaw status criterion;

[0051] The processing module is used to estimate the meltwater runoff generation of the active surface layer based on the freeze-thaw cycle state and geothermal gradient data, permafrost thickness data, surface roughness data and initial pore water saturation data, and to estimate the comprehensive runoff flow in combination with rainfall forecast data;

[0052] The processing module is configured to dynamically calculate a surface unit scouring force based on the integrated runoff flow, and determine whether the surface unit scouring force reaches an erosion initiation state based on a soil instability limit shear threshold at a freeze-thaw interface;

[0053] The output module is used to trigger a soil erosion warning when the surface freeze-thaw conversion state superimposed on the effective meltwater runoff reaches a preset percentile and the surface unit scouring force exceeds the soil instability limit shear threshold.

[0054] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs any of the methods described above.

[0055] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions. When the instructions are executed, any one of the methods described above is executed.

[0056] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0057] 1. This application dynamically determines the freeze-thaw cycle changes of the active surface layer by collecting surface temperature data and freeze-thaw status data in real time, and estimates the meltwater runoff generation of the active layer by combining geothermal gradient data, permafrost thickness data, surface roughness data and initial pore water saturation data, and integrates rainfall forecast data to estimate the comprehensive runoff flow. It further dynamically calculates the surface unit scouring force based on the comprehensive runoff flow, and determines in real time whether it exceeds the soil instability limit shear threshold at the freeze-thaw interface, thereby triggering a soil erosion warning in advance when the surface freeze-thaw transformation superimposed on the effective meltwater runoff reaches a risk level. It can accurately capture the conditions for the occurrence of soil erosion disasters in high-altitude cold areas under the combined effects of permafrost degradation caused by climate warming and extreme rainfall, and realize continuous dynamic response and forward-looking risk warning for climate change processes.

[0058] 2. Through the high-frequency acquisition of multi-source surface temperature data and geothermal depth gradient data, combined with the setting of freeze-thaw state criteria, the freezing state, melting state and critical transition state of the active surface layer can be dynamically identified with high precision, ensuring that the freeze-thaw cycle state judgment has fine-grained temporal and spatial resolution, laying an accurate foundation for subsequent meltwater runoff estimation and erosion risk analysis.

[0059] 3. Based on the freeze-thaw cycle status and geothermal gradient data, the effective thaw thickness is estimated, and the meltwater runoff process of the active layer is dynamically estimated. This can accurately characterize the meltwater release and runoff behavior of the surface soil under the freeze-thaw alternating environment, and improve the physical authenticity and dynamic response capability of the meltwater runoff generation estimation.

[0060] 4. By introducing rainfall forecast data and combining it with dynamically corrected runoff curves, the linear superposition results of rainfall runoff and meltwater runoff are calculated, realizing real-time dynamic prediction of comprehensive runoff under extreme rainfall superimposed freeze-thaw and meltwater scenarios, significantly improving the integrity and timeliness of comprehensive hydrodynamic input.

[0061] 5. Calculate the surface free water thickness based on the comprehensive runoff flow, apply the Manning formula to calculate the surface flow velocity, further calculate the surface unit scour force and compare the soil instability limit shear threshold in real time, realize dynamic identification of the erosion initiation state of the surface active layer, effectively capture the critical conditions for soil instability and erosion, and improve the sensitivity and reliability of erosion risk monitoring.

[0062] 6. By real-time monitoring of the surface freeze-thaw conversion status and accumulating the effective meltwater runoff, combined with the simultaneous determination of the surface unit scouring force and the ultimate shear threshold of soil instability, it is possible to trigger a dual warning of soil and water loss, effectively avoid misjudgment of a single indicator, and achieve accurate identification and risk prevention of complex erosion processes in high-altitude freeze-thaw areas.

[0063] 7. Introduce an uncertainty management mechanism after the soil erosion warning is triggered, quantify the monitoring data error in real time, and dynamically update the soil erosion risk probability based on particle filtering or sequential Monte Carlo deduction methods. Through the coordinated adjustment of multi-level warning levels and response measures, the adaptability, robustness and response accuracy of the overall risk management system under extreme climate change conditions can be improved, and full-cycle closed-loop dynamic risk control can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 This is a flow chart of a method for early warning of soil and water loss based on climate change disclosed in an embodiment of the present application;

[0065] Figure 2 This is a module diagram of a soil and water loss early warning device based on climate change disclosed in an embodiment of the present application;

[0066] Figure 3 This is a structural diagram of an electronic device disclosed in an embodiment of the present application.

[0067] Explanation of the reference numerals: 201, acquisition module; 202, processing module; 203, output module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0068] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0069] In the description of the embodiments of this application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "for example" or "for instance" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0070] In the description of the embodiments of the present application, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0071] Soil and water loss early warning is to continuously monitor the surface environmental status, hydrological and meteorological changes, and soil erosion mechanisms, and based on physical models, statistical inference or machine learning methods, to assess soil erosion risks in real time and issue alarms and linkage responses before the threshold is reached, aiming to prevent the degradation of soil and water resources and ecological disasters; soil and water loss early warning based on climate change in high-altitude and cold areas targets permafrost degradation and thermal thaw collapse caused by climate warming. By monitoring the dynamics of freeze-thaw cycles, effective meltwater runoff, and changes in surface mechanics, it identifies high-risk states of erosion and debris flows induced by extreme rainfall in advance, providing efficient and scientific support for ecological protection, engineering safety, and climate adaptability management.

[0072] This embodiment discloses a soil and water loss early warning method based on climate change. Figure 1 , including the following steps S110-S140:

[0073] S110 , collecting surface temperature data and freeze-thaw status data of the target area in real time, and determining the freeze-thaw cycle status of the active surface layer based on a set freeze-thaw status criterion.

[0074] The embodiment of the present application discloses a method for early warning of soil and water loss based on climate change, which is applied to a server. The server includes, but is not limited to, electronic devices such as mobile phones, tablet computers, wearable devices, and personal computers (PCs), and can also be a background server running the method for early warning of soil and water loss based on climate change. The server can be implemented as a standalone server or a server cluster consisting of multiple servers.

[0075] In one possible implementation, surface temperature data and freeze-thaw status data of a target area are collected in real time, and the freeze-thaw cycle status of the active surface layer is determined based on a set freeze-thaw status criterion. Specifically, the method includes: deploying a surface temperature sensor array and a geothermal depth gradient probe in the target area, combining satellite remote sensing observation data to form a multi-source surface temperature data and freeze-thaw status data joint collection system, wherein the surface temperature data is collected at the same frequency as the freeze-thaw status data; and determining, based on the surface temperature data, that the active surface layer is frozen when the surface temperature is continuously lower than a preset first temperature for a preset period of time and the geothermal gradient change rate is on a downward trend. When the surface temperature is continuously higher than the preset second temperature and the rate of change of the geothermal gradient changes from negative to positive, and the daily average surface temperature rise reaches the preset threshold, and the surface freeze-thaw interface position is detected to have moved upward by more than the preset thickness change value according to the freeze-thaw status data, the surface active layer is determined to be in a melting state; when the surface temperature is between the preset second temperature and the preset first temperature, and the daily amplitude of the geothermal gradient exceeds the preset amplitude, the surface active layer is determined to be in a critical transition state; or, when the upward movement rate of the surface freeze-thaw interface presents an unstable fluctuation state, and there are high-frequency alternating changes between the frozen state and the melted state in the freeze-thaw status data, the surface active layer is determined to be in a critical transition state.

[0076] Specifically, first, a surface temperature sensor array and a geothermal depth gradient probe are deployed in the target area. The surface temperature sensor array is used to collect surface temperature data of the target area, and the geothermal depth gradient probe is used to measure the soil temperature at different depths to form a data curve of ground temperature changing with depth. At the same time, combined with satellite remote sensing observation data, including but not limited to microwave remote sensing freeze-thaw discrimination products and surface temperature remote sensing inversion data, a joint collection system for multi-source surface temperature data and freeze-thaw status data is established to ensure that the collected data are consistent in spatial range and temporal resolution. The collection frequency of surface temperature data and the collection frequency of freeze-thaw status data are set to the same value, preferably once an hour, to achieve high-efficiency tracking of freeze-thaw cycle dynamics.

[0077] Subsequently, based on the real-time collected surface temperature data, the continuous time series is analyzed. When it is detected that the surface temperature is continuously lower than the preset first temperature within the preset time period, and the geothermal gradient change rate shows a downward trend, it indicates that the downward conduction of surface heat is reduced and the freezing is intensified. At this time, according to the set freeze-thaw state judgment criteria, the surface active layer is judged to be in a frozen state. In the frozen state, the soil structure strength is increased and the water migration is restricted, providing a dynamic monitoring basis for the subsequent melting stage and the accumulation of erosion risks.

[0078] Then, when the surface temperature continues to be higher than the set preset second temperature, and the geothermal gradient change rate changes from negative to positive, that is, the geothermal gradient from the surface to the shallow layer is reversed, and the daily average surface temperature rise reaches the set temperature rise threshold, at the same time, combined with the freeze-thaw status data, it is detected that the position of the surface freeze-thaw interface moves upward and exceeds the preset thickness change value. It is comprehensively determined that large-scale melting of the surface permafrost has started, and according to the set freeze-thaw status criteria, the surface active layer is determined to be in a melting state. At this time, the soil strength drops rapidly, the water release accelerates, and the surface becomes extremely prone to erosion response.

[0079] When the surface temperature is between the preset second temperature and the preset first temperature, where the preset second temperature is lower than the preset first temperature, that is, the surface is in a fluctuation range near zero degrees Celsius, and at the same time the daily amplitude of the geothermal gradient exceeds the set amplitude threshold, it indicates that melting during the day and freezing at night alternate repeatedly and are accompanied by strong thermal dynamic disturbances. According to the set freeze-thaw state criterion, the surface active layer is judged to be in a critical transition state. In the critical transition state, the soil structure stability is extremely low, and the micro cracks expand rapidly, which can easily become the starting point of subsequent concentrated erosion.

[0080] Alternatively, during the monitoring process, when the upward movement rate of the surface freeze-thaw interface shows an obvious unstable fluctuation state, that is, the freeze-thaw interface shows abnormal ups and downs in the time series, and the freeze-thaw state data identifies the phenomenon of high-frequency alternation of the frozen state and the melted state on a short time scale, based on the above information and the set freeze-thaw state criteria, the surface active layer is also determined to be in a critical transition state. In this case, the surface physical structure is extremely fragile, and it is a sensitive window period for extreme weather to trigger erosion disasters.

[0081] S120, based on the freeze-thaw cycle status and geothermal gradient data, permafrost thickness data, surface roughness data and initial pore water saturation data, estimates the meltwater runoff generation of the active surface layer, and combines it with rainfall forecast data to estimate the comprehensive runoff flow.

[0082] In one possible implementation, the meltwater runoff generation of the surface active layer is estimated based on the freeze-thaw cycle state and geothermal gradient data, permafrost thickness data, surface roughness data and initial pore water saturation data, specifically including: judging whether the surface active layer is currently in a frozen state, a melted state or a critical transition state according to the freeze-thaw cycle state, wherein the frozen state corresponds to a meltwater flow rate of zero, and the melted state and the critical transition state trigger the meltwater runoff generation estimation; estimating the heat flux change trend from the surface to the frozen interface according to the geothermal gradient data, dynamically calculating the daily movement rate of the freeze-thaw interface using the heat diffusion formula, and determining the effective melting thickness of the surface active layer in combination with the permafrost thickness data; based on the effective melting thickness, combining the surface roughness data and the initial pore water saturation, Based on the data, an active layer meltwater runoff model is constructed, in which the initial effective porosity, capillary suction and permeability coefficient inside the surface active layer are dynamically adjusted according to the initial pore water saturation data and surface roughness data. In each time step, the potential meltwater generation per unit area caused by the movement of the freeze-thaw interface is first calculated according to the active layer meltwater runoff model, and the soil moisture replenishment status is judged according to the initial pore water saturation. If the soil pores are not saturated, part of the meltwater is converted into soil moisture storage. If the soil pores are saturated, the remaining meltwater is converted into surface runoff. The surface resistance coefficient is calculated according to the surface roughness data, and the runoff rate per unit area is corrected. The active layer meltwater runoff generation per unit time is accumulated, and the meltwater runoff generation is continuously updated in real time based on the latest collected data.

[0083] Specifically, geothermal gradient data refers to the gradient of soil temperature with depth from the surface to a certain depth. This data reflects the direction and intensity of surface heat transfer and is a fundamental parameter for determining the dynamic evolution of the frozen interface and the rate of freeze-thaw processes. Permafrost thickness data describes the vertical distance from the surface to the bottom of permafrost or seasonally frozen soil and is used to define the effective thaw volume of the active layer. Surface roughness data quantitatively describes the effects of surface microtopography, vegetation cover, and soil surface structural properties on water flow, and is a key factor influencing the formation and velocity of surface runoff. Initial pore water saturation data indicates the proportion of soil pore space filled with water at the beginning of the freeze-thaw process, determining whether the soil can continue to absorb meltwater or directly generate runoff. Meltwater runoff generation in the active surface layer refers to the per-unit area flow rate of frozen water released from the soil due to the upward movement of the freeze-thaw interface and the rise in surface temperature, which is converted into surface runoff after the soil pores are saturated or supersaturated. It is the direct driving force of energy and material transport in the soil erosion process.

[0084] First, based on the real-time determination of the freeze-thaw cycle status, it is determined whether the active surface layer is currently in a frozen state, a melted state, or a critical transition state. When the active surface layer is in a frozen state, the soil pore structure is filled with ice crystals, the water is consolidated, and the permeability is extremely low, so the meltwater flow rate is determined to be zero. When the active surface layer is in a melted state or a critical transition state, the permafrost structure is loosened, the pore connectivity is enhanced, and the water release and migration are intensified. It is determined that it is necessary to perform meltwater runoff generation calculation to provide initial input for subsequent hydrodynamic evolution.

[0085] Subsequently, based on the collected geothermal gradient data, the heat flux change trend between the surface and the frozen interface was calculated. A one-dimensional heat diffusion model was used to dynamically calculate the vertical movement rate of the freeze-thaw interface on a daily scale, combining the geothermal gradient and soil thermal conductivity parameters. The specific calculation is based on the following formula:

[0086]

[0087] Among them, v interface It represents the daily vertical movement rate of the freeze-thaw interface, measured in meters per day, and is used to describe the speed at which the freezing or thawing interface advances or retreats over time in the vertical space from the surface to the underground. λ represents the thermal conductivity of the soil, measured in watts per meter per Kelvin, and is used to measure the ability of the soil to conduct heat during freezing or thawing. Soil moisture content, porosity, and soil texture significantly affect this parameter value. w Indicates the density of water, with the unit being kilograms per cubic meter, usually one thousand kilograms per cubic meter. It indicates the mass of water per unit volume and is a basic physical constant in hydrodynamic and thermodynamic calculations. f It represents the latent heat of water, with the unit of joule per kilogram. It represents the energy absorbed or released when one kilogram of water is converted from solid to liquid or vice versa. The standard value is about 334,000 joules per kilogram. It represents the geothermal gradient in degrees Celsius per meter, which indicates the rate of temperature change within a unit depth range. It is a key parameter for describing the direction and intensity of thermal driving from the surface to the shallow freeze-thaw interface.

[0088] At the same time, combined with the permafrost thickness data, the effective melting thickness of the active layer is determined. The effective melting thickness refers to the thickness of the soil layer between the surface and the frozen interface that has reached water thaw and has the ability to produce runoff, which serves as an important basis for the subsequent calculation of meltwater volume.

[0089] Then, based on the effective melting thickness, combined with the surface roughness data and the initial pore water saturation data, an active layer meltwater runoff model was constructed. The meltwater runoff model is based on the modified Green-Ampt model. On the framework of the traditional Green-Ampt infiltration model, a real-time adjustment mechanism for parameters such as the initial effective porosity, capillary suction and permeability coefficient of the soil is dynamically introduced. Among them, the initial effective porosity is determined based on the mapping of the initial pore water saturation data, and the capillary suction and permeability coefficient are corrected based on the surface roughness data, ensuring that the runoff process can reflect the non-steady-state evolution characteristics of the soil hydraulic properties caused by freeze-thaw cycles.

[0090] In each time step, the potential meltwater generation per unit area is calculated based on the movement rate of the freeze-thaw interface. The potential meltwater generation is the product of the amount of water released by the change in effective melting thickness and the unit area. It is calculated using the following formula:

[0091] M potential =Δh melt ×θ i

[0092] Among them, M potential It represents the potential meltwater generation per unit area, in meters, and is used to quantify the ratio of the volume of liquid water released during the melting of frozen soil to the surface area. melt Represents the change in effective thawing thickness, in meters, which refers to the net change in the thickness of the soil thawing area caused by the upward movement of the freeze-thaw interface in a specific time step. i It represents the initial soil ice content, and the unit is a dimensionless ratio value. It refers to the ratio of the volume of ice contained in a unit volume of soil to the total soil volume when the soil is frozen. It is a direct reflection of the potential meltwater storage in frozen soil.

[0093] Subsequently, the soil moisture replenishment status is judged based on the initial pore water saturation. When the soil pores are not fully saturated, potential meltwater is used first to replenish the soil moisture reserve without generating surface runoff. When the soil pores reach a saturated or supersaturated state, the remaining potential meltwater is converted into surface runoff, providing actual flow production for the hydrodynamic scouring process.

[0094] In the process of estimating surface runoff formation, the surface resistance coefficient is further estimated based on the surface roughness data. The surface resistance coefficient describes the degree of obstruction of surface water flow and has a direct impact on the free water velocity and runoff rate. The runoff rate per unit area is corrected in combination with the surface roughness and water flow characteristics. Finally, the active layer meltwater runoff generation per unit time is accumulated. At each time step, the active layer meltwater runoff generation is dynamically updated based on the latest collected ground temperature, surface state and hydraulic data to ensure that the estimated results reflect changes in the surface environment in real time.

[0095] First, based on the rainfall forecast data, the rainfall intensity time series corresponding to the target area is obtained. The rainfall intensity time series is a continuous rainfall intensity data set collected or forecasted at fixed time intervals, with the unit of millimeters per hour. It can reflect the rainfall change trend in the target area within a specific time period in the future and is one of the core inputs for estimating rainfall runoff and comprehensive runoff flow.

[0096] Subsequently, based on the surface roughness data and initial pore water saturation data, the initial runoff curve number of the target area is dynamically corrected. The initial runoff curve number is used to characterize the surface runoff characteristics. The standard runoff curve number is preset according to the land use type, soil type and slope classification. During the dynamic correction process, by analyzing the impact of the current surface roughness level and the initial pore water saturation level on the runoff potential, the roughness correction factor and the saturation correction factor are respectively applied to perform weighted adjustment on the standard runoff curve number to obtain the corrected runoff curve number reflecting the actual surface hydrodynamic conditions.

[0097] After obtaining the modified runoff curve number, the initial rainfall loss is calculated based on the relationship formula between the runoff curve number and the initial loss. The initial rainfall loss represents the sum of the rainfall interception and infiltration that the surface must first meet before runoff is generated. Its value is equal to the corresponding parameters of the modified runoff curve number converted by a certain ratio. The specific conversion ratio is determined according to the empirical formula in the runoff curve method to ensure that the initial rainfall loss is dynamically consistent with the surface runoff response characteristics. Among them, the initial rainfall loss is specifically calculated by the following formula:

[0098]

[0099] Among them, I a It indicates the initial rainfall loss in millimeters. It is used to quantify the amount of rainfall that needs to be intercepted, infiltrated or retained by the soil before runoff occurs. It is the basic condition for judging whether effective rainfall runoff is generated. adj The modified runoff curve number is a dimensionless ratio value. It is based on the standard runoff curve number after dynamic adjustment according to surface roughness data and initial pore water saturation data. It is used to comprehensively reflect the actual surface runoff response characteristics. The value of 0.2 is based on empirical settings. This value indicates that under normal circumstances, the initial loss of rainfall is approximately 20% of the maximum possible water storage capacity. This ratio is determined based on statistical analysis of a large amount of measured runoff data. It reflects the rainfall required for non-runoff processes such as interception, initial infiltration, and surface retention that occur at the beginning of rainfall under natural surface conditions.

[0100] The values 25400 and 254 are derived from the standard runoff curve number theory. The value 25400 is the unit conversion factor, which is millimeters multiplied by the dimensionless value. It is used to convert the dimensionless modified runoff curve number into a maximum water storage value with actual physical dimensions. The value 254 is the empirical correction constant used to standardize the initial loss characteristics of different land uses and soil types during the derivation of the formula. These two values ensure that the Curve Number value corresponds to the actual soil water capacity range that can generate runoff within a reasonable physical range (usually between 40 and 100).

[0101] Subsequently, effective rainfall is judged based on the rainfall intensity time series and the initial rainfall loss. In each time step, if the current rainfall intensity is less than or equal to the initial rainfall loss, it is determined that no effective rainfall runoff is generated and the rainfall runoff volume is zero; if the current rainfall intensity is greater than the initial rainfall loss, the rainfall runoff volume is calculated. The rainfall runoff volume is the square of the remainder after deducting the initial rainfall loss from the rainfall intensity, and then divided by the sum of the difference between the rainfall intensity after deducting the initial rainfall loss and the corresponding parameter of the modified runoff curve number, to ensure that the rainfall runoff volume estimation conforms to the nonlinear runoff characteristics. It is calculated specifically by the following formula:

[0102]

[0103] Among them, Q rain It represents the rainfall runoff in millimeters, and is used to quantify the surface runoff per unit area under the effective rainfall intensity after considering the initial damage. rain Indicates rainfall intensity in millimeters per hour. It is derived from rainfall forecast data or measured data and indicates the amount of rainfall in the target area per unit time. It is the direct driving parameter for estimating rainfall runoff. a Indicates the initial rainfall loss. adj Indicates the number of modified runoff curves. If the rainfall intensity P rain Less than or equal to the initial rainfall loss I a , then determine the rainfall runoff Q rain Zero means no effective rainfall runoff is generated.

[0104] Finally, in each time step, the estimated active layer meltwater runoff generation is used as the basic meltwater runoff input and linearly superimposed with the rainfall runoff. Linear superposition is the direct addition operation of the two values to obtain the comprehensive runoff. The comprehensive runoff can simultaneously reflect the comprehensive contribution of the meltwater process and the rainfall process to the total surface runoff, providing continuous dynamic input for the subsequent dynamic calculation of the surface unit scour force and the determination of the erosion initiation state.

[0105] S130, based on the comprehensive runoff flow, dynamically calculate the surface unit scouring force, and determine whether the surface unit scouring force reaches the erosion initiation state based on the soil instability limit shear threshold at the freeze-thaw interface.

[0106] In one possible implementation, the surface unit scouring force is dynamically calculated based on the integrated runoff flow, and whether the surface unit scouring force reaches the erosion initiation state is determined based on the soil instability limit shear threshold at the freeze-thaw interface. Specifically, the following steps are performed: based on the integrated runoff flow, the surface free water flow thickness is calculated, and the surface free water flow thickness is determined according to the functional relationship between the integrated runoff flow and the local surface slope; based on the surface free water flow thickness, the surface slope data and the surface roughness data are combined to calculate the surface flow velocity using the Manning formula, and the surface flow velocity is dynamically updated as the surface free water flow thickness changes; based on the surface free water flow thickness and the surface flow velocity, the surface free water flow thickness is calculated based on the Manning formula. , combining water density with gravitational acceleration and the tangent value of the surface slope to calculate the surface hydrodynamic shear force; according to the surface hydrodynamic shear force and the surface area of the surface active layer, the surface unit scouring force is obtained; the surface unit scouring force is compared with the soil instability limit shear threshold in real time, and the soil instability limit shear threshold is preset or dynamically adjusted according to the soil structural characteristics, water content, temperature state and porosity; when the surface unit scouring force is greater than or equal to the soil instability limit shear threshold, it is determined that the surface active layer has reached the erosion initiation state; when the surface unit scouring force is less than the soil instability limit shear threshold, it is determined that the surface active layer has not reached the erosion initiation state.

[0107] Specifically, surface scouring force refers to the shear force generated on a unit area of the surface during the flow of surface water due to gravity and slope action. Its physical essence is the tangential driving force exerted by hydrodynamic force on soil particles along the surface direction. It is the core hydraulic parameter that drives the detachment and movement of surface soil particles and forms an erosion process. The magnitude of surface scouring force mainly depends on the thickness of surface free water flow, surface water flow velocity, water density, surface slope and gravitational acceleration. The larger the value, the easier it is for the surface soil to be destroyed and enter an erosion state. Therefore, surface scouring force is not only a direct basis for judging the initiation of erosion, but also a key control variable for establishing a dynamic model of soil and water loss and implementing early warning responses.

[0108] First, the surface free water thickness is calculated based on the comprehensive runoff flow. The surface free water thickness refers to the thickness of the water body formed by surface runoff per unit area, and the unit is meter. The functional relationship between the comprehensive runoff flow and the local surface slope is comprehensively considered during the calculation. Specifically, based on the assumption that the surface runoff is a shallow and uniform flow state, the comprehensive runoff flow is converted into the surface free water thickness in the corresponding time step through an empirical formula. The local surface slope data is used to adjust the free water accumulation and outflow rate, so as to accurately determine the instantaneous surface free water thickness at each location.

[0109] Subsequently, based on the surface free water flow thickness, combined with the surface slope data and surface roughness data, the Manning formula was applied to calculate the surface flow velocity. The Manning formula is a classic formula for shallow open channel flow. It can calculate the water flow velocity when the surface free water flow thickness, surface slope and surface roughness parameters are known. During the calculation process, the surface flow velocity is dynamically updated in real time as the surface free water flow thickness changes, reflecting the continuous response characteristics of the surface hydrodynamic state to changes in the comprehensive runoff flow and surface physical properties.

[0110] Next, based on the calculated surface free water flow thickness and surface flow velocity, combined with water density, gravitational acceleration and surface slope tangent, the surface hydrodynamic shear force is calculated. Surface hydrodynamic shear force is defined as the tangential force per unit area of surface affected by water flow. The calculation formula uses a water density of one thousand kilograms per cubic meter, a gravitational acceleration of nine point eight meters per second squared, and a surface slope tangent calculated in real time based on surface elevation data to ensure that the hydrodynamic shear force can accurately reflect the actual stress state of the surface.

[0111] After obtaining the surface hydrodynamic shear force, the surface unit scouring force is calculated based on the surface hydrodynamic shear force and the surface area of the surface active layer. The surface unit scouring force refers to the intensity of the hydrodynamic shear force on the surface per unit area, and the unit is Pascal. During the calculation process, the surface hydrodynamic shear force is divided by the surface area to ensure that the scouring force value has spatial standardization characteristics and can be directly used to judge the initiation of erosion.

[0112] The surface unit scouring force is compared with the ultimate shear threshold of soil instability in real time. The ultimate shear threshold of soil instability is preset based on the soil structural characteristics, moisture content, temperature state and porosity, or dynamically adjusted based on real-time data. When the soil structure is loose, the pore water is saturated or the temperature rises, the ultimate shear threshold of soil instability decreases and the soil's anti-scouring ability weakens. Dynamic adjustment ensures that the judgment standard matches the current surface physical environment.

[0113] When the real-time calculated surface unit scouring force is greater than or equal to the soil instability limit shear threshold, the surface active layer is judged to have reached the erosion initiation state, which means that the surface soil has lost sufficient anti-scouring capacity and erosion damage will occur rapidly under the action of hydrodynamics; when the surface unit scouring force is less than the soil instability limit shear threshold, the surface active layer is judged to have not reached the erosion initiation state, and real-time monitoring and dynamic updating of the comparison results of the scouring force and the instability threshold are continued to continuously capture the evolution of erosion risk.

[0114] S140: When the surface freeze-thaw conversion state superimposed on the effective meltwater runoff reaches the preset percentile, and the surface unit scouring force exceeds the soil instability limit shear threshold, a soil erosion warning is triggered.

[0115] In one possible implementation, when the surface freeze-thaw conversion state superimposed on the effective meltwater runoff reaches a preset percentile and the surface unit scouring force exceeds the soil instability limit shear threshold, a soil erosion warning is triggered, specifically including: real-time monitoring of the surface freeze-thaw conversion state, when the surface active layer changes from a frozen state to a melted state or a critical transition state and maintains a preset time, it is determined that the surface freeze-thaw conversion state is completed, and the starting time point of the completion of the surface freeze-thaw conversion state is recorded; starting from the starting time point, the meltwater runoff generation of the surface active layer is continuously accumulated, and compared with the rainfall runoff in the corresponding time step The effective meltwater runoff is obtained by linear superposition, which is the linear superposition result of the meltwater runoff generation in the active layer and the rainfall runoff. Based on the historical statistical samples, the percentile distribution of the effective meltwater runoff in the target area is set, and the preset percentile corresponding to the specific risk level is selected. When the effective meltwater runoff reaches the flow value corresponding to the preset percentile, it is determined that the first trigger condition is met. In each time step, when the surface unit scouring force is greater than the soil instability limit shear threshold, it is determined that the second trigger condition is met. When the first trigger condition and the second trigger condition are met at the same time, the soil and water loss warning is triggered.

[0116] Specifically, first, the surface freeze-thaw conversion state is monitored in real time. The surface freeze-thaw conversion state is the complete process of the surface active layer changing from a frozen state to a melted state or a critical transition state; specifically, the state evolution of the active layer is judged by surface temperature data and geothermal gradient data. When the melting state criterion or the critical transition state criterion is met in a continuous time, and the continuous maintenance time exceeds a preset time threshold, such as twelve hours or twenty-four hours, the surface freeze-thaw conversion state is judged to be completed, and the completion time point is recorded as the starting time mark for the subsequent accumulation of dynamic indicators.

[0117] Subsequently, starting from the starting time point when the surface freeze-thaw conversion state is completed, the meltwater runoff generation of the surface active layer is continuously estimated in each time step, and the meltwater runoff generation is numerically added to the rainfall runoff in the same time step to form the effective meltwater runoff. The effective meltwater runoff is the linear superposition result of the meltwater runoff generation of the active layer and the rainfall runoff, and the unit is millimeter. It reflects that the surface runoff process is affected by both freeze-thaw and rainfall drive, and is the core hydrological indicator to characterize the intensity of soil erosion conditions.

[0118] Next, a percentile distribution model of effective meltwater runoff in the target area is constructed based on historical statistical sample data. The percentile distribution model is generated by statistically sorting the effective meltwater runoff data measured or simulated over many years, reflecting the probability of occurrence of hydrological processes of different intensities in historical samples. According to the set risk management level, a preset percentile (for example, the 90th percentile, the 95th percentile, or the 99th percentile) is selected as the high-risk threshold for soil and water loss. When the real-time accumulated effective meltwater runoff reaches or exceeds the flow value corresponding to the preset percentile at any time step, it is determined that the first trigger condition is met.

[0119] At the same time, within each time step, the surface unit scouring force is dynamically calculated and compared with the soil instability limit shear threshold corresponding to the current moment in real time. When the surface unit scouring force value is greater than or equal to the soil instability limit shear threshold, it indicates that the shear effect of the hydrodynamics on the soil structure has reached the critical level that triggers erosion damage, and it is determined that the second trigger condition is met. The comparison process is based on the real-time updated surface hydrodynamics and soil physical state parameters to ensure that the threshold judgment is always accurate and effective in complex environmental changes.

[0120] When the first trigger condition and the second trigger condition are met at the same time in any time step, that is, the effective meltwater runoff exceeds the corresponding value of the preset percentile and the surface unit scouring force exceeds the soil instability limit shear threshold, the soil erosion warning is immediately triggered and the high-risk state response process is entered. The warning result will link the downstream response mechanism for subsequent risk notification, graded disposal or emergency control.

[0121] Furthermore, when the surface freeze-thaw conversion state superimposed on the effective meltwater runoff reaches a preset percentile, and the surface unit scouring force exceeds the soil instability limit shear threshold, after the soil erosion warning is triggered, the method also includes: establishing an uncertainty management mechanism, the uncertainty management mechanism is used to receive various monitoring data of the target area in real time, and uniformly quantify the measurement error, inference error and environmental disturbance error of the monitoring data; based on the uncertainty quantification results, a particle filtering method or a sequential Monte Carlo deduction method is used to generate multiple groups of erosion process scenario samples, and the comparison results of the surface unit scouring force and the soil instability limit shear threshold and the probability of the erosion initiation state under each scenario are respectively calculated, so as to form a dynamic analysis of the erosion probability. The system calculates the soil and water loss risk probability at the current moment in real time. The soil and water loss risk probability indicates the possibility of the active surface layer entering an erosion state under the current environmental and data uncertainty conditions. It sets multi-level soil and water loss warning level thresholds, including normal state, level 1 warning state, level 2 warning state, and level 3 warning state. Each warning state corresponds to an erosion probability interval. When the soil and water loss risk probability enters a different erosion probability interval, the corresponding soil and water loss warning level is dynamically adjusted. The soil and water loss risk probability is continuously and dynamically updated, and the soil and water loss warning level and response measures are adjusted until the environmental data stabilizes and falls back to a safe range and the erosion probability is lower than the warning release threshold, and the current soil and water loss warning state is released.

[0122] Specifically, first, after the soil erosion warning is triggered, an uncertainty management mechanism is established. The uncertainty management mechanism is used to centrally receive various monitoring data of the target area, including but not limited to surface temperature data, geothermal gradient data, permafrost thickness data, surface roughness data, initial pore water saturation data, rainfall intensity data, meltwater runoff generation data, rainfall runoff data, comprehensive runoff data, surface free water flow thickness data, surface flow velocity data, surface unit scour force data and soil instability limit shear threshold data, and conduct unified quantitative modeling for the measurement errors, model calculation errors and external environmental disturbance errors in the above data to form an error covariance structure that can be used for dynamic prediction.

[0123] Subsequently, based on the established uncertainty quantification model, the particle filtering method or the sequential Monte Carlo deduction method is used to generate a large number of erosion process scenario samples in each time step. The scenario samples are potential surface response trajectories simulated and evolved under different disturbance conditions. Each sample corresponds to a set of comparison results between the surface unit scouring force and the ultimate shear threshold of soil instability. By counting the proportion of triggered erosion initiation states in all scenario samples, the probability of occurrence of the erosion initiation state at the current moment is calculated, thereby constructing a dynamic distribution curve of surface erosion probability, forming a basis for risk situation perception.

[0124] In each real-time calculation cycle, the statistical expectation or confidence interval mean is extracted according to the dynamic distribution curve of the erosion probability, and the soil and water loss risk probability at the current moment is calculated. The soil and water loss risk probability indicates the probability that the active surface layer will enter an erosion state under the current environmental and data uncertainty conditions. It is a probabilistic representation of the stability of the scouring force-threshold structure under disturbance conditions, and the unit is percentage.

[0125] Based on long-term experience accumulation and regional prevention and control needs, multi-level soil and water loss warning level thresholds are pre-set. The typical settings include four levels: normal state, level one warning state, level two warning state and level three warning state. Each soil and water loss warning level corresponds to a soil and water loss risk probability interval. For example, the normal state corresponds to zero to twenty percent, the level one warning state corresponds to twenty to forty percent, the level two warning state corresponds to forty to seventy percent, and the level three warning state corresponds to more than seventy percent.

[0126] When the probability of soil and water loss risk enters any preset soil and water loss risk probability interval, the current soil and water loss warning level will be adjusted dynamically immediately. The change in warning level will trigger the corresponding response instruction module, including increasing monitoring frequency, issuing risk notices, activating drainage facilities, closing specific areas or initiating personnel evacuation and other differentiated response measures to ensure that emergency response resources match the current risk level.

[0127] Uncertainty sampling, erosion probability update and warning level judgment operations are continuously performed at fixed time intervals, and risk judgment results are dynamically corrected during continuous changes in environmental parameters. When the real-time collected data shows that all core parameters have stabilized and fallen back to the historical safety range and the current soil and water loss risk probability is lower than the preset release threshold (for example, ten percent), the current soil and water loss warning status is automatically released and restored to normal monitoring mode, completing a complete closed-loop response cycle.

[0128] This embodiment also discloses a soil and water loss early warning device based on climate change, referring to Figure 2 , comprising an acquisition module 201, a processing module 202 and an output module 203, the device is used to execute any of the above-mentioned soil and water loss early warning methods based on climate change, wherein:

[0129] The acquisition module 201 is used to collect the surface temperature data and freeze-thaw status data of the target area in real time, and determine the freeze-thaw cycle status of the active surface layer based on the set freeze-thaw status criterion.

[0130] The processing module 202 is used to estimate the meltwater runoff generation of the active surface layer based on the freeze-thaw cycle status and geothermal gradient data, permafrost thickness data, surface roughness data and initial pore water saturation data, and to estimate the comprehensive runoff flow in combination with rainfall forecast data.

[0131] The processing module 202 is used to dynamically calculate the surface unit scouring force based on the comprehensive runoff flow, and determine whether the surface unit scouring force reaches the erosion initiation state based on the soil instability limit shear threshold at the freeze-thaw interface.

[0132] The output module 203 is used to trigger a soil erosion warning when the surface freeze-thaw conversion state superimposed on the effective meltwater runoff reaches a preset percentile and the surface unit scouring force exceeds the soil instability limit shear threshold.

[0133] In one possible embodiment, the acquisition module 201 is used to form a joint acquisition system for multi-source surface temperature data and freeze-thaw status data by deploying a surface temperature sensor array and a geothermal depth gradient probe in the target area and combining satellite remote sensing observation data. The acquisition frequency of the surface temperature data is the same as the acquisition frequency of the freeze-thaw status data.

[0134] The processing module 202 is configured to determine, based on the surface temperature data, that the surface active layer is in a frozen state when the surface temperature is continuously lower than a preset first temperature within a preset period of time and the rate of change of the geothermal gradient is in a downward trend.

[0135] Processing module 202 is used to determine that the surface active layer is in a melting state when the surface temperature is continuously higher than the preset second temperature and the geothermal gradient change rate changes from negative to positive, and the daily average surface temperature rise reaches a preset threshold, and when it is detected based on the freeze-thaw status data that the position of the surface freeze-thaw interface moves upward by more than a preset thickness change value.

[0136] The processing module 202 is configured to determine that the surface active layer is in a critical transition state when the surface temperature is between a preset second temperature and a preset first temperature and the daily amplitude of the geothermal gradient exceeds a preset amplitude.

[0137] Alternatively, when the upward movement rate of the surface freeze-thaw interface presents an unstable fluctuating state and there are high-frequency alternating changes between the frozen state and the melted state in the freeze-thaw state data, the surface active layer is judged to be in a critical transition state.

[0138] In one possible embodiment, the processing module 202 is used to determine whether the active surface layer is currently in a frozen state, a melted state, or a critical transition state based on the freeze-thaw cycle state, wherein the frozen state corresponds to a meltwater flow rate of zero, and the melted state and the critical transition state trigger the calculation of meltwater runoff generation.

[0139] Processing module 202 is used to infer the trend of heat flux changes from the surface to the frozen interface based on geothermal gradient data, dynamically calculate the daily movement rate of the freeze-thaw interface using the heat diffusion formula, and determine the effective melting thickness of the surface active layer in combination with the permafrost thickness data.

[0140] Processing module 202 is used to construct an active layer meltwater runoff model based on the effective melt thickness, combined with surface roughness data and initial pore water saturation data, wherein the initial effective porosity, capillary suction and permeability coefficient inside the surface active layer are dynamically adjusted according to the initial pore water saturation data and surface roughness data.

[0141] The processing module 202 is used to first calculate the potential meltwater generation per unit area caused by the movement of the freeze-thaw interface based on the active layer meltwater runoff model in each time step, and judge the soil moisture replenishment status based on the initial pore water saturation. If the soil pores are not saturated, part of the meltwater is converted into soil moisture storage. If the soil pores are saturated, the remaining meltwater is converted into surface runoff.

[0142] The processing module 202 is used to calculate the surface resistance coefficient based on the surface roughness data, correct the runoff rate per unit area, accumulate the active layer meltwater runoff generation per unit time, and continuously update the meltwater runoff generation in real time based on the latest collected data.

[0143] In a possible implementation, the processing module 202 is configured to obtain a rainfall intensity time series corresponding to a target area based on rainfall forecast data.

[0144] The processing module 202 is used to dynamically correct the initial runoff curve number of the target area based on the surface roughness data and the initial pore water saturation data to obtain the corrected runoff curve number.

[0145] The processing module 202 is used to calculate the initial rainfall loss according to the modified runoff curve number, wherein the initial rainfall loss is a preset ratio of the parameter corresponding to the modified runoff curve number.

[0146] Processing module 202 is used to determine whether the rainfall intensity generates effective runoff based on the rainfall intensity time series and the initial rainfall loss. When the rainfall intensity is greater than the initial rainfall loss, the rainfall runoff volume is calculated. The rainfall runoff volume is the square of the remainder after deducting the initial rainfall loss from the rainfall intensity, divided by the rainfall intensity and the initial rainfall loss, and then added with the difference between the corresponding parameters of the modified runoff curve number.

[0147] The processing module 202 is used to use the meltwater runoff generation as the basic meltwater runoff input, and linearly superimpose it with the rainfall runoff to obtain the comprehensive runoff.

[0148] In a possible implementation, the processing module 202 is configured to estimate the surface free water flow thickness based on the integrated runoff flow, where the surface free water flow thickness is determined based on a functional relationship between the integrated runoff flow and the local surface slope.

[0149] The processing module 202 is used to calculate the surface flow velocity based on the surface free water flow thickness, combined with the surface slope data and the surface roughness data, using the Manning formula. The surface flow velocity is dynamically updated as the surface free water flow thickness changes.

[0150] The processing module 202 is used to calculate the surface hydrodynamic shear force based on the surface free water flow thickness and the surface flow velocity, combined with the water density, gravity acceleration and the surface slope tangent value.

[0151] The processing module 202 is used to obtain the surface unit scour force according to the surface hydrodynamic shear force and the surface area of the surface active layer.

[0152] The processing module 202 is used to compare the surface unit scouring force with the soil instability limit shear threshold in real time. The soil instability limit shear threshold is preset or dynamically adjusted based on soil structural characteristics, moisture content, temperature state and porosity.

[0153] The processing module 202 is configured to determine that the surface active layer has reached an erosion initiation state when the surface unit scour force is greater than or equal to the soil instability limit shear threshold.

[0154] The processing module 202 is configured to determine that the surface active layer has not reached the erosion initiation state when the surface unit scour force is less than the soil instability limit shear threshold.

[0155] In one possible embodiment, the acquisition module 201 is used to monitor the surface freeze-thaw conversion state in real time. When the surface active layer changes from a frozen state to a melted state or a critical transition state and maintains a preset time continuously, it is determined that the surface freeze-thaw conversion state is completed, and the starting time point of the completion of the surface freeze-thaw conversion state is recorded.

[0156] Processing module 202 is used to continuously accumulate the meltwater runoff generated in the surface active layer from the starting time point, and superimpose it with the rainfall runoff in the corresponding time step to obtain the effective meltwater runoff. The effective meltwater runoff is the linear superposition result of the meltwater runoff generated in the active layer and the rainfall runoff.

[0157] Processing module 202 is used to set the percentile distribution of the effective meltwater runoff in the target area based on historical statistical samples, select the preset percentile corresponding to the specific risk level, and when the effective meltwater runoff reaches the flow value corresponding to the preset percentile, it is determined that the first trigger condition is met.

[0158] The processing module 202 is configured to determine that a second trigger condition is satisfied when the surface unit scour force is greater than a soil instability limit shear threshold in each time step.

[0159] The output module 203 is configured to trigger a soil and water loss warning when the first trigger condition and the second trigger condition are simultaneously met.

[0160] In one possible implementation, the processing module 202 is used to establish an uncertainty management mechanism, which is used to receive various monitoring data of the target area in real time and uniformly quantify the measurement error, inference error and environmental disturbance error of the monitoring data.

[0161] The processing module 202 is used to generate multiple sets of erosion process scenario samples based on the uncertainty quantification results, using the particle filtering method or the sequential Monte Carlo deduction method, and respectively calculate the comparison results of the surface unit scouring force and the soil instability limit shear threshold under each scenario and the probability of the erosion initiation state, thereby forming a dynamic distribution curve of the erosion probability.

[0162] The processing module 202 is used to calculate the soil and water loss risk probability at the current moment in real time. The soil and water loss risk probability represents the possibility of the surface active layer entering an erosion state under the current environment and data uncertainty conditions.

[0163] The processing module 202 is used to set multiple levels of soil and water loss warning thresholds, including normal state, level 1 warning state, level 2 warning state and level 3 warning state, each warning state corresponding to an erosion probability interval.

[0164] The output module 203 is used to dynamically adjust the corresponding soil and water loss warning level when the soil and water loss risk probability enters different erosion probability intervals.

[0165] The output module 203 is used to continuously and dynamically update the soil erosion risk probability and adjust the soil erosion warning level and response measures until the environmental data stabilizes and falls back to the safe range and the erosion probability is lower than the warning cancellation threshold, thereby canceling the current soil erosion warning state.

[0166] It should be noted that the above embodiments provide devices that implement their functions using only the division of the above functional modules as examples. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0167] This embodiment also discloses an electronic device, referring to Figure 3 The electronic device may include: at least one processor 301 , at least one communication bus 302 , a user interface 303 , a network interface 304 , and at least one memory 305 .

[0168] The communication bus 302 is used to implement the connection and communication between these components.

[0169] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0170] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0171] The processor 301 may include one or more processing cores. The processor 301 utilizes various interfaces and lines to connect various parts of the entire server. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and calling data stored in the memory 305, the processor 301 performs various server functions and processes data. Optionally, the processor 301 may be implemented in at least one hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 301 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily handles operations, user interfaces, and applications; the GPU is responsible for rendering and drawing the content to be displayed on the display; and the modem is used to handle wireless communications. It is understood that the modem may not be integrated into the processor 301 and may be implemented separately on a single chip.

[0172] Among them, the memory 305 may include a random access memory (RAM) or a read-only memory (ROM). Optionally, the memory includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing operations, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also be at least one storage device located away from the aforementioned processor 301. The memory 305 as a computer storage medium may include an operation, a network communication module, a user interface 303 module and an application for a soil and water loss early warning method based on climate change.

[0173] exist Figure 3 In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 301 can be used to call an application stored in the memory 305 for a soil and water loss early warning method based on climate change. When executed by one or more processors 301, the electronic device executes one or more methods in the above embodiments.

[0174] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0175] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0176] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into one another, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0177] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0178] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0179] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory 305 and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory 305 includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disk.

[0180] The present application also discloses a computer-readable storage medium storing instructions, which, when executed by one or more processors 301 , enable an electronic device to execute one or more methods in the above embodiments.

[0181] The above are merely exemplary embodiments of the present disclosure and are not intended to limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not recorded in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A soil and water loss early warning method based on climate change, characterized in that: The method comprises: Real-time collection of surface temperature data and freeze-thaw status data in the target area, and determination of the freeze-thaw cycle status of the active surface layer based on the set freeze-thaw status criteria; Based on the freeze-thaw cycle state and geothermal gradient data, permafrost thickness data, surface roughness data and initial pore water saturation data, the meltwater runoff generation of the active surface layer is estimated, and the comprehensive runoff flow is estimated in combination with rainfall forecast data; Based on the comprehensive runoff flow, dynamically calculating the surface unit scouring force, and determining whether the surface unit scouring force reaches an erosion initiation state based on the soil instability limit shear threshold at the freeze-thaw interface; When the surface freeze-thaw conversion state superimposed on the effective meltwater runoff reaches a preset percentile, and the surface unit scouring force exceeds the soil instability limit shear threshold, a soil erosion warning is triggered.

2. The method for early warning of soil and water loss based on climate change according to claim 1, characterized in that: The real-time collection of surface temperature data and freeze-thaw status data of the target area and determination of the freeze-thaw cycle status of the active surface layer based on a set freeze-thaw status criterion specifically include: By deploying a surface temperature sensor array and a geothermal depth gradient probe in the target area, a multi-source surface temperature data and freeze-thaw status data joint collection system is formed in combination with satellite remote sensing observation data, wherein the surface temperature data is collected at the same frequency as the freeze-thaw status data; When, according to the surface temperature data, the surface temperature is continuously lower than a preset first temperature within a preset time period and the rate of change of the geothermal gradient is in a downward trend, determining that the surface active layer is in a frozen state; When the surface temperature is continuously higher than the preset second temperature and the geothermal gradient change rate changes from negative to positive, and the daily average surface temperature rise reaches a preset threshold, and the surface freeze-thaw interface position is detected to move upward by more than a preset thickness change value according to the freeze-thaw state data, the surface active layer is determined to be in a melting state; When the surface temperature is between the preset second temperature and the preset first temperature, and the daily amplitude of the geothermal gradient exceeds a preset amplitude, it is determined that the surface active layer is in a critical transition state; Alternatively, when the upward movement rate of the surface freeze-thaw interface presents an unstable fluctuating state and there are high-frequency alternating changes between the frozen state and the melted state in the freeze-thaw state data, the surface active layer is determined to be in a critical transition state.

3. The method for early warning of soil and water loss based on climate change according to claim 2, characterized in that: The method of estimating the meltwater runoff generation of the active surface layer based on the freeze-thaw cycle state and geothermal gradient data, frozen soil thickness data, surface roughness data, and initial pore water saturation data specifically includes: Determining whether the active surface layer is currently in the frozen state, the thawed state, or the critical transition state based on the freeze-thaw cycle state, wherein the frozen state corresponds to zero meltwater runoff, and the thawed state and the critical transition state trigger meltwater runoff generation estimation; The heat flux variation trend from the ground surface to the frozen interface is calculated based on the geothermal gradient data, the daily movement rate of the freeze-thaw interface is dynamically calculated using the heat diffusion formula, and the effective thawing thickness of the surface active layer is determined in combination with the permafrost thickness data; Based on the effective melt thickness, combined with the surface roughness data and the initial pore water saturation data, an active layer meltwater runoff model is constructed, wherein the initial effective porosity, capillary suction and permeability coefficient within the surface active layer are dynamically adjusted according to the initial pore water saturation data and the surface roughness data; At each time step, the potential meltwater generation per unit area caused by the movement of the freeze-thaw interface is first calculated based on the active layer meltwater runoff model. The soil water replenishment state is determined based on the initial pore water saturation. If the soil pores are not saturated, part of the meltwater is converted into soil water storage. If the soil pores are saturated, the remaining meltwater is converted into surface runoff. The surface resistance coefficient is calculated based on the surface roughness data, the runoff rate per unit area is corrected, and the active layer meltwater runoff generation per unit time is accumulated, and the meltwater runoff generation is continuously updated in real time based on the latest collected data.

4. The method for early warning of soil and water loss based on climate change according to claim 3, characterized in that: The method of estimating the meltwater runoff generation of the active surface layer based on the freeze-thaw cycle state and geothermal gradient data, permafrost thickness data, surface roughness data, and initial pore water saturation data, and estimating the comprehensive runoff flow in combination with rainfall forecast data, specifically includes: Obtaining a rainfall intensity time series corresponding to the target area based on the rainfall forecast data; Dynamically correcting the initial runoff curve number of the target area based on the surface roughness data and the initial pore water saturation data to obtain a corrected runoff curve number; Calculating the initial rainfall loss according to the modified runoff curve number, wherein the initial rainfall loss is a preset ratio of the parameter corresponding to the modified runoff curve number; Determining whether the rainfall intensity generates effective runoff based on the rainfall intensity time series and the initial rainfall loss; and calculating the rainfall runoff volume when the rainfall intensity is greater than the initial rainfall loss. The rainfall runoff volume is the square of the remainder after deducting the initial rainfall loss from the rainfall intensity, divided by the rainfall intensity and the initial rainfall loss, and then adding the difference between the parameters corresponding to the modified runoff curve number. The meltwater runoff generation amount is used as the basic meltwater runoff input, and linearly superimposed with the rainfall runoff amount to obtain the comprehensive runoff flow.

5. The method for early warning of soil and water loss based on climate change according to claim 1, characterized in that: The method of dynamically calculating the surface unit scouring force based on the comprehensive runoff flow and determining whether the surface unit scouring force reaches an erosion initiation state based on the soil instability limit shear threshold at the freeze-thaw interface specifically includes: Calculating the thickness of the surface free water flow based on the comprehensive runoff flow, wherein the thickness of the surface free water flow is determined according to a functional relationship between the comprehensive runoff flow and the local surface slope; Based on the surface free water flow thickness, combined with surface slope data and surface roughness data, the surface flow velocity is calculated using the Manning formula, and the surface flow velocity is dynamically updated as the surface free water flow thickness changes; Calculating the surface hydrodynamic shear force based on the surface free water flow thickness and the surface flow velocity, combined with the water density, gravitational acceleration, and the tangent value of the surface slope; deriving a surface unit scour force according to the surface hydrodynamic shear force and the surface area of the surface active layer; Comparing the surface unit scouring force with the soil instability limit shear threshold in real time, wherein the soil instability limit shear threshold is preset or dynamically adjusted based on soil structural characteristics, moisture content, temperature state and porosity; When the surface unit scouring force is greater than or equal to the soil instability limit shear threshold, it is determined that the surface active layer has reached the erosion initiation state; When the surface unit scouring force is less than the soil instability limit shear threshold, it is determined that the surface active layer has not reached the erosion initiation state.

6. The method for early warning of soil and water loss based on climate change according to claim 2, characterized in that: When the surface freeze-thaw conversion state and the effective meltwater runoff reach a preset percentile, and the surface unit scouring force exceeds the soil instability limit shear threshold, a soil erosion warning is triggered, specifically including: monitoring the surface freeze-thaw conversion state in real time, and determining that the surface freeze-thaw conversion state is complete when the surface active layer changes from the frozen state to the melted state or the critical transition state and maintains the state for the preset time continuously, and recording the starting time point of the completion of the surface freeze-thaw conversion state; Starting from the starting time point, the meltwater runoff generated by the active surface layer is continuously accumulated and superimposed with the rainfall runoff in the corresponding time step to obtain the effective meltwater runoff, which is the linear superposition result of the meltwater runoff generated by the active layer and the rainfall runoff; Based on historical statistical samples, the percentile distribution of the effective meltwater runoff in the target area is set, and a preset percentile corresponding to a specific risk level is selected. When the effective meltwater runoff reaches the flow value corresponding to the preset percentile, it is determined that the first trigger condition is met; In each time step, when the surface unit scouring force is greater than the soil instability limit shear threshold, it is determined that the second trigger condition is met; When the first trigger condition and the second trigger condition are met at the same time, a soil and water loss warning is triggered.

7. The method for early warning of soil and water loss based on climate change according to claim 1, characterized in that: After triggering the soil and water loss warning when the surface freeze-thaw conversion state superimposed on the effective meltwater runoff reaches a preset percentile and the surface unit scouring force exceeds the soil instability limit shear threshold, the method further includes: Establishing an uncertainty management mechanism, the uncertainty management mechanism is used to receive various monitoring data of the target area in real time and uniformly quantify the measurement error, inference error and environmental disturbance error of the monitoring data; Based on the uncertainty quantification results, a particle filter method or sequential Monte Carlo deduction method is used to generate multiple sets of erosion process scenario samples. The comparison results of the surface unit scour force and the soil instability limit shear threshold under each scenario and the probability of the erosion initiation state are calculated respectively, forming a dynamic distribution curve of erosion probability. Real-time calculation of the soil erosion risk probability at the current moment, which indicates the likelihood of the active surface layer entering an erosion state under current environmental and data uncertainty conditions; Set multiple levels of soil and water loss warning thresholds, including normal state, level 1 warning state, level 2 warning state, and level 3 warning state. Each warning state corresponds to an erosion probability interval. When the soil and water loss risk probability enters different erosion probability intervals, dynamically adjusting the corresponding soil and water loss warning level; The soil and water loss risk probability is continuously and dynamically updated, and the soil and water loss warning level and response measures are adjusted until the environmental data stabilizes and falls back to a safe range and the erosion probability is lower than the warning cancellation threshold, thereby canceling the current soil and water loss warning status.

8. A soil and water loss early warning device based on climate change, characterized in that: The device is used to execute the soil and water loss early warning method based on climate change according to any one of claims 1 to 7, and the device comprises an acquisition module (201), a processing module (202) and an output module (203), wherein: The acquisition module (201) is used to collect surface temperature data and freeze-thaw status data of the target area in real time, and determine the freeze-thaw cycle status of the active surface layer based on a set freeze-thaw status criterion; The processing module (202) is used to estimate the meltwater runoff generation of the surface active layer based on the freeze-thaw cycle state and geothermal gradient data, frozen soil thickness data, surface roughness data and initial pore water saturation data, and to estimate the comprehensive runoff flow in combination with rainfall forecast data; The processing module (202) is used to dynamically calculate the surface unit scouring force based on the comprehensive runoff flow, and determine whether the surface unit scouring force reaches an erosion initiation state based on the soil instability limit shear threshold at the freeze-thaw interface; The output module (203) is used to trigger a soil and water loss warning when the surface freeze-thaw conversion state superimposed on the effective meltwater runoff reaches a preset percentile and the surface unit scouring force exceeds the soil instability limit shear threshold.

9. An electronic device, characterized in that: The electronic device comprises a processor (301), a communication bus (302), a user interface (303), a network interface (304) and a memory (305), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are both used to communicate with other devices, the communication bus (302) is used to realize connection and communication between components in the electronic device, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

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