Method for estimating water leakage of soil in high and cold sandy land

Abstract

The invention relates to the technical field of alpine sand soil moisture leakage estimation technologies, and particularly discloses an alpine sand soil moisture leakage estimation method which comprises the following steps: S1, multi-source data acquisition: acquiring 0-110cm layered soil moisture content and temperature data by using a cold-resistant soil hydrothermal sensor array, and calculating the moisture content and temperature data of the layered soil; synchronously acquiring Sentinel-2 remote sensing data to extract vegetation coverage and land surface temperature, and recording regional freezing and thawing cycle times and biological crust coverage types; s2, key parameter inversion: calculating a permeability coefficient correction factor kFTC = alpha * N (1 / 2) through a multi-expression programming model based on the freezing and thawing cycle number N. According to the method for estimating the soil moisture leakage amount of the high and cold sand land, the limitation of a traditional model based on steady-state water flow and empirical parameters is broken through by quantifying the dynamic effect of freezing and thawing cycles on the porosity of a 30-80 mm soil layer and the water blocking effect of 23.41%-30.39% when the biological crust coverage degree exceeds 30%; and the influence of the low-temperature freezing and thawing cycle and the biological crust on the leakage mechanism of the high and cold sand land is accurately depicted.

Description

Technical Field

[0001] The present invention relates to the technical field of estimating soil moisture leakage in alpine sandy lands, and in particular to a method for estimating soil moisture leakage in alpine sandy lands. Background Art

[0002] In alpine sandy ecosystems, soil moisture infiltration is a key parameter of water resource circulation. Its accurate estimation is of great significance to regional water resource management, ecological restoration project design, and climate change response. The unique low temperature environment and frequent freeze-thaw cycles of alpine sandy areas, coupled with the significant impact of biological crusts on soil hydrological processes, make the soil moisture infiltration mechanism in this area extremely complex.

[0003] Traditional soil water infiltration estimation methods, such as the Hydrus-1D model, are often based on steady-state water flow assumptions and empirical parameters, and fail to fully consider the dynamic effects of freeze-thaw cycles on soil pore structure and permeability. Studies have shown that freeze-thaw cycles can redistribute soil particles, causing significant changes in porosity at a depth of 30-80 mm, which in turn affects water infiltration characteristics. However, existing methods struggle to accurately quantify these changes. Furthermore, the widespread distribution of biological crusts in alpine sandy areas has a significant water-blocking effect. When the coverage of biological crusts exceeds 30%, the soil infiltration rate can be reduced by 23.41%-30.39%. Traditional models often simplify this, resulting in large errors in the estimated results.

[0004] In terms of data acquisition and processing, existing technologies often rely on single-point in-situ monitoring or a single remote sensing data source, making it impossible to achieve continuous spatiotemporal estimation of seepage in the 0-110 cm soil layer. Furthermore, traditional interpolation methods and statistical models struggle to effectively integrate heterogeneous data from multiple sources, failing to meet the demand for high-precision estimates in the complex environment of alpine sandy lands. Therefore, a method is urgently needed that can comprehensively consider specific factors such as freeze-thaw cycles and biological crusts, and combine multi-source data to achieve accurate spatiotemporal estimation, thereby filling the gap in existing technology in the study of soil water seepage in alpine sandy lands. Summary of the Invention

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for estimating soil moisture leakage in alpine sandy land, comprising the following steps:

[0006] S1. Multi-source data acquisition: Utilize a cold-resistant soil water and heat sensor array to collect soil moisture and temperature data at 0-110 cm layers. Simultaneously acquire Sentinel-2 remote sensing data to extract vegetation cover and surface temperature, and record the number of freeze-thaw cycles and biocrust cover types in the region.

[0007] S2, key parameter inversion: based on the number of freeze-thaw cycles N, the permeability coefficient correction factor k_FTC=α·N^(1 / 2) is calculated through the multi-expression programming model, where α is the soil texture coefficient, and the water blocking factor β is determined according to the coverage of biological crust, when the coverage is > 30%, β=0.72-0.85;

[0008] S3, coupling model construction: the correction factor in step S2 is embedded into the improved Hydrus-1D model to establish a water balance equation considering the freeze-thaw-porosity dynamics;

[0009] S4, leakage calculation: the Koschke formula is combined to iteratively solve the seepage rate of each soil layer, and the random forest algorithm is used to fuse remote sensing and in-situ data to continuously estimate the temporal and spatial seepage of the 0-110cm soil layer;

[0010] S5, error correction, when the deviation is > 15%, the k_FTC and β are iteratively corrected by comparing the measured value of porosity by CT scanning with the predicted value of the model.

[0011] Preferably, the cold-resistant sensor array in step S1 includes TDR sensors buried at depths of 10, 30, 50, 70, 90, 110cm, the data acquisition interval is ≤30min, and the sensor has a working capacity of-30℃ to 50℃.

[0012] Preferably, the determination method of the permeability coefficient correction factor in step S2 includes:

[0013] S21. Freeze-thaw cycle test on undisturbed soil sample;

[0014] S22. Test the particle size distribution of the soil sample by laser particle size analyzer and calculate the change Δm of sand, silt and clay content;

[0015] S23. Use MEP algorithm to establish the permeability coefficient change model.

[0016] Preferably, the freeze-thaw-porosity dynamic relationship in step S3 is determined by CT scanning, when the freeze-thaw cycle number N≤10, the total porosity of the soil first decreases and then increases, and there is a porosity sensitive zone in the 30-80mm soil layer depth, the relationship between the porosity change rate Δn and the freeze-thaw cycle number is: Δn=0.017·sin(0.3N)+0.023.

[0017] Preferably, the multi-source data fusion algorithm in step S4 uses a DBN deep learning model, the input variables include NDVI, land surface temperature and soil initial moisture content reduced to 30m, and the output is 30m resolution daily seepage data, and the root mean square error RMSE≤0.048m 3 / m 3 .

[0018] An alpine sandy soil water infiltration amount estimation system, comprising:

[0019] A data acquisition module is configured to acquire soil water and heat data, remote sensing data and freeze-thaw cycle parameters.

[0020] A parameter inversion module is configured to calculate a freeze-thaw correction factor and a biological soil crust water resistance factor.

[0021] A model calculation module integrates an improved Hydrus-1D model and a multi-source data fusion algorithm, and realizes the specific method of the spatial and temporal continuous estimation described in S4 of claim 1.

[0022] An output module is configured to generate a spatial and temporal continuous infiltration amount estimation result.

[0023] Preferably, the parameter inversion module is built-in MEP algorithm engine, which can calculate the correction value of the permeability coefficient according to the number of freeze-thaw cycles and the change of soil particle size.

[0024] Preferably, the model calculation module supports user-defined input of alpine sandy land type parameters, and when applied to mobile dunes, automatically enables a 110cm deep infiltration threshold correction parameter.

[0025] The system has the following beneficial effects:

[0026] The present application quantifies the dynamic effect of freeze-thaw cycles on 30-80mm soil porosity and the water resistance effect of biological soil crust coverage of more than 30%, breaks through the limitations of traditional models based on steady-state water flow and empirical parameters, accurately depicts the influence of alpine sandy land low-temperature freeze-thaw cycles and biological soil crust on infiltration mechanism, has lower error compared with traditional methods, and realizes spatial and temporal continuous estimation of 30m resolution of infiltration amount of 0-110cm soil layer per day through multi-source data fusion of Sentinel-2 remote sensing data and cold-resistant sensor array, combined with inverse distance weighted spatial interpolation, LSTM time series prediction algorithm, and the like. 3 / m 3 , has high prediction accuracy for extreme freeze-thaw events, in addition, the freeze-thaw number and the power function relationship of the permeability coefficient, the biological soil crust resistance function and other innovative parameters are embedded into the Hydrus-1D model for the first time, forming a special estimation system suitable for low-temperature environment of-6.0~2.7℃, providing key technical support for water resource management, ecological restoration engineering design and climate change response strategy in alpine sandy areas with annual average potential evaporation of 1500-2400mm in the northeast of Qinghai-Tibet Plateau. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present application will be clearly and completely described below. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work belong to the protection scope of the present application.

[0028] The present application provides a technical solution: a high-cold sandy soil water infiltration amount estimation method, characterized in that it comprises the following steps:

[0029] S1, multi-source data acquisition: using a cold-resistant soil water and heat sensor array to collect 0-110cm layered soil moisture content and temperature data, synchronously acquiring Sentinel-2 remote sensing data to extract vegetation coverage and surface temperature, recording the number of freeze-thaw cycles and biological crust coverage types in the region, the cold-resistant sensor array includes TDR sensors buried at depths of 10, 30, 50, 70, 90 and 110cm, the data acquisition interval is ≤30min, and the sensor has a working capacity of-30℃ to 50℃;

[0030] S2, key parameter inversion: based on the number of freeze-thaw cycles N, the penetration coefficient correction factor k_FTC is calculated through a multi-expression programming model, k_FTC=α·N^(1 / 2), wherein α is a soil texture coefficient, and a water blocking factor β is determined according to the biological crust coverage, when the coverage is >30%, β=0.72-0.85, and the determination method of the penetration coefficient correction factor comprises:

[0031] S21, freeze-thaw cycle test on undisturbed soil samples, the specific steps are as follows:

[0032] S211, sample collection and preparation: selecting a representative area in the high-cold sandy land, collecting undisturbed soil samples of 0-110cm soil layer, cutting the soil samples into cylinders with a diameter of 5cm and a height of 10cm, and preparing 3 parallel samples for each soil layer;

[0033] S212, initial parameter determination: using the cutting ring method to determine the initial bulk density of the soil sample, using the drying method to determine the initial moisture content, and using the mercury intrusion method to obtain the initial pore structure parameters of the soil sample;

[0034] S213, freeze-thaw cycle setting: placing the soil sample in a high-low temperature alternating test box, setting the freezing temperature to-15℃, the freezing time to 12 hours, the thawing temperature to 5℃, and the thawing time to 12 hours, to form a complete freeze-thaw cycle period;

[0035] S214, cycle process monitoring: recording the temperature of the soil sample every 2 hours during each freeze-thaw cycle, and using a displacement sensor to monitor the volume change of the soil sample during the freeze-thaw process;

[0036] S215, post-circulation treatment: after completing the predetermined number of freeze-thaw cycles (1, 3, 5, 7, 10 times), the soil sample is removed, naturally air-dried to constant weight, and the bulk density, water content and pore structure parameters of the soil sample are measured again;

[0037] S22, the laser particle size analyzer is used to test the particle distribution of the soil sample, and the changes in the contents of sand, silt and clay are calculated;

[0038] S23, the MEP algorithm is used to establish a permeability coefficient change model:

[0039]

[0040] where k0 is the initial permeability coefficient, a1, a2, a3 are fitting coefficients;

[0041] S3, coupling model construction: the correction factor of step S2 is embedded into the improved Hydrus-1D model to establish a water balance equation considering freeze-thaw-porosity dynamics:

[0042]

[0043] where k(θ) is the temperature-corrected permeability coefficient, ψ is the soil water potential, and f(crust) is the biological crust resistance function;

[0044] The freeze-thaw-porosity dynamic relationship is determined by CT scanning. When the freeze-thaw cycle number N is less than or equal to 10, the total porosity of the soil first decreases and then increases, and there is a porosity sensitive zone in the 30-80mm soil layer depth. The relationship between the porosity change rate Δn and the freeze-thaw cycle number is: Δn = 0.017·sin(0.3N) + 0.023;

[0045] S4, leakage calculation: the Kostiakov formula is used to iteratively solve the leakage rate of each soil layer, and the random forest algorithm is used to fuse remote sensing and in-situ data to continuously estimate the temporal and spatial leakage of the 0-110cm soil layer. The specific method is as follows:

[0046] S41, the preprocessed multi-source data is preprocessed, and the quality control algorithm is used to remove abnormal data, and the time and space interpolation method is used to fill in the missing data. The inverse distance weighting method is used for spatial interpolation, and the cubic spline interpolation method is used for time interpolation;

[0047] S42, the preprocessed data is divided according to the 30m×30m grid, and a spatial grid database is established;

[0048] S43, based on historical data, the seasonal decomposition algorithm is used to decompose the leakage time series, and the trend item, seasonal item and random item are separated;

[0049] S44. For each spatial grid, a long short-term memory network (LSTM) model is used, using the decomposed trend term, seasonal term, and environmental parameters of the current grid (vegetation cover, surface temperature, initial soil moisture content, etc.) as input to predict the seepage rate of each soil layer in the future time period;

[0050] S45. Integrate the prediction results of each spatial grid to generate continuous spatiotemporal data of seepage volume at a resolution of 30 m per day for the 0-110 cm soil layer;

[0051] The multi-source data fusion algorithm uses the DBN deep learning model. The input variables include NDVI, surface temperature, and initial soil moisture content downscaled to 30m. The output is daily seepage data with a resolution of 30m. The root mean square error (RMSE) is ≤0.048m. 3 / m 3 .

[0052] S5. Error correction: By comparing the measured porosity values ​​from CT scans with the model-predicted values, when the deviation is >15%, k_FTC and β are iteratively corrected. The correction formula is:

[0053]

[0054] Where γ is the correction coefficient, n_CT is the porosity measured by CT, and n_model is the porosity calculated by the model.

[0055] Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field and related fields without making creative efforts should fall within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described and explained in the present invention shall be implemented in accordance with conventional means in the field unless otherwise specified or limited.

Claims

1. A method for estimating soil moisture leakage in alpine sandy land, characterized in that: The following steps are involved: S1. Multi-source data acquisition: Utilize a cold-resistant soil water and heat sensor array to collect soil moisture and temperature data at 0-110 cm layers. Simultaneously acquire Sentinel-2 remote sensing data to extract vegetation cover and surface temperature, and record the number of freeze-thaw cycles and biocrust cover types in the region. S2. Key parameter inversion: Based on the number of freeze-thaw cycles N, a multi-expression programming model is used to calculate the permeability correction factor k_FTC = α·N^(1 / 2), where α is the soil texture coefficient. The water resistance factor β is determined based on the coverage of biological crusts. When the coverage is >30%, β = 0.72-0.85; S3, coupled model construction: embed the correction factor of step S2 into the improved Hydrus-1D model to establish a water balance equation considering freeze-thaw-porosity dynamics; S4. Leakage calculation: The seepage rate of each soil layer is iteratively solved by combining the Koskogaff formula. The remote sensing and in-situ data are integrated through the random forest algorithm to perform spatiotemporal continuous estimation of the seepage rate of the 0-110 cm soil layer. S5. Error correction: By comparing the measured porosity values ​​from CT scans with the model predicted values, k_FTC and β are iteratively corrected when the deviation is >15%.

2. The method for estimating soil moisture leakage in alpine sandy land according to claim 1, characterized in that: In step S1, the cold-resistant sensor array includes TDR sensors buried at depths of 10, 30, 50, 70, 90, and 110 cm, with a data collection interval of ≤30 min, and the sensors have the ability to work at -30°C to 50°C.

3. The method for estimating soil moisture leakage in alpine sandy land according to claim 1, characterized in that: The method for determining the permeability coefficient correction factor in step S2 includes: S21. Conduct freeze-thaw cycle test on undisturbed soil samples; S22. Use a laser particle size analyzer to measure the particle distribution of the soil sample and calculate the change in sand, silt, and clay content (Δm); S23. Use the MEP algorithm to establish a permeability coefficient change model.

4. The method for estimating soil moisture leakage in alpine sandy land according to claim 1, characterized in that: In step S3, the freeze-thaw-porosity dynamic relationship is determined by CT scanning. When the freeze-thaw frequency N is less than or equal to 10 times, the total soil porosity first decreases and then increases. There is a porosity-sensitive zone at a soil depth of 30-80 mm, and the relationship between the porosity change rate Δn and the freeze-thaw frequency is: Δn = 0.017·sin(0.3N) + 0.

023.

5. The method for estimating soil moisture leakage in alpine sandy land according to claim 1, characterized in that: The multi-source data fusion algorithm in step S4 adopts the DBN deep learning model. The input variables include NDVI, surface temperature, and initial soil moisture content downscaled to 30m. The output is daily seepage data with a resolution of 30m. The root mean square error (RMSE) is ≤ 0.048m. 3 / m 3 .

6. A system for estimating soil moisture leakage in alpine sandy areas, characterized by: include: Data acquisition module, used to obtain soil water and heat data, remote sensing data and freeze-thaw cycle parameters; Parameter inversion module, used to calculate freeze-thaw correction factor and biocrust water blocking factor; A model calculation module, integrating the improved Hydrus-1D model and the multi-source data fusion algorithm, and implementing the specific method of spatiotemporal continuous estimation described in S4 of claim 1; Output module generates spatiotemporally continuous leakage estimation results.

7. The alpine sandy land soil moisture leakage estimation system according to claim 6 is characterized in that: The parameter inversion module has a built-in MEP algorithm engine, which can calculate the permeability coefficient correction value in real time according to the number of freeze-thaw cycles and the change of soil particle size.

8. The alpine sandy land soil moisture leakage estimation system according to claim 6, characterized in that: The model calculation module supports user-defined input of alpine sand type parameters. When applied to mobile sand dunes, the 110 cm depth seepage threshold correction parameter is automatically enabled.

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