A method and system for analyzing the evolution characteristics of soil moisture

Abstract

This invention discloses a method and system for analyzing soil moisture evolution characteristics, belonging to the field of soil analysis technology. The method includes: determining the analysis area and period, and acquiring soil moisture-related data for the analysis area within the analysis period; constructing and debugging a VIC model based on the acquired data to obtain a VIC model with a preset accuracy; simulating soil moisture using the determined VIC model, and extracting soil moisture evolution characteristics in time and space based on the acquired data to obtain the spatiotemporal evolution law of soil moisture; analyzing the influencing factors of the obtained spatiotemporal evolution law of soil moisture to obtain the analysis results of soil moisture evolution characteristics. This invention can solve the problem of insufficient existing measured data, which affects the accuracy and comprehensiveness of soil moisture evolution characteristic analysis results, and thus impacts water resource management and decision-making.

Description

Technical Field

[0001] This invention belongs to the field of soil analysis technology, specifically relating to a method and system for analyzing the evolution characteristics of soil moisture. Background Technology

[0002] Soil moisture is a crucial factor influencing the water cycle and water balance, playing a significant role in the temporal and spatial evolution of climate and vegetation. Its temporal and spatial characteristics have a substantial impact on watershed precipitation, runoff, and evaporation, particularly on climate. In hydrology, soil moisture, as a comprehensive indicator, not only participates in the hydrological cycle, influencing infiltration and runoff, but also affects the Earth's biological cycles. Soil moisture also plays a vital role in climatology, moderating climate through albedo, sensible heat, and latent heat at the Earth's surface. In ecology, soil moisture is the primary factor for vegetation growth, determining its distribution and evolution.

[0003] In recent years, the water demand in the study area has been continuously increasing in order to promote socio-economic development. Furthermore, due to global warming and human development and utilization, ecological and environmental problems have become increasingly prominent, and water scarcity has become more severe. Therefore, research on the spatiotemporal variation of soil moisture is essential, not only as a key issue in eco-hydrology but also for the great significance of future ecological security and sustainable development. Changes in soil moisture are the result of the combined effects of various climatic and geographical factors, with different factors having different driving forces. Therefore, in-depth research into the patterns and driving factors of soil moisture variation, revealing its intrinsic causes, is not only crucial for the water cycle but also essential for the efficient utilization, scientific planning and management of watershed water resources, and agricultural development, ultimately contributing to the high-quality development of the study area.

[0004] In recent years, with a growing understanding of the importance of soil moisture to ecosystems and agricultural production, researchers both domestically and internationally have conducted extensive studies on soil moisture changes at different scales and their influencing factors, achieving a series of important research results. These studies not only cover the soil moisture cycle mechanisms at the macro scale, but also the relationship between soil moisture and factors such as climate, land use, and vegetation cover at the regional scale.

[0005] However, while existing research has provided insights into soil moisture dynamics, systematic studies on the spatiotemporal characteristics and driving factors of soil moisture changes at smaller scales, such as small and medium-sized watersheds, remain relatively lacking. At the watershed scale, current research often focuses on analyzing the impact of single environmental factors on soil moisture, such as precipitation patterns or land use change, while rarely considering the effects of interactions between factors. This results in an incomplete understanding of the complex relationship between soil moisture and environmental factors. For example, soil moisture is not only directly affected by precipitation but is also related to factors such as temperature, vegetation type, soil type, and human activities. These factors are often intertwined, jointly influencing the dynamic changes in soil moisture.

[0006] Furthermore, soil moisture research often lacks long-term and comprehensive measured data, which limits our understanding of soil moisture variation trends and periodic characteristics. Without sufficient data, it is difficult to distinguish the effects of natural variations and anthropogenic disturbances, and it is also difficult to accurately predict future soil moisture trends. Therefore, strengthening long-term, systematic research at the watershed scale and exploring the comprehensive impact of multiple environmental factors on soil moisture is of great significance for improving our understanding of the dynamic mechanisms of soil moisture change and for water resource management. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and system for analyzing soil moisture evolution characteristics, thereby solving the problem that insufficient measured data affects the accuracy and comprehensiveness of soil moisture evolution characteristic analysis results, and consequently affects water resource management and decision-making.

[0008] To solve the above-mentioned technical problems, the present invention is implemented using the following technical solution:

[0009] In a first aspect, the present invention provides a method for analyzing the evolution characteristics of soil moisture, including:

[0010] S1: Determine the analysis area and period, and obtain soil moisture data related to the analysis area within the analysis period;

[0011] S2: Construct and debug the VIC model based on the data obtained in step S1 to obtain a VIC model with preset accuracy;

[0012] S3: Simulate soil moisture based on the VIC model determined in step S2, and combine it with the data obtained in step S1 to extract the soil moisture evolution characteristics in time and space, so as to obtain the spatiotemporal evolution law of soil moisture.

[0013] S4: Analyze the influencing factors of the spatiotemporal evolution of soil moisture obtained in step S3, and obtain the analysis results of soil moisture evolution characteristics.

[0014] In the aforementioned method for analyzing soil moisture evolution characteristics, step S1 includes the following data related to soil moisture in the analysis area during the analysis period: measured soil moisture data, meteorological data, land use type data, elevation data, and vegetation data. Step S1 also includes preprocessing the acquired data to the input format required by the VIC model.

[0015] In the aforementioned method for analyzing soil moisture evolution characteristics, step S2 involves debugging the VIC model by using simulated soil moisture and measured soil moisture data output by the VIC model to calculate the Nash-Sutcliffe efficiency coefficient, relative error, and correlation coefficient, and evaluating the accuracy level of the VIC model based on the calculation results.

[0016] The aforementioned method for analyzing soil moisture evolution characteristics includes step S3, which includes:

[0017] The Mann-Kendall trend test was used to analyze the changing trend of soil moisture, and the Mann-Kendall mutation test was used to identify the mutation points and their years of occurrence, thus obtaining the temporal evolution characteristics of soil moisture. The semi-variogram was used to analyze the spatial variability of soil moisture, and the spatial structure parameters were obtained by fitting the theoretical model. The spatial clustering of soil moisture was detected by combining spatial autocorrelation analysis, thus obtaining the spatial distribution characteristics of soil moisture. By combining the temporal evolution characteristics and spatial distribution characteristics of soil moisture, the spatiotemporal evolution law of soil moisture was obtained.

[0018] The aforementioned method for analyzing soil moisture evolution characteristics includes step S4, which includes:

[0019] The factors influencing soil moisture are divided into climatic factors and geographical factors. The influence of climatic factors and geographical factors on the evolution of soil moisture is analyzed separately to obtain the dominant influencing factors and their effects on the evolution of soil moisture. Among them, climatic factors include precipitation, temperature, relative humidity and evapotranspiration, and geographical factors include altitude, slope, aspect, vegetation cover and land use type. The dominant influencing factors include dominant climatic factors, dominant climatic factor groups, dominant geographical factors and dominant geographical factor groups.

[0020] The aforementioned method for analyzing soil moisture evolution characteristics includes analyzing the influence of climatic factors on soil moisture evolution, such as:

[0021] This study analyzes the impact of individual climatic factors and their pairwise interactions on soil moisture changes. Specifically, the analysis of individual climatic factors on soil moisture changes includes: using full-cycle and local-cycle correlation analysis to assess the time-varying impact of each climatic factor on soil moisture; and combining a geographic detector and multi-scale geographic weighted regression to analyze the spatial driving effect of each climatic factor on soil moisture. The analysis of the impact of pairwise interactions of climatic factors on soil moisture changes includes: climatic factors interact to form multiple climatic factor groups; using an interaction geographic detector and multi-scale geographic weighted regression to analyze the synergistic impact of each climatic factor group on soil moisture changes.

[0022] The aforementioned method for analyzing the evolution characteristics of soil moisture includes the following: full-cycle correlation analysis: using the Pearson correlation coefficient method to calculate the global correlation between each climate factor and soil moisture, and using the t-test to evaluate the statistical significance of the correlation coefficient, thereby obtaining the correlation strength and direction over the entire time series; local periodic correlation analysis includes: using cross wavelet transform to analyze the local correlation between each climate factor and soil moisture in the time and frequency domain, and identifying significant resonance periods and phase relationships.

[0023] The aforementioned method for analyzing soil moisture evolution characteristics, specifically the combination of a geographic detector and multi-scale geographic weighted regression, includes: using a geographic detector to quantify the explanatory power of each climate factor on soil moisture, identifying the dominant climate factor among the climate factors based on the calculated explanatory power; analyzing the spatial non-stationarity of the dominant climate factor through multi-scale geographic weighted regression to obtain the scale of influence and local regression coefficient distribution of the heterogeneity of the dominant climate factor; and employing an interaction geographic detector and multi-scale geographic weighted regression, specifically: using an interaction geographic detector to quantify the explanatory power of each climate factor group on soil moisture, identifying the dominant climate factor group among the climate factor groups based on the calculated explanatory power; analyzing the spatial non-stationarity of the dominant climate factor group through multi-scale geographic weighted regression to obtain the scale of influence and local regression coefficient distribution of the heterogeneity of the dominant climate factor group.

[0024] The aforementioned method for analyzing soil moisture evolution characteristics, wherein the analysis of the influence of geographical factors on soil moisture evolution includes:

[0025] This study analyzes the impact of various geographic factors and their pairwise interactions on soil moisture changes. Specifically, the analysis of individual geographic factors on soil moisture changes includes: quantifying the explanatory power of each geographic factor on soil moisture using a geographic detector, identifying the dominant geographic factor based on the calculated explanatory power, and analyzing the spatial non-stationarity of the dominant geographic factor through multi-scale geographic weighted regression analysis to obtain the scale of influence and local regression coefficient distribution of the heterogeneity of the dominant geographic factor. The analysis of the impact of pairwise interactions of geographic factors on soil moisture changes includes: geographic factors interact to form multiple geographic factor groups; quantifying the explanatory power of each geographic factor group on soil moisture using an interaction geographic detector, identifying the dominant geographic factor group within the geographic factor group based on the calculated explanatory power, and analyzing the spatial non-stationarity of the dominant geographic factor group through multi-scale geographic weighted regression analysis to obtain the scale of influence and local regression coefficient distribution of the heterogeneity of the dominant geographic factor group.

[0026] Secondly, the present invention provides a soil moisture evolution characteristic analysis system, including a data acquisition module, a model determination module, a pattern acquisition module, and a result analysis module;

[0027] The data acquisition module is used to: determine the analysis area and acquire data related to soil moisture in the analysis area; the model determination module is used to: construct and debug the VIC model based on the data acquired by the data acquisition module to obtain a VIC model with a preset accuracy; the pattern acquisition module is used to: simulate soil moisture based on the VIC model determined by the model determination module, and extract soil moisture evolution characteristics in time and space in combination with the data acquired by the data acquisition module to obtain the spatiotemporal evolution pattern of soil moisture; the result analysis module is used to: analyze the influencing factors of the spatiotemporal evolution pattern of soil moisture acquired by the pattern acquisition module to obtain the soil moisture evolution characteristic analysis results.

[0028] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0029] This invention employs a sufficiently accurate VIC model to provide the soil moisture data required for soil moisture evolution characteristic analysis. It first obtains the spatiotemporal evolution law of soil moisture itself, and then uses this law as a result guide to further analyze the causes of the results and the influence of influencing factors on soil moisture changes. This yields the soil moisture evolution characteristic analysis results, improving the accuracy and comprehensiveness of soil moisture analysis results. It solves the problem that existing soil moisture analysis is limited by insufficient measured data, affecting the accuracy and comprehensiveness of soil moisture analysis results, and consequently impacting water resource management and decision-making.

[0030] This invention uses three methods—Nash-Sutcliffe efficiency coefficient, relative error, and correlation coefficient—to calibrate the accuracy of the VIC model, enabling it to obtain sufficiently accurate and continuous soil moisture data, providing reliable data support for subsequent analysis.

[0031] Furthermore, this invention employs the Mann-Kendall trend test to monitor the time-series changes in soil moisture; identifies significant turning points in soil moisture changes through variance analysis; and utilizes semivariogram and spatial autocorrelation analysis to explore the spatial dependence and clustering characteristics of soil moisture. These analyses comprehensively reveal the complexity of soil moisture changes and derive the spatiotemporal evolution patterns of soil moisture itself.

[0032] Furthermore, based on the characteristics of the influencing factors themselves, this invention divides the influencing factors into geographical factors and climatic factors, takes soil moisture as the dependent variable and the influencing factors as independent variables, and conducts an in-depth analysis of the influence of the independent variables on the dependent variables to obtain the dominant influencing factors of soil moisture evolution and the effects of the dominant influencing factors.

[0033] This invention considers the magnitude of the influence of single climate factors on soil moisture, and uses full-cycle and local-cycle correlation analysis to evaluate the time-varying influence of each climate factor on soil moisture; combined with geographic detectors and multi-scale geographic weighted regression, it analyzes the spatial driving effect of each climate factor on soil moisture, and obtains the dominant climate factor and its effect.

[0034] This invention considers the influence of pairwise interactions of climate factors on soil moisture. It uses an interaction geospatial detector and multi-scale geographic weighted regression to analyze the synergistic effects of each group of climate factors on soil moisture changes, and obtains the dominant climate factor group and its effects.

[0035] This invention considers the influence of single geographical factors on soil moisture, uses geographical detectors and multi-scale geographical weighted regression to analyze the spatial driving effect of each geographical factor on soil moisture, and obtains the dominant geographical factors and their effects.

[0036] This invention considers the influence of pairwise interactions of geographical factors on soil moisture. It uses an interaction geospatial detector and multi-scale geographical weighted regression to analyze the synergistic effects of each group of geographical factors on soil moisture changes, and obtains the dominant geographical factor groups and their effects.

[0037] This invention employs spatial autocorrelation (Moran's I, LISA) to first reveal the spatial distribution pattern (aggregation / dispersion) of soil moisture, providing analytical direction for geospatial detectors. Geospatial detectors (q-values, interactions) then interpret the causes of the patterns (e.g., altitude-dominated moisture differentiation), verifying the rationality of the spatial autocorrelation results.

[0038] This invention overcomes the challenges encountered in existing research on the driving forces of soil moisture change, such as the difficulty in obtaining long-term continuous data, insufficient research on the interaction of multiple factors, and limitations in the precision of the research scale, which affect the accuracy and comprehensiveness of soil moisture analysis results. It yields more accurate and comprehensive soil moisture analysis results, which can optimize water resource allocation, reduce disaster risks, and promote sustainable water-food-ecology coordinated management, which is of great significance to achieving the United Nations Sustainable Development Goal (SDG 6). Attached Figure Description

[0039] Figure 1 This is a schematic diagram of a method for analyzing the evolution characteristics of soil moisture in Embodiment 1 of the present invention. Detailed Implementation

[0040] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations thereof. In the absence of conflict, the embodiments and technical features in the embodiments can be combined with each other.

[0041] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0042] Example 1:

[0043] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings and examples.

[0044] In this example, the present invention provides a method for analyzing the evolution characteristics of soil moisture, such as... Figure 1 As shown, the method includes the following steps:

[0045] S1: Determine the analysis area and period, and obtain soil moisture data related to the analysis area within the analysis period;

[0046] S2: Construct and debug the VIC model based on the data obtained in step S1 to obtain a VIC model with preset accuracy;

[0047] S3: Simulate soil moisture based on the VIC model determined in step S2, and combine it with the data obtained in step S1 to extract the soil moisture evolution characteristics in time and space, so as to obtain the spatiotemporal evolution law of soil moisture.

[0048] S4: Analyze the influencing factors of the spatiotemporal evolution of soil moisture obtained in step S3, and obtain the analysis results of soil moisture evolution characteristics.

[0049] This embodiment uses a sufficiently accurate VIC model to provide the soil moisture data required for soil moisture evolution characteristic analysis, solving the problem of insufficient measured data in existing technologies. The specific implementation of the method of this invention is described below:

[0050] In step S1, the soil moisture-related data for the analysis area during the analysis period includes:

[0051] Measured soil moisture data, meteorological data, land use type data, elevation data, and vegetation data;

[0052] Step S1 includes preprocessing the acquired data into the input format required by the VIC model.

[0053] Step S1 specifically includes:

[0054] Collect and organize data from the analyzed area, including raw elevation data, vegetation database files and land cover data, land surface vegetation type distribution and land cover type data, measured flow from hydrological stations, soil data, meteorological data, etc., and preprocess the data.

[0055] Preprocessing of hydrological and meteorological data includes:

[0056] Step 11: To ensure data quality and consistency, precipitation and evaporation data are preprocessed, including removing outliers, filling missing values, and transforming the data temporal and spatial scales; then, a mask is used to extract the analysis area, and the soil texture classification is described.

[0057] Step 12: Resample the DEM, slope, aspect, land use type, NDVI, and temperature data to the same spatial resolution and ensure that the geographic coordinate system is consistent; Based on the watershed digital elevation model (DEM), use ArcGIS software to divide the study area into independent cell grids. In this embodiment, the analysis area is divided into grids with a resolution of 0.083°×0.083°.

[0058] Step 13: Normalize the precipitation and other data.

[0059] In step S2, debugging the VIC model includes calculating the Nash-Sutcliffe efficiency coefficient, relative error, and correlation coefficient using the simulated soil moisture and measured soil moisture data output by the VIC model, and evaluating the accuracy level of the VIC model based on the calculation results.

[0060] Step S2 includes:

[0061] A VIC hydrological model was constructed in the study area. The model inputs included a global control file, a meteorological drive file, a soil parameter file, a vegetation pool file, and a vegetation parameter file. The model outputs included daily runoff, baseflow, and soil moisture content data for each grid. The simulation process was divided into two phases: a calibration phase and a validation phase. Nash efficiency coefficient, relative error, and correlation coefficient were selected to evaluate the model simulation results. The specific steps included:

[0062] Step 21: Establish a database of geographical environmental factors such as watershed climate (rainfall, runoff, evaporation), altitude, land use type (vegetation), and soil moisture.

[0063] Step 22: Successful operation of the VIC model requires input of various files, such as: global control file, meteorological drive file, soil parameter file, vegetation library file, and vegetation parameter file.

[0064] The vegetation parameter files were selected based on global 1km resolution land cover type data published by the University of Maryland, focusing on vegetation types 1 through 11. In the VIC model, vegetation parameters were described using a vegetation library file and a vegetation parameter file. The parameters requiring calibration in the vegetation parameter library file included: structural impedance, minimum stomatal impedance, leaf area index, albedo, roughness, and zero-plane displacement. The vegetation parameter files reflected the distribution of various vegetation types across the grid, including the proportion of each grid cell, root zone depth, root zone depth in the three soil layers, and leaf area index from January to December.

[0065] The soil parameter file uses the global 5 arcminute (approximately 10 km) resolution soil dataset provided by NOAA. The soil parameter file describes the geological and geomorphological information of each grid cell, mainly including soil type parameters and DEM, etc.

[0066] One type of parameter in the model, such as saturated soil water potential, saturated soil hydraulic conductivity, and the exponential parameter b used to describe unsaturated flow and saturated soil volumetric water content, is related to soil properties and does not need to be modified once determined.

[0067] Another type of parameter is determined by the degree of fit between simulation and measured data, such as the depth of three soil layers. Water storage capacity curve and three parameters related to the base current. and These parameters have a significant impact on runoff generation, and they are calibrated using measured runoff data from hydrological stations. The range is 0.02~0.18. The range is 0.0009~0.15. The range is 1.5 to 7.5. The range is 0.61~0.85. The range is 0.1~2. The range is 2 to 5.

[0068] Meteorological forcing data is used to describe parameters such as daily precipitation, temperature, and wind speed within each grid analysis period of the analysis area. Meteorological station data is selected and interpolated into the grid using the inverse distance weighting method. The model integration step size is daily. Based on the water balance principle, only daily precipitation, daily maximum temperature, daily minimum temperature, and daily average wind speed data need to be input.

[0069] The aforementioned inverse distance weighting method is based on the first law of geography, namely, "things that are closer together are more closely related." It assumes that the attribute values ​​of an unknown point are influenced by the attribute values ​​of surrounding known points, and the degree of influence is inversely proportional to the distance; the closer a known point is to the unknown point, the greater its contribution to the estimation of its attribute values.

[0070] The global control file is used to set core parameters such as the model's time step, simulation start and end dates, water balance mode, input data path, and output result path.

[0071] The VIC model runs continuously across the grid of the study watershed, simulating and generating daily runoff, baseflow, soil moisture, and other data for each grid at a specified time.

[0072] Step 23: The VIC model simulation process consists of two phases: a calibration phase and a validation phase. The accuracy of the model is validated and calibrated based on measured data, thereby simulating long-term soil moisture. The Nash-Sutcliffe efficiency coefficient (NSE), relative error (Er), and correlation coefficient (r) are selected to evaluate the model's simulation performance.

[0073] The simulated values ​​of the VIC model were verified using measured values. The corresponding data were extracted according to latitude and longitude, and correlation analysis and scatter plot were performed to prove the reliability of the simulated values.

[0074] Step S3 includes:

[0075] The Mann-Kendall trend test was used to analyze the changing trend of soil moisture, and the Mann-Kendall mutation test was used to identify the mutation points of soil moisture and their years of occurrence, so as to obtain the temporal evolution characteristics of soil moisture.

[0076] The spatial variability of soil moisture was analyzed by using a semi-variogram function, the spatial structure parameters were obtained by fitting a theoretical model, and the spatial clustering of soil moisture was detected by combining spatial autocorrelation analysis to obtain the spatial distribution characteristics of soil moisture.

[0077] By combining the temporal evolution characteristics and spatial distribution characteristics of soil moisture, the spatiotemporal evolution law of soil moisture is obtained.

[0078] Step 3 specifically includes:

[0079] Step 31, Time Series Variation Feature Analysis:

[0080] The nonparametric Mann-Kendall trend test was used to analyze the trend of long-term soil moisture data. The significance of the trend was determined by calculating the standardized Z-value (p<0.05), and the rate of change was quantified using Sen's slope estimation method. Furthermore, the Mann-Kendall catastrophe test was applied to identify the years of abrupt changes in soil moisture by constructing forward (UF) and reverse (UB) statistic curves. Cross-validation was performed using a sliding t-test, ultimately revealing the temporal evolution pattern and key turning points of soil moisture.

[0081] Step 32, Spatial Variation Feature Analysis:

[0082] Based on the theory of semivariograms, the experimental semivariogram values ​​are calculated and the optimal theoretical model (Gaussian model, exponential model, or spherical model) is fitted. The goodness of fit of the model is evaluated by the coefficient of determination (R²) and the sum of squared residuals (RSS). Structural parameters such as nugget value (C0), sill value (C0+C), and range (a) are extracted, and the nugget coefficient (C0 / (C0+C)) is calculated to quantify the degree of spatial variability.

[0083] By combining global Moran's I index and local Moran's I (LISA) index analysis with spatial autocorrelation analysis, the spatial autocorrelation characteristics and clustering patterns of soil moisture are revealed at both global and local scales. This reveals the spatial distribution patterns of soil moisture.

[0084] Moran's I is in the range [-1, 1]. When the exponent is positive, the larger the value, the stronger the positive spatial correlation. When the exponent is negative, the smaller the value, the stronger the negative spatial correlation. When the exponent is 0, it means that there is no obvious spatial correlation.

[0085] Step 33, Spatiotemporal Analysis:

[0086] By conducting spatiotemporal correlation analysis between temporal abrupt change points and spatial variation parameters, we can explore the impact mechanism of key climate events or human activities on the spatiotemporal pattern of soil moisture, and finally construct a spatiotemporal evolution law of soil moisture that combines "temporal evolution-spatial differentiation".

[0087] Step S4 includes:

[0088] The factors influencing soil moisture are divided into climatic factors and geographical factors. The influence of climatic factors and geographical factors on the evolution of soil moisture is analyzed separately to obtain the dominant influencing factors and their effects on the evolution of soil moisture.

[0089] Among them, climate factors include precipitation, temperature, relative humidity and evapotranspiration, and geographical factors include altitude, slope, aspect, vegetation cover and land use type.

[0090] The dominant influencing factors include dominant climate factors, dominant climate factor groups, dominant geographical factors, and dominant geographical factor groups.

[0091] In this embodiment, vegetation cover and land use type are used to reflect the impact of human activities on soil moisture. To quantitatively analyze vegetation cover and land use type, this embodiment uses the Normalized Difference Vegetation Index (NDVI) to quantify the dynamic changes in vegetation cover and constructs a land transfer matrix to analyze the characteristics of land use type transformation.

[0092] When analyzing the spatiotemporal changes in soil moisture, this embodiment assumes that climate factors have temporal dynamics while geographical factors remain relatively stable within the analysis period.

[0093] Therefore, the influence of climatic factors on soil moisture is considered using both sequence variation analysis and spatial heterogeneity analysis; while the influence of geographical factors on soil moisture is considered using only spatial heterogeneity analysis, in order to distinguish the contributions of different influencing factors.

[0094] Detailed analysis of climate factors:

[0095] The analysis of the influence of climatic factors on soil moisture evolution includes:

[0096] Analyze the effects of various climatic factors and their interactions on soil moisture changes.

[0097] in,

[0098] The analysis of the impact of various climatic factors on soil moisture changes includes:

[0099] We used full-cycle and local-cycle correlation analysis to assess the temporal impact of various climatic factors on soil moisture; and combined geographic detectors and multi-scale geographic weighted regression to analyze the spatial driving effect of various climatic factors on soil moisture.

[0100] Full-cycle correlation analysis includes:

[0101] The global correlation between each climate factor and soil moisture was calculated using the Pearson correlation coefficient method, and the statistical significance of the correlation coefficient was evaluated by the t-test to obtain the correlation strength and direction over the entire time series.

[0102] Correlation analysis is one of the most common and widely used methods for measuring the relationship between things. The strength of the correlation is determined by testing its significance. The purpose of the significance test is to judge the calculated correlation coefficient. Is it due to random factors, or does it truly reflect a significant linear relationship between the two variables?

[0103] Local periodic correlation analysis includes:

[0104] Based on cross-wavelet transform analysis, the local correlation between various climate factors and soil moisture in the time and frequency domain is analyzed to identify significant resonance periods and phase relationships.

[0105] The relationship between soil moisture and various climatic factors varies at different time scales. However, the Pearson correlation coefficient method can only reveal the overall or long-term changes between soil moisture and each influencing factor, and cannot effectively analyze the correlation between variables at specific local time scales. Therefore, we use wavelet correlation analysis to study the oscillation characteristics of soil moisture and various climatic factors in the analysis area at the whole cycle and local cycle time scales.

[0106] There are temporal differences between changes in soil moisture and various climatic factors, and these factors also exhibit certain correlations at different time scales. Cross-wavelet transform, which combines continuous wavelet and cross-spectral analysis, eliminates the influence of the non-stationarity of the sequence itself by decomposing the signal at local time scales, and can effectively reveal the correlation and relative phase relationship between soil moisture and various climatic factors at different time scales.

[0107] The combination of geographic detectors and multi-scale geographic weighted regression includes:

[0108] Geographic detectors are used to quantify the explanatory power (q-value) of each climate factor on soil moisture, and the dominant climate factor is identified based on the magnitude of the calculated explanatory power (q-value).

[0109] By using multi-scale geographically weighted regression analysis to analyze the spatial nonstationarity of dominant climate factors, the scale of influence of the heterogeneity of dominant climate factors and the distribution of local regression coefficients are obtained.

[0110] Geographic detectors are used to screen dominant factors and identify whether they are important (global explanatory power); MGWR is used to quantify spatial nonstationarity and reveal where they are important and how they function (local mechanisms).

[0111] For the identified dominant climate factors, a multi-scale geographically weighted regression (MGWR) model was constructed to analyze their spatial heterogeneity characteristics. The process included:

[0112] Multi-scale modeling: Adaptive bandwidth optimization technique is used to assign independent heterogeneous action scales to each climate factor (e.g., precipitation bandwidth = 80km, temperature bandwidth = 120km), and the optimal action scale (bandwidth) combination is determined by the AICc criterion.

[0113] Nonstationarity analysis: The distribution of local regression coefficients in the output spatial variation reveals the spatial heterogeneity of the analysis region driving the direction.

[0114] Significance validation: The significant region of the coefficient was identified based on the F-test (p<0.05), and the model reliability was ensured by combining residual spatial autocorrelation analysis (Moran's I<0.2). The final results can be used to formulate soil moisture management strategies for climate-differentiated conditions.

[0115] The analysis of the effects of pairwise interactions of climatic factors on soil moisture changes includes:

[0116] Climate factors interact in pairs to form multiple climate factor groups. Using an interaction geospatial detector and multi-scale geographic weighted regression, the synergistic effects of each climate factor group on soil moisture changes are analyzed.

[0117] The use of interactive geographic detectors and multi-scale geographic weighted regression includes:

[0118] An interactive geodetector was used to quantify the explanatory power (q-value) of each climate factor group on soil moisture, and the dominant climate factor group was identified based on the magnitude of the calculated explanatory power (q-value).

[0119] By using multi-scale geographically weighted regression analysis to assess the spatial nonstationarity of the dominant climate factor group, the scale of influence of the heterogeneity of the dominant climate factor group and the distribution of local regression coefficients are obtained.

[0120] Detailed analysis of geographical factors:

[0121] Compared to the diverse changes in climatic factors during the analysis period, geographical factors are relatively stable. Therefore, this embodiment does not consider the temporal variation of geographical factors on soil moisture, but only the spatial heterogeneous influence of geographical factors on soil moisture.

[0122] The analysis of the influence of geographical factors on soil moisture evolution includes:

[0123] Analyze the effects of various geographical factors and their pairwise interactions on soil moisture changes.

[0124] in,

[0125] The analysis of the impact of various geographical factors on soil moisture changes includes:

[0126] Geographic detectors are used to quantify the explanatory power (q-value) of each geographic factor on soil moisture, and the dominant geographic factor is identified based on the magnitude of the calculated explanatory power (q-value).

[0127] By using multi-scale geographical weighted regression analysis to analyze the spatial nonstationarity of dominant geographical factors, the scale of influence of the heterogeneity of dominant geographical factors and the distribution of local regression coefficients are obtained.

[0128] The analysis of the pairwise interactions of geographical factors on soil moisture changes includes:

[0129] Geographic factors interact in pairs to form multiple geographic factor groups. An interaction geographic detector is used to quantify the explanatory power (q value) of each geographic factor group on soil moisture. The dominant geographic factor group is identified based on the calculated explanatory power (q value).

[0130] By using multi-scale geographic weighted regression analysis to analyze the spatial nonstationarity of the dominant geographic factor group, the scale of influence of the heterogeneity of the dominant geographic factor group and the distribution of local regression coefficients are obtained.

[0131] In one specific embodiment, the method of this embodiment was applied to the soil moisture evolution characteristics analysis of the Yihe River Basin from 1970 to 2020. Some of the analysis process and results are as follows:

[0132] The annual average soil moisture in the Yi River Basin exhibits a fluctuating trend, which is closely related to the interannual fluctuations in precipitation. In years with more precipitation, the soil moisture is higher; while in dry years with little rainfall, the soil moisture content is significantly reduced.

[0133] Over the past 50 years, the annual runoff in the basin has decreased significantly, the response to precipitation fluctuations has been slow, and the interannual fluctuations have been large, with obvious seasonal variations. Soil moisture is high in summer and changes are intense, while soil moisture decreases in autumn and winter and changes are relatively gentle.

[0134] Spatially, soil moisture varies significantly in the Yi River Basin. Generally, soil moisture is lower in the eastern and southern regions, while it is higher in the northern and western regions, exhibiting a spatial and temporal pattern of "high in summer and low in winter, low in mountainous areas and high in plains."

[0135] Regarding driving factors, precipitation and relative humidity have a greater impact on soil moisture changes than air temperature and evapotranspiration. Soil moisture is significantly correlated with precipitation, air temperature, relative humidity, and evapotranspiration, exhibiting different resonance cycles across different time scales and time periods. On short time scales, the relationship between soil moisture and these factors is unstable, but on long time scales, soil moisture shows a in-phase relationship with precipitation and relative humidity, and an out-of-phase relationship with air temperature and evapotranspiration.

[0136] Among geographical environmental factors, altitude is the dominant factor influencing the spatial variation of soil moisture. In mountainous and hilly areas, due to steep terrain, surface runoff from precipitation is rapid, making it difficult to effectively store soil moisture; while in plains, the flat terrain facilitates precipitation infiltration and soil moisture retention. Among climatic factors, precipitation has the greatest impact on the spatial variation of soil moisture, making it the dominant climatic factor. The amount, intensity, and distribution of rainfall directly determine the amount and range of soil moisture replenishment. Furthermore, the interaction between any two factors exhibits a reinforcing relationship: precipitation and temperature form the dominant climatic factor group; altitude and land use type form the dominant geographical factor group. Overall, climatic factors have a stronger driving force than geographical environmental factors.

[0137] Example 2:

[0138] Based on the same inventive concept as Embodiment 1, this embodiment introduces a soil moisture evolution characteristic analysis system, including a data acquisition module, a model determination module, a pattern acquisition module, and a result analysis module. The data acquisition module is used to: determine the analysis area and acquire data related to soil moisture in the analysis area; the model determination module is used to: construct and debug a VIC model based on the data acquired by the data acquisition module to obtain a VIC model with a preset accuracy; the pattern acquisition module is used to: simulate soil moisture based on the VIC model determined by the model determination module, and, combined with the data acquired by the data acquisition module, extract soil moisture evolution characteristics in time and space to obtain the spatiotemporal evolution pattern of soil moisture; the result analysis module is used to: analyze the influencing factors of the spatiotemporal evolution pattern of soil moisture acquired by the pattern acquisition module to obtain the soil moisture evolution characteristic analysis results.

[0139] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0140] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0141] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0142] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0143] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method of analyzing the evolution of the characteristics of the humidity of a soil, characterized in that, include: S1: Determine the analysis area and period, and obtain soil moisture data related to the analysis area within the analysis period; S2: Construct and debug the VIC model based on the data obtained in step S1 to obtain a VIC model with preset accuracy; S3: Simulate soil moisture based on the VIC model determined in step S2, and combine it with the data obtained in step S1 to extract the soil moisture evolution characteristics in time and space, so as to obtain the spatiotemporal evolution law of soil moisture. S4: Analyze the influencing factors of the spatiotemporal evolution of soil moisture obtained in step S3, and obtain the analysis results of soil moisture evolution characteristics; Step S4 includes: The factors influencing soil moisture are divided into climatic factors and geographical factors. The influence of climatic factors and geographical factors on the evolution of soil moisture is analyzed separately to obtain the dominant influencing factors and their effects on the evolution of soil moisture. Among them, climate factors include precipitation, temperature, relative humidity and evapotranspiration, and geographical factors include altitude, slope, aspect, vegetation cover and land use type. The dominant influencing factors include dominant climatic factors, dominant climatic factor groups, dominant geographical factors, and dominant geographical factor groups; The analysis of the impact of climate factors on soil moisture evolution includes: Analyze the effects of various climatic factors and their interactions on soil moisture changes. in, The analysis of the impact of various climatic factors on soil moisture changes includes: We used full-cycle and local-cycle correlation analysis to assess the temporal impact of various climatic factors on soil moisture; and combined geographic detectors and multi-scale geographic weighted regression to analyze the spatial driving effect of various climatic factors on soil moisture. The analysis of the effects of pairwise interactions of climatic factors on soil moisture changes includes: Climate factors interact in pairs to form multiple climate factor groups. Using an interaction geospatial detector and multi-scale geographic weighted regression, the synergistic effects of each climate factor group on soil moisture changes are analyzed. The combination of geographic detectors and multi-scale geographic weighted regression includes: Geographic detectors are used to quantify the explanatory power of each climate factor on soil moisture, and the dominant climate factor is identified based on the calculated explanatory power. By using multi-scale geographically weighted regression analysis to analyze the spatial nonstationarity of dominant climate factors, the scale of influence of the heterogeneity of dominant climate factors and the distribution of local regression coefficients can be obtained. The use of interactive geographic detectors and multi-scale geographic weighted regression includes: Interactive geodetectors are used to quantify the explanatory power of each climate factor group on soil moisture, and the dominant climate factor group is identified based on the calculated explanatory power. By using multi-scale geographically weighted regression analysis to assess the spatial nonstationarity of the dominant climate factor group, the scale of influence of the heterogeneity of the dominant climate factor group and the distribution of local regression coefficients are obtained.

2. The method of claim 1, wherein, In step S1, the data related to soil moisture in the analysis area during the analysis period include: measured soil moisture data, meteorological data, land use type data, elevation data, and vegetation data; Step S1 includes preprocessing the acquired data into the input format required by the VIC model.

3. The method of claim 2, wherein, In step S2, debugging the VIC model includes calculating the Nash-Sutcliffe efficiency coefficient, relative error, and correlation coefficient using the simulated soil moisture and measured soil moisture data output by the VIC model, and evaluating the accuracy level of the VIC model based on the calculation results.

4. The method of claim 1, wherein, Step S3 includes: The Mann-Kendall trend test was used to analyze the changing trend of soil moisture, and the Mann-Kendall mutation test was used to identify the mutation points of soil moisture and their years of occurrence, so as to obtain the temporal evolution characteristics of soil moisture. The spatial variability of soil moisture was analyzed by using a semi-variogram function, the spatial structure parameters were obtained by fitting a theoretical model, and the spatial clustering of soil moisture was detected by combining spatial autocorrelation analysis to obtain the spatial distribution characteristics of soil moisture. By combining the temporal evolution characteristics and spatial distribution characteristics of soil moisture, the spatiotemporal evolution law of soil moisture is obtained.

5. The method for analyzing soil moisture evolution characteristics according to claim 1, characterized in that, Full-cycle correlation analysis includes: The global correlation between each climate factor and soil moisture was calculated using the Pearson correlation coefficient method, and the statistical significance of the correlation coefficient was evaluated by the t-test to obtain the correlation strength and direction over the entire time series. Local periodic correlation analysis includes: Based on cross-wavelet transform analysis, the local correlation between various climate factors and soil moisture in the time and frequency domain is analyzed to identify significant resonance periods and phase relationships.

6. The method of claim 1, wherein, The analysis of the influence of geographical factors on soil moisture evolution includes: Analyze the effects of various geographical factors and their pairwise interactions on soil moisture changes. in, The analysis of the impact of various geographical factors on soil moisture changes includes: Geographic detectors are used to quantify the explanatory power of each geographic factor on soil moisture, and the dominant geographic factors are identified based on the calculated explanatory power. By using multi-scale geographical weighted regression analysis to analyze the spatial nonstationarity of dominant geographical factors, the scale of influence of the heterogeneity of dominant geographical factors and the distribution of local regression coefficients are obtained. The analysis of the pairwise interactions of geographical factors on soil moisture changes includes: Geographic factors interact in pairs to form multiple geographic factor groups. An interaction geographic detector is used to quantify the explanatory power of each geographic factor group on soil moisture. The dominant geographic factor group is identified based on the calculated explanatory power. By using multi-scale geographic weighted regression analysis to analyze the spatial nonstationarity of the dominant geographic factor group, the scale of influence of the heterogeneity of the dominant geographic factor group and the distribution of local regression coefficients are obtained.

7. A soil moisture evolution profile analysis system, characterized by, The method according to any one of claims 1 to 6 includes a data acquisition module, a model determination module, a pattern acquisition module, and a result analysis module; The data acquisition module is used to: determine the analysis area and acquire data related to the soil moisture of the analysis area; The model determination module is used to: construct and debug a VIC model based on the data acquired by the data acquisition module, and obtain a VIC model with a preset accuracy; The pattern acquisition module is used to: simulate soil moisture according to the VIC model determined by the model determination module, and extract the soil moisture evolution characteristics in time and space by combining the data obtained by the data acquisition module, so as to obtain the spatiotemporal evolution pattern of soil moisture. The result analysis module is used to: analyze the influencing factors of the spatiotemporal evolution of soil moisture obtained by the pattern acquisition module, and obtain the soil moisture evolution characteristic analysis results.

Images