Soil humidity evolution characteristic analysis method and system

Abstract

The invention discloses a soil humidity evolution characteristic analysis method and system, and belongs to the technical field of soil analys.The method comprises the steps that an analysis area and an analysis period are determined, and data related to soil humidity of the analysis area in the analysis period are obtained; constructing and debugging a VIC model according to the acquired data to obtain a VIC model with preset precision; simulating soil humidity according to the determined VIC model, and combining the obtained data to extract soil humidity evolution characteristics in different time and space to obtain a soil humidity spatio-temporal evolution rule; and analyzing the obtained influence factors of the soil humidity spatio-temporal evolution law to obtain a soil humidity evolution characteristic analysis result. The method can solve the problem that the accuracy and comprehensiveness of the soil humidity evolution characteristic analysis result are affected and further the water resource management and decision-making are affected due to the insufficient actual measurement data volume in the prior art.

Description

Technical Field

[0001] The present invention belongs to the technical field of soil analysis, and in particular relates to a method and system for analyzing soil moisture evolution characteristics. Background Art

[0002] Soil moisture is a key factor influencing the water cycle and water balance, and plays a significant role in the temporal and spatial evolution of climate and plants. Its temporal and spatial characteristics significantly influence basin precipitation, runoff, and evaporation, and particularly climate. In hydrology, soil moisture, as a comprehensive indicator, not only participates in the hydrological cycle, influencing infiltration and runoff, but also influences the Earth's biological cycles. Soil moisture also plays a crucial role in climatology, regulating climate through albedo, surface sensible heat, and latent heat. In ecology, soil moisture is the primary factor in vegetation growth and plays a decisive role in its distribution and evolution.

[0003] In recent years, water demand in the study area has continued to increase to promote socioeconomic development. Furthermore, due to climate warming and human development and utilization, ecological and environmental issues have become increasingly prominent, and water resource shortages have become more severe. Therefore, studying the spatiotemporal variations in soil moisture is essential. This is not only a key issue in ecohydrology, but also holds significant significance for future ecological security and sustainable development. Soil moisture variations are the result of the combined effects of various climatic and geographical factors, with different driving forces varying. Therefore, in-depth research on the patterns and driving factors of soil moisture variations, and revealing the underlying causes of these variations, is crucial not only for the water cycle but also for the efficient use, scientific planning and management of water resources in the basin, and for agricultural development. It is also conducive to promoting high-quality development in the study area.

[0004] In recent years, with growing awareness of the importance of soil moisture to ecosystems and agricultural production, researchers at home and abroad have conducted extensive research on soil moisture variations and the factors influencing them at various scales, achieving a series of important results. These studies not only examine the macroscopic soil moisture cycle but also the relationships 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 of the spatiotemporal characteristics of soil moisture changes and their driving factors at smaller scales, such as small and medium-sized watersheds, are still relatively lacking. At the watershed scale, current research often focuses on analyzing the impact of a single environmental factor on soil moisture, such as precipitation patterns or land use change, while rarely considering the interactions between these 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 and jointly influence the dynamic changes in soil moisture.

[0006] Furthermore, soil moisture research often lacks long-term, comprehensive, field-measured data, which limits our understanding of soil moisture trends and cyclical characteristics. Without sufficient data, it is difficult to distinguish the impacts of natural variability from human interference, and it is also difficult to accurately predict future soil moisture trends. Therefore, strengthening long-term, systematic research at the watershed scale and in-depth exploration of the combined impacts of multiple environmental factors on soil moisture are crucial for improving our understanding of the dynamic mechanisms of soil moisture changes and managing water resources. Summary of the Invention

[0007] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a soil moisture evolution characteristic analysis method and system to solve the problem that the amount of existing measured data is insufficient, which affects the accuracy and comprehensiveness of the soil moisture evolution characteristic analysis results and thus affects water resource management and decision-making.

[0008] In order to solve the above technical problems, the present invention is implemented by adopting the following technical solutions:

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

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

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

[0012] S3: Simulating soil moisture according to the VIC model determined in step S2, combining the data obtained in step S1, extracting soil moisture evolution characteristics in time and space, and obtaining the spatiotemporal evolution law of soil moisture;

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

[0014] In the aforementioned soil moisture evolution characteristic analysis method, 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 form required by the VIC model.

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

[0016] In the aforementioned soil moisture evolution characteristic analysis method, step S3 includes:

[0017] The Mann-Kendall trend test method was used to analyze the changing trend of soil moisture, and the Mann–Kendall mutation test method was used to identify the mutation points of soil moisture and the years of their occurrence, thereby obtaining the temporal evolution characteristics of soil moisture. The semivariogram was used to analyze the spatial variability of soil moisture, and the theoretical model was fitted to obtain the spatial structure parameters. The spatial autocorrelation analysis was combined to detect the spatial aggregation of soil moisture, thereby obtaining the spatial distribution characteristics of soil moisture. The temporal evolution characteristics of soil moisture and the spatial distribution characteristics of soil moisture were combined to obtain the spatiotemporal evolution law of soil moisture.

[0018] In the aforementioned soil moisture evolution characteristic analysis method, step S4 includes:

[0019] The influencing factors of soil moisture are divided into climatic factors and geographical factors. The effects of climatic factors and geographical factors on soil moisture evolution are analyzed respectively, and the dominant influencing factors of soil moisture evolution and the effects of the dominant influencing factors are obtained. 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] In the aforementioned soil moisture evolution characteristic analysis method, the analysis of the impact of climate factors on soil moisture evolution includes:

[0021] Analyze the impact of various climate factors and the interaction between two climate factors on soil moisture changes; among them, the analysis of the impact of various climate factors on soil moisture changes includes: using full-cycle and local-cycle correlation analysis to evaluate the time-varying impact of various climate factors on soil moisture; combining geographic detectors and multi-scale geographic weighted regression to analyze the spatial driving effect of various climate factors on soil moisture; analyzing the impact of pairwise interactions of climate factors on soil moisture changes includes: climate factors interact with each other to form multiple climate factor groups, and using interactive geographic detectors and multi-scale geographic weighted regression to analyze the synergistic impact of various climate factor groups on soil moisture changes.

[0022] The aforementioned soil moisture evolution characteristic analysis method includes the following: full-cycle correlation analysis: using the Pearson correlation coefficient method to calculate the global correlation between each climatic factor and soil moisture, and evaluating the statistical significance of the correlation coefficient through the t-test to obtain the correlation strength and direction of the entire time series; local-cycle correlation analysis includes: analyzing the local correlation between each climatic factor and soil moisture in the time-frequency domain based on the cross-wavelet transform, and identifying significant resonance periods and phase relationships.

[0023] The aforementioned soil moisture evolution characteristic analysis method, the combination of geographic detectors and multi-scale geographic weighted regression includes: using geographic detectors to quantify the explanatory power of each climate factor on soil moisture, and 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, and obtaining the scale of action and local regression coefficient distribution of the heterogeneity of the dominant climate factor; the use of interactive geographic detectors and multi-scale geographic weighted regression includes: using interactive geographic detectors to quantify the explanatory power of each climate factor group on soil moisture, and 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, and obtaining the scale of action and local regression coefficient distribution of the heterogeneity of the dominant climate factor group.

[0024] In the aforementioned soil moisture evolution characteristic analysis method, the analysis of the impact of geographical factors on soil moisture evolution includes:

[0025] Analyze the impact of various geographical factors and the interaction between two geographical factors on soil moisture changes; among them, analyzing the impact of various geographical factors on soil moisture changes includes: using geographical detectors to quantify the explanatory power of each geographical factor on soil moisture, and identifying the dominant geographical factors among the geographical factors based on the calculated explanatory power; analyzing the spatial non-stationarity of the dominant geographical factors through multi-scale geographical weighted regression, and obtaining the scale of action and local regression coefficient distribution of the heterogeneity of the dominant geographical factors; analyzing the impact of the interaction between two geographical factors on soil moisture changes includes: geographical factors interact with each other to form multiple geographical factor groups, using interactive geographical detectors to quantify the explanatory power of each geographical factor group on soil moisture, and identifying the dominant geographical factor group among the geographical factor groups based on the calculated explanatory power; analyzing the spatial non-stationarity of the dominant geographical factor group through multi-scale geographical weighted regression, and obtaining the scale of action and local regression coefficient distribution of the heterogeneity of the dominant geographical factor group.

[0026] In a second aspect, the present invention provides a soil moisture evolution characteristic analysis system, comprising a data acquisition module, a model determination module, a law acquisition module, and a result analysis module;

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

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] The present invention adopts a VIC model with sufficient accuracy to provide soil moisture data required for soil moisture evolution characteristic analysis. The spatiotemporal evolution law of soil moisture itself is first obtained. Then, the spatiotemporal evolution law of soil moisture obtained is used as the result guide, and the causes leading to the results are further analyzed. The influence of influencing factors on soil moisture changes is analyzed to obtain soil moisture evolution characteristic analysis results, thereby improving the accuracy and comprehensiveness of soil moisture analysis results and solving the problem that existing soil moisture analysis is limited by insufficient measured data, which affects the accuracy and comprehensiveness of soil moisture analysis results and thus affects water resource management and decision-making.

[0030] The present invention uses three methods, namely Nash-Sutcliffe efficiency coefficient, relative error and correlation coefficient, to calibrate the accuracy of the VIC model, so that the present invention can obtain sufficiently accurate and continuous soil moisture data, providing reliable data support for subsequent analysis.

[0031] Furthermore, the present invention uses the Mann-Kendall trend test to monitor time series changes in soil moisture; uses variation point analysis to identify significant turning points in soil moisture changes; and uses semivariogram and spatial autocorrelation analysis to explore the spatial dependencies and clustering characteristics of soil moisture. These analyses fully reveal the complexity of soil moisture changes and reveal the temporal and spatial evolution patterns of soil moisture itself.

[0032] Furthermore, the present invention divides the influencing factors into geographical factors and climatic factors according to their own characteristics, takes soil moisture as the dependent variable and the influencing factors as the independent variable, and deeply analyzes the influence of the independent variables on the corresponding variables to obtain the dominant influencing factors of soil moisture evolution and the effects of the dominant influencing factors.

[0033] This paper considers the influence of a single climate factor on soil moisture and uses full-cycle and local-cycle correlation analysis to evaluate the time-varying impact of each climate factor on soil moisture. It also combines geographic detectors and multi-scale geographically weighted regression to analyze the spatial driving effect of each climate factor on soil moisture and obtain the dominant climate factor and its effect.

[0034] The present invention considers the influence of the interaction between two climate factors on soil moisture, uses interactive geographic detectors and multi-scale geographical weighted regression to analyze the synergistic effects of various climate factor groups on soil moisture changes, and obtains the dominant climate factor group and its effect;

[0035] The present invention considers the influence of a single geographical factor 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 factor and its effect;

[0036] The present invention considers the influence of the interaction between geographical factors on soil moisture, adopts interactive geographical detector and multi-scale geographical weighted regression to analyze the synergistic influence of each geographical factor group on soil moisture change, and obtains the dominant geographical factor group and its effect.

[0037] This method uses spatial autocorrelation (Moran's I, LISA) to first reveal the spatial distribution pattern (aggregation / dispersion) of soil moisture, providing analysis direction for the geographic detector. The geographic detector (q-value, interaction) then explains the causes of the pattern (such as the elevation-dominated moisture variation) and verifies 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 analysis, such as the difficulty in obtaining long-term continuous data, insufficient research on the interactive effects of multiple factors, and limitations on research scale precision, which affect the accuracy and comprehensiveness of soil moisture analysis results. It obtains more accurate and comprehensive soil moisture analysis results, which can optimize water resource allocation, reduce disaster risks, and promote sustainable water-food-ecological coordinated management, which is of great significance to the realization of the United Nations Sustainable Development Goals (SDG 6). BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a flow chart of a soil moisture evolution characteristic analysis method according to Example 1 of the present invention. DETAILED DESCRIPTION

[0040] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. Unless there is a conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0041] The term "and / or" in this document simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. Additionally, the character " / " in this document generally indicates an "or" relationship between the related objects.

[0042] Example 1:

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

[0044] In this example, the present invention provides a soil moisture evolution characteristic analysis method, such as Figure 1 As shown, the method includes the following steps:

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

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

[0047] S3: Simulating soil moisture according to the VIC model determined in step S2, combining the data obtained in step S1, extracting soil moisture evolution characteristics in time and space, and obtaining the spatiotemporal evolution law of soil moisture;

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

[0049] This embodiment uses a sufficiently accurate VIC model to provide soil moisture data required for soil moisture evolution characteristic analysis, solving the problem of the prior art being limited by insufficient measured data. The specific implementation of the method of the present invention is described below:

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

[0051] Soil moisture measured data, meteorological data, land use type data, elevation data and vegetation data;

[0052] Step S1 includes pre-processing the acquired data into the input form required by the VIC model.

[0053] Step S1 specifically includes:

[0054] Collect and organize data for the analyzed area, including original elevation data, vegetation database files and land cover data, land surface vegetation type distribution maps and land cover type data, measured flow at hydrological stations, soil data, meteorological data, etc., and pre-process the data;

[0055] The preprocessing of hydrological and meteorological data includes:

[0056] Step 11: To ensure data quality and consistency, the precipitation and evaporation data are preprocessed, including removing outliers, filling missing values, and converting the data into time and space 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 consistency in the geographic coordinate system. Based on the digital elevation model (DEM) of the watershed, use ArcGIS software to divide the study area into independent cell grids. In this example, the analysis area is divided into grids with a resolution of 0.083° × 0.083°.

[0058] Step 13: normalize 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 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 driver file, a soil parameter file, a vegetation library file, and a vegetation parameter file. The model output included daily runoff, baseflow, and soil moisture data for each grid. The simulation process was divided into two phases: a proving phase and a validation phase. The Nash efficiency coefficient, relative error, and correlation coefficient were used to evaluate the model's simulation results. The following steps were performed:

[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, the successful operation of the VIC model requires the input of various files, such as: global control file, weather driver file, soil parameter file, vegetation library file and vegetation parameter file.

[0064] The vegetation parameter file was selected based on global 1km-resolution land cover data published by the University of Maryland, with vegetation types 1 through 11 selected for study. In the VIC model, vegetation parameters are described using a vegetation library file and a vegetation parameter file. Parameters that require calibration within the vegetation parameter file include structural impedance, minimum stomatal impedance, leaf area index, albedo, roughness, and zero-plane displacement. The vegetation parameter file describes the distribution of various vegetation types within each grid, including the percentage of grid occupied, root zone depth percentage, root zone depth in three soil layers, and leaf area index from January to December.

[0065] The soil parameter file uses the global 5 arc minute (about 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.

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

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

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

[0069] The inverse distance weighting method is based on the first law of geography, which states that "closer things are, the more strongly they are related." It assumes that the attribute value of an unknown point is influenced by the attribute values of surrounding known points, and the degree of influence is inversely proportional to distance. The closer the known points are to the unknown point, the greater their contribution to the estimated attribute value.

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

[0071] The VIC model runs continuously on the grids of the study basin, simulating and generating daily runoff, base flow, soil moisture and other data for each grid at a specified time.

[0072] In step 23, the VIC model simulation process consists of two phases: calibration and validation. The model's accuracy is verified and calibrated using field data, resulting in a simulated long-term soil moisture series. The Nash-Sutcliffe efficiency coefficient (NSE), relative error (Er), and correlation coefficient (r) are used 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 longitude and latitude, and correlation analysis and scatter plots were performed to prove the reliability of the simulated values.

[0074] Step S3 includes:

[0075] The Mann-Kendall trend test method was used to analyze the changing trend of soil moisture, and the Mann–Kendall mutation test method was used to identify the mutation points of soil moisture and their occurrence years, and the temporal evolution characteristics of soil moisture were obtained;

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

[0077] By integrating the temporal evolution characteristics of soil moisture and the 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 change characteristics analysis:

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

[0081] Step 32: Spatial variation feature analysis:

[0082] Based on semivariogram theory, experimental semivariogram values were calculated and fitted to the optimal theoretical model (Gaussian, exponential, or spherical). Model fit was assessed using the coefficient of determination (R²) and residual sum of squares (RSS). Structural parameters such as the nugget value (C0), sill value (C0+C), and range (a) were extracted, and the nugget coefficient (C0 / (C0+C)) was calculated to quantify the degree of spatial variability.

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

[0084] Among them, Moran's I is between [-1,1]. When the index is positive, the larger the value, the stronger the positive spatial correlation; when the index is negative, the smaller the value, the stronger the negative spatial correlation; when the index is 0, it means that there is no obvious spatial correlation.

[0085] Step 33, spatiotemporal analysis:

[0086] The temporal and spatial correlation analysis of temporal mutation points and spatial variation parameters was carried out to explore the impact mechanism of key climate events or human activities on the spatiotemporal pattern of soil moisture, and finally the spatiotemporal evolution law of soil moisture combining "temporal evolution-spatial differentiation" was constructed.

[0087] Step S4 includes:

[0088] The factors affecting soil moisture are divided into climatic factors and geographical factors. The effects of climatic factors and geographical factors on soil moisture evolution are analyzed respectively, and the dominant influencing factors of soil moisture evolution and the effects of the dominant influencing factors are obtained.

[0089] Among them, climatic 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 climatic factors, dominant climatic 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 dynamic changes in vegetation cover and constructs a land transfer matrix to analyze the characteristics of land use type conversion.

[0092] When analyzing the spatiotemporal variation of soil moisture, this embodiment assumes that the climate factor has temporal dynamics while the geographical factor remains relatively stable within the analysis period.

[0093] Therefore, the impact of climatic factors on soil moisture takes into account both sequence variation analysis and spatial heterogeneity analysis, while the impact of geographical factors on soil moisture only takes into account spatial heterogeneity analysis, in order to distinguish the contributions of different influencing factors.

[0094] Specific analysis of climate factors:

[0095] The analysis of the impact of climate factors on soil moisture evolution includes:

[0096] Analyze the impact of various climate factors and the interaction between climate factors on soil moisture changes;

[0097] in,

[0098] Analysis of the impact of various climate factors on soil moisture changes includes:

[0099] The full-cycle and local-cycle correlation analysis was used to assess the temporal influence of various climate factors on soil moisture. The spatial driving effect of various climate factors on soil moisture was analyzed by combining geographic detectors and multi-scale geographically weighted regression.

[0100] Full-cycle correlation analysis includes:

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

[0102] Correlation analysis is one of the most commonly used and common methods to measure the relationship between things. The strength of the correlation is completed by testing its significance. The purpose of the significance test is to determine the calculated correlation coefficient. Is it due to accidental factors, or does it really reflect the existence of a significant linear relationship between the two variables?

[0103] Local periodic correlation analysis includes:

[0104] The local correlation between various climate factors and soil moisture in the time and frequency domains was analyzed based on cross wavelet transform, and significant resonance periods and phase relationships were identified.

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

[0106] There are temporal differences between soil moisture changes and various climate factors, and there are also certain correlations between them at different time scales. The cross-wavelet transform combines continuous wavelet and cross-spectral analysis. By decomposing the signal at the local time scale and eliminating the influence of the sequence's inherent non-stationarity, it can effectively reveal the correlation and relative phase relationship between soil moisture and various climate factors at different time scales.

[0107] The combined geographic detector and multi-scale geographically weighted regression method includes:

[0108] The geographical detector was used to quantify the explanatory power (q value) of each climate factor on soil moisture, and the dominant climate factor among the climate factors was identified according to the calculated explanatory power (q value);

[0109] The spatial non-stationarity of the dominant climate factors was analyzed by multi-scale geographically weighted regression to obtain the action scale and local regression coefficient distribution of the heterogeneity of the dominant climate factors.

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

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

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

[0113] Non-stationarity analysis: Outputs the distribution of local regression coefficients of spatial variation, revealing the spatial heterogeneity of the analyzed area in terms of driving direction.

[0114] Significance Verification: The F-test was used to identify significant regions of the coefficient (p < 0.05), and residual spatial autocorrelation analysis (Moran's I < 0.2) was used to ensure model reliability. The final results can be used to develop climate-specific soil moisture management strategies.

[0115] Analysis of the effects of pairwise interactions of climate factors on soil moisture changes includes:

[0116] The climate factors interacted with each other to form multiple climate factor groups. The interactive geographic detector and multi-scale geographically weighted regression were used to analyze the synergistic effects of each climate factor group on soil moisture changes.

[0117] The interactive geographic detector and multi-scale geographically weighted regression method include:

[0118] The interactive geographic detector 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 according to the calculated explanatory power (q value).

[0119] The spatial non-stationarity of the dominant climate factor group was analyzed by multi-scale geographically weighted regression to obtain the action scale and local regression coefficient distribution of the heterogeneity of the dominant climate factor group.

[0120] Specific analysis of geographical factors:

[0121] Compared with the diversity of climatic factors during the analysis period, geographical factors are relatively stable during the analysis period. Therefore, this example does not consider the temporal changes of geographical factors on soil moisture, but only considers the spatial heterogeneity of geographical factors on soil moisture.

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

[0123] Analyze the effects of various geographical factors and the interactions between geographical factors on soil moisture changes;

[0124] in,

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

[0126] The geographical detector was used to quantify the explanatory power (q value) of each geographical factor on soil moisture, and the dominant geographical factor among the geographical factors was identified according to the calculated explanatory power (q value);

[0127] The spatial non-stationarity of the dominant geographical factors is analyzed through multi-scale geographically weighted regression, and the effect scale and local regression coefficient distribution of the heterogeneity of the dominant geographical factors are obtained.

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

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

[0130] The spatial non-stationarity of the dominant geographical factor group is analyzed through multi-scale geographically weighted regression, and the effect scale and local regression coefficient distribution of the heterogeneity of the dominant geographical factor group are obtained.

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

[0132] The annual average soil moisture in the Yi River Basin shows a fluctuating trend, which is closely related to the interannual fluctuations in precipitation. In years with abundant precipitation, soil moisture is higher; in years with little rain, soil moisture content is significantly lower.

[0133] In the past 50 years, the annual runoff in the basin has decreased significantly, and the response to precipitation fluctuations has been slow. There are large interannual fluctuations and obvious seasonal changes. In summer, the soil moisture is high and the soil moisture changes strongly. In autumn and winter, the soil moisture decreases and the soil moisture changes relatively slowly.

[0134] From a spatial perspective, soil moisture varies significantly across the Yi River Basin. Generally speaking, soil moisture is lower in the eastern and southern regions, while higher in the northern and western regions. This demonstrates a spatiotemporal pattern of "high in summer, low in winter, and low in mountainous areas, high in plains."

[0135] In terms of driving factors, precipitation and relative humidity have a greater impact on soil moisture changes than temperature and evapotranspiration. Soil moisture is significantly correlated with precipitation, temperature, relative humidity, and evapotranspiration, exhibiting different resonance periods at different time scales and time periods. On short timescales, the relationships between soil moisture and these factors are unstable. However, on long timescales, soil moisture is in phase with precipitation and relative humidity, and out of phase with temperature and evapotranspiration.

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

[0137] Example 2:

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

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

[0140] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0141] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0142] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0143] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A soil moisture evolution characteristics analysis method, characterized in that: include: S1: Determine the analysis area and period, and obtain data related to soil moisture in the analysis area within the analysis period; S2: construct and debug a VIC model based on the data obtained in step S1 to obtain a VIC model with a preset accuracy; S3: Simulating soil moisture according to the VIC model determined in step S2, combining the data obtained in step S1, extracting soil moisture evolution characteristics in time and space, and obtaining the spatiotemporal evolution law of soil moisture; S4: Analyze the factors affecting the spatiotemporal evolution of soil moisture obtained in step S3 to obtain the analysis results of soil moisture evolution characteristics.

2. The soil moisture evolution characteristic analysis method according to claim 1, characterized in that: 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 pre-processing the acquired data into the input form required by the VIC model.

3. The soil moisture evolution characteristic analysis method according to claim 2, characterized in that: 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 of the VIC model based on the calculation results.

4. The soil moisture evolution characteristic analysis method according to claim 1, characterized in that: Step S3 includes: The Mann-Kendall trend test method was used to analyze the changing trend of soil moisture, and the Mann–Kendall mutation test method was used to identify the mutation points of soil moisture and their occurrence years, and the temporal evolution characteristics of soil moisture were obtained; The spatial variability of soil moisture was analyzed using semivariogram, and the spatial structure parameters were obtained by fitting theoretical models. The spatial clustering of soil moisture was detected by combining spatial autocorrelation analysis to obtain the spatial distribution characteristics of soil moisture. By integrating the temporal evolution characteristics of soil moisture and the spatial distribution characteristics of soil moisture, the spatiotemporal evolution law of soil moisture is obtained.

5. The soil moisture evolution characteristic analysis method according to claim 4, characterized in that: Step S4 includes: The factors affecting soil moisture are divided into climatic factors and geographical factors. The effects of climatic factors and geographical factors on soil moisture evolution are analyzed respectively, and the dominant influencing factors of soil moisture evolution and the effects of the dominant influencing factors are obtained. 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.

6. The soil moisture evolution characteristic analysis method according to claim 5, characterized in that: The analysis of the impact of climate factors on soil moisture evolution includes: Analyze the impact of various climate factors and the interaction between climate factors on soil moisture changes; in, Analysis of the impact of various climate factors on soil moisture changes includes: The full-cycle and local-cycle correlation analysis was used to assess the temporal influence of various climate factors on soil moisture. The spatial driving effect of various climate factors on soil moisture was analyzed by combining geographic detectors and multi-scale geographically weighted regression. Analysis of the effects of pairwise interactions of climate factors on soil moisture changes includes: The climate factors interacted with each other to form multiple climate factor groups. The interactive geographic detector and multi-scale geographically weighted regression were used to analyze the synergistic effects of each climate factor group on soil moisture changes.

7. The soil moisture evolution characteristic analysis method according to claim 6, characterized in that: Full-cycle correlation analysis includes: The Pearson correlation coefficient method was used to calculate the global correlation between each climate factor and soil moisture, and the statistical significance of the correlation coefficient was evaluated by t-test to obtain the correlation strength and direction over the entire time series. Local periodic correlation analysis includes: The local correlation between various climate factors and soil moisture in the time and frequency domains was analyzed based on cross wavelet transform, and significant resonance periods and phase relationships were identified.

8. The soil moisture evolution characteristic analysis method according to claim 6, characterized in that: The combined geographic detector and multi-scale geographically weighted regression method includes: The geographical detector is used to quantify the explanatory power of each climate factor on soil moisture, and the dominant climate factor among the climate factors is identified based on the calculated explanatory power. The spatial non-stationarity of the dominant climate factors was analyzed by multi-scale geographically weighted regression to obtain the heterogeneous effect scale and local regression coefficient distribution of the dominant climate factors. The interactive geographic detector and multi-scale geographically weighted regression method include: The interactive geographic detector was used to quantify the explanatory power of each climate factor group on soil moisture, and the dominant climate factor group was identified according to the calculated explanatory power. The spatial non-stationarity of the dominant climate factor group was analyzed by multi-scale geographically weighted regression to obtain the action scale and local regression coefficient distribution of the heterogeneity of the dominant climate factor group.

9. The soil moisture evolution characteristic analysis method according to claim 5, characterized in that: The analysis of the impact of geographical factors on soil moisture evolution includes: Analyze the effects of various geographical factors and the interactions between geographical factors on soil moisture changes; in, Analysis of the impact of various geographical factors on soil moisture changes includes: The geographical detector is used to quantify the explanatory power of each geographical factor on soil moisture, and the dominant geographical factor among the geographical factors is identified according to the calculated explanatory power. The spatial non-stationarity of the dominant geographical factors is analyzed through multi-scale geographically weighted regression, and the effect scale and local regression coefficient distribution of the heterogeneity of the dominant geographical factors are obtained. The analysis of the effects of pairwise interactions of geographical factors on soil moisture changes includes: Geographic factors interact with each other to form multiple geographical factor groups. The interaction geographical detector is used to quantify the explanatory power of each geographical factor group on soil moisture. The dominant geographical factor group in the geographical factor group is identified according to the calculated explanatory power. The spatial non-stationarity of the dominant geographical factor group is analyzed through multi-scale geographically weighted regression, and the effect scale and local regression coefficient distribution of the heterogeneity of the dominant geographical factor group are obtained.

10. A soil moisture evolution characteristic analysis system, characterized in that: It includes data acquisition module, model determination module, rule acquisition module and result analysis module; The data acquisition module is used to: determine an 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 according to the data acquired by the data acquisition module to obtain a VIC model of preset accuracy; The law acquisition module is used to simulate soil moisture according to the VIC model determined by the model determination module, extract soil moisture evolution characteristics by time and space in combination with the data obtained by the data acquisition module, and obtain the spatiotemporal evolution law of soil moisture; The result analysis module is used to analyze the influencing factors of the spatiotemporal evolution law of soil moisture obtained by the law acquisition module, and obtain the soil moisture evolution characteristic analysis results.

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