A method, system, medium, and equipment for attributing soil erosion in plateau regions

Abstract

This invention relates to the field of environmental assessment technology, and provides a method, system, medium, and equipment for attributing soil erosion in plateau areas. The method includes: calculating the soil erosion rate of each computational unit within the study area based on multi-source geospatial data and using a soil erosion model; the soil erosion rate is the product of rainfall erosivity, soil erodibility, slope length and gradient, vegetation cover management, and soil and water conservation measures; using rainfall erosivity, soil erodibility, slope length and gradient, vegetation cover management, and / or soil and water conservation measures as key driving factors, and employing the logarithmic mean División index method to calculate the degree of control of these key driving factors on changes in the soil erosion rate; wherein, the degree of control is assigned a weighting coefficient for each computational unit during the calculation process, and the weighting coefficient is constructed based on the difference between each computational unit and a preset benchmark area. This method can accurately separate the independent contributions of each factor to erosion changes.

Description

Technical Field

[0001] This invention belongs to the field of environmental assessment technology, and in particular relates to a method, system, medium and equipment for attributing soil erosion in plateau areas. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Soil erosion is a major global environmental problem, particularly prominent in plateau regions. It not only leads to a sharp decline in land productivity in key areas such as alpine meadows and river terraces, disrupting the fragile ecological balance of the plateau, but also significantly interferes with the stability of permafrost carbon pools and desert soil carbon pools through erosion-deposition processes, profoundly affecting terrestrial carbon cycles and regional and even global climate change.

[0004] Under the dual influence of climate change and human activities, the driving mechanisms of soil erosion on the plateau are becoming increasingly complex. On the one hand, increased glacial meltwater and frequent extreme rainfall events caused by warming may exacerbate erosion by enhancing runoff dynamics. On the other hand, vegetation restoration brought about by ecological protection projects (such as increased alpine grassland coverage) and local soil and water conservation measures (such as the construction of river valley terraces) may mitigate erosion by enhancing surface protection. These driving factors not only often act in opposite directions, but also interact strongly due to the unique geographical environment of the plateau. For example, the erosion-inhibiting effect of vegetation greening in steep slope areas may be offset or even reversed by meltwater runoff triggered by concurrent extreme rainfall.

[0005] However, existing methods (such as simple correlation analysis or regression models) have limited ability to quantitatively separate the independent contributions and relative importance of such complex factors. In particular, they are unable to distinguish the specific impact weights of "natural drivers (such as rainfall and meltwater)" and "human interventions (such as vegetation restoration and engineering measures)," which makes it impossible to clearly reveal the dominant mechanisms of soil erosion changes on the plateau.

[0006] More importantly, the plateau has a huge difference in elevation and significant spatial differentiation in soil and vegetation (for example, from the foot of the mountain to the plateau surface, the soil erodibility changes from easily eroded sandy soil to erosion-resistant permafrost, and the vegetation cover decreases sharply from forest to alpine desert). Traditional attribution methods use uniform weights for analysis, which makes it difficult to reflect the inherent differences between different regions, further exacerbating the bias in quantifying the contribution of driving factors. Summary of the Invention

[0007] To address the technical problems mentioned above, this invention provides a method, system, medium, and equipment for attributing soil erosion in plateau regions. It employs the Logarithmic Mean Divisia Index (LMDI) method to quantify the control level of key driving factors. This method offers advantages such as zero residuals and scalability in factor decomposition, accurately separating the independent contributions of each factor to erosion changes. Furthermore, it corrects for regional differences through weighted coefficients constructed based on the differences between the study area and a preset benchmark area. This effectively eliminates the inherent heterogeneity of different computational units in terms of rainfall dynamics, soil background, and vegetation conditions, thereby improving attribution accuracy.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] The first aspect of the present invention provides a method for attributing soil erosion in plateau regions, comprising:

[0010] Acquire multi-source geospatial data for each computing unit within the study area;

[0011] Based on multi-source geospatial data, the soil erosion rate of each computational unit in the study area was calculated using a soil erosion model. The soil erosion rate is the product of rainfall erosivity factor, soil erodibility factor, slope length and gradient factor, vegetation cover management factor, and soil and water conservation measure factor.

[0012] Rainfall erosivity, soil erodibility, slope length and gradient, vegetation cover management, and / or soil and water conservation measures were used as key driving factors. The log-mean Divisia index method was employed to calculate the degree of control of these key driving factors on changes in soil erosion rate. In the calculation process, a weighting coefficient was set for each calculation unit, and the weighting coefficient was constructed based on the differences between each calculation unit in the study area and the preset benchmark area.

[0013] Furthermore, the multi-source geospatial data includes digital elevation model data, soil attribute data, land use data, daily-scale meteorological data, and daily-scale vegetation remote sensing data.

[0014] Furthermore, the rainfall erosivity factor is:

[0015] ;

[0016] ;

[0017] ;

[0018] ;

[0019] ;

[0020] Where R represents the rainfall erosivity factor; R r Indicates the runoff erosion coefficient. Indicates surface runoff, Indicates peak flow; R p denoted by , where i represents different semi-monthly periods; k is the number of days in the i-th semi-month; P j It represents the erosive daily rainfall on day j during the half-month period; α1 and β1 are intermediate parameters; and These refer to average daily erosive rainfall and average annual erosive rainfall, respectively.

[0021] Furthermore, if vegetation cover management factors are taken as key driving factors, then the weighting coefficient of the i-th calculation unit is:

[0022] ;

[0023] in, = i / 基准 , i This represents the rainfall erosivity factor of the i-th computational unit. 基准 Indicates the rainfall erosivity factor for the reference area; = i / 基准 , i This represents the soil erodibility factor of the i-th computational unit. 基准 Indicates the soil erodibility factor of the reference area; =C i / 基准 , i This represents the vegetation cover management factor of the i-th computational unit. 基准 This indicates the vegetation cover management factor for the baseline area.

[0024] Furthermore, it also includes: calculating soil carbon loss caused by soil erosion in each computational unit within the study area based on the soil erosion rate of each computational unit within the study area; using rainfall erosivity factor, soil erodibility factor, slope length and gradient factor, vegetation cover management factor and / or soil and water conservation measure factor as key driving factors, and employing the logarithmic mean Divisia index method to calculate the degree of control of key driving factors on changes in soil carbon loss; wherein, the degree of control is weighted by weighting coefficients for each computational unit during the calculation process, and the weighting coefficients are constructed based on the differences between each computational unit in the study area and the preset benchmark area.

[0025] Furthermore, the soil carbon loss is as follows:

[0026] ;

[0027] ;

[0028] ;

[0029] ;

[0030] Where m represents the number of computational units, Indicates the carbon production of eroded soil. Indicates sediment yield, Indicates the soil carbon enrichment ratio. This indicates the concentration of soil organic carbon, soil inorganic carbon, or total carbon. Indicates soil carbon density; Indicates soil thickness; Bulk density; Indicates the rate of soil erosion. This indicates the sediment transport ratio.

[0031] Furthermore, the soil carbon density is determined by a functional relationship of the sediment connectivity index.

[0032] A second aspect of the present invention provides a soil erosion attribution system for plateau regions, comprising:

[0033] The data acquisition module is configured to acquire multi-source geospatial data from each computing unit within the study area.

[0034] The erosion calculation module is configured to: calculate the soil erosion rate of each calculation unit in the study area based on multi-source geospatial data and through a soil erosion model; wherein the soil erosion rate is the product of rainfall erosivity factor, soil erodibility factor, slope length and slope factor, vegetation cover management factor and soil and water conservation measure factor.

[0035] The erosion attribution module is configured to: use rainfall erosivity factor, soil erodibility factor, slope length and gradient factor, vegetation cover management factor and / or soil and water conservation measure factor as key driving factors, and use the log-mean Divisia index method to calculate the degree of control of key driving factors on changes in soil erosion rate; wherein, the degree of control is assigned a weighting coefficient for each calculation unit during the calculation process, and the weighting coefficient is constructed based on the differences between each calculation unit in the study area and the preset benchmark area.

[0036] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method for attributing soil erosion in plateau regions.

[0037] A fourth aspect of the present invention provides a computer device including a computer-readable storage medium, a processor, and a computer program stored on the computer-readable storage medium and executable on the processor, wherein the processor executes the program to implement the steps in the above-described method for attributing soil erosion in plateau regions.

[0038] Compared with the prior art, the beneficial effects of the present invention are:

[0039] This invention employs the Log-Mean Divisia Index (LMDI) method to quantify the degree of control of key driving factors. This method has advantages such as no residuals and the ability to accumulate in factor decomposition, and can accurately separate the independent contributions of each factor to erosion changes. Furthermore, it corrects regional differences through weighted coefficients, which are constructed based on the differences between the study area and the preset benchmark area. This effectively eliminates the inherent heterogeneity of different calculation units in terms of rainfall dynamics, soil background, vegetation conditions, etc., and improves the accuracy of attribution.

[0040] This invention, by clarifying the degree of control of each driving factor, can directly identify the dominant factors affecting soil erosion changes, providing a scientific basis for accurately formulating soil and water conservation measures and optimizing resource allocation, and combining theoretical research depth with practical guidance significance.

[0041] This invention not only distinguishes between the loss of soil organic carbon and soil inorganic carbon, but also innovatively introduces sediment transport ratio and carbon enrichment ratio to estimate the net carbon flux entering water bodies laterally, making carbon loss assessment more comprehensive and scientific, and providing more reliable data support for regional carbon budget assessment.

[0042] This invention introduces runoff erosion coefficient and rainfall erosion coefficient, comprehensively considering the erosive force directly generated by rainfall and the contribution of surface runoff (including peak flow) formed by rainfall to erosion. It breaks through the traditional single-dimensional calculation logic that only relies on rainfall amount or rainfall intensity, and more comprehensively reflects the chain process of "rainfall-runoff-erosion", which is highly consistent with the actual mechanism of hydraulic erosion. Attached Figure Description

[0043] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0044] Figure 1 This is a flowchart of a method for attributing soil erosion in plateau areas according to Embodiment 1 of the present invention;

[0045] Figure 2 This is a verification chart showing the accuracy of the average annual soil erosion rate calculation in Embodiment 1 of the present invention;

[0046] Figure 3 This is a schematic diagram of the structure of a computer device according to Embodiment 4 of the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0048] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0049] Example 1

[0050] This embodiment provides a method for attributing soil erosion in plateau regions.

[0051] This embodiment provides a soil erosion attribution method for plateau regions. By integrating high-precision data, refined carbon loss calculation, and advanced attribution models, it can significantly improve the accuracy and depth of mechanism explanation in assessing soil erosion and carbon cycle processes, providing strong scientific basis and technical support for regional ecological environmental protection, carbon budget assessment, and climate change adaptation management.

[0052] This embodiment provides a method for attributing soil erosion in plateau areas, such as... Figure 1 As shown, it includes the following steps:

[0053] Step 1: Calculation of soil erosion rate.

[0054] Step 101: Obtain multi-source geospatial data for each computing unit within the study area.

[0055] Among them, multi-source geospatial data includes at least: digital elevation model (DEM) data, soil property data, land use data, daily meteorological data, and daily vegetation remote sensing data.

[0056] All spatial data is processed to a uniform grid resolution before computation.

[0057] This embodiment provides a soil erosion attribution method for plateau regions. By employing high-resolution meteorological (rainfall) and vegetation (NDVI) data at the daily scale, it can more precisely capture the impact of dynamic changes in rainfall and vegetation on the soil erosion process, thereby significantly improving the accuracy and timeliness of soil erosion rate estimation.

[0058] Step 102: Based on multi-source geospatial data, a soil erosion model is used to calculate the time series of soil erosion rates for each computational unit within the study area.

[0059] In this embodiment, the soil erosion model adopts the modified Universal Soil Loss Equation (RUSLE), and its calculation formula is as follows:

[0060] ;

[0061] Where A represents the average annual soil erosion rate (t·ha) -1 ·a -1 For a certain plateau region, the average annual soil erosion rate was calculated. Figure 2 To verify the accuracy of the calculation.

[0062] Where R is the rainfall erosivity factor, calculated based on rainfall data from daily meteorological data. The R factor represents the probability of rainfall causing hydraulic erosion, and the daily rainfall erosion rate model is widely used due to its high temporal accuracy, strong data continuity, and ease of acquisition. In this embodiment, the R factor is calculated using the daily rainfall erosion rate model:

[0063] ;

[0064] ;

[0065] ;

[0066] ;

[0067] ;

[0068] Where R represents the hydraulic erosion coefficient (MJ·mm·ha) -1 ·h -1 ·a -1 ); R r Indicates the runoff erosion coefficient. Indicates surface runoff, Indicates peak flow; Rp denoted by , where i represents different semi-monthly periods; k is the number of days in the i-th semi-month; P j It is the erosive daily rainfall on day j during the half-month period; the standard for erosive daily rainfall is 12 mm, therefore, when the daily rainfall is <12 mm, P j The value is 0; parameters α1 and β1 are determined in this rainfall erosivity model, while and These refer to the average daily erosive rainfall (≥12 mm) and the average annual erosive rainfall, respectively.

[0069] In this embodiment, both runoff erosion caused by glacial meltwater and rainfall erosion are statistically analyzed, and weighting parameters are set for each. and Taking the Qinghai-Tibet Plateau region as an example, runoff erosion caused by glacial meltwater is dominant, therefore The weighted coefficient for rainfall erosion is 0.8. It is 0.2.

[0070] Wherein, K is the soil erodibility factor, calculated based on soil property data. The K factor reflects the impact of erosion on soil stripping and transport. In this embodiment, soil type, soil texture, and organic carbon content of the study area (e.g., the Yellow River water source area) are extracted from the World Soil Database (HWSD v1.2). The K factor for each calculation unit is calculated according to the formula in the erosion-productivity impact model:

[0071] ;

[0072] In the formula, The number of freeze-thaw cycles is represented by San, Sil, Cla and TOC, which represent sand content (0.05-2mm), silt content (0.002-0.05mm), clay content (<0.002mm) and organic carbon content, respectively. The intermediate parameter SN1 = 1-0.01San.

[0073] Wherein, LS is the slope length-gradient factor, calculated based on DEM data. The LS factor is a parameter reflecting the influence of topographic changes on the physical processes of water erosion within the study area, and its specific formula is as follows:

[0074] ;

[0075] ;

[0076] ;

[0077] ;

[0078] ;

[0079] ;

[0080] In the formula, L represents the slope length coefficient, S represents the slope coefficient, λ is the slope length, G is the slope length index related to the slope, β2 is the conversion coefficient, and θ is the slope angle.

[0081] Wherein, C represents the vegetation cover management factor, calculated based on daily-scale vegetation remote sensing data. The C factor is an indicator measuring the impact of vegetation cover and management measures on soil erosion. This embodiment uses a C factor evaluation model based on the Normalized Difference Vegetation Index (NDVI), and the specific calculation formula is as follows:

[0082] ;

[0083] In the formula, α3 is assigned a value of 2, and β3 is assigned a value of 1. The NDVI value ranges from 0 to 1; NDVI data from May to September in the study area were used to synthesize extreme values ​​to eliminate the influence of low-value noise; since water bodies and bare rock land use types lack vegetation cover and do not participate in the soil and water loss process, the C factor of these land use types is assigned a value of 0 according to the land use map.

[0084] Taking the Qinghai-Tibet Plateau as an example, the Normalized Difference Vegetation Index (NDVI) can provide the most accurate long-term data to reflect vegetation conditions.

[0085] Wherein, P is the soil and water conservation measures factor, determined based on land use data and / or field survey data. The P factor represents the ratio of soil loss with and without soil protection measures, and its value ranges from 0 to 1, which can be determined according to the land use type. In this embodiment, a value is assigned to land use data with a resolution of 1 kilometer.

[0086] Taking the Qinghai-Tibet Plateau as an example, based on the actual soil protection measures implemented and combined with previous research results on the P factor, the values ​​were assigned as follows: paddy fields were 0.01 and dry land was 0.35; for grassland, forest land, other forested land and unused land (referring to land that is difficult to use), the values ​​were 1, 1, 0.7 and 1, respectively.

[0087] Step 2: Calculation of erosion-induced carbon loss.

[0088] Carbon displacement due to soil erosion is not the same as carbon loss, because this carbon may simply be transferred to other locations and reburied. Soil carbon loss (C loss Soil carbon loss is considered a lateral transport of carbon to rivers. Carbon in the soil is displaced by soil erosion and subsequently transported into rivers, resulting in soil carbon loss. Therefore, this embodiment innovatively constructs a soil carbon loss equation, defining soil carbon loss caused by soil erosion as follows:

[0089] ;

[0090] ;

[0091] ;

[0092] ;

[0093] Where m represents the number of computational units, and the value of each computational unit is the mean of the values ​​of all pixels within that unit. (kg·C·ha -1 ·a -1 ) represents the carbon production of the eroded soil in the i-th computational unit. (t·ha -1 ·a -1 ) indicates sediment yield, This represents the soil carbon enrichment ratio of the i-th computational unit. (kg·C·kg -1 •Soil) refers to the concentration of soil organic carbon (SOC), soil inorganic carbon (SIC), or total carbon (TC). (kg·C·m -2 () indicates soil carbon density; (m) represents the soil thickness. Since the calculation involves surface soil erosion, only a 1-meter-thick soil layer is considered. (kg·m -3 () represents the bulk density; (t·ha -1 ·a -1 ) represents the soil erosion rate of the i-th computational unit, while This represents the sediment transport ratio (SDR) of the i-th computational unit. Furthermore, 5.22 is set to... The theoretical upper limit.

[0094] Among them, soil carbon concentration The calculations are based on soil carbon density, soil thickness, and soil bulk density within a specific depth range of the Earth's surface.

[0095] SDR is a key parameter for predicting watershed sediment yield because it represents the ratio of sediment yield to soil erosion. SDR can be determined as a function of the sediment connectivity index (IC).

[0096] ;

[0097] ;

[0098] ;

[0099] in, The sediment connectivity index represents the i-th computational unit; and These represent the uphill and downhill components of connectivity, respectively; This represents the average weighting factor for the uphill contribution area; (m·m -1 () represents the average slope gradient of the upslope contribution area; (m 2 () indicates the area of ​​the uphill contribution region; (m) represents the length from the i-th calculation unit to the downstream main channel; Represents the C factor of the i-th computational unit; This represents the slope gradient of the i-th calculation unit; This represents the maximum theoretical SDR, with a default value of 0.8 when detailed soil information is lacking. and These are calibration parameters. and The best-fit values ​​in the Qinghai-Tibet Plateau region are 0.5 and 2, respectively; It is a weighting factor specifically designed for high-altitude areas; slope is the gradient.

[0100] The above equations are applicable to plateau regions (such as the Qinghai-Tibet Plateau) and are used to deduce the spatial distribution of carbon loss caused by erosion.

[0101] The losses of soil organic carbon (SOC) and soil inorganic carbon (SIC) were estimated based on the distribution maps of SOC and SIC, respectively. Total carbon (TC) loss represents the combined loss of SOC and SIC. Therefore, soil carbon loss is quantified using the above equations.

[0102] This embodiment provides a soil erosion attribution method for plateau regions, which not only distinguishes between the loss of soil organic carbon (SOC) and soil inorganic carbon (SIC), but also innovatively introduces sediment transport ratio (SDR) and carbon enrichment ratio (ER) to estimate the net carbon flux entering water bodies laterally, making the carbon loss assessment more comprehensive and scientific, and providing more reliable data support for regional carbon budget assessment.

[0103] Step 3: Dynamic change analysis.

[0104] Statistical analysis methods were used to identify the long-term trends and / or key abrupt change years of the soil erosion rate time series obtained in step 1 and the soil carbon loss time series obtained in step 2.

[0105] The statistical analysis methods included the Mann-Kendall trend test and the Pettitt mutation point test.

[0106] This embodiment provides a soil erosion attribution method for plateau regions. By combining trend analysis and abrupt change detection technology, it can clearly reveal the evolution characteristics and key turning points of soil erosion and its carbon effects over long timescales, which helps to deepen the understanding of the response mechanisms of surface processes to climate change and human activities.

[0107] Step 4: Driver factor attribution analysis.

[0108] Key driving factors influencing soil erosion were selected, including at least rainfall erosivity factor R and vegetation cover management factor C. A pre-defined attribution model was used to quantitatively analyze the relative contributions of key driving factors to changes in soil erosion rate and / or carbon loss.

[0109] The following section uses the relative contribution to changes in soil erosion rate as an example to illustrate the calculation of the relative contribution to changes in carbon loss.

[0110] To conduct attribution analysis on changes in soil erosion, the Log-Mean División Index (LMDI) method was employed within the RUSLE model framework to explore the driving factors of soil erosion. LMDI quantifies the relative importance of these factors—that is, the percentage control each factor (rainfall erosivity and vegetation cover management) has on soil erosion—by comparing the control effects of rainfall erosivity and vegetation cover management on soil erosion. This achieves a complete and accurate decomposition without residuals, ensuring the integrity of the attribution results. Given the multiplicative nature of the RUSLE model, the LMDI method is a simple yet powerful approach. It accurately handles the nonlinear relationships between factors such as rainfall erosivity and vegetation cover management, while inherently considering the interactions between factors. By employing the log-mean, LMDI preserves these nonlinear characteristics during the attribution process, avoiding biases that might be introduced when controlling variables individually. This makes it possible to accurately and without residuals analyze the relative importance of rainfall erosivity and vegetation cover management on changes in soil erosion.

[0111] To apply the LMDI model within the RUSLE framework to attribute the effects of rainfall and vegetation, relevant data for the selected time period must first be collected. Then, this method is used to decompose soil erosion into the contributions of rainfall erosion and the relative importance of vegetation cover management. This approach clearly reveals how these two factors independently control soil erosion under different environmental and temporal conditions.

[0112] Assuming V represents the total soil erosion estimated by the RUSLE model, the total soil erosion V is expressed as the soil erosion amount V in each calculation unit. i The sum, assuming there are m computational units and n factors controlling soil erosion, each factor is associated with a quantifiable variable, that is, the i-th computational unit has n variables x.1,i x 2,i ... x n,i subscript This indicates a specific subcategory in the aggregation that requires analysis of structural changes.

[0113] At the subcategory level, relation V =x 1,i ×x 2,i ×…×x n,i The identity of general exponential decomposition analysis (IDA) is:

[0114] ;

[0115] The change in V is driven by the variation of different factors (ΔV) x1 ,ΔV x2 ,…,ΔV xn ):

[0116] ;

[0117] Against this backdrop This represents the overall change in the target variable V; The target variable represents the baseline period (labeled as period 0), while This represents the target variable in the expected period T; (k=1,2,3,…,n) represents the change in the target variable caused by each corresponding factor xk.

[0118] In the multiplicative factorization method, the proportion of each factor (D) x1 D x2 ,…,D xn The breakdown is as follows:

[0119] ;

[0120] in, This represents the ratio of the target variable between the planning period T and the base period 0; (k = 1,2,3,…,n) represents the rate of change of the target variable related to each of the factors (x1,x2,x3,…,xn).

[0121] The relative importance of each factor to the target variable is shown below:

[0122] ;

[0123] ;

[0124] in, and These represent the control effect of the k-th factor on the target variable V; This represents the value of the k-th factor at the base period 0, while Let L(a,b) represent the value of the k-th factor at planning period T; L(a,b) is a function representing the logarithmic mean of a and b. ; Let represent the target variable of the i-th computational unit during the baseline period, and Let represent the target variable of the i-th calculation unit in the expected period T.

[0125] The LMDI model was introduced to quantitatively assess the percentage control (percentage) of C and R factors over soil erosion, as shown in the following equation:

[0126] ;

[0127] in, Indicates the control magnitude (%) of factor C; This represents the soil erosion rate in year a. This represents the soil erosion rate in year b. This represents the vegetation cover management in year a. This indicates the vegetation cover management in year b; is the weighting coefficient for the i-th computational unit.

[0128] As one implementation method, the weighting coefficient of the i-th computational unit ;

[0129] As another implementation, the weighting coefficient of the i-th computational unit ; = i / 基准 , i Represents the R factor of the i-th computational unit. 基准 This indicates the R factor for the set reference region; = i / 基准 , i Denotes the K-factor of the i-th computational unit. 基准 This represents the K-factor of the set reference region; =C i / 基准 , i Represents the C factor of the i-th computational unit. 基准The C factor represents the set baseline region.

[0130] The control amount of the R factor is calculated using the following equation:

[0131] ;

[0132] in, Indicates the control magnitude of the R factor (%). Indicates the erosivity of rainfall in year a. This indicates the erosivity of rainfall in year b. Weighting coefficients:

[0133] As one implementation method, the weighting coefficient of the i-th computational unit ;

[0134] As another implementation, the weighting coefficient of the i-th computational unit .

[0135] The weighting coefficients in this embodiment are constructed by leveraging the differences between the R (rainfall erosivity), K (soil erodibility), and C (vegetation cover management) factors and the baseline area. This allows for precise and coordinated correction of regional heterogeneity in rainfall dynamics, soil background, and vegetation protection. By customizing the baseline area to adapt to different research scales, it dynamically reflects the changes in erosion control brought about by soil and water conservation measures. This makes the quantitative assessment of soil erosion control more realistic, providing accurate data support for identifying key areas for erosion intervention and optimizing prevention and control measures. It combines physical mechanism adaptability with decision-making application value.

[0136] This embodiment provides a soil erosion attribution method for plateau regions. Using the LMDI model, it can effectively and quantitatively separate the relative contributions of key driving factors such as changes in rainfall erosivity and vegetation cover to changes in soil erosion and carbon loss without residuals. This provides strong quantitative evidence for identifying dominant driving factors and understanding the relative strength of different driving forces (such as the impact of climate change and the effectiveness of ecological restoration).

[0137] The soil erosion attribution method provided in this embodiment has a solid theoretical foundation and wide applicability in its core models (RUSLE, SDR model, LMDI). Most of the required multi-source data (remote sensing data, meteorological data, soil data, etc.) can be obtained through public channels or investigated by conventional means, which makes the method of this embodiment more operable and has the potential to be promoted and applied to other regions.

[0138] The soil erosion attribution method provided in this embodiment is no longer a simple application of a general model. Instead, it introduces key physical processes such as snowmelt, freeze-thaw cycles, non-photosynthetic vegetation, and permafrost to create a new model with an inherent dynamic response mechanism specifically designed for high-altitude and cold plateau regions, which greatly improves the scientificity and accuracy of the assessment results.

[0139] This embodiment provides a soil erosion attribution method for plateau regions. By integrating snowmelt erosion power (RTP) and quantifying the daily protection effect of vegetation (CTP), this invention overcomes the "blindness" problem of traditional models during the spring snowmelt period and non-growing seasons, and can capture the complete erosion dynamics throughout the year.

[0140] This embodiment provides a soil erosion attribution method for plateau regions, which can distinguish and quantify the loss processes of "organic carbon" and "inorganic carbon" with different sources and properties, providing key technical support for accurately assessing regional carbon budget under the background of permafrost degradation.

[0141] Example 2

[0142] This embodiment provides a soil erosion attribution system for plateau regions, which specifically includes:

[0143] The data acquisition module is configured to acquire multi-source geospatial data from each computing unit within the study area.

[0144] The erosion calculation module is configured to: calculate the soil erosion rate of each calculation unit in the study area based on multi-source geospatial data and through a soil erosion model; wherein the soil erosion rate is the product of rainfall erosivity factor, soil erodibility factor, slope length and slope factor, vegetation cover management factor and soil and water conservation measure factor.

[0145] The erosion attribution module is configured to: use rainfall erosivity factor, soil erodibility factor, slope length and gradient factor, vegetation cover management factor and / or soil and water conservation measure factor as key driving factors, and use the log-mean Divisia index method to calculate the degree of control of key driving factors on changes in soil erosion rate; wherein, the degree of control is assigned a weighting coefficient for each calculation unit during the calculation process, and the weighting coefficient is constructed based on the differences between each calculation unit in the study area and the preset benchmark area;

[0146] The carbon loss calculation module is configured to calculate the soil carbon loss caused by soil erosion in each calculation unit within the study area, based on the soil erosion rate of each calculation unit within the study area.

[0147] The carbon loss attribution module is configured to use rainfall erosivity, soil erodibility, slope length and gradient, vegetation cover management, and / or soil and water conservation measures as key driving factors, and employ the log-mean Divisia index method to calculate the degree of control of key driving factors on soil carbon loss changes. The degree of control is calculated by weighting each calculation unit with a weighting coefficient, and the weighting coefficient is constructed based on the differences between each calculation unit in the study area and the preset benchmark area.

[0148] The multi-source geospatial data includes digital elevation model data, soil property data, land use data, daily meteorological data, and daily vegetation remote sensing data.

[0149] Among them, the rainfall erosivity factor is:

[0150] ;

[0151] ;

[0152] ;

[0153] ;

[0154] ;

[0155] Where R represents the rainfall erosivity factor; R r Indicates the runoff erosion coefficient. Indicates surface runoff, Indicates peak flow; R p denoted by , where i represents different semi-monthly periods; k is the number of days in the i-th semi-month; P j It represents the erosive daily rainfall on day j during the half-month period; α1 and β1 are intermediate parameters; and These refer to average daily erosive rainfall and average annual erosive rainfall, respectively.

[0156] If vegetation cover management factors are taken as key driving factors, then the weighting coefficient of the i-th calculation unit is:

[0157] ;

[0158] in, = i / 基准 , i This represents the rainfall erosivity factor of the i-th computational unit. 基准Indicates the rainfall erosivity factor for the reference area; = i / 基准 , i This represents the soil erodibility factor of the i-th computational unit. 基准 Indicates the soil erodibility factor of the reference area; =C i / 基准 , i This represents the vegetation cover management factor of the i-th computational unit. 基准 This indicates the vegetation cover management factor for the baseline area.

[0159] Among them, soil carbon loss is:

[0160] ;

[0161] ;

[0162] ;

[0163] ;

[0164] Where m represents the number of computational units, Indicates the carbon production of eroded soil. Indicates sediment yield, Indicates the soil carbon enrichment ratio. This indicates the concentration of soil organic carbon, soil inorganic carbon, or total carbon. Indicates soil carbon density; Indicates soil thickness; Bulk density; Indicates the rate of soil erosion. This indicates the sediment transport ratio.

[0165] Soil carbon density is determined through a functional relationship of the sediment connectivity index.

[0166] It should be noted that each module in this embodiment corresponds one-to-one with each step in Embodiment 1, and their specific implementation processes are the same, so they will not be repeated here.

[0167] Example 3

[0168] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the soil erosion attribution method for plateau regions as described in Embodiment 1 above.

[0169] Example 4

[0170] This embodiment provides a computer device, such as... Figure 3 As shown, the system includes a computer-readable storage medium 1003, a processor 1001, a communication interface 1002, and a computer program stored on the computer-readable storage medium 1003 and executable on the processor 1001. The processor 1001, communication interface 1002, and computer-readable storage medium 1003 can be connected via a bus or other means. The communication interface 1002 is used to receive and send data. When the processor 1001 executes the program, it implements the steps in the soil erosion attribution method for plateau areas as described in Embodiment 1 above.

[0171] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for attributing soil erosion in plateau regions, characterized in that, include: Acquire multi-source geospatial data for each computing unit within the study area; Based on multi-source geospatial data, the soil erosion rate of each computational unit in the study area was calculated using a soil erosion model. The soil erosion rate is the product of rainfall erosivity factor, soil erodibility factor, slope length and gradient factor, vegetation cover management factor, and soil and water conservation measure factor. Rainfall erosivity, soil erodibility, slope length and gradient, vegetation cover management, and / or soil and water conservation measures were used as key driving factors. The log-mean Divisia index method was employed to calculate the degree of control of key driving factors on changes in soil erosion rate. In the calculation process, a weighting coefficient was set for each calculation unit, and the weighting coefficient was constructed based on the differences between each calculation unit in the study area and the preset benchmark area. The weighting coefficient of the i-th computational unit ; = i / 基准 , i Represents the R factor of the i-th computational unit. 基准 This indicates the R factor for the set reference region; = i / 基准 , i Denotes the K-factor of the i-th computational unit. 基准 This represents the K-factor of the set reference region; =C i / 基准 , i Represents the C factor of the i-th computational unit. 基准 The C factor represents the baseline area set; R represents the rainfall erosivity factor, K represents the soil erodibility factor, and C represents the vegetation cover management factor. Based on the soil erosion rate of each computational unit within the study area, the soil carbon loss caused by soil erosion in each computational unit within the study area is calculated. The soil carbon loss is: ； ； ； ； Where m represents the number of computational units, Indicates the carbon production of eroded soil. Indicates sediment yield, Indicates the soil carbon enrichment ratio. This indicates the concentration of soil organic carbon, soil inorganic carbon, or total carbon. Indicates soil carbon density; Indicates soil thickness; Bulk density; Indicates the rate of soil erosion. This indicates the sediment transport ratio.

2. The method for attributing soil erosion in plateau areas as described in claim 1, characterized in that, The multi-source geospatial data includes digital elevation model data, soil property data, land use data, daily-scale meteorological data, and daily-scale vegetation remote sensing data.

3. The method for attributing soil erosion in plateau areas as described in claim 1, characterized in that, The rainfall erosivity factor is: ； ； ； ； ； Where R represents the rainfall erosivity factor; R r Indicates the runoff erosion coefficient. Indicates surface runoff, Indicates peak flow; R p denoted by , where i represents different semi-monthly periods; k is the number of days in the i-th semi-month; P j It represents the erosive daily rainfall on day j during the half-month period; α1 and β1 are intermediate parameters; and These refer to average daily erosive rainfall and average annual erosive rainfall, respectively. Indicates the runoff erosion weighting factor; This represents the weighted coefficient for rainfall erosion.

4. The method for attributing soil erosion in plateau areas as described in claim 1, characterized in that, If vegetation cover management factors are taken as key driving factors, then the weighting coefficient of the i-th calculation unit is: ； in, = i / 基准 , i This represents the rainfall erosivity factor of the i-th computational unit. 基准 Indicates the rainfall erosivity factor for the reference area; = i / 基准 , i This represents the soil erodibility factor of the i-th computational unit. 基准 Indicates the soil erodibility factor of the reference area; =C i / 基准 , i This represents the vegetation cover management factor of the i-th computational unit. 基准 This indicates the vegetation cover management factor for the baseline area.

5. The method for attributing soil erosion in plateau areas as described in claim 1, characterized in that, Rainfall erosivity, soil erodibility, slope length and gradient, vegetation cover management, and / or soil and water conservation measures were used as key driving factors. The log-mean Divisia index method was employed to calculate the degree of control of these key driving factors on changes in soil carbon loss. The degree of control was calculated by weighting each calculation unit using weighting coefficients, which were constructed based on the differences between each calculation unit in the study area and the preset baseline area.

6. The method for attributing soil erosion in plateau areas as described in claim 1, characterized in that, The soil carbon density is determined by a functional relationship of the sediment connectivity index.

7. A soil erosion attribution system for plateau regions, characterized in that, include: The data acquisition module is configured to acquire multi-source geospatial data from each computing unit within the study area. The erosion calculation module is configured to: calculate the soil erosion rate of each calculation unit in the study area based on multi-source geospatial data and through a soil erosion model; wherein the soil erosion rate is the product of rainfall erosivity factor, soil erodibility factor, slope length and gradient factor, vegetation cover management factor and soil and water conservation measure factor. The erosion attribution module is configured to: use rainfall erosivity factor, soil erodibility factor, slope length and gradient factor, vegetation cover management factor and / or soil and water conservation measure factor as key driving factors, and use the log-mean Divisia index method to calculate the degree of control of key driving factors on changes in soil erosion rate; wherein, the degree of control is assigned a weighting coefficient for each calculation unit during the calculation process, and the weighting coefficient is constructed based on the differences between each calculation unit in the study area and the preset benchmark area; The weighting coefficient of the i-th computational unit ; = i / 基准 , i Represents the R factor of the i-th computational unit. 基准 This indicates the R factor for the set reference region; = i / 基准 , i Denotes the K-factor of the i-th computational unit. 基准 This represents the K-factor of the set reference region; =C i / 基准 , i Represents the C factor of the i-th computational unit. 基准 The C factor represents the baseline area set; R represents the rainfall erosivity factor, K represents the soil erodibility factor, and C represents the vegetation cover management factor. Based on the soil erosion rate of each computational unit within the study area, the soil carbon loss caused by soil erosion in each computational unit within the study area is calculated. The soil carbon loss is: ； ； ； ； Where m represents the number of computational units, Indicates the carbon production of eroded soil. Indicates sediment yield, Indicates the soil carbon enrichment ratio. This indicates the concentration of soil organic carbon, soil inorganic carbon, or total carbon. Indicates soil carbon density; Indicates soil thickness; Bulk density; Indicates the rate of soil erosion. This indicates the sediment transport ratio.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the soil erosion attribution method for plateau areas as described in any one of claims 1-6.

9. A computer device comprising a computer-readable storage medium, a processor, and a computer program stored on the computer-readable storage medium and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the soil erosion attribution method for plateau areas as described in any one of claims 1-6.

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