A method for assessing the response of soil carbon concentration in karst regions to rainfall events

Abstract

The present application relates to the technical field of environment simulation calculation, and particularly relates to a kind of evaluation methods of karst region soil carbon concentration-rainfall event response.A kind of evaluation methods of karst region soil carbon concentration-rainfall event response, comprising the following steps: S1: obtaining extreme rainfall event and non-extreme rainfall event, dominant mode of explicit path is determined according to the extreme rainfall event, carbon loss intensity of implicit path is determined according to the non-extreme rainfall event;S2: according to the extreme rainfall event, determine the excitation discharge intensity of soil carbon pool;S3: based on the dominant mode of explicit path, carbon loss intensity of implicit path and excitation discharge intensity of soil carbon pool, comprehensive integration analysis and uncertainty evaluation are carried out using Monte Carlo simulation.The present application constructs multi-path carbon loss evaluation framework, fuses explicit and implicit path quantification and Monte Carlo simulation, and improves the analysis accuracy and risk assessment ability of carbon cycle in karst area.

Description

Technical Field

[0001] This invention relates to the field of environmental simulation and calculation technology, and in particular to a method for assessing the soil carbon concentration-rainfall event response in karst regions. Background Technology

[0002] Karst regions, as typical ecologically fragile areas, exhibit thin soil layers and strong infiltration characteristics, making carbon cycling processes extremely sensitive to rainfall responses. Existing research has achieved basic correlation analysis between soil carbon concentration and rainfall events through the deployment of soil monitoring networks and hydrological sensors. A preliminary carbon loss prediction model established based on statistical regression methods provides data support for understanding the mechanisms of karst carbon cycling.

[0003] However, prediction models based on deterministic parameters cannot effectively handle the high heterogeneity of karst systems, resulting in insufficient accuracy of risk assessment results under extreme rainfall scenarios; existing analytical frameworks have failed to achieve synergistic assessment of physical migration pathways and biochemical processes, limiting the accuracy of constructing a comprehensive characterization of carbon loss processes.

[0004] Therefore, it is necessary to construct an assessment method that can integrate multiple carbon loss mechanisms and quantify their uncertainties, and improve the ability to analyze the response of soil carbon cycle to rainfall in karst areas by integrating the multidimensional characteristics of hydrological and biogeochemical processes. Summary of the Invention

[0005] To overcome the low accuracy of multi-path collaborative assessment, this invention provides a method for assessing the soil carbon concentration-rainfall event response in karst regions.

[0006] The technical implementation of this invention is: a method for assessing the soil carbon concentration-rainfall event response in karst regions, comprising the following steps:

[0007] S1: Obtain extreme rainfall events and non-extreme rainfall events, determine the dominant mode of the explicit pathway based on the extreme rainfall events, and determine the carbon loss intensity of the implicit pathway based on the non-extreme rainfall events;

[0008] S2: Determine the induced emission intensity of the soil carbon pool based on the extreme rainfall events described;

[0009] S3: Based on the dominant patterns of explicit pathways, the carbon loss intensity of implicit pathways, and the induced emission intensity of soil carbon pools, Monte Carlo simulation is used for integrated analysis and uncertainty assessment.

[0010] Preferably, the step of acquiring extreme rainfall events and non-extreme rainfall events, determining the dominant mode of the explicit pathway based on the extreme rainfall events, and determining the carbon loss intensity of the implicit pathway based on the non-extreme rainfall events includes:

[0011] The extreme rainfall event refers to a short-duration high-intensity precipitation event with a maximum rainfall intensity exceeding a preset threshold, while the non-extreme rainfall event refers to all continuous precipitation events with a maximum rainfall intensity not exceeding the preset threshold.

[0012] Based on the extreme rainfall events, data on carbon concentration differences and soil loss in the topsoil were obtained. Based on the carbon concentration difference data and soil loss data, the dominant patterns of the overt pathways were determined. The overt pathways refer to the main migration channels that use surface runoff as the driving force to remove particulate organic carbon from the terrestrial ecosystem through physical scouring.

[0013] Soluble organic carbon concentration data of soil profiles are obtained based on the non-extreme rainfall events. Infiltration flux is determined based on the soluble organic carbon concentration data and a hydrological model. Carbon loss intensity of hidden pathways is determined based on the infiltration flux. The hidden pathways refer to the main migration mechanisms that transport soluble organic carbon to deep soil or groundwater through chemical leaching, using water infiltration as a carrier.

[0014] Preferably, determining the dominant pattern of the overt pathway based on the carbon concentration difference data and soil loss data includes:

[0015] Iterate through all extreme rainfall event samples and extract the following numerical features from each extreme rainfall event sample: rainfall driving features: total rainfall, maximum rainfall intensity, average rainfall intensity, and rainfall duration; soil carbon loss response features: difference in surface soil carbon concentration and soil loss per unit area; construct an event feature vector based on the rainfall driving features and soil carbon loss response features.

[0016] An event feature matrix is ​​constructed from the event feature vectors of all extreme rainfall event samples. Each row of the event feature matrix corresponds to an event, and each column corresponds to a feature. The event feature matrix is ​​then standardized using Z-score.

[0017] The standardized event feature matrix is ​​input into the K-means clustering algorithm. The optimal number of clusters K is determined according to the elbow rule. Cluster analysis is performed, and the cluster label corresponding to each event and the coordinates of the K cluster centers are output.

[0018] Based on the coordinate characteristics of the K cluster centers, typical rainfall-carbon loss event types are defined.

[0019] Preferably, defining typical rainfall-carbon loss event types based on the coordinate characteristics of the K cluster centers includes:

[0020] Based on the coordinate characteristics of the K cluster centers, identify and define typical dominant patterns of explicit paths;

[0021] The typical dominant modes include high-intensity scouring and large-volume leaching.

[0022] The high-intensity erosion type refers to an explicit path pattern driven by the kinetic energy of short-duration, high-intensity rainfall, which directly splashes and strips soil particles from the surface, resulting in rapid physical erosion.

[0023] The large-volume leaching type refers to an explicit path pattern in which long-duration, large-volume rainfall leads to soil moisture saturation and groundwater level rise, which in turn triggers runoff erosion and soil collapse.

[0024] Preferably, the step of determining the infiltration flux based on the soluble organic carbon concentration data and a hydrological model, and determining the carbon loss intensity via hidden pathways based on the infiltration flux, includes: the hydrological model formula is:

[0025] ;

[0026] in, For infiltration flux, For precipitation, Evaporation rate Surface runoff, The change in soil water storage is given by the formula for soluble organic carbon flux:

[0027] ;

[0028] in, This refers to the flux of soluble organic carbon loss. This represents the average concentration of soluble organic carbon. This refers to the infiltration flux;

[0029] The carbon loss intensity of the hidden pathway is determined based on the soluble organic carbon loss flux.

[0030] Preferably, determining the induced emission intensity of the soil carbon pool based on the extreme rainfall event includes:

[0031] The rapid initiation period is defined as the time from the onset of an extreme rainfall event to the peak soil respiration rate.

[0032] The time it takes for the peak soil respiration rate to decay to the baseline threshold is defined as the sustained excitation period.

[0033] The time during which the soil respiration rate stabilizes below the baseline threshold is defined as the baseline recovery period.

[0034] From records of non-extreme rainfall periods, time periods when soil moisture was below a specific threshold were selected, the average soil respiration rate during the selected time periods was calculated, and the average value was used as the baseline threshold.

[0035] The carbon loss during the rapid excitation period and the carbon loss during the sustained excitation period are determined based on the rapid excitation period and the sustained excitation period.

[0036] Preferably, determining the carbon loss during the rapid excitation period and the carbon loss during the sustained excitation period based on the rapid excitation period and the sustained excitation period includes:

[0037] The carbon loss during the rapid activation period is obtained by integrating the difference between the soil respiration rate and the baseline threshold during the rapid activation period; the carbon loss during the rapid activation period refers to the total amount of additional carbon emitted during the rapid activation period due to the strong activation of microbial and root activity.

[0038] The carbon loss during the continuous induction period is obtained by integrating the difference between the soil respiration rate and the baseline threshold during the continuous induction period; the carbon loss during the continuous induction period refers to the total amount of additional carbon emitted during the continuous induction period due to the continuous activity of the microbial community.

[0039] The induced emission intensity of the soil carbon pool is determined based on the carbon loss during the rapid induced period and the carbon loss during the sustained induced period.

[0040] Preferably, determining the induced emission intensity of the soil carbon pool based on the carbon loss during the rapid induced emission period and the carbon loss during the sustained induced emission period includes:

[0041] The induced emission intensity of the soil carbon pool is a composite index characterized by total induced carbon loss, average induced rate, and sustained induced contribution ratio.

[0042] The duration of the sustained excitation period is taken as the effective duration.

[0043] The average excitation rate is defined as the ratio of carbon loss during the sustained excitation period to the effective duration.

[0044] The sum of carbon loss during the rapid activation period and carbon loss during the sustained activation period is taken as the total activation carbon loss;

[0045] The ratio of carbon loss during the sustained excitation period to total carbon loss during excitation is used as the sustained excitation contribution ratio.

[0046] Preferably, the dominant pattern based on explicit pathways, the carbon loss intensity of implicit pathways, and the induced emission intensity of the soil carbon pool are comprehensively analyzed and uncertainties assessed using Monte Carlo simulation, including:

[0047] The typical dominant mode of the explicit pathway is used as the scenario constraint, and the composite index of carbon loss intensity and induced emission intensity of the implicit pathway is set as the probability distribution input.

[0048] By simulating the joint probability distribution of rainfall-carbon loss in different karst vulnerable areas through random sampling, the contribution ratio and coupling effect of the two pathways and the excitation emission process to the total soil carbon loss are quantified, and finally, the probability prediction and risk range of total soil carbon loss under extreme precipitation scenarios are generated.

[0049] Preferably, the quantification of the contribution ratio and coupling effect of the two pathways and the induced emission process to the total soil carbon loss includes: the coupling effect quantification formula is:

[0050] ;

[0051] in, The coupling effect coefficient is... The total contribution ratio of the physical migration path is obtained by summing the contribution ratios of high-intensity scouring and high-volume leaching in the explicit paths. The total contribution ratio of biochemical migration pathways is obtained by summing the contribution ratio corresponding to the carbon loss intensity of the latent pathways with the contribution ratio corresponding to the total activated carbon loss of the activated emission processes. The contribution ratio of high-intensity scouring in the dominant pathway is calculated based on the carbon loss caused by high-intensity scouring, which is the typical dominant mode in Monte Carlo simulations. The contribution ratio of the large-volume leaching type in the explicit pathway is calculated based on the carbon loss generated by the large-volume leaching type, which is the typical dominant mode in Monte Carlo simulations.

[0052] Beneficial Effects: This invention achieves a systematic analysis of soil carbon cycle processes in karst regions by constructing a multi-pathway carbon loss assessment framework encompassing physical erosion, chemical leaching, and biological activation. Clustering technology is used to accurately identify dominant patterns of explicit pathways, and hydrological models and integral calculations are combined to quantify the combined indicators of carbon loss intensity and activated emissions from implicit pathways, forming a multi-dimensional set of carbon migration characteristics. By introducing Monte Carlo simulation to establish a multi-parameter probability sampling mechanism, the interactions between pathways are quantified into coupling effect coefficients, and probability predictions and risk intervals under extreme rainfall scenarios are generated. This invention overcomes the limitations of traditional single-pathway analysis and deterministic assessment, significantly improving the accuracy of constructing a comprehensive portrait of carbon loss, and providing scientific and reliable technical support for carbon cycle management and ecological risk assessment in vulnerable karst areas. Attached Figure Description

[0053] Figure 1 This is a flowchart of the method for assessing the soil carbon concentration-rainfall event response in karst regions according to the present invention.

[0054] Figure 2 This is a schematic diagram of the clustering analysis process for the explicit path-dominant pattern of the present invention.

[0055] Figure 3 This is a schematic diagram illustrating the period division and calculation process for the soil carbon pool-induced emission intensity of the present invention;

[0056] Figure 4 This is a schematic diagram of the carbon loss probability prediction and risk assessment process based on Monte Carlo simulation of the present invention. Detailed Implementation

[0057] The present invention will be further described below with reference to specific embodiments. The illustrative embodiments and descriptions herein are used to explain the present invention, but are not intended to limit the present invention.

[0058] Example 1: A method for assessing the soil carbon concentration-rainfall event response in karst regions, such as... Figures 1-4 As shown, it includes the following steps:

[0059] S1: Obtain extreme rainfall events and non-extreme rainfall events, determine the dominant mode of the explicit pathway based on the extreme rainfall events, and determine the carbon loss intensity of the implicit pathway based on the non-extreme rainfall events;

[0060] The extreme rainfall event refers to a short-duration high-intensity precipitation event with a maximum rainfall intensity exceeding a preset threshold, while the non-extreme rainfall event refers to all continuous precipitation events with a maximum rainfall intensity not exceeding the preset threshold.

[0061] It should be noted that in carbon cycle assessment in karst regions, traditional methods, limited to single physical erosion pathways, struggle to comprehensively capture the multi-process responses driven by rainfall. Therefore, this step establishes a differentiated analytical foundation by collecting data on extreme and non-extreme rainfall events, such as sampling points in severely, moderately, lightly eroded, and non-karst areas. Extreme rainfall events are defined as short-duration, high-intensity precipitation based on a preset threshold for maximum rainfall intensity, dynamically calibrated using the statistical quantile (e.g., the 95th percentile) of the region's historical maximum rainfall intensity sequence. Non-extreme rainfall encompasses continuous precipitation events where the maximum rainfall intensity does not exceed the threshold. After acquiring event data using deployed meteorological sensors and a soil carbon monitoring network, the dominant patterns of explicit pathways are further analyzed to characterize physical erosion mechanisms, while the intensity of carbon loss through implicit pathways is quantified to reveal chemical leaching fluxes. This design overcomes the limitations of traditional single-pathway analysis, achieving the synergistic quantification of physical migration and chemical leaching processes, providing key input parameters for integrated assessment of multi-pathway carbon loss.

[0062] Data on carbon concentration differences and soil loss in the topsoil were obtained based on the extreme rainfall events, and the dominant patterns of the overt pathways were determined based on the carbon concentration difference data and soil loss data.

[0063] Iterate through all extreme rainfall event samples and extract the following numerical features from each extreme rainfall event sample: rainfall driving features: total rainfall, maximum rainfall intensity, average rainfall intensity, and rainfall duration; soil carbon loss response features: difference in surface soil carbon concentration and soil loss per unit area; construct an event feature vector based on the rainfall driving features and soil carbon loss response features.

[0064] An event feature matrix is ​​constructed from the event feature vectors of all extreme rainfall event samples. Each row of the event feature matrix corresponds to an event, and each column corresponds to a feature. The event feature matrix is ​​then standardized using Z-score.

[0065] The standardized event feature matrix is ​​input into the K-means clustering algorithm. The optimal number of clusters K is determined according to the elbow rule. Cluster analysis is performed, and the cluster label corresponding to each event and the coordinates of the K cluster centers are output.

[0066] Based on the coordinate characteristics of the K cluster centers, typical rainfall-carbon loss event types are defined.

[0067] It should be noted that the reference Figure 2 To overcome the limitations of traditional carbon loss assessments in pattern recognition, this step involves collecting topsoil data after extreme rainfall using a soil monitoring network. The data is then categorized into severely, moderately, lightly affected areas, and areas without karst topography to enhance the regional representativeness of the pattern recognition. Carbon concentration difference data is obtained by measuring the difference in carbon concentration before and after rainfall; for example, a decrease from 2.5% to 2.0% is recorded as a difference of -0.5%. Soil loss data is measured using runoff collection devices, determining the loss per unit area, such as 10 tons per hectare. These data collectively form the quantitative basis for physical erosion.

[0068] Rainfall-driven characteristics—including total precipitation, maximum intensity, average intensity, and duration—describe external stress factors; soil carbon loss response characteristics—including carbon concentration differences and loss per unit area—characterize the system output. These two are integrated to construct a six-dimensional feature vector, for example, [100mm, 50mm / h, 20mm / h, 5h, -0.5%, 10t / ha]. All event vectors form a feature matrix for structured data processing. After Z-score standardization to eliminate the influence of dimensions, the matrix is ​​input into the K-means algorithm to determine the number of clusters using the elbow rule. The output includes event cluster labels and center point coordinates. For example, cluster centers [90, 45, 18, 4.5, -0.4, 9] represent high-intensity erosion, and [120, 60, 25, 6, -0.6, 12] correspond to large-volume leaching. [Precipitation unit: mm, intensity unit: mm / h, time unit: h, carbon concentration unit: %, loss unit: t / ha]. By analyzing the features of cluster centers, typical event types are defined, breaking through the limitations of traditional single-mode approaches and achieving accurate identification of explicit path multimodalities.

[0069] Based on the coordinate characteristics of the K cluster centers, identify and define typical dominant patterns of explicit paths;

[0070] The typical dominant modes include high-intensity scouring and large-volume leaching.

[0071] The high-intensity erosion type refers to an explicit path pattern driven by the kinetic energy of short-duration, high-intensity rainfall, which directly splashes and strips soil particles from the surface, resulting in rapid physical erosion.

[0072] The large-volume leaching type refers to an explicit path pattern in which long-duration, large-volume rainfall leads to soil moisture saturation and groundwater level rise, which in turn triggers runoff erosion and soil collapse.

[0073] The explicit pathways refer to the main migration channels that use surface runoff as a driving force to remove particulate organic carbon from terrestrial ecosystems through physical scouring.

[0074] It should be noted that, addressing the limitations of the singular approach to physical erosion pathways in traditional carbon loss assessments, this step utilizes cluster center coordinate analysis to achieve multimodal identification of explicit pathways. When the values ​​of maximum and average rainfall intensity are more prominent than the values ​​of total rainfall and duration in the cluster center feature vector, it represents a high-intensity erosion-type dominant mode. For example, in the center coordinates 90,45,18,4.5,-0.4,9, the values ​​of maximum intensity (45 mm / h) and average intensity (18 mm / h) are more prominent than the total rainfall (90 mm) and duration (4.5 h). When the dimensions of total rainfall and duration are prominent, it corresponds to a large-volume leaching-type mode, such as the characteristics of 120 mm total rainfall and 6 h duration in the coordinates [120,60,25,6,-0.6,12]. These two typical models respectively characterize the differentiated physical processes of kinetic energy-driven instantaneous scouring and water-saturated progressive erosion. By quantifying the relative weights of the values ​​of each dimension of the cluster centers, the dominant mechanism of particulate organic carbon migration under extreme rainfall conditions is analyzed. This breaks through the shortcomings of traditional methods in treating physical paths as homogeneous and provides a classification basis for multi-path coupling analysis.

[0075] Soluble organic carbon concentration data of soil profiles are obtained based on the non-extreme rainfall events. Infiltration flux is determined based on the soluble organic carbon concentration data and a hydrological model. Carbon loss intensity of hidden pathways is determined based on the infiltration flux.

[0076] The hydrological model formula is:

[0077] ;

[0078] in, For infiltration flux, For precipitation, Evaporation rate Surface runoff, The change in soil water storage is given by the formula for soluble organic carbon flux:

[0079] ;

[0080] in, This refers to the flux of soluble organic carbon loss. This represents the average concentration of soluble organic carbon. This refers to the infiltration flux;

[0081] The carbon loss intensity of the hidden pathway is determined based on the soluble organic carbon loss flux.

[0082] The aforementioned hidden pathway refers to the main migration mechanism by which soluble organic carbon is transported to deep soil or groundwater through chemical leaching, using water infiltration as a carrier.

[0083] It should be noted that, to fill the quantitative gap in chemical leaching pathways in traditional carbon loss assessments, this step collects soluble organic carbon concentration data from soil profiles. Combined with the permeability characteristics of different karst vulnerable areas (e.g., heavily, moderately, lightly, and non-karst areas), dissolved carbon content is determined using profile sampling and liquid chromatography. For example, a detection value of 5 mg / L characterizes the dissolved carbon load per unit volume of water. These data are coupled with a hydrological model, which uses the water balance equation... Calculate the infiltration flux, including precipitation. Driven by the infiltration process, evapotranspiration Surface runoff Changes in soil water storage This constitutes a water dissipation term. When =200mm =60mm =40mm When the infiltration flux is 20 mm, the infiltration flux is... Reaching 80 mm reflects the intensity of water migration to deeper layers. Based on this, the formula for soluble organic carbon flux... To establish the relationship between concentration and migration, the calculation uses the equation 1 mm flux is equivalent to 1 L / m 2 The unit conversion for water volume is as follows: =5mg / L =80mm, then =400mg / m 2 This value is directly defined as the intensity of carbon loss through hidden pathways. A higher intensity value indicates a greater contribution of chemical leaching to carbon loss, thus breaking through the traditional assessment framework dominated by physical pathways and achieving synergistic quantification of carbon migration through multiple pathways.

[0084] Example 2, Reference Figure 3 S2: Determine the induced emission intensity of the soil carbon pool based on the extreme rainfall events;

[0085] The rapid initiation period is defined as the time from the onset of an extreme rainfall event to the peak soil respiration rate.

[0086] The time it takes for the peak soil respiration rate to decay to the baseline threshold is defined as the sustained excitation period.

[0087] The time during which the soil respiration rate stabilizes below the baseline threshold is defined as the baseline recovery period.

[0088] From records of non-extreme rainfall periods, time periods when soil moisture was below a specific threshold were selected, the average soil respiration rate during the selected time periods was calculated, and the average value was used as the baseline threshold.

[0089] The carbon loss during the rapid excitation period and the carbon loss during the sustained excitation period are determined based on the rapid excitation period and the sustained excitation period.

[0090] It should be noted that, to address the shortcomings of traditional carbon loss assessments in quantifying bio-initiation processes, this step focuses on the dynamic storage of organic carbon in the soil carbon pool. Baseline thresholds are established for different karst-vulnerable areas (e.g., severely, moderately, lightly, and non-karst areas). By analyzing soil respiration dynamics induced by extreme rainfall, the additional carbon release resulting from a surge in microbial activity is characterized, and the intensity of bio-initiation emissions is determined accordingly. This intensity is based on total bio-initiation carbon loss as the core indicator, for example, 50 gC / m³. 2 The quantitative results reflect the contribution of biological processes to carbon flux. Periods with soil moisture below a specific threshold were selected from records of non-extreme rainfall periods. This specific threshold was obtained by collecting historical soil moisture data (e.g., daily values ​​for at least 5 years) and calculating the 20th percentile of its numerical sequence (i.e., the values ​​at the 20th percentile after sorting). Then, all periods with soil moisture below this quantile were selected, and the average soil respiration rate during these periods was calculated as the baseline threshold. .

[0091] Based on the characteristics of soil respiration rate changes, the response process was divided into three time-series stages: the rapid excitation period, covering the period from the start of rainfall to the peak respiration (e.g., 0-24 hours), was used to capture the transient responses of microorganisms and roots; the sustained excitation period corresponds to the decay of the peak to the baseline threshold range (e.g., 24-120 hours), characterizing the sustained effects of community metabolism; and the baseline recovery period defines the stage where respiration stabilizes below the threshold (e.g., after 120 hours), indicating that the system has returned to steady state. The baseline threshold was established by extracting the average respiration rate during periods of low soil moisture in non-extreme rainfall, for example, 2 μmol / m³. 2 The baseline value is / s. This classification is based on the phased characteristics of respiratory dynamics, with carbon loss during the rapid excitation phase (e.g., 30 gC / m²). 2 This characterizes short-term high-intensity emissions and the carbon loss during the sustained activation period (e.g., 20 gC / m³). 2 The assessment of long-term cumulative effects, together with the assessment of the emission intensity, forms the basis for the calculation of the emission intensity, thereby improving the multi-path collaborative assessment system for carbon loss.

[0092] The carbon loss during the rapid activation period is obtained by integrating the difference between the soil respiration rate and the baseline threshold during the rapid activation period; the carbon loss during the rapid activation period refers to the total amount of additional carbon emitted during the rapid activation period due to the strong activation of microbial and root activity.

[0093] The carbon loss during the continuous induction period is obtained by integrating the difference between the soil respiration rate and the baseline threshold during the continuous induction period; the carbon loss during the continuous induction period refers to the total amount of additional carbon emitted during the continuous induction period due to the continuous activity of the microbial community.

[0094] The induced emission intensity of the soil carbon pool is determined based on the carbon loss during the rapid induced period and the carbon loss during the sustained induced period.

[0095] It should be noted that, to overcome the shortcomings of insufficient quantification of bioactivation processes in traditional carbon loss assessments, this step analyzes soil respiration dynamics through integral calculations. Carbon loss during the rapid activation period is calculated using the following formula. Calculation, where Instantaneous value of soil respiration rate (μmol CO2 / m 2 / s), Represents the baseline threshold (μmol CO2 / m 2 / s), to Define the duration of the rapid activation period. Carbon loss during the sustained activation period is determined by... The characterization, whose parameter definitions are consistent with the previous formula, to Define the duration of the sustained excitation period. When The higher the instantaneous value, the greater the total carbon loss; and and The more significantly the excitation effect accumulates, the more synergistically the magnitude and duration of the difference are enhanced. Baseline threshold differential computation can strip away background carbon fluxes and accurately capture additional emissions caused by surges in microbial activity, thereby addressing the problem of traditional methods ignoring biological pathways and improving the integrated assessment mechanism for multi-pathway carbon loss.

[0096] The carbon loss obtained from the integral calculation is in μmol CO2 / m 2 It needs to be converted to gC / m 2 The conversion formula is: ,in, The conversion factor (1 μmol CO2 corresponds to 12 × 10⁻⁶) is the conversion factor. -6 gC). Similarly, carbon loss during the sustained activation period. Perform the same conversion as well. .

[0097] The induced emission intensity of the soil carbon pool is a composite index characterized by total induced carbon loss, average induced rate, and sustained induced contribution ratio.

[0098] The duration of the sustained excitation period is taken as the effective duration.

[0099] The average excitation rate is defined as the ratio of carbon loss during the sustained excitation period to the effective duration.

[0100] The sum of carbon loss during the rapid activation period and carbon loss during the sustained activation period is taken as the total activation carbon loss;

[0101] The ratio of carbon loss during the sustained excitation period to total carbon loss during excitation is used as the sustained excitation contribution ratio.

[0102] It should be noted that, addressing the limitation of traditional carbon loss assessments that rely on a single bio-initiation process characterization, this scheme employs a multi-dimensional approach to synergistically characterize the dynamic response of the soil carbon pool. Total induced carbon loss integrates additional emissions during both rapid and sustained phases, such as 50 gC / m³. 2 The numerical values ​​reflect the scale of carbon flux driven by microbial activity; the average excitation rate is calculated as the ratio of carbon loss during duration to effective duration, such as 0.208 gC / m². 2 The quantitative results per h reveal the intensity of carbon emissions per unit time; the sustained excitation contribution ratio is characterized by the proportion of sustained loss, and if it reaches 40%, it indicates that long-term metabolic processes play a significant role in carbon loss. These three types of indicators construct a complete picture of excitation emissions from the dimensions of total amount, rate, and structure, respectively, overcoming the shortcomings of traditional methods that only focus on cumulative amounts, and providing a multi-scale analytical basis for the quantification of biochemical pathways.

[0103] Example 3, based on Examples 1-2, with reference to Figure 4 S3: Based on the dominant patterns of explicit pathways, the carbon loss intensity of implicit pathways, and the induced emission intensity of soil carbon pools, Monte Carlo simulation is used for integrated analysis and uncertainty assessment.

[0104] The typical dominant mode of the explicit pathway is used as the scenario constraint, and the composite index of carbon loss intensity and induced emission intensity of the implicit pathway is set as the probability distribution input.

[0105] By simulating the joint probability distribution of rainfall-carbon loss in different karst vulnerable areas through random sampling, the contribution ratio and coupling effect of the two pathways and the excitation emission process to the total soil carbon loss are quantified, and finally, the probability prediction and risk range of total soil carbon loss under extreme precipitation scenarios are generated.

[0106] It should be noted that, to address the shortcomings of traditional assessment methods in handling the heterogeneity of karst systems, this step introduces Monte Carlo simulation technology. By setting probability distribution inputs in different karst vulnerable areas (e.g., heavily, moderately, lightly, and non-karst areas), the uncertainty of carbon loss processes is quantified. In specific implementation, the typical dominant patterns of explicit pathways serve as scenario constraints to define the basic framework of physical migration; while the composite index of carbon loss intensity and induced emissions from implicit pathways is transformed into probability distribution inputs to characterize the inherent volatility of biochemical processes. By establishing a multi-parameter probability sampling mechanism, this method can simulate carbon loss responses under different scenarios. Based on this, contribution ratio analysis clarifies the degree of influence of each pathway on the total loss, and coupling effect coefficients are used to quantify the strength of synergistic or antagonistic effects between physical migration and biochemical processes. Through tens of thousands of random samplings, probabilistic prediction results are finally generated; for example, at a 90% confidence level, the predicted range for total soil carbon loss is 110–165 gC / m³. 2 This probabilistic risk assessment effectively overcomes the limitations of traditional deterministic forecasting, providing a reliable basis for decision-making in carbon cycle management in karst regions.

[0107] It should be noted that before calculating the coupling effect coefficients, it is necessary to check the case where the contribution ratio of each path is zero. , , or If the value is zero, then replace it with a very small value (such as 10). -6 This ensures the mathematical validity of logarithmic operations.

[0108] The formula for quantifying coupling effects is:

[0109] ;

[0110] in, The coupling effect coefficient is... The total contribution ratio of the physical migration path is obtained by summing the contribution ratios of high-intensity scouring and high-volume leaching in the explicit paths. The total contribution ratio of biochemical migration pathways is obtained by summing the contribution ratio corresponding to the carbon loss intensity of the latent pathways with the contribution ratio corresponding to the total activated carbon loss of the activated emission processes. The contribution ratio of high-intensity scouring in the dominant pathway is calculated based on the carbon loss caused by high-intensity scouring, which is the typical dominant mode in Monte Carlo simulations. The contribution ratio of the large-volume leaching type in the explicit pathway is calculated based on the carbon loss generated by the large-volume leaching type, which is the typical dominant mode in Monte Carlo simulations.

[0111] It should be noted that, to address the insufficient quantification of multi-pathway synergistic mechanisms in traditional carbon loss assessments, this scheme proposes a coupling effect quantification formula. This formula systematically characterizes the interaction strength between pathways by constructing a composite index. Among these, the total contribution ratio of physical migration pathways is... It integrates the synergistic effects of two explicit pathways. The contribution ratio of high-intensity scouring in the explicit pathway. The contribution ratio of large-volume leaching in the explicit pathway, and the total contribution ratio of the biochemical pathway. This combines the combined effects of leaching and emission activation. The contribution ratio corresponding to the intensity of carbon loss through hidden pathways. The contribution ratio corresponding to the total activated carbon loss in the activated emission process.

[0112] During the Monte Carlo simulation, the carbon loss along each path was obtained through tens of thousands of random samplings: This indicates the amount of carbon loss under the typical dominant mode of high-intensity scouring. The typical dominant mode is carbon loss under high-volume leaching. This represents the sum of carbon loss through all pathways. The contribution ratio of each pathway is then calculated based on this sum. and These ratios accurately reflect the relative importance of different dominant modes in the carbon loss process.

[0113] When the contribution of each path is closer to the equilibrium state... and The closer the ratio is to 1, the stronger the coupling coefficient. The value then approaches 1. This multi-path synergistic quantification mechanism effectively overcomes the limitations of traditional single-path analysis and provides a new analytical dimension for the study of multi-process coupling of carbon cycling in karst regions.

[0114] It should be noted that the coefficients in the formula The coefficients are used to calculate the arithmetic mean of two synergy indicators, with initial values ​​set based on preliminary data analysis and mathematical simplification principles. In practical applications, predicted data are generated through extensive Monte Carlo simulations and compared with actual observed carbon loss data. Least squares regression analysis is used to optimize the coefficients to minimize prediction errors. The updated coefficients replace the initial values ​​for subsequent simulations.

[0115] Building upon Example 3, to illustrate how Monte Carlo simulations quantify contribution ratios and coupling effects, a concise example is provided below: Input probability distribution: The intensity of carbon loss via the hidden pathway is set to follow a normal distribution N(0.25, 0.05) gC / m². 2The total carbon loss from induced emissions is set to follow a normal distribution N(3.6,0.5)gC / m³. 2 The two constitute a joint probability distribution. The dominant pattern of the explicit pathway serves as a constraint for the "large-volume leaching" scenario. Simulation and quantitative analysis were performed: Total soil carbon loss was simulated through 10,000 random samplings. The contribution ratio of each pathway was calculated for each sampling result, and its distribution was statistically analyzed. The statistical results of this example simulation show: Contribution ratio: The average contribution ratios of explicit pathways, induced emissions, and implicit pathways to the total loss are 78.5%, 20.0%, and 1.5%, respectively. Coupling effect: The coupling effect coefficient (based on the above contribution ratios) is calculated. The average value is 0.15, indicating a weak interaction between physical migration and biochemical pathways. This example demonstrates that the present invention achieves probabilistic quantification of the contribution ratio of multiple pathways and coupling effects through Monte Carlo simulations.

[0116] Following the aforementioned Monte Carlo simulation process, the carbon loss from implicit pathways, induced emissions, and explicit pathways obtained from each sampling are summed to obtain a possible value for the total soil carbon loss under extreme precipitation scenarios. Statistical analysis of tens of thousands (e.g., 10,000) simulation results generates a probability prediction and risk range. For example, the average total soil carbon loss output from a typical simulation is 15.2 gC / m³. 2 Its 90% confidence interval is [12.5, 18.1] gC / m 2 The results indicate that, under the extreme rainfall scenario described, there is a 90% probability that the total soil carbon loss will fall between 12.5 and 18.1 gC / m³. 2 This provides an intuitive and quantitative probabilistic basis for assessing the carbon loss risk in karst regions.

[0117] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for assessing the soil carbon concentration-rainfall event response in karst regions, characterized in that, Includes the following steps: S1: Obtain information on extreme and non-extreme rainfall events, determine the dominant patterns of overt pathways based on the extreme rainfall events, and determine the carbon loss intensity of covert pathways based on the non-extreme rainfall events; the overt pathways refer to the main migration channels that use surface runoff as the driving force to remove particulate organic carbon from terrestrial ecosystems through physical scouring; the covert pathways refer to the main migration mechanisms that use water infiltration as a carrier to transport soluble organic carbon to deeper soil or groundwater through chemical leaching. S2: Determining the activation emission intensity of the soil carbon pool based on the extreme rainfall event includes: defining the period from the start of the extreme rainfall event to the peak soil respiration rate as the rapid activation period; defining the period from the peak soil respiration rate to the baseline threshold as the sustained activation period; defining the period from the stable soil respiration rate below the baseline threshold as the baseline recovery period; selecting periods from records of non-extreme rainfall periods where soil moisture is below a specific threshold, calculating the average soil respiration rate during these periods, and using this average value as the baseline threshold; determining the carbon loss during the rapid activation period and the carbon loss during the sustained activation period based on the rapid activation period and the sustained activation period; the determination of the carbon loss during the rapid activation period and the carbon loss during the sustained activation period based on the rapid activation period and the sustained activation period includes: performing an integral operation on the difference between the soil respiration rate and the baseline threshold during the rapid activation period to obtain the carbon loss during the rapid activation period; the carbon loss during the rapid activation period refers to the carbon loss incurred during the rapid activation period due to the depletion of microbial and root activity. The total amount of additional carbon emitted due to intense excitation; the carbon loss during the sustained excitation period is obtained by integrating the difference between the soil respiration rate and the baseline threshold during the sustained excitation period; the sustained excitation period carbon loss refers to the total amount of additional carbon emitted due to the continuous activity of the microbial community during the sustained excitation period; the excitation emission intensity of the soil carbon pool is determined based on the carbon loss during the rapid excitation period and the carbon loss during the sustained excitation period; the determination of the excitation emission intensity of the soil carbon pool based on the carbon loss during the rapid excitation period and the carbon loss during the sustained excitation period includes: the excitation emission intensity of the soil carbon pool is a composite index characterized by the total excitation carbon loss, the average excitation rate, and the sustained excitation contribution ratio; the duration of the sustained excitation period is used as the effective duration; the ratio of the sustained excitation period carbon loss to the effective duration is used as the average excitation rate; the sum of the carbon loss during the rapid excitation period and the carbon loss during the sustained excitation period is used as the total excitation carbon loss; the ratio of the sustained excitation period carbon loss to the total excitation carbon loss is used as the sustained excitation contribution ratio. S3: Based on the dominant patterns of explicit pathways, the carbon loss intensity of implicit pathways, and the induced emission intensity of soil carbon pools, Monte Carlo simulation is used for integrated analysis and uncertainty assessment.

2. The method for assessing the soil carbon concentration-rainfall event response in karst areas according to claim 1, characterized in that, The process of acquiring extreme and non-extreme rainfall events, determining the dominant patterns of overt pathways based on the extreme rainfall events, and determining the carbon loss intensity of covert pathways based on the non-extreme rainfall events includes: The extreme rainfall event refers to a short-duration high-intensity precipitation event with a maximum rainfall intensity exceeding a preset threshold, while the non-extreme rainfall event refers to all continuous precipitation events with a maximum rainfall intensity not exceeding the preset threshold. Data on carbon concentration differences and soil loss in the topsoil were obtained based on the extreme rainfall events, and the dominant patterns of the overt pathways were determined based on the carbon concentration difference data and soil loss data. Soluble organic carbon concentration data of soil profiles are obtained based on the non-extreme rainfall events. Infiltration flux is determined based on the soluble organic carbon concentration data and a hydrological model. Carbon loss intensity of hidden pathways is determined based on the infiltration flux.

3. The method for assessing the soil carbon concentration-rainfall event response in karst areas according to claim 2, characterized in that, The process of determining the dominant pattern of the overt pathway based on the carbon concentration difference data and soil loss data includes: Iterate through all extreme rainfall event samples and extract the following numerical features from each extreme rainfall event sample: rainfall driving features: total rainfall, maximum rainfall intensity, average rainfall intensity, and rainfall duration; soil carbon loss response features: difference in surface soil carbon concentration and soil loss per unit area; construct an event feature vector based on the rainfall driving features and soil carbon loss response features. An event feature matrix is ​​constructed from the event feature vectors of all extreme rainfall event samples. Each row of the event feature matrix corresponds to an event, and each column corresponds to a feature. The event feature matrix is ​​then standardized using Z-score. The standardized event feature matrix is ​​input into the K-means clustering algorithm. The optimal number of clusters K is determined according to the elbow rule. Cluster analysis is performed, and the cluster label corresponding to each event and the coordinates of the K cluster centers are output. Based on the coordinate characteristics of the K cluster centers, typical rainfall-carbon loss event types are defined.

4. The method for assessing the soil carbon concentration-rainfall event response in karst areas according to claim 3, characterized in that, The definition of typical rainfall-carbon loss event types based on the coordinate characteristics of the K cluster centers includes: Based on the coordinate characteristics of the K cluster centers, typical dominant patterns of explicit paths are identified and defined; the typical dominant patterns include high-intensity scouring and high-volume leaching. The high-intensity erosion type refers to an explicit path pattern driven by the kinetic energy of short-duration, high-intensity rainfall, which directly splashes and strips soil particles from the surface, resulting in rapid physical erosion. The large-volume leaching type refers to an explicit path pattern in which long-duration, large-volume rainfall leads to soil moisture saturation and groundwater level rise, which in turn triggers runoff erosion and soil collapse.

5. The method for assessing the soil carbon concentration-rainfall event response in karst areas according to claim 2, characterized in that, The process of determining infiltration flux based on the soluble organic carbon concentration data and a hydrological model, and determining the carbon loss intensity via hidden pathways based on the infiltration flux, includes: the hydrological model formula is as follows: ； in, For infiltration flux, For precipitation, Evaporation rate Surface runoff, The change in soil water storage is given by the formula for soluble organic carbon flux: ； in, This refers to the flux of soluble organic carbon loss. This represents the average concentration of soluble organic carbon. The carbon loss intensity of the hidden pathway is determined based on the soluble organic carbon loss flux.

6. The method for assessing the soil carbon concentration-rainfall event response in karst areas according to claim 1, characterized in that, The dominant patterns based on explicit pathways, the intensity of carbon loss through implicit pathways, and the induced emission intensity of the soil carbon pool were comprehensively integrated and analyzed using Monte Carlo simulations, along with uncertainty assessments. The typical dominant mode of the explicit pathway is used as the scenario constraint, and the composite index of carbon loss intensity and induced emission intensity of the implicit pathway is set as the probability distribution input. By simulating the joint probability distribution of rainfall-carbon loss in different karst vulnerable areas through random sampling, the contribution ratio and coupling effect of the two pathways and the excitation emission process to the total soil carbon loss are quantified, and finally, the probability prediction and risk range of total soil carbon loss under extreme precipitation scenarios are generated.

7. The method for assessing the soil carbon concentration-rainfall event response in karst areas according to claim 6, characterized in that, The quantification of the contribution ratio and coupling effect of the two pathways and the induced emission process to the total soil carbon loss includes: the coupling effect quantification formula is as follows: ； in, The coupling effect coefficient is... The total contribution ratio of the physical migration path is obtained by summing the contribution ratios of high-intensity scouring and high-volume leaching in the explicit paths. The total contribution ratio of biochemical migration pathways is obtained by summing the contribution ratio corresponding to the carbon loss intensity of the latent pathways with the contribution ratio corresponding to the total activated carbon loss of the activated emission processes. The contribution ratio of high-intensity scouring in the dominant pathway is calculated based on the carbon loss caused by high-intensity scouring, which is the typical dominant mode in Monte Carlo simulations. The contribution ratio of the large-volume leaching type in the explicit pathway is calculated based on the carbon loss generated by the large-volume leaching type, which is the typical dominant mode in Monte Carlo simulations.

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