A method for estimating soil organic carbon storage in karst areas based on black carbon correction

Abstract

The application discloses a kind of based on black carbon correction's karst region soil organic carbon storage estimation method, it is related to karst region soil organic carbon storage estimation technical field, to solve the problem that existing estimation method cannot be in the soil organic carbon storage calculation of missing soil type classification area or in the case where data volume is limited, realize greater scale soil organic carbon metering expansion provides basis problem.Step one, the determination of research area;Step two, sample point arrangement: arrange spatial distribution grid on 1:10000 research area topographic map, utilize handheld GPS, compass instrument and sample point distribution topographic map to carry out sampling point positioning;Step three, sample collection: dig soil profile at each sampling point, collect soil sample by stratified strip sampling, the background information of each sampling point needs to be recorded, and the indexes of soil bulk density, soil thickness, rock exposure rate are determined;Step four, sample processing and determination analysis;Step five, the calculation of soil organic carbon storage.

Description

Technical Field

[0001] This invention relates to the field of soil organic carbon storage estimation technology in karst areas, specifically a method for estimating soil organic carbon storage in karst areas based on black carbon correction. Background Technology

[0002] Soil is the largest and most active carbon reservoir in terrestrial ecosystems, and a core component of global carbon cycle and climate change research. Therefore, accurately estimating soil organic carbon storage is extremely important. Currently, the main methods for estimating soil carbon storage include typological methods and geostatistical methods. GIS, as an emerging technology, is rapidly expanding its application areas, and its combination with other methods is continuously improving the accuracy and efficiency of soil organic carbon calculations.

[0003] Current research on GIS spatial interpolation simulation methods mainly focuses on large-scale areas, with limited research on watershed-scale methods, especially in karst regions with complex topography. Geostatistical methods consider the spatial heterogeneity of soil properties, using a limited number of sampling points to estimate unobserved points within the region. Through raster computation, they estimate soil carbon storage at medium and small scales, offering greater accuracy than typological methods. However, when estimating carbon storage in highly heterogeneous ecosystems, geostatistical methods also suffer from significant uncertainties due to factors such as sampling density and sampling methods. Karst ecosystems are highly heterogeneous ecosystems constrained by unique geological backgrounds, with topographic and geomorphological conditions, hydrothermal conditions, vegetation site conditions, and soil development conditions all differing from non-karst regions. However, many studies using geostatistical methods to study discontinuous soils in karst regions, when estimating soil organic carbon storage, treat bare rock patches and gravel content as soil distribution patches, significantly overestimating soil organic carbon storage in karst areas.

[0004] Black carbon (BC) is a continuous carbonaceous substance produced by the incomplete combustion of biomass and fossil fuels, and it is widely present in soil environmental media. However, soil black carbon is not soil organic carbon. Due to the complex characteristics of karst landforms, the calculation of soil organic carbon storage in karst areas does not deduct soil black carbon, resulting in a large error in soil organic carbon storage. At the same time, the unique habitat characteristics of karst areas determine that the carbon storage and carbon density estimation methods for non-karst areas are not suitable for this region. Although current methods consider indicators such as rock exposure rate and soil layer thickness when estimating soil organic carbon storage in karst areas, they still do not consider the combined influence of black carbon content, gravel content, and rock exposure rate on the estimation of soil organic carbon storage, thus failing to meet current needs. To address this, we propose a black carbon-corrected method for estimating soil organic carbon storage in karst areas. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the existing defects and provide a method for estimating soil organic carbon storage in karst areas based on black carbon correction, so as to solve the problem that the current estimation methods proposed in the background art cannot provide a basis for calculating soil organic carbon storage in areas lacking soil type classification or for expanding soil organic carbon measurement to a larger scale when the amount of data is limited.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for estimating soil organic carbon storage in karst areas based on black carbon correction, wherein:

[0008] Step 1: Determining the study area: Selecting a suitable karst watershed region;

[0009] Step 2: Sampling Point Layout: Using the ArcGIS 9.3 software platform, a spatially distributed grid was laid out on a 1:10000 topographic map of the study area, based on the grid method principle. The study area is 75 km². 2 The actual grid size is 0.15 × 0.15 km. 2 If sampling points are set at the center of the grid, there are theoretically 3333 sampling points. During the field soil sample collection and baseline information survey, handheld GPS, compass and topographic map of sampling point distribution are used to locate the sampling points.

[0010] Step 4: Soil Sample Collection and Basic Data Determination: At each sampling point, a soil profile is excavated. Soil samples are collected using stratified strip sampling, employing a bottom-up stratified sampling method. The excavation depth of the soil profile is ≤100cm, with shallow excavations reaching bedrock or parent material, and deeper excavations reaching 100cm. Layers are created according to depths of 0–5cm, 5–10cm, 10–15cm, 15–20cm, 20–30cm, 30–40cm, 40–50cm, 50–60cm, 70–80cm, 80–90cm, and 90–100cm, for a total of 1–12 layers. Records must be recorded at each sampling point. Baseline information was collected, and soil bulk density, soil thickness, and rock exposure rate were measured. Soil bulk density was measured layer by layer along the soil profile from top to bottom using the ring cutter method. Soil thickness was measured separately for each microhabitat type using the probe method. Two types of iron probes, 60cm and 120cm in length, were used to measure soil at different depths. The average soil thickness was represented by measuring 8-10 points. Rock exposure rate was measured using the transect method and represented by the percentage of vegetation cover area and the percentage of exposed rock area within the sampling point range. Gravel content was represented by the percentage of volume occupied by gravel larger than 2mm. Soil distribution area was statistically analyzed using a combination of remote sensing data verification and field survey.

[0011] Step 5: Determination of Soil Organic Carbon and Black Carbon: First, the soil samples collected in the field are air-dried, then ground to prepare the samples required for the test. Soil organic carbon is determined using the potassium dichromate-sulfuric acid oxidation method. Soil black carbon is determined using a laboratory thermo-optical organic carbon elemental carbon analyzer. The specific steps are as follows: the soil sample is filtered onto a membrane, and then the membrane carrying the particulate matter is heated in a pure gas environment. Heating continues in a He environment containing O2. At this time, the elemental carbon (EC1, EC2, EC3) in the membrane is oxidized to CO2. The CO2 needs to be reduced to CH4 in a methane conversion furnace and finally detected by a flame ionization detector (FID). A laser is used to irradiate a quartz membrane, and the reflection or radiation signal of the membrane is measured by a photodetector. The change in the signal indicates the starting point of elemental carbon oxidation. During the carbonization process of organic carbon, optically cracked carbon (OPC) is formed, and then the content of black carbon is calculated.

[0012] Step 6: Calculation of soil organic carbon storage.

[0013] Preferably, the optimized formula for calculating soil organic carbon storage in step six includes:

[0014] (1) Traditional spatial interpolation methods:

[0015] ①

[0016] TSOC1 is the soil organic carbon storage of the sample plot calculated by the traditional spatial interpolation method, 0.01 is the unit conversion factor, 22500 is the sampling area, SOCj is the soil organic carbon content in j-unit grid, BDj is the soil bulk density in j-unit grid, and Tj is the soil depth in j-unit grid. After interpolation, the soil depth is greater than 100cm, so T = 100cm is taken, and n is the number of grids. Kriging interpolation of SOC content and soil bulk density is performed using the Geostatistical Analyst module in ArcGIS, and soil depth is interpolated using the IDW method of the SpatialAnalyst module in ArcGIS.

[0017] (2) Spatial interpolation method based on rock exposure rate, gravel content, and soil black carbon correction:

[0018] ②

[0019] SOCD2 is the topsoil organic carbon storage calculated using a raster algorithm based on rock exposure rate, gravel content, and soil black carbon correction. 0.01, 22500, SOCj, BDj, and Tj are the same as δ. j G represents the rock exposure rate of the j-th type of partition. j H represents the percentage (%) of gravel larger than 2 mm in the j-th type partition. jThe content of black carbon in the soil of the j-th type zone;

[0020] (3) Soil profile summation method based on rock exposure rate, gravel content, and soil black carbon correction:

[0021] Karst regions have diverse soil types, and their soil organic carbon content, bulk density, and thickness exhibit significant variability. Therefore, stratified calculation of soil organic carbon density is necessary. The soil profile is divided into 1 to 12 layers. The soil organic carbon density of each layer is calculated based on its corresponding organic carbon content, bulk density, and thickness, thus obtaining the spatial characteristic values ​​of soil organic carbon density in the small watershed. Then, the organic carbon storage is calculated stratified using the soil organic carbon density and the distribution area of ​​various soil types, ultimately yielding the total soil organic carbon storage in the study area. To eliminate estimation errors caused by rocks and black carbon in karst regions and to correct the soil organic carbon storage estimation results to be closer to the true value, gravel content, rock exposure rate, and black carbon in the soil are used for correction. The formulas for calculating soil organic carbon density and its storage are presented below.

[0022] ③

[0023] SOCD i,j Let be the soil organic carbon density of the i-th layer in the j-th soil profile; ρ represents the soil organic carbon content of the i-th layer for the j-th soil type; i,j T is the soil bulk density of the i-th layer of the j-th soil type; i,j S is the thickness of the i-th soil layer of the j-th soil type; 10⁻² is the unit conversion factor; SOCS is the total organic carbon storage of the soil in the study area; S j δ represents the soil distribution area of ​​the j-th soil genus; 103 is the unit conversion factor. j G represents the rock exposure rate of the j-th type of partition. j H represents the volume percentage of gravel larger than 2 mm in the j-th type partition. j The content of black carbon in the soil of the j-th type zone;

[0024] (4) Formula for calculating the miscalculation rate (e) of organic carbon storage

[0025] ④

[0026] In the formula: k takes the values ​​1 and 2 respectively.

[0027] Preferably, the number of sampling points can be reduced based on the obstacles within the study area.

[0028] Compared with existing technologies, the beneficial effects of this invention are: the number of sampling points can be appropriately reduced according to obstacles in the study area, resulting in the most representative, least sampling workload, and most easily operable method for estimating organic carbon storage in karst soils. This is of great significance for reducing working costs and improving the accuracy of storage estimation, and has the following advantages:

[0029] This invention provides a preliminary understanding of the distribution characteristics and internal structure of data through descriptive statistical analysis, revealing the inherent patterns between soil organic carbon and influencing factors in karst regions, thus laying the foundation for further overcoming the heterogeneity among karst data. The spatial interpolation method based on corrections for rock exposure rate, gravel content, and soil black carbon is superior to traditional spatial interpolation methods, significantly reducing errors caused by black carbon content, rock exposure rate, and gravel content. This is particularly evident in estimating the organic carbon reserves of surface soil in karst regions, where the errors caused by black carbon content and rock exposure rate are greatly reduced. Furthermore, calculations of the organic carbon reserve miscalculation rate show that the spatial interpolation method based on corrections for rock exposure rate and gravel content is closer to the actual values ​​of the sample area, especially when estimating organic carbon reserves on karst slopes with high rock exposure. Traditional geostatistical methods, on the other hand, tend to mistakenly classify soil black carbon as soil organic carbon, making them unsuitable for estimating soil organic carbon reserves in karst regions. Attached Figure Description

[0030] Figure 1 This is a descriptive statistical characteristic diagram of soil organic carbon content, soil bulk density, and gravel content according to the present invention.

[0031] Figure 2 This is a statistical diagram of soil reserves at different soil depths according to the present invention;

[0032] Figure 3 The semivariance function model and parameter diagram of organic carbon storage at different soil depths in this invention;

[0033] Figure 4 A comparison chart of organic carbon reserves using different estimation methods of the present invention;

[0034] Figure 5 This is a flowchart of the method for estimating soil organic carbon storage in karst areas according to the present invention. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] Please see Figure 1 The present invention provides the following technical solution: a method for estimating soil organic carbon storage in karst areas based on black carbon correction, characterized by comprising the following steps:

[0037] Step 1: Determining the study area: Selecting a suitable karst watershed region;

[0038] Step 2: Sampling Point Layout: Using the ArcGIS 9.3 software platform, a spatially distributed grid was laid out on a 1:10000 topographic map of the study area, based on the grid method principle. The study area is 75 km². 2 The actual grid size is 0.15 × 0.15 km. 2 If sampling points are set at the center of the grid, there are theoretically 3333 sampling points. During the field soil sample collection and baseline information survey, handheld GPS, compass and topographic map of sampling point distribution are used to locate the sampling points.

[0039] Step 3: Sample Collection: At each sampling point, a soil profile is excavated, and soil samples are collected through stratified strip sampling. Soil samples are collected using a bottom-up stratified sampling method. The soil profile excavation depth is ≤100cm, with shallow excavation to bedrock or parent material and deeper excavation to 100cm. The soil layers are divided into 12 layers: 0-5cm, 5-10cm, 10-15cm, 15-20cm, 20-30cm, 30-40cm, 40-50cm, 50-60cm, 70-80cm, 80-90cm, and 90-100cm. The baseline information for each sampling point must be recorded, and the soil bulk density, soil thickness, and rock exposure rate must be measured.

[0040] Step 4: Sample Processing and Analysis: First, the soil samples collected in the field were air-dried, then ground to prepare the samples required for the experiments. Soil organic carbon was determined using the potassium dichromate-sulfuric acid oxidation method. Soil distribution area was statistically analyzed using a combination of remote sensing data verification and field surveys. Soil bulk density was measured layer by layer along the soil profile from top to bottom using the ring cutter method. Soil thickness was measured separately according to microhabitat type using the probe method. Two types of iron probes, 60cm and 120cm in length, were used to measure soil at different depths. The average value of soil layer thickness was expressed as the result of measuring 8-10 points. Rock exposure rate was determined using the transect method and expressed as the percentage of vegetation cover area and rock exposure area within the sample point range. Gravel content was expressed as the volume percentage of gravel larger than 2mm.

[0041] Step 5: Determination of Soil Organic Carbon and Black Carbon: First, the soil samples collected in the field are air-dried, then ground to prepare the samples required for the test. Soil organic carbon is determined using the potassium dichromate-sulfuric acid oxidation method. Soil black carbon is determined using a laboratory thermo-optical organic carbon elemental carbon analyzer. The specific steps are as follows: the soil sample is filtered onto a membrane, and then the membrane carrying the particulate matter is heated in a pure gas environment. Heating continues in a He environment containing O2. At this time, the elemental carbon (EC1, EC2, EC3) in the membrane is oxidized to CO2. The CO2 needs to be reduced to CH4 in a methane conversion furnace and finally detected by a flame ionization detector (FID). A laser is used to irradiate a quartz membrane, and the reflection or radiation signal of the membrane is measured by a photodetector. The change in the signal indicates the starting point of elemental carbon oxidation. During the carbonization process of organic carbon, optically cracked carbon (OPC) is formed, and then the content of black carbon is calculated.

[0042] Step 6: Calculation of soil organic carbon storage.

[0043] The optimized formula for calculating soil organic carbon storage in step six includes:

[0044] (1) Traditional spatial interpolation methods:

[0045] ①

[0046] TSOC1 is the soil organic carbon storage of the sample plot calculated by the traditional spatial interpolation method, 0.01 is the unit conversion factor, 22500 is the sampling area, SOCj is the soil organic carbon content in j-unit grid, BDj is the soil bulk density in j-unit grid, and Tj is the soil depth in j-unit grid. After interpolation, the soil depth is greater than 100cm, so T = 100cm is taken, and n is the number of grids. Kriging interpolation of SOC content and soil bulk density is performed using the Geostatistical Analyst module in ArcGIS, and soil depth is interpolated using the IDW method of the SpatialAnalyst module in ArcGIS.

[0047] (2) Spatial interpolation method based on rock exposure rate, gravel content, and soil black carbon correction:

[0048] ②

[0049] SOCD2 is the topsoil organic carbon storage calculated using a raster algorithm based on rock exposure rate, gravel content, and soil black carbon correction. 0.01, 22500, SOCj, BDj, and Tj are the same as δ. j G represents the rock exposure rate of the j-th type of partition. j H represents the percentage (%) of gravel larger than 2 mm in the j-th type partition. jThe content of black carbon in the soil of the j-th type zone;

[0050] (3) Soil profile summation method based on rock exposure rate, gravel content, and soil black carbon correction:

[0051] Karst regions have diverse soil types, and their soil organic carbon content, bulk density, and thickness exhibit significant variability. Therefore, stratified calculation of soil organic carbon density is necessary. The soil profile is divided into 1 to 12 layers. The soil organic carbon density of each layer is calculated based on its corresponding organic carbon content, bulk density, and thickness, thus obtaining the spatial characteristic values ​​of soil organic carbon density in the small watershed. Then, the organic carbon storage is calculated stratified using the soil organic carbon density and the distribution area of ​​various soil types, ultimately yielding the total soil organic carbon storage in the study area. To eliminate estimation errors caused by rocks and black carbon in karst regions and to correct the soil organic carbon storage estimation results to be closer to the true value, gravel content, rock exposure rate, and soil black carbon are used for correction. The formulas for calculating soil organic carbon density and its storage are presented below.

[0052] ③

[0053] SOCD i,j Let be the soil organic carbon density of the i-th layer in the j-th soil profile; ρ represents the soil organic carbon content of the i-th layer for the j-th soil type; i,j T is the soil bulk density of the i-th layer of the j-th soil type; i,j S is the thickness of the i-th soil layer of the j-th soil type; 10⁻² is the unit conversion factor; SOCS is the total organic carbon storage of the soil in the study area; S j δ represents the soil distribution area of ​​the j-th soil genus; 103 is the unit conversion factor. j G represents the rock exposure rate of the j-th type of partition. j H represents the volume percentage of gravel larger than 2 mm in the j-th type partition. j The content of black carbon in the soil of the j-th type zone;

[0054] (4) Formula for calculating the miscalculation rate (e) of organic carbon storage

[0055] ④

[0056] In the formula: k takes the values ​​1 and 2 respectively.

[0057] The number of sampling points can be reduced based on obstacles within the study area.

[0058] The study area, the Houzhaihe small watershed in Puding County, Guizhou Province, is a plateau-type karst small watershed, covering Chengguan Town, Maguan Town, and Baiyan Town in the county, with a drainage area of ​​75 km². 2The region lies between 105°40′43″ and 105°48′2″E, and 26°12′29″ and 26°17′15″N, with an altitude ranging from 1223.4 to 1567.4 meters and an air pressure between 806.1 and 883.8 hPa. The average annual temperature is 15.3℃, the average annual rainfall is over 1170.9 mm, and the annual evaporation is 920 mm. The soil is primarily limestone-dolomite soil, yellow soil, and paddy soil, with a heavy texture and poor resistance to erosion. Vegetation includes cypress, poplar, toon, and sand pear; crops include rice, corn, soybeans, sunflowers, and sweet potatoes, with crop rotation of corn, soybeans, rice, and sunflowers, and intercropping of sweet potatoes.

[0059] Materials and methods involved:

[0060] Sampling point layout: Using the ArcGIS 9.3 software platform, a spatially distributed grid was laid out on the 1:10000 topographic map of the study area according to the grid method principle. The actual grid size was 0.15 × 0.15 km. 2 Sampling points were set at the center of the grid, and the study area was 75 km. 2 Theoretically, there should be 3333 sampling points, but due to some sampling points being located near rivers, roads, and residences, the actual number of sampling points is 2755. During the field soil sampling and baseline information survey, handheld GPS, compasses, and topographic maps of the sampling point distribution were used to locate the sampling points.

[0061] Sample processing and analysis: First, the soil samples collected in the field were air-dried and then ground to prepare the samples required for the test. Soil organic carbon was determined by potassium dichromate-sulfuric acid oxidation method

[19] ; ​​the soil distribution area was statistically analyzed by a combination of remote sensing data verification and field survey; soil bulk density was measured by ring cutter method along the soil profile from top to bottom; soil thickness was measured by inserting rods according to microhabitat type, with two rod lengths of 60cm and 120cm, which are suitable for measuring soil at different depths, and the average value of soil layer thickness at 8-10 points was used; rock exposure rate was determined by transect method, and expressed by the percentage of vegetation coverage area and rock exposure area within the sample point range; gravel content was expressed as the volume percentage of gravel larger than 2mm.

[0062] Soil organic carbon storage calculation and formula optimization:

[0063] Traditional spatial interpolation methods:

[0064] ①

[0065] In the formula: TSOC1 is the soil organic carbon storage (kg) of the sample plot calculated by the traditional spatial interpolation method, 0.01 is the unit conversion factor, and 22500 is the area of ​​the sampling area (m²). 2SOCj represents the soil organic carbon content (g / kg) within grid unit j, and BDj represents the soil bulk density (g / cm³) within grid unit j. 3 Tj represents the soil depth (cm) within unit raster j; if the interpolated soil depth is greater than 100cm, T = 100cm is used), and n is the number of raster cells. This paper uses the Geostatistical Analyst module in ArcGIS to perform Kriging interpolation on SOC content and soil bulk density, and uses the IDW method in the SpatialAnalyst module of ArcGIS to interpolate soil depth. All interpolation is performed using data from 2755 soil patch sampling points.

[0066] Spatial interpolation method based on rock exposure rate, gravel content, and soil black carbon correction:

[0067] ②

[0068] SOCD2 is the topsoil organic carbon storage calculated using a raster algorithm corrected for rock exposure rate and gravel content. 0.01, 22500, SOCj, BDj, and Tj are the same as δ. j G represents the rock exposure rate of the j-th type of partition. j H represents the percentage (%) of gravel larger than 2 mm in the j-th type partition. j The content of black carbon in the soil of the j-th type zone;

[0069] Soil profile summation method based on rock exposure rate, gravel content, and black carbon correction:

[0070] Considering the diverse soil types in karst regions and the significant variability in soil organic carbon content, bulk density, and thickness, stratified calculation of soil organic carbon density is necessary. The soil profile is divided into 12 layers, and the soil organic carbon density of each layer is calculated based on its corresponding organic carbon content, bulk density, and thickness, thus obtaining the spatial characteristic values ​​of soil organic carbon density in the Puding Houzhai River watershed. Then, the organic carbon storage is calculated stratified using soil organic carbon density and the distribution area of ​​various soil types, ultimately yielding the total soil organic carbon storage in the study area. To eliminate estimation errors caused by the rock component in karst regions and to correct the soil organic carbon storage estimation results to be closer to the true value, corrections are made based on gravel content and rock exposure rate. The formula for calculating soil organic carbon density and its storage is ③.

[0071] ③

[0072] SOCD i,j Let be the soil organic carbon density of the i-th layer in the j-th soil profile; ρ represents the soil organic carbon content of the i-th layer for the j-th soil type;i,j T is the soil bulk density of the i-th layer of the j-th soil type; i,j S is the thickness of the i-th soil layer of the j-th soil type; 10⁻² is the unit conversion factor; SOCS is the total organic carbon storage of the soil in the study area; S j δ represents the soil distribution area of ​​the j-th soil genus; 103 is the unit conversion factor. j G represents the rock exposure rate of the j-th type of partition. j H represents the volume percentage of gravel larger than 2 mm in the j-th type partition. j The content of black carbon in the soil of the j-th type zone.

[0073] Formula for calculating the miscalculation rate (e) of organic carbon reserves

[0074] ④

[0075] In the formula: k takes the values ​​1 and 2 respectively.

[0076] Results and Analysis:

[0077] Depend on Figure 1 It can be seen that descriptive statistical analysis of soil-related indicators can provide a preliminary understanding of the distribution characteristics and internal structure of the data, reveal the patterns of variable changes, and lay the foundation for further analysis. Routine statistical analysis was performed on data from 2755 soil profiles and 23536 soil samples. The average organic carbon content was highest in the 0–5 cm soil layer (29.66 g / kg), followed by the 5–10 cm layer (25.77 g / kg). The average organic carbon content decreased with increasing soil depth, reaching a minimum of 5.25 g / kg in the 90–100 cm layer. The average soil bulk density ranged from 1.12 to 1.40 g / cm³. 3 The maximum value is 1.25 times the minimum value. As the soil depth increases, the soil bulk density first increases and then tends to stabilize, reaching its maximum at a depth of 70-80 cm, at 1.41 g / cm³. 3 The minimum soil bulk density at a depth of 0-5cm is 1.12g / cm³. 3 The average gravel content ranges from 0 to 20.15%. As the soil layer deepens, the gravel content gradually decreases and eventually drops to zero. The gravel content is highest in the 0-5cm layer, at 20.15%, while it is lowest in the 80-90cm and 90-100cm layers, at zero.

[0078] The soil and rock exposure rates are interspersed across different areas within the watershed, dividing the soil cover into patches of varying sizes, resulting in discontinuity. There are certain differences in exposure rates among different soil types. Rocks and soils are horizontally and spatially interspersed, with varying intervals between them, and the proportion of exposed rock also varies. There are also certain differences in rock exposure rates among different soil genus zones. Figure 2 Through field investigation, it was found that the exposed rocks in the Houzhai River Basin are distributed in a fragmented and patchy spatial pattern. In the upper reaches of the basin, peak forests, peak clusters and small depressions are interspersed. There are only a few depressions. The soil layer in the depressions is deep, and the rock exposure rate and gravel content are low. There are many high-value patches of exposed rocks in the upper reaches. Peak forests and peak clusters are distributed in the northern and southern areas of the middle reaches. The terrain in the middle reaches is relatively flat, mainly consisting of hills and depressions. Only a few high-value areas appear near Longcha Mountain and other places in the middle reaches. The terrain in the lower reaches of the basin is even flatter, mostly consisting of depressions, with only a small number of peak clusters and isolated peaks. Therefore, the overall soil and rock exposure rate in the lower reaches is low, with high-value areas only appearing near Longtou Mountain. There are certain differences in the rock exposure rate among different soil types. The highest is 43.34% in the black limestone soil area, while the lowest is 29.22% in the large mud soil area of ​​cultivated land. The three major cultivated land areas of yellow clay soil, large mud field, and yellow mud field have almost no exposed rocks, and the rock exposure rate is counted as 0. The distribution characteristics of gravel content are basically consistent with the distribution of rock exposure rate. The gravel content of large mud field and yellow mud field is zero. The gravel content of other soil types is in the following order: black limestone soil > yellow limestone soil > white sandy soil > white large mud soil > small mud soil > large mud soil > yellow clay soil.

[0079] Distribution characteristics of soil carbon storage under different soil layers: Based on data quality control and validity analysis, statistical analysis was performed on the valid data of soil organic carbon storage at different layers, and descriptive statistical results of soil organic carbon storage at different layers in the Houzhai River Basin were obtained. Figure 2 Due to the complex characteristics of karst regions, such as exposed bedrock, limited soil reserves, and diverse micro-topography, many sample plots consist of discontinuous, shallow soil layers of varying thickness, resulting in different sample numbers at each soil depth. Furthermore, the sample number gradually decreases with increasing soil depth. Therefore, this study investigated the spatial heterogeneity of organic carbon storage in karst soils at different soil depths. The organic carbon storage at a depth of 10 cm was 1.48 × 10⁸ kg, at 20 cm it was 2.65 × 10⁸ kg, at 30 cm it was 3.43 × 10⁸ kg, and at 100 cm it was 5.39 × 10⁸ kg. The spatial variability of soil organic carbon storage in each layer exhibits moderate intensity. The coefficient of variation for soil organic carbon storage in each layer increases with increasing soil depth, with the following coefficients: 0–10 cm: 0.46, 0–20 cm: 0.49, 0–30 cm: 0.51, and 0–100 cm: 0.60. The coefficient of variation for soil organic carbon storage is highest in the 0–100 cm depth layer.

[0080] Geostatistical analysis of soil organic carbon storage at different levels: semivariogram model and related parameters of soil organic carbon storage in karst watersheds ( Figure 2The results show that the nugget value C0 of soil organic carbon storage in each layer increases with soil depth, with the largest nugget value in the 0-100cm layer. This indicates that when structural and random factors jointly influence soil heterogeneity, structural factors are the dominant factor. The proportions of the nugget value of soil organic carbon storage in the sill values ​​of each layer in the karst watershed are 87.75% for 0-10cm, 85.92% for 0-20cm, 86.13% for 0-30cm, and 89.05% for 0-100cm. The range 'a' of soil organic carbon storage in each layer of the karst watershed is greater than the lag distance, indicating a correlation between soil organic carbon storage in each layer over a relatively large range. The lag distance of the semivariogram function of soil organic carbon storage in each layer of the watershed shows a sequence of 0-30cm < 0-20cm < 0-10cm < 0-100cm. The lag distances for soil organic carbon storage in each layer are relatively large, indicating significant variability in soil organic carbon storage within a small watershed scale. This suggests that there are correlations among soil organic carbon storage layers within the small watershed. The semivariogram fitting curves for soil organic carbon storage in each layer exhibit periodic fluctuations around the sill value after exceeding the lag distance.

[0081] The spatial variability model of soil organic carbon storage with different soil layer thicknesses in the watershed showed the highest model fit when described by the Gaussian model. Figure 2 The coefficients of determination were 0.91 for 0-10cm, 0.92 for 0-20cm, 0.93 for 0-30cm, and 0.86 for 0-100cm. Figure 2 The spatial variability of soil in the watershed was evaluated using nugget value (C0), sill value (C0+C), and range (a) of the semivariogram (h). The results showed that the nugget value (C0) of soil organic carbon storage increased with increasing soil thickness, indicating that the spatial variability caused by random factors in soil organic carbon storage increased with increasing soil thickness. The ratio of nugget value to sill value reflected the proportion of variation caused by random factors in the total variation. When the C0 / C0+C ratio was <25%, the spatial correlation was strong; between 25% and 75%, it showed a moderate degree of spatial correlation; and >75%, the spatial correlation was weak. In this study, soil organic carbon storage under different soil layer thicknesses exhibited a moderate spatial correlation.

[0082] Spatial Distribution Pattern of Soil Organic Carbon Storage at Different Layers: Based on the Kriging interpolation method, organic carbon storage data from various soil profiles were interpolated and calculated. The spatial distribution pattern of soil organic carbon storage at different layers in the watershed showed varying degrees of difference, indicating complex spatial variability due to the combined influence of natural and anthropogenic factors. Soil organic carbon storage at all layers in the watershed was generally higher in the east than in the west. Topsoil organic carbon storage was not concentrated, exhibiting spatial variability only within a small area. It gradually decreased from east to west, with small patches of high and low values ​​concentrated in the eastern and western parts of the study area, respectively. Areas with soil organic carbon storage less than 1.48 × 10⁸ kg accounted for 20% of the total study area. With increasing soil depth, the difference in low-value ranges of soil organic carbon storage was not significant, but the area of ​​organic carbon storage less than 2112.12–20500.41 kg decreased with each layer. Overall, the organic carbon storage of the four soil thicknesses has similar spatial distribution characteristics, showing a trend of low levels in the central part, high levels around the edges, higher levels in the east, and lowest levels in the south.

[0083] Comparison of soil organic carbon storage under different estimation methods: See [link to comparison of soil organic carbon storage under different estimation methods] Figure 3 The soil organic carbon storage at different soil thicknesses in the watershed was estimated using traditional spatial interpolation methods to be 17.73 × 10⁸ kg, 31.43 × 10⁸ kg, 40.53 × 10⁸ kg, and 65.12 × 10⁸ kg, respectively, for soil depths of 0–10 cm, 0–20 cm, 0–30 cm, and 0–100 cm. The soil organic carbon storage in the sample area was calculated using spatial interpolation methods corrected for rock exposure rate and gravel content to be 1.68 × 10⁸ kg. The organic carbon storage of the soil in the sample area was calculated by the soil profile summation method. The organic carbon storage of the soil at soil thicknesses of 0-10cm, 0-20cm, 0-30cm, and 0-100cm were 1.48×108kg, 2.65×108kg, 3.43×108kg, and 5.39×108kg, respectively. Traditional spatial interpolation is a classic method for estimating carbon storage in non-karst regions. However, in this study, the traditional spatial interpolation method was 1.05 times (0–10 cm), 1.02 times (0–20 cm), and 1.01 times (0–30 cm) of the traditional method for different soil layer thicknesses, respectively. This indicates that the estimation results based on rock exposure rate and gravel content have a significant impact on the soil organic carbon storage in karst regions. In contrast, the spatial interpolation method based on rock exposure rate and gravel content is superior to the traditional spatial interpolation method, especially in estimating the organic carbon storage and carbon density of surface soil in karst regions.

[0084] The Houzhai River Basin is characterized by widespread bare rock and discontinuous soil blocks. When estimating organic carbon storage using the soil profile summation method, the carbon storage of a single soil profile or soil body is calculated first, and then all soil profiles are summed to obtain the final organic carbon storage of the area. In estimating the regional soil organic carbon storage, the soil layer depth, rock exposure rate, and soil block area of ​​each soil patch were investigated in detail to obtain the soil organic carbon storage of the sample area. This method can reflect the true value of the soil organic carbon storage of the sample area. Therefore, the soil profile summation method is used as the true value in this study. The traditional spatial interpolation method for different soil thicknesses yielded 11.98 times (0–10 cm), 11.86 times (0–20 cm), 11.80 times (0–30 cm), and 12.08 times (0–100 cm) the soil profile summation method. In contrast, the spatial interpolation method based on rock exposure rate and gravel content correction yielded 1.14 times (0–10 cm), 1.16 times (0–20 cm), 1.16 times (0–30 cm), and 1.19 times (0–100 cm) the soil profile summation method. Both methods estimated soil organic carbon storage higher than the soil profile summation method, indicating that the spatial interpolation method based on rock exposure rate and gravel content correction is closer to the actual values ​​of the sample area. It is particularly applicable when estimating surface organic carbon storage on karst slopes with high rock exposure, while traditional geostatistical methods are not suitable for estimating organic carbon storage and carbon density in high bare rock mountainous areas.

[0085] The above are merely embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for estimating soil organic carbon storage in karst areas based on black carbon correction, characterized in that, Includes the following steps: Step 1: Determining the study area: Selecting a suitable karst watershed region; Step 2: Sampling Point Layout: Using the ArcGIS 9.3 software platform, a spatially distributed grid was laid out on a 1:10000 topographic map according to the grid method principle. The study area is 75km². 2 The actual grid size is 0.15 × 0.15 km. 2 Sampling points were set up at the center of the grid, with a theoretical number of 3,333 sampling points. During the field soil sample collection and baseline information survey, handheld GPS, compass and topographic map of sampling point distribution were used to locate the sampling points. Step 3: Sample Collection: Excavate a soil profile at each sampling point and collect soil samples through stratified strip sampling. Soil sample collection adopts the bottom-up stratified sampling method of soil profile. The soil profile excavation depth is ≤100cm, with shallow excavation to bedrock or parent material and deep excavation to 100cm. The soil layers are divided into 1 to 12 layers: 0~5cm, 5~10cm, 10~15cm, 15~20cm, 20~30cm, 30~40cm, 40~50cm, 50~60cm, 70~80cm, 80~90cm, and 90~100cm. The baseline information of each sampling point must be recorded, and the soil bulk density, soil thickness, and rock exposure rate must be measured. Step 4: Measurement and Analysis: Soil distribution area was statistically analyzed using a combination of remote sensing data verification and field surveys; soil bulk density was measured layer by layer along the soil profile from top to bottom using the ring cutter method; soil thickness was measured separately according to microhabitat type using the probe method, with two probe lengths of 60cm and 120cm, suitable for measuring soil at different depths, and the average soil layer thickness was expressed as the average of 8-10 points; rock exposure rate was measured using the transect method, expressed as the percentage of vegetation cover area and rock exposure area within the sampling point range; gravel content was expressed as the volume percentage of gravel larger than 2mm. Step 5: Determination of Soil Organic Carbon and Black Carbon: First, the soil samples collected in the field are air-dried, then ground to prepare the samples required for the test. Soil organic carbon is determined using the potassium dichromate-sulfuric acid oxidation method. Soil black carbon is determined using a laboratory thermo-optical organic carbon elemental carbon analyzer. The steps are as follows: the soil sample is placed on a filter membrane, and then the filter membrane carrying the particulate matter is heated in a pure gas environment. Heating is continued in a He environment containing O2. At this time, the elemental carbon in the filter membrane is oxidized to CO2. The elemental carbons are EC1, EC2, and EC3. The CO2 needs to be reduced to CH4 in a methane conversion furnace. Finally, it is detected by a hydrogen ion flame detector (FID). A laser is used to irradiate a quartz filter membrane, and the reflection or refraction signal of the filter membrane is measured by a photodetector. The change in the signal indicates the starting point of elemental carbon oxidation. During the carbonization process of organic carbon, optically pyrolyzed carbon (OPC) is formed. Then, the content of black carbon is calculated. Step Six: Calculation of Soil Organic Carbon Storage: An optimized formula for calculating soil organic carbon storage, wherein: (1) Traditional spatial interpolation method: ① SOCD1 represents the soil organic carbon storage of the sample plot calculated using the traditional spatial interpolation method, with 0.01 as the unit conversion factor, 22500 as the sampling area, SOCj as the soil organic carbon content within a j-unit raster, BDj as the soil bulk density within a j-unit raster, and Tj as the soil depth within a j-unit raster. Since the interpolated soil depth is greater than 100 cm, T = 100 cm is used, and n is the number of raster cells. Kriging interpolation of SOC content and soil bulk density was performed using the Geostatistical Analyst module in the ArcGIS 9.3 software platform, while soil depth was interpolated using the IDW method of the SpatialAnalyst module in the ArcGIS 9.3 software platform. (2) Spatial interpolation method based on rock exposure rate, gravel content, and soil black carbon correction: ② SOCD2 represents the topsoil organic carbon storage calculated using a raster algorithm based on rock exposure rate, gravel content, and soil black carbon correction. The values ​​0.01, 22500, SOCj, BDj, and Tj are the same as those in traditional spatial interpolation methods. This represents the rock exposure rate of the j-th type of partition. This represents the volume percentage of gravel larger than 2 mm in the j-th type of partition. The content of black carbon in the soil of the j-th type zone; (3) Soil profile summation method based on rock exposure rate, gravel content, and soil black carbon correction: Karst regions have diverse soil types, and soil organic carbon content, bulk density, and thickness exhibit significant variability. Therefore, stratified calculation of soil organic carbon density is necessary. The soil profile is divided into 1 to 12 layers. Soil organic carbon density is calculated for each layer based on its organic carbon content, bulk density, and thickness, yielding spatial characteristics of soil organic carbon density in the small watershed. Then, organic carbon storage is calculated using soil organic carbon density and the distribution area of ​​various soil types, ultimately resulting in the total soil organic carbon storage for the study area. To eliminate estimation errors caused by rocks and black carbon in karst regions and to correct the soil organic carbon storage estimation results to more closely approximate the true value, gravel content, rock exposure rate, and soil black carbon are used for correction. The formulas for calculating soil organic carbon density and its storage are presented below. ③ Let be the soil organic carbon density of the i-th layer in the j-th soil profile; The soil organic carbon content of the i-th layer for the j-th soil type; It is the soil bulk density of the i-th layer of the j-th soil type; It is the thickness of the i-th soil layer of the j-th soil type; 10 -2 The unit conversion factor is given, and SOCD3 represents the total organic carbon storage in the soil of the study area. This represents the soil distribution area of ​​the j-th soil genus; 10 3 For unit conversion factor, This represents the rock exposure rate of the j-th type of partition. This represents the volume percentage of gravel larger than 2 mm in the j-th type of partition. The content of black carbon in the soil of the j-th type zone; (4) Formula for calculating the miscalculation rate (e) of organic carbon reserves ④ In the formula: k takes the values ​​1 and 2 respectively.

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