A method for constructing a sand or dust parameterization scheme considering biological soil crust

Abstract

The application provides a biological soil crust sand or dust emission parameterization scheme construction method, and belongs to the field of sand and dust numerical simulation.The method comprises the following steps: obtaining biological suitable areas and biological distribution areas based on investigation and sampling data of a research area, and obtaining a crust distribution area by intersecting the biological distribution area and the biological suitable areas; dividing the research area into grids, and obtaining soil crust coverage of each biological according to the crust distribution area in each grid; constructing a critical friction wind speed growth rate calculation formula, and calculating the critical friction wind speed growth rate in combination with the soil crust coverage of each biological; constructing a soil crust dust emission formula, calculating dust emission according to the critical friction wind speed growth rate, and completing the sand or dust emission parameterization scheme construction.The application solves the problems that the existing sand emission scheme does not consider the influence of biological surface crust on the sand emission process, and the biological soil crust plot sand or dust emission simulation accuracy and reliability are low.

Description

Technical Field

[0001] This invention belongs to the field of numerical simulation of sand and dust, and particularly relates to a method for constructing a parameterized scheme for sand or dust generation that takes into account biological soil crusting. Background Technology

[0002] Dust is a significant component of aerosols. During its transport, it scatters solar radiation and absorbs long-wave radiation, affecting the optical properties of the atmosphere and indirectly altering cloud properties and precipitation rates through aerosol-cloud interactions. Dust contains nutrients and organic matter from its source region, which, upon deposition, enhance the productivity of marine and terrestrial ecosystems, thereby altering regional and global biogeochemical cycles. When dust settles on snow and glacier surfaces, it enhances the absorption of solar radiation, accelerating snowmelt and impacting freshwater supply and alpine vegetation phenology. Dust transport near the Earth's surface increases particulate matter concentration, causing air pollution and reducing visibility. Dust lifted to a certain height is easily transported over long distances, affecting air quality in transport corridors. Dust can also impact human health by carrying pathogens, triggering respiratory and cardiovascular diseases.

[0003] Biocrusts are photosynthetic autotrophic communities composed of cyanobacteria, lichens, and mosses, coexisting with heterotrophic bacteria, archaea, and fungi on the surface of arid soils, forming a crust layer. Within the top few millimeters of the soil, biocrusts protect the soil surface by binding soil particles together through their rhizomes and hyphae. Many organisms that form biocrusts secrete extracellular polymers composed of amino acids, polysaccharides, and other carbohydrates, creating a robust binding layer that enhances the soil's resistance to erosion. Current dust storm forecasting models do not consider the impact of biocrusts on the dust storm process. Dynamically incorporating biocrust information into dust storm / sand storm parameterization schemes is crucial for improving the accuracy of dust storm / sand storm simulations. Summary of the Invention

[0004] To address the aforementioned shortcomings in the existing technology, this invention provides a method for constructing a parameterized scheme for sand or dust generation that considers biological soil crusts. This method solves the problems of existing sand generation schemes not considering the influence of biological surface crusts on the sand generation process and the low accuracy and reliability of sand or dust generation simulations for plots with biological soil crusts.

[0005] To achieve the above objectives, the technical solution adopted by this invention is: a method for constructing a parameterized scheme for sand or dust generation considering biological soil crusting, comprising the following steps:

[0006] S1. Based on the survey sampling of the study area, the maximum entropy model is modeled to obtain the suitable biological habitat. Based on the satellite remote sensing data of the study area, the biological distribution area is obtained by calculating the crust index. The intersection of the biological distribution area and the suitable biological habitat area is used to obtain the crust distribution area.

[0007] S2. Divide the study area into grids, and obtain the coverage rate of biological soil crusts for the preset types based on the crust distribution area in each grid.

[0008] S3. Based on wind tunnel experiments, construct the response relationship between biological soil crust coverage and critical frictional wind speed growth rate, obtain the calculation formula for critical frictional wind speed growth rate, and calculate the critical frictional wind speed growth rate according to the biological soil crust coverage of preset types in the grid.

[0009] S4. Construct a formula for soil crust dust emission and calculate the dust emission amount based on the critical friction wind speed growth rate, thus completing the construction of a parameterized scheme for sand or dust generation.

[0010] The beneficial effects of this invention are as follows: This invention utilizes environmental parameters and remote sensing data to invert the distribution of biological soil crusts, and corrects the critical friction wind speed through the action of biological crusts, thereby improving the spatiotemporal resolution, accuracy, and reliability of sand or dust simulation. The overall parameterization scheme construction process is simple and easy to implement, and has real-time and universality.

[0011] Further, S1 includes the following steps:

[0012] S101. Based on the survey sampling in the study area, the maximum entropy model is modeled according to the surface biological survey data and the environmental climate data of the survey sampling points;

[0013] S102. Based on the maximum entropy model, and according to the environmental information of the study area, a suitable biota containing cyanobacteria, lichens and mosses is obtained.

[0014] S103. Based on satellite remote sensing data of the study area, the distribution area of ​​organisms including cyanobacteria, lichens and mosses is obtained by calculating the crust index. The intersection of the distribution area and the suitable habitat area is then used to obtain the crust distribution area.

[0015] The beneficial effects of the above-mentioned further solutions are as follows: This invention retrieves the distribution of biological soil crusts by inverting environmental parameters and remote sensing data. Specifically, it obtains the suitable biological habitat area through the maximum entropy model and obtains the biological distribution area by calculating the crust index through satellite remote sensing data. Then, by combining the suitable biological habitat area and the biological distribution area, the accuracy of the obtained crust distribution area is improved.

[0016] Furthermore, S2 specifically refers to:

[0017] The study area was divided into grids, and the soil crust coverage rate of the three organisms, namely cyanobacteria, lichens and mosses, was obtained based on the area occupied by the crust distribution zone of cyanobacteria, lichens and mosses in each grid.

[0018] The beneficial effects of the above-mentioned further scheme are as follows: By utilizing the suitable habitat conditions of surface organisms in the study area, this invention obtains the soil crust coverage rate of different organisms, improves the prediction accuracy of biological soil crust coverage rate, and provides excellent data support for subsequent calculation of the critical frictional wind speed growth rate.

[0019] Furthermore, S3 specifically refers to:

[0020] Based on wind tunnel experiments, by obtaining the critical friction wind speed under preset types of soil crust coverage, the response relationship between biological soil crust coverage and the critical friction wind speed growth rate is constructed, and the calculation formula of the critical friction wind speed growth rate under different biological conditions is obtained.

[0021] The study area was divided into grids, and the critical frictional wind speed growth rate was calculated by combining the soil crust coverage rate of preset types of organisms in each grid.

[0022] Furthermore, the formula for calculating the critical frictional wind speed growth rate is as follows:

[0023] ;

[0024] in, This represents the critical frictional wind speed growth rate. Indicates the maximum biogenic soil crust coverage. This represents the rate constant that changes as the biofilm coverage of soil increases.

[0025] The beneficial effects of the above-mentioned further solutions are as follows: By constructing the response relationship between the biological soil crust coverage rate and the critical friction wind speed growth rate, the present invention obtains a more accurate critical friction wind speed growth rate, thereby accurately calculating the amount of dust or sand.

[0026] Furthermore, step S4 includes the following steps:

[0027] S401. Based on the critical frictional wind speed and frictional wind speed in each grid, construct the soil crust dust emission formula.

[0028] S402. Based on the critical frictional wind speed growth rate, the dust emission amount is calculated using the soil crust dust emission formula, and the parameterization scheme for dust or sand generation is constructed.

[0029] Furthermore, the formula for soil crust dust emission is as follows:

[0030] ;

[0031] ;

[0032] in, This indicates the vertical emission volume of dust. Indicates a constant. This represents the air density in each grid cell of the study area. Represents gravitational acceleration. Indicates the frictional wind speed. This represents the critical frictional wind speed. This indicates the soil particle size in each grid of the study area. Indicates soil moisture content, This indicates the horizontal emission level of dust. This indicates the percentage of permafrost. Indicates snow coverage. This indicates the percentage of dust particles of a specific size emitted into the air. This indicates the percentage of clay.

[0033] The beneficial effects of the above-mentioned further solutions are as follows: By utilizing the critical frictional wind speed growth rate obtained from the biological soil crust coverage rate, the present invention constructs a soil crust dust emission formula, corrects the critical frictional wind speed, and improves the accuracy, reliability and efficiency of dust simulation. Attached Figure Description

[0034] Figure 1 This is a flowchart of the method of the present invention.

[0035] Figure 2 This is a comparison chart of the detected and predicted values ​​of a sandstorm event in this embodiment. Detailed Implementation

[0036] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0037] Example

[0038] like Figure 1 As shown, this invention provides a method for constructing a parameterized scheme for sand or dust generation considering biological soil crusting, the implementation method of which is as follows:

[0039] S1. Based on the survey sampling of the study area, a maximum entropy model is built to obtain the suitable biological habitat area. Then, based on the satellite remote sensing data of the study area, the crust index is calculated to obtain the biological distribution area. The intersection of the biological distribution area and the suitable biological habitat area is used to obtain the crust distribution area. The specific steps are as follows:

[0040] S101. Based on the survey sampling in the study area, the maximum entropy model is modeled according to the surface biological survey data and the environmental climate data of the survey sampling points;

[0041] S102. Based on the maximum entropy model, and according to the environmental information of the study area, a suitable biota containing cyanobacteria, lichens and mosses is obtained.

[0042] S103. Based on satellite remote sensing data of the study area, the distribution area of ​​organisms including cyanobacteria, lichens and mosses is obtained by calculating the crust index. The intersection of the distribution area and the suitable habitat area is then used to obtain the crust distribution area.

[0043] In this embodiment, a survey and sampling were conducted in the study area. Surface biological survey data and environmental climate data from the survey sampling points were used to model the maximum entropy model using the maximum entropy model tool. Based on the maximum entropy model and the environmental information of the study area, suitable habitats for three organisms, namely cyanobacteria, lichens, and mosses, were obtained respectively.

[0044] Based on satellite remote sensing data, the distribution areas of three organisms—cyanobacteria, lichens, and mosses—were obtained by calculating the crust index.

[0045] The calculation of the crust index specifically involves crust inversion, and the specific steps are as follows:

[0046] The cyanobacterial crust was inverted, and the crust index (CI) was calculated. When the CI value was between 0.625 and 0.725, the surface cover could be identified as cyanobacterial crust. The calculation formula is as follows:

[0047] ;

[0048] in, Indicates the crust index, Indicates red band reflectivity. Indicates blue band reflectivity;

[0049] The lichen crust was inverted, and the Biocrust Index (BSCI) was calculated. The BSCI distinguishes between biocrusts dominated by lichens. The calculation formula is as follows:

[0050] ;

[0051] in, Indicates the reflectivity of the green band. This indicates an adjustment parameter used for amplification. and The absolute difference between them This represents the average reflectance in the green, red, and near-infrared bands;

[0052] Inverting the moss crust data and calculating the Sandy Ratio Crust Index (SRCI), the SRCI monitors moss-dominated biocrusts in sandy areas. The calculation formula is as follows:

[0053] ;

[0054] in, This indicates the reflectivity of the 8NIR band, which refers to the 8th sub-band of the near-infrared spectrum, i.e., a wavelength of 950 mm. This indicates the reflectivity of band 6RE2, where 6RE2 refers to the second subdivision of the red-edge band, i.e., a wavelength of 740mm. This indicates the reflectivity of band 5RE1, which refers to the fifth sub-band of red-edge band 1, i.e., the wavelength of 750mm.

[0055] The distribution areas of the three organisms were intersected with their suitable habitats to obtain the crust distribution areas of the three organisms that simultaneously satisfy environmental information prediction and satellite remote sensing data calculation.

[0056] S2. Divide the study area into grids, and obtain the coverage rate of the preset types of biological soil crust based on the crust distribution areas within each grid, specifically:

[0057] The study area was divided into grids, and the soil crust coverage rate of the three organisms, namely cyanobacteria, lichens and mosses, was obtained based on the area occupied by the crust distribution zone of cyanobacteria, lichens and mosses in each grid.

[0058] In this embodiment, the study area is divided into multiple grids. The soil crust coverage rate of the three organisms, including cyanobacteria, lichens and mosses, can be obtained by dividing the area occupied by the three organisms in each grid by the area of ​​the entire grid.

[0059] S3. Based on wind tunnel experiments, the response relationship between biocrust coverage and the critical frictional wind speed growth rate is constructed, the calculation formula for the critical frictional wind speed growth rate is obtained, and the critical frictional wind speed growth rate is calculated according to the biocrust coverage of preset types in the grid, specifically:

[0060] Based on wind tunnel experiments, by obtaining the critical friction wind speed under preset types of soil crust coverage, the response relationship between biological soil crust coverage and the critical friction wind speed growth rate is constructed, and the calculation formula of the critical friction wind speed growth rate under different biological conditions is obtained.

[0061] The study area was divided into grids, and the critical frictional wind speed growth rate was calculated by combining the soil crust coverage rate of preset types of organisms in each grid.

[0062] In this embodiment, wind tunnel experiments were conducted to measure the critical frictional wind speed of samples with different biocrust coverage rates. This critical frictional wind speed was compared with the frictional wind speed measured under bare soil conditions to obtain the growth rate of the critical frictional wind speed. This yielded the critical frictional wind speed under different biocrust coverage rates, and a response relationship between biocrust coverage and the growth rate of the critical frictional wind speed was constructed. The calculation formula for the growth rate of the critical frictional wind speed is shown below:

[0063] ;

[0064] in, This represents the growth rate of critical frictional wind speed, indicating the rate of increase in critical frictional wind speed as the biocrust cover changes. Indicates the coverage of biological soil crust. The rate constant representing the change in critical frictional wind speed with increasing biocrust coverage is obtained through wind tunnel experiments. and The value is determined by the current organism.

[0065] S4. Construct a formula for soil crust dust emission and calculate the dust emission amount based on the critical friction wind speed growth rate. Complete the construction of a parameterized scheme for sand or dust generation. The specific steps are as follows:

[0066] S401. Based on the critical frictional wind speed and frictional wind speed in each grid, construct the soil crust dust emission formula.

[0067] S402. Based on the critical frictional wind speed growth rate, the dust emission amount is calculated using the soil crust dust emission formula, and the parameterization scheme for dust or sand generation is constructed.

[0068] In this embodiment, based on the MB (Marticorena-Bergametti) dust initiation scheme, the critical frictional wind speed growth rate is introduced according to the critical frictional wind speed and frictional wind speed in each grid, and the dust emission formula in the MB (Marticorena-Bergametti) dust initiation scheme is improved to construct the soil crust dust emission formula, the expression of which is as follows:

[0069] ;

[0070] ;

[0071] in, This indicates the vertical emission volume of dust. Indicates a constant. This represents the air density in each grid cell of the study area. Represents gravitational acceleration. Indicates the frictional wind speed. This represents the critical frictional wind speed. This indicates the soil particle size in each grid of the study area. Indicates soil moisture content, This indicates the horizontal emission level of dust. This indicates the percentage of permafrost. Indicates snow coverage. This indicates the percentage of dust particles of a specific size emitted into the air. Indicates the proportion of clay;

[0072] Based on the critical frictional wind speed growth rate, the dust emission amount was calculated using the soil crust dust emission formula, thus completing the construction of a parameterized scheme for sand or dust generation.

[0073] This embodiment uses the critical frictional wind speed corrected for biological soil crust coverage to calculate dust emissions, thereby improving calculation accuracy.

[0074] In this embodiment, as Figure 2 As shown, the critical frictional wind speed was optimized based on the biocrust coverage, and the atmospheric particulate matter concentration was further simulated. For a dust storm event in the experimental area in early April 2023, the daily average PM10 concentration monitoring values ​​of three monitoring points on April 4, 2023 were extracted and compared with the predicted values ​​without considering the surface crust and the predicted values ​​considering the surface crust. The predicted values ​​are shown in Table 1.

[0075] Table 1

[0076]

[0077] As shown in Table 1, the predicted daily average PM10 concentration considering the action of biocrust is closer to the monitoring value than the predicted value without considering the action of biocrust. Practice has proven that for plots with crust, the parameterization scheme construction method of the present invention is more accurate and reliable than the parameterization scheme construction method for sand or dust generation without considering biocrust.

[0078] Furthermore, practical applications are based on remote sensing data inversion results, which do not require a large amount of on-site measurement work. With sufficient computing power, it can also optimize the dust source particulate matter emission inventory and more accurately predict atmospheric particulate matter concentration.

Claims

1. A method for constructing a parameterized scheme for sand or dust generation considering biological soil crusting, characterized in that, Includes the following steps: S1. Based on the survey sampling of the study area, the maximum entropy model is modeled to obtain the suitable biological habitat. Based on the satellite remote sensing data of the study area, the biological distribution area is obtained by calculating the crust index. The intersection of the biological distribution area and the suitable biological habitat area is used to obtain the crust distribution area. S2. Divide the study area into grids, and obtain the coverage rate of biological soil crusts for the preset types based on the crust distribution area in each grid. S3. Based on wind tunnel experiments, construct the response relationship between biological soil crust coverage and critical frictional wind speed growth rate, obtain the calculation formula for critical frictional wind speed growth rate, and calculate the critical frictional wind speed growth rate according to the biological soil crust coverage of preset types in the grid. The formula for calculating the critical frictional wind speed growth rate is as follows: in, This represents the critical frictional wind speed growth rate. Indicates the coverage of biological soil crust. The rate constant representing the change as the biofilm coverage of soil increases; S4. Construct a formula for soil crust dust emission and calculate the dust emission amount based on the critical friction wind speed growth rate, thus completing the construction of a parameterized scheme for sand or dust generation. The formula for soil crust dust emission is as follows: in, This indicates the vertical emission volume of dust. Indicates a constant. This represents the air density in each grid cell of the study area. Represents gravitational acceleration. This indicates the frictional wind speed. This represents the critical frictional wind speed. This indicates the soil particle size in each grid of the study area. Indicates soil moisture content, This represents the critical frictional wind speed growth rate. Indicates the maximum biogenic soil crust coverage. This indicates the horizontal emission level of dust. This indicates the percentage of permafrost. Indicates snow coverage. This indicates the percentage of dust particles of a specific size emitted into the air. This indicates the percentage of clay.

2. The method for constructing a parameterized scheme for sand or dust generation considering biological soil crusting according to claim 1, characterized in that, S1 includes the following steps: S101. Based on the survey sampling in the study area, the maximum entropy model is modeled according to the surface biological survey data and the environmental climate data of the survey sampling points; S102. Based on the maximum entropy model, and according to the environmental information of the study area, a suitable biota containing cyanobacteria, lichens and mosses is obtained. S103. Based on satellite remote sensing data of the study area, the distribution area of ​​organisms including cyanobacteria, lichens and mosses is obtained by calculating the crust index. The intersection of the distribution area and the suitable habitat area is then used to obtain the crust distribution area.

3. The method for constructing a parameterized scheme for sand or dust generation considering biological soil crusting according to claim 1, characterized in that, Specifically, S2 is: The study area was divided into grids, and the soil crust coverage rate of the three organisms, namely cyanobacteria, lichens and mosses, was obtained based on the area occupied by the crust distribution zone of cyanobacteria, lichens and mosses in each grid.

4. The method for constructing a parameterized scheme for sand or dust generation considering biological soil crusting according to claim 1, characterized in that, Specifically, S3 is: Based on wind tunnel experiments, by obtaining the critical friction wind speed under preset types of soil crust coverage, the response relationship between biological soil crust coverage and the critical friction wind speed growth rate is constructed, and the calculation formula of the critical friction wind speed growth rate under different biological conditions is obtained. The study area was divided into grids, and the critical frictional wind speed growth rate was calculated by combining the soil crust coverage rate of preset types of organisms in each grid.

5. The method for constructing a parameterized scheme for sand or dust generation considering biological soil crusting according to claim 1, characterized in that, S4 includes the following steps: S401. Based on the critical frictional wind speed and frictional wind speed in each grid, construct the soil crust dust emission formula. S402. Based on the critical frictional wind speed growth rate, the dust emission amount is calculated using the soil crust dust emission formula, and the parameterization scheme for dust or sand generation is constructed.

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