Soil erosion by wind is a global environmental problem. China is one of the countries most seriously affected by wind erosion in the world, and wind erosion is the primary process of land desertification in arid, semi-arid and partly humid areas of China. The area of wind erosion and desertification in China reaches 160.74×104km2, accounting for 16.7% of the total land area, which seriously affects the resource development and the sustainable and stable development of social economy in these areas. Since the 1980s, soil wind erosion, as the primary link of desertification, has received unprecedented attention. A large number of experimental researches have been carried out successively, revealing the influence of various factors on the wind erosion process, especially the intensification of human factors on wind erosion. And put forward the wind erosion control measures in different areas. The mountainous areas in western China are ecologically fragile, and the phenomenon of soil erosion is relatively serious, threatening the ecological security of the country. The estimation of local wind erosion modulus has certain significance for maintaining the sustainable development of its ecological environment.
The construction of soil erosion model can better explore the causes of erosion, so as to carry out a series of prediction work on soil erosion and reduce its impact on the ecological environment. Due to the complexity of the erosion process, modeling needs to fully consider various factors, such as meteorology, hydrology, geological environment, soil conditions, etc. The revised wind erosion equation (revised wind erosion equation, RWEQ) is widely used in soil wind erosion prediction. The model, developed by the Agricultural Research Service (Agricultural Research Service) organization of the United States Department of Agriculture (USDA), is an empirical model based on process simulation. The RWEQ model can successfully simulate the effects of field management measures and different crop rotations on wind erosion.
This content will explain the operation of the RWEQ model and related attribution analysis with a case. First introduce the basic principles and data requirements of the RWEQ model, then introduce the basic functions of ArcGIS, and explain the method of regional geospatial database construction in the RWEQ model and the principles and methods of each parameter extraction in combination with cases, and finally give a comprehensive explanation in combination with typical cases in the Sanjiangyuan area Wind erosion modulus estimation, mapping and attribution analysis
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【Introduction】:
Chapter 1, Theoretical Basis:
1. Basic principles of soil erosion
- Soil erosion: The whole process of soil and soil parent material being destroyed, denuded, transported and deposited under the action of external forces such as water, wind, freeze-thaw, and gravity.
- Classification of soil erosion: water erosion, gravity erosion, freeze-thaw erosion and wind erosion, etc.
- The hazards and causes of soil erosion: The vast area of mountains and hills in China, large terrain fluctuations, loose and deep ground materials, high rainfall intensity, long history of reclamation, and low vegetation coverage are all important factors that cause soil erosion. Different combinations of various factors determine the type, degree, regional distribution and potential danger of soil erosion.
2. Soil wind erosion model
- Mechanism of Soil Wind Erosion
- Influencing factors of soil wind erosion: 1) wind speed; 2) physical properties of surface soil; 3) surface cover and roughness.
- Soil wind erosion assessment model:
- Wind Erosion Equation (WEQ)
- The wind erosion equation model (WEQ) was proposed by Woodruff and Siddoway in 1965, aiming to analyze the influence of field surface conditions and field management measures on erosion rate, and then effectively prevent wind erosion of farmland. WEQ is used to predict the annual wind erosion (kg/ha-1) of farmland in the United States.
- WEQ is the first model for estimating annual wind erosion in fields, which contains 11 variables in 5 groups: climatic factors, soil erodibility, soil surface roughness, field length, and crop residues. Among them, soil erodibility and climate factors are the most important dependent variables.
- WEQ can be expressed by the following formula: E=f(I,K,C,L,V)
- Among them, E is the annual wind erosion amount (t / acre, 1 acre= 4046.86m2); f is the functional relationship; I is the soil erodibility (t / acre); K is the soil roughness factor; C is the climate factor; L is the bare length of the field (ft, 1 ft =30.48 cm); V is the vegetation factor.
- Revised Wind Erosion Equation Model (Revised Wind Erosion Equation, RWEQ)
The revised wind erosion equation (RWEQ) is a long-term series estimation of regional soil wind erosion with high spatial and temporal resolution, so as to effectively predict the amount of wind erosion, which can provide a basis for land desertification prevention and control.
Chapter two:
1. ArcGIS software introduction and installation, common function introduction
- ArcGIS version introduction, installation;
- ArcGIS software interface, common function introduction;
- ArcGIS workspace environment settings
2. ArcGIS spatial analysis and mapping
- How ArcGIS defines the coordinate system
- ArcGIS Spatial Analysis
In the spatial analysis toolbox of ArcGIS software, a large number of raster data processing tools are provided, among which the raster data smoothing tool plays a very important role in removing the salt and pepper noise on the image
- Extraction analysis: extraction by attribute or spatial position, extraction by pixel value;
- Map Algebra : language rules of map algebra ;
- Local analysis: raster data overlay analysis, pixel statistics , classification, frequency value ;
- Neighborhood analysis: neighborhood shape, neighborhood statistics type, point statistics;
- Regional analysis: regional geometric statistics, regional statistics, area tabulation, regional histogram ;
- Interpolation analysis: inverse distance weighting method, natural neighbor method, trend surface method, spline function method, kriging method;
- Sampling and resampling: fishnet analysis, random point sampling, reclassification, lookup tables, etc.;
- ArcGIS layout design
- ArcGIS basic map service use: configure map server; add and use online map
- Production and design of maps, eagle-eye maps, range indicators, grids, tables, charts, and more.
- Those pits that have been stepped on in the past - common mistakes and precautions for use, etc.
Chapter 3: RWEQ Model Data Support
- Acquisition and preprocessing of vector data
- Knowledge of vector data
- Vector data creation, conversion, editing
- Acquisition and preprocessing of raster data
- Knowledge of raster data
- Raster data input, output and conversion
- Awareness of spatial resolution
- Raster data resampling
- Acquisition and processing of text data based on Python
- Building a Python development environment
- Installation and explanation of Python code library
- Read and write operations on *.txt files
- Merge of data
- Text Data to Raster
- Raster data to text
- Transformation of one-dimensional matrix
- Batch data projection definition and conversion
- Acquisition and processing of NetCDF data
- Understanding and Reading of NC Data
- Components of ArcGIS ModelBuilder
- ArcGIS new toolbox and custom tools
Chapter 4 , RWEQ model parameter extraction:
- Climatic factor WF extraction
Climatic conditions such as wind speed, temperature, rainfall, solar radiation, and snow cover days all affect the soil wind erosion modulus, which together constitute climate factors.
The climate factor WF characterizes the ability of wind to transport soil particles under the conditions of considering factors such as rainfall, temperature, sunshine and snow cover, and its expression is as follows:
In the formula, WF is the meteorological factor (kg/m); WE is the wind field intensity factor (m3/s3), which is composed of the monitoring wind speed μ2 (m/s), the sand-emission wind speed μ1 (assumed to be 5 m/s) and the observation period The number of days Nd is calculated; ρ is the air density (kg/m3), calculated from the altitude EL (km) and the absolute temperature T (K); g is the acceleration of gravity (m/s2); S is the soil moisture factor (dimensionless ); R is the rainfall (mm); I is the amount of irrigation (mm); Rd is the number of rainfall and (or) irrigation days; ETP is the potential relative evaporation of the surface (mm), which is determined by the solar radiation SR (cal / cm2 ) and The average temperature DT (°C) is calculated; SD is the snow cover factor (dimensionless); P is the probability that the snow cover depth (Hsnow) is greater than 25.4 mm during the calculation period.
- Soil erodibility factor EF extraction
Soil erodibility refers to the susceptibility of soil to erosion. For soil types with different mechanical composition and physical and chemical properties, the smaller the particle size, the lower the organic matter content, the greater the erodibility of the soil, and the easier it is to be eroded; on the contrary, the coarser the particle size, the higher the organic matter content, and the smaller the erodibility , the less likely to be eroded.
- Soil crust factor SCF extraction
Soil crust refers to the microlayer formed by the interaction between some lower organisms and the soil surface or the precipitation splashed on the soil surface. Generally, according to the mechanism of formation, it can be divided into biological crust and physical crust. Among them, the biological crust is beneficial to resist soil wind erosion; the physical crust is fragile, but accelerates the process of soil being eroded by wind.
- Vegetation coverage factor C extraction
Different vegetation has different root systems, and thus has different water-fixing and sand-fixing abilities. Vegetation coverage factor indicates the inhibitory effect on soil wind erosion under certain vegetation coverage conditions. According to the LUCC classification map of the study area, the vegetation is divided into five vegetation types: woodland, shrubs, grassland, farmland, and bare land, and each vegetation coverage factor is calculated according to different coefficients.
In the formula, ai is the coefficient of different vegetation types, among which, forest land is -0.153 5, shrub is -0.092 1, grassland is -0.151 1, farmland is -0.043 8, and bare land is -0.076 8; SC is the vegetation coverage ( dimensionless), calculated from the NDVI dataset.
- Extraction of Surface Roughness Factor K'
Surface roughness refers to the influence of land surface roughness caused by topography on soil wind erosion
In the formula, Kr is the terrain roughness length (cm) caused by terrain undulations; Crr is the random roughness factor, which is taken as 0; ΔH is the altitude difference within the distance L (m), according to different undulating terrain conditions, have different values.
- Calculation of Soil Wind Erosion
SL is the soil wind erosion amount (thm-2a-1); Qmax is the maximum sand transfer amount (kg/m); S is the key plot length (m); z is the maximum wind erosion distance in the downwind direction (m); WF is the climate Factor (kg/m); K' is surface roughness factor; EF is soil erodibility factor; SCF is soil crust factor; C is vegetation cover factor.
Chapter Four, Attribution Analysis:
- Analysis on Spatial Distribution of Soil Wind Erosion in Three River Sources
The change of soil wind erosion intensity is the result of many factors. Meteorological factors, underlying surface roughness, vegetation coverage, soil water content and other factors restrict the difference in soil wind erosion intensity. Land use and cover changes are important factors affecting soil wind erosion.
Based on the extraction and analysis of land use and cover change information in the study area and other related research results, the spatial distribution characteristics of the study area will be statistically analyzed, and in-depth analysis will be made for soil wind erosion prevention and control measures.
- correlation analysis
Existing studies have shown that the grid method is a simple and effective research method in the correlation study of the spatial distribution of multiple factors. Use the ArcGIS fishnet tool to create a grid of a certain size in the research area, and perform map segmentation, sampling analysis, and division of research units. Establish the scatter diagrams of factors such as vegetation and potential wind erosion, actual wind erosion and windbreak and sand fixation in the Three Rivers Headwaters area by grid method, and analyze the scatter diagrams
Perform optimal function fitting and explore its correlation in spatial distribution.
- path analysis
Taking the annual soil wind erosion in the Sanjiangyuan area in 2015 as the dependent variable, and taking climate factors and vegetation coverage as independent variables to conduct path analysis, the direct and indirect joint contributions of each factor were quantitatively analyzed.
- Factor detection analysis -- geographic detector
The spatial distribution of wind erosion is not caused by a single geographical, climatic or human factor, its formation is inseparable from the joint action of multiple factors, so the factors that contribute more to its effect will determine its actual spatial distribution. The Geographic Detector Model (GDM) is proposed based on the theory of spatial differentiation and the spatial analysis technology of geographic information system (GIS). It is often used to study the factors that affect the heterogeneity of spatial hierarchy and their underlying mechanisms.
- factor detector
The factor detector can evaluate the contribution of a certain influencing factor to the wind erosion amount, and the specific formula is as follows:
Among them, D is a certain impact factor, H is the amount of wind erosion, Q is the contribution of the impact factor to the amount of wind erosion, the value range is [0-1], N, σ2 are the sample size and its variance, h is the number of sample layers , L is the classification number of impact factors. When the Q value is larger, it indicates that the contribution to wind erosion is greater.
- Interaction Detector
The interaction detector can evaluate the contribution of the two influencing factors to the wind erosion in the study area, so as to more accurately analyze the actual contribution of multiple influencing factors.
- R-based geodetector implementation:
- Independent and dependent variable data preparation;
- Geographic probe operation preparation;
- R software and package installation, basic settings, etc.;
- Geoprobe runs code parsing;
- Analysis and visualization of factor detector results;
- Interactive detector results and visualization;
Chapter 5 , SCI paper writing skills related to RWEQ model :
- Science Paper Structure
- introduction
- Are the scientific questions clear?
- Is the logical reasoning rigorous?
- Writing Skills for Literature Review
- Examples of Introduction Writing
- Summary and Conclusion
- English abstract writing requirements
- Five Elements of a Summary
- How to construct a summary abstract for an SCI paper
- Difference Between Abstract and Conclusion
- Data source and preprocessing
- Model factor extraction method
- discuss
- writing points for discussion
- Discuss common problems in writing
- Analysis of paper submission skills
- SCI paper case analysis