Table of contents
Topic 3 RWEQ Model Data Support
Topic 4 RWEQ Model Parameter Extraction
Topic 6 SCI paper writing skills related to RWEQ model
Combined with the case to explain the operation of the RWEQ model and related attribution analysis, it will give practical explanations on the soil wind erosion from the aspects of principle, data, method and attribution analysis. Principles: Introduce the basic principles and main models of soil erosion and soil wind erosion, and analyze and explain the latest scientific research results at home and abroad; Data: Introduce the acquisition methods of meteorological, vegetation, soil and other data; explain the characteristics and application of various data Methods: Introduce the methods and key points of each parameter extraction in the RWEQ model; use the operating environment that effectively combines Python and ArcGIS, make full use of Python's efficient data processing capabilities, and ArcGIS' powerful spatial analysis and visualization capabilities, through code analysis and tools Practical drills, explaining diversified model parameter extraction methods; analysis chapter: analyzing the distribution characteristics of soil wind erosion and its influencing factors; explaining the principles of attribution analysis methods such as Tongjing analysis and geographic detectors, which are implemented in R, SPSS and other environments Attribution analysis and visualization of soil wind erosion.
You will gain a deep understanding of the principles and driving factors of the wind erosion model through the full-process practical exercise of the RWEQ model operation; master the techniques in wind erosion modulus estimation such as multi-source heterogeneous data processing, model parameter extraction, and attribution analysis; in specific practice In the case, learn to use the above principles and technical methods to improve the application ability level of wind erosion model.
Topic 1 Theoretical Basis
1. Basic principles of soil erosion
Soil erosion: the whole process of soil and soil parent material being destroyed, eroded, 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 characteristics of surface soil; 3) surface coverage and roughness.
Soil wind erosion evaluation model:
Wind Erosion Equation (WEQ)
Wind Erosion Equation (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 erodible 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 (RWEQ) 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 The model can provide a basis for land desertification prevention and control.
Topic 2 Platform Basics
1. ArcGIS software introduction and installation, common function introduction
ArcGIS version introduction, installation;
ArcGIS software interface, common function introduction;
ArcGIS workspace environment setting
2. ArcGIS spatial analysis and mapping
①How to define the coordinate system in ArcGIS
②ArcGIS spatial analysis
In the spatial analysis toolbox of ArcGIS software, a large number of raster data processing tools are provided, among which the tools for smoothing raster data are used to remove images. Salt and pepper noise plays a very important role
(1) Extraction analysis: extraction by attribute or spatial position, extraction by cell value; (
2) Map algebra: language rules of map algebra;
(3) Local analysis: raster data overlay (4) Neighborhood analysis
: neighborhood shape, neighborhood statistics type, point statistics;
(5) Regional analysis: partition geometric statistics, partition statistics, area tabulation, area Histogram;
(6) Interpolation analysis: inverse distance weighting method, natural neighbor method, trend surface method, spline function method, Kriging method; (7) Sampling and resampling:
fishnet analysis, random point sampling, reclassification , lookup table, etc.;
③ArcGIS layout design
ArcGIS basic map service use: configure map server; add and use online maps
Production and design of maps, eagle-eye maps, range indicators, grids, tables, charts, etc.
Those pitfalls that have been stepped on in the past —Common errors and usage precautions, etc.
Topic 3 RWEQ Model Data Support
1. Acquisition and preprocessing of vector data
Knowledge of
vector data Creation, conversion and editing of vector data
2. Acquisition and preprocessing of raster data
Understanding
of raster data Input, output and conversion of raster data
Understanding of spatial resolution
Raster data resampling
3. Remote sensing cloud platform data acquisition
Introduction of remote sensing cloud platform data
Basic syntax of remote sensing cloud platform
Data acquisition of remote sensing cloud platform
4. Acquisition and processing of NetCDF data
Recognition and reading of NC data
Composition of ArcGIS model builder
ArcGIS new toolbox and custom tools
5. Acquisition and processing of meteorological data based on Python
Introduction to meteorological data
Python development environment Building
Python code library installation and explanation
Reading and writing of text, vector, raster and other files
Python data cleaning
Conversion of text data and raster data
NC Data and *.TIF data conversion
Batch data projection definition and conversion
Topic 4 RWEQ Model Parameter Extraction
1. Climatic factor WF extraction
Climatic conditions such as wind speed, temperature, rainfall, solar radiation, and snow cover days will all affect the soil wind erosion modulus, which together constitute climatic 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.
Wf factor
ETp factor
SW factor
WF factor
2. Extraction of soil erodibility factor EF
Soil erodibility refers to the sensitivity 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. The calculation formula for soil erodibility factor is as follows:
3. Extraction of soil crust factor SCF
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, it can be divided into biological crust and physical crust according to the production mechanism. 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. Its calculation formula is as follows:
4. Extraction of vegetation coverage factor C
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, shrub, 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.
5. Extraction of surface roughness factor K'
Surface roughness refers to the influence of land surface roughness caused by terrain on soil wind erosion.
In the formula, Kr is the length of terrain roughness (cm) caused by terrain undulations; Crr is random roughness Factor, take 0; ΔH is the altitude difference (m) within the range of distance L, and L has different values according to different undulating terrain conditions.
6. 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.
Topic 5 Attribution Analysis
1. Statistical Analysis
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.
2. Correlation analysis
Fishnet analysis: 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.
Correlation analysis: establish the scatter diagrams of factors such as vegetation in the Three Rivers Headwaters area and the potential wind erosion amount, actual wind erosion amount, and windbreak and sand fixation amount by using the grid method, and perform optimal function fitting on the scatter diagram to explore its spatial distribution on the correlation.
3. Path analysis
The annual soil wind erosion in the Sanjiangyuan area in 2015 was used as the dependent variable, and the climate factors and vegetation coverage were used as the independent variables to carry out path analysis to quantitatively analyze the joint contribution of the direct and indirect effects of each factor.
4. Factor detection analysis--
The spatial distribution of wind erosion by geographic detectors is not caused by a single geographical, climatic or human factor. 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 amount of wind erosion, 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 when they interact, so as to more accurately analyze the actual contribution of multiple influencing factors.
Geographic detector based on R realizes
independent variable and dependent variable data preparation;
geographic detector operation preparation;
R software and program package installation, basic settings, etc.;
geographic detector operation code analysis;
factor detector result analysis and visualization;
interaction Detector Results and Visualization
Topic 6 SCI paper writing skills related to RWEQ model
1. Structure of scientific paper
2. Introduction
Is the scientific problem clear?
Is the logical reasoning rigorous?
Writing skills of literature review
Examples of introduction writing
3. Abstract and conclusion
Requirements for
writing an English abstract Five elements of abstract
How to construct a summary abstract of an SCI paper
Difference between abstract and conclusion
Data source and preprocessing
Model factor extraction method
4.
Discussion Discussion
5. Analysis of paper submission skills 6.
Case analysis of SCI papers
Note: Please prepare your own computer and install the required software in advance
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