Universal Soil Loss Equation (USLE) ❖ Concept: The Universal Soil Loss Equation (USLE) is a mathematical model used to predict the average rate of soil erosion caused by rainfall and surface runoff in a particular area. It was developed by a team of scientists at the U.S. Department of Agriculture (USDA) in the 1960s and has since been widely used for soil conservation and land management purposes. The USLE is particularly useful in assessing the potential impact of different land uses and management practices on soil erosion. • USLE was developed by Wischmeier and Smith in 1950s. • USLE estimates the long-term average annual rate of erosion on a field slope based on rainfall pattern, soil type, topography, crop system and management practices (OMAFRA, 2012). ❖ Formula: A=RK LS * C* P • Where, A is the average annual soil loss (tons ha 1year 1), • R is the rainfall erosivity (MJmm ha1 h1 year 1). • K is the soil erodibility factor (tons ha1 R unit1). • LS is the topographic factor (dimensionless), • C is the cropping management factors (dimensionless), • P is the practice support factor (dimensionless) ❖ Factors: Let’s briefly explain each factor: 1. Rainfall Erosivity Factor(R): This factor represents the erosive power of rainfall in a particular region. It takes into account the amount and intensity of rainfall. Rainfall erosivity is the kinetic energy of raindrop’s impact and the rate of associated runoff. ▪ Spatial coverage: World ▪ Pixel size: 30 arc-seconds (~ 1 km at the equator) ▪ Limitation: It can overestimate the soil erosion. Data source: Global Rainfall Erosivity (https://esdac.jrc.ec.europa.eu/content/gloreda#tabs-0-description=0 ) 2. Soil Erodibility Factor(K): This factor reflects the susceptibility of a specific soil type to erosion. It considers soil texture, structure, organic matter content, and permeability. Soil erodibility represents the effect of soil properties and soil profile characteristics on soil loss. ▪ Spatial coverage: World ▪ Pixel size: 30 arc-seconds (~ 1 km at the equator) ▪ Limitation: It can overestimate the soil erosion. Data source: Harmonized World Soil Database v 1.2 (https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soildatabase-v12/en/ • Note that the table in the guide accounts for % organic matter (OM), not just organic carbon (OC). • If we do not know the conversion value for the area, the value OC is multiplied by 1.72 to get OM. • OM=1.72OC • The references for conversion factors are given in IPCC-AFOLU report 2006. 3. Topographic Factor (LS): Topographic factors LS consist of slope length L and slope steepness S. These two factors are combined to account for the effect of the slope on erosion. The longer and steeper the slope, the higher the potential for erosion. • Increase in the slope length (L) causes increase in erosion due to a progressive accumulation of runoff in the direction of downslope. • Increase in slope steepness factor (S) increase the soil erosion as a result of increasing velocity. ▪ DEM data source: SRTM (30 m) (https://dwtkns.com/srtm30m ) ▪ Formula: o L = [(FA * cell size)/22.13]m (Moore and Wilson, 1992) where, FA is flow accumulation, cell size is the size of DEM and m ranges from 0.2-0.6. o S= [(sinẞ* 0.01745)/0.09] n where, ẞ is slope angle in percentage, n ranges from 1.0-1.3. o LS = (LS)/100 4. Crop /vegetation and management factor (C): This factor considers the type and amount of vegetation cover and the effectiveness of land management practices in reducing erosion. ▪ Used to determine the relative effectiveness of soil and crop management systems in preventing soil loss. ▪ Value can be assigned for different landcover classes from look-up table in literatures. Landcover data source: Sentinel-2 Land Use/Land Cover (10 m) (https://www.arcgis.com/apps/instant/media/index.html?appid=fc92d38533d440078f17678ebc20e8e2 • Several references on estimating these factors can be found online: • USLE Fact Sheet • http://www.omafra.gov.on.ca/english/engineer/facts/12- 051.html • U.N. Food and Agriculture Organization http://www.fao.org/docrep/T1765E/t1765e0c.html • RUSLE handbook (Renard et al., 1997) 5. Support practice factor (P): This factor represents the effect of erosion control practices, such as terracing or contour plowing. ▪ It reflects the effects of practices that will reduce the amount and rate of the water runoff and thus reduce the amount of erosion. ▪ Values are obtained from literatures based on the farmers practices. ▪ For easy interpretation, we can used 1 irrespective of landcover classes. Step to perform this operation are as follow: 1. Rainfall Erosivity Factor(R): a. Open the link (https://esdac.jrc.ec.europa.eu/content/gloreda#tabs-0- description=0 ) in chrome →and then fill up the Request form → get the mail in Gmail then click on Link to access the dataset . And download the zip file of Global R Dataset (Global Rainfall Erosivity database (GloREDa) and monthly R-factor data at 1km spatial resolution). b.After the download is complete, extract the zip file and open the GlobalR dataset into Arcmap. c. Now extract the Global R dataset by Sundarban Block.shp data. Now go to properties → symbology → Classify → Define the classes (5) → Choose a colour ramp → Click on Apply → ok. 2. Soil Erodibility Factor(K): a. Open the link (https://www.fao.org/soils-portal/data-hub/soil-maps-anddatabases/harmonized-world-soil-database-v12/en/ ) in chrome and download the dataset Download database (.mdb) and HWSD Raster . b.After the download is complete, extract the zip file and open the HWSD.bil dataset into Arcmap. c. Now extract the HWSD dataset by Sundarban Block.shp data. Now go to properties → symbology → Unique Values → Choose a colour ramp → Click on Apply → Ok. d. Then go to the HWSD data folder → Right click in blank space → New → Microsoft Excel Worksheet → Open the new sheet → File →Save As → Type: CSV (Comma Delimiated) → Save → Open → Select the downloaded HWSD.mdb data. e. Delete all columns keeping MU_GLOBAL, T_SAND, T_SILT, T_CLAY, T_OC (respectively, classification code, sand, silt, clay, organic carbon). f. Open the HWSD Raster data in ArcGIS → Classify using Unique Values → Find these unique values in the excel table and keep only these values. g. Now we will calculate soil organic matter through the formula 1.72organic carbon. h. Now from this chart we will match the above values and find the soil composition. i. Now from the chart we will match the organic material percentage and soil type and will find out the Mean K value. We get the following table after calculation. j. Now we will go to ArcGIS → We will search for the tool Raster to Polygon and run it on the HWSD raster. k. Open attribute table of raster layer → Table options → Add field K → Editor → Start Editing → Select K_raster to vector layer → add the values from excel sheet. l. Polygon to Raster m. After the process is complete go to properties → symbology → unique value → chose colour ramp → Apply → Ok. 3. Topographic Factor (LS): a. Open DEM → Fill, Flow Direction, Flow Accumulation. b. Raster Calculator → Use the following formula to get the value of L. L= Power((“Flow_Accu_sundarban”30)/22.13,0.5) c. Search Slope and run. d. Raster Calculator → Use the following formula to get the value of S. S= pow((Sin(“slope”0.01745)/0.09),1.3) e. Raster Calculator →Use the following formula to get the value of LS. LS=(LS)/100 4. Crop /vegetation and management factor (C): a. Open the Global LULC data in ArcGIS and mask it with Kerala Boundary → Raster to polygon → Right click on the layer → Geoprocessing → Dissolve using Gridcode →Right click on dissolved layer → Table options → Add field C. b. We will provide C values in the attribute table from the following chart. The final table is the following: c. Polygon to raster on the basis of field C. d. Then resample R, K, C factor and create a proper layout. 5. Calculation Of A: Now we will use the formula A=RKLSC*P to calculate the final annual soil loss. Due to unavailability of the P data, we will use 1 in place of P. The final soil loss map is the following: Interpretation: This map shows the annual soil loss of Sundarban. Mostly very low soil Erosion is observable but due to presence of water-bodies sundarban is very much Flood- Prone and extreme soil erosion is observable in many areas in form of deep blue patches