Simulating the impact of climate change on soil erosion in sub-tropical monsoon dominated watershed based on RUSLE, SCS runoff and MIROC5 climatic model

摘要

Climate change due to precipitation is one of the important dominant variables that determine the trend of soil loss in future period. In the present study the MIROC5 model of RCP 2.6, 4.5, 6.0 and 8.5 scenarios have been used to estimate the future period precipitation in storm rainfall event. Statistical downscaling approaches have been applied to estimate the precipitation for the time period of 1900 to 2010 and 2070 to 2100. Then the rainfall and runoff erosivity (R) factor has been estimated from the predicted precipitation scenario with the help of Modified Fourier Index from 2070 to 2100 in different return period such as 5 year, 10 year and 15 year return period. SRTM (Shuttle Radar Topographic Mission) DEM (Digital Elevation Model) and Landsat 8 OLI (Operational Land Imager) have been used to prepare the necessary thematic inputs for RUSLE (Revised Universal Soil Loss Equation) model in GIS environment. In this study the information regarding the soil characteristics have been accounted based on the primary information. In the present study, 2 sq km * 2 sq km grids of the entire basin have been taken into consideration randomly for collecting the soil samples within this region. Then the soil texture has been estimated and identified through the automatic sieve shaker in laboratory. Despite the soil texture, the soil pH and organic matter have also been estimated in the laboratory for estimating the soil erodibility factor (Kfactor) more accurately. Slope length and steepness factor (LS) have been estimated from the SRTM DEM in GIS environment. NDVI (Normalized Difference Vegetation Index) was derived from the Landsat 8 OLI data for estimating the cover and management factor (C), support practice factor (P) related to slope direction has been estimated based on the primary observation during the field visit. Apart from that the SCS curve number values and the weighted curve number values for each and every individual LULC classes have been derived to estimate the R factor. It was revealed that the average annual soil loss in the severe region (very high) in the base year is 12.6% and it would be 25.12% (5 year return period), 26.48% (10 year return period), 27.59% (15 year return period) in RCP 2.6 scenario. The average annual soil loss in this region would be 28.53% (5 year return period), 30.00% (10 year return period), 30.97% (15 year return period) in RCP 4.5 scenario. The amount of soil in the severe region would be 32.19% (5 year return period), 33.48% (10 year return period), 34.05% (15 year return period) in RCP 6.0 scenario. In the RCP 8.5 scenario the average annual soil loss would be 32.78% (5 year return period), 32.97% (10 year return period), 33.28% (15 year return period). This type of study is more helpful for the decision makers and regional planner for adopting the suitable measures with keeping in the view the local environment. (C) 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.