1,126
Views
32
CrossRef citations to date
0
Altmetric
Original Articles

A comparative study of soil erosion modelling by MMF, USLE and RUSLE

, &
Pages 89-103 | Received 07 Jan 2016, Accepted 07 Aug 2016, Published online: 20 Sep 2016
 

Abstract

The quantitative assessment of spatial soil erosion is valuable information to control the erosion. The study area in a part of Narmada river in central India is selected. The main objective is to assess and compare the results obtained from three soil erosion models using GIS platform. Variation in the rate of erosion of the three models is compared considering varying slope, soil and land use of the area. Three models selected are Morgan–Morgan–Finney (MMF), Universal Soil Loss Equation (USLE) and Revised Universal Soil Loss Equation (RUSLE). The best fit or the most reliable model for the study area is selected after validation with the observed sedimentation data. The results give –39.45%, –9.60% and 4.80% difference in the values of sedimentation by MMF, USLE and RUSLE, respectively, from the observed data. Finally, RUSLE model has been found to be most reliable for the study area.

Acknowledgement

The authors are grateful to the National Remote Sensing Centre (NRSC) for the satellite images and to the India Meteorological Department (IMD) for the rainfall data, National Bureau of Soil Science (NBSS) for soil data and to the Central Water Commission (CWC) for the observed sediment load data. The authors also show gratitude to the Council of Scientific & Industrial Research (CSIR) for financial assistance in the research. Authors are thankful to the METI and the NASA for the ASTER DEM.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access
  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart
* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.