Publication Date:
2023-01-13
Description:
Quantifying soil erosion rates (ERs) in developing countries is necessary to accomplish the Goal 15 of the UN 2030 Agenda for Sustainable Development and is relevant to meet the technical challenges of future hyperresolution models. This study proposes using the RUSLE model on the basis of the Generalized Likelihood Uncertainty Estimation (GLUE) methodology to estimate ERs at country scale for developing countries, which commonly exhibit spatio-temporal limitations/lack of groundbased measurements or field relations potentially inducing high uncertainty in the estimates of the R and C factors. In this context, ERs are calibrated with area-specific sediment yield data and global soil erosion model outputs. The method is successfully applied to Peru, and subsequently 5-km resolution ER maps are obtained for the years 1990, 2000, and 2010 which suggest that the steady increase of soil erosion rates are mainly induced by anthropogenic controls (e.g. changes in land use). In the light of our results, we believe that the method has the potential to be used as a standard method to estimate ERs and provide decision makers for an objective information to better manage soil resources in developing countries.
Keywords:
MULT; Multiple investigations; Peru
Type:
Dataset
Format:
application/zip, 22 MBytes
