Abstract
Abstract. Rainfall erosivity is a major causal factor of soil erosion, and it is included in many prediction models. Maps of rainfall erosivity indices are required for assessing soil erosion at the regional scale. In this study a comparison is made between several techniques for mapping the rainfall erosivity indices: i) the RUSLE R factor and ii) the average EI30 index of the erosive events over the Ebro basin (NE Spain). A spatially dense precipitation data base with a high temporal resolution (15 min) was used. Global, local and geostatistical interpolation techniques were employed to produce maps of the rainfall erosivity indices, as well as mixed methods. To determine the reliability of the maps several goodness-of-fit and error statistics were computed, using a cross-validation scheme, as well as the uncertainty of the predictions, modeled by Gaussian geostatistical simulation. All methods were able to capture the general spatial pattern of both erosivity indices. The semivariogram analysis revealed that spatial autocorrelation only affected at distances of ~15 km around the observatories. Therefore, local interpolation techniques tended to be better overall considering the validation statistics. All models showed high uncertainty, caused by the high variability of rainfall erosivity indices both in time and space, what stresses the importance of having long data series with a dense spatial coverage.
Highlights
Soil erosion has become a major environmental threat due to the growth of the World’s population, and is one of the main consequences of projected land use and climate change sce-narios (Gobin et al, 2004)
Isolating the role of different natural and management factors on soil erosion has been one of the major research topics. The combination of those factors in the form of a parametric model allowed the development of tools such as the USLE (Wischmeier and Smith, 1978; Kinnell and Risse, 1998), which can be used for predicting the effect of different management strategies on soil erosion rates
Rainfall erosivity is an indicator of the precipitation aggressiveness, and depends on the rainfall energy and the intensity of the storm event
Summary
Soil erosion has become a major environmental threat due to the growth of the World’s population, and is one of the main consequences of projected land use and climate change sce-narios (Gobin et al, 2004). Isolating the role of different natural and management factors on soil erosion has been one of the major research topics. The combination of those factors in the form of a parametric model allowed the development of tools such as the USLE (Wischmeier and Smith, 1978; Kinnell and Risse, 1998), which can be used for predicting the effect of different management strategies on soil erosion rates. Maps showing the spatial distribution of natural and management related erosion factors are of great value in the early stages of land management plans, allowing identify preferential areas where action against soil erosion is more urgent or where the remediation effort will have highest revenue. With the advent of Geographic Information Systems (GIS), studies of this kind have become more and more frequent
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