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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 13, issue 10
Hydrol. Earth Syst. Sci., 13, 1907–1920, 2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 13, 1907–1920, 2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.

  19 Oct 2009

19 Oct 2009

Mapping rainfall erosivity at a regional scale: a comparison of interpolation methods in the Ebro Basin (NE Spain)

M. Angulo-Martínez1, M. López-Vicente3, S. M. Vicente-Serrano2, and S. Beguería1 M. Angulo-Martínez et al.
  • 1Department of Soil and Water, Aula Dei Experimental Station – CSIC, 1005 Avda. Montañana, 50080-Zaragoza, Spain
  • 2Department of Geo-environmental Processes and Global Change, Pyrenean Institute of Ecology – CSIC, 1005 Avda. Montañana, 50080-Zaragoza, Spain
  • 3Department of Earth and Environmental Sciences, Katholieke Universiteit Leuven, Celestijnenlaan 200E, 3001 Leuven-Heverlee, Belgium

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.

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