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

被引:93
作者
Angulo-Martinez, M. [1 ]
Lopez-Vicente, M. [3 ]
Vicente-Serrano, S. M. [2 ]
Begueria, S. [1 ]
机构
[1] Aula Dei Expt Stn CSIC, Dept Soil & Water, Zaragoza 50080, Spain
[2] Pyrenean Inst Ecol CSIC, Dept Geoenvironm Proc & Global Change, Zaragoza 50080, Spain
[3] Katholieke Univ Leuven, Dept Earth & Environm Sci, B-3001 Louvain, Belgium
关键词
SOIL-EROSION; MONTHLY PRECIPITATION; EXTREME RAINFALL; AIR-TEMPERATURE; SPATIAL INTERPOLATION; SOLAR-RADIATION; REGRESSION; PREDICTION; RUSLE; GIS;
D O I
10.5194/hess-13-1907-2009
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
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 similar to 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.
引用
收藏
页码:1907 / 1920
页数:14
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