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. 2017 Feb 1:579:1298-1315.
doi: 10.1016/j.scitotenv.2016.11.123. Epub 2016 Nov 30.

Mapping monthly rainfall erosivity in Europe

Cristiano Ballabio  1 , Pasquale Borrelli  2 , Jonathan Spinoni  3 , Katrin Meusburger  4 , Silas Michaelides  5 , Santiago Beguería  6 , Andreas Klik  7 , Sašo Petan  8 , Miloslav Janeček  9 , Preben Olsen  10 , Juha Aalto  11 , Mónika Lakatos  12 , Anna Rymszewicz  13 , Alexandru Dumitrescu  14 , Melita Perčec Tadić  15 , Nazzareno Diodato  16 , Julia Kostalova  17 , Svetla Rousseva  18 , Kazimierz Banasik  19 , Christine Alewell  4 , Panos Panagos  20
Affiliations

Mapping monthly rainfall erosivity in Europe

Cristiano Ballabio et al. Sci Total Environ. .

Abstract

Rainfall erosivity as a dynamic factor of soil loss by water erosion is modelled intra-annually for the first time at European scale. The development of Rainfall Erosivity Database at European Scale (REDES) and its 2015 update with the extension to monthly component allowed to develop monthly and seasonal R-factor maps and assess rainfall erosivity both spatially and temporally. During winter months, significant rainfall erosivity is present only in part of the Mediterranean countries. A sudden increase of erosivity occurs in major part of European Union (except Mediterranean basin, western part of Britain and Ireland) in May and the highest values are registered during summer months. Starting from September, R-factor has a decreasing trend. The mean rainfall erosivity in summer is almost 4 times higher (315MJmmha-1h-1) compared to winter (87MJmmha-1h-1). The Cubist model has been selected among various statistical models to perform the spatial interpolation due to its excellent performance, ability to model non-linearity and interpretability. The monthly prediction is an order more difficult than the annual one as it is limited by the number of covariates and, for consistency, the sum of all months has to be close to annual erosivity. The performance of the Cubist models proved to be generally high, resulting in R2 values between 0.40 and 0.64 in cross-validation. The obtained months show an increasing trend of erosivity occurring from winter to summer starting from western to Eastern Europe. The maps also show a clear delineation of areas with different erosivity seasonal patterns, whose spatial outline was evidenced by cluster analysis. The monthly erosivity maps can be used to develop composite indicators that map both intra-annual variability and concentration of erosive events. Consequently, spatio-temporal mapping of rainfall erosivity permits to identify the months and the areas with highest risk of soil loss where conservation measures should be applied in different seasons of the year.

Keywords: Cubist; K-means clustering; Modelling; R-factor; REDES; Seasonal rainfall intensity; Soil erosion.

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Figures

Image 1
Graphical abstract
Fig. 1
Fig. 1
Rainfall stations included in the Rainfall Erosivity Database at European Scale (REDES).
Fig. 2
Fig. 2
Observed vs predicted values of R-factor.
Fig. 3
Fig. 3
Maps of estimated R-factor from January to April.
Fig. 4
Fig. 4
Maps of estimated R-factor from May to August.
Fig. 5
Fig. 5
Maps of estimated R-factor from September to December.
Fig. 6
Fig. 6
Rainfall erosivity (MJ mm ha− 1 h− 1) per season (winter – spring – summer –autumn).
Fig. 7
Fig. 7
Spatial distribution of R-factor clusters (left) compared to Köppen-Geiger climate zones (right).
Fig. 8
Fig. 8
Monthly R-factor contribution in each cluster. The horizontal axis expresses the value of the R-factor in MJ mm ha− 1 h− 1.
Fig. 9
Fig. 9
Monthly R-factor, Precipitation and Erosivity Density contribution in each cluster. The horizontal axis is month of the year. The shaded regions represent the 0.95 confidence intervals.
Fig. 10
Fig. 10
Monthly R-factor, Precipitation and Erosivity Density by Köppen-Geiger main climatic zones (BS: Steppe, BW: Desert; Cf: Temperate without dry season; Cs: Temperate, dry summer; Df: Cold without dry season; Ds: Cold, dry summer; E: Polar). The shaded regions represent the 0.95 confidence intervals.
Fig. 11
Fig. 11
Ratio between the least and the most erosive month R-factor.
Fig. 12
Fig. 12
Map of the Coefficient of Variation of the Monthly Erosivity Density. Areas with values < 1 are subject to more evenly distributed events, while areas where CV > 1 are subject to a more heterogeneous precipitation regime during the year.
Fig. 13
Fig. 13
Weighted Erosivity Density (WED). Areas with the highest WED are more subject to extreme erosive events.
Fig. 14
Fig. 14
Map of the month of the year with the highest value of R (left) and lowest (right).
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