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. 2017 Jun 23;7(1):4175.
doi: 10.1038/s41598-017-04282-8.

Global Rainfall Erosivity Assessment Based on High-Temporal Resolution Rainfall Records

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Free PMC article

Global Rainfall Erosivity Assessment Based on High-Temporal Resolution Rainfall Records

Panos Panagos et al. Sci Rep. .
Free PMC article

Abstract

The exposure of the Earth's surface to the energetic input of rainfall is one of the key factors controlling water erosion. While water erosion is identified as the most serious cause of soil degradation globally, global patterns of rainfall erosivity remain poorly quantified and estimates have large uncertainties. This hampers the implementation of effective soil degradation mitigation and restoration strategies. Quantifying rainfall erosivity is challenging as it requires high temporal resolution(<30 min) and high fidelity rainfall recordings. We present the results of an extensive global data collection effort whereby we estimated rainfall erosivity for 3,625 stations covering 63 countries. This first ever Global Rainfall Erosivity Database was used to develop a global erosivity map at 30 arc-seconds(~1 km) based on a Gaussian Process Regression(GPR). Globally, the mean rainfall erosivity was estimated to be 2,190 MJ mm ha-1 h-1 yr-1, with the highest values in South America and the Caribbean countries, Central east Africa and South east Asia. The lowest values are mainly found in Canada, the Russian Federation, Northern Europe, Northern Africa and the Middle East. The tropical climate zone has the highest mean rainfall erosivity followed by the temperate whereas the lowest mean was estimated in the cold climate zone.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
(a) Global distribution of rainfall erosivity stations (red dots) compiled in the Global Rainfall Erosivity Database (GloREDa); (b) Distribution of rainfall erosivity stations by continent. Maps generated with ESRI ArcGIS ver. 10.4 (http://www.esri.com).
Figure 2
Figure 2
(a) Global Rainfall Erosivity map (spatial resolution 30 arc-seconds). Erosivity classes correspond to quantiles. Map generated with ESRI ArcGIS ver. 10.4 (http://www.esri.com); (b) number and cumulative percentage of GloREDa stations grouped by erosivity; (c) mean erosivity by continent; (d) mean erosivity by climate zone.
Figure 3
Figure 3
R-factor descriptive statistics per Kopper-Geiger climate type. Colour bars are the mean values per climate zone. Error bars represent the standard deviation. Percentages below each main climate category represent its proportion within the study area. Climate zones: Af (tropical rainforest), Am (tropical monsoon), Aw (tropical savannah), BWh (hot desert), BWk (cold desert), BSh (hot steppe), BSk (cold steppe), Csa (dry hot summer), Csb (dry warm summer), Cwa (subtropical dry winter), Cwb (dry winter and dry summer), Cfa (temperate without dry season and hot summer), Cfb (temperate without dry season and warm summer), Cfc (temperate without dry season and cold summer), DSa (cold and dry hot summer), Dsb (cold and dry warm summer), Dsc (cold and dry cold summer), Dwa (cold and dry winter, and hot summer), Dwb (cold and dry winter, and warm summer), Dwc (cold and dry winter, and cold summer), Dwd (cold and dry winter, and very cold winter), Dfa (cold without dry season and hot summer), Dfb (cold without dry season and warm summer), Dfc (cold without dry season and cold summer), Dfd (cold without dry season and very cold winter), E (polar).
Figure 4
Figure 4
Comparison of predicted vs. measured R-factor values (values below 10,000 MJ mm−1 ha−1 yr−1) for the three previous and the presented global models. Grey line is the result of an optimal model (Intercept = 0 and regression coefficient = 1); Blue line is the regression result of each model; Grey zone is the 99% confidence interval for the coefficient.

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