Monthly Rainfall Erosivity: Conversion Factors for Different Time Resolutions and Regional Assessments

Meusburger

et al. 2017

Sci Rep

Self Cite

409

300

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.

Section: Methodsmentioning

confidence: 99%

“…REDES has been the data source for the erosivity map of Europe 28 and was updated in 2015 to estimate monthly erosivity in Europe 13, 33 .…”

Section: Methodsmentioning

confidence: 99%

Global rainfall erosivity assessment based on high-temporal resolution rainfall records

Panagos

Meusburger

et al. 2017

Sci Rep

Self Cite

409

300

“…Then, it was decided to transform all the calculated monthly R-factors to a common resolution of 30 min. For this reasons, Panagos et al (2016b) have developed monthly calibration factors at different time resolutions. Finally, 18,816 monthly R-factor values are serving for making the interpolations and developing the monthly R-factor maps.…”

Section: Methodsmentioning

confidence: 99%

Mapping monthly rainfall erosivity in Europe

Ballabio

Science of The Total Environment

Spinoni

et al. 2017

Self Cite

151

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 (315 MJ mm ha− 1 h− 1) compared to winter (87 MJ mm ha− 1 h− 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.

“…As pointed out by Agnese et al (2006), the ideal data for at‐site estimation of erosivity is high temporal resolution data, such as hyetograph data (Yin et al, 2007; Bonilla and Vidal, 2011), 1‐min (Yin et al, 2015; Xie et al, 2016), 5‐min (Fiener et al, 2013), 6‐min (Yu, 1998; Lu and Yu, 2002), 10‐min (Verstraeten and Poesen, 2006; Meusburger et al, 2012; Shiono et al, 2013; Ma et al, 2014), 15‐min (USDA–ARS, 2013; Angulo‐Martinez and Beguería, 2012; Klik and Konecny, 2013; Klik et al, 2015), 20‐min (Capolongo et al, 2008), 30‐min (Panagos et al, 2015, 2016b), and 60‐min (Renard et al, 1997) interval data. Conversion factors are generally used to adjust the R factor based on the different intervals of rain data to that based on the hyetograph data (Yin et al, 2007; Panagos et al, 2016a; Porto, 2016).…”

Section: Estimation Of Erosivitymentioning

confidence: 99%

Rainfall Erosivity: An Overview of Methodologies and Applications

Yin

Nearing

et al. 2017

Vadose Zone Journal

Self Cite

The rainfall erosivity factor (R factor) is one of six erosion factors in the Universal Soil Loss Equation (USLE), which together reflect the combined effects that cause soil loss by rill and interrill erosion on hillslopes by precipitation. It is defined as the summation of event EI 30 (the product of kinetic energy and maximum 30-min intensity) over a year and calculated based on rainfall hyetograph data. The R factor was developed in the various versions of the USLE, including the definition of the individual event and the criterion for selecting events used in the calculation, the equation used to estimate the unit kinetic energy from the rainfall intensity, the estimation of erosivity from the snowmelt and thaw, and erosivity mapping. Most research on rainfall erosivity deals with any of three aspects: developing estimation methods for deriving erosivity from courser resolution rainfall data (such as daily, monthly, and annual) but with greater spatial and temporal coverages than those from hyetograph data; preparing erosivity maps including those for annual average, monthly, and 10-yr recurrence erosivity; and documenting temporal trends in erosivity. Rainfall erosivity research on these three aspects is summarized to provide a greater understanding of the R factor.