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| Titel |
Geo-statistical model of Rainfall erosivity by using high temporal resolution precipitation data in Europe |
| VerfasserIn |
Panos Panagos, Cristiano Ballabio, Pasquale Borrelli, Katrin Meusburger, Christine Alewell |
| Konferenz |
EGU General Assembly 2015
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 17 (2015) |
| Datensatznummer |
250113826
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| Publikation (Nr.) |
 EGU/EGU2015-14900.pdf |
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| Zusammenfassung |
| Rainfall erosivity (R-factor) is among the 6 input factors in estimating soil erosion risk by
using the empirical Revised Universal Soil Loss Equation (RUSLE). R-factor is a driving
force for soil erosion modelling and potentially can be used in flood risk assessments,
landslides susceptibility, post-fire damage assessment, application of agricultural
management practices and climate change modelling. The rainfall erosivity is extremely
difficult to model at large scale (national, European) due to lack of high temporal
resolution precipitation data which cover long-time series. In most cases, R-factor
is estimated based on empirical equations which take into account precipitation
volume.
The Rainfall Erosivity Database on the European Scale (REDES) is the output of an extensive
data collection of high resolution precipitation data in the 28 Member States of the European
Union plus Switzerland taking place during 2013-2014 in collaboration with national
meteorological/environmental services. Due to different temporal resolutions of the data (5,
10, 15, 30, 60 minutes), conversion equations have been applied in order to homogenise the
database at 30-minutes interval. The 1,541 stations included in REDES have been
interpolated using the Gaussian Process Regression (GPR) model using as covariates the
climatic data (monthly precipitation, monthly temperature, wettest/driest month) from
WorldClim Database, Digital Elevation Model and latitude/longitude. GPR has been selected
among other candidate models (GAM, Regression Kriging) due the best performance
both in cross validation (R2=0.63) and in fitting dataset (R2=0.72). The highest
uncertainty has been noticed in North-western Scotland, North Sweden and Finland
due to limited number of stations in REDES. Also, in highlands such as Alpine
arch and Pyrenees the diversity of environmental features forced relatively high
uncertainty.
The rainfall erosivity map of Europe available at 500m resolution plus the standard error and
the erosivity density (Rainfall erosivity per mm of precipitation) are available in the European
Soil Data Centre (ESDAC). The highest erosivity has been found in the mediterrean countries
(Italy, Western Greece, Spain, Northern Portugal), South Austria, Slovenia, Croatia and
Western United Kingdom. |
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