The indicator measures the capacity of ecosystems to avoid soil erosion assigning values ranging from 0 to 1 at pixel level, covering the EU-28 territory. This indicator is related to the capacity of a given land cover type to provide soil protection.The level of detail of this indicator is per NUTS0 and NUTS2.
1. Van der Knijff, J., Jones, R., Montanarella, L. (1999). "Soil erosion risk assessment in Italy".
Joint Research Centre. European Commission. http://www.preventionweb.net/files/1581_ereurnew2.pdf
- Joachim Maes
How to cite
Lavalle, Carlo; Perpina Castillo, Carolina; Mari Rivero, Ines; Maes, Joachim (2015): LF521 - Capacity of ecoystems to avoid soil erosion (LUISA Platform REF2014). European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/jrc-luisa-lf521-capacity-of-ecoystems-to-avoid-soil-erosion-ref-2014
The compressed zip file contains the projected capacity of ecosystems to avoid soil erosion
at NUTS0 and NUTS2 , from 2010 to 2050. The data is stored in .csv format.
The compressed zip file contains the projected capacity of ecosystems to avoid soil erosion for the Danube region at NUTS0 and NUTS2 , from 2010 to 2050. The data is stored in .csv format.
LUISA webpage (European Commission - JRC Science Hub)
- AMER GEOPHYSICAL UNION, WASHINGTON, UNITED STATES OF AMERICA
A statistical bias correction technique is applied to a set of high resolution climate change simulations for Europe from 11 state-of-theart regional climate models (RCMs) from the project ENSEMBLES. Modelled and observed daily values of mean, minimum and maximum temperature and total precipitation are used to construct transfer functions for the period 1961-1990, which are then applied to the decade 1991-2000, where the results are evaluated. By using a large ensembles of model runs and a long construction period, we take into account both inter-model variability, and longer (e.g. decadal) natural climate variability. Results show that the technique performs successfully for all variables over large part of the European continent, for all seasons. In particular, the probability distribution functions (PDFs) of both temperature and precipitation are greatly improved, especially in the tails, i.e., increasing the capability of reproducing extreme events. When the statistics of bias corrected results are ensemble-averaged, the result is very close to the observed ones. The bias correction technique is also able to improve statistics that depend strongly on the temporal sequence of the original field, such as the number of consecutive dry days and the total amount of precipitation in consecutive heavy precipitation episodes, which are quantities that may have a large influence on e.g. hydrological or crop impact models. Bias-corrected projections of RCMs are hence found to be potentially useful for the assessment of impacts of climate change over Europe.
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- Environment, Science and technology
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