Soil retention, is calculated as soil loss without vegetation cover minus soil loss including the current land use/cover pattern. Specifically, this indicator takes into account climate data (observed measurements for rainfall and modelled for snow), topographic aspects, soil properties and the presence or not of the vegetation cover.The level of detail of this indicator is per NUTS0 and NUTS2.
Additional Publications:
1. Wischmeier W.H. and Smith D.D. (1978). "Predicting Rainfall Erosion Losses – A Guide to Conservation Planning". Agriculture Handbook, No. 537, USDA, Washington DC.http://naldc.nal.usda.gov/download/CAT79706928/PDF
2. Bosco C., de Rigo D., Dewitte O. and Montarella L.. (2011). "Towards the reproducibility in soil erosion modeling: a new Pan-European soil map". Wageningen conferences on applied Soil Science, 18-22 September 2011. http://figshare.com/articles/Towards_the_reproducibility_in_soil_erosion_modelling_a_new_Pan_European_soil_erosion_map/936872
Lavalle, Carlo; Perpina Castillo, Carolina; Mari Rivero, Ines; Maes, Joachim (2026): LF522 - Soil retention (LUISA Platform REF2014). European Commission, Joint Research Centre [Dataset] doi: 10.2905/JRC.0QMXFG8 PID: http://data.europa.eu/89h/jrc-luisa-lf522-soil-retention-ref-2014
CLC 2006EU Reference Scenario 2014LUISAMedREMNUTSNUTS0NUTS2RUSLE equationSoil erosionSoil framework directiveState
The compressed zip file contains the projected soil retention at NUTS0 and NUTS2 , from 2010 to 2050. The data is stored in .csv format.
The compressed zip file contains the projected soil retention 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)
A statistical bias correction technique is applied to twelve high-resolution climate change simulations of temperature and precipitation over Europe, under the SRES A1B scenario, produced for the EU project ENSEMBLES. The bias correction technique is based on a transfer function, estimated on current climate, which affects the whole Probability Distribution Function (PDF) of variables, and which is assumed constant between the current and future climate. The impact of bias correction on 21st Century projections, their inter-model variability, and the climate change signal is investigated, with focus being on discrepancies between the original and the bias-corrected results. As assessing the impact of climate change is significantly dependent on the frequency of extreme events, we also analyze the evolution of the shape of the PDFs, and extreme events indices. Results show that the ensemble mean climate change signal and its inter-model variability are generally conserved. However, the impact of the bias correction varies amongst regions, seasons and models, and differences up to 0.5 C for the summer temperature climate change signal are found in Southern Europe. Finally the bias correction is found to influence the probability of extreme events like extremely hot or frost days, which also impacts the climate change signal.
| From date | To date |
|---|---|
| 2010-01-01 | 2050-12-31 |
