  Soil erosion by water is a widespread phenomenon throughout Europe and has
the potentiality, with his on-site and off-site effects, to affect water
quality, food security and floods. Despite the implementation of numerous and
different models for estimating soil erosion by water in Europe, there is still
a lack of harmonization of assessment methodologies.
  Often, different approaches result in soil erosion rates significantly
different. Even when the same model is applied to the same region the results
may differ. This can be due to the way the model is implemented (i.e. with the
selection of different algorithms when available) and/or to the use of datasets
having different resolution or accuracy. Scientific computation is emerging as
one of the central topic of the scientific method, for overcoming these
problems there is thus the necessity to develop reproducible computational
method where codes and data are available.
  The present study illustrates this approach. Using only public available
datasets, we applied the Revised Universal Soil loss Equation (RUSLE) to locate
the most sensitive areas to soil erosion by water in Europe.
  A significant effort was made for selecting the better simplified equations
to be used when a strict application of the RUSLE model is not possible. In
particular for the computation of the Rainfall Erosivity factor (R) the
reproducible research paradigm was applied. The calculation of the R factor was
implemented using public datasets and the GNU R language. An easily
reproducible validation procedure based on measured precipitation time series
was applied using MATLAB language. Designing the computational modelling
architecture with the aim to ease as much as possible the future reuse of the
model in analysing climate change scenarios is also a challenging goal of the
research.
