Soil erosion by water is one of the most widespread forms of soil  degradation. The loss of soil as a result of erosion can lead to decline in  organic matter and nutrient contents, breakdown of soil structure and  reduction of the water-holding capacity. Measuring soil loss across the whole  landscape is impractical and thus research is needed to improve methods of  estimating soil erosion with computational modelling, upon which integrated  assessment and mitigation strategies may be based. Despite the efforts, the  prediction value of existing models is still limited, especially at regional  and continental scale, because a systematic knowledge of local climatological  and soil parameters is often unavailable. A new approach for modelling soil  erosion at regional scale is here proposed. It is based on the joint use of  low-data-demanding models and innovative techniques for better estimating  model inputs. The proposed modelling architecture has at its basis the  semantic array programming paradigm and a strong effort towards computational  reproducibility. An extended version of the Revised Universal Soil Loss  Equation (RUSLE) has been implemented merging different empirical  rainfall-erosivity equations within a climatic ensemble model and adding a  new factor for a better consideration of soil stoniness within the model.  Pan-European soil erosion rates by water have been estimated through the use  of publicly available data sets and locally reliable empirical relationships.  The accuracy of the results is corroborated by a visual plausibility check  (63% of a random sample of grid cells are accurate, 83% at least  moderately accurate, bootstrap p ≤ 0.05). A comparison with country-level  statistics of pre-existing European soil erosion maps is also provided.