The conservation of soil resources is increasingly becoming a critical issue worldwide, with growing interest in carbon stocks and water storage within the soil. Hungary is no exception, and there has been a demand for a country level soil erosion map that incorporates digital information available from the latest surveys and digital mapping campaigns. The map presented in this paper in based on the extremely wet year of 2010, and thus provides users a 1:100,000 scale 'worst case scenario' of soil erosion risk in Hungary (see Main Map). Results from both the Universal Soil Loss Equation and the Pan-European Soil Erosion Risk Assessment models were combined in order to achieve a map that can be used by a wide range of professionals. Both models estimate soil erosion by water in tonnes per hectare per year. ARTICLE HISTORY

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Soil erosion by water is one of the most significant forms of soil degradation globally, especially in Europe. A new soil erosion risk map of Hungary has been compiled and published recently, using the combined outputs of the Universal Soil Loss Equation and Pan‐European Soil Erosion Risk Assessment models. Our study aimed at providing evaluation of the map by using semiquantitative validation data obtained from the Hungarian Soil Degradation Subsystem of the National Environmental Information System. The Soil Degradation Subsystem database contained information at farm level as well as indicators based on laboratory data for 5‐ha representative plots. On the basis of the semiquantitative analysis, the map results align well with the farm‐based degradation data and provide viable information not only at the regional scale but also at the farm scale. However, indicators from representative plots did not support model results, indicating possible conflict between farm‐ and plot‐level data. Cross‐comparison of these indicators showed only limited correlation between farm‐ and plot‐level indicators.

As soil erosion is still a global threat to soil resources, the estimation of soil loss, particularly at a spatiotemporal setting, is still an existing challenge. The primary aim of our study is the assessment of changes in soil erosion potential in Hungary from 1990 to 2018, induced by the changes in land use and land cover based on CORINE Land Cover data. The modeling scheme included the application and cross-valuation of two internationally applied methods, the Universal Soil Loss Equation (USLE) and the Pan-European Soil Erosion Risk Assessment (PESERA) models. Results indicate that the changes in land cover resulted in a general reduction in predicted erosion rates, by up to 0.28 t/ha/year on average. Analysis has also revealed that the combined application of the two models has reduced the occurrence of extreme predictions, thus, increasing the robustness of the method. Random Forest regression analysis has revealed that the differences between the two models are mainly driven by their sensitivity to slope and land cover, followed by soil parameters. The resulting spatial predictions can be readily applied for qualitative spatial analysis. However, the question of extreme predictions still indicates that quantitative use of the output results should only be carried out with sufficient care.

The development of the recent European and global initiatives resulted in an increasing demand for harmonized digital soil information. One of the major limitations of harmonization is the great variation of field and analytical methods and classification systems. Since 1998, the World Reference Base for Soil Resources (WRB) is the global correlation scheme for soil classification and international communication. The one to one correlation of units, however, is difficult, if not impossible. Another problem is that the correct correlation of national units to WRB units might have spatial consequences. If the original map units need to be maintained, it is important to express the extent to which certain national units match with the WRB units. Taxonomic distance measurements were applied successfully to express numerically the correlation between the brown forest soil types (BFS) of the Hungarian Soil Classification System (HSCS) and WRB Reference Soil Groups (RSGs).