## 3 Results and Discussion
### National Differences Based on USLE Input Parameters  
The differences in mean or median erosion for the different sites and methods and the field specific differences result generally from different calculations of the USLE factors and in case of the field differences their spatial distribution. For the \(R\) factor, it is first of all the differences in defining erosive events. While the BY and AT methods define the first criterion of an erosive event as events with more than 10 mm rainfall (following the Bavarian USLE adaptation by [PERSON] et al. [12]), the CZ method uses the original threshold of 12.7 mm [16]. Differences in the definitions of the second criterion for an erosive event are even larger (minimum 15 min intensity of at least 25.4 mm h\({}^{-1}\), minimum 30 min intensity of at least 10 mm h\({}^{-1}\), and minimum intensity of 10 mm h\({}^{-1}\) for CZ, BY, and AT, respectively). This is only the first substantial difference which is the basis for very different techniques used to calculate site specific \(R\) factors. A further substantial difference is that in BY and AT, a regression between \(R\) factors, derived from a limited number of stations and for different historic periods, and mean annual or mean summer rainfall are used to calculated \(R\) factors for lager areas or more recent periods. In contrast, the \(R\) factor in CZ is calculated directly from high resolution rainfall data (1 min) of the Czech Hydro-Meteorological Institute, which in principal should be more precise than using \(R\)-precipitation-relations. In general, both methods to calculate test catchment specific \(R\) factors have advantages and disadvantages: Deriving \(R\) factors via regressions from annual rainfall is much easier as gridded yearly rainfall data are often easily available and well quality-checked (including the used interpolation techniques) by national weather services (e.g., [25]). The problem might be that the regressions between historic rainfall erosivity data (partly from the 1960s to the 1970s; BY) and annual rainfall might not be stable under changing erosivity due to climate change [51; 52]. The disadvantage of the more precise direct calculation of \(R\) factors is that spatially distributed data need to be derived using spatial interpolation techniques, which might be weak compared to the gridded data of annual rainfall from weather services, which take elevation, instrument changes, etc. into account [25]. There are newer developments using corrected rainfall radar data (spatial and temporal resolution 1 km \(\times\) 1 km and 5 min) to derive rainfall erosivity [53] which could be operationally used in USLE-based erosion modelling. However, this will not help in a first attempt to harmonize the \(R\) factor calculations in Europe. Given the size of the test catchments, a potentially varying \(R\) factor within the catchments is anyway small and hence will not substantially affect field-specific differences in erosion from the different methods. Another substantial source of differences in modelled mean field erosion are the different approaches to derive the \(C\) factor, which varies up to a factor of 2.8 between methods (Table 3). All calculations refer to USLE methodology whereas the seasonality in soil cover from different crops is combined with seasonality of the \(R\) factor. However, for practical reasons, this time-consuming and data-intensive approach is then either simplified and empirical equations to derive \(C\) factors for specific crop rotations implemented [44], or even more complex models are used to first of all create a seasonality of soil cover [45] before using RUSLE procedures [47]. These different approaches might fit perfectly for the different regions but would require more testing before transferring from one region to another. A main reason for the larger variability in field-specific erosion in case of the AT method results from using field-specific \(C\) factor values instead of using an average \(C\) factor for all fields (Figure 2; Table 3).