Validation of official erosion modelling based on high‐resolution radar rain data by aerial photo erosion classification

Abstract

The universal soil loss equation (USLE) is the most frequently applied erosion prediction model and it is also implemented as an official decision‐making instrument for agricultural regulations. The USLE itself has been already validated using different approaches. Additional errors, however, arise from input data and interpolation procedures that become necessary for field‐specific predictions on a national scale for administrative purposes. In this study, predicted event soil loss using the official prediction system in Bavaria (Germany) was validated by comparison with aerial photo erosion classifications of 8100 fields. Values for the USLE factors were mainly taken from the official Bavarian high‐resolution (5 × 5 m2) erosion cadastre. As series of erosion events were examined, the cover and management factor was replaced by the soil loss ratio. The event erosivity factor was calculated from high‐resolution (1 × 1 km2, 5 min), rain gauge‐adjusted radar rain data (RADOLAN). Aerial photo erosion interpretation worked sufficiently well and average erosion predictions and visual classifications correlated closely. This was also true for data broken down to individual factors and different crops. There was no reason to assume a general invalidity of the USLE and the official parametrization procedures. Event predictions mainly suffered from errors in the assumed crop stage period and tillage practices, which do not reflect interannual and farm‐specific variation. In addition, the resolution of radar data (1 km2) did not seem to be sufficient to predict short‐term erosion on individual fields given the strong spatial gradients within individual rains. The quality of the input data clearly determined prediction quality. Differences between USLE predictions and observations are most likely caused by parametrization weaknesses but not by a failure of the model itself. Copyright © 2017 John Wiley & Sons, Ltd.

Rain erosivity map for Germany derived from contiguous radar rain data

“…Second, even long records of rain gauge data may miss the largest events that occurred in close proximity to a rain gauge and thus underestimate rain erosivity. This is illustrated nicely by the data of Fischer et al (2018b). They showed that the largest event erosivity, which was recorded by contiguous measurements over only 2 months, was more than twice as large as the largest erosivity recorded by 115 rain gauges over 16 years and the same area.…”

“…Rain erosivity strongly depends on intensity peaks. Fischer et al (2018b) have shown that these peaks increasingly disappear the lower the spatial and temporal resolution becomes. This can be accounted for by scaling factors but these scaling factors can only adjust to an average behavior, while the factors may either be too large or too small for a specific event.…”

“…Recent research has shown that the erosivity of single events exhibits strong spatial gradients (Fiener and Auerswald, 2009;Fischer et al, 2016Fischer et al, , 2018bKrajewski et al, 2003;Pedersen et al, 2010;Peleg et al, 2016). This is due to the small spatial extent of convective rain cells, which is typical for erosive rains.…”

Recent and Future Changes in Rainfall Erosivity and Implications for the Soil Erosion Risk in Brandenburg, NE Germany

“…Better regional and national datasets are unavailable. Nonetheless, the data quality is continuously improving, including radar data [48,49].…”

Radar-Based Precipitation Climatology in Germany—Developments, Uncertainties and Potentials

“…Despite the fact that these temporally (up to 5 min) and spatially (1 km 2 ) highly resolved data have been available for fourteen years now, RADOLAN is still sparsely used in scientific studies. Studies using RADOLAN comprise the analysis of extreme flash floods and heavy rainfalls [26,27], forecasting of water levels and floods [12,28], the intercomparison to satellite-based QPEs [16,18] and the estimation of rainfall erosivity [29,30]. However, an increased use of RADOLAN data could be observed as most of these studies have been published within the last four years.…”