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Open AccessArticle

Comparing Rainfall Erosivity Estimation Methods Using Weather Radar Data for the State of Hesse (Germany)

1
Institute of Physical Geography and Landscape Ecology, Leibniz Universität Hannover, Schneiderberg 50, 30167 Hannover, Germany
2
Hessian Agency for Nature Conservation, Environment and Geology, 65203 Wiesbaden, Germany
3
Institute of Bio- and Geosciences IBG-3, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
*
Author to whom correspondence should be addressed.
Water 2020, 12(5), 1424; https://doi.org/10.3390/w12051424
Received: 9 April 2020 / Revised: 12 May 2020 / Accepted: 14 May 2020 / Published: 16 May 2020
(This article belongs to the Special Issue Soil Water Erosion)
Rainfall erosivity exhibits a high spatiotemporal variability. Rain gauges are not capable of detecting small-scale erosive rainfall events comprehensively. Nonetheless, many operational instruments for assessing soil erosion risk, such as the erosion atlas used in the state of Hesse in Germany, are still based on spatially interpolated rain gauge data and regression equations derived in the 1980s to estimate rainfall erosivity. Radar-based quantitative precipitation estimates with high spatiotemporal resolution are capable of mapping erosive rainfall comprehensively. In this study, radar climatology data with a spatiotemporal resolution of 1 km2 and 5 min are used alongside rain gauge data to compare erosivity estimation methods used in erosion control practice. The aim is to assess the impacts of methodology, climate change and input data resolution, quality and spatial extent on the R-factor of the Universal Soil Loss Equation (USLE). Our results clearly show that R-factors have increased significantly due to climate change and that current R-factor maps need to be updated by using more recent and spatially distributed rainfall data. Radar climatology data show a high potential to improve rainfall erosivity estimations, but uncertainties regarding data quality and a need for further research on data correction approaches are becoming evident. View Full-Text
Keywords: R-factor; soil erosion; USLE; rainfall intensity; modeling; radar climatology; RADKLIM; rain gauge R-factor; soil erosion; USLE; rainfall intensity; modeling; radar climatology; RADKLIM; rain gauge
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Kreklow, J.; Steinhoff-Knopp, B.; Friedrich, K.; Tetzlaff, B. Comparing Rainfall Erosivity Estimation Methods Using Weather Radar Data for the State of Hesse (Germany). Water 2020, 12, 1424.

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