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@ARTICLE{Kreklow:887689,
      author       = {Kreklow, Jennifer and Steinhoff-Knopp, Bastian and
                      Friedrich, Klaus and Tetzlaff, Björn},
      title        = {{C}omparing {R}ainfall {E}rosivity {E}stimation {M}ethods
                      {U}sing {W}eather {R}adar {D}ata for the {S}tate of {H}esse
                      ({G}ermany)},
      journal      = {Water},
      volume       = {12},
      number       = {5},
      issn         = {2073-4441},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2020-04350},
      pages        = {1424 -},
      year         = {2020},
      abstract     = {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.},
      cin          = {IBG-3},
      ddc          = {690},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255)},
      pid          = {G:(DE-HGF)POF3-255},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000555915200203},
      doi          = {10.3390/w12051424},
      url          = {https://juser.fz-juelich.de/record/887689},
}