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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 19, issue 10
Hydrol. Earth Syst. Sci., 19, 4113–4126, 2015
https://doi.org/10.5194/hess-19-4113-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 19, 4113–4126, 2015
https://doi.org/10.5194/hess-19-4113-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 09 Oct 2015

Research article | 09 Oct 2015

Rainfall erosivity estimation based on rainfall data collected over a range of temporal resolutions

S. Yin1,2, Y. Xie1,2, B. Liu1,2, and M. A. Nearing3 S. Yin et al.
  • 1State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
  • 2School of Geography, Beijing Normal University, Beijing 100875, China
  • 3USDA-ARS Southwest Watershed Research Center, Tucson, AZ 85719, USA

Abstract. Rainfall erosivity is the power of rainfall to cause soil erosion by water. The rainfall erosivity index for a rainfall event (energy-intensity values – EI30) is calculated from the total kinetic energy and maximum 30 min intensity of individual events. However, these data are often unavailable in many areas of the world. The purpose of this study was to develop models based on commonly available rainfall data resolutions, such as daily or monthly totals, to calculate rainfall erosivity. Eleven stations with 1 min temporal resolution rainfall data collected from 1961 through 2000 in the eastern half of China were used to develop and calibrate 21 models. Seven independent stations, also with 1 min data, were utilized to validate those models, together with 20 previously published equations. The models in this study performed better or similar to models from previous research to estimate rainfall erosivity for these data. Using symmetric mean absolute percentage errors and Nash–Sutcliffe model efficiency coefficients, we can recommend 17 of the new models that had model efficiencies ≥ 0.59. The best prediction capabilities resulted from using the finest resolution rainfall data as inputs at a given erosivity timescale and by summing results from equations for finer erosivity timescales where possible. Results from this study provide a number of options for developing erosivity maps using coarse resolution rainfall data.

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