Preprints
https://doi.org/10.5194/esurf-2022-1
https://doi.org/10.5194/esurf-2022-1
 
01 Feb 2022
01 Feb 2022
Status: this preprint is currently under review for the journal ESurf.

Comparison of rainfall generators with regionalisation for the estimation of rainfall erosivity at ungauged sites

Ross Pidoto1, Nejc Bezak2, Hannes Müller-Thomy3,4,, Bora Shehu1, Ana Claudia Callau-Beyer5, Katarina Zabret2, and Uwe Haberlandt1 Ross Pidoto et al.
  • 1Institute of Hydrology and Water Resources Management, Leibniz University Hannover, Germany
  • 2University of Ljubljana, Faculty of Civil and Geodetic Engineering, Ljubljana, Slovenia
  • 3Leichtweiß Institute for Hydraulic Engineering and Water Resources, Department of Hydrology, Water Management and Water Protection, Technische Universität Braunschweig, Brunswick, Germany
  • 4Institute of Hydraulic Engineering and Water Resources Management, Vienna University of Technology, Austria
  • 5Institute of Horticultural Production Systems, Leibniz University Hannover, Germany
  • previously published under the name Hannes Müller

Abstract. The assessment of rainfall erosivity is one of the main inputs in determining soil erosion. To calculate mean annual rainfall erosivity (R), long-term high-resolution observed rainfall time series are required, which are often not available. To overcome the issue of limited data availability in space, four methods are employed: the direct regionalisation of R, the regionalisation of 5 minute rainfall, the disaggregation of daily rainfall into 5 minute timesteps, and the use of a regionalised stochastic rainfall model. In addition the minimum time series length necessary to adequately estimate R is investigated. The impact of station density is considered for each of the methods. The study is carried out using 159 recording and 150 non-recording (daily) rainfall stations in the federal state of Lower Saxony, Germany. Results show that the direct regionalisation of mean annual erosivity leads to the best results in terms of relative bias and relative root mean square error (RMSE). This is followed by the regionalisation of the 5 minute rainfall data, which yields better results than the rainfall generation models, namely an alternating renewal model (ARM) and a multiplicative cascade model (Disagg). However, a key advantage of using regionalised rainfall models is the generation of rainfall time series that can be used for the estimation of the erosive event characteristics, which is not possible through the direct regionalisation of R. Using the stochastic ARM it can be shown that in most cases more than 60 years of data is needed in order to obtain a stable estimate of the annual rainfall erosivity. Estimation of soil erosion based on only 5 or 10 years of data can lead to uncertain values. Such short time series are often used when regionalisation is applied. Moreover, it was also found that resolution of measuring device has a significant effect on the rainfall erosivity and coarser data resolution can lead to high relative bias.

Ross Pidoto et al.

Status: open (until 24 May 2022)

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Ross Pidoto et al.

Ross Pidoto et al.

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Short summary
Erosion is a threat for soils with rainfall as driving force. The annual rainfall erosivity factor quantifies rainfall impact by analysing high-resolution rainfall time series (~5 min). Due to the lack of measuring stations alternatives for its estimation are analysed in this study. The best results are obtained for the regionalization of the erosivity factor itself. However, the identified minimum of 60 years time series length suggests to use rainfall generators as done in this study as well.