Preprints
https://doi.org/10.5194/nhess-2021-150
https://doi.org/10.5194/nhess-2021-150

  01 Jul 2021

01 Jul 2021

Review status: this preprint is currently under review for the journal NHESS.

Adaptation and Application of the large LAERTES-EU RCM Ensemble for Modeling Hydrological Extremes: A pilot study for the Rhine basin

Florian Ehmele1, Lisa-Ann Kautz1, Hendrik Feldmann1, Yi He2, Martin Kadlec3, Fanni Dora Kelemen1,a, Hilke Simone Lentink1, Patrick Ludwig1, Desmond Manful2, and Joaquim Ginete Pinto1 Florian Ehmele et al.
  • 1Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), Hermann–von–Helmholtz–Platz 1, 76344 Eggenstein–Leopoldshafen, Germany
  • 2Tyndall Centre for Climate Change Research, School of Environmental Science, University of East Anglia (UEA), Norwich, United Kingdom
  • 3Impact Forecasting, Aon, Prague, Czech Republic
  • anow at: Institute for Atmospheric and Environmental Sciences, Goethe University Frankfurt, Frankfurt am Main, Germany

Abstract. Enduring and extensive heavy precipitation associated with widespread river floods are among the main natural hazards affecting Central Europe. Since such events are characterized by long return periods, it is difficult to adequately quantify their frequency and intensity solely based on the available observations of precipitation. Furthermore, long-term observations are rare, not homogeneous in space and time, and thus not suitable to run hydrological models (HMs) with respect to extremes. To overcome this issue, we make use of the recently introduced LAERTES-EU (LArge Ensemble of Regional climaTe modEl Simulations for EUrope) data set, which is an ensemble of regional climate model simulations providing over 12.000 simulated years. LAERTES-EU is adapted for the use in an HM to calculate discharges for large river basins by applying a quantile mapping with a fixed density function to correct the mainly positive bias in model precipitation. The Rhine basin serves as a pilot area for calibration and validation. The results show clear improvements in the representation of both precipitation (e.g., annual cycle and intensity distributions) and simulated discharges by the HM after the bias correction. Furthermore, the large size of LAERTES-EU improves the statistical representativeness also for high return values above 100 years of discharges. We conclude that the bias-corrected LAERTES-EU data set is generally suitable for hydrological applications and posterior risk analyses. The results of this pilot study will soon be applied to several large river basins in Central Europe.

Florian Ehmele et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-150', Anonymous Referee #1, 13 Aug 2021
    • AC1: 'Reply on RC1', Florian Ehmele, 11 Nov 2021
  • RC2: 'Comment on nhess-2021-150', Anonymous Referee #2, 18 Oct 2021
    • AC2: 'Reply on RC2', Florian Ehmele, 11 Nov 2021

Florian Ehmele et al.

Florian Ehmele et al.

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Short summary
For various applications, it is crucial to have profound knowledge of the frequency, severity, and risk of extreme flood events. Such events are characterized by very long return periods which observations can not cover. We use a large ensemble of regional climate model simulations as input for a hydrological model. Precipitation data were post-processed to reduce systematic errors. The representation of precipitation and discharge is improved and estimates of long return periods become robust.
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