Adaptation and application of the large LAERTES-EU RCM ensemble for modeling hydrological extremes: A pilot study for the Rhine basin

Florian Ehmele (Lead Author), Lisa-Ann Kautz, Hendrik Feldmann, Yi He, Martin Kadlec, Fanni D. Kelemen, Hilke S. Lentink, Patrick Ludwig, Desmond Manful, Joaquim G. Pinto

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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.
Original languageEnglish
Pages (from-to)677–692
Number of pages16
JournalNatural Hazards and Earth System Sciences
Volume22
Issue number2
DOIs
Publication statusPublished - 3 Mar 2022

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