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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 13, issue 2
Nat. Hazards Earth Syst. Sci., 13, 263–277, 2013
https://doi.org/10.5194/nhess-13-263-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Nat. Hazards Earth Syst. Sci., 13, 263–277, 2013
https://doi.org/10.5194/nhess-13-263-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 08 Feb 2013

Research article | 08 Feb 2013

Simulating future precipitation extremes in a complex Alpine catchment

C. Dobler1,2, G. Bürger3,4, and J. Stötter1,2 C. Dobler et al.
  • 1Institute of Geography, University of Innsbruck, Innrain 52, Innsbruck, Austria
  • 2alpS – Centre for Climate Change Adaptation Technologies, Grabenweg 68, Innsbruck, Austria
  • 3Pacific Climate Impact Consortium, University of Victoria, 2489 Sinclair Road, Victoria, Canada
  • 4Institute of Earth and Environmental Science, University of Potsdam, Karl-Liebknecht-Str. 24-25, Potsdam-Golm, Germany

Abstract. The objectives of the present investigation are (i) to study the effects of climate change on precipitation extremes and (ii) to assess the uncertainty in the climate projections. The investigation is performed on the Lech catchment, located in the Northern Limestone Alps. In order to estimate the uncertainty in the climate projections, two statistical downscaling models as well as a number of global and regional climate models were considered. The downscaling models applied are the Expanded Downscaling (XDS) technique and the Long Ashton Research Station Weather Generator (LARS-WG). The XDS model, which is driven by analyzed or simulated large-scale synoptic fields, has been calibrated using ECMWF-interim reanalysis data and local station data. LARS-WG is controlled through stochastic parameters representing local precipitation variability, which are calibrated from station data only. Changes in precipitation mean and variability as simulated by climate models were then used to perturb the parameters of LARS-WG in order to generate climate change scenarios. In our study we use climate simulations based on the A1B emission scenario. The results show that both downscaling models perform well in reproducing observed precipitation extremes. In general, the results demonstrate that the projections are highly variable. The choice of both the GCM and the downscaling method are found to be essential sources of uncertainty. For spring and autumn, a slight tendency toward an increase in the intensity of future precipitation extremes is obtained, as a number of simulations show statistically significant increases in the intensity of 90th and 99th percentiles of precipitation on wet days as well as the 5- and 20-yr return values.

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