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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 20, issue 3 | Copyright
Hydrol. Earth Syst. Sci., 20, 1031-1047, 2016
https://doi.org/10.5194/hess-20-1031-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 08 Mar 2016

Research article | 08 Mar 2016

Sensitivity analysis of runoff modeling to statistical downscaling models in the western Mediterranean

Benjamin Grouillet1, Denis Ruelland1, Pradeebane Vaittinada Ayar2, and Mathieu Vrac2 Benjamin Grouillet et al.
  • 1CNRS, Laboratoire HydroSciences, Place Eugene Bataillon, 34095 Montpellier, France
  • 2LSCE, Laboratoire des Sciences du Climat et de l'Environnement, UMR CEA-CNRS-UVSQ 1572, CE Saclay l'Orme des Merisiers, 91191 Gif-sur-Yvette, France

Abstract. This paper analyzes the sensitivity of a hydrological model to different methods to statistically downscale climate precipitation and temperature over four western Mediterranean basins illustrative of different hydro-meteorological situations. The comparison was conducted over a common 20-year period (1986&ndsh;2005) to capture different climatic conditions in the basins. The daily GR4j conceptual model was used to simulate streamflow that was eventually evaluated at a 10-day time step. Cross-validation showed that this model is able to correctly reproduce runoff in both dry and wet years when high-resolution observed climate forcings are used as inputs. These simulations can thus be used as a benchmark to test the ability of different statistically downscaled data sets to reproduce various aspects of the hydrograph. Three different statistical downscaling models were tested: an analog method (ANALOG), a stochastic weather generator (SWG) and the cumulative distribution function–transform approach (CDFt). We used the models to downscale precipitation and temperature data from NCEP/NCAR reanalyses as well as outputs from two general circulation models (GCMs) (CNRM-CM5 and IPSL-CM5A-MR) over the reference period. We then analyzed the sensitivity of the hydrological model to the various downscaled data via five hydrological indicators representing the main features of the hydrograph. Our results confirm that using high-resolution downscaled climate values leads to a major improvement in runoff simulations in comparison to the use of low-resolution raw inputs from reanalyses or climate models. The results also demonstrate that the ANALOG and CDFt methods generally perform much better than SWG in reproducing mean seasonal streamflow, interannual runoff volumes as well as low/high flow distribution. More generally, our approach provides a guideline to help choose the appropriate statistical downscaling models to be used in climate change impact studies to minimize the range of uncertainty associated with such downscaling methods.

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This original paper provides a guideline to select statistical downscaling methods (SDMs) in climate change impact studies (CCIS) to minimize uncertainty from downscaling. Three SDMs were applied to NCEP reanalysis and 2 GCM data values. We then analyzed the sensitivity of the hydrological model to the various downscaled data via 5 hydrological indicators representing the main features of the hydrograph. Our results enable selection of the appropriate SDMs to be used to build climate scenarios.
This original paper provides a guideline to select statistical downscaling methods (SDMs) in...
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