Articles | Volume 17, issue 2
Hydrol. Earth Syst. Sci., 17, 705–720, 2013

Special issue: Precipitation uncertainty and variability: observations, ensemble...

Hydrol. Earth Syst. Sci., 17, 705–720, 2013

Research article 19 Feb 2013

Research article | 19 Feb 2013

Benefits from using combined dynamical-statistical downscaling approaches – lessons from a case study in the Mediterranean region

N. Guyennon1, E. Romano1, I. Portoghese2, F. Salerno3, S. Calmanti4, A. B. Petrangeli1, G. Tartari3, and D. Copetti3 N. Guyennon et al.
  • 1National Research Council, Water Research Institute, Roma, Italy
  • 2National Research Council, Water Research Institute, UOS Bari, Bari, Italy
  • 3National Research Council, Water Research Institute, UOS Brugherio, Brugherio, Italy
  • 4ENEA – Energy and Environment Modeling Technical Unit, Roma, Italy

Abstract. Various downscaling techniques have been developed to bridge the scale gap between global climate models (GCMs) and finer scales required to assess hydrological impacts of climate change. Such techniques may be grouped into two downscaling approaches: the deterministic dynamical downscaling (DD) and the statistical downscaling (SD). Although SD has been traditionally seen as an alternative to DD, recent works on statistical downscaling have aimed to combine the benefits of these two approaches. The overall objective of this study is to assess whether a DD processing performed before the SD permits to obtain more suitable climate scenarios for basin scale hydrological applications starting from GCM simulations. The case study presented here focuses on the Apulia region (South East of Italy, surface area about 20 000 km2), characterised by a typical Mediterranean climate; the monthly cumulated precipitation and monthly mean of daily minimum and maximum temperature distribution were examined for the period 1953–2000. The fifth-generation ECHAM model from the Max-Planck-Institute for Meteorology was adopted as GCM. The DD was carried out with the Protheus system (ENEA), while the SD was performed through a monthly quantile-quantile correction. The SD resulted efficient in reducing the mean bias in the spatial distribution at both annual and seasonal scales, but it was not able to correct the miss-modelled non-stationary components of the GCM dynamics. The DD provided a partial correction by enhancing the spatial heterogeneity of trends and the long-term time evolution predicted by the GCM. The best results were obtained through the combination of both DD and SD approaches.