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

Abstract

Various downscaling techniques have been developedto bridge the scale gap between global climate models(GCMs) and finer scales required to assess hydrological impactsof climate change. Such techniques may be groupedinto two downscaling approaches: the deterministic dynamicaldownscaling (DD) and the statistical downscaling (SD).Although SD has been traditionally seen as an alternative toDD, recent works on statistical downscaling have aimed tocombine the benefits of these two approaches. The overallobjective of this study is to assess whether a DD processingperformed before the SD permits to obtain more suitableclimate scenarios for basin scale hydrological applicationsstarting from GCM simulations. The case study presentedhere focuses on the Apulia region (South East of Italy,surface area about 20 000 km2), characterised by a typicalMediterranean climate; the monthly cumulated precipitationand monthly mean of daily minimum and maximum temperaturedistribution were examined for the period 1953-2000. The fifth-generation ECHAM model from the Max-Planck-Institute for Meteorology was adopted as GCM. TheDD was carried out with the Protheus system (ENEA), whilethe SD was performed through a monthly quantile-quantilecorrection. The SD resulted efficient in reducing the meanbias in the spatial distribution at both annual and seasonalscales, but it was not able to correct the miss-modelled nonstationarycomponents of the GCM dynamics. The DD provideda partial correction by enhancing the spatial heterogeneityof trends and the long-term time evolution predictedby the GCM. The best results were obtained through thecombination of both DD and SD approaches.


Autore Pugliese

Tutti gli autori

  • Guyennon N.; Romano E.; Portoghese I.; Salerno F.; Calmanti S.; Petrangeli A.B.; Tartari G.; Copetti D.

Titolo volume/Rivista

Hydrology and earth system sciences


Anno di pubblicazione

2013

ISSN

1027-5606

ISBN

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