Statistical downscaling of general circulation model outputs to catchment scale hydroclimatic variables: issues, challenges and possible solutions

The aim of this paper is to discuss the issues and challenges associated with statistical downscaling of general circulation model (GCM) outputs to hydroclimatic variables at catchment scale and also to discuss potential solutions to address these issues and challenges. Outputs of GCMs (predictors o...

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Bibliographic Details
Published in:Journal of water and climate change 2014-12, Vol.5 (4), p.496-525
Main Authors: SACHINDRA, D. A, HUANG, F, BARTON, A, PERERA, B. J. C
Format: Article
Language:English
Subjects:
Atmospheric models
Catchment area
Catchment scale
Catchments
Climatology. Bioclimatology. Climate change
Earth sciences
Earth, ocean, space
Exact sciences and technology
External geophysics
General circulation models
Greenhouse gases
Hydroclimatology
Hydrology
Hydrology. Hydrogeology
Mathematical models
Meteorology
Robustness (mathematics)
Scale (ratio)
Statistical analysis
Statistical models
Uncertainty
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Summary:The aim of this paper is to discuss the issues and challenges associated with statistical downscaling of general circulation model (GCM) outputs to hydroclimatic variables at catchment scale and also to discuss potential solutions to address these issues and challenges. Outputs of GCMs (predictors of statistical downscaling models) suffer a considerable degree of uncertainty, mainly due to the lack of theoretical robustness caused by the limited understanding of various physical processes of the atmosphere and the incomplete mathematical representation of those processes in GCMs. The presence of several future GHG emission scenarios with equal likelihood of occurrence leads to scenario uncertainty. Outputs of a downscaling study are dependent on the quality and the length of the record of field observations, as statistical downscaling models are calibrated and validated against these observations of the hydroclimatic variables (predictands of statistical downscaling models). The downscaled results vary from one statistical downscaling technique to another due to different representations of the predictor-predictand relationships. Also different techniques used in selecting the predictors for statistical downscaling models influence the model outputs. Although statistical downscaling faces these issues, it is still considered as a potential method of predicting the catchment scale hydroclimatology from GCM outputs.
ISSN:2040-2244
2408-9354
DOI:10.2166/wcc.2014.056