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Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling
Author(s) -
Fowler H. J.,
Blenkinsop S.,
Tebaldi C.
Publication year - 2007
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.1556
Subject(s) - downscaling , climate change , environmental science , computer science , probabilistic logic , climatology , environmental resource management , artificial intelligence , ecology , geology , biology
There is now a large published literature on the strengths and weaknesses of downscaling methods for different climatic variables, in different regions and seasons. However, little attention is given to the choice of downscaling method when examining the impacts of climate change on hydrological systems. This review paper assesses the current downscaling literature, examining new developments in the downscaling field specifically for hydrological impacts. Sections focus on the downscaling concept; new methods; comparative methodological studies; the modelling of extremes; and the application to hydrological impacts. Consideration is then given to new developments in climate scenario construction which may offer the most potential for advancement within the ‘downscaling for hydrological impacts’ community, such as probabilistic modelling, pattern scaling and downscaling of multiple variables and suggests ways that they can be merged with downscaling techniques in a probabilistic climate change scenario framework to assess the uncertainties associated with future projections. Within hydrological impact studies there is still little consideration given to applied research; how the results can be best used to enable stakeholders and managers to make informed, robust decisions on adaptation and mitigation strategies in the face of many uncertainties about the future. It is suggested that there is a need for a move away from comparison studies into the provision of decision‐making tools for planning and management that are robust to future uncertainties; with examination and understanding of uncertainties within the modelling system. Copyright © 2007 Royal Meteorological Society