Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling
H. J. Fowler,a* S. Blenkinsopa and C. Tebaldi b
a Water Resource Systems Research Laboratory, School of Civil Engineering and Geosciences, Newcastle University, UK
b Institute for the Study of Society and Environment, National Center for Atmospheric Research, Boulder, CO, USA
* Correspondence to: H. J. Fowler, Water Resource Systems Research Laboratory, School of Civil Engineering and Geosciences, Cassie Building, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK. E-mail: h.j.fowler@ncl.ac.uk
INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 27: 1547–1578 (2007)
Abstract:
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.
KEY WORDS downscaling; climate change; hydrological impacts; comparative studies; extremes; uncertainty
INTRODUCTION
General circulation models (GCMs) are an important tool in the assessment of climate change. These numerical coupled models represent various earth systems including the atmosphere, oceans, land surface and sea-ice and offer considerable potential for the study of climate change and variability. However, they remain relatively coarse in resolution and are unable to resolve significant subgrid scale features (Grotch and MacCracken, 1991) such as topography, clouds and land use. For example, the Hadley Centre’s HadCM3 model is resolved at a spatial resolution of 2.5° latitude by 3.75° longitude whereas a spatial resolution of 0.125° latitude and longitude is required by hydrologic simulations of monthly flow in mountainous catchments (Salathe, 2003). Bridging the gap between ´ the resolution of climate models and regional and local scale processes represents a considerable problem for the impact assessment of climate change, including the application of climate change scenarios to hydrological models. Thus, considerable effort in the climate community has focussed on the development of techniques to bridge the gap, known as ‘downscaling’.
A number of papers have previously reviewed downscaling concepts, including Hewitson and Crane (1996); Wilby and Wigley (1997); Zorita and von Storch (1997); Xu (1999); Wilby et al. (2004); and regionally for Scandinavia in Hanssen-Bauer et al. (2005). This paper differs from previous reviews as it focuses on recent developments in downscaling methods for hydrological impact studies, updating and extending the methodological study of Xu (1999). In the next two sections the concept of downscaling, the development of new methods, comparative methodological studies and the modelling of extremes are discussed. The application of downscaling to the field of climate change impacts on hydrological modelling is reviewed in “Downscaling for hydrological impact studies”. “Incorporating new developments” reviews new developments in climate scenario construction, 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. The last section “Summary and next steps” draws these themes together to make some recommendations on future work in the field, providing an example of how probabilistic climate scenarios can be linked with downscaling methods for hydrological, and other, impact studies.
In particular, this review will try to answer five questions that we believe must be addressed for the successful use of downscaling methods in hydrological impact assessment, in both the downscaling research community and for practitioners:
1. What more (if anything) can be learnt from downscaling method comparison studies?
2. Can dynamical downscaling contribute advantages that can not be conferred by statistical downscaling?
3. Can realistic climate change scenarios be produced from dynamically downscaled output for periods outside the time period of simulation using methods such as pattern scaling?
4. What new methods can be used together with downscaling to assess uncertainties in hydrological response?
5. How can downscaling methods be better utilized within the hydrological impacts community?
Whilst this review aims to discuss recent developments in the application of climate change scenarios, through downscaling methods, to assess hydrological impacts, it will not provide a comprehensive review of all published studies. Instead it aims to concentrate on those studies that address new concepts and real advances in downscaling for hydrological impact assessment, particularly those that address the quantification of uncertainty in the estimation of climate change impacts. Therefore, the review will concentrate on studies that compare different downscaling approaches, the outputs from multiple climate models or ensembles, and multiple emissions scenarios.
CONCLUSIONS
In conclusion, since the 1990s there has been a thorough exploration of the strengths and weakness of different downscaling methods within the literature and there is no need for additional comparison studies. Although there has been a huge expansion of the downscaling literature only about one third of all downscaling studies consider impacts, and only half of these consider hydrological impacts. Within studies considering hydrological impacts 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.
As many of the impacts of climate change will not be detectable in the near future (e.g. Wilby, 2006), there is a need for decision-making tools for planning and management that are robust to future uncertainties. In hydrological impacts research there is a need for a move away from comparison studies into the provision of such tools based on the selection of robust, possibly impactspecific, downscaling methods. This is essential, together with the examination and understanding of uncertainties within the downscaling and modelling system, as for example, attempted by Wilby and Harris (2006). Probabilistic methods seem to offer a more robust way of assessing climate change impacts, but much research is still needed on the best way to apply such methods for different impacts and in different locations. It is still unclear who exactly should be responsible for generating probabilistic hydrologic scenarios. If this is to be the decision-maker or practitioner rather than the scientist, then this implies a need for more tools where the ‘hard’ science and data is embedded and hidden, such as EARWIG (Kilsby et al., 2007b). Nevertheless, as these allow the inclusion of uncertainty estimates using a multi-model approach which can be used in the planning of adaptation measures, they seem to offer the most potential for advancement within both the ‘downscaling for hydrological impacts’ science community and for practitioners.
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