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Regional Climate Models for Hydrological Impact Studies at the Catchment Scale: A Review of Recent Modeling Strategies
Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology. Uppsala Universitet, Sverige.ORCID iD: 0000-0002-3344-2468
Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology. Department of Geography, University of Zurich.
2010 (English)In: Geography Compass, ISSN 1749-8198, E-ISSN 1749-8198, Vol. 4, no 7, 834-860 p.Article in journal (Refereed) Published
Abstract [en]

This article reviews recent applications of regional climate model (RCM) output for hydrological impact studies. Traditionally, simulations of global climate models (GCMs) have been the basis of impact studies in hydrology. Progress in regional climate modeling has recently made the use of RCM data more attractive, although the application of RCM simulations is challenging due to often considerable biases. The main modeling strategies used in recent studies can be classified into (i) very simple constructed modeling chains with a single RCM (S-RCM approach) and (ii) highly complex and computing-power intensive model systems based on RCM ensembles (E-RCM approach). In the literature many examples for S-RCM can be found, while comprehensive E-RCM studies with consideration of several sources of uncertainties such as different greenhouse gas emission scenarios, GCMs, RCMs and hydrological models are less common. Based on a case study using control-run simulations of fourteen different RCMs for five Swedish catchments, the biases of and the variability between different RCMs are demonstrated. We provide a short overview of possible bias-correction methods and show that inter-RCM variability also has substantial consequences for hydrological impact studies in addition to other sources of uncertainties in the modeling chain. We propose that due to model bias and inter-model variability, the S-RCM approach is not advised and ensembles of RCM simulations (E-RCM) should be used. The application of bias-correction methods is recommended, although one should also be aware that the need for bias corrections adds significantly to uncertainties in modeling climate change impacts.

Place, publisher, year, edition, pages
2010. Vol. 4, no 7, 834-860 p.
Keyword [en]
climate, uncertainty, modeling, hydrological and fluvial processes, global warming, climate dynamics and variability, simulation
National Category
Meteorology and Atmospheric Sciences Oceanography, Hydrology, Water Resources
Research subject
Physical Geography
Identifiers
URN: urn:nbn:se:su:diva-52619DOI: 10.1111/j.1749-8198.2010.00357.xOAI: oai:DiVA.org:su-52619DiVA: diva2:388442
Funder
Formas, 2007-1433
Available from: 2011-01-17 Created: 2011-01-17 Last updated: 2017-12-11Bibliographically approved
In thesis
1. Hydrological Modeling for Climate Change Impact Assessment: Transferring Large-Scale Information from Global Climate Models to the Catchment Scale
Open this publication in new window or tab >>Hydrological Modeling for Climate Change Impact Assessment: Transferring Large-Scale Information from Global Climate Models to the Catchment Scale
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A changing climate can severely perturb regional hydrology and thereby affect human societies and life in general. To assess and simulate such potential hydrological climate change impacts, hydrological models require reliable meteorological variables for current and future climate conditions. Global climate models (GCMs) provide such information, but their spatial scale is too coarse for regional impact studies. Thus, GCM output needs to be downscaled to a finer scale either through statistical downscaling or through dynamic regional climate models (RCMs). However, even downscaled meteorological variables are often considerably biased and therefore not directly suitable for hydrological impact modeling. This doctoral thesis discusses biases and other challenges related to incorporating climate model output into hydrological studies and evaluates possible strategies to address them. An analysis of possible sources of uncertainty stressed the need for full ensembles approaches, which should become standard practice to obtain robust and meaningful hydrological projections under changing climate conditions. Furthermore, it was shown that substantial biases in current RCM simulations exist and that correcting them is an essential prerequisite for any subsequent impact simulation. Bias correction algorithms considerably improved RCM output and subsequent streamflow simulations under current conditions. In addition, differential split-sample testing was highlighted as a powerful tool for evaluating the transferability of bias correction algorithms to changed conditions. Finally, meaningful projections of future streamflow regimes could be realized by combining a full ensemble approach with bias correction of RCM output: Current flow regimes in Sweden with a snowmelt-driven spring flood in April will likely change to rather damped flow regimes that are dominated by large winter streamflows.

Place, publisher, year, edition, pages
Stockholm: Department of Physical Geography and Quaternary Geology, Stockholm University, 2013. 44 p.
Series
Dissertations from the Department of Physical Geography and Quaternary Geology, ISSN 1653-7211 ; 34
Keyword
Bias Correction, Climate Change, Climate Models, Ensembles, GCM, HBV, Hydrological Modeling, Precipitation, RCM, Split Sample Test, Streamflow, Sweden, Temperature, Uncertainty
National Category
Oceanography, Hydrology, Water Resources Climate Research
Research subject
Physical Geography
Identifiers
urn:nbn:se:su:diva-84197 (URN)978-91-7447-622-4 (ISBN)
Public defence
2013-02-15, Nordenskiöldsalen, Geovetenskapens hus, Svante Arrhenius väg 12, Stockholm, 13:00 (English)
Opponent
Supervisors
Funder
Formas, 2007-1433
Available from: 2013-01-24 Created: 2012-12-18 Last updated: 2014-01-31Bibliographically approved

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