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Bias correction of regional climate model simulations for hydrological climate change impact studies: review and evaluation of different methods
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. Uppsala Universitet, Sverige.
2012 (English)In: Journal of Hydrology, ISSN 0022-1694, E-ISSN 1879-2707, Vol. 456-457, 12-29 p.Article, review/survey (Refereed) Published
Abstract [en]

Despite the increasing use of regional climate model (RCM) simulations in hydrological climate-change impact studies, their application is challenging due to the risk of considerable biases. To deal with these biases, several bias correction methods have been developed recently, ranging from simple scaling to rather sophisticated approaches. This paper provides a review of available bias correction methods and demonstrates how they can be used to correct for deviations in an ensemble of 11 different RCM-simulated temperature and precipitation series. The performance of all methods was assessed in several ways: At first, differently corrected RCM data was compared to observed climate data. The second evaluation was based on the combined influence of corrected RCM-simulated temperature and precipitation on hydrological simulations of monthly mean streamflow as well as spring and autumn flood peaks for five catchments in Sweden under current (1961-1990) climate conditions. Finally, the impact on hydrological simulations based on projected future (2021-2050) climate conditions was compared for the different bias correction methods. Improvement of uncorrected RCM climate variables was achieved with all bias correction approaches. While all methods were able to correct the mean values, there were clear differences in their ability to correct other statistical properties such as standard deviation or percentiles. Simulated streamflow characteristics were sensitive to the quality of driving input data: Simulations driven with bias-corrected RCM variables fitted observed values better than simulations forced with uncorrected RCM climate variables and had more narrow variability bounds.

Place, publisher, year, edition, pages
2012. Vol. 456-457, 12-29 p.
Keyword [en]
RCM, Bias correction, Downscaling, Hydrology, HBV, Streamflow
National Category
Oceanography, Hydrology, Water Resources
Research subject
Physical Geography
Identifiers
URN: urn:nbn:se:su:diva-81296DOI: 10.1016/j.jhydrol.2012.05.052ISI: 000308060100002OAI: oai:DiVA.org:su-81296DiVA: diva2:563250
Funder
Formas, 2007-1433
Note

AuthorCount:2;

Available from: 2012-10-29 Created: 2012-10-15 Last updated: 2017-12-07Bibliographically 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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