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Hydrological Modeling for Climate Change Impact Assessment: Transferring Large-Scale Information from Global Climate Models to the Catchment Scale
Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
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 [en]
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: urn:nbn:se:su:diva-84197ISBN: 978-91-7447-622-4 (print)OAI: oai:DiVA.org:su-84197DiVA: diva2:578765
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
List of papers
1. Evaluation of different downscaling techniques for hydrological climate-change impact studies at the catchment scale
Open this publication in new window or tab >>Evaluation of different downscaling techniques for hydrological climate-change impact studies at the catchment scale
2011 (English)In: Climate Dynamics, ISSN 0930-7575, E-ISSN 1432-0894, Vol. 37, no 9-10, 2087-2105 p.Article in journal (Refereed) Published
Abstract [en]

Hydrological modeling for climate-change impact assessment implies using meteorological variables simulated by global climate models (GCMs). Due to mismatching scales, coarse-resolution GCM output cannot be used directly for hydrological impact studies but rather needs to be downscaled. In this study, we investigated the variability of seasonal streamflow and flood-peak projections caused by the use of three statistical approaches to downscale precipitation from two GCMs for a meso-scale catchment in southeastern Sweden: (1) an analog method (AM), (2) a multi-objective fuzzy-rule-based classification (MOFRBC) and (3) the Statistical DownScaling Model (SDSM). The obtained higher-resolution precipitation values were then used to simulate daily streamflow for a control period (1961-1990) and for two future emission scenarios (2071-2100) with the precipitation-streamflow model HBV. The choice of downscaled precipitation time series had a major impact on the streamflow simulations, which was directly related to the ability of the downscaling approaches to reproduce observed precipitation. Although SDSM was considered to be most suitable for downscaling precipitation in the studied river basin, we highlighted the importance of an ensemble approach. The climate and streamflow change signals indicated that the current flow regime with a snowmelt-driven spring flood in April will likely change to a flow regime that is rather dominated by large winter streamflows. Spring flood events are expected to decrease considerably and occur earlier, whereas autumn flood peaks are projected to increase slightly. The simulations demonstrated that projections of future streamflow regimes are highly variable and can even partly point towards different directions.

Keyword
gcm, statistical downscaling, hydrological impact modeling, precipitation, temperature, streamflow, hbv, climate change, sweden, atmospheric circulation patterns, change scenarios, central sweden, daily precipitation, analog method, runoff model, gcm output, uncertainty, predictions, calibration
National Category
Oceanography, Hydrology, Water Resources Climate Research
Research subject
Physical Geography
Identifiers
urn:nbn:se:su:diva-67695 (URN)10.1007/s00382-010-0979-8 (DOI)000296476600023 ()
Funder
Formas, 2007-1433
Note

840XY Times Cited:1 Cited References Count:61

Available from: 2011-12-30 Created: 2011-12-30 Last updated: 2017-12-08Bibliographically approved
2. Regional Climate Models for Hydrological Impact Studies at the Catchment Scale: A Review of Recent Modeling Strategies
Open this publication in new window or tab >>Regional Climate Models for Hydrological Impact Studies at the Catchment Scale: A Review of Recent Modeling Strategies
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.

Keyword
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:nbn:se:su:diva-52619 (URN)10.1111/j.1749-8198.2010.00357.x (DOI)
Funder
Formas, 2007-1433
Available from: 2011-01-17 Created: 2011-01-17 Last updated: 2017-12-11Bibliographically approved
3. Bias correction of regional climate model simulations for hydrological climate change impact studies: review and evaluation of different methods
Open this publication in new window or tab >>Bias correction of regional climate model simulations for hydrological climate change impact studies: review and evaluation of different methods
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.

Keyword
RCM, Bias correction, Downscaling, Hydrology, HBV, Streamflow
National Category
Oceanography, Hydrology, Water Resources
Research subject
Physical Geography
Identifiers
urn:nbn:se:su:diva-81296 (URN)10.1016/j.jhydrol.2012.05.052 (DOI)000308060100002 ()
Funder
Formas, 2007-1433
Note

AuthorCount:2;

Available from: 2012-10-29 Created: 2012-10-15 Last updated: 2017-12-07Bibliographically approved
4. Is bias correction of Regional Climate Model (RCM) simulations possible for non-stationary conditions?
Open this publication in new window or tab >>Is bias correction of Regional Climate Model (RCM) simulations possible for non-stationary conditions?
2012 (English)In: Hydrology and Earth System Sciences Discussions, ISSN 1812-2108, E-ISSN 1812-2116, Vol. 9, no 11, 12765-12795 p.Article in journal (Other academic) Published
Abstract [en]

In hydrological climate-change impact studies, Regional Climate Models (RCMs) are commonly used to transfer large-scale Global Climate Model (GCM) data to smaller scales and to provide more detailed regional information. However, there are often considerable biases in RCM simulations, which have led to the development of a number of bias correction approaches to provide more realistic climate simulations for impact studies. Bias correction procedures rely on the assumption that RCM biases do not change over time, because correction algorithms and their parameterizations are derived for current climate conditions and assumed to apply also for future climate conditions. This underlying assumption of bias stationarity is the main concern when using bias correction procedures. It is in principle not possible to test whether this assumption is actually fulfilled for future climate conditions. In this study, however, we demonstrate that it is possible to evaluate how well bias correction methods perform for conditions different from those used for calibration. For five Swedish catchments, several time series of RCM simulated precipitation and temperature were obtained from the ENSEMBLES data base and different commonly-used bias correction methods were applied.  We then performed a differential split-sample test by dividing the data series into cold and warm respective dry and wet years. This enabled us to evaluate the performance of different bias correction procedures under systematically varying climate conditions. The differential split-sample test resulted in a large spread and a clear bias for some of the correction methods during validation years. More advanced correction methods such as distribution mapping performed relatively well even in the validation period, whereas simpler approaches resulted in the largest deviations and least reliable corrections for changed conditions. Therefore, we question the use of simple bias correction methods such as the widely used delta-change approach and linear scaling for RCM-based climate-change impact studies and recommend using higher-skill bias correction methods.

Keyword
RCM, bias correction, downscaling, hydrology, differential split-sample test
National Category
Oceanography, Hydrology, Water Resources Climate Research
Research subject
Physical Geography
Identifiers
urn:nbn:se:su:diva-84190 (URN)10.5194/hessd-9-12765-2012 (DOI)
Funder
Formas, 2007-1433
Available from: 2012-12-18 Created: 2012-12-18 Last updated: 2017-12-06Bibliographically approved

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