Distribution-based scaling to improve usability of regional climate model projections for hydrological climate change impacts studies

Yang, Wei; Andreasson, Johan; Graham, L. Phil; Olsson, Jonas; Rosberg, Jörgen; Wetterhall, F.

doi:10.2166/nh.2010.004

Cited by 249 publications

(227 citation statements)

References 5 publications

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“…Both projections simulated effects of the A1B emission scenario (Nakićenović et al, 2000), and the GCM results were dynamically downscaled to 50 km using the regional climate model (RCM) called RCA3 (Samuelsson et al, 2011). Thereafter, daily surface temperatures (at 2 m) and precipitation were further downscaled to 4 km, and bias was corrected using the distribution-based scaling method (Yang et al, 2010) with reference data from the 4 km grid-based observations for 1981-2010. Finally, gridded values were transferred to each subbasin for the period 1961-2100 to force the S-HYPE model.…”

Section: Model Approach To the Past And The Futurementioning

confidence: 99%

“…Statistical downscaling and bias correction techniques involve empirical correction of simulated climate variables (e.g., precipitation and temperature) by fitting simulated means and quantiles to the available observations and applying the same correction to future simulations (e.g., Yang et al, 2010). Consequently, it is assumed that the observed biases in the mean and variability of those climate parameters are systematic and will be the same in the future, but it remains to be determined whether the climate model errors are static over time (Maraun et al, 2010).…”

Section: Downscaling and Bias Correctionmentioning

confidence: 99%

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Climate impact on floods: changes in high flows in Sweden in the past and the future (1911–2100)

Arheimer

Lindström

2015

Hydrol. Earth Syst. Sci.

132

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Abstract. There is an ongoing discussion whether floods occur more frequently today than in the past, and whether they will increase in number and magnitude in the future. To explore this issue in Sweden, we merged observed time series for the past century from 69 gauging sites throughout the country (450 000 km 2 ) with high-resolution dynamic model projections of the upcoming century. The results show that the changes in annual maximum daily flows in Sweden oscillate between dry and wet periods but exhibit no significant trend over the past 100 years. Temperature was found to be the strongest climate driver of changes in river high flows, which are related primarily to snowmelt in Sweden. Annual daily high flows may decrease by on average −1 % per decade in the future, mainly due to lower peaks from snowmelt in the spring (−2 % per decade) as a result of higher temperatures and a shorter snow season. In contrast, autumn flows may increase by +3 % per decade due to more intense rainfall. This indicates a shift in floodgenerating processes in the future, with greater influence of rain-fed floods. Changes in climate may have a more significant impact on some specific rivers than on the average for the whole country. Our results suggest that the temporal pattern in future daily high flow in some catchments will shift in time, with spring floods in the northern-central part of Sweden occurring about 1 month earlier than today. High flows in the southern part of the country may become more frequent. Moreover, the current boundary between snow-driven floods in northern-central Sweden and rain-driven floods in the south may move toward higher latitudes due to less snow accumulation in the south and at low altitudes. The findings also indicate a tendency in observations toward the modeled projections for timing of daily high flows over the last 25 years. Uncertainties related to both the observed data and the complex model chain of climate impact assessments in hydrology are discussed.

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Section: Model Approach To the Past And The Futurementioning

confidence: 99%

Section: Downscaling and Bias Correctionmentioning

confidence: 99%

Climate impact on floods: changes in high flows in Sweden in the past and the future (1911–2100)

Arheimer

Lindström

2015

Hydrol. Earth Syst. Sci.

132

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“…parts of the variable distribution). This is sometimes referred to as distribution-based scaling (DBS; Yang et al 2010;van Roosmalen et al 2011). Déqué (2007 and Piani et al (2010) gave a detailed description of the method.…”

Section: Bias Correction Methodsmentioning

confidence: 99%

Projected Change—Models and Methodology

Wibig

Maraun

Benestad

et al. 2015

Regional Climate Studies

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General (global) circulation models (GCMs) are a useful tool for studying how climate may change in the future. Although GCMs have high temporal resolution, their spatial resolution is low. To simulate the future climate of the Baltic Sea region, it is necessary to downscale GCM data. This chapter describes the two conceptually different ways of downscaling: regional climate models (RCMs) nested in GCMs and using empirical and/or statistical relations between large-scale variables from GCMs and small-scale variables. There are many uncertainties in climate models, including uncertainty related to future land use and atmospheric greenhouse gas concentrations, limits on the amount of input data and their accuracy, and the chaotic nature of weather. The skill of methods for describing regional climate futures is also limited by natural climate variability. For the Baltic Sea area, the lack of an oceanic component in RCMs and poor representation of forcing by aerosols and changes in land use are major limitations. IntroductionThe development of general circulation models (GCMs) has created a useful tool for projecting how climate may change in the future. Such models describe the climate at a set of grid points, regularly distributed in space and time and with the same density over land and ocean. Their temporal resolution is relatively high, but their spatial resolution is limited by computing power. Many important processes, such as cloud formation, convection, and precipitation, occur at spatial scales much smaller than the distance between grid points. This means that these so-called sub-grid processes are not explicitly simulated by the models, but must be approximated with simplifying algorithms referred to as parameterisations. The low spatial resolution also means that the topography, coastline, and processes at the land-air, ocean-air, and land-ocean boundaries are coarsely represented in GCMs.

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“…Previous research (e.g., Hawkins & Sutton, 2009) has shown that towards the end of the 21st century, the emission scenarios (here RCPs) are the dominant source of uncertainty in climate projections; hence, our focus is on addressing only this source of uncertainty. The RCM projections (mean daily precipitation and temperature) were bias corrected against the APHRODITE and AphroTEMP datasets using the Distribution Based Scaling, DBS, statistical method (Yang et al, 2010) to obtain a reliable impression of the climate change. In brief, DBS aims to match the quantile distribution of bias-corrected data (precipitation and temperature) to the one of the reference data.…”

Section: Climate Scenariosmentioning

confidence: 99%

“…Three projections using emission scenarios RCP2.6, RCP4.5 and RCP8.5 are examined to provide information on the uncertainty in the climate projections. The state of the art bias-correction method Distribution Based Scaling (Yang et al, 2010) was used to improve on relevant biases for hydrological impact modelling in the area. Finally, the bias-corrected CORDEX-SA projections were introduced into the HYPE (HYdrological Predictions for the Environment) hydrological model to assess the impact of climate change on water over the Luni region.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the climate change impacts on the water resources of the Luni region, India

Pechlivanidis

Olsson²,

Sharma³

et al. 2015

Global NEST Journal

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Climate change is expected to have a strong impact on water resources at the local, regional and global scales. In this study, the impact of climate change on the hydro-climatology of the Luni region, India, is investigated by comparing statistics of current and projected future fluxes resulting from three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5). The use of different scenarios allows for the estimation of uncertainty of future impacts. The projections are based on the CORDEXSouth Asia framework and are bias-corrected using the DBS method before being entered into the HYPE (HYdrological Predictions for the Environment) hydrological model to generate predictions of runoff, evapotranspiration, soil moisture deficit, and applied irrigation water to soil. Overall, the high uncertainty in the climate projections is propagated in the impact model, and as a result the spatiotemporal distribution of change is subject to the climate change scenario. In general, for all scenarios, results show a -20 to +20% change in the long-term average precipitation and evapotranspiration, whereas more pronounced impacts are expected for runoff (-40 to +40% change). Climate change can also affect other hydro-climatic components, however, at a lower impact. Finally, the flow dynamics in the Luni River are substantially affected in terms of shape and magnitude.

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Distribution-based scaling to improve usability of regional climate model projections for hydrological climate change impacts studies

Cited by 249 publications

References 5 publications

Climate impact on floods: changes in high flows in Sweden in the past and the future (1911–2100)

Climate impact on floods: changes in high flows in Sweden in the past and the future (1911–2100)

Projected Change—Models and Methodology

Assessment of the climate change impacts on the water resources of the Luni region, India

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