Uncertainty analysis of statistical downscaling methods

Khan, Mohammad Sajjad; Coulibaly, Paulin; Dibike, Yonas

doi:10.1016/j.jhydrol.2005.06.035

Cited by 312 publications

(213 citation statements)

References 13 publications

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“…SDSM simulated a generally increasing trend in mean daily precipitation amount and variability not reproduced by the WG. Similar results were obtained by Khan et al (2006) in a comparison of SDSM, LARS-WG and an ANN method. SDSM was found to perform the best, with the ANN method producing the poorest results.…”

Section: Inter-comparison Of Statistical Downscaling Methodssupporting

confidence: 84%

“…Overall, WGs proved more skilful, with ANNs the worst because of the overestimation of wet days; use of the direct output from the HadCM2 GCM was superior to one ANN model on 12 out of 14 occasions. Indeed, ANNs have been shown repeatedly to perform poorly in the simulation of daily precipitation, particularly for wet-day occurrence (Wilby and Wigley, 1997;Wilby et al, 1998;Zorita and von Storch, 1999;Khan et al, 2006) due to a simplistic treatment of days with zero amounts, although they perform adequately for monthly precipitation (Schoof and Pryor, 2001). Harpham and Wilby (2005) addressed this by using variants of ANNs which, analogous to weather generation methods, treat the occurrence and amount of precipitation separately.…”

Section: Inter-comparison Of Statistical Downscaling Methodsmentioning

confidence: 99%

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Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling

Fowler

Blenkinsop

Tebaldi

2007

Intl Journal of Climatology

1,927

1,491

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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.

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Section: Inter-comparison Of Statistical Downscaling Methodssupporting

confidence: 84%

Section: Inter-comparison Of Statistical Downscaling Methodsmentioning

confidence: 99%

Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling

Fowler

Blenkinsop

Tebaldi

2007

Intl Journal of Climatology

1,927

1,491

View full text Add to dashboard Cite

show abstract

“…SDSM is a hybrid of a regression method and weather generator (Wilby et al 1999(Wilby et al , 2002(Wilby et al , 2003. Many comparative studies Wilby et al 1998;Khan et al 2006;Harpham and Wilby 2005;Dibike and Coulibaly 2005) have shown that this method is simple to handle and has, by and large, superior capability and is, therefore, widely applied (Wilby and Harris 2006).…”

Section: Introductionmentioning

confidence: 99%

Statistical downscaling of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River, China

Chu

Xia

et al. 2009

Theor Appl Climatol

247

158

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A statistical downscaling method (SDSM) was evaluated by simultaneously downscaling air temperature, evaporation, and precipitation in Haihe River basin, China. The data used for evaluation were large-scale atmospheric data encompassing daily NCEP/NCAR reanalysis data and the daily mean climate model results for scenarios A2 and B2 of the HadCM3 model. Selected as climate variables for downscaling were measured daily mean air temperature, pan evaporation, and precipitation data from 11 weather stations in the Haihe River basin. The results obtained from SDSM showed that: (1) the pattern of change in and numerical values of the climate variables can be reasonably simulated, with the coefficients of determination between observed and downscaled mean temperature, pan evaporation, and precipitation being 99%, 93%, and 73%, respectively; (2) systematic errors existed in simulating extreme events, but the results were acceptable for practical applications; and (3) the mean air temperature would increase by about 0.7°C during 2011~2040; the total annual precipitation would decrease by about 7% in A2 scenario but increase by about 4% in B2 scenario; and there were no apparent changes in pan evaporation. It was concluded that in the next 30 years, climate would be warmer and drier, extreme events could be more intense, and autumn might be the most distinct season among all the changes.

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“…CGCM3.1 outputs have been used as potential predictors for downscaling temperatures and precipitation in Quebec, Canada (e.g. Khan et al 2006;Dibike and Coulibaly 2006;Jeong et al 2012). The datasets were obtained through the Data Access Integration (DAI) portal (http://climat-quebec.qc.ca/CC-DEV/trunk/index.php/pages/dai) from Environment Canada (DAI 2008).…”

Section: Datasetsmentioning

confidence: 99%

Effects of variance adjustment techniques and time-invariant transfer functions on heat wave duration indices and other metrics derived from downscaled time-series. Study case: Montreal, Canada

Gaitán

2016

Nat Hazards

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Statistical downscaling techniques are often used to generate finer-scale projections of climate variables affected by local-scale processes not resolved by coarse resolution numerical models like global climate models (GCMs). Statistical downscaling models rely on several assumptions in order to produce finer-/local-scale projections of the variable of interest; one of these assumptions is the time-invariance of the relationships between predictors (e.g. coarse resolution GCM output) and the local-scale predictands (e.g. gridded observation-based time-series or weather station observations). However, in the absence of future observations, statistical downscaling studies use historical data to evaluate their models and assume that these historical simulation skills will be retained in the future. In addition, regression-based downscaling models fail to reproduce the observed variance, and hence their projections need to be adjusted accordingly. Two approaches are usually employed to perform this adjustment: randomization and variance inflation. Here, we study the effect of the stationarity assumption when downscaling daily maximum temperatures and using the downscaled information to estimate historical and future metrics like return periods and heat waves durations over Montreal, Canada; and the effect of the two variance adjustment techniques on the historical and future time-series. To do so, we used regional climate model (RCM) output from the Canadian RCM 4.2, as proxies of historical and future local climates, and daily maximum temperatures obtained from the Canadian GCM 3.1. The results show that the root-mean-squared errors between the pseudo-observations and the statistically downscaled time-series (historical and future) varied over time, with higher errors in the future period; and the effects of randomization and variance inflation on the tails of the statistically downscaled time-series.

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Uncertainty analysis of statistical downscaling methods

Cited by 312 publications

References 13 publications

Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling

Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling

Statistical downscaling of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River, China

Effects of variance adjustment techniques and time-invariant transfer functions on heat wave duration indices and other metrics derived from downscaled time-series. Study case: Montreal, Canada

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