Uncertainty of downscaling method in quantifying the impact of climate change on hydrology

Chen, Jie; Brissette, François; Leconte, Robert

doi:10.1016/j.jhydrol.2011.02.020

Cited by 587 publications

(348 citation statements)

References 40 publications

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“…Even though our evaluation illustrated two simple regression-type statistical downscaling models, the main conclusions may also be valid for more complicated models, like the nonlinear classification and regression models used by Gaitan et al (2014b) to downscale daily precipitation. Our results also corroborate the cautionary notes from Chen et al (2011) and Ouyang et al (2014) regarding the confidence that should be attributed to climate change impact studies based on only one downscaling method.…”

Section: Summary and Recommendationssupporting

confidence: 87%

“…For example, when comparing 28 climate change projections from seven GCMs and three scenarios, over Central Quebec, Chen et al (2011) concluded that the uncertainty envelopes from the downscaling methods were similar to the envelopes from the emission scenarios. Therefore, it is possible that if we add the (unaccounted) non-stationarity uncertainty to the downscaling uncertainty, this envelope could become the predominant one, as the regression-based statistical downscaling methods employed by Chen et al (2011) contributed significantly to the uncertainty envelope.…”

Section: Summary and Recommendationsmentioning

confidence: 99%

“…Anandhi et al 2008;Ahrens 2003;Chen et al 2011), biologists (e.g. Cossarini et al 2008Bucklin et al 2012;Saba et al 2012), agronomists, economists (e.g.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Summary and Recommendationssupporting

confidence: 87%

Section: Summary and Recommendationsmentioning

confidence: 99%

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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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“…dynamical) downscaling procedures. However, Chen et al (2011) claimed that there are many sources of uncertainty involved in climate change studies and the major sources of uncertainty are linked to GCM and greenhouse gasses emissions scenarios (GGES) in addition to the uncertainties that stem from a downscaling method. Therefore, to avoid the GCM and downscaling uncertainties and challenges, many studies (e.g., Tigkas et al 2012;Vangelis et al 2013;Reis et al 2016) proposed delta perturbation (change factor) concepts, in which plausible alterations in meteorological variables are assumed.…”

Section: Introductionmentioning

confidence: 99%

Adaptation Strategy to Mitigate the Impact of Climate Change on Water Resources in Arid and Semi-Arid Regions: a Case Study

Mohammed

Scholz

2017

Water Resour Manage

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Climate change and drought phenomena impacts have become a growing concern for water resources engineers and policy makers, mainly in arid and semi-arid areas. This study aims to contribute to the development of a decision support tool to prepare water resources managers and planners for climate change adaptation. The Hydrologiska Byråns Vattenbalansavdelning (The Water Balance Department of the Hydrological Bureau) hydrologic model was used to define the boundary conditions for the reservoir capacity yield model comprising daily reservoir inflow from a representative example watershed with the size of 14,924 km 2 into a reservoir with the capacity of 6.80 Gm 3 . The reservoir capacity yield model was used to simulate variability in climate change-induced differences in reservoir capacity needs and performance (operational probability of failure, resilience, and vulnerability). Owing to the future precipitation reduction and potential evapotranspiration increase during the worst case scenario (−40% precipitation and +30% potential evapotranspiration), substantial reductions in streamflow of between −56% and −58% are anticipated for the dry and wet seasons, respectively. Furthermore, model simulations recommend that as a result of future climatic conditions, the reservoir operational probability of failure would generally increase due to declined reservoir inflow. The study developed preparedness plans to combat the consequences of climate change and drought.

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“…The uncertainty of the climate impact studies can be attributed in the first place to the climate models and the emission scenarios. Recent studies (Chen et al, 2011;Stoll et al, 2011;Crosbie et al, 2011) showed that apart from the uncertainty of the climate models also the downscaling process contributes significantly to the uncertainty of the results of impact studies. The fraction of uncertainty related to the hydrological part Published by Copernicus Publications on behalf of the European Geosciences Union.…”

Section: Introductionmentioning

confidence: 99%

What can we learn from long-term groundwater data to improve climate change impact studies?

Stoll

Franssen

Barthel

et al. 2011

Hydrol. Earth Syst. Sci.

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Abstract. Future risks for groundwater resources, due to global change are usually analyzed by driving hydrological models with the outputs of climate models. However, this model chain is subject to considerable uncertainties. Given the high uncertainties it is essential to identify the processes governing the groundwater dynamics, as these processes are likely to affect groundwater resources in the future, too. Information about the dominant mechanisms can be achieved by the analysis of long-term data, which are assumed to provide insight in the reaction of groundwater resources to changing conditions (weather, land use, water demand). Referring to this, a dataset of 30 long-term time series of precipitation dominated groundwater systems in northern Switzerland and southern Germany is collected. In order to receive additional information the analysis of the data is carried out together with hydrological model simulations. High spatio-temporal correlations, even over large distances could be detected and are assumed to be related to large-scale atmospheric circulation patterns. As a result it is suggested to prefer innovative weather-type-based downscaling methods to other stochastic downscaling approaches. In addition, with the help of a qualitative procedure to distinguish between meteorological and anthropogenic causes it was possible to identify processes which dominated the groundwater dynamics in the past. It could be shown that besides the meteorological conditions, land use changes, pumping activity and feedback mechanisms governed the groundwater dynamics. Based on these findings, recommendations to improve climate change impact studies are suggested.

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Uncertainty of downscaling method in quantifying the impact of climate change on hydrology

Cited by 587 publications

References 40 publications

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

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

Adaptation Strategy to Mitigate the Impact of Climate Change on Water Resources in Arid and Semi-Arid Regions: a Case Study

What can we learn from long-term groundwater data to improve climate change impact studies?

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