Which downscaled rainfall data for climate change impact studies in urban areas? Review of current approaches and trends

Bi, Eustache Gooré; Gachon, Philippe; Vrac, Mathieu; Monette, Frédéric

doi:10.1007/s00704-015-1656-y

Cited by 34 publications

(10 citation statements)

References 105 publications

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“…Regression analyses are powerful tools for the determination of temporal precipitation trends at station locations. As with the Mann-Kendall test, above, Bi et al (2015) provide further examples of research that uses regression analyses in their review of precipitation trend studies. This study differs in that it performs additional regression analyses for the spatial interpolation of values between stations with the use of physiographic parameters.…”

Section: Discussionmentioning

confidence: 99%

Extreme precipitation time trends in Ontario, 1960–2010

Soulis

Sarhadi

Tinel

et al. 2016

Hydrological Processes

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The impact of climate change on the behaviour of intensity–duration–frequency curves is critical to the estimation of design storms, and thus to the safe design of drainage infrastructure. The present study develops a regional time trend methodology that detects the impact of climate change on extreme precipitation from 1960 to 2010. The regional time trend linear regression method is fitted to different durations of annual maximum precipitation intensities derived from multiple sites in Ontario, Canada. The results show the relationship between climate change and increased extreme precipitation in this province. The regional trend analysis demonstrates, under nonstationary conditions arising from climate change, that the intensity of extreme precipitation increased decennially between 1.25% for the 30‐min storm and 1.82% for the 24‐h storm. A comparison of the results with a regional Mann–Kendall test validates the found regional time‐trend results. The results are employed to extrapolate the intensity–duration–frequency curves temporally and spatially for future decades across the province. The results of the regional time trend assessment help with the establishment of new safety margins for infrastructure design in Ontario. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Discussionmentioning

confidence: 99%

Extreme precipitation time trends in Ontario, 1960–2010

Soulis

Sarhadi

Tinel

et al. 2016

Hydrological Processes

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“…An observation-based cross-validation is applied to evaluate the (Räty et al, 2014;Schmith et al, 2021). In the observation-based cross-validation, also called the holdout method (Piani et al, 2010;Dosio and Paruolo, 2011) and perfect predictor experiment (Maraun et al, 2019a, b), observations are regarded as being pseudoclimate model data. The validation period is defined from 1971 to 2000, which is the same as the historical period of the GCMs.…”

Section: Validation Of Statistical Downscaling Methodsmentioning

confidence: 99%

“…Several research indicators can be used to assess the statistical downscaling methods for the impact analysis of climate change on drought. In combination with total precipitation (water supply), one could consider evapotranspiration (water demand) to assess dryness (Greve et al, 2019;Tabari, 2020) and water availability (Tabari et al, 2015;Konapala et al, 2020). Furthermore, additional indicators can be used to study dry spells.…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

“…To meet the demand of high spatiotemporal results for the hydrological impact analysis of climate change, the use of statistical downscaling methods has recently increased (e.g. Sunyer et al, 2015;Onyutha et al, 2016;Gooré Bi et al, 2017;Smid and Costa, 2018;Van Uytven, 2019;De Niel et al, 2019;Hosseinzadehtalaei et al, 2020). The results of statistical downscaling methods are, nevertheless, often compromised with bias and limitations due to assumptions and approximations made within each method (Trzaska and Schnarr, 2014;Maraun et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Comparison of statistical downscaling methods for climate change impact analysis on precipitation-driven drought

Tabari

Paz

Buekenhout

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. General circulation models (GCMs) are the primary tools for evaluating the possible impacts of climate change; however, their results are coarse in temporal and spatial dimensions. In addition, they often show systematic biases compared to observations. Downscaling and bias correction of climate model outputs is thus required for local applications. Apart from the computationally intensive strategy of dynamical downscaling, statistical downscaling offers a relatively straightforward solution by establishing relationships between small- and large-scale variables. This study compares four statistical downscaling methods of bias correction (BC), the change factor of mean (CFM), quantile perturbation (QP) and an event-based weather generator (WG) to assess climate change impact on drought by the end of the 21st century (2071–2100) relative to a baseline period of 1971–2000 for the weather station of Uccle located in Belgium. A set of drought-related aspects is analysed, i.e. dry day frequency, dry spell duration and total precipitation. The downscaling is applied to a 28-member ensemble of Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs, each forced by four future scenarios of SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5. A 25-member ensemble of CanESM5 GCM is also used to assess the significance of the climate change signals in comparison to the internal variability in the climate. A performance comparison of the downscaling methods reveals that the QP method outperforms the others in reproducing the magnitude and monthly pattern of the observed indicators. While all methods show a good agreement on downscaling total precipitation, their results differ quite largely for the frequency and length of dry spells. Using the downscaling methods, dry day frequency is projected to increase significantly in the summer months, with a relative change of up to 19 % for SSP5–8.5. At the same time, total precipitation is projected to decrease significantly by up to 33 % in these months. Total precipitation also significantly increases in winter, as it is driven by a significant intensification of extreme precipitation rather than a dry day frequency change. Lastly, extreme dry spells are projected to increase in length by up to 9 %.

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“…To meet the demand of high spatiotemporal results for the hydrological impact analysis of climate change, the use of statistical downscaling methods (SDMs) has recently increased (e.g., Sunyer et al, 2015;Gooré Bi et al, 2017;Smid and https://doi.org/10.5194/hess-2020-506 Preprint. Discussion started: 19 October 2020 c Author(s) 2020.…”

Section: Introductionmentioning

confidence: 99%

Comparison of statistical downscaling methods for climate change impact analysis on drought

Tabari

Buekenhout

Willems

2020

Preprint

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Abstract. General circulation models (GCMs) are the primary tools to evaluate the possible impacts of climate change; however, their results are coarse in temporal and spatial dimensions. In addition, they often show systematic biases compared to observations. Downscaling and bias correction of climate model outputs is thus required for local applications. Besides the computationally intensive strategy of dynamical downscaling, statistical downscaling offers a relatively straightforward solution by establishing relationships between small and large scale variables. This study compares four statistical downscaling methods (SDMs) of bias correction (BC), change factor of mean (CFM), quantile perturbation (QP) and event based weather generator (EBWG) to assess climate change impact on drought by the end of the 21st century (2071–2100) relative to a baseline period of 1971–2000. A set of drought related aspects is analysed: dry day frequency, dry spell duration and total precipitation. The downscaling is applied to a 14-member ensemble of CMIP6 GCMs, each powered by four future scenarios of SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5. A 25-member ensemble of CanESM5 GCM is also used to assess the significance of the climate change signals in comparison to the internal variability of the climate. While all methods show a good agreement on downscaling total precipitation, the CFM method fails to downscale dry day frequency well. The QP method outperforms the others in downscaling dry spells. Using this method, dry day frequency is projected to increase significantly in the summer months, with relative changes of up to 20.4 % in the worst-case climate change scenario. At the same time, total precipitation is projected to decrease significantly by up to 41.9 % in these months. Lastly, extreme dry spells are projected to increase in length by up to 7.4 %.

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Which downscaled rainfall data for climate change impact studies in urban areas? Review of current approaches and trends

Cited by 34 publications

References 105 publications

Extreme precipitation time trends in Ontario, 1960–2010

Extreme precipitation time trends in Ontario, 1960–2010

Comparison of statistical downscaling methods for climate change impact analysis on precipitation-driven drought

Comparison of statistical downscaling methods for climate change impact analysis on drought

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