Using raw regional climate model outputs for quantifying climate change impacts on hydrology

Chen, Jie; Brissette, François; Liu, Pan; Xia, Jun

doi:10.1002/hyp.11368

Cited by 18 publications

(13 citation statements)

References 66 publications

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“…It is a common practice to bias correct RCM data to ensure that calibrated hydrological models are driven by realistic meteorological conditions (Muerth et al 2013). However, there is some debate as to whether an RCM output should be, or not be, bias-corrected prior to drive a hydrological model, as bias correction may introduce further uncertainty into future hydrological simulations (Chen et al 2017;Clark et al 2016). Therefore, raw RCM outputs may be preferred to drive hydrological models for some applications, as when Lucas-Picher et al (2015) reconstructed the Richelieu River flooding of spring 2011, one of the most important flood that occurred in Québec over the last years.…”

Section: Introductionmentioning

confidence: 99%

The ClimEx Project: A 50-Member Ensemble of Climate Change Projections at 12-km Resolution over Europe and Northeastern North America with the Canadian Regional Climate Model (CRCM5)

Leduc

Mailhot

Frigon

et al. 2019

Journal of Applied Meteorology and Climatology

102

158

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The Canadian Regional Climate Model (CRCM5) Large Ensemble (CRCM5-LE) consists of a dynamically downscaled version of the CanESM2 50-member initial-conditions ensemble (CanESM2-LE). The downscaling was performed at 12-km resolution over two domains, Europe (EU) and northeastern North America (NNA), and the simulations extend from 1950 to 2099, following the RCP8.5 scenario. In terms of validation, warm biases are found over the EU and NNA domains during summer, whereas during winter cold and warm biases appear over EU and NNA, respectively. For precipitation, simulations are generally wetter than the observations but slight dry biases also occur in summer. Climate change projections for 2080–99 (relative to 2000–19) show temperature changes reaching 8°C in summer over some parts of Europe, and exceeding 12°C in northern Québec during winter. For precipitation, central Europe will become much dryer during summer (−2 mm day−1) and wetter during winter (>1.2 mm day−1). Similar changes are observed over NNA, although summer drying is not as prominent. Projected changes in temperature interannual variability were also investigated, generally showing increasing and decreasing variability during summer and winter, respectively. Temperature variability is found to increase by more than 70% in some parts of central Europe during summer and to increase by 80% in the northernmost part of Québec during the month of May as the snow cover becomes subject to high year-to-year variability in the future. Finally, CanESM2-LE and CRCM5-LE are compared with respect to extreme precipitation, showing evidence that the higher resolution of CRCM5-LE allows a more realistic representation of local extremes, especially over coastal and mountainous regions.

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Section: Introductionmentioning

confidence: 99%

The ClimEx Project: A 50-Member Ensemble of Climate Change Projections at 12-km Resolution over Europe and Northeastern North America with the Canadian Regional Climate Model (CRCM5)

Leduc

Mailhot

Frigon

et al. 2019

Journal of Applied Meteorology and Climatology

102

158

View full text Add to dashboard Cite

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“…Future climate variables coupled with hydrological models are used worldwide to assess the climate change impacts on water resources at river basin scale (e.g., Burlando & Rosso, ; Chen, Brissette, Liu, & Xia, ; Sato, Kojiri, Michihiro, Suzuki, & Nakakita, ; Teutschbein & Seibert, ; Vezzoli, Mercogliano, Pecora, Zollo, & Cacciamani, ; Yu & Wang, ). In almost all these studies, the uncertainty in the climate input and, hence, in the results arises, recommending the use of several climate projections (combination of GCMs, RCMs, and forcing scenarios) to obtain a range of likely future conditions (Hunt, Walker, Westenbroek, Hay, & Markstrom, ; Teutschbein & Seibert, ).…”

Section: Introductionmentioning

confidence: 99%

Quantifying the impacts of climate change on water resources in northern Tuscany, Italy, using high‐resolution regional projections

D’Oria

Ferraresi

Tanda

2019

Hydrological Processes

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This paper investigates the potential impacts of climate change on water resources in northern Tuscany, Italy. A continuous hydrological model for each of the seven river basins within the study area was calibrated using historical data. The models were then driven by downscaled and bias‐corrected climate projections of an ensemble of 13 regional climate models (RCMs), under two different scenarios of representative concentration pathway (RCP4.5 and RCP8.5). The impacts were examined at medium term (2031–2040) and long term (2051–2060) in comparison with a reference period (2003–2012); the changes in rainfall, streamflow, and groundwater recharge were investigated. A high degree of uncertainty characterized the results with a significant intermodel variability, the period being equal. For the sake of brevity, only the results for the Serchio River basin were presented in detail. According to the RCM ensemble mean and the RCP4.5, a moderate decrease in rainfall, with reference to 2003–2012, is expected at medium term (−0.6%) and long term (−2.8%). Due to the warming of the study area, the reduction in the streamflow volume is two times the precipitation decrease (−1.1% and −6.8% at medium and long term, respectively). The groundwater recharge is mainly affected by the changes in climate with expected percolation volume variations of −3.3% at 2031–2040 and −8.1% at 2051–2060. The impacts on the Serchio River basin water resources are less significant under the RCP8.5 scenario. The presence of artificial structures, such as dam‐reservoir systems, can contribute to mitigate the effects of climate change on water resources through the implementation of appropriate regulation strategies.

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“…This may be because the bias of intervariable correlation simulated by climate models is not stationary, and the observed intervariable correlation itself is also not invariable. Previous studies (Chen et al, 2017; Hui et al, 2018; Maraun, 2012) have shown that bias correction methods can deteriorate the original climate simulations when bias directions are different between future and historical periods (or calibration and validation periods) or when future biases reduce to less than half the calibration biases. The nonstationarity of intervariable correlation bias of climate models can be observed in Figure S7.…”

Section: Discussionmentioning

confidence: 99%

Impacts of Using State‐of‐the‐Art Multivariate Bias Correction Methods on Hydrological Modeling Over North America

Guo

Chen

Zhang

et al. 2020

Water Resources Research

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Bias correction techniques are widely used to bridge the gap between climate model outputs and input requirements of hydrological models to assess the climate change impacts on hydrology. In addition to univariate bias correction methods, several multivariate bias correction methods were proposed recently, which can not only correct the biases in marginal distributions of individual climate variables but also properly adjust the biased intervariable correlations simulated by climate models. Due to the diversities of climate regime and climate model bias, hydrological simulation for watersheds under different climate conditions may show various sensitivities to the correction of intervariable correlations. Therefore, it is of great importance to investigate (1) whether the correction of intervariable correlations has impacts on the hydrological modeling and (2) how these impacts vary with watersheds under different climate conditions. To achieve these goals, this study evaluates behaviors and their spatial variability of multiple state-of-the-art multivariate bias correction methods in hydrological modeling over 2,840 watersheds distributed in different climate regimes in North America. The results show that, compared to using a quantile mapping univariate bias correction method, applying multivariate methods can improve the simulation of snow proportion, snowmelt, evaporation, and several streamflow variables. In addition, this improvement is more clear for watersheds with arid and warm temperate climates in southern regions, while it is limited for northern snow-characterized watersheds. Overall, this study demonstrates the importance of using multivariate bias correction methods instead of univariate methods in hydrological climate change impact studies, especially for watersheds with arid and warm temperate climates.

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Using raw regional climate model outputs for quantifying climate change impacts on hydrology

Cited by 18 publications

References 66 publications

The ClimEx Project: A 50-Member Ensemble of Climate Change Projections at 12-km Resolution over Europe and Northeastern North America with the Canadian Regional Climate Model (CRCM5)

The ClimEx Project: A 50-Member Ensemble of Climate Change Projections at 12-km Resolution over Europe and Northeastern North America with the Canadian Regional Climate Model (CRCM5)

Quantifying the impacts of climate change on water resources in northern Tuscany, Italy, using high‐resolution regional projections

Impacts of Using State‐of‐the‐Art Multivariate Bias Correction Methods on Hydrological Modeling Over North America

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