Impact of bias correction and downscaling through quantile mapping on simulated climate change signal: a case study over Central Italy

Sangelantoni, Lorenzo; Russo, Aniello; Gennaretti, Fabio

doi:10.1007/s00704-018-2406-8

Cited by 34 publications

(26 citation statements)

References 42 publications

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“…, 99) and then interpolated to adjust all the physical values (not only those corresponding to the quantiles where the function has been derived). In this case, it is used in a 3-hourly configuration of a purely empirical approach as in References [27,35,49,50,[71][72][73], where a daily-based QM configuration was used.…”

Section: Methods and Statistical Bias Correction Of The Climate Simulmentioning

confidence: 99%

“…However, even though the post-processing techniques produce simulations' statistical properties close to the observed climate, they can potentially modify original simulations of the Climate Change Signal (CCS, i.e., difference/ratio between climate statistics of a future and a historical temporal segment). The modification of the original CCS implicitly alters the results of impact models [27][28][29][30][31][32][33][34][35]. The plausibility of letting bias correction alter the original simulated CCS is currently strongly debated.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, we derived the QM correction functions considering observed products at a station scale. This application implicitly takes into account systematic biases and the representativeness error arising from the spatial-scale gap between simulations and observations such as in References [26,34,35,[49][50][51]. We take advantage of five high-resolution (~12 km) climate simulations provided by the Euro-CORDEX initiative [52].…”

Section: Introductionmentioning

confidence: 99%

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On the Use of Original and Bias-Corrected Climate Simulations in Regional-Scale Hydrological Scenarios in the Mediterranean Basin

et al. 2019

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The response of Mediterranean small catchments hydrology to climate change is still relatively unexplored. Regional Climate Models (RCMs) are an established tool for evaluating the expected climate change impact on hydrology. Due to the relatively low resolution and systematic errors, RCM outputs are routinely and statistically post-processed before being used in impact studies. Nevertheless, these techniques can impact the original simulated trends and then impact model results. In this work, we characterize future changes of a small Apennines (Central Italy) catchment hydrology, according to two radiative forcing scenarios (Representative Concentration Pathways, RCPs, 4.5 and 8.5). We also investigate the impact of a widely used bias correction technique, the empirical Quantile Mapping (QM) on the original Climate Change Signal (CCS), and the subsequent alteration of the original Hydrological Change Signal (HCS). Original and bias-corrected simulations of five RCMs from Euro-CORDEX are used to drive the CETEMPS hydrological model CHyM. HCS is assessed by using monthly mean discharge and a hydrological-stress index. HCS shows a large spatial and seasonal variability where the summer results are affected by the largest decrease of mean discharge (down to −50%). QM produces a small alteration of the original CCS, which generates a generally wetter HCS, especially during the spring season.

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Section: Methods and Statistical Bias Correction Of The Climate Simulmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the Use of Original and Bias-Corrected Climate Simulations in Regional-Scale Hydrological Scenarios in the Mediterranean Basin

et al. 2019

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“…Hagemann et al (2011), investigating hydrological changes globally, has noted that for some regions the magnitude of change in climate change signal due to bias correction can be greater than the magnitude of the signal itself, such that bias correction uncertainty can be as large as climate model uncertainty. Several subsequent studies have investigated how bias correction modifies the 5 rainfall climate change signal (Dosio et al, 2012;Ivanov et al, 2018;Mbaye et al, 2016;Potter et al, 2018;Sangelantoni et al, 2018;Themeßl et al, 2012). Themeßl et al (2012) concluded that QQM-BC is likely to improve the reliability of projected changes if the climate model biases are related to the shape of the distribution i.e.…”

Section: Catchment Scale Simulationsmentioning

confidence: 99%

“…There is also debate and uncertainty due to the modifications in the climate change signal (CCS) caused by the bias 10 correction (Ivanov and Kotlarski, 2017;Hagemann et al, 2011;Dosio et al, 2012;Themeßl et al, 2012;Velázquez et al, 2015;Mbaye et al, 2016;Switanek et al, 2017;Ivanov et al, 2018;Sangelantoni et al, 2018). Teng et al (2015) determined that bias correction altered the change signal for many characteristics, including high rainfall amounts, which had a significant impact on simulated runoff and particularly high flows.…”

Section: Introductionmentioning

confidence: 99%

Impact of downscaled rainfall biases on projected runoff changes

Charles¹,

Chiew²,

Potter³

et al. 2019

Preprint

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Realistic projections of changes to daily rainfall frequency and magnitude, at catchment scales, are required to assess the potential impacts of climate change on regional water supply. We show that quantile-quantile matched (QQM) bias-corrected daily rainfall from dynamically downscaled WRF simulations of current climate produce biased hydrological 10 simulations, in a case study for the State of Victoria, Australia (237,629 km 2 ). While the QQM bias correction can remove bias in daily rainfall distributions at each 10 km 2 grid point across Victoria, the GR4J rainfall-runoff model underestimates runoff when driven with QQM bias-corrected daily rainfall. We compare simulated runoff differences using bias-corrected and empirically scaled rainfall for several key water supply catchments across Victoria and discuss the implications for confidence in the magnitude of projected changes for mid-century. Our results highlight the imperative for methods that can 15 correct for temporal and spatial biases in dynamically downscaled daily rainfall if they are to be suitable for hydrological projection.

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RoCliB– bias‐corrected CORDEX RCMdataset over Romania

Dumitrescu

Amihăesei

Cheval

2022

Geoscience Data Journal

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Four climate parameters (i.e. maximum, mean and minimum air temperature and precipitation amount) from 10 regional climate models, provided by the EURO‐CORDEX initiative, are adjusted using as reference the ROCADA gridded dataset. The adjustment was performed on a daily temporal resolution for the historical period (1971–2005), as well as for climate change scenarios based on two Representative Concentration Pathways (RCP4.5 and RCP8.5). The most accurately method for bias‐correction (BC) was selected following a 2‐fold cross‐validation approach, which was performed on historical data using two methods: Quantile Mapping (QMAP) and Multivariate Bias Correction with N‐dimensional probability (MBCn). The performances of the two methods are very similar when analysing the frequency distribution of each selected variable, whereas the comparison between (1) the intervariables correlation of the adjusted datasets, and (2) the reference dataset revealed much smaller differences for the dataset adjusted with the multivariate method, hence this was used for producing the BC scenario dataset. Based on the MBCn adjusted dataset, a climate change analysis over Romania was tested at the seasonal and annual scale. Overall, for the multimodel ensemble mean, at the country level, a substantial temperature increase is reported for both scenarios and no significant trend is revealed for precipitation amount. The adjusted RCMs are provided without any restrictions via an open‐access repository in netCDF CF‐1.4‐compliant file format. The BC climate models are archived at the 0.1° spatial resolution, in the WGS‐84 coordinate system, at a daily temporal resolution.

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Impact of bias correction and downscaling through quantile mapping on simulated climate change signal: a case study over Central Italy

Cited by 34 publications

References 42 publications

On the Use of Original and Bias-Corrected Climate Simulations in Regional-Scale Hydrological Scenarios in the Mediterranean Basin

On the Use of Original and Bias-Corrected Climate Simulations in Regional-Scale Hydrological Scenarios in the Mediterranean Basin

Impact of downscaled rainfall biases on projected runoff changes

RoCliB– bias‐corrected CORDEX RCMdataset over Romania

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