Selection of Bias Correction Methods to Assess the Impact of Climate Change on Flood Frequency Curves

Soriano, Enrique; Mediero, Luis; Garijo, Carlos

doi:10.3390/w11112266

Cited by 44 publications

(27 citation statements)

References 25 publications

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“…In order to correct this and to improve the performance of the model, the rainfall dataset for HadGEM2-ES was bias-corrected. Bias correction is the process of scaling climate model outputs to account for their systematic errors, in order to improve their fitting to observations [35]. The evaluation of other climatic parameters shows satisfactory results as the biases are close to zero and NSE is greater than 0.7, so they were not bias corrected.…”

Section: Bias Correction Of Projected Rainfallmentioning

confidence: 99%

Modelling Maize Yield and Water Requirements under Different Climate Change Scenarios

Durodola

Mourad

2020

Climate

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African countries such as Nigeria are anticipated to be more susceptible to the impacts of climate change due to large dependence on rainfed agriculture and to several uncertainties in the responses of crop production to climate change. The impacts of climate change on crop water requirements (CWR), irrigation water requirements (IWR), yields and crop water productivity (CWP) of rainfed maize in Ogun-Osun River Basin, Nigeria were evaluated for a baseline period (1986–2015) and future projection period (2021–2099) under Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios. For the baseline period, there is no significant trend within the variables studied. However, IWR is projected to increase significantly by up to 140% in the future period, while yield might likely decline under both scenarios up to −12%. This study shows that in the future periods, supplemental irrigation has little impact in improving yields, but an increase in soil fertility can improve yields and CWP by up to 80% in 2099. This paper offers useful information on suitable adaptation measures which could be implemented by stakeholders and policymakers to counterbalance the effects of climate change on crop production.

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Section: Bias Correction Of Projected Rainfallmentioning

confidence: 99%

Modelling Maize Yield and Water Requirements under Different Climate Change Scenarios

Durodola

Mourad

2020

Climate

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“…Similar studies have identified climate models as being the dominant source (e.g. Sulis et al, 2012;Hattermann et al, 2018). Indeed, Addor et al (2014) highlight that there seems to be a general agreement in the literature on the dominant contribution of climate models to the uncertainty in discharge projections.…”

Section: Discussionmentioning

confidence: 53%

Uncertainties and their interaction in flood hazard assessment with climate change

Meresa

Murphy

Fealy

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. The assessment of future impacts of climate change is associated with a cascade of uncertainty linked to the modelling chain employed in assessing local-scale changes. Understanding and quantifying this cascade is essential for developing effective adaptation actions. We evaluate and quantify uncertainties in future flood quantiles associated with climate change for four catchments, incorporating within our modelling chain uncertainties associated with 12 global climate models contained in the Coupled Model Intercomparison Project Phase 6, five different bias correction approaches, hydrological model parameter uncertainty and the use of three different extreme value distributions for flood frequency analysis. Results indicate increased flood hazard in all catchments for different Shared Socioeconomic Pathways (SSPs), with changes in flooding consistent with changes in annual maximum precipitation. We use additive chains and analysis of variance (ANOVA) to quantify and decompose uncertainties and their interactions in estimating selected flood quantiles for each catchment. We find that not only do the contributions of different sources of uncertainty vary by catchment, but that the dominant sources of uncertainty can be very different on a catchment-by-catchment basis. While uncertainties in future projections are widely assumed to be dominated by the ensemble of climate models used, we find that in one of our catchments uncertainties associated with bias correction methods dominate, while in another the uncertainty associated with the use of different extreme value distributions outweighs the uncertainty associated with the ensemble of climate models. These findings highlight the inability to generalise a priori about the importance of different components of the cascade of uncertainty in future flood hazard at the catchment scale. Moreover, we find that the interaction of components of the modelling chain employed are substantial (> 20 % of overall uncertainty in two catchments). While our sample is small, there is evidence that the dominant components of the cascade of uncertainty may be linked to catchment characteristics and rainfall–runoff processes. Future work that seeks to further explore the characteristics of the uncertainty cascade as they relate to catchment characteristics may provide insight into a priori identifying the key components of modelling chains to be targeted in climate change impact assessments.

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“…Joseph et al (2018) also considered hydrological parameters (VIC) and climate models (RCP) uncertainty assessment in seasonal flow projections. Soriano et al (2019) evaluated the uncertainty of bias correction methods on flood frequency and found that the climate model's bias correction is significant and brought significant change in the magnitude of flood design values. Feng and Beighley (2020) also identified the uncertainties from both climate forcings and hydrologic model components as well as associated parameterization.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, the biases correction of GCMs output was applied on a grid commensurate with daily climate model output. Nowadays, many researchers have been used and addressed the importance of climate bias correction methods (Kay et al 2009;Saini et al 2015;Soriano et al 2019). Quantile mapping, empirical mapping, simple statistical transformation, and joint probability are the most popular and widely applied for climate model bias correction and related to observed climate time series.…”

Section: Introductionmentioning

confidence: 99%

Climate change impact on extreme precipitation and peak flood magnitude and frequency: observations from CMIP6 and hydrological models

Meresa

Tischbein

Mekonnen³

2022

Nat Hazards

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Changes in climate intensity and frequency, including extreme events, heavy and intense rainfall, have the greatest impact on water resource management and flood risk management. Significant changes in air temperature, precipitation, and humidity are expected in future due to climate change. The influence of climate change on flood hazards is subject to considerable uncertainty that comes from the climate model discrepancies, climate bias correction methods, flood frequency distribution, and hydrological model parameters. These factors play a crucial role in flood risk planning and extreme event management. With the advent of the Coupled Model Inter-comparison Project Phase 6, flood managers and water resource planners are interested to know how changes in catchment flood risk are expected to alter relative to previous assessments. We examine catchment-based projected changes in flood quantiles and extreme high flow events for Awash catchments. Conceptual hydrological models (HBV, SMART, NAM and HYMOD), three downscaling techniques (EQM, DQM, and SQF), and an ensemble of hydrological parameter sets were used to examine changes in peak flood magnitude and frequency under climate change in the mid and end of the century. The result shows that projected annual extreme precipitation and flood quantiles could increase substantially in the next several decades in the selected catchments. The associated uncertainty in future flood hazards was quantified using aggregated variance decomposition and confirms that climate change is the dominant factor in Akaki (C2) and Awash Hombole (C5) catchments, whereas in Awash Bello (C4) and Kela (C3) catchments bias correction types is dominate, and Awash Kuntura (C1) both climate models and bias correction methods are essential factors. For the peak flow quantiles, climate models and hydrologic models are two main sources of uncertainty (31% and 18%, respectively). In contrast, the role of hydrological parameters to the aggregated uncertainty of changes in peak flow hazard variable is relatively small (5%), whereas the flood frequency contribution is much higher than the hydrologic model parameters. These results provide useful knowledge for policy-relevant flood indices, water resources and flood risk control and for studies related to uncertainty associated with peak flood magnitude and frequency.

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Selection of Bias Correction Methods to Assess the Impact of Climate Change on Flood Frequency Curves

Cited by 44 publications

References 25 publications

Modelling Maize Yield and Water Requirements under Different Climate Change Scenarios

Modelling Maize Yield and Water Requirements under Different Climate Change Scenarios

Uncertainties and their interaction in flood hazard assessment with climate change

Climate change impact on extreme precipitation and peak flood magnitude and frequency: observations from CMIP6 and hydrological models

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