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/ecws-3-05809

Cited by 8 publications

(10 citation statements)

References 6 publications

(7 reference statements)

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“…This concern introduces larger uncertainty when high return periods T are considered. Such studies point out future changes in flood regimes affecting not only frequency and magnitude of floods, but also a shift in their timing, which might increase the seasonality effects in statistical terms [60][61][62][63].…”

Section: Discussionmentioning

confidence: 99%

Effect of Seasonality on the Quantiles Estimation of Maximum Floodwater Levels in a Reservoir and Maximum Outflows

Aranda

García-Bartual

2020

Water

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Certain relevant variables for dam safety and downstream safety assessments are analyzed using a stochastic approach. In particular, a method to estimate quantiles of maximum outflow in a dam spillway and maximum water level reached in the reservoir during a flood event is presented. The hydrological system analyzed herein is a small mountain catchment in north Spain, whose main river is a tributary of Ebro river. The ancient Foradada dam is located in this catchment. This dam has no gates, so that flood routing operation results from simple consideration of fixed crest spillway hydraulics. In such case, both mentioned variables (maximum outflow and maximum reservoir water level) are basically derived variables that depend on flood hydrograph characteristics and the reservoir’s initial water level. A Monte Carlo approach is performed to generate very large samples of synthetic hydrographs and previous reservoir levels. The use of extreme value copulas allows the ensembles to preserve statistical properties of historical samples and the observed empirical correlations. Apart from the classical approach based on annual periods, the modelling strategy is also applied differentiating two subperiods or seasons (i.e., summer and winter). This allows to quantify the return period distortion introduced when seasonality is ignored in the statistical analysis of the two relevant variables selected for hydrological risk assessment. Results indicate significant deviations for return periods over 125 years. For the analyzed case study, ignoring seasonal statistics and trends, yields to maximum outflows underestimation of 18% for T = 500 years and 29% for T = 1000 years were obtained.

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Section: Discussionmentioning

confidence: 99%

Effect of Seasonality on the Quantiles Estimation of Maximum Floodwater Levels in a Reservoir and Maximum Outflows

Aranda

García-Bartual

2020

Water

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“…In the Barrios de Luna, Porma and Riaño catchments, the polynomial bias correction method is selected. However, in Camporredondo, the linear correction provides the best results (Soriano et al 2019).…”

Section: Bias Correctionmentioning

confidence: 97%

“…The results provided by the 12 combinations of GCMs and RCMs of the EURO-CORDEX programme in the four catchments selected as case studies present significant biases in the control period compared with the observations in the same period. Soriano et al (2019) identified the best bias correction techniques for precipitation and temperature projections in the four catchments that have been applied in this study. The daily temperature projections in the control and future periods have been corrected by using a monthly bias correction that depends on the climate model considered (Eq.…”

Section: Bias Correctionmentioning

confidence: 99%

“…The best combination of bias correction methodologies was selected by considering the flood quantiles in the control period (Soriano et al 2019). The benefits of correcting the bias in temperature and rainfall time series are shown in Figs.…”

Section: Bias Correctionmentioning

confidence: 99%

“…Distribution mapping to correct the distribution function of climate values to fit observations by using a transfer function has been used to reduce bias errors (Boe et al 2007). Soriano et al (2018) identified the best bias correction method to improve the flood frequency curve fitting in the control period in four catchments of the River Duero Basin. Such study concluded that the best bias correction methodologies are the monthly mean correction for temperature series and polynomial quantile mapping correction for precipitation series.…”

Section: Introductionmentioning

confidence: 99%

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Quantification of Expected Changes in Peak Flow Quantiles in Climate Change by Combining Continuous Hydrological Modelling with the Modified Curve Number Method

Soriano

Mediero

Garijo

2020

Water Resour Manage

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Climate projections point to modifications in the magnitude, frequency and timing of floods in the future. However, robust methodologies to quantify how climate change will modify the catchment response in flood events are required. Continuous hydrological modelling usually smooth magnitudes of extreme events. This paper proposes a methodology to improve the assessment of flood changes in the future driven by climate change. Climate change projections of the EURO-CORDEX programme obtained under the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) supplied are used. Four catchments located on the Douro River Basin have been considered as case studies. Precipitation and temperature projections have been bias corrected to reduce errors with observations in the control period (1971-2004). The HBV continuous hydrological simulation model has been used to simulate the soil moisture content on the day of occurrence of the maximum annual rainfalls in the four catchments. The modified curve number method has been utilized to obtain the changes expected in the future in flood magnitudes, considering the initial soil moisture contents estimated with the HBV model and the expected changes in annual maximum rainfalls. The methodology has been applied to the control period (1971-2004) to check the validity of the process. Then, the methodology has been applied to the future period (2011-2095), to quantify the changes expected in the future in flood magnitudes under climate change conditions. The combined use of the HBV continuous hydrological simulation with the modified curve number method improves the results provided by the HBV model. The proposed methodology allows a better characterization of the response of catchments in flood events. It also considers the expected variation in the antecedent moisture content in catchments in the future, driven by increasing temperatures and decreasing mean annual precipitations in the future. The results show that flood quantiles will increase in three of the four catchments considered.

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On future flood magnitudes and estimation uncertainty across 151 catchments in mainland China

Yin

Wang

et al. 2020

Intl Journal of Climatology

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As atmospheric moisture holding capacity is positively dependent on temperatures, a large intensification of precipitation extremes is projected under foreseeable climate warming. Flooding that is mainly attributed to extreme storms usually accounts for an ambitious target in weather-related hazard mitigation over China. Previous works seldom focused on flooding evolution patterns under climate change at a national scale, and fewer flooding projections considered the estimation uncertainty sourced from limited samples. This study systematically projected changes in flood quantiles based on annual maximum series and seasonality and also evaluated the variations of sampling uncertainty for 151 catchments over mainland China under the emission scenario of representative concentration pathway (RCP) 8.5. In order to project future streamflow series, the bias-corrected outputs of six global climate models (GCMs) were input into a best-performing hydrological model, which was selected from four calibrated hydrological models based on the KGE criteria. The Pearson type-III (P-III) distribution and L-moments (L-M) method were employed to derive the flood quantiles for different return periods during historical (1961-2005) and future (2056-2100) periods, and the bootstrapping method was applied to estimate the sampling uncertainty. A regression trend method was used to track the variations of flood seasonality in the context of climate warming. Our results project earlier flood timing and larger flood quantiles for most catchments in future period than those in the historical period, despite being accompanied by substantial spatial variations. We also project that the sampling uncertainty in estimating flood quantiles is increased in a warming future. Many catchments are exposed to dramatic changes in both flood quantile and estimation uncertainty by over 50%, while only a few catchments are projected to have decreasing flood risks. These results suggest an urgent need to improve the functionality of early warning systems and increase societal resilience to warming climates over China.

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

Cited by 8 publications

References 6 publications

Effect of Seasonality on the Quantiles Estimation of Maximum Floodwater Levels in a Reservoir and Maximum Outflows

Effect of Seasonality on the Quantiles Estimation of Maximum Floodwater Levels in a Reservoir and Maximum Outflows

Quantification of Expected Changes in Peak Flow Quantiles in Climate Change by Combining Continuous Hydrological Modelling with the Modified Curve Number Method

On future flood magnitudes and estimation uncertainty across 151 catchments in mainland China

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