Impact of Bias-Correction Methods in Assessing the Potential Flood Frequency Change in the Bago River

The increasing flood risks in the Bago River due to rapid urbanization and climate change have great implications on the local development and quality of life in the basin. Therefore, the current flood hazard and potential future changes in flooding due to climate change must be assessed. This study investigates the potential flood frequency change in the Bago River and its sensitivity to the bias-correction method used in climate projections from the downscaled Global Climate Model (GCM) output. A pseudo-global warming method using MIROC5 RCP 8.5 was employed to produce 12-km 30-y historical and future climate projections. Empirical quantile mapping (EQM), gamma quantile mapping (GQM), and the multiplicative scaling method (SCM) were used for bias-correcting the rainfall input of the water-energy budget distributed hydrological model (WEB-DHM). The impacts of bias-correction methods used in reproducing the annual maximum series in the frequency analysis are sensitive to the trend of potential future changes in flood discharge frequency estimation. All methods exhibited decreases in the flood peak discharge for 50-yr and 100-yr flood predictions, which may primarily be due to the MIROC5 GCM used. However, the variation in the magnitude of the change is wide. This demonstrates the uncertainty of the frequency analysis for flood magnitude due to the employed bias-correction method. This uncertainty has significant implications on risk quantification conducted using downscaled climate projections. The effect of the uncertainty of the bias-correction method on the annual maximum rainfall time series should be communicated properly when conducting risk and hazard assessment studies.

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Climate Projections for Precipitation and Temperature Indicators in the Douro Wine Region: The Importance of Bias Correction

Agronomy ◽

10.3390/agronomy11050990 ◽

2021 ◽

Vol 11 (5) ◽

pp. 990

Author(s):

Joana Martins ◽

Helder Fraga ◽

André Fonseca ◽

João Andrade Santos

Keyword(s):

Climate Change ◽

Bias Correction ◽

Climate Model ◽

Correction Method ◽

Maximum Temperature ◽

Lower Sensitivity ◽

Climate Projections ◽

Quantile Mapping ◽

Bias Correction Method ◽

Wine Region

The implications of weather and climate extremes on the viticulture and winemaking sector can be particularly detrimental and acquire more relevance under a climate change context. A four-member ensemble of the Regional Climate Model-Global Climate Model chain simulations is used to evaluate the potential impacts of climate change on indices of extreme temperature and precipitation, as well as on agroclimatic indices of viticultural suitability in the Douro Wine Region, Portugal, under current and future climate conditions, following the RCP8.5 anthropogenic radiative forcing scenario. Historical (1989–2005) and future (2051–2080) periods are considered for this purpose. Although model outputs are bias-corrected to improve the accuracy of the results, owing to the sensitivity of the climatic indicators to the specific bias correction method, the performance of the linear and quantile mapping methods are compared. The results hint at the importance of choosing the most accurate method (quantile mapping), not only in replicating extremes events but also in reproducing the accumulated agroclimatic indices. Significant differences between the bias correction methods are indeed found for the number of extremely warm days (maximum temperature > 35 °C), number of warm spells, number of warm spell days, number of consecutive dry days, the Dryness Index, and growing season precipitation. The Huglin Index reveals lower sensitivity, thus being more robust to the choice of the method. Hence, an unsuitable bias correction method may hinder the accuracy of climate change projections in studies heavily relying on derived extreme indices and agroclimatic indicators, such as in viticulture. Regarding the climate change signal, significant warming and drying trends are projected throughout the target region, which is supported by previous studies, but also accompanied by an increase of intensity, frequency, and duration of extreme events, namely heatwaves and dry spells. These findings thereby corroborate the need to adopt timely and effective adaptation strategies by the regional winemaking sector to warrant its future sustainability and enhance climate resilience.

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

Proceedings ◽

10.3390/ecws-3-05809 ◽

2018 ◽

Vol 7 (1) ◽

pp. 14 ◽

Cited By ~ 1

Author(s):

Enrique Soriano ◽

Luis Mediero ◽

Carlos Garijo

Keyword(s):

Climate Change ◽

Bias Correction ◽

Correction Method ◽

Flood Frequency ◽

Precipitation Extremes ◽

Bias Correction Method ◽

Simulated Precipitation ◽

The Future ◽

The Impact ◽

Frequency Curves

Annual maximum daily rainfalls will change in the future because of climate change, according to climate projections provided by EURO-CORDEX. This study aims at understanding how the expected changes in precipitation extremes will affect the flood behavior in the future. Hydrological modeling is required to characterize the rainfall-runoff process adequately in a changing climate to estimate flood changes. Precipitation and temperature projections given by climate models in the control period usually do not fit the observations in the same period exactly from a statistical point of view. To correct such errors, bias correction methods are used. This paper aims at finding the most adequate bias correction method for both temperature and precipitation projections, minimizing the errors between observed and simulated precipitation and flood frequency curves. Four catchments located in central western Spain have been selected as case studies. The HBV hydrological model has been calibrated, using the observed precipitation, temperature, and streamflow data available at a daily scale. Expected changes in precipitation extremes are usually smoothed by the reduction of soil moisture content due to expected increases in temperatures and decreases in mean annual precipitation. Consequently, rainfall is the most significant input to the model and polynomial quantile mapping is the best bias correction method.

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Bias-Corrected CMIP5-Derived Single-Forcing Future Wind-Wave Climate Projections toward the End of the Twenty-First Century

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-19-0297.1 ◽

2020 ◽

Vol 59 (9) ◽

pp. 1393-1414

Author(s):

Gil Lemos ◽

Alvaro Semedo ◽

Mikhail Dobrynin ◽

Melisa Menendez ◽

Pedro M. A. Miranda

Keyword(s):

Bias Correction ◽

Correction Method ◽

Wave Climate ◽

First Century ◽

Climate Projections ◽

Wave Direction ◽

Quantile Mapping ◽

Bias Correction Method ◽

Twenty First Century ◽

Projected Changes

AbstractA quantile-based bias-correction method is applied to a seven-member dynamic ensemble of global wave climate simulations with the aim of reducing the significant wave height HS, mean wave period Tm, and mean wave direction (MWD) biases, in comparison with the ERA5 reanalysis. The corresponding projected changes toward the end of the twenty-first century are assessed. Seven CMIP5 EC-EARTH runs (single forcing) were used to force seven wave model (WAM) realizations (single model), following the RCP8.5 scenario (single scenario). The biases for the 1979–2005 reference period (present climate) are corrected using the empirical Gumbel quantile mapping and empirical quantile mapping methods. The same bias-correction parameters are applied to the HS, Tm (and wave energy flux Pw), and MWD future climate projections for the 2081–2100 period. The bias-corrected projected changes show increases in the annual mean HS (14%), Tm (6.5%), and Pw (30%) in the Southern Hemisphere and decreases in the Northern Hemisphere (mainly in the North Atlantic Ocean) that are more pronounced during local winter. For the upper quantiles, the bias-corrected projected changes are more striking during local summer, up to 120%, for Pw. After bias correction, the magnitude of the HS, Tm, and Pw original projected changes has generally increased. These results, albeit consistent with recent studies, show the relevance of a quantile-based bias-correction method in the estimation of the future projected changes in swave climate that is able to deal with the misrepresentation of extreme phenomena, especially along the tropical and subtropical latitudes.

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

Water ◽

10.3390/w11112266 ◽

2019 ◽

Vol 11 (11) ◽

pp. 2266 ◽

Cited By ~ 4

Author(s):

Enrique Soriano ◽

Luis Mediero ◽

Carlos Garijo

Keyword(s):

Climate Change ◽

Bias Correction ◽

Climate Models ◽

Climate Model ◽

Control Period ◽

Flood Frequency ◽

Climate Projections ◽

The Future ◽

Flood Quantiles ◽

The Impact

Climate projections provided by EURO-CORDEX predict changes in annual maximum series of daily rainfall in the future in some areas of Spain because of climate change. Precipitation and temperature projections supplied by climate models do not usually fit exactly the statistical properties of the observed time series in the control period. Bias correction methods are used to reduce such errors. This paper seeks to find the most adequate bias correction techniques for temperature and precipitation projections that minimizes the errors between observations and climate model simulations in the control period. Errors in flood quantiles are considered to identify the best bias correction techniques, as flood quantiles are used for hydraulic infrastructure design and safety assessment. In addition, this study aims to understand how the expected changes in precipitation extremes and temperature will affect the catchment response in flood events in the future. Hydrological modelling is required to characterize rainfall-runoff processes adequately in a changing climate, in order to estimate flood changes expected in the future. Four catchments located in the central-western part of Spain have been selected as case studies. The HBV hydrological model has been calibrated in the four catchments by using the observed precipitation, temperature and streamflow data available on a daily scale. Rainfall has been identified as the most significant input to the model, in terms of its influence on flood response. The quantile mapping polynomial correction has been found to be the best bias correction method for precipitation. A general reduction in flood quantiles is expected in the future, smoothing the increases identified in precipitation quantiles by the reduction of soil moisture content in catchments, due to the expected increase in temperature and decrease in mean annual precipitations.

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Analysis of Rainfall-Runoff Characteristics on Bias Correction Method of Climate Change Scenarios

Journal of Korean Society on Water Environment ◽

10.15681/kswe.2015.31.3.241 ◽

2015 ◽

Vol 31 (3) ◽

pp. 241-252 ◽

Cited By ~ 1

Author(s):

Donghyuk Kum ◽

Younsik Park ◽

Young Hun Jung ◽

Min Hwan Shin ◽

Jichul Ryu ◽

...

Keyword(s):

Climate Change ◽

Bias Correction ◽

Correction Method ◽

Climate Change Scenarios ◽

Rainfall Runoff ◽

Bias Correction Method

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Estimating monthly rainfall in rural river basins under climate change: an improved bias-correcting statistical downscaling approach

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-6847-2013 ◽

2013 ◽

Vol 10 (6) ◽

pp. 6847-6896

Author(s):

D. L. Jayasekera ◽

J. J. Kaluarachchi

Keyword(s):

Climate Change ◽

General Circulation Model ◽

River Basin ◽

Bias Correction ◽

General Circulation ◽

Circulation Model ◽

Correction Method ◽

Annual Rainfall ◽

Bias Correction Method ◽

Time Space

Abstract. This study extended the work of Kim et al. (2008) to generate future rainfall under climate change using a discrete-time/space Markov chain based on historical conditional probabilities. A bias-correction method is proposed by fitting suitable statistical distributions to transform rainfall from the general circulation model (GCM) scale to watershed scale. The demonstration example used the Nam Ngum River Basin (NNRB) in Laos which is a rural river basin with high potential for hydropower generation and significant rain-fed agriculture supporting rural livelihoods. This work generated weekly rainfall for a 100 yr period using historical rainfall data from 1961 to 2000 for ten selected weather stations. The bias-correction method showed the ability to reduce bias of the mean values of GCMs when compared to the observed mean amount at each station. The simulated rainfall series is perturbed using the delta change estimated at each station to project future rainfall for the Special Report on Emission Scenarios (SRES) A2. GCMs consisting of third generation coupled general circulation model (CGCM3.1 T63) and European center Hamburg model (ECHAM5) projected an increasing trend of mean annual rainfall in the NNRB. Seasonal rainfall percent changes showed an increase in the wet and dry seasons with the highest increase in the dry season mean rainfall of about 31% from 2051 to 2090. While the GCM projections showed good results with appropriate bias corrections, the Providing REgional Climates for Impacts Studies (PRECIS) regional climate model significantly underestimated historical behavior and produced higher mean absolute errors compared to the corresponding GCM predictions.

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High-Resolution Integrated Hydrological Modeling of Climate Change Impacts on a Semi-arid Urban Watershed in Niamey, Niger

10.20944/preprints201912.0329.v1 ◽

2019 ◽

Author(s):

Abdou Boko Boubacar ◽

Konaté Moussa ◽

Nicaise Yalo ◽

Steven J. Berg ◽

Andre R. Erler ◽

...

Keyword(s):

Climate Change ◽

Bias Correction ◽

Hydrological Modeling ◽

Correction Method ◽

Groundwater Table ◽

Annual Rainfall ◽

Urban Watershed ◽

Bias Correction Method ◽

The Impact ◽

Semi Arid

This study evaluated the impact of climate change on water resources in a large semi-arid urban watershed located in Niamey Republic of Niger, West Africa. The watershed was modeled using the fully integrated surface-subsurface HydroGeoSpheremodel at a high spatial resolution. Historical (1980-2005) and projected (2020-2050) climate scenario derived from the outputs of three Regional Climate Models (RCM) under the RCP 4.5 scenario were statistically downscaled using the multiscale quantile mapping bias correction method. Results show that the bias correction method is optimum at daily and monthly scales, and increased RCM resolution does not improve the performance of the model. The three RCMs predict increases of up to 1.6% in annual rainfall and of 1.58°C for mean annual temperatures between the historical and projected periods. The durations of the Minimum Environmental Flow (MEF) conditions, required to supply drinking and agriculture water, were found to be sensitive to changes in runoff resulting from climate change. MEF occurrences and durations are likely to be greater for (2020-2030), and then they will be reduced for (2030-2050). All three RCMs consistently project a rise in groundwater table of more than 10 meters in topographically high zones where the groundwater table is deep and an increase of 2 meters in the shallow groundwater table.

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