scholarly journals A Quantile Mapping Bias Correction Method Based on Hydroclimatic Classification of the Guiana Shield

Sensors ◽  
2017 ◽  
Vol 17 (6) ◽  
pp. 1413 ◽  
Author(s):  
Justine Ringard ◽  
Frederique Seyler ◽  
Laurent Linguet
Keyword(s):  
Bias Correction ◽  
Correction Method ◽  
Guiana Shield ◽  
Quantile Mapping ◽  
Bias Correction Method ◽  
Mapping Bias

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

Agronomy ◽  
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.

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

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.

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

2020 ◽  
Vol 15 (3) ◽  
pp. 288-299
Author(s):  
Ralph Allen E. Acierto ◽  
Akiyuki Kawasaki ◽  
Win Win Zin ◽  
◽  
Keyword(s):  
Climate Change ◽  
Frequency Analysis ◽  
Bias Correction ◽  
Correction Method ◽  
Flood Frequency ◽  
Climate Projections ◽  
Frequency Change ◽  
Annual Maximum ◽  
Quantile Mapping ◽  
Bias Correction Method

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.

BIAS CORRECTION METHOD FOR d4PDF TYPHOON TRACK DATASET AND FUTURE CHANGES IN TYPHOON CENTRAL PRESSURE

2021 ◽  
Vol 77 (2) ◽  
pp. I_973-I_978
Author(s):  
Junichi ARIMURA ◽  
Zhongrui QIU ◽  
Tetsuya OKAYASU ◽  
Koutarou CHICHIBU ◽  
Kunihiro WATANABE ◽  
...  
Keyword(s):  
Bias Correction ◽  
Correction Method ◽  
Central Pressure ◽  
Bias Correction Method ◽  
Typhoon Track

scholarly journals Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand

2007 ◽  
Vol 11 (4) ◽  
pp. 1373-1390 ◽  
Author(s):  
D. Sharma ◽  
A. Das Gupta ◽  
M. S. Babel
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Hydrological Modeling ◽  
Correction Method ◽  
Hydrologic Model ◽  
Global Climate Models ◽  
Model Parameters ◽  
Bias Correction Method ◽  
Spatial Disaggregation ◽  
Grid Nodes

Abstract. Global Climate Models (GCMs) precipitation scenarios are often characterized by biases and coarse resolution that limit their direct application for basin level hydrological modeling. Bias-correction and spatial disaggregation methods are employed to improve the quality of ECHAM4/OPYC SRES A2 and B2 precipitation for the Ping River Basin in Thailand. Bias-correction method, based on gamma-gamma transformation, is applied to improve the frequency and amount of raw GCM precipitation at the grid nodes. Spatial disaggregation model parameters (β,σ2), based on multiplicative random cascade theory, are estimated using Mandelbrot-Kahane-Peyriere (MKP) function at q=1 for each month. Bias-correction method exhibits ability of reducing biases from the frequency and amount when compared with the computed frequency and amount at grid nodes based on spatially interpolated observed rainfall data. Spatial disaggregation model satisfactorily reproduces the observed trend and variation of average rainfall amount except during heavy rainfall events with certain degree of spatial and temporal variations. Finally, the hydrologic model, HEC-HMS, is applied to simulate the observed runoff for upper Ping River Basin based on the modified GCM precipitation scenarios and the raw GCM precipitation. Precipitation scenario developed with bias-correction and disaggregation provides an improved reproduction of basin level runoff observations.

Sign in / Sign up

Export Citation Format

Share Document