Application of Quantile Mapping Bias Correction for Mid-Future Precipitation Projections over Vietnam

Abstract

The Quantile Mapping (QM) bias correction (BC) technique was applied for the first time to address biases in the simulated precipitation over Vietnam from the Regional Climate Model (RegCM) driven by five Coupled Model Intercomparison Project Phase 5 (CMIP5) Global Climate Model (GCM) products. The QM process was implemented for the period 1986−2005, and subsequently applied to the mid-future period 2046−2065 under both Representative Concentration Pathway (RCP) 4.5 and RCP 8.5. Comparison with the original model outputs during the independent validation period shows a large bias reduction from 45% to 3% over Vietnam and significant improvements in representing precipitation indices (PI) after applying the QM technique. Moreover, the ensemble average of the BC products generally performed better than an individual BC member in capturing the spatial distribution of the PIs. A drier condition with a longer rainfall break, and shorter consecutive rainfall events are anticipated over Northern and Central Vietnam during their respective wet seasons in the mid-future. Furthermore, this study showed that the QM method minimally modified the future changes in PIs over most of Vietnam; thus, these corrected projections could be used in climate impacts and adaptation studies.

Impact of Climate and Land Use/Land Cover Change on the Water Resources of a Tropical Inland Valley Catchment in Uganda, East Africa

“…The varying patterns observed in annual precipitation from the models even after bias correction could be inherent model errors, which can be either random or systematic [63]. These findings are consistent with previous studies by [32,64] conducted in tropical climates. Thus, application of the chosen bias correction approaches for the study area offer representative precipitation and temperature projections, which can be used in future climate change impact studies as well as for the assessment of the potential hydrological changes for decision making and management strategies.…”

Performance Evaluation of Bias Correction Methods for Climate Change Monthly Precipitation Projections over Costa Rica

“…After individual bias correction of future GCM-RCM pairs, the bias corrected ensemble-mean was selected to provide future projections of precipitation over Costa Rica. This approach has repeatedly been implemented in literature [5,57,64,93,113,114]. For future projections, the monthly averaged precipitation anomalies between the mean values of the observed control period and the bias corrected multimodel ensemble-mean were calculated and expressed as percentage-change (PC).…”

Investigation of The Effect of Climate Change on Extreme Precipitation: Tekirdağ Case

“…QM method is based on calibrating, CDF of the modeled data into the CDF of observed data by using a transfer function. Studies support that quantile mapping or CDF matching technique yields sufficient results for rainfall data (Piani et al, 2010;Gudmundsson et al, 2012;Chen et al, 2013;Trinh-Tuan et al, 2019;Mendez et al, 2020). Piani et al (2010) reveal that QM is well represented the simulated daily rainfall across Europe.…”

“…Non-parametric quantile mapping (QM) using robust empirical quantiles (REQ) and quantile mapping using parametric transformations (PT) are used for bias correction. The QM non-parametric technique which has the additional advantage of not relying on any predetermined statistical distribution of the data, is used in this study (Gudmundsson et al, 2012;Trinh-Tuan et. al., 2019).…”