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

Abstract

Six bias correction (BC) methods; delta-method (DT), linear scaling (LS), power transformation of precipitation (PTR), empirical quantile mapping (EQM), gamma quantile mapping (GQM) and gamma-pareto quantile mapping (GPQM) were applied to adjust the biases of historical monthly precipitation outputs from five General Circulation Models (GCMs) dynamically downscaled by two Regional Climate Models (RCMs) for a total of seven different GCM-RCM pairs over Costa Rica. High-resolution gridded precipitation observations were used for the control period 1951-1980 and validated over the period 1981-1995. Results show that considerable biases exist between uncorrected GCM-RCM outputs and observations, which largely depend on GCM-RCM pair, seasonality, climatic region and spatial resolution. After the application of bias correction, substantial biases reductions and comparable performances among most BC methods were observed for most GCM-RCM pairs; with EQM and DT marginally outperforming the remaining methods. Consequently, EQM and DT were selectively applied to correct the biases of precipitation projections from each individual GCM-RCM pair for a near-future (2011-2040), mid-future (2041-2070) and far-future (2071-2100) period under Representative Concentration Pathways (RCPs) 2.6, 4.5 and 8.5 using the control period . Results from the bias-corrected future ensemble-mean anticipate a marked decreasing trend in precipitation from near to far-future periods during the dry season (December, January, February (DJF) and March, April, May (MAM)) for RCP4.5 and 8.5; with pronounced drier conditions for those climatic regions draining towards the Pacific Ocean. In contrast, mostly wetter conditions are expected during the dry season under RCP2.6, particularly for the Caribbean region. In most of the country, the greatest decrease in precipitation is projected at the beginning of the rainy season (June, July, August (JJA)) for the far-future period under RCP8.5, except for the Caribbean region where mostly wetter conditions are anticipated. Regardless of future period, slight increases in precipitation with higher radiative forcing are expected for SON excluding the Caribbean region, where precipitation is likely to increase with increasing radiative forcing and future period. This study demonstrates that bias correction should be considered before direct application of GCM-RCM precipitation projections over complex territories such as Costa Rica.

Appraisal of Climate Change and Its Impact on Water Resources of Pakistan: A Case Study of Mangla Watershed

“…The GCMs, MirocESM, and ESMLR were found to be more accurate than other GCMs, and distribution mapping downscaling was found to be a more suitable downscaling technique for the transboundary of Mangla watershed to study climatic parameters such as maximum and minimum temperature and precipitation. The linear scaling downscaling technique is found to be less accurate than change factor downscaling [51]. The findings of this research consistent with previous studies such as [51] predicted that linear scaling is less suitable than change factor, whereas [52] studied that the distribution mapping performed better than other downscaling techniques.…”

“…The linear scaling downscaling technique is found to be less accurate than change factor downscaling [51]. The findings of this research consistent with previous studies such as [51] predicted that linear scaling is less suitable than change factor, whereas [52] studied that the distribution mapping performed better than other downscaling techniques. CMHyd software was used for linear scaling and distribution mapping, in which data were used directly in NetCDF files format [53].…”

“…Five GCMs, along with their ensembles, were selected, and three types of downscaling techniques were compared to estimate the change in precipitation and temperature. After a comparison of these downscaling techniques with base data, it was concluded that the distribution mapping is the best downscaling technique to study climatic parameters within the watershed, however, linear scaling downscaling was found to be a less effective downscaling technique, as the results are in close agreement with previous studies [51]. Downscaled data were used to generate stream flows using a semi-distributed hydrological model SWAT.…”

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.…”

Erosion Quantification in Runoff Agriculture Plots by Multitemporal High-Resolution UAS Digital Photogrammetry

“…PBIAS values were used to evaluate the relative difference between collected and estimated sediments, where a negative value means an underprediction and a positive value indicates an overprediction. The correlation coefficient (R) was used to estimate the trend in the correlation of the collected and estimated observations [46]- [47].…”