Importance of Bias Correction and its Techniques: Multi-Model Ensemble vs. Single Climatology - ISLAM, MD. TARIKUL; ISLAM, MD. AMIRUL; CHOWDHURY, SARWAR JAHAN; KHONDAKER , EMAMUL HAQUE (Journal of the Indian Society of Coastal Agricultural Research, 2026)
In connection with investigation of the sea level rise dynamics along the coast of Bangladesh, predicted data of precipitation, temperature and evaporation data are needed for hydrological modeling. The study focuses on assessing the impacts of climate change on the Ganges-Brahmaputra-Meghna (GBM) basins in South Asia, by simulating meteorological parameters which do not usually fit exactly with the observed time series in the control period. This paper seeks to find the most adequate bias correction techniques to minimize the errors between observations and climate model simulations in the control period. The analysis revealed a significant bias in the single model climatology, including over estimation of precipitation during the dry season and under estimation during the monsoon season. The CORDEX - South Asia project evaluated six regional climate models by comparing them with observations and a single climatology to detect wet and dry biases caused by incorrect predictions of rainfall and temperature in future climate. Bias correction based on cumulative distribution function was applied to reduce these errors. The bias-corrected ensemble mean of six CORDEX-SA driving GCM experiments was used for hydrological modeling to predict runoff and evapotranspiration for future periods under two Representative Concentration Pathways scenarios (RCP4.5 and RCP8.5). The study revealed that the eastern area, which represents the Ganges basins, had vigorous underestimation during monsoon, particularly the glaciers of Ganges, while the Brahmaputra results were acceptable. The bias correction led to an improvement in under estimation during peak monsoon over the GBM basins, especially in the Brahmaputra and Meghna Basins, and over estimation during the dry season, resulting in reliable predictions in accordance with observed values. The bias-corrected model validations showed a perfection of future predictions up to 0.5 in case of precipitation simulations (not for all sub-catchments), indicating the reliability of the results.