Comparison of bias correction methods for climate change projections in the lower Shivaliks of Punjab - Kuldeep Kaur; Navneet Kaur (Journal of Water and Climate Change, 2023)
The study evaluates the performance of bias correction techniques by dividing the observed climate data period into calibration and validation sets. For this purpose, the daily data of temperature, rainfall, and solar radiation from 2010–2095 for lower Shivaliks of Punjab (Ballowal Saunkhri) were downloaded from Marksim weather generators using outputs of CSIRO-Mk3-6-0 climate model under four RCP scenarios (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5). The bias correction of model data (temperature, rainfall, and solar radiation) was done by developing correction functions (using a model and observed data from 2010 to 2015) from different bias correction methods (difference method, Leander and Buishand method, modified difference method, linear scaling, variance scaling, and quantile mapping). The corrected model data for the year 2016–2020 were validated against the observed data. The difference method was found to be best for bias correction due to low error and high efficiency. The corrected future model data (2021–2095) analysis on an annual and seasonal basis predicted a rise in maximum temperature and minimum temperature by 1.3–2.8 °C and 0.5–3.0 °C, respectively, under different scenarios. The study predicted more increase in rainfall and solar radiation under RCP 8.5 followed by RCP 6.0, RCP 4.5, and RCP 2.6 scenarios. HIGHLIGHTS The study analyzed the implications of bias correction methods on the projection of climate change.; The difference method of bias correction was better as compared to other methods in terms of different statistical tests.; Validation results using the difference method showed a reduction in differences between observed and model data after correction.; Increase in temperature is observed as compared to the historical period by the end of the 21st century.;