Author(s): Aditi Bhadra; Arishinaro Longkumer; Liza Kiba; Arnab Bandyopadhyay

Linked Author(s): Aditi Bhadra

Keywords: SWE; Dibang; RCP scenarios; ERA5; Bias correction

Abstract: The climate change impact assessment on snow water equivalent (SWE) were conducted for Dibang river basin of Arunachal Pradesh. This study uses the ERA5 snow water equivalent reanalysis data from 1986 to 2015, and five Coupled Model Intercomparison Project Phase 5 models: Beijing Climate Centre Climate System Model, version 1.1 (BCC-CSM1.1), Commonwealth Scientific and Industrial Research Organisation, version 3.6 (CSIRO-MK3.6), Canadian Earth System Model, second generation (CanESM2), Max Planck Institute Earth System Model running on low resolution (MPI-ESM-LR) and Max Planck Institute Earth System Model running on medium resolution (MPI-ESM-MR). The time slices were presented from 1986-2005 as the baseline period, 2006-2015 as the validation period and 2020-2039, 2050-2069 and 2080-2099 as the future analysis periods under the two Representative Concentration Pathway (RCP) scenarios (RCP 4.5 and RCP 8.5). The bias correction technique known as the Equidistant Quantile Mapping Method (EDQM) was applied to address the biases in the simulated SWE over the Dibang Valley from the five Global Climate Models (GCM) products against the reanalysis SWE data (ERA5) for the period of 1986-2005 and the bias corrected values were obtained for the other future years. The validation of climate projected SWE conducted for the period of 2006-2015 gave better performance for the three climatic models (CanESM2, MPI-ESM- and CSIRO-MK3.6) out of the 5 GCMs w.r.t reanalysis SWE data from ERA5. The closest similarity was from MPI-ESM-MR for the month of January where observed mean value was 0.117m and the model mean value was 0.118m under RCP 4.5. For RCP 8.5, the observed mean value was 0.201m and the model mean value was 0.200m for the month of February. These three GCMs were considered for the further analysis. The projections indicated a decreasing trend under both the future scenarios, the lowest values occurring under RCP 8.5 scenario. Relative changes w.r.t baseline was performed for the climate models individually as well as for the ensemble. Individual GCM results showed an overall negative increment in SWE except for CSIRO-MK3.6 in 2020s under RCP 8.5, where the change is more than the 2050s and 2080s. Considering the ensemble, it showed a negative increment with the largest changes occurring in the 2080s for both scenarios (-52.793% under RCP 4.5 and -74.591% under RCP 8.5). The overall projection indicated that SWE will eventually decrease under both the future scenarios; with a steady decrease under RCP 4.5 and a higher reduction during high GHG emission level under RCP 8.5. The monthly average SWE projected by ensemble GCM w.r.t the baseline SWE in snow months showed a reduction in the future time slices for the ensemble SWE values w.r.t baseline except for the month of April in 2050s which is greater than 2020s in both the scenarios.

DOI: https://doi.org/10.3850/IAHR-39WC2521711920229

Year: 2022