The objective of this research is to assess the mathematical downscaling of the union-wise administrative area of Bangladesh that simulations and future projections of rainfall and mean temperature of CMIP6 (SSP2–4.5 and 5–8.5). Models were used to determine uncertainty with spatiotemporal variability of rainfall and mean temperature projections. Model data NETCDF file has been converted to Raster with cell size of 1, 1 decimal degree which means that each cell contains 100 km x 100 km area coverage. After preparing the dataset of 0.01, 0.01 decimal degree cell size (1km x1km), the dataset of Bangladesh has been extracted union-wise by the Bilinear resampling technique. An average value has been generated from the multiple values belonging to the specific union. After that, the dataset of Bangladesh has been generated. Mathematical downscaling and bias correction are made for the selected 16 model runs. The CMIP6 models for the model and observed values of rainfall show Kling-Gupta Efficiency (KGE) values in a range of 0.58-0.72 and for mean temperature in a range of 0.85- 0.90. The CMIP6 models show Pearson's correlation coefficient (R) in the range of 0.83-0.90 for rainfall and in a range of 0.86-0.93 for mean temperature. Also, CMIP6 models showed Nash Sutcliffe in the range of 0.06-0.78 for rainfall and 0.73-0.89 for mean temperature from the model and observed value. The projected change of future rainfall and mean temperature in the study increases the rainfall intensities due to the increment of temperature.
No takes yet. Share an insight, caveat, or question.
Karim et al. (2023) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: