Selection of CMIP6 Models for Regional Precipitation Projection and Climate Change Assessment in the Jhelum and Chenab River Basins

TL;DR

This study develops a machine learning-based method to select the most suitable CMIP6 climate models for regional precipitation projections in the Jhelum and Chenab River Basins, aiding water resource management.

Contribution

Introduces a novel envelope-based model selection approach for CMIP6 GCMs using machine learning, applied for the first time to SSP scenarios in this region.

Findings

Identified NorESM2 LM and FGOALS g3 as optimal models for the study region.

Highlighted vulnerable areas in Punjab, Jammu, and Kashmir due to climate change.

Found no significant difference between CMIP5 and CMIP6 precipitation projections under RCP and SSP scenarios.

Abstract

Effective water resource management depends on accurate projections of flows in water channels. For projected climate data, use of different General Circulation Models (GCM) simulates contrasting results. This study shows selection of GCM for the latest generation CMIP6 for hydroclimate change impact studies. Envelope based method was used for the selection, which includes components based on machine learning techniques, allowing the selection of GCMs without the need for in-situ reference data. According to our knowledge, for the first time, such a comparison was performed for the CMIP6 Shared Socioeconomic Pathway (SSP) scenarios data. In addition, the effect of climate change under SSP scenarios was studied, along with the calculation of extreme indices. Finally, GCMs were compared to quantify spatiotemporal differences between CMIP5 and CMIP6 data. Results provide NorESM2 LM, FGOALS g3 as selected models for the Jhelum and Chenab River. Highly vulnerable regions under the effect of climate change were highlighted through spatial maps, which included parts of Punjab, Jammu, and Kashmir. Upon comparison of CMIP5 and CMIP6, no discernible difference was found between the RCP and SSP scenarios precipitation projections. In the future, more detailed statistical comparisons could further reinforce the proposition.