Selection of CMIP6 Models for Regional Precipitation Projection and Climate Change Assessment in the Jhelum and Chenab River Basins
2026-02-13 • Machine Learning
Machine Learning
AI summaryⓘ
The authors studied how to pick the best climate models from the newest CMIP6 set to predict water flow changes in rivers without needing on-site data. They used machine learning to select models and looked at how climate change might affect extreme weather in certain river regions. They found two models that work well for the Jhelum and Chenab Rivers and identified vulnerable areas in Punjab, Jammu, and Kashmir. When comparing older CMIP5 data with CMIP6, they saw no big difference in rainfall forecasts, but suggest more detailed studies are needed.
General Circulation ModelsCMIP6Shared Socioeconomic Pathway (SSP)machine learninghydroclimateJhelum RiverChenab Riverclimate change impactextreme weather indicesCMIP5
Authors
Saad Ahmed Jamal, Ammara Nusrat, Muhammad Azmat, Muhammad Osama Nusrat
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.