The climate change scenarios, especially global warming, have raised significant concerns, and the Himalayan regions such as Uttarakhand are highly vulnerable to such climatic shifts. Considering 10 Coordinated Regional Climate Downscaling Experiments in South Asia (CORDEX-SA), experiments with 3 regional climate models (RCMs), driven by 13 global climate models, historical estimates and future projections are analyzed from the mid-century (MC) i.e., from 2021–2050 to the end of the century (EC) i.e., from 2070–2099 to characterize annual and seasonal variations in precipitation and temperature. The analysis shows a decrease in the annual average precipitation by 5.92% at MC and an increase of 5.97% at EC for the Representative Climate Pathway (RCP) 4.5, while precipitation may likely increase from 2.83% to 15.89% towards MC and EC in the RCP 8.5. The maximum temperature may likely increase from 0.42 °C to 3.07 °C from MC to EC in the RCP 4.5 and from 0.83 °C to 5.49 °C in the RCP 8.5. In addition, the minimum temperature may increase from 0.80 °C to 3.25 °C from MC to EC in the RCP 4.5 and from 0.30 °C to 5.86 °C from MC to EC in the RCP 8.5. Notably, a decrease in the pre-monsoon precipitation at EC and a higher increase in the maximum temperature during the monsoon season are observed. An increase in the maximum temperature along with precipitation may lead to an increase in the frequency of the monsoon season’s extreme rainfall events.

Landslides are a major geological hazard that poses serious risks to infrastructure, transportation networks, and populations. This study develops a framework for evaluating landslide risk in the Uttarkashi District of Uttarakhand, India, using GIS and machine learning. With topographic, geological, hydrological, environmental, and anthropogenic characteristics, a landslide inventory comprising 1,994 landslide locations and the same number of non-landslide locations was prepared and evaluated using fourteen conditioning factors. Models like Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) were trained and evaluated using spatial five-fold cross-validation and an independent test dataset. Elevation, distance from roads, NDVI, aspect, lithology, and land-use/land-cover were found to be among the most significant predictors using feature-importance analysis, albeit their relative significance differed between models. The mean AUC values for RF, XGBoost and LightGBM were 0.8805, 0.8846, and 0.8840, respectively, based on the spatial cross-validation data. LightGBM obtained an AUC of 0.8950 on the independent test dataset, whereas RF and XGBoost obtained an AUC of 0.8988. High- and very-high-susceptibility zones were frequently identified on the resulting susceptibility maps along the Yamuna and Bhagirathi River systems as well as important transportation routes like the Yamunotri and Gangotri highways. Additionally, this study shows how machine learning combined with remote sensing and GIS can effectively assess the susceptibility of a region to landslides. In the Himalayan context, it offers spatial data to support infrastructure management, slope stabilization, land-use planning, and disaster risk reduction.