The alteration in river flow patterns, particularly those that originate in the Himalaya, has been caused by the increased temperature and rainfall variability brought on by climate change. Due to the impending intensification of extreme climate events, as predicted by the Intergovernmental Panel on Climate Change (IPCC) in its Sixth Assessment Report, it is more essential than ever to predict changes in streamflow for future periods. Despite the fact that some research has utilised machine-learning- and deep-learning-based models to predict streamflow patterns in response to climate change, very few studies have been undertaken for a mountainous catchment, with the number of studies for the western Himalaya being minimal. This study investigates the capability of five different machine learning (ML) models and one deep learning (DL) model, namely the Gaussian linear regression model (GLM), Gaussian generalised additive model (GAM), multivariate adaptive regression splines (MARSs), artificial neural network (ANN), random forest (RF), and 1D convolutional neural network (1D-CNN), in streamflow prediction over the Sutlej River basin in the western Himalaya during the periods 2041–2070 (2050s) and 2071–2100 (2080s). Bias-corrected data downscaled at a grid resolution of 0.25^∘ × 0.25^∘ from six general circulation models (GCMs) of the Coupled Model Intercomparison Project Phase 6 GCM framework under two greenhouse gas (GHG) trajectories (SSP245 and SSP585) were used for this purpose. Four different rainfall scenarios (R₀, R₁, R₂, and R₃) were applied to the models trained with daily data (1979–2009) at Kasol (the outlet of the basin) in order to better understand how catchment size and the geo-hydromorphological aspects of the basin affect runoff. The predictive power of each model was assessed using six statistical measures, i.e. the coefficient of determination (R²), the ratio of the root mean square error to the standard deviation of the measured data (RSR), the mean absolute error (MAE), the Kling–Gupta efficiency (KGE), the Nash–Sutcliffe efficiency (NSE), and the percent bias (PBIAS). The RF model with rainfall scenario R₃, which outperformed other models during the training (R² = 0.90; RSR = 0.32; KGE = 0.87; NSE = 0.87; PBIAS = 0.03) and testing (R² = 0.78; RSR = 0.47; KGE = 0.82; NSE = 0.71; PBIAS = −0.31) period, therefore was chosen to simulate streamflow in the Sutlej River in the 2050s and 2080s under the SSP245 and SSP585 scenarios. Bias correction was further applied to the projected daily streamflow in order to generate a reliable times series of the discharge. The mean ensemble of the model results shows that the mean annual streamflow of the Sutlej River is expected to rise between 2050s and 2080s by 0.79 % to 1.43 % for SSP585 and by 0.87 % to 1.10 % for SSP245. In addition, streamflow will increase during the monsoon (9.70 % to 11.41 % and 11.64 % to 12.70 %) in the 2050s and 2080s under both emission scenarios, but it will decrease during the pre-monsoon (−10.36 % to −6.12 % and −10.0 % to −9.13 %), post-monsoon (−1.23 % to −0.22 % and −5.59 % to −2.83 %), and during the winter (−21.87 % to −21.52 % and −21.87 % to −21.11 %). This variability in streamflow is highly correlated with the pattern of precipitation and temperature predicted by CMIP6 GCMs for future emission scenarios and with physical processes operating within the catchment. Predicted declines in the Sutlej River streamflow over the pre-monsoon (April to June) and winter (December to March) seasons might have a significant impact on agriculture downstream of the river, which is already having problems due to water restrictions at this time of year. The present study will therefore assist in strategy planning to ensure the sustainable use of water resources downstream by acquiring knowledge of the nature and causes of unpredictable streamflow patterns.

Driven by the strength in local land‐atmosphere coupling, inter‐annual variability of larger‐scale atmospheric circulations primarily determines a glacier's response to warming in high Asia. In this study, micrometeorological measurements in conjunction with regional reanalysis dataset were analyzed to examine seasonal land‐atmosphere coupling strength at a typical Central Himalayan (CH) glacier where the influence of Indian summer monsoon (ISM) predominates relative to winter‐westerlies. Energy‐water (E–W) exchange and coupling behaviour were studied for the Pindari glacier based on sub‐hourly measurements of radiative‐convective flux, state parameters, and sub‐surface thermal profiles using cross‐correlations between various E–W balance components. Coupling was positive in summer‐ and winter‐accumulation seasons. However, it remained strongest during ISM. Coupling reversed during seasonal transition phases concurrent with distinct seasonality in E–W components. Lead‐lag relation between some variables showed strong association at diurnal‐scale (VPD–Rn; VPD–LE; Rn–G), whereas some persisted beyond months (Rn–LE; Bowen ratio‐precipitation; surface‐air temperature). Weak association of variation of latent heat flux (LE) and rainfall was found during ISM at local‐scale than at regional‐scale, but with a lag, which was more prominent at regional‐scale. These observations indicate a seasonally variable coupling between E–W balance components through response–feedback mechanisms. Cross‐correlations of daily mean values of energy fluxes and meteorological variables reveal that Rn and air temperature are the prime drivers of energy balance. Net radiative energy (Rn) dominates energy exchanges at the glacier‐atmosphere interface (governed primarily by the variation in net shortwave radiation), contributed on average 62% of the melt energy. However, sub‐surface heat flux along with the turbulent fluxes was the energy sinks of 24% and 15%, respectively. This study would help understand and parameterize E–W exchange pathways for ISM dominated CH glaciers in coupled glacier‐climate models.

A cloudburst event occurred at around 04:00hrs (IST), on 18, August 2019, elicited landslides and flash floods, at Tikochi and Makudi village under the Arakot region of Uttarkashi District, Uttarakhand, India. This heavy rain has caused a massive flash flood in Arakot Nala and induced a major landslide downstream. This flash flood has caused the death of 19 people, 8 were missings, and 15 were injured, affecting 38 villages (70 km² area) and stranding more than 400 people at various places. These flash floods have caused irreparable damage to the livestock, infrastructures, environment, and properties in the Uttarakhand and Himachal states and estimated economic losses of about Rs ∼175 crores. During the same period, heavy rainfall also made havoc across north India. Meanwhile, the rescue and evacuation process in the region has been carried out by the Indo-Tibetan Border Police (ITBP), National Disaster Response Force (NDRF), and State Disaster Response Force (SDRF). However, it will take a lot of time to bring the situation in the region to normalcy. The study has suggested that pragmatic check dam policy and logical shifting of the people should be exercised in a safe and stable area before the onset of the monsoon season in the hazard-prone areas. An early warning system (EWS), installation of Doppler radar, and lightning sensors, which would be helpful for policymakers, planners, practitioners, and technologists will aid to minimize the disaster risk and sustainable development within the region. Although an EWS alone can not stop a similar type of disaster but would be very helpful in minimizing the loss of human lives, livestock, biodiversity, infrastructure (civilian and strategic), and land holding in the hilly region.

Warming-induced expansion in vegetation coverage and activity can accelerate the montane hydrological regimes. However, the climate impacts on ecohydrology of forested valleys of the Himalaya are uncertain. In this study, utilizing results of about three centuries of cellulose isotope chronologies (δ¹³C andδ¹⁸O) of dominant tree species, geo-chronological proxies, bio-geophysical dataset and simulations including satellite observations, we show an activation in the ecophysiological processes including evapotranspiration (ET) since the 1950s. Observation suggests rapid greening, while isotopic records indicate enhanced assimilation and transpiration in deciduous species vis-à-vis conifers post 1950s. Given strong vegetation-precipitation feedback and superimposed on the increasing trends of conducive atmospheric factors affecting valley-scale convective processes, intensification in forest ET is manifesting in a progressive enhancement in extreme rainfall events (EREs) since the last few decades. Results suggest that representation of ecophysiological processes and dynamics of seasonal moisture loading in observational and modelling framework is critical for understanding EREs under climate change.