Vishal Singh

With the growing availability of global glacier thickness data sets, there is a need for fast and adaptable approach to assess how glacier thickness changes over time. This study demonstrates how machine learning (ML) models can be trained using existing thickness data sets and later applied with updated inputs to quantify the changes in glacier thickness. This study introduces an innovative skeletonization distance approach, which converts glacier outlines into thin central structure and computes each pixel's distance from this structure to represent glacier geometry more effectively. This new input variable, combined with elevation and slope, was used to train four tree‐based ML models: Random Forest, XGBoost (XGB), AdaBoost‐RF, and AdaBoost‐DT. Among them, XGB performed best, achieving R ² values of 0.82–0.88 (training) and 0.73–0.82 (testing). The best‐performing XGB model was then used to analyze glacier thickness changes between 2000 and 2015 for the Bara Shigri, Gangotri, and Zemu glaciers. The analysis revealed heterogeneous thinning concentrated in debris‐covered ablation zones of the study glaciers. Incorporating skeletonization improved test accuracy by up to 13%. To test transferability, the approach was applied to Aletsch (Switzerland), Koxkar (China), and Saskatchewan (Canada) glaciers, effectively capturing regional thinning patterns consistent with previous studies. The trained models can be directly applied with Digital Elevation Models and glacier boundaries of different years, allowing researchers to estimate glacier thickness changes.

Recent studies show variations in precipitation‐gridded data set accuracy with changing geographical parameters. Ensemble precipitation products, combining diverse data sets, offer global‐scale effectiveness, but applying them to regional studies, particularly in small to medium‐sized sub‐basins, presents challenges in addressing precipitation dependence on specific geographical conditions. Here, we present a newly developed Clusters Based‐Minimum Error approach to assimilate different open‐source gridded precipitation data sets for forming an accurate precipitation product over small to medium‐sized hilly terrain basins, with limited precipitation gauges. This methodology generates the New Gridded Precipitation Data Set (NGPD) from 1991 to 2022 for the Upper Ganga Basin in the western Himalaya, covering approximately 22,292 km ² . The study utilizes nine open‐source gridded precipitation data sets and 11 observed precipitation gauges, NGPD is evaluated through station‐wise, grid‐wise, and elevation‐wise analyses using statistical parameters, quantile‐quantile plots, daily coefficient of determination, Rainfall Anomaly Index, and seasonality/precipitation pattern analyses. Results demonstrate the superior performance of NGPD compared to other gridded precipitation sources across various evaluation metrics. Nash‐Sutcliffe Efficiency (NSE), Coefficient of determination ( R ² ), and Root mean squared error (RMSE) range from 0.67 to 0.90, 0.73–0.93, and 4.4–10.69 mm/day, respectively, w.r.t 11 observed precipitation gauges. NGPD outperforms the widely used IMD data set in India, exhibiting a monthly scale improvement of 18.47% and 17.7% in average NSE and R ² values, respectively. Additionally, the methodology is also successfully applied to the Tamor Basin in Nepal, proving its reliability for various Himalayan regions. This approach reliably creates accurate gridded precipitation data sets for hilly sub‐basins, especially in Himalayan regions with limited station data.

This article investigates the current institutional arrangement concerning water governance in Nainital, India, located in the Western Himalayas. It assesses the availability of water resources, the functioning of the supply system, and sheds light on the role of institutions in managing water resources, supply, and distribution. Through qualitative methods (such as focus group discussions and key informant interviews with officials from water management institutions, citizens, etc.) supplemented by a literature review, the study reveals a complex network of institutions and stakeholders involved in water management. This complexity results in overlapping jurisdictions, leading to accountability gaps and various issues. The study identifies that in Nainital, existing institutions operate independently, impacting water governance in terms of water supply, resources, and environmental aspects. Considering the fragile biophysical environment of Nainital, the study also recommends integration of nature-centric approaches into institutional frameworks, which can be helpful in improving the resilience of water systems and promoting sustainable water governance.

Air pollution is a key environmental issue affecting the urban population in the urban cities of Hindu Kush Himalaya (HKH) countries. It is particularly detrimental to marginalized occupation groups like street vendors, labourers and drivers who work outdoors for their livelihood. There are mitigation strategies to reduce the brunt of air pollution that work in the long run. However, these strategies will need time to implement and operationalize. Adaptation behaviours and measures, in this context, are urgently required and become vital to cope with the impacts of air pollution exposure especially for highly exposed informal workers who have very little means of avoiding it. Adaptation behaviour is very complex and depends on socioeconomic and psychological factors. In this paper, we assess the impact of psychological factors like perception and motivation on the adaptive behaviour of the informal workers using Protection Motivation Theory (PMT). Our findings from Dehradun show that concern behaviour towards air pollution was strongly affected by motivation and perception factors. Adaptive behaviour in the form of both concern behavior and the extent of use of additional protective measures is dependent on how the risks of air pollution and related adaptation measures are perceived by the workers. In addition to this, certain adaptation behaviours like changing or adjusting the daily normal behaviour to avoid air pollution exposure are not feasible as they have direct implications on daily wage earnings.

The rise in temperatures over the Himalayan regions due to climate change have shown profound effects on the seasonality and long‐term availability of snow melt runoff and glacier melt runoff. As per the sixth assessment report by the Intergovernmental Panel on Climate Change, the Himalayan glaciers are retreating and the fraction of precipitation falling as snow is altering. This study analyses the effect of snow‐glacier changes on the melt runoff (1986–2020) over the Upper Ganga river basin (up to Rishikesh) using a fully distributed model namely, Spatial Process in Hydrology (SPHY), which governs by Temperature Index Model approach with varying degree‐day factors. This study performs the two‐step calibration approach for model parameterization and to evaluate the snow and glacier melt runoff utilizing the observed discharge and real time satellite datasets. The SPHY derived snow cover was compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) derived snow covers. Results show that the SPHY modelled fractional snow covers found comparable to MODIS derived snow covers (~70%–90% correlation). The R² based comparison of SPHY modelled runoff (Q) and observed Q at two gauges namely, Maneri (~0.85) and Rishikesh (~0.80) found reasonable. For the assessment of glacier changes and corresponding melt runoff, temporal glacier maps (i.e., for years 1990, 2000, 2010, 2020) were prepared utilizing the Landsat imageries. Results showed a noticeable reduction in the glacier areas (~5% mass reduction in 30 years) and an increase in glacier melt runoff at different stations (e.g., at Bhojwasa ~3%–4% and at Rishikesh ~1%–2%). Similarly, a decrease in snow melt runoff (~3%–4%) and an increase in rainfall runoff (~3%) were computed. A month wise percent of change analysis has shown significant fluctuations in the magnitude and seasonality of all runoff components (i.e., snow Q, glacier Q, base Q and rainfall induced Q) in the Himalayan Upper Ganga basin.