Due to climate change the drop in spring-water discharge poses a serious issue in the Himalayan region, especially in the higher of Himachal Pradesh. This study used different climatic factors along with long-term rainfall data to understand the decreasing trend in spring-water discharge. It was determined which climate parameter was most closely correlated with spring discharge volumes using a general as well as partial correlation plot. Based on 40 years (1981-2021) of daily average rainfall data, a rainfall-runoff model was utilised to predict and assess trends in spring-water discharge using the MIKE 11 NAM hydrological model. The model's effectiveness was effectively proved by the validation results (NSE = 0.79, R2 = 0.944, RMSE = 0.23, PBIAS = 32%). Model calibration and simulation revealed that both observed and simulated spring-water runoff decreased by almost 29%, within the past 40 years. Consequently, reduced spring-water discharge is made sensitive to the hydrological (groundwater stress, base flow, and stream water flow) and environmental entities (drinking water, evaporation, soil moisture, and evapotranspiration). This study will help researchers and policymakers to think and work on the spring disappearance and water security issues in the Himalayan region.

The present study proposes a rapid visual screening methodology for multi-hazard vulnerability assessment (termed as MH-RVS) of reinforced concrete (RC) buildings in the Indian Himalayan region considering earthquakes, debris flow, debris flood, and soil subsidence. An extensive field survey of 1200 buildings was conducted in three hill towns situated in the Northwestern Indian Himalayan region to identify prevalent multi-hazard vulnerability attributes. The presented MH-RVS methodology is statistically developed based on the information obtained from the current field survey and existing post-hazard reconnaissance studies. The proposed methodology effectively addresses the concern of underpredicting the expected damage states of RC buildings situated in hilly regions subjected to multi-hazard scenarios when they are assessed using RVS methodologies of seismic vulnerability assessment. Further, a simplified MH-RVS form is developed to collect field data and conveniently segregate the RC buildings based on their expected damage state under multi-hazard scenarios involving earthquakes, debris flow, debris flood, and soil subsidence. Stakeholders and decision-makers can use the proposed MH-RVS methodology to assess the perceived vulnerability of RC buildings in the Indian Himalayan region and devise timely strategies for structural strengthening and risk mitigation.

The Lesser Himalayan regions face significant geotechnical challenges due to unstable and erosive soil. This study investigates stabilizing these soils with Nano-silica (NS), a reliant additive that has been demonstrated to enhance soil mechanical properties. A comprehensive set of investigations, including multiple laboratory analyses, was conducted to evaluate the mechanical and physical characteristics of problematic soil stabilized with NS. The study also includes reliability analysis to assess the long-term performance and durability of the treated soil. The results of the experiment showed that adding NS greatly increased the soil's compressibility. More precisely, the right amount of NS increased the strength of the problematic soil and resulted in a notable rise in compressibility. According to the durability test results, stabilizing problematic soil with NS and allowing it to cure preserves its improved properties for an extended length of time. Reliability research utilizing probabilistic methodologies showed that applying NS considerably decreased the likelihood of problematic soil failure. The findings show that NS has the potential to be a stable, troublesome soil stabilizer that can lower the probability of soil failure in the Lesser Himalayan regions over the long run. This work provides a foundational understanding for future applications and paves the way for the construction of more robust infrastructure in mountainous terrain.

Studies in aerosol properties, types and sources in the Himalayas are important for atmospheric and climatic issues due to high aerosol loading in the neighboring plains. This study uses in situ measurements of aerosol optical and microphysical properties obtained during the Ganges Valley Aerosol eXperiment (GVAX) at Nainital, India over the period June 2011-March 2012, aiming to identify key aerosol types and mixing states for two particle sizes (PM1 and PM10). Using a classification matrix based on SAE vs. AAE thresholds (scattering vs. absorption Angstrom exponents, respectively), seven aerosol types are identified, which are highly dependent on particle size. An aerosol type named large/BC mix dominates in both PM1 (45.4%) and PM10 (46.9%) mass, characterized by aged BC mixed with other aerosols, indicating a wide range of particle sizes and mixing states. Small particles with low spectral dependence of the absorption (AAE < 1) account for 31.6% and BC-dominated aerosols for 14.8% in PM1, while in PM10, a large fraction (39%) corresponds to large/low-absorbing aerosols and only 3.9% is characterized as BC-dominated. The remaining types consist of mixtures of dust and local emissions from biofuel burning and display very small fractions. The main optical properties e.g. spectral scattering, absorption, single scattering albedo, activation ratio, as well as seasonality and dependence on wind speed and direction of identified types are examined, revealing a large influence of air masses originating from the Indo-Gangetic Plains. This indicates that aerosols over the central Himalayas are mostly composed by mixtures of processed and transported polluted plumes from the plains. This is the first study that identifies key aerosol populations in the central Indian Himalayas based on in situ measurements and the results are highly important for aerosol-type inventories, chemical transport models and reducing the uncertainty in aerosol radiative forcing over the third pole. (C) 2020 Elsevier B.V. All rights reserved.