Rapid urbanization in Nepal is lead to unplanned land development, posing significant challenges for sustainable urban growth. This study conducted a comprehensive land use suitability analysis for urban development in Lalitpur District, Nepal, using Geographic Information System (GIS) and Multi-criteria Analysis (MCA) techniques including the weighted overlay and Analytic Hierarchy Process (AHP). By examining factors such as proximity to infrastructure which include land cover, population density, flood sensitivity, slope, Digital Elevation Model (DEM), proximity to brick factory and Central Business District (CBD), the study identified suitable areas for urban development in Lalitpur District. The study identified five suitability classes ranging from very low/restricted, low suitability, marginal suitability, moderate suitability and high suitability, with the weighted overlay method aligning more closely with real-world data using flood sensitivity and land cover data. The analysis identified five suitability classes for urban development: existing built-up area (34.36 km²), high suitability (57.66 km² ), moderate suitability (130.48 km² ), marginal suitability (106.12 km² ), low suitability (47.40 km² ) and very low/ restricted suitability (20.33 km² ). The findings of the analysis emphasize the importance of integrating environmental factors, risk factors in land use planning and urban development to preserve green spaces, protect water bodies, and ensure the long-term well-being of residents. The study provides valuable insights for decision-makers and urban planners, enabling them to make informed decisions and effective resource allocation for sustainable urban development.

In mountainous countries like Nepal, floods are a major challenge due to complex topography, intense snowmelt, and highly variable monsoon rainfall that drive frequent flooding events. This study focuses on the Hilly and Himalayan regions of Nepal, where flood monitoring and risk management are increasingly important for safeguarding vulnerable communities and infrastructure.

Our study focuses on Bhutan, a highly mountainous country where government authorities are strengthening air pollution monitoring efforts. To support further analysis and the monitoring strategy, we present the first high-resolution air quality simulations with the chemistry transport model WRF-CHIMERE over the western region of Bhutan at a spatial resolution of roughly 1 km. Increasing the horizontal resolution of the model improves its performance and reduces potential errors caused by excessive spatial averaging of meteorological and emission data with high spatial variability. However, the air pollutant emissions must be improved at a fine scale with better proxy, particularly for industries where improvements are still required. For the first time, we propose high resolution maps of air pollution (concentrations and deposition fields). Our simulations confirm that Bhutan valleys also suffer from air pollution mainly due to PM2.5 (concentrations exceeding 20 µg m−3) dominated by carbonaceous species, largely above the World Health Organization guidelines. Wildfires and anthropogenic activities release large amount of carbonaceous species and can also impact the glaciers by atmospheric fallout. Wildfires can locally contribute to 20 % of the total PM2.5 concentrations over a 15 d period, and theoretically, black carbon can be transported up to the highest peaks. Ecosystems are at risks with deposition fluxes of sulfur and nitrogen species comparable with other locations at risk in the world.

Extreme hydrometeorological events are increasingly threatening high-mountain environments such as the Himalayas. Sediment-rich debris floods in high-mountain environment pose challenges for satellite-based flood detection, as high turbidity and sediment mixing obscure conventional water spectral signature. This study evaluates the performance of multiple Sentinel-2 spectral indices (normalized difference vegetation index [NDVI], normalized differences water index [NDWI], modified NDWI [MNDWI], and water ratio index [WRI]) for mapping the June 2021 Melamchi River debris flood in central Nepal and assesses post-event vegetation dynamics using NDVI time-series analysis (2019–2024) within Google Earth Engine (GEE). Index-based threshold calibration using stratified random sampling captured open-water areas but tended to overestimate inundation under turbid conditions. In contrast, NDVI decline delineated zones of vegetation removal and sediment burial associated with high-energy debris flows, achieving an overall mapping accuracy of 83% with lower commission error relative to water indices. NDVI is therefore interpreted not as direct water detectors. but as a disturbance proxy in sediment-dominated flood environment. Seasonally consistent NDVI composites were used to assess multi-year vegetation trends. Results indicated that 73.85% of the mapped flood corridors exhibited negative NDVI slope between 2019 and 2024 with degradation spatially concentrated within flood-affected zones. While regional climate variability may also influence vegetation dynamics, the spatial coincidence of degradation with mapped sediment-dominated flood corridors suggests strong geomorphic disturbance effects. The study demonstrates a reproducible, cloud-based workflow for integrating flood extent mapping with multi-year vegetation monitoring in data-scarce mountain regions. These findings highlight the importance of combining disturbance sensitive vegetation indices with conventional water indices when assessing sediment-dominated flood events.

Our study focuses on Bhutan, a highly mountainous country where governmental authorities are increasingly monitoring air pollution. To support further analysis and the monitoring strategy, we present the first high-resolution air quality simulations with the chemistry transport model WRF-CHIMERE over the western region of Bhutan at a spatial resolution of roughly 1 km. Increasing the horizontal resolution of the model improve the performances, decreases potential errors due to too important spatial average of meteorological and emissions data having an high spatial variability. However, the air pollutant emissions must be improved at fine scale with better proxy, particularly for industries where improvement are still required. For the first time, we propose high resolution maps of air pollution (concentrations and deposition fields). Our simulations confirm that Bhutan valleys also suffer from air pollution mainly due to PM_2.5 (concentrations exceeding 20 µg m⁻³) dominated by carbonaceous species, largely above the World Health Organization guidelines. Wildfires and anthropogenic activities release large amount of carbonaceous species and can also impact the glaciers by atmospheric fallout. Wildfires can locally contribute to 20 % of the total PM_2.5 concentrations over a 15 days period, and theoretically, black carbon can be transported up to the highest peaks. Ecosystems are at risks with deposition fluxes of sulfur and nitrogen species comparable with other locations at risk in the world.