The Himalayan region, a major source of fresh water, is recognized as a water tower of the world. Many perennial rivers originate from Nepal Himalaya, located in the central part of the Himalayan region. Snowmelt water is essential freshwater for living, whereas it poses flood disaster potential, which is a major challenge for sustainable development. Climate change also largely affects snowmelt hydrology. Therefore, river discharge measurement requires crucial attention in the face of climate change, particularly in the Himalayan region. The snowmelt runoff model (SRM) is a frequently used method to measure river discharge in snow-fed mountain river basins. This study attempts to investigate snowmelt contribution in the overall discharge of the Budhi Gandaki River Basin (BGRB) using satellite remote sensing data products through the application of the SRM model. The model outputs were validated based on station measured river discharge data. The results show that SRM performed well in the study basin with a coefficient of determination (R²) >0.880. Moreover, this study found that the moderate resolution imaging spectroradiometer (MODIS) snow cover data and European Centre for Medium-Range Weather Forecasts (ECMWF) meteorological datasets are highly applicable to the SRM in the Himalayan region. The study also shows that snow days have slightly decreased in the last three years, hence snowmelt contribution in overall discharge has decreased slightly in the study area. Finally, this study concludes that MOD10A2 and ECMWF precipitation and two-meter temperature products are highly applicable to measure snowmelt and associated discharge through SRM in the BGRB. Moreover, it also helps with proper freshwater planning, efficient use of winter water flow, and mitigating and preventive measures for the flood disaster.

Understanding the pattern of species distribution and the underlying mechanism is essential for conservation planning. Several climatic variables determine the species diversity, and the dependency of species on climate motivates ecologists and bio-geographers to explain the richness patterns along with elevation and environmental correlates. We used interpolated elevational distribution data to examine the relative importance of climatic variables in determining the species richness pattern of 26 species of gymnosperms in the longest elevation gradients in the world. Thirteen environmental variables were divided into three predictors set representing each hypothesis model (energy-water, physical-tolerance, and climatic-seasonality); to explain the species richness pattern of gymnosperms along the elevational gradient. We performed generalized linear models and variation partitioning to evaluate the relevant role of environmental variables on species richness patterns. Our findings showed that the gymnosperms’ richness formed a hump-shaped distribution pattern. The individual effect of energy-water predictor set was identified as the primary determinant of species richness. While, the joint effects of energy-water and physical-tolerance predictors have explained highest variations in gymnosperm distribution. The multiple environmental indicators are essential drivers of species distribution and have direct implications in understanding the effect of climate change on the species richness pattern.

Although substantial information had been generated on the effects of land use change on soil organic carbon (SOC) and total nitrogen (TN) storage, studies are absent on multifactorial effects of land use types, land use age, and elevation on SOC and TN storage. SOC and TN were therefore investigated in 30 field sites comprising natural forests, planted forests, shrub, and grasslands. SOC and TN stocks differed and correlated significantly with land use age; the C stocks correlates significantly with land use change compared the TN stocks. However, there was no relation between the C and N stocks with elevation, implying that SOC and TN are solely dependent on land use age. SOC sequestration potentials of the sampled ecosystems were 345.86, 293.19, 266.45, and 251.23 t ha⁻¹ for the natural forests, planted forests, shrub, and grasslands with total mean value of 289.18 t·ha⁻¹ (1,060.42 t·ha⁻¹ CO₂₋eq). A significant SOC stock loss (17.96%, 29.80%, and 37.66%) occurred in converting natural forests to planted forests, shrub, and grasslands, whereas gains (27.36%, 14.31%, and 5.71%) would occur in reconverting grassland to natural forests, planted forests, and shrublands. Therefore, the C that was lost during deforestation and conversion of natural forests into other land use types could not match the carbon gains thereafter. Our results suggest that land use change and land use age have influenced soil C and N stocks. Moreover, natural forests are better in ecological conservation and restoration of degraded lands. This study provides baseline information for C and N management in ecologically restored and degraded lands.