Understanding the hydrology in the upstream mountainous part of the Karnali basin in Nepal is vital, considering the importance of streamflow for downstream nature conservation and water supply. We use a fully distributed hydrological model to understand the current hydrology, the associated vulnerability of the basin, and the importance of the different hydrological components in regulating flow. Downscaled ERA5 meteorological data is used to force the model for the period 1991–2022 at a high spatial resolution (500 meters). We calibrate our model using observed discharges, and the model performance is considered good with a reported Kling-Gupta efficiency of 0.84 and a bias of −3.33%. Our results show that 40% of the overall discharge generated in the Karnali basin originates from rain runoff, 35% from baseflow, 24% from snowmelt, and a negligible 0.8% from glaciers. The water balance components vary spatially in magnitude, but the overall monthly patterns are comparable. On average, the basin receives 1,485 mm/year of precipitation, peaking in July, and is a pronounced southwest region. The annual average evapotranspiration in the basin is 574 mm/year, and discharge is 914 mm/year. Analysis of anomalies reveals that the discharge has become increasingly more variable over the last decades and, therefore, less predictable. Our results also reveal that the basin is frequently experiencing meteorological droughts, often translating into a hydrological drought with a lag time of a month. The average duration of a hydrological drought period in the basin was about 6 months. Snow storage plays an important role in modulating these droughts, and variability in initial snow storage impacts basin streamflow for up to 6 months. A climate change-induced shift from snow to rain may therefore impact the climate resilience of the Karnali considerably.

The aerodynamic roughness of heat, moisture, and momentum of a natural surface are important parameters in atmospheric models, as they co-determine the intensity of turbulent transfer between the atmosphere and the surface. Unfortunately this parameter is often poorly known, especially in remote areas where neither high-resolution elevation models nor eddy-covariance measurements are available. In this study we adapt a bulk drag partitioning model to estimate the aerodynamic roughness length (z0m) such that it can be applied to 1D (i.e. unidirectional) elevation profiles, typically measured by laser altimeters. We apply the model to a rough ice surface on the K-transect (west Greenland Ice Sheet) using UAV photogrammetry, and we evaluate the modelled roughness against in situ eddy-covariance observations. We then present a method to estimate the topography at 1 m horizontal resolution using the ICESat-2 satellite laser altimeter, and we demonstrate the high precision of the satellite elevation profiles against UAV photogrammetry. The currently available satellite profiles are used to map the aerodynamic roughness during different time periods along the K-transect, that is compared to an extensive dataset of in situ observations. We find a considerable spatiooral variability in z0m, ranging between 10-4 m for a smooth snow surface and 10-1 m for rough crevassed areas, which confirms the need to incorporate a variable aerodynamic roughness in atmospheric models over ice sheets.

Glacier mass loss is recognized as a major contributor to current sea level rise. However, large uncertainties remain in projections of glacier mass loss on global and regional scales. We present an ensemble of 288 glacier mass and area change projections for the 21st century based on 11 glacier models using up to 10 general circulation models and four Representative Concentration Pathways (RCPs) as boundary conditions. We partition the total uncertainty into the individual contributions caused by glacier models, general circulation models, RCPs, and natural variability. We find that emission scenario uncertainty is growing throughout the 21st century and is the largest source of uncertainty by 2100. The relative importance of glacier model uncertainty decreases over time, but it is the greatest source of uncertainty until the middle of this century. The projection uncertainty associated with natural variability is small on the global scale but can be large on regional scales. The projected global mass loss by 2100 relative to 2015 (79 ± 56 mm sea level equivalent for RCP2.6, 159 ± 86 mm sea level equivalent for RCP8.5) is lower than, but well within, the uncertainty range of previous projections.