Snow sublimation is a loss of water from the snowpack to the atmosphere. So far, snow sublimation has remained unquantified in the Himalaya, prohibiting a full understanding of the water balance and glacier mass balance. Hence, we measured surface latent heat fluxes with an eddy covariance system on Yala Glacier (5,350 m a.s.l) in the Nepalese Himalaya to quantify the role snow sublimation plays in the water and glacier mass budget. Observations reveal that cumulative sublimation is 32 mm for a 32-day period from October to November 2016, which is high compared to observations in other regions in the world. Multiple turbulent flux parameterizations were subsequently tested against this observed sublimation. The bulk-aerodynamic method offered the best performance, and we subsequently used this method to estimate cumulative sublimation and evaporation at the location of the eddy covariance system for the 2016-2017 winter season, which is 125 and 9 mm respectively. This is equivalent to 21% of the annual snowfall. In addition, the spatial variation of total daily sublimation over Yala Glacier was simulated with the bulk-aerodynamic method for a humid and non-humid day. Required spatial fields of meteorological variables were obtained from high-resolution WRF simulations of the region in combination with field observations. The cumulative daily sublimation at the location of the eddy covariance system equals the simulated sublimation averaged over the entire glacier. Therefore, this location appears to be representative for Yala Glacier sublimation. The spatial distribution of sublimation is primarily controlled by wind speed. Close to the ridge of Yala Glacier cumulative daily sublimation is a factor 1.7 higher than at the location of the eddy covariance system, whereas it is a factor 0.8 lower at the snout of the glacier. This illustrates that the fraction of snowfall returned to the atmosphere may be much higher than 21% at wind-exposed locations. This is a considerable loss of water and illustrates the importance and need to account for sublimation in future hydrological and mass balance studies in the Himalaya.

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Temperature index (TI) models are convenient for modelling glacier ablation since they require only a few input variables and rely on simple empirical relations. The approach is generally assumed to be reliable at lower elevations (below 3500 m above sea level, a.s.l) where air temperature ( T a ) relates well to the energy inputs driving melt. We question this approach in High Mountain Asia (HMA). We study in-situ meteorological drivers of glacial ablation at two sites in central Nepal, between 2013 and 2017, using data from six automatic weather stations (AWS). During the monsoon, surface melt dominates ablation processes at lower elevations (between 4950 and 5380 m a.s.l.). As net shortwave radiation ( SW net ) is the main energy input at the glacier surface, albedo ( α ) and cloudiness play key roles while being highly variable in space and time. For these cases only, ablation can be calculated with a TI model, or with an Enhanced TI (ETI) model that includes a shortwave radiation ( SW ) scheme and site specific ablation factors. In the ablation zone during other seasons and during all seasons in the accumulation zone, sublimation and other wind-driven ablation processes also contribute to mass loss, and remain unresolved with TI or ETI methods.

Surface energy balance models are common tools to estimate melt rates of debris-covered glaciers. In the Himalayas, radiative fluxes are occasionally measured, but very limited observations of turbulent fluxes on debris-covered tongues exist to date. We present measurements collected between 26 September and 12 October 2016 from an eddy correlation system installed on the debris-covered Lirung Glacier in Nepal during the transition between monsoon and post-monsoon. Our observations suggest that surface energy losses through turbulent fluxes reduce the positive net radiative fluxes during daylight hours between 10 and 100%, and even lead to a net negative surface energy balance after noon. During clear days, turbulent flux losses increase to over 250 W m −2 mainly due to high sensible heat fluxes. During overcast days the latent heat flux dominates the turbulent losses and together they reach just above 100 W m −2. Subsequently, we validate the performance of three bulk approaches in reproducing the observations from the eddy correlation system. Large differences exist between the approaches, and accurate estimates of surface temperature, wind speed, and surface roughness are necessary for their performance to be reasonable. Moreover, the tested bulk approaches generally overestimate turbulent latent heat fluxes by a factor 3 on clear days, because the debris-covered surface dries out rapidly, while the bulk equations assume surface saturation. Improvements to bulk surface energy models should therefore include the drying process of the surface. A sensitivity analysis suggests that, in order to be useful in distributed melt models, an accurate extrapolation of wind speed, surface temperature and surface roughness in space is a prerequisite. By applying the best performing bulk model over a complete melt period, we show that turbulent fluxes reduce the available energy for melt at the debris surface by 17% even at very low wind speeds. Overall, we conclude that turbulent fluxes play an essential role in the surface energy balance of debris-covered glaciers and that it is essential to include them in melt models.

Abstract. Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However, snow cover data provide no direct information on the actual amount of water stored in a snowpack, i.e., the snow water equivalent (SWE). Therefore, in this study remotely sensed snow cover was combined with in situ observations and a modified version of the seNorge snow model to estimate (climate sensitivity of) SWE and snowmelt runoff in the Langtang catchment in Nepal. Snow cover data from Landsat 8 and the MOD10A2 snow cover product were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an ensemble Kalman filter (EnKF). Independent validations of simulated snow depth and snow cover with observations show improvement after data assimilation compared to simulations without data assimilation. The approach of modeling snow depth in a Kalman filter framework allows for data-constrained estimation of snow depth rather than snow cover alone, and this has great potential for future studies in complex terrain, especially in the Himalayas. Climate sensitivity tests with the optimized snow model revealed that snowmelt runoff increases in winter and the early melt season (December to May) and decreases during the late melt season (June to September) as a result of the earlier onset of snowmelt due to increasing temperature. At high elevation a decrease in SWE due to higher air temperature is (partly) compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature.

Seasonal snow cover is an important source of melt water for irrigation and hydropower production in many regions of the world, but can also be a cause of disasters, such as avalanches and floods. In the remote Himalayan environment there is a great demand for up-to-date information on the snow conditions for the purposes of planned hydropower development and disaster risk reduction initiatives. We describe and evaluate a snow mapping setup for the remote Langtang Valley in the Nepal Himalayas, which can deliver data for snow and water availability mapping all year round. The setup utilizes (1) robust and almost maintenance-free in-situ instrumentation with satellite transmission, (2) a freely available numerical snow model, and (3) estimation of model key parameters from local meteorological and snow observations as well as from freely available climatological data. Novel features in the model include the estimation of melt parameters and solid precipitation from passive gamma-radiation based snow sensor data, as well as improved parameterization and estimation of melt water refreezing (36% of total melt) within, and sublimation/evaporation (57 mm yr −1 ) from the snow pack. Evaluation of the model results show a reasonable fit with snow cover data from satellite images. As many of the high-mountain regions in central and eastern Nepal show high correlation (>0.8) with the estimated snow line elevation in the Langtang catchment, the results may provide a first-order approximation of the snow conditions for these areas too.

Seasonal snow is an important component of the Himalayan hydrological system, but a lack of observations at high altitude hampers understanding and forecasting of water availability in this region. Here, we use a passive gamma ray sensor that measures snow water equivalent (SWE) and complementary meteorological instruments installed at 4962 m a.s.l. in the Nepal Himalayas to quantify the evolution of SWE and snow depth over a 2-year period. We assess the accuracy, spatial representativeness and the applicability of the SWE and snow depth measurements using time-lapse camera imagery and field observations. The instrument setup performs well for snowpacks >50 mm SWE, but caution must be applied when interpreting measurements from discontinuous, patchy snow cover or those that contain lenses of refrozen meltwater. Over their typical ∼6-month lifetime, snowpacks in this setting can attain up to 200 mm SWE, of which 10-15% consists of mixed precipitation and rain-on-snow events. Precipitation gauges significantly underrepresent the solid fraction of precipitation received at this elevation by almost 40% compared to the gamma ray sensor. The application of sub-daily time-lapse camera imagery can help to correctly interpret and increase the reliability and representativeness of snowfall measurements. Our monitoring approach provides high quality, continuous, near-real time information that is essential to develop snow models in this data scarce region. We recommend that a similar instrument setup be extended into remote Himalayan environments to facilitate widespread snowpack monitoring and further our understanding of the high-altitude water cycle.

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