Spatial and temporal patterns of snowmelt refreezing in a Himalayan catchment

Recent progress has been made in quantifying snowmelt in the Himalaya. Although the conditions are favorable for refreezing, little is known about the spatial variability of meltwater refreezing, hindering a complete understanding of seasonal snowmelt dynamics. This study aims to improve our understanding about how refreezing varies in space and time. We simulated refreezing with the seNorge (v2.0) snow model for the Langtang catchment, Nepalese Himalaya, covering a 5-year period. Meteorological forcing data were derived from a unique elaborate network of meteorological stations and high-resolution meteorological simulations. The results show that the annual catchment average refreezing amounts to 122 mm w.e. (21% of the melt), and varies strongly in space depending on elevation and aspect. In addition, there is a seasonal altitudinal variability related to air temperature and snow depth, with most refreezing during the early melt season. Substantial intra-annual variability resulted from fluctuations in snowfall. Daily refreezing simulations decreased by 84% (annual catchment average of 19 mm w.e.) compared to hourly simulations, emphasizing the importance of using sub-daily time steps to capture melt-refreeze cycles. Climate sensitivity experiments revealed that refreezing is highly sensitive to changes in air temperature as a 2°C increase leads to a refreezing decrease of 35%

The Importance of Turbulent Fluxes in the Surface Energy Balance of a Debris-Covered Glacier in the Himalayas

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

The Importance of Snow Sublimation on a Himalayan Glacier

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

Climate change decisive for Asia’s snow meltwater supply

Streamflow in high-mountain Asia is influenced by meltwater from snow and glaciers, and determining impacts of climate change on the region’s cryosphere is essential to understand future water supply. Past and future changes in seasonal snow are of particular interest, as specifics at the scale of the full region are largely unknown. Here we combine models with observations to show that regional snowmelt is a more important contributor to streamflow than glacier melt, that snowmelt magnitude and timing changed considerably during 1979–2019 and that future snow meltwater supply may decrease drastically. The expected changes are strongly dependent on the degree of climate change, however, and large variations exist among river basins. The projected response of snowmelt to climate change indicates that to sustain the important seasonal buffering role of the snowpacks in high-mountain Asia, it is imperative to limit future climate change

Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

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 83.1ĝ€% 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

Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

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 83.1ĝ€% 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

Energy and mass balance dynamics of the seasonal snowpack at two high-altitude sites in the Himalaya

Snow dynamics play a crucial role in the hydrology of alpine catchments in the Himalaya. However, studies based on in-situ observations that elucidate the energy and mass balance of the snowpack at high altitude in this region are scarce. In this study, we use meteorological and snow observations at two high-altitude sites in the Nepalese Himalaya to quantify the mass and energy balance of the seasonal snowpack. Using a data driven experimental set-up we aim to understand the main meteorological drivers of snowmelt, illustrate the importance of accounting for the cold content dynamics of the snowpack, and gain insight into the role that snow meltwater refreezing plays in the energy and mass balance of the snowpack. Our results show an intricate relation between the sensitivity of melt and refreezing on the albedo, the importance of meltwater refreezing, and the amount of positive net energy used to overcome the cold content of the snowpack. The net energy available at both sites is primarily driven by the net shortwave radiation, and is therefore extremely sensitive to snow albedo measurements. We conclude that, based on observed snowpack temperatures, 21% of the net positive energy is used to overcome the cold content build up during the night. We also show that at least 32–34% of the snow meltwater refreezes again for both sites. Even when the cold content and refreezing are accounted for, excess energy is available beyond what is needed to melt the snowpack. We hypothesize that this excess energy may be explained by uncertainties in the measurement of shortwave radiation, an underestimation of refreezing due to a basal ice layer, a cold content increase due to fresh snowfall and the ground heat flux. Our study shows that in order to accurately simulate the mass balance of seasonal snowpacks in Himalayan catchments, simple temperature index models do not suffice and refreezing and the cold content needs to be accounted for

Climate change decisive for Asia’s snow meltwater supply

Streamflow in high-mountain Asia is influenced by meltwater from snow and glaciers, and determining impacts of climate change on the region’s cryosphere is essential to understand future water supply. Past and future changes in seasonal snow are of particular interest, as specifics at the scale of the full region are largely unknown. Here we combine models with observations to show that regional snowmelt is a more important contributor to streamflow than glacier melt, that snowmelt magnitude and timing changed considerably during 1979–2019 and that future snow meltwater supply may decrease drastically. The expected changes are strongly dependent on the degree of climate change, however, and large variations exist among river basins. The projected response of snowmelt to climate change indicates that to sustain the important seasonal buffering role of the snowpacks in high-mountain Asia, it is imperative to limit future climate change

Evaluation of a sub-kilometre NWP system in an Arctic fjord-valley system in winter

Terrain challenges the prediction of near-surface atmospheric conditions, even in kilometre-scale numerical weather prediction (NWP) models. In this study, the ALADIN-HIRLAM NWP system with 0.5 km horizontal grid spacing and an increased number of vertical levels is compared to the 2.5-km model system similar to the currently operational NWP system at the Norwegian Meteorological Institute. The impact of the increased resolution on the forecasts’ ability to represent boundary-layer processes is investigated for the period from 12 to 16 February 2018 in an Arctic fjord-valley system in the Svalbard archipelago. Model simulations are compared to a wide range of observations conducted during a field campaign. The model configuration with sub-kilometre grid spacing improves both the spatial structure and overall verification scores for the near-surface temperature and wind forecasts compared to the 2.5-km experiment. The sub-kilometre experiment successfully captures the wind channelling through the valley and the temperature field associated with it. In a situation of a cold-air pool development, the sub-kilometre experiment has a particularly high near-surface temperature bias at low elevations. The use of measurement campaign data, however, reveals some encouraging results, e.g. the sub-kilometre system has a more realistic vertical profile of temperature and wind speed, and the surface temperature sensitivity to the net surface energy is closer to the observations. This work demonstrates the potential of sub-kilometre NWP systems for forecasting weather in complex Arctic terrain, and also suggests that the increase in resolution needs to be accompanied with further development of other parts of the model system