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Seasonal snow contributes significantly to the annual runoff in the Himalaya and both timing and volume are important for downstream users. In polar regions, meltwater refreezing within snowpacks has been well-studied. While the conditions in the Himalaya are considered favorable for refreezing, little is known about refreezing in this region, hindering a complete understanding of seasonal snowmelt dynamics. In this study, we simulated refreezing with the seNorge (v2.0) snow model for the Langtang catchment in the Nepalese Himalaya covering a 5-year period. Thereby, we aim to improve our understanding about how refreezing varies in space and time and to provide a framework for future snow modeling studies. The first part of this study focuses extensively on developing meteorological forcing data, which were derived from an unique elaborate network of meteorological stations and high-resolution meteorological simulations. The snow model was validated against in-situ snow observations and snow cover satellite data. In the second part of this study, we analyze the spatial and temporal refreezing patterns, and attempt to identify possible driving factors. The results show that the annual average refreezing amounts to 122 mm (21% of the total melt). We found that the magnitude of refreezing varies strongly in space depending on elevation and aspect. In addition, there is a strong seasonal altitudinal variability related to air temperature and snow depth, with most refreezing during the early melt season. We also found a substantial intra-annual variability, which mainly results from fluctuations of snowfall, highlighting the importance of using multi-year time series in refreezing assessments. Daily refreezing simulations decreased by 84% (to an average 19 mm year-1) compared to hourly simulations, emphasizing the importance of using sub-daily time steps to capture diurnal melt-refreeze cycles. Climate sensitivity experiments revealed that refreezing is highly sensitive to future changes in air temperature, as a temperature increase of 2°C leads to a refreezing decrease of 35%. We conclude that including refreezing with a sub-daily temporal resolution is highly relevant for understanding snow dynamics in the current and future climate of 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.

This is chapter 8 of the State of Environmental Science in Svalbard (SESS) report 2020 (https://sios-svalbard.org/SESS_Issue3). We document differences and similarities between three satellite-based and three model-based snow cover datasets, showing the geographical distribution and amount of snow across Svalbard for several periods from 1957 to 2020. The study shows that the datasets have many differences and that work needs to be done to accurately represent the snow cover in Svalbard. Low resolution datasets tend to predict longer winters than higher resolution datasets. We studied differences between the datasets and suggest methods to improve each dataset. Satellite data have been available since 1978, but early sensors had low resolution, and can only provide correct information over larger areas. Current sensors, available since 2016, have high resolution. Older low-resolution data may be improved by utilising overlapping time-series of high- and low-resolution data since local snow distribution patterns recur annually with a time-shift depending on average temperature and precipitation during the winter. The snow models predict in general the amount of snow (Snow Water Equivalent or SWE), but the timing of snow disappearance predicted by the models can be compared with estimates from satellite snow cover observations. Since the snow models depend on uncertain models of precipitation and temperature to estimate SWE there is potential to integrate satellite data to improve the models for snow in the future.

The Langtang catchment is a high mountain, third order catchment in the Gandaki basin in the Central Himalaya (28.2°N, 85.5°E), that eventually drains into the Ganges. The catchment spans an elevation range from 1400 to 7234 m a.s.l. and approximately one quarter of the area is glacierized. Numerous research projects have been conducted in the valley during the last four decades, with a strong focus on the cryospheric components of the catchment water balance. Since 2012 multiple weather stations and discharge stations provide measurements of atmospheric and hydrologic variables. Full weather stations are used to monitor at an hourly resolution all four radiation components (incoming and outgoing shortwave and longwave radiation; SWin/out and LWin/out), air temperature, humidity, wind speed and direction, and precipitation, and cover an elevational range of 3862–5330 m a.s.l. Air temperature and precipitation are monitored along elevation gradients for investigations of the spatial variability of the high mountain meteorology. Dedicated point‐scale observations of snow cover, depth and water equivalent as well as ice loss have been carried out over multiple years and complement the observations of the water cycle. All data presented is openly available in a database and will be updated annually., Multiple years of meteorological and hydrological observations as well as the cryosphere from a high mountain catchment in the Central Himalaya are available. Data includes multiple full weather stations throughout the catchment, measuring all components of the energy balance as well as precipitation. Discharge is monitored at the outlet of the catchment.

This report was commissioned by the Norwegian Environment Agency in order to provide basic information for use in climate change adaptation in Svalbard. It includes descriptions of historical, as well as projections for the future climate development in the atmosphere, hydrosphere, cryosphere and ocean, and it includes effects on the physical nature e.g. on permafrost and various types of landslides and avalanches. The projections for the future climate are based on results in the IPCCs fifth assessment report. The report is to a large degree an assessment of existing literature and model results. New results from atmosphere, ocean and hydrological models are, however, also presented. The report may be downloaded from the Norwegian Centre for Climate Service’s web portal www.klimaservicesenter.no.

This report was commissioned by the Norwegian Environment Agency in order to provide basic information for use in climate change adaptation in Svalbard. It includes descriptions of historical, as well as projections for the future climate development in the atmosphere, hydrosphere, cryosphere and ocean, and it includes effects on the physical nature e.g. on permafrost and various types of landslides and avalanches. The projections for the future climate are based on results in the IPCCs fifth assessment report. The report is to a large degree an assessment of existing literature and model results. New results from atmosphere, ocean and hydrological models are, however, also presented. The report may be downloaded from the Norwegian Centre for Climate Service’s web portal www.klimaservicesenter.no. Norwegian Environment Agency (Miljødirektoratet) Published Refereed Current 13 14 15 Sea Ice Sea state Sea surface temperature Subsurface temperature Surface currents Subsurface currents Ocean surface heat flux Manual (incl. handbook, guide, cookbook etc)

This report was commissioned by the Norwegian Environment Agency in order to provide basic information for use in climate change adaptation in Svalbard. It includes descriptions of historical, as well as projections for the future climate development in the atmosphere, hydrosphere, cryosphere and ocean, and it includes effects on the physical nature e.g. on permafrost and various types of landslides and avalanches. The projections for the future climate are based on results in the IPCCs fifth assessment report. The report is to a large degree an assessment of existing literature and model results. New results from atmosphere, ocean and hydrological models are, however, also presented. publishedVersion

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.

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.

Results for the projected future average snow depth are presented for the 19 counties of Norway at different elevations and at the time of four different popular holiday seasons during the winter and spring. The results show, among others, that the overall absolute changes in snow depth from 1971-2000 to 2071-2100 seem to be largest in the west coast counties (Rogaland to Møre og Romsdal) as well as in Nordland, and smallest in the inland counties of eastern Norway (Hedmark, Oppland, Buskerud).

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.