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An uncertain factor in assessing future sea level rise is the meltwater runoff buffering capacity of snow and firn on glaciers and ice caps. Field studies have resulted in observations of perennial firn aquifers (PFAs), which are bodies of water present deep in the firn layer and sheltered from cold surface conditions. PFAs can store surface melt, thereby acting as a buffer against sea level rise, and influence the thermodynamics of the firn layer. Furthermore, ice dynamics might be affected by the presence of liquid water through hydrofracturing and water transport to the bed, influencing bed properties and ice flow. In this study, we present results of applying the US Geological Survey (USGS) Modular Hydrological Model MODFLOW 6 to an observed perennial firn aquifer on the Lomonosovfonna ice cap in central Svalbard. The observations span a 3-year period, where a ground-penetrating radar (GPR) was used to measure the water table depth of the aquifer. We calibrate our model against these observations to infer a hydraulic conductivity of firn snow of  6.4 x 10^-4 m s⁻¹ and then use the model to project the aquifer evolution over the period 1957–2019. We find that the aquifer was present in 1957 and that it steadily grew over the modeled period with a relative increase of about 15 % in water table depth. On an annual basis, the aquifer exhibits sharp water table increases during the melt season, followed by slow seepage through the cold season.

This is chapter 5 of the State of Environmental Science in Svalbard (SESS) report 2022.

Snowpack covers 60-100% of all land in Svalbard, depending on the season, and it is very sensitive to changes in climate. Knowledge about the snowpack is important not just in itself, but also to understand how snow cover affects other components of Svalbard’s natural environment – land, sea, permafrost, glaciers, and the ecosystems that they support. Monitoring the evolution of Svalbard’s snow cover will be crucial as the world’s climate continues to warm.

Ground-penetrating radars (GPRs) towed by snowmobile across glaciers and snowfields provide vital information about snowpack thickness and structure. Ideally, such surveys should be repeated annually for continuous monitoring of climate-induced change. Three decades ago, a GPR programme catalogued regional variations in snow accumulation. This should be repeated and expanded to cover all of Svalbard. The GPR method should also be further developed e.g. by mounting GPRs on drones, giving access to parts of glaciers that are too dangerous for researchers to visit. Lastly, women are encouraged to join the field of GPR-based research on snow.

Most of the GPR data collected so far are not currently available in any data repository. The comprehensive compilation of available studies presented in this report, and the recommendations for metadata and data quality, are important first steps to making GPR data more accessible.

Snow in the mountains is essential for the water cycle in cold regions. The complexity of the snow processes in such an environment makes it challenging for accurate snow and runoff predictions. Various snow modelling approaches have been developed, especially to improve snow predictions. In this study, we compared the ability to improve runoff predictions in the Överuman Catchment, Northern Sweden, using different parametric representations of snow distribution. They included a temperature-based method, a snowfall distribution (SF) function based on wind characteristics and a snow depletion curve (DC). Moreover, we assessed the benefit of using distributed snow observations in addition to runoff in the hydrological model calibration. We found that models with the SF function based on wind characteristics better predicted the snow water equivalent (SWE) close to the peak of accumulation than models without this function. For runoff predictions, models with the SF function and the DC showed good performances (median Nash–Sutcliffe efficiency equal to 0.71). Despite differences among the calibration criteria for the different snow process representations, snow observations in model calibration added values for SWE and runoff predictions.

Reliable and accurate mapping of snow cover are essential in applications such as water resource management, hazard forecasting, calibration and validation of hydrological models and climate impact assessments. Optical remote sensing has been utilized as a tool for snow cover monitoring over the last several decades. However, consistent long-term monitoring of snow cover can be challenging due to differences in spatial resolution and retrieval algorithms of the different generations of satellite-based sensors. Snow models represent a complementary tool to remote sensing for snow cover monitoring, being able to fill in temporal and spatial data gaps where a lack of observations exist. This study utilized three optical remote sensing datasets and two snow models with overlapping periods of data coverage to investigate the similarities and discrepancies in snow cover estimates over Nordenskiöld Land in central Svalbard. High-resolution Sentinel-2 observations were utilized to calibrate a 20-year MODIS snow cover dataset that was subsequently used to correct snow cover fraction estimates made by the lower resolution AVHRR instrument and snow model datasets. A consistent overestimation of snow cover fraction by the lower resolution datasets was found, as well as estimates of the first snow-free day (FSFD) that were, on average, 10–15 days later when compared with the baseline MODIS estimates. Correction of the AVHRR time series produced a significantly slower decadal change in the land-averaged FSFD, indicating that caution should be exercised when interpreting climate-related trends from earlier lower resolution observations. Substantial differences in the dynamic characteristics of snow cover in early autumn were also present between the remote sensing and snow model datasets, which need to be investigated separately. This work demonstrates that the consistency of earlier low spatial resolution snow cover datasets can be improved by using current-day higher resolution datasets.

Projected climate warming and wettening will have a major impact on the state of glaciers and seasonal snow in High Arctic regions. Following up on a historical simulation (1957–2018) for Svalbard, we make future projections of glacier climatic mass balance (CMB), snow conditions on glaciers and land, and runoff, under Representative Concentration Pathways (RCP) 4.5 and 8.5 emission scenarios for 2019–60. We find that the average CMB for Svalbard glaciers, which was weakly positive during 1957–2018, becomes negative at an accelerating rate during 2019–60 for both RCP scenarios. Modelled mass loss is most pronounced in southern Svalbard, where the equilibrium line altitude is predicted to rise well above the hypsometry peak, leading to the first occurrences of zero accumulation-area ratio already by the 2030s. In parallel with firn line retreat, the total pore volume in snow and firn drops by as much as 70–80% in 2060, compared to 2018. Total refreezing remains largely unchanged, despite a marked change in the seasonal pattern towards increased refreezing in winter. Finally, we find pronounced shortening of the snow season, while combined runoff from glaciers and land more than doubles from 1957–2018 to 2019–60, for both scenarios.

Although many convincing, diverse, and sometimes competing models of glacier surging have been proposed, the observed behavior of surging glaciers does not fit into distinct categories, and suggests the presence of a universal mechanism driving all surges. On the one hand, recent simulations of oscillatory flow behavior through the description of transient basal drag hint at a fundamental underlying process. On the other hand, the proposition of a unified model of oscillatory flow through the concept of enthalpy adopts a systems based view, in an attempt to rather unify different mechanisms through a single universal measure. While these two general approaches differ in perspective, they are not mutually exclusive, and seem likely to complement each other. A framework incorporating both approaches would see the mechanics of basal drag describing ice flow velocity and surge propagation as a function of forcing by conditions at the glacier bed, in turn modulated through the unified measure of enthalpy.

Under a warming climate, an improved understanding of the water stored in snowpacks is becoming increasingly important for hydropower planning, flood risk assessment and water resource management. Due to inaccessibility and a lack of ground measurement networks, accurate quantification of snow water storage in mountainous terrains still remains a major challenge. Remote sensing can provide dynamic observations with extensive spatial coverage, and has proved a useful means to characterize snow water equivalent (SWE) at a large scale. However, current SWE products show very low quality in the mountainous areas due to very coarse spatial resolution, complex terrain, large spatial heterogeneity and deep snow. With more high-quality satellite data becoming available from the development of satellite sensors and platforms, it provides more opportunities for better estimation of snow conditions. Meanwhile, machine learning provides an important technique for handling the big data offered from remote sensing. Using the overuman Catchment in Northern Sweden as a case study, this paper explores the potentials of machine learning for improving the estimation of mountain snow water storage using satellite observations, topographic factors, land cover information and ground SWE measurements from the spatially distributed snow survey. The results show that significantly improved SWE estimation close to the peak of snow accumulation can be achieved in the catchment using the random forest regression. This study demonstrates the potentials of machine learning for better understanding the snow water storage in mountainous areas.