The ice-free topography of Svalbard

We present a first version of the Svalbard ice-free topography (SVIFT1.0) using a mass-conserving approach for mapping glacier ice thickness. SVIFT1.0 is informed by more than 900’000 point-measurements of glacier thickness, totalling almost 8’300 km of thickness profiles. It is publicly available for download. Our estimate for the total ice volume is 6’253km3, equivalent to 1.6cm sea-level rise. The thickness map suggests that 13% of the glacierised area is grounded below sea-level. Thickness values are provided together with a map of error estimates that comprise uncertainties in the thickness surveys as well as in other input variables. Aggregated error estimates are used to define a likely ice-volume range of 5’200-7’400km3. The ice-front thickness of marine-terminating glaciers is a key quantity for ice-loss attribution because it controls the potential ice discharge by iceberg calving into the ocean. We find a mean ice-front thickness of 133m for the archipelago

Modeling the snow depth variability with a high-resolution Lidar data set and nonlinear terrain dependency

In the mountains of Norway, snow depth (SD) is highly variable due to strong winds and open terrain. To investigate snow conditions on one of Europe's largest mountain plateaus, Hardangervidda, we conducted snow measurement campaigns in spring 2008 and 2009 using airborne lidar scanning at the approximate time of annual snow maximum (mid‐April). From 658 empirical distributions of SD at Hardangervidda, each comprised about 4,000 SD values sampled from a grid cell of 0.5 km2, quantitative tests have shown that the gamma distribution is a better fit for SD than the normal and log‐normal distributions. When aggregating snow and terrain data from 10 × 10 m to 0.5 km2, we find that the standard deviation of the terrain parameter squared slope, land cover, and the mean SD are highly correlated (0.7, 0.52, and 0.89) to the standard deviation of SD. A model for SD variance is proposed that, in addition to addressing the dependencies between the variability of SD and the terrain characteristics, also takes into account the observed nonlinear relationship between the mean and the standard deviation of SD. When validated against observed SD variance retrieved from the same area, the model explains 81–83% of the observed variance for spatial scales of 0.5 and 5.1 km2, which compares favorably to previous models. The model parameters can be determined from a GIS analysis of a detailed digital terrain and land cover model and will hence not increase the number of calibration parameters when implemented in environmental models

Multiscale spatial variability of lidar-derived and modeled snow depth on Hardangervidda, Norway

This study presents results from an Airborne Laser Scanning (ALS) mapping survey of snow depth on the mountain plateau Hardangervidda, Norway, in 2008 and 2009 at the approximate time of maximum snow accumulation during the winter. The spatial extent of the survey area is >240 km2. Large variability is found for snow depth at a local scale (2 m2), and similar spatial patterns in accumulation are found between 2008 and 2009. The local snow-depth measurements were aggregated by averaging to produce new datasets at 10, 50, 100, 250 and 500 m2 and 1 km2 resolution. The measured values at 1 km2 were compared with simulated snow depth from the seNorge snow model (www.senorge.no), which is run on a 1 km2 grid resolution. Results show that the spatial variability decreases as the scale increases. At a scale of about 500 m2 to 1 km2 the variability of snow depth is somewhat larger than that modeled by seNorge. This analysis shows that (1) the regional-scale spatial pattern of snow distribution is well captured by the seNorge model and (2) relatively large differences in snow depth between the measured and modeled values are present

Glaciers in Svalbard : mass balance, runoff and freshwater flux

Gain or loss of the freshwater stored in Svalbard glaciers has both global implications for sea level and, on a more local scale, impacts upon the hydrology of rivers and the freshwater flux to fjords. This paper gives an overview of the potential runoff from the Svalbard glaciers. The freshwater flux from basins of different scales is quantified. In small basins (A < 10 km2), the extra runoff due to the negative mass balance of the glaciers is related to the proportion of glacier cover and can at present yield more than 20% higher runoff than if the glaciers were in equilibrium with the present climate. This does not apply generally to the ice masses of Svalbard, which are mostly much closer to being in balance. The total surface runoff from Svalbard glaciers due to melting of snow and ice is roughly 25 ± 5 km3 a?1, which corresponds to a specific runoff of 680 ± 140 mm a?1, only slightly more than the annual snow accumulation. Calving of icebergs from Svalbard glaciers currently contributes significantly to the freshwater flux and is estimated to be 4 ± 1 km3 a?1 or about 110 mm a?1

Integrating a glacier retreat model into a hydrological model – Case studies of three glacierised catchments in Norway and Himalayan region

Glaciers are crucial in many countries where meltwater from glaciers is an important source of water for drinking water supply, irrigation, hydropower generation and the ecological system. Glaciers are also important indicators of climate change. They have been significantly altered due to the global warming and have subsequently affected the regional hydrological regime. However, few models are able to parameterise the dynamics of the glacier system and consequent runoff processes in glacier fed basins with desirable performance measures. To narrow this gap, we have developed an integrated approach by coupling a hydrological model (HBV) and a glacier retreat model (Δh-parameterisation) and tested this approach in three basins with different glacier coverage and subject to different climate and hydrologic regimes. Results show that the coupled model is able to give satisfactory estimations of runoff and glacier mass balance in the Nigardsbreen basin where the measured data are available to verify the results. In addition, the model can provide maps of snowpack distribution and estimate runoff components from glaciers