40Large volcanic eruptions on Earth commonly occur with collapse of the roof of a crustal magma 41 reservoir, forming a caldera. Only a few such collapses occur per century and lack of detailed 42 observations has obscured insight on mechanical interplay between collapse and eruption. We use Calderas are 1 -100 km diameter depressions found in volcanic regions of Earth and other planets. basaltic andesite) intrusive activity and eruptions (2,(9)(10)(11)(12). 59The consensus from field and modelling studies is that caldera collapse progresses from initial 60 surface downsag to fault-controlled subsidence (1, 8, 13, 14). The pre-collapse topography is obtained by subtracting the subsidence observed at the surface. As we recorded the caldera subsidence mainly on the ice (Fig. 1, Fig. S1), we made corrections and (Fig. 3A). We therefore conclude that suggestions of a large increase in ice flow out of the caldera 147 during these events (25) cannot be fitted with our data. 148Bedrock subsidence exceeding 1 m occurred within an area of 110 km 2 that extended beyond the 149 pre-existing caldera (Fig. 1, Fig. S1). After termination of collapse the total subsidence at the pre-150 existing caldera rims amounted to 3 to 11 meters ( Fig. 1D and 1E). Using subglacial radio-echo GPS station in the center of the caldera (Fig. 1A), including the rate of vertical rate of ice surface Cumulative number of M>4 caldera earthquakes, with magnitude evolution colored in red, blue and 176 grey representing clusters on the southern rim, the northern rim and smaller clusters, respectively 177 (see Fig. S5). E) Cumulative seismic moment for M>4 caldera earthquakes. from analysis of subaerial gas measurements (Fig. 4). This depth concurs with our regional on FTIR and Multi-GAS measurements (24). 194Seismicity and subsurface structure 195 We used seismic data and Distinct Element Method (DEM) numerical modelling (24), to 196 characterize the deeper collapse structure as the reactivation of a steeply-inclined ring fault (Fig. 5). 197We mostly observed seismicity at depths of 0-9 km beneath the northern and southern caldera rims 198( Fig. 5B), with earthquakes being more numerous on the northern rim. This spatial pattern of 199 seismicity is consistent with fracturing above a deflating magma reservoir that was elliptical in (Fig. 5C, D). Our best fitting models had preexisting faults dipping out at 80-85¡ from the caldera 207 center on the north side and at 85-90¡ toward the caldera center on the south side. The modeled pre- 208existing faults lay at 1-2 km below the surface on the north side and 3-4 km on the south side. 209Modeling of a more complex fault geometry or the inclusion of greater material heterogeneity may 210 further improve the data fit, but presently lacks robust geophysical constraints. components of the observed earthquakes at B ‡rdarbunga. We, however, narrowed down on 222 plausible solutions by using the micro-earthquakes (Fig. 5A). The moment tensor solutions are well 223 constrained, but the inferred d...

Abstract. In response to climate change, most glaciers are losing mass and hence contribute to sea-level rise. Repeated and accurate mapping of their surface topography is required to estimate their mass balance and to extrapolate/calibrate sparse field glaciological measurements. In this study we evaluate the potential of sub-meter stereo imagery from the recently launched Pléiades satellites to derive digital elevation models (DEMs) of glaciers and their elevation changes. Our five evaluation sites, where nearly simultaneous field measurements were collected, are located in Iceland, the European Alps, the central Andes, Nepal and Antarctica. For Iceland, the Pléiades DEM is also compared to a lidar DEM. The vertical biases of the Pléiades DEMs are less than 1 m if ground control points (GCPs) are used, but reach up to 7 m without GCPs. Even without GCPs, vertical biases can be reduced to a few decimetres by horizontal and vertical co-registration of the DEMs to reference altimetric data on ice-free terrain. Around these biases, the vertical precision of the Pléiades DEMs is ±1 m and even ±0.5 m on the flat glacier tongues (1σ confidence level). Similar precision levels are obtained in the accumulation areas of glaciers and in Antarctica. We also demonstrate the high potential of Pléiades DEMs for measuring seasonal, annual and multiannual elevation changes with an accuracy of 1 m or bet-

Abstract. In this paper we describe how recent high-resolution digital elevation models (DEMs) can be used to extract glacier surface DEMs from old aerial photographs and to evaluate the uncertainty of the mass balance record derived from the DEMs. We present a case study for Drangajökull ice cap, NW Iceland. This ice cap covered an area of 144 km2 when it was surveyed with airborne lidar in 2011. Aerial photographs spanning all or most of the ice cap are available from survey flights in 1946, 1960, 1975, 1985, 1994 and 2005. All ground control points used to constrain the orientation of the aerial photographs were obtained from the high-resolution lidar DEM. The lidar DEM was also used to estimate errors of the extracted photogrammetric DEMs in ice- and snow-free areas, at nunataks and outside the glacier margin. The derived errors of each DEM were used to constrain a spherical semivariogram model, which along with the derived errors in ice- and snow-free areas were used as inputs into 1000 sequential Gaussian simulations (SGSims). The simulations were used to estimate the possible bias in the entire glaciated part of the DEM and the 95 % confidence level of this bias. This results in bias correction varying in magnitude between 0.03 m (in 1975) and 1.66 m (in 1946) and uncertainty values between ±0.21 m (in 2005) and ±1.58 m (in 1946). Error estimation methods based on more simple proxies would typically yield 2–4 times larger error estimates. The aerial photographs used were acquired between late June and early October. An additional seasonal bias correction was therefore estimated using a degree-day model to obtain the volume change between the start of 2 glaciological years (1 October). This correction was largest for the 1960 DEM, corresponding to an average elevation change of −3.5 m or approx. three-quarters of the volume change between the 1960 and the 1975 DEMs. The total uncertainty of the derived mass balance record is dominated by uncertainty in the volume changes caused by uncertainties of the SGSim bias correction, the seasonal bias correction and the interpolation of glacier surface where data are lacking. The record shows a glacier-wide mass balance rate of Ḃ  = −0.26 ± 0.04 m w.e. a−1 for the entire study period (1946–2011). We observe significant decadal variability including periods of mass gain, peaking in 1985–1994 with Ḃ  = 0.27 ± 0.11 m w.e. a−1. There is a striking difference when Ḃ  is calculated separately for the western and eastern halves of Drangajökull, with a reduction of eastern part on average  ∼  3 times faster than the western part. Our study emphasizes the need for applying rigorous geostatistical methods for obtaining uncertainty estimates of geodetic mass balance, the importance of seasonal corrections of DEMs from glaciers with high mass turnover and the risk of extrapolating mass balance record from one glacier to another even over short distances.

Abstract. Sub-meter resolution, stereoscopic satellite images allow for the generation of accurate and high-resolution digital elevation models (DEMs) over glaciers and ice caps. Here, repeated stereo images of Drangajökull ice cap (NW Iceland) from Pléiades and WorldView2 (WV2) are combined with in situ estimates of snow density and densification of firn and fresh snow to provide the first estimates of the glacier-wide geodetic winter mass balance obtained from satellite imagery. Statistics in snow- and ice-free areas reveal similar vertical relative accuracy (<  0.5 m) with and without ground control points (GCPs), demonstrating the capability for measuring seasonal snow accumulation. The calculated winter (14 October 2014 to 22 May 2015) mass balance of Drangajökull was 3.33 ± 0.23 m w.e. (meter water equivalent), with ∼ 60 % of the accumulation occurring by February, which is in good agreement with nearby ground observations. On average, the repeated DEMs yield 22 % less elevation change than the length of eight winter snow cores due to (1) the time difference between in situ and satellite observations, (2) firn densification and (3) elevation changes due to ice dynamics. The contributions of these three factors were of similar magnitude. This study demonstrates that seasonal geodetic mass balance can, in many areas, be estimated from sub-meter resolution satellite stereo images.