Abstract
Glacier hydrology describes water movement over, through and under glaciers and ice sheets. Water reaching the ice bed influences ice motion and ice dynamical models, therefore requiring a good understanding of glacier hydrology, particularly water pressures and pathways. However, as in situ observations are sparse and methods for direct observations of water pathways and internal pressures are lacking, our understanding of the aforementioned pathways and pressure remains limited. Here, we present a method that allows the reconstruction of planar subsurface water flow paths and spatially reference water pressures. We showcase this method by reconstructing the 2D topology and the water pressure distribution of an englacial channel in Austre Brøggerbreen (Svalbard). The approach uses inertial measurements from submersible sensing drifters and reconstructs the flow path between given start and end coordinates. Validation on a supraglacial channel shows an average length error of 3.9 m (5.3 %). At the englacial channel, the average length error is 107 m (11.6 %) and the average pressure error 3.4 hPa (0.3 %). Our method allows mapping sub- and englacial flow paths and the pressure distribution within, thereby facilitating hydrological model validation. Further, our method also allows the reconstruction of other, previously unexplored, subsurface fluid flow paths.
Highlights
Water movement through and under glaciers and ice sheets in en- and subglacial drainage systems is an essential factor in the control of ice dynamics (Hubbard and Nienow, 1997; Fountain and Walder, 1998; Irvine-Fynn et al, 2011)
We tested our approach with the reconstruction of a supraglacial channel with known geometry (fig. 2(c))
We showed the topological reconstruction of a supra- and englacial channel on Austre Brøggerbreen (Svalbard)
Summary
Water movement through and under glaciers and ice sheets in en- and subglacial drainage systems is an essential factor in the control of ice dynamics (Hubbard and Nienow, 1997; Fountain and Walder, 1998; Irvine-Fynn et al, 2011) Such systems vary in space and time, and their configuration is traditionally inferred using the physical principles (Röthlisberger, 1972) and concepts of hydraulic potential (Shreve, 1972) developed 50 years ago (see review by Flowers (2015)). Direct observations to validate such hydrological models are sparse, increasing their uncertainty, and recent approaches have utilized Bayesian inversion modeling to fit hydrological models to sparse observations (Brinkerhoff et al, 2021; Irarrazaval et al, 2021) These approaches, still require field data (Brinkerhoff et al, 2021), which are hard to obtain.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have