HydroFirn: A numerical model for large-scale multidimensional firn hydrology

TL;DR

HydroFirn introduces a novel large-scale, multidimensional firn hydrology model that improves understanding of meltwater dynamics and ice layer formation on the Greenland Ice Sheet.

Contribution

The paper presents a new efficient multidimensional, multiphase model for firn hydrology that accounts for heterogeneity and dynamic ice layer formation, surpassing previous one-dimensional models.

Findings

Model shows excellent agreement with analytic solutions for coupled flows.

Lateral heterogeneities significantly affect melt percolation depth.

Application to Greenland data enhances understanding of meltwater and ice layer processes.

Abstract

Observations show the multidimensional dynamics of meltwater and distribution of ice layers in the firn on the Greenland Ice Sheet. However, state-of-the-art large-scale models for firn hydrology are essentially one-dimensional, limiting their ability to explain observed datasets and contributing to uncertainty in surface mass balance and sea-level rise estimates. Here, we present a large-scale, multidimensional, multiphase, and thermomechanical model for the subsurface hydrology of firn. The model is highly efficient due to a novel algorithm in which an extra equation for pressure is solved only in saturated regions. Furthermore, the model can apply spatially heterogeneous boundary conditions to the unsaturated-saturated domain and allows for the dynamic formation of fully impermeable ice layers. The numerical results show excellent comparisons against analytic solutions to one- and two-dimensional problems that involve coupled unsaturated-saturated flows, thermodynamics, and phase change. We further apply the model to investigate field data from southwest Greenland and find that lateral heterogeneities strongly influence the depth of melt percolation and ice layer formation. Improved understanding of these local, multidimensional processes will provide physics-based constraints on firn densification, reduce uncertainty in converting altimetric elevation change to mass change, and improve estimates of freshwater fluxes to the ocean under a warming climate.