Brief communication: Inferring Glacier Equilibrium Line Altitudes in the Europe Alps with FROST

Abstract

The current pace of glacier retreat in the European Alps is unprecedented in the observational record and has significant implications for water resources and downstream ecosystems. Quantifying the future evolution of these systems requires physically based glacier models that are calibrated against observational data. Using the open-source Framework for assimilating Remote-sensing Observations for Surface mass balance Tuning (FROST), we infer mean Equilibrium Line Altitudes (ELAs) and other surface Mass Balance (SMB) parameters for 409 Alpine glaciers for the time period 2000–2019 using an Ensemble Kalman Filter. The method combines an elevation-dependent SMB model with ice dynamics from the Instructed Glacier Model (IGM). Validation against ELA estimates from in-situ measurements and end-of-summer snowline data shows good agreement, with Pearson correlation coefficients of r=0.74 and r=0.64, respectively. These results demonstrate that FROST enables satellite-based calibration of SMB parameters at regional scale. This study serves as a first step toward a more general framework for transient data assimilation in glacier modeling.