Assigned Session: #AGM28: Generic Meeting Session
Mapping glacier ice thickness in Chile
Abstract ID: 28.7444 | Accepted as Poster | Poster | 2025-02-28 12:45 - 14:15 | Ágnes‐Heller‐Haus/Small Lecture Room
Jorge Andres Berkhoff Leal (0)
Fürst, Johaness (1), Sommer, Christian Sommer (1), Farias, David (2), Schaefer, Marius (3), Rodriguez, Jose Luis (3), Uribe, Jose (4)
Jorge Andres Berkhoff Leal ((0) Friedrich-Alexander-Universität, Wetterkreuz, 91058, Erlangen, Baviera, DE)
Fürst, Johaness (1), Sommer, Christian Sommer (1), Farias, David (2), Schaefer, Marius (3), Rodriguez, Jose Luis (3), Uribe, Jose (4)
(0) Friedrich-Alexander-Universität, Wetterkreuz, 91058, Erlangen, Baviera, DE
(1) Friedrich-Alexander-Universität, Wetterkreuz, 91058, Erlangen, Baviera, DE
(2) Universidad de Concepción, Concepción, Bio-Bio, CL
(3) Universidad Austral de Chile, Valdivia, Los Rios, CL
(4) Centro de Estudios Cientificos del sur (CEC), Valdivia, Los Rios, CL
(1) Friedrich-Alexander-Universität, Wetterkreuz, 91058, Erlangen, Baviera, DE
(2) Universidad de Concepción, Concepción, Bio-Bio, CL
(3) Universidad Austral de Chile, Valdivia, Los Rios, CL
(4) Centro de Estudios Cientificos del sur (CEC), Valdivia, Los Rios, CL
(2) Universidad de Concepción, Concepción, Bio-Bio, CL
(3) Universidad Austral de Chile, Valdivia, Los Rios, CL
(4) Centro de Estudios Cientificos del sur (CEC), Valdivia, Los Rios, CL
Knowledge of ice thickness is essential for understanding past and predicting future changes of glaciers systems in response to climatic changes. Various methods exist on how to best estimate ice thickness from surface information in data sparse regions. These estimates are vital as they serve as starting point for future glacier evolution under different climatic scenarios.
These projections serve to determine future sea-level contribution or to inform adaptation or mitigation strategies required in response to glacier retreat.
Methods for mapping glacier ice thickness typically utilize surface information and combine it with the perfect plasticity assumption, mass-conservation or the stress balance to infer the unknown thickness distribution. In data sparse regions, estimates remain largely unconstrained and might deviate considerably not only on local scales. Several maps of glacier ice thickness have been presented for Chile. Most of them however had global or at least a larger target region. So often site-specific measurements were not considered or at most for loose validation.
This presents the first systematic effort to integrate local field measurements conducted by the Chilean Water Directorate (DGA) between 2012 and 2014 into an ice thickness reconstruction. These measurements of a constant basal shear stress (τy) at the ice-bedrock interface to infer ice thickness and subglacial topography. This approach avoids overly complex parameterization and is particularly well-suited for data-sparse regions. For this study, ice thickness was reconstructed using surface elevation , glacier outlines and extensive GPR measurements.
Validation results demonstrated achieving a root mean square error of 4.7 meters and a bias of 0.65 meters compared to other evaluated models. These findings underscore the importance of integrating local measurements with advanced modeling techniques to enhance the accuracy of ice-thickness maps in Chile.
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