Integrating a global glacier model into local hydrological modeling: Impacts on melt contributions
- 1Oeschger Centre for Climate Change Research (Bern, Switzerland)
Abstract
Snow and glacier meltwater are critical water resources for mountain regions, yet their accurate representation in hydrological models remains challenging. As climate change alters the timing and magnitude of melt contributions, accurate partitioning between snow and glacier sources becomes increasingly important. To address this challenge, this study couples the hydrological model HBV (as implemented in the Raven modeling framework) with the Global Glacier Evolution Model GloGEM and integrates a snow redistribution scheme for application across 14 glaciated headwater catchments in Switzerland. At this local catchment scale, we investigate whether glacier constraints and the addition of snow redistribution reduce parameter equifinality and increase the reliability of melt contribution estimates. Simulations are evaluated against observed streamflow, gridded snow water equivalent data, and glacier melt data based on observed mass balance. The gravitational snow redistribution algorithm successfully prevents unrealistic high-elevation snow accumulation and improves catchment average SWE simulation performance. Uncoupled HBV configurations outperform coupled HBV-GloGEM setups according to streamflow metrics. However, this superior performance is achieved by simulating glacier melt rates exceeding GloGEM estimates and glacier storage change data by factors of 2–3 in some catchments, effectively using glacier ice to offset precipitation biases in forcing data, which can be critical for climate change impact studies. Within a single model configuration, glacier melt contributions can vary by more than a factor of three among the best-performing parameter sets, and by up to an order of magnitude in weakly glacierized catchments, highlighting severe parameter equifinality. Coupling the HBV model with GloGEM (calibrated using glacier-specific geodetic mass balance) produces glacier melt consistent with observations and substantially improves identifiability of both glacier and snowmelt contributions. Despite lower streamflow performance metrics, glacier melt constraints prevent compensatory errors that would compromise projections as melt dynamics shift under climate change. Applying this framework to future climate scenarios and integrating additional constraints such as snow observations may further improve the reliability of melt contribution projections.
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BibTeX
@article{berg2026integrating,
title = {{Integrating a global glacier model into local hydrological modeling: Impacts on melt contributions}},
author = {Justine Berg and Pascal Horton and Martina Kauzlaric and Alexandra von der Esch and Bettina Schaefli},
journal = {Hydrology and Earth System Sciences},
year = {2026},
month = oct,
doi = {10.5194/hess-30-6511-2026},
url = {https://doi.org/10.5194/hess-30-6511-2026},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Integrating a global glacier model into local hydrological modeling: Impacts on melt contributions
AU - Justine Berg
AU - Pascal Horton
AU - Martina Kauzlaric
AU - Alexandra von der Esch
AU - Bettina Schaefli
PY - 2026
DA - 2026/10/23
JO - Hydrology and Earth System Sciences
DO - 10.5194/hess-30-6511-2026
UR - https://doi.org/10.5194/hess-30-6511-2026
AB - Snow and glacier meltwater are critical water resources for mountain regions, yet their accurate representation in hydrological models remains challenging. As climate change alters the timing and magnitude of melt contributions, accurate partitioning between snow and glacier sources becomes increasingly important. To address this challenge, this study couples the hydrological model HBV (as implemented in the Raven modeling framework) with the Global Glacier Evolution Model GloGEM and integrates a snow redistribution scheme for application across 14 glaciated headwater catchments in Switzerland. At this local catchment scale, we investigate whether glacier constraints and the addition of snow redistribution reduce parameter equifinality and increase the reliability of melt contribution estimates. Simulations are evaluated against observed streamflow, gridded snow water equivalent data, and glacier melt data based on observed mass balance. The gravitational snow redistribution algorithm successfully prevents unrealistic high-elevation snow accumulation and improves catchment average SWE simulation performance. Uncoupled HBV configurations outperform coupled HBV-GloGEM setups according to streamflow metrics. However, this superior performance is achieved by simulating glacier melt rates exceeding GloGEM estimates and glacier storage change data by factors of 2–3 in some catchments, effectively using glacier ice to offset precipitation biases in forcing data, which can be critical for climate change impact studies. Within a single model configuration, glacier melt contributions can vary by more than a factor of three among the best-performing parameter sets, and by up to an order of magnitude in weakly glacierized catchments, highlighting severe parameter equifinality. Coupling the HBV model with GloGEM (calibrated using glacier-specific geodetic mass balance) produces glacier melt consistent with observations and substantially improves identifiability of both glacier and snowmelt contributions. Despite lower streamflow performance metrics, glacier melt constraints prevent compensatory errors that would compromise projections as melt dynamics shift under climate change. Applying this framework to future climate scenarios and integrating additional constraints such as snow observations may further improve the reliability of melt contribution projections.
SN - 1607-7938
SN - 1027-5606
ER -