Articles | Volume 9, issue 2
https://doi.org/10.5194/gmd-9-633-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-9-633-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
ESCIMO.spread (v2): parameterization of a spreadsheet-based energy balance snow model for inside-canopy conditions
T. Marke
CORRESPONDING AUTHOR
Institute of Geography, University of Innsbruck, Innsbruck, Austria
E. Mair
Institute of Geography, University of Innsbruck, Innsbruck, Austria
K. Förster
Institute of Geography, University of Innsbruck, Innsbruck, Austria
alpS – Centre for Climate Change Adaptation, Innsbruck, Austria
F. Hanzer
Institute of Geography, University of Innsbruck, Innsbruck, Austria
alpS – Centre for Climate Change Adaptation, Innsbruck, Austria
J. Garvelmann
Institute of Meteorology and Climate Research (IMK-IFU), Karlsruhe Institute of Technology (KIT), Garmisch-Partenkirchen, Germany
S. Pohl
Hydrology Department, University of Freiburg, Freiburg, Germany
M. Warscher
Institute of Meteorology and Climate Research (IMK-IFU), Karlsruhe Institute of Technology (KIT), Garmisch-Partenkirchen, Germany
U. Strasser
Institute of Geography, University of Innsbruck, Innsbruck, Austria
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Cited
9 citations as recorded by crossref.
- Revisiting Forest Effects on Winter Air Temperature and Wind Speed—New Open Data and Transfer Functions M. Klein et al. https://doi.org/10.3390/atmos12060710
- Inverse modelling of snow depths U. Schlink & D. Hertel https://doi.org/10.1016/j.envsoft.2018.01.010
- ‘Teflon Basin’ or Not? A High-Elevation Catchment Transit Time Modeling Approach J. Schmieder et al. https://doi.org/10.3390/hydrology6040092
- ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks G. Krinner et al. https://doi.org/10.5194/gmd-11-5027-2018
- Climate forcing controls on carbon terrestrial fluxes during shale weathering L. Stolze et al. https://doi.org/10.1073/pnas.2400230121
- Modelling forest snow processes with a new version of WaSiM K. Förster et al. https://doi.org/10.1080/02626667.2018.1518626
- openAMUNDSEN v1.0: an open-source snow-hydrological model for mountain regions U. Strasser et al. https://doi.org/10.5194/gmd-17-6775-2024
- Energy demand and yield enhancement for roof mounted photovoltaic snow mitigation systems I. Frimannslund et al. https://doi.org/10.1016/j.enbuild.2022.112602
- What Can We Learn from Comparing Glacio-Hydrological Models? E. Stoll et al. https://doi.org/10.3390/atmos11090981
9 citations as recorded by crossref.
- Revisiting Forest Effects on Winter Air Temperature and Wind Speed—New Open Data and Transfer Functions M. Klein et al. https://doi.org/10.3390/atmos12060710
- Inverse modelling of snow depths U. Schlink & D. Hertel https://doi.org/10.1016/j.envsoft.2018.01.010
- ‘Teflon Basin’ or Not? A High-Elevation Catchment Transit Time Modeling Approach J. Schmieder et al. https://doi.org/10.3390/hydrology6040092
- ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks G. Krinner et al. https://doi.org/10.5194/gmd-11-5027-2018
- Climate forcing controls on carbon terrestrial fluxes during shale weathering L. Stolze et al. https://doi.org/10.1073/pnas.2400230121
- Modelling forest snow processes with a new version of WaSiM K. Förster et al. https://doi.org/10.1080/02626667.2018.1518626
- openAMUNDSEN v1.0: an open-source snow-hydrological model for mountain regions U. Strasser et al. https://doi.org/10.5194/gmd-17-6775-2024
- Energy demand and yield enhancement for roof mounted photovoltaic snow mitigation systems I. Frimannslund et al. https://doi.org/10.1016/j.enbuild.2022.112602
- What Can We Learn from Comparing Glacio-Hydrological Models? E. Stoll et al. https://doi.org/10.3390/atmos11090981
Saved (final revised paper)
Latest update: 24 Jun 2026
Short summary
This article describes the extension of the ESCIMO.spread spreadsheet-based point energy balance snow model by (i) an advanced approach for precipitation phase detection, (ii) a concept for cold and liquid water storage consideration and (iii) a canopy sub-model that allows one to quantify the effect of a forest canopy on the meteorological conditions inside the forest as well as the simulation of snow accumulation and ablation inside a forest stand.
This article describes the extension of the ESCIMO.spread spreadsheet-based point energy balance...