Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 5.154 IF 5.154
  • IF 5-year value: 5.697 IF 5-year
    5.697
  • CiteScore value: 5.56 CiteScore
    5.56
  • SNIP value: 1.761 SNIP 1.761
  • IPP value: 5.30 IPP 5.30
  • SJR value: 3.164 SJR 3.164
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 59 Scimago H
    index 59
  • h5-index value: 49 h5-index 49
Volume 9, issue 2
Geosci. Model Dev., 9, 633–646, 2016
https://doi.org/10.5194/gmd-9-633-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 9, 633–646, 2016
https://doi.org/10.5194/gmd-9-633-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Model description paper 16 Feb 2016

Model description paper | 16 Feb 2016

ESCIMO.spread (v2): parameterization of a spreadsheet-based energy balance snow model for inside-canopy conditions

T. Marke et al.

Related authors

ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks
Gerhard Krinner, Chris Derksen, Richard Essery, Mark Flanner, Stefan Hagemann, Martyn Clark, Alex Hall, Helmut Rott, Claire Brutel-Vuilmet, Hyungjun Kim, Cécile B. Ménard, Lawrence Mudryk, Chad Thackeray, Libo Wang, Gabriele Arduini, Gianpaolo Balsamo, Paul Bartlett, Julia Boike, Aaron Boone, Frédérique Chéruy, Jeanne Colin, Matthias Cuntz, Yongjiu Dai, Bertrand Decharme, Jeff Derry, Agnès Ducharne, Emanuel Dutra, Xing Fang, Charles Fierz, Josephine Ghattas, Yeugeniy Gusev, Vanessa Haverd, Anna Kontu, Matthieu Lafaysse, Rachel Law, Dave Lawrence, Weiping Li, Thomas Marke, Danny Marks, Martin Ménégoz, Olga Nasonova, Tomoko Nitta, Masashi Niwano, John Pomeroy, Mark S. Raleigh, Gerd Schaedler, Vladimir Semenov, Tanya G. Smirnova, Tobias Stacke, Ulrich Strasser, Sean Svenson, Dmitry Turkov, Tao Wang, Nander Wever, Hua Yuan, Wenyan Zhou, and Dan Zhu
Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018,https://doi.org/10.5194/gmd-11-5027-2018, 2018
Short summary
The Rofental: a high Alpine research basin (1890–3770 m a.s.l.) in the Ötztal Alps (Austria) with over 150 years of hydrometeorological and glaciological observations
Ulrich Strasser, Thomas Marke, Ludwig Braun, Heidi Escher-Vetter, Irmgard Juen, Michael Kuhn, Fabien Maussion, Christoph Mayer, Lindsey Nicholson, Klaus Niedertscheider, Rudolf Sailer, Johann Stötter, Markus Weber, and Georg Kaser
Earth Syst. Sci. Data, 10, 151–171, https://doi.org/10.5194/essd-10-151-2018,https://doi.org/10.5194/essd-10-151-2018, 2018
Short summary
The importance of snowmelt spatiotemporal variability for isotope-based hydrograph separation in a high-elevation catchment
Jan Schmieder, Florian Hanzer, Thomas Marke, Jakob Garvelmann, Michael Warscher, Harald Kunstmann, and Ulrich Strasser
Hydrol. Earth Syst. Sci., 20, 5015–5033, https://doi.org/10.5194/hess-20-5015-2016,https://doi.org/10.5194/hess-20-5015-2016, 2016
Short summary
Multilevel spatiotemporal validation of snow/ice mass balance and runoff modeling in glacierized catchments
Florian Hanzer, Kay Helfricht, Thomas Marke, and Ulrich Strasser
The Cryosphere, 10, 1859–1881, https://doi.org/10.5194/tc-10-1859-2016,https://doi.org/10.5194/tc-10-1859-2016, 2016
Short summary
An open-source MEteoroLOgical observation time series DISaggregation Tool (MELODIST v0.1.1)
Kristian Förster, Florian Hanzer, Benjamin Winter, Thomas Marke, and Ulrich Strasser
Geosci. Model Dev., 9, 2315–2333, https://doi.org/10.5194/gmd-9-2315-2016,https://doi.org/10.5194/gmd-9-2315-2016, 2016
Short summary

Related subject area

Hydrology
Automated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0)
Zhen Yin, Sebastien Strebelle, and Jef Caers
Geosci. Model Dev., 13, 651–672, https://doi.org/10.5194/gmd-13-651-2020,https://doi.org/10.5194/gmd-13-651-2020, 2020
Short summary
glmGUI v1.0: an R-based graphical user interface and toolbox for GLM (General Lake Model) simulations
Thomas Bueche, Marko Wenk, Benjamin Poschlod, Filippo Giadrossich, Mario Pirastru, and Mark Vetter
Geosci. Model Dev., 13, 565–580, https://doi.org/10.5194/gmd-13-565-2020,https://doi.org/10.5194/gmd-13-565-2020, 2020
Short summary
The Canadian Hydrological Model (CHM) v1.0: a multi-scale, multi-extent, variable-complexity hydrological model – design and overview
Christopher B. Marsh, John W. Pomeroy, and Howard S. Wheater
Geosci. Model Dev., 13, 225–247, https://doi.org/10.5194/gmd-13-225-2020,https://doi.org/10.5194/gmd-13-225-2020, 2020
Short summary
WAYS v1: a hydrological model for root zone water storage simulation on a global scale
Ganquan Mao and Junguo Liu
Geosci. Model Dev., 12, 5267–5289, https://doi.org/10.5194/gmd-12-5267-2019,https://doi.org/10.5194/gmd-12-5267-2019, 2019
TOPMELT 1.0: a topography-based distribution function approach to snowmelt simulation for hydrological modelling at basin scale
Mattia Zaramella, Marco Borga, Davide Zoccatelli, and Luca Carturan
Geosci. Model Dev., 12, 5251–5265, https://doi.org/10.5194/gmd-12-5251-2019,https://doi.org/10.5194/gmd-12-5251-2019, 2019
Short summary

Cited articles

Blöschl, G. and Kirnbauer, R.: Point snowmelt models with different degrees of complexity – internal processes, J. Hydrol., 129, 127–147, 1991.
Braun, L. N.: Simulation of Snowmelt-Runoff in Lowland and Lower Alpine Regions of Switzerland, PhD thesis, ETH Zurich, Zürich, 1984.
Breuer, L., Eckhardt, K., and Frede, H.-G.: Plant parameter values for models in temperate climates, Ecol. Model., 169, 237–293, 2003.
Brock, B. W. and Arnold, N. S.: A spreadsheet-based (Microsoft Excel) point surface energy balance model for glacier and snow melt studies, Earth Surf. Proc. Land., 25, 649–658, 2000.
Buck, A. L.: New equations for computing vapor pressure and enhancement factor, J. Appl. Meteorol., 20, 1527–1532, 1981.
Publications Copernicus
Download
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...
Citation