ESCIMO.spread (v2): parameterization of a spreadsheet-based energy balance snow model for inside-canopy conditions
- 1Institute of Geography, University of Innsbruck, Innsbruck, Austria
- 2Institute of Meteorology and Climate Research (IMK-IFU), Karlsruhe Institute of Technology (KIT), Garmisch-Partenkirchen, Germany
- 3alpS – Centre for Climate Change Adaptation, Innsbruck, Austria
- 4Hydrology Department, University of Freiburg, Freiburg, Germany
Abstract. 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 method for cold content and liquid water storage consideration and (iii) a canopy sub-model that allows the quantification of canopy effects on the meteorological conditions inside the forest as well as the simulation of snow accumulation and ablation inside a forest stand. To provide the data for model application and evaluation, innovative low-cost snow monitoring systems (SnoMoS) have been utilized that allow the collection of important meteorological and snow information inside and outside the canopy. The model performance with respect to both, the modification of meteorological conditions as well as the subsequent calculation of the snow cover evolution, are evaluated using inside- and outside-canopy observations of meteorological variables and snow cover evolution as provided by a pair of SnoMoS for a site in the Black Forest mountain range (southwestern Germany). The validation results for the simulated snow water equivalent with Nash–Sutcliffe model efficiency values of 0.81 and 0.71 and root mean square errors of 8.26 and 18.07 mm indicate a good overall model performance inside and outside the forest canopy, respectively. The newly developed version of the model referred to as ESCIMO.spread (v2) is provided free of charge together with 1 year of sample data including the meteorological data and snow observations used in this study.