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

Abstract

Abstract. This article describes the extension of the ES-CIMO.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 insideand 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.

‘Teflon Basin’ or Not? A High-Elevation Catchment Transit Time Modeling Approach

“…We used the modified version [32] of the surface energy-balance model ESCIMO [43] to simulate the rain, snow and ice melt input fluxes and the respective δ 18 O values for 100 m elevation bands. ESCIMO has been successfully applied at low and high mountain sites, e.g., [44,45]. A similar setup like in our study has been already applied successfully [4,31,32], where snow melt δ 18 O was simulated as the weighted average of individual snowfall events without addressing isotopic fractionation explicitly.…”

ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks

“…Snowmelt timing is a critical climatic metric that is often incorrectly simulated by climate and dedicated snow models, but it is difficult to untangle the effects of the simulation of snow albedo from other processes because of the strong feedbacks involved Qu and Hall, 2014). An experiment in which snow albedo is fixed to 0.7, which is a typical snow pre-melt albedo (Harding and Pomeroy, 1996;Melloh et al, 2002;Wang et al, 2016), will enable evaluation of the effect of seasonal snow albedo variations and biases.…”

Revisiting Forest Effects on Winter Air Temperature and Wind Speed—New Open Data and Transfer Functions

“…The effective leaf area index (LAI) is the most important factor of the metadata because it summarizes the distribution of leaves, which has great influence on biological and physical processes in the forest and, thus, on micrometeorology [12]. The existing transfer functions apply the effective LAI [15], referring to the definition according to [12], which determines that tree trunks, branches, and leaves are included, but not aggregating effects, which describe that leaves are not randomly distributed and cover each other. The LAI value mentioned in this article and contained in the dataset adheres to this definition.…”

“…Empirical transfer functions for air temperature and wind speed are already available in the literature [11,[13][14][15][20][21][22][23] and used in climate and snow models.…”