&amp;lt;i&amp;gt;ESCIMO.spread&amp;lt;/i&amp;gt; – a spreadsheet-based point snow surface energy balance model to calculate hourly snow water equivalent and melt rates for historical and changing climate conditions

Strasser, Ulrich; Marke, Thomas

doi:10.5194/gmd-3-643-2010

Cited by 23 publications

(25 citation statements)

References 18 publications

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“…Due to a lack of suitable snow data for the calibration of a simple parameterised snow model and the availability of the appropriate climatic input data, a point-energy-balance based approach was chosen. This study uses a modified implementation of ESCIMO (Energy Balance Snow Cover Integrated Model by Strasser and Marke, 2010), based on ESCIMO.spread and requires hourly input values for air temperature, precipitation …”

Section: Distributed Snow Modellingmentioning

confidence: 99%

Reevaluation of transit time distributions, mean transit times and their relation to catchment topography

Seeger

Weiler

2014

Hydrol. Earth Syst. Sci.

158

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Abstract. The transit time of water is a fundamental property of catchments, revealing information about the flow pathways, source of water and storage in a single integrated measure. While several studies have investigated the relationship between catchment topography and transit times, few studies expanded the analysis to a wide range of catchment properties and assessed the influence of the selected transfer function (TF) model. We used stable water isotopes from mostly baseflow samples with lumped convolution models of time-invariant TFs to estimate the transit time distributions of 24 meso-scale catchments covering different geomorphic and geologic regions in Switzerland. The sparse network of 13 precipitation isotope sampling sites required the development of a new spatial interpolation method for the monthly isotopic composition of precipitation. A pointenergy-balance based snow model was adapted to account for the seasonal water isotope storage in snow dominated catchments. Transit time distributions were estimated with three established TFs (exponential, gamma distribution and two parallel linear reservoirs). While the exponential TF proved to be less suitable to simulate the isotopic signal in most of the catchments, the gamma distribution and the two parallel linear reservoirs transfer function reached similarly good model fits to the fortnightly observed isotopic compositions in discharge, although in many catchments the transit time distributions implied by equally well fitted models differed markedly from each other and in extreme cases, the resulting mean transit time (MTT) differed by orders of magnitude. A more thorough comparison showed that equally suited models corresponded to agreeing values of cumulated transit time distributions only between 3 and 6 months. The short-term ( < 30 days) component of the transit time distributions did not play a role because of the limited temporal resolution of the available input data. The long-term component (> 3 years) could hardly be assessed by means of stable water isotopes, resulting in ambiguous MTT and hence questioning the relevance of an MTT determined with stable isotopes. Finally we investigated the relation between MTT estimates based on the three different TF types as well as other transit time properties and a range of topographical catchment characteristics. Depending on the selected transfer model, we found a weak correlation between transit time properties and the ratio between flow path length over the flow gradient, drainage density and the mean discharge. The catchment storage derived from MTTs and mean discharge did not show a clear relation to any catchment properties, indicating that in many studies the mean annual discharge may bias the MTT estimates.

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Section: Distributed Snow Modellingmentioning

confidence: 99%

Reevaluation of transit time distributions, mean transit times and their relation to catchment topography

Seeger

Weiler

2014

Hydrol. Earth Syst. Sci.

158

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“…-ESCIMO (Energy balance Snow Cover Integrated MOdel, see, Strasser et al, 2002;Strasser and Marke, 2010) was originally developed for point-scale applications. The model was extended to the fully distributed hydro-climatological model AMUNDSEN (Alpine MUltiscale Numerical Distributed Simulation ENgine, Strasser et al, 2004Strasser et al, , 2008, which includes detailed process descriptions, e.g., for topographydependent radiative transfer, gravitational and windinduced redistribution of snow, technical snow production (Hanzer et al, 2014) and runoff concentration.…”

Section: Snow Modelsmentioning

confidence: 99%

Effect of meteorological forcing and snow model complexity on hydrological simulations in the Sieber catchment (Harz Mountains, Germany)

Förster

Meon

Marke

et al. 2014

Hydrol. Earth Syst. Sci.

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Abstract. Detailed physically based snow models using energy balance approaches are spatially and temporally transferable and hence regarded as particularly suited for scenario applications including changing climate or land use. However, these snow models place high demands on meteorological input data at the model scale. Besides precipitation and temperature, time series of humidity, wind speed, and radiation have to be provided. In many catchments these time series are rarely available or provided by a few meteorological stations only. This study analyzes the effect of improved meteorological input on the results of four snow models with different complexity for the Sieber catchment (44.4 km 2 ) in the Harz Mountains, Germany. The Weather Research and Forecast model (WRF) is applied to derive spatial and temporal fields of meteorological surface variables at hourly temporal resolution for a regular grid of 1.1 km × 1.1 km. All snow models are evaluated at the point and the catchment scale. For catchment-scale simulations, all snow models were integrated into the hydrological modeling system PANTA RHEI. The model results achieved with a simple temperature-index model using observed precipitation and temperature time series as input are compared to those achieved with WRF input. Due to a mismatch between modeled and observed precipitation, the observed melt runoff as provided by a snow lysimeter and the observed streamflow are better reproduced by application of observed meteorological input data. In total, precipitation is simulated statistically reasonably at the seasonal scale but some single precipitation events are not captured by the WRF data set. Regarding the model efficiencies achieved for all simulations using WRF data, energy balance approaches generally perform similarly compared to the temperature-index approach and partially outperform the latter.

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“…The new version of ESCIMO.spread (v2) builds upon the ESCIMO.spread model as published by Strasser and Marke (2010). It is a 1-D, one-layer process model that calculates snow accumulation and melt for a snow cover assumed to be a single and homogeneous pack.…”

Section: General Descriptionmentioning

confidence: 99%

“…To do so, it solves the energy and mass balance equations for the snow surface applying simple parameterizations of the relevant processes. The energy balance of the snow surface is calculated for each hourly time step considering shortwave and longwave radiation, sensible and latent heat fluxes, energy conducted by solid or liquid precipitation, as well as sublimation/resublimation and a constant soil heat flux (Strasser and Marke, 2010). Thereby, absorbed and reflected shortwave radiation is calculated from incoming shortwave radiation on the basis of the snow albedo, which is estimated for each hourly time step using an albedo ageing curve approach.…”

Section: General Descriptionmentioning

confidence: 99%

“…To develop a free and easy-to-use tool for the simulation of the temporal evolution of the snow cover with explicit consideration of these complex snow-canopy-atmosphere interactions, the ESCIMO.spread spreadsheet-based point energy balance snow model developed by Strasser and Marke (2010) (in the following referred to as ESCIMO.spread (v1)) has been extended with a canopy sub-model. Moreover, the model has been improved by integrating an advanced algorithm for precipitation phase detection that applies wet-bulb temperature as a criterion to distinguish solid and liquid precipitation.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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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.

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<i>ESCIMO.spread</i> – a spreadsheet-based point snow surface energy balance model to calculate hourly snow water equivalent and melt rates for historical and changing climate conditions

Cited by 23 publications

References 18 publications

Reevaluation of transit time distributions, mean transit times and their relation to catchment topography

Reevaluation of transit time distributions, mean transit times and their relation to catchment topography

Effect of meteorological forcing and snow model complexity on hydrological simulations in the Sieber catchment (Harz Mountains, Germany)

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

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