Potential of a low‐cost sensor network to understand the spatial and temporal dynamics of a mountain snow cover

Pohl, S.; Garvelmann, Jakob; Wawerla, Jens; Weiler, Markus

doi:10.1002/2013wr014594

Cited by 32 publications

(45 citation statements)

References 84 publications

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“…In winters with little snow, snow sublimation losses become dominant and, consequently, the snow lasts longer in the open than inside the forest, mainly for northern exposures (in the Northern Hemisphere). Similar patterns were observed by Pohl et al (2014) in the Black Forest region.…”

Section: Introductionsupporting

confidence: 82%

“…1) with a mostly temperate snow cover at an elevation of 800 m a.s.l. The applied hydrometeorological data have been recorded by a set of low-cost snow monitoring systems (SnoMoS) recently developed by Pohl et al (2014). The model can be downloaded from www.alpinehydroclimatology.net together with 1 year of example meteorological recordings and snow observations.…”

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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Section: Introductionsupporting

confidence: 82%

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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“…Shading reduces snowmelt compared to direct sunlight. Enhanced emitted long wave radiation due to warm bare rocks or trees increases the melt rate (Garvelmann et al, 2013;Pohl et al, 2014).…”

Section: Theoretical Background Of Snow Cover Variationsmentioning

confidence: 99%

A conceptual, distributed snow redistribution model

Frey

Holzmann

2015

Hydrol. Earth Syst. Sci.

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Abstract. When applying conceptual hydrological models using a temperature index approach for snowmelt to high alpine areas often accumulation of snow during several years can be observed. Some of the reasons why these "snow towers" do not exist in nature are vertical and lateral transport processes. While snow transport models have been developed using grid cell sizes of tens to hundreds of square metres and have been applied in several catchments, no model exists using coarser cell sizes of 1 km 2 , which is a common resolution for meso-and large-scale hydrologic modelling (hundreds to thousands of square kilometres). In this paper we present an approach that uses only gravity and snow density as a proxy for the age of the snow cover and land-use information to redistribute snow in alpine basins. The results are based on the hydrological modelling of the Austrian Inn Basin in Tyrol, Austria, more specifically the Ötztaler Ache catchment, but the findings hold for other tributaries of the river Inn. This transport model is implemented in the distributed rainfall-runoff model COSERO (Continuous Semidistributed Runoff). The results of both model concepts with and without consideration of lateral snow redistribution are compared against observed discharge and snow-covered areas derived from MODIS satellite images. By means of the snow redistribution concept, snow accumulation over several years can be prevented and the snow depletion curve compared with MODIS (Moderate Resolution Imaging Spectroradiometer) data could be improved, too. In a 7-year period the standard model would lead to snow accumulation of approximately 2900 mm SWE (snow water equivalent) in high elevated regions whereas the updated version of the model does not show accumulation and does also predict discharge with more accuracy leading to a Kling-Gupta efficiency of 0.93 instead of 0.9. A further improvement can be shown in the comparison of MODIS snow cover data and the calculated depletion curve, where the redistribution model increased the efficiency (R 2 ) from 0.70 to 0.78 (calibration) and from 0.66 to 0.74 (validation).

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“…The probable controlling factor of underestimation by SNODAS in this region is the sub-kilometer-scale heterogeneity of snow distribution caused by both vegetation and topography. Furthermore, canopy interception remains an important aspect of the mountain snow energy balance that is still not well understood (Marks et al, 2008;Pohl et al, 2014), adding uncertainty to the assimilation model framework. SNODAS has been found to underestimate snow depths in similar forested alpine terrain (Anderson, 2011), so this result is not unexpected.…”

Section: Region #3: Rabbit Ears Passmentioning

confidence: 99%

Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements

et al. 2015

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Abstract. Repeated light detection and ranging (lidar) surveys are quickly becoming the de facto method for measuring spatial variability of montane snowpacks at high resolution. This study examines the potential of a 750 km 2 lidar-derived data set of snow depths, collected during the 2007 northern Colorado Cold Lands Processes Experiment (CLPX-2), as a validation source for an operational hydrologic snow model. The SNOw Data Assimilation System (SNODAS) model framework, operated by the US National Weather Service, combines a physically based energy-and-mass-balance snow model with satellite, airborne and automated groundbased observations to provide daily estimates of snowpack properties at nominally 1 km resolution over the conterminous United States. Independent validation data are scarce due to the assimilating nature of SNODAS, compelling the need for an independent validation data set with substantial geographic coverage.Within 12 distinctive 500 × 500 m study areas located throughout the survey swath, ground crews performed approximately 600 manual snow depth measurements during each of the CLPX-2 lidar acquisitions. This supplied a data set for constraining the uncertainty of upscaled lidar estimates of snow depth at the 1 km SNODAS resolution, resulting in a root-mean-square difference of 13 cm. Upscaled lidar snow depths were then compared to the SNODAS estimates over the entire study area for the dates of the lidar flights. The remotely sensed snow depths provided a more spatially continuous comparison data set and agreed more closely to the model estimates than that of the in situ measurements alone. Finally, the results revealed three distinct areas where the differences between lidar observations and SNODAS estimates were most drastic, providing insight into the causal influences of natural processes on model uncertainty.

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Potential of a low‐cost sensor network to understand the spatial and temporal dynamics of a mountain snow cover

Cited by 32 publications

References 84 publications

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

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

A conceptual, distributed snow redistribution model

Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements

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