Snow cover characteristics in a glacierized catchment in the Tyrolean Alps - Improved spatially distributed modelling by usage of Lidar data

Schöber, Johannes; Schneider, Kirsten; Schattan, Paul; Achleitner, Stefan; Schöberl, Friedrich; Kirnbauer, R.

doi:10.1016/j.jhydrol.2013.12.054

Cited by 44 publications

(43 citation statements)

References 62 publications

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“…Besides temperature, precipitation gradients are often adjusted to fit observed and modelled target variables (e.g. snow patterns or runoff) (Huss et al, 2009b;Schöber et al, 2014). Justification for doing so is the general lack of gauging stations in the summit regions (Daly et al, 1994(Daly et al, , 2008 along with the high error of precipitation gauges (Rasmussen et al, 2011;Williams et al, 1998).…”

Section: Modelling Approachesmentioning

confidence: 99%

“…Since our model is following the empirical approach, too, the presented paper concentrates on that approach. Snow accumulation gradients determined by airborne lidar measurements (Helfricht et al, 2012) were used by Schöber et al (2014) to improve hydrological modelling using the distributed energy balance model SES (Snow and Ice Melt; Asztalos, 2004). Lidar data, however, are relatively expensive to obtain.…”

Section: S Frey and H Holzmann: A Conceptual Distributed Snow Redimentioning

confidence: 99%

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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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Section: Modelling Approachesmentioning

confidence: 99%

Section: S Frey and H Holzmann: A Conceptual Distributed Snow Redimentioning

confidence: 99%

A conceptual, distributed snow redistribution model

Frey

Holzmann

2015

Hydrol. Earth Syst. Sci.

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“…For hydropower generation it is interesting to know if a winter season is above or below average regarding the accumulation of snow. For water management demands such as efficient hydropower production, large efforts have been made to measure SWE in catchments of reservoirs (Painter et al, 2016;Krajči et al, 2017;Schattan et al, 2017), to simulate distributed SWE in basins of reservoirs and water intakes Hanzer et al, 2016), to improve flood forecasts with distributed SWE data (Schöber et al, 2014), and to model future runoff under climate change conditions in snow-and ice-melt-dominated catchments (Barnett et al, 2005;Finger et al, 2012;Hanzer et al, 2017). Gridded SWE data used for initialization of a process-based hydrological model improved predictions of SWE with lead times up to 1 month (Jörg-Hess et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Retrospective forecasts of the upcoming winter season snow accumulation in the Inn headwaters (European Alps)

Förster

Hanzer

Stoll³

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. This article presents analyses of retrospective seasonal forecasts of snow accumulation. Re-forecasts with 4 months' lead time from two coupled atmosphere-ocean general circulation models (NCEP CFSv2 and MetOffice GloSea5) drive the Alpine Water balance and Runoff Estimation model (AWARE) in order to predict mid-winter snow accumulation in the Inn headwaters. As snowpack is hydrological storage that evolves during the winter season, it is strongly dependent on precipitation totals of the previous months. Climate model (CM) predictions of precipitation totals integrated from November to February (NDJF) compare reasonably well with observations. Even though predictions for precipitation may not be significantly more skilful than for temperature, the predictive skill achieved for precipitation is retained in subsequent water balance simulations when snow water equivalent (SWE) in February is considered. Given the AWARE simulations driven by observed meteorological fields as a benchmark for SWE analyses, the correlation achieved using GloSea5-AWARE SWE predictions is r = 0.57. The tendency of SWE anomalies (i.e. the sign of anomalies) is correctly predicted in 11 of 13 years. For CFSv2-AWARE, the corresponding values are r = 0.28 and 7 of 13 years. The results suggest that some seasonal prediction of hydrological model storage tendencies in parts of Europe is possible.

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“…The Alpine snow cover and glacier ice are important water reservoirs for catchment areas (Verbunt et al, 2003;Schöber et al 2014). Snow and glacier ice have different time scales for storage dynamics and can be compensatory or cumulative for catchment runoffs.…”

Section: Introductionmentioning

confidence: 99%

“…Currently, the judgement of the impact of storage capacities of the cryosphere for runoff relies solely on point observations of snowpack parameter (mainly just snow depth) and glacier properties (ablation stakes) in combination with spatial interpolations and model assessments (Zemp et al, 2009). To overcome deficits in measurements of snow accumulation (in snow water equivalent SWE), in many cases simple parameterizations are applied to convert for accumulated masses from snow depths (Jonas et al, 2009;Schöber et al, 2014). However, precipitation regimes and winter accumulation are widely affected through changes in climate and temperature with the consequence of increased melt and extreme events even for high alpine sites (Barnett et al, 2005;Trujillo and Molotch, 2014).…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Wet Snow and Accumulations at High Alpine Glaciers Using Radar Technologies

Wendleder

Heilig

Schmitt

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Conventional studies to assess the annual mass balance for glaciers rely on single point observations in combination with model and interpolation approaches. Just recently, airborne and spaceborne data is used to support such mass balance determinations. Here, we present an approach to map temporal changes of the snow cover in glaciated regions of Tyrol, Austria, using SAR-based satellite data. Two dual-polarized SAR images are acquired on 22 and 24 September 2014. As X and C-band reveal different backscattering properties of snow, both TerraSAR-X and RADARSAT-2 images are analysed and compared to ground truth data. Through application of filter functions and processing steps containing a Kennaugh decomposition, ortho-rectification, radiometric enhancement and normalization, we were able to distinguish between dry and wet parts of the snow surface. The analyses reveal that the wet-snow can be unambiguously classified by applying a threshold of -11 dB. Bare ice at the surface or a dry snowpack does not appear in radar data with such low backscatter values. From the temporal shift of wet-snow, a discrimination of accumulation areas on glaciers is possible for specific observation dates. Such data can reveal a periodic monitoring of glaciers with high spatial coverage independent from weather or glacier conditions.

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Snow cover characteristics in a glacierized catchment in the Tyrolean Alps - Improved spatially distributed modelling by usage of Lidar data

Cited by 44 publications

References 62 publications

A conceptual, distributed snow redistribution model

A conceptual, distributed snow redistribution model

Retrospective forecasts of the upcoming winter season snow accumulation in the Inn headwaters (European Alps)

Monitoring of Wet Snow and Accumulations at High Alpine Glaciers Using Radar Technologies

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