The relevance of glacier melt in the water cycle of the Alps: the example of Austria

Koboltschnig, Gernot; Schöner, Wolfgang

doi:10.5194/hess-15-2039-2011

Cited by 38 publications

(34 citation statements)

References 16 publications

(35 reference statements)

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“…They estimated the glacier melt component in runoff by comparing discharges of the glacierized and non-glacierized catchments with similar precipitation and catchment attributes, and developed an empirical function relating proportionality of glacier melt in runoff to glacier area ratio (GAR). Koboltschnig and Schoner (2011) compiled a dataset of glacier melt contribution to runoff for thirty-eight catchments in Austria. The dataset was compiled from diverse sources of glacier melt estimation, e.g., the glacier mass balance observation, simulation of different kind of hydrologic models, and etc.…”

Section: Introductionmentioning

confidence: 99%

Using glacier area ratio to quantify effects of melt water on runoff

Zhang

Luo

Sun

et al. 2016

Journal of Hydrology

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Keywords:Glacier area ratio Glacier melt Runoff coefficient Coefficient of variation of runoff SWAT s u m m a r y Twenty-four headwater catchments with varying glacier area ratios (GARs) in the Eastern and Central Tian Shan Mountains were simulated by the glacier-enhanced Soil and Water Assessment Tool (SWAT) model from 1961 to 2007. The mean catchment GAR ranges between 0.7% and 44.8% with a mean of 8.6%. Through synthetic analysis of the glacio-hydrological processes simulation results of the catchments, it was found that the GAR is an effective index that can be used to interpret quantitatively the varying influences of glaciers on runoff across catchments. Among the twenty-four catchments, the ratio of glacier melt contribution (RGMC) to runoff varies between 3.5% and 67.5% with a mean of 24.0%; the ratio of ice melt contribution (RIMC) between 1.4% and 35.8% with a mean of 10.5%; the ratio of ice melt in glacier melt between 33.4% and 59.1% with a mean of 43.4%; the runoff coefficient (RC) between 0.24 and 0.90 with a mean of 0.52; and the coefficient of variation (CV) of runoff between 0.10 and 0.29 with a mean of 0.18. Based on synthetic analysis, it was found that (1) power functions fit the relations between RGMC, RIMC, RC, and CV and GAR with high certainty; (2) the CV decreases with increasing GAR while others increase; and (3) these power functions change sensitively with GAR when GAR is less than 10%, implicating that a small change in GAR may cause remarkable changes in RGMC, RIMC, RC, and CV in the less glacierized catchments.

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Section: Introductionmentioning

confidence: 99%

Using glacier area ratio to quantify effects of melt water on runoff

Zhang

Luo

Sun

et al. 2016

Journal of Hydrology

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“…The water input due to snow and glacier meltwater during spring and summer is relevant for the yearly runoff regime of streams and groundwater in high-elevation Alpine areas (Koboltschnig and Schöner, 2011). Particularly, inner valleys of the Alps are characterized by relatively low amounts of liquid precipitation and significantly benefit from the water contribution provided by lateral valleys where snowmelt and/or glacier melt dominate streamflow and feed groundwater.…”

Section: Penna Et Al: Tracer-based Analysis Of Spatial and Tempormentioning

confidence: 99%

Tracer-based analysis of spatial and temporal variations of water sources in a glacierized catchment

Penna

Engel

Mao

et al. 2014

Hydrol. Earth Syst. Sci.

109

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Abstract. Snow-dominated and glacierized catchments are important sources of fresh water for biological communities and for populations living in mountain valleys. Gaining a better understanding of the runoff origin and of the hydrological interactions between meltwater, streamflow and groundwater is critical for natural risk assessment and mitigation as well as for effective water resource management in mountain regions. This study is based on the use of stable isotopes of water and electrical conductivity as tracers to identify the water sources for runoff and groundwater and their seasonal variability in a glacierized catchment in the Italian Alps. Samples were collected from rainfall, snow, snowmelt, ice melt, spring and stream water (from the main stream at different locations and from selected tributaries) in 2011, 2012 and 2013. The tracer-based mixing analysis revealed that, overall, snowmelt and glacier melt were the most important endmembers for stream runoff during late spring, summer and early fall. The temporal variability of the tracer concentration suggested that stream water was dominated by snowmelt at the beginning of the melting season (May-June), by a mixture of snowmelt and glacier melt during mid-summer (Julyearly August), and by glacier melt during the end of the summer (end of August-September). The same seasonal pattern observed in streamflow was also evident for groundwater, with the highest electrical conductivity and least negative isotopic values found during cold or relatively less warm periods, when the melt of snowpack and ice was limited. Particularly, the application of a two-component mixing model to data from different springs showed that the snowmelt contribution to groundwater recharge varied between 21 % (±3 %) and 93 % (±1 %) over the season, and the overall contribution during the three study years ranged between 58 % (±24 %) and 72 % (±19 %). These results provided new insights into the isotopic characterization of the study catchment presenting further understanding of the spatio-temporal variability of the main water sources contributing to runoff.

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“…This would mean a shift towards a more rainfall-influenced regime, though snowmelt processes are projected to still play an important role [52]. Other studies focusing on runoff contribution from glaciers [18,19] show that the ongoing glacier retreat in the future (after 2040) might lead to a shift in the runoff regime from glacial to nival for small and highly glaciated catchments [53].…”

Section: Changes In Streamflow Seasonality Under Climate Changementioning

confidence: 99%

Impacts of Climate Change on the Hydrological Regime of the Danube River and Its Tributaries Using an Ensemble of Climate Scenarios

Stagl

Hattermann

2015

Water

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Information about the potential impacts of climate change on river runoff is needed to prepare efficient adaptation strategies. This study presents scenario projections for the future hydrological runoff regime in the Danube River Basin. The eco-hydrological watershed model Soil and Water Integrated Model (SWIM) was applied for the entire Danube River catchment, considering 1224 subbasins. After calibration and validation of the model, a set of high-resolution climate projections (bias-corrected and non-bias-corrected) served as meteorological drivers with which future daily river discharge under different climate warming scenario conditions was simulated. Despite existing uncertainties, robust trends could be identified. In the next 30 years, the seasonal stream-flow regime of the Danube and its tributaries is projected to change considerably. Our results show a general trend towards a decrease in summer runoff for the whole Danube basin and, additionally, in autumn runoff for the Middle and Lower Danube basin, aggravating the existing low flow periods. For the winter and early spring seasons, mainly January-March, an increase in river runoff is projected. Greater uncertainties show up in particular for winter runoff in the Dinaric Alps and the Lower Danube basin. The existing trends become very distinct until the end of the 21st century, especially for snow-influenced river regimes.

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The relevance of glacier melt in the water cycle of the Alps: the example of Austria

Cited by 38 publications

References 16 publications

Using glacier area ratio to quantify effects of melt water on runoff

Using glacier area ratio to quantify effects of melt water on runoff

Tracer-based analysis of spatial and temporal variations of water sources in a glacierized catchment

Impacts of Climate Change on the Hydrological Regime of the Danube River and Its Tributaries Using an Ensemble of Climate Scenarios

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