Global Warming in the Alps: Vulnerability and Climatic Dependency of Alpine Springs in Italy, Regione Valle d’Aosta and Switzerland, Canton Valais

Amanzio, Gianpiero; Bertolo, Davide; Maio, Marina De; Lodi, Laura Pia; Pitet, L.; Suozzi, Enrico

doi:10.1007/978-3-319-09048-1_263

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…The Alps are considered to be the “water towers of Europe” [ European Environment Agency , , p. 30] and thereby provide much of the water from snowmelt to surrounding European countries. Research in this mountain chain has been conducted to determine how snowpack response to climate change [ Amanzio et al , ] will influence water availability, flooding, and hydroelectric power generation. Since the presence of snow influences skiing and winter recreation in this region, changes in the snowpack will affect the duration of time that ski resorts can remain open during the winter season [ Gobiet et al , ].…”

Section: Introductionmentioning

confidence: 99%

Measurement of the physical properties of the snowpack

Kinar

Pomeroy

2015

Reviews of Geophysics

196

202

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This paper reviews measurement techniques and corresponding devices used to determine the physical properties of the seasonal snowpack from distances close to the ground surface. The review is placed in the context of the need for scientific observations of snowpack variables that provide inputs for predictive hydrological models that help to advance scientific understanding of geophysical processes related to snow in the near-surface cryosphere. Many of these devices used to measure snow are invasive and require the snowpack to be disrupted, thereby precluding the possibility for multiple measurements to be made at the same sampling location. Moreover, many devices rely on the use of empirical calibration equations that may not be valid at all geographic locations. The spatial density of observations with most snow measurement devices is often inadequate. There is a need for improved automation of snowpack measurement instrumentation with an emphasis on field-based feedback of measurement validity in lieu of postprocessing of samples or data at a lab or office location. The scientific future of snow measurement instrumentation thereby requires a synthesis between science and engineering principles that takes into consideration geophysics and the physics of device operation.

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Section: Introductionmentioning

confidence: 99%

Measurement of the physical properties of the snowpack

Kinar

Pomeroy

2015

Reviews of Geophysics

196

202

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“…The duration of the exhaustion period, the time between snowmelt peaks and the annual minimum point before the new recharge influence the α value. The minimum estimated value for α was 0.0094 (2011( -2012 and the maximum value was 0.0206 (2014( -2015. The fluctuation depended mainly on the amount of snow fall in winter (Tab.…”

Section: Recession Curvementioning

confidence: 97%

SOURCE: A Semi-automatic Tool for Spring Monitoring Data Analysis and Aquifer Characterization

Russo

Suozzi

Gizzi

et al. 2021

Preprint

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It has become increasingly necessary to optimise mountain groundwater resource management and comprehend resource recharging systems from a hydrogeological perspective in order to formulate adequate resource protection strategies. Analysing mountain spring behaviour and aquifer characteristics can be time consuming, so new automated techniques and software tools are needed to estimate hydrogeological parameters and understand exhaustion dynamics of groundwater resources. This paper introduces SOURCE, a new semi-automatic tool that automates the hydrogeological characterisation of water springs and provides proper estimations of the vulnerability index, as well as autocorrelation and cross-correlation statistical coefficients. SOURCE rapidly processed input data from the Mascognaz 1 spring (Aosta Valley) water probes and meteorological station to provide graphical outputs and values for the main hydrodynamic parameters. Having a single software package that contains all the main methods of water spring analysis could potentially reduce analysis times from a few days to a few hours.

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SOURCE: a semi-automatic tool for spring-monitoring data analysis and aquifer characterisation

et al. 2021

Self Cite

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It has become increasingly necessary to optimise mountain groundwater resource management and comprehend resource-recharging systems from a hydrogeological perspective to formulate adequate resource protection strategies. Analysing mountain spring behaviour and aquifer characteristics can be time-consuming, so new automated techniques and software tools are needed to estimate hydrogeological parameters and understand the exhaustion dynamics of groundwater resources. This paper introduces SOURCE, a new semi-automatic tool that automates the hydrogeological characterisation of water springs and provides proper estimations of the vulnerability index, as well as autocorrelation and cross-correlation statistical coefficients. SOURCE rapidly processed input data from the Mascognaz 1 spring (Aosta Valley) water probes and meteorological station to provide graphical outputs and values for the main hydrodynamic parameters. Having a single software package that contains all the main methods of water spring analysis could potentially reduce analysis times from a few days to a few hours.

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Global Warming in the Alps: Vulnerability and Climatic Dependency of Alpine Springs in Italy, Regione Valle d’Aosta and Switzerland, Canton Valais

Cited by 4 publications

References 3 publications

Measurement of the physical properties of the snowpack

Measurement of the physical properties of the snowpack

SOURCE: A Semi-automatic Tool for Spring Monitoring Data Analysis and Aquifer Characterization

SOURCE: a semi-automatic tool for spring-monitoring data analysis and aquifer characterisation

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