Winter snow deficit was a harbinger of summer 2022 socio-hydrologic drought in the Po Basin, Italy

Avanzi, Francesco; Munerol, Francesca; Milelli, Massimo; Gabellani, Simone; Massari, Christian; Girotto, Manuela; Cremonese, Edoardo; Galvagno, Marta; Bruno, Giulia; Morra di Cella, Umberto; Rossi, Lauro; Altamura, Marco; Ferraris, Luca

doi:10.1038/s43247-024-01222-z

Cited by 7 publications

(2 citation statements)

References 78 publications

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“…Snow on the ground is an important component of the hydrological cycle, the climate system, and mountain ecosystems throughout the world (Beaumet et al, 2021;Beniston et al, 2018;Gobiet et al, 2014;Notarnicola, 2022). The timing of snowfall, along with its accumulation, has profound implications on water resources (Viviroli et al, 2011;Colombo et al, 2023;Avanzi et al, 2024), mountain tourism (Elsasser and Bürki, 2002), and mountain hazards such as avalanches (Marty et al, 2017). Understanding the impact that climate change has on this valuable resource is therefore essential in order to assist regional planning and preparedness.…”

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confidence: 99%

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Switanek

2024

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Section: Introductionmentioning

confidence: 99%

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Switanek

2024

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Section: Introductionmentioning

confidence: 99%

Snow depth sensitivity to mean temperature, precipitation, and elevation in the Austrian and Swiss Alps

Switanek,

Resch,

Gobiet

et al. 2024

Preprint

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Abstract. Snow depth is an incredibly important component of the climatic and hydrological cycles. Previous studies have shown predominantly decreasing trends of average seasonal snow depth across the European Alps. Additionally, prior work has shown bivariate statistical relationships between average seasonal snow depth and mean air temperature or precipitation. Building upon existing research, our study uses observational records of in situ station data across Austria and Switzerland to better quantify the sensitivity of historical changes in seasonal snow depth through a multivariate framework that depends on elevation, mean temperature and precipitation. These historical sensitivities, which are obtained over the 1901/02–1970/01 period, are then used to forecast snow depths over the more recent period 1971/72–2020/21. We find that the year-to-year forecasts of snow depths, which are derived from an empirical-statistical model (SnowSens), that rely solely on the historical sensitivities are nearly as skillful as the operational physically based SNOWGRID-CL model used by the weather service at GeoSphere Austria. Furthermore, observed long-term changes over the last 50 years are in better agreement with SnowSens than with SNOWGRID-CL. These results indicate that historical sensitivities between snow depth with temperature and precipitation are quite robust over decadal-length scales of time, and they can be used to effectively translate expected long-term changes in temperature and precipitation to changes in seasonal snow depth.

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Spatio‐temporal wet snow dynamics from model simulations and remote sensing: A case study from the Rofental, Austria

Rottler,

Warscher,

Hanzer

et al. 2024

Hydrological Processes

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The formation and concentration of liquid water (LW) in the snowpack constitute key processes linking snow and runoff. Hence, the LW content of the snowpack represents a crucial target variable to investigate for snowmelt‐induced runoff predictions. In this study, we capture the wet snow dynamics at higher than hectometre resolution in the alpine headwater catchment Rofental, Tyrol, Austria (98.1 km2) by means of distributed model simulations and remote sensing data for the 5 year period 10/2017–09/2022. The model simulations are conducted using the intermediate complexity open‐source snow‐hydrological model openAMUNDSEN. Simulation results are compared to wet snow maps (WSM) derived from Sentinel‐1 data. Our investigations indicate that distributed snow models of intermediate complexity, such as openAMUNDSEN and satellite‐based wet snow data are well capable of capturing the wet snow dynamics in high spatial and temporal resolutions. The areal extents of wet snow as well as the upward movement of the wet snow line to higher elevation with progressing snowmelt are captured well by both approaches. In order to evaluate the snow simulations, we use fractional snow cover (FSC) data based on Sentinel‐2, which proved to provide valuable small‐scale snow and snow redistribution patterns in alpine catchments. The comparison of model simulations with FSC maps with more than 50% of the non‐glaciated area being cloud‐free (i.e. 364 images) results in an accuracy of 0.91. This study represents a further step towards a serviceable operational snow‐hydrological monitoring and modelling framework for mountain regions including wet snow dynamics in high spatial and temporal resolutions.

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Winter snow deficit was a harbinger of summer 2022 socio-hydrologic drought in the Po Basin, Italy

Cited by 7 publications

References 78 publications

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Snow depth sensitivity to mean temperature, precipitation, and elevation in the Austrian and Swiss Alps

Spatio‐temporal wet snow dynamics from model simulations and remote sensing: A case study from the Rofental, Austria

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