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European Alps Snow Depth Observations

Spatial and Long-term Trends 1971-2019

By [source]

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About this dataset

This dataset contains a wealth of knowledge on snow depth and snow cover duration across the European Alps. It includes monthly and daily station observations from more than 2,000 stations located in Austria, Germany, France, Italy, Switzerland and Slovenia. With data spanning 1971 to 2019 this dataset provides researchers development agencies with insights into long-term trends and spatial patterns in the region. In particular it provides key information on average yearly snow depth (HNsum), mean (HSmean) & maximum (HSmax) values; as well as covering related statistics such as snow cover duration at varying levels of depth (SCD1-100). Furthermore the dataset also includes gap-filled values for some variables for improved accuracy – provided by HSmean_gapfill/ HSmax_gapfill/ SCD1_gapfill /SCD1gt_gapfill /SCD10_gapfill / SCD20_gapfill/ SCD30_gapfill/ SCD50_

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How to use the dataset

This dataset provides an important source of information on snow depth and the depth of the snowpack in the European Alps. This data can be used to analyze long-term trends in snow cover and to identify spatial patterns in the Alpine landscape, as well as to inform decisions related to skiing, winter tourism, climate change research, ecology, hydrology and more.

To use this dataset effectively, it is important that you understand each of the columns contained within it:

Name - this column contains information regarding what station data was collected at.
HNsum - this column gives a measurement of total snow depth (in centimeters).
HSmean - this column gives a measurement of mean snow depth (in centimeters).
HSmax - this column gives a measurement of maximum snow depth (in centimeters).
SCD1gt –this column records duration in days for which the average daily temperature was below 0°C and visible or detectable levels of ground cover existed. Other SCD columns provide similar measurements with different thresholds for deeper depths according to their labels i.e., 10 cm is referred to as SCD10 etc.).
Gapfilled values – these columns indicate how many observations occurred with gaps filled using another observation e.g., HSmean_gapfill indicates how many observations had missing values replaced by similar measurements collected from other sites nearby. Frac_gapfilled indicates what fraction of values were gapfilled when one or more observational items per month has missing data points at one station location but no gap-fillable data within that same month exists elsewhere due to temporal changes upstream/downstream/downwind through extrapolation or interpolation over multiple years among other sources that have similar seasonal weather fluctuations e /weather balloons can measure wind speed etc.).
Month & Year – these two fields contain time-related meta-data about when measurements were taken e.g., January 1970 up until December 2019 across each record item respectively meaning observations may sometimes span two years if collected before January 1st yet still belong under their start date year so those need manually assigning between duplicates later depending on usage case downstream from CSV preparation via macros–we used automated system reconciliations here whereas manual efforts such as careful field number calculations would otherwise be needed here if hand counts instead being processed from logfile reports etc.).

By understanding each column within this dataset, you will gain valuable insight into long-term trends and spatial patterns for alpine conditions measuring accurate snow depths across multiple metrics such as average daily temperature

Research Ideas

• Examining trends in snow depth and depth of snowfall in the European Alps over time.
• Investigating the spatial patterns of mountain snow cover variation across different countries in the region.
• Analyzing long-term changes in regional snow melt rates and associated climate impacts on alpine ecosystems

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: data_monthly_IT_BZ.csv

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File: data_monthly_IT_LOMBARDIA.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .