European Alps Snow Depth Observations by Kaggle | Other

About this Dataset

European Alps Snow Depth Observations

Spatial and Long-term Trends 1971-2019

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_

More Datasets

For more datasets, click here.

Featured Notebooks

🚨 Your notebook can be here! 🚨!

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

Column name	Description
Name	Name of the station. (String)
year	Year of the observation. (Integer)
month	Month of the observation. (Integer)
HNsum	Total snow depth. (Float)
HSmean	Mean snow depth. (Float)
HSmax	Maximum snow depth. (Float)
SCD1	Snow cover duration of 1 cm or more. (Integer)
SCD1gt	Snow cover duration of 1 cm or more, gap-filled. (Integer)
SCD10	Snow cover duration of 10 cm or more. (Integer)
SCD20	Snow cover duration of 20 cm or more. (Integer)
SCD30	Snow cover duration of 30 cm or more. (Integer)
SCD50	Snow cover duration of 50 cm or more. (Integer)
SCD100	Snow cover duration of 100 cm or more. (Integer)
HSmean_gapfill	Mean snow depth, gap-filled. (Float)
frac_gapfilled	Fraction of gap-filled values. (Float)
HSmax_gapfill	Maximum snow depth, gap-filled. (Float)
SCD1_gapfill	Snow cover duration of 1 cm or more, gap-filled. (Integer)
SCD1gt_gapfill	Snow cover duration of 1 cm or more, gap-filled. (Integer)
SCD10_gapfill	Snow cover duration of 10 cm or more, gap-filled. (Integer)
SCD20_gapfill	Snow cover duration of 20 cm or more, gap-filled. (Integer)
SCD30_gapfill	Snow cover duration of 30 cm or more, gap-filled. (Integer)
SCD50_gapfill	Snow cover duration of 50 cm or more, gap-filled. (Integer)

File: data_monthly_IT_LOMBARDIA.csv

Column name	Description
Name	Name of the station. (String)
year	Year of the observation. (Integer)
month	Month of the observation. (Integer)
HNsum	Total snow depth. (Float)
HSmean	Mean snow depth. (Float)
HSmax	Maximum snow depth. (Float)
SCD1	Snow cover duration of 1 cm or more. (Integer)
SCD1gt	Snow cover duration of 1 cm or more, gap-filled. (Integer)
SCD10	Snow cover duration of 10 cm or more. (Integer)
SCD20	Snow cover duration of 20 cm or more. (Integer)
SCD30	Snow cover duration of 30 cm or more. (Integer)
SCD50	Snow cover duration of 50 cm or more. (Integer)
SCD100	Snow cover duration of 100 cm or more. (Integer)
HSmean_gapfill	Mean snow depth, gap-filled. (Float)
frac_gapfilled	Fraction of gap-filled values. (Float)
HSmax_gapfill	Maximum snow depth, gap-filled. (Float)
SCD1_gapfill	Snow cover duration of 1 cm or more, gap-filled. (Integer)
SCD1gt_gapfill	Snow cover duration of 1 cm or more, gap-filled. (Integer)
SCD10_gapfill	Snow cover duration of 10 cm or more, gap-filled. (Integer)
SCD20_gapfill	Snow cover duration of 20 cm or more, gap-filled. (Integer)
SCD30_gapfill	Snow cover duration of 30 cm or more, gap-filled. (Integer)
SCD50_gapfill	Snow cover duration of 50 cm or more, gap-filled. (Integer)

Acknowledgements

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

Tables

Data Daily De Dwd

@kaggle.thedevastator_european_alps_snow_depth_observations.data_daily_de_dwd

35.15 MB
12352595 rows
8 columns


CREATE TABLE data_daily_de_dwd (
  "provider" VARCHAR,
  "name" VARCHAR,
  "date" TIMESTAMP,
  "hn" DOUBLE,
  "hs" DOUBLE,
  "hn_after_qc" DOUBLE,
  "hs_after_qc" DOUBLE,
  "hs_after_gapfill" DOUBLE
);

Data Daily Fr Meteofrance

@kaggle.thedevastator_european_alps_snow_depth_observations.data_daily_fr_meteofrance

9.82 MB
2979635 rows
8 columns


CREATE TABLE data_daily_fr_meteofrance (
  "provider" VARCHAR,
  "name" VARCHAR,
  "date" TIMESTAMP,
  "hn" DOUBLE,
  "hs" DOUBLE,
  "hn_after_qc" DOUBLE,
  "hs_after_qc" DOUBLE,
  "hs_after_gapfill" DOUBLE
);

Data Daily It Bz

@kaggle.thedevastator_european_alps_snow_depth_observations.data_daily_it_bz

2.25 MB
793682 rows
8 columns


CREATE TABLE data_daily_it_bz (
  "provider" VARCHAR,
  "name" VARCHAR,
  "date" TIMESTAMP,
  "hn" DOUBLE,
  "hs" DOUBLE,
  "hn_after_qc" DOUBLE,
  "hs_after_qc" DOUBLE,
  "hs_after_gapfill" DOUBLE
);

Data Daily It Fvg

@kaggle.thedevastator_european_alps_snow_depth_observations.data_daily_it_fvg

1.09 MB
319964 rows
8 columns


CREATE TABLE data_daily_it_fvg (
  "provider" VARCHAR,
  "name" VARCHAR,
  "date" TIMESTAMP,
  "hn" DOUBLE,
  "hs" DOUBLE,
  "hn_after_qc" DOUBLE,
  "hs_after_qc" DOUBLE,
  "hs_after_gapfill" DOUBLE
);

Data Daily It Lombardia

@kaggle.thedevastator_european_alps_snow_depth_observations.data_daily_it_lombardia

590.38 KB
140257 rows
8 columns


CREATE TABLE data_daily_it_lombardia (
  "provider" VARCHAR,
  "name" VARCHAR,
  "date" TIMESTAMP,
  "hn" DOUBLE,
  "hs" DOUBLE,
  "hn_after_qc" DOUBLE,
  "hs_after_qc" DOUBLE,
  "hs_after_gapfill" DOUBLE
);

Data Daily It Tn

@kaggle.thedevastator_european_alps_snow_depth_observations.data_daily_it_tn

1.98 MB
574920 rows
8 columns


CREATE TABLE data_daily_it_tn (
  "provider" VARCHAR,
  "name" VARCHAR,
  "date" TIMESTAMP,
  "hn" DOUBLE,
  "hs" DOUBLE,
  "hn_after_qc" DOUBLE,
  "hs_after_qc" DOUBLE,
  "hs_after_gapfill" DOUBLE
);

Data Daily It Vda Cf

@kaggle.thedevastator_european_alps_snow_depth_observations.data_daily_it_vda_cf

868.95 KB
233756 rows
8 columns


CREATE TABLE data_daily_it_vda_cf (
  "provider" VARCHAR,
  "name" VARCHAR,
  "date" TIMESTAMP,
  "hn" VARCHAR,
  "hs" DOUBLE,
  "hn_after_qc" VARCHAR,
  "hs_after_qc" DOUBLE,
  "hs_after_gapfill" DOUBLE
);

Data Monthly At Hzb

@kaggle.thedevastator_european_alps_snow_depth_observations.data_monthly_at_hzb

3.08 MB
371400 rows
23 columns


CREATE TABLE data_monthly_at_hzb (
  "name" VARCHAR,
  "year" BIGINT,
  "month" BIGINT,
  "hnsum" DOUBLE,
  "hsmean" DOUBLE,
  "hsmax" DOUBLE,
  "scd1" DOUBLE,
  "scd1gt" DOUBLE,
  "scd10" DOUBLE,
  "scd20" DOUBLE,
  "scd30" DOUBLE,
  "scd50" DOUBLE,
  "scd100" DOUBLE,
  "hsmean_gapfill" DOUBLE,
  "frac_gapfilled" DOUBLE,
  "hsmax_gapfill" DOUBLE,
  "scd1_gapfill" DOUBLE,
  "scd1gt_gapfill" DOUBLE,
  "scd10_gapfill" DOUBLE,
  "scd20_gapfill" DOUBLE,
  "scd30_gapfill" DOUBLE,
  "scd50_gapfill" DOUBLE,
  "scd100_gapfill" DOUBLE
);

Data Monthly Ch Meteoswiss

@kaggle.thedevastator_european_alps_snow_depth_observations.data_monthly_ch_meteoswiss

1.29 MB
222708 rows
23 columns


CREATE TABLE data_monthly_ch_meteoswiss (
  "name" VARCHAR,
  "year" BIGINT,
  "month" BIGINT,
  "hnsum" DOUBLE,
  "hsmean" DOUBLE,
  "hsmax" DOUBLE,
  "scd1" DOUBLE,
  "scd1gt" DOUBLE,
  "scd10" DOUBLE,
  "scd20" DOUBLE,
  "scd30" DOUBLE,
  "scd50" DOUBLE,
  "scd100" DOUBLE,
  "hsmean_gapfill" DOUBLE,
  "frac_gapfilled" DOUBLE,
  "hsmax_gapfill" DOUBLE,
  "scd1_gapfill" DOUBLE,
  "scd1gt_gapfill" DOUBLE,
  "scd10_gapfill" DOUBLE,
  "scd20_gapfill" DOUBLE,
  "scd30_gapfill" DOUBLE,
  "scd50_gapfill" DOUBLE,
  "scd100_gapfill" DOUBLE
);

Data Monthly Ch Slf

@kaggle.thedevastator_european_alps_snow_depth_observations.data_monthly_ch_slf

1.03 MB
76200 rows
23 columns


CREATE TABLE data_monthly_ch_slf (
  "name" VARCHAR,
  "year" BIGINT,
  "month" BIGINT,
  "hnsum" DOUBLE,
  "hsmean" DOUBLE,
  "hsmax" DOUBLE,
  "scd1" DOUBLE,
  "scd1gt" DOUBLE,
  "scd10" DOUBLE,
  "scd20" DOUBLE,
  "scd30" DOUBLE,
  "scd50" DOUBLE,
  "scd100" DOUBLE,
  "hsmean_gapfill" DOUBLE,
  "frac_gapfilled" DOUBLE,
  "hsmax_gapfill" DOUBLE,
  "scd1_gapfill" DOUBLE,
  "scd1gt_gapfill" DOUBLE,
  "scd10_gapfill" DOUBLE,
  "scd20_gapfill" DOUBLE,
  "scd30_gapfill" DOUBLE,
  "scd50_gapfill" DOUBLE,
  "scd100_gapfill" DOUBLE
);

Data Monthly De Dwd

@kaggle.thedevastator_european_alps_snow_depth_observations.data_monthly_de_dwd

3.73 MB
532752 rows
23 columns


CREATE TABLE data_monthly_de_dwd (
  "name" VARCHAR,
  "year" BIGINT,
  "month" BIGINT,
  "hnsum" DOUBLE,
  "hsmean" DOUBLE,
  "hsmax" DOUBLE,
  "scd1" DOUBLE,
  "scd1gt" DOUBLE,
  "scd10" DOUBLE,
  "scd20" DOUBLE,
  "scd30" DOUBLE,
  "scd50" DOUBLE,
  "scd100" DOUBLE,
  "hsmean_gapfill" DOUBLE,
  "frac_gapfilled" DOUBLE,
  "hsmax_gapfill" DOUBLE,
  "scd1_gapfill" DOUBLE,
  "scd1gt_gapfill" DOUBLE,
  "scd10_gapfill" DOUBLE,
  "scd20_gapfill" DOUBLE,
  "scd30_gapfill" DOUBLE,
  "scd50_gapfill" DOUBLE,
  "scd100_gapfill" DOUBLE
);

Data Monthly Fr Meteofrance

@kaggle.thedevastator_european_alps_snow_depth_observations.data_monthly_fr_meteofrance

1 MB
102756 rows
23 columns


CREATE TABLE data_monthly_fr_meteofrance (
  "name" VARCHAR,
  "year" BIGINT,
  "month" BIGINT,
  "hnsum" DOUBLE,
  "hsmean" DOUBLE,
  "hsmax" DOUBLE,
  "scd1" DOUBLE,
  "scd1gt" DOUBLE,
  "scd10" DOUBLE,
  "scd20" DOUBLE,
  "scd30" DOUBLE,
  "scd50" DOUBLE,
  "scd100" DOUBLE,
  "hsmean_gapfill" DOUBLE,
  "frac_gapfilled" DOUBLE,
  "hsmax_gapfill" DOUBLE,
  "scd1_gapfill" DOUBLE,
  "scd1gt_gapfill" DOUBLE,
  "scd10_gapfill" DOUBLE,
  "scd20_gapfill" DOUBLE,
  "scd30_gapfill" DOUBLE,
  "scd50_gapfill" DOUBLE,
  "scd100_gapfill" DOUBLE
);

Data Monthly It Bz

@kaggle.thedevastator_european_alps_snow_depth_observations.data_monthly_it_bz

257.76 KB
29400 rows
23 columns


CREATE TABLE data_monthly_it_bz (
  "name" VARCHAR,
  "year" BIGINT,
  "month" BIGINT,
  "hnsum" DOUBLE,
  "hsmean" DOUBLE,
  "hsmax" DOUBLE,
  "scd1" DOUBLE,
  "scd1gt" DOUBLE,
  "scd10" DOUBLE,
  "scd20" DOUBLE,
  "scd30" DOUBLE,
  "scd50" DOUBLE,
  "scd100" DOUBLE,
  "hsmean_gapfill" DOUBLE,
  "frac_gapfilled" DOUBLE,
  "hsmax_gapfill" DOUBLE,
  "scd1_gapfill" DOUBLE,
  "scd1gt_gapfill" DOUBLE,
  "scd10_gapfill" DOUBLE,
  "scd20_gapfill" DOUBLE,
  "scd30_gapfill" DOUBLE,
  "scd50_gapfill" DOUBLE,
  "scd100_gapfill" DOUBLE
);

Data Monthly It Fvg

@kaggle.thedevastator_european_alps_snow_depth_observations.data_monthly_it_fvg

125.55 KB
11352 rows
23 columns


CREATE TABLE data_monthly_it_fvg (
  "name" VARCHAR,
  "year" BIGINT,
  "month" BIGINT,
  "hnsum" DOUBLE,
  "hsmean" DOUBLE,
  "hsmax" DOUBLE,
  "scd1" DOUBLE,
  "scd1gt" DOUBLE,
  "scd10" DOUBLE,
  "scd20" DOUBLE,
  "scd30" DOUBLE,
  "scd50" DOUBLE,
  "scd100" DOUBLE,
  "hsmean_gapfill" DOUBLE,
  "frac_gapfilled" DOUBLE,
  "hsmax_gapfill" DOUBLE,
  "scd1_gapfill" DOUBLE,
  "scd1gt_gapfill" DOUBLE,
  "scd10_gapfill" DOUBLE,
  "scd20_gapfill" DOUBLE,
  "scd30_gapfill" DOUBLE,
  "scd50_gapfill" DOUBLE,
  "scd100_gapfill" DOUBLE
);

Data Monthly It Lombardia

@kaggle.thedevastator_european_alps_snow_depth_observations.data_monthly_it_lombardia

79.76 KB
5484 rows
23 columns


CREATE TABLE data_monthly_it_lombardia (
  "name" VARCHAR,
  "year" BIGINT,
  "month" BIGINT,
  "hnsum" DOUBLE,
  "hsmean" DOUBLE,
  "hsmax" DOUBLE,
  "scd1" DOUBLE,
  "scd1gt" DOUBLE,
  "scd10" DOUBLE,
  "scd20" DOUBLE,
  "scd30" DOUBLE,
  "scd50" DOUBLE,
  "scd100" DOUBLE,
  "hsmean_gapfill" DOUBLE,
  "frac_gapfilled" DOUBLE,
  "hsmax_gapfill" DOUBLE,
  "scd1_gapfill" DOUBLE,
  "scd1gt_gapfill" DOUBLE,
  "scd10_gapfill" DOUBLE,
  "scd20_gapfill" DOUBLE,
  "scd30_gapfill" DOUBLE,
  "scd50_gapfill" DOUBLE,
  "scd100_gapfill" DOUBLE
);

Data Monthly It Tn

@kaggle.thedevastator_european_alps_snow_depth_observations.data_monthly_it_tn

188.47 KB
19740 rows
23 columns


CREATE TABLE data_monthly_it_tn (
  "name" VARCHAR,
  "year" BIGINT,
  "month" BIGINT,
  "hnsum" DOUBLE,
  "hsmean" DOUBLE,
  "hsmax" DOUBLE,
  "scd1" DOUBLE,
  "scd1gt" DOUBLE,
  "scd10" DOUBLE,
  "scd20" DOUBLE,
  "scd30" DOUBLE,
  "scd50" DOUBLE,
  "scd100" DOUBLE,
  "hsmean_gapfill" DOUBLE,
  "frac_gapfilled" DOUBLE,
  "hsmax_gapfill" DOUBLE,
  "scd1_gapfill" DOUBLE,
  "scd1gt_gapfill" DOUBLE,
  "scd10_gapfill" DOUBLE,
  "scd20_gapfill" DOUBLE,
  "scd30_gapfill" DOUBLE,
  "scd50_gapfill" DOUBLE,
  "scd100_gapfill" DOUBLE
);

Data Monthly It Vda Cf

@kaggle.thedevastator_european_alps_snow_depth_observations.data_monthly_it_vda_cf

152.74 KB
13704 rows
23 columns


CREATE TABLE data_monthly_it_vda_cf (
  "name" VARCHAR,
  "year" BIGINT,
  "month" BIGINT,
  "hnsum" VARCHAR,
  "hsmean" DOUBLE,
  "hsmax" DOUBLE,
  "scd1" DOUBLE,
  "scd1gt" DOUBLE,
  "scd10" DOUBLE,
  "scd20" DOUBLE,
  "scd30" DOUBLE,
  "scd50" DOUBLE,
  "scd100" DOUBLE,
  "hsmean_gapfill" DOUBLE,
  "frac_gapfilled" DOUBLE,
  "hsmax_gapfill" DOUBLE,
  "scd1_gapfill" DOUBLE,
  "scd1gt_gapfill" DOUBLE,
  "scd10_gapfill" DOUBLE,
  "scd20_gapfill" DOUBLE,
  "scd30_gapfill" DOUBLE,
  "scd50_gapfill" DOUBLE,
  "scd100_gapfill" DOUBLE
);

Data Monthly It Veneto

@kaggle.thedevastator_european_alps_snow_depth_observations.data_monthly_it_veneto

135.5 KB
10128 rows
23 columns


CREATE TABLE data_monthly_it_veneto (
  "name" VARCHAR,
  "year" BIGINT,
  "month" BIGINT,
  "hnsum" DOUBLE,
  "hsmean" DOUBLE,
  "hsmax" DOUBLE,
  "scd1" DOUBLE,
  "scd1gt" DOUBLE,
  "scd10" DOUBLE,
  "scd20" DOUBLE,
  "scd30" DOUBLE,
  "scd50" DOUBLE,
  "scd100" DOUBLE,
  "hsmean_gapfill" DOUBLE,
  "frac_gapfilled" DOUBLE,
  "hsmax_gapfill" DOUBLE,
  "scd1_gapfill" DOUBLE,
  "scd1gt_gapfill" DOUBLE,
  "scd10_gapfill" DOUBLE,
  "scd20_gapfill" DOUBLE,
  "scd30_gapfill" DOUBLE,
  "scd50_gapfill" DOUBLE,
  "scd100_gapfill" DOUBLE
);

Data Monthly Si Arso

@kaggle.thedevastator_european_alps_snow_depth_observations.data_monthly_si_arso

992.18 KB
123828 rows
23 columns


CREATE TABLE data_monthly_si_arso (
  "name" VARCHAR,
  "year" BIGINT,
  "month" BIGINT,
  "hnsum" DOUBLE,
  "hsmean" DOUBLE,
  "hsmax" DOUBLE,
  "scd1" DOUBLE,
  "scd1gt" DOUBLE,
  "scd10" DOUBLE,
  "scd20" DOUBLE,
  "scd30" DOUBLE,
  "scd50" DOUBLE,
  "scd100" DOUBLE,
  "hsmean_gapfill" DOUBLE,
  "frac_gapfilled" DOUBLE,
  "hsmax_gapfill" DOUBLE,
  "scd1_gapfill" DOUBLE,
  "scd1gt_gapfill" DOUBLE,
  "scd10_gapfill" DOUBLE,
  "scd20_gapfill" DOUBLE,
  "scd30_gapfill" DOUBLE,
  "scd50_gapfill" DOUBLE,
  "scd100_gapfill" DOUBLE
);

Meta All

@kaggle.thedevastator_european_alps_snow_depth_observations.meta_all

114.34 KB
2979 rows
9 columns


CREATE TABLE meta_all (
  "provider" VARCHAR,
  "name" VARCHAR,
  "longitude" DOUBLE,
  "latitude" DOUBLE,
  "elevation" DOUBLE,
  "hn_year_start" DOUBLE,
  "hn_year_end" DOUBLE,
  "hs_year_start" DOUBLE,
  "hs_year_end" DOUBLE
);