Catchment characteristics and water chemistry of over 5000 nationally monitored Swedish lakes
https://doi.org/10.5878/rxj2-ks21
This dataset contains data for catchment characteristics and lake water chemistry for lakes in the Swedish Lake Survey, SLS (omdrevstationer), and trend lake (trendlakestationer) monitoring programs. Both surveys, the Swedish Lake Survey and the trend lake survey, are compiled at the Swedish University of Agricultural Sciences (SLU) as part of the university's environmental monitoring and assessment programme Lakes and Watercourses. This makes up a dataset of 5140 lakes (110 of which are trend lakes monitored 4 times a year and 5030 survey lakes monitored every 6 years, some lakes are in both programs), all >1 ha in lake area. For each of these programs this dataset includes (1) time series of water chemistry for all the stations, (2) shapefile with the catchment delineation and station coordinates, and (3) a file summarizing catchment characteristics. Catchment characteristics include land cover, soil depth, peat coverage, ditch density, ditch density in peat areas, NDVI, lake water chemistry, atmospheric deposition, precipitation, temperature, precipitation as snow, percentage above the high coast line, and runoff. An extra file, 'significance.csv', was added in version 2 of this dataset containing the significance (p-value) of slope estimators, where applicable.
This dataset also includes the code used to extract the data used to generate this time series and characteristics. As such this data can be reproduced, and extended from the information contained in the dataset.
The following files are included in the dataset:
**Documentation**
readme_overview.md: An overview of what the dataset contains (further elaborated in readme_data.md) and how it was generated (further elaborated in readme_code.md).
readme_data.md: Documentation of the final datasets files, including units, reference to the code used to generate, and original data source.
readme_code.md: Documentation of the code and functions used to generate the dataset. A static version can be found in the dataset as git_freeze and a live version of the code can be found at https://github.com/aalackner/DOC_catchments.
**Data**
chemistry_SLS.csv: 14107 x 46, 4.8MB
chemistry_trend.csv: 16018 x 46, 5.1MB
characteristics_SLS.csv: 5030 x 81, 6MB
characteristics_trend.csv: 110 x 93, 0.16MB
significance.csv: 5137 × 27, 1.21 MB
SLS.zip: 6194 polygons, 20MB
trend.zip: 111 polygons, 0.3MB
etc.zip: 114 MB
etc.zip is a folder with additional timeseries data used in the calculation of characteristics.
Documentation files
Documentation files
Citation and access
Citation and access
Data access level:
Creator/Principal investigator(s):
Research principal:
Principal's reference number:
- SLU.ma.2026.4.2.IÄ-1
Data contains personal data:
No
Citation:
Language:
Method and outcome
Method and outcome
Time period(s) investigated:
Variables:
72
Data format/data structure:
Data collection - Automated data extraction
Data collection - Automated data extraction
Mode of collection:
Automated data extraction
Description of the mode of collection:
Extraction and summarization of catchment characteristics, taken from multiple different original sources, including land cover, climate data, runoff in the catchment, among others. Variables with a temporal resolution were summarized as start, change and variation, using the period 2007-2012 for start and 2013-2024 for the change and variation. All methods used to extract and summarize data for this code can be found in the code section of this dataset.
Time period(s) for data collection:
2024 - 2024
Data collector:
- Swedish University of Agricultural Sciences
Opens a new window at ror.org.
ROR
Source of the data:
- Other
Data collection - Automated data extraction: API query
Data collection - Automated data extraction: API query
Mode of collection:
Automated data extraction: API query
Description of the mode of collection:
Water chemistry was extracted from the MVM data portal via API, and then processed into easily understandable time series. This was done using a script 'get_water_chemistry.R' included in the dataset.
Time period(s) for data collection:
2025 - 2025
Data collector:
- Swedish University of Agricultural Sciences
Opens a new window at ror.org.
ROR
Source of the data:
- Other
Temporal resolution:
1 year
Geographic coverage
Geographic coverage
Geographic location:
Geographic description:
This dataset covers the catchments of 5030 Swedish lakes, spread across the country.
Administrative information
Administrative information
Responsible department/unit:
Department of Aquatic Sciences and Assessment
Funding
Funding
Funding agency:
- Swedish Research Council for Environment Agricultural Sciences and Spatial Planning
Opens a new window at ror.org.
ROR
Award number:
2022-01207
Award title:
Förbättring av evidensbasen för förståelsen och hantering av ytvattenbrunifiering
Funding agency:
- Swedish Research Council for Environment Agricultural Sciences and Spatial Planning
Opens a new window at ror.org.
ROR
Award number:
Grant No. 2022–00942
Topic and keywords
Topic and keywords
Swedish Standard Classification of Research Subjects 2025:
INSPIRE topic categories:
Relations
Relations
Is new version of:
Versions
Versions
Version:
2
Data added:
File significance.csv added, which contains the p-values for all the Theil-Sen slope estimators, which are included in characteristics_SLS.csv and characteristics_trend.csv. The documentation files were updated accordingly.
Published:
Metadata
Metadata
Versions
Versions
Version:
2
Data added:
File significance.csv added, which contains the p-values for all the Theil-Sen slope estimators, which are included in characteristics_SLS.csv and characteristics_trend.csv. The documentation files were updated accordingly.
Published:
