The Surface Water Chemistry (SWatCh) database: a standardized global database of water chemistry to facilitate large-sample hydrological research

• Published in: Earth System Science Data
• Main Authors: L. Rotteveel, F. Heubach, S. M. Sterling
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2022
• Subjects: geo; envir; Canada; McMurdo Dry Valleys
• Online Access: https://doi.org/10.5194/essd-14-4667-2022; https://essd.copernicus.org/articles/14/4667/2022/essd-14-4667-2022.pdf; https://doaj.org/article/b039b7e8735d4444ad9cf72f372cd3c8

Openly accessible global-scale surface water chemistry datasets are urgently needed to detect widespread trends and problems, to help identify their possible solutions, and to determine critical spatial data gaps where more monitoring is required. Existing datasets are limited with respect to availability, sample size and/or sampling frequency, and geographic scope. These limitations inhibit researchers from tackling emerging transboundary water chemistry issues – for example, the detection and understanding of delayed recovery from freshwater acidification. Here, we begin to address these limitations by compiling the global Surface Water Chemistry (SWatCh) database, available on Zenodo (https://doi.org/10.5281/zenodo.6484939; Rotteveel and Heubach, 2021). We collect, clean, standardize, and aggregate open-access data provided by six national and international programs and research groups (United Nations Environment Programme; Hartmann et al., 2019; Environment and Climate Change Canada; the United States of America National Water Quality Monitoring Council; the European Environment Agency; and the United States National Science Foundation McMurdo Dry Valleys Long-Term Ecological Research Network) in order to compile a database containing information on sites, methods, and samples, and a geospatial information system (GIS) shapefile of site locations. We remove poor-quality data (e.g., values flagged as “suspect” or “rejected”), standardize variable naming conventions and units, and perform other data cleaning steps required for statistical analysis. The database contains water chemistry data for streams, rivers, canals, ponds, lakes, and reservoirs across seven continents, 24 variables, 33 722 sites, and over 5 million samples collected between 1960 and 2022. Similar to prior research, we identify critical spatial data gaps on the African and Asian continents, highlighting the need for more data collection and sharing initiatives in these areas, especially considering that freshwater ecosystems in these ...