Abstract
Abstract. Large-scale hydrological studies are often limited by the lack of available observation data with a good spatiotemporal coverage. This has affected the reproducibility of previous studies and the potential improvement of existing hydrological models. In addition to the observation data themselves, insufficient or poor-quality metadata have also discouraged researchers from integrating the already-available datasets. Therefore, improving both the availability and quality of open water quality data would increase the potential to implement predictive modeling on a global scale. The Global River Water Quality Archive (GRQA) aims to contribute to improving water quality data coverage by aggregating and harmonizing five national, continental and global datasets: CESI (Canadian Environmental Sustainability Indicators program), GEMStat (Global Freshwater Quality Database), GLORICH (GLObal RIver CHemistry), Waterbase and WQP (Water Quality Portal). The GRQA compilation involved converting observation data from the five sources into a common format and harmonizing the corresponding metadata, flagging outliers, calculating time series characteristics and detecting duplicate observations from sources with a spatial overlap. The final dataset extends the spatial and temporal coverage of previously available water quality data and contains 42 parameters and over 17 million measurements around the globe covering the 1898–2020 time period. Metadata in the form of statistical tables, maps and figures are provided along with observation time series. The GRQA dataset, supplementary metadata and figures are available for download on the DataCite- and OpenAIRE-enabled Zenodo repository at https://doi.org/10.5281/zenodo.5097436 (Virro et al., 2021).
Highlights
Human-driven loads of nutrients to aquatic ecosystems have become the main driver of eutrophication in waterways and coastal zones (Desmit et al, 2018; Sinha et al, 2019)
The Global River Water Quality Archive (GRQA) dataset consists of observation time series for 42 different water quality parameters provided in tabular form as CSV files
– a data catalog (GRQA_data_catalog.pdf) with maps showing the spatiotemporal coverage and graphs describing the distribution of all 42 parameters along with a README file describing the dataset structure
Summary
Human-driven loads of nutrients to aquatic ecosystems have become the main driver of eutrophication in waterways and coastal zones (Desmit et al, 2018; Sinha et al, 2019). Agricultural production is already one of the major forces behind environmental degradation (Foley et al, 2011), and population growth is increasing that pressure (Mueller et al, 2012). The use of nitrogen (N) and phosphorus (P) fertilizers to increase agricultural productivity is predicted to increase 3fold by 2050 unless more efficient fertilizer use can be implemented (Tilman et al, 2001). In order to achieve the UN Sustainable Development Goal (SDG) 6, we need better understanding of our water resources and water quality. Monitoring and modeling the hydrochemical properties of rivers is essential for understanding and mitigating water quality deterioration due to agricultural and industrial non-point-source pollution (Krysanova et al, 1998; Leon et al, 2001; Wu and Chen, 2013).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
