A step towards FAIR water quality data – lessons learned from the OGC Water Quality Interoperability Experiment

Assessing the status and trends of water quality in inland water bodies requires access to reliable water quality monitoring data and associated metadata such as the monitoring locations, sampling methods, monitoring equipment and analytical methods. Many environmental agencies and research organizations collect water quality monitoring data, but unlike in other environmental domains and due to a lack of common best practices and standards, most organizations use their own data models, formats and controlled vocabularies to store and share these data. As a result, large-scale water quality analyses with a transboundary, continental or global scope require significant efforts to collect the necessary monitoring data from different sources and to harmonize the different data structures. Several international initiatives such as the UNEP Global Environment Monitoring System for Freshwater (GEMS/Water)¹ or research activities such as the Global River Water Quality Archive (GRQA)² have compiled global water quality datasets to facilitate large-scale hydrological studies, all facing the same challenges and often duplicating data processing efforts.
Over the last 20 years, the observing community has developed data models and semantic ontologies such as the OGC Observations, Measurements, and Samples (OMS)³ standard or the OGC/W3C Semantic Sensor Network (SSN)⁴ ontology to describe observations and associated metadata. These form the basis of several standards for the exchange of hydrological observation data such as the WaterML 2.0 family of standards. However, water quality specific aspects such as the description of sampling activities and associated metadata have not yet been included in these water specific standards. 
To address this issue, several government agencies and research organizations have started a Water Quality Interoperability Experiment (WQIE) within the Open Geospatial Consortium (OGC) in 2022. Several use cases for the exchange of water quality monitoring data of physical and chemical parameters monitored in surface and groundwater bodies using in-situ (sensor) or ex-situ (laboratory) monitoring were developed and described as object diagrams in UML based on the OMS conceptual model. Based on this exercise, a physical data model was developed by extending the OGC SensorThingsAPI (STA)⁵ with a plugin for the open source FROST server⁶. Several WQIE participants deployed pilot instances of water quality enabled FROST servers, making their water quality data publicly available. A web client was developed to facilitate access to the various STA endpoints and to enable data visualisation⁷. 
This presentation will give an overview of the developments of the OGC Water Quality Interoperability Experiment, highlighting achievements, outstanding challenges and future development plans. 

References:

¹ https://www.unep.org/explore-topics/water/monitoring-water-quality

² Virro, H., Amatulli, G., Kmoch, A., Shen, L., and Uuemaa, E.: GRQA: Global River Water Quality Archive, Earth Syst. Sci. Data, 13, 5483–5507, https://doi.org/10.5194/essd-13-5483-2021, 2021.

³ https://docs.ogc.org/as/20-082r4/20-082r4.html

⁴ https://www.w3.org/TR/vocab-ssn/

⁵ https://www.ogc.org/publications/standard/sensorthings/

⁶ https://github.com/hylkevds/FROST-Server.Plugin.WaterQualityIE/tree/main

⁷ https://api4inspire.k8s.ilt-dmz.iosb.fraunhofer.de/servlet/is/226/