Integrating Inland and Coastal Water Quality Data for Actionable Knowledge

Abstract

Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision making.

Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing

“…This systematic review is relevant to multiple research domains, including, but not limited to RS, geographic information science, computer science, data science, information science, geoscience, hydrology, and water resource management. This paper does not attempt to review the application of RS to water resources and hydrology more generally; for recent reviews of these topics, see [ 13 , 21 , 22 , 23 , 24 ]. A survey of DL applications in hydrology and water resources can be found in [ 25 ]; a survey of AI in the water domain can be found in [ 26 ]; and a survey of water quality applications using satellite data solely focused on ML can be found in [ 27 ].…”

“…As emphasized in [ 13 , 14 ], one of the major current challenges for water resource management is the integration of water quality data and indices from multiple sources into usable and meaningful insights for actionable management decisions. Geovisualization, also known as geographic visualization, uses the visual representations of geospatial data and the use of cartographic techniques to facilitate thinking, understanding, knowledge construction, and decision support about human and physical environments at geographic scales of measurement [ 102 , 103 ].…”

“… Simply applying (or with minor modifications) existing AI/ML/CV/DL algorithms/methods to RS big data imagery-based problems is still very far away from producing real-world applications that meet water management professionals’ and policymakers’ needs. As echoed in [ 13 ], “[…] realizing the full application potential of emerging technologies requires solutions for merging various measurement techniques and platforms into useful information for actionable management decisions, requiring effective communication between data providers and water resource managers” [ 116 ]. Much more multidisciplinary and integrative collaboration in terms of depth and breadth are in high demand.…”

“…More specifically, automated monitoring of water bodies is critical for adapting to climate change, water resources, ecosystem services, and the hydrological cycle, as well as for urban hydrology, which can facilitate timely flood protection planning and water quality control for public safety and health [ 10 , 11 , 12 ]. Accurate water quality monitoring is essential for developing sustainable water resource management strategies and ensuring the health of communities, ecosystems, and economies [ 13 ]. However, current knowledge of water quality is often disconnected in time and space across different measurement techniques and platforms that may fail to capture dynamic ecosystem changes.…”

“…This disconnection indicates an inefficiency and redundancy in research and monitoring activities. A major challenge for water resource management is how to integrate multiple sources of water quality data and indices into usable and actionable information of environmental, social, economic, and infrastructural value [ 13 , 14 ].…”