Integrating inland and coastal water quality data for actionable knowledge

El Serafy, Ghada Y.H.; Schaeffer, Blake A.; Neely, Merrie-Beth; Spinosa, Anna; Odermatt, Daniel; Weathers, Kathleen C.; Baracchini, Theo; Bouffard, Damien; Carvalho, Laurence; Conmy, Robyn N.; Keukelaere, Liesbeth De; Hunter, Peter D.; Jamet, Cédric; Joehnk, Klaus D.; Johnston, John M.; Knudby, Anders; Minaudo, Camille; Pahlevan, Nima; Reusen, Ils; Rose, Kevin C.; Schalles, John; Tzortziou, Maria. 2021 Integrating inland and coastal water quality data for actionable knowledge [in special issue: Big Earth data and remote sensing in coastal environments] Remote Sensing, 13 (15), 2899. 24, pp.

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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

Item Type: Publication - Article
Digital Object Identifier (DOI):
UKCEH and CEH Sections/Science Areas: Water Resources (Science Area 2017-)
ISSN: 2072-4292
Additional Information. Not used in RCUK Gateway to Research.: Open Access paper - full text available via Official URL link.
Additional Keywords: water quality, remote sensing, lake, estuary, coastal, sensors, management, interoperability, integration
NORA Subject Terms: Ecology and Environment
Data and Information
Date made live: 05 Oct 2021 16:29 +0 (UTC)

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