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.
Highlights
Considered the most valuable natural resource, water is essential to human life and the health of the environment
Discrete sampling has been used for decades to obtain information about physical, chemical, and biogeochemical parameters related to water quality [17]
Recent improvements in technology have enabled the integration of optical sensors for measurements of irradiance, spectral absorption, fluorescence, and backscattering from buoys [32] from flow-through systems installed on-board ships (Figure 4) [19], and from profiling floats (BCG-Argo; [33]), or gliders [34]
Summary
Considered the most valuable natural resource, water is essential to human life and the health of the environment. These data (Figure 1) include in situ discrete samples, from professional and citizen science/volunteer (or, community) monitoring programs, automatic in situ sensors, satellite, drone, or aircraft remote sensing, and model outputs. GEO AquaWatch defined the iterative development model into five stages: (1) defining end-user needs; (2) generating or obtaining data required to meet the needs; (3) aggregating or transforming the data into usable products and information; (4) distributing and enabling access to the products and information; and, (5) translating the products and information into actionable knowledge through data assimilation of water quality derived from multiple space and time observations and platforms (Figure 2)
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