Integrating Inland and Coastal Water Quality Data for Actionable Knowledge

Area of Science:

• Environmental Science
• Water Resource Management
• Remote Sensing
• Data Assimilation

Background:

• Water quality data are collected through discrete sampling and continuous sensor networks for inland and coastal waters.
• Water quality parameters can also be derived from model outputs and remote sensing technologies.
• Integrating these varied data sources offers a more comprehensive understanding of dynamic aquatic ecosystems.

Purpose of the Study:

• To review existing data sources and integration frameworks for water quality data.
• To identify gaps in current frameworks for scaling water quality data across regions.
• To propose a new integration framework to enhance global capacity for water quality data utility and access.

Main Methods:

• Review of current water quality data sources (e.g., monitoring programs, sensors, remote sensing, models).
• Analysis of existing data integration frameworks and their limitations.
• Development of a proposed integration framework aligned with the Group on Earth Observations (GEO) AquaWatch Initiative.

Main Results:

• Current methods for scaling water quality data are emerging, driven by global satellite data availability.
• Gaps exist in frameworks for translating localized data into globally applicable, actionable knowledge.
• The proposed framework aims to bridge these gaps by enhancing data integration and accessibility.

Conclusions:

• Integrating diverse water quality data is crucial for improved ecosystem characterization and water resource management.
• A standardized, scalable integration framework is needed to leverage global data, especially from remote sensing.
• The proposed framework supports equitable access to water quality information for policy and decision-making.