Improving global water quality information by combining in-situ data, remote sensing and modeling

Achieving good ambient water quality for rivers, lakes and groundwater is anchored in the Sustainable Development Goals (SDGs). Poor water quality has considerable impacts on ecosystem integrity, human health, and food security. Information on the state of water quality is the basis for decision-making on pollution reduction measures.

To date, water quality information has mostly relied on data from on-site sampling and, increasingly, sensor-based monitoring stations. Despite the increasing amount of in-situ data and growing efforts to make these data easily accessible, spatial coverage and temporal consistency are not sufficient to provide comprehensive water quality information worldwide. In-situ data are particularly missing in low-income countries and regions known for their lack of data sharing policy . Therefore, it is necessary to tap into additional methods to obtain water quality information worldwide.

Data from satellites can provide information on optical water quality parameters such as turbidity and chlorophyll. Water quality models integrate observational data and build on the relationships between the state of water quality and its drivers such as agricultural practices and/or the discharge of untreated municipal wastewater. Models provide spatially and temporally consistent information and are the only tool that allows forecasts and projection of possible future water quality scenarios.

Combining information from these three sources (in situ data, satellite data, modeled data) helps to overcome specific limitations of each data source; and provides complementary information on the state of water quality parameters. 

We present the outcome of the GlobeWQ project (www.globewq.info) that has developed a prototype of a web-based platform that provides access to global and regional water quality information. The platform combines data from in-situ observations, satellite-based remote sensing, and water quality modeling to provide robust and timely water quality information. GlobeWQ provides global water quality information based on the WorldQual model, data-driven approaches and by incorporating in-situ data from the GEMStat water quality database (https://gemstat.org). At European scale the long-term nitrogen surplus has been reconstructed for more than a century (1850–2019) to assist modeling of nitrogen exports in European river catchments. 

Regional case studies have been established in a co-design process so that the data products are tailored to the needs of the regional users.

We demonstrate the capability of the “ triangulation” approach that combines the best available information from in-situ data , remote sensing and water quality modeling to improve the availability of water quality for the regional case studies (e.g.: Lake Victoria, Lake Sevan, Elbe River Basin). At the global scale, water quality modeling results are used to provide spatially and temporally resolved and consistent water quality information.