Moving beyond the limits of in-situ water-quality observations: employing data from modelling and remote sensing opens new horizons to enhance global water-quality information.
Key messages
- Data from in-situ observations is often not available at sufficient spatial coverage and temporal resolution to provide reliable information on water quality.
- Integration of in-situ data, remote sensing and modelling is first proposed as an innovative approach for improving water-quality information, especially in data-scarce regions, and is demonstrated for Lake Victoria.
Achieving good ambient water quality to safeguard human and ecosystem health is rooted in SDG 6. Global information on freshwater quality is based almost exclusively on in-situ data, which can be regarded as the “gold standard” for monitoring and assessment. However, in many regions of the world, there is a lack of water-quality monitoring programmes and data-sharing policies. In the 2021 progress update on SDG 6.3.2 (UNEP 2021), water quality could not be assessed for about three billion people because of a lack of water-quality data.
To provide timely, consistent information on the state and trends of water quality globally, especially under rapidly changing conditions, we need to tap into additional data sources.
One key additional data source comes from satellite remote sensing that offers unprecedented opportunities in terms of coverage and consistency as the same methods can be practically applied everywhere to observe optical water-quality parameters such as turbidity and Chlorophyll-a. Another important cornerstone of getting more consistent water-quality information are water-quality models. These models use observational data and link them to drivers of water quality. They can provide spatially and temporarily consistent water-quality information, as well as enable the connection of the drivers of water quality to the state and impacts.
Figure 1. The World Water Quality Assessment triangle is suggested as an innovative approach benefiting from the strengths of integrating in-situ, modelling and remote-sensing data for better water-quality assessment across scales (Source: GlobeWQ project).
The Lake Victoria Use Case
The Lake Victoria Use Case is one of three African Use Cases piloted by the World Water Quality Alliance to demonstrate the capabilities of current water-quality information services.
Lake Victoria is the largest in Africa and the second-largest freshwater lake in the world. It contributes substantially to the economies and livelihoods of the riparian countries (Kenya, Tanzania, Uganda). Fisheries, in particular, are an important industry for the entire region (Njiru et al. 2018). Despite its importance, the lake's water quality has come under pressure from oil spills, discharge of untreated wastewater, solid waste inputs, and diffuse nutrient inputs (Hecky et al. 2010). High nutrient loadings are one of the main factors causing harmful algal blooms (Hecky et al. 2010). Algal blooms directly threaten fish populations when they result in low-oxygen conditions (Ochumba 1990). In addition, cyanobacteria that produce harmful algal blooms can lead to the accumulation of microcystin in fish, which at high concentrations can pose risks to human health (Mchau et al. 2019; Roegner et al. 2020). WWQA stakeholder workshops for Lake Victoria recognized the need to better monitor the occurrence of algal blooms.
Data from satellite remote sensing (Landsat and Sentinel 2) are being used to provide timely and aerial information on algal blooms across the lake surface. An important source of in-situ observations on Lake Victoria is the GEMStat database, which provides additional water quality parameters and longer time series. Nevertheless, this database covers only a limited area of the lake. Water quality modelling shows that nutrient inputs from five tributary catchments contribute to more than 70 per cent of the annual inputs to Lake Victoria.
Combining the information shows that Winam Gulf is susceptible to high chlorophyll-a concentrations due to its limited water exchange with the main lake on the one hand, and nutrient inputs from the Nyando and Sondu catchments on the other. Figure 2 shows the spatial pattern and time series of the Harmful Algal Bloom Indicator (HAB) in Winam Gulf.
Water quality information platform for Lake Victoria
Regularly updated remote sensing data, in-situ data from the GEMstat database and model results are available on the GlobeWQ platform.
https://portal.globewq.info/globewq-webapp/react-geo-baseclient/build/index.html?applicationId=297