Finalist 2020

Early Algal Warning System


A system to monitor water quality and health risks in near real time

Water is a precious resource for recreational and commercial activities for everyone. We developed a system to monitor water quality and health risks in near real time reducing reporting turnaround time from weeks to days.

The Early Algal Warning System is a combined satellite-based detection and data visualisation system that monitors algal activity, and it visualises trends to report on water health. The system monitors rising concentrations of algae across areas of jurisdiction, and enables local councils and state authorities to issue alerts and address mitigation as quickly as possible. It has been in use by CSIRO scientists since 2016.

Design Excellence

Diversity – Our team is multidisciplinary and distributed. It comprises a visualisation designer (VIC), a data engineer and two water scientists (ACT), and a remote sensing researcher (QLD).

Functionality – We conducted face-to-face design workshops to sketch out the shape of the tool, and identified the key functionalities required to mitigate the long reporting time.

Aesthetics – The user interface adopted a minimalistic approach. It highlights water health as the first priority. The visualisation leverages the conventional red-amber-green traffic light for ease of understanding.

Overall: The system enables a higher water safety, quality and environmental sustainability in Australia.

Design Impact

Triple bottom line: The system mitigates risk of toxic water being consumed by alerting the local council authorities to problematic water sources in advance. The goal is to prevent potentially lethal water sources from being consumed by farmed cattle or humans.

From a social standpoint, healthier water contributes to our high quality of living, and allows event organisers to switch water sources with sufficient notice. From an environmental standpoint, it allows interventions to occur much earlier on, improving our access to more water. From a commercial point of view, it reduces business risk for everyone as a preventative technology.

Design Transformation

The Early Algal Warning System integrates web technology, data visualisation, water monitoring, and remote sensing into one seamless platform that has benefited Australia through a creative technology and human-centred approach. It has been presented to national conferences and also received international praise through the geospatial community.

Our technology design has inspired other countries within the Open Data Cube initiative ( to implement their own remote sensing solutions for water health monitoring. As of September 2020, Australia is one of the world’s only three countries to have an operational data cube centre, among Colombia and Switzerland.

Design Innovation

Differentiator – The Early Algal Warning System is one of the first monitoring platforms that visualised water health near real-time based on satellite imagery down to individual squares of metres resolution, whereas most current solutions only offered one data point per lake.

Human-in-the-loop – Our team included two water research scientists in the design journey, who served as both domain experts and our target audience. The team was able to rapidly prototype and iterate with their feedback after using various versions of the system. By the time the system was released as “version 1”, we went through 19 iterations over one year.

Other Key Features

The system provides an instant search result with lakes by names. It shows the overall trends of algae at the state level, and the timeline for when the next satellites pass over. The user can scrub through historical satellite analyses to better understand seasonal changes, and a sense of progression.

This research was supported by a joint research project between CSIRO’s Land and Water, NSW Water, as well as CSIRO’s eResearch program. The team is distributed, and the interface and visualisation designer is Victoria-based. The system speculates the future of water monitoring in a global context.

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