Any stakeholders in the catchment who would benefit can access the data. This includes catchment groups, iwi and hapu, scientists and farmers. Photo / Supplied
Any stakeholders in the catchment who would benefit can access the data. This includes catchment groups, iwi and hapu, scientists and farmers. Photo / Supplied
Automated water quality sensors in the Manga-o-tama catchment in Waikato are collecting hourly information about the state of water quality.
The data is transferred to an online dashboard for visualisation, which is accessible by the project stakeholders.
The sensors are gathering information for the Manga-o-tama Ōhaupō Peat Lakes to Waipā River Connection project, a partnership that involves iwi partners, Nga Iwi Tōpū O Waipā - Ngāti Apakura, Ngāti Hikairo, Ngāti Mahanga, local farmers and stakeholders including Waipā District Council, Waikato Regional Council, NZ Landcare Trust and the Manga-o-tama Wetland Restoration Trust.
The Manga-o-tama catchment project is supported by Living Water, a partnership between the Department of Conservation (DoC) and Fonterra, who funded the sensors. Living Water has been working to improve the water quality in the Lake Ruatuna catchment, a peat lake in Waikato.
Environmental IoT (Internet of Things) monitoring specialists, Adroit installed sensors in two tributaries that feed into the Manga-o-tama wetland. Photo / Supplied
The work previously has involved collecting monthly water samples in person, and then sending them to a laboratory to be analysed for nutrients and contaminants, according to the Waikato Regional Council State of the Environment monitoring standards.
Living Water freshwater science lead Dr Katie Collins says for the Manga-o-tama project, Living Water wanted to ensure water quality information could be collected easily, reliably and accurately.
"Monthly data provides good information for establishing long-term trends in large, stable bodies of water, but results can be highly weather dependant and collection of samples is time-consuming," says Collins.
"Real-time water quality monitoring allows us to see daily patterns and changes as they happen, including the impacts of high rainfall events and different flows."
In May 2022, Environmental IoT (Internet of Things) monitoring specialists Adroit installed sensors in two tributaries that feed into the Manga-o-tama wetland. Every hour the sensors record levels of nitrates (NO3), turbidity, total suspended solids, total dissolved solids, dissolved oxygen, water temperature, conductivity and pH.
The data is transferred via the Spark IoT CAT-M1 network to an online dashboard, accessible via a smartphone or computer. Any stakeholders in the catchment who would benefit can access the data. This includes catchment groups, iwi and hapū, scientists and farmers.
Collins says the real-time monitoring will allow Living Water to understand long and short-term water quality trends in the catchment.
The data is transferred via the Spark IoT CAT-M1 network to an online dashboard, accessible via a smartphone or computer. Photo / Supplied
"There can be a large amount of variation in water quality from day to day and in different flows, from natural causes and from land use," says Collins.
"We don't have a lot of information around these short-term trends because previously there was no real way to collect the data."
The Manga-o-tama catchment is an important migration route for native fish species such as short-finned and long-finned eels. Other native fish recorded in the Manga-o-tama stream include banded kōkopu, black mudfish, common bully, ran's bully, koura, smelt and torrent fish.
Several rare terrestrial species have also been recorded at the site including pūweto/spotless crake and pekapeka-tou-roa/long-tailed bat, one of only two remaining species of native land mammals in New Zealand.
Living Water has learnt from previous projects that a lack of good quality data is a barrier to better catchment management. The Manga-o-tama catchment project group, along with other parties, will have access to the same data, enabling evidence-based decisions on interventions and land-use changes.
Collins says the real-time data will confirm if interventions within the catchments are working.
"The new sensor data will let us know if the interventions like riparian planting, wetland restoration, fencing setbacks and on-farm changes are having an impact", says Collins.
"Many of the interventions like wetland restoration and riparian planting reduce sediment entering waterways in high rainfall when it's dangerous to gather water samples. These sensors are designed to withstand high flow conditions and will continue to provide real-time data in even the worst conditions."
