Hindcasting a groundwater system to pre-European conditions using mātauranga Māori
GNS Science
2021 - 2023
Team
Tara Forstner, Dr Leanne Morgan, Dr Catherine Moore (GNS), Dr Wes Kitlasten (GNS), Dr Amber Aranui (Te Papa)
The Problem
Worldwide there is a push for indigenous led governance and a decolonization of water resource management practices, many of which are calling for various restoration actions. Groundwater modelling plays a critical role in the future of management practices, as many indigenous cultural sites are groundwater supported. One approach to setting restoration targets is to determine baseline conditions prior to European settlement. Hindcasting, modelling past conditions, is often confounded by the lack of historical data, in addition to untangling of the effects of climate change and anthropogenic impacts over the last century. This research aims to use multiple lines of evidence from western data (ie. aerial photos, reconstruction of wetland extents) and Mātauranga Māori sources (ie. oral stories, indigenous ontologies) to model various periods in catchment history.
Research approach
Our research aims to explore the nexus of colonialism, indigenous communities, and economic developments on groundwater supported ecosystem depletion. We are collaborating with indigenous scholars and communities to provide outcomes both at a local-scale for an iwi community, but also with global implications by exploring novel perspectives on groundwater depletion quantification. This research is also in collaboration with the GNS-funded Te Whakaheke o Te Wai research programme which aims to better support water management based on the understanding of flow sources, pathways and lags. Through this programme, groundwater and surface water Mātauranga Māori and Mōhiotanga Māori is being explored and approaches for combining this knowledge, with other sources of information, are being developed to improve groundwater management. This represents a unique combination of western science and indigenous knowledge and is demonstrating the importance of combining the two knowledge systems.
Project Partners:
