National Digital Twin for Flood Resilience in New Zealand

Develop and test the specifications needed for a New Zealand flood resilience digital twin

The Challenge

Flood inundation is a frequent, widespread, and impactful hazard, which regularly causes damage to housing and infrastructure along with disruption to communities and businesses. Further, flood risk is expected to increase in future because of climate change through increased storminess and due to rapid urbanisation. Flood risk management and mitigation requires substantial amounts of spatial data related to infrastructure and the environment, making it challenging and expensive to develop suitable risk assessments or scenarios. A “digital twin” comprising of three-waters (drinking water, wastewater, and stormwater), flood mitigation and other infrastructure, high-resolution topography and land cover would enable these assessments to be completed more rapidly and at lower cost, and will facilitate detailed, standardised risk assessments at the national scale. This project developed and tested the specifications needed for a New Zealand flood resilience digital twin and implement it for selected urban areas.


The project partners were the University of Canterbury, Land Information New Zealand, and the National Institute of Water and Atmospheric Research Limited.

The Solution

This project aimed to develop and test a New Zealand digital twin for flood resilience with the following four objectives:

  1. To assess existing and/or develop new standards and specifications for spatial data of relevance to flood resilience in urban areas, including but not limited to infrastructure such as pipes, storm water drainage systems, streamlines, culverts and stopbanks (levees), topographic data from terrestrial LiDAR, channel bathymetry, land cover and other infrastructure of relevance such as buildings and roads.
  2. To test these standards within an interoperability experiment based on those of the Open Geospatial Consortium (OGC).
  3. To use the specifications to develop a “digital twin” and implement this for automated generation of flood inundation models for rapid flood risk assessment, based on new methods of machine learning for automated feature extraction.
  4. To test and demonstrate this digital twin for an urban flood event.

Deliverables included review and research papers, open-source code for the flood resilience digital twin and a roadmap for user uptake and next steps.


The project developed leading flood risk research at the national scale in New Zealand, building directly from previous research. It helped the potential value of geospatial information for flood risk assessment to be realised. In addition, methods developed will aid the spatial industry to develop applications in other important areas including, for example, improved planning for urban development. It is expected the use case will become an exemplar for other industry sectors and the experience gained will assist industry in developing digital twins for other important applications.


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