This research investigates how landslides disrupt New Zealand's road network. By analysing past landslides, linking landslide severity to road impacts, and combining these relationships with landslide forecasts, the project aims to predict future road closures, improve emergency planning, reduce community isolation, and enhance public safety during severe weather and earthquakes.

This research improves flood prediction by analysing data from more than 3,000 rivers worldwide and using local fitting techniques to compare similar weather events. By relying on relevant historical data rather than human intuition, the model aims to produce more accurate flood forecasts and strengthen disaster preparedness under climate change.

This research improves weather and climate forecasting by studying how dry air mixes into thunderstorm clouds, a process called entrainment. Using satellite observations, radar data, and interpretable machine learning, the work refines outdated cloud physics models, helping scientists better predict severe weather, hurricanes, and long-term climate behavior.