ProfessorMatthew Wilson

Matt Wilson is a Professor in the School of Earth and Environment | Te Kura Aronukurangi and, since 2018, Director of the Geospatial Research Institute | Toi Hangarau (GRI). His research examines how water moves over and through the land surface in a changing climate, and how human activity shapes, and is shaped by, those processes, from floods and soil moisture to the transport of sediment and pollutants from land to rivers and coasts. To address these questions, he develops and applies process-based models, Earth observation and artificial intelligence, integrated in open environmental digital twins that keep uncertainty explicit.

Current work includes field-scale soil moisture for agricultural water management across Australia and New Zealand, combining ground GNSS sensors, the Rongowai airborne mission and satellites with machine learning; flood risk under climate change and nature-based solutions; and tracing water, sediment and pollutants from catchment to harbour with Ngāti Toa Rangatira, to support the restoration of Te Awarua o Porirua. With his PhD students, he has shown how earthquakes can reshape rivers and flood hazard, and how levees, dams and urban growth alter flood risk. He led the uncertainty theme of the MBIE Endeavour programme that produced New Zealand's first nationwide flood hazard and risk maps.

Matt teaches hydrology, spatial data science and environmental modelling, and coordinates GEOG409 Coasts and Rivers. He is a Technical Programme Chair for IEEE IGARSS 2028 and a member of the NZS 9421 Flood Modelling Standard committee.

Before joining UC in 2017, he founded the Department of Geography at the University of the West Indies, Trinidad & Tobago, working on water resources and flood risk in the Caribbean, after studying floodplain inundation in Amazonia at the Universities of Bristol and Exeter.

Prospective students: Matt welcomes enquiries from prospective PhD students interested in floods, soil moisture, land-surface water processes, remote sensing and machine learning.