Climate change increases landslide susceptibility in Aotearoa New Zealand: Development and application of a national-scale model using machine learning
Abstract. Rainfall-induced landslides (RILs) pose a major hazard to infrastructure, ecosystems, and communities across Aotearoa New Zealand, with events such as Cyclone Gabrielle underscoring the potential scale of their impacts. In this study, we develop a relatively high-resolution national-scale RIL susceptibility model that includes both conditioning and triggering variables and use it to assess the impacts of climate change on RIL susceptibility. The model utilises machine learning (ML) (gradient boosted decision trees) to predict RIL susceptibility in response to extreme rainfall events under current and future climate scenarios at 25 m spatial resolution. We use a training dataset of observed landslides triggered by Cyclone Gabrielle in the Hawke's Bay and Gisborne/Tairāwhiti regions. Predictor variables include topographic, geologic, and environmental factors, with rainfall intensity serving as the primary trigger. Model performance is evaluated using Shapley additive explanations (SHAP) analysis, alongside standard error metrics, achieving a receiver operating characteristic area under the curve (ROC-AUC) of 0.94. We then apply the model nationally to estimate RIL susceptibility under six current and 24 future storm scenarios based on NIWA’s high-intensity rainfall design system (HIRDS) datasets and modelled temperature changes under different shared socioeconomic pathways (SSPs). Results show a substantial increase in RIL susceptibility under warmer climate futures, with susceptibility increasing disproportionately to rainfall increase. Forest cover is found to play an important role in mitigating susceptibility. This work presents a robust framework for national-scale RIL susceptibility assessment under specific storm scenarios and provides a national-scale dataset suitable to support climate-resilient land use planning and nature-based mitigation strategies.