| 17 Feb 2026
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.
The manuscript focuses on the development of a national-scale rainfall-induced landslide model for New Zealand and the projected changes in landslide susceptibility under future climate scenarios. The topic is of clear practical relevance. The authors attempt to incorporate triggering rainfall into the model and further combine the modelling results with future climate scenarios and exposure analyses for roads and buildings, thereby building a national-scale application framework. From a methodological perspective, the manuscript include landslide sample extraction, predictor selection, LightGBM modelling, SHAP interpretation, and scenario-based projection. The amount of work is substantial, and the results have reference value. However, there are several important issues still require further clarification.
1. The training data are based on landslides triggered by Cyclone Gabrielle in Hawke’s Bay and Gisborne/Tairāwhiti. The authors also acknowledge that the South Island, especially the Southern Alps, differs substantially from the training area in both lithology and dominant slope processes. Pages 21-22 further state that the South Island is dominated by harder metamorphic rocks, and that the Southern Alps commonly feature scree/talus slopes, rock avalanches, and large deep-seated landslides, which are not equal to the shallow rainfall-induced landslides that are more typical of the North Island. The manuscript also notes that “the interpretation of RIL susceptibility maps for the Southern Alps should be undertaken cautiously.” It is suggested that the authors either state the applicable scope more accurately in the title and main conclusions, or add cross-regional validation.
2. The authors identify bare-ground change using a ΔNDVI threshold derived from pre- and post-event multi-temporal Sentinel-2 imagery, and then filter landslide pixels using a slope threshold of >10°. As explicitly stated on page 5, this approach “includes both the initiation point and run-out zone.” This means that the model is not learning purely the conditions of landslide initiation, but rather the spatial pattern of terrain affected after the event. On page 20, the authors also state that the model “does not separate the RIL initiation point from the debris run-out zone.” This has direct implications for the physical interpretation of predictors such as slope, curvature, TWI, and soil depth, because the geomorphic characteristics of runout zones differ from those of initiation zones. The authors should clarify more explicitly whether the model output is closer to initiation susceptibility, event-affected area probability, or a mixed footprint probability.
3. The authors use slope >10° to distinguish landslide-related bare-ground change from non-landslide areas. On page 13, in the SHAP interpretation, the manuscript itself states that the lower susceptibility below 10° “may also be a consequence of the RIL mapping exercise.” This indicates that part of the slope effect is directly embedded in the sample construction procedure, rather than being entirely learned by the model from independent observations of the process. Since slope is identified as the second most important predictor, this issue should be explained more carefully in the Results and Discussion.
4.The authors first hold out 20% of the landslide polygons, then randomly sample non-landslide pixels from the full region, and use a 10 m exclusion buffer and a 72 m buffer to reduce spatial leakage. Even so, the training and testing data still come from the same Cyclone Gabrielle event and the same broader regional context. In addition, the 10-fold cross-validation used in the manuscript is random cross-validation and does not explicitly account for spatial autocorrelation. It is suggested that the authors further evaluate the model using spatial cross-validation.
5. The future scenarios are derived from HIRDS rainfall data at 2000 m resolution, statistically downscaled to 25 m, and then combined with the change factors in Table 2 and the national mean temperature changes in Table 3 to construct the SSP2-4.5 and SSP3-7.0 scenarios. On the one hand, the future intensification signal is based on national mean warming rather than finer-scale regional climate differences. On the other hand, the manuscript does not systematically report the uncertainty range across the six climate models, but instead directly uses the national mean warming signal. The resulting outputs can be considered as national-scale scenario projections, but they may not be sufficient to support more detailed local-scale interpretations. It is suggested that the authors add corresponding statements or uncertainty discussion in the Abstract, Discussion, and Conclusion.
6.The presentation of the SSP scenarios should be made consistent throughout the manuscript. It would be better to use SSP2-4.5 and SSP3-7.0 consistently, rather than 2-4.5 and 3-7.0.