Research projects

Underway

Hōretireti Whenua | Sliding Lands Programme

The Hōretireti Whenua | Sliding Lands programme is an Endeavour Fund initiative (2023 to 2028) led by GNS Science and Massey University, focusing on hazard, risk, and impact modelling for fast-moving landslides in New Zealand. The programme aims to develop nationally applicable models that deliver a comprehensive quantifiable assessment of rapid landslide hazard and risk, and therefore, allow for smarter targeted risk management. To do this requires the seamless linkage of triggering scenarios with geospatial susceptibility and the size and runout of different landslide types, coupled with appropriate and defensible multi-faceted vulnerability models to deliver the landslide nowcasts and forecasts of impacts to decision-makers.

Background information is available here:https://www.gns.cri.nz/research-projects/sliding-lands/

Lead Researchers: Saskia de Vilder, Chris Massey (GNS Science), Emma Hudson-Doyle (Massey University)
Dates: 2023-2028

Landslide Watch Aotearoa

This Endeavour Fund initiative aims to move away from expensive local reactive (post-event) in-situ monitoring to pro-active (pre-event) space-based observation across all Aotearoa. Led by GNS Science, this work will be in collaboration with NIWA, University of Waikato, University of Canterbury, University of Leeds (UK), University of Oregon (US), University of California (US), University of Washington (US) and the Disaster Prevention Research Institute, Kyoto University (Japan). While the Sliding Lands work pays most attention to rapid, first-time slope failures that create obvious scars in the landscape and have immediate consequences, this second programme focuses on a slightly different hazard. Many landslides are pre-existing, large, deep, slow-moving and persist for generations; they damage homes, infrastructure and sometimes accelerate to fail catastrophically. Forecasting when a landslide might transition from slow to fast depends on our ability to identify the movement, constrain its mechanism, and model that movement under different driving conditions. Until now, methods used to find and predict the occurrence of damaging landslides faced considerable limitations, with traditional ground-based monitoring being too costly, time-consuming, and offering limited spatial coverage. The team propose to use satellite data (InSAR) to detect slow-moving landslides, link their movement patterns to the climatic drivers and characterise their behaviour before they cause damaging and/or catastrophic impacts. This ambitious approach will enable landslides to be identified nationwide, link their movement patterns to climatic drivers, and characterise their behaviour before they cause damage. Background information is available here: https://www.linkedin.com/posts/gnsscience_introducing-landslide-watch-aotearoa-activity-7244126737914060800-kHcq/

Lead Researchers: Chris Massey, Ian Hamling (GNS Science)
Dates: 2024-2029

Landslide Risk Management Guidelines (AGS2007 update project)

The AGS guidelines represent current best practice for landslide risk management in Australasia. The New Zealand Geotechnical Society and the Australian Geomechanics Society are working in partnership on an update to the guidelines to reflect lessons learned since they were published in 2007. This work is expected to be approximately 80% complete by April 2026. Background information is available here: https://landsliderisk.org/

PhD – Auckland West Coast Landslides

This research focuses on developing a hybrid model to assess landslide susceptibility and hazard mapping in the Muriwai and West Coast areas. This involves studying the geological, environmental, and anthropogenic factors contributing to landslides in this region. By integrating geospatial modeling, engineering surveys, and remote sensing data, the project aims to provide a comprehensive understanding of landslide mechanisms, particularly under the influence of climate change. Ultimately, the goal is to deliver a tool to enhance prediction accuracy to guide policymakers in devising effective mitigation strategies and minimize the risks to infrastructure and communities.
Progress Update (Jan 2025)
Data Collection: Geological, environmental, and geospatial data have been gathered from various databases. Systematic Literature Review: A review titled “Evolution and Integration of Hybrid Models for Landslide Susceptibility Assessment: A Systematic Review with Focus on Climate Change Impacts” is currently in progress and is expected to be completed soon. Creating Inventory Map: Using the existing data of the Muriwai and West Coast area, an inventory map of historical landslides is being developed to establish baseline data for analysis.
Next Steps
Initial Analysis: Perform detailed geospatial modeling and analysis of the inventory map and other collected data, focusing on identifying critical landslide factors in the Muriwai region.
Model Refinement and Validation: Develop the hybrid model further, validate it with additional data, and compare its performance with existing methods.

Researcher: Yousef Adeeb Chamachaei (Auckland University of Technology)
Supervisor(s): Dr Roo Kalatehjari (Auckland University of Technology) & Martin Brook (University of Auckland)
Dates: 2024-2027

PhD – Enhancing Landslide Susceptibility Mapping with Geotechnical and Climate Change Data: A Case Study from Titirangi, Auckland, New Zealand

Research Questions

How do geotechnical parameters influence the accuracy and reliability of landslide susceptibility maps within existing models?
How do climate change scenarios and their impact on key geotechnical parameters affect landslide susceptibility analysis?
What innovative machine-learning approaches can be implemented in landslide susceptibility analysis to best leverage local geotechnical and climate data?
In what ways does the proposed model improve our understanding of landslide susceptibility in Titirangi for local land use and development policies?

Objectives of the Study

Based on these research questions, this study aims to achieve three major objectives. Firstly, identify the strengths and weaknesses of geotechnical engineering-based approaches in improving landslide susceptibility mapping accuracy. After generating geotechnical-based landslide susceptibility maps, compare the results with existing susceptibility maps. Secondly, the study aims to focus on the impact of climate change data integration in landslide susceptibility analyses. The main idea is to combine geotechnical-based models with climate change data. This objective directly addresses research question two. Thirdly, innovative machine-learning approaches will be investigated with combination of geotechnical and climate data. In addition to these main objectives, by comparing the proposed models with existing ones, the study aims to understand how they enhance landslide susceptibility prediction.

Researcher: Rajitha Subhasinghe (Auckland University of Technology)
Supervisor(s): Dr Roo Kalatehjari (Auckland University of Technology)
Dates: 2024-2027

Past projects

Earthquake-Induced Landscape Dynamics

Large earthquakes, like the November 2016 Mw 7.8 Kaikōura earthquake, can generate thousands of landslides, landslide dams and damage hillslopes that are susceptible to failure during rainstorms and aftershocks. This debris, when mobilised, creates new hazards, including further landslides, landslide dams, rapid aggradation and formation of alluvial fans and floodplains, and increased river channel instability, as the debris cascades from hillslope to sea. These hazards may persist for decades and therefore represent a prolonged risk that must be managed by the impacted communities and stakeholders.

Earthquake-induced landscape dynamics was funded by the New Zealand Ministry for Business, Innovation and Employment Endeavour fund. The five year programme (2018-2023) was led by GNS Science in association with a number of research partners. The research was directed to effectively manage earthquake- and post-earthquake landslide risk using an integrated set of predictive tools guided by an evidence-based decision making framework by determining over what time scales do landscapes heal after major earthquakes. The Kaikōura earthquake provided a laboratory to quantify post-earthquake landscape dynamics.

More information is available here: https://slidenz.net/

SLIDE – Stability of Land in Dynamic Environments

Stability of Land in Dynamic Environments (SLIDE) is a GNS Science research project that investigates the impact of landslides in Aotearoa New Zealand. It aims to improves resilience of Wellington’s infrastructure through improving knowledge of the behaviour of anthropogenic slopes and developing strategies for more robust remediation approaches.

More information is available here: https://www.gns.cri.nz/research-projects/slide-stability-of-land-in-dynamic-environments/