Preprints
https://doi.org/10.5194/nhess-2022-88
https://doi.org/10.5194/nhess-2022-88
 
25 May 2022
25 May 2022
Status: this preprint is currently under review for the journal NHESS.

Multi-event assessment of typhoon-triggered landslide susceptibility in the Philippines

Joshua N. Jones1,2, Georgina L. Bennett2, Claudia Abancó3, Mark M. A. Matera4, and Fibor J. Tan4 Joshua N. Jones et al.
  • 1AECOM, East Wing Plumer House, Plymouth, PL6 5DH, United Kingdom
  • 2College of Life and Environmental Sciences, University of Exeter, Exeter, EX4 4RJ, United Kingdom
  • 3Faculty of Earth Sciences, University of Barcelona, 08028 Barcelona, Spain
  • 4School of Civil, Environmental and Geological Engineering, Mapúa University, Manila, Philippines

Abstract. There is a clear and pressing need to improve and update landslide susceptibility models across the Philippines. This is challenging, as landslides in this region are frequently triggered by temporally and spatially disparate typhoon events, and it remains unclear whether such spatially and/or temporally distinct typhoon events cause similar landslide responses, i.e., whether the landslide susceptibility for one typhoon event is similar for another. Here, we use logistic regression techniques (implemented alongside a LASSO (Least Absolute Shrinkage and Selection Operator) for independent variable selection) to develop four landslide susceptibility models based on three typhoon-triggered landslide inventories. These inventories are of landslides triggered by the 2009 Typhoon Parma (local name Typhoon Pepeng), the 2018 Typhoon Mangkhut (local name Typhoon Ompong), and the 2019 Typhoon Kammuri (local name Typhoon Tisoy). The 2009 and 2018 inventories were mapped across the same 150 km2 region of Itogon in the Benguet Province, whilst the 2019 event was mapped across a 490 km2 region of Abuan in the Isabela Province. The four susceptibility models produced are for the 2009, 2018 and 2019 inventories separately, and for the 2009 and 2018 inventories combined. By comparing the susceptibility model outputs across all four models, we are then able to assess the similarity in landslide response between the different typhoon events. Furthermore, using AUROC validation and 30 % of the landslide inventories saved for independent testing, we quantify the degree to which susceptibility models derived from one event can forecast/hindcast the landslides triggered by the other events. The logistic regression approach produced susceptibility models with 65–82 % accuracy, with the 2009, 2018, and combined 2009–2018 models being considerably more accurate (78–82 %) than the 2019 model (65 %). Furthermore, we find that the three typhoon events caused quite different landslide responses. Most notably, landslides in Itogon triggered by the 2018 and 2009 typhoons were heavily distributed across E/SE/S-facing slopes and at slope angles >30°, whereas landslides in Abuan triggered by the 2019 typhoon occurred across all aspects and slope angles. Finally, the AUROC validation shows that using a susceptibility model for one typhoon event to forecast/hindcast another leads to a 6–10 % reduction in model accuracy compared to the accuracy obtained when modelling and validating each event separately. However, using a susceptibility model for two combined typhoon events (2009 + 2018) to forecast/hindcast each typhoon event separately led to just a 1–3 % reduction in model accuracy. This suggests that combined multi-event typhoon triggered landslide susceptibility models will be more accurate and reliable for the forecasting of future typhoon-triggered landslides.

Joshua N. Jones et al.

Status: open (until 08 Jul 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-88', Anonymous Referee #1, 01 Jul 2022 reply

Joshua N. Jones et al.

Joshua N. Jones et al.

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Short summary
We modelled where landslides occur in the Philippines using landslide data from three typhoon events in 2009, 2018 and 2019. These models show where landslides occurred within the landscape. By comparing the different models, we found that the 2019 landslides were occurring all across the landscape, whereas the 2009 and 2018 landslides were mostly occurring at specific slope angles and aspects. This shows that landslide susceptibility must be considered variable through space and time.
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