Preprints
https://doi.org/10.5194/nhessd-3-4871-2015
https://doi.org/10.5194/nhessd-3-4871-2015
17 Aug 2015
 | 17 Aug 2015
Status: this preprint was under review for the journal NHESS. A revision for further review has not been submitted.

Landslide inventory development in a data sparse region: spatial and temporal characteristics of landslides in Papua New Guinea

J. C. Robbins and M. G. Petterson

Abstract. In Papua New Guinea (PNG) earthquakes and rainfall events form the dominant trigger mechanisms capable of generating many landslides. Large volume and high density landsliding can result in significant socio-economic impacts, which are felt particularly strongly in the largely subsistence-orientated communities which reside in the most susceptible areas of the country. As PNG has undergone rapid development and increased external investment from mining and other companies, population and settled areas have increased, hence the potential for damage from landslides has also increased. Information on the spatial and temporal distribution of landslides, at a regional-scale, is critical for developing landslide hazard maps and for planning, sustainable development and decision making. This study describes the methods used to produce the first, country-wide landslide inventory for PNG and analyses of landslide events which occurred between 1970 and 2013. The findings illustrate that there is a strong climatic control on landslide-triggering events and that the majority (~ 61 %) of landslides in the PNG landslide inventory are initiated by rainfall related triggers. There is also large year to year variability in the annual occurrence of landslide events and this is related to the phase of El Niño Southern Oscillation (ENSO) and mesoscale rainfall variability. Landslide-triggering events occur during the north-westerly monsoon season during all phases of ENSO, but less landslide-triggering events are observed during drier season months (May to October) during El Niño phases, than either La Niña or ENSO neutral periods. This analysis has identified landslide hazard hotspots and relationships between landslide occurrence and rainfall climatology and this information can prove to be very valuable in the assessment of trends and future behaviour, which can be useful for policy makers and planners.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
J. C. Robbins and M. G. Petterson
 
Status: closed (peer review stopped)
Status: closed (peer review stopped)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
 
Status: closed (peer review stopped)
Status: closed (peer review stopped)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
J. C. Robbins and M. G. Petterson
J. C. Robbins and M. G. Petterson

Viewed

Total article views: 1,807 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,195 510 102 1,807 91 91
  • HTML: 1,195
  • PDF: 510
  • XML: 102
  • Total: 1,807
  • BibTeX: 91
  • EndNote: 91
Views and downloads (calculated since 17 Aug 2015)
Cumulative views and downloads (calculated since 17 Aug 2015)

Cited

Saved

Latest update: 21 Nov 2024
Download
Short summary
This paper outlines approaches for developing a regional landslide inventory in a data sparse region, with a specific focus on Papua New Guinea (PNG). In addition, analyses of landslide events which occurred in PNG between 1970 and 2013 are reviewed in detail. Results show that there is a strong climatic control on landslide-triggering events (at a range of temporal and spatial scales) and that the majority of landslides in the PNG landslide inventory are initiated by rainfall related triggers.
Altmetrics