English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  Landslide initiation thresholds in data-sparse regions: application to landslide early warning criteria in Sitka, Alaska, USA

Patton, A. I., Luna, L., Roering, J. J., Jacobs, A., Korup, O., Mirus, B. B. (2023): Landslide initiation thresholds in data-sparse regions: application to landslide early warning criteria in Sitka, Alaska, USA. - Natural Hazards and Earth System Sciences, 23, 10, 3261-3284.
https://doi.org/10.5194/nhess-23-3261-2023

Item is

Files

show Files
hide Files
:
patton_2023_nhess-23-3261-2023.pdf (Publisher version), 9MB
Name:
patton_2023_nhess-23-3261-2023.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-

Locators

show

Creators

show
hide
 Creators:
Patton, Annette I.1, Author
Luna, Lisa2, Author              
Roering, Joshua J.1, Author
Jacobs, Aaron1, Author
Korup, Oliver1, Author
Mirus, Benjamin B.1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

Content

show
hide
Free keywords: -
 Abstract: Probabilistic models to inform landslide early warning systems often rely on rainfall totals observed during past events with landslides. However, these models are generally developed for broad regions using large catalogs, with dozens, hundreds, or even thousands of landslide occurrences. This study evaluates strategies for training landslide forecasting models with a scanty record of landslide-triggering events, which is a typical limitation in remote, sparsely populated regions. We evaluate 136 statistical models trained on a precipitation dataset with five landslide-triggering precipitation events recorded near Sitka, Alaska, USA, as well as > 6000 d of non-triggering rainfall (2002–2020). We also conduct extensive statistical evaluation for three primary purposes: (1) to select the best-fitting models, (2) to evaluate performance of the preferred models, and (3) to select and evaluate warning thresholds. We use Akaike, Bayesian, and leave-one-out information criteria to compare the 136 models, which are trained on different cumulative precipitation variables at time intervals ranging from 1 h to 2 weeks, using both frequentist and Bayesian methods to estimate the daily probability and intensity of potential landslide occurrence (logistic regression and Poisson regression). We evaluate the best-fit models using leave-one-out validation as well as by testing a subset of the data. Despite this sparse landslide inventory, we find that probabilistic models can effectively distinguish days with landslides from days without slide activity. Our statistical analyses show that 3 h precipitation totals are the best predictor of elevated landslide hazard, and adding antecedent precipitation (days to weeks) did not improve model performance. This relatively short timescale of precipitation combined with the limited role of antecedent conditions likely reflects the rapid draining of porous colluvial soils on the very steep hillslopes around Sitka. Although frequentist and Bayesian inferences produce similar estimates of landslide hazard, they do have different implications for use and interpretation: frequentist models are familiar and easy to implement, but Bayesian models capture the rare-events problem more explicitly and allow for better understanding of parameter uncertainty given the available data. We use the resulting estimates of daily landslide probability to establish two decision boundaries that define three levels of warning. With these decision boundaries, the frequentist logistic regression model incorporates National Weather Service quantitative precipitation forecasts into a real-time landslide early warning “dashboard” system (https://sitkalandslide.org/, last access: 9 October 2023). This dashboard provides accessible and data-driven situational awareness for community members and emergency managers.

Details

show
hide
Language(s): eng - English
 Dates: 2023-10-182023-10-18
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.5194/nhess-23-3261-2023
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Extremes
Regional keyword: North America
Model / method: Quantitative Methods
MDB-ID: pending
OATYPE: Gold Open Access
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Natural Hazards and Earth System Sciences
Source Genre: Journal, SCI, Scopus, p3, oa
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 23 (10) Sequence Number: - Start / End Page: 3261 - 3284 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals352
Publisher: Copernicus