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Rainfall-triggered landslides are a globally occurring hazard that cause several thousand fatalities per year on average and lead to economic damages by destroying buildings and infrastructure and blocking transportation networks. For people living and governing in susceptible areas, knowing not only where, but also when landslides are most probable is key to inform strategies to reduce risk, requiring reliable assessments of weather-related landslide hazard and adequate warning. Taking proper action during high hazard periods, such as moving to higher levels of houses, closing roads and rail networks, and evacuating neighborhoods, can save lives. Nevertheless, many regions of the world with high landslide risk currently lack dedicated, operational landslide early warning systems.
The mounting availability of temporal landslide inventory data in some regions has increasingly enabled data-driven approaches to estimate landslide hazard on the basis of rainfall conditions. In other areas, however, such data remains scarce, calling for appropriate statistical methods to estimate hazard with limited data. The overarching motivation for this dissertation is to further our ability to predict rainfall-triggered landslides in time in order to expand and improve warning. To this end, I applied Bayesian inference to probabilistically quantify and predict landslide activity as a function of rainfall conditions at spatial scales ranging from a small coastal town, to metropolitan areas worldwide, to a multi-state region, and temporal scales from hourly to seasonal. This thesis is composed of three studies.
In the first study, I contributed to developing and validating statistical models for an online landslide warning dashboard for the small town of Sitka, Alaska, USA. We used logistic and Poisson regressions to estimate daily landslide probability and counts from an inventory of only five reported landslide events and 18 years of hourly precipitation measurements at the Sitka airport. Drawing on community input, we established two warning thresholds for implementation in the dashboard, which uses observed rainfall and US National Weather Service forecasts to provide real-time estimates of landslide hazard.
In the second study, I estimated rainfall intensity-duration thresholds for shallow landsliding for 26 cities worldwide and a global threshold for urban landslides. I found that landslides in urban areas occurred at rainfall intensities that were lower than previously reported global thresholds, and that 31% of urban landslides were triggered during moderate rainfall events. However, landslides in cities with widely varying climates and topographies were triggered above similar critical rainfall intensities: thresholds for 77% of cities were indistinguishable from the global threshold, suggesting that urbanization may harmonize thresholds between cities, overprinting natural variability. I provide a baseline threshold that could be considered for warning in cities with limited landslide inventory data.
In the third study, I investigated seasonal landslide response to annual precipitation patterns in the Pacific Northwest region, USA by using Bayesian multi-level models to combine data from five heterogeneous landslide inventories that cover different areas and time periods. I quantitatively confirmed a distinctly seasonal pattern of landsliding and found that peak landslide activity lags the annual precipitation peak. In February, at the height of the landslide season, landslide intensity for a given amount of monthly rainfall is up to ten times higher than at the season onset in November, underlining the importance of antecedent seasonal hillslope conditions.
Together, these studies contributed actionable, objective information for landslide early warning and examples for the application of Bayesian methods to probabilistically quantify landslide hazard from inventory and rainfall data.
Glacial deposits on the high-altitude, arid southern Central Andean Plateau (CAP), the Puna in northwestern Argentina, document past changes in climate, but the associated geomorphic features have rarely been directly dated. This study provides direct age control of glacial moraine deposits from the central Puna (24 degrees S) at elevations of 3900-5000 m through surface exposure dating with cosmogenic nuclides. Our results show that the most extensive glaciations occurred before 95 ka and an additional major advance occurred between 46 and 39 ka. The latter period is synchronous with the highest lake levels in the nearby Pozuelos basin and the Minchin (Inca Huasi) wet phase on the Altiplano in the northern CAP. None of the dated moraines produced boulder ages corresponding to the Tauca wet phase (24-15 ka). Additionally, the volcanic lithologies of the deposits allow us to establish production ratios at low latitude and high elevation for five different nuclide and mineral systems: Be-10, Ne-21, and Al-26 from quartz (11 or 12 samples) and He-3 and Ne-21 from pyroxene (10 samples). We present production ratios for all combinations of the measured nuclides and cross-calibrated production rates for 21Ne in pyroxene and quartz for the high, (sub-)tropical Andes. The production rates are based on our Be-10-normalized production ratios and a weighted mean of reference 10Be production rates calibrated in the high, tropical Andes (4.02 +/- 0.12 at g(-1) yr(-1)). These are, Ne-21(qtz): 18.1 +/- 1.2 at g(-1) yr(-1) and Ne-21(px): 36.6 +/- 1.8 at g(-1) yr(-1) (En(88-94)) scaled to sea level and high latitude using the Lal/Stone scheme, with 1 sigma uncertainties. As He-3 and Al-26 have been directly calibrated in the tropical Andes, we recommend using those rates. Finally, we compare exposure ages calculated using all measured cosmogenic nuclides from each sample, including 11 feldspar samples measured for Cl-36, and a suite of previously published production rates. (C) 2018 Published by Elsevier B.V.
Seasonal variations in landslide activity remain understudied compared to recent advances in landslide early warning at hourly to daily timescales.
Here, we learn the seasonal pattern of monthly landslide activity in the Pacific Northwest from five heterogeneous landslide inventories with differing spatial and temporal coverage and reporting protocols combined in a Bayesian multi-level model.
We find that landslide activity is distinctly seasonal, with credible increases in landslide intensity, inter-annual variability, and probability marking the onset of the landslide season in November.
Peaks in landslide probability in January and intensity in February lag the annual peak in mean monthly precipitation and landslide activity is more variable in winter than in summer, when landslides are rare.
For a given monthly rainfall, landslide intensity at the season peak in February is up to 10 times higher than at the onset in November, underlining the importance of antecedent seasonal hillslope conditions.