Geomorphology and initiation mechanisms of the 2020 Haines, Alaska landslide

Darrow, Margaret M.; Nelson, Victoria A.; Grilliot, Michael; Wartman, Joseph; Jacobs, Aaron; Baichtal, James F.; Buxton, Cindy

doi:10.1007/s10346-022-01899-3

Cited by 9 publications

(3 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Climate change may alter the flow of OC across the land‐ocean interface in the NPCTR. Glacier lake outburst floods (Harrison et al., 2018) and landslides associated with both atmospheric rivers (Darrow et al., 2022) and glacier recession are projected to increase in frequency (Holm et al., 2004). These events deliver large volumes of sediment via rivers to the coast, where freshwater plumes can extend more than 50 km down fjord ecosystems and impact coastal C cycling and marine food webs (Geertsema et al., 2022; Meerhoff et al., 2019).…”

Section: Resultsmentioning

confidence: 99%

Small, Coastal Temperate Rainforest Watersheds Dominate Dissolved Organic Carbon Transport to the Northeast Pacific Ocean

McNicol

Hood

Butman

et al. 2023

Geophysical Research Letters

View full text Add to dashboard Cite

The northeast Pacific Coastal Temperate Rainforest (NPCTR) is a region of dramatic elevation gradients including steep and subdued terrain and the largest remaining icefields in North America (Bidlack et al., 2021;O'Neel et al., 2015). Ecosystems within the NPCTR are characterized by slow and incomplete decomposition of organic carbon (OC), resulting in one of the densest soil carbon stocks on Earth (228 ± 111 Mg ha −1 ; McNicol et al., 2019). The regional proximity to frontal storms from the Gulf of Alaska leads to extreme rates of precipitation (>6 m yr −1 at high elevations) and an annual land-to-ocean freshwater flux of roughly 1,300 km 3 (runoff = 0.9 m yr −1 ; Hill et al., 2015;Morrison et al., 2012). This freshwater discharge from the 1.4 M km 2 NPCTR drainage basin is 60% greater than that from the 3.4 M km 2 Mississippi River (Dai & Trenberth, 2002), and it provides an important vector for lateral transport of dissolved organic carbon (DOC) across the terrestrial-marine interface.

show abstract

Section: Resultsmentioning

confidence: 99%

Small, Coastal Temperate Rainforest Watersheds Dominate Dissolved Organic Carbon Transport to the Northeast Pacific Ocean

McNicol

Hood

Butman

et al. 2023

Geophysical Research Letters

View full text Add to dashboard Cite

show abstract

“…This includes 135 volumetric soil water content and positive pore-water pressures measured in two shallow soil pits that are less than 10 meters apart from each other on a steep hillslope. The period shown (Figure 1) encompasses responses to several major storm events that triggered landsliding across the region, one of which ultimately culminated in a fatal landslide in Haines, Alaska (refer to Darrow et al, 2022), roughly 200 kilometers to the northeast of the monitoring site in Sitka. In Soil Pit 1, the matric potential and soil-moisture sensors show some variations in near-surface conditions and flashy piezometer response only 140 during peak rainfall.…”

Section: Continuous Field Monitoring For Comprehension Of Triggering ...mentioning

confidence: 99%

Invited Perspectives: Integrating hydrologic information into the next generation of landslide early warning systems

Mirus,

Bogaard,

Greco

et al. 2024

Preprint

View full text Add to dashboard Cite

Abstract. Although rainfall-triggered landslides are initiated by subsurface hydro-mechanical processes related to the loading, weakening, and eventual failure of slope materials, most landslide early warning systems (LEWS) have relied solely on rainfall event information. In previous decades, several studies demonstrated the value of integrating proxies for subsurface hydrologic information to improve rainfall-based forecasting of shallow landslides. More recently, broader access to commercial sensors and telemetry for real-time data transmission has invigorated new research into hydrometeorological thresholds for LEWS. Given the increasing number of studies across the globe using hydrologic monitoring, mathematical modeling, or both in combination, it is now possible to make some insights into the advantages versus limitations of this approach. The extensive progress demonstrates the value of in situ hydrologic information for reducing both failed and false alarms, through the ability to characterize infiltration during, as well as the drainage and drying processes between major storm events. There are also some areas for caution surrounding the long-term sustainability of subsurface monitoring in landslide-prone terrain, as well as unresolved questions in hillslope hydrologic modeling, which relies heavily on the assumptions of diffuse flow and vertical infiltration but often ignores preferential flow and lateral drainage. Here, we share a collective perspective based on our previous collaborative work across Europe, North America, Africa, and Asia to discuss these challenges and provide some guidelines for integrating knowledge of hydrology and climate into the next generation of LEWS. We propose that the greatest opportunity for improvement is through a measure-and-model approach to develop an understanding of landslide hydro-climatology that accounts for local controls on subsurface storage dynamics. Additionally, new efforts focused on the subsurface hydrology are complementary to existing rainfall-based methods, so leveraging these with near-term precipitation forecasts is also a priority for increasing lead times.

show abstract

“…Hydrometeorological thresholds for landslide initiation have been proposed for nearby remote areas of British Columbia (Jakob et 120 al., 2006) and suburban Vancouver (Jakob et al, 2012) in Canada, but no systematic landslide warning threshold currently exists at either local scales for towns within southeast Alaska or at the regional scale for southeast Alaska as a whole, despite its high susceptibility to slope failures (e.g., Darrow et al, 2022;Patton et al, 2022). Generations of knowledge in southeast Alaska and close observation of the natural environment provide rich understanding of landslides and other natural processes, but southeast Alaska lacks extensive written records of landslide occurrence with daily timestamps and sub-daily, spatially distributed 125 precipitation records.…”

Section: Developing Precipitation Thresholds For Landslide Warningmentioning

confidence: 99%

Landslide initiation thresholds in data sparse regions: Application to landslide early warning criteria in Sitka, Alaska, USA

Patton

Luna

Roering

et al. 2023

Preprint

View full text Add to dashboard Cite

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 train and evaluate 136 statistical models with a rainfall dataset with five landslide-triggering rainfall events recorded near Sitka, Alaska, USA, as well as >6,000 days of non-triggering rainfall (2002–2020). We use Akaike, Bayesian, and leave-one-out information criteria to compare models trained on cumulative precipitation at timescales ranging from 1 hour 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 with 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. Although frequentist and Bayesian inference 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. Three-hour 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 combined with the limited role of antecedent conditions reflects the rapid draining of porous colluvial soils on very steep hillslopes around Sitka. We use the resulting estimates of daily landslide probability to establish two decision boundaries for 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 (sitkalandslide.org). This dashboard provides accessible and data-driven situational awareness for community members and emergency managers.

show abstract

Geomorphology and initiation mechanisms of the 2020 Haines, Alaska landslide

Cited by 9 publications

References 30 publications

Small, Coastal Temperate Rainforest Watersheds Dominate Dissolved Organic Carbon Transport to the Northeast Pacific Ocean

Small, Coastal Temperate Rainforest Watersheds Dominate Dissolved Organic Carbon Transport to the Northeast Pacific Ocean

Invited Perspectives: Integrating hydrologic information into the next generation of landslide early warning systems

Landslide initiation thresholds in data sparse regions: Application to landslide early warning criteria in Sitka, Alaska, USA

Contact Info

Product

Resources

About