An official website of the United States government
Abstract Regional‐scale characterization of shallow landslide hazards is important for reducing their destructive impact on society. These hazards are commonly characterized by (a) their location and likelihood using susceptibility maps, (b) landslide size and frequency using geomorphic scaling laws, and (c) the magnitude of disturbance required to cause landslides using initiation thresholds. Typically, this is accomplished through the use of inventories documenting the locations and triggering conditions of previous landslides. In the absence of comprehensive landslide inventories, physics‐based slope stability models can be used to estimate landslide initiation potential and provide plausible distributions of landslide characteristics for a range of environmental and forcing conditions. However, these models are sometimes limited in their ability to capture key mechanisms tied to discrete three‐dimensional (3D) landslide mechanics while possessing the computational efficiency required for broad‐scale application. In this study, the RegionGrow3D (RG3D) model is developed to broadly simulate the area, volume, and location of landslides on a regional scale (≥1,000 km2) using 3D, limit‐equilibrium (LE)‐based slope stability modeling. Furthermore, RG3D is incorporated into a susceptibility framework that quantifies landsliding uncertainty using a distribution of soil shear strengths and their associated probabilities, back‐calculated from inventoried landslides using 3D LE‐based landslide forensics. This framework is used to evaluate the influence of uncertainty tied to shear strength, rainfall scenarios, and antecedent soil moisture on potential landsliding and rainfall thresholds over a large region of the Oregon Coast Range, USA.
more »
« less
Mechanistic insights from emergent landslides in physical experiments
Abstract Landslides pose a major natural hazard, and heterogeneous conditions and limited data availability in the field make it difficult to connect mapped landslide inventories to the underlying mass-failure mechanics. To test and build predictive links between landslide observations and mechanics, we monitored 67.89 h of physical experiments in which an incising and laterally migrating river generated landslides by undercutting banks of moist sand. Using overhead photos (every 20 s) and 1-mm-resolution laser topographic scans (every 15–30 min), we quantified the area, width, length, depth, volume, and time of every visible landslide, as well as the scarp angles for those within 3 min prior to a topographic scan. Both the landslide area–frequency distribution and area–volume relationship are consistent with those from field data. Cohesive strength controlled the peak in landslide area–frequency distribution. These results provide experimental support for inverting landslide inventories to recover the mechanical properties of hillslopes, which can then be used to improve hazard predictions.
more »
« less
- Award ID(s):
- 1944782
- PAR ID:
- 10263902
- Date Published:
- Journal Name:
- Geology
- Volume:
- 49
- Issue:
- 4
- ISSN:
- 0091-7613
- Page Range / eLocation ID:
- 392 to 396
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract. Rapid detection of landslides is critical for emergency response, disaster mitigation, and improving our understanding of landslide dynamics. Satellite-based synthetic aperture radar (SAR) can be used to detect landslides, often within days of a triggering event, because it penetrates clouds, operates day and night, and is regularly acquired worldwide. Here we present a SAR backscatter change approach in the cloud-based Google Earth Engine (GEE) that uses multi-temporal stacks of freely available data from the Copernicus Sentinel-1 satellites to generate landslide density heatmaps for rapid detection. We test our GEE-based approach on multiple recent rainfall- and earthquake-triggered landslide events. Our ability to detect surface change from landslides generally improves with the total number of SAR images acquired before and after a landslide event, by combining data from both ascending and descending satellite acquisition geometries and applying topographic masks to remove flat areas unlikely to experience landslides. Importantly, our GEE approach does not require downloading a large volume of data to a local system or specialized processing software, which allows the broader hazard and landslide community to utilize and advance these state-of-the-art remote sensing data for improved situational awareness of landslide hazards.more » « less
-
Abstract. We developed a new approach for mapping landslide hazards by combiningprobabilities of landslide impacts derived from a data-driven statisticalapproach and a physically based model of shallow landsliding. Ourstatistical approach integrates the influence of seven site attributes (SAs) onobserved landslides using a frequency ratio (FR) method. Influential attributesand resulting susceptibility maps depend on the observations of landslidesconsidered: all types of landslides, debris avalanches only, or source areasof debris avalanches. These observational datasets reflect the detection ofdifferent landslide processes or components, which relate to differentlandslide-inducing factors. For each landslide dataset, a stability index (SI) is calculated as a multiplicative result of the frequency ratios for all attributes and is mapped across our study domain in the North Cascades National Park Complex (NOCA), Washington, USA. A continuous function is developed to relate local SI values to landslide probability based on a ratio of landslide and non-landslide grid cells. The empirical model probability derived from the debris avalanche source area dataset is combined probabilistically with a previously developed physically based probabilistic model. A two-dimensional binning method employs empirical andphysically based probabilities as indices and calculates a joint probabilityof landsliding at the intersections of probability bins. A ratio of thejoint probability and the physically based model bin probability is used asa weight to adjust the original physically based probability at each gridcell given empirical evidence. The resulting integrated probability oflandslide initiation hazard includes mechanisms not captured by the infinite-slope stability model alone. Improvements in distinguishing potentiallyunstable areas with the proposed integrated model are statisticallyquantified. We provide multiple landslide hazard maps that land managers canuse for planning and decision-making, as well as for educating the publicabout hazards from landslides in this remote high-relief terrain.more » « less
-
Abstract. We developed a new rule-based, cellular-automaton algorithm for predicting the hazard extent, sediment transport, and topographic change associated with the runout of a landslide. This algorithm, which we call MassWastingRunout (MWR), is coded in Python and implemented as a component for the package Landlab. MWR combines the functionality of simple runout algorithms used in landscape evolution and watershed sediment yield models with the predictive detail typical of runout models used for landslide inundation hazard mapping. An initial digital elevation model (DEM), a regolith depth map, and the location polygon of the landslide source area are the only inputs required to run MWR to model the entire runout process. Runout relies on the principle of mass conservation and a set of topographic rules and empirical formulas that govern erosion and deposition. For the purpose of facilitating rapid calibration to a site, MWR includes a calibration utility that uses an adaptive Bayesian Markov chain Monte Carlo algorithm to automatically calibrate the model to match observed runout extent, deposition, and erosion. Additionally, the calibration utility produces empirical probability density functions of each calibration parameter that can be used to inform probabilistic implementation of MWR. Here we use a series of synthetic terrains to demonstrate basic model response to topographic convergence and slope, test calibrated model performance relative to several observed landslides, and briefly demonstrate how MWR can be used to develop a probabilistic runout hazard map. A calibrated runout model may allow for region-specific and more insightful predictions of landslide impact on landscape morphology and watershed-scale sediment dynamics and should be further investigated in future modeling studies.more » « less
-
Landslides are a significant hazard and dominant feature throughout the landscape of the Pacific Northwest. However, the hazard and risk posed by coseismic landslides triggered by great Cascadia Subduction Zone (CSZ) earthquakes is highly uncertain due to a lack of local and global data. Despite a wealth of other geologic evidence for past earthquakes on the Cascadia Subduction Zone, no landslides have been definitively linked to such earthquakes, even in areas otherwise highly susceptible to failure. While shallow landslides may not leave a lasting topographical signature in the landscape, there are thousands of deep-seated landslides in Cascadia, and these deposits often persist for hundreds of years and multiple earthquake cycles. Synthesizing newly developed inventories of dated large deep-seated landslides in the Oregon Coast Range, we use statistical methods to estimate the proportion of these types of landslides that could have been triggered during past great Cascadia Subduction Zone earthquakes. Statistical analysis of high-precision dendrochronology ages of landslide-dammed lakes and surface roughness-dated bedrock landslides reveal Cascadia Subduction Zone earthquakes may have triggered 0–15 % of large deep-seated landslides in the Oregon Coast Range over multiple earthquake cycles. Our results refine estimates from previous studies and further suggest that coseismic triggering accounts for a small fraction of the total deep-seated bedrock landslides mapped in coastal Cascadia. However, if the real rate of coseismic landslide triggering during CSZ earthquakes is near our estimated upper bound for the 1700 CSZ earthquake, we estimate up to 2400 coseismic large deep-seated landslides could occur in the Oregon Coast Range in a single earthquake. These findings suggest Cascadia is consistent with global observations from other subduction zones and that coseismic landslides may still represent a serious geohazard in the region.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1130/G47875.1
