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1. A new approach to mapping landslide hazards: a probabilistic integration of empirical and physically based models in the North Cascades of Washington, USA

   https://doi.org/10.5194/nhess-19-2477-2019  

   Strauch, Ronda ; Istanbulluoglu, Erkan ; Riedel, Jon ( January 2019 , Natural Hazards and Earth System Sciences)

   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 modelmore »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.« less
2. Landslides caused by the Mw7.8 Kaikōura earthquake and the immediate response

   https://doi.org/10.5459/bnzsee.50.2.106-116  

   Dellow, Sally ; Massey, Chris ; Cox, Simon ; Archibald, Garth ; Begg, John ; Bruce, Zane ; Carey, Jon ; Davidson, Jonathan ; Pasqua, Fernando Della ; Glassey, Phil ; et al ( June 2017 , Bulletin of the New Zealand Society for Earthquake Engineering)

   Tens of thousands of landslides were generated over 10,000 km2 of North Canterbury and Marlborough as a consequence of the 14 November 2016, Mw7.8 Kaikōura Earthquake. The most intense landslide damage was concentrated in 3500 km2 around the areas of fault rupture. Given the sparsely populated area affected by landslides, only a few homes were impacted and there were no recorded deaths due to landslides. Landslides caused major disruption with all road and rail links with Kaikōura being severed. The landslides affecting State Highway 1 (the main road link in the South Island of New Zealand) and the South Island main trunk railway extended from Ward in Marlborough all the way to the south of Oaro in North Canterbury. The majority of landslides occurred in two geological and geotechnically distinct materials reflective of the dominant rock types in the affected area. In the Neogene sedimentary rocks (sandstones, limestones and siltstones) of the Hurunui District, North Canterbury and around Cape Campbell in Marlborough, first-time and reactivated rock-slides and rock-block slides were the dominant landslide type. These rocks also tend to have rock material strength values in the range of 5-20 MPa. In the Torlesse ‘basement’ rocks (greywacke sandstones and argillite) ofmore »the Kaikōura Ranges, first-time rock and debris avalanches were the dominant landslide type. These rocks tend to have material strength values in the range of 20-50 MPa. A feature of this earthquake is the large number (more than 200) of valley blocking landslides it generated. This was partly due to the steep and confined slopes in the area and the widely distributed strong ground shaking. The largest landslide dam has an approximate volume of 12(±2) M m3 and the debris from this travelled about 2.7 km2 downslope where it formed a dam blocking the Hapuku River. The long-term stability of cracked slopes and landslide dams from future strong earthquakes and large rainstorms are an ongoing concern to central and local government agencies responsible for rebuilding homes and infrastructure. A particular concern is the potential for debris floods to affect downstream assets and infrastructure should some of the landslide dams breach catastrophically. At least twenty-one faults ruptured to the ground surface or sea floor, with these surface ruptures extending from the Emu Plain in North Canterbury to offshore of Cape Campbell in Marlborough. The mapped landslide distribution reflects the complexity of the earthquake rupture. Landslides are distributed across a broad area of intense ground shaking reflective of the elongate area affected by fault rupture, and are not clustered around the earthquake epicentre. The largest landslides triggered by the earthquake are located either on or adjacent to faults that ruptured to the ground surface. Surface faults may provide a plane of weakness or hydrological discontinuity and adversely oriented surface faults may be indicative of the location of future large landslides. Their location appears to have a strong structural geological control. Initial results from our landslide investigations suggest predictive models relying only on ground-shaking estimates underestimate the number and size of the largest landslides that occurred.« less
3. Modelling of the tsunami from the December 22, 2018 lateral collapse of Anak Krakatau volcano in the Sunda Straits, Indonesia

   https://doi.org/10.1038/s41598-019-48327-6  

   Grilli, Stephan T. ; Tappin, David R. ; Carey, Steven ; Watt, Sebastian F. L. ; Ward, Steve N. ; Grilli, Annette R. ; Engwell, Samantha L. ; Zhang, Cheng ; Kirby, James T. ; Schambach, Lauren ; et al ( August 2019 , Scientific Reports)

   On Dec. 22, 2018, at approximately 20:55–57 local time, Anak Krakatau volcano, located in the Sunda Straits of Indonesia, experienced a major lateral collapse during a period of eruptive activity that began in June. The collapse discharged volcaniclastic material into the 250 m deep caldera southwest of the volcano, which generated a tsunami with runups of up to 13 m on the adjacent coasts of Sumatra and Java. The tsunami caused at least 437 fatalities, the greatest number from a volcanically-induced tsunami since the catastrophic explosive eruption of Krakatau in 1883 and the sector collapse of Ritter Island in 1888. For the first time in over 100 years, the 2018 Anak Krakatau event provides an opportunity to study a major volcanically-generated tsunami that caused widespread loss of life and significant damage. Here, we present numerical simulations of the tsunami, with state-of the-art numerical models, based on a combined landslide-source and bathymetric dataset. We constrain the geometry and magnitude of the landslide source through analyses of pre- and post-event satellite images and aerial photography, which demonstrate that the primary landslide scar bisected the Anak Krakatau volcano, cutting behind the central vent and removing 50% of its subaerial extent. Estimated submarine collapse geometriesmore »result in a primary landslide volume range of 0.22–0.30 km³, which is used to initialize a tsunami generation and propagation model with two different landslide rheologies (granular and fluid). Observations of a single tsunami, with no subsequent waves, are consistent with our interpretation of landslide failure in a rapid, single phase of movement rather than a more piecemeal process, generating a tsunami which reached nearby coastlines within ~30 minutes. Both modelled rheologies successfully reproduce observed tsunami characteristics from post-event field survey results, tide gauge records, and eyewitness reports, suggesting our estimated landslide volume range is appropriate. This event highlights the significant hazard posed by relatively small-scale lateral volcanic collapses, which can occuren-masse, without any precursory signals, and are an efficient and unpredictable tsunami source. Our successful simulations demonstrate that current numerical models can accurately forecast tsunami hazards from these events. In cases such as Anak Krakatau’s, the absence of precursory warning signals together with the short travel time following tsunami initiation present a major challenge for mitigating tsunami coastal impact.

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4. Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine

   https://doi.org/10.5194/nhess-22-753-2022  

   Handwerger, Alexander L. ; Huang, Mong-Han ; Jones, Shannan Y. ; Amatya, Pukar ; Kerner, Hannah R. ; Kirschbaum, Dalia B. ( January 2022 , Natural Hazards and Earth System Sciences)

   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.
5. A new method to detect changes in displacement rates of slow-moving landslides using InSAR time series

   https://doi.org/10.1007/s10346-022-01913-8  

   Urgilez Vinueza, Alexandra ; Handwerger, Alexander L. ; Bakker, Mark ; Bogaard, Thom ( June 2022 , Landslides)

   Slow-moving landslides move downslope at velocities that range from mm year⁻¹to m year⁻¹. Such deformations can be measured using satellite-based synthetic aperture radar interferometry (InSAR). We developed a new method to systematically detect and quantify accelerations and decelerations of slowly deforming areas using InSAR displacement time series. The displacement time series are filtered using an outlier detector and subsequently piecewise linear functions are fitted to identify changes in the displacement rate (i.e., accelerations or decelerations). Grouped accelerations and decelerations are inventoried as indicators of potential unstable areas. We tested and refined our new method using a high-quality dataset from the Mud Creek landslide, CA, USA. Our method detects accelerations and decelerations that coincide with those previously detected by manual examination. Second, we tested our method in the region around the Mazar dam and reservoir in Southeast Ecuador, where the time series data were of considerably lower quality. We detected accelerations and decelerations occurring during the entire study period near and upslope of the reservoir. Application of our method results in a wealth of information on the dynamics of the surface displacement of hillslopes and provides an objective way to identify changes in displacement rates. The displacement rates, their spatialmore »variation, and the timing of accelerations and decelerations can be used to study the physical behavior of a slow-moving slope or for regional hazard assessment by linking the timing of changes in displacement rates to landslide causal and triggering factors.

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