@phdthesis{Marc2016,
  author    = {Marc, Odin},
  title     = {Earthquake-induced landsliding},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-96808},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xvi, 171},
  year      = {2016},
  abstract  = {Earthquakes deform Earth's surface, building long-lasting topographic features and contributing to landscape and mountain formation. However, seismic waves produced by earthquakes may also destabilize hillslopes, leading to large amounts of soil and bedrock moving downslope. Moreover, static deformation and shaking are suspected to damage the surface bedrock and therefore alter its future properties, affecting hydrological and erosional dynamics. Thus, earthquakes participate both in mountain building and stimulate directly or indirectly their erosion. Moreover, the impact of earthquakes on hillslopes has important implications for the amount of sediment and organic matter delivered to rivers, and ultimately to oceans, during episodic catastrophic seismic crises, the magnitude of life and property losses associated with landsliding, the perturbation and recovery of landscape properties after shaking, and the long term topographic evolution of mountain belts. Several of these aspects have been addressed recently through individual case studies but additional data compilation as well as theoretical or numerical modelling are required to tackle these issues in a more systematic and rigorous manner. This dissertation combines data compilation of earthquake characteristics, landslide mapping, and seismological data interpretation with physically-based modeling in order to address how earthquakes impact on erosional processes and landscape evolution. Over short time scales (10-100 s) and intermediate length scales (10 km), I have attempted to improve our understanding and ability to predict the amount of landslide debris triggered by seismic shaking in epicentral areas. Over long time scales (1-100 ky) and across a mountain belt (100 km) I have modeled the competition between erosional unloading and building of topography associated with earthquakes. Finally, over intermediate time scales (1-10 y) and at the hillslope scale (0.1-1 km) I have collected geomorphological and seismological data that highlight persistent effects of earthquakes on landscape properties and behaviour. First, I compiled a database on earthquakes that produced significant landsliding, including an estimate of the total landslide volume and area, and earthquake characteristics such as seismic moment and source depth. A key issue is the accurate conversion of landslide maps into volume estimates. Therefore I also estimated how amalgamation - when mapping errors lead to the bundling of multiple landslide into a single polygon - affects volume estimates from various earthquake-induced landslide inventories and developed an algorithm to automatically detect this artifact. The database was used to test a physically-based prediction of the total landslide area and volume caused by earthquakes, based on seismological scaling relationships and a statistical description of the landscape properties. The model outperforms empirical fits in accuracy, with 25 out of 40 cases well predicted, and allows interpretation of many outliers in physical terms. Apart from seismological complexities neglected by the model I found that exceptional rock strength properties or antecedent conditions may explain most outliers. Second, I assessed the geomorphic effects of large earthquakes on landscape dynamics by surveying the temporal evolution of precipitation-normalized landslide rate. I found strongly elevated landslide rates following earthquakes that progressively recover over 1 to 4 years, indicating that regolith strength drops and recovers. The relaxation is clearly non-linear for at least one case, and does not seem to correlate with coseismic landslide reactivation, water table level increase or tree root-system recovery. I suggested that shallow bedrock is damaged by the earthquake and then heals on annual timescales. Such variations in ground strength must be translated into shallow subsurface seismic velocities that are increasingly surveyed with ambient seismic noise correlations. With seismic noise autocorrelation I computed the seismic velocity in the epicentral areas of three earthquakes where I constrained a change in landslide rate. We found similar recovery dynamics and timescales, suggesting that seismic noise correlation techniques could be further developed to meaningfully assess ground strength variations for landscape dynamics. These two measurements are also in good agreement with the temporal dynamics of post-seismic surface displacement measured by GPS. This correlation suggests that the surface healing mechanism may be driven by tectonic deformation, and that the surface regolith and fractured bedrock may behave as a granular media that slowly compacts as it is sheared or vibrated. Last, I compared our model of earthquake-induced landsliding with a standard formulation of surface deformation caused by earthquakes to understand which parameters govern the competition between the building and destruction of topography caused by earthquakes. In contrast with previous studies I found that very large (Mw>8) earthquakes always increase the average topography, whereas only intermediate (Mw ~ 7) earthquakes in steep landscapes may reduce topography. Moreover, I illustrated how the net effect of earthquakes varies with depth or landscape steepness implying a complex and ambivalent role through the life of a mountain belt. Further I showed that faults producing a Gutenberg-Richter distribution of earthquake sizes, will limit topography over a larger range of fault sizes than faults producing repeated earthquakes with a characteristic size.},
  language  = {en}
}
@phdthesis{Brune2009,
  author    = {Brune, Sascha},
  title     = {Landslide generated tsunamis : numerical modeling and real-time prediction},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-32986},
  school      = {Universit{\"a}t Potsdam},
  year      = {2009},
  abstract  = {Submarine landslides can generate local tsunamis posing a hazard to human lives and coastal facilities. Two major related problems are: (i) quantitative estimation of tsunami hazard and (ii) early detection of the most dangerous landslides. This thesis focuses on both those issues by providing numerical modeling of landslide-induced tsunamis and by suggesting and justifying a new method for fast detection of tsunamigenic landslides by means of tiltmeters. Due to the proximity to the Sunda subduction zone, Indonesian coasts are prone to earthquake, but also landslide tsunamis. The aim of the GITEWS-project (German-Indonesian Tsunami Early Warning System) is to provide fast and reliable tsunami warnings, but also to deepen the knowledge about tsunami hazards. New bathymetric data at the Sunda Arc provide the opportunity to evaluate the hazard potential of landslide tsunamis for the adjacent Indonesian islands. I present nine large mass movements in proximity to Sumatra, Java, Sumbawa and Sumba, whereof the largest event displaced 20 km³ of sediments. Using numerical modeling, I compute the generated tsunami of each event, its propagation and runup at the coast. Moreover, I investigate the age of the largest slope failures by relating them to the Great 1977 Sumba earthquake. Continental slopes off northwest Europe are well known for their history of huge underwater landslides. The current geological situation west of Spitsbergen is comparable to the continental margin off Norway after the last glaciation, when the large tsunamigenic Storegga slide took place. The influence of Arctic warming on the stability of the Svalbard glacial margin is discussed. Based on new geophysical data, I present four possible landslide scenarios and compute the generated tsunamis. Waves of 6 m height would be capable of reaching northwest Europe threatening coastal areas. I present a novel technique to detect large submarine landslides using an array of tiltmeters, as a possible tool in future tsunami early warning systems. The dislocation of a large amount of sediment during a landslide produces a permanent elastic response of the earth. I analyze this response with a mathematical model and calculate the theoretical tilt signal. Applications to the hypothetical Spitsbergen event and the historical Storegga slide show tilt signals exceeding 1000 nrad. The amplitude of landslide tsunamis is controlled by the product of slide volume and maximal velocity (slide tsunamigenic potential). I introduce an inversion routine that provides slide location and tsunamigenic potential, based on tiltmeter measurements. The accuracy of the inversion and of the estimated tsunami height near the coast depends on the noise level of tiltmeter measurements, the distance of tiltmeters from the slide, and the slide tsunamigenic potential. Finally, I estimate the applicability scope of this method by employing it to known landslide events worldwide.},
  language  = {en}
}
@phdthesis{Nikolaeva2013,
  author    = {Nikolaeva, Elena},
  title     = {Landslide kinematics and interactions studied in central Georgia by using synthetic aperture radar interferometry, optical imagery and inverse modeling},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-70406},
  school      = {Universit{\"a}t Potsdam},
  year      = {2013},
  abstract  = {Landslides are one of the biggest natural hazards in Georgia, a mountainous country in the Caucasus. So far, no systematic monitoring and analysis of the dynamics of landslides in Georgia has been made. Especially as landslides are triggered by extrinsic processes, the analysis of landslides together with precipitation and earthquakes is challenging. In this thesis I describe the advantages and limits of remote sensing to detect and better understand the nature of landslide in Georgia. The thesis is written in a cumulative form, composing a general introduction, three manuscripts and a summary and outlook chapter. In the present work, I measure the surface displacement due to active landslides with different interferometric synthetic aperture radar (InSAR) methods. The slow landslides (several cm per year) are well detectable with two-pass interferometry. In same time, the extremely slow landslides (several mm per year) could be detected only with time series InSAR techniques. I exemplify the success of InSAR techniques by showing hitherto unknown landslides, located in the central part of Georgia. Both, the landslide extent and displacement rate is quantified. Further, to determine a possible depth and position of potential sliding planes, inverse models were developed. Inverse modeling searches for parameters of source which can create observed displacement distribution. I also empirically estimate the volume of the investigated landslide using displacement distributions as derived from InSAR combined with morphology from an aerial photography. I adapted a volume formula for our case, and also combined available seismicity and precipitation data to analyze potential triggering factors. A governing question was: What causes landslide acceleration as observed in the InSAR data? The investigated area (central Georgia) is seismically highly active. As an additional product of the InSAR data analysis, a deformation area associated with the 7th September Mw=6.0 earthquake was found. Evidences of surface ruptures directly associated with the earthquake could not be found in the field, however, during and after the earthquake new landslides were observed. The thesis highlights that deformation from InSAR may help to map area prone landslides triggering by earthquake, potentially providing a technique that is of relevance for country wide landslide monitoring, especially as new satellite sensors will emerge in the coming years.},
  language  = {en}
}
@phdthesis{Luna2023,
  author    = {Luna, Lisa Victoria},
  title     = {Rainfall-triggered landslides: conditions, prediction, and warning},
  doi       = {10.25932/publishup-60092},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-600927},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xix, 119},
  year      = {2023},
  abstract  = {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.},
  language  = {en}
}