@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{Luna2023,
  author    = {Luna, Lisa},
  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}
}
@phdthesis{Teshebaeva2016,
  author    = {Teshebaeva, Kanayim},
  title     = {SAR interferometry analysis of surface processes in the Pamir - Tien Shan active orogens - emphasis on coseismic deformation and landslides},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-96743},
  school      = {Universit{\"a}t Potsdam},
  pages     = {128},
  year      = {2016},
  abstract  = {This thesis presents new approaches of SAR methods and their application to tectonically active systems and related surface deformation. With 3 publications two case studies are presented: (1) The coseismic deformation related to the Nura earthquake (5th October 2008, magnitude Mw 6.6) at the eastern termination of the intramontane Alai valley. Located between the southern Tien Shan and the northern Pamir the coseismic surface displacements are analysed using SAR (Synthetic Aperture RADAR) data. The results show clear gradients in the vertical and horizontal directions along a complex pattern of surface ruptures and active faults. To integrate and to interpret these observations in the context of the regional active tectonics a SAR data analysis is complemented with seismological data and geological field observations. The main moment release of the Nura earthquake appears to be on the Pamir Frontal thrust, while the main surface displacements and surface rupture occurred in the footwall and along of the NE-SW striking Irkeshtam fault. With InSAR data from ascending and descending satellite tracks along with pixel offset measurements the Nura earthquake source is modelled as a segmented rupture. One fault segment corresponds to high-angle brittle faulting at the Pamir Frontal thrust and two more fault segments show moderate-angle and low-friction thrusting at the Irkeshtam fault. The integrated analysis of the coseismic deformation argues for a rupture segmentation and strain partitioning associated to the earthquake. It possibly activated an orogenic wedge in the easternmost segment of the Pamir-Alai collision zone. Further, the style of the segmentation may be associated with the presence of Paleogene evaporites. (2) The second focus is put on slope instabilities and consequent landslides in the area of prominent topographic transition between the Fergana basin and high-relief Alai range. The Alai range constitutes an active orogenic wedge of the Pamir - Tien Shan collision zone that described as a progressively northward propagating fold-and-thrust belt. The interferometric analysis of ALOS/PALSAR radar data integrates a period of 4 years (2007-2010) based on the Small Baseline Subset (SBAS) time-series technique to assess surface deformation with millimeter surface change accuracy. 118 interferograms are analyzed to observe spatially-continuous movements with downslope velocities up to 71 mm/yr. The obtained rates indicate slow movement of the deep-seated landslides during the observation time. We correlated these movements with precipitation and seismic records. The results suggest that the deformation peaks correlate with rainfall in the 3 preceding months and with one earthquake event. In the next step, to understand the spatial pattern of landslide processes, the tectonic morphologic and lithologic settings are combined with the patterns of surface deformation. We demonstrate that the lithological and tectonic structural patterns are the main controlling factors for landslide occurrence and surface deformation magnitudes. Furthermore active contractional deformation in the front of the orogenic wedge is the main mechanism to sustain relief. Some of the slower but continuously moving slope instabilities are directly related to tectonically active faults and unconsolidated young Quaternary syn-orogenic sedimentary sequences. The InSAR observed slow moving landslides represent active deep-seated gravitational slope deformation phenomena which is first time observed in the Tien Shan mountains. Our approach offers a new combination of InSAR techniques and tectonic aspects to localize and understand enhanced slope instabilities in tectonically active mountain fronts in the Kyrgyz Tien Shan.},
  language  = {en}
}