@phdthesis{Oeztuerk2018,
  author    = {{\"O}zt{\"u}rk, Ugur},
  title     = {Learning more to predict landslides},
  doi       = {10.25932/publishup-42643},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-426439},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xxi, 104},
  year      = {2018},
  abstract  = {Landslides are frequent natural hazards in rugged terrain, when the resisting frictional force of the surface of rupture yields to the gravitational force. These forces are functions of geological and morphological factors, such as angle of internal friction, local slope gradient or curvature, which remain static over hundreds of years; whereas more dynamic triggering events, such as rainfall and earthquakes, compromise the force balance by temporarily reducing resisting forces or adding transient loads. This thesis investigates landslide distribution and orientation due to landslide triggers (e.g. rainfall) at different scales (6-4∙10^5 km^2) and aims to link rainfall movement with the landslide distribution. It additionally explores the local impacts of the extreme rainstorms on landsliding and the role of precursory stability conditions that could be induced by an earlier trigger, such as an earthquake. Extreme rainfall is a common landslide trigger. Although several studies assessed rainfall intensity and duration to study the distribution of thus triggered landslides, only a few case studies quantified spatial rainfall patterns (i.e. orographic effect). Quantifying the regional trajectories of extreme rainfall could aid predicting landslide prone regions in Japan. To this end, I combined a non-linear correlation metric, namely event synchronization, and radial statistics to assess the general pattern of extreme rainfall tracks over distances of hundreds of kilometers using satellite based rainfall estimates. Results showed that, although the increase in rainfall intensity and duration positively correlates with landslide occurrence, the trajectories of typhoons and frontal storms were insufficient to explain landslide distribution in Japan. Extreme rainfall trajectories inclined northwestwards and were concentrated along some certain locations, such as coastlines of southern Japan, which was unnoticed in the landslide distribution of about 5000 rainfall-triggered landslides. These landslides seemed to respond to the mean annual rainfall rates. Above mentioned findings suggest further investigation on a more local scale to better understand the mechanistic response of landscape to extreme rainfall in terms of landslides. On May 2016 intense rainfall struck southern Germany triggering high waters and landslides. The highest damage was reported at the Braunsbach, which is located on the tributary-mouth fan formed by the Orlacher Bach. Orlacher Bach is a ~3 km long creek that drains a catchment of about ~6 km^2. I visited this catchment in June 2016 and mapped 48 landslides along the creek. Such high landslide activity was not reported in the nearby catchments within ~3300 km^2, despite similar rainfall intensity and duration based on weather radar estimates. My hypothesis was that several landslides were triggered by rainfall-triggered flash floods that undercut hillslope toes along the Orlacher Bach. I found that morphometric features such as slope and curvature play an important role in landslide distribution on this micro scale study site (<10 km^2). In addition, the high number of landslides along the Orlacher Bach could also be boosted by accumulated damages on hillslopes due karst weathering over longer time scales. Precursory damages on hillslopes could also be induced by past triggering events that effect landscape evolution, but this interaction is hard to assess independently from the latest trigger. For example, an earthquake might influence the evolution of a landscape decades long, besides its direct impacts, such as landslides that follow the earthquake. Here I studied the consequences of the 2016 Kumamoto Earthquake (MW 7.1) that triggered some 1500 landslides in an area of ~4000 km^2 in central Kyushu, Japan. Topography, i.e. local slope and curvature, both amplified and attenuated seismic waves, thus controlling the failure mechanism of those landslides (e.g. progressive). I found that topography fails in explaining the distribution and the preferred orientation of the landslides after the earthquake; instead the landslides were concentrated around the northeast of the rupture area and faced mostly normal to the rupture plane. This preferred location of the landslides was dominated mainly by the directivity effect of the strike-slip earthquake, which is the propagation of wave energy along the fault in the rupture direction; whereas amplitude variations of the seismic radiation altered the preferred orientation. I suspect that the earthquake directivity and the asymmetry of seismic radiation damaged hillslopes at those preferred locations increasing landslide susceptibility. Hence a future weak triggering event, e.g. scattered rainfall, could further trigger landslides at those damaged hillslopes.},
  language  = {en}
}
@phdthesis{Agarwal2018,
  author    = {Agarwal, Ankit},
  title     = {Unraveling spatio-temporal climatic patterns via multi-scale complex networks},
  doi       = {10.25932/publishup-42395},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-423956},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xxix, 153},
  year      = {2018},
  abstract  = {The climate is a complex dynamical system involving interactions and feedbacks among different processes at multiple temporal and spatial scales. Although numerous studies have attempted to understand the climate system, nonetheless, the studies investigating the multiscale characteristics of the climate are scarce. Further, the present set of techniques are limited in their ability to unravel the multi-scale variability of the climate system. It is completely plausible that extreme events and abrupt transitions, which are of great interest to climate community, are resultant of interactions among processes operating at multi-scale. For instance, storms, weather patterns, seasonal irregularities such as El Ni{\~n}o, floods and droughts, and decades-long climate variations can be better understood and even predicted by quantifying their multi-scale dynamics. This makes a strong argument to unravel the interaction and patterns of climatic processes at different scales. With this background, the thesis aims at developing measures to understand and quantify multi-scale interactions within the climate system. In the first part of the thesis, I proposed two new methods, viz, multi-scale event synchronization (MSES) and wavelet multi-scale correlation (WMC) to capture the scale-specific features present in the climatic processes. The proposed methods were tested on various synthetic and real-world time series in order to check their applicability and replicability. The results indicate that both methods (WMC and MSES) are able to capture scale-specific associations that exist between processes at different time scales in a more detailed manner as compared to the traditional single scale counterparts. In the second part of the thesis, the proposed multi-scale similarity measures were used in constructing climate networks to investigate the evolution of spatial connections within climatic processes at multiple timescales. The proposed methods WMC and MSES, together with complex network were applied to two different datasets. In the first application, climate networks based on WMC were constructed for the univariate global sea surface temperature (SST) data to identify and visualize the SSTs patterns that develop very similarly over time and distinguish them from those that have long-range teleconnections to other ocean regions. Further investigations of climate networks on different timescales revealed (i) various high variability and co-variability regions, and (ii) short and long-range teleconnection regions with varying spatial distance. The outcomes of the study not only re-confirmed the existing knowledge on the link between SST patterns like El Ni{\~n}o Southern Oscillation and the Pacific Decadal Oscillation, but also suggested new insights into the characteristics and origins of long-range teleconnections. In the second application, I used the developed non-linear MSES similarity measure to quantify the multivariate teleconnections between extreme Indian precipitation and climatic patterns with the highest relevance for Indian sub-continent. The results confirmed significant non-linear influences that were not well captured by the traditional methods. Further, there was a substantial variation in the strength and nature of teleconnection across India, and across time scales. Overall, the results from investigations conducted in the thesis strongly highlight the need for considering the multi-scale aspects in climatic processes, and the proposed methods provide robust framework for quantifying the multi-scale characteristics.},
  language  = {en}
}