@phdthesis{Vogel2013,
  author    = {Vogel, Kristin},
  title     = {Applications of Bayesian networks in natural hazard assessments},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-69777},
  school      = {Universit{\"a}t Potsdam},
  year      = {2013},
  abstract  = {Even though quite different in occurrence and consequences, from a modeling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding: uncertainty about the modeling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Nevertheless deterministic approaches are still widely used in natural hazard assessments, holding the risk of underestimating the hazard with disastrous effects. The all-round probabilistic framework of Bayesian networks constitutes an attractive alternative. In contrast to deterministic proceedings, it treats response variables as well as explanatory variables as random variables making no difference between input and output variables. Using a graphical representation Bayesian networks encode the dependency relations between the variables in a directed acyclic graph: variables are represented as nodes and (in-)dependencies between variables as (missing) edges between the nodes. The joint distribution of all variables can thus be described by decomposing it, according to the depicted independences, into a product of local conditional probability distributions, which are defined by the parameters of the Bayesian network. In the framework of this thesis the Bayesian network approach is applied to different natural hazard domains (i.e. seismic hazard, flood damage and landslide assessments). Learning the network structure and parameters from data, Bayesian networks reveal relevant dependency relations between the included variables and help to gain knowledge about the underlying processes. The problem of Bayesian network learning is cast in a Bayesian framework, considering the network structure and parameters as random variables itself and searching for the most likely combination of both, which corresponds to the maximum a posteriori (MAP score) of their joint distribution given the observed data. Although well studied in theory the learning of Bayesian networks based on real-world data is usually not straight forward and requires an adoption of existing algorithms. Typically arising problems are the handling of continuous variables, incomplete observations and the interaction of both. Working with continuous distributions requires assumptions about the allowed families of distributions. To "let the data speak" and avoid wrong assumptions, continuous variables are instead discretized here, thus allowing for a completely data-driven and distribution-free learning. An extension of the MAP score, considering the discretization as random variable as well, is developed for an automatic multivariate discretization, that takes interactions between the variables into account. The discretization process is nested into the network learning and requires several iterations. Having to face incomplete observations on top, this may pose a computational burden. Iterative proceedings for missing value estimation become quickly infeasible. A more efficient albeit approximate method is used instead, estimating the missing values based only on the observations of variables directly interacting with the missing variable. Moreover natural hazard assessments often have a primary interest in a certain target variable. The discretization learned for this variable does not always have the required resolution for a good prediction performance. Finer resolutions for (conditional) continuous distributions are achieved with continuous approximations subsequent to the Bayesian network learning, using kernel density estimations or mixtures of truncated exponential functions. All our proceedings are completely data-driven. We thus avoid assumptions that require expert knowledge and instead provide domain independent solutions, that are applicable not only in other natural hazard assessments, but in a variety of domains struggling with uncertainties.},
  language  = {en}
}
@techreport{AgarwalBoessenkoolFischeretal.2016,
  author    = {Agarwal, Ankit and Boessenkool, Berry and Fischer, Madlen and Hahn, Irene and K{\"o}hn, Lisei and Laudan, Jonas and Moran, Thomas and {\"O}zt{\"u}rk, Ugur and Riemer, Adrian and R{\"o}zer, Viktor and Sieg, Tobias and Vogel, Kristin and Wendi, Dadiyorto and Bronstert, Axel and Thieken, Annegret},
  title     = {Die Sturzflut in Braunsbach, Mai 2016},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-394881},
  pages     = {20},
  year      = {2016},
  abstract  = {Im Graduiertenkolleg NatRiskChange der Universit{\"a}t Potsdam und anderen Forschungseinrichtungen werden beobachtete sowie zuk{\"u}nftig m{\"o}gliche Ver{\"a}nderungen von Naturgefahren untersucht. Teil des strukturierten Doktorandenprogramms sind sogenannte Task-Force-Eins{\"a}tze, bei denen die Promovierende zeitlich begrenzt ein aktuelles Ereignis auswerten. Im Zuge dieser Aktivit{\"a}t wurde die Sturzflut vom 29.05.2016 in Braunsbach (Baden-W{\"u}rttemberg) untersucht. In diesem Bericht werden erste Auswertungen zur Einordnung der Niederschl{\"a}ge, zu den hydrologischen und geomorphologischen Prozessen im Einzugsgebiet des Orlacher Bachs sowie zu den verursachten Sch{\"a}den beleuchtet. Die Region war Zentrum extremer Regenf{\"a}lle in der Gr{\"o}ßenordnung von 100 mm innerhalb von 2 Stunden. Das 6 km² kleine Einzugsgebiet hat eine sehr schnelle Reaktionszeit, zumal bei vorges{\"a}ttigtem Boden. Im steilen Bachtal haben mehrere kleinere und gr{\"o}ßere Hangrutschungen {\"u}ber 8000 m³ Ger{\"o}ll, Schutt und Schwemmholz in das Gew{\"a}sser eingetragen und m{\"o}glicherweise kurzzeitige Aufstauungen und Durchbr{\"u}che verursacht. Neben den großen Wassermengen mit einer Abflussspitze in einer Gr{\"o}ßenordnung von 100 m³/s hat gerade die Geschiebefracht zu großen Sch{\"a}den an den Geb{\"a}uden entlang des Bachlaufs in Braunsbach gef{\"u}hrt.},
  language  = {de}
}
@misc{PrahlRybskiBoettleetal.2016,
  author    = {Prahl, Boris F. and Rybski, Diego and Boettle, Markus and Kropp, J{\"u}rgen},
  title     = {Damage functions for climate-related hazards},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {534},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-41018},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-410184},
  pages     = {15},
  year      = {2016},
  abstract  = {Most climate change impacts manifest in the form of natural hazards. Damage assessment typically relies on damage functions that translate the magnitude of extreme events to a quantifiable damage. In practice, the availability of damage functions is limited due to a lack of data sources and a lack of understanding of damage processes. The study of the characteristics of damage functions for different hazards could strengthen the theoretical foundation of damage functions and support their development and validation. Accordingly, we investigate analogies of damage functions for coastal flooding and for wind storms and identify a unified approach. This approach has general applicability for granular portfolios and may also be applied, for example, to heat-related mortality. Moreover, the unification enables the transfer of methodology between hazards and a consistent treatment of uncertainty. This is demonstrated by a sensitivity analysis on the basis of two simple case studies (for coastal flood and storm damage). The analysis reveals the relevance of the various uncertainty sources at varying hazard magnitude and on both the microscale and the macroscale level. Main findings are the dominance of uncertainty from the hazard magnitude and the persistent behaviour of intrinsic uncertainties on both scale levels. Our results shed light on the general role of uncertainties and provide useful insight for the application of the unified approach.},
  language  = {en}
}
@phdthesis{Korzeniowska2017,
  author    = {Korzeniowska, Karolina},
  title     = {Object-based image analysis for detecting landforms diagnostic of natural hazards},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-402240},
  school      = {Universit{\"a}t Potsdam},
  pages     = {XV, 139},
  year      = {2017},
  abstract  = {Natural and potentially hazardous events occur on the Earth's surface every day. The most destructive of these processes must be monitored, because they may cause loss of lives, infrastructure, and natural resources, or have a negative effect on the environment. A variety of remote sensing technologies allow the recoding of data to detect these processes in the first place, partly based on the diagnostic landforms that they form. To perform this effectively, automatic methods are desirable. Universal detection of natural hazards is challenging due to their differences in spatial impacts, timing and longevity of consequences, and the spatial resolution of remote-sensing data. Previous studies have reported that topographic metrics such as roughness, which can be captured from digital elevation data, can reveal landforms diagnostic of natural hazards, such as gullies, dunes, lava fields, landslides and snow avalanches, as these landforms tend to be more heterogeneous than the surrounding landscape. A single roughness metric is often limited in such detections; however, a more complex approach that exploits the spatial relation and the location of objects, such as object-based image analysis (OBIA), is desirable. In this thesis, I propose a topographic roughness measure derived from an airborne laser scanning (ALS) digital terrain model (DTM) and discuss its performance in detecting landforms principally diagnostic of natural hazards. I further develop OBIA-based algorithms for the detection of snow avalanches using near-infrared (NIR) aerial images, and the size (changes) of mountain lakes using LANDSAT satellite images. I quantitatively test and document how the level of difficulty in detecting these very challenging landforms depends on the input data resolution, the derivatives that could be evaluated from images and DTMs, the size, shape and complexity of landforms, and the capabilities of obtaining the information in the data. I demonstrate that surface roughness is a promising metric for detecting different landforms in diverse environments, and that OBIA assists significantly in detecting parts of lakes and snow avalanches that may not be correctly assigned by applying only the thresholding of spectral properties of data and their derivatives. The curvature-based surface roughness parameter allows the detection of gullies, dunes, lava fields and landslides with a user's accuracy of 0.63, 0.21, 0.53, and 0.45, respectively. The OBIA algorithms for detecting lakes and snow avalanches obtained user's accuracy of 0.98, and 0.78, respectively. Most of the analysed landforms constituted only a small part of the entire dataset, and therefore the user's accuracy is the most appropriate performance measure that should be given in a such classification, because it tells how many automatically-extracted pixels in fact represent the object that one wants to classify, and its calculation does not take the second (background) class into account. One advantage of the proposed roughness parameter is that it allows the extraction of the heterogeneity of the surface without the need for data detrending. The OBIA approach is novel in that it allows the classification of lakes regardless of the physical state of their water, and also allows the separation of frozen lakes from glaciers that have very similar water indices used in purely optical remote sensing applications. The algorithm proposed for snow avalanches allows the detection of release zones, tracks, and deposition zones by verifying the snow heterogeneity based on a roughness metric evaluated from a water index, and by analysing the local relation of segments with their neighbouring objects. This algorithm contains few steps, which allows for the simultaneous classification of avalanches that occur on diverse mountain slopes and differ in size and shape. This thesis contributes to natural hazard research as it provides automatic solutions to tracking six different landforms that are diagnostic of natural hazards over large regions. This is a step toward delineating areas susceptible to the processes producing these landforms and the improvement of hazard maps.},
  language  = {en}
}
@phdthesis{Golly2017,
  author    = {Golly, Antonius},
  title     = {Formation and evolution of channel steps and their role for sediment dynamics in a steep mountain stream},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-411728},
  school      = {Universit{\"a}t Potsdam},
  pages     = {180},
  year      = {2017},
  abstract  = {Steep mountain channels are an important component of the fluvial system. On geological timescales, they shape mountain belts and counteract tectonic uplift by erosion. Their channels are strongly coupled to hillslopes and they are often the main source of sediment transported downstream to low-gradient rivers and to alluvial fans, where commonly settlements in mountainous areas are located. Hence, mountain streams are the cause for one of the main natural hazards in these regions. Due to climate change and a pronounced populating of mountainous regions the attention given to this threat is even growing. Although quantitative studies on sediment transport have significantly advanced our knowledge on measuring and calibration techniques we still lack studies of the processes within mountain catchments. Studies examining the mechanisms of energy and mass exchange on small temporal and spatial scales in steep streams remain sparse in comparison to low-gradient alluvial channels. In the beginning of this doctoral project, a vast amount of experience and knowledge of a steep stream in the Swiss Prealps had to be consolidated in order to shape the principal aim of this research effort. It became obvious, that observations from within the catchment are underrepresented in comparison to experiments performed at the catchment's outlet measuring fluxes and the effects of the transported material. To counteract this imbalance, an examination of mass fluxes within the catchment on the process scale was intended. Hence, this thesis is heavily based on direct field observations, which are generally rare in these environments in quantity and quality. The first objective was to investigate the coupling of the channel with surrounding hillslopes, the major sources of sediment. This research, which involved the monitoring of the channel and adjacent hillslopes, revealed that alluvial channel steps play a key role in coupling of channel and hillslopes. The observations showed that hillslope stability is strongly associated with the step presence and an understanding of step morphology and stability is therefore crucial in understanding sediment mobilization. This finding refined the way we think about the sediment dynamics in steep channels and motivated continued research of the step dynamics. However, soon it became obvious that the technological basis for developing field tests and analyzing the high resolution geometry measured in the field was not available. Moreover, for many geometrical quantities in mountain channels definitions and a clear scientific standard was not available. For example, these streams are characterized by a high spatial variability of the channel banks, preventing straightforward calculations of the channel width without a defined reference. Thus, the second and inevitable part of this thesis became the development and evaluation of scientific tools in order to investigate the geometrical content of the study reach thoroughly. The developed framework allowed the derivation of various metrics of step and channel geometry which facilitated research on the a large data set of observations of channel steps. In the third part, innovative, physically-based metrics have been developed and compared to current knowledge on step formation, suggested in the literature. With this analyses it could be demonstrated that the formation of channel steps follow a wide range of hydraulic controls. Due to the wide range of tested parameters channel steps observed in a natural stream were attributed to different mechanisms of step formation, including those based on jamming and those based on key-stones. This study extended our knowledge on step formation in a steep stream and harmonized different, often time seen as competing, processes of step formation. This study was based on observations collected at one point in time. In the fourth part of this project, the findings of the snap-shot observations were extended in the temporal dimension and the derived concepts have been utilized to investigate reach-scale step patterns in response to large, exceptional flood events. The preliminary results of this work based on the long-term analyses of 7 years of long profile surveys showed that the previously observed channel-hillslope mechanism is the responsible for the short-term response of step formation. The findings of the long-term analyses of step patterns drew a bow to the initial observations of a channel-hillslope system which allowed to join the dots in the dynamics of steep stream. Thus, in this thesis a broad approach has been chosen to gain insights into the complex system of steep mountain rivers. The effort includes in situ field observations (article I), the development of quantitative scientific tools (article II), the reach-scale analyses of step-pool morphology (article III) and its temporal evolution (article IV). With this work our view on the processes within the catchment has been advanced towards a better mechanistic understanding of these fluvial system relevant to improve applied scientific work.},
  language  = {en}
}
@article{SchwanghartRyanKorup2018,
  author    = {Schwanghart, Wolfgang and Ryan, Marie and Korup, Oliver},
  title     = {Topographic and seismic constraints on the vulnerability of himalayan hydropower},
  series = {Geophysical research letters},
  volume    = {45},
  journal   = {Geophysical research letters},
  number    = {17},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {0094-8276},
  doi       = {10.1029/2018GL079173},
  pages     = {8985 -- 8992},
  year      = {2018},
  abstract  = {Plain Language Summary The 2015 Gorkha earthquake in Nepal caused severe losses in the hydropower sector. The country temporarily lost similar to 20\% of its hydropower capacity, and >30 hydropower projects were damaged. The projects hit hardest were those that were affected by earthquake-triggered landslides. We show that these projects are located along very steep rivers with towering sidewalls that are prone to become unstable during strong seismic ground shaking. A statistical classification based on a topographic metric that expresses river steepness and earthquake ground acceleration is able to approximately predict hydropower damage during future earthquakes, based on successful testing of past cases. Thus, our model enables us to estimate earthquake damages to hydropower projects in other parts of the Himalayas. We find that >10\% of the Himalayan drainage network may be unsuitable for hydropower infrastructure given high probabilities of high earthquake damages.},
  language  = {en}
}
@misc{OlenBookhagen2018,
  author    = {Olen, Stephanie M. and Bookhagen, Bodo},
  title     = {Mapping Damage-Affected Areas after Natural Hazard Events Using Sentinel-1 Coherence Time Series},
  series = {remote sensing},
  journal   = {remote sensing},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-417766},
  pages     = {19},
  year      = {2018},
  abstract  = {The emergence of the Sentinel-1A and 1B satellites now offers freely available and widely accessible Synthetic Aperture Radar (SAR) data. Near-global coverage and rapid repeat time (6-12 days) gives Sentinel-1 data the potential to be widely used for monitoring the Earth's surface. Subtle land-cover and land surface changes can affect the phase and amplitude of the C-band SAR signal, and thus the coherence between two images collected before and after such changes. Analysis of SAR coherence therefore serves as a rapidly deployable and powerful tool to track both seasonal changes and rapid surface disturbances following natural disasters. An advantage of using Sentinel-1 C-band radar data is the ability to easily construct time series of coherence for a region of interest at low cost. In this paper, we propose a new method for Potentially Affected Area (PAA) detection following a natural hazard event. Based on the coherence time series, the proposed method (1) determines the natural variability of coherence within each pixel in the region of interest, accounting for factors such as seasonality and the inherent noise of variable surfaces; and (2) compares pixel-by-pixel syn-event coherence to temporal coherence distributions to determine where statistically significant coherence loss has occurred. The user can determine to what degree the syn-event coherence value (e.g., 1st, 5th percentile of pre-event distribution) constitutes a PAA, and integrate pertinent regional data, such as population density, to rank and prioritise PAAs. We apply the method to two case studies, Sarpol-e, Iran following the 2017 Iran-Iraq earthquake, and a landslide-prone region of NW Argentina, to demonstrate how rapid identification and interpretation of potentially affected areas can be performed shortly following a natural hazard event.},
  language  = {en}
}
@misc{TostEhmelHeidmannetal.2018,
  author    = {Tost, Jordi and Ehmel, Fabian and Heidmann, Frank and Olen, Stephanie M. and Bookhagen, Bodo},
  title     = {Hazards and accessibility},
  series = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  number    = {710},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-42785},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-427853},
  pages     = {8},
  year      = {2018},
  abstract  = {The assessment of natural hazards and risk has traditionally been built upon the estimation of threat maps, which are used to depict potential danger posed by a particular hazard throughout a given area. But when a hazard event strikes, infrastructure is a significant factor that can determine if the situation becomes a disaster. The vulnerability of the population in a region does not only depend on the area's local threat, but also on the geographical accessibility of the area. This makes threat maps by themselves insufficient for supporting real-time decision-making, especially for those tasks that involve the use of the road network, such as management of relief operations, aid distribution, or planning of evacuation routes, among others. To overcome this problem, this paper proposes a multidisciplinary approach divided in two parts. First, data fusion of satellite-based threat data and open infrastructure data from OpenStreetMap, introducing a threat-based routing service. Second, the visualization of this data through cartographic generalization and schematization. This emphasizes critical areas along roads in a simple way and allows users to visually evaluate the impact natural hazards may have on infrastructure. We develop and illustrate this methodology with a case study of landslide threat for an area in Colombia.},
  language  = {en}
}
@article{OlenBookhagen2018,
  author    = {Olen, Stephanie M. and Bookhagen, Bodo},
  title     = {Mapping Damage-Affected Areas after Natural Hazard Events Using Sentinel-1 Coherence Time Series},
  series = {remote sensing},
  volume    = {10},
  journal   = {remote sensing},
  number    = {8},
  publisher = {Molecular Diversity Preservation International (MDPI)},
  address   = {Basel},
  issn      = {2072-4292},
  doi       = {10.3390/rs10081272},
  pages     = {1 -- 19},
  year      = {2018},
  abstract  = {The emergence of the Sentinel-1A and 1B satellites now offers freely available and widely accessible Synthetic Aperture Radar (SAR) data. Near-global coverage and rapid repeat time (6-12 days) gives Sentinel-1 data the potential to be widely used for monitoring the Earth's surface. Subtle land-cover and land surface changes can affect the phase and amplitude of the C-band SAR signal, and thus the coherence between two images collected before and after such changes. Analysis of SAR coherence therefore serves as a rapidly deployable and powerful tool to track both seasonal changes and rapid surface disturbances following natural disasters. An advantage of using Sentinel-1 C-band radar data is the ability to easily construct time series of coherence for a region of interest at low cost. In this paper, we propose a new method for Potentially Affected Area (PAA) detection following a natural hazard event. Based on the coherence time series, the proposed method (1) determines the natural variability of coherence within each pixel in the region of interest, accounting for factors such as seasonality and the inherent noise of variable surfaces; and (2) compares pixel-by-pixel syn-event coherence to temporal coherence distributions to determine where statistically significant coherence loss has occurred. The user can determine to what degree the syn-event coherence value (e.g., 1st, 5th percentile of pre-event distribution) constitutes a PAA, and integrate pertinent regional data, such as population density, to rank and prioritise PAAs. We apply the method to two case studies, Sarpol-e, Iran following the 2017 Iran-Iraq earthquake, and a landslide-prone region of NW Argentina, to demonstrate how rapid identification and interpretation of potentially affected areas can be performed shortly following a natural hazard event.},
  language  = {en}
}
@article{MerzKuhlickeKunzetal.2020,
  author    = {Merz, Bruno and Kuhlicke, Christian and Kunz, Michael and Pittore, Massimiliano and Babeyko, Andrey and Bresch, David N. and Domeisen, Daniela I. and Feser, Frauke and Koszalka, Inga and Kreibich, Heidi and Pantillon, Florian and Parolai, Stefano and Pinto, Joaquim G. and Punge, Heinz J{\"u}rgen and Rivalta, Eleonora and Schr{\"o}ter, Kai and Strehlow, Karen and Weisse, Ralf and Wurpts, Andreas},
  title     = {Impact forecasting to support emergency management of natural hazards},
  series = {Reviews of geophysics},
  volume    = {58},
  journal   = {Reviews of geophysics},
  number    = {4},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {8755-1209},
  doi       = {10.1029/2020RG000704},
  pages     = {52},
  year      = {2020},
  abstract  = {Forecasting and early warning systems are important investments to protect lives, properties, and livelihood. While early warning systems are frequently used to predict the magnitude, location, and timing of potentially damaging events, these systems rarely provide impact estimates, such as the expected amount and distribution of physical damage, human consequences, disruption of services, or financial loss. Complementing early warning systems with impact forecasts has a twofold advantage: It would provide decision makers with richer information to take informed decisions about emergency measures and focus the attention of different disciplines on a common target. This would allow capitalizing on synergies between different disciplines and boosting the development of multihazard early warning systems. This review discusses the state of the art in impact forecasting for a wide range of natural hazards. We outline the added value of impact-based warnings compared to hazard forecasting for the emergency phase, indicate challenges and pitfalls, and synthesize the review results across hazard types most relevant for Europe.},
  language  = {en}
}