@article{SiegThieken2022,
  author    = {Sieg, Tobias and Thieken, Annegret},
  title     = {Improving flood impact estimations},
  series = {Environmental research letters},
  volume    = {17},
  journal   = {Environmental research letters},
  number    = {6},
  publisher = {IOP Publ. Ltd.},
  address   = {Bristol},
  issn      = {1748-9326},
  doi       = {10.1088/1748-9326/ac6d6c},
  pages     = {16},
  year      = {2022},
  abstract  = {A reliable estimation of flood impacts enables meaningful flood risk management and rapid assessments of flood impacts shortly after a flood. The flood in 2021 in Central Europe and the analysis of its impacts revealed that these estimations are still inadequate. Therefore, we investigate the influence of different data sets and methods aiming to improve flood impact estimates. We estimated economic flood impacts to private households and companies for a flood event in 2013 in Germany using (a) two different flood maps, (b) two approaches to map exposed objects based on OpenStreetMap and the Basic European Asset Map, (c) two different approaches to estimate asset values, and (d) tree-based models and Stage-Damage-Functions to describe the vulnerability. At the macro scale, water masks lead to reasonable impact estimations. At the micro and meso-scale, the identification of affected objects by means of water masks is insufficient leading to unreliable estimations. The choice of exposure data sets is most influential on the estimations. We find that reliable impact estimations are feasible with reported numbers of flood-affected objects from the municipalities. We conclude that more effort should be put in the investigation of different exposure data sets and the estimation of asset values. Furthermore, we recommend the establishment of a reporting system in the municipalities for a fast identification of flood-affected objects shortly after an event.},
  language  = {en}
}
@article{ZaliOhrnbergerScherbaumetal.2021,
  author    = {Zali, Zahra and Ohrnberger, Matthias and Scherbaum, Frank and Cotton, Fabrice and Eibl, Eva P. S.},
  title     = {Volcanic tremor extraction and earthquake detection using music information retrieval algorithms},
  series = {Seismological research letters},
  volume    = {92},
  journal   = {Seismological research letters},
  number    = {6},
  publisher = {Seismological Society of America},
  address   = {Boulder, Colo.},
  issn      = {0895-0695},
  doi       = {10.1785/0220210016},
  pages     = {3668 -- 3681},
  year      = {2021},
  abstract  = {Volcanic tremor signals are usually observed before or during volcanic eruptions and must be monitored to evaluate the volcanic activity. A challenge in studying seismic signals of volcanic origin is the coexistence of transient signal swarms and long-lasting volcanic tremor signals. Separating transient events from volcanic tremors can, therefore, contrib-ute to improving upon our understanding of the underlying physical processes. Exploiting the idea of harmonic-percussive separation in musical signal processing, we develop a method to extract the harmonic volcanic tremor signals and to detect tran-sient events from seismic recordings. Based on the similarity properties of spectrogram frames in the time-frequency domain, we decompose the signal into two separate spec-trograms representing repeating (harmonic) and nonrepeating (transient) patterns, which correspond to volcanic tremor signals and earthquake signals, respectively. We reconstruct the harmonic tremor signal in the time domain from the complex spectrogram of the repeating pattern by only considering the phase components for the frequency range in which the tremor amplitude spectrum is significantly contribut-ing to the energy of the signal. The reconstructed signal is, therefore, clean tremor signal without transient events. Furthermore, we derive a characteristic function suitable for the detection of tran-sient events (e.g., earthquakes) by integrating amplitudes of the nonrepeating spectro-gram over frequency at each time frame. Considering transient events like earthquakes, 78\% of the events are detected for signal-to-noise ratio = 0.1 in our semisynthetic tests. In addition, we compared the number of detected earthquakes using our method for one month of continuous data recorded during the Holuhraun 2014-2015 eruption in Iceland with the bulletin presented in Agustsdottir et al. (2019). Our single station event detection algorithm identified 84\% of the bulletin events. Moreover, we detected a total of 12,619 events, which is more than twice the number of the bulletin events.},
  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}
}
@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}
}
@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}
}