TY - JOUR A1 - Rözer, Viktor A1 - Müller, Meike A1 - Bubeck, Philip A1 - Kienzler, Sarah A1 - Thieken, Annegret A1 - Pech, Ina A1 - Schröter, Kai A1 - Buchholz, Oliver A1 - Kreibich, Heidi T1 - Coping with Pluvial Floods by Private Households JF - Water N2 - Pluvial floods have caused severe damage to urban areas in recent years. With a projected increase in extreme precipitation as well as an ongoing urbanization, pluvial flood damage is expected to increase in the future. Therefore, further insights, especially on the adverse consequences of pluvial floods and their mitigation, are needed. To gain more knowledge, empirical damage data from three different pluvial flood events in Germany were collected through computer-aided telephone interviews. Pluvial flood awareness as well as flood experience were found to be low before the respective flood events. The level of private precaution increased considerably after all events, but is mainly focused on measures that are easy to implement. Lower inundation depths, smaller potential losses as compared with fluvial floods, as well as the fact that pluvial flooding may occur everywhere, are expected to cause a shift in damage mitigation from precaution to emergency response. However, an effective implementation of emergency measures was constrained by a low dissemination of early warnings in the study areas. Further improvements of early warning systems including dissemination as well as a rise in pluvial flood preparedness are important to reduce future pluvial flood damage. KW - pluvial floods KW - surface water flooding KW - emergency response KW - early warning KW - preparedness KW - damage KW - mitigation Y1 - 2016 U6 - https://doi.org/10.3390/w8070304 SN - 2073-4441 VL - 8 PB - MDPI CY - Basel ER - TY - JOUR A1 - Reil, Daniela A1 - Imholt, Christian A1 - Rosenfeld, Ulrike M. A1 - Drewes, Stephan A1 - Fischer, S. A1 - Heuser, Emil A1 - Petraityte-Burneikiene, Rasa A1 - Ulrich, R. G. A1 - Jacob, J. T1 - Validation of the Puumala virus rapid field test for bank voles in Germany JF - Epidemiology and infection N2 - Puumala virus (PUUV) causes many human infections in large parts of Europe and can lead to mild to moderate disease. The bank vole (Myodes glareolus) is the only reservoir of PUUV in Central Europe. A commercial PUUV rapid field test for rodents was validated for bank-vole blood samples collected in two PUUV-endemic regions in Germany (North Rhine-Westphalia and Baden-Wurttemberg). A comparison of the results of the rapid field test and standard ELISAs indicated a test efficacy of 93-95%, largely independent of the origin of the antigens used in the ELISA. In ELISAs, reactivity for the German PUUV strain was higher compared to the Swedish strain but not compared to the Finnish strain, which was used for the rapid field test. In conclusion, the use of the rapid field test can facilitate short-term estimation of PUUV seroprevalence in bank-vole populations in Germany and can aid in assessing human PUUV infection risk. KW - Antibody detection KW - early warning KW - Europe KW - hantavirus KW - Myodes glareolus Y1 - 2017 U6 - https://doi.org/10.1017/S0950268816002557 SN - 0950-2688 SN - 1469-4409 VL - 145 IS - 3 SP - 434 EP - 439 PB - Cambridge Univ. Press CY - New York ER - TY - GEN A1 - Rözer, Viktor A1 - Müller, Meike A1 - Bubeck, Philip A1 - Kienzler, Sarah A1 - Thieken, Annegret A1 - Pech, Ina A1 - Schröter, Kai A1 - Buchholz, Oliver A1 - Kreibich, Heidi T1 - Coping with pluvial floods by private households N2 - Pluvial floods have caused severe damage to urban areas in recent years. With a projected increase in extreme precipitation as well as an ongoing urbanization, pluvial flood damage is expected to increase in the future. Therefore, further insights, especially on the adverse consequences of pluvial floods and their mitigation, are needed. To gain more knowledge, empirical damage data from three different pluvial flood events in Germany were collected through computer-aided telephone interviews. Pluvial flood awareness as well as flood experience were found to be low before the respective flood events. The level of private precaution increased considerably after all events, but is mainly focused on measures that are easy to implement. Lower inundation depths, smaller potential losses as compared with fluvial floods, as well as the fact that pluvial flooding may occur everywhere, are expected to cause a shift in damage mitigation from precaution to emergency response. However, an effective implementation of emergency measures was constrained by a low dissemination of early warnings in the study areas. Further improvements of early warning systems including dissemination as well as a rise in pluvial flood preparedness are important to reduce future pluvial flood damage. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 355 KW - pluvial floods KW - surface water flooding KW - emergency response KW - early warning KW - preparedness KW - damage KW - mitigation Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-400465 ER - TY - JOUR A1 - Olen, Stephanie M. A1 - Bookhagen, Bodo T1 - Applications of SAR interferometric coherence time series BT - satiotemporal dynamics of geomorphic transitions in the South-Central Andes JF - Journal of geophysical research : Earth surface N2 - Sediment transport domains in mountain landscapes are characterized by fundamentally different processes and rates depending on several factors, including geology, climate, and biota. Accurately identifying where transitions between transport domains occur is an important step to quantify the past, present, and future contribution of varying erosion and sedimentation processes and enhance our predictive capabilities. We propose a new methodology based on time series of synthetic aperture radar (SAR) interferometric coherence images to map sediment transport regimes across arid and semiarid landscapes. Using 4 years of Sentinel-1 data, we analyze sediment transport regimes for the south-central Andes in northwestern Argentina characterized by steep topographic and climatic gradients. We observe seasonally low coherence during the regional wet season, particularly on hillslopes and in alluvial channels. The spatial distribution of coherence is compared to drainage areas extracted from digital topography to identify two distinct transitions within watersheds: (a) a hillslope-to-fluvial and (b) a fluvial-to-alluvial transition. While transitions within a given basin can be well-constrained, the relative role of each sediment transport domain varies widely over the climatic and topographic gradients. In semiarid regions, we observe larger relative contributions from hillslopes compared to arid regions. Across regional gradients, the range of coherence within basins positively correlates to previously published millennial catchment-wide erosion rates and to topographic metrics used to indicate long-term uplift. Our study suggests that a dense time series of interferometric coherence can be used as a proxy for surface sediment movement and landscape stability in vegetation-free settings at event to decadal timescales. KW - Copernicus KW - SAR KW - critical infrastructure resilience KW - early warning KW - landslides Y1 - 2020 U6 - https://doi.org/10.1029/2019JF005141 SN - 2169-9003 SN - 2169-9011 VL - 125 IS - 3 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Merz, Bruno A1 - Kuhlicke, Christian A1 - Kunz, Michael A1 - Pittore, Massimiliano A1 - Babeyko, Andrey A1 - Bresch, David N. A1 - Domeisen, Daniela I. A1 - Feser, Frauke A1 - Koszalka, Inga A1 - Kreibich, Heidi A1 - Pantillon, Florian A1 - Parolai, Stefano A1 - Pinto, Joaquim G. A1 - Punge, Heinz Jürgen A1 - Rivalta, Eleonora A1 - Schröter, Kai A1 - Strehlow, Karen A1 - Weisse, Ralf A1 - Wurpts, Andreas T1 - Impact forecasting to support emergency management of natural hazards JF - Reviews of geophysics N2 - 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. KW - impact forecasting KW - natural hazards KW - early warning Y1 - 2020 U6 - https://doi.org/10.1029/2020RG000704 SN - 8755-1209 SN - 1944-9208 VL - 58 IS - 4 PB - American Geophysical Union CY - Washington ER - TY - THES A1 - Luna, Lisa Victoria T1 - Rainfall-triggered landslides: conditions, prediction, and warning N2 - 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. N2 - Durch Regenfälle ausgelöste Erdrutsche sind eine weltweit auftretende Gefahr, die im Durchschnitt mehrere tausend Todesopfer pro Jahr fordern und zu wirtschaftlichen Schäden führen, indem sie Gebäude und Infrastrukturen zerstören und Verkehrsnetze blockieren. Für Bewohner, sowie lokale Regierungen in potentiell gefährdeten Gebieten, ist es entscheidend zu wissen, nicht nur wo, sondern auch wann Erdrutsche am wahrscheinlichsten sind, um Strategien zur Verringerung des Risikos zu entwickeln. Dies erfordert zuverlässige Bewertungen der wetterbedingten Erdrutschgefahr und eine angemessene Warnung. Angemessene Maßnahmen während Hochrisikoperioden, wie der Umzug in höhere Etagen, die Sperrung von Straßen und Schienennetzen, sowie die Evakuierung von Wohngebieten, können Leben retten. In vielen Regionen mit hohem Erdrutschrisiko gibt es jedoch derzeit keine spezifischen, einsatzfähigen Frühwarnsysteme für Erdrutsche. In einigen Regionen ermöglichte die zunehmende Verfügbarkeit von zeitlich-aufgelösten Erdrutschdaten datengestützte Ansätze zur Abschätzung der Erdrutschgefahr auf Grundlage von Niederschlagsbedingungen. In anderen Gebieten sind solche Daten jedoch nach wie vor spärlich, sodass geeignete statistische Methoden erforderlich sind, um die Gefährdung trotz einer begrenzten Datenmenge abzuschätzen. Die übergreifende Motivation für diese Dissertation besteht darin, unsere Fähigkeit zur rechtzeitigen Vorhersage von niederschlagsbedingten Erdrutschen zu verbessern, um Frühwarnsysteme zu erweitern und optimieren. Zu diesem Zweck habe ich Bayes'sche Inferenz angewandt, um die Erdrutschaktivität in Abhängigkeit von den Niederschlagsbedingungen probabilistisch zu quantifizieren und vorherzusagen. Meine Studien decken dabei sowohl eine breite räumliche Skala, welche von einer lokalen bis regionalen Betrachtung reicht, als auch eine von stündlich bis saisonal reichende zeitliche Skala ab. Diese Dissertation setzt sich aus drei Studien zusammen. In der ersten Studie habe ich zur Entwicklung und Validierung statistischer Modelle für ein Online-Dashboard zur Erdrutschwarnung in der Kleinstadt Sitka, Alaska, USA, beigetragen. Wir verwendeten logistische und Poisson-Regressionen zur Einschätzung der täglichen Erdrutschwahrscheinlichkeit und der Anzahl der Erdrutsche auf Grundlage von nur fünf dokumentierten Erdrutschereignissen und 18 Jahren stündlicher Niederschlagsmessungen am Flughafen von Sitka. Basierend auf Hinweisen aus der Bevölkerung legten wir zwei Warnschwellenwerte für die Umsetzung des Dashboards fest, welches wiederum beobachtete Niederschläge und Vorhersagen des US-amerikanischen Wetterdienstes (US National Weather Service) nutzt, um Echtzeiteinschätzungen der Erdrutschgefahr zu liefern. In der zweiten Studie habe ich Schwellenwerte für die Niederschlagsintensität und -dauer für Erdrutsche in 26 Städten weltweit, sowie einen globalen Schwellenwert für urbane Erdrutsche ermittelt. Dabei stellte ich fest, dass Erdrutsche in urbanen Gebieten bei Niederschlagsintensitäten auftreten, die unter den zuvor gemeldeten globalen Schwellenwerten liegen, und dass 31 % der Erdrutsche in Städten durch moderate Niederschlagsereignisse ausgelöst wurden. Erdrutsche in Städten mit sehr unterschiedlichen klimatischen und topografischen Bedingungen wurden jedoch bei vergleichbaren kritischen Niederschlagsintensitäten ausgelöst: Für 77 % der Städte unterschieden sich die lokalen Schwellenwerte nicht von den globalen Schwellenwerten, was darauf hindeutet, dass eine zunehmende Urbanisierung die Schwellenwerte zwischen Städten angleicht und natürliche Schwankungen überlagern kann. Ich habe einen Basisschwellenwert festgelegt, der für die Warnung in Städten mit begrenzten Erdrutschdaten in Betracht gezogen werden könnte. In der dritten Studie untersuchte ich saisonale Reaktionen von Erdrutschen auf jährliche Niederschlagsmuster im pazifischen Nordwesten der USA. Dafür verwendete ich Bayes'sche Mehrebenenmodelle, um Daten aus fünf heterogenen Erdrutschinventaren zu kombinieren, welche unterschiedliche Gebiete und Zeiträume abdecken. Ich fand heraus, dass Erdrutsche deutlich saisonabhängig sind und dass der Höhepunkt der Erdrutschaktivität mit einem zeitlichen Versatz auf den jährlichen Niederschlagsspitzenwert folgt. Im Februar, auf dem Höhepunkt der Erdrutschsaison, ist die Erdrutschintensität bei einer gegebenen monatlichen Niederschlagsmenge bis zu zehnmal höher als zu Beginn der Saison im November. Dies unterstreicht die Bedeutung von vorherigen saisonalen Hangbedingungen. Zusammengefasst liefern die in dieser Dissertation vorgestellten Studien umsetzbare, objektive Informationen für die Frühwarnung vor Erdrutschen und Beispiele für die Anwendung von Bayes'schen Methoden zur probabilistischen Quantifizierung der Erdrutschgefahr mittels Bestands- und Niederschlagsdaten. T2 - Durch Regenfälle ausgelöste Erdrutsche: Bedingungen, Vorhersage und Warnung KW - landslide KW - natural hazards KW - Bayesian statistics KW - early warning KW - geomorphology KW - Bayessche Statistik KW - Erdrutsch KW - Naturgefahren KW - Frühwarnung KW - Geomorphologie Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-600927 ER -