TY - JOUR A1 - Weatherill, Graeme A1 - Cotton, Fabrice Pierre T1 - A ground motion logic tree for seismic hazard analysis in the stable cratonic region of Europe BT - regionalisation, model selection and development of a scaled backbone approach JF - Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering N2 - Regions of low seismicity present a particular challenge for probabilistic seismic hazard analysis when identifying suitable ground motion models (GMMs) and quantifying their epistemic uncertainty. The 2020 European Seismic Hazard Model adopts a scaled backbone approach to characterise this uncertainty for shallow seismicity in Europe, incorporating region-to-region source and attenuation variability based on European strong motion data. This approach, however, may not be suited to stable cratonic region of northeastern Europe (encompassing Finland, Sweden and the Baltic countries), where exploration of various global geophysical datasets reveals that its crustal properties are distinctly different from the rest of Europe, and are instead more closely represented by those of the Central and Eastern United States. Building upon the suite of models developed by the recent NGA East project, we construct a new scaled backbone ground motion model and calibrate its corresponding epistemic uncertainties. The resulting logic tree is shown to provide comparable hazard outcomes to the epistemic uncertainty modelling strategy adopted for the Eastern United States, despite the different approaches taken. Comparison with previous GMM selections for northeastern Europe, however, highlights key differences in short period accelerations resulting from new assumptions regarding the characteristics of the reference rock and its influence on site amplification. KW - ground motion models KW - stable craton KW - regionalisation KW - epistemic KW - uncertainty KW - Europe Y1 - 2020 U6 - https://doi.org/10.1007/s10518-020-00940-x SN - 1570-761X SN - 1573-1456 VL - 18 IS - 14 SP - 6119 EP - 6148 PB - Springer Science + Business Media B.V. CY - Dordrecht ER - TY - GEN A1 - Rolinski, Susanne A1 - Rammig, Anja A1 - Walz, Ariane A1 - von Bloh, Werner A1 - van Oijen, M. A1 - Thonicke, Kirsten T1 - A probabilistic risk assessment for the vulnerability of the European carbon cycle to weather extremes BT - The ecosystem perspective T2 - Postprints der Universität Potsdam : Mathematisch naturwissenschaftliche Reihe (487) N2 - Extreme weather events are likely to occur more often under climate change and the resulting effects on ecosystems could lead to a further acceleration of climate change. But not all extreme weather events lead to extreme ecosystem response. Here, we focus on hazardous ecosystem behaviour and identify coinciding weather conditions. We use a simple probabilistic risk assessment based on time series of ecosystem behaviour and climate conditions. Given the risk assessment terminology, vulnerability and risk for the previously defined hazard are estimated on the basis of observed hazardous ecosystem behaviour. We apply this approach to extreme responses of terrestrial ecosystems to drought, defining the hazard as a negative net biome productivity over a 12-month period. We show an application for two selected sites using data for 1981-2010 and then apply the method to the pan-European scale for the same period, based on numerical modelling results (LPJmL for ecosystem behaviour; ERA-Interim data for climate). Our site-specific results demonstrate the applicability of the proposed method, using the SPEI to describe the climate condition. The site in Spain provides an example of vulnerability to drought because the expected value of the SPEI is 0.4 lower for hazardous than for non-hazardous ecosystem behaviour. In northern Germany, on the contrary, the site is not vulnerable to drought because the SPEI expectation values imply wetter conditions in the hazard case than in the non-hazard case. At the pan-European scale, ecosystem vulnerability to drought is calculated in the Mediterranean and temperate region, whereas Scandinavian ecosystems are vulnerable under conditions without water shortages. These first model- based applications indicate the conceptual advantages of the proposed method by focusing on the identification of critical weather conditions for which we observe hazardous ecosystem behaviour in the analysed data set. Application of the method to empirical time series and to future climate would be important next steps to test the approach. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 487 KW - global vegetation model KW - climate extremes KW - fire emissions KW - drought KW - forest KW - productivity KW - reduction KW - events KW - assimilation KW - uncertainty Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-407999 SN - 1866-8372 IS - 487 SP - 1813 EP - 1831 ER - TY - JOUR A1 - Sureth, Michael A1 - Kalkuhl, Matthias A1 - Edenhofer, Ottmar A1 - Rockström, Johan T1 - A welfare economic approach to planetary boundaries JF - Jahrbücher für Nationalökonomie und Statistik N2 - The crises of both the climate and the biosphere are manifestations of the imbalance between human extractive, and polluting activities and the Earth’s regenerative capacity. Planetary boundaries define limits for biophysical systems and processes that regulate the stability and life support capacity of the Earth system, and thereby also define a safe operating space for humanity on Earth. Budgets associated to planetary boundaries can be understood as global commons: common pool resources that can be utilized within finite limits. Despite the analytical interpretation of planetary boundaries as global commons, the planetary boundaries framework is missing a thorough integration into economic theory. We aim to bridge the gap between welfare economic theory and planetary boundaries as derived in the natural sciences by presenting a unified theory of cost-benefit and cost-effectiveness analysis. Our pragmatic approach aims to overcome shortcomings of the practical applications of CEA and CBA to environmental problems of a planetary scale. To do so, we develop a model framework and explore decision paradigms that give guidance to setting limits on human activities. This conceptual framework is then applied to planetary boundaries. We conclude by using the realized insights to derive a research agenda that builds on the understanding of planetary boundaries as global commons. KW - cost-benefit analysis KW - cost-effectiveness analysis KW - global commons KW - planetary boundaries KW - precautionary principle KW - shadow price KW - uncertainty KW - welfare economics Y1 - 2023 U6 - https://doi.org/10.1515/jbnst-2022-0022 SN - 0021-4027 SN - 2366-049X VL - 243 IS - 5 SP - 477 EP - 542 PB - De Gruyter Oldenbourg CY - Berlin ER - TY - GEN A1 - Korup, Oliver T1 - Bayesian geomorphology T2 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - The rapidly growing amount and diversity of data are confronting us more than ever with the need to make informed predictions under uncertainty. The adverse impacts of climate change and natural hazards also motivate our search for reliable predictions. The range of statistical techniques that geomorphologists use to tackle this challenge has been growing, but rarely involves Bayesian methods. Instead, many geomorphic models rely on estimated averages that largely miss out on the variability of form and process. Yet seemingly fixed estimates of channel heads, sediment rating curves or glacier equilibrium lines, for example, are all prone to uncertainties. Neighbouring scientific disciplines such as physics, hydrology or ecology have readily embraced Bayesian methods to fully capture and better explain such uncertainties, as the necessary computational tools have advanced greatly. The aim of this article is to introduce the Bayesian toolkit to scientists concerned with Earth surface processes and landforms, and to show how geomorphic models might benefit from probabilistic concepts. I briefly review the use of Bayesian reasoning in geomorphology, and outline the corresponding variants of regression and classification in several worked examples. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1348 KW - Bayes’ rule KW - probability KW - uncertainty KW - prediction Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-539892 SN - 1866-8372 IS - 1 ER - TY - JOUR A1 - Korup, Oliver T1 - Bayesian geomorphology JF - Earth surface processes and landforms : the journal of the British Geomorphological Research Group N2 - The rapidly growing amount and diversity of data are confronting us more than ever with the need to make informed predictions under uncertainty. The adverse impacts of climate change and natural hazards also motivate our search for reliable predictions. The range of statistical techniques that geomorphologists use to tackle this challenge has been growing, but rarely involves Bayesian methods. Instead, many geomorphic models rely on estimated averages that largely miss out on the variability of form and process. Yet seemingly fixed estimates of channel heads, sediment rating curves or glacier equilibrium lines, for example, are all prone to uncertainties. Neighbouring scientific disciplines such as physics, hydrology or ecology have readily embraced Bayesian methods to fully capture and better explain such uncertainties, as the necessary computational tools have advanced greatly. The aim of this article is to introduce the Bayesian toolkit to scientists concerned with Earth surface processes and landforms, and to show how geomorphic models might benefit from probabilistic concepts. I briefly review the use of Bayesian reasoning in geomorphology, and outline the corresponding variants of regression and classification in several worked examples. KW - Bayes' rule KW - probability KW - uncertainty KW - prediction Y1 - 2020 U6 - https://doi.org/10.1002/esp.4995 SN - 0197-9337 SN - 1096-9837 VL - 46 IS - 1 SP - 151 EP - 172 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - McDowell, Michelle A1 - Kause, Astrid T1 - Communicating uncertainties about the effects of medical interventions using different display formats JF - Risk analysis : an international journal N2 - Communicating uncertainties in scientific evidence is important to accurately reflect scientific knowledge , increase public understanding of uncertainty, and to signal transparency and honesty in reporting. While techniques have been developed to facilitate the communication of uncertainty, many have not been empirically tested, compared for communicating different types of uncertainty, or their effects on different cognitive, trust, and behavioral outcomes have not been evaluated. The present study examined how a point estimate, imprecise estimate, conflicting estimates, or a statement about the lack of evidence about treatment effects, influenced participant's responses to communications about medical evidence. For each type of uncertainty, we adapted three display formats to communicate the information: tables, bar graphs, and icon arrays. We compared participant's best estimates of treatment effects, as well as effects on recall, subjective evaluations (understandability and usefuleness), certainty perceptions, perceptions of trustworthiness of the information, and behavioral intentions. We did not find any detrimental effects from communicating imprecision or conflicting estimates relative to a point estimate across any outcome. Furthermore, there were more favorable responses to communicating imprecision or conflicting estimates relative to lack of evidence, where participants estimated the treatment would improve outcomes by 30-50% relative to a placebo. There were no differences across display formats, suggesting that, if well-designed, it may not matter which format is used. Future research on specific display formats or uncertainty types and with larger sample sizes would be needed to detect small effects. Implications for the communication of uncertainty are discussed. KW - risk communication KW - uncertainty KW - visual displays Y1 - 2021 U6 - https://doi.org/10.1111/risa.13739 SN - 0272-4332 SN - 1539-6924 VL - 41 IS - 12 SP - 2220 EP - 2239 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Dormann, Carsten F. A1 - Schymanski, Stanislaus J. A1 - Cabral, Juliano Sarmento A1 - Chuine, Isabelle A1 - Graham, Catherine A1 - Hartig, Florian A1 - Kearney, Michael A1 - Morin, Xavier A1 - Römermann, Christine A1 - Schröder-Esselbach, Boris A1 - Singer, Alexander T1 - Correlation and process in species distribution models: bridging a dichotomy JF - Journal of biogeography N2 - Within the field of species distribution modelling an apparent dichotomy exists between process-based and correlative approaches, where the processes are explicit in the former and implicit in the latter. However, these intuitive distinctions can become blurred when comparing species distribution modelling approaches in more detail. In this review article, we contrast the extremes of the correlativeprocess spectrum of species distribution models with respect to core assumptions, model building and selection strategies, validation, uncertainties, common errors and the questions they are most suited to answer. The extremes of such approaches differ clearly in many aspects, such as model building approaches, parameter estimation strategies and transferability. However, they also share strengths and weaknesses. We show that claims of one approach being intrinsically superior to the other are misguided and that they ignore the processcorrelation continuum as well as the domains of questions that each approach is addressing. Nonetheless, the application of process-based approaches to species distribution modelling lags far behind more correlative (process-implicit) methods and more research is required to explore their potential benefits. Critical issues for the employment of species distribution modelling approaches are given, together with a guideline for appropriate usage. We close with challenges for future development of process-explicit species distribution models and how they may complement current approaches to study species distributions. KW - Hypothesis generation KW - mechanistic model KW - parameterization KW - process-based model KW - species distribution model KW - SDM KW - uncertainty KW - validation Y1 - 2012 U6 - https://doi.org/10.1111/j.1365-2699.2011.02659.x SN - 0305-0270 VL - 39 IS - 12 SP - 2119 EP - 2131 PB - Wiley-Blackwell CY - Hoboken ER - TY - THES A1 - Schröter, Kai T1 - Improved flood risk assessment BT - new data sources and methods for flood risk modelling N2 - Rivers have always flooded their floodplains. Over 2.5 billion people worldwide have been affected by flooding in recent decades. The economic damage is also considerable, averaging 100 billion US dollars per year. There is no doubt that damage and other negative effects of floods can be avoided. However, this has a price: financially and politically. Costs and benefits can be estimated through risk assessments. Questions about the location and frequency of floods, about the objects that could be affected and their vulnerability are of importance for flood risk managers, insurance companies and politicians. Thus, both variables and factors from the fields of hydrology and sociol-economics play a role with multi-layered connections. One example are dikes along a river, which on the one hand contain floods, but on the other hand, by narrowing the natural floodplains, accelerate the flood discharge and increase the danger of flooding for the residents downstream. Such larger connections must be included in the assessment of flood risk. However, in current procedures this is accompanied by simplifying assumptions. Risk assessments are therefore fuzzy and associated with uncertainties. This thesis investigates the benefits and possibilities of new data sources for improving flood risk assessment. New methods and models are developed, which take the mentioned interrelations better into account and also quantify the existing uncertainties of the model results, and thus enable statements about the reliability of risk estimates. For this purpose, data on flood events from various sources are collected and evaluated. This includes precipitation and flow records at measuring stations as well as for instance images from social media, which can help to delineate the flooded areas and estimate flood damage with location information. Machine learning methods have been successfully used to recognize and understand correlations between floods and impacts from a wide range of data and to develop improved models. Risk models help to develop and evaluate strategies to reduce flood risk. These tools also provide advanced insights into the interplay of various factors and on the expected consequences of flooding. This work shows progress in terms of an improved assessment of flood risks by using diverse data from different sources with innovative methods as well as by the further development of models. Flood risk is variable due to economic and climatic changes, and other drivers of risk. In order to keep the knowledge about flood risks up-to-date, robust, efficient and adaptable methods as proposed in this thesis are of increasing importance. N2 - Flüsse haben seit jeher ihre Auen überflutet. In den vergangenen Jahrzehnten waren weltweit über 2,5 Milliarden Menschen durch Hochwasser betroffen. Auch der ökonomische Schaden ist mit durchschnittlich 100 Milliarden US Dollar pro Jahr erheblich. Zweifelsohne können Schäden und andere negative Auswirkungen von Hochwasser vermieden werden. Allerdings hat dies einen Preis: finanziell und politisch. Kosten und Nutzen lassen sich durch Risikobewertungen abschätzen. Dabei werden in der Wasserwirtschaft, von Versicherungen und der Politik Fragen nach dem Ort und der Häufigkeit von Überflutungen, nach den Dingen, die betroffen sein könnten und deren Anfälligkeit untersucht. Somit spielen sowohl Größen und Faktoren aus den Bereichen der Hydrologie und Sozioökonmie mit vielschichtigen Zusammenhängen eine Rolle. Ein anschauliches Beispiel sind Deiche entlang eines Flusses, die einerseits in ihrem Abschnitt Überflutungen eindämmen, andererseits aber durch die Einengung der natürlichen Vorländer den Hochwasserabfluss beschleunigen und die Gefährdung für die Anlieger flussab verschärfen. Solche größeren Zusammenhänge müssen in der Bewertung des Hochwasserrisikos einbezogen werden. In derzeit gängigen Verfahren geht dies mit vereinfachenden Annahmen einher. Risikoabschätzungen sind daher unscharf und mit Unsicherheiten verbunden. Diese Arbeit untersucht den Nutzen und die Möglichkeiten neuer Datensätze für eine Verbesserung der Hochwasserrisikoabschätzung. Es werden neue Methoden und Modelle entwickelt, die die angesprochenen Zusammenhänge stärker berücksichtigen und auch die bestehenden Unsicherheiten der Modellergebnisse beziffern und somit die Verlässlichkeit der getroffenen Aussagen einordnen lassen. Dafür werden Daten zu Hochwasserereignissen aus verschiedenen Quellen erfasst und ausgewertet. Dazu zählen neben Niederschlags-und Durchflussaufzeichnungen an Messstationen beispielsweise auch Bilder aus sozialen Medien, die mit Ortsangaben und Bildinhalten helfen können, die Überflutungsflächen abzugrenzen und Hochwasserschäden zu schätzen. Verfahren des Maschinellen Lernens wurden erfolgreich eingesetzt, um aus vielfältigen Daten, Zusammenhänge zwischen Hochwasser und Auswirkungen zu erkennen, besser zu verstehen und verbesserte Modelle zu entwickeln. Solche Risikomodelle helfen bei der Entwicklung und Bewertung von Strategien zur Minderung des Hochwasserrisikos. Diese Werkzeuge ermöglichen darüber hinaus Einblicke in das Zusammenspiel verschiedener Faktoren sowie Aussagen zu den zu erwartenden Folgen auch von Hochwassern, die das bisher bekannte Ausmaß übersteigen. Diese Arbeit verzeichnet Fortschritte in Bezug auf eine verbesserte Bewertung von Hochwasserrisiken durch die Nutzung vielfältiger Daten aus unterschiedlichen Quellen mit innovativen Verfahren sowie der Weiterentwicklung von Modellen. Das Hochwasserrisiko unterliegt durch wirtschaftliche Entwicklungen und klimatische Veränderungen einem steten Wandel. Um das Wissen über Risiken aktuell zu halten sind robuste, leistungs- und anpassungsfähige Verfahren wie sie in dieser Arbeit vorgestellt werden von zunehmender Bedeutung. T2 - Verbesserte Hochwasserrisikobewertung: Neue Datenquellen und Methoden für die Risikomodellierung KW - flood KW - risk KW - vulnerability KW - machine learning KW - uncertainty KW - Hochwasser KW - Risiko KW - Vulnerabilität KW - Maschinelles Lernen KW - Unsicherheiten Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-480240 ER - TY - THES A1 - Zurell, Damaris T1 - Integrating dynamic and statistical modelling approaches in order to improve predictions for scenarios of environmental change T1 - Integration dynamischer und statistischer Modellansätze zur Verbesserung von Arealvorhersagen für Szenarien globalen Wandels N2 - Species respond to environmental change by dynamically adjusting their geographical ranges. Robust predictions of these changes are prerequisites to inform dynamic and sustainable conservation strategies. Correlative species distribution models (SDMs) relate species’ occurrence records to prevailing environmental factors to describe the environmental niche. They have been widely applied in global change context as they have comparably low data requirements and allow for rapid assessments of potential future species’ distributions. However, due to their static nature, transient responses to environmental change are essentially ignored in SDMs. Furthermore, neither dispersal nor demographic processes and biotic interactions are explicitly incorporated. Therefore, it has often been suggested to link statistical and mechanistic modelling approaches in order to make more realistic predictions of species’ distributions for scenarios of environmental change. In this thesis, I present two different ways of such linkage. (i) Mechanistic modelling can act as virtual playground for testing statistical models and allows extensive exploration of specific questions. I promote this ‘virtual ecologist’ approach as a powerful evaluation framework for testing sampling protocols, analyses and modelling tools. Also, I employ such an approach to systematically assess the effects of transient dynamics and ecological properties and processes on the prediction accuracy of SDMs for climate change projections. That way, relevant mechanisms are identified that shape the species’ response to altered environmental conditions and which should hence be considered when trying to project species’ distribution through time. (ii) I supplement SDM projections of potential future habitat for black grouse in Switzerland with an individual-based population model. By explicitly considering complex interactions between habitat availability and demographic processes, this allows for a more direct assessment of expected population response to environmental change and associated extinction risks. However, predictions were highly variable across simulations emphasising the need for principal evaluation tools like sensitivity analysis to assess uncertainty and robustness in dynamic range predictions. Furthermore, I identify data coverage of the environmental niche as a likely cause for contrasted range predictions between SDM algorithms. SDMs may fail to make reliable predictions for truncated and edge niches, meaning that portions of the niche are not represented in the data or niche edges coincide with data limits. Overall, my thesis contributes to an improved understanding of uncertainty factors in predictions of range dynamics and presents ways how to deal with these. Finally I provide preliminary guidelines for predictive modelling of dynamic species’ response to environmental change, identify key challenges for future research and discuss emerging developments. N2 - Das Vorkommen von Arten wird zunehmend bedroht durch Klima- und Landnutzungswandel. Robuste Vorhersagen der damit verbundenen Arealveränderungen sind ausschlaggebend für die Erarbeitung dynamischer und nachhaltiger Naturschutzstrategien. Habitateignungsmodelle erstellen statistische Zusammenhänge zwischen dem Vorkommen einer Art und relevanten Umweltvariablen und erlauben zügige Einschätzungen potentieller Arealveränderungen. Dabei werden jedoch transiente Dynamiken weitgehend ignoriert sowie demographische Prozesse und biotische Interaktionen. Daher wurden Vorschläge laut, diese statistischen Modelle mit mechanistischeren Ansätzen zu koppeln. In der vorliegenden Arbeit zeige ich zwei verschiedene Möglichkeiten solcher Kopplung auf. (i) Ich beschreibe den sogenannten ‚Virtuellen Ökologen’-Ansatz als mächtiges Validierungswerkzeug, in dem mechanistische Modelle virtuelle Testflächen bieten zur Erforschung verschiedener Probenahmedesigns oder statistischer Methoden sowie spezifischer Fragestellungen. Auch verwende ich diesen Ansatz, um systematisch zu untersuchen wie sich transiente Dynamiken sowie Arteigenschaften und ökologische Prozesse auf die Vorhersagegüte von Habitateignungsmodellen auswirken. So kann ich entscheidende Prozesse identifizieren welche in zukünftigen Modellen Berücksichtigung finden sollten. (ii) Darauf aufbauend koppele ich Vorhersagen von Habitateignungsmodellen mit einem individuen-basierten Populationsmodell, um die Entwicklung des Schweizer Birkhuhnbestandes unter Klimawandel vorherzusagen. Durch die explizite Berücksichtigung der Wechselwirkungen zwischen Habitat und demographischer Prozesse lassen sich direktere Aussagen über Populationsentwicklung und damit verbundener Extinktionsrisiken treffen. Allerdings führen verschiedene Simulationen auch zu hoher Variabilität zwischen Vorhersagen, was die Bedeutung von Sensitivitätsanalysen unterstreicht, um Unsicherheiten und Robustheit von Vorhersagen einzuschätzen. Außerdem identifiziere ich Restriktionen in der Datenabdeckung des Umweltraumes als möglichen Grund für kontrastierende Vorhersagen verschiedener Habitateignungsmodelle. Wenn die Nische einer Art nicht vollständig durch Daten beschrieben ist, kann dies zu unrealistischen Vorhersagen der Art-Habitat-Beziehung führen. Insgesamt trägt meine Arbeit erheblich bei zu einem besseren Verständnis der Auswirkung verschiedenster Unsicherheitsfaktoren auf Vorhersagen von Arealveränderungen und zeigt Wege auf, mit diesen umzugehen. Abschließend erstelle ich einen vorläufigen Leitfaden für Vorhersagemodelle und identifiziere Kernpunkte für weitere Forschung auf diesem Gebiet. KW - species distribution models KW - dynamic population models KW - climate change KW - prediction KW - uncertainty KW - Habitatmodelle KW - dynamische Populationsmodelle KW - Klimawandel KW - Vorhersage KW - Unsicherheit Y1 - 2011 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-56845 ER - TY - THES A1 - Zhou, Xiangqian T1 - Modeling of spatially distributed nitrate transport to investigate the effects of drought and river restoration in the Bode catchment, Central Germany N2 - The European Water Framework Directive (WFD) has identified river morphological alteration and diffuse pollution as the two main pressures affecting water bodies in Europe at the catchment scale. Consequently, river restoration has become a priority to achieve the WFD's objective of good ecological status. However, little is known about the effects of stream morphological changes, such as re-meandering, on in-stream nitrate retention at the river network scale. Therefore, catchment nitrate modeling is necessary to guide the implementation of spatially targeted and cost-effective mitigation measures. Meanwhile, Germany, like many other regions in central Europe, has experienced consecutive summer droughts from 2015-2018, resulting in significant changes in river nitrate concentrations in various catchments. However, the mechanistic exploration of catchment nitrate responses to changing weather conditions is still lacking. Firstly, a fully distributed, process-based catchment Nitrate model (mHM-Nitrate) was used, which was properly calibrated and comprehensively evaluated at numerous spatially distributed nitrate sampling locations. Three calibration schemes were designed, taking into account land use, stream order, and mean nitrate concentrations, and they varied in spatial coverage but used data from the same period (2011–2019). The model performance for discharge was similar among the three schemes, with Nash-Sutcliffe Efficiency (NSE) scores ranging from 0.88 to 0.92. However, for nitrate concentrations, scheme 2 outperformed schemes 1 and 3 when compared to observed data from eight gauging stations. This was likely because scheme 2 incorporated a diverse range of data, including low discharge values and nitrate concentrations, and thus provided a better representation of within-catchment heterogenous. Therefore, the study suggests that strategically selecting gauging stations that reflect the full range of within-catchment heterogeneity is more important for calibration than simply increasing the number of stations. Secondly, the mHM-Nitrate model was used to reveal the causal relations between sequential droughts and nitrate concentration in the Bode catchment (3200 km2) in central Germany, where stream nitrate concentrations exhibited contrasting trends from upstream to downstream reaches. The model was evaluated using data from six gauging stations, reflecting different levels of runoff components and their associated nitrate-mixing from upstream to downstream. Results indicated that the mHM-Nitrate model reproduced dynamics of daily discharge and nitrate concentration well, with Nash-Sutcliffe Efficiency ≥ 0.73 for discharge and Kling-Gupta Efficiency ≥ 0.50 for nitrate concentration at most stations. Particularly, the spatially contrasting trends of nitrate concentration were successfully captured by the model. The decrease of nitrate concentration in the lowland area in drought years (2015-2018) was presumably due to (1) limited terrestrial export loading (ca. 40% lower than that of normal years 2004-2014), and (2) increased in-stream retention efficiency (20% higher in summer within the whole river network). From a mechanistic modelling perspective, this study provided insights into spatially heterogeneous flow and nitrate dynamics and effects of sequential droughts, which shed light on water-quality responses to future climate change, as droughts are projected to be more frequent. Thirdly, this study investigated the effects of stream restoration via re-meandering on in-stream nitrate retention at network-scale in the well-monitored Bode catchment. The mHM-Nitrate model showed good performance in reproducing daily discharge and nitrate concentrations, with median Kling-Gupta values of 0.78 and 0.74, respectively. The mean and standard deviation of gross nitrate retention efficiency, which accounted for both denitrification and assimilatory uptake, were 5.1 ± 0.61% and 74.7 ± 23.2% in winter and summer, respectively, within the stream network. The study found that in the summer, denitrification rates were about two times higher in lowland sub-catchments dominated by agricultural lands than in mountainous sub-catchments dominated by forested areas, with median ± SD of 204 ± 22.6 and 102 ± 22.1 mg N m-2 d-1, respectively. Similarly, assimilatory uptake rates were approximately five times higher in streams surrounded by lowland agricultural areas than in those in higher-elevation, forested areas, with median ± SD of 200 ± 27.1 and 39.1 ± 8.7 mg N m-2 d-1, respectively. Therefore, restoration strategies targeting lowland agricultural areas may have greater potential for increasing nitrate retention. The study also found that restoring stream sinuosity could increase net nitrate retention efficiency by up to 25.4 ± 5.3%, with greater effects seen in small streams. These results suggest that restoration efforts should consider augmenting stream sinuosity to increase nitrate retention and decrease nitrate concentrations at the catchment scale. N2 - Die Europäische Wasserrahmenrichtlinie hat die morphologischen Veränderungen der Flüsse und die diffuse Verschmutzung als die Hauptprobleme der Gewässer in Europa identifiziert. Um diese Probleme anzugehen, hat die Renaturierung von Fließgewässern hohe Priorität. Es ist jedoch nur wenig darüber bekannt, wie sich Veränderungen der Flussform, wie z. B. die Re-Mäanderung, auf die Nitratrückhaltung im Fließgewässer auswirken. Deutschland hat in den letzten Jahren Dürreperioden erlebt, die zu Veränderungen der Nitratkonzentration in den Fließgewässern geführt haben. Es gibt jedoch nur wenig Erkenntnisse darüber, wie sich diese Dürreperioden auf die Nitratkonzentration auswirken. Zur Untersuchung dieser Einflüsse kann eine Modellierung des Nitrat-Transports und -Rückhalts in Einzugsgebieten wichtige Hinweise zu wirksamen Reduzierungsmaßnahmen liefern. In dieser Studie wurde ein prozessbasiertes hydrologisches Wasserqualitätsmodell (mHM-Nitrate) verwendet, um die Nitratdynamik im Einzugsgebiet der Bode (3200 km2) zu simulieren. Das Modell wurde anhand von Daten aus verschiedenen Teileinzugsgebieten kalibriert und bewertet. Es wurden drei Kalibrierungsvarianten entwickelt, die die Flächennutzung, die Ordnung der Fließgewässer und die mittleren Nitratkonzentrationen mit unterschiedlichem Detaillierungsgrad berücksichtigten. Die Modellierungsgüte für den Abfluss war bei allen drei Kalibrierungsvarianten ähnlich, während sich bei den Nitratkonzentrationen deutliche Unterschiede ergaben. Die Studie zeigte, dass die Auswahl von Messstationen, die die charakteristischen Gebietseigenschaften widerspiegeln, für die Nitrat-Kalibrierung wichtiger ist als die reine Anzahl der Messstationen. Das Modell wurde auch verwendet, um die Beziehung zwischen Dürreperioden und der Nitratdynamik im Bodegebiet zu untersuchen. Das Modell gab die Dynamik von Abfluss und Nitrat sehr gut wider und erfasste hierbei auch die räumlichen Unterschiede in den Nitratkonzentrationen sehr gut. Die Studie ergab, dass Dürreperioden zu niedrigeren Nitratkonzentrationen in den landwirtschaftlich genutzten Gebieten im Tiefland führten, was auf einen geringeren terrestrischen Export und einen erhöhten Rückhalt in den Fließgewässern zurückzuführen war. Die Untersuchung liefert Erkenntnisse über die Auswirkungen von Dürren auf den Nitrataustrag, was für das Verständnis der Auswirkungen des künftigen Klimawandels wichtig ist. Darüber hinaus untersuchte die Studie die Auswirkungen der Renaturierung von Fließgewässern, insbesondere der Re-Mäanderung, auf die Nitratrückhaltung im Fließgewässernetz. Die Untersuchung zeigte, dass der gewässerinterne Nitratrückhalt in landwirtschaftlichen Tieflandgebieten höher war als in bewaldeten Gebieten. Die Wiederherstellung natürlich meandrierender Fließgewässer erhöhte die Nitratretention und verringerte die Nitratkonzentration im Fließgewässer, insbesondere in kleinen Bächen in landwirtschaftlichen Gebieten. Dies deutet darauf hin, dass bei Sanierungsmaßnahmen die Erhöhung der Gewässersinuosität berücksichtigt werden sollte, um den Nitratrückhalt und die Wasserqualität insbesondere in den Teiflandgebieten zu erhöhen. KW - multi-site calibration KW - spatiotemporal validation KW - uncertainty KW - parameter transferability KW - drought KW - catchment hydrology Water quality model KW - river restoration KW - stream sinuosity KW - mHM-Nitrate model KW - stream denitrification KW - assimilatory uptake KW - Kalibrierung an mehreren Standorten KW - räumlich-zeitliche Validierung KW - Ungewissheit KW - Übertragbarkeit der Parameter KW - Dürre KW - Einzugsgebietshydrologie Wasserqualitätsmodell KW - Restaurierung von Flüssen KW - Strömungsneigung KW - mHM-Nitrat-Modell KW - Bachdenitrifikation KW - assimilatorische Aufnahme Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-621059 ER - TY - JOUR A1 - Pathiraja, Sahani Darschika A1 - Leeuwen, Peter Jan van T1 - Multiplicative Non-Gaussian model error estimation in data assimilation JF - Journal of advances in modeling earth systems : JAMES N2 - Model uncertainty quantification is an essential component of effective data assimilation. Model errors associated with sub-grid scale processes are often represented through stochastic parameterizations of the unresolved process. Many existing Stochastic Parameterization schemes are only applicable when knowledge of the true sub-grid scale process or full observations of the coarse scale process are available, which is typically not the case in real applications. We present a methodology for estimating the statistics of sub-grid scale processes for the more realistic case that only partial observations of the coarse scale process are available. Model error realizations are estimated over a training period by minimizing their conditional sum of squared deviations given some informative covariates (e.g., state of the system), constrained by available observations and assuming that the observation errors are smaller than the model errors. From these realizations a conditional probability distribution of additive model errors given these covariates is obtained, allowing for complex non-Gaussian error structures. Random draws from this density are then used in actual ensemble data assimilation experiments. We demonstrate the efficacy of the approach through numerical experiments with the multi-scale Lorenz 96 system using both small and large time scale separations between slow (coarse scale) and fast (fine scale) variables. The resulting error estimates and forecasts obtained with this new method are superior to those from two existing methods. KW - model uncertainty KW - non-Gaussian KW - data-driven KW - uncertainty KW - quantification KW - Lorenz 96 KW - sub-grid scale Y1 - 2022 U6 - https://doi.org/10.1029/2021MS002564 SN - 1942-2466 VL - 14 IS - 4 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Buss, Martin A1 - Kearney, Eric T1 - Navigating the unknown BT - uncertainty moderates the link between visionary leadership, perceived meaningfulness, and turnover intentions JF - Journal of occupational and organizational psychology N2 - Visionary leadership is considered to be one of the most important elements of effective leadership. Among other things, it is related to followers' perceived meaningfulness of their work. However, little is known about whether uncertainty in the workplace affects visionary leadership's effects. Given that uncertainty is rising in many, if not most, workplaces, it is vital to understand whether this development influences the extent to which visionary leadership is associated with followers' perceived meaningfulness. In a two-source, lagged design field study of 258 leader-follower dyads from different settings, we show that uncertainty moderates the relation between visionary leadership and followers' perceived meaningfulness such that this relation is more strongly positive when uncertainty is high, rather than low. Moreover, we show that with increasing uncertainty, visionary leadership is more negatively related to followers' turnover intentions via perceived meaningfulness. This research broadens our understanding of how visionary leadership may be a particularly potent tool in times of increasing uncertainty. KW - follower turnover intentions KW - perceived meaningfulness KW - uncertainty KW - visionary leadership Y1 - 2024 U6 - https://doi.org/10.1111/joop.12500 SN - 0963-1798 SN - 2044-8325 PB - Wiley CY - Hoboken, NJ ER - TY - JOUR A1 - Wagener, Thorsten A1 - Reinecke, Robert A1 - Pianosi, Francesca T1 - On the evaluation of climate change impact models JF - Wiley interdisciplinary reviews : Climate change N2 - In-depth understanding of the potential implications of climate change is required to guide decision- and policy-makers when developing adaptation strategies and designing infrastructure suitable for future conditions. Impact models that translate potential future climate conditions into variables of interest are needed to create the causal connection between a changing climate and its impact for different sectors. Recent surveys suggest that the primary strategy for validating such models (and hence for justifying their use) heavily relies on assessing the accuracy of model simulations by comparing them against historical observations. We argue that such a comparison is necessary and valuable, but not sufficient to achieve a comprehensive evaluation of climate change impact models. We believe that a complementary, largely observation-independent, step of model evaluation is needed to ensure more transparency of model behavior and greater robustness of scenario-based analyses. This step should address the following four questions: (1) Do modeled dominant process controls match our system perception? (2) Is my model's sensitivity to changing forcing as expected? (3) Do modeled decision levers show adequate influence? (4) Can we attribute uncertainty sources throughout the projection horizon? We believe that global sensitivity analysis, with its ability to investigate a model's response to joint variations of multiple inputs in a structured way, offers a coherent approach to address all four questions comprehensively. Such additional model evaluation would strengthen stakeholder confidence in model projections and, therefore, into the adaptation strategies derived with the help of impact models. This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models Assessing Impacts of Climate Change > Evaluating Future Impacts of Climate Change KW - adaptation KW - sensitivity analysis KW - uncertainty KW - validation Y1 - 2022 U6 - https://doi.org/10.1002/wcc.772 SN - 1757-7780 SN - 1757-7799 VL - 13 IS - 3 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - van der Aa, Han A1 - Leopold, Henrik A1 - Weidlich, Matthias T1 - Partial order resolution of event logs for process conformance checking JF - Decision support systems : DSS N2 - While supporting the execution of business processes, information systems record event logs. Conformance checking relies on these logs to analyze whether the recorded behavior of a process conforms to the behavior of a normative specification. A key assumption of existing conformance checking techniques, however, is that all events are associated with timestamps that allow to infer a total order of events per process instance. Unfortunately, this assumption is often violated in practice. Due to synchronization issues, manual event recordings, or data corruption, events are only partially ordered. In this paper, we put forward the problem of partial order resolution of event logs to close this gap. It refers to the construction of a probability distribution over all possible total orders of events of an instance. To cope with the order uncertainty in real-world data, we present several estimators for this task, incorporating different notions of behavioral abstraction. Moreover, to reduce the runtime of conformance checking based on partial order resolution, we introduce an approximation method that comes with a bounded error in terms of accuracy. Our experiments with real-world and synthetic data reveal that our approach improves accuracy over the state-of-the-art considerably. KW - process mining KW - conformance checking KW - partial order resolution KW - data KW - uncertainty Y1 - 2020 U6 - https://doi.org/10.1016/j.dss.2020.113347 SN - 0167-9236 SN - 1873-5797 VL - 136 PB - Elsevier CY - Amsterdam [u.a.] ER - TY - THES A1 - Kox, Thomas T1 - Perception and use of uncertainty in severe weather warnings T1 - Wahrnehmung und Verwendung von Unsicherheit in Wetterwarnungen N2 - Uncertainty is an essential part of atmospheric processes and thus inherent to weather forecasts. Nevertheless, weather forecasts and warnings are still predominately issued as deterministic (yes or no) forecasts, although research suggests that providing weather forecast users with additional information about the forecast uncertainty can enhance the preparation of mitigation measures. Communicating forecast uncertainty would allow for a provision of information on possible future events at an earlier time. The desired benefit is to enable the users to start with preparatory protective action at an earlier stage of time based on the their own risk assessment and decision threshold. But not all users have the same threshold for taking action. In the course of the project WEXICOM (‘Wetterwarnungen: Von der Extremereignis-Information zu Kommunikation und Handlung’) funded by the Deutscher Wetterdienst (DWD), three studies were conducted between the years 2012 and 2016 to reveal how weather forecasts and warnings are reflected in weather-related decision-making. The studies asked which factors influence the perception of forecasts and the decision to take protective action and how forecast users make sense of probabilistic information and the additional lead time. In a first exploratory study conducted in 2012, members of emergency services in Germany were asked questions about how weather warnings are communicated to professional endusers in the emergency community and how the warnings are converted into mitigation measures. A large number of open questions were selected to identify new topics of interest. The questions covered topics like users’ confidence in forecasts, their understanding of probabilistic information as well as their lead time and decision thresholds to start with preparatory mitigation measures. Results show that emergency service personnel generally have a good sense of uncertainty inherent in weather forecasts. Although no single probability threshold could be identified for organisations to start with preparatory mitigation measures, it became clear that emergency services tend to avoid forecasts based on low probabilities as a basis for their decisions. Based on this findings, a second study conducted with residents of Berlin in 2014 further investigated the question of decision thresholds. The survey questions related to the topics of the perception of and prior experience with severe weather, trustworthiness of forecasters and confidence in weather forecasts, and socio-demographic and social-economic characteristics. Within the questionnaire a scenario was created to determine individual decision thresholds and see whether subgroups of the sample lead to different thresholds. The results show that people’s willingness to act tends to be higher and decision thresholds tend to be lower if the expected weather event is more severe or the property at risk is of higher value. Several influencing factors of risk perception have significant effects such as education, housing status and ability to act, whereas socio-demographic determinants alone are often not sufficient to fully grasp risk perception and protection behaviour. Parallel to the quantitative studies, an interview study was conducted with 27 members of German civil protection between 2012 and 2016. The results show that the latest developments in (numerical) weather forecasting do not necessarily fit the current practice of German emergency services. These practices are mostly carried out on alarms and ground truth in a reactive manner rather than on anticipation based on prognosis or forecasts. As the potential consequences rather than the event characteristics determine protective action, the findings support the call and need for impact-based warnings. Forecasters will rely on impact data and need to learn the users’ understanding of impact. Therefore, it is recommended to enhance weather communication not only by improving computer models and observation tools, but also by focusing on the aspects of communication and collaboration. Using information about uncertainty demands awareness about and acceptance of the limits of knowledge, hence, the capabilities of the forecaster to anticipate future developments of the atmosphere and the capabilities of the user to make sense of this information. N2 - Obwohl atmosphärische Prozesse wesentlich durch Unsicherheit gekennzeichnet sind, werden Wettervorhersagen und Warnungen in Deutschland überwiegend noch als deterministische (ja oder nein) Vorhersagen herausgegeben. Dagegen legen jüngere Forschungsergebnisse nahe, dass durch die frühzeitige Bereitstellung von Informationen über die Vorhersageunsicherheit, die Vorbereitung von Vorsorgemaßnahmen verbessert werden kann. Der gewünschte Vorteil bestünde darin, es der Empfängerin und dem Empfänger zu ermöglichen auf Grundlage der eigenen Risikobewertung zu einem möglichen früheren Zeitpunkt mit Schutzmaßnahmen zu beginnen. Offen ist dabei, ob diese mit probabilistischen Wettervorhersagen, der Unsicherheit und den Implikationen durch die zusätzliche Vorlaufzeit umgehen können, wie sich Vorhersagen und Warnungen in wetterbezogenen Entscheidungen widerspiegeln und welche Einflussfaktoren auf die Entscheidung wirken. Im Rahmen des vom Deutschen Wetterdienst (DWD) geförderten Projektes WEXICOM (Wetterwarnungen: Von der Extremereignis-Information zu Kommunikation und Handlung) wurden dazu zwischen den Jahren 2012 und 2016 drei Studien durchgeführt. In einer ersten explorativen Studie wurden Vertreterinnen und Vertreter des deutschen Bevölkerungsschutzes gefragt, wieWetterwarnungen an sie kommuniziert werden und wie sie diese Informationen in Maßnahmen umsetzten. Die Studie bestand aus mehreren offenen Fragen, um möglichst viele neue Themen zu identifizieren. Thematische Schwerpunkte waren das Vertrauen in Wettervorhersagen, das Verständnis von Wahrscheinlichkeiten und die benötigte Vorlaufzeit für vorbereitende Maßnahmen. Die Ergebnisse zeigen, dass die Befragten im Allgemeinen die Unsicherheit in Wettervorhersagen gut einschätzen. Obwohl kein eindeutiger Wahrscheinlichkeitswert identifiziert werden konnte, bei dem sie mit vorbereitenden Maßnahmen beginnen würden, wurde deutlich, dass Feuerwehren und Rettungsdienste es vermeiden ihre Entscheidungen auf Grund von Vorhersagen mit niedriger Eintrittswahrscheinlichkeit zu treffen. In einer zweiten Fragebogenstudie wurde das Thema der Schwellenwerte für Entscheidungen mit Bewohnern von Berlin weiter untersucht. Es wurde geprüft, welche mögliche Faktoren die individuellen Wahrscheinlichkeitswerte beeinflussen. Der Fragebogen beinhaltete dafür ein fiktives Entscheidungs-Szenario und weitere Fragen zur Wahrnehmung und vorherigen Erfahrung mit Unwetter, der Einschätzung der Vertrauenswürdigkeit von Forecastern und das Vertrauen inWettervorhersagen sowie soziodemografische und sozioökonomische Merkmale. Die Ergebnisse zeigen, dass die Handlungsbereitschaft tendenziell höher ist und die Schwellenwerte tendenziell niedriger ausfallen, wenn das erwartete Wetterereignis schwerwiegender ist oder das gefährdete Eigentum einen höheren Wert hat. Mehrere Faktoren beeinflussen die Risikowahrnehmung und den Schwellenwert. Darunter Bildung, Wohnstatus und Selbstwirksamkeitserwartung. Wohingegen soziodemografische Merkmale nicht ausreichen, um Risikowahrnehmung und Schutzverhalten vollständig zu erfassen. Parallel zu den quantitativen Studien wurde eine Interviewstudie mit 27 Vertreterinnen und Vertretern des deutschen Bevölkerungsschutzes durchgeführt. Die Ergebnisse zeigen, dass die technologischen Entwicklungen durch die numerische Wettervorhersage nicht zwangsläufig durch die derzeitige Praxis des deutschen Bevölkerungsschutzes aufgegriffen werden können. Dieser reagiert zumeist auf Grundlage von Alarmen und Bestätigungen durch Lagebilder von Ort, anstatt auf der Grundlage von (Wetter-) Vorhersagen zu arbeiten. Die Ergebnisse zeigen insgesamt, dass letztendlich die Konsequenzen von Wetter und nicht das Wetterereignis an sich die Handlungen prägen. Impact-basierende Warnungen können hier aufgrund der Praxisnähe eine hilfreiche Unterstützung für einige Empfängergruppen darstellen. Für diese Form der Warnung benötigen die Wetterdienste Daten zu den Auswirkungen von Wetter. Notwendig dafür ist eine verbesserte Kommunikation und Zusammenarbeit zwischen Wetterdiensten und professionellen Empfängergruppen. So erlernen die Wetterdienste die Bedeutung von Auswirkungen für den einzelnen Empfänger und unterstützen gleichzeitig das Verständnis von numerischen Wettervorhersagen. Die Kommunikation von Vorhersageunsicherheiten erfordert grundlegend ein Bewusstsein und Verständnis über die Grenzen des Wissens und somit über die Fähigkeiten der Forecaster, zukünftige Entwicklungen der Atmosphäre vorherzusagen und über die Fähigkeiten der Empfängerinnen und Empfänger, diese Informationen zu verstehen und umzusetzen. KW - uncertainty KW - risk KW - warning KW - Unsicherheit KW - Risiko KW - Warnung Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-411541 ER - TY - THES A1 - Schaber, Jörg T1 - Phenology in Germany in the 20th century : methods, analyses and models N2 - Die Länge der Vegetationsperiode (VP) spielt eine zentrale Rolle für die interannuelle Variation der Kohlenstoffspeicherung terrestrischer Ökosysteme. Die Analyse von Beobachtungsdaten hat gezeigt, dass sich die VP in den letzten Jahrzehnten in den nördlichen Breiten verlängert hat. Dieses Phänomen wurde oft im Zusammenhang mit der globalen Erwärmung diskutiert, da die Phänologie von der Temperatur beeinflusst wird. Die Analyse der Pflanzenphänologie in Süddeutschland im 20. Jahrhundert zeigte: - Die starke Verfrühung der Frühjahrsphasen in dem Jahrzehnt vor 1999 war kein singuläres Ereignis im 20. Jahrhundert. Schon in früheren Dekaden gab es ähnliche Trends. Es konnten Perioden mit unterschiedlichem Trendverhalten identifiziert werden. - Es gab deutliche Unterschiede in den Trends von frühen und späten Frühjahrsphasen. Die frühen Frühjahrsphasen haben sich stetig verfrüht, mit deutlicher Verfrühung zwischen 1931 und 1948, moderater Verfrühung zwischen 1948 und 1984 und starker Verfrühung zwischen 1984 und 1999. Die späten Frühjahrsphasen hingegen, wechselten ihr Trendverhalten in diesen Perioden von einer Verfrühung zu einer deutlichen Verspätung wieder zu einer starken Verfrühung. Dieser Unterschied in der Trendentwicklung zwischen frühen und späten Frühjahrsphasen konnte auch für ganz Deutschland in den Perioden 1951 bis 1984 und 1984 bis 1999 beobachtet werden. Der bestimmende Einfluss der Temperatur auf die Frühjahrsphasen und ihr modifizierender Einfluss auf die Herbstphasen konnte bestätigt werden. Es zeigt sich jedoch, dass - die Phänologie bestimmende Funktionen der Temperatur nicht mit einem globalen jährlichen CO2 Signal korreliert waren, welches als Index für die globale Erwärmung verwendet wurde - ein Index für grossräumige regionale Zirkulationsmuster (NAO-Index) nur zu einem kleinen Teil die beobachtete phänologischen Variabilität erklären konnte. Das beobachtete unterschiedliche Trendverhalten zwischen frühen und späten Frühjahrsphasen konnte auf die unterschiedliche Entwicklung von März- und Apriltemperaturen zurückgeführt werden. Während sich die Märztemperaturen im Laufe des 20. Jahrhunderts mit einer zunehmenden Variabilität in den letzten 50 Jahren stetig erhöht haben, haben sich die Apriltemperaturen zwischen dem Ende der 1940er und Mitte der 1980er merklich abgekühlt und dann wieder deutlich erwärmt. Es wurde geschlussfolgert, dass die Verfrühungen in der Frühjahrsphänologie in den letzten Dekaden Teile multi-dekadischer Fluktuationen sind, welche sich nach Spezies und relevanter saisonaler Temperatur unterscheiden. Aufgrund dieser Fluktuationen konnte kein Zusammenhang mit einem globalen Erwärmungsignal gefunden werden. Im Durchschnitt haben sich alle betrachteten Frühjahrsphasen zwischen 1951 und 1999 in Naturräumen in Deutschland zwischen 5 und 20 Tagen verfrüht. Ein starker Unterschied in der Verfrühung zwischen frühen und späten Frühjahrsphasen liegt an deren erwähntem unterschiedlichen Verhalten. Die Blattverfärbung hat sich zwischen 1951 und 1999 für alle Spezies verspätet, aber nach 1984 im Durchschnitt verfrüht. Die VP hat sich in Deutschland zwischen 1951 und 1999 um ca. 10 Tage verlängert. Es ist hauptsächlich die Änderung in den Frühjahrphasen, die zu einer Änderung in der potentiell absorbierten Strahlung (PAS) führt. Darüber hinaus sind es die späten Frühjahrsphasen, die pro Tag Verfrühung stärker profitieren, da die zusätzlichen Tage länger undwärmer sind als dies für die frühen Phasen der Fall ist. Um die relative Änderung in PAS im Vergleich der Spezies abzuschätzen, müssen allerdings auch die Veränderungen in den Herbstphasen berücksichtigt werden. Der deutliche Unterschied zwischen frühen und späten Frühjahrsphasen konnte durch die Anwendung einer neuen Methode zur Konstruktion von Zeitreihen herausgearbeitet werden. Der neue methodische Ansatz erlaubte die Ableitung verlässlicher 100-jähriger Zeitreihen und die Konstruktion von lokalen kombinierten Zeitreihen, welche die Datenverfügbarkeit für die Modellentwicklung erhöhten. Ausser analysierten Protokollierungsfehlern wurden mikroklimatische, genetische und Beobachtereinflüsse als Quellen von Unsicherheit in phänologischen Daten identifiziert. Phänologischen Beobachtungen eines Ortes können schätzungsweise 24 Tage um das parametrische Mittel schwanken.Dies unterstützt die 30-Tage Regel für die Detektion von Ausreissern. Neue Phänologiemodelle, die den Blattaustrieb aus täglichen Temperaturreihen simulieren, wurden entwickelt. Diese Modelle basieren auf einfachen Interaktionen zwischen aktivierenden und hemmenden Substanzen, welche die Entwicklungsstadien einer Pflanze bestimmen. Im Allgemeinen konnten die neuen Modelle die Beobachtungsdaten besser simulieren als die klassischen Modelle. Weitere Hauptresultate waren: - Der Bias der klassischen Modelle, d.h. Überschätzung von frühen und Unterschätzung von späten Beobachtungen, konnte reduziert, aber nicht vollständig eliminiert werden. - Die besten Modellvarianten für verschiedene Spezies wiesen darauf hin, dass für die späten Frühjahrsphasen die Tageslänge eine wichtigere Rolle spielt als für die frühen Phasen. - Die Vernalisation spielte gegenüber den Temperaturen kurz vor dem Blattaustrieb nur eine untergeordnete Rolle. N2 - The length of the vegetation period (VP) plays a central role for the interannual variation of carbon fixation of terrestrial ecosystems. Observational data analysis has indicated that the length of the VP has increased in the last decades in the northern latitudes mainly due to an advancement of bud burst (BB). This phenomenon has been widely discussed in the context of Global Warming because phenology is correlated to temperatures. Analyzing the patterns of spring phenology over the last century in Southern Germany provided two main findings: - The strong advancement of spring phases especially in the decade before 1999 is not a singular event in the course of the 20th century. Similar trends were also observed in earlier decades. Distinct periods of varying trend behavior for important spring phases could be distinguished. - Marked differences in trend behavior between the early and late spring phases were detected. Early spring phases changed as regards the magnitude of their negative trends from strong negative trends between 1931 and 1948 to moderate negative trends between 1948 and 1984 and back to strong negative trends between 1984 and 1999. Late spring phases showed a different behavior. Negative trends between 1931 and 1948 are followed by marked positive trends between 1948 and 1984 and then strong negative trends between 1984 and 1999. This marked difference in trend development between early and late spring phases was also found all over Germany for the two periods 1951 to 1984 and 1984 to 1999. The dominating influence of temperature on spring phenology and its modifying effect on autumn phenology was confirmed in this thesis. However, - temperature functions determining spring phenology were not significantly correlated with a global annual CO2 signal which was taken as a proxy for a Global Warming pattern. - an index for large scale regional circulation patterns (NAO index) could only to a small part explain the observed phenological variability in spring. The observed different trend behavior of early and late spring phases is explained by the differing behavior of mean March and April temperatures. Mean March temperatures have increased on average over the 20th century accompanied by an increasing variation in the last 50 years. April temperatures, however, decreased between the end of the 1940s and the mid-1980s, followed by a marked warming after the mid-1980s. It can be concluded that the advancement of spring phenology in recent decades are part of multi-decadal fluctuations over the 20th century that vary with the species and the relevant seasonal temperatures. Because of these fluctuations a correlation with an observed Global Warming signal could not be found. On average all investigated spring phases advanced between 5 and 20 days between 1951 and 1999 for all Natural Regions in Germany. A marked difference be! tween late and early spring phases is due to the above mentioned differing behavior before and after the mid-1980s. Leaf coloring (LC) was delayed between 1951 and 1984 for all tree species. However, after 1984 LC was advanced. Length of the VP increased between 1951 and 1999 for all considered tree species by an average of ten days throughout Germany. It is predominately the change in spring phases which contributes to a change in the potentially absorbed radiation. Additionally, it is the late spring species that are relatively more favored by an advanced BB because they can additionally exploit longer days and higher temperatures per day advancement. To assess the relative change in potentially absorbed radiation among species, changes in both spring and autumn phenology have to be considered as well as where these changes are located in the year. For the detection of the marked difference between early and late spring phenology a new time series construction method was developed. This method allowed the derivation of reliable time series that spanned over 100 years and the construction of locally combined time series increasing the available data for model development. Apart from analyzed protocolling errors, microclimatic site influences, genetic variation and the observers were identified as sources of uncertainty of phenological observational data. It was concluded that 99% of all phenological observations at a certain site will vary within approximately 24 days around the parametric mean. This supports to the proposed 30-day rule to detect outliers. New phenology models that predict local BB from daily temperature time series were developed. These models were based on simple interactions between inhibitory and promotory agents that are assumed to control the developmental status of a plant. Apart from the fact that, in general, the new models fitted and predicted the observations better than classical models, the main modeling results were: - The bias of the classical models, i.e. overestimation of early observations and underestimation of late observations, could be reduced but not completely removed. - The different favored model structures for each species indicated that for the late spring phases photoperiod played a more dominant role than for early spring phases. - Chilling only plays a subordinate role for spring BB compared to temperatures directly preceding BB. KW - Phänologie KW - kombinierte Zeitreihen KW - Physiologie-basierte Modelle KW - Unsicherheit KW - Variabilität KW - Ausreisser KW - Trends KW - Naturräume KW - Trendwendepunkte KW - Fag KW - phenology KW - combined time series KW - physiology-based models KW - uncertainty KW - variability KW - outliers KW - trends KW - Natural Regions KW - trend turning points KW - Fagus syl Y1 - 2002 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-0000532 ER - TY - THES A1 - Rözer, Viktor T1 - Pluvial flood loss to private households T1 - Schäden durch urbane Sturzfluten in Privathaushalten N2 - Today, more than half of the world’s population lives in urban areas. With a high density of population and assets, urban areas are not only the economic, cultural and social hubs of every society, they are also highly susceptible to natural disasters. As a consequence of rising sea levels and an expected increase in extreme weather events caused by a changing climate in combination with growing cities, flooding is an increasing threat to many urban agglomerations around the globe. To mitigate the destructive consequences of flooding, appropriate risk management and adaptation strategies are required. So far, flood risk management in urban areas is almost exclusively focused on managing river and coastal flooding. Often overlooked is the risk from small-scale rainfall-triggered flooding, where the rainfall intensity of rainstorms exceeds the capacity of urban drainage systems, leading to immediate flooding. Referred to as pluvial flooding, this flood type exclusive to urban areas has caused severe losses in cities around the world. Without further intervention, losses from pluvial flooding are expected to increase in many urban areas due to an increase of impervious surfaces compounded with an aging drainage infrastructure and a projected increase in heavy precipitation events. While this requires the integration of pluvial flood risk into risk management plans, so far little is known about the adverse consequences of pluvial flooding due to a lack of both detailed data sets and studies on pluvial flood impacts. As a consequence, methods for reliably estimating pluvial flood losses, needed for pluvial flood risk assessment, are still missing. Therefore, this thesis investigates how pluvial flood losses to private households can be reliably estimated, based on an improved understanding of the drivers of pluvial flood loss. For this purpose, detailed data from pluvial flood-affected households was collected through structured telephone- and web-surveys following pluvial flood events in Germany and the Netherlands. Pluvial flood losses to households are the result of complex interactions between impact characteristics such as the water depth and a household’s resistance as determined by its risk awareness, preparedness, emergency response, building properties and other influencing factors. Both exploratory analysis and machine-learning approaches were used to analyze differences in resistance and impacts between households and their effects on the resulting losses. The comparison of case studies showed that the awareness around pluvial flooding among private households is quite low. Low awareness not only challenges the effective dissemination of early warnings, but was also found to influence the implementation of private precautionary measures. The latter were predominately implemented by households with previous experience of pluvial flooding. Even cases where previous flood events affected a different part of the same city did not lead to an increase in preparedness of the surveyed households, highlighting the need to account for small-scale variability in both impact and resistance parameters when assessing pluvial flood risk. While it was concluded that the combination of low awareness, ineffective early warning and the fact that only a minority of buildings were adapted to pluvial flooding impaired the coping capacities of private households, the often low water levels still enabled households to mitigate or even prevent losses through a timely and effective emergency response. These findings were confirmed by the detection of loss-influencing variables, showing that cases in which households were able to prevent any loss to the building structure are predominately explained by resistance variables such as the household’s risk awareness, while the degree of loss is mainly explained by impact variables. Based on the important loss-influencing variables detected, different flood loss models were developed. Similar to flood loss models for river floods, the empirical data from the preceding data collection was used to train flood loss models describing the relationship between impact and resistance parameters and the resulting loss to building structures. Different approaches were adapted from river flood loss models using both models with the water depth as only predictor for building structure loss and models incorporating additional variables from the preceding variable detection routine. The high predictive errors of all compared models showed that point predictions are not suitable for estimating losses on the building level, as they severely impair the reliability of the estimates. For that reason, a new probabilistic framework based on Bayesian inference was introduced that is able to provide predictive distributions instead of single loss estimates. These distributions not only give a range of probable losses, they also provide information on how likely a specific loss value is, representing the uncertainty in the loss estimate. Using probabilistic loss models, it was found that the certainty and reliability of a loss estimate on the building level is not only determined by the use of additional predictors as shown in previous studies, but also by the choice of response distribution defining the shape of the predictive distribution. Here, a mix between a beta and a Bernoulli distribution to account for households that are able to prevent losses to their building’s structure was found to provide significantly more certain and reliable estimates than previous approaches using Gaussian or non-parametric response distributions. The successful model transfer and post-event application to estimate building structure loss in Houston, TX, caused by pluvial flooding during Hurricane Harvey confirmed previous findings, and demonstrated the potential of the newly developed multi-variable beta model for future risk assessments. The highly detailed input data set constructed from openly available data sources containing over 304,000 affected buildings in Harris County further showed the potential of data-driven, building-level loss models for pluvial flood risk assessment. In conclusion, pluvial flood losses to private households are the result of complex interactions between impact and resistance variables, which should be represented in loss models. The local occurrence of pluvial floods requires loss estimates on high spatial resolutions, i.e. on the building level, where losses are variable and uncertainties are high. Therefore, probabilistic loss estimates describing the uncertainty of the estimate should be used instead of point predictions. While the performance of probabilistic models on the building level are mainly driven by the choice of response distribution, multi-variable models are recommended for two reasons: First, additional resistance variables improve the detection of cases in which households were able to prevent structural losses. Second, the added variability of additional predictors provides a better representation of the uncertainties when loss estimates from multiple buildings are aggregated. This leads to the conclusion that data-driven probabilistic loss models on the building level allow for a reliable loss estimation at an unprecedented level of detail, with a consistent quantification of uncertainties on all aggregation levels. This makes the presented approach suitable for a wide range of applications, from decision support in spatial planning to impact- based early warning systems. N2 - Über die Hälfte der Weltbevölkerung lebt heute in Städten. Mit einer hohen Dichte an Menschen, Gütern und Gebäuden sind Städte nicht nur die wirtschaftlichen, politischen und kulturellen Zentren einer Gesellschaft, sondern auch besonders anfällig gegenüber Naturkatastrophen. Insbesondere Hochwasser und Überflutungen stellen in Folge von steigenden Meeresspiegeln und einer erwarteten Zunahme von Extremwettereignissen eine wachsende Bedrohung in vielen Regionen dar. Um die möglichen Folgen dieser Entwicklung zu vermeiden bzw. zu reduzieren, ist es notwendig sich der steigenden Gefahr durch geeignete Maßnahmen anzupassen. Bisher ist der Hochwasserschutz in Städten beinahe ausschließlich auf Überflutungen durch Flusshochwasser oder Sturmfluten fokussiert. Dabei werden sogenannte urbane Sturzfluten, die in den letzten Jahren vermehrt zu hohen Schäden in Städten geführt haben, nicht berücksichtigt. Bei urbanen Sturzfluten führen lokale Starkniederschläge mit hohen Regenmengen zu einer Überlastung des städtischen Abwassersystems und damit zu einer direkten, oft kleinräumigen Überflutung innerhalb eines bebauten Gebiets. Mit einer prognostizierten Zunahme von Starkniederschlägen, sowie einer baulichen Verdichtung und damit einhergehender Flächenversiegelung in vielen Städten, ist mit einer Zunahme von urbanen Sturzfluten zu rechnen. Dies verlangt die Einbindung des Risikos durch urbane Sturzfluten in bestehende Hochwasserschutzkonzepte. Bisher fehlen allerdings sowohl detaillierte Daten als auch Methoden um das Risiko durch urbane Sturzfluten und die dadurch verursachten Schäden, etwa an Wohngebäuden, zuverlässig abzuschätzen. Aus diesem Grund beschäftigt sich diese Arbeit hauptsächlich mit der Entwicklung von Verfahren und Modellen zur Abschätzung von Schäden an Privathaushalten durch urbane Sturzfluten. Dazu wurden detaillierte Daten durch Telefon- und Online-Umfragen nach urbanen Sturzflutereignissen in Deutschland und in den Niederlanden erhoben und ausgewertet. Die Erkenntnisse aus den detaillierten Analysen zu Vorsorge, Notmaßnahmen und Wiederherstellung, vor, während und nach urbanen Sturzflutereignissen, wurden genutzt um eine neue Methode zur Schätzung von Schäden an Wohngebäuden zu entwickeln. Dabei werden neben Angaben wie Dauer und Höhe der Überflutung, auch Eigenschaften von Haushalten, wie etwa deren Risikobewusstsein, in die Schätzung miteinbezogen. Nach lokaler Validierung wurde die neuentwickelte Methode beispielhaft zur Schätzung von Wohngebäudeschäden nach einem urbanen Sturzflutereignis im Großraum Houston (Texas, USA) erfolgreich angewendet. Anders als bei bisherigen Ansätzen wird der geschätzte Schaden eines Wohngebäudes nicht als einzelner Wert angegeben, sondern als Verteilung, welche die Bandbreite möglicher Schäden und deren Wahrscheinlichkeit angibt. Damit konnte die Zuverlässigkeit von Schadensschätzungen im Vergleich zu bisherigen Verfahren erheblich verbessert werden. Durch die erfolgreiche Anwendung sowohl auf der Ebene einzelner Gebäude als auch für gesamte Städte, ergibt sich ein breites Spektrum an Nutzungsmöglichkeiten, etwa als Entscheidungsunterstützung in der Stadtplanung oder für eine verbesserte Frühwarnung vor urbanen Sturzfluten. KW - Schadensmodellierung KW - Unsicherheiten KW - Starkregen KW - Privathaushalte KW - damage modeling KW - economic impacts KW - uncertainty KW - private households KW - probabilistic KW - pluvial flooding Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-429910 ER - TY - JOUR A1 - Kreibich, Heidi A1 - Botto, Anna A1 - Merz, Bruno A1 - Schröter, Kai T1 - Probabilistic, Multivariable Flood Loss Modeling on the Mesoscale with BT-FLEMO JF - Risk analysis N2 - Flood loss modeling is an important component for risk analyses and decision support in flood risk management. Commonly, flood loss models describe complex damaging processes by simple, deterministic approaches like depth-damage functions and are associated with large uncertainty. To improve flood loss estimation and to provide quantitative information about the uncertainty associated with loss modeling, a probabilistic, multivariable Bagging decision Tree Flood Loss Estimation MOdel (BT-FLEMO) for residential buildings was developed. The application of BT-FLEMO provides a probability distribution of estimated losses to residential buildings per municipality. BT-FLEMO was applied and validated at the mesoscale in 19 municipalities that were affected during the 2002 flood by the River Mulde in Saxony, Germany. Validation was undertaken on the one hand via a comparison with six deterministic loss models, including both depth-damage functions and multivariable models. On the other hand, the results were compared with official loss data. BT-FLEMO outperforms deterministic, univariable, and multivariable models with regard to model accuracy, although the prediction uncertainty remains high. An important advantage of BT-FLEMO is the quantification of prediction uncertainty. The probability distribution of loss estimates by BT-FLEMO well represents the variation range of loss estimates of the other models in the case study. KW - Damage modeling KW - multiparameter KW - probabilistic KW - uncertainty KW - validation Y1 - 2016 U6 - https://doi.org/10.1111/risa.12650 SN - 0272-4332 SN - 1539-6924 VL - 37 IS - 4 SP - 774 EP - 787 PB - Wiley CY - Hoboken ER - TY - THES A1 - Sieg, Tobias T1 - Reliability of flood damage estimations across spatial scales T1 - Verlässlichkeit von Hochwasserschadensschätzungen über räumliche Skalen N2 - Extreme Naturereignisse sind ein integraler Bestandteil der Natur der Erde. Sie werden erst dann zu Gefahren für die Gesellschaft, wenn sie diesen Ereignissen ausgesetzt ist. Dann allerdings können Naturgefahren verheerende Folgen für die Gesellschaft haben. Besonders hydro-meteorologische Gefahren wie zum Beispiel Flusshochwasser, Starkregenereignisse, Winterstürme, Orkane oder Tornados haben ein hohes Schadenspotential und treten rund um den Globus auf. Einhergehend mit einer immer wärmer werdenden Welt, werden auch Extremwetterereignisse, welche potentiell Naturgefahren auslösen können, immer wahrscheinlicher. Allerdings trägt nicht nur eine sich verändernde Umwelt zur Erhöhung des Risikos von Naturgefahren bei, sondern auch eine sich verändernde Gesellschaft. Daher ist ein angemessenes Risikomanagement erforderlich um die Gesellschaft auf jeder räumlichen Ebene an diese Veränderungen anzupassen. Ein essentieller Bestandteil dieses Managements ist die Abschätzung der ökonomischen Auswirkungen der Naturgefahren. Bisher allerdings fehlen verlässliche Methoden um die Auswirkungen von hydro-meteorologischen Gefahren abzuschätzen. Ein Hauptbestandteil dieser Arbeit ist daher die Entwicklung und Anwendung einer neuen Methode, welche die Verlässlichkeit der Schadensschätzung verbessert. Die Methode wurde beispielhaft zur Schätzung der ökonomischen Auswirkungen eines Flusshochwassers auf einzelne Unternehmen bis hin zu den Auswirkungen auf das gesamte Wirtschaftssystem Deutschlands erfolgreich angewendet. Bestehende Methoden geben meist wenig Information über die Verlässlichkeit ihrer Schätzungen. Da diese Informationen Entscheidungen zur Anpassung an das Risiko erleichtern, wird die Verlässlichkeit der Schadensschätzungen mit der neuen Methode dargestellt. Die Verlässlichkeit bezieht sich dabei nicht nur auf die Schadensschätzung selber, sondern auch auf die Annahmen, die über betroffene Gebäude gemacht werden. Nach diesem Prinzip kann auch die Verlässlichkeit von Annahmen über die Zukunft dargestellt werden, dies ist ein wesentlicher Aspekt für Prognosen. Die Darstellung der Verlässlichkeit und die erfolgreiche Anwendung zeigt das Potential der Methode zur Verwendung von Analysen für gegenwärtige und zukünftige hydro-meteorologische Gefahren. N2 - Natural extreme events are an integral part of nature on planet earth. Usually these events are only considered hazardous to humans, in case they are exposed. In this case, however, natural hazards can have devastating impacts on human societies. Especially hydro-meteorological hazards have a high damage potential in form of e.g. riverine and pluvial floods, winter storms, hurricanes and tornadoes, which can occur all over the globe. Along with an increasingly warm climate also an increase in extreme weather which potentially triggers natural hazards can be expected. Yet, not only changing natural systems, but also changing societal systems contribute to an increasing risk associated with these hazards. These can comprise increasing exposure and possibly also increasing vulnerability to the impacts of natural events. Thus, appropriate risk management is required to adapt all parts of society to existing and upcoming risks at various spatial scales. One essential part of risk management is the risk assessment including the estimation of the economic impacts. However, reliable methods for the estimation of economic impacts due to hydro-meteorological hazards are still missing. Therefore, this thesis deals with the question of how the reliability of hazard damage estimates can be improved, represented and propagated across all spatial scales. This question is investigated using the specific example of economic impacts to companies as a result of riverine floods in Germany. Flood damage models aim to describe the damage processes during a given flood event. In other words they describe the vulnerability of a specific object to a flood. The models can be based on empirical data sets collected after flood events. In this thesis tree-based models trained with survey data are used for the estimation of direct economic flood impacts on the objects. It is found that these machine learning models, in conjunction with increasing sizes of data sets used to derive the models, outperform state-of-the-art damage models. However, despite the performance improvements induced by using multiple variables and more data points, large prediction errors remain at the object level. The occurrence of the high errors was explained by a further investigation using distributions derived from tree-based models. The investigation showed that direct economic impacts to individual objects cannot be modeled by a normal distribution. Yet, most state-of-the-art approaches assume a normal distribution and take mean values as point estimators. Subsequently, the predictions are unlikely values within the distributions resulting in high errors. At larger spatial scales more objects are considered for the damage estimation. This leads to a better fit of the damage estimates to a normal distribution. Consequently, also the performance of the point estimators get better, although large errors can still occur due to the variance of the normal distribution. It is recommended to use distributions instead of point estimates in order to represent the reliability of damage estimates. In addition current approaches also mostly ignore the uncertainty associated with the characteristics of the hazard and the exposed objects. For a given flood event e.g. the estimation of the water level at a certain building is prone to uncertainties. Current approaches define exposed objects mostly by the use of land use data sets. These data sets often show inconsistencies, which introduce additional uncertainties. Furthermore, state-of-the-art approaches also imply problems of missing consistency when predicting the damage at different spatial scales. This is due to the use of different types of exposure data sets for model derivation and application. In order to face these issues a novel object-based method was developed in this thesis. The method enables a seamless estimation of hydro-meteorological hazard damage across spatial scales including uncertainty quantification. The application and validation of the method resulted in plausible estimations at all spatial scales without overestimating the uncertainty. Mainly newly available data sets containing individual buildings make the application of the method possible as they allow for the identification of flood affected objects by overlaying the data sets with water masks. However, the identification of affected objects with two different water masks revealed huge differences in the number of identified objects. Thus, more effort is needed for their identification, since the number of objects affected determines the order of magnitude of the economic flood impacts to a large extent. In general the method represents the uncertainties associated with the three components of risk namely hazard, exposure and vulnerability, in form of probability distributions. The object-based approach enables a consistent propagation of these uncertainties in space. Aside from the propagation of damage estimates and their uncertainties across spatial scales, a propagation between models estimating direct and indirect economic impacts was demonstrated. This enables the inclusion of uncertainties associated with the direct economic impacts within the estimation of the indirect economic impacts. Consequently, the modeling procedure facilitates the representation of the reliability of estimated total economic impacts. The representation of the estimates' reliability prevents reasoning based on a false certainty, which might be attributed to point estimates. Therefore, the developed approach facilitates a meaningful flood risk management and adaptation planning. The successful post-event application and the representation of the uncertainties qualifies the method also for the use for future risk assessments. Thus, the developed method enables the representation of the assumptions made for the future risk assessments, which is crucial information for future risk management. This is an important step forward, since the representation of reliability associated with all components of risk is currently lacking in all state-of-the-art methods assessing future risk. In conclusion, the use of object-based methods giving results in the form of distributions instead of point estimations is recommended. The improvement of the model performance by the means of multi-variable models and additional data points is possible, but small. Uncertainties associated with all components of damage estimation should be included and represented within the results. Furthermore, the findings of the thesis suggest that, at larger scales, the influence of the uncertainty associated with the vulnerability is smaller than those associated with the hazard and exposure. This leads to the conclusion that for an increased reliability of flood damage estimations and risk assessments, the improvement and active inclusion of hazard and exposure, including their uncertainties, is needed in addition to the improvements of the models describing the vulnerability of the objects. KW - hydro-meteorological risk KW - damage modeling KW - uncertainty KW - probabilistic approach KW - economic impacts KW - OpenStreetMap KW - hydro-meteorologische Risiken KW - Schadensmodellierung KW - Unsicherheiten KW - probabilistischer Ansatz KW - ökonomische Auswirkungen KW - OpenStreetMap Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-426161 ER - TY - GEN A1 - Sieg, Tobias A1 - Vogel, Kristin A1 - Merz, Bruno A1 - Kreibich, Heidi T1 - Seamless Estimation of Hydrometeorological Risk Across Spatial Scales T2 - Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe N2 - Hydrometeorological hazards caused losses of approximately 110 billion U.S. Dollars in 2016 worldwide. Current damage estimations do not consider the uncertainties in a comprehensive way, and they are not consistent between spatial scales. Aggregated land use data are used at larger spatial scales, although detailed exposure data at the object level, such as openstreetmap.org, is becoming increasingly available across the globe.We present a probabilistic approach for object-based damage estimation which represents uncertainties and is fully scalable in space. The approach is applied and validated to company damage from the flood of 2013 in Germany. Damage estimates are more accurate compared to damage models using land use data, and the estimation works reliably at all spatial scales. Therefore, it can as well be used for pre-event analysis and risk assessments. This method takes hydrometeorological damage estimation and risk assessments to the next level, making damage estimates and their uncertainties fully scalable in space, from object to country level, and enabling the exploitation of new exposure data. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 743 KW - spatial scales KW - risk assessment KW - hydro-meterological hazards KW - object-based damage modeling KW - uncertainty KW - probabilistic approaches Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-435341 SN - 1866-8372 IS - 743 SP - 574 EP - 581 ER -