@misc{TrietDungMerzetal.2018,
  author    = {Triet, Nguyen Van Khanh and Dung, Nguyen Viet and Merz, Bruno and Apel, Heiko},
  title     = {Towards risk-based flood management in highly productive paddy rice cultivation},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {931},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-44603},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-446032},
  pages     = {2859 -- 2876},
  year      = {2018},
  abstract  = {Flooding is an imminent natural hazard threatening most river deltas, e.g. the Mekong Delta. An appropriate flood management is thus required for a sustainable development of the often densely populated regions. Recently, the traditional event-based hazard control shifted towards a risk management approach in many regions, driven by intensive research leading to new legal regulation on flood management. However, a large-scale flood risk assessment does not exist for the Mekong Delta. Particularly, flood risk to paddy rice cultivation, the most important economic activity in the delta, has not been performed yet. Therefore, the present study was developed to provide the very first insight into delta-scale flood damages and risks to rice cultivation. The flood hazard was quantified by probabilistic flood hazard maps of the whole delta using a bivariate extreme value statistics, synthetic flood hydrographs, and a large-scale hydraulic model. The flood risk to paddy rice was then quantified considering cropping calendars, rice phenology, and harvest times based on a time series of enhanced vegetation index (EVI) derived from MODIS satellite data, and a published rice flood damage function. The proposed concept provided flood risk maps to paddy rice for the Mekong Delta in terms of expected annual damage. The presented concept can be used as a blueprint for regions facing similar problems due to its generic approach. Furthermore, the changes in flood risk to paddy rice caused by changes in land use currently under discussion in the Mekong Delta were estimated. Two land-use scenarios either intensifying or reducing rice cropping were considered, and the changes in risk were presented in spatially explicit flood risk maps. The basic risk maps could serve as guidance for the authorities to develop spatially explicit flood management and mitigation plans for the delta. The land-use change risk maps could further be used for adaptive risk management plans and as a basis for a cost-benefit of the discussed land-use change scenarios. Additionally, the damage and risks maps may support the recently initiated agricultural insurance programme in Vietnam.},
  language  = {en}
}
@article{KhuranaHesseKleidonHildebrandtetal.2022,
  author    = {Khurana, Swamini and Hesse, Falk and Kleidon-Hildebrandt, Anke and Thullner, Martin},
  title     = {Should we worry about surficial dynamics when assessing nutrient cycling in the groundwater?},
  series = {Frontiers in water},
  volume    = {4},
  journal   = {Frontiers in water},
  publisher = {Frontiers Media},
  address   = {Lausanne},
  issn      = {2624-9375},
  doi       = {10.3389/frwa.2022.780297},
  pages     = {17},
  year      = {2022},
  abstract  = {The fluxes of water and solutes in the subsurface compartment of the Critical Zone are temporally dynamic and it is unclear how this impacts microbial mediated nutrient cycling in the spatially heterogeneous subsurface. To investigate this, we undertook numerical modeling, simulating the transport in a wide range of spatially heterogeneous domains, and the biogeochemical transformation of organic carbon and nitrogen compounds using a complex microbial community with four (4) distinct functional groups, in water saturated subsurface compartments. We performed a comprehensive uncertainty analysis accounting for varying residence times and spatial heterogeneity. While the aggregated removal of chemical species in the domains over the entire simulation period was approximately the same as that in steady state conditions, the sub-scale temporal variation of microbial biomass and chemical discharge from a domain depended strongly on the interplay of spatial heterogeneity and temporal dynamics of the forcing. We showed that the travel time and the Damkohler number (Da) can be used to predict the temporally varying chemical discharge from a spatially heterogeneous domain. In homogeneous domains, chemical discharge in temporally dynamic conditions could be double of that in the steady state conditions while microbial biomass varied up to 75\% of that in steady state conditions. In heterogeneous domains, the interquartile range of uncertainty in chemical discharge in reaction dominated systems (log(10)Da > 0) was double of that in steady state conditions. However, high heterogeneous domains resulted in outliers where chemical discharge could be as high as 10-20 times of that in steady state conditions in high flow periods. And in transport dominated systems (log(10)Da < 0), the chemical discharge could be half of that in steady state conditions in unusually low flow conditions. In conclusion, ignoring spatio-temporal heterogeneities in a numerical modeling approach may exacerbate inaccurate estimation of nutrient export and microbial biomass. The results are relevant to long-term field monitoring studies, and for homogeneous soil column-scale experiments investigating the role of temporal dynamics on microbial redox dynamics.},
  language  = {en}
}
@misc{SiegVogelMerzetal.2019,
  author    = {Sieg, Tobias and Vogel, Kristin and Merz, Bruno and Kreibich, Heidi},
  title     = {Seamless Estimation of Hydrometeorological Risk Across Spatial Scales},
  series = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  number    = {743},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-43534},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-435341},
  pages     = {574 -- 581},
  year      = {2019},
  abstract  = {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.},
  language  = {en}
}
@article{SiegVogelMerzetal.2019,
  author    = {Sieg, Tobias and Vogel, Kristin and Merz, Bruno and Kreibich, Heidi},
  title     = {Seamless Estimation of Hydrometeorological Risk Across Spatial Scales},
  series = {Earth's Future},
  volume    = {7},
  journal   = {Earth's Future},
  number    = {5},
  publisher = {Wiley-Blackwell},
  address   = {Hoboken, NJ},
  issn      = {2328-4277},
  doi       = {10.1029/2018EF001122},
  pages     = {574 -- 581},
  year      = {2019},
  abstract  = {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.},
  language  = {en}
}
@phdthesis{Sieg2018,
  author    = {Sieg, Tobias},
  title     = {Reliability of flood damage estimations across spatial scales},
  doi       = {10.25932/publishup-42616},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-426161},
  school      = {Universit{\"a}t Potsdam},
  pages     = {XIII, 115},
  year      = {2018},
  abstract  = {Extreme Naturereignisse sind ein integraler Bestandteil der Natur der Erde. Sie werden erst dann zu Gefahren f{\"u}r die Gesellschaft, wenn sie diesen Ereignissen ausgesetzt ist. Dann allerdings k{\"o}nnen Naturgefahren verheerende Folgen f{\"u}r die Gesellschaft haben. Besonders hydro-meteorologische Gefahren wie zum Beispiel Flusshochwasser, Starkregenereignisse, Winterst{\"u}rme, Orkane oder Tornados haben ein hohes Schadenspotential und treten rund um den Globus auf. Einhergehend mit einer immer w{\"a}rmer werdenden Welt, werden auch Extremwetterereignisse, welche potentiell Naturgefahren ausl{\"o}sen k{\"o}nnen, immer wahrscheinlicher. Allerdings tr{\"a}gt nicht nur eine sich ver{\"a}ndernde Umwelt zur Erh{\"o}hung des Risikos von Naturgefahren bei, sondern auch eine sich ver{\"a}ndernde Gesellschaft. Daher ist ein angemessenes Risikomanagement erforderlich um die Gesellschaft auf jeder r{\"a}umlichen Ebene an diese Ver{\"a}nderungen anzupassen. Ein essentieller Bestandteil dieses Managements ist die Absch{\"a}tzung der {\"o}konomischen Auswirkungen der Naturgefahren. Bisher allerdings fehlen verl{\"a}ssliche Methoden um die Auswirkungen von hydro-meteorologischen Gefahren abzusch{\"a}tzen. Ein Hauptbestandteil dieser Arbeit ist daher die Entwicklung und Anwendung einer neuen Methode, welche die Verl{\"a}sslichkeit der Schadenssch{\"a}tzung verbessert. Die Methode wurde beispielhaft zur Sch{\"a}tzung der {\"o}konomischen Auswirkungen eines Flusshochwassers auf einzelne Unternehmen bis hin zu den Auswirkungen auf das gesamte Wirtschaftssystem Deutschlands erfolgreich angewendet. Bestehende Methoden geben meist wenig Information {\"u}ber die Verl{\"a}sslichkeit ihrer Sch{\"a}tzungen. Da diese Informationen Entscheidungen zur Anpassung an das Risiko erleichtern, wird die Verl{\"a}sslichkeit der Schadenssch{\"a}tzungen mit der neuen Methode dargestellt. Die Verl{\"a}sslichkeit bezieht sich dabei nicht nur auf die Schadenssch{\"a}tzung selber, sondern auch auf die Annahmen, die {\"u}ber betroffene Geb{\"a}ude gemacht werden. Nach diesem Prinzip kann auch die Verl{\"a}sslichkeit von Annahmen {\"u}ber die Zukunft dargestellt werden, dies ist ein wesentlicher Aspekt f{\"u}r Prognosen. Die Darstellung der Verl{\"a}sslichkeit und die erfolgreiche Anwendung zeigt das Potential der Methode zur Verwendung von Analysen f{\"u}r gegenw{\"a}rtige und zuk{\"u}nftige hydro-meteorologische Gefahren.},
  language  = {en}
}
@article{KreibichBottoMerzetal.2016,
  author    = {Kreibich, Heidi and Botto, Anna and Merz, Bruno and Schr{\"o}ter, Kai},
  title     = {Probabilistic, Multivariable Flood Loss Modeling on the Mesoscale with BT-FLEMO},
  series = {Risk analysis},
  volume    = {37},
  journal   = {Risk analysis},
  number    = {4},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {0272-4332},
  doi       = {10.1111/risa.12650},
  pages     = {774 -- 787},
  year      = {2016},
  abstract  = {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.},
  language  = {en}
}
@phdthesis{Roezer2018,
  author    = {R{\"o}zer, Viktor},
  title     = {Pluvial flood loss to private households},
  doi       = {10.25932/publishup-42991},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-429910},
  school      = {Universit{\"a}t Potsdam},
  pages     = {XXII, 109},
  year      = {2018},
  abstract  = {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.},
  language  = {en}
}
@phdthesis{Schaber2002,
  author    = {Schaber, J{\"o}rg},
  title     = {Phenology in Germany in the 20th century : methods, analyses and models},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-0000532},
  school      = {Universit{\"a}t Potsdam},
  year      = {2002},
  abstract  = {Die L{\"a}nge der Vegetationsperiode (VP) spielt eine zentrale Rolle f{\"u}r die interannuelle Variation der Kohlenstoffspeicherung terrestrischer {\"O}kosysteme. Die Analyse von Beobachtungsdaten hat gezeigt, dass sich die VP in den letzten Jahrzehnten in den n{\"o}rdlichen Breiten verl{\"a}ngert hat. Dieses Ph{\"a}nomen wurde oft im Zusammenhang mit der globalen Erw{\"a}rmung diskutiert, da die Ph{\"a}nologie von der Temperatur beeinflusst wird. Die Analyse der Pflanzenph{\"a}nologie in S{\"u}ddeutschland im 20. Jahrhundert zeigte: - Die starke Verfr{\"u}hung der Fr{\"u}hjahrsphasen in dem Jahrzehnt vor 1999 war kein singul{\"a}res Ereignis im 20. Jahrhundert. Schon in fr{\"u}heren Dekaden gab es {\"a}hnliche Trends. Es konnten Perioden mit unterschiedlichem Trendverhalten identifiziert werden. - Es gab deutliche Unterschiede in den Trends von fr{\"u}hen und sp{\"a}ten Fr{\"u}hjahrsphasen. Die fr{\"u}hen Fr{\"u}hjahrsphasen haben sich stetig verfr{\"u}ht, mit deutlicher Verfr{\"u}hung zwischen 1931 und 1948, moderater Verfr{\"u}hung zwischen 1948 und 1984 und starker Verfr{\"u}hung zwischen 1984 und 1999. Die sp{\"a}ten Fr{\"u}hjahrsphasen hingegen, wechselten ihr Trendverhalten in diesen Perioden von einer Verfr{\"u}hung zu einer deutlichen Versp{\"a}tung wieder zu einer starken Verfr{\"u}hung. Dieser Unterschied in der Trendentwicklung zwischen fr{\"u}hen und sp{\"a}ten Fr{\"u}hjahrsphasen konnte auch f{\"u}r ganz Deutschland in den Perioden 1951 bis 1984 und 1984 bis 1999 beobachtet werden. Der bestimmende Einfluss der Temperatur auf die Fr{\"u}hjahrsphasen und ihr modifizierender Einfluss auf die Herbstphasen konnte best{\"a}tigt werden. Es zeigt sich jedoch, dass - die Ph{\"a}nologie bestimmende Funktionen der Temperatur nicht mit einem globalen j{\"a}hrlichen CO2 Signal korreliert waren, welches als Index f{\"u}r die globale Erw{\"a}rmung verwendet wurde - ein Index f{\"u}r grossr{\"a}umige regionale Zirkulationsmuster (NAO-Index) nur zu einem kleinen Teil die beobachtete ph{\"a}nologischen Variabilit{\"a}t erkl{\"a}ren konnte. Das beobachtete unterschiedliche Trendverhalten zwischen fr{\"u}hen und sp{\"a}ten Fr{\"u}hjahrsphasen konnte auf die unterschiedliche Entwicklung von M{\"a}rz- und Apriltemperaturen zur{\"u}ckgef{\"u}hrt werden. W{\"a}hrend sich die M{\"a}rztemperaturen im Laufe des 20. Jahrhunderts mit einer zunehmenden Variabilit{\"a}t in den letzten 50 Jahren stetig erh{\"o}ht haben, haben sich die Apriltemperaturen zwischen dem Ende der 1940er und Mitte der 1980er merklich abgek{\"u}hlt und dann wieder deutlich erw{\"a}rmt. Es wurde geschlussfolgert, dass die Verfr{\"u}hungen in der Fr{\"u}hjahrsph{\"a}nologie 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 Erw{\"a}rmungsignal gefunden werden. Im Durchschnitt haben sich alle betrachteten Fr{\"u}hjahrsphasen zwischen 1951 und 1999 in Naturr{\"a}umen in Deutschland zwischen 5 und 20 Tagen verfr{\"u}ht. Ein starker Unterschied in der Verfr{\"u}hung zwischen fr{\"u}hen und sp{\"a}ten Fr{\"u}hjahrsphasen liegt an deren erw{\"a}hntem unterschiedlichen Verhalten. Die Blattverf{\"a}rbung hat sich zwischen 1951 und 1999 f{\"u}r alle Spezies versp{\"a}tet, aber nach 1984 im Durchschnitt verfr{\"u}ht. Die VP hat sich in Deutschland zwischen 1951 und 1999 um ca. 10 Tage verl{\"a}ngert. Es ist haupts{\"a}chlich die {\"A}nderung in den Fr{\"u}hjahrphasen, die zu einer {\"A}nderung in der potentiell absorbierten Strahlung (PAS) f{\"u}hrt. Dar{\"u}ber hinaus sind es die sp{\"a}ten Fr{\"u}hjahrsphasen, die pro Tag Verfr{\"u}hung st{\"a}rker profitieren, da die zus{\"a}tzlichen Tage l{\"a}nger undw{\"a}rmer sind als dies f{\"u}r die fr{\"u}hen Phasen der Fall ist. Um die relative {\"A}nderung in PAS im Vergleich der Spezies abzusch{\"a}tzen, m{\"u}ssen allerdings auch die Ver{\"a}nderungen in den Herbstphasen ber{\"u}cksichtigt werden. Der deutliche Unterschied zwischen fr{\"u}hen und sp{\"a}ten Fr{\"u}hjahrsphasen konnte durch die Anwendung einer neuen Methode zur Konstruktion von Zeitreihen herausgearbeitet werden. Der neue methodische Ansatz erlaubte die Ableitung verl{\"a}sslicher 100-j{\"a}hriger Zeitreihen und die Konstruktion von lokalen kombinierten Zeitreihen, welche die Datenverf{\"u}gbarkeit f{\"u}r die Modellentwicklung erh{\"o}hten. Ausser analysierten Protokollierungsfehlern wurden mikroklimatische, genetische und Beobachtereinfl{\"u}sse als Quellen von Unsicherheit in ph{\"a}nologischen Daten identifiziert. Ph{\"a}nologischen Beobachtungen eines Ortes k{\"o}nnen sch{\"a}tzungsweise 24 Tage um das parametrische Mittel schwanken.Dies unterst{\"u}tzt die 30-Tage Regel f{\"u}r die Detektion von Ausreissern. Neue Ph{\"a}nologiemodelle, die den Blattaustrieb aus t{\"a}glichen 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. {\"U}bersch{\"a}tzung von fr{\"u}hen und Untersch{\"a}tzung von sp{\"a}ten Beobachtungen, konnte reduziert, aber nicht vollst{\"a}ndig eliminiert werden. - Die besten Modellvarianten f{\"u}r verschiedene Spezies wiesen darauf hin, dass f{\"u}r die sp{\"a}ten Fr{\"u}hjahrsphasen die Tagesl{\"a}nge eine wichtigere Rolle spielt als f{\"u}r die fr{\"u}hen Phasen. - Die Vernalisation spielte gegen{\"u}ber den Temperaturen kurz vor dem Blattaustrieb nur eine untergeordnete Rolle.},
  language  = {en}
}
@phdthesis{Kox2018,
  author    = {Kox, Thomas},
  title     = {Perception and use of uncertainty in severe weather warnings},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-411541},
  school      = {Universit{\"a}t Potsdam},
  pages     = {154},
  year      = {2018},
  abstract  = {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.},
  language  = {en}
}
@article{vanderAaLeopoldWeidlich2020,
  author    = {van der Aa, Han and Leopold, Henrik and Weidlich, Matthias},
  title     = {Partial order resolution of event logs for process conformance checking},
  series = {Decision support systems : DSS},
  volume    = {136},
  journal   = {Decision support systems : DSS},
  publisher = {Elsevier},
  address   = {Amsterdam [u.a.]},
  issn      = {0167-9236},
  doi       = {10.1016/j.dss.2020.113347},
  pages     = {12},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}