@phdthesis{Kemter2022,
  author    = {Kemter, Matthias},
  title     = {River floods in a changing world},
  doi       = {10.25932/publishup-55856},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-558564},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xvii, 120},
  year      = {2022},
  abstract  = {River floods are among the most devastating natural hazards worldwide. As their generation is highly dependent on climatic conditions, their magnitude and frequency are projected to be affected by future climate change. Therefore, it is crucial to study the ways in which a changing climate will, and already has, influenced flood generation, and thereby flood hazard. Additionally, it is important to understand how other human influences - specifically altered land cover - affect flood hazard at the catchment scale. The ways in which flood generation is influenced by climatic and land cover conditions differ substantially in different regions. The spatial variability of these effects needs to be taken into account by using consistent datasets across large scales as well as applying methods that can reflect this heterogeneity. Therefore, in the first study of this cumulative thesis a complex network approach is used to find 10 clusters of similar flood behavior among 4390 catchments in the conterminous United States. By using a consistent set of 31 hydro-climatological and land cover variables, and training a separate Random Forest model for each of the clusters, the regional controls on flood magnitude trends between 1960-2010 are detected. It is shown that changes in rainfall are the most important drivers of these trends, while they are regionally controlled by land cover conditions. While climate change is most commonly associated with flood magnitude trends, it has been shown to also influence flood timing. This can lead to trends in the size of the area across which floods occur simultaneously, the flood synchrony scale. The second study is an analysis of data from 3872 European streamflow gauges and shows that flood synchrony scales have increased in Western Europe and decreased in Eastern Europe. These changes are attributed to changes in flood generation, especially a decreasing relevance of snowmelt. Additionally, the analysis shows that both the absolute values and the trends of flood magnitudes and flood synchrony scales are positively correlated. If these trends persist in the future and are not accounted for, the combined increases of flood magnitudes and flood synchrony scales can exceed the capacities of disaster relief organizations and insurers. Hazard cascades are an additional way through which climate change can influence different aspects of flood hazard. The 2019/2020 wildfires in Australia, which were preceded by an unprecedented drought and extinguished by extreme rainfall that led to local flooding, present an opportunity to study the effects of multiple preceding hazards on flood hazard. All these hazards are individually affected by climate change, additionally complicating the interactions within the cascade. By estimating and analyzing the burn severity, rainfall magnitude, soil erosion and stream turbidity in differently affected tributaries of the Manning River catchment, the third study shows that even low magnitude floods can pose a substantial hazard within a cascade. This thesis shows that humanity is affecting flood hazard in multiple ways with spatially and temporarily varying consequences, many of which were previously neglected (e.g. flood synchrony scale, hazard cascades). To allow for informed decision making in risk management and climate change adaptation, it will be crucial to study these aspects across the globe and to project their trajectories into the future. The presented methods can depict the complex interactions of different flood drivers and their spatial variability, providing a basis for the assessment of future flood hazard changes. The role of land cover should be considered more in future flood risk modelling and management studies, while holistic, transferable frameworks for hazard cascade assessment will need to be designed.},
  language  = {en}
}
@phdthesis{Brill2022,
  author    = {Brill, Fabio Alexander},
  title     = {Applications of machine learning and open geospatial data in flood risk modelling},
  doi       = {10.25932/publishup-55594},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xix, 124},
  year      = {2022},
  abstract  = {Der technologische Fortschritt erlaubt es, zunehmend komplexe Vorhersagemodelle auf Basis immer gr{\"o}ßerer Datens{\"a}tze zu produzieren. F{\"u}r das Risikomanagement von Naturgefahren sind eine Vielzahl von Modellen als Entscheidungsgrundlage notwendig, z.B. in der Auswertung von Beobachtungsdaten, f{\"u}r die Vorhersage von Gefahrenszenarien, oder zur statistischen Absch{\"a}tzung der zu erwartenden Sch{\"a}den. Es stellt sich also die Frage, inwiefern moderne Modellierungsans{\"a}tze wie das maschinelle Lernen oder Data-Mining in diesem Themenbereich sinnvoll eingesetzt werden k{\"o}nnen. Zus{\"a}tzlich ist im Hinblick auf die Datenverf{\"u}gbarkeit und -zug{\"a}nglichkeit ein Trend zur {\"O}ffnung (open data) zu beobachten. Thema dieser Arbeit ist daher, die M{\"o}glichkeiten und Grenzen des maschinellen Lernens und frei verf{\"u}gbarer Geodaten auf dem Gebiet der Hochwasserrisikomodellierung im weiteren Sinne zu untersuchen. Da dieses {\"u}bergeordnete Thema sehr breit ist, werden einzelne relevante Aspekte herausgearbeitet und detailliert betrachtet. Eine prominente Datenquelle im Bereich Hochwasser ist die satellitenbasierte Kartierung von {\"U}berflutungsfl{\"a}chen, die z.B. {\"u}ber den Copernicus Service der Europ{\"a}ischen Union frei zur Verf{\"u}gung gestellt werden. Große Hoffnungen werden in der wissenschaftlichen Literatur in diese Produkte gesetzt, sowohl f{\"u}r die akute Unterst{\"u}tzung der Einsatzkr{\"a}fte im Katastrophenfall, als auch in der Modellierung mittels hydrodynamischer Modelle oder zur Schadensabsch{\"a}tzung. Daher wurde ein Fokus in dieser Arbeit auf die Untersuchung dieser Flutmasken gelegt. Aus der Beobachtung, dass die Qualit{\"a}t dieser Produkte in bewaldeten und urbanen Gebieten unzureichend ist, wurde ein Verfahren zur nachtr{\"a}glichenVerbesserung mittels maschinellem Lernen entwickelt. Das Verfahren basiert auf einem Klassifikationsalgorithmus der nur Trainingsdaten von einer vorherzusagenden Klasse ben{\"o}tigt, im konkreten Fall also Daten von {\"U}berflutungsfl{\"a}chen, nicht jedoch von der negativen Klasse (trockene Gebiete). Die Anwendung f{\"u}r Hurricane Harvey in Houston zeigt großes Potenzial der Methode, abh{\"a}ngig von der Qualit{\"a}t der urspr{\"u}nglichen Flutmaske. Anschließend wird anhand einer prozessbasierten Modellkette untersucht, welchen Einfluss implementierte physikalische Prozessdetails auf das vorhergesagte statistische Risiko haben. Es wird anschaulich gezeigt, was eine Risikostudie basierend auf etablierten Modellen leisten kann. Solche Modellketten sind allerdings bereits f{\"u}r Flusshochwasser sehr komplex, und f{\"u}r zusammengesetzte oder kaskadierende Ereignisse mit Starkregen, Sturzfluten, und weiteren Prozessen, kaum vorhanden. Im vierten Kapitel dieser Arbeit wird daher getestet, ob maschinelles Lernen auf Basis von vollst{\"a}ndigen Schadensdaten einen direkteren Weg zur Schadensmodellierung erm{\"o}glicht, der die explizite Konzeption einer solchen Modellkette umgeht. Dazu wird ein staatlich erhobener Datensatz der gesch{\"a}digten Geb{\"a}ude w{\"a}hrend des schweren El Ni{\~n}o Ereignisses 2017 in Peru verwendet. In diesem Kontext werden auch die M{\"o}glichkeiten des Data-Mining zur Extraktion von Prozessverst{\"a}ndnis ausgelotet. Es kann gezeigt werden, dass diverse frei verf{\"u}gbare Geodaten n{\"u}tzliche Informationen f{\"u}r die Gefahren- und Schadensmodellierung von komplexen Flutereignissen liefern, z.B. satellitenbasierte Regenmessungen, topographische und hydrographische Information, kartierte Siedlungsfl{\"a}chen, sowie Indikatoren aus Spektraldaten. Zudem zeigen sich Erkenntnisse zu den Sch{\"a}digungsprozessen, die im Wesentlichen mit den vorherigen Erwartungen in Einklang stehen. Die maximale Regenintensit{\"a}t wirkt beispielsweise in St{\"a}dten und steilen Schluchten st{\"a}rker sch{\"a}digend, w{\"a}hrend die Niederschlagssumme in tiefliegenden Flussgebieten und bewaldeten Regionen als aussagekr{\"a}ftiger befunden wurde. L{\"a}ndliche Gebiete in Peru weisen in der pr{\"a}sentierten Studie eine h{\"o}here Vulnerabilit{\"a}t als die Stadtgebiete auf. Jedoch werden auch die grunds{\"a}tzlichen Grenzen der Methodik und die Abh{\"a}ngigkeit von spezifischen Datens{\"a}tzen and Algorithmen offenkundig. In der {\"u}bergreifenden Diskussion werden schließlich die verschiedenen Methoden - prozessbasierte Modellierung, pr{\"a}diktives maschinelles Lernen, und Data-Mining - mit Blick auf die Gesamtfragestellungen evaluiert. Im Bereich der Gefahrenbeobachtung scheint eine Fokussierung auf neue Algorithmen sinnvoll. Im Bereich der Gefahrenmodellierung, insbesondere f{\"u}r Flusshochwasser, wird eher die Verbesserung von physikalischen Modellen, oder die Integration von prozessbasierten und statistischen Verfahren angeraten. In der Schadensmodellierung fehlen nach wie vor die großen repr{\"a}sentativen Datens{\"a}tze, die f{\"u}r eine breite Anwendung von maschinellem Lernen Voraussetzung ist. Daher ist die Verbesserung der Datengrundlage im Bereich der Sch{\"a}den derzeit als wichtiger einzustufen als die Auswahl der Algorithmen.},
  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{Nguyen2021,
  author    = {Nguyen, Van Khanh Triet},
  title     = {Flood dynamics in the Vietnamese Mekong Delta},
  doi       = {10.25932/publishup-51283},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-512830},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xx, 113},
  year      = {2021},
  abstract  = {Today, the Mekong Delta in the southern of Vietnam is home for 18 million people. The delta also accounts for more than half of the country's food production and 80\% of the exported rice. Due to the low elevation, it is highly susceptible to the risk of fluvial and coastal flooding. Although extreme floods often result in excessive damages and economic losses, the annual flood pulse from the Mekong is vital to sustain agricultural cultivation and livelihoods of million delta inhabitants. Delta-wise risk management and adaptation strategies are required to mitigate the adverse impacts from extreme events while capitalising benefits from floods. However, a proper flood risk management has not been implemented in the VMD, because the quantification of flood damage is often overlooked and the risks are thus not quantified. So far, flood management has been exclusively focused on engineering measures, i.e. high- and low- dyke systems, aiming at flood-free or partial inundation control without any consideration of the actual risks or a cost-benefit analysis. Therefore, an analysis of future delta flood dynamics driven these stressors is valuable to facilitate the transition from sole hazard control towards a risk management approach, which is more cost-effective and also robust against future changes in risk. Built on these research gaps, this thesis investigates the current state and future projections of flood hazard, damage and risk to rice cultivation, the most important economic activity in the VMD. The study quantifies the changes in risk and hazard brought by the development of delta-based flood control measures in the last decades, and analyses the expected changes in risk driven by the changing climate, rising sea-level and deltaic land subsidence, and finally the development of hydropower projects in the Mekong Basin. For this purpose, flood trend analyses and comprehensive hydraulic modelling were performed, together with the development of a concept to quantify flood damage and risk to rice plantation. The analysis of observed flood levels revealed strong and robust increasing trends of peak and duration downstream of the high-dyke areas with a step change in 2000/2001, i.e. after the disastrous flood which initiated the high-dyke development. These changes were in contrast to the negative trends detected upstream, suggested that high-dyke development has shifted flood hazard downstream. Findings of the trend's analysis were later confirmed by hydraulic simulations of the two recent extreme floods in 2000 and 2011, where the hydrological boundaries and dyke system settings were interchanged. However, the high-dyke system was not the only and often not the main cause for a shift of flood hazard, as a comparative analysis of these two extreme floods proved. The high-dyke development was responsible for 20-90\% of the observed changes in flood level between 2000 and 2011, with large spatial variances. The particular flood hydrograph of the two events had the highest contribution in the northern part of the delta, while the tidal level had 2-3 times higher influence than the high-dyke in the lower-central and coastal areas downstream of high-dyke areas. The impact of the high-dyke development was highest in the areas closely downstream of the high-dyke area just south of the Cambodia-Vietnam border. The hydraulic simulations also validated that the concurrence of the flood peak with spring tides, i.e. high sea level along the coast, amplified the flood level and inundation in the central and coastal regions substantially. The risk assessment quantified the economic losses of rice cultivation to USD 25.0 and 115 million (0.02-0.1\% of the total GDP of Vietnam in 2011) corresponding to the 10-year and the 100-year floods, with an expected annual damage of about USD 4.5 million. A particular finding is that the flood damage was highly sensitive to flood timing. Here, a 10-year event with an early peak, i.e. late August-September, could cause as much damage as a 100-year event that peaked in October. This finding underlines the importance of a reliable early flood warning, which could substantially reduce the damage to rice crops and thus the risk. The developed risk assessment concept was furthermore applied to investigate two high-dyke development alternatives, which are currently under discussion among the administrative bodies in Vietnam, but also in the public. The first option favouring the utilization of the current high-dyke compartments as flood retention areas instead for rice cropping during the flood season could reduce flood hazard and expected losses by 5-40\%, depending on the region of the delta. On the contrary, the second option promoting the further extension of the areas protected by high-dyke to facilitate third rice crop planting on a larger area, tripled the current expected annual flood damage. This finding challenges the expected economic benefit of triple rice cultivation, in addition to the already known reducing of nutrient supply by floodplain sedimentation and thus higher costs for fertilizers. The economic benefits of the high-dyke and triple rice cropping system is further challenged by the changes in the flood dynamics to be expected in future. For the middle of the 21st century (2036-2065) the effective sea-level rise an increase of the inundation extent by 20-27\% was projected. This corresponds to an increase of flood damage to rice crops in dry, normal and wet year by USD 26.0, 40.0 and 82.0 million in dry, normal and wet year compared to the baseline period 1971-2000. Hydraulic simulations indicated that the planned massive development of hydropower dams in the Mekong Basin could potentially compensate the increase in flood hazard and agriculture losses stemming from climate change. However, the benefits of dams as mitigation of flood losses are highly uncertain, because a) the actual development of the dams is highly disputed, b) the operation of the dams is primarily targeted at power generation, not flood control, and c) this would require international agreements and cooperation, which is difficult to achieve in South-East Asia. The theoretical flood mitigation benefit is additionally challenged by a number of negative impacts of the dam development, e.g. disruption of floodplain inundation in normal, non-extreme flood years. Adding to the certain reduction of sediment and nutrient load to the floodplains, hydropower dams will drastically impair rice and agriculture production, the basis livelihoods of million delta inhabitants. In conclusion, the VMD is expected to face increasing threats of tidal induced floods in the coming decades. Protection of the entire delta coastline solely with "hard" engineering flood protection structures is neither technically nor economically feasible, adaptation and mitigation actions are urgently required. Better control and reduction of groundwater abstraction is thus strongly recommended as an immediate and high priority action to reduce the land subsidence and thus tidal flooding and salinity intrusion in the delta. Hydropower development in the Mekong basin might offer some theoretical flood protection for the Mekong delta, but due to uncertainties in the operation of the dams and a number of negative effects, the dam development cannot be recommended as a strategy for flood management. For the Vietnamese authorities, it is advisable to properly maintain the existing flood protection structures and to develop flexible risk-based flood management plans. In this context the study showed that the high-dyke compartments can be utilized for emergency flood management in extreme events. For this purpose, a reliable flood forecast is essential, and the action plan should be materialised in official documents and legislation to assure commitment and consistency in the implementation and operation.},
  language  = {en}
}
@phdthesis{MetinUsta2021,
  author    = {Metin Usta, Ay{\c{s}}e Duha},
  title     = {The role of risk components and spatial dependence in flood risk estimations},
  doi       = {10.25932/publishup-49255},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-492554},
  school      = {Universit{\"a}t Potsdam},
  pages     = {XIX, 97},
  year      = {2021},
  abstract  = {Flooding is a vast problem in many parts of the world, including Europe. It occurs mainly due to extreme weather conditions (e.g. heavy rainfall and snowmelt) and the consequences of flood events can be devastating. Flood risk is mainly defined as a combination of the probability of an event and its potential adverse impacts. Therefore, it covers three major dynamic components: hazard (physical characteristics of a flood event), exposure (people and their physical environment that being exposed to flood), and vulnerability (the elements at risk). Floods are natural phenomena and cannot be fully prevented. However, their risk can be managed and mitigated. For a sound flood risk management and mitigation, a proper risk assessment is needed. First of all, this is attained by a clear understanding of the flood risk dynamics. For instance, human activity may contribute to an increase in flood risk. Anthropogenic climate change causes higher intensity of rainfall and sea level rise and therefore an increase in scale and frequency of the flood events. On the other hand, inappropriate management of risk and structural protection measures may not be very effective for risk reduction. Additionally, due to the growth of number of assets and people within the flood-prone areas, risk increases. To address these issues, the first objective of this thesis is to perform a sensitivity analysis to understand the impacts of changes in each flood risk component on overall risk and further their mutual interactions. A multitude of changes along the risk chain are simulated by regional flood model (RFM) where all processes from atmosphere through catchment and river system to damage mechanisms are taken into consideration. The impacts of changes in risk components are explored by plausible change scenarios for the mesoscale Mulde catchment (sub-basin of the Elbe) in Germany. A proper risk assessment is ensured by the reasonable representation of the real-world flood event. Traditionally, flood risk is assessed by assuming homogeneous return periods of flood peaks throughout the considered catchment. However, in reality, flood events are spatially heterogeneous and therefore traditional assumption misestimates flood risk especially for large regions. In this thesis, two different studies investigate the importance of spatial dependence in large scale flood risk assessment for different spatial scales. In the first one, the "real" spatial dependence of return period of flood damages is represented by continuous risk modelling approach where spatially coherent patterns of hydrological and meteorological controls (i.e. soil moisture and weather patterns) are included. Further the risk estimations under this modelled dependence assumption are compared with two other assumptions on the spatial dependence of return periods of flood damages: complete dependence (homogeneous return periods) and independence (randomly generated heterogeneous return periods) for the Elbe catchment in Germany. The second study represents the "real" spatial dependence by multivariate dependence models. Similar to the first study, the three different assumptions on the spatial dependence of return periods of flood damages are compared, but at national (United Kingdom and Germany) and continental (Europe) scales. Furthermore, the impacts of the different models, tail dependence, and the structural flood protection level on the flood risk under different spatial dependence assumptions are investigated. The outcomes of the sensitivity analysis framework suggest that flood risk can vary dramatically as a result of possible change scenarios. The risk components that have not received much attention (e.g. changes in dike systems and in vulnerability) may mask the influence of climate change that is often investigated component. The results of the spatial dependence research in this thesis further show that the damage under the false assumption of complete dependence is 100 \% larger than the damage under the modelled dependence assumption, for the events with return periods greater than approximately 200 years in the Elbe catchment. The complete dependence assumption overestimates the 200-year flood damage, a benchmark indicator for the insurance industry, by 139 \%, 188 \% and 246 \% for the UK, Germany and Europe, respectively. The misestimation of risk under different assumptions can vary from upstream to downstream of the catchment. Besides, tail dependence in the model and flood protection level in the catchments can affect the risk estimation and the differences between different spatial dependence assumptions. In conclusion, the broader consideration of the risk components, which possibly affect the flood risk in a comprehensive way, and the consideration of the spatial dependence of flood return periods are strongly recommended for a better understanding of flood risk and consequently for a sound flood risk management and mitigation.},
  language  = {en}
}
@phdthesis{Hartmann2024,
  author    = {Hartmann, Anne},
  title     = {Tracing the evolution of hillslope structure and hillslope hydrological response over ten millennia in two glacial forefields of different geology},
  doi       = {10.25932/publishup-62862},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-628629},
  school      = {Universit{\"a}t Potsdam},
  pages     = {XVIII, 138, XLIV},
  year      = {2024},
  abstract  = {Assessing the impact of global change on hydrological systems is one of the greatest hydrological challenges of our time. Changes in land cover, land use, and climate have an impact on water quantity, quality, and temporal availability. There is a widespread consensus that, given the far-reaching effects of global change, hydrological systems can no longer be viewed as static in their structure; instead, they must be regarded as entire ecosystems, wherein hydrological processes interact and coevolve with biological, geomorphological, and pedological processes. To accurately predict the hydrological response under the impact of global change, it is essential to understand this complex coevolution. The knowledge of how hydrological processes, in particular the formation of subsurface (preferential) flow paths, evolve within this coevolution and how they feed back to the other processes is still very limited due to a lack of observational data. At the hillslope scale, this intertwined system of interactions is known as the hillslope feedback cycle. This thesis aims to enhance our understanding of the hillslope feedback cycle by studying the coevolution of hillslope structure and hillslope hydrological response. Using chronosequences of moraines in two glacial forefields developed from siliceous and calcareous glacial till, the four studies shed light on the complex coevolution of hydrological, biological, and structural hillslope properties, as well as subsurface hydrological flow paths over an evolutionary period of 10 millennia in these two contrasting geologies. The findings indicate that the contrasting properties of siliceous and calcareous parent materials lead to variations in soil structure, permeability, and water storage. As a result, different plant species and vegetation types are favored on siliceous versus calcareous parent material, leading to diverse ecosystems with distinct hydrological dynamics. The siliceous parent material was found to show a higher activity level in driving the coevolution. The soil pH resulting from parent material weathering emerges as a crucial factor, influencing vegetation development, soil formation, and consequently, hydrology. The acidic weathering of the siliceous parent material favored the accumulation of organic matter, increasing the soils' water storage capacity and attracting acid-loving shrubs, which further promoted organic matter accumulation and ultimately led to podsolization after 10 000 years. Tracer experiments revealed that the subsurface flow path evolution was influenced by soil and vegetation development, and vice versa. Subsurface flow paths changed from vertical, heterogeneous matrix flow to finger-like flow paths over a few hundred years, evolving into macropore flow, water storage, and lateral subsurface flow after several thousand years. The changes in flow paths among younger age classes were driven by weathering processes altering soil structure, as well as by vegetation development and root activity. In the older age class, the transition to more water storage and lateral flow was attributed to substantial organic matter accumulation and ongoing podsolization. The rapid vertical water transport in the finger-like flow paths, along with the conductive sandy material, contributed to podsolization and thus to the shift in the hillslope hydrological response. In contrast, the calcareous site possesses a high pH buffering capacity, creating a neutral to basic environment with relatively low accumulation of dead organic matter, resulting in a lower water storage capacity and the establishment of predominantly grass vegetation. The coevolution was found to be less dynamic over the millennia. Similar to the siliceous site, significant changes in subsurface flow paths occurred between the young age classes. However, unlike the siliceous site, the subsurface flow paths at the calcareous site only altered in shape and not in direction. Tracer experiments showed that flow paths changed from vertical, heterogeneous matrix flow to vertical, finger-like flow paths after a few hundred to thousands of years, which was driven by root activities and weathering processes. Despite having a finer soil texture, water storage at the calcareous site was significantly lower than at the siliceous site, and water transport remained primarily rapid and vertical, contributing to the flourishing of grass vegetation. The studies elucidated that changes in flow paths are predominantly shaped by the characteristics of the parent material and its weathering products, along with their complex interactions with initial water flow paths and vegetation development. Time, on the other hand, was not found to be a primary factor in describing the evolution of the hydrological response. This thesis makes a valuable contribution to closing the gap in the observations of the coevolution of hydrological processes within the hillslope feedback cycle, which is important to improve predictions of hydrological processes in changing landscapes. Furthermore, it emphasizes the importance of interdisciplinary studies in addressing the hydrological challenges arising from global change.},
  language  = {en}
}
@phdthesis{Angermann2018,
  author    = {Angermann, Lisa},
  title     = {Hillslope-stream connectivity across scales},
  doi       = {10.25932/publishup-42454},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-424542},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xix, 193},
  year      = {2018},
  abstract  = {The concept of hydrologic connectivity summarizes all flow processes that link separate regions of a landscape. As such, it is a central theme in the field of catchment hydrology, with influence on neighboring disciplines such as ecology and geomorphology. It is widely acknowledged to be an important key in understanding the response behavior of a catchment and has at the same time inspired research on internal processes over a broad range of scales. From this process-hydrological point of view, hydrological connectivity is the conceptual framework to link local observations across space and scales. This is the context in which the four studies this thesis comprises of were conducted. The focus was on structures and their spatial organization as important control on preferential subsurface flow. Each experiment covered a part of the conceptualized flow path from hillslopes to the stream: soil profile, hillslope, riparian zone, and stream. For each study site, the most characteristic structures of the investigated domain and scale, such as slope deposits and peat layers were identified based on preliminary or previous investigations or literature reviews. Additionally, further structural data was collected and topographical analyses were carried out. Flow processes were observed either based on response observations (soil moisture changes or discharge patterns) or direct measurement (advective heat transport). Based on these data, the flow-relevance of the characteristic structures was evaluated, especially with regard to hillslope to stream connectivity. Results of the four studies revealed a clear relationship between characteristic spatial structures and the hydrological behavior of the catchment. Especially the spatial distribution of structures throughout the study domain and their interconnectedness were crucial for the establishment of preferential flow paths and their relevance for large-scale processes. Plot and hillslope-scale irrigation experiments showed that the macropores of a heterogeneous, skeletal soil enabled preferential flow paths at the scale of centimeters through the otherwise unsaturated soil. These flow paths connected throughout the soil column and across the hillslope and facilitated substantial amounts of vertical and lateral flow through periglacial slope deposits. In the riparian zone of the same headwater catchment, the connectivity between hillslopes and stream was controlled by topography and the dualism between characteristic subsurface structures and the geomorphological heterogeneity of the stream channel. At the small scale (1 m to 10 m) highest gains always occurred at steps along the longitudinal streambed profile, which also controlled discharge patterns at the large scale (100 m) during base flow conditions (number of steps per section). During medium and high flow conditions, however, the impact of topography and parafluvial flow through riparian zone structures prevailed and dominated the large-scale response patterns. In the streambed of a lowland river, low permeability peat layers affected the connectivity between surface water and groundwater, but also between surface water and the hyporheic zone. The crucial factor was not the permeability of the streambed itself, but rather the spatial arrangement of flow-impeding peat layers, causing increased vertical flow through narrow "windows" in contrast to predominantly lateral flow in extended areas of high hydraulic conductivity sediments. These results show that the spatial organization of structures was an important control for hydrological processes at all scales and study areas. In a final step, the observations from different scales and catchment elements were put in relation and compared. The main focus was on the theoretical analysis of the scale hierarchies of structures and processes and the direction of causal dependencies in this context. Based on the resulting hierarchical structure, a conceptual framework was developed which is capable of representing the system's complexity while allowing for adequate simplifications. The resulting concept of the parabolic scale series is based on the insight that flow processes in the terrestrial part of the catchment (soil and hillslopes) converge. This means that small-scale processes assemble and form large-scale processes and responses. Processes in the riparian zone and the streambed, however, are not well represented by the idea of convergence. Here, the large-scale catchment signal arrives and is modified by structures in the riparian zone, stream morphology, and the small-scale interactions between surface water and groundwater. Flow paths diverge and processes can better be represented by proceeding from large scales to smaller ones. The catchment-scale representation of processes and structures is thus the conceptual link between terrestrial hillslope processes and processes in the riparian corridor.},
  language  = {en}
}
@phdthesis{Duy2023,
  author    = {Duy, Nguyen Le},
  title     = {Hydrological processes in the Vietnamese Mekong Delta},
  doi       = {10.25932/publishup-60260},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-602607},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xxi, 153},
  year      = {2023},
  abstract  = {Understanding hydrological processes is of fundamental importance for the Vietnamese national food security and the livelihood of the population in the Vietnamese Mekong Delta (VMD). As a consequence of sparse data in this region, however, hydrologic processes, such as the controlling processes of precipitation, the interaction between surface and groundwater, and groundwater dynamics, have not been thoroughly studied. The lack of this knowledge may negatively impact the long-term strategic planning for sustainable groundwater resources management and may result in insufficient groundwater recharge and freshwater scarcity. It is essential to develop useful methods for a better understanding of hydrological processes in such data-sparse regions. The goal of this dissertation is to advance methodologies that can improve the understanding of fundamental hydrological processes in the VMD, based on the analyses of stable water isotopes and monitoring data. The thesis mainly focuses on the controlling processes of precipitation, the mechanism of surface-groundwater interaction, and the groundwater dynamics. These processes have not been fully addressed in the VMD so far. The thesis is based on statistical analyses of the isotopic data of Global Network of Isotopes in Precipitation (GNIP), of meteorological and hydrological data from Vietnamese agencies, and of the stable water isotopes and monitoring data collected as part of this work. First, the controlling processes of precipitation were quantified by the combination of trajectory analysis, multi-factor linear regression, and relative importance analysis (hereafter, a model-based statistical approach). The validity of this approach is confirmed by similar, but mainly qualitative results obtained in other studies. The total variation in precipitation isotopes (δ18O and δ2H) can be better explained by multiple linear regression (up to 80\%) than single-factor linear regression (30\%). The relative importance analysis indicates that atmospheric moisture regimes control precipitation isotopes rather than local climatic conditions. The most crucial factor is the upstream rainfall along the trajectories of air mass movement. However, the influences of regional and local climatic factors vary in importance over the seasons. The developed model-based statistical approach is a robust tool for the interpretation of precipitation isotopes and could also be applied to understand the controlling processes of precipitation in other regions. Second, the concept of the two-component lumped-parameter model (LPM) in conjunction with stable water isotopes was applied to examine the surface-groundwater interaction in the VMD. A calibration framework was also set up to evaluate the behaviour, parameter identifiability, and uncertainties of two-component LPMs. The modelling results provided insights on the subsurface flow conditions, the recharge contributions, and the spatial variation of groundwater transit time. The subsurface flow conditions at the study site can be best represented by the linear-piston flow distribution. The contributions of the recharge sources change with distance to the river. The mean transit time (mTT) of riverbank infiltration increases with the length of the horizontal flow path and the decreasing gradient between river and groundwater. River water infiltrates horizontally mainly via the highly permeable aquifer, resulting in short mTTs (<40 weeks) for locations close to the river (<200 m). The vertical infiltration from precipitation takes place primarily via a low-permeable overlying aquitard, resulting in considerably longer mTTs (>80 weeks). Notably, the transit time of precipitation infiltration is independent of the distance to the river. All these results are hydrologically plausible and could be quantified by the presented method for the first time. This study indicates that the highly complex mechanism of surface-groundwater interaction at riverbank infiltration systems can be conceptualized by exploiting two-component LPMs. It is illustrated that the model concept can be used as a tool to investigate the hydrological functioning of mixing processes and the flow path of multiple water components in riverbank infiltration systems. Lastly, a suite of time series analysis approaches was applied to examine the groundwater dynamics in the VMD. The assessment was focused on the time-variant trends of groundwater levels (GWLs), the groundwater memory effect (representing the time that an aquifer holds water), and the hydraulic response between surface water and multi-layer alluvial aquifers. The analysis indicates that the aquifers act as low-pass filters to reduce the high-frequency signals in the GWL variations, and limit the recharge to the deep groundwater. The groundwater abstraction has exceeded groundwater recharge between 1997 and 2017, leading to the decline of groundwater levels (0.01-0.55 m/year) in all considered aquifers in the VMD. The memory effect varies according to the geographical location, being shorter in shallow aquifers and flood-prone areas and longer in deep aquifers and coastal regions. Groundwater depth, season, and location primarily control the variation of the response time between the river and alluvial aquifers. These findings are important contributions to the hydrogeological literature of a little-known groundwater system in an alluvial setting. It is suggested that time series analysis can be used as an efficient tool to understand groundwater systems where resources are insufficient to develop a physical-based groundwater model. This doctoral thesis demonstrates that important aspects of hydrological processes can be understood by statistical analysis of stable water isotope and monitoring data. The approaches developed in this thesis can be easily transferred to regions in similar tropical environments, particularly those in alluvial settings. The results of the thesis can be used as a baseline for future isotope-based studies and contribute to the hydrogeological literature of little-known groundwater systems in the VMD.},
  language  = {en}
}
@phdthesis{Schoppa2023,
  author    = {Schoppa, Lukas},
  title     = {Dynamics in the flood vulnerability of companies},
  doi       = {10.25932/publishup-59242},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-592424},
  school      = {Universit{\"a}t Potsdam},
  pages     = {X, 165},
  year      = {2023},
  abstract  = {River flooding is a constant peril for societies, causing direct economic losses in the order of \$100 billion worldwide each year. Under global change, the prolonged concentration of people and assets in floodplains is accompanied by an emerging intensification of flood extremes due to anthropogenic global warming, ultimately exacerbating flood risk in many regions of the world. Flood adaptation plays a key role in the mitigation of impacts, but poor understanding of vulnerability and its dynamics limits the validity of predominant risk assessment methods and impedes effective adaptation strategies. Therefore, this thesis investigates new methods for flood risk assessment that embrace the complexity of flood vulnerability, using the understudied commercial sector as an application example. Despite its importance for accurate risk evaluation, flood loss modeling has been based on univariable and deterministic stage-damage functions for a long time. However, such simplistic methods only insufficiently describe the large variation in damage processes, which initiated the development of multivariable and probabilistic loss estimation techniques. The first study of this thesis developed flood loss models for companies that are based on emerging statistical and machine learning approaches (i.e., random forest, Bayesian network, Bayesian regression). In a benchmarking experiment on basis of object-level loss survey data, the study showed that all proposed models reproduced the heterogeneity in damage processes and outperformed conventional stage-damage functions with respect to predictive accuracy. Another advantage of the novel methods is that they convey probabilistic information in predictions, which communicates the large remaining uncertainties transparently and, hence, supports well-informed risk assessment. Flood risk assessment combines vulnerability assessment (e.g., loss estimation) with hazard and exposure analyses. Although all of the three risk drivers interact and change over time, such dependencies and dynamics are usually not explicitly included in flood risk models. Recently, systemic risk assessment that dissolves the isolated consideration of risk drivers has gained traction, but the move to holistic risk assessment comes with limited thoroughness in terms of loss estimation and data limitations. In the second study, I augmented a socio-hydrological system dynamics model for companies in Dresden, Germany, with the multivariable Bayesian regression loss model from the first study. The additional process-detail and calibration data improved the loss estimation in the systemic risk assessment framework and contributed to more accurate and reliable simulations. The model uses Bayesian inference to quantify uncertainty and learn the model parameters from a combination of prior knowledge and diverse data. The third study demonstrates the potential of the socio-hydrological flood risk model for continuous, long-term risk assessment and management. Using hydroclimatic ad socioeconomic forcing data, I projected a wide range of possible risk trajectories until the end of the century, taking into account the adaptive behavior of companies. The study results underline the necessity of increased adaptation efforts to counteract the expected intensification of flood risk due to climate change. A sensitivity analysis of the effectiveness of different adaptation measures and strategies revealed that optimized adaptation has the potential to mitigate flood risk by up to 60\%, particularly when combining structural and non-structural measures. Additionally, the application shows that systemic risk assessment is capable of capturing adverse long-term feedbacks in the human-flood system such as the levee effect. Overall, this thesis advances the representation of vulnerability in flood risk modeling by offering modeling solutions that embrace the complexity of human-flood interactions and quantify uncertainties consistently using probabilistic modeling. The studies show how scarce information in data and previous experiments can be integrated in the inference process to provide model predictions and simulations that are reliable and rich in information. Finally, the focus on the flood vulnerability of companies provides new insights into the heterogeneous damage processes and distinct flood coping of this sector.},
  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}
}