@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}
}
@article{BryantDaviesSoletal.2022,
  author    = {Bryant, Seth and Davies, Evan and Sol, David and Davis, Sandy},
  title     = {The progression of flood risk in southern Alberta since the 2013 flood},
  series = {Journal of flood risk management},
  volume    = {15},
  journal   = {Journal of flood risk management},
  number    = {3},
  publisher = {Wiley-Blackwell},
  address   = {Oxford},
  issn      = {1753-318X},
  doi       = {10.1111/jfr3.12811},
  pages     = {18},
  year      = {2022},
  abstract  = {After a century of semi-restricted floodplain development, Southern Alberta, Canada, was struck by the devastating 2013 Flood. Aging infrastructure and limited property-level floodproofing likely contributed to the \$4-6 billion (CAD) losses. Following this catastrophe, Alberta has seen a revival in flood management, largely focused on structural protections. However, concurrent with the recent structural work was a 100,000+ increase in Calgary's population in the 5 years following the flood, leading to further densification of high-hazard areas. This study implements the novel Stochastic Object-based Flood damage Dynamic Assessment (SOFDA) model framework to quantify the progression of the direct-damage flood risk in a mature urban neighborhood after the 2013 Flood. Five years of remote-sensing data, property assessment records, and inundation simulations following the flood are used to construct the model. Results show that in these 5 years, vulnerability trends (like densification) have increased flood risk by 4\%; however, recent structural mitigation projects have reduced overall flood risk by 47\% for this case study. These results demonstrate that the flood management revival in Southern Alberta has largely been successful at reducing flood risk; however, the gains are under threat from continued development and densification absent additional floodproofing regulations.},
  language  = {en}
}
@article{DevittNealWageneretal.2021,
  author    = {Devitt, Laura and Neal, Jeffrey and Wagener, Thorsten and Coxon, Gemma},
  title     = {Uncertainty in the extreme flood magnitude estimates of large-scale flood hazard models},
  series = {Environmental research letters : ERL / Institute of Physics},
  volume    = {16},
  journal   = {Environmental research letters : ERL / Institute of Physics},
  number    = {6},
  publisher = {IOP Publ. Ltd.},
  address   = {Bristol},
  issn      = {1748-9326},
  doi       = {10.1088/1748-9326/abfac4},
  pages     = {15},
  year      = {2021},
  abstract  = {The growing worldwide impact of flood events has motivated the development and application of global flood hazard models (GFHMs). These models have become useful tools for flood risk assessment and management, especially in regions where little local hazard information is available. One of the key uncertainties associated with GFHMs is the estimation of extreme flood magnitudes to generate flood hazard maps. In this study, the 1-in-100 year flood (Q100) magnitude was estimated using flow outputs from four global hydrological models (GHMs) and two global flood frequency analysis datasets for 1350 gauges across the conterminous US. The annual maximum flows of the observed and modelled timeseries of streamflow were bootstrapped to evaluate the sensitivity of the underlying data to extrapolation. Results show that there are clear spatial patterns of bias associated with each method. GHMs show a general tendency to overpredict Western US gauges and underpredict Eastern US gauges. The GloFAS and HYPE models underpredict Q100 by more than 25\% in 68\% and 52\% of gauges, respectively. The PCR-GLOBWB and CaMa-Flood models overestimate Q100 by more than 25\% at 60\% and 65\% of gauges in West and Central US, respectively. The global frequency analysis datasets have spatial variabilities that differ from the GHMs. We found that river basin area and topographic elevation explain some of the spatial variability in predictive performance found in this study. However, there is no single model or method that performs best everywhere, and therefore we recommend a weighted ensemble of predictions of extreme flood magnitudes should be used for large-scale flood hazard assessment.},
  language  = {en}
}
@phdthesis{Falter2016,
  author    = {Falter, Daniela},
  title     = {A novel approach for large-scale flood risk assessments},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-90239},
  school      = {Universit{\"a}t Potsdam},
  pages     = {95},
  year      = {2016},
  abstract  = {In the past, floods were basically managed by flood control mechanisms. The focus was set on the reduction of flood hazard. The potential consequences were of minor interest. Nowadays river flooding is increasingly seen from the risk perspective, including possible consequences. Moreover, the large-scale picture of flood risk became increasingly important for disaster management planning, national risk developments and the (re-) insurance industry. Therefore, it is widely accepted that risk-orientated flood management ap-proaches at the basin-scale are needed. However, large-scale flood risk assessment methods for areas of several 10,000 km² are still in early stages. Traditional flood risk assessments are performed reach wise, assuming constant probabilities for the entire reach or basin. This might be helpful on a local basis, but where large-scale patterns are important this approach is of limited use. Assuming a T-year flood (e.g. 100 years) for the entire river network is unrealistic and would lead to an overestimation of flood risk at the large scale. Due to the lack of damage data, additionally, the probability of peak discharge or rainfall is usually used as proxy for damage probability to derive flood risk. With a continuous and long term simulation of the entire flood risk chain, the spatial variability of probabilities could be consider and flood risk could be directly derived from damage data in a consistent way. The objective of this study is the development and application of a full flood risk chain, appropriate for the large scale and based on long term and continuous simulation. The novel approach of 'derived flood risk based on continuous simulations' is introduced, where the synthetic discharge time series is used as input into flood impact models and flood risk is directly derived from the resulting synthetic damage time series. The bottleneck at this scale is the hydrodynamic simu-lation. To find suitable hydrodynamic approaches for the large-scale a benchmark study with simplified 2D hydrodynamic models was performed. A raster-based approach with inertia formulation and a relatively high resolution of 100 m in combination with a fast 1D channel routing model was chosen. To investigate the suitability of the continuous simulation of a full flood risk chain for the large scale, all model parts were integrated into a new framework, the Regional Flood Model (RFM). RFM consists of the hydrological model SWIM, a 1D hydrodynamic river network model, a 2D raster based inundation model and the flood loss model FELMOps+r. Subsequently, the model chain was applied to the Elbe catchment, one of the largest catchments in Germany. For the proof-of-concept, a continuous simulation was per-formed for the period of 1990-2003. Results were evaluated / validated as far as possible with available observed data in this period. Although each model part introduced its own uncertainties, results and runtime were generally found to be adequate for the purpose of continuous simulation at the large catchment scale. Finally, RFM was applied to a meso-scale catchment in the east of Germany to firstly perform a flood risk assessment with the novel approach of 'derived flood risk assessment based on continuous simulations'. Therefore, RFM was driven by long term synthetic meteorological input data generated by a weather generator. Thereby, a virtual time series of climate data of 100 x 100 years was generated and served as input to RFM providing subsequent 100 x 100 years of spatially consistent river discharge series, inundation patterns and damage values. On this basis, flood risk curves and expected annual damage could be derived directly from damage data, providing a large-scale picture of flood risk. In contrast to traditional flood risk analysis, where homogenous return periods are assumed for the entire basin, the presented approach provides a coherent large-scale picture of flood risk. The spatial variability of occurrence probability is respected. Additionally, data and methods are consistent. Catchment and floodplain processes are repre-sented in a holistic way. Antecedent catchment conditions are implicitly taken into account, as well as physical processes like storage effects, flood attenuation or channel-floodplain interactions and related damage influencing effects. Finally, the simulation of a virtual period of 100 x 100 years and consequently large data set on flood loss events enabled the calculation of flood risk directly from damage distributions. Problems associated with the transfer of probabilities in rainfall or peak runoff to probabilities in damage, as often used in traditional approaches, are bypassed. RFM and the 'derived flood risk approach based on continuous simulations' has the potential to provide flood risk statements for national planning, re-insurance aspects or other questions where spatially consistent, large-scale assessments are required.},
  language  = {en}
}
@article{Hudson2020,
  author    = {Hudson, Paul},
  title     = {The affordability of flood risk property-level adaptation measures},
  series = {Risk Analysis},
  volume    = {40},
  journal   = {Risk Analysis},
  number    = {6},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {0272-4332},
  doi       = {10.1111/risa.13465},
  pages     = {1151 -- 1167},
  year      = {2020},
  abstract  = {The affordability of property-level adaptation measures against flooding is crucial due to the movement toward integrated flood risk management, which requires the individuals threatened by flooding to actively manage flooding. It is surprising to find that affordability is not often discussed, given the important roles that affordability and social justice play regarding flood risk management. This article provides a starting point for investigating the potential rate of unaffordability of flood risk property-level adaptation measures across Europe using two definitions of affordability, which are combined with two different affordability thresholds from within flood risk research. It uses concepts of investment and payment affordability, with affordability thresholds based on residual income and expenditure definitions of unaffordability. These concepts, in turn, are linked with social justice through fairness concerns, in that, all should have equal capability to act, of which affordability is one avenue. In doing so, it was found that, for a large proportion of Europe, property owners generally cannot afford to make one-time payment of the cost of protective measures. These can be made affordable with installment payment mechanisms or similar mechanisms that spread costs over time. Therefore, the movement toward greater obligations for flood-prone residents to actively adapt to flooding should be accompanied by socially accessible financing mechanisms.},
  language  = {en}
}
@article{HudsonHagedoornBubeck2020,
  author    = {Hudson, Paul and Hagedoorn, Liselotte and Bubeck, Philip},
  title     = {Potential linkages between social capital, flood risk perceptions, and self-efficacy},
  series = {International journal of disaster risk science},
  volume    = {11},
  journal   = {International journal of disaster risk science},
  number    = {3},
  publisher = {Springer},
  address   = {Berlin},
  issn      = {2095-0055},
  doi       = {10.1007/s13753-020-00259-w},
  pages     = {251 -- 262},
  year      = {2020},
  abstract  = {A growing focus is being placed on both individuals and communities to adapt to flooding as part of the Sendai Framework for Disaster Risk Reduction 2015-2030. Adaptation to flooding requires sufficient social capital (linkages between members of society), risk perceptions (understanding of risk), and self-efficacy (self-perceived ability to limit disaster impacts) to be effective. However, there is limited understanding of how social capital, risk perceptions, and self-efficacy interact. We seek to explore how social capital interacts with variables known to increase the likelihood of successful adaptation. To study these linkages we analyze survey data of 1010 respondents across two communities in Thua Tien-Hue Province in central Vietnam, using ordered probit models. We find positive correlations between social capital, risk perceptions, and self-efficacy overall. This is a partly contrary finding to what was found in previous studies linking these concepts in Europe, which may be a result from the difference in risk context. The absence of an overall negative exchange between these factors has positive implications for proactive flood risk adaptation.},
  language  = {en}
}
@article{HudsonPhamBubeck2019,
  author    = {Hudson, Paul and Pham, My and Bubeck, Philip},
  title     = {An evaluation and monetary assessment of the impact of flooding on subjective well-being across genders in Vietnam},
  series = {Climate \& development},
  volume    = {11},
  journal   = {Climate \& development},
  number    = {7},
  publisher = {Routledge, Taylor \& Francis Group},
  address   = {Abingdon},
  issn      = {1756-5529},
  doi       = {10.1080/17565529.2019.1579698},
  pages     = {623 -- 637},
  year      = {2019},
  abstract  = {The intangible impacts of floods on welfare are not well investigated, even though they are important aspects of welfare. Moreover, flooding has gender based impacts on welfare. These differing impacts create a gender based flood risk resilience gap. We study the intangible impacts of flood risk on the subjective well-being of residents in central Vietnam. The measurement of intangible impacts through subjective well-being is a growing field within flood risk research. We find an initial drop in welfare through subjective well-being across genders when a flood is experienced. Male respondents tended to recover their welfare losses by around 80\% within 5 years while female respondents were associated with a welfare recovery of around 70\%. A monetization of the impacts floods have on an individual's subjective well-being shows that for the average female respondent, between 41\% to 86\% of annual income would be required to compensate subjective well-being losses after 5 years of experiencing a flood. The corresponding value for males is 30\% to 57\% of annual income. This shows that the intangible impacts of flood risk are important (across genders) and need to be integrated into flood (or climate) risk assessments to develop more socially appropriate risk management strategies.},
  language  = {en}
}
@misc{HudsonPhamBubeck2019,
  author    = {Hudson, Paul and Pham, My and Bubeck, Philip},
  title     = {An evaluation and monetary assessment of the impact of flooding on subjective well-being across genders in Vietnam},
  series = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  number    = {736},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-43341},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-433414},
  pages     = {623 -- 637},
  year      = {2019},
  abstract  = {The intangible impacts of floods on welfare are not well investigated, even though they are important aspects of welfare. Moreover, flooding has gender based impacts on welfare. These differing impacts create a gender based flood risk resilience gap. We study the intangible impacts of flood risk on the subjective well-being of residents in central Vietnam. The measurement of intangible impacts through subjective well-being is a growing field within flood risk research. We find an initial drop in welfare through subjective well-being across genders when a flood is experienced. Male respondents tended to recover their welfare losses by around 80\% within 5 years while female respondents were associated with a welfare recovery of around 70\%. A monetization of the impacts floods have on an individual's subjective well-being shows that for the average female respondent, between 41\% to 86\% of annual income would be required to compensate subjective well-being losses after 5 years of experiencing a flood. The corresponding value for males is 30\% to 57\% of annual income. This shows that the intangible impacts of flood risk are important (across genders) and need to be integrated into flood (or climate) risk assessments to develop more socially appropriate risk management strategies.},
  language  = {en}
}
@misc{HudsonThiekenBubeck2019,
  author    = {Hudson, Paul and Thieken, Annegret and Bubeck, Philip},
  title     = {The challenges of longitudinal surveys in the flood risk domain},
  series = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  number    = {759},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-43409},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-434092},
  pages     = {23},
  year      = {2019},
  abstract  = {There has been much research regarding the perceptions, preferences, behaviour, and responses of people exposed to flooding and other nat- ural hazards. Cross-sectional surveys have been the predominant method applied in such research. While cross-sectional data can provide a snapshot of a respondent's behaviour and perceptions, it cannot be assumed that the respondent's perceptions are constant over time. As a result, many important research questions relating to dynamic processes, such as changes in risk perceptions, adaptation behaviour, and resilience cannot be fully addressed by cross-sectional surveys. To overcome these shortcomings, there has been a call for developing longitudinal (or panel) datasets in research on natural hazards, vulnerabilities, and risks. However, experiences with implementing longitudinal surveys in the flood risk domain (FRD), which pose distinct methodological challenges, are largely lacking. The key problems are sample recruitment, attrition rate, and attrition bias. We present a review of the few existing longitudinal surveys in the FRD. In addition, we investigate the potential attrition bias and attrition rates in a panel dataset of flood-affected households in Germany. We find little potential for attrition bias to occur. High attrition rates across longitudinal survey waves are the larger concern. A high attrition rate rapidly depletes the longitudinal sample. To overcome high attrition, longitudinal data should be collected as part of a multisector partnership to allow for sufficient resources to implement sample retention strategies. If flood-specific panels are developed, different sample retention strategies should be applied and evaluated in future research to understand how much-needed longitudinal surveying techniques can be successfully applied to the study of individuals threatened by flooding.},
  language  = {en}
}
@article{MerzBassoFischeretal.2022,
  author    = {Merz, Bruno and Basso, Stefano and Fischer, Svenja and Lun, David and Bloeschl, Guenter and Merz, Ralf and Guse, Bjorn and Viglione, Alberto and Vorogushyn, Sergiy and Macdonald, Elena and Wietzke, Luzie and Schumann, Andreas},
  title     = {Understanding heavy tails of flood peak distributions},
  series = {Water resources research},
  volume    = {58},
  journal   = {Water resources research},
  number    = {6},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {0043-1397},
  doi       = {10.1029/2021WR030506},
  pages     = {37},
  year      = {2022},
  abstract  = {Statistical distributions of flood peak discharge often show heavy tail behavior, that is, extreme floods are more likely to occur than would be predicted by commonly used distributions that have exponential asymptotic behavior. This heavy tail behavior may surprise flood managers and citizens, as human intuition tends to expect light tail behavior, and the heaviness of the tails is very difficult to predict, which may lead to unnecessarily high flood damage. Despite its high importance, the literature on the heavy tail behavior of flood distributions is rather fragmented. In this review, we provide a coherent overview of the processes causing heavy flood tails and the implications for science and practice. Specifically, we propose nine hypotheses on the mechanisms causing heavy tails in flood peak distributions related to processes in the atmosphere, the catchment, and the river system. We then discuss to which extent the current knowledge supports or contradicts these hypotheses. We also discuss the statistical conditions for the emergence of heavy tail behavior based on derived distribution theory and relate them to the hypotheses and flood generation mechanisms. We review the degree to which the heaviness of the tails can be predicted from process knowledge and data. Finally, we recommend further research toward testing the hypotheses and improving the prediction of heavy tails.},
  language  = {en}
}