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Seit 1990 waren mehrere der großen Flussgebiete Mitteleuropas wiederholt von extremen Hochwassern betroffen. Da sowohl die Landoberfläche als auch die Flusssysteme weiter Teile Mitteleuropas in der Vergangenheit weitreichenden Eingriffen ausgesetzt gewesen sind, wird bei der Suche nach den Ursachen für diese Häufung von Extremereignissen auch die Frage nach der Verantwortung des Menschen hierfür diskutiert. Gewässerausbau, Flächenversiegelung, intensive landwirtschaftliche Bodenbearbeitung, Flurbereinigung und Waldschäden sind nur einige Beispiele und Folgen der anthropogenen Eingriffe in die Landschaft. Aufgrund der Vielfalt der beteiligten Prozesse und deren Wechselwirkungen gibt es allerdings bislang nur Schätzungen darüber, wie sehr sich die Hochwassersituation hierdurch verändert hat. Vorrangiges Ziel dieser Arbeit ist es, mit Hilfe eines hydrologischen Modells systematisch darzustellen, in welcher Weise, in welcher Größenordnung und unter welchen Umständen die Art der Landnutzung auf die Hochwasserentstehung Einfluss nimmt. Dies wird anhand exemplarischer Modellanwendungen in der hydrologischen Mesoskala untersucht. Zu diesem Zweck wurde das deterministische und flächendifferenzierte hydrologische Modell wasim-eth ausgewählt, das sich durch eine ausgewogene Mischung aus physikalisch begründeten und konzeptionellen Ansätzen auszeichnet. Das Modell wurde im Rahmen dieser Arbeit um verschiedene Aspekte erweitert, die für die Charakterisierung des Einflusses der Landnutzung auf die Hochwasserentstehung wichtig sind: (1) Bevorzugtes Fließen in Makroporen wird durch eine Zweiteilung des Bodens in Makroporen und Bodenmatrix dargestellt, die schnelle Infiltration und Perkolation jenseits der hydraulischen Leitfähigkeit der Bodenmatrix ermöglicht. (2) Verschlämmung äußert sich im Modell abhängig von Niederschlagsintensität und Vegetationsbedeckungsgrad als Verschlechterung der Infiltrationsbedingungen an der Bodenoberfläche. (3) Das heterogene Erscheinungsbild bebauter Flächen mit einer Mischung aus versiegelten Bereichen und Freiflächen wird berücksichtigt, indem jede Teilfläche je nach Versiegelungsgrad in einen unversiegelten Bereich und einen versiegelten Bereich mit Anschluss an die Kanalisation aufgeteilt wird. (4) Dezentraler Rückhalt von Niederschlagswasser kann sowohl für natürliche Mulden als auch für gezielt angelegte Versickerungsmulden mit definierten Infiltrationsbedingungen simuliert werden. Das erweiterte Modell wird exemplarisch auf drei mesoskalige Teileinzugsgebiete des Rheins angewandt. Diese drei Gebiete mit einer Fläche von zwischen 100 und 500 km² wurden im Hinblick darauf ausgewählt, dass jeweils eine der drei Hauptlandnutzungskategorien Bebauung, landwirtschaftliche Nutzung oder Wald dominiert. Für die drei Untersuchungsgebiete sind räumlich explizite Landnutzungs- und Landbedeckungsszenarien entworfen worden, deren Einfluss auf die Hochwasserentstehung mit Hilfe des erweiterten hydrologischen Modells simuliert wird. Im Einzelnen werden die Auswirkungen von Verstädterung, Maßnahmen zur Niederschlagsversickerung in Siedlungsgebieten, Stilllegung agrarisch genutzter Flächen, veränderter landwirtschaftlicher Bodenbearbeitung, Aufforstung sowie von Sturmschäden in Wäldern untersucht. Diese Eingriffe beeinflussen die Interzeption von Niederschlag, dessen Infiltration, die oberflächennahen unterirdischen Fließprozesse sowie, zum Beispiel im Fall der Kanalisation, auch die Abflusskonzentration. Die hydrologischen Simulationen demonstrieren, dass die Versiegelung einer Fläche den massivsten Eingriff in die natürlichen Verhältnisse darstellt und deshalb die stärksten (negativen) Veränderungen der Hochwassersituation hervorbringt. Außerdem wird deutlich, dass eine bloße Änderung des Interzeptionsvermögens zu keinen wesentlichen Veränderungen führt, da die Speicherkapazität der Pflanzenoberflächen im Verhältnis zum Volumen hochwasserauslösender Niederschläge eher klein ist. Stärkere Veränderungen ergeben sich hingegen aus einer Änderung der Infiltrationsbedingungen. Die Grenzen der entwickelten Methodik zeigen sich am deutlichsten bei der Simulation veränderter landwirtschaftlicher Bewirtschaftungsmethoden, deren mathematische Beschreibung und zahlenmäßige Charakterisierung aufgrund der Komplexität der beteiligten Prozesse mit großen Unsicherheiten behaftet ist. Die Modellierungsergebnisse belegen darüber hinaus, dass pauschale Aussagen zum Einfluss der Landnutzung auf die Hochwasserentstehung aufgrund der entscheidenden Bedeutung der klimatischen und physiographischen Randbedingungen unzulässig sind. Zu den klimatischen Randbedingungen zählen sowohl Niederschlagsintensität und -dauer als auch die Feuchtebedingungen vor einem hochwasserauslösenden Niederschlag. Die physiographischen Randbedingungen sind von der geomorphologischen und geologischen Ausstattung des Gebiets vorgegeben. Weiterhin muss der räumliche und zeitliche Maßstab, über den Aussagen getroffen werden, klar definiert sein, da sich mit steigender Einzugsgebietsgröße die relative Bedeutung sowohl der verschiedenen Niederschlagstypen als auch der physiographischen Eigenschaften verschiebt. Dies wird in der vorliegenden Arbeit im Gegensatz zu vielen anderen Untersuchungen konsequent berücksichtigt. In Abhängigkeit von Randbedingungen und räumlichen Maßstab sind aufgrund der gewonnen Erkenntnisse folgende Aussagen zum Einfluss von Landnutzungsänderungen auf die Hochwasserentstehung möglich: (1) Für intensive konvektive Niederschlagsereignisse mit tendenziell geringer Vorfeuchte ist der Einfluss der Landnutzung größer als für langanhaltende advektive Niederschläge geringer Intensität, da im ersten Fall veränderte Infiltrationsbedingungen stärker zum Tragen kommen als bei kleinen Niederschlagsintensitäten. (2) In kleinen Einzugsgebieten, wo kleinräumige Konvektivzellen zu Hochwassern führen können, ist der Einfluss der Landnutzung dementsprechend größer als in großen Flussgebieten wie dem Rheingebiet, wo vor allem langanhaltende advektive Ereignisse (unter Umständen verbunden mit Schneeschmelze) relevant sind. (3) In Gebieten mit guten Speichereigenschaften wie mächtigen, gut durchlässigen Böden und gut durchlässigem Gesteinsuntergrund ist der Einfluss der Landnutzung größer als in Gebieten mit geringmächtigen Böden und geringdurchlässigem Festgestein. Dies ist darin begründet, dass in Gebieten mit guten Speichereigenschaften bei einer Verschlechterung der Infiltrationsbedingungen mehr Speicherraum für Niederschlag verloren geht als in anderen Gebieten.
River reaches protected by dikes exhibit high damage potential due to strong value accumulation in the hinterland areas. While providing an efficient protection against low magnitude flood events, dikes may fail under the load of extreme water levels and long flood durations. Hazard and risk assessments for river reaches protected by dikes have not adequately considered the fluvial inundation processes up to now. Particularly, the processes of dike failures and their influence on the hinterland inundation and flood wave propagation lack comprehensive consideration. This study focuses on the development and application of a new modelling system which allows a comprehensive flood hazard assessment along diked river reaches under consideration of dike failures. The proposed Inundation Hazard Assessment Model (IHAM) represents a hybrid probabilistic-deterministic model. It comprises three models interactively coupled at runtime. These are: (1) 1D unsteady hydrodynamic model of river channel and floodplain flow between dikes, (2) probabilistic dike breach model which determines possible dike breach locations, breach widths and breach outflow discharges, and (3) 2D raster-based diffusion wave storage cell model of the hinterland areas behind the dikes. Due to the unsteady nature of the 1D and 2D coupled models, the dependence between hydraulic load at various locations along the reach is explicitly considered. The probabilistic dike breach model describes dike failures due to three failure mechanisms: overtopping, piping and slope instability caused by the seepage flow through the dike core (micro-instability). The 2D storage cell model driven by the breach outflow boundary conditions computes an extended spectrum of flood intensity indicators such as water depth, flow velocity, impulse, inundation duration and rate of water rise. IHAM is embedded in a Monte Carlo simulation in order to account for the natural variability of the flood generation processes reflected in the form of input hydrographs and for the randomness of dike failures given by breach locations, times and widths. The model was developed and tested on a ca. 91 km heavily diked river reach on the German part of the Elbe River between gauges Torgau and Vockerode. The reach is characterised by low slope and fairly flat extended hinterland areas. The scenario calculations for the developed synthetic input hydrographs for the main river and tributary were carried out for floods with return periods of T = 100, 200, 500, 1000 a. Based on the modelling results, probabilistic dike hazard maps could be generated that indicate the failure probability of each discretised dike section for every scenario magnitude. In the disaggregated display mode, the dike hazard maps indicate the failure probabilities for each considered breach mechanism. Besides the binary inundation patterns that indicate the probability of raster cells being inundated, IHAM generates probabilistic flood hazard maps. These maps display spatial patterns of the considered flood intensity indicators and their associated return periods. Finally, scenarios of polder deployment for the extreme floods with T = 200, 500, 1000 were simulated with IHAM. The developed IHAM simulation system represents a new scientific tool for studying fluvial inundation dynamics under extreme conditions incorporating effects of technical flood protection measures. With its major outputs in form of novel probabilistic inundation and dike hazard maps, the IHAM system has a high practical value for decision support in flood management.
Flood hazard estimations are conducted with a variety of methods. These include flood frequency analysis (FFA), hydrologic and hydraulic modelling, probable maximum discharges as well as climate scenarios. However, most of these methods assume stationarity of the used time series, i.e., the series must not exhibit trends. Against the background of climate change and proven significant trends in atmospheric circulation patterns, it is questionable whether these changes are also reflected in the discharge data. The aim of this PhD thesis is therefore to clarify, in a spatially-explicit manner, whether the available discharge data derived from selected German catchments exhibit trends. Concerning the flood hazard, the suitability of the currently used stationary FFA approaches is evaluated for the discharge data. Moreover, dynamics in atmospheric circulation patterns are studied and the link between trends in these patterns and discharges is investigated. To tackle this research topic, a number of different analyses are conducted. The first part of the PhD thesis comprises the study and trend test of 145 discharge series from catchments, which cover most of Germany for the period 1951–2002. The seasonality and trend pattern of eight flood indicators, such as maximum series and peak-over-threshold series, are analyzed in a spatially-explicit manner. Analyses are performed on different spatial scales: at the local scale, through gauge-specific analyses, and on the catchment-wide and basin scales. Besides the analysis of discharge series, data on atmospheric circulation patterns (CP) are an important source of information, upon which conclusions about the flood hazard can be drawn. The analyses of these circulation patterns (after Hess und Brezowsky) and the study of the link to peak discharges form the second part of the thesis. For this, daily data on the dominant CP across Europe are studied; these are represented by different indicators, which are tested for trend. Moreover, analyses are performed to extract flood triggering circulation patterns and to estimate the flood potential of CPs. Correlations between discharge series and CP indicators are calculated to assess a possible link between them. For this research topic, data from 122 meso-scale catchments in the period 1951–2002 are used. In a third part, the Mulde catchment, a mesoscale sub-catchment of the Elbe basin, is studied in more detail. Fifteen discharge series of different lengths in the period 1910–2002 are available for the seasonally differentiated analysis of the flood potential of CPs and flood influencing landscape parameters. For trend tests of discharge and CP data, different methods are used. The Mann-Kendall test is applied with a significance level of 10%, ensuring statistically sound results. Besides the test of the entire series for trend, multiple time-varying trend tests are performed with the help of a resampling approach in order to better differentiate short-term fluctuations from long-lasting trends. Calculations of the field significance complement the flood hazard assessment for the studied regions. The present thesis shows that the flood hazard is indeed significantly increasing for selected regions in Germany during the winter season. Especially affected are the middle mountain ranges in Central Germany. This increase of the flood hazard is attributed to a longer persistence of selected CPs during winter. Increasing trends in summer floods are found in the Rhine and Danube catchments, decreasing trends in the Elbe and Weser catchments. Finally, a significant trend towards a reduced diversity of CPs is found causing fewer patterns with longer persistence to dominate the weather over Europe. The detailed study of the Mulde catchment reveals a flood regime with frequent low winter floods and fewer summer floods, which bear, however, the potential of becoming extreme. Based on the results, the use of instationary approaches for flood hazard estimation is recommended in order to account for the detected trends in many of the series. Through this methodology it is possible to directly consider temporal changes in flood series, which in turn reduces the possibility of large under- or overestimations of the extreme discharges, respectively.
Even though quite different in occurrence and consequences, from a modeling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding: uncertainty about the modeling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Nevertheless deterministic approaches are still widely used in natural hazard assessments, holding the risk of underestimating the hazard with disastrous effects. The all-round probabilistic framework of Bayesian networks constitutes an attractive alternative. In contrast to deterministic proceedings, it treats response variables as well as explanatory variables as random variables making no difference between input and output variables. Using a graphical representation Bayesian networks encode the dependency relations between the variables in a directed acyclic graph: variables are represented as nodes and (in-)dependencies between variables as (missing) edges between the nodes. The joint distribution of all variables can thus be described by decomposing it, according to the depicted independences, into a product of local conditional probability distributions, which are defined by the parameters of the Bayesian network. In the framework of this thesis the Bayesian network approach is applied to different natural hazard domains (i.e. seismic hazard, flood damage and landslide assessments). Learning the network structure and parameters from data, Bayesian networks reveal relevant dependency relations between the included variables and help to gain knowledge about the underlying processes. The problem of Bayesian network learning is cast in a Bayesian framework, considering the network structure and parameters as random variables itself and searching for the most likely combination of both, which corresponds to the maximum a posteriori (MAP score) of their joint distribution given the observed data. Although well studied in theory the learning of Bayesian networks based on real-world data is usually not straight forward and requires an adoption of existing algorithms. Typically arising problems are the handling of continuous variables, incomplete observations and the interaction of both. Working with continuous distributions requires assumptions about the allowed families of distributions. To "let the data speak" and avoid wrong assumptions, continuous variables are instead discretized here, thus allowing for a completely data-driven and distribution-free learning. An extension of the MAP score, considering the discretization as random variable as well, is developed for an automatic multivariate discretization, that takes interactions between the variables into account. The discretization process is nested into the network learning and requires several iterations. Having to face incomplete observations on top, this may pose a computational burden. Iterative proceedings for missing value estimation become quickly infeasible. A more efficient albeit approximate method is used instead, estimating the missing values based only on the observations of variables directly interacting with the missing variable. Moreover natural hazard assessments often have a primary interest in a certain target variable. The discretization learned for this variable does not always have the required resolution for a good prediction performance. Finer resolutions for (conditional) continuous distributions are achieved with continuous approximations subsequent to the Bayesian network learning, using kernel density estimations or mixtures of truncated exponential functions. All our proceedings are completely data-driven. We thus avoid assumptions that require expert knowledge and instead provide domain independent solutions, that are applicable not only in other natural hazard assessments, but in a variety of domains struggling with uncertainties.
Large Central European flood events of the past have demonstrated that flooding can affect several river basins at the same time leading to catastrophic economic and humanitarian losses that can stretch emergency resources beyond planned levels of service. For Germany, the spatial coherence of flooding, the contributing processes and the role of trans-basin floods for a national risk assessment is largely unknown and analysis is limited by a lack of systematic data, information and knowledge on past events. This study investigates the frequency and intensity of trans-basin flood events in Germany. It evaluates the data and information basis on which knowledge about trans-basin floods can be generated in order to improve any future flood risk assessment. In particu-lar, the study assesses whether flood documentations and related reports can provide a valuable data source for understanding trans-basin floods. An adaptive algorithm was developed that systematically captures trans-basin floods using series of mean daily discharge at a large number of sites of even time series length (1952-2002). It identifies the simultaneous occurrence of flood peaks based on the exceedance of an initial threshold of a 10 year flood at one location and consecutively pools all causally related, spatially and temporally lagged peak recordings at the other locations. A weighted cumulative index was developed that accounts for the spatial extent and the individual flood magnitudes within an event and allows quantifying the overall event severity. The parameters of the method were tested in a sensitivity analysis. An intensive study on sources and ways of information dissemination of flood-relevant publications in Germany was conducted. Based on the method of systematic reviews a strategic search approach was developed to identify relevant documentations for each of the 40 strongest trans-basin flood events. A novel framework for assessing the quality of event specific flood reports from a user’s perspective was developed and validated by independent peers. The framework was designed to be generally applicable for any natural hazard type and assesses the quality of a document addressing accessibility as well as representational, contextual, and intrinsic dimensions of quality. The analysis of time-series of mean daily discharge resulted in the identification of 80 trans-basin flood events within the period 1952-2002 in Germany. The set is dominated by events that were recorded in the hydrological winter (64%); 36% occurred during the summer months. The occurrence of floods is characterised by a distinct clustering in time. Dividing the study period into two sub-periods, we find an increase in the percentage of winter events from 58% in the first to 70.5% in the second sub-period. Accordingly, we find a significant increase in the number of extreme trans-basin floods in the second sub-period. A large body of 186 flood relevant documentations was identified. For 87.5% of the 40 strongest trans-basin floods in Germany at least one report has been found and for the most severe floods a substantial amount of documentation could be obtained. 80% of the material can be considered grey literature (i.e. literature not controlled by commercial publishers). The results of the quality assessment show that the majority of flood event specific reports are of a good quality, i.e. they are well enough drafted, largely accurate and objective, and contain a substantial amount of information on the sources, pathways and receptors/consequences of the floods. The inclusion of this information in the process of knowledge building for flood risk assessment is recommended. Both the results as well as the data produced in this study are openly accessible and can be used for further research. The results of this study contribute to an improved spatial risk assessment in Germany. The identified set of trans-basin floods provides the basis for an assessment of the chance that flooding occurs simultaneously at a number of sites. The information obtained from flood event documentation can usefully supplement the analysis of the processes that govern flood risk.
In the last decade, the number and dimensions of catastrophic flooding events in the Niger River Basin (NRB) have markedly increased. Despite the devastating impact of the floods on the population and the mainly agriculturally based economy of the riverine nations, awareness of the hazards in policy and science is still low. The urgency of this topic and the existing research deficits are the motivation for the present dissertation.
The thesis is an initial detailed assessment of the increasing flood risk in the NRB. The research strategy is based on four questions regarding (1) features of the change in flood risk, (2) reasons for the change in the flood regime, (3) expected changes of the flood regime given climate and land use changes, and (4) recommendations from previous analysis for reducing the flood risk in the NRB.
The question examining the features of change in the flood regime is answered by means of statistical analysis. Trend, correlation, changepoint, and variance analyses show that, in addition to the factors exposure and vulnerability, the hazard itself has also increased significantly in the NRB, in accordance with the decadal climate pattern of West Africa. The northern arid and semi-arid parts of the NRB are those most affected by the changes.
As potential reasons for the increase in flood magnitudes, climate and land use changes are attributed by means of a hypothesis-testing framework. Two different approaches, based on either data analysis or simulation, lead to similar results, showing that the influence of climatic changes is generally larger compared to that of land use changes. Only in the dry areas of the NRB is the influence of land use changes comparable to that of climatic alterations.
Future changes of the flood regime are evaluated using modelling results. First ensembles of statistically and dynamically downscaled climate models based on different emission scenarios are analyzed. The models agree with a distinct increase in temperature. The precipitation signal, however, is not coherent. The climate scenarios are used to drive an eco-hydrological model. The influence of climatic changes on the flood regime is uncertain due to the unclear precipitation signal. Still, in general, higher flood peaks are expected. In a next step, effects of land use changes are integrated into the model. Different scenarios show that regreening might help to reduce flood peaks. In contrast, an expansion of agriculture might enhance the flood peaks in the NRB. Similarly to the analysis of observed changes in the flood regime, the impacts of climate- and land use changes for the future scenarios are also most severe in the dry areas of the NRB.
In order to answer the final research question, the results of the above analysis are integrated into a range of recommendations for science and policy on how to reduce flood risk in the NRB. The main recommendations include a stronger consideration of the enormous natural climate variability in the NRB and a focus on so called “no-regret” adaptation strategies which account for high uncertainty, as well as a stronger consideration of regional differences. Regarding the prevention and mitigation of catastrophic flooding, the most vulnerable and sensitive areas in the basin, the arid and semi-arid Sahelian and Sudano-Sahelian regions, should be prioritized. Eventually, an active, science-based and science-guided flood policy is recommended. The enormous population growth in the NRB in connection with the expected deterioration of environmental and climatic conditions is likely to enhance the region´s vulnerability to flooding. A smart and sustainable flood policy can help mitigate these negative impacts of flooding on the development of riverine societies in West Africa.
Flood generation at the scale of large river basins is triggered by the interaction of the hydrological pre-conditions and the meteorological event conditions at different spatial and temporal scales. This interaction controls diverse flood generating processes and results in floods varying in magnitude and extent, duration as well as socio-economic consequences. For a process-based understanding of the underlying cause-effect relationships, systematic approaches are required. These approaches have to cover the complete causal flood chain, including the flood triggering meteorological event in combination with the hydrological (pre-)conditions in the catchment, runoff generation, flood routing, possible floodplain inundation and finally flood losses.
In this thesis, a comprehensive probabilistic process-based understanding of the causes and effects of floods is advanced. The spatial and temporal dynamics of flood events as well as the geophysical processes involved in the causal flood chain are revealed and the systematic interconnections within the flood chain are deciphered by means of the classification of their associated causes and effects. This is achieved by investigating the role of the hydrological pre-conditions and the meteorological event conditions with respect to flood occurrence, flood processes and flood characteristics as well as their interconnections at the river basin scale.
Broadening the knowledge about flood triggers, which up to now has been limited to linking large-scale meteorological conditions to flood occurrence, the influence of large-scale pre-event hydrological conditions on flood initiation is investigated. Using the Elbe River basin as an example, a classification of soil moisture, a key variable of pre-event conditions, is developed and a probabilistic link between patterns of soil moisture and flood occurrence is established. The soil moisture classification is applied to continuously simulated soil moisture data which is generated using the semi-distributed conceptual rainfall-runoff model SWIM. Applying successively a principal component analysis and a cluster analysis, days of similar soil moisture patterns are identified in the period November 1951 to October 2003.
The investigation of flood triggers is complemented by including meteorological conditions described by a common weather pattern classification that represents the main modes of atmospheric state variability. The newly developed soil moisture classification thereby provides the basis to study the combined impact of hydrological pre-conditions and large-scale meteorological event conditions on flood occurrence at the river basin scale.
A process-based understanding of flood generation and its associated probabilities is attained by classifying observed flood events into process-based flood types such as snowmelt floods or long-rain floods. Subsequently, the flood types are linked to the soil moisture and weather patterns. Further understanding of the processes is gained by modeling of the complete causal flood chain, incorporating a rainfall-runoff model, a 1D/2D hydrodynamic model and a flood loss model. A reshuffling approach based on weather patterns and the month of their occurrence is developed to generate synthetic data fields of meteorological conditions, which drive the model chain, in order to increase the flood sample size. From the large number of simulated flood events, the impact of hydro-meteorological conditions on various flood characteristics is detected through the analysis of conditional cumulative distribution functions and regression trees.
The results show the existence of catchment-scale soil moisture patterns, which comprise of large-scale seasonal wetting and drying components as well as of smaller-scale variations related to spatially heterogeneous catchment processes. Soil moisture patterns frequently occurring before the onset of floods are identified. In winter, floods are initiated by catchment-wide high soil moisture, whereas in summer the flood-initiating soil moisture patterns are diverse and the soil moisture conditions are less stable in time. The combined study of both soil moisture and weather patterns shows that the flood favoring hydro-meteorological patterns as well as their interactions vary seasonally. In the analysis period, 18 % of the weather patterns only result in a flood in the case of preceding soil saturation. The classification of 82 past events into flood types reveals seasonally varying flood processes that can be linked to hydro-meteorological patterns. For instance, the highest flood potential for long-rain floods is associated with a weather pattern that is often detected in the presence of so-called ‘Vb’ cyclones. Rain-on-snow and snowmelt floods are associated with westerly and north-westerly wind directions. The flood characteristics vary among the flood types and can be reproduced by the applied model chain. In total, 5970 events are simulated. They reproduce the observed event characteristics between September 1957 and August 2002 and provide information on flood losses. A regression tree analysis relates the flood processes of the simulated events to the hydro-meteorological (pre-)event conditions and highlights the fact that flood magnitude is primarily controlled by the meteorological event, whereas flood extent is primarily controlled by the soil moisture conditions.
Describing flood occurrence, processes and characteristics as a function of hydro-meteorological patterns, this thesis is part of a paradigm shift towards a process-based understanding of floods. The results highlight that soil moisture patterns as well as weather patterns are not only beneficial to a probabilistic conception of flood initiation but also provide information on the involved flood processes and the resulting flood characteristics.
The heavy rainfall events in recent years have caused great damage, which has increased the public awareness of the topic of heavy rainfall. For this reason, this article discusses how a systematic integration of heavy rainfall within the framework of the European Floods Directive would be possible and reasonable. For this purpose, a matrix covering possible synergies and barriers was created for all steps of the directive, which were then examined in 15 semi-structured interviews with representatives from specialized administration, the private sector and academia. Although there are some synergies, the additional effort required, especially regarding the identification of the risk areas and the higher level of detail required for risk modeling, would be so high that the European Floods Directive cannot be deemed to be an appropriate framework for heavy rainfall risk management. Nevertheless, there is a need for action, e.g. in the field of self-protection, improved risk communication to the population, combined with increased public and interagency cooperation.
Floods are among the most costly natural hazards that affect Europe and Germany, demanding a continuous adaptation of flood risk management. While social and economic development in recent years altered the flood risk patterns mainly with regard to an increase in flood exposure, different flood events are further expected to increase in frequency and severity in certain European regions due to climate change. As a result of recent major flood events in Germany, the German flood risk management shifted to more integrated approaches that include private precaution and preparation to reduce the damage on exposed assets. Yet, detailed insights into the preparedness decisions of flood-prone households remain scarce, especially in connection to mental impacts and individual coping strategies after being affected by different flood types.
This thesis aims to gain insights into flash floods as a costly hazard in certain German regions and compares the damage driving factors to the damage driving factors of river floods. Furthermore, psychological impacts as well as the effects on coping and mitigation behaviour of flood-affected households are assessed. In this context, psychological models such as the Protection Motivation Theory (PMT) and methods such as regressions and Bayesian statistics are used to evaluate influencing factors on the mental coping after an event and to identify psychological variables that are connected to intended private flood mitigation. The database consists of surveys that were conducted among affected households after major river floods in 2013 and flash floods in 2016.
The main conclusions that can be drawn from this thesis reveal that the damage patterns and damage driving factors of strong flash floods differ significantly from those of river floods due to a rapid flow origination process, higher flow velocities and flow forces. However, the effects on mental coping of people that have been affected by flood events appear to be weakly influenced by different flood types, but yet show a coherence to the event severity, where often thinking of the respective event is pronounced and also connected to a higher mitigation motivation. The mental coping and preparation after floods is further influenced by a good information provision and a social environment, which encourages a positive attitude towards private mitigation.
As an overall recommendation, approaches for an integrated flood risk management in Germany should be followed that also take flash floods into account and consider psychological characteristics of affected households to support and promote private flood mitigation. Targeted information campaigns that concern coping options and discuss current flood risks are important to better prepare for future flood hazards in Germany.
Rivers have always flooded their floodplains. Over 2.5 billion people worldwide have been affected by flooding in recent decades. The economic damage is also considerable, averaging 100 billion US dollars per year. There is no doubt that damage and other negative effects of floods can be avoided. However, this has a price: financially and politically. Costs and benefits can be estimated through risk assessments. Questions about the location and frequency of floods, about the objects that could be affected and their vulnerability are of importance for flood risk managers, insurance companies and politicians. Thus, both variables and factors from the fields of hydrology and sociol-economics play a role with multi-layered connections. One example are dikes along a river, which on the one hand contain floods, but on the other hand, by narrowing the natural floodplains, accelerate the flood discharge and increase the danger of flooding for the residents downstream. Such larger connections must be included in the assessment of flood risk. However, in current procedures this is accompanied by simplifying assumptions. Risk assessments are therefore fuzzy and associated with uncertainties.
This thesis investigates the benefits and possibilities of new data sources for improving flood risk assessment. New methods and models are developed, which take the mentioned interrelations better into account and also quantify the existing uncertainties of the model results, and thus enable statements about the reliability of risk estimates. For this purpose, data on flood events from various sources are collected and evaluated. This includes precipitation and flow records at measuring stations as well as for instance images from social media, which can help to delineate the flooded areas and estimate flood damage with location information. Machine learning methods have been successfully used to recognize and understand correlations between floods and impacts from a wide range of data and to develop improved models.
Risk models help to develop and evaluate strategies to reduce flood risk. These tools also provide advanced insights into the interplay of various factors and on the expected consequences of flooding. This work shows progress in terms of an improved assessment of flood risks by using diverse data from different sources with innovative methods as well as by the further development of models. Flood risk is variable due to economic and climatic changes, and other drivers of risk. In order to keep the knowledge about flood risks up-to-date, robust, efficient and adaptable methods as proposed in this thesis are of increasing importance.