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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 repeated occurrence of exceptional floods within a few years, such as the Rhine floods in 1993 and 1995 and the Elbe and Danube floods in 2002 and 2013, suggests that floods in Central Europe may be organized in flood-rich and flood-poor periods. This hypothesis is studied by testing the significance of temporal clustering in flood occurrence (peak-over-threshold) time series for 68 catchments across Germany for the period 1932-2005. To assess the robustness of the results, different methods are used: Firstly, the index of dispersion, which quantifies the departure from a homogeneous Poisson process, is investigated. Further, the time-variation of the flood occurrence rate is derived by non-parametric kernel implementation and the significance of clustering is evaluated via parametric and non-parametric tests. Although the methods give consistent overall results, the specific results differ considerably. Hence, we recommend applying different methods when investigating flood clustering. For flood estimation and risk management, it is of relevance to understand whether clustering changes with flood severity and time scale. To this end, clustering is assessed for different thresholds and time scales. It is found that the majority of catchments show temporal clustering at the 5% significance level for low thresholds and time scales of one to a few years. However, clustering decreases substantially with increasing threshold and time scale. We hypothesize that flood clustering in Germany is mainly caused by catchment memory effects along with intra- to inter-annual climate variability, and that decadal climate variability plays a minor role. (C) 2016 Elsevier B.V. All rights reserved.
To understand past flood changes in the Rhine catchment and in particular the role of anthropogenic climate change in extreme flows, an attribution study relying on a proper GCM (general circulation model) downscaling is needed. A downscaling based on conditioning a stochastic weather generator on weather patterns is a promising approach. This approach assumes a strong link between weather patterns and local climate, and sufficient GCM skill in reproducing weather pattern climatology. These presuppositions are unprecedentedly evaluated here using 111 years of daily climate data from 490 stations in the Rhine basin and comprehensively testing the number of classification parameters and GCM weather pattern characteristics. A classification based on a combination of mean sea level pressure, temperature, and humidity from the ERA20C reanalysis of atmospheric fields over central Europe with 40 weather types was found to be the most appropriate for stratifying six local climate variables. The corresponding skill is quite diverse though, ranging from good for radiation to poor for precipitation. Especially for the latter it was apparent that pressure fields alone cannot sufficiently stratify local variability. To test the skill of the latest generation of GCMs from the CMIP5 ensemble in reproducing the frequency, seasonality, and persistence of the derived weather patterns, output from 15 GCMs is evaluated. Most GCMs are able to capture these characteristics well, but some models showed consistent deviations in all three evaluation criteria and should be excluded from further attribution analysis.
Variational methods are employed in situations where exact Bayesian inference becomes intractable due to the difficulty in performing certain integrals. Typically, variational methods postulate a tractable posterior and formulate a lower bound on the desired integral to be approximated, e.g. marginal likelihood. The lower bound is then optimised with respect to its free parameters, the so-called variational parameters. However, this is not always possible as for certain integrals it is very challenging (or tedious) to come up with a suitable lower bound. Here, we propose a simple scheme that overcomes some of the awkward cases where the usual variational treatment becomes difficult. The scheme relies on a rewriting of the lower bound on the model log-likelihood. We demonstrate the proposed scheme on a number of synthetic and real examples, as well as on a real geophysical model for which the standard variational approaches are inapplicable.