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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.
Trends in precipitation over Germany and the Rhine basin related to changes in weather patterns
(2017)
Precipitation as the central meteorological feature for agriculture, water security, and human well-being amongst others, has gained special attention ever since. Lack of precipitation may have devastating effects such as crop failure and water scarcity. Abundance of precipitation, on the other hand, may as well result in hazardous events such as flooding and again crop failure. Thus, great effort has been spent on tracking changes in precipitation and relating them to underlying processes. Particularly in the face of global warming and given the link between temperature and atmospheric water holding capacity, research is needed to understand the effect of climate change on precipitation.
The present work aims at understanding past changes in precipitation and other meteorological variables. Trends were detected for various time periods and related to associated changes in large-scale atmospheric circulation. The results derived in this thesis may be used as the foundation for attributing changes in floods to climate change. Assumptions needed for the downscaling of large-scale circulation model output to local climate stations are tested and verified here.
In a first step, changes in precipitation over Germany were detected, focussing not only on precipitation totals, but also on properties of the statistical distribution, transition probabilities as a measure for wet/dry spells, and extreme precipitation events.
Shifting the spatial focus to the Rhine catchment as one of the major water lifelines of Europe and the largest river basin in Germany, detected trends in precipitation and other meteorological variables were analysed in relation to states of an ``optimal'' weather pattern classification. The weather pattern classification was developed seeking the best skill in explaining the variance of local climate variables.
The last question addressed whether observed changes in local climate variables are attributable to changes in the frequency of weather patterns or rather to changes within the patterns itself. A common assumption for a downscaling approach using weather patterns and a stochastic weather generator is that climate change is expressed only as a changed occurrence of patterns with the pattern properties remaining constant. This assumption was validated and the ability of the latest generation of general circulation models to reproduce the weather patterns was evaluated.
% Paper 1
Precipitation changes in Germany in the period 1951-2006 can be summarised briefly as negative in summer and positive in all other seasons. Different precipitation characteristics confirm the trends in total precipitation: while winter mean and extreme precipitation have increased, wet spells tend to be longer as well (expressed as increased probability for a wet day followed by another wet day). For summer the opposite was observed: reduced total precipitation, supported by decreasing mean and extreme precipitation and reflected in an increasing length of dry spells.
Apart from this general summary for the whole of Germany, the spatial distribution within the country is much more differentiated. Increases in winter precipitation are most pronounced in the north-west and south-east of Germany, while precipitation increases are highest in the west for spring and in the south for autumn. Decreasing summer precipitation was observed in most regions of Germany, with particular focus on the south and west.
The seasonal picture, however, was again differently represented in the contributing months, e.g.\ increasing autumn precipitation in the south of Germany is formed by strong trends in the south-west in October and in the south-east in November. These results emphasise the high spatial and temporal organisation of precipitation changes.
% Paper 2
The next step towards attributing precipitation trends to changes in large-scale atmospheric patterns was the derivation of a weather pattern classification that sufficiently stratifies the local climate variables under investigation. Focussing on temperature, radiation, and humidity in addition to precipitation, a classification based on mean sea level pressure, near-surface temperature, and specific humidity was found to have the best skill in explaining the variance of the local variables. A rather high number of 40 patterns was selected, allowing typical pressure patterns being assigned to specific seasons by the associated temperature patterns. While the skill in explaining precipitation variance is rather low, better skill was achieved for radiation and, of course, temperature.
Most of the recent GCMs from the CMIP5 ensemble were found to reproduce these weather patterns sufficiently well in terms of frequency, seasonality, and persistence.
% Paper 3
Finally, the weather patterns were analysed for trends in pattern frequency, seasonality, persistence, and trends in pattern-specific precipitation and temperature. To overcome uncertainties in trend detection resulting from the selected time period, all possible periods in 1901-2010 with a minimum length of 31 years were considered. Thus, the assumption of a constant link between patterns and local weather was tested rigorously. This assumption was found to hold true only partly. While changes in temperature are mainly attributable to changes in pattern frequency, for precipitation a substantial amount of change was detected within individual patterns.
Magnitude and even sign of trends depend highly on the selected time period. The frequency of certain patterns is related to the long-term variability of large-scale circulation modes.
Changes in precipitation were found to be heterogeneous not only in space, but also in time - statements on trends are only valid for the specific time period under investigation. While some part of the trends can be attributed to changes in the large-scale circulation, distinct changes were found within single weather patterns as well.
The results emphasise the need to analyse multiple periods for thorough trend detection wherever possible and add some note of caution to the application of downscaling approaches based on weather patterns, as they might misinterpret the effect of climate change due to neglecting within-type trends.