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In freshwater sciences, nitrogen gained increasing attention in the past as an important resource potentially influencing phytoplankton growth and thus eutrophication. Most studies and all management approaches, however, are still restricted to dissolved inorganic nitrogen (DIN = nitrate + nitrite + ammonium) since dissolved organic nitrogen (DON) was considered to be refractory for most of the photoautotrophs. In the meantime this assumption has been disproved for all aquatic systems. While research on DON in marine ecosystems substantially increased, in freshwater a surprisingly small number of investigations has been carried out on DON utilization by phytoplankton or even the occurrence and seasonal development of total DON or its compounds in lakes. Therefore, our present knowledge on DON utilization by phytoplankton is often based on single species experiments using a sole, usually low molecular weight DON component, often in unnaturally high amounts mainly carried out with marine phytoplankton species. Thus, we know that some phytoplankton species can take up different DON fractions if they are available in high concentrations and as sole nitrogen source. This does not necessarily imply that phytoplankton would perform likewise in natural environments. In addition, it will be difficult to draw conclusions on the behavior of freshwater phytoplankton from experiments with marine phytoplankton since the nutrient regime in marine environments differs from that of freshwater. In the light of the parallel availability of inorganic and organic nitrogen species in natural freshwater ecosystems, several questions must be raised: "If inorganic nitrogen is available, would phytoplankton really rely on an organic nitrogen source? Could a connection be detected between the seasonal development of DON and changes in the phytoplankton community composition as found for inorganic nitrogen? And if we reduce the input of inorganic nitrogen in lakes and rivers would the importance of DON as nitrogen source for phytoplankton increase, counteracting all management efforts or even leading to undesired effects due to changes in phytoplankton physiology and biodiversity?" I experimentally addressed the questions whether those DON compounds differentially influence growth, physiology and composition of phytoplankton both as sole available nitrogen source and in combination with other nitrogen compounds. I hypothesized that all offered DON - compounds (urea, natural organic matter (NOM), dissolved free and combined amino acids (DFAA, DCAA)) could be utilized by phytoplankton at natural concentrations. However, I assumed that the availability would decrease with increasing compound complexity. I furthermore hypothesized that the occurrence of low DIN concentrations would not affect the utilization of DON negatively. The nitrogen source, whatsoever, would have an impact on phytoplankton physiology as well as community composition. To investigate these questions and assumptions I conducted bioassays with algae monocultures as well as phytoplankton communities testing the utilization of various DON compounds by several freshwater phytoplankton species. Especially the potential utilization of NOM, a complex DON compound mainly consisting of humic substances is of interest, since it is usually regarded to be refractory. In order to be able to use natural concentrations of DON - compounds for my experiments the concentration of total DON and some DON - compounds (urea, humic substances, heigh molecular weight substances) was assessed in Lake Müggelsee. All compounds were able to support algae growth in the low natural concentrations supplied. However, I found that the offered DON compounds differ in their availability to various algae species, both, as sole nitrogen source or in combination with low DIN concentrations. As expected, the availability decreased with increasing complexity of the nitrogen compound. Furthermore, I could show that changes in algal physiology (nitrogen storage, metabolism) occur depending on the utilized nitrogen source. Especially the secondary photosynthetic pigment composition, heterocyst frequency and C:N - ratio of the algae were affected. The uptake and usage of certain nitrogen compounds might be more costly, potentially resulting in those physiology changes. Whereas laboratory experiments with single species revealed strong effects of DON, algal responses to DON in a multi-species situation remain unclear. Experiments with phytoplankton communities from Lake Müggelsee revealed that the nitrogen pool composition does influence the phytoplankton community structure. The findings furthermore show that several species combined might utilize the supplied nitrogen completely different than monocultures in the laboratory. Thus, besides the actual ability of algae to use the offered nitrogen sources other factors, such as interspecific competition, may be of importance. I further investigated, if the results of the laboratory experiments, can be verified in the field. Here, I surveyed the seasonal development of several dissolved organic matter (DOM) components (urea, high molecular weight substances (HMWS), humic substances (HS)) and associated parameters (Specific UV-absorption (SUVA), C:N - ratio) in Lake Müggelsee between 2011 and 2013. Furthermore, data from the long term measurements series of Lake Müggelsee such as physical (temperature, light, pH, O2) and chemical parameters (nitrogen, phosphorous, silica, inorganic carbon), zooplankton and phytoplankton data were used to investigate how much of the variability of the phytoplankton composition in Lake Müggelsee can be explained by DON/DOM concentration and composition, relative to the other groups of explanatory variables. The results show that DON mainly consists of rather complex compounds such as humic substances and biopolymers (80 %) and that only slight seasonal trends are detectable. Using variance partitioning I could show, that the usually investigated nutrients (DIN, silica, inorganic carbon, phosphorous) and abiotic factors together explain most of the algae composition as was to be expected (57.1 % of modeled variance). However, DOM and the associated parameters uniquely explain 10.3 % of the variance and thus slightly more than zooplankton with 9.3 %. I could therefore prove, that the composition of DOM (nitrogen and carbon) is connected to the algae composition in an eutrophic lake such as Lake Müggelsee. DON - compounds such as urea, however, could not be correlated with the occurrence of specific phytoplankton species. Overall, the results of this study imply that DON can be a valuable nitrogen source for freshwater phytoplankton. DON is used by various species even when DIN is available in low concentrations. Through the reduction of DIN in lakes and rivers, the DON:DIN ratio might be changed, resulting even in an increased importance of DON as phytoplankton nitrogen source. My work suggests that not only N2-fixation but also DON utilization might compensate for reduced N - input. Changes from DIN to DON as main nitrogen source might also promote certain, potentially undesired algae species and influence the biodiversity of a limnic ecosystem through changes in the phytoplankton community structure. Thus, DON, especially urea, should be included in calculations concerning total available nitrogen and when determining nitrogen threshold values. Furthermore, the input-reduction of DON, for example from waste-water treatment plants should also be evaluated and the results of my thesis should find consideration when planning to reduce the nitrogen input in freshwater.
The TeV binary system LS I +61 degrees 303 is known for its regular, non-thermal emission pattern that traces the orbital period of the compact object in its 26.5 day orbit around its B0 Ve star companion. The system typically presents elevated TeV emission around apastron passage with flux levels between 5% and 15% of the steady flux from the Crab Nebula (> 300 GeV). In this article, VERITAS observations of LS I + 61 degrees. 303 taken in late 2014 are presented, during which bright TeV flares around apastron at flux levels peaking above 30% of the Crab Nebula flux were detected. This is the brightest such activity from this source ever seen in the TeV regime. The strong outbursts have rise and fall times of less than a day. The short timescale of the flares, in conjunction with the observation of 10 TeV photons from LS I + 61 degrees 303 during the flares, provides constraints on the properties of the accelerator in the source.
The Galactic Center ridge has been observed extensively in the past by both GeV and TeV gamma-ray instruments revealing a wealth of structure, including a diffuse component and the point sources G0.9+0.1 (a composite supernova remnant) and Sgr A* (believed to be associated with the supermassive black hole located at the center of our Galaxy). Previous very high energy (VHE) gamma-ray observations with the H.E.S.S.. experiment have also detected an extended TeV gamma-ray component along the Galactic plane in the >300 GeV gamma-ray regime. Here we report on observations of the Galactic Center ridge from 2010 to 2014 by the VERITAS telescope array in the >2 TeV energy range. From these observations we (1) provide improved measurements of the differential energy spectrum for Sgr A* in the >2 TeV gamma-ray regime, (2) provide a detection in the >2 TeV gamma-ray emission from the composite SNR G0.9+0.1 and an improved determination of its multi-TeV gamma-ray energy spectrum, and. (3) report on the detection of VER J1746-289, a localized enhancement of >2 TeV gamma-ray emission along the Galactic plane.
The F-type star KIC. 8462852 has recently been identified as an exceptional target for search for extraterrestrial intelligence (SETI) observations. We describe an analysis methodology for optical SETI, which we have used to analyze nine hours of serendipitous archival observations of KIC. 8462852 made with the VERITAS gamma-ray observatory between 2009 and 2015. No evidence of pulsed optical beacons, above a pulse intensity at the Earth of approximately 1 photon m(-2), is found. We also discuss the potential use of imaging atmospheric Cherenkov telescope arrays in searching for extremely short duration optical transients in general.
In the study of relativistic jets one of the key open questions is their interaction with the environment on the microscopic level. Here, we study the initial evolution of both electron-proton (e(-)-p(+)) and electron-positron (e(+/-)) relativistic jets containing helical magnetic fields, focusing on their interaction with an ambient plasma. We have performed simulations of "global" jets containing helical magnetic fields in order to examine how helical magnetic fields affect kinetic instabilities such as the Weibel instability, the kinetic Kelvin-Helmholtz instability (kKHI) and the Mushroom instability (MI). In our initial simulation study these kinetic instabilities are suppressed and new types of instabilities can grow. In the e(-)-p(+) jet simulation a recollimation-like instability occurs and jet electrons are strongly perturbed. In the e(+/-) jet simulation a recollimation-like instability occurs at early times followed by a kinetic instability and the general structure is similar to a simulation without helical magnetic field. Simulations using much larger systems are required in order to thoroughly follow the evolution of global jets containing helical magnetic fields.
The very high energy (VHE; E > 100 GeV) blazar Markarian 501 was observed between April 17 and May 5 (MJD 54 938-54 956), 2009, as part of an extensive multiwavelength campaign from radio to VHE. Strong VHE yray activity was detected on May 1st with Whipple and VERITAS, when the flux (E > 400 GeV) increased to 10 times the preflare baseline flux (3.9 x 10(-11) ph cm(-2) s(-1)), reaching five times the flux of the Crab Nebula. This coincided with a decrease in the optical polarization and a rotation of the polarization angle by 15. This VHE flare showed a fast flux variation with an increase of a factor similar to 4 in 25 min, and a falling time of similar to 50 min. We present the observations of the quiescent state previous to the flare and of the high state after the flare, focusing on the flux and spectral variability from Whipple, VERITAS, Fermi-LAT, RXTE, and Swift combined with optical and radio data.
About a quarter of anthropogenic CO2 emissions are currently taken up by the oceans, decreasing seawater pH. We performed a mesocosm experiment in the Baltic Sea in order to investigate the consequences of increasing CO2 levels on pelagic carbon fluxes. A gradient of different CO2 scenarios, ranging from ambient (similar to 370 mu atm) to high (similar to 1200 mu atm), were set up in mesocosm bags (similar to 55m(3)). We determined standing stocks and temporal changes of total particulate carbon (TPC), dissolved organic carbon (DOC), dissolved inorganic carbon (DIC), and particulate organic carbon (POC) of specific plankton groups. We also measured carbon flux via CO2 exchange with the atmosphere and sedimentation (export), and biological rate measurements of primary production, bacterial production, and total respiration. The experiment lasted for 44 days and was divided into three different phases (I: t0-t16; II: t17-t30; III: t31-t43). Pools of TPC, DOC, and DIC were approximately 420, 7200, and 25 200 mmol Cm-2 at the start of the experiment, and the initial CO2 additions increased the DIC pool by similar to 7% in the highest CO2 treatment. Overall, there was a decrease in TPC and increase of DOC over the course of the experiment. The decrease in TPC was lower, and increase in DOC higher, in treatments with added CO2. During phase I the estimated gross primary production (GPP) was similar to 100 mmol C m(-2) day(-1), from which 75-95% was respired, similar to 1% ended up in the TPC (including export), and 5-25% was added to the DOC pool. During phase II, the respiration loss increased to similar to 100% of GPP at the ambient CO2 concentration, whereas respiration was lower (85-95% of GPP) in the highest CO2 treatment. Bacterial production was similar to 30% lower, on average, at the highest CO2 concentration than in the controls during phases II and III. This resulted in a higher accumulation of DOC and lower reduction in the TPC pool in the elevated CO2 treatments at the end of phase II extending throughout phase III. The "extra" organic carbon at high CO2 remained fixed in an increasing biomass of small-sized plankton and in the DOC pool, and did not transfer into large, sinking aggregates. Our results revealed a clear effect of increasing CO2 on the carbon budget and mineralization, in particular under nutrient limited conditions. Lower carbon loss processes (respiration and bacterial remineralization) at elevated CO2 levels resulted in higher TPC and DOC pools than ambient CO2 concentration. These results highlight the importance of addressing not only net changes in carbon standing stocks but also carbon fluxes and budgets to better disentangle the effects of ocean acidification.
About a quarter of anthropogenic CO2 emissions are currently taken up by the oceans, decreasing seawater pH. We performed a mesocosm experiment in the Baltic Sea in order to investigate the consequences of increasing CO2 levels on pelagic carbon fluxes. A gradient of different CO2 scenarios, ranging from ambient (similar to 370 mu atm) to high (similar to 1200 mu atm), were set up in mesocosm bags (similar to 55m(3)). We determined standing stocks and temporal changes of total particulate carbon (TPC), dissolved organic carbon (DOC), dissolved inorganic carbon (DIC), and particulate organic carbon (POC) of specific plankton groups. We also measured carbon flux via CO2 exchange with the atmosphere and sedimentation (export), and biological rate measurements of primary production, bacterial production, and total respiration. The experiment lasted for 44 days and was divided into three different phases (I: t0-t16; II: t17-t30; III: t31-t43). Pools of TPC, DOC, and DIC were approximately 420, 7200, and 25 200 mmol Cm-2 at the start of the experiment, and the initial CO2 additions increased the DIC pool by similar to 7% in the highest CO2 treatment. Overall, there was a decrease in TPC and increase of DOC over the course of the experiment. The decrease in TPC was lower, and increase in DOC higher, in treatments with added CO2. During phase I the estimated gross primary production (GPP) was similar to 100 mmol C m(-2) day(-1), from which 75-95% was respired, similar to 1% ended up in the TPC (including export), and 5-25% was added to the DOC pool. During phase II, the respiration loss increased to similar to 100% of GPP at the ambient CO2 concentration, whereas respiration was lower (85-95% of GPP) in the highest CO2 treatment. Bacterial production was similar to 30% lower, on average, at the highest CO2 concentration than in the controls during phases II and III. This resulted in a higher accumulation of DOC and lower reduction in the TPC pool in the elevated CO2 treatments at the end of phase II extending throughout phase III. The "extra" organic carbon at high CO2 remained fixed in an increasing biomass of small-sized plankton and in the DOC pool, and did not transfer into large, sinking aggregates. Our results revealed a clear effect of increasing CO2 on the carbon budget and mineralization, in particular under nutrient limited conditions. Lower carbon loss processes (respiration and bacterial remineralization) at elevated CO2 levels resulted in higher TPC and DOC pools than ambient CO2 concentration. These results highlight the importance of addressing not only net changes in carbon standing stocks but also carbon fluxes and budgets to better disentangle the effects of ocean acidification.
Zircon (ZrSiO4), hafnon (HfSiO4) and five intermediate compositions were synthesized from a Pb silicate melt. The resulting crystals were 20-300 mu m in size and displayed sector and growth zoning. Raman spectra were acquired at locations in the sample for which preceding electron microprobe (EMP) analyses revealed sufficient compositional homogeneity. The dataset documents shifts of Raman bands with changing composition. In this study, bands that have previously not been reported were found for the intermediate compositions and for pure hafnon, in particular at wavenumbers less than 200 cm(-1). For these external modes, the dataset provides new insight into the compositional dependence of their frequencies. Density-functional theory calculations support the observations and are used for a detailed interpretation of the spectra. The pitfalls of the EMP analysis along the zircon-hafnon join are highlighted.
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.
Flood loss modeling is an important component for risk analyses and decision support in flood risk management. Commonly, flood loss models describe complex damaging processes by simple, deterministic approaches like depth-damage functions and are associated with large uncertainty. To improve flood loss estimation and to provide quantitative information about the uncertainty associated with loss modeling, a probabilistic, multivariable Bagging decision Tree Flood Loss Estimation MOdel (BT-FLEMO) for residential buildings was developed. The application of BT-FLEMO provides a probability distribution of estimated losses to residential buildings per municipality. BT-FLEMO was applied and validated at the mesoscale in 19 municipalities that were affected during the 2002 flood by the River Mulde in Saxony, Germany. Validation was undertaken on the one hand via a comparison with six deterministic loss models, including both depth-damage functions and multivariable models. On the other hand, the results were compared with official loss data. BT-FLEMO outperforms deterministic, univariable, and multivariable models with regard to model accuracy, although the prediction uncertainty remains high. An important advantage of BT-FLEMO is the quantification of prediction uncertainty. The probability distribution of loss estimates by BT-FLEMO well represents the variation range of loss estimates of the other models in the case study.
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.
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.