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Complete protection against flood risks by structural measures is impossible. Therefore flood prediction is important for flood risk management. Good explanatory power of flood models requires a meaningful representation of bio-physical processes. Therefore great interest exists to improve the process representation. Progress in hydrological process understanding is achieved through a learning cycle including critical assessment of an existing model for a given catchment as a first step. The assessment will highlight deficiencies of the model, from which useful additional data requirements are derived, giving a guideline for new measurements. These new measurements may in turn lead to improved process concepts. The improved process concepts are finally summarized in an updated hydrological model. In this thesis I demonstrate such a learning cycle, focusing on the advancement of model evaluation methods and more cost effective measurements. For a successful model evaluation, I propose that three questions should be answered: 1) when is a model reproducing observations in a satisfactory way? 2) If model results deviate, of what nature is the difference? And 3) what are most likely the relevant model components affecting these differences? To answer the first two questions, I developed a new method to assess the temporal dynamics of model performance (or TIGER - TIme series of Grouped Errors). This method is powerful in highlighting recurrent patterns of insufficient model behaviour for long simulation periods. I answered the third question with the analysis of the temporal dynamics of parameter sensitivity (TEDPAS). For calculating TEDPAS, an efficient method for sensitivity analysis is necessary. I used such an efficient method called Fourier Amplitude Sensitivity Test, which has a smart sampling scheme. Combining the two methods TIGER and TEDPAS provided a powerful tool for model assessment. With WaSiM-ETH applied to the Weisseritz catchment as a case study, I found insufficient process descriptions for the snow dynamics and for the recession during dry periods in late summer and fall. Focusing on snow dynamics, reasons for poor model performance can either be a poor representation of snow processes in the model, or poor data on snow cover, or both. To obtain an improved data set on snow cover, time series of snow height and temperatures were collected with a cost efficient method based on temperature measurements on multiple levels at each location. An algorithm was developed to simultaneously estimate snow height and cold content from these measurements. Both, snow height and cold content are relevant quantities for spring flood forecasting. Spatial variability was observed at the local and the catchment scale with an adjusted sampling design. At the local scale, samples were collected on two perpendicular transects of 60 m length and analysed with geostatistical methods. The range determined from fitted theoretical variograms was within the range of the sampling design for 80% of the plots. No patterns were found, that would explain the random variability and spatial correlation at the local scale. At the watershed scale, locations of the extensive field campaign were selected according to a stratified sample design to capture the combined effects of elevation, aspect and land use. The snow height is mainly affected by the plot elevation. The expected influence of aspect and land use was not observed. To better understand the deficiencies of the snow module in WaSiM-ETH, the same approach, a simple degree day model was checked for its capability to reproduce the data. The degree day model was capable to explain the temporal variability for plots with a continuous snow pack over the entire snow season, if parameters were estimated for single plots. However, processes described in the simple model are not sufficient to represent multiple accumulation-melt-cycles, as observed for the lower catchment. Thus, the combined spatio-temporal variability at the watershed scale is not captured by the model. Further tests on improved concepts for the representation of snow dynamics at the Weißeritz are required. From the data I suggest to include at least rain on snow and redistribution by wind as additional processes to better describe spatio-temporal variability. Alternatively an energy balance snow model could be tested. Overall, the proposed learning cycle is a useful framework for targeted model improvement. The advanced model diagnostics is valuable to identify model deficiencies and to guide field measurements. The additional data collected throughout this work helps to get a deepened understanding of the processes in the Weisseritz catchment.
The business problem of having inefficient processes, imprecise process analyses, and simulations as well as non-transparent artificial neuronal network models can be overcome by an easy-to-use modeling concept. With the aim of developing a flexible and efficient approach to modeling, simulating, and optimizing processes, this paper proposes a flexible Concept of Neuronal Modeling (CoNM). The modeling concept, which is described by the modeling language designed and its mathematical formulation and is connected to a technical substantiation, is based on a collection of novel sub-artifacts. As these have been implemented as a computational model, the set of CoNM tools carries out novel kinds of Neuronal Process Modeling (NPM), Neuronal Process Simulations (NPS), and Neuronal Process Optimizations (NPO). The efficacy of the designed artifacts was demonstrated rigorously by means of six experiments and a simulator of real industrial production processes.
Cyber-physical systems achieve sophisticated system behavior exploring the tight interconnection of physical coupling present in classical engineering systems and information technology based coupling. A particular challenging case are systems where these cyber-physical systems are formed ad hoc according to the specific local topology, the available networking capabilities, and the goals and constraints of the subsystems captured by the information processing part. In this paper we present a formalism that permits to model the sketched class of cyber-physical systems. The ad hoc formation of tightly coupled subsystems of arbitrary size are specified using a UML-based graph transformation system approach. Differential equations are employed to define the resulting tightly coupled behavior. Together, both form hybrid graph transformation systems where the graph transformation rules define the discrete steps where the topology or modes may change, while the differential equations capture the continuous behavior in between such discrete changes. In addition, we demonstrate that automated analysis techniques known for timed graph transformation systems for inductive invariants can be extended to also cover the hybrid case for an expressive case of hybrid models where the formed tightly coupled subsystems are restricted to smaller local networks.
Biomimicry is the art of mimicking nature to overcome a particular technical or scientific challenge. The approach studies how evolution has found solutions to the most complex problems in nature. This makes it a powerful method for science. In combination with the rapid development of manufacturing and information technologies into the digital age, structures and material that were before thought to be unrealizable can now be created with simple sketch and the touch of a button. This doctoral thesis had as its primary goal to investigate how digital tools, such as programming, modelling, 3D-Design tools and 3D-Printing, with the help from biomimicry, could lead to new analysis methods in science and new medical devices in medicine.
The Electrical Discharge Machining (EDM) process is applied commonly to deform or mold hard metals that are difficult to work using normal machinery. A workpiece submerged in an electrolyte is deformed while being in close vicinity to an electrode. When high voltage is put between the workpiece and the electrode it will cause sparks that create cavitations on the substrate which in turn removes material and is flushed away by the electrolyte. Usually, such surfaces are analysed based on roughness, in this work another method using a novel curvature analysis method is presented as an alternative. In addition, to better understand how the surface changes during process time of the EDM process, a digital impact model was created which created craters on ridges on an originally flat substrate. These substrates were then analysed using the curvature analysis method at different processing times of the modelling. It was found that a substrate reaches an equilibrium at around 10000 impacts. The proposed curvature analysis method has potential to be used in the design of new cell culture substrates for stem cell.
The Venus flytrap can shut its jaws at an amazing speed. The shutting mechanism may be interesting to use in science and is an example of a so-called mechanical bi-stable system – there are two stable states. In this work two truncated pyramid structures were modelled using a non-linear mechanical model called the Chained Beam Constraint Model (CBCM). The structure with a slope angle of 30 degrees is not bi-stable and the structure with a slope angle of 45 degrees is bi-stable. Developing this idea further by using PEVA, which has a shape-memory effect, the structure which is not bi-stable could be programmed to be bi-stable and then turned off again. This could be used as an energy storage system. Another species which has interesting mechanism is the tapeworm. Some species of this animal has a crown of hooks and suckers located on its side. The parasite commonly is found in mammals in the lower intestine and attaches to the walls by using its suckers. When the tapeworm has found a suitable spot, it ejects its hooks and permanently attaches to the wall. This function could be used in minimally invasive medicine to have better control of implants during the implantation process. By using the CBCM model and a 3D-printer capable of tuning how hard or soft a printed part is, a design strategy was developed to investigate how one could create a device that mimics the tapeworm. In the end a prototype was created which was able attach to a pork loin at an under pressure of 20 kPa and to ejects its hooks at an under pressure of 50 kPa or above.
These three projects is an exhibit of how digital tools and biomimicry can be used together to come up with applicable solutions in science and in medicine.
To investigate the reliability and stability of spherical harmonic models based on archeo/-paleomagnetic data, 2000 Geomagnetic models were calculated. All models are based on the same data set but with randomized uncertainties. Comparison of these models to the geomagnetic field model gufm1 showed that large scale magnetic field structures up to spherical harmonic degree 4 are stable throughout all models. Through a ranking of all models by comparing the dipole coefficients to gufm1 more realistic uncertainty estimates were derived than the authors of the data provide.
The derived uncertainty estimates were used in further modelling, which combines archeo/-paleomagnetic and historical data. The huge difference in data count, accuracy and coverage of these two very different data sources made it necessary to introduce a time dependent spatial damping, which was constructed to constrain the spatial complexity of the model. Finally 501 models were calculated by considering that each data point is a Gaussian random variable, whose mean is the original value and whose standard deviation is its uncertainty. The final model arhimag1k is calculated by taking the mean of the 501 sets of Gauss coefficients. arhimag1k fits different dependent and independent data sets well. It shows an early reverse flux patch at the core-mantle boundary between 1000 AD and 1200 AD at the location of the South Atlantic Anomaly today. Another interesting feature is a high latitude flux patch over Greenland between 1200 and 1400 AD. The dipole moment shows a constant behaviour between 1600 and 1840 AD.
In the second part of the thesis 4 new paleointensities from 4 different flows of the island Fogo, which is part of Cape Verde, are presented. The data is fitted well by arhimag1k with the exception of the value at 1663 of 28.3 microtesla, which is approximately 10 microtesla lower than the model suggest.
Im Zuge der Covid-19 Pandemie werden zwei Werte täglich diskutiert: Die zuletzt gemeldete Zahl der neu Infizierten und die sogenannte Reproduktionsrate. Sie gibt wieder, wie viele weitere Menschen ein an Corona erkranktes Individuum im Durchschnitt ansteckt. Für die Schätzung dieses Wertes gibt es viele Möglichkeiten - auch das Robert Koch-Institut gibt in seinem täglichen Situationsbericht stets zwei R-Werte an: Einen 4-Tage-R-Wert und einen weniger schwankenden 7-Tage-R-Wert. Diese Arbeit soll eine weitere Möglichkeit vorstellen, einige Aspekte der Pandemie zu modellieren und die Reproduktionsrate zu schätzen.
In der ersten Hälfte der Arbeit werden die mathematischen Grundlagen vorgestellt, die man für die Modellierung benötigt. Hierbei wird davon ausgegangen, dass der Leser bereits ein Basisverständnis von stochastischen Prozessen hat. Im Abschnitt Grundlagen werden Verzweigungsprozesse mit einigen Beispielen eingeführt und die Ergebnisse aus diesem Themengebiet, die für diese Arbeit wichtig sind, präsentiert. Dabei gehen wir zuerst auf einfache Verzweigungsprozesse ein und erweitern diese dann auf Verzweigungsprozesse mit mehreren Typen. Um die Notation zu erleichtern, beschränken wir uns auf zwei Typen. Das Prinzip lässt sich aber auf eine beliebige Anzahl von Typen erweitern.
Vor allem soll die Wichtigkeit des Parameters λ herausgestellt werden. Dieser Wert kann als durchschnittliche Zahl von Nachfahren eines Individuums interpretiert werden und bestimmt die Dynamik des Prozesses über einen längeren Zeitraum. In der Anwendung auf die Pandemie hat der Parameter λ die gleiche Rolle wie die Reproduktionsrate R.
In der zweiten Hälfte dieser Arbeit stellen wir eine Anwendung der Theorie über Multitype Verzweigungsprozesse vor. Professor Yanev und seine Mitarbeiter modellieren in ihrer Veröffentlichung Branching stochastic processes as models of Covid-19 epidemic development die Ausbreitung des Corona Virus' über einen Verzweigungsprozess mit zwei Typen. Wir werden dieses Modell diskutieren und Schätzer daraus ableiten: Ziel ist es, die Reproduktionsrate zu ermitteln. Außerdem analysieren wir die Möglichkeiten, die Dunkelziffer (die Zahl nicht gemeldeter Krankheitsfälle) zu schätzen. Wir wenden die Schätzer auf die Zahlen von Deutschland an und werten diese schließlich aus.
Mathematical modeling of biological phenomena has experienced increasing interest since new high-throughput technologies give access to growing amounts of molecular data. These modeling approaches are especially able to test hypotheses which are not yet experimentally accessible or guide an experimental setup. One particular attempt investigates the evolutionary dynamics responsible for today's composition of organisms. Computer simulations either propose an evolutionary mechanism and thus reproduce a recent finding or rebuild an evolutionary process in order to learn about its mechanism. The quest for evolutionary fingerprints in metabolic and gene-coexpression networks is the central topic of this cumulative thesis based on four published articles. An understanding of the actual origin of life will probably remain an insoluble problem. However, one can argue that after a first simple metabolism has evolved, the further evolution of metabolism occurred in parallel with the evolution of the sequences of the catalyzing enzymes. Indications of such a coevolution can be found when correlating the change in sequence between two enzymes with their distance on the metabolic network which is obtained from the KEGG database. We observe that there exists a small but significant correlation primarily on nearest neighbors. This indicates that enzymes catalyzing subsequent reactions tend to be descended from the same precursor. Since this correlation is relatively small one can at least assume that, if new enzymes are no "genetic children" of the previous enzymes, they certainly be descended from any of the already existing ones. Following this hypothesis, we introduce a model of enzyme-pathway coevolution. By iteratively adding enzymes, this model explores the metabolic network in a manner similar to diffusion. With implementation of an Gillespie-like algorithm we are able to introduce a tunable parameter that controls the weight of sequence similarity when choosing a new enzyme. Furthermore, this method also defines a time difference between successive evolutionary innovations in terms of a new enzyme. Overall, these simulations generate putative time-courses of the evolutionary walk on the metabolic network. By a time-series analysis, we find that the acquisition of new enzymes appears in bursts which are pronounced when the influence of the sequence similarity is higher. This behavior strongly resembles punctuated equilibrium which denotes the observation that new species tend to appear in bursts as well rather than in a gradual manner. Thus, our model helps to establish a better understanding of punctuated equilibrium giving a potential description at molecular level. From the time-courses we also extract a tentative order of new enzymes, metabolites, and even organisms. The consistence of this order with previous findings provides evidence for the validity of our approach. While the sequence of a gene is actually subject to mutations, its expression profile might also indirectly change through the evolutionary events in the cellular interplay. Gene coexpression data is simply accessible by microarray experiments and commonly illustrated using coexpression networks where genes are nodes and get linked once they show a significant coexpression. Since the large number of genes makes an illustration of the entire coexpression network difficult, clustering helps to show the network on a metalevel. Various clustering techniques already exist. However, we introduce a novel one which maintains control of the cluster sizes and thus assures proper visual inspection. An application of the method on Arabidopsis thaliana reveals that genes causing a severe phenotype often show a functional uniqueness in their network vicinity. This leads to 20 genes of so far unknown phenotype which are however suggested to be essential for plant growth. Of these, six indeed provoke such a severe phenotype, shown by mutant analysis. By an inspection of the degree distribution of the A.thaliana coexpression network, we identified two characteristics. The distribution deviates from the frequently observed power-law by a sharp truncation which follows after an over-representation of highly connected nodes. For a better understanding, we developed an evolutionary model which mimics the growth of a coexpression network by gene duplication which underlies a strong selection criterion, and slight mutational changes in the expression profile. Despite the simplicity of our assumption, we can reproduce the observed properties in A.thaliana as well as in E.coli and S.cerevisiae. The over-representation of high-degree nodes could be identified with mutually well connected genes of similar functional families: zinc fingers (PF00096), flagella, and ribosomes respectively. In conclusion, these four manuscripts demonstrate the usefulness of mathematical models and statistical tools as a source of new biological insight. While the clustering approach of gene coexpression data leads to the phenotypic characterization of so far unknown genes and thus supports genome annotation, our model approaches offer explanations for observed properties of the coexpression network and furthermore substantiate punctuated equilibrium as an evolutionary process by a deeper understanding of an underlying molecular mechanism.
The standing stock and production of organismal biomass depends strongly on the organisms’ biotic environment, which arises from trophic and non-trophic interactions among them. The trophic interactions between the different groups of organisms form the food web of an ecosystem, with the autotrophic and bacterial production at the basis and potentially several levels of consumers on top of the producers. Feeding interactions can regulate communities either by severe grazing pressure or by shortage of resources or prey production, termed top-down and bottom-up control, respectively. The limitations of all communities conglomerate in the food web regulation, which is subject to abiotic and biotic forcing regimes arising from external and internal constraints. This dissertation presents the effects of alterations in two abiotic, external forcing regimes, terrestrial matter input and long-lasting low temperatures in winter. Diverse methodological approaches, a complex ecosystem model study and the analysis of two whole-lake measurements, were performed to investigate effects for the food web regulation and the resulting consequences at the species, community and ecosystem scale. Thus, all types of organisms, autotrophs and heterotrophs, at all trophic levels were investigated to gain a comprehensive overview of the effects of the two mentioned altered forcing regimes. In addition, an extensive evaluation of the trophic interactions and resulting carbon fluxes along the pelagic and benthic food web was performed to display the efficiencies of the trophic energy transfer within the food webs. All studies were conducted in shallow lakes, which is worldwide the most abundant type of lakes. The specific morphology of shallow lakes allows that the benthic production contributes substantially to the whole-lake production. Further, as shallow lakes are often small they are especially sensitive to both, changes in the input of terrestrial organic matter and the atmospheric temperature. Another characteristic of shallow lakes is their appearance in alternative stable states. They are either in a clear-water or turbid state, where macrophytes and phytoplankton dominate, respectively. Both states can stabilize themselves through various mechanisms.
These two alternative states and stabilizing mechanisms are integrated in the complex ecosystem model PCLake, which was used to investigate the effects of the enhanced terrestrial particulate organic matter (t-POM) input to lakes. The food web regulation was altered by three distinct pathways: (1) Zoobenthos received more food, increased in biomass which favored benthivorous fish and those reduced the available light due to bioturbation. (2) Zooplankton substituted autochthonous organic matter in their diet by suspended t-POM, thus the autochthonous organic matter remaining in the water reduced its transparency. (3) T-POM suspended into the water and reduced directly the available light. As macrophytes are more light-sensitive than phytoplankton they suffered the most from the lower transparency. Consequently, the resilience of the clear-water state was reduced by enhanced t-POM inputs, which makes the turbid state more likely at a given nutrient concentration. In two subsequent winters long-lasting low temperatures and a concurrent long duration of ice coverage was observed which resulted in low overall adult fish biomasses in the two study lakes – Schulzensee and Gollinsee, characterized by having and not having submerged macrophytes, respectively. Before the partial winterkill of fish Schulzensee allowed for a higher proportion of piscivorous fish than Gollinsee. However, the partial winterkill of fish aligned both communities as piscivorous fish are more sensitive to low oxygen concentrations. Young of the year fish benefitted extremely from the absence of adult fish due to lower predation pressure. Therefore, they could exert a strong top-down control on crustaceans, which restructured the entire zooplankton community leading to low crustacean biomasses and a community composition characterized by copepodites and nauplii. As a result, ciliates were released from top-down control, increased to high biomasses compared to lakes of various trophic states and depths and dominated the zooplankton community. While being very abundant in the study lakes and having the highest weight specific grazing rates among the zooplankton, ciliates exerted potentially a strong top-down control on small phytoplankton and particle-attached bacteria. This resulted in a higher proportion of large phytoplankton compared to other lakes. Additionally, the phytoplankton community was evenly distributed presumably due to the numerous fast growing and highly specific ciliate grazers. Although, the pelagic food web was completely restructured after the subsequent partial winterkills of fish, both lakes were resistant to effects of this forcing regime at the ecosystem scale. The consistently high predation pressure on phytoplankton prevented that Schulzensee switched from the clear-water to the turbid state. Further mechanisms, which potentially stabilized the clear-water state, were allelopathic effects by macrophytes and nutrient limitation in summer. The pelagic autotrophic and bacterial production was an order of magnitude more efficient transferred to animal consumers than the respective benthic production, despite the alterations of the food web structure after the partial winterkill of fish. Thus, the compiled mass-balanced whole-lake food webs suggested that the benthic bacterial and autotrophic production, which exceeded those of the pelagic habitat, was not used by animal consumers. This holds even true if the food quality, additional consumers such as ciliates, benthic protozoa and meiobenthos, the pelagic-benthic link and the potential oxygen limitation of macrobenthos were considered. Therefore, low benthic efficiencies suggest that lakes are primarily pelagic systems at least at the animal consumer level.
Overall, this dissertation gives insights into the regulation of organism groups in the pelagic and benthic habitat at each trophic level under two different forcing regimes and displays the efficiency of the carbon transfer in both habitats. The results underline that the alterations of external forcing regimes affect all hierarchical level including the ecosystem.
Functional traits determine biomass dynamics, coexistence and energetics in plankton food webs
(2022)
Plankton food webs are the basis of marine and limnetic ecosystems. Especially aquatic ecosystems of high biodiversity provide important ecosystem services for humankind as providers of food, coastal protection, climate regulation, and tourism. Understanding the dynamics of biomass and coexistence in these food webs is a first step to understanding the ecosystems. It also lays the foundation for the development of management strategies for the maintenance of the marine and freshwater biodiversity despite anthropogenic influences.
Natural food webs are highly complex, and thus often equally complex methods are needed to analyse and understand them well. Models can help to do so as they depict simplified parts of reality. In the attempt to get a broader understanding of the complex food webs, diverse methods are used to investigate different questions.
In my first project, we compared the energetics of a food chain in two versions of an allometric trophic network model. In particular, we solved the problem of unrealistically high trophic transfer efficiencies (up to 70%) by accounting for both basal respiration and activity respiration, which decreased the trophic transfer efficiency to realistic values of ≤30%. Next in my second project I turned to plankton food webs and especially phytoplankton traits. Investigating a long-term data set from Lake Constance we found evidence for a trade-off between defence and growth rate in this natural phytoplankton community. I continued working with this data set in my third project focusing on ciliates, the main grazer of phytoplankton in spring. Boosted regression trees revealed that temperature and predators have the highest influence on net growth rates of ciliates. We finally investigated in my fourth project a food web model inspired by ciliates to explore the coexistence of plastic competitors and to study the new concept of maladaptive switching, which revealed some drawbacks of plasticity: faster adaptation led to higher maladaptive switching towards undefended phenotypes which reduced autotroph biomass and coexistence and increased consumer biomass.
It became obvious that even well-established models should be critically questioned as it is important not to forget reality on the way to a simplistic model. The results showed furthermore that long-term data sets are necessary as they can help to disentangle complex natural processes. Last, one should keep in mind that the interplay between models and experiments/ field data can deliver fruitful insights about our complex world.
Durch die anthropogene Nutzung sind viele Auen in Mitteleuropa verändert worden, wobei insbesondere die Retentionsflächen stark verringert wurden. Während Auen seit längerem im Fokus der wissenschaftlichen Bearbeitung stehen, gibt es bisher große Wissensdefizite in der Frage der Auenreaktivierungen. Zum einen sind derartige Projekte bisher kaum verwirklicht und zum anderen ist ein langfristiges Monitoring notwendig, um die Anpassung von Biozönosen an die veränderten Standortbedingungen beobachten zu können. Um die Folgen derartiger Eingriffe zu analysieren, bieten sich computergestützte Modellierungen der Landschaftsentwicklung an, wie sie in der vorliegenden Arbeit verwirklicht wurden. Ziel der Arbeit war, mit Hilfe eines Geografischen Informationssystems (GIS) das Entwicklungspotenzial der Landschaft bei verschiedenen Rückdeichungsvarianten auf der Ebene der Biotoptypen darzustellen. Dabei ging es nicht um die Erstellung eines allgemein gültigen Auenmodells sondern um die Erarbeitung eines Modells für einen konkreten Anwendungsfall. Der erarbeitete Ansatz sollte zudem für die landschaftsplanerische Praxis geeignet sein. Als Beispielgebiete wurden Flächen an der Mittleren Elbe bei Rogätz und Sandau, beide im nördlichen Teil von Sachsen-Anhalt, ausgewählt. Die vorliegende Arbeit gliedert sich in zwei Teile. Im ersten Teil werden Erhebungen und Auswertungen als Grundlage der Modellentwicklung dargestellt. Dazu wurden die Biotoptypen der Beispielgebiete flächendeckend erhoben und mit punktuellen Vegetationserhebungen ergänzt. Aus dem Forschungsprojekt "Rückgewinnung von Retentionsflächen und Altauenreaktivierung an der Mittleren Elbe in Sachsen-Anhalt" des Bundesministeriums für Bildung und Forschung (BMBF) standen standortökologische Daten der Hydrologie und Bodenkunde zur Verfügung. Ziel der Auswertung war, Schlüsselfaktoren für Hydrologie und Bodenbedingungen innerhalb der rezenten Aue zu identifizieren, die zur Ausprägung bestimmter Biotoptypen führen. Im zweiten Teil der Arbeit wurde ein Modell für Biotoptypenpotenziale auf den geplanten Rück–deichungsflächen entwickelt. Das Modell bearbeitet die Datenbank der verwendeten GIS-Dateien, die auf Daten zum Bestand beruht und um solche der Prognose der Standortökologie (Hydrologie und Boden) im Rückdeichungsfalle aus dem BMBF-Projekt erweitert wurde. Weitere Voraussetzung für die Modellierung war die Erarbeitung von Leitbildern, in denen unterschiedliche Nutzungsszenarios für die Landschaft nach Deichrückverlegung hypothetisch festgelegt wurden. Insbesondere die Nutzungsintensität wurde variiert, von einer Variante intensiver land- und forstwirtschaftlicher Nutzung über sogenannte integrierte Entwicklungsziele aus dem BMBF-Projekt bis hin zu einer Variante der Naturschutznutzung. Zusätzlich wurde eine zukünftige Potentielle Natürliche Vegetation modelliert. Eine Überprüfung des Modell fand für den Raum der rezenten Aue in der intensiven Nutzungsvariante statt, die der gegenwärtigen Nutzung am nächsten kommt. Werden Informationen des Bestandsbiotoptyps als Korrekturgröße in das Modell einbezogen, konnte für viele Biotoptypen eine Trefferquote von über 90 % erreicht werden. Bei flächenmäßig weniger bedeutenden Bio–toptypen lag dieser Wert aufgrund der schmaleren Datenbasis zwischen 20 und 40 %. Als Ergebnis liegt für unterschiedliche Deichvarianten und Leitbilder in den Beispielgebieten die Landschaftsentwicklung als Biotoppotenzial vor. Als eine vereinfachte Regionalisierung der punktuellen Vegetationsdaten wurde im Modell geprüft, inwieweit die modellierten Biotopflächen der Charakteristik der pflanzensoziologischen Aufnahmen aus der rezenten Aue entsprechen. In dem Falle wurde die Pflanzengesellschaft der jeweiligen ökologisch im Rahmen der Untersuchung einheitlichen Flächeneinheit zugeordnet. Anteilig lässt sich damit die Biotopprognosefläche pflanzensoziologisch konkretisieren. Die vorliegende Arbeit gehört zu den bisher wenigen Arbeiten, die sich mit den Folgen von Auenreaktivierung auf die Entwicklung der Landschaft auseinandersetzen. Sie zeigt eine Möglichkeit auf, Prognosemodelle für Biotoptypen und Vegetation anhand begrenzter Felduntersuchungen zu entwerfen. Derartige Modelle können zum Verständnis von Eingriffen in den Naturhaushalt, wie sie die Deichrückverlegungen darstellen, beitragen und eine Folgenabschätzung unterstützen.