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Der Rangsdorfer See (A = 2,44 km² , z(max) = 6 m, z(mean) = 1,930 m) im Landkreis Teltow Fläming ist einer von vielen Gewässern in Brandenburg, die derzeit den nach EU-Wasserrahmenrichtlinie geforderten guten Zustand nicht erreichen. Bekanntlich gilt Phosphor für viele Gewässer als der bedeutendste produktionslimitierende Nährstoff und ist somit aussichtsreicher Steuerfaktor für eine erfolgreiche Seentherapie.
Ziel dieser Arbeit war es, die Gewässergüte des Rangsdorfer Sees nach trophischen Aspekten zu bewerten, Phosphor-Eintragspfade zu identifizieren, welche die höchsten Frachten verursachen sowie Therapiemaßnahmen zu finden, die eine langfristige Zustandsverbesserung ermöglichen. In einer Szenarioanalyse wurde das modifizierte Einbox Modell angewendet, um die Wirksamkeit externer und interner Therapiemaßnahmen abzuschätzen. Nach Abschluss der Studienarbeiten können folgende Schlüsse gezogen werden:
Der Rangsdorfer See ist aufgrund seiner Morphometrie ein naturgegebenes nährstoffreiches Gewässer und war das auch schon, bevor anthropogene Einflüsse auf ihn einwirkten. Langjährige Nährstoffeinträge verschiedener Herkunft (Abwassereinleitungen, Fischintensivhaltung, Rieselfelder) führten jedoch zu einer übermäßigen Produktivität. Viele Belastungsquellen wurden ausgeschaltet, es findet jedoch immer noch ein relevanter Nährstoffaustrag aus dem Einzugsgebiet statt. Unter Verwendung von Phosphor-Bilanzmodellen und seetypspezifischen kritischen Phosphor-Seekonzentrationen zeigt sich, dass die aktuell stattfindende externe Phosphor-Belastung den kritischen Phosphor-Eintrag zur mutmaßlichen Erreichung des guten ökologischen Zustandes überschreitet. Anteilig die größte Fracht wird über den natürlichen Hauptzufluss in den Rangsdorfer See transportiert. Sanierungsmaßnahmen in dessen Einzugsgebiet stellen ein effektives Mittel dar. Eine technische Lösung zur Nährstoffminderung im Zufluss (Eliminierungsanlage) kann unterstützend eingesetzt werden, muss aber dann bei unveränderter hoher Phosphor-Konzentration im Zufluss dauerhaft betrieben werden. Das Einbox Modell stellte sich als hilfreiches Instrument zur Vorauswahl geeigneter Therapiemaßnahmen heraus.
The temporal dynamics of climate processes are spread across different timescales and, as such, the study of these processes at only one selected timescale might not reveal the complete mechanisms and interactions within and between the (sub-) processes. To capture the non-linear interactions between climatic events, the method of event synchronization has found increasing attention recently. The main drawback with the present estimation of event synchronization is its restriction to analysing the time series at one reference timescale only. The study of event synchronization at multiple scales would be of great interest to comprehend the dynamics of the investigated climate processes. In this paper, the wavelet-based multi-scale event synchronization (MSES) method is proposed by combining the wavelet transform and event synchronization. Wavelets are used extensively to comprehend multi-scale processes and the dynamics of processes across various timescales. The proposed method allows the study of spatio-temporal patterns across different timescales. The method is tested on synthetic and real-world time series in order to check its replicability and applicability. The results indicate that MSES is able to capture relationships that exist between processes at different timescales.
We evaluate the spatial and temporal evolution of Earth's long-wavelength surface dynamic topography since the Jurassic using a series of high-resolution global mantle convection models. These models are Earth-like in terms of convective vigour, thermal structure, surface heat-flux and the geographic distribution of heterogeneity. The models generate a degree-2-dominated spectrum of dynamic topography with negative amplitudes above subducted slabs (i.e. circum-Pacific regions and southern Eurasia) and positive amplitudes elsewhere (i.e. Africa, north-western Eurasia and the central Pacific). Model predictions are compared with published observations and subsidence patterns from well data, both globally and for the Australian and southern African regions. We find that our models reproduce the long-wavelength component of these observations, although observed smaller-scale variations are not reproduced. We subsequently define "geodynamic rules" for how different surface tectonic settings are affected by mantle processes: (i) locations in the vicinity of a subduction zone show large negative dynamic topography amplitudes; (ii) regions far away from convergent margins feature long-term positive dynamic topography; and (iii) rapid variations in dynamic support occur along the margins of overriding plates (e.g. the western US) and at points located on a plate that rapidly approaches a subduction zone (e.g. India and the Arabia Peninsula). Our models provide a predictive quantitative framework linking mantle convection with plate tectonics and sedimentary basin evolution, thus improving our understanding of how subduction and mantle convection affect the spatio-temporal evolution of basin architecture.
Background: Inferring regulatory interactions between genes from transcriptomics time-resolved data, yielding reverse engineered gene regulatory networks, is of paramount importance to systems biology and bioinformatics studies. Accurate methods to address this problem can ultimately provide a deeper insight into the complexity, behavior, and functions of the underlying biological systems. However, the large number of interacting genes coupled with short and often noisy time-resolved read-outs of the system renders the reverse engineering a challenging task. Therefore, the development and assessment of methods which are computationally efficient, robust against noise, applicable to short time series data, and preferably capable of reconstructing the directionality of the regulatory interactions remains a pressing research problem with valuable applications.
Results: Here we perform the largest systematic analysis of a set of similarity measures and scoring schemes within the scope of the relevance network approach which are commonly used for gene regulatory network reconstruction from time series data. In addition, we define and analyze several novel measures and schemes which are particularly suitable for short transcriptomics time series. We also compare the considered 21 measures and 6 scoring schemes according to their ability to correctly reconstruct such networks from short time series data by calculating summary statistics based on the corresponding specificity and sensitivity. Our results demonstrate that rank and symbol based measures have the highest performance in inferring regulatory interactions. In addition, the proposed scoring scheme by asymmetric weighting has shown to be valuable in reducing the number of false positive interactions. On the other hand, Granger causality as well as information-theoretic measures, frequently used in inference of regulatory networks, show low performance on the short time series analyzed in this study.
Conclusions: Our study is intended to serve as a guide for choosing a particular combination of similarity measures and scoring schemes suitable for reconstruction of gene regulatory networks from short time series data. We show that further improvement of algorithms for reverse engineering can be obtained if one considers measures that are rooted in the study of symbolic dynamics or ranks, in contrast to the application of common similarity measures which do not consider the temporal character of the employed data. Moreover, we establish that the asymmetric weighting scoring scheme together with symbol based measures (for low noise level) and rank based measures (for high noise level) are the most suitable choices.