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Institute
Highlights
• Germany plans more long-distances water transfers to secure drinking water supply.
• Long-distance water transfers can unfold lock-ins that limit adaptive water governance.
• Our interdisciplinary case study shows how lock-ins emerge over different spaces and times.
• Commercialisation of water but also local protests contributed to various lock-ins.
• We therefore call for context-specific assessments of potentials and risks of LDWT.
Abstract
Germany plans to expand water transfers over long distances in the light of numerous and pressing challenges for drinking water supply. Research on inter- and intrabasin water transfers warns, however, that major investments in large-scale infrastructure systems accompanied by institutional logics and political interests often lead to a so-called lock-in. As a consequence, long-distance water transfers can limit the potential for adaptive water governance in the involved supply areas over decades with negative impacts for people and the environment. By using a case study in Germany as an example, we researched when, where and how such lock-ins around long-distance water transfers emerge. In the infrastructural development of the Elbaue-Ostharz transfer system we found various lock-ins that overlap in space and time. Some are located at the centre others at the margins of the infrastructure and commercialization of the water sector as well as hydraulic and hygienic concerns interlock with local protests in a way that the expansion of the long-distance water transfer infrastructure is presented continuously as imperative. Our findings contribute to a relational understanding of lock-ins of long-distance water transfers as contingent and diverse processes. Given the widespread occurrence of lock-ins, we argue for a context-specific assessment of potentials and risks of long-distance water transfers in times of multiple crises.
Highlights
• We present the first results of a deep learning model based on a convolutional neural network for earthquake magnitude estimation, using HR-GNSS displacement time series.
• The influence of different dataset configurations, such as station numbers, epicentral distances, signal duration, and earthquake size, were analyzed to figure out how the model can be adapted to various scenarios.
• The model was tested using real data from different regions and magnitudes, resulting in the best cases with 0.09 ≤ RMS ≤ 0.33.
Abstract
High-rate Global Navigation Satellite System (HR-GNSS) data can be highly useful for earthquake analysis as it provides continuous high-frequency measurements of ground motion. This data can be used to analyze diverse parameters related to the seismic source and to assess the potential of an earthquake to prompt strong motions at certain distances and even generate tsunamis. In this work, we present the first results of a deep learning model based on a convolutional neural network for earthquake magnitude estimation, using HR-GNSS displacement time series. The influence of different dataset configurations, such as station numbers, epicentral distances, signal duration, and earthquake size, were analyzed to figure out how the model can be adapted to various scenarios. We explored the potential of the model for global application and compared its performance using both synthetic and real data from different seismogenic regions. The performance of our model at this stage was satisfactory in estimating earthquake magnitude from synthetic data with 0.07 ≤ RMS ≤ 0.11. Comparable results were observed in tests using synthetic data from a different region than the training data, with RMS ≤ 0.15. Furthermore, the model was tested using real data from different regions and magnitudes, resulting in the best cases with 0.09 ≤ RMS ≤ 0.33, provided that the data from a particular group of stations had similar epicentral distance constraints to those used during the model training. The robustness of the DL model can be improved to work independently from the window size of the time series and the number of stations, enabling faster estimation by the model using only near-field data. Overall, this study provides insights for the development of future DL approaches for earthquake magnitude estimation with HR-GNSS data, emphasizing the importance of proper handling and careful data selection for further model improvements.
Die Welt im Wasserstress
(2024)
Wie haben sich die Wasserresourcen in den letzten 120 Jahren verändert? Und was passiert, wenn es bis Ende des 21. Jahrhunderts noch einmal zwei Grad wärmer wird als heute? Fragen wie diese beantwortet das globale Wasser-Modell WaterGAP, das maßgeblich vom Institut für Physische Geographie der Goethe-Universität und von der Ruhr-Universität Bochum entwickelt wird. Bislang ließen sich die damit erzeugten Daten nur von Expertinnen und Experten nutzen. Eine neue Web-App ändert das nun. Entwickelt wurde sie von dem französischen Geodaten-Unternehmen Ageoce, das dafür mit der Goethe-Universität kooperierte.
We present a new experimental dataset on the impact of the heavy halogens chlorine, bromine and iodine on the Raman water bands concerning pressure and their concentration at room temperature. These experiments were conducted at ambient temperature, with variations in halogen concentration and pressure ranging from 0 to 1.4 GPa.
The strength of the Raman water band shift change increases with the ionic size from chlorine, over bromine, to iodine. Our experiments further demonstrate that increased pressure diminishes the impact of the halogen shift change to a varying extent for each of the three halogens. This finding can have significant implications for the salinity calculation of fluid inclusions in minerals such as quartz or olivine. Particularly in the low salinity range, the concentration can be markedly underestimated if the pressure effect is neglected. For experiments in diamond anvil cells involving halogens dissolved in water, the change in Raman water band shifts can serve either as a new tool to monitor pressure, or to monitor the salinity.
Unsichtbare Winzlinge
(2023)
Local climate change risk assessments (LCCRAs) are best supported by a quantitative integration of physical hazards, exposures and vulnerabilities that includes the characterization of uncertainties. We propose to use Bayesian Networks (BNs) for this task and show how to integrate freely-available output of multiple global hydrological models (GHMs) into BNs, in order to probabilistically assess risks for water supply. Projected relative changes in hydrological variables computed by three GHMs driven by the output of four global climate models were processed using MATLAB, taking into account local information on water availability and use. A roadmap to set up BNs and apply probability distributions of risk levels under historic and future climate and water use was co-developed with experts from the Maghreb (Tunisia, Algeria, Morocco) who positively evaluated the BN application for LCCRAs. We conclude that the presented approach is suitable for application in the many LCCRAs necessary for successful adaptation to climate change world-wide.
Herein, the high-pressure/high-temperature synthesis (11 GPa, 650 °C) of Tb3B10O17(OH)5 in a modified Walker-type multianvil device is presented. The structure of this rare-earth borate was determined by single-crystal X-ray diffraction methods and was found to crystallize orthorhombically in the space group Pmn21 (no. 31) with the unit cell parameters a = 16.2527(4), b = 4.4373(1), and c = 8.8174(2) Å. The new compound was further characterized using infrared spectroscopy, energy-dispersive X-ray spectroscopy, second harmonic generation (SHG) measurements, and temperature-dependent X-ray powder diffraction. Tb3B10O17(OH)5 decomposes to β-Tb(BO2)3 at temperatures higher than 460 °C. With increasing temperatures, the formation of μ-TbBO3 was observed, which transforms to π-TbBO3 upon cooling.
Die nachfolgende Dissertation wurde an der Goethe-Universität Frankfurt am Institut für Geowissenschaften (FB 11) in der Arbeitsgruppe Kristallographie und Mineralogie (AG Winkler) verfasst. Die Betreuung der hier durchgeführten Arbeiten erfolgte hauptsächlich durch Prof. B. Winkler in Zusammenarbeit mit Dr. L. Bayarjargal, PD Dr. E. Haussühl und PD Dr. V. Vinograd. Bei dem vorliegenden Manuskript handelt es sich um eine kumulative bzw. publikationsbasierte Dissertation, welche die Forschungsergebnisse verschiedener bereits veröffentlichter wissenschaftlicher Fachartikel zusammenfasst.
Die Arbeit beschreibt verschiedene Synthesen und Untersuchungen an Carbonaten und teilt sich im Wesentlichen in zwei Abschnitte. Zum einen wurden Experimente mit Carbonaten bei Extrembedingungen bzw. unter hohen Drücken und hohen Temperaturen durchgeführt, wie sie auch im Inneren der Erde zu finden sind. Im zweiten Teil wurden Carbonate bei Raumbedingungen synthetisiert und der Einbau von Seltenerdelementen untersucht. Grundsätzlich werden jedoch in beiden Teilen dieser Arbeit die Strukturen und Eigenschaften verschiedener Carbonate und eine mögliche Kationensubstitution bzw. die Synthese isostruktureller Verbindungen erforscht.