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Highlights
• Constrictional structures range from dome-and-basin folds to coeval folds and boudins.
• Under bulk constriction, the competent layer rotates slower than a passive plane.
• Extension-parallel and –perpendicular folds grow simultaneously.
• Extension-perpendicular folds affect previous boudins.
Abstract
We conducted scaled analogue modelling to show the influence of varying single layer initial orientation on the geometry of folds and boudins in a bulk constrictional strain field. The initial angle between the plane of shortening and the competent layer (θZ(i)) was incrementally increased from 0° to 90° by multiples of 11.25°. While the amount of layer thickening decreased with increasing θZ(i), the deformation structures produced range from pure dome-and-basin folds to coeval folds and boudins. Based on the attitude of fold axes, there are extension-parallel (FEPR) and extension-perpendicular (FEPP) folds, with axes subparallel and subperpendicular to the principal stretching axis (X), respectively. Coeval growth of FEPR folds and boudins occurred when θZ(i) > ca. 25°. The FEPP folds can be subdivided into a first type which affect the entire layer (if θZ(i) ranges between 11.25 and 78.75°) and a second type, referred to as FBEPP folds, which are affecting pre-existing boudins if θZ(i) > 45°. The interlimb angle of all types of folds increases with increasing θZ(i). Folds and boudins similar to the ones produced in this study can be found in salt domes and in tectonites of subduction zones.
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.
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.
Non-matrix-matched calibration of laser ablation ICPMS (trace/major) element data is a common quantification strategy. However, LA sampling is associated with downhole elemental fractionation, potentially causing inaccuracies if the magnitude of fractionation between the sample and reference material (RM) differs. Here, we estimate fractionation factors (FFs) for different elements (El) in a range of RMs relative to NIST SRM610/612 (FFEl/Ca-NIST) and evaluate element-specific corrections for downhole fractionation using these measured FFEl/Ca-NIST. Significantly different mean El/Ca values were observed before and after correction, particularly for the alkali elements (all RMs), and B, Fe, and Zn (some RMs), notably improving accuracy, especially for the alkali elements. In cases where this methodology does not result in an accuracy improvement, this may help identify underlying issues in reported/reference values for RMs, given that this phenomenon should be accounted for. Overall, we recommend considering routine assessment of FFs and applying a FF correction to enhance data quality.
While high-quality climate reconstructions of some past warm periods in the Cenozoic era now exist, the geological processes responsible for driving the observed longterm changes in atmospheric CO2 are not sufficiently well understood. The long-term change in atmospheric CO2 across the Cenozoic has been proposed to be driven by processes such as terrestrial weathering, organic carbon production and burial, reverse weathering, and volcanic degassing. One way of constraining the relative importance of the various driving forces proposed so far is to better understand the degree to which ocean chemistry has changed because the chemistry of seawater responds to geologic processes that drive atmospheric CO2. In addition, knowledge of the concentration of the major elements in seawater is crucial for accurately applying proxies such as those based on the boron isotopic composition and Mg/Ca of marine carbonates (a proxy for palaeo pH/CO2 and palaeotemperature, respectively). Previously reported records of seawater composition are primarily derived from fluid inclusions in marine evaporites; however, the results are sparse due to the limited availability of such deposits. In this thesis, changes in the Eocene seawater chemistry were reconstructed using trace element (elements/Ca) and isotopic (δ26Mg) proxies in a Larger Benthic Foraminifera (LBFs), i.e., Nummulites sp., to constrain the driving processes of long-term changes in seawater chemistry.
To achieve the objective of this thesis, first, a measurement protocol was established using LA-ICPMS to measure the K/Ca ratio simultaneously with other element/calcium ratios, which is challenging due to the interference of ArH+ on K+. Utilising this newly established measurement protocol, laboratory-cultured Operculina ammonoides grown at different seawater calcium concentrations ([Ca2+]), repeated at different temperatures, as well as modern O. ammonoides collected from different regions exhibiting a range of seawater parameters, were investigated. A significant correlation was observed between K/Casw and K/CaLBF, allowing K/CaLBF to potentially be used as a proxy for seawater major ion reconstructions. In addition, modern O. ammonoides demonstrated no significant influence of most seawater parameters (temperature, salinity, pH, or [CO32-]) on K/CaLBF. Modern O.
ammonoides were also assessed for their Mg isotopic composition (δ26Mg), revealing no significant effect of temperature or salinity on δ26MgLBF. Furthermore, the Mg isotopic fractionation in O. ammonoides was found to be close to that of inorganic calcite, indicating minimal vital effects in these large benthic foraminifera.
Operculina ammonoides is the nearest living relative of the abundant Eocene genus Nummulites, enabling the reconstruction of seawater chemistry using the calibration based on O. ammonoides. The trace elemental/calcium proxies, including Na/Ca, K/Ca, and Mg/Ca, as well as the δ26Mg proxy, were investigated in Eocene Nummulites. The result showed that during the Eocene, [Ca2+]sw was 1.6-2 times higher, while [K+]sw was ~2 times lower than the modern seawater composition. Furthermore, [Mg2+]sw decreased from the early Eocene (54.3− +9 7..69 mmol kg-1 at ~55 Ma) to Late Eocene (37.8− +4 4..3 4 mmol kg-1 at ~31 Ma), followed by
an increase toward modern seawater [Mg]. In contrast, the variability in δ26Mgsw values remained within a narrow range of ~0.3 ‰ throughout the Cenozoic. The reconstructed [Ca2+]sw agrees with the suggestion that Cenozoic seawater chemistry changes can be explained via a change in the seafloor spreading rate. When combined with existing records, the observed minimal change in δ26Mgsw with an increase in [Mg2+]sw suggests an additional possible role of a decrease in the formation of authigenic clay minerals coincident with the Cenozoic decline in deep ocean temperature, which is also supported by the increase in the [K+]sw reconstructed here for the first time. This finding highlights that the reduction in seafloor-spreading rate and decline in reverse weathering during the Cenozoic era has played a significant role in the evolution of seawater chemistry, emphasizing the importance of these processes in driving long-term changes in the carbon cycle.
In the deep sea, interactions between benthic fauna and seafloor sediment primarily occur through bioturbation that can be preserved as traces (i.e. lebensspuren). Lebensspuren are common features of deep-sea landscapes and are more abundant than the organisms that produce them (i.e. tracemakers), rendering lebensspuren promising proxies for inferring biodiversity. The density and diversity relationships between lebensspuren and benthic fauna remain unclear, and contradicting correlations have been proposed (i.e. negative, positive, or even null correlations). To approach these variable correlations, lebensspuren and benthic fauna were characterized taxonomically at eight deep-sea stations in the Kuril-Kamchatka Trench area, together with two novel categories: tracemakers (specific epibenthic fauna that produce these traces) and degrading fauna (benthic fauna that can erase lebensspuren). No general correlation (overall study area) was observed between diversities of lebensspuren, tracemakers, degrading fauna, and fauna. However, a diversity correlation was observed at specific stations, showing both negative and positive correlations depending on: (1) the number of unknown tracemakers (especially significant for dwelling lebensspuren); (2) the lebensspuren with multiple origins; and (3) tracemakers that can produce different lebensspuren. Lebensspuren and faunal density were not correlated. However, lebensspuren density was either positively or negatively correlated with tracemaker densities, depending on the lebensspuren morphotypes. A positive correlation was observed for resting lebensspuren (e.g. ophiuroid impressions, Actiniaria circular impressions), while negative correlations were observed for locomotion-feeding lebensspuren (e.g. echinoid trails). In conclusion, lebensspuren diversity may be a good proxy for tracemaker biodiversity when the lebensspuren–tracemaker relationship can be reliable characterized. Lebensspuren–density correlations vary depending on the specific lebensspuren residence time, tracemaker density, and associated behaviour (rate of movement). Overall, we suggest that lebensspuren density and diversity correlations should be studied with tracemakers rather than with general benthic fauna. On a global scale, abiotic (e.g. hydrodynamics, substrate consistency) and other biotic factors (e.g. microbial degradation) may also play an important role.
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 investigate the applicability of the well-known multilevel Monte Carlo (MLMC) method to the class of density-driven flow problems, in particular the problem of salinisation of coastal aquifers. As a test case, we solve the uncertain Henry saltwater intrusion problem. Unknown porosity, permeability and recharge parameters are modelled by using random fields. The classical deterministic Henry problem is non-linear and time-dependent, and can easily take several hours of computing time. Uncertain settings require the solution of multiple realisations of the deterministic problem, and the total computational cost increases drastically. Instead of computing of hundreds random realisations, typically the mean value and the variance are computed. The standard methods such as the Monte Carlo or surrogate-based methods are a good choice, but they compute all stochastic realisations on the same, often, very fine mesh. They also do not balance the stochastic and discretisation errors. These facts motivated us to apply the MLMC method. We demonstrate that by solving the Henry problem on multi-level spatial and temporal meshes, the MLMC method reduces the overall computational and storage costs. To reduce the computing cost further, parallelization is performed in both physical and stochastic spaces. To solve each deterministic scenario, we run the parallel multigrid solver ug4 in a black-box fashion.
Highlights
• Extreme weather events (floods, droughts, extreme heat) impact freshwater ecosystems.
• Effects of extreme events are site-specific, varying by organism traits and site hydrography.
• Cumulative impact of events is more significant than single events' magnitude.
• Temporal dynamics and biological characteristics are crucial for evaluating impacts.
• Freshwater ecosystem resilience depends on complex factors, not just event severity.
Abstract
Understanding the impacts of extreme weather events on freshwater ecosystems is imperative during a time when a multitude of challenges compromises these environments' health. Exploring how such events affect macroinvertebrate communities in rivers sheds light on the resilience of freshwater ecosystems, which is essential for human well-being and biodiversity conservation. In this study, long-term time series of benthic macroinvertebrate communities from four sites along three freshwater streams within the Rhine-Main-Observatory Long-Term Ecological Research site in Germany were analyzed. Each of them was sampled annually over a span of ~20 years to assess the impacts of extreme weather events (floods, droughts, and extreme heat) on macroinvertebrate communities. The findings reveal that the effects of extreme events are site-specific, suggesting that the impacts of an extreme event can vary based on several potential factors, including the life history traits of the organisms within the community and, among others, the hydrography of the site. Moreover, the analysis highlights that the cumulative impact of these events over time is more significant than the impact of a single event's magnitude, while following distinct temporal dynamics. This underscores the importance of considering both the temporal dynamics and the biological characteristics of communities when evaluating the consequences of extreme weather events on biodiversity, illustrating that the resilience of freshwater ecosystems and their biodiversity under such conditions depends on a complex interplay of factors rather than the severity of individual events.
First-principles modeling techniques offer the ability to simulate a wide range of systems under different physical conditions, such as temperature, pressure, and composition, without relying on empirical knowledge. Density functional theory (DFT), a quantum mechanical method, has become an exceptionally successful framework for materials science modeling. Employing DFT makes it possible to gain valuable insights into the fundamental state of a system, enabling the reliable determination of equilibrium crystal structures. Over time, DFT has become an essential tool that can be incorporated into various schemes for predicting the properties of a material related to its structure, insulating/metallic behavior, magnetism, and optics. DFT is regularly applied in numerous fields, spanning from fundamental subjects in condensed matter physics to the study of large-scale phenomena in geosciences. In the latter, the effectiveness of DFT stems from its ability to simulate the properties found on the Earth, other planets, and meteorites, which may pose challenges for their direct study or laboratory investigation.
In this thesis, a comprehensive examination of a family of monosulfides and a perovskite heterostructure was conducted. These materials are relevant for their potential applications in technology, energy harvesting, and in the case of monosulfides, their speculated abundance on the planet Mercury.
Firstly, a DFT approach was used to analyze two non-magnetic monosulfides, CaS and MgS. We determined their structural properties and then focused on the modeling of their reflectivity in the infrared region. The calculation of the reflectivity considered both harmonic and anharmonic contributions. In the harmonic limit, the non-analytic correction was employed to accurately determine the LO/TO splitting, which is necessary to delimit the retstrahlend band, that is, the maximum of the reflectivity. The anharmonic effects given by up to three-phonon and isotopic scatterings, which were included using perturbation theory, primarily smeared the reflectivity spectra edges in the high-wave region.
Secondly, four polymorphs of MnS were studied using a combination of first-principles methods to simulate their antiferromagnetic (AFM) and paramagnetic (PM) states. The integration of DFT+$U$ with special quasirandom structures (SQS) supercells, and occupation matrix control techniques was crucial for achieving convergence, structural optimization accuracy, and obtaining finite energy band gaps and local magnetic moments in the PM phases. The addition of the Hubbard $U$ correction was necessary to treat the highly-correlated Mn $d$-electrons. The success of our approach was clear based on our electronic structure predictions for the PM rock-salt B1-MnS polymorph. Experimentally this phase has been observed to be an insulator, but multiple \emph{ab initio} works resulted previously in metallic behavior. Our computations, on the other hand, predicted insulating and magnetic properties that compare well with available measurements. Additionally, the pressure-field stability of the four MnS polymorphs was studied. In the case of the PM phases, B1-MnS was identified to be the most stable up to about 21 GPa, then transforming into the B31-MnS polymorph. This finding was in close agreement with high-pressure experiments reporting a similar phase transformation. The optical properties of B1-, B4-, and B31-MnS were also simulated. The SQS technique was used to obtain soft-mode-free phonon band structures within the harmonic approximation. Then, the anharmonic effects were included, and the reflectivity was calculated for B1-MnS and B4-MnS. In both cases, a good agreement for the LO/TO splitting was achieved in comparison to experimental results.
Lastly, the oxygen-deficient heterostructure of LaAlO$_{3-\delta}$ /SrTiO$_{3-\delta}$ was investigated also employing DFT+$U$, with a particular emphasis on the potential impact of vacancy clustering at the interface. Six distinct configurations of pairs of vacancies were studied and their energies were compared to find the most stable one. The orbital reconstruction of Ti orbitals was also examined based on their location with respect to the vacancies and the local magnetic moments were calculated. The final results showed that linearly arranged vacancies located opposite to Ti ions give the most energetically stable configuration.