ALBERT

Description: The Tawa River is one among the major southern tributary of the Narmada River in India, exhibiting substantial variation in rainfall, altering the water availability. The variation in water availability effect the hydrological characteristics such groundwater recharge, soil moisture level, water balance. Studying rainfall-runoff conversion is necessary for proper and sustainable surface and ground water resources planning and management. In the changing climate, it becomes imperative to understand the hydrological response and project water availability for sustainable water management. In this work, the MIKE 11 NAM model is calibrated and validated to evaluate the climate change impact on availability of water. Using the outputs of downscaled, bias-corrected data of CMIP6, the future projection of runoff is done for the near century, mid-century and end century under the scenario SSP245 and SSP 585. MPI-ESM1-2-HRand EC-EARTH 3-VEG climate models were selected. Temporal analysis was performed to evaluate the impact on the availability of water at the 50%, 75% and 90% percentage dependability flow. The annual analysis revealed that scenario SSP5-8.5 has a higher increase in the runoff than SSP2- 4.5, mainly for the end century, as depicted by both the models. Monthly analysis revealed strong intra-seasonal variations and highlighted that August is projected as the most active month of the year, and wet seasons displayed a larger change than dry seasons. The findings indicate that climate change shall significantly influence hydrological processes in the Tawa watershed and potentially impact the availability of water in the Tawa River basin. Our study underscores the imperative need to adapt water resource planning and management strategies to mitigate the potential impacts of climate change on the availability of water in the Tawa River basin.

Description: Precise control over the crystalline phase and crystallographic orientation within thin films of metal–organic frameworks (MOFs) is highly desirable. Here, we report a comparison of the liquid- and vapour-phase film deposition of two copper-dicarboxylate MOFs starting from an oriented metal hydroxide precursor. X-ray diffraction revealed that the vapour- or liquid-phase reaction of the linker with this precursor results in different crystalline phases, morphologies, and orientations. Pole figure analysis showed that solution-based growth of the MOFs follows the axial texture of the metal hydroxide precursor, resulting in heteroepitaxy. In contrast, the vapour-phase method results in non-epitaxial growth with uniplanar texture only.

Description: The prediction of landslide deformation is an important part of landslide early warning systems. Displacement prediction based on geotechnical in-situ monitoring performs well, but its high costs and spatial limitations hinder frequent use within large areas. Here, we propose a novel physically-based and cost-effective landslide displacement prediction framework using the combination of Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) and machine learning techniques. We first extract displacement time series for the landslide from spaceborne Copernicus Sentinel-1A SAR imagery by MT-InSAR. Using wavelet transform, we then decompose the nonlinear displacement time series into trend terms, periodic terms, and noises. The advanced machine learning method of Gated Recurrent Units (GRU) is utilized to predict the trend and periodic displacements, respectively. The modeling inputs for trend and periodic displacement predictions are determined by analyzing their corresponding influencing factors. The total displacements are finally predicted by summing the predicted displacements of trend and periodic items. The Shuping and Muyubao landslides, identified as seepage-driven and buoyancy-driven, respectively, in the Three Gorges Reservoir area in China are selected as case studies to evaluate the performance of our methodology. The prediction results demonstrate that machine learning algorithms can accurately establish the nonlinear relationship between the landslide deformation and its triggers. GRU outperforms the algorithms of Long Short-Term Memory networks and Kernel-based Extreme Learning Machine, and the Adam algorithm can effectively optimize the model hyperparameters. The root mean square error and mean absolute percentage error are 3.817 and 0.022 in Shuping landslide, and 5.145 and 0.020 in Muyubao landslide, respectively. By integrating the advantages of MT-InSAR and machine learning techniques, our proposed prediction framework, considering the physics principles behind landslide deformation, can predict landslide displacement cost-effectively within large areas.

Description: The Eocene-Oligocene Transition (EOT) marks the passage from Eocene greenhouse to Oligocene icehouse conditions. It holds keys to our understanding of the behavior of climate systems under major pCO2 shifts. While the environmental impact of the EOT is rather homogenous in oceans, it is much more heterogeneous on continents. Although little to no changes are recorded in some regions, several EOT studies in western Eurasia suggest an increase in seasonal climatic contrast (e.g., higher amplitude of changes in mean temperature or precipitation), along with a higher sensitivity of the climate to orbital variations. However, these variations remain to be properly documented through changes in sedimentary facies and structures and forcing mechanisms. Here we investigate the depocenter of the Mulhouse Basin (Upper Rhine Graben; URG) revealing a prominent transition from massive mudstones to laminated sediments and varves, alongside the emergence of astronomically-forced mudstone-evaporite alternations. These changes are identified in the distal and proximal parts of the southern URG, where they consist of millimeter-thick mudstone-evaporite couplets and siliciclastic-carbonate couplets. The elemental composition and micro-facies analysis of the laminae show a recurrent depositional pattern consistent with a seasonal depositional process, which suggests that they are varves. We propose that the occurrence of varved sediments, together with the observed orbital cyclicity in the southern URG, reflects an increase in seasonal climatic contrast, and an increase in the sensitivity of climate to orbital variations across the EOT. We show that similar changes were noticed in the Rennes and Bourg-en-Bresse basins, and that of other western Eurasian records for similar climatic conditions. This work emphasizes the potential of high-resolution sedimentary structures to serve as markers of climate change across the EOT.

Description: This data set includes the results of high-resolution digital elevation models (DEM) and digital image correlation (DIC) analysis applied to analogue modelling experiments. Twenty generic analogue models are extended on top of a rubber sheet. Two benchmark experiments are also reported. Detailed descriptions of the experiments can be found in Liu et al. (submitted) to which this data set is supplement. The data presented here are visualized as topography and the horizontal cumulative surface strain (principal strain and slip rake).

Description: Rapidly growing seismic and macroseismic databases and simplified access to advanced machine learning methods have in recent years opened up vast opportunities to address challenges in engineering and strong motion seismology from novel, datacentric perspectives. In this thesis, I explore the opportunities of such perspectives for the tasks of ground motion modeling and rapid earthquake impact assessment, tasks with major implications for long-term earthquake disaster mitigation. In my first study, I utilize the rich strong motion database from the Kanto basin, Japan, and apply the U-Net artificial neural network architecture to develop a deep learning based ground motion model. The operational prototype provides statistical estimates of expected ground shaking, given descriptions of a specific earthquake source, wave propagation paths, and geophysical site conditions. The U-Net interprets ground motion data in its spatial context, potentially taking into account, for example, the geological properties in the vicinity of observation sites. Predictions of ground motion intensity are thereby calibrated to individual observation sites and earthquake locations. The second study addresses the explicit incorporation of rupture forward directivity into ground motion modeling. Incorporation of this phenomenon, causing strong, pulse like ground shaking in the vicinity of earthquake sources, is usually associated with an intolerable increase in computational demand during probabilistic seismic hazard analysis (PSHA) calculations. I suggest an approach in which I utilize an artificial neural network to efficiently approximate the average, directivity-related adjustment to ground motion predictions for earthquake ruptures from the 2022 New Zealand National Seismic Hazard Model. The practical implementation in an actual PSHA calculation demonstrates the efficiency and operational readiness of my model. In a follow-up study, I present a proof of concept for an alternative strategy in which I target the generalizing applicability to ruptures other than those from the New Zealand National Seismic Hazard Model. In the third study, I address the usability of pseudo-intensity reports obtained from macroseismic observations by non-expert citizens for rapid impact assessment. I demonstrate that the statistical properties of pseudo-intensity collections describing the intensity of shaking are correlated with the societal impact of earthquakes. In a second step, I develop a probabilistic model that, within minutes of an event, quantifies the probability of an earthquake to cause considerable societal impact. Under certain conditions, such a quick and preliminary method might be useful to support decision makers in their efforts to organize auxiliary measures for earthquake disaster response while results from more elaborate impact assessment frameworks are not yet available. The application of machine learning methods to datasets that only partially reveal characteristics of Big Data, qualify the majority of results obtained in this thesis as explorative insights rather than ready-to-use solutions to real world problems. The practical usefulness of this work will be better assessed in the future by applying the approaches developed to growing and increasingly complex data sets.

Description: Das rapide Wachstum seismischer und makroseismischer Datenbanken und der vereinfachte Zugang zu fortschrittlichen Methoden aus dem Bereich des maschinellen Lernens haben in den letzen Jahren die datenfokussierte Betrachtung von Fragestellungen in der Seismologie ermöglicht. In dieser Arbeit erforsche ich das Potenzial solcher Betrachtungsweisen im Hinblick auf die Modellierung erdbebenbedingter Bodenerschütterungen und der raschen Einschätzung von gesellschaftlichen Erdbebenauswirkungen, Disziplinen von erheblicher Bedeutung für den langfristigen Erdbebenkatastrophenschutz in seismisch aktiven Regionen. In meiner ersten Studie nutze ich die Vielzahl an Bodenbewegungsdaten aus der Kanto Region in Japan, sowie eine spezielle neuronale Netzwerkarchitektur (U-Net) um ein Bodenbewegungsmodell zu entwickeln. Der einsatzbereite Prototyp liefert auf Basis der Charakterisierung von Erdbebenherden, Wellenausbreitungspfaden und Bodenbeschaffenheiten statistische Schätzungen der zu erwartenden Bodenerschütterungen. Das U-Net interpretiert Bodenbewegungsdaten im räumlichen Kontext, sodass etwa die geologischen Beschaffenheiten in der Umgebung von Messstationen mit einbezogen werden können. Auch die absoluten Koordinaten von Erdbebenherden und Messstationen werden berücksichtigt. Die zweite Studie behandelt die explizite Berücksichtigung richtungsabhängiger Verstärkungseffekte in der Bodenbewegungsmodellierung. Obwohl solche Effekte starke, impulsartige Erschütterungen in der Nähe von Erdbebenherden erzeugen, die eine erhebliche seismische Beanspruchung von Gebäuden darstellen, wird deren explizite Modellierung in der seismischen Gefährdungsabschätzung aufgrund des nicht vertretbaren Rechenaufwandes ausgelassen. Mit meinem, auf einem neuronalen Netzwerk basierenden, Ansatz schlage ich eine Methode vor, umdieses Vorhaben effizient für Erdbebenszenarien aus dem neuseeländischen seismischen Gefährdungsmodell für 2022 (NSHM) umzusetzen. Die Implementierung in einer seismischen Gefährdungsrechnung unterstreicht die Praktikabilität meines Modells. In einer anschließenden Machbarkeitsstudie untersuche ich einen alternativen Ansatz der auf die Anwendbarkeit auf beliebige Erdbebeszenarien abzielt. Die abschließende dritte Studie befasst sich mit dem potenziellen Nutzen der von makroseismischen Beobachtungen abgeleiteten pseudo-Erschütterungsintensitäten für die rasche Abschätzung von gesellschaftlichen Erdbebenauswirkungen. Ich zeige, dass sich aus den Merkmalen solcher Daten Schlussfolgerungen über die gesellschaftlichen Folgen eines Erdbebens ableiten lassen. Basierend darauf formuliere ich ein statistisches Modell, welches innerhalb weniger Minuten nach einem Erdbeben die Wahrscheinlichkeit für das Auftreten beachtlicher gesellschaftlicher Auswirkungen liefert. Ich komme zu dem Schluss, dass ein solches Modell, unter bestimmten Bedingungen, hilfreich sein könnte, um EntscheidungsträgerInnen in ihren Bestrebungen Hilfsmaßnahmen zu organisieren zu unterstützen. Die Anwendung von Methoden des maschinellen Lernens auf Datensätze die sich nur begrenzt als Big Data charakterisieren lassen, qualifizieren die Mehrheit der Ergebnisse dieser Arbeit als explorative Einblicke und weniger als einsatzbereite Lösungen für praktische Fragestellungen. Der praktische Nutzen dieser Arbeit wird sich in erst in Zukunft an der Anwendung der erarbeiteten Ansätze auf wachsende und zunehmend komplexe Datensätze final abschätzen lassen.

Description: Enhanced knowledge of the Pamir salient formation can contribute to comprehending the tectonic evolution of Himalaya-Tibetan orogen. However, whether the Pamir salient formed along a linear or a curved southern Asian margin between the Tarim and Tajik cratons remains controversial. Likewise, the role of the two craton blocks during the evolution of the Pamir salient is unclear. Here we present three sandbox experiments exploring the effect of the geometry of the southern Asian margin, as well as the presence of Tarim and Tajik cratons. The results show that the highly curved shape of the Pamir salient, transpressional faults in its wings and strike-slip faults within its interior only form along a curved southern Asian margin. A westward-deflecting arcuate thrust wedge formed along the asymmetric curved southern Asian margin. Together with the Tarim craton and the Tajik craton, this wedge facilitated the westward transfer of materials in the Pamir, and resulted in the westward deflection of the velocity field in Pamir and the formation of the Tajik fold-thrust belt. The oblique slip of arcuate thrust wedge along the western edge of the Tarim craton generated the Kongur extensional system. Moreover, the Tarim and Tajik cratons concentrated deformation mainly along the non-cratonic continental margin and promoted the formation of transpressional faults surrounding the Pamir and the strike-slip faults within the Pamir.