ALBERT

Description: Watershed management requires an understanding of key hydrochemical processes. The Pra Basin is one of the five major river basins in Ghana with a population of over 4.2 million people. Currently, water resources management faces challenges due to surface water pollution caused by the unregulated release of untreated household and industrial waste into aquatic ecosystems and illegal mining activities. This has increased the need for groundwater as the most reliable water supply. Our understanding of groundwater recharge mechanisms and chemical evolution in the basin has been inadequate, making effective management difficult. Therefore, the main objective of this work is to gain insight into the processes that determine the hydrogeochemical evolution of groundwater quality in the Pra Basin. The combined use of stable isotope, hydrochemistry, and water level data provides the basis for conceptualizing the chemical evolution of groundwater in the Pra Basin. For this purpose, the origin and evaporation rates of water infiltrating into the unsaturated zone were evaluated. In addition, Chloride Mass Balance (CMB) and Water Table Fluctuations (WTF) were considered to quantify groundwater recharge for the basin. Indices such as water quality index (WQI), sodium adsorption ratio (SAR), Wilcox diagram, and salinity (USSL) were used in this study to determine the quality of the resource for use as drinking water and for irrigation purposes. Due to the heterogeneity of the hydrochemical data, the statistical techniques of hierarchical cluster and factor analysis were applied to subdivide the data according to their spatial correlation. A conceptual hydrogeochemical model was developed and subsequently validated by applying combinatorial inverse and reaction pathway-based geochemical models to determine plausible mineral assemblages that control the chemical composition of the groundwater. The interactions between water and rock determine the groundwater quality in the Pra Basin. The results underline that the groundwater is of good quality and can be used for drinking water and irrigation purposes. It was demonstrated that there is a large groundwater potential to meet the entire Pra Basin’s current and future water demands. The main recharge area was identified as the northern zone, while the southern zone is the discharge area. The predominant influence of weathering of silicate minerals plays a key role in the chemical evolution of the groundwater. The work presented here provides fundamental insights into the hydrochemistry of the Pra Basin and provides data important to water managers for informed decision-making in planning and allocating water resources for various purposes. A novel inverse modelling approach was used in this study to identify different mineral compositions that determine the chemical evolution of groundwater in the Pra Basin. This modelling technique has the potential to simulate the composition of groundwater at the basin scale with large hydrochemical heterogeneity, using average water composition to represent established spatial groupings of water chemistry.

Description: Die Bewirtschaftung von Wassereinzugsgebieten erfordert ein Verständnis der wichtigsten hydrochemischen Prozesse. Das Pra-Becken ist eines der fünf großen Flusseinzugsgebiete Ghanas mit einer Bevölkerung von über 4,2 Millionen Menschen. Die Bewirtschaftung der Wasserressourcen wird derzeit durch die Verschmutzung der Oberflächengewässer erschwert, die durch die unkontrollierte Einleitung von unbehandelten Haushalts- und Industrieabfällen in die aquatischen Ökosysteme und durch illegale Bergbauaktivitäten entsteht. Dies hat den Bedarf an Grundwasser als zuverlässigste Wasserversorgung erhöht. Unser Verständnis der Mechanismen der Grundwasserneubildung und der chemischen Entwicklung im Einzugsgebiet ist bislang unzureichend, was eine wirksame Bewirtschaftung erschwert. Daher ist das Hauptziel dieser Arbeit Einblicke in die Prozesse zu bekommen, welche die hydrogeochemische Entwicklung der Grundwasserqualität im Pra-Becken bestimmen. Die kombinierte Verwendung von Daten stabiler Isotope, der Hydrochemie und von Wasserständen bildet die Grundlage für die Konzeption der chemischen Entwicklung des Grundwassers im Pra-Becken. Dafür wurden die Herkunft und die Verdunstungsraten des in die ungesättigte Zone infiltrierenden Wassers bewertet. Darüber hinaus wurden die Chlorid-Massenbilanz und die Wasserspiegelschwankungen betrachtet, um die Grundwasserneubildung für das Einzugsgebiet zu quantifizieren. Indizes wie der Wasserqualitätsindex (WQI), das Natriumadsorptionsverhältnis (SAR), das Wilcox-Diagramm und der Salzgehalt (USSL) wurden in dieser Studie verwendet, um die Qualität der Ressource für die Verwendung als Trinkwasser und zu Bewässerungszwecken zu bestimmen. Aufgrund der Heterogenität der hydrochemischen Daten wurden die statistischen Verfahren der hierarchischen Cluster- und Faktorenanalyse angewandt, um die Daten entsprechend ihrer räumlichen Korrelation zu unterteilen. Ein konzeptionelles hydrogeochemisches Modell wurde entwickelt und anschließend durch Anwendung kombinatorischer inverser und reaktionspfadbasierter geochemischer Modelle validiert, um plausible mineralische Assemblagen zu bestimmen, welche die chemische Zusammensetzung des Grundwassers kontrollieren. Die Wechselwirkungen zwischen Wasser und Gestein bestimmen die Grundwasserqualität im Pra-Becken. Die Ergebnisse unterstreichen, dass das Grundwasser eine gute Qualität aufweist und als Trinkwasser und für Bewässerungszwecke genutzt werden kann. Es wurde nachgewiesen, dass ein großes Grundwasserpotenzial vorhanden ist, um den derzeitigen und künftigen Wasserbedarf des gesamten Pra-Beckens zu decken. Als Hauptneubildungsgebiet wurde die nördliche Zone im Gebiet identifiziert, während die südliche Zone das Abflussgebiet ist. Der vorherrschende Einfluss der Verwitterung von Silikatmineralen spielt bei der chemischen Entwicklung des Grundwassers eine zentrale Rolle. Die hier vorgestellte Arbeit gibt grundlegende Einblicke in die Hydrochemie des Pra-Beckens und liefert für das Wassermanagement wichtige Daten für eine fundierte Entscheidungsfindung bei der Planung und Zuweisung von Wasserressourcen für verschiedene Zwecke. Ein neuartiger Ansatz zur inversen Modellierungwurde in dieser Studie eingesetzt, um unterschiedliche Mineralzusammensetzungen zu ermitteln, welche die chemische Entwicklung des Grundwassers im Pra-Becken bestimmen. Diese Modellierungstechnik hat das Potenzial, die Zusammensetzung eines Grundwassers auf der Skala eines Beckens mit großer hydrochemischer Heterogenität zu simulieren, wobei die durchschnittliche Wasserzusammensetzung zur Darstellung der etablierten räumlichen Gruppierungen der Wasserchemie verwendet wird.

Description: Watershed management requires an understanding of key hydrochemical processes. The Pra Basin is one of the five major river basins in Ghana with a population of over 4.2 million people. Currently, water resources management faces challenges due to surface water pollution caused by the unregulated release of untreated household and industrial waste into aquatic ecosystems and illegal mining activities. This has increased the need for groundwater as the most reliable water supply. Our understanding of groundwater recharge mechanisms and chemical evolution in the basin has been inadequate, making effective management difficult. Therefore, the main objective of this work is to gain insight into the processes that determine the hydrogeochemical evolution of groundwater quality in the Pra Basin. The combined use of stable isotope, hydrochemistry, and water level data provides the basis for conceptualizing the chemical evolution of groundwater in the Pra Basin. For this purpose, the origin and evaporation rates of water infiltrating into the unsaturated zone were evaluated. In addition, Chloride Mass Balance (CMB) and Water Table Fluctuations (WTF) were considered to quantify groundwater recharge for the basin. Indices such as water quality index (WQI), sodium adsorption ratio (SAR), Wilcox diagram, and salinity (USSL) were used in this study to determine the quality of the resource for use as drinking water and for irrigation purposes. Due to the heterogeneity of the hydrochemical data, the statistical techniques of hierarchical cluster and factor analysis were applied to subdivide the data according to their spatial correlation. A conceptual hydrogeochemical model was developed and subsequently validated by applying combinatorial inverse and reaction pathway-based geochemical models to determine plausible mineral assemblages that control the chemical composition of the groundwater. The interactions between water and rock determine the groundwater quality in the Pra Basin. The results underline that the groundwater is of good quality and can be used for drinking water and irrigation purposes. It was demonstrated that there is a large groundwater potential to meet the entire Pra Basin’s current and future water demands. The main recharge area was identified as the northern zone, while the southern zone is the discharge area. The predominant influence of weathering of silicate minerals plays a key role in the chemical evolution of the groundwater. The work presented here provides fundamental insights into the hydrochemistry of the Pra Basin and provides data important to water managers for informed decision-making in planning and allocating water resources for various purposes. A novel inverse modelling approach was used in this study to identify different mineral compositions that determine the chemical evolution of groundwater in the Pra Basin. This modelling technique has the potential to simulate the composition of groundwater at the basin scale with large hydrochemical heterogeneity, using average water composition to represent established spatial groupings of water chemistry.

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: Large parts of the Earth’s interior are inaccessible to direct observation, yet global geodynamic processes are governed by the physical material properties under extreme pressure and temperature conditions. It is therefore essential to investigate the deep Earth’s physical properties through in-situ laboratory experiments. With this goal in mind, the optical properties of mantle minerals at high pressure offer a unique way to determine a variety of physical properties, in a straight-forward, reproducible, and time-effective manner, thus providing valuable insights into the physical processes of the deep Earth. This thesis focusses on the system Mg-Fe-O, specifically on the optical properties of periclase (MgO) and its iron-bearing variant ferropericlase ((Mg,Fe)O), forming a major planetary building block. The primary objective is to establish links between physical material properties and optical properties. In particular the spin transition in ferropericlase, the second-most abundant phase of the lower mantle, is known to change the physical material properties. Although the spin transition region likely extends down to the core-mantle boundary, the ef-fects of the mixed-spin state, where both high- and low-spin state are present, remains poorly constrained. In the studies presented herein, we show how optical properties are linked to physical properties such as electrical conductivity, radiative thermal conductivity and viscosity. We also show how the optical properties reveal changes in the chemical bonding. Furthermore, we unveil how the chemical bonding, the optical and other physical properties are affected by the iron spin transition. We find opposing trends in the pres-sure dependence of the refractive index of MgO and (Mg,Fe)O. From 1 atm to ~140 GPa, the refractive index of MgO decreases by ~2.4% from 1.737 to 1.696 (±0.017). In contrast, the refractive index of (Mg0.87Fe0.13)O (Fp13) and (Mg0.76Fe0.24)O (Fp24) ferropericlase increases with pressure, likely because Fe Fe interactions between adjacent iron sites hinder a strong decrease of polarizability, as it is observed with increasing density in the case of pure MgO. An analysis of the index dispersion in MgO (decreasing by ~23% from 1 atm to ~103 GPa) reflects a widening of the band gap from ~7.4 eV at 1 atm to ~8.5 (±0.6) eV at ~103 GPa. The index dispersion (between 550 and 870 nm) of Fp13 reveals a decrease by a factor of ~3 over the spin transition range (~44–100 GPa). We show that the electrical band gap of ferropericlase significantly widens up to ~4.7 eV in the mixed spin region, equivalent to an increase by a factor of ~1.7. We propose that this is due to a lower electron mobility between adjacent Fe2+ sites of opposite spin, explaining the previously observed low electrical conductivity in the mixed spin region. From the study of absorbance spectra in Fp13, we show an increasing covalency of the Fe-O bond with pressure for high-spin ferropericlase, whereas in the low-spin state a trend to a more ionic nature of the Fe-O bond is observed, indicating a bond weakening effect of the spin transition. We found that the spin transition is ultimately caused by both an increase of the ligand field-splitting energy and a decreasing spin-pairing energy of high-spin Fe2+.

Description: Geodynamische Prozesse werden von den physikalischen Materialeigenschaften unter den extremen Druck- und Temperaturbedingungen des Erdinneren gesteuert, gerade diese Areale sind aber faktisch nicht für direkte Beobachtungen zugänglich. Umso wichtiger ist es, die physikalischen Eigenschaften unter Bedingungen des Erdinneren zu untersuchen. Mit diesem Ziel vor Augen erlaubt das Studium der optischen Eigenschaften von Mineralen des Erdmantels, eine große Bandbreite an physikalischen Materialeigenschaften, in einer einfachen, reproduzierbaren und effizienten Art und Weise zu bestimmen. Dadurch bieten sich wichtige Einblicke in die physikalischen Prozessen des Erdinneren. Die vorliegende Arbeit konzentriert sich auf das System Mg-Fe-O, im Speziellen auf Periklas (MgO) und seine Eisen-haltige Variante Ferroperiklas ((Mg,Fe)O), ein wichtiger Baustein planetarer Körper. Das Hauptziel der Arbeit besteht darin Verbindungen zwischen optischen Eigenschaften und physikalischen Materialeigenschaften zu finden. Gerade der Spin-Übergang in Ferroperiklas, der zweithäufigsten Phase des unteren Erdmantels, ist dabei von Bedeutung, da damit Veränderungen in den physikalischen Materialeigenschaften einhergehen. Obwohl sich der Spinübergangsbereich vermutlich bis zur Kern-Mantel-Grenze erstreckt, sind die Auswirkungen des gemischten Spin-Zustandes, bei dem sowohl Hoch- als auch Tief-Spin präsent sind, nur unzureichend untersucht. Die hier vorgestellten Studien zeigen, wie optische Eigenschaften mit anderen wichtigen physikalischen Eigenschaften wie elektrischer und thermischer Leitfähigkeit, Viskosität oder auch mit der chemischen Bindung verbunden sind. Daraus lässt sich auch ableiten wie der Spin-Übergang in Ferroperiklas diese Eigenschaften beeinflusst. Von Raumbedingungen bis zu ~140 GPa sinkt der Brechungsindex von MgO um ~2.4 % von 1.737 auf 1.696 (±0.017). Im Gegensatz dazu steigt der Brechungsindex von (Mg0.87Fe0.13)O (Fp13) und (Mg0.76Fe0.24)O (Fp24) Ferroperiklas mit dem Druck an. Dies ist auf Fe-Fe Wechselwirkungen zwischen benachbarten Eisenpositionen zurückzuführen, die eine starke Verringerung der Polarisierbarkeit, wie im Falle von reinem MgO mit zunehmender Dichte, behindern. Eine Analyse der Dispersion des Brechungsindexes von MgO (Abnahme um ~23 % von 1 Atm zu ~103 GPa) offenbart eine Verbreiterung der Bandlücke von ~7.4 eV bei 1 Atm zu ~8.5 (±0.6) eV bei ~103 GPa. Die Messung der Dispersion (zwischen 550 und 870 nm) in Fp13 zeigt eine starke Abnahme über den Bereich des Spin-Überganges (~44–100 GPa) bis zu einem Faktor von ~3. Die Bandlücke nimmt in der Region des gemischten Spin-Zustandes signifikant auf bis zu ~4.7 eV zu (entspricht einer Zunahme um den Faktor ~1.7). Dies deutet auf eine Verringerung der Elektronen-Mobilität zwischen benachbarten Fe2+-Positionen mit unterschiedlichem Spin-Zustand hin, was die bereits in früheren Arbeiten beobachtete Abnahme der elektrischen Leitfähigkeit im Bereich des gemischten Spin-Zustandes erklärt. Absorptionsspektren an Fp13 zeigen eine Druck-bedingte Zunahme der Kovalenz der Fe-O Bindung für Ferroperiklas im Hoch-Spin Zustand, wohingegen Tief-Spin Ferroperiklas einen Trend zu einer mehr ionischen Fe-O Bindung auf-weist, was auf einen Bindungs-schwächenden Effekt des Spin-Wechsels hinweist. Der Übergang von Hoch- zu Tiefspin ist letztlich auf eine Zunahme der Ligandenfeldaufspaltungsenergie sowie eine abnehmende Spinpaarungsenergie von Hoch-Spin Fe2+ zurückzuführen.

Description: The world should redouble its efforts on the SDGs, not abandon them. Here’s how to progress the United Nations’ agenda towards 2050.

Description: The Arctic sea ice (ASI) is expected to decrease with further global warming. However, considerable uncertainty remains regarding the temperature range that would lead to a completely ice-free Arctic. Here, we combine satellite data and a large suite of models from the latest phase of the Coupled Model Intercomparison Project (CMIP6) to develop an empirical, observation-based projection of the September ASI area for increasing global mean surface temperature (GMST) values. This projection harnesses two simple linear relationships that are statistically supported by both observations and model data. First, we show that the September ASI area is linearly proportional to the area inside a specific northern hemisphere January–September mean temperature contour Tc. Second, we use observational data to show how zonally averaged temperatures have followed a positive linear trend relative to the GMST, consistent with Arctic amplification. To ensure the reliability of these observations throughout the rest of the century, we validate this trend by employing the CMIP6 ensemble. Combining these two linear relationships, we show that the September ASI area decrease will accelerate with respect to the GMST increase. Our analysis of observations and CMIP6 model data suggests a complete loss of the September ASI (area below 10〈sup〉8〈/sup〉 km〈sup〉2〈/sup〉) for global warming between 1.5 C and 2.2 C above pre-industrial GMST levels.

Description: The loss of Arctic sea ice (ASI) represents a major transformation in the Arctic region, impacting regional and global climate, ecosystems, and socio-economic structures. Observational and reanalysis data have consistently shown a notable shift in polar environmental conditions over recent decades, marked by a substantial reduction in the ASI area and a rise in the variability in its coverage and distribution. Utilizing data from the latest Coupled Model Intercomparison Project phase, our study reveals a consistent pattern highlighting a fundamental shift in ASI dynamics preceding total loss. We observe increasing fluctuations in the September ASI area as the threshold for an ice-free Arctic is approached across various scenarios and models. This pattern is particularly concentrated in the Central Arctic (CA) sub-region. Spatial analyses reveal increasing variance along the CA's northern coastlines, accompanied by a substantial increase in open water coverage, underscoring the shift from stable to highly variable ice conditions in this region. Additionally, our findings suggest a potential link between increased ASI fluctuations and variability in surface wind speeds. These specific results underscore the urgency of multidisciplinary approaches in addressing the challenges posed by ASI variability, with implications for marine ecosystems, Indigenous communities, and navigational safety.

Description: Detection of critical slowing down (CSD) is the dominant avenue for anticipating critical transitions from noisy time-series data. Most commonly, changes in variance and lag-1 autocorrelation [AC(1)] are used as CSD indicators. However, these indicators will only produce reliable results if the noise driving the system is white and stationary. In the more realistic case of time-correlated red noise, increasing (decreasing) the correlation of the noise will lead to spurious (masked) alarms for both variance and AC(1). Here, we propose two new methods that can discriminate true CSD from possible changes in the driving noise characteristics. We focus on estimating changes in the linear restoring rate based on Langevin-type dynamics driven by either white or red noise. We assess the capacity of our new estimators to anticipate critical transitions and show that they perform significantly better than other existing methods both for continuous-time and discrete-time models. In addition to conceptual models, we apply our methods to climate model simulations of the termination of the African Humid Period. The estimations rule out spurious signals stemming from nonstationary noise characteristics and reveal a destabilization of the African climate system as the dynamical mechanism underlying this archetype of abrupt climate change in the past.

Description: Ionospheric disturbances are associated with the propagation of seismic waves generated by earthquakes. We analyze total electron content (TEC) fluctuations from Global Navigation Satellite Systems (GNSS) data. This study investigates the global earthquakes from December 2004 to March 2020 with magnitude (Mw) 4.0 to 9.1. Data of 65 permanent GNSS stations are used to analyze the impacts of these earthquakes on the ionosphere. The experimental data based on the cluster sampling method ensure strict conditions such as accuracy, the distance from the monitoring stations to the epicenter, and the depth of the hypocenter. At least three GNSS stations near the epicenter participate in the analysis of each earthquake. Probability and statistics are applied to remove outliers and rough errors in the input data, to select datasets with similar quality, and to analyze TEC anomalies. The results show that when a strong earthquake occurs, the TEC values calculated at different GNSS stations surrounding the earthquake region tend to exhibit similar variations. Depending on the magnitude, epicenter, and hypocenter depth, these fluctuations can range from ± 3.2 TECU to ± 14.5 TECU for large earthquakes. These TEC fluctuations occur from 30 min to almost two hours before the mainshock of the earthquakes and last to the aftershock period. For earthquakes with a magnitude greater than 6.0 Mw, the TEC fluctuations are significant. The findings of this study contribute to GNSS applications in studying earthquakes in the future.

Description: In deformation analysis, irregularly spaced data, extreme values, and anomalies in time series can lead to misleading simulations for forecast models, such as overfitting and underfitting. Therefore, K-fold cross-validation is one of the hyperparameter tuning techniques used in machine learning (ML) to deal with these problems. In this study, we use data from 22 permanent GNSS stations to predict the motion trajectory of the Earth’s crust. Lag functions and sampling techniques are applied to generate 924-time series samples. Time series standardization techniques are also performed to improve the quality of data. To test the efficiency of the K-fold cross-validation method, we investigate 26 mathematical models based on six ML algorithms. The optimal K values are selected through trial methods. Root mean squared error (RMSE) of validation and test is the basis for determining the overfitting and underfitting models. The investigations show that the optimal intervals of K-fold range from five to ten folds for the GNSS time series with many anomalies, jumps, and significant variations, from three to ten for stable time series. The sensitivity of cross-validation is more effective on the time series of the Up component than those of the North and East components. In addition, cross-validation can also detect effectively overfitting and underfitting for forecast models in motion of permanent GNSS stations.

Description: Understanding the phase behavior and structural properties of salt water at high pressures is essential for understanding the dynamics and physical characteristics of icy planets. In this study, we employed high-pressure experimental and ab initio simulation techniques to investigate the impact of CaCl2 on the structure of ice VII. Our findings reveal that 1.8 mol% CaCl2 can be incorporated into the ice VII structure above 10 GPa. This CaCl2-bearing ice VII (Cb VII) exhibits a lower O-H stretching frequency in the Raman spectra as well as a reduced volume of the unit cell compared to pure ice VII. In contrast to doping ice VII with other salts such as LiCl and NaCl that leads to an increase of the ice VII to ice X transition pressure occurring at 100–150 GPa, CaCl2 doping stands out by reducing the transition pressure. It shifts the transition to a pressure of 52 GPa, which is significantly lower than the transition pressure of 80 GPa in the pure H2O ice system. This notable distinction highlights the unique influence of CaCl2 on the phase behavior of water under high pressure, and we attribute these effects to the phenomenon of chemical pressure induced by CaCl2 within the ice VII structure. Our study suggests that the presence of a modified ice VII phase, contaminated with salt and referred to as Cb VII, may influence the composition, structure, and evolution of planets.