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Institut
- Institut für Geowissenschaften (304) (entfernen)
Terrestrial landscape dynamics are dominated by the production, mobilisation, transfer and deposition of sediment. Numerous chemical elements are carried by sediments, making them a key component for ecological processes, as soil constitution and thus plants and animal ecosystems depends on them, and by extension the human species. They are also essential for climate evolution and regulation as marine sedimentation acts as a carbon sink. However, the processes at the origin of their production, mobilisation, transfer can occur suddenly with high-energy content – such as volcanic eruption, mass wasting or flooding events and wildfires – shaking ecosystems and shaping landforms. Besides, in the last era, the human species has shown its ability to disturb landscape dynamics and change sediments cycles. Thus, there is a need for predictive understanding of the processes involved. This relies on un-derstanding of the mechanisms of key processes and their controls, and knowledge of the state and evolution of the Earth’s surface. Classic approaches to these challenges include empirical observations and numerical modeling of geochemical fluxes and surface processes, as well as the study of terrestrial sedimentary archives to better understand the parameters at stake in landscape dynamics and climate changes and the different actions and retroactions between the production, mobilisation, transfer and deposition of sediments which ultimately shape landscapes. Environmental seismology complements these approaches.
Environmental seismology is the science topic investigating the source functions and propagation properties of seismic vibrations triggered by processes happening at or near the Earth’s surface, below and above it - cryosphere, hydrosphere, atmosphere, human habitat, biosphere, etc., to obtain insight into all these physical processes. Indeed, from mass wasting event to rivers, from wild species to hu-man, all these processes are generating seismic waves. Environmental seismology is a rather recent field, with new branches rapidly expanding and at various stages of scientific progress. This thesis is motivated by the goal of learning more on two major natural process hazards (river bedload transport and mass wasting) as well as on human-generated acoustic hazard, while covering the axis of funda-mental research progression, from data exploration and method and theory development to proof of concept, with the twin aims of developing a better understanding of the operation of these specific processes, and of advancing the methods we have at our disposal to study them.
First, I provide a benchmark for assessment of the reliability of existing seismic bedload model inver-sion to retrieve bedload flux from seismic data. Bedload flux measurements are essential to better understand river dynamics, and this can be achieved with environmental seismology. However, due to a lack of well-constrained validation data, the accuracy of the resulting inversions is unknown. I address this gap in Chapter 2.2, reporting a seismic field experiment, and comparing the results to high-quality independent bedload measurements to constrain a seismic bedload model. The study shows that the quality of bedload flux estimates from seismic data strongly depends on the quality of the input data for the model. Direct measurements of relevant parameters, chiefly seismic ground proper-ties needed for the Green's function and the grain size distribution of the moving bedload, considerably improve the model quality over generic approaches using empirical or theoretical functions. I also pro-vide a numerical tool to facilitate the use of water turbulence and bedload seismic inversion models: seismic models for bedload flux and water turbulence require painstaking work to constrain parame-ters describing the ground properties by active seismic study or analysis of passive seismic data, and the grain size distribution via independent measurements. Reasonable predictions can be achieved by using a Monte Carlo approach to optimize the free parameters with respect to the target parameters. The validation of the tool, in Chapter 2.3, with independent measurements of water depth and bedload flux at a study site on the Eshtemoa river in Israël makes it available for reliable use at other sites. The work reported in this chapter has been published in Lagarde et al. 2021 and Dietze et al. 2019b.
In a second study, reported in Chapter 3, I investigate the formation of a failure plane prior to a rock-slide. A better understanding of the dynamics of the preparation phase is essential to determine the timing, volume and mobilization mechanism of a rock slope failure, and this can be achieved with envi-ronmental seismology. I take advantage of a network of seismic stations close to an instable slope recording cracking signals prior to the slope failure, and use a machine learning technique based on hidden Markov models to isolate these signals from the seismic data, retrieving the cumulative number of cracking events in a period of 20 days prior to a large rockslide and 10 days after. The trajectory of the cumulative number of cracks shifts from a rather linear shape in the two weeks prior to the rock-slide to an S-shaped development in the last 27 h before failure. I interpret this change as a switch from initial distributed cracking to localised damage accumulation in the hours prior to the failure. I develop a simple physical model to explain the temporal evolution of crack activity during the S-shape phase, revealing the importance of an internal parameter, the total crack boundary length as the dominant control parameter of failure plane evolution. This study has been published as Lagarde et al. 2023.
Third, I develop a model converting acoustic signals to seismic signals. A part of the acoustic vibrations generated on the Earth’s surface is converted to seismic signals at the ground interface. Consequently, noise pollution may be translated to slope fatigue and rock micro (or macro) fracturing resulting in a degrading effect on landforms. Moreover, this pollution can have negative impacts, such as physical, physiological as well as psychological effects on animal species. At present, the impact of seismic pol-lution generated by acoustic sources is difficult to evaluate. In Chapter 4, I improve and implement a model converting the acoustic pressure generated by a source in the atmosphere to the corresponding seismic signal for a receiver within the ground. The ground is considered to be a porous elastic medium in which wave behaviour can be approximated by the Biot-Stoll model. The model is extended for use of a temporal pressure pulse as an input, and to produce output on a plan 2D map, where the wind effect on the acoustic to seismic coupling can be reproduced. I invest extensive effort in making the model user-friendly, as the project aims at reaching a large audience, comprising, for example, geo-morphologists, biologists and sociologists. Finally, the model is subjected to synthetic testing as well as a qualitative comparison of the predicted ground particle velocity and the seismic signal of a real heli-copter flight as a source of acoustic input.
These studies advance understanding of the operation of specific natural processes in channels and on hillslopes, and bring us closer to designing functioning early warning systems for mass-wasting and flood events. This thesis also raises questions that have not been considered before, such as the con-tribution of human acoustic pollution to the seismic hum and its impact on the natural environment, or the importance of cracks in the self-development of the failure plane prior to slope. Together, these studies question general assumptions usually made regarding the triggering of mass wasting or the hillslope-channel connectivity. Beyond this, the thesis covers the axis of fundamental research progres-sion, from data exploration and method and theory development to proof of concept, and shows how in the rapidly developing field of environmental seismology, an active awareness of progress can help strengthen and accelerate general advances.
The European Alps are amongst the regions with highest glacier mass loss rates over the last decades. Under the threat of ongoing climate change, the ability to predict glacier mass balance changes for water and risk management purposes has become imperative. This raises an urgent need for reliable glacier models. The European Alps do not only host glaciers, but also numerous caves containing carbonate formations, called speleothems. Previous studies have shown that those speleothems also grew during times when the cave was covered by a warm-based glacier. In this thesis, I utilise speleothems from the European Alps as archives of local, environmental conditions related to mountain glacier evolution.
Previous studies have shown that speleothem isotope data from the Alps can be strongly affected by in-cave processes. Therefore, part of this thesis focusses on developing an isotope evolution model, which successfully reproduces differences between contemporaneous growing speleothems. The model is used to propose correction approaches for prior calcite precipitation effects on speleothem oxygen isotopes (δ18O). Applications on speleothem records from caves outside of the Alps demonstrate that corrected δ18O agrees better with other records and climate model simulations.
Existing speleothem growth histories and carbon isotope (δ13C) records from Alpine caves located at different elevations are used to infer soil vs. glacier cover and the thermal regime of the glacier over the last glacial cycle. The compatibility with glacier evolution models is statistically assessed. A general agreement between speleothem δ13C-derived information on soil vs. glacier presence and modelled glacier coverage is found. However, glacier retreat during Marine Isotope Stage (MIS) 3 seems to be underestimated by the model. Furthermore, speleothem data provides evidence of surface temperature above the freezing point which is, however, not fully reproduced by the simulations.
History of glacier cover and their thermal regime is explored for the high-elevation cave system Melchsee-Frutt in the Swiss Alps. Based on new (MIS 9b – MIS 7b, MIS 2) and available speleothem δ13C (MIS 7a – 5d) data, warm-based glacier cover is inferred for MIS 8, 7d, 6, and 2. Also a short period of cold-based ice coverage is found for early MIS 6. In a detailed multi-proxy analysis (δ18O, δ13C, Mg/Ca and Sr/Ca), millennial-scale changes in the glacier-related source of the water infiltrating in the karst during MIS 8 and 7d are found and linked to Northern Hemisphere climate variability.
While speleothem records from high-elevation cave sites in the Alps exhibit huge potential for glacier reconstruction, several limitations remain, which are discussed throughout this thesis. Ultimately, recommendations are given to further leverage subglacial speleothems as an archive of glacier dynamics.
Plate tectonic boundaries constitute the suture zones between tectonic plates. They are shaped by a variety of distinct and interrelated processes and play a key role in geohazards and georesource formation. Many of these processes have been previously studied, while many others remain unaddressed or undiscovered. In this work, the geodynamic numerical modeling software ASPECT is applied to shed light on further process interactions at continental plate boundaries. In contrast to natural data, geodynamic modeling has the advantage that processes can be directly quantified and that all parameters can be analyzed over the entire evolution of a structure. Furthermore, processes and interactions can be singled out from complex settings because the modeler has full control over all of the parameters involved. To account for the simplifying character of models in general, I have chosen to study generic geological settings with a focus on the processes and interactions rather than precisely reconstructing a specific region of the Earth.
In Chapter 2, 2D models of continental rifts with different crustal thicknesses between 20 and 50 km and extension velocities in the range of 0.5-10 mm/yr are used to obtain a speed limit for the thermal steady-state assumption, commonly employed to address the temperature fields of continental rifts worldwide. Because the tectonic deformation from ongoing rifting outpaces heat conduction, the temperature field is not in equilibrium, but is characterized by a transient, tectonically-induced heat flow signal. As a result, I find that isotherm depths of the geodynamic evolution models are shallower than a temperature distribution in equilibrium would suggest. This is particularly important for deep isotherms and narrow rifts. In narrow rifts, the magnitude of the transient temperature signal limits a well-founded applicability of the thermal steady-state assumption to extension velocities of 0.5-2 mm/yr. Estimation of the crustal temperature field affects conclusions on all temperature-dependent processes ranging from mineral assemblages to the feasible exploitation of a geothermal reservoir.
In Chapter 3, I model the interactions of different rheologies with the kinematics of folding and faulting using the example of fault-propagation folds in the Andean fold-and-thrust belt. The evolution of the velocity fields from geodynamic models are compared with those from trishear models of the same structure. While the latter use only geometric and kinematic constraints of the main fault, the geodynamic models capture viscous, plastic, and elastic deformation in the entire model domain. I find that both models work equally well for early, and thus relatively simple stages of folding and faulting, while results differ for more complex situations where off-fault deformation and secondary faulting are present. As fault-propagation folds can play an important role in the formation of reservoirs, knowledge of fluid pathways, for example via fractures and faults, is crucial for their characterization.
Chapter 4 deals with a bending transform fault and the interconnections between tectonics and surface processes. In particular, the tectonic evolution of the Dead Sea Fault is addressed where a releasing bend forms the Dead Sea pull-apart basin, while a restraining bend further to the North resulted in the formation of the Lebanese mountains. I ran 3D coupled geodynamic and surface evolution models that included both types of bends in a single setup. I tested various randomized initial strain distributions, showing that basin asymmetry is a consequence of strain localization. Furthermore, by varying the surface process efficiency, I find that the deposition of sediment in the pull-apart basin not only controls basin depth, but also results in a crustal flow component that increases uplift at the restraining bend.
Finally, in Chapter 5, I present the computational basis for adding further complexity to plate boundary models in ASPECT with the implementation of earthquake-like behavior using the rate-and-state friction framework. Despite earthquakes happening on a relatively small time scale, there are many interactions between the seismic cycle and the long time spans of other geodynamic processes. Amongst others, the crustal state of stress as well as the presence of fluids or changes in temperature may alter the frictional behavior of a fault segment. My work provides the basis for a realistic setup of involved structures and processes, which is therefore important to obtain a meaningful estimate for earthquake hazards.
While these findings improve our understanding of continental plate boundaries, further development of geodynamic software may help to reveal even more processes and interactions in the future.
Ecosystems play a pivotal role in addressing climate change but are also highly susceptible to drastic environmental changes. Investigating their historical dynamics can enhance our understanding of how they might respond to unprecedented future environmental shifts. With Arctic lakes currently under substantial pressure from climate change, lessons from the past can guide our understanding of potential disruptions to these lakes. However, individual lake systems are multifaceted and complex. Traditional isolated lake studies often fail to provide a global perspective because localized nuances—like individual lake parameters, catchment areas, and lake histories—can overshadow broader conclusions. In light of these complexities, a more nuanced approach is essential to analyze lake systems in a global context.
A key to addressing this challenge lies in the data-driven analysis of sedimentological records from various northern lake systems. This dissertation emphasizes lake systems in the northern Eurasian region, particularly in Russia (n=59). For this doctoral thesis, we collected sedimentological data from various sources, which required a standardized framework for further analysis. Therefore, we designed a conceptual model for integrating and standardizing heterogeneous multi-proxy data into a relational database management system (PostgreSQL). Creating a database from the collected data enabled comparative numerical analyses between spatially separated lakes as well as between different proxies.
When analyzing numerous lakes, establishing a common frame of reference was crucial. We achieved this by converting proxy values from depth dependency to age dependency. This required consistent age calculations across all lakes and proxies using one age-depth modeling software. Recognizing the broader implications and potential pitfalls of this, we developed the LANDO approach ("Linked Age and Depth Modelling"). LANDO is an innovative integration of multiple age-depth modeling software into a singular, cohesive platform (Jupyter Notebook). Beyond its ability to aggregate data from five renowned age-depth modeling software, LANDO uniquely empowers users to filter out implausible model outcomes using robust geoscientific data. Our method is not only novel but also significantly enhances the accuracy and reliability of lake analyses.
Considering the preceding steps, this doctoral thesis further examines the relationship between carbon in sediments and temperature over the last 21,000 years. Initially, we hypothesized a positive correlation between carbon accumulation in lakes and modelled paleotemperature. Our homogenized dataset from heterogeneous lakes confirmed this association, even if the highest temperatures throughout our observation period do not correlate with the highest carbon values. We assume that rapid warming events contribute more to high accumulation, while sustained warming leads to carbon outgassing. Considering the current high concentration of carbon in the atmosphere and rising temperatures, ongoing climate change could cause northern lake systems to contribute to a further increase in atmospheric carbon (positive feedback loop). While our findings underscore the reliability of both our standardized data and the LANDO method, expanding our dataset might offer even greater assurance in our conclusions.
Today, near-surface investigations are frequently conducted using non-destructive or minimally invasive methods of applied geophysics, particularly in the fields of civil engineering, archaeology, geology, and hydrology. One field that plays an increasingly central role in research and engineering is the examination of sedimentary environments, for example, for characterizing near-surface groundwater systems. A commonly employed method in this context is ground-penetrating radar (GPR). In this technique, short electromagnetic pulses are emitted into the subsurface by an antenna, which are then reflected, refracted, or scattered at contrasts in electromagnetic properties (such as the water table). A receiving antenna records these signals in terms of their amplitudes and travel times. Analysis of the recorded signals allows for inferences about the subsurface, such as the depth of the groundwater table or the composition and characteristics of near-surface sediment layers. Due to the high resolution of the GPR method and continuous technological advancements, GPR data acquisition is increasingly performed in three-dimensional (3D) fashion today.
Despite the considerable temporal and technical efforts involved in data acquisition and processing, the resulting 3D data sets (providing high-resolution images of the subsurface) are typically interpreted manually. This is generally an extremely time-consuming analysis step. Therefore, representative 2D sections highlighting distinctive reflection structures are often selected from the 3D data set. Regions showing similar structures are then grouped into so-called radar facies. The results obtained from 2D sections are considered representative of the entire investigated area. Interpretations conducted in this manner are often incomplete and highly dependent on the expertise of the interpreters, making them generally non-reproducible.
A promising alternative or complement to manual interpretation is the use of GPR attributes. Instead of using the recorded data directly, derived quantities characterizing distinctive reflection structures in 3D are applied for interpretation. Using various field and synthetic data sets, this thesis investigates which attributes are particularly suitable for this purpose. Additionally, the study demonstrates how selected attributes can be utilized through specific processing and classification methods to create 3D facies models. The ability to generate attribute-based 3D GPR facies models allows for partially automated and more efficient interpretations in the future. Furthermore, the results obtained in this manner describe the subsurface in a reproducible and more comprehensive manner than what has typically been achievable through manual interpretation methods.
A comprehensive study on seismic hazard and earthquake triggering is crucial for effective mitigation of earthquake risks. The destructive nature of earthquakes motivates researchers to work on forecasting despite the apparent randomness of the earthquake occurrences. Understanding their underlying mechanisms and patterns is vital, given their potential for widespread devastation and loss of life. This thesis combines methodologies, including Coulomb stress calculations and aftershock analysis, to shed light on earthquake complexities, ultimately enhancing seismic hazard assessment.
The Coulomb failure stress (CFS) criterion is widely used to predict the spatial distributions of aftershocks following large earthquakes. However, uncertainties associated with CFS calculations arise from non-unique slip inversions and unknown fault networks, particularly due to the choice of the assumed aftershocks (receiver) mechanisms. Recent studies have proposed alternative stress quantities and deep neural network approaches as superior to CFS with predefined receiver mechanisms. To challenge these propositions, I utilized 289 slip inversions from the SRCMOD database to calculate more realistic CFS values for a layered-half space and variable receiver mechanisms. The analysis also investigates the impact of magnitude cutoff, grid size variation, and aftershock duration on the ranking of stress metrics using receiver operating characteristic (ROC) analysis. Results reveal the performance of stress metrics significantly improves after accounting for receiver variability and for larger aftershocks and shorter time periods, without altering the relative ranking of the different stress metrics.
To corroborate Coulomb stress calculations with the findings of earthquake source studies in more detail, I studied the source properties of the 2005 Kashmir earthquake and its aftershocks, aiming to unravel the seismotectonics of the NW Himalayan syntaxis. I simultaneously relocated the mainshock and its largest aftershocks using phase data, followed by a comprehensive analysis of Coulomb stress changes on the aftershock planes. By computing the Coulomb failure stress changes on the aftershock faults, I found that all large aftershocks lie in regions of positive stress change, indicating triggering by either co-seismic or post-seismic slip on the mainshock fault.
Finally, I investigated the relationship between mainshock-induced stress changes and associated seismicity parameters, in particular those of the frequency-magnitude (Gutenberg-Richter) distribution and the temporal aftershock decay (Omori-Utsu law). For that purpose, I used my global data set of 127 mainshock-aftershock sequences with the calculated Coulomb Stress (ΔCFS) and the alternative receiver-independent stress metrics in the vicinity of the mainshocks and analyzed the aftershocks properties depend on the stress values. Surprisingly, the results show a clear positive correlation between the Gutenberg-Richter b-value and induced stress, contrary to expectations from laboratory experiments. This observation highlights the significance of structural heterogeneity and strength variations in seismicity patterns. Furthermore, the study demonstrates that aftershock productivity increases nonlinearly with stress, while the Omori-Utsu parameters c and p systematically decrease with increasing stress changes. These partly unexpected findings have significant implications for future estimations of aftershock hazard.
The findings in this thesis provides valuable insights into earthquake triggering mechanisms by examining the relationship between stress changes and aftershock occurrence. The results contribute to improved understanding of earthquake behavior and can aid in the development of more accurate probabilistic-seismic hazard forecasts and risk reduction strategies.
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.
The Central Andean region is characterized by diverse climate zones with sharp transitions between them. In this work, the area of interest is the South-Central Andes in northwestern Argentina that borders with Bolivia and Chile. The focus is the observation of soil moisture and water vapour with Global Navigation Satellite System (GNSS) remote-sensing methodologies. Because of the rapid temporal and spatial variations of water vapour and moisture circulations, monitoring this part of the hydrological cycle is crucial for understanding the mechanisms that control the local climate. Moreover, GNSS-based techniques have previously shown high potential and are appropriate for further investigation. This study includes both logistic-organization effort and data analysis. As for the prior, three GNSS ground stations were installed in remote locations in northwestern Argentina to acquire observations, where there was no availability of third-party data.
The methodological development for the observation of the climate variables of soil moisture and water vapour is independent and relies on different approaches. The soil-moisture estimation with GNSS reflectometry is an approximation that has demonstrated promising results, but it has yet to be operationally employed. Thus, a more advanced algorithm that exploits more observations from multiple satellite constellations was developed using data from two pilot stations in Germany. Additionally, this algorithm was slightly modified and used in a sea-level measurement campaign. Although the objective of this application is not related to monitoring hydrological parameters, its methodology is based on the same principles and helps to evaluate the core algorithm. On the other hand, water-vapour monitoring with GNSS observations is a well-established technique that is utilized operationally. Hence, the scope of this study is conducting a meteorological analysis by examining the along-the-zenith air-moisture levels and introducing indices related to the azimuthal gradient.
The results of the experiments indicate higher-quality soil moisture observations with the new algorithm. Furthermore, the analysis using the stations in northwestern Argentina illustrates the limits of this technology because of varying soil conditions and shows future research directions. The water-vapour analysis points out the strong influence of the topography on atmospheric moisture circulation and rainfall generation. Moreover, the GNSS time series allows for the identification of seasonal signatures, and the azimuthal-gradient indices permit the detection of main circulation pathways.
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.
Volcanic hydrothermal systems are an integral part of most volcanoes and typically involve a heat source, adequate fluid supply, and fracture or pore systems through which the fluids can circulate within the volcanic edifice. Associated with this are subtle but powerful processes that can significantly influence the evolution of volcanic activity or the stability of the near-surface volcanic system through mechanical weakening, permeability reduction, and sealing of the affected volcanic rock. These processes are well constrained for rock samples by laboratory analyses but are still difficult to extrapolate and evaluate at the scale of an entire volcano. Advances in unmanned aircraft systems (UAS), sensor technology, and photogrammetric processing routines now allow us to image volcanic surfaces at the centimeter scale and thus study volcanic hydrothermal systems in great detail. This thesis aims to explore the potential of UAS approaches for studying the structures, processes, and dynamics of volcanic hydrothermal systems but also to develop methodological approaches to uncover secondary information hidden in the data, capable of indicating spatiotemporal dynamics or potentially critical developments associated with hydrothermal alteration. To accomplish this, the thesis describes the investigation of two near-surface volcanic hydrothermal systems, the El Tatio geyser field in Chile and the fumarole field of La Fossa di Vulcano (Italy), both of which are among the best-studied sites of their kind. Through image analysis, statistical, and spatial analyses we have been able to provide the most detailed structural images of both study sites to date, with new insights into the driving forces of such systems but also revealing new potential controls, which are summarized in conceptual site-specific models. Furthermore, the thesis explores methodological remote sensing approaches to detect, classify and constrain hydrothermal alteration and surface degassing from UAS-derived data, evaluated them by mineralogical and chemical ground-truthing, and compares the alteration pattern with the present-day degassing activity. A significant contribution of the often neglected diffuse degassing activity to the total amount of degassing is revealed and constrains secondary processes and dynamics associated with hydrothermal alteration that lead to potentially critical developments like surface sealing. The results and methods used provide new approaches for alteration research, for the monitoring of degassing and alteration effects, and for thermal monitoring of fumarole fields, with the potential to be incorporated into volcano monitoring routines.