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The Greenland Ice Sheet is the second-largest mass of ice on Earth. Being almost 2000 km long, more than 700 km wide, and more than 3 km thick at the summit, it holds enough ice to raise global sea levels by 7m if melted completely. Despite its massive size, it is particularly vulnerable to anthropogenic climate change: temperatures over the Greenland Ice Sheet have increased by more than 2.7◦C in the past 30 years, twice as much as the global mean temperature. Consequently, the ice sheet has been significantly losing mass since the 1980s and the rate of loss has increased sixfold since then. Moreover, it is one of the potential tipping elements of the Earth System, which might undergo irreversible change once a warming threshold is exceeded. This thesis aims at extending the understanding of the resilience of the Greenland Ice Sheet against global warming by analyzing processes and feedbacks relevant to its centennial to multi-millennial stability using ice sheet modeling.
One of these feedbacks, the melt-elevation-feedback is driven by the temperature rise with decreasing altitudes: As the ice sheet melts, its thickness and surface elevation decrease, exposing the ice surface to warmer air and thus increasing the melt rates even further. The glacial isostatic adjustment (GIA) can partly mitigate this melt-elevation feedback as the bedrock lifts in response to an ice load decrease, forming the negative GIA feedback. In my thesis, I show that the interaction between these two competing feedbacks can lead to qualitatively different dynamical responses of the Greenland Ice Sheet to warming – from permanent loss to incomplete recovery, depending on the feedback parameters. My research shows that the interaction of those feedbacks can initiate self-sustained oscillations of the ice volume while the climate forcing remains constant.
Furthermore, the increased surface melt changes the optical properties of the snow or ice surface, e.g. by lowering their albedo, which in turn enhances melt rates – a process known as the melt-albedo feedback. Process-based ice sheet models often neglect this melt-albedo feedback. To close this gap, I implemented a simplified version of the diurnal Energy Balance Model, a computationally efficient approach that can capture the first-order effects of the melt-albedo feedback, into the Parallel Ice Sheet Model (PISM). Using the coupled model, I show in warming experiments that the melt-albedo feedback almost doubles the ice loss until the year 2300 under the low greenhouse gas emission scenario RCP2.6, compared to simulations where the melt-albedo feedback is neglected,
and adds up to 58% additional ice loss under the high emission scenario RCP8.5. Moreover, I find that the melt-albedo feedback dominates the ice loss until 2300, compared to the melt-elevation feedback.
Another process that could influence the resilience of the Greenland Ice Sheet is the warming induced softening of the ice and the resulting increase in flow. In my thesis, I show with PISM how the uncertainty in Glen’s flow law impacts the simulated response to warming. In a flow line setup at fixed climatic mass balance, the uncertainty in flow parameters leads to a range of ice loss comparable to the range caused by different warming levels.
While I focus on fundamental processes, feedbacks, and their interactions in the first three projects of my thesis, I also explore the impact of specific climate scenarios on the sea level rise contribution of the Greenland Ice Sheet. To increase the carbon budget flexibility, some warming scenarios – while still staying within the limits of the Paris Agreement – include a temporal overshoot of global warming. I show that an overshoot by 0.4◦C increases the short-term and long-term ice loss from Greenland by several centimeters. The long-term increase is driven by the warming at high latitudes, which persists even when global warming is reversed. This leads to a substantial long-term commitment of the sea level rise contribution from the Greenland Ice Sheet.
Overall, in my thesis I show that the melt-albedo feedback is most relevant for the ice loss of the Greenland Ice Sheet on centennial timescales. In contrast, the melt-elevation feedback and its interplay with the GIA feedback become increasingly relevant on millennial timescales. All of these influence the resilience of the Greenland Ice Sheet against global warming, in the near future and on the long term.
Environmental pollution by microplastics has become a severe problem in terrestrial and aquatic ecosystems and, according to actual prognoses, problems will further increase in the future. Therefore, assessing and quantifying the risk for the biota is crucial. Standardized short-term toxicological procedures as well as methods quantifying potential toxic effects over the whole life span of an animal are required. We studied the effect of the microplastic polystyrene on the survival and reproduction of a common freshwater invertebrate, the rotifer Brachionus calyciflorus, at different timescales. We used pristine polystyrene spheres of 1, 3, and 6 µm diameter and fed them to the animals together with food algae in different ratios ranging from 0 to 50% nonfood particles. As a particle control, we used silica to distinguish between a pure particle effect and a plastic effect. After 24 h, no toxic effect was found, neither with polystyrene nor with silica. After 96 h, a toxic effect was detectable for both particle types. The size of the particles played a negligible role. Studying the long-term effect by using life table experiments, we found a reduced reproduction when the animals were fed with 3 µm spheres together with similar-sized food algae. We conclude that the fitness reduction is mainly driven by the dilution of food by the nonfood particles rather than by a direct toxic effect.
Seismology, like many scientific fields, e.g., music information retrieval and speech signal pro- cessing, is experiencing exponential growth in the amount of data acquired by modern seismo- logical networks. In this thesis, I take advantage of the opportunities offered by "big data" and by the methods developed in the areas of music information retrieval and machine learning to predict better the ground motion generated by earthquakes and to study the properties of the surface layers of the Earth. In order to better predict seismic ground motions, I propose two approaches based on unsupervised deep learning methods, an autoencoder network and Generative Adversarial Networks. The autoencoder technique explores a massive amount of ground motion data, evaluates the required parameters, and generates synthetic ground motion data in the Fourier amplitude spectra (FAS) domain. This method is tested on two synthetic datasets and one real dataset. The application on the real dataset shows that the substantial information contained within the FAS data can be encoded to a four to the five-dimensional manifold. Consequently, only a few independent parameters are required for efficient ground motion prediction. I also propose a method based on Conditional Generative Adversarial Networks (CGAN) for simulating ground motion records in the time-frequency and time domains. CGAN generates the time-frequency domains based on the parameters: magnitude, distance, and shear wave velocities to 30 m depth (VS30). After generating the amplitude of the time-frequency domains using the CGAN model, instead of classical conventional methods that assume the amplitude spectra with a random phase spectrum, the phase of the time-frequency domains is recovered by minimizing the observed and reconstructed spectrograms. In the second part of this dissertation, I propose two methods for the monitoring and characterization of near-surface materials and site effect analyses. I implement an autocorrelation function and an interferometry method to monitor the velocity changes of near-surface materials resulting from the Kumamoto earthquake sequence (Japan, 2016). The observed seismic velocity changes during the strong shaking are due to the non-linear response of the near-surface materials. The results show that the velocity changes lasted for about two months after the Kumamoto mainshock. Furthermore, I used the velocity changes to evaluate the in-situ strain-stress relationship. I also propose a method for assessing the site proxy "VS30" using non-invasive analysis. In the proposed method, a dispersion curve of surface waves is inverted to estimate the shear wave velocity of the subsurface. This method is based on the Dix-like linear operators, which relate the shear wave velocity to the phase velocity. The proposed method is fast, efficient, and stable. All of the methods presented in this work can be used for processing "big data" in seismology and for the analysis of weak and strong ground motion data, to predict ground shaking, and to analyze site responses by considering potential time dependencies and nonlinearities.
A local and flexible definition of the monsoon season based on hydrological evidence is important for the understanding and management of Himalayan water resources. Here, we present an objective statistical method to retrieve seasonal hydrometeorological transitions. Applied to daily rainfall data (1951-2015), this method shows an average longitudinal delay of similar to 15 days, with later monsoon onset and earlier withdrawal in the western Himalaya, consistent with the continental progression of wet air masses. This delay leads to seasons of different length along the Himalaya and biased precipitation amounts when using uniform calendric monsoon boundaries. In the Central Himalaya annual precipitation has increased, due primarily to an increase of premonsoon precipitation. These findings highlight issues associated with a static definition of monsoon boundaries and call for a deeper understanding of nonmonsoonal precipitation over the Himalayan water tower. <br /> Plain Language Summary Precipitation in the Himalayas determines water availability for the Indian foreland with large socioeconomic implications. Despite its importance, spatial and temporal patterns of precipitation are poorly understood. Here, we estimate the long-term average and trends of seasonal precipitation at the scale of individual catchments draining the Himalayas. We apply a statistical method to detect the timing of hydrometeorological seasons from local precipitation measurements, focusing on monsoon onset and withdrawal. We identify longitudinal and latitudinal delays, resulting in seasons of different length along and across the Himalayas. These spatial patterns and the annual variability of the monsoon boundaries mean that oft-used, fixed calendric dates, for example, 1 June to 30 September, may be inadequate for retrieving monsoon rainfall totals. Moreover, we find that, despite its prominent contribution to annual rainfall totals, the Indian summer monsoon cannot explain the increase of the annual precipitation over the Central Himalayas. Instead, this appears to be mostly driven by changes in premonsoon and winter rainfall. So far, little attention has been paid to premonsoon precipitation, but governed by evaporative processes and surface water availability, it may be enhanced by irrigation and changed land use in the Gangetic foreland.
Most hydrological studies rely on a model calibrated using discharge alone. However, judging the model reliability based on such calibration is problematic, as it does not guarantee the correct representation of internal hydrological processes. This study aims (a) to develop a comprehensive multi-objective calibration framework using remote sensing vegetation data and hydrological signatures (flow duration curve - FDC, and baseflow index) in addition to discharge, and (b) to apply this framework for calibration of the Soil and Water Assessment Tool (SWAT) in a typical Andean catchment. Overall, our calibration approach outperformed traditional discharge-based and FDC signature-based calibration strategies in terms of vegetation, streamflow, and flow partitioning simulation. New hydrological insights for the region are the following: baseflow is the main component of the streamflow sustaining the long dry-season flow, and pasture areas offer higher water yield and baseflow than other land-cover types. The proposed approach could be used in other data-scarce regions with complex topography.
Sediment transport domains in mountain landscapes are characterized by fundamentally different processes and rates depending on several factors, including geology, climate, and biota. Accurately identifying where transitions between transport domains occur is an important step to quantify the past, present, and future contribution of varying erosion and sedimentation processes and enhance our predictive capabilities. We propose a new methodology based on time series of synthetic aperture radar (SAR) interferometric coherence images to map sediment transport regimes across arid and semiarid landscapes. Using 4 years of Sentinel-1 data, we analyze sediment transport regimes for the south-central Andes in northwestern Argentina characterized by steep topographic and climatic gradients. We observe seasonally low coherence during the regional wet season, particularly on hillslopes and in alluvial channels. The spatial distribution of coherence is compared to drainage areas extracted from digital topography to identify two distinct transitions within watersheds: (a) a hillslope-to-fluvial and (b) a fluvial-to-alluvial transition. While transitions within a given basin can be well-constrained, the relative role of each sediment transport domain varies widely over the climatic and topographic gradients. In semiarid regions, we observe larger relative contributions from hillslopes compared to arid regions. Across regional gradients, the range of coherence within basins positively correlates to previously published millennial catchment-wide erosion rates and to topographic metrics used to indicate long-term uplift. Our study suggests that a dense time series of interferometric coherence can be used as a proxy for surface sediment movement and landscape stability in vegetation-free settings at event to decadal timescales.
Large rock slope failures play a pivotal role in long-term landscape evolution and are a major concern in land use planning and hazard aspects. While the failure phase and the time immediately prior to failure are increasingly well studied, the nature of the preparation phase remains enigmatic. This knowledge gap is due, to a large degree, to difficulties associated with instrumenting high mountain terrain and the local nature of classic monitoring methods, which does not allow integral observation of large rock volumes. Here, we analyse data from a small network of up to seven seismic sensors installed during July-October 2018 (with 43 days of data loss) at the summit of the Hochvogel, a 2592 m high Alpine peak. We develop proxy time series indicative of cyclic and progressive changes of the summit. Modal analysis, horizontal-to-vertical spectral ratio data and end-member modelling analysis reveal diurnal cycles of increasing and decreasing coupling stiffness of a 260,000 m(3) large, instable rock volume, due to thermal forcing. Relative seismic wave velocity changes also indicate diurnal accumulation and release of stress within the rock mass. At longer time scales, there is a systematic superimposed pattern of stress increased over multiple days and episodic stress release within a few days, expressed in an increased emission of short seismic pulses indicative of rock cracking. Our data provide essential first order information on the development of large-scale slope instabilities towards catastrophic failure. (c) 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd
Deep geological repositories represent a promising solution for the final disposal of nuclear waste. Due to its low permeability, high sorption capacity and self-sealing potential, Opalinus Clay (OPA) is considered a suitable host rock formation for the long-term storage of nuclear waste in Switzerland and Germany. However, the clay formation is characterized by compositional and structural variabilities including the occurrence of carbonate- and quartz-rich layers, pronounced bedding planes as well as tectonic elements such as pre-existing fault zones and fractures, suggesting heterogeneous rock mass properties.
Characterizing the heterogeneity of host rock properties is therefore essential for safety predictions of future repositories. This includes a detailed understanding of the mechanical and hydraulic properties, deformation behavior and the underlying deformation processes for an improved assessment of the sealing integrity and long-term safety of a deep repository in OPA. Against this background, this thesis presents the results of deformation experiments performed on intact and artificially fractured specimens of the quartz-rich, sandy and clay-rich, shaly facies of OPA. The experiments focus on the influence of mineralogical composition on the deformation behavior as well as the reactivation and sealing properties of pre-existing faults and fractures at different boundary conditions (e.g., pressure, temperature, strain rate).
The anisotropic mechanical properties of the sandy facies of OPA are presented in the first section, which were determined from triaxial deformation experiments using dried and resaturated samples loaded at 0°, 45° and 90° to the bedding plane orientation. A Paterson-type deformation apparatus was used that allowed to investigate how the deformation behavior is influenced by the variation of confining pressure (50 – 100 MPa), temperature (25 – 200 °C), and strain rate (1 × 10-3 – 5 × 10-6 s-1). Constant strain rate experiments revealed brittle to semi-brittle deformation behavior of the sandy facies at the applied conditions. Deformation behavior showed a strong dependence on confining pressure, degree of water saturation as well as bedding orientation, whereas the variation of temperature and strain rate had no significant effect on deformation. Furthermore, the sandy facies displays higher strength and stiffness compared to the clay-rich shaly facies deformed at similar conditions by Nüesch (1991). From the obtained results it can be concluded that cataclastic mechanisms dominate the short-term deformation behavior of dried samples from both facies up to elevated pressure (<200 MPa) and temperature (<200 °C) conditions.
The second part presents triaxial deformation tests that were performed to investigate how structural discontinuities affect the deformation behavior of OPA and how the reactivation of preexisting faults is influenced by mineral composition and confining pressure. To this end, dried cylindrical samples of the sandy and shaly facies of OPA were used, which contained a saw-cut fracture oriented at 30° to the long axis. After hydrostatic pre-compaction at 50 MPa, constant strain rate deformation tests were performed at confining pressures of 5, 20 or 35 MPa. With increasing confinement, a gradual transition from brittle, highly localized fault slip including a stress drop at fault reactivation to semi-brittle deformation behavior, characterized by increasing delocalization and non-linear strain hardening without dynamic fault reactivation, can be observed. Brittle localization was limited by the confining pressure at which the fault strength exceeded the matrix yield strength, above which strain partitioning between localized fault slip and distributed matrix deformation occurred. The sandy facies displayed a slightly higher friction coefficient (≈0.48) compared to the shaly facies (≈0.4). In addition, slide-hold-slide tests were conducted, revealing negative or negligible frictional strengthening, which suggests stable creep and long-term weakness of faults in both facies of OPA. The conducted experiments demonstrate that dilatant brittle fault reactivation in OPA may be favored at high overconsolidation ratios and shallow depths, increasing the risk of seismic hazard and the creation of fluid pathways.
The final section illustrates how the sealing capacity of fractures in OPA is affected by mineral composition. Triaxial flow-through experiments using Argon-gas were performed with dried samples from the sandy and shaly facies of OPA containing a roughened, artificial fracture. Slate, graywacke, quartzite, natural fault gouge, and granite samples were also tested to highlight the influence of normal stress, mineralogy and diagenesis on the sustainability of fracture transmissivity. With increasing normal stress, a non-linear decrease of fracture transmissivity can be observed that resulted in a permanent reduction of transmissivity after stress release. The transmissivity of rocks with a high portion of strong minerals (e.g., quartz) and high unconfined compressive strength was less sensitive to stress changes. In accordance with this, the sandy facies of OPA displayed a higher initial transmissivity that was less sensitive to stress changes compared to the shaly facies. However, transmissivity of rigid slate was less sensitive to stress changes than the sandy facies of OPA, although the slate is characterized by a higher phyllosilicate content. This demonstrates that in addition to mineral composition, other factors such as the degree of metamorphism, cementation and consolidation have to be considered when evaluating the sealing capacity of phyllosilicate-rich rocks.
The results of this thesis highlighted the role of confining pressure on the failure behavior of intact and artificially fractured OPA. Although the quartz-rich sandy facies may be considered as being more favorable for underground constructions due to its higher shear strength and stiffness than the shaly facies, the results indicate that when fractures develop in the sandy facies, they are more conductive and remain more permeable compared to fractures in the clay-dominated shaly facies at a given stress. The results may provide the basis for constitutive models to predict the integrity and evolution of a future repository. Clearly, the influence of composition and consolidation, e.g., by geological burial and uplift, on the mechanical sealing behavior of OPA highlights the need for a detailed site-specific material characterization for a future repository.
Bayesian geomorphology
(2020)
The rapidly growing amount and diversity of data are confronting us more than ever with the need to make informed predictions under uncertainty. The adverse impacts of climate change and natural hazards also motivate our search for reliable predictions. The range of statistical techniques that geomorphologists use to tackle this challenge has been growing, but rarely involves Bayesian methods. Instead, many geomorphic models rely on estimated averages that largely miss out on the variability of form and process. Yet seemingly fixed estimates of channel heads, sediment rating curves or glacier equilibrium lines, for example, are all prone to uncertainties. Neighbouring scientific disciplines such as physics, hydrology or ecology have readily embraced Bayesian methods to fully capture and better explain such uncertainties, as the necessary computational tools have advanced greatly. The aim of this article is to introduce the Bayesian toolkit to scientists concerned with Earth surface processes and landforms, and to show how geomorphic models might benefit from probabilistic concepts. I briefly review the use of Bayesian reasoning in geomorphology, and outline the corresponding variants of regression and classification in several worked examples.
Classic inversion methods adjust a model with a predefined number of parameters to the observed data. With transdimensional inversion algorithms such as the reversible-jump Markov chain Monte Carlo (rjMCMC), it is possible to vary this number during the inversion and to interpret the observations in a more flexible way. Geoscience imaging applications use this behaviour to automatically adjust model resolution to the inhomogeneities of the investigated system, while keeping the model parameters on an optimal level. The rjMCMC algorithm produces an ensemble as result, a set of model realizations, which together represent the posterior probability distribution of the investigated problem. The realizations are evolved via sequential updates from a randomly chosen initial solution and converge toward the target posterior distribution of the inverse problem. Up to a point in the chain, the realizations may be strongly biased by the initial model, and must be discarded from the final ensemble. With convergence assessment techniques, this point in the chain can be identified. Transdimensional MCMC methods produce ensembles that are not suitable for classic convergence assessment techniques because of the changes in parameter numbers. To overcome this hurdle, three solutions are introduced to convert model realizations to a common dimensionality while maintaining the statistical characteristics of the ensemble. A scalar, a vector and a matrix representation for models is presented, inferred from tomographic subsurface investigations, and three classic convergence assessment techniques are applied on them. It is shown that appropriately chosen scalar conversions of the models could retain similar statistical ensemble properties as geologic projections created by rasterization.