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Highlights
• We present the first results of a deep learning model based on a convolutional neural network for earthquake magnitude estimation, using HR-GNSS displacement time series.
• The influence of different dataset configurations, such as station numbers, epicentral distances, signal duration, and earthquake size, were analyzed to figure out how the model can be adapted to various scenarios.
• The model was tested using real data from different regions and magnitudes, resulting in the best cases with 0.09 ≤ RMS ≤ 0.33.
Abstract
High-rate Global Navigation Satellite System (HR-GNSS) data can be highly useful for earthquake analysis as it provides continuous high-frequency measurements of ground motion. This data can be used to analyze diverse parameters related to the seismic source and to assess the potential of an earthquake to prompt strong motions at certain distances and even generate tsunamis. In this work, we present the first results of a deep learning model based on a convolutional neural network for earthquake magnitude estimation, using HR-GNSS displacement time series. The influence of different dataset configurations, such as station numbers, epicentral distances, signal duration, and earthquake size, were analyzed to figure out how the model can be adapted to various scenarios. We explored the potential of the model for global application and compared its performance using both synthetic and real data from different seismogenic regions. The performance of our model at this stage was satisfactory in estimating earthquake magnitude from synthetic data with 0.07 ≤ RMS ≤ 0.11. Comparable results were observed in tests using synthetic data from a different region than the training data, with RMS ≤ 0.15. Furthermore, the model was tested using real data from different regions and magnitudes, resulting in the best cases with 0.09 ≤ RMS ≤ 0.33, provided that the data from a particular group of stations had similar epicentral distance constraints to those used during the model training. The robustness of the DL model can be improved to work independently from the window size of the time series and the number of stations, enabling faster estimation by the model using only near-field data. Overall, this study provides insights for the development of future DL approaches for earthquake magnitude estimation with HR-GNSS data, emphasizing the importance of proper handling and careful data selection for further model improvements.
PolarCAP – A deep learning approach for first motion polarity classification of earthquake waveforms
(2022)
Highlights
• We present PolarCAP, a deep learning model that can classify the polarity of a waveform with a 98% accuracy.
• The first-motion polarity of seismograms is a useful parameter, but its manual determination can be laborious and imprecise.
• We demonstrate that in several cases the model can assign trace polar-ity more accurately than a human analyst.
Abstract
The polarity of first P-wave arrivals plays a significant role in the effective determination of focal mechanisms specially for smaller earthquakes. Manual estimation of polarities is not only time-consuming but also prone to human errors. This warrants a need for an automated algorithm for first motion polarity determination. We present a deep learning model - PolarCAP that uses an autoencoder architecture to identify first-motion polarities of earth-quake waveforms. PolarCAP is trained in a supervised fashion using more than 130,000 labelled traces from the Italian seismic dataset (INSTANCE) and is cross-validated on 22,000 traces to choose the most optimal set of hyperparameters. We obtain an accuracy of 0.98 on a completely unseen test dataset of almost 33,000 traces. Furthermore, we check the model generalizability by testing it on the datasets provided by previous works and show that our model achieves a higher recall on both positive and negative polarities.
Highlights
• Germany plans more long-distances water transfers to secure drinking water supply.
• Long-distance water transfers can unfold lock-ins that limit adaptive water governance.
• Our interdisciplinary case study shows how lock-ins emerge over different spaces and times.
• Commercialisation of water but also local protests contributed to various lock-ins.
• We therefore call for context-specific assessments of potentials and risks of LDWT.
Abstract
Germany plans to expand water transfers over long distances in the light of numerous and pressing challenges for drinking water supply. Research on inter- and intrabasin water transfers warns, however, that major investments in large-scale infrastructure systems accompanied by institutional logics and political interests often lead to a so-called lock-in. As a consequence, long-distance water transfers can limit the potential for adaptive water governance in the involved supply areas over decades with negative impacts for people and the environment. By using a case study in Germany as an example, we researched when, where and how such lock-ins around long-distance water transfers emerge. In the infrastructural development of the Elbaue-Ostharz transfer system we found various lock-ins that overlap in space and time. Some are located at the centre others at the margins of the infrastructure and commercialization of the water sector as well as hydraulic and hygienic concerns interlock with local protests in a way that the expansion of the long-distance water transfer infrastructure is presented continuously as imperative. Our findings contribute to a relational understanding of lock-ins of long-distance water transfers as contingent and diverse processes. Given the widespread occurrence of lock-ins, we argue for a context-specific assessment of potentials and risks of long-distance water transfers in times of multiple crises.
Highlights
• New fumarole and thermal water data for Askja and Kverkfjöll volcanoes, Iceland.
• Data compared to modelled compositions and fluxes of magmatic gas.
• Fumarole compositions compatible with origin of CO2 and S from degassing intrusions.
• Intrusive magmatic fluxes sufficient to sustain hydrothermal fluxes of CO2 and S in Iceland
• Magma degassing insignificant/minor source of H2O and Cl to Icelandic hydrothermal fluids
Abstract
Mantle volatiles are transported to Earth's crust and surface by basaltic volcanism. During subaerial eruptions, vast amounts of carbon, sulfur and halogens can be released to the atmosphere during a short time-interval, with impacts ranging in scale from the local environment to the global climate. By contrast, passive volatile release at the surface originating from magmatic intrusions is characterized by much lower flux, yet may outsize eruptive volatile quantities over long timescales. Volcanic hydrothermal systems (VHSs) act as conduits for such volatile release from degassing intrusions and can be used to gauge the contribution of intrusive magmatism to global volatile cycles. Here, we present new compositional and isotopic (δD and δ18O-H2O, 3He/4He, δ13C-CO2, Δ33S-δ34S-H2S and SO4) data for thermal waters and fumarole gases from the Askja and Kverkfjöll volcanoes in central Iceland. We use the data together with magma degassing modelling and mass balance calculations to constrain the sources of volatiles in VHSs and to assess the role of intrusive magmatism to the volcanic volatile emission budgets in Iceland.
The CO2/ΣS (10−30), 3He/4He (8.3–10.5 RA; 3He/4He relative to air), δ13C-CO2 (−4.1 to −0.2 ‰) and Δ33S-δ34S-H2S (−0.031 to 0.003 ‰ and −1.5 to +3.6‰) values in high-gas flux fumaroles (CO2 > 10 mmol/mol) are consistent with an intrusive magmatic origin for CO2 and S at Askja and Kverkfjöll. We demonstrate that deep (0.5–5 kbar, equivalent to ∼2–18 km crustal depth) decompression degassing of basaltic intrusions in Iceland results in CO2 and S fluxes of 330–5060 and 6–210 kt/yr, respectively, which is sufficient to account for the estimated CO2 flux of Icelandic VHSs (3365–6730 kt/yr), but not the VHS S flux (220–440 kt/yr). Secondary, crystallization-driven degassing from maturing intrusions and leaching of crustal rocks are suggested as additional sources of S. Only a minor proportion of the mantle flux of Cl is channeled via VHSs whereas the H2O flux remains poorly constrained, because magmatic signals in Icelandic VHSs are masked by a dominant shallow groundwater component of meteoric water origin. These results suggest that the bulk of the mantle CO2 and S flux to the atmosphere in Iceland is supplied by intrusive, not eruptive magmatism, and is largely vented via hydrothermal fields.
Highlights
• Subcrustal earthquakes detected beneath Fogo volcano, Cape Verde.
• At the focal depth of 40 km temperatures are likely too high for brittle failure.
• The earthquakes may originate from magma injection into a deep subcrustal reservoir.
• This observation indicates a distinct magma supply system of Fogo volcano.
Abstract
Fogo volcano belongs to the Cape Verde hotspot and its most recent eruption occurred from November 2014 to February 2015. From January to December 2016 we operated a temporary seismic network and array on Fogo and were able to locate 289 earthquakes in total. Array analysis shows that most of the events occur within the crust at distances >25 km near the neighboring island of Brava. However, on 15th August 2016 the network recorded an isolated cluster of >20 earthquakes, 13 of which could be located beneath the southern part of Fogo. The differences between S- and P-wave arrival times at steep incidence clearly indicate focal depths between approximately 38 and 44 km whereas receiver-function analyses place the Moho discontinuity at depths between 11 and 14 km. Thus, the earthquakes are located well within the upper mantle directly beneath Fogo. In view of the elevated upper-mantle temperatures within a hotspot regime, we propose that fracturing induced by magmatic injection is the most likely cause for the observed deep earthquakes.
Highlights
• Full automatized analysis of teleseismic XKS shear wave splitting.
• Rapid analysis of large seismological data sets.
• Automated window selection and quality classification.
• Application to the USArray Transportable Array including expansion to Alaska.
• Improved statistical evidence and objectivity of derived effective splitting.
Abstract
Recent technological advances have led to community wide use of large-scale seismic experiments which produce seismic data on previously impossible scales. Standard processing procedures thus require automatization to facilitate a fast and objective analysis of the data. Among these, XKS-splitting is an important tool to derive first insights into the Earth's deformation regimes at depth by studying seismic anisotropy. Most often, shear-wave splitting is interpreted to represent crystallographic preferred orientation (CPO) of mantle minerals like olivine as dominating feature and can thus be used as a proxy of mantle flow processes. Here, we introduce an addition to the MATLAB®-based SplitRacer tool box (Reiss and Rümpker 2017) which automatizes the entire XKS-splitting procedure. This is achieved by the automatization of 1) choosing a time window based on spectral analyses and 2) categorization of results based on three different XKS-splitting methods (energy minimization, rotation correlation and splitting intensity). This provides effective and objective results for splitting as well as null-measurement results. This extension allows to use SplitRacer without a graphical interface and introduces a bootstrapping statistics as error estimate of the single layer joint splitting method. The procedures are designed to allow a fast and more objective analysis of a vast amount of data, as produced by recent seismic deployments (e.g. USArray, AlpArray). We test this automatization by applying the analysis to the USArray data set, which has approximately 1900 stations with between two to fifteen years of data. We can reproduce the general pattern of the results from former studies with the more objective automatic analysis. Based on a joint-splitting approach, we approximate the splitting effect at individual stations by a single anisotropic layer. As we include null-measurements as well as a larger data set as previous studies, we can provide improved statistical evidence for these effective splitting parameters.
Highlights
• We show the first observations of seismo-acoustic tremor at Oldoinyo Lengai, the world's only active carbonatite volcano.
• We observe significant changes in seismic and acoustic tremor properties and their correlation in one year of data collection.
• Using satellite-based thermal data, we identify different volcanic processes (degassing, lava pond dynamics and spattering).
Abstract
We analyze volcanic tremor from Oldoinyo Lengai, Tanzania, which is currently the only active volcano on Earth producing carbonatitic lavas. Here, we use data from the recent SEISVOL deployment and focus on a co-located seismic and infrasound station about 200 m below the summit. We show the very first observations of seismo-acoustic tremor caused by carbonatitic eruptions. This seismo-acoustic tremor is highly variable throughout the ∼one year of data which we characterize by analyzing its seismic amplitude, duration, recurrence, dominant seismic frequency and harmonics. Frequency gliding occurs frequently and over short (minutes to hours) to long time scales (hours to days) and likely reflects different time-dependent mechanisms, such as evenly-spaced repeating events with a change in inter-event times, crater dynamics that alter resonators, and dike intrusions. Seismic and acoustic wavefields correlate well for stronger eruptive sequences but are only partially coherent which suggests that high-frequency seismic tremor (up to 25 Hz) may be caused by the low viscosity of the carbonatitic melt and not by ground-coupled airwaves. In addition, the comparison between seismic-acoustic and satellite InfraRed thermal data allows us to infer different volcanic activity styles which partially alternate throughout the year: intrusive activity and the construction of hornitos, degassing, activity from a lava pond, and varying styles of extrusive activity, in particular spattering. Our study provides important insights into the eruption dynamics of this peculiar volcano which suggests shallow melt storage within the crater floor.
Although global- and catchment-scale hydrological models are often shown to accurately simulate long-term runoff time-series, far less is known about their suitability for capturing hydrological extremes, such as droughts. Here we evaluated simulations of hydrological droughts from nine catchment scale hydrological models (CHMs) and eight global scale hydrological models (GHMs) for eight large catchments: Upper Amazon, Lena, Upper Mississippi, Upper Niger, Rhine, Tagus, Upper Yangtze and Upper Yellow. The simulations were conducted within the framework of phase 2a of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). We evaluated the ability of the CHMs, GHMs and their respective ensemble means (Ens-CHM and Ens-GHM) to simulate observed hydrological droughts of at least one month duration, over 31 years (1971–2001). Hydrological drought events were identified from runoff-deficits and the Standardised Runoff Index (SRI). In all catchments, the CHMs performed relatively better than the GHMs, for simulating monthly runoff-deficits. The number of drought events identified under different drought categories (i.e. SRI values of -1 to -1.49, -1.5 to -1.99, and ≤-2) varied significantly between models. All the models, as well as the two ensemble means, have limited abilities to accurately simulate drought events in all eight catchments, in terms of their occurrence and magnitude. Overall, there are opportunities to improve both CHMs and GHMs for better characterisation of hydrological droughts.
Semi-arid African ecosystems influence trends and variability in global terrestrial carbon dynamics. However, there are uncertainties in potential effects of future climates for semi-arid ecosystems, especially for niche ecosystems. At the same time, African ecosystems provide the livelihoods and ecosystem services for around 1.4 billion people. Future population growth and associated changes in land use pose a challenge for the protection of African biodiversity. Therefore, this work focussed on future impacts of climate change on African ecosystems and carbon dynamics and also for African protected areas (PAs), where they may cooccur with other global change factors. Another focus was on uncertainties associated with future projections and with modelling the Nama Karoo, as an example of a semi-arid niche ecosystem. Dynamic vegetation models (DVMs) were the main research tool.
In Chapter 2, we analysed climate change impacts on African ecosystems and carbon pools until the end of the 21st century and associated uncertainties based on an ensemble of vegetation simulations with the DVM adaptive dynamic vegetation model (aDGVM). We investigated the impact of increased atmospheric CO2 concentrations and two climate change scenarios (medium (RCP4.5) and high emissions (RCP8.5); RCP - representative concentration pathway) on vegetation changes. Differences in the simulated vegetation were primarily driven by assumptions about the influence of CO2 on plants. Elevated CO2 concentrations led to increased total aboveground vegetation biomass and shrub encroachment into grasslands and savannas for both climate scenarios. In simulations without the direct influence of CO2 on plants, there was hardly any shrub encroachment and vegetation biomass decreased or varied between a slight decrease in some cases and a slight increase in others. Based on these results, biome changes due to climate change are likely in Africa in the future. Due to the large uncertainties in future projections, strategies to adapt to climate change must be flexible.
The simulated vegetation in Chapter 2 represented potential, natural vegetation and is particularly suitable to investigate PAs. However, PAs do not exist isolated from their environment and social developments. In Chapter 3, the vegetation projections with CO2 effect from Chapter 2 were combined with projections for population density and land use. Except for many PAs in North Africa, most PAs were adversely affected by at least one of the three drivers by the end of the 21st century in both investigated scenarios ("middle-of-the-road" and "fossil-fuelled development"). Cooccurrence of the drivers varied by region and scenario for PAs. Both scenarios implied increasing challenges for the conservation of African biodiversity in PAs. The impact of climate change on vegetation is likely to be exacerbated by socio-economic change for most African PAs. Strong mitigation of future climate change together with equitable societal development may facilitate successful ecosystem conservation.
The simulations in Chapters 2 and 3 showed large-scale patterns of vegetation change, but their low resolution makes them unsuitable for local analyses. In Chapter 4, the challenges of simulating smaller scale, semi-arid ecosystems and their carbon cycle were analysed for the Nama Karoo with the aDGVM2 and its shrub module. The aDGVM2 is based on the aDGVM, but represents plants more flexibly. In all tested aDGVM2 configurations, the carbon fluxes improved compared to initial simulations but still overestimated them. The measured morphology of the dwarf shrubs and soil water dynamics were not reproduced in aDGVM2. Semi-arid soil water dynamics and coping strategies of semi-arid dwarf shrubs under drought stress are not adequately implemented in the aDGVM2. Further field research on semi-arid water and carbon dynamics of vegetation is necessary to parameterise the aDGVM2 for dwarf shrubs. If these challenges are overcome, DVMs can be a powerful tool for much-needed research on the impacts of climate change on the Nama Karoo.
The analyses have shown that climate change under medium to high emission scenarios is likely to lead to large-scale changes in ecosystems and the carbon balance in Africa. Because lower emissions scenarios come with less uncertainty, climate change adaptation strategies likely need to be less complex or extensive if climate change is minimised. For African PAs, the challenges of climate change may be exacerbated by socio-economic factors to a regionally varying extent. This research suggests that successful ecosystem conservation depends on climate change mitigation measures and ensuring equitable, sustainable development. The shown uncertainties, e.g., in the implementation of the CO2 effect on plants or vegetation dynamics in more niche ecosystems, help to focus future research efforts and increase our understanding of the range of plausible futures we may need to adapt to.
Climatology of morphology and cloud-radiative properties of marine low-level mixed-phase clouds
(2023)
Marine stratocumuli cover about 40 - 60% of the ocean surface. They self-organize into different morphological regimes. The two organized cellular regimes are called open and closed mesoscale-cellular convective (MCC) clouds. In mid-to-high latitudes, open and closed cells are the two most frequent types of MCC clouds. In particular, many MCC clouds consist of a mixture of vapor, liquid droplets, and ice particles, referred to as mixed-phase clouds (MPCs). Even for the same cloud fraction, the albedo of open cells is, on average, lower than that of closed MCC clouds. Cloud phase and morphology individually influence the cloud radiative effect. Thus, this thesis investigates the relationships between the cloud phase, MCC organization, cell size, and differences regarding the cloud-radiative effect.
This thesis focuses on space-borne retrievals to achieve extensive temporal and spatial coverage. The liDAR-raDAR (DARDAR) version 2 product collocates two active and one passive satellite: CloudSat, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and Moderate Resolution Imaging Spectroradiometer (MODIS). The cloud phase of DARDAR is vertically integrated to establish a single cloud phase at each data point. The MCC classification data set based on the liquid water path (LWP) of MODIS scenes is collocated with the DARDAR product to determine the MCC organization. Cell-size statistics of both MCC clouds are obtained using a marker-based image segmentation method on MODIS reflectance scenes. In addition, based on MODIS reflectance scenes, a convolutional neural network (CNN) is developed to classify open and closed MCC scenes to avoid missing mature MPCs with a low LWP.
The first part of this thesis explores the relationships between cloud phase, morphology, and cloud albedo in the Southern Ocean (SO). At a given cloud-top temperature (CTT), seasonal changes in the mixed-phase fraction, defined as the number of MPCs divided by the sum of MPC and supercooled liquid cloud (SLC) pixels, are stronger than the morphological changes. Therefore, external factors seem to influence these changes instead of morphology. The dependence of cloud phase on cloud-top height (CTH) is more substantial than on CTT in clouds with CTHs below 2.5 km. The previously observed acceleration of closed-to-open transition in MPCs, known as preconditioning, is not the primary driver of climatological cloud morphology statistics in the SO. The morphological differences in cloud albedo are more pronounced in SLCs than in MPCs. This change in albedo alters the cloud radiative effect in the SO by 21Wm−2 to 39Wm−2 depending onseason and cloud phase.
Open and closed MCC clouds exhibit larger equivalent cell diameters in the MPCs than in SLCs in austral summer, whereas, in austral winter, the SLCs are larger. The cell’s aspect ratio accounts for varying CTHs. Closed cells have smaller aspect ratios than open cells, so their cell diameter is smaller, independent of CTH. While the seasonal differences in closed cells are due to changes in CTH, the seasonal aspect ratio differences in open cells are mainly caused by MPCs. With increasing aspect ratios, the cloud albedo decreases in both open and closed MCC clouds, with the most substantial decrease in open MPCs clouds. This leads to cloud-radiative changes of 60 - 75Wm−2 in the SO, depending on cloud phase and aspect ratio.
The established CNN exhibits a good accuracy of 80.6%, with even higher accuracies in the Open (85.5%) and Closed (87.3%) categories. The global MCC climatology based on the CNN generally agrees well with previous MCC distributions. The most notable difference occurs in the Northern Hemisphere (NH) in boreal winter, with a higher occurrence frequency of closed and open MCC clouds. This might indicate missing MPCs in previous studies based on the LWP and some restricted to warm cloud scenes. Thus, the developed CNN seems to better represent the different morphologies in MPCs than in previous classifications.
In conclusion, this thesis shows that understanding the dependencies of cloud phase, cloud morphology, and cell size is important to enhance predictions of the cloud-radiative effect and thus, it is important to evaluate how cloud phase, cloud morphology, and cellsize change in a warming climate.