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The 11 July 1889 Chilik earthquake (M-w 8.0-8.3) forms part of a remarkable sequence of large earthquakes in the late nineteenth and early twentieth centuries in the northern Tien Shan. Despite its importance, the source of the 1889 earthquake remains unknown, though the macroseismic epicenter is sited in the Chilik valley, similar to 100 km southeast of Almaty, Kazakhstan (similar to 2 million population). Several short fault segments that have been inferred to have ruptured in 1889 are too short on their own to account for the estimated magnitude. In this paper we perform detailed surveying and trenching of the similar to 30 km long Saty fault, one of the previously inferred sources, and find that it was formed in a single earthquake within the last 700 years, involving surface slip of up to 10 m. The scarp-forming event, likely to be the 1889 earthquake, was the only surface-rupturing event for at least 5000 years and potentially for much longer. From satellite imagery we extend the mapped length of fresh scarps within the 1889 epicentral zone to a total of similar to 175 km, which we also suggest as candidate ruptures from the 1889 earthquake. The 175 km of rupture involves conjugate oblique left-lateral and right-lateral slip on three separate faults, with step overs of several kilometers between them. All three faults were essentially invisible in the Holocene geomorphology prior to the last slip. The recurrence interval between large earthquakes on any of these faults, and presumably on other faults of the Tien Shan, may be longer than the timescale over which the landscape is reset, providing a challenge for delineating sources of future hazard.
This case study evaluates the suitability of radar-based quantitative precipitation estimates (QPEs) for the simulation of streamflow in the Marikina River Basin (MRB), the Philippines. Hourly radar-based QPEs were produced from reflectivity that had been observed by an S-band radar located about 90 km from the MRB. Radar data processing and precipitation estimation were carried out using the open source library wradlib. To assess the added value of the radar-based QPE, we used spatially interpolated rain gauge observations (gauge-only (GO) product) as a benchmark. Rain gauge observations were also used to quantify rainfall estimation errors at the point scale. At the point scale, the radar-based QPE outperformed the GO product in 2012, while for 2013, the performance was similar. For both periods, estimation errors substantially increased from daily to the hourly accumulation intervals. Despite this fact, both rainfall estimation methods allowed for a good representation of observed streamflow when used to force a hydrological simulation model of the MRB. Furthermore, the results of the hydrological simulation were consistent with rainfall verification at the point scale: the radar-based QPE performed better than the GO product in 2012, and equivalently in 2013. Altogether, we could demonstrate that, in terms of streamflow simulation, the radar-based QPE can perform as good as or even better than the GO product - even for a basin such as the MRB which has a comparatively dense rain gauge network. This suggests good prospects for using radar-based QPE to simulate and forecast streamflow in other parts of the Philippines where rain gauge networks are not as dense.
Six N-alkylpyridinium salts [CnPy](2)[MCl4] (n = 4 or 12 and M = Co, Cu, Zn) were synthesized, and their structure and thermal properties were studied. The [C4Py](2)[MCl4] compounds are monoclinic and crystallize in the space group P2(1)/n. The crystals of the longer chain analogues [C12Py](2)[MCl4] are triclinic and crystallize in the space group P (1) over bar. Above the melting temperature, all compounds are ionic liquids (ILs). The derivatives with the longer C12 chain exhibit liquid crystallinity and the shorter chain compounds only show a melting transition. Consistent with single-crystal analysis, electron paramagnetic resonance spectroscopy suggests that the [CuCl4](2-) ions in the Cu-based ILs have a distorted tetrahedral geometry.
Six N-alkylpyridinium salts [CnPy](2)[MCl4] (n = 4 or 12 and M = Co, Cu, Zn) were synthesized, and their structure and thermal properties were studied. The [C4Py](2)[MCl4] compounds are monoclinic and crystallize in the space group P2(1)/n. The crystals of the longer chain analogues [C12Py](2)[MCl4] are triclinic and crystallize in the space group P (1) over bar. Above the melting temperature, all compounds are ionic liquids (ILs). The derivatives with the longer C12 chain exhibit liquid crystallinity and the shorter chain compounds only show a melting transition. Consistent with single-crystal analysis, electron paramagnetic resonance spectroscopy suggests that the [CuCl4](2-) ions in the Cu-based ILs have a distorted tetrahedral geometry.
The mechanisms by which climate and vegetation affect erosion rates over various time scales lie at the heart of understanding landscape response to climate change. Plot-scale field experiments show that increased vegetation cover slows erosion, implying that faster erosion should occur under low to moderate vegetation cover. However, demonstrating this concept over long time scales and across landscapes has proven to be difficult, especially in settings complicated by tectonic forcing and variable slopes. We investigate this problem by measuring cosmogenic Be-10-derived catchment-mean denudation rates across a range of climate zones and hillslope gradients in the Kenya Rift, and by comparing our results with those published from the Rwenzori Mountains of Uganda. We find that denudation rates from sparsely vegetated parts of the Kenya Rift are up to 0.13 mm/yr, while those from humid and more densely vegetated parts of the Kenya Rift flanks and the Rwenzori Mountains reach a maximum of 0.08 mm/yr, despite higher median hillslope gradients. While differences in lithology and recent land-use changes likely affect the denudation rates and vegetation cover values in some of our studied catchments, hillslope gradient and vegetation cover appear to explain most of the variation in denudation rates across the study area. Our results support the idea that changing vegetation cover can contribute to complex erosional responses to climate or land-use change and that vegetation cover can play an important role in determining the steady-state slopes of mountain belts through its stabilizing effects on the land surface.
Metabolically active microbial communities are present in a wide range of subsurface environments. Techniques like enumeration of microbial cells, activity measurements with radiotracer assays and the analysis of porewater constituents are currently being used to explore the subsurface biosphere, alongside with molecular biological analyses. However, many of these techniques reach their detection limits due to low microbial activity and abundance. Direct measurements of microbial turnover not just face issues of insufficient sensitivity, they only provide information about a single specific process but in sediments many different process can occur simultaneously. Therefore, the development of a new technique to measure total microbial activity would be a major improvement. A new tritium-based hydrogenase-enzyme assay appeared to be a promising tool to quantify total living biomass, even in low activity subsurface environments. In this PhD project total microbial biomass and microbial activity was quantified in different subsurface sediments using established techniques (cell enumeration and pore water geochemistry) as well as a new tritium-based hydrogenase enzyme assay. By using a large database of our own cell enumeration data from equatorial Pacific and north Pacific sediments and published data it was shown that the global geographic distribution of subseafloor sedimentary microbes varies between sites by 5 to 6 orders of magnitude and correlates with the sedimentation rate and distance from land. Based on these correlations, global subseafloor biomass was estimated to be 4.1 petagram-C and ~0.6 % of Earth's total living biomass, which is significantly lower than previous estimates. Despite the massive reduction in biomass the subseafloor biosphere is still an important player in global biogeochemical cycles. To understand the relationship between microbial activity, abundance and organic matter flux into the sediment an expedition to the equatorial Pacific upwelling area and the north Pacific Gyre was carried out. Oxygen respiration rates in subseafloor sediments from the north Pacific Gyre, which are deposited at sedimentation rates of 1 mm per 1000 years, showed that microbial communities could survive for millions of years without fresh supply of organic carbon. Contrary to the north Pacific Gyre oxygen was completely depleted within the upper few millimeters to centimeters in sediments of the equatorial upwelling region due to a higher supply of organic matter and higher metabolic activity. So occurrence and variability of electron acceptors over depth and sites make the subsurface a complex environment for the quantification of total microbial activity. Recent studies showed that electron acceptor processes, which were previously thought to thermodynamically exclude each other can occur simultaneously. So in many cases a simple measure of the total microbial activity would be a better and more robust solution than assays for several specific processes, for example sulfate reduction rates or methanogenesis. Enzyme or molecular assays provide a more general approach as they target key metabolic compounds. Since hydrogenase enzymes are ubiquitous in microbes, the recently developed tritium-based hydrogenase radiotracer assay is applied to quantify hydrogenase enzyme activity as a parameter of total living cell activity. Hydrogenase enzyme activity was measured in sediments from different locations (Lake Van, Barents Sea, Equatorial Pacific and Gulf of Mexico). In sediment samples that contained nitrate, we found the lowest cell specific enzyme activity around 10^(-5) nmol H_(2) cell^(-1) d^(-1). With decreasing energy yield of the electron acceptor used, cell-specific hydrogenase activity increased and maximum values of up to 1 nmol H_(2) cell^(-1) d^(-1) were found in samples with methane concentrations of >10 ppm. Although hydrogenase activity cannot be converted directly into a turnover rate of a specific process, cell-specific activity factors can be used to identify specific metabolism and to quantify the metabolically active microbial population. In another study on sediments from the Nankai Trough microbial abundance and hydrogenase activity data show that both the habitat and the activity of subseafloor sedimentary microbial communities have been impacted by seismic activities. An increase in hydrogenase activity near the fault zone revealed that the microbial community was supplied with hydrogen as an energy source and that the microbes were specialized to hydrogen metabolism.
Subsurface microbial communities undertake many terminal electron-accepting processes, often simultaneously. Using a tritium-based assay, we measured the potential hydrogen oxidation catalyzed by hydrogenase enzymes in several subsurface sedimentary environments (Lake Van, Barents Sea, Equatorial Pacific, and Gulf of Mexico) with different predominant electron-acceptors. Hydrogenases constitute a diverse family of enzymes expressed by microorganisms that utilize molecular hydrogen as a metabolic substrate, product, or intermediate. The assay reveals the potential for utilizing molecular hydrogen and allows qualitative detection of microbial activity irrespective of the predominant electron-accepting process. Because the method only requires samples frozen immediately after recovery, the assay can be used for identifying microbial activity in subsurface ecosystems without the need to preserve live material. We measured potential hydrogen oxidation rates in all samples from multiple depths at several sites that collectively span a wide range of environmental conditions and biogeochemical zones. Potential activity normalized to total cell abundance ranges over five orders of magnitude and varies, dependent upon the predominant terminal electron acceptor. Lowest per-cell potential rates characterize the zone of nitrate reduction and highest per-cell potential rates occur in the methanogenic zone. Possible reasons for this relationship to predominant electron acceptor include (i) increasing importance of fermentation in successively deeper biogeochemical zones and (ii) adaptation of H(2)ases to successively higher concentrations of H-2 in successively deeper zones.
Subsurface microbial communities undertake many terminal electron-accepting processes, often simultaneously. Using a tritium-based assay, we measured the potential hydrogen oxidation catalyzed by hydrogenase enzymes in several subsurface sedimentary environments (Lake Van, Barents Sea, Equatorial Pacific, and Gulf of Mexico) with different predominant electron-acceptors. Hydrogenases constitute a diverse family of enzymes expressed by microorganisms that utilize molecular hydrogen as a metabolic substrate, product, or intermediate. The assay reveals the potential for utilizing molecular hydrogen and allows qualitative detection of microbial activity irrespective of the predominant electron-accepting process. Because the method only requires samples frozen immediately after recovery, the assay can be used for identifying microbial activity in subsurface ecosystems without the need to preserve live material. We measured potential hydrogen oxidation rates in all samples from multiple depths at several sites that collectively span a wide range of environmental conditions and biogeochemical zones. Potential activity normalized to total cell abundance ranges over five orders of magnitude and varies, dependent upon the predominant terminal electron acceptor. Lowest per-cell potential rates characterize the zone of nitrate reduction and highest per-cell potential rates occur in the methanogenic zone. Possible reasons for this relationship to predominant electron acceptor include (i) increasing importance of fermentation in successively deeper biogeochemical zones and (ii) adaptation of H(2)ases to successively higher concentrations of H-2 in successively deeper zones.
The subsurface harbors a large fraction of Earth's living biomass, forming complex microbial ecosystems. Without a profound knowledge of the ongoing biologically mediated processes and their reaction to anthropogenic changes it is difficult to assess the long-term stability and feasibility of any type of geotechnical utilization, as these influence subsurface ecosystems. Despite recent advances in many areas of subsurface microbiology, the direct quantification of turnover processes is still in its infancy, mainly due to the extremely low cell abundances. We provide an overview of the currently available techniques for the quantification of microbial turnover processes and discuss their specific strengths and limitations. Most techniques employed so far have focused on specific processes, e.g. sulfate reduction or methanogenesis. Recent studies show that processes that were previously thought to exclude each other can occur simultaneously, albeit at very low rates. Without the identification of the respective processes it is impossible to quantify total microbial activity. Even in cases where all simultaneously occurring processes can be identified, the typically very low rates prevent quantification. In many cases a simple measure of total microbial activity would be a better and more robust measure than assays for several specific processes. Enzyme or molecular assays provide a more general approach as they target key metabolic compounds. Depending on the compound targeted a broader spectrum of microbial processes can be quantified. The two most promising compounds are ATP and hydrogenase, as both are ubiquitous in microbes. Technical constraints limit the applicability of currently available ATP-assays for subsurface samples. A recently developed hydrogenase radiotracer assay has the potential to become a key tool for the quantification of subsurface microbial activity.
The behaviour of individuals, businesses, and government entities before, during, and immediately after a disaster can dramatically affect the impact and recovery time. However, existing risk-assessment methods rarely include this critical factor. In this Perspective, we show why this is a concern, and demonstrate that although initial efforts have inevitably represented human behaviour in limited terms, innovations in flood-risk assessment that integrate societal behaviour and behavioural adaptation dynamics into such quantifications may lead to more accurate characterization of risks and improved assessment of the effectiveness of risk-management strategies and investments. Such multidisciplinary approaches can inform flood-risk management policy development.
Numerical simulation of fluid-flow processes in a 3D high-resolution carbonate reservoir analogue
(2014)
A high-resolution three-dimensional (3D) outcrop model of a Jurassic carbonate ramp was used in order to perform a series of detailed and systematic flow simulations. The aim of this study was to test the impact of small- and large-scale geological features on reservoir performance and oil recovery. The digital outcrop model contains a wide range of sedimentological, diagenetic and structural features, including discontinuity surfaces, shoal bodies, mud mounds, oyster bioherms and fractures. Flow simulations are performed for numerical well testing and secondary oil recovery. Numerical well testing enables synthetic but systematic pressure responses to be generated for different geological features observed in the outcrops. This allows us to assess and rank the relative impact of specific geological features on reservoir performance. The outcome documents that, owing to the realistic representation of matrix heterogeneity, most diagenetic and structural features cannot be linked to a unique pressure signature. Instead, reservoir performance is controlled by subseismic faults and oyster bioherms acting as thief zones. Numerical simulations of secondary recovery processes reveal strong channelling of fluid flow into high-permeability layers as the primary control for oil recovery. However, appropriate reservoir-engineering solutions, such as optimizing well placement and injection fluid, can reduce channelling and increase oil recovery.
The climate is a complex dynamical system involving interactions and feedbacks among different processes at multiple temporal and spatial scales. Although numerous studies have attempted to understand the climate system, nonetheless, the studies investigating the multiscale characteristics of the climate are scarce. Further, the present set of techniques are limited in their ability to unravel the multi-scale variability of the climate system. It is completely plausible that extreme events and abrupt transitions, which are of great interest to climate community, are resultant of interactions among processes operating at multi-scale. For instance, storms, weather patterns, seasonal irregularities such as El Niño, floods and droughts, and decades-long climate variations can be better understood and even predicted by quantifying their multi-scale dynamics. This makes a strong argument to unravel the interaction and patterns of climatic processes at different scales. With this background, the thesis aims at developing measures to understand and quantify multi-scale interactions within the climate system.
In the first part of the thesis, I proposed two new methods, viz, multi-scale event synchronization (MSES) and wavelet multi-scale correlation (WMC) to capture the scale-specific features present in the climatic processes. The proposed methods were tested on various synthetic and real-world time series in order to check their applicability and replicability. The results indicate that both methods (WMC and MSES) are able to capture scale-specific associations that exist between processes at different time scales in a more detailed manner as compared to the traditional single scale counterparts.
In the second part of the thesis, the proposed multi-scale similarity measures were used in constructing climate networks to investigate the evolution of spatial connections within climatic processes at multiple timescales. The proposed methods WMC and MSES, together with complex network were applied to two different datasets.
In the first application, climate networks based on WMC were constructed for the univariate global sea surface temperature (SST) data to identify and visualize the SSTs patterns that develop very similarly over time and distinguish them from those that have long-range teleconnections to other ocean regions. Further investigations of climate networks on different timescales revealed (i) various high variability and co-variability regions, and (ii) short and long-range teleconnection regions with varying spatial distance. The outcomes of the study not only re-confirmed the existing knowledge on the link between SST patterns like El Niño Southern Oscillation and the Pacific Decadal Oscillation, but also suggested new insights into the characteristics and origins of long-range teleconnections.
In the second application, I used the developed non-linear MSES similarity measure to quantify the multivariate teleconnections between extreme Indian precipitation and climatic patterns with the highest relevance for Indian sub-continent. The results confirmed significant non-linear influences that were not well captured by the traditional methods. Further, there was a substantial variation in the strength and nature of teleconnection across India, and across time scales.
Overall, the results from investigations conducted in the thesis strongly highlight the need for considering the multi-scale aspects in climatic processes, and the proposed methods provide robust framework for quantifying the multi-scale characteristics.
Sea surface temperature (SST) patterns can – as surface climate forcing – affect weather and climate at large distances. One example is El Niño-Southern Oscillation (ENSO) that causes climate anomalies around the globe via teleconnections. Although several studies identified and characterized these teleconnections, our understanding of climate processes remains incomplete, since interactions and feedbacks are typically exhibited at unique or multiple temporal and spatial scales. This study characterizes the interactions between the cells of a global SST data set at different temporal and spatial scales using climate networks. These networks are constructed using wavelet multi-scale correlation that investigate the correlation between the SST time series at a range of scales allowing instantaneously deeper insights into the correlation patterns compared to traditional methods like empirical orthogonal functions or classical correlation analysis. This allows us to identify and visualise regions of – at a certain timescale – similarly evolving SSTs and distinguish them from those with long-range teleconnections to other ocean regions. Our findings re-confirm accepted knowledge about known highly linked SST patterns like ENSO and the Pacific Decadal Oscillation, but also suggest new insights into the characteristics and origins of long-range teleconnections like the connection between ENSO and Indian Ocean Dipole.
Sea surface temperature (SST) patterns can – as surface climate forcing – affect weather and climate at large distances. One example is El Niño-Southern Oscillation (ENSO) that causes climate anomalies around the globe via teleconnections. Although several studies identified and characterized these teleconnections, our understanding of climate processes remains incomplete, since interactions and feedbacks are typically exhibited at unique or multiple temporal and spatial scales. This study characterizes the interactions between the cells of a global SST data set at different temporal and spatial scales using climate networks. These networks are constructed using wavelet multi-scale correlation that investigate the correlation between the SST time series at a range of scales allowing instantaneously deeper insights into the correlation patterns compared to traditional methods like empirical orthogonal functions or classical correlation analysis. This allows us to identify and visualise regions of – at a certain timescale – similarly evolving SSTs and distinguish them from those with long-range teleconnections to other ocean regions. Our findings re-confirm accepted knowledge about known highly linked SST patterns like ENSO and the Pacific Decadal Oscillation, but also suggest new insights into the characteristics and origins of long-range teleconnections like the connection between ENSO and Indian Ocean Dipole.
The quantification of spatial propagation of extreme precipitation events is vital in water resources planning and disaster mitigation. However, quantifying these extreme events has always been challenging as many traditional methods are insufficient to capture the nonlinear interrelationships between extreme event time series. Therefore, it is crucial to develop suitable methods for analyzing the dynamics of extreme events over a river basin with a diverse climate and complicated topography. Over the last decade, complex network analysis emerged as a powerful tool to study the intricate spatiotemporal relationship between many variables in a compact way. In this study, we employ two nonlinear concepts of event synchronization and edit distance to investigate the extreme precipitation pattern in the Ganga river basin. We use the network degree to understand the spatial synchronization pattern of extreme rainfall and identify essential sites in the river basin with respect to potential prediction skills. The study also attempts to quantify the influence of precipitation seasonality and topography on extreme events. The findings of the study reveal that (1) the network degree is decreased in the southwest to northwest direction, (2) the timing of 50th percentile precipitation within a year influences the spatial distribution of degree, (3) the timing is inversely related to elevation, and (4) the lower elevation greatly influences connectivity of the sites. The study highlights that edit distance could be a promising alternative to analyze event-like data by incorporating event time and amplitude and constructing complex networks of climate extremes.
Hydrologic regionalization deals with the investigation of homogeneity in watersheds and provides a classification of watersheds for regional analysis. The classification thus obtained can be used as a basis for mapping data from gauged to ungauged sites and can improve extreme event prediction. This paper proposes a wavelet power spectrum (WPS) coupled with the self-organizing map method for clustering hydrologic catchments. The application of this technique is implemented for gauged catchments. As a test case study, monthly streamflow records observed at 117 selected catchments throughout the western United States from 1951 through 2002. Further, based on WPS of each station, catchments are classified into homogeneous clusters, which provides a representative WPS pattern for the streamflow stations in each cluster.
The temporal dynamics of climate processes are spread across different timescales and, as such, the study of these processes at only one selected timescale might not reveal the complete mechanisms and interactions within and between the (sub-) processes. To capture the non-linear interactions between climatic events, the method of event synchronization has found increasing attention recently. The main drawback with the present estimation of event synchronization is its restriction to analysing the time series at one reference timescale only. The study of event synchronization at multiple scales would be of great interest to comprehend the dynamics of the investigated climate processes. In this paper, the wavelet-based multi-scale event synchronization (MSES) method is proposed by combining the wavelet transform and event synchronization. Wavelets are used extensively to comprehend multi-scale processes and the dynamics of processes across various timescales. The proposed method allows the study of spatio-temporal patterns across different timescales. The method is tested on synthetic and real-world time series in order to check its replicability and applicability. The results indicate that MSES is able to capture relationships that exist between processes at different timescales.
Quantifying the roles of single stations within homogeneous regions using complex network analysis
(2018)
Regionalization and pooling stations to form homogeneous regions or communities are essential for reliable parameter transfer, prediction in ungauged basins, and estimation of missing information. Over the years, several clustering methods have been proposed for regional analysis. Most of these methods are able to quantify the study region in terms of homogeneity but fail to provide microscopic information about the interaction between communities, as well as about each station within the communities. We propose a complex network-based approach to extract this valuable information and demonstrate the potential of our approach using a rainfall network constructed from the Indian gridded daily precipitation data. The communities were identified using the network-theoretical community detection algorithm for maximizing the modularity. Further, the grid points (nodes) were classified into universal roles according to their pattern of within- and between-community connections. The method thus yields zoomed-in details of individual rainfall grids within each community.
Hydrometric networks play a vital role in providing information for decision-making in water resource management. They should be set up optimally to provide as much information as possible that is as accurate as possible and, at the same time, be cost-effective. Although the design of hydrometric networks is a well-identified problem in hydrometeorology and has received considerable attention, there is still scope for further advancement. In this study, we use complex network analysis, defined as a collection of nodes interconnected by links, to propose a new measure that identifies critical nodes of station networks. The approach can support the design and redesign of hydrometric station networks. The science of complex networks is a relatively young field and has gained significant momentum over the last few years in different areas such as brain networks, social networks, technological networks, or climate networks. The identification of influential nodes in complex networks is an important field of research. We propose a new node-ranking measure – the weighted degree–betweenness (WDB) measure – to evaluate the importance of nodes in a network. It is compared to previously proposed measures used on synthetic sample networks and then applied to a real-world rain gauge network comprising 1229 stations across Germany to demonstrate its applicability. The proposed measure is evaluated using the decline rate of the network efficiency and the kriging error. The results suggest that WDB effectively quantifies the importance of rain gauges, although the benefits of the method need to be investigated in more detail.