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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.
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.
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.
This study aims to compare impacts of climate change on streamflow in four large representative African river basins: the Niger, the Upper Blue Nile, the Oubangui and the Limpopo. We set up the eco-hydrological model SWIM (Soil and Water Integrated Model) for all four basins individually. The validation of the models for four basins shows results from adequate to very good, depending on the quality and availability of input and calibration data.
For the climate impact assessment, we drive the model with outputs of five bias corrected Earth system models of Coupled Model Intercomparison Project Phase 5 (CMIP5) for the representative concentration pathways (RCPs) 2.6 and 8.5. This climate input is put into the context of climate trends of the whole African continent and compared to a CMIP5 ensemble of 19 models in order to test their representativeness. Subsequently, we compare the trends in mean discharges, seasonality and hydrological extremes in the 21st century. The uncertainty of results for all basins is high. Still, climate change impact is clearly visible for mean discharges but also for extremes in high and low flows. The uncertainty of the projections is the lowest in the Upper Blue Nile, where an increase in streamflow is most likely. In the Niger and the Limpopo basins, the magnitude of trends in both directions is high and has a wide range of uncertainty. In the Oubangui, impacts are the least significant. Our results confirm partly the findings of previous continental impact analyses for Africa. However, contradictory to these studies we find a tendency for increased streamflows in three of the four basins (not for the Oubangui). Guided by these results, we argue for attention to the possible risks of increasing high flows in the face of the dominant water scarcity in Africa. In conclusion, the study shows that impact intercomparisons have added value to the adaptation discussion and may be used for setting up adaptation plans in the context of a holistic approach.
Climate or Land Use?
(2015)
This study intends to contribute to the ongoing discussion on whether land use and land cover changes (LULC) or climate trends have the major influence on the observed increase of flood magnitudes in the Sahel. A simulation-based approach is used for attributing the observed trends to the postulated drivers. For this purpose, the ecohydrological model SWIM (Soil and Water Integrated Model) with a new, dynamic LULC module was set up for the Sahelian part of the Niger River until Niamey, including the main tributaries Sirba and Goroul. The model was driven with observed, reanalyzed climate and LULC data for the years 1950-2009. In order to quantify the shares of influence, one simulation was carried out with constant land cover as of 1950, and one including LULC. As quantitative measure, the gradients of the simulated trends were compared to the observed trend. The modeling studies showed that for the Sirba River only the simulation which included LULC was able to reproduce the observed trend. The simulation without LULC showed a positive trend for flood magnitudes, but underestimated the trend significantly. For the Goroul River and the local flood of the Niger River at Niamey, the simulations were only partly able to reproduce the observed trend. In conclusion, the new LULC module enabled some first quantitative insights into the relative influence of LULC and climatic changes. For the Sirba catchment, the results imply that LULC and climatic changes contribute in roughly equal shares to the observed increase in flooding. For the other parts of the subcatchment, the results are less clear but show, that climatic changes and LULC are drivers for the flood increase; however their shares cannot be quantified. Based on these modeling results, we argue for a two-pillar adaptation strategy to reduce current and future flood risk: Flood mitigation for reducing LULC-induced flood increase, and flood adaptation for a general reduction of flood vulnerability.
Sediment-discharge hysteresis loops are frequently analyzed to facilitate the understanding of sediment transport processes. Hysteresis patterns, however, are often complex and their interpretation can be complicated. Particularly, quantifying hysteresis patterns remains a problematic issue. Moreover, it is currently unknown how much data is required for analyzing sediment-discharge hysteresis loops in a given area. These open questions and challenges motivated us to develop a new method for quantifying suspended-sediment hysteresis. Subsequently, we applied the new hysteresis index to three suspended-sediment and discharge datasets from a small tropical rainforest catchment. The datasets comprised a different number of events and sampling sites. Our analyses show three main findings: (1) datasets restricted to only few events, which is typical for rapid assessment surveys, were always sufficient to identify the dominating hysteresis pattern in our research area. Furthermore, some of these small datasets contained multiple-peak events that allowed identifying intra-event exhaustion effects and hence, limitations in sediment supply. (2) Datasets comprising complete hydrological years were particularly useful for analyzing seasonal dynamics of hysteresis. These analyses revealed an exhaustion of hysteresis on the inter-event scale which also points to a limited sediment supply. (3) Datasets comprising measurements from two consecutive gauges installed at the catchment outlet and on a slope within that catchment allowed analyzing the change of hysteresis patterns along the flowpath. On the slope, multiple-peak events showed a stronger intra-event exhaustion of hysteresis than at the catchment outlet. Furthermore, exhaustion of hysteresis on the inter-event scale was not evident on the slope but occurred at the catchment outlet. Our results indicate that even small sediment datasets can provide valuable insights into sediment transport processes of small catchments. Furthermore, our results may serve as a first guideline on what to expect from an analysis of hysteresis patterns for datasets of varying quality and quantity. (c) 2014 Elsevier B.V. All rights reserved.
Samples of 474 forest stands in Germany were analysed for concentrations of polycyclic aromatic hydrocarbons (PAHs) in three sampling depths. Enhanced concentrations were mainly found at spots relatively close to densely industrialized and urbanized regions and at some topographically elevated areas. Average enrichment factors between mineral soil and humic layer depend on humus type i.e. decrease from mull via moder to more Based on their compound-patterns, the observed samples could be assigned to three main clusters. For some parts of our study area a uniform assignment of samples to clusters over larger regions could be identified. For instance, samples taken at vicinity to brown-coal strip-mining districts are characterized by high relative abundances of low-molecular-weight PAHs. These results suggest that PAHs are more likely originated from local and regional emitters rather than from long-range transport and that specific source-regions can be identified based on PAH fingerprints. (C) 2015 Elsevier Ltd. All rights reserved.