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Temperature is a key factor controlling plant growth and vitality in the temperate climates of the mid-latitudes like in vast parts of the European continent. Beyond the effect of average conditions, the timings and magnitudes of temperature extremes play a particularly crucial role, which needs to be better understood in the context of projected future rises in the frequency and/or intensity of such events. In this work, we employ event coincidence analysis (ECA) to quantify the likelihood of simultaneous occurrences of extremes in daytime land surface temperature anomalies (LSTAD) and the normalized difference vegetation index (NDVI). We perform this analysis for entire Europe based upon remote sensing data, differentiating between three periods corresponding to different stages of plant development during the growing season. In addition, we analyze the typical elevation and land cover type of the regions showing significantly large event coincidences rates to identify the most severely affected vegetation types. Our results reveal distinct spatio-temporal impact patterns in terms of extraordinarily large co-occurrence rates between several combinations of temperature and NDVI extremes. Croplands are among the most frequently affected land cover types, while elevation is found to have only a minor effect on the spatial distribution of corresponding extreme weather impacts. These findings provide important insights into the vulnerability of European terrestrial ecosystems to extreme temperature events and demonstrate how event-based statistics like ECA can provide a valuable perspective on environmental nexuses.
Climatic variations and human activity now and increasingly in the future cause land cover changes and introduce perturbations in the terrestrial carbon reservoirs in vegetation, soil and detritus. Optical remote sensing and in particular Imaging Spectroscopy has shown the potential to quantify land surface parameters over large areas, which is accomplished by taking advantage of the characteristic interactions of incident radiation and the physico-chemical properties of a material. The objective of this thesis is to quantify key soil parameters, including soil organic carbon, using field and Imaging Spectroscopy. Organic carbon, iron oxides and clay content are selected to be analyzed to provide indicators for ecosystem function in relation to land degradation, and additionally to facilitate a quantification of carbon inventories in semiarid soils. The semiarid Albany Thicket Biome in the Eastern Cape Province of South Africa is chosen as study site. It provides a regional example for a semiarid ecosystem that currently undergoes land changes due to unadapted management practices and furthermore has to face climate change induced land changes in the future. The thesis is divided in three methodical steps. Based on reflectance spectra measured in the field and chemically determined constituents of the upper topsoil, physically based models are developed to quantify soil organic carbon, iron oxides and clay content. Taking account of the benefits limitations of existing methods, the approach is based on the direct application of known diagnostic spectral features and their combination with multivariate statistical approaches. It benefits from the collinearity of several diagnostic features and a number of their properties to reduce signal disturbances by influences of other spectral features. In a following step, the acquired hyperspectral image data are prepared for an analysis of soil constituents. The data show a large spatial heterogeneity that is caused by the patchiness of the natural vegetation in the study area that is inherent to most semiarid landscapes. Spectral mixture analysis is performed and used to deconvolve non-homogenous pixels into their constituent components. For soil dominated pixels, the subpixel information is used to remove the spectral influence of vegetation and to approximate the pure spectral signature coming from the soil. This step is an integral part when working in natural non-agricultural areas where pure bare soil pixels are rare. It is identified as the largest benefit within the multi-stage methodology, providing the basis for a successful and unbiased prediction of soil constituents from hyperspectral imagery. With the proposed approach it is possible (1) to significantly increase the spatial extent of derived information of soil constituents to areas with about 40 % vegetation coverage and (2) to reduce the influence of materials such as vegetation on the quantification of soil constituents to a minimum. Subsequently, soil parameter quantities are predicted by the application of the feature-based soil prediction models to the maps of locally approximated soil signatures. Thematic maps showing the spatial distribution of the three considered soil parameters in October 2009 are produced for the Albany Thicket Biome of South Africa. The maps are evaluated for their potential to detect erosion affected areas as effects of land changes and to identify degradation hot spots in regard to support local restoration efforts. A regional validation, carried out using available ground truth sites, suggests remaining factors disturbing the correlation of spectral characteristics and chemical soil constituents. The approach is developed for semiarid areas in general and not adapted to specific conditions in the study area. All processing steps of the developed methodology are implemented in software modules, where crucial steps of the workflow are fully automated. The transferability of the methodology is shown for simulated data of the future EnMAP hyperspectral satellite. Soil parameters are successfully predicted from these data despite intense spectral mixing within the lower spatial resolution EnMAP pixels. This study shows an innovative approach to use Imaging Spectroscopy for mapping of key soil constituents, including soil organic carbon, for large areas in a non-agricultural ecosystem and under consideration of a partially vegetation coverage. It can contribute to a better assessment of soil constituents that describe ecosystem processes relevant to detect and monitor land changes. The maps further provide an assessment of the current carbon inventory in soils, valuable for carbon balances and carbon mitigation products.
The Seismic Hazard Inferred from Tectonics based on the Global Strain Rate Map (SHIFT_GSRM) earthquake forecast was designed to provide high-resolution estimates of global shallow seismicity to be used in seismic hazard assessment. This model combines geodetic strain rates with global earthquake parameters to characterize long-term rates of seismic moment and earthquake activity. Although SHIFT_GSRM properly computes seismicity rates in seismically active continental regions, it underestimates earthquake rates in subduction zones by an average factor of approximately 3. We present a complementary method to SHIFT_GSRM to more accurately forecast earthquake rates in 37 subduction segments, based on the conservation of moment principle and the use of regional interface seismicity parameters, such as subduction dip angles, corner magnitudes, and coupled seismogenic thicknesses. In seven progressive steps, we find that SHIFT_GSRM earthquake-rate underpredictions are mainly due to the utilization of a global probability function of seismic moment release that poorly captures the great variability among subduction megathrust interfaces. Retrospective test results show that the forecast is consistent with the observations during the 1 January 1977 to 31 December 2014 period. Moreover, successful pseudoprospective evaluations for the 1 January 2015 to 31 December 2018 period demonstrate the power of the regionalized earthquake model to properly estimate subduction-zone seismicity.
One third of the world's population lives in areas where earthquakes causing at least slight damage are frequently expected. Thus, the development and testing of global seismicity models is essential to improving seismic hazard estimates and earthquake-preparedness protocols for effective disaster-risk mitigation. Currently, the availability and quality of geodetic data along plate-boundary regions provides the opportunity to construct global models of plate motion and strain rate, which can be translated into global maps of forecasted seismicity. Moreover, the broad coverage of existing earthquake catalogs facilitates in present-day the calibration and testing of global seismicity models. As a result, modern global seismicity models can integrate two independent factors necessary for physics-based, long-term earthquake forecasting, namely interseismic crustal strain accumulation and sudden lithospheric stress release.
In this dissertation, I present the construction of and testing results for two global ensemble seismicity models, aimed at providing mean rates of shallow (0-70 km) earthquake activity for seismic hazard assessment. These models depend on the Subduction Megathrust Earthquake Rate Forecast (SMERF2), a stationary seismicity approach for subduction zones, based on the conservation of moment principle and the use of regional "geodesy-to-seismicity" parameters, such as corner magnitudes, seismogenic thicknesses and subduction dip angles. Specifically, this interface-earthquake model combines geodetic strain rates with instrumentally-recorded seismicity to compute long-term rates of seismic and geodetic moment. Based on this, I derive analytical solutions for seismic coupling and earthquake activity, which provide this earthquake model with the initial abilities to properly forecast interface seismicity. Then, I integrate SMERF2 interface-seismicity estimates with earthquake computations in non-subduction zones provided by the Seismic Hazard Inferred From Tectonics based on the second iteration of the Global Strain Rate Map seismicity approach to construct the global Tectonic Earthquake Activity Model (TEAM). Thus, TEAM is designed to reduce number, and potentially spatial, earthquake inconsistencies of its predecessor tectonic earthquake model during the 2015-2017 period. Also, I combine this new geodetic-based earthquake approach with a global smoothed-seismicity model to create the World Hybrid Earthquake Estimates based on Likelihood scores (WHEEL) model. This updated hybrid model serves as an alternative earthquake-rate approach to the Global Earthquake Activity Rate model for forecasting long-term rates of shallow seismicity everywhere on Earth.
Global seismicity models provide scientific hypotheses about when and where earthquakes may occur, and how big they might be. Nonetheless, the veracity of these hypotheses can only be either confirmed or rejected after prospective forecast evaluation. Therefore, I finally test the consistency and relative performance of these global seismicity models with independent observations recorded during the 2014-2019 pseudo-prospective evaluation period. As a result, hybrid earthquake models based on both geodesy and seismicity are the most informative seismicity models during the testing time frame, as they obtain higher information scores than their constituent model components. These results support the combination of interseismic strain measurements with earthquake-catalog data for improved seismicity modeling. However, further prospective evaluations are required to more accurately describe the capacities of these global ensemble seismicity models to forecast longer-term earthquake activity.
Hyperspectral remote sensing of the spatial and temporal heterogeneity of low Arctic vegetation
(2019)
Arctic tundra ecosystems are experiencing warming twice the global average and Arctic vegetation is responding in complex and heterogeneous ways. Shifting productivity, growth, species composition, and phenology at local and regional scales have implications for ecosystem functioning as well as the global carbon and energy balance. Optical remote sensing is an effective tool for monitoring ecosystem functioning in this remote biome. However, limited field-based spectral characterization of the spatial and temporal heterogeneity limits the accuracy of quantitative optical remote sensing at landscape scales. To address this research gap and support current and future satellite missions, three central research questions were posed:
• Does canopy-level spectral variability differ between dominant low Arctic vegetation communities and does this variability change between major phenological phases?
• How does canopy-level vegetation colour images recorded with high and low spectral resolution devices relate to phenological changes in leaf-level photosynthetic pigment concentrations?
• How does spatial aggregation of high spectral resolution data from the ground to satellite scale influence low Arctic tundra vegetation signatures and thereby what is the potential of upcoming hyperspectral spaceborne systems for low Arctic vegetation characterization?
To answer these questions a unique and detailed database was assembled. Field-based canopy-level spectral reflectance measurements, nadir digital photographs, and photosynthetic pigment concentrations of dominant low Arctic vegetation communities were acquired at three major phenological phases representing early, peak and late season. Data were collected in 2015 and 2016 in the Toolik Lake Research Natural Area located in north central Alaska on the North Slope of the Brooks Range. In addition to field data an aerial AISA hyperspectral image was acquired in the late season of 2016. Simulations of broadband Sentinel-2 and hyperspectral Environmental and Mapping Analysis Program (EnMAP) satellite reflectance spectra from ground-based reflectance spectra as well as simulations of EnMAP imagery from aerial hyperspectral imagery were also obtained.
Results showed that canopy-level spectral variability within and between vegetation communities differed by phenological phase. The late season was identified as the most discriminative for identifying many dominant vegetation communities using both ground-based and simulated hyperspectral reflectance spectra. This was due to an overall reduction in spectral variability and comparable or greater differences in spectral reflectance between vegetation communities in the visible near infrared spectrum.
Red, green, and blue (RGB) indices extracted from nadir digital photographs and pigment-driven vegetation indices extracted from ground-based spectral measurements showed strong significant relationships. RGB indices also showed moderate relationships with chlorophyll and carotenoid pigment concentrations. The observed relationships with the broadband RGB channels of the digital camera indicate that vegetation colour strongly influences the response of pigment-driven spectral indices and digital cameras can track the seasonal development and degradation of photosynthetic pigments.
Spatial aggregation of hyperspectral data from the ground to airborne, to simulated satel-lite scale was influenced by non-photosynthetic components as demonstrated by the distinct shift of the red edge to shorter wavelengths. Correspondence between spectral reflectance at the three scales was highest in the red spectrum and lowest in the near infra-red. By artificially mixing litter spectra at different proportions to ground-based spectra, correspondence with aerial and satellite spectra increased. Greater proportions of litter were required to achieve correspondence at the satellite scale.
Overall this thesis found that integrating multiple temporal, spectral, and spatial data is necessary to monitor the complexity and heterogeneity of Arctic tundra ecosystems. The identification of spectrally similar vegetation communities can be optimized using non-peak season hyperspectral data leading to more detailed identification of vegetation communities. The results also highlight the power of vegetation colour to link ground-based and satellite data. Finally, a detailed characterization non-photosynthetic ecosystem components is crucial for accurate interpretation of vegetation signals at landscape scales.
The first step in the estimation of probabilistic seismic hazard in a region commonly consists of the definition and characterization of the relevant seismic sources. Because in low-seismicity regions seismicity is often rather diffuse and faults are difficult to identify, large areal source zones are mostly used. The corresponding hypothesis is that seismicity is uniformly distributed inside each areal seismic source zone. In this study, the impact of this hypothesis on the probabilistic hazard estimation is quantified through the generation of synthetic spatial seismicity distributions. Fractal seismicity distributions are generated inside a given source zone and probabilistic hazard is computed for a set of sites located inside this zone. In our study, the impact of the spatial seismicity distribution is defined as the deviation from the hazard value obtained for a spatially uniform seismicity distribution. From the generation of a large number of synthetic distributions, the correlation between the fractal dimension D and the impact is derived. The results show that the assumption of spatially uniform seismicity tends to bias the hazard to higher values. The correlation can be used to determine the systematic biases and uncertainties for hazard estimations in real cases, where the fractal dimension has been determined. We apply the technique in Germany (Cologne area) and in France (Alps).
Ground-motion prediction equations (GMPE) are essential in probabilistic seismic hazard studies for estimating the ground motions generated by the seismic sources. In low-seismicity regions, only weak motions are available during the lifetime of accelerometric networks, and the equations selected for the probabilistic studies are usually models established from foreign data. Although most GMPEs have been developed for magnitudes 5 and above, the minimum magnitude often used in probabilistic studies in low-seismicity regions is smaller. Disaggregations have shown that, at return periods of engineering interest, magnitudes less than 5 may be contributing to the hazard. This paper presents the testing of several GMPEs selected in current international and national probabilistic projects against weak motions recorded in France (191 recordings with source-site distances up to 300 km, 3:8 <= M-w <= 4:5). The method is based on the log-likelihood value proposed by Scherbaum et al. (2009). The best-fitting models (approximately 2:5 <= LLH <= 3:5) over the whole frequency range are the Cauzzi and Faccioli (2008), Akkar and Bommer (2010), and Abrahamson and Silva (2008) models. No significant regional variation of ground motions is highlighted, and the magnitude scaling could be the predominant factor in the control of ground-motion amplitudes. Furthermore, we take advantage of a rich Japanese dataset to run tests on randomly selected low-magnitude subsets, and confirm that a dataset of similar to 190 observations, the same size as the French dataset, is large enough to obtain stable LLH estimates. Additionally we perform the tests against larger magnitudes (5-7) from the Japanese dataset. The ranking of models is partially modified, indicating a magnitude scaling effect for some of the models, and showing that extrapolating testing results obtained from low-magnitude ranges to higher magnitude ranges is not straightforward.
In low-seismicity regions, such as France or Germany, the estimation of probabilistic seismic hazard must cope with the difficult identification of active faults and with the low amount of seismic data available. Since the probabilistic hazard method was initiated, most studies assume a Poissonian occurrence of earthquakes. Here we propose a method that enables the inclusion of time and space dependences between earthquakes into the probabilistic estimation of hazard. Combining the seismicity model Epidemic Type Aftershocks-Sequence (ETAS) with a Monte Carlo technique, aftershocks are naturally accounted for in the hazard determination. The method is applied to the Pyrenees region in Southern France. The impact on hazard of declustering and of the usual assumption that earthquakes occur according to a Poisson process is quantified, showing that aftershocks contribute on average less than 5 per cent to the probabilistic hazard, with an upper bound around 18 per cent
The results of this study clearly identify four key parameters controlling the estimation of probabilistic seismic hazard assessment (PSHA) in France in the framework of the Cornell-McGuire method. Results in terms of peak ground acceleration demonstrate the equally high impact, at all return periods, of the choice of truncation of the predicted ground-motion distribution (at + 2sigma) and of the choice between two different magnitude-intensity correlations. The choice of minimum magnitude (3.5/4.5) on hazard estimates can have an important impact at small return periods (<1000 years), whereas the maximum magnitude (6.5/7.0), on the other hand, is not a key parameter even at large return periods (10,000 years). This hierarchy of impacts is maintained at lower frequencies down to 5 Hz. Below 5 Hz, the choice of the maximum magnitude has a much greater impact, whereas the impact due to the choice of the minimum magnitude disappears. Moreover, variability due to catalog uncertainties is also quantified; these uncertainties that underly all hazard results can engender as high a variability as the controlling parameters. Parameter impacts, calculated at the centers of each source zone, show a linear trend with the seismicity models of the zone, demonstrating the lack of contributions coming from neighboring zones. Indeed, the region of influence that contributes to the PSHA estimate at a given site decreases with increasing return periods. The resulting overall variability in hazard estimates due to input uncertainties is quantified through a logic tree, obtained coefficients of variation vary between 10% and 20%. Until better physical models are obtained, the uncertainty on hazard estimates may be reduced by working on an appropriate magnitude-intensity correlation
New age data have been obtained to time constrain the recent Quaternary volcanism of El Hierro (Canary Islands) and to estimate its recurrence rate. We have carried out Ar-40/Ar-39 geochronology on samples spanning the entire volcanostratigraphic sequence of the island and C-14 geochronology on the most recent eruption on the northeast rift of the island: 2280 +/- 30 yr BP. We combine the new absolute data with a revision of published ages onshore, some of which were identified through geomorphological criteria (relative data). We present a revised and updated chronology of volcanism for the last 33 ka that we use to estimate the maximum eruptive recurrence of the island. The number of events per year determined is 9.7 x 10(-4) for the emerged part of the island, which means that, as a minimum, one eruption has occurred approximately every 1000 years. This highlights the need of more geochronological data to better constrain the eruptive recurrence of El Hierro. (C) 2015 Elsevier Ltd. All rights reserved.
Beta diversity is a conceptual link between diversity at local and regional scales. Various additional methodologies of quantifying this and related phenomena have been applied. Among them, measures of pairwise (dis)similarity of sites are particularly popular. Undersampling, i.e. not recording all taxa present at a site, is a common situation in ecological data. Bias in many metrics related to beta diversity must be expected, but only few studies have explicitly investigated the properties of various measures under undersampling conditions. On the basis of an empirical data set, representing near-complete local inventories of the Lepidoptera from an isolated Pacific island, as well as simulated communities with varying properties, we mimicked different levels of undersampling. We used 14 different approaches to quantify beta diversity, among them dataset-wide multiplicative partitioning (i.e. true beta diversity') and pairwise site x site dissimilarities. We compared their values from incomplete samples to true results from the full data. We used these comparisons to quantify undersampling bias and we calculated correlations of the dissimilarity measures of undersampled data with complete data of sites. Almost all tested metrics showed bias and low correlations under moderate to severe undersampling conditions (as well as deteriorating precision, i.e. large chance effects on results). Measures that used only species incidence were very sensitive to undersampling, while abundance-based metrics with high dependency on the distribution of the most common taxa were particularly robust. Simulated data showed sensitivity of results to the abundance distribution, confirming that data sets of high evenness and/or the application of metrics that are strongly affected by rare species are particularly sensitive to undersampling. The class of beta measure to be used should depend on the research question being asked as different metrics can lead to quite different conclusions even without undersampling effects. For each class of metric, there is a trade-off between robustness to undersampling and sensitivity to rare species. In consequence, using incidence-based metrics carries a particular risk of false conclusions when undersampled data are involved. Developing bias corrections for such metrics would be desirable.
The seismicity pattern along the San Andreas fault near Parkfield and Cholame, California, varies distinctly over a length of only fifty kilometres. Within the brittle crust, the presence of frictionally weak minerals, fault-weakening high fluid pressures and chemical weakening are considered possible causes of an anomalously weak fault northwest of Parkfield(1-4). Non-volcanic tremor from lower-crustal and upper-mantle depths(5-7) is most pronounced about thirty kilometres southeast of Parkfield and is thought to be associated with high pore-fluid pressures at depth(8). Here we present geophysical evidence of fluids migrating into the creeping section of the San Andreas fault that seem to originate in the region of the uppermost mantle that also stimulates tremor, and evidence that along-strike variations in tremor activity and amplitude are related to strength variations in the lower crust and upper mantle. Interconnected fluids can explain a deep zone of anomalously low electrical resistivity that has been imaged by magnetotelluric data southwest of the Parkfield-Cholame segment. Near Cholame, where fluids seem to be trapped below a high-resistivity cap, tremor concentrates adjacent to the inferred fluids within a mechanically strong zone of high resistivity. By contrast, sub-vertical zones of low resistivity breach the entire crust near the drill hole of the San Andreas Fault Observatory at Depth, northwest of Parkfield, and imply pathways for deep fluids into the eastern fault block, coincident with a mechanically weak crust and the lower tremor amplitudes in the lower crust. Fluid influx to the fault system is consistent with hypotheses of fault-weakening high fluid pressures in the brittle crust.
Eight d-metal-containing N-butylpyridinium ionic liquids (ILs) with the nominal composition (C4Py)2[Ni0.5M0.5Cl4] or (C4Py)2[Zn0.5M0.5Cl4] (M = Cu, Co, Mn, Ni, Zn; C4Py = N-butylpyridinium) were synthesized, characterized, and investigated for their optical properties. Single crystal and powder X-ray analysis shows that the compounds are isostructural to existing examples based on other d-metal ions. Inductively coupled plasma optical emission spectroscopy measurements confirm that the metal/metal ratio is around 50 : 50. UV-Vis spectroscopy shows that the optical absorption can be tuned by selection of the constituent metals. Moreover, the compounds can act as an optical sensor for the detection of gases such as ammonia as demonstrated via a simple prototype setup.
Causes for slow weathering and erosion in the steep, warm, monsoon-subjected Highlands of Sri Lanka
(2018)
In the Highlands of Sri Lanka, erosion and chemical weathering rates are among the lowest for global mountain denudation. In this tropical humid setting, highly weathered deep saprolite profiles have developed from high-grade metamorphic charnockite during spheroidal weathering of the bedrock. The spheroidal weathering produces rounded corestones and spalled rindlets at the rock-saprolite interface. I used detailed textural, mineralogical, chemical, and electron-microscopic (SEM, FIB, TEM) analyses to identify the factors limiting the rate of weathering front advance in the profile, the sequence of weathering reactions, and the underlying mechanisms. The first mineral attacked by weathering was found to be pyroxene initiated by in situ Fe oxidation, followed by in situ biotite oxidation. Bulk dissolution of the primary minerals is best described with a dissolution – re-precipitation process, as no chemical gradients towards the mineral surface and sharp structural boundaries are observed at the nm scale. Only the local oxidation in pyroxene and biotite is better described with an ion by ion process. The first secondary phases are oxides and amorphous precipitates from which secondary minerals (mainly smectite and kaolinite) form. Only for biotite direct solid state transformation to kaolinite is likely. The initial oxidation of pyroxene and biotite takes place in locally restricted areas and is relatively fast: log J = -11 molmin/(m2 s). However, calculated corestone-scale mineral oxidation rates are comparable to corestone-scale mineral dissolution rates: log R = -13 molpx/(m2 s) and log R = -15 molbt/(m2 s). The oxidation reaction results in a volume increase. Volumetric calculations suggest that this observed oxidation leads to the generation of porosity due to the formation of micro-fractures in the minerals and the bedrock allowing for fluid transport and subsequent dissolution of plagioclase. At the scale of the corestone, this fracture reaction is responsible for the larger fractures that lead to spheroidal weathering and to the formation of rindlets. Since these fractures have their origin from the initial oxidational induced volume increase, oxidation is the rate limiting parameter for weathering to take place. The ensuing plagioclase weathering leads to formation of high secondary porosity in the corestone over a distance of only a few cm and eventually to the final disaggregation of bedrock to saprolite. As oxidation is the first weathering reaction, the supply of O2 is a rate-limiting factor for chemical weathering. Hence, the supply of O2 and its consumption at depth connects processes at the weathering front with erosion at the surface in a feedback mechanism. The strength of the feedback depends on the relative weight of advective versus diffusive transport of O2 through the weathering profile. The feedback will be stronger with dominating diffusive transport. The low weathering rate ultimately depends on the transport of O2 through the whole regolith, and on lithological factors such as low bedrock porosity and the amount of Fe-bearing primary minerals. In this regard the low-porosity charnockite with its low content of Fe(II) bearing minerals impedes fast weathering reactions. Fresh weatherable surfaces are a pre-requisite for chemical weathering. However, in the case of the charnockite found in the Sri Lankan Highlands, the only process that generates these surfaces is the fracturing induced by oxidation. Tectonic quiescence in this region and low pre-anthropogenic erosion rate (attributed to a dense vegetation cover) minimize the rejuvenation of the thick and cohesive regolith column, and lowers weathering through the feedback with erosion.
In the Sri Lankan Highlands erosion and chemical weathering rates are among the lowest for global mountain denudation. In this tropical humid setting, highly weathered deep saprolite profiles have developed from high-grade metamorphic charnockite during spheroidal weathering of the bedrock. The spheroidal weathering produces rounded corestones and spalled rindlets at the rock-saprolite interface. We used detailed textural, mineralogical and chemical analyses to reconstruct the sequence of weathering reactions and their causes. The first mineral attacked by weathering was found to be pyroxene initiated by in situ Fe oxidation. Volumetric calculations suggest that this oxidation leads to the generation of porosity due to the formation of micro-fractures allowing for fluid transport and subsequent dissolution of biotite and plagioclase. The rapid ensuing plagioclase weathering leads to formation of high secondary porosity in the corestone over a distance of only a few cm and eventually to the final disaggregation of bedrock to saprolite. The first secondary phases are oxides or amorphous precipitates from which secondary minerals (mainly gibbsite, kaolinite and goethite) form. As oxidation is the first weathering reaction, the supply of O-2 is a rate-limiting factor for chemical weathering. Hence, the supply of O-2 and its consumption at depth connects processes at the weathering front with those at the Earth's surface in a feedback mechanism. The strength of the feedback depends on the relative weight of advective versus diffusive transport of O-2 through the weathering profile. The feedback will be stronger with dominating diffusive transport. The low weathering rate is explained by the nature of this feedback that is ultimately dependent on the transport of O-2 through the whole regolith, and on lithological factors such as low bedrock porosity and the amount of Fe-bearing primary minerals. Tectonic quiescence in this region and low pre-development erosion rate (attributed to a dense vegetation cover) minimize the rejuvenation of the thick and cohesive regolith column, finally leading to low denudation rates. (C) 2015 Elsevier B.V. All rights reserved.
The Misho complex in Northwest Iran is a prominent topographic massif bounded by well known active faults. Our new structural analysis of this area indicates that faulting has important role in the exhumation of this complex. The conjugate orientation of the North and South Misho Faults caused uplift in the Misho and exhumation of the Precambrian crystalline basement. Our structural and stratigraphic data shows that rapid uplift could have been initiation since the 21-22 Ma and exhumation rate was about 0.16 to 0.24 km/Ma. To refine this age, we performed U/Pb analysis of detrital zircon from the Upper Red Formation using LA-ICP-MS. We conducted AFT analysis on 6 basement samples from the hanging wall and 1 sample from the Upper Red Formation in the footwall NMF. Uplift in the hanging wall of NMF led to resting of sample 916 marl. This geochronologic and thermochronologic data shows that exhumation in the MC is diachronously along strike and affected by faults. The phase of exhumation is documented in the study area and entire Iranian plateau is related to the final closure of the Neo-Tethys and northward motion of the Arabian Plate.
Growing attention to phytoplankton mixotrophy as a trophic strategy has led to significant revisions of traditional pelagic food web models and ecosystem functioning. Although some empirical estimates of mixotrophy do exist, a much broader set of in situ measurements are required to (i) identify which organisms are acting as mixotrophs in real time and to (ii) assess the contribution of their heterotrophy to biogeochemical cycling. Estimates are needed through time and across space to evaluate which environmental conditions or habitats favour mixotrophy: conditions still largely unknown. We review methodologies currently available to plankton ecologists to undertake estimates of plankton mixotrophy, in particular nanophytoplankton phago-mixotrophy. Methods are based largely on fluorescent or isotopic tracers, but also take advantage of genomics to identify phylotypes and function. We also suggest novel methods on the cusp of use for phago-mixotrophy assessment, including single-cell measurements improving our capacity to estimate mixotrophic activity and rates in wild plankton communities down to the single-cell level. Future methods will benefit from advances in nanotechnology, micromanipulation and microscopy combined with stable isotope and genomic methodologies. Improved estimates of mixotrophy will enable more reliable models to predict changes in food web structure and biogeochemical flows in a rapidly changing world.
There are far-reaching conceptual similarities between bi-static surface georadar and post-stack, "zero-offset" seismic reflection data, which is expressed in largely identical processing flows. One important difference is, however, that standard deconvolution algorithms routinely used to enhance the vertical resolution of seismic data are notoriously problematic or even detrimental to the overall signal quality when applied to surface georadar data. We have explored various options for alleviating this problem and have tested them on a geologically well-constrained surface georadar dataset. Standard stochastic and direct deterministic deconvolution approaches proved to be largely unsatisfactory. While least-squares-type deterministic deconvolution showed some promise, the inherent uncertainties involved in estimating the source wavelet introduced some artificial "ringiness". In contrast, we found spectral balancing approaches to be effective, practical and robust means for enhancing the vertical resolution of surface georadar data, particularly, but not exclusively, in the uppermost part of the georadar section, which is notoriously plagued by the interference of the direct air- and groundwaves. For the data considered in this study, it can be argued that band- limited spectral blueing may provide somewhat better results than standard band-limited spectral whitening, particularly in the uppermost part of the section affected by the interference of the air- and groundwaves. Interestingly, this finding is consistent with the fact that the amplitude spectrum resulting from least-squares-type deterministic deconvolution is characterized by a systematic enhancement of higher frequencies at the expense of lower frequencies and hence is blue rather than white. It is also consistent with increasing evidence that spectral "blueness" is a seemingly universal, albeit enigmatic, property of the distribution of reflection coefficients in the Earth. Our results therefore indicate that spectral balancing techniques in general and spectral blueing in particular represent simple, yet effective means of enhancing the vertical resolution of surface georadar data and, in many cases, could turn out to be a preferable alternative to standard deconvolution approaches.