The Oeschinensee rock avalanche,Bernese Alps,Switzerland: a co-seismic failure 2300 years ago?

Landslide deposits dam Lake Oeschinen (Oeschinensee), located above Kandersteg, Switzerland. However, past confusion differentiating deposits of multiple landslide events has confounded efforts to quantify the volume, age, and failure dynamics of the Oeschinensee rock avalanche. Here we combine field and remote mapping, topographic reconstruction, cosmogenic surface exposure dating, and numerical runout modeling to quantify salient parameters of the event. Differences in boulder lithology and deposit morphology reveal that the landslide body damming Oeschinensee consists of debris from both an older rock avalanche, possibly Kandertal, as well as the Oeschinensee rock avalanche. We distinguish a source volume for the Oeschinensee event of 37 Mm³, resulting in an estimated deposit volume of 46 Mm³, smaller than previous estimates that included portions of the Kandertal mass. Runout modeling revealed peak and average rock avalanche velocities of 65 and 45 m/s, respectively, and support a single-event failure scenario. ³⁶Cl surface exposure dating of deposited boulders indicates a mean age for the rock avalanche of 2.3 ± 0.2 kyr. This age coincides with the timing of a paleo-seismic event identified from lacustrine sediments in Swiss lakes, suggesting an earthquake trigger. Our results help clarify the hazard and geomorphic effects of rare, large rock avalanches in alpine settings.     

Extreme waves induced by cyclone Nargis at Myanmar coast: numerical modeling versus satellite observations

Natural Hazards - The deltaic coast of Myanmar was severely hit by tropical cyclone Nargis in May 2008. In the present study, a top-down numerical simulation approach using the Weather Research and...     

Age and origin of groundwater resources in the Ararat Valley,Armenia: a baseline study applying hydrogeochemistry and environmental tracers

Within the Ararat Valley (Armenia), a continuously growing water demand (for irrigation and fish farming) and a simultaneous decline in groundwater recharge (due to climate change) result in increasing stress on the local groundwater resources. This detrimental development is reflected by groundwater-level drops and an associated reduction of the area with artesian conditions in the valley centre. This situation calls for increasing efforts aimed at more sustainable water resources management. The aim of this baseline study was the collection of data that allows for study on the origin and age distribution of the Ararat Valley groundwater based on environmental tracers, namely stable (δ²H, δ¹⁸O) and radioactive (³⁵S, ³H) isotopes, as well as physical-chemical indicators. The results show that the Ararat Valley receives modern recharge, despite its (semi-)arid climate. While subannual groundwater residence times could be disproved (³⁵S), the detected ³H pattern suggests groundwater ages of several decades, with the oldest waters being recharged around 60 years ago. The differing groundwater ages are reflected by varying scatter of stable isotope and hydrochemical signatures. The presence of young groundwater (i.e., younger that the 1970s), some containing nitrate, indicates groundwater vulnerability and underscores the importance of increased efforts to achieve sustainable management of this natural resource. Since stable isotope signatures indicate the recharge areas to be located in the mountains surrounding the valley, these efforts must not be limited to the central part of the valley where most of the abstraction wells are located.

     

The curse of physiology—challenges and opportunities in the interpretation of geochemical data from mollusk shells

Physiology corrupts geochemical records of mollusk shells in many ways, e.g., by actively controlling the incorporation of trace elements in the skeleton. However, the effects of variable biomineralization rates and growth cessation have largely remained unconsidered. Mediated by endogenous timekeeping mechanisms, mollusks stop growing skeletal material on a regular basis ranging from ultradian to annual timescales. During growth cessation, the shells do not record environmental conditions. Shell growth also stops when environmental conditions are beyond the physiological tolerance of the organism, e.g., above and below genetically determined, species-specific thermal extremes where shell growth slows and eventually ceases. Such growth disruptions can occur at non-periodic time intervals. Due to growth retardations and halts, proxy records of mollusk shells are thus incomplete, and reconstructed environmental amplitudes prone to truncation. Furthermore, environmental records are biased toward the physiological optimum of the animal. Favorable environmental conditions increase shell growth, whereas adverse environmental conditions result in reduced shell production and lowered overall metabolism. Not least, the duration of the growing season and overall growth rate decrease as the mollusk grows older. Mathematical modeling approaches can significantly improve proxy records obtained from mollusk shells. For example, if the duration of growth cessation is known, it may be possible to model the missing environmental record. It is also fairly easy to account for age-related growth trends, or variable time-averaging in different portions of the shell. However, a major premise for a reliable interpretation of proxy records from a mollusk shell or other organisms secreting biogenic hard parts is a proper understanding of the physiology, and of course, a high-resolution record of the many different environmental factors that may influence physiology and shell growth. The present paper reviews examples from the literature, and unpublished data on how physiology influences geochemical proxy records from mollusk shells, and presents methods how to eliminate such adverse effects.     

Reduced Hamiltonian formalism for general-relativistic adiabatic fluids and applications

For the dynamics of general-relativistic adiabatic fluids a reduced Hamiltonian formalism is presented. For slowly moving fluids an approximation scheme is defined. The first steps of the scheme are worked out explicitly. Radiation damping and emission, and backscattering are discussed. The usefulness of the formalism for numerical relativity is pointed out.     

Using Statistical Downscaling to Quantify the GCM-Related Uncertainty in Regional Climate Change Scenarios： A Case Study of Swedish Precipitation

There are a number of sources of uncertainty in regional climate change scenarios. When statistical downscaling is used to obtain regional climate change scenarios, the uncertainty may originate from the uncertainties in the global climate models used, the skill of the statistical model, and the forcing scenarios applied to the global climate model. The uncertainty associated with global climate models can be evaluated by examining the differences in the predictors and in the downscaled climate change scenarios based on a set of different global climate models. When standardized global climate model simulations such as the second phase of the Coupled Model Intercomparison Project （CMIP2） are used, the difference in the downscaled variables mainly reflects differences in the climate models and the natural variability in the simulated climates. It is proposed that the spread of the estimates can be taken as a measure of the uncertainty associated with global climate models. The proposed method is applied to the estimation of global-climate-model-related uncertainty in regional precipitation change scenarios in Sweden. Results from statistical downscaling based on 17 global climate models show that there is an overall increase in annual precipitation all over Sweden although a considerable spread of the changes in the precipitation exists. The general increase can be attributed to the increased large-scale precipitation and the enhanced westerly wind. The estimated uncertainty is nearly independent of region. However, there is a seasonal dependence. The estimates for winter show the highest level of confidence, while the estimates for summer show the least.     

Boosting for Mineral Prospectivity Modeling: A New GIS Toolbox

With an increasing demand for raw materials, predictive models that support successful mineral exploration targeting are of great importance. We evaluated different machine learning techniques with an emphasis on boosting algorithms and implemented them in an ArcGIS toolbox. Performance was tested on an exploration dataset from the Iberian Pyrite Belt (IPB) with respect to accuracy, performance, stability, and robustness. Boosting algorithms are ensemble methods used in supervised learning for regression and classification. They combine weak classifiers, i.e., classifiers that perform slightly better than random guessing to obtain robust classifiers. Each time a weak learner is added; the learning set is reweighted to give more importance to misclassified samples. Our test area, the IPB, is one of the oldest mining districts in the world and hosts giant volcanic-hosted massive sulfide (VMS) deposits. The spatial density of ore deposits, as well as the size and tonnage, makes the area unique, and due to the high data availability and number of known deposits, well-suited for testing machine learning algorithms. We combined several geophysical datasets, as well as layers derived from geological maps as predictors of the presence or absence of VMS deposits. Boosting algorithms such as BrownBoost and Adaboost were tested and compared to Logistic Regression (LR), Random Forests (RF) and Support Vector machines (SVM) in several experiments. We found performance results relatively similar, especially to BrownBoost, which slightly outperformed LR and SVM with respective accuracies of 0.96 compared to 0.89 and 0.93. Data augmentation by perturbing deposit location led to a 7% improvement in results. Variations in the split ratio of training and test data led to a reduction in the accuracy of the prediction result with relative stability occurring at a critical point at around 26 training samples out of 130 total samples. When lower numbers of training data were introduced accuracy dropped significantly. In comparison with other machine learning methods, Adaboost is user-friendly due to relatively short training and prediction times, the low likelihood of overfitting and the reduced number of hyperparameters for optimization. Boosting algorithms gave high predictive accuracies, making them a potential data-driven alternative for regional scale and/or brownfields mineral exploration.

     

Experimental impact cratering: A summary of the major results of the MEMIN research unit

This paper reviews major findings of the Multidisciplinary Experimental and Modeling Impact Crater Research Network (MEMIN). MEMIN is a consortium, funded from 2009 till 2017 by the German Research Foundation, and is aimed at investigating impact cratering processes by experimental and modeling approaches. The vision of this network has been to comprehensively quantify impact processes by conducting a strictly controlled experimental campaign at the laboratory scale, together with a multidisciplinary analytical approach. Central to MEMIN has been the use of powerful two-stage light-gas accelerators capable of producing impact craters in the decimeter size range in solid rocks that allowed detailed spatial analyses of petrophysical, structural, and geochemical changes in target rocks and ejecta. In addition, explosive setups, membrane-driven diamond anvil cells, as well as laser irradiation and split Hopkinson pressure bar technologies have been used to study the response of minerals and rocks to shock and dynamic loading as well as high-temperature conditions. We used Seeberger sandstone, Taunus quartzite, Carrara marble, and Weibern tuff as major target rock types. In concert with the experiments we conducted mesoscale numerical simulations of shock wave propagation in heterogeneous rocks resolving the complex response of grains and pores to compressive, shear, and tensile loading and macroscale modeling of crater formation and fracturing. Major results comprise (1) projectile–target interaction, (2) various aspects of shock metamorphism with special focus on low shock pressures and effects of target porosity and water saturation, (3) crater morphologies and cratering efficiencies in various nonporous and porous lithologies, (4) in situ target damage, (5) ejecta dynamics, and (6) geophysical survey of experimental craters.     

Hydraulic roughness over simple subaqueous dunes

Detailed studies of flow over subaqueous dunes in laboratory flumes were used to suggest a virtual near-bed layer of twice the dune height in which the mean velocity is accelerated towards the crest by contraction. The mean flow velocity in this layer above the crest, transformed into friction velocity by means of the surface skin roughness, is shown to give values consistent with measured values. The resulting dimensionless shear stress due to skin friction is depth-independent, in contrast to that derived by means of often cited traditional methods. As a result of the relationship between dune height and the thickness of the near-bed layer, an expression for the expansion loss behind dunes was formulated and used to relate form resistance directly to dune height.     

Thermophysical properties of Almahata Sitta meteorites (asteroid 2008 TC3) for high‐fidelity entry modeling

Asteroid 2008 TC₃ was characterized in a unique manner prior to impacting Earth's atmosphere, making its October 7, 2008, impact a suitable field test for or validating the application of high‐fidelity re‐entry modeling to asteroid entry. The accurate modeling of the behavior of 2008 TC₃ during its entry in Earth's atmosphere requires detailed information about the thermophysical properties of the asteroid's meteoritic materials at temperatures ranging from room temperature up to the point of ablation (T ~ 1400 K). Here, we present measurements of the thermophysical properties up to these temperatures (in a 1 atm. pressure of argon) for two samples of the Almahata Sitta meteorites from asteroid 2008 TC₃: a thick flat‐faced ureilite suitably shaped for emissivity measurements and a thin flat‐faced EL6 enstatite chondrite suitable for diffusivity measurements. Heat capacity was determined from the elemental composition and density from a 3‐D laser scan of the sample. We find that the thermal conductivity of the enstatite chondrite material decreases more gradually as a function of temperature than expected, while the emissivity of the ureilitic material decreases at a rate of 9.5 × 10^?5 K^?1 above 770 K. The entry scenario is the result of the actual flight path being the boundary to the load the meteorite will be affected with when entering. An accurate heat load prediction depends on the thermophysical properties. Finally, based on these data, the breakup can be calculated accurately leading to a risk assessment for ground damage.