Climate change effects on landslides along the southwest coast of British Columbia

Antecedent rainfall and short-term intense rainfall both contribute to the temporal occurrence of landslides in British Columbia. These two quantities can be extracted from the precipitation regimes simulated by climate models. This makes such models an attractive tool for use in the investigation of the effect of global warming on landslide frequencies.In order to provide some measure of the reliability of models used to address the landslide question, the present-day simulation of the antecedent precipitation and short-term rainfall using the daily data from the Canadian Centre for Climate Modelling and Analysis model (CGCM) is compared to observations along the south coast of British Columbia. This evaluation showed that the model was reasonably successful in simulating statistics of the antecedent rainfall but was less successful in simulating the short-term rainfall.The monthly mean precipitation data from an ensemble of 19 of the world's global climate models were available to study potential changes in landslide frequencies with global warming. Most of the models were used to produce simulations with three scenarios with different levels of prescribed greenhouse gas concentrations during the twenty-first century. The changes in the antecedent precipitation were computed from the resulting monthly and seasonal means. In order to deal with models' suspected difficulties in simulating the short-term precipitation and lack of daily data, a statistical procedure was used to relate the short-term precipitation to the monthly means.The qualitative model results agree reasonably well, and when averaged over all models and the three scenarios, the change in the antecedent precipitation is predicted to be about 10% and the change in the short-term precipitation about 6%. Because the antecedent precipitation and the short-term precipitation contribute to the occurrence of landslides, the results of this study support the prediction of increased landslide frequency along the British Columbia south coast during the twenty-first century.     

Near‐Well Nonlinear Flow Identified by Various Displacement Well Response Testing

The impact of nonlinear flow phenomena on well response tests is still not completely understood today. With the present paper, a set of 10 well response tests is investigated. The tests were conducted in a fractured Devonian limestone formation close to the western national border of Germany. The test design incorporates a packer as well as different solid cylinders to initiate a series of slug‐injection and slug‐withdrawal tests by various initial displacements. Nonlinear response characteristics were observed in the course of the tests, which cannot be explained by tubing‐controlled flow inside the cased well. The analysis shows that the nonlinear response characteristics are likely to be either formation controlled according to non‐Darcian flow developing in a high‐conductivity fracture compartment of the tested limestone formation or a consequence of a severe well inefficiency caused by some sort of screen clogging. This inference is obtained from analyzing the data by a nonlinear well response test model, which differentiates between wellbore internal hydraulic head losses and a generalized rate‐dependent skin effect accounting for nonlinear flow processes in the vicinity of the well. The potential of identifying near‐well nonlinear flow by various displacement well response testing may indicate this methodology to be a valuable complement to modern high‐resolution borehole imaging techniques used when characterizing fractured reservoirs and the tightness of fractured reservoir cap rocks.     

Quantifying prehistoric soil erosion—A review of soil loss methods and their application to a Celtic square enclosure (Viereckschanze) in Southern Germany

This paper discusses the strengths and weaknesses of three different methods for quantifying prehistoric soil erosion. Method A estimates erosion by determining the amount of colluvium stored downhill. Method B involves reconstructing a former erosion surface using truncated soil horizons. Method C compares the elevation of a paleosol beneath an earthwork with the modern surface in the surrounding area. Each method was applied to a Celtic earthwork (Viereckschanze) at Poign (near Regensburg) in Southern Germany in order to cross‐check the different results. For an erosion area of 3.6 ha and during 300 years of agricultural usage, Method A calculates a minimum erosion rate of 20.8 t/ha/a. Method B computes 10 t/ha/a of soil loss. Method C yields the highest rate of erosion with 24.2 t/ha/a. We have confidence in Method C, which implies an underassesment of soil loss in using methods A or B. © 2007 Wiley Periodicals, Inc.     

Dark Energy in the Universe

I 24 Studying the Nature of Dark Energy with Galaxy Clusters I 50 Constraining Dark Energy via Baryon Acoustic Oscillations I 65 Constraining Dark Energy with Redshift Surveys I 103 Dark Energy: Necessity, Models and Expectations I 177 Searching for galaxy clusters through weak lensing, X‐rays and the SZ observations I 181 SNIa and Dark Energy     

Hydro-meteorological influences and multimodal suspended particle size distributions in the Belgian nearshore area (southern North Sea)

Suspended particulate matter (SPM) concentration and particle size distribution (PSD) were assessed in a coastal turbidity maximum area (southern North Sea) during a composite period of 37 days in January–April 2008. PSDs were measured with a LISST 100X and classified using entropy analysis in terms of subtidal alongshore flow. The PSDs during tide-dominated conditions showed distinct multimodal behaviour due to flocculation, revealing that the building blocks of flocs consist of primary particles (<3 μm) and flocculi (15 μm). Flocculi comprise clusters of clay minerals, whereas primary particles have various compositions (calcite, clays). The PSDs during storms with a NE-directed alongshore subtidal current (NE storms) are typically unimodal and characterised by mainly granular material (silt, sand) resuspended from the seabed. During storms with a SW-directed alongshore subtidal current (SW storms), by contrast, mainly flocculated material can be identified in the PSDs. The findings emphasise the importance of wind-induced advection, alongshore subtidal flow and highly concentrated mud suspensions (HCMSs) as regulating mechanisms of SPM concentration, as well as other SPM characteristics (cohesiveness or composition of mixed sediment particles) and size distribution in a high-turbidity area. The direction of subtidal alongshore flow during SW storm events results in an increase in cohesive SPM concentration, HCMS formation, and the armouring of sand; by contrast, there is a decrease in cohesive SPM concentration, no HCMS formation, and an increase in sand and silt in suspension during NE storms.     

Comparison of polynomial chaos and Gaussian process surrogates for uncertainty quantification and correlation estimation of spatially distributed open-channel steady flows

Data assimilation is widely used to improve flood forecasting capability, especially through parameter inference requiring statistical information on the uncertain input parameters (upstream discharge, friction coefficient) as well as on the variability of the water level and its sensitivity with respect to the inputs. For particle filter or ensemble Kalman filter, stochastically estimating probability density function and covariance matrices from a Monte Carlo random sampling requires a large ensemble of model evaluations, limiting their use in real-time application. To tackle this issue, fast surrogate models based on polynomial chaos and Gaussian process can be used to represent the spatially distributed water level in place of solving the shallow water equations. This study investigates the use of these surrogates to estimate probability density functions and covariance matrices at a reduced computational cost and without the loss of accuracy, in the perspective of ensemble-based data assimilation. This study focuses on 1-D steady state flow simulated with MASCARET over the Garonne River (South-West France). Results show that both surrogates feature similar performance to the Monte-Carlo random sampling, but for a much smaller computational budget; a few MASCARET simulations (on the order of 10–100) are sufficient to accurately retrieve covariance matrices and probability density functions all along the river, even where the flow dynamic is more complex due to heterogeneous bathymetry. This paves the way for the design of surrogate strategies suitable for representing unsteady open-channel flows in data assimilation.     

Detection of point scatterers using diffraction imaging and deep learning

Diffracted waves carry high-resolution information that can help interpreting fine structural details at a scale smaller than the seismic wavelength. However, the diffraction energy tends to be weak compared to the reflected energy and is also sensitive to inaccuracies in the migration velocity, making the identification of its signal challenging. In this work, we present an innovative workflow to automatically detect scattering points in the migration dip angle domain using deep learning. By taking advantage of the different kinematic properties of reflected and diffracted waves, we separate the two types of signals by migrating the seismic amplitudes to dip angle gathers using prestack depth imaging in the local angle domain. Convolutional neural networks are a class of deep learning algorithms able to learn to extract spatial information about the data in order to identify its characteristics. They have now become the method of choice to solve supervised pattern recognition problems. In this work, we use wave equation modelling to create a large and diversified dataset of synthetic examples to train a network into identifying the probable position of scattering objects in the subsurface. After giving an intuitive introduction to diffraction imaging and deep learning and discussing some of the pitfalls of the methods, we evaluate the trained network on field data and demonstrate the validity and good generalization performance of our algorithm. We successfully identify with a high-accuracy and high-resolution diffraction points, including those which have a low signal to noise and reflection ratio. We also show how our method allows us to quickly scan through high dimensional data consisting of several versions of a dataset migrated with a range of velocities to overcome the strong effect of incorrect migration velocity on the diffraction signal.     

Natural Stimuli Calibration with Fining Direction Regularization in an Integrated Hydrologic Model

The interaction between surface water and groundwater during flood events is a complex process that has traditionally been described using simplified analytical solutions, or abstracted numerical models. To make the problem tractable, it is common to idealize the flood event, simplify river channel geometry, and ignore bank soil heterogeneity, often resulting in a model that only loosely represents the site, thus limiting its applicability to any specific river cross-section. In this study, we calibrate a site-specific fully-integrated surface and subsurface HydroGeoSphere model using flood events for a cross-section along the South River near Waynesboro, VA. The calibration approach presented in this study demonstrates the incorporation of fining direction regularization with a highly parameterized inversion driven by natural stimuli, to develop several realistic realizations of hydraulic conductivity fields that reflect the depositional history of the system. Specifically, we calibrate a model with 365 unique material zones to multiple flood events recorded in a dense well network while incorporating possible fining sequences consistent with the depositional history of the riverbank. Over 25,000 individual simulations were completed using calibration software and a cloud platform specifically designed for highly parallelized computing environments. The results of this study demonstrate the use of fining direction regularization during model calibration to generate multiple calibrated model realizations that account for the depositional environment of the system.     

A statistic of water mass intrusions in the south Indian Ocean between 100° E and 110° E

Ocean Dynamics - The concept of water type richness wtr and inversion count inv is introduced and applied to high-resolution Argo float data in a meridional strip in the southern Indian Ocean as a...     

Place cookies and setting spiders in dream cartography

This article contributes to understanding the difference between objective space and subjective place. New data models and visual methods, which make possible the comparison between dream settings, are necessary to an exploratory analysis of dreams. The subjective perception of settings is decomposed by studying dream reports, by applying a survey, and by considering related scientific literature. This leads to the construction of two data models, which are applied in dream cartography. The place cookie model features the dreamer's familiarity with the setting, being visualized in the form of concentric circles. The setting spider model is based on 26 variables, extensively characterizing the setting. These are grouped into eight factors, and visualized in a compact radar chart with eight “legs.” As a superordinate system of the setting spider, the event spider is developed, describing the whole dream scene. The proposed models and visualization methods can be transferred for real‐life events (settings).