Long-term spatio-temporal hydrochemical and 222Rn tracing to investigate groundwater flow and water–rock interaction in the Gran Sasso (central Italy) carbonate aquifer

In the Gran Sasso fissured carbonate aquifer (central Italy), a long-term (2001–2007) spatio-temporal hydrochemical and ²²²Rn tracing survey was performed with the goal to investigate groundwater flow and water–rock interaction. Analyses of the physico-chemical parameters, and comparisons of multichemical and characteristic ratios in space and time, and subsequent statistical analyses, permitted a characterisation of the hydrogeology. At the regional scale, groundwater flows from recharge areas to the springs located at the aquifer boundaries, with a gradual increase of mineralisation and temperature along its flowpaths. However, the parameters of each group of springs may significantly deviate from the regional trend owing to fast flows and to the geological setting of the discharge spring areas, as corroborated by statistical data. Along regional flowpaths, the effects of seasonal recharge and lowering of the water table clearly cause changes in ion concentrations over time. This conceptual model was validated by an analysis of the ²²²Rn content in groundwater. ²²²Rn content, for which temporal variability depends on seasonal fluctuations of the water table, local lithology and the fracture network at the spring discharge areas, was considered as a tracer of the final stages of groundwater flowpaths.     

Displacement trends of slow-moving landslides: Classification and forecasting

A framework is proposed to characterize and forecast the displacement trends of slow-moving landslides, defined as the reactivation stage of phenomena in rocks or fine-grained soils, with movements localized along one or several existing shear surfaces. The framework is developed based on a thorough analysis of the scientific literature and with reference to significant reported case studies for which a consistent dataset of continuous displacement measurements is available. Three distinct trends of movement are defined to characterize the kinematic behavior of the active stages of slow-moving landslides in a velocity-time plot： a linear trend-type I, which is appropriate for stationary phenomena; a convex shaped trend-type II, which is associated with rapid increases in pore water pressure due to rainfall, followed by a slow decrease in the groundwater level with time; and a concave shaped trend-type III, which denotes a non-stationary process related to the presence of new boundary conditions such as those associated with the development of a newly formed local slip surface that connects with the main existing slip surface. Within the proposed framework, a model is developed to forecast future displacements for active stages of trend-type II based on displacement measurements at the beginning of the stage. The proposed model is validated by application to two case studies.     

Simplified Wave Models Applicability to Shallow Mud Flows Modeled as Power-Law Fluids

Simplified wave models- such as kinematic,diffusion and quasi-steady- are widely employed as a convenient replacement of the full dynamic one in the analysis of unsteady open-channel flows,and especially for flood routing.While their use may guarantee a significant reduction of the computational effort,it is mandatory to define the conditions in which they may be confidently applied.The present paper investigates the applicability conditions of the kinematic,diffusion and quasisteady dynamic shallow wave models for mud flows of power-law fluids.The power-law model describes in an adequate and convenient way fluids that at low shear rates fluids do not posses yield stress,such as clay or kaolin suspensions,which are frequently encountered in Chinese rivers.In the framework of a linear analysis,the propagation characteristics of a periodic perturbation of an initial steady uniform flow predicted by the simplified models are compared with those of the full dynamic one.Based on this comparison,applicability criteria for the different wave approximations for mud flood of power-law fluids are derived.The presented results provide guidelines for selecting the appropriate approximation for a given flow problem,and therefore they may represent a useful tool for engineering predictions.     

Investigating the time dynamics of monthly rainfall time series observed in northern Lebanon by means of the detrended fluctuation analysis and the Fisher-Shannon method

We investigate the time dynamics of monthly rainfall series intermittently recorded on seven climatic stations in northern Lebanon from 1939 to 2010 using the detrending fluctuation analysis (DFA) and the Fisher-Shannon (FS) method. The DFA is employed to study the scaling properties of the series, while the FS method to analyze their order/organization structure. The obtained results indicate that most all the stations show a significant persistent behavior, suggesting that the dynamics of the rainfall series is governed by positive feedback mechanisms. Furthermore, we found that the Fisher Information Measure (the Shannon entropy power) seems to decrease (increase) with the height of the rain gauge; this indicates that the rainfall series appear less organized and less regular for higher-located stations. Such findings could be useful for a better comprehension of the climatic regimes governing northern Lebanon.     

Period-luminosity relations for Cepheid variables: from mid-infrared to multi-phase

This paper discusses two aspects of current research on the Cepheid period-luminosity (P-L) relation: the derivation of mid-infrared (MIR) P-L relations and the investigation of multi-phase P-L relations.     

Machine learning for cross-gazetteer matching of natural features

ABSTRACT

Defining and identifying duplicate records in a dataset is a challenging task which grows more complex when the modeled entities themselves are hard to delineate. In the geospatial domain, it may not be clear where a mountain, stream, or valley ends and begins, a problem carried over when such entities are catalogued in gazetteers. In this paper, we take two gazetteers, GeoNames and SwissNames3D, and perform matching – identifying records in each that are about the same entity – across a sample of natural feature records. We first perform rule-based matching, establishing competitive results, then apply machine learning using Random Forests, a method well-suited to the matching task. We report on the performance of a wider array of matching features than has been previously studied, including domain-specific ones such as feature type, land cover class, and elevation. Our results show an increase in performance using machine learning over rules, with a notable performance gain from considering feature types, but negligible gains from other specialized matching features. We argue that future work in this area should strive to be more reproducible and report results on a realistic testing pipeline including candidate selection, feature extraction, and classification.     

Electrical resistivity and seismic refraction tomography to detect buried cavities

Near‐surface cavities can pose serious hazards to human safety, especially in highly urbanized town centres. The location of subsurface voids, the estimation of their size and the evaluation of the overburden thickness are necessary to assess the risk of collapse. In this study, electrical resistivity tomography (ERT) and seismic refraction tomography data are integrated in a joint interpretation process for cavity location in the city of Rome. ERT is a well established and widely employed method for cavity detection. However, additional information provided by seismic refraction tomography is capable of eliminating some potential pitfalls in resistivity data interpretation. We propose that the structure of the cavities defined by ERT can be used as a base to optimize seismic refraction tomography investigations within the framework of a joint interpretation process. Data integration and the insertion of a priori information are key issues for reducing the uncertainties associated with the inversion process and for optimizing both acquisition procedures and computation time. Herein, the two geophysical methods are tested on both synthetic and real data and the integration of the results is found to be successful in detecting isolated cavities and in assessing their geometrical characteristics. The cavity location inferred by geophysical non‐invasive methods has been subsequently confirmed by direct inspection.     

Visibility Graph Analysis of the 2003–2012 Earthquake Sequence in the Kachchh Region of Western India

A visibility graph (VG) is a rather novel statistical method in earthquake sequence analysis; it maps a time series into networks or graphs, converting dynamical properties of the time series into topological properties of networks. By using the VG approach, we defined the parameter window mean interval connectivity time <T_c>, that informs about the mean linkage time between earthquakes. We analysed the time variation of <T_c> in the aftershock-depleted catalogue of Kachchh Gujarat (Western India) seismicity from 2003 to 2012, and we found that <T_c>: i) changes through time, indicating that the topological properties of the earthquake network are not stationary; and, ii) appeared to significantly decrease before the largest shock (M5.7) that occurred on March 7, 2006 near the Gedi fault, an active fault in the Kachchh region.     

A numerical model for the dynamic analysis of inclined pile groups

The paper presents a numerical model for the dynamic analysis of pile groups with inclined piles in horizontally layered soil deposits. Piles are modelled with Euler–Bernoulli beams, while the soil is supposed to be constituted by independent infinite viscoelastic horizontal layers. The pile–soil–pile interaction as well as the hysteretic and geometric damping is taken into account by means of two‐dimensional elastodynamic Green's functions. Piles cap is considered by introducing a rigid constraint; the condensation of the problem permits a consistent derivation of both the dynamic impedance matrix of the soil–foundation system and the foundation input motion. These quantities are those used to perform inertial soil–structure interaction analyses in the framework of the substructure approach. Furthermore, the model allows evaluating the kinematic stress resultants in piles resulting from waves propagating in the soil deposit, taking into account the pile–soil–pile interactions. The model validation is carried out by performing accuracy analyses and comparing results in terms of dynamic impedance functions, kinematic response parameters and pile stress resultants, with those furnished by 3D refined finite element models. To this purpose, classical elastodynamic solutions are adopted to define the soil–pile interaction problem. The model results in low computational demands without significant loss of precision, compared with more rigorous approaches or refined finite element models. Copyright © 2015 John Wiley & Sons, Ltd.     

Bedform effect on the reorganization of surface and subsurface grain size distribution in gravel bedded channels

Quantification of river bedform variability and complexity is important for sediment transport modeling as well as for characterization of river morphology. Alluvial bedforms are shown to exhibit highly nonlinear dynamics across a range of scales, affect local bed roughness, and vary with local hydraulic, hydrologic, and geomorphic properties. This paper examines sediment sorting on the crest and trough of gravel bedforms and relates it to bed elevation statistics. The data analysed here are the spatial and temporal series of bed elevation, grain size distribution of surface and subsurface bed materials, and sediment transport rates from flume experiments. We describe surface topography through bedform variability in height and wavelength and multiscale analysis of bed elevations as a function of discharge. We further relate bedform migration to preferential distribution of coarse and fine sediments on the troughs and crests, respectively, measuring directly surface and subsurface grain size distributions, and indirectly the small scale roughness variations as estimated from high resolution topographic scans.