Modelling shallow landslide susceptibility by means of a subsurface flow path connectivity index and estimates of soil depth spatial distribution

Topographic index-based hydrological models have gained wide use to describe the hydrological control on the triggering of rainfall-induced shallow landslides at the catchment scale. A common assumption in these models is that a spatially continuous water table occurs simultaneously across the catchment. However, during a rainfall event isolated patches of subsurface saturation form above an impeding layer and their hydrological connectivity is a necessary condition for lateral flow initiation at a point on the hillslope. <br><br> Here, a new hydrological model is presented, which allows us to account for the concept of hydrological connectivity while keeping the simplicity of the topographic index approach. A dynamic topographic index is used to describe the transient lateral flow that is established at a hillslope element when the rainfall amount exceeds a threshold value allowing for (a) development of a perched water table above an impeding layer, and (b) hydrological connectivity between the hillslope element and its own upslope contributing area. A spatially variable soil depth is the main control of hydrological connectivity in the model. The hydrological model is coupled with the infinite slope stability model and with a scaling model for the rainfall frequency–duration relationship to determine the return period of the critical rainfall needed to cause instability on three catchments located in the Italian Alps, where a survey of soil depth spatial distribution is available. The model is compared with a quasi-dynamic model in which the dynamic nature of the hydrological connectivity is neglected. The results show a better performance of the new model in predicting observed shallow landslides, implying that soil depth spatial variability and connectivity bear a significant control on shallow landsliding

Snow water equivalent modeling components in NewAge-JGrass

This paper presents a package of modified temperature-index-based snow water equivalent models as part of the hydrological modeling system NewAge-JGrass. Three temperature-based snow models are integrated into the NewAge-JGrass modeling system and use many of its components such as those for radiation balance (short wave radiation balance, SWRB), kriging (KRIGING), automatic calibration algorithms (particle swarm optimization) and tests of goodness of fit (NewAge-V), to build suitable modeling solutions (MS). Similarly to all the NewAge-JGrass components, the models can be executed both in raster and in vector mode. The simulation time step can be daily, hourly or sub-hourly, depending on user needs and availability of input data. The MS are applied on the Cache la Poudre River basin (CO, USA) using three test applications. First, daily snow water equivalent is simulated for three different measurement stations for two snow model formulations. Second, hourly snow water equivalent is simulated using all the three different snow model formulae. Finally, a raster mode application is performed to compute snow water equivalent maps for the whole Cache la Poudre Basin

A multi-layer edge-on single photon counting silicon microstrip detector for innovative techniques in diagnostic radiology

A three-layer detector prototype, obtained by stacking three edge-on single photon counting silicon microstrip detectors, has been developed and widely tested. This was done in the framework of the Synchrotron Radiation for Medical Physics/Frontier Radiology (SYRMEP/FRONTRAD) collaboration activities, whose aim is to improve the quality of mammographic examinations operating both on the source and on the detector side. The active surface of the device has been fully characterized making use of an edge-scanning technique and of a well-collimated laminar synchrotron radiation beam. The obtained data (interlayer distances, channel correspondence, etc.) have then been used to combine information coming from each detector layer, without causing any loss in spatial and contrast resolution of the device. Contrast and spatial resolution have also been separately evaluated for each detector layer. Moreover, imaging techniques (phase contrast, refraction, and scatter imaging), resulting in an increased visibility of low absorbing details, have been implemented, and their effectiveness has been tested on a biological sample. Finally, the possibility of simultaneously acquiring different kind of images with the different detector layers is discussed. This would result in maximizing the information extracted from the sample, while at the same time the high absorption efficiency of the detector device would allow a low dose delivery

Cellular Models for River Networks

A cellular model introduced for the evolution of the fluvial landscape is revisited using extensive numerical and scaling analyses. The basic network shapes and their recurrence especially in the aggregation structure are then addressed. The roles of boundary and initial conditions are carefully analyzed as well as the key effect of quenched disorder embedded in random pinning of the landscape surface. It is found that the above features strongly affect the scaling behavior of key morphological quantities. In particular, we conclude that randomly pinned regions (whose structural disorder bears much physical meaning mimicking uneven landscape-forming rainfall events, geological diversity or heterogeneity in surficial properties like vegetation, soil cover or type) play a key role for the robust emergence of aggregation patterns bearing much resemblance to real river networks.Comment: 7 pages, revtex style, 14 figure

Use of XR-QA2 radiochromic films for quantitative imaging of a synchrotron radiation beam

This work investigates the use of XR-QA2 radiochromic films for quantitative imaging of a synchrotron radiation (SR) beam. Pieces (200 7 30 mm2) of XR-QA2 film were irradiated in a plane transverse to the beam axis, at the SYRMEP beamline at ELETTRA (Trieste), with a monochromatic beam of size 170 7 3.94 mm2 (H 7 V) and energy of 28, 35, 38 or 40 keV. The response was calibrated in terms of average air kerma (1\uf02d20 mGy), measured with a calibrated ionization chamber. Films were digitized in reflectance mode using a flatbed scanner. The 16-bit red channel was used. The net\uf020reflectance was then converted to photon fluence per unit air kerma (mm-2 mGy-1). The SR beam profile was acquired also with a scintillator (GOS) based, fiberoptic coupled CCD camera as well as with a scintillator based flat panel detector. Horizontal profiles obtained with the two modalities were compared, evaluated in a ROI of 17.71 7 0.59 mm2, across the beam centre. Once corrected for flat field, the CCD profile was scaled in order to have the same average value as the normalized profile acquired with the gafchromic film. The same procedure was followed for the beam images acquired with the flat panel detector. Horizontal and vertical line profiles acquired with the radiochromic film show an uneven 2D distribution of the beam intensity, with variations in the order of 15\uf02d20% in the horizontal direction, while the statistical uncertainties evaluated for the radiochromic dose measurements were 6% at 28 keV. Larger variations up to 64% were observed in the vertical direction. The response of the radiochromic film is comparable to that of the other imaging detectors, within less than 5% variation

Disorder-induced critical behavior in driven diffusive systems

Using dynamic renormalization group we study the transport in driven diffusive systems in the presence of quenched random drift velocity with long-range correlations along the transport direction. In dimensions $d\mathopen< 4$ we find fixed points representing novel universality classes of disorder-dominated self-organized criticality, and a continuous phase transition at a critical variance of disorder. Numerical values of the scaling exponents characterizing the distributions of relaxation clusters are in good agreement with the exponents measured in natural river networks

Multiple Imputation for Multilevel Data with Continuous and Binary Variables

We present and compare multiple imputation methods for multilevel continuous and binary data where variables are systematically and sporadically missing. The methods are compared from a theoretical point of view and through an extensive simulation study motivated by a real dataset comprising multiple studies. The comparisons show that these multiple imputation methods are the most appropriate to handle missing values in a multilevel setting and why their relative performances can vary according to the missing data pattern, the multilevel structure and the type of missing variables. This study shows that valid inferences can only be obtained if the dataset includes a large number of clusters. In addition, it highlights that heteroscedastic multiple imputation methods provide more accurate inferences than homoscedastic methods, which should be reserved for data with few individuals per cluster. Finally, guidelines are given to choose the most suitable multiple imputation method according to the structure of the data