Intercomparison of measurements of bulk snow density and water equivalent of snow cover with snow core samplers: Instrumental bias and variability induced by observers

Manually collected snow data are often considered as ground truth for many applications such as climatological or hydrological studies. However, there are many sources of uncertainty that are not quantified in detail. For the determination of water equivalent of snow cover (SWE), different snow core samplers and scales are used, but they are all based on the same measurement principle. We conducted two field campaigns with 9 samplers commonly used in observational measurements and research in Europe and northern America to better quantify uncertainties when measuring depth, density and SWE with core samplers. During the first campaign, as a first approach to distinguish snow variability measured at the plot and at the point scale, repeated measurements were taken along two 20 m long snow pits. The results revealed a much higher variability of SWE at the plot scale (resulting from both natural variability and instrumental bias) compared to repeated measurements at the same spot (resulting mostly from error induced by observers or very small scale variability of snow depth). The exceptionally homogeneous snowpack found in the second campaign permitted to almost neglect the natural variability of the snowpack properties and focus on the separation between instrumental bias and error induced by observers. Reported uncertainties refer to a shallow, homogeneous tundra-taiga snowpack less than 1 m deep (loose, mostly recrystallised snow and no wind impact). Under such measurement conditions, the uncertainty in bulk snow density estimation is about 5% for an individual instrument and is close to 10% among different instruments. Results confirmed that instrumental bias exceeded both the natural variability and the error induced by observers, even in the case when observers were not familiar with a given snow core sampler.     

Assessment of soil factors controlling ephemeral gully erosion on agricultural fields

The soil factor is crucial in controlling and properly modeling the initiation and development of ephemeral gullies (EGs). Usually, EG initiation has been related to various soil properties (i.e. sealing, critical shear stress, moisture, texture, etc.); meanwhile, the total growth of each EG (erosion rate) has been linked with proper soil erodibility. But, despite the studies to determine the influence of soil erodibility on (ephemeral) gully erosion, a universal approach is still lacking. This is due to the complex relationship and interactions between soil properties and the erosive process. A feasible soil characterization of EG erosion prediction on a large scale should be based on simple, quick and inexpensive tests to perform. The objective of this study was to identify and assess the soil properties – easily and quickly to determine – which best reflect soil erodibility on EG erosion. Forty‐nine different physical–chemical soil properties that may participate in establishing soil erodibility were determined on agricultural soils affected by the formation of EGs in Spain and Italy. Experiments were conducted in the laboratory and in the field (in the vicinity of the erosion paths). Because of its importance in controlling EG erosion, five variables related to antecedent moisture prior to the event that generated the gullies and two properties related to landscape topography were obtained for each situation. The most relevant variables were detected using multivariate analysis. The results defined 13 key variables: water content before the initiation of EGs, organic matter content, cation exchange capacity, relative sealing index, two granulometric and organic matter indices, seal permeability, aggregates stability (three index), crust penetration resistance, shear strength and an erodibility index obtained from the Jet Test erosion apparatus. The latter is proposed as a useful technique to evaluate and predict soil loss caused by EG erosion. Copyright © 2018 John Wiley & Sons, Ltd.     

The Italian guidelines for seismic risk classification of constructions: technical principles and validation

The Italian “Guidelines for the seismic risk classification of constructions” approved in February 2017 define the technical principles for exploiting tax deductions with respect to seismic strengthening interventions on existing buildings (Sismabonus). Tax deductions represent a unique opportunity to improve the seismic safety of the existing Italian building stock. The guidelines are very simple and allow practitioners to deal with the sophisticated concepts behind modern seismic design, such as expected annual losses (EAL) and repair costs (expressed as a fraction of the Reconstruction Cost: %RC). The seismic risk classes of buildings and the class upgrade due to strengthening interventions can be assessed using the principles included in the guidelines. The seismic risk class is the minimum between the class defined by the building safety index at the ultimate limit state and the one related to the EAL. The latter class depends on the area under the curve of the expected losses, which is easily obtained by computing the safety index converted in the return period (annual frequency) at different limit states and the relevant %RC. This paper illustrates the technical principles at the base of the guidelines and the procedure used to calibrate the repair costs associated with the different limit states using the actual repair costs monitored in the reconstruction process following recent Italian earthquakes. Finally, simple tools to estimate the cost of the strengthening interventions to improve the seismic capacity at the life-safety limit states are provided.     

Automatic Change Detection in Very High Resolution Images With Pulse-Coupled Neural Networks

A novel approach based on pulse-coupled neural networks (PCNNs) for image change detection is presented. PCNNs are based on the implementation of the mechanisms underlying the visual cortex of small mammals, and, with respect to more traditional NNs architectures, such as multilayer perceptron, own interesting advantages. In particular, they are unsupervised and context sensitive. This latter property may be particularly useful when very high resolution images are considered as, in this case, an object analysis might be more suitable than a pixel-based one. The qualitative and more quantitative results are reported. The performance of the algorithm has been evaluated on a pair of QuickBird images taken over the test area of Tor Vergata University, Rome.      

Spatial vent opening probability map of Etna volcano (Sicily, Italy)

We produce a spatial probability map of vent opening (susceptibility map) at Etna, using a statistical analysis of structural features of flank eruptions of the last 2?ky. We exploit a detailed knowledge of the volcano structures, including the modalities of shallow magma transfer deriving from dike and dike-fed fissure eruptions analysis on historical eruptions. Assuming the location of future vents will have the same causal factors as the past eruptions, we converted the geological and structural data in distinct and weighted probability density functions, which were included in a non-homogeneous Poisson process to obtain the susceptibility map. The highest probability of new eruptive vents opening falls within a N-S aligned area passing through the Summit Craters down to about 2,000?m?a.s.l. on the southern flank. Other zones of high probability follow the North-East, East-North-East, West, and South Rifts, the latter reaching low altitudes (～400?m). Less susceptible areas are found around the faults cutting the upper portions of Etna, including the western portion of the Pernicana fault and the northern extent of the Ragalna fault. This structural-based susceptibility map is a crucial step in forecasting lava flow hazards at Etna, providing a support tool for decision makers.