Michele Barsanti

Heterogeneous large total CO2 abundance in the shallow magmatic system of Kilauea volcano, Hawaii

[1] Due to its very low solubility in silicate melts, CO 2 concentrations in melt inclusions (MIs) within crystals are commonly orders of magnitude less than the total concentration in the multiphase magma, strongly limiting the possibility to constrain CO 2 abundance based on the dissolved quantities. Here we develop a statistical method to process MI data, which allows analytical uncertainties to be taken into account together with the peculiar features of the local saturation surface. The method developed leads to retrieve total H 2 O and CO 2 concentrations in magma as well as the gas phase abundance at the time of magma crystallization. Application to a set of 29 high-resolution secondary ion mass spectrometry (SIMS) MI data from a single specimen of the 1842-1844 eruption of Kilauea, Hawaii, reveals the existence of heterogeneous total CO 2 abundance, and of at least 2-6 wt % total CO 2 in some magma batches, two orders of magnitude higher than the dissolved amounts and 30-50 times more abundant than the corresponding total H 2 O content. Heterogeneous total volatile concentrations are interpreted as due to a combination of degassing and gas flushing in magma subject to convective motion at shallow depth where P 1 wt % is likely to characterize the >30 km deep magma, not represented in the analyzed inclusions, from which a CO 2 -rich gas phase exsolves and decouples from the liquid.

Shallow landslides susceptibility assessment in different environments

ABSTRACT The spatial distribution of shallow landslides is strongly influenced by different climatic conditions and environmental settings. This makes difficult the implementation of an exhaustive monitoring technique for correctly assessing the landslide susceptibility in different environmental contexts. In this work, a unique methodological strategy, based on the statistical implementation of the generalized additive model (GAM), was performed. This method was used to investigate the shallow landslide predisposition of four sites with different geological, geomorphological and land-use characteristics: the Rio Frate and the Versa catchments (Southern Lombardy) and the Vernazza and the Pogliaschina catchments (Eastern Liguria). A good predictive overall accuracy was evaluated computing by the area under the ROC curve (AUROC), with values ranging from 0.76 to 0.82 and estimating the mean accuracy of the model (0.70–0.75). The method showed a high flexibility, which led to a good identification of the most significant predisposing factors for shallow landslide occurrence in the different investigated areas. In particular, detailed susceptibility maps were obtained, allowing to identify the shallow landslide prone areas. This methodology combined with the use of the rainfall thresholds for triggering shallow landslides may provide an innovative tool useful for the improvement of spatial planning and early warning systems.

First-order correction to counter the effect of eccentricity on the hole-drilling integral method with strain-gage rosettes:

The offset between the hole and the centre of the strain-gage rosette is unavoidable, although usually small, in the hole-drilling technique for residual stress evaluation. In this article, we revised the integral method described in the ASTM E837 standard and we recalculated the calibration coefficients. The integral method was then extended by taking into account the two eccentricity components, and a more general procedure was proposed including the first-order correction. A numerical validation analysis was used to consolidate the procedure and evaluate the residual error after implementing the correction. The values of this error resulted limited to a few percentage points, even for eccentricities larger than the usual experimental values. The narrow eccentricity limit claimed by the standard, to keep the maximum error lower than 10%, can now be considered extended by approximately a factor of 10, after implementing the proposed correcting procedure, proving that the effect of the eccentricity is mainly linear within a relatively large range.

Rainfall Intensity-Duration Thresholds for Triggering Shallow Landslides in the Eastern Ligurian Riviera (Italy)

The Eastern Ligurian Riviera, including the famous Cinque Terre, is frequently hit by heavy rainfall, which often induces shallow landslides and floods, causing damage and sometimes death. In this context, the assessment of the rainfall thresholds for shallow landslides initiation is very important in order to improve forecasting and to arrange efficient warning systems. With this purpose, a detailed analysis of the main rainstorms was carried out. The hourly rainfall recorded from 1967 to 2006 was analysed and compared with the occurrence of shallow landslides. Critical threshold curves were defined, in terms of duration and intensity, applying statistical techniques (logistic regression) in order to separate rainfall events that induced failures and events that did not. The rainfall thresholds obtained in this work were compared with the local and regional curves proposed by various authors. The results of this analysis suggest that in the study area landslide activity initiation requires higher amount of rainfall and intensity than elsewhere.

Comparison of statistical methods and multi-time validation for the determination of the shallow landslide rainfall thresholds

Shallow landslides are unforeseeable phenomena often resulting in critical conditions in terms of people’s safety and damage. The main purpose of this paper is the comparison of different statistical methods used to determine the rainfall thresholds for the shallow landslide occurrence. Rainfall data over a 46-year period were collected for one rain gauge located in a test area of northwest Italy (Riviera Spezzina; RS). In the RS, intense rainfalls often induce shallow landslides causing damage and sometimes casualties. The rainfall events occurred in the 1967–2006 period were classified as events inducing shallow landslides (SLEs1967–2006) and events that did not trigger shallow landslides (NSLEs1967–2006). Thresholds for various percentiles of SLEs1967–2006 were computed by identifying the lower limit above which shallow landslides occurred. Another set of thresholds, corresponding to different probabilities of occurrence, was determined using SLEs1967–2006 and NSLEs1967–2006. The least-squares linear fit (LSF) and the quantile regression (QR) techniques were employed in the former approach, while the logistic regression (LR) was applied in the latter. The thresholds were validated with the same data used for their definition and with the data recorded in the 2008–2014 period. Contingency tables were created and contingencies and skill scores were computed. The 10% probability threshold obtained using the LR method is characterized by the best values of at least two skill scores for both periods considered; therefore, it may be considered the “best” threshold for the RS. The results of this work can help the choice of the best statistical method to determine the shallow landslide rainfall thresholds.

Statistical Methods for the Assessment of Rainfall Thresholds for Triggering Shallow Landslides: A Case Study

La Spezia Province (880 km2; Liguria, northwestern Italy) is frequently hit by intense rainfalls, which often cause shallow landslides and damage to population and environment. In this regard, the Provincial Administration of La Spezia and the Earth Sciences Department, University of Pisa, promoted a study to define the rainfall thresholds for shallow landslides occurrence. In fact, on 25 October 2011 a very intense rainfall hit two parts of the provincial territory (Cinque Terre-Riviera area and Vara Valley) causing at least 3500 shallow landslides. This event was analyzed together with other 134 shallow landslide events occurred from 2008 to 2014. The rainfall conditions of these events were determined using an algorithm implemented by the CNR-IRPI of Perugia. The rainfall thresholds at different exceedance probability levels of landslide were defined using two statistical techniques: least-squares linear fit (LSF) and Quantile Regression (QR). The results highlight that the LSF thresholds seems to be the best performing from a statistical point of view and, consequently, the “best” for the study area.

Analysis of the Predisposing Factors for Different Landslide Types Using the Generalized Additive Model

In this paper, a semi-parametric nonlinear regression technique, known as Generalized Additive Model (GAM), was implemented for the landslide susceptibility assessment in the Gravegnola catchment (Northern Apennines, Eastern Liguria, Italy), which was affected by more than 500 shallow landslides on the 25 October 2011 intense rainfall event. Twelve explanatory variables derived from DEM with 5-m resolution, river network, land use and geological maps were considered to investigate their influence on landslide type occurrence. The predictive performance of different combinations of explanatory variables has been evaluated through a cross-validation technique and ROC curve analysis. Different susceptibility maps for each landslide type were finally produced and the results were compared. The preliminary results show the higher ability of GAM than a single regression technique in selecting the most influent predisposing factors on the basis of the type of movement involved in landsliding.

Integral method coefficients for the ring-core technique to evaluate non-uniform residual stresses

The ring-core technique allows for the determination of non-uniform residual stresses from the surface up to relatively higher depths as compared to the hole-drilling technique. The integral method, which is usually applied to hole-drilling, can also be used for elaborating the results of the ring-core test since these two experimental techniques share the axisymmetric geometry and the 0°–45°–90° layout of the strain gage rosette. The aim of this article is to provide accurate coefficients which can be used for evaluating the residual stress distribution by the ring-core integral method. The coefficients have been obtained by elaborating the results of a very refined plane harmonic axisymmetric finite element model and verified with an independent three-dimensional model. The coefficients for small depth steps were initially provided, and then the values for multiple integer step depths were also derived by manipulating the high-resolution coefficient matrices, thus showing how the present results can be practically used for obtaining the residual stresses according to different depth sequences, even non-uniform. This analysis also allowed the evaluation of the eccentricity effect which turned out to be negligible due to the symmetry of the problem. An applicative example was reported in which the input of the experimentally measured relaxed strains was elaborated with different depth resolutions, and the obtained residual stress distributions were compared.

Remarks on the Role of Landslide Inventories in the Statistical Methods Used for the Landslide Susceptibility Assessment

Open image in new window In this work, the role of different landslide inventories in susceptibility assessment was evaluated using a non linear regression technique, namely the generalized additive model (GAM).The investigation was carried out in three study areas: the Versa catchment (Oltrepo Pavese, Southern Lombardy, Italy), the Vernazza catchment (Cinque Terre, Eastern Liguria, Italy) and the Pogliaschina catchment (Vara Valley, Eastern Liguria, Italy). Two landslide inventories related to the 2009 and 2013 rainfall events were taken into account in the Versa catchment, whereas two landslide inventories (referred to the same 2011 rainfall event) which differ for methods of detection and criteria adopted for the landslide mapping were considered in the Vernazza and Pogliaschina catchments. The predictive performance of GAM for each landslide inventory was evaluated. The results related to different inventories were compared. The results show that the predictive capability of the model and the landslide susceptibility are significantly influenced by the type of landslide inventory. Thus, the work highlights that a standard criterion for preparing inventories should be adopted in order to produce landslide susceptibility assessment as reliable as possible.

GIS-Based Deterministic and Statistical Modelling of Rainfall-Induced Landslides: A Comparative Study

Open image in new window In this paper three different approaches for landslide susceptibility modeling—Shallow Landslide Stability model (SHALSTAB), Likelihood Ratio (LR) and Generalized Additive Model (GAM)—are compared. They are based on deterministic and statistical methods, respectively. These methods were tested in the Pogliaschina catchment (25 km2 wide; Northern Apennines, Eastern Liguria, Italy), heavily hit by an intense rainfall on 25 October 2011, that caused hundreds of shallow landslides, human losses and severe damage to infrastructure and buildings. The paper focuses on the assessment of the predictive performance of the three methods through a two-fold cross-validation technique and prediction rate curves (PRCs) analysis. The preliminary results have revealed that statistical methods have a higher predictive capability than the deterministic one.