Mattia De Amicis

Database of geo-hydrological disasters for civil protection purposes

This paper presents the results of a research concerning available historical information about natural hazards (landslides and floods) and consequent disasters in the Consortium of Mountain Municipalities of Valtellina di Tirano, in Northern Italy. A geo-referenced database, collecting information till 2008, was designed with the aim of using available data of historical events for hazard estimation and the definition of risk scenarios as a basis for Civil Protection planning and emergency management purposes. This database and related statistics about landslides and floods are shown, and a brief overview of historical disasters caused by natural hazards in the study area is presented. A case study showing how useful the database can be to define a simple but realistic scenario is described. Information availability and reliability is discussed and possible uncertainties are underlined. The study shows that collecting and making use of historical information for the definition of hypothetical scenarios and the evaluation of territorial threats is a fundamental source of knowledge to deal with future emergencies.

Spatiotemporal Pattern of Social Vulnerability in Italy

Evaluation of social vulnerability (SV) against natural hazards remains a big challenge for disaster risk reduction. Spatiotemporal analysis of SV is important for successful implementation of prevision and prevention measures for risk mitigation. This study examined the spatiotemporal pattern of SV in Italy, and also analyzed socioeconomic factors that may influence how the Italian population reacts to catastrophic natural events. We identified 16 indicators that quantify SV and collected data for the census years 1991, 2001, and 2011. We created a social vulnerability index (SVI) for each year by using principal component analysis outputs and an additive method. Exploratory spatial data analysis, including global and local autocorrelations, was used to understand the spatial patterns of social vulnerability across the country. Specifically, univariate local Moran’s index was performed for the SVI of each of the three most recent census years in order to detect changes in spatial clustering during the whole study period. The original contribution of this Italy case study was to use a bivariate spatial correlation to describe the spatiotemporal correlation between the threes annual SV indices. The temporal analysis shows that the percentage of municipalities with medium social vulnerability in Italy increased from 1991 to 2011 and those with very high social vulnerability decreased. Spatial analysis provided evidence of clusters that maintained significant high values of social vulnerability throughout the study periods. The SVI of many areas in the center and the south of the peninsula remained stable, and the people living there have continued to be potentially vulnerable to natural hazards.

A Web-Based Inventory of Landslides Occurred in Italy in the Period 2012–2015

Open image in new window Landslides in Italy are extremely frequent and cause a high number of casualties and damage to structures and infrastructures. A landslides database has been developed through the Google Alert’s service from January 2012 to December 2015. In total, 10947 notifications have been received, read and analyzed, allowing to create an inventory of 1054 landslide events occurred in Italy in the studied period. For each landslide, the main event location, the number of people and the damages to the structure have been inserted into a relational database together with information about following facts. In addition, a large number of information related to events occurred before the studied period have been inserted into the database. Starting from this inventory, we studied the spatial and temporal distribution of landslide events in Italy, and the relationship with geo-environmental factors, in order to recognize the most significant controlling factors, such as lithology, land use, slope gradient and aspect. As a conclusion, we found that Google Alert is a valuable tool for the study of landslide events, even if the resulting inventory is not complete in remote inhabited areas. Moreover, in order to become a useful tool for landslide risk analysis, the web sources need to be integrated with more technical information existing in other databases.

A GRASS GIS module to obtain an estimation of glacier behavior under climate change

The aim of this work is to integrate the Minimal Glacier Model in a Geographic Information System Python module in order to obtain spatial simulations of glacier retreat and to assess the future scenarios with a spatial representation. The Minimal Glacier Models are a simple yet effective way of estimating glacier response to climate fluctuations. This module can be useful for the scientific and glaciological community in order to evaluate glacier behavior, driven by climate forcing. The module, called r.glacio.model, is developed in a GRASS GIS (GRASS Development Team, 2016) environment using Python programming language combined with different libraries as GDAL, OGR, CSV, math, etc. The module is applied and validated on the Rutor glacier, a glacier in the south-western region of the Italian Alps. This glacier is very large in size and features rather regular and lively dynamics. The simulation is calibrated by reconstructing the 3-dimensional dynamics flow line and analyzing the difference between the simulated flow line length variations and the observed glacier fronts coming from ortophotos and DEMs. These simulations are driven by the past mass balance record. Afterwards, the future assessment is estimated by using climatic drivers provided by a set of General Circulation Models participating in the Climate Model Inter-comparison Project 5 effort. The approach devised in r.glacio.model can be applied to most alpine glaciers to obtain a first-order spatial representation of glacier behavior under climate change. HighlightsA module to integrate the Minimal Glacier Model in a GIS is proposed.The aim is to obtain spatial simulations to assess the future scenarios.The module is applied and validated on the Rutor glacier, in the Italian Alps.The simplicity of the model makes it applicable for a large amount of glaciers.

Research paperA GRASS GIS module to obtain an estimation of glacier behavior under climate change: A pilot study on Italian glacier

The aim of this work is to integrate the Minimal Glacier Model in a Geographic Information System Python module in order to obtain spatial simulations of glacier retreat and to assess the future scenarios with a spatial representation. The Minimal Glacier Models are a simple yet effective way of estimating glacier response to climate fluctuations. This module can be useful for the scientific and glaciological community in order to evaluate glacier behavior, driven by climate forcing. The module, called r.glacio.model, is developed in a GRASS GIS (GRASS Development Team, 2016) environment using Python programming language combined with different libraries as GDAL, OGR, CSV, math, etc. The module is applied and validated on the Rutor glacier, a glacier in the south-western region of the Italian Alps. This glacier is very large in size and features rather regular and lively dynamics. The simulation is calibrated by reconstructing the 3-dimensional dynamics flow line and analyzing the difference between the simulated flow line length variations and the observed glacier fronts coming from ortophotos and DEMs. These simulations are driven by the past mass balance record. Afterwards, the future assessment is estimated by using climatic drivers provided by a set of General Circulation Models participating in the Climate Model Inter-comparison Project 5 effort. The approach devised in r.glacio.model can be applied to most alpine glaciers to obtain a first-order spatial representation of glacier behavior under climate change. HighlightsA module to integrate the Minimal Glacier Model in a GIS is proposed.The aim is to obtain spatial simulations to assess the future scenarios.The module is applied and validated on the Rutor glacier, in the Italian Alps.The simplicity of the model makes it applicable for a large amount of glaciers.