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With the intent to be compliant with the European Floods Directive (2007/60/EC) duties, the Po River District Authority gathered the expertise of 20 Italian Universities and the Italian National Research Council (CNR) active on hydrology (including climate change effects), hydraulics and damage modelling. Within this research framework, the MOVIDA project is developed by a consortium dedicated to flood damage modelling (8 Universities and CNR) and intends to provide an Information System capable to perform an analytical evaluation and mapping of expected damage, overcoming the limitations of present maps where the evaluation of risk remains highly qualitative and subjective. Proper damage assessment tools were identified for all the five categories of exposed elements foreseen by the EU Directive: population, infrastructures, economic activities, environmental and cultural heritage, and na-tech sites. A dedicated Open Source Geographic Information System (i.e. QGIS plugin) was developed and transferred to Regional Authorities for flood damage evaluation and mapping to all areas at significant risk in the Po District. In particular, the methodology defined for road and railway infrastructures qualitatively estimates the potential damage impacts of a flood event for those infrastructures. Different impact classes (High, Medium, Low and Null) are assigned in relation to roads category (i.e., Highways, Main, Secondary, Service, Other) and railways type (high-speed train or not), taking into account both the importance of the infrastructure itself (as well as its topographic characteristics: e.g. tunnel, bridge, etc.) and the magnitude of the expected event (i.e., hazard). The results enable to spatially identify the extent of the impacted infrastructures, as to drive the evaluation of potential mitigation measures and to support the competent bodies in the organization of the rescue.

The Po River District Authority has recently signed a research agreement with 20 Italian Universities and the Italian National Research Council (CNR) for updating the Flood Risk Management Plan, in compliance with the European Floods Directive (2007/60/EC). Specifically, the MOVIDA project envisages the efforts of a research consortium dedicated to flood damage modelling (8 Universities and CNR) to provide an Information System capable to perform an analytical evaluation and mapping of expected damage, overcoming the limitations of present maps where the evaluation of risk remains highly qualitative and subjective. Proper damage assessment tools were identified for all the five categories of exposed elements foreseen by the EU Directive: population, infrastructures, economic activities, environmental and cultural heritage, and na-tech sites. A dedicated Open Source Geographic Information System (i.e. QGIS plugin) was developed and transferred to Regional Authorities for flood damage evaluation and mapping to all areas at significant risk in the Po District. Focusing on road and railway infrastructures, the methodology proposed adopts information coming from different data sources (Regional Geoportals, Open Street Map, etc.) and allows to qualitatively estimate the potential damage impacts of a flood event. Different impact classes (High, Medium, Low and Null) are assigned in relation to the roads category (i.e., Highways, Main, Secondary, Service, Other) or railways type (high-speed train or not), taking into account both the importance of the infrastructure itself (as well as its topographical characteristics: e.g. tunnel, bridge, etc.) and the magnitude of the expected event (i.e., hazard).The results enable to spatially identify the extent of the impacted infrastructures and the locations of the flooded railway stations, information necessary to identify potential mitigation and support measures to the competent bodies in the organization of the rescue.

A collaborative open-source IT infrastructure is designed and implemented to optimize the process of geological field data collection, integration, validation, and sharing. Firstly, field data collection is carried out by multiple users using free and open-source GIS-based tools for mobile devices according to a predefined database structure; then, data integration is automatically performed in a central server, where the collected geological information is stored and validated; finally, data are shared over the Internet, providing users with up-to-date information. The IT infrastructure is currently being employed to accomplish surveys for the realization of the “Brescia” geological map within the New Geological Map of Italy, scale 1:50,000 (CARG Project). Users are only required to run the field data collection application on their mobile devices, add different geometric features to predefined thematic layers and fill in the dialogue forms with the required information to store the new structured and georeferenced data in the central database. The major advantage of the proposed IT infrastructure consists of guaranteeing the operational continuity between field surveys and the finalization of geological or geothematic maps leveraging field data collection tools that are operational both online and offline to ensure the overall system resilience.

The evaluation of flood impacts to economic activities is characterized by obstacles like the paucity of available data to characterise the enterprises, the lack of high-quality data to derive new models or validate existing ones, and the high variability of activity types which hampers generalisation. This study contributes at improving knowledge about types and extent of impacts of flood events on economic activities through the analysis of empirical data, focusing on direct damage and with specific reference to the Italian context. The collaboration among different research groups allowed to collect around a thousand of observed damage records after four flood events in Italy: the inundations that occurred in the town of Lodi (Lombardia Region) in 2002, in Sardegna Region in 2013, and in the Emilia-Romagna Region caused by the Secchia (2014) and Enza (2017) Rivers. The analysis of these data lead to a better knowledge of the types of losses suffered by economic activities, to a representation of damage composition and to the computation of reference damage values. Such a result supports the identification of the more vulnerable elements within the business sector as well as the estimation of the order of magnitude of potential damage, orienting modellers’ and decision-makers’ choices.

The recent upsurge in the incidence of extreme wildfire events, the expected impact of climate change on the frequency and severity of fires, and the progressive expansion of wildland-urban interface areas highlight the tangible need for improvement in our ability to predict, mitigate and manage the growing risk to which communities are exposed. The aim of this research is to contribute to deepen the knowledge on the spatial simulation of complex dynamics of wildland surface fire behaviour through the development and validation of a spatially distributed predictive model for the simulation of wildland surface fire spread intended for operational purposes. Given the position of one or more ignition points, the developed model allows to (i) obtain near real time dynamic estimates of the geo-environmental variables that control the fire spread, (ii) compute the direction and intensity of the maximum rate of fire spread in heterogeneous environments, and (iii) simulate the surface fire spread using agent-based models. The final aim is to provide competent authorities with timely information on the expected evolution of the flame front to optimise decision-making processes. The model, developed under synthetic conditions, is then applied to case studies recorded in the territory of the Autonomous Region of Sardinia, that offers institutional information on the ignition location, the evolution of the flame front, and the completed fire suppression activities, which are implemented in the model as well. Overall, the model showed a promising predictive capacity evaluated in quantitative terms of morphological matching between the observed and predicted fire spread patterns, returning more accurate results in areas with less complex morphologies and dominated by herbaceous rather than shrubby fuels. The model also made it possible to obtain accurate simulations in rapid processing times, compatible with its operational application as a tool for optimising and planning fire risk prevention and mitigation strategies and policies as well as fire management activities. Future research will be addressed at improving the predictive capacity of the model and estimating the propagation of the parametric uncertainty of the geo-environmental variables within the model.

“Mappa Italia” is a WebGIS service, issued by ANIA Servizi (Associazione Nazionale fra le Imprese Assicuratrici), conceived to support the business activity of insurance companies. It returns a “Risk Index” for the entire Italian territory by analyzing national institutional geo-hazard maps and the physical exposure of the assets located in each terrain unit used in this study. The analysis has been performed by processing data from census tracts, land use map and flood and landslide hazard maps. Census tracts provides data about population, buildings (residential, commercial and industrial) and firms (number of companies and employees). Data about land use (urban and rural areas), have been extracted from the Corine LandCover. Data about flood and landslide hazard have been obtained from institutional maps published by ISPRA (Italian Institute for Environmental Protection and Research). The assets have been classified into four classes (D1, D2, D3, D4) considering their type and amount in each terrain unit using a k-means clustering technique.Then, four different risk matrixes were applied to get a Risk Index one for each asset class (D1, D2, D3, D4), by plotting the classified asset classes against the probability of occurrence of a given flood/landslide event. Then, a total Risk Index was derived for each terrain unit, by summing up the Risk Index of each asset class.Insurance companies may access results by a WebApp and a Web Service by authentication Service providing login and password.This work has to be considered a starting point targeted to assess and make available information on exposure, hazard and of the vulnerable elements.

The study of flood impacts on the different sectors that compose the built environment and the society is crucial to implement actions of prevention, mitigation, and cautious planning. In such a context, the sector of businesses assumes a critical role, both for its importance for the wellbeing of the society and because of the high losses it suffers in case of inundations. Nevertheless, flood damage modelling to businesses is still a challenging task: the large number of different commercial activities, their specific geographical and economic contexts and the few observed damage data are just some of the reasons for that. In Italy, for example, a shared methodology to assess damage to enterprises is not present; building knowledge about types and dimensions of impacts of flood events to economic activities is then even more impelling. This contribution presents the analysis of about a thousand observed damage records regarding industrial and commercial activities, collected by four research groups after different flood events in Italy: the inundation occurred in the town of Lodi (Lombardia Region) in 2002, the one in Sardegna Region in 2013, and the floods caused in the Emilia-Romagna Region by Secchia (2014) and Enza (2017) Rivers. Data retrieved from the local and regional authorities responsible for damage compensation present different levels of detail and aggregation, according to the case study investigated. In all cases, they refer to the direct damage only and, for each case study, they have been first organised according to the activity types (e.g. trade, manufacturing, construction, finance) and per affected components: i.e. structure, equipment and stock. Data analysis has been led by some questions, we identified as key to start developing knowledge for damage modelling: are there similarities in the different case studies? Which are the more affected business sectors in case of flood? Which component suffers the highest damage among structure, equipment, and stock? Is there an empirical trend of damage with hazard parameters? Results were first compared with the socio-economic context of the affected area, to have a first confirmation of data quality and reliability; then, the analysis focused on searching information and relationships between damage and activity type, activity dimension and water level. Results support the identification of the more vulnerable elements within the business sector, orienting modellers’ and decision-makers’ choices.

We present Flood-SHE, a data-driven, statistically-based procedure for the delineation of areas expected to be inundated by river floods. We applied Flood-SHE in the 23 River Basin Authorities (RBAs) in Italy using information on the presence or absence of inundations obtained from existing flood zonings as the dependent variable, and six hydro-morphometric variables computed from a 10 m × 10 m DEM as covariates. We trained 96 models for each RBA using 32 combinations of the hydro-morphometric covariates for the three return periods, for a total of 2208 models, which we validated using 32 model sets for each of the covariate combinations and return periods, for a total of 3072 validation models. In all the RBAs, Flood-SHE delineated accurately potentially inundated areas that matched closely the corresponding flood zonings defined by physically-based hydro-dynamic flood routing and inundation models. Flood-SHE delineated larger to much larger areas as potentially subject of being inundated than the physically-based models, depending on the quality of the flood information. Analysis of the sites with flood human consequences revealed that the new data-driven inundation zones are good predictors of flood risk to the population of Italy. Our experiment confirmed that a small number of hydro-morphometric terrain variables is sufficient to delineate accurate inundation zonings in a variety of physiographical settings, opening to the possibility of using Flood-SHE in other areas. We expect the new data-driven inundation zonings to be useful where flood zonings built on hydrological modelling are not available, and to decide where improved flood hazard zoning is needed.

The Third United Nations World Conference on Disaster Risk Reduction, held in Sendai in 2015, has defined a global strategy directed at enhancing risk-exposed communities’ resilience. In line with those needs, the study intends to improve and optimize decision-making processes in wildfire risk management by implementing predictive spatially distributed models of wildfire behaviour.The proposed methodology has been applied to simulate some large and fully documented wildfire events in Umbria and Sardinia regions, in Central and Southern Italy respectively. The predictive model for wildfire behaviour is based on the reviewed Rothermel’s quasi-empirical mathematical model, which investigates propagation-driving parameters, i.e. the local geomorphometrical and meteorological parameters along with the pyrological and phenological characteristics of the local plant communities, to estimate the rate of spread of the fire. Propagation-driving parameters and their spatiotemporal variability have been estimated in the pre-fire environment by applying and adapting empirical relationships well-established in literature. Remote sensing-derived data have been analysed over phenologically distinct periods, along with ancillary data, to elicit information necessary to distinguish the mosaic of fuel model types and to monitor spatiotemporal variations in either live or dead fuel moisture content. According to input data availability, the methodology has been adapted to different case studies, focusing major attention on MODIS instrument by NASA on board the Terra satellite as well as on Sentinel constellations of satellites of the ESA Copernicus programme due to their accessibility and to their medium-high spatial and temporal resolution. A two-dimensional Agent-Based Model with a hexagonal grid, which, given a map of the rate of spread and an ignition point as inputs, returns a map of the cumulative propagation time, has been developed in order to simulate the wildland surface fire behaviour.Satellite estimated propagation-driving parameters have been compared with information collected in the field and recorded by the regional annual reports on wildfire events, revealing a good predictive ability. Likewise, the wildfire behaviour model has provided accurate predictions, up to 70% in terms of morphological matching between obtained simulations and respective documented historical events boundaries, also if compared with results from other well-known wildfire simulation toolset and software. Obtained results suggest the developed wildfire behaviour model could represent a promising tool in prioritizing firefighting interventions in near-real time.

The increasing availability of free-access satellite data represents a relevant opportunity for the analysis and assessment of natural hazards. The systematic acquisition of spaceborne imagery allows monitoring areas prone to geo-hydrological disasters, providing relevant information for risk evaluation and management. In case of major landslide events, for example, spaceborne radar data can provide an innovative solution for the detection of slope failures, even in case of persistent cloud cover. The information about extension and location of the landslide-affected areas may support decision-making processes during the emergency responses. In this paper, we present a semi-automatic procedure, based on Sentinel-1 Synthetic Aperture Radar (SAR) images, aimed to facilitate the detection of rapid-moving landslides. The procedure evaluates changes of radar backscattered signals associated to land cover modifications, that may be also caused by mass movements. The procedure requires an initial manual selection of some parameters, and is able to execute automatically the download and pre-processing of images, the detection of SAR amplitude changes, and the identification of areas potentially affected by landslides, which are then displayed in a geo-referenced map. This map should help decision-makers and emergency managers to organize field investigations. The processes automatization is implemented with specific scripts running on a GNU/Linux operating system and exploiting modules of Open Source software. We tested the processing chain, in back analysis, on an area of about 3000 km2 in central Papua New Guinea that in February/March 2018 was struck by a severe seismic sequence that triggered numerous widespread landslides. In the area, we simulated a periodic survey of about seven months, from 12 November 2017 to 6 June 2018, downloading 36 Sentinel-1 images and performing 17 change detection analyses automatically. The procedure resulted in statistical and graphical evidences of widespread land cover changes occurred just after the most severe seismic events. Most of them can be interpreted as mass movements triggered by the main seismic shocks.

Planning in advance to prepare for and respond to a natural hazard-induced disaster-related emergency is a key action that allows decision makers to mitigate unexpected impacts and potential damage. To further this aim, a collaborative, modular, and information and communications technology-based Spatial Data Infrastructure (SDI) called SIRENE—Sistema Informativo per la Preparazione e la Risposta alle Emergenze (Information System for Emergency Preparedness and Response) is designed and implemented to access and share, over the Internet, relevant multisource and distributed geospatial data to support decision makers in reducing disaster risks. SIRENE flexibly searches and retrieves strategic information from local and/or remote repositories to cope with different emergency phases. The system collects, queries, and analyzes geographic information provided voluntarily by observers directly in the field (volunteered geographic information (VGI) reports) to identify potentially critical environmental conditions. SIRENE can visualize and cross-validate institutional and research-based data against VGI reports, as well as provide disaster managers with a decision support system able to suggest the mode and timing of intervention, before and in the aftermath of different types of emergencies, on the basis of the available information and in agreement with the laws in force at the national and regional levels. Testing installations of SIRENE have been deployed in 18 hilly or mountain municipalities (12 located in the Italian Central Alps of northern Italy, and six in the Umbria region of central Italy), which have been affected by natural hazard-induced disasters over the past years (landslides, debris flows, floods, and wildfire) and experienced significant social and economic losses.

The delimitation of flood-prone areas is an important non-structural measure that proves to be effective in the long term in reducing food risk.In Italy, more than 20 basin’s Units of Management (UoMs) were in charge to delineate the flood hazard zoning (FHZ) for three different flood return periods. Mostly, FHZ was prepared using physically based models i.e., considering the rainfall-runoff transformation and simulating the flood discharge through the river network. Physically-based models require many inputs and boundary conditions including: hydro-meteorological data, detailed characterization of the geometry of the riverbeds, roughness, infiltration parameters and also real hydrometric measurements in order to be calibrated. Physically based modelling is therefore a long, time consuming and resource intensive process that should be frequently updated to take into account the river channel changes. As a consequence, the Italian FHZ suffers from an underlying lack of homogeneity across the different UoMs, resulting in significant differences on the percentage of the river network for which the flood-prone areas were delineated.As alternatives to physically based models, in recent years many authors have produced maps of flood susceptibility or hazard using expert (e.g. Analytic Hierarchy Process) or data-driven (e.g. multivariate statistics or machine learning) approaches. Such methods were mostly used in ungauged territories where hydro-meteorological data is not available.Here we present a procedure, named Flood-SHE (Flood - Statistical Hazard Evaluation), which is aimed at the delineation of flood-prone areas and the corresponding expected water depth, using a multivariate statistical classification model. Flood-SHE was applied to the entire Italian territory with the aim to integrate the UOMs FHZ where it is not available or incomplete. The classification model was trained exploiting the existing UoMs FHZ and using, as independent variables, a set of geomorphometric layers (derived at 10x10 meters ground resolution) which includes the distance and height to the closest rivers and to the basins outlets, the local DEM slope, a stream order classification criterion and the DEM local roughness. Random training and validation areas were used for the classification model in order to obtain an estimation of the uncertainty of the values of the predictive performance indexes. Results highlight (i) the significance of the the variables distance and height to the closest rivers, roughness and stream order in predicting the flood-prone areas, (ii) the impact of the UoMs morphology and the quality of UoMs FHZ on the reliability of the statistically modeled flood-prone areas.

The purpose of this paper is to model July 1987 flooding event occurred in Valtellina (central Alps, northern Italy): at that time, a set of meteo-hydrogeological phenomena affected the study area causing huge direct and indirect physical and socio-economic consequences. The methodology proposed in this study combines GIS and CAD systems to model the geometric and structural features of the morphological and anthropic elements and HEC - RAS to model depth and velocity of the flow. A crucial input for the simulations is the Digital Terrain Model (DTM) of the study area at the time of event that was not initially available. So, a complex methodology for its realization has been designed and applied. Then, a sensitivity analysis on the Manning's coefficients was performed in order to obtain the best correspondence between observed and modeled flooded area.

Il contributo, dopo aver analizzato i fattori che influenzano la capacità di affrontare e superare efficacemente situazioni di emergenza legate a rischi naturali e/o antropici, propone una soluzione implementata nell'ambito del progetto "SIMULATOR_ADS: un Sistema Integrato ModULAre per la gesTione e prevenziOne dei Rischi - Arricchito con Dati Satellitari", una piattaforma ICT originale, con un'architettura basata su servizi Web, a supporto degli operatori e delle autorità locali di Protezione Civile nelle fasi di preparazione e di gestione delle emergenze.

Debris flow hazard modelling at medium (regional) scale has been subject of various studies in recent years. In this study, hazard zonation was carried out, incorporating information about debris flow initiation probability (spatial and temporal), and the delimitation of the potential runout areas. Debris flow hazard zonation was carried out in the area of the Consortium of Mountain Municipalities of Valtellina di Tirano (Central Alps, Italy). The complexity of the phenomenon, the scale of the study, the variability of local conditioning factors, and the lacking data limited the use of process-based models for the runout zone delimitation. Firstly, a map of hazard initiation probabilities was prepared for the study area, based on the available susceptibility zoning information, and the analysis of two sets of aerial photographs for the temporal probability estimation. Afterwards, the hazard initiation map was used as one of the inputs for an empirical GIS-based model (Flow-R), developed at the University of Lausanne (Switzerland). An estimation of the debris flow magnitude was neglected as the main aim of the analysis was to prepare a debris flow hazard map at medium scale. A digital elevation model, with a 10m resolution, was used together with landuse, geology and debris flow hazard initiation maps as inputs of the Flow-R model to restrict potential areas within each hazard initiation probability class to locations where debris flows are most likely to initiate. Afterwards, runout areas were calculated using multiple flow direction and energy based algorithms. Maximum probable runout zones were calibrated using documented past events and aerial photographs. Finally, two debris flow hazard maps were prepared. The first simply delimits five hazard zones, Correspondence to: J. Blahut (blahut@irsm.cas.cz) while the second incorporates the information about debris flow spreading direction probabilities, showing areas more likely to be affected by future debris flows. Limitations of the modelling arise mainly from the models applied and analysis scale, which are neglecting local controlling factors of debris flow hazard. The presented approach of debris flow hazard analysis, associating automatic detection of the source areas and a simple assessment of the debris flow spreading, provided results for consequent hazard and risk studies. However, for the validation and transferability of the parameters and results to other study areas, more testing is needed.

Adaptation to complex and unforeseen events requires enhancing the links between planning and preparedness phases to reduce future risks in the most efficient way. In this context, the legal–administrative and cultural context has to be taken into account. This is why four case study areas of the CHANGES1 project (Nehoiu Valley in Romania, Ubaye Valley in France, Val Canale in Italy, and Wieprzówka catchment in Poland) serve as examples to highlight currently implemented risk management strategies for land-use planning and emergency preparedness. The focus is particularly on flood and landslide hazards. The strategies described in this paper were identified by means of exploratory and informal interviews in each study site. Results reveal that a dearth or, in very few cases, a weak link exists between spatial planners and emergency managers. Management strategies could benefit from formally intensifying coordination and cooperation between emergency services and spatial planning authorities. Moreover, limited financial funds urge for a more efficient use of resources and better coordination towards long-term activities. The research indicates potential benefits to establishing or, in some cases, strengthening this link through contextual changes, e.g., in organizational or administrative structures, that facilitate proper interaction between risk management and spatial planning. It also provides suggestions for further development in the form of information and decision support systems as a key connection point. 1 Marie Curie ITN CHANGES – Changing Hydro-meteorological Risks as Analyzed by a New Generation of European Scientists

The present short paper describes the functionalities of a desktop tool enabling non-expert users to develop downstream services for the periodic download and processing of data from Sentinel sources.

This study had three aims. The first was to assess the performance of the weights-of-evidence (WofE) landslide susceptibility model in areas that are very different in terms of size, geoenvironmental settings, and landslide types. The second was to test the appropriate strategies to sample the mapped landslide polygon. The final aim was to evaluate the performance of the method to changes in the landslide sample size used to train the model. The method was applied to two areas: the Fella River basin (eastern Italian Alps) containing debris flows, and Buzau County (Romanian Carpathians) with shallow landslides. The three landslide sampling strategies used were: (1) the landslide scarp centroid, (2) points populating the scarp on a 50-m grid, and (3) the entire scarp polygon. The highest success rates were obtained when sampling shallow landslides as 50-m grid-points and debris flow scarps as polygons. Prediction rates were highest when using the entire scarp polygon method for both landslide types. The sample size test using the landslide centroids showed that a sample of 104 debris flow scarps was sufficient to predict the remaining 941 debris flows in the Fella River basin, while 161 shallow landslides was the minimum required number to predict the remaining 1451 scarps in Buzau County. Below these landslide sample thresholds, model performance was too low. However, using more landslides than the threshold produced a plateau effect with little to no increase in the model performance rates.

The paper presents a Spatial Decision Support System (SDSS) able to support decision makers in managing preparedness and response activities in the field of Civil Protection. The system takes advantage of a database containing all the information required during the emergency response phase, that is structures, resources and address book.The SDSS supports decision makers providing them with the flow of actions to execute, strictly controlled by the laws and directives in force at a national, regional and local level. The procedure is formalized using Petri Nets, a formal model able to represents concurrent and distributed systems. When the operator chooses the right procedure, related to the actual risk, the system allows executing it and, at the same time, the system writes a log file containing all the executed actions and a timestamps. Moreover, it is possible to access to a list of instructions explaining each action of the procedure. If those instructions contain references to some actors stored in the database, the system will show them to the operator (name, phone numbers, traceability, etc.). Furthermore, at each action, the system will show all the documents to issue related to that action and will allow opening them using the text editors available on the operators' computer.

I principali prodotti del progetto comprendono: originali stime dell'esposizione economica (beni capitali e fatturato) delle imprese italiane a livello comunale; le prime stime in Italia della 'disponibilità a pagare' per la riduzione del rischio (alluvioni e terremoti) e connesse stime della VoSL (Value of Statistical Life); l'utilizzo dei dati OMI Osservatorio del Mercato Immobiliare) per stime ai valori di mercato di danno e rischio per le abitazioni a scala sub-comunale; un originale indice sintetico di rischio socio-economico a livello comunale (hazard, esposizioni e vulnerabilità/resilienza socio-economica); un'originale mappatura della pericolosità idraulica su scala nazionale; un tool originale per la valutazione del danno socio-economico atteso da alluvione; mappature di suscettibilità e non suscettibilità a frana per il territorio nazionale integrate con i dati OMI per l'analisi del rischio economico da frana; originali stime delle perdite di valori immobiliari nel terremoto emiliano 2012 e, in via preliminare, nel terremoto in Centro Italia 2016-2017.

Two complementary GIS-based functions are designed and implemented to assess the expected degree of loss due to the occurrence of flood events. Each function processes institutional thematic layers and allows decision makers first to quantify the physical and the economic exposure of the elements at risk in a given study region and then to assess the expected degree of economic loss in relation to the flood water depth chosen for the analysis. The functions are implemented using QGIS with GRASS Python API extension and the workflow is exposed as a QGIS plug-in. The GUI is built over QT multi-platform framework and, therefore, the results are consistently integrated into the QGIS system.

Quantitative landslide risk assessment requires information about the temporal, spatial and intensity probability of hazardous processes both regarding their initiation as well as their run-out. This is followed by an estimation of the physical consequences inflicted by the hazard, preferentially quantified in monetary values. For that purpose, deterministic hazard modelling has to be coupled with information about the value of the elements at risk and their vulnerability. Dynamic run-out models for debris flows are able to determine physical outputs (extension, depths, velocities, impact pressures) and to determine the zones where the elements at risk can suffer an impact. These results can then be applied for vulnerability and risk calculations. Debris flow risk has been assessed in the area of Tresenda in the Valtellina Valley (Lombardy Region, northern Italy). Three quantitative hazard scenarios for different return periods were prepared using available rainfall and geotechnical data. The numerical model FLO-2D was applied for the simulation of the debris flow propagation. The modelled hazard scenarios were consequently overlaid with the elements at risk, represented as building footprints. The expected physical damage to the buildings was estimated using vulnerability functions based on flow depth and impact pressure. A qualitative correlation between physical vulnerability and human losses was also proposed. To assess the uncertainties inherent in the analysis, six risk curves were obtained based on the maximum, average and minimum values and direct economic losses to the buildings were estimated, in the range of 0.25-7.7 million EUR, depending on the hazard scenario and vulnerability curve used. © 2013 Springer-Verlag Berlin Heidelberg.

Groundwater level forecasting is a useful tool for a more efficient and sustainable groundwater resource management. Developing models that can accurately reproduce groundwater level response to meteorological conditions can lead to a better understanding of the groundwater resource availability. Here an autoregressive neural network (NNARx) approach is proposed and compared with autoregressive linear models with exogenous input (ARx) in order to forecast groundwater level in an aquifer system where a linear groundwater level response to recharge by rainfall is observed. A well known problem regarding neural networks consists in the high risk of overfitting. Here, three NNARx model were trained using different methods to avoid overfitting: Early stopping, Bayesian regularization and a combination of both. The results show that on the short term forecasting (up to 15 days) the performance of NNARx and ARx are comparable but the ARx model generalizes better, while the NNARx trained with Bayesian regularization outperforms the linear models and the other NNARx models on longer scenarios on the test set. As linear models are less time demanding and do not require high computational power, they can be considered as suitable tools for short term groundwater level forecasting in linear systems while when longer scenarios are needed neural networks can be considered more reliable, and training them with Bayesian regularization allows to minimize the risk of overfitting.

Il lavoro ha avuto come obiettivo la definizione di una metodologia finalizzata alla delineazione delle aree inondabili in funzione di diversi tempi di ritorno applicabile a tutto il territorio nazionale. Le aree inondabili sono generalmente definite utilizzando modelli di flusso mono e bi-dimensionali, l'applicazione di questi modelli è condizionata dalla disponibilità di dati accurati e sono generalmente molto costosi in termini di disponibilità dei dati, tempo di calcolo e risorse umane impiegate. Per questo motivo la loro applicazione è limitata a studi specifici di singoli tratti delle aste fluviali. Per ovviare a queste limitazioni alcuni autori ((Degiorgis et al., 2012); (De Risi, Jalayer, & De Paola, 2015); (Manfreda et al., 2015)) hanno sperimentato l'utilizzo dei metodi statistici finalizzati all'individuazione delle aree inondabili, applicandoli con risultati soddisfacenti a bacini idrografici di dimensioni limitate utilizzati come aree test. In ragione dell'estensione dell'area di studio, corrispondente all'intero territorio nazionale, e della numerosità dei corsi d'acqua da valutare, si è scelto di far uso di un approccio statistico simile a quelli proposti in letteratura e precedentemente elencati. Sulla base di tali lavori, nel corso del biennio di progetto, IRPI ha sviluppato una procedura statistica che utilizza modelli statistici di tipo «machine- learning» per classificare il territorio (i.e. inondabile vs non-inondabile) basandosi sull'uso di variabili morfometriche derivate dai modelli digitali di elevazione (DEM). In pratica, l'approccio proposto per la determinazioni delle aree inondabili si basa su criteri di tipo geomorfologico. La modellazione ha previsto due fasi distinte: (i) fase di calibrazione del modello; (ii) fase di validazione del modello. Per la calibrazione sono stati utilizzati i dati relativi alle aree inondabili resi disponibili nei PAI ed elaborati ai sensi del D.Lgs 49/2010 di recepimento della Direttiva Flood. La procedura statistica elaborata nell'ambito del progetto e l'applicazione su scala nazionale ha consentito l'individuazione delle aree inondabili delle aste fluviali e dei bacini secondari che non sono stati oggetto delle perimetrazioni della pericolosità idraulica elaborate ai sensi del D.Lgs 49/2010 che costituiscono lo scenario di pericolosità istituzionale di riferimento. Il risultato finale è uno scenario di pericolosità modellato, ovvero una zonazione sinottica della propensione del territorio ad essere inondato (suscettibilità da inondazione), per vari tempi di ritorno, uniforme e confrontabile per tutto il territorio nazionale basato su criteri di tipo geomorfologico Questo primo tentativo di modellazione statistica a scala nazionale finalizzata alla perimetrazione della pericolosità idraulica è da considerarsi un notevole valore aggiunto alla conoscenza della pericolosità idraulica del territorio italiano, complementare, e non sostituibile, agli scenari istituzionali dei PAI e delle perimetrazioni ai sensi D.Lgs 49/2010.

The Maldives islands in recent decades have experienced dramatic land-use change. Uninhabited islands were turned into new resort islands; evergreen tropical forests were cut, to be replaced by fields and new built-up areas. All these changes happened without a proper monitoring and urban planning strategy from the Maldivian government due to the lack of national land-use and land-cover (LULC) data. This study aimed to realize the first land-use map of the entire Maldives archipelago and to detect land-use and land-cover change (LULCC) using high-resolution satellite images and socioeconomic data. Due to the peculiar geographic and environmental features of the archipelago, the land-use map was obtained by visual interpretation and manual digitization of land-use patches. The images used, dated 2011, were obtained from Digital Globe’s WorldView 1 and WorldView 2 satellites. Nine land-use classes and 18 subclasses were identified and mapped. During a field survey, ground control points were collected to test the geographic and thematic accuracy of the land-use map. The final product’s overall accuracy was 85%. Once the accuracy of the map had been checked, LULCC maps were created using images from the early 2000s derived from Google Earth historical imagery. Post-classification comparison of the classified maps showed that growth of built-up and agricultural areas resulted in decreases in forest land and shrubland. The LULCC maps also revealed an increase in land reclamation inside lagoons near inhabited islands, resulting in environmental impacts on fragile reef habitat. The LULC map of the Republic of the Maldives produced in this study can be used by government authorities to make sustainable land-use planning decisions and to provide better management of land use and land cover.