The HuT (The Human-Tech Nexus) project, funded by Horizon Europe initiative, is focused on risk assessment and disaster risk reduction for distinct types of hazards (wildfires, landslide, droughts, etc.), over the European territory, by means of a series of demonstrators representing a multi-hazard arena. In the framework of this project, we present an innovative Catastrophe Bond (Cat Bond) designed to enhance disaster risk reduction strategies. Cat Bonds are a key financial instrument for transferring the risk of extreme events from insurers to capital markets, thereby increasing resilience and reducing the economic impact of disasters. Our approach lies in the use of the recently developed E3CI (European Extreme Events Climate Index) as the trigger mechanism for the bond. The E3CI is a suite of indicators designed to monitor and quantify the occurrence and intensity of climate extremes across Europe. It integrates multiple variables into a single, scientifically robust metric, enabling consistent and transparent assessment of climate-related risks. By using E3CI as the trigger for our Cat Bond, we ensure that payouts are based on objective, observed climate conditions rather than loss estimates, improving reliability and fairness in risk transfer mechanisms. The coupon here reckoned for the Cat Bond are based on hypothetical portfolios over the Italian territory. The proposed Cat Bond ensures transparency, objectivity, and a strong link to observed climate extremes. This solution represents an interesting case study in integrating climate science into risk financing solutions, supporting both insurers and communities in managing the growing risks associated with climate change.

In recent years, Europe has experienced a notable increase in the frequency and severity of extreme weather events, including heatwaves, floods, droughts, storms, and wildfires. These events have had profound impacts on human life, infrastructure, ecosystems, and economies, highlighting the urgent need to develop effective climate services to prevent, manage and timely respond to associated impacts. The aim of this paper is to describe and validate the European Extreme Climate Index (E3CI), designed as a climate service that allows monitoring the occurrence and severity of extreme weather dynamics across Europe. Starting from data provided by ERA5, the fifth generation of atmospheric reanalyzes produced by the European Center for Medium-Range Weather Forecasts (ECMWF), which covers the entire globe from 1940, E3CI provides insights regarding variations and trends in seven specific dynamics associated with extreme weather events: Extreme Maximum Temperatures, Extreme Minimum Temperatures, Extreme Precipitations, Extreme Winds, Hail, Droughts, and Fires. For each of these, the E3CI collects information at monthly scale in terms of anomalies compared to a reference climatological time span (1981–2010), therefore enabling back-analyses on specific territories, highlighting areas subject to significant weather-induced criticalities. E3CI has proven effective in identifying the spatial distribution and intensity of the extreme precipitation and extreme wind events in the areas affected by powerful storms Dudley, Eunice, and Franklin, as well as the significant heat waves in the summer of 2022 and the extreme precipitation in Emilia Romagna in May 2023. Such index offers numerous application opportunities for businesses, institutions, and communities, in the vein of profound innovation in climate risk management and extreme weather events policies. Its flexible and dynamic nature allows for the production of customizable analysis based on the specific user needs including the ability to refine geographic areas to a radius of approximately 30 km. The data are made available for visualization and download through a dedicated portal, which is updated monthly. Additionally, a Newsletter for subscribers provides information about major events across Europe and their assessment by the corresponding components of E3CI.

The HuT (The Human-Tech Nexus) project aims at finding effective strategies to manage the risks associated with extreme climate events by means of specific demonstrators over the European territory in which different Disaster Risk Reduction strategies are prototyped and tested. In this context, we show here two distinct innovative insurance prototypes to cope with risks associated with wildfires and landslides over two peculiar areas in Sardinia and Campania regions (Italy). While the hazard posed by the two perils show distinct characteristics and origins, in both cases an insurance product can play a crucial role in the aftermath of the events for communities and private stakeholders. Since the risk assessment is crucial both in terms of financial structure and pricing strategies of a natural hazard insurance product, prototypes are developed through a Nat Cat modeling-based hazard assessment, while the vulnerability and finance considerations are related to the specific characteristics of the area of interest. Eventually, two prototypes are fully developed: “Landslide First Rescue”, a semi-parametric product designed to cope with the immediate economic needs after a landslide events; and “Fire Safe Community”, proposed as a community-based efficient tools to restore the economic losses related to wildfires. The prototypes present specific discounts if the policy holders are willing to implement risk reduction solutions to cope with the specific natural hazard. Results prove that the final premium associated with the products would be affordable and several consultations with interested stakeholders have shown how these kinds of products could also play a role in the development of nature-based solutions over broader regions.

Recent studies show how geomorphic descriptors, retrieved from digital elevation models (DEMs), can be used for flood hazard mapping. As they strictly depend on the accuracy of the input DEMs and reference flood hazard maps used for training, DEM-based flood hazard models may display severe inconsistencies. Our study shows the application of two advanced DEM-based models to a large study area, and presents two main innovative points. First, the delicate tasks of appropriately selecting the input DEM and flood hazard map are specifically addressed with newly defined methods. Second, the ability of DEM-based models to exploit their natural features to enhance flood hazard mapping over the study region is investigated. Our results show (a) the benefits of considering multiple geomorphic descriptors, (b) the potential of DEM-based models for completing the information of imperfect reference flood hazard maps, and (c) the advantages of continuous representation of hazard over binary flood maps.

Recent literature shows several examples of simplified approaches that perform flood hazard (FH) assessment and mapping across large geographical areas on the basis of fast-computing geomorphic descriptors. These approaches may consider a single index (univariate) or use a set of indices simultaneously (multivariate). What is the potential and accuracy of multivariate approaches relative to univariate ones? Can we effectively use these methods for extrapolation purposes, i.e., FH assessment outside the region used for setting up the model? Our study addresses these open problems by considering two separate issues: (1) mapping flood-prone areas and (2) predicting the expected water depth for a given inundation scenario. We blend seven geomorphic descriptors through decision tree models trained on target FH maps, referring to a large study area (∼ 105 km2). We discuss the potential of multivariate approaches relative to the performance of a selected univariate model and on the basis of multiple extrapolation experiments, where models are tested outside their training region. Our results show that multivariate approaches may (a) significantly enhance flood-prone area delineation (accuracy: 92 %) relative to univariate ones (accuracy: 84 %), (b) provide accurate predictions of expected inundation depths (determination coefficient ∼ 0.7), and (c) produce encouraging results in extrapolation.

Every year flood events cause worldwide vast economic losses, as well as heavy social and environmental impacts, which have been steadily increasing for the last five decades due to the complex interaction between climate change and anthropogenic pressure (i.e. land-use and land-cover modifications). As a result, the body of literature on flood risk assessment is constantly and rapidly expanding, aiming at developing faster, computationally lighter and more efficient methods relative to the traditional and resource-intensive hydrodynamic numerical models. Recent and reliable fast-processing techniques for flood hazard assessment and mapping consider binary geomorphic classifiers retrieved from the analysis of Digital Elevation Models (DEMs). These procedures (termed herein “DEM-based methods”) produce binary maps distinguishing between floodable and non-floodable areas based on the comparison between the local value of the considered geomorphic classifier and a threshold, which in turn is calibrated against existing flood hazard maps. Previous studies have shown the reliability of DEM-based methods using a single binary classifier, they also highlighted that different classifiers are associated with different performance, depending on the geomorphological, climatic and hydrological characteristics of the study area. The present study maps flood-prone areas and predicts water depth associated with a given non-exceedance probability by combining several geomorphic classifiers and terrain features through regression trees and random forests. We focus on Northern Italy (c.a. 100000 km2, including Po, Adige, Brenta, Bacchiglione and Reno watersheds), and we consider the recently compiled MERIT (Multi-Error Removed Improved-Terrain) DEM, with 3sec-resolution (~90m at the Equator). We select the flood hazard maps provided by (i) the Italian Institute for Environmental Protection and Research (ISPRA), and (ii) the Joint Research Centre (JRC) of the European Commission as reference maps. Our findings (a) confirm the usefulness of machine learning techniques for improving univariate DEM-based flood hazard mapping, (b) enable a discussion on potential and limitations of the approach and (c) suggest promising pathways for further exploring DEM-based approaches for predicting a likely water depth distribution with flood-prone areas.

ABSTRACTExtreme rainfall events have large impacts on society and are likely to increase in intensity under climate change. For design and management decisions, particularly regarding hydraulic works, accurate estimates of precipitation magnitudes are needed at different durations. In this article, an objective approach of the regional frequency analysis (RFA) has been applied to precipitation data for the island of Sicily, Italy. Annual maximum series for rainfall with durations of 1, 3, 6, 12, and 24 h from about 130 rain gauges were used. The RFA has been implemented using principal component analysis (PCA) followed by a clustering analysis, through thek‐means algorithm, to identify statistically homogeneous groups of stations for the derivation of regional growth curves. Three regional probability distributions were identified as appropriate from an initial wider selection of distributions and were compared – the three‐parameter log‐normal distribution (LN3), the generalized extreme value (GEV) distribution, and the two component extreme value (TCEV) distribution. The regional parameters of these distributions were estimated using L‐moments and considering a hierarchical approach. Finally, assessment of the accuracy of the growth curves was achieved by means of the relative bias and relative root‐mean‐square error (RMSE) using a simulation analysis of regional L‐moments. Results highlight that for the lower return periods, all distributions showed the same accuracy while for higher return periods the LN3 distribution provided the best result. The study provides an updated resource for the estimation of extreme precipitation quantiles for Sicily through the derivation of growth curves needed to obtain depth–duration–frequency (DDF) curves.

In this paper, a novel convolutional neural network model for blind deconvolution of images is proposed. The structure of the model is based on two sub models devoted, respectively, to deblurring and denoising of an input image. The model has been designed to restore a picture affected by different kinds of noise. The main innovation is the introduction of a regularization term in the training cost function, based on a blurred/non-blurred classification tool. Results show interesting features of the model, particularly regarding the robustness of results. The comparison with other state-of-the-art models confirms the value of the model proposed in this study.

Critical rainfall thresholds for landslides are powerful tools for preventing landslide hazard. The thresholds are commonly estimated empirically starting from rainfall events that triggered landslides in the past. The creation of the appropriate rainfall–landslide database is one of the main efforts in this approach. In fact, an accurate agreement between the landslide and rainfall information, in terms of location and timing, is essential in order to correctly estimate the rainfall–landslide relationships. A further issue is taking into account the average moisture conditions prior the triggering event, which reasonably may be crucial in determining the sufficient amount of precipitation. In this context, the aim of this paper is exploiting historical landslide and rainfall data in a spatial database for the derivation of critical rainfall thresholds for landslide occurrence in Sicily, southern Italy. The hourly rainfall events that caused landslides occurred in the twentieth century were specifically identified and reconstructed. A procedure was proposed to automatically convert rain guages charts recorded on paper tape into digital format and then to provide the cumulative rainfall hyetograph in digital format. This procedure is based on a segmentation followed by signal recognition techniques which allow to digitalize and to recognize the hyetograph automatically. The role of rainfall prior to the landslide events was taken into account by including in the analysis the rainfall occurred 5, 15 and 30 days before each landslide. Finally, cumulated rainfall duration thresholds for different exceedance probability levels were determined. The obtained thresholds resulted in agreement with the regional curves proposed by other authors for the same area; antecedent rainfall turned out to be particularly important in triggering landslides.

Integrative information models for filling/reconstructing hydro-climatic time-series are required for a variety of practical applications. A GIS-based model for a rapid and reliable assessment of monthly time-series of several key hydro-climatic variables at the basin scale, is here developed as plug-in and applied to the entire region of Sicily (Italy). The plug-in, once the desired basin outlet section and time-window are selected, uses appropriate spatial techniques and algorithms to identify its drainage area and estimate the corresponding mean areal rainfall and temperatures time-series. A recent regional regressive rainfall-runoff model is successively applied for the assessment of the runoff time-series. Finally, a consolidated temperature-based method is applied to estimate monthly potential evapotranspiration time-series, while, actual evapotranspiration and soil moisture storage time-series are derived through a classical water balance model. The tool, supported by a preliminarily developed database, includes automatic procedures for data retrieving and processing and a user friendly interface.

•Potentials of a single polarization X-band radar have been evaluated.•Data from a disdrometer and a rain gauge network have been used for the analyses.•Three procedures blending radar data with other instruments have been developed.•Meteorological and hydrological suitability of these procedures has been evaluated.•The Z–R relation has been studied by means of a one year-long disdrometer recordset.

The development of Web-based information systems coupled with advanced monitoring systems could prove to be extremely useful in landslide risk management and mitigation. A new frontier in the field of rainfall-triggered landslides (RTLs) lies in the real-time modelling of the relationship between rainfall and slope stability; this requires an intensive monitoring of some key parameters that could be achieved through the use of modern and often low-cost technologies. This work describes an integrated information system for early warning of RTLs that has been deployed and tested, in a prototypal form, for an Italian pilot site. The core of the proposed system is a wireless sensor network collecting meteorological, hydrological and geotechnical data. Data provided by different sensors and transmitted to a Web-based platform are used by an opportunely designed artificial neural network performing a stability analysis in near real-time or in forecast modality. The system is able to predict whether and when landslides could occur, providing early warnings of potential slope failures. System infrastructure, designed on three interacting levels, encompasses a sensing level, integrating different Web-based sensors, a processing level, using Web standard interoperability services and specifically implemented algorithms, and, finally, a warning level, providing warning information through Web technologies.

An exhaustive comparison among different spatial interpolation algorithms was carried out in order to derive annual and monthly air temperature maps for Sicily (Italy). Deterministic, data-driven and geostatistics algorithms were used, in some cases adding the elevation information and other physiographic variables to improve the performance of interpolation techniques and the reconstruction of the air temperature field. The dataset is given by air temperature data coming from 84 stations spread around the island of Sicily. The interpolation algorithms were optimized by using a subset of the available dataset, while the remaining subset was used to validate the results in terms of the accuracy and bias of the estimates. Validation results indicate that univariate methods, which neglect the information from physiographic variables, significantly entail the largest errors, while performances improve when such parameters are taken into account. The best results at the annual scale have been obtained using the the ordinary kriging of residuals from linear regression and from the artificial neural network algorithm, while, at the monthly scale, a Fourier-series algorithm has been used to downscale mean annual temperature to reproduce monthly values in the annual cycle.

Precipitation is one of the major variables for many applications and disciplines related to water resources and the geophysical Earth system. Satellite retrieval systems, rain-gauge networks, and radar systems are complementary to each other in terms of their coverage and capability of monitoring precipitation. Satellite-rainfall estimate systems produce data with global coverage that can provide information in areas for which data from other sources are unavailable. Without referring to ground measurements, satellite-based estimates can be biased and, although some gauge-adjusted satellite-precipitation products have been already developed, an effective way of integrating multi-sources of precipitation information is still a challenge.In this study, a specific area, the Sicilia Island (Italy), has been selected for the evaluation of satellite-precipitation products based on rain-gauge data. This island is located in the Mediterranean Sea, with a particular climatology and morphology, which can be considered an interesting test site for satellite-precipitation products in the European mid-latitude area. Four satellite products (CMORPH, PERSIANN, PERSIANN-CCS, and TMPA-RT) and two GPCP-adjusted products (TMPA and PERSIANN Adjusted) have been selected. Evaluation and comparison of selected products is performed with reference to data provided by the rain-gauge network of the Island Sicilia and by using statistical and graphical tools. Particular attention is paid to bias issues shown both by only-satellite and adjusted products. In order to investigate the current and potential possibilities of improving estimates by means of adjustment procedures using GPCC ground precipitation, the data have been retrieved separately and compared directly with the reference rain-gauge network data set of the study area.Results show that bias is still considerable for all satellite products, then some considerations about larger area climatology, PMW-retrieval algorithms, and GPCC data are discussed to address this issue, along with the spatial and seasonal characterization of results. (C) 2013 Elsevier B.V. All rights reserved.