Anna Bozza

Developing an integrated framework to quantify resilience of urban systems against disasters

AbstractUrban resilience against disasters represents a key issue for contemporary society. The increasing complexity of cities along with more severe threats induced by climate change is pressing modern societies to search for new paths to prevention, preparedness and rapid recovery. As a result, resilience is triggering an increasing interest within many scientific contexts to explore the capabilities of communities to withstand extreme events. The present study proposes a framework aimed at quantifying disaster resilience of urban systems while ensuring an adequate level of sustainability, all according to a social and human-centric perspective. Urban networks are modelled as hybrid social–physical networks (HSPNs) by merging both physical and social components, and engineering measures are performed on HSPNs, as a measure of urban efficiency, within a multi-scale approach. Thence, social indicators are identified in order to characterise quality of life in the aftermath of a catastrophic event. Both efficiency and quality of life indicators are evaluated using a time–discrete approach before and after an extreme event occurs and during the recovery phase in order to measure inhabitant happiness and environmental sustainability. This approach allows handling different kinds of information simultaneously, being potentially implemented both in peacetime and during the recovery process. The former can be effective for urban coping capacity assessment in order to reduce risks as a mitigation instrument. The latter can be used in the post-event to identify the best recovery paths needing to be followed for adaptation.

A methodological framework assessing disaster resilience of city ecosystems to enhance resource use efficiency

Resilience represents a key issue for modern societies. Resilience is related to sustainability as safeguarding environmental assets can reduce risk and provide resources to facilitate recovery. The methodology quantifies the disaster resilience of city ecosystems by measuring urban efficiency in the event of shocks, according to an anthropocentric perspective. Cities are modelled as hybrid social–physical networks (HSPNs), accounting for interrelations between the physical and social components. Seismic scenarios are run on diverse urban shapes and on the real case study of the city of Sarno. Results on synthetic HSPNs show consistency with the real case study, therefore the validity of the model is highlighted. Urban shapes or forms prove to have a significant impact on urban efficiency in terms of service delivery, and on urban disaster resilience. The methodology proposed in this paper could be a useful tool for urban planners, particularly in cities with high exposure to natural disasters.

Alternative Resilience Indices for City Ecosystems Subjected to Natural Hazards

Prompt and efficient responses against natural hazards are needed to build cities capable of withstanding disasters, namely resilient cities. This study aims at presenting and testing synthetic resilience indices over a real urban center threatened by multiple hazards, for which a global overview of city performance is requested. An integrated framework is proposed for quantitative resilience assessment by way of time-independent synthetic indices. The approach proposed is in accordance to the complex network theory and uses a global indicator of the system connectivity to assess the city functioning also in case of network disruption. Resilience is evaluated as a proxy for systemic urban damage by modeling a city ecosystem as a hybrid social–physical network. Seismic and landslide scenario analyses are performed for the city of Sarno, Italy. A probability-based approach is used to compute urban vulnerability. Subsequently, to highlight changes in results according to the type of disaster, a recovery strategy is simulated to assess efficiency and damage states in each recovery stage, and urban resilience.

National-level prediction of expected seismic loss based on historical catalogue

The frequency and severity of natural catastrophes have increased significantly over the last few years, with countries around the world having to face huge economic and human losses. Italy in particular is very seismic-prone, being located in the precise area of convergence between the African and Eurasian lithospheric plates. In addition, most Italian cities are densely populated and have many old and historical buildings, making the country even more exposed and vulnerable in terms of potential losses. Recently, new regulations gave householders the chance to buy insurance against earthquakes. Unfortunately, the widespread risk perception in Italy is very low among the population. In addition, insurance premiums can be extremely high given the low-probability-high-risk of cash flow and insolvency problems potentially incurred by an insurance company if there is a high magnitude earthquake. The aim of the methodology proposed in this paper is to give insurers an engineering instrument with which to quantify expected losses in the case of an earthquake. This will enable insurance companies to model innovative and more affordable financial products by optimizing the quantification of premiums. Seismic events with a magnitude greater than 4 from 217 a.C. to 2012 have been selected from the historical catalogue of the National Institute of Geophysics and Volcanology, and statistical simulations of earthquake scenarios are performed for each of them. In particular, the peak ground acceleration is simulated, based on the ground motion prediction equation of Bindi et al. (Bull Earthq Eng 7(3):591–608, 2009). Actual exposure is assumed for the Italian building stock, which is modelled according to the database of the National Institute of Statistics. Finally, in order to compute the total losses for the entire national building stock, the annual expected losses are quantified according to the procedure demonstrated in Asprone et al. (Struct Saf 44:70–79, 2013).

CATASTROPHE RESILIENCE RELATED TO URBAN NETWORK SHAPE: PRELIMINARY ANALYSIS.

Abstract. People living in a city represent the most important agents of the urban system. In fact, people organize bits of the city, while organizing their own lives, hence directly influencing much of the city structure, from both a human point of view and a topological one. Such a self-organizing process reflects on both safety and life quality of citizens and efficiency of city services. As a result, in order to better manage a city one should know its inhabitants’ behaviour and its topological configuration too. An ambitious goal that can be pursued in the sense of complex networks theory approach, studying the urban centre as a hybrid social-physical network made by both human and physical components (HSPN)[1]. In this study different topological structures and geometric shapes of cities are investigated, focusing on the efficiency of cities themselves, and their resilience. Moreover, due to the current increasing risk of natural and human-induced disaster threatening local communities, urban societies are suffering a gradual reduction of their actual and potential resilience, as their ability to cope and withstand with external events. To this purpose, seismic events are simulated for each investigated urban geometry, referring to the most common shapes existing worldwide. A novel systemic measure of the expected damage state is here defined, which allows for a vectorial measure of the city efficiency in its entirety. Urban resilience is assessed as an integral measure, before, during and after an extreme event occurs. Thence, a recovery strategy is hypothesized and the efficiency of the HSPN network and the resilience of the city are then evaluated and compared in a timediscrete analysis.

Role of Urban Interactions and Damage in Seismic Resilience of Historical Centers.

Historical centers are places where local identity principles and contemporary dynamics of urbanization coexist. They conserve cultural heritage, hence the presence of many historical assets makes these places highly vulnerable and exposed.

HOW CAN INSURERS GET PREPARED TO CATASTROPHES? ASSESSING EARTHQUAKE EXPECTED LOSSES FROM HISTORICAL CATALOGUE.

Abstract. Countries around the world have had to face huge economic losses due to natural disasters over the past decade. This, of course represents a source of great concern for National Governments and even more so for the insurance industry. In the aftermath of a natural disaster, insurance and reinsurance markets are prone to severe insolvencies and destabilization. Therefore, the finance industry is looking for more reliable loss estimation procedures and insurance models, as effective means for resilience improvement. The present paper proposes an engineering-based methodology as a support for innovative insurance models. The study aims at defining a scientific instrument supporting insurers and reinsurers in forecasting expected losses and in mitigating the potential lack of financial capacity. This allows for catastrophe-linked modeling to be performed according to a riskbased framework. The proposed methodology is applied to the Italian residential building stock subjected to seismic risk. Expected losses are evaluated following the procedure outlined in Asprone et al. (2013)[1] for earthquake scenarios from the catalogue of historical earthquakes, of the National Institute of Volcanology and Geology (INGV) [2] and assuming present-day exposure characteristics. Hence the procedure can be implemented anywhere else a detailed catalogue collecting information about earthquakes from the past is available, as for Italy. Statistical simulations of ground motion intensity (peak ground acceleration, PGA) using multivariate normal distributions are performed for each earthquake. The simulated PGA values are calculated based on the ground motion prediction equation of Sabetta and Pugliese (1996)[3], whose coefficient are re-estimated by Bindi et al. (2009)[4], for each Italian Municipality. A set of different fragility curves from the literature has been selected and averaged for each building type, also accounting for seismic and non-seismic design. In the next step, the annual expected losses for insurers are evaluated and the results are aggregated in order to calculate total losses for the entire National building stock. Linear regression analysis is performed for predicting the expected loss as a function of earthquake magnitude. The resulting loss model can be used for efficient and rapid loss estimation for a given earthquake scenario.