Suchergebnisse

Abstract. The damage triggered by different flood events costs the Italian economy millions of euros each year. This cost is likely to increase in the future due to climate variability and economic development. In order to avoid or reduce such significant financial losses, risk management requires tools which can provide a reliable estimate of potential flood impacts across the country. Flood loss functions are an internationally accepted method for estimating physical flood damage in urban areas. In this study, we derived a new flood loss function for Italian residential structures (FLF-IT), on the basis of empirical damage data collected from a recent flood event in the region of Emilia-Romagna. The function was developed based on a new Australian approach (FLFA), which represents the confidence limits that exist around the parameterized functional depth–damage relationship. After model calibration, the performance of the model was validated for the prediction of loss ratios and absolute damage values. It was also contrasted with an uncalibrated relative model with frequent usage in Europe. In this regard, a three-fold cross-validation procedure was carried out over the empirical sample to measure the range of uncertainty from the actual damage data. The predictive capability has also been studied for some sub-classes of water depth. The validation procedure shows that the newly derived function performs well (no bias and only 10 % mean absolute error), especially when the water depth is high. Results of these validation tests illustrate the importance of model calibration. The advantages of the FLF-IT model over other Italian models include calibration with empirical data, consideration of the epistemic uncertainty of data, and the ability to change parameters based on building practices across Italy.

This paper discusses the role played by decentralized, voluntary multi-stakeholder partnerships between public authorities and agencies and/or public authorities and civil society for disaster risk reduction. We pay attention to Public – Public Partnerships (PuP), a term coined for public alliances in the early 2000s although arguably building upon community-based natural resource management (CBNRM) and disaster risk reduction (CBDRR), as well as other cooperative initiatives. In many respects PuPs became known as a counterpart of PPPs and quickly spread in public water and health service provision. While the concept of PuPs match to some extent the European Union's efforts to expand horizontal cooperation and collaboration, it appears too narrow to capture the sense of European initiatives. In particular, the strict exclusion of business and commercial undertakings in the essence of PuPs by early scholars is not compatible with the call for truly cooperative multi-governance arrangements. The paper examines the concept of PuP, its objectives and defining characteristics, partners involved and relationship tying them. It then moves to understand to what extent partnerships meant to improve cooperation and coordination have permeated the EU legislation and policies, focusing especially on the role of inclusive governance and territorial cooperation. The analysis is complemented by examples of PuPs addressed in the ENHANCE case studies in which disaster risk reduction plays a role.

Abstract. In March 2015, a new international blueprint for disaster risk reduction (DRR) was adopted in Sendai, Japan, at the end of the Third UN World Conference on Disaster Risk Reduction (WCDRR, 14–18 March 2015). We review and discuss the agreed commitments and targets, as well as the negotiation leading the Sendai Framework for DRR (SFDRR) and discuss briefly its implication for the later UN-led negotiations on sustainable development goals and climate change.

This book assesses both the effectiveness and efficiency of implemented Economic Policy Instruments (EPIs) in order to achieve water policy goals, and identifies the preconditions under which they outperform alternative (e.g. regulatory) policy instruments and/or can complement them as part of complex policy mixes. The development of a consolidated assessment framework helps clarify (and where possible, quantify) the effectiveness of each EPI on the basis of different criteria. Outcome-oriented criteria describe how the EPIs perform. They include intended and unintended economic and environmental outcomes and the distribution of benefits and costs among the affected parties. These steps consider the application of cost effectiveness and cost benefits analysis, e.g. to assess ex-post performance of the EPI. Process criteria describe the institutional conditions (legislative, political, cultural, etc.) affecting the formation and operation of the EPI studied (particularly relevant for assessing the possible impacts of using economic instruments), the transaction costs involved in implementing and enforcing the instruments, and the process of implementation. Case studies from Cyprus, Denmark, France, Germany, Hungary, Italy, the Netherlands, Spain, and the United Kingdom, as well as from Australia, Chile, Israel and the USA are presented in this book. A wide variety of EPIs are also covered, including water-pricing schemes (tariffs, environmental taxes, environmental charges or fees, subsidies on products and practices), trading schemes (tradable permits for abstraction and pollution), and cooperation mechanisms

This paper estimates the direct and indirect socio-economic impacts of the 2000 flood that took place in the Po river basin (Italy) using a combination of Computable General Equilibrium (CGE) model and Spatial and Multi-Criteria Analysis. A risk map for the whole basin is generated as a function of hazard, exposure and vulnerability. The indirect economic losses are assessed using the CGE model, whereas the direct social and economic impacts are estimated with spatial analysis tools combined with Multi-Criteria Analysis. The social impact is expressed as a function of physical characteristics of the extreme event, social vulnerability and adaptive capacity. The results indicate that the highest risk areas are located in the mountainous and in the most populated portions of the basin, which are consistent with the high values of hazard and vulnerability. Considerably economic damages occurred to the critical infrastructure of all the sectors with the industry/commercial sector having the biggest impact. A negative variation in the country and industry Gross Domestic Product (GDP) was also reported. Our study is of great interest to those who are interested in estimating the economic impact of flood events. It can also assist decision makers in pinpointing factors that threaten the sustainability and stability of a risk-prone area and more specifically, to help them understand how to reduce social vulnerability to flood events.

Abstract. The combined effect of global sea level rise and land subsidence phenomena
poses a major threat to coastal settlements. Coastal flooding events are
expected to grow in frequency and magnitude, increasing the potential
economic losses and costs of adaptation. In Italy, a large share of the
population and economic activities are located along the low-lying coastal
plain of the North Adriatic coast, one of the most sensitive areas to relative sea
level changes. Over the last half a century, this stretch of coast has
experienced a significant rise in relative sea level, the main component of
which was land subsidence; in the forthcoming decades, climate-induced sea
level rise is expected to become the first driver of coastal inundation
hazard. We propose an assessment of flood hazard and risk linked with
extreme sea level scenarios, under both historical conditions and sea level
rise projections in 2050 and 2100. We run a hydrodynamic inundation model on
two pilot sites located along the North Adriatic coast of Emilia-Romagna:
Rimini and Cesenatico. Here, we compare alternative extreme sea level
scenarios accounting for the effect of planned and hypothetical seaside
renovation projects against the historical baseline. We apply a flood damage
model to estimate the potential economic damage linked to flood scenarios,
and we calculate the change in expected annual damage according to changes
in the relative sea level. Finally, damage reduction benefits are evaluated
by means of cost–benefit analysis. Results suggest an overall profitability
of the investigated projects over time, with increasing benefits due to
increased probability of intense flooding in the near future.

Abstract. Flood risk management generally relies on economic assessments performed by
using flood loss models of different complexity, ranging from simple
univariable models to more complex multivariable models. The latter account for a
large number of hazard, exposure and vulnerability factors, being
potentially more robust when extensive input information is available. We
collected a comprehensive data set related to three recent major flood events
in northern Italy (Adda 2002, Bacchiglione 2010 and Secchia 2014), including
flood hazard features (depth, velocity and duration), building
characteristics (size, type, quality, economic value) and reported losses.
The objective of this study is to compare the performances of expert-based
and empirical (both uni- and multivariable) damage models for estimating the
potential economic costs of flood events to residential buildings. The
performances of four literature flood damage models of different natures and
complexities are compared with those of univariable, bivariable and
multivariable models trained and tested by using empirical records from
Italy. The uni- and bivariable models are developed by using linear,
logarithmic and square root regression, whereas multivariable models are
based on two machine-learning techniques: random forest and artificial neural networks. Results provide important insights about the choice of the
damage modelling approach for operational disaster risk management. Our
findings suggest that multivariable models have better potential for
producing reliable damage estimates when extensive ancillary data for flood
event characterisation are available, while univariable models can be
adequate if data are scarce. The analysis also highlights that expert-based
synthetic models are likely better suited for transferability to other areas
compared to empirically based flood damage models.

We describe a climate risk index that has been developed to inform national climate adaptation planning in Italy and that is further elaborated in this paper. The index supports national authorities in designing adaptation policies and plans, guides the initial problem formulation phase, and identifies administrative areas with higher propensity to being adversely affected by climate change. The index combines (i) climate change-amplified hazards; (ii) high-resolution indicators of exposure of chosen economic, social, natural and built- or manufactured capital (MC) assets and (iii) vulnerability, which comprises both present sensitivity to climate-induced hazards and adaptive capacity. We use standardized anomalies of selected extreme climate indices derived from high-resolution regional climate model simulations of the EURO-CORDEX initiative as proxies of climate change-altered weather and climate-related hazards. The exposure and sensitivity assessment is based on indicators of manufactured, natural, social and economic capital assets exposed to and adversely affected by climate-related hazards. The MC refers to material goods or fixed assets which support the production process (e.g. industrial machines and buildings); Natural Capital comprises natural resources and processes (renewable and non-renewable) producing goods and services for well-being; Social Capital (SC) addressed factors at the individual (people's health, knowledge, skills) and collective (institutional) level (e.g. families, communities, organizations and schools); and Economic Capital (EC) includes owned and traded goods and services. The results of the climate risk analysis are used to rank the subnational administrative and statistical units according to the climate risk challenges, and possibly for financial resource allocation for climate adaptation. This article is part of the theme issue 'Advances in risk assessment for climate change adaptation policy'.

Purpose
The purpose of this paper is to discuss how, despite increasing data availability from a wide range of sources unlocks unprecedented opportunities for disaster risk reduction, data interoperability remains a challenge due to a number of barriers. As a first step to enhancing data interoperability for disaster risk reduction is to identify major barriers, this paper presents a case study on data interoperability in disaster risk reduction in Europe, linking current barriers to the regional initiative of the European Science and Technology Advisory Group.


Design/methodology/approach
In support of Priority 2 ("Strengthening disaster risk governance to manage disaster risk") of the Sendai Framework and SDG17 ("Partnerships for the goals"), this paper presents a case study on barriers to data interoperability in Europe based on a series of reviews, surveys and interviews with National Sendai Focal Points and stakeholders in science and research, governmental agencies, non-governmental organizations and industry.


Findings
For a number of European countries, there remains a clear imbalance between long-term disaster risk reduction and short-term preparation and the dominant role of emergency relief, response and recovery, pointing to the potential of investments in ex ante measures with better inclusion and exploitation of data.


Originality/value
Modern society is facing a digital revolution. As highlighted by the International Council of Science and the Committee on Data for Science and Technology, digital technology offers profound opportunities for science to discover unsuspected patterns and relationships in nature and society, on scales from the molecular to the cosmic, from local health systems to global sustainability. It has created the potential for disciplines of science to synergize into a holistic understanding of the complex challenges currently confronting humanity; the Sustainable Development Goals are a direct reflectance of this. Interdisciplinary is obtained with integration of data across relevant disciplines. However, a barrier to realization and exploitation of this potential arises from the incompatible data standards and nomenclatures used in different disciplines. Although the problem has been addressed by several initiatives, the following challenge still remains: to make online data integration a routine.

Purpose – The purpose of this paper is to discuss how, despite increasing data availability from a wide range of sources unlocks unprecedented opportunities for disaster risk reduction, data interoperability remains a challenge due to a number of barriers. As a first step to enhancing data interoperability for disaster risk reduction is to identify major barriers, this paper presents a case study on data interoperability in disaster risk reduction in Europe, linking current barriers to the regional initiative of the European Science and Technology Advisory Group. Design/methodology/approach – In support of Priority 2 ("Strengthening disaster risk governance to manage disaster risk") of the Sendai Framework and SDG17 ("Partnerships for the goals"), this paper presents a case study on barriers to data interoperability in Europe based on a series of reviews, surveys and interviews with National Sendai Focal Points and stakeholders in science and research, governmental agencies, non-governmental organizations and industry. Findings – For a number of European countries, there remains a clear imbalance between long-term disaster risk reduction and short-term preparation and the dominant role of emergency relief, response and recovery,pointing to the potential of investments in ex ante measures with better inclusion and exploitation of data. Originality/value – Modern society is facing a digital revolution. As highlighted by the International Council of Science and the Committee on Data for Science and Technology, digital technology offers profound opportunities for science to discover unsuspected patterns and relationships in nature and society, on scales from the molecular to the cosmic, from local health systems to global sustainability. It has created the potential for disciplines of science to synergize into a holistic understanding of the complex challenges currently confronting humanity; the Sustainable Development Goals are a direct reflectance of this. Interdisciplinary is obtained with integration of data across relevant disciplines. However, a barrier to realization and exploitation of this potential arises from the incompatible data standards and nomenclatures used in different disciplines. Although the problem has been addressed by several initiatives, the following challenge still remains: to make online data integration a routine.