935 resultados para Flood insurance


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Shipping list no.: 99-0035-P.

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Mode of access: Internet.

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Description based on: 1977

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"July 1983."

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Relief shown by spot heights.

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Significant numbers of homes within the UK are at risk of flooding. Although community level flood protection schemes are the first line of defence for mitigating flood risk, not all properties are protectable. Property-Level Flood Protection (PLFP) provides those unprotected homeowners with an approach for protecting their homes from flooding. This study sought to establish why property-level flood protection is needed and secondly assess the extent of take up using Worcester as the study area. An exploratory questionnaire survey was conducted to achieve these objectives. After consultation of available literature it was established that the introduction of PLFP protection provided numerous benefits including limiting the health & psychological effects flooding poses, the direct financial benefits and also the possible influence on gaining flood insurance. Despite the benefits and the recognition given to PLFP by the government it was found that the overall take up of the measures was low, findings which were further backed up by data collected in the study area of Worcester with only 23% of the sample having introduced PLFP measures. Reasoning for the low take up numbers typically included; unawareness of the measures, low risk of flood event, installation costs and inability to introduce due to tenancy. Age was noted as a significant impacting factor in the study area with none of the respondents under 25 suggesting they had “a good amount of knowledge of PLFP measures” even when they claimed their properties to be at risk of flooding. Guidance and support is especially recommended to those who are unable to manage their own flood risk for e.g. social housing/rental tenants.

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States' option on federal flood insurance.

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Mitigation pays. It includes any activities that prevent an emergency, reduce the chance of an emergency happening, or lessen the damaging effects of unavoidable emergencies. Investing in mitigation steps now such as constructing barriers such as levees and purchasing flood insurance will help reduce the amount of structural damage to your home and financial loss from building and crop damage should a flood or flash flood occur.

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Mitigation pays. It includes any activities that prevent an emergency, reduce the chance of an emergency happening, or lessen the damaging effects of unavoidable emergencies. Investing in mitigation steps now such as constructing barriers such as levees and purchasing flood insurance will help reduce the amount of structural damage to your home and financial loss from building and crop damage should a flood or flash flood occur.

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Catastrophe risk models used by the insurance industry are likely subject to significant uncertainty, but due to their proprietary nature and strict licensing conditions they are not available for experimentation. In addition, even if such experiments were conducted, these would not be repeatable by other researchers because commercial confidentiality issues prevent the details of proprietary catastrophe model structures from being described in public domain documents. However, such experimentation is urgently required to improve decision making in both insurance and reinsurance markets. In this paper we therefore construct our own catastrophe risk model for flooding in Dublin, Ireland, in order to assess the impact of typical precipitation data uncertainty on loss predictions. As we consider only a city region rather than a whole territory and have access to detailed data and computing resources typically unavailable to industry modellers, our model is significantly more detailed than most commercial products. The model consists of four components, a stochastic rainfall module, a hydrological and hydraulic flood hazard module, a vulnerability module, and a financial loss module. Using these we undertake a series of simulations to test the impact of driving the stochastic event generator with four different rainfall data sets: ground gauge data, gauge-corrected rainfall radar, meteorological reanalysis data (European Centre for Medium-Range Weather Forecasts Reanalysis-Interim; ERA-Interim) and a satellite rainfall product (The Climate Prediction Center morphing method; CMORPH). Catastrophe models are unusual because they use the upper three components of the modelling chain to generate a large synthetic database of unobserved and severe loss-driving events for which estimated losses are calculated. We find the loss estimates to be more sensitive to uncertainties propagated from the driving precipitation data sets than to other uncertainties in the hazard and vulnerability modules, suggesting that the range of uncertainty within catastrophe model structures may be greater than commonly believed.

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Global flood hazard maps can be used in the assessment of flood risk in a number of different applications, including (re)insurance and large scale flood preparedness. Such global hazard maps can be generated using large scale physically based models of rainfall-runoff and river routing, when used in conjunction with a number of post-processing methods. In this study, the European Centre for Medium Range Weather Forecasts (ECMWF) land surface model is coupled to ERA-Interim reanalysis meteorological forcing data, and resultant runoff is passed to a river routing algorithm which simulates floodplains and flood flow across the global land area. The global hazard map is based on a 30 yr (1979–2010) simulation period. A Gumbel distribution is fitted to the annual maxima flows to derive a number of flood return periods. The return periods are calculated initially for a 25×25 km grid, which is then reprojected onto a 1×1 km grid to derive maps of higher resolution and estimate flooded fractional area for the individual 25×25 km cells. Several global and regional maps of flood return periods ranging from 2 to 500 yr are presented. The results compare reasonably to a benchmark data set of global flood hazard. The developed methodology can be applied to other datasets on a global or regional scale.

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Flooding can have a devastating impact on businesses, especially on small- and medium-sized enterprises (SMEs) who may be unprepared and vulnerable to the range of both direct and indirect impacts. SMEs may tend to focus on the direct tangible impacts of flooding, limiting their ability to realise the true costs of flooding. Greater understanding of the impacts of flooding is likely to contribute towards increased uptake of flood protection measures by SMEs, particularly during post-flood property reinstatement. This study sought to investigate the full range of impacts experienced by SMEs located in Cockermouth following the floods of 2009. The findings of a questionnaire survey of SMEs revealed that businesses not directly affected by the flooding experienced a range of impacts and that short-term impacts were given a higher significance. A strong correlation was observed between direct, physical flood impacts and post-flood costs of insurance. Significant increases in the costs of property insurance and excesses were noted, meaning that SMEs will be exposed to increased losses in the event of a future flood event. The findings from the research will enable policy makers and professional bodies to make informed decisions to improve the status of advice given to SMEs. The study also adds weight to the case for SMEs to consider investing in property-level flood risk adaptation measures, especially during the post flood reinstatement process. © 2012 Blackwell Publishing Ltd and The Chartered Institution of Water and Environmental Management (CIWEM).

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Worldwide floods have become one of the costliest weather-related hazards, causing large-scale human, economic, and environmental damage during the recent past. Recent years have seen a large number of such flood events around the globe, with Europe and the United Kingdom being no exception. Currently, about one in six properties in England is at risk of flooding (EA, 2009), and the risk is expected to further increase in the future (Evans et al., 2004). Although public spending on community-level flood protection has increased and some properties are protected by such protection schemes, many properties at risk of flooding may still be left without adequate protection. As far as businesses are concerned, this has led to an increased need for implementing strategies for property-level flood protection and business continuity, in order to improve their capacity to survive a flood hazard. Small and medium-sized enterprises (SMEs) constitute a significant portion of the UK business community. In the United Kingdom, more than 99% of private sector enterprises fall within the category of SMEs (BERR, 2008). They account for more than half of employment creation (59%) and turnover generation (52%) (BERR, 2008), and are thus considered the backbone of the UK economy. However, they are often affected disproportionately by natural hazards when compared with their larger counterparts (Tierney and Dahlhamer, 1996; Webb, Tierney, and Dahlhamer, 2000; Alesch et al., 2001) due to their increased vulnerability. Previous research reveals that small businesses are not adequately prepared to cope with the risk of natural hazards and to recover following such events (Tierney and Dahlhamer, 1996; Alesch et al., 2001; Yoshida and Deyle, 2005; Crichton, 2006; Dlugolecki, 2008). For instance, 90% of small businesses do not have adequate insurance coverage for their property (AXA Insurance UK, 2008) and only about 30% have a business continuity plan (Woodman, 2008). Not being adequately protected by community-level flood protection measures as well as property- and business-level protection measures threatens the survival of SMEs, especially those located in flood risk areas. This chapter discusses the potential effects of flood hazards on SMEs and the coping strategies that the SMEs can undertake to ensure the continuity of their business activities amid flood events. It contextualizes this discussion within a survey conducted under the Engineering and Physical Sciences Research Council (EPSRC) funded research project entitled “Community Resilience to Extreme Weather — CREW”.

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There is a wealth of literature on the design of ex post compensation mechanisms for natural disasters. However, more research needs to be done on the manner in which these mechanisms could steer citizens toward adopting individual-level preventive and protection measures in the face of flood risks. We have provided a comparative legal analysis of the financial compensation mechanisms following floods, be it through insurance, public funds, or a combination of both, with an empirical focus on Belgium, the Netherlands, England, and France. Similarities and differences between the methods in which these compensation mechanisms for flood damages enhance resilience were analyzed. The comparative analysis especially focused on the link between the recovery strategy on the one hand and prevention and mitigation strategies on the other. There is great potential within the recovery strategy for promoting preventive action, for example in terms of discouraging citizens from living in high-risk areas, or encouraging the uptake of mitigation measures, such as adaptive building. However, this large potential has yet to be realized, in part because of insufficient consideration and promotion of these connections within existing legal frameworks. We have made recommendations about how the linkages between strategies can be further improved. These recommendations relate to, among others, the promotion of resilient reinstatement through recovery mechanisms and the removal of legal barriers preventing the establishment of link-inducing measures.

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This paper applies Hierarchical Bayesian Models to price farm-level yield insurance contracts. This methodology considers the temporal effect, the spatial dependence and spatio-temporal models. One of the major advantages of this framework is that an estimate of the premium rate is obtained directly from the posterior distribution. These methods were applied to a farm-level data set of soybean in the State of the Parana (Brazil), for the period between 1994 and 2003. The model selection was based on a posterior predictive criterion. This study improves considerably the estimation of the fair premium rates considering the small number of observations.