Spatial risk assessment for extreme river flows
The UK has in recent years experienced a series of fluvial flooding events which have simultaneously affected communities over different parts of the country. For the co-ordination of flood mitigation activities and for the insurance and reinsurance industries, knowledge of the spatial characteristi...
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Veröffentlicht in: | Applied statistics 2009-12, Vol.58 (5), p.601-618 |
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description | The UK has in recent years experienced a series of fluvial flooding events which have simultaneously affected communities over different parts of the country. For the co-ordination of flood mitigation activities and for the insurance and reinsurance industries, knowledge of the spatial characteristics of fluvial flooding is important. Past research into the spatiotemporal risk of fluvial flooding has largely been restricted to empirical estimates of risk measures. A weakness with such an approach is that there is no basis for extrapolation of these estimates to rarer events, which is required as empirical evidence suggests that larger events tend to be more localized in space. We adopt a model-based approach using the methods of Heffernan and Tawn. However, the large proportion of missing data over a network of sites makes direct application of this method highly inefficient. We therefore propose an extension of the Heffernan and Tawn method which accounts for missing values. Furthermore, as the risk measures are spatiotemporal an extension of the Heffernan and Tawn method is also required to handle temporal dependence. We illustrate the benefits of the procedure with a simulation study and by assessing spatial dependence over four fluvial sites in Scotland. |
doi_str_mv | 10.1111/j.1467-9876.2009.00672.x |
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For the co-ordination of flood mitigation activities and for the insurance and reinsurance industries, knowledge of the spatial characteristics of fluvial flooding is important. Past research into the spatiotemporal risk of fluvial flooding has largely been restricted to empirical estimates of risk measures. A weakness with such an approach is that there is no basis for extrapolation of these estimates to rarer events, which is required as empirical evidence suggests that larger events tend to be more localized in space. We adopt a model-based approach using the methods of Heffernan and Tawn. However, the large proportion of missing data over a network of sites makes direct application of this method highly inefficient. We therefore propose an extension of the Heffernan and Tawn method which accounts for missing values. Furthermore, as the risk measures are spatiotemporal an extension of the Heffernan and Tawn method is also required to handle temporal dependence. We illustrate the benefits of the procedure with a simulation study and by assessing spatial dependence over four fluvial sites in Scotland.</description><identifier>ISSN: 0035-9254</identifier><identifier>EISSN: 1467-9876</identifier><identifier>DOI: 10.1111/j.1467-9876.2009.00672.x</identifier><identifier>CODEN: APSTAG</identifier><language>eng</language><publisher>Oxford, UK: Oxford, UK : Blackwell Publishing Ltd</publisher><subject>Acceleration of convergence ; Analytical estimating ; Applications ; Confidence interval ; Datasets ; Estimation methods ; Exact sciences and technology ; Extreme value theory ; Floods ; Gaussian distributions ; Insurance, economics, finance ; Markov processes ; Mathematics ; Missing data ; Modeling ; Multivariate analysis ; Multivariate extreme values ; Numerical analysis ; Numerical analysis. Scientific computation ; Parametric models ; Probability and statistics ; Probability theory and stochastic processes ; Reinsurance ; Reliability, life testing, quality control ; Risk assessment ; Risk management ; River flows ; Rivers ; Sciences and techniques of general use ; Simulations ; Spatial risk assessment ; Spatiotemporal extremal dependence ; Statistical methods ; Statistics ; Stream flow ; Studies ; Water management ; Watersheds</subject><ispartof>Applied statistics, 2009-12, Vol.58 (5), p.601-618</ispartof><rights>Copyright 2009 The Royal Statistical Society and Blackwell Publishing Ltd.</rights><rights>2009 Royal Statistical Society</rights><rights>2009 INIST-CNRS</rights><rights>Copyright Blackwell Publishing Ltd. 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For the co-ordination of flood mitigation activities and for the insurance and reinsurance industries, knowledge of the spatial characteristics of fluvial flooding is important. Past research into the spatiotemporal risk of fluvial flooding has largely been restricted to empirical estimates of risk measures. A weakness with such an approach is that there is no basis for extrapolation of these estimates to rarer events, which is required as empirical evidence suggests that larger events tend to be more localized in space. We adopt a model-based approach using the methods of Heffernan and Tawn. However, the large proportion of missing data over a network of sites makes direct application of this method highly inefficient. We therefore propose an extension of the Heffernan and Tawn method which accounts for missing values. Furthermore, as the risk measures are spatiotemporal an extension of the Heffernan and Tawn method is also required to handle temporal dependence. We illustrate the benefits of the procedure with a simulation study and by assessing spatial dependence over four fluvial sites in Scotland.</description><subject>Acceleration of convergence</subject><subject>Analytical estimating</subject><subject>Applications</subject><subject>Confidence interval</subject><subject>Datasets</subject><subject>Estimation methods</subject><subject>Exact sciences and technology</subject><subject>Extreme value theory</subject><subject>Floods</subject><subject>Gaussian distributions</subject><subject>Insurance, economics, finance</subject><subject>Markov processes</subject><subject>Mathematics</subject><subject>Missing data</subject><subject>Modeling</subject><subject>Multivariate analysis</subject><subject>Multivariate extreme values</subject><subject>Numerical analysis</subject><subject>Numerical analysis. Scientific computation</subject><subject>Parametric models</subject><subject>Probability and statistics</subject><subject>Probability theory and stochastic processes</subject><subject>Reinsurance</subject><subject>Reliability, life testing, quality control</subject><subject>Risk assessment</subject><subject>Risk management</subject><subject>River flows</subject><subject>Rivers</subject><subject>Sciences and techniques of general use</subject><subject>Simulations</subject><subject>Spatial risk assessment</subject><subject>Spatiotemporal extremal dependence</subject><subject>Statistical methods</subject><subject>Statistics</subject><subject>Stream flow</subject><subject>Studies</subject><subject>Water management</subject><subject>Watersheds</subject><issn>0035-9254</issn><issn>1467-9876</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>X2L</sourceid><recordid>eNqNkt9v0zAQxyMEEmXwJyAiJHhLdrbPP_LAA6pY2VSBRDfxeHJSB5KlTbHTrf3vcZapD7wMS-dE_n6-l8udkyRlkLO4ztucodJZYbTKOUCRAyjN88OzZHYSniczACGzgkt8mbwKoYW4GOAsOV_t7NDYLvVNuE1tCC6EjdsOad371B0G7zYuanfOp3XX34fXyYvadsG9eXyeJTcXX67nX7Pl98Xl_PMyq5RCnqHRTEChHBSlZRyRl6VyWhtT1vXaCclgLQ1ahWs0pULmENfMOoNSCO2MOEs-Tnl3vv-zd2GgTRMq13V26_p9IKF5gQyKp0EJWKCRT4J8zMbl-On3_4Btv_fb-LfEgenYYzZCZoIq34fgXU0732ysPxIDGgdDLY39p7H_NA6GHgZDh2i9mqze7Vx18pWdbXsfQkV3JKw0cTvGeLAK24xnMXYxFDBSzNDvYROTfXgs1obKdrW326oJp6ScgwaUELlPE3ffdO7438XSj9VqHt-i_-3kb8PQ-5MfQSJTTEc9m_QmDO5w0q2_JaWFlvTz24KWml1zvBC0iPy7ia9tT_ZXvH10s4rtFcBUwSRT4i-1_Nip</recordid><startdate>200912</startdate><enddate>200912</enddate><creator>Keef, Caroline</creator><creator>Tawn, Jonathan</creator><creator>Svensson, Cecilia</creator><general>Oxford, UK : Blackwell Publishing Ltd</general><general>Blackwell Publishing Ltd</general><general>Wiley-Blackwell</general><general>Royal Statistical Society</general><general>Oxford University Press</general><scope>FBQ</scope><scope>BSCLL</scope><scope>IQODW</scope><scope>DKI</scope><scope>X2L</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8BJ</scope><scope>8FD</scope><scope>FQK</scope><scope>JBE</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7U1</scope><scope>7U2</scope><scope>C1K</scope></search><sort><creationdate>200912</creationdate><title>Spatial risk assessment for extreme river flows</title><author>Keef, Caroline ; Tawn, Jonathan ; Svensson, Cecilia</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c6642-48713096e09ba12442bb6e7788bffde3510d584a64d48b641e44d1ae845337e83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Acceleration of convergence</topic><topic>Analytical estimating</topic><topic>Applications</topic><topic>Confidence interval</topic><topic>Datasets</topic><topic>Estimation methods</topic><topic>Exact sciences and technology</topic><topic>Extreme value theory</topic><topic>Floods</topic><topic>Gaussian distributions</topic><topic>Insurance, economics, finance</topic><topic>Markov processes</topic><topic>Mathematics</topic><topic>Missing data</topic><topic>Modeling</topic><topic>Multivariate analysis</topic><topic>Multivariate extreme values</topic><topic>Numerical analysis</topic><topic>Numerical analysis. Scientific computation</topic><topic>Parametric models</topic><topic>Probability and statistics</topic><topic>Probability theory and stochastic processes</topic><topic>Reinsurance</topic><topic>Reliability, life testing, quality control</topic><topic>Risk assessment</topic><topic>Risk management</topic><topic>River flows</topic><topic>Rivers</topic><topic>Sciences and techniques of general use</topic><topic>Simulations</topic><topic>Spatial risk assessment</topic><topic>Spatiotemporal extremal dependence</topic><topic>Statistical methods</topic><topic>Statistics</topic><topic>Stream flow</topic><topic>Studies</topic><topic>Water management</topic><topic>Watersheds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Keef, Caroline</creatorcontrib><creatorcontrib>Tawn, Jonathan</creatorcontrib><creatorcontrib>Svensson, Cecilia</creatorcontrib><collection>AGRIS</collection><collection>Istex</collection><collection>Pascal-Francis</collection><collection>RePEc IDEAS</collection><collection>RePEc</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>Technology Research Database</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Risk Abstracts</collection><collection>Safety Science and Risk</collection><collection>Environmental Sciences and Pollution Management</collection><jtitle>Applied statistics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Keef, Caroline</au><au>Tawn, Jonathan</au><au>Svensson, Cecilia</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Spatial risk assessment for extreme river flows</atitle><jtitle>Applied statistics</jtitle><date>2009-12</date><risdate>2009</risdate><volume>58</volume><issue>5</issue><spage>601</spage><epage>618</epage><pages>601-618</pages><issn>0035-9254</issn><eissn>1467-9876</eissn><coden>APSTAG</coden><abstract>The UK has in recent years experienced a series of fluvial flooding events which have simultaneously affected communities over different parts of the country. For the co-ordination of flood mitigation activities and for the insurance and reinsurance industries, knowledge of the spatial characteristics of fluvial flooding is important. Past research into the spatiotemporal risk of fluvial flooding has largely been restricted to empirical estimates of risk measures. A weakness with such an approach is that there is no basis for extrapolation of these estimates to rarer events, which is required as empirical evidence suggests that larger events tend to be more localized in space. We adopt a model-based approach using the methods of Heffernan and Tawn. However, the large proportion of missing data over a network of sites makes direct application of this method highly inefficient. We therefore propose an extension of the Heffernan and Tawn method which accounts for missing values. Furthermore, as the risk measures are spatiotemporal an extension of the Heffernan and Tawn method is also required to handle temporal dependence. We illustrate the benefits of the procedure with a simulation study and by assessing spatial dependence over four fluvial sites in Scotland.</abstract><cop>Oxford, UK</cop><pub>Oxford, UK : Blackwell Publishing Ltd</pub><doi>10.1111/j.1467-9876.2009.00672.x</doi><tpages>18</tpages><oa>free_for_read</oa></addata></record> |
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source | Jstor Complete Legacy; Oxford University Press Journals All Titles (1996-Current); RePEc; Wiley Online Library Journals Frontfile Complete; Business Source Complete; JSTOR Mathematics & Statistics |
subjects | Acceleration of convergence Analytical estimating Applications Confidence interval Datasets Estimation methods Exact sciences and technology Extreme value theory Floods Gaussian distributions Insurance, economics, finance Markov processes Mathematics Missing data Modeling Multivariate analysis Multivariate extreme values Numerical analysis Numerical analysis. Scientific computation Parametric models Probability and statistics Probability theory and stochastic processes Reinsurance Reliability, life testing, quality control Risk assessment Risk management River flows Rivers Sciences and techniques of general use Simulations Spatial risk assessment Spatiotemporal extremal dependence Statistical methods Statistics Stream flow Studies Water management Watersheds |
title | Spatial risk assessment for extreme river flows |
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