Consequences to flood management of using different probability distributions to estimate extreme rainfall

This data was imported from PubMed:

Authors: Esteves, L.S.

http://eprints.bournemouth.ac.uk/20931/

Journal: J Environ Manage

Volume: 115

Pages: 98-105

eISSN: 1095-8630

DOI: 10.1016/j.jenvman.2012.11.013

The design of flood defences, such as pumping stations, takes into consideration the predicted return periods of extreme precipitation depths. Most commonly these are estimated by fitting the Generalised Extreme Value (GEV) or the Generalised Pareto (GP) probability distributions to the annual maxima series or to the partial duration series. In this paper, annual maxima series of precipitation depths obtained from daily rainfall data measured at three selected stations in southeast UK are analysed using a range of probability distributions. These analyses demonstrate that GEV or GP distributions do not always provide the best fit to the data, and that extreme rainfall estimates for long return periods (e.g. 1 in 100 years) can differ by more than 40% depending on the distribution model used. Since a large number of properties in the UK and elsewhere currently benefit from flood defences designed using the GEV or GP probability distributions, the results from this study question whether the level of protection they offer are appropriate in locations where data demonstrate clearly that alternative probability distributions may have a better fit to the local rainfall data. This work: (a) raises awareness of the limitations of common practices in extreme rainfall analysis; (b) suggests a simple way forward to incorporate uncertainties that is easily applicable to local rainfall data worldwide; and thus (c) contributes to improve flood risk management.

This source preferred by Luciana Esteves

This data was imported from Scopus:

Authors: Esteves, L.S.

http://eprints.bournemouth.ac.uk/20931/

Journal: Journal of Environmental Management

Volume: 115

Pages: 98-105

eISSN: 1095-8630

ISSN: 0301-4797

DOI: 10.1016/j.jenvman.2012.11.013

The design of flood defences, such as pumping stations, takes into consideration the predicted return periods of extreme precipitation depths. Most commonly these are estimated by fitting the Generalised Extreme Value (GEV) or the Generalised Pareto (GP) probability distributions to the annual maxima series or to the partial duration series. In this paper, annual maxima series of precipitation depths obtained from daily rainfall data measured at three selected stations in southeast UK are analysed using a range of probability distributions. These analyses demonstrate that GEV or GP distributions do not always provide the best fit to the data, and that extreme rainfall estimates for long return periods (e.g. 1 in 100 years) can differ by more than 40% depending on the distribution model used. Since a large number of properties in the UK and elsewhere currently benefit from flood defences designed using the GEV or GP probability distributions, the results from this study question whether the level of protection they offer are appropriate in locations where data demonstrate clearly that alternative probability distributions may have a better fit to the local rainfall data. This work: (a) raises awareness of the limitations of common practices in extreme rainfall analysis; (b) suggests a simple way forward to incorporate uncertainties that is easily applicable to local rainfall data worldwide; and thus (c) contributes to improve flood risk management. © 2012 Elsevier Ltd.

This data was imported from Web of Science (Lite):

Authors: Esteves, L.S.

http://eprints.bournemouth.ac.uk/20931/

Journal: JOURNAL OF ENVIRONMENTAL MANAGEMENT

Volume: 115

Pages: 98-105

eISSN: 1095-8630

ISSN: 0301-4797

DOI: 10.1016/j.jenvman.2012.11.013

This data was imported from Europe PubMed Central:

Authors: Esteves, L.S.

http://eprints.bournemouth.ac.uk/20931/

Journal: Journal of environmental management

Volume: 115

Pages: 98-105

eISSN: 1095-8630

ISSN: 0301-4797

The design of flood defences, such as pumping stations, takes into consideration the predicted return periods of extreme precipitation depths. Most commonly these are estimated by fitting the Generalised Extreme Value (GEV) or the Generalised Pareto (GP) probability distributions to the annual maxima series or to the partial duration series. In this paper, annual maxima series of precipitation depths obtained from daily rainfall data measured at three selected stations in southeast UK are analysed using a range of probability distributions. These analyses demonstrate that GEV or GP distributions do not always provide the best fit to the data, and that extreme rainfall estimates for long return periods (e.g. 1 in 100 years) can differ by more than 40% depending on the distribution model used. Since a large number of properties in the UK and elsewhere currently benefit from flood defences designed using the GEV or GP probability distributions, the results from this study question whether the level of protection they offer are appropriate in locations where data demonstrate clearly that alternative probability distributions may have a better fit to the local rainfall data. This work: (a) raises awareness of the limitations of common practices in extreme rainfall analysis; (b) suggests a simple way forward to incorporate uncertainties that is easily applicable to local rainfall data worldwide; and thus (c) contributes to improve flood risk management.

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