Skip to main content Accessibility help
×
Hostname: page-component-848d4c4894-5nwft Total loading time: 0 Render date: 2024-04-30T23:53:23.819Z Has data issue: false hasContentIssue false

5 - New plotting position rule for flood records considering historical data and palaeologic information

Published online by Cambridge University Press:  07 May 2010

Guo Sheng Lian
Affiliation:
Department of Engineering Hydrology, University College Galway, Ireland, on leave from Wuhan University of Hydraulic and Electric Engineering, Wuhan, People's Republic of China
Zbigniew W. Kundzewicz
Affiliation:
World Meteorological Organization, Geneva
Get access

Summary

ABSTRACT The graphical curve fitting procedure has been favoured by many hydrologists and engineers, and the plotting positions are required both for the display of flood records and for the quantile estimation. The existing plotting position formulae which consider historical floods and palaeologic information are reviewed and discussed. The plotting positions for systematically recorded floods below the threshold of perception must be adjusted to reflect the additional information provided by the pre-gauging period if the historical flood data and the systematic records are to be analyzed jointly in a consistent and statistically efficient manner. However, all available formulae are unlikely to adjust these plotting positions properly. It is felt that the traditional rule and exceedance rule assumptions are inconsistent with the floods over and below the threshold of perception of historical floods. A new type of formula is proposed and examined. Simulation studies and numerical examples show that the new formula type performs better than the traditional rule and competitive to the exceedance rule. The Weibull based formulae result in large bias in quantile estimation. If an unbiased plotting position formula were required, then the proposed modified exceedance Cunnane formula would be the best selection.

INTRODUCTION

Probability plots are much used in hydrology as a diagnostic tool to indicate the degree to which data conform to a specific probability distribution, as a means of identifying outlier, in order to infer quantile values.

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 1995

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×