A model for stochastic simulation of daily streamflow

Sandhya Patidar, Kazi Hassan, Heather Haynes, Gareth Pender, Douglas Pender

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Assessment of fluvial flood risk is typically based on a single 1:N year extreme flow event, which cannot assure the long-term sustainability of flood defense assets. To achieve long-term sustainability, the estimation of daily streamflow time series is of paramount importance. Traditional, indirect approaches, combining stochastic simulation of rainfall with hydrological rainfall-runoff models are limited by uncertainties in model calibration and computational expense. For stochastic modelling of daily streamflow, this paper presents a simple, direct, approach that combines a Hidden Markov Model with an extreme value distribution. Model has been validated across three hydrologically distinct catchments in the UK. Results show that the model produces excellent performance (relative mean absolute differences of < 2%), appropriately captures extreme events, and is generically applicable across a range of hydrological regimes. Thus, proposed model can be readily applied to a range of catchment types for various flood risk studies.
Original languageEnglish
Title of host publicationRiver FLow 2016
EditorsGeorge Constantinescu, Marcelo Garcia, Dan Hanes
PublisherCRC Press
Pages1910-1915
ISBN (Electronic)978-1-138-02913-2
Publication statusPublished - 11 Jul 2016
EventRiver Flow 2016: Eighth International Conference on Fluvial Hydraulics - St. Louis, Mo., United States
Duration: 12 Jul 201615 Jul 2016

Conference

ConferenceRiver Flow 2016
Abbreviated titleRIVER FLOW 2016
CountryUnited States
CitySt. Louis, Mo.
Period12/07/1615/07/16

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