Stochastic modelling of flow sequences for improved prediction of fluvial flood hazards

Sandhya Patidar, Deonie Allen, Rick Haynes, Heather Haynes

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

Abstract

The availability of historical streamflow data of the desired length is often limited and, in these situations, the ability to synthetically generate statistically significant datasets becomes important. We previously developed a highly efficient stochastic modelling approach for the synthetic generation of daily streamflow sequences using the systematic combination of a hidden Markov model with the generalized Pareto distribution (the HMM-GP model). Daily streamflow sequences provide limited information on various significant small duration flooding events exceeding the peak over threshold values, but these are averaged out in the daily datasets. These small duration intense flooding events are often capable of causing significant damage and are important in conducting thorough flood risk management and flood risk assessment studies. This paper presents upgrades to our HMM-GP stochastic modelling approach and examines its efficiency in simulating streamflow at a temporal resolution of 15 minutes. The potential of the HMM-GP model in simulating a synthetic 15-minute streamflow series is investigated by comparing various statistical characteristics (e.g. percentiles, the probability density distribution and the autocorrelation function) of the observed streamflow records with 100 synthetically simulated streamflow time series. The proposed modelling schematics are robustly validated across case studies in four UK rivers (the Don, Nith, Dee and Tweed).
LanguageEnglish
Title of host publicationRiver to Reservoir
Subtitle of host publicationGeoscience to Engineering
PublisherGeological Society of London
ISBN (Electronic)9781786204004
DOIs
Publication statusPublished - 18 Dec 2018

Publication series

NameGeological Society Special Publications
PublisherGeological Society of London
Volume488
ISSN (Print)0305-8719
ISSN (Electronic)2041-4927

Fingerprint

streamflow
hazard
prediction
modeling
flooding
autocorrelation
risk assessment
time series
damage
river

Keywords

  • Hidden Markov Model
  • HMM-GP
  • River flow

Cite this

Patidar, S., Allen, D., Haynes, R., & Haynes, H. (2018). Stochastic modelling of flow sequences for improved prediction of fluvial flood hazards. In River to Reservoir: Geoscience to Engineering (Geological Society Special Publications; Vol. 488). Geological Society of London. https://doi.org/10.1144/SP488.4
Patidar, Sandhya ; Allen, Deonie ; Haynes, Rick ; Haynes, Heather. / Stochastic modelling of flow sequences for improved prediction of fluvial flood hazards. River to Reservoir: Geoscience to Engineering. Geological Society of London, 2018. (Geological Society Special Publications).
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Patidar, S, Allen, D, Haynes, R & Haynes, H 2018, Stochastic modelling of flow sequences for improved prediction of fluvial flood hazards. in River to Reservoir: Geoscience to Engineering. Geological Society Special Publications, vol. 488, Geological Society of London. https://doi.org/10.1144/SP488.4

Stochastic modelling of flow sequences for improved prediction of fluvial flood hazards. / Patidar, Sandhya; Allen, Deonie; Haynes, Rick; Haynes, Heather.

River to Reservoir: Geoscience to Engineering. Geological Society of London, 2018. (Geological Society Special Publications; Vol. 488).

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

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T1 - Stochastic modelling of flow sequences for improved prediction of fluvial flood hazards

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N2 - The availability of historical streamflow data of the desired length is often limited and, in these situations, the ability to synthetically generate statistically significant datasets becomes important. We previously developed a highly efficient stochastic modelling approach for the synthetic generation of daily streamflow sequences using the systematic combination of a hidden Markov model with the generalized Pareto distribution (the HMM-GP model). Daily streamflow sequences provide limited information on various significant small duration flooding events exceeding the peak over threshold values, but these are averaged out in the daily datasets. These small duration intense flooding events are often capable of causing significant damage and are important in conducting thorough flood risk management and flood risk assessment studies. This paper presents upgrades to our HMM-GP stochastic modelling approach and examines its efficiency in simulating streamflow at a temporal resolution of 15 minutes. The potential of the HMM-GP model in simulating a synthetic 15-minute streamflow series is investigated by comparing various statistical characteristics (e.g. percentiles, the probability density distribution and the autocorrelation function) of the observed streamflow records with 100 synthetically simulated streamflow time series. The proposed modelling schematics are robustly validated across case studies in four UK rivers (the Don, Nith, Dee and Tweed).

AB - The availability of historical streamflow data of the desired length is often limited and, in these situations, the ability to synthetically generate statistically significant datasets becomes important. We previously developed a highly efficient stochastic modelling approach for the synthetic generation of daily streamflow sequences using the systematic combination of a hidden Markov model with the generalized Pareto distribution (the HMM-GP model). Daily streamflow sequences provide limited information on various significant small duration flooding events exceeding the peak over threshold values, but these are averaged out in the daily datasets. These small duration intense flooding events are often capable of causing significant damage and are important in conducting thorough flood risk management and flood risk assessment studies. This paper presents upgrades to our HMM-GP stochastic modelling approach and examines its efficiency in simulating streamflow at a temporal resolution of 15 minutes. The potential of the HMM-GP model in simulating a synthetic 15-minute streamflow series is investigated by comparing various statistical characteristics (e.g. percentiles, the probability density distribution and the autocorrelation function) of the observed streamflow records with 100 synthetically simulated streamflow time series. The proposed modelling schematics are robustly validated across case studies in four UK rivers (the Don, Nith, Dee and Tweed).

KW - Hidden Markov Model

KW - HMM-GP

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Patidar S, Allen D, Haynes R, Haynes H. Stochastic modelling of flow sequences for improved prediction of fluvial flood hazards. In River to Reservoir: Geoscience to Engineering. Geological Society of London. 2018. (Geological Society Special Publications). https://doi.org/10.1144/SP488.4