Statistical modelling approach for integrating probabilistic climate projections with the river flow data

Sandhya Patidar, Kazi Hassan, Heather Haynes, Gareth Pender

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

1 Citation (Scopus)


To address some of the key challenges arising due to the uncertain climate change, paper proposes
suits of novel stochastic modelling techniques, capable of integrating probabilistic climate information
within the synthetic streamflow series generation procedure. To achieve this, a thorough statistical analysis of
the climate information, available from the UK daily rainfall data, part of the Met Office Integrated Data Archive
System (MIDAS), the UK Climate Projections (UKCP09) and the daily streamflow time series of the
case study river (the Dee, of the UK) has been conducted. The model is first calibrated, using the historic flow
and climate timeseries, and then simulated with the probabilistic climate projections available from UKCP09.
Stochastic modeling techniques used are mainly the Hidden Markov Models (HMM) combined with the Generalised
Pareto (GP) distribution and Probability Distribution models. Potential impacts of the climate change
on the statistical characteristics of the river flow data has been explored.
Original languageEnglish
Title of host publicationRiver Flow 2016
EditorsGeorge Constantinescu, Marcelo Garcia, Dan Hanes
PublisherCRC Press
ISBN (Electronic)978-1-138-02913-2
Publication statusPublished - Jun 2016
EventRiver Flow 2016: Eighth International Conference on Fluvial Hydraulics - St. Louis, Mo., United States
Duration: 12 Jul 201615 Jul 2016


ConferenceRiver Flow 2016
Abbreviated titleRIVER FLOW 2016
Country/TerritoryUnited States
CitySt. Louis, Mo.


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