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A Volterra Process Model for River Water Temperature

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

Abstract

Water temperature as a key indicator of river environments evolves dynamically as well as stochastically. We introduce a new mathematical framework for modeling river water temperature at an observation station based on a Volterra process: a non-Markovian model. The non-Markovian nature arises from a non-exponential autocorrelation function, which is effectively captured by a completely monotone kernel. Parameters of the model are identified for a study site in Japan where the water temperature has been measured at a 10-min interval. We show that the observed time series of the water temperature is separated into seasonal and stochastic parts, and that the non-exponential autocorrelation function is fitted well by the proposed model. A brief application of the model to statistical evaluation of the river water temperature subject to misspecification is finally presented.

Original languageEnglish
Title of host publicationSustainable Development of Water and Environment - Proceedings of the ICSDWE2022
EditorsHan-Yong Jeon
PublisherSpringer Science and Business Media Deutschland GmbH
Pages95-106
Number of pages12
ISBN (Print)9783031074998
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event5th International Conference on Sustainable Development of Water and Environment, ICSDWE 2022 - Virtual, Online
Duration: Mar 17 2022Mar 18 2022

Publication series

NameEnvironmental Science and Engineering
ISSN (Print)1863-5520
ISSN (Electronic)1863-5539

Conference

Conference5th International Conference on Sustainable Development of Water and Environment, ICSDWE 2022
CityVirtual, Online
Period3/17/223/18/22

Keywords

  • Markovian lift
  • Model identification
  • River water temperature

ASJC Scopus subject areas

  • Environmental Engineering
  • Information Systems

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