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  • Mindham and Tych_2019_submitted

    Rights statement: This is the author’s version of a work that was accepted for publication in Environmental Modelling & Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling & Software, 121, 2019 DOI: 10.1016/j.envsoft.2019.104503

    Accepted author manuscript, 613 KB, PDF document

    Embargo ends: 19/08/20

    Available under license: CC BY-NC-ND

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Dynamic harmonic regression and irregular sampling; avoiding pre-processing and minimising modelling assumptions

Research output: Contribution to journalJournal article

Published
Article number104503
<mark>Journal publication date</mark>1/11/2019
<mark>Journal</mark>Environmental Modelling and Software
Volume121
Number of pages11
Publication statusPublished
Early online date19/08/19
Original languageEnglish

Abstract

Many environmental time-series measurements are characterised by irregular sampling. A significant improvement of the Dynamic Harmonic Regression (DHR) modelling technique to accommodate irregular sampled time-series, without the need for data pre-processing, has been developed. Taylor's series is used to obtain the time-step state increments, modifying the transition equation matrices. This allows the user to avoid artefacts arising and insertion of assumptions from interpolation and regularisation of the data to a regular time-base and makes DHR more consistent with the Data-Based Mechanistic approach to modelling environmental systems. The new technique implemented as a Matlab package has been tested on demanding simulated data-sets and demonstrated on various environmental time-series data with significantly varying sampling times. The results have been compared with standard DHR, where possible, and the method reduces analysis time and produces unambiguous results (by removing the need for pre-processing – always based on assumptions) based only on the observed environmental data.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Environmental Modelling & Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling & Software, 121, 2019 DOI: 10.1016/j.envsoft.2019.104503