Refining linear trend estimates from one dimensional time series data with autoregressive covariance modelling - An application to GRACE total water storage time series data

Refining linear trend estimates from one dimensional time series data with autoregressive covariance modelling - An application to GRACE total water storage time series data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Refining linear trend estimates from one dimensional time series data with autoregressive covariance modelling - An application to GRACE total water storage time series data Lukas Jendges, Jan Martin Brockmann This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5439927/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Jun, 2025 Read the published version in Stochastic Environmental Research and Risk Assessment → Version 1 posted 8 You are reading this latest preprint version Abstract Deriving trend functions from observed time series is one of the main tasks in the field of signal processing. Separating the observation into a deterministic function, a stochastic residual signal and a stochastic noise component using dedicated model representations enables to further study these individual components, e.g. screening for climate signals. Whereas the deterministic part is modelled as a linear combination of basis functions, the use of autoregressive processes to model the noise and signal is proposed. Within an iterative estimation scheme, the uncertainty information of the observed variables is properly modelled and carefully propagated to the resulting parameters. This enables the use of statistical testing and Least-Squares Collocation in further investigations of the separated signal components. In this study, the proposed iterative procedure is applied to relatively short total water storage time series derived from measurements of the satellite mission GRACE. The trend in total water storage is for instance relevant for climate studies, identifying regions getting drier or wetter. Accounting for the aforementioned covariance information based on autoregressive processes allows to use Hypothesis tests to identify regions with significant trends. On the contrary, the smoothed stochastic signal components are required to identify extreme/anomalous events like floods and droughts in the observed time series. Additionally, to improve the estimation of the stochastic signal, data from numerical models are used to estimate the process characteristics. autoregressive processessignal separationtrend estimationcovariance modellingtotal water storageGRACE Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Jun, 2025 Read the published version in Stochastic Environmental Research and Risk Assessment → Version 1 posted Editorial decision: Revision requested 23 Apr, 2025 Reviews received at journal 22 Apr, 2025 Reviewers agreed at journal 18 Mar, 2025 Reviewers agreed at journal 29 Dec, 2024 Reviewers invited by journal 29 Dec, 2024 Editor assigned by journal 14 Nov, 2024 Submission checks completed at journal 13 Nov, 2024 First submitted to journal 12 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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