Bayesian spatio-temporal models for stream networks

Edgar Santos-Fernandez, Jay M. Ver Hoef, Erin E. Peterson, James McGree, Daniel J. Isaak and Kerrie Mengersen

Computational Statistics & Data Analysis, 2022, vol. 170, issue C

Abstract: Spatio-temporal models are widely used in many research areas including ecology. The recent proliferation of the use of in-situ sensors in streams and rivers supports space-time water quality modelling and monitoring in near real-time. A new family of spatio-temporal models is introduced. These models incorporate spatial dependence using stream distance while temporal autocorrelation is captured using vector autoregression approaches. Several variations of these novel models are proposed using a Bayesian framework. The results show that our proposed models perform well using spatio-temporal data collected from real stream networks, particularly in terms of out-of-sample RMSPE. This is illustrated considering a case study of water temperature data in the northwestern United States.

Keywords: Bayesian model; Space-time; Linear regression; Branching network; Vector autoregression (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:170:y:2022:i:c:s0167947322000263

DOI: 10.1016/j.csda.2022.107446

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