Bayesian spatio-temporal models for stream networks

TL;DR

This paper introduces new Bayesian spatio-temporal models tailored for stream networks, effectively capturing spatial dependence via stream distance and temporal autocorrelation, demonstrated through a water temperature case study.

Contribution

The paper presents a novel family of Bayesian spatio-temporal models specifically designed for stream networks, incorporating stream distance and vector autoregression.

Findings

Models perform well with real stream network data

Out-of-sample RMSPE shows improved accuracy

Effective in near real-time water quality monitoring

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.