  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.
