Bayesian spatio-temporal models for stream networks

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.

Citation

Santos-Fernandez, Edgar; Ver Hoef, Jay M.; Peterson, Erin E.; McGree, James; Isaak, Daniel J.; Mengersen, Kerrie. 2022. Bayesian spatio-temporal models for stream networks. Computational Statistics and Data Analysis. 170: 107446.