Bayesian spatio-temporal models for stream networks

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. (C)& nbsp;2022 Elsevier B.V. All rights reserved.

Automated summary

Spatio-temporal models are widely used in various research fields, including ecology. The use of in-situ sensors in streams and rivers has recently increased, enabling near real-time water quality modeling and monitoring. A new family of spatio-temporal models is introduced, incorporating spatial dependence and temporal autocorrelation. Using a Bayesian framework, several variations of these models are proposed. The results demonstrate the good performance of the proposed models using spatio-temporal data from real stream networks, particularly in terms of out-of-sample RMSPE. A case study on water temperature data in the northwestern United States is presented.