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.

30 pages, 10 figs

References in corpus (1)

• Space-Time Covariance Models on Networks with An Application on Streams

Cited by in corpus (3)

• A Deep Learning Synthetic Likelihood Approximation of a Non-stationary Spatial Model for Extreme Streamflow Forecasting
• Monitoring pollution pathways in river water by predictive path modelling using untargeted GC-MS measurements
• Modeling Extremal Streamflow using Deep Learning Approximations and a Flexible Spatial Process