Space-Time Modeling of Vector Hydrologic Sequences

<p> Stochastic modeling of vector hydrologic sequences is examined with a general class of space&hyphen;time autoregressive integrated moving average (STARIMA) models. The models describe spatial and temporal autocorrelatjon, through dependent variables lagged both in space and time. The model structures incorporate a hierarchical ordering scheme to map the vector of observations into a network configuration. The neighboring structure used introduces a physical/geographical hierarchy to enable the model identification procedures to assist in determining appropriate correlative relationships. The three&hyphen;stage iterative space&hyphen;time model building procedure is illustrated using average monthly streamfiow data for a four&hyphen;station network of the Southeastern Hydropower System.</p>