SPACE‐TIME MODELING OF VECTOR HYDROLOGIC SEQUENCES1

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Abstract
Stochastic modeling of vector hydrologic sequences is examined with a general class of space‐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‐stage iterative space‐time model building procedure is illustrated using average monthly streamfiow data for a four‐station network of the Southeastern Hydropower System.