A Bayesian Hierarchical Network Model for Daily Streamflow Ensemble Forecasting | CU Experts

A novel Bayesian Hierarchical Network Model (BHNM) for ensemble; forecasts of daily streamflow that uses the spatial dependence induced; by the river network topology and hydrometeorological variables from the; upstream contributing area between station gauges is presented. Model; parameters are allowed to vary with time as functions of selected; covariates for each day. Using the network structure to incorporate flow; information from upstream gauges and precipitation from the immediate; contributing area as covariates allows one to model the spatial; correlation of flows simultaneously and parsimoniously. An application; to daily monsoon period (July-August) streamflow at four gauges in the; Narmada basin in central India for the period 1978 – 2014 is presented.; The covariates include daily streamflow from upstream gauges or from the; gauge above of the upstream gauges depending on travel times and daily,; 2-day, or 3-day precipitation from the area between two stations. The; model validation indicates that the model is highly skillful relative to; climatology and relative to a null-model of linear regression. We; applied the BHNM out of sample to two high flooding years. High skill in; both the timing and magnitude of the events is demonstrated.

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