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A Bayesian Hierarchical Network Model for Daily Streamflow Ensemble Forecasting
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  • Alvaro Ossandon,
  • Balaji Rajagopalan,
  • Upmanu Lall,
  • Nanditha J. S.,
  • Vimal Mishra
Alvaro Ossandon
University of Colorado Boulder

Corresponding Author:[email protected]

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Balaji Rajagopalan
University of Colorado Boulder
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Upmanu Lall
Columbia University
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Nanditha J. S.
Indian Institute of Technology Gandhinagar
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Vimal Mishra
Indian Institute of Technology, Gandhinagar
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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.
Sep 2021Published in Water Resources Research volume 57 issue 9. 10.1029/2021WR029920