A Bayesian Hierarchical Network Model for Daily Streamflow Ensemble Forecasting

Ossandón, Álvaro; Rajagopalan, Balaji; Lall, Upmanu; Nanditha, J. S.; Mishra, Vimal

doi:10.1029/2021wr029920

i

A Bayesian Hierarchical Network Model for Daily Streamflow Ensemble Forecasting

Supporting Files

2021
By Ossandón, Álvaro ; Rajagopalan, Balaji ; Lall, Upmanu ; ...

Details

Journal Title:

Water Resources Research
Personal Author:

Ossandón, Álvaro ; Rajagopalan, Balaji ; Lall, Upmanu ; Nanditha, J. S. ; Mishra, Vimal
NOAA Program & Office:

CIRES (Cooperative Institute for Research in Environmental Sciences)
Description:

A novel Bayesian Hierarchical Network Model (BHNM) for ensemble forecasts of daily streamflow is presented that uses the spatial dependence induced by the river network topology and hydrometeorological variables from the upstream contributing area between station gauges. 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
Keywords:

Water Science and Technology
Source:

Water Resources Research, 57(9)
DOI:

https://doi.org/10.1029/2021wr029920
ISSN:

0043-1397 ; 1944-7973
Format:

PDF
Publisher:

American Geophysical Union (AGU)
Document Type:

Journal Article
Rights Information:

Other
Compliance:

Library
Main Document Checksum:

urn:sha256:51b5481df4676f7e3b65bc31a6d70a1f2eca299f87a448579f53cbad315ca78e
Download URL:

https://repository.library.noaa.gov/view/noaa/54275/noaa_54275_DS1.pdf
File Type:

[PDF - 3.82 MB ]

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