Dayflow-PR: High-Resolution Streamflow Reanalysis for Puerto Rico, Version 1.0

Ghimire, Ganesh; Gangrade, Sudershan; Kao, Shih-Chieh; Hansen, Carly; DeNeale, Scott

doi:10.13139/OLCF/2583869

Title: Dayflow-PR: High-Resolution Streamflow Reanalysis for Puerto Rico, Version 1.0

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Abstract

This dataset provides a high-resolution historical streamflow reanalysis for NHDPlusV2 stream reaches across Puerto Rico (PR) spanning 1950–2019. The reanalysis is generated using the VIC-RAPID hydrologic modeling framework, forced with both sub-daily and daily meteorological forcings from Daymet. Runoff is simulated on 1- and 6-km grids, and the resulting total runoff is routed through the NHDPlusV2 river network using the RAPID routing model to produce Naturalized Streamflow Reanalysis. Where complete observational records are available, streamflows are assimilated (substituted) and subsequently routed downstream through the river network to produce Assimilated Streamflow Reanalysis. The dataset includes streamflow outputs from eight distinct hydrologic modeling configurations along with key performance evaluation metrics at daily and monthly scales, supporting a wide range of water resource applications.This dataset is derived to support the "Non-Powered Dam Assessment", as well as "9505 Secure Water Assessment" projects for the US Department of Energy (DOE) Water Power Technologies Office (WPTO). For further details, refer to Ghimire et al. (2023), Kao et al. (2024), and Ghimire et al. (2025).

Authors:

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Publication Date:: Wed Oct 01 04:00:00 UTC 2025

DOE Contract Number:: AC05-00OR22725

Research Org.:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE

OSTI Identifier:: 3001985

DOI:: https://doi.org/10.13139/OLCF/2583869

Citation Formats


                    Ghimire, Ganesh, Gangrade, Sudershan, Kao, Shih-Chieh, Hansen, Carly, and DeNeale, Scott. Dayflow-PR: High-Resolution Streamflow Reanalysis for Puerto Rico, Version 1.0.  United States: N. p., 2025. 
        Web.  doi:10.13139/OLCF/2583869.

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                    Ghimire, Ganesh, Gangrade, Sudershan, Kao, Shih-Chieh, Hansen, Carly, & DeNeale, Scott. Dayflow-PR: High-Resolution Streamflow Reanalysis for Puerto Rico, Version 1.0.  United States.  doi:https://doi.org/10.13139/OLCF/2583869

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                    Ghimire, Ganesh, Gangrade, Sudershan, Kao, Shih-Chieh, Hansen, Carly, and DeNeale, Scott. 2025.  
"Dayflow-PR: High-Resolution Streamflow Reanalysis for Puerto Rico, Version 1.0".  United States.  doi:https://doi.org/10.13139/OLCF/2583869.  https://www.osti.gov/servlets/purl/3001985. Pub date:Wed Oct 01 04:00:00 UTC 2025

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@article{osti_3001985,

  title        = {Dayflow-PR: High-Resolution Streamflow Reanalysis for Puerto Rico, Version 1.0},

  author       = {Ghimire, Ganesh and Gangrade, Sudershan and Kao, Shih-Chieh and Hansen, Carly and DeNeale, Scott},

  abstractNote = {This dataset provides a high-resolution historical streamflow reanalysis for NHDPlusV2 stream reaches across Puerto Rico (PR) spanning 1950–2019. The reanalysis is generated using the VIC-RAPID hydrologic modeling framework, forced with both sub-daily and daily meteorological forcings from Daymet. Runoff is simulated on 1- and 6-km grids, and the resulting total runoff is routed through the NHDPlusV2 river network using the RAPID routing model to produce Naturalized Streamflow Reanalysis. Where complete observational records are available, streamflows are assimilated (substituted) and subsequently routed downstream through the river network to produce Assimilated Streamflow Reanalysis. The dataset includes streamflow outputs from eight distinct hydrologic modeling configurations along with key performance evaluation metrics at daily and monthly scales, supporting a wide range of water resource applications.This dataset is derived to support the "Non-Powered Dam Assessment", as well as "9505 Secure Water Assessment" projects for the US Department of Energy (DOE) Water Power Technologies Office (WPTO). For further details, refer to Ghimire et al. (2023), Kao et al. (2024), and Ghimire et al. (2025).},

  doi          = {10.13139/OLCF/2583869},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Wed Oct 01 04:00:00 UTC 2025},

  month        = {Wed Oct 01 04:00:00 UTC 2025}

}

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DOI: https://doi.org/10.13139/OLCF/2583869

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