High-Resolution Fire Weather Index Data for the Conterminous US (1980–2099), Version 1

Zhang, Tianqi; Mao, Jiafu; Kao, Shih-Chieh

doi:10.13139/OLCF/2533549

Title: High-Resolution Fire Weather Index Data for the Conterminous US (1980–2099), Version 1

Dataset
Other Related Research

Abstract

This dataset presents a suite of high-resolution fire weather index datasets calculated from observation (gridMet, Livneh, Daymet V4), reanalysis (AgERA5), downscaled hydro-climate projections over the conterminous United States (CONUS) based on multiple selected Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Aside from the daily FWI datasets, we also include a set of FWI extreme indicators at annual, seasonal, and monthly scales, including 1) fwixx: maximum FWI; 2) fwisa: mean FWI. For annual fwisa, it refers to the season with the maximum seasonal average; 3) fwils: Length of fire season over a specified period, where fire season is defined as the days exceeding the median value of the normalized FWI during the reference period (1980-1984); 4) fwixd: Number of extreme fire weather days over a specified period, where extreme day is defined as the day with FWI > the 95th percentile of the FWI during the reference period (1980-1984). All FWI datasets cover 1980-2020 baseline and the model simulated products including the downscaled products additionally include 2021-2099 near-future periods under the high-end (SSP585) emission scenario.

Authors:

Zhang, Tianqi; Mao, Jiafu; Kao, Shih-Chieh

Oak Ridge National Laboratory; ORNL
Oak Ridge National Laboratory

Publication Date:: Mon Apr 21 00:00:00 EDT 2025

DOE Contract Number:: AC05-00OR22725

Research Org.:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: Office of Science (SC); DOE Grid Modernization Initiative

Subject:: 54 ENVIRONMENTAL SCIENCES; 58 GEOSCIENCES; 97 MATHEMATICS AND COMPUTING

OSTI Identifier:: 2533549

DOI:: https://doi.org/10.13139/OLCF/2533549

Citation Formats


                    Zhang, Tianqi, Mao, Jiafu, and Kao, Shih-Chieh. High-Resolution Fire Weather Index Data for the Conterminous US (1980–2099), Version 1.  United States: N. p., 2025. 
        Web.  doi:10.13139/OLCF/2533549.

Copy to clipboard


                    Zhang, Tianqi, Mao, Jiafu, & Kao, Shih-Chieh. High-Resolution Fire Weather Index Data for the Conterminous US (1980–2099), Version 1.  United States.  doi:https://doi.org/10.13139/OLCF/2533549

Copy to clipboard


                    Zhang, Tianqi, Mao, Jiafu, and Kao, Shih-Chieh. 2025.  
"High-Resolution Fire Weather Index Data for the Conterminous US (1980–2099), Version 1".  United States.  doi:https://doi.org/10.13139/OLCF/2533549.  https://www.osti.gov/servlets/purl/2533549. Pub date:Mon Apr 21 00:00:00 EDT 2025

Copy to clipboard


                    
@article{osti_2533549,

  title        = {High-Resolution Fire Weather Index Data for the Conterminous US (1980–2099), Version 1},

  author       = {Zhang, Tianqi and Mao, Jiafu and Kao, Shih-Chieh},

  abstractNote = {This dataset presents a suite of high-resolution fire weather index datasets calculated from observation (gridMet, Livneh, Daymet V4), reanalysis (AgERA5), downscaled hydro-climate projections over the conterminous United States (CONUS) based on multiple selected Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Aside from the daily FWI datasets, we also include a set of FWI extreme indicators at annual, seasonal, and monthly scales, including 1) fwixx: maximum FWI; 2) fwisa: mean FWI. For annual fwisa, it refers to the season with the maximum seasonal average; 3) fwils: Length of fire season over a specified period, where fire season is defined as the days exceeding the median value of the normalized FWI during the reference period (1980-1984); 4) fwixd: Number of extreme fire weather days over a specified period, where extreme day is defined as the day with FWI > the 95th percentile of the FWI during the reference period (1980-1984). All FWI datasets cover 1980-2020 baseline and the model simulated products including the downscaled products additionally include 2021-2099 near-future periods under the high-end (SSP585) emission scenario.},

  doi          = {10.13139/OLCF/2533549},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Mon Apr 21 00:00:00 EDT 2025},

  month        = {Mon Apr 21 00:00:00 EDT 2025}

}

Copy to clipboard

Dataset:

View Dataset

DOI: https://doi.org/10.13139/OLCF/2533549

Save / Share:

Export Metadata

Save to My Library

Similar records in DOE Data Explorer and OSTI.GOV collections:

CMIP6-based Multi-model Streamflow Projections over the Conterminous US, Version 1.1

Dataset Kao, Shih-Chieh ; Ghimire, Ganesh R. ; Gangrade, Sudershan

This dataset presents an ensemble of streamflow projections covering the conterminous United States (CONUS), developed to support the SECURE Water Act Section 9505 Assessment for the US Department of Energy (DOE) Water Power Technologies Office (WPTO). Multiple Coupled Models Intercomparison Project phase 6 (CMIP6) Global Climate Models (GCMs) were downscaled using either statistical (DBCCA) or dynamical (RegCM) downscaling methods, based on two meteorological reference datasets (Daymet and Livneh). Subsequently, the downscaled precipitation, temperature, and wind speed data were used to drive two calibrated hydrologic models (VIC and PRMS), with total runoff routed through the Routing Application for Parallel computatIon ofmore »« less
CMIP6-based Multi-model Hydroclimate Projection over the Conterminous US, Version 1.1

Dataset Kao, Shih-Chieh ; Ashfaq, Moetasim ; Rastogi, Deeksha ; ...

This dataset presents a suite of high-resolution downscaled hydro-climate projections over the conterminous United States (CONUS) based on multiple selected Global Climate Models (GCMs) from the Coupled Models Intercomparison Project phase 6 (CMIP6). The CMIP6 GCMs are downscaled using either statistical (DBCCA) or dynamical (RegCM) downscaling approaches based on two meteorological reference datasets (Daymet and Livneh). Subsequently, the downscaled precipitation, temperature, and wind speed are employed to drive two calibrated hydrologic models (VIC and PRMS), enabling the simulation of projected future hydrologic responses across the CONUS. Each ensemble member covers the 1980-2019 baseline and 2020-2059 near-future periods under the high-endmore »« less
CMIP6-based Multi-model Hydropower Projection over the Conterminous US, Version 1.1

Dataset Voisin, Nathalie ; Kao, Shih-Chieh ; Broman, Daniel ; ...

This dataset presents a suite of hydropower projections for the conterminous United States (CONUS), derived from multiple downscaled and bias-corrected Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The CMIP6 GCMs are downscaled using either statistical (DBCCA) or dynamical (RegCM) approaches, based on two meteorological reference datasets (Daymet and Livneh). The resulting downscaled precipitation, temperature, and wind speed data are then used to drive two calibrated hydrologic models (VIC and PRMS), enabling simulations of projected future hydrologic responses across the CONUS. Simulated total runoff is subsequently employed to drive two hydropower models (WMP, now implementedmore »« less
CMIP6-based Multi-model Hydropower Projection over the Conterminous US, Version 1.1

Dataset Voisin, Nathalie ; Kao, Shih-Chieh ; Broman, Daniel ; ...

This dataset presents a suite of hydropower projections for the conterminous United States (CONUS), derived from multiple downscaled and bias-corrected Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The CMIP6 GCMs are downscaled using either statistical (DBCCA) or dynamical (RegCM) approaches, based on two meteorological reference datasets (Daymet and Livneh). The resulting downscaled precipitation, temperature, and wind speed data are then used to drive two calibrated hydrologic models (VIC and PRMS), enabling simulations of projected future hydrologic responses across the CONUS. Simulated total runoff is subsequently employed to drive two hydropower models (WMP, now implementedmore »« less
Drought index using micrometeorological data from Embrapa weather station at Adolpho Ducke Reserve in Manaus, Brazil

Dataset Pastorello, Gilberto ; Lima, Adriano ; Meng, Lin ; ...

This dataset includes daily resolution time series data including precipitation, minimum and maximum daily air temperature, and air relative humidity downloaded from the Embrapa InfoClima portal (https://www.cnpaf.embrapa.br/infoclima/), with data for the Adolpho Ducke Reserve climatological station in Manaus, Brazil, for the period of January 1, 1980 to December 31, 2016. Using this precipitation record, a Standardized Precipitation Index (SPI) was calculated and added to the dataset, using daily resolution for 180 day intervals and with 20 years for calibration (1980-1999) and adopting a gamma distribution. These data were applied as a proxy for analyzes of precipitation and SPI for themore »« less

Similar Records