Spatially-referenced data used in the study "Evaluating the Factors Responsible for Post-Fire Water Quality Response in Forests of the Western USA"

The practice of fire suppression across the western United States over the past century has led to dense forests, and when coupled with drought has contributed to an increase in large and destructive wildfires. Forest management efforts aimed at reducing flammable fuels through various fuel treatments can help to restore frequent fire regimes and increase forest resilience. Our research examines how different fuel treatments influenced burn severity and post-fire vegetative stand dynamics on the San Carlos Apache Reservation, in east-central Arizona, U.S.A. Our methods included the use of multitemporal remote sensing data and cloud computing to evaluate burn severity and post-fire vegetation conditions as well as statistical analyses. We investigated how forest thinning, commercial harvesting, prescribed burning, and resource benefit burning (managed wildfire) related to satellite measured burn severity (the difference Normalized Burn Ratio – dNBR) following the 2013 Creek Fire and used spectral measures of post-fire stand dynamics to track changes in land surface characteristics (i.e., brightness, greenness and wetness). This dataset includes all of the attribute information for each point, including if the location of the point intersects a treatment type or combination of treatments as well as a KML file showing the location of each point.

The practice of fire suppression across the western United States over the past century has led to dense forests, and when coupled with drought has contributed to an increase in large and destructive wildfires. Forest management efforts aimed at reducing flammable fuels through various fuel treatments can help to restore frequent fire regimes and increase forest resilience. Our research examines how different fuel treatments influenced burn severity and post-fire vegetative stand dynamics on the San Carlos Apache Reservation, in east-central Arizona, U.S.A. Our methods included the use of multitemporal remote sensing data and cloud computing to evaluate burn severity and post-fire vegetation conditions as well as statistical analyses. We investigated how forest thinning, commercial harvesting, prescribed burning, and resource benefit burning (managed wildfire) related to satellite measured burn severity (the difference Normalized Burn Ratio – dNBR) following the 2013 Creek Fire and used spectral measures of post-fire stand dynamics to track changes in land surface characteristics (i.e., brightness, greenness and wetness). This dataset includes all of the attribute information for each point, including if the location of the point intersects a treatment type or combination of treatments as well as a KML file showing the location of each point.

After wildfires occurred in the western United States during 2020, in-stream water quality monitors and automatic samplers were deployed in four burned watersheds and one unburned watershed. In-stream water temperature, turbidity, and fluorescent dissolved organic matter (fDOM) were measured at high frequency, and the fDOM data were corrected for temperature and turbidity effects. Automatic samplers were triggered during storm events, which captured turbid conditions in the wildfire affected streams. Laboratory experiments with storm event samples informed site-specific turbidity correction coefficients for fDOM data. An iterative solver approach also was developed to verify turbidity correction coefficients. This data release contains laboratory experiment data, as well as in-stream water temperature, turbidity, uncorrected fDOM, temperature-corrected fDOM, and temperature- and turbidity-corrected fDOM data. An example of the iterative solver code is also provided.

After wildfires occurred in the western United States during 2020, in-stream water quality monitors and automatic samplers were deployed in four burned watersheds and one unburned watershed. In-stream water temperature, turbidity, and fluorescent dissolved organic matter (fDOM) were measured at high frequency, and the fDOM data were corrected for temperature and turbidity effects. Automatic samplers were triggered during storm events, which captured turbid conditions in the wildfire affected streams. Laboratory experiments with storm event samples informed site-specific turbidity correction coefficients for fDOM data. An iterative solver approach also was developed to verify turbidity correction coefficients. This data release contains laboratory experiment data, as well as in-stream water temperature, turbidity, uncorrected fDOM, temperature-corrected fDOM, and temperature- and turbidity-corrected fDOM data. An example of the iterative solver code is also provided.

Spatial data used in the study "Characterization and Evaluation of Controls on Post-Fire Streamflow Response Across Western U.S. Watersheds".

Spatial data used in the study "Characterization and Evaluation of Controls on Post-Fire Streamflow Response Across Western U.S. Watersheds".

Wildfire effects on Stream discharge and suspended sediments. This dataset is associated with the following publication: Beyene, M.T., S.G. Leibowitz, and M.J. Pennino. Parsing Weather Variability and Wildfire Effects on the Post-Fire Changes in Daily Stream Flows: A Quantile-Based Statistical Approach and Its Application. WATER RESOURCES RESEARCH. American Geophysical Union, Washington, DC, USA, 57(10): e2020WR028029, (2021).

Wildfire effects on Stream discharge and suspended sediments. This dataset is associated with the following publication: Beyene, M.T., S.G. Leibowitz, and M.J. Pennino. Parsing Weather Variability and Wildfire Effects on the Post-Fire Changes in Daily Stream Flows: A Quantile-Based Statistical Approach and Its Application. WATER RESOURCES RESEARCH. American Geophysical Union, Washington, DC, USA, 57(10): e2020WR028029, (2021).

This dataset represents percent area burned in each burn severity class for wildfires within individual local and accumulated upstream catchments for NHDPlusV2 Waterbodies for each year for 1984-2018.The Monitoring Trends in Burn Severity MTBS project assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (includes wildfire, wildland fire use, and prescribed fire) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico from the beginning of the Landsat Thematic Mapper archive to the present. See: https://catalog.data.gov/dataset/monitoring-trends-in-burn-severity-burned-area-boundaries-feature-layer-27201 and https://www.mtbs.gov/product-descriptions