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).

These data were compiled for/to provide an example and assess methods and results of pre-fire estimation of predicted differenced normalized burn ration (dNBR) for predicting post-fire debris flow hazard classification. Objective(s) of our study were to develop predictive models for burn severity, using variables of pre-fire conditions, for two large wildfires from 2020 in Colorado, USA. These data represent pre-fire predictions of post-fire differenced normalized burn ratio (dNBR) as a proxy of burn severity and further understand pre-fire modeling of burn severity. These data were collected/created in the fire perimeters the East Troublesome Fire (10/14/2020 – 11/30/2020) and the Grizzly Creek Fire (8/10/2020 – 12/18/2020), Colorado, USA. These data were collected/created by use of random forest modeling of variables representing pre-fire conditions (satellite spectral data, landscape biophysical data, GIS topographic data, and meteorological/climate data) against observed estimates of post-fire difference in normalized burn ratio (dNBR). These data can be used to provide estimates of burn severity for post-fire hazard analysis.

These data were compiled for/to provide an example and assess methods and results of pre-fire estimation of predicted differenced normalized burn ration (dNBR) for predicting post-fire debris flow hazard classification. Objective(s) of our study were to develop predictive models for burn severity, using variables of pre-fire conditions, for two large wildfires from 2020 in Colorado, USA. These data represent pre-fire predictions of post-fire differenced normalized burn ratio (dNBR) as a proxy of burn severity and further understand pre-fire modeling of burn severity. These data were collected/created in the fire perimeters the East Troublesome Fire (10/14/2020 – 11/30/2020) and the Grizzly Creek Fire (8/10/2020 – 12/18/2020), Colorado, USA. These data were collected/created by use of random forest modeling of variables representing pre-fire conditions (satellite spectral data, landscape biophysical data, GIS topographic data, and meteorological/climate data) against observed estimates of post-fire difference in normalized burn ratio (dNBR). These data can be used to provide estimates of burn severity for post-fire hazard analysis.

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.

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

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

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

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

These data were compiled for/to estimate predicted pre-fire burn severity for estimating hazard of post-fire debris flow for conservation populations of blue-lineage Colorado River Cutthroat Trout. Objective(s) of our study were to predicted burn severity. These data represent predicted pre-fire differenced Normalized Burn Ratio (dNBR) for portions of Colorado, Utah, and Wyoming and were created for the extent of historic distribution of blue-lineage Colorado River Cutthroat Trout in 2016-2022. These data were created by the U.S. Geological Survey, Southwest Biological Science Center using remote sensing and ecological modeling processes and techniques. These data can be used to compare observations of post wildfire burn severity and/or observed post-fire dNBR for independent validation and for estimating potential post-fire debris-flow in unburned areas.