These data were used to examine how post-fire sedimentation might change in western USA watersheds with future fire from the decade of 2001-10 through 2041-50. The data include previously published projections (Hawbaker and Zhu, 2012a, b) of areas burned by future wildfires for several climate change scenarios and general circulation models (GCMs) that we summarized for 471 watersheds of the western USA. The data also include previously published predictions (Miller et al., 2011) of first year post-fire hillslope soil erosion from GeoWEPP that we summarized for 471 watersheds of the western USA. We synthesized these summarized data in order to project sediment yield from future fires for 471 watersheds through the year 2050 at the hydrologic unit 8 (HUC8) scale. The detailed methods, results, and original data sources (i.e.: Hawbaker and Zhu, 2012a, b; Miller et al., 2011) were reported in the manuscript.

These data were used to examine how post-fire sedimentation might change in western USA watersheds with future fire from the decade of 2001-10 through 2041-50. The data include previously published projections (Hawbaker and Zhu, 2012a, b) of areas burned by future wildfires for several climate change scenarios and general circulation models (GCMs) that we summarized for 471 watersheds of the western USA. The data also include previously published predictions (Miller et al., 2011) of first year post-fire hillslope soil erosion from GeoWEPP that we summarized for 471 watersheds of the western USA. We synthesized these summarized data in order to project sediment yield from future fires for 471 watersheds through the year 2050 at the hydrologic unit 8 (HUC8) scale. The detailed methods, results, and original data sources (i.e.: Hawbaker and Zhu, 2012a, b; Miller et al., 2011) were reported in the manuscript.

These data were used to examine how post-fire sedimentation might change in western USA watersheds with future fire from the decade of 2001-10 through 2041-50. The data include previously published projections (Hawbaker and Zhu, 2012a, b) of areas burned by future wildfires for several climate change scenarios and general circulation models (GCMs) that we summarized for 471 watersheds of the western USA. The data also include previously published predictions (Miller et al., 2011) of first year post-fire hillslope soil erosion from GeoWEPP that we summarized for 471 watersheds of the western USA. We synthesized these summarized data in order to project sediment yield from future fires for 471 watersheds through the year 2050 at the hydrologic unit 8 (HUC8) scale. The detailed methods, results, and original data sources (i.e.: Hawbaker and Zhu, 2012a, b; Miller et al., 2011) were reported in the manuscript.

These data show all the postfire erosion results affiliated with this data release summed by wildfire and attached to a polygon of each fire perimeter, as defined by Monitoring Trends in Burn Severity (MTBS). The results are shown as attributes for each polygon of wildfire perimeter. Some of the original MTBS data (name, ignition date, and ID) were preserved to allow for joining to other MTBS data. Results include WEPP modeling results of hillslope and channel erosion, a sum of postfire debris flow modeling results and field-based measurements, and a few derived results such as total sediment and total yield (mass per area).

This is a shapefile containing polygons of watersheds that were burned in wildfires that occurred in California between 1984 and 2021. The Water Erosion Prediction Project (WEPP) model for postfire erosion was run on all watersheds for the first year following wildfire and the results of this modeling effort are included as attributes of each watershed polygon.

This is a shapefile containing polygons of watersheds that were burned in wildfires that occurred in California between 1984 and 2021. The Water Erosion Prediction Project (WEPP) model for postfire erosion was run on all watersheds for the first year following wildfire and the results of this modeling effort are included as attributes of each watershed polygon.

Following wildfire, mountainous areas of the western United States are susceptible to enhanced runoff and erosion and an increased vulnerability to debris flow during intense rainfall. Convective storms that can generate debris flows in recently burned areas may occur during or immediately after the wildfire, leaving insufficient time for development and implementation of risk mitigation strategies. We present a method for estimating post-fire debris-flow hazards prior to wildfire using historical data to define the range of potential fire severity for a given location based on the statistical distribution of severity metrics obtained from remote sensing. Estimates of debris-flow likelihood, magnitude and triggering rainfall threshold based upon the statistically simulated fire severity data provide hazard predictions consistent with those calculated from fire severity data collected after wildfire. Simulated fire severity data also produce hazard estimates that replicate observed debris-flow occurrence, rainfall conditions, and magnitude at a monitored site in the San Gabriel Mountains of southern California. Future applications of this method should rely upon a range of potential fire severity scenarios for improved pre-fire estimates of debris-flow hazard. The method presented here is also applicable to modeling other post-fire hazards, such as flooding and erosion risk, and for quantifying historic trends in fire severity in a changing climate. This release contains the data used to derive the historical distributions of fire severity, including a) the data used to derive a Weibull cumulative distribution function to historical measures of the differenced normalized burn ratio for fires >= 4 square kilometers (1000 acres) that burned between 2001 and 2014 in the western United States, b) the shape and scale parameters for the Weibull cumulative distribution function for every class of existing vegetation type, and the statistics describing goodness-of-fit of the Weibull distribution to these data, and c) the data used to determine the BARC4 threshold defining the break between pixels burned at low and moderate or high severity.

Following wildfire, mountainous areas of the western United States are susceptible to enhanced runoff and erosion and an increased vulnerability to debris flow during intense rainfall. Convective storms that can generate debris flows in recently burned areas may occur during or immediately after the wildfire, leaving insufficient time for development and implementation of risk mitigation strategies. We present a method for estimating post-fire debris-flow hazards prior to wildfire using historical data to define the range of potential fire severity for a given location based on the statistical distribution of severity metrics obtained from remote sensing. Estimates of debris-flow likelihood, magnitude and triggering rainfall threshold based upon the statistically simulated fire severity data provide hazard predictions consistent with those calculated from fire severity data collected after wildfire. Simulated fire severity data also produce hazard estimates that replicate observed debris-flow occurrence, rainfall conditions, and magnitude at a monitored site in the San Gabriel Mountains of southern California. Future applications of this method should rely upon a range of potential fire severity scenarios for improved pre-fire estimates of debris-flow hazard. The method presented here is also applicable to modeling other post-fire hazards, such as flooding and erosion risk, and for quantifying historic trends in fire severity in a changing climate. This release contains the data used to derive the historical distributions of fire severity, including a) the data used to derive a Weibull cumulative distribution function to historical measures of the differenced normalized burn ratio for fires >= 4 square kilometers (1000 acres) that burned between 2001 and 2014 in the western United States, b) the shape and scale parameters for the Weibull cumulative distribution function for every class of existing vegetation type, and the statistics describing goodness-of-fit of the Weibull distribution to these data, and c) the data used to determine the BARC4 threshold defining the break between pixels burned at low and moderate or high severity.

This dataset represents 25 parallel longitudinal profiles that were extracted from terrestrial lidar point clouds taken during six survey periods. The six lidar surveys were conducted between 7 October 2010 and 8 October 2013. Over that time a colluvial hollow eroded into a fluvial channel. The longitudinal profiles show the topography of the colluvial hollow for each survey period. The width of the original colluvial hollow was approximately 1.25 m, and a longitudinal profile was extracted every 5 cm for the entire length of the hollow, resulting in 25 parallel longitudinal profiles. These data can be used to observe the transition of the colluvial hollow to a fluvial channel and furthermore they show the development of alternating steps and plunge pools. Within each of the 25 files, the column header describes the point number of the longitudinal profile, the distance downstream (meters), and the date of data collection. In addition, the file numbers represent profiles from west (1) to east (25).

This data set contains annual modeled estimates of wildland fire emissions at 0.01 degree (~1-km) spatial resolution from the Wildland Fire Emissions Information System (WFEIS v0.5) for the conterminous U.S. (CONUS) and Alaska for 2001 through 2013. WFEIS is a web-based tool that provides resources to quantify emissions from past fires and output results as spatial data files (French et al., 2014). The data set includes emissions estimates of carbon (C), carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), other non-methane hydrocarbons (NMHC), and particulate matter (PM) as well as estimates of above-ground biomass, total fuel availability, and consumption estimates.