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.

Debris Flow, Precipitation, and Volume Measurements in the Grizzly Creek Burn Perimeter June 2021-September 2022 https://doi.org/10.5066/P9Z7RROL This data release contains data summarizing observations within and adjacent to the Grizzly Creek Fire, which burned from 10 August to 18 December 2020. This monitoring data summarizes precipitation, observations of debris flows, and the volume of sediment eroded during debris flows triggered during the summer monsoonal period in 2021 and 2022. Summary rainfall data 2021 (1a_Storm_matrix_2021_gr1mmhr.csv) are provided in a comma-separated value (CSV) file. These data represent the maximum measured rainfall intensities during the monsoon months of 2021 (June-Sept). The columns in the csv file are: Date (m/dd/yy), Name (11 columns have unique gage names), Max 15 min (this is the maximum 15-minute rainfall intensity in mm/h for the unique gauge), Maximum Value for All Gages (this is the maximum rainfall intensity for all of the gauges in units of either mm/h or in/15 min), Peak 15-minute Intensity (in/15 min) (this is the total inches of rainfall in 15 minutes), Debris Flow (this can be 0 indicating no debris flow response, or 1 indicating a debris flow response). Note that we only display gauges that record data sufficient to produce a 15-minute rainfall intensity. Gauges with longer recording rates (e.g., 1 hour) cannot be used to compute the 15-minute rainfall intensity and are not displayed in this table. A null value (‘n/a’) populates the entries where the rain gauge did not measure a 15-minute rainfall intensity greater than 1 mm/hr. Time series rainfall data from the gauges are provided in the child item: Precipitation Data Grizzly Creek Burn Perimeter. Summary rainfall data 2022 (1b_Storm_matrix_2022_gr1mmhr.csv) are provided in a comma-separated value (CSV) file. These data represent the maximum measured rainfall intensities during the monsoon months of 2022 (June-Sept). The columns in the csv file are: Date (m/dd/yy), Name (7 columns have unique gage names), Max 15 min (this is the maximum 15-minute rainfall intensity in mm/h), Peak 15-minute Intensity (in/15 min) (this is the total inches of rainfall in 15 minutes), Debris Flow (this can be 0 indicating no debris flow response, or 1 indicating a debris flow response). Note that we only display gauges that record data sufficient to produce a 15-minute rainfall intensity. Gauges with longer recording rates (e.g., 1 hour) cannot be used to compute the 15-minute rainfall intensity and are not displayed in this table. A null value (‘n/a’) populates the entries where the rain gauge did not measure a 15-minute rainfall intensity greater than 1 mm/hr. Time series rainfall data from the gauges are provided in the child item: Precipitation Data Grizzly Creek Burn Perimeter. Debris Flow Observation data 2021 (2a_All_Verification_2021.csv) are provided in a comma-separated value (CSV) file. The columns in the csv file are: Year (yyyy), State, Fire Name, Fire_ID (index for the fire developed during the USGS debris flow hazard assessment), Fire_SegID (a specific index assigned by the USGS debris flow hazard assessment to the channel segment that produced the debris flow), Site Name (the name of the nearest milemarker on interstate 70), ObservationDate_mmddyyyy, ObservationLatitude_DD, ObservationLongitude_DD, DebrisFlowResponse (this can be 0 indicating no debris flow response, or 1 indicating a debris flow response), SourceOfObservation (name of the observer), StormDate_mmddyyyy, GaugeName, GaugeLatitude_DD, GaugeLongitude_DD, GaugeDist_km (distance from watershed of the debris flow observation to the nearest rain gage in km), StormAccum_mm (the total rainfall during a storm in millimeters), StormDuration_hr (the total duration of a storm in hours), Peak_I15_mm/h (the maximum 15 minute rainfall intensity in mm/h), Peak_I30_mm/h (the maximum 30 minute rainfall intensity in mm/h), Peak_I60_mm/h (the maximum 60 minute rainfall intensity in

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).

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.

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.

This product is a Model Archive for Green-Ampt method infiltration modeling following wildfire in the Boulder Creek watershed, in the Colorado Front Range, USA. The models contained in this archive simulate infiltration and consequent runoff generation through 7 years of recovery following a wildfire in 2010. These simulations provide insight to changes in the timing of runoff generation that have implications for water quantity and quality following wildfire, with direct impacts on water supply.

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.

These data were compiled so that annual wildfire could be modelled across the sagebrush region in the western United States. Our goal was to understand how wildfire probability relates to climate and fuel conditions across the entire sagebrush region. To do this we developed a statistical model that represents the relationship between annual wildfire probability and a small number of climate and fuel variables. Specifically, created predictions of wildfire probability using a biologically plausible logistic regression model that related wildfire probability to mean temperature, annual precipitation, the proportion summer precipitation (PSP), and aboveground biomass of annual herbaceous plants and perennial herbaceous plants. The biomass variables were used as proxies for fine fuel availability. These data represent annual fire occurrence in 1 km pixels (i.e. did a given pixel burn that year), predicted wildfire probability, as well as the three year running average (i.e. average across the current and previous two years) of climate and vegetation variables. These data were collected across the sagebrush region (the extent of the study area is provided by the cell_number_ids.tif file). The climate and vegetation data were compiled using a existing gridded dataset (Daymet) of daily precipitation and temperature, and vegetation data were summaries of annual estimates of aboveground biomass of annual and perennial herbaceous plants from the Rangeland Analysis Platform (https://rangelands.app/). These data can be used to understand spatial and temporal variability in wildfire occurrence and modelled wildfire probability between 1988 and 2019 and how that variability relates to spatial and temporal variability in climate and vegetation.

Wildfire can impact soil-physical and soil-hydraulic properties, with major implications for hydrologic and ecologic response. The durations of these soil impacts are poorly characterized for some forested environments. This dataset sheds light on the first four years of recovery of soil-physical properties of bulk density, loss on ignition (measure of soil organic matter), ground cover, and soil particle size distribution and of soil-hydraulic properties of sorptivity and field-saturated hydraulic conductivity. The dataset also includes a simple infiltration model used to examine infiltration as the sites recover from fire. Sample locations within the 2013 Black Forest Fire study area are: BF1, UTM-Easting (m) 532027, UTM-Northing (m) 4323210, Approximate elevation (± 5m) 2288; BF2, UTM-Easting (m) 532139, UTM-Northing (m) 4323273, Approximate elevation (± 5m) 2288; BF3, UTM-Easting (m) 532015, UTM-Northing (m) 4323416, Approximate elevation (± 5m) 2285; BF4, UTM-Easting (m) 532166, UTM-Northing (m) 4323576, Approximate elevation (± 5m) 2292; BF5, UTM-Easting (m) 532370, UTM-Northing (m) 4323194, Approximate elevation (± 5m) 2293; BF6, UTM-Easting (m) 532712, UTM-Northing (m) 4323283, Approximate elevation (± 5m) 2300; UTM is Universal Transverse Mercator, Zone 13, NAD83 datum, GRS80 geodetic reference system.