Post-fire debris-flow hazard model output files, Santa Fe Municipal Watershed
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Wildfires are increasing in size and severity due to warmer drought climate change combined with overstocked forests. Fire increases the likelihood of debris flows, posing significant threats to life, property, and water supplies. Post-fire debris flows are a substantial, increasing hazard in the Santa Fe Municipal Watershed and other similar forested watersheds across the western United States. The Santa Fe Municipal Watershed in northern New Mexico is of vital importance to the water supply for the city of Santa We conducted a debris-flow hazard assessment for the Santa Fe Municipal Watershed (SFMW) in north-central New Mexico. We modeled post-fire debris flow probability and volume in 103 sub-basins for 2-year, 5-year, and Probable Maximum Precipitation rainfalls following modeled low-, moderate-, and high-severity wildfires. Crown fire potential was modeled with FlamMap (Finney, 2006), after Tillery et al. (2014). To satisfy data input requirements of the debris flow model that includes burn severity classes (low, moderate, and high), the modeled crown fire activity was first converted to dNBR (French et al. 2008). This conversion was calibrated based on burn severities from the 2011 Pacheco Fire that burned in a nearby watershed (approximately 5 km north of the SFMW). Data files are numbered 1-5. Spatial files provided in this data release include: 1) polygon of the study area; 2) 103 sub-basins within the study area; 3) Thematic Burn Severity Mosaic for New Mexico in 2011; and 4) complete post-fire debris flow probability and volume data of every rainfall event and wildfire scenario for 103 sub-basins; and tabulated data provided in this data release include: 5) calculated burn severity percentages for Pacheco Canyon Fire.
Field-verified inventory of postfire debris flows for the 2021 Dixie Fire following a 23-25 October 2021 atmospheric river storm and 12 June 2022 thunderstorm
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This data release is a field-verified inventory of postfire debris flows for the 2021 Dixie Fire following a 23-25 October 2021 atmospheric river storm and 12 June 2022 thunderstorm. The “README.txt” file describes the fields for the “Inventory.csv” file. The “Chambers” and “Chips” rain gage data referenced in the inventory are included as: “Chambers-Oct2021-Storm.csv”, “Chambers-Jun2022-Storm.csv”, “Chips-Oct2021-Storm.csv”, and “Chips-Jun2022-Storm.csv.” The fields for the rain gage data, which includes the geographic locations of the gages, are also described in the “README.txt” file. Fields with value “-9999” indicate that data are not available or do not exist.
Data supporting an analysis of the recurrence interval of post-fire debris-flow generating rainfall in the southwestern United States
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This data release supports the analysis of the recurrence interval of post-fire debris-flow generating rainfall in the southwestern United States. We define the recurrence interval of the peak 15-, 30-, and 60-minute rainfall intensities for 316 observations of post-fire debris-flow occurrence in 18 burn areas, 5 U.S. states, and 7 climate types (as defined by Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., & Wood, E. F. (2018). Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data, 5(1), 180214. doi:10.1038/sdata.2018.214).
Data supporting an analysis of the recurrence interval of post-fire debris-flow generating rainfall in the southwestern United States
공공데이터포털
This data release supports the analysis of the recurrence interval of post-fire debris-flow generating rainfall in the southwestern United States. We define the recurrence interval of the peak 15-, 30-, and 60-minute rainfall intensities for 316 observations of post-fire debris-flow occurrence in 18 burn areas, 5 U.S. states, and 7 climate types (as defined by Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., & Wood, E. F. (2018). Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data, 5(1), 180214. doi:10.1038/sdata.2018.214).
Post-wildfire debris-flow monitoring data, Arroyo Seco, 2009 Station Fire, Los Angeles County, California, November 2009 to March 2010.
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This data release includes time-series data from a monitoring site located in a small drainage basin in the Arroyo Seco watershed in Los Angeles County, CA, USA (N3788964 E389956, UTM Zone 11, NAD83). The site was established after the 2009 Station Fire and recorded a series debris flows in the first winter after the fire. The data include three types of time-series: (1) 1-minute time series of rainfall, soil water content, channel bed pore pressure and temperature, and flow stage recorded by radar and laser distance meters (ArroyoSecoContinuous.csv); (2) 10-Hz time series of flow stage recorded by the laser distance meter during rain storms (ArroyoSecoStormLaser.csv), and (3) 2-second time series of rainfall and channel bed pore pressure and temperature during rain storms (ArroyoSecoStormPressureRain.csv). The laser and radar distance meters are suspended above the pore pressure sensor mounted in the bedrock of the channel. The equations for converting the distance measurements into flow stage above the pressure sensor (or stage of the stationary bed surface during times of no flow) are given by the equations Stage_laser (meters) = 2.107 meters – Distance_laser (meters), and Stage_radar (meters) = 2.156 meters – Distance_radar (feet)*0.3048 Details of this study are described in the journal article: Kean, J. W., D. M. Staley, and S. H. Cannon (2011), In situ measurements of post-fire debris flows in southern California: Comparisons of the timing and magnitude of 24 debris-flow events with rainfall and soil moisture conditions, J. Geophys. Res., 116, F04019, doi:10.1029/2011JF002005.
Burn probability predictions for the state of California, USA using an optimal set of spatio-temporal features.
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Burn probability (BP) models the likelihood that a location could burn. However, predicting BP is extremely challenging, because fire behavior varies strongly among landscapes and with changing weather conditions and wildfire spread simulations are computationally intensive and require integration of data with large spatial and temporal variability. In this data release we include the monthly BP estimation for the state of California, USA for the 2015-2019 period produced using a machine learning model and two different sets of input features. For the first case, the baseline, the model used all available input features to predict BP. The second output set corresponds to the BP predictions when the model used only the set of optimal features as determined in the cited paper.
Post-wildfire debris-flow monitoring data, 2014 Silverado Fire, Orange County, California, November 2014 to January 2016.
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This data release includes time-series data from two monitoring stations in a small drainage basin burned in the 2014 Silverado Fire, Orange County, California. One station (upper station) is located in the headwaters of the study area (33 45’39.10”N, 117 35’17.48”W, WGS84). The other station (lower station) is located at the outlet of the study area (33 45’04.61”N, 117 35’12.54”W). The data were collected between November 15, 2014 and January 14, 2016. The data include continuous 1-minute time series of rainfall and soil water content recorded at the both stations and intermittent (during rain storms) 50-Hz time series of flow-induced ground vibrations recorded by geophones at the lower station. The soil water content measurements were made at 2 depths below the ground surface (5 and 10 cm) between 2014-11-15 and 2015-04-24, and 4 depths below the ground surface (5, 10, 15, and 20 cm) between 2015-04-24 and 2016-01-14. The ground vibrations were measured by two 4.5 Hz vertical axis geophones (Geospace SNG 11D/PC902/OPEN-30m) located approximately 3 m from the channel bank and separated by 11.8 m in the streamwise direction. Details of this study are described in the journal article: McGuire, L.A., Rengers, F.K., Kean, J.W., Staley, D.M., and Mirus B.B., (2017), Incorporating spatially heterogeneous infiltration capacity into hydrologic models with applications for simulating post-wildfire debris flow initiation, Hydrologic Processes.
Post-wildfire debris-flow monitoring data, 2014 Silverado Fire, Orange County, California, November 2014 to January 2016.
공공데이터포털
This data release includes time-series data from two monitoring stations in a small drainage basin burned in the 2014 Silverado Fire, Orange County, California. One station (upper station) is located in the headwaters of the study area (33 45’39.10”N, 117 35’17.48”W, WGS84). The other station (lower station) is located at the outlet of the study area (33 45’04.61”N, 117 35’12.54”W). The data were collected between November 15, 2014 and January 14, 2016. The data include continuous 1-minute time series of rainfall and soil water content recorded at the both stations and intermittent (during rain storms) 50-Hz time series of flow-induced ground vibrations recorded by geophones at the lower station. The soil water content measurements were made at 2 depths below the ground surface (5 and 10 cm) between 2014-11-15 and 2015-04-24, and 4 depths below the ground surface (5, 10, 15, and 20 cm) between 2015-04-24 and 2016-01-14. The ground vibrations were measured by two 4.5 Hz vertical axis geophones (Geospace SNG 11D/PC902/OPEN-30m) located approximately 3 m from the channel bank and separated by 11.8 m in the streamwise direction. Details of this study are described in the journal article: McGuire, L.A., Rengers, F.K., Kean, J.W., Staley, D.M., and Mirus B.B., (2017), Incorporating spatially heterogeneous infiltration capacity into hydrologic models with applications for simulating post-wildfire debris flow initiation, Hydrologic Processes.