Shortly before the beginning of the 2017–2018 winter rainy season, one of the largest fires in California (USA) history (Thomas fire) substantially increased the susceptibility of steep slopes in Santa Barbara and Ventura Counties to debris flows. On 9 January 2018, before the fire was fully contained, an intense burst of rain fell on the portion of the burn area above Montecito, California. The rainfall and associated runoff triggered a series of debris flows that mobilized ∼680,000 m3 of sediment (including boulders >6 m in diameter) at velocities up to 4 m/s down coalescing urbanized alluvial fans. The resulting destruction (including 23 fatalities, at least 167 injuries, and 408 damaged homes) underscores the need for improved understanding of debris-flow runout in the built environment, and the need for a comprehensive framework to assess the potential loss from debris flows following wildfire. We present observations of the inundation, debris-flow dynamics, and damage from the event. The data include field measurements of flow depth and deposit characteristics made within the first 12 days after the event (before ephemeral features of the deposits were lost to recovery operations); an inventory of building damage; estimates of flow velocity; information on flow timing; soil-hydrologic properties; and post-event imagery and lidar. Together, these data provide rare spatial and dynamic constraints for testing debris-flow runout models, which are needed for advancing post-fire debris-flow hazard assessments. Our analysis also outlines a framework for translating the results of these models into estimates of economic loss based on an adaptation of the U.S. Federal Emergency Management Agency’s Hazus model for tsunamis.

The post–Thomas Fire debris flows of 9 January 2018 killed 23 people, damaged 558 structures, and caused severe damage to infrastructure in Montecito and Carpinteria, CA. U.S. Highway 101 was closed for 13 days, significantly impacting transportation and commerce in the region. A narrow cold frontal rain band generated extreme rainfall rates within the western burn area, triggering runoff-driven debris flows that inundated 5.6 km2 of coastal land in eastern Santa Barbara County. Collectively, this series of debris flows is comparable in magnitude to the largest documented post-fire debris flows in the state and cost over a billion dollars in debris removal and damages to homes and infrastructure. This study summarizes observations and analyses on the extent and magnitude of inundation areas, debris-flow velocity and volume, and sources of debris-flow material on the south flank of the Santa Ynez Mountains. Additionally, we describe the atmospheric conditions that generated intense rainfall and use precipitation data to compare debris-flow source areas with spatially associated peak 15 minute rainfall amounts. We then couple the physical characterization of the event with a compilation of debris-flow damages to summarize economic impacts.

Wildfire risk has increased in recent decades over many regions, due to warming climate and other factors. Increased sediment export from recently burned landscapes can jeopardize downstream infrastructure and water resources, but physical landscape response to fire has not been quantified for some at-risk areas, including much of northern California, USA. We measured sediment yield from three watersheds (13-29 km 2 ) that drain to Whiskeytown Lake, California, within the area burned by the 2018 Carr Fire. Structure-from-Motion photogrammetry on aerial images combined with sonar bathymetric mapping of submerged areas indicated first-year post-fire sediment yields of 4,080 ± 598 t/km 2 (Brandy Creek), 2,700 ± 527 t/km 2 (Boulder Creek), and 305 ± 58.0 t/km 2 (Whiskey Creek)-some of the first post-fire yields measured in northern California and 64, 42, and 4.8 times greater than pre-fire yields, respectively. These were measured during a wet year and resulted largely from rilling erosion and fluvial sediment transport, without post-fire debris flows. Rilling preferentially developed in contact with dirt roads, aided by thin soils and exposed bedrock, and on slopes vegetated by Accepted ArticleThis article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

Following wildfires, the probability of flooding and debris flows increase, posing risks to human lives, downstream communities, infrastructure, and ecosystems. In southern California (USA), the Rowe, Countryman, and Storey (RCS) 1949 methodology is an empirical method that is used to rapidly estimate post‐fire peak streamflow. We re‐evaluated the accuracy of RCS for 33 watersheds under current conditions. Pre‐fire peak streamflow prediction performance was low, where the average R2 was 0.29 and average RMSE was 1.10 cms/km2 for the 2‐ and 10‐year recurrence interval events, respectively. Post‐fire, RCS performance was also low, with an average R2 of 0.26 and RMSE of 15.77 cms/km2 for the 2‐ and 10‐year events. We demonstrated that RCS overgeneralizes watershed processes and does not adequately represent the spatial and temporal variability in systems affected by wildfire and extreme weather events and often underpredicted peak streamflow without sediment bulking factors. A novel application of machine learning was used to identify critical watershed characteristics including local physiography, land cover, geology, slope, aspect, rainfall intensity, and soil burn severity, resulting in two random forest models with 45 and five parameters (RF‐45 and RF‐5, respectively) to predict post‐fire peak streamflow. RF‐45 and RF‐5 performed better than the RCS method; however, they demonstrated the importance and reliance on data availability. The important parameters identified by the machine learning techniques were used to create a three‐dimensional polynomial function to calculate post‐fire peak streamflow in small catchments in southern California during the first year after fire (R2 = 0.82; RMSE = 6.59 cms/km2) which can be used as an interim tool by post‐fire risk assessment teams. We conclude that a significant increase in data collection of high temporal and spatial resolution rainfall intensity, streamflow, and sediment loading in channels will help to guide future model development to quantify post‐fire flood risk.

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