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Abstract Wildfire alters the hydrologic cycle, with important implications for water supply and hazards including flooding and debris flows. In this study we use a combination of electrical resistivity and stable water isotope analyses to investigate the hydrologic response during storms in three catchments: one unburned and two burned during the 2020 Bobcat Fire in the San Gabriel Mountains, California, USA. Electrical resistivity imaging shows that in the burned catchments, rainfall infiltrated into the weathered bedrock and persisted. Stormflow isotope data indicate that the amount of mixing of surface and subsurface water during storms was similar in all catchments, despite higher streamflow post-fire. Therefore, both surface runoff and infiltration likely increased in tandem. These results suggest that the hydrologic response to storms in post-fire environments is dynamic and involves more surface-subsurface exchange than previously conceptualized, which has important implications for vegetation regrowth and post-fire landslide hazards for years following wildfire. 

This data release contains two debris-flow inventories summarizing observations from burned and unburned areas in the western Cascade Range of Oregon (OR). The burned inventory focuses on debris flows that occurred during the first two years after the 2020 Archie Creek, Holiday Farm, Beachie Creek/Lionshead, and Riverside fires (OR_field_observations.csv). The unburned inventory (1995-2022) focuses on debris flows in the same areas (excluding the Riverside Fire). The inventories are derived from field observations (OR_field_observations.csv) and aerial imagery (OR_imagery_observations.csv). They include mapped debris-flow initiation locations, descriptions of the inferred initiation process, other notable site characteristics, and rainfall data. Locations of debris flows observed after wildfires are also linked to USGS postfire debris-flow hazard assessments (USGS, 2022; Staley and others, 2017; Thomas and others 2023). Rainfall characteristics for each debris flow in the inventory are derived from the closest rainfall gage to an observed debris flow (gage_locations.csv). Peak rainfall rates during the known time window of debris-flow initiation are reported for durations of 15 minutes, 30 minutes, 60 minutes, 12 hours, 24 hours, 36 hours, and 48 hours. More detailed explanations of the headers for each of these csv files can be found within the README_csvname.txt file. References: Landslide Hazards Program. (n.d.). Emergency assessment of post-fire debris-flow hazards. U.S. Geological Survey. https://landslides.usgs.gov/hazards/postfire_debrisflow Staley, D. M., Negri, J. A., Kean, J. W., Laber, J. L., Tillery, A. C., and Youberg, A. M., 2017, Prediction of spatially explicit rainfall intensity–duration thresholds for post-fire debris-flow generation in the western United States. Geomorphology, 278, 149–162. https://doi.org/10.1016/j.geomorph.2016.10.019 Thomas, M. A., Kean, J. W., McCoy, S. W., Lindsay, D. N., Kostelnik, J., Cavagnaro, D. B., Rengers, F. K., East, A. E., Schwartz, J. Y., Smith, D. P., and Collins, B. D., 2023, Postfire hydrologic response along the Central California (USA) coast: insights for the emergency assessment of postfire debris-flow hazards. Landslides, 20, 2421-2436. https://doi.org/10.1007/s10346-023-02106-7 

Abstract Debris flows pose a significant hazard to communities in mountainous areas, and there is a continued need for methods to delineate hazard zones associated with debris-flow inundation. In certain situations, such as scenarios following wildfire, where there could be an abrupt increase in the likelihood and size of debris flows that necessitates a rapid hazard assessment, the computational demands of inundation models play a role in their utility. The inability to efficiently determine the downstream effects of anticipated debris-flow events remains a critical gap in our ability to understand, mitigate, and assess debris-flow hazards. To better understand the downstream effects of debris flows, we introduce a computationally efficient, reduced-complexity inundation model, which we refer to as the Progressive Debris-Flow routing and inundation model (ProDF). We calibrate ProDF against mapped inundation from five watersheds near Montecito, CA, that produced debris flows shortly after the 2017 Thomas Fire. ProDF reproduced 70% of mapped deposits across a 40 km 2 study area. While this study focuses on a series of post-wildfire debris flows, ProDF is not limited to simulating debris-flow inundation following wildfire and could be applied to any scenario where it is possible to estimate a debris-flow volume. However, given its ability to reproduce mapped debris-flow deposits downstream of the 2017 Thomas Fire burn scar, and the modest run time associated with a simulation over this 40 km 2 study area, results suggest ProDF may be particularly promising for post-wildfire hazard assessment applications. 

Tens of thousands of landslides were generated over 10,000 km2 of North Canterbury and Marlborough as a consequence of the 14 November 2016, Mw7.8 Kaikōura Earthquake. The most intense landslide damage was concentrated in 3500 km2 around the areas of fault rupture. Given the sparsely populated area affected by landslides, only a few homes were impacted and there were no recorded deaths due to landslides. Landslides caused major disruption with all road and rail links with Kaikōura being severed. The landslides affecting State Highway 1 (the main road link in the South Island of New Zealand) and the South Island main trunk railway extended from Ward in Marlborough all the way to the south of Oaro in North Canterbury. The majority of landslides occurred in two geological and geotechnically distinct materials reflective of the dominant rock types in the affected area. In the Neogene sedimentary rocks (sandstones, limestones and siltstones) of the Hurunui District, North Canterbury and around Cape Campbell in Marlborough, first-time and reactivated rock-slides and rock-block slides were the dominant landslide type. These rocks also tend to have rock material strength values in the range of 5-20 MPa. In the Torlesse ‘basement’ rocks (greywacke sandstones and argillite) of the Kaikōura Ranges, first-time rock and debris avalanches were the dominant landslide type. These rocks tend to have material strength values in the range of 20-50 MPa. A feature of this earthquake is the large number (more than 200) of valley blocking landslides it generated. This was partly due to the steep and confined slopes in the area and the widely distributed strong ground shaking. The largest landslide dam has an approximate volume of 12(±2) M m3 and the debris from this travelled about 2.7 km2 downslope where it formed a dam blocking the Hapuku River. The long-term stability of cracked slopes and landslide dams from future strong earthquakes and large rainstorms are an ongoing concern to central and local government agencies responsible for rebuilding homes and infrastructure. A particular concern is the potential for debris floods to affect downstream assets and infrastructure should some of the landslide dams breach catastrophically. At least twenty-one faults ruptured to the ground surface or sea floor, with these surface ruptures extending from the Emu Plain in North Canterbury to offshore of Cape Campbell in Marlborough. The mapped landslide distribution reflects the complexity of the earthquake rupture. Landslides are distributed across a broad area of intense ground shaking reflective of the elongate area affected by fault rupture, and are not clustered around the earthquake epicentre. The largest landslides triggered by the earthquake are located either on or adjacent to faults that ruptured to the ground surface. Surface faults may provide a plane of weakness or hydrological discontinuity and adversely oriented surface faults may be indicative of the location of future large landslides. Their location appears to have a strong structural geological control. Initial results from our landslide investigations suggest predictive models relying only on ground-shaking estimates underestimate the number and size of the largest landslides that occurred.