The hazardous impact and erosive potential of slow-moving landslides depends on landslide properties including velocity, size, and frequency of occurrence. However, constraints on size, in particular, subsurface geometry, are lacking because these types of landslides rarely fully evacuate material to create measurable hillslope scars. Here we use pixel offset tracking with data from the NASA/JPL Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) to measure the three-dimensional surface deformation of 134 slow-moving landslides in the northern California Coast Ranges. We apply volume conservation to infer the actively deforming thickness, volume, geometric scaling, and frictional strength of each landslide. These landslides move at average rates between ~0.1–3 m/yr and have areas of ~6.1 x 10^3–2.35 x 10^6 m^2, inferred mean thicknesses of ~1.1–25 m, and volumes of ~7.01 x 103–9.75 x 10^6 m^3. The best-fit volume-area geometric scaling exponent is γ ~ 1.2–1.5, indicating that these landslides fall between typical soil and bedrock landslide scaling. A rollover in the scaling relationship suggests that the largest landslide complexes in our dataset become large primarily by increasing in area rather than thickness. In addition, the slow-moving landslides display scale-dependent frictional strength, such that large landslide tend to be weaker than small landslides. This decrease in frictional strength with landslide size is likely because larger landslides are composed of higher proportions of weak material. Our work shows how state-of-the-art remote sensing techniques can be used to better understand landslide processes and quantify their contribution to landscape evolution and hazards to human safety.

In steep wildfire-burned terrains, intense rainfall can produce large volumes of runoff that can trigger highly destructive debris flows. The ability to accurately characterize and forecast debris-flow hazards in burned terrains, however, remains limited. Here, we augment the Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) to simulate both overland and channelized flows and assess postfire debris-flow hazards over a regional domain. We perform hindcast simulations using high-resolution weather radar-derived precipitation and reanalysis data to drive non-burned baseline and burn scar sensitivity experiments. Our simulations focus on January 2021 when an atmospheric river triggered numerous debris flows within a wildfire burn scar in Big Sur – one of which destroyed California’s famous Highway 1. Compared to the baseline, our burn scar simulation yields dramatic increases in total and peak discharge, and shorter lags between rainfall onset and peak discharge. At Rat Creek, where Highway 1 was destroyed, discharge volume increases eight-fold and peak discharge triples relative to the baseline. For all catchments within the burn scar, we find that the median catchment-area normalized discharge volume increases nine-fold after incorporating burn scar characteristics, while the 95th percentile volume increases 13-fold. Catchments with anomalously high hazard levels correspond well with post-event debris flow observations. Our results demonstrate that WRF-Hydro provides a compelling new physics-based tool to investigate and potentially forecast postfire hydrologic hazards at regional scales.

Slow-moving landslides are hydrologically driven. Yet, landslide sensitivity to precipitation, and in particular, precipitation extremes, is difficult to constrain because landslides occur under diverse hydroclimatological conditions. Here we use standardized open-access satellite radar interferometry data to quantify the sensitivity of 38 landslides to both a record drought and extreme rainfall that occurred in California between 2015 and 2020. These landslides are hosted in similar rock types, but span more than ~2 m/yr in mean annual rainfall. Despite the large differences in hydroclimate, we found these landslides exhibited surprisingly similar behaviors and hydrologic sensitivity, which was characterized by faster (slower) than average velocities during wetter (drier) than average years, once the impact of the drought diminished. Our findings may be representative of future landslide behaviors in California where precipitation extremes are predicted to become more frequent with climate change.