Search for: All records

Abstract During December 2022–January 2023, nine atmospheric rivers (ARs) struck California consecutively, causing catastrophic flooding and 600+ landslides. The extensive footprints of landslide‐triggering storms and their diverse hydrometeorological forcings highlight the urgent need to incorporate regional‐scale hydrometeorology into landslide research. Here, using a meteorologically‐informed hydrologic model, we simulate the time‐evolving water budget during the nine‐AR event and identify hydrometeorological conditions that contributed to widespread landslide occurrences across California. Our analysis reveals that 89% of observed landslides occurred under excessively wet conditions, driven by precipitation exceeding the capacities of infiltration, storage, evapotranspiration, and soil drainage. Using K‐means clustering, we identify three distinct hydrometeorological pathways that increased landslide potential: intense precipitation‐induced runoff (∼32% of reported landslides), rain on pre‐wetted soils (∼53%), and snowmelt and soil ice thawing (∼15%). Our findings highlight the importance of constraining the compounding factors that influence slope stability over spatial scales consistent with landslide‐triggering weather systems. 

Residential wood combustion (RWC) is a primary heating fuel in just 2% of US homes. However, the 2023 release of the US Environmental Protection Agency’s National Emissions Inventory (NEI) found that RWC contributes ~28% of total wintertime fine particulate matter (PM_2.5) emissions, suggesting that ambient PM_2.5concentrations could be substantially reduced if RWC were curtailed. Despite its contribution to PM_2.5emissions, an assessment of the air quality, health, and distributional impacts of RWC using the updated NEI has not been performed. Here, we use a high-resolution (4 kilometers) air quality model and the updated NEI to evaluate wintertime RWC impacts over the contiguous United States. We find that RWC contributes 2.43 micrograms per cubic meter (21.9%) of winter population-weighted mean PM_2.5concentrations, leading to ~8600 (confidence interval: 6500 to 9600) premature deaths annually. Moreover, nonwhite communities are disproportionately affected by RWC-related PM_2.5and associated mortality, especially in urban areas. We suggest that policies targeting RWC could substantially reduce air pollution, improve health, and address distributional disparities. 

Abstract Electric vehicle adoption strategies have the potential to reduce greenhouse gas and air pollutant emissions. However, the effectiveness of this transition may depend on which vehicles are electrified, and where. To assess the efficacy of different modes of transportation electrification, we apply a watts-to-wheel analysis framework that accounts for upstream emission increases from battery charging and downstream reductions in tailpipe emissions. Using the WRF-CMAQ chemical transport model at ∼1 km²resolution, we compare the greenhouse gas, air quality, and public health impacts of electrifying 30% of light-duty vehicles (eLDVs) versus 30% of heavy-duty vehicles (eHDVs) across a U.S. Midwestern domain. Both electrification scenarios achieve net reductions in CO₂emissions despite increased emissions from electricity generation units, with greater total reductions from eLDVs (∼7 Mt CO₂/year, −4.5%) than eHDVs (∼1.6 Mt CO₂/year, −1.1%). However, air quality benefits are greater in the eHDV scenario, where cumulative reductions in health-harming air pollutants such as nitrogen dioxide (NO₂) and elemental carbon (EC) exceed those in the eLDV scenario. Both scenarios show modest increases in daily 8 h average ozone (MDA8 O₃), with disbenefits largest in the eHDV scenario. Estimated health benefits of the eHDV scenario exceed those of the eLDV scenario, with 70 (50) more avoided premature deaths annually from reduced NO₂(EC), offset by 50 additional deaths from MDA8 O₃increases. In both scenarios, the largest health benefits occur in communities with higher proportions of Black and Hispanic residents. However, long-standing relative exposure disparities persist. On a per-vehicle basis, we find that electrifying one HDV yields nearly 5× more CO₂reduction-based economic benefits and 23× more NO₂reduction-based economic health benefits than a single eLDV. Our results demonstrate that multi-modal and multi-pollutant assessments are critical for informing more effective and equitable decarbonization and air pollutant remediation strategies. 

Abstract Interannual precipitation variability profoundly influences society via its effects on agriculture, water resources, infrastructure, and disaster risks. In this study, we use dailyin situprecipitation observations from the global historical climatology network-daily (GHCN-D) to assess the ability of 21 Coupled Model Intercomparison Project Phase 6 (CMIP6) models, including the 50-member fifth-generation Canadian Earth System Model single model initial-condition large ensemble (CanESM5_SMILE), to realistically simulate historical interannual precipitation variability trends within 17 regions of the contiguous United States (CONUS). We assess how accurately the CMIP6 simulations align with observational data across annual, summer, and winter periods, focusing on four key hydrometeorological metrics, including interannual precipitation variability, relative interannual precipitation variability (coefficient of variation), annual mean precipitation, and annual wet day frequency. Our findings reveal that CMIP6 ensemble members generally reproduce the spatial patterns of observed trends in annual mean precipitation. In most regions, models agree well with the signs of observed changes in annual mean precipitation, though discrepancies in trend magnitude are evident. Further, observed trends in winter mean precipitation broadly exhibit a spatial pattern similar to that of the observed annual mean. However, analysis of the CanESM5_SMILE shows that trends in precipitation variability may primarily be the result of model-simulated internal variability, suggesting caution in interpreting multi-model single-realization ensemble results. Challenges in accurately simulating interannual precipitation variability underscore the need for ongoing model refinement and validation to enhance climate projections, especially in regions vulnerable to extreme precipitation events. 

Abstract Post‐fire debris flows alter impacted fluvial systems, but few studies quantify the magnitude and timing of reach‐scale channel response to these events. In August 2020, the Big Creek watershed along California's central coast burned in the Dolan Fire; in January 2021, an atmospheric river event triggered post‐fire debris flows in steep tributaries to the Big Creek. Here, we characterize the evolution of fluvial morphology and grain size in Big Creek, a cascade and step‐pool channel downstream of tributaries in which post‐fire debris flows initiated, using pre‐ and post‐fire structure from motion (SfM) and airborne lidar surveys. We also make comparisons to Devil's Creek, an adjacent basin which burned but did not experience post‐fire debris flows. We observe grain size fining following debris flows in Big Creek, but the coarsest 40% of the grain size distribution remained essentially unchanged despite reorganization of channel structure. Changes in grain size and elevated post‐fire peak flows account for approximately equal portions of a substantial increase in modeled bedload transport capacity one year post‐fire. In Big Creek, geomorphic recovery is well underway just two years post‐fire. A valley‐spanning log jam, which formed during debris flows, acts as a sediment trap upstream of our Big Creek study reach, and is partially responsible for accelerating recovery processes. In contrast, Devil's Creek exhibited little change in morphology or grain size despite elevated post‐fire peak flows. This period of geomorphic dynamism following the Dolan Fire has complex ecological impacts, notably for the threatened anadromous salmonid spawning habitat in Big Creek. 

This Review synthesizes progress and outlines a new framework for understanding how land surface hazards interact and propagate as sediment cascades across Earth’s surface, influenced by interactions among the atmosphere, biosphere, hydrosphere, and solid Earth. Recent research highlights a gap in understanding these interactions on human timescales, given rapid climatic change and urban expansion into hazard-prone zones. We review how surface processes such as coseismic landslides and post-fire debris flows form a complex sequence of events that exacerbate hazard susceptibility. Moreover, innovations in modeling, remote sensing, and critical zone science can offer new opportunities for quantifying cascading hazards. Looking forward, societal resilience can increase by transforming our understanding of cascading hazards through advances in integrating data into comprehensive models that link across Earth systems.