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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. 

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. 

Abstract Heavy-duty vehicles (HDVs) disproportionately contribute to the creation of air pollutants and emission of greenhouse gases—with marginalized populations unequally burdened by the impacts of each. Shifting to non-emitting technologies, such as electric HDVs (eHDVs), is underway; however, the associated air quality and health implications have not been resolved at equity-relevant scales. Here we use a neighbourhood-scale (~1 km) air quality model to evaluate air pollution, public health and equity implications of a 30% transition of predominantly diesel HDVs to eHDVs over the region surrounding North America’s largest freight hub, Chicago, IL. We find decreases in nitrogen dioxide (NO₂) and fine particulate matter (PM_2.5) concentrations but ozone (O₃) increases, particularly in urban settings. Over our simulation domain NO₂and PM_2.5reductions translate to ~590 (95% confidence interval (CI) 150–900) and ~70 (95% CI 20–110) avoided premature deaths per year, respectively, while O₃increases add ~50 (95% CI 30–110) deaths per year. The largest pollutant and health benefits simulated are within communities with higher proportions of Black and Hispanic/Latino residents, highlighting the potential for eHDVs to reduce disproportionate and unjust air pollution and associated air-pollution attributable health burdens within historically marginalized populations. 

Abstract High-resolution air quality data products have the potential to help quantify inequitable environmental exposures over space and across time by enabling the identification of hotspots, or areas that consistently experience elevated pollution levels relative to their surroundings. However, when different high-resolution data products identify different hotspots, the spatial sparsity of ‘gold-standard’ regulatory observations leaves researchers, regulators, and concerned citizens without a means to differentiate signal from noise. This study compares NO₂hotspots detected within the city of Chicago, IL, USA using three distinct high-resolution (1.3 km) air quality products: (1) an interpolated product from Microsoft Research’s Project Eclipse—a dense network of over 100 low-cost sensors; (2) a two-way coupled WRF-CMAQ simulation; and (3) a down-sampled product using TropOMI satellite instrument observations. We use the Getis-OrdG_i^*statistic to identify hotspots of NO₂and stratify results into high-, medium-, and low-agreement hotspots, including one consensus hotspot detected in all three datasets. Interrogating medium- and low-agreement hotspots offers insights into dataset discrepancies, such as sensor placement and model physics considerations, data retrieval caveats, and the potential for missing emission inventories. When treated as complements rather than substitutes, our work demonstrates that novel air quality products can enable researchers to address discrepancies in data products and can help regulators evaluate confidence in policy-relevant insights. 

Abstract Electric vehicles (EVs) constitute just a fraction of the current U.S. transportation fleet; however, EV market share is surging. EV adoption reduces on-road transportation greenhouse gas emissions by decoupling transportation services from petroleum, but impacts on air quality and public health depend on the nature and location of vehicle usage and electricity generation. Here, we use a regulatory-grade chemical transport model and a vehicle-to-electricity generation unit electricity assignment algorithm to characterize neighborhood-scale (∼1 km) air quality and public health benefits and tradeoffs associated with a multi-modal EV transition. We focus on a Chicago-centric regional domain wherein 30% of the on-road transportation fleet is instantaneously electrified and changes in on-road, refueling, and power plant emissions are considered. We find decreases in annual population-weighted domain mean NO₂(−11.83%) and PM_2.5(−2.46%) with concentration reductions of up to −5.1 ppb and −0.98µg m⁻³in urban cores. Conversely, annual population-weighted domain mean maximum daily 8 h average ozone (MDA8O₃) concentrations increase +0.64%, with notable intra-urban changes of up to +2.3 ppb. Despite mixed pollutant concentration outcomes, we find overall positive public health outcomes, largely driven by NO₂concentration reductions that result in outsized mortality rate reductions for people of color, particularly for the Black populations within our domain.