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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 A rheological model for loose granular media is developed to capture both solid-like and fluid-like responses during shearing. The proposed model is built by following the mathematical structure of an extended Kelvin–Voigt model, where an elastic spring and plastic slider act in parallel to a viscous damper. This arrangement requires the partition of the total stress into rate-independent and rate-dependent stress components. To model the solid-like behavior, a simple frictional plasticity model is adopted without modifications, thus contributing to the rate-independent stress. Instead, the fluid-like or rate-dependent stress is further decomposed into deviatoric and volumetric parts, by proposing a new formulation based on a combination of the m(I) relation, originally developed under pressure-controlled shear, with a pressure-shear rate relation derived under volume-controlled shear. The proposed formulation allows the model to capture both the increase in the friction coefficient and the enhanced dilation at high shear rates. High-fidelity simulation data, obtained from discrete element method and multiscale modelling, are used to evaluate the performance of the proposed constitutive model. The model provides accurate results under both drained and undrained simple shear paths across a wide range of shear rates. Furthermore, it successfully reproduces at much lower computational cost the flowslide mobility computed through multiscale simulations, which is primarily regulated by the shear rate dependence of the material properties during the dynamic runout stage. 

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. 

Abstract A hierarchical multiscale modeling framework is proposed to simulate flowslide triggering and runout. It couples a system‐scale sliding‐consolidation model (SCM) resolving hydro‐mechanical feedbacks within a flowslide with a local‐scale solver based on the discrete element method (DEM) replicating the sand deformation response in the liquefied regime. This coupling allows for the simulation of a seamless transition from solid‐ to fluid‐like behavior following liquefaction, which is controlled by the grain‐scale dynamics. To investigate the role of grain‐scale interactions, the DEM simulations replace the constitutive model within the SCM framework, enabling the capture of the emergent rate‐dependent behavior of the sand during the inertial regime of motion. For this purpose, a novel algorithm is proposed to ensure the accurate passage of the strain rate from the global analysis to the local DEM solver under both quasi‐static (pre‐triggering) and dynamic (post‐triggering) regimes of motion. Our findings demonstrate that the specifics of the coupling algorithm do not bear significant consequences to the triggering analysis, in that the grain‐scale dynamics is negligible. By contrast, major differences between the results obtained with traditional algorithms and the proposed algorithm are found for the post‐triggering stage. Specifically, the existing algorithms suffer from loss of convergence and require proper numerical treatment to capture the micro‐inertial effects arising from the post‐liquefaction particle agitation responsible for viscous‐like effects that spontaneously regulate the flowslide velocity. These findings emphasize the important role of rate‐dependent feedback for the analysis of natural hazards involving granular materials, especially for post‐failure propagation analysis. 

Abstract Landslide motion is often simulated with interface‐like laws able to capture changes in frictional strength caused by the growth of the pore water pressure and the consequent reduction of the effective stress normal to the plane of sliding. Here it is argued that, although often neglected, the evolution of all the 3D stress components within the basal shear zone of landslides also contributes to changes in frictional strength and must be accounted for to predict changes in seasonal velocity. For this purpose, an augmented sliding‐consolidation model is proposed which allows for the computation of excess pore pressure development and downslope sliding with any constitutive law with 3D stress evolution. Simulations of idealised infinite slope models subjected to hydrologic forcing are used to study the role of in‐situ stress conditions and stress rate multiaxiality. Specifically, a Drucker‐Prager perfectly plastic model is used to replicate frictional failure and shear deformation at the base of landslides. The model reveals that conditions amenable to the shearing of a frictional interface are met only after numerous rainfall cycles, that is, when multiaxial stress rates are suppressed. In this case, the landslide is predicted to move through a seasonal ratcheting controlled only by the effective stress component normal to the plane of sliding. By contrast, in newly formed landslides, the multiaxial stress evolution is found to produce further regimes of motion, from plastic shakedown to cyclic failure, neither of which can be captured by interface‐like frictional laws. Notably, the model suggests that a transition across these regimes can emerge in response to an aggravation of the magnitude of forcing, implying that (i) fluctuations in climate may alter the seasonal trends of motion observed today; (ii) our ability to quantify landslide‐induced risks is impaired unless proper geomechanical models are used to examine their long‐term dynamics. 

Abstract Accurate soil moisture and streamflow data are an aspirational need of many hydrologically relevant fields. Model simulated soil moisture and streamflow hold promise but models require validation prior to application. Calibration methods are commonly used to improve model fidelity but misrepresentation of the true dynamics remains a challenge. In this study, we leverage soil parameter estimates from the Soil Survey Geographic (SSURGO) database and the probability mapping of SSURGO (POLARIS) to improve the representation of hydrologic processes in the Weather Research and Forecasting Hydrological modeling system (WRF‐Hydro) over a central California domain. Our results show WRF‐Hydro soil moisture exhibits increased correlation coefficients (r), reduced biases, and increased Kling‐Gupta Efficiencies (KGEs) across seven in situ soil moisture observing stations after updating the model's soil parameters according to POLARIS. Compared to four well‐established soil moisture data sets including Soil Moisture Active Passive data and three Phase 2 North American Land Data Assimilation System land surface models, our POLARIS‐adjusted WRF‐Hydro simulations produce the highest mean KGE (0.69) across the seven stations. More importantly, WRF‐Hydro streamflow fidelity also increases, especially in the case where the model domain is set up with SSURGO‐informed total soil thickness. The magnitude and timing of peak flow events are better captured,rincreases across nine United States Geological Survey stream gages, and the mean KGE across seven of the nine gages increases from 0.12 to 0.66. Our pre‐calibration parameter estimate approach, which is transferable to other spatially distributed hydrological models, can substantially improve a model's performance, helping reduce calibration efforts and computational costs. 

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.