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Accurate prediction of shallow landslide occurrence and the subsequent motion range after transformation into debris flows is crucial for reducing disaster‐induced losses. The use of Cellular Automata‐based (CA) hydrodynamic models has seen increasing application in predicting landslides, debris flows, and other related hazards. However, previous CA‐based models have primarily focused on the motion and evolution process of debris flows, lacking detailed description about the dynamic instability associated with shallow landslides. In this study, we propose a comprehensive CA‐based model that is based on existing theories and improved models for simulating hydrological processes, sliding surface identification algorithms, threshold‐based mechanical interactions, material entrainment, and deposition. The model was applied to the Yindongzi (YDZ) landslides in Sichuan, China. The accuracy of the model was validated through comparisons with field investigation results and calculations based on shallow water equations. This model enables efficient and rapid prediction of shallow landslide occurrence time, volume, spatial distribution, and runout distance. Evaluation of the model's predictive performance reveals an error range of −23.12% to +44.26% for YDZ landslides. Moreover, the influence of different shallow landslide failure patterns on the deposition and erosion of debris flows was analyzed. The results indicate that the failure patterns of shallow landslides significantly affect the deposition and entrainment capacity of debris flows. This study provides a novel approach for predicting the occurrence of shallow landslides and the subsequent motion, entrainment, and deposition after transformation into debris flows. Key Points: A unified framework of cellular‐automaton model is proposed for predicting mass release and extent of shallow landslidesThe model reproduces the occurrence time, volume, spatial distribution, and extent of shallow landslides and debris flowThe deposition and entrainment processes of debris flow are influenced by different landslide failure patterns [ABSTRACT FROM AUTHOR]

We have developed a joint tomographic inversion method with seismic surface wave dispersion and gravity data for obtaining more reliable crustal 3-D shear wave structures. We take the eikonal-based direct surface wave tomographic method and adaptive gravity modelling method in spherical coordinates in the inverse problem. Based on the empirical relations between seismic velocity and density parameters, our method combines surface wave dispersion curves (i.e. surface wave traveltimes at different periods) and Bouguer gravity anomaly data together to invert for 3-D shear wave velocity structures. In our method, off-great-circle propagation of the surface wave and the earth's curvature is considered in the forward modelling. Synthetic tests suggest that the joint tomographic method could improve the reliability and obtain more convincing results than individual seismic surface wave tomography. The gravity data can provide more constraints into the model resolution and help restore the crustal anomalies better. The inversion results in the eastern Tibetan Plateau and Sichuan basin indicate complex distributions of low-velocity zone in the mid-crust of the eastern Tibetan Plateau and a craton-like basement of the Sichuan basin, which supports the crust channel flow model. Although both the 3-D shear wave velocity model from joint inversion and the individual seismic surface wave inversion can fit the surface wave data almost equally well, the joint inversion model can better match the gravity data We also found that the 3-D model from joint inversion in this study shows similar structural characteristics with the surface wave tomographic model, which indicates the icing on the cake effects of gravity data. [ABSTRACT FROM AUTHOR]

Determining a detailed 3-D velocity model with high resolution for the sedimentary layer in the Sichuan Basin is potentially beneficial both to the industrial oil/gas exploration and earthquake hazards' mitigation. In this study, we apply the ambient noise tomography method to construct a 3-D S -wave velocity model. This model focuses on the sedimentary layer of the Sichuan Basin, with a 0.3° × 0.3° grid precision. Dispersion curves of both group and phase velocities of Rayleigh wave at 4 to 40 s periods are utilized, which are extracted from 87 broad-band stations in the Sichuan Basin and the surrounding areas. The 3-D model reveals a thick sedimentary layer of the Sichuan Basin with S -wave velocity ranging from ∼2.0 to 3.4 km s−1. The sediment thickness in the margins of the Sichuan Basin is generally greater than the typical values of 6–10 km in the central areas due to surrounding orogenic activities, with a maximum depth of ∼13 km in the northwestern margin. Moreover, a prominent low S -wave velocity anomaly in the margins may be caused by the sediment accumulations from large-scale landslides and pronounced denudation of the surrounding orogenic belts. Major geologic units in the sedimentary layer are delineated in this study. The S -wave velocity values within each geologic unit and their bottom interfaces are obtained. Based on our model, we calculate synthetic ground motions for the 2013 Lushan earthquake and obtain the distribution of the peak ground acceleration from the earthquake epicentre to the western Sichuan Basin. The result clearly illustrates the basin amplification effect on the seismic waves. [ABSTRACT FROM AUTHOR]

The role of meteorological conditions has long been recognized in modulating regional air quality. The impact of near-surface turbulence, nevertheless, remains poorly understood. To curb the spread of COVID-19, a variety of lockdown measures were implemented, providing us an unprecedented opportunity to examine the joint impact of emission control and meteorology on regional air quality. Here we examined the variations of planetary boundary layer (PBL) height, PM 2.5 concentrations, turbulence kinetic energy (TKE), vertical wind shear, and their associations in Chengdu, Sichuan province in Southwest China between January 13 and February 24, 2020, by synergistically using micro pulse lidar, ground-level meteorological and PM 2.5 measurements, as well as ultrasonic anemometer observations. During the study period, Sichuan basin was primarily regulated by the straight west wind, with an averaged wind speed of 2–3 m/s at 850 hPa, indicative of a relatively stable atmospheric dispersion condition. TKE was positively correlated with PBL height but negatively correlated with PM 2.5. The PM 2.5 concentration varied dramatically during pre- and post-lockdown periods but remained near constant at a relatively low level during the lockdown period. Meanwhile, the negative correlation between TKE and PM 2.5 was much stronger during the lockdown and post-lockdown periods, when aerosol emissions were significantly reduced. Moreover, the correlation between TKE and PM 2.5 exhibited large diurnal variability, with the strongest correlation observed during the daytime when solar radiation and turbulent mixing generally reached their peaks. Overall, the observational results in Chengdu underscore the non-negligible impact of turbulence on regional PM 2.5 concentrations, which could help better understand the variation of regional air pollution events. • Lockdown measures during COVID-19 helps improve the air quality in Chengdu. • TKE positively correlated with PBL height and had a negative correlation with PM 2.5 • Near-surface turbulence has significant impact on the variation of aerosol concentration. [ABSTRACT FROM AUTHOR]