Conventional outputs of physics-based landslide forecasting models are presented as deterministic warnings by calculating the safety factor ( F _s ) of potentially dangerous slopes. However, these models are highly dependent on variables such as cohesion force and internal friction angle which are affected by a high degree of uncertainty especially at a regional scale, resulting in unacceptable uncertainties of F _s . Under such circumstances, the outputs of physical models are more suitable if presented in the form of landslide probability values. In order to develop such models, a method to link the uncertainty of soil parameter values with landslide probability is devised. This paper proposes the use of Monte Carlo methods to quantitatively express uncertainty by assigning random values to physical variables inside a defined interval. The inequality F _s <1 is tested for each pixel in n simulations which are integrated in a unique parameter. This parameter links the landslide probability to the uncertainties of soil mechanical parameters and is used to create a physics-based probabilistic forecasting model for rainfall-induced shallow landslides. The prediction ability of this model was tested in a case study, in which simulated forecasting of landslide disasters associated with heavy rainfalls on 9 July 2013 in the Wenchuan earthquake region of Sichuan province, China, was performed. The proposed model successfully forecasted landslides in 159 of the 176 disaster points registered by the geo-environmental monitoring station of Sichuan province. Such testing results indicate that the new model can be operated in a highly efficient way and show more reliable results, attributable to its high prediction accuracy. Accordingly, the new model can be potentially packaged into a forecasting system for shallow landslides providing technological support for the mitigation of these disasters at regional scale.

During the summer fire season in the Greater Khingan Mountains, weak synoptic winds allow local mountain–valley breeze systems to predominantly influence fire spread. However, their dynamic effects remain insufficiently quantified, limiting fire forecasting accuracy. This study analyzes a decade of summer meteorological data and a high-resolution WRF-Fire simulation of a 2023 wildfire to investigate wind patterns and their impact on fire behavior. Results reveal pronounced diurnal and spatial wind variability, with low directional persistence and concentrated nighttime distributions. Under low-wind conditions, mountain–valley breezes shift from upslope during the day to downslope flows at night. Simulations and observations indicate higher nighttime wind speeds on slopes and higher daytime speeds in valleys, reflecting the combined effects of thermal circulation and gravitational acceleration. The WRF-Fire model effectively reproduced the wildfire’s macro-scale spread pattern, showing strong agreement with satellite-derived burn scars in mountainous regions. Fire progression was influenced by five distinct phases, with nocturnal mountain winds and topographic channeling accelerating spread. These highlight the role of terrain-driven mountain–valley breezes in fire propagation and provide insights to improve fire forecasting and management strategies in mountainous regions. Firefighting strategies must account for the diurnal cycle of wind, particularly the contrast between strong nighttime winds at higher altitudes and stable valley conditions.

The terrain of Changbai Mountain has great influence on the distribution of atmospheric flows and the occurrence and development of precipitation. This study quantitatively analyzes the regional characteristic of topographic perturbations and the relationship between terrain, wind and precipitation in Changbai Mountain region, by using a spectral analysis of the two-dimensional discrete cosine transform. Three domains with relatively heavy summer precipitation are selected as the study region. The results indicate that the overall terrain of Changbai Mountain region exhibits anisotropic characteristics. The terrain spectra of domain B are less than those of domain A and C across the whole wavelength (λ) bands, indicating that the large-scale topographic perturbations of domain B are relatively weak. The largest topographic spectral peak of domain C shows the most pronounced undulation of terrain among the three domains. The dominant wavelengths of terrain height variance for domain A and C, both close to the respective maximum wavelengths, indicate more prominent large-scale topographic perturbations. For domain A, the variation of the precipitation spectra is consistent with that of the wind spectra at the wavelength bands of λ < 390 km, showing a high correlation between wind field and the occurrence of rainfall. The inverse relationship at larger wavelengths indicates that multiple factors contribute to the occurrence of rainfall. For domain B, there is consistency in the fluctuations of terrain spectra, precipitation spectra, and wind spectra at the wavelength bands of λ < 278.3 km, implying that the smaller-scale terrain has an important effect on the occurrence of summer precipitation. For domain C, the variations of terrain spectra, precipitation spectra, and wind spectra are almost consistent across the whole wavelength bands, indicating that the large-scale terrain and minor terrain both play a crucial role in atmospheric uplift and the occurrence and development of summer rainfall.

The terrain of Changbai Mountain has great influence on the distribution of atmospheric flows and the occurrence and development of precipitation. However, quantitative studies on the real terrain characteristics and the terrain effect on precipitation distribution in this region are scant at present. This study quantitatively analyzes the regional characteristic of topographic perturbations and the relationship between terrain, wind, and precipitation in Changbai Mountain region by using a spectral analysis of the two-dimensional discrete cosine transform. Three domains with relatively heavy summer precipitation are selected as the study region. The results indicate that the overall terrain of the Changbai Mountain region exhibits anisotropic characteristics. The terrain spectra of domain B are less than those of domains A and C across the whole wavelength (λ) bands, indicating that the large-scale topographic perturbations of domain B are relatively weak. The largest topographic spectral peak of domain C shows the most pronounced undulation of terrain among the three domains. The dominant wavelengths of terrain height variance for domains A and C, both close to the respective maximum wavelengths, indicate more prominent large-scale topographic perturbations. For domain A, the variation of the precipitation spectra is consistent with that of the wind spectra at the wavelength bands of λ < 390 km, showing a high correlation between wind field and the occurrence of rainfall. The inverse relationship at larger wavelengths indicates that multiple factors contribute to the occurrence of rainfall. For domain B, there is consistency in the fluctuations of terrain spectra, precipitation spectra, and wind spectra at the wavelength bands of λ < 278.3 km, implying that the smaller-scale terrain has an important effect on the occurrence of summer precipitation. For domain C, the variations of terrain spectra, precipitation spectra, and wind spectra are almost consistent across the whole wavelength bands, indicating that the large-scale terrain and minor terrain both play a crucial role in atmospheric uplift and the occurrence and development of summer rainfall.

Conventional outputs of physics-based landslide forecasting models are presented as deterministic warnings by calculating the safety factor (Fs) of potentially dangerous slopes. However, these models are highly dependent on variables such as cohesion force and internal friction angle which are affected by high degree of uncertainty especially at a regional scale, which result in unacceptable uncertainties of Fs. Under such circumstances, the outputs of physical models are more suitable if presented in the form of landslide probability values. In order to develop such models, a method to link the uncertainty of soil parameter values with landslide probability is devised. This paper proposes the use of Monte Carlo method to quantitatively express uncertainty by assigning random values to physical variables inside a defined interval. The inequality Fs<1 is tested for each pixel in n simulations which are integrated in a unique parameter. This parameter links the landslide probability to the uncertainties of soil mechanical parameters and is used to create a physics-based probabilistic forecasting model for rainfall-induced shallow landslides. The prediction ability of this model was tested in a case study, in which simulated forecasting of landslide disasters associated to heavy rainfalls on July 9 of 2013 in the Wenchuan earthquake region of Sichuan province, China was performed. The proposed model successfully forecasted landslides in 159 of the 176 disaster points registered by the geo-environmental monitoring station of Sichuan province. Such testing results indicate that the new model can be operated in a high efficient way and show more reliable results attributing to its high prediction accuracy. Accordingly, the new model can be potentially packaged into a forecasting system for shallow landslides providing technological support for the mitigation of these disasters at regional scale.