The rapid development of rural regions, the mountainous landscape, and frequent subtropical-typhoon-related rainfall have collectively contributed to a high incidence of cut slope-induced landslides in the coastal areas of eastern China. Despite the escalating risk, there has been a noticeable absence of comprehensive hazard assessments and targeted management measures for private housing and road construction in these rural environments. This paper introduces a novel approach for mitigating such risks by employing a susceptibility evaluation framework grounded in machine learning and uncertainty methods, combined with a double-index rainfall intensity-duration (I-D) threshold model. The proposed Intelligent Slope Prevention System operates through a sequential four-step process: (i) Site-specific landslide susceptibility is assessed through cut slope feature investigations and the use of three machine learning algorithms, namely, Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN); (ii) the double-index model calculates rainfall thresholds, accounting for both prolonged continuous rainfall and short-term heavy rainfall events; (iii) the integration of rainfall thresholds with susceptibility assessments allows for the categorization of hazard levels; and (iv) tailored management strategies are deployed for data collection and early warning issuance. The study demonstrates that the SVM achieved the highest prediction accuracy across soil, rock-soil mixed, and rock slopes. The double-index model further enhanced the system's performance by predicting all 20 rainfall-induced landslides, with 15 of them falling under high or very high warning levels. An empirical evaluation during a heavy rainfall event on 29th June 2021 confirmed the system's effectiveness in identifying high-hazard areas and issuing timely warnings, thus significantly mitigating potential damage. Implemented in the coastal mountain basins of eastern China, the Intelligent Slope Prevention System leverages the gathered knowledge to manage and regulate slope hazards effectively, thereby enhancing the safety of both residential and infrastructural assets.

Empirical orthogonal function (EOF) and correlation analyses were employed to investigate the winter and spring snow depth in Eurasia and its relationship with Eastern China precipitation based on the observed and reanalyzed data from 1980 to 2016. The results show that the winter and spring snow cover in Eurasia not only highlights a decreasing trend due to global warming (the first EOF mode, its variance accounted for 24.4% and 22.6% of the total variance) but also exhibits notable interdecadal variation (the second EOF mode, its variance accounted for 10.2% and 11.5% of the total variance). The second EOF mode of winter snow depth in Eurasia is characterized by a west-east dipole pattern. It was observed that the spatial correlation pattern between the EOF2 of Eurasian snow depth and summer precipitation in China closely resembles the meridional quadrupole structure of the third EOF mode of summer precipitation in China. This pattern is characterized by excessive rainfall in Northeast China and the lower-middle reaches of the Yangtze River, and less rainfall over the Yellow River basin and southern China. The EOF mode of spring snow depth not only reflects the declining trend but also regulates precipitation in Eastern China. The possible mechanisms by which snow depth causes changes in soil moisture and subsequently affects atmospheric circulation are then explored from the perspective of the hydrological effects of snow cover. Decreased (Increased) snow depth in Eurasia during the winter and spring directly leads to diminished (increased) soil moisture while increasing (decreasing) net radiation and sensible heat flux at the surface. The meridional distribution of surface temperature also exhibits a dipole pattern, leading to enhanced subtropical westerly jet in the upper troposphere. The Eurasian snow cover anomalies pattern triggered an anomalous mid-latitude Eurasian wave train, which strengthened significantly in the Western Siberian Plain. It then splits into two branches, one continuing to propagate eastward at high latitudes and the other shifting towards East Asia, thereby impacting precipitation in Eastern China. This work indicates that the second EOF mode of Eurasian snow cover can impact the precipitation variability in Eastern China during the same period and in summer on an interdecadal scale.

Surface freezing and thawing processes pose significant influences on surface water and energy balances, which, in turn, affect vegetation growth, soil moisture, carbon cycling, and terrestrial ecosystems. At present, the changes in surface freezing and thawing states are hotspots of ecological research, but the variations of surface frozen days (SFDs) are less studied, especially in the permafrost areas covered with boreal forest, and the influence of the environmental factors on the SFDs is not clear. Utilizing the Advanced Microwave Scanning Radiometer for EOS (AMSRE) and Microwave Scanning Radiometer 2 (AMSR2) brightness temperature data, this study applies the Freeze-Thaw Discriminant Function Algorithm (DFA) to explore the spatiotemporal variability features of SFDs in the Northeast China Permafrost Zone (NCPZ) and the relationship between the permafrost distribution and the spatial variability characteristics of SFDs; additionally, the Optimal Parameters-based Geographical Detector is employed to determine the factors that affect SFDs. The results showed that the SFDs in the NCPZ decreased with a rate of -0.43 d/a from 2002 to 2021 and significantly decreased on the eastern and western slopes of the Greater Khingan Mountains. Meanwhile, the degree of spatial fluctuation of SFDs increased gradually with a decreasing continuity of permafrost. Snow cover and air temperature were the two most important factors influencing SFD variability in the NCPZ, accounting for 83.9% and 74.8% of the spatial variation, respectively, and SFDs increased gradually with increasing snow cover and decreasing air temperature. The strongest explanatory power of SFD spatial variability was found to be the combination of air temperature and precipitation, which had a coefficient of 94.2%. Moreover, the combination of any two environmental factors increased this power. The findings of this study can be used to design ecological environmental conservation and engineer construction policies in high-latitude permafrost zones with forest cover.

Accurate delineation of spatiotemporal variations in ground surface soil freeze and thaw (F/T) states is essential to appraise many geoscience issues, such as the hydrological circulation and land surface-atmosphere feedbacks. Recently, an Improved Dual-index algorithm (DIA) method was proposed by accounting for the influence of soil moisture variations on the discrimination accuracy with passive microwave remote sensing (RS) data products. Compared with the original DIA, the Improved DIA method has proven to be a more practical approach on surface soil F/T states discrimination. However, the method has only been applied and verified in cold regions of high-altitude (e.g., Tibetan Plateau), it's applicability and effectiveness in the cold areas in mid-high latitudes, where the geographic and climatic conditions are quite different, yet remained to be further explored. The present study investigated the feasibility of using AMSR-E (the Advanced Microwave Scanning Radiometer-EOS) and AMSR2 (the second Advance Microwave Scanning Radiometer) passive microwave RS data products to discriminate the F/T states of the ground surface for a long period from 2002 to 2019 by means of the Improved DIA method over a typical mid-high latitude cold region of Northeastern China. Seasonal variation characteristics of soil moisture in mid-high latitude areas were similar with those in high-altitude areas, even though the spatial heterogeneity of soil water content was significant in different regions. Discriminating surface soil F/T states with the Improved DIA method derived overall discriminating accuracy of about 91.6% in the study area, which demonstrated excellent feasibility of the Improved DIA method in mid-high latitude cold regions. The mapping results shown surface soil F/T cycle in Northeastern China responding to climate change was examined from the perspective of regional average, both the proportion of frozen soil area and frozen days showed significant decreasing trends continuously with differed quite spatially. The discriminating accuracy of the Improved DIA method was found to be lower in plain areas with dense populations and large farmland areas compared to mountainous areas when human activities were not taken into consideration, as quantifying human activities can be challenging. The Improved DIA method has been well verified in both high-altitude and high-latitude regions; it has great potential in global scale research.

Permafrost monitoring using remote sensing techniques is an effective approach at present. Permafrost mostly occurs below the land surface, which limits permafrost monitoring by optical remote sensing. Considering the specific hydrothermal relations between permafrost and its active layer, we developed a permafrost monitoring and classification method that integrated the ground surface soil freeze/thaw states determined by the dual-index algorithm (DIA) and the permafrost classification method based on thermal stability. The modified frost index was introduced into the method as a link between the DIA and the permafrost classification method. Northeastern China was selected to establish and verify the proposed method and to examine the changes in regional permafrost against the background of global warming from 2002 to 2017. The results showed that the ground surface soil freeze/thaw states were significantly correlated with the permafrost distribution. The spatial continuity of permafrost and its sensitivity to climate change could be effectively reflected by the modified frost index. The proposed method had a high accuracy with a classification error smaller than 3%, compared with static permafrost maps. Moreover, the proportion of permafrost decreased from 29% at the beginning of the 21st century to 22.5% at present in northeastern China over the study period. The southern permafrost boundary in the study area generally moved northward approximately 25-75 km. Additionally, the method was applied to the Northern Hemisphere (30 degrees N - 90 degrees N), which demonstrated its effectiveness and extended applicability.

The fine-mode aerosol absorption optical depth (AAOD) retrieved from the Aerosol Robotic Network (AERONET) has been used in previous studies to calculate the radiative forcing of black carbon (BC) aerosol, assuming that the absorption by fine-mode aerosols (diameter >= 1 mu m) is primarily from BC while the absorption by larger particles (diameter > 1 mu m) is principally from dust. In the present study, the Community Earth System Model was used to simulate and quantify the contribution of fine-mode dust to fine-mode AAOD in eastern China (29-41 degrees N, 104-122 degrees E)-an area where concentrations of BC are high. The simulated fine-mode dust concentrations were constrained by observations from nine sites belonging to the Chinese Meteorological Administration Atmosphere Watch Network. Averaged over eastern China, the simulated annual mean fine-mode dust AAOD was 3.6 x 10(-3), with the maximum AAOD in spring and the minimum value in winter. The contribution of fine-mode dust to the total fine-mode AAOD (sum of fine-mode dust, BC, and organic carbon) in winter, spring, summer, and autumn was 3.4%, 25.2%, 12.5%, and 14.9%, respectively, with an annual mean value of 15.1%. The results indicate the importance of removing fine dust AAOD when the AERONET fine-mode AAOD is used for calculating the radiative forcing of BC in eastern China.