Multitemporal geohazard susceptibility analysis can not only provide reliable results but can also help identify the differences in the mechanisms of different elements under different temporal and spatial backgrounds, so as to better accurately prevent and control geohazards. Here, we studied the 12 counties (cities) that were severely affected by the Wenchuan earthquake of 12 May 2008. Our study was divided into four time periods: 2008, 2009–2012, 2013, and 2014–2017. Common geohazards in the study area, such as landslides, collapses and debris flows, were taken into account. We constructed a geohazard susceptibility index evaluation system that included topography, geology, land cover, meteorology, hydrology, and human activities. Then we used a random forest model to study the changes in geohazard susceptibility during the Wenchuan earthquake, the following ten years, and its driving mechanisms. We had four main findings. (1) The susceptibility of geohazards from 2008 to 2017 gradually increased and their spatial distribution was significantly correlated with the main faults and rivers. (2) The Yingxiu-Beichuan Fault, the western section of the Jiangyou-Dujiangyan Fault, and the Minjiang and Fujiang rivers were highly susceptible to geohazards, and changes in geohazard susceptibility mainly occurred along the Pingwu-Qingchuan Fault, the eastern section of the Jiangyou-Dujiangyan Fault, and the riparian areas of the Mianyuan River, Zagunao River, Tongkou River, Baicao River, and other secondary rivers. (3) The relative contribution of topographic factors to geohazards in the four different periods was stable, geological factors slowly decreased, and meteorological and hydrological factors increased. In addition, the impact of land cover in 2008 was more significant than during other periods, and the impact of human activities had an upward trend from 2008 to 2017. (4) Elevation and slope had significant topographical effects, coupled with the geological environmental effects of engineering rock groups and faults, and river-derived effects, which resulted in a spatial aggregation of geohazard susceptibility. We attributed the dynamic changes in the areas that were highly susceptible to geohazards around the faults and rivers to the changes in the intensity of earthquakes and precipitation in different periods.

The Qinghai–Tibet Plateau (QTP) is a sensor of global climate change and regional human activities, and drought monitoring will help to achieve its ecological protection and sustainable development. In order to effectively control the geospatial scale effect, we divided the study area into eight geomorphological sub-regions, and calculated the Temperature-Vegetation Drought Index (TVDI) of each geomorphological sub-region based on MODIS Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data, and synthesized the TVDI of the whole region. We employed partial and multiple correlation analyses to identify the relationship between TVDI and temperature and precipitation. The random forest model was further used to study the driving mechanism of TVDI in each geomorphological division. The results of the study were as follows: (1) From 2000 to 2019, the QTP showed a drought trend, with the most significant drought trend in the central region. The spatial pattern of TVDI changes of QTP was consistent with the gradient changes of precipitation and temperature, both showing a gradual trend from southeast to northwest. (2) There was a risk of drought in the four seasons of the QTP, and the seasonal variation of TVDI was significant, which was characterized by being relatively dry in spring and summer and relatively humid in autumn and winter. (3) Drought in the QTP was mainly driven by natural factors, supplemented by human factors. The driving effect of temperature and precipitation factors on TVDI was stable and significant, which mainly determined the spatial distribution and variation of TVDI of the QTP. Geomorphological factors led to regional intensification and local differentiation effects of drought, especially in high mountains, flat slopes, sunny slopes and other places, which had a more significant impact on TVDI. Human activities had local point-like and linear impacts, and grass-land and cultivated land that were closely related to the relatively high impacts on TVDI of human grazing and farming activities. In view of the spatial-temporal patterns of change in TVDI in the study area, it is important to strengthen the monitoring and early warning of changes in natural factors, optimize the spatial distribution of human activities, and scientifically promote ecological protection and restoration.

Analysis of the driving mechanism of wetland change can help identify the spatial differences in the mechanisms of different elements in order to better scientifically prevent and utilize wetlands. The Beijing-Tianjin-Hebei Plain (BTHP) was selected as the study area, and the dynamic degree model and change intensity index were utilized to examine the spatial and temporal changes of wetlands based on four periods of land use data. After establishing a wetland change evaluation system that included topography, geology, meteorological, and human activities, we utilized a random forest model to study the driving mechanism of wetland change from 1990–2020. Based on the developed zoning map, we then offered specific protection policies. We have four major findings: (1) the wetlands reduced significantly in the BTHP and underwent a change process from decreasing to increasing, and reservoirs and rivers, particularly along the Bohai Rim, were the primary determinants of wetland changes; (2) the impact of topographic factors such as elevation showed a significant gradient effect, the impact of geological factors such as hydrogeological division was low and indirect, the impact of meteorological factors was nonlinear, and the impact of anthropic factors was most significant and showed clear spatial directivity; (3) the urban expansion and agricultural reclamation had significant dominant effects, coupled with the topographical effects of elevation and slope, the geological environmental effects of surface subsidence and hydrogeological division, and the cumulative effects of temperature and precipitation, which resulted in the spatial change of wetlands; and (4) protecting wetland integrity, dynamic monitoring, restricting human activities, and establishing wetland buffer zones should be applied to the general area, natural factors area, anthropic factors area, and significant comprehensive area, respectively.

Land cover change (LCC)-climate interactions could amplify or mitigate climate change in the Arctic and Boreal regions. Revealing the impacts of LCC on climate extremes helps to improve risk assessment capability and provides targeted adaptation and mitigation strategies to enhance regional climate resilience. In this study, we parameterized remotely-sensed land cover maps into the Community Earth System Model version 2 (CESM2) to examine the biogeophysical effects of LCC on temperature and precipitation extremes in the Arctic–Boreal Vulnerability Experiment (ABoVE) core domain, where the contribution of substantial LCC to abnormal climate variations has been little explored. Results showed that LCC had varying degrees of impact on regional climate extremes. Alteration in surface albedo induced net radiation changes, which further resulted in variation in maximum temperature (Tmax), with ice days (ID) and warm spell duration index (WSDI) increased by more than 3 days, and the maximum value of daily maximum temperature (TXx) decreased by more than 0.6 °C. The LCC-induced combined effects of water vapor supply and atmospheric dynamics resulted in a decreasing trend in precipitation extreme, with the number of days with precipitation exceeding 10 mm (R10mm) and annual total precipitation in days with precipitation exceeding 1 mm (PRCPTOT) decreased by more than 3 days and 15 mm, respectively, while consecutive dry days (CDD) increased by more than 3 days. These findings highlight the importance of LCC for regional climate variability in the Arctic and Boreal regions, contribute to a better understanding of LCC-climate interactions.

Accurate simulation of climate extremes is critical for climate mitigation and adaptation. The role of land cover and land use change (LCLUC) on climate extremes has been well documented. Yet, current modeling studies can barely capture how LCLU evolves in the real world, limiting their ability to reliably simulate climate extremes. This study integrated high‐resolution, accurate, dynamic remotely‐sensed LCLU data from 1982 to 2013 into the second version of the Community Earth System Model (CESM) to enhance its performance in simulating precipitation extremes in China. Two CESM experiments, EXP‐LUH2 with default annual Land‐Use Harmonization (LUH2) data and EXP‐RS with annual remotely‐sensed LCLU data, were compared. The results demonstrate that EXP‐RS outperforms EXP‐LUH2 in simulating precipitation extremes with lower bias and root mean square error for 11 out of the 12 extreme precipitation indices. This improved performance, to a large extent, is attributed to the reduction in bias associated with leaf area index and soil moisture, the enhancement of evapotranspiration, and the subsequent improvement in simulation of total precipitable water. These findings highlight the importance of incorporating realistic, time‐series LCLU data to better represent LCLUC in climate models, especially when satellite‐derived LCLU data is available, which can further benefit climate projections and land management strategies.