Dr. Liangzhi Chen

Persistent multiyear drought (MYD) events pose a growing threat to nature and humans in a changing climate. We identified and inventoried global MYDs by detecting spatiotemporally contiguous climatic anomalies, showing that MYDs have become drier, hotter, and led to increasingly diminished vegetation greenness. The global terrestrial land affected by MYDs has increased at a rate of 49,279 ± 14,771 square kilometers per year from 1980 to 2018. Temperate grasslands have exhibited the greatest declines in vegetation greenness during MYDs, whereas boreal and tropical forests have had comparably minor responses. With MYDs becoming more common, this global quantitative inventory of the occurrence, severity, trend, and impact of MYDs provides an important benchmark for facilitating more effective and collaborative preparedness toward mitigation of and adaptation to such extreme events.

Amid global climate warming and increased humidity, thermokarst landslides (TLs) have become frequent in the Qinghai-Tibet Plateau (QTP), significantly impacting the ecological environment and infrastructure. Early identification of potential TLs is crucial but currently lacks effective methods. This paper introduces an automatic early identification method for potential TLs in the QTP. We first evaluated various atmospheric correction methods, finding that the quadtree segmentation method is particularly effective in QTP's permafrost regions. Using Intermittent Small Baseline Subset (ISBAS) with this method and non-landslide area masking, we obtained a deformation rate map. Potential TLs areas were then identified by extracting anomalous deformation points and applying density clustering and shape algorithms to delineate boundaries. A 20 km × 30 km region was selected for validation. We also analyzed deformation evolution, finding a strong positive correlation between average temperature and deformation rate, with a noticeable lag between rainfall increases and deformation rate changes. The instability mechanism of TLs was further explored by calculating the critical sliding depth through shear experiments at the ice-soil interface. Results suggest that slopes with high moisture and low bulk density may become unstable before reaching maximum thawing depth, indicating that early warning systems should focus on the period before this maximum depth is achieved.

The sensitivity strength of air temperature (T) to surface soil temperature (sST) (namely β hereafter) constitutes a significant factor in how global climate models quantify changes in the climate. This study examines how this sensitivity is represented in the CMIP6 models. Results show regional differences and even contrasts in the β trends at interannual scales between climate models and two reanalysis products during 1980–2014. At high latitudes in the Northern Hemisphere (NH), β is stronger in the CMIP6 models than in the reanalysis data. Additionally, the β trends differ between the CMIP6 and reanalysis data, which may be related to the different precipitation (PR) and soil water availability (PR-ET) trends between the models. In the regions of increasing β intensity at high latitudes in the NH, sST is more sensitive to PR and PR-ET. Consequently, increasing PR and PR-ET leads to slower sST changes, potentially making β intensity stronger in CMIP6 models. However, in the reanalysis data, decreases in PR and PR-ET accelerate sST changes, leading to a weakening of the β intensity. A resulting implication is that β, based on high-emission scenarios, shows a stronger trend during 2015–2100, although this trend could potentially be overestimated. The findings contribute to a better understanding of the sensitivity of T to sST and facilitate the assessment of energy exchange between the land surface and the atmosphere in climate models.

An active-layer detachment slide (ALDS) occurred on September 21, 2018, in the Fenghuoshan mountains of the Qinghai-Tibet Plateau (QTP) (34°39.1′N, 92°53.5′E). With the Sentinel-1A image from Copernicus Open Access Hub, we use small baseline subset to achieve the time series deformation map to analyze the thermo-spatial creep feature, motion pattern, trigger mechanism, and correlation of environmental changes in the ALDS. The SBAS (the Small Baselines Subset) results show that the trailing part of ALDS has the largest downward deformation rate; however, the leading area was small, and the creep feature shows a clear seasonal change corresponding to the freeze-thaw cycle. We also divide the motion pattern into three stages: moderate creep, steady creep, and rapid collapse, based on the deformation rate. Meteorological observation and reanalysis data, as well as borehole data, show that heavy precipitation in the summer of 2017 and 2018 promote the formation of underground ice, while high air temperatures allow the thaw plane to reach the ice-rich zone, and confined water generated by the two-way freezing process result in ALDS. Moreover, there exists a temporal delay of approximately one month in the association between deformation rate and both precipitation and temperature. Furthermore, there is a clear correlation between variations in thawing depth and deformation, which serves as the primary catalyst for ALDS in permafrost regions. Finally, we also identify that ALDS is a mixed-type landslide and that cumulative deformation and creep damage play the main roles in triggering ALDS.

Thermokarst landslides (TLs), which are made up of retrogressive thaw slumps (RTSs) and active-layer detachments slides (ALDSs), are quickly increasing in the central Qinghai-Tibet Plateau (QTP) permafrost area. TLs induce many environmental problems and threaten the safety of infrastructure. A landslide susceptibility map is crucial to prevent the negative impacts of these landslides. However, traditional thermokarst landslides susceptibility (TLS) evaluations do not consider real-time surface deformation information. Therefore, we propose a novel method that integrates a conventional machine learning model with ground surface deformation. Nine influencing factors, including slope, normalized difference vegetation index, elevation, precipitation, thawing degree days, soil content, active layer thickness, water content, and vegetation type were selected based on the q-index detector, and support vector machine was employed to obtain an initial model (IM). We obtained surface deformation in the study area using the enhanced Small Baseline Subset (SBAS) method. The accuracy of the InSAR results was validated through comparison with data from two field monitoring cross-sections. Subsequently, we established an integrated model (ITM) by combining the initial model with surface deformation using the contribution matrix, and confirmed the fusion model's higher rationality and accuracy through comparison with the IM. Furthermore, we examined the impact of the quadtree segmentation method on atmospheric correction and validated the TLS results obtained using the ITM with high-resolution optical remote sensing imagery from GF6. Finally, the influencing factors and distribution characteristics of TLS was analyzed, and the results indicated that climatic conditions are the primary factors affecting the distribution of TLS.