Lanzhou New District is the first and largest national-level new district in the Loess Plateau region of China. Large-scale land creation and rapid utilization of the land surface for construction has induced various magnitudes of land subsidence in the region, which is posing an increasing threat to the built environment and quality of life. In this study, the spatial and temporal evolution of surface subsidence in Lanzhou New District was assessed using Persistent Scatterer Interferometric Synthetic Aperture radar (PSInSAR) to process the ENVISAT SAR images from 2003–2010, and the Small Baseline Subset (SBAS) InSAR to process the Sentinel-1A images from 2015–2016. We found that the land subsidence exhibits distinct spatiotemporal patterns in the study region. The spatial pattern of land subsidence has evidently extended from the major urban zone to the land creation region. Significant subsidence of 0–55 mm/year was detected between 2015 and 2016 in the land creation and urbanization area where either zero or minor subsidence of 0–17.2 mm/year was recorded between 2003 and 2010. The change in the spatiotemporal pattern appears to be dominated mainly by the spatial heterogeneity of land creation and urban expansion. The spatial associations of subsidence suggest a clear geological control, in terms of the presence of compressible sedimentary deposits; however, subsidence and groundwater fluctuations are weakly correlated. We infer that the processes of land creation and rapid urban construction are responsible for determining subsidence over the region, and the local geological conditions, including lithology and the thickness of the compressible layer, control the magnitude of the subsidence process. However, anthropogenic activities, especially related to land creation, have more significant impacts on the detected subsidence than other factors. In addition, the higher collapsibility and compressibility of the loess deposits in the land creation region may be the underlying mechanism of macro-subsidence in Lanzhou New District. Our results provide a useful reference for land creation, urban planning and subsidence mitigation in the Loess Plateau region, where the large-scale process of bulldozing mountains and valley infilling to create level areas for city construction is either underway or forthcoming.

Geological conditions along the Karakorum Highway (KKH) promote the occurrence of frequent natural disasters, which pose a serious threat to its normal operation. Landslide susceptibility mapping (LSM) provides a basis for analyzing and evaluating the degree of landslide susceptibility of an area. However, there has been limited analysis of actual landslide activity processes in real-time. The SBAS-InSAR (Small Baseline Subsets-Interferometric Synthetic Aperture Radar) method can fully consider the current landslide susceptibility situation and, thus, it can be used to optimize the results of LSM. In this study, we compared the results of LSM using logistic regression and Random Forest models along the KKH. Both approaches produced a classification in terms of very low, low, moderate, high, and very high landslide susceptibility. The evaluation results of the two models revealed a high susceptibility of land sliding in the Gaizi Valley and the Tashkurgan Valley. The Receiver Operating Characteristic (ROC) curve and historical landslide verification points were used to compare the evaluation accuracy of the two models. The Area under Curve (AUC) value of the Random Forest model was 0.981, and 98.79% of the historical landslide points in the verification points fell within the range of high and very high landslide susceptibility degrees. The Random Forest evaluation results were found to be superior to those of the logistic regression and they were combined with the SBAS-InSAR results to conduct a new LSM. The results showed an increase in the landslide susceptibility degree for 2808 cells. We conclude that this optimized landslide susceptibility mapping can provide valuable decision support for disaster prevention and it also provides theoretical guidance for the maintenance and normal operation of KKH.

Under global climate change, glaciers on the Tibetan Plateau are experiencing severe retreat, which significantly impacts the regional water cycle and the occurrence of natural hazards. However, detailed insights into the spatiotemporal patterns of this retreat and its climatic drivers remain insufficiently explored. In this study, an adaptive glacier extraction index (AGEI) is proposed based on the analysis of multispectral Landsat images integrated with the Google Earth Engine, and comprehensive and high-resolution mapping of glaciers on the Tibetan Plateau is realized at 5-year intervals from 1988 to 2022. Subsequently, the ERA5-Land air temperature and precipitation data are downscaled to a finer 1 km resolution. Finally, the impacts of the annual and seasonal changes in the downscaled meteorological factors on the glacier extent are quantified. Results demonstrate a rapid yet heterogeneous pattern of glacier retreat across the Tibetan Plateau between 1988 and 2022, with retreat rates ranging from 0.14 ± 0.07 % to 0.51 ± 0.09 % annually. A notable trend is observed: most glaciers experienced a decrease in extent from 1990 to 2000 followed by a slight increase from 2000 to 2010. From 2010, a majority of the glaciers exhibited either a static state or minimal retreat. The most pronounced impact of annual temperature on glacier retreat is observed in the southern Himalayas, with a value of −9.34 × 10³ km² °C⁻¹, and the most restraining impact of precipitation on glacier retreat reaches 261 km² mm⁻¹, which is observed in the Karakoram Range for the spring season. These insights are pivotal in comprehending the temporal and spatial heterogeneity of glacier retreats and in understanding the effects of climatic variations on the state of glaciers on the Tibetan Plateau.