The Tibetan Plateau, a critical region influencing both local and global atmospheric circulation, climate dynamics, hydrology and terrestrial ecosystems, is undergoing climate-driven changes, including glacial retreat, permafrost thaw and groundwater changes. Despite its importance, implementing continuous and systematic observations has been challenging due to the area's high altitude and extreme climate conditions. In this context, seismic interferometry emerges as a cost-effective method for the continuous monitoring of subsurface structural changes driven by environmental factors and internal geophysical processes. We investigate subsurface evolution using four years of seismic data from nine stations on the northeastern Tibetan Plateau, by applying coda wave interferometry across multiple frequency bands. Our findings highlight seismic velocity changes within the frequency bands 5-10, 0.77-1.54, and 0.25-0.51 Hz, revealing depth-dependent seasonal and long-term changes. Near-surface and deeper strata exhibit similar seasonal patterns, with velocities increasing in winter and decreasing in summer driven by changes in hydrological processes, while intermediate ice-water phase strata show contrasting behaviour due to thermal elastic strain. Long-term trends suggest that the upper subsurface layer is affected by melting water and precipitation originating from Kunlun Mountains, whereas deeper layer reflect groundwater level variations influenced by climate change and human activities. This study provides insights into the environmental evolution of the Tibetan Plateau and its impact on managing local groundwater resources.

Increased tree mortality rates have been observed worldwide in connection to climate warming-related processes, such as drought, heat, fire, and insect pest outbreaks. An understanding of the drivers of tree mortality during the Anthropocene is urgently needed to estimate forest vulnerability in a warmer climate. In this study, we assessed the drivers of tree mortality in an urban recreational boreal forest area in Helsinki, Finland, of approximately 830 ha, where increased tree mortality rates have been recently observed. A time series of aerial images was used to quantify tree mortality over the area to detect dead trees from 2005 to 2021 at seven timestamps. In total, 6008 dead trees were observed from the aerial images collected during the monitoring period. Forest environmental and climatic variables were used to explore the tree mortality drivers for individual trees and tree communities using logistic regression and correlation analysis. Our results showed that droughtrelated variables, i.e., the standardised precipitation evapotranspiration index and the Palmer drought severity index, were linked with increased tree mortality rates. We found that the stand-level basal area predicted tree mortality risk and was linked to site type; smaller basal area stands were located on rocky dry soils, resulting in a greater probability of tree mortality. We also observed that trees at high elevations or on steep slopes showed a greater mortality risk. Our results can increase the understanding of tree mortality in urban areas and help the planning of built and green areas in a changing climate.

Time-series interferometric synthetic aperture radar (InSAR) provides a unique tool for measuring large-scale and long-term land surface deformation. Under the assumption of a single linear deformation model in conventional InSAR, it is difficult to quantify and interpret the impacts of multiple environmental factors that presumably induce nonlinear deformations. In this paper, we propose a SAR-Transformer method to decompose InSAR time-series signals into various physics-related components and apply the method to evaluate the deformation of the world's longest cross-sea bridge, the Hong Kong-Zhuhai-Macao Bridge (HZMB). We first developed an improved bridge geometry-based InSAR network to monitor the deformation of the HZMB using Sentinel-1 and COSMO-SkyMed images from 2019 to 2022, which were validated using the leveling and GPS data. The SAR-Transformer model was trained using synthetic InSAR time-series samples and applied to decompose the monitored InSAR measurements. Compared with that of conventional curve-fitting and seasonal-trend decomposition using LOESS, SAR-Transformer reduced the mean absolute error at least by 58.32% and mean absolute percentage error at least by 8.84% for time-series signal reconstruction. We evaluated the decomposed patterns according to the geotechnical, meteorological, and marine processes, and found that: 1) Seasonal thermal expansion owing to temperature changes was significant in all parts of the bridge, and deflection due to concrete shrinkage and creep was observed on cable-stayed bridges. 2) The artificial islands experienced evident ground subsidence with a decelerating trend. In particular, the newly adopted non-dredged reclamation method resulted in a lower decelerated settlement than that of fully-dredged reclamation areas. 3) The seawall showed linear horizontal movement from the outward stretching of the reclaimed soil consolidation and periodic displacement related to sea tidal loading. Furthermore, typhoons and coastal earthquakes had limited effects on the permanent movement of the bridge. These results improve the understanding of the interactions between artificial super-infrastructures and environmental factors, and provide valuable guidelines for the maintenance and management of the HZMB.

Aufeis is a common phenomenon in cold regions of the Northern Hemisphere that develops during winter by successive water overflow and freezing on ice-covered surfaces. Most studies on aufeis occurrence focus on regions in North America and Siberia, while research in High Mountain Asia (HMA) is still in an exploratory phase. This study investigates the extent and dynamics of icing processes and aufeis in the Tso Moriri basin, eastern Ladakh, India. Based on a combination of 235 Landsat 5 TM/8 OLI and Sentinel-2 imagery from 2008 to 2021 the occurrence of icing and aufeis was classified using a random forest classifier. A total of 27 frequently occurring aufeis fields with an average maximum extent of 9 km(2) were identified, located at a mean elevation of 4,700 m a.s.l. Temporal patterns show a distinct accumulation phase (icing) between November and April, and a melting phase lasting from May until July. Icing is characterized by high seasonal and inter-annual variability. Successive water overflow mainly occurs between January and March and seems to be related to diurnal freeze-thaw-cycles, whereas higher daytime temperatures result in larger icing areas. Aufeis feeding sources are often located within or in close vicinity to wetland areas, while vegetation is largely absent on surfaces with frequent aufeis formation. These interactions require more attention in future research. In addition, this study shows the high potential of a machine learning approach to monitor icing processes and aufeis, which can be transferred to other regions.

As an important indicator of permafrost degradation, surface deformation is often used to monitor the thawing and freezing process in the permafrost active layer. However, due to the large area of the continuous permafrost of the Qinghai-Tibet Plateau (QTP) and the large amount of data processed by conventional time-series InSAR, previous studies have mostly focused on local area investigations, and regional characteristics of surface deformation of the continuous permafrost area on the QTP are still unclear. In this paper, we characterized surface deformation in space and time over the main continuous permafrost area on the QTP, by analyzing 11 ascending and 8 descending orbits of Sentinel-1 SAR data acquired between 2018 and 2021 with the time-series InSAR processing system LiCSAR. The reliability of the InSAR deformation results was verified by a combination of leveling measurement data, the intercomparison of overlapping area results, and field verification. The results show that the permafrost regions of the central QTP exhibited the most significant linear subsidence trend. The subsidence trend of permafrost on the QTP was mainly related to the thermal stability of permafrost, and the regions with larger subsidence rates were concentrated in sub-stable, transitional and unstable permafrost areas. We also found that, according to analysis of time-series displacement, the beginning and ending times of permafrost thawing were highly spatially heterogeneous, with the time of maximum thawing depth varying between mid-October and mid-November, which was probably attributed to the active layer thickness (ALT), water content in the active layer, and vegetation cover in these regions. This study is of great significance for understanding the changing trend of permafrost on the QTP under the background of climate change. In addition, this study also demonstrates that combination of Sentinel-1 SAR images with the LiCSAR system has significant potential for detecting permafrost deformation with high accuracy and high efficiency at regional and global scales.

We investigate permafrost surface features revealed from satellite radar data in the Siberian arctic at the Yamal peninsula. Surface dynamics analysis based on SRTM and TanDEM-X DEMs shows up to 2 m net loss of surface relief between 2000 and 2014 indicating a highly dynamic landscape. Surface features for the past 14 years reflect an increase in small stream channels and a number of new lakes that developed, likely caused by permafrost thaw. We used Sentinel-1 SAR imagery to measure permafrost surface changes. Owing to limited observation data we analyzed only 2 years. The InSAR time-series has detected surface displacements in three distinct spatial locations during 2017 and 2018. At these three locations, 60-120 mm/yr rates of seasonal surface permafrost changes are observed. Spatial location of seasonal ground displacements aligns well with lithology. One of them is located on marine sediments and is linked to anthropogenic impact on permafrost stability. Two other areas are located within alluvial sediments and are at the top of topographic elevated zones. We discuss the influence of the geologic environment and the potential effect of local upwelling of gas. These combined analyses of InSAR time-series with analysis of geomorphic features from DEMs present an important tool for continuous process monitoring of surface dynamics as part of a global warming risk assessment.

Human activities have substantially altered present-day flow regimes. The Headwater Area of the Yellow River (HAYR, above Huanghe'yan Hydrological Station, with a catchment area of 21,000 km(2) and an areal extent of alpine permafrost at similar to 86%) on the northeastern Qinghai-Tibet Plateau, Southwest China has been undergoing extensive changes in streamflow regimes and groundwater dynamics, permafrost degradation, and ecological deterioration under a warming climate. In general, hydrological gauges provide reliable flow records over many decades and these data are extremely valuable for assessment of changing rates and trends of streamflow. In 1998-2003, the damming of the Yellow River by the First Hydropower Station of the HAYR complicated the examination of the relations between hydroclimatic variables and streamflow dynamics. In this study, the monthly streamflow rate of the Yellow River at Huanghe'yan is reconstructed for the period of 1955-2019 using the double mass curve method, and then the streamflow at Huagnhe'yan is forecasted for the next 20 years (2020-2040) using the Elman neural network time-series method. The dam construction (1998-2000) has caused a reduction of annual streamflow by 53.5-68.4%, and a more substantial reduction of 71.8-94.4% in the drier years (2003-2005), in the HAYR. The recent removal of the First Hydropower Station of the HAYR dam (September 2018) has boosted annual streamflow by 123-210% (2018-2019). Post-correction trends of annual maximum (Q(Max)) and minimum (Q(Min)) streamflow rates and the ratio of the Q(Max)/Q(Min) of the Yellow River in the HAYR (0.18 and 0.03 m(3).(-)s(-1).yr(-1) and -0.04 yr(-1), respectively), in comparison with those of precorrection values (-0.11 and -0.004 m(3).s(-1).yr(-1) and 0.001 yr(-1), respectively), have more truthfully revealed a relatively large hydrological impact of degrading permafrost. Based on the Elman neural network model predictions, over the next 20 years, the increasing trend of flow in the HAYR would generally accelerate at a rate of 0.42 m(3).s(-1).yr(-1). Rising rates of spring (0.57 m(3).s(-1).yr(-1)) and autumn (0.18 m(3).s(-1).yr(-1)) discharge would see the benefits from an earlier snow-melt season and delayed arrival of winter conditions. This suggests a longer growing season, which indicates ameliorating phonology, soil nutrient availability, and hydrothermal environments for vegetation in the HAYR. These trends for hydrological and ecological changes in the HAYR may potentially improve ecological safety and water supplies security in the HAYR and downstream Yellow River basins.