Uncertainty analysis of environmental loads response for fixed offshore platform in climate change condition

Peer Reviewe

Northern Hemisphere permafrost map based on TTOP modelling for 2000-2016 at 1 km²scale

Permafrost is a key element of the cryosphere and an essential climate variable in the Global Climate Observing System. There is no remote-sensing method available to reliably monitor the permafrost thermal state. To estimate permafrost distribution at a hemispheric scale, we employ an equilibrium state model for the temperature at the top of the permafrost (TTOP model) for the 2000–2016 period, driven by remotely-sensed land surface temperatures, down-scaled ERA-Interim climate reanalysis data, tundra wetness classes and landcover map from the ESA Landcover Climate Change Initiative (CCI) project. Subgrid variability of ground temperatures due to snow and landcover variability is represented in the model using subpixel statistics. The results are validated against borehole measurements and reviewed regionally. The accuracy of the modelled mean annual ground temperature (MAGT) at the top of the permafrost is ±2 °C when compared to permafrost borehole data. The modelled permafrost area (MAGT 0) is around 21 × 106 km2 (22% of exposed land area), which is approximately 2 × 106 km2 less than estimated previously. Detailed comparisons at a regional scale show that the model performs well in sparsely vegetated tundra regions and mountains, but is less accurate in densely vegetated boreal spruce and larch forests

Northern Hemisphere permafrost map based on TTOP modelling for 2000–2016 at 1 km2 scale

Permafrost is a key element of the cryosphere and an essential climate variable in the Global Climate Observing System. There is no remote-sensing method available to reliably monitor the permafrost thermal state. To estimate permafrost distribution at a hemispheric scale, we employ an equilibrium state model for the temperature at the top of the permafrost (TTOP model) for the 2000–2016 period, driven by remotely- sensed land surface temperatures, down-scaled ERA-Interim climate reanalysis data, tundra wetness classes and landcover map from the ESA Landcover Climate Change Initiative (CCI) project. Subgrid variability of ground temperatures due to snow and landcover variability is represented in the model using subpixel statistics. The results are validated against borehole measurements and reviewed regionally. The accuracy of the modelled mean annual ground temperature (MAGT) at the top of the permafrost is ±2 °C when compared to permafrost borehole data. The modelled permafrost area (MAGT 0) is around 21 × 106 km2 (22% of exposed land area), which is approximately 2 × 106 km2 less than estimated previously. Detailed comparisons at a regional scale show that the model performs well in sparsely vegetated tundra regions and mountains, but is less accurate in densely vegetated boreal spruce and larch forests

Uncertainty analysis of environmental loads response for fixed offshore platform in climate change condition

Peer Reviewe

Vegetation Mapping in the Permafrost Region: A Case Study on the Central Qinghai-Tibet Plateau

An accurate and detailed vegetation map is of crucial significance for understanding the spatial heterogeneity of subsurfaces, which can help to characterize the thermal state of permafrost. The absence of an alpine swamp meadow (ASM) type, or an insufficient resolution (usually km-level) to capture the spatial distribution of the ASM, greatly limits the availability of existing vegetation maps in permafrost modeling of the Qinghai-Tibet Plateau (QTP). This study generated a map of the vegetation type at a spatial resolution of 30 m on the central QTP. The random forest (RF) classification approach was employed to map the vegetation based on 319 ground-truth samples, combined with a set of input variables derived from the visible, infrared, and thermal Landsat-8 images. Validation using a train-test split (i.e., 70% of the samples were randomly selected to train the RF model, while the remaining 30% were used for validation and a total of 1000 runs) showed that the average overall accuracy and Kappa coefficient of the RF approach were 0.78 (0.68–0.85) and 0.69 (0.64–0.74), respectively. The confusion matrix showed that the overall accuracy and Kappa coefficient of the predicted vegetation map reached 0.848 (0.844–0.852) and 0.790 (0.785–0.796), respectively. The user accuracies for the ASM, alpine meadow, alpine steppe, and alpine desert were 95.0%, 83.3%, 82.4%, and 86.7%, respectively. The most important variables for vegetation type prediction were two vegetation indices, i.e., NDVI and EVI. The surface reflectance of visible and shortwave infrared bands showed a secondary contribution, and the brightness temperature and the surface temperature of the thermal infrared bands showed little contribution. The dominant vegetation in the study area is alpine steppe and alpine desert. The results of this study can provide an accurate and detailed vegetation map, especially for the distribution of the ASM, which can help to improve further permafrost studies

Vegetation Mapping in the Permafrost Region: A Case Study on the Central Qinghai-Tibet Plateau

An accurate and detailed vegetation map is of crucial significance for understanding the spatial heterogeneity of subsurfaces, which can help to characterize the thermal state of permafrost. The absence of an alpine swamp meadow (ASM) type, or an insufficient resolution (usually km-level) to capture the spatial distribution of the ASM, greatly limits the availability of existing vegetation maps in permafrost modeling of the Qinghai-Tibet Plateau (QTP). This study generated a map of the vegetation type at a spatial resolution of 30 m on the central QTP. The random forest (RF) classification approach was employed to map the vegetation based on 319 ground-truth samples, combined with a set of input variables derived from the visible, infrared, and thermal Landsat-8 images. Validation using a train-test split (i.e., 70% of the samples were randomly selected to train the RF model, while the remaining 30% were used for validation and a total of 1000 runs) showed that the average overall accuracy and Kappa coefficient of the RF approach were 0.78 (0.68–0.85) and 0.69 (0.64–0.74), respectively. The confusion matrix showed that the overall accuracy and Kappa coefficient of the predicted vegetation map reached 0.848 (0.844–0.852) and 0.790 (0.785–0.796), respectively. The user accuracies for the ASM, alpine meadow, alpine steppe, and alpine desert were 95.0%, 83.3%, 82.4%, and 86.7%, respectively. The most important variables for vegetation type prediction were two vegetation indices, i.e., NDVI and EVI. The surface reflectance of visible and shortwave infrared bands showed a secondary contribution, and the brightness temperature and the surface temperature of the thermal infrared bands showed little contribution. The dominant vegetation in the study area is alpine steppe and alpine desert. The results of this study can provide an accurate and detailed vegetation map, especially for the distribution of the ASM, which can help to improve further permafrost studies

The Impact of Permafrost Degradation on Lake Changes in the Endorheic Basin on the Qinghai–Tibet Plateau

Lakes on the Qinghai–Tibetan Plateau (QTP) have experienced significant changes, especially the prevailing lake expansion since 2000 in the endorheic basin. The influence of permafrost thawing on lake expansion is significant but rarely considered in previous studies. In this study, based on Landsat images and permafrost field data, the spatial-temporal area changes of lakes of more than 5 km2 in the endorheic basin on the QTP during 2000–2017 is examined and the impact of permafrost degradation on lake expansion is discussed. The main results are that permafrost characteristics and its degradation trend have close relationships with lake changes. Lake expansion in the endorheic basin showed a southwest–northeast transition from shrinking to stable to rapidly expanding, which corresponded well with the permafrost distribution from island-discontinuous to seasonally frozen ground to continuous permafrost. A dramatic lake expansion in continuous permafrost showed significant spatial differences; lakes expanded significantly in northern and eastern continuous permafrost with a higher ground ice content but slightly in southern continuous permafrost with a lower ground ice content. This spatial pattern was mainly attributed to the melting of ground ice in shallow permafrost associated with accelerating permafrost degradation. Whereas, some lakes in the southern zones of island-discontinuous permafrost were shrinking, which was mainly because the extended taliks arising from the intensified permafrost degradation have facilitated surface water and suprapermafrost groundwater discharge to subpermafrost groundwater and thereby drained the lakes. Based on observation and simulated data, the melting of ground ice at shallow depths below the permafrost table accounted for 21.2% of the increase in lake volume from 2000 to 2016

Effective Two-Photon Excited Photodynamic Therapy of Xenograft Tumors Sensitized by Water-Soluble Bis(arylidene)cycloalkanone Photosensitizers

A series of bis(arylidene)cydoalkanone photo-sensitizers modified by polyethylene glycol (PEG) have been studied for two-photon excited photodynamic therapy (2PE-PDT). As compared with their prototype compounds, these PEGylated photosensitizers show enhanced water solubilities while their photophysical and photochemical properties, including linear absorption, two-photon absorption, fluorescence, and singlet oxygen quantum yield, remain unaltered. In vitro behaviors (cellular uptake, subcellular localization, photocytotomicity in both PDT and 2PE-PDT) of these photosensitizers reveal that an optimized lipid-water partition coefficient can be obtained by adjusting the length and position of the PEG chains. Among them, the photosensitizer modified asymmetrically by two tetraethylene glycol chains presents the best performance as a 2PE-PDT candidate. Selective blood-vessel closure and obvious therapeutic effect in inhibiting the growth of tumors are confirmed by in vivo 2PE-PDT after intravenous injection of this photosensitiezer. The survival periods of treated tumor-bearing mice are significantly prolonged. This study demonstrates the feasibility of using a simple molecule to construct a potential candidate for 2PE-PDT

Ground Deformation and Permafrost Degradation in the Source Region of the Yellow River, in the Northeast of the Qinghai-Tibet Plateau

The source region of the Yellow River (SRYR) is situated on the permafrost boundary in the northeast of the Qinghai-Tibet Plateau (QTP), which is an area highly sensitive to climate change. As a result of increasing global temperatures, the permafrost in this region has undergone significant degradation. In this study, we utilized Sentinel-1 to obtain ground surface deformation data in the SRYR from June 2017 to January 2022. We then analyzed the differences in terrain deformation under various environmental conditions. Our findings indicated an overall subsidence trend in the SRYR, with a long-term deformation velocity of −4.2 mm/a and seasonal deformation of 8.85 mm. Furthermore, the results showed that terrain deformation varied considerably from region to region, and that the Huanghe’ yan sub-basin with the highest permafrost coverage among all sub-basins significantly higher subsidence rates than other regions. Topography strongly influenced ground surface deformation, with flat slopes exhibiting much higher subsidence rates and seasonal deformation. Moreover, the ground temperature and ground ice richness played a certain role in the deformation pattern. This study also analyzed regional deformation details from eight boreholes and one profile line covering different surface conditions, revealing the potential for refining the permafrost boundary. Overall, the results of this study provide valuable insights into the evolution of permafrost in the SRYR region