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Climate warming causes changes in permafrost distribution, which affects the surface energy balance, hydrologic cycle and carbon flux in cold regions. In this study, the Surface Frost Number model was applied to examine permafrost distribution on the Qinghai–Tibet Plateau (QTP) under the four RCPs (RCP2.6, RCP4.5, RCP6.0, and RCP8.5). The Kappa statistic was used to evaluate model results by comparing simulations of baseline permafrost distribution (1981–2010) with the existing frozen soil maps. The comparison shows that the Surface Frost Number model is suitable for simulating the general characteristics of permafrost distribution on the QTP. Simulated results suggest that areas of permafrost degradation would be the smallest in the near-term (2011‒2040) with the rates of 17.17%, 18.07%, 12.95% and 15.66% under RCP2.6, RCP4.5, RCP6.0 and RCP8.5, respectively. The rate of permafrost degradation would be faster in the mid-term (2041‒2070), especially under the RCP8.5 scenario (about 41.42%). Areas of permafrost degradation would be the largest in the long-term (2071‒2099) relative to baseline conditions, with a modelled 64.31% decrease in permafrost distribution using the RCP8.5 scenario. Our results would help the decision‒making for engineering construction program on the QTP, and support local units in their efforts to adapt climate change.

In this study, the updated Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) dataset for growing season (April to October), which can better reflect the vegetation vigor, was used to investigate the interannual variations in NDVI and its relationship with climatic factors, in order to preliminarily understand the climate impact on vegetation and provide theoretical basis for the response of ecosystem to climate change. Multivariate linear regression models, including the Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR), were adopted to analyze the correlation between NDVI and climatic factors (temperature and precipitation) together. Average growing-season NDVI significantly increased at a rate of 0.0015/year from 1982 to 2013, larger than several regions in China. On the whole, its relationship with temperature is positive and also stronger than precipitation, which indicated that temperature may be a limiting factor for the vegetation growth in the Karst region. Moreover, the correlation coefficients between grassland NDVI and climatic factors are the largest. Under the background of NDVI increasing trend from 1982 to 2013, the period of 2009–2012 was chosen to investigate the influencing factors of a sharp decline in NDVI. It can be found that the reduced temperature and solar radiation, caused by the increase in cloud cover and precipitation, may play important roles in the vegetation cover change. All in all, the systematic research on the interannual variations of growing-season NDVI and its relationship with climate revealed the heterogeneity and variability in the complicated climate change in the Karst ecosystem for the study area. It is the Karst characteristics that hinder obtaining more representative conclusions and tendencies in this region. Hence, more attention should be paid to promoting Karst research in the future.

Climate change is a major driver of vegetation activity, and thus their complex processes become a frontier and difficulty in global change research. To understand this relationship between climate change and vegetation activity, the spatial distribution and dynamic characteristics of the response of NDVI to climate change were investigated by the geographically weighted regression (GWR) model during 1982 to 2013 in China. This model was run based on the combined datasets of satellite vegetation index (NDVI) and climate observation (temperature and moisture) from meteorological stations nationwide. The results showed that the spatial non-stationary relationship between NDVI and surface temperature has appeared in China: the significant negative temperature-vegetation relationship was located in Northeast, Northwest and Southeast China, while the positive correlation was more concentrated from southwest to northeast. By comparing the normalized regression coefficients from GWR model for different climate factors, it presented the regions with moisture dominants for NDVI were in North China and the Tibetan Plateau, and the areas of temperature dominants were distributed in East, Central and Southwest China, where the annual mean maximum temperature accounted for the largest areas. In addition, regression coefficients from GWR model between NDVI dynamics and climate variability indicated that the higher warming rate could result in the weakened vegetation activity through some mechanisms such as enhanced drought, while the moisture variability could mediate the hydrothermal conditions for the variation of vegetation activity. When the increasing rate of photosynthesis exceeded that of respiration, the positive correlation between vegetation dynamics and climate variability was reflected. However, the continuous and dynamic process of vegetation activity response to climate change will be determined by spatially heterogeneous conditions in climate change and vegetation cover. Furthermore, the dynamic description of climate-induced vegetation activity from its rise to decline in different regions is expected to provide a scientific basis for initiating ecosystem-based adaptation strategies in response to global climate change.

Extreme sea levels resulting from storm surges are a major contributor to coastal flood risk and are often assessed using outputs from global storm surge models. However, these models exhibit systematic errors, particularly in data‐sparse regions. Here, we present a novel framework to improve simulated extreme water levels. The method applies quantile mapping to peaks‐over‐threshold extremes, is calibrated using paired observed and simulated annual maxima at tide gauges, and transfers the correction models from reference stations to nearby sites within a defined radius. Results show that the approach achieves consistent improvements within about 400 km of gauges, with median relative improvements of −0.84 in mean squared error and −0.60 in normalized root‐mean‐square error. Systematic errors are reduced most in high‐latitude, low‐wind regions. These findings highlight the transferability of the correction models and show that even sparse observations can improve extreme sea‐level estimates for global coastal hazard assessment.

The development of bone repair scaffolds has long been a research hotspot in tissue engineering. Owing to its unique capability for personalized customization of scaffold geometry and microstructure, 3D printing technology has been extensively adopted for fabricating bone repair scaffolds. Poly-L-lactic acid (PLLA), endowed with favorable biodegradability, excellent biocompatibility, and reliable in vivo safety, is widely used as a matrix material for 3D printed bone repair scaffolds. PLLA is a bioinert polymer characterized by inferior cell adhesion and osteogenic differentiation capabilities. To mitigate this bioinertness limitation, the present study employed N-methylpyrrolidone (NMP) etching to modify the surface of 3D-printed PLLA bone repair scaffolds. Following NMP etching for 1–24 h, the originally smooth scaffold surface evolved into a hierarchical, petal-like gradient microstructure, accompanied by a marked increase in surface roughness. Correspondingly, the hydrophilicity of the treated scaffolds was also enhanced. Differential scanning calorimetry (DSC) and X-ray diffraction (XRD) analyses further confirmed that the crystallinity of PLLA in the scaffolds was significantly enhanced. Concomitantly, the modified scaffolds exhibited a marked improvement in adsorption capacity for green fluorescent protein (GFP), while the adhesion and proliferation of MC3T3-E1 on their surface were also significantly promoted. In vivo animal experiments demonstrated that the NMP-etched scaffolds could accelerate the process of bone defect repair. Collectively, surface modification of 3D-printed PLLA bone scaffolds via NMP etching enables precise modulation of their physicochemical properties, thereby effectively mitigating the inherent bioinertness limitation of PLLA scaffolds. Graphical Abstract

Understanding how ecosystem vulnerability in response to climate change is generated and distributed is an important topic; however, quantitative assessments of the vulnerability process remain relatively rare. The Intergovernmental Panel on Climate Change (IPCC) regards ecosystem vulnerability as sensitivity or susceptibility to harm, lacking the capacity to cope and adapt. Through analysis of the concept of vulnerability, we propose a response-based quantitative method to assess ecosystem vulnerability, integrating the concepts of sensitivity and adaptability. This method was applied to assess ecosystem vulnerability in China from 1981 to 2050. The results indicated that vulnerability would develop in nearly 30% of the terrestrial ecosystems of China. Vulnerability would be severe in the grassland and desert ecosystems, distributed mainly in the Tianshan Mountain and the Inner Mongolia Plateau. As the effects of sensitivity and adaptability to temperature and precipitation differ, obvious regional differences exist between the ecosystem vulnerability zones in China. For example, the effects of climate warming would be severe in the northwest of the Loess Plateau, with the vulnerability caused mainly by high sensitivity to warming. However, the vulnerability in the southeastern part would be caused mainly by low adaptability to increasing precipitation. Compared with previous research, our method emphasizes the critical role of adaptability, which enhances the scientificity and reasonableness of assessing ecosystem vulnerability, and enriches the methodology adopted under the IPCC framework. However, as we did not consider fully the autonomous adaptation mechanism in ecosystem vulnerability, this aspect should be combined with other factors in future study.

Evapotranspiration (ET) is a key process of the climate system because it links water, energy and carbon cycles. In this study we modified a Penman-Monteith based algorithm to estimate ET on the Tibetan Plateau at a 1 km spatial resolution for the period 2000-2010 using meteorological and satellite remote sensing data. The results showed that the average annual ET on the Tibetan Plateau was 350.3 mm year−1 and decreased from the southeast toward the northwest. The highest ET value was found in open water bodies (680.9 mm year−1) and the lowest ET value was found in open shrubland (254.0 mm year−1). Overall, the inter-annual ET decreased from 2000 to 2010 and there was significant negative ET trend over 42% of the region, primarily in the northwest of the Tibetan Plateau. Relative humidity was the dominant factor in controlling long-term variations of ET in the arid northwest plateau. But under moist conditions, leaf area index or temperature drove ET. In addition, P-ET on the Tibetan Plateau significantly increased and about 37% of the region showed strong positive P-ET trend primarily in the central of the Tibetan Plateau. The positive P-ET trend in four seasons suggested that the Tibetan Plateau might have become wetter during the past decade.

There is increasing evidence of environmental change impacts on ecosystem processes and services, yet poor understanding of the relative contributions of land use and climate change to ecosystem services variations. Based on detailed meteorological, hydrological records and satellite data over the Yellow River Source Area (YRSA) in Tibetan Plateau from 1980s to 2008, together with a water-yield module of Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and also a Residual Trends (RESTREND) method, we assessed the water supply variations in YRSA during the past three decades and disentangled the relative contributions of land use and climate change. Results show that water supply significantly decreased from 1980 to 2005 and then increased from 2005 to 2008. The quantity slightly decreased from 283.01 mm in 1980 to 276.95 mm in 1995, 270.12 mm in 2000 and 267.97 mm in 2005, and it then rebounded slightly to 275.26 mm in 2008. The water supply variation ranged from 283.01 mm to 267.97 mm. Climate change contributed dominantly to water supply decrease from 1980 to 1995, which accounts for approximately 64% of the decrease. During 1995 to 2000, land use contributed more and about 58% to the water supply decrease as the intense human activities. From 2000 to 2005, climate change became a positive contribution to the water supply as the increased precipitation, but the land use still contributed negatively. From 2005 to 2008, both climate and land use have positive impacts, but land use contributed about 61% to the water supply increase. The implementation of the Three Rivers Source Area Ecological Protection Project has greatly improved the vegetation coverage conditions and the water retention ability during this period. We recommend that the implementation of ecological projects, grazing policies and artificial improvement of degraded grassland would help to conserve the water retention ability and increase water supply.

Earthquakes often cause secondary disasters in mountainous areas, forming the typical earthquake-landslide-debris flow disaster chain for a long time that results in a series of losses. It is important to improve the risk assessment method from the perspective of cascading effect of such a disaster chain, by strengthening quantitative research on hazards of the debris flows which are affected by landslide volume and rainstorm intensity. Taking Wenchuan County as an example, the risk assessment method for population loss of the disaster chain is established and the risks are evaluated in this paper. The results show that the population loss risk is 2.59–2.71 people/km2 under the scenarios of the Wenchuan Ms8.0 earthquake and four rainstorm intensities. The impacts of landslide and debris flow after the earthquake were long-term and profound. A comparison of risks caused by each element of the chain revealed that the risk associated with the earthquake accounted for the highest proportion, and landslide and debris flow accounted for 38.82–37.18% and 3.42–7.50%, respectively. As the earthquake intensity increases, the total risk posed by the disaster chain increases significantly. The risk caused by the earthquake is the highest in high earthquake intensity zones; while in the lower-intensity zones, landslides and debris flows pose relatively high risks. The risk assessment results were verified through comparison with actual data, indicating that the simulation results are quite consistent with the existing disaster information and that the risk assessment method based on the earthquake-landslide-debris flow cascade process is significant for future risk estimation.

In the context of urbanization, frequent flood event have become the most common natural disasters, posing a significant challenge to human society. Considering the effects of urbanization on flood risk is critical for flood risk reduction and reasonable land planning strategies at the city scale. This study proposes an integrated approach based on remote sensing data using CA, Markov, and simplified hydrodynamic (FloodMap) models to accurately and effectively assess flood risk under urbanization. Taking Chongqing City as a case study, this paper analyzes the temporal and spatial variations in land use/land cover (LULC) in 2010, 2015, and 2018 and predicts the LULC for 2030, based on historic trends. Flood risk is assessed by combining the hazard, exposure, and modified vulnerability. The results suggest that the area of built-up land will increase significantly from 19.56% in 2018 to 25.21% in 2030. From 2010 to 2030, the area of medium and high inundation depths will increase by 10 and 16 times, respectively. Flood damage varies remarkably according to the LULC and return period. The expected annual damage (EAD) has been estimated to increase from USD 68 million in 2010 to USD 200 million in 2030. Flood risk is proportional to population and is significantly inversely proportional to socioeconomic level. The approach used here can provide a comprehensive understanding of flood risk and is significant for land-use policymaking and the management of flood control facilities.