Wen He

Earthquakes disrupt terrestrial ecosystem carbon storage by triggering landslides and altering land cover, yet a global quantification of these carbon losses has been lacking. Here we quantify the global earthquake-induced carbon losses by combining vegetation models, land cover, carbon density, and earthquake records. Focusing on strong earthquakes worldwide between 2006 and 2018, we find that these events cause a cumulative loss of 38.86 ± 6.23 teragrams of carbon by 2022. Forest ecosystems require at least six years to begin recovery. These carbon losses are unevenly distributed across countries, with 84.6% of affected countries being emerging and developing economies. In Nepal and Ecuador, cumulative earthquake-induced carbon losses exceed half of their cumulative land-use change emissions during the post-earthquake period. Our study establishes a global benchmark for carbon losses from geophysical disturbances, offering a crucial reference for national carbon accounting and disaster resilience strategies.

Ecosystem multifunctionality (EMF) is a crucial factor in protecting biodiversity and regulating climate dynamics. However, the global and regional dynamics of EMF are currently limited to field experiments, resulting in an incomplete understanding of the spatial patterns and driving mechanisms of EMF. We focused on biogeochemical cycles to quantify the spatiotemporal dynamics of EMF in China and determine the impact of environmental factors on EMF. Our findings indicate that climate and topography are the primary drivers of EMF. More importantly, over 66% of the study area is expected to experience a decline in EMF by the end of the twenty‐first century across various future climate scenarios. Under high‐emission scenarios in particular, EMF could be on an unsustainable trajectory, putting increased pressure on terrestrial ecosystems and exacerbating their degradation. These results highlight that more robust policies need to be designed to mitigate ecosystem degradation triggered by climate change.

Earthquakes disrupt local organic carbon distribution by stripping vegetation, destabilizing soil, and triggering landslides, leading to immediate carbon loss and potential long-term climate impacts. While remote sensing techniques effectively assess post-earthquake vegetation loss, they fail to capture subsurface carbon dynamics along vertical profiles. This study quantifies ecosystem carbon loss from the Luding Earthquake by integrating field sampling, UAV-based LiDAR, and machine learning models to assess vegetation and soil carbon stocks. Field investigations were conducted at landslide deposits, debris flow deposits, and undisturbed sites to analyze soil organic carbon and biomass carbon content. UAV-derived point cloud data improved vegetation biomass estimation, reducing sample plot overestimation by 30.4% due to uneven vegetation distribution. The results indicate that landslides and debris flows caused an 83.9–95.9% reduction in carbon storage, with the total ecosystem carbon loss estimated at 7.36 × 10⁵ Mg. This study provides a comprehensive assessment of earthquake-triggered carbon loss, offering critical insights for carbon budget research on natural disasters and the development of post-earthquake ecological restoration policies.

Flash flood can cause dramatic damages to the infrastructure of transportation networks. Because of the uncertainty of flash floods and the access function of roads, assessing the impact of flash floods on transportation systems has been a great challenge. In this study, we proposed a framework for evaluating the risk posed by flash floods to transportation network, which considers the interruption probability of road links and the expected impact on the access function of transportation. To assess the probability of interruption, we used the random forest model to output the flash floods susceptibility. Then, a multiple centrality model was used to evaluate the expected impact when a link was interrupted. The risk takes into account the flash floods susceptibility and road multiple centrality, and it identify critical links. Using the natural break points method, the result of flash floods susceptibility, road multiple centrality and risk were divided into five levels: extremely low, low, moderate, high and extremely high. Approximately 30.8% of the roads were found to be at high and extremely high risk, and most of these roads were located in Sichuan and Yunnan provinces. The flash floods susceptibility contributes more to the risk than the road multiple centrality, thus, further study should be focused on the areas with high flash floods susceptibility. We discussed different disaster prevention and mitigation measures for the different types of high risk links. The results can improve our understanding of the risks posed by flash floods to road networks. The proposed generalized network risk analysis method provides a new perspective and tool for analyzing the impact of flash floods on road network.

Climate change, population increase, and urban expansion have increased the risk of flooding. Therefore, accurately identifying future changing patterns in the flood risk is essential. For this purpose, this study elaborated a new framework for a basin scale that employs a future land-use simulation model, a factor spatialization technique, and a novel hybrid model for scenario-based flood risk assessment in 2030 and 2050. Three land-use scenarios (i.e., natural growth scenario, cropland protection scenario, and ecological protection scenario) were set and applied in Jinjiang Basin to explore the changes in future flood risk under these scenarios. The results indicate the different degrees of increase in flood risk that will occur in the three scenarios. Under the natural growth (NG) scenario, the city will expand rapidly with the growth of population and economy, and the total area with high and very high flood risk will increase by 371.30 km² by 2050, as compared to 2020. However, under the ecological protection (EP) scenario, woodlands will be protected, and the growth in population, economy, and built-up lands will slow down with slightly increased risk of flooding. In this scenario, the total area with high and very high flood risk will increase by 113.75 km² by 2050. Under the cropland protection (CP) scenario, the loss of croplands will have been effectively stopped, and the flood risk will not show a significant increase under this scenario, with an increase by only 90.96 km² by 2050, similar to the EP scenario. Spatially, these increased flood risks mainly locate at the periphery of existing built-up lands, and the high-flood-risk zones are mainly distributed in the southeast of the Jinjiang Basin. The information about increasing flood risk determined by the framework provides insight into the spatio-temporal characteristics of future flood-prone areas, which facilitates reasonable flood mitigation measures to be developed at the most critical locations in the region.

Variations in surface physicochemical properties and spatial structures can prominently transform surface albedo which conversely influence surface energy balances and global climate, making it crucial to continuously monitor and quantify surface dynamics at fine scales. Here, we made two improvements to propose an algorithm for the simultaneous retrieval of 30-m Landsat albedo, based on the coupling of Landsat-8 and MODIS BRDF. First, two kinds of prior knowledge were added to disaggregate BRDF, including the Anisotropic Flat Index (AFX) and the Albedo-to-Nadir reflectance ratio (AN ratio), from MODIS scales into Landsat scales. Second, a simplified data fusion method was used to simulate albedo for the same, subsequent, or antecedent dates. Finally, we validated the reliability and correlations of the algorithm at six sites of the Surface Radiation (SURFRAD) budget network and intercompared the results with another algorithm called the ‘concurrent approach’. The results showed that the proposed algorithm had favorable usability and robustness, with a root mean square error (RMSE) of 0.015 (8%) and a mean bias of −0.005; while the concurrent approach had a RMSE of 0.026 (14%) and a mean bias of −0.018. The results emphasized that the proposed algorithm has captured subtle changes in albedo over a 16-day period.

Grassland degradation in Altay Prefecture is of considerable concern as it is a threat that hinders the sustainable development of the local economy and the stable operation of the livestock industry. Quantitative assessment of the relative contributions of climate change and human activities, which are considered as the dominant triggers of grassland degradation, to grassland variation is crucial for understanding the grassland degradation mechanism and mitigating the degraded grassland in Altay Prefecture. In this paper, the Carnegie-Ames-Stanford Approach model and the Thornthwaite memorial model were adopted to simulate the actual net primary productivity (NPPA) and potential net primary productivity (NPPP) in the Altay Prefecture from 2000 to 2019. Meanwhile, the difference between potential NPP and actual NPP was employed to reflect the effects of human activities (NPPH) on the grassland. On this basis, we validated the viability of the simulated NPP using the Pearson correlation coefficient, investigated the spatiotemporal variability of grassland productivity, and established comprehensive scenarios to quantitatively assess the relative roles of climate change and human activities on grassland in Altay prefecture. The results indicate three main points. (1) The simulated NPPA was highly consistent with the MOD17A3 dataset in spatial distribution. (2) Regions with an increased NPPA accounted for 70.53% of the total grassland, whereas 29.47% of the total grassland area experienced a decrease. At the temporal scale, the NPPA presented a slightly increasing trend (0.83 g C m–2 yr–1) over the study period, while the trends of NPPP and NPPH were reduced (–1.31 and –2.15 g C m–2 yr–1). (3) Compared with climate change, human activities played a key role in the process of grassland restoration, as 66.98% of restored grassland resulted from it. In contrast, inter-annual climate change is the primary cause of grassland degradation, as it influenced 55.70% of degraded grassland. These results could shed light on the mechanisms of grassland variation caused by climate change and human activities, and they can be applied to further develop efficient measures to combat desertification in Altay Prefecture.

Quantitative evaluation and driving mechanism analysis of vegetation dynamics are essential for promoting regional sustainable development. In the past 20 years, the ecological environment in Altay Prefecture has changed significantly due to global warming. Meanwhile, with increasing human activities, the spatiotemporal pattern and driving forces of vegetation variation in the area are uncertain and difficult to accurately assess. Hence, we quantified the vegetation growth by using the Normalized Difference Vegetation Index (NDVI) on the Google Earth Engine (GEE). Then, the spatiotemporal patterns of vegetation from 2000 to 2019 were analyzed at the pixel scale. Finally, significance threshold segmentation was performed using meteorological data based on the correlation analysis results, and the contributions of climate change and human activities to vegetation variation were quantified. The results demonstrated that the vegetation coverage in Altay Prefecture is mainly concentrated in the north. The vegetation areas representing significant restoration and degradation from 2000 to 2019 accounted for 24.08% and 1.24% of Altay Prefecture, respectively. Moreover, spatial correlation analysis showed that the areas with significant correlations between NDVI and temperature, precipitation and sunlight hours accounted for 3.3%, 6.9% and 20.3% of Altay Prefecture, respectively. In the significant restoration area, 18.94% was dominated by multiple factors, while 3.4% was dominated by human activities, and 1.74% was dominated by climate change. Within the significant degradation area, abnormal degradation and climate change controlled 1.07% and 0.17%, respectively. This study revealed the dynamic changes of vegetation and their driving mechanisms in Altay Prefecture, and can provide scientific support for further research on life community mechanism theory and key remediation technology of mountain-water-forest-farmland-lake-grass in Altay Prefecture.