The alpine timberline, an ecosystem ecotone, indicates climatic change and is tending to shift toward higher altitudes because of an increase in global warming. However, spatiotemporal variations of the alpine timberline are not consistent on a global scale. The abundant and highest alpine timberline, located on the Tibetan Plateau, is less subject to human activity and disturbance. Although many studies have investigated the alpine timberline on the Tibetan Plateau, large-scale monitoring of spatial-temporal dynamics and driving mechanisms of the alpine timberline remain uncertain and inaccurate. Hence, the Gongga Mountain on the southeastern Tibetan Plateau was chosen as the study area because of the most complete natural altitudinal zonation. We used the Otsu method on Google Earth Engine to extract the alpine timberline from 1987–2019 based on the normalized difference vegetation index (NDVI). Then, the alpine timberline spatiotemporal patterns and the effect of topography on alpine timberline distribution were explored. Four hillsides on the western Gongga Mountain were selected to examine the hillside differences and drivers of the alpine timberline based on principal component analysis (PCA) and multiple linear regression (MLR). The results indicated that the elevation range of alpine timberline was 3203–4889 m, and the vegetation coverage increased significantly (p < 0.01) near the alpine timberline ecotone on Gongga Mountain. Moreover, there was spatial heterogeneity in dynamics of alpine timberline, and some regions showed no regular trend in variations. The spatial pattern of the alpine timberline was generally high in the west, low in the east, and primarily distributed on 15–55° slopes. Besides, the drivers of the alpine timberline have the hillside differences, and the sunny and shady slopes possessed different driving factors. Thus, our results highlight the effects of topography and climate on the alpine timberline on different hillsides. These findings could provide a better approach to study the dynamics and formation of alpine timberlines.

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.