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Meteorological elements are important for various fields related to human activities, including scientific research. Using the Tibetan Areas of West Sichuan Province (TAOWS) as an example, this study examined the estimation methods for near-surface air temperature (Ta), vapour pressure deficit (VPD), and atmospheric pressure (P) and their distribution characteristics in areas with complex terrains and sparse stations. An improved satellite-based approach, combining an artificial neural network and inverse distance weighting (ANN-IDW), is proposed for estimating Ta and VPD with high-accuracy under all weather conditions from Moderate Resolution Imaging Spectroradiometer (MODIS) data. The data of 41 meteorological stations in TAOWS and its adjacent areas were used for the training and validation of the ANN-IDW. For Ta and VPD, the mean absolute errors (MAEs) of the ANN-IDW are 1.45°C to 2.15°C and 0.54 hPa to 0.87 hPa, respectively. Also, the detailed features of the distribution of the estimated Ta and VPD are prominent and closely related to the terrain. The accuracy of the method was also verified indirectly. In addition, the improved method based on the existing method was applied for estimating P. The results confirm that (1) the ANN-IDW is suitable for estimating Ta and VPD in areas with complex terrain and sparse stations under all weather conditions; (2) the improved method is more suitable for estimating P at high-elevation. Moreover, the distribution characteristics of meteorological elements in TAOWS were also analysed. These elements influence agricultural production and animal husbandry and have a high application value. The results further show that topography is the most important factor affecting the spatial distribution and complexity of meteorological elements over complex terrains, but the degree of influence of topography varies greatly across different seasons. [ABSTRACT FROM AUTHOR]

The focus of this paper is the selection of paths for the maritime transportation of crude oil. In addition to transportation cost, the nodes in a maritime transportation network are always affected by extreme events. Therefore, connectivity reliability must be considered during path selection. In this paper, uncertainty variables are introduced to describe the connectivity reliability of nodes under the influence of extreme events, and an uncertain bi-objective programming model with connectivity reliability maximization and transportation cost minimization as objectives is established for path selection. China’s maritime transportation network for crude oil imports is used as a case study to validate the model, the effects of variations in the model parameters on path selection, transportation cost and connectivity reliability are discussed in the case study. The research results provide a basis on which for government decision makers to better plan crude oil transportation strategies. [ABSTRACT FROM AUTHOR]

In the past decades, a multilayer transshipment network of bulk shipping has been formed along the Yangtze River so as to support the fast development of Chinese iron and steel industry. Yet with the decrease of iron ore trade in China since 2014, the bulk port system on Yangtze River probably will be subject to change in the future. Under this background, this paper aims to analyze the development potential of Yangtze River bulk ports system with a focus on iron ore transshipment. To realize this objective, we firstly sort out the main transshipment patterns of bulk shipping, and optimize the bulk-shipping network with an optimization model. Several scenarios are then proposed and examined in the model by changing corresponding parameters. Cooperation among ports as one scenario is analyzed by applying core theory in cooperative game. Based on the changes of transshipment plans observed under the various scenarios, we finally ascertain the different development potentials of the system, and provide suggestions to the port operators and local maritime authorities. [ABSTRACT FROM PUBLISHER]