The spatio-temporal pattern of the global water resource has significantly changed with climate change and intensified human activities. The regional economy and ecological environment are highly affected by terrestrial water storage (TWS), especially in arid areas. To investigate the variation of TWS and its influencing factors under changing environments, the response relationships between TWS and changing environments (climate change and human activities) in Central Asia have been analyzed based on the Gravity Recovery and Climate Experiment (GRACE) data, Climatic Research Unit (CRU) climate data and Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data products (MOD16A2, MOD13A3 and MCD12Q1) from 2003 to 2013. The slope and Pearson correlation analysis methods were used. Results indicate that: (1) TWS in about 77 % of the study area has decreased from 2003 to 2013. The total change volume of TWS is about 2915.6 × 10⁸ m³. The areas of decreased TWS are mainly distributed in the middle of Central Asia, while the areas of increased TWS are concentrated in the middle-altitude regions of the Kazakhstan hills and Tarim Basin. (2) TWS in about 5.91% of areas, mainly distributed in the mountain and piedmont zones, is significantly positively correlated with precipitation, while only 3.78% of areas show significant correlation between TWS and temperature. If the response time was delayed by three months, there would be a very good correlation between temperature and TWS. (3) There is a significantly positive relationship between TWS and Normalized Difference Vegetation Index (NDVI) in 13.35% of the study area. (4) The area of significantly positive correlation between TWS and evapotranspiration is about 31.87%, mainly situated in mountainous areas and northwestern Kazakhstan. The reduction of regional TWS is related to precipitation more than evaporation. Increasing farmland area may explain why some areas show increasing precipitation and decreasing evapotranspiration. (5) The influences of land use on TWS are still not very clear. This study could provide scientific data useful for the estimation of changes in TWS with climate change and human activities.

Colombo port and Hambantota port in Sri Lanka play a key role in transiting and supporting the shipping trade of “the 21st-Century Maritime Silk Road”. In recent years, Chinese enterprises have made huge investments in the infrastructure construction of Colombo port and Hambantota port. The construction progress and development trend of Colombo port and Hambantota port have been attracting the attention of Chinese investment enterprises and the society. In this paper, multi-temporal high spatial resolution remote sensing images are used to monitor the infrastructure construction condition of Colombo port and Hambantota port from 2010 to 2017. According to the interpreted infrastructure information of the two ports, the international container terminal of Colombo and Hambantota port have completed their constructions. By the end of 2017, the international container terminal of Colombo built the container yards with 28.8 ha and roads with 32.6 ha. At the south of the international container terminal of Colombo, the 62.2 ha of reclamation area were built for the planned port city. In Hambantota port, 77 ha of container yards, 48 ha of roads and 2.9 ha of oil storage areas were constructed during this period. Meanwhile, the analysis of potential storage capacity of Colombo port and Hambantota port shows that the throughput of Colombo port may increase by 3 million tons per year while the throughput of Hambantota port will be over its designed 2.5 million tons per year. These analysis results are able to provide a useful reference for Chinese investment enterprises and the related research of “the Belt and Road”.

As is well-known, Nansha Islands in the South China Sea (SCS) are of important strategic position and invaluable ecological value. Therefore, many attentions are paid to either the political and legal aspects of exposed features that matter in the maritime delimitation, or the physical and geographical characteristics of coral reefs that are the predominant structures of these islands. However, it seems that they consistently lose the connection of sciences and humanities in the research of Nansha Islands in the SCS. In this study, we carry out a combinative research, based on remote sensing data using satellite imagery analysis together with historical materials using literature investigation, so as to reconsider to the geographical characteristics of Nansha Islands from a standpoint of the United Nations Convention on the Law of the Sea (UNCLOS). After thoroughly appraising the statuses of these newly formed sand cays, several high tide features of Nansha Islands are identified. By means of the comparison of satellite images coupled with reference to nautical charting and sailing directions during different time periods, we can conclude that the statuses of these high tide features formed on reef platforms are relatively stable due to the growth of reef-building organisms although their migration patterns are subject to external disturbances, and hence the cursory judgment pursuant to the relevant provisions of the UNCLOS about whether a coral reef belongs to a low tide elevation only based on outdated references or ex parte evidences is not reliable. Moreover, it is accordingly justified to improve further development and perfection of the maritime legislation by eliminating such ambiguities with the growth of coral reefs and the evolution of sand cays being both taken into account.

Investigating the influence of international events on global maritime networks is a challenging task that must comprehensively incorporate geographical, political, and maritime sciences. Understanding global maritime network dynamics is an initial and critical step in this investigation. This study proposes an automatic identification system (AIS)-based approach to understanding maritime network dynamics before and after international events. In this approach, a spatiotemporal modeling method is introduced to measure the similarity in shipping trends before and after international events. Then, a spatiotemporal analytic framework is proposed to understand the maritime network dynamics by grouping similar situation, and assessing possible indirect effects within a network. Finally, three case studies of international events, military conflict, lifted economic sanctions, and government elections, were used to investigate the observed network dynamics possibly affected by international events. The results indicate that container, tanker, and bulk shipping between India and its connected countries all declined more than 69% after military conflicts between India and Pakistan in August 2015. Tanker shipping between Iran and the United Arab Emirates increased 51% after economic sanctions on Iran were lifted. Container shipping between Sri Lanka and Singapore, Malaysia, and India increased more than 74% after the general election in Sri Lanka. These investigations demonstrate the feasibility of the proposed approach in assessing the possible effects of international events on maritime network dynamics.

This paper uses data for the period 1950-2050 compiled by the United Nations Population Division together with methods including spatial autocorrelation analysis, hierarchical cluster analysis and the standard deviational ellipse, to analyze the spatio-temporal evolution of population and urbanization in the 75 countries located along the routes of the Silk Road Economic Belt and the 21st-century Maritime Silk Road, to identify future population growth and urbanization hotspots. The results reveal the following: First, in 2015, the majority of Belt and Road countries in Europe, South Asia and Southeast Asia had high population densities, whereas most countries in Central Asia, North Africa and West Asia, as well as Russia and Mongolia, had low population densities; the majority of countries in South Asia, Southeast Asia, Central Asia, West Asia and North Africa had rapid population growth, whereas many countries in Europe had negative population growth; and five Belt and Road countries are in the initial stage of urbanization, 44 countries are in the acceleration stage of urbanization, and 26 are in the terminal stage of urbanization. Second, in the century from 1950 to 2050, the mean center of the study area’s population is consistently located in the border region between India and China. Prior to 2000, the trajectory of the mean center was from northwest to southeast, but from 2000 it is on a southward trajectory, as the population of the study area becomes more concentrated. Future population growth hotspots are predicted to be in South Asia, West Asia and Southeast Asia, and hotspot countries for the period 2015-2030 include India, China, Pakistan and Indonesia, though China will move into negative population growth after 2030. Third, the overall urban population of Belt and Road countries increased from 22% in 1950 to 49% in 2015, and it is expected to gradually catch up with the world average, reaching 64% in 2050. The different levels of urbanization in different countries display significant spatial dependency, and in the hundred-year period under consideration, this dependency increases before eventually weakening. Fourth, between 2015 and 2030, urban population hotspots will include Thailand, China, Laos and Albania, while Kuwait, Cyprus, Qatar and Estonia will be urban “coldspots.” Fifth, there were 293 cities with populations over 1 million located along the Belt and Road in 2015, but that number is expected to increase to 377 by 2030. Of those, 43 will be in China, with many of the others located in India, Indonesia and the eastern Mediterranean.

The construction of China-Mongolia-Russia high-speed railways is a strategic move to promote transportation infrastructure inter-connectivity between these countries, which will accelerate the implementation of the China-Mongolia-Russia Economic Corridor. However, well-planned China-Mongolia-Russia high-speed railways demand accurately identifying construction risks, scientifically evaluating risk levels, and mapping the spatial distribution of these risks. Therefore, this study established the integrated risk evaluation model (IREM) to scientifically evaluate the economic, social, and ecological risks of China-Mongolia-Russia high-speed railway construction and determine their magnitude and spatial distribution pattern. Based on this analysis, we propose designs for the east and west China-Mongolia-Russia high-speed railways and policy suggestions to mitigate construction risks. Suggestions include developing innovative cooperation of the "high-speed railway for resources and market", strengthening communication and technology dissemination, and applying innovative engineering techniques and setting buffers; establishing collaborative prevention and control systems to mitigate the three major ecological risks in the China, Mongolia, and Russia trans-border areas; and promoting economic integration by improving strategic coordination. In summary, this study provides scientific support for designing the China-Mongolia-Russia high-speed railways minimizing construction risks.

The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current research mainly focuses on the coarse-grained, holistic cargo ship transportation network while ignoring the structural diversity of different sub-networks. In this paper, we evaluate the robustness of the global cargo ship transportation network based on the most recent Automatic Identification System (AIS) data available. First, we subdivide three typical cargo ship transportation networks (i.e., oil tanker, container ship and bulk carrier) from the original cargo ship transportation network. Then, we design statistical indices based on complex network theory and employ four attack strategies, including random attack and three intentional attacks (i.e., degree-based attack, betweenness-based attack and flux-based attack) to evaluate the robustness of the three typical cargo ship transportation networks. Finally, we compare the integrity of the remaining ports of the network when a small proportion of ports lose their function. The results show that 1) compared with the holistic cargo ship transportation network, the fine-grain-based cargo ship transportation networks can fully reflect the pattern and process of global cargo transportation; 2) different cargo ship networks behave heterogeneously in terms of their robustness, with the container network being the weakest and the bulk carrier network being the strongest; and 3) small-scale intentional attacks may have significant influence on the integrity of the container network but a minor impact on the bulk carrier and oil tanker transportation networks. These conclusions can help improve the decision support capabilities in maritime transportation planning and emergency response and facilitate the establishment of a more reliable maritime transportation system.

Geographical circumstances are the fundamental background for all kinds of geopolitical events. The geopolitical environment system (GES) refers to a system that combines both physical and anthropogenic subsystems. Research on the geopolitical environment system simulation is a key to understanding the international geopolitical phenomenon. The theory of GES arose from the integration of the traditional geopolitics and earth system sciences. As an interdisciplinary system composed of many different fields, integrated reviews and a metadata study of GES are urgently needed. This paper presents a comprehensive view into the origination and advance of the GES theory. The conceptual framework of the GES is described in detail. The methodology for simulating and forecasting geopolitical events is also provided. It is proposed that the core topics of the GES science may include, but are not limited to, issues as data acquisition technologies; principles on the interactions between multiple subsystems (or factors) at different scales; evaluating and mitigating the global geopolitical risks, including the political risks, economic risks, the social risks, the environmental risks and the technological risks; and forecasting the geopolitical events with machine learning and artificial intelligence techniques.

In this study, information is collected on the weather, soils, field management and agricultural statistics in the Bangladesh, India and Myanmar (BIM) region. Crop growth parameters within the EPIC (Environmental Policy Integrated Climate) model are calibrated using cultivar data and regional experimental records of indica hybrid rice Fyou498 and Fengliangyou4 in China. Potential yields of rice are then simulated in the BIM region from 1996 to 2005. The effects of local irrigation and fertilization levels on super hybrid rice yield are examined. The potential yields of Chinese hybrid rice at local irrigation and fertilization levels in 2000 and at full irrigation and rational fertilization levels are found to be 10.22 t/ha and 11.33 t/ha, respectively. The potential for increasing monsoon rice production in the study region is 227.71 million tons. The eastern Indo-Gangetic Plain in India, the southeast coast of India Peninsula and the Ayeyarwady Delta in Myanmar have the largest potentials for monsoon rice production. The northeastern and southwestern areas of the Deccan Plateau and the northwestern region of the Indo-Gangetic Plain need to improve irrigation equipment to meet the water-use requirements of high-yield rice. The central and southern plains in Myanmar and northeastern India need greater access to nitrogen fertilization for high-yield rice.

Quantitative analysis of the impact factors in energy-related CO₂ emissions serves as an important guide for reducing carbon emissions and building an environmentally-friendly society. This paper aims to use LMDI method and a modified STIRPAT model to research the conventional energy-related CO₂ emissions in Kazakhstan after the collapse of the Soviet Union. The results show that the trajectory of CO₂ emissions displayed U-shaped curve from 1992 to 2013. Based on the extended Kaya identity and additive LMDI method, we decomposed total CO₂ emissions into four influencing factors. Of those, the economic active effect is the most influential factor driving CO₂ emissions, which produced 110.86 Mt CO₂ emissions, with a contribution rate of 43.92%. The second driving factor is the population effect, which led to 11.87 Mt CO₂ emissions with a contribution rate of 4.7%. On the contrary, the energy intensity effect is the most inhibiting factor, which caused -110.90 Mt CO₂ emissions with a contribution rate of -43.94%, followed by the energy carbon structure effect resulting in -18.76 Mt CO₂ emissions with a contribution rate of -7.43%. In order to provide an in-depth examination of the change response between energy-related CO₂ emissions and each impact factor, we construct a modified STIRPAT model based on ridge regression estimation. The results indicate that for every 1% increase in population size, economic activity, energy intensity and energy carbon structure, there is a subsequent increase in CO₂ emissions of 3.13%, 0.41%, 0.30% and 0.63%, respectively.