Climate change investigation at a watershed-scale plays a significant role in revealing the historical evolution and future trend of the runoff variation in watershed. This study examines the multisource hydrological and meteorological variables over the source area of the Yellow River (SAYR) from 1961 to 2012 and the future climate scenarios in the region during 2006-2100 based on the CMIP5 projection data. It recognizes the significant characteristics of the recent climate change in the SAYR and predicts the change trend of future flow in the region. It is found that (1) The climate in the SAYR has experienced a significant warm-wet change since the early 2000s, which is very different from the antecedent warm-dry trend since the late 1980s; (2) The warm-wet trend in the northwestern SAYR (the headwater area of the Yellow River (HAYR), is more obvious than that in the whole SAYR; (3) With precipitation increase, the runoff in the region also experienced an increasing process since 2006. The runoff variations in the region are sensitive to the changes of precipitation, PET and maximum air temperature, but not very sensitive to changes in mean and minimum air temperatures; (4) Based on the CMIP5 projection data, the warm-wet climate trend in SAYR are likely to continue until 2049 if considering three different (i.e. RCP2.6, RCP4.5 and RCP8.5) greenhouse gas emission scenarios, and the precipitation in SAYR will not be less than the current level before 2100; however, it is estimated that the recent flow increase in the SAYR is likely to be the decadal change and it will at most continue until the 2020s; (5) The inter-annual variations of the East Asian winter monsoon are found to be closely related to the variations of annual precipitation in the region. Meanwhile, the increased precipitation as well as the increase of potential evapotranspiration (PET) being far less than that of precipitation in the recent period are the main climate causes for the flow increase in the region.
Sixty water samples (35 groundwater samples, 22 surface water samples and three hot-spring water samples) were collected at 36 points from villages and towns in Lhasa city, Nagchu (Nagqu) prefecture, Ali (Ngari) prefecture and Shigatse (Xigaze) prefecture (Tibet) in 2013 to study the hydrochemical characteristics and element contents of natural waters. The concentrations of elements were determined in the water samples and compared with the concentrations in water samples from other regions, such as southeast Qinghai, south Xinjiang, east Sichuan and west Tibet. The hydrochemical species in different areas were also studied. Water in most parts of Tibet reaches the requirements of the Chinese national standard and the World Health Organization international standard. The pH values of the water samples ranged from 6.75 to 8.21 and the value for the mean total dissolved solids was 225.54 mg/L. The concentration of arsenic in water from Ali prefecture exceeded the limit of both the Chinese national standard and the international standard and the concentration of fluoride in water from Shuanghu exceeded the limit of both the Chinese national standard and the international standard. The main hydrochemical species in water of Tibet is Ca (HCO3)2. From south to north, the main cation in water changes from Ca2+ to Na+, whereas the main anions in water change from HCO3- to Cl- and SO42-. The chemistry of river water and melt water from ice and snow is dominated by the rocks present at their source, whereas the chemistry of groundwater is affected by many factors. Tectonic divisions determine the concentrations of the main elements in water and also affect the hydrochemical species present.
The Manasarovar Basin in southern Tibet, which is considered a holy land in Buddhism, has drawn international academic attention because of its unique geographical environment. In this study, based on actual measurements of major ion concentrations in 43 water samples collected during the years 2005 and 2012, we analyzed systemically the spatial- temporal patterns of water chemistry and its controlling factors in the lake and inflowing rivers. The results reveal that the water in the Manasarovar Basin is slightly alkaline, with a pH ranging between 7.4-7.9. The amounts of total dissolved solids (TDS) in lake and river waters are approximately 325.4 and 88.7 mg/l, respectively, lower than that in most of the surface waters in the Tibetan Plateau. Because of the long-term effect of evaporative crystallization, in the lake, Na+ and HCO3- have the highest concentrations, accounting for 46.8% and 86.8% of the total cation and anion content. However, in the inflowing rivers, the dominant ions are Ca2+ and HCO3-, accounting for 59.6% and 75.4% of the total cation and anion content. The water exchange is insufficient for such a large lake, resulting in a remarkable spatial variation of ion composition. There are several large inflowing rivers on the north side of the lake, in which the ion concentrations are significantly higher than that on the other side of the lake, with a TDS of 468.9 and 254.9 mg/l, respectively. Under the influence of complicated surroundings, the spatial variations in water chemistry are even more significant in the rivers, with upstreams exhibiting a higher ionic content. The molar ratio between (Ca2++Mg2+) and (Na++K+) is much higher than 1.0, revealing that the main source of ions in the waters is carbonate weathering. Although natural processes, such as rock weathering, are the major factors controlling main ion chemistry in the basin, in the future we need to pay more attention to the anthropogenic influence.
Based on SPOT-5 images, 1:1 million topographic maps, the maps of the returning farmland to forest project and the Chongqing forest project, social and economic statistics, etc., this paper identifies the features and factors influencing farmland marginalization. The results showed: (1) During 2002-2012, the rate of farmland marginalization was 16.18%, which was mainly found in the high areas of northern Qiyao mountains and the medium-altitude areas of southern Qiyao mountains. And this farmland marginalization will increase, associated with non-agriculturalization of rural labourers and aging of the remaining labourers. (2) Elevation, distance radius from villages and road connections had a great influence on farmland marginalization. Farmland marginalization rates showed an increasing trend with the increase of elevation, and 60.88% of the total farmland marginalization area is found at an altitude greater than 1000 m above sea level. The marginalization trend also increases with slope and distance from the distribution network. (3) Farmland area per labourer and the average age of farm labourers were major factors driving farmland marginalization. Farmland transfer and small agricultural machinery sets affect farmland marginalization with respect to management and productivity efficiency. (4) Farmland with “comparative- disadvantage-dominated marginalization” accounted for 55.32% of the total farmland marginalization area, followed by “location-dominated marginalization” (33.80%). (5) According to the specifics of each real situation, different policies are suggested to mitigate the marginalization. A “continuous marginalization” policy will encourage the return of farmland to forest in “terrain-dominated marginalization”. An “anti-marginalization” policy is suggested to create new rural accommodation and improve the rural road system to counteract “terrain-dominated marginalization”. And another “anti-marginalization” policy is planned to improve management and micro-mechanization for “comparative-disadvantage-dominated marginalization”. A new idea was developed to integrate high resolution remote sensing and statistical data with survey information to identify land marginalization and its driving forces in mountainous areas.
Transport infrastructure plays an important role in shaping the configuration of spatial socio-economic structures and influencing regional accessibility. Although China’s transport infrastructure has been experiencing a rapid development in the last 100 years, there lacks a systematic examination of the complete evolution history of China’s transport development, particularly with all kinds of transport modes. This paper first aims to clarify the history of China’s transportation from 1910 to 2012, and divides its evolution process into five periods (1911, 1935, 1953, 1981 and 2012) whereby each period represents the preliminary development time for each transport mode. Second, the paper calculates the transport dominance and analyses its spatial distribution in each period, with county as the basic analysis unit. Transport dominance here is defined as an integrated indicator for evaluating regional transport conditions. The results demonstrate the following: (1) areas with relative good transport dominance have expanded from scattered dots such as Tianjin, Shanghai, Guangzhou in 1911 to extensive areas with each provincial city as cores in 2012; (2) transport development is improved by advances in transportation technology. The construction of modern transport infrastructures such as seaports, airports, high-speed rails (HSRs), and freeways lead the expansion of national territorial areas with good and excellent transport dominance and the increase of the value of transport dominance over time; (3) transport dominance is spatially unevenly distributed and exhibits resemblance with the expansion of transport network, which is closely related to China’s socio-economic development and policies.
Due to its great strategic significance in integrating regional coordinated development and enhancing the rise of Central China, urban agglomeration in the middle reaches of Changjiang (Yangtze) River has attracted much attention from both theoretical and practical aspects. Such research into the area’s economic network structure is beneficial for the formation of an urban- and regional-development strategy. This paper constructs an economic tie model based on a modified gravitation model. Subsequently, referring to social network analysis, the paper empirically studies the network density, network centrality, subgroups and structural holes of the middle reaches of Changjiang River’s urban agglomeration economic network. The findings are fourfold: (1) an economic network of urban agglomeration in the middle reaches of Changjiang River has been formed, and economic ties between the cities in this network are comparatively dense; (2) the urban agglomeration in the middle reaches of Changjiang River can be divided into four significant subgroups, with each subgroup having its own obvious economic communications, while there is less economic-behavioral heterogeneity among subgroups - this is especially true for the two subgroups that exist in the Poyang Lake Ecological Economic Zone; (3) an economy pattern driven by the central cities of Wuhan, Changsha and Nanchang has emerged in the urban agglomeration of the middle reaches of Changjiang River, while these three capital cities have exerted great radiation abilities to their surrounding cities, the latter are less able to absorb resources from the former; (4) the Wuhan Metropolitan Areas and the Poyang Lake Ecological Economic Zone have more structural holes than the Ring of Changsha, Zhuzhou and the Xiangtan City Clusters, meaning that cities at the periphery of these two areas are easily constrained by central cities. The Ring of Changsha, Zhuzhou and the Xiangtan City Clusters have fewer structural holes; thus, the cities in this area will not face as many constraints as those in the other two areas.
Urban population during the daytime and at night and their spatial distribution are important bases for planning urban infrastructure, public services and disaster relief. As current population statistics cannot distinguish urban population during the daytime from that at night, existed research in this field are quite limited. This paper tries to advance studies at this aspect by establishing a relationship model for the three components of ‘population, land use and time (daytime or night)’ to explore the temporal and spatial characteristics of different types of population, which is aimed to estimate urban population during the daytime and at night and to analyze their spatial characteristics at grid scale. Furthermore, an empirical case study has been carried out at the Haidian District in Beijing, China to test the model. The results are as follows: (1) The spatial structure of urban population during the daytime is significantly different from that at night. The spatial distribution of urban population during the daytime is more extensive and more agglomerated that that at night. (2) Several types of spatial coupling relationship between population during the daytime and that at night have been identified, such as sandwich mode, symmetry mode, convergence mode and single mode, etc. (3) The spatial distribution of daytime and nighttime population also reflects certain factors during the development of China, such as the distribution of old residential areas, the construction of new industrial districts, and the differences between urban and rural areas, which can provide reference points for studies in this field and other regional research.