In this study, we analyzed the spatiotemporal variation of cold surges in Inner Mongolia between 1960 and 2012 and their possible driving factors using daily minimum temperature data from 121 meteorological stations in Inner Mongolia and the surrounding areas. These data were analyzed utilizing a piecewise regression model, a Sen+Mann- Kendall model, and a correlation analysis. Results demonstrated that (1) the frequency of single-station cold surges decreased in Inner Mongolia during the study period, with a linear tendency of -0.5 times/10a (-2.4 to 1.2 times/10a). Prior to 1991, a significant decreasing trend of -1.1 times/10a (-3.3 to 2.5 times/10a) was detected, while an increasing trend of 0.45 times/10a (-4.4 to 4.2 times/10a) was found after 1991. On a seasonal scale, the trend in spring cold surges was consistent with annual values, and the most obvious change in cold surges occurred during spring. Monthly cold surge frequency displayed a bimodal structure, and November witnessed the highest incidence of cold surge. (2) Spatially, the high incidence of cold surge is mainly observed in the northern and central parts of Inner Mongolia, with a higher occurrence observed in the northern than in the central part. Inter-decadal characteristic also revealed that high frequency and low frequency regions presented decreasing and increasing trends, respectively, between 1960 and 1990. High frequency regions expanded after the 1990s, and regions exhibiting high cold surge frequency were mainly distributed in Tulihe, Xiao’ergou, and Xi Ujimqin Banner. (3) On an annual scale, the cold surge was dominated by AO, NAO, CA, APVII, and CQ. However, seasonal differences in the driving forces of cold surges were detected. Winter cold surges were significantly correlated with AO, NAO, SHI, CA, TPI, APVII, CW, and IZ, indicating they were caused by multiple factors. Autumn cold surges were mainly affected by CA and IM, while spring cold surges were significantly correlated with CA and APVII.
Seasonal water-level fluctuations (WLF) play a dominate role in lacustrine ecosystems. River-lake interaction is a direct factor in changes of seasonal lake WLF, especially for those lakes naturally connected to upstream and downstream rivers. During the past decade, the modification of WLF in the Poyang Lake (the largest freshwater lake in China) has caused intensified flood and irrigation crises, reduced water availability, compromised water quality and extensive degradation of the lake ecosystem. There has been a conjecture as to whether the modification was caused by its interactions with Yangtze River. In this study, we investigated the variations of seasonal WLF in China’s Poyang Lake by comparing the water levels during the four distinct seasons (the dry season, the rising season, the flood season, and the retreating season) before and after 2003 when the Three Gorge Dam operated. The Water Surface Slope (WSS) was used as a representative parameter to measure the changes in river-lake interaction and its impacts on seasonal WLF. The results showed that the magnitude of seasonal WLF has changed considerably since 2003; the seasonal WLF of the Poyang Lake have been significantly altered by the fact that the water levels both rise and retreat earlier in the season and lowered water levels in general. The fluctuations of river-lake interactions, in particular the changes during the retreating season, are mainly responsible for these variations in magnitude of seasonal WLF. This study demonstrates that WSS is a representative parameter to denote river-lake interactions, and the results indicate that more emphasis should be placed on the decrease of the Poyang Lake caused by the lowered water levels of the Yangtze River, especially in the retreating season.
Detecting variation trend in dry-wet conditions can provide information for developing strategic measures to mitigate the impacts of global warming, particularly in dry regions. Taking the hilly region of northern Shaanxi on the Loess Plateau as a case area, this study analyzed the trend of aridity variation during 1981-2012, and explored the effect of vegetation restoration promoted by the Grain-for-Green (GFG) program implemented in 1999. The results indicated that the aridity in the region was non-significantly increased by 0.88% per year during 1981-2012, showing a drying trend. This drying trend and amplitude were changed by the influence of vegetation restoration promoted by the GFG program, based on two findings. The first one was that the aridity variation tended to increase during 1981-1999 while it turned to decrease during 2000-2012, with the regional mean relative change rate changed from 2.45% to -1.06%. This distinction was more remarkable in the loess gully region, where the vegetation was improved more obviously. The second one was that the mean vegetation coverage as indicated by EVI increased by 0.90% to 4.32% per year at county level, while the aridity decreased by 0.14% to 2.32% per year during 2000-2012. The regression analysis using the mean county data indicated that the change rate of aridity was negatively related to that of EVI with the coefficient of determination (R2) of 0.56, illustrating that around half of the aridity decline was explained by the EVI change. The mechanism of this effect was complicated, but it was found that the wind speed decline induced by the vegetation improvement could be an important contributor. It is concluded that the region became drier during 1981-2012, but the eco-restoration reduced the drying speed. However, this conclusion is involved in uncertainties, and further study based on experiments is needed to confirm the effect of the GFG-promoted vegetation restoration.
We present the first quantitative estimation of monsoon precipitation during the late glacial-Holocene in the sandy land of northern China, based on organic carbon isotopic composition data from a loess-sand sequence at margin of the Mu Us sandy land. We use the relationship between monsoon precipitation and the carbon isotopic composition of modern soils as an analogue, with a minor modification, to reconstruct precipitation back to c. 47 ka ago. The preliminary results indicate that annual monsoon precipitation was high after 8 ka, with an average of 435 mm; and it decreased during 18 and 8 ka with a mean value of 194 mm. The precipitation value of 47-18 ka varied between the two. We compare the reconstructed precipitation with other records and paleoclimatic modeling results, showing that our record agrees with reconstructions of the monsoon precipitation from other sources, even capturing short climatic events such as the Younger Dryas. We suggest that solar irradiance, high-latitude temperature/ice volume and local evaporation have together modified moistures in the sandy land.
Runoff calculation is one of the key components in the hydrological modeling. For a certain spatial scale, runoff is a very complex nonlinear process. Currently, the runoff yield model in different hydrological models is not unique. The Chinese LCM model and the American SCS model describe runoff at the macroscopic scale, taking into account the relationship between total actual retention and total rainfall and having a certain similarity. In this study, by comparing the two runoff yield models using theoretical analyses and numerical simulations, we have found that: (1) the SCS model is a simple linear representation of the LCM model, and the LCM model reflects more significantly the nonlinearity of catchment runoff. (2) There are strict mathematical relationships between parameters (R, r) of the LCM model and between parameters (S) of the SCS model, respectively. Parameters (R, r) of the LCM can be determined using the research results of the SCS model parameters. (3) LCM model parameters (R, r) can be easily obtained by field experiments, while SCS parameters (S) are difficult to measure. Therefore, parameters (R, r) of the LCM model also can provide the foundation for the SCS model. (4) The SCS model has a linear relationship between the reciprocal of total actual retention and the reciprocal of total rainfall during runoff period. The one-order terms of a Taylor series expansion of the LCM model describe the same relationship, which is worth further study.
According to the highway data and some socioeconomic data of 1990, 1994, 2000, 2005 and 2009 of county units in the Pearl River Delta, this paper measured urban integrated power of different counties in different years by factor analysis, and estimated each county’s potential in each year by means of expanded potential model. Based on that, the spatio-temporal association patterns and evolution of county potential were analyzed using spatio-temporal autocorrelation methods, and the validity of spatio-temporal association patterns was verified by comparing with spatial association patterns and cross-correlation function. The main results are shown as follows: (1) The global spatio-temporal association of county potential showed a positive effect during the study period. But this positive effect was not strong, and it had been slowly strengthened during 1994-2005 and decayed during 2005-2009. The local spatio-temporal association characteristics of most counties’ potential kept relatively stable and focused on a positive autocorrelation, however, there were obvious transformations in some counties among four types of local spatio-temporal association (i.e., HH, LL, HL and LH). (2) The distribution difference and its change of local spatio-temporal association types of county potential were obvious. Spatio-temporal HH type units were located in the central zone and Shenzhen-Dongguan region of the eastern zone, but the central spatio-temporal HH area shrunk to the Guangzhou-Foshan core metropolitan region only after 2000; the spatio-temporal LL area in the western zone kept relatively stable with a surface-shaped continuous distribution pattern, new LL type units emerged in the south-central zone since 2005, the eastern LL area expanded during 1994-2000, but then gradually shrunk and scattered at the eastern edge in 2009; the spatio-temporal HL and LH areas varied significantly. (3) The local spatio-temporal association patterns of county potential among the three zones presented significant disparity, and obvious difference between the eastern and central zones tended to decrease, whereas that between the western zone and the central and eastern zones further expanded. (4) Spatio-temporal autocorrelation methods can efficiently mine the spatio-temporal association patterns of county potential, and can better reveal the complicated spatio-temporal interaction between counties than ESDA methods.
A network perspective has increasingly become an organizational paradigm for understanding regional spatial structures. Based on a critical overview of existing empirical models for estimating intercity networks based on firm linkages, this study extends the recently proposed regional corporate city model algorithm by proposing a new method for approximating urban networks based on the locational strategies of firms. The new method considers both regional and hierarchical network features and avoids the information loss associated with the conversion from two-mode firm-city networks to one-mode city-city networks. In addition, networks estimated by using the method proposed herein are suitable when employing social network analysis. Finally, this method is empirically validated by examining intercity firm networks formed by advanced producer services firms in China’s two largest metropolitan areas, namely the Yangtze River Delta and Pearl River Delta. The presented empirical analysis suggests two main findings. First, in contrast to conventional methods (e.g., the interlocking city network model), our new method produces regional and hierarchical urban networks that more closely resemble reality. Second, the new method allows us to use social network analysis to assess betweenness and closeness centralities. However, regardless of the model applied, the validity of any method that measures urban networks depends on the soundness of its underlying assumptions about how network actors (firms, in our case) interact.
Using a sample of 14 prefecture-level cities in Liaoning Province, this study first explored the spatial hierarchy and structural characteristics of energy efficiency from the following three viewpoints: energy technical efficiency based on data envelopment analysis, energy consumption per unit of GDP, and energy utilization efficiency combining the previous two indexes. After measuring and analyzing the advancement, rationality, and concentration of the industrial structure in each city, we made some generalizations about the coupling features of the energy efficiency and industrial structure in Liaoning, using the coupling degree rating model. Some of our conclusions are as follows: (1) The 14 cities differ significantly in their energy efficiency, with Shenyang, Dalian, Anshan, and Jinzhou enjoying the highest energy efficiency. Northwestern Liaoning and other heavy-industrial cities such as Fushun and Benxi belong to low-efficiency and high-consumption areas. (2) In areas with higher efficiency, the spatial patterns of the energy technical efficiency, energy consumption per unit of GDP, and energy utilization efficiency are, respectively, "π"-,"Ⅱ"- and "H"- shaped. Geographically, the energy utilization efficiency shows different trends from east to west and from north to south. Factors such as the binuclear structure of economic development have a major effect on this spatial pattern of energy efficiency. (3) Southeastern Liaoning enjoys a highly advanced industrial structure. Areas with a highly rational industrial structure form an “H” shape, with Shenyang and Dalian at the two poles. The urban agglomerations in middle and southern Liaoning have a highly concentrated industrial structure. (4) Overall, the coupling between energy efficiency and industrial structure is low in Liaoning, except for Shenyang and Dalian at both ends, where the coupling between an advanced industrial structure and energy efficiency is higher than in other cities.
This paper investigated several stages in the formation of the geopolitical influence of oil and gas, including the basis of its gestation, the means of transformation, and the formation and exercise of power. Based on this theoretical framework, a system for assessing the geopolitical influence of oil and gas was developed. This system is comprised of 13 indicators, each with its own method of measurement. Then 21 representative oil and gas importing, exporting, and transit countries were selected as assessment subjects. A quantitative assessment of the geopolitical influence of oil and gas in the selected countries was carried out using the proposed indicators, and factor analysis was used to obtain the main factors of these indicators and the composite score of each country. The empirical results showed that the 13 indicators could be summarized into five main factors, in the order of contribution rate, specifically comprehensive national strength, energy, transportation, risk, and geopolitics, each with its own variance contribution rate. Results of the assessment indicated that the selected countries could be classified into five categories in terms of oil and gas geopolitical influence: strong, relatively strong, moderate, relatively weak, and weak.