Among various different measures which can be used to make the promotion of regional economic development, the role of innovation cannot be ignored. Over the past 20 years, the proportion of the total number of researchers in the three northeastern provinces (including Heilongjiang, Jilin and Liaoning) of China has dropped 6.6 percent. Besides, the R & D / GDP of the three northeastern provinces increased by only 0.09%, which is far below the national average (1.51%). According to the current researches, it can be found that the economic growth achieved through the innovation capacity in the northeastern provinces accounts for 49.3%, which indicates that innovation plays an important role during the process of promoting the economic development.Innovation has turned into more and more important for regional development in China. On the basis of the panel data of China ranging from 1995 to 2013, Malmquist index model was used to make the analysis on the spatial and temporal differences of innovation TFP in the three provinces of Northeastearn China. The PVAR model was constructed on decomposed data. The change trend of TFP and its decomposition inthese three provinces of Northeast Chinaareforecasted. In accordance with the above process, three conclusions can be made as follows:1) The innovation TFP growth speed of Northeast China is slightly higher than that of Western China while is lower than Central China and Eastern China. 2) From the inside of the three northeastern provinces, the innovation TFP of Jilin Province, which is driven by the improvement of the management level and the adjustment of the system (PEC), grew fastest from 1995 to 2013. Secondly, the major driving force of innovation TFP of Liaoning Province came from technology. However, its technological progress has been limited by the scale of innovation. The growth rate of innovation TFP in Heilongjiang Province is the slowest, which is mainly due to the limited growth rate of endogenous power (SEC, PTC, STC, PEC). At the same time, the result shows that the promotion of the system and management level in these three northeastern provinces is far greater than the technological progress and the enlargement of the scale. 3) Based on the forecast results, the three provinces in the Northeast innovation TFP growth rate may continue to slow down in the near future. From the perspective of unit changes, STC and SEC unit changes play the dominant roles in improving TFP of the three northeastern provinces. In terms of the degree of influence, the innovation of TFP in Northeast China is mainly affected by the change of PEC and STC.
Based on the coupling model and the quadrant diagram method, the element agglomeration degree and ecological environment evolution and their relationship were analyzed from the perspective of the overall and internal in Haerbin-Dalian giant urban belt. The results showed that: 1) The element agglomeration degree is ‘firstly decreased, and then increased’, while the ecological environment is ‘first increased quickly and then dropped slowly’, the relationship between the degree of element agglomeration and the level of the ecological environment become more harmonious. Further analysis found that the evolution of industrial structure, resource consumption level, economic density are main elements, which contribute the coordination degree to higher, and the industrial structure evolution is most remarkable. 2) Within internal elements of urban agglomeration, ‘four cities’ pattern has been formed, present a ‘core-edge’ structure. The relationship between the degree of internal element agglomeration and the level of the ecological environment can be divided into four kinds, which are highly coordinated, element agglomeration lagged, low coordinated and ecological environment lagged. 3) In 2000-2012, the relationship between the degree of element agglomeration and the level of the ecological environment of different cities are in continuous evolution, and the center cities main transformed into ecological environment lagged; the surrounding areas of center cities transformed into element agglomeration lagged; resource-dependent and heavy industrial cities present low coordinated.
Spatial interaction model is an important research field. Existing studies indicate spatial structure of destinations has a significant impact on spatial flow. Thus, traditional spatial interaction models suffer model misspecification problem because the absence of spatial structure variable. Among the modified models introduced to solve the misspecification problem, the competing destinations model is the most widely-used one.The competing destinations modelassumes that the travelers’ destinations selecting process adopts a hierarchical information processing strategy.Based on this strategy, the spatial decision process is divided into two stages. In the first stage, travelers select a destinations cluster containing a set of destinations; in the second stage, travelers select an individual destination from the cluster selected in the first stage. The competing destinations model has been empirically applied in numerous studies in foreign countries.However, the empirical conclusions with respect to the validity of the competing destinations model are still far from agreement. Moreover, none empirical study of this model has been conductedin China. This study applies the competing destinations model based on intercity railway passenger data in 2010 in China, and test its validity by comparing it with traditional spatial interaction models. The estimations of the competing destinations model as well as the traditional spatial interaction model are conducted by the maximum likelihood method, which is calculated by a new method distinguishing from existing studies, i.e. the Particle Swarm Optimization (PSO) algorithm. The conclusions can be drawn as follows. 1) Spatial structure has a significant impact on intercity railway passenger flow of China, and there exists a significant competing effect among destinations both in the system-wide estimation results and in the origin-specific estimation results. The system-wide distance-decay parameter estimated in the competing destinations model (-1.165) is more negative than in the traditional spatial interaction model (-1.108). In the other hand, 124out of a total number of 177 (the ratio is 70% ) origin-specific distance-decay parameter estimationsare more negative in the competing destinations model than in the traditional spatial interaction model, while 140 out of 177 (79%) origin-specific destinations accessibility indicator estimations are negative in the competing destinations model. These characteristics have not ever been reported in Chinese context in existing studies. 2) The competing destinations model reduces the spatial autocorrelation among distance-decay parameters, thus significantly corrects the misspecification problem of traditional spatial interaction models. These results illustrate that the competing destinations model performs significantly better than the traditional spatial interaction model, and thus the improvements by the competing destinations model are empirically valid in Chinese context. 3) The parameters estimation and empirical analysis of traditional spatial interaction models (i.e. gravity model) in existing literature may be biased, while the competing destinations model is an efficient improvement and can play an important part in empirical analysis.
Chinese top 500 enterprises are the concentrated reflection of Chinese outstanding enterprises, which determine the development direction of Chinese future industry and even economy. On the basis of ‘Chinese top 500 enterprises’ data derived from Fortunechina.com, and having taken the number of enterprises and operating incomes as the main assessment index, this article analyzed the characteristics of industrial structure and regional distribution of Chinese top 500 enterprises by statistics, and then probed into the spatial pattern of provincial industrial structure. There were three key findings.First, during the period of 2009-2014, a declining trend has showed in the proportion of listed companies and its operating incomes of manufacturing industry, but manufacturing industry still dominant in the 13 kinds of industries, and even be the indispensable pillar of Chinese economy. Another four industries like financial, construction, wholesale and retail trade and real estate, have a strong momentum of development and shoulder the burden of leading industries. As a technology-intensive industry, information transmission and software industry has some difference with the above industries. For instance, it has a low proportion of listed enterprises and operating incomes, while the proportion trends to rise again in the recent three years. So the information transmission and software industry can be regarded as the pioneer industry, and have the possibility to be the leading industryin the future spell. The second finding is that the geographical concentration of Chinese top 500 enterprises upgrades continuously, specifically, the concentration areas of Chinese top 500 enterprises are mainly in eastern region, especially in Beijing City, Shanghai City and Guangdong Province, and Hong Kong Special Administrative Region. The last key finding is about spatial pattern of provincial industrial structure, which manifests that industrial structure tends to be diverse, simplified and single which distributed in ladder pattern, and that there is a great gap in the proportion of provincial manufacturing industry and enterprises belonging to services industry tend to concentrate in eastern provinces. Meanwhile, it is palpable of the disparity between provincial benefit of industrial structure. At the end of the article, some feasible measures are advised to out of the dilemma suffered by Chinese industry, likely, attach equal importance to producer services and strategic emerging industry, which could guide the industry to climb to the high-end of the value train and further upgrade the industrial structure.
In order to break through the segmentation between urban and rural land markets, and achieve the target of integrated land use of ‘mountain-water-forest-cropland-lake’, integrated urban and rural land markets need to be developed. This study explored the mechanism impacts of urban land market, informal rural collective construction land market, and rural agricultural land circulation market on land use/land cover change (LUCC). The results show that, first, the segmentation between urban and rural land markets still existed, and resulted in a great effect on LUCC. Second, the different development degree of urban state-owned construction land market has a direct impact on relationship between land supply and demand, as well as land use structure, and even on the efficiency of land use allocation. Third, the rural collective construction land market has appeared, which accelerated the speed of the transformation of land use types because of the vagueness of property rights and the lack of legal supervision and protection. Fourth, the segmentation of urban land rural land markets affected the sustainability of land use systems and the ecosystem and environmental systems. Finally, on the basis of above results, we thought that the future research will strengthen the analysis of the integration of urban land rural land markets. Moreover, the response mechanism (pattern, process and effect) of the integration of urban and rural land markets on LUCC will be revealed.
Along with the accelerating urbanization, there are more and more contradictions between the number of cars and urban transportation facilities. The congestion time and congested roads in cities are increasing. Intelligent urban traffic management platform is the effective method to alleviate the increasingly serious urban congestion problems. By using prediction results of traffic flow big data, the platform can guide users to adjust the travel plan, and ease the traffic pressure effectively. How to use a large number of spatio-temporal data related to traffic activities to predict the traffic flow is the key to realizing traffic guidance. In this article, a distributed incremental aggregation method for traffic flow data is studied. The method combines the distributed incremental data aggregation method with the traffic flow data cleaning rules, makes cleaning and counting of traffic flow big data, and provides data for traffic flow forecast. With the analysis of traffic flow correlation in the network of upstream and downstream, this article uses the multi-order of turning rate in the intersection to quantize the correlation, builds the spatial weight matrix based on the road network correlation, and improves the STARIMA model. In this article, two groups of contrast experiments were made. Through the contrast experiment between MapReduce method and MPI method, the result proves that the method proposed in this article is better than the MPI method in the development cycle and stable operation. The method’s efficiency can meet the need of traffic flow data aggregation. The traffic flow statistics can be used as the basis of traffic flow forecasting. Through the contrast experiment between the Improved STARIMA model and the Dynamic STARIMA model, the result proves that the Improved STARIMA model, which considers the multi-order correlation between the upstream and downstream sections, matches the distribution rules of traffic flow in road network better. Therefore, the forecast results are more accurate. In conclusion, the method of this article is a new method of traffic flow forecasting in the background of big data, and it can realize accurate prediction.
China has been a long-period fast economic growth after its opening policy. The whole degree of poverty in China has decreased sharply, which plays an important role in fulfilling Millennium Development Goals (MDG) made by the United Nations. As a developing country, however, China still has a challenge of reducing poverty and promoting regional development. Rural poverty is still a serious problem in rural China, especially in mountainous or ethnic areas. Different scales of governments in China develop much poverty-alleviation policy, but the efficacy of these policy are sometimes low because “one size fits all” policy always neglect regional difference in poverty resulting from different contexts of different places. Spatial patterns and determinants of regional poverty is a key theme for scholars from many disciplines. Giving that determinants of rural poverty in different places are different and the effects of significant factor are dependent on spatial scales, there is a need for more empirical evidences at different scales or in different regions. Furthermore there is little study to explore the spatial variations of effects of determinants. The present article can fill these gap to some extent through analyzing the determinants of county-level poverty and its spatial variation of their effects within Guizhou Province in the southwestern China. The rate of county-level poverty is largely different within this province. Based on methods of OLS regression, spatial econometric and geographic weighted regression (GWR), this article studies spatial variations and determinants of rural poverty at the county level. The results show that rate of rural poverty is higher in the eastern, southern, western counties than middle and northern counterparts. There is a significant spatial autocorrelation of rural poverty, for index of Moran’s I is between 0.45 and 0.55, which indicates that poverty of neighboring counties have a positive effect on the poverty of a specific county. Some counties with a high-high poor pattern fall into spatial trap of poverty based on results of index of Local Moran’s I. These counties are located at southeast and southern parts of Guizhou and have a high proportion of ethnic minorities’ population. For the determinants, the OLS estimation results show that topographic slope, distance to a local urban center, the percentage of teenagers, the percentage of ethnic minorities are key determinants of spatial variations in rural poverty at the county level. The effects of these four factors are found to have different spatial patterns based on GWR analysis. There is no significant effect for distance to the provincial capital on the rural poverty. The above results have important policy implication. The core implication is to combine place-based and people-based policy, which surpass the current Poverty-Targeting-Alleviation (jing zhun fu pin) initiatives dominating poverty-reduction policy of China’s governments.
Geographers increasingly focus on spatio-temporal patterns of shopping behaviors and its influencing factors in China. However, few researchers try to unscramble the matching pattern of housing-shopping space based on microcosmic behaviors. Based on residents’ daily shopping behaviors closely related with their basic need and living quality under the macro-background, the evaluation of the pattern of housing-shopping space in communities of Guangzhou will contribute a lot to the construction of community commercial facilities. In this article, firstly we figure out the matching relationship between residents' daily shopping and the supply of community commercial facilities through a major index of distance. Then based on a multiple linear regressive model, we try to quantitatively explore those factors influencing the above matching pattern. The paper takes into consideration four major factors for further analysis: institutional-structural factors, the supply of surrounding commercial facilities, preference of daily shopping destination, social-economic properties of individual and family. Several results are carried out as follow: Generally the matching relationship of housing-shopping space behaves well, in which the construction of community facilities within 1 500 meters counts a lot. However, the relationship in the suburbs is much worse than that in the center. System-space-social changes have different effects on the above relationship in the suburbs, ranging from the marketization of housing, the reform of housing system, suburbanization to the changing of commerce types. Those significant factors can be summarized to two parts: objective restrict and subjective decision. On the one hand, the article verifies the effects of house type, macro-location of dwelling and supply of its surrounding commercial facilities. House type and macro-location of dwelling both play an indirect role .What really matters is the supply of specific type of commercial facilities within a certain distance. In other words, it’s more effective to improve the comprehensive commercial facilities within 1 500 meters away from home. On the other hand, social-economic properties of residents and their preference are also influencing factors. It’s worth noting that residents’ preference for shopping complex is increasingly visible with the retail format transition and lifestyle change. During the transition period, it’s common to find such kind of mismatch of housing-shopping space in the suburbs in Guangzhou. So some suggestions are given to make it possible to solve the above problem of mismatching effectively. Not only should we improve the spatial relationship according to different location characteristic, community type and group attribute, but we should also reinforce the construction of commercial complex within walking distance.
By taking 126 national traditional villages of Guangdong Provinces as samples, a database about spatial properties of these villages was established by the application of a vector map of Guangdong Province, with a scale of 1∶500 000, and ArcGIS. Then, the proximal point index, geography coefficient of concentration, spatial gini coefficient, and unbalanced index were combined and used to analyze the spatial distribution and controlling factors of these villages. The results are as follows: 1) The calculation of nearest-neighbour distance reveals that the spatial distribution of these national traditional villages in Guangdong Province presents a type of cluster; while the calculation of geography coefficient of concentration reveals that in the scale of a city, these villages are mainly distributed in the following four cities: Meizhou, Qingyuan, Guangzhou, and Zhanjiang. The calculation of spatial gini coefficient reveals that these villages present an unbalanced distribution pattern, they are mainly distributed in the north of Guangdong, then is the Pearl River Delta. In addition, the kernel density estimation reveals that there are two high density regions of these villages in Meizhou and Qingyuan, three sub-high density regions in Guangzhou, Zhanjiang and Dongguan, respectively. 2) The spatial distribution of these national traditional villages in Guangdong is affected by natural environment, social economy, history and culture. In terms of natural geographic environment, these villages are mainly distributed in the northern mountainous area of Guangdong, the Pearl River Delta, and the coastal area of Zhanjiang. It indicates more villages are distributed in relatively isolated mountainous area and plain area with good farming conditions. The reason is that the geographical environment of mountainous area is relatively independent, so traditional villages have formed their own characteristics in a relatively closed environment and preserved relatively complete; plain areas generally have ample water, land conditions and abundant land resources, so it is the first choice for people to live and work. Moreover, the spatial distribution of these villages significantly correlates with economic developing level of different regions, they are either distributed in relatively under-developed or developed regions. Because, people and government have money to protect traditional villages in developed regions; and in relatively under-developed regions, the transportation, communication, information exchange, economic development are relatively backward, where traditional villages were not destroyed but existed. Furthermore, the accumulation of history and culture is another important factor that affects the distribution density of these villages. Historical regions with distinct cultural identity in Guangdong are found to have more traditional villages.
With the help of statistical analysis of the number of place-name and spatial smoothing analysis in GIS based on the point distance operation of mobile search method, this research objectively and accurately reflects the fact that the cultural landscape of rural place-name in Chengde area is rooted in the unique natural&cultural geographical characteristics of it. At the same time, it also reflects the blending&integration of diverse cultures of Chengde area in historical period. The research discovered that there are differences in the spatial distribution of cultural landscape place-name of various categories in Chengde area. The cultural landscape place-name of military activities category are distributed in Weichang County, Luanping County, Chengde County and Pingquan County of Chengde. These areas are just the places of battles and border defences in the history of Chengde. The cultural landscape place-name of economic activities are mainly distributed in Weichang County. Since in the reign of Guangxu in Qing Dynasty, paddocks were opened and farmlands were forbidden, a great number of people bought lands for reclamation to develop agricultural economy. The subsequent economic activities such as stores, power houses, crock kilns, brick kilns, distilleries, oil mills, dye-works, sugar mills and Blacksmith furnaces increased. These activities in this area were much more than other areas and place-name named after these activities were common. The spatial distribution of cultural landscape place-name of buildings category is positively correlated with the rural population density of Chengde area. This is demonstrated by the fact that in the southern area of Chengde where the population density is relatively high, the spatial distribution of cultural landscape place-name of buildings category is basically concentrated. As for the spatial distribution state of cultural landscape place-name of commemoration category, there is no obvious concentration area and they are distributed everywhere, which shows that local people all yearn for a beautiful life. Most of the spatial distribution areas of cultural landscape place-name of Manchu language, Mongolian language and dialects are concentrated in Pingquan County. This fully reflects the regional characteristic of Pingquan that it’s located at the place where different nationalities depend on and interact with each other. Moreover, this demonstrates the brands of the activities between Manchu people, Mongolian people and the Han people while they live together here. From the angle of the spatial diversity of place-name culture, the in-depth characteristics of the culture in Chengde area were further recognized. In today when the traditional culture is disappearing rapidly, applying new methods for the quantitative identification of the spatial distribution characteristics of the cultural landscape of place-name is the basis of the protection&development of the intangible cultural heritage of place-name. It also reflects the features of folk custom in this whole area and the deep historical and cultural background. Its researches reflect the blending of diverse cultures of Chengde area in historical period to some extent. It also has reference significance to the exploration of the protection of the intangible cultural heritage of place-name in Chengde area.
Under the background of rapid urbanization in the Qinhuai River Watershed, models of land use change are primary tools for analyzing the causes and consequences of land use changes. We choose CLUE-S model to simulate the land use situation of it in 2020. We use linear regression model, Markov model and the gray GM (1, 1) model respectively to predict the demand for land use which is needed by the non-spatial module of CLUE-S model, then we compared the three forecast results.In order to further verify the influence of policy on land use change, two prediction scenarios were established, one is "natural development" scenario where land use will change according to historical trend and the other is "optimization" scenario which considered the effects of planning policy. We simulated the Qinhuai River Watershed land use pattern in 2020 under different scenarios, and analyze the landscape pattern of it. The results shows that the Kappa index of Linear regression model, Markov model, the gray GM (1, 1) model are 0.866, 0.849, 0.867 respectively, so three methods all satisfy the requirements of model accuracy; In “natural development” scenario, the water area, paddy field, forest land, urban land and the dry farm change, compared to 2010, by 21.5%, 15.3%, 9.0%, 9.0%, 9.0%, respectively, while in “optimization” scenario water area, paddy field, forest land, urban land and the dry farm change by 3.1%, 1.6%, 10.8%, 6.3%, 10.8%, respectively; Under the “optimization” scenario, the land use condition can meet the requirement of protection of basic farmland and ecological land, increasing infiltration capacity of rainwater, and alleviating the urban heat island effect. This work could be the reference for the choice of the method of non-spatial module and provide scientific support for land use planning and managements of the watershed.
Heavy metals (including As, Cd, Pb, V, Cr, Mn, Ni, Cu and Zn) concentrations in sediment from the lower reaches of Shaliuhe River in Qinghai Lake watershed were investigated by inductively coupled plasma mass spectrometry. The pollution coefficient (Pi), enrichment factor (EF), geoaccumulation index (Igeo) and potential ecological risk index (RI) were applied for assessing the status of sediment heavy metal pollution and the extent of potential ecological risk. The results showed that the mean concentrations of As, Cd, Pb, V, Cr, Mn, Ni, Cu and Zn were 10.4±1.5 mg/kg, 0.12±0.05 mg/kg, 18.7±3.5 mg/kg, 63.6±7.7 mg/kg, 49.1±8.9 mg/kg, 618±100 mg/kg, 24.9±4.8 mg/kg, 19.7±6.6 mg/kg and 68.1±13.7 mg/kg, respectively. These heavy metals concentrations in both spatial and temporal sediments are lower than the average soil content of Qinghai Lake watershed. Both Pi and EF of all the elements in sediment samples are lower than 2, and most of them are lower than 1. The heavy metals that have high value of Pi and EF are mainly Cd, Cu, Ni and Zn. The Igeo of most elements are negative except Cd and Cu in QB-19. Besides Cd in three surface sediment samples (QB-19, QB-23 and QB-30), the potential ecological risk factor (Ei) of the heavy metals are lower than 30, and the RI of the heavy metals are lower than 70 except for the three surface sediment samples mentioned above. The results suggest that sediment in the lower reaches of Shaliuhe River has not been polluted, and it has low potential ecological risk. However, anthropogenic contribution for heavy metals (especially Cd, Cu, Ni and Zn) do exists in the Shaliuhe River watershed, and it was significant in modern times.
Investigation on the pedogenesis of soils from the upper reaches of the Minjiang River is necessary for ecological reconstruction and vegetation restoration in this region. The soils development from the valley of the upper Minjiang River and the Jiuding Mountain were investigated based on the physical and chemical properties such as grain size distribution, oxide of iron, organic matter, carbonate, pH, organic stable isotope, cation exchange capacity, and clay mineralogy. The well development and eluviation prevailed in the soils from the Sanjiang to the Yingxiou, demonstrated by the higher values of clay/silt, iron free degree, clay and organic mater contents and lower values of carbonate content, pH and organic δ13C, indicating an environmental property of warm-wet climatic conditions and prevailed trees. However, the soils from the Caopo to the Fengyi were characterized by the higher sand and carbonate contents, pH and organic δ13C, and lower values of iron free degree and organic mater content, showing an arid climate in where C4 vegetation prevail. The clay/silt and organic mater content increase, iepidocrocite arising and calcite lack occurred in the high altitude region of the Jiuding Mountain, indicating a wet climatic condition. While the soils under 2000 metre above sea level presented an arid climatic feature, as demonstrated by the lower values of clay/silt and contents of clay and organic mater, as well as the higher calcite content and pH.
Based on MOD16 products from remote sensing and its precision verification, this article analyzed the spatiotemporal characteristics of interannual and annual evapotranspiration and variation characteristics of evapotranspiration under different land cover types from 2000 to 2014, using statistical analysis of GIS, linear trend method and variation coefficient method et al. The results showed as follows: 1) In 2000-2014, the 15-year averaged PET was 1 476 mm and the spatial distribution of PET was decreasing trend from southeast to northwest while the 15-year averaged ET was 654 mm and the spatial distribution of ET in the west and south was higher than in the east and north. The averaged PET and ET are in the opposite order under different land cover types. 2) The interannual variability of PET was 13.63 mm/a and showed a weak increasing trend while ET was -2.3 mm/a and showed a weak decreasing trend. The gap of PET and ET showed an increasing trend, which indicated that water resources in the Hanjiang River Basin was decreasing. Spatial variation trend of PET was “the increasing in the East and the decreasing in the West” while ET was “the decreasing in the East and the increasing in the West”. There was the tendency of drought in the northeast. 3) Interannual PET and ET was a single peak. PET was the largest in June and ET was the largest in July. Both of them were the smallest in December. The maximum gap between PET and ET was the spring drought from April to June. Monthly averages of PET and ET showed a single peak type under different land cover types. The difference of monthly ET for different land cover types was obvious in the vegetation growing season when monthly ET in forestland was the fastest growth rate. 4) Seasonal averages of PET and ET had obvious differences among the four seasons. Spatial distribution of seasonal ET was very significant which greatly attributed to the transpiration of forestland because it was greatest contribution to the annual ET. In 2000-2014, spatial change of seasonal ET was significantly increasing in mountainous western region of the Hanjiang River Basin, while decreasing in eastern region. On the whole, the annual change of seasonal ET was the most obvious in winter, and the weakest in spring.
Daily precipitation and temperature data of 74 weather stations of the Pearl River Basin in 1952-2013 were analyzed to characterize spatiotemporal characteristics of extreme precipitation in terms magnitude, frequency and occurrence time by using the peaks-over-threshold approach (POT), Mann-Kendall test(MK) and Poisson Regression Model for the whole year, wet season(April-September) and dry season(October-March). In addition, causes behind extreme precipitation changes were investigated by relating spatiotemporal patterns of extreme precipitation to ENSO and large-scale moisture circulation. The results indicate that:1) Extreme precipitation over the Pearl River Basin is in uneven. Precipitation extreme mainly occurs from April to September and highest extreme precipitation regimes can be found in June; 2) The frequency of extreme precipitation across the Pearl River Basin during wet season and whole year is even. However, occurrence rates of extreme precipitation within the Pearl River Delta during dry season tends to be uneven with significant difference in annual frequency of extreme precipitation;3) Magnification of extreme precipitation can be observed during dry season with respect to precipitation magnitude and occurrence rates. Besides, occurrence timing of extreme precipitation regimes tends to be earlier. However, no significant trend can be identified in extreme precipitation during wet season and during the entire year. Winter flood has the potential to causeagriculturalloss. Therefore, wetting tendency of dry season can pose new challenges for regional water resources management; 4) Warming climate across the Pearl River Basin and variations of warm and cold episodes of ENSO have significant effects on extreme precipitation processes. From the perspective of the ENSO’s influence, magnitude and frequency of extreme precipitation over the west of the Pearl River Basin increases in El Ni?o years with decreased magnitude of extreme precipitation in the lower Pearl River Basin.
The response of structure and function of terrestrial ecosystem to global climate change has become a major point. Vegetation is an essential component of the terrestrial ecosystem which has proved to be sensitive to climate change. Normalized Difference Vegetation Index (NDVI) is widely recognized as a good indicator of vegetation coverage and productivity, has been widely used to indicate vegetation activity and dynamics, also vegetation growth, ecosystem structure and functions respond to climate change. Climate warming has important influence on the vegetation coverage, and alpine grassland is one of the most significant vegetation type on the Qinghai-Tibet Plateau. This study used GIMMS NDVI data sets and climate data from 40 meteorological stations to investigate spatial and temporal variations of alpine grassland cover and the response of NDVI to climatic variables on the Qinghai-Tibet Plateau in 1982-2013. The results showed that the average growing season NDVI is high in the southeast and low in the northwest. As a whole, the alpine grassland cover tended to increase on the Qinghai-Tibet Plateau, with the rate of 0.000 3/a (p<0.05) in the past 32 years. Spatially, the tendency of alpine grassland NDVI showed great heterogeneity, with the significantly NDVI increased mainly distributed in the northeastern part of Qinghai-Tibet Plateau dominated by alpine meadow which approximates to 26.0% (p<0.05) of the total study area. The area with significantly decreased area accounted for 4.7%, mainly emerged in the western part where the grassland was dominated by alpine steppe. In the regional scale, the variation in alpine grassland cover was more closely related to precipitation than other climate factors. The spatial characteristics of the relationship between growing season NDVI and climatic variables were analyzed based on the eco-geographical regions. Significant lagged correlations between precipitation and seasonal NDVI were found for the alpine steppe. The results suggested that precipitation was the key limit variable in the northeastern part of Qinghai-Tibet Plateau with higher annual mean temperature. But in the eastern and central eco-regions with the more rainfall, temperature could limit the growth of grassland vegetation. In the southern plateau with more precipitation and higher temperature compared with other regions, the correlations between alpine grassland cover and climatic factors were significant positive. The change of alpine grassland cover was not significantly relevant to climatic variables in the middle and western part of Qinghai-Tibet Plateau.
In this article, luminescence chronology of ‘Old Red Sand’(ORS) in Haitan Island are discussed in Fujian Province. Combined with the published OSL data, the development process of ORS and the relationship between the process and the change of sea-level and East Asia Monsoon(EAM) are discussed on the standard chronology. The results show that, the development age of QFS section is 110-33 ka, of which the initial development age extend to the Last Interglacial period which mainly covers Marine Isotope Stage(MIS)5c-MIS3, and there are 4 stages of rapid accumulation. We hold opinion that the conclusions of other researches, who consider ORS is the aeolian sediment since the last glacial period, should be corrected. 2) The development process of ORS in South China is closely tied with the change of regional sea-level. ORS of South China is broadly forming at the Last Interglacial period and the Last glacial period, of which high (the height of sea-level is between -70 m and -50 m) or higher(the height of sea-level is more than -50 m) sea-level is one of important premises. Synthetic impact of regional crustal uplift and sea-level change creates the diversity of sedimentary rate of ORS of South China: there is a rapid accumulation in upstate firstly at the high or higher sea-level period of the regressive process, and there is a rapid accumulation in south area firstly at the high or higher sea-level period of the process of marine transgression. At the same time, its a remarkable face that the sediment is absent at the period of Last Glacial Maximum(LGM) (the height of sea-level is under -70 m ). 3) The depth-age pattern of ORS reflects not merely the control action of solar radiation on east Asian winter monsoon but also the change of global ice volume which is the response of solar radiation. 4) The variation of ORS material source of South China are changed with glacial-interglacial cycle, and the main part of ORS is the regional response to the global climate change, especially the East Asia Winter Monsoon(EAWM), meanwhile there are secondary response process of global sea-level change and regional structure. Sedimentary rate of ORS mainly embodied the double restriction of climate and sea level changes on conveying of material source.
Land is the most basic natural resources that mankind depend on for existence and development. Land cover change is the most obvious expression of the dynamic impact of human activities on land surface system, which will have an important impact on regional ecological environment and will influence the global environment cumulatively.
Source regions of the Yangtze River (SRYA)and the Yellow River (SRYE) are important ecological protective screen in China,located in the Three-River Headwaters Region, which is known as the "Chinese water tower". In recent years, the land cover situation changed a lot due to climate change/human activities and some other factors. Based on the land cover datasets of middle and late 1970s, early 1990s, 2004 and 2012, this article analyzed the change characteristics of land cover and ecological situation and their differences in the SRYA and SRYE over the past 30 years, using the methods of transfer matrix, land cover situation index and land cover change index, and discussed the driving forces. The results showed that grassland were the main land cover types in both regions in 2012, but percentage of grassland in the SRYE was 17% more than that in the SRYA. In addition, there were glacier and desert in the SRYA, which was none in the SRYE. In the past 30 years, the land cover situation of the SRYE was better than that of the SRYA all the time. In terms of land cover change, the land cover and ecological situation of the SRYA became better, and had overall experienced a process of degeneration, slight melioration, and continuous melioration; while that of the SRYE became worse, and had overall experienced a process of degeneration, obvious degeneration and slight melioration, and the overall change in magnitude in the SRYE was more dramatic than that in the SRYA. Since there were huge glacier and permafrost in the SRYA, the warmer temperature since early 1990s made glaciers and permafrost melt, which made the water body and marsh expand and the land cover situation become better. The fact that land cover situation of the SRYE became worse in the period of 1970s and 2004 mainly because of overgrazing, warm and dry climate, while since 2004, the regional climate became warm and wet and that was helpful for natural ecosystem recovery. Meanwhile, reduction of livestock and the implementation of ecological project since 2004 also had certain positive effects on vegetation restoration in both source regions.
Analysis on the different characteristics of land cover changes in the SRYA and SRYE will enhance our understanding of ecological environmental issues and changes in the SRYA and SRYE, and provide reference for the rehabilitation and reconstruction of integrated ecosystem functions in different areas.
