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Wang C.,CAS Institute of Geographical Sciences and Natural Resources Research | Ducruet C.,French National Center for Scientific Research
Journal of Transport Geography | Year: 2012

Planned as Shanghai's new port, Yangshan is currently expanding its roles as transhipment hub and integrated logistics/industrial center in the Asia-Pacific region. This paper examines the impact of the emergence of Yangshan on the spatial pattern of the Yangtze River Delta since the 1970s, with reference to existing port system spatial evolutionary models. While this emergence confirms the trend of offshore hub development and regionalization processes observed in other regions, we also discuss noticeable deviations due to territorial and governance issues. Strong national policies favoring Shanghai's vicinity rather than Ningbo as well as the growth of Yangshan beyond sole transhipment functions all contribute to Shanghai's transformation into a global city. © 2012 Elsevier Ltd. Source


Xu J.,CAS Institute of Geographical Sciences and Natural Resources Research
Quaternary International | Year: 2011

Response of river runoff to climate change and human activity is an important issue in hydrological sciences. Considering the Wudinghe River, a tributary of the middle Yellow River, a study has been made on the influence of climate change and human activity on annual runoff. Both the measured and natural annual runoffs showed a decreasing trend, and the areas of four types of soil and water conservation measures and water diversion increased. A comparison between the "base-line" period (1956-1971) and the "measure" period indicates that, for the former period, 78.6% and 72.9% of variations in the measured annual runoff and natural runoff respectively can be explained by variation in annual precipitation; but for the latter period, they lowered to 42.5% and 50.2%, indicating that after soil and water conservation measures, the control of precipitation on runoff generation became much weaker. There were close negative correlations between annual runoff and the areas of four types of soil and water conservation measures. Step-wise multiple regression analysis has been performed, and the contributions from water diversion, maximum 30-day cumulative precipitation, maximum 1-day precipitation, annual precipitation and the weighted average of the area of soil and water conservation measures to the variation in annual runoff were calculated as 37.5%, 26.9%, 9.4%, 14.5% and 11.8% respectively. © 2010 Elsevier Ltd and INQUA. Source


Pei T.,CAS Institute of Geographical Sciences and Natural Resources Research
Mathematical Geosciences | Year: 2011

The task of discriminating between heterogeneity and complete spatial randomness (CSR) for a given point process can be divided into three subtasks: the identification of the point pattern; the determination of the sizes of clusters; and the estimation of the numbers of events in dominant clusters. Many studies have been performed regarding the first and second subtasks. However, limited work has been done on the third aspect; hence, the determination of the number of events in each dominant cluster is still an unsolved problem. In this paper, we provide a solution by constructing a new index which is defined as the ratio between the variance of the (k+1)th nearest distance and that of the kth nearest distance. Our method can be divided into two phases: the detection of point pattern and the estimation of the numbers of events in dominant clusters. These phases can be estimated by the values at which the index abruptly decreases to be less than 1. A comparative study between the existing indices and our index shows the following: (i) our index can indicate the numbers of events in dominant clusters in a relatively objective way, which is different from the K-function revealing the sizes of clustered patterns; (ii) it is a nonparametric index and is easy to implement; and (iii) it demonstrates the highest detection power for differentiating between heterogeneity and CSR. The simulations and two seismic case studies also confirmed the correctness of our method. © 2011 International Association for Mathematical Geosciences. Source


Tao F.,CAS Institute of Geographical Sciences and Natural Resources Research | Zhang Z.,Beijing Normal University
Climatic Change | Year: 2011

Projections of future climate change are plagued with uncertainties from global climate models and emission scenarios, causing difficulties for impact assessments and for planners taking decisions on adaptation measure. Here, we developed an approach to deal with the uncertainties and to project the changes of maize productivity and water use in China using a process-based crop model, against a global mean temperature (GMT) increase scale relative to 1961-1990 values. From 20 climate scenarios output from the Intergovernmental Panel on Climate Change Data Distribution Centre, we adopted the median values of projected changes in monthly mean climate variables for representative stations and driven the CERES-Maize model to simulate maize production under baseline and future climate scenarios. Adaptation options such as automatic planting, automatic application of irrigation and fertilization were considered, although cultivars were assumed constant over the baseline and future. After assessing representative stations across China, we projected changes in maize yield, growing period, evapotranspiration, and irrigation-water use for GMT changes of 1°C, 2°C, and 3°C, respectively. Results indicated that median values of projected decreases in the yields of irrigated maize without (with) consideration of CO2-fertilization effects ranged from 1.4% to 10.9% (1.6% to 7.8%), 9.8% to 21.7% (10.2% to 16.4%), and 4.3% to 32.1% (3.9% to 26.6%) for GMT changes of 1°C, 2°C, and 3°C, respectively. Median values of projected changes in irrigation-water use without (with) consideration of CO2-fertilization effects ranged from -1.3% to 2.5% (-18.8% to 0.0%), -43.6% to 2.4% (-56.1% to -18.9%), and -19.6% to 2.2% (-50.6% to -34.3%), which were ascribed to rising CO2 concentration, increased precipitation, as well as reduced growing period with GMT increasing. For rainfed maize, median values of projected changes in yields without (with) consideration of CO2-fertilization effects ranged from -22.2% to -1.0% (-10.8% to 0.7%), -27.6% to -7.9% (-18.1% to -5.6%), and -33.7% to -4.6% (-25.9% to -1.6%). Approximate comparisons showed that projected maize yield losses were larger than previous estimates, particularly for rainfed maize. Our study presents an approach to project maize productivity and water use with GMT increases using process-based crop models and multiple climate scenarios. The resultant impact function is fundamental for identifying which climate change level is dangerous for food security. © 2010 Springer Science+Business Media B.V. Source


Tao F.,CAS Institute of Geographical Sciences and Natural Resources Research | Zhang Z.,Beijing Normal University
Journal of Applied Meteorology and Climatology | Year: 2013

The impact of climate change on rice productivity in China remains highly uncertain because of uncertainties from climate change scenarios, parameterizations of biophysical processes, and extreme temperature stress in crop models. Here, the Model to Capture the Crop-Weather Relationship over a Large Area (MCWLA)-Rice crop model was developed by parameterizing the process-based general crop model MCWLAfor rice crop. Bayesian probability inversion and a Markov chain Monte Carlo technique were then applied to MCWLA-Rice to analyze uncertainties in parameter estimations and to optimize parameters. Ensemble hindcasts showed that MCWLA-Rice could capture the interannual variability of the detrended historical yield series fairly well, especially over a large area. A superensemble-based probabilistic projection system (SuperEPPS) coupled to MCWLA-Rice was developed and applied to project the probabilistic changes of rice productivity and water use in eastern China under scenarios of future climate change. Results showed that across most cells in the study region, relative to 1961-90 levels, the rice yield would change on average by 7.5%-17.5% (from 210.4% to 3.0%), 0.0%-25.0% (from 226.7% to 2.1%), and from 210.0% to 25.0% (from 239.2% to 26.4%) during the 2020s, 2050s, and 2080s, respectively, in response to climate change, with (without) consideration of CO2 fertilization effects. The rice photosynthesis rate, biomass, and yield would increase as a result of increases in mean temperature, solar radiation, and CO2 concentration, although the rice development rate could accelerate particularly after the heading stage. Meanwhile, the risk of high-temperature stress on rice productivity would also increase notably with climate change. The effects of extreme temperature stress on rice productivity were explicitly parameterized and addressed in the study. © 2013 American Meteorological Society. Source

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