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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.


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.


Tao F.,CAS Institute of Geographical Sciences and Natural Resources Research | Zhang Z.,Beijing Normal University
European Journal of Agronomy | Year: 2010

Adaptation is a key factor that will shape the future severity of climate change impacts on food production. We need to evaluate the relative potential of adaptation strategies, and to develop effective adaptation strategies to cope with climate risk. Here, we apply a super-ensemble-based probabilistic projection system (SuperEPPS) to project maize productivity and evapotranspiration (ET) over growing period during 2050s in the North China Plain, and to examine the relative contributions of adaptation options. Based on a large number of simulation outputs from the super-ensemble-based projection, our results show that without adaptation maize yield could decrease averagely by 13.2-19.1%, and ET during growing period could decrease by 15.6-21.8% during 2050s, relative to 1961-1990. In comparison with the experiment without adaptation, using high-temperature sensitive varieties, maize yield could averagely increase by 1.0-6.0%, 9.9-15.2%, and 4.1-5.6%, by adopting adaptation options of early planting, fixing variety growing duration, and late planting, respectively. ET could averagely increase by 1.9-4.4%, 1.9-3.7%, and -2.9% to -0.7%, respectively. In contrast, using high-temperature tolerant varieties, maize yield could averagely increase by -2.4% to -1.4%, 34.7-45.6%, and 5.7-6.1%, respectively. ET could averagely increase by 0.7-0.9%, 9.4-11.6%, and -0.4% to 0.2%, respectively. The spatial patterns show that the relative contributions of adaptation options can be geographically quite different, depending on the climate and variety properties. The biggest benefits will result from the development of new crop varieties that are high-temperature tolerant and have high thermal requirements. © 2010 Elsevier B.V.


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

Ensemble-based probabilistic projection is an effective approach to deal with the uncertainties in climate change impact assessments and to inform adaptations. Here, the crop model MCWLA-Wheat was firstly developed by adapting the process-based general crop model, MCWLA [Tao, F., Yokozawa, M., Zhang, Z., 2009a. Modelling the impacts of weather and climate variability on crop productivity over a large area: a new process-based model development, optimization, and uncertainties analysis. Agric. For. Meteorol. 149, 831-850], to winter wheat. Then the Bayesian probability inversion and a Markov chain Monte Carlo (MCMC) technique were applied to the MCWLA-Wheat to analyse uncertainties in parameters estimations, and to optimize parameters. Ensemble hindcasts showed that the MCWLA-Wheat could capture the interannual variability of detrended historical yield series fairly well, especially over a large area. Finally, based on the MCWLA-Wheat, a super-ensemble-based probabilistic projection system was developed and applied to project the probabilistic responses of wheat productivity and water use in the North China Plain (NCP) to future climate change. The system used 10 climate scenarios consisting of the combinations of five global climate models and two greenhouse gases emission scenarios (A1FI and B1), the corresponding atmospheric CO2 concentration range, and multiple sets of crop model parameters representing the biophysical uncertainties from crop models. The results showed that winter wheat yields in the NCP could increase with high probability in future due to climate change. During 2020s, 2050s, and 2080s, with (without) CO2 fertilization effects, relative to 1961-1990 level, simulated wheat yields would increase averagely by up to 37.7% (18.6%), 67.8% (23.1%), and 87.2% (34.4%), respectively, across 80% of the study area; simulated changes in evaportranspiration during wheat growing period would range generally from -6% to 6% (-0.6% to 10%), from -10% to 8% (-1.0% to 17%), and from -17% to 4% (7-12%), respectively, across the study area. Further analyses suggested that the improvements in heat and water resources and rising atmospheric CO2 concentration ([CO2]) could contribute notably to wheat productivity increase in future. Climate change could enhance the development and photosynthesis rate; however the duration of reproductive period could be less affected than that of vegetative period, and wheat productivity could benefit from enhanced photosynthesis due to climate change and rising [CO2]. Furthermore, wheat could become mature earlier, which could prevent it from severe high temperature stress. Our study parameterized explicitly the effects of high temperature stress on productivity, accounted for a wide range of crop cultivars with contrasting phenological and thermal characteristics, and presented new findings on the probabilistic responses of wheat productivity and water use to climate change in the NCP. © 2011 Elsevier B.V.


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.


Zhang S.,CAS Institute of Geographical Sciences and Natural Resources Research | Tao F.,CAS Institute of Geographical Sciences and Natural Resources Research
European Journal of Agronomy | Year: 2013

Crop models have been widely used in simulating and predicting changes in rice phenology in the major rice production regions of China, however the uncertainties in simulating crop phenology at a large scale and from different models were rarely investigated. In the present study, five rice phenological models/modules (i.e., CERES-Rice, ORYZA2000, RCM, Beta Model, SIMRIW) were firstly calibrated and validated based on a large number of rice phenological observations across China during 1981-2009. The inner workings of the models, as well as the simulated phenological response to climate change/variability, were compared to determine if the models adequately handled climatic changes and climatic variability. Results showed these models simulated rice phenological development over a large area fairly well after calibration, although the relative performance of the models varied in different regions. The simulated changes in rice phenology were generally consistent when temperatures were below the optimum; however varied largely when temperatures were above the optimum. The simulated rice growing season under future climate scenarios was shortened by about 0.45-5.78 days; but in northeastern China, increased temperature variability may prolong the growing season of rice. We concluded more modeling and experimental studies should be conducted to accelerate understanding of rice phenology development under extreme temperatures. © 2012 Elsevier B.V.


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.


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.


Xiao D.,CAS Institute of Geographical Sciences and Natural Resources Research | Tao F.,CAS Institute of Geographical Sciences and Natural Resources Research
European Journal of Agronomy | Year: 2014

The detailed field experiment data from 1980 to 2009 at four stations in the North China Plain (NCP), together with a crop simulation model, were used to disentangle the relative contributions of cultivars renewal, fertilization management and climate change to winter wheat yield, as well as the relative impacts of different climate variables on winter wheat yield, in the past three decades. We found that during 1980-2009 cultivars renewal contributed to yield increase by 12.2-22.6%; fertilization management contributed to yield increase by 2.1-3.6%; and climate change contributed to yield generally by -3.0-3.0%, however by -15.0% for rainfed wheat in southern part of the NCP. Modern cultivars and agronomic management played dominant roles in yield increase in the past three decades, nevertheless the estimated impacts of climate change on yield accounted for as large as -23.8-25.0% of observed yield trends. During the study period, increase in temperature increased winter wheat yield by 3.0-6.0% in northern part of the NCP, however reduced rainfed winter wheat yield by 9.0-12.0% in southern part of the NCP. Decrease in solar radiation reduced wheat yield by 3.0-12.0% across the stations. The impact of precipitation change on winter wheat yield was slight because there were no pronounced trends in precipitation. Our findings highlight that modern cultivars and agronomic management contributed dominantly to yield increase in the past three decades, nevertheless the impacts of climate change were large enough in some areas to affect a significant portion of observed yield trends in the NCP. © 2013 Elsevier B.V.


Hu M.-G.,CAS Institute of Geographical Sciences and Natural Resources Research | Wang J.-F.,CAS Institute of Geographical Sciences and Natural Resources Research
Environmental Modelling and Software | Year: 2011

The density and distribution of spatial samples heavily affect the precision and reliability of estimated population attributes. An optimization method based on Mean of Surface with Nonhomogeneity (MSN) theory has been developed into a computer package with the purpose of improving accuracy in the global estimation of some spatial properties, given a spatial sample distributed over a heterogeneous surface; and in return, for a given variance of estimation, the program can export both the optimal number of sample units needed and their appropriate distribution within a specified research area. © 2010 Elsevier Ltd.

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