Shi Pei-jun

Climate change regionalization in China (1961-2010)

Since climatic condition is the important foundation for human subsistence and development and the key factor in sustainable development of economy and society, climate change has been a global issue attracting great attentions of politicians, scientists, governments, and the public alike throughout the world. Existing climate regionalization in China aims to characterize the regional differences in climate based on years of the mean value of different climate indexes. However, with the accelerating climate change nowadays, existing climate regionalization cannot represent the regional difference of climate change, nor can it reflect the disasters and environmental risks incurred from climate changes. This paper utilizes the tendency value and fluctuation value of temperature and precipitation from 1961 to 2010 to identify the climate change quantitatively, and completes the climate change regionalization in China (1961–2010) with county administrative regionalization as the unit in combination with China’s terrain feature. Level-I regionalization divides China’s climate change (1961–2010) into five tendency zones based on the tendency of temperature and precipitation, which are respectively Northeast China-North China warm-dry trend zone, East China-Central China wet-warm trend zone, Southwest China-South China dry-warm trend zone, Southeast Tibet-Southwest China wet-warm trend zone, and Northwest China-Qinghai-Tibet Plateau warm-wet trend zone; level-II regionalization refers to fourteen fluctuation regions based on level-I regionalization according to the fluctuation of temperature and precipitation.

A combined approach for estimating vegetation cover in urban/suburban environments from remotely sensed data

The spatio-temporal distribution of vegetation is an important component of the urban/suburban environment. Therefore, correct estimation of vegetation cover in urban/suburban areas is fundamental in land use studies. In this study, the potential of extracting fractional vegetation cover (FVC) from remotely sensed data and ground measurements is explored. Based on the assumption that pixel has a mosaic structure, sub-pixel models for FVC estimation are first introduced. Then a combined approach of using different sub-pixel models for FVC estimation based on land cover classification is proposed. The experimental result, derived from a case study in Haidian district, Beijing, indicates that the accuracy of FVC estimation using the proposed method can be up to 80.7%. The results suggest that this method may be generally useful for FVC estimation in urban and suburban areas. r 2005 Elsevier Ltd. All rights reserved.

Estimation of daily evapotranspiration using a two‐layer remote sensing model

A two‐layer remote sensing model for estimating daily evapotranspiration over a large area was developed. The model followed an energy balance approach, where evapotranspiration is estimated as a residual when net radiation, sensible heat flux and ground flux are known. The accuracy of the model outputs was determined using harmonized surface conditions derived from measurements from ground stations. An accurate agreement (r 2 = 0.844, n = 48) between estimates and ground‐based measurement was obtained.

Modeling the impacts of drying trend scenarios on land systems in northern China using an integrated SD and CA model

Climate-induced drought has exerted obvious impacts on land systems in northern China. Although recent reports by the Intergovernmental Panel on Climate Change (IPCC) have suggested a high possibility of climate-induced drought in northern China, the potential impacts of such drying trends on land systems are still unclear. Land use models are powerful tools for assessing the impacts of future climate change. In this study, we first developed a land use scenario dynamic model (iLUSD) by integrating system dynamics and cellular automata. Then, we designed three drying trend scenarios (reversed drying trend, gradual drying trend, and acceleration of drying trend) for the next 25 years based on the IPCC emission scenarios and considering regional climatic predictions in northern China. Finally, the impacts of drying trend scenarios on the land system were simulated and compared. An accuracy assessment with historic data covering 2000 to 2005 indicated that the developed model is competent and reliable for understanding complex changes in the land use system. The results showed that water resources varied from 441.64 to 330.71 billion m3 among different drying trend scenarios, suggesting that future drying trends will have a significant influence on water resource and socioeconomic development. Under the pressures of climate change, water scarcity, and socioeconomic development, the ecotone (i.e., transition zone between cropping area and nomadic area) in northern China will become increasingly vulnerable and hotspots for land-use change. Urban land and grassland would have the most prominent response to the drying trends. Urban land will expand around major metropolitan areas and the conflict between urban and cultivated land will become more severe. The results also show that previous ecological control measures adopted by the government in these areas will play an important role in rehabilitating the environment. In order to achieve a sustainable development in northern China, issues need to be addressed such as how to arrange land use structure and patterns rationally, and how to adapt to the pressures of climate change and socioeconomic development together.

Correlation analysis of Normalized Different Vegetation Index (NDVI) difference series and climate variables in the Xilingole steppe, China from 1983 to 1999

There is a crucial need in the study of global change to understand how terrestrial ecosystems respond to the climate system. It has been demonstrated by many researches that Normalized Different Vegetation Index (NDVI) time series from remotely sensed data, which provide effective information of vegetation conditions on a large scale with highly temporal resolution, have a good relation with meteorological factors. However, few of these studies have taken the cumulative property of NDVI time series into account. In this study, NDVI difference series were proposed to replace the original NDVI time series with NDVI difference series to reappraise the relationship between NDVI and meteorological factors. As a proxy of the vegetation growing process, NDVI difference represents net primary productivity of vegetation at a certain time interval under an environment controlled by certain climatic conditions and other factors. This data replacement is helpful to eliminate the cumulative effect that exist in original NDVI time series, and thus is more appropriate to understand how climate system affects vegetation growth in a short time scale. By using the correlation analysis method, we studied the relationship between NOAA/AVHRR ten-day NDVI difference series and corresponding meteorological data from 1983 to 1999 from 11 meteorological stations located in the Xilingole steppe in Inner Mongolia. The results show that: (1) meteorological factors are found to be more significantly correlation with NDVI difference at the biomass-rising phase than that at the falling phase; (2) the relationship between NDVI difference and climate variables varies with vegetation types and vegetation communities. In a typical steppe dominated by Leymus chinensis, temperature has higher correlation with NDVI difference than precipitation does, and in a typical steppe dominated by Stipa krylovii, the correlation between temperature and NDVI difference is lower than that between precipitation and NDVI difference. In a typical steppe dominated by Stipa grandis, there is no significant difference between the two correlations. Precipitation is the key factor influencing vegetation growth in a desert steppe, and temperature has poor correlation with NDVI difference; (3) the response of NDVI difference to precipitation is fast and almost simultaneous both in a typical steppe and desert steppe, however, mean temperature exhibits a time-lag effect especially in the desert steppe and some typical steppe dominated by Stipa krylovii; (4) the relationship between NDVI difference and temperature is becoming stronger with global warming.

Estimation of regional evapotranspiration over Northwest China using remote sensing

It is a very complicated problem to estimate evapotranspiration (ET) over a large area of land surface. In this paper, the evapotranspiration estimation models for dense vegetation and bare soil are presented, based on the information of parameters like vegetation cover-degree and surface albedo. Combined with vegetation cover-degree data, a model for regional evapotranspiration estimation over the heterogeneous landscape is derived. Through a case study using remote sensing data over Northwest China, the accuracy of the model for regional evapotranspiration estimation is checked. The result shows that the accuracy of the model is satisfactory. The features of evapotranspiration over Northwest China are also discussed with the application of the model.

Assessment on typical drought risk for wheat production in China based on natural vulnerability.

Food security is the base of human survival and national stability.,of which food supply-demand balance variation and its induced food price fluctuation are the two major concerns.Global food production has been kept on increase since 2000,though in a slow pace,which should be able to meet the basic diet requirement.According to the 4th IPCC report,however,from the end of 2007,the worldwide food supply problem has been paid great attention to due to a sky rocket of food price.The earth is getting warmer and the frequency of drought is increased. Consequently,the intensification of water stress to crops,resulted from both the long term climate change and increase of extreme weather events,may lead to the reduction of crop yield and the increase of drought disasters. To understand the mechanism of crop drought disaster process is the key to the understanding of food security and hence a possible solution to the problem.In the past researches,the concepts of physical vulnerability and social vulnerability are not always yet explicitly distinguished or separated regarding to agricultural drought disaster risk assessment,which restricts the understanding of process-based mechanism of disaster risk dramatically.The major focuses of studies on drought disaster risk assessment range from statistical analysis and probability analysis to prediction of drought hazard or disaster losses,whilst few attached importance to the quantitative separation of physical and social vulnerabilities. The goals of this paper are to explore and quantify the physical vulnerability and drought hazard and risk.The detailed process is as follows:Firstly,agricultural drought disaster risk assessment model is developed based on the physical vulnerability of crops.This model covers agricultural drought hazard possibility distribution estimation, physical vulnerability curve estimation,land use-based exposure assessment,coping capacity integration and risk assessment.Secondly,a case study on two typical wheat species in China is carried out,and a quantitative physical vulnerability to agricultural drought hazards is analyzed using a physical-process based crop growth model and Erosion Productivity Impact Calculator(EPIC).At the beginning,the one-dimension(point scale) crop growth model EPIC with a capability of depicting water stress and temperature stress is extended to a two-dimension(area scale) model,and the Spatial Erosion Productivity Impact Calculator(S-EPIC) can be used to simulate crop yield under different intensity scenarios of drought.Two extreme scenarios for crop growth and yield simulation were investigated. Finally,the maps of drought risk occurring every 2,5,10 and 20 years were charted separately based on the calculated probable distribution of drought hazard and the vulnerability curve.The results reveal that the wheat yield loss caused by drought is decreased from the northwestern part to the southeastern part of China.

Understanding cultivated land dynamics and its driving forces in northern China during 1983-2001

Based on the long-term serial NOAA/NDVI dataset during 1983–1999 and SPOT/VGT dataset in 2001, the land use/cover change information in the 13 provinces of northern China was extracted based on the analysis of the cultivated landscape characteristics at first, then the effects of human activities on cultivated land process were explored by GIS and the driving forces of cultivated land change were investigated. The conclusions can be drawn as follows: (1) The constant increase of weak ecological function land as desert and cultivated land and the decrease of the ecological function land of forest and shrub were the main characteristics of the land use/cover change in the 13 provinces from 1983 to 1999, which showed the effects on the ecological adjustment function. However, such situations were changed to some extent in the 2000s because of the eco-construction policy of the government. (2) From 1983 to 2001, the Barycenter of cultivated land tended to move from northeast to southwest with the topography and transportation situations being the main influences on the cultivated land distribution. It is found that the cultivated land use intensity decreased noticably with the increase of distance from the main communication arteries. (3) The improvement of the people’s living standard is closely related with the cultivated land change. The structural adjustment in the agricultural land caused by economic development and the improvement of the people’s living standard is an important factor affecting the cultivated land change in northern China from 1983 to 2001.

Mapping and ranking global mortality, affected population and GDP loss risks for multiple climatic hazards

Coping with extreme climate events and its related climatic disasters caused by climate change has become a global issue and drew wide attention from scientists, policy-makers and public. This paper calculated the expected annual multiple climatic hazards intensity index based on the results of nine climatic hazards including tropical cyclone, flood, landslide, storm surge, sand-dust storm, drought, heat wave, cold wave and wildfire. Then a vulnerability model involving the coping capacity indicator with mortality rate, affected population rate and GDP loss rate, was developed to estimate the expected annual affected population, mortality and GDP loss risks. The results showed that: countries with the highest risks are also the countries with large population or GDP. To substantially reduce the global total climatic hazards risks, these countries should reduce the exposure and improving the governance of integrated climatic risk; Without considering the total exposure, countries with the high mortality rate, affected population rate or GDP loss rate, which also have higher or lower coping capacity, such as the Philippines, Bangladesh and Vietnam, are the hotspots of the planning and strategy making for the climatic disaster risk reduction and should focus on promoting the coping capacity.

The change of NDVI time series based on change vector analysis in China

Change vector analysis (CVA) in NDVI time-trajectories space is a powerful tool to analyze land-cover change. The magnitude of the change vector indicates the amplitude of the change, while its direction indicates the nature of the change. CVA is applied to two remotely sensed indicators of land surface conditions, NDVI and spatial structure, in order to improve the capability to detect and categorize land-cover change. The magnitude and type of changes are calculated in China from 1989 to 1999. Through the research, the main conclusions are: 1) the change of NDVI is very different between east China and west China, and change scope in the east is bigger than in the west. The trend in NDVI time series is a smooth increase, the increases happened mostly in Taiwan, Fujian, Sichuan, Henan province and the decreases are located in Yunnan, Xinjiang province. 2) The spatial structure indicator is able to detect changes in the seasonal ecosystem dynamic for spatially heterogeneous landscapes. Most of spatial structure changes, which occurred in South China, correlated with vegetation growth processes and the directions of strike of the mountains.