摘要 :
The possible response of the carbon (C) balance of China's forests to an increase in atmospheric CO_2 concentration and climate change was investigated through a series of simulations using the Integrated Terrestrial Ecosystem Car...
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The possible response of the carbon (C) balance of China's forests to an increase in atmospheric CO_2 concentration and climate change was investigated through a series of simulations using the Integrated Terrestrial Ecosystem Carbon (InTEC) model, which explicitly represents the effects of climate, CO_2 concentration, and nitrogen deposition on future C sequestration by forests. Two climate change scenarios (CGCM2-A2 and -B2) were used to drive the model. Simulations showed that China's forests were a C sink in the 1990s, averaging 189Tg Cyr~(-1) (about 13% of the global total). This sink peaks around 2020 and then gradually declines to 33.5Tg Cyr~(-1) during 2091-2100 without climate and CO_2 changes. Effects of pure climate change of CGCM2-A2 and -B2 without allowing CO_2 effects on C assimilation in plants might reduce the average net primary productivity (NPP) of China's forests by 29% and 18% during 2091-2100, respectively. Total soil C stocks might decrease by 16% and 11% during this period. China's forests might broadly act as C sources during 2091-2100, with values of about 50 g Cm~(-2) yr~(-1) under the moderate warming of CGCM2-B2 and 50-200 g Cm~(-2) yr~(-1) under the warmer scenario of CGCM2-A2. An increase in CO_2 might broadly increase future C sequestration of China's forests. However, this CO_2 fertilization effect might decline with time. The CO_2 fertilization effects on NPP by the end of this century are 349.6 and 241.7 Tg Cyr~(-1) under CGCM2-A2 and -B2 increase scenarios, respectively. These effects increase by 199.1 and 126.6 Tg Cyr~(-1) in the first 50 years, and thereafter, by 150.5 and 115.1 Tg Cyr~(-1) in the second 50 years under CGCM2-A2 and -B2 increase scenarios, respectively.
Under a CO_2 increase without climate change, the majority of China's forests would be C sinks during 2091-2100, ranging from 0 to 100 g C m~(-2) yr~(-1). The positive effect of CO_2 fertilization on NPP and net ecosystem productivity would be exceeded by the negative effect of climate change after 2050. Under the CGCM2-A2 climate scenario and with direct CO_2 effects, China's forests may be a small C source of 7.6Tg Cyr~(-1) during 2091-2100. Most forests act as C sources of 0-40 g Cm~(-2) yr~(-1). Under the CGCM2-B2 climate scenario and with direct CO_2 effects, China's forests might be a small C sink of 10.5 Tg Cyr~(-1) during 2091-2100, with C sequestration of most forests ranging from 0 to 40 g Cm~(-2) yr~(-1). Stand age structure plays a more dominant role in determining future C sequestration than CO_2 and climate change. The prediction of future C sequestration of China's forests is very sensitive to the Q~(10) value used to estimate maintenance respiration and to soil water availability and less sensitive to N deposition scenario.
The results are not yet comprehensive, as no forest disturbance data were available or predicted after 2001. However, the results indicate a range of possible responses of the C balance of China's forests to various scenarios of increase in CO_2 and climate change. These results could be useful for assessing measures to mitigate climate change through reforestation.
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This research attempts to model the complexity of planting trees to increase China's CO_2 sequestration potential by using a GIS-based integrated assessment (IA) approach. We use the IA model to assess the impact of China's Grain ...
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This research attempts to model the complexity of planting trees to increase China's CO_2 sequestration potential by using a GIS-based integrated assessment (IA) approach. We use the IA model to assess the impact of China's Grain for Green reforestation and afforestation program on farmer and state incomes as well as CO_2 sequestration in Liping County, Guizhou Province. The IA model consists of five sub-models for carbon sequestration, crop income, timber income, Grain for Green, and carbon credits. It also includes a complementary qualitative module for assessing program impacts by gender and ethnicity. Using four scenarios with various assumptions about types of trees planted, crop incomes by township, CO_2 credit prices, state subsidies, methods for estimating carbon sequestered, and harvesting of trees, we find great variation in the impact of the Grain for Green program on incomes and on carbon sequestered over a 48 year period at both the county and township levels.
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This paper reports the annual carbon (C) balance of China's forests during 1901-2001 estimated using the Integrated Terrestrial Ecosystem C-budget model (InTEC). Annual carbon source and sink distributions are simulated for the sa...
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This paper reports the annual carbon (C) balance of China's forests during 1901-2001 estimated using the Integrated Terrestrial Ecosystem C-budget model (InTEC). Annual carbon source and sink distributions are simulated for the same period using various spatial datasets including land cover and leaf area index (LAI) obtained from remote sensing, soil texture, climate, forest age, and nitrogen deposition. During 1901-1949, China's forests were a source of 21.0 ± 7.8TgCyr~(-1) due to disturbances (human activities). Its size increased to 122.3 ± 25.3 Tg Cyr~(-1) during 1950-1987 due to intensified human activities in the late 1950s, early 1960s, 1970s and early 1980s. The forests became large sinks of 176.7 ± 44.8 Tg Cyr~(-1) during 1988-2001, owing to large-scale plantation and forest regrowth in previously disturbed areas as well as growth stimulation by nondisturbance factors such as climatic warming, atmospheric CO_2 fertilization, and N deposition. From 1901 to 2001, China's forests were a small carbon source of 3.32 PgC, about 32.9 ± 22.3 Tg Cyr~(-1). The overall C balance in biomass from InTEC generally agrees with previous results derived from forest inventories of China's forests. InTEC results also include C stock variation in soils and are therefore more comprehensive than previous results. The uncertainty in InTEC results is still large, but it can be reduced if a detailed forest age map becomes available.
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Local topography significantly affects spatial variations of climatic variables and soil water movement in complex terrain. Therefore, the distribution and productivity of ecosystems are closely linked to topography. Using a coupl...
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Local topography significantly affects spatial variations of climatic variables and soil water movement in complex terrain. Therefore, the distribution and productivity of ecosystems are closely linked to topography. Using a coupled terrestrial carbon and hydrological model (BEPS-TerrainLab model), the topographic effects on the net primary productivity (NPP) are analyzed through four modelling experiments for a 5700 km area in Baohe River basin, Shaanxi Province, northwest of China. The model was able to capture 81% of the variability in NPP estimated from tree rings, with a mean relative error of 3.1%. The average NPP in 2003 for the study area was 741 gCm~(-2) yr~(-1) from a model run including topographic effects on the distributions of climate variables and lateral flow of ground water. Topography has considerable effect on NPP, which peaks near 1350 m above the sea level. An elevation increase of 100 m above this level reduces the average annual NPP by about 25 g C m~(-2). The terrain aspect gives rise to a NPP change of 5% for forests located below 1900 m as a result of its influence on incident solar radiation. For the whole study area, a simulation totally excluding topographic effects on the distributions of climatic variables and ground water movement overestimated the average NPP by 5%.
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The terrestrial carbon cycle is one of the foci in global climate change research. Simulating net primary productivity (NPP) of terrestrial ecosystems is important for carbon cycle research. In this study, China's terrestrial NPP ...
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The terrestrial carbon cycle is one of the foci in global climate change research. Simulating net primary productivity (NPP) of terrestrial ecosystems is important for carbon cycle research. In this study, China's terrestrial NPP was simulated using the Boreal Ecosystem Productivity Simulator (BEPS), a carbon-water coupled process model based on remote sensing inputs. For these purposes, a national-wide database (including leaf area index, land cover, meteorology, vegetation and soil) at a 1 km resolution and a validation database were established. Using these databases and BEPS, daily maps of NPP for the entire China's landmass in 2001 were produced, and gross primary productivity (GPP) and autotrophic respiration (RA) were estimated. Using the simulated results, we explore temporal-spatial patterns of China's terrestrial NPP and the mechanisms of its responses to various environmental factors. The total NPP and mean NPP of China's landmass were 2.235GtC and 235.2gCm~(-2) yr~(-1), respectively; the total GPP and mean GPP were 4.418 GtC and 465 gC m~(-2) yr~(-1); and the total RA and mean RA were 2.227 GtC and 234 gC m~(-2) yr~(-1), respectively. On average, NPP was 50.6% of GPP. In addition, statistical analysis of NPP of different land cover types was conducted, and spatiotemporal patterns of NPP were investigated. The response of NPP to changes in some key factors such as LAI, precipitation, temperature, solar radiation, VPD and AWC are evaluated and discussed.
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We modeled net primary productivity (NPP) at high spatial resolution using an advanced spaceborne thermal emission and reflection radiometer (ASTER) image of a Qilian Mountain study area using the boreal ecosystem productivity sim...
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We modeled net primary productivity (NPP) at high spatial resolution using an advanced spaceborne thermal emission and reflection radiometer (ASTER) image of a Qilian Mountain study area using the boreal ecosystem productivity simulator (BEPS). Two key driving variables of the model, leaf area index (LAI) and land cover type, were derived from ASTER and moderate resolution imaging spectroradiometer (MODIS) data. Other spatially explicit inputs included daily meteorological data (radiation, precipitation, temperature, humidity), available soil water holding capacity (AWC), and forest biomass. NPP was estimated for coniferous forests and other land cover types in the study area. The result showed that NPP of coniferous forests in the study area was about 4.4tC ha~(-1) y~(-1). The correlation coefficient between the modeled NPP and ground measurements was 0.84, with a mean relative error of about 13.9%.
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Leaf area index (LAI) is an important ecological and environmental parameter. A new LAI algorithm is developed using the principles of ground LAI measurements based on canopy gap fraction. First, the relationship between LAI and g...
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Leaf area index (LAI) is an important ecological and environmental parameter. A new LAI algorithm is developed using the principles of ground LAI measurements based on canopy gap fraction. First, the relationship between LAI and gap fraction at various zenith angles is derived from the definition of LAI. Then, the directional gap fraction is acquired from a remote sensing bidirectional reflectance distribution function (BRDF) product. This acquisition is obtained by using a kernel driven model and a large-scale directional gap fraction algorithm. The algorithm has been applied to estimate a LAI distribution in China in mid-July 2002. The ground data acquired from two field experiments in Changbai Mountain and Qilian Mountain were used to validate the algorithm. To resolve the scale discrepancy between high resolution ground observations and low resolution remote sensing data, two TM images with a resolution approaching the size of ground plots were used to relate the coarse resolution LAI map to ground measurements. First, an empirical relationship between the measured LAI and a vegetation index was established. Next, a high resolution LAI map was generated using the relationship. The LAI value of a low resolution pixel was calculated from the area-weighted sum of high resolution LAIs composing the low resolution pixel. The results of this comparison showed that the inversion algorithm has an accuracy of 82%. Factors that may influence the accuracy are also discussed in this paper.
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Cushioning characteristics are essential for a substrate material to prevent serious damage on impact. The energy that can cause fracture propagation must be dissipated and transmitted. This study designed a sandwich structure tha...
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Cushioning characteristics are essential for a substrate material to prevent serious damage on impact. The energy that can cause fracture propagation must be dissipated and transmitted. This study designed a sandwich structure that was made of nonwoven fabrics and laid filaments to dissipate and transmit the energy under impact. The compound nonwoven fabric was placed at the multi-layer Kevlar fabrics to form the Kevlar complex fabric. The cushion effect was evaluated by the impact energy measured in a drop-weight impact test. This work changed the content of low melt-temperature polyester staple fiber, content of polyester filament per unit area and needle-punched density to discuss the influences of these parameters on the fracture propagation energy for multi-layer Kevlar complex fabrics. The cushion effect was evaluated by the deformation of the oil clay after bullet-shooting test. The results demonstrate that the compound nonwoven fabric exerts a more powerful buffer effect under suitable process conditions than the original plain weave polyamide fabric.
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Urbanization is one of the most important aspects of global change. The process of urbanization has a significant impact on the terrestrial ecosystem carbon cycle. The Yangtze Delta region has one of the highest rates of urbanizat...
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Urbanization is one of the most important aspects of global change. The process of urbanization has a significant impact on the terrestrial ecosystem carbon cycle. The Yangtze Delta region has one of the highest rates of urbanization in China. In this study, carried out in Jiangyin County as a representative region within the Yangtze Delta, land use and land cover changes were estimated using Landsat TM and ETM + imagery. With these satellite data and the BEPS process model (Boreal Ecosystem Productivity Simulator), the impacts of urbanization on regional net primary productivity (NPP) and annual net primary production were assessed for 1991 and 2002. Landsat-based land cover maps in 1991 and 2002 showed that urban development encroached large areas of cropland and forest. Expansion of residential areas and reduction of vegetated areas were the major forms of land transformation in Jiangyin County during this period. Mean NPP of the total area decreased from 818 to 699gCm~(-2) yr~(-1) during the period of 1991 to 2002. NPP of cropland was only reduced by 2.7% while forest NPP was reduced by 9.3%. Regional annual primary production decreased from 808 GgC in 1991 to 691 Gg C in 2002, a reduction of 14.5%. Land cover changes reduced regional NPP directly, and the increasing intensity and frequency of human-induced disturbance in the urbanized areas could be the main reason for the decrease in forest NPP.
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We developed algorithms for spatial scaling of leaf area index (LAI) using sub-pixel information. The study area is located near Liping County, Guizhou Province, in China. Methods for LAI spatial scaling were investigated on LAI i...
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We developed algorithms for spatial scaling of leaf area index (LAI) using sub-pixel information. The study area is located near Liping County, Guizhou Province, in China. Methods for LAI spatial scaling were investigated on LAI images with 960 m resolution derived in two ways. LAI from distributed calculation (LAID) was derived using Landsat ETM+ data (30 m), and LAI from lumped calculation (LAIL) was obtained from the coarse (960 m) resolution data derived through resampling the ETM + data. We found that lumped calculations can be considerably biased compared to the distributed (ETM + ) case, suggesting that global and regional LAI maps can be biased if surface heterogeneity within the mapping resolution is ignored. Based on these results, we developed algorithms for removing the biases in lumped LAI maps using sub-pixel land cover-type information, and applied these to correct one coarse resolution LAI product which greatly improved its accuracy.
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