scholarly journals Research Advances in Net Primary Productivity of Terrestrial Ecosystem

2020 ◽  
Vol 08 (08) ◽  
pp. 48-54
Author(s):  
Yixin Xu ◽  
Xiaoling Hu ◽  
Zhao Liu ◽  
Huayong Zhang
Keyword(s):  
Primary Productivity ◽  
Net Primary Productivity ◽  
Terrestrial Ecosystem

Modeling net primary productivity of the terrestrial ecosystem in China from 1961 to 2005

2013 ◽  
Vol 24 (1) ◽  
pp. 3-17 ◽  
Author(s):  
Quanzhi Yuan ◽  
Shaohong Wu ◽  
Dongsheng Zhao ◽  
Erfu Dai ◽  
Li Chen ◽  
...  
Keyword(s):  
Primary Productivity ◽  
Net Primary Productivity ◽  
Terrestrial Ecosystem

scholarly journals Roles of Climate Change and Increasing CO2 in Driving Changes of Net Primary Productivity in China Simulated Using a Dynamic Global Vegetation Model

2019 ◽  
Vol 11 (15) ◽  
pp. 4176 ◽  
Author(s):  
Qing Huang ◽  
Weimin Ju ◽  
Fangyi Zhang ◽  
Qian Zhang
Keyword(s):  
Climate Change ◽  
Interannual Variability ◽  
Primary Productivity ◽  
Southwest China ◽  
Net Primary Productivity ◽  
Terrestrial Ecosystem ◽  
Co2 Concentration ◽  
Series Data ◽  
Geographical Regions ◽  
The Future

Net primary productivity (NPP) is the key component of the terrestrial carbon cycle, and terrestrial NPP trends under increasing CO2 and climate change in the past and future are of great significance in the study of the global carbon budget. Here, the LPJ-DGVM was employed to simulate the magnitude and pattern of China’s terrestrial NPP using long-term series data to understand the response of terrestrial NPP to increasing CO2 concentration and climate change. The results showed that total NPP of China’s terrestrial ecosystem increased from 2.8 to 3.6 Pg C yr−1 over the period of 1961–2016, with an annual average of 3.1 Pg C yr−1. The average NPP showed a gradient decrease from the southeast to northwest. Southwest China and Northwest China, comprising mostly arid and semi-arid regions, exhibited the largest increase rate in total NPP among the six geographical regions of China. Additionally, large interannual variability around the NPP trends was presented, and NPP anomalies in China’s terrestrial ecosystem are strongly associated with the El Niño-Southern Oscillation (ENSO). Southwest China made the largest contribution to the interannual variability of national total NPP. The total NPP of China’s terrestrial ecosystem continuously increased with the concurrent increase in the CO2 concentration and climate change under different scenarios in the future. During the period from 2091 to 2100, the average total NPP under the A2 and RCP85 scenarios would reach 4.9 and 5.1 Pg C yr−1 respectively, higher than 4.2 and 3.9 Pg C yr−1 under the B1 and RCP45 scenarios. Forests, especially temperate forests, make the largest contribution to the future increase in NPP. The increase in CO2 concentration would play a dominant role in driving further NPP increase in China’s terrestrial ecosystems, and climate change may slightly attenuate the fertilization effect of CO2 on NPP.

scholarly journals GRASSLAND NPP MONITORING BASED ON MULTI-SOURCE REMOTE SENSING DATA FUSION

2018 ◽  
Vol XLII-3 ◽  
pp. 113-118
Author(s):  
Y. R. Cai ◽  
J. H. Zheng ◽  
M. J. Du ◽  
C. Mu ◽  
J. Peng
Keyword(s):  
Carbon Cycle ◽  
Primary Productivity ◽  
Vegetation Index ◽  
Net Primary Productivity ◽  
Terrestrial Ecosystem ◽  
Remote Sensing Data ◽  
Terrestrial Ecosystems ◽  
The United States ◽  
Estimation Accuracy ◽  
Modis Ndvi

Vegetation is an important part of the terrestrial ecosystem. It plays an important role in the energy and material exchange of the ground-atmosphere system and is a key part of the global carbon cycle process.Climate change has an important influence on the carbon cycle of terrestrial ecosystems. Net Primary Productivity (Net Primary Productivity)is an important parameter for evaluating global terrestrial ecosystems. For the Xinjiang region, the study of grassland NPP has gradually become a hot issue in the ecological environment.Increasing the estimation accuracy of NPP is of great significance to the development of the ecosystem in Xinjiang. Based on the third-generation GIMMS AVHRR NDVI global vegetation dataset and the MODIS NDVI (MOD13A3) collected each month by the United States Atmospheric and Oceanic Administration (NOAA),combining the advantages of different remotely sensed datasets, this paper obtained the maximum synthesis fusion for New normalized vegetation index (NDVI) time series in 2006–2015.Analysis of Net Primary Productivity of Grassland Vegetation in Xinjiang Using Improved CASA Model The method described in this article proves the feasibility of applying data processing, and the accuracy of the NPP calculation using the fusion processed NDVI has been greatly improved. The results show that: (1) The NPP calculated from the new normalized vegetation index (NDVI) obtained from the fusion of GIMMS AVHRR NDVI and MODIS NDVI is significantly higher than the NPP calculated from these two raw data; (2) The grassland NPP in Xinjiang Interannual changes show an overall increase trend; interannual changes in NPP have a certain relationship with precipitation.

scholarly journals Spatiotemporal Dynamics of Vegetation Net Primary Productivity and Its Response to Climate Change in Inner Mongolia from 2002 to 2019

2021 ◽  
Vol 13 (23) ◽  
pp. 13310
Author(s):  
Lei Hao ◽  
Shan Wang ◽  
Xiuping Cui ◽  
Yongguang Zhai
Keyword(s):  
Climate Change ◽  
Solar Radiation ◽  
Primary Productivity ◽  
Inner Mongolia ◽  
Net Primary Productivity ◽  
Terrestrial Ecosystem ◽  
Climate Factors ◽  
Vegetation Types ◽  
Casa Model ◽  
Close Relationship

Understanding vegetation dynamics and their responses to climate change are essential to enhance the carbon sequestration of the terrestrial ecosystem under global warming. Although some studies have identified that there is a close relationship between vegetation net primary productivity and climate change, it is unclear whether this response exists in ecologically fragile areas, especially in Inner Mongolia, in which multiple ecological ecotones are related to vegetation types. This study uses the Carnegie–Ames–Stanford Approach (CASA) model to estimate vegetation NPP in Inner Mongolia from 2002 to 2019 and focuses on the spatial and temporal changes of NPP of different vegetation types and their responses to three typical climate factors: precipitation, temperature, and solar radiation. The results show that the NPP estimated by the CASA model agrees well with the observed NPP (R2 = 0.66, p < 0.001). The vegetation NPP in Inner Mongolia decreases gradually from northeast to southwest, and the average NPP is 223.50 gC ∙ m−2. From 2002 to 2019, the NPP of all vegetation types trended upward, but exhibiting different rates. The vegetation types, ranked in order of decreasing NPP, are forest, cropland, grassland, and desert. The NPP response of different vegetation types to climate factors possesses significant differences. The cropland NPP and grassland NPP are mainly affected by precipitation, the desert NPP is controlled by both precipitation and solar radiation, and the forest NPP is determined by all three climate factors.

A CLASSIFICATION INDICES-BASED MODEL FOR NET PRIMARY PRODUCTIVITY (NPP) AND POTENTIAL PRODUCTIVITY OF VEGETATION IN CHINA

2012 ◽  
Vol 05 (03) ◽  
pp. 1260009 ◽  
Author(s):  
HUILONG LIN ◽  
JUN ZHAO ◽  
TIANGANG LIANG ◽  
JAN BOGAERT ◽  
ZHENQING LI
Keyword(s):  
Primary Productivity ◽  
Large Scale ◽  
Net Primary Productivity ◽  
Global Climate ◽  
Terrestrial Ecosystem ◽  
Terrestrial Vegetation ◽  
Moisture Index ◽  
Potential Productivity ◽  
Climate Conditions ◽  
Factors Affecting

Net Primary Productivity (NPP) is an important parameter, which is closely connected with global climate change, the global carbon balance and cycle. The study of climate-vegetation interaction is the basis for research on the responses of terrestrial ecosystem to global change and mainly comprises two important components: climate vegetation classification and the NPP of the natural vegetation. Comparing NPP estimated from the classification indices-based model with NPP derived from measurements at 3767 sites in China indicated that the classification indices-based model was capable of estimating large scale NPP. Annual cumulative temperature above 0°C and a moisture index, two main factors affecting NPP, were spatially plotted with the ArcGIS grid tool based on measured data in 2348 meteorological stations from 1961 to 2006. The distribution of NPP for potential vegetation classes under present climate conditions was simulated by the classification indices-based model. The model estimated the total NPP of potential terrestrial vegetation of China to fluctuate between 1.93 and 4.54 Pg C year-1. It provides a reliable means for scaling-up from site to regional scales, and the findings could potentially favor China's position in reducing global warming gases as outlined in the Kyoto Protocol in order to fulfill China's commitment of reducing greenhouse gases.

Climate-driven uncertainties in modeling terrestrial ecosystem net primary productivity in China

2017 ◽  
Vol 246 ◽  
pp. 123-132 ◽  
Author(s):  
Fengxue Gu ◽  
Yuandong Zhang ◽  
Mei Huang ◽  
Bo Tao ◽  
Zhengjia Liu ◽  
...  
Keyword(s):  
Primary Productivity ◽  
Net Primary Productivity ◽  
Terrestrial Ecosystem

The change of global terrestrial ecosystem net primary productivity (NPP) and its response to climate change in CMIP5

2014 ◽  
Vol 121 (1-2) ◽  
pp. 319-335 ◽  
Author(s):  
Suosuo Li ◽  
Shihua Lü ◽  
Yongjun Zhang ◽  
Yuanpu Liu ◽  
Yanhong Gao ◽  
...  
Keyword(s):  
Climate Change ◽  
Primary Productivity ◽  
Net Primary Productivity ◽  
Terrestrial Ecosystem

scholarly journals The allocation of ecosystem net primary productivity in tropical forests

2011 ◽  
Vol 366 (1582) ◽  
pp. 3225-3245 ◽  
Author(s):  
Yadvinder Malhi ◽  
Christopher Doughty ◽  
David Galbraith
Keyword(s):  
Primary Productivity ◽  
Fine Roots ◽  
Net Primary Productivity ◽  
Fine Root ◽  
Terrestrial Ecosystem ◽  
Ecosystem Models ◽  
Woody Tissue ◽  
Trade Off ◽  
The Mean ◽  
Main Components

The allocation of the net primary productivity (NPP) of an ecosystem between canopy, woody tissue and fine roots is an important descriptor of the functioning of that ecosystem, and an important feature to correctly represent in terrestrial ecosystem models. Here, we collate and analyse a global dataset of NPP allocation in tropical forests, and compare this with the representation of NPP allocation in 13 terrestrial ecosystem models. On average, the data suggest an equal partitioning of allocation between all three main components (mean 34 ± 6% canopy, 39 ± 10% wood, 27 ± 11% fine roots), but there is substantial site-to-site variation in allocation to woody tissue versus allocation to fine roots. Allocation to canopy (leaves, flowers and fruit) shows much less variance. The mean allocation of the ecosystem models is close to the mean of the data, but the spread is much greater, with several models reporting allocation partitioning outside of the spread of the data. Where all main components of NPP cannot be measured, litterfall is a good predictor of overall NPP ( r 2 = 0.83 for linear fit forced through origin), stem growth is a moderate predictor and fine root production a poor predictor. Across sites the major component of variation of allocation is a shifting allocation between wood and fine roots, with allocation to the canopy being a relatively invariant component of total NPP. This suggests the dominant allocation trade-off is a ‘fine root versus wood’ trade-off, as opposed to the expected ‘root–shoot’ trade-off; such a trade-off has recently been posited on theoretical grounds for old-growth forest stands. We conclude by discussing the systematic biases in estimates of allocation introduced by missing NPP components, including herbivory, large leaf litter and root exudates production. These biases have a moderate effect on overall carbon allocation estimates, but are smaller than the observed range in allocation values across sites.

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