Estimation of regional net primary productivity (NPP) using a process-based ecosystem model: How important is the accuracy of climate data?

2004 ◽  
Author(s):  
B MATSUSHITA
Keyword(s):  
Primary Productivity ◽  
Net Primary Productivity ◽  
Ecosystem Model ◽  
Climate Data

Estimation of regional net primary productivity (NPP) using a process-based ecosystem model: How important is the accuracy of climate data?

2004 ◽  
Vol 178 (3-4) ◽  
pp. 371-388 ◽  
Author(s):  
Bunkei Matsushita ◽  
Ming Xu ◽  
Jin Chen ◽  
Satoshi Kameyama ◽  
Masayuki Tamura
Keyword(s):  
Primary Productivity ◽  
Net Primary Productivity ◽  
Ecosystem Model ◽  
Climate Data

scholarly journals Impacts of 1.5 °C and 2 °C Global Warming on Net Primary Productivity and Carbon Balance in China’s Terrestrial Ecosystems

2020 ◽  
Vol 12 (7) ◽  
pp. 2849
Author(s):  
Li Yu ◽  
Fengxue Gu ◽  
Mei Huang ◽  
Bo Tao ◽  
Man Hao ◽  
...  
Keyword(s):  
Global Warming ◽  
Primary Productivity ◽  
Net Primary Productivity ◽  
Climate Adaptation ◽  
Coupled Model ◽  
Terrestrial Ecosystems ◽  
Northern China ◽  
Ecosystem Model ◽  
Intercomparison Project ◽  
Potential Risks

Assessing potential impacts of 1.5 °C and 2 °C global warming and identifying the risks of further 0.5 °C warming are crucial for climate adaptation and disaster risk management. Four earth system models in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and a process-based ecosystem model are used in this study to assess the impacts and potential risks of the two warming targets on the carbon cycle of China’s terrestrial ecosystems. Results show that warming generally stimulates the increase of net primary productivity (NPP) and net ecosystem productivity (NEP) under both representative concentration pathway (RCP) 4.5 and RCP8.5 scenarios. The projected increments of NPP are higher at 2 °C warming than that at 1.5 °C warming for both RCP4.5 and RCP8.5 scenarios; approximately 13% and 19% under RCP4.5, and 12.5% and 20% under RCP8.5 at 1.5 °C and 2 °C warming, respectively. However, the increasing rate of NPP was projected to decline at 2 °C warming under the RCP4.5 scenario, and the further 0.5 °C temperature rising induces the decreased NPP linear slopes in more than 81% areas of China’s ecosystems. The total NEP is projected to be increased by 53% at 1.5 °C, and by 81% at 2 °C warming. NEP was projected to increase approximately by 28% with the additional 0.5 °C warming. Furthermore, the increasing rate of NEP weakens at 2 °C warming, especially under the RCP8.5 scenario. In summary, China’s total NPP and NEP were projected to increase under both 1.5 °C and 2 °C warming scenarios, although adverse effects (i.e., the drop of NPP growth and the reduction of carbon sequestration capacity) would occur in some regions such as northern China in the process of global warming.

Influence of spring phenology on seasonal net primary productivity in the alpine grassland on the Tibetan Plateau

2020 ◽  
Author(s):  
Zhoutao Zheng ◽  
Wenquan Zhu ◽  
Yangjian Zhang ◽  
Ke Huang ◽  
Nan Cong
Keyword(s):  
Primary Productivity ◽  
Net Primary Productivity ◽  
Growing Season ◽  
Ecosystem Model ◽  
Alpine Grassland ◽  
The Tibetan Plateau ◽  
Field Observations ◽  
Vegetation Phenology ◽  
Spring Phenology ◽  
The Relationship

<p>Vegetation phenology is recognized to exert crucial influences on carbon sequestration and the role of vegetation phenology in mediating carbon cycle varies with ecosystem type. However, the relationship between vegetation phenology and productivity has not been fully understood in the alpine ecosystem due to a lack of field observations, poor model performances and their complex mechanisms. In this study, we examined the spatio-temporal variation in beginning of growing season (BGS) and net primary productivity (NPP) for the alpine grassland on the Tibetan Plateau (TP) and the regulation effects of spring phenology on seasonal NPP by integrating field observations, remote sensing monitoring and ecosystem model simulation. The ecosystem model performances were improved by optimizing ecosystem parameters from field observations. The results indicated a significant advance in BGS with a rate of 0.31 days/yr (P < 0.1) in the alpine grassland during 2001-2015 while the annual NPP increased significantly at a rate of 1.25 gC/m<sup>2</sup>/yr (P < 0.01). With regard to the relationship between BGS and NPP, large spatial heterogeneities were identified. Overall, a negative but non-significant correlation (R = -0.34, P > 0.1) was observed between BGS and annual NPP for the entire grassland ecosystem on the TP. But responses of NPP to BGS varied with seasons. Specifically, BGS showed significant negative correlation with spring NPP (R = -0.73, P < 0.01), and advanced spring led to increased spring NPP. The positive effects of advanced BGS on NPP tended to weaken in summer. Moreover, BGS was significantly and positively correlated with autumn NPP in some relatively arid zones of the southwestern TP, suggesting the suppressing effects of earlier spring on carbon assimilation during the later growing season in water limited areas. This study improved our understanding on the impacts of biotic factors on carbon cycles of the alpine ecosystem and implies that the effects of phenology can’t be concluded simply for an annual sum, and their relationships for each separate season are also critical.</p>

scholarly journals Empirical and model-based estimates of spatial and temporal variations in net primary productivity in semi-arid grasslands of Northern China

2017 ◽  
Author(s):  
Shengwei Zhang ◽  
Rui Zhang ◽  
Tingxi Liu ◽  
Xin Song ◽  
Mark A. Adams
Keyword(s):  
Primary Productivity ◽  
Net Primary Productivity ◽  
Northern China ◽  
Ecosystem Model ◽  
Temporal Variations ◽  
Temperature And Precipitation ◽  
Summer Temperatures ◽  
Arid Grasslands ◽  
Annual Scale ◽  
Pixel Scale

Abstract. Spatiotemporal variations in net primary productivity (NPP) of vegetation offer insights to surface water and carbon dynamics, and are closely related to temperature and precipitation. We employed the Carnegie-Ames-Stanford Approach ecosystem model to estimate NPP of semiarid grassland in northern China between 2001 and 2013. Model estimates were strongly linearly correlated with observed values (R2 = 0.67, RMSE = 35 g C m−2 year−1). We also quantified inter-annual changes in NPP over the 13-year study period. NPP varied between 141 and 313 g C m−2 year−1, with a mean of 240 g C m−2 year−1. NPP increased from west to east each year, and mean precipitation in each county was significantly positively correlated with NPP in annually, summer and autumn. Mean precipitation was also positively correlated with NPP in spring, but the correlation was not significant. Annual and summer temperatures were mostly negatively correlated with NPP, but temperature was positively correlated with spring and autumn NPP. Spatial correlation and partial correlation analyses at the pixel scale confirmed precipitation as a major driver of NPP. Temperature was negatively correlated with NPP in 99 % of the regions at the annual scale, but after removing the effect of precipitation, temperature was positively correlated with the NPP in 77 % of the regions.

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