Changes in the trends of vegetation net primary productivity in China between 1982 and 2015

Feng, Yuhao; Zhu, Jun; Zhao, Xia; Tang, Zhiyao; Zhu, Jieshun; Fang, Jingyun

doi:10.1088/1748-9326/ab4cd8

Cited by 41 publications

(32 citation statements)

References 49 publications

(51 reference statements)

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“…This dataset was derived from a widely used Moderate Resolution Imaging Spectroradiometer (MODIS) product, and was calculated using the C5 MOD17 algorithm with data validation from flux towers (Zhao et al 2005; Zhao & Running 2010). For comparison, we obtained a flux‐based GPP dataset (Yao et al 2018) from 1982 to 2015 estimated with the Model Tree Ensemble algorithm at 0.1° resolution and an improved NPP dataset (Feng et al 2019) from 1982 to 2015 estimated with the Carnegie‐Ames‐Stanford Approach (CASA) at 8‐km resolution. We also obtained biomass‐estimated NPP data for 1099 forest stands in China from a recent publication (Michaletz et al 2014).…”

Section: Methodsmentioning

confidence: 99%

Leaf size of woody dicots predicts ecosystem primary productivity

Reich

Schmid

et al. 2020

Ecology Letters

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A key challenge in ecology is to understand the relationships between organismal traits and ecosystem processes. Here, with a novel dataset of leaf length and width for 10 480 woody dicots in China and 2374 in North America, we show that the variation in community mean leaf size is highly correlated with the variation in climate and ecosystem primary productivity, independent of plant life form. These relationships likely reflect how natural selection modifies leaf size across varying climates in conjunction with how climate influences canopy total leaf area. We find that the leaf size-primary productivity functions based on the Chinese dataset can predict productivity in North America and vice-versa. In addition to advancing understanding of the relationship between a climate-driven trait and ecosystem functioning, our findings suggest that leaf size can also be a promising tool in palaeoecology for scaling from fossil leaves to palaeo-primary productivity of woody ecosystems.

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Section: Methodsmentioning

confidence: 99%

Leaf size of woody dicots predicts ecosystem primary productivity

Reich

Schmid

et al. 2020

Ecology Letters

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“…Normally, linear regression is used to analyze the changing trend of a time series. However, previous studies have indicated that ordinary linear regression models cannot describe long-term vegetation changes due to climate change and human activities [30][31][32]. In this study, some linear regression models were transformed into piecewise regression models according to the Akaike information criterion (AIC).…”

Section: Piecewise Regression Analysismentioning

confidence: 99%

“…Taking the annual cumulative precipitation, the annual average air temperature, the annual cumulative sunshine duration, and the proportion of vegetation as input data, the multiple linear regression model can simulate NPP change [30]. We built a multiple linear regression model like Equation (2):…”

Section: Contribution Of Variables Based On Multiple Regression Modelmentioning

confidence: 99%

Spatiotemporal Dynamics of Net Primary Productivity in China’s Urban Lands during 1982–2015

et al. 2021

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The rapid urbanization process has threatened the ecological environment. Net primary productivity (NPP) can effectively indicate vegetation growth status in an urban area. In this paper, we evaluated the change in NPP in China and China’s urban lands and assessed the impact of temperature, precipitation, the sunshine duration, and vegetation loss due to urban expansion on NPP in China’s three fast-growing urban agglomerations and their buffer zones (~5–20 km). The results indicated that the NPP in China exhibited an increasing trend. In contrast, the NPP in China’s urban lands showed a decreasing trend. However, after 1997, China’s increasing trend in NPP slowed (from 9.59 Tg C/yr to 8.71 Tg C/yr), while the decreasing trend in NPP in China’s urban lands weakened. Moreover, we found that the NPP in the Beijing–Tianjin–Hebei urban agglomeration (BTHUA), the Yangtze River Delta urban agglomeration (YRDUA), and the Pearl River Delta urban agglomeration (PRDUA) showed a decreasing trend. The NPP in the BTHUA showed an increasing trend in the buffer zones, which was positively affected by temperature and sunshine duration. Additionally, nonsignificant vegetation loss could promote the increase of NPP. In the YRDUA, the increasing temperature was the main factor that promoted the increase of NPP. The effect of temperature on NPP could almost offset the inhibition of vegetation reduction on the increase of NPP as the buffer zone expanded. In PRDUA, sunshine duration and vegetation loss were the main factors decreasing NPP. Our results will support future urban NPP prediction and government policymaking.

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“…Relatively abundant studies have focused on the impacts of temperature and precipitation changes on greening and browning using satellite measured normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI) (Sarmah et al 2018;Du et al 2019;Feng et al 2019b;Liu et al 2019;Qian et al 2019;Sun et al 2019;Wang et al 2019;Li et al 2020a;Parida et al 2020;Yuan et al 2020). Studies using other indicators of vegetation are relatively limited and some of them are mentioned here, e.g., LAI (Piao et al 2015;Chen et al 2019), foliar projective coverage (FPC, Cuo et al 2016), tree ring (Shi et al 2019), ecosystem indices (Fu et al 2019), simulated NPP (Zhuang et al 2010;Piao et al 2012;Bao et al 2019;Feng et al 2019b), modeled carbon and water use efficiency (El Masri et al 2019), and observed or surveyed phenological states and NPP (Niu et al 2019;Li et al 2020b). Few have studied the effects of cloud cover and CO 2 changes (Piao et al 2012;Cuo et al 2016) on vegetation.…”

Section: Introductionmentioning

confidence: 99%

Decadal change and inter-annual variability of net primary productivity on the Tibetan Plateau

et al. 2021

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Net primary productivity (NPP) is an important indicator of plant dynamics and the net carbon exchange between the terrestrial ecosystem and atmosphere. Both the long-term shifts in climate mean (climate change) and short-term variations around the climate mean (climate variability) have impacts on NPP but studies examining both aspects of climate variations are rare especially in the data-scarce regions such as the Tibetan Plateau (TP). Here, we used a dynamic vegetation model to investigate the impacts of the changes and variabilities in temperature, precipitation, cloud cover and CO2 on NPP on the TP. The simulated NPP was evaluated using field and Moderate-Resolution Imaging Spectroradiometer NPP and was found to be reasonable. At monthly time scale, NPP significantly correlated concurrently and at 1-month lag with temperature, precipitation and cloud cover (coefficient of determination, R2, in 0.52–0.77). Annual NPP variability was high (low) where mean annual NPP was low (high). The effects of annual precipitation, cloud cover and temperature variability on annual NPP variability were spatially heterogeneous, and temperature variability appeared to be the dominant factor (R2 of 0.74). Whereas, NPP changes were very similar to CO2 increases across the TP (spatial correlation of 0.60), indicating that long-term changes in NPP were dominated by CO2 increases. For both variability and long-term changes in NPP, temperature was the major factor of influence (highest spatial correlation of 0.67). These findings could assist in making informed mitigation policies on the impacts of climate change and variability on ecosystem and local nomadic communities.

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Changes in the trends of vegetation net primary productivity in China between 1982 and 2015

Cited by 41 publications

References 49 publications

Leaf size of woody dicots predicts ecosystem primary productivity

Leaf size of woody dicots predicts ecosystem primary productivity

Spatiotemporal Dynamics of Net Primary Productivity in China’s Urban Lands during 1982–2015

Decadal change and inter-annual variability of net primary productivity on the Tibetan Plateau

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