Responses of Terrestrial Ecosystems’ Net Primary Productivity to Future Regional Climate Change in China

Zhao, Dongsheng; Wu, Shaohong; Yin, Yongguang

doi:10.1371/journal.pone.0060849

Cited by 27 publications

(28 citation statements)

References 30 publications

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“…Model input includes monthly mean air temperature, total precipitation, number of wet days and percentage sunshine hours (or surface radiation), annual CO 2 concentration, and soil texture class. In our previous studies, the LPJ model was modified by adjusting the bioclimatic limits of plant function type, adopting a Penman‐Monteith method to calculate potential evapotranspiration, and defining two novel PFTs to ensure the model is applicable to the particular alpine environment of the QTP (Zhao, Wu, Yin, & Yin, , ).…”

Section: Methodsmentioning

confidence: 99%

Projected Changes in Permafrost Active Layer Thickness Over the Qinghai‐Tibet Plateau Under Climate Change

Zhao

2019

Water Resources Research

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Climate warming would increase the active layer thickness (ALT) of permafrost, which can cause changes in the hydrological cycle, ecological processes, and carbon flux in cold regions. However, changes in permafrost ALT due to climate warming remain poorly quantified, which limits our understanding of environmental change in cold regions. In this study, the Lund‐Postam‐Jena dynamic vegetation model is coupled to the Kudryavtsev model to examine changes in permafrost ALT on the Qinghai‐Tibet Plateau (QTP) under climate change scenarios based on the representative concentration pathways (RCPs). The results suggest that the permafrost ALT on the QTP would exhibit a significant increasing trend under the climate change scenarios, and a large increment of ALT may occur in the northwestern QTP. In the near‐term (2011–2040), the change in ALT on the QTP is different under the RCP2.6, RCP4.5, RCP6.0, and RCP8.5 emissions scenarios, with an increment of 5–30 cm. In the mid‐term (2041–2070), ALT would deepen further with an obvious reduction in permafrost area, while the increase in ALT decreases from south to north. In the long‐term (2071–2099), the average increment of ALT would be greater than 30 cm under the RCP4.5, RCP6.0, and RCP8.5 scenarios, and the increment in ALT increases from south to north, with a remarkable retreat in permafrost area from south to north.

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Section: Methodsmentioning

confidence: 99%

Projected Changes in Permafrost Active Layer Thickness Over the Qinghai‐Tibet Plateau Under Climate Change

Zhao

2019

Water Resources Research

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“…Projections of net primary productivity (NPP) under climate change indicate that there is likely to be a high level of regional variation (Zhao et al 2013). Using a process model and climate scenario projections, Peters et al (2013) predicted that average regional productivity in forests in the Great Lakes region of North America could increase from 67 to 142 %, runoff could potentially increase from 2 to 22 % and net N mineralization from 10 to 12 %.…”

Section: Ecosystem Responses To Climatementioning

confidence: 99%

Climate change impacts and adaptation in forest management: a review

Keenan

2015

Annals of Forest Science

483

303

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Abstract& Key message Adaptation of forest management to climate change requires an understanding of the effects of climate on forests, industries and communities; prediction of how these effects might change over time; and incorporation of this knowledge into management decisions. This requires multiple forms of knowledge and new approaches to forest management decisions. Partnerships that integrate researchers from multiple disciplines with forest managers and local actors can build a shared understanding of future challenges and facilitate improved decision making in the face of climate change.

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“…For better simulation in China, LPJ-DGVM was carefully improved by adding shrub and cold grass PFTs, which were parameterized based on various inventory and observational data in accordance with the characteristics of ecosystems in China (Zhao et al, 2013). The simulated NPP by the modified model was validated with the data of observed sites in China, where the correlation coefficient (R 2 =0.64, p<0.01) was better than 15 the original LPJ-DGVM data (R 2 =0.10) used by Ni (2003).…”

Section: Lund-potsdam-jena Dynamic Global Vegetation Model (Lpj-dgvm)mentioning

confidence: 99%

Past and future influence of climate change on spatially heterogeneous vegetation activity in China

Gao

Jiao

et al. 2017

Preprint

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Abstract. Climate change is a major driver of vegetation activity, and thus their complex relationships become a frontier and 10 difficulty in global change research. In this paper, the spatial distribution and dynamic variation of climate change impacts on vegetation activity from 1980s to 2050 in China were investigated by the Geographically Weighted Regression (GWR) model, based on the combined datasets of satellite vegetation index, climate observation and projection, and future vegetation productivity simulation. Our results noted that the significant positive precipitation-vegetation relationship was and will be almost distributed in the north of China, except the northeast and northwest China with significant but varying 15 influence of temperature rising, while the regions with temperature dominated vegetation activity mainly located in the southern part of China. There will be different climatic dominators for vegetation activity in some regions such as northwest China, and even opposite correlation in the northeast China, and further the responding patterns of vegetation activity to precipitation variation will be moving southward in the future three decades. It is indicated that although high warming rate will restrain the vegetation activity, precipitation variability can mediate the hydrothermal conditions for vegetation activity, 20 for example the enhanced vegetation activity in the Tibetan Plateau and the weakened vegetation activity in the East and Middle China in the future. Furthermore, coupling the responding patterns and the dynamic variation, it can be found that during the period from 2021 to 2050, vegetation in most of north China may adapt to an arid environment, while in many southern parts it will be repressed due to water shortage. However, the continuous and dynamic responding process of vegetation activity to climate change will be determined by the spatial heterogeneity in climate change and vegetation cover. 25

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Responses of Terrestrial Ecosystems’ Net Primary Productivity to Future Regional Climate Change in China

Cited by 27 publications

References 30 publications

Projected Changes in Permafrost Active Layer Thickness Over the Qinghai‐Tibet Plateau Under Climate Change

Projected Changes in Permafrost Active Layer Thickness Over the Qinghai‐Tibet Plateau Under Climate Change

Climate change impacts and adaptation in forest management: a review

Past and future influence of climate change on spatially heterogeneous vegetation activity in China

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