Responses of vegetation activity to the daytime and nighttime warming in Northwest China

Du, Zheng; Zhao, Jie; Pan, Huan-Huan; Wu, Zhitao; Zhang, Hong

doi:10.1007/s10661-019-7855-8

Cited by 24 publications

(18 citation statements)

References 30 publications

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“…Besides, low-temperature extremes will cause ice cover and vegetation death. In addition, TX90p was found to be closely related to NDVI, because daytime warming can directly affect vegetation activities [93]. Some studies suggest that this is due to the different effects of daytime temperature warming on vegetation, which is regulated by insect herbivores [94,95].…”

Section: Discussionmentioning

confidence: 99%

Vegetation Change and Its Response to Climate Extremes in the Arid Region of Northwest China

2021

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Changes in climate extremes have a profound impact on vegetation growth. In this study, we employed the Moderate Resolution Imaging Spectroradiometer (MODIS) and a recently published climate extremes dataset (HadEX3) to study the temporal and spatial evolution of vegetation cover, and its responses to climate extremes in the arid region of northwest China (ARNC). Mann-Kendall test, Anomaly analysis, Pearson correlation analysis, Time lag cross-correlation method, and Least absolute shrinkage and selection operator logistic regression (Lasso) were conducted to quantitatively analyze the response characteristics between Normalized Difference Vegetation Index (NDVI) and climate extremes from 2000 to 2018. The results showed that: (1) The vegetation in the ARNC had a fluctuating upward trend, with vegetation significantly increasing in Xinjiang Tianshan, Altai Mountain, and Tarim Basin, and decreasing in the central inland desert. (2) Temperature extremes showed an increasing trend, with extremely high-temperature events increasing and extremely low-temperature events decreasing. Precipitation extremes events also exhibited a slightly increasing trend. (3) NDVI was overall positively correlated with the climate extremes indices (CEIs), although both positive and negative correlations spatially coexisted. (4) The responses of NDVI and climate extremes showed time lag effects and spatial differences in the growing period. (5) Precipitation extremes were closely related to NDVI than temperature extremes according to Lasso modeling results. This study provides a reference for understanding vegetation variations and their response to climate extremes in arid regions.

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Section: Discussionmentioning

confidence: 99%

Vegetation Change and Its Response to Climate Extremes in the Arid Region of Northwest China

2021

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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).…”

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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“…The rise of night temperatures is influenced by various factors, such as increased cloud cover resulting in reduced back radiation (Alward, Detling, & Milchunas, 1999; Braganza, Karoly, & Arblaster, 2004; Huang, Dickinson, & Chameides, 2006), desertification and changes in land use (Gallo, Easterling, & Peterson, 1996; Karl et al, 1991), aerosols (Ramanathan et al, 2007), higher evaporation and precipitation (Dai, Fung, & Del Genio, 1997) or the differential depth of the planetary boundary layer (Davy et al, 2017). The night temperature increase is especially severe in crop‐growing regions such as Central and South Asia or North America (Du, Zhao, Pan, Wu, & Zhang, 2019; Klein Tank et al, 2006; Ma, Xia, & Meng, 2019; Peng et al, 2004; Peng et al, 2013; Rao, Chowdary, Sandeep, Rao, & Venkateswarlu, 2014; Welch et al, 2010; Zhao & Fitzgerald, 2013; Y. Zhou & Ren, 2011).…”

Section: Introductionmentioning

confidence: 99%

Physiological and molecular attributes contribute to high night temperature tolerance in cereals

Schaarschmidt

Lawas

Kopka

et al. 2021

Plant Cell & Environment

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Asymmetric warming resulting in a faster increase in night compared to day temperatures affects crop yields negatively. Physiological characterization and agronomic findings have been complemented more recently by molecular biology approaches including transcriptomic, proteomic, metabolomic and lipidomic investigations in crops exposed to high night temperature (HNT) conditions. Nevertheless, the understanding of the underlying mechanisms causing yield decline under HNT is still limited. The discovery of significant differences between HNT‐tolerant and HNT‐sensitive cultivars is one of the main research directions to secure continuous food supply under the challenge of increasing climate change. With this review, we provide a summary of current knowledge on the physiological and molecular basis of contrasting HNT tolerance in rice and wheat cultivars. Requirements for HNT tolerance and the special adaptation strategies of the HNT‐tolerant rice cultivar Nagina‐22 (N22) are discussed. Putative metabolite markers for HNT tolerance useful for marker‐assisted breeding are suggested, together with future research directions aimed at improving food security under HNT conditions.

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Responses of vegetation activity to the daytime and nighttime warming in Northwest China

Cited by 24 publications

References 30 publications

Vegetation Change and Its Response to Climate Extremes in the Arid Region of Northwest China

Vegetation Change and Its Response to Climate Extremes in the Arid Region of Northwest China

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

Physiological and molecular attributes contribute to high night temperature tolerance in cereals

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