Temperature dependence of variations in the end of the growing season from 1982 to 2012 on the Qinghai–Tibetan Plateau

Ding, M. D.; Chen, Qian; Li, Lanhui; Zhang, Yili; Wang, Zhaofeng; Liu, Linshan; Sun, Xiaomin

doi:10.1080/15481603.2015.1120371

Cited by 29 publications

(32 citation statements)

References 52 publications

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“…Among the multiple preseason (here preseason refers to the period preceding a certain phonological date in which a climate factor has strongest impact on that phonology event; details are given in the method section) meteorological factors that could explain these changes, low temperature was identified as a major driver of leaf senescence in autumn (Cong, Shen, & Piao, ; Ding et al, ; Ge et al, ; Jeong et al, ; Liu, Fu, Zeng, et al, ; Liu, Fu, Zhu, et al, ; Marchand et al, ; Yang et al, ; Zeng et al, ; Zhao et al, ). In most of these studies, the interannual variations in the date of autumn phenology onset were related to daily mean temperature.…”

Section: Introductionmentioning

confidence: 99%

“…The Tibetan Plateau is home to unique alpine vegetation that is sensitive to climatic warming (H. Chen et al, ; Shen, Piao, Dorji, et al, ). Recent studies have revealed significant control of the end of the growing season (EOS) by the preseason mean daily temperature for alpine vegetation on the Tibetan Plateau (Cong, Shen, Piao, ; Ding et al, ; Liu, Fu, Zeng, et al, ; Yang et al, ). It has been shown that the minimum daily temperature ( T min , nighttime) and maximum daily temperature ( T max , daytime) exerted different impacts on the start of the growing season (SOS) on the Tibetan Plateau (Shen et al, ), probably because higher T min could mitigate low‐temperature constraints and because the impacts of increasing T max could be limited by low water availability on the Plateau, which mostly has an arid or semiarid environment (Shen et al, ; Shen, Tang, et al, ; Shen, Cong, et al, ; Shen et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…These studies suggest that vegetation activity on the Tibetan Plateau may respond differently to changes in T max and T min and that the response will depend on a combination of temperature and moisture responses. However, little is known about the impacts of changes in T min and T max on the EOS of the Tibetan Plateau (Ding et al, ) until a recent study (Yang et al, ) shows that higher August and September mean of daily minimum temperature could significantly postpone EOS based on tree‐ring data on the Tibetan Plateau. Nevertheless, we yet did not know the response of EOS of the widespread grasslands to preseason asymmetric warming on the plateau.…”

Section: Introductionmentioning

confidence: 99%

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Asymmetric Responses of the End of Growing Season to Daily Maximum and Minimum Temperatures on the Tibetan Plateau

et al. 2017

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Climate warming has delayed the end of the growing season (EOS) in temperate and cold ecosystems. However, it is unclear whether asymmetric warming (higher warming at night than during the day) has triggered different responses in the timing of EOS. Here we used satellite‐observed EOS of alpine vegetation to reveal its asymmetric responses to nighttime and daytime warming on the Tibetan Plateau. Increased preseason minimum temperature could postpone EOS by 7.92 day K−1 (P < 0.01), probably by slowing low‐temperature induced leaf senescence, whereas increased preseason maximum temperature could advance EOS by 3.57 day K−1 (P < 0.05), likely due to the confounding effects of water limitations. The delaying effect of nighttime warming was stronger in more arid areas of the plateau, where daytime warming has a stronger advancing effect on EOS. Our results provide new insights into understanding and modeling autumn vegetation phenology on the Tibetan Plateau and grassland ecosystems in other temperate and cold regions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Asymmetric Responses of the End of Growing Season to Daily Maximum and Minimum Temperatures on the Tibetan Plateau

et al. 2017

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“…Considerate research efforts have been directed toward exploring vegetation dynamics under the influence of 1 digitalcommons.unl.edu climate change (Chang et al 2014;Song and Ma 2011;Nemani et al 2003). Numerous studies indicated that the relationship between vegetation dynamics and climate change differed with a combination of climatic zones, altitudinal gradients, vegetation types, land management activities, and other microenvironments (Nemani et al 2003;Qiu et al 2013b;Ding et al 2015;Yuan, Wang, and Mitchell 2014;Song and Ma 2011;Qiu et al 2014b). Given the complexity of vegetation dynamic patterns due to environmental heterogeneity, its quantification required dedicated monitoring tools.…”

Section: Introductionmentioning

confidence: 99%

“…Considerable studies have focused on temperature and precipitation (Qiu et al 2013b;Song and Ma 2011;Yuan, Wang, and Mitchell 2014;Ding et al 2015;Cuba et al 2013). Other microclimatic variables, such as sunshine duration and humidity, are also important influential factors for vegetation photosynthesis (Krishnaswamy, John, and Joseph 2014).…”

Section: Introductionmentioning

confidence: 99%

A multi-scale spatiotemporal modeling approach to explore vegetation dynamics patterns under global climate change

Qiu

Wang

Tang

et al. 2016

GIScience & Remote Sensing

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Given the complexity of vegetation dynamic patterns under global climate change, multi-scale spatiotemporal explicit models are necessary in order to account for environmental heterogeneity. However, there is no efficient time-series tool to extract, reconstruct and analyze the multi-scale vegetation dynamic patterns under global climate change. To fill this gap, a Multi-Scale Spatio-Temporal Modeling (MSSTM) framework which can incorporate the pixel, scale, and time-specific heterogeneity was proposed. The MSSTM method was defined on proper time-series models for multitemporal components through wavelet transforms. The proposed MSSTM approach was applied to a subtropical mountainous and hilly agro-forestry ecosystem in southeast China using the moderate resolution imaging spectroradiometer enhanced vegetation index (EVI) time-series data sets from 2001 to 2011. The MSSTM approach was proved to be efficient in characterizing and forecasting the complex vegetation dynamic patterns. It provided good estimates of the peaks and valleys of the observed EVI and its average percentages of relative absolute errors of reconstruction was low (6.65). The complexity of the relationship between vegetation dynamics and meteorological parameters was also revealed through the MSSTM method: (1) at seasonal level, vegetation dynamic patterns are strongly associated with climatic variables, primarily the temperature and then precipitation, with correlations slight decreasing (EVI-temperature)/increasing (EVI-precipitation) with altitudinal gradients. (2) At inter-annual scale, obvious positive correlations were primarily observed between EVI and temperature. (3) Despite very low-correlation coefficients observed at intraseasonal scales, considerable proportions of EVI anomalies are associated with climatic variables, principally the precipitation and sunshine durations.

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Improving daily precipitation estimates for the Qinghai‐Tibetan plateau based on environmental similarity

Wang

Kong

Chen

et al. 2020

Intl Journal of Climatology

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Due to the scarcity of gauge observations and inaccuracy of satellite estimation, obtaining reliable daily precipitation estimates over the Qinghai-Tibetan Plateau (QTP) remains challenging. In this article, an integrated scheme is developed based on the assumption that in a specific climatic region, the similarity of environmental conditions related to precipitation (SEP) in two locations is positively correlated to the similarities of occurrence and magnitude of precipitation between them. First, the QTP was divided into the northwestern arid, middle semi-arid/semi-humid, and southeastern humid climatic subregions by grouping analysis. Second, based on modified weighted k-nearest neighbour model, daily precipitation of target locations in these climatic subregions were predicted by weighted regression of a group of gauge observations that have the largest SEP with the target locations. SEP was calculated by the following auxiliary environmental factors: longitude, latitude, elevation, Normalized Difference Vegetation Index, relative humidity, and CMORPH (Climate Prediction Center's morphing technique) daily precipitation estimates (original CMORPH). The validation results demonstrate the effectiveness of the proposed scheme. Compared with the original CMORPH and PDFcalibrated CMORPH (CMORPH calibrated by probability density function matching plus optimal interpolation method) at daily, monthly, and yearly scales, the scheme improves the rain/no rain detection capacity and the accuracy of daily precipitation estimates. In addition, the daily precipitation estimates obtained from this scheme can present significant discrimination over specific geographic units, particularly the Qaidam Basin, the great bend of the Brahmaputra River, and Hengduan Mountain.

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Temperature dependence of variations in the end of the growing season from 1982 to 2012 on the Qinghai–Tibetan Plateau

Cited by 29 publications

References 52 publications

Asymmetric Responses of the End of Growing Season to Daily Maximum and Minimum Temperatures on the Tibetan Plateau

Asymmetric Responses of the End of Growing Season to Daily Maximum and Minimum Temperatures on the Tibetan Plateau

A multi-scale spatiotemporal modeling approach to explore vegetation dynamics patterns under global climate change

Improving daily precipitation estimates for the Qinghai‐Tibetan plateau based on environmental similarity

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