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The Sensitivity of Vegetation Phenology to Extreme Climate Indices in the Loess Plateau, China

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  • Tingting Pei

    (College of Management, Gansu Agricultural University, Lanzhou 730070, China)

  • Zhenxia Ji

    (College of Resources and Environment, Gansu Agricultural University, Lanzhou 730070, China)

  • Ying Chen

    (College of Management, Gansu Agricultural University, Lanzhou 730070, China
    College of Resources and Environment, Gansu Agricultural University, Lanzhou 730070, China)

  • Huawu Wu

    (Key Laboratory of Grassland Ecosystem, Gansu Agricultural University, Lanzhou 730070, China
    Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China)

  • Qingqing Hou

    (College of Resources and Environment, Gansu Agricultural University, Lanzhou 730070, China)

  • Gexia Qin

    (College of Resources and Environment, Gansu Agricultural University, Lanzhou 730070, China)

  • Baopeng Xie

    (College of Management, Gansu Agricultural University, Lanzhou 730070, China)

Abstract

Climate changes, especially increased temperatures, and precipitation changes, have significant impacts on vegetation phenology. However, the response of vegetation phenology to the extreme climate in the Loess Plateau in Northwest China remains poorly quantified. The research described here analyzed the spatial change in vegetation phenology and the response of vegetation phenology to climate change in the Loess Plateau from 2001 to 2018, using data from seven extreme climate indices based on the ridge regression method. The results showed that extreme climate indexes, TNn (yearly minimum value of the daily minimum temperature), TXx (yearly maximum value of the daily maximum temperature), and RX5day (yearly maximum consecutive five-day precipitation) progressively increased from 2001 to 2018 in the Loess Plateau region, but decrease trend was found in DRT (diurnal temperature range). The start of the growing season (SOS) of vegetation gradually advanced with precipitation from northwest to southeast, and the rate was +0.38 d/a. The overall vegetation end of the growing season (EOS) was delayed, and the trend was −2.83 d/a. The sensitivity of the different vegetation phenology to different extreme weather indices showed obvious spatial differences, the sensitivity coefficient of SOS being mainly positive in the region, whereas the sensitivity coefficient of EOS was negative generally. More sensitivity was found in the EOS to extreme climate indexes than in the SOS. Forest, shrubland and grassland have similar responses to DRT and TNn; namely, both SOS and EOS are advanced with the increase in DRT and delayed with the increase in TNn (the sensitivity coefficient is quite different) but have different responses to RX5day and TXx. These results reveal that extreme climate events have a greater impact on vegetation EOS than on vegetation SOS, with these effects varying with vegetation types. This research can provide a scientific basis for formulating a scientific basis for regional vegetation restoration strategies and disaster prediction on the Loess Plateau.

Suggested Citation

  • Tingting Pei & Zhenxia Ji & Ying Chen & Huawu Wu & Qingqing Hou & Gexia Qin & Baopeng Xie, 2021. "The Sensitivity of Vegetation Phenology to Extreme Climate Indices in the Loess Plateau, China," Sustainability, MDPI, vol. 13(14), pages 1-18, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:14:p:7623-:d:590440
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    1. Trevor F. Keenan & Josh Gray & Mark A. Friedl & Michael Toomey & Gil Bohrer & David Y. Hollinger & J. William Munger & John O’Keefe & Hans Peter Schmid & Ian Sue Wing & Bai Yang & Andrew D. Richardson, 2014. "Net carbon uptake has increased through warming-induced changes in temperate forest phenology," Nature Climate Change, Nature, vol. 4(7), pages 598-604, July.
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    Cited by:

    1. Zhaozhe Li & Ranghui Wang & Bo Liu & Zhonghua Qian & Yongping Wu & Cheng Li, 2022. "Responses of Vegetation Autumn Phenology to Climatic Factors in Northern China," Sustainability, MDPI, vol. 14(14), pages 1-13, July.

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