Ecological Indicators (Mar 2021)

Relationship between net primary production and climate change in different vegetation zones based on EEMD detrending – A case study of Northwest China

  • Huiyu Liu,
  • Junhe Jia,
  • Zhenshan Lin,
  • Zhaoyue Wang,
  • Haibo Gong

Journal volume & issue
Vol. 122
p. 107276

Abstract

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The relationship between vegetation Net primary production (NPP) and climate change is critical for understanding the driving forces of vegetation changes, while less were studied based on detrending analysis and Bioclimatic variables. In this study, detrending analysis based on Ensemble Empirical Mode Decomposition (EEMD) method was adopted to assess the relationship between NPP and climate change in different vegetation zones in Northwest China. The results indicated that: (1) although monotonic increasing was the main type of vegetation NPP trend (49.42%), shifted trend accounted for 36.02% of the whole area. There were some risks of vegetation degradation in temperate desert and alpine region of the Qinghai Tibet Platea, but chances for vegetation recovery in temperate grassland and warm temperate deciduous broad-leaved forest zones; (2) EEMD-detrending analysis performed much better than linear detrending analysis for assessing the relationship between climate change and vegetation NPP; (3) compared with no detrending, EEMD-detrending reduced the importance of BIO1 (annual mean temperature) and BIO2 (mean temperature diurnal range) for vegetation NPP, but enhanced those of BIO13 (precipitation of the wettest month) and BIO15 (precipitation seasonality); (4) BIO1, BIO2, BIO12 (annual precipitation), and BIO13 mainly showed positive relationships with interannual NPP variations, except that BIO1 and BIO13 mainly showed negative relationship in temperate grassland and warm temperate deciduous broad-leaved forest zones. Interannual NPP variations were dominated by BIO12 and BIO13, except that alpine region of Tibet plateau was dominated by BIO1 and BIO2. Our results demonstrated that EEMD-detrending analysis and Bioclimatic variables can better explore the interannual vegetation-climate relationship.

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