Editorial Type:
Article
Article Type:
Research Article

Vegetation Dynamics on the Tibetan Plateau (1982–2006): An Attribution by Ecohydrological Diagnostics

Danlu Cai * Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China
Max-Planck-Institute for Meteorology, Hamburg, Germany

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Klaus Fraedrich Max-Planck-Institute for Meteorology, Hamburg, Germany

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Frank Sielmann Meteorological Institute, University of Hamburg, Hamburg, Germany

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Ling Zhang Max-Planck-Institute for Meteorology, Hamburg, Germany

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Xiuhua Zhu KlimaCampus, Hamburg University, Hamburg, Germany

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Shan Guo * Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China

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Yanning Guan * Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China

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Print Publication:
01 Jun 2015
DOI:
https://doi.org/10.1175/JCLI-D-14-00692.1
Page(s):
4576–4584
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Abstract

Vegetation greenness distributions [based on remote sensing normalized difference vegetation index (NDVI)] and their change are analyzed as functional vegetation–climate relations in a two-dimensional ecohydrological state space spanned by surface flux ratios of energy excess (U; loss by sensible heat H over supply by net radiation N) versus water excess (W; loss by discharge Ro over gain by precipitation P). An ecohydrological ansatz attributes state change trajectories in (U, W) space to external (or climate) and internal (or anthropogenic) causes jointly with vegetation greenness interpreted as an active tracer. Selecting the Tibetan Plateau with its complex topographic, climate, and vegetation conditions as target area, ERA-Interim weather data link geographic and (U, W) state space, into which local remote sensing Global Inventory Modeling and Mapping Studies (GIMMS) data (NDVI) are embedded; a first and second period (1982–93 and 1994–2006) are chosen for change attribution analysis. The study revealed the following results: 1) State space statistics are characterized by a bimodal distribution with two distinct geobotanic regimes (semidesert and steppe) of low and moderate vegetation greenness separated by gaps at aridity D ~ 2 (net radiation over precipitation) and greenness NDVI ~ 0.3. 2) Changes between the first and second period are attributed to external (about 70%) and internal (30%) processes. 3) Attribution conditioned joint distributions of NDVI (and its change) show 38.2% decreasing (61.8% increasing) area cover with low (moderate) greenness while high greenness areas are slightly reduced. 4) Water surplus regions benefit most from climate change (showing vegetation greenness growth) while the energy surplus change is ambiguous, because ecohydrological diagnostics attributes high mountainous regions (such as the Himalayas) as internal without considering the heat storage deficit due to increasing vegetation.

Corresponding author address: Yanning Guan, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beigen West Road #1, Beijing 100101, China. E-mail: guanyn@radi.ac.cn

Abstract

Vegetation greenness distributions [based on remote sensing normalized difference vegetation index (NDVI)] and their change are analyzed as functional vegetation–climate relations in a two-dimensional ecohydrological state space spanned by surface flux ratios of energy excess (U; loss by sensible heat H over supply by net radiation N) versus water excess (W; loss by discharge Ro over gain by precipitation P). An ecohydrological ansatz attributes state change trajectories in (U, W) space to external (or climate) and internal (or anthropogenic) causes jointly with vegetation greenness interpreted as an active tracer. Selecting the Tibetan Plateau with its complex topographic, climate, and vegetation conditions as target area, ERA-Interim weather data link geographic and (U, W) state space, into which local remote sensing Global Inventory Modeling and Mapping Studies (GIMMS) data (NDVI) are embedded; a first and second period (1982–93 and 1994–2006) are chosen for change attribution analysis. The study revealed the following results: 1) State space statistics are characterized by a bimodal distribution with two distinct geobotanic regimes (semidesert and steppe) of low and moderate vegetation greenness separated by gaps at aridity D ~ 2 (net radiation over precipitation) and greenness NDVI ~ 0.3. 2) Changes between the first and second period are attributed to external (about 70%) and internal (30%) processes. 3) Attribution conditioned joint distributions of NDVI (and its change) show 38.2% decreasing (61.8% increasing) area cover with low (moderate) greenness while high greenness areas are slightly reduced. 4) Water surplus regions benefit most from climate change (showing vegetation greenness growth) while the energy surplus change is ambiguous, because ecohydrological diagnostics attributes high mountainous regions (such as the Himalayas) as internal without considering the heat storage deficit due to increasing vegetation.

Corresponding author address: Yanning Guan, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beigen West Road #1, Beijing 100101, China. E-mail: guanyn@radi.ac.cn
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