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Assessing spatio-temporal variations of precipitation-use efficiency over Tibetan grasslands using MODIS and in-situ observations

Authors
  • Liu, Zhengjia1, 2
  • Huang, Mei1
  • 1 Chinese Academy of Sciences, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Beijing, 100101, China , Beijing (China)
  • 2 Chinese Academy of Sciences, State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Beijing, 100101, China , Beijing (China)
Type
Published Article
Journal
Frontiers of Earth Science
Publisher
Higher Education Press
Publication Date
Mar 24, 2016
Volume
10
Issue
4
Pages
784–793
Identifiers
DOI: 10.1007/s11707-016-0566-3
Source
Springer Nature
Keywords
License
Yellow

Abstract

Clarifying the spatial and temporal variations in precipitation-use efficiency (PUE) is helpful for advancing our knowledge of carbon and water cycles in Tibetan grassland ecosystems. Here we use an integrated remote sensing normalized difference vegetation index (NDVI) and in-situ above-ground net primary production (ANPP) measurements to establish an empirical exponential model to estimate spatial ANPP across the entire Tibetan Plateau. The spatial and temporal variations in PUE (the ratio of ANPP to mean annual precipitation (MAP)), as well as the relationships between PUE and other controls, were then investigated during the 2001–2012 study period. At a regional scale, PUE increased from west to east. PUE anomalies increased significantly (>0.1 g·m–2·mm–1/10 yr) in the southern areas of the Tibetan Plateau yet decreased (>0.02 g·m–2·mm–1/10 yr) in the northeastern areas. For alpine meadow, we obtained an obvious breaking point in trend of PUE against elevation gradients at 3600 m above the sea level, which showed a contrasting relationship. At the inter-annual scale, PUE anomalies were smaller in alpine steppe than in alpine meadow. The results show that PUE of Tibetan grasslands is generally high in dry years and low in wet years.

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