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Copula-Based Abrupt Variations Detection in the Relationship of Seasonal Vegetation-Climate in the Jing River Basin, China

1
State Key Laboratory of Eco-hydraulics in Northwest Arid Region of China, Xi’an University of Technology, Xi’an 710048, China
2
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
3
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
4
Environmental Change Institute, University of Oxford, Oxford OX1 3QY, UK
5
School of Geography and the Environment, University of Oxford, Oxford OX1 3QY, UK
6
Department of Geography, University of Munich (LMU), 80333 Munich, Germany
7
Max Planck Institute for Meteorology, 20146 Hamburg, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(13), 1628; https://doi.org/10.3390/rs11131628
Received: 22 May 2019 / Revised: 24 June 2019 / Accepted: 5 July 2019 / Published: 9 July 2019
(This article belongs to the Special Issue Observations, Modeling, and Impacts of Climate Extremes)
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Abstract

Understanding the changing relationships between vegetation coverage and precipitation/temperature (P/T) and then exploring their potential drivers are highly necessary for ecosystem management under the backdrop of a changing environment. The Jing River Basin (JRB), a typical eco-environmentally vulnerable region of the Loess Plateau, was chosen to identify abrupt variations of the relationships between seasonal Normalized Difference Vegetation Index (NDVI) and P/T through a copula-based method. By considering the climatic/large-scale atmospheric circulation patterns and human activities, the potential causes of the non-stationarity of the relationship between NDVI and P/T were revealed. Results indicated that (1) the copula-based framework introduced in this study is more reasonable and reliable than the traditional double-mass curves method in detecting change points of vegetation and climate relationships; (2) generally, no significant change points were identified during 1982–2010 at the 95% confidence level, implying the overall stationary relationship still exists, while the relationships between spring NDVI and P/T, autumn NDVI and P have slightly changed; (3) teleconnection factors (including Arctic Oscillation (AO), Pacific Decadal Oscillation (PDO), Niño 3.4, and sunspots) have a more significant influence on the relationship between seasonal NDVI and P/T than local climatic factors (including potential evapotranspiration and soil moisture); (4) negative human activities (expansion of farmland and urban areas) and positive human activities (“Grain For Green” program) were also potential factors affecting the relationship between NDVI and P/T. This study provides a new and reliable insight into detecting the non-stationarity of the relationship between NDVI and P/T, which will be beneficial for further revealing the connection between the atmosphere and ecosystems. View Full-Text
Keywords: copula-based method; NDVI and precipitation/temperature; change points; teleconnection factors; double cumulative curve method copula-based method; NDVI and precipitation/temperature; change points; teleconnection factors; double cumulative curve method
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zhao, J.; Huang, S.; Huang, Q.; Wang, H.; Leng, G.; Peng, J.; Dong, H. Copula-Based Abrupt Variations Detection in the Relationship of Seasonal Vegetation-Climate in the Jing River Basin, China. Remote Sens. 2019, 11, 1628.

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