|Title||Causal relationship in the interaction between land cover change and underlying surface climate in the grassland ecosystems in China|
|Author(s)||Li, Zhouyuan; Wang, Zezhong; Liu, Xuehua; Fath, Brian D.; Liu, Xiaofei; Xu, Yanjie; Hutjes, Ronald; Kroeze, Carolien|
|Source||Science of the Total Environment 647 (2019). - ISSN 0048-9697 - p. 1080 - 1087.|
Earth System Science
Alterra - Climate change and adaptive land and water management
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||Cause-effect - Correlation analysis - Eco-climatology - Grassland - Land-climate - Remote sensing|
Land-climate interactions are driven by causal relations that are difficult to ascertain given the complexity and high dimensionality of the systems. Many methods of statistical and mechanistic models exist to identify and quantify the causality in such highly-interacting systems. Recent advances in remote sensing development allowed people to investigate the land-climate interaction with spatially and temporally continuous data. In this study, we present a new approach to measure how climatic factors interact with each other under land cover change. The quantification method is based on the correlation analysis of the different order derivatives, with the canonical mathematical definitions developed from the theories of system dynamics and practices of the macroscopic observations. We examined the causal relationship between the interacting variables on both spatial and temporal dimensions based on macroscopic observations of land cover change and surface climatic factors through a comparative study in the different grassland ecosystems of China. The results suggested that the interaction of land-climate could be used to explain the temporal lag effect in the comparison of the three grassland ecosystems. Significant spatial correlations between the vegetation and the climatic factors confirmed feedback mechanisms described in the theories of eco-climatology, while the uncertain temporal synchronicity reflects the causality among the key indicators. This has been rarely addressed before. Our research show that spatial correlations and the temporal synchronicity among key indicators of the land surface and climatic factors can be explained by a novel method of causality quantification using derivative analysis.