Assessing the Causal Effects of Climate Change on Vegetation Dynamics in Northeast China Using Convergence Cross-Mapping
The patterns of interaction between terrestrial vegetation and the atmosphere are complex, and some are poorly understood. Linear or general linear methods have been widely used to explore the dynamics of vegetation and climate changes. However, linear thinking may hinder our understanding of comple...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10287326/ |
| _version_ | 1827783666198118400 |
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| author | Jiapei Wu Yuke Zhou Han Wang Xiaoying Wang Jiaojiao Wang |
| author_facet | Jiapei Wu Yuke Zhou Han Wang Xiaoying Wang Jiaojiao Wang |
| author_sort | Jiapei Wu |
| collection | DOAJ |
| description | The patterns of interaction between terrestrial vegetation and the atmosphere are complex, and some are poorly understood. Linear or general linear methods have been widely used to explore the dynamics of vegetation and climate changes. However, linear thinking may hinder our understanding of complex nonlinear systems, and it is difficult to extract the underlying causality of linear correlations directly from observational data. In this study, we aimed to quantify the interactions between vegetation and climate, using nonlinear dynamical methods based on state-space reconstruction and datasets from Chinese meteorological stations and remote sensing data during 1982–2015 in Northeast China (NEC). Specifically, we identified the causal links between meteorological factors (temperature and precipitation) and the vegetation index (NDVI) by reconstructing the state space from historical records. During the study period, vegetation exhibited a strong bidirectional causal relationship with both temperature and precipitation across Northeast China. The NDVI can be accurately reconstructed from the state information of meteorological factors (temperature and precipitation). The results of the multivariate EDM scenarios reveal varying sensitivities of different vegetation types to meteorological factors. Overall, slight temperature changes have a stronger impact on vegetation compared to precipitation. Mixed forest and broad-leaved forest demonstrate lower sensitivity to precipitation changes compared to other vegetation types. This study on the causal relationship between vegetation and meteorological factors in Northeast China contributes to a deeper understanding of climate change and vegetation feedback in middle and high latitudes. Our work demonstrates that the EDM-based convergent cross-mapping nonlinear causal analysis method is valuable for comprehending the interactions within complex systems in earth science. |
| first_indexed | 2024-03-11T15:50:41Z |
| format | Article |
| id | doaj.art-25ec81bcd37d4b80a9d46b81a59bc449 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T15:50:41Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-25ec81bcd37d4b80a9d46b81a59bc4492023-10-25T23:00:35ZengIEEEIEEE Access2169-35362023-01-011111536711537910.1109/ACCESS.2023.332548510287326Assessing the Causal Effects of Climate Change on Vegetation Dynamics in Northeast China Using Convergence Cross-MappingJiapei Wu0https://orcid.org/0009-0007-0038-0823Yuke Zhou1https://orcid.org/0000-0002-2559-0241Han Wang2Xiaoying Wang3Jiaojiao Wang4https://orcid.org/0000-0002-7098-7504Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic and Natural Resources Research, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic and Natural Resources Research, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic and Natural Resources Research, Chinese Academy of Sciences, Beijing, ChinaInstitute of Atmospheric Environment, CMA, Shenyang, ChinaState Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, ChinaThe patterns of interaction between terrestrial vegetation and the atmosphere are complex, and some are poorly understood. Linear or general linear methods have been widely used to explore the dynamics of vegetation and climate changes. However, linear thinking may hinder our understanding of complex nonlinear systems, and it is difficult to extract the underlying causality of linear correlations directly from observational data. In this study, we aimed to quantify the interactions between vegetation and climate, using nonlinear dynamical methods based on state-space reconstruction and datasets from Chinese meteorological stations and remote sensing data during 1982–2015 in Northeast China (NEC). Specifically, we identified the causal links between meteorological factors (temperature and precipitation) and the vegetation index (NDVI) by reconstructing the state space from historical records. During the study period, vegetation exhibited a strong bidirectional causal relationship with both temperature and precipitation across Northeast China. The NDVI can be accurately reconstructed from the state information of meteorological factors (temperature and precipitation). The results of the multivariate EDM scenarios reveal varying sensitivities of different vegetation types to meteorological factors. Overall, slight temperature changes have a stronger impact on vegetation compared to precipitation. Mixed forest and broad-leaved forest demonstrate lower sensitivity to precipitation changes compared to other vegetation types. This study on the causal relationship between vegetation and meteorological factors in Northeast China contributes to a deeper understanding of climate change and vegetation feedback in middle and high latitudes. Our work demonstrates that the EDM-based convergent cross-mapping nonlinear causal analysis method is valuable for comprehending the interactions within complex systems in earth science.https://ieeexplore.ieee.org/document/10287326/Vegetation dynamicclimate changecausal linksconvergent cross mappingempirical dynamic model |
| spellingShingle | Jiapei Wu Yuke Zhou Han Wang Xiaoying Wang Jiaojiao Wang Assessing the Causal Effects of Climate Change on Vegetation Dynamics in Northeast China Using Convergence Cross-Mapping IEEE Access Vegetation dynamic climate change causal links convergent cross mapping empirical dynamic model |
| title | Assessing the Causal Effects of Climate Change on Vegetation Dynamics in Northeast China Using Convergence Cross-Mapping |
| title_full | Assessing the Causal Effects of Climate Change on Vegetation Dynamics in Northeast China Using Convergence Cross-Mapping |
| title_fullStr | Assessing the Causal Effects of Climate Change on Vegetation Dynamics in Northeast China Using Convergence Cross-Mapping |
| title_full_unstemmed | Assessing the Causal Effects of Climate Change on Vegetation Dynamics in Northeast China Using Convergence Cross-Mapping |
| title_short | Assessing the Causal Effects of Climate Change on Vegetation Dynamics in Northeast China Using Convergence Cross-Mapping |
| title_sort | assessing the causal effects of climate change on vegetation dynamics in northeast china using convergence cross mapping |
| topic | Vegetation dynamic climate change causal links convergent cross mapping empirical dynamic model |
| url | https://ieeexplore.ieee.org/document/10287326/ |
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