Overridingly increasing vegetation sensitivity to vapor pressure deficit over the recent two decades in China
Vapor pressure deficit (VPD) shows significant spatial and temporal variability in the context of global climate change, which is important for studying the implications of climate change on the structure and function of ecosystems to analyze the effects of VPD on vegetation dynamics. Spatial patter...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-04-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X24004345 |
| _version_ | 1797203068251537408 |
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| author | Miao Liu Guijun Yang Wenping Yuan Zhenhong Li Meiling Gao Yun Yang Huiling Long Yang Meng Changchun Li Haitang Hu Heli Li Zhanliang Yuan |
| author_facet | Miao Liu Guijun Yang Wenping Yuan Zhenhong Li Meiling Gao Yun Yang Huiling Long Yang Meng Changchun Li Haitang Hu Heli Li Zhanliang Yuan |
| author_sort | Miao Liu |
| collection | DOAJ |
| description | Vapor pressure deficit (VPD) shows significant spatial and temporal variability in the context of global climate change, which is important for studying the implications of climate change on the structure and function of ecosystems to analyze the effects of VPD on vegetation dynamics. Spatial patterns of vegetation sensitivity to VPD have been recently investigated, however, the feedback of different vegetation types to VPD may vary depending on physiological characteristics, it is unclear how different types influence the sensitivity to VPD. In this study, the ERA5-Land reanalysis time-series dataset was used to analyze the spatial and temporal trends of VPD under different vegetation types. It was found that VPD showed an increasing trend in vegetated areas over the past 20 years with large spatial heterogeneity, generally increasing with drying conditions. On this basis, the spatial patterns of vegetation sensitivity to VPD and temporal trends in sensitivity were evaluated over the past 20 years in China using the enhanced vegetation index (EVI) and near-infrared reflectance of vegetation (NIRv) which can describe vegetation dynamics. The results show that the sensitivities under the two indices have high spatial consistency, with northeastern and central China showing positive sensitivities and southern China showing negative sensitivities, respectively. The positive sensitivities are relatively high for Deciduous Broadleaf Forests (DBF), Deciduous Needleleaf Forests (DNF), Grasslands (GL), and Croplands (CL) types, while the negative sensitivities are larger for Shrublands (SL) and Savannas (SA) types. Under different climatic zones, the sensitivity of CL and GL are independent of climatic zones (both showing positive), while the sensitivity of SL is negative in the Humid zone and positive in the Semi-Arid zone. Temporally, the sensitivity showed a slow increasing trend over the last 20 years. In terms of vegetation types, sensitivities of Evergreen Broadleaf Forests (EBF), DBF, GL and CL types showed a significant increasing trend (p < 0.05), except for the SL type, which showed a significant decreasing trend (p < 0.05). The trends of sensitivity are not affected by the differences in vegetation types (all of them show an increasing trend) under arid and semi-arid conditions, while dry sub-humid and humid have a greater impact on sensitivity trends. The finding of an overall increase in sensitivity suggests a mechanism of erratic change in vegetation growth under climate change. Notably, the increased sensitivity of certain vegetation types (especially GL and CL) suggests that these may become progressively vulnerable to increased VPD as global climate change persists, with the risk of moving from facilitation to inhibition of photosynthesis. |
| first_indexed | 2024-04-24T08:13:27Z |
| format | Article |
| id | doaj.art-fa3866bc87af49f1b0356d178167b21a |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-04-24T08:13:27Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
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| series | Ecological Indicators |
| spelling | doaj.art-fa3866bc87af49f1b0356d178167b21a2024-04-17T04:48:51ZengElsevierEcological Indicators1470-160X2024-04-01161111977Overridingly increasing vegetation sensitivity to vapor pressure deficit over the recent two decades in ChinaMiao Liu0Guijun Yang1Wenping Yuan2Zhenhong Li3Meiling Gao4Yun Yang5Huiling Long6Yang Meng7Changchun Li8Haitang Hu9Heli Li10Zhanliang Yuan11College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China; Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaCollege of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China; Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Corresponding author at: College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China.School of Atmospheric Sciences, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Zhuhai Key Laboratory of Dynamics Urban Climate and Ecology, Sun Yat-sen University, Zhuhai, Guangdong 510245, ChinaCollege of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, ChinaCollege of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, ChinaCollege of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, ChinaKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing, Jiangsu 210095, ChinaResearch Institute of Quantitative Remote Sensing and Smart Agriculture, School of Surveying and Mapping Land Information Engineering, Henan Polytechnic University, Jiaozuo, Henan 454000, ChinaKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaResearch Institute of Quantitative Remote Sensing and Smart Agriculture, School of Surveying and Mapping Land Information Engineering, Henan Polytechnic University, Jiaozuo, Henan 454000, ChinaVapor pressure deficit (VPD) shows significant spatial and temporal variability in the context of global climate change, which is important for studying the implications of climate change on the structure and function of ecosystems to analyze the effects of VPD on vegetation dynamics. Spatial patterns of vegetation sensitivity to VPD have been recently investigated, however, the feedback of different vegetation types to VPD may vary depending on physiological characteristics, it is unclear how different types influence the sensitivity to VPD. In this study, the ERA5-Land reanalysis time-series dataset was used to analyze the spatial and temporal trends of VPD under different vegetation types. It was found that VPD showed an increasing trend in vegetated areas over the past 20 years with large spatial heterogeneity, generally increasing with drying conditions. On this basis, the spatial patterns of vegetation sensitivity to VPD and temporal trends in sensitivity were evaluated over the past 20 years in China using the enhanced vegetation index (EVI) and near-infrared reflectance of vegetation (NIRv) which can describe vegetation dynamics. The results show that the sensitivities under the two indices have high spatial consistency, with northeastern and central China showing positive sensitivities and southern China showing negative sensitivities, respectively. The positive sensitivities are relatively high for Deciduous Broadleaf Forests (DBF), Deciduous Needleleaf Forests (DNF), Grasslands (GL), and Croplands (CL) types, while the negative sensitivities are larger for Shrublands (SL) and Savannas (SA) types. Under different climatic zones, the sensitivity of CL and GL are independent of climatic zones (both showing positive), while the sensitivity of SL is negative in the Humid zone and positive in the Semi-Arid zone. Temporally, the sensitivity showed a slow increasing trend over the last 20 years. In terms of vegetation types, sensitivities of Evergreen Broadleaf Forests (EBF), DBF, GL and CL types showed a significant increasing trend (p < 0.05), except for the SL type, which showed a significant decreasing trend (p < 0.05). The trends of sensitivity are not affected by the differences in vegetation types (all of them show an increasing trend) under arid and semi-arid conditions, while dry sub-humid and humid have a greater impact on sensitivity trends. The finding of an overall increase in sensitivity suggests a mechanism of erratic change in vegetation growth under climate change. Notably, the increased sensitivity of certain vegetation types (especially GL and CL) suggests that these may become progressively vulnerable to increased VPD as global climate change persists, with the risk of moving from facilitation to inhibition of photosynthesis.http://www.sciencedirect.com/science/article/pii/S1470160X24004345Vapor pressure deficit (VPD)Aridity index (AI)EVINIRvVegetationSensitivity |
| spellingShingle | Miao Liu Guijun Yang Wenping Yuan Zhenhong Li Meiling Gao Yun Yang Huiling Long Yang Meng Changchun Li Haitang Hu Heli Li Zhanliang Yuan Overridingly increasing vegetation sensitivity to vapor pressure deficit over the recent two decades in China Ecological Indicators Vapor pressure deficit (VPD) Aridity index (AI) EVI NIRv Vegetation Sensitivity |
| title | Overridingly increasing vegetation sensitivity to vapor pressure deficit over the recent two decades in China |
| title_full | Overridingly increasing vegetation sensitivity to vapor pressure deficit over the recent two decades in China |
| title_fullStr | Overridingly increasing vegetation sensitivity to vapor pressure deficit over the recent two decades in China |
| title_full_unstemmed | Overridingly increasing vegetation sensitivity to vapor pressure deficit over the recent two decades in China |
| title_short | Overridingly increasing vegetation sensitivity to vapor pressure deficit over the recent two decades in China |
| title_sort | overridingly increasing vegetation sensitivity to vapor pressure deficit over the recent two decades in china |
| topic | Vapor pressure deficit (VPD) Aridity index (AI) EVI NIRv Vegetation Sensitivity |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X24004345 |
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