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Liu S, Xue L, Xiao Y, Yang M, Liu Y, Han Q, Ma J. Dynamic process of ecosystem water use efficiency and response to drought in the Yellow River Basin, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 934:173339. [PMID: 38763192 DOI: 10.1016/j.scitotenv.2024.173339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 05/01/2024] [Accepted: 05/16/2024] [Indexed: 05/21/2024]
Abstract
Ecosystem water use efficiency (WUE) is a crucial indicator of the impact of climate change on terrestrial ecosystems, reflecting the balance between biological processes (photosynthesis and transpiration) and physical processes (evapotranspiration). However, the response mechanisms and driving processes of WUE to drought remain to be further understood. In this study, we analyzed the spatial and temporal dynamics and response mechanisms of WUE in the Yellow River Basin (YRB) using data on Gross Primary Productivity (GPP), Evapotranspiration (ET) and Standardized Precipitation Evapotranspiration Index (SPEI), which revealed the cumulative effect of drought on WUE and assessed the ecosystem's resilience. The study results showed that (1) GPP, ET and WUE in the YRB exhibited a significant increasing trend, with 63.04 % of the area showing a marked increase in WUE. (2) GPP was the dominant factor influencing WUE in 65.36 % of the area, particularly in cropland and grassland, while ET was more influential in forested areas. Vapor pressure deficit (VPD) was identified as the principal driver affecting vegetation GPP in semi-arid and semi-humid regions of the YRB. In contrast, soil moisture (SM) was the limiting factor in arid areas. (3) 71.00 % of the WUE in the basin was affected by drought cumulative effects, with an average cumulative duration of 4.5 months. Arid regions experienced the most extended duration of 7.29 months, compared to 3.05 months in semi-humid regions. (4) 74.85 % of the regional ecosystems exhibited ecological resilience to drought, particularly in the source areas of the western basin of the YRB. Shrublands have the highest drought resilience among vegetation types, while grasslands have the lowest. The resilience of each climatic zone was in the order of semi-humid, semi-arid, and arid order. This study comprehensively analyzed of the spatial and temporal dynamics and response mechanisms of WUE in the YRB, offering a new perspective and scientific basis for understanding and predicting the ecosystem response to climate change.
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Affiliation(s)
- SaiHua Liu
- College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
| | - Lianqing Xue
- College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China; School of Hydraulic Engineering, Wanjiang University of Technology, Anhui 243031, China.
| | - Ying Xiao
- College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
| | - Mingjie Yang
- College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
| | - Yuanhong Liu
- College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
| | - Qiang Han
- College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
| | - Jingtian Ma
- College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
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Zhao Y, Xiong L, Yin J, Zha X, Li W, Han Y. Understanding the effects of flash drought on vegetation photosynthesis and potential drivers over China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 931:172926. [PMID: 38697519 DOI: 10.1016/j.scitotenv.2024.172926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 04/26/2024] [Accepted: 04/29/2024] [Indexed: 05/05/2024]
Abstract
Flash droughts characterized by rapid onset and intensification are expected to be a new normal under climate change and potentially affect vegetation photosynthesis and terrestrial carbon sink. However, the effects of flash drought on vegetation photosynthesis and their potential dominant driving factors remain uncertain. Here, we quantify the susceptibility and response magnitude of vegetation photosynthesis to flash drought across different ecosystems (i.e., forest, shrubland, grassland, and cropland) in China based on reanalysis and satellite observations. By employing the extreme gradient boosting model, we also identify the dominant factors that influence these flash drought-photosynthesis relationships. We show that over 51.46 % of ecosystems across China are susceptible to flash drought, and grasslands are substantially suppressed, as reflected in both sensitivity and response magnitude (with median gross primary productivity anomalies of -0.13). We further demonstrate that background climate differences (e.g., mean annual temperature and aridity) predominantly regulate the response variation in forest and shrubland, with hotter/colder or drier ecosystems being more severely suppressed by flash drought. However, in grasslands and croplands, the differential vegetation responses are attributed to the intensity of abnormal hydro-meteorological conditions during flash drought (e.g., vapor pressure deficit (VPD) and temperature anomalies). The effects of flash droughts intensify with increasing VPD and nonmonotonically relate to temperature, with colder or hotter temperatures leading to more severe vegetation loss. Our results identify the vulnerable ecological regions under flash drought and enable a better understanding of vegetation photosynthesis response to climate extremes, which may be useful for developing effective management strategies.
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Affiliation(s)
- Yue Zhao
- State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, PR China.
| | - Lihua Xiong
- State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, PR China.
| | - Jiabo Yin
- State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, PR China.
| | - Xini Zha
- Changjiang Water Resources Protection Institute, Wuhan 430051, PR China; Key Laboratory of Ecological Regulation of Non-point Source Pollution in Lake and Reservoir Water Sources, Changjiang Water Resources Commission, Wuhan 430051, PR China.
| | - Wenbin Li
- State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, PR China.
| | - Yajing Han
- State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, PR China.
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Li Y, Huang S, Fang P, Liang Y, Wang J, Xiong N. Vegetation net primary productivity in urban areas of China responded positively to the COVID-19 lockdown in spring 2020. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 916:169998. [PMID: 38220011 DOI: 10.1016/j.scitotenv.2024.169998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 12/28/2023] [Accepted: 01/05/2024] [Indexed: 01/16/2024]
Abstract
To prevent the spread of COVID-19, China implemented large-scale lockdown measures in early 2020, resulting in a marked reduction in human activities over a short period. Studies have explored environmental changes during lockdowns, lacking analysis of response of net primary productivity (NPP) to lockdowns, especially for diverse vegetation types. Correlation between NPP and impact factors during lockdowns remains unclear. Through Google Earth Engine, we evaluated spatial-temporal changes in spring NPP at multiple scales during lockdown period (LD, 2020) compared with unlocked period (UL, 2017-2019) by remote sensing data in urban areas of China. Changes in four impact factors, aerosol optical depth (AOD) and photosynthetically active radiation (PAR) (via remote sensing data), alongside temperature (TEM) and precipitation (PRE) (via meteorological data) were explored. Additionally, geodetector, a valuable statistical tool for detecting the driving ability of various elements, was employed to explore the underlying causes of vegetation changes during LD. In the spring of LD: 1) National urban NPP generally increased (+6.50 %), notably in Northeast China (NE), North China (N) and East China (E). Besides, overall urban AOD decreased (-3.64 %), notably in N and Central China (C). National urban PAR increased (+2.7 %), particularly in C and Northwest China (NW). However, overall urban TEM (-0.06 %) and PRE (-1.21 %) changed negatively. 2) NPP in all three vegetation types in urban areas enhanced, with change rates: croplands > forests > grasslands. Evident enhancements occurred in the forests and croplands in N, and the grasslands in NE. 3) Through geodetector, during LD, AOD (q = 0.223) and TEM (q = 0.272) emerged as the dominant factors for NPP. Compared with UL, the explanatory power of AOD and PAR on NPP increased during LD. This study provides valuable insights into understanding the effects of short-term human activities on vegetation productivity, offering reference for the formulation of ecological and environmental policies.
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Affiliation(s)
- Yujie Li
- Beijing Key Laboratory of Precision Forestry, Beijing Forestry University, Beijing 100083, China; Institute of GIS, RS & GPS, Beijing Forestry University, Beijing 100083, China
| | - Shaodong Huang
- Beijing Key Laboratory of Precision Forestry, Beijing Forestry University, Beijing 100083, China; Institute of GIS, RS & GPS, Beijing Forestry University, Beijing 100083, China
| | - Panfei Fang
- Beijing Key Laboratory of Precision Forestry, Beijing Forestry University, Beijing 100083, China; Institute of GIS, RS & GPS, Beijing Forestry University, Beijing 100083, China
| | - Yuying Liang
- Beijing Key Laboratory of Precision Forestry, Beijing Forestry University, Beijing 100083, China; Institute of GIS, RS & GPS, Beijing Forestry University, Beijing 100083, China
| | - Jia Wang
- Beijing Key Laboratory of Precision Forestry, Beijing Forestry University, Beijing 100083, China; Institute of GIS, RS & GPS, Beijing Forestry University, Beijing 100083, China.
| | - Nina Xiong
- Beijing Key Laboratory of Precision Forestry, Beijing Forestry University, Beijing 100083, China; Institute of GIS, RS & GPS, Beijing Forestry University, Beijing 100083, China.
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