Saturation response of enhanced vegetation productivity attributes to intricate interactions

Lian, Xihong; Jiao, Limin; Liu, Zejin

doi:10.1111/gcb.16522

Cited by 14 publications

(5 citation statements)

References 75 publications

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“…One possible explanation is that the greening stimulated by carbon dioxide (i.e., an augmentation in leaf area index), surpasses the water‐saving effect (i.e., the reduction in stomatal conductance and transpiration), aggravating water scarcity in arid regions and thereby amplifying the E WA‐GPP at shorter time scales, including 3‐, 7‐, and 17‐year time scales (Chen et al., 2022; Ueyama et al., 2020). However, in ENF, EBF, DBF, and MF, we observed a weakening E WA‐GPP (both positive and negative) for GPP NIRv , which is consistent with previous studies that there is a weakening impact of WA induced by carbon dioxide fertilization effects (Lian et al., 2023; Wang et al., 2020), whereas GPP FLUX and GPP EC‐LUE exhibited an increasing E WA‐GPP . Considering the better response of NIRv to high vegetation cover areas, we believe that the effect of WA on forests is weakening, which is more plausible, but of course, this needs to be further confirmed by other metrics such as biomass (Wang et al., 2021; Yang, Wang, et al., 2022).…”

Section: Discussionsupporting

confidence: 92%

The strengthened impact of water availability at interannual and decadal time scales on vegetation GPP

Liang,

Zhang,

Wang

et al. 2024

Global Change Biology

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Water availability (WA) is a key factor influencing the carbon cycle of terrestrial ecosystems under climate warming, but its effects on gross primary production (EWA‐GPP) at multiple time scales are poorly understood. We used ensemble empirical mode decomposition (EEMD) and partial correlation analysis to assess the WA‐GPP relationship (RWA‐GPP) at different time scales, and geographically weighted regression (GWR) to analyze their temporal dynamics from 1982 to 2018 with multiple GPP datasets, including near‐infrared radiance of vegetation GPP, FLUXCOM GPP, and eddy covariance–light‐use efficiency GPP. We found that the 3‐ and 7‐year time scales dominated global WA variability (61.18% and 11.95%), followed by the 17‐ and 40‐year time scales (7.28% and 8.23%). The long‐term trend also influenced 10.83% of the regions, mainly in humid areas. We found consistent spatiotemporal patterns of the EWA‐GPP and RWA‐GPP with different source products: In high‐latitude regions, RWA‐GPP changed from negative to positive as the time scale increased, while the opposite occurred in mid‐low latitudes. Forests had weak RWA‐GPP at all time scales, shrublands showed negative RWA‐GPP at long time scales, and grassland (GL) showed a positive RWA‐GPP at short time scales. Globally, the EWA‐GPP, whether positive or negative, enhanced significantly at 3‐, 7‐, and 17‐year time scales. For arid and humid zones, the semi‐arid and sub‐humid zones experienced a faster increase in the positive EWA‐GPP, whereas the humid zones experienced a faster increase in the negative EWA‐GPP. At the ecosystem types, the positive EWA‐GPP at a 3‐year time scale increased faster in GL, deciduous broadleaf forest, and savanna (SA), whereas the negative EWA‐GPP at other time scales increased faster in evergreen needleleaf forest, woody savannas, and SA. Our study reveals the complex and dynamic EWA‐GPP at multiple time scales, which provides a new perspective for understanding the responses of terrestrial ecosystems to climate change.

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Section: Discussionsupporting

confidence: 92%

The strengthened impact of water availability at interannual and decadal time scales on vegetation GPP

Liang,

Zhang,

Wang

et al. 2024

Global Change Biology

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“…According to a new study, the driving forces of vegetation productivity in China have changed from being dominated by a single factor to being influenced by the interactive effects of multiple factors [21]. In some regions, the interactive effects of driving forces behind vegetation variations were seen, but the dominant factors and the types of interactions varied between regions [17,23].…”

Section: Driving Factors For the Spatial Heterogeneity Of Vegetation ...mentioning

confidence: 99%

“…In addition to change trends, significant spatial heterogeneity exists in the vegetation, and various settings have varied vegetation features [18,19]. Meanwhile, it is not always the case that one factor has an independent effect on vegetation development [20,21]. Geographical detectors can effectively identify the influencing factors of spatial differences in vegetation, as well as the importance of each factor and how those factors interact with one another [17,22].…”

Section: Introductionmentioning

confidence: 99%

Spatial Heterogeneity and the Increasing Trend of Vegetation and Their Driving Mechanisms in the Mountainous Area of Haihe River Basin

Cao,

Wang,

Zhang

et al. 2024

Remote Sensing

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In addition to serving as North China’s water supply and ecological barrier, the mountainous area of the Haihe River basin (MHRB) is a crucial location for the application of ecological engineering. Vegetation is an important component in the ecological conservation and eco-hydrological progress of the MHRB. A better understanding of regional vegetation growth can be achieved by a thorough investigation of vegetation indicators. In this research, the leaf area index (LAI) and gross primary productivity (GPP) were chosen as vegetation indicators. The characteristics and driving forces of the spatiotemporal variations of LAI and GPP in the MHRB were explored through Sen’s slope, the Mann–Kendall test, the optimal parameter-based geographical detector model, and correlation analysis. From 2001 to 2018, the annual LAI and GPP increased significantly on the regional scale. The areas with significantly increased vegetation accounted for more than 81% of the MHRB. Land use was the most influential element for the spatial heterogeneity of LAI and GPP, and the humidity index was the most crucial one among climate indicators. Non-linear enhancement or bivariate enhancement was discovered between any two factors, and the strongest interaction was from land use and humidity index. The lowest vegetation cover was found in dry regions with annual precipitation below 407 mm and the humidity index under 0.41; while in both forests and large undulating mountains, higher LAI and GPP were observed. About 87% of the significantly increased vegetation was found in areas with unaltered land use. The increase in vegetation in the MHRB from 2001 to 2018 was promoted by the increased precipitation and humidity index and the reduced vapor pressure deficit. The sensitivity of GPP to climate change was stronger than that of LAI. These findings can serve as a theoretical guide for the application of ecological engineering and ecological preservation in the MHRB.

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“…The results showed that the partial correlation coefficients of the NDVI and albedo with LST are significant except in summer (Table 3). This may be due to the saturation effect of the summer vegetation and albedo contributions on the LST [48], resulting in their insignificant trend of contribution. The individual contribution of albedo to LST was larger than that of NDVI at all timescales, especially in spring and autumn.…”

Section: Vegetation Impacts On Lst At Different Time Scalesmentioning

confidence: 99%

Annual and Seasonal Trends of Vegetation Responses and Feedback to Temperature on the Tibetan Plateau since the 1980s

Wang

Darvishzadeh

et al. 2023

Remote Sensing

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The vegetation–temperature relationship is crucial in understanding land–atmosphere interactions on the Tibetan Plateau. Although many studies have investigated the connections between vegetation and climate variables in this region using remote sensing technology, there remain notable gaps in our understanding of vegetation–temperature interactions over different timescales. Here, we combined site-level air temperature observations, information from the global inventory modeling and mapping studies (GIMMS) dataset, and moderate-resolution imaging spectroradiometer (MODIS) products to analyze the spatial and temporal patterns of air temperature, vegetation, and land surface temperature (LST) on the Tibetan Plateau at annual and seasonal scales. We achieved these spatiotemporal patterns by using Sen’s slope, sequential Mann–Kendall tests, and partial correlation analysis. The timescale differences of vegetation-induced LST were subsequently discussed. Our results demonstrate that a breakpoint of air temperature change occurred on the Tibetan Plateau during 1994–1998, dividing the study period (1982–2013) into two phases. A more significant greening response of NDVI occurred in the spring and autumn with earlier breakpoints and a more sensitive NDVI response occurred in recent warming phase. Both MODIS and GIMMS data showed a common increase in the normalized difference vegetation index (NDVI) on the Tibetan Plateau for all timescales, while the former had a larger greening area since 2000. The most prominent trends in NDVI and LST were identified in spring and autumn, respectively, and the largest areas of significant variation in NDVI and LST mostly occurred in winter and autumn, respectively. The partial correlation analysis revealed a significant negative impact of NDVI on LST during the annual scale and autumn, and it had a significant positive impact during spring. Our findings improve the general understanding of vegetation–climate relationships at annual and seasonal scales.

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Saturation response of enhanced vegetation productivity attributes to intricate interactions

Cited by 14 publications

References 75 publications

The strengthened impact of water availability at interannual and decadal time scales on vegetation GPP

The strengthened impact of water availability at interannual and decadal time scales on vegetation GPP

Spatial Heterogeneity and the Increasing Trend of Vegetation and Their Driving Mechanisms in the Mountainous Area of Haihe River Basin

Annual and Seasonal Trends of Vegetation Responses and Feedback to Temperature on the Tibetan Plateau since the 1980s

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