A warmer winter followed by a colder summer contributed to a longer recovery time in the high latitudes of Northeast China

Ying, Yu; Liu, Yanxu; Fu, Bojie; Wang, Yijia; Wang, Yaping; Chen, Peng; Zhan, Tianyu

doi:10.1016/j.agrformet.2022.108979

Cited by 7 publications

(5 citation statements)

References 96 publications

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“…Fourth, the impacts of drought on ecosystems leave marks in multiple ways that can be observed from leaves to tree rings to ecosystems (Kannenberg et al, 2019). However, current studies mostly evaluate the recovery of ecosystems to drought from the perspective of productivity (GPP) and vegetation greenness (LAI, NDVI) (Liu et al, 2019; Yao, Liu, et al, 2022). Therefore, we emphasize the use of flux station data, tree ring data and remote sensing data to explore the drought recovery mechanism of terrestrial ecosystems from multiple perspectives.…”

Section: Discussionmentioning

confidence: 99%

“…Third, we quantified the recovery time based on two calculation methods (Figure 1b). One was that the recovery time started when the detrended vegetation data reached a minimum and ended when the detrended vegetation data were greater than −0.5 SD (Ingrisch & Bahn, 2018; Li et al, 2019; Yao, Liu, et al, 2022). The second was that recovery time began from the end of drought and ended when the detrended vegetation data were greater than −0.5 SD (Schwalm et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

“…We employed partial correlation analysis based on the five main factors of recovery time to determine the spatial pattern of the dominant factors. This method enable detection of the correlation between two variables without the influence of other factors (Li et al, 2022; Yao, Liu, et al, 2022). Specifically, we calculated the partial correlation coefficient between recovery time and Rec SM while eliminating the influence of drought duration, vegetation loss, Rec VPD and Rec TEM within a spatial moving window of 7 × 7 pixels.…”

Section: Methodsmentioning

confidence: 99%

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Soil moisture determines the recovery time of ecosystems from drought

Ying

Liu

Zhou

et al. 2023

Global Change Biology

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Drought refers to the climate extreme caused by moisture deficiency that affects the hydrological cycle, increases the risk of forest death, causes land degradation and even reduces the biodiversity of terrestrial ecosystems (Sturm et al., 2022;Vicente-Serrano et al., 2020;Zhang et al., 2022). Previous research pointed that negatively affected by warm drought, negative gross primary productivity (GPP) extremes of northern mid-latitude ecosystems increased by 10.6% in 2000-2016 compared with the period from 1982 to 1998 (Gampe et al., 2021), which greatly weakened the carbon sink of ecosystems and increased the risks to ecosystems. Suffered from droughts, resilience of ecosystem to disturbance plays roles in recovering from negative vegetation anomalies caused by droughts (Forzieri et al., 2022;Smith et al., 2022). Under increasing global drought risks, revealing how ecosystems recover from droughts and further clarifying the key factors affecting recovery could provide critical support to maintain the stability of

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Soil moisture determines the recovery time of ecosystems from drought

Ying

Liu

Zhou

et al. 2023

Global Change Biology

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“…Regions with monthly NDVI values <0.1 are considered sparse vegetation and are discarded from the final analyses. LAI is widely used measurements for describing plant canopy structure (Yao, Fu, et al., 2022), and kNDVI is close relations to vegetation productivity and density (Yao, Liu, et al., 2022). In the subsequent section, we independently calculated drought resistance and resilience using different vegetation indices to enhance the robustness of results.…”

Section: Methodsmentioning

confidence: 99%

“…(2022) and Yao, Liu, et al. (2022) used the leaf area index (LAI) from Global Inventory Monitoring and Modeling System (GIMMS) to assess the drought resistance and resilience of forest and grassland ecosystems, and revealed that forest ecosystems were high resistance‐low resilience but grassland ecosystems were low resistance‐high resistance. In European ecosystems, Ivits et al.…”

Section: Introductionmentioning

confidence: 99%

Declining Tradeoff Between Resistance and Resilience of Ecosystems to Drought

Yao,

Liu,

Song

et al. 2024

Earth's Future

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Resistance and resilience are widely used to characterize ecosystem drought stability. Tradeoff between resistance and resilience have been reported, but its long‐term trends remain uncertain at global scale. Based on remotely sensed vegetation indices, we assessed the spatiotemporal dynamics of drought resistance and resilience. Result revealed that there was a significant decline in the tradeoff between resistance and resilience, corresponding to a substantial increase in the proportion of areas with high resistance‐high resilience or low resistance‐low resilience. In the South Sahel, South Africa and Central China, the increased precipitation and vegetation coverage contribute to enhanced drought stability constructed by resistance and resilience; while rising temperature, decreased water availability and deforestation lead to declined stability in northeastern North America, South America and the Congo region. Increases in the areas with low resistance‐low resilience resulting from declining tradeoffs warn of increased regional ecosystem vulnerability.

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