Evaluation of ecosystem resilience to drought based on drought intensity and recovery time

Ying, Yu; Fu, Bojie; Liu, Yanxu; Li, Yan; Wang, Shuai; Zhan, Tianyu; Wang, Yijia; Gao, Dexin

doi:10.1016/j.agrformet.2022.108809

Cited by 58 publications

(52 citation statements)

References 84 publications

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“…Several studies in fact support the notion that a higher drought frequency decreases both resistance (Bose et al, 2020;Xu et al, 2021) and recovery (Gao et al, 2018;Peltier & Ogle, 2019;Szejner et al, 2020;Jiao et al, 2021;Serra-Maluquer et al, 2021). However, the opposite, that is a higher drought frequency leading to a higher resilience, has also been shown (Yao et al, 2022; see also the above section on adaptations shaping the resilience to a subsequent drought event). Also, increasing drought severity is expected to decrease resistance to and recovery from a subsequent drought (Figure 4).…”

Section: Water-use Efficiencymentioning

confidence: 96%

Drought legacies and ecosystem responses to subsequent drought

Müller

Bahn

2022

Global Change Biology

117

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Section: Water-use Efficiencymentioning

confidence: 96%

Drought legacies and ecosystem responses to subsequent drought

Müller

Bahn

2022

Global Change Biology

117

View full text Add to dashboard Cite

show abstract

“…The area of resilience curves in this study has the following ecological implications (Yao et al, 2022): Within the same recovery time, the larger the curve area, the lower the sensitivity of the forest to drought, and the higher the forest resilience to drought.…”

Section: Interpretation Of the Resilience Curve And Resilience Qualif...mentioning

confidence: 83%

“…The leaf area index (LAI) is a key vegetation structure variable that is generally defined as half of the sum of green leaf surface area per unit surface area (Chen et al, 1991) and is critical in vegetation feedback to climate (Fang et al, 2019;Yan et al, 2019;Yao et al, 2022) (Wang et al, 2018).…”

Section: Vegetation Datamentioning

confidence: 99%

Altitudinal differentiation of forest resilience to drought in a dryland mountain

Gao

et al. 2023

Preprint

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Drought is the major climate disaster experienced by dryland mountains. However, the altitudinal differentiation of forest resilience to drought and its dominant climate factors in dryland mountains is largely unknown, which is crucial to predict whether the primary forests will collapse under future global warming. In this study, we selected the leaf area index (LAI) and the standardized precipitation evapotranspiration index (SPEI), fitted forest resilience curves based on drought intensity and recovery time, calculated the curve area to define resilience along elevation zones in Qilian Mountains from 1982 to 2020, and analysed the characteristics of resilience altitudinal differentiation and dominant factors. The results showed that the humid characteristics and trends towards humidification in forests were more significant at higher elevations. Forests in the elevation range of 2600–3900 m exhibited faster recovery rates and greater resilience to drought than those in 1700–2600 m. This altitudinal differentiation was related to precipitation non-monotonically. Meanwhile, potential evapotranspiration and cloud cover were the most important climate factors for resilience temporal changes in different altitude zones. The findings of this study confirm the importance of the altitudinal perspective in studies of the dryland mountain evolution under future global change conditions.

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“…The grassland vegetation response to drought is not instantaneous but is caused by a cumulative water deficit over time, as reflected in the response time of vegetation to drought (lag effect) ( Zhao et al., 2020 ; Zhang et al., 2022 ). When faced with prolonged drought, grasses also adopt different strategies to adapt to water scarcity, such as closing stomata ( Li and Liu, 2022 ), evolving stronger roots, and even having drought memory ( Yao et al., 2022 ). Among them, photosynthesis of vegetation is the most important link, which regulates its own growth and development to adapt to drought.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic assessment of drought stress vulnerability in grasslands of Xinjiang, China

et al. 2023

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In the process of climate warming, drought has increased the vulnerability of ecosystems. Due to the extreme sensitivity of grasslands to drought, grassland drought stress vulnerability assessment has become a current issue to be addressed. First, correlation analysis was used to determine the characteristics of the normalized precipitation evapotranspiration index (SPEI) response of the grassland normalized difference vegetation index (NDVI) to multiscale drought stress (SPEI-1 ~ SPEI-24) in the study area. Then, the response of grassland vegetation to drought stress at different growth periods was modeled using conjugate function analysis. Conditional probabilities were used to explore the probability of NDVI decline to the lower percentile in grasslands under different levels of drought stress (moderate, severe and extreme drought) and to further analyze the differences in drought vulnerability across climate zones and grassland types. Finally, the main influencing factors of drought stress in grassland at different periods were identified. The results of the study showed that the spatial pattern of drought response time of grassland in Xinjiang had obvious seasonality, with an increasing trend from January to March and November to December in the nongrowing season and a decreasing trend from June to October in the growing season. August was the most vulnerable period for grassland drought stress, with the highest probability of grassland loss. When the grasslands experience a certain degree of loss, they develop strategies to mitigate the effects of drought stress, thereby decreasing the probability of falling into the lower percentile. Among them, the highest probability of drought vulnerability was found in semiarid grasslands, as well as in plains grasslands and alpine subalpine grasslands. In addition, the primary drivers of April and August were temperature, whereas for September, the most significant influencing factor was evapotranspiration. The results of the study will not only deepen our understanding of the dynamics of drought stress in grasslands under climate change but also provide a scientific basis for the management of grassland ecosystems in response to drought and the allocation of water in the future.

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Evaluation of ecosystem resilience to drought based on drought intensity and recovery time

Cited by 58 publications

References 84 publications

Drought legacies and ecosystem responses to subsequent drought

Drought legacies and ecosystem responses to subsequent drought

Altitudinal differentiation of forest resilience to drought in a dryland mountain

Probabilistic assessment of drought stress vulnerability in grasslands of Xinjiang, China

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