Ensemble empirical mode decomposition for analyzing phenological responses to warming

Guan, Biing T.

doi:10.1016/j.agrformet.2014.03.010

Cited by 26 publications

(24 citation statements)

References 32 publications

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“…Guan, Wright, Chung, & Chang, 2012;Guan et al, 2018;Zhang & Chen, 2017) and phenological data (Guan, 2014). Guan, Wright, Chung, & Chang, 2012;Guan et al, 2018;Zhang & Chen, 2017) and phenological data (Guan, 2014).…”

Section: Time Series Decompositionmentioning

confidence: 99%

“…North Atlantic Oscillation (NAO) or El Niño-Southern Oscillation (ENSO)) are related to changes in ocean temperatures and global atmospheric phenomena (Hurrell, 1995). The ability of large-scale indices to outperform proxies of local climatic conditions in explaining variation in ecological processes has been demonstrated in plant phenology (Guan, 2014;Post & Stenseth, 1999) and tree growth (Camarero, 2011;Lo, Blanco, & Guan, 2017;Madrigal-González et al, 2018;Piovesan & Schirone, 2000;Rozas, Camarero, Sangüesa-Barreda, Souto, & García-González, 2015). Nevertheless, to the best of our knowledge, no research has analysed the effect of global atmospheric-oceanic circulation patterns on leaf litter quantity and quality dynamics.…”

Section: Introductionmentioning

confidence: 99%

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ENSO and NAO affect long‐term leaf litter dynamics and stoichiometry of Scots pine and European beech mixedwoods

Andrés

Blanco

Imbert

et al. 2019

Global Change Biology

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Litterfall dynamics (production, seasonality and nutrient composition) are key factors influencing nutrient cycling. Leaf litter characteristics are modified by species composition, site conditions and water availability. However, significant evidence on how large‐scale, global circulation patterns affect ecophysiological processes at tree and ecosystem level remains scarce due to the difficulty in separating the combined influence of different factors on local climate and tree phenology. To fill this gap, we studied links between leaf litter dynamics with climate and other forest processes, such as tree‐ring width (TRW) and intrinsic water‐use efficiency (iWUE) in two mixtures of Scots pine (Pinus sylvestris L.) and European beech (Fagus sylvatica L.) in the south‐western Pyrenees. Temporal series (18 years) of litterfall production and elemental chemical composition were decomposed following the ensemble empirical mode decomposition method and relationships with local climate, large‐scale climatic indices, TRW and Scots pine's iWUE were assessed. Temporal trends in N:P ratios indicated increasing P limitation of soil microbes, thus affecting nutrient availability, as the ecological succession from a pine‐dominated to a beech‐dominated forest took place. A significant influence of large‐scale patterns on tree‐level ecophysiology was explained through the impact of the North Atlantic Oscillation (NAO) and El Niño–Southern Oscillation (ENSO) on water availability. Positive NAO and negative ENSO were related to dry conditions and, consequently, to early needle shedding and increased N:P ratio of both species. Autumn storm activity appears to be related to premature leaf abscission of European beech. Significant cascading effects from large‐scale patterns on local weather influenced pine TRW and iWUE. These variables also responded to leaf stoichiometry fallen 3 years prior to tree‐ring formation. Our results provide evidence of the cascading effect that variability in global climate circulation patterns can have on ecophysiological processes and stand dynamics in mixed forests.

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Section: Time Series Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ENSO and NAO affect long‐term leaf litter dynamics and stoichiometry of Scots pine and European beech mixedwoods

Andrés

Blanco

Imbert

et al. 2019

Global Change Biology

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“…Empirical mode decomposition (EMD) [Huang et al, 1998] is an algorithm to iteratively extract the intrinsic time scale components from any given series. It has already been applied in studies of climate variability [Brisson et al, 2015;Coughlin and Tung, 2005;Molla et al, 2011;Pegram et al, 2008] and climate effects on plant phenology [Guan, 2014].…”

Section: Interannual Extraction Tools 411 Ensemble Empirical Mode mentioning

confidence: 99%

Vegetation response to precipitation variability in East Africa controlled by biogeographical factors

et al. 2016

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Ecosystem sensitivity to climate variability varies across East Africa, and identifying the determinant factors of this sensitivity is crucial to assessing region‐wide vulnerability to climate change and variability. Such assessment critically relies on spatiotemporal data sets with inherent uncertainty, on new processing techniques to extract interannual variability at a priori unknown time scales and on adequate statistical models to test for biogeographical effects on vegetation‐precipitation relationships. In this study, interannual variability in long‐term records of normalized difference vegetation index and satellite‐based precipitation estimates was detected using ensemble empirical mode decomposition and standardized precipitation index with varying accumulation periods. Environmental effect modeling using additive models with spatially correlated effects showed that ecosystem sensitivity is primarily predicted by biogeographical factors such as annual precipitation distribution (reaching maximum sensitivity at 500 mm yr−1), vegetation type and structure, ocean‐climate coupling, and elevation. The threat of increasing climate variability and extremes impacting productivity and stability of ecosystems is most imminent in semiarid grassland and mixed cropland ecosystems. The influence of oceanic phenomena such as El Niño–Southern Oscillation and Indian Ocean Dipole is foremost reflected in precipitation variability, but prolonged episodes also pose risks for long‐term degradation of tree‐rich ecosystems in the East African Great Lakes region.

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“…The extracted secular trend, either monotonic or containing only one extremum, does not follow a priori shape and varies with time after the removal of intrinsic variability or the extension of new data, allowing the extracted trend to reveal more underlying information on the nonlinear and nonstationary time series (Ji, Wu, Huang, & Chassignet, 2014). Thus, EEMD has a wide range of applications in climatic research such as analyzing the evolution of land surface air temperature trends (Ji et al, 2014), phenological responses to warming (Guan, 2014), interannual vegetation dynamics (Hawinkel et al, 2016), and nonlinear variations in forest LAI (Yin et al, 2017).…”

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confidence: 99%

Vegetation dynamic trends and the main drivers detected using the ensemble empirical mode decomposition method in East Africa

Wei

Wang

et al. 2018

Land Degrad Dev

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Understanding vegetation changes and the underlying causes are critical to predict the future ecosystem dynamics. Water‐limited ecosystems in East Africa have been identified as particularly vulnerable to the impacts of increased environmental change, but relatively little is known about the vegetation dynamic trends and the main drivers. Taking the satellite‐derived normalized difference vegetation index (NDVI) as a proxy of vegetation growth, we detected the spatiotemporal evolution of NDVI trends and explored the main drivers based on the ensemble empirical mode decomposition method. Results showed that greening (restoration) and browning (degradation) coexisted in East Africa during 1982–2013. However, the early greening stalled or even reversed as the browning continuously intensified over time. Greening‐to‐browning prevailed in East Africa after 2000. Generally, more than half of the study area (54.77%, 13.77% with a significant decrease) experienced a browning trend during 1982–2013, with an average change rate of (−0.12 ± 1.10) × 10−3. We also found that the percentage of greening area in each land cover type overall decreased. Furthermore, the remarkable spatiotemporal correlations between NDVI and soil water suggested that water was a dominant factor governing the vegetation changes in East Africa. The effectiveness of different types of protected areas (PAs) on vegetation change varied, depending to some extent on management policy (legal laws and on‐site patrols). Therefore, a big challenge for sustainable management of protected areas is how to balance conservation and development objectives in East Africa.

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Ensemble empirical mode decomposition for analyzing phenological responses to warming

Cited by 26 publications

References 32 publications

ENSO and NAO affect long‐term leaf litter dynamics and stoichiometry of Scots pine and European beech mixedwoods

ENSO and NAO affect long‐term leaf litter dynamics and stoichiometry of Scots pine and European beech mixedwoods

Vegetation response to precipitation variability in East Africa controlled by biogeographical factors

Vegetation dynamic trends and the main drivers detected using the ensemble empirical mode decomposition method in East Africa

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