Impacts of a strong El Niño event on leaf phenology and carbon dioxide exchange in a secondary dry dipterocarp forest

Kaewthongrach, Rungnapa; Chidthaisong, Amnat; Charuchittipan, Doojdao; Vitasse, Yann; Sanwangsri, Montri; Varnakovida, Pariwate; Diloksumpun, Sapit; Panuthai, Samreong; Pakoktom, Tiwa; Suepa, Tanita; Leclerc, Monique Y.

doi:10.1016/j.agrformet.2020.107945

Cited by 9 publications

(14 citation statements)

References 30 publications

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“…Drought‐induced suppression on GPP , NEP , and ET were also significant in the rainy season. However, due to the different environmental preferences and phenology, we only found a decrease in the NF's GPP and NEP during the senescence phase in the drier cool‐dry I season (Kaewthongrach et al., 2020). Detto et al.…”

Section: Discussionmentioning

confidence: 75%

“…The largest decrease rates of GPP, ET, and LUE occurred in the warm-dry season for both land-use systems (Figures 5 and 6), attributed to a late leaf shedding in early 2016 (Figure S1 in Supporting Information S1). The longer deciduousness at the NF was also found by the litterfall measurements (Kaewthongrach et al, 2020). Under the prolonged drought, the limited supply of subsurface water storage from the rainy season could not have enough capacity to satisfy plant water demands in the subsequent dry season (Guan et al, 2015).…”

Section: Ecosystem Response To Droughtmentioning

confidence: 67%

“…Drought-induced suppression on GPP, NEP, and ET were also significant in the rainy season. However, due to the different environmental preferences and phenology, we only found a decrease in the NF's GPP and NEP during the senescence phase in the drier cool-dry I season (Kaewthongrach et al, 2020). Detto et al (2018) highlighted this elevated leaf fall of tropical dry forests prior to the warm-dry phase in response to the El Niño-Southern Oscillation based on 30-year field observations.…”

Section: Ecosystem Response To Droughtmentioning

confidence: 73%

“…As a prevailing cash crop, rubber ( Hevea brasiliensis ) plantations have expanded extensively over the region and have provided more than 85% of the global natural rubber at the expense of NF loss (Ahrends et al., 2015; Grogan et al., 2019). Although reforestation has largely expanded the area of second‐growth (secondary) forests, these areas still face competition with the plantations for the cleared forestland (Kaewthongrach et al., 2020). As a result, rubber plantations are still rapidly expanding from the humid equatorial zone northward into the non‐traditional planting areas over the mainland (Chambon et al., 2016).…”

Section: Introductionmentioning

confidence: 99%

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Carbon and Water Cycling in Two Rubber Plantations and a Natural Forest in Mainland Southeast Asia

et al. 2022

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Rubber plantations have rapidly replaced natural forests (NFs) in Mainland Southeast Asia, yet the relevant impacts on the terrestrial carbon cycle remain uncertain especially with an increase in drought frequency. Our study compared eddy‐covariance measurements of carbon and water fluxes from two rubber monoculture plantations (at a northern marginal site and a southern traditional plantation site) with a second‐growth NF between 2015 and 2018, and their responses to a prolonged drought during 2015/2016. The NF had higher light use efficiency, water use efficiency and gross primary productivity (GPP, 2.94 ± 0.41 kg C m−2 yr−1) than the northern rubber (NR) monoculture (2.45 ± 0.17 kg C m−2 yr−1), while lower ecosystem carbon use efficiency (eCUE) caused a lower net ecosystem productivity (NEP, 0.75 ± 0.25 kg C m−2 yr−1) compared to the plantation (1.19 ± 0.22 kg C m−2 yr−1). Drought decreased the NF eCUE by 23% with significant carbon uptake restrictions across multiple seasons, while the rubber GPP reduction was only substantial in the warm‐dry season with an overall 17% decline in eCUE. The NR site's GPP was mainly controlled by soil water content throughout the year. Higher light availability offset the negative effect of drier conditions on the rubber GPP, resulting in larger carbon uptake compared to the southern plantation (GPP, 2.12 ± 0.12 kg C m−2 yr−1; NEP, 1.07 ± 0.14 kg C m−2 yr−1). In contrast, the NF GPP was mainly restricted by vapor pressure deficit, especially during the drought.

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Section: Discussionmentioning

confidence: 75%

Section: Ecosystem Response To Droughtmentioning

confidence: 67%

Section: Ecosystem Response To Droughtmentioning

confidence: 73%

Section: Introductionmentioning

confidence: 99%

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Carbon and Water Cycling in Two Rubber Plantations and a Natural Forest in Mainland Southeast Asia

et al. 2022

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“…This El Niño significantly reduced the amount of rainfall in Southeast Asia and worldwide, and was associated with higher temperatures when compared with the long-term average climate [ 6 ]. Related research [ 7 ] found the event had significant impacts on forest carbon uptake and species responses which did not appear in the long-term average climate because forests can absorb atmospheric carbon dioxide through photosynthesis.…”

Section: Introductionmentioning

confidence: 99%

Characterizing and forecasting the responses of tropical forest leaf phenology to El Nino by machine learning algorithms

et al. 2021

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Climate change and global warming have serious adverse impacts on tropical forests. In particular, climate change may induce changes in leaf phenology. However, in tropical dry forests where tree diversity is high, species responses to climate change differ. The objective of this research is to analyze the impact of climate variability on the leaf phenology in Thailand’s tropical forests. Machine learning approaches were applied to model how leaf phenology in dry dipterocarp forest in Thailand responds to climate variability and El Niño. First, we used a Self-Organizing Map (SOM) to cluster mature leaf phenology at the species level. Then, leaf phenology patterns in each group along with litterfall phenology and climate data were analyzed according to their response time. After that, a Long Short-Term Memory neural network (LSTM) was used to create model to predict leaf phenology in dry dipterocarp forest. The SOM-based clustering was able to classify 92.24% of the individual trees. The result of mapping the clustering data with lag time analysis revealed that each cluster has a different lag time depending on the timing and amount of rainfall. Incorporating the time lags improved the performance of the litterfall prediction model, reducing the average root mean square percent error (RMSPE) from 14.35% to 12.06%. This study should help researchers understand how each species responds to climate change. The litterfall prediction model will be useful for managing dry dipterocarp forest especially with regards to forest fires.

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Untangling winter chilling and spring forcing effects on spring phenology of subtropical tree seedlings

Zeng

Vitasse

2023

Agricultural and Forest Meteorology

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Impacts of a strong El Niño event on leaf phenology and carbon dioxide exchange in a secondary dry dipterocarp forest

Cited by 9 publications

References 30 publications

Carbon and Water Cycling in Two Rubber Plantations and a Natural Forest in Mainland Southeast Asia

Carbon and Water Cycling in Two Rubber Plantations and a Natural Forest in Mainland Southeast Asia

Characterizing and forecasting the responses of tropical forest leaf phenology to El Nino by machine learning algorithms

Untangling winter chilling and spring forcing effects on spring phenology of subtropical tree seedlings

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