Next Article in Journal
Effect of Stand Age on Soil CO2 Emissions in Pedunculate Oak (Quercus robur L.) Forests
Previous Article in Journal
Chemical Ecology in Forests
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 15
Issue 9
10.3390/f15091572
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Heat and Drought Have Exacerbated the Midday Depression Observed in a Subtropical Fir Forest by a Geostationary Satellite

by
Qianqian Xie
1,†,
Kexin Chen
1,†,
Tong Li
1,2,
Jia Liu
1,
Yuqiu Wang
1 and
Xiaolu Zhou
1,2,*
1
School of Geographic Sciences, Hunan Normal University, Changsha 410081, China
2
Hunan Provincial Key Laboratory for Eco-Environmental Changes and Carbon Sequestration of the Dong-Ting Lake Basin, Hunan Normal University, Changsha 410081, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Forests 2024, 15(9), 1572; https://doi.org/10.3390/f15091572
Submission received: 22 July 2024 / Revised: 31 August 2024 / Accepted: 4 September 2024 / Published: 7 September 2024
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
Browse Figures
Review Reports Versions Notes

Abstract

Recently, increasing heat and drought events have threatened the resilience of Chinese fir forests. Trees primarily respond to these threats by downregulating photosynthesis including through stomatal limitation that causes a drop in productivity at noon (known as the midday depression). However, the effects of these events on midday and afternoon GPP inhibition are rarely analyzed on a fine timescale. This may result in negligence of critical responses. Here, we investigated the impact of climatic events on the midday depression of photosynthesis at a subtropical fir forest in Huitong from 2016 to 2022 using data from the Himawari 8 meteorological satellite and flux tower. Our results indicated that the highest number of midday depression occurred in 2022 (126 times) with the highest average temperature (29.1 °C). A higher incidence of midday depression occurred in summer and autumn, with 48 and 34 occurrences, respectively. Compound drought, heat, and drought events induced increases in midday depression at 74.3%, 66.0%, and 47.5%. Thus, trees are more likely to adopt midday depression as an adaptive strategy during compound drought and heat events. This study can inform forest management and lead to improvements in Earth system models.

1. Introduction

Chinese fir forests, extensively cultivated in subtropical regions of China, are the leading type of artificial forests in terms of both planting area and standing biomass [1]. This forest type is more susceptible to the impacts of climate change than natural forests [2,3]. Over the past four decades, subtropical regions of China have experienced a profound impact on vegetation productivity due to the increasing frequency and severity of drought events [4]. Concurrently, heatwaves have emerged with greater regularity, exacerbating the stress on these ecosystems [5]. Compound drought and heat events have shown a notable rise, posing a dual threat to the resilience of Chinese fir forests [6].
In the face of this threat, trees primarily respond to drier and warmer conditions by downregulating photosynthesis, a response triggered by stomatal limitations [7]; restrictions in electron transport [8]; decreases in photosynthetic pigments [9]; and the deactivation of Rubisco (ribulose-1,5-bisphosphate carboxylase/oxygenase) [10]. These adjustments result in oscillations of the net photosynthetic rate throughout the day. It is essential to acknowledge that this daily timescale is where environmental factors exert the most substantial impact on the dynamics of photosynthesis, including the efficiency of carbon capture and water utilization [11,12].
Numerous previous studies have adequately explained the dynamic response of photosynthesis to stomatal behavior. The diurnal dynamic of photosynthesis is usually a unimodal or asymmetrical bimodal profile, with the bimodal profile often characterizing the occurrence of midday depression [13,14]. Furthermore, midday depression in plants is defined as the absence of an increase in carbon uptake with an increase in photosynthetically active radiation over the timescale of a day, leading to a decrease in productivity during the midday period [15]. Midday depression is a physiological phenomenon that plants have used to adapt to environmental stresses over a long period of evolution [16]. Although it regulates and protects the entire life cycle of plants, it comes at the cost of low energy utilization of sunlight at midday and temporary downregulation of physiological and biochemical activities in plants, leading to reduced carbon uptake and thus, lower photosynthetic yield [14,17,18], which can be reduced by 20 to 50 percent or more [19]. Therefore, the phenomenon of midday depression can be discerned by examining the Gross Primary Productivity (GPP) of plants, with the quantification of its impact on photosynthesis achieved through the analysis of characteristic daily fluctuations in GPP [20]. However, the meteorological conditions for the effect of midday depression on the daily fluctuations in GPP remain unclear based on observations of long-term positioning. Although advancements in remote sensing technologies and model simulations have rendered the high-resolution temporal simulation of GPP feasible in conjunction with remote sensing data [21,22], the effects of high temperature and drought on midday and afternoon GPP inhibition are rarely analyzed on a finer timescale for several consecutive years. Negligence of critical responses may occur during coarser timescale analysis.
In this study, we investigated the impact of climatic events on the midday depression of photosynthesis at a subtropical fir forest in Huitong, China. We used the photosynthetically active radiation data (ten minutes scale) from the Himawari 8 meteorological satellite in conjunction with ERA5-land data to estimate the GPP for this site from 2016 to 2022 and compared these estimates with the actual flux data measured using the eddy covariance technique at the site to verify the accuracy of the estimated GPP. Depending on the relevance between drought events and GPP fluctuations in the typical Chinese fir forest ecosystem, we hypothesize that drought events may increase the frequency of midday dormancy and thus reduce GPP in subtropical areas that have been stressed by summer drought for a long time. The purposes of this study are as follows: (1) to elucidate the intra-annual and inter-annual temporal characteristics and trends of midday depression; and (2) to estimate the impacts of various climatic events on the frequency of midday depression.

2. Materials and Methods

2.1. Study Site

This study was conducted in the Huitong fir forest located in Hunan Province, which is characterized by a subtropical monsoon climate with high temperatures and abundant rainfall in summer, and mild weather with less precipitation in winter. This area has a mean annual temperature of 16.8 °C and a mean annual precipitation of 1268 mm. The flux tower for this research is situated at 26°47′ N latitude and 109°35′ E longitude, with an elevation of 337 m (Figure 1). The tower stands at a height of 32.5 m, with the eddy covariance observation system instruments installed at a height of 26.7 m, and the canopy height of the fir forest below the tower is approximately 15 m.
The study area is roughly located in the center of the subtropical Chinese fir forest distribution, which can reflect the average level of terrain, climate, and soil in the Chinese fir planting area. The underlying geographical surface of this region is relatively homogenous and has unique climatic characteristics. There are highly frequent, intense temperature events accompanied by drought that often occur in the second half of summer. Therefore, we used the observation of the only flux tower in this area as the reference level of carbon flux in this typical Chinese fir ecoregion to verify the temporal variation pattern and frequency of midday depression in the forest.

2.2. Data Collection

We used the ERA5-land dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF; https://cds.climate.copernicus.eu (accessed on 19 January 2024)), which offers a high spatial resolution of 0.1° × 0.1°. We downloaded hourly data for temperature, relative humidity, and potential evapotranspiration for the period from 2016 to 2022. Recognizing the coarse spatial resolution of the ERA5-land hourly data was inadequate for the precision required by our research, we employed a linear interpolation method to refine the data to a half-hourly resolution, thereby enhancing the accuracy of GPP estimation. Additionally, the vapor pressure deficit (VPD) was calculated using temperature and relative humidity data, and the aridity index was determined using the potential evapotranspiration (ET) data.
Our study employed the photosynthetically active radiation (PAR) dataset provided by the Himawari 8 meteorological satellite, a geostationary satellite operated by the Japan Meteorological Agency (JMA). Himawari 8 offers a spatial resolution of 500 m in the visible band and 2 km in the infrared band. Covering the entire East Asia and Western Pacific region, including Japan, China, Korea, Southeast Asia, and Australia, the satellite provides comprehensive coverage for our region of interest. For the period from 2016 to 2022, we extracted solar shortwave radiation (SW) and PAR data at ten minutes intervals from the raster points. The SW data were utilized for radiation correction, while the PAR data served as input for the variables required in the GPP model.
To validate the accuracy of daily GPP simulated by remote sensing data, we compared it with the measured flux data from the Huitong Chinese fir forest. Briefly, ecosystem-scale carbon-based gas fluxes were measured using the eddy covariance (EC) technique with the EC system installed in a relatively flat area. The system comprises an open-path methane infrared gas analyzer (LI-7700, LI-COR, Lincoln, Nebraska, USA), an open-path carbon dioxide and water vapor infrared gas analyzer (LI-7500A, LI-COR, Lincoln, Nebraska, USA), and a three-dimensional sonic anemometer. Positioned 26.7 m above the soil surface, these sensors recorded raw data at a frequency of 10 Hz, which were then stored by a data logger in half-hourly intervals.
In brief, all data were obtained from (i) satellite datasets, (ii) model output based on satellite datasets, and (iii) field data from the flux tower. Additionally, we did not use field measurement from forest plots.

2.3. Data Analysis

To ensure the quality of flux data in our study, we implemented a series of quality control measures. We excluded data affected by the CSAT3 anemometer’s support structure and non-forest ecosystems surrounding our Huitong fir forest site. We also eliminated records with low RSSI indicators during rainfall events. Additionally, we removed data with excessive variance in methane flux and applied a threshold for friction wind velocity to ensure reliable observations. After these stringent controls, 34.8% of the original data remained for analysis. The method for calculating GPP was adopted from Landsberg and Waring (1997) [23].
Subsequently, we used three circadian metrics to identify and quantify the midday depression occurring between 2016 and 2022, including the daily center of mass of GPP (CGPP), the GPP peak hour (Hourpeak), and the ratio of GPP in the morning and the afternoon (Ratio M/A). This method was adopted from Li et al. (2023) [24]. If the CGPP is less than 12, it can be regarded as one of the determining conditions for the occurrence of midday depression. Hourpeak refers to the moment when the GPP value is at its maximum between 7 a.m. and 5 p.m. If the Hourpeak occurs before 12 p.m., it can be regarded as a determining condition for the occurrence of midday depression. Additionally, the Ratio M/A exceeding 1 indicates that afternoon GPP exceeds that of the morning, and vice versa. A ratio below 1 can be regarded as a determining condition for the occurrence of midday depression.
We analyzed the impact of three climatic events on the midday depression of the fir forest, including heat, drought, and compound drought and heat events. A heat event was defined as a day when the maximum temperature exceeded the 90th percentile of all daily maximum temperatures recorded throughout the study period. Considering the lag effect of drought on plants, a drought event was defined as a day when the cumulative precipitation over the previous seven days was less than 14 mm. Furthermore, a day was defined as a compound event if both a heat event and a drought event occurred on the same day. Subsequently, we examined the relationship between the frequency of climatic events and the occurrence of midday depression through linear regression analysis. We estimated the probability of midday depression being triggered by each climatic event by dividing the number of days with both the event and midday depression by the total days of the event.

3. Results

We integrated the PAR data from the Himawari 8 meteorological satellite with ERA5-land meteorological data to calculate the diurnal variations in GPP for the fir forest in Huitong from 2016 to 2022. Our results were compared with those obtained from the flux tower measurements. The fitting performance over the seven-year period was satisfactory, with an R² value of 0.605 for the satellite data and 0.631 for the flux tower data, indicating that geostationary satellite data can be effectively utilized to identify the midday depression in plant photosynthesis (Figure 2).
Between 2016 and 2022, seasonal trends in GPP (measured every half-hour), temperature, and precipitation exhibited similar patterns across the years (Figure 3). GPP values typically reached their annual nadir in January, then increased gradually, reaching their zenith in the months of July through September, after which they began to decrease (Figure 3a). The maximum annual GPP was recorded in 2017. Temperatures reached their zenith during June to August each year, with heatwaves predominantly occurring in this period (Figure 3b). The fir forest in Huitong experienced its highest precipitation during June to July, with a notable decrease during the winter (Figure 3c). The average daily temperatures during heat, drought, as well as compound drought and heat events were 22.6 °C, 17.5 °C, and 23.0 °C, respectively (Figure 3d). The average daily precipitation during heat events was 3.0 mm, followed by 0.8 mm during drought, and the least at 0.5 mm during compound drought and heat events (Figure 3e).
Figure 4 shows the number of midday depression occurrences at the Huitong fir forest site across different years, months, and seasons. The year 2022 had the highest number of occurrences with 126, followed by 2020 with 119, and 2016 with 113 times (Figure 4a). The year with the fewest occurrences was 2019, with 89 times. From 2018 to 2022, there was an overall increasing trend in the annual count of midday depression. The number of occurrences exhibited a unimodal pattern, with the fewest times in January, a gradual increase to a peak in August, and a subsequent decline (Figure 4b). Notably, the midday depression was most frequent during July, August, September, and October, each with more than 10 occurrences. The fir forest in this region experienced a higher incidence of midday depression in the summer and autumn, with 48 and 34 occurrences, respectively, and fewer events in the spring and winter seasons.
The mean annual occurrences of midday depression at Huitong fir forest from 2016 to 2022 demonstrated a significant positive correlation with the mean annual occurrences of heat, drought, and compound heat and drought events (Figure 5). Specifically, the number of midday depression occurrences in photosynthesis increased with the frequency of heat events (R² = 0.92, p = 0. 004; Figure 5a). Although the relationship between drought events and midday depression was the weakest among the climatic events, it still reached a substantial R² value of 0.68 (Figure 5b). The compound drought and heat events significantly increased the occurrence of midday depression (R² = 0.79, p = 0.024; Figure 5c). Figure 6 shows the probability of midday depression being concurrently triggered by these climatic events. Among them, compound drought and heat events were most likely to induce midday depression, with a probability of 74.3%, followed by heat events at 66.0%, and drought events at 47.5%.

4. Discussion

We used high-frequency data from geostationary orbit satellites to capture the daily fluctuations in GPP, which serve as indicators of the diurnal patterns of photosynthesis in subtropical Chinese fir forest ecosystems. By comparing this satellite-based estimation method with field measurements from flux towers, its accuracy has been validated [25]. This level of consistency is crucial for subsequent analyses because it ensures that the diurnal variation patterns attributed to the midday depression phenomenon are not artifacts of the estimation process, but rather, reflections of actual physiological processes occurring within the forest stand [26]. Our results suggest that using ten minutes resolution geostationary satellite data to estimate GPP and identify midday depression would lead to a better understanding of the diurnal variation of photosynthetic rates and their response to different climatic events at regional and continental scales [20]. Compared to the most commonly used methods that estimate photosynthetic rates based on ecological models, our methodological framework can provide explanations based on stomatal physiology.
Subsequently, we identified the midday depression of photosynthesis in the Huitong fir forest from 2016 to 2022 and found significant changes in the annual occurrence of this phenomenon. The highest number of midday depression occurrences was recorded in 2022 with 126, followed by 119 in 2020, and 113 in 2016. The intensification of midday depression in forest photosynthesis in 2022 may be related to the unprecedented high temperatures experienced during the summer of that year. The average temperature in the region during this summer reached 29.1 °C, with an average of 58.2 days of temperatures exceeding 35 °C, ranking first in historical temperature records since 1961 [27]. High temperatures directly affect the stomatal conductance and photosynthetic capacity of plants, leading to a reduction in carbon assimilation at midday. The drought conditions throughout 2020 also exacerbated the scarcity of water resources, with the region receiving only 852.5 mm of precipitation from April to December, which was 22.9% less than the average precipitation for this period, further putting pressure on plants and possibly contributing to the increase in observed midday depression [28,29]. Similarly, the drought in autumn of 2016 may also be the main factor inducing midday depression in fir trees [30]. These drought events intensified the scarcity of water resources, bringing additional stress to plants, and may have contributed to the observed increases in midday depression.
From July to October each year, the occurrence of midday depression in the Huitong fir forest showed a clear peak. In the summer, increased solar radiation and higher temperatures may have led to an increase in transpiration rates [31]. If soil moisture is insufficient to meet the water needs of plants, this could trigger midday depression as a protective mechanism to prevent excessive water loss and potential damage to photosynthetic organs [32]. In contrast, the number of midday depression occurrences was lower during the colder seasons of spring and winter. The lower temperatures during these periods led to reduced transpiration and evaporation rates, which may have alleviated some of the water stress that causes stomata to close at midday. Additionally, the physiological activity of plants may be inherently lower during the colder months, which could also be a reason for the observed decrease in the number of midday depression occurrences in trees [33].
We further explored the relationship between heat, drought, and their combined events with the occurrence of midday depression from 2016 to 2022. Our results indicated that heat events had the strongest correlation with midday depression compared to other climatic events. Heat stress is the primary cause of the suppression of plant photosynthesis at midday [34]. When temperatures exceed the optimal temperature for photosynthesis, they inhibit the activity of photosynthetic enzymes [35], including Rubisco [36], a key enzyme in photosynthesis. Heat stress can also damage components of the photosynthetic electron transport chain, affecting the conversion and utilization of light energy [37]. Furthermore, heat stress intensifies the process of photorespiration, a metabolic pathway that occurs under high oxygen and low CO2 concentrations, consuming some of the energy and fixed carbon produced by photosynthesis [38]. Lastly, heat stress also increases the evaporation of internal water in plants, thereby reducing their water content [39]. To maintain water balance, plants close their stomata, further leading to the midday depression of photosynthesis [40].
Drought events had the weakest relationship with midday depression compared to other climatic events. Drought does not directly damage the biochemical and photochemical systems of photosynthesis, but primarily downregulates photosynthesis by increasing the diffusive resistance to CO2 entry into chloroplasts [37]. Under drought conditions, guard cells on leaves shrink due to the loss of turgor pressure and close the openings of stomata to reduce water evaporation [41]. This response, while effectively reducing water loss, also limits the entry of CO2, leading to a decrease in the rate of photosynthesis, and affects the carbon fixation and growth of the plant [42,43]. In addition, stomatal closure may also activate a series of signal transduction pathways within the plant, such as the accumulation of abscisic acid, further strengthening the stomatal closure response to adapt to the water-deficient environment [44].
Midday depression in subtropical fir trees is most likely to occur during compound drought and heat events. The combination of drought and heat exacerbates their individual impact on plants [45]. Firstly, superimposed water stress leads to a more severe water shortage in plants [46,47]. Drought reduces the moisture in soil and the atmosphere, limiting the water absorption of plants, while heat stress increases plant transpiration, further accelerating the loss of water [48]. This dual effect poses a significant challenge for plants in obtaining water and maintaining the water balance. Secondly, the superimposed temperature stress damages the cellular structure and function of plants, reducing the efficiency of photosynthesis. When drought and heat occur simultaneously, plant heat tolerance is reduced, resulting in higher susceptibility to heat damage, which further affects the energy balance within plants [49]. In addition, the complexity of physiological regulation increases the difficulty for plants to adapt to environmental stress. Faced with the dual pressures of drought and heat, plants need to regulate multiple physiological processes such as water absorption, transpiration, and photosynthesis simultaneously, which not only increases the complexity of regulation but also raises the difficulty of maintaining physiological balance [15,50]. Therefore, compared to single drought or heat events, plants are more likely to adopt midday depression as an adaptive strategy during compound drought and heat events to reduce water transpiration, lower energy consumption, and protect themselves from heat damage, thereby increasing their chances of survival in extreme environments [51].
The current GPP model lacks comprehensive factors, such as soil nutrients and tree species, and does not account for the effects of latent and sensible heat. This oversight can lead to inaccuracies in reflecting carbon flux dynamics, particularly in capturing rapid, instantaneous changes. As an empirical formula, it introduces uncertainties in quantifying GPP and assessing the midday dormancy state of trees. The complexity of field-flux observations can result in the incorporation of various interfering factors into the dataset. Despite employing specialized commercial software for data cleaning, it remains challenging to eliminate all uncertainties.
The robust validation of the GPP model is an additional challenge. Our study is dedicated to deepening the comprehension of the carbon flux dynamics within the subtropical fir forest regions of China, with a particular emphasis on modeling and estimating within the fir ecological zone. Regrettably, the scarcity of flux towers, with only one available for the entire ecological zone, limits our capacity to precisely delineate regional flux variations at the landscape scale. Consequently, discrepancies in the midday depression pattern may manifest across this broader spatial context.
Looking forward, it is anticipated that advancements in observation techniques and platforms will yield more precise data, enhancing the accuracy and reliability of GPP measurements and models.

5. Conclusions

We used the data from geostationary orbit satellites to capture the daily fluctuations in GPP and the diurnal patterns of photosynthesis in subtropical Chinese fir forest ecosystems. By comparison with field-measured flux data, the accuracy of these data has been validated. Using ten minutes resolution geostationary satellite data to identify midday depression provides a better understanding of the diurnal variation of photosynthetic rates and their response to different climatic events at regional and continental scales. Our methodological framework has shown a better fitting of data. Furthermore, there were significant changes in the annual occurrence of midday depression. The highest number of midday depression occurrences was identified in 2022 (126 times) with the highest average temperature (29.1 °C). A higher incidence of midday depression occurred in summer and autumn, with 48 and 34 occurrences, respectively. In contrast, the number of midday depression occurrences was lower during the colder seasons of spring and winter. Compound drought, heat, and drought events induced increases in midday depression at 74.3%, 66.0%, and 47.5%. Heat events had the strongest correlation with midday depression compared to other climatic events. Heat stress is the primary cause of the suppression of plant photosynthesis at midday. Trees are more likely to adopt midday depression as an adaptive strategy during compound drought and heat events. This study can inform forest management and contribute to improvements in earth system models.

Author Contributions

Conceptualization, Q.X. and X.Z.; methodology, Q.X. and K.C.; software, Q.X. and J.L.; validation, Q.X., K.C. and X.Z.; formal analysis, K.C.; investigation, Q.X. and T.L.; data curation, Q.X. and Y.W.; writing—original draft preparation, Q.X.; writing—review and editing, K.C., T.L., and X.Z.; visualization, Q.X.; supervision, X.Z.; project administration, X.Z.; funding acquisition, T.L. and X.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Joint Fund for Regional Innovation and Development of the National Science Foundation (U22A20570), the Science and Technology Innovation Program of Hunan Province of China (2022RC4027), the National Natural Science Foundation of China (42201064), and the Natural Science Foundation of Hunan Province of China (2023JJ40440).

Data Availability Statement

Data will be made available on request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Su, X.; Li, S.; Wan, X.; Huang, Z.; Liu, B.; Fu, S.; Kumar, P.; Chen, H.Y. Understory vegetation dynamics of Chinese fir plantations and natural secondary forests in subtropical China. For. Ecol. Manag. 2021, 483, 118750. [Google Scholar] [CrossRef]
  2. Dorman, M.; Perevolotsky, A.; Sarris, D.; Svoray, T. The effect of rainfall and competition intensity on forest response to drought: Lessons learned from a dry extreme. Oecologia 2015, 177, 1025–1038. [Google Scholar] [CrossRef]
  3. Song, L.; Li, M.; Zhu, J.; Zhang, J. Comparisons of radial growth and tree-ring cellulose δ 13 C for Pinus sylvestris var. mongolica in natural and plantation forests on sandy lands. J. For. Res. 2017, 22, 160–168. [Google Scholar] [CrossRef]
  4. Huang, Y.; Guo, M.; Bai, P.; Li, J.; Liu, L.; Tian, W. Warming intensifies severe drought over China from 1980 to 2019. Int. J. Clim. 2023, 43, 1980–1992. [Google Scholar] [CrossRef]
  5. Lloret, F.; Batllori, E. Climate-Induced Global Forest Shifts due to Heatwave-Drought. In Ecological Studies; Canadell, J.G., Jackson, R.B., Eds.; Springer International Publishing, Cham, Switzerland, 2021; pp. 155–186. [Google Scholar] [CrossRef]
  6. Qu, L.; De Boeck, H.J.; Fan, H.; Dong, G.; Chen, J.; Xu, W.; Ge, Z.; Huang, Z.; Shao, C.; Hu, Y. Diverging responses of two subtropical tree species (Schima superba and Cunninghamia lanceolata) to heat waves. Forests 2020, 11, 513. [Google Scholar] [CrossRef]
  7. Maai, E.; Nishimura, K.; Takisawa, R.; Nakazaki, T. Light stress-induced chloroplast movement and midday depression of photosynthesis in sorghum leaves. Plant Prod. Sci. 2020, 23, 172–181. [Google Scholar] [CrossRef]
  8. Wang, H.; Prentice, I.C.; Davis, T.W. Biophsyical constraints on gross primary production by the terrestrial biosphere. Biogeosciences 2014, 11, 5987–6001. [Google Scholar] [CrossRef]
  9. Zhang, Y.J.; Xie, Z.K.; Wang, Y.J.; Su, P.X.; An, L.P.; Gao, H. Effect of water stress on leaf photosynthesis, chlorophyll content, and growth of oriental lily. Russ. J. Plant Physiol. 2011, 58, 844–850. [Google Scholar] [CrossRef]
  10. Flexas, J.; Badger, M.; Chow, W.S.; Medrano, H.L.; Osmond, C.B. Analysis of the Relative Increase in Photosynthetic O2 Uptake When Photosynthesis in Grapevine Leaves Is Inhibited Following Low Night Temperatures and/or Water Stress. Plant Physiol. 1999, 121, 675–684. [Google Scholar] [CrossRef]
  11. Damm, A.; Elbers, J.; Erler, A.; Gioli, B.; Hamdi, K.; Hutjes, R.; Kosvancova, M.; Meroni, M.; Miglietta, F.; Moersch, A.; et al. Remote sensing of sun-induced fluorescence to improve modeling of diurnal courses of gross primary production (GPP). Glob. Chang. Biol. 2009, 16, 171–186. [Google Scholar] [CrossRef]
  12. Bucci, S.J.; Silletta, L.M.C.; Garré, A.; Cavallaro, A.; Efron, S.T.; Arias, N.S.; Goldstein, G.; Scholz, F.G. Functional relationships between hydraulic traits and the timing of diurnal depression of photosynthesis. Plant Cell Environ. 2019, 42, 1603–1614. [Google Scholar] [CrossRef] [PubMed]
  13. Okamoto, A.; Koyama, K.; Bhusal, N. Diurnal change of the photosynthetic light-response curve of buckbean (Menyanthes trifoliata), an emergent aquatic plant. Plants 2022, 11, 174. [Google Scholar] [CrossRef] [PubMed]
  14. Tanizaki, T.; Yokoyama, G.; Kitano, M.; Yasutake, D. Contribution of diffusional and non-diffusional limitations to the midday depression of photosynthesis which varies dynamically even under constant environmental conditions. Int. Agrophysics 2022, 36, 207–212. [Google Scholar] [CrossRef]
  15. Guo, X.Y.; Peng, C.H.; Li, T.; Huang, J.J.; Song, H.X.; Zhu, Q.A.; Wang, M. The effects of drought and re-watering on non-structural carbohydrates of Pinus tabulaeformis seedlings. Biology 2021, 10, 281. [Google Scholar] [CrossRef]
  16. Clarendon, G. The Emerald Planet: How Plants Changed Earth’s History. Quart. Rev. Biol. 2008, 83, 117–118. [Google Scholar] [CrossRef]
  17. Ferrar, P.; Slatyer, R.; Vranjic, J. Photosynthetic Temperature Acclimation in Eucalyptus Species from Diverse Habitats, and a Comparison with Nerium oleander. Funct. Plant Biol. 1989, 16, 199–217. [Google Scholar] [CrossRef]
  18. Xu, H.; Xiao, J.; Zhang, Z. Heatwave effects on gross primary production of northern mid-latitude ecosystems. Environ. Res. Lett. 2020, 15, 074027. [Google Scholar] [CrossRef]
  19. Koyama, K.; Takemoto, S. Morning reduction of photosynthetic capacity before midday depression. Sci. Rep. 2014, 4, 4389. [Google Scholar] [CrossRef]
  20. Xiao, J.F.; Fisher, J.; Hashimoto, H.; Ichii, K.; Parazoo, N.C. Emerging satellite observations for diurnal cycling of ecosystem processes. Nat. Plants 2021, 7, 877–887. [Google Scholar] [CrossRef]
  21. Yang, X.; Mustard, J.; Tang, J.; Xu, H. Regional-scale phenology modeling based on meteorological records and remote sensing observations. J. Geophys. Res. Biogeosci. 2012, 117, G03029. [Google Scholar] [CrossRef]
  22. Sun, Y.; Wen, J.; Gu, L.; Joiner, J.; Chang, C.Y.; van der Tol, C.; Porcar-Castell, A.; Magney, T.; Wang, L.; Hu, L.; et al. From remotely sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part I-Harnessing theory. Glob. Chang. Biol. 2023, 29, 2926–2952. [Google Scholar] [CrossRef]
  23. Landsberg, J.J.; Waring, R.H. A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning. For. Ecol. Manag. 1997, 95, 209–228. [Google Scholar] [CrossRef]
  24. Li, X.; Ryu, Y.; Xiao, J.; Dechant, B.; Liu, J.; Li, B.; Jeong, S.; Gentine, P. New-generation geostationary satellite reveals widespread midday depression in dryland photosynthesis during 2020 western U.S. heatwave. Sci. Adv. 2023, 9, eadi0775. [Google Scholar] [CrossRef] [PubMed]
  25. Consoli, S.; Vanella, D. Comparisons of satellite-based models for estimating evapotranspiration fluxes. J. Hydrol. 2014, 513, 475–489. [Google Scholar] [CrossRef]
  26. Li, X.; Xiao, J.; Fisher, J.B.; Baldocchi, D.D. ECOSTRESS estimates gross primary production with fine spatial resolution for different times of day from the International Space Station. Remote Sens. Environ. 2021, 258, 112360. [Google Scholar] [CrossRef]
  27. Hunan Meteorological Bureau. Top 10 Weather and Climate Events in Hunan Province in 2022. Available online: https://www.hengyang.gov.cn/xxgk/dtxx/tzgg/gsgg/20230323/i2966725.html (accessed on 24 May 2024).
  28. China Meteorological Administration. Top 10 Domestic and Foreign Weather and Climate Events of 2022. Available online: https://www.cma.gov.cn/2011xwzx/2011xqxxw/2011xqxyw/202301/t20230109_5247477.html (accessed on 24 May 2024).
  29. China Meteorological Administration. CHINA CLIMATE BULLETIN (2020). Available online: https://www.cma.gov.cn/zfxxgk/gknr/qxbg/202104/t20210406_3051288.html (accessed on 24 May 2024).
  30. Wang, S.; Wang, S.; Feng, J. Drought Events and Its Influence in Autumn of 2016 in China. J. Arid. Metcorology 2016, 34, 1099–1104. (In Chinese) [Google Scholar] [CrossRef]
  31. Wang, L.; Yu, M.; Ye, S.; Yan, J. Seasonal patterns of carbon and water flux responses to precipitation and solar radiation variability in a subtropical evergreen forest, South China. Agric. For. Meteorol. 2023, 342, 109760. [Google Scholar] [CrossRef]
  32. Liu, F.; Zhao, Y.; Wang, X.; Wang, B.; Xiao, F.; He, K. Physiological response and drought resistance evaluation of Gleditsia sinensis seedlings under drought-rehydration state. Sci. Rep. 2023, 13, 19963. [Google Scholar] [CrossRef]
  33. Wang, Y.; Wang, J.; Sarwar, R.; Zhang, W.; Geng, R.; Zhu, K.M.; Tan, X.L. Research progress on the physiological response and molecular mechanism of cold response in plants. Front. Plant Sci. 2024, 15, 1334913. [Google Scholar] [CrossRef]
  34. Zhao, J.; Lu, Z.; Wang, L.; Jin, B. Plant Responses to Heat Stress: Physiology, Transcription, Noncoding RNAs, and Epigenetics. Int. J. Mol. Sci. 2020, 22, 117. [Google Scholar] [CrossRef]
  35. Li, B.; Gao, K.; Ren, H.; Tang, W. Molecular mechanisms governing plant responses to high temperatures. J. Integr. Plant Biol. 2018, 60, 757–779. [Google Scholar] [CrossRef] [PubMed]
  36. Jajoo, A.; Allakhverdiev, S.I. High Temperature Stress in Plants: Consequences and Strategies for Protecting Photosynthetic Machinery. In Plant Stress Physiology; CABI: Wallingford, UK, 2017; pp. 138–154. [Google Scholar] [CrossRef]
  37. Rennenberg, H.; Loreto, F.; Polle, A.; Brilli, F.; Fares, S.; Beniwal, R.S.; Gessler, A. Physiological Responses of Forest Trees to Heat and Drought. Plant Biol. 2006, 8, 556–571. [Google Scholar] [CrossRef]
  38. Moroney, J.V.; Jungnick, N.; Dimario, R.J.; Longstreth, D.J. Photorespiration and carbon concentrating mechanisms: Two adaptations to high O2, low CO2 conditions. Photosynth. Res. 2013, 117, 121–131. [Google Scholar] [CrossRef] [PubMed]
  39. Zhao, Y.; Liu, S.; Yang, K.; Hu, X.; Jiang, H. Fine-control of growth and thermotolerance in plant response to heat stress. J. Integr. Agric. 2024, in press. [Google Scholar] [CrossRef]
  40. Murata, Y.; Mori, I.C. Stomatal regulation of plant water status. In Plant Abiotic Stress; John, Wiley & Sons: Hoboken, NJ, USA, 2013; pp. 47–67. [Google Scholar] [CrossRef]
  41. Zahra, N.; Hafeez, M.B.; Kausar, A.; Al Zeidi, M.; Asekova, S.; Siddique, K.H.M.; Farooq, M. Plant photosynthetic responses under drought stress: Effects and management. J. Agron. Crop. Sci. 2023, 209, 651–672. [Google Scholar] [CrossRef]
  42. Earl, H.J. Stomatal and non-stomatal restrictions to carbon assimilation in soybean (Glycine max) lines differing in water use efficiency. Environ. Exp. Bot. 2002, 48, 237–246. [Google Scholar] [CrossRef]
  43. Drake, J.E.; Power, S.A.; Duursma, R.A.; Medlyn, B.E.; Aspinwall, M.J.; Choat, B.; Creek, D.; Eamus, D.; Maier, C.; Pfautsch, S.; et al. Stomatal and non-stomatal limitations of photosynthesis for four tree species under drought: A comparison of model formulations. Agric. For. Meteorol. 2017, 247, 454–466. [Google Scholar] [CrossRef]
  44. Liu, H.; Song, S.; Zhang, H.; Li, Y.; Niu, L.; Zhang, J.; Wang, W. Signaling Transduction of ABA, ROS, and Ca2+ in Plant Stomatal Closure in Response to Drought. Int. J. Mol. Sci. 2022, 23, 14824. [Google Scholar] [CrossRef]
  45. Hussain, S.; Ulhassan, Z.; Brestic, M.; Zivcak, M.; Zhou, W.J.; Allakhverdiev, S.I.; Yang, X.H.; Safdar, M.E.; Yang, W.Y.; Liu, W.G. Photosynthesis research under climate change. Photosynth. Res. 2021, 150, 5–19. [Google Scholar] [CrossRef]
  46. Ye, Z.P. Nonlinear optical absorption of photosynthetic pigment molecules in leaves. Photosynth. Res. 2012, 112, 31–37. [Google Scholar] [CrossRef]
  47. Zhang, Y.J.; Gao, H.; Li, Y.H.; Wang, L.; Kong, D.S.; Guo, Y.Y.; Yan, F.; Wang, Y.W.; Lu, K.; Tian, J.W.; et al. Effect of Water Stress on Photosynthesis, Chlorophyll Fluorescence Parameters and Water Use Efficiency of Common Reed in the Hexi Corridor. Russ. J. Plant Physiol. 2019, 66, 556–563. [Google Scholar] [CrossRef]
  48. Wang, J.; Yu, Q.; Li, J.; Li, L.H.; Li, X.G.; Yu, G.R.; Sun, X.M. Simulation of diurnal variations of CO2, water and heat fluxes over winter wheat with a model coupled photosynthesis and transpiration. Agric. For. Meteorol. 2006, 137, 194–219. [Google Scholar] [CrossRef]
  49. Wang, C.; Li, Z.; Chen, Y.; Ouyang, L.; Li, Y.; Sun, F.; Liu, Y.; Zhu, J. Drought-heatwave compound events are stronger in drylands. Weather Clim. Extrem. 2023, 42, 100632. [Google Scholar] [CrossRef]
  50. Choat, B.; Brodribb, T.J.; Brodersen, C.R.; Duursma, R.A.; López, R.; Medlyn, B.E. Triggers of tree mortality under drought. Nature 2018, 558, 531–539. [Google Scholar] [CrossRef] [PubMed]
  51. Yu, R.; Zhai, P. More frequent and widespread persistent compound drought and heat event observed in China. Sci. Rep. 2020, 10, 14576. [Google Scholar] [CrossRef]
Forests 15 01572 g001
Figure 1. The site location of the flux tower in the Huitong fir forest site.
Figure 1. The site location of the flux tower in the Huitong fir forest site.
Forests 15 01572 g001
Forests 15 01572 g002
Figure 2. Comparison of GPP estimates with measured values of the flux tower between 2016 and 2022. The number of available samples was 14633. Each sample represents a half-hourly observation. (a) Prediction using observed climate data from the flux tower. The black dashed line is the data fit line. The red solid line is the 45° tangent. The color intensity of the right bar represents the density of data points. Unit for vertical coordinates is g C m−2 h−1. (b) Residual distribution, RMSE, and MAE corresponding to (a). (c) Prediction using the satellite data. (d) Residual distribution, RMSE, and MAE corresponding to (c).
Figure 2. Comparison of GPP estimates with measured values of the flux tower between 2016 and 2022. The number of available samples was 14633. Each sample represents a half-hourly observation. (a) Prediction using observed climate data from the flux tower. The black dashed line is the data fit line. The red solid line is the 45° tangent. The color intensity of the right bar represents the density of data points. Unit for vertical coordinates is g C m−2 h−1. (b) Residual distribution, RMSE, and MAE corresponding to (a). (c) Prediction using the satellite data. (d) Residual distribution, RMSE, and MAE corresponding to (c).
Forests 15 01572 g002
Forests 15 01572 g003
Figure 3. Observed variables at the Huitong fir forest site from 2016 to 2022. (a) GPP value, (b) air temperature, (c) precipitation. (d) Average daily temperature and (e) precipitation during climatic events. In (d,e), 1 represents average daily temperature or precipitation during heat events, 2 indicates drought events, 3 indicates compound events, and Ave. indicates overall climatic event averages.
Figure 3. Observed variables at the Huitong fir forest site from 2016 to 2022. (a) GPP value, (b) air temperature, (c) precipitation. (d) Average daily temperature and (e) precipitation during climatic events. In (d,e), 1 represents average daily temperature or precipitation during heat events, 2 indicates drought events, 3 indicates compound events, and Ave. indicates overall climatic event averages.
Forests 15 01572 g003
Forests 15 01572 g004
Figure 4. The number of midday depression occurrences in different years (a), different months (b), and different seasons (c) at the Huitong fir forest site from 2016 to 2022.
Figure 4. The number of midday depression occurrences in different years (a), different months (b), and different seasons (c) at the Huitong fir forest site from 2016 to 2022.
Forests 15 01572 g004
Forests 15 01572 g005
Figure 5. Regression between the number of midday depression occurrences and the number of climatic event occurrences from 2016 to 2022. (a) Regression between the number of midday depression occurrences and the number of heat event occurrences. (b) Regression between the number of midday depression occurrences and the number of drought event occurrences. (c) Regression between the number of midday depression occurrences and the number of compositeevents. (d) Mean annual number of climatic event occurrences from 2016 to 2022. The C event, D event, and HT event represent composite events (compound drought and heat), drought events, and heat events, respectively.
Figure 5. Regression between the number of midday depression occurrences and the number of climatic event occurrences from 2016 to 2022. (a) Regression between the number of midday depression occurrences and the number of heat event occurrences. (b) Regression between the number of midday depression occurrences and the number of drought event occurrences. (c) Regression between the number of midday depression occurrences and the number of compositeevents. (d) Mean annual number of climatic event occurrences from 2016 to 2022. The C event, D event, and HT event represent composite events (compound drought and heat), drought events, and heat events, respectively.
Forests 15 01572 g005
Forests 15 01572 g006
Figure 6. Probability of midday depression being concurrently triggered by climatic events between 2016 and 2022. HT event, D event, and C event represent heat events, drought events, and composite events (compound drought and heat), respectively.
Figure 6. Probability of midday depression being concurrently triggered by climatic events between 2016 and 2022. HT event, D event, and C event represent heat events, drought events, and composite events (compound drought and heat), respectively.
Forests 15 01572 g006
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Xie, Q.; Chen, K.; Li, T.; Liu, J.; Wang, Y.; Zhou, X. Heat and Drought Have Exacerbated the Midday Depression Observed in a Subtropical Fir Forest by a Geostationary Satellite. Forests 2024, 15, 1572. https://doi.org/10.3390/f15091572

AMA Style

Xie Q, Chen K, Li T, Liu J, Wang Y, Zhou X. Heat and Drought Have Exacerbated the Midday Depression Observed in a Subtropical Fir Forest by a Geostationary Satellite. Forests. 2024; 15(9):1572. https://doi.org/10.3390/f15091572

Chicago/Turabian Style

Xie, Qianqian, Kexin Chen, Tong Li, Jia Liu, Yuqiu Wang, and Xiaolu Zhou. 2024. "Heat and Drought Have Exacerbated the Midday Depression Observed in a Subtropical Fir Forest by a Geostationary Satellite" Forests 15, no. 9: 1572. https://doi.org/10.3390/f15091572

APA Style

Xie, Q., Chen, K., Li, T., Liu, J., Wang, Y., & Zhou, X. (2024). Heat and Drought Have Exacerbated the Midday Depression Observed in a Subtropical Fir Forest by a Geostationary Satellite. Forests, 15(9), 1572. https://doi.org/10.3390/f15091572

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop