Next Article in Journal
Research on Spatial–Temporal Characteristics and Driving Factors of Urban Development Intensity for Pearl River Delta Region Based on Geodetector
Previous Article in Journal
The Impact of Opening a High-Speed Railway on Urban Innovation: A Comparative Perspective of Traditional Innovation and Green Innovation
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Land
Volume 12
Issue 9
10.3390/land12091672
land-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 thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Divergent Resistance and Resilience of Forest and Grassland Ecosystems to Extreme Summer Drought in Carbon Sequestration

by
Jie Lu
1,2 and
Fengqin Yan
2,*
1
Henan Agricultural Remote Sensing Big Data Development and Innovation Laboratory, Department of Surveying and Planning, Shangqiu Normal University, Shangqiu 476000, China
2
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Land 2023, 12(9), 1672; https://doi.org/10.3390/land12091672
Submission received: 12 July 2023 / Revised: 23 August 2023 / Accepted: 25 August 2023 / Published: 27 August 2023
(This article belongs to the Section Land–Climate Interactions)
Browse Figures
Versions Notes

Abstract

:
It is projected that extreme drought events will become more frequent and more severe across many regions of the globe by the end of the 21st century. Despite the substantial efforts that have been made to explore the impacts of droughts on terrestrial ecosystems, our understanding of the response of diverse ecosystems, including resistance and resilience, remains unclear. A total of 16 site years of eddy covariance-based carbon flux data were used to reveal the different responses of forest and grassland ecosystems to two extreme summer droughts. We found that the carbon fluxes of the forest, namely gross primary productivity (GPP), ecosystem respiration (Re), and the net ecosystem carbon exchange (NEE), exhibited distinct seasonal patterns with a single peak. However, GPP and NEE of grassland showed multiple peaks owing to hay harvesting throughout one year. Meanwhile, all climate factors jointly affected the seasonal dynamics in the NEE of the forest, whereas solar radiation only dominated the variability in the NEE of grassland. Moreover, the optimal response relationship was quadratic between the vapor pressure deficit (VPD) and the NEE, with the thresholds being 5.46 and 5.84 for forest and grassland, respectively. Owing to the large increase in VPD during the droughts of 2003 and 2018, the carbon sequestration of forest and grassland reduced sharply and even altered from carbon sink to carbon source. Compared with grassland, forest GPP showed stronger resistance with weaker resilience to droughts. However, larger resilience appeared for both forest and grassland NEE relative to their resistance. All analyses reflect the different adaptive strategies among plant functional types, which is crucial to evaluate ecosystem carbon sequestration to overcome future climate change.

1. Introduction

Future global climate change is expected to continue causing changes in precipitation and heat patterns and, as a result, increasing the occurrence and intensity of droughts across many regions around the world [1,2]. In recent years, exceptional weather events, particularly droughts, heatwaves, and floods, which led to severe damage in agriculture, forestry, and stock farming, have attracted more and more attention among ecologists and decision makers [3]. These extreme drought events will also exert more significant impacts on terrestrial ecosystem structures and functions and severely disturb global carbon budgets [4,5]. Therefore, understanding the effect and response strategy of droughts is crucial for maintaining the ecological integrity and sustainability of terrestrial ecosystems.
As fossil fuels remain the major source of energy before 2035, global atmospheric carbon dioxide (CO2) concentration will continuously increase in the next few decades [6]. However, terrestrial vegetation significantly contributes to mitigating climate change by absorbing CO2 from the atmosphere [7,8]. Among the diverse terrestrial ecosystems, forests play a crucial role in global carbon cycles through photosynthetic activity and storing it in biomass and soils, which accounted for 1.1 ± 0.8 Pg C year−1 [9]. In addition, grassland covers an area of 52.5 million km2 of Earth’s land surface and also acts as an important carbon sink, with the carbon stored as root biomass and soil organic carbon [10]. Nevertheless, extreme climate events, particularly droughts, seriously threaten terrestrial ecosystem structures and functions that could essentially weaken the role of forests and grasslands as carbon sinks and even make ecosystems become carbon sources [11,12].
Recent studies have been conducted to explore the effects of droughts under different periods and severities on terrestrial ecosystem carbon cycling by affecting the balance between carbon uptake and carbon release [2,13,14]. Overall, the impacts of drought on ecosystem carbon cycles are complicated and depend on a variety of biotic and abiotic factors. For example, drought can result in decreased carbon uptake and productivity in forests and grasslands by reducing their stomatal conductance to limit water loss through transpiration [15,16]. Additionally, drought can cause plant mortality, which can result in the release of a mass of stored carbon back into the atmosphere in a short period [5,17]. During extreme drought, plant mortality owing to the hydraulic transport failure and the restriction of the carbohydrate supply to C-dependent metabolic, defence, or hydraulic functions, will lead to not only a sharp decrease in gross primary productivity (GPP) but also an increase in carbon emission from decomposition (Re), thereby altering the net ecosystem carbon exchange (NEE) [18].
The 2003 European drought was one of the most severe and prolonged heatwaves in recent history, with temperatures reaching record highs across most regions of Europe [19]. Subsequently, the 2018 drought even exceeded the hot drought of 2003 [20]. Because drought can alter terrestrial ecosystem functions and cause the vulnerability of carbon sequestration potential, future trends of drought represent the potential risk for achieving global climate change mitigation goals. Recently, a global study on the basis of 352 site-year eddy covariance-based observations revealed that hydraulic diversity mediated drought resilience in forests [21]. Although a great number of attempts have been made to explore the effects of droughts on ecosystem carbon sequestration, little attention has been paid to the response mechanism of specific vegetation such as forest and grassland to such events, which includes resistance (sensitivity to drought) and resilience (post-drought recovery rate), respectively [22,23].
Therefore, this study specifically aimed to (1) examine the dynamics of climate factors and ecosystem carbon fluxes before, during, and after two extreme drought events; (2) reveal the effects of climate factors on forest and grassland ecosystem carbon sequestrations; and (3) compare the resistance and resilience of two different ecosystems in face of drought extremes. All these analyses will enhance our understanding of the interactions between terrestrial ecosystem functions and climate systems.

2. Materials and Methods

2.1. Description of the Flux Sites

As illustrated in Figure 1, in this study, we used the eddy covariance (EC)-based measurements of ecosystem carbon fluxes and climate data at the forest (CH-Dav: 2002–2005 and 2017–2020) and grassland (CH-Oe1: 2002–2005; CH-Cha: 2017–2020) sites before, during, and after the droughts of 2003 and 2018. These data can be accessed from http://www.europe-fluxdata.eu/ (accessed on 8 March 2023). Both sites belong to the Swiss FluxNet national eddy covariance network [3]. The climate of this region is temperate, with elevation as the confounding factor, particularly for the subalpine evergreen forest in Davos. The CH-Dav flux site (9°51′21.3″ E, 46°48′55.2″ N) is located in Davos, Switzerland, at an elevation of approximately 1600 m above sea level. The vegetation type is dominated by the evergreen needle-leaf forest of mostly Norway spruce. A 45-meter tall tower was equipped at this site. Multi-year mean temperature and annual precipitation are 3.4 °C and 992 mm, respectively [24]. The CH-Oe1 flux site (7°43′55.9″ E, 47°17′8.1″ N) is located on the Central Swiss Plateau near Oensingen, Switzerland, at an elevation of about 452 m above sea level. The flux tower was built up in an intensively managed grassland at 1.2 m above ground with the dominant species of English ryegrass, meadow foxtail, and white clover. The grassland was cut typically four times per year. Multi-year mean temperature and annual precipitation are 9.5 °C and 1184 mm, respectively [25,26]. The dominant vegetation for the low-elevation grassland site (CH-Cha) is a mixture of Italian ryegrass and white clover, predominantly used for fodder production and occasional winter grazing by sheep [20]. Multi-year mean precipitation and temperature are 1136 mm and 9.5 °C, respectively. The flux site (8°24′38″ E, 47°12′37″ N) was operational since July of 2005.

2.2. Flux Measurements and Data Processing

As an indispensable part of the global network of micrometeorological flux observations, Swiss FluxNet comprises a collection of research sites that were constructed to better understand the exchange of carbon dioxide and other gases between the atmosphere and terrestrial ecosystems, as well as the impacts of climate change [3]. At each flux site, an open-path infrared CO2/H2O gas analyser (Li-7500, LI-COR, Lincoln, NE, USA) and three-dimensional (3D) sonic anemometer (models Solent R3-50 and HS, Gill Instruments, Lymington, UK) are jointly used as the EC-based micrometeorological measurement system. An automatic meteorological station (AWS) is also installed on the flux tower and continuously measures the local meteorological variables, namely the incoming shortwave radiation solar radiation (Rg), near-surface air temperature (Ta), relative humidity, the vapour pressure deficit (VPD), soil temperature and volumetric soil water content (Ts and SWC), and the amount of precipitation (Pre). In this study, we used the variability in the VPD to represent atmospheric dryness, which is defined as the difference between saturated water vapour pressure and actual water vapour pressure [27,28]. Thus, the VPD means how far the air is from thermodynamic equilibrium, with higher VPD related to stronger atmospheric demand for water from the land surface.
The raw flux data are recorded at 10 Hz. After the flux measurements are taken, the data must be processed to correct for errors and calculate the fluxes. Eddy covariance fluxes were calculated using the free and open source software EddyPro (V6, LI-COR) and then made available as part of the ICOS Ecosystem Thematic Centre (ETC) dataset [29,30]. The data processing techniques followed the FLUXNET data processing pipeline: (1) the sonic anemometer data are rotated to account for any misalignment between the anemometer and the vertical axis; (2) the data are filtered to remove any high-frequency noise and periods of unstable atmospheric conditions; (3) the data are checked for errors and outliers, and any suspect data are removed; (4) the fluxes of carbon dioxide, water vapour, and energy are calculated using standard equations; and (5) any missing data are gap-filled by using interpolation or other techniques. In this study, the standardised gap-filling and partitioning procedures of CO2 fluxes were performed using the method proposed by Reichstein et al. [31], with the marginal distribution sampling (MDS) gap-filling algorithm and flux partitioning methodology based on a short-term temperature-dependent approach by separating NEE into GPP and Re. More details about the data processing can be found in Gharun et al. [20].

2.3. Resistance and Resilience Indexes

The response of forest and grassland ecosystems to extreme drought events can be assessed using both the sensitivity to drought (resistance, Rt) and the post-drought recovery rate (resilience, Rs) [22,32]. Rt means the capacity for a specific ecosystem to remain stable during the disturbance or stress, and Rs represents the indices for a particular ecosystem to restore to its initial state before the disturbance or stress happens [33,34]. Hence, Rt and Rs can be calculated for each disturbance event according to the definitions proposed by Isbell et al. [35]:
R t = Y n ¯ Y e − Y n ¯
R s = Y e − Y n ¯ Y e + 2 − Y n ¯
where Y n ¯ is the mean GPP, Re, and NEE during the normal years with no droughts; Y e is the GPP, Re, and NEE during a drought year; and Y e + 2 represents the GPP, Re, and NEE during the two years after the drought event. Previous research has found that the impact of extreme drought events on forest ecosystems lasted for two years, and most vegetation can recover from drought disturbance within two years [36,37]. Therefore, we used 2 years to evaluate the Rs of forest and grassland ecosystems here. Stronger Rt indicates that the ecosystem functions are less reduced owing to the disturbance, whereas higher Rs indicates that ecosystem functions recover quickly to their initial state after disturbance.

2.4. Statistical Analysis

We used the Pearson correlation analysis to explore the relationships between climate factors, namely Rg, Ta, VPD, and Pre, and ecosystem carbon fluxes, namely GPP, Re, and NEE, at the forest and grassland sites. This step was conducted using the IBM SPSS Statistics 21 software [38,39]. The Pearson coefficient represents the strength of the relationship among the two parameters in this study, with ** and * as proxies for such correlations being significant at the 0.01 level and 0.05 level, respectively. All figures were created using ArcGIS 10.2 (ESRI, Redlands, CA, USA) and Origin 8.0 (Origin Lab Corporation, Northampton, MA, USA).

3. Results

3.1. Seasonal Dynamics of Climate Factors and Ecosystem Carbon Fluxes

The variability in climate factors, namely Rg, Ta, VPD, and Pre, as well as the carbon fluxes, namely GPP, Re, and NEE, at the forest and grassland sites before, during, and after the two extreme drought events are illustrated in Figure 2, Figure 3, Figure 4 and Figure 5. Essentially, the dynamics of terrestrial ecosystem carbon sequestration depended on both GPP and Re, which were directly relevant to the interannual fluctuations of climate conditions. The seasonal dynamics of GPP and Re strongly co-varied with Ta, which exhibited a distinct inverted V-like curve, despite several sharp decreases in GPP at the grassland sites owing to hay harvesting throughout one year. Interestingly, the Re of grassland did not proportionally reduce like GPP. During the dormant period, with the week of the year (WOY) ranging from 1 to 10 and from 46 to 52 for the forest, and from 1 to 9 and from 47 to 52 for grassland, low temperature and frozen soils hindered the vegetation photosynthesis as well as respiration, which led to both forest and grassland acting as weak carbon sources. However, with the increase in Ta and Rg during the growing season, vegetation started to grow and both GPP and Re gradually rose. As a result, GPP exceeded Re and the vegetation changed from carbon source to carbon sink. The NEE of the forest ecosystem reached its peak at WOY 21 with about −2.81 g C m−2 d−1, whereas the NEE of the grassland ecosystems showed several peaks, with the largest one at WOY 17 with about −6.60 g C m−2 d−1 for the CH-Oe1 site, and at WOY 15 with about −3.71 g C m−2 d−1 for the CH-Cha site, respectively (Figure 3 and Figure 5). Meanwhile, owing to haying, the carbon sequestration capacity of grassland fluctuated dramatically around the growing season. This study also found that, during the extreme droughts of 2003 and 2018, the variability in VPD exhibited obvious abnormalities in summer, with relatively larger values in comparison with multi-year mean VPD, particularly for the grassland ecosystem (Figure 2 and Figure 4).

3.2. Effects of Climate Factors on Ecosystem Carbon Fluxes

The Pearson correlation analysis was used to examine the relationships between climate factors and ecosystem carbon fluxes at the forest and grassland sites for each weekly period, respectively (Table 1). We found that all climate factors, namely Rg, Ta, VPD, and Pre, were strongly and positively correlated with GPP, Re, and the carbon sequestration capacity (the absolute value of NEE) of the forest ecosystem. In contrast, the main components, i.e., both GPP and Re, showed strong correlations with these climate factors except Pre, which may be ascribed to the multiple haying throughout one year at the grassland sites. We also noticed that the grassland NEE only exhibited a strong negative correlation with Rg (r = −0.212, p < 0.01). Meanwhile, with regard to the forest, Ta was the dominant climate factor of GPP (r = 0.864, p < 0.01) and Re (r = 0.728, p < 0.01), whereas Rg dominated NEE (r = −0.632, p < 0.01). However, the variabilities of GPP and NEE were mainly dominated by Rg, and Ta exerted the largest effect on Re (r = 0.635, p < 0.01) for the grassland ecosystem. Therefore, all climate factors jointly affected the seasonal dynamics in the NEE of the forest ecosystem, and only Rg dominated the variability in the NEE of grassland.
Given that the VPD was a good proxy for atmospheric dryness, we especially explored the response relationships between the VPD and carbon fluxes, namely GPP, Re, and NEE, at the forest and grassland ecosystems, respectively. Multiple different methods, namely linear, quadratic, and exponential, were used for analysis. As shown in Figure 6, there existed contrasting response relationships to the variability in the VPD for forest and grassland ecosystems, particularly for GPP and Re. Specifically, both GPP and Re rose with the increase in VPD during the early stage in the grassland ecosystem and then tended to decline. However, there was not a downward trend for the forest ecosystem, and a linear relationship was observed between the VPD and GPP or Re. Nevertheless, both forest and grassland NEE showed a quadratic polynomial form, with the thresholds being 5.46 and 5.84, respectively. Once the VPD exceeded such thresholds, the carbon sequestration capacity of forest and grassland ecosystems reduced gradually.
We also examined the relative deviations of changes in climate and ecosystem carbon fluxes between the multi-year means during the adjacent normal years and the two extreme drought events at the forest and grassland sites. As shown in Table 2 and Table 3, owing to larger Ta and less Pre in 2003 and 2018, the VPD increased by 28.1% and 15.0% compared with the normal years in the forest ecosystem, which led to a severe decrease in NEE from carbon sink to carbon source. Nevertheless, we found that both GPP and Re increased in this period, but Re increased more than GPP and resulted in an alteration in forest carbon sequestration function. Meanwhile, the drought extremes exerted a more significant effect on the VPD by increasing 60.4% and 38.8% at the CH-Oe1 and CH-Cha grassland sites, respectively, which was mainly ascribed to the sharp decrease in Pre. Contrastingly, both GPP and Re at this site decreased substantially. The larger reduction in GPP led to a sharp decrease in NEE by approximately −64.3% during the drought in 2003 (Table 2), and the carbon release more than doubled during the drought in 2018 (Table 3), respectively. All analyses indicated that such drought events led to more adverse results in the grassland than in the forest ecosystem.

3.3. Responses of Ecosystem Carbon Fluxes to Drought

As illustrated in Figure 7 and Figure 8, in this study, we compared the resistance and resilience of two different ecosystems in the face of two extreme drought events. In comparison with the grassland ecosystem, the forest ecosystem presented a higher GPP-derived Rt of about 21.4 but a lower Re-derived Rt of about 5.1, whereas a lower GPP-derived Rt of about 5.63 but a higher Re-derived Rt of about 10.1 were observed in the grassland ecosystem during the drought 2003 (Figure 7). A similar phenomenon was found during the drought of 2018 (Figure 8). However, the GPP-derived Rs of forest was relatively weaker than that of the grassland with 2.65 vs. 16.74 during the 2003 drought, and 2.73 vs. 13.43 during the 2018 drought, respectively. The Re-derived Rs of grassland was apparently lower than its Rt for both drought periods. Meanwhile, this study revealed that, although the NEE-derived Rt of the forest was weak, the NEE-derived Rs values were strong with 0.05 vs. 3.54 during the 2003 drought and 0.92 vs. 41.25 during the 2018 drought, respectively. Similar to the GPP-derived Rt and Rs, the NEE-derived Rt and Rs of the grassland sites exhibited weak resistance but strong resilience for both droughts.

4. Discussion

A deep understanding of the interactions between global climate change, particularly for drought extremes, and terrestrial ecosystems is critical for predicting the future trends of terrestrial carbon sequestration. With the increase in air temperature and changes in relative humidity, the changing climate has altered the VPD dynamics in recent decades [27,40]. As a result of the increased VPD, recent researchers have projected that Earth is facing a severe increase in atmospheric dryness in the next few decades [41,42], which importantly contributes to the CO2 and water vapour fluxes between the land surface and atmosphere. Thus, the VPD is a vital environmental driver for vegetation structures and functions. The variability in ecosystem evapotranspiration owing to the VPD will then affect soil moisture dynamics [43,44]. But plant stomatal conductance is directly related to the VPD, which leads to stomata closure in order to prevent excessive water consumption [45,46]. Meanwhile, the increase in VPD will severely limit the stomatal conductance and constrain vegetation photosynthetic activity, resulting in a decline in vegetation productivity and carbon uptake [42,47]. Our study also demonstrated the severe consequences of drought events on the carbon sequestration of two different ecosystems (Table 2 and Table 3). The forest ecosystem changed from carbon sink to carbon source, and the carbon sequestration capacity of the grassland ecosystem was also severely altered. Meanwhile, we noted that the optimal response relationship was quadratic between the VPD and the NEE for both forest and grassland ecosystems. Below the threshold, the carbon uptakes of these two different ecosystems increased with the increase in the VPD. But when the VPD exceeded the thresholds, the carbon sequestration capacity of both forest and grassland gradually decreased (Figure 6). Green et al. (2020) also indicated that, in most parts of the wet Amazon rainforest, the ecosystem GPP tended to enhance together with the VPD increase owing to the changes in canopy characteristics [48]. Moreover, the studies based on various drought indexes revealed similar findings indicating that vegetation carbon sinks exhibited nonlinear relationships with the severity of the drought [49,50].
Recent evidence has highlighted the necessity to account for the feedback between soil moisture and atmospheric dryness (VPD) when evaluating the response mechanism of global carbon cycles to the changing climate, and when investigating the field-scale response of specific ecosystems to drought events [51,52]. Moreover, most of the interannual variations in global terrestrial carbon sequestration were driven by the effect of the VPD regulated by soil moisture status [44]. When extreme drought events occurred, low soil water content hindered ecosystem evapotranspiration, which is the most efficient cooling effect of land surface [53]. The alterations in energy balance raised air temperature, lowered the relative humidity, and essentially increased the VPD [54]. We further examined the relationships between the VPD and soil moisture at the grassland sites. As shown in Figure 9, we found inverse seasonal patterns in the VPD and SWC over each weekly interval. With the decrease in the SWC during the growing season, the VPD exhibited an upward trend throughout the periods of 2002 and 2005, as well as in 2017 and 2020. Meanwhile, it was noted that the lowest SWC was 17.2% as the VPD reached up to 19.1 during the 2003 drought, which was over twice the VPD during the normal years. The scatter plots between the VPD and SWC at the grassland sites (Figure 10) also proved the close relationships, with the correlation coefficients being −0.74 and −0.58 at the CH-Oe1 site and the CH-Cha site, respectively. Such analyses again implied the reasonability by using the VPD as a proxy for drought conditions.
Resistance and resilience metrics are often used to assess the sensitivity of ecosystems to climate extremes [55,56]. Whether forest and grassland follow similar or unique patterns regarding GPP, Re, and NEE resistance and resilience to drought remains unclear. Ecological theories and observations suggest that there is a trade-off between the resistance and resilience of ecosystem structures and functions [57]. This is because of the different life and evolutionary histories of resident plant species in grasslands and forests that result in different functional responses to extreme drought. Generally, forests have a higher resistance to drought than grasslands, because of their deeper root systems and greater water storage capacity [58]. In addition, forests have a greater ability to regulate their water use through stomatal control, which enables them to maintain their water balance even during periods of drought [3]. The higher resistance of forests to drought means that they are better able to maintain their structure and function and continue to sequester carbon, even during periods of drought [59]. This study also confirmed this point, as shown in Figure 7 and Figure 8. Xu et al. (2018) also indicated that grasslands were vulnerable to hydroclimatic differences [60]. Nevertheless, many studies suggest that grasslands have a higher resilience to drought than forests, because of their ability to respond quickly to changes in environmental conditions [61,62]. Grasslands can quickly re-establish themselves after a drought, due to their ability to rapidly allocate resources to new growth. In addition, grasslands have a more flexible root system that enables them to adapt to changing soil moisture conditions. The higher resilience of grasslands to drought means that they are able to recover more quickly from the impacts of drought and continue to provide ecosystem services. This study highlighted the stronger Rs of grassland ecosystems to droughts compared with forest ecosystems when derived from GPP (Figure 7 and Figure 8). However, both forest and grassland sites exhibited weaker NEE-derived Rt but stronger NEE-derived Rs. Although this study revealed many interesting findings, more flux sites of diverse ecosystems are necessary to consolidate these results in future work.

5. Conclusions

In summary, on the basis of tower-based flux measurements at the forest and grassland sites, this study revealed the different effects of extreme drought events on forest and grassland carbon sequestrations. Although the NEE values of both ecosystems exhibited a sharp decrease during the drought years 2003 and 2018, the underlying mechanisms were different. When facing the disturbance of a drought event, both GPP and Re of the forest ecosystem even exhibited a slight increase, but the large increase in Re led to the final decrease in the NEE. However, the drought severely decreased the GPP and Re of the grassland, and a larger reduction in GPP eventually resulted in a sharp decline in the NEE. The resistance and resilience of these two ecosystems to extreme drought events were also contrasting. Generally, forest GPP exhibited stronger resistance to drought with weaker resilience. However, both the Re and NEE of the forest showed relatively lower resistance and higher resilience to the two drought events. Meanwhile, although the GPP-derived resilience and NEE-derived resilience of grassland were strong, their drought resistance was poor. Conversely, the grassland Re-derived resistance was stronger with relatively weaker resilience. The resistance and resilience of forest and grassland to drought differ due to differences in root systems, water storage capacity, and water use regulation. Therefore, our findings offer new insights into divergent resistance and resilience of forest and grassland ecosystems to extreme droughts for carbon sequestration.

Author Contributions

Conceptualisation and methodology, F.Y.; formal analysis and writing, J.L.; review and funding acquisition, F.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was jointly funded by the National Key Research and Development Program of China (Grant No. 2022YFF0802101) and the Youth Project of Innovation LREIS, Chinese Academy of Sciences (Grant No. YPI001).

Data Availability Statement

All data can be accessed through the provided website.

Acknowledgments

We are grateful to all persons in the maintenance, operation, and collection of the flux data of the Swiss FluxNet in the present study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Spinoni, J.; Vogt, J.V.; Naumann, G.; Barbosa, P.; Dosio, A. Will drought events become more frequent and severe in Europe? Int. J. Climatol. 2018, 38, 1718–1736. [Google Scholar] [CrossRef]
  2. Wang, M.; Ding, Z.; Wu, C.; Song, L.; Ma, M.; Yu, P.; Lu, B.; Tang, X. Divergent responses of ecosystem water-use efficiency to extreme seasonal droughts in Southwest China. Sci. Total Environ. 2021, 760, 143427. [Google Scholar] [CrossRef] [PubMed]
  3. Wolf, S.; Eugster, W.; Ammann, C.; Häni, M.; Zielis, S.; Hiller, R.; Stieger, J.; Imer, D.; Merbold, L.; Buchmann, N. Contrasting response of grassland versus forest carbon and water fluxes to spring drought in Switzerland. Environ. Res. Lett. 2013, 8, 035007. [Google Scholar] [CrossRef]
  4. Wang, Y.; Wang, Y.; Chen, Y.; Chen, H.; Li, X.; Ding, Z.; Han, X.; Tang, X. Spatial and temporal characteristics of drought events in southwest China over the past 120 years. Remote Sens. 2023, 15, 3008. [Google Scholar] [CrossRef]
  5. Liu, Q.Y.; Peng, C.H.; Schneider, R.; Cyr, D.; McDowell, N.G.; Kneeshaw, D. Drought-induced increase in tree mortality and corresponding decrease in the carbon sink capacity of Canada’s boreal forests from 1970 to 2020. Glob. Chang. Biol. 2023, 29, 2274–2285. [Google Scholar] [CrossRef]
  6. Friedlingstein, P.; Jones, M.W.; Sullivan, M.O.; Andrew, R.M.; Bakker, D.C.E.; Hauck, J.; Le Quéré, C.; Peters, G.P.; Peters, W. Global Carbon Budget 2021. Earth Syst. Sci. Data 2022, 14, 1917–2005. [Google Scholar] [CrossRef]
  7. Song, Y.; Jiao, W.Z.; Wang, J.; Wang, L.X. Increased global vegetation productivity despite rising atmospheric dryness over the last two decades. Earth’s Future 2022, 10, e2021EF002634. [Google Scholar] [CrossRef]
  8. Tang, X.G.; Xiao, J.F.; Ma, M.G.; Yang, H.; Li, X.; Ding, Z.; Yu, P.J.; Zhang, Y.G.; Wu, C.Y.; Huang, J.; et al. Satellite evidence for China’s leading role in restoring vegetation productivity over global karst ecosystems. For. Ecol. Manag. 2022, 507, 120000. [Google Scholar] [CrossRef]
  9. Pan, Y.; Birdsey, R.; Fang, J.; Houghton, R.; Kauppi, P.; Kurz, W.; Phillips, O.; Shvidenko, A.; Lewis, S.; Canadell, J.; et al. A large and persistent carbon sink in the World’s forests. Science 2011, 333, 988–992. [Google Scholar] [CrossRef]
  10. Ghosh, P.K.; Mahanta, S.K. Carbon sequestration in grassland systems. Range Manag. Agrofor. 2014, 35, 173–181. [Google Scholar]
  11. Zhang, W.; Koch, J.; Wei, F.; Zeng, Z.; Fang, Z.; Fensholt, R. Soil moisture and atmospheric aridity impact spatio-temporal changes in evapotranspiration at a global scale. J. Geophys. Res. Atmos. 2023, 128, e2022JD038046. [Google Scholar] [CrossRef]
  12. Brando, P.M.; Paolucci, L.; Ummenhofer, C.C.; Ordway, E.M.; Hartmann, H.; Cattau, M.E.; Balch, J. Droughts, wildfires, and forest carbon cycling: A pantropical synthesis. Annu. Rev. Earth Planet Sci. 2019, 47, 555–581. [Google Scholar] [CrossRef]
  13. Wang, Q.; Yang, Y.; Liu, Y.; Tong, L.; Zhang, Q.; Li, J. Assessing the impacts of drought on grassland net primary production at the global scale. Sci. Rep. 2019, 9, 14041. [Google Scholar] [CrossRef]
  14. Ye, C.; Sun, J.; Liu, M.; Xiong, J.; Zong, N.; Hu, J.; Huang, Y.; Duan, X.; Tsunekawa, A. Concurrent and lagged effects of extreme drought induce net reduction in vegetation carbon uptake on Tibetan Plateau. Remote Sens. 2020, 12, 2347. [Google Scholar] [CrossRef]
  15. Zhang, M.; Yuan, X. Rapid reduction in ecosystem productivity caused by flash droughts based on decade-long FLUXNET observations. Hydrol. Earth Syst. Sci. 2020, 24, 5579–5593. [Google Scholar] [CrossRef]
  16. Xie, S.; Mo, X.; Liu, S.; Hu, S. Plant hydraulics improves predictions of ET and GPP responses to drought. Water Resour. Res. 2023, 59, e2022WR033402. [Google Scholar] [CrossRef]
  17. Anderegg, W.R.L.; Kane, J.M.; Anderegg, L.D.L. Consequences of widespread tree mortality triggered by drought and temperature stress. Nat. Clim. Chang. 2013, 3, 30–36. [Google Scholar] [CrossRef]
  18. McDowell, N.G.; Sapes, G.; Pivovaroff, A.; Adams, H.D.; Allen, C.D.; Anderegg, W.R.L.; Arend, M.; Breshears, D.D.; Brodribb, T.; Choat, B.; et al. Mechanisms of woody-plant mortality under rising drought, CO2 and vapour pressure deficit. Nat. Rev. Earth Environ. 2022, 3, 294–308. [Google Scholar] [CrossRef]
  19. Ciais, P.; Reichstein, M.; Viovy, N.; Granier, A.; Ogee, J.; Allard, V.; Aubinet, M.; Buchmann, N.; Bernhofer, C.; Carrara, A.; et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 2005, 437, 529–533. [Google Scholar] [CrossRef]
  20. Gharun, M.; Hörtnagl, L.; Paul-Limoges, E.; Ghiasi, S.; Feigenwinter, I.; Burri, S.; Marquardt, K.; Etzold, S.; Zweifel, R.; Eugster, W.; et al. Physiological response of Swiss ecosystems to 2018 drought across pant types and elevation. Philos. Trans. R. Soc. B Biol. Sci. 2020, 375, 20190521. [Google Scholar] [CrossRef]
  21. Anderegg, W.R.; Konings, A.G.; Trugman, A.T.; Yu, K.; Bowling, D.R.; Gabbitas, R.; Karp, D.S.; Pacala, S.; Sperry, J.S.; Sulman, B.N. Hydraulic diversity of forests regulates ecosystem resilience during drought. Nature 2018, 561, 538–541. [Google Scholar] [CrossRef] [PubMed]
  22. Li, X.; Piao, S.; Wang, K.; Wang, X.; Wang, T.; Ciais, P.; Chen, A.; Lian, X.; Peng, S.; Peñuelas, J. Temporal trade-off between gymnosperm resistance and resilience increases forest sensitivity to extreme drought. Nat. Ecol. Evol. 2020, 4, 1075–1083. [Google Scholar] [CrossRef]
  23. Castagneri, D.; Vacchiano, G.; Hacket-Pain, A.; DeRose, R.J.; Klein, T.; Bottero, A. Meta-analysis reveals different competition effects on tree growth resistance and resilience to drought. Ecosystems 2022, 25, 30–43. [Google Scholar] [CrossRef]
  24. Zweifel, R.; Eugster, W.; Etzold, S.; Dobbertin, M.; Buchmann, N.; Häsler, R. Link between continuous stem radius changes and net ecosystem productivity of a subalpine norway spruce forest in the Swiss Alps. New Phytol. 2010, 187, 819–830. [Google Scholar] [CrossRef]
  25. Ammann, C.; Flechard, C.R.; Leifeld, J.; Neftel, A.; Fuhrer, J. The carbon budget of newly established temperate grassland depends on management intensity. Agric. Ecosyst. Environ. 2007, 121, 5–20. [Google Scholar] [CrossRef]
  26. Ammann, C.; Spirig, C.; Leifeld, J.; Neftel, A. Assessment of the nitrogen and carbon budget of two managed temperate grassland fields. Agric. Ecosyst. Environ. 2009, 133, 150–162. [Google Scholar] [CrossRef]
  27. Ficklin, D.L.; Novick, K.A. Historic and projected changes in vapor pressure deficit suggest a continental-scale drying of the United States atmosphere. J. Geophys. Res. Atmos. 2017, 122, 2061–2079. [Google Scholar] [CrossRef]
  28. Lian, X.; Piao, S.; Chen, A.; Huntingford, C.; Fu, B.; Li, L.Z.X.; Huang, J.; Sheffield, J.; Berg, A.M.; Keenan, T.F.; et al. Multifaceted characteristics of dryland aridity changes in a warming world. Nat. Rev. Earth Environ. 2021, 2, 232–250. [Google Scholar] [CrossRef]
  29. Mauder, M.; Foken, T.; Clement, R.; Elbers, J.A.; Eugster, W.; Grünwald, T.; Heusinkveld, B.; Kolle, O. Quality control of CarboEurope flux data—Part 2: Inter-comparison of eddy-covariance software. Biogeosciences 2008, 5, 451–462. [Google Scholar] [CrossRef]
  30. Drought 2018 Team & ICOS Ecosystem Thematic Centre. Drought-2018 Ecosystem Eddy Covariance Flux Product for 52 Stations in FLUXNET-Archive Format. Available online: https://doi.org/10.18160/YVR0-4898 (accessed on 8 March 2023).
  31. Reichstein, M.; Falge, E.; Baldocchi, D.; Papale, D.; Aubinet, M.; Berbigier, P.; Bernhofer, C.; Buchmann, N.; Gilmanov, T.; Granier, A.; et al. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: Review and improved algorithm. Glob. Chang. Biol. 2005, 11, 1424–1439. [Google Scholar] [CrossRef]
  32. Bellanthudawa, B.K.A.; Chang, N.B. Spectral index-based time series analysis of canopy resistance and resilience in a watershed under intermittent weather changes. Ecol. Inform. 2022, 69, 101666. [Google Scholar] [CrossRef]
  33. Chang, N.B.; Wen, D. Enhanced resilience and resistance assessment with virtual ecoexergy for a subtropical lake ecosystem under the intermittent impact of hurricanes and droughts. Ecol. Inform. 2017, 39, 68–83. [Google Scholar] [CrossRef]
  34. DeRose, R.J.; Long, J.N. Resistance and resilience: A conceptual framework for silviculture. For. Sci. 2014, 60, 1205–1212. [Google Scholar] [CrossRef]
  35. Isbell, F.; Crave, D.; Connolly, J.; Loreau, M.; Schmid, B.; Beierkuhnlein, C.; Bezemer, T.M.; Bonin, C.; Bruelheide, H.; de Luca, E.; et al. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 2015, 526, 574–577. [Google Scholar] [CrossRef]
  36. Schwalm, C.R.; Anderegg, W.R.L.; Michalak, A.M.; Fisher, J.B.; Biondi, F.; Koch, G.; Litvak, M.; Ogle, K.; Shaw, J.D.; Wolf, A. Global patterns of drought recovery. Nature 2017, 548, 202–205. [Google Scholar] [CrossRef]
  37. Anderegg, W.R.L.; Schwalm, C.; Biondi, F.; Camarero, J.J.; Koch, G.; Litvak, M.; Ogle, K.; Shaw, J.D.; Shevliakova, E.; Williams, A.P.; et al. Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models. Science 2015, 349, 520–532. [Google Scholar] [CrossRef]
  38. Chen, Y.; Ding, Z.; Yu, P.; Yang, H.; Song, L.; Fan, L.; Han, X.; Ma, M.; Tang, X. Quantifying the variability in water use efficiency from the canopy to ecosystem scale across main croplands. Agric. Water Manag. 2022, 262, 107427. [Google Scholar] [CrossRef]
  39. Li, G.; Wu, C.; Chen, Y.; Huang, C.; Zhao, Y.; Wang, Y.; Ma, M.; Ding, Z.; Yu, P.; Tang, X. Increasing temperature regulates the advance of peak photosynthesis timing in the boreal ecosystem. Sci. Total Environ. 2023, 882, 163587. [Google Scholar] [CrossRef]
  40. Grossiord, C.; Buckley, T.N.; Cernusak, L.A.; Novick, K.A.; Poulter, B.; Siegwolf, R.T.W.; Sperry, J.S.; McDowell, N.G. Plant responses to rising vapor pressure deficit. New Phytol. 2020, 226, 1550–1566. [Google Scholar] [CrossRef]
  41. Yuan, W.; Zheng, Y.; Piao, S.; Ciais, P.; Lombardozzi, D.L.; Wang, Y.; Ryu, Y.; Chen, G.; Dong, W.; Hu, Z.; et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 2019, 5, eaax1396. [Google Scholar] [CrossRef]
  42. Lopez, J.; Way, D.A.; Sadok, W. Systemic effects of rising atmospheric vapor pressure deficit on plant physiology and productivity. Glob. Chang. Biol. 2021, 27, 1704–1720. [Google Scholar] [CrossRef]
  43. Liu, X.; Feng, X.; Fu, B. Changes in global terrestrial ecosystem water use efficiency are closely related to soil moisture. Sci. Total Environ. 2020, 698, 134165. [Google Scholar] [CrossRef] [PubMed]
  44. Humphrey, V.; Berg, A.; Ciais, P.; Gentine, P.; Jung, M.; Reichstein, M.; Seneviratne, S.I.; Frankenberg, C. Soil moisture—Atmosphere feedback dominates land carbon uptake variability. Nature 2021, 592, 65–69. [Google Scholar] [CrossRef]
  45. Oren, R.; Sperry, J.S.; Katul, G.; Pataki, D.E.; Ewers, B.; Phillips, N.; Schäfer, K. Survey and synthesis of intra-and interspecific variation in stomatal sensitivity to vapour pressure deficit. Plant Cell Environ. 1999, 22, 1515–1526. [Google Scholar] [CrossRef]
  46. McAdam, S.A.; Brodribb, T.J. The evolution of mechanisms driving the stomatal response to vapor pressure deficit. Plant Physiol. 2015, 167, 833–843. [Google Scholar] [CrossRef] [PubMed]
  47. He, B.; Chen, C.; Lin, S.; Yuan, W.; Chen, H.W.; Chen, D.; Zhang, Y.; Guo, L.; Zhao, X.; Liu, X.; et al. Worldwide impacts of atmospheric vapor pressure deficit on the interannual variability of terrestrial carbon sinks. Natl. Sci. Rev. 2022, 9, nwab150. [Google Scholar] [CrossRef]
  48. Green, J.K.; Berry, J.; Ciais, P.; Zhang, Y.; Gentine, P. Amazon rainforest photosynthesis increases in response to atmospheric dryness. Sci. Adv. 2020, 6, eabb7232. [Google Scholar] [CrossRef] [PubMed]
  49. Cao, S.; Yi, H.; Zhang, L.; Sun, Q.; Zhang, Y.; Li, H.; Wei, X.; Liu, Y. Spatiotemporal dynamics of vegetation net ecosystem productivity and its response to drought in northwest China. GISci. Remote Sens. 2023, 60, 2194597. [Google Scholar] [CrossRef]
  50. Huang, K.; Xia, J.; Wang, Y.; Ahlström, A.; Chen, J.; Cook, R.B.; Cui, E.; Fang, Y.; Fisher, J.B.; Huntzinger, D.N.; et al. Enhanced peak growth of global vegetation and its key mechanisms. Nat. Ecol. Evol. 2018, 2, 1897–1905. [Google Scholar] [CrossRef]
  51. Piao, S.; Wang, X.; Wang, K.; Li, X.; Bastos, A.; Canadell, J.G.; Ciais, P.; Friedlingstein, P.; Sitch, S. Interannual variation of terrestrial carbon cycle: Issues and perspectives. Glob. Chang. Biol. 2020, 26, 300–318. [Google Scholar] [CrossRef]
  52. Cox, P.M.; Pearson, D.; Booth, B.B.; Friedlingstein, P.; Huntingford, C.; Jones, C.D.; Luke, C.M. Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature 2013, 494, 341–344. [Google Scholar] [CrossRef]
  53. Bateni, S.M.; Entekhabi, D. Relative efficiency of land surface energy balance components. Water Resour. Res. 2012, 48, W04510. [Google Scholar] [CrossRef]
  54. Cotlier, G.I.; Jimenez, J.C. The extreme heat wave over western North America in 2021: An assessment by means of land surface temperature. Remote Sens. 2022, 14, 561. [Google Scholar] [CrossRef]
  55. Lloret, F.; Keeling, E.G.; Sala, A. Components of tree resilience: Effects of successive low-growth episodes in old ponderosa pine forests. Oikos 2011, 120, 1909–1920. [Google Scholar] [CrossRef]
  56. Stuart-Haëntjens, E.; De Boeck, H.J.; Lemoine, N.P.; Mänd, P.; Kröel-Dulay, G.; Schmidt, I.K.; Jentsch, A.; Stampfli, A.; Anderegg, W.R.; Bahn, M.; et al. Mean annual precipitation predicts primary production resistance and resilience to extreme drought. Sci. Total Environ. 2018, 636, 360–366. [Google Scholar] [CrossRef]
  57. Petrakis, R.; Wu, Z.; McVay, J.; Middleton, B.; Dye, D.; Vogel, J. Vegetative response to water availability on the San Carlos Apache reservation. For. Ecol. Manag. 2016, 378, 14–23. [Google Scholar] [CrossRef]
  58. MacGillivray, C.; Grime, J.; Team, T.I.S.P. Testing predictions of the resistance and resilience of vegetation subjected to extreme events. Funct. Ecol. 1995, 9, 640–649. [Google Scholar] [CrossRef]
  59. Cao, S.; Zhang, L.; He, Y.; Zhang, Y.; Chen, Y.; Yao, S.; Yang, W.; Sun, Q. Effects and contributions of meteorological drought on agricultural drought under different climatic zones and vegetation types in northwest China. Sci. Total Environ. 2022, 821, 153270. [Google Scholar] [CrossRef]
  60. Xu, H.; Wang, X.; Zhao, C.; Yang, X. Diverse Responses of vegetation growth to meteorological drought across climate zones and land biomes in northern China from 1981 to 2014. Agric. For. Meteorol. 2018, 262, 1–13. [Google Scholar] [CrossRef]
  61. Hoover, D.L.; Knapp, A.K.; Smith, M.D. Resistance and resilience of a grassland ecosystem to climate extremes. Ecology 2014, 95, 2646–2656. [Google Scholar] [CrossRef]
  62. Liu, Y.; You, C.; Zhang, Y.; Chen, S.; Zhang, Z.; Li, J.; Wu, Y. Resistance and resilience of grasslands to drought detected by SIF in Inner Mongolia, China. Agric. For. Meteorol. 2021, 308–309, 108567. [Google Scholar] [CrossRef]
Land 12 01672 g001
Figure 1. Location of the flux sites used in this study. The full names of CH-Dav, CH-Oe1, and CH-Cha are Davos (evergreen needle-leaf forest), Oensingen1 (grassland), and Chamau (grassland), respectively. The base map is derived from the AVHRR 1 km land-cover map.
Figure 1. Location of the flux sites used in this study. The full names of CH-Dav, CH-Oe1, and CH-Cha are Davos (evergreen needle-leaf forest), Oensingen1 (grassland), and Chamau (grassland), respectively. The base map is derived from the AVHRR 1 km land-cover map.
Land 12 01672 g001
Land 12 01672 g002
Figure 2. Seasonal dynamics of climate factors, namely solar radiation (Rg) (a,b), air temperature (Ta) (c,d), vapour pressure deficit (VPD) (e,f), and precipitation (Pre) (g,h) at the forest (left: CH-Dav) and grassland (right: CH-Oe1) sites for each weekly period. The blue and red lines represent multi-year means during the adjacent normal years and the drought year 2003, respectively. The error bars represent ± 1 standard deviation. For the x-axis, the data refer to the week of the year (WOY) ranging from 1 to 52.
Figure 2. Seasonal dynamics of climate factors, namely solar radiation (Rg) (a,b), air temperature (Ta) (c,d), vapour pressure deficit (VPD) (e,f), and precipitation (Pre) (g,h) at the forest (left: CH-Dav) and grassland (right: CH-Oe1) sites for each weekly period. The blue and red lines represent multi-year means during the adjacent normal years and the drought year 2003, respectively. The error bars represent ± 1 standard deviation. For the x-axis, the data refer to the week of the year (WOY) ranging from 1 to 52.
Land 12 01672 g002
Land 12 01672 g003
Figure 3. Seasonal dynamics of carbon fluxes, namely gross primary productivity (GPP) (a,b), ecosystem respiration (Re) (c,d), and net ecosystem exchange (NEE) (e,f) at the forest (left: CH-Dav) and grassland (right: CH-Oe1) sites for each weekly period, respectively. The blue and red lines represent multi-year means during the adjacent normal years and the drought year 2003, respectively. The error bars represent ± 1 standard deviation. For the x-axis, the data refer to the week of the year (WOY) ranging from 1 to 52.
Figure 3. Seasonal dynamics of carbon fluxes, namely gross primary productivity (GPP) (a,b), ecosystem respiration (Re) (c,d), and net ecosystem exchange (NEE) (e,f) at the forest (left: CH-Dav) and grassland (right: CH-Oe1) sites for each weekly period, respectively. The blue and red lines represent multi-year means during the adjacent normal years and the drought year 2003, respectively. The error bars represent ± 1 standard deviation. For the x-axis, the data refer to the week of the year (WOY) ranging from 1 to 52.
Land 12 01672 g003
Land 12 01672 g004
Figure 4. Seasonal dynamics of climate factors, namely solar radiation (Rg) (a,b), air temperature (Ta) (c,d), vapour pressure deficit (VPD) (e,f), and precipitation (Pre) (g,h) at the forest (left: CH-Dav) and grassland (right: CH-Oe1) sites for each weekly period. The blue and red lines represent multi-year means during the adjacent normal years and the drought year 2018, respectively. The error bars represent ± 1 standard deviation. For the x-axis, the data refer to the week of the year (WOY) ranging from 1 to 52.
Figure 4. Seasonal dynamics of climate factors, namely solar radiation (Rg) (a,b), air temperature (Ta) (c,d), vapour pressure deficit (VPD) (e,f), and precipitation (Pre) (g,h) at the forest (left: CH-Dav) and grassland (right: CH-Oe1) sites for each weekly period. The blue and red lines represent multi-year means during the adjacent normal years and the drought year 2018, respectively. The error bars represent ± 1 standard deviation. For the x-axis, the data refer to the week of the year (WOY) ranging from 1 to 52.
Land 12 01672 g004
Land 12 01672 g005
Figure 5. Seasonal dynamics of carbon fluxes, namely gross primary productivity (GPP) (a,b), ecosystem respiration (Re) (c,d), and net ecosystem exchange (NEE) (e,f), at the forest (left: CH-Dav) and grassland (right: CH-Cha) sites for each weekly period, respectively. The blue and red lines represent multi-year means during the adjacent normal years and the drought year 2018, respectively. The error bars represent ± 1 standard deviation. For the x-axis, the data refer to the week of the year (WOY) ranging from 1 to 52.
Figure 5. Seasonal dynamics of carbon fluxes, namely gross primary productivity (GPP) (a,b), ecosystem respiration (Re) (c,d), and net ecosystem exchange (NEE) (e,f), at the forest (left: CH-Dav) and grassland (right: CH-Cha) sites for each weekly period, respectively. The blue and red lines represent multi-year means during the adjacent normal years and the drought year 2018, respectively. The error bars represent ± 1 standard deviation. For the x-axis, the data refer to the week of the year (WOY) ranging from 1 to 52.
Land 12 01672 g005
Land 12 01672 g006
Figure 6. Relationships between VPD and ecosystem carbon fluxes, namely GPP (a,b), Re (a,b), and NEE (c,d), at the forest (Left) and grassland (Right) sites for each weekly period.
Figure 6. Relationships between VPD and ecosystem carbon fluxes, namely GPP (a,b), Re (a,b), and NEE (c,d), at the forest (Left) and grassland (Right) sites for each weekly period.
Land 12 01672 g006
Land 12 01672 g007
Figure 7. Differences in resistance (Rt) and resilience (Rs) of GPP, Re, and NEE at the forest (CH-Dav) and grassland (CH-Oe1) ecosystem to the 2003 drought.
Figure 7. Differences in resistance (Rt) and resilience (Rs) of GPP, Re, and NEE at the forest (CH-Dav) and grassland (CH-Oe1) ecosystem to the 2003 drought.
Land 12 01672 g007
Land 12 01672 g008
Figure 8. Differences in resistance (Rt) and resilience (Rs) of GPP, Re, and NEE at the forest (CH-Dav) and grassland (CH-Cha) ecosystem to the 2018 drought.
Figure 8. Differences in resistance (Rt) and resilience (Rs) of GPP, Re, and NEE at the forest (CH-Dav) and grassland (CH-Cha) ecosystem to the 2018 drought.
Land 12 01672 g008
Land 12 01672 g009
Figure 9. The variability in VPD and soil water content (SWC) at the two grassland flux sites ((a) CH-Oe1; (b) CH-Cha) over each weekly period. Owing to the data availability, only the grassland sites were analysed here.
Figure 9. The variability in VPD and soil water content (SWC) at the two grassland flux sites ((a) CH-Oe1; (b) CH-Cha) over each weekly period. Owing to the data availability, only the grassland sites were analysed here.
Land 12 01672 g009
Land 12 01672 g010
Figure 10. Scatter plots between VPD and soil water content (SWC) at the two grassland flux sites ((a) CH-Oe1; (b) CH-Cha) over each weekly period. Here, r means the correlation coefficient. Owing to the data availability, only the grassland sites were analysed here.
Figure 10. Scatter plots between VPD and soil water content (SWC) at the two grassland flux sites ((a) CH-Oe1; (b) CH-Cha) over each weekly period. Here, r means the correlation coefficient. Owing to the data availability, only the grassland sites were analysed here.
Land 12 01672 g010
Table 1. The Pearson correlation analysis between climate factors and ecosystem carbon fluxes at the forest and grassland sites for each weekly period, respectively.
Table 1. The Pearson correlation analysis between climate factors and ecosystem carbon fluxes at the forest and grassland sites for each weekly period, respectively.
Vegetation TypeCarbon FluxesRg
(W m−2)		Ta
(℃)		VPD
(hPa)		Pre
(mm d−1)
ForestGPP
(g C m−2 d−1)		0.770 **0.864 **0.646 **0.223 **
Re
(g C m−2 d−1)		0.547 **0.728 **0.624 **0.151 **
NEE
(g C m−2 d−1)		−0.632 **−0.559 **−0.310 **−0.179 **
GrasslandGPP
(g C m−2 d−1)		0.711 **0.643 **0.520 **−0.042
Re
(g C m−2 d−1)		0.615 **0.635 **0.488 **−0.001
NEE
(g C m−2 d−1)		−0.212 **−0.007−0.0440.087
** The correlation is significant at the 0.01 level.
Table 2. Relative deviations of changes in climate and ecosystem carbon fluxes between the multi-year means during the adjacent normal years and the drought 2003 at the forest (CH-Dav) and grassland (CH-Oe1) sites, respectively.
Table 2. Relative deviations of changes in climate and ecosystem carbon fluxes between the multi-year means during the adjacent normal years and the drought 2003 at the forest (CH-Dav) and grassland (CH-Oe1) sites, respectively.
VariablesForest SiteGrassland Site
Multi-Year MeansDrought 2003Multi-Year MeansDrought 2003
Climate factorsRg
(W m−2)		150.53163.04135.1149.2
8.3%10.4%
Ta
(°C)		4.224.729.239.56
11.8%3.6%
VPD
(hPa)		3.134.012.834.54
28.1%60.4%
Pre
(mm y−1)		862.1663.21276.5890.3
−23.1%−30.3%
Carbon fluxesGPP
(g C m−2 y−1)		1541.961614.052003.241647.57
4.68%−17.8%
Re
(g C m−2 y−1)		1534.761835.671530.431378.82
19.6%−9.9%
NEE
(g C m−2 y−1)		−10.76218.8−441.3−157.3
−2133.5%−64.3%
Table 3. Relative deviations of changes in climate and ecosystem carbon fluxes between the multi-year means during adjacent normal years and the drought 2018 at the forest (CH-Dav) and grassland sites (CH-Cha), respectively.
Table 3. Relative deviations of changes in climate and ecosystem carbon fluxes between the multi-year means during adjacent normal years and the drought 2018 at the forest (CH-Dav) and grassland sites (CH-Cha), respectively.
VariablesForest SiteGrassland Site
Multi-Year MeansDrought 2018Multi-Year MeansDrought 2018
Climate factorsRg
(W m−2)		150.53155.16141.05149.66
3.1%6.1%
Ta
(°C)		4.225.239.8111.10
23.9%13.1%
VPD
(hPa)		3.133.602.763.83
15.0%38.8%
Pre
(mm y−1)		862.1818.31158.5872.8
−5.1%−24.7%
Carbon fluxesGPP
(g C m−2 y−1)		1238.321405.863313.062752.37
13.5%−16.9%
Re
(g C m−2 y−1)		1133.31416.493564.13290.6
24.9%−7.7%
NEE
(g C m−2 y−1)		−108.629.17217.07464.6
−108.4%114.0%
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

Lu, J.; Yan, F. The Divergent Resistance and Resilience of Forest and Grassland Ecosystems to Extreme Summer Drought in Carbon Sequestration. Land 2023, 12, 1672. https://doi.org/10.3390/land12091672

AMA Style

Lu J, Yan F. The Divergent Resistance and Resilience of Forest and Grassland Ecosystems to Extreme Summer Drought in Carbon Sequestration. Land. 2023; 12(9):1672. https://doi.org/10.3390/land12091672

Chicago/Turabian Style

Lu, Jie, and Fengqin Yan. 2023. "The Divergent Resistance and Resilience of Forest and Grassland Ecosystems to Extreme Summer Drought in Carbon Sequestration" Land 12, no. 9: 1672. https://doi.org/10.3390/land12091672

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