Contrasting variations of ecosystem gross primary productivity during flash droughts caused by competing water demand and supply

Flash drought events (FDEs) are projected to increase frequently in a warming world, significantly impacting ecosystem productivity and the global carbon cycle. The development of FDEs, induced by anomalies in different environmental variables, may cause different responses to the ecosystem’s gross primary productivity (GPP). However, the GPP variations and underlying mechanisms during the FDEs have rarely been quantified. This study collected long-term (>10 years) high-quality flux observations from the FLUXNET 2015 dataset to investigate GPP variations and their driving mechanisms during FDEs. Results showed that all vegetation types have two contrasting GPP variations during FDEs. One variation is a decreasing then increasing standardized GPP anomaly (V-shape response). The other shows an increase followed by decreasing standardized GPP anomaly (inverted V-shape response). The V-shape GPP response to FDEs was induced by increased soil water content deficit at the onset stage of FDEs. In contrast, the inverted V-shape GPP response to FDEs was induced by increased net radiation at the onset of FDEs. Such results indicated competing moisture supply and atmospheric moisture demand at the onset of FDEs, controlling the two contrasting ecosystem’s carbon responses with its development. Moreover, the contribution of water use efficiency to the magnitude of the V-shape GPP response (64.5 ± 22.4%) is greater than that to the inverted V-shape GPP response (47.6 ± 18.7%). This study identified the two contrasting types of GPP variations during FDEs and their driving mechanisms across multiple ecosystem types which can improve our ability to predict the future effects of more frequent FDEs on ecosystem productivity.


Introduction
Flash drought is a type of drought characterized by a sudden onset and dramatic rate of intensification, occurring more frequently under global warming (Ford and Labosier 2017, Otkin et al 2018, Nguyen et al 2019, Yuan et al 2019, 2023, Christian et al 2021).Flash drought events (FDEs) have strong and complex impacts on the ecosystem's carbon dynamics, such as gross primary productivity (GPP) (Zhang et al 2020).For example, the 2012 summer FDE over US Midwest reduced total regional GPP by 63 Tg C compared to other years between 2010 and 2014 (Jin et al 2019).There have been many studies to assess the changes in GPP before the onset of FDEs and after the end of FDEs (von Buttlar et al 2018, Xu et al 2019, Zhang et al 2020, Zhu et al 2021).However, The variations in GPP during FDEs have rarely been studied.The variations of GPP are critical signals on revealing the adaptation and response processes of vegetation to water and heat stresses during droughts.Understanding the variations of GPP during FDEs can elucidate the internal adaptation mechanisms of ecosystems to droughts (Flack-Prain et al 2019).It can also further help to develop appropriate anthropogenic mitigation strategies to reduce carbon sink losses under increasingly frequent FDEs conditions in a warming future.
Several studies have reported that GPP responded oppositely at the onset of FDEs at different sites worldwide.Wolf et al (2016) reported that standardized GPP anomaly (SGPPA) increased with warm climate at the onset of FDE occurring over the Midwestern Plains in the USA in 2012.Xie et al (2016) reported that SGPPA decreased rapidly with decreasing soil moisture at the onset of the FDE occurring over southern China in 2013.Wolf et al (2013) found that SGPPA reduced at the lowland grassland sites while it enhanced at the montane grassland and forests at the onset of an FDE that occurred over Switzerland in 2011.Water-use efficiency (WUE) substantially increased in forests, but not in grasslands during this FDE.A recent study based on FLUXNET observations showed SGPPA exhibited rapid reduction at the beginning of soil moisture flash droughts (Zhang and Yuan 2020).However, the development and mechanism of the two GPP variations during FDEs have not been systematically analyzed.Thus our knowledge about GPP response to FDEs in different stages across different ecosystems is still limited.
There was increasing evidence showing that the GPP variation was mainly driven by vapor pressure deficit (VPD), temperature, and soil water content (SWC), especially during FDEs (Amilcare et al 2004, Soudani et al 2014, Ford et al 2015, Mo and Lettenmaier 2015, Cleverly et al 2016, Zhang et al 2019, Christian et al 2021).VPD and SWC influence GPP responses mainly via their controls on plants' stomatal behavior during droughts (Sack and Holbrook 2006, Anderegg et al 2020, Liu et al 2020, Lawson and Matthews 2020, López et al 2021).GPP may increase with rising VPD during FDEs for transporting more water from roots to the atmosphere by increasing stomatal conductance.However, GPP would decrease slowly above optimal stomatal conductance (Monteith 1995, Ocheltree et al 2014, Konings et al 2017).Wolf et al (2016) reported that GPP rapidly increased due to increased VPD at the beginning of the FDE over the Midwestern Plain in the USA in 2012.However, Goodrich et al (2015) found that GPP decreased slowly with increasing VPD when it exceeds 10 hPa in a Southern Hemisphere bog.SWC deficit can reduce enzymatic activity or mesophyll and stomatal conductance controlled by the difference between soil and leaf water potentials (Novick et al 2016).Mahto and Mishra (2020), Xie et al (2016) reported that a drastic SWC deficit induced GPP reductions over India during the monsoon seasons and over southern China in 2013, respectively.Moreover, photosynthetically active radiation (PAR) also significantly affects the GPP variation through changes in air temperature and photosynthetic photon flux density during FDEs (Dang et al 2022).Generally, increases in PAR will enhance GPP via stronger photosynthesis (Lin et al 2018, Xi andYuan 2022).Overall, multiple environmental factors strongly impacted the GPP variation during FDEs.However, the important factors of the different GPP variations during FDEs and whether the dominant factor will change with the development of FDEs in different ecosystems remain unclear.
Water use efficiency (WUE), the carbon assimilation per unit of water loss by ecosystems (Keenan et al 2013, van der Sleen et al 2015, Cheng et al 2017), can play a regulatory role in GPP, especially during FDEs.WUE directly reflects the magnitude of the ecosystem's internal water-carbon responses to water stress (Peters et al 2018).Hasselquist et al (2010) demonstrated that dry-deciduous trees constrained stomatal conductance to reduce transpiration losses and further affected carbon assimilation rates by maintaining a relatively large WUE.Moreover, evergreen trees maintained a relatively high transpiration loss and carbon assimilation by reducing WUE under dry conditions in the northern Yucatan Peninsula, Mexico.Yang et al (2016) reported that WUE mainly drives biological processes (i.e.assimilation) in semiarid/sub-humid regions.Therefore, disentangling the contribution of WUE to the GPP variation magnitude during FDEs is important for a deeper understanding of the internal mechanisms of GPP responses at the ecosystem scale (Zhou et al 2014(Zhou et al , 2015)).
To better understand the GPP response to FDEs, FLUXNET 2015 dataset was used to investigate GPP responses over different stages of FDEs across different ecosystems.Identification of FDEs followed the method proposed by Christian et al (2019).Based on the dynamic time warping (DTW) method, GPP responses were analyzed with SWC, VPD, and net radiation (Rn) to identify the major external drivers of GPP variations during the FDE development.Based on the Lindeman-Merenda-Gold (LMG) method, the quantitative contribution of WUE was analyzed to identify ecosystem internal driving mechanisms of GPP responses to FDEs.The objectives of this study were: (1) to detect the GPP variation during the FDEs across multiple ecosystems using flux measurements; (2) to identify key environmental factors affecting the GPP variation during the FDE development; and (3) to investigate the contribution of WUE to the magnitude of the GPP variation during FDEs.

Identification of FDEs
FDEs were identified using a method proposed by Christian et al (2019).Main steps how to identify FDEs were described as follow.
(I) Calculation of the daily evaporative stress ratio (ESR).The ESR represents the degree of regional evaporative stress, calculated by the following formula (Christian et al 2021): where ET is the evapotranspiration converted from observed latent heat flux (LE) from a unit of W m −2 to the unit in the equivalent depth of water in mm d where SESR ip (referred to as SESR) is the standardized value of ESR at a specific data site i for a specific pentad p (one year has 73 pentads); ESR ip is the mean ESR at a specific data site i for the (p − 1)th, pth, (p + 1)th pentads for all years; and σ ESR ip is the standard deviation of ESR at a specific data site i for the (p − 1)th, pth, (p + 1)th pentads for all years.The shortterm temporal change in SESR was calculated and standardized as follows: where ∆SESR ip is the standardized value of the change in SESR from one pentad p to its next pentad (p + 1) at a specific data site i; is the mean change in SESR values at a specific data site i for the (p − 1)th, pth, (p + 1)th pentads for all years; SESR and ∆SESR were detrended before standardizing to account for changes that may have occurred in the drought threshold (SESR) or individual pentad changes (∆SESR

Quantification of the GPP and WUE responses
SGPPA was estimated to investigate the response of GPP during FDEs using the standardized method as follows: The variability of SGPPA during FDEs can effectively reflect the impact of FDEs on GPP.
The ecosystem WUE reflects the degree of coupling between carbon assimilation and water loss during FDEs.In this study, WUE is defined as follows: This study used GPP based on the nighttime partitioning method (GPP_NT_VUT_REF) to estimate SGPPA and SWUEA.
The T a , VPD, PAR, and SWC were the major factors driving the GPP variations during FDEs.Because the availability of PAR is rare in the FLUXNET 2015 dataset, Rn data was used to substitute PAR for subsequent analysis.Anomalies of these four factors were standardized using the same way as the calculation of SESR, denoted as STAA, SVPDA, SRnA, and SSWCA, respectively.All daily hydrometeorological variables and carbon fluxes were aggregated or averaged to a pentad (5 d) timescale.

Remote sensing GPP data
MODIS GPP product (i.e.MOD17A2HGF, Version 6.1) was collected to test whether GPP responses identified from the remote sensing data are consistent with those based on the flux data.The MOD17A2HGF is a cumulative 8 d composite of values with 500 m pixel size based on the radiation use efficiency concept (Running et al 2021).As this dataset has been quality controlled, the original GPP was used directly.The 8 d GPP series was interpolated to pentad values linearly.

DTW method
The DTW method, as implemented in the R (v 4.1.3)package dtw v 1.23-1 (Giorgino 2009), is widely used to compare the similarity of two sequences based on dynamic programming and Euclidean distance.DTW finds the optimal alignment, resulting in a dissimilarity measure (Sakoe and Chiba 1978).The smaller the dissimilarity measure calculated by DTW, the smaller the difference between the two sequences.This study used this method to calculate the similarity between the variation of the three variables (SWC, VPD, and Rn) and GPP to analyze the main environmental factor affecting GPP variation during FDEs.Main steps how to calculate the DTW cumulative distances were described in Text S2.

LMG method
The LMG method, as implemented in the R (v4.1.3)package relaimpo v 2.2-6, is widely used to quantify the contribution of WUE and ET to the GPP variation.Firstly, this method quantifies the changes in the residual sums of squares by removing each independent variable from a bivariate regression between WUE and ET as follows: where RI v is the relative importance of variable v; RSS v is the residual sum of squares of the regression of GPP with variable v; RSS WUE, ET is the residual sum of squares of the regression of GPP against WUE and GPP, and TSS is the total sum of squares.More information on LMG distance can be found in Grömping (2006).

Variations of GPP during FDEs
Based on the FLUXNET 2015 dataset, 53 FDEs with durations longer than or equal to 6 pentads (30 d) were identified during the growing season at 30 sites.Figure 1(a) shows the distribution of the 30 sites with 8 vegetation types (see table S1), mainly distributed over North America and Europe.The number of FDEs with croplands (CRO), deciduous broadleaf forests (DBF), evergreen broad-leaved forests, evergreen needle-leaved forests (ENF), mixed forests, grasslands, woody savannas, and wetlands were 7, 12, 5, 21, 3, 2, 2, and 1, respectively (figure S1(b)).The mean duration of FDEs ranged from 30 to 40 d (figure S1(c)).Only three vegetation types that experienced more than five FDEs (CRO, DBF, ENF) were used in the subsequent analysis to reduce the uncertainty caused by small sample sizes.In total, 40 FDEs were studied.
In these 40 FDEs, two types of GPP variations were identified.One was that SGPPA decreased and then increased (from now on, denoted as V-shape response).The other was that SGPPA increased then decreased (from now on, denoted as an inverted Vshape response).The number of FDEs with V-shape and inverted V-shape responses was 20 (50%) during FDEs (table S1).Figures 1(a), (c) and (e) show the Vshape response at US-Ne3 (CRO), IT-Col (DBF), and CA-Man (ENF) flux sites, respectively.Figures 1(b),  (d) and (f) show the inverted V-shape response at US-Ne1 (CRO), US-WCr (DBF), and FR-LBr (ENF) flux sites, respectively.Therefore, the GPP variations during FDEs have two types.
For the different vegetation types, the GPP variations during FDEs had the same two types but differed in magnitude (see figure S3).For the V-shape response, SGPPA of DBF exhibited a slight decreases (0.12) from onset to middle of FDEs, different from SGPPA of CRO and ENF.Because drought resistance of the DBFs is high (Li et al 2021).There is a small reduction in stomatal opening of the remaining leaves of the DBFs to maintain a relatively high carbon assimilation rate when face with droughts (Hasselquist et al 2010).SGPPA of DBF exhibited a greater increase (0.47) during middle to end of FDEs.This was due to reduced transpiration under poor water supply condition (see table S3).On the basis of only a slight decrease in stomatal conductance in the previous phase, the proportion of photosynthesis increased.For the inverted V-shape response, SGPPA of CRO exhibited virtually no change (0.01) during GPP increasing phase.Because drought resilience and resistance of the CROs is low, and a part of crops may die during FDEs (Trifilò et al 2021).Therefore, SGPPA of CRO falled faster during in the next phase during FDEs.For the test using the remote sensing data, there were 24 FDEs at 17 locations allow us to do a comparison analysis as shown in figure S6.Results show that 20 of the 24 FDEs have the same GPP response types identified from both flux measurements and remote sensing data.Therefore, it is promising to investigate the GPP response to FDEs using remote sensing data at global scale.And the number and distribution of V-shape and inverted V-shape responses of all vegetation types could be further compared to explore the resistance of different ecosystems to FDEs.

Drivers of two contrasting GPP responses to FDEs
Figure S4 shows the goodness of fit (R 2 ) and normalized distance calculated by DTW (ND-DTW) between the four factors (SSWCA, SVPDA, SRnA, STAA) and SGPPA during FDEs for V-shape and inverted V-shape responses, respectively.For the Vshape response, the R 2 between the four factors and SGPPA were similar.The ND-DTW between the four factors and SGPPA were 0.29 ± 0.19, 0.47 ± 0.21, and 0.45 ± 0.23, 0.42 ± 0.19, respectively.For the similarity of variations during FDEs, the variation of SGPPA was more similar to that of SSWCA.The results about the goodness of fit and similarity indicated that the GPP variation was driven mainly by SWC for the Vshape response.For the inverted V-shape response, the correlation relationship between the SGPPA and SRnA was better And for similarity of variation, these results were the same as that for the goodness of fit. Figure S4 indicated that the GPP variation was driven mainly by Rn for the V-shape response.
Figure 2 displays ND-DTW between SGPPA and the four factors in two developing phases of FDEs for the V-shape and inverted V-shape responses.For the V-shape response, the value of ND-DTW between SGPPA and SSWCA was 0.30 ± 0.28, far lower than that between SGPPA and the other factors during SGPPA decreasing phase.During SGPPA increasing phase, ND-DTW between SGPPA and SSWCA was the same as during the previous phase.But the variation of SVPDA, SRnA and STAA became more similar to the variation of SGPPA (figure 2(a)).Those results indicated that SWC largely drove the GPP variation in the decreasing and increasing phases of the V-shape response.Moreover, the effect of T a , Rn and VPD on GPP variations was greater with the development of FDEs.For the inverted V-shape response, ND-DTW between SGPPA and SRnA was 0.25 ± 0.06, lower than that between SGPPA and other factors during the increasing phase.During the decreasing phase, the value of ND-DTW between SGPPA and SSWCA was 0.25 ± 0.13, far lower than that between SGPPA and other factors (figure 2(b)).Therefore, the increase in Rn drove the GPP variation in the increasing phase, SWC deficit drove the GPP variation in the decreasing phase for inverted V-shape response.
Essentially, the V-shape GPP response to FDEs was induced by increased SWC deficit at the onset stage of FDEs, whereas the inverted V-shape GPP response to FDEs was induced by increased Rn or T a at the onset stage of FDEs.

Contribution of WUE to the responses of GPP to FDEs
Figure 3(a) shows ND-DTW between SGPPA and both SWUEA and SETA during FDEs for the V-shape and inverted V-shape response, respectively.For the V-shape response, the variation of SGPPA was more similar to that of SWUEA.For the inverted V-shape response, the variation of SGPPA was more similar to that of SETA.This result was consistent with the results obtained for the contribution of WUE to the GPP variation (figure 3(b)).For the V-shape response, the contribution of WUE to the GPP variation was 64.5 ± 22.4%, whereas it was 47.6 ± 18.7% for the inverted V-shape response.Figure 3(c) shows the contribution of WUE to the GPP variation in SGPPA decreasing and increasing phase for the Vshape and inverted V-shape responses.For the Vshape response, the contribution of WUE to the GPP variation was 52.5 ± 25.6% and 51.8 ± 26.0% in the decreasing and increasing phases, respectively.For the inverted V-shape response, the contribution of WUE to the GPP variation was 42.7 ± 20.9% and 40.0 ± 22.1% in the decreasing and increasing phases, respectively.In different phases during the FDEs, the contribution of WUE to the GPP variation was almost the same.These results about contribution to the GPP variation implied that the variation of the ecosystem's internal response to FDEs (WUE) dominated the magnitude of the GPP variation during FDEs for the V-shape response.In contrast, water loss (ET) dominated the magnitude of the GPP variation during FDEs for the inverted V-shape response.

Mechanisms of two response types during FDEs
During the decreasing phase of the V-shape response, the GPP variation was largely induced by SWC deficit (figure 2) and mainly contributed by WUE variation (figure 3(b)).When vegetation was under soil water stress for 69.3% of FDEs (table S2), leaf stomata was limited by plants to reduce water loss, as shown in figure 4(a).GPP is reduced by decreasing WUE. McDowell (2011) demonstrated that plant growth declines with nonstructural carbohydrates surplus during water stress.These phenomena indicated that occurrences of V-shape response were induced by negative water supply.During the increasing phase, SWC continued to decline for 61.5% of FDEs (table S3); SWC was no longer the only dominant factor (see figure 2(a)) because hydraulic responses to soil water potential (Knipfer et al 2020) were lost due to turgor loss of leaf cells as the second phase of figure 4(a).Carbon assimilation was increased by the increasing VPD or Rn for 95.0% of FDEs (see table S3).Moreover, the non-monotonic changes in GPP with VPD may influence the analysis results (Su et al 2023, Wan et al 2023), as shown in figures 2 and S4.However, previous studies have shown that the optimum VPD was mostly greater than 10 hPa for the three vegetation types (Monteith 1995, Patane 2010, Ocheltree et al 2014, Kröber et al 2015).In this study, the mean of VPD was less than 10 hPa for all identified FDEs (see figure S5).Theoretically, an increase in VPD led to increased GPP during the identified FDEs (Wang and Yuan 2021, Noguera et al 2022), as shown in figure 4(a).Increasing VPD is often caused by increasing temperature.
For the inverted V-shape response, the GPP responses were induced by increasing Rn during the increasing phase, as shown in figure 2. Under comparatively small water stress for 58.3% of FDEs (see table S4), the growth rate of GPP increased from photosynthesis by increasing Rn with increasing water loss (Cheng et al 2011, Lei et al 2023).These phenomena indicated that occurrences of the inverted V-shape response resulted from a positive anomaly in water demand accompanied by an increasing Rn.When SWC started to drop dramatically for 58.3% of FDEs (see table S5 Therefore, occurrences of the V-shape and inverted V-shape responses were induced by two variations of moisture conditions.These two conditions were negative anomaly water supply dominated by SWC and positive anomaly atmospheric water demand dominated by Rn or VPD.These two variations of moisture conditions were also the reasons for FDEs induced by competing water supply and demand.

Conclusion
This study investigated the GPP variation of three vegetation types during 40 flash droughts (FDEs) based on the FLUXNET 2015 dataset during growing season.Two contrasting GPP variations were identified, i.e.V-shape and an inverted V-shape responses.Based on the DTW method, results show the V-shape and the inverted V-shape GPP responses to FDEs were induced by decreased SWC (water supply) and increased net radiation (water demand) at the onset of FDEs, respectively.Moreover, we found the magnitude of the V-shape and inverted V-shape responses was dominated by ecosystem's internal WUE and water loss during FDEs, respectively.These results implied the V-shape response was caused by WUE induced by change in water demand, while the inverted V-shape response was caused by shift of FDE driving factors from increasing water demand dominance to decreasing water supply dominance.This study can help advance our understanding and ability to predict the future effects of frequent FDEs on ecosystem productivity.
) over time due to climate change.(III) Identification of FDEs.Four criteria were used in identifying FDEs (Basara et al 2019, Christian et al 2019):The criteria arex The duration of one FDE is greater than 5 pentads and less than 18 pentads (about 3 months).y The final SESR value (SESR ip ) of one FDE must be below the 20th percentile of SESR values for the (p − 1)th, pth, (p + 1)th of all years.z No more than one ∆SESR value (∆SESR ip ) during the entire FDE could be above the 40th percentile of individual ∆SESR values for the (p − 1)th, pth, (p + 1)th of all years.{ The mean change in SESR during the entire length of the FDE (e.g. from p pentad to p + n pentads) must be less than the 25th percentile of the ∆SESR values for the pth, (p + 1)th, …, (p + n)th of all years.(IV) Extraction of FDEs during the growing season.The GPP variations to FDEs were explored only during the growing season due to low GPP during the non-growing season.In this study, growing season extraction followed Kong et al (2020) using the 'phenofit' package in R (Kong et al 2022) based on the 16 d Global Inventory Modeling and Mapping Studies-Normalized Difference Vegetation Index (GIMMS-NDVI) dataset.
observations Data of 62 sites from the FLUXNET 2015 dataset with more than 10 years of high-quality observations from 1996 to 2014 were used.The FLUXNET 2015 dataset is the latest collection of global eddy flux observations (Pastorello et al 2020).Daily air temperature (T a ), VPD, atmospheric pressure (PA), wind speed (u 2 ), and Rn were collected to calculate the daily PET.The corrected LE data by the energy balance closure ratio and the Bowen ratio were used in this study.

Figure 1 .
Figure 1.Example time series of standardized gross primary productivity anomaly (SGPPA) and standardized evaporative stress ratio (SESR) during a single FDE of CRO, DBF, ENF sites.Left ((a), (c) and (e)) and right ((b), (d) and (f)) panels show the V-shape and inverted V-shape responses of SGPPA during FDEs, respectively.The black dots represent SGPPA, the blue dots represent SESR.The red shaded zone represents period of the FDE.The two black solid vertical lines represent the beginning and ending time of FDE.

Figure 3 .
Figure 3. (a) Barplots of DTW normalized distance between two variables (SSWCA (green), SEETA (orange)) and SGPPA during FDEs for V-shape and inverted V-shape response.(b) Barplots of the contribution of variation of WUE to the magnitude of the V-shape and inverted V-shape GPP responses.(c) Barplots of the contribution of variability of WUE to the GPP responses during two developing phases of FDEs for the V-shape and inverted V-shape GPP response.
), ecosystems were forced to respond to rapidly decreasing SWC caused by high-intensity FDEs during the decreasing phase (Otkin et al 2018, Yuan et al 2019, Parker et al 2021).The growth rate of GPP decreased with SWC (figure 4(b)).

Figure 4 .
Figure 4. Schematic diagram of the main mechanisms (a) for the V-shape GPP response and (b) for the inverted V-shape GPP response GPP response during flash droughts.

Calculation of pentad (5 d) standardized ESR (SESR) and ∆SESR. The
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