Evaluating drought-induced mortality risk for Robinia pseudoacacia plantations along the precipitation gradient on the Chinese Loess Plateau

Agricultural and Forest Meteorology 284 (2020) 107897

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Evaluating drought-induced mortality risk for Robinia pseudoacacia plantations along the precipitation gradient on the Chinese Loess Plateau

T

⁎

Zhongdian Zhanga,b, Mingbin Huangb,c, , Yingnan Yanga,b, Xiaofang Zhaoa,b a

College of Natural Resources and Environment, Northwest A & F University, Yangling 712100, China State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A & F University, Yangling 712100, China c CAS Center for Excellence in Quaternary Science and Global Change, Xian, Shaanxi 710061, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Soil desiccation Tree mortality Plant hydraulics Biome-BGC model Precipitation gradient

Extensive afforestation with exotic species like Robinia pseudoacacia on the Chinese Loess Plateau are facing high drought-induced mortality risk due to the large fluctuations in annual precipitation and severe soil desiccation. The aim of this study was to assess the risk of drought-induced mortality for R. pseudoacacia plantations on the Loess Plateau based on plant hydraulics. We modified the routines of soil-plant-atmosphere water transfer in the Biome BioGeochemistry model (Biome-BGC) using a plant hydraulic model based on the supply-demand theory. The modified model efficiently captured the dynamics of canopy transpiration, soil moisture, leaf water potential, and regional variation in leaf area index in R. pseudoacacia stands on the Loess Plateau. We simulated the 50-year (1968–2017) plant hydraulic dynamics at 14 sites along a precipitation gradient on the Loess Plateau. The results indicated that annual average percentage loss of whole-plant hydraulic conductance (APLK) showed strong temporal variation due to climatic variability, which was positively correlated with annual potential evapotranspiration (PET) and the aridity index (the ratio of PET to annual precipitation). Along the precipitation gradient, the maximum APLK increased linearly with decreasing mean annual precipitation (MAP) and could exceed 60% at sites with MAP <446.1 mm. The sustainable growth of R. pseudoacacia plantations at these sites would face a severe threat. We analyzed the effect of soil desiccation on drought-induced mortality risk further. Soil desiccation increased the sensitivity of plant hydraulic safety to precipitation variability considerably, and the effect was more significant in areas with lower MAP. These quantitative findings should be helpful for evaluating and promoting the sustainability of plantation forests on the Loess Plateau.

1. Introduction The Chinese Loess Plateau is reputed to have the most severe soil erosion in the world. Afforestation practices have been implemented since the 1950s to reduce severe soil erosion and to improve environmental quality (Jia et al., 2017). Robinia pseudoacacia L. (black locust) was widely chosen as a pioneer afforestation species due to its fast growth and high tolerance to drought and poor soil fertility (Li et al., 2018). Thus far, R. pseudoacacia has been planted on >10 million hm2 on the Loess Plateau, which accounted for nearly 90% of artificial forests in this area (Ma et al., 2017a). However, the soil moisture was found to decline quickly after afforestation with R. pseudoacacia due to its high water consumption, and there has widely existed a dry soil layer in the deep soil across the Loess Plateau (Wang et al., 2013b; Wang et al., 2011). The dry soil layer is characterized as a soil

desiccation phenomenon that forms below the mean annual infiltration depth. It is mainly caused by the excessive depletion of deep soil water by the water-intensive species combined with long-term insufficient amounts of precipitation (Wang et al., 2011). The soil moisture content (SMC) in a dry soil layer ranges between the permanent wilting point (PWP) and the stable field capacity, which is generally considered to be equivalent to 60% of the field capacity. After the formation of a dry soil layer, the capability of the “soil reservoir” to supply water to plants reduces markedly, and plant requirements for water would mostly rely on precipitation (Chen et al., 2008). The precipitation presents high intra- and inter-annual variability on the Loess Plateau (Zhang et al., 2015b), therefore R. pseudoacacia trees suffer more frequent and severe drought stress after the formation of a dry soil layer. Recently, there have been reports that the drought-induced growth decline and the risk of mortality for R. pseudoacacia stands have increased in this area

⁎ Corresponding author at: State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A & F University, Yangling 712100, China. E-mail address: [email protected] (M. Huang).

https://doi.org/10.1016/j.agrformet.2019.107897 Received 6 June 2019; Received in revised form 22 October 2019; Accepted 30 December 2019 0168-1923/ © 2019 Elsevier B.V. All rights reserved.

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Fig. 1. The location of the precipitation contours, study area, and 14 study sites that include the four representative sites on the Loess Plateau, China. The boundary of the study area was determined by the border of counties.

of the plant hydraulic model with more eco-hydrological processes to improve the prediction of drought-induced tree mortality. The Biome BioGeochemical cycle model (Biome-BGC) simulates the cycling of energy, water, and nitrogen within ecosystems using general stand information and daily meteorological data (Thornton et al., 2002). The Biome-BGC model has been applied widely in diverse biomes around the world (Kimball et al., 1997; Wang et al., 2005; Zhang et al., 2015b), and numerous modelers participated in its improvements to incorporate the latest advances in the study of ecophysiological mechanisms. Zhang et al. (2015b) successfully simulated tree growth and hydrological processes for R. pseudoacacia on the Chinese Loess Plateau with the Biome-BGC model, and all physiological input parameters were calibrated and validated using field data. Therefore, we adopted the Biome-BGC model to incorporate the Sperry model for the coupled simulations of plant hydraulic traits with water, carbon, and nitrogen cycles. The objectives of this study were (1) to modify the routines of SPAC water transfer in the Biome-BGC model with the Sperry model and to evaluate its application for R. pseudoacacia trees, (2) to quantify the spatial pattern of drought-induced mortality risk for R. pseudoacacia on the Loess Plateau by simulating long-term plant hydraulic dynamics, and (3) to analyze the effect of soil desiccation on the drought-induced mortality risk with the modified model.

(Wei et al., 2018). Moreover, drought intensity and duration are expected to increase in the future due to climate change (Peng et al., 2017; Yan et al., 2017), which presents a serious threat to sustainable plant growth and benefits for soil and water conservation. There have been an increasing number of studies that attempt to assess the risk of drought-induced mortality for plantation forests. Most of those assessments were based on climatic indicators that included soil moisture (Zhao et al., 2015), the number of consecutive days without effective rainfall (daily rainfall > 5 mm in summer and > 3 mm in spring and autumn (Wang et al., 2017). However, the predictive power of these climatic water deficit terms is limited due to the lack of physiological mechanisms that underlie drought-induced tree mortality (Tai et al., 2017). According to previous studies, the mechanisms involved in droughtinduced tree mortality included the failure of water transport in the xylem, depletion of carbohydrate reserves over prolonged drought, and increased vulnerability to pests and pathogens (McDowell et al., 2013). It has been accepted that plant hydraulic traits occupy a central role in determining survival during drought and influencing carbon dynamics through stomatal regulation (Choat et al., 2018). Currently, hydraulic failure has been much better modelled than other mechanisms (McDowell et al., 2018), and becomes a promising way to predict drought-induced mortality. Sperry et al. (1998) developed a resistancenetwork model to describe water transport in the soil-plant-atmosphere continuum (SPAC) mechanistically, and they upgraded the model further to include stomatal control of transpiration and leaf water potential (Sperry et al., 2016) based on the supply-demand theory (abbreviated as Sperry model). The model efficiently provided a mechanisticbased way to quantify stomatal responses to soil and atmospheric drought, and the model has been applied successfully in a wide range of species (Sperry et al., 2016). By incorporating the Sperry model into the Terrestrial Regional Ecosystem Exchange Simulator (TREES), Tai et al. (2017) proved that employing plant hydraulic traits greatly improved the ability to explain mortality patterns of aspen (Populus tremuloides Michx.) in the southwestern USA compared with using soil moisture alone. The success of these studies encourages the integration

2. Materials and methods 2.1. Study area and study sites The Loess Plateau is situated between longitudes 102° and 114° East and latitudes 35° and 41° North and covers an area of approximately 640,000 km2. Under the extensive monsoonal influence, the mean annual precipitation (MAP, mm) ranges from 700 mm in the southeast to 200 mm in the northwest, 55–78% of which falls from June to September. The mean annual temperature (MAT,°C) ranges from 14.3 °C in the southeast to 3.6 °C in the northwest. Soil textures also follow a similar gradient, being more clayey in the southeast and 2

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hydrological condition, SPAC water transport, plant growth, and plant hydraulic traits. The original code of the BBGC-SPERRY model is provided in the supplementary file. Complete details on the Biome-BGC and Sperry models can be found in previous reports (Sperry and Love, 2015; Sperry et al., 2016; Thornton et al., 2002), and a brief summary of relevant equations is provided here. In the BBGC-SPERRY model, daily precipitation is routed to snowpack or soil based on air temperature. Snowfall is not intercepted by the vegetation canopy and is passed directly to the surface. The sublimation and snowmelt of the snowpack is estimated based on the daily air temperature and net solar radiation. Rainfall is intercepted by the canopy using a prescribed interception coefficient based on leaf area index and then evaporated from the canopy using the Penman method. Transpiration rate is calculated based on the supplydemand theory. The supply function (E(Ψleaf)) describes the steadystate relationship between rising transpiration rate and decreasing leaf water potential (Ψleaf) for constant soil water potential (Ψsoil). The soilplant-atmosphere continuum is divided into leaf, stem, root, and rhizosphere components in series. The steady-state flow rate through each component, Ei, is related to the flow-induced water potential drop from the upstream (Ψup) to the downstream (Ψdown), which was calculated by the integral transform of the component's vulnerability curve (k (Ψ)i):

Table 1 Summary of climatic and soil variables of the four studied stands along the precipitation gradient on the Loess Plateau, China. Site code

YL

CW

AS

MZ

Coordinates

34°18′ N 108°02′ E 344.7 13.6 639.8 862.1

35°14′ N 107°41′ E 1186.7 10.3 583 845.7

36°51′ N 109°19′ E 1155.2 9.7 512.4 962.3

37°51′N 110°11′E 944.2 10.5 445.6 1073.4

1.40 7.28 65.61 27.11 18 6.5 9.5

1.29 6.30 76.78 16.93 15 7.2 8.4

1.28 33.90 56.97 9.13 15 5.9 8.8

1.24 33.66 56.58 9.76 20 3.6 10.2

Elevation (m a.s.l.) Mean annual temperature (°C) Mean annual precipitation (mm) Mean annual potential evapotranspiration Soil bulk density (g cm−3) Sand content (%) Silt content (%) Clay content (%) Stand age (years) Mean tree height (m) Mean diameter at breast height (cm)

Note: Sites: YL, Yangling; CW, Changwu; AS, Ansai; MZ, Mizhi.

sandier in the northwest (Zhang et al., 2015b). Based on the distribution of R. pseudoacacia and the spatial variation in MAP and soil texture, we selected a transect 545.2 km-long and 479.6 km-wide that followed a precipitation gradient on the central Loess Plateau (Fig. 1). The transect belongs to the major distribution area of R. pseudoacacia, and covered the main soil textural types and topographical features. Based on the availability of long-term daily meteorological data and field measurements of soil properties, we chose 14 sites along the transect in this study. MAP ranged from 390 mm to 650 mm and MAT ranged from 9 °C to 14 °C at these sites. To evaluate the performance of the model, flat black locust stands were selected at four representative sites (Fig. 1, Table 1) with different MAPs—Yangling (MAP = 639.8 mm, YL), Changwu (583.0 mm, CW), Ansai (512.4 mm, AS), and Mizhi (445.6 mm, MZ). The stands were 15–20 years old and had similar densities (1600–1700 trees ha−1). One 10 m × 10 m plot was established at each site, and the diameter at breast height of trees differed little within the plot at each site (Table 1). In July 2016, the maximum seasonal leaf area index (LAImax) was determined with an LAI-2200 plant canopy analyzer (Li-Cor, Inc., Lincoln, NE, USA). At the CW site, a time-series of canopy transpiration, soil moisture, and leaf water potential were monitored during the growing season (May to October) in 2018.

Ei =

Ψup

∫Ψ

down

k (Ψ)i d Ψ

(1)

where k(Ψ)i quantifies the decline of hydraulic conductance with decreasing water potential (Ψ) for each component. Hydraulic conductance of xylem components is calculated using a two-parameter Weibull function (b, c): c

ki = k x,max e−(−Ψx / b)

(2)

where kx,max is the maximum hydraulic conductance, and Ψx is water potential of the xylem components (leaf, stem, root). The ‘vulnerability curve’ of the rhizosphere is described with a van Genuchten function (van Genuchten, 1980):

ki = k s,max v (n − 1)/2n [(1 − v )(n − 1)/ n − 1]2

(3)

v = [(α Ψsoil)n + 1]−1

(4)

where ks,max is the maximum hydraulic conductance of the rhizosphere components, n and α are texture-specific parameters. The E(Ψleaf) supply function is calculated by solving Ψleaf as E is increased from zero for the entire continuum. The demand function calculates E and leaf diffusive conductance to water vapor from D, maximum diffusive conductance (Gmax), and the supply function. Stomata are assumed to regulate the water potential drop ΔΨ = Ψs - Ψc based on the fractional drop in soil-plant hydraulic conductance from its maximum:

2.2. The incorporation of the Biome-BGC model and the Sperry model The Sperry model solves transpiration rate (E), diffuse conductance (G), soil-plant hydraulic conductance (k), and the distribution of water potential and conductance in the SPAC system relied on soil water potential profile and vapor pressure deficit (D) as inputs at each time step. For the long-term continuous simulations, the soil water potential profile in root zone is highly variable as affected by multiple eco-hydrological processes including plant growth, precipitation infiltration, soil water evaporation, root water uptake, etc. (Sulis et al., 2019), which should be simulated with the related water cycle and plant growth routines. The Biome-BGC model fully incorporated these routines after the improvements by numerous researchers (Kimball et al., 1997; Wang et al., 2005; Zhang et al., 2015b), providing a good platform to integrate the Sperry model for simulating plant hydraulic dynamics. Thus, we incorporated the Sperry model into the Biome-BGC model in this study. The SPAC routines including root water uptake, canopy transpiration, etc. in the original Biome-BGC model were replaced with the Sperry model (Fig. S1). Sperry model derived root water uptake from each layer. This flux was fed back to Biome-BGC to update the soil water potential profile at every time step by combining with the water cycle routines. On this basis, the BBGC-SPERRY model could synchronously simulate the long-term dynamics of site

ΔΨ = ΔΨ′[(dE′/ d Ψleaf )/(dE / d Ψmax)]

(5)

where ΔΨ′ is the unregulated water potential drop that is derived from the supply function with unregulated transpiration rate: E′=DGmax. This regulated ΔΨ yields the regulated values for E based on the supply function. G is calculated by E/D and further coupled to the photosynthesis routine of Biome-BGC. The root and rhizosphere components are partitioned into five layers with equal roots based on the root distribution function. The five root surface water potential and the root crown water potential at the downstream junction can be solved using the multidimensional Newton-Rhaphson method as described in Sperry et al. (2016). Then, root water uptake rate at each layer is calculated using Eq. (1), and further coupled to the water flow sub-model of Biome-BGC, which has been modified by Huang et al. (2013) for simulating soil water movement. 3

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Table 2 Major parameters of the modified model values for Robinia pseudoacacia used in this study. Parameter (a) Sperry model Weibull function b and c for root, stem, leaf Maximum soil-plant hydraulic conductance per leaf area (mmol s−1 m−2 MPa−1) Maximum diffusive conductance to water vapor (mmol s−1 m−2) Average % resistance in rhizosphere (%) Root depth coefficient Number of root and soil layers (b) Biome-BGC model Transfer growth period fraction of growing season (%) Litterfall fraction of growing season (%) Annual leaf and fine root turnover fraction (year−1) Annual live wood turnover fraction (year−1) Annual fire mortality fraction (year−1) Allocation new fine root C: new leaf C Allocation new stem C: new leaf C Allocation new live wood C: new total wood C Allocation new croot C: new stem C Allocation current growth proportion (%) C:N of leaves C:N of leaf litter C:N of fine roots C:N of live wood C:N of dead wood Leaf litter labile proportion Leaf litter cellulose proportion Leaf litter lignin proportion Fine root labile proportion Fine root cellulose proportion Fine root lignin proportion Dead wood cellulose proportion Dead wood lignin proportion Canopy water interception coefficient (LAI−1 day−1) Canopy light extinction coefficient All-sided to projected leaf area ratio SLA (projected area basis) (m2 kg−1 C) Ratio of shaded SLA:sunlit SLA Fraction of leaf N in Rubisco Cuticular conductance (m s−1) Boundary layer conductance (m s−1) a b c d e f g

Value

b = 2.0, c = 4.1 4.2 143.4 20.5 0.99a 5 0.2 0.2 1 0.7 0.0025 1 2.2 0.209b 0.22 0.5 18.8c 32.2d 16.3e 50 550 0.345 0.4 0.255f 0.34 0.44 0.22 0.68 0.32 0.045 0.54 2 39.5g 1.27 0.14 0.00006 0.01

Fig. 2. Stem vulnerability curve measured by the bench top method. The solid line is the fitted line with a Weibull function. PLC, the percentage loss of xylem hydraulic conductivity.

Instruments Co), which equated to stem water potential (Ψstem). Soon after excising the leaves, six 2-cm-long stem segments were harvested under water from each shoot. The PLC of each stem segment was measured with the low pressure flow meter as described by Tyree et al. (1993) and modified by Wang et al. (2014). The stem segment was mounted on a conductivity system filled with ultra-pure, degassed 0.1 mol L−1 KCl solution pre-filtered to 10 nm and post-filtered to 0.3 um. Initial conductivity (Ki) was measured by flowing KCl solution from a reservoir through the segment and onto a computerinterfaced balance with a pressure difference of about 3 kPa. The stems were then flushed with a pressure of 150 kPa for 2 min to remove air bubbles. The hydraulic conductivity was determined again and the flushing was repeated until attaining a maximum conductivity (Kmax). PLC was calculated by:

PLC = 1 −

Ki Kmax

(6)

The VC was constructed by plotting stem PLCs against corresponding stem water potential (Fig. 2), and further fitted with the Weibull function as follows:

Zhang et al. (2018b). White et al. (2000). Ma et al. (2017b). Aber and Melillo (1982). Chen et al. (2018). Aber and Melillo (1982). Liu (2008).

4.1

PLC = 1 − e−(−Ψstem/2.6) (R2 = 0.96; RMSE = 6.82%)

(7)

The parameters of VC were assumed to be the same for leaf, stem, and root. Maximum soil-plant hydraulic conductance (kmax), Gmax, and average proportion of rhizosphere resistance were estimated from observed transpiration and leaf water potential in 2018 at the CW site (Sperry et al., 2016; Tai et al., 2018; Wolfe et al., 2016). The ecophysiological parameters in the Biome-BGC model were obtained from published data for R. pseudoacacia. Daily meteorological parameters that included temperature, humidity, and precipitation were obtained from the local weather station, and radiation was estimated by the MT-CLIM model (Thornton et al., 2002). Distribution of soil particle size at each site was collected from the measurements of Li et al. (1985) and Zhang et al. (2018b), and soil–water retention curves and saturated hydraulic conductivity were collected from the measurements of Wang et al. (2015, 2013a). Soil–water retention curves were determined using the centrifugation method, and they were further fitted with the van Genuchten model using the RETC program (van Genuchten, 1992). Volumetric soil moisture contents at field capacity (FC) and PWP were determined at −0.033 and −1.5 MPa based on the results of Wu (2010) and (Zhang et al., 2015a), respectively. Saturated hydraulic conductivity was determined using the constant head method (Klute and Dirksen, 1986).

2.3. Eco-physiological, meteorological and soil parameters Major parameters and values used in this study are summarized in Table 2. At the CW site, xylem vulnerability curve (VC) was measured using the bench top dehydration method as described by Sperry et al. (1988a), with some modification by Wang et al. (2014) for R. pseudoacacia. The VC quantifies the increase of the percentage loss of xylem hydraulic conductivity (PLC) with decreasing xylem water potential. In May 2018, 12 current-year shoots of 1.4–2 m long were excised from the southern crown of the trees in the morning when xylem tension was low. The shoots were sprayed with water and enclosed in humidified black plastic bags before excising to minimize water loss. After excision, the shoots were brought to the laboratory within half an hour, and submerged in water for at least 30 min to release the xylem tension. Shoots were dehydrated on a bench at room temperature to obtain a range of water potentials, and then tightly wrapped in a black plastic bag for at least 1 h to ensure the equilibration between leaves and stem. Three leaves were then excised from the shoot to measure leaf water potential with pressure chamber (model 1505D; PMS 4

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2.4. Model evaluation The BBGC-SPERRY model was firstly evaluated by comparing the simulated daily transpiration (Td, mm d−1), soil moisture, predawn (Ψpd) and midday (Ψmd) leaf water potentials with field measurements at the CW site. We were not able to measure these variables at the four sites simultaneously since the field observations were labor-intensive and time-consuming, and the reported data were not available at sites of YL, AS, and MZ. Stem sap flux measurements were conducted on six trees using Granier-type thermal dissipation sensors (Dynamax. Inc., Houston, Texas, USA) from May 8 to October 31 in 2018. Thirty-minute averages of temperature difference data were computed and stored in data loggers (CR1000, Campbell Scientific Inc.). The sensor signal was converted to sap flux density using the calibrated Granier's equation of Ma et al. (2017a) for R. pseudoacacia. Js was scaled and converted to the stand-scale daily transpiration using tree sapwood area per unit ground area (Ewers et al., 2002). The sapwood area of each tree in the plot was calculated using the allometric equation developed in this area (Ma et al., 2017a). During the growing season (May to October) in 2018, a total of 19 soil moisture measurements from the 0 to 5.4 m soil profile were taken with a soil coring method. Ψpd and Ψmd were measured using the pressure chamber with 6 replications, and the measurements were conducted once a month on sunny days. Initial soil water conditions in the 0–5.4 m soil profile were taken from the first measurements made at the beginning of the experimental period (May). Initial aboveground biomass was estimated from the measured tree height, diameter at breast height, and planting density using an allometric regression (Zhang et al., 2018a). Biomass was divided into leaf, stem, and root C contents based on the allocation ratios in Table 2, and initial N concentrations of different components of the plant were calculated using the ratios of C:N (Table 2). We further compared the simulated and observed values of LAImax in 2016 at the four representative sites along the precipitation gradient. The initial conditions were obtained with the ‘spin-up mode’ using the 50-y meteorological data (1968–2017) at each site (Thornton and Rosenbloom, 2005).

Fig. 3. The cumulative frequency distribution curve for annual precipitation (AP) during 1968–2017 at the four representative sites. Sites: YL, Yangling; CW, Changwu; AS, Ansai; MZ, Mizhi.

For each typical hydrological year, we simulated APLK under four levels of initial soil moisture content (SMCini): 80%FC, 60%FC, 40%FC, and PWP. We analyzed the changes in APLK with annual precipitation under different initial soil water conditions. 2.6. Data analysis In this study, we adopted annual precipitation (AP), potential evapotranspiration (PET), and aridity index (ϕ = =PET/AP) to characterize the annual water conditions at each site. PET was calculated using the Penman (1948, 1956) with collected meteorological parameters. To illustrate the intensity of soil desiccation in R. pseudoacacia stands, we collected the data from SMC profiles in cropland and R. pseudoacacia stands (15–20 y old) at the four representative sites from previous studies (Duan, 2017; Jia et al., 2017; Li et al., 2008; Suo et al., 2017). The R. pseudoacacia stands were located in the same small watershed with the cropland at each site, and SMC measurements were conducted at the same time point, so the data were good for comparative analysis. The model performance was evaluated with the coefficient of determination (R2) and root mean square error (RMSE). RMSE was calculated using

2.5. Quantification of drought-induced mortality risk To quantify the spatial pattern of drought-induced mortality risk for R. pseudoacacia on the Loess Plateau, we conducted a 50-y (1968–2017) simulation with the BBGC-SPERRY model at the 14 sites along the precipitation gradient. The initial conditions were obtained with the ‘spin-up mode’ at each site. Daily percentage loss of soil-plant hydraulic conductance (PLK) was calculated by:

PLK = 1 −

k k max

RMSE =

(8)

1 n

n

∑ (Oi − Pi)2 i=1

(9)

where Oi and Pi are the observed and predicted values, respectively. At the four representative sites, descriptive statistical parameters (e.g., minimum, maximum, mean, standard deviation, and coefficient of variation) were calculated to illustrate the basic trends in APLK. We examined the temporal trend in APLK and its relationship with climatic variables by correlation analysis. The maximum APLK during the simulation period (APLKmax) was regressed with climatic variables for the 14 sites along the precipitation gradient transect to quantify the spatial variation in APLKmax. All analyses were conducted with SPSS 11.0 (SPSS Inc., Chicago, USA).

Then, the average PLK during the growing season (APLK) was calculated for each year. According to the previous studies, the probability of mortality increased dramatically when APLK was >60% (McDowell et al., 2013; Sperry and Love, 2015; Tai et al., 2018). This threshold was used to assess the risk of drought-induced mortality in this study. Based on previous studies, we assumed that the xylem embolism in R. pseudoacacia could not be reversed during the growing season (Wang et al., 2014), while xylem embolism could recover at the start of next growing season due to the generation of root and/or stem pressure and new growth of plant tissues in the spring (McDowell et al., 2013; Sperry et al., 1988b). To examine the impact of soil desiccation on drought-induced mortality risk, we simulated the changes in APLK under different initial soil water conditions and hydrological years at the four representative sites. Based on the cumulative frequency distribution curve of the annual precipitation in 1968–2017 (Fig. 3), we selected seven typical hydrological years with the cumulative frequency that ranged between 0–5%, 10–15%, 25–30%, 50–55%, 75–80%, 90–95%, and 95–100%.

3. Results 3.1. Climatic variability and soil desiccation along the precipitation gradient The time series of annual precipitation, potential evapotranspiration, and aridity index in 1968–2017 showed that they were highly variable at each site (Fig. 4). AP exhibited strong temporal variation with the coefficient of variation ranging from 22% to 24%. There were 5

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Fig. 4. Time series of annual precipitation (AP), potential evapotranspiration (PET), and aridity index (ϕ) during 1968–2017 at four representative sites. The dash line represents the criterion of the dry years. According to Chinese “grades of meteorological drought”, the years in which precipitation anomaly percentage less than −15% were classified as dry years. Sites: YL, Yangling; CW, Changwu; AS, Ansai; MZ, Mizhi.

RMSE = 0.13 MPa). The modified model provided better predictions of Td and soil moisture content than the original Biome-BGC model. In the Biome-BGC model, the R2 and RMSE between simulated and observed values were 0.70 and 0.929 mm d−1 for Td, respectively, 0.66 and 0.007 cm−3 for SMC, respectively. In BBGC-SPERRY model, R2 and RMSE between simulated and observed values were 0.75 and 0.793 mm d−1 for Td, respectively, 0.69 and 0.005 cm−3 for soil moisture content, respectively. There was a significant over-prediction in SMC since the 232th day by the original Biome-BGC model (Fig. 6). Along the precipitation gradient, the simulated LAImax values in 2016 also agreed with the measurements at the four representative sites (Fig. 7, R2 = 0.85, RMSE = 0.30 MPa), except for the higher estimates at MZ.

12, 13, 14, and 15 dry years (yearly precipitation anomaly percentage less than −15% according to the grades of meteorological drought classified by Standardization Administration of the P.R.C., 2017) at YL, CW, AS, and MZ, respectively, which represented 24–30% of the 50 y at each site. PET exhibited an opposite pattern than AP and increased significantly with time at each site (P < 0.01); the coefficient of variation was 7%–8%. ϕ showed a similar trend to PET, although it exhibited higher annual variation, and the coefficient of variation was 26%–29%. ϕ increased significantly at YL. Both the high fluctuation in AP and the increasing PET could result in severe drought stress on plants. R. pseudoacacia is known as a typical water-intensive species (Ma et al., 2017a; Wang et al., 2013b). The general pattern of soil desiccation in R. pseudoacacia plantations at each site is shown in Fig. 5. Afforestation with R. pseudoacacia has resulted in a severe decline of soil moisture in different climatic zones of the Loess Plateau (Fig. 5). SMC in R. pseudoacacia plantations was generally lower than in croplands in the 5-m soil profile, and the difference were larger below 1.5 m where precipitation barely recharged. The profile-averaged SMC in R. pseudoacacia plantations was 59.3% of FC at YL and close to PWP at the other sites.

3.3. Long-term simulation of plant hydraulic dynamics Both Ψmd and PLK exhibited high intra- and inter-annual variation with the fluctuations of climatic factors and SMC (Fig. 8; Fig. S2), and appeared considerable differences among sites. During the growing season, Ψmd generally decreased from nearly −0.5 MPa to the minimum value in summer, and then recovered gradually due to the increase in SMC (Fig. S2) and the decrease in D. Ψmd could recover to nearly −0.5 MPa by the end of growing season in most years. However, Ψmd could not fully recover in a few years due to the shortage of precipitation in late autumn, and had a low value at the start of next growing season. At different sites, Ψmd could decrease with decreasing MAP in most years. PLK exhibited an opposite variation with Ψmd. During the growing season, PLK generally increased rapidly and reached a maximum with the decline of SMC (Fig. S2), and then

3.2. Model evaluation At CW, the simulated canopy transpiration and soil moisture by BBGC-SPERRY model were in good agreement with the measured values, whereas the model reasonably captured the dynamics of Ψpd and Ψmd (Fig. 6). The BBGC-SPERRY model closely followed the observed Ψpd (R2 = 0.92; RMSE = 0.10 MPa) and Ψmd (R2 = 0.71; 6

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Fig. 5. Volumetric soil moisture profiles for R. pseudoacacia plantations and croplands at the four representative sites. Dash and solid lines represent the permanent wilting point and field capacity, respectively. Sites: YL, Yangling; CW, Changwu; AS, Ansai; MZ, Mizhi.

plant hydraulic safety was more sensitive to fluctuations in AP with more severe soil desiccation that led to higher risk of drought-induced mortality. The effect was more significant in the areas with lower MAP, and this could easily cause large-scale tree mortality.

dropped partially due to the increase of SMC and recovery of soil hydraulic conductivity. PLK never reached 100% at any site during the simulation period, but the maximum PLK increased with decreasing MAP. The maximum PLK was 42.9%, 61.6%, 76.8%, and 91.9% at YL, CW, AS, and MZ, respectively, which suggested a higher risk of hydraulic failure with decreasing MAP. APLK exhibited high annual variation during the simulation period, and the coefficient of variation ranged between 78% and 112% (the ratio of the standard deviation to the mean of APLK; Table 3) at the four sites. At YL, CW, and AS, APLK increased significantly over time (P < 0.05). APLK generally showed an opposite fluctuation with AP at each site, although there were some inconsistencies between them (Figs. 4 and 9). APLK correlated significantly with PET and ϕ at each site (P < 0.05). The average APLK at MZ was significantly higher than YL and CW (Table 3). APLK was >60% in 2 y at MZ (1999, 2001), and the maximum APLK at AS was close to 60% in 2000. APLK was <60% at the other sites. The maximum APLK (APLKmax) at the 14 sites along the precipitation gradient showed a significant negative correlation with MAP (r = −0.84, P < 0.01; Fig. 10), and we fitted the relationship with a linear function as:

APLC max = −0.184MAP + 142.13(R2 = 0.71, RMSE = 8.64%)

4. Discussion Drought-induced tree mortality has received extensive attention worldwide in recent years under the background of global climate change (Choat et al., 2018; McDowell et al., 2018; Sun et al., 2018). In the Loess Plateau, assessing drought-induced mortality risk of plantation forests is essential for guiding large-scale afforestation. In this study, we incorporated the Sperry model and Biome-BGC model to provide an efficient tool for coupled simulation of plant hydraulic dynamics with water, carbon, and nitrogen cycles. We simulated the temporospatial patterns of plant hydraulic safety in R. pseudoacacia trees at the regional scale with the modified model, and examined its correlation with climatic factors. We further decoupled the effect of soil desiccation and AP variability on APLK, and highlighted the importance of maintaining water storage in deep soil. These quantitative findings should be helpful for assessing and reducing drought-induced mortality risk of plantation forests in this area.

(10)

According to the function, APLKmax was >60% when MAP was <446.1 mm. APLKmax did not exhibit an obvious functional relationship with mean annual PET and the aridity index in this study (P > 0.05).

4.1. Model evaluation and applications In this study, we modified the routines of SPAC water transfer in the Biome-BGC model with the Sperry model. The modified model provided a more mechanistic approach to simulate SPAC water transport and higher predictive precision than the original Biome-BGC model (Fig. 6). The Sperry model quantifies the stomatal response to soil and atmospheric drought based on the supply-demand theory, which formalizes the concept that stomatal closure in response to water deficit is associated with protecting the xylem from excessive cavitation (Sperry and Love, 2015). The model has been evaluated in multiple species, and we verified its applicability in R. pseudoacacia for the first time in this paper. In the traditional SPAC models, the routines of root water uptake and canopy transpiration generally adopt empirical functions in numerous mathematical forms to describe the response to soil and atmospheric drought (Gong, 2005; Sperry et al., 2016). The coupling between root water uptake and canopy transpiration is loose and lacks mechanisms (Tai et al., 2018). The Sperry model explicitly solves the

3.4. Effect of soil desiccation on drought-induced mortality risk APLK generally decreased with increased AP under different SMCini (Fig. 11). When SMCini was >60%FC, APLK was <20% and changed little in different hydrological years at each site. When SMCini was <60%FC, the values of APLK increased considerably and showed larger variation with decreasing AP, which suggested a stronger impact of variation in precipitation on plant hydraulic safety with soil desiccation. When SMCini reached PWP, APLK was <60% in all hydrological years at YL. At CW and AS, APLK was >60% when AP was <498.7 mm and <430.0 mm, respectively, which corresponded to the probability of 25.5% and 24.8%, respectively. At MZ, APLK was >60% in each hydrological year. In summary, sufficient soil water storage efficiently reduced the threat of large variability in AP for plant survival, whereas 7

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Fig. 6. Comparison between observed and simulated values by BBGC-SPERRY model and Biome-BGC model for daily transpiration (a), average soil moisture content (SMC) for the 0–5.4 m soil profile (b), predawn leaf water potential (c) and midday leaf water potential (d) at the Changwu site. Bars indicate ± SE, n = 6.

water transport through SPAC using the physics of flow through soil and xylem. The model efficiently defines the response of plant water use to soil and atmospheric drought and incorporates eco-physiological properties that include root distribution, hydraulic redistribution, cavitation vulnerability, and cavitation reversal (Sperry et al., 2016), which demonstrates the best understanding of soil-plant hydraulic processes. Most parameters of the model are measurable, and there are fewer empirical parameters. More importantly, the model calculates a series of unmeasurable internal hydraulic traits that include soil-plant hydraulic conductance, maximum plant transpiration potential, hydraulic safety margin, etc. Previous studies suggested that APLK could well anticipate droughtinduced mortality of trees (McDowell et al., 2011; McDowell et al., 2013; Tai et al., 2018). The physiological mechanism of drought-induced tree mortality is highly complex and variable, in which chronically high PLK is an important risk factor. According to the ‘chronic stress hypothesis’, chronically high PLK could lead to substantial

decrease in stomatal conductance, photosynthetic rate and productivity, hence limit cell expansion, reduce membrane permeability, disrupt phloem transport, which further increase the susceptibility to heat stress, light stress and pests (Choat et al., 2018; Sperry and Love, 2015). All of these stresses tended to precede mortality. APLK could well quantify the long-term plant hydraulic status during the growing seasons (Tai et al., 2018). Based on the analyses conducted by Sperry and Love (2015) and Adams et al. (2017), APLK at or above 60% provided a generally supported starting point for multiple species, beyond which the probability of mortality increased. Currently, there is still a lack of APLK threshold for the drought-induced mortality in R. pseudoacacia. In this study, we adopted the threshold of APLK > 60% for a preliminary analysis of the drought-induced tree mortality risk. There is an urgent need to combine the model simulations with field experiments and surveys to determine the exact APLK threshold in the further studies.

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4.3. The effect of soil desiccation on drought-induced mortality risk In the Loess Plateau, the thickness of loess was >100 m in most areas, which is regarded as a “soil reservoir” with a high soil water holding capacity that mitigates drought stress effectively (Chen et al., 2008). However, the improper type and exorbitant density of plantation forests have induced severe soil desiccation and the formation of a dry soil layer in the deep soil (Jia et al., 2019). This issue has become one of the most prominent limitations for ecological service functions of artificial vegetation, and this has received extensive attention from researchers in the last 20 y (Chen et al., 2008; Jia et al., 2019; Wang et al., 2010). Previous studies mainly relied on field surveys and suggested that soil desiccation could be related to the decline in growth and increased mortality in this area (Shangguan, 2007; Wang et al., 2008; Wei et al., 2018). In this study, we moved forward to analyze the quantitative relationship between soil desiccation and drought-induced mortality risk based on a process-based model. The results indicated that APLK generally decreased with increasing AP under different SMCini, but there also existed some inconsistent fluctuations. For instance, APLK exhibited a slight increase as AP increased from 516.2 to 565.7 mm at MZ (Fig. 11). This phenomenon could be attributed to the different seasonal distributions of precipitation. In this study, we assumed that xylem embolism could not be recovered during the growing season as suggested by previous studies (Wang et al., 2014). Therefore, if precipitation was low and this induced severe xylem embolism in the early stage of the growing season, the higher precipitation in the later stage did not reduce xylem PLK and resulted in the relatively high value of APLK. According to the variation in APLK with AP under different SMCini, plant hydraulic safety was more sensitive to AP fluctuations with more severe soil desiccation. A significant increase in droughtinduced mortality risk was found when SMCini was <60%FC at each site, which strongly supported adopting 60%FC to define a dry soil layer in previous studies (Wang et al., 2010; Wang et al., 2011). The simulations indicated that R. pseudoacacia trees faced high risk of drought-induced mortality at most sites when SMCini reached PWP. As suggested by previous studies, after drought stress led to the death of plantation forests, the difficulty in vegetation renewal and reafforestation was much higher in this area (Chen et al., 2008; Shangguan, 2007). Therefore, it is urgent to take effective measures to alleviate soil desiccation, which would be helpful for reducing the mortality risk of plantation forests due to the high fluctuations of AP in this area.

Fig. 7. Observed and estimated seasonal maximum leaf area index (LAImax) at the four representative sites along the precipitation gradient. Sites: YL, Yangling; CW, Changwu; AS, Ansai; MZ, Mizhi.

4.2. Long-term simulation of plant hydraulic dynamics Long-term simulation of plant eco-physiological properties with process-based models can efficiently take into account climatic variability, which provides a useful tool for evaluating the sustainability of artificial forests (Alam et al., 2018; Huang et al., 2013; Huang et al., 2011). In this study, we simulated the 50-y plant hydraulic dynamics at 14 sites along the precipitation gradient on the Loess Plateau with the BBGC-SPERRY model. APLK exhibited strong temporal fluctuations and significant regional differences due to the temporospatial variation in environmental factors. APLK and PET exhibited a significant increasing trend with time, and there existed strong positive correlations between them at the four representative sites. Therefore, it could be inferred that the increasing PET drove the increase in APLK with time. PET was predicted to continue to increase in the future due to the elevation of atmosphere CO2 concentration (Peng et al., 2017), which could further worsen plant hydraulic safety and lead to tree mortality at a large scale in this area. During the simulation period, APLKmax increased considerably with decreasing MAP, and they exhibited significant negative correlations (Fig. 10). According to previous studies, MAP correlated strongly with other climatic and soil variables and has been widely used to examine environmental trends in this area (Wang et al., 2010; Zhang et al., 2015b; Zhang et al., 2018b), which makes it a promising predictor to quantify spatial variation in APLKmax. As suggested by Clifford et al. (2013), the precipitation threshold could well anticipate the spatial pattern of pinyon pine (Pinus edulis Engelm.) die-off at the regional scale. The regression analysis suggested that the APLKmax of R. pseudoacacia trees could exceed 60% at sites that MAP was less than 446.1 mm. According to the field investigation of Liu (2008), the growth of R. pseudoacacia declined with decreasing MAP without tree death at Chunhua (MAP = 585.7 mm), Yichuan (MAP = 516.1 mm), and Ansai (MAP = 512.4 mm), but they found a large-area die-off at the Suide site where MAP was 410.6 mm. Wang and Li (2004) also suggested that it was not suitable for the growth of R. pseudoacacia trees at the Wuqi, Suide, and Mizhi sites where MAP was 456.2 mm, 410.6 mm, and 445.6 mm, respectively. These results suggested that the model predications in this study matched well with the previous field surveys on the Loess Plateau. The determination of APLK threshold leading to drought-induced mortality of R. pseudoacacia trees would greatly help to determine the exact MAP threshold in the further studies.

4.4. Methodological limitations There are still some limitations in this study that should be further improved. Firstly, the vulnerability curves of xylem were vital for the plant hydraulic simulations. In this study, the VC constructed by the bench top dehydration technique displayed an S-shaped curve (Fig. 2), which was in line with previous studies (An et al., 2018; Li et al., 2012; Wang et al., 2014). We also noticed that some studies presented Rshaped VCs in R. pseudoacacia with the centrifuge method (An et al., 2018; Wang et al., 2014), in which PLK increased quickly from zero tension. Wang et al. (2014) compared three methods including centrifuge method, air injection method, and bench top dehydration method for measuring VCs, and they further combined the VCs with the water relationship in R. pseudoacacia. The results suggested that the Rshaped VCs obtained by the centrifuge method were probably artefacts and invalid in R. pseudoacacia, and the S-shaped VC constructed by bench top method was mostly realistic since it fitted best with the water relationship in R. pseudoacacia. R. pseudoacacia is a typical long-vessel, ring-porous species. According to the results of Wang et al. (2014), the mean vessel length ranged from 5 to 33 cm and the maximum vessel length could be 61 cm in R. pseudoacacia. When measuring VCs with the centrifuge method, there was large percent of vessels extend from the stem end to near the segment center and beyond, i.e. ‘open-to-center 9

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Fig. 8. Simulated temporal changes in midday leaf water potential (Ψmd) and percentage loss of whole-plant hydraulic conductance (PLK) during 1968–2017 at the four representative sites. Sites: YL, Yangling; CW, Changwu; AS, Ansai; MZ, Mizhi.

injection chamber as suggested by previous studies (Ennajeh et al., 2011). In the bench top dehydration method, the xylem embolism was induced naturally by transpiration of leaves, and has been regarded as a ‘gold standard’ for measuring VC. In this study, we adopted the Sshaped VC constructed by the bench top dehydration method for modelling study. However, the curve was obtained with multiple branches, and this method indeed neglected the variation in the hydraulic traits of different branches, which should be examined in the further studies. Secondly, the tree density could substantially influence the mortality risk of trees during drought stress (Liang et al., 2019). Unfortunately, both the original Biome-BGC model and the BBGC-SPERRY model established in this study mainly focused on the stand-level ecophysiological processes and initialed with a steady state condition. The models could not simulate the impact of different tree densities on drought-induced tree mortality and the other inter-tree competitive processes. In this study, the investigated R. pseudoacacia stands had good representativeness in stand density in this area, which guaranteed

Table 3 Descriptive statistics of seasonal average percentage loss of whole-plant hydraulic conductance at four representative sites along the precipitation gradient. Site code YL CW AS MZ

Minimum (%) 0.55 0.39 0.76 0.92

Maximum (%) 26.92 44.20 59.07 66.13

Mean (%)* c

5.95 12.42bc 16.72ab 23.82a

S.D. (%)

CV (%)

6.68 10.91 15.48 18.74

112.29 87.81 92.56 78.65

S.D.: standard deviation; CV: coefficients of variation. Sites: YL, Yangling; CW, Changwu; AS, Ansai; MZ, Mizhi. ⁎ Different letters indicate a significant difference at the 0.05 level.

vessels’. The air bubbles or solid particles could travel to the center of the axis of rotation in a centrifuge where the highest tension exists, thus inducing the pre-mature embolism and resulting in the R-shaped curves (Sperry et al., 2012). Similarly, the air injection method could also overestimate the vulnerability when vessels were longer than the 10

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Fig. 9. Temporal variation in seasonal average percentage loss of whole-plant hydraulic conductance (APLK) during 1968-2017 at four representative sites. The dash line represents the temporal trend of APLK (P < 0.05). Sites: YL, Yangling; CW, Changwu; AS, Ansai; MZ, Mizhi.

pressure gradient and surface air flow on the snow grains, etc. (Zwaaftink et al., 2011). The original Biome-BGC model did not consider wind speed when calculating snow sublimation (White et al., 2000). It should be improved in the further studies to better simulate the hydrological processes. Besides, although the model captured the decreasing trend in LAImax with decreasing MAP for the four representative sites, the model overpredicted LAImax at MZ (Fig. 7). The major reason could be that the model did not simulate drought-induced leaf shedding adequately with the present routines. The drought-induced leaf shedding was supposed to be associated with plant hydraulic traits (Wolfe et al., 2016). With the development of drought stress, leaf shedding efficiently reduced the water demand of plants, thus maintaining the safety of plant water transport and avoiding hydraulic failure (Tyree and Sperry, 1988). On the basis of revealing the hydraulic threshold that corresponded to leaf shedding, coupling plant hydraulic with the routines of plant growth would improve the predictive accuracy of the leaf area index. Fig. 10. Relationship between maximum seasonal average percentage loss of soil-plant hydraulic conductance (APLKmax) with mean annual precipitation (MAP) along the precipitation gradient transect. Dash line is the fitted line.

5. Conclusion In this study, the Sperry model was integrated with the carbon and nitrogen processes of the Biome-BGC model. The modified model was capable of simulating canopy transpiration, soil moisture, leaf water potential and leaf area index in R. pseudoacacia stands on the Loess Plateau, and it served as a useful tool for simulating climate–soil–vegetation interactions in this region. The long-term simulations of plant hydraulic traits revealed the strong annual variation in APLK due to climatic variability and an increasing trend in APLK with the increase of PET during 1968–2017. Along the precipitation gradient transect, APLKmax increased linearly with decreasing MAP, and could be higher than 60% at sites with MAP less than 446.1 mm. Based on the effect of

the validity of the modelling study in current state on the Loess Plateau. The model should be improved in the further studies to quantify the effect of differential tree densities on drought-induced tree mortality. Finally, the routines in the C, N and water cycles should be further upgraded to incorporate the latest advances in eco-hydrology and microclimatology. For instance, wind speed plays an important role in snow sublimation. However, the effect of wind on snow sublimation is still poorly quantified (Gustafson et al., 2010). Especially the drifting snow sublimation is a complicated physical process as affected by multiple factors including specific surface area of snow grains, vapor 11

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Fig. 11. Changes of seasonal average percentage loss of soil-plant hydraulic conductance (APLK) with annual precipitation (AP) under different initial soil moisture contents at the four representative sites. FC, field capacity; PWP, permanent wilting point. Sites: YL, Yangling; CW, Changwu; AS, Ansai; MZ, Mizhi.

the online version, at doi:10.1016/j.agrformet.2019.107897.

soil desiccation on drought-induced mortality risk, soil desiccation could lead to a considerable increase in the sensitivity of plant hydraulic safety to precipitation variability. The results highlighted the role of maintaining soil water storage in deep soil to reduce mortality risk under high temporal variation in AP in this area. These findings should be helpful for guiding vegetation restoration and for promoting sustainability of plantation forests on the Loess Plateau. In further studies, other indicators involved in the mechanisms of carbon starvation and biotic attack should be adopted for anticipating mortality. Besides, more detailed descriptions of hydrological processes should also be integrated into the model to deal with the complex geological and hydrogeological conditions in this area.

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Declaration of Competing Interest None. Acknowledgements This research was financially supported by the National Natural Science Foundation of China (Nos. 41571213 and 41571130082). The authors thank the members of the Changwu Ecology Station, Chinese Academy of Sciences, and Ministry of Water Resources for their assistance. We are very grateful to Professor John Sperry (University of Utah) and the group of the numerical terradynamic simulation of University of Montana for freely providing the plant hydraulic and Biome-BGC models, respectively. Special thanks are given to the editor and two anonymous reviewers for their comments and suggestions. Supplementary materials Supplementary material associated with this article can be found, in 12

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