Introduction
Terrestrial plants assimilate carbon dioxide (CO2) through
photosynthesis accompanied by a loss of water (H2O) in transpiration.
Both processes are strongly regulated by local environmental conditions and
plant physiology (e.g. stomatal conductance; gs). Plants
protect themselves from excessive water losses (diffusion out of the leaf)
under water-limited environments through a reduction of stomatal conductance,
which in turn leads to less carbon uptake (diffusion of CO2 into the
leaf) and possibly subsequent physiological stress (McDowell et al., 2008;
Will et al., 2013).
Soil water deficit can induce a reduction of transpiration (Bréda et al.,
1993; Clenciala et al., 1998; Granier et al., 2007; Irvine et al., 1998), and
it has been recognized as the main environmental factor limiting plant
photosynthesis on the global scale (Nemani et al., 2003). Even though the
occurrence of drought is low in northern Europe, the summer of 2006 in
Finland was extremely dry and 24.4 % of the 603 forest
health observation sites over entire Finland showed drought damage symptoms
by visual examination, in comparison to 2–4 % damaged sites in a normal
year (Muukkonen et al., 2015). According to the simulated regional soil
moisture, the summer drought in 2006 in southern Finland was the most severe
one over the past 30 years (1981–2010), and the spatial distribution of the
drought damage has been found to be closely related to the plant available
soil moisture (Gao et al., 2016).
Water use efficiency (WUE) is a critical metric that quantifies the trade-off
between photosynthetic carbon assimilation and transpiration at the leaf
level (Farquhar et al., 1982). WUE can be used to study ecosystem functioning which is in close connection to the global cycles of water, energy, and carbon
(Keenan et al., 2013). With the use of the eddy covariance technique (EC) and
associated data processing, i.e. the derivation of gross primary production
(GPP) and evapotranspiration (ET) from measurements of CO2 flux and
latent heat flux, WUE can be calculated on the ecosystem scale as the
ecosystem level water use efficiency (EWUE), which is the ratio of GPP to ET.
EWUE is broadly adopted as a surrogate for the leaf level WUE in many
studies, because more data are available at the ecosystem level than at the
leaf level (Arneth et al., 2006; Law et al., 2002; Lloyd et al., 2002).
Reichstein et al. (2007) observed a small decrease in EWUE in the majority of
the 11 studied EC sites during the 2003 summer heatwave in Europe. However,
their findings are at odds with many models that describe the environmental
controls on stomatal conductance, with increased EWUE predicted during
drought periods (Schulze et al., 2005). Many of those models are based on the
optimality theory by Cowan and Farquhar (1977) who proposed that plants are
able to regulate stomatal conductance in order to maximize WUE. Granier
et al. (2008) reported that EWUE increased linearly with soil water deficit
duration and intensity at a young beech forest site in north-eastern France.
Moreover, EWUE also increased substantially at two forest sites, but not at
grassland sites, during the 2011 spring drought in Switzerland (Wolf et al.,
2013). However, no differences in EWUE were shown between abundant- and
low-rainfall years at a boreal Scots pine forest site in south-eastern
Finland, even though GPP was reduced during low-rainfall years with
long-lasting drought periods (Ge et al., 2014). Therefore, the impact of
drought on EWUE remains unclear. Beer et al. (2009) concluded that the impact
of vapour pressure deficit (VPD) on canopy conductance disturbs responses of
both GPP and ET to changing environmental conditions and proposed the
ecosystem level inherent water use efficiency (IWUE), which is a quantity
defined as EWUE multiplied with mean daytime VPD. IWUE has been found to
increase during short-term moderate drought (Beer et al., 2009). Moreover,
based on IWUE and an optimality-theory-based (Cowan and Farquhar, 1977) stomatal model with the assumptions suggested by Farquhar et al. (1993) and
Lloyd and Farquhar (1994), the underlying water use efficiency (uWUE) was
introduced to exclude the nonlinear dependence of IWUE on VPD, and the linear
relationship between GPP multiplied with a square root of VPD and ET was
found on the half-hourly time scale by Zhou et al. (2014). Later on, the
appropriateness of uWUE on the daily time scale was also demonstrated (Zhou
et al., 2015).
Given the need to understand and project feedbacks between climate change and
plant physiological responses, it is crucial to be able to realistically
model the plant controls of stomatal conductance, and photosynthesis and
transpiration responses under water stress (Berry et al., 2010; Knauer
et al., 2015; Zhou et al., 2013). The various land ecosystem model
simulations highlight the current uncertainty about plant physiology (water
use) in response to drought in models (Huang et al., 2015; Jung et al.,
2007).
The objectives of this study are (1) to understand the environmental controls
on GPP and ET fluxes during a summer drought in boreal Scots pine
(Pinus sylvestris) forests at a EC flux site in southern Finland;
(2) to investigate the drought impact on WUE metrics, including EWUE, IWUE
and uWUE; and (3) to evaluate how adequately the JSBACH land surface model
captures plant responses to changes in environmental variables.
Key characteristics relevant to this study from observation and the
parameter settings in the JSBACH site level simulation at the Hyytiälä
site.
Observation
Site
Location
Vegetation
LAI
Canopy
Measurement
Annual mean air
Soil type
Analysed
References
type
(m2m-2)
height
height (m)
temperature (∘C)
measurement
(all-sided,
(m)
and precipitation
depth of soil
annual)
(mm) (30 year
moisture (cm)
averages)
Hyytiälä
61∘51′ N,
Scots pine
6
13–16
23
2.9; 709
Mineral
5–23;
Markkanen et al.
24∘17′ E
(Haplic podzol)
23–60
(2001); Vesala
et al. (2005)
Settings in JSBACH
Site
PFT
Maximum
Maximum electron
Maximum
Soil type
Analysed depth of
Soil depth
Root depth
LAI
transport rate
carboxylation
soil moisture (cm)
(m)
(m)
(m2m-2)
(Vmax) at 25 ∘C
rate at 25 ∘C
Hyytiälä
Evergreen
16
37.5
71.3
Loamy sand
Average of layer-2
5.416
1.265
needleleaf
(6.5–31.9) and layer-3
forest
(31.9–123.2)
Data and methods
Study sites
The Hyytiälä flux site is located in southern Finland
(61∘51′ N, 24∘17′ E; 180 ma.s.l.) at the
SMEAR-II (Station for Measuring Ecosystem–Atmosphere Relations) field
measurement station (Hari and Kulmala, 2005). The site is dominated by
55 year-old boreal Scots pine (Pinus sylvestris), which is
homogeneous about 200 m in all directions from the site and extends
to the north for about 1 km (Mammarella et al., 2007). The canopy
height of trees is about 13–16 m and the mean all-sided leaf area
index (LAI) is 6 m2m-2. The soil at the site is Haplic podzol
on glacial till (FAO-UNESCO, 1990). The 30 year (1961–1990) averaged annual
mean air temperature is 2.9 ∘C and precipitation is 709 mm
at the site (Vesala et al., 2005). Those details about the site are listed in
Table 1. The ground vegetation consists mainly of blueberry
(Vaccinium myrtillus), lingonberry (Vaccinium vitis-idaea),
feather moss (Pleurozium schreberi) and other bryophytes (Kolari
et al., 2009). We analysed the summer (June–August) from an 11-year period
(1999–2009) according to data availability.
Flux measurement and data processing
Ecosystem carbon and water fluxes at the site were measured with the
micrometeorological EC method. Turbulent fluxes were calculated as
half-hourly averages following standard methodology (Aubinet et al., 2012)
with EddyUH software (Mammarella et al., 2016). The vertical CO2 flux
was obtained as the covariance of high-frequency (10 Hz) observations
of vertical wind speed and the CO2 concentration (Baldocchi, 2003).
The CO2 flux was corrected for storage change to obtain net ecosystem
CO2 exchange (NEE), which was then partitioned into total ecosystem
respiration (TER) and GPP according to Kolari et al. (2009). Data quality of 30 min values of NEE and latent heat flux (LE) was ensured by
excluding records with low turbulent mixing (friction velocity below
0.25 ms-1) as described in Markkanen et al. (2001), Mammarella
et al. (2007), and Ilvesniemi et al. (2010). TER was modelled using an
exponential equation with temperature at a depth of 2 cm in the soil
organic layer as the explanatory factor. The value of GPP was then directly
derived as residual from the measured NEE. When NEE was missing, GPP was
gap-filled according to Kolari et al. (2009). LE was gap-filled using
a linear regression against net radiation in a moving window of 5 days, and
then ET was inferred from LE.
In addition to the EC measurements, a set of supporting meteorological
variables were adopted as half-hourly averages; incoming shortwave radiation
(Rs) and longwave radiation, air temperature
(Ta), atmospheric humidity, and precipitation were used as
meteorological forcing for the site level simulation. The soil moisture was
monitored at 1 h intervals by the time domain reflectometry method
(Tektronix 1502 C cable radar, Tektronix Inc., Redmond, USA). Three layers of
mineral soil (0–5, 5–23, and 23–60 cm) were measured, as well as
the organic layer on the top (-4 to 0 cm). In this study, soil
moisture at the two lower levels of mineral soil (5–23 and
23–60 cm) at Hyytiälä was averaged over a day to represent
daily soil moisture dynamics in the root zone at the site. The reason to
exclude layer 1 soil moisture is that it is too sensitive to temperature and
precipitation variations.
The half-hourly data of GPP and ET, as well as meteorological variables were
averaged over the selected time periods in a day. Prior to averaging, rainy
days and a number of dry days after the rainy days were firstly excluded from
the data. The number of excluded dry days was determined by the ratio of
daily precipitation to potential evapotranspiration (PET). When precipitation
was smaller than PET, no dry day after rainy day was excluded. When
precipitation was equal or larger than twice that of PET, two dry days following
the rainy day were excluded. Additionally, when precipitation was larger than
PET but with the ratio less than 2, one dry day after the rainy day was
excluded. PET was calculated using the Penman–Monteith equation and the
“Evapotranspiration” package in R software was used (Guo et al., 2016).
Second, in order to capture the daily time periods of effective
photosynthesis, only half-hourly data with Rs larger than
100 Wm-2 were selected. Finally, the half-hourly data of
Rs, VPD, and Ta were also averaged over the
selected time periods to get their daytime mean values respective to the GPP
and ET data. The same data processing method was used for the simulation
results.
JSBACH land surface model
JSBACH (Raddatz et al., 2007; Reick et al., 2013) is the land surface model of the Max
Planck Institute for Meteorology Earth System Model (MPI–ESM) (Roeckner
et al., 1996; Stevens et al., 2013). The land physics of JSBACH mainly follow
those of the global atmosphere circulation model ECHAM5 (Roeckner et al.,
2003), and the biogeochemical components are mostly taken from the biosphere
model BETHY (Knorr, 2000). In JSBACH, land vegetation cover is described as
plant functional types (PFTs) and a set of properties (e.g. maximum LAI
and albedo) is attributed to each PFT with respect to the processes that are
accounted for by JSBACH. The phenology model (Logistic Growth Phenology;
LoGro-P) of JSBACH simulates the LAI dynamics to compute photosynthetic
production (Böttcher et al., 2016). The models of Farquhar et al. (1980)
and Collatz et al. (1992) are used for photosynthesis of C3 and C4 plants,
respectively. A five-layer soil hydrology scheme was implemented in JSBACH by
Hagemann and Stacke (2015). Gao et al. (2016) has demonstrated that JSBACH
with its five-layer soil hydrology scheme is able to capture the soil
moisture dynamics at sites and on the regional scale of Finland.
The stomatal conductance model in JSBACH
The current version of the stomatal conductance model in JSBACH considers the
limitation from soil water availability on stomatal conductance
(gs), which further impacts on carbon assimilation and
transpiration.
Firstly, the net assimilation rate (An; molm-2s-1) and gs
(molm-2s-1) are calculated without water limitation as
the unstressed net assimilation rate (An, pot; molm-2s-1)
and the unstressed stomatal conductance (gs, pot; molm-2s-1). The An, pot
is calculated using the photosynthesis model in JSBACH, for which the
intercellular CO2 concentration under unstressed condition
(Ci, pot; molmol-1) is needed. The Ci,
pot is prescribed using the atmospheric CO2 concentration
(Ca; molmol-1), where Ci,
pot=0.87Ca for C3 plants and Ci, pot=0.67Ca for C4 plants (Knorr,
2000). After the An, pot is determined, the gs, pot
is derived using the following equation:
gs, pot=1.6An, potCa-Ci,
pot.
Then, an empirical water stress factor, which is a function of volumetric
soil moisture, is used to derive gs
(molm-2s-1) from gs, pot as follows:
gs=βgs, pot.
where
β=1θ≥θcritθ-θwiltθcrit-θwiltθwilt<θ<θcrit0θ≤θwilt,
herein, θ (m3m-3) is the volumetric soil moisture,
θcrit (m3m-3) is the critical point, and
θwilt (m3m-3) is the permanent wilting point.
Finally, the intercellular CO2 concentration (Ci) and
An are resolved using gs. The canopy conductance
(Gc; molm-2s-1) and canopy-scale
An are integrated over the leaf area. Unlike the BETHY approach
(Knorr, 2000), the control of gs in JSBACH does not include the
influence of atmospheric humidity.
Site level simulation by JSBACH
For the site simulation, JSBACH was forced with the half-hourly local
meteorological observations. Based on the site-specific information, PFT was
assigned as evergreen needleleaf forest and the soil type was set as loamy
sand in JSBACH. The modelled LAI reached values close to the observed LAI
when the parameter maximum LAI was set to 16 m2m-2. Also, the
maximum carboxylation rate (Jmax) and maximum electron transport rate
(Vmax) at 25 ∘C were adjusted, for the simulated GPP to match
the magnitude of the observed GPP. The Vmax was set to be 37.5 and the
Jmax was 71.3. The soil depth and root depth at the site were derived from
maps for the regional JSBACH simulation presented in Gao et al. (2016) (see
also Hagemann and Stacke, 2015). Those parameter settings in the JSBACH site
level simulation for the site are listed in Table 1. Prior to the actual
simulations, a 30 year spin-up run was conducted by cycling meteorological
forcing that was used for the actual simulation to obtain equilibrium for
soil water and soil heat balances.
Soil Moisture Index (SMI)
In this study, the soil moisture dynamics are represented by SMI (also
referred to as Relative Extractable Water – REW), which has been
demonstrated to represent summer drought in boreal forests in Finland (Gao
et al., 2016). The SMI describes the ratio of plant available soil moisture
to the maximum volume of water available to plants in the soil (Betts, 2004;
Seneviratne et al., 2010):
SMI=θ-θWILT/θFC-θWILT,
where θ is the volumetric soil moisture
(m3 H2O m-3), θFC is the field
capacity (m3 H2O m-3), and
θWILT is the permanent wilting point
(m3 H2O m-3). When θ exceeds
θFC, soil water cannot be retained against gravitational
drainage, while below θWILT, the soil water is strongly
held by the soil matrix and cannot be extracted by plants (Hillel, 1998). In
this study, soil moisture conditions were classified into five groups according
to SMI values with an interval of 0.2: very dry, 0≤SMI<0.2;
moderate dry, 0.2≤SMI<0.4; mid-range, 0.4≤SMI<0.6; moderate wet, 0.6≤SMI<0.8; and very wet, 0.8≤SMI<1.
From simulations, we used the average of the second layer (layer-2;
6.5–31.9 cm) and the third layer (layer-3; 31.9–123.2 cm)
soil moisture together with model soil parameters to determine the simulated
SMI for Hyytiälä, with the aim to correspond with the observed SMI
that calculated with measured soil moisture at the two lower levels of
mineral soil at the site. The layer 1 soil moisture was excluded in
determining both simulated and observed SMIs because it is too sensitive to
temperature and precipitation variations. For the observed SMI, the measured
soil parameters derived based on water retention curves determined from soil
samples taken at the site were adopted (i.e. volumetric soil moisture at
saturation (θSAT)=0.50 m3 H2O m-3, θFC=0.30 m3 H2O m-3, and θWILT=0.08 m3 H2O m-3). As θFC
acts as a proxy for θSAT in the five-layer soil hydrology
scheme in JSBACH (Hagemann and Stacke, 2015), θSAT was used
instead of θFC for consistency when calculating SMI based
on the observed soil moisture data.
Ecosystem water use efficiency (EWUE), inherent water use efficiency (IWUE), and underlying water use efficiency (uWUE)
The EWUE is calculated as,
EWUE=GPP/ET,
IWUE is defined as EWUE multiplied by daytime mean VPD in Beer et al. (2009),
IWUE=GPP×VPD/ET,
uWUE is derived based on IWUE and an optimality-theory-based (Cowan and Farquhar,
1977) stomatal model with the assumptions suggested by Farquhar
et al. (1993) and Lloyd and Farquhar (1994) in Zhou et al. (2014). The
formulation of uWUE is,
uWUE=GPP×VPD0.5/ET
From EC data, EWUE and IWUE can only be calculated with ET, which, in addition
to transpiration, contains evaporation of water intercepted by surfaces and
soil evaporation. However, process-based ecosystem models do resolve
evaporation and transpiration which together compose ET. Therefore,
transpiration-based EWUE, IWUE, and uWUE can also be calculated using
simulated transpiration instead of ET in those equations.
(a) Daily mean soil moisture index (SMI) at
Hyytiälä from observation and the JSBACH simulation for
the summer months (June, July, August) in the 11-year study period (from 1999 to 2009). (b) Daily mean SMI at
Hyytiälä from observation and the JSBACH simulation for the summer months in 2006; the two black dashed lines
represent the averaged daily SMI in the summer months over the 11-year study period from observation and the JSBACH
simulation. (c) Daily mean air temperature (Ta) in the summer months of 2006 and the averaged daily
mean Ta in the summer months over the 11-year study period at Hyytiälä from observation, meanwhile,
the daily precipitation amount in 2006 is shown as the bar plot.
Results
Soil moisture drought at Hyytiälä in 2006
In the summer of 2006, a period with evidently lower SMI values (<0.2)
than in any other year during the 11-year time series was shown (Fig. 1a).
According to the in situ observation, in the summer of 2006, there were 37
consecutive days (23 July–28 August) with SMI lower than 0.2, and 17
consecutive days (1–17 August) with SMI lower than 0.15. The observed SMI
reached its minimum of 0.115 on 16 August 2006. The simulated SMI was
generally smaller than the observed SMI in the summer of 2006, showing 42
consecutive days (17 July–27 August) with SMI lower than 0.2, and 33
consecutive days (26 July–27 August) with SMI lower than 0.15. The lowest
SMI from simulation was 0.052 on 15 August. The simulated SMI agreed well
with in situ observed SMI over the 11-year study period, with a correlation
coefficient of 0.63 and a root-mean-square error (RMSE) of 0.23. However, the
simulated SMI showed a larger amplitude and a faster response to changes in
climate conditions in comparison to the observed SMI. Nevertheless, a very
good correlation coefficient of 0.97 between simulated and observed SMIs was
found for the year 2006 (Fig. 1b), despite the simulated SMI being systematically
lower than the observed SMI (RMSE =0.12).
Concurrently with the low soil moisture, a high Ta anomaly was
observed in August 2006 (Fig. 1c). In all the days in August 2006, the daily
mean in situ Ta was higher than the 11-year averaged daily mean
Ta. The monthly mean Ta in August 2006 (18.1±1.9 ∘C) was 3.1 ∘C higher than that of the 11-year average
(15.0±1.63 ∘C). Also, the daily mean VPD in August 2006 was
higher than the 11-year averaged daily mean VPD in August in general (not
shown), except on the days with precipitation. Especially, the mean value of
the daily mean VPD in the period from 31 July to 16 August (1.067±0.361 kPa) was substantially higher than the mean of the 11-year
averaged daily mean VPD over this period (0.582±0.200 kPa). The
biggest difference between the daily mean VPD and the 11-year averaged daily
mean VPD reached 1.054 kPa on 5 August that was the day with highest
Ta in August 2006. The daily mean Rs in the
summer of 2006 was overall higher than the 11-year averaged daily mean
Rs, with the monthly mean values by 15.4, 31.2, and 21.4 %
higher in June, July, and August, respectively.
Relationship between the daytime averaged gross primary production
(GPP in µgCm-2s-1) and
evapotranspiration (ET in mg H2O m-2s-1) at Hyytiälä in the summer months (June, July,
August) of the 11-year study period (from 1999 to 2009) from (a) observation and (b) the JSBACH
simulation. Data are categorized according to daily mean soil moisture index (SMI), daytime mean incoming shortwave
radiation (Rs), daytime mean air temperature (Ta), and daytime mean vapour pressure deficit
(VPD). In the observation, the group of data under the very dry soil moisture condition showing GPP values
lower than other days is marked with a grey circle.
The precipitation events have a strong impact on the temporal pattern of SMI.
The cumulative in situ precipitation of 34 mm in July 2006 was the
lowest during the 11-year study period with the July average of 91±31 mm. In contrast, the highest total precipitation in July was in
2007, reaching 146 mm. The cumulative precipitation of 48 mm
in August 2006 was not as low as in July when compared to the 11-year average
of 71±43 mm. However, the lack of precipitation since the end
of July led to the continuous drop of SMI till mid August 2006, followed by
a small increase in soil moisture after a light precipitation event. The SMI
increased to be above 0.2 in the end of August with a heavy precipitation
event exceeding 25 mm in one day. Moreover, the precipitation in
June 2006 was also less than the 11-year average (45 vs. 70±24 mm) and temporally unevenly distributed, with only a small amount
at the beginning of June and a large amount in the end of June. Therefore,
there was a continuous decrease in soil moisture from the beginning of June
and an abrupt increase in SMI of more than 0.1 at the end of June.
The relationship of GPP to ET categorized by environmental variables
In general, the daytime averaged GPP and ET from observations at
Hyytiälä showed a non-linear relationship (Fig. 2a). When categorized
according to environmental variables, there is a group of data under the very
dry soil moisture condition (encircled in grey in Fig. 2a) showing
GPP values lower than other days. The ET values of this group are also
located in the lower end, but just partly lower than ET values on other days.
It is found that the days in this group are with SMI smaller than 0.15.
Moreover, there are only two days with SMI values smaller than 0.15 that are
not included in the encircled group due to their slightly higher GPP values.
Most of the days in the group have high daytime mean Ta
(18–24 ∘C), sufficient daytime mean Rs (mostly above
300 Wm-2), and relatively high daytime mean VPD (above
1 kPa).
The non-linear relationship between the daytime averaged GPP and ET was also
found in the JSBACH simulated result (Fig. 2b). The decline of both GPP and
ET during low SMI was captured by the model. However, under the very low soil
moisture condition (SMI <0.15) during the summer drought in 2006, the
model simulated a much less reduction of GPP, while the ET decreased to be
lower than the observation in a few days. The non-linear relationship between
simulated daytime averaged GPP and transpiration (Fig. S1 in Supplement) is
similar to the relationship between simulated daytime averaged GPP and ET,
which demonstrates that transpiration composes a large fraction of ET during daytime at the site, especially under soil water stress. Except the drought
events, GPP and ET both increased with increasing Rs and VPD in
the simulation, which was more evident than in the observational data.
Response of daytime mean gross primary production (GPP in
µgCm-2s-1) and evapotranspiration
(ET in mg H2O m-2s-1) to daytime mean incoming shortwave radiation (Rs), daytime mean
air temperature (Ta), daytime mean vapour pressure deficit (VPD), and daily mean soil moisture index (SMI) at
Hyytiälä, categorized by daily mean soil moisture index (SMI) in the summer months (June, July, August) of the
11-year study period (from 1999 to 2009) from (a) observation and (b) the JSBACH simulation. The
regression lines are fitted for the five SMI groups (very dry, 0≤SMI<0.2; moderate dry, 0.2≤SMI<0.4; mid-range, 0.4≤SMI<0.6; moderate wet, 0.6≤SMI<0.8; and very wet, 0.8≤SMI<1).
Response of GPP and ET to environmental variables categorized by SMI
The dependence of GPP and ET on environmental variables was further
investigated for different SMI ranges (Fig. 3). The exclusion of the night-time
and the days affected by rain (see details in Sect. 2.2) also removed the
small values of GPP and ET. Linear regressions were fitted between GPP
(ET) and environmental variables for each soil moisture group to emphasize
the deviating differences of dependence of GPP (ET) on environmental
variables under different soil moisture conditions. The regression
parameters, correlation coefficient and statistical significance are
summarized in Table S1 in the Supplement.
The very dry soil (0≤SMI<0.2) led to a response of observed
daytime mean GPP and ET to daytime mean Rs, Ta,
and VPD that deviated considerably from the responses of the daily mean SMI
values greater than 0.2 (Fig. 3a). Under the very dry soil moisture
condition, GPP decreased with the declining SMI with a high correlation of
0.79, whereas the other SMI groups showed a more scattered relationship between
GPP and SMI. Unlike the other SMI groups, GPP was the most negatively
correlated with Ta and VPD under the very dry soil moisture
condition. Moreover, the group with SMI values less than 0.2 displayed lower
GPP values (on average 97.6 µgCm-2s-1) than the other
groups (on average 151 µgCm-2s-1). The response
patterns of the observed ET to environmental variables were similar to those
of GPP. As with GPP, the group under the very dry soil moisture condition
deviated strongly from the other SMI groups. However, the decrease in ET
under severe soil moisture drought was not as pronounced as in GPP.
For the simulated GPP and ET too, the group under the very dry soil moisture
condition deviated from the other SMI groups, but not to the same extent as
that in the observed GPP and ET. Under other soil moisture conditions
(SMI >0.2), the simulated GPP had stronger positive linear relationships
with daytime mean Rs, Ta, and VPD than the
observed GPP. Compared to the observed ET, some differences existed in the
response of the simulated ET to environmental variables. First, the
dependence of simulated ET on Rs tended to be more linear than
the observed ET and Rs relationship. Second, unlike observed
ET, the simulated ET increased concomitantly with VPD at high VPD.
Nevertheless, simulated ET of the group under severe soil moisture drought
deviated strongly from the other SMI groups, but to a lesser extent than
observed ET.
The dependence of the product of daytime mean gross primary
production (GPP in µgCm-2s-1) and
daytime mean vapour pressure deficit (VPD) on evapotranspiration (ET in mg H2O m-2s-1) (i.e.
GPP × VPD/ET, which represents the inherent water use efficiency, IWUE), and the dependence of the production of
GPP and the square root of VPD on ET (i.e. GPP×VPD0.5/ET, which represents the
underlying water use efficiency, uWUE) in the summer months (June, July, August) of the 11-year study period (from 1999 to
2009) from (a) observation and (b) the JSBACH simulation. Data are categorized according to daily mean
soil moisture index (SMI). The fitted lines for the dependence of the product of GPP and VPD on ET are for the data under
SMI <0.2 (red line) and the data under 0.2≤SMI<1 (blue line); both fittings are
statistically significant (p value <0.05). No lines were fitted for the dependence of the production of GPP and the
square root of VPD on ET, as the data under SMI <0.2 and data under 0.2≤SMI<1 are more converged in
a line in comparison to the dependence of the product of GPP and VPD on ET.
Soil moisture drought impacts on EWUE, IWUE, and uWUE
From the observation, the decrease in GPP was much stronger than the decrease
in ET during the soil moisture drought, which resulted in a largely decreased
EWUE that reached the recorded minimum during the severe soil moisture
drought (Figs. 2 and S2). In contrast to EWUE, IWUE increased from
3.25 µgCkPamg-1 H2O (the mean value for the
days with SMI equal or larger than 0.2) to
3.93 µgCkPamg-1 H2O (the mean value for the
days with SMI smaller than 0.2), and uWUE did not change under the severe
soil moisture drought at Hyytiälä (Fig. 4a). The simulated EWUE
decreased less and the simulated IWUE increased more (from 3.62 to
5.17 µgCkPamg-1 H2O) than the observation,
which is mainly because of a smaller decrease in the simulated GPP than its
observed counterpart during the soil moisture drought (Fig. 4b). The
simulated uWUE remained insensitive to the severe soil moisture drought. In
addition, the transpiration-based EWUE, IWUE, and uWUE (Fig. S3 in Supplement)
showed similar results to those three metrics calculated with ET.
Discussion
Drought impacts on GPP and ET
Both GPP and ET were suppressed when there was the severe soil moisture
drought in the summer of 2006 at Hyytiälä. In addition, the response
of GPP and ET to the changes in environmental variables under severe water
stress differed from those under other soil moisture conditions. The dominant
reason is that low soil moisture leads to stomatal regulation of the plants,
which limits plant carbon assimilation and transpiration. The decreased ET
due to soil moisture drought may increase atmospheric VPD, which could in
turn intensify stomatal closure (Eamus et al., 2013; Jarvis, 1976). Moreover,
the GPP and ET were decoupled and EWUE decreased due to the soil moisture
drought. Unlike EWUE, IWUE increased but uWUE showed no changes during
the severe soil moisture drought at Hyytiälä. IWUE depends on the
difference between ambient partial pressure of CO2 (Ca)
and a weighted average of inner leaf partial pressure of CO2
(Ci) through the canopy within the tower footprint (Beer
et al., 2009). It has been shown that the term
(1-Ci/Ca) increases as VPD increases (Wong et al.,
1979). Thus, the increase in IWUE during drought was a result of decreased
stomatal conductance due to increased VPD. The uWUE was formulated to be more
independent of a varying VPD than IWUE. According to Xie et al. (2016), both
IWUE and uWUE at a flux site increased and reached their maximum values over
the long-term during a severe drought in central and southern China in the summer
of 2013. In this work, the unchanged uWUE during this drought event
demonstrate that the trade-off between carbon assimilation and transpiration
of the boreal Scots pine forest was not disturbed by drought at the study
site, even though the stomatal conductance decreased.
Differences between observations and site simulations
The model showed the limitations on GPP and ET under the very dry soil
moisture condition (0≤SMI<0.2) at Hyytiälä. However,
the discrepancies in response between observed and simulated GPP and ET to
changing environmental variables were obvious. This is because the
formulation for stomatal conductance in JSBACH does not include a response to
air humidity, and therefore the stomatal conductance in JSBACH is insensitive
to atmospheric VPD (Knauer et al., 2015). In Knauer et al. (2015), Ball–Berry
model (Ball et al., 1987) has been found to be the best among a few stomatal
conductance models in its response to atmospheric drought under non-limited
soil moisture conditions. In reality, low soil moisture and high
Ta during drought are closely coupled with high atmospheric
VPD. Our results indicate that the combined effects of soil moisture and
atmospheric drought on stomatal conductance have to be taken into account.
Moreover, model performance could be improved through the inclusion of
non-stomatal limitations on plant photosynthesis, which have been considered
to be important for the simulation of short-term plant responses to drought
(Egea et al., 2011; Manzoni et al., 2011; Zhou et al., 2013). However, JSBACH
is being continuously developed and the effect of soil water stress is to be
accounted for according to Egea et al. (2011) for both stomatal and non-stomatal
processes, affecting both conductance and photosynthesis parameters.
Moreover, when comparing results from the EC data and simulations, it should
be kept in mind that the EC method has its uncertainties. Due to the
stochastic nature of the turbulent flow, there is always a random error
component in the observations. In addition, imperfect spectral corrections
and gap-filling procedures as well as calibration problems may be sources of
systematic errors (Richardson et al., 2012; Wilson et al., 2002). The
uncertainty of EC flux data is typically 20–30 % for annual carbon
budget (Aubinet et al., 2012; Baldocchi, 2003). Nevertheless, the
uncertainties of the GPP and ET estimated from EC measurements are likely to
have negligible impacts on our findings of the three WUE metrics, as the same
data with the same uncertainties were used.
Conclusions
In this study, the impact of the severe soil moisture drought in
the summer of 2006 on the water use efficiency of a boreal Scots pine forest
ecosystem at Hyytiälä flux site in southern Finland was investigated
using both ground-based observations from a flux tower and the site-level
simulation by the JSBACH land surface model. The SMI was used to indicate the soil moisture condition at the site. Finland is
a high-latitude country and drought is uncommon. Nevertheless, the summer
drought in 2006 caused severe forest damage in southern Finland (Muukkonen
et al., 2015). The SMI calculated from regional soil moisture simulations
over the past 30 years (1981–2010) indicated that such extreme drought
affecting forest health was rare in Finland, and the summer drought in 2006
in southern Finland was the most severe one in the 30 year study period (Gao
et al., 2016). According to climate scenarios, regardless of the anticipated
increase in precipitation, a modest drying of soil is foreseen in
northern Europe during the 21st century because of intensifying
evapotranspiration (Ruosteenoja et al., 2017).
The impacts from the severe soil moisture drought on plant functioning at the
site were clearly seen in the GPP and ET values. From both the observation and simulation results, the
GPP and ET reached the recorded minimums during the drought event.
The EWUE decreased, whereas the IWUE increased and the uWUE was unchanged during the severe soil moisture drought at the site. The
EWUE is very sensitive to the daily changes of GPP and ET. The increase in
IWUE during drought was due to the decreased stomatal conductance of plants
under increased VPD. The unchanged uWUE indicates that the carbon
assimilation and transpiration coupling of the boreal Scots pine forest was
not disturbed by the drought event at this site, although the stomatal
conductance of plants decreased.
The simulated response in plant functioning to the severe soil moisture
drought predicted by JSBACH was weaker than those in the observed dataset,
even though the strong limitation on GPP and ET through stomatal closure were
seen at the very dry soil moisture condition (0≤SMI<0.2) as
in the observed data. The differences between the observed and the model
results suggest that, in order to adequately simulate effects of drought on
plant functioning, the combined effects of atmospheric and soil moisture
drought on stomatal conductance have to be included in the stomatal
conductance model in JSBACH. Moreover, inclusion of non-stomatal limitations
on photosynthesis during drought, e.g. reduced mesophyll conductance or
carboxylation capacity, may additionally improve the model results (Keenan
et al., 2010).
This study gives a view of the response of water use efficiency to a summer
drought event in a boreal Scots pine forest in Finland, and further suggests
that improving our knowledge of ecosystem processes in land surface models
are of great importance when estimating biosphere–atmosphere feedbacks of
terrestrial ecosystems under climate change.