The co-occurrence of droughts and heatwaves can have
significant impacts on many socioeconomic and environmental systems.
Groundwater has the potential to moderate the impact of droughts and
heatwaves by moistening the soil and enabling vegetation to maintain higher
evaporation, thereby cooling the canopy. We use the Community Atmosphere
Biosphere Land Exchange (CABLE) land surface model, coupled to a groundwater
scheme, to examine how groundwater influences ecosystems under conditions of
co-occurring droughts and heatwaves. We focus specifically on south-east
Australia for the period 2000–2019, when two significant droughts and
multiple extreme heatwave events occurred. We found groundwater plays an
important role in helping vegetation maintain transpiration, particularly in
the first 1–2 years of a multi-year drought. Groundwater impedes
gravity-driven drainage and moistens the root zone via capillary rise. These
mechanisms reduced forest canopy temperatures by up to 5 ∘C during
individual heatwaves, particularly where the water table depth is shallow.
The role of groundwater diminishes as the drought lengthens beyond 2 years
and soil water reserves are depleted. Further, the lack of deep roots or
stomatal closure caused by high vapour pressure deficit or high temperatures
can reduce the additional transpiration induced by groundwater. The capacity
of groundwater to moderate both water and heat stress on ecosystems during
simultaneous droughts and heatwaves is not represented in most global
climate models, suggesting that model projections may overestimate the risk of
these events in the future.
Introduction
Droughts and heatwaves are important socioeconomic and environmental
phenomena, impacting regional food production (Kim et al., 2019; Lesk et
al., 2016), water resources (Leblanc et al., 2009; Orth and Destouni, 2018),
and the resilience of ecosystems (Ibáñez et al., 2019; Ruehr et al.,
2019; Sandi et al., 2020). When droughts and heatwaves co-occur (a
“compound event”) the consequences can be particularly severe, reducing
the terrestrial carbon sink (Ciais et al., 2005), potentially accelerating
tree die-off (Allen et al., 2010, 2015; Birami et al., 2018), and setting
conditions conducive to wildfires (Jyoteeshkumar reddy et al., 2021). One
region experiencing severe coincident heatwaves and drought is Australia
(Mitchell et al., 2014). Drought in Australia is associated with large-scale
modes of variability, including the El Niño–Southern Oscillation and the
Indian Ocean Dipole (van Dijk et al., 2013), and periods of below-average
rainfall can extend for multiple years (Verdon-Kidd and Kiem, 2009).
Heatwaves are commonly synoptically driven, associated with blocking events
that can be sustained over many days (Perkins-Kirkpatrick et al., 2016;
Perkins, 2015). Modes of variability and synoptic situations are important
in setting up conditions conducive to drought and heatwave. However, once a
heatwave or drought has become established, land–atmosphere interactions can
intensify and prolong both heatwaves and droughts (Miralles et al., 2019),
affect their intensity, and influence the risk of their co-occurrence
(Mukherjee et al., 2020). The role of the land surface in amplifying or
dampening heatwaves and droughts is associated with the partitioning of
available energy between sensible and latent heat (Fischer et al., 2007;
Hirsch et al., 2019) and is regulated by subsurface water availability
(Teuling et al., 2013; Zhou et al., 2019). As soil moisture becomes more
limiting, more of the available energy is converted into sensible heat,
reducing evaporative cooling via latent heat. Changes in the surface
turbulent energy fluxes influence the humidity in the boundary layer, the
formation of clouds, incoming solar radiation, and the generation of rainfall
(D'Odorico and Porporato, 2004; Seneviratne et al., 2010; Zhou et al.,
2019). The sensible heat fluxes warm the boundary layer, leading to heat
that can accumulate over several days and exacerbate heat extremes (Miralles
et al., 2014), which can in turn increase the atmospheric demand for water
and intensify drought (Miralles et al., 2019; Schumacher et al., 2019).
Vegetation access to groundwater has the potential to alter these
land–atmosphere feedbacks by maintaining vegetation function during extended
dry periods, supporting transpiration and moderating the impact of droughts
and heatwaves (Marchionni et al., 2020; Miller et al., 2010). Where the
water table is relatively shallow, capillarity may bring water from the
groundwater towards the surface root zone, increasing plant water
availability. Where the water table is deeper, phreatophytic vegetation with
tap roots can directly access groundwater (Zencich et al., 2002). The
presence of groundwater and the access to groundwater by vegetation are therefore likely to buffer vegetation drought and heatwave stress. For
example, groundwater may help vegetation sustain transpiration and
consequently cool plant canopies via evaporation. This is particularly
critical during compound events where cessation of transpiration would
increase the risk of impaired physiological function and the likelihood that
plants would exceed thermal limits and risk mortality (Geange et al., 2021;
O'sullivan et al., 2017; Sandi et al., 2020).
Quantifying the influence of groundwater on vegetation function has remained
challenging as concurrent observations of groundwater dynamics, soil
moisture, and energy and water fluxes are generally lacking over most of
Australia and indeed many parts of the world. Land surface models (LSMs)
provide an alternative tool for studying the interactions between
groundwater, vegetation, and surface fluxes in the context of heatwaves and
droughts (Gilbert et al., 2017; Martinez et al., 2016a; Maxwell et al.,
2011; Shrestha et al., 2014). However, there has been very little work
focused on the influence of groundwater on droughts and heatwaves occurring
at the same time (Keune
et al., 2016; Zipper et al., 2019). Our key goal in this paper is therefore
to examine the timescales and extent to which vegetation utilises
groundwater during drought and heatwaves and determine the degree to which
groundwater can mitigate the impacts of compound extremes. We focus on
droughts and heatwaves occurring over south-eastern (SE) Australia during
2000–2019 using the Community Atmosphere Biosphere Land Exchange (CABLE)
LSM. SE Australia is an ideal case study since its forest and woodland
ecosystems are known to be dependent on groundwater (Eamus and Froend, 2006;
Kuginis et al., 2016; Zencich et al., 2002), and it has experienced two
multi-year droughts and record-breaking heatwaves over the last 2 decades.
By examining the role of groundwater in influencing droughts and heatwaves
and by understanding how well CABLE can capture the relevant processes, we
aim to build confidence in the simulations of land–atmosphere interactions
for future droughts and heatwaves.
MethodsStudy area
The climate over SE Australia varies from humid temperate near the coast
to semi-arid in the interior. In the last 20 years, SE Australia
experienced the 9-year Millennium Drought during 2001–2009 (van Dijk et
al., 2013), where rainfall dropped from a climatological average (1970–1999)
of 542 to 449 mm yr-1, and a 3-year intense recent drought
during 2017–2019 where rainfall dropped to 354 mm yr-1 (Fig. S1). It
has also suffered record-breaking summer heatwaves in 2009, 2013, 2017, and
2019 (Bureau of Meteorology, 2013, 2017, 2019; National Climate Centre,
2009). Here we investigate groundwater interactions during the period
2000–2019, focusing on the Millennium Drought (MD, 2001–2009) and the
recent drought (RD, 2017–2019).
Overview of CABLE
CABLE is a process-based LSM that simulates the interactions between
climate, plant physiology, and hydrology (Wang et al., 2011). Above ground,
CABLE simulates the exchange of carbon, energy, and water fluxes, using a
single-layer, two-leaf (sunlit and shaded) canopy model (Wang and Leuning,
1998), with a treatment of within-canopy turbulence (Raupach, 1994; Raupach
et al., 1997). CABLE includes a six-layer soil model (down to 4.6 m) with soil
hydraulic and thermal characteristics dependent on the soil type and soil
moisture content. CABLE has been extensively evaluated (e.g. Abramowitz et
al., 2008; Wang et al., 2011; Zhang et al., 2013) and benchmarked
(Abramowitz, 2012; Best et al., 2015) at global and regional scales.
Here we adopt a version of CABLE (Decker, 2015; Decker et al., 2017) which
includes a dynamic groundwater component with aquifer water storage. This
version, CABLE-GW, has been previously evaluated by Decker (2015), Ukkola et
al. (2016b), and Mu et al. (2021) and shown to perform well for simulating
water fluxes. CABLE code is freely available upon registration
(https://trac.nci.org.au/trac/cable/wiki, last access: 29 August 2021); here we use CABLE SVN revision
7765.
Hydrology in CABLE-GW
The hydrology scheme in CABLE-GW solves the vertical redistribution of soil
water via a modified Richards equation (Zeng and
Decker, 2009):
∂θ∂t=-∂∂zK∂∂zΨ-ΨE-Fsoil,
where θ is the volumetric water content of the soil (mm3 mm-3), K is the hydraulic conductivity (mm s-1), z is the
soil depth (mm), Ψ and ΨE are the soil matric potential (mm)
and the equilibrium soil matric potential (mm), and Fsoil is a sink
term related to subsurface runoff and transpiration (s-1)
(Zeng and Decker, 2009; Decker, 2015). To simulate
groundwater dynamics, an unconfined aquifer is added to the bottom of the
soil column with a simple water balance model:
dWaqdt=qre-qaq,sub,
where Waq is the mass of water in the aquifer (mm), qaq,sub is the
subsurface runoff in the aquifer (mm s-1), and qre is the water
flux between the aquifer and the bottom soil layer (mm s-1) computed
by the modified Darcy's law:
qre=(Kaq+Kbot)2Ψaq-Ψn-ΨE,aq-ΨE,nzwtd-zn,
where Kaq and Kbot are the hydraulic conductivity in the aquifer
and in the bottom soil layer (mm s-1), Ψaq and ΨE,aq
are the soil matric potentials for the aquifer (mm), and Ψn and ΨE,n are the soil matric potentials for the bottom soil layer (mm).
zwtd and zn are the depth of the water table (mm) and the lowest
soil layer (mm), respectively. The positive qre refers to the downward
water flow from soil column to aquifer (i.e. vertical drainage, Dr), and the
negative qre is the upward water movement from the aquifer to the soil column
(i.e. recharge, Qrec). CABLE-GW assumes that the groundwater aquifer sits above
impermeable bedrock, giving a bottom boundary condition of
qout=0.
CABLE-GW computes the subsurface runoff (qsub, mm s-1) using
qsub=sindzdl‾q^sube-zwtdfp,
where dzdl‾ is the mean subgrid-scale slope,
q^sub is the maximum rate of subsurface drainage (mm s-1), and
fp is a tunable parameter. qsub is generated from the aquifer and
the saturated deep soil layers (below the third soil layer).
Experiment design
To explore how groundwater influences droughts and heatwaves, we designed
two experiments, with and without groundwater dynamics, driven by the same
3 h time-evolving meteorology forcing and the same land surface
properties (see Sect. 2.5 for datasets) for the period 1970–2019 at a
0.05∘ spatial resolution with a 3 h time step. To correct a
tendency for high soil evaporation, we implemented a parameterisation of
soil evaporation resistance that has previously been shown to improve the
model (Decker et al., 2017; Mu et al., 2021).
Groundwater experiment (GW)
This simulation uses the default CABLE-GW model, which includes the
unconfined aquifer to hold the groundwater storage and simulates the water
flux between the bottom soil layer and the aquifer. We first ran the default
CABLE-GW with fixed CO2 concentrations at 1969 levels for 90 years by
looping the time-evolving meteorology forcing over 1970–1999. At the end of
the 90-year spin-up, moisture in both the soil column and the groundwater
aquifer reached an effective equilibrium when averaged over the study area.
We then ran the model from 1970 to 2019 with time varying CO2. We omit
the first 30 years of this period and analyse the period 2000–2019 to allow
for further equilibrium with the time-evolving CO2.
Free drainage experiment (FD)
Many LSMs, including those used in the Coupled Model Intercomparison Project
5 (CMIP5), still use a free drainage assumption and neglect the
parameterisation of the unconfined aquifer. To test the impact of this
assumption we decoupled the aquifer from the bottom soil layer and thus
removed the influence of groundwater dynamics (experiment FD). In FD, at the
interface between the bottom soil layer and the aquifer, soil water can only
move downwards as vertical drainage at the rate defined by the bottom soil
layer's hydraulic conductivity:
qre=Kbot.
This vertical drainage is added to the subsurface runoff flux:
qsub=qsub+qre.
The simulated water table depth (WTD, i.e. zwtd) in CABLE-GW affects
the water potential gradient between the soil layers via ΨE
(Zeng and Decker, 2009) and impacts qsub
(Eq. 5). However, in FD, decoupling the soil column from the aquifer
and adding vertical drainage directly to subsurface runoff causes an
artificial and unrealistic decline in WTD. To solve this problem, we assume
a fixed WTD in the FD simulations at 10 m in order to remove this artefact
from the simulation of ΨE and qsub.
The FD simulations are initialised from the near-equilibrated state at the
end of the 90-year spin-up used in GW. The period 1970–2019 is then
simulated using varying CO2 and the last 20 years are used for
analysis.
Deep root experiment (DR)
The parameterisation of roots, including the prescription of root parameters
in LSMs, is very uncertain (Arora and Boer, 2003;
Drewniak, 2019) and LSMs commonly employ root distributions that are too
shallow (Wang and Dickinson, 2012). The
vertical distribution of roots influences the degree to which plants can
utilise groundwater, and potentially the role groundwater plays in
influencing droughts and heatwaves. To explore the uncertainty associated
with root distribution, we added a “deep root” (DR) experiment by
increasing the effective rooting depth in CABLE for tree areas. In common
with many LSMs, CABLE-GW defines the root distribution following
Gale and Grigal (1987):
froot=1-βrootz,
where froot is the cumulative root fraction (between 0 and 1) from the
soil surface to depth z (cm), and βroot is a fitted parameter
specified for each plant functional type (PFT)
(Jackson et al., 1996). In CABLE, the tree areas in our study region are simulated as
evergreen broadleaf PFT (Fig. S2a) with a βroot= 0.962,
implying that only 8 % of the simulated roots are located below the soil
depth of 0.64 m (Fig. S2b). However, field observations
(Canadell et al., 1996; Eberbach and Burrows, 2006; Fan et al., 2017; Griffith et al.,
2008) suggest that the local trees tend to have a far deeper root system,
possibly to help cope with the high climate variability. We therefore
increased βroot for the evergreen broadleaf PFT to 0.99, which
assumes 56 % of roots are located at depths below 0.64 m and 21 % of
roots below 1.7 m (Fig. S2b). This enables the roots to extract larger
quantities of deep soil water moisture, which is more strongly influenced by
groundwater.
Given that we lack the detailed observations to set root distributions across
SE Australia, we undertake the DR experiment as a simple sensitivity
study. We only run this experiment during January 2019, when the
record-breaking heatwaves compound with the severe recent drought. The DR
experiment uses identical meteorology forcing and land surface properties as
GW and FD and is initialised by the state of the land surface on the
31 December 2018 from the GW experiment.
Datasets
Our simulations are driven by the atmospheric forcing from the Australian
Water Availability Project (AWAP), which provides daily gridded data
covering Australia at 0.05∘ spatial resolution
(Jones et al., 2009). This dataset has
been widely used to force LSMs for analysing the water and carbon balances
in Australia (Haverd
et al., 2013; De Kauwe et al., 2020; Raupach et al., 2013; Trudinger et al.,
2016). The AWAP forcing data include observed fields of precipitation, solar
radiation, minimum and maximum daily temperatures, and vapour pressure at 09:00 and 15:00. Since AWAP forcing does not include wind and air pressure we
adopted the near-surface wind speed data from
McVicar et al. (2008) and assume a
fixed air pressure of 1000 hPa. Due to missing observations before 1990, the
solar radiation input for 1970–1989 was built from the 1990–1999 daily
climatology. Similarly, wind speeds for 1970–1974 are built from the
30-year climatology from 1975 to 2004. We translated the daily data into
3-hourly resolution using a weather generator (Haverd et al., 2013).
The land surface properties for our simulations are prescribed based on
observational datasets. Land cover type is derived from the National Dynamic
Land Cover Data of Australia (DLCD)
(https://www.ga.gov.au/scientific-topics/earth-obs/accessing-satellite-imagery/landcover, last access: 29 August 2021).
We classify DLCD's land cover types to five CABLE PFTs: crop
(irrigated/rainfed crop, pasture, and sugar DLCD classes), broadleaf
evergreen forest (closed/open/scattered/sparse tree), shrub
(closed/open/scattered/sparse shrubs and open/scattered/sparse chenopod
shrubs), grassland (open/sparse herbaceous), and barren land (bare areas). We
then resample the DLCD dataset from the 250 m resolution to the
0.05∘ resolution. The leaf area index (LAI) in CABLE is prescribed
using a monthly climatology derived from the Copernicus Global Land Service
product (https://land.copernicus.eu/global/products/lai, last access: 29 August 2021). The climatology
was constructed by first creating a monthly time series by taking the
maximum of the 10 daily time steps each month and then calculating a
climatology from the monthly data over the period 1999–2017. The LAI data
were resampled from the original 1 km resolution to the 0.05∘
resolution following De Kauwe et al. (2020). Soil parameters are derived from the soil texture information
(sand, clay, and silt fraction) from SoilGrids
(Hengl et
al., 2017) via the pedotransfer functions in Cosby et al. (1984) and
resampled from 250 m to 0.05∘ resolution.
To evaluate the model simulations, we use monthly total water storage
anomaly (TWSA) at 0.5∘ spatial resolution from the Gravity
Recovery and Climate Experiment (GRACE) and GRACE Follow On products
(Landerer et al., 2020; Watkins et al., 2015; Wiese et al., 2016, 2018). The RLM06M
release is used for February 2002–June 2017 and for June 2018–December 2019. We also use total land evapotranspiration from the 2000–2018 monthly
Derived Optimal Linear Combination Evapotranspiration (DOLCE version 2,
Hobeichi et al., 2021a) at 0.25∘ resolution, as well as the
2000–2019 daily Global Land Evaporation Amsterdam Model (GLEAM version 3.5,
https://www.gleam.eu/;
Martens et al., 2017; Miralles et al., 2011) at 0.5∘ spatial resolution.
For daytime land surface temperature (LST) we use the Moderate Resolution
Imaging Spectroradiometer (MODIS) datasets from Terra and Aqua satellites
(products MOD11A1 and MYD11A1, Wan and Li,
1997; Wan et al., 2015a, b) at 1 km spatial resolution. We only consider pixels
and time steps identified as good quality (QC flags 0). Only the daytime
LST values are used due to the lack of good-quality nighttime LST data. The
Terra overpass occurs at 10:00 and Aqua at 14:00 local time. To analyse the
compound events in January 2019, we linearly interpolate the 3-hourly model
outputs to 14:00 to match the overpass time of the Aqua LST. The GRACE, GLEAM, and MODIS datasets were resampled to the AWAP resolution using bilinear
interpolation.
To evaluate model performance during heatwaves, we identify heatwave events
using the excess heat factor index (EHF, Nairn
and Fawcett, 2014). EHF is calculated using the daily AWAP maximum
temperature, as the product of the difference of the previous 3 d mean to
the 90th percentile of the 1970–1999 climatology and the difference of the
previous 3 d mean to the preceding 30 d mean. A heatwave occurs when the
EHF index is greater than 0 for at least 3 consecutive days. We only
focus on summer heatwaves occurring between December and February of the
following year.
ResultsSimulations for the Millennium Drought and the recent drought
Previous studies have shown that simulations by LSMs diverge as the soil
dries (Ukkola et al., 2016a),
associated with systematic biases in evaporative fluxes and soil moisture
states in the models (Mu
et al., 2021; Swenson and Lawrence, 2014; Trugman et al., 2018). We
therefore first evaluate how well CABLE-GW captures the evolution of
terrestrial water variability during two recent major droughts.
Figure 1a shows the total water storage anomaly during 2000–2019 observed
by GRACE and simulated in GW and FD. Both GW and FD accurately capture the
interannual variability in total water storage for SE Australia (r= 0.96 in GW and 0.90 in FD). Both model configurations simulate a decline in
TWSA through the first drought period (up to 2009, see Fig. S1), the rapid
increase in TWSA from 2010 associated with higher rainfall, a decline from
around 2012 due to the re-emergence of drought conditions, and the rapid
decline during the recent drought after conditions had eased in 2016 (Fig. S1). FD underestimates the magnitude of monthly TWSA variance (standard
deviation, SD = 37.18 mm) compared to GRACE (47.74 mm) or GW (47.67 mm),
particularly during the wetter periods (2000, 2011–2016) and the first
∼2 years of the droughts (2001–2002, 2017–2018) (Fig. 1a). This
underestimation in FD compared to GW is linked to the lack of aquifer
water storage in the FD simulations, which provides a reservoir of water that
changes slowly and has a memory of previous wet/dry climate conditions
(Fig. 1a).
(a) Total water storage anomaly (TWSA) during 2000–2019 and (b)
accumulated P–E for the two droughts over SE Australia. In panel (a),
observations from GRACE are shown in black, the GW simulation in green, FD
in orange, and the aquifer water storage anomaly in GW in blue. The shading
in panel (a) highlights the two drought periods. The left top corner of
panel (a) displays the correlation (r) between GRACE and GW/FD, as well as
the standard deviation (SD, mm) of GRACE, GW, and FD over the periods when
GRACE and the simulations coincide. Panel (b) shows the accumulated P–E for
two periods; the dark lines show the 2001–2009 Millennium Drought (MD) and
the light lines show the 2017–2019 recent drought (RD). The correlation (r)
between the P and E is shown in the legend of panel (b).
Figure 1b shows the accumulated precipitation (P) minus evapotranspiration
(E) over the two drought periods. GW increases the evapotranspiration
relative to FD such that the accumulated P–E decreases from about 786 to
455 mm during the Millennium Drought, which is within the range of DOLCE
(460 mm) and GLEAM (97 mm) estimates. A similar result, although over a much
shorter period, is also apparent for the recent drought (Fig. 1b). The
lower P–E in GW suggests that the presence of groundwater storage can
alleviate the vegetation water stress during droughts and reduces the
reliance of E on P, indicated by a small reduction in the correlation (r)
between E and P from 0.28 in FD to 0.24 in GW for MD and a reduction from
0.42 to 0.37 for RD (Fig. 1b). Although the evapotranspiration products
display some differences, the GW simulations are closer overall to both the
DOLCE and the GLEAM observationally constrained estimates. The better match
of GW than FD to the two evapotranspiration products implies that adding
groundwater improves the simulations during droughts, whilst the remaining
mismatch would tend to suggest further biases in simulated
evapotranspiration arising from multiple sources (e.g. a mismatch in leaf
area index or contributions from the understorey). The difference in E is
also demonstrated spatially in Fig. S3. During the Millennium Drought, the
GW simulations show a clear improvement over FD in two aspects. GW shows
smaller biases in E along the coast where FD underestimates E strongly
(Fig. S3b–c). The areas where E is underestimated are also smaller in
extent in GW, suggesting that GW overall reduces the dry bias. The magnitude
of the bias in GW reaches around 300 mm over small areas of SE Australia, while in the FD simulations biases are larger, reaching 400 mm over a larger
area. Plant photosynthesis assimilation rates are associated with
transpiration via stomata conductance. Figure S4 presents the spatial maps
of gross primary productivity (GPP) during the two droughts. GW simulations
increase carbon uptake by 50–300 g C yr-1 along
the coasts (Fig. S4c, f). However, since CABLE uses a prescribed LAI and
does not simulate any feedback between water availability and plant growth
(e.g. defoliation) and its impact on GPP, we only focus on how GW
influences evapotranspiration and the surface energy balance in the
subsequent sections.
Overall, Figs. 1 and S3 indicates that representing groundwater in the
model improves the simulation of the interannual variability in the
terrestrial water cycle and storage, particularly during droughts.
The role of groundwater in sustaining evapotranspiration during droughts
We next explore the mechanisms by which including groundwater modifies the
simulation of evapotranspiration. Figure 2 displays the overall influence of
groundwater on water fluxes during the recent drought. GW simulates 50–200 mm yr-1 more E over coastal regions where there is high tree cover
(Fig. 2a; see Fig. S2a for land cover). Adding groundwater also
increases E in most other regions, although the impact is negligible in many
inland and non-forested regions (i.e. west of 145∘ E). We
identified a clear connection between E (Fig. 2a) and the simulated WTD in
the GW simulations (Fig. S5). GW simulates 110 mm yr-1 more E when
the WTD is shallower than 5 m deep, 22 mm yr-1 when the WTD is 5–10 m
deep, but only 3 mm yr-1 more when the WTD is below 10 m. Higher
transpiration (Et) in GW explains 78 % of the evapotranspiration
difference between GW and FD where WTD is shallower than 5 m (Fig. 2b).
This is confirmed by the change in the soil evaporation (ΔEs)
(Fig. 2c) where adding groundwater increases Es by negligible amounts over
most of SE Australia but by up to 25 mm yr-1 in regions underlain by
shallow groundwater (Fig. S5), which is consistent with field observations
that indicate that Es can be substantial under conditions of a very shallow
water table (Thorburn et al.,
1992). In the very shallow WTD areas, the excess Es in GW results from the
capillary rise of moisture from the shallow groundwater to the surface.
The overall influence of groundwater during the recent drought.
Panels (a)–(e) are the difference (GW–FD) in evapotranspiration (ΔE),
transpiration (ΔEt), soil evaporation (ΔEs), vertical
drainage (ΔDr) and recharge from the aquifer to soil column (ΔQrec), respectively. In the bottom right of panels (a)–(e), the average of
each variable over selected water table depths (WTDs) is provided. Panel (f) is the
maximum nighttime water stress factor difference (Δβ)
between 03:00 (i.e. predawn when the soil is relatively moist following
capillary lift overnight) and 21:00 the previous day. We only include
rainless nights in January 2019 to calculate Δβ to remove any
influence of overnight rainfall.
A significant factor in explaining how groundwater influences E is through
changes in vertical drainage and recharge from the aquifer to the soil
column. Figure 2d shows that the vertical drainage (Dr) both increases and
decreases depending on the location. The addition of groundwater reduces
vertical drainage by 74 mm yr-1 where WTD is shallower than 5 m. In
some regions, the drainage increases with the inclusion of groundwater by up
to 100 mm yr-1, especially in the areas where WTD is ∼5 m. This is associated with the WTD being slightly below the bottom of the
soil column (4.6 m). When the groundwater aquifer is nearly full in GW, the
wetter soil in the bottom layer leads to a much higher hydraulic
conductivity in GW than in FD, leading to higher vertical drainage in GW and
a positive ΔDr. Inland, where the WTD tends to be much deeper there
is no significant difference in Dr between GW and FD.
Figure 2e shows the difference in recharge into the upper soil column
(ΔQrec) between GW and FD. The recharge from the aquifer into the
bottom soil layer provides 17 mm yr-1 of extra moisture in GW, where the WTD is between 5–10 m, and 10 mm yr-1 where the WTD is < 10 m, partially explaining the changes in E and Et in areas with a deep WTD. However,
there is no significant ΔQrec in regions with a shallow WTD
(∼5 mm yr-1), suggesting the influence of groundwater is
mainly via reduced drainage in these locations. Recharge from the aquifer to
the soil column can only occur when WTD is below the soil column (bottom
boundary at 4.6 m depth). If WTD is shallow and within the soil column, the
interface is saturated and no recharge from the aquifer to the soil column
can occur and water only moves downwards by gravity.
The combined impact of reduced drainage in GW (Fig. 2d) and recharge from
the aquifer into the root zone (Fig. 2e) is an increased water potential
gradient between the drier top soil layers and the wetter deep soil layers,
encouraging overnight capillary rise. Taking the hot and dry January 2019 as
an example, when the compound events occurred, Fig. 2f shows the maximum
water stress factor difference (Δβ) overnight (between 21:00 and 03:00, i.e. predawn when soil is relatively moist following capillary
lift overnight). We only consider rainless nights to exclude the impact of
drainage induced by precipitation. The water stress factor (β) is
based on the root distribution and moisture availability in each soil layer
and represents the soil water stress on transpiration as water becomes
limiting. Figure 2f implies that while the redistribution of moisture is
small overall, in some locations it can reduce moisture stress by up to
4 %–6 %.
The impact of groundwater during heatwaves
We next explore whether the higher available moisture due to the inclusion
of groundwater enables the canopy to cool itself via evapotranspiration
during heatwaves by examining the temperature difference between the
simulated canopy temperature (Tcanopy, ∘C) and the forced air
temperature (Tair, ∘C). We focus on the forested regions (Fig. S2a) as the role of groundwater in enhancing plant water availability was
shown to be largest in these regions (Fig. 2).
Figure 3a shows the average Tcanopy-Tair (ΔT,
∘C) over the forested regions for summer heatwaves from the
GW and FD simulations, with the grey line indicating the median ΔT
difference. During heatwaves, the inclusion of groundwater moistens the soil
and supports higher transpiration, cooling the canopy and reducing ΔT relative to FD by up to 0.76 ∘C (e.g. summer heatwaves in
2013). As the drought lengthens in time, the depletion of moisture gradually
reduces this effect, from an average reduction of 0.52 ∘C of the
first 3 years to 0.16 ∘C of the last 3 years in the Millennium Drought
(Fig. 3a). The impact of groundwater is clear in the evaporative fraction
(Fig. 3b) where in periods of higher rainfall (e.g. 2010–2011; Fig. S1) and at the beginning of a drought (2001, 2017) the evaporative fraction (EF) is higher (0.03
to 0.18). This implies that more of the available energy is exchanged with the
atmosphere in the form of latent rather than sensible heat. However, the
strength of the cooling effect decreases as the droughts extend and the
transpiration difference (ΔEt, mm d-1) diminishes quickly
(Fig. 3c) because the vegetation becomes increasingly water-stressed
(Fig. 3d) which consequently limits transpiration. For all variables
(ΔT, EF, Et, and β), the difference between GW and FD is
greatest during the wetter periods (e.g. 2013) and in the first 1–2 years
of the multi-year drought (2001–2002 for the Millennium Drought or
2017–2018 for the recent drought). After the drought becomes well
established, the FD and GW simulations converge as depleting soil moisture
reservoirs reduce the impact of groundwater on canopy cooling and
evaporative fluxes.
Groundwater-induced differences in (a)Tcanopy-Tair
(ΔT), (b) evaporative fraction (EF), (c) transpiration (Et), and (d) water stress factor (β) during 2000–2019 summer heatwaves over
forested areas (the green region in Fig. S2a). The left y axis is the
scale for boxes. The blue boxes refer to the GW experiment and the red boxes
to FD. For each box, the middle line is the median, the upper border is the
75th percentile, and the lower border is 25th percentile. The
right y axis is the scale for the grey lines which display the difference in
the medians (GW–FD). The shadings highlight the two drought periods.
Land response to heatwaves during the recent drought. Panels
(a)–(c) are the mean Tcanopy-Tair (ΔT), evaporative
fraction (EF), and soil water stress factor (β) in GW, respectively,
during 2017–2019 summer heatwaves. Panels (d)–(f) are the difference (GW–FD)
of Tcanopy, EF, and β. In the bottom right of each plot, the
average of each variable over selected water table depths (WTDs) is provided.
Note that the colour bar is switched between (d) and (e)–(f).
Figure 4a shows the spatial map of ΔT simulated in GW during
heatwaves in the 2017–2019 drought. It indicates that both land cover type
(Fig. S2a) and WTD (Fig. S5) contribute to the ΔT pattern. The
evaporative cooling via transpiration is stronger over the forested areas
compared to cropland or grassland and stronger in the regions with a wetter
soil associated with a shallower WTD. However, EF is mainly determined by
WTD (compare Figs. 4b and S5). Inland, where the WTD is deeper and
the soil is drier, most of the net radiation absorbed by the land surface is
partitioned into sensible rather than latent heat (Fig. 4b). However, in
the coastal regions with a shallow WTD, the wetter soil reduces the water
stress (Fig. 4c), enables a higher EF (Fig. 4b), and alleviates heat
stress on the leaves (Fig. 4a). Along the coast where WTD is shallow, GW
simulates a cooler canopy temperature due to the higher evaporative cooling
(Fig. 4e), which is the consequence of a lower soil water stress (Fig. 4f) linked to the influence of groundwater (Fig. S5).
Figure 5 shows the density scatter plot of ΔT versus WTD in SE
Australia forested areas during heatwaves in 2000–2019. A shallow WTD
moderates the temperature difference between the canopy and the ambient air
during heatwaves leading to a smaller temperature difference. Meanwhile, as
the WTD increases, due to the limited rooting depth in the model, the
ability of the groundwater to support transpiration and offset the impact of
high air temperatures is reduced. Figure 5 shows a large amount of
variations but nonetheless implies a threshold of ∼6 m, whereafter there is a decoupling and little influence from groundwater
during heatwaves. However, the absolute value of the threshold is likely
CABLE-specific and associated with the assumption of a 4.6 m soil depth,
which also sets the maximum rooting depth (roots can only extend to the
bottom of the soil and cannot directly access the groundwater aquifer in
CABLE). The CABLE soil depth comes from observational evidence of most roots
being situated within the top 4.6 m (Canadell et al. 1996). Since the model
assumes no roots exist in the groundwater aquifer, when the water table is
below this depth, the water fluxes become largely uncoupled between the soil
column and the groundwater aquifer, leading to a negligible impact of GW
below ∼6 m depth.
A density scatter plot of Tcanopy-Tair (ΔT)
versus water table depth (WTD) in GW simulations over forested areas in all
heatwaves during 2000–2019. Every tree pixel on each heatwave day accounts
for one record, and the darker colours show higher recorded densities.
The impact of groundwater during the drought and heatwave compound events
To examine the influence of groundwater on heatwaves occurring simultaneously
with drought, we focus on a case study of the record-breaking heatwaves in
January 2019, which is the hottest month on record for the study region
(Bureau of Meteorology, 2019). The
unprecedented prolonged heatwave period started in early December 2018 and
continued through January 2019 with three peaks. We select 2 d (15 and 25 January 2019), when heatwaves spread across the
study region, from the second and third heatwave phases (Fig. S6).
We evaluate CABLE Tcanopy against MODIS LST observations, concentrating
on forested areas where MODIS LST should more closely reflect vegetation
canopy temperatures, but note that this comparison is not direct as the
satellite estimate will contain contributions from the understorey and soil.
Figure 6a–b show the good-quality MODIS LST minus Tair at 14:00 (ΔTMOD_14:00) over forested regions on the
15 and 25 January 2019, and Fig. 6c–d display the matching
GW-simulated ΔT at 14:00 (ΔTGW_14:00).
Overall, ΔTGW_14:00 increases from the coast to
the interior in both heatwaves, consistent with the ΔTMOD_14:00 pattern in both heatwaves, although ΔTGW_14:00 appears to be biased high relative to ΔTMOD_14:00 along the coastal forests (Fig. S7a–b).
The simulation of two heatwaves on 15 (left column) and
25 January 2019 (right column). The first row shows the difference
between MODIS land surface temperature (LST) and Tair at 14:00 (ΔTMOD_14:00) (only forested areas with good LST quality
data are displayed). The second row is the GW simulation of ΔT at 14:00 (ΔTGW_14:00). The third row is the difference
of ΔT at 14:00 between GW and FD simulations (ΔTGW_14:00-ΔTFD_14:00). The
last row is the same as the third row but for the difference between the DR
and GW simulations (ΔTDR_14:00-ΔTGW_14:00). Note that the comparison between GW/FD/DR and
MODIS LST is shown in Fig. S7.
Figure 6e–f show the ΔT14:00 difference between GW and FD.
Access to groundwater can reduce canopy temperature by up to
5 ∘C, in particular where the WTD is shallow. While
reductions of 5 ∘C are clearly limited in spatial extent,
the overall pattern of cooling is quite widespread and coincident with the
groundwater-induced Et increase (Fig. S8a–b), implying a reduction in heat
stress along coastal regions with a shallow WTD during heatwaves. Generally,
GW matches MODIS LST better than FD despite the bias in both simulations
(compare Figs. S7a–b and S7c–d). Nevertheless, the temperature
reduction between GW and FD is still modest (< 1 ∘C)
for most of the forested regions. This may be related to the shallow root
distribution assumed in many LSMs, which prevents roots from directly
accessing the moisture stored in the deeper soil (note, CABLE assumes 92 %
of a forest's roots are in the top 0.64 m, Fig. S2b). To examine this
possibility, we performed the deep root (DR) sensitivity experiment which
prescribed more roots in the deeper soil for forests (56 % below 0.64 m
depth). Figure 6g–h illustrate the difference between ΔT14:00 in
DR and ΔTGW_14:00. By enabling access to moisture
in the deeper soil, the LSM simulates further cooling by 0.5–5 ∘C
across the forests associated with an Et increase of 25–250 W m-2
(Fig. S8c–d). The prescribed deeper roots also lead to an overall better
simulation of ΔT at 20:00 relative to the MODIS LST (Figs. S7e–f vs. Fig. S7a–b).
Figure 7 shows the diurnal cycles of ΔT for the two selected regions
(red boxes in Fig. 6) compared with the MODIS LST estimates. The region
highlighted for the 15 January (Fig. 7a) has a WTD of 4–7 m, while
the region highlighted for the 25 January (Fig. 7b) has a WTD < 4 m (Fig. S5). In both regions, the simulated ΔT is
highest in FD, lower in GW and lowest in DR. Where the WTD is 4–7 m (Fig. 7a), the three simulated ΔT are slightly lower than ΔT
calculated by MODIS LST (red squares). However, in the shallower WTD region
(Fig. 7b), the simulated ΔT between experiments is more dispersed
across experiments and exceeds the MODIS ΔT at both time points,
implying that neglecting groundwater dynamics and deep roots is more likely
to cause an overestimation of heat stress in the shallower WTD region. The
shallower WTD region (Fig. 7b) tends to have a high LAI coverage, implying
that the MODIS LST represents a good approximation of the canopy temperature
over this region. Consequently, the lower MODIS ΔT implies that
CABLE is likely underestimating transpiration, leading to an overestimation
of ΔT in all three simulations.
Diurnal cycle of ΔT on 15 (left column) and
25 January 2019 (right column) over the selected regions shown in
Fig. 6. The shadings show the uncertainty in every simulation defined as
one standard deviation (SD) among the selected pixels. The red dots are
MODIS LST minus Tair with the uncertainty shown by the red error bars.
For both regions, only pixels available in MODIS are shown.
Constraints on groundwater mediation during the compound events
We finally probe the reasons for the apparent contradiction between the
large impact of groundwater on E during drought (Fig. 2a) but a smaller
impact on ΔT during the compound events (Fig. 7). Figure 8 shows
three factors (β, vapour pressure deficit (D), and Tair) that constrain the impact of groundwater on ΔT in CABLE during heatwaves
in January 2019. Figure 8a shows the difference in ΔT between GW and
FD as a function of Δβ, suggesting that the inclusion of
groundwater has a large impact on ΔT when there is a coincidental
and large difference in β between the GW and FD simulations. Figure 8b
indicates a clear threshold at D= 3 kPa where GW and FD converge, while
Fig. 8c shows a convergence threshold when Tair exceeds
32 ∘C. Above these two thresholds, access to groundwater
seemingly becomes less important in mitigating plant heat stress. There are
two mechanisms in CABLE that explain this behaviour. First, as D increases,
CABLE predicts that stomata begin to close following a square root
dependence (De Kauwe et al., 2015; Medlyn et al., 2011). Second, as Tair increases,
photosynthesis becomes inhibited as the temperature exceeds the optimum for
photosynthesis. In both instances, evaporative cooling is reduced,
regardless of the root zone moisture state dictated by groundwater access.
That is to say, access to groundwater has limited capacity to directly
mediate the heat stress on plants during a compound event when the air is
very dry or very hot.
Density scatter plots showing the three factors that influence the
difference in Tcanopy-Tair between GW and FD (ΔT,
expressed as GW–FD difference). Panel (a) is ΔT difference against the
β difference (GW–FD) (Δβ), (b) is ΔT
difference against vapour pressure deficit (D), and (c) is ΔT
difference against Tair. Each point corresponds to a tree pixel on a
heatwave day in January 2019. The darker colours illustrate where the
records are denser. The correlation (r) between the x and y axes is
shown in the bottom left of each panel.
Discussion
In the absence of direct measurements, we used the CABLE-GW LSM, constrained
by satellite observations to investigate how groundwater influences
ecosystems under conditions of co-occurring droughts and heatwaves. We found
that the influence of groundwater was most important during the wetter
periods and the first ∼ 2 years of a multi-year drought
(∼2001–2002 and 2017–2018; Figs. 1 and 3). This primarily
occurred via impedance of gravity-driven drainage (Fig. 2d) but also via
capillary rise from the groundwater aquifer (Fig. 2e). This moistening
enabled the vegetation to sustain higher E for at least a year (Fig. S9).
As the droughts progressed into multi-year events, the impact of groundwater
diminished due to a depletion of soil moisture stores regardless of whether
groundwater dynamics were simulated.
When a heatwave occurs during a drought, and in particular early in a
drought, the extra transpiration enabled by representing groundwater
dynamics helps reduce the heat stress on vegetation (e.g. the reduction of
0.64 ∘C of ΔT over the forests in 2002, Fig. 3a).
This effect is particularly pronounced in regions with a shallower WTD (e.g.
where the groundwater was within the first 5 m, there was a
0.5 ∘C mean reduction in ΔT in the recent drought; Fig. 4d). Importantly, the role played by groundwater diminishes as the
drought lengthens beyond 2 years (Fig. 3). Additionally, either the lack
of deep roots or stomatal closure caused by high D/Tair can reduce the
additional transpiration induced by groundwater. The latter plant physiology
feedback dominates during heatwaves co-occurring with drought, even if the
groundwater's influence has increased root zone water availability.
Our results highlight the impact of groundwater on both land surface states
(e.g. soil moisture) and on surface fluxes and how this impact varies with
the length and intensity of droughts and heatwaves. The results imply that
the dominant mechanism by which groundwater buffered transpiration was
through impeding gravity-driven drainage. We found a limited role for upward
water movement from the aquifer due to simulated shallow WTD (which was broadly
consistent with the observations in Fan et al.,
2013). Further work will be necessary to understand how groundwater
interacts with droughts and heatwaves and what these interactions mean for
terrestrial ecosystems and the occurrence of the compound extreme events,
particularly under the projection of intensifying droughts
(Ukkola et al., 2020) and heatwaves
(Cowan et al., 2014).
Changes in the role of groundwater in multi-year droughts
Groundwater is the slowest part of the terrestrial water cycle to change
(Condon et al., 2020) and can have a memory
of multi-year variations in rainfall
(Martínez-de
la Torre and Miguez-Macho, 2019; Martinez et al., 2016a). Our results show
that the effect of groundwater on the partitioning of available energy
between latent and sensible heat fluxes is influenced by the length of
drought. As the drought extends in time, the extra E sustained by
groundwater decreases (e.g. during the Millennium Drought, Fig. S9). The
role of a drying landscape in modifying the partitioning of available energy
between latent and sensible heat fluxes is well known and has been
extensively studied
(Fan,
2015; Miralles et al., 2019; Seneviratne et al., 2010). Our results add to
the knowledge by quantifying the extent of the groundwater control, and
eliciting the timescales of influence and the mechanisms at play. The
importance of vegetation–groundwater interactions on multi-year timescales
has been identified previously.
Humphrey et al. (2018)
hypothesised that climate models may underestimate the amplitude of global
net ecosystem exchange because of a lack of deep-water access. Our regionally based results support this hypothesis and in particular highlight the
importance of groundwater for explaining the amplitude of fluxes in wet
periods as well as sustaining evapotranspiration during drought (Fig. 1).
Implications for land–atmosphere feedbacks during compound events
Our results show that during drought–heatwave compound events, the existence
of groundwater eases the heat stress on the forest canopy and reduces the
sensible heat flux to the atmosphere. This has the potential to reduce heat
accumulating in the boundary layer and help ameliorate the intensity of a
heatwave (Keune
et al., 2016; Zipper et al., 2019). The presence of groundwater helps dampen
a positive feedback loop whereby during drought–heatwave compound events,
the high exchange of sensible and low exchange of latent heat can heat the
atmosphere and increase the atmospheric demand for water
(De Boeck et
al., 2010; Massmann et al., 2019), intensifying drying
(Miralles et al., 2014). The lack of
groundwater in many LSMs suggests a lack of this moderating process and
consequently a risk of overestimating the positive feedback on the boundary
layer in coupled climate simulations. Our results show that neglecting
groundwater leads to an average overestimate in canopy temperature by
0.2–1 ∘C where the WTD is shallow (Fig. 4d) but as much as
5 ∘C in single heatwave events (Fig. 6e–f), leading to an
increase in the sensible heat flux (Fig. 4e).
The capacity of groundwater to moderate this positive land–atmosphere
feedback is via modifying soil water availability. Firstly, soil water
availability influenced by WTD affects how much water is available for E. In
the shallow WTD regions, the higher soil water is likely to suppress the
mutual enhancement of droughts and heatwaves
(Keune et al., 2016; Zipper et al., 2019), particularly early in a drought.
However, this suppression becomes weaker as the WTD deepens, in particular
at depths beneath the root zone (e.g. 4.6 m in CABLE-GW) or as a drought
lengthens. Our results imply that the land amplification of heatwaves is likely
stronger in the inland regions (Hirsch et al., 2019), where the WTD is lower than 5 m and the influence of groundwater diminishes
(Fig. S5), and once a drought has intensified significantly.
On a dry and hot heatwave afternoon, plant physiology feedbacks to high D
and high Tair dominate transpiration and reduce the influence of
groundwater in moderating heatwaves. In CABLE, stomatal closure occurs
either directly due to high D (> 3 kPa)
(De Kauwe et al. 2015) or indirectly
due to biochemical feedbacks on photosynthesis at high Tair
(> 32 ∘C) (Kowalczyk et al.,
2006); both processes reduce transpiration to near zero, eliminating the
buffering effect of groundwater on canopy temperatures. While the timing of
the onset of these physiology feedbacks varies across LSMs due to different
parameterised sensitivities of stomatal conductance to atmospheric demand
(Ball et al., 1987; Leuning et al., 1995) and different temperature dependence
parameterisations (Badger and
Collatz, 1977; Bernacchi et al., 2001; Crous et al., 2013), importantly,
stomatal closure during heat extremes would be model invariant.
Uncertainties and future directions
Our study uses a single LSM, and consequently the parameterisations included
in CABLE-GW influence the quantification of the role of groundwater on
droughts and heatwaves. We note CABLE-GW has been extensively evaluated for
water cycle processes (Decker,
2015; Decker et al., 2017; Mu et al., 2021; Ukkola et al., 2016b), but
evaluation for groundwater interactions remains limited due to the lack of
suitable observations (e.g. regional WTD monitoring or detailed knowledge of
the distribution of root depths). Figure 1 gives us confidence that CABLE-GW
is performing well, based on the evaluation against the GRACE, DOLCE, and
GLEAM products, as well as previous work that showed the capacity of
CABLE-GW to simulate E well (Decker, 2015; Decker et al.,
2017). However, we also note that key model parameterisations that may
influence the role of groundwater are particularly uncertain.
We need to be cautious about the “small” groundwater impact on the canopy
temperature and associated turbulent energy fluxes during high D or high
Tair (Figs. 3, 4, 6 and 7). The thresholds of D and Tair
currently assumed by LSMs are in fact likely to be species specific.
Australian trees in particular have evolved a series of physiological
adaptations to reduce the negative impact of heat extremes. It is important
to note that most LSMs parameterise their stomatal response to D for
moderate ranges (< 2 kPa), which leads to significant biases at high D (Yang et al., 2019), a feature
common in Australia and during heatwaves in general. New theory is needed to
ensure that models adequately capture the full range of stomatal response to
variability in D (low and high ranges). Similarly, while there is strong
evidence to suggest that the optimum temperature for photosynthesis does not
vary predictably with the climate of species origin
(Kumarathunge et al.,
2019) (implying model parameterisations do not need to vary with species),
findings from studies do vary (Cunningham and Reed, 2002; Reich et al., 2015).
Moreover, evidence that plants acclimate their photosynthetic temperature
response is strong
(Kattge
and Knorr, 2007; Kumarathunge et al., 2019; Mercado et al., 2018; Smith et
al., 2016; Smith and Dukes, 2013). As a result, it is likely that LSMs
currently underestimate groundwater influence during heatwaves due to the
interaction with plant physiology feedbacks. This is a key area requiring
further investigation. For example,
Drake
et al. (2018) demonstrated that during a 4 d heatwave > 43 ∘C, Australian Eucalyptus parramattensis trees did not reduce transpiration to zero as models
would commonly predict, allowing the trees to persist unharmed in a
whole-tree chamber experiment. Although
De Kauwe et al. (2019) did not find strong support for this phenomenon across eddy
covariance sites, if this physiological response is common across Australian
woodlands, it would change our view on the importance of soil water
availability (therefore groundwater) for the evolution of heatwave or even
compound events. Coupled model sensitivity experiments may be important to
determine the magnitude that such a physiological feedback would present and
could guide the direction of future field/manipulation experiments.
Root distribution and root function and thereby how roots utilise
groundwater are uncertain in models (Arora
and Boer, 2003; Drewniak, 2019; Wang et al., 2018; Warren et al., 2015) and
indeed in observations
(Fan et al., 2017; Jackson et al., 1996; Schenk and Jackson, 2002). Models often
ignore how roots forage for water and respond to moisture heterogeneity,
limiting the model's ability to accurately reflect the plant usage of
groundwater (Warren et al., 2015). In LSMs,
roots are typically parameterised using a fixed distribution and normally
ignore water uptake from deep roots. This assumption neglects any
climatological impact of root distribution and the differentiation in root
morphology and function (fine roots vs. tap roots), leading to a potential
underestimation of groundwater utilisation in LSMs (see our deep root
experiment, Fig. 6g–h). This assumption may be particularly problematic in
Australia where vegetation has developed significant adaptation strategies
to cope with both extreme heat and drought, including deeply rooted
vegetation that can access groundwater (Bartle
et al., 1980; Dawson and Pate, 1996; Eamus et al., 2015; Eberbach and
Burrows, 2006; Fan et al., 2017). We also note that CABLE does not directly
consider hydraulic redistribution, defined as the passive water movement via
plant roots from moister to drier soil layers (Burgess
et al., 1998; Richards and Caldwell, 1987). Neglecting hydraulic
redistribution has the potential to underestimate the groundwater
transported upwards and understate the importance of groundwater on
ecosystems.
On the atmosphere side, the existence of groundwater increases the water
flux from the land to the atmosphere, particularly in regions of shallow WTD,
during the first 1–2 years of a drought. This has the potential to moisten
the lower atmosphere and may encourage precipitation
(Anyah
et al., 2008; Jiang et al., 2009; Martinez et al., 2016b; Maxwell et al.,
2011). However, our experiments are uncoupled from the atmosphere so while
there is the potential for the higher E to affect the boundary layer
moisture (Bonetti
et al., 2015; Gilbert et al., 2017; Maxwell et al., 2007), clouds, and
precipitation, we cannot conclude that it would until we undertake future
coupled simulations.
Finally, we note we have focused on the role of groundwater in a natural
environment. Humans extract large quantities of groundwater in many regions
(Döll et al., 2014; Wada, 2016). Adding human management of groundwater
into LSMs enables an examination of how this affects the vulnerability of
ecosystems to heatwaves and drought and may ultimately identify those
vulnerable ecosystems close to tipping points that are priorities for
protection.
Conclusions
In conclusion, we used the CABLE LSM, constrained by satellite observations,
to explore the timescales and extent to which groundwater influences
vegetation function and turbulent energy fluxes during multi-year droughts.
We showed that groundwater moistened the soil during the first
∼ 2 years of a multi-year drought, which enabled the
vegetation to sustain higher evaporation (50–200 mm yr-1 over the
coastal forest regions) during drought onset. This cooled the forest canopy
on average by 0.03–0.76 ∘C in heatwaves during 2001–2019 and by as much
as 5 ∘C in regions of shallow water table depths in the heatwave in
January 2019, helping to moderate the heat stress on vegetation during
heatwaves. However, the ability of groundwater to buffer vegetation function
varied with the length and intensity of droughts and heatwaves, with its
influence decreasing with prolonged drought conditions. Importantly, we also
demonstrated that the capacity of the groundwater to buffer evaporative
fluxes during heatwaves is constrained by plant physiology feedbacks which
regulate stomatal control, irrespective of soil water status. Given the increased risk of regional heatwaves and droughts in the future, the role of
groundwater on land–atmosphere feedbacks and on terrestrial ecosystems needs
to be better understood in order to constrain future projections.
Code and data availability
The CABLE code is available at https://trac.nci.org.au/trac/cable (NCI, 2021) after registration. Here, we use CABLE
revision r7765. Scripts for plotting and processing model outputs are
available at 10.5281/zenodo.5158498 (Mu, 2021). The DOLCE
version 2 dataset is available from the NCI data catalogue at
10.25914/5f1664837ef06 (Hobeichi et al., 2021b). The GRACE dataset is available
at 10.5067/TEMSC-3JC62 (Wiese et al., 2019). The GLEAM version 3.5 dataset is available at https://www.gleam.eu/ (GLEAM, 2021). The datasets of MOD11A1 (10.5067/MODIS/MOD11A1.006, Wan et al., 2015a) and MYD11A1 (10.5067/MODIS/MYD11A1.006, Wan et al., 2015b) were acquired from the NASA Land Processed Distributed Active Archive Center (LP DAAC), located in the USGS Earth Resources Observation and Science (EROS) Center in Sioux Falls, South Dakota, USA (https://lpdaacsvc.cr.usgs.gov/appeears/, LP DAAC, 2021).
The supplement related to this article is available online at: https://doi.org/10.5194/esd-12-919-2021-supplement.
Author contributions
MM, MGDK, AJP, and AMU conceived the study, designed the model experiments,
investigated the simulations, and drafted the paper. SH and PRB provided
the evaluation and the meteorology forcing datasets. All authors
participated in the discussion and revision of the paper.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Special issue statement
This article is part of the special issue “Understanding compound weather and climate events and related impacts (BG/ESD/HESS/NHESS inter-journal SI)”. It is not associated with a conference.
Acknowledgements
Mengyuan Mu, Martin G. De Kauwe, Andy J. Pitman, Anna M. Ukkola, and Sanaa Hobeichi acknowledge support from the Australian Research Council (ARC)
Centre of Excellence for Climate Extremes (CE170100023). Mengyuan Mu
acknowledges support from the UNSW University International Postgraduate
Award (UIPA) scheme. Martin G. De Kauwe and Andy J. Pitman acknowledge
support from the ARC Discovery Grant (DP190101823) and Anna M. Ukkola
support from the ARC Discovery Early Career Researcher Award (DE200100086).
Martin G. De Kauwe acknowledges support from the NSW Research Attraction and
Acceleration Program (RAAP). We thank the National Computational
Infrastructure at the Australian National University, an initiative of the
Australian Government, for access to supercomputer resources. Mengyuan Mu
thanks the University of Nanjing for hosting her research through 2020.
Financial support
This research has been supported by the Australian Research Council (grant nos. CE170100023, DP190101823, and DE200100086) and the NSW Research Attraction and Acceleration Program.
Review statement
This paper was edited by Jakob Zscheischler and reviewed by three anonymous referees.
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