We describe developments to the land surface model JULES,
allowing for flexible user-prescribed harvest regimes of various perennial
bioenergy crops or natural vegetation types. Our aim is to integrate the
most useful aspects of dedicated bioenergy models into dynamic global
vegetation models, in order that assessment of bioenergy options can benefit
from state-of-the-art Earth system modelling. A new plant functional type
(PFT) representing Miscanthus is also presented. The Miscanthus PFT fits well with growth
parameters observed at a site in Lincolnshire, UK; however, global observed
yields of Miscanthus are far more variable than is captured by the model, primarily
owing to the model's lack of representation of crop age and establishment
time. Global expansion of bioenergy crop areas under a 2 ∘C
emissions scenario and balanced greenhouse gas mitigation strategy from the
IMAGE integrated assessment model (RCP2.6-SSP2) achieves a mean yield of 4.3 billion tonnes of dry matter per year over 2040–2099, around 30 % higher
than the biomass availability projected by IMAGE. In addition to perennial
grasses, JULES-BE can also be used to represent short-rotation coppicing,
residue harvesting from cropland or forestry and rotation forestry.
Introduction
A large supply of biomass energy, from diverse sources, is an essential
component of most strategies to avoid dangerous climate change (Rose et
al., 2013; Daioglou et al., 2019). Biomass is important both as a versatile
energy source (e.g. used for heat and electricity production and transport
fuels), and as part of bioenergy with carbon capture and storage (BECCS),
the most feasible mechanism by which large amounts of CO2 may be
actively removed from the atmosphere (Smith et al., 2015; Bauer et al.,
2017; Daioglou et al., 2019).
“Second-generation” bioenergy crops, comprising lignocellulosic perennial
grasses, tree species managed as short-rotation coppice and residues from
forestry and agriculture, are the assumed preferred candidates to meet
future biomass energy demand (Chum et al.,
2011). They are preferred over “first-generation” biofuels such as maize
and sugarcane which require higher nutrient inputs and have undesirable
interactions with the food production systems (since they are food crops and
must be grown on cropland; Tilman et al., 2009).
A wide range of estimates of future bioenergy supply exists, but most 2 ∘C or lower scenarios feature BECCS being rolled out at scale in
the next 10–20 years (Fuss et al., 2014; Clarke et al., 2014; Rogelj et
al., 2020), with bioenergy crops delivering 100–400 EJ yr-1
(primary energy) by 2100 (Huppmann et al., 2018). The impacts of
large-scale bioenergy production on the land surface and Earth system could
be significant, because changes to vegetation cover across the Earth can
change climate systems through biophysical effects such as changes to
albedo, evaporation and runoff or through biogeochemical effects like
disturbance or priming of soil carbon (Fontaine et al., 2004).
The importance of bioenergy expansion to future efforts to limit climate
change, combined with relative lack of understanding of its environmental
effects, strongly motivates further efforts to improve our understanding of
this process. Earth system modelling, a method by which we study many
aspects of global environmental change, provides a robust framework for
simulating and interrogating large-scale land-use change such as bioenergy
cropland expansion.
Dedicated bioenergy crop models may be used to project yields and responses
to environmental stressors at site or regional level (Robertson et
al., 2015). MISCANFOR (Hastings et al., 2009) is one example of a
Miscanthus growth model that has been applied at a global scale (Pogson et al.,
2013). These models tend to have simple or limited representation of soil
carbon cycling, hydrology and climate. Dynamic global vegetation models
(DGVMs), by contrast, are models specifically developed to address questions
about large-scale vegetation patterns and productivity, and their links with
the climate and Earth system (particularly as part of the Earth system
models of which they form the terrestrial components; Sitch et al., 2008). However, this
typically occurs at the expense of representation of specific plant species
and detailed site and management information. There are differences between
DGVMs (and ESMs) in representation of bioenergy crops and calculation of
harvests (Krause et al., 2018): although some feature explicit
representation of bioenergy crops and harvesting (e.g. LPJml,
Beringer et al., 2011; Boysen et al., 2016;
ORCHIDEE-MICT-BIOENERGY, Li et al., 2018b), others use
approximations based on generic plant functional types (PFTs) and calculate
harvests as a fixed proportion of productivity (e.g. NorESM,
Muri, 2018).
Currently the Joint UK Land Environment Simulator (JULES) uses generic C3
and C4 grasses to simulate bioenergy productivity, with harvest taken from
30 % of litter (Harper et al., 2018a). In this paper,
we describe new functionality developed within the JULES land surface model
to represent the growth and harvest cycles of specific perennial bioenergy
crops including lignocellulosic grasses (Miscanthus) and trees used in short-rotation
coppice regimes (poplar SRC), as well as forest management (Table 1),
hereafter called JULES-BE. JULES-BE represents the yield mechanistically by
removing the above-ground biomass, reducing the plant's height and leaf area
and allowing it to regrow. The parametrisation of a new PFT to represent
Miscanthus is also presented. The aim of these functional developments is to simulate
yields of biomass for energy feedstocks, and to evaluate the impacts of
bioenergy cropping on the global carbon cycle and climate system. Therefore,
this study fits best with the DGVM approach, which allows analysis of the
impacts of bioenergy on climate and land surface processes. JULES has been
used to model bioenergy systems before (Hughes et al., 2010; Black et al.,
2012; Oliver et al., 2015), at site level, but these approaches have not been
integrated into TRIFFID, the DGVM within JULES which links plant
productivity to soil carbon and the global carbon cycle. The improved
representation of harvesting and yield we present here is unique because it
facilitates the assessment of impacts of bioenergy crops on the carbon cycle
and climate system in a way that has not been shown before using the JULES
model.
Functionality and applications of JULES-BE.
FunctionSimulated applicationsContinuous harvestForest management (without felling) Biomass removal from agricultural landPeriodic harvestHarvests of perennial crops (e.g. Miscanthus) Short-rotation coppicing (e.g. poplar) Forestry rotationsAssisted expansionPlanting-out of new agricultural areas with bioenergy crops or treesTechnical developmentExisting model description
JULES is a community land surface model that can be run stand-alone (as
described here) or used as the land surface component of the Met Office's
Earth system models (Collins et al., 2011).
JULES is described in Best et al. (2011) and Clark et al. (2011). JULES
calculates the surface energy and water fluxes, along with gross and net
primary productivity, on a half-hourly or hourly time step. The net primary
productivity (NPP) for each PFT is accumulated during each time step, to be
later used for calculating changes in vegetation structure and coverage in
TRIFFID, the dynamic global vegetation model built into JULES. TRIFFID is
called at the end of a user-defined number of days (typically 1 or 10 d),
and the accumulated NPP is allocated between “growth” and “spreading”.
The former is used for increasing leaf area index (LAI) and canopy height,
while the latter is used to allow PFTs to take up more space in a grid cell.
Competition for space is determined based on PFT heights: the tallest plants
get first access to space in a grid cell, but may not be able to compete if
their NPP is too low.
In JULES, crops are represented in one of two ways. Major food crops such as
wheat, maize and soya are represented by the JULES-crop module
(Osborne et al., 2015). However, JULES-crop is suitable only
for annual seed crops, and is not compatible with TRIFFID and the wider
carbon cycle representation within JULES. Therefore, the TRIFFID-crop module
was developed to represent crops within the carbon cycle and climate system.
When the TRIFFID-crop option is enabled within JULES, multiple types of
agricultural land are represented separately. The user defines the fraction
of each grid cell dedicated to food crops, pasture and bioenergy. The
fractions can vary in time with new values prescribed annually or less
frequently. Each of these crop area types forms a separate “land class”
for which specific PFTs are allocated. TRIFFID-crop requires height-based
competition (Harper et al., 2018b), which allows for a
flexible number of PFTs. Each PFT is assigned to only one land class and
competes only with PFTs of the same land class, within the defined fraction.
Any land within the fraction that cannot be filled by the assigned PFTs is
occupied by bare soil. Multiple identically parametrised PFTs may be used if
the same type of plant (e.g. C3 grass) is desired in multiple land classes
(e.g. natural, food crops and pasture). TRIFFID-crop also introduces
harvesting of biomass from crop areas, described in Sect. 2.2.1 as
“continuous harvest”. JULES-BE describes a set of options within JULES,
building upon the TRIFFID-crop functionality to enable periodic harvesting
and assisted expansion of the bioenergy PFT area.
Harvesting regimes
Two methods of representing crop harvest are used. A new TRIFFID parameter,
harvest_type (Table 3), may be set to 0, 1 or 2 for each PFT. A value of 0 represents no
harvest; the two harvest types are described below.
Continuous harvest (type 1)
This harvest type is used and described by Harper et al. (2018a) and represented in Eqs. (1) and (2). A fixed percentage (currently
hardcoded as 30 %) of the PFT's litter production (litc) is rerouted
to a harvest pool (harvest) on a continuous basis. The remaining litter
fraction (currently 70 %) enters the soil pool as normal. Setting the
harvest to 30 % of litter production approximates the estimate of 8.2 Pg C yr-1 of human-appropriated net primary production from crop
harvests globally in 2000 (Haberl et al., 2007). Future
development of JULES-BE will allow the harvest rate to be user-prescribed
for each PFT.
1harvestt=0.3×litct2litct=0.7×litct
Periodic harvest (type 2)
At defined intervals, specified in days by the user, the PFT is reduced to a
short height, also specified by the user (see Table 2 for a list of
parameters). New values for wood (woodC), leaf (leafC) and root
(rootC) biomass are calculated based on this height, per Eqs. (46), (56)–(58) and
(60) given by Clark et al. (2011) and
reproduced in the Supplement. The difference between old and new
above-ground carbon is allocated to the harvest pool (Eq. 3), whereas the
change in root (below-ground) carbon is added to the plant litter flux
(litc), as given in Eq. (4). A time coefficient (Δt) is used to
convert stocks to fluxes.
3harvestt=leafCt-1+woodCt-1-leafCt+woodCtΔt4litct=litct+(rootCt-1-rootCt)Δt
Since the model describes a constant perfect correlation between PFT height
and balanced-growth LAI, minimum LAI must also be set low enough to
accommodate the prescribed harvest_ht (Table 2). The PFT then begins to regrow again
from its new shorter height.
TRIFFID parameters required for JULES-BE. An explanation of the use
of these parameters is given in Sect. 2.2.
ParameterTypeValuesDefinitioncropinteger0Natural land1Food crop2Pasture3Bioenergy cropsharvest_typeinteger0No harvest1Continuous harvest2Periodic harvestharvest_freqinteger0Placeholder for harvest types 0 or 1>0Interval in days between harvestsharvest_doyinteger0Placeholder for harvest types 0 or 1>0Day of year on which harvest takes placeharvest_htreal0Placeholder for harvest types 0 or 1>0Height to which crop is reduced on harvest (metres)ag_expandinteger0No automatic increase of PFT fraction when land class fraction increases1Automatically plant out new crop areas with target PFTsAssisted expansion
This section describes new functionality which directs the model to simulate
planting of new agricultural areas. In the existing scheme, when the
fractional area of a land class increases, the new area is covered by bare
soil, until the existing vegetation expands into it. Expansion of PFTs in
the absence of competition follows Eq. (5). Equation (5) is a simplified
version of Eq. (52) in Clark et al. (2011), assuming that only one PFT is assigned to the land class, the PFT
occupies at least 1 % of the total grid cell and the plant has already
reached its maximum height. Cveg represents the PFT's biomass density, and
garea is a constant parameter representing total mortality.
Δfrac=frac×NPPCveg-garea
This arrangement represents competition and growth in natural landscapes,
but where land is dedicated to a specific purpose such as bioenergy crops,
it is less realistic to represent it as such; it is equivalent to humans
clearing an area of land for cropping but then neglecting to plant anything.
Where the agricultural areas consist of ordinary C3 and C4 grasses, this
does not pose much of a problem since Cveg is usually small relative to
NPP during the growing season; therefore, NPPCveg can attain
sufficient size to allow the grass to increase its area. The problem is more
significant in the case of high-density lignocellulosic bioenergy grasses,
in which NPP may be 1–3 times that of an ordinary grass but Cveg is
5–10 times larger. Annual harvesting also reduces the capacity of crop
grasses to increase their area, since more of their NPP is dedicated to
increasing their height (i.e. one of the assumptions of Eq. (5) does not
hold for much of the year).
Therefore, in order to represent the establishment of new agricultural
areas, without sacrificing the benefits of dynamic vegetation, i.e. that
plants can die off where the environment is unsuitable, a new planting
mechanism has been implemented. This mechanism, activated using the switch
l_ag_expand globally and the ag_expand switch on individual PFTs (Table 2), alters the value of
Δfrac returned by TRIFFID. Land class fractions may change once per
year, whereas TRIFFID (where plant competition and fractional allocation
takes place) is run once per simulation day. At each grid cell, the current
land class fraction is compared to the value used at the last TRIFFID call.
Where the land class fraction has increased, the assisted expansion function
is activated. Δfrac is calculated as it would have been without land-use change (Δfracna in Eq. (6), which could be positive or
negative), but then the value of the increase (Δfracag)
is added to it. This is equivalent to assuming that agricultural expansion
is accompanied by planting new crops. Δfrac is then added to the
previous PFT fraction. If two or more PFTs (for which assisted expansion is
enabled) share the same land class, the new area is divided equally between
them (NPFTag). This process is also illustrated in Fig. 1.
Δfrac=Δfracna+ΔfracagNPFTag
Schematic of the agricultural expansion functionality in JULES-BE.
A full description of the process is provided in Sect. 2.3. The area marked with ∗
represents the change in the plant functional type (PFT) area that would
occur without the change in crop area (Δfracna in Eq. 6). The area marked with †
represents the newly available agricultural area (Δfracag in Eq. 6), which is immediately populated
with the crop PFT where assisted expansion is enabled, or left bare where it
is disabled.
New PFT parametrisation
A new bioenergy PFT was developed representing Miscanthus, a perennial grass of
particular interest in the bioenergy literature due to its robust growth and
low input requirements (Heaton et al., 2008; Zub and Brancourt-Hulmel,
2010; McCalmont et al., 2017). An earlier representation of Miscanthus in JULES
(Hughes et al., 2010) focused on realistic representation
of height and LAI, and estimated yields based on NPP. In the new method of
periodic harvesting, above-ground biomass (AGB) is the most important factor
determining yields, and therefore this aspect was emphasised in the
development of this PFT (Fig. 2d; Fig. S1 in the Supplement).
(a, b) Modelled leaf area index (LAI) and height of
Miscanthus, compared against observations at Lincolnshire, UK, for the period
2010–2013. (c) Relationship between height and LAI, model compared against
observations at Lincolnshire, UK. (d) Relationship between height and
above-ground biomass (AGB), with the generic equation from the model compared against
observations from the UK (Christian et al., 2008), Poland
(Jeżowski et al., 2011) and Italy (Cosentino
et al., 2007).
In the current version of JULES, around 90 PFT parameters and 13 TRIFFID
parameters govern a PFT's response to its environment, although they are not
all used at once because many parameters are only required by specific
configurations. The Miscanthus PFT presented here was developed based on a generic C4
grass in the 9-PFT JULES scheme (Harper et al., 2016),
with 14 parameters redefined specifically for this study. Table 3 gives an
overview of the main features of the Miscanthus PFT. A full list of parameters and
their relevance in JULES is given in the Supplement (see also
Harper et al., 2018b for further information about PFT
parameter selection).
PFT parameters distinguishing the Miscanthus PFT used in this study. “C4
Grass” parameters are taken from Harper et al. (2018b);
“Miscanthus (Hughes)” are given by Hughes et al. (2010). A full list of parameters,
with definitions and explanations for values used, is given in the
Supplement. “–” indicates parameters that were not yet introduced in the
older version of JULES used by Hughes et al. (2010). Parameters described as
“Allometry” were determined via an iterative process to improve the
relationships between above-ground biomass, leaf area index (LAI) and
height, as described in the Supplement. Parameters described as “BETYdb”
were taken from observations in the Biofuel Ecophysiological Traits and
Yields database (LeBauer et al., 2018). “GPP calibration”
indicates tlow was determined via an iterative process to improve the fit of
modelled gross primary productivity (GPP) to the flux data obtained from the
Lincolnshire site. “Litter calibration” indicates g_leaf_0 was determined via an
iterative process to approximate the observed ratio of leaf litter to yield
(Amougou et al., 2012). Details of these calculations are provided
in the Supplement.
ParameterC4 GrassMiscanthusMiscanthusRationale(Harper)(Hughes)(this study)a_wl0.0050.0140.07Allometrya_ws10.91Non-woody plant (100 % live stem)alpha0.040.0670.067Hughes et al. (2010)b_wl1.6671.6672Allometryeta_sl0.010.010.08Allometrylma0.137–0.065Feng et al. (2012)tlow137.8512.8GPP calibrationlai_max3310BETYdblai_min10.60.1LAI at minimum height (harvest_ht =0.1)nmass0.0113–0.0217BETYdbnr0.0084–0.0228BETYdbnsw0.0202–0.0101BETYdbg_leaf_03–2Litter calibration
JULES-BE can represent any type of plant as a bioenergy crop. In addition to
perennial grasses, short-rotation coppicing (SRC) with willow or poplar can
be simulated, or softwood or hardwood trees for forestry (Table 1). This
study introduces examples of tree types grown for biomass or bioenergy in
Sect. 3.4, using two poplar PFTs developed for JULES by Oliver et al. (2015).
Methods of evaluation
Simulations were carried out to evaluate and illustrate the new
functionality in JULES-BE. These simulations were all based on the JULES-ES
configuration, a set of options designed for best representation of carbon
cycle and climate dynamics over decadal to centennial timescales. All
simulations began with initial conditions from a spin-up to equilibrium,
then included a transient spin-up period prior to the main run.
Lincolnshire site data
Adjustment of PFT parameters for Miscanthus was performed using observational data
collected from a commercial Miscanthus plantation in Lincolnshire, UK. The site is on
a compacted loam soil previously used to grow wheat and oilseed rape. The
site had mean annual temperature of 9.8 ∘C and mean annual
precipitation of 621 mm. The net ecosystem exchange of CO2 was measured
by eddy covariance methodology. Gross primary productivity (GPP) was
calculated using the REddyProc method described by Robertson et al. (2017), after Reichstein et al. (2005). Manual measurements of
height and LAI were taken over the growing season (Fig. 2).
JULES requires meteorological and soil ancillary (time-invariant) data to
drive the model. Meteorological data were collected at the site on an hourly
basis during 2006–2013 (shortwave and longwave radiation, wind speed,
precipitation, temperature, air pressure and specific humidity). Physical
soil properties (soil albedo, heat capacity, thermal conductivity, hydraulic
conductivity at saturation, soil moisture at saturation, soil moisture at
critical point, soil moisture at wilting point, Brooks–Corey exponent for
soil hydraulic calculations and soil matric suction at saturation) were derived
from measurements taken at the site between 2009 and 2010. The site and data
collection are described in greater detail by Robertson et al. (2016, 2017).
Global bioenergy yield dataset
In order to further explore the suitability of this Miscanthus PFT for simulating
biomass yields, a comparison was conducted against observed yields.
Li et al. (2018a) have compiled a comprehensive
global dataset of bioenergy crop yields as reported in scientific
literature. It includes 981 observations of Miscanthus yields, from the United States
and Europe, with and without irrigation and fertiliser.
For comparison with modelled Miscanthus yields produced by JULES-BE, the observations
of Miscanthus from this dataset were combined into sixty-eight 0.5∘×0.5∘
grid cells. Observed sites using fertiliser or irrigation were found not to
differ significantly in yield from untreated sites, and were therefore
included in the comparison (JULES-BE is not currently configured to support
irrigation or nitrogen fertilisation). JULES-BE was then run at the same 68 grid cells over the period 1980–1999, using meteorological driving data
from WATCH at 0.5∘×0.5∘ (Weedon et al., 2010, 2011).
Future simulation
To evaluate the implications of the new representation of bioenergy crops for
climate mitigation, a 21st century simulation of bioenergy crop area
under SSP2-RCP2.6 is shown here. Meteorological driving data from HadGEM2-ES
ISIMIP simulations (for RCP2.6) were used, downscaled to 0.5∘ and
bias-corrected to calibrate with WATCH observed climatology over 1960–1999
(Hempel et al., 2013). Atmospheric CO2 concentrations followed
the RCP2.6 CO2 concentration pathway, covering the period 2006–2099,
generated by IMAGE for SSP2. The land-use scenario is generated by the IMAGE
3.0 integrated assessment model (Stehfest et al., 2014).
The RCP2.6-SSP2 scenario (Doelman et al., 2018; Daioglou et al., 2019)
features a rapid scale-up of global bioenergy crop area in the tropics over
2025–2045 to around 250 million hectares (Mha), followed by gradual
expansion into temperate regions over the rest of the century, with
fluctuations in crop area driven by bioenergy demand (Fig. 7). Figure 6,
which shows yields across the global land surface, is generated using the
same driving data, though bioenergy crops are not grown on all grid cells in
the RCP2.6-SSP2 simulation.
Forestry and short-rotation coppice demonstrations
Three simulations were carried out to demonstrate the functionality of
JULES-BE for harvesting of woody biomass: short-rotation coppicing (SRC),
permanent (non-felling) forest management with residue harvesting and
rotation forestry plantation. They are presented as illustrative cases to
inform future model development, and are thus intentionally idealised
scenarios.
These three simulations were carried out for a single point, a FLUXNET site
in Italy (IT-CA1, Castel d'Asso; http://sites.fluxdata.org/IT-CA1, last access: 20 June 2019; Sabbatini et al., 2016),
at which poplar is grown on a short-rotation coppicing regime.
Meteorological data was collected on-site from 2011 to 2014 on a half-hourly
basis. Over this period, the mean annual temperature at this site was 15 ∘C and the mean annual precipitation was 736 mm. Site soil
properties were also used. The local biome (IGBP class) is temperate
deciduous forest.
All three simulations were run for a 60-year cycle, using looped
meteorological driving data from 2011 to 14:
Poplar SRC: two species of Poplar, Populus nigra and P.×euramericana, parametrised and evaluated by
Oliver et al. (2014). Harvesting occurs on a 3-year rotation on day 270 of
the year, when trees are cut to 1 m height, allowing sufficient
remaining biomass for rapid regrowth the following year. The PFT and TRIFFID
parameters for the poplar PFTs are given in the Supplement.
Residue harvesting forestry: two tree species, a broadleaf deciduous tree and a
needleleaf evergreen tree. Generic PFT tree parameters as per
Harper et al. (2018b; reproduced in the Supplement).
Continuous harvesting (50 % of litter production from wood only) is
applied to represent residues.
Rotation forestry: two tree species, a broadleaf deciduous tree and a needleleaf
evergreen tree. Generic PFT tree parameters as per Harper et
al. (2018b; reproduced in the Supplement), with lai_min adjusted to 0.1 to allow
for harvest cutting. Harvesting occurs on a 40-year rotation on day 364 of
the year, when trees are cut to 1.5 m height.
ResultsLincolnshire site
Model results from the Lincolnshire site are shown in Figs. 2a–c and 3,
compared against observational data from the site. The seasonal cycle of
growth through to harvest in mid-February is illustrated by the seasonal
fluctuation of height and LAI (Fig. 2a–b). The observations show more
year-to-year variation in peak seasonal height and LAI than the model. The
modelled peak heights (2.4–2.55 m during 2010–2012) and LAIs (2.75–2.9)
are also generally lower than those observed (height: 2.8–3.1 m; LAI:
3.1–4.1), although observed height and LAI tended to decline after their
peaks to values closer to those produced by the model. The modelled crop
also increased in height and LAI earlier in the season compared to
observations. The correlation between observed and modelled GPP at this site
is excellent (R=0.956; Fig. 3).
Modelled gross primary productivity of Miscanthus, compared against
observations at Lincolnshire, UK, for the period 2008–2012.
The mean modelled yield was 6.0±0.5 t C ha-1 yr-1, equivalent to a dry matter (DM) yield of 12.4±1.1 t DM ha-1 yr-1 assuming 48 % carbon in dry biomass
(Baxter et al., 2014). This significantly exceeds the
observed yields of 7.6±1.6 t DM ha-1 yr-1 at
this site (Robertson et al., 2017), though it sits squarely within the
range of yields observed in the UK (12.4±5.9 t DM ha-1 yr-1; 11 studies compiled by Li et al., 2018a).
Modelled Miscanthus yields against observations
A comparison of yields was conducted between the JULES-BE model results and
observed Miscanthus yields compiled from the literature by Li
et al. (2018a). The results of this comparison are given in Figs. 4 and 5.
Across all sites and years, observed yields were much more variable, with a
mean ± SD of 12.5±9 t DM ha-1 yr-1 (n=981), compared to 14.3±7 t DM ha-1 yr-1 for the modelled yields (n=1360). In a few cases, yields up
to 51 t DM ha-1 yr-1 were observed, exceeding the
maximum modelled yield of 37 t DM ha-1 yr-1; but more
significantly, low yields of less than 4 t DM ha-1 yr-1 were much more common in the observations (Fig. 5b).
Comparison of modelled Miscanthus yields against observations from
Li et al. (2018a).
Modelled yields compared to observed yields of Miscanthus collated by
Li et al. (2018a). In (a), the error bars give the
range of data at each half-degree grid cell. The observed range (horizontal
error bars) accounts for variation between sites (different sites may exist
within a grid cell area), years and fertiliser or irrigation treatment; the
modelled range (vertical error bars) reflects interannual variability only.
Panel (b) shows the range of values by relative frequency. This figure may be
compared to Fig. 3e–f from Li et al. (2018b).
The modelled yields showed a consistent positive correlation with both mean
annual precipitation (R=0.752) and mean annual temperature (R=0.718; Fig. S2). For wider comparison, Fig. 6 shows simulated yields of
Miscanthus across the global land surface. For the observed yields, the correlation
with precipitation was much weaker (R=0.094), and while correlation with
mean annual temperature was weak overall (R=0.252), yield appears to peak
around 14–15 ∘C and decline with higher temperatures (Fig. S2).
This difference between modelled and observed results is clearly illustrated
in the southern United States, where modelled yields are as much as 20 t DM ha-1 yr-1 higher than observations (Fig. 4).
These observations were of Miscanthus×giganteus, a cultivar that produces very high yields in
temperate climates but appears less well-adapted to high temperatures
(Fedenko et al., 2013). Other perennial grasses may be
more appropriate for hot climates. The model PFT would benefit from some
further tuning to better represent properties such as stomatal conductance
and photosynthetic temperature response, particularly the tupp and vsl parameters
to better calibrate the relationship between leaf temperature and maximum
rate of carboxylation of Rubisco (Vcmax; Sect. S1 in the Supplement).
Modelled yields of the Miscanthus PFT, averaged over 2010–2019.
Figure 6 shows modelled yields for the whole Earth area, averaged over
2010–2019, in order to show the general spatial pattern of productivity of
Miscanthus. Yields of 8–20 t DM ha-1 yr-1 are typical for most
temperate climates, increasing to a maximum of about 35 t DM ha-1 yr-1 in the humid tropics. Yields are positively
correlated with both temperature and precipitation (Fig. S2). This may help
to contextualise the yields shown in Fig. 4.
Assisted expansion, global and future yields
To assess the impact of the assisted expansion feature on simulated global
Miscanthus crop area, Fig. 7 shows total Miscanthus crop area in the RCP2.6-SSP2 scenario
(van Vuuren et al., 2017). This scenario features a
rapid increase in bioenergy crop area (“Available area”; black) from 29 Mha in 2025 to 282 Mha in 2045. “Natural expansion” (green) represents the
Miscanthus PFT parametrised as discussed here, without using the new agricultural
expansion functionality. In this scenario, Miscanthus occupies 13 Mha of the bioenergy
crop area in 2025, increasing to 104 Mha in 2045 – leaving 178 Mha as bare
soil. In 2035, only 31 Mha, or 25 % of the bioenergy crop area, is
occupied by Miscanthus. With “Assisted expansion” (blue), the Miscanthus PFT occupies a
consistently larger proportion of the available area throughout this period
of rapid increase. In 2035, the PFT covers 119 Mha, 96 % of the available
area. The proportion of area covered begins to decline after 2040, as the
bioenergy production area shifts from the tropics into temperate biomes
which are somewhat less favourable for growth in this representation of
Miscanthus. The difference in crop area between the old and new expansion methods
declines toward the end of the simulation, as the crop area begins to
stabilise and the two simulations begin to converge.
Modelled area of Miscanthus under RCP2.6-SSP2, showing the effect of the
agricultural expansion functionality. “Assisted expansion” shows the model
run using the assisted expansion function, compared to “Natural expansion”
in which this function is disabled. “Available area” shows the total
available area for bioenergy crops under this scenario.
In Fig. 8, the total global Miscanthus yield is shown, using the “assisted
expansion” method shown in Fig. 7. The bioenergy crop yield supplied in the
IMAGE model is shown for reference (Huppmann et al., 2018; Doelman et al.,
2018; Daioglou et al., 2019). Following the rapid increase in bioenergy crop
area, from 2040 to 2099, bioenergy crop yields remain fairly steady in JULES-BE
at 4.3 Gt DM yr-1 globally, compared to 3.3 Gt DM yr-1
in IMAGE over the same period. IMAGE uses a management factor when
projecting energy yields, assuming that yields are currently used
inefficiently (typical values are 60 % in 2020) but that improvements to
crop breeding and management will increase yields to 120 %–140 % of
physical potential by 2100 (Stehfest et al., 2014). This
accounts for a portion of the gap in the early years of this scenario, which
closes between the two models by the 2090s. The Miscanthus PFT in JULES-BE probably
overestimates yields in hot climates (Fig. 4); as such, the yields
projected by IMAGE may be more reliable. This scenario, and the comparison
between JULES-BE and IMAGE, will be explored in greater detail in a future
publication.
Bioenergy crop yield from Miscanthus under the land-use scenario
RCP2.6-SSP2, compared to equivalent bioenergy yield in IMAGE (Huppmann et
al., 2018).
Demonstrations of forestry and short-rotation coppicing
Figure 9 shows illustrative simulations of short-rotation coppicing and
managed forestry using JULES-BE. Over the 20 harvest cycles of poplar SRC,
the yield was 2.4±0.3 t C ha-1 yr-1 (P. Nigra) and
2.2±0.5 t C ha-1 yr-1 (P.×euramericana). This falls within
the range observed by Sabbatini et al. (2016) over the
2011–2012 growing seasons (3.1±1.5 t C ha-1 yr-1) at the IT-CA1 site (growing Populus×canadensis on a 2-year coppicing
rotation). The site received some supplemental irrigation during dry spells,
which is not represented in the model; this may account for some
underestimation of yields. Residue harvesting based on wood litter produced
generally small yields of 0.15–0.25 t C ha-1 yr-1 in
addition to forest carbon stock accumulation of 45–80 t C ha-1
over the 60-year period. For rotation forestry, the yield over the 40-year
rotation was 41 t C ha-1 for broadleaf and 69 t C ha-1 for
needleleaf, equivalent to 1.0 and 1.7 t C ha-1 yr-1
respectively. This is higher than the average productivity for European
forests (around 0.8 t C ha-1 yr-1, assuming 250 kg C m-3 of harvested roundwood; Payn et al., 2015), but
lower than recent estimates from France for Douglas fir of 3.1 t C ha-1 yr-1 following a 40-year rotation
(Bréda and Brunette, 2019). These examples show that with
appropriate tuning and validation of the PFT and harvest parameters,
JULES-BE could be used to facilitate decision-making on questions such as
species selection, harvesting regime, harvest frequency and timing.
Illustrated cumulative harvests and vegetation regrowth over a
60-year period, showing the application of JULES-BE to short-rotation
coppicing (a, b); permanent forestry with harvest of wood litter (c, d),
and rotation forestry (e, f).
DiscussionMain findings and limitations
The modelled yields of Miscanthus were broadly consistent with observations from sites
in the USA and Europe, but showed much less variability. A major reason for
this is that the harvest frequency is fixed in the model, with no option for
irregular frequency or for harvests to be skipped. For example, in practice
Miscanthus is generally allowed 1–2 years after planting to establish before being
harvested annually, followed by 1–2 years of low yields. The largest yields
generally occur during years 4–10 and decline thereafter, with a typical
rotation length of 20 years (Zub and Brancourt-Hulmel, 2010).
In the model, there is no representation of a plant's age, so it is not
possible to establish an age-dependent harvest regime. Another reason for
reduced variability in yields is that the root system reverts to the same
small size after each harvest (Eq. 4), dropping its surplus biomass into
the soil C pool. In reality, a relatively small proportion of root biomass
is shed at harvest, and the mature plant gets a regrowth benefit from an
established root system. The model currently relies on a fixed relationship
between above-ground height and root biomass, and breaking this link would
create other problems in the model relating to PFT scaling. Future versions
of JULES will use the Reduced Ecosystem Demography (RED) approach, which
represents separate mass classes within a PFT (Moore et al.,
2018). Alternatively, an approach could be implemented similar to that of
Black et al. (2012), in which three PFTs are used to represent
different age classes of sugarcane, although this would not be compatible
with dynamic vegetation. Given these difficulties, and the fact that JULES
is a global model, accurate average yields with reduced variability compared
to observations is likely to be an acceptable compromise for most
applications of JULES-BE.
The Miscanthus PFT has not been tested with other advanced modules within TRIFFID,
such as nitrogen cycling or layered soil carbon (Burke et al.,
2017), and will likely require additional updating and tuning of parameters
to yield useful results with other functions. Since nitrogen content is
recorded for the harvested biomass, with appropriate tuning JULES-BE could
also be used to quantify nitrogen loss from bioenergy crop ecosystems due to
harvesting.
Further work
An example of rotation forestry has been shown in Fig. 9 for a single point.
To represent forestry on a country, regional or global scale, further
development of the model is required. The harvest frequency and timing are
currently fixed for each PFT, meaning that all grid cells are harvested at
the same time. Over a large number of grid cells, this would not be
realistic and would produce undesirable hydrological and climatic effects.
Further improvements to the model could enable the user to stagger the
timing of harvesting. Allowing harvest frequency to vary regionally would
better represent rotation forestry and increase yield by enabling the user
to choose a regionally appropriate harvest frequency (shorter for more
productive regions). Allowing harvest day-of-year to vary regionally would
improve global-scale assessment of any bioenergy crop, since harvest timing
is dependent on local climatology and affects local land surface properties,
such as roughness length, albedo and transpiration rate, which in turn
affect the climate. This functionality may be best implemented by allowing
these variables to be user-prescribed for each grid cell. However, providing
these data may be burdensome for the user, and some predictive algorithms
based on climatology and growth, built into the model, may be more
appropriate.
The algorithms for competition between PFTs within TRIFFID can potentially
be used to determine the most suitable type of bioenergy crop in each grid
cell. However, some modifications would need to be made to the existing
code. In the simulations presented in this study, TRIFFID competition was
enabled, allowing the bioenergy PFT to adjust its area to scale with its
productivity – for example, allowing a crop to die back in response to an
unsuitable environment. The current competition scheme is not useful for
allowing different types of bioenergy PFTs to compete with each other within
a grid cell, since it is based on height. This favours plants that can gain
height easily, rather than shorter species with greater biomass density. A
competition scheme based on above-ground biomass rather than height would be
the first modification to make. This could help select between species
within a harvesting regime, e.g. helping determine the best perennial grass for
annual harvesting, or the best tree species for short-rotation coppicing.
However, this may not necessarily select for the highest-yielding plant,
because above-ground biomass is only a good proxy for yield at the end of
the growing season, and competition for area is invoked at every iteration
of TRIFFID (once per day in these simulations). Also, this development would
not be useful for mixing PFTs with different harvest frequency or harvest
day-of-year, since it would continue to bias competition towards the PFT
that has been harvested less recently. The best solution would be to
reapportion the bioenergy crop area between PFTs once per harvest cycle,
based on the previous cycle's yield, but that would be a complex development
given the existing model structure. Ultimately however, a yield-based
competition scheme would still ignore the biophysical, economic and
environmental factors that influence choice of crop type. As such, JULES-BE
may always be more useful for informing these land-use decisions based on
its output, rather than integrating these decisions into the existing model.
Conclusions
This study presents new functionality to represent second-generation
bioenergy cropping and harvests in JULES. This is the first step to getting
such processes represented mechanistically within Earth system models, in
order that the effects of bioenergy cropping on the carbon cycle and climate
system can be evaluated. JULES-BE allows for flexible parametrisation of
many types of bioenergy PFTs, although only Miscanthus has been fully developed here.
Yields of the Miscanthus PFT were within the range generally observed in the United
States and Europe, though the model failed to capture the large variability
in observed yields across and within sites.
Applications for JULES-BE include short-rotation coppicing, rotation
forestry and residue harvesting from forests or agricultural systems. Future
development will focus on improving the competition scheme so that multiple
bioenergy PFTs can be represented simultaneously, and adding features to the
harvest timing mechanism that improve representation of forest harvesting at
regional or global scale.
Implications of this model functionality include the ability to study
bioenergy cropping and harvests within a land surface model. Ultimately,
this should facilitate climate change mitigation and climate modelling
research to evaluate future low-carbon energy systems featuring bioenergy
crops for their impacts on hydrology, climate and carbon storage.
Code availability
This work was based on a version of JULES5.1 with additional developments
that will be included in a future release of JULES. The code is available
from the JULES FCM repository: https://code.metoffice.gov.uk/trac/jules (last access: 27 February 2019)
(registration required). The version used was r12164_biotiles_harvest (located in the repository at
branches/dev/emmalittleton/r12164_biotiles_harvest).
The supplement related to this article is available online at: https://doi.org/10.5194/gmd-13-1123-2020-supplement.
Author contributions
The aims and objectives of the project were jointly developed by EWL, ABH,
NEV and TML. EWL developed the model code with help from ABH and MCDR and
designed and performed the validation simulations with advice from ABH, NEV,
RJO and TML. EWL and ABH prepared the manuscript with contributions from
NEV, RJO, MCDR and TML.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This work is part of FAB GGR (Feasibility of Afforestation and Biomass
energy with carbon capture and storage for Greenhouse Gas Removal), a
project funded by the UK Natural Environment Research Council
(NE/P019951/1), part of a wider Greenhouse Gas Removal research programme
(http://www.fab-ggr.org/, last access: 20 June 2019). Thanks are due to Jon Finch for carrying out the data collection at
the Lincolnshire Miscanthus site. The authors thank Eddy Robertson and Andy Wiltshire
for providing consultation and advice on the model development. Thanks also
to Sarah Chadburn for feedback on draft figures. Maps were produced using
map data from http://naturalearthdata.com (last access: 20 June 2019).
Financial support
This research has been supported by the Natural Environment Research Council (grant no. NE/P019951/1), the Engineering and Physical Sciences Research Council (grant no. EP/N030141/1) and the Horizon 2020 programme (CRESCENDO; grant no. 641816).
Review statement
This paper was edited by David Lawrence and reviewed by two anonymous referees.
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