Introduction
If the trend in global carbon dioxide (CO2) emissions observed over the
last 2 decades continues, the atmospheric CO2 concentration is expected
to exceed 900 ppm at the end of the 21st century, resulting in a surface
temperature increase of several degrees (Friedlingstein et al., 2014; Le
Quéré et al., 2015; Peters et al., 2013). However, during the COP21
climate conference in Paris 2015, participating parties agreed to limit
global warming to 2 ∘C or less relative to the pre-industrial era,
and by today, 169 countries have ratified the agreement
(http://unfccc.int/paris_agreement/items/9485.php, accessed 2 November
2017). The < 2 ∘C warming goal requires greenhouse gas
(GHG) concentrations to approximately follow or stay below the representative
concentration pathway 2.6 (RCP2.6, van Vuuren et al., 2011), which will
require serious reductions in CO2 (and other GHG) emissions across all
sectors. Present projections indicate that (1) without substantial net
negative CO2 emissions later this century, the Paris goal will not be
achievable (Fuss et al., 2014; Rogelj et al., 2015), and (2) some negative
emissions need to be realized in 10–20 years (Anderson and Peters, 2016).
The total carbon dioxide removal (CDR) necessary to achieve the 2 ∘C target is typically around 100–230 GtC (Rogelj et al., 2015; Smith et
al., 2016) depending on the future CO2 emission pathway and including
the need to avoid carbon (C) emissions from further land clearance. Two main
strategies of land-based climate change mitigation are commonly discussed
for CDR: growth of bioenergy crops in combination with carbon capture and
storage (BECCS), and avoided deforestation in combination with afforestation
and reforestation (ADAFF) (Humpenöder et al., 2014; van Vuuren et
al., 2013; Williamson, 2016). BECCS involves the planting of bioenergy crops
or trees, which are burned in power stations or converted to biofuels, and
the released CO2 being captured for long-term underground storage in
geological reservoirs. ADAFF utilizes the natural C uptake of forest
ecosystems in biomass and soil by maintaining and expanding global forest
area.
The total land demand and spatial patterns of these mitigation strategies
are highly uncertain due to strong dependencies on underlying assumptions
about future environmental and socio-economic changes (Boysen et al.,
2017; Popp et al., 2017; Slade et al., 2014). BECCS and ADAFF will likely
increase pressure on food-producing agricultural areas and, in the case of
BECCS, natural ecosystems. Moreover, similar to other mitigation
technologies, the feasibility and effectiveness of BECCS and ADAFF are
debated (Keller et al., 2014; Williamson, 2016). For instance, in boreal
and many temperate regions tree cover reduces surface albedo, thereby
causing local warming (Alkama and Cescatti, 2016). Additionally,
reduced CO2 emissions through forest protection and expansion might be
counteracted by cropland expansion in non-forest areas (Popp et al., 2014). BECCS includes substantial economic costs in
its CCS component (Smith et al., 2016) and is currently far from being
deployable at the commercial scale (Peters et al., 2017; Reiner, 2016).
It will also require sufficient safe geologic C storage capacities
(Scott et al., 2015). Additionally, the efficiency of BECCS
might diminish when C emissions from deforestation (Wiltshire and
Davies-Barnard, 2015) or nitrous oxide (N2O) emissions from bioenergy
crops (Crutzen et al., 2008) are considered (with the latter
often being accounted for in BECCS scenarios, e.g. Humpenöder et al.,
2014).
But even if land-based measures were to be successful with respect to their
primary goal of permanently and substantially reducing atmospheric CO2
levels to mitigate climate change, impacts on ecosystems and societies are
likely to be complex (Bennett et al., 2009; Creutzig et al., 2015; Foley
et al., 2005; Smith and Torn, 2013; Smith et al., 2013; Viglizzo et al.,
2012) and include effects far away from the original land-use (LU) location
(DeFries et al., 2004; Rodriguez et al., 2006). The multiplicity of
environmental implications caused by large-scale CO2 removal have so
far been largely neglected (Williamson, 2016). The relevance of
negative emission technologies, combined with our limited knowledge of their
feasibility and risks, encourages the exploration of potential synergies and
trade-offs between terrestrial ecosystem services (ESs, defined as
benefits that people obtain from ecosystems; MEA, 2005) that are affected in
land-based mitigation projects. Such work will facilitate decision-making as
to whether the realization of such projects is desirable for society.
In this study, we utilize projections of future LU from one integrated
assessment model (IAM, IMAGE) and one LU model (MAgPIE), that are created
based on three large-scale land-based mitigation options (BECCS, ADAFF, and a
combination of both). Each of these target a CDR of 130 GtC (only
CO2 carbon, omitting other greenhouse gases) by the end of the century,
which is approximately equivalent to the cumulative deforestation CO2
emissions from the late 19th century to today, or around 60 ppm (Le
Quéré et al., 2015). We use these spatially explicit LU patterns as
input for simulations with the LPJ-GUESS dynamic vegetation model to analyse
effects on a variety of ecosystem functions that serve as indicators for
important ecosystem services. By using LU patterns from two different LU
models we explore some of the uncertainty in indicators of ESs arising from
different model assumptions concerning the land demand of land-based
mitigation. The main research questions we address in this study are as
follows.
What are the impacts of land management for carbon uptake on other ecosystem
service indicators?
Do the effects of land-based climate change mitigation on ecosystem service
indicators differ based on the mitigation approach (BECCS, ADAFF, or a
combination of both)?
If so, can a mitigation approach be identified in which trade-offs between
other ecosystem service indicators are less pronounced than in the other
approaches?
What are the spatial and temporal patterns of the impacts of land-based
mitigation on ecosystem service indicators?
This is to our knowledge the first time that global LU scenarios are being
used as input to a process-based ecosystem model to assess changes in
ecosystem function and effects on multiple ES indicators.
Methods
LPJ-GUESS
The process-based dynamic global vegetation model (DGVM) LPJ-GUESS simulates
vegetation dynamics in response to climate, land-use change (LUC),
atmospheric CO2, and nitrogen (N) input (Olin et al., 2015a; Smith et
al., 2014). The model distinguishes between natural, pasture and cropland
land-cover types (Lindeskog et al., 2013), all of which include C–N dynamics
(Olin et al., 2015a; Smith et al., 2014). Vegetation dynamics in natural land
cover are characterized by the establishment, competition, and mortality of
12 plant functional types (PFTs, 10 groups of tree species, C3 and
C4 grasses) in a number of replicate patches (10 in this study for
primary vegetation, 2 for abandoned agricultural areas). Vertical forest
structure is accounted for by the use of different age classes for woody
PFTs. When forests are cleared for agriculture, 20 % of the woody biomass
enters a product pool (turnover time of 25 years), with the rest being
oxidized (74 %) or transferred to the litter (6 %). Pastures are
populated by C3 or C4 grasses which are annually harvested
(50 % of above-ground biomass) (Lindeskog et al., 2013). Croplands are
represented by prescribed fractions of five crop functional types (CFTs, see Table S1 in the
Supplement), which are moderately tilled, fertilized, and harvested (Olin et
al., 2015a), and are prescribed to be either irrigated or rain-fed (Lindeskog
et al., 2013). Specific bioenergy crops are currently not represented. While
LPJ-GUESS does not assume yield increases due to technological progress (in
contrast to IMAGE and MAgPIE), climate change adaption is simulated by using
a dynamic potential heat unit (PHU) calculation (Lindeskog et al., 2013). The
PHU sum needed for the full development of a crop determines its harvesting
time. For irrigated crops, water supply is assumed to be available as
required to fulfil the plant's water demand. Unmanaged cover grass (C3
or C4 type depending on climate) is allowed to grow in croplands between
growing seasons.
The IMAGE and MAgPIE models and the provided land-use scenarios
IMAGE is an IAM model framework that includes several sub-models representing
the energy system, agricultural economy, LU, natural vegetation, and climate
system (Stehfest et al., 2014). Socio-economic parameters are usually
calculated for 26 world regions, and most environmental parameters are
modelled on a 0.5∘ × 0.5∘ grid at annual time steps.
LU dynamics are driven by demand for and supply of crops, animal products,
and bioenergy. Bioenergy demand to achieve a specific CDR target is
determined by the energy system sub-model which uses land availability from
the LU sub-model following a set of sustainability criteria (Hoogwijk et al.,
2003). For this study, bioenergy crops are included as fast-growing C4
grasses (Doelman et al., 2017) as these produce higher yields than woody
plants in many locations. The level of agricultural intensification required
to free up land for afforestation to achieve a specific CDR target is
estimated using a stepwise approach of increasing yields and livestock
efficiencies. This implies that reduced crop and pasture areas go with higher
yields and livestock efficiencies, thereby allowing the same food production
as in the baseline. Afforestation is assumed to occur first in grid cells
with high potential for forest growth. IMAGE also represents degraded areas
(calibrated so that, together with areas cleared for agriculture, FAO
deforestation statistics are met) which can be reforested as part of the
afforestation activities (Doelman et al., 2017). Natural vegetation regrowth
trajectories as well as crop yields, C, and water dynamics are modelled
dynamically by the internally coupled DGVM LPJmL (Bondeau et al., 2007;
Stehfest et al., 2014).
MAgPIE is a global multi-regional partial equilibrium model of the
agricultural sector (Lotze-Campen et al., 2008; Popp et al., 2014). The
model aims to minimize the global costs for agricultural production
throughout the 21st century at a 5-year time step (recursive dynamic
optimization) and is driven by demand for agricultural commodities and
associated costs in 10 world regions. The cost minimization is subject to
various spatially explicit biophysical factors such as land and water
availability as well as crop yields (provided by LPJmL). Major options to
fulfil increasing demand are intensification (yield-increasing
technologies), expansion (LUC), and international trade. Demand for CDR
enters the model at the global scale, while the spatial distribution of
bioenergy production or afforestation is derived endogenously in the model
(involving economic and biophysical factors). Bioenergy demand is fulfilled
chiefly through the growth and harvest of grassy energy crops; woody
bioenergy in this study is grown only on less than 1 % of the area used
for bioenergy. Actual bioenergy yields are derived from potential LPJmL
yields (using information about observed LU intensity and agricultural area
for initialization) but can exceed LPJmL yields over time due to
technological progress (Humpenöder et al., 2014). Afforestation is
assumed to occur as managed regrowth of natural vegetation according to
parameterized s-shaped growth curves towards a maximum potential natural
vegetation C density as provided by LPJmL, with soil C increasing linearly
towards its potential maximum within 20 years (Humpenöder et al.,
2014). For simplicity, we refer to both IMAGE and MAgPIE as LU models (LUMs)
in the following.
As input to our study we use the baseline projections (without land-based
mitigation) from IMAGE and MAgPIE, and three land-based mitigation scenarios,
each calculated by both LUMs, based on the assumption of a cumulative CDR
target of 130 GtC by the year 2100. In the “BECCS” scenario this is
achieved via bioenergy plant cultivation and subsequent CCS, the “ADAFF”
scenario involves maintaining and expanding global forest area, and in
“BECCS-ADAFF” the CDR demand is fulfilled in equal parts via both options.
While the CDR target in ADAFF is achieved via terrestrial C uptake (CDR =Δ vegetation C +Δ soil C +Δ product pool),
in BECCS it is fulfilled solely via CCS (CDR = cumulative CCS) and thus
did not account for changes in vegetation and soil C. The baseline scenario
(“BASE”) involves no land-based mitigation but LUC takes place in response
to, among other factors, increasing food demand, dependent on population and
GDP growth. LUC was provided by the LUMs as net land-cover transitions. Wood
harvest was not accounted for in the data provided by the LUMs. All scenarios
were developed with RCP2.6 climate produced by the IPSL-CM5A-LR general
circulation model (GCM), bias corrected to the 1960–1999 historical period
(Hempel et al., 2013). The LU scenarios were created using harmonized
assumptions about climate change, atmospheric composition, and socio-economic
development and thus did not include C cycle feedbacks. As it seems currently
unlikely that the RCP2.6 pathway can be achieved without any land-based
mitigation (Fuss et al., 2014), the BASE scenario should rather be regarded
as a diagnostic scenario to isolate the LU effects induced by the mitigation
scenarios from other factors. CO2 fertilization effects on plant growth
were simulated in the LUMs' crop growth and vegetation models. Both LUMs
harmonized their cropland and pasture LU patterns to the spatially explicit
HYDE 3.1 dataset (Klein Goldewijk et al., 2011) in the year 1995 (MAgPIE) or
2005 (IMAGE), with small deviations in the area of the land-cover classes
occurring due to different land masks and calibration routines. The
simulation period was 1970–2100 in IMAGE and 1995–2100 in MAgPIE.
Socio-economic developments as input to the LUMs were based on the Shared
Socioeconomic Pathway 2 (SSP2, “Middle of the Road”) (O'Neill et al., 2014;
Popp et al., 2017). We only used spatially explicit LU and land management
(irrigation and synthetic plus organic N fertilizer) patterns from the LUMs
as input to the LPJ-GUESS simulations; other variables also available from
the LUMs (e.g. C stocks or crop production) were calculated with LPJ-GUESS.
Details about the conversion of IMAGE and MAgPIE-LU data to LPJ-GUESS input
data can be found in Supplement Sect. S1.
Time series (2000–2100) of area under natural vegetation (including
afforested area), pasture (including degraded forest area for IMAGE), and
cropland (including bioenergy production area) for the different scenarios,
for IMAGE (a) and MAgPIE (b).
(a) Fraction of grid cell under natural vegetation
(including afforested area but not degraded forests) by the end of the
century (2090–2099) in the BASE scenario for IMAGE (left) and MAgPIE
(right). (b) Difference in the natural vegetation fraction between
the ADAFF and the BASE scenario by the end of the century (2090–2099).
(c) Same as panel (b) but between the BECCS and the BASE
scenario.
Even though MAgPIE and IMAGE derive crop yields and C densities from the same
DGVM (LPJmL; Bondeau et al., 2007), the land demand to meet the same CDR
target is larger in IMAGE than in MAgPIE. This reflects different model
approaches: while in IMAGE bioenergy cultivation can only be established in
unproductive regions not needed for food production, in MAgPIE there is a
competition for land between food production and land-based mitigation.
Concerning afforestation, managed regrowth (according to prescribed growth
curves) is assumed in MAgPIE while in IMAGE natural regrowth dynamically
calculated within LPJmL is implemented. Consequently, bioenergy production in
MAgPIE is located in regions with mostly higher yields compared to IMAGE, and
forest regrowth occurs at a faster rate, resulting in less LUC and mitigation
actions starting later in the MAgPIE scenarios (Fig. 1, Table S2). In the
BASE scenario, the area under natural vegetation decreases throughout the
future for both IMAGE and MAgPIE (Fig. 1, Table S2), but more so for IMAGE
due to the representation of degraded forests (which are treated as grassland
in IMAGE; see Supplement Sect. S1). Substantial regional differences between
both LUMs exist by the end of the century in the BASE scenario (Fig. 2a).
Avoided deforestation and afforestation in the ADAFF scenarios is chiefly
located in the tropics (Fig. 2b) and afforestation typically takes place on
pastures or degraded forests in IMAGE but on croplands in MAgPIE (Table S2).
Bioenergy production area in BECCS is increased mainly at the expense of
natural vegetation in IMAGE but taken also from existing agricultural land in
MAgPIE. Total cropland area increases in the scenario combining both
strategies (BECCS-ADAFF) compared to BASE for IMAGE but decreases for MAgPIE
BECCS-ADAFF (Fig. 1). IMAGE uses a slightly larger grid list than MAgPIE and
accounts for the water fraction of a grid cell; but as the impacts on
land-based mitigation in LPJ-GUESS turned out to be small (< 2 GtC
over the simulation period) we only included grid cells in our simulations
for which LU data were provided by both LUMs (assuming 100 % land cover)
to facilitate comparison of the results.
Simulations setup
The LPJ-GUESS simulations were forced by daily atmospheric climate variables
(surface temperature, precipitation, shortwave radiation) extracted from
bias-corrected simulated IPSL-CM5A-LR RCP2.6 climate (1950–2099) from the
first phase of ISI-MIP project (Warszawski et al., 2014). For the historical
period we randomly chose years from the period 1950–1959 to generate climate
data for the years 1901–1949. A repeating climate cycle from the 1901–1930
period was used for the model's spin-up. The global average surface
temperature increase in IPSL-CM5A-LR is 1.3 ∘C (1.6 ∘C on
land) by the end of the century (2070–2099) compared to present-day
(1980–2009) for RCP2.6. This value is in the middle of an ensemble of a
wider range of GCM models used in ISI-MIP (Warszawski et al., 2014).
Historical (1901–2005) and future (RCP2.6, 2006–2099) atmospheric CO2
mixing ratios were taken from Meinshausen et al. (2011). The year 1901 value
(296 ppmv) was used for the spin-up. Future atmospheric CO2 mixing
ratio peaks at 443 ppmv in year 2052 and drops to ∼ 424 ppmv by the
end of the century (Meinshausen et al., 2011). Gridded N deposition rates
were available as decadal monthly averages for the historical and future
(RCP2.6) period (Lamarque et al., 2010, 2011). N deposition for year 1901 was
used for the spin-up. Spatially explicit LU patterns and N fertilization were
adopted from IMAGE and MAgPIE (see also Supplement Sect. S1). We used the
year 1901 land-cover map for the spin-up, thereby omitting LUC occurring
before the 20th century as we assumed legacy effects from pre-1901 LUC on the
future C cycle to be small.
Linking ecosystem functions to ecosystem services (ESs). An increase
in an ecosystem function can be interpreted positive (+), negative (-),
zero (0), or either positive or negative (+/-), depending on the background
conditions or perspective. Effects can be small (+ or -) or large (++
or --). Regional effects are shown without brackets and global effects,
where relevant, in brackets. Indirect effects that are more directly
represented by another ecosystem function considered here are not shown. The
table is based on evidence from the literature in cases where the link is not
directly clear (see footnotes).
Ecosystem function
ES – climate change
ES – water
ES – flood
ES – water
ES – air
ES – food
mitigation
availability
protection
quality
quality
production
C storage ↑
++ (++)
Surface albedo ↑
++ (+)a
Evapotranspiration ↑
++ (+/-)b
Annual runoff ↑
++
-
0/+c
Peak monthly runoff ↑
0/+d
--
0/-e
0/-f
Crop production ↑
++ (++)
N loss ↑
+/- (+/-)g
--g
- (-)g
BVOC emissions ↑
+/- (+/-)h
0/-- (0/-)i
a The global effects of LU-driven albedo changes seem
to be small (e.g. de Noblet-Ducoudre et al., 2012).b Local
surface cooling as heat is needed to evaporate water. On larger scales, the
effect could be either a warming due to increases in atmospheric water vapour
(Boucher et al., 2004) or a cooling due to increased planetary albedo
resulting from more cloudiness (Bala et al., 2007; Ban-Weiss et al., 2011).c High flows imply more volume for dilution, prevent algae
growth,
and maintain
oxygen levels (Whitehead et al., 2009).d Effect of peak monthly runoff on water availability is
dependent on seasonal rainfall distribution and regional water storage
capacity. Annual runoff is the clearer indicator.e Soil erosion and associated remobilization of metals is
enhanced during flood events (Whitehead et al., 2009).f Due to flood damage in croplands (Posthumus et al., 2009).g LPJ-GUESS at present calculates total N loss and does not
differentiate between leaching and gaseous loss. Thus, we indicate several
effects that would arise from N emitted as N2O (a greenhouse gas), as
NOX or NH3 (affecting air quality and aerosol formation), or as
dissolved N. The net effect of N loss on climate
has been estimated to be a small cooling (Erisman et al., 2011), but
uncertainties are
large.h The net impact of BVOC emissions is very uncertain. On the
global scale, increased BVOC emissions might result in a warming (Unger,
2014).i BVOCs often increase ozone and aerosol formation, primarily
locally (Rosenkranz et al., 2015), with principally opposite warming and
cooling effects (Unger, 2014).
Analysed ecosystem service indicators
We analysed the implications of future LU patterns for the following ES
indicators: C storage (as an indicator for global climate change mitigation),
surface albedo and evapotranspiration (indicators for regional climate
effects in response to land-cover change), annual runoff (indicator for water
availability), peak monthly runoff (indicator for flood protection), crop
production (excluding cotton, forage crops, and pasture harvest; indicator
for food production), N loss (in LPJ-GUESS currently not differentiated into
dissolved N vs. N lost to the atmosphere; indicator for water or air
quality, or GHG losses), and emissions of the most common biogenic volatile
organic compounds (BVOCs) – isoprene and monoterpenes (indicator for air
quality). With the exception of C storage and crop production these variables
were not available from the LUMs. Most variables are direct outputs from
LPJ-GUESS simulations. Calculations for ES indicators not taken directly from
model outputs (C storage via CCS, crop production scaled to EarthStat,
albedo) or different from the standard model setup (BVOCs) are provided in
the Supplement Sects. S2–S5.
Global net-total values ± standard deviations (over 10 years) of
all analysed ecosystem functions as simulated by LPJ-GUESS for all scenarios
and different time periods and for LPJGIMAGE (top) and
LPJGMAgPIE (bottom). Total C is the sum of vegetation C, soil C,
product C (wood removed during deforestation but not immediately oxidized),
and cumulative CCS.
Ecosystem function
BASE
ADAFF
BECCS-ADAFF
BECCS
2000–2009
2090–2099
Vegetation C
380 ± 1
415 ± 2
478 ± 4
444 ± 3
391 ± 2
(GtC)
393 ± 2
459 ± 2
496 ± 5
476 ± 3
450 ± 2
Soil and litter C
1575 ± 1
1578 ± 1
1588 ± 1
1580 ± 1
1567 ± 1
(GtC)
1585 ± 1
1587 ± 1
1599 ± 2
1592 ± 2
1583 ± 1
Product C
5.7 ± 0.4
1.5 ± 0.1
0.4 ± 0.0
1.0 ± 0.1
2.4 ± 0.2
(GtC)
4.6 ± 0.2
0.3 ± 0.0
0.4 ± 0.0
0.3 ± 0.0
0.6 ± 0.1
Cumulative CCS
–
–
–
52.1 ± 3.4
100.0 ± 6.6
(GtC)
–
–
–
34.7 ± 2.5
73.5 ± 5.6
Total C
1961 ± 2
1995 ± 3
2067 ± 5
2077 ± 7
2060 ± 7
(GtC)
1983 ± 2
2047 ± 3
2096 ± 7
2103 ± 7
2108 ± 8
January albedo
0.250 ± 0.004
0.240 ± 0.002
0.237 ± 0.002
0.238 ± 0.002
0.241 ± 0.002
0.249 ± 0.004
0.240 ± 0.002
0.238 ± 0.002
0.240 ± 0.002
0.240 ± 0.002
July albedo
0.182 ± 0.001
0.179 ± 0.001
0.177 ± 0.001
0.178 ± 0.001
0.180 ± 0.001
0.182 ± 0.001
0.179 ± 0.001
0.177 ± 0.001
0.178 ± 0.001
0.179 ± 0.001
Evapotranspiration
58.6 ± 0.7
57.9 ± 1.2
59.1 ± 1.2
58.6 ± 1.2
57.7 ± 1.2
(1000 km3 yr-1∗)
58.9 ± 0.7
58.8 ± 1.2
59.5 ± 1.2
59.3 ± 1.2
58.9 ± 1.2
Annual runoff
52.5 ± 3.1
55.1 ± 2.8
53.9 ± 2.8
54.4 ± 2.8
55.3 ± 2.8
(1000 km3 yr-1)
52.2 ± 3.1
54.3 ± 2.8
53.7 ± 2.8
53.9 ± 2.8
54.2 ± 2.8
Peak monthly runoff
17.9 ± 1.0
18.9 ± 1.2
18.7 ± 1.2
18.8 ± 1.2
19.0 ± 1.2
(1000 km3 month-1)
17.9 ± 1.0
18.8 ± 1.2
18.6 ± 1.2
18.7 ± 1.2
18.8 ± 1.2
Crop production
28.9 ± 0.5
35.9 ± 0.5
34.7 ± 0.5
34.0 ± 0.5
33.5 ± 0.5
(Ecal)
27.5 ± 0.9
45.2 ± 0.4
29.3 ± 2.0
35.5 ± 0.7
40.8 ± 0.5
N loss
60.3 ± 7.1
109.7 ± 13.2
102.3 ± 12.5
103.6 ± 12.3
98.4 ± 11.5
(TgN yr-1)
73.3 ± 6.8
119.0 ± 8.0
103.2 ± 8.4
108.1 ± 7.9
110.0 ± 7.0
Isoprene emissions
477 ± 8
419 ± 9
529 ± 11
469 ± 10
382 ± 8
(TgC yr-1)
503 ± 9
495 ± 10
578 ± 13
532 ± 11
483 ± 10
Monoterpene emissions
40.7 ± 0.6
38.9 ± 0.9
40.2 ± 1.0
39.4 ± 0.9
38.2 ± 0.9
(TgC yr-1)
41.9 ± 0.7
40.5 ± 0.9
41.6 ± 1.0
40.9 ± 0.9
40.4 ± 0.9
∗ 1000 km3 are equal to 1 Eg of water.
The analysed ES indicators can serve as proxies for several ESs linked to
human well-being. Table 1 gives a qualitative overview of how these ES
indicators and corresponding ESs are interlinked. We do not aim to value and
rank individual ES indicators and thus do not assess here how relative
changes could be differently prioritized in decision-making for land
management. While this is certainly too simple of a generalization for fully
assessing the implications of such scenarios, ranking or prioritizing
individual ES indicators is a substantial challenge, which is beyond the
scope of this study. A given relative change can be more crucial for some
indicators than for others, and their importance can also vary across regions
and parties concerned. ESs will be influenced by changes in climate,
atmospheric chemistry, and LU even in the absence of land management for C
mitigation. To separate these non-mitigation effects from those effects
associated with a mitigation approach, we compared changes in ES indicators
in the BASE simulations over the 21st century to the changes that occur
when a mitigation approach is implemented. Land-based mitigation may thus
potentially enhance or degrade ESs to human societies.
Global relative changes in analysed ecosystem functions simulated by
LPJ-GUESS for different LU scenarios from IMAGE and MAgPIE. Changes are
capped at ±40 % for clarity reasons, and values exceeding 40 % are
written below the bar. (a) Changes in the BASE simulation from
2000–2009 to 2090–2099. (b) Changes from BASE to ADAFF by the
2090-2099 period. (c) Same as panel (b) but from BASE to
BECCS. (d) Same as panel (b) but from BASE to BECCS-ADAFF.
Results
In the following, the expressions “LPJGIMAGE” and “LPJGMAgPIE”
refer to results from LPJ-GUESS simulations driven by LU patterns from IMAGE
and MAgPIE, plus climate, CO2, and N deposition from RCP2.6. At some
points we refer to output directly taken from the IMAGE and MAgPIE
scenarios, in which case this is explicitly stated (“in the original
results/directly from the LUMs /the LUMs report”).
Carbon storage
Total global C pools simulated with LPJ-GUESS are generally lower for
LPJGIMAGE than for LPJGMAgPIE for all scenarios (Table 2, Fig. S1a). This difference is mainly a result of the representation of degraded
forests as grasslands in IMAGE-LU patterns (see Table S2), while MAgPIE does
not include degraded forests. Moreover, some temperate croplands that are
specified in the MAgPIE-LU patterns to grow fodder are represented in
LPJ-GUESS by rain-fed or irrigated, harvested grass. This crop type
increases soil C relative to cereal crops because the larger
below-ground / above-ground biomass ratio results in less C being removed
during harvest and thus more C input to the soil. C sequestration is
calculated by LPJ-GUESS for both BASE simulations within the 21st century, resulting in total C pools of 1995 (LPJGIMAGE) and 2047
(LPJGMAgPIE) GtC by 2090–2099 (Table 2). The combined effects of LU,
changing climate, N deposition, and atmospheric CO2 levels thus enhance
total C pools by 1.7 and 3.2 % (33 and 64 Gt) between the beginning
and the end of the century (Fig. 3a).
As expected from the overall scenario objective, total, vegetation, and soil
C pools are higher in the ADAFF simulations relative to the respective BASE
at the end of the century (Table 2, Fig. S1a–c). The additional C uptake for
ADAFF is larger for LPJGIMAGE (3.6 % or 72 GtC in year
2090–2099, 76 GtC in year 2099) than for LPJGMAgPIE (2.4 %
or 49 GtC in year 2090–2099, 55 GtC in year 2099, Fig. 3b). This reflects
the larger afforestation area and earlier afforestation activities in IMAGE
(Figs. 1, 2b). The largest changes in total C are found in tropical regions,
especially in Africa (+15 and +9 %, Fig. 4b) and/or tropical forests
(+13 and +8 %, Fig. S2b), mostly due to increases in vegetation C.
The BECCS scenario focusing on bioenergy crops and CCS as a climate change
mitigation strategy removes slightly less C from the atmosphere than ADAFF
for LPJGIMAGE but removes more C for LPJGMAgPIE (Table 2, Fig. 3c). Interestingly, LPJGIMAGE ADAFF accumulates more C than
LPJGIMAGE BECCS within the first half of the century, while BECCS
catches up during the second half of the century (Fig. S1a); this
acceleration of the BECCS sink is related to a steady increase in bioenergy
area throughout the century. The additional total C storage achieved by the
period 2090–2099 (compared to BASE 2090–2099) is 66 GtC (74 GtC in year
2099) for LPJGIMAGE and 61 GtC (69 GtC in year 2099) for
LPJGMAgPIE. Within these totals, cumulative C storage via CCS
(harvested C from bioenergy crops) is 100 and 74 GtC by the end of the
century (Table 2), but total C uptake is less than cumulative CCS as
LPJ-GUESS simulates a loss of vegetation and soil C from expanded
agricultural land. C storage in the combined bioenergy–avoided deforestation
and afforestation case (BECCS–ADAFF) mostly lies between the BECCS and the
ADAFF case but for LPJGIMAGE exceeds both ADAFF and BECCS by the end of
the century (Table 2, Figs. 3d, S1a, S3).
Regional relative changes in analysed ecosystem functions as
simulated by LPJ-GUESS for IMAGE-LU (left) and MAgPIE-LU (right). Changes are
capped at ±50 % for clarity reasons, values exceeding ±50 %
are written upon or below the bar. Regions are aggregated Global Fire
Emissions Database regions (Giglio et al., 2010) and are North America, South
America, Europe, Middle East, Africa, North Asia, Central Asia, South Asia,
and Oceania. (a) Changes in the BASE simulation from 2000–2009 to
2090–2099. (b) Changes from BASE to ADAFF by the 2090–2099 period.
(c) Same as panel (b) but from BASE to BECCS.
Albedo
Globally averaged January albedo under present-day conditions is
significantly higher (∼ 0.25) than July albedo
(∼ 0.18) due to the extensive northern hemispheric snow cover
in January. Both values decrease throughout the 21st century in the
BASE simulations, but more so for January (-4.1 and -3.7 % for
LPJGIMAGE and LPJGMAgPIE, respectively) than for July (-1.7
and -1.8 %) as a result of northward vegetation shifts and reductions in
snow cover (Table 2, Figs. 3a, S1d–e). Regionally, for both months and
both LUMs, the greatest reductions occur in high latitudes (Fig. 4a).
An increase in forested area as in the ADAFF scenario results in further
albedo reductions that are – at least for July albedo – comparable in
magnitude to the changes in BASE throughout the century (Table 2, Fig. 3b).
Only small increases compared to BASE occur in the BECCS simulations (Fig. 3c) as the land demand for bioenergy crop cultivation is relatively small.
BECCS-ADAFF results in a decrease in January and July albedo for both LUMs.
Evapotranspiration
Global evapotranspiration in the BASE simulations decreases much more for
LPJGIMAGE (-1.2 %) than for LPJGMAgPIE (0.1 %; Table 2,
Figs. 3a, S1f) due to different deforestation rates. There is large spatial
variability with evapotranspiration decreasing in some regions but
increasing in others (Fig. 4a), mainly driven by shifting rainfall patterns
(not shown).
As expected from the generally high evapotranspiration rates of forests,
end-of-century evapotranspiration in ADAFF is 2.1 and 1.3 % higher
than in BASE for LPJGIMAGE and LPJGMAgPIE, respectively (Fig. 3b),
with the largest increase occurring in Africa (Fig. 4b). BECCS results in a
change of -0.4 and +0.2 % for LPJGIMAGE and LPJGMAgPIE,
respectively, and BECCS-ADAFF in an increase of 1.3 and 0.8 % compared
to BASE.
Runoff
In the BASE simulations, global annual runoff increases by 4.9 and
4.1 % by the end of the century for LPJGIMAGE and LPJGMAgPIE,
respectively, with a slightly larger increase of 5.2 and 5.0 % in peak
monthly runoff (Table 2, Fig. 3a). This increase is mainly driven by
precipitation changes, but forest loss and increased water use efficiency
simulated under elevated CO2 levels also play a role. Similar to
evapotranspiration, spatial patterns are heterogeneous, with generally
larger changes in annual runoff than in peak monthly runoff in high
latitudes and reverse patterns in parts of the (sub-) tropics (Figs. 4a, S2a).
Changes in runoff in the mitigation simulations are opposite to
evapotranspiration changes (Figs. 3b–d, 4b–c), and the effects of
land-based mitigation on annual runoff are often larger than on peak monthly
runoff. ADAFF reduces annual runoff by 2.2 and 1.1 % (LPJGIMAGE
and LPJGMAgPIE) and peak monthly runoff by 1.3 and 0.7 %, while
BECCS increases annual runoff by 0.3 and 0.2 % and peak monthly runoff
by 0.2 and 0.0 %.
Crop production
Globally, total crop production simulated by LPJ-GUESS averages
∼ 29 and 27 Ecal yr-1 over the years 2000–2009 and
increases by 24 and 64 % to 36 and 45 Ecal yr-1 by the end of the
century for the LPJGIMAGE and LPJGMAgPIE BASE simulations,
respectively (Table 2, Fig. S1i) (for comparison, the increase is 78 and
96 % in the original IMAGE and MAgPIE results, respectively). The large
differences in crop production increase between LPJGIMAGE and
LPJGMAgPIE can be explained by variations in management and crop types
(e.g. whether the LUMs assume C3 or C4 crops to be grown in certain
regions), and the area and location of managed land, which differs
considerably by the end of the century, especially in Africa (Fig. 2a).
Sensitivity simulations in which N fertilizer rates, cropland area,
atmospheric CO2 mixing ratio, or the dynamic PHU calculation (i.e.
adaption to climate change via selecting suitable crop varieties, see Sect. 2.1) were fixed at year 2009 levels indicate that around 62 and 39 %
(LPJGIMAGE and LPJGMAgPIE, respectively) of the crop production
increase in the BASE simulations can be attributed to increases in N
fertilizer rates, 22 and 74 % to cropland expansion, 26 and 10 %
to increased atmospheric CO2 levels, and 9 and 4 % to dynamic
PHU calculation (Fig. S4a). The numbers do not add up to 100 % due to
non-linear effects, interdependencies between variables (crop
area/fertilization), and additional influences we did not analyse (e.g.
climate, N deposition, crop types, and irrigation).
Crop production calculated with LPJ-GUESS is reduced in all mitigation
simulations compared to BASE, by contrast to a set requirement in the LUMs
to retain annual production at similar levels to BASE: in the LUMs this is
achieved through further technology increases (for example through improved
management, inputs, pest control, and better crop varieties) compared to BASE.
The decline simulated in LPJ-GUESS, which is larger for LPJGMAgPIE than
for LPJGIMAGE, especially for ADAFF (LPJGIMAGE -3 % for the
2090–2099 period compared to 2090–2099 BASE; LPJGMAgPIE -35 %),
occurs because LPJ-GUESS captures only yield increases achieved through
higher N input, which only covers a part of the additional technological
yield increase assumed by the LUMs for the mitigation scenarios (and which
therefore allows for shrinking production area, see Table S2).
Nitrogen loss
Global N loss in the BASE simulations increases strongly over the 21st
century by 82 % for LPJGIMAGE and 62 % for
LPJGMAgPIE (Fig. 3a). Most of the increase is caused by
fertilization but increasing N deposition contributes as well (+19 %
over the century). N loss is higher for LPJGMAgPIE than for
LPJGIMAGE at the beginning and end of the 21st century, but
higher for LPJGIMAGE around mid-century (Table 2, Fig. S1j). As
total fertilizer application is higher for LPJGMAgPIE throughout
the entire century, these differences can be explained by spatial
heterogeneity (e.g. in India, where fertilization has a large impact on N
loss, fertilizer rates are generally higher for LPJGIMAGE than
for LPJGMAgPIE). Increases in N losses correspond roughly to
increases in N application, and to crop production increases in the original
LUMs. This indicates that crops in LPJ-GUESS approach N saturation, and
cannot use the additional N for higher yields, and thus that N application
rates, while consistent with LUM yield levels, are too high for LPJ-GUESS
yields. Sensitivity simulations indicate that most of the N loss increase
between 2000–2009 and 2090–2099 is induced by increased fertilizer
application and cropland expansions, while
increasing atmospheric CO2 and dynamic PHU calculation reduce N loss
(Fig. S4b).
N loss in ADAFF decreases by 6.7 % for LPJGIMAGE and 13.2 % for
LPJGMAgPIE compared to BASE 2090–2099 (Fig. 3b), but with large
variability across regions (Fig. 4b). The decrease can be attributed to
lower global fertilizer amounts in ADAFF than in BASE for both LUMs, as
forests are not fertilized. In the BECCS simulations the decrease is larger
for LPJGIMAGE (-10.3 %) than for LPJGMAgPIE (-7.6 %),
including substantial regional variations, especially in South America (Fig. 4c). The fertilization of bioenergy crops (for which low fertilizer rates
are assumed in the LUMs) adds N to the system; however, crop N uptake and
subsequent removal during harvest are also enhanced, resulting in a net N
removal in LPJ-GUESS (and thus less N available to leave the system via
leaching or in gaseous form). N loss reductions in BECCS-ADAFF lie between
ADAFF and BECCS for LPJGMAgPIE (-9.2 %) but are smallest amongst all
mitigation simulations for LPJGIMAGE (-5.5 %).
BVOCs
Changes in BVOC emissions are dominated by isoprene emissions, which are, by
weight, an order of magnitude higher than those of monoterpenes (Table 2,
Fig. S1k–l). In the BASE simulations, total BVOC emissions from 2000–2009 to
2090–2099 decrease by 11 % for LPJGIMAGE but only by 2 % for
LPJGMAgPIE (Fig. 3a). Spatially, BVOC emissions generally increase in
high latitudes but decrease in the tropics (Fig. 4a), corresponding to
northward forest shifts and deforestation or forest degradation concentrated
in low latitudes (not shown). The tropics dominate the overall response due
to much higher typical emission rates.
As expected from the generally high emission potential of woody vegetation
(compared with herbaceous), BVOC emissions increase in the ADAFF simulations
(24 and 16 % for LPJGIMAGE and LPJGMAgPIE, respectively).
Following the spatial change in forest cover, the increase mainly occurs in
the tropics (Fig. 4b). In the BECCS simulations, BVOC emissions decrease by
8 % for LPJGIMAGE and by 2 % for LPJGMAgPIE (Fig. 3c) due to
the low emissions of grassy bioenergy crops (corn in LPJ-GUESS). BECCS-ADAFF
results in 11 and 7 % higher emissions for LPJGIMAGE and
LPJGMAgPIE, respectively (Fig. 3d).
Discussion
Modelling uncertainties under present-day and future climate
The ES indicators analysed in this study are subject to uncertainties
arising from knowledge gaps, simplified modelling assumptions, and the need
to use parameterizations suited for global simulations. LPJ-GUESS has been
extensively evaluated against present-day C fluxes and stocks, both for
natural and agricultural systems, at site scale and against global estimates
(e.g. Fleischer et al., 2015; Piao et al., 2013; Pugh et al., 2015; Smith
et al., 2014). The use of forcing climate data from only one climate model
can be a major source of uncertainty as shown by the large variability in
future terrestrial C stocks introduced by different climate change
realizations even for the same emissions pathway (Ahlstrom et al., 2012).
As we use the low-emission scenario RCP2.6 here, we expect this effect to be
relatively small. The albedo calculation in this study was not used
previously, but patterns simulated by LPJ-GUESS under present-day conditions
(Fig. S5) broadly agree with Fig. 3 in Boisier et al. (2013).
Evapotranspiration and runoff in LPJ were evaluated by Gerten et al. (2004). Global total runoff calculated in this study for the 1961–1990
period is 26 % higher than their results. Simulation biases against global
estimates and observations from large river basins in the Gerten study were
mainly attributed to uncertainties in climate input data and to human
activities such as LUC (which is now accounted for) and human water
withdrawal. Spatial runoff patterns as simulated by the current LPJ-GUESS
version (Fig. S6.) seem to reveal some improvements compared to the biases
reported in Gerten et al. (2004) in mid- and high latitudes, but the model
still overestimates runoff in parts of the tropics. With respect to crop
production, simulated crop yields in LPJ-GUESS are constrained by N and
water limitation, but not by local management decisions, crop
varieties or breeds, diseases, and weeds (Lindeskog et al., 2013; Olin et
al., 2015b), and future improvement in plant breeding are ignored. While we
accounted for the additional restrictions by scaling simulated present-day
yields to observations, applying the unscaled LPJ-GUESS yield changes into
the future might create substantial underestimation of future yields and
crop production, as the only yield-augmenting factor for a given crop type
in LPJ-GUESS is increased N input. Global N-leaching rates are highly
uncertain but the annual rate simulated with LPJ-GUESS (if all N losses are
assumed to be via leaching) is within the range of published studies
(Olin et al., 2015a). Future modelled N leaching may also be affected by
ignoring improvements in plant breeds, as the current representation of crops
may not be able to absorb the N input computed in the LUMs for improved
varieties and management. For BVOCs, global datasets for evaluation are not
available (Arneth et al., 2007; Schurgers et al., 2009). Spatial emission
patterns are in good agreement with other simulations (Hantson
et al., 2017).
While LPJ-GUESS has thus been evaluated as comprehensively as possible, a
further next step for multi-process evaluation would be adopting a
formalized benchmarking system that also allows model performance to be
scored (Kelley et al., 2013). Likewise, large uncertainties reside in the actual
LUMs, which differ to a large degree in their estimates of main land-cover
classes for the present day (Alexander et al., 2017; Prestele et al.,
2016), and for which evaluation against observations has been identified as
a challenge (van Vliet et al., 2016).
Climate regulation via biogeochemical and biophysical effects
Our LPJGIMAGE simulations are slightly more effective than the
LPJGMAgPIE simulations in terms of simulated C uptake, but all
simulations diverge from the CDR target initially implemented in the LUMs
(see Sect. 4.7). Land-based mitigation might also impact the emissions of
other GHGs (e.g. N2O; see Table 1), but future fertilizer application
rates and emissions from bioenergy crops are highly uncertain
(Davidson and Kanter, 2014). While N2O contributes to global
warming, the net effect of reactive N might be a cooling when accounting for
short-lived pollutants and interactions with the C cycle (Erisman et al.,
2011). In our LPJ-GUESS simulations, reductions in N losses suggest a
decrease in gaseous N emissions for both ADAFF and BECCS; however, no
quantifications are possible as LPJ-GUESS does not yet differentiate between
different forms of N losses.
Climate effects of well-mixed GHG are global, whereas biophysical effects
are primarily felt on the local scale (Alkama and Cescatti, 2016).
Surface albedo in regions with seasonal snow cover is expected to decrease
significantly for afforestation scenarios (Bala et al., 2007; Bathiany et
al., 2010; Betts, 2000; Davies-Barnard et al., 2014), thereby opposing the
biogeochemical cooling effect. Effects of enhanced forest cover are less
pronounced in lower latitudes (Li et al., 2015) and for BECCS scenarios
(Smith et al., 2016). A modelling study by Hallgren et al. (2013) found that while albedo effects and C emissions from
deforestation for biofuel production might balance on the global scale,
biophysical effects can be large locally. In our BECCS simulations, albedo
changes are relatively small. However, we find noticeable albedo reductions
in ADAFF despite the fact that for both LUMs afforestation was concentrated
in snow-free regions where satellites rarely observe albedo differences
between forests and open land exceeding 0.05 (Li et al., 2015).
High evapotranspiration rates, often observed in forests, cool the local
surface. In tropical regions, this cooling effect exceeds the warming effect
from lower albedo (Alkama and Cescatti, 2016; Li et al., 2015). Current
anthropogenic land-cover changes have been estimated to reduce terrestrial
evapotranspiration by ∼ 5 % (Sterling et al.,
2013). In our simulations, impacts of land-based mitigation on global
evapotranspiration range from -0.4 % (LPJGIMAGE BECCS) to +2.1 %
(LPJGIMAGE ADAFF). On the regional scale this can translate to absolute
changes of more than 100 mm yr-1 in some tropical areas (e.g. central
Africa). While these changes seem relatively small compared to the mean
differences between forests and non-forests reported by Li et al. (2015)
(141 mm yr-1 20–50∘ N, 238 mm yr-1
20–50∘ S, 428 mm yr-1 20∘ S–20∘ N), our results still suggest that reducing emissions from
deforestation and forest degradation (REDD) activities would not only help
mitigate global climate change via avoided C losses but could provide
additional local cooling, serving as a “payback” for tropical countries.
The simulated evaporative water loss due to ADAFF at the end of the century
(∼ 1200 km3 yr-1 for LPJGIMAGE and 750 km3 yr-1 for LPJGMAgPIE for a C sequestration rate of
∼ 0.8 and 1.4 GtC yr-1, respectively) is higher than
estimated by Smith et al. (2016) (370 km3 yr-1 for a C
sequestration rate of ∼ 1.1 GtC yr-1). Furthermore, Smith
et al. (2016) assumed that dedicated rain-fed bioenergy crops consume
more water than the replaced vegetation (with additional water required for
CCS), while in our simulations bioenergy crops had little impact on
evapotranspiration as they were represented as corn. LU-driven changes in
evapotranspiration rates can also modify the amount of atmospheric water
vapour and cloud cover, with consequences for direct radiative forcing,
planetary albedo, and precipitation (e.g. Sampaio et al., 2007, see also
Table 1); however, such interactions cannot be captured by our model setup.
BVOCs influence climate via their influence on tropospheric ozone, methane,
and secondary organic aerosol formation (Arneth et al., 2010; Scott et
al., 2014), which depend strongly on local conditions such as levels of
nitrogen oxides (NOX) or background aerosol (Carslaw et al., 2010;
Rosenkranz et al., 2015). BVOC emissions also impact climate directly by
reducing terrestrial C stocks, but the magnitude is small (< 0.5 %)
compared to total GPP. While enhanced leaf-level BVOC emissions are driven
by warmer temperatures, uncertainties arise from additional CO2 effects
(which suppress leaf emissions). On the canopy scale, isoprene emissions
generally decrease for deforestation scenarios (Hantson et
al., 2017) but increase for woody biofuel plantations, which tend to use
high-emission tree species (Rosenkranz et al., 2015). In our simulations, we
find increases in BVOC emissions for ADAFF but not so for BECCS as bioenergy
crops were grown as low-emission corn. The high spatial and temporal
variability of the BVOC emissions, complications of atmospheric transport,
and gaps in our knowledge of the reactions involved make it difficult to
judge whether an increase in BVOC emissions results in a warming or cooling. The
global effect (assuming present-day air pollution in 1850 and excluding
aerosol–cloud interactions) of historic (1850s–2000s) reductions in BVOC
emissions (20–25 %) due to deforestation has been estimated to be a
cooling of -0.11 ± 0.17 W m-2 (Unger, 2014). Accordingly,
the substantial increase in BVOC emissions in our ADAFF simulations (16
and 24 %) might induce a warming of similar magnitude.
Water availability
Forests generally reduce local river flow compared to grass- and croplands.
Based on 26 catchment datasets including 504 observations worldwide, Farley
et al. (2005) reported an average decrease of 44 and 31 % in annual
stream flow caused by woody plantations replacing grasslands and shrublands,
respectively, with large variability across different plantation ages.
Simulations by Sterling et al. (2013) suggest that historic land-cover
changes were responsible for a 7 % increase in total runoff. The
reduction in global annual runoff due to ADAFF
(1200 and 600 km3 yr-1
compared to BASE 2090–2099) corresponds to around 16–32 % of human
runoff withdrawal (Oki and Kanae, 2006), which could be seen as a potential
risk to freshwater supply. Regional changes range from -5.2 to +0.4 %
across all scenarios, but in many cases impacts on irrigation (the largest
consumer of freshwater) potential in fact might be small: modelling work
suggests that renewable water supply will exceed the irrigation demand in
most regions by the end of the century for RCP8.5 (Elliott et al., 2014).
However, Elliott et al. (2014) also found that regions with the largest
potential for yield increases from increased irrigation are also the regions
most likely to suffer from water limitations. Patterns will be different in
an RCP2.6 world as CO2 fertilization significantly reduced global
irrigation demand (8–15 % on presently irrigated area) in the Elliott et
al. crop models and climate impacts are expected to be less severe in RCP2.6.
In uncoupled simulations, such as those carried out here, atmospheric feedbacks related
to higher evapotranspiration cannot be captured. At regional or continental
scale, there is evidence that afforestation might actually increase runoff
as the larger evapotranspiration rates enhance precipitation (Ellison et
al., 2012). However, based on regional climate modelling, Jackson et al. (2005) concluded that atmospheric feedbacks were not likely to offset water
losses in temperate regions where the additional atmospheric moisture cannot
be lifted high enough to form clouds.
Changing runoff affects water supply but can also contribute to changes in
flood risks. Bradshaw et al. (2007), using a multi-model approach and
data from 56 developing countries, calculated a 4–28 % increase in flood
frequency and a 4–8 % increase in flood duration for a hypothetical
reduction of 10 % natural forest cover, while van Dijk et al. (2009), for example, questioned forest potential to reduce large-scale flooding and
argued that the frequency of reported floods can be mainly explained by
population density. Ferreira and Ghimire (2012) extended the original
Bradshaw sample to all countries (129) that reported at least one large
flood between 1990 and 2009 and included socio-economic factors in their
analyses. They did not find a statistically significant correlation between
forest cover and reported floods. In our simulations, peak monthly runoff is
generally reduced for ADAFF; however, given maximum regional changes of
-3.6 % (Africa, LPJGIMAGE ADAFF) and presuming that floods are
largely controlled by other factors than forest cover, we expect LU effects
on flooding to be limited.
Food production
Increasing food production in a sustainable way to feed a growing population
is a major challenge of the modern world (Tilman et al., 2002).
Population and income growth (in SSP2 population peaks in 2070 at 9.4 billion people, and per capita GDP
continues to increase until 2100; Dellink et al., 2017; Samir and Lutz, 2017) are projected to be
accompanied by an increased need of total calories and shifts in diets
(Popp et al., 2017). For SSP2, economic modelling suggests that global
food crop demand will increase by 50–97 % between 2005 and 2050 (Valin
et al., 2014). In the present study, the corresponding increase reported
directly from the LUMs is 38 % for IMAGE and 52 % for MAgPIE in 2050
(78 and 96 % in year 2100). In our LPJ-GUESS BASE simulations we find
crop production increases of 22 and 45 % (LPJGIMAGE and LPJGMAgPIE, respectively) by
2050 and 24 and 64 % by the end of the century (corresponding to a per capita
increase for MAgPIE but a decrease for IMAGE). However, the production
increase is significantly reduced in the mitigation simulations, especially
for LPJGMAgPIE ADAFF, due to production shifts and the abandonment of
croplands for reforestation. Similar results have been reported by Reilly et
al. (2012) who found that afforestation substantially
increases prices for agricultural products, while the cultivation of
biofuels has little impact on agricultural prices due to benefits of
avoided environmental damage offsetting higher mitigation costs. Crop yields
in LPJ-GUESS are a function of environmental conditions, fertilizers,
irrigation, and adaption to climate change by selecting suitable varieties.
In our BASE simulations, the combined effect is an average yield increase of
∼ 17 and ∼ 41 % (LPJGIMAGE and
LPJGMAgPIE) between 2000–2009 and 2090–2099. In the LUMs the mitigation
scenarios are characterized by additional yield increases compared to BASE,
triggered by increased land prices. This intensification is to some extent
reflected in the fertilizer rates (derived from yields) provided by the
LUMs; however, other management improvements and investments in research and
development leading to higher-yielding varieties also impact future yield
increases. Additional assumptions about yield increases driven by
technological progress can thus not be captured by LPJ-GUESS. The simulated
decline in productivity in response to shrinking cropland area in the
mitigation scenarios suggests that, when adapting N fertilization,
irrigation and cropland area, and location from the LUMs, additional yield
increases of up to 6.6 and 35 % (LPJGIMAGE and LPJGMAgPIE)
would be required between the 2000s and the 2090s to produce the same amount
of food crops as in the BASE scenario, equivalent to ∼ 0.07 and 0.33 % per year.
Water and air quality
Managed agricultural systems directly impact freshwater quality.
Historically, approximately 20 % of reactive N moved into aquatic
ecosystems (Galloway et al., 2004), causing drinking water pollution and
eutrophication. As N loss in LPJ-GUESS is largely driven by fertilization
(Blanke et al., 2017), the much higher future fertilization
rates compared to present-day (+78 % for LPJGIMAGE; +95 % for
LPJGMAgPIE) lead to an increase in N loss of 82 and 62 % in BASE.
Such a large increase would have severe impacts on waterways and coastal
zones, where current levels of N pollution are already having substantial
effects (Camargo and Alonso, 2006). However, as discussed above, the N
application rates are derived from crop yields in the LUMs, and can only be
partially utilized by LPJ-GUESS due to its lower yield levels. Increasing
crop yields by increased N inputs leads to a strong decline in nutrient use
efficiency and declining returns on yields (Cassman et al., 2002; Mueller
et al., 2017). In contrast to the BASE simulations, the mitigation
simulations result in somewhat lower N losses because less fertilizer is
applied (ADAFF) or because bioenergy harvest removes more N than is added via
bioenergy crop fertilization (BECCS). Simulated N losses in LPJ-GUESS are
affected by different assumptions about N fertilizers and inconsistencies
between the models: fertilizer rates in the LUMs were calculated to support
the estimated crop yields (and hence the ensuing N demand). The resulting
grid-cell averages available to LPJ-GUESS did not take into account
differences in N application across crop types in a grid cell
(Mueller et al., 2012). Additionally, IMAGE and MAgPIE
simulate further increases in crop productivity and N use efficiency and
therefore nutrient recovery in harvested biomass, which may only be partly
captured by LPJ-GUESS (see Sect. 4.4).
Although we do not explicitly simulate emissions of N gases, increased N
losses suggest an excess of soil N, which increases the likelihood of gaseous
reactive N emissions such as NOX and ammonia (NH3) pollution,
contributing to particulate matter formation, visibility degradation, and
atmospheric N deposition (Behera et al., 2013). The chemical form and level
of these emissions will strongly depend on soil water status (Liu et al.,
2007). Improvements in air quality, e.g. via reductions in tropospheric ozone
(O3), are not only relevant for human health but can also enhance plant
productivity and crop yields (Wilkinson et al., 2012). The response of
O3 to BVOC emissions changes depends on the local NOX : BVOC
ratio (Sillman, 1999). An increase in BVOC emissions slightly suppresses
O3 concentration in regions of low NOX background but promotes it
in polluted regions (Pyle et al., 2011). Ganzeveld et al. (2010) used a
chemistry–climate model to study the effects of LUC in the SRES A2 scenario
(tropical deforestation) on atmospheric chemistry. By year 2050, they found
increases in boundary layer ozone mixing ratios of up to 9 ppb (20 %).
Changes in the concentration of the hydroxyl radical resulting from
deforestation (the primary atmospheric oxidant, and main determinant of
atmospheric methane lifetime) are much less clear due to uncertainties in
isoprene oxidation chemistry (Fuchs et al., 2013; Hansen et al., 2017;
Lelieveld et al., 2008), but O3 concentrations were not sensitive to
this uncertainty (Pugh et al., 2010). ADAFF describes a reverse scenario,
with forest expansion being largely concentrated in the tropics. The sign of
changes in the ADAFF simulations is reverse to changes in Ganzeveld et
al. (2010): by mid-century, global N loss in ADAFF decreases by ∼ 8 and
4 % and isoprene emissions increase by ∼ 14 and 4 % compared to
BASE. Consequently, we would expect tropospheric O3 burden in ADAFF to
decrease in the tropics but to increase in large parts of the mid-latitudes.
However, changes in overall air quality will likely be dominated by
anthropogenic emissions rather than LUC (Val Martin et al., 2015). BVOC
emissions might also increase in bioenergy scenarios (Rosenkranz et al.,
2015) but this does not happen in our study as the LUMs assumed grasses to be
the predominant bioenergy crop.
Potential impacts on biodiversity
Global-scale approaches that link changes in LU, climate, and other drivers
to effects on biodiversity are scarce, and burdened with high uncertainty,
though some approaches exist (Alkemade et al., 2009; Visconti et al.,
2011). Biodiversity, whether it is being perceived as a requisite for the
provision of ESs or an ES per se, with its own intrinsic value (Liang et
al., 2016; Mace et al., 2012), has not been considered in our analysis.
Nevertheless, it is evident that biodiversity can be in critical conflict
with demands for land resources such as food or timber (Behrman et al.,
2015; Murphy and Romanuk, 2014). LUC has been the most critical driver of
recent species loss (Jantz et al., 2015; Newbold et al., 2014). This has
led to substantial concerns that land requirements for bioenergy crops would
be competing with conservation areas directly or by leakage. Santangeli et
al. (2016) found around half of today's global bioenergy production
potential to be located either in already protected areas or in land that
has highest priority for protection, indicating a high risk for biodiversity
in the absence of strong regulatory conservation efforts.
In principle, avoided deforestation and reforestation/afforestation should
maintain and enhance habitat and species richness, since forests are amongst
the most diverse ecosystems (Liang et al., 2016). Forestation could also
support the restoration of degraded ecosystems. However, success of
large-scale reforestation–afforestation programs under a C-uptake as well as
a biodiversity perspective will depend critically on the types of forests
promoted and so far show mixed results (Cunningham et al., 2015; Hua et
al., 2016). Likewise, even under a globally implemented forest conservation
scheme there may be cropland expansion into non-forested regions that could
well be C-rich (implying reduced overall C mitigation) but also diverse such
as savannas or natural grasslands.
Role of model assumptions on carbon uptake via land-based mitigation and
implications for other ecosystem services
Our simulations show that trade-offs between C uptake and other ESs are to be
expected. Consequently, the question of whether land-based mitigation projects
should be realized depends not only on the effects on ESs, but also on the
magnitude of C uptake that will be achieved. However, our study suggests
that potential C uptake is highly model-dependent: C uptake in the three
land-based mitigation options in LPJ-GUESS is lower than the target value
used in the LUMs. When the underlying reasons for model–model discrepancies
are explored, a number of reasons can be identified such as bioenergy
yields, forest regrowth, legacy effects from past LUC, and recovery of soil
carbon in response to reforestation. Additionally, in the BECCS scenarios,
the CDR target was implemented as a CCS target which does not account for
additional LUC emissions, partly explaining the lower CDR values.
For forest regrowth, the current model configuration of LPJ-GUESS simulates
natural forest succession, including the representation of different age
classes. Krause et al. (2016) showed that the recovery of C in ecosystems
following different agricultural LU histories broadly agreed with site-based
measurements. LPJ-GUESS also has N (and soil water availability) as an
explicit constraint on forest growth and has been successfully tested
against a broad range of observations (Fleischer et al., 2015; Smith et
al., 2014). These studies indicate an overall realistic rate of forest
growth under natural succession. However, much of the afforestation may
occur with management facilitating fast built-up of C stocks (as assumed in
MAgPIE), but LPJ-GUESS does not implement plantations and has thus not been
evaluated against this type of regrowth. Forest (re)growth is simulated very
differently in LPJ-GUESS (where different age classes and their competition
are simulated), IMAGE (where in this study the dynamically coupled LPJmL
DGVM simulates natural regrowth in one individual per PFT) and MAgPIE (where
managed regrowth is prescribed towards potential C densities from LPJmL, see
Sect. 2.2). LPJmL also does not yet consider N constraints on vegetation
regrowth. C losses from deforestation and maximum C uptake following
reforestation depend on potential C densities which are likely different in
LPJmL and LPJ-GUESS. In the LUMs, the model's algorithm adopts C pools from
LPJmL and can thus decide to reforest the most suitable areas, while in
LPJ-GUESS other regions might have more reforestation potential. Finally,
soil C sequestration rates are likely different between LPJ-GUESS and LPJmL,
especially for MAgPIE-LPJmL where the assumption of soil C recovering within
20 years is likely overoptimistic (see Krause et al., 2016).
For BECCS, LPJ-GUESS simulates CCS rates of ∼ 2.2 and
1.8 GtC yr-1 (LPJGIMAGE and LPJGMAgPIE) by the
end of the 21st century, compared to ∼ 2.8 GtC yr-1 reported
from the LUMs directly. The number from the LUMs is close to the mean removal
rate of 3.3 GtC yr-1 reported in Smith et al. (2016) for scenarios of
similar production area (380–700, vs. 493 and 363 Mha in our IMAGE and
MAgPIE BECCS scenarios, respectively) and slightly larger CO2
concentrations (430–480 ppmv vs. 424 ppmv). Discrepancies between the
models arise mainly from differences in assumptions about bioenergy crop
yields. In our LPJ-GUESS simulations we grew bioenergy crops as corn (i.e. a
crop functional type with parameters taken from corn). By the end of the century, simulated bioenergy
yields are higher for LPJGMAgPIE BECCS (on average
13.8 t dry mass ha-1 yr-1, 10 % of total above-ground
biomass remaining on-site) than for LPJGIMAGE BECCS
(12.2 t dry mass ha-1 yr-1) due to different fertilizer rates
and production locations. Bioenergy crop yields in LPJ-GUESS might be
influenced by inconsistencies between the models about fertilization of
bioenergy crops: while the LUMs generally assume high N application,
fertilizer rates are reduced in areas used for bioenergy production because
bioenergy crops are less N-demanding. Consequently, the fertilizer rates from
the LUMs might be insufficient to fulfil the N demand of the corn-based
bioenergy crop in LPJ-GUESS, which responds strongly to fertilization (Blanke
et al., 2017). In contrast, bioenergy crops in the LUMs are represented by
dedicated lignocellulosic energy grasses. Reported yields of dedicated
bioenergy crops under present-day conditions show large variability
(miscanthus × giganteus:
5–44 t dry mass ha-1 yr-1; switchgrass:
1–35 t ha-1 yr-1; woody species:
0–51 t ha-1 yr-1), depending on location, plot size, and
management (Searle and Malins, 2014). By the end of the century, the LUMs
report average bioenergy yields of ∼ 15.0 t ha-1 yr-1
(IMAGE) and ∼ 20.3 t ha-1 yr-1 (MAgPIE), but how bioenergy
yields will evolve in reality when averaged across regions (including more
marginal land) is highly uncertain (Creutzig, 2016; Searle and Malins, 2014;
Slade et al., 2014).
Legacy effects from historic LU might also impact future C uptake as the
soil C balance continues to respond to LUC decades or even centuries after
(Krause et al., 2016; Pugh et al., 2015). We assessed the contribution of
legacy effects by comparing an LPJ-GUESS simulation in which LU (but not
climate and CO2) was held constant from year 1970 for IMAGE and 1995
for MAgPIE (consistent with the scenario starting years in each model) with
a run with fixed LU from year 1901 on. The differences then seen over the
21st century between these two simulations would arise chiefly from
legacy fluxes of 20th century LUC. These were found to be
∼ 17–18 GtC (not shown), accounting for part of the difference
in uptake between LPJ-GUESS and the LUMs. In the LUMs, harmonization to
history has been done with respect to land cover, but this was not possible
with respect to changes in vegetation and soil C pools (prior to 1970/1995).
Our results show that assumptions about forest growth and C densities,
bioenergy crop yields, and timescales of soil processes can critically
influence the C removal potential of land-based mitigation. Large
uncertainties about forest regrowth trajectories in different DGVMs (Pongratz
et al., in preparation) and BECCS potential to remove C from the atmosphere
(Creutzig et al., 2015; Kemper, 2015) have been reported before, including
the importance of second-generation bioenergy crops (Kato and Yamagata, 2014)
and LU-driven C losses in vegetation and soils (Wiltshire and Davies-Barnard,
2015). This is clearly an important subject for future research. Additional
analyses about the difference in C removal between the LUMs and LPJ-GUESS,
including results from additional DGVMs, are ongoing and will be published in
a separate paper (Krause et al., 2017).