Future changes in the climate system could have significant impacts on the
natural environment and human activities, which in turn affect changes in
the climate system. In the interaction between natural and human systems
under climate change conditions, land use is one of the elements that play
an essential role. On the one hand, future climate change will affect the
availability of water and food, which may impact land-use change. On the
other hand, human-induced land-use change can affect the climate system
through biogeophysical and biogeochemical effects. To investigate these
interrelationships, we developed MIROC-INTEG-LAND (MIROC INTEGrated LAND
surface model version 1), an integrated model that combines the land surface
component of global climate model MIROC (Model for Interdisciplinary
Research on Climate) with water resources, crop production, land ecosystem,
and land-use models. The most significant feature of MIROC-INTEG-LAND is
that the land surface model that describes the processes of the energy and
water balance, human water management, and crop growth incorporates a land
use decision-making model based on economic activities. In MIROC-INTEG-LAND,
spatially detailed information regarding water resources and crop yields is
reflected in the prediction of future land-use change, which cannot be
considered in the conventional integrated assessment models. In this paper,
we introduce the details and interconnections of the submodels of
MIROC-INTEG-LAND, compare historical simulations with observations, and
identify various interactions between the submodels. By evaluating the
historical simulation, we have confirmed that the model reproduces the
observed states well. The future simulations indicate that changes in
climate have significant impacts on crop yields, land use, and irrigation
water demand. The newly developed MIROC-INTEG-LAND could be combined with
atmospheric and ocean models to develop an integrated earth system model to
simulate the interactions among coupled natural–human earth system
components.
Introduction
The problems associated with climate change are related to the various
processes involved in natural and human systems, as well as their interconnections.
Changes in the climate system are caused by greenhouse gas emissions and
changes in land use resulting from human activity (Collins et al., 2013). At
the same time, climate change impacts natural and human systems in a variety
of ways (e.g., Arent et al., 2014; Porter et al., 2014; Romero-Lankao et al., 2014). According to research on the
linkage of various risks caused by climate change (e.g., Yokohata et al.,
2019), changes in the climate system affect the natural environment, leading
to changes in the socioeconomic system and finally impacting human lives.
One of the factors that plays an essential role in the interaction between
the natural and human systems is land use (van Vuuren et al., 2012;
Rounsevell et al., 2014; Lawrence et al., 2016). In general, changes in land
use are driven by changes in various socioeconomic factors, such as an
increase in food demand (Foley et al., 2011; Weinzettel et al., 2013;
Alexander et al., 2015). At the same time, changes in the climate system
affect the water resources available to agriculture and the size of the food
supply through changes in crop yield (Rosenzweig et al., 2014; Liu et al., 2016; Pugh et al., 2016), significantly affecting human land use (Parry et
al., 2004; Howden et al., 2007). Furthermore, climate mitigation measures
often include the use of biofuel crops, which can significantly influence
human land use (Smith et al., 2013; Humpenöder et al., 2015; Popp et
al., 2017). On the other hand, land-use change is known to have
biogeophysical and biogeochemical effects on the earth system (Mahmood et
al., 2014; Chen and Dirmeyer, 2016; Smith et al., 2016), as changes in land
use bring about changes in surface heat and water budgets, which, in turn,
affect air temperature and precipitation (Feddema et al., 2005; Findell et
al., 2017; Hirsch et al., 2018). Changes in land use also affect the
terrestrial carbon budget, thereby influencing the concentration of
greenhouse gases (GHGs) in the atmosphere (Brovkin et al., 2013; Lawrence et
al., 2016; Le Quéré et al., 2018). It seems clear, then, that climate
change induces land-use change by affecting various human activities, and
that human land-use change affects changes in the climate system (Hibbard et
al., 2010; van Vuuren et al., 2012; Alexander et al., 2017; Calvin and
Bond-Lamberty 2018, Robinson et al., 2018).
Various numerical models have been developed to describe the interaction
between natural and human systems in order to project future conditions as
they relate to climate change (van Vuuren et al., 2012; Calvin and
Bond-Lamberty 2018). Generally, in models dealing with the details of
natural systems, elements related to human activity are simplified, and in
models dealing with the details of human activities, elements related to
natural systems tend to be likewise simplified (Müller-Hansen et al., 2017;
Robinson et al., 2018). An earth system model (ESM) describes in detail the
physical and carbon cycle processes in a natural system. A number of ESMs
take human activities into consideration (Calvin and Bond-Lamberty 2018).
The iESM project (Collins et al., 2015) is based on a CESM (Community Earth System Model
Project, 2019) that incorporates GCAM (Calvin, 2011; Wise et al., 2014), an
integrated assessment model (IAM) that provides a comprehensive description
of human economic activities. With iESM, it is possible to capture the
various interactions between the natural environment and human economic
activities (Collins et al., 2015), but the model used to indicate the impact
of climate change on water resources and crops is rather simplified
(Thornton et al., 2017; Robinson et al., 2018; Calvin and Bond-Lamberty
2018).
IAMs consider supply and demand equations across the entire range of
economic transactions and calculate the changes in surface air temperature
resulting from increased GHGs in the atmosphere (Moss et al., 2010). IAMs
can also project future changes in human land use (Wise and Calvin, 2011;
Letourneau et al., 2012; Hasegawa et al., 2017). In general, however, IAMs
simplify processes related to the natural environment (water resources, the
ecosystem, crop growth, etc.) (Robinson et al., 2018) and thus do not
explore the interactions between the natural and human systems on a
spatially disaggregated basis (Alexander et al., 2018).
Many models for predicting changes in human land use have been developed
(e.g., Hurtt et al., 2006; Lotze-Campen et al., 2008; Havlík et al., 2011;
Wise and Calvin 2011; Meiyappan et al., 2014; Dietrich et al., 2019). Among
these, the LPJ-GUESS and PLUMv2 coupled models are able to consider spatially
specific interactions between changes in vegetation, irrigation, crop
growth, and land use (Engström et al., 2016;
Alexander et al., 2018). However, LPJ-GUESS (Olin et al., 2015) is a dynamic
vegetation model that is incapable of exploring interactions related to
physical processes, such as biogeophysical effects or future changes in
water resources. On the other hand, LPJmL is a well-established global
dynamical vegetation, hydrology, and crop growth model that can also
consider the nitrogen and carbon cycles (Rolinski et al., 2018; von Bloh et
al., 2018). The output of LPJmL (Bondeau et al., 2007), such as crop yield,
land and/or water constraints, and vegetation and soil carbon, is used in the land-use model MAgPIE (Lotze-Campen et al., 2008; Popp et al., 2011; Dietrich et
al., 2013; Kriegler and Lucht 2015; Dietrich et al., 2019). Although the
gridded information of LPJmL is linked to MAgPIE (Alexander et al., 2018),
the land-use change calculated by MAgPIE is not communicated to LPJmL
(one-way coupling), making interactive calculations using the dynamic
vegetation, hydrology, crop growth, and land-use models impossible.
In this study, we develop a global model that can evaluate the spatially
detailed interactions between physical and biological processes, human water
use, crop production, and land use related to economic activities. The model
is based on the land surface component of global climate model MIROC (Model
for Interdisciplinary Research on Climate, version 5.0; Watanabe et al., 2010),
into which we have incorporated water resources, land ecosystem, crop
growth, and land-use models. In the integrated model, which we call
MIROC-INTEG-LAND (MIROC INTEGrated LAND surface model version 1), the budgets
of energy, water, and carbon are determined by consistently considering the
processes related to land surface physics, ecosystems, and human activities.
Section 2 in this paper explains the overall structure of MIROC-INTEG-LAND.
The component models of MIROC-INTEG-LAND (climate, land ecosystem, water
resource, crop growth, and land use), here called “submodels”, are
described in detail in Sect. 3. Special attention is given to the land-use
submodel, as it was specifically developed for inclusion into MIROC-INTEG-LAND
and is expected to play a pivotal role. The other submodels – the climate,
water resources, crop growth, and land ecosystem models – are based on
models developed in the course of previous research. Section 3 outlines how
the submodels used here differ from the original models. Section 4 explains
the numerical procedure used to combine the submodels in the integrated
model. Section 5 describes the data used for the various inputs and boundary
conditions required to operate the integrated model. Section 6 verifies
model reliability by comparing historical simulation results with various
observational data. A summary of the results from simulations by
MIROC-INTEG-LAND of future conditions and a discussion of the interactions
between climate and water resources, crops, land use, and ecosystem are
presented in Sect. 7. Finally, in Sect. 8, we discuss possible research
themes regarding the interaction between natural and human systems that can
be addressed using MIROC-INTEG-LAND.
Relationships among variables in MIROC-INTEG-LAND. Components of
the integrated model (submodels) are shown as colored boxes. Climate (land
surface) and water resource components are represented with HiGWMAT (Pokhrel et al., 2012a),
which is based on the land surface model MATSIRO (Nitta et al., 2014) in a
global climate model MIROC (Watanabe et al., 2010). Land ecosystem and crop
growth components are represented with VISIT (Ito and Inatomi 2012) and PRYSBI2 (Sakurai et
al., 2014), respectively. The land-use model TeLMO (Terrestrial
Land-use MOdel) is developed in this
study. Inputs into the model are shown as boxes of climate and
socioeconomic scenarios. Solid arrows between the boxes indicate the
exchange of variables between the submodels. Dashed arrows indicate the
input variables of the submodels.
Overall features of MIROC-INTEG-LANDModel structure
The distinctive feature of MIROC-INTEG-LAND (Fig. 1) is that it couples
human activity models to the land surface component of MIROC, a
state-of-the-art global climate model (Watanabe et al., 2010). The MIROC
series is a global atmosphere–land–ocean coupled global climate model, which is one
of the models contributing to the Coupled Model Intercomparison Project
(CMIP). MIROC's land surface component MATSIRO (Minimal Advanced Treatments
of Surface Interaction and Runoff; Takata et al., 2003; Nitta et al., 2014)
can consider the energy and water budgets consistently on the land grid with
a spatial resolution of 1∘. MIROC-INTEG-LAND performs its calculations
over the global land area only, and neither the atmosphere nor ocean
components of MIROC are coupled. One of the advantages of running only the
land surface model is that it can be used to assess the impacts of land on
climate change, taking into account the uncertainties of future atmospheric
projections.
Human activity models are included in MIROC-INTEG-LAND: HiGWMAT (Pokhrel et
al., 2012b), which is a global land surface model with human water management modules,
and PRYSBI2 (Sakurai et al., 2014), which is a global crop model. In HiGWMAT, models
of human water regulation such as water withdrawals from rivers, dam
operations, and irrigation (Hanasaki et al., 2006, 2008a, b; Pokhrel et
al., 2012a, b) are incorporated into MATSIRO, the abovementioned global
land surface model. In PRYSBI2, the growth and yield of four crops (wheat,
maize, soybean, rice) are calculated. In addition, TeLMO (Terrestrial
Land-use MOdel), a global land-use model developed for the present study,
calculates the grid ratio of cropland (food and bioenergy crops), pasture,
and forest (managed and unmanaged) as well as their transitions. The land-use
transition matrix calculated by TeLMO is used in the process-based
terrestrial ecosystem model VISIT (Vegetation Integrative SImulator for
Trace gases; Ito and Inatomi 2012).
In MIROC-INTEG-LAND, various socioeconomic variables are given as the input
data for future projections. For example, domestic and industrial water
demand is used in HiGWMAT. The crop growth model PRYSBI2 uses future GDP
projections in order to estimate the “technological factor” that
represents crop yield increase due to technological improvement. The land-use model TeLMO uses future demand for food, bioenergy, pasture, and roundwood, as well as future GDP and population estimates. For future
socioeconomic projections, we use the scenarios associated with shared
socioeconomic pathways (SSPs; O'Neil et al., 2017) and representative
concentration pathways (RCPs; van Vuuren et al., 2011). These are generated
by an integrated assessment model: AIM/CGE (Asia-Pacific Integrated Model/Computable General Equilibrium; Fujimori et al., 2012, 2017b).
Interactions of the natural environment and human activities are evaluated
through the exchange of variables in MIROC-INTEG-LAND (Fig. 1). The
calculations in HiGWMAT are based on atmospheric variables (e.g., surface
air temperature, humidity, wind, and precipitation) that serve as boundary
conditions. The HiGWMAT model calculates the land surface and underground
physical variables for three tiles (natural vegetation, rainfed cropland, and
irrigated cropland) in each grid; a grid average is calculated by
multiplying the areal weight of the three tiles. In HiGWMAT, water is taken
from rivers or groundwater based on water demand (domestic, industrial, and
agricultural). Agricultural demand is calculated endogenously in HiGWMAT,
and withdrawn water is supplied to the irrigated cropland area, which
modifies the soil moisture. The operation of dams and storage reservoirs
also modifies the flow of the river. Using the soil moisture and temperature
calculated in HiGWMAT, the crop model PRYSBI2 simulates crop growth and
yield. PRYSBI2 also uses the same atmospheric variables that are used as
input data in HiGWMAT.
The land-use model TeLMO uses the yield calculated by PRYSBI2. In TeLMO, the
ratios of food plus bioenergy crop, pasture, and forest in each grid are
calculated based on socioeconomic input variables such as the demand for
food, bioenergy, pasture, and roundwood, as well as crop yield and ground
slope. TeLMO also calculates the transition matrix of land usage (e.g.,
forest to cropland, cropland to pasture), which is passed to the terrestrial
ecosystem model VISIT to evaluate the carbon cycle. The land uses calculated
by TeLMO are also used as the grid ratios of natural vegetation and cropland
area (rainfed and irrigated) in HiGWMAT.
Novelty of MIROC-INTEG-LAND
An important feature of MIROC-INTEG-LAND is that the land allocation model
is coupled to the state-of-the-art land surface model, and that the impact
of future climate and socioeconomic changes on water resources and land use
can be considered consistently. In general, future land-use changes are
often assessed by using an IAM. However, as mentioned earlier, IAMs are not
grid based, but rather they divide the world into dozens of regions and
describe the entirety of economic activity in these regions. Therefore,
IAMs have a simplified description of the processes related to water
resources and crop growth. In contrast, MIROC-INTEG-LAND provides
capabilities to calculate complex physical processes over the land and
considers the changes in water resources, taking into account human
activities such as irrigation and reservoir operation. Furthermore,
process-based crop models allow for an explicit and detailed consideration
of growth processes of five different crops.
For the projection of future land use, IAMs usually (1) calculate the area of
agricultural land by using yield information averaged over these regions
based on the balance between supply and demand and (2) allocate the
agricultural land by using a downscaling approach (e.g., Hasegawa et al., 2017). As pointed out in previous studies (Alexander et al., 2017), the
problem with this method is that it does not allow for an explicit
consideration of spatiotemporal information such as yield and production
cost when determining land-use change. The Food Cropland Model in TeLMO
addresses this issue by making it possible to consistently consider the
spatiotemporal information such as crop yields and the balance between
supply and demand when allocating the agricultural land, by using the Food
Cropland Down-scale Module and the International Trade Module as explained
in Appendix B.
As for the projection of future land-use change, TeLMO enables the
calculation of future land-use change as an offline simulation, by using the
crop yield data calculated in advance. On the other hand, crop yield depends
on the water resource availability that is affected by the changes in soil
physical processes due to future climate change, as well as the changes in
irrigated cropland area caused by the increases in future food demands.
MIROC-INTEG-LAND couples the models of physical land-surface processes, human water
management, and crop growth processes with the land-use allocation model to
consider these various interactions, as explained above.
SubmodelsGlobal land surface model with human water management HiGWMAT
The HiGWMAT model (Pokhrel et al., 2015) is a global land surface model
(LSM) that simulates surface and subsurface hydrologic processes
considering both the natural and anthropogenic flow of water globally
(1∘ in latitude and longitude). It incorporates human water
management schemes (Pokhrel et al., 2012a, b) into the
global LSM MATSIRO (Minimal Advanced Treatments of Surface Interaction and
Runoff) (Takata et al., 2003). In MIROC-INTEG-LAND, HiGWMAT calculates the
physical states (based on the changes in the energy and water budgets),
including human water use and management. In HiGWMAT, the biophysical fluxes
are updated after water use and management processes are simulated (Pokhrel
et al., 2012a). Since our previous publications provide a detailed
description of the MATSIRO model (Takata et al., 2003), groundwater scheme
(Koirala et al., 2014), and the human impact representations (Pokhrel et
al., 2012a, b, 2015), we include here
only a brief overview of these models or schemes.
MATSIRO land surface model
MATSIRO (Takata et al., 2003; Nitta et al., 2014) was developed at The
University of Tokyo and the National Institute for Environmental Studies in
Japan as the land surface component of the MIROC (K-1 Model Developers, 2004;
Watanabe et al., 2010) general circulation model (GCM) framework. MATSIRO
estimates the exchange of energy, water vapor, and momentum between the land
surface and the atmosphere on a physical basis. The effects of vegetation on
the surface energy balance are calculated based on the multilayer canopy
model of Watanabe (1994) and the photosynthesis-stomatal conductance model
of Collatz et al. (1991) following the scheme in the SiB2 model (Sellers et
al., 1996). The vertical movement of soil moisture is estimated by
numerically solving the Richards equation (Richards, 1931) for soil layers
in the unsaturated zone. The original version of MATSIRO (Takata et al.,
2003) did not include an explicit representation of water table dynamics. To
represent surface and subsurface runoff processes, a simplified TOPMODEL
(Beven and Kirkby 1979; Stieglitz et al., 1997) is used. The surface heat
balances are solved by an implicit scheme at the ground and canopy surfaces
in the snow-free and snow-covered portions (i.e., four different surfaces
within a grid cell) to determine ground surface and canopy temperature. The
temperature of snow is prognosticated by using a thermal conduction
equation, and the snow water equivalent (SWE) is prognosticated by using the
mass balance equation considering snowfall, snowmelt, and freeze. The number
of snow layers in each grid cell is determined from SWE. The albedo of snow
in the model is varied using an aging factor (Wiscombe and Warren 1980) and
in accordance with the time since the last snowfall and snow temperature,
considering the densification, metamorphism, and soilage of the snow.
Human water management schemes
The original MATSIRO was enhanced by Pokhrel et al. (2012a, b) through
the incorporation of a river-routing model and human water management
schemes (i.e., irrigation, reservoir operation, water withdrawal, and
environmental flow requirement). The irrigation scheme is based on the soil
moisture deficit in the top 1 m (i.e., the root zone) of the soil column;
that is, irrigation demand is estimated as the difference between the target
soil moisture set for each crop type and the actual simulated soil moisture
(Pokhrel et al., 2012b). Irrigation water is added as sprinkler irrigation
on top of vegetation; a part of this is lost as evapotranspiration, and the
rest returns back to the soil column. Subgrid variability of vegetation is
represented by partitioning each grid cell into three tiles: natural
vegetation, rainfed, and irrigated cropland.
The crop growth module for irrigation water is based on the H08 model
(Hanasaki et al., 2008a, b), where the crop vegetation formulations and
parameters are adopted from the Soil and Water Integrated Model (SWIM)
(Krysanova et al., 1998). The crop growth module for irrigation water in
HiGWMAT estimates the cropping period that is necessary to obtain mature and
optimal total plant biomass for 18 different crop types. Irrigation is
activated during the entire growing season but only for the irrigated
portion of a grid cell using a tile approach (Pokhrel et al., 2012a).
Crop growth considered in the irrigation scheme is simulated within the
HiGWMAT model using a crop growth module, which differs from the crop scheme
in PRYSBI2 that simulates crop yields (Sect. 3.2). The reasons why
different crop models are used to calculate irrigation water (HiGWMAT) and
crop yields (PRYSBI2) are that (1) HiGWMAT has been used as a crop model based
on SWIM (and it has been validated that the water withdrawal in various
regions is consistent with the statistical data; Pokhrel et al., 2012b), and
(2) PRYSBI2 has been used as a crop model based on SWAT, and crop yield in
PRYSBI2 has been calibrated using the agricultural statistics; Sakurai et
al. (2014). MIROC-INTEG-LAND uses different crop models to obtain realistic
water withdrawal in HiGWMAT and to calculate realistic crop yields in
PRYSBI2. The differences in the formulation between the crop models in
PRYSBI2 and HiGWMAT are that the former uses more detailed crop modeling of
the two-layer crop canopy, Farquhar photosynthetic CO2 assimilation,
and the cropping period based on Sacks et al. (2010) (see details in
Sect. A2), while the latter employs the simpler crop modeling of the
single-layer crop canopy, radiation-use efficiency-type biomass
accumulation, and the hypothetical planting date that gives the highest
yield under the given weather conditions (Okada et al., 2015).
The reservoir operation and environmental flow requirement schemes are based
on the H08 model (Hanasaki et al., 2008a, b). The reservoir operation
scheme (Hanasaki et al., 2006) is integrated within the TRIP global river-routing model (Oki and Sud, 1998) to simulate reservoir storage and release
for grid cells that contain reservoirs. The reservoir database is taken from
Lehner et al. (2011). Large reservoirs having a storage capacity greater
than 1 km3 are explicitly simulated; medium-sized reservoirs with a
storage capacity ranging from 3×106 to 1×109 m3 (Hanasaki et al., 2010) are considered to be ponds holding water
temporarily and releasing it entirely during the dry season. The withdrawal
module extracts the total (domestic, industrial, and agricultural) water
requirements: first from river channels and surface reservoirs and then from
groundwater; the lower threshold of river discharge prescribed as the
environmental flow requirement is considered when extracting water from
river channels. While irrigation demand is simulated by the irrigation
module, domestic and industrial water uses are prescribed based on the
AQUASTAT database of the Food and Agricultural Organization (FAO; see
Pokhrel et al., 2012b). We use the same prescribed values for domestic and
industrial water uses in both historical and future simulations, as future
projections of water withdrawal are not available.
Global crop growth model PRYSBI2
PRYSBI2 (Process-based Regional-scale crop Yield Simulator with Bayesian
Inference 2) (version 2.2) is a semi-process-based global-scale crop growth
model in which the daily biomass growth and resulting crop yield are
calculated for the same grid cell as HiGWMAT (1∘ in latitude and
longitude) (Sakurai et al., 2014). In MIROC-INTEG-LAND, PRYSIB2 is used to
calculate crop yields. The target crops are maize, soybeans, wheat, and
rice. Daily biomass growth is calculated using daily meteorological data
(precipitation, temperature, wind speed, humidity, solar radiation, and
atmospheric CO2 concentration) according to the photosynthetic rate
calculated by a simple big leaf model (Monsi and Saeki, 1953) and the enzyme
kinetics model developed by Farquhar et al. (1980). To determine the water
stress, the soil moisture and temperature calculated by HiGWMAT (Sect. 3.1) are used. In PRYSBI2, the planting date is given by using the data of
Sacks et al. (2010). The harvesting date is determined by when the crops
accumulate their total number of heat units (THU) up to the threshold
values. Crop yields for each year are calculated from the aboveground
biomass and harvest indexes (Sect. A2).
The process of fertilizer input is not included in this model. Rather,
parameters relating to technological factors that include the effect of
fertilizer are set and input into the model (Sect. A7). We call this
model a semi-process-based model, because some of the parameters, including
the parameters relevant to technological factors, are statistically
estimated using historical crop yield data (Iizumi et al., 2014) for each
grid cell by the DREAM (DiffeRential Evolution Adaptive Metropolis)
algorithm (Vrugt et al., 2009). The parameters were estimated by Markov
chain Monte Carlo (MCMC) methods with 20 000 steps for each grid cell
(Sakurai et al., 2014). The parameter values of the technological factors in
future scenarios are estimated as a linear function of the gross domestic
product (GDP) of each shared socioeconomic pathway (SSP) for each country
(see details in Sect. A7).
In the original photosynthesis model by Farquhar et al. (1980), the
photosynthesis rate is directly stimulated by the increase in CO2
concentration, which is called the CO2 fertilization effect. However,
it is also known that the CO2 fertilization effect is downregulated by
environmental limitations such as sink–source balance and nitrogen supply
(Ainsworth and Long, 2005). In this model, the downregulation of the
CO2 fertilization effect is described as a function of atmospheric
CO2 concentration, in which the potential photosynthesis rate (maximum
carboxylation rate of Rubisco and the potential rate of electron transport)
gradually decreases according to the increase in CO2 concentration (see Sect. A6).
The crop model used in this study is an updated version (version 2.2) of the
model described in Sakurai et al. (2014) (which gives a detailed
description of PRYSBI2 version 2.0) and Müller et al. (2017) (which
gives a brief description of version 2.1). The structure of the model is
quite similar to versions 2.0 and 2.1. However, there are some parts of the
version 2.2 structure that are slightly different. In Appendix A, we present
a summary of the model and identify the elements that differ from the
earlier versions.
Global land ecosystem model VISIT
The functions of the natural land ecosystem and their environmental
responses are simulated by the submodel VISIT (Vegetation Integrative
SImulator for Trace gases) (Ito, 2010; Ito et al., 2018). In
MIROC-INTEG-LAND, VISIT is used to calculate the carbon and
nitrogen cycles. VISIT is a process-based terrestrial biogeochemical model
that simulates the atmosphere–land-surface exchange of greenhouse gases such
as CO2 and CH4 and trace gases such as biogenic volatile organic compounds. Carbon, nitrogen, and associated water cycles are fully simulated
in the model using ecophysiological relationships but in a simplified
manner. The model operates at the global scale with a spatial resolution of
0.5∘×0.5∘. The ecosystem carbon cycle is
simulated using a box-flow scheme composed of three plant carbon pools
(leaf, stem, and root) and two soil carbon pools (litter and humus).
Photosynthetic carbon acquisition is a function of the leaf area index,
light absorptance, and photosynthetic capacity, which respond to
temperature, ambient CO2, and humidity. Soil carbon dynamics are
simplified by the litter and humus scheme but works well to simulate microbial
decomposition and carbon storage. The model has two layers, i.e., natural
vegetation and cropland, at each grid that are weighted by a land-cover
fraction to obtain the total grid-based budget. Impacts of land-use change
on the ecosystem carbon budget are taken into account using a simple scheme
by McGuire et al. (2001) in which typical fractionation factors are applied
to deforested biomass (e.g., immediate emission, 1-, 10-, and 100-year
pools). The difference in carbon emissions from primary and secondary
forests is included by using a different biomass density; regrowth of
abandoned croplands is also simulated as the recovery of the mean biomass of
the natural vegetation in the same grid. For brevity, croplands are
categorized into three types (rice paddy, other C3 crops such as wheat,
and C4 crops such as maize); the crop calendar and management practices
such as fertilizer input are simulated within the VISIT model (i.e.,
independent of PRYSBI2) in a conventional manner. Planting and harvest dates
are determined by monthly mean temperature; country-specific fertilizer
inputs derived from the FAO country statistics (FAOSTAT; FAO, 2019) are used.
In PRYSBI2, the effects of fertilizer are included in the technological
factors, and crop yields are calibrated based on the technological factors,
as described in Sects. 3.2 and A7. On the other hand, VISIT has
been applied and validated at various scales from flux measurement sites to
the global scale (e.g., Ito et al., 2017) based on the treatment of
fertilizer input, as described above. The consistent treatment of fertilizer
processes in PRYSBI2 and VISIT should be important future work.
Land-use model TeLMO
In the course of developing the integrated terrestrial model
MIROC-INTEG-LAND, we developed the Terrestrial Land-use MOdel (TeLMO) for
projecting global land use with a resolution of 0.5∘×0.5∘. TeLMO projects land use in each grid cell based on
socioeconomic data such as demand for food and biofuel crops obtained from
the AIM/CGE (Fujimori et al., 2012, 2017b). In MIROC-INTEG-LAND, TeLMO is
used to estimate land-use change. For long-term projections, TeLMO assumes
that there is a preferential order to land use by humans (i.e., urban, food
cropland, bioenergy cropland, pasture land, and managed forests). That is,
it assumes that land is used in the order of highest to lowest value added
per unit area. After allocating land use in this manner, TeLMO calculates a
transition matrix for each grid in order to evaluate the impact of land-use
change on terrestrial ecosystems. Details of the five models comprising
TeLMO – (1) the Food Cropland Model, (2) the Bioenergy Cropland Model, (3) the Pastureland Model, (4) the managed forest model, and (5) the land-use
Transition Matrix Model – are explained in Appendix B.
The numerical simulation procedure in MIROC-INTEG-LAND. The order
of the numerical integration is (1) TeLMO, (2) HiGWMAT + PRYSIB2, and (3) VISIT as described in Sect. 4. Boxes indicate the submodels and data. For
the submodels, the name and time step of the models are indicated in the
boxes. In the “Data” box, the name of the variable saved as a file is
indicated. In the “Input data” box, information regarding the input data
is indicated.
Numerical procedure of model coupling
In MIROC-INTEG-LAND, submodels with different time steps are executed
simultaneously by exchanging variables as shown in Fig. 1. The numerical
procedure for exchanging variables between the submodels is shown in Fig. 2. Exchanging variables among submodels is accomplished in one of two ways:
online coupling or offline coupling (Collins et al., 2015). In online
coupling, the values calculated by a submodel are exchanged with other
submodels via internal memory (i.e., the values calculated in one
subroutine are passed directly to other subroutines). In offline coupling,
the output of a particular submodel is written to a file; the other
submodels then read the file as needed. The far-right “Data” box in Fig. 2 indicates the files used for saving submodel output data. The arrows show
the exchanges that are made. The arrows between one submodel box and
another indicate online coupling; those between a submodel box and the
data box indicate offline coupling. The flow of submodel calculations is
described below.
TeLMO
The land-use model TeLMO (Sect. 3.4) calculates the areal fraction of each
land use within a grid (natural vegetation, cropland, pasture, etc.) and the
transitions among them once a year, using the decadal average of crop yields
calculated by PRYSBI2. The start year of TeLMO calculation is 2005. Since
the exchange of variables is not so frequent, TeLMO is coupled to the other
models via offline coupling (as shown in Fig. 2). That is, the output of
TeLMO (grid fraction of land uses and transitions) is written to files, and
the other submodels read the files as necessary. As shown in the figure,
TeLMO reads the output files of PRYSBI2 (crop yields) for its calculations.
HiGWMAT + PRYSBI2
HiGWMAT (Sect. 3.1), the global land surface model that considers human
water management, is used to calculate the physical states (surface and soil
temperature and moisture, as well as energy and water fluxes) at hourly to
daily time steps. The crop model PRYSBI2 (Sect. 3.2) is used to calculate
crop yields at daily time steps using the soil moisture and temperature
values generated by HiGWMAT. Since the exchange of variables between HiGWMAT
and PRYSBI2 is very frequent (i.e., daily), these two submodels are joined
through online coupling.
As shown in Fig. 2, in the future simulations, the MIROC-INTEG-LAND
calculations start with TeLMO (TeLMO is switched off before 2004). After the
output of TeLMO is written to files, the online-coupled HiGWMAT and PRYSBI2
make their calculations using the land-use grid ratio produced by TeLMO.
Once the output of the HiGWMAT-PRYSBI2 combination is written to files,
TeLMO again starts it calculations for the next year using the 10-year output.
The exchange continues in this fashion.
VISIT
As shown in Fig. 2, VISIT (Sect. 3.3), the terrestrial ecosystem model,
calculates the carbon and nitrogen cycles using the output of the land-use
model TeLMO. In MIROC-INTEG-LAND, no variable exchange between
HiGWMAT-PRYSBI2 and VISIT is performed at this stage since the structures of
these two submodels differ significantly. In the current version of
MIROC-INTEG-LAND, we first calculate the TeLMO-HiGWMAT-PRYSBI2 calculations until the year 2100,
and then perform the VISIT calculations from preindustrial time (including
spin-up simulations) to the end of the 21st century by using the TeLMO
output. (TeLMO is used only for the future period, and LUH (Land Use Harmonized) data are used for
other periods.)
Model coupling
The proper choice of coupling method depends on the specific features of the
variable exchange between submodels (Collins et al., 2015). One of the
advantages of offline coupling is that the structure of the original model
(e.g., the relationships between the main program and the subroutines) can
be preserved, at least to some extent, in the coupling. This is not the case
for online coupling. For example, for online coupling, either the main
program of the original model needs to be modified in order for it to serve
as a subroutine or a special program for connecting stand-alone models
(i.e., a coupler) needs to be developed. In MIROC-INTEG, offline coupling
is suitable for coupling TeLMO since the model structure of TeLMO is
different from the other submodels (TeLMO solves equations with various
spatial resolutions: global 30 s, 0.5∘, and 17 regions; see Appendix B
for details) and data exchange occurs only once per year (so that the
calculation cost for the input/output procedure can be minimized). On the
other hand, online coupling is appropriate for connecting HiGWMAT and
PRYSBI2, since the structure of the two submodels is similar (spatial
resolution with a global 1∘ grid), and the exchange of variables
is frequent (daily). In MIROC-INTEG, some of the subroutines of the original
PRYSBI2 models that calculate the crop growth processes are called from
HiGWMAT.
Experimental settings
Since MIROC-INTEG-LAND is based on a global land surface model, atmospheric
boundary data (hereafter “forcing” data) are required to operate the
model. The global land surface model with human water management HiGWMAT
uses atmospheric temperature, humidity, wind, and surface precipitation as
the forcing data to calculate the physical processes. In this study, we use
forcing data from the Inter-Sectoral Impact Model Intercomparison Project
(ISIMIP) Fast Track (Hempel et al., 2013). In ISIMIP, historical and future
climate simulations by five global climate models (GCMs) with bias
correction are used as the distributed forcing data. The methodology of bias
correction is described in Hempel et al. (2013). The five GCMs include
GFDL-ES2M (Dunne et al., 2012), HadGEM2-ES (Jones et al., 2011),
IPSL-CM5A-LR (Dufresne et al., 2012), Nor-ESM (Bentsen et al., 2013), and
MIROC-ESM-CHEM (Watanabe et al., 2011). Uncertainties in the atmospheric
predictions of the model can be considered by using the output data from the
various GCMs. In ISIMIP data, correction for model bias is based on
historical observations (Hempel et al., 2013). Thus, we can expect that
over- and underestimation errors are removed (at least to some extent).
Since the time interval in the original ISIMIP data is daily and the time
step in the land surface model HiGWMAT is subdaily, we generated
3-hourly data from the ISIMIP Fast Track daily data, based on the
methods described in Debele et al. (2007) and Willet et al. (2007), where
diurnal variations are generated based on the daily mean data.
In order to obtain a stable state of model variables, we performed spin-up
simulations following the procedure defined in the ISIMIP Fast Track
protocols. We first generated detrended 20-year data using 1951–1970
forcing data. The 20-year dataset was then replicated and assembled
back-to-back to obtain an extended dataset. The order of years was reversed
in every other copy of the 20-year block in order to minimize potential
discontinuities in low-frequency variability. The time duration of the
spin-up simulations was 400 years for the land surface model HiGWMAT and the
crop growth model PRYSBI2 and 3000 years (repeated 100 times using the
first 30 years detrended climate) for the terrestrial ecosystem model
VISIT. The spin-up time of VISIT is longer than that for the other
submodels, because it requires more time to reach a stable state, especially
in the case of soil organic carbon.
After the spin-up simulations, we performed historical (1951–2005) and
future (2006–2100) simulations based on the ISIMIP Fast Track protocols. For
the future simulations, we used the forcing data of the five global climate
models based on four RCPs (van Vuuren et al., 2011) – RCP2.6, 4.5, 6.0, and
8.5 – corresponding to radiative forcings of 2.6, 4.5, 6.0, and 8.5 W m-2 in the year 2100, respectively.
In the historical simulations of HiGWMAT, we used the land-use data (grid
ratio of natural vegetation, rainfed, and irrigated cropland) provided by the
Land Use Harmonized (LUH) project (LUHv2h; Lawrence et al., 2016); TeLMO was
switched off. In the future simulations of HiGWMAT, the rainfed and
irrigation cropland area is varied according to the output of TeLMO (Sect. 3.4). Since TeLMO projects the future total cropland area (irrigated plus
rainfed), the future irrigated area is calculated by multiplying the grid
irrigation ratio (irrigated / (rainfed + irrigated)) and the total
cropland area calculated by TeLMO. The grid irrigation ratio is calculated
by using the irrigated and rainfed cropland area determined by LUHv2h in
2005 and is fixed throughout the future simulation period. Although TeLMO
also calculates the future bioenergy cropland area, we assume that
bioenergy cropland is all rainfed.
TeLMO starts its calculations in 2005. As input data for TeLMO, we use the
output variables based on the shared socioeconomic pathways (SSPs; O'Neil
et al., 2017) calculated by an integrated assessment model (AIM/CGE;
Fujimori et al., 2017b). In this study, we use outputs of the SSP2 scenario
calculated by AIM/CGE (Fujimori et al., 2017b). Since the RCP8.5 scenario is not
available in SSP2, we use the output of the baseline scenario by AIM/CGE for the
calculation of RCP8.5. TeLMO uses future projections of GDP per capita,
demand for food and bioenergy crops, pasture, and roundwood (Sect. 3.4,
Appendix B). AIM/CGE calculates the aggregated transactions associated with
the activities of economic actors; the energy system is represented in
detail by dividing the globe into 17 regions (Fujimori et al., 2012).
The terrestrial ecosystem model VISIT is forced by the same ISIMIP forcing
data used in HiGWMAT (Hempel et al., 2013). In the historical simulations,
VISIT uses the historical land-use data from LUHv2h (Lawrence et al., 2016),
as described above. In the VISIT future simulations, the output variables
calculated by TeLMO, such as land use (cropland, pasture, forest) and the
transition matrix describing transitions from one use to another (see
Sect. 3.4 for details) are used as the forcing data.
It should be noted that the socioeconomic scenario that is used in climate
forcing data by ISIMIP Fast Track (Hempel et al., 2013) does not match
exactly the SSP scenarios (O'Neil et al., 2017), because the former is based
on CMIP Phase 5 (CMIP5; Taylor et al., 2012) and the latter on CMIP Phase 6
(CMIP6; Eyring et al., 2016). This should not be a serious problem because
the atmospheric processes are not coupled, and the radiative forcing (i.e.,
the RCP scenarios) used in ISIMIP Fast Track and the SSP scenarios is
consistent. The ISIMIP phase 3 (ISIMIP3;
https://www.isimip.org/protocol/#isimip3b, last access: 20 July 2020), which recently started
distributing the climate forcing data, uses CMIP6 GCM simulations based on
the SSP scenarios and is consistent with the present study.
Comparison of historical terrestrial water storage (TWS) simulated
by MIROC-INTEG-LAND with GRACE satellite data. For each river basin, the
panel to the right shows the seasonal cycle. The GRACE data shown are the
mean of the mass concentration products from two processing centers: CSR and
JPL. Simulated results are the average of five climate model simulations.
Grey shading indicates the uncertainty range shown by 1 standard deviation
from the mean.
Historical simulations and comparisons with observationsHiGWMAT
Offline simulations from the original MATSIRO and HiGWMAT models have been
extensively validated with ground- and satellite-based observations of
various hydrologic fluxes and forms of storage (e.g., river discharge,
irrigation water use, water table depth, and terrestrial water storage
(TWS)) at varying spatial domains and temporal scales in numerous
global-scale studies (Felfelani et al., 2017; Pokhrel et al., 2016, 2017, 2015, 2012a, b; Veldkamp et al., 2018; Zaherpour et al., 2018; Zhao et al., 2017).
For completeness, we provide here a brief evaluation of TWS and irrigation
simulations, since TWS is an indicator of overall water availability in a
region and a primary determinant of terrestrial water fluxes (e.g., evapotranspiration (ET) and
river discharge), and irrigation is an important component of the global
freshwater systems that share the largest fraction of human water use
globally (Hanasaki et al., 2008a; Pokhrel et al., 2016). Figure 3 plots the
comparison of simulated TWS with observations by the Gravity Recovery and
Climate Experiment (GRACE) satellite for the 2002–2005 period. The results
shown are spatial averages over 18 major global river basins selected by
considering a wide coverage of geographical and climate regions (Felfelani
et al., 2017; Koirala et al., 2014). For the GRACE data, we use the mean of
mass concentration (mascon) products from the Center for Space Research
(CSR; Save et al., 2016) at the University of Texas at Austin and the Jet
Propulsion Laboratory (JPL; Watkins et al., 2015; Wiese, 2016; Yuan et al., 2016)
at the California Institute of Technology. It is evident from Fig. 3 that
the model accurately captures the temporal variations as well as the
seasonal cycle of TWS in most basins. Certain differences between model and
GRACE can be seen in basins such as the Brahmaputra, Huang He, and Volga
river basins, but such disagreements have been commonly reported in the
literature owing to limitations in model parameterizations in simulating TWS
components (e.g., the representation of snow physics and human activities)
and inherent uncertainties in GRACE data (Felfelani et al., 2017; Scanlon et
al., 2018; Chaudhari et al., 2019).
Comparison of irrigation demands simulated by MIROC-INTEG-LAND (a) with the results from offline simulations using HiGWMAT and (b) forced by
observed climate forcing data (Pokhrel et al., 2015) for 1∘×1∘ grids shown as the mean for the 1998–2002 period.
Figure 4 compares the irrigation water demand simulated by MIROC-INTEG-LAND
with the results from offline HIGWMAT simulation obtained from Pokhrel et
al. (2015), which is forced by the observed climate data. It is evident
from this comparison that the broad spatial patterns seen in the offline
simulations are clearly captured by MIROC-INTEG-LAND. Certain disagreements
are, however, apparent. For example, MIROC-INTEG-LAND tends to overestimate
irrigation demand over highly irrigated areas in the central United States,
northwestern India, parts of Pakistan, and northern and eastern China, which
is likely due to the drier and warmer climate simulated by MIROC
(Watanabe et al., 2010) in these regions. The total global irrigation demand
simulated by MIROC-INTEG-LAND is 1750 km3, which is greater than the 1238±67 km3 from the offline simulations but falls near the upper bound
of estimates by various other global studies (see Table 1 in Pokhrel et al.,
2015). The overestimation comes primarily from the highly irrigated regions
noted above. Given that our meteorological forcing data are from
GCM simulations, we consider our results for both TWS and irrigation demand
to be acceptable.
PRYSBI2
Figure 5 shows historical simulation results for crop yield using ISIMIP
forcing data as the baseline climate during the period from 1981 to 2005.
The historical simulation results were compared with the gridded global dataset of historical yield (Iizumi et al., 2013; Iizumi, 2017), which is a hybrid of
satellite-derived vegetation index data and FAOSTAT (FAO, 2019). The spatial
aggregation to the country scale was conducted by using the harvested area
(Monfreda et al., 2008). The area of wheat was separated into spring and
winter wheat by using their production proportions (United States
Department of Agriculture, 1994).
Comparison of model estimation with reference data on average
yield during the period 1981–2005 for the top 10 countries producing each
crop. The box plots show the median and range of model results estimated
from the five GCM outcomes. The main production countries were identified
according to the country-based harvested area for each crop.
The results of the comparison of crop yields show that the simulated yields
in most countries were underestimated to some degree (Fig. 5). Notably,
using WATCH Forcing Data as the reference data in the bias correction for
the ISIMIP dataset tends to underestimate solar radiation compared to the
observation data (Iizumi et al., 2014; Famien et al., 2018), which in turn
causes an underestimation of crop yields. The uncertainty of the projected
yields as measured by the differences in outcomes for the five climate
forcings was relatively small. The reason for this is that ISIMIP climate
forcing data were bias corrected using the same historical weather dataset
and the same method. For all crops, most of the correlations between the
simulated and reported data were distributed along the 1 : 1 line. These
results indicate that the model is capable of capturing the relative spatial
difference of long-term average crop yield across countries.
Comparison of latitudinal distribution of gross primary
production (GPP) in 2000–2010 with upscaled flux measurements (Model-Tree
Ensemble (MTE); Beer et al., 2010) and satellite observation (MODIS; Zhao et
al., 2005).
VISIT
The VISIT model captured the spatial and temporal patterns of terrestrial
ecosystem productivity and carbon budget with satisfactory accuracy. Figure 6 shows the latitudinal distribution of gross primary production for the
2000–2010 period in comparison to upscaled flux measurements (Beer et al.,
2010) and satellite observation (Zhao et al., 2005). High productivity in
the humid tropics and low productivity in the arid middle latitudes and arid,
cold high latitudes were effectively reproduced by the model simulation,
although mean global total GPP was slightly higher than the observation
(127.5 Pg C yr-1 by VISIT, 114.0 Pg C yr-1 by flux upscaling, and
121.7 Pg C yr-1 by satellite). Global carbon stocks in vegetation and
soil organic matter were estimated as 499 and 1308 Pg C, respectively, in
2010; this is comparable to the contemporary synthesis (Ciais et al., 2013).
Because of historical atmospheric CO2 rise, climate change, and
land-use change, substantial changes in terrestrial ecosystem properties
were simulated (not shown). As demonstrated by model validation and
intercomparison studies, the VISIT model allows us to effectively capture
the terrestrial ecosystem functions under changing environmental conditions.
TeLMO
In Fig. 7, the cropland area simulated by TeLMO in MIROC-INTEG-LAND is
compared with the cropland area reported in FAOSTAT (FAO, 2019) and to the
area simulated by AIM/CGE (Fujimori et al., 2017b), whose output of food
demand and GDP per capita is used as input in TeLMO. Output of the TeLMO
0.5∘ grid data is aggregated by country to facilitate comparison
with the FAOSTAT data. In order to also compare the TeLMO 0.5∘
grid data with the AIM/CGE cropland area, we used 0.5∘
downscaled land-use data based on the AIM/CGE calculation. (The methodology
of downscaling is described in Fujimori et al., 2017a.) With the adjustment
parameter Cj, the cropland area in TeLMO in 2005 is the same as that of
LUH (Lawrence et al., 2016). As shown in Fig. 7, MIROC-INTEG-LAND roughly
reproduces the cropland area by country shown in FAOSTAT (FAO, 2019). The
differences in the five climate forcings given to MIROC-INTEG-LAND cause
variance in crop yields, which in turn results in the variance in cropland
area results shown in Fig. 7.
Comparison of historical cropland area simulated by
MIROC-INTEG-LAND (red), AIM/CGE (blue), and FAOSTAT (black), using the ratio of
cropland area to total area. For MIROC-INTEG-LAND simulations, the cropland area
results for the five different climate forcings are shown.
Same as Fig. 7 but for the comparison of historical
pasture area simulated by MIROC-INTEG-LAND (red), AIM/CGE (blue), and LUH
(black), using the ratio of pasture area to total area.
In Russia, Brazil, and Australia, the recorded cropland area (i.e., FAOSTAT)
is within the range of the MIROC-INTEG-LAND cropland area simulations using
the different climate forcings. In Brazil and Russia, the variations in
cropland area are mainly due to the difference in climate forcings. In the
United States, the reported cropland area in FAOSTAT (FAO, 2019) is closely
reproduced by MIROC-INTEG-LAND until around 2010; however, the declining
trend of cropland area in the second half is not effectively reproduced. The
reason for the overestimation seen here may be related to the
underestimating of crop yield in PRYSBI2 (Sect. 6.3). The slight
overestimation of the global cropland area trend (Fig. 7h) may stem from
the same cause. Also, in China, although there is a declining trend of
cropland area in MIROC-INTEG-LAND, in reality the cropland area remained
nearly constant until 2014 and increased slightly thereafter. The increase
of cropland area in China is considered to be influenced by policy, which is
not considered in TeLMO.
In MIROC-INTEG-LAND, TeLMO uses the food demand and GDP per capita
calculated by AIM/CGE under the socioeconomic scenario SSP2 (Fujimori et
al., 2017b). Therefore, the difference between TeLMO and AIM/CGE is due to
the difference in crop yield as well as the mechanism for the allocation of
agricultural land. As explained in Sect. B1, TeLMO can consider the
spatial distribution of crop yield when allocating agricultural land. On the
other hand, in AIM/CGE, land-use change is calculated by aggregating crop
yield information in the regions where the model calculation is performed
(AIM/CGE divides the world into 17 regions). In large countries such as
Australia, Brazil, and Russia, the allocation method in TeLMO shows good
performance.
Same as Fig. 7 but for the historical forest area
simulated by MIROC-INTEG-LAND (red), AIM/CGE (blue), and FAO (black), using the
ratio of forest area to total area.
Time series of changes in the climate system based on the
forcings of the five climate models. Results shown are for (a) surface air
temperature (K); (b) soil moisture in the top 300 mm of the soil column
(mm), shown as an anomaly from first 20-year average; (c) irrigation water
supply (km3 yr-1). Thin curves indicate the global
average of results for each of the five climate model forcings. Thick curves
show the overall average of results based on the five forcings. The colors
indicate RCP2.6 (blue), RCP4.5 (green), RCP6.0 (orange), and RCP8.5 (red).
Figure 8 shows a comparison of TeLMO, AIM, and LUH data for pasture. Unlike
cropland, pastures are compared with LUH data, because there are no long-term
global observation data. TeLMO calculates pasturelands such that the area
matches that in the AIM for the AIM calculation domain (17 regions around
the world). Because AIM treats China and the United States as one region,
the results of TeLMO and AIM for China, the United States, and the globe are
almost the same. On the other hand, in Australia, TeLMO is closer to LUH.
Similarly, Fig. 9 shows a comparison between TeLMO, AIM, and FAO data of
forest area. TeLMO refers to MODIS data and calculates forest area by taking
into account deforestation and changes in crop area. Some differences can be
seen between TeLMO and FAO, probably because TeLMO refers to MODIS and not
to FAO; however, the differences are relatively small. Given that its
performance is similar to that of AIM/CGE, the TeLMO submodel in
MIROC-INTEG-LAND can be considered useful for future land-use prediction.
Future simulations and interaction of submodels
In the MIROC-INTEG-LAND future simulations, the RCP2.6, 4.5, 6.0, and 8.5
scenarios provided by ISIMIP1 (Hempel et al., 2013) serve as the climate
scenario, while the output of AIM/CGE (demand for food and bioenergy crops,
pasture, wood, etc.) according to the four RCPs under SSP2 (Fujimori et al., 2017b) serves as the socioeconomic scenario. The results in this section
provide an understanding of the interactions between climate, water
resources, crops, ecosystems, and land use that MIROC-INTEG-LAND
accommodates.
Time series of changes in crop yield (unit: t ha-1)
based on the forcings of the five climate models. Results shown are for (a) winter wheat, (b) spring wheat, (c) maize, (d) soybean, (e) rice, and (f) grid maximum value for the five crop types. Thin curves indicate the global
average of results for each of the five climate model forcings. Thick curves
show the overall average of results based on the five forcings. The colors
indicate RCP2.6 (blue), RCP4.5 (green), RCP6.0 (orange), and RCP8.5 (red).
Figure 10 shows the various time series related to climate system change.
Figure 10a depicts the change in surface air temperature used as forcing
data in MIROC-INTEG-LAND. It is displayed as the deviation from the average
value of the 10-year period around the start year of the future simulations (2005). As shown in Fig. 10a, the increase in average global land surface
air temperature in 2100 is approximately 6 ∘C for RCP8.5, 3 ∘C for RCP6.0, 2.5 ∘C for RCP4.5, and 1 ∘C
for RCP2.6. Figure 10b shows the change in soil moisture calculated by
MIROC-INTEG-LAND. Although the annual variation of soil moisture is
considerable, the global land average soil moisture content tends to
decrease in the 21st century. The reduction in soil moisture is largest in
the RCP8.5 scenario, where the rise in surface air temperature is
substantial. Results for the irrigation water supply are shown in Fig. 10c.
As indicated in Sect. 3.1, water is supplied from rivers to the soil
through irrigation until the ratio of soil moisture reaches a certain
threshold. The irrigated area is calculated by multiplying the cropland area
(as calculated by TeLMO) by the irrigation ratio, a fixed value
corresponding to the ratio of irrigation cropland area to the total cropland
area in 2005. Therefore, the changes in irrigation water supply in Fig. 10c reflect the changes in the irrigation area and the irrigation water
supplied from rivers to the soil to compensate for the decrease in soil
moisture. Although the global total cropland area increases in the first
half of the 21st century (Fig. 12), in regions with a high irrigation
ratio (e.g., India, China), cropland area decreases by the end of the
century (Fig. 12). As a consequence, the irrigation area in
MIROC-INTEG-LAND decreases, and, accordingly, the irrigation water supply
also decreases, as shown in Fig. 10c.
Changes in crop yield calculated for the various future scenarios are shown
in Fig. 11. The crop growth model PRYSBI2 in MIROC-INTEG-LAND can
calculate the yields (t ha-1) of four crops (wheat, maize, soybean, rice),
with a clear distinction between winter and spring wheat (meaning five crops
in all). In Fig. 11f, the global average of the grid maximum yield value
among the crops, which is used in the TeLMO calculation, is also shown. As
described in Sect. 3.2, the future simulations by PRYSBI2 take into
account the effects of climate change, as well as the CO2 fertilization
effects due to rising greenhouse gas concentrations (Sect. A6) and the
increase in technical coefficients due to future technological improvement
(Sect. A7).
Time series of changes in cropland area based on the
forcings of the five climate models. The vertical axis is the cropland area
as a fraction of total land area. The results are for (a) food cropland
area and (b) food + bioenergy cropland area. Thin curves
indicate the global average of results for each of the five climate model
forcings. Thick curves show the overall average of results based on the five
forcings. The colors indicate RCP2.6 (blue), RCP4.5 (green), RCP6.0
(orange), and RCP8.5 (red).
As shown in Fig. 11a–e, the yields of each of the crops rise over the
first half of the 21st century. This is due to the CO2 fertilization
effect and technological improvement. In general, the increase in yield is
more significant in the high-GHG scenarios such as RCP8.5 than in the
low-GHG scenarios such as RCP2.6. Such differences can be considered to be due to
the fertilization effect and impact of climate change, since all the RCPs
feature the same technological coefficient under the same SSP scenario
(i.e., SSP2). On the other hand, in the latter half of the 21st
century, the negative impact of climate change on crop yield is evident. In
the RCP8.5 scenario, in particular, crop yields decline sharply. PRYSBI2
results show that the crop type most sensitive to climate change is maize:
in 2100, the yield of maize under RCP2.6 is highest, while the yield of
maize under RCP8.5 is lowest.
Figure 12a shows the change in the food cropland area calculated by TeLMO.
As described in Sect. 3.4 and Appendix B, TeLMO uses the yield calculated
by PRYSBI2 (grid maximum value as shown in Fig. 11f) and the food demand
output of AIM/CGE. As shown in the Fig. 12a, crop area increases to meet
the increase in food demand in the first half of the 21st century. Compared
to other RCP scenarios during this time period, the RCP2.6 scenario requires
more food cropland area, since the increase in crop yield is smaller in the
RCP2.6 scenario. In the second half of the 21st century, the food cropland
area tends to decrease as crop yield increases more than food demand. The
decrease is smallest under RCP2.6 and largest under RCP6.0, and RCP8.5
actually requires an increase in food cropland area; as in this scenario,
crop yields decline late in the century. Although there are differences
among the results using the five different climate model forcings (the thin
lines in Fig. 12a), using the average value lines (the thick lines in the
figure) for comparison indicates that, by the end of the 21st century, the
food cropland area is largest under RCP8.5.
Figure 12b shows the time series of the sum of food and bioenergy cropland
area calculated by TeLMO. As described in Sect. 3.4, TeLMO calculates the
distribution of the global bioenergy cropland area needed to meet the
bioenergy demand calculated by AIM/CGE. It is known that the future
bioenergy cropland area will change substantially depending on crop yield,
and it should be noted that the setting in which crop yield is calculated
can significantly affect the bioenergy cropland area (Kato and Yamagata,
2014). As shown in Fig. 12b, the bioenergy cropland area is significantly
increased under RCP2.6 and RCP4.5. These climate scenarios require large
areas of bioenergy crops for future climate mitigation. Although the food
cropland area tends to decrease in the late 21th century (except in the
RCP8.5 scenario), more cropland area will be needed if we consider both food cropland and bioenergy cropland.
Spatial distribution of land-use change (units: a ratio
of the grid box area). The results are for (a, b) food cropland area and
(c, d) bioenergy cropland area. Average of the five climate projection-based
simulations under (a, c) RCP2.6 and (b, d) RCP8.5 scenarios in the 2090s.
Temporal change in global carbon stock in (a, b)
vegetation biomass and (c, d) soil organic carbon, with (red) and
without (green) land-use change under (a, c) RCP2.6 and (b, d) RCP8.5 scenarios.
Thick lines show the median and light zones show the maximum to minimum range
of the five climate projection-based simulations.
Figure 13 shows the global distribution of changes in food and bioenergy
cropland areas, using the difference in 10-year averages around 2100 and
2005. As described in Fig. 12a, RCP2.6 tends to reduce the food cropland
area in the latter half of the 21st century. Figure 13a and b show that
the food cropland area decreases in Africa, India, and China. As is
explained in Appendix B, TeLMO relies on the premise that the distribution
of food cropland area is determined by changes in crop yield, food prices,
wages (corresponding to changes in GDP per capita), and the demand for food.
Thus the decreases in food cropland area shown in Fig. 13a and b are due
to the increase in yield (meaning demand can be met with less cropland area)
and the increase in GDP per capita (which means the population engaged in
agriculture decreases due to development) in the SSP2 scenario. It should be
noted that the change in cropland area at a particular grid is not
determined solely by food production (the product of cropland area and crop
yield) at that grid, as TeLMO considers the food trade among the 17 regions.
As shown in Fig. 12 and noted earlier, the food cropland area will
increase in the late 21st century in the RCP8.5 scenario. Accordingly, in
comparison to the RCP2.6 scenario, the food cropland area in South America
and central Africa increases in the RCP8.5 scenario.
Spatial distribution of land-use-induced changes in
terrestrial ecosystem carbon stock. Results are for (a, b) vegetation
biomass and (c, d) soil carbon stock. Average of the five climate
projection-based simulations under (a, c) RCP2.6 and (b, d) RCP8.5 scenarios
in the 2090s.
As shown in Fig. 13, bioenergy cropland areas increase in various regions,
especially in the RCP2.6 scenario. As discussed in Appendix B, TeLMO
assumes that biofuel cropland is allocated based on the Agricultural
Suitability Index (Eq. B14), which is a function of the yield and price of
the bioenergy crop, GDP per capita, etc. At the same time, TeLMO also
assumes that regions with high biodiversity are protected, and calculations
are performed so as not to allocate biofuel cropland to the protected areas
as shown in Fig. B2 (Wu et al., 2019). As a result, bioenergy cropland
area is allocated to regions where the agricultural index is
high – northwest and southern South America, central Africa, and
Australia – but it cannot be allocated to protected areas such as the
Amazon.
Figures 14 and 15 show the effects of changes in food and bioenergy cropland
area on the terrestrial ecosystem calculated by VISIT in MIROC-INTEG-LAND.
The impact of land-use change on terrestrial ecosystems is evaluated by
comparing the calculation with and without considering the land-use change.
The global time sequence (Fig. 14) shows that the changes in food and
bioenergy cropland area have a significant impact on terrestrial ecosystems,
especially in RCP2.6, where the aboveground biomass will decrease by
approximately 50 Pg C (about 10 % of the present biomass stock) by 2100
due to deforestation for land-use conversion. The decrease in soil carbon
after deforestation is much smaller than the decrease in aboveground
biomass, as the carbon supply from crop residue compensates for the soil
carbon loss. Consequently, this simulation implies that the impacts of
land-use change occur heterogeneously and differ in their magnitude and
direction between vegetation and soil. Figure 15 shows the global
distribution of the effect of land-use change on aboveground biomass and
soil carbon. The impact on aboveground biomass is projected to be greater
in northwest South America, central Africa, northeast North America, and
Australia, where the bioenergy cropland area is expanding. In these regions,
even under the mitigation-oriented scenario, considerable declines in
ecosystem structure and functions would occur, leading to deterioration, for
example, of habitats for natural organisms, water holding capacity, and soil
nutrients. Consequently, these functional degradations would degrade
ecosystem services such as biodiversity, regulation, and provision. On the
other hand, in Asia, the decrease in food cropland area tends to increase
the aboveground biomass in both the RCP2.6 and RCP8.5 scenarios, possibly
leading to the enhancement of aboveground biomass and thus
ecosystem services.
Figure 16 shows the results of simulations to evaluate the effects of
climate change on crop yield, land use, and water demand. In Fig. 16, the
RCP8.5 simulations with climatic factors (temperature, water vapor, wind
speed, soil moisture, soil temperature) and CO2 concentration fixed at
2006 (noCL + noFE), those with fixed climatic factors (noCL + FE), and those
with variable climatic factors and CO2 concentrations (CL + FE) are
compared. The CL + FE simulations are the same as the RCP8.5 results shown
in Fig. 11f (crop yield), Fig. 12a (food cropland area), and Fig. 10c (irrigation
demand).
As shown in Fig. 16a, the crop yield is significantly larger in the
noCL + FE experiment than in the CL + FE experiment. This result indicates
that climate change can significantly reduce crop yields. One of the reasons
for the observed reduction in crop yield in the CL + FE experiment is that
the growing season is shortened due to an increase in surface air
temperature, which adversely affects crop growth (Sakurai et al., 2014). The
impacts of climate change on crop growth increase with increasing
temperature, and in 2100, crop yields in the CL + FE experiment are
projected to decrease by approximately 60 % relative to the yields in the
noCL + FE experiments.
As shown in Fig. 16a, the crop yield was much smaller in the noCL + noFE
simulations than that in the CL + FE simulations. The reason for the yield
in the noCL + noFE experiment being smaller than that in the CL + FE
experiment is because the crop yield increases due to the CO2
fertilization effect in the latter. The increase in crop yield in the
noCL + noFE experiment is due to technological developments (Sects. 3.2 and A7). Although there is a great deal of uncertainty regarding the
treatment of CO2 fertilizer effects in crop models (Sakurai et al., 2014), the increase in crop yields due to the CO2 fertilizer effect is
significant in the simulations of MIROC-INTEG-LAND.
Due to the changes in crop yields resulting from the changes in climate and
fertilization effects, future cropland area and irrigation demand will also
change significantly. In the CL + FE experiment, the food cropland area
(Fig. 16b) and irrigation demands (Fig. 16c) become larger than those in
the noCL + FE experiments because of the larger decrease in crop yields due
to the impacts of climate change (Fig. 16a). On the other hand, the
noCL + noFE experiment requires more food cropland area (Fig. 16b) and
irrigation demand (Fig. 16c) compared to the CL + FE experiment because
of the smaller increase in crop yields, mainly due to the absence of CO2
fertilization effects (Fig. 16a). In summary, the changes in climate and
CO2 fertilization effects are expected to have marked impacts on crop
yields, land use, and water demands in the future.
Implications and future research
With MIROC-INTEG-LAND, it is possible to calculate the interaction between
climate, water resources, crops, land use, and ecosystems. The discussion in
Sect. 7 suggests the type of feedback processes that can occur. As shown
in Fig. 11, future climate change can affect crop yields. Especially under
a scenario of large temperature increases (RCP8.5), crop yields will
decrease in the latter half of the 21st century (Fig. 11). Here, the
influence of the CO2 fertilization effect is also a very important
factor affecting future changes in crop yields (Fig. 16a). Changes in crop
yields due to climate change also have a large impact on cropland area
(Figs. 12, 16b). Future cropland area may increase in response to an
increase in food demand due to population growth, as well as due to
increases in biofuel crop cultivation in response to global warming
countermeasures. Such an increase in cropland area will cause a concomitant
increase in water demand due to an increase in irrigated cropland area
(Figs. 10, 16c). In addition, an increase in cropland area can affect
carbon uptake in terrestrial ecosystems (Fig. 14). Increased human water
use and changes in terrestrial carbon uptake can further affect the water,
crop yields, and carbon budgets on the land surface. A real novelty of
MIROC-INTEG-LAND is that the availability of both water and agricultural
land can be consistently considered in conjunction with changes in climate
conditions.
While this study showed only the results of the SSP2 scenario, in the SSP3
scenario, where the world is divided, the demand for food will be greater
and more cropland area will be needed (O'Neill et al., 2017). Investigating
the impacts of various natural and socioeconomic factors (climate,
irrigation, fertilization effects, population, food demands, etc.) on land-use change and land ecosystems is an important future research direction as
an extension of the present study.
Time series of changes in (a) cropland yield (maximum
across five crops in each grid, t ha-1), (b) food cropland area (a fraction of
total land area), and irrigation demand (km3 yr-1)
based on the forcings of the five climate models under the RCP8.5 scenario.
Simulations with climatic factors and CO2
concentrations fixed at 2006 (light green, noCL + noFE), those with
climatic factors fixed (cyan, noCL + FE), and those with varying
climate and CO2 concentrations (red,
CL + FE).
In addition to analyzing interactions, it is crucial to analyze the impacts
of climate change and the effectiveness of countermeasures using
MIROC-INTEG-LAND. The combined impacts of climate change on water resources,
crops, land use, and ecosystems can be mitigated by enhancing various
adaptation measures. For example, the use of water resources to control crop
yield loss, changes in cropping calendars, and breeding can reduce the
adverse effects of climate change on food and land use. With
MIROC-INTEG-LAND, it is possible to assess the efficiency of adaptation
measures designed to address the impacts of climate change on water
resources, crops, land use, and ecosystems (Alexander et al., 2018). With
consistent consideration of climate change, water resources, and land use,
the competition between water, food, and bioenergy use can be analyzed
(e.g., Smith et al., 2010). The model also provides useful insights into the
trade-offs of biodiversity loss from land-use change and the benefits of
climate mitigation.
MIROC-INTEG-LAND provides a way to integrate various human activity models
based on the global climate model as shown in Sect. 4. This paper
introduced illustrative simulation results produced by our application of
MIROC-INTEG-LAND as a land surface model driven by meteorological forcing
data. We plan to extend the model by enabling it to consider the physical
processes and carbon and nitrogen cycles in the atmosphere and ocean. The MIROC
community has developed MIROC-ES2L, an earth system model for CMIP6 (Hajima
et al., 2020). By incorporating the water resource model (HiGWMAT), the crop
growth model (PRYSBI2), and the land-use model (TeLMO) used in
MIROC-INTEG-LAND into MIROC-ES2L, we are developing an integrated earth
system model that we call MIROC-INTEG-ES. In MIROC-INTEG-ES, the
interactions between the earth system and human activities are consistently
considered. By using this integrated earth system model, the impact of
land-use changes on the climate system, including biogeophysical and
biogeochemical effects (Lawrence et al., 2016), can be more consistently
investigated.
Description of crop model PRYSBI2 version 2.2
In the following description, we present a summary of the crop model used in
MIROC-INTEG-LAND (PRYSBI2 version 2.2) and identify the elements that differ
from the earlier versions (version 2.0, Sakurai et al., 2014, and version 2.1,
Müller et al., 2017).
Input data
As input data, PRYSIB2 version 2.2 uses the cropping period based on the
planting and harvesting date by Sacks et al. (2010). Soil field capacity
(Scholes and Brown de Colstoun, 2011) and atmospheric data (average, maximum, and minimum
daily temperature; daily shortwave and longwave radiation; daily humidity;
and CO2 concentration) are also used as input data. We use the same
atmospheric data as HiGWMAT described in Sect. 5 (i.e., ISIMIP Fast Track
data by Hempel et al., 2013).
Growing period, maturity, and harvest
The time of seedling emergence after the planting date is determined by a
parameter relevant to the average period between planting and emergence
(lemerge). The period from emergence to maturity is determined by the
total number of heat units (THU) (Neitsch et al., 2005). The crop is
mature when THU is equal to a threshold value (thutotal), at which point
it is harvested. THU thresholds were estimated for each grid by performing
calibration between 1980 and 2006, so that harvest dates fit the data from
Sacks et al. (2010). If future projections are performed using this
threshold value, then the harvest date will deviate from Sacks et al. (2010)
because of the temperature rise in future climates (i.e., harvest dates
become earlier due to the increase in temperature). Using the biomass values
obtained at the time of crop maturity, the yield is calculated as follows:
Yield=hibase⋅BIOabove(maturity),
where “Yield” is the crop yield (kg ha-1), hibase is the harvest index, and
BIOabove(maturity) (kg ha-1) is the aboveground biomass at the time
of crop maturity. Although the harvest index changes according to
atmospheric CO2 concentration in version 2.0, in version 2.2, for
simplicity, it is fixed.
Photosynthesis
The photosynthesis processes in version 2.2 are the same as in the previous
versions. The photosynthesis rate is calculated according to the daily
meteorological data. The instantaneous global radiation and temperature at
time (t) of the day are estimated from the daily global radiation and daily
maximum and minimum temperature on a given day (td) according to the method
described by Goudriaan and van Laar (1994). The amount of photosynthetically
active radiation, PARt,td (MJ m-2 s-1), intercepted by the leaf
at time t on a given day td, is calculated using Beer's law (Monsi and Saeki,
1953). We used the model described by Baldocchi (1994) to calculate the
photosynthetic rate.
Temperature stress
The equations for the effects of temperature on the maximum carboxylation
rate of Rubisco and dark respiration rate are changed from those in version
2.0. The influence of temperature on the maximum carboxylation rate of
Rubisco and the potential rate of electron transport is given as follows
(Kaschuk et al., 2012; Medlyn et al., 2002):
Cvcmax(t,td)=expTMt,td-25⋅epvcmax298⋅R⋅(273+TMt,td),Cjmax(t,td)=expEjmaxTMt,td-25298⋅R⋅TMt,td+273⋅1+exp298⋅Sjmax-Hjmax298⋅R1+expTMt,td+273⋅Sjmax-HjmaxTMt,td+273⋅R,
where Cvcmax(t,td) and Cjmax(t,td) represent the
effect of temperature on the maximum carboxylation rate of Rubisco and the
potential rate of electron transport, respectively; TMt,td is the air
temperature (∘C) at time t on day td; epvcmax, Ejmax,
Sjmax, and Hjmax are parameters that describe the shape of the curve
(Kaschuk et al., 2012; Medlyn et al., 2002); and R is the universal gas
constant (8.314 J mol-1 K-1).
The influence of temperature on the dark respiration of leaves is given as
Cdark(t,td)=expTMt,td-25⋅eprd298⋅R⋅(273+TMt,td),
where Cdark(t,td) represents the effect of temperature on dark
respiration at time t on day td, and eprd is the parameter that describes the
shape of the curve (Kaschuk et al., 2012).
The maximum carboxylation rate of Rubisco, the potential rate of electron
transport, and the dark respiration rate are modified by temperature
effects:
A5Vcmax(t,td)=Θ⋅ξV⋅Cvcmax(t,td)⋅vcmax⋅Wstress(td),A6Jmax(t,td)=Θ⋅ξJ⋅Cjmax(t,td)⋅jmax⋅Wstresstd,
where Vcmax(t,td) is the maximum carboxylation rate of
Rubisco, Jmax(t,td) is the potential rate of electron
transport, and vcmax and jmax are the potential maximum carboxylation
rate and the potential rate of electron transport, respectively.
Wstress(td) represents water stress, which is
explained in Sect. A5. Θ is the compensation variable (0–1) that
represents the discrepancy between the ideal photosynthetic potential and
the actual one. ξV and ξJ are photosynthesis
compensation variables that change according to CO2 concentration.
These variables (Θ, ξV, and ξJ) are described in
the following section. The dark respiration rate is calculated as follows:
Rd(t,td)=rd⋅Cdarkt,td⋅vcmax,
where Rd(t,td) is the dark respiration rate (µmol m-2 s-1), and rd is the leaf respiration factor (Collatz et al.,
1991; Sellers et al., 1996a, b). The maintenance respiration and growth
respiration are also considered. The formulations of the respiration models
are also the same as those of the previous versions.
Soil water balance and water stress
In PRYSBI2, the calculation of water stress follows the SWAT (Neitsch et
al., 2005) algorithm. In SWAT, the daily water stress is calculated
according to soil water, soil characteristics (field capacity and water
content at saturation), root depth, and crop field evapotranspiration.
PRYSBI2 uses the soil water calculated in HiGWMAT as explained in Sect. 3.2. The crop field evapotranspiration is calculated in SWAT according to
the leaf area index.
Correction of parameters according to CO2
concentration
The correction of parameters based on CO2 concentration is included in the model using the following equations:
A8ξV=1-rϕ1ca-cbase+rmax1-rϕ1ca-cbase+rmax12-4rθrϕ1rmax1ca-c12rθ,A9ξJ=1-rϕ2ca-cbase+rmax2-rϕ1ca-cbase+rmax22-4rθrϕ1rmax2ca-cbase2rθ,A10rϕ1=drvcmaxcbase,A11rϕ2=drjmaxcbase,A12rmax1=drvcmax600cbase-1,A13rmax2=drjmax600cbase-1,
where ξV and ξJ are photosynthesis compensation
variables, drvcmax and drjmax describe the parameters, ca is
atmospheric CO2 concentration (mol mol-1), and cbase is the
baseline atmospheric CO2 concentration (mol mol-1). In this model,
if drvcmax and drjmax>0, ξV and ξJ decrease
linearly with increasing atmospheric CO2. If drvcmax and
drjmax=0, ξV and ξJ do not depend on atmospheric
CO2. In these equations, rmax1 and rmax2
are the respective asymptotic lines. rθ is the parameter
that determines the curvature of the lines; we set rθ=0.99. The parameters drvcmax and drjmax are based on the results
of Ainsworth and Long (2005).
Time trend of the parameter relevant to agricultural management
When using historical yield data to calibrate model parameters, we need to
consider temporal trends in the effects of non-climatic factors. Crop yield
should improve from year to year because of agricultural factors, such as
the decrease in harvest loss and the use of improved crop cultivars and
pesticides. We, therefore, assumed the following linear trend in
non-climatic effects when evaluating the long-term yield data:
Θ=θbase+θtrendYear-ybase,
where Θ is the compensation variable (0–1) that represents
the discrepancy between the ideal photosynthetic potential and the actual
one, which is used in Eqs. (A5) and (A6); θbase is the value of
Θ in year ybase and must be calibrated for each cell of
the grid; θtrend is the annual increase in Θ
due to non-climatic factors (which also must be calibrated for each cell of
the grid); “Year” is the year; and ybase is the criterion year (2006). In this
study, we analyzed the relationship between θbase and GDP for each crop and used the estimated relationship
for future prediction.
Description of land-use model TeLMOFood Cropland Model
For each grid, TeLMO first allocates the area for urban use; it then
allocates the area for food cropland. For the allocation of the urban area,
we use the Land Use Harmonization phase 2 future data that are used in
Coupled Model Intercomparison Project Phase 6 (CMIP6) (LUH2f; Lawrence et
al., 2016). It is generally expected that the food cropland area is
determined by the balance between the supply and demand for food crops. The
estimation of the supply potential of food crops requires the spatial
distribution of crop production, which is related to the natural
environment. On the other hand, the balance between the supply and demand
for food crops is influenced by socioeconomic factors (e.g., populations,
crop prices) related to international food trade. For this reason, TeLMO
projects future land-use change by allowing the Food Cropland Down-scale
Module (Sect. B1.1), which projects the global cropland distribution at a
resolution of 0.5∘ by considering environmental factors, to
interact with the International Trade Module (Sect. B1.2), which describes food
supply and demand based on the General Equilibrium Model by dividing the
world into 17 countries/regions. The primary objective of using TeLMO is to
describe the long-term trend in land-use change and not the detailed
year-to-year variations in land-use change. Therefore, we use 10-year
average values as input to the model.
A major feature of TeLMO is that it does not project the local cropland
distribution by unidirectionally downscaling the total cropland area for
countries/regions obtained by integrated assessment models. This is because
the total cropland area for each country/region depends on the local
distribution of the cropland area. Therefore, TeLMO consistently treats the
cropland distribution calculated by the Food Cropland Down-scale Module and
the total cropland area for countries/regions obtained from the
International Trade Module to project future land-use change. The Food
Cropland Down-scale Module and International Trade Module are explained
below.
Food Cropland Down-scale Module
The Food Cropland Down-scale Module divides the earth into 0.5∘×0.5∘ (latitude × longitude) grid cells
(hereinafter “0.5∘ cells”) and calculates the percentage of each
cell occupied by cropland. The percentage of cropland is estimated by
calculating the probability that each 30′′×30′′ grid cell
(hereinafter “30′′ cell”) is used as cropland and averaging these
probabilities over the entire 0.5∘ cell. A 30′′ cell allocated to
urban use is not used for cropland. The probability ri of a given 30′′
cell being used as cropland is calculated as
ri=11+exp1.228+0.237ϕi-0.206pkyj/wkCj,
where ϕ is the slope, y is the yield per unit area (t ha-1), p is
the price of food crops, w is the wage, and C is an adjustment
parameter. The subscript i identifies the 30′′ cell, j identifies the
0.5∘ cell containing the ith grid cell, and k identifies the
country/region containing the ith and jth grid cell. The definition of
countries/regions is the same as that used in AIM/CGE (Fujimori et al.,
2012, 2017b). Equation (B1) is formulated based on the fact that the cropland
area is determined as a function of slope, crop price, yield, and the
wages of farmers. The first term of Eq. (B1) is defined as the agricultural
suitability index (ASI), which represents the relationship between cropland
area and the explanatory variables. The adjustment parameter Cj is used
to reproduce the cropland area of LUH (Lawrence et al., 2016) in the base
year 2005 and to connect the future TeLMO projection with the historical
simulation.
The ASI is derived from a logistic regression analysis using past
statistical data. We use the global 0.5∘ MODIS cropland area
(Friedl et al., 2010) as the objective variable, and the Global 30
Arc-Second Elevation (GTOPO30; Verdin and Greenlee 1996), the FAOSTAT food
crop yield and price (FAO, 2019), and GDP per capita as the explanatory
variables. GDP per capita rather than the wages of farmers is used for the
reason indicated in the discussion of Eq. (B4) below. The logistic regression
coefficient was derived from 23 000 data values that were randomly selected
from the set of global 0.5∘ grids at year 2005. A comparison of
the MODIS cropland areas and the calculated ASI values is shown in Fig. B1. The 23 000 randomly selected cropland area values were sorted in
descending order and divided into 10 categories, and the average MODIS
cropland area and the average ASI-based cropland area in each category were
compared. As shown in Fig. B1, the values calculated by the logistic
regression effectively reproduce the distribution of the MODIS cropland area
data.
In the MIROC-INTEG simulations, GTOPO30 (Verdin and Greenlee, 1996) is used
for the slope ϕi, and the food price pk and wage wk are
obtained in the International Trade Module as explained in Sect. B1.2. PRYSBI2
results (1.0∘ resolution, Sect. 3.2), converted to a resolution
of 0.5∘, are used for the yield yj. In TeLMO, total food
cropland area is projected by using the maximum yield across the five cereal
types (winter and spring wheat, maize, soybean, and rice). The reason for
this formulation is explained in Sect. B1.2. yj in Eq. (B1) is
calculated from the yields of the five cereal types by PRYSBI2. As
discussed above, TeLMO is a model that evaluates the long-term trend in
land-use change. Therefore, the crop yield and wage wk in Eq. (B1) is
the average value of 10 years (using the data from the 1 year to the 10 years before the calculation year).
Comparison of the global MODIS cropland area and the
calculated area using the agricultural suitability index (ASI). Here, 23 000
randomly selected cropland area values are arranged in descending order and
divided into 10 categories; the average value of MODIS (black) and ASI
values calculated by TeLMO (red) in each category are compared. The
horizontal axis is the higher percentile of cropland area data that is
randomly selected from the global 0.5∘ grids at year 2005.
Global distribution of areas protected from bioenergy
production.
The 0.5∘ cell cropland area (Rj) is calculated by averaging
the cropland probability in each of the 30′′ cells (ri) as follows:
Rj=∑iJiriJi,
where Ji is the number of i cells (3600) in each 0.5∘ cell.
The adjustment parameter Cj in Eq. (B1) is set so that the cropland
area in the first year of calculation equals the data from LUH2f (Lawrence
et al., 2016).
As explained above, the cropland distribution Rj projected at a spatial
resolution of 0.5∘ by the Food Cropland Down-scale Module is used
in calculations in the International Trade Module (Sect. B1.2).
International Trade Module
Our model was developed by extending one of the simplest of the basic
models – the Ricardian model. The Ricardian model is a one-production-factor
(productivity per capita), two-country/two-commodity (food and non-food) model
that attempts to describe the essence of free trade behavior based on the
theory of comparative advantage. Because of its simple structure, the
Ricardian model can be extended to a multi-country and multi-commodity model
(Ejiri, 2008). In the International Trade Module, we extend the Ricardian
model to be a multi-country (the entire world)/two-commodity (food and
non-food) general equilibrium model. In addition, we account for decreasing
returns in terms of production efficiency following the approach of Ejiri
(2008). That is to say, we assume that agricultural production efficiency
declines with increasing cropland area (and, conversely, that agricultural
production efficiency increases as cropland area decreases). For this
reason, industrial specialization, which has been pointed out as a problem
of the Ricardian model, is unlikely to occur.
In order to construct a multi-country/two-commodity model, the subscript k
was used to indicate country/region (the same 17 countries/regions defined
in AIM/CGE), and subscripts 1 and 2 were added to indicate agricultural and
nonagricultural sectors, respectively. The prices and wages in Eq. (B1)
are those in the agricultural sector, which are represented by p1,k and
w1,k, respectively.
First, wages in the agricultural sector, w1,k, are defined by using
labor input and gross domestic production (GDP). In the International Trade
Module, economic variables (e.g., food prices, wages, labor, and GDP) are
described as the relative ratio to the base year (2005), which is the first year of
calculation. Here, we assume that the total labor population ratio (relative
to the base year) equals the total population ratio (relative to the base
year).
l1,k+l2,k=Lk,
where l1,k, and l2,k are the labor input of the agricultural and
nonagricultural sectors, respectively, and Lk is the total labor
population (Murakami and Yamagata, 2019). GDP can then be described as total
domestic income:
GDPk=w1,k⋅l1+w2,k⋅l2,
where the value calculated by AIM/CGE is used for GDPk (units: USD).
If we assume that the wage (ratio relative to the base year) for the
nonagricultural sector is the same as that of the agricultural sector, the
agricultural worker wage w1,k is calculated as
w1,k=GDPkl1,k+l2,k=GDPkLk.
In other words, it is assumed that the change in agricultural worker wage
(relative to the base year) is equal to the change in per capita GDP. It is
known that the employment rate have changed by a small percentage in the
past. However, it is difficult to project the future changes in the
employment rate, and thus the employment rate is assumed to be constant in
the standard CGE (Computable General Equilibrium) models (e.g., Fujimori et al., 2012). Similarly, it is not
easy to confirm the historical changes in wages for each country nor to
estimate their future change; thus, similar to that for employment rate, the
future changes in wages are usually kept constant in the CGE models (e.g.,
Fujimori et al., 2012). It should be noted that a small increase in
employment rate (compared to the base year) can slightly decrease the wages
as indicated in Eq. (B4), possibly leading to an increase in cropland area
(Eq. B1).
Next, the price for agricultural sector p1,k is calculated using the
multi-country/two-commodity general equilibrium model. The prices for
agricultural and nonagricultural sectors are calculated using Eqs. (B5)
and (B6), respectively:
B6p1,k=w1,kl1,kx1,k,B7p2,k=w2,kl2,kx2,k,
where x1,k and x2,k are the production index in the agricultural
and nonagricultural sectors, respectively. Here, the production index in
the agricultural sector in region k (x1,k) can be calculated as the
sum of the products of 0.5∘ crop yield yj and cropland area
Rj using Eq. (7):
x1,k=∑jKjyjRj,
where Kj indicates the number of 0.5∘ cells within the
country/region k (3600). As described above, the cropland distribution
Rj generated by the Food Cropland Down-scale Module (Sect. B1.1) is used in
Eq. (B7). The domestic price p in Eqs. (B6) and (B7) is expressed in
terms of the local currency unit (LCU). This is converted to the
international price P (USD) using the exchange rate π (LCU per USD) in
Eqs. (B8) and (B9):
B9p1,k=πk⋅P1,k,B10p2,k=πk⋅P2,k.
The price p and production index x can then be connected using a
relational equation for the trade budget as follows. Imposing the condition
that the international budget for any country is zero results in Eq. (B10)
for the international balance of payments:
p1,k⋅x1,k-X1,k+p2,k⋅x2,k-X2,k=0,
where X1,k and X2,k are the demands for each good in
each region. As described previously, the output generated by AIM/CGE based
on the socioeconomic scenario is used for food demand X1,k. In this
study, livestock feed demand is not included in X1,k. The international
balance of payments as shown in Eq. (B10) consists of the current, capital,
and financial accounts. The imbalance in the international budget
corresponds to foreign exchange reserve. The foreign exchange reserve
changes over periods longer than 10 years, but it is not possible to predict
its future variation and thus is not considered in the standard CGE
models (e.g., Ejiri, 2008). In the real world, if foreign exchange reserve
increases, the amount of import goods tends to be decreased, because money is not
used for them. Consequently, in food importing countries, food production
tends to be increased, possibly leading to an increase in cropland area.
In addition, the price p and product index x can be related through Eq. (B11) by expressing economic growth in terms of GDP:
GDPk=P1,k⋅x1,k+P2,k⋅x2,k.
In Eq. (B3) and Eqs. (B5)–(B11) above, the eight unknown values are
p1,k, p2,k, x1,k, x2,k, l1,k, l2,k, πk, and X2,k. Of these, because the
reference for the international price P is the United States (region index
k=1), P1,1 and P2,1 (along with p1,1, p2,1) cannot be
set. For this reason, the condition is imposed that total global net exports
and imports are equal to zero:
B13∑k=1Kallx1,k-X1,k=0,B14∑k=1Kallx2,k-X2,k=0.
As explained above, TeLMO uses 10-year averages as input to the model to
represent long-term trends in land-use change (Sect. B1.1). We assumed that the
global total production is equal to consumption, i.e., the total global net
exports and imports equal to zero. In reality, there are certainly stock
changes in various goods, but it would not be counterfactual to assume that
they are net zero at longer timescale. The unknown values for p1,k, p2,k, x1,k, x2,k, l1,k, l2,k, πk, and X2,k are calculated by simultaneously solving
eight equations, Eq. (B3) and Eqs. (B5)–(B11), for all 17 regions
(k=1-17) subject to the conditions imposed by Eqs. (B12) and (B13). The p1,k, and w1,k values obtained from Eq. (B4) are
entered into Eq. (B1). Finally, the share of cropland for each
0.5∘ cell Rj can then be calculated using Eq. (B2).
As explained in Sect. B1.1, TeLMO uses the maximum yield of five cereal
types to project the total cropland area. Alternatively, it is possible to
increase the number of agricultural sectors in Eqs. (B3)–(B12), solve
the prices for each crops, and allocate the cropland area according to the
ASI values for each crop. Although we attempted this formulation in the course of
our development of TeLMO, it was found that the results were similar to
those obtained from the current formulation. On the other hand, the solution
of general equilibrium models did not converge in some cases, because the
number of sectors increases in the equations. For this reason, we decided to
adopt the current formulation while recognizing that calculating cropland
areas for each crop is an important future work.
Bioenergy Cropland Model
The Bioenergy Cropland Model uses 30′′ cells that are not assigned to urban
use or food cropland use. Whereas adjustment parameter Cj in the Food
Cropland Model (Eq. B1) could be set using observed cropland area for the
first year of the TeLMO calculation (the base year 2005), there is no
corresponding adjustment parameter in the case of bioenergy cropland,
because sufficient cropland devoted to biofuel crops did not exist in the
base year. Accordingly, the Bioenergy Cropland Model allocates bioenergy
cropland around the globe so that the global total biofuel crop production
equals the global total biofuel crop demand obtained by AIM/CGE. The
Bioenergy Cropland Model uses the same formularization to that in the Food
Cropland Down-scale Module (Sect. B1.1) to evaluate the probability of bioenergy
cropland in 30′′ cells using the following equation:
rbio,i=Cbio1+exp1.228+0.237ϕi-0.206pbio,kybio,j/w1,k,
where ϕi is the slope in 30′′ cell i, pbio,k is the biofuel crop
price in region k, ybio,j is the yield (t ha-1) of biofuel crops in
0.5∘ cells, and w1,k is the agricultural sector wage in
region k. For the biofuel crop price pbio,k, the values generated by
AIM/CGE are used. For biofuel crop yield ybio,j, the yield for
miscanthus or switchgrass, whichever is greater in a given cell, is
calculated for the entire globe by using the biofuel crop model developed in
Kato and Yamagata (2014). The biofuel crop model in Kato and Yamagata (2014)
considers the future changes in climate based on the RCP scenarios. In this
study, we also consider the future changes in fertilizer input based on the
SSPs adopted in Mori et al. (2018). Because of the uncertainty in future
fertilizer application for crop management, we set the high end of the N
fertilizer input threshold according to Tilman et al. (2011). The nitrogen
fertilizer application was set to increase from the current level according
to the increasing rate of GDP in the SSP2 scenario up to 160 kg(N) ha-1 yr-1 if the fertilizer input at the country level was below 160 kg(N) ha-1 yr-1 in the 2000s. Also, the phosphorus fertilizer input in
each country was set to follow the same annual increase rate as the nitrogen
fertilizer application.
Our use of the same formularization for the Food Cropland Model and the
Bioenergy Cropland Model is based on the assumption that the factors
determining both cropland areas are similar.
The adjustment parameter Cbio is set so that the global total biofuel
crop production volume (product of yield and cropland area) equals the
global total biofuel crop demand calculated by AIM/CGE:
∑kKallXbio,k=∑jJallybio,jRbio,j,
where Xbio,k is the biofuel crop demand for region k calculated by
AIM/CGE and Kall and Jall are the total number of regions (17) and
the total number of 0.5∘ cells (259 200), respectively.
Rbio,j is the average percentage of bioenergy cropland for all 30′′
cells in a given 0.5∘ cell, where the individual 30′′ cell
percentages are determined by Eq. (B14).
If bioenergy cropland were allocated based on the principle described
above, a massive development of bioenergy cropland would occur in regions
with high ecosystem production such as the Amazon. For this reason, the
model accounts for protected areas that cannot be allocated as bioenergy
cropland as shown in Fig. B2. Two sources were used for protected areas
(Wu et al., 2019): the World Database for Protected Areas (WDPA) (IUCN and
UNEP-WCMC, 2018) and the World Database of Key Biodiversity Areas (KBA)
(BirdLife International, 2017). As of 2018, the WDPA covered an area of 33.6×106 km2, and the KBA covered an area of 19.9×106 km2. In
this study, we did not consider the protected area for the calculation of
the food cropland and pasture under the assumption that food has a higher
priority than ecosystem protection.
Pastureland Model
Whereas the Food Cropland Model uses statistical relationships between
cropland area, yield, and economic variables, because reliable statistical
data do not exist for pastureland, a simpler approach is taken to estimate
pastureland. The probability of pastureland in each 30′′ cell is determined
based on net primary production (NPP) and slope, which is given by
rpast,i=Cpast,j×NPPj1+ϕ/20.
The denominator in Eq. (B16) reflects the fact that the use of land as
pasture decreases with the angle of inclination, as is shown in the LUH2f
data (Lawrence et al., 2016). The results of an offline simulation by VISIT
(Ito and Inatomi 2012), assuming the entire world to be grassland, are used
here for NPPj. The boundary condition of the VISIT offline
simulations is fixed at year 2005. Cpast,j is the adjustment parameter
for 0.5∘ cells. The value of Cpast,j changes from year to
year. The adjustment parameter for the base year, Cpast,j(t=0), is set
so that the pastureland distribution equals that of LUH2f (Lawrence et al.,
2016) for the base year (2005). Adjustment parameters for years other than
the base year, Cpast,j(t), are set by applying a
proportionality factor, α(t), to the base-year parameter:
Cpast,j(t)=α(t)×Cpast,j(t=0),
where α(t) is set so that regional total pastureland
area equals the regional total pastureland demand calculated by AIM/CGE. In
other words, α(t) is set so that the condition
Spast,k(t)=∑jJkRpast,j(t)
is met, where Spast,k(t) is the pastureland demand calculated by
AIM/CGE for region k; Rpast,j(t) is the average of percentage of
pastureland for all 30′′ cells (from Eq. B16) in a given 0.5∘
cell; and Jk is the total number of 0.5∘ cells in each region
k.
Managed Forest Model
In the Managed Forest Model, satellite data are used to determine forest
area; the share of forest area where timber harvesting occurs is allocated
as managed forest in the manner described below. The distribution of managed
forests in 0.5∘ cells, Rmfr,j(t), is formularized in terms of
the area of managed forests in the base year and the population density:
Rmfr,j=Afr,j×ρj*Cmanfr,k+ρj*,
where Afr,j is the area of managed forest in the 0.5∘ cells in
the base year (2005) and ρj* is the mean population density in the
5×5 grid (2.5∘ cell) of cells centered on the
0.5∘ cell in question. Larger 2.5∘ cells were used
instead of 0.5∘ cells based on the assumption that harvested
timber is transported within an approximately 100 km radius and that the
amount of harvested timber is determined by the population density in each
2.5∘ cell. The 100 km radius is estimated from the distance where
the transportation cost of timber (∼ USD 1 km-1 t-1) is
balanced with the price of timber (∼ USD 100 t-1). Here, the
transportation cost and price of timber are estimated using the FAOSTAT data
(FAO, 2019). Moderate Resolution Imaging Spectroradiometer (MODIS) satellite
data (Friedl et al., 2010) are used for the base-year forest area (2005),
and data from Murakami and Yamagata (2019) are used for the population
density (ρj*). Cmfr,k is an adjustment parameter that is
set for each of the 17 regions (k) so that the managed forest area
conforms to the roundwood demand Xmfr,k (kg yr-1) calculated by AIM/CGE.
We use the region-level adjustment factors for managed forest (Cmfr,k),
because the grid-level reference data are not available. In other words,
Cmfr,k is set so that the total regional amount of harvested timber
equals the regional total roundwood demand:
Xmfr,k=∑jJkRmfr,j×BjLj,
where Bj is the distribution of forest biomass (kg m-2) in
0.5∘ cells, calculated by VISIT (Ito and Inatomi, 2012) offline
simulations assuming the entire world to be forest with the fixed boundary
conditions (2005). Jk is the total number of 0.5∘ cells in
each region k. Lj is the harvesting period (years), which is estimated
as follows, based on theNPPj for 0.5∘ cells obtained from
VISIT (Ito and Inatomi, 2012).
Lj=∞NPPj<4500/NPPj4≤NPPj≤252025<NPPjLj reflects the fact that the harvesting period decreases with increases in net
primary production, as is shown in the LUH2v data (Lawrence et al., 2016).
The amount of forest harvested in a given year can also be calculated as
Rmfr,j×Bj/Lj (kg yr-1)
based on the distribution of managed forests Rmfr,j, forest biomass
Bj, and the felling period Lj for 0.5∘ cells.
Formulation of the Transition Matrix Model
Evaluating the impact of land-use change on terrestrial ecosystems requires
not only the spatial distribution of land use but also information on the
land-use transition. For example, in areas where shifting cultivation is
practiced, a particular area may be cleared as cropland while another area is
abandoned even though the overall cropland area within a cell does not
change. In such cases, there is a transition from cropland to secondary
land, which impacts the aboveground biomass and carbon budget. Thus, matrix
information regarding the transition from one land use to another land use
is essential.
For the land-cover types used in the transition matrix, we use the five
classes (urban, cropland, pasture, secondary/primary land) used in the VISIT
terrestrial ecosystem model (Ito and Inatomi, 2012). TeLMO forecasts eight
land-cover types, including the previously described urban, cropland (food
and bioenergy), pasture, managed forest, and unmanaged forest classes as
well as “grassland” (obtained from MODIS satellite data, Friedl et al.,
2010) and “other” land-cover types that are not used by humans (e.g., glaciers, lakes, and marshes, as defined by MODIS satellite data;
Friedl et al., 2010). The correspondence between the land-cover types used in
TeLMO and those used in the land-use transition matrix is presented in Table B1.
Correspondence of land-cover type in land-use model and
transition matrix.
Land-cover type inLand-cover type inland-use modeltransition matrixUrbanUrbanCropland (food)CroplandCropland (biofuel crop)PasturePastureManaged forestSecondary landUnmanaged forestPrimary landSecondary landGrasslandPrimary landSecondary landOther–
The primary/secondary land classes in the land-use Transition Matrix Model
are defined as land that has never been used by humans or land that has been
used at least once by humans, respectively. Here, unmanaged forest and
grassland are classified as primary or secondary land based on data from
LUH2f supplied by LUH2v (Lawrence et al., 2016). Unmanaged forest or
grassland areas that are classified as secondary land in the base year
(2005) remain classified as secondary land in subsequent years. In the case
in which unmanaged forest or grassland area is classified as primary land
in the base year, it is classified as
secondary land if the area is converted to cropland or pasture and then
later returned to being unmanaged forest or grassland. In TeLMO, land classified as other is considered the land
that cannot be used by humans and is therefore not included in the land-use
transition matrices.
The method used to create the land-use transition matrices is shown in
Fig. B3. As explained above, TeLMO assumes that land is used in order of
highest to lowest value added per unit area (i.e., urban, food cropland,
bioenergy cropland, pastureland, managed forest, and unmanaged forest).
Aligning these land-use classes with corresponding classes in the transition
matrix (Table B1), the preferential order of the latter becomes urban,
cropland (food + bioenergy), pasture, secondary land, and primary land. To
calculate land-use transition matrices, the percent areas of the different
land-cover types in each 0.5∘ cell in a given year are first sorted
in order of preference (“Pre” in Fig. B3). In Fig. B3, the length of
each colored bar represents the percent area of a given land-cover type. The
sum of the percent areas for all land-use classes is 100 %. Next, the
percent areas of different land-cover types in each 0.5∘ cell in
the following year are again sorted in order of preference (“Post” in Fig. B3).
Schematic diagram of land-cover transition. Details are
explained in the main text.
As shown in Fig. B3, the percent areas of transitioned land defined in
transition matrices can be calculated by comparing the percent areas for
each land-cover type in a given year and the next year. For example, the area
indicated in column “a” in Fig. B3 corresponds to the percent area of
land that transitioned from pasture to cropland. Similarly, the area
indicated in column “b” in Fig. B3 corresponds to the percent area of
land that transitioned from secondary land to pasture. In this manner, it is
possible to calculate the transition between land-cover types by assuming a
preferential order to land use.
Shifting cultivation is taken into account when making the land-use
transition matrices. We assume that the share of cultivated land does not
change over time on the larger (i.e., 0.5∘ cell) scale. Data from
Butler (1980) are used for the global allocation of shifting cultivation on
this larger scale. Furthermore, in regions where shifting cultivation is
practiced, we assume that cropland is used sequentially with a fixed
rotation (Butler, 1980). Under this assumption, in areas where shifting
cultivation is practiced, 1/15 of the cropland area is newly cultivated, and
1/15 of the cropland area is abandoned each year. Thus, 1/15 of the cropland
area is transitioned from secondary land to cropland, and 1/15 of the
cropland area is transitioned from cropland to secondary land. These
transitions are added to the transition matrices for areas where shifting
cultivation is practiced.
Code and data availability
The MIROC-INTEG-LAND source code for this study is available to those who conduct
collaborative research with the model users under license from the copyright
holders. For further information on how to obtain the code, please contact
the corresponding author. The data from the model simulations and
observations used in the analyses are available from the corresponding
author upon request.
Author contributions
TY was responsible for the development and description of MIROC-INTEG-LAND and TeLMO. TY and AI carried out the experimental study and analyzed the results of numerical experiments of historical and future simulations. TK was responsible for the development and description of TeLMO. GS, MN, and TI were responsible for the development and description of PRYSBI2. AI was responsible for the development and description of VISIT. MO analysed the output of PRYSIB2 historical simulations. YS calculated the forcing data. EK carried out the simulations for the bioenergy crop yields. YP, FF, TN, YM, and NH were responsible for the development and description of HiGWMAT and analyzed the output of HiGWMAT historical simulations. SF and KT were responsible for the input data by AIM/CGE. YY, SE, and TY proposed the development of MIROC-INTEG-LAND and led the project of model development. All authors have read and approved the final article.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This research is supported by the “Integrated Research Program for
Advancing Climate Models (TOUGOU Program)” sponsored by the Ministry of
Education, Culture, Sports, Science, and Technology (MEXT), Japan. It was
carried out as part of the Integrated Climate Assessment–Risks,
Uncertainties, and Society (ICA-RUS) project funded by the Environment
Research and Technology Development Fund (S-10) of the
Ministry of the Environment of Japan. Model simulations were performed on
the SGI UV20 at the National Institute for Environmental Studies. We
gratefully acknowledge the helpful discussions with Kaoru Tachiiri, Tomohiro Hajima, Takashi Arakawa, Junichi Tsutsui, and Michio Kawamiya. The authors
are much indebted to Keita Matsumoto, Kuniyasu Hamada, Kenryou Kataumi, Eiichi Hirohashi, Futoshi Takeuchi, Nobuaki Morita, and
Kenji Yoshimura at NEC Corporation for their support in model
development.
Financial support
This research has been supported by the Ministry of Education, Culture, Sports, Science and Technology of Japan (Integrated Research Program for Advancing Climate Models) (grant no. JPMXD0717935715) and the Ministry of the Environment of Japan (The Environment Research and Technology Development Fund (S-10, grant no. JPMEERF12S11000)).
Review statement
This paper was edited by Olivier Marti and reviewed by two anonymous referees.
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