Demeter is a community spatial downscaling model that
disaggregates land use and land cover changes projected by integrated
human–Earth system models. Demeter has not been intensively calibrated, and
we still lack good knowledge about its sensitivity to key parameters and
parameter uncertainties. We used long-term global satellite-based land
cover records to calibrate key Demeter parameters. The results identified the
optimal parameter values and showed that the parameterization substantially
improved the model's performance. The parameters of intensification ratio and
selection threshold were the most sensitive and needed to be carefully tuned,
especially for regional applications. Further, small parameter uncertainties
after calibration can be inflated when propagated into future scenarios,
suggesting that users should consider the parameterization equifinality to
better account for the uncertainties in Demeter-downscaled products. Our
study provides a key reference for Demeter users and ultimately contributes
to reducing the uncertainties in Earth system model simulations.
Introduction
Land use and land cover change (LULCC) represents one of the most important
human impacts on the Earth system (Hibbard et al., 2017). Besides its
socioeconomic effects, LULCC is directly linked to many natural land surface
processes, such as land surface energy balance and the carbon and water cycle (e.g.,
Piao et al., 2007; Law et al., 2018; Sleeter et al., 2018; Pongratz et al.,
2006), and indirectly affects the climate system (e.g., Dickinson and
Kennedy, 1992; Findell et al., 2017; Costa and Foley, 2000). Thus, LULCC has
been considered a key process in simulating Earth system dynamics, and
LULCC inputs at appropriate time steps and spatial resolutions are required
to match the setup of Earth system models (ESMs) and the nature of the
spatial heterogeneity of Earth system processes (Brovkin et al., 2013;
Lawrence et al., 2016; Prestele et al., 2017).
While recent historical LULCC information can be obtained by ground
investigation or satellite remote sensing (Friedl et al., 2002; Hansen et
al., 2000; Loveland et al., 2000; Zhang et al., 2003), projections of future
LULCC largely rely on mathematical models that bring socioeconomic and other
diverse sectoral information together in a coherent framework to simulate the
interactions between natural and human systems. However, these integrated
models project LULCC at subregional level, i.e., the basic spatial units that
have uniform properties for every sector (e.g., agricultural, energy, and
water, etc.), typically ranging from a few hundred to millions of square
kilometers (Edmonds et al., 2012). For example, the Global Change Assessment Model (GCAM) has been
widely used to explore future societal and environmental scenarios under
different climate mitigation policies, which provides LULCC projections at
the regional–agroecological or water basin level (Edmonds et al., 1997; Edmonds and
Reilly, 1985; Kim et al., 2006). ESMs divide the Earth's surface into a number
of grid cells and the forcing data have to be available at the same spatial
resolution to drive the ESMs (Taylor et al., 2012). Therefore, spatial
downscaling of subregional LULCC has become a critical step for linking
models like GCAM and ESMs to investigate the effects of LULCC on
processes in the natural world and further the interactions between
human and natural systems (Hibbard and Janetos, 2013; Lawrence et al., 2012).
There have been a few spatial disaggregation studies for LULCC, e.g., the
Global Land Use Model (Hurtt et al., 2011) and a dynamic global land use
model (Meiyappan et al., 2014), with various geographical and socioeconomic
assumptions. In previous studies, we have developed a new simple and
efficient LULCC downscaling model, named Demeter (version 1.0.0), to bridge
GCAM and ESMs (Le Page et al., 2016; Vernon et al., 2018; West et al., 2014)
and made it available online at 10.5281/zenodo.1214342. Compared to
other models, Demeter makes minimal assumptions on socioeconomic impacts.
Instead, it uses a few parameters to implicitly characterize the spatial
patterns of land use changes (see introductions in Sect. 2.1). Demeter has
been successfully applied at both global (Le Page et al., 2016) and regional
(West et al., 2014) levels for downscaling GCAM-projected land use and land
cover changes and has been further developed with an extensible output
module that streamlines the production of specific output formats required by
various ESMs (Vernon et al., 2018). However, Demeter's parameters (discussed
in Sect. 2.1), which include many geographic patterns of long-term land
cover changes such as intensification and expansion, are difficult to
determine by either literature review or simple mathematical calculations.
Therefore, Demeter's parameter values were empirically determined, and a
complete analysis of Demeter's parametric sensitivity and uncertainties, as
well as a rigorous model calibration, has not been conducted to help minimize
the propagation of downscaling errors. In recent years, a growing number of
long-term global remote-sensing-based LULCC datasets have been made available
(e.g., the Land Cover project of the European Space Agency (ESA) Climate Change
Initiative, MODIS Land Cover product collection 6), so it has become possible to
use these datasets to calibrate Demeter parameters. The major objective of
this study is to develop a framework for calibrating the key parameters of
Demeter, testing and quantifying the parameter sensitivities and
uncertainties, and demonstrating how the parameter uncertainties would affect
downscaled products.
MethodDemeter
Demeter is a land use and land cover change downscaling model, which is
designed to disaggregate projections of land allocations generated by GCAM
and other models. For example, GCAM projects land cover areas in each of its
spatial units (e.g., regional agroecological zones, referred to here as regional AEZs) for each land
cover type, and Demeter uses gridded observational land cover data (e.g.,
satellite-based land cover product) as the reference spatial distribution for
land cover types and allocates the GCAM-projected land area changes to grid
level at a target spatial resolution, following some user-defined rules and
spatial constraints (Fig. S1). Below we briefly summarize the key processes
of Demeter, and the detailed algorithms can be found in three earlier
publications (Le Page et al., 2016; Vernon et al., 2018; West et al., 2014).
Demeter first reconciles the land cover classes defined in the parent model
and the reference dataset to user-defined unified final land types (FLTs).
Downscaled land cover types will be presented in FLTs. For example, if
Demeter reclassifies the 22 GCAM land cover types and the 16 International
Geosphere–Biosphere Programme (IGBP) land cover types from the reference
dataset into seven FLTs (forest, shrub, grass, crops, urban, and sparse), the
seven
FLTs will be the land types represented in Demeter's outputs by default.
Demeter then harmonizes the GCAM-projected land cover areas and the reference
dataset at the first time step (or “base year”) to make sure they are
consistent with the GCAM spatial units and allocates the projected land cover
changes by intensification and extensification. Intensification is the
process of increasing a particular land cover in a grid cell in which it already
exists, while extensification creates new land cover in grid cells in which it
does not yet exist but is in proximity to an existing allocation. The order
of transitions among land cover types is defined by “transition priorities”
during the processes of intensification and extensification. A parameter
(r, from 0 to 1) is defined as the ratio of intensification, and thus 1-r
of the land cover change is for extensification. Proximal relationships are
defined by spatial constraints that determine the probability that a grid
cell may contain a particular land use or land cover class. The current
Demeter setup includes three spatial constraints: kernel density (KD), soil
workability (SW), and nutrient availability (NA). KD measures the probability
density of a land cover type around a given grid cell, and SW and NA are
normalized scalars (0–1) for agricultural suitability. For each land cover
type and grid cell, KD is calculated by the spatial distance (D) at the
runtime, and SW and NA are estimated from the Harmonized World Soil Database
(HWSD, FAO/IIASA/ISRIC/ISSCAS/JRC, 2012). A suitability index (SI) from 0 to
1 is defined as the weighted average of the three spatial constraints to
assess how suitable a grid cell is to receive a land cover type:
SI=(wK×KD+wS×SW+wN×NA)/(wK+wS+wN),
where wK, wS, and wN are the weights for KD,
SW,
and NA, respectively, and the sum of them is 1. In the process of
extensification, Demeter ranks candidate grid cells based on their
suitability indices and selects the most suitable candidate grid cells
following a user-defined threshold percentage (τ) for extensification.
In other words, τ determines the number of grid cells to be selected
and used for the tentative and actual conversion of land cover types.
Transition priorities by analyzing the 24-year global land cover
records from the Land Cover CCI project of the European Space Agency Climate
Change Initiative. The rows and columns represent the origins and
destinations of the transitions, respectively. The smaller numbers indicate
higher transition priorities.
Final land types (origins)Final land types (destinations) ForestShrubGrassCropsUrbanSnowSparseForest0231456Shrub2031456Grass1203564Crops2310564Urban1432065Snow2341506Sparse2341560Calibrate Demeter with historical land cover record and
sensitivity analysis
As indicated above, users should define a few parameters, including the
treatment order, the transition priorities for allocating the land cover
changes, the intensification ratio r, the selection threshold τ, the
radius for calculating kernel density D, and weights for the spatial
constraints (wK, wS, and wN), in order to use
Demeter for downscaling projected land cover change. These parameters were
determined empirically in previous studies. Here we calibrated these
parameters for Demeter using a time series of global land cover records from
the Land Cover project of the European Space Agency Climate Change Initiative
(referred to as CCI-LC products hereafter). The CCI-LC products have been
generated by critically revisiting all algorithms required for the generation
of a global land cover product from various Earth observation (EO)
instruments, thus providing a globally consistent land cover record over
2 decades (1992–2015). The CCI-LC products are available at 300 m
spatial resolution with an annual time step and classify the global land cover
into 38 groups. We reclassified the CCI-LC products into the seven default FLTs
(Table S1) and resampled them into 0.25∘ resolution with the official
software tools, following the description of CCI-LC products in the user
guide
(http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf, last access: 18 April 2019). Figure 1 shows large interannual global
changes for the seven FLT areas, especially for the forests and croplands, which
have decreased and increased over 0.6 million km2 over the past
2 decades, respectively. We used the gridded 0.25∘ CCI-LC over the
24-year period as observational data (below referred to “LC-grid-obs”)
and aggregated them into GCAM's regional AEZ level to produce a synthetic
GCAM-projected land cover change (below referred to “LC-AEZ-syn”). In this
way, we can apply Demeter to LC-AEZ-syn to calibrate Demeter with the
LC-grid-obs by tuning the parameters of Demeter.
Interannual changes in global final land type (FLT) areas over
1992–2015 relative to 1992, as indicated by the ESA CCI-LC product.
A preliminary sensitivity analysis of Demeter indicated that the downscaled
results are less sensitive to treatment order and transition priorities (Le
Page et al., 2016), and thus we used the default treatment order, i.e., from
least to greatest: urban, snow, sparse, crops, forest, grass, shrub. We
decided the transition priorities by sorting the probabilities of
transitioning one FLT to another based on the 24-year CCI-LC record
(Table 1). To calibrate the other six parameters (r, τ, wK,
wS, wN, and D), we sampled their values at equal
intervals (Table 2) and generated all possible combinations (23 100 in total)
for a Monte Carlo ensemble Demeter downscaling experiment using LC-AEZ-syn
as the input. The Monte Carlo experiment generated 23 100 sets of downscaled
0.25∘ global land use and land cover areas, which were compared
against LC-grid-obs to calculate their similarities to the observational
data, ranked by their discrepancies from the least to greatest to determine
the likelihood of the parameters. We calculated the discrepancies as the root
mean square error (Ey) between the downscaled and observed land cover
areas for each year,
Ey=1G1L∑gG∑lLAdy,l,g-Aoy,l,g2,
and the average of the discrepancies over the years (E):
E=1Y∑yYEy,
where g is the index for G grid cells over the globe (G= 265 852),
l is the index for the L FLTs (L=8), and y is the index for Y years.
We chose 1992, 2000, 2005, 2010, and 2015 to be consistent with the GCAM
time steps, and thus Y=5. Ady,l,g and Aoy,l,g are the
downscaled and observational land cover areas for grid cell g, FLT l, and
year y. The unit for Ey and E is km2.
To test the model sensitivity to these key parameters, we conducted a
sensitivity analysis using the results from the Monte Carlo experiment. The
first-order and total-order Sobol sensitivity indices were used to identify
the model sensitivity to each of the six parameters (Saltelli et al., 2004).
Let θi denote the ith parameter (i=1,…,n, here n=6), and
ε is the model outputs (i.e., the discrepancies between
downscaled and observed land cover areas); the first-order Sobol index
(Si) is defined as
Si=VarEε|θiVar(ε).
Here Var and E are the statistical variance and expectation. And the
total-order Sobol index (ST,i) is defined as the sum of
sensitivity indices at any order involved parameter θi, where
Si,j,k,…,n denotes the nth-order sensitivity index.
5ST,i=Si+∑j=1,j≠inSi,j+∑j,k=1,j,k≠inSi,j,k+…+∑j,k,…,n=1,j,k,…,n≠inSi,j…,n
The first-order Sobol index represents the contribution to the output
variance of the main effect of θi, and therefore it measures the
effect of varying θi alone; the total-order Sobol index
measures the contribution to output variance of θi and includes
all variance caused by its interactions with other parameters. Larger Sobol
indices indicate higher parameter sensitivities.
Key parameters and their sampling range and steps for calibration
in this study.
NameDefinitionMinMaxSampling stepwNWeight of soil nutrient availability for calculating suitability index010.2wSWeight of soil workability for calculating suitability index010.2wKWeight of kernel density for calculating suitability index010.2rIntensification ratio010.1τSelection threshold010.1DKernel radius1010010Propagate the parameter uncertainties to GCAM LULCC downscaling
We selected parameter combinations that produced the smallest 5 % and 10 % of E values
based on their rankings from the Monte Carlo experiment and used them as
“acceptable” parameters to represent the parameter uncertainties after
calibration (Fig. 2). We used Demeter with these parameters to downscale the
GCAM-projected LULCC at a 5-year time step from 2005 to 2100 under a reference
scenario to examine the uncertainties of land cover areas for each FLT to
demonstrate how different downscaled LULCC can be induced by
uncertain parameters. The reference scenario is a business-as-usual case with
no explicit climate mitigation efforts that reaches a high radiative
forcing level of over 7 W m-2 in 2100. We only saved the downscaling
results in 2005, 2010, 2050, and 2100 considering the size of the output files
and computational cost. Finally, we calculated the standard deviation across
the downscaled land cover areas for each FLT driven by different parameter
combinations, which indicates the parameter-induced model uncertainties.
ResultsParameter estimation and sensitivity
The Monte Carlo Demeter experiment driven by the 23 100 ensemble parameter
sets produced diverse downscaled LULCC realizations. As shown in Fig. 2a, the
disagreements between the downscaled FLT fraction and the reference record,
measured by the average root mean square error (E, Eq. 3) for all the FLTs
and grid cells over the five years (1992, 2000, 2005, 2010, and 2015), are
mainly distributed between 8 and 17 km2 (about 1 %–3 % of the
area of a 0.25∘ grid cell).
(a) Histogram of E values, i.e., the global average
discrepancies between the downscaled and observed land cover areas with the
23 100 ensemble parameter sets; the vertical dashed line in (a)
shows the interval of the “acceptable” 5 % parameters, as described in
Sect. 2.3. (b) The probability density of each of the acceptable
5 % parameters, as shown by the violin plots; the black lines across the
six parameters show all the acceptable 5 % parameter sets, and the
red line indicates the global optimal parameter values; the box plots and
horizontal bar inside the violin plots indicate the interquartile ranges and
the mean of the parameter values, respectively. (c) Same
as (b) but showing the “best” 10 % parameter sets. Note that the
values of D were divided by 100 for the purpose of illustration in
(b) and (c).
Figure 3 shows the relationship between the values of the six parameters and
their corresponding E values resulting from the Monte Carlo experiment. We found
that the E values are significantly correlated with all six parameters (p<0.01). The intensification ratio (r) has the strongest linear correlation
with the E values (R2=0.64), followed by the selection threshold (τ)
(R2=0.24). Overall, the parameters wK and τ are
positively correlated with E values (positive slopes of the trend lines), while
wN, wS, r, and D hold negative correlations,
indicating that smaller wK and τ, and larger wN,
wS, r, and D, are associated with smaller E values.
Relationships between the six Demeter parameters and the global
average discrepancies between the downscaled and observed land cover areas
(E values) resulted from the Monte Carlo ensemble experiment. Box plots shows
the distributions of the E values, and the solid lines show the linear trends.
Figure 4 shows the first-order and total-order Sobol indices calculated with
the parameter ensemble and the associated E values. As indicated by the
first-order Sobol indices, the intensification ratio r directly contributes
about 59 % to the variability of the E values, followed by the selection
threshold τ and kernel radius D, which directly contribute 29 %
and 1 % to the variability of the E values. The other parameters
(wN, wS, and wK) have few direct
contributions to the E variability. The total-order Sobol indices showed
a similar order of parameter importance; r and its interactions with other
parameters contributed about 70 % of the E variability, τ
contributed about 40 %, D contributed about 3 %, and wN,
wS, and wK contributed 2 % each. It is clear
that the downscaling error is most sensitive to the intensification ratio,
followed by the selection threshold, but not sensitive to the kernel radius
and the weighting factors of the spatial constraints.
Sobol sensitivity indices for the six Demeter parameters. Higher
indices indicate higher sensitivities.
We identified the “best” parameters, which are associated with the lowest
E, and marked them as the red line in Fig. 2b. We also selected
acceptable parameters that have E values lower than the 5 % quantile in
Fig. 2a (hereafter referred to as the “top 5 % parameters”) and thus have
a similar performance as the best parameters (differences of E<1 %); we used them to represent the uncertainty of the parameters shown
as the probability density distributions in Fig. 2b. The best wN,
wS, wK, r, τ, and D are 0, 0.6, 0.4, 1, 0.6, and
100, respectively. All the parameters are constrained with the calibration
compared to their uniform prior distributions. The intensification ratio r
has been constrained into a small range (0.9–1.0 and mostly 1.0) from 0 to
1.0. Constraints on the other parameters are relatively weaker:
wN, wS, and wK have been narrowed to the ranges
of 0–0.8, 0.2–1.0, and 0–0.8 and primarily distributed in 0–0.4,
0.2–0.6, and 0–0.4 (the first and third quantiles), respectively; τ
and D have been constrained into the range of 0.2–1.0 and 30–100 with the
first and third quantiles being 0.2–0.8 and 40–90, respectively. This
analysis again indicates that r is the most sensitive parameter, and therefore
its posterior distribution can be significantly narrowed through the
calibration. In addition, we also selected the acceptable parameters that
have E values lower than the 10 % quantile (top 10 % parameters), as shown in
Fig. 2a and c. Similar distributions of the top 10 % parameters are found as
those of the top 5 % parameters, with some small extension on the ranges
of 5 % parameters.
Performance of Demeter in downscaling LULCC
Demeter generally performs well in downscaling synthetic land use and
land cover change with small disagreements with the reference data. For all
FLTs, the disagreements between the downscaled FLT fraction and the reference
record in 1992 (i.e., E1992 in Eq. 2) are close to zero since we used
it as the harmonization year. The disagreements in 2000 (E2000) are
mainly distributed in a range between 5 and 15 km2 (about
1 %–2 % of a 0.25∘ grid cell), with the median about
10 km2 and the mean slightly above 12 km2 (Fig. 5h). The
disagreements increase over years at a rate of about 1 km2 per 5-year
time step and reach 13–24 km2 (median: 15 km2; mean:
18 km2) in 2015. Overall, the average disagreements over the five years
(E) are mainly distributed in 8–17 km2 (also shown in Fig. 2a), with
a median of about 10 km2 and a mean of about 12 km2.
Possibility densities for the E values between downscaled and
observational final land type areas for 1992, 2000, 2005, 2010, 2015, and the
mean of the five time steps. The box plots and horizontal bar inside the
violin plots indicate the interquartile ranges and the mean of the parameter
values, respectively. Note that the E values for snow are close to 0 and thus not
visible in the figure.
The errors for each of the FLTs follow the same increasing trend over the
years. Forest and crops have the largest disagreements between the downscaled
and reference distributions with the errors primarily located in the
range of 20–40 km2 on average over the five time steps (Fig. 5a, d).
The errors for sparse lands are relatively smaller, which mainly fall into
the range of 10–20 km2 (Fig. 5g), followed by grass, shrub, and urban,
with the errors mainly distributed in 0–10 km2 on average over the
five years. Errors for snow are near zero since there was little areal change
for this FLT in the CCI-LC record (Fig. 1), and little LULCC allocation was
needed in the downscaling process over the years.
Comparison between the observed and downscaled final land types with
optimal parameters over the 265 852 0.25∘ grid cells in 2015. The
blue solid lines show the 1 : 1 line, and the red dashed lines show the
95 % confidence intervals.
Figure 6 shows the comparison between reference gridded CCI-LC FLTs and the
downscaled FLTs driven by the best parameters (see Sect. 3.1) among the
265 852 0.25∘ grid cells in 2015. Except for urban, the downscaled
land cover of other FLTs matches the reference record very well (all R2
are above 0.98). The R2 is 1 for snow due to little change in snow and
ice area in the CCI-LC record. Figure 7 demonstrates the spatial distribution
of FLT fraction from the reference data and the best downscaled results, together
with their differences, using crops as an example. We find that the downscaled
results have successfully reproduced the spatial pattern of crops from the
reference data, and similar conclusions can be drawn for other FLTs (see
Figs. S2–S6; figure for snow was not shown because of little change for this
FLT). However, the misallocation of land cover change takes places in most
regional AEZs, especially where LULCC was significant (e.g., Brazil, eastern
China, temperate Africa, and northern Eurasia; Figs. 7 and S1–S5) over the
study years, likely due to the application of an improper global ratio of
intensification. For example, the North China Plain has experienced
extensive urbanization by converting a large area of cropland into urban
development
during the past few decades (Liu et al., 2010). However, since the calibrated
intensification ratio is high (Fig. 2), Demeter tends to underestimate
urban expansion and thus overestimate cropland area that should be
urbanized. Similarly, cropland has been largely expanded and thus applying a
high intensification ratio could not capture such changes.
Spatial pattern of the observed and downscaled crop densities
(measured by percentage fraction of the grid cell) and their differences in
2015. The grey dotted lines show the boundaries of the GCAM regional AEZs.
Uncertainty propagation
While applying the acceptable parameters (top 5 % and 10 %) in
downscaling GCAM projections of LULCC under the reference scenario, we found
that these well-constrained parameters induced considerable uncertainties in
the downscaled results. For each grid cell, we calculated the standard
deviation (σ) of the downscaled land cover areas with different
parameters for each FLT. Figure 8 shows the mean σ of the 265 852
0.25∘ grid cells over the globe for 2005, 2010, 2050, and 2100, as
well as the spatial variability of σ (calculated as the standard
deviation over the grid cells and shown as the shaded area in Fig. 8). As
shown by the grey lines and shading in Fig. 8, the uncertainty of the top 5 %
parameters has a minor effect on downscaled urban and snow areas, since GCAM
projected little areal change in urban and snow. Downscaled sparse areas
were slightly affected by the choice of parameters, indicated by small mean
σ (about 2 km2 per grid cell). However, the other FLTs,
including forest, shrub, grass, and crops, have larger σ values, which also
showed an increasing trend over time. The global mean σ for forest
and shrub reached about 3 to 4 km2 per grid cell and about 6 to
8 km2 for grass and crops in 2100. The spatial variability of σ
was also larger for these FLTs; for example, the standard deviation of
σ reached over 15 km2 per grid cell in 2100 for crops, and the
maximum σ can be over 350 km2 per grid cell in some grid cells
(Fig. S7). Similar results can be found by using the top 10 % parameters
but with slightly higher magnitudes (red lines and shaded areas in Figs. 8
and S8).
The mean (shown as the solid lines) and standard deviations
(σ, shown as the shaded area) for the downscaled final land type (FLT)
areas when propagating the parameter uncertainties into the GCAM-projected
land use and land cover change downscaling in the 21st century. The black and
red colors represent using the top 5 % and 10 % parameters,
respectively.
Discussion
To date, there have been only a handful of methods for downscaling projected
global land use and land cover change. For example, Hoskins et al. (2016)
fitted a statistical model relating coarse-scaled spatial patterns in land
cover classes to finer-scaled land cover and other explaining variables. Many
more studies have used a complex land use modeling approach (e.g., Houet et al.,
2017; Hoskins et al., 2016; Meiyappan et al., 2014; Hurtt et al., 2011; Souty
et al., 2012) that combines a variety of socioeconomic processes to provide
global-scale land use allocations. Our results demonstrated that Demeter is
an effective tool for downscaling global land use and land cover change,
although it adapts a relatively simpler approach. However, choices of
parameter values are critically important for a simple model, since it is
possible that some complicated processes are simplified and represented by
a single parameter. Although an uncalibrated Demeter can lead to noticeable
errors and uncertainties in downscaled land cover areas, our results have
shown the effectiveness of the calibration efforts in minimizing
downscaling errors and constraining uncertainties.
A central purpose of our study is to make suggestions for setting up
parameters for Demeter's global applications, shown as the global optimal
values in Fig. 2. Interestingly, we found that the parameters of
intensification ratio (r) and selection threshold (τ) strongly
affected the downscaled results, while the weights of the spatial constraints
and kernel radius showed small impacts on the results. This indicates
that the selected spatial constraints (soil workability and nutrient
availability) and spatial autocorrelation (measured by kernel density)
provide loose constraints on land allocation in the downscaling process,
and therefore users should focus more on the quality of other parameters such
as r and τ to which the model is more sensitive. In addition, the
intensification ratio has been strictly constrained to a range close to 1.0,
suggesting that the intensification of land cover, especially cropland, may
be the major contributor to global land use and land cover change; thus,
spatial constraints on extensification are not very effective. We also
noticed that the optimal weight for soil nutrient availability for
calculating the suitability indices is zero (Fig. 2). A
possible reason is that soil nutrient availability has a similar spatial
distribution as cropland in CCI-LC data, thus providing little additional
information on constraining the downscaling processes (Fig. S10). This result
suggests that users could ignore the input of soil nutrient availability
if it is not available or difficult to collect, and the quantification of the
downscaling uncertainty is not required.
There have been a number of numerical methods for model calibration, such as
gradient methods (Ypma, 1995), evolutionary algorithms (Ashlock, 2006), and
data assimilation techniques (Kalnay, 2002). Our calibration method is
relatively simpler, and the sampling steps are relatively coarse. As a
result, it is possible that the calibrated parameters can be further improved
with a more rigorous calibration strategy, although these biases should be
small since the sampling bins are narrow and the sensitive parameters are
well constrained (Fig. 2). However, our method has a few advantages for this
particular global land use and land cover change downscaling model
calibration problem. First, we sampled the whole parameter space, and thus our
Monte Carlo downscaling experiments can represent the parameter
uncertainties well. Second, the other methods mentioned above typically adjust
model parameters and run the model iteratively to find the parameters to hit
the local or global minimum cost function value (Chong and Zak, 2013);
this can be very time-consuming due to the size of the datasets and the
difficulty of algorithm parallelization. The Monte Carlo ensemble runs of
Demeter in our method can be easily parallelized, and it is thus computationally
efficient. Finally, the saved downscaled results from the global Monte Carlo
downscaling experiment can be reused for regional applications. Our study
provided an optimal set of Demeter parameters. It is worth noting that these
parameters are optimized to minimize the average discrepancies between the
downscaled and historically observed land cover areas at the global scale,
and
they may thus need to be recalibrated when Demeter is applied to a particular
region. For example, the best estimate of the intensification ratio is 1 for
a global downscaling experiment, probably due to the fact that intensification is a
more common phenomenon than extensification for land use and land
cover change in the past 2 decades as recorded by the CCI-LC data.
However, this high intensification ratio for crops may be more realistic for
regions with a long-term agricultural history (e.g., India), while it
should become lower for the United States (US) where cropland extensification
happened rapidly in the past century. We extracted grid cells in the
conterminous US (grid cells between 25 and 50∘ N and between 125 and
65∘ W) and India (grid cells between 7 and 33∘ N and between 68
and 98∘ E) and used them together with the same method as the
global calibration to determine the optimal parameters for the US and India,
which clearly showed that the intensification ratio remained 1 for India but
moved towards lower values for the US (Fig. S9). Therefore, we recommend
future effort to examine reginal parameterization for
Demeter's applications at specific regional AEZ levels. Since some of the key
parameters have a clear physical definition (e.g., the intensification ratio)
and
while the global optimal values could be used as a starting point, it would
be helpful to review local historical land use change to infer these
parameters when applying Demeter to a specific region.
In addition, although the downscaled urban land use can capture most of the
variability in reality, it is clear that Demeter's performance for urban is
not as good as that for other land cover types (Fig. 6). On the other hand,
accurate projection of the spatial extent and pattern of urbanization is
becoming more important for a better understanding of its environmental,
ecological,
and socioeconomic impacts in such an era of rapid urbanization (Georgescu et
al., 2012; Jones et al., 1990; Merckx et al., 2018; Zhang et al., 2018).
Thus, future effort should be made to improve the downscaling
accuracy of urban land use. The relatively larger errors could be due to
the limited consideration of complex urbanization processes and the lack of
specific parameterization of the urban land cover type. While incorporating
a better representation of urbanization in Demeter can be more complicated, it
is possible to improve the model performance by further parameterizing the
model with more historical urban data. For example, global satellite-observed
nightlights have been used for mapping urban areas (Elvidge et al., 2009; Li
and Zhou, 2017b; Zhou et al., 2014) and producing a global record of annual
urban dynamics (1992–2013) (Li and Zhou, 2017a), which will be particularly
useful for the future calibration of Demeter on urban dynamics.
Model calibration can usually provide several sets of parameters to allow the
calibrated model to give similar results, which is called equifinality (Beven
and Freer, 2001). As a result, the calibrated parameters become another
source of uncertainty in model-simulated results. Equifinality also
exists in our calibrations. We observed noticeable growing uncertainties
in downscaled land cover areas while propagating the parameter uncertainties
into the Demeter downscaling practices with GCAM-projected LULCC in the 21st
century. Therefore, while calibration can remarkably reduce the uncertainty
of the parameters, it may be better to use sets of constrained parameters
rather than a single set of “best” parameters in the practice of Demeter
for the purpose of accounting for parameter uncertainty and providing
more reliable land use and land cover change downscaling. Moreover, it is
worth noting that the calibrated parameters are tuned for FLTs, which we
believe have covered most land cover types and are directly useful in most
cases. When users need to consider more FLTs in their global
applications, the optimal values introduced in this study can be used as a
starting point for further tuning.
Conclusions
We developed a Monte Carlo ensemble experiment for Demeter, a land use and
land cover change downscaling model of GCAM, analyzed the model's
sensitivity to its key parameters, and calibrated the parameters to minimize
the mismatch between model-downscaled and satellite-observed land use
and land cover change in the past 2 decades. We identified the optimal
parameter values for global applications of Demeter and showed that the
parameterization of Demeter substantially improved the model's performance
in downscaling global land use and land cover change. The intensification
ratio and selection threshold turned out to be the most sensitive
parameters and thus need to be carefully tuned, especially when Demeter is used
for regional applications. Further, the small uncertainty of parameters
after calibration can result in considerably larger uncertainties in the
results when propagating them into the practice of downscaling GCAM
projections, suggesting that Demeter users consider parameterization
equifinality to better account for the uncertainties in Demeter-downscaled
land use and land cover changes.
Code availability
The source code of GCAM and Demeter is available at
https://github.com/JGCRI/gcam-core (last access: 18 April 2019) and
10.5281/zenodo.1214342 (Vernon, 2019). The scripts
for performing the calibration and analysis are available at
10.5281/zenodo.2634584 (Chen et al., 2019a).
Data availability
The ESA-CCI data were downloaded from
https://www.esa-landcover-cci.org/ (last access: 3 April 2017). Other data, e.g., input and configuration files for Demeter
experiments in this paper, are available at
10.25584/data.2019-04.715/1505616 (Chen et al., 2019b).
The supplement related to this article is available online at: https://doi.org/10.5194/gmd-12-1753-2019-supplement.
Author contributions
MC conceived the study, and all the authors contributed to designing the
study. MC led the data acquisition and performed the experiment and analysis
with technical assistance from CRV; MC wrote the paper with input
from all the coauthors.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
This research was supported by the U.S. Department of Energy, Office of
Science, as part of research in the MultiSector Dynamics, Earth and
Environmental System Modeling Program.
Review statement
This paper was edited by David Lawrence and reviewed by two
anonymous referees.
References
Ashlock, D.: Evolutionary Computation for Modeling and Optimization,
Springer-Verlag, New York, 2006.Beven, K. and Freer, J.: Equifinality, data assimilation, and uncertainty
estimation in mechanistic modelling of complex environmental systems using
the GLUE methodology, J. Hydrol., 249, 11–29,
10.1016/S0022-1694(01)00421-8, 2001.Brovkin, V., Boysen, L., Arora, V. K., Boisier, J. P., Cadule, P., Chini, L.,
Claussen, M., Friedlingstein, P., Gayler, V., van den Hurk, B. J. J. M.,
Hurtt, G. C., Jones, C. D., Kato, E., de Noblet-Ducoudré, N., Pacifico,
F., Pongratz, J., and Weiss, M.: Effect of Anthropogenic Land-Use and
Land-Cover Changes on Climate and Land Carbon Storage in CMIP5 Projections
for the Twenty-First Century, J. Clim., 26, 6859–6881,
10.1175/JCLI-D-12-00623.1, 2013.Chen, M., Vernon, C. R., Huang, M., Calvin, K. V., and Kraucunas, I. P.:
IMMM-SFA/chen_et_al_gmd_2019: Chen et al. 2019, GMD supporting code,
10.5281/zenodo.2634584, 2019a.Chen, M., Vernon, C. R., Huang, M., Calvin, K. V., and Kraucunas, I. P.:
IMMM-SFA/chen_et_al_gmd_2019: Chen et al. 2019, GMD supporting data,
10.25584/data.2019-04.715/1505616, 2019b.
Chong, E. K. P. and Zak, S. H.: An introduction to optimization, 4th edn.,
John Wiley & Sons, Inc., Hoboken, NJ, 2013.Costa, M. H. and Foley, J. A.: Combined Effects of Deforestation and Doubled
Atmospheric CO2 Concentrations on the Climate of Amazonia, J.
Climate, 13, 18–34, 10.1175/1520-0442(2000)013<0018:CEODAD>2.0.CO;2,
2000.Dickinson, R. E. and Kennedy, P.: Impacts on regional climate of Amazon
deforestation, Geophys. Res. Lett., 19, 1947–1950, 10.1029/92GL01905,
1992.
Edmonds, J. and Reilly, J.: Global Energy: Assessing the Future, Oxford
University Press, New York, 1985.Edmonds, J., Wise, M., Pitcher, H., Richels, R., Wigley, T., and Maccracken,
C.: An integrated assessment of climate change and the accelerated
introduction of advanced energy technologies, Mitig. Adapt. Strateg. Glob.
Chang., 1, 311–339, 10.1007/BF00464886, 1997.
Edmonds, J. A., Calvin, K. V, Clarke, L. E., Janetos, A. C., Kim, S. H.,
Wise, M. A., and McJeon, H. C.: Integrated Assessment Modeling, in
Encyclopedia of Sustainability Science and Technology, edited by: Meyers, R.
A., Springer New York, New York, NY, 5398–5428, 2012.
Elvidge, C. D., Sutton, P. C., Tuttle, B. T., Ghosh, T., and Baugh, K. E.:
Global urban mapping based on nighttime lights, Glob. Mapp. Hum. Settl.,
129–144, 2009.
FAO/IIASA/ISRIC/ISSCAS/JRC: Harmonized World Soil Database (version 1.2),
FAO, Rome, Italy and IIASA, Laxenburg, Austria, 2012.Findell, K. L., Berg, A., Gentine, P., Krasting, J. P., Lintner, B. R.,
Malyshev, S., Santanello, J. A., and Shevliakova, E.: The impact of
anthropogenic land use and land cover change on regional climate extremes,
Nat. Commun., 8, 989, 10.1038/s41467-017-01038-w, 2017.Friedl, M. A., McIver, D. K., Hodges, J. C. F., Zhang, X. Y., Muchoney, D.,
Strahler, A. H., Woodcock, C. E., Gopal, S., Schneider, A., Cooper, A.,
Baccini, A., Gao, F., and Schaaf, C.: Global land cover mapping from MODIS:
algorithms and early results, Remote Sens. Environ., 83, 287–302,
10.1016/S0034-4257(02)00078-0, 2002.Georgescu, M., Moustaoui, M., Mahalov, A., and Dudhia, J.: Summer-time
climate impacts of projected megapolitan expansion in Arizona, Nat. Clim.
Chang., 3, 37–41, 10.1038/nclimate1656, 2012.Hansen, M. C., Defries, R. S., Townshend, J. R. G., and Sohlberg, R.: Global
land cover classification at 1 km spatial resolution using a classification
tree approach, Int. J. Remote Sens., 21, 1331–1364,
10.1080/014311600210209, 2000.Hibbard, K. A. and Janetos, A. C.: The regional nature of global challenges:
a need and strategy for integrated regional modeling, Clim. Change, 118,
565–577, 10.1007/s10584-012-0674-3, 2013.
Hibbard, K. A., Hoffman, F. M., Huntzinger, D., and West, T. O.: Changes in
land cover and terrestrial biogeochemistry, in: Climate Science Special
Report: Fourth National Climate Assessment, Volume I, edited by: Wuebbles, D.
J., Fahey, D. W., Hibbard, K. A., Dokken, D. J., Stewart, B. C., and Maycock,
T. K., U.S. Global Change Research Program, Washington, DC, USA, 277–302,
2017.Hoskins, A. J., Bush, A. , Gilmore, J. , Harwood, T. , Hudson, L. N., Ware,
C., Williams, K. J. and Ferrier, S.: Downscaling land-use data to provide
global 30′′ estimates of five land-use classes, Ecol. Evol., 6, 3040–3055,
10.1002/ece3.2104, 2016.Houet, T., Grémont, M., Vacquié, L., Forget, Y., Marriotti, A.,
Puissant, A., Bernardie, S., Thiery, Y., Vandromme, R., and Grandjean, G.:
Downscaling scenarios of future land use and land cover changes using a
participatory approach: an application to mountain risk assessment in the
Pyrenees (France), Reg. Environ. Chang., 17, 2293–2307,
10.1007/s10113-017-1171-z, 2017.Hurtt, G., Chini, L., Frolking, S., Betts, R., Feddema, J., Fischer, G.,
Fisk, J., Hibbard, K., Houghton, R., Janetos, A., Jones, C., Kindermann, G.,
Kinoshita, T., Klein Goldewijk, K., Riahi, K., Shevliakova, E., Smith, S.,
Stehfest, E., Thomson, A., Thornton, P., van Vuuren, D., and Wang, Y.:
Harmonization of land-use scenarios for the period 1500–2100: 600 years of
global gridded annual land-use transitions, wood harvest, and resulting
secondary lands, Clim. Change, 109, 117–161, 10.1007/s10584-011-0153-2,
2011.Jones, P. D., Groisman, P. Y., Coughlan, M., Plummer, N., Wang, W.-C., and
Karl, T. R.: Assessment of urbanization effects in time series of surface air
temperature over land, Nature, 347, 169–172, 10.1038/347169a0, 1990.
Kalnay, E.: Atmospheric modeling, data assimilation and predictability,
Cambridge University Press, 2002.
Kim, S. H., Edmonds, J., Lurz, J., Smith, S. J., and Wise, M.: The ObjECTS
Framework for Integrated Assessment: Hybrid Modeling of Transportation, The
Energy Journal, International Association for Energy Economics, 63–92, 2006.
Law, B. E., Hudiburg, T. W., Berner, L. T., Kent, J. J., Buotte, P. C., and
Harmon, M. E.: Land use strategies to mitigate climate change in carbon dense
temperate forests, P. Natl. Acad. Sci. USA, 115, 3663–3668, 2018.Lawrence, D. M., Hurtt, G. C., Arneth, A., Brovkin, V., Calvin, K. V., Jones,
A. D., Jones, C. D., Lawrence, P. J., de Noblet-Ducoudré, N., Pongratz,
J., Seneviratne, S. I., and Shevliakova, E.: The Land Use Model
Intercomparison Project (LUMIP) contribution to CMIP6: rationale and
experimental design, Geosci. Model Dev., 9, 2973–2998,
10.5194/gmd-9-2973-2016, 2016.Lawrence, P. J., Feddema, J. J., Bonan, G. B., Meehl, G. A., O'Neill, B. C.,
Oleson, K. W., Levis, S., Lawrence, D. M., Kluzek, E., Lindsay, K., and
Thornton, P. E.: Simulating the Biogeochemical and Biogeophysical Impacts of
Transient Land Cover Change and Wood Harvest in the Community Climate System
Model (CCSM4) from 1850 to 2100, J. Clim., 25, 3071–3095,
10.1175/JCLI-D-11-00256.1, 2012.Le Page, Y., West, T. O., Link, R., and Patel, P.: Downscaling land use and
land cover from the Global Change Assessment Model for coupling with Earth
system models, Geosci. Model Dev., 9, 3055–3069,
10.5194/gmd-9-3055-2016, 2016.Li, X. and Zhou, Y.: A Stepwise Calibration of Global DMSP/OLS Stable
Nighttime Light Data (1992–2013), Remote Sens., 9, 637,
10.3390/rs9060637, 2017a.Li, X. and Zhou, Y.: Urban mapping using DMSP/OLS stable night-time light: a
review, Int. J. Remote Sens., 38, 6030–6046,
10.1080/01431161.2016.1274451, 2017b.Liu, J., Zhang, Z., Xu, X., Kuang, W., Zhou, W., Zhang, S., Li, R., Yan, C.,
Yu, D., Wu, S., and Jiang, N.: Spatial patterns and driving forces of land
use change in China during the early 21st century, J. Geogr. Sci., 20,
483–494, 10.1007/s11442-010-0483-4, 2010.Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., Yang, L.,
and Merchant, J. W.: Development of a global land cover characteristics
database and IGBP DISCover from 1 km AVHRR data, Int. J. Remote Sens., 21,
1303–1330, 10.1080/014311600210191, 2000.Meiyappan, P., Dalton, M., O'Neill, B. C., and Jain, A. K.: Spatial modeling
of agricultural land use change at global scale, Ecol. Modell., 291,
152–174, 10.1016/j.ecolmodel.2014.07.027, 2014.Merckx, T., Souffreau, C., Kaiser, A., Baardsen, L. F., Backeljau, T., Bonte,
D., Brans, K. I., Cours, M., Dahirel, M., Debortoli, N., De Wolf, K.,
Engelen, J. M. T., Fontaneto, D., Gianuca, A. T., Govaert, L., Hendrickx, F.,
Higuti, J., Lens, L., Martens, K., Matheve, H., Matthysen, E., Piano, E.,
Sablon, R., Schön, I., Van Doninck, K., De Meester, L., and Van Dyck, H.:
Body-size shifts in aquatic and terrestrial urban communities, Nature, 558,
113–116, 10.1038/s41586-018-0140-0, 2018.Piao, S., Friedlingstein, P., Ciais, P., de Noblet-Ducoudré, N., Labat,
D., and Zaehle, S.: Changes in climate and land use have a larger direct
impact than rising CO2 on global river runoff trends, P. Natl. Acad.
Sci. USA, 104, 15242–15247, 2007.Pongratz, J., Bounoua, L., DeFries, R. S., Morton, D. C., Anderson, L. O.,
Mauser, W., and Klink, C. A.: The Impact of Land Cover Change on Surface
Energy and Water Balance in Mato Grosso, Brazil, Earth Interact., 10, 1–17,
10.1175/EI176.1, 2006.Prestele, R., Arneth, A., Bondeau, A., de Noblet-Ducoudré, N., Pugh, T.
A. M., Sitch, S., Stehfest, E., and Verburg, P. H.: Current challenges of
implementing anthropogenic land-use and land-cover change in models
contributing to climate change assessments, Earth Syst. Dynam., 8, 369–386,
10.5194/esd-8-369-2017, 2017.
Saltelli, A., Tarantola, S., Campolongo, F., and Ratto, M.: Sensitivity
Analysis in Practice: A Guide to Assessing Scientific Models, Wiley, 2004.Sleeter, B. M., Liu, J., Daniel, C., Rayfield, B., Sherba, J., Hawbaker, T.
J., Zhu, Z., Selmants, P. C., and Loveland, T. R.: Effects of contemporary
land-use and land-cover change on the carbon balance of terrestrial
ecosystems in the United States, Environ. Res. Lett., 13, 45006,
10.1088/1748-9326/aab540, 2018.Souty, F., Brunelle, T., Dumas, P., Dorin, B., Ciais, P., Crassous, R.,
Müller, C., and Bondeau, A.: The Nexus Land-Use model version 1.0, an
approach articulating biophysical potentials and economic dynamics to model
competition for land-use, Geosci. Model Dev., 5, 1297–1322,
10.5194/gmd-5-1297-2012, 2012.Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and
the Experiment Design, B. Am. Meteorol. Soc., 93, 485–498,
10.1175/BAMS-D-11-00094.1, 2012.Vernon, C.: IMMM-SFA/demeter: Demeter – Version 1.0.0, 10.5281/zenodo.1214342,
2019.Vernon, C. R., Le Page, Y., Chen, M., Huang, M., Calvin, K. V, Kraucunas, I.
P., and Braun, C. J.: Demeter – A Land Use and Land Cover Change
Disaggregation Model, J. Open Res. Softw., 6, 15, 10.5334/jors.208, 2018.West, T. O., Le Page, Y., Huang, M., Wolf, J., and Thomson, A. M.:
Downscaling global land cover projections from an integrated assessment model
for use in regional analyses: results and evaluation for the US from 2005 to
2095, Environ. Res. Lett., 9, 64004, 10.1088/1748-9326/9/6/064004, 2014.Ypma, T.: Historical Development of the Newton–Raphson Method, SIAM Rev.,
37, 531–551, 10.1137/1037125, 1995.Zhang, W., Villarini, G., Vecchi, G. A., and Smith, J. A.: Urbanization
exacerbated the rainfall and flooding caused by hurricane Harvey in Houston,
Nature, 563, 384–388, 10.1038/s41586-018-0676-z, 2018.Zhang, X., Friedl, M. A., Schaaf, C. B., Strahler, A. H., Hodges, J. C. F.,
Gao, F., Reed, B. C., and Huete, A.: Monitoring vegetation phenology using
MODIS, Remote Sens. Environ., 84, 471–475,
10.1016/S0034-4257(02)00135-9, 2003.Zhou, Y., Smith, S. J., Elvidge, C. D., Zhao, K., Thomson, A., and Imhoff,
M.: A cluster-based method to map urban area from DMSP/OLS nightlights,
Remote Sens. Environ., 147, 173–185, 10.1016/j.rse.2014.03.004, 2014.