Introduction
Recent ongoing global warming is expected to change current
hydroclimatological environments at the global scale. Since fresh
water is essential for various industrial and social activities of
human beings, its availability plays a crucial role in the sustainable
development of society.
Agriculture is one of the human activities that are highly susceptible
to hydroclimatological conditions.
Irrigated water is supplied to cropland
to compensate for the deficit in the soil water content,
which affects crop growth.
Since soil water is primarily consumed through evapotranspiration,
which is sensitive to meteorological conditions,
the amount of required irrigation water varies
with the meteorological conditions.
According to
(Tables 7.3 and 7.4), the total amount of
global freshwater withdrawal was 3560 km3yr-1 for
1995–2000, 2480 km3yr-1 (70 % of the total
withdrawal) of which was supplied for agricultural use, and the consumption
through evapotranspiration from irrigated cropland amounted to
1210 km3yr-1 (34 % of the total human
withdrawal and 49 % of the total agricultural withdrawal).
In Asia, a larger proportion of abstracted water is consumed through
evapotranspiration (52 % of the total human withdrawal and
59 % of the total agricultural withdrawal) than the global
average. Moreover, the volume of irrigation water is expected to
increase in the future because of an increase in evapotranspiration
from cropland under warmer climates e.g.,
and the expansion of irrigated
cropland to meet the increasing demand for food owing to the increase
in the world population e.g.,.
Precise estimation of the amount of
irrigation water abstraction is crucial for the sustainable use of
available water in the future.
To quantitatively evaluate future irrigation water, we must
substantially rely on hydrological simulation. However, there are
fundamental difficulties in the estimation because there are many
possible errors and uncertainties in the data sets (meteorological
data sets, land use data, etc.), calculation schemes
(evapotranspiration, runoff, river flow, etc.) and parameters.
Moreover, there are also difficulties in incorporating
irrigation schemes that are able to represent
realistic irrigation management and performance.
In fact, different general circulation models (GCMs) and global
hydrological models (GHMs) give different estimates.
showed that the discrepancies in the estimation
stem from both meteorological and irrigated area data. Recently, the
Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) set the
estimation of uncertainties in both GCMs and GHMs through intermodel
comparison as one of its goals .
GCM biases are one of the substantial sources of uncertainty in future
climate projections. For over a decade, we have made considerable
effort to remove GCM biases from the temperature and precipitation
data because these meteorological elements are crucial for analyzing
the impact of climate change. However, hydrological simulations
require other meteorological elements in addition to these elements.
Solving water and heat budgets at the ground surface basically
requires seven meteorological elements (atmospheric temperature,
precipitation, short- and longwave downward radiation, wind velocity,
pressure and humidity). Less attention has been paid to GCM biases of
meteorological elements other than temperature and
precipitation. intensively examined the
compound effects of the bias correction of radiation, wind and
humidity, and showed that bias correction has an impact on absolute
values of evapotranspiration but less impact on relative changes.
Moreover, global humidity observation data sets contain uncertainties originating
from the accuracy of measurements, grid sampling
and the spatial variability within land cells. Knowing the sensitivity of
irrigation water to humidity conditions at different locations would
help clarify the maximum expected uncertainty ranges in the estimation
of irrigation water and their geographical susceptibility.
In the framework of the recent research project
on climate change impact assessment, the
ISI-MIP has provided GCM-generated meteorological data sets that were adjusted by
a sophisticated bias correction method developed by
. Although most of the meteorological elements
used in GHMs have been corrected by this method, the relative humidity
remains uncorrected. It is important to quantitatively evaluate the
size of the humidity biases existing in the original GCM data and the
extent to which they affect the estimation of irrigation water.
In this study, we examine possible uncertainty sources in estimating
irrigation water consumption via evapotranspiration
by focusing on the propagation of
uncertainties in humidity data. We also examine whether uncertainties
in irrigation water consumption across GCMs can be reduced if bias
correction is applied to the humidity.
The data and analysis methods are described in Sect. 2 and the results
and discussion are given in Sects. 3 and 4, respectively.
Bias-corrected meteorological data used in this study.
The data sets were distributed by the ISI-MIP,
after bias correction by the method proposed by .
Element
Bias correction
Average temperature
additive
Total precipitation
multiplicative
Snowfall
multiplicative
Shortwave radiation
multiplicative
Longwave radiation
multiplicative
Near-surface wind speed
multiplicative
Surface pressure
multiplicative
Relative humidity
uncorrected
Data and methods
Bias-corrected meteorological data
We used bias-corrected meteorological data sets distributed by the
ISI-MIP for driving GHM H08 (details of the model are given in
Sect. 2.2). Five GCMs based on the Coupled Model Intercomparison
Project Phase 5 (CMIP5) were used: GFDL-ESM2M (NOAA Geophysical
Fluid Dynamics Laboratory), HadGEM2-ES (Met Office Hadley Centre
with contribution by Instituto Nacional de Pesquisas Espaciais),
IPSL-CM5A-LR (Institut Pierre-Simon Laplace), MIROC-ESM-CHEM
(Japan Agency for Marine-Earth Science and Technology, Atmosphere
and Ocean Research Institute (University of Tokyo) and
National Institute for Environmental Studies) and NorESM1-M
(Norwegian Climate Centre). Hereafter, we abbreviate these GCMs
to GFDL, HadGEM, IPSL, MIROC and NorESM, respectively. Bias
correction was applied to the meteorological elements listed in
Table using the method of
with observation-based WATCH meteorological data sets
for 1960–1999. The bias in relative
humidity in the GCMs has remained uncorrected because of
difficulties in preserving physical consistency between
humidity-related variables (relative/specific humidity, vapor
pressure), the atmospheric temperature and the pressure after bias
correction . The geographical resolution of all
meteorological data was commonly adjusted to 0.5∘×0.5∘. Future projections were made under four
representative concentration pathways (RCPs 2.6, 4.5, 6.0 and 8.5)
.
Hydrological model
The hydrological model used in this study was H08
. The model solves both
the water and energy balances at a time step of 1 day with
global coverage at a resolution of 0.5∘×0.5∘. The model consists of six submodels (land surface
hydrology, river routing, crop growth, water abstraction,
reservoir operation and environmental flow requirement), but only
the first four submodels were employed in this study. The land
surface hydrology submodel solves the water and energy balances.
The submodel solves the water balance using simple and basic
physical hydrological processes that are suitable for global-scale
simulation. A 1 m leaky bucket is assumed in the model: the
soil moisture in each land cell is expressed as water stored in
this bucket, and the water slowly drains from the bucket to
express the subsurface runoff. The crop growth submodel is
a process-based model that is used to estimate
the crop-growing season globally. The water abstraction submodel estimates
the human impacts of irrigational,
municipal and industrial water abstraction from rivers for
consumptive use.
The consumptive use of irrigation water
was estimated from the deficit in the soil water content
compared with a target level
in irrigated cropland during the growing season.
Details are described in the second half of this section.
The water is abstracted from rivers
as the first choice if the riverine water is available;
the rest of the required water is limitlessly supplied
from non-renewable and non-local blue water resources
(e.g., groundwater or long-distance transported water;
see ).
Values for the consumptive use of municipal and industrial water
were taken from country-based AQUASTAT data .
Municipal and industrial water consumption
at each land cell were weighted by the population
using the Gridded Population of the World, version 3 (GPWv3)
.
Socioeconomic conditions (e.g., the population and irrigated area)
were fixed at those in the year 2000.
To stabilize the initial
conditions, the hydrological model was spun up using data from
1950 to 1959.
We assumed that irrigation water is supplied to irrigated
cropland under the condition that crops are not affected by water
stress. The soil water content was maintained at 75 % of
the field capacity for all crops except rice (100 %)
during the growing season and for 30 days before the
planting date.
If there is a deficit relative to this threshold,
soil water content was assumed to be supplied by irrigation.
The soil water of cropland is consumed through
evapotranspiration and lost through runoff. The former was calculated
from both the meteorological conditions and the soil water content
(see Sect. 2.3), whereas the latter was assumed to vary
with the soil water content. The spatial distribution of the
irrigated area was fixed at that for the year 2000 based on the
data of throughout the analysis period. We
separately calculated the results for three different water
management schemes corresponding to three types of agricultural land use:
double-cropping irrigated cropland (we refer to this water
management scheme as Mosaic 1 hereafter), single-cropping
irrigated cropland (Mosaic 2) and rain-fed cropland (Mosaic 3).
Their geographical distributions are shown
in Fig. .
Information on double and single croppings was taken
from the cropping intensity reported by .
We aggregated the three types of water
management into a land cell (Mosaic 0) in consideration of their
areal fractions in each land cell.
We considered the 19 crops (18 crops plus “others”)
used in Table 7 in
but with an updated geographical distribution for the year 2000
.
The crop parameters used to calculate their growth were
based on the SWIM code .
Geographical distribution of irrigated croplands – (a)
double cropping each year (Mosaic 1), (b) single cropping each year
(Mosaic 2) and (c) rain-fed cropland (Mosaic 3) – used in this
study. The distributions are indicated in black.
In this study, we evaluated two quantities
regarding the irrigation water
(hereafter, the water volume is reported on a consumption basis):
irrigation water demand (IWD)
and irrigation water consumption from rivers (IWCR).
The IWD gives the cumulative amount of water
to be supplied over cropland
to compensate for the deficit relative to a threshold
soil water content.
The soil water is primarily supplied by precipitation
under natural conditions
and consumed via evapotranspiration, drained by runoff and so forth.
Since we assumed that the soil water
should be kept at a certain level by irrigation
(described in Sect. 2.3 in detail),
IWD gives the additional amount of water
required to prevent crops from suffering water stress
under given meteorological conditions.
In other words, the IWD gives the maximum water consumption
while maintaining the current agricultural maneuver
(geographical distribution of irrigated cropland, cultivars,
water management in irrigated cropland, etc.)
under idealized conditions without fear of water shortage.
The IWCR gives the irrigation water consumption that can be supplied from
rivers and is defined as a proportion of the IWD. In practice, irrigation
water is abstracted from various resources (e.g., rivers, local reservoirs,
groundwater). Among them, rivers are the largest water resource and their
flow is vulnerable to future climate change. Thus, it is important to examine
the proportion of IWD that can be supplied from rivers. By taking this
situation into consideration, our calculation scheme was based on the
assumption that water is primarily abstracted from rivers
. Through evaluation of IWCR under restrictions of
riverine water availability, we estimated the extent to which humidity biases
affect hydrological variables that are not determined only from
meteorological conditions.
Evapotranspiration calculation scheme
Various formulae for estimating potential
evapotranspiration have been developed (e.g., ),
and researchers have utilized suitable
formulae for their own research purposes. These formulae are
classified into two basic categories: physical and empirical
formulae. The former describe potential evapotranspiration from
the viewpoint of the energy balance at the land surface, and such
formulae are suitable for (micro)meteorological studies requiring
a high temporal resolution. Thus, this type of formula requires
several meteorological elements such as the surface temperature,
humidity, radiation and wind speed. On the other hand, the
latter describe climatological conditions for less time-varying
phenomena in a simplified manner and, in general, require only
two or three meteorological elements. Thus, the latter are
suitable for sites where meteorological observation data are
limited. Examples of evapotranspiration formulae are given in the
Appendix.
The calculation scheme for potential evapotranspiration
Epot employed in H08 is the bulk formula
Epot=ρCDU(qsat(Ts)-q),
where ρ, CD and U are the air density, bulk transfer
coefficient (0.003) and wind speed, respectively. Thus,
Epot is proportional to the difference between the
saturated specific humidity at the surface temperature
qsat(Ts) and the specific humidity of the air q.
Since bias correction was independently applied to each
meteorological element except for the relative humidity, the
physical consistency among meteorological elements guaranteed in
the original GCMs might be lost. In this study, we recalculated
q to maintain local physical consistency between the
bias-corrected temperature and uncorrected relative humidity.
Actual evapotranspiration is estimated by multiplying by
a function of the soil water content W. If W is less than
three-quarters of the field capacity Wfc,
Eact linearly decreases with decreasing W:
Eact=βEpot,
where
β=1(W≥0.75Wfc)W0.75Wfc(W<0.75Wfc).
The soil water content in irrigated cropland
was assumed to be maintained at 0.75Wfc (Wfc for rice)
to prevent crops from suffering water stress.
That is, evapotranspiration from irrigated cropland
is not suppressed by a decrease in the soil water content
(i.e., Eact=Epot) during the growing season.
Although the actual threshold
may be different for different types of irrigation
(e.g., sprinklers, drip irrigation, ditch irrigation)
or irrigation management,
global information on such variation is unavailable.
The adopted irrigation scheme based on the soil water content
is simple but applicable for global-scale simulations
e.g.,.
Experiment design of this study
To investigate the effects of bias correction of the humidity, we
designed three sets of experiments in this study: (1) a reference
experiment, (2) a sensitivity experiment and (3) a bias-corrected
experiment. In the reference experiment, a hydrological
simulation was performed with the uncorrected humidity data
described in Sect. 2.1. We evaluated the evapotranspiration and
irrigation water for both present and future periods. The
results were also used as a reference for the other two sets of
experiments, details of which are given below.
Sensitivity experiment with hypothetical bias
in humidity
Measurement of the atmospheric humidity inevitably involves
errors. Observation-based humidity data sets, which are often
used as reference data for bias correction, might contain
a certain level of error. Moreover, the sensitivity of the
amount of irrigation water to atmospheric humidity varies
geographically or seasonally because irrigation water depends
not only on meteorological conditions but also on the areal
fraction of irrigated cropland, irrigation management,
irrigation techniques
and the cultivation maneuver (crop
type, crop calendar, etc.) in each land cell.
To evaluate the sensitivity of the amount of irrigation water
to atmospheric humidity, we carried out a sensitivity experiment
in which we introduced a pair of constant biases so that the data
were higher and lower than the original GCM-based humidity data
and investigated the effect of the biases on irrigation water.
The sensitivity is also helpful for predicting the size of the
error in the simulation of irrigation water. In this
experiment, we introduced “hypothetical” biases into the
relative humidity by simply adding biases of ±5 % RH as a worst case (discussed below) homogeneously
to all the land cells. (Hereafter, to discriminate between the
unit of relative humidity and a general percentage, we use
% RH for the unit of humidity.) When the relative
humidity exceeded 100 % RH or became negative, we used
values of 100 and 0 % RH, respectively. The other
meteorological elements were unchanged. This experiment was
carried out for the both present and future periods.
Total number of days
when the humidity is oversaturated (> 100 % RH)
in the original regridded GCM data.
Both the annual and seasonal sums are given
as the mean over all land cells
(67 420 cells). The total numbers of days are
given in parentheses in the header.
GCMs
1971–2000
2070–2099 RCP8.5
Annual
DJF
MAM
JJA
SON
Annual
DJF
MAM
JJA
SON
(10 958)
(2708)
(2760)
(2760)
(2730)
(10 957)
(2707)
(2760)
(2760)
(2730)
GFDL-ESM2M
715.6
330.4
99.0
20.8
265.5
692.4
342.5
87.9
27.9
234.1
HadGEM2-ES
738.6
416.1
165.5
10.1
146.9
417.8
263.7
78.9
3.1
72.2
IPSL-CM5A-LR
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
MIROC-ESM-CHEM
636.8
310.9
187.9
14.6
123.3
228.5
122.7
62.8
12.8
30.2
NorESM1-M
290.9
169.7
48.7
0.5
72.0
130.0
91.7
20.5
0.8
16.8
Ensemble mean
476.4
245.4
100.2
9.2
121.5
293.7
164.1
50.0
8.9
70.7
Total number of days
when the humidity is truncated at 100 % RH
during adjustment by our primitive bias correction
described in Sect. 2.4.2.
Both the annual and seasonal sums are given
by the mean over all land cells
(67 420 cells). The total numbers of days are
given in parentheses in the header.
GCMs
1971–2000
2070–2099 RCP8.5
Annual
DJF
MAM
JJA
SON
Annual
DJF
MAM
JJA
SON
(10 958)
(2708)
(2760)
(2760)
(2730)
(10 957)
(2707)
(2760)
(2760)
(2730)
GFDL-ESM2M
526.7
256.0
117.8
37.5
115.4
563.0
280.6
115.6
49.9
116.8
HadGEM2-ES
528.2
313.4
89.9
11.6
113.2
333.1
202.4
52.7
10.1
67.7
IPSL-CM5A-LR
186.2
92.3
32.2
27.9
33.8
141.0
72.5
24.3
20.9
23.3
MIROC-ESM-CHEM
411.6
245.6
74.5
7.3
84.3
170.5
99.4
27.8
10.7
32.5
NorESM1-M
277.2
167.2
41.8
10.0
58.2
159.9
103.9
23.7
7.9
24.3
Ensemble mean
386.0
214.9
71.2
18.9
81.0
273.5
151.8
48.8
19.9
52.9
Total number of days
when the humidity is truncated at 0 % RH
during adjustment by our primitive bias correction
described in Sect. 2.4.2.
Both the annual and seasonal sums are given
by the mean over all land cells
(67 420 cells). The total numbers of days are
given in parentheses in the header.
GCMs
1971–2000
2070–2099 RCP8.5
Annual
DJF
MAM
JJA
SON
Annual
DJF
MAM
JJA
SON
(10 958)
(2708)
(2760)
(2760)
(2730)
(10 957)
(2707)
(2760)
(2760)
(2730)
GFDL-ESM2M
28.34
3.19
7.53
10.42
7.20
39.35
4.25
10.25
15.35
9.51
HadGEM2-ES
2.34
0.11
1.34
0.38
0.49
5.20
0.39
2.56
1.31
0.95
IPSL-CM5A-LR
1.03
0.04
0.61
0.19
0.19
4.94
0.74
2.50
0.80
0.90
MIROC-ESM-CHEM
7.79
1.23
2.01
2.04
2.50
17.21
2.63
4.98
3.65
5.95
NorESM1-M
10.62
2.61
3.82
2.45
1.74
18.80
2.80
9.46
4.43
2.11
Ensemble mean
10.02
1.44
3.06
3.10
2.42
17.10
2.16
5.95
5.11
3.88
In fact, reported that the maximum
uncertainties in humidity measurements with dry- and wet-bulb
thermometers amounted to 2.75 and 5 % RH at
temperatures of 0 and -10 ∘C, respectively.
summarized the errors for various measurement
equipment: for example, advanced equipment based on the
capacitive method has an accuracy of 2 % RH (for
a humidity of 10–80 % RH) to 3 % RH (for
a humidity of 80–ca. 100 % RH). By considering these
reports, we set ±5 % RH as the worst case in this
study.
Through such sensitivity experiments, we are able to estimate the
largest possible ranges of uncertainty in irrigation water
consumption due to an uncertainty in the relative humidity of
α % RH because the uncertainty for irrigation
water in the case of geographically random biases within
±α % RH necessarily lies between those for the two
extremes of the globally homogeneous bias of ±α % RH. Recall that,
because of the supply of irrigation water,
Eact=Epot
for irrigated cropland during the growing season. If we
artificially add positive (negative) biases to the relative
humidity without changing other elements, both ρ and
qsat(Ts)-q on the right-hand side of the bulk
formula (Eq. ) will decrease (increase),
resulting in a decrease (increase) in potential
evapotranspiration. The increase in Epot via
qsat(Ts) is
smaller than the direct decrease in Epot resulting from
introducing a hypothetical bias of α % RH.
Therefore, Eact has a monotonic dependence on the
humidity bias: Eact becomes smaller (larger) for
positive (negative) biases in the relative humidity.
We note that this simple relation holds only for irrigated
cropland during the crop-growing season when irrigation water is
limitlessly supplied. In rain-fed cropland or irrigation-free seasons,
evapotranspiration has a complex dependence on meteorological
conditions because Eact also
depends on the soil moisture content (Eq. ).
The sensitivity experiment was also carried out for a future period because
different GCMs project different future climates. Even if the biases in
meteorological elements were completely removed for the present period, the
future temperature or precipitation would still differ across the GCMs. Since
evapotranspiration is also sensitive to temperature conditions, the future
sensitivity may be different from the present sensitivity and also vary among
the GCMs. The sensitivity experiment for a future period will help clarify
the propagation of humidity biases into the amount of irrigation water even
under different future climates projected by different GCMs.
Bias-corrected experiment
If we introduce bias correction of the humidity, does it affect
hydrological projections and have any advantages? To examine this
effect, we prepared another set of meteorological data for which
the humidity data were bias-corrected with a primitive methodology
that adjusts only the monthly climatology. Using this
bias-corrected humidity data set and the original bias-corrected
meteorological data sets for the other elements, we recalculated
the hydrological process in the same way and compared the results
with the uncorrected ones (i.e., those of the reference
experiment). This experiment was carried out for both the present
and future periods.
The bias correction methodology was based on additive adjustment
in order to preserve the range of variability
in the relative humidity because the evapotranspiration
obtained by a physical formula (see Appendix)
is sensitive to the vapor pressure deficit.
First, we obtained the monthly climatological relative humidity at
all land cells for each GCM by averaging the relative humidity
data for the same month of the year over the period 1960–1999.
By subtracting the monthly climatological relative humidity in the
GCM for the same period from those in the WATCH observational
data, we determined the climatological monthly adjustments. Then,
we compiled daily bias-corrected humidity data by simply adding
the climatological monthly adjustments to the original GCM daily
humidity data. Values of less than 0 % RH and greater
than 100 % RH were set to 0 and 100 % RH,
respectively.
We summarize the statistics of the truncated humidity data before and during
our bias correction in Table 2. The original regridded data already
contain supersaturation (greater than 100 % RH), except for IPSL
(Table ). Most of the supersaturation data were obtained
at high northern latitudes in winter where the atmospheric temperature was
well below 0 ∘C. The number of truncated humidity data at
100 % RH during our primitive bias correction
(Table ) is less than the number of supersaturation data
in the original regridded data except for IPSL, particularly in the boreal
winter, because a certain proportion of the oversaturated data in the GCMs
were adjusted to undersaturated data by the bias correction when the monthly
climatological humidity of the GCMs was larger than that of the WATCH data.
In contrast, the number of truncated humidity data at 0 % RH is
very small (Table ). These truncations were observed in
highly dry regions, such as deserts. Generally, the number of truncated data
at 100 % RH in the future projection (RCP8.5) is smaller than that
in the present, whereas the number at 0 % RH is larger than the
present number.
We expect the errors in evapotranspiration due to these truncations to be
marginal and not to cause major problems in the interpretation of our results
on hydrological variables. In fact, the evapotranspiration under the
low-temperature conditions typically seen at high northern latitudes in
winter approaches zero. Moreover, few crops are cultivated in the winter, and
irrigated agriculture is not practiced in these regions. Similarly,
evapotranspiration in and around desert areas (except in limited areas with
intensive irrigation) is also very small.
Global average of monthly SD ( % RH)
in relative humidity,
shown in Fig. , for each land use.
GCMs
Mosaic 0
Mosaic 1
Mosaic 2
Mosaic 3
GFDL-ESM2M
20.3
26.2
23.9
17.6
HadGEM2-ES
11.7
24.3
20.1
13.4
IPSL-CM5A-LR
15.0
34.8
27.0
13.6
MIROC-ESM-CHEM
20.4
18.0
20.0
17.4
NorESM1-M
17.9
20.4
18.8
13.4
Results
Comparison of performance of meteorological elements between GCMs
We first examine the differences in the meteorological elements
between the five GCMs in the framework of the reference
experiment to search for existing GCM-inherent biases and
compare them with the WATCH observation-based meteorological
elements to evaluate the performance of bias correction.
Figure shows the monthly difference
worldwide averaged over each type of land use (mosaic). Monthly
profiles of the atmospheric temperature, precipitation and
shortwave downward radiation for the five GCMs agree with those
of WATCH. Note that the 30-year analysis period
(1971–2000) is slightly different from the bias correction
period (1960–1999). For the wind speed data, although the
monthly profile of MIROC is slightly larger than that of WATCH
over Mosaic 1, we consider the overall performance of bias
correction to be reasonably good for the wind data.
In contrast, the monthly profiles of the relative humidity,
which contain GCM-inherent biases, show a large dispersion
between the five GCMs and also deviate from those of WATCH.
The global-mean relative humidity over
Mosaic 1 shows a larger dispersion
than those over the other mosaics: the
largest difference in the relative humidity between the monthly
GCMs reaches 19.8 % RH in both January and October
with a minimum of 11.0 % RH in May for Mosaic 1.
Monthly profiles of meteorological elements
used in this study for 1971–2000. The results are aggregated
over each type of land use, identified by the mosaic number.
Profiles of the five GCMs are indicated in different colors:
(red) GFDL, (green) HadGEM, (blue) IPSL, (dark yellow) MIROC
and (light blue) NorESM. Profiles of the WATCH data
are shown as black lines with dots.
Such differences in the uncorrected relative humidity cause the
deviation of the potential evapotranspiration and
evapotranspiration between the five GCMs.
Figure shows their monthly profiles.
Different GCMs have different monthly profiles and peak months.
The difference in the potential evapotranspiration among the
GCMs for Mosaic 1 reaches a maximum of 1.23 mmday-1
in June with a minimum of 0.56 mmday-1 in December.
The difference exceeds 0.9 mmday-1 from March to
October. Since the temperature, shortwave downward radiation
and wind speed, which are required for the calculation of the
potential evapotranspiration (Eq. ), are
successfully bias-corrected (Fig. ), these
differences in the potential evapotranspiration are considered
to be mainly due to GCM biases in the relative humidity. NorESM
tends to have a small but positive bias of the potential
evapotranspiration and a small negative bias of the
evapotranspiration during the summer. However, no clear biases
of the relative humidity can be observed in
Fig. .
Monthly profiles of the potential evapotranspiration
and evapotranspiration for 1971–2000
calculated in this study. The results are aggregated
over the same land use.
Profiles of the five GCMs are indicated in different colors:
(red) GFDL, (green) HadGEM, (blue) IPSL, (dark yellow) MIROC
and (light blue) NorESM.
Next, we determine the geographical distribution of the GCM
biases with respect to the WATCH data because regional
deviations with opposite signs may cancel each other when
calculating the global mean. Figures ,
and
show the SD of 12-month climatological data
of the relative humidity, atmospheric temperature and precipitation
of the GCMs
with respect to the WATCH data, respectively. Strong regional
patterns were detected in the relative humidity
(Fig. ). Figure also shows that
the relative humidity in high mountainous areas (Rocky Mountains, Andes
and Himalayas) have larger deviations from the WATCH data for
all GCMs. Each GCM has a different geographical distribution.
For example, GFDL exhibits large differences over the world.
HadGEM and IPSL have large differences in Eurasia but good
performance in Australia. MIROC has high deviations in inland
regions of Asia and Australia. NorESM has small differences in
Europe and the eastern United States but large differences in
Australia.
Geographical distribution of the SD
from the WATCH data for the relative humidity.
The SD was evaluated
from 12-month climatological (1971–2000) data
for each land cell.
In contrast, uniformly distributed small biases (less than
0.5 ∘C for most of the world) were
observed for the temperature (Fig. ).
The SD for the precipitation (Fig. )
is less than 0.2 mmday-1 for most of the world
and around 0.5 mmday-1 for humid areas (e.g., Southeast Asia).
Although exceptions are seen in the Amazonian inland,
where a large SD is observed for GFDL and IPSL,
these contributions are considered to be marginal
when taking the large annual precipitation (greater than 2000 mm)
and the smaller amount of cropland (see Fig. )
into account. These
results also indicate that the bias corrections of the
atmospheric temperature and precipitation were successful
at the regional scale.
Same as Fig. but for the atmospheric temperature.
Same as Fig. but for the precipitation.
We averaged the monthly SD over the land cells of each mosaic and summarized
the results in Table . HadGEM has the smallest
deviation from WATCH over all land cells (Mosaic 0). However,
MIROC and NorESM have superior performance for Mosaic 1 and 2.
Since both Mosaic 1 and 2 are irrigated cropland, differences
in the potential evapotranspiration directly affect differences
in the amount of irrigation water. Errors in the humidity
are one possible error source when calculating evapotranspiration.
In this sense, small humidity
biases over irrigated cropland are beneficial for suppressing
their effects on irrigation water
provided that other meteorological elements are successfully bias-corrected.
GCM features and their propagation into future projections
Next, we examine the extent to which GCM-inherent features in the
relative humidity affect the estimation of irrigation water and
propagate into a future period (2070–2099) in the framework of
the reference experiment. If the effects are not negligible,
bias correction of the humidity, as well as other meteorological
elements, is highly recommended.
Monthly profiles of meteorological elements
used in this study for 2070–2099 under RCP8.5. The results are aggregated
over each type of land use, identified by the mosaic number.
Profiles of the five GCMs are indicated in different colors:
(red) GFDL, (green) HadGEM, (blue) IPSL, (dark yellow) MIROC
and (light blue) NorESM.
Figure shows future monthly profiles
of the five GCMs. Since the meteorological variables are bias-corrected
for 1960–1999, the GCM-inherent future climate trends
diverge from their monthly profiles. The monthly profiles
of the atmospheric temperature and precipitation
show small differences but have similar shapes across the GCMs.
Shortwave downward radiation
and wind have little dispersion among the GCMs.
In contrast, the relative humidity has large dispersion among the GCMs.
For each GCM, in comparison with Fig. ,
the monthly profiles of the relative humidity
for the present and future periods
have similar shapes.
Monthly anomalies with respect to
the ensemble mean of five GCMs for 1971–2000.
The results are aggregated over each land use.
The anomaly in each GCM is indicated in different colors:
(red) GFDL, (green) HadGEM, (blue) IPSL, (dark yellow) MIROC
and (light blue) NorESM. The panels from top to bottom show
the relative humidity, potential evapotranspiration,
evapotranspiration, irrigation water demand (IWD)
and irrigation water consumption from rivers (IWCR).
Same as in Fig.
but for the future period (2070–2099) under RCP8.5.
To easily perceive the
differences between the GCMs, we evaluate the relative anomaly of
the five GCMs with respect to their ensemble mean. The results
of anomalies in the relative humidity and related hydrological
elements (potential evapotranspiration, evapotranspiration, IWD
and IWCR) are shown in Figs. and
.
First, Fig. shows that the monthly anomaly
profiles of the potential evapotranspiration, evapotranspiration
and IWD are similar but vertically opposite those of the relative
humidity. This relation is expected from
Eq. (), while other meteorological conditions
are fixed. We note that although the evapotranspiration from
rain-fed cropland (Mosaic 3) also depends on the soil moisture,
GCM-inherent features are weakly observed in the monthly profile
of evapotranspiration.
Second, Fig. shows that the future monthly
anomaly profiles of the relative humidity are very similar to the
present ones (Fig. ) for all GCMs. This
implies that the GCM-inherent biases propagate into future
projections. As a result, the future monthly profiles of other
hydrological elements also resemble the present ones.
Geographical distribution of the monthly anomaly
of the relative humidity with respect to the ensemble mean
of five GCMs for January (left two columns) and July (right two columns).
Each pair of adjoining panels shows results
for the present (1971–2000) and future (2070–2099, RCP8.5) periods,
respectively.
Since IWCR is limited by the availability of riverine water,
GCM-inherent features are weakened but remain. For example,
larger positive anomalies in HadGEM and IPSL and negative ones in
MIROC during boreal fall for 1971–2000
(Fig. ) are similarly observed in the future
projections (Fig. ).
Geographical distribution of the monthly anomaly
of the relative humidity (Fig. )
also shows that GCM-inherent biases are propagated
into future projections. For all GCMs,
the anomaly pattern for the future periods
resembles that for 1971–2000.
The results imply that, if we adequately remove
the GCM-inherent biases of the humidity,
their propagation into future projections
can be alleviated.
Results of the present (1971–2000) estimation and future
(2070–2099) projection of irrigation water demand (IWD). The values in
parentheses are changes (%) relative to the present values. The range (the
difference between the maximum and minimum) of the five GCMs is given in the
bottom line.
Global sum of IWD (km3yr-1) and relative change (%)
GCMs
Mosaic 0
present
future (2070–2099)
(1971–2000)
RCP2.6
RCP4.5
RCP6.0
RCP8.5
GFDL-ESM2M
1324.7
1425.4
(+7.60)
1426.9
(+7.71)
1485.1
(+12.11)
1569.7
(+18.49)
HadGEM2-ES
1295.1
1289.5
(-0.43)
1370.9
(+5.85)
1345.7
(+3.90)
1435.7
(+10.85)
IPSL-CM5A-LR
1435.5
1484.5
(+3.41)
1507.7
(+5.03)
1585.4
(+10.44)
1703.7
(+18.68)
MIROC-ESM-CHEM
1161.3
1265.4
(+8.96)
1249.4
(+7.59)
1389.1
(+19.61)
1377.0
(+18.57)
NorESM1-M
1152.6
1182.2
(+2.56)
1211.4
(+5.10)
1238.1
(+7.41)
1322.6
(+14.75)
Ensemble mean
1273.8
1329.4
(+4.36)
1353.3
(+6.24)
1408.7
(+10.59)
1481.7
(+16.32)
Range
282.9
302.3
296.3
347.3
381.1
Results of the present (1971–2000) estimation and future
(2070–2099) projection of irrigation water consumption from rivers (IWCR).
The values in parentheses are changes (%) relative to the present values.
The range (the difference between the maximum and minimum) of the five GCMs
is given in the bottom line.
Global sum of IWCR (km3 yr-1) and relative change (%)
GCMs
Mosaic 0
present
future (2070–2099)
(1971–2000)
RCP2.6
RCP4.5
RCP6.0
RCP8.5
GFDL-ESM2M
522.5
525.1
(+0.49)
526.9
(+0.83)
527.9
(+1.02)
540.0
(+3.35)
HadGEM2-ES
524.9
515.7
(-1.75)
522.8
(-0.39)
518.4
(-1.23)
532.0
(+1.35)
IPSL-CM5A-LR
551.4
542.4
(-1.62)
542.7
(-1.57)
539.6
(-2.13)
550.5
(-0.15)
MIROC-ESM-CHEM
511.6
513.5
(+0.36)
507.4
(-0.83)
519.0
(+1.44)
506.0
(-1.10)
NorESM1-M
497.7
500.3
(+0.53)
502.6
(+0.99)
507.3
(+1.93)
514.4
(+3.36)
Ensemble mean
521.6
519.4
(-0.42)
520.5
(-0.21)
522.4
(+0.15)
528.6
(+1.34)
Range
53.7
42.1
40.1
32.3
44.5
Uncertainties in absolute values of irrigation water across GCMs
In Table 4, we summarize the results of the reference experiment on present
and future values of the global
sum of irrigation water, focusing on their ranges across the
GCMs. Note that the global sum of irrigation water (Mosaic 0)
is equivalent to the sum of those for Mosaic 1 and 2 because no
irrigation is applied to Mosaic 3. IWD (Table ) ranges between
1152.6 and 1435.5 km3yr-1 for 1971–2000. A larger
increase of ca. 20 % in the future (2070–2099) is
projected under a higher concentration of greenhouse gases such as
under RCP8.5. Both absolute values and relative changes show
a large dispersion between the GCMs.
Since it is
difficult to validate these results with observed data because of
the lack of global census data, we compare the results with those
in previous studies. reviewed past studies on
irrigation water consumption (in their Table S1), which was in
the range of 1029–1772 km3yr-1 at the end (or the
last few decades) of the 20th century.
reported that global blue water consumption for irrigation use
was 1364 km3yr-1. Our estimations of
IWD are close to these reported results.
In contrast to IWD, future changes in IWCR (Table ) relative to the
1971–2000 values show a small increase of at most 3.4 %.
Several pairs of GCM-RCPs show a small decrease in the future.
Since IWCR is strongly constrained by water availability from
rivers, these results reflect the future river flow. In other
words, current irrigation maneuvers cannot be sustained by only
riverine water under a future warming climate for these scenarios
because, despite increasing demand for irrigation water
(Table ),
water consumption that can be supplied from rivers cannot
meet the demand (Table ) at the global scale.
Monthly profiles of the global sum of present and future
irrigation water consumption from rivers (IWCR).
Black, blue and red lines show the results
of the present (1971–2000) estimation
and future (2070–2099) projections under RCPs 2.6 and 8.5, respectively.
We note that MIROC and NorESM, whose relative humidity shows small
deviations from the observation (see Sect. 3.1), tend to have the
smallest IWD and IWCR values among the five GCMs.
Monthly profiles of the global sum of the present and future IWCR
(Fig. ) differ among the GCMs. Since most
irrigated croplands are distributed in the Northern Hemisphere,
the global sum of IWCR has a peak in boreal summer of
approximately 3 times the value in boreal winter. Despite
large differences in the absolute monthly values between the GCMs,
all GCMs show a future increase in IWCR in boreal summer and
a decrease in boreal spring under a future warmer climate.
Although in April the global sum of the future IWD is
approximately the same as that of the present IWD (not shown), the
future IWCR is expected to decrease in boreal spring
(Fig. ). This result indicates that the future
decrease in IWCR is attributable to a deficit in irrigation
water that can be supplied from rivers, not to an increase in
evapotranspiration demand from cropland.
Monthly profiles of the global sum
of irrigation water consumption from rivers (IWCR)
for the reference and sensitivity experiments
with artificial biases of ±5 % RH.
The analysis period is 1971–2000.
Sensitivity experiment with hypothetical biases
Present period (1971–2000)
We investigate the effect of humidity biases on irrigation water by examining
the results of the sensitivity experiment by adding biases of ±5 % RH homogeneously all over the world. Table 5 shows that biases
of ±5 % RH approximately correspond to changes in IWD of ±6.5 to ±7.5 % and IWCR of ±3.5 to ±5.0 % as
the maximum error range. Monthly profiles of IWCR with biased humidity also
deviate from the original profiles (Fig. ). The
effect of the artificial bias is clearly observed during boreal summer. In
the comparison of Tables 4 and 5 or Figs. and
, changes with ±5 % RH biases are
comparable to, or sometimes larger than, future changes in IWCR under RCP8.5.
Results of the reference and sensitivity experiments
with artificial biases of ±5 % RH – irrigation water demand (IWD).
The values in parentheses are changes (%)
relative to the original values.
Global sum of IWD (km3yr-1) and relative change (%)
GCMs
Mosaic 0
original
-5 % RH
+5 % RH
RCP8.5
-5 % RH
+5 % RH
(1971–2000)
(1971–2000)
(1971–2000)
(2070–2099)
(2070–2099)
(2070–2099)
GFDL-ESM2M
1324.7
1414.0
(+6.74)
1238.4
(-6.51)
1569.7
1673.2
(+6.59)
1469.7
(-6.37)
HadGEM2-ES
1295.1
1388.8
(+7.24)
1204.5
(-7.00)
1435.7
1536.8
(+7.04)
1338.0
(-6.80)
IPSL-CM5A-LR
1435.5
1531.6
(+6.69)
1342.3
(-6.49)
1703.7
1813.9
(+6.47)
1596.8
(-6.28)
MIROC-ESM-CHEM
1161.3
1249.9
(+7.63)
1075.9
(-7.35)
1377.0
1477.6
(+7.31)
1280.0
(-7.04)
NorESM1-M
1152.6
1236.6
(+7.28)
1071.6
(-7.03)
1322.6
1415.2
(+7.00)
1233.1
(-6.77)
Results of the reference and sensitivity experiments
with artificial biases of ±5 % RH – irrigation water consumption from rivers (IWCR).
The values in parentheses are changes (%)
relative to the original values.
Global sum of IWCR (km3yr-1) and relative change (%)
GCMs
Mosaic 0
original
-5 % RH
+5 % RH
RCP8.5
-5 % RH
+5 % RH
(1971–2000)
(1971–2000)
(1971–2000)
(2070–2099)
(2070–2099)
(2070–2099)
GFDL-ESM2M
522.5
543.8
(+4.08)
500.9
(-4.14)
540.0
559.3
(+3.57)
520.3
(-3.65)
HadGEM2-ES
524.9
549.4
(+4.68)
499.7
(-4.79)
532.0
554.1
(+4.15)
509.6
(-4.21)
IPSL-CM5A-LR
551.4
572.4
(+3.81)
529.8
(-3.92)
550.5
569.1
(+3.36)
531.7
(-3.42)
MIROC-ESM-CHEM
511.6
535.7
(+4.71)
486.9
(-4.84)
506.0
525.2
(+3.78)
486.5
(-3.86)
NorESM1-M
497.7
521.4
(+4.76)
473.7
(-4.82)
514.4
535.7
(+4.13)
492.9
(-4.18)
Figure shows the geographical distribution
of the sensitivity (i.e., the change in IWD or IWCR per unit
change in the relative humidity (1 % RH)) for June and
August. The negative sensitivity of IWD, as expected from
Eq. (), is observed, particularly in India and
East China, where both double-cropping and single-cropping
irrigated croplands are intensely distributed. In contrast,
midlatitudes (Europe to Central Asia and North America) show
smaller negative sensitivity than India and East China. This
implies that IWD in India and East China is more sensitive to
small changes in the relative humidity than other regions of the
world, possibly due to the high temperature in summer and the high
areal fraction of irrigated cropland.
The sensitivity of IWCR shows a similar geographical distribution
to that of IWD but with a smaller magnitude. In June, the
negative sensitivity of IWCR is markedly weaker than that of IWD
in India and East China. These features are considered to be due
to the limited water availability in river flow, which results in
less dependence on the atmospheric humidity. In fact, the rainy
season starts in June in India and in June and July in southern
and northern China, respectively.
From these results, to effectively and efficiently reduce the
uncertainty of irrigation water consumption, more stringent
accuracy for the atmospheric humidity data is required for India
and East China.
Geographical distribution of sensitivity,
given by change in IWD or IWCR per change of 1 % RH
in the relative humidity.
HadGEM results for June and August are shown.
Future period (2070–2099)
Table 5 also shows the results of the sensitivity experiment for the future
period. We observed slightly smaller sensitivities (±6.0 to ±7.5 % for IWD and ±3.0 to ±4.5 % for IWCR) for
the future period than for the present. Readers are reminded that these
sensitivities were evaluated under GCM-inherent future climate trends because
only the relative humidity was artificially changed around its future
projected value while the other variables were fixed to their future
projected values in the experiment.
The differences in the relative humidity among the GCMs for the future period
(Fig. ) are sufficiently large for the estimates
of IWD and IWCR to diverge. The differences in the relative humidity between
the GCMs are one of the marked uncertainty sources in the future projection
of hydrological variables.
Monthly anomalies with respect to
the ensemble mean in five GCMs with bias-corrected humidity data
for 1971–2000.
The results are aggregated over each land use.
The anomaly in each GCM is indicated in different colors:
(red) GFDL, (green) HadGEM, (blue) IPSL, (dark yellow) MIROC
and (light blue) NorESM. The panels from top to bottom show
the relative humidity, potential evapotranspiration,
evapotranspiration, irrigation water demand (IWD)
and irrigation water consumption from rivers (IWCR).
Bias-corrected experiment
and effects of reduction of uncertainty across GCMs
Next, we examine the extent to which uncertainties are reduced by
bias correction of the humidity data (Sect. 2.4.2).
Figure shows monthly anomalies of hydrological
elements with respect to the GCM-ensemble means. In comparison
with Fig. , the relative humidity of all GCMs
is in good agreement, implying that bias correction, even with
a primitive method, is effective. The potential
evapotranspiration is also similar among the GCMs except for
NorESM, which has a positive bias. NorESM also had a positive
bias in Fig. . The monthly profiles of the
evapotranspiration, IWD and IWCR are confined in narrower ranges
than those for the uncorrected humidity data. For example, IWD
remains within ±20 % from the ensemble mean
throughout the year, in clear contrast to the range of
approximately ±30 % in Fig. .
Future projections (Fig. , in comparison with
Fig. ) also show the advantageousness of
reducing differences in projected hydrological elements across the
GCMs by bias correction of the humidity data.
Same as in Fig.
but for the future period (2070–2099) under RCP8.5.
Bias correction of the humidity data also reduces the
uncertainties (i.e., the range between the maximum and minimum) in
the monthly IWD and IWCR for the five GCMs
(Fig. ). Hereafter, the monthly reduction in
uncertainties is quantified as the ratio of the range with
bias-corrected humidity data to that with uncorrected humidity
data for IWD and IWCR. For 1971–2000, the range of IWD projected
with the bias-corrected humidity data is smaller than that with
the uncorrected data: the range of the corrected data is
12 % for the best month (January) and 84 % for
the worst month (June). Even for future projections under RCP8.5,
the range of IWD with the bias-corrected data is 35 %
(best month, January) to 89 % (worst month, August) of that
with the uncorrected data. The results for IWCR, which is
governed by riverine water availability, also suggest the
advantageousness of bias correction of the humidity data: for
1971–2000, the range of IWCR with the bias-corrected data is
reduced to as little as 29 % of that with the uncorrected
data (February), although the range is increased in June and July
(110 and 102 %, respectively).
Global sum of irrigation water demand (IWD) and irrigation water
consumption from rivers (IWCR) (km3yr-1) with bias-corrected
humidity data. See Table 4 for comparison with uncorrected humidity data. The
values in parentheses are changes (%) relative to present values. The range
(the difference between the maximum and minimum) of the five GCMs is given in
the bottom line.
GCMs
IWD (km3yr-1)
IWCR (km3yr-1)
present
RCP8.5
present
RCP8.5
1971–2000
2070–2099
1971–2000
2070–2099
GFDL-ESM2M
1282.3
1516.4
(+18.26)
525.4
544.4
(+3.62)
HadGEM2-ES
1312.4
1462.5
(+11.44)
542.0
542.3
(+0.06)
IPSL-CM5A-LR
1283.5
1526.8
(+18.96)
521.1
522.8
(+0.33)
MIROC-ESM-CHEM
1196.0
1412.6
(+18.12)
522.3
515.7
(-1.25)
NorESM1-M
1145.4
1312.0
(+14.55)
501.9
517.2
(+3.06)
Ensemble mean
1243.9
1446.1
(+16.26)
522.5
528.5
(+1.15)
Range
167.0
214.8
40.1
28.7
The reduction in uncertainty by bias correction of the humidity
was also clearly observed in the absolute annual values of IWD and
IWCR. Table shows the annual values of IWD
and IWCR and their ranges across the GCMs. The uncertainty ranges
with bias-corrected humidity data (bottom line), in comparison
with those in Table 4, are reduced from 282.9 to
167.0 km3yr-1 and from 53.7 to
40.1 km3yr-1 for the present IWD and IWCR,
respectively. Similarly, the range decreases from 381.1 to
214.8 km3yr-1 and from 44.5 to
28.7 km3yr-1 for future (RCP8.5, 2070–2099)
projections of IWD and IWCR, respectively. Absolute values
estimated using a single GCM were also affected by bias correction
of the humidity. For example, IPSL shows a large reduction in IWD
as a result of bias correction. This indicates that the large IWD
values for IPSL in Table 4 can be attributed to
biased humidity data.
Changes in monthly ranges of
irrigation water demand (IWD)
and irrigation water consumption from rivers (IWCR)
after correcting humidity bias.
Broken black and solid red lines show the results
with uncorrected and bias-corrected humidity data, respectively.
Each pair of lines gives the maximum and minimum values
for the five GCMs.
Discussion
Necessity of bias correction of humidity data
It is widely known that bias correction is necessary for
hydrological simulations with GCM meteorological data because the
raw meteorological outputs of GCMs deviate from meteorological
observations. The probability density functions of meteorological
elements generated by GCMs for a past period often deviate from
those of observed elements (e.g., ).
Since these GCM-inherent features in the
humidity affect other hydroclimatological elements and propagate
in future projections, we are convinced that bias correction of
the humidity, as well as the atmospheric temperature and
precipitation, is crucial for analyzing the impact of climate
change and also beneficial for dampening GCM-inherent features in
projections of evapotranspiration and irrigation water
consumption.
Owing to the successful removal of GCM biases except for humidity
by employing a state-of-the-art methodology
(Fig. ), we can focus on the effects of GCM
biases in the humidity in this study. For the present period,
since the GCM biases are
negligible in other meteorological elements (such as temperature
and precipitation), we consider that the differences
in evapotranspiration and irrigation water
consumption (Figs. and )
among the GCMs are primarily attributable to GCM biases in the
relative humidity.
For the future period (Fig. ),
both the humidity biases
and the differences in GCM-inherent climate change trends
in temperature and/or precipitation can cause
differences in evapotranspiration and irrigation water consumption.
However, since the monthly anomaly profiles of evapotranspiration
and irrigation water consumption tend to show the opposite dependence
to that of relative humidity (Fig. ),
and since future monthly anomaly profiles of relative humidity
tend to preserve present monthly anomaly profiles
(by comparing Fig.
with Fig. ), we consider that
biases in relative humidity have a considerable effect
on differences in evapotranspiration and irrigation water consumption
across the GCMs. The sensitivity results obtained under future climate conditions
(Table 5) by projecting existing humidity differences
into the future (Fig. )
also support this hypothesis.
Although considerable attention has been paid to GCM biases in the
temperature and precipitation, less attention has been paid to GCM
biases in the humidity. A pioneering study by
examined the compound effects of bias
corrections of shortwave and longwave radiation, humidity and
wind, in contrast to our analysis focusing on the bias correction
effects of humidity. They compared hydrological simulations
driven by bias-corrected and uncorrected meteorological data and
showed that bias correction of radiation, humidity and wind speed
increases the agreement with baseline simulations. They also
pointed out that bias correction significantly affects the
absolute values of simulated runoff and evapotranspiration. In
this sense, our results are in agreement with their results. On
the other hand, they used four GHMs implementing different
potential evapotranspiration formulae (see also Appendix); three
of them, LPJmL, WaterGAP (Priestley–Taylor) and MPI-HM
(Thornthwaite), are empirical-type formulae that are independent
of the atmospheric humidity. Only VIC (Penman–Monteith) is
a physical type and dependent on the humidity. Thus, we consider
that GHMs with empirical formula are insensitive to uncertainties
in humidity data. We will discuss the problem of the GHM
dependence on humidity data from a different viewpoint in the next
subsection.
Figure implies that the high sensitivity of
humidity data over India and East China plays a key role in the
uncertainty in the global sum of irrigation water. In these
regions, the areal fraction of irrigated cropland is higher than
in other regions. Even if the evapotranspiration over a unit area
of irrigated cropland was the same over the globe, the total
amount of water consumption via evapotranspiration over a unit land area
would be larger over densely distributed irrigated cropland than
over sparsely distributed irrigated cropland. Moreover, the
potential evapotranspiration has higher sensitivity to the
atmospheric humidity at higher temperatures than at lower
temperatures; since air is able to contain more vapor at higher
temperatures, the vapor pressure deficit for a given relative
humidity is also larger at higher temperatures.
Moreover, in both India and East China, since future water
availability is expected to worsen in these regions owing to an
increase in the population and increasing demand for agricultural
production, it is highly desirable to accurately estimate future
water demand. Some studies have
warned that a large volume of irrigation water in excess of
recharge is being abstracted from groundwater in India. Water
availability is determined by the balance between water supply and
demand. Reducing the uncertainties in future projections of
irrigation water demand, as well as other factors such as future
socioeconomic scenarios and agricultural maneuvers, will help
obtain reliable estimates of future water availability. This
statement also applies to monthly water availability. In fact,
some studies have shown that water availability (or water stress)
varies from month to month .
Caveats on different sensitivities of evapotranspiration
to atmospheric humidity
In climate impact studies on evapotranspiration, since GCM outputs
are used as GHM inputs, both GCMs and GHMs may be sources of
intermodel differences. Different evapotranspiration formulae
adopted in different GHMs may be a source of differences in
evapotranspiration among GHMs. The performance of the various
evapotranspiration formulae that have been proposed has been
primarily examined in comparison with the results of in situ
observation
(e.g., )
at various geographical
scales. As described in Sect. 2.3 and the Appendix,
evapotranspiration formulae are classified into physical and
empirical formulae. In practice, since the former formulae
require more meteorological elements (e.g., wind, humidity)
than the latter, the availability of observed meteorological data
is the key to choosing which type of potential evapotranspiration
formula to implement.
The existence of these two types of potential evapotranspiration formula
indicates that GHMs implementing physical potential
evapotranspiration formulae (referred to as phGHMs hereafter) are
sensitive to the atmospheric humidity, whereas GHMs implementing
empirical formulae (emGHMs hereafter) are insensitive to the
humidity. Thus, uncertainties in the humidity affect phGHMs but
not emGHMs.
Recently, studies on irrigation water published as ISI-MIP Fast
Track results have reported future changes in its seasonality
and the possibility of reduced water
availability in river basins due to increasing demand for
irrigation water . In both papers, the
authors reported that there are large differences in the future
projections of hydrological elements among the GHMs. As
summarized in , the next impact studies should
explore the reasons for intermodel differences to better
understand the mechanisms underlying the impact of climate change.
However, since each GHM is an assemblage of software modules,
every scheme and parameter adopted in each GHM may be a source of
intermodel differences. For example, as tabulated in Table S3 of
, intermodel differences in the global sum of
irrigation water withdrawal among the GHMs are ascribed to
differences in not only the evapotranspiration but also the total
area of irrigated cropland adopted in the GHMs. We are a long way
from identifying possible sources of intermodel differences.
To examine the contributions to uncertainty from smaller
components of software, we can take top-down and bottom-up
approaches. In the former approach, we first evaluate the overall
differences, then we allocate them into smaller differences
originating from smaller components of software modules. One
example of this approach can be seen in , who
classified the overall uncertainties into three possible sources
(GHMs, GCMs and RCPs) based on the method of
. In the latter approach, as shown in this
study, we first obtain differences generated by a single software
component and estimate their overall differences. This is
laborious but advantageous for identifying contributions from each
component or from the calculation process.
We note that a special care should be taken to account for the
different sensitivity to the humidity between phGHMs and emGHMs
when using a top-down approach. For example, if we deal with both
phGHMs and emGHMs together without special care, GCM-inherent
humidity biases can be misinterpreted as GHM-inherent features
because of the different sensitivity to the humidity.
Other factors contributing to uncertainty in future projections
of hydroclimatological environments
Evapotranspiration plays a key role in global water circulation
. Under global warming, the
global hydrological cycle is considered to be strengthened owing
to intensified precipitation and increasing evapotranspiration.
The global energy cycle
can be altered by changes in the hydrological cycle via latent
heat transported by water vapor flux. It has been a matter of
controversy whether the surface humidity will change with climate
change. found that the relative humidity averaged
over the global land area remained almost constant during
1976–2004, whereas the specific humidity increased owing to the
increasing surface temperature. showed that
the significant increase in surface specific humidity is mainly
attributable to human influence by performing
a detection-and-attribution analysis. If the climatological
relative humidity changes in the future, we should also consider
its effects on assessments of the impact of climate change by
applying a suitable methodology for bias correction.
We consider that, in a practical sense, bias correction is still
necessary for analyzing the impact of climate change to remove
GCM-inherent biases. posed the controversial
but important question of whether we should prioritize the
application of bias correction to meteorological inputs because
most bias correction methodologies independently correct biases of
different elements without considering their mutual physical
relations. In fact, as described in Sect. 2.1, humidity-related
variables are strictly linked to the atmospheric temperature (and
pressure). Moreover, the atmospheric humidity closely interacts
with weather conditions (e.g., the humidity is high on rainy
days). In this study, sacrificing stringency, we attempted to
adjust the monthly climatology of the relative humidity by
applying a primitive additive bias correction (Sect. 2.4.2). Even
without an advanced methodology, bias correction of the humidity
is advantageous in reducing uncertainties in irrigation water
across the GCMs. The development of next-generation methodologies
of bias correction with physical consistency among meteorological
variables would greatly increase the reliability of future
projections of hydroclimatological environments.
Although we primarily focused on irrigation water in this study,
we did not fully discuss the reduction in evapotranspiration
caused by a low soil water content (see Eq. ) in
an explicit manner.
Recent studies have addressed the possibility of deficit irrigation,
where the irrigation water use is below the optimal level,
for irrigated cropland in water-limited areas .
Future changes in global evapotranspiration,
including those in areas of rain-fed cropland and natural
vegetation, require a more complex discussion because
evapotranspiration is determined by not only atmospheric
conditions but also soil moisture conditions, which vary with the
soil properties and topography. However, the latter has higher
geographical diversity than the former because of the dependence
on topographical and geological conditions. The in situ
observation of soil moisture often shows significant differences
at two sites separated by a small distance .
showed that the declining trend in global land
evapotranspiration since 1998 is attributable to limited soil
moisture.
Changes in land use (e.g., transition to irrigated cropland),
which are not considered in this study nor in the ISI-MIP Fast Track
results, also alter the regional water flux between the land
and the atmosphere . However, such
anthropogenic effects are highly dependent on future
socioeconomic scenarios, which still contain large uncertainties.
If future changes in land use are large, we cannot neglect the
feedback process from the land to the atmosphere, and the validity
of offline simulation (i.e., GHMs able to run separately with
GCMs), which is frequently used in climate impact studies, might
become limited.
Conclusions
We have quantitatively investigated the propagation of
uncertainties in humidity data into the estimation of the amount of
irrigation water under ongoing climate change. We used bias-corrected
meteorological data sets (except for the
atmospheric humidity) of five GCMs distributed by the ISI-MIP.
We used H08 for hydrological simulation at the global scale for
both present and future periods under four RCPs. H08 employs the
bulk formula, which is sensitive to the atmospheric humidity, to
calculate the potential evapotranspiration. This study
was based on the principle that we should examine one of the possible
uncertainty sources in the evaluation of hydrological elements
after the removal of bias from the temperature or
precipitation by a state-of-the-art bias correction methodology.
The monthly relative humidity of the five GCMs deviated from the
observed meteorological data sets (WATCH) by up to
ca. 20 % RH for 1970–2000 over global land cells
(Mosaic 0). Monthly profiles of the relative humidity showed the
characteristics of each GCM, which propagate into monthly
profiles of hydrological elements such as evapotranspiration and
irrigation water demand obtained by both historical and future
simulations. The global sums of irrigation water demand (IWD)
and irrigation water consumption from rivers (IWCR), where the
latter is constrained by riverine water availability, were
evaluated as a reference when we used uncorrected humidity data.
The obtained values were widely spread from 1152.6 to
1435.5 km3yr-1 (range = 282.9 km3yr-1) for IWD and from 497.7 to
551.4 km3yr-1 (53.7 km3yr-1) for
IWCR between the five GCMs for the present period (1971–2000).
Estimations of IWD and IWCR under RCP8.5 (2070–2099) varied
from 1322.6 to 1703.7 km3yr-1
(381.1 km3yr-1) and from 506.0 to
550.5 km3yr-1 (44.5 km3yr-1)
between the GCMs, respectively.
A sensitivity experiment involving the uniform addition of
hypothetical biases of ±5 % to the humidity data
over all land areas showed that the hypothetical biases cause the
global sum of IWCR to deviate by ±3.5 to ±4.5 %.
High sensitivity to bias was observed in India
and East China, where intensively irrigated cropland is
distributed, during the crop-growing season.
We also found that the bias correction of humidity data can
reduce uncertainties in the estimation of IWD and IWCR across the
GCMs. Even for a primitive bias correction method that adjusts
the monthly climatological humidity of each land cell, we
observed a reduction in uncertainties. The ranges across the
GCMs for the present and future (RCP8.5) periods were reduced to
167.0 and 214.8 km3yr-1 for IWD and 40.1 and
28.7 km3yr-1 for IWCR, respectively.
Their ensemble means were less affected by the bias correction.
Therefore, the bias correction of the humidity has a merit
to narrow the uncertainty range of estimated irrigation water across GCMs.
The absolute
values obtained using a single GCM were also improved by the bias
correction.
We conclude that GCM biases in the humidity propagate into the
present and future estimation of hydroclimatological factors such
as evapotranspiration and irrigation water.
The humidity is one of the important uncertainty sources
in evaluating hydrological variables.
However, after the successful removal of biases
from other meteorological variables, biases in the humidity
become a significant uncertainty source.
Thus, bias correction of
the humidity can reduce uncertainties in the estimation of
irrigation water across the GCMs. The results indicate that it
is desirable to apply bias correction to not only the atmospheric
temperature and precipitation but also the humidity.
Reliable future projections for IWCR are crucial for future
projections of water availability, particularly in water-limited
regions where different purposes of water abstraction conflict
with each other. People living in some river basins have been or
will be obliged to make difficult decisions regarding the
allocation of water for various purposes in their society
because of the increasing demand for water under limited riverine
water availability.
Recently, many authors have pointed out the problem of
uncertainties in assessing the impact of climate change through
research projects such as the ISI-MIP. It is not an easy
task to identify possible uncertainty sources from a huge
assemblage of models, calculation schemes and parameters.
Investigations such as this study will be helpful for identifying
sources of uncertainty underlying assessments on the impact of
climate change. Although we have a long way to go, reducing the
possible uncertainties in studies on the impact of climate change
is necessary to obtain a better understanding of future
hydroclimatological environments and is an important next step.