HESSHydrology and Earth System SciencesHESSHydrol. Earth Syst. Sci.1607-7938Copernicus PublicationsGöttingen, Germany10.5194/hess-22-6043-2018A simple tool for refining GCM water availability projections, applied to Chinese catchmentsRefining GCM water availability projectionsOsborneJoe M.j.m.osborne@exeter.ac.ukhttps://orcid.org/0000-0002-1128-5975LambertF. HugoCollege of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UKJoe M. Osborne (j.m.osborne@exeter.ac.uk)27November201822116043605728March201827April201816October20181November2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://hess.copernicus.org/articles/22/6043/2018/hess-22-6043-2018.htmlThe full text article is available as a PDF file from https://hess.copernicus.org/articles/22/6043/2018/hess-22-6043-2018.pdf
There is a growing desire for reliable 21st-century projections of water
availability at the regional scale. Global climate models (GCMs) are
typically used together with global hydrological models (GHMs) to generate
such projections. GCMs alone are unsuitable, especially if they have biased
representations of aridity. The Budyko framework represents how water
availability varies as a non-linear function of aridity and is used here to
constrain projections of runoff from GCMs, without the need for
computationally expensive GHMs. Considering a Chinese case study, we first
apply the framework to observations to show that the contribution of direct
human impacts (water consumption) to the significant decline in Yellow River
runoff was greater than the contribution of aridity change by a factor of
approximately 2, although we are unable to rule out a significant
contribution from the net effect of all other factors. We then show that the
Budyko framework can be used to narrow the range of Yellow River runoff
projections by 34 %, using a multi-model ensemble and the high-end Representative
Concentration Pathway (RCP8.5) emissions scenario. This increases confidence that the Yellow River will see
an increase in runoff due to aridity change by the end of the 21st century.
Yangtze River runoff projections change little, since aridity biases in GCMs
are less substantial. Our approach serves as a quick and inexpensive tool to
rapidly update and correct projections from GCMs alone. This could serve as a
valuable resource when determining the water management policies required to
alleviate water stress for future generations.
Introduction
Climate change is a global problem, but the impacts and associated
vulnerability are not homogeneous. There is therefore a demand for robust
projections of changes in regional climate, particularly water availability.
At the largest scales, the majority of the literature on projected changes in
aridity suggests a global land-drying tendency
: a consequence of ubiquitous increases in
potential evapotranspiration (Ep) but mixed signals in precipitation (P).
This has been challenged, however, by some recent studies
. At the river catchment scale,
direct human impacts (non-climatic, human interventions directly affecting
the partitioning of P into runoff – Q – and evapotranspiration – E) are
already having a significant, but poorly quantified, effect on water
availability . For
the Indus River catchment showed that current direct
human impacts on water availability (decreases due to water consumption for
irrigation) are expected to be greater in magnitude than end-of-21st-century
climatic impacts on water availability. Increasing E due to irrigation is
commonly observed in heavily populated catchments, especially across southern
and eastern Asia .
The literature on future water availability projections has typically been framed
around the net atmospheric supply of water versus the net demand for water
resulting from direct human impacts (land-use change, dam construction and
reservoir operation, and surface water and groundwater consumption for
irrigation). Recent studies have considered (1) the projected human
water demand using integrated assessment models, with water supply fixed to
present conditions ; (2) the projected water supply using
global climate models (GCMs) to force offline global hydrological models (GHMs),
with human water demand fixed to present conditions
; or (3) both projected water supply and projected
human water demand .
Using GCM output alone in hydrological projections is not considered
suitable, because GCMs have coarse resolution; simplified land surface
schemes; and, crucially, biases in simulating hydrological cycle components.
The usual approach for generating hydrological projections is to use
bias-corrected and downscaled GCM output to force offline GHMs .
Using GHMs in addition to GCMs greatly increases the computational expense of
a study. Here, we propose an approach for refining projections of water
availability from GCM models participating in phase 5 of the Coupled Model
Intercomparison Project (CMIP5). We use Q as a measure of water
availability. The term “refine” is used in the sense that we expect to
generate projections of Q on an improved physical footing, compared to
using GCM output directly. The approach uses model-simulated aridity and a
bias correction, within the Budyko framework . We do not
consider future human water demand, only future net atmospheric supply of
water, of which aridity is a key determinant.
We consider a simple water balance and assume that changes in storage are negligible:
P=Q+E.
GCM variables require bias correction to be of value at catchment scales
. Bias-correcting a GCM-simulated future Q or E is a
complex process. To illustrate this point, we introduce the Budyko framework
. Within this framework the partitioning of (annual to
long-term mean) P into Q and E scales as a non-linear function of
aridity. Aridity, within the Budyko framework, is the dimensionless ratio
of Ep to P. The evaporative index, the dimensionless ratio of E to P,
is dependent on Ep/P. The relationship is represented by the non-linear
Budyko curve, which is constrained by the physical limits of the atmospheric
demand for water (E<Ep; the red dashed 1:1 line in
Fig. ) and the atmospheric supply of water (E<P; the blue
dashed horizontal line in Fig. ).
The traditional Budyko curve (solid black curve), corresponding to
ω=2.6 in Eq. (). The ω=2.6±1 curves are also shown
(dot-dashed grey curves bounding the shaded region). The atmospheric supply
limit (E<P; horizontal dashed blue line) and atmospheric demand limit
(E<Ep; diagonal dashed red line) are shown. Energy-limited
conditions are represented to the left of the vertical dashed black line
(EpP<1), and water-limited conditions are represented to the right
(Ep/P>1).
The original deterministic and non-parametric Budyko formula was developed
using data mainly from European river catchments :
EP=EpPtanhPEp1-exp-EpP1/2.
The trajectory taken in the Budyko space due to a change in P, Ep, or E
is dependent on the initial values of these three fluxes (the mean state)
. Therefore, an accurate representation of the
observed climatology is important in any modelling study looking at
hydrological projections, especially since changes in variables are typically
small compared with climatology. If the present-day aridity is biased, then
the future-minus-present changes in runoff (ΔQ) and
evapotranspiration (ΔE) will also be biased, even if the
future-minus-present changes in precipitation (ΔP) and
evapotranspiration (ΔEp) are correctly simulated.
Section S1 in the Supplement further demonstrates this point with an example that, although
arbitrary, is illustrative of the magnitude of aridity biases in CMIP5 models.
Recent work has shown that aridity can only explain part of the differences
between catchments e.g.. This has led to the derivation
of a number of parametric forms of the Budyko curve. One of the more popular
forms is the Fu equation , a one-parameter function
expressed as
EP=1+EpP-1+EpPω1/ω,
where ω is an empirical parameter that is calibrated against local
data. The traditional Budyko curve (Eq. ) corresponds to
ω=2.6 in Eq. () .
Here, an attempt is made to utilise biased but plentiful GCM output without
the need for GHMs. We apply our approach to two major catchments in China,
the Yangtze and the Yellow. There is a wealth of literature that uses the
Budyko framework to understand water changes in other Chinese basins
. The Yangtze and Yellow rivers dominate the
wetter south and drier north, respectively (Fig. ). The
spatial variability in P means that the north of the country, which is
poleward of the east Asian monsoon rains, is more water-stressed than the
south. This is exacerbated by the fact that the north has 65 % of the total
arable land in China .
Precipitation climatology for 1961–1990 using the CRU precipitation
dataset. This dataset is spatially interpolated, using available in situ
observations, to give complete global land coverage. The location of the
Yangtze and Yellow River catchments within China is shown.
The mismatch in water supply versus water demand could be the reason behind
the stark decline in Yellow River streamflow (the temporally lagged, spatial
integral of upstream Q) seen in recent years . The
contributions of climate change (which incorporates not only aridity change but also
changes in seasonality, snow dynamics, storminess, and many other factors;
) and direct human impacts to this “drying up” are
widely discussed in the recent literature .
Most studies suggest a significant contribution of direct human impacts,
including afforestation and land-use change ,
although methods and attributed contributions vary.
We can also use the Budyko framework to quantify the contribution of aridity
change alone (changes in P and Ep only) to the 20th-century decrease
in Q in the Yellow River catchment. Qualitative agreement with previous studies will
serve to validate the use of the Budyko framework in refining 21st-century
projections, while hopefully shedding new light on 20th-century observed
changes. The 20th-century Budyko framework estimate is compared with an
estimate of Q simulated by an offline land surface model (LSM) that does
not include a representation of direct human impacts, with the exception of
land-use change. If these estimates reconcile, it suggests that the Budyko
framework is suitable for this attribution, since Q simulated by the LSM
should largely reflect changes in P and Ep only. Further, we ask if the
difference between the total change in Q and the component attributed to
aridity change, for the Yellow River catchment, is in close agreement with a simple
estimate of the change in Q due to direct human impacts.
Schematic of how the Budyko framework is used to improve our
understanding of 20th-century historical changes and 21st-century projected
changes.
Section details the observed and modelled data used, and
Sect. describes the methodology. Results are presented in
Sect. , first applying the Budyko framework to the 20th-century
observed water availability (Sect. ), before extending the approach
to constrain 21st-century model projections of water availability
(Sect. ). We finish with a discussion (Sect. ) and
conclusions (Sect. ) The two-pronged approach of this paper
is summarised in Fig. . It shows how the approaches share the same
theory and use many of the same equations but are independent in their
objectives. However, the 20th-century application of the Budyko framework
supports the suitability of the 21st-century application, as indicated (Fig. ).
Data and methodsData20th-century historical changes
We use the Global River Flow and Continental Discharge
Dataset to calculate observed Q for the Yangtze and Yellow River catchments. This
dataset aimed to use the farthest downstream gauging station (to maximise
spatial representation) that had good temporal coverage. Q is calculated by
dividing river discharge at a gauging station by the upstream catchment area.
In keeping with many other hydrological studies we use annual mean values
throughout, but we consider the water year (October–September). Data are
available for October 1950 to September 2000.
To ensure an accurate comparison between observed P and Q, we produce
high-resolution catchment masks on a 0.5∘× 0.5∘
grid to match that of the P dataset used (Fig. ). We
select the latest Climatic Research Unit (CRU) high-resolution P dataset,
CRU TS3.23 . The interpolated version of the dataset is
used, which offers complete global terrestrial coverage. This allows for
direct comparison with the spatial and temporal coverage of observed Yangtze
and Yellow River Q. By restricting our analysis of observations to 1951–2000, we
find that our conclusions are not sensitive to using either the interpolated
version or raw version of the precipitation dataset (see Sect. S2 and Fig. S1
in the Supplement). We then calculate E as P-Q (Eq. ).
Likewise, we use the CRU TS3.23 Ep dataset (0.5∘× 0.5∘
resolution), which is estimated from variables such as
temperature, vapour pressure, cloud cover, and wind speed, using a variant of
the Penman–Monteith equation. This Ep estimator is computed from variables
that are often poorly observed, both spatially and temporally. An energy-only
Ep estimator would be preferable , but
required observations are not available.
We also use Q output from the Lund–Potsdam–Jena (LPJ) LSM
. This is forced over the 1951–2000 historical
period with observed CRU P, as well as other observed CRU climate variables
and changing CO2 concentrations (more details are given
in Sect. S3). The run used in our primary analyses was also
driven by historical land-use changes, calculated from the History Database
of the Global Environment (HYDE) . A separate run
excludes the HYDE dataset, so that we are able to test the sensitivity to
land-use changes. Assuming that any sensitivity is minimal, we only comment
on this separate run briefly. Simulated Q is available at a monthly
frequency at 0.5∘× 0.5∘ resolution. The LPJ LSM is
chosen from a multi-model ensemble that forms the TRENDY intercomparison
project because it simulates a long-term mean (1951–2000)
runoff coefficient (Q/P) that is closest to that observed for both major
Chinese river catchments (not shown).
21st-century projected changes
We use data from 34 GCMs participating in CMIP5 . These are
listed in Sect. S4. We consider data for historical (1951–2005) and two
21st-century Representative Concentration Pathway emissions scenario (RCP4.5 and RCP8.5; 2006–2100)
experiments. Only one ensemble member was used for each model and
experiment (the first: r1i1p1). Simulated data are regridded to
0.5∘× 0.5∘ resolution and masked to the two Chinese river
catchments. We calculate Q as P-E (Eq. ).
An energy-only Ep estimator is used for CMIP5 models. Ep, being a
hypothetical construct, is not a standard output of CMIP5 models. We follow
recent work e.g. and estimate Ep
directly from net surface radiation (Rn):
Ep=Rnλ,
where λ is the latent heat of vaporisation (λ≈2.45 MJ kg-1).
This simple energy-only Ep estimator has been shown to
perform well compared to more complicated estimators, particularly under
significant climate change .
Methods20th-century historical changes
The Budyko framework can be used to estimate the aridity change contribution
to the overall change in Q. We have to first calibrate ω against
local data for each catchment. Using the observed annual mean P, E,
and Ep for 1951–1960, ω is calculated as the value that minimises the
mean squared errors between the observed annual mean E/P ratios and those
modelled using Eq. (), for each catchment. Following
the objective function is
Obj=min∑iEiPi-1+EpiPi-1+EpiPiω1/ω2,
where i is the year. The period 1951–1960, in this context, is considered
to be representative of natural Q (minimal water consumption or regulation
by human activities). There will be some direct human impacts on Q at this
time, with a substantial Chinese land area equipped for irrigation even in
the 1950s , although it does predate major dam
construction; the Sanmenxia dam was the first major dam in the Yellow
River catchment and was completed in 1960. In calculating the Ep/P (aridity
change) contribution to the change in E/P (Eq. ) we take ω
to be constant over the period 1951–2000. Our results are not
qualitatively affected by the length of period chosen to represent natural Q
(analyses are repeated for 5-, 15-, and 20-year periods, all starting in 1951).
We use ω values of 1.74 and 2.29 for the Yangtze and Yellow River catchments,
respectively. Combining Eq. () with Eq. () gives
Qa=-Ep+P1+EpPω1/ω,
where Qa is the runoff due to aridity change (changes in P and Ep
only), and so ω is taken to be constant. This separates aridity change
from changes in all other climatic factors besides aridity change, as well as
changes in all non-climatic factors. All other climatic and non-climatic
factors are integrated by ω. This aridity change component is
sometimes referred to as the natural Q in other studies .
However, this can be misleading since changes in P and Ep include both
changes due to natural variability and, potentially, human-induced changes
.
We also estimate the runoff due to direct human impacts (Qh) for the
Yellow River catchment only, since previous work suggests that Qh contributes
significantly to the measured runoff (Qm) here .
Time series of water consumption are derived to estimate Qh. Water
consumption is defined as the water withdrawn for human use that leaves a
catchment . Agricultural sector irrigation accounts for a large
proportion of total water consumption and, in turn, Qh. A year 2000 water
consumption estimate of 0.082 mm day-1 for the Yellow River catchment (48 %
of the 1951–1960 mean Qm) is scaled with a 1951–2000 time
series of Chinese irrigated area . Irrigated area in China
increased 3-fold between 1951 and 2000, and we assume that Yellow River
catchment irrigated area has changed in proportion with national changes.
Accurate quantification of past (and even present) water consumption is
immensely difficult, but using estimates of past irrigated area offers a
means of making pseudo-quantitative statements about Qh.
With the change in runoff due to aridity change defined as ΔQa, the
measured change in runoff (ΔQm) can be approximated as the sum
of ΔQa, the change in runoff due to direct human
impacts (ΔQh), and the change in runoff due to all other climatic and non-climatic factors
besides aridity change and direct human impacts (ΔQo):
ΔQm=ΔQa+ΔQh+ΔQo,
with changes over the historical period (1951–2000) calculated as the linear
trend. We note that our conclusions are not affected by using the difference
between either 10- or 20-year means at the beginning and end of the historical
period. The Budyko framework can only separate the contribution of aridity
change to the measured decrease in Yellow River runoff from the contribution
of all other factors besides aridity change (time-varying ω),
represented by the residual ΔQh+ΔQo in
Eq. (). The parameter ω integrates all other factors, so a
significant residual represents a significant net contribution from these
factors. Changes in climatic factors besides aridity – such as seasonality,
snow dynamics, and storminess – and non-climatic factors besides direct human
impacts, such as land surface characteristics and the physiological response
of plants to increasing CO2 (CO2 fertilisation, CO2 stomatal
closure, and water-use efficiency), could all play a role. However, the previous literature
suggests that ΔQh has been significant in the Yellow River catchment. We
therefore decompose the residual in Eq. () into a component
due to direct human impacts and a component due to all other factors besides
both aridity change and direct human impacts. Since water is being diverted
from the river and heavily consumed, we expect ΔQh to be negative.
We reconcile Qa with Q simulated by the LPJ LSM. Although the LSM is
unable to simulate water resources with the complexity of a GHM, it does
include a representation of some of the factors integrated by ω,
particularly non-climatic factors such as changes in land use and land cover,
the response of stomata to rising CO2 concentrations, CO2
fertilisation, and soil moisture controls on transpiration (see Sect. S3 and
). The representation of these other factors means that we
do not truly compare like for like when reconciling Qa with Q simulated
by the LPJ LSM. However, we still expect aridity change to be the dominant
driver of runoff in the LPJ LSM and so define the change in runoff simulated
by the LPJ LSM as ΔQal. That is to say, ΔQal should
be dominated by changes in P and Ep and show strong agreement
with ΔQa. We specifically test the sensitivity to land-use changes since
they are excluded in a separate run of the LPJ LSM model. This is the only
change between the two runs, so we can elucidate the influence of land-use
changes by simply taking the difference between them.
21st-century projected changes
Equation () is also used to constrain projections of Q in
CMIP5 models, instead substituting P with a corrected P (P′) and Ep
with a corrected Ep (Ep′):
Q*=-Ep′+P′1+Ep′P′ω1/ω,
where Q*, the Budyko-corrected runoff, is calculated for the period 1951–2100.
An asterisk (rather than a prime) is used to show that Q has
been corrected using the Budyko framework and not directly using a simple
bias correction. The bias correction technique chosen to calculate P′
and Ep′ is covered in Sect. . This is because the results of
exploratory data analyses on ΔP and ΔEp, and how these
relate to climatology biases across the CMIP5 models, will inform the choice
of correction technique. In Eq. () we use
ω values calculated using observed data and Eq. () for the
1951–2000 period (1.77 and 2.44 for the Yangtze and Yellow River catchments, respectively).
We compare Q* with the original CMIP5 model-simulated Q, calculated as
P-E. Data for Q are also directly available for 28 of the 34 GCMs.
Conclusions should not be sensitive to using either direct Q output or
water-balance-derived Q if changes in storage are negligible.
, however, showed evidence for long-term systematic changes
in water storage in some CMIP5 models. Although not a primary analysis, it is
sensible to test the sensitivity of our results to the choice of Q.
Observed runoff and precipitation anomalies for the
Yangtze (a) and Yellow (b) River catchments for 1951–2000,
relative to 1961–1990. The dot-dashed lines show linear fits to the time
series.
Results20th-century historical changes
The drying of the Yellow River has been one of the most notable aspects of
hydrological change in China over recent decades .
There has been a significant negative linear trend in Yellow River Q
between 1951 and 2000 (-0.26±0.06 mm day-1 century-1,
p<0.05; range is the 5 %–95 % range, taken as ±1.64 SD),
while the decrease in P over the equivalent period is not
significant at the 95 % (p=0.05) confidence level (-0.17±0.21 mm day-1 century-1)
(Fig. ). The decrease in Q is
particularly notable since about 1970. Despite a substantial human water
demand in the second half of the 20th century there has been a slight,
non-significant, increase in Q in the Yangtze River catchment (0.04±0.29 mm day-1 century-1)
that is closely matched by a slight, non-significant, increase in P (0.02±0.34 mm day-1 century-1).
The evaporative index against aridity for the Yangtze (red) and
Yellow (blue) River catchments. The symbols represent observed annual mean
data for 1951–2000 with darker shades for the more recent years. The
traditional Budyko curve is fitted, corresponding to ω=2.6 in
Eq. ().
The Yangtze River shows no tendency to shift towards a distinct new area of
the Budyko space between 1951 and 2000 (Fig. ). The
Yellow River, however, seems to shift towards larger E/P values (smaller Q/P).
Within the Budyko framework this could be expected under a shift
towards greater aridity (larger Ep/P values), or increases in ω.
A systematic shift towards greater aridity is not obvious in Fig. .
There is a significant positive linear trend in
Yellow River E/P between 1951 and 2000 (0.22±0.05 per century), but
the positive trend in Ep/P (0.52±0.50 per century) is only
significant at the 90 % (p=0.10) confidence level. This suggests that all
other factors (ω) may also be a key driver of changes in E/P over
this time period in the Yellow River catchment. Given this evidence and the
significant negative linear trend in Yellow River Q, we investigate further
the contributions of aridity change and all other factors to the decrease in Q.
ΔQa is noticeably different to ΔQm for the Yellow River catchment
(-0.07±0.08 and -0.26±0.06 mm day-1 century-1, respectively), with a significantly less negative
trend (ΔQa-ΔQm is equal to
0.19±0.07 mm day-1 century-1, p<0.05) (Fig. ). We reconcile our
ΔQa calculations with ΔQal. The linear trends are
statistically consistent (-0.07±0.08 and
-0.05±0.06 mm day-1 century-1 for ΔQa
and ΔQal, respectively). This also holds when considering the LPJ LSM run
without land-use changes, for which ΔQal is
-0.06±0.06 mm day-1 century-1. Our results are not sensitive
to fixed or varying land use.
Runoff anomalies for the Yangtze (a) and
Yellow (b) River catchments for 1951–2000, relative to 1961–1990.
Shown are measured runoff (Qm), runoff due to aridity
change (Qa), runoff simulated by the LPJ
LSM (Qal), and (for the Yellow River only) the
difference between Qm and runoff due to direct human
impacts (Qh). The dashed lines show linear fits to the time
series.
If aridity change and direct human impacts have dominated the measured change
in Yellow River runoff, so that the change in runoff due to all other factors
is negligible, from Eq. () we get ΔQa≈ΔQm-ΔQh.
We calculate ΔQh as -0.11±0.01 mm day-1 century-1
for the Yellow River (note that the uncertainty
range is artificially small due to the limited temporal resolution of the
irrigated-area time series of ). Therefore,
ΔQm-ΔQh (-0.15±0.07 mm day-1 century-1) does not fully
reconcile our estimates of ΔQa and ΔQal
(-0.07±0.08 and -0.05±0.06 mm day-1 century-1, respectively).
ΔQh only accounts for 59 % and 54 %
of ΔQm-ΔQa and ΔQm-ΔQal, respectively.
This imbalance could suggest a significant contribution from ΔQo, or
be explained by an underestimate of the year 2000 water consumption. We
calculate the year 2000 water consumption that balances ΔQa=ΔQm-ΔQh
to be 0.140 mm day-1, a 70 % increase on the estimate
of . This closely matches a year 2000 water consumption
estimate by of 0.137 mm day-1. Calculating the relative
contribution of aridity change to the measured decrease in Yellow River
runoff as (ΔQa/ΔQm)×100 % returns a value of 27 %.
Using the two estimates of year 2000 water consumption of 0.082 and
0.137 mm day-1, the relative contribution of direct human impacts to
the measured decrease in Yellow River runoff ((ΔQh/ΔQm)×100 %)
is 43 % and 71 %, respectively.
We account for between 70 % and 98 % of ΔQm with
ΔQa+ΔQh, using the low and high water consumption estimates,
respectively. Using this information with Eq. (), we could
suggest that the contribution from ΔQo is either negligible (using
the high water consumption estimate) or significant (using the low water
consumption estimate). Instead, it shows that there is considerable
uncertainty in quantifying water consumption and, in turn, the contribution
of ΔQh to ΔQm. Nevertheless, the close agreement
of ΔQa and ΔQal suggests that direct human impacts have played a
larger role than aridity change in causing the water availability crisis in
the Yellow River catchment. The contribution of direct human impacts would appear
to be greater, by a factor of approximately 2, than the contribution of
aridity change. It is worth remembering that ΔQa will
reflect not only natural variability but also human-induced changes; Ep has
increased due to human-induced warming , and P has changed due
to various anthropogenic forcings .
21st-century projected changes
From the Budyko framework, changes in Q are dependent not only on changes
in P, Ep, and E but also on the initial values of these three fluxes.
This means that we should view Q projections cautiously if there are biases
in key hydrological cycle variables in CMIP5 models. Consistent with previous
work , we find that the spatial pattern of P over China is
reproduced by CMIP5 models but annual mean P is overestimated in most
regions, compared to CRU climatology. This is evident in the multi-model mean P
bias (Fig. ), with the greatest wet biases seen in the the
western parts of the Yangtze and Yellow River catchments (the eastern Tibetan Plateau).
As a result of these P biases most CMIP5 models do not fall in the same
region of the Budyko space as observations for the Yellow River catchment
(Fig. ). Although P is overestimated in the Yangtze River catchment
for 1951–2000 (3.78±0.97 and 2.74 mm day-1 for
CMIP5 and observations, respectively), there is little multi-model mean bias
in Ep/P (0.88±0.28 and 0.85 for CMIP5 and observations,
respectively), implying that Ep is also overestimated (3.24±0.47 and
2.30 mm day-1 for CMIP5 and observations, respectively).
In contrast, there is considerable multi-model mean bias in Ep/P in the
Yellow River catchment (1.35±0.52 and 2.27 for CMIP5 and observations,
respectively), with models (on average) simulating a humid rather than a
semi-arid climate zone, according to a widely used aridity classification
. In fact, only one of 34 models considered simulates an
aridity greater than 2.0 (MRI-CGCM3). This misrepresentation is a result of a
significant overestimate of P for 1951–2000 (2.18±0.91 and
1.10 mm day-1 for CMIP5 and observations, respectively) and a less
biased simulation of Ep (2.75±0.43 and 2.45 mm day-1
for CMIP5 and observations, respectively).
Absolute (a) and relative (b) multi-model mean
precipitation climatology bias for 1961–1990. The location of the Yangtze
and Yellow River catchments within China is shown. Desert regions
(<200 mm yr-1), as determined from CRU climatology (1961–1990), are
masked in white.
Figure shows the multi-model mean ΔP and ΔEp
(using 1980–1999 and 2080–2099 as present-day and future climates,
respectively) in RCP8.5. Consistent with the previous literature ,
P increases in CMIP5 projections throughout China, with significant
increases across most of the Yellow River catchment. P also increases across the
Yangtze River catchment, although fewer models simulate significant increases here.
As discussed by , significant Ep increases are ubiquitous.
CMIP5 multi-model mean ΔP is 0.36±0.56 and 0.35±0.30 mm day-1
for the Yangtze and Yellow River catchments, respectively. Respective values for ΔEp
are 0.33±0.23 and 0.25±0.19 mm day-1.
Although we expect model-simulated ΔQ and ΔE to be erroneous
due to climatology biases (as highlighted in the hypothetical example in
Sect. S1), we assume that ΔP and ΔEp in the
CMIP5 multi-model ensemble are not dependent on the biases in climatology
described above (Fig. ). Figure shows
how ΔP and ΔEp relate to the climatology of P (P‾)
and Ep (Ep‾), respectively, across the 34 CMIP5 models. There
are weak but significant correlations between ΔP and P‾
in RCP8.5 for both the Yangtze and Yellow River catchments, but significance is
lost with the exclusion of an outlying model in each case. The weak but
significant correlation between ΔP and P‾ in RCP4.5 for
the Yangtze River catchment is also dependent on an outlying model. There is little
evidence for significant correlations between ΔEp and Ep‾.
If there were strong evidence for relationships between ΔP and P‾
and/or ΔEp and Ep‾, then a simple
multiplicative correction, applied to catchment annual mean P and Ep,
would be appropriate . For P,
PGCM′=PGCM×P‾CRUP‾GCM,
where the subscript GCM is an individual model from the CMIP5 ensemble, the
subscript CRU is the observed data, and P′ is the corrected P. The
period 1980–1999 is used to calculate the climatologies. Using a multiplicative
correction factor preserves the relative rather than absolute trends in model-simulated P and Ep.
CMIP5 P and Ep biases for the Yangtze and Yellow River catchments are, on
average, positive and substantial (Fig. ). As such, the
correction factor in Eq. () is, on average, less than unity.
A multiplicative correction factor would therefore narrow the ranges of ΔP
and ΔEp across the CMIP5 ensemble. The assumption that we
can use absolute ΔP and ΔEp from CMIP5 models seems valid in
the absence of strong evidence for relationships between ΔP and P‾
and/or ΔEp and Ep‾. We instead use a
simple additive correction . Temporally constant offsets
(the absolute differences between observed and simulated climatologies) are
added to model-simulated P and Ep. For P,
PGCM′=PGCM+P‾CRU-P‾GCM.
We adjust P and Ep in the 34 CMIP5 models for 1951–2100 to eliminate
the biases in simulating the observed CRU climatologies, while retaining
absolute ΔP and ΔEp. Positivity constraints on P and Ep
can render additive corrections inappropriate, but this is not a problem at
the spatial (catchment) and temporal (annual) resolutions considered here.
The evaporative index against aridity for the Yangtze (a)
and Yellow (b) River catchments. The shaded blue regions represent
the density of CMIP5 annual mean data for the 1951–2000 period, with darker
shades meaning more data in a given region of the Budyko space. The symbols
represent observed data, with darker shades for the more recent years.
ω values are calculated for the 1951–2000 period using
Eq. ().
CMIP5 model-simulated (ΔQ) and CMIP5 Budyko-corrected (ΔQ*) future-minus-present runoff changes
(mm day-1) for 2080–2099, relative to 1980–1999. The multi-model mean
and 5 %–95 % ranges across the individual models are listed (based on a
Gaussian assumption). For comparison, values for a subset of 28 (from 34)
CMIP5 models for which Q is directly simulated are also shown. CMIP5 model
directly simulated future-minus-present runoff changes
(ΔQdirect) are used to verify the suitability of
calculating ΔQ as ΔP-ΔE (water-balance-derived).
Q (as simulated by the CMIP5 models and calculated using P-E) differs
considerably from Q* (Fig. ) as calculated with Eq. (),
particularly for the Yellow River catchment. The Budyko-corrected future-minus-present change in runoff (ΔQ*; recall that
the future-minus-present change is the mean of 2080–2099 minus the mean
of 1980–1999) is similar to ΔQ for the Yangtze River catchment in both RCP4.5
and RCP8.5 across the CMIP5 ensemble (Table ). In the Yellow River
catchment (RCP8.5) the multi-model mean ΔQ* matches that of the
multi-model mean ΔQ (both 0.09 mm day-1). The 5 %–95 % range,
however, is reduced by 34 % (±0.14 to ±0.09 mm day-1).
Similar results are found with RCP4.5, with little change in the
multi-model mean from 0.07 to 0.06 mm day-1 but a decrease
of 35 % in the 5 %–95 % range from ±0.11 to ±0.07 mm day-1
for ΔQ and ΔQ*, respectively. These findings are
not sensitive to using directly simulated runoff instead (Fig. and
Table ). The small differences between Q and Q*,
and ΔQ and ΔQ*, for the Yangtze River catchment are expected given
that CMIP5 models broadly fall in the correct region of the Budyko space
(Fig. ). For the Yellow River catchment, the uncertainties in
runoff projections have been reduced considerably. The CMIP5 multi-model
mean ΔQ* in RCP8.5 is significantly different from zero at the 90 %
confidence level. Such a level of confidence is not achieved for ΔQ.
Discussion
Before using the Budyko framework in tandem with CMIP5 output, we considered
whether it could be used to quantify the contribution of aridity change to
the measured decrease in Yellow River runoff between 1951 and 2000.
Encouragingly, for both the Yangtze and Yellow River catchments, the Q trend due
to aridity change was found to be near-identical to that simulated using the
LPJ LSM (which is forced by observed P and Ep). This suggests that the
Budyko framework is suitable for determining the relative contribution of
aridity change to the measured decrease in Yellow River runoff, calculated as
27 %. Therefore, the relative contribution of all other factors besides
aridity to the measured decrease in Yellow River runoff is expected to equal 73 %.
With time series of water consumption derived using low and high year 2000 water
consumption estimates, the component due to direct human impacts is
calculated as 43 % and 71 %, respectively. Therefore, we can account for
nearly all of the measured decrease in Yellow River runoff (98 %) using
aridity change and the high consumption estimate alone, but we stress that such
estimates are highly uncertain. We are not able to dismiss a significant
contribution from the net effect of all other factors (besides aridity and
direct human impacts), which ranges from 2 % to 30 %. Given that the
estimate of the contribution of aridity change appears to be the most robust
result, we can instead state that the majority of the measured decrease in
Yellow River runoff appears to be due to direct human impacts and all other
factors. Also, despite the uncertain water consumption estimates, the
contribution from direct human impacts is approximately 2 times greater than
the contribution from aridity change. Other studies have estimated the
climate change (all non-human) and human components.
attribute 55 % of the reduction in Yellow River water discharge to humans,
with giving a value of 49 %, compared to our range of 43 %
to 71 %. Note that these studies use different methods and periods to
estimate the contributions of the two components but focus on the second
half of the 20th century. Our estimate of the component due to direct human
impacts is consistent with these previous estimates, although we add detail
by finding that this contribution is markedly greater than the contribution
from aridity change alone.
Multi-model mean future-minus-present changes (2080–2099 minus
1980–1999) in P(a) and Ep(b) in RCP8.5.
Stippling indicates where fewer than 50 % of the CMIP5 models show
significant change, as determined with a t test comparing present-day and
future climates. Absence of stippling indicates where more than 50 % of the
models show significant change and more than 80 % of the significant models
agree on the sign. Grey indicates where more than 50 % of the models show
significant change but fewer than 80 % of the significant models agree on
the sign. This method follows . Desert regions
(<200 mm yr-1), as determined from CRU climatology (1961–1990), are
masked in white for P.
Future-minus-present changes (2080–2099 minus 1980–1999) in
P(a) and Ep(b) against P and
Ep climatologies (1980–1999), respectively, for 34 CMIP5 models.
The dashed vertical lines show the observed climatologies for the Yellow
(red) and Yangtze (blue) River catchments. The values in the top left of each panel
refer to correlation coefficients for the catchment and RCP emissions
scenario listed in the legend. Those with asterisks are significant at the
95 % (p=0.05) confidence level.
CMIP5 model-simulated (Q; orange) and CMIP5 Budyko-corrected
(Q*; blue) runoff anomalies for 1951–2100, relative to 1980–1999, for
the Yangtze (a) and Yellow (b) River catchments in the
historical and RCP8.5 experiments. Shown are the 5-year running multi-model
mean (thick line) and 5 %–95 % ranges (shading) across the CMIP5
ensemble. The box plots (mean, ±1 SD ranges,
5 %–95 % ranges, and minimum to maximum ranges) are given for 2080–2099
(Table ). Also shown, for comparison, are box plots for a
subset of 28 (from 34) CMIP5 models for which Q is directly simulated (not
limited to being calculated as P-E). The unfilled box plot shows CMIP5
model directly simulated runoff for 2080–2099.
Although estimates of water consumption are highly uncertain, there are also
uncertainties in our estimate of the aridity change contribution to Q
change. This estimate, as well as runoff simulated by the LPJ LSM, rely on an
uncertain observed Ep dataset (see Sect. ). An energy-only
EP estimator is expected to be more appropriate but is not
available because of insufficient observed data. Meanwhile, the observed P
dataset is likely to contain biases and inhomogeneities .
Many grid boxes in China are poorly gauged (some not at all) in the period
investigated (see Fig. S1), especially in the mountainous
Tibetan Plateau region, where P is scarce but highly variable
. These are largely insurmountable obstacles facing all
hydroclimatological studies.
Within the Budyko framework all climatic and non-climatic factors besides
aridity are integrated by the ω parameter. In Eq. ()
we separate this “residual” into a component due to direct human impacts
and a component due to all other climatic and non-climatic factors besides
aridity change and direct human impacts. The low water consumption estimate
means that we are not able to dismiss a significant contribution from the net
effect of all other factors. Support for a negligible contribution from all
other factors comes from the strength of agreement between Qa
and Qal. This is because the LPJ LSM includes a realistic representation of
vegetation, which has been shown to be a useful indicator of these other
factors that are integrated by ω (although this may only hold for
larger catchments) (see Sect. S3). Further,
Fig. S3 shows that CMIP5 models simulate no obvious changes in ω over
the second half of the 20th century.
In estimating direct human impacts from just water consumption there remains
the possibility that other direct human impacts could account for a
significant contribution to the decrease in Yellow River runoff. We present
evidence that the contribution from land-use change is negligible. On the
other hand, catchment runoff can abruptly decrease during the filling of
large reservoirs following dam construction, causing anomalously low annual
runoff. Following filling, runoff should return to pre-dam levels, and such
projects are only thought to affect seasonal water storage and not introduce
trends in long-term runoff. Rather, dam and reservoir construction
facilitates access to water resources and leads to more water withdrawal and
consumption. The influence of dams and reservoirs are likely accounted for in
the water consumption estimates .
The agreement between the Budyko framework and the LPJ LSM for the observed
period also increases our confidence in using the Budyko framework for
projections. The CMIP5 Budyko-corrected projected changes in runoff rely on
the assumption that 21st-century changes in P and Ep are not dependent
on existing climatology biases in CMIP5 models. Across the CMIP5 multi-model
ensemble we did not find compelling evidence for relationships, supporting
this assumption (Fig. ). This is broadly consistent with
expectations, given recent research showing that the “wet gets wetter, dry
gets drier” paradigm does not hold over global land surfaces
. However, the mean state can undoubtedly have some
influence on the simulated changes in P and Ep due to land–atmosphere
feedbacks . We note that when correcting Ep
(Eq. ; with P replaced with Ep) we calculate the
correction offset as the observed climatology (Penman–Monteith estimator)
minus the model-simulated climatology (energy-only estimator). Using these
different estimators will likely introduce some error in the calculation.
It is also important to note some potential limitations of using Eq. ()
to separate the measured decrease in Yellow River runoff
into various components. This approach assumes a linear relationship and
therefore that the individual components are independent.
showed that cross-correlations exist between many of the factors suggested to
influence runoff through ω. Testing for dependencies between ΔQh
and other components is unfortunately limited by the poor temporal
resolution of the irrigated-area time series of . Although
we find that interannual variations in Qa and the residual Qh+Qo are
correlated (-0.35), this correlation is weak and reverses sign when
considering multi-year means. Further, our approach considers long-term
trends/changes in runoff, which means that any dependencies at shorter
timescales should not influence conclusions.
In calculating Q* (Eq. ) ω values are
calculated for the 1951–2000 period, using Eq. (), then
taken to be constant for the period 1951–2100. While the relationships of
variations in Q with variations in such catchment-specific parameters are
understood , the full complexity of the
influence of changes in catchment properties on these parameters is not.
However, showed that, for large catchments, the long-term
averaged annual vegetation coverage explains as much as 63 % of the variance
in the catchment-specific ω. With 21st-century increases in total
vegetation coverage projected , we expect this parameter
will increase in magnitude. This is found to be the case in the CMIP5
multi-model ensemble, and these increases in ω need to be included when
verifying the Budyko framework on the CMIP5 models themselves (see
Sect. S3 and Figs. S2–S4). The influence of changes in ω
on projected changes in Q is small compared to the influence of
correcting Ep and P (see Sect. S3 and Fig. S5). Demonstrating this,
the CMIP5 multi-model mean ΔQ* for the Yellow River catchment in RCP8.5
with constant ω (0.09±0.09 mm day-1) is not significantly
different to the CMIP5 multi-model mean ΔQ* for the Yellow River catchment
in RCP8.5 with time-varying ω (0.07±0.08 mm day-1).
Therefore, our conclusions are not sensitive to the choice of ω
(constant or time-varying).
We show that aridity change (changes in P and Ep only) is of greatest
importance in shaping projected changes in runoff in CMIP5 models, and all
other factors (ω) play a secondary role. We expect our CMIP5
Budyko-corrected Q projections to be substantially more reliable than the original
CMIP5 model-simulated Q projections. In the case of the Yellow River catchment,
the 5 %–95 % range of the future-minus-present (2080–2099 minus 1980–1999)
change in Q is reduced by 34 % and 35 % in RCP8.5 and RCP4.5, respectively.
Importantly, constraining Q projections using the Budyko framework
increases confidence that the Yellow River catchment will see increases in Q by
the end of the 21st century – the best-guess (CMIP5 multi-model mean) change
of 0.09 mm day-1 is significantly different from zero at the 90 %
confidence level. Greater confidence in the range of Yellow River catchment water
availability projections could be of great value to policymakers. More
generally, the Budyko framework serves as an inexpensive tool to rapidly
update projections from biased GCM simulations without the need for offline
GHMs. However, further research is needed. Specifically, we believe that an
ensemble of GHMs, driven by at least one set of bias-corrected and downscaled
GCM projections, should be used as a means of verification.
Most applications of the Budyko framework consider spatial rather than
temporal variations. demonstrate that spatial and
temporal variations are not necessarily tradable. We stress that the Budyko
framework is not employed here to robustly determine interannual variability
in water availability but is instead used to understand long-term trends
(Sect. ) or the difference between 20-year means at the end of the
20th and 21st centuries (Sect. ).
Conclusions
We have demonstrated how the Budyko framework can be used to place water
availability projections from readily available GCM output onto a more
physical basis by correcting for biases in aridity, using the example of the
Yangtze and Yellow River catchments in China. The approach is inexpensive,
does not need the use of offline GHMs, and could be used to provide rapid
updates on water availability projections for new GCM scenarios. Wherever
GCMs simulate significant biases in representing observed aridity, we expect
to generate significantly altered projections. In the Yellow River catchment,
considerable negative biases in simulated aridity lead to a substantial
narrowing of the range of future GCM projections. In catchments where GCMs
simulate positive biases, we would expect to see broadening of the range of
GCM projections. Meanwhile, in the Yangtze River catchment, simulated aridity
biases are small, meaning that projections are little changed by our approach.
We stress again that these refined water availability projections account for
aridity change only. In the hypothetical case where future aridity change is
known, the projected Q will not be realised due to the effect of all other factors, especially
highly uncertain future changes in direct human impacts
(these are not represented in CMIP5 models). Current human impacts on Q are
possibly greater than end-of-21st-century aridity change impacts on Q in
the Yellow River catchment . Therefore, the current water
shortages are not likely to be alleviated without improved agricultural
practices and water management. Importantly though, reducing the range of
water availability projections gives planners an improved idea of what needs
to be done to reduce water stress in the Yellow River catchment for future
generations. Moreover, our conclusions underline the need for imminent action
and highlight the fact that increases in Q due to aridity change will not
offer much relief in the absence of serious and concerted action to minimise
direct human impacts.
Chinese authorities have recently attempted to alleviate the drying in the
north of China, by diverting water there from the wetter south (the
South-to-North Water Diversion Project). It remains to be seen whether this
will reduce the imbalance in atmospheric water supply and human water demand
across China and whether it could even place additional water stress on the
more resilient south . Generating refined water
availability projections in these two key river catchments should underpin
decisions made on future engineering projects.
The CMIP5 data can be accessed via the Web portal
https://esgf-node.llnl.gov/projects/esgf-llnl/.
For the observed datasets, CRU TS3.23 P and Ep can be found at
http://www.cru.uea.ac.uk/data/, and the Global
River Flow and Continental Discharge Dataset Q can be found at
http://www.cgd.ucar.edu/cas/catalog/surface/dai-runoff/. Data from
the LPJ LSM experiments are available from the authors upon request.
The supplement related to this article is available online at: https://doi.org/10.5194/hess-22-6043-2018-supplement.
JMO and FHL designed the study and discussed results.
JMO performed the research, analysed the data and wrote the manuscript.
The authors declare that they have no conflict of interest.
Acknowledgements
This work was supported by the Natural Environment Research Council
grant NE/M006123/1 and the UK–China Research & Innovation Partnership Fund through
the Met Office Climate Science for Service Partnership (CSSP) China as part
of the Newton Fund. We acknowledge the World Climate Research Programme's
Working Group on Coupled Modelling, which is responsible for CMIP, and we
thank the climate modelling groups for producing and making available their
model output. For CMIP the US Department of Energy's Program for Climate
Model Diagnosis and Intercomparison provides coordinating support and led
development of software infrastructure in partnership with the Global
Organization for Earth System Science Portals.
Edited by: Luis Samaniego
Reviewed by: two anonymous referees
ReferencesAdam, J. C., Clark, E. A., Lettenmaier, D. P., and Wood, E. F.: Correction of
Global Precipitation Products for Orographic Effects, J. Climate, 19, 15–38,
10.1175/JCLI3604.1, 2006.Barnett, J., Rogers, S., Webber, M., Finlayson, B., and Wang, M.: Sustainability:
Transfer project cannot meet China's water needs, Nature, 527, 295–297,
10.1038/527295a, 2015.Berg, A., Findell, K., Lintner, B., Giannini, A., Seneviratne, S. I., van den
Hurk, B., Lorenz, R., Pitman, A., Hagemann, S., Meier, A., Cheruy, F., Ducharne,
A., Malyshev, S., and Milly, P. C. D.: Land-atmosphere feedbacks amplify aridity
increase over land under global warming, Nat. Clim. Change, 6, 869–874,
10.1038/nclimate3029, 2016.Berghuijs, W. R. and Woods, R. A.: Correspondence: Space-time asymmetry
undermines water yield assessment, Nat. Commun., 7, 11603, 10.1038/ncomms11603, 2016.Biemans, H., Haddeland, I., Kabat, P., Ludwig, F., Hutjes, R. W. A., Heinke, J.,
von Bloh, W., and Gerten, D.: Impact of reservoirs on river discharge and
irrigation water supply during the 20th century, Water Resour. Res., 47, W03509,
10.1029/2009WR008929, 2011.Bring, A., Asokan, S. M., Jaramillo, F., Jarsjö, J., Levi, L., Pietroń,
J., Prieto, C., Rogberg, P., and Destouni, G.: Implications of freshwater flux
data from the CMIP5 multimodel output across a set of Northern Hemisphere
drainage basins, Earth's Future, 3, 206–217, 10.1002/2014EF000296, 2015.
Budyko, M. I.: Climate and Life, Academic Press, New York, 1974.Burke, C. and Stott, P.: Impact of anthropogenic climate change on the East
Asian summer monsoon, J. Climate, 30, 5205–5220, 10.1175/JCLI-D-16-0892.1, 2017.Chen, L. and Frauenfeld, O. W.: A comprehensive evaluation of precipitation
simulations over China based on CMIP5 multimodel ensemble projections, J.
Geophys. Res., 119, 5767–5786, 10.1002/2013JD021190, 2014.Cook, B. I., Smerdon, J. E., Seager, R., and Coats, S.: Global warming and 21st
century drying, Clim. Dynam., 43, 2607–2627, 10.1007/s00382-014-2075-y, 2014.Dai, A.: Increasing drought under global warming in observations and models,
Nat. Clim. Change, 3, 52–58, 10.1038/nclimate1633, 2013.Dai, A., Qian, T., Trenberth, K. E., and Milliman, J. D.: Changes in Continental
Freshwater Discharge from 1948 to 2004, J. Climate, 22, 2773–2792,
10.1175/2008JCLI2592.1, 2009.Destouni, G., Jaramillo, F., and Prieto, C.: Hydroclimatic shifts driven by
human water use for food and energy production, Nat. Clim. Change, 3, 213–217,
10.1038/nclimate1719, 2013.
Freydank, K. and Siebert, S.: Towards mapping the extent of irrigation in the
last century: Time series of irrigated area per country, Technical Report
Frankfurt Hydrology Paper 08, Insititue of Physical Geography, University of
Frankfurt, Frankfurt, 2008.
Fu, B.: On the calculation of the evaporation from land surface, Sci. Atmos.
Sin., 1, 23–31, 1981.Gerten, D., Rost, S., von Bloh, W., and Lucht, W.: Causes of change in
20th century global river discharge, Geophys. Res. Lett., 35, L20405, 10.1029/2008GL035258, 2008.Gordon, L. J., Steffen, W., Jönsson, B. F., Folke, C., Falkenmark, M., and
Johannessen, Å.: Human modification of global water vapor flows from the
land surface, P. Natl. Acad. Sci. USA, 102, 7612–7617, 10.1073/pnas.0500208102, 2005.Greve, P. and Seneviratne, S. I.: Assessment of future changes in water
availability and aridity, Geophys. Res. Lett., 42, 5493–5499, 10.1002/2015GL064127, 2015.
Greve, P., Orlowsky, B., Mueller, B., Sheffield, J., Reichstein, M., and
Seneviratne, S. I.: Global assessment of trends in wetting and drying over land,
Nat. Geosci., 7, 716–721, 2014.Greve, P., Roderick, M. L., and Seneviratne, S. I.: Simulated changes in aridity
from the last glacial maximum to 4 ×CO2, Environ. Res.
Lett., 12, 114021, 10.1088/1748-9326/aa89a3, 2017.Gudmundsson, L., Greve, P., and Seneviratne, S. I.: The sensitivity of water
availability to changes in the aridity index and other factors – A probabilistic
analysis in the Budyko space, Geophys. Res. Lett., 43, 6985–6994, 10.1002/2016GL069763, 2016.Haddeland, I., Heinke, J., Biemans, H., Eisner, S., Flörke, M., Hanasaki,
N., Konzmann, M., Ludwig, F., Masaki, Y., Schewe, J., Stacke, T., Tessler, Z.
D., Wada, Y., and Wisser, D.: Global water resources affected by human
interventions and climate change, P. Natl. Acad. Sci. USA, 111, 3251–3256,
10.1073/pnas.1222475110, 2014.Harris, I., Jones, P., Osborn, T., and Lister, D.: Updated high-resolution grids
of monthly climatic observations – the CRU TS3.10 dataset, Int. J. Climatol.,
34, 623–642, 10.1002/joc.3711, 2014.
Hejazi, M., Edmonds, J., Clarke, L., Kyle, P., Davies, E., Chaturvedi, V.,
Wise, M., Patel, P., Eom, J., Calvin, K., Moss, R., and Kim, S.: Long-term
global water projections using six socioeconomic scenarios in an integrated
assessment modeling framework, Technol. Forecast. Soc., 81, 205–226, 2014a.Hejazi, M. I., Edmonds, J., Clarke, L., Kyle, P., Davies, E., Chaturvedi, V.,
Wise, M., Patel, P., Eom, J., and Calvin, K.: Integrated assessment of global
water scarcity over the 21st century under multiple climate change mitigation
policies, Hydrol. Earth Syst. Sci., 18, 2859–2883, 10.5194/hess-18-2859-2014, 2014b.Held, I. M. and Soden, B. J.: Robust responses of the hydrological cycle to
global warming, J. Climate, 19, 5686–5699, 10.1175/JCLI3990.1, 2006.Hempel, S., Frieler, K., Warszawski, L., Schewe, J., and Piontek, F.: A
trend-preserving bias correction – the ISI-MIP approach, Earth Syst. Dynam.,
4, 219–236, 10.5194/esd-4-219-2013, 2013.Huang, M., Zhang, L., and Gallichand, J.: Runoff responses to afforestation in
a watershed of the Loess Plateau, China, Hydrol. Process., 17, 2599–2609,
10.1002/hyp.1281, 2003.Klein Goldewijk, K. and Verburg, P. H.: Uncertainties in global-scale
reconstructions of historical land use: an illustration using the HYDE data
set, Landscape Ecol., 28, 861–877, 10.1007/s10980-013-9877-x, 2013.Li, D., Pan, M., Cong, Z., Zhang, L., and Wood, E.: Vegetation control on water
and energy balance within the Budyko framework, Water Resour. Res., 49, 969–976,
10.1002/wrcr.20107, 2013.Liang, W., Bai, D., Wang, F., Fu, B., Yan, J., Wang, S., Yang, Y., Long, D.,
and Feng, M.: Quantifying the impacts of climate change and ecological
restoration on streamflow changes based on a Budyko hydrological model in
China's Loess Plateau, Water Resour. Res., 51, 6500–6519, 10.1002/2014WR016589, 2015.Liu, M., Tian, H., Chen, G., Ren, W., Zhang, C., and Liu, J.: Effects of land
use and land cover change on evapotranspiration and water yield in China during
the 20th century, J. Am. Water Resour. Assoc., 44, 1193–1207, 10.1111/j.1752-1688.2008.00243.x, 2008.Miao, C., Ni, J., Borthwick, A. G., and Yang, L.: A preliminary estimate of
human and natural contributions to the changes in water discharge and sediment
load in the Yellow River, Global Planet. Change, 76, 196–205, 10.1016/j.gloplacha.2011.01.008, 2011.
Middleton, N. and Thomas, D.: World Atlas of Desertification, 2nd Edn., Arnold, London, 1997.Milly, P. C. D. and Dunne, K. A.: Potential evapotranspiration and continental
drying, Nat. Clim. Change, 6, 946–949, 10.1038/NCLIMATE3046, 2016.Nilsson, C., Reidy, C. A., Dynesius, M., and Revenga, C.: Fragmentation and
Flow Regulation of the World's Large River Systems, Science, 308, 405–408,
10.1126/science.1107887, 2005.Osborne, J. M. and Lambert, F. H.: The missing aerosol response in
twentieth-century mid-latitude precipitation observations, Nat. Clim. Change,
4, 374–378, 10.1038/nclimate2173, 2014.Osborne, J. M., Lambert, F. H., Groenendijk, M., Harper, A. B., Koven, C. D.,
Poulter, B., Pugh, T. A. M., Sitch, S., Stocker, B. D., Wiltshire, A., and
Zaehle, S.: Reconciling Precipitation with Runoff: Observed Hydrological Change
in the Midlatitudes, J. Hydrometeorol., 16, 2403–2420, 10.1175/JHM-D-15-0055.1, 2015.Padrón, R. S., Gudmundsson, L., Greve, P., and Seneviratne, S. I.:
Large-Scale Controls of the Surface Water Balance Over Land: Insights From a
Systematic Review and Meta-Analysis, Water Resour. Res., 53, 9659–9678,
10.1002/2017WR021215, 2017.Piao, S., Ciais, P., Huang, Y., Shen, Z., Peng, S., Li, J., Zhou, L., Liu, H.,
Ma, Y., Ding, Y., Friedlingstein, P., Liu, C., Tan, K., Yu, Y., Zhang, T., and
Fang, J.: The impacts of climate change on water resources and agriculture in
China, Nature, 467, 43–51, 10.1038/nature09364, 2010.Qiu, G. Y., Yin, J., Tian, F., and Geng, S.: Effects of the “Conversion of
Cropland to Forest and Grassland Program” on the water budget of the Jinghe
River catchment in China, J. Environ. Qual., 40, 1745–1755, 10.2134/jeq2010.0263, 2011.Roderick, M. L. and Farquhar, G. D.: A simple framework for relating variations
in runoff to variations in climatic conditions and catchment properties, Water
Resour. Res., 47, W00G07, 10.1029/2010WR009826, 2011.Roderick, M. L., Greve, P., and Farquhar, G. D.: On the assessment of aridity
with changes in atmospheric CO2, Water Resour. Res., 51, 5450–5463,
10.1002/2015WR017031, 2015.Scheff, J. and Frierson, D. M. W.: Terrestrial Aridity and Its Response to
Greenhouse Warming across CMIP5 Climate Models, J. Climate, 28, 5583–5600,
10.1175/JCLI-D-14-00480.1, 2015.Scheff, J., Seager, R., Liu, H., and Coats, S.: Are Glacials Dry? Consequences
for Paleoclimatology and for Greenhouse Warming, J. Climate, 30, 6593–6609,
10.1175/JCLI-D-16-0854.1, 2017.Schewe, J., Heinke, J., Gerten, D., Haddeland, I., Arnell, N. W., Clark, D. B.,
Dankers, R., Eisner, S., Fekete, B. M., Colón-Gonález, F. J., Gosling,
S. N., Kim, H., Liu, X., Masaki, Y., Portmann, F. T., Satoh, Y., Stacke, T.,
Tang, Q., Wada, Y., Wisser, D., Albrecht, T., Frieler, K., Piontek, F.,
Warszawski, L., and Kabat, P.: Multimodel assessment of water scarcity under
climate change, P. Natl. Acad. Sci. USA, 111, 3245–3250, 10.1073/pnas.1222460110, 2014.Schneck, R., Reick, C. H., Pongratz, J., and Gayler, V.: The mutual importance
of anthropogenically and climate-induced changes in global vegetation cover for
future land carbon emissions in the MPI-ESM CMIP5 simulations, Global Biogeochem.
Cy., 29, 1816–1829, 10.1002/2014GB004959, 2015.Sheffield, J., Wood, E. F., and Roderick, M. L.: Little change in global drought
over the past 60 years, Nature, 491, 435–438, 10.1038/nature11575, 2012.Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W.,
Kaplan, J. O., Levis, S., Lucht, W., Sykes, M. T., Thonicke, K., and Venevsky,
S.: Evaluation of ecosystem dynamics, plant geography and terrestrial carbon
cycling in the LPJ dynamic global vegetation model, Global Change Biol., 9,
161–185, 10.1046/j.1365-2486.2003.00569.x, 2003.Sitch, S., Friedlingstein, P., Gruber, N., Jones, S. D., Murray-Tortarolo, G.,
Ahlström, A., Doney, S. C., Graven, H., Heinze, C., Huntingford, C., Levis,
S., Levy, P. E., Lomas, M., Poulter, B., Viovy, N., Zaehle, S., Zeng, N., Arneth,
A., Bonan, G., Bopp, L., Canadell, J. G., Chevallier, F., Ciais, P., Ellis, R.,
Gloor, M., Peylin, P., Piao, S. L., Le Quéré, C., Smith, B., Zhu, Z.,
and Myneni, R.: Recent trends and drivers of regional sources and sinks of
carbon dioxide, Biogeosciences, 12, 653–679, 10.5194/bg-12-653-2015, 2015.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.Tebaldi, C., Arblaster, J. M., and Knutti, R.: Mapping model agreement on future
climate projections, Geophys. Res. Lett., 38, L23701, 10.1029/2011GL049863, 2011.van der Velde, Y., Vercauteren, N., Jaramillo, F., Dekker, S. C., Destouni, G.,
and Lyon, S. W.: Exploring hydroclimatic change disparity via the Budyko
framework, Hydrol. Process., 28, 4110–4118, 10.1002/hyp.9949, 2014.Wang, H., Yang, Z., Saito, Y., Liu, J. P., and Sun, X.: Interannual and seasonal
variation of the Huanghe (Yellow River) water discharge over the past 50 years:
Connections to impacts from ENSO events and dams, Global Planet. Change, 50,
212–225, 10.1016/j.gloplacha.2006.01.005, 2006.Wood, A. W., Leung, L. R., Sridhar, V., and Lettenmaier, D. P.: Hydrologic
Implications of Dynamical and Statistical Approaches to Downscaling Climate
Model Outputs, Climatic Change, 62, 189–216, 10.1023/B:CLIM.0000013685.99609.9e, 2004.Xu, K., Milliman, J. D., and Xu, H.: Temporal trend of precipitation and runoff
in major Chinese Rivers since 1951, Global Planet. Change, 73, 219–232,
10.1016/j.gloplacha.2010.07.002, 2010.
Xu, X., Yang, D., Yang, H., and Lei, H.: Attribution analysis based on the
Budyko hypothesis for detecting the dominant cause of runoff decline in Haihe
basin, J. Hydrol., 510, 530–540, 10.1016/j.jhydrol.2013.12.052, 2014.Yang, D., Li, C., Hu, H., Lei, Z., Yang, S., Kusuda, T., Koike, T., and Musiake,
K.: Analysis of water resources variability in the Yellow River of China during
the last half century using historical data, Water Resour. Res., 40, W06502,
10.1029/2003WR002763, 2004.Yang, D., Sun, F., Liu, Z., Cong, Z., Ni, G., and Lei, Z.: Analyzing spatial
and temporal variability of annual water-energy balance in nonhumid regions
of China using the Budyko hypothesis, Water Resour. Res., 43, W04426, 10.1029/2006WR005224, 2007.Yang, H., Yang, D., Lei, Z., and Sun, F.: New analytical derivation of the mean
annual water-energy balance equation, Water Resour. Res., 44, W03410, 10.1029/2007WR006135, 2008.Zhang, L., Dawes, W. R., and Walker, G. R.: Response of mean annual
evapotranspiration to vegetation changes at catchment scale, Water Resour. Res.,
37, 701–708, 10.1029/2000WR900325, 2001.Zhang, L., Hickel, K., Dawes, W. R., Chiew, F. H. S., Western, A. W., and
Briggs, P. R.: A rational function approach for estimating mean annual
evapotranspiration, Water Resour. Res., 40, W02502, 10.1029/2003WR002710, 2004.Zhang, X., Zwiers, F. W., Hegerl, G. C., Lambert, F. H., Gillett, N. P., Solomon,
S., Stott, P. A., and Nozawa, T.: Detection of human influence on twentieth-century
precipitation trends, Nature, 448, 461–465, 10.1038/nature06025, 2007.Zhang, X., Zhang, L., Zhao, J., Rustomji, P., and Hairsine, P.: Responses of
streamflow to changes in climate and land use/cover in the Loess Plateau, China,
Water Resour. Res., 44, W00A07, 10.1029/2007WR006711, 2008.
Zhu, Z., Giordano, M., Cai, X., Molden, D., Shangchi, H., Huiyan, Z., Yu, L.,
Huian, L., Xuecheng, Z., Xinghai, Z., and Yunpeng, X.: Yellow river comprehensive
assessment: Basin features and issues – Collaborative research between
International Water Management Institute (IWMI) and Yellow River Conservancy
Commission (YRCC), IWMI working paper, International Water Management Institute, 2003.