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Hydrol. Earth Syst. Sci., 15, 279–294, 2011
www.hydrol-earth-syst-sci.net/15/279/2011/
doi:10.5194/hess-15-279-2011
© Author(s) 2011. CC Attribution 3.0 License.
Hydrology and
Earth System
Sciences
A comparative analysis of projected impacts of climate change on
river runoff from global and catchment-scale hydrological models
S. N. Gosling1, R. G. Taylor2, N. W. Arnell3, and M. C. Todd4
1School of Geography, University of Nottingham, UK
2Department of Geography, UCL, UK
3Walker Institute for Climate System Research, University of Reading, UK
4Department of Geography, University of Sussex, UK
Received: 24 August 2010 – Published in Hydrol. Earth Syst. Sci. Discuss.: 23 September 2010
Revised: 14 December 2010 – Accepted: 3 January 2011 – Published: 21 January 2011
Abstract. We present a comparative analysis of projected
impacts of climate change on river runoff from two types of
distributed hydrological model, a global hydrological model
(GHM) and catchment-scale hydrological models (CHM).
Analyses are conducted for six catchments that are global
in coverage and feature strong contrasts in spatial scale as
well as climatic and developmental conditions. These in-
clude the Liard (Canada), Mekong (SE Asia), Okavango (SW
Africa), Rio Grande (Brazil), Xiangxi (China) and Harper’s
Brook (UK). A single GHM (Mac-PDM.09) is applied to
all catchments whilst different CHMs are applied for each
catchment. The CHMs include SLURP v. 12.2 (Liard),
SLURP v. 12.7 (Mekong), Pitman (Okavango), MGB-IPH
(Rio Grande), AV-SWAT-X 2005 (Xiangxi) and Cat-PDM
(Harper’s Brook). The CHMs typically simulate water re-
source impacts based on a more explicit representation of
catchment water resources than that available from the GHM
and the CHMs include river routing, whereas the GHM does
not. Simulations of mean annual runoff, mean monthly
runoff and high (Q5) and low (Q95) monthly runoff under
baseline (1961–1990) and climate change scenarios are pre-
sented. We compare the simulated runoff response of each
hydrological model to (1) prescribed increases in global-
mean air temperature of 1.0, 2.0, 3.0, 4.0, 5.0 and 6.0◦C rel-
ative to baseline from the UKMO HadCM3 Global Climate
Model (GCM) to explore response to different amounts of
climate forcing, and (2) a prescribed increase in global-mean
air temperature of 2.0 ◦C relative to baseline for seven GCMs
to explore response to climate model structural uncertainty.
We find that the differences in projected changes of
mean annual runoff between the two types of hydrological
model can be substantial for a given GCM (e.g. an absolute
Correspondence to: S. N. Gosling
(simon.gosling@nottingham.ac.uk)
GHM-CHM difference in mean annual runoff percentage
change for UKMO HadCM3 2◦C warming of up to 25%),
and they are generally larger for indicators of high and low
monthly runoff. However, they are relatively small in com-
parison to the range of projections across the seven GCMs.
Hence, for the six catchments and seven GCMs we consid-
ered, climate model structural uncertainty is greater than the
uncertainty associated with the type of hydrological model
applied. Moreover, shifts in the seasonal cycle of runoff with
climate change are represented similarly by both hydrologi-
cal models, although for some catchments the monthly tim-
ing of high and low flows differs. This implies that for stud-
ies that seek to quantify and assess the role of climate model
uncertainty on catchment-scale runoff, it may be equally as
feasible to apply a GHM (Mac-PDM.09 here) as it is to ap-
ply a CHM, especially when climate modelling uncertainty
across the range of available GCMs is as large as it cur-
rently is. Whilst the GHM is able to represent the broad
climate change signal that is represented by the CHMs, we
find however, that for some catchments there are differences
between GHMs and CHMs in mean annual runoff due to dif-
ferences in potential evapotranspiration estimation methods,
in the representation of the seasonality of runoff, and in the
magnitude of changes in extreme (Q5, Q95) monthly runoff,
all of which have implications for future water management
issues.
1 Introduction
1.1 Classification of hydrological models
Numerically-based hydrological models can be classified as
either deterministic or stochastic (Beven, 2001; Abbott and
Refsgaard, 1996). Deterministic models permit a single out-
come from a simulation with one set of inputs and parameter
Published by Copernicus Publications on behalf of the European Geosciences Union.
280 S. N. Gosling et al.: Comparing global and catchment-scale hydrological models
values, whereas stochastic models allow for an element of
randomness in the outcomes due to uncertainties associated
with the input variables, boundary conditions or model pa-
rameters. With deterministic models, two main approaches
to modelling may be adopted, the lumped approach or the
distributed approach (Breuer et al., 2009; Beven, 2001;
Abbott and Refsgaard, 1996). Lumped hydrological mod-
els consider the whole system (catchment, sub-catchment,
aquifer, etc.) as a single unit and typically represent state
variables, such as average storage in the saturated zone, as an
average over the entire catchment. A limitation of the lumped
approach is that the models are not able to consider the spa-
tial diversity of hydrological processes over large spatial do-
mains, associated with heterogeneity in land cover/use and
soil properties, for example. In contrast, distributed hydro-
logical models typically incorporate spatial variable datasets
(e.g., land use, land and soil characteristics and forcing in-
put) and discretize the catchment into sub-units (e.g. grid
cells). As such, distributed models are able to provide a more
representative description of catchment-scale processes than
lumped models (Abbott and Refsgaard, 1996). Indeed, sev-
eral studies show that distributed models demonstrate higher
skill than lumped models in simulations of runoff (Refsgaard
and Knudsen, 1996; Boyle et al. 2001; Carpenter and Geor-
gakakos, 2006).
Distributed models feature a range of complexities. Fully-
distributed models (e.g. MGB-IPOH, Collischonn et al.,
2007) typically divide the catchment into a uniform grid and
are the most complex but they are often criticized because an
a priori estimation of model parameters is difficult (Breuer et
al., 2009). Semi-distributed models with less complex spa-
tial resolution simulate all hydrological processes within spa-
tially non-explicit Hydrological Response Units (HRU); re-
sults for each HRU are lumped within sub-catchments and
routed downstream. Examples include SWAT (Arnold et al.,
1998) and SLURP (Kite, 1995). Furthermore, distributed
models are applied at a range of spatial scales, from a few
tens of meters grid cell resolution for small basins and ur-
ban areas (e.g. the DSHVM model, Cuo et al., 2008), to
the size of medium-size sub-catchments using catchment-
scale hydrological models (CHMs, e.g. the SLURP model,
Thorne, 2010) and up to the global-scale with global hy-
drological models (GHMs, e.g. the WaterGAP model, D¨
oll
et al., 2003). The explicit representation of catchment wa-
ter resources (e.g., soil water, groundwater, snow/ice, river
channel losses) typically differ depending upon model scale.
For instance, CHMs usually simulate water resource impacts
based on a more explicit representation of catchment water
resources than that available from GHMs.
1.2 The opportunity for a novel comparison of a GHM
with a CHM
Whilst a variety of earlier studies have inter-compared dis-
tributed versus lumped model simulations (Carpenter and
Fig. 1. The four stages of a climate change hydrological impact
assessment and the inherent uncertainties. The shaded areas denote
the uncertainties we considered in this analysis.
Georgakakos, 2006; Boyle et al., 2001; Refsgaard and Knud-
sen, 1996) or differences between several models that have
been designed to operate at similar spatial scales (Jones et
al., 2006), the comparison of distributed model simulations
from a GHM with a CHM has not yet been explored. Fur-
thermore, the opportunity exists to explore how these two
types of model respond to consistent climate change forcing.
The comparison is novel and significant because GHMs typi-
cally aggregate catchment-scale measures of water resources
to calculate national, regional, or global-scale indicators of
water resources (Arnell, 2004a; Alcamo et al., 2003). Such
a comparison should demonstrate the potential feasibility of
applying a GHM to evaluate catchment-scale indicators of
water resources, which are usually assessed by CHMs.
1.3 Uncertainties in climate change hydrological impact
assessment
Climate change will affect the global terrestrial hydrological
system (Kundzewicz et al., 2007) and there is evidence that it
has already responded to the observed warming over recent
decades (Bates et al., 2008). The most common method for
assessing the magnitude of this impact is to run a hydrologi-
cal model driven by various climate projections from general
circulation models (GCMs, i.e. global-scale climate models)
as input forcing data (e.g. Gosling et al., 2010). The simula-
tions of key hydrological indicators, such as river runoff, can
then be used to assess the potential impact of climate change
and to inform policy- and decision-making. However, there
are a number of uncertainties associated with making such
projections.
Figure 1 summarises the four main stages of performing
a climate change hydrological impact assessment, which is
broadly similar to other climate change impact sector assess-
ments (Gosling et al., 2009). The first stage is to determine
the greenhouse gas emissions scenarios with which the cli-
mate model (e.g. a GCM) will be driven with, in order to
produce the climate change projections (the second stage).
GCMs typically represent the atmosphere, ocean, land sur-
face, cryosphere, and biogeochemical processes, and solve
the equations governing their evolution on a geographical
grid covering the globe. Some processes are represented ex-
plicitly within GCMs, large-scale circulations for instance,
while others are represented by simplified parameterisations.
Hydrol. Earth Syst. Sci., 15, 279–294, 2011 www.hydrol-earth-syst-sci.net/15/279/2011/
S. N. Gosling et al.: Comparing global and catchment-scale hydrological models 281
Liard
Mekong
Okavango
Xiangxi
Rio Grande
Harper's Brook
Fig. 2. Maps showing the 0.5◦×0.5◦model grid cells located
within the catchments we investigated. The number of cells in-
cluded within each catchment is shown in Table 1.
The use of these parameterisations is sometimes due to pro-
cesses taking place on scales smaller than the typical grid
size of a GCM (a horizontal resolution of between 250 and
600 km) or sometimes to the current limited understanding of
these processes. Different climate modelling institutions will
use different plausible representations of the climate system,
which is why climate projections for a single greenhouse gas
emissions scenario will differ between modelling institutes.
Two main methods can be used to sample this so called “cli-
mate model structural uncertainty”. The first is to use a range
of climate projections from ensembles of plausible GCMs,
to produce an ensemble of impact projections for compari-
son. Such multi-model datasets are often described as “en-
sembles of opportunity”, e.g. the World Climate Research
Programme Third Coupled Model Intercomparison Project
(WCRP CMIP3; Meehl et al., 2007). A second approach
generates a “perturbed physics ensemble” (PPE) that intro-
duces perturbations to the physical parameterisation schemes
of a single climate model, leading to many plausible versions
of the same underlying model. If sufficient computer power
is available, then very large ensembles can be generated in
this way. For example, Stainforth et al. (2005) ran an ensem-
ble of 2578 simulations that sampled combinations of low,
intermediate, and high values of 6 parameters. As well as
climate model structural uncertainty, climate models are sen-
sitive to the initial conditions with which the models are ini-
tialised, which adds a further level of uncertainty.
The third stage of a climate change hydrological impact
assessment is to downscale the climate model output to a
finer resolution, suitable for application to a hydrological
model. Two approaches are typically available, statistical
downscaling and dynamical downscaling. The former uses
statistical relationships to convert the large-scale projections
from a GCM to fine scales. Different statistical methods
can be applied for the downscaling, which introduces uncer-
tainty. The latter approach uses a dynamic model similar to
a GCM to cover a region. The dynamic model is then forced
at its lateral boundaries using results from the coarse scale
GCM. The dynamic method is typically more computation-
ally expensive but does not rely on the central assumption of
most statistical downscaling, that the downscaling relation-
ship derived for the present day will also hold in the future.
In the final stage, the downscaled climate data is applied
to a hydrological model. Uncertainty at this stage can arise
from the application of different hydrological models, e.g.
CHMs and GHMs (similar in essence to the uncertainty that
can be sampled from a GCM ensemble of opportunity), and
from different parameters sets and perturbations within a
given hydrological model, i.e. parameter uncertainty (simi-
lar in essence to the uncertainty that can be sampled from a
GCM PPE).
For six catchments, we compare the simulated runoff re-
sponse of a GHM and CHM to projected future climate asso-
ciated with (1) several prescribed increases in global-mean
temperature from a single GCM to explore simulated re-
sponse to different amounts of climate forcing, and (2) a
prescribed increase in global-mean temperature of 2.0◦C for
seven GCMs to explore response to climate model structural
uncertainty. The main sources of uncertainty sampled by this
methodological framework are shaded in Fig. 1. Note that
emissions uncertainty and downscaling uncertainty are not
sampled, i.e. they are held constant, and nor do we consider
GCM perturbed physics or hydrological model parameter un-
certainty.
2 Data and methods
In this section, we first describe the GHM and CHMs applied
in this study. We then describe the climate data that was used
to drive the hydrological models. Finally, we describe the
hydrological indicators calculated for the comparison.
2.1 River catchments and hydrological models
The six catchments we considered for the comparison are
global in coverage and feature strong contrasts in spatial
scale as well as climatic and developmental conditions. They
include: the Liard (Canada), Mekong (SE Asia), Okavango
(SW Africa), Rio Grande (Brazil), Xiangxi (China) and
Harper’s Brook (UK) – see Fig. 2. Catchments were selected
where international researchers had already established lo-
cally calibrated, distributed CHMs derived from previous and
on-going research projects (Todd et al., 2010). The CHMs
are described in detail in each of the papers in this issue and
a summary is provided in Table 1. Note that a different, sin-
gle CHM was applied to each catchment respectively.
All the CHMs had already been calibrated typically us-
ing local gauge networks. For each catchment, the CHM
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282 S. N. Gosling et al.: Comparing global and catchment-scale hydrological models
Table 1. List of the catchments and their characteristics included in this study and the CHMs applied to each respective catchment. References
for the re-calibrated version of each CHM applied in this study are given in the far right column, next to the the Nash-Sutcliffe model
efficiency coefficients (E) (Nash and Sutcliffe, 1970) that were calculated in validation exercises presented by those studies. ndenotes the
number of 0.5 ◦×0.5◦model grid cells located within each catchment.
Catchment Area nCatchment Climatic E Reference in
(km2)Hydrological Model zone(s) this issue
Liard (a tributary of 275 000 164 SLURP (v. 12.2) Arctic and 0.75 Thorne (2010)
the MacKenzie river, semi-distributed sub-Arctic
Canada) 35 sub-basins
(Kite et al., 1994)
Mekong 569 410 192 SLURP (v. 12.7) high-altitude 0.89, 0.78, 0.44 Kingston et al. (2010)
(Southeast Asia) semi-distributed sub-tropical, (three gauging stations)
13 sub-basins humid tropical
(Kite, 1995)
Okavango 226 256 80 Pitman humid and 0.11–0.83 Hughes et al. (2010)
(south-west Africa) semi-distributed semi-arid tropical (range across 14
14 sub-basins gauging stations)
(Hughes et al., 2006)
Rio Grande 145000 75 MGB-IPH (VIC) humid tropical 0.69 N´
obrega et al. (2010)
(a tributary of distributed
the Parana river, Brazil) (Collischonn et al., 2007)
Xiangxi (a tributary of 3099 9 AV-SWAT-X 2005 humid sub-tropical 0.56 Xu et al. (2010)
the Yangzte river, China) semi-distributed
(Arnold et al., 1998)
Harper’s Brook 74 1 Cat-PDM humid, temperate 0.58 Arnell (2010)
(a tributary of distributed
the Nene river, UK) (Arnell, 2003b; Arnell, 2004b)
was re-calibrated for use with gridded (0.5◦×0.5◦) climate
data from the CRU TS 3.0 dataset (Mitchell and Jones, 2005)
for the period 1961–1990. This dataset was the baseline for
all analyses presented here and for the papers listed in Ta-
ble 1. Importantly, the climate change scenarios (described
in Sect. 2.2.) are compatible with the baseline (Todd et al.,
2010), which is why each CHM was re-calibrated against the
baseline. This process is described in each of the individ-
ual papers in this issue, listed in Table 1. A summary of the
Nash-Sutcliffe model efficiency coefficients (E) (Nash and
Sutcliffe, 1970) that were calculated in validation exercises
presented by each paper is also presented in Table 1. Accord-
ing to the classifcation scheme of Henriksen et al. (2008), the
CHMs generally performed “fair” to “excellent”, although
for a very small number of gauging stations in the Okavango
and Mekong, the performance was “poor” (see Hughes et al.,
2010, and Kingston et al., 2010, for more details).
We applied the Mac-PDM.09 (“Mac” for “macro-scale”
and “PDM” for “probability distributed moisture model”)
GHM in this study. Detailed descriptions of Mac-PDM.09
which simulates runoff across the world at a spatial reso-
lution of 0.5◦×0.5◦, are provided by Gosling and Arnell
(2010) and Arnell (1999, 2003a). The model has been shown
to perform as well as other GHMs in a recent GHM inter-
model comparison exercise (Haddeland et al., 2011). In
brief, Mac-PDM.09 calculates the water balance in each of
65000 land surface 0.5◦×0.5◦cells on a daily basis, treat-
ing each cell as an independent catchment. It is implicit in the
model formulation that these cells are equivalent to medium-
sized catchment areas (i.e., 100 to 5000 km2). River runoff
is generated from precipitation falling on the portion of the
cell that is saturated, and by drainage from water stored in
the soil. A basin-specific calibration of Mac-PDM.09 was
not performed; instead, the model was calibrated by ‘tuning’
it to help set parameter values. This involved tests of pre-
cipitation datasets and potential evaporation calculations and
was done against long-term average runoff and long-term av-
erage within-year runoff patterns for a small number of ma-
jor river basins and for a large number of small basins (see
Arnell, 1999). Model parameters describing soil and vegeta-
tion characteristics are taken from spatial land cover data sets
(de Fries et al., 1998; FAO, 1995). For comparison with the
CHMs, river runoff was simply aggregated for all grid cells
within the boundaries of the river catchments applicable to
each CHM respectively as shown in Fig. 2. Hereafter, we
refer to Mac-PDM.09 as the GHM. The GHM simulations
were performed on the University of Reading Campus Grid
by high-throughput computing (Gosling et al., 2010).
Hydrol. Earth Syst. Sci., 15, 279–294, 2011 www.hydrol-earth-syst-sci.net/15/279/2011/
S. N. Gosling et al.: Comparing global and catchment-scale hydrological models 283
Table 2. GCMs that were pattern-scaled by ClimGen and applied in this study.
GCM Climate modelling centre and location
UKMO HadCM3 Hadley Centre for Climate Prediction and Research (UK)
CCCMA CGCM3.1 Canadian Centre for Climate Modelling and Analysis (Canada)
IPSL CM4 Institut Pierre Simon Laplace (France)
ECHAM5 Max Planck Institute for Meteorology (Germany)
NCAR CCSM3 National Centre for Atmospheric Research (USA)
UKMO HadGEM1 Hadley Centre for Climate Prediction and Research (UK)
CSIRO MK3.0 CSIRO Atmospheric Research (Australia)
2.2 Climate data
To facilitate the model comparison, consistent climate
change forcing data were applied to the CHMs and GHM re-
spectively. Monthly meteorological variables for the present-
day climate – hereafter referred to as the baseline – were ob-
tained from the gridded (0.5◦×0.5◦) CRU TS 3.0 data set
(Mitchell and Jones, 2005) for the period 1961-1990. Be-
cause the spatial resolution of climate change scenarios de-
rived from GCMs is coarse compared to that of the hydrolog-
ical processes simulated by GHMs and CHMs, climate data
needed to be downscaled to a finer resolution. For exam-
ple, the UK is covered by only 4 land cells and 2 ocean cells
within the UKMO HadCM3 GCM. To this end, the climate
change scenarios applied to the GHM and CHMs were gener-
ated using ClimGen, a spatial climate scenario generator that
uses the pattern-scaling approach (Mitchell, 2003) to gen-
erate spatial climate change information for a given global-
mean temperature change from the baseline and a given
GCM. ClimGen includes a statistical downscaling algorithm
that calculates climate change scenarios at 0.5◦×0.5◦reso-
lution, taking account of higher resolution surface variabil-
ity in doing so. A detailed description of the pattern-scaling
technique applied by ClimGen is given by Todd et al. (2010).
To explore the effect of various degrees of global-mean
warming on simulated runoff, climate change patterns for
the UKMO HadCM3 GCM associated with prescribed in-
creases in global-mean temperature of 1.0, 2.0, 3.0, 4.0,
5.0 and 6.0◦C relative to the baseline were used. Also,
to explore the effects of climate model structural uncer-
tainty on simulated runoff, climate change patterns from
seven GCMs included in the Coupled Model Intercompar-
ison Project (CMIP3) archive (Meehl et al., 2007) associ-
ated with a prescribed increase in global-mean temperature
of 2.0◦C relative to the baseline were used – see Table 2.
The prior uncertainty from climate model structural uncer-
tainty could be reduced by comparing the GCM simulations
of baseline climate with observations (e.g. Gleckler et al.,
2008) but the calculation of single indices of model perfor-
mance can be misleading because it hides a more complex
picture of the relative merits of different GCMs (see Arnell
(2010) for a more detailed discussion). Therefore all seven
GCMs are assumed to be equally credible in this analysis.
ClimGen generates 30-year long monthly timeseries of
forcing data for a given GCM and prescribed increase in
global-mean temperature (e.g. UKMO HadCM3 2.0◦C).
This means that the 30-year long climate change scenar-
ios for a given GCM are representative of a world that is
warmer from baseline by a prescribed temperature, but they
are not assigned a specific time period in years, which is ar-
bitrary. Therefore the runoff simulations are also presented
for arbitrary 30-year periods, representative of worlds where
global-mean temperature is a prescribed amount warmer than
baseline (1.0, 2.0, 3.0◦C etc.). Most of the CHMs and
the GHM required daily forcing data. Therefore a weather
generator was applied to create daily data from monthly
data. Detailed descriptions of the generator are provided by
Todd et al. (2010).
2.3 Hydrological indicators
To investigate GHM-CHM differences in simulated runoff
we calculated three indicators of hydrological performance
for each CHM and GHM simulation respectively; (1) mean
annual runoff, (2) mean monthly runoff and (3) high and low
monthly runoff, expressed as Q5 and Q95 respectively, where
for example, Q5 is the monthly runoff exceeded only 5% of
the time, and thus high. To facilitate model comparisons,
we express the mean monthly runoff as percentages of the
simulated mean annual total runoff.
3 Results
3.1 Precipitation changes
Precipitation is the main driver of runoff (Chiew et al., 2009)
so it is important to understand the magnitude by which it
changes in each of the climate change scenarios we con-
sidered. Figure 3 shows the percentage change from base-
line in total-annual precipitation for UKMO HadCM3 pre-
scribed warming of 1–6 ◦C, for each catchment. The greatest
changes in precipitation are observed for the Liard (around
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284 S. N. Gosling et al.: Comparing global and catchment-scale hydrological models
Liard
1 2 3 4 5 6
0
10
20
30
40
Mekong
1 2 3 4 5 6
0
5
10
15
20
Rio Grande
1 2 3 4 5 6
0
10
20
30
Okavango
1 2 3 4 5 6
−50
−40
−30
−20
−10
0
Xiangxi
1 2 3 4 5 6
0
10
20
30
40
Harper’s Brook
1 2 3 4 5 6
−10
−5
0
Fig. 3. Change in total-annual precipitation relative to baseline (ver-
tical axis; %) for UKMO HadCM3 prescribed warming of 1–6 ◦C
(horizontal axis), for each catchment.
+33% with 6◦C prescribed warming), Xiangxi (around
+31% with 6 ◦C prescribed warming) and Okavango (around
−44% with 6◦C prescribed warming). Harper’s Brook is
associated with a small change in precipitation with 6◦C
prescribed warming (−7%). Analyses in Sect. 3.2. demon-
strate how the simulated changes in precipitation from each
prescribed increase in global-mean air temperature are re-
alised in changes in runoff.
Figure 4 shows the percentage change from baseline in to-
tal annual precipitation projected by seven GCMs for a pre-
scribed increase in global-mean air temperature of 2◦C, for
each catchment. Whilst all GCMs simulate increases in pre-
cipitation with climate change for the Liard, there is not con-
sensus in the sign of precipitation change across the seven
GCMs for the remaining catchments. For instance, with the
Mekong, four GCMs simulate increases in precipitation with
climate change and three GCMs simulate decreases. It could
be argued that this precludes a hydrological analysis using all
seven GCMs. However, given the large dependence of runoff
on precipitation (Chiew et al., 2009) and that complex non-
linear interactions are common between climate forcing and
runoff (Majone et al., 2010), it is important to demonstrate
how the uncertainty in the projections of precipitation across
GCMs translates into runoff projections. Moreover, the con-
sequent uncertainty across runoff simulations could have im-
portant implications for water resources management. Anal-
yses in Section 3.3. demonstrate how the simulated changes
in precipitation from each GCM are realised in changes in
runoff.
3.2 Hydrological model responses to different amounts
of forcing projected by UKMO HadCM3
3.2.1 Mean annual river flow
Figure 5 shows the GHM and CHM changes in simulated
mean annual runoff relative to baseline for UKMO HadCM3
prescribed warming of 1–6◦C. The GHM and CHMs sim-
ulate increased runoff with global-mean warming for the
Liard, Rio Grande and Xiangxi catchments. There is also
agreement between the CHM and GHM that runoff decreases
with global warming for the Okavango. The absolute GHM-
CHM differences in mean annual runoff percentage change
for 2◦C warming are 12% (Liard), 9% (Mekong), 1% (Rio
Grande), 6% (Okavango), 10% (Xiangxi) and 25% (Harper’s
Brook). Even under large increases in global mean air tem-
perature (>4◦C) the GHM-CHM differences are relatively
small for the Rio Grande (<10%) and Okavango (<20%) but
the GHM estimates a substantially greater change in runoff
relative to the CHM for the Liard (>20%) and underesti-
mates it for the Xiangxi (>30%). There are stark differences
in simulated annual runoff between the CHM and GHM
for the Mekong and Harper’s Brook catchments. With the
Mekong, the GHM simulates a largely linear relationship be-
tween global-mean temperature and runoff, whilst the CHM
simulates no major change from baseline. With Harper’s
Brook, the GHM simulates steady decreases in runoff with
global warming of up to −40%, whereas the CHM simulates
steady increases of up to +20%.
3.2.2 The seasonal cycle
Figure 6 shows the mean monthly runoff (expressed as a
percentage of the annual total), for the baseline conditions
and projected using climate fields from the UKMO HadCM3
2◦C prescribed warming scenario, simulated by the GHM
and CHMs. First, it is clear that for most catchments, espe-
cially those in the tropics, the amplitude of the seasonal cycle
as simulated by the GHM is much greater than that simulated
by the CHM. The CHMs were calibrated locally and so the
simulated seasonal cycle is close to the observed seasonal cy-
cle (see papers listed in Table 1). Hence the GHM tends to
overestimate the seasonal cycle. The GHM and CHM simu-
late peak (Q5) and low (Q95) runoff as occurring in identical
months for the Mekong and Harper’s Brook. However, there
is a tendency for the GHM to simulate the month of lowest
runoff 1–2 months earlier than the CHM for the Rio Grande
(August for GHM and September for CHM) and Okavango
(September for GHM and November for CHM). Peak runoff
is also simulated by the GHM one month earlier than the
CHM for the Liard (May for the GHM and June for the
CHM).
For the Rio Grande and Okavango, monthly runoff as a
proportion of the annual total remains relatively unaltered
with global warming; even up to 6◦C the absolute difference
in monthly runoff as a percentage of the annual total is small
(<3%) for any given month. However, climate change af-
fects this proportion in the other catchments. For instance,
with the Liard, the GHM and CHM consistently show an
increase in springtime runoff with climate change (>10%
in April with the GHM and >5% in May with the CHM).
There are subtle GHM-CHM differences for the Mekong;
July–September proportional runoff decreases with climate
change for both hydrological models (by up to 3% of the
Hydrol. Earth Syst. Sci., 15, 279–294, 2011 www.hydrol-earth-syst-sci.net/15/279/2011/
S. N. Gosling et al.: Comparing global and catchment-scale hydrological models 285
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10 1 = UKMO HadCM3
2 = CCCMA CGCM3.
1
3 = IPSL CM4
4 = MPI ECHAM5
5 = NCAR CCSM3
6 = UKMO HadGEM1
7 = CSIRO MK3.0
Fig. 4. Change in total-annual precipitation relative to baseline (ver-
tical axis; %) for for the 7 GCMs under 2 ◦C prescribed warming
(horizontal axis), for each catchment.
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Fig. 5. Change in mean annual runoff relative to baseline (verti-
cal axis; %) for the 6 prescribed warming temperatures (horizontal
axis), as simulated by the GHM and CHM respectively, for each
catchment.
annual total) but April–June runoff increases relative to base-
line using the CHM (up to 3% of the annual total), whereas
as it remains almost unchanged from the baseline using the
GHM. With the Xiangxi, the GHM shows much greater de-
creases in proportional summer runoff (up to 5% of the an-
nual total) with global warming compared with smaller de-
creases simulated by the CHM (<2% of the annual total).
However, the GHM and CHM are consistent in showing a
shift of the peak runoff season from summer (July–August)
to autumn (September–October) with climate change. For
Harper’s Brook, global warming induces a slight strengthen-
ing of the seasonal cycle, which even under baseline climate
is more pronounced with the CHM than the GHM. For ex-
ample, under 6◦C warming the CHM simulates that January
runoff presents 23% of the mean annual total runoff (16% for
baseline) whilst the GHM simulates 17% of the total (11%
for baseline).
3.2.3 Peak high and low monthly river flows
Figure 7 shows the percentage change from simulated base-
line in Q5 (high flow) and Q95 (low flow) monthly runoff un-
der six degrees of prescribed global warming for each catch-
ment and the GHM and CHM respectively. The GHM and
CHM are consistent in showing an increase in the magnitude
Fig. 6. Mean-monthly runoff, expressed as a percentage of the
mean annual total runoff, simulated by the GHM and CHM re-
spectively, for the baseline (black lines) and UKMO HadCM3 pre-
scribed warming of 1◦C and 6◦C. The range in simulated runoff
between 1 ◦C and 6◦C prescribed warming is shaded.
of the change with the magnitude of global warming for all
catchments, although there are differences between the GHM
and CHMs in the sign of change in some cases such as the
Mekong (Q5), Harper’s Brook (Q5) and Rio Grande (Q95).
The sign and magnitude of projected changes to high and
low flows and the sensitivity to degree of global warming
(with the UKMO HadCM3 driving fields) is generally sim-
ilar to that for mean annual flow (Fig. 5), with some no-
table exceptions. For the Mekong Q95 increases are smaller
than those for mean annual flow; for the Rio Grande Q95
decreases with increasing global warming under the GHM
simulations. With some catchments, the projected changes in
low flows are high, such as with the Xiangxi, where the GHM
and CHM simulate changes of +75% and +95% in Q95 rel-
ative to baseline with 6◦C prescribed warming. Even under
large increases in global mean air temperature (>4◦C) ab-
solute differences in simulated percentage changes between
GHM and CHM are relatively small (<20%) for some catch-
ments (e.g. Q95 for the Xiangxi, Q5 for the Rio Grande)
whereas for other catchments, the differences are substantial
(>30%; Q5 for the Xiangxi and Liard).
The GHM-CHM differences in simulated changes in ex-
treme flows can be substantially greater than they are for
changes in mean annual runoff. For instance, comparing
Fig. 5 with Fig. 7, for each catchment, with 2 ◦C warming,
the GHM-CHM differences in mean annual runoff (Q5 and
Q95 differences respectively in parenthesis) are 12% (14%,
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286 S. N. Gosling et al.: Comparing global and catchment-scale hydrological models
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Fig. 7. Percentage change from baseline in GHM- and CHM-
simulated Q5 and Q95 monthly runoff (vertical axis) with UKMO
HadCM3 prescribed warming of 1–6◦C (horizontal axis), for each
catchment.
10%; Liard), 9% (12%, 3%; Mekong), 1% (9%, 22%; Rio
Grande), 6% (7%, 20%; Okavango), 10% (11%, 5%; Xi-
angxi) and 25% (38%, 6%; Harper’s Brook).
3.3 Hydrological model responses to climate modelling
uncertainty
3.3.1 Mean annual river flow
Figure 8 shows the GHM and CHM changes in simulated
mean annual runoff relative to baseline for prescribed global
warming of 2◦C for seven GCMs. There are two important
observations to make. Firstly, there is little overall consensus
in the sign of runoff change, be it an increase or decrease,
across all seven GCMs for any of the catchments. For in-
stance, with the Rio Grande, the CHM and GHM are consis-
tent in showing decreases in runoff with climate change for
three GCMs – CCCMA CGCM3.1 (−3% and −3% [GHM
and CHM respectively]), IPSL CM4 (−29% and −19%) and
UKMO HadGEM1 (−10% and −1%) – but for four GCMs
the CHM and GHM simulate increases in runoff – UKMO
HadCM3 (+15% and +16%), MPI ECHAM5 (+20% and
+18%), NCAR CCSM3 (+1% and +3%) and CSIRO MK3.0
(+3% and +7%). Projected differences between GCMs may
be large. For example, NCAR CCSM3 driving climate data
simulates a +26% and +29% change in runoff for the Oka-
1 = UKMO HadCM3
2 = CCCMA CGCM3.1
3 = IPSL CM4
4 = MPI ECHAM5
5 = NCAR CCSM3
6 = UKMO HadGEM1
7 = CSIRO MK3.0
GHM
CHM
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Fig. 8. Change in mean annual runoff relative to baseline (vertical
axis; %) for the 7 GCMs under 2 ◦C prescribed warming (horizontal
axis), as simulated by the GHM and CHM respectively, for each
catchment.
vango (using the GHM and CHM respectively) and UKMO
HadCM3 forcing simulates changes of −40% and −30%
(using the GHM and CHM respectively). The greatest abso-
lute differences in the percentage changes from baseline be-
tween any two GCMs with 2◦C prescribed warming for the
GHM (CHM in parenthesis) for each catchment respectively
are 28% (17%; Liard), 30% (23%; Mekong), 48% (36%; Rio
Grande), 62% (58%; Okavango), 34% (15%; Xiangxi) and
30% (31%; Harper’s Brook). Only for the Xiangxi and Liard
catchments do most of the simulations show a consistent (in-
creased runoff) signal across most of the GCMs (see Todd et
al. (2010) for further discussion of this).
Secondly, for a given GCM, the GHM and CHM are gen-
erally consistent in simulating the same sign of runoff change
relative to baseline. This is true where the simulated changes
in runoff are greater than ±10%. For cases where pro-
jected runoff changes are small (<10%), the CHM and GHM
may simulate runoff changes that are different in sign (e.g.
Liard with UKMO HadGEM1 forcing and Xiangxi with MPI
ECHAM 5 forcing). The one exception to this is Harper’s
Brook with UKMO HadCM3 and CSIRO MK3.0 forcing.
Generally, the differences in projected changes to mean an-
nual runoff between the two types of hydrological model are
relatively small, in comparison to the range of projections
across GCMs. In some cases, the difference in the absolute
magnitude of the projected percentage change between the
GHM and CHM may be as small as 1% (e.g. Rio Grande with
UKMO HadCM3 forcing and Xiangxi with NCAR CCSM3
forcing).
3.3.2 The seasonal cycle
Figure 9 shows the mean monthly runoff for each catchment
when the GHM and CHM are forced with the seven GCMs
under a 2◦C rise in global mean air temperature; the en-
semble mean, calculated from the mean of the seven projec-
tions, is also displayed for the GHM and CHM respectively,
with the inter-GCM range of projections shaded. For the
Okavango and Rio Grande catchments, the inter-GCM range
is relatively small, compared to that for other catchments
Hydrol. Earth Syst. Sci., 15, 279–294, 2011 www.hydrol-earth-syst-sci.net/15/279/2011/
S. N. Gosling et al.: Comparing global and catchment-scale hydrological models 287
Fig. 9. Baseline and projected mean-monthly runoff simulated by
the GHM and CHM respectively (expressed as a percentage of the
mean annual total runoff) when they are forced by 7 GCMs under
2◦C prescribed warming, for each catchment. The light grey and
dark grey lines show the ensemble mean across the 7 GCMs for the
CHM and GHM respectively, with the shaded region denoting the
inter-GCM range.
and the ensemble mean is very close to baseline. However,
note that to aid hydrological model comparisons, Fig. 9 dis-
plays the mean monthly runoff as a percentage of the mean
annual-total runoff – if the absolute values are plotted, the
inter-GCM range would appear larger, similar to what is
displayed in Fig. 9. There is consistency across GCMs in
important changes in the seasonal cycle of runoff to a 2◦C
prescribed increase in global-mean air temperature. For in-
stance, an increase relative to baseline in springtime runoff
for the Liard is represented by all seven GCMs, and so is
a shift in peak runoff season from summer (July–August)
to autumn (September–October) for the Xiangxi. Also, the
GCMs suggest a move in the month of peak runoff from
August to September with 2◦C prescribed warming for the
Mekong.
3.3.3 Peak high and low monthly river flows
Figure 10 shows the percentage change from baseline in Q5
and Q95 monthly runoff (vertical axis) for the 7 GCMs with
2◦C prescribed warming simulated by the GHM and CHM
respectively, for each catchment. Two observations, which
are consistent across the six catchments, are noteworthy.
Firstly, for a given GCM, the GHM and CHM tend to agree in
the sign of simulated change for high and low flows respec-
tively. In some cases, the difference between the GHM and
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Fig. 10. Percentage change from baseline in Q5 and Q95 monthly
runoff (vertical axis) for the 7GCMs (horizontal axis) under 2 ◦C
prescribed warming, as simulated by the GHM and CHM respec-
tively, for each catchment.
CHM projected changes are relatively small (<5%), such as
for the Xiangxi with the NCAR CCSM3 GCM, where GHM
and CHM both project a 38% change in Q5 relative to base-
line. However, in a small number of cases, the differences
may be larger, such as with the CCCMA CGCM3.1 simula-
tions of Q95 change for the Liard, which are 22% (GHM)
and 3% (CHM). Also, there are some GCMs where the two
hydrological models simulate changes that are different in
sign, e.g., CSIRO MK3.0 (Liard Q5 and Rio Grande Q95)
and UKMO HadGEM1 (Liard Q5 and Q95, Rio Grande Q5,
Okavango Q5, Xiangxi Q5 and Q95 and Harper’s Brook Q5).
Secondly, for a given hydrological model, the sign of pro-
jected change is not consistent across all seven GCMs for
any catchment and indicator (with exception to Q95 for the
Liard). For any given hydrological model, the differences
between GCMs tend to be large. For instance, for the Oka-
vango, NCAR CCSM3 suggests that the change in Q5 is
+30% to +38% (GHM and CHM respectively) and CSIRO
MK3.0 suggests the change is −40% to −30%. Generally,
for any given catchment, the difference between the GHM
and CHM simulated change for any given GCM is smaller
than the difference in projections between the seven GCMs.
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288 S. N. Gosling et al.: Comparing global and catchment-scale hydrological models
4 Discussion
The simulations of response to prescribed global-mean
warming with UKMO HadCM3 suggest that the GHM sim-
ulates similar changes to the CHM for some hydrological
indicators and catchments but substantial differences for
others. For instance, the GHM-CHM absolute differ-
ences between simulated percentage changes in mean an-
nual runoff are relatively small for the Rio Grande (<10%)
and Okavango (<20%). However, the GHM estimates sub-
stantially greater changes in mean annual runoff relative to
the CHM for the Liard (>30%) and lower estimates for the
Xiangxi under large increases in global mean air tempera-
ture (>4◦C), whilst for Harper’s Brook and the Mekong, the
GHM and CHM simulate changes that are opposite in sign.
Likewise, the GHM-CHM differences in simulated changes
of extreme monthly runoff are relatively small (<10%) for
some catchments (e.g. Q95 for the Xiangxi, Q5 for the Rio
Grande) whereas for other catchments, the differences are
larger (>30%; e.g. Q5 for the Xiangxi and Liard), whilst
for the Mekong (Q5) and Rio Grande (Q95) the simulated
changes are opposite in sign between the two models.
Although GHM-CHM differences are apparent for the
UKMO HadCM3 GCM, when 2◦C prescribed warming
across all seven GCMs is considered, there is generally a
higher level of agreement, for a given GCM, between the
two hydrological models in the sign and magnitude of the
mean annual and monthly extreme runoff change for the six
catchments. The results imply that the GHM we applied here
may be a useful and complimentary tool to the set of CHMs
we applied for assessing catchment-scale changes in runoff
where ensembles (instead of a single GCM) of GCMs are
applied. A potential advantage of this approach is that un-
less a single CHM is calibrated for each catchment – which
can be a time-consuming and demanding exercise – when
runoff simulations for several catchments are required, the
inherent uncertainty derived from applying different CHMs
for each catchment can be removed. For instance, within this
study the CHMs applied included SLURP (v. 12.2; Kite et
al., 1994), SLURP (v. 12.7; Kite, 1995), Pitman (Hughes et
al., 2006), MGB-IPH (Collischonn et al., 2007), AV-SWAT-
X 2005 (Arnold et al., 1998) and Cat-PDM (Arnell, 2003b,
2004b), all of which include their own specific parameter-
isation schemes. By applying a GHM to several catch-
ments, the parameterisation scheme remains the same for all
catchments. Importantly, however, an element of uncertainty
would still remain, given that any model parameter is uncer-
tain. Only detailed sensitivity analyses such as multi-method
global sensitivity analysis (MMGSA; Cloke et al. 2007) or
parameter perturbations (Gosling and Arnell, 2010; Hughes
et al., 2010; Arnell, 2010) can demonstrate the sensitivity of
simulated runoff to a given parameterisation scheme.
Although the difference in simulated response of annual
runoff to 2◦C prescribed warming between the GHM and
CHM are generally small across the 7 GCMs for all catch-
ments, the response to 1–6 ◦C UKMO HadCM3 forcing
differs greatly between GHM and CHM with the Harper’s
Brook and Mekong catchments. These two catchments are
associated with the smallest changes in annual precipita-
tion with climate change of the six catchments investigated
– around −7% (Harper’s Brook) and +19% (Mekong) with
UKMO HadCM3 6◦C (see Fig. 3). The inter-hydrological
model differences here can be explained by differences in
the seasonal cycle of runoff change simulated by each model
– in particular the peak runoff – which are associated with
differences in the relative dominance of potential evapotran-
spiration (PET) over precipitation.
For instance, with Harper’s Brook, there are increases in
winter precipitation and decreases in summer precipitation
with climate change (see Arnell, 2010). However, there are
subtle differences between the GHM and CHM in the role
of the dominance of increased PET over precipitation with
global warming. The CHM simulates a greater reduction in
summer (JJA) runoff relative to the GHM and at 6◦C pre-
scribed warming; the late-summer runoff simulated by the
CHM is almost 0% of the annual total. Furthermore, the
CHM simulates comparatively much greater winter (DJF)
runoff increases with climate change than simulated by the
GHM. The net effect is that annual runoff decreases with cli-
mate change with the GHM whereas it increases slightly with
the CHM because of the relative ‘strengthening’ of its sea-
sonal cycle.
Similarly, for the Mekong, the CHM simulates a greater
decrease in peak runoff (August–September) with climate
change than the GHM but the slight increases in early sea-
son runoff (April–July) simulated by each model are similar.
Differences arise, in part, from the application of different al-
gorithms for estimating evapotranspiration. During the cali-
bration of the CHM, Kingston et al. (2010) found that substi-
tuting the Penman-Monteith method of estimating PET with
a less data-intensive, temperature-based method (Linacre) re-
duced the overestimation of runoff and improved the repre-
sentation of seasonal flows by the CHM. Indeed, as shown by
Kingston et al. (2009) and Gosling and Arnell (2010), choice
of PET algorithm can substantially influence terrestrial wa-
ter balances. The GHM we applied employs the Penman-
Monteith method, so runoff for the Mekong is likely overes-
timated by the GHM. The net effect for the Mekong is that
annual runoff increases with climate change using the GHM
but remains relatively unchanged using the CHM. This may
also explain why there is such a large discrepancy in simu-
lated high and low monthly flows (Q5 and Q95) between the
GHM and CHM for this catchment.
Changes in the seasonal cycle related to the dominance
of PET over precipitation by each hydrological model are
important and perhaps even more so where the change in
annual precipitation with climate change is minor. Further-
more, the nature of the response of runoff to climate change
is complex and the common use of mean annual runoff as a
measure of the response of hydrological systems to climate
Hydrol. Earth Syst. Sci., 15, 279–294, 2011 www.hydrol-earth-syst-sci.net/15/279/2011/
S. N. Gosling et al.: Comparing global and catchment-scale hydrological models 289
change is over-simplistic. The analysis presented here and
by others (N´
obrega et al., 2010 Hughes et al., 2010; Ar-
nell, 2010; Xu et al., 2010) shows that mean annual runoff
can mask considerably greater seasonal variations which are
of fundamental importance to water management and our
understanding of freshwater availability.
An important result is that even though the magnitudes
of simulated changes in mean annual runoff with climate
change differ considerably between GCMs, there is consis-
tency in simulated directional shifts of the seasonal cycle.
For instance, the increase in spring runoff associated with
increased snow-melt and an increase in autumn runoff due
to increased precipitation with climate change for the Liard
is represented by all seven GCMs, and so is the shift of the
peak runoff season from summer (July–August) to autumn
(September–October) with climate change for the Xiangxi.
This means that for some catchments, whilst there is consid-
erable uncertainty in the magnitude of projected mean an-
nual and monthly extreme runoff change across the 7 GCMs,
there is higher confidence in directional shifts ofthe seasonal
cycle. Furthermore, the GHM simulates such changes that
are consistent with the CHM, which means despite the gen-
eralisations GHMs need to make in order to be run over the
global domain, the GHM we applied can be as useful as, and
complimentary to, the CHMs we considered for assessments
of catchment-scale shifts in the seasonal cycle.
However, it should be noted that whilst the GHM rep-
resents the sub-arctic nival regime of the Liard fairly well,
compared with the CHM, the GHM simulates peak runoff
one month behind the CHM. This is an inherent limitation of
the GHM applied here and Gosling and Arnell (2010) have
shown that the GHM we applied tends to simulate the peak
monthly runoff one month early relative to observations with
other sub-arctic catchments such as the Don (central Russia,
378000km2), MacKenzie (central Canada 1570000 km2),
and Ob (western Siberia, 2 949 998 km2). Also, the GHM
has previously been shown to simulate peak runoff one
month ahead of observations for very large catchments such
as the Amazon (4640300km2), Volga (1 360000 km2), and
Ob (2949998km2)because runoff is not routed from one
model cell to another (Gosling and Arnell, 2010). The
largest catchment considered here, however, is 795000km2
(Mekong), which is why there is no discrepancy in the
months of peak runoff between the GHM and CHM for
catchments other than the Liard.
The CHMs applied in this study were calibrated using his-
torical data – see individual papers listed in Table 1 for fur-
ther details on the calibration methods employed by each
CHM. A catchment-specific calibration of the GHM was not
performed. Instead, the GHM was calibrated by ‘tuning’ it
to help set parameter values. This involved tests of precipita-
tion datasets and potential evaporation calculations and was
done against long-term average runoff and long term aver-
age within-year runoff patterns (Arnell, 1999). It is acknowl-
edged that a catchment-specific calibration of the GHM pa-
rameters could lead to reductions in the magnitude of some
of the GHM-CHM differences presented. For instance, the
application of the Linacre method for PET estimation to the
GHM instead of Penman-Monteith could reduce the magni-
tude of the GHM-CHM differences in mean annual runoff
and Q5 and Q95 that we present for the Mekong. How-
ever, it is important to note that the GHM parameter cali-
bration process is sensitive to uncertainties in the observed
data (Biemans et al., 2009).
For any given catchment, the difference in simulated
change in mean annual runoff (Fig. 5) or Q5 and Q95 (Fig. 7)
between the GHM and CHM for UKMO HadCM3 2◦C pre-
scribed warming is smaller than the difference across the
seven GCMs for either the GHM or CHM (Figs. 8 and
10). For instance, with UKMO HadCM3 2 ◦C prescribed
warming, the absolute GHM-CHM differences in mean an-
nual runoff change are 12% (Liard), 9% (Mekong), 1% (Rio
Grande), 6% (Okavango), 10% (Xiangxi) and 25% (Harper’s
Brook), whilst the greatest absolute differences between any
two GCMs with 2◦C prescribed warming for the GHM
(CHM) for each catchment respectively are 28% (17%), 30%
(23%), 48% (36%), 62% (58%), 34% (15%) and 30% (31%).
Indeed, an important conclusion to draw from our analysis
is that there is little overall consensus in the sign of mean an-
nual and monthly Q5 and Q95 runoff change across all seven
GCMs for any of the catchments, even though the GHM and
CHM tend to agree on the magnitude and sign of change
for any given GCM. The differences in projected changes of
mean annual and Q5 and Q95 runoff between the two types
of hydrological model are relatively small, in comparison
to the range of projections across GCMs. This result sup-
ports previous findings that climate modelling structural un-
certainty is greater than hydrological modelling uncertainty
with simulations of runoff under climate change scenarios
(Kay et al., 2009; Bl¨
oschl and Montanari, 2010; Kingston
and Taylor, 2010; Hughes et al., 2010; Arnell, 2010). This
suggests that it may be equally feasible to apply a GHM, as
it is to apply a CHM, to explore catchment-scale changes
in runoff with climate change from ensembles of currently
available GCM projections, where inter-GCM climate pro-
jection differences are typically large due to climate mod-
elling uncertainty. However, given that the uncertainty range
across the 7 GCMs for the CHM is generally slightly smaller
than the range across the GHM, then should advances in cli-
mate modelling over the coming decades mean that climate
modelling uncertainty is substantially reduced, then the role
of hydrological model (and land-surface model) uncertainty
will become more important and the application of a CHM
over a GHM may be appropriate.
Indeed, it should be noted that whilst Figs. 8, 9 and 10
show that GHM-CHM differences are generally relatively
small when a range of GCMs is considered, and that the
GHM is able to represent the broad climate change signal
that is represented by the CHMs, Figs. 5 and 7 show that
for a few catchments and hydrological indicators, when a
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290 S. N. Gosling et al.: Comparing global and catchment-scale hydrological models
single forcing GCM is considered, the CHM and GHM can
disagree substantially. Hence, for a given single GCM, the
GHM we applied is no more feasible than a CHM for esti-
mating catchment-scale runoff changes under global warm-
ing scenarios.
The substantial GHM-CHM differences observed for some
catchments in mean annual runoff, Q5 and Q95 monthly
runoff and in the seasonal cycle, has implications for future
water management issues, such as, for example, in the plan-
ning of dams and reservoirs for dealing with high and low
flows. The results suggest larger GHM-CHM differences for
indicators of high and low extreme monthly runoff (Q5 and
Q95) than for mean annual runoff (although the magnitude
of this difference is still smaller than the difference across
GCMs) so careful thought should be given in whether to ap-
ply a CHM or GHM when measures of extreme hydrological
behaviour are sought. This, however, in unsurprising given
that extremes of hydrological behaviour are notoriously dif-
ficult to simulate. We postulate that if another CHM were
included for each catchment, the difference between the new
CHM and the CHM presented in this study, in simulated
changes in Q5 and Q95 with climate change, might be com-
parable to that of the differences between GHM and CHM
presented here. Indeed, in a discussion of the role of uncer-
tainty in climate change impacts assessment and hydrology,
Bl¨
oschl and Montanari (2010) suggest that when two experts
estimate the 100-year flood in a small ungauged catchment,
chances are that their estimates are very different. A recent
inter-model comparison confirms the case in point (Ludwig
et al., 2009), suggesting that the difference in simulated dis-
charge under climate change scenarios for a 10-year flood
event and given catchment between hydrological models of
different complexity may be over 200%.
The discrepancy in sign of simulated change across the
7 GCMs has implications for policy- and decision-making.
Whilst one should be cautious with results based on projec-
tions from a single GCM because mistaken management de-
cisions may follow (N´
obrega et al., 2010), decision-makers
are faced with a challenging prospect when approached with
a range of projections from several GCMs that are different
in sign. In the case of the Liard, where 6 of 7 GCMs sug-
gest very little change or an increase in runoff with climate
change, the GCM that suggests a decrease in annual runoff
may arguably be considered as an outlier (Todd et al., 2010).
However, where around half the GCMs suggest a substantial
increase in annual runoff with climate change and the other
half a substantial decrease (e.g. the Mekong and Rio Grande),
then the decision-making process is more complex. Sum-
mary statistics such as the ensemble-mean are inappropriate
with such projections because “the mean of equal increases
and decreases is no change”.
A key conclusion is that climate model uncertainty dom-
inates hydrological model uncertainty. However, it is ac-
knowledged that this conclusion is based on the prior un-
certainty assigned to both climate and hydrological models.
Moreover, we have not sampled downscaling uncertainty,
emissions uncertainty, and hydrological model parameter un-
certainty (see Fig. 1). Therefore, we are likely underestimat-
ing the magnitude of climate and hydrological uncertainty
in our analysis. Given the constraints of computational re-
sources, we considered seven climate models and two hy-
drological models for each catchment. It can be argued that
the application of seven climate models presents a reason-
able representation of climate model structural uncertainty,
given that previous climate change hydrological impact as-
sessments have tended to apply a similar or lower number
of climate models (Arnell et al., 2011; Hayashi et al., 2010;
Prudhomme et al., 2003). The prior uncertainty from climate
model structural uncertainty could be reduced by compar-
ing GCM simulations of baseline climate with observations.
Such considerations have led to the calculation of perfor-
mance metrics for GCMs, such as ranking them according
to a measure of relative error (Gleckler et al., 2008). Form-
ing a single index of model performance, however, can be
misleading in that it hides a more complex picture of the rel-
ative merits of different models. Furthermore, for one spe-
cific region, Chiew et al. (2009) concluded that there was no
clear difference in rainfall projections between the “better”
and “poorer” 23 GCMs included in the CMIP3 archive (7
of which we applied here) based on their abilities to repro-
duce observed historical rainfall. Therefore in their analysis,
using only the better GCMs or weights to favour the better
GCMs gave similar runoff impact assessment results as the
use of all the 23GCMs. Moreover, on a conceptual level,
it has been argued that, because of deep and structural un-
certainty, it is not appropriate to seek to estimate the relative
weight of different GCMs, and to do so would lead to signifi-
cant over-interpretation of model-based scenarios (Stainforth
et al., 2007): all models are only partial representations of a
complex world, and miss important processes. For these rea-
sons, in the present analysis, we assumed that all the GCMs
are equally credible, although they are not completely inde-
pendent.
The computational resources required to perform multiple
GHM simulations are relatively small compared with those
required to run multiple CHMs because in previous work
ClimGen was integrated with the GHM and adapted to run
by high throughput computing (HTC) on the University of
Reading Campus Grid, which reduced simulation time by a
factor of over 80 relative to running on a single compute node
(see Gosling et al., 2010). A more thorough consideration
of downscaling uncertainty would apply climate projections
from regional climate models (RCMs), which have been dy-
namically downscaled, and/or a range of different statistical
downscaling algorithms other than that included in ClimGen
(e.g. see Maraun et al., 2010). However, this would effec-
tively at least double the computing and time resources re-
quired from what was used in the present analysis.
A more thorough consideration of hydrological model un-
certainty would explore (1) hydrological model parameter
Hydrol. Earth Syst. Sci., 15, 279–294, 2011 www.hydrol-earth-syst-sci.net/15/279/2011/
S. N. Gosling et al.: Comparing global and catchment-scale hydrological models 291
perturbations, and (2) the application of several CHMs for
each catchment. However, this would be demanding in terms
of computational and human resources. For instance, to
address the latter suggestion above, each CHM (SLURP,
SWAT, etc.) would need to be calibrated for each individual
catchment (Liard, Mekong etc.) and would then involve per-
forming 216 CHM simulations (6 CHMs ×6 catchments×6
increases in global-mean air temperature) for a single GCM
pattern. As such, a computer cluster with around 216 nodes
would be ideal, but each CHM would need to be adapted for
running by HTC. This is not straightforward; see Gosling
et al. (2010) for a detailed discussion on the issues regarding
adapting a hydrological model to run by HTC. To address the
former suggestion, Multi-Method Global Sensitivity Analy-
sis (MMGSA; Cloke et al., 2007) presents a method for sys-
tematically perturbing all model parameters systematically
but again, the extensive computing resources required for
this precluded such an analysis here. Moreover, each CHM
and GHM will include different parameters, so a like-with-
like comparison is not straightforward. Nevertheless, Arnell
(2010) demonstrates that the uncertainty associated with 100
CHM model parameter sets is vastly smaller than the uncer-
tainty across 21 GCM climate projections, which supports
our conclusion that climate model uncertainty dominates
hydrological model uncertainty. Moreover, evidence from
other climate change impact assessment sectors (e.g. agri-
culture; Challinor et al., 2009) suggests that climate model
uncertainty is effectively damped once other non-climatic
uncertainties, such as decision-making processes or socio-
economic uncertainties are considered, in a wider decision-
making framework.
Our analysis demonstrates that the GHM is able to repre-
sent the broad climate change signal that is represented by
the CHMs, for each catchment. Therefore where future cli-
mate change impacts assessments seek to quantify and assess
the range of hydrological projections across an ensemble of
GCMs, it may be as equally feasible to apply a GHM as it is
to apply a CHM to explore catchment-scale changes in runoff
with global warming. However, in the present analysis, we
only considered only one GHM, Mac-PDM.09 (Gosling and
Arnell, 2010). Recent work highlights that there is uncer-
tainty across different GHMs in the simulation of runoff
(Haddeland et al., 2011), so it can not be assumed that all
GHMs will perform in the same way as the GHM presented
here.
5 Conclusions
We have presented a comparative analysis of projected im-
pacts of global warming on river runoff from a GHM (Mac-
PDM.09; Gosling and Arnell, 2010) and a set of catchment-
specific CHMs for six catchments, which are global in cov-
erage and feature strong contrasts in spatial scale as well as
climatic and developmental conditions. For some catchments
and simulated hydrological indicators, particularly with in-
dicators of high and low extreme monthly runoff, the GHM-
CHM difference for a single GCM and climate forcing can be
substantial. This highlights firstly, that it is important to con-
sider more than only the simulated mean annual runoff when
comparing different hydrological models, and secondly, that
for a given single GCM, the GHM we applied is no more
feasible than a CHM for estimating catchment-scale runoff
changes under global warming scenarios. Whilst for some
catchments there is considerable uncertainty in the magni-
tude of projected mean annual runoff and Q5 and Q95 change
across the seven GCMs, there is higher confidence in direc-
tional shifts of the seasonal cycle, such as increases in spring
and autumn runoff with the Liard catchment, although the
GHM does, for some catchments, estimate the month of peak
or low runoff one or two months ahead or behind the CHM.
Perhaps the most important conclusion to draw from our
analysis is that the differences in projected changes of mean
annual as well as high (Q5) and low (Q95) monthly runoff
between the two types of hydrological model are generally
relatively small in comparison to the range of projections
across the seven GCMs. For example, with UKMO HadCM3
2◦C prescribed warming, the absolute GHM-CHM differ-
ences in mean annual runoff change are 12% (Liard), 9%
(Mekong), 1% (Rio Grande), 6% (Okavango), 10% (Xi-
angxi) and 25% (Harper’s Brook), whilst the greatest abso-
lute differences between any two GCMs with 2◦C prescribed
warming for the GHM (CHM) for each catchment respec-
tively are 28% (17%), 30% (23%), 48% (36%), 62% (58%),
34% (15%) and 30% (31%). This implies that climate model
structural uncertainty is greater than the uncertainty associ-
ated with the type of hydrological model applied. There-
fore, where future climate change impacts assessments seek
to quantify and assess the range of hydrological projections
across an ensemble of GCMs, it may be as equally feasi-
ble to apply a GHM (Mac-PDM.09 here) as it is to apply
a CHM to explore catchment-scale changes in runoff with
global warming. Given that there is a growing acceptance
that climate change impacts assessments should consider the
range of uncertainty inherent in the currently available set
of GCMs available to the modelling community, this is a
poignant finding. However, although the GHM is able to rep-
resent the broad climate change signal that is represented by
the CHMs, across seven GCMs, when a single forcing GCM
is considered, the CHM and GHM can disagree substantially,
for a few catchments and hydrological indicators, especially
with indicators of extreme monthly runoff. These differences
have implications for future water management issues, such
as, for example, in the planning of dams and reservoirs for
dealing with high and low flows. As such, our analysis sug-
gests that given the choice, there is no evidence to suggest
that the application of a GHM would be more favourable than
the application of a CHM, for the estimation of changes in
catchment-scale runoff under climate change scenarios.
www.hydrol-earth-syst-sci.net/15/279/2011/ Hydrol. Earth Syst. Sci., 15, 279–294, 2011
292 S. N. Gosling et al.: Comparing global and catchment-scale hydrological models
Acknowledgements. This work was supported by a grant from the
Natural Environment Research Council (NERC), under the QUEST
programme (grant no. NE/E001890/1). Thank you to the following
for performing the catchment-scale hydrological model simulations
of runoff, for the Liard (Robin Thorne, School of Geography
and Earth Sciences, McMaster University, Canada), Mekong
(Daniel G. Kingston, Department of Geography, University of
Otago, New Zealand), Okavango (Denis A. Hughes, Institute for
Water Research, Rhodes University, South Africa), Rio Grande
(M´
arcio T. N´
obrega, Walter Collischonn, Carlos E. M. Tucci and
Adriano R. da Paz, Instituto de Pesquisas Hidr´
aulicas, Universidade
Federal do Rio Grande do Sul, Brazil) and Xiangxi (Hongmei
Xu, Laboratory for Climate Studies of China Meteorological
Administration, National Climate Center, China). Thank you to
Dan Bretherton (ESSC, University of Reading, UK), University of
Reading IT Services and the entire University of Reading Campus
Grid team for their support in setting-up Mac-PDM.09 to run by
high throughput computing on the University of Reading Campus
Grid. The climate model projections were taken from the WCRP
CMIP3 dataset (http://www-pcmdi.llnl.gov/ipcc/about ipcc.php).
ClimGen was developed by Tim Osborn at the Climatic Research
Unit (CRU) at the University of East Anglia (UEA), UK. Hannah
Cloke (Department of Geography, King’s College London) and an
anonymous reviewer are thanked for their very helpful comments
on a previous version of the manuscript.
Edited by: J. Vrugt
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