Comparing projections of future changes in runoff and water resources from hydrological and ecosystem models in ISI-MIP

Abstract

Projections of future changes in runoff can have important implications
for water resources and flooding. In this study, runoff projections from
ISI-MIP (Inter-sectoral Impact Model Intercomparison Project)
simulations forced with HadGEM2-ES bias-corrected climate data under the
Representative Concentration Pathway 8.5 have been analysed. Projections
of change from the baseline period (1981-2010) to the future (2070-2099)
from a number of different ecosystems and hydrological models were
studied. The differences between projections from the two types of model
were looked at globally and regionally. Typically, across different
regions the ecosystem models tended to project larger increases and
smaller decreases in runoff than the hydrological models. However, the
differences varied both regionally and seasonally. Sensitivity
experiments were also used to investigate the contributions of varying
CO2 and allowing vegetation distribution to evolve on
projected changes in runoff. In two out of four models which had data
available from CO2 sensitivity experiments, allowing
CO2 to vary was found to increase runoff more than keeping
CO2 constant, while in two models runoff decreased. This
suggests more uncertainty in runoff responses to elevated CO2
than previously considered. As CO2 effects on
evapotranspiration via stomatal conductance and leaf-area index are more
commonly included in ecosystems models than in hydrological models, this
may partially explain some of the difference between model types.
Keeping the vegetation distribution static in JULES runs had much less
effect on runoff projections than varying CO2, but this may
be more pronounced if looked at over a longer timescale as vegetation
changes may take longer to reach a new state.

Full text

Earth Syst. Dynam., 4, 359–374, 2013
www.earth-syst-dynam.net/4/359/2013/
doi:10.5194/esd-4-359-2013
© Author(s) 2013. CC Attribution 3.0 License.
Earth System
Dynamics
Open Access
Comparing projections of future changes in runoff from
hydrological and biome models in ISI-MIP
J. C. S. Davie
1
, P. D. Falloon
1
, R. Kahana
1
, R. Dankers
1
, R. Betts
1
, F. T. Portmann
2, 9
, D. Wisser
3
, D. B. Clark
4
,
A. Ito
5
, Y. Masaki
5
, K. Nishina
5
, B. Fekete
6
, Z. Tessler
6
, Y. Wada
7
, X. Liu
8
, Q. Tang
8
, S. Hagemann
10
, T. Stacke
10
,
R. Pavlick
11
, S. Schaphoff
12
, S. N. Gosling
13
, W. Franssen
14
, and N. Arnell
15
1
Met Office Hadley Centre, Exeter, UK
2
Biodiversity and Climate Research Centre (LOEWE BiK-F) & Senckenberg Research Institute and Natural History
Museum, Frankfurt am Main, Germany
3
Center for Development Research, University of Bonn, Germany
4
Centre for Ecology and Hydrology, Wallingford, UK
5
Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Japan
6
Civil Engineering Department, The City College of New York CUNY, New York, USA
7
Department of Physical Geography, Faculty of Geosciences, Utrecht University, the Netherlands
8
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
9
Institute of Physical Geography, Goethe University Frankfurt, Frankfurt am Main, Germany
10
Max Planck Institute for Meteorology, Hamburg, Germany
11
Max Planck Institute for Biogeochemistry, Jena, Germany
12
Potsdam Institute for Climate Impact Research, Potsdam, Germany
13
School of Geography, University of Nottingham, Nottingham, UK
14
Wageningen University and Research Centre, Wageningen, the Netherlands
15
Walker Institute, University of Reading, UK
Correspondence to: J. C. S. Davie (jemma.davie@metoffice.gov.uk) and P. D. Falloon (pete.falloon@metoffice.gov.uk)
Received: 31 January 2013 – Published in Earth Syst. Dynam. Discuss.: 13 February 2013
Revised: 23 August 2013 – Accepted: 28 August 2013 – Published: 10 October 2013
Abstract. Future changes in runoffcanhave important impli-
cations for water resources and flooding. In this study, runoff
projections from ISI-MIP (Inter-sectoral ImpactModel Inter-
comparison Project) simulations forced with HadGEM2-ES
bias-corrected climate data under the Representative Con-
centration Pathway 8.5 have been analysed for differences
between impact models. Projections of change from a base-
line period (1981–2010) to the future (2070–2099) from 12
impacts models which contributed to the hydrological and
biomes sectors of ISI-MIP were studied. The biome mod-
els differed from the hydrological models by the inclusion of
CO
2
impacts and most also included a dynamic vegetation
distribution. The biome and hydrological models agreed on
the sign of runoff change for mostregions of the world. How-
ever, in West Africa, the hydrological models projected dry-
ing, and the biome models a moistening. The biome models
tended to produce larger increases and smaller decreases in
regionally averaged runoff than the hydrological models, al-
though there is large inter-modelspread.Thetiming of runoff
change was similar, but there were differences in magnitude,
particularly at peak runoff. The impact of vegetation distri-
bution change was much smaller than the projected change
over time, while elevated CO
2
had an effect as large as the
magnitude of change over time projected by some models in
some regions. The effect of CO
2
on runoff was not consis-
tent across the models, with two models showing increases
and two decreases. There was also more spread in projec-
tions from the runs with elevated CO
2
than with constant
CO
2
. The biome models which gave increased runoff from
elevated CO
2
were also those which differed most from the
hydrological models. Spatially, regions with most difference
between model types tended to be projected to have most
Published by Copernicus Publications on behalf of the European Geosciences Union.

360 J. C. S. Davie et al.: Runoff projections from hydrological and biome models in ISI-MIP
effect from elevated CO
2
, and seasonal differences were also
similar, so elevated CO
2
can partly explain the differences
between hydrological and biome model runoff change pro-
jections. Therefore, this shows that a range of impact models
should be considered to give the full range of uncertainty in
impacts studies.
1 Introduction
Assessments of future hydrological changes are important
due to the effects that changes in water availability, flooding
and drought can have on society (Kundzewicz et al., 2007).
At the global scale, projections of future freshwater avail-
ability may be provided by a number of different modelling
approaches (Bates et al., 2008), each of which may poten-
tially produce different results, even when drivenby the same
forcing data. For example, the WaterMIP intercomparison
(Haddeland et al., 2011) studied two types of water models.
They classified the models into global hydrological models
(GHMs, which tend to be focused on water resources and
represent lateral transfers of water), and land surface models
(LSMs, which typically calculate vertical exchanges of heat,
carbon and water), although these categories are not exclu-
sive and some GHMs contain features of LSMs and vice-
versa. These two categories of model showed differences in
simulating aspects of the present-day water balance (Hadde-
land et al., 2011), linked both to the representation of snow
processes in mid–high latitudes, and canopy evaporation over
the Amazon. Similarly, a recent study comparing multiple
GHMs driven by an ensemble of GCMs (Hagemann et al.,
2013) found a large spread in future runoff responses, with
GHM choice being an important factor. The spread in fu-
ture runoff projections was dominated by GHM choice over
central Amazonia and the high latitudes (Hagemann et al.,
2013). This suggests that differences between models are a
major source of uncertainty, and that climate change impact
studies need to consider both multiple climate models and
multiple impact models.
The Inter-Sectoral Impact Model Intercomparison Project
(ISI-MIP) (Warszawski et al., 2013) is a community-driven
modelling effort with the goal of providing cross-sectoral
global impact assessments, based on the newly developed
climate [representative concentration pathways (RCPs)] and
socio-economic [shared socio-economic pathways (SSPs)]
scenarios (Moss et al., 2010). Based on common background
scenarios (climate and socio-economic), a quantitative esti-
mate of impacts and uncertainties for different sectors and
from multiple impact models were derived. Within ISI-MIP,
future projections of runoff (Schewe et al., 2013) were pro-
vided by both models contributing to the hydrological sector
(which mostly do not include vegetation dynamics) and the
biome sector (which do include vegetation dynamics).
1.1 Impact of vegetation change on runoff
Vegetation dynamics may alter the future response of runoff
since changing vegetation patterns (in response to future
climate) may alter the fluxes of energy and water in sev-
eral ways. Firstly, plant structural changes, such as chang-
ing plant functional types (PFTs), or changes in leaf area
index (LAI) may alter evapotranspiration rates and albedo.
Secondly, changes in plant productivity and leaf area index
may result from the changing climate, which may similarly
alter evapotranspiration rates and albedo. Thirdly, increased
CO
2
concentrations will alter plant growth, photosynthesis,
and water use efficiency, which may also alter evapotranspi-
ration rates (Falloon and Betts, 2006; Gedney et al., 2006;
Betts et al., 2007), and albedo. Since any changes in evap-
otranspiration caused by plant responses to increasing CO
2
have to be balanced by runoff, changes in runoff may result.
Elevated CO
2
is generally considered to have two op-
posing impacts on runoff through changes to evapotranspi-
ration. Firstly, CO
2
fertilisation of photosynthesis, may in-
crease plant productivity and leaf area index, thereby also
increasing the possible evapotranspiration from the canopy
(Betts et al., 2007; Alo and Wang, 2008), and thus decreas-
ing runoff. Secondly, CO
2
may also inhibit evapotranspira-
tion by reducing stomatal conductance at the leaf level (Ged-
ney et al., 2006; Betts et al., 2007; Cao et al., 2010). Re-
cent studies have generally found overall increases in runoff
resulting from elevated CO
2
concentrations (Gedney et al.,
2006; Betts et al., 2007), although the relative size of the two
opposing effects may vary (Alkama et al., 2010), particularly
regionally and seasonally. The CO
2
fertilisation of photosyn-
thesis and reduced stomatal conductance can also lead to in-
creased soil moisture contents (Niklaus and Falloon, 2006),
leading to further increases in NPP (Friend et al., 2013). Even
within one impact model, estimates of future water stress
have been found to be highly sensitive to CO
2
impacts on
runoff (Wiltshire et al., 2013).
1.2 Present study
The aims of this study are set out in the following questions:
– How do the runoff responses projected by biome and
hydrological models, from the ISI-MIP ensemble, dif-
fer in terms of the direction, magnitude, spatial and
seasonal patterns of change?
– How does the inclusion of elevated CO
2
and its effects
in the biome models affect the runoff response in the
direction, magnitude and pattern of change?
– How does the inclusion of a dynamic vegetation distri-
bution affect the runoff response in direction, magni-
tude and pattern of change?
Earth Syst. Dynam., 4, 359–374, 2013 www.earth-syst-dynam.net/4/359/2013/

J. C. S. Davie et al.: Runoff projections from hydrological and biome models in ISI-MIP 361
– Can the effects on runoff of elevated CO
2
and a chang-
ing vegetation distribution explain the differences be-
tween hydrological and biome models’ runoff projec-
tions?
2 Methodology
2.1 Forcing data
Runoff data was analysed from all impacts models, con-
tributing to the hydrological or biomes sector, that provided
monthly output fields to the ISI-MIP archive from simula-
tions forced with HadGEM2-ES (Collins et al., 2011; Jones
et al., 2011; Martin et al., 2011) bias-corrected climate data
(Hempel et al., 2013) for the historical period (1971–2004)
and the RCP 8.5 future climate scenario (2005–2099). We
focussed only on simulations driven by the HadGEM2-ES
RCP8.5 experiments for several reasons. This setup provided
the largest data set for analysis in ISI-MIP, and the largest im-
pacts of vegetation change on runoff may be expected under
the stronger RCP8.5 forcing scenario. While the application
of non-bias-corrected GCM data can result in large uncer-
tainty in impact simulations (Gosling et al., 2010; Ehret et al.,
2012), the application of bias correction in the ISI-MIP forc-
ing data set may largely have removed any impact of differ-
ences between GCMs in the present-day baseline (Hempel
et al.
, 2013). Unrouted runoff, as opposed to (routed) dis-
charge was analysed in the present study since discharge data
was not available from all of the biome models studied here.
For 2100 compared to the baseline period (1861–1990), in
the original HadGEM2-ES simulations, global mean temper-
atures increased by approximately 6K and precipitation by
around 6% (Caesar et al., 2012).
2.2 Models
The models whose data was used are described in Table 1. In
this study, the models were assigned to two groups, named
biome and hydrological models. If a model contributed to the
biome sector or both the biome and hydrological sectors of
ISI-MIP, it was classified as a biome model. If a model only
contributed to the hydrological sector, then it was a hydro-
logical model for the purposes of this study (Fig. 1). This
method of grouping the impact models was used to sepa-
rate the models including vegetation effects on runoff from
those which do not. VISIT did not include vegetation dy-
namics, but did include CO
2
impacts, hence its inclusion as
a biome model. Due to this, JULES and LPJmL were classi-
fied as biome models because their inclusion of CO
2
impacts
and dynamic vegetation distributions, although they are also
full hydrology models. The data used here were global grid-
ded data sets mainly on a 0.5
◦
latitude-longitude grid, with
JULES and JeDi on a 1.25
◦
× 1.875
◦
latitude-longitude grid.
J. C. S. Davie et al.: 13
Table 2. Model simulations analysed in the present study (all driven by ISI-MIP forcing data for HadGEM2-ES historic and RCP8.5
scenarios) - (a) nosoc: naturalized runs, with no human impact, no irrigation, and no population/GDP data prescribed; nolu = no human land
use assumed
Model name Main
simulations (a)
Sensitivity experiments
Vegetation
dynamics
CO
2
impacts
Fixed
vegetation
Dynamic
vegetation
Fixed CO
2
Varying
CO
2
Fixed CO
2
Varying
CO
2
Hydrological models
DBH nosoc - - - - - -
VIC nosoc - - - - - -
WBM nosoc - - - - - -
Mac-PDM.09 nosoc - - - - - -
MPI-HM nosoc - - - - - -
WaterGAP nosoc - - - - - -
H08 nosoc - - - - - -
PCR-GLOBWB nosoc - - - - - -
Biome models
LPJmL nolu Yes Yes - - Yes Yes
JULES nolu Yes Yes Yes Yes Yes Yes
VISIT nolu - Yes Yes Yes - -
JeDI nolu Yes Yes - - Yes Yes
Fig. 1. Venn Diagram to show the grouping of the impact models - The black circles show which models contributed to the biomes and
hydrological sectors of ISI-MIP, with the overlap showing models which contributed output for variables in both sectors. The boxes with
coloured outlines show the classification of models into groups within this study - the models within the blue box are included as hydrological
models and the models listed within the green box are included as biome models.
Fig. 1. Venn diagram to show the grouping of the impact models –
the black circles show which models contributed to the biomes and
hydrological sectors of ISI-MIP, with the overlap showing models
which contributed output for variables in both sectors. The boxes
with coloured outlines show the classification of models into groups
within this study – the models within the blue box are included as
hydrological models and the models listed within the green box are
included as biome models.
2.3 Experimental setup
The model runs were set up according to the ISI-MIP simula-
tion protocol (Warszawski et al., 2013) so they were run with
comparable settings. As common forcing data was used in
all of the model runs, differences between their output came
from differences in the impact models – and therefore show
the uncertainty in projections based only on the model se-
lected or the setup of the model in the case of sensitivity ex-
periments. The main simulations analysed in this study were
the core ISI-MIP runs, provided by the largest set of impact
models (Warszawski et al., 2013). For hydrological models,
these were naturalised runs with no human impact, and for
biome models, these were runs with varying CO
2
concentra-
tion, specified by the RCP scenario. Sensitivity experiment
runs using the biome models were further analysed to in-
vestigate the importance of including individual processes.
These included model runs with either constant CO
2
(con-
centration kept constant from 2000), static vegetation distri-
bution or both. Table 2 gives an overview of the experiments
analysed in this study and shows which models carried out
sensitivity experiments. The aim of this study is to show that
impact models including carbon dioxide impacts and/or veg-
etation dynamics may give differing projections to models
not considering these, and therefore they should be included
in hydrological impact assessments.
2.4 Evaluation of simulated present-day runoff
Simple validation of the modelled runoff was carried out,
firstly by comparing averaged historical runoff from the
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362 J. C. S. Davie et al.: Runoff projections from hydrological and biome models in ISI-MIP
Table 1. Models used in the present study and their main characteristics (in part, after Haddeland et al. (2011)) – (a) R =rainfall
rate; S =snowfall rate; P = precipitation (rain or snow distinguished in the model); T =air temperature; Tmax=maximum daily air
temperature; Tmin =minimum daily air temperature; W =windspeed; Q =specific humidity; LW= longwave radiation flux (downward);
LWnet= longwave radiation flux (net); SW= shortwave radiation flux (downward); and SP=surface pressure; (b) Bulk formula: Bulk trans-
fer coefficients are used when calculating the turbulent heat fluxes; (c) Beta function: runoff is a nonlinear function of soil moisture.
Model Name Model
time
step
Meteorological
forcing
variables (a)
Energy
ba-
lance
ET
scheme
(b)
Runoff
scheme (c)
Snow
scheme
Vegetation
dyna-
mics
CO
2
impacts
References
Hydrological models
DBH 1h P , T , W , Q,
LW, SW, SP
Yes Energy
balance
Infiltration
excess
Energy
balance
No No Tang et al. (2006, 2007)
VIC Daily/3h P , Tmax,
Tmin, W , Q,
LW, SW, SP
No Penman–
Monteith
Saturation
excess/beta
function
Energy
balance
No No Liang et al. (1994)
WBM Daily P , T No Hamon Saturation
excess
Empirical
T and
P based
formula
No No Vörösmarty et al. (1998)
Mac-
PDM.09
Daily P , T , LWnet,
SW
No Penman–
Monteith
Saturation
excess/beta
function
Degree-
day
No No Gosling et al. (2010);
Gosling and Arnell (2011)
MPI-HM Daily P , T , W , Q,
LW, SW, SP
No Penman–
Monteith
Saturation
excess/beta
function
Degree-
day
No No Hagemann and Gates
(2003); Stacke and
Hagemann (2012)
WaterGAP Daily P , T , LWnet,
SW
No Priestley–
Taylor
Beta function Degree-
day
No No Alcamo et al. (2003); Döll
et al. (2003, 2012); Flörke
et al. (2013)
H08 Daily R, S, T , W , Q,
LW, SW, SP
Yes Bulk
formula
Saturation
excess/beta
function/
subsurface
flow
Energy
balance
No No Hanasaki et al. (2008a, b)
PCR-
GLOBWB
Daily P , T No Hamon Saturation ex-
cess/infiltration
excess
Degree-
day
No No Wada et al., 2011, 2013a;
van Beek et al., 2011
Biome models
LPJmL Daily P , T , LWnet,
SW
No Priestley–
Taylor
Saturation
excess
Degree
day
Yes Yes Bondeau et al. (2007);
Rost et al. (2008)
JULES 1h R, S, T , W , Q,
LW, SW, SP
Yes Penman–
Monteith
Infiltration
excess/Darcy
Energy
balance
Yes Yes Clark et al. (2011); Best
et al. (2011)
VISIT Monthly P , T , Q, SW Yes Penman–
Monteith
Bucket (sim-
plified satura-
tion excess)
Ambient
tempera-
ture
No Yes Ito and Inatomi (2011)
JeDi Daily P , T , LW, SW No Priestley–
Taylor
Saturation
excess/beta
function
Degree-
day
Yes Yes Pavlick et al. (2013)
Earth Syst. Dynam., 4, 359–374, 2013 www.earth-syst-dynam.net/4/359/2013/

J. C. S. Davie et al.: Runoff projections from hydrological and biome models in ISI-MIP 363
Table 2. Model simulations analysed in the present study (all driven by ISI-MIP forcing data for HadGEM2-ES historic and RCP8.5 scenar-
ios) – (a) nosoc: naturalized runs, with no human impact, no irrigation, and no population/GDP data prescribed; nolu =no human land use
assumed.
Model name Main
simulations (a)
Sensitivity experiments
Vegetation
dynamics
CO
2
impacts
Fixed
vegetation
Dynamic
vegetation
Fixed
CO
2
Varying
CO
2
Fixed
CO
2
Varying
CO
2
Hydrological models
DBH nosoc – – – – – –
VIC nosoc – – – – – –
WBM nosoc – – – – – –
Mac-PDM.09 nosoc – – – – – –
MPI-HM nosoc – – – – – –
WaterGAP nosoc – – – – – –
H08 nosoc – – – – – –
PCR-GLOBWB nosoc – – – – – –
Biome models
LPJmL nolu Yes Yes – – Yes Yes
JULES nolu Yes Yes Yes Yes Yes Yes
VISIT nolu – Yes Yes Yes – –
JeDI nolu Yes Yes – – Yes Yes
impact models, with the ISLSCP II UNH/GRDC composite
monthly runoff (Fekete et al., 2002; Hall et al., 2006; Fekete
et al., 1999; Fekete and Vorosmarty, 2011) by calculating
global and regional annual averages (Table 3). This showed
that globally, the impact models tend to predict higher runoff
totals than the GRDC data set. Regionally the impact mod-
els also tended to overestimate runoff, with very few model
and region combinations giving lower runoff values than the
composite runoff field. This, however, is strongly related to
the GCM precipitation input; simulated runoff driven by ob-
served precipitation has given values more similar to the
GRDC data set in previous studies (
van Beek et al., 2011).
Secondly, the annual cycles of runoff from the Fekete com-
posite runoff field, for a group of Giorgi regions have been
overplotted on annual cycle plots of modelled runoff to com-
pare the timing of runoff throughout the year, which show
whether the impact models captured observed seasonality.
The timing of runoff projected by the models matches well
with ISLSCP II UNH/GRDC composite runoff (Fekete et al.,
2002, 1999; Hall et al., 2006; Vörösmarty et al., 1998), al-
though the magnitudes are different, particularly at the peaks,
where the models (mainly the biome models) gave generally
higher runoff than the composite data set in some regions.
2.5 Analysis
Using the full model data set described above, 30yr averages
of annual and monthly runoff for 1981–2010 and 2070–2099
were calculated and the difference between them analysed.
Precipitation was largely identical in all of the models since
they were driven by the common forcing data, which had
a global mean of 893 mm yr
−1
for the land surface during
the baseline period (1981–2010), which is within the range
of 743–926mmyr
−1
suggested by
Biemans et al. (2009), al-
though the latter used a baseline period of 1979–1999. Very
minor differences in the precipitation havearisen through dif-
ferences in model setup, including grid resolution.
Data was analysed on annual and monthly timescales for
land Giorgi regions (Supplementary Fig. 1: Giorgi and Bi,
2005; Ruosteenoja, 2003), in order to compare differences
between models across large regions with different climates.
As discussed in Meehl et al. (2007), the Giorgi regions have
simple shapes and are no smaller than the horizontal scales
on which current global climate models are useful for cli-
mate simulations (typically judged to be roughly 1000km).
This means that the whole global land area could be covered
using a manageable number of similarly sized boxes, giving a
broader global picture than a selection of river basins. Using
regions of similar size also means that in scatter plots with
a point representing each region, results are less biased to-
wards giving smaller basins relatively more effect visually
per unit area than larger basins. Despite these benefits of
using Giorgi regions rather than river basins, regional aver-
ages over Giorgi regions may have some deficiencies (Meehl
et al., 2007). These are discussed in Sect. 4.
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364 J. C. S. Davie et al.: Runoff projections from hydrological and biome models in ISI-MIP
Table 3. Globally and regionally averaged runoff calculated from the ISLSCP II UNH/GRDC (Fekete and Vorosmarty, 2011) data set (1986–1995 average) and impacts models
(1981–1990 average) in mmyr
−1
.
Global AMZ WAF SAS ALA TIB CAS WNA CNA ENA CAM SSA NEU SEU SAH EAF SAF EAS SEA NAU
Fekete composite 292 921 409 480 137 64 63 151 159 437 323 139 260 123 1 193 181 222 1379 41
LPJmL 415 920 616 765 320 85 105 329 363 565 443 290 438 228 7 305 278 371 1376 184
VISIT 254 887 689 730 262 88 114 325 436 638 469 323 443 232 7 323 277 363 1538 167
JeDi 384 729 510 658 450 65 84 350 340 668 294 169 564 207 5 193 189 325 1105 162
DBH 520 1153 637 859 421 78 122 375 471 784 574 430 564 303 13 334 357 548 1782 257
JULES 423 1016 555 815 311 55 77 274 415 655 465 304 377 204 12 299 243 418 1324 246
H08 396 954 558 767 393 101 86 342 295 524 385 239 412 230 2 275 257 352 1262 143
Mac-PDM.09 403 981 539 741 317 91 107 282 349 587 400 306 388 224 10 268 290 398 1267 175
MPI-HM 362 915 495 617 235 53 60 269 300 569 377 268 411 214 2 223 173 375 1332 77
VIC 340 728 393 699 339 98 107 291 335 507 332 238 353 212 4 201 186 380 980 136
WBM 336 789 400 668 237 69 60 281 347 604 404 251 325 203 2 204 248 308 1257 169
WaterGAP 345 795 354 702 334 73 80 310 245 518 330 226 372 245 13 154 178 363 1221 156
PCR-GLOBWB 357 776 407 675 290 122 103 284 377 537 397 322 354 233 9 221 252 387 1131 92
Earth Syst. Dynam., 4, 359–374, 2013 www.earth-syst-dynam.net/4/359/2013/

J. C. S. Davie et al.: Runoff projections from hydrological and biome models in ISI-MIP 365
Fig. 2. Ensemble consensus for runoff change between 1981–2010
and 2070–2099 for (a) hydrological models, (b) biome models and
(c) all models when forced with HadGEM2-ES RCP8.5 climate.
Each colour shows the category of runoff change, while lighter
(darker) shades indicate the proportion of models agreeing with
that category of change. Runoff changes were calculated individ-
ually for each model, and then the consensus across these individ-
ual model changes were calculated for hydrological models, biome
models and all models.
In order to identify spatial patterns of model agreement,
consensus plots (Kaye et al., 2011; McSweeney and Jones,
2013) were created for the biome models and hydrological
models separately as well as for the full set of models. These
show the proportion of models which agreed on a particu-
lar category of runoff change. This was done since averag-
ing over model groups may compromise the physical con-
sistency between variables, and does not show the true be-
haviour of any particular model outcome (Taylor et al., 2013;
Ehret et al., 2012).
16 J. C. S. Davie et al.:
Fig. 3. Scatter plot of precipitation change against runoff change between 1981-2010 and 2070-2099 in mm day-1 for the Giorgi regions –
including results from all models forced with HadGEM2-ES RCP8.5 climate. Solid 1:1 line. Dashed x=0 line and y=0 line.
Fig. 3. Scatter plot of precipitation change against runoff change
between 1981–2010 and 2070–2099 in mm day
−1
for the Giorgi
regions – including results from all models forced with HadGEM2-
ES RCP8.5 climate. Solid 1:1 line. Dashed x = 0 line and y = 0
line.
3 Results and discussion
3.1 Runoff changes across all models
There were differences between the runoff projections from
the hydrological and biome models (Fig. 2). However, in
common with Hagemann et al. (2013) there was a large
spread of projections between models (Fig. 3). Within each
model category the spread was larger than the difference be-
tween the two categories, as well as there being considerable
overlap, so the differences largely result from intermodel un-
certainty. Haddeland et al. (2011) also found that differences
between models in each class were larger than inter-class dif-
ferences. The direction of projected runoff change tended to
be the same for each type of model, but with different magni-
tude of change (Figs. 2 and 3). The approximately linear pos-
itive relationship between annual mean precipitation change
and annual mean runoff change showing the dominance of
precipitation in controlling the runoff changes (Fig. 3), is in
agreement with Betts et al. (2007).
Regional differences in other processes affecting runoff
changes are apparent from the dispersion of points about the
1 : 1 line in Fig. 3. An exception to the direction of change
being consistent between the model types is in parts of cen-
tral Africa where biome models showed consensus for in-
creased runoff, while the hydrological models showed con-
sensus for decreased runoff (Fig. 2). In some regions includ-
ing Europe, central Africa and the Amazon, the hydrologi-
cal models gave consensus for a drying, whereas the biome
models had little agreement as to the projected change.
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366 J. C. S. Davie et al.: Runoff projections from hydrological and biome models in ISI-MIP
J. C. S. Davie et al.: 17
Fig. 4. Annual cycles of runoff for selected Giorgi regions using ISI-MIP (Warszawski et al., Submitted) model runs forced with HadGEM2-
ES RCP8.5 climate. a-d: absolute values for 1981-1990, e-h: absolute values for 2081-2090, i-l: absolute changes between 1981-1990 and
2081-2090 .
Fig. 4. Annual cycles of runoff for selected Giorgi regions using ISI-MIP (Warszawski et al., 2013) model runs forced with HadGEM2-ES
RCP8.5 climate. (a–d): absolute values for 1981–1990, (e–h): absolute values for 2081–2090, (i–l): absolute changes between 1981–1990
and 2081–2090.
The biome models tended to have more increased and less
decreased runoff between 1981–2010 and 2070–2099 than
the hydrological models, particularly in regions with a large
model spread, and when large change was projected (Fig. 3).
However, this was not the case for all of the models, as JeDi
and VISIT projected larger decreases in some regions. The
seasonal patterns of runoff change were reasonably similar
for the biome and hydrological models, with the main dif-
ference between the model types being the magnitude of
changes (Fig. 4). For example, the annual cycle of runoff
change for Amazonia shows that the two types of model had
a similar shape to the seasonal cycle, but the hydrological
models projected larger decreases than the biome models.
For Amazonia, Southern Asia and West Africa, regions with
pronounced differences, there was most difference between
model types at times of peak runoff. Haddeland et al. (2011)
found that runoff results for the Amazon were sensitive to
the representation of canopy evaporation. Hagemann et al.
(2013) also found that spread in runoff projections largely
came from model choice over the Amazon and high latitudes.
However, it is more difficult to determine differences in the
seasonal pattern for Alaska and Western Canada, but both
types of model gavea shift to an earlier month of peak runoff.
3.2 The impact of varying CO
2
in biome models
The biome models tended to be consistent in their indi-
vidual projections for the direction of runoff change over
time, regardless of whether CO
2
varied or remained constant
(Fig. 5). The projected changes in runoff from the constant
CO
2
runs tended to be within the range of projected changes
18 J. C. S. Davie et al.:
Fig. 5. Scatter plot of precipitation change against runoff change between 1981-2010 and 2070-2099 in mm day-1 for the Giorgi regions –
for models including both varying and constant CO
2
forced with HadGEM2-ES RCP8.5 climate. Solid 1:1 line. Dashed x=0 line and y=0
line.
Fig. 5. Scatter plot of precipitation change against runoff change
between 1981–2010 and 2070–2099 in mm day
−1
for the Giorgi re-
gions – for models including both varying and constant CO
2
forced
with HadGEM2-ES RCP8.5 climate. Solid 1 : 1 line. Dashed x = 0
line and y = 0 line.
Earth Syst. Dynam., 4, 359–374, 2013 www.earth-syst-dynam.net/4/359/2013/

J. C. S. Davie et al.: Runoff projections from hydrological and biome models in ISI-MIP 367
J. C. S. Davie et al.: 19
Fig. 6. Annual cycles of runoff for selected Giorgi regions using runs from models including both varying CO
2
and constant CO
2
. Lines
represent each varying CO2 run and each constant CO2 run : a-d: absolute values for 1981-1990, e-h: absolute values for 2081-2090, i-l:
absolute changes between 1981-1990 and 2081-2090
Fig. 6. Annual cycles of runoff for selected Giorgi regions using runs from models including both varying CO
2
and constant CO
2
. Lines
represent each varying CO2 run and each constant CO
2
run: (a–d): absolute values for 1981–1990, (e–h): absolute values for 2081–2090,
(i–l): absolute changes between 1981–1990 and 2081–2090.
from the varying CO
2
runs, so the changes in the varying
CO
2
runs were more spread with smaller and larger magni-
tude changes than under constant CO
2
. The biome models
did not agree, however, on the direction of change in runoff
due to elevated CO
2
, with two of the models (JULES and
LPJmL) showing larger increases and smaller decreases in
runoff and the other two (JeDi and VISIT) showing the re-
verse. The increase in CO
2
has competing effects on runoff,
and the comparative strengths of these control whether there
will be increased or decreased runoff due to elevated CO
2
.
Therefore, these models must have had differently related
strengths to produce the opposite overall effects in runoff.
Compared to the other biome models, JeDi has a weaker
coupling between CO
2
and stomatal conductance, leading
to smaller reductions in transpiration under increased CO
2
.
However, it produces a similar strength CO
2
fertilisation ef-
fect to the other models, so the balance between the opposing
influences on runoff led to higher transpiration and reduced
runoff. Wada et al. (2013b) found reduced irrigation water
demand for LPJmL projections with elevated CO
2
compared
to the constant CO
2
projection, which is consistent with our
findings. The biome models which differed most from the
hydrological models in their runoff projections (Fig. 3) were
also those which projected higher runoff from varying CO
2
than constant CO
2
(Fig. 5). The more similar changes pro-
jected by the models’ constant CO
2
runs showed that some
of the uncertainty in biome models’ runoff projections was
related to processes linking CO
2
with runoff.
The effect of elevated CO
2
on runoff change was of
as large a magnitude as the change projected over time
for some models and regions. For example, in the JULES
runs, Amazonia (AMZ) was projected to have an aver-
age change of −88.26mmyr
−1
with varying CO
2
and
−191.51mmyr
−1
with constant CO
2
. Spatially, the areas
where runoff change was most affected by elevated CO
2
were very similar between the four biome models (Ama-
zonia, eastern North America, Southeast Asia and central
Africa), however with opposing directions of change be-
tween models in these regions.
Seasonally, the timing of change in runoff was very simi-
lar for model runs with varying CO
2
as for those with con-
stant CO
2
, and the main difference was the magnitude of
change at different times of year (Fig. 6). In Amazonia, West
Africa and Southern Asia, there was most difference between
the varying CO
2
and fixed CO
2
runoff change projections
at times of peak runoff. During the rainy season, evapotran-
spiration is not limited by soil moisture availability so that
plants usually may transpire at their potential rate. Thus, lim-
its on transpiration imposed by the stomatal conductance will
directly impact the total amounts of evapotranspiration, and
hence runoff.
3.3 The impact of varying vegetation and CO
2
in
JULES
The relative effects of elevated CO
2
and changing vegeta-
tion on runoff change were analysed using sensitivity exper-
iments carried out with JULES. As in the previous Sect. 3.2,
JULES projected greater increases and smaller decreases un-
der elevated CO
2
. This was regardless of the inclusion of
vegetation change, which had a much smaller magnitude
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368 J. C. S. Davie et al.: Runoff projections from hydrological and biome models in ISI-MIP
20 J. C. S. Davie et al.:
Fig. 7. Scatter plot of precipitation change against runoff change between 1981-2010 and 2070-2099 in mm day-1 for the Giorgi regions –
for the four JULES simulations forced with HadGEM2-ES RCP8.5 climate. Solid 1:1 line. Dashed x=0 line and y=0 line.
Fig. 7. Scatter plot of precipitation change against runoff change
between 1981–2010 and 2070–2099 in mm day
−1
for the Giorgi
regions – for the four JULES simulations forced with HadGEM2-
ES RCP8.5 climate. Solid 1 : 1 line. Dashed x = 0 line and y = 0
line.
impact on the projections. The impact of vegetation distri-
bution change on projected runoff change varied in direction
for different regions (Fig. 7).
There was relatively little change in the vegetation dis-
tribution in the model runs (Supplementary Figs. 4 and 5)
which accounts for the small effect on runoff. This agrees
with Falloon et al. (2012a) who found only small impacts
of vegetation change on future (2080s) surface climate. In
contrast, in studies where larger vegetation changes were ap-
plied, either in palaeoclimate (O’ishi and Abe-Ouchi, 2012;
Micheels et al., 2009), at equilibrium in the future (Jones
et al., 2009, 2010) or synthetically (Fraedrich et al., 2005),
larger impacts on surface climate were observed. There was
a larger effect of vegetation change on relative runoff change
in regions with lower precipitation (Supplementary Fig. 3),
which was also found by Leipprand and Gerten (2006). As
well as the magnitude, the seasonal pattern of the effects on
runoff change of the two factors also differed. The impact of
elevated CO
2
was relatively even throughout the year, while
the impact of vegetation change varied more seasonally. For
example, there was most effect from vegetation change in
West Africa between July and September in both the ele-
vated and constant CO
2
runs and for Amazonia, there was
most effect between January and April in the constant CO
2
projection (Fig. 8).
Seasonally, the effect of a changing vegetation distribu-
tion varied between regions (Fig. 8). For example, vegetation
change gave a larger shift to an earlier peak in spring runoff
for Alaska and Western Canada, while the effect was less on
the timing of the seasonal cycle and more on the magnitude
of the changes for some other regions. Considering the effect
of vegetation distribution change and the timing of high and
low runoff throughout the year, some regions were projected
to experience an increasing effect during high runoff (WAF),
and some a decreasing effect (SAS).
Spatially, there was mostly higher runoff projected by the
run with dynamic vegetation,particularly over Amazonia and
Southeast Asia, but lower runoff projected in a few places
than the run with a static vegetation distribution. Over Ama-
zonia (AMZ), there were projected to be smaller decreases
in runoff in the JULES run with changing vegetation than the
run with static vegetation, with a change from shrubs to trees.
Annual evaporation is generally higher in forested catch-
ments compared to non-forested catchments (Zhang et al.,
2001), so this change from shrub to trees would be expected
to reduce runoff. Therefore, reduced transpiration rates due
to elevated CO
2
outweighed increases in evapotranspiration
due to change in vegetation cover. However, over Europe and
parts of eastern North America, the effect on runoff of the
change in vegetation type was not outweighed by the effects
of CO
2
on stomatal conductance. In these regions, a change
from needleleaf to broadleaf trees was projected along with
reduced runoff, as when fully leafed out, broadleaf trees
have twice the albedo and 50–80% greater evapotranspira-
tion rates than needleleaf trees (Swann et al., 2010).
3.4 Linking vegetation effects and model differences
Two of the biome models in this study (JULES and LPJmL)
had runoff change projections which were more dissimilar
in magnitude of change to the hydrological models’ projec-
tions than the other two. These were also the biome mod-
els which projected increased runoff with elevated CO
2
, so
the inclusion of CO
2
processes contributed to the differences
between the hydrological models and the biome models in
this study. The larger spread of projections from the biome
model runs with varying CO
2
than with constant CO
2
added
to the uncertainty of projections and so it is important not
to discount these models in hydrological impact studies if
the full range of possible outcomes is to be considered. The
differences in runoff change projections between runs with
varying CO
2
and constant CO
2
were as large as the change
over time in some regions in some model projections. The
spatial pattern of where there was most difference between
biome and hydrological models’ projections and the pattern
of where there was most difference by varying CO
2
over-
lapped in Amazonia, central Africa, eastern North Amer-
ica and Southern Asia. Seasonally, both for differences be-
tween biome and hydrological models and between varying
and constant CO
2
model runs, the main differences were
the magnitude of changes, rather than the timing. The two
comparisons also showed the common pattern that there was
most difference at the peaks of runoff for Amazonia, South-
ern Asia and West Africa. Vegetation change, however, had a
much smaller effect on the runoff projections, so contributed
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J. C. S. Davie et al.: Runoff projections from hydrological and biome models in ISI-MIP 369
J. C. S. Davie et al.: 21
Fig. 8. Annual cycles of runoff for selected Giorgi regions using sensitivity experiment runs from JULES forced with HadGEM2-ES RCP8.5
climate: a-d: absolute values for 1981-1990 (solid lines) and 2081-2090 (dashed lines); e-h: absolute changes between 1981-1990 and
2081-2090
Fig. 8. Annual cycles of runoff for selected Giorgi regions using sensitivity experiment runs from JULES forced with HadGEM2-ES RCP8.5
climate: (a–d): absolute values for 1981–1990 (solid lines) and 2081–2090 (dashed lines); (e–h): absolute changes between 1981–1990 and
2081–2090.
less to the differences between the biome and hydrological
models’ runoff change projections.
4 Limitations and future work
Only changes in annual and monthly means were consid-
ered, which do not account for changes in extremes linked
to runoff, such as floods (Dankers et al., 2013) and drought
(Taylor et al., 2013; Prudhomme et al., 2013). Differences
between biome and hydrological model projections may not
show the same patterns for the extremes as they did for
the mean changes. Nevertheless, in the ISI-MIP simulations,
Prudhomme et al. (2013) noted smaller runoff deficits (less
time is projected to be spent with runoff values below the
Q
90
threshold of daily runoff calculated for the reference pe-
riod) projected by the impact model JULES under elevated
CO
2
, compared to fixed CO
2
, while JULES with both fixed
vegetation distribution and constant CO
2
behaved most like
the hydrological models.
Use of spatial means using Giorgi region averages was
beneficial for the aims of this study, however has some de-
ficiencies. For instance in some cases, the simple boxes
used result in spatial averaging over regions where precipi-
tation is projected to increase and decrease. On a sub-region
scale within Giorgi regions, there may be robust and plau-
sible hydrological responses, which would not be captured
through spatial averaging. Other papers have also used rel-
atively large regions rather than river basins; for example,
Betts et al. (2007) and Gedney et al. (2006) both consider
runoff at the continental scale rather than at a river basin
scale. When comparing results from other ISI-MIP papers
which considered runoff or discharge with our general find-
ings, the choice of Giorgi region scale rather than river basin
scale would be unlikely to alter the overall conclusions. For
example, (Schewe et al., 2013) considered runoff at a coun-
try scale (calculated using basins) and global scale, and found
that JULES and LPJmL had a lower proportion of the global
population under water stress than the other hydrological
models in the future, which is in agreement with our find-
ings. Prudhomme et al. (2013) considered runoff at a global
scale and GEO sub-region scale, and drew similar conclu-
sions when considering JULES in relation to the other hy-
drological models.
We have found that there were differences in runoff pro-
jections between models, but in order to determine the causes
of these differences, other variables contributing to runoff
rate such as evapotranspiration, snow mass, leaf area index
and plant functional type fractions could be investigated sys-
tematically (Haddeland et al., 2011), even though the com-
plicated interactions between the various processes make it
infeasible to explain the causes of many simulation differ-
ences in detail, as noted in previous model intercomparisons
(e.g. Koster and Milly, 1997).
Key uncertainties in projections of future runoff come
from the possible changes in climate (GCM uncertainty),
changes in vegetation and the runoff responses determined
by the impacts models. As these findings used bias-corrected
HadGEM2-ES climate forcing data, runoff responses using
forcing data which has not been bias corrected may dif-
fer (Kahana et al., 2013) and using forcing data from other
GCMs and representative concentration pathways may also
influence runoff projections differently to HadGEM2-ES
RCP 8.5 (Schewe et al., 2013). Although the present study
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370 J. C. S. Davie et al.: Runoff projections from hydrological and biome models in ISI-MIP
has only considered one future scenario (RCP8.5), Tang and
Lettenmaier (2012) found that spatial patterns of runoff sen-
sitivity are stable across emissions scenarios, which suggests
that there would be spatial similarities if this analysis were
repeated for a different scenario. In a similar analysis for
RCP2.6, the spatial patterns of changes, as well as the dif-
ferences between the two types of model, were indeed fairly
similar, although the magnitude of changes were smaller un-
der the mitigation scenario (Davie et al., 2013).
The methods we have used for validation within this study
were only to give a broad picture of how the models per-
form compared with observationally constrained data and
hence conclusions are limited, as the models were not driven
with observed precipitation, which explains some of the dif-
ference in magnitude, and also the ISLSCP II UNH/GRDC
composite field would not ideally be used as a whole for val-
idation. Therefore, more detailed comparison of simulated
water balance terms with observational data (e.g. Haddeland
et al., 2011; Falloon et al., 2011) would provide further in-
sight into the reasons for differences between the model pro-
jections discussed here. Many of the impact models con-
sidered in this study have been extensively validated previ-
ously (Falloon et al., 2011; Hagemann et al., 2013; Hadde-
land et al., 2011). However, for land surface processes, vali-
dation does not necessarily help to constrain the future spread
of projections – a wide range of future outcomes may result,
despite reasonable simulation of present-day values (e.g. for
water: Haddeland et al., 2011; Hagemann et al., 2013; Wada
et al., 2013b, and for ecosystems and the carbon cycle: Good
et al., 2012; Nishina et al., 2013).
This study has only assessed runoff projections and not
any of the associated socioeconomic impacts (for example,
assessing impacts on water stress – Schewe et al., 2013). Hu-
man interventions through land use change, irrigation and
construction of dams and reservoirs may also affect future
runoff, but have not been considered. Different impacts may
also result from biome and hydrological models when fully
coupled to GCMs as feedbacks can have a significant ef-
fect on projections (Falloon et al., 2012b; Martin and Levine,
2012).
5 Summary and conclusions
Our study has found notable differences in runoff projec-
tions between hydrological and biome models. In general,
the biome models tended to produce larger increases and
smaller decreases in regionally averaged annual mean runoff
than hydrological models. However, there was much spread
between the model projections within each category. Con-
sensus for both types of model agreed on the sign of change
across most of the world’s land area. However in West Africa,
the hydrological models tend to project drying whereas the
biome models project a moistening. In some regions large
differences in projections of changes in average runoff were
found between impacts models, despite using common cli-
mate forcing data. The projected timing of runoff change for
each category is similar, with the main difference being the
magnitude at times of peak runoff.
The JULES simulations of sensitivity experiments with
static vegetation distributions showed that the impacts of
vegetation distribution change on runoff were generally
much smaller than overall future projected changes in the
period considered to 2100. We found that in some regions,
runoff changed in the direction which would be expected for
the change in vegetation type, however in others it did not, so
other factors outweighed the influence of vegetation change
on runoff.
Interestingly, the impact of elevated CO
2
on runoff in the
four biome models studied here was not consistent. Two
models showed increases and two decreases, with a larger
spread between the projections with varying CO
2
than con-
stant CO
2
. These differences in model behaviour are affected
by two competing processes, which vary in strength across
the models, that of elevated CO
2
on stomatal conductance
and the fertilising impact on transpiration. In some regions,
models projected differences between the varying CO
2
and
constant CO
2
runs which were as large as the magnitude of
change over time. The differences were largest at times of
high runoff and the timing of runoff change throughout the
year was similar.
The biome models which increased runoff from varying
CO
2
, JULES and LPJmL, were also most dissimilar to the
hydrological models in their projections. Therefore, the ef-
fects of CO
2
on runoff add to the uncertainty in model projec-
tions, and partly explain differences between the hydrologi-
cal and biome models’ projections. The spatial and seasonal
patterns of runoff change are also similar. Broadly, regions
which showed most difference between the biome and hy-
drological models also projected most difference between the
varying and constant CO
2
runs. Seasonally the differences
between model types or sensitivity experiments tended to be
greatest at times of high runoff. The impact of varying CO
2
was much larger than the impact of a changing vegetation
distribution and so contributes more to explaining the differ-
ences between the biome and hydrological models.
To account for the full range of uncertainty, climate im-
pact studies should consider a range of impact models. In
planning studies of water resource management into the fu-
ture, biome models which include CO
2
effects and dynamic
vegetation should be used in conjunction with hydrological
models, as this will better show the full range of uncertainty
in these projections which should not be ignored.
Supplementary material related to this article is
available online at http://www.earth-syst-dynam.net/4/
359/2013/esd-4-359-2013-supplement.pdf.
Earth Syst. Dynam., 4, 359–374, 2013 www.earth-syst-dynam.net/4/359/2013/

J. C. S. Davie et al.: Runoff projections from hydrological and biome models in ISI-MIP 371
Acknowledgements. This work has been conducted under the
framework of ISI-MIP. The ISI-MIP Fast Track project was funded
by the German Federal Ministry of Education and Research
(BMBF) with project funding reference number 01LS1201A.
Responsibility for the content of this publication lies with the
author. The role of J. C. S. Davie, P. D. Falloon, R. Kahana, R. Betts
and R. Dankers was also supported by the Joint DECC/Defra Met
Office Hadley Centre Climate Programme (GA01101). A. Ito,
Y. Masaki and K. Nishina were supported by the Environment
Research and Technology Development Fund (S-10) of the
Ministry of the Environment, Japan. We acknowledge the World
Climate Research Programme’s Working Group on Coupled
Modelling, which is responsible for CMIP, and we thank the
HadGEM2-ES climate modeling group at the Met Office Hadley
Centre 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 partner-
ship with the Global Organization for Earth System Science Portals.
Edited by: W. Lucht
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