Multimodel Estimate of the Global Terrestrial Water Balance: Setup and First Results

TLDR: The Water Model Intercomparison Project (WaterMIP) as discussed by the authors was the first attempt to compare simulation results of these different classes of models in a consistent way, and the results showed that differences between models are a major source of uncertainty.

Abstract: Six land surface models and five global hydrological models participate in a model intercomparison project [Water Model Intercomparison Project (WaterMIP)], which for the first time compares simulation results of these different classes of models in a consistent way. In this paper, the simulation setup is described and aspects of the multimodel global terrestrial water balance are presented. All models were run at 0.58 spatial resolution for the global land areas for a 15-yr period (1985–99) using a newly developed global meteorological dataset. Simulated global terrestrial evapotranspiration, excluding Greenland and Antarctica, ranges from 415 to 586 mm yr 21 (from 60 000 to 85 000 km 3 yr 21 ), and simulated runoff ranges from 290 to 457 mm yr 21 (from 42 000 to 66 000 km 3 yr 21 ). Both the mean and median runoff fractions for the land surface models are lower than those of the global hydrological models, although the range is wider. Significant simulation differences between land surface and global hydrological models are found to be caused by the snow scheme employed. The physically based energy balance approach used by land surface models generally results in lower snow water equivalent values than the conceptual degreeday approach used by global hydrological models. Some differences in simulated runoff and evapotranspiration are explained by model parameterizations, although the processes included and parameterizations used are not distinct to either land surface models or global hydrological models. The results show that differences between models are a major source of uncertainty. Climate change impact studies thus need to use not only multiple climate models but also some other measure of uncertainty (e.g., multiple impact

Figures

FIG. 6. River basin mean model predicted runoff and ET values (mm yr21). LSMs are represented with solid orange symbols, GHMs are represented with open blue symbols, and the same symbols as in Fig. 2 are used. The dashed gray lines show long-term multimodel mean annual runoff, ET, and precipitation, and the dotted lines show the range in multimodel mean annual runoff. Observed mean annual runoff (vertical black line; mm yr21) for the 15-yr simulation period is included for all basins except the Congo and Murray–Darling River basins, where the long-term average is used because there were no or insufficient data available for the period in question. The shaded area indicates the range in observed runoff for the period in question. For the Amazon, Congo, Lena, and Brahmaputra basins, the maximum or minimum observed annual runoff falls outside the runoff range included on the x axis. The box plots represent runoff fractions for all models combined and for the LSMs and GHMs separately, and they illustrate the smallest simulated runoff fractions, lower quartiles, medians, upper quartiles, and the largest simulated runoff fractions. Outliers are represented by circles. All terms are calculated for the basin area upstream of the discharge gauge.

FIG. 9. Box plots illustrating the smallest simulated runoff fractions, lower quartiles, medians, upper quartiles, and the largest simulated runoff fractions for all cells within the five main Köppen climate zones. (a)–(e) All participating models are included: that is, the same grouping as in Table 1 is used. (f)–(j) The same subgroups as in Fig. 7 are used: that is, GWAVA, LPJmL, and MacPDM are included in the GHM subgroup (GHMsub) and HTESSEL, JULES, and MATSIRO are included in the LSM subgroup (LSMsub).

FIG. 1. (a) Mean annual precipitation; multimodel mean annual (b) ET and (c) runoff fraction; and CV of the model means of (d) snowfall, (e) ET, and (f) runoff.

FIG. 4. Location of river basins and discharge gauges.

FIG. 5. Simulated mean annual PET in the Niger, Oranje, Murray– Darling, Amazon, and Congo River basins for a subset of GHMs. For calculating PET, GWAVA, and MacPDM, use the Penman– Monteith equation (open symbols); for calculating LPJmL and WaterGAP, use the Priestley–Taylor equation (solid symbols).

FIG. 8. Mean monthly river basin results. To highlight main simulation differences, only selected model results are shown in each panel. Model results mentioned in the text are represented by colored lines; the others are represented by gray lines. Shaded area shows the range of the model results. (a) Snowfall, (b) SWE, and (c) snow sublimation and evaporation values in the Lena River basin. (d) Canopy evaporation, (e) ET, and (f) runoff in the Amazon River basin. (g) ET, (h) runoff, and (i) net radiation in the Brahmaputra River basin. Water fluxes are in mm day21, storage terms are in mm, and radiation values are in W m22.

Full text

Multimodel estimate of the global
terrestrial water balance: setup and first
results
Article
Published Version
Haddeland, I., Clark, D. B., Franssen, W., Ludwig, F., Voß, F.,
Arnell, N. W., Bertrand, N., Best, M., Folwell, S., Kabat, P.,
Koirala, S., Oki, T., Polcher, J., Stacke, T., Viterbo, P.,
Weedon, G. P., Yehm, P., Gerten, D., Gomes, S., Gosling, S.
N., Hagemann, S., Hanasaki, N., Harding, R. and Heinke, J.
(2011) Multimodel estimate of the global terrestrial water
balance: setup and first results. Journal of Hydrometeorology,
12 (5). pp. 869-884. ISSN 1525-7541 doi:
https://doi.org/10.1175/2011JHM1324.1 Available at
https://centaur.reading.ac.uk/24011/
It is advisable to refer to the publisher’s version if you intend to cite from the
work. See Guidance on citing
.
To link to this article DOI: http://dx.doi.org/10.1175/2011JHM1324.1
Publisher: American Meteorological Society
Publisher statement: © Copyright 2011 American Meteorological Society (AMS).
Permission to use figures, tables, and brief excerpts from this work in scientific
and educational works is hereby granted provided that the source is
acknowledged. Any use of material in this work that is determined to be “fair use”
under Section 107 of the U.S. Copyright Act or that satisfies the conditions
specified in Section 108 of the U.S. Copyright Act (17USC §108, as revised by

P.L. 94-553) does not require the AMS’s permission. Republication, systematic
reproduction, posting in electronic form, such as on a web site or in a searchable
database, or other uses of this material, except as exempted by the above
statement, requires written permission or a license from the AMS. Additional
details are provided in the AMS Copyright Policy, available on the AMS Web site
located at (http://www.ametsoc.org/) or from the AMS at 617-227-2425 or
copyright@ametsoc.org.
All outputs in CentAUR are protected by Intellectual Property Rights law,
including copyright law. Copyright and IPR is retained by the creators or other
copyright holders. Terms and conditions for use of this material are defined in
the End User Agreement
.
www.reading.ac.uk/centaur
CentAUR
Central Archive at the University of Reading
Reading’s research outputs online

Multimodel Estimate of the Global Terrestrial Water Balance:
Setup and First Results
INGJERD HADDELAND,
a
DOUGLAS B. CLARK,
b
WIETSE FRANSSEN,
c
FULCO LUDWIG,
c
FRANK VOß,
d
NIGEL W. ARNELL,
e
NATHALIE BERTRAND,
f
MARTIN BEST,
g
SONJA FOLWELL,
b
DIETER GERTEN,
h
SANDRA GOMES,
i
SIMON N. GOSLING,
j
STEFAN HAGEMANN,
k
NAOTA HANASAKI,
l
RICHARD HARDING,
b
JENS HEINKE,
h
PAVEL KABAT,
c
SUJAN KOIRALA,
m
TAIKAN OKI,
m
JAN POLCHER,
f
TOBIAS STACKE,
k
PEDRO VITERBO,
i
GRAHAM P. WEEDON,
g
and PAT YEH
m
a
Norwegian Water Resources and Energy Directorate, Oslo, Norway, and Wageningen University and
Research Centre, Wageningen, Netherlands
b
Centre for Ecology and Hydrology, Wallingford, United Kingdom
c
Wageningen University and Research Centre, Wageningen, Netherlands
d
Center for Environmental Systems Research, University of Kassel, Kassel, Germany
e
Walker Institute for Climate System Research, University of Reading, Reading, United Kingdom
f
Laboratorie de Meteorologie Dynamique, Paris, France
g
Met Office Hadley Centre, Joint Centre for Hydrometeorological Research, Wallingford, United Kingdom
h
Potsdam Institute for Climate Research, Potsdam, Germany
i
Centro de Geofisica da Universidade de Lisboa, Lisbon, Portugal
j
School of Geography, University of Nottingham, Nottingham, United Kingdom
k
Max Planck Institute for Meteorology, Hamburg, Germany
l
National Institute for Environmental Studies, Tsukuba, Japan
m
University of Tokyo, Tokyo, Japan
(Manuscript received 4 June 2010, in final form 4 February 2011)
ABSTRACT
Six land surface models and five global hydrological models participate in a model intercomparison
project [Water Model Intercomparison Project (WaterMIP)], which for the first time compares simulation
results of these different classes of models in a consistent way. In this paper, the simulation setup is de-
scribed and aspects of the multimodel global terrestrial water balance are presented. All models were run
at 0.58 spatial resolution for the global land areas for a 15-yr period (1985–99) using a newly developed
global meteorological dataset. Simulated global terrestrial evapotranspiration, excluding Greenland and
Antarctica, ranges from 415 to 586 mm yr
21
(from 60 000 to 85 000 km
3
yr
21
), and simulated runoff
ranges from 290 to 457 mm yr
21
(from 42 000 to 66 000 km
3
yr
21
). Both the mean and median runoff
fractions for the land surface models are lower than those of the global hydrological models, although the
range is wider. Significant simulation differences between land surface and global hydrological models are
found to be caused by the snow scheme employed. The physically based energy balance approach used by
land surface models generally results in lower snow water equivalent values than the conceptual degree-
day approach used by global hydrological models. Some differences in simulated runoff and evapotrans-
piration are explained by model parameterizations, although the processes included and parameterizations
used are not distinct to either land surface models or global hydrological models. The results show that
differences between models are a major source of uncertainty. Climate change impact studies thus need to
use not only multiple climate models but also some other measure of uncertainty (e.g., multiple impact
models).
Corresponding author address: Ingjerd Haddeland, Norwegian Water Resources and Energy Directorate, P.O. Box 5091 Maj., 0301
Oslo, Norway.
E-mail: ingjerd.haddeland@nve.no
O
CTOBER 2011 HADDELAND ET AL. 869
DOI: 10.1175/2011JHM1324.1
Ó 2011 American Meteorological Society

1. Introduction
The global water balance has been the subject of
modeling studies for decades, both from a climate per-
spective where the main interest is the influence of the
water balance on surface heat fluxes and from a hydro-
logical perspective focusing on water availability and
use. However, there are still many uncertainties in our
understanding of the current water cycle, and to date the
results of land surface models (LSMs) and global hy-
drology models (GHMs) have not been compared in
a consistent way. LSMs, which can be coupled to atmo-
spheric models, tend to describe the vertical exchanges of
heat, water, and sometimes carbon, in considerable de-
tail. In contrast, GHMs are traditionally more focused on
water resources and lateral transfer of water.
There have been several previous model intercom-
parisons: for example, Spatial Variability of Land Surface
Processes (SLAPS) (Polcher et al. 1996), the Project to
Intercompare Land surface Parameterization Schemes
(PILPS) (Henderson-Sellers et al. 1995; Pitman and
Henderson-Sellers 1998), and the Global Soil Wetness
Project (GSWP) (Dirmeyer et al. 1999, 2006). The focus
in these projects has been on LSMs and the simulations
of surface water and energy balances. Results on water
availability and stress from different GHMs have ap-
peared in the scientific literature (e.g., Alcamo et al. 2003;
Arnell 2004), as have results on anthropogenic water
uses at the global scale (e.g., Do
¨
ll and Siebert 2002;
Hanasaki et al. 2008b; Rost et al. 2008). However, com-
parison of these numbers, their uncertainties, and the
causes thereof has been limited. The GHM community
has recently started the process of systematically com-
piling and comparing results through the GWSP and the
Green Blue Water Initiative (Voß et al. 2008; Hoff et al.
2010).
The Water and Global Change (WATCH) project,
funded under the European Union (EU) Sixth Frame-
work Programme (FP6), brings together the hydrol-
ogical, water resources, and climate communities to
analyze, quantify, and predict the components of the
current and future global water cycles and related water
resources states. An important part of WATCH is a
model intercomparison project in which both LSMs and
GHMs participate. WATCH and GWSP have recently
combined their model intercomparison efforts in a joint
project called the Water Model Intercomparison Proj-
ect (WaterMIP). WaterMIP includes both LSMs and
GHMs, and many of the participating models include
the possibility of taking into account anthropogenic
impacts such as water withdrawals and dams. Hence,
WaterMIP provides an opportunity to compare results
of LSMs and GHMs, focusing on differences between
the two model strategies, while additionally investi-
gating the effects of anthropogenic impacts on the global
terrestrial water balance. Estimates of water availability
and stress, as well as the uncertainties thereof, will also
be compared for both current and future conditions.
Using a range of model simulations, the aim is to im-
prove our understanding of current and future water
availability and water stress at the global scale, with an
emphasis on the available water resources of major river
systems at the subannual time scale. Water demands
involve strong seasonal variations; hence, both annual
water volumes and seasonal timing are important factors.
Through integrated model intercomparison and evalua-
tion, participating models will improve the parameteri-
zation of human interactions with the global terrestrial
water cycle. In related activities within WATCH, global
consumptive water use in different sectors—not only for
irrigation but also for domestic, manufacturing, and
livestock farming purposes—will be considered.
This paper is the first in a series presenting the results
of WaterMIP. It gives an overview of the participating
models, describes the experimental setup, and discusses
the results of naturalized model simulations (i.e., with-
out taking water management like reservoirs and water
withdrawals into account) for historic climate. It also
identifies reasons for some of the differences between
model results. Understanding how the models perform
differently for naturalized conditions and current cli-
mate provides important information with which to
understand why some models might respond differently
in future runs using climate projections. The models
participating in WaterMIP cover a wide range of char-
acteristics, ranging from physically based models run at
subhourly time steps to more conceptual models run at
daily time steps. An objective of WaterMIP is to bring
together researchers from the climate and water re-
sources communities, because there have been few
comparisons of water balance results between these
communities. The main hypothesis tested in this paper is
whether there is a consistent difference in simulations of
the global terrestrial water cycle between LSMs and
GHMs. Explaining all the differences is beyond the
scope of this paper. Subsequent papers will present re-
sults of model simulations including human influences
and the impacts of climate change on global water re-
sources.
2. Simulation setup and model descriptions
In this first stage of WaterMIP, we assess the com-
ponents of the contemporary global terrestrial water
balance under naturalized conditions: that is, human
impacts such as storage in man-made reservoirs and
870 JOURNAL OF HYDROMETEOROLOGY VOLUME 12

agricultural water withdrawal are not included in the
model runs. The spatial resolution of the forcing data and
the model simulations is 0.58 in latitude and longitude,
covering the land area defined by the Climate Research
Unit of the University of East Anglia (CRU) global land
mask. The land mask does not include Antarctica. Models
that include lateral routing of streamflow all use the
DDM30 routing network (Do
¨
ll and Lehner 2002), which
was slightly modified to match the CRU land mask. A
total of 11 models participated in this round of WaterMIP
(see Table 1, which includes a description of the models’
main characteristics). The models use their default soil
and vegetation information and no attempt was made to
standardize these parameters.
A key difference between the models is whether they
solve both the water and the energy balances at the land
surface or only the water balance. The models that solve
the energy balance have to be run using a subdaily time
step, whereas participating models run in water bal-
ance mode alone all run with daily time steps. The
models differ in their choice of evapotranspiration (ET)
and runoff schemes (see Table 1) and vary substantially in
complexity. For example, there are differences in the
number of components of evapotranspiration that are
considered: for example, interception evaporation, veg-
etation transpiration, open-water evaporation, and the
level of detail given to vegetation description and pro-
cesses. Other model differences concern the complexity
of the representation of runoff processes, groundwater,
snow, and frozen soil. The snow schemes are based on
either the degree-day approach, which is used by all
models run at daily time step, or an energy balance ap-
proach, which is used by all models run at subdaily time
steps. Detailed information on each participating model
can be found in the references listed in Table 1. Although,
traditionally, LSMs have been developed within the cli-
mate community and GHMs have been developed within
the hydrologic community, there are similarities in par-
ticular areas between individual models from the differ-
ent groups; thus, the grouping shown in Table 1 is a useful
device but is not necessarily definitive. Other classifica-
tions are undoubtedly possible by other aspects of the
TABLE 1. Participating models, including their main characteristics.
Model
name
a
Model
time step
Meteorological
forcing variables
b
Energy
balance ET scheme
c
Runoff scheme
d
Snow scheme Reference(s)
GWAVA Daily P, T, W, Q,LW
net
,
SW, SP
No Penman–
Monteith
Saturation excess/
beta function
Degree-day Meigh et al. 1999
H08 6h R, S, T, W, Q,LW,
SW, SP
Yes Bulk formula Saturation excess/
beta function
Energy
balance
Hanasaki et al. 2008a
HTESSEL 1h R, S, T, W, Q,LW,
SW, SP
Yes Penman–
Monteith
Infiltration excess/
Darcy
Energy
balance
Balsamo et al. 2009
JULES 1h R, S, T, W, Q,LW,
SW, SP
Yes Penman–
Monteith
Infiltration excess/
Darcy
Energy
balance
Cox et al. 1999;
Essery et al. 2003
LPJmL Daily P, T,LW
net
, SW No Priestley–
Taylor
Saturation excess Degree-day Bondeau et al. 2007;
Rost et al. 2008
MacPDM Daily P, T, W, Q,LW
net
, SW No Penman–
Monteith
Saturation excess/
beta function
Degree-day Arnell 1999;
Gosling and
Arnell 2010
MATSIRO 1h R, S, T, W, Q,LW,
SW, SP
Yes Bulk formula Infiltration and
saturation excess/
groundwater
Energy
balance
Takata et al. 2003;
Koirala 2010
MPI-HM Daily P, T No Thornthwaite Saturation excess/
beta function
Degree-day Hagemann and Gates
2003; Hagemann
and Du
¨
menil 1998
Orchidee 15 min R, S, T, W, Q,SW,
LW, SP
Yes Bulk formula Saturation excess Energy
balance
De Rosnay and
Polcher 1998
VIC Daily/3h P, T
max
, T
min
, W,
Q, LW, SW, SP
Snow
season
Penman–Monteith Saturation excess/
beta function
Energy
balance
Liang et al. 1994
WaterGAP Daily P, T,LW
net
, SW No Priestley–Taylor Beta function Degree-day Alcamo et al. 2003
a
Model names written in bold are classified as LSMs in this paper; the other models are classified as GHMs.
b
R 5 rainfall rate; S 5 snowfall rate; P 5 precipitation (rain or snow distinguished in the model); T 5 air temperature; T
max
5 maximum
daily air temperature; T
min
5 minimum daily air temperature; W 5 wind speed; Q 5 specific humidity; LW 5 longwave radiation flux
(downward); LW
net
5 longwave radiation flux (net); SW 5 shortwave radiation flux (downward); and SP 5 surface pressure.
c
Bulk formula: Bulk transfer coefficients are used when calculating the turbulent heat fluxes.
d
Beta function: Runoff is a nonlinear function of soil moisture.
O
CTOBER 2011 HADDELAND ET AL. 871

Frequently asked questions

Q1. What contributions have the authors mentioned in the paper "Multimodel estimate of the global terrestrial water balance: setup and first results" ?

In this paper, the simulation setup is described and aspects of the multimodel global terrestrial water balance are presented. 

Q2. What future works have the authors mentioned in the paper "Multimodel estimate of the global terrestrial water balance: setup and first results" ?

More information about WaterMIP and related modeling activities within WATCH, including information on the protocol and possibilities of obtaining forcing data and modeling results, can be found online ( at http: // www. eu-watch. org/watermip ). 

Q3. How many runoff fractions are simulated in the tropics?

the simulated range in runoff values is nearly 25 000 km3 yr21 (or 45% of the mean simulated runoff), with the results of the LSMs appearing at both the wet and dry ends of the range. 

Q4. What is the spatial resolution of the forcing data and the model simulations?

The spatial resolution of the forcing data and the model simulations is 0.58 in latitude and longitude, covering the land area defined by the Climate Research Unit of the University of East Anglia (CRU) global land mask. 

Q5. What is the main interest of the global water balance?

The global water balance has been the subject of modeling studies for decades, both from a climate perspective where the main interest is the influence of the water balance on surface heat fluxes and from a hydrological perspective focusing on water availability and use. 

Q6. Why were the forcing variables adjusted sequentially?

surface pressure, specific humidity, and downward longwave radiation were adjusted sequentially in that order because they are interdependent via the elevation adjustment. 

Q7. Why is it not appropriate to compare the models with observed discharge in all basins?

1. Because all simulations were for naturalized conditions, meaning that dams and water withdrawals that change the dynamics of the water cycle are not taken into account, it is not appropriate to compare the models with observed discharge at subannual time scales in all basins. 

Q8. How much water is lost to snow sublimation and evaporation in the winter?

In some of the models (H08, HTESSEL, and JULES) about 30 mm of water is lost to snow sublimation and evaporation in the snow accumulation season, and these models correspond to the models simulating the lowest SWE values in March.