Hydrological Sciences–Journal–des Sciences Hydrologiques, 46(6) December 2001
Special issue: Can Science and Society Avert the World Water Crisis in the 21st Century?

983

Global assessment of current water resources
using total runoff integrating pathways
TAIKAN OKI, YASUSHI AGATA, SHINJIRO KANAE,TAKAO SARUHASHI, DAWEN YANG & KATUMI MUSIAKE
Institute of Industrial Science, University of Tokyo, 4-6-1 Komaba, Meguro-ku,
Tokyo 153-8505, Japane-mail: taikan@iis.u-tokyo.ac.jp

Abstract The anticipated water scarcity in the first half of this century is one of theinternational issues of most concern, which needs to be adequately addressed.
However, even though the issue has an international impact and worldwide
monitoring is critical, there are limited global estimates at present. In this study,
annual water availability has been derived from annual runoff estimated by land
surface models using total runoff integrating pathways (TRIP) with 0.5° by 0.5°
longitude/latitude resolution globally. The global distribution of abstraction was estimated for each sector at the same spatial resolution based on country-based statistics
of municipal water use, industrial water use, and agricultural intakes, using a global
geographical information system with global distribution of population and irrigatedcrop land area. The total population under water stress estimated for 1995
corresponded very well with earlier estimates. However, the number is highly
dependent on how one assumes the volume of water from upstream of a region, which
can be considered as “available” water resources within the region. Therefore it isimportant, even for global scale analysis, to evaluate the regional water quality
deterioration and the real consumption of water resources in the upper part of thestream, as well as the accessibility of water. Further studies should be promoted by an
integrated approach to improve the accuracy of future projections on both the naturaland social aspects of water resources.
Key words water resources assessment; water scarcity; water availability; abstraction; demand
for water

Evaluation des ressources en eau à l’échelle mondiale à l’aide deschemins intégrateurs de l’écoulement total
Résumé La pénurie d’eau à laquelle on s’attend au cours de la première moitié de ce
siècle est un problème international particulièrement inquiétant qui devrait fairel’objet d’une évaluation adéquate. Il n’y a cependant à l’heure actuelle que peu
d’estimations à l’échelle mondiale, en dépit de la dimension internationale duproblème et alors que le niveau de l’observation hydrologique est partout critique.
Dans cette étude, la disponibilité de l’eau à l’échelle annuelle a été déduite del’écoulement annuel estimé par des modèles de surface utilisant les cheminsintégrateurs de l’écoulement total selon une résolution de 0.5° de longitude par 0.5° delatitude. La répartition des prélèvements, selon la même résolution spatiale, a été
estimée sur la base des statistiques par pays des utilisations municipales, industrielles
et agricoles de l’eau grâce à l’utilisation d’un système d’information géographiquemondial de la répartition de la population et des surfaces irriguées. L’importance de lapopulation souffrant d’un déficit en eau estimée pour 1995 s’accorde très bien aux
anciennes estimations mais cette estimation dépend dans une large mesure deshypothèses concernant la fraction de l’eau venant de l’amont qui peut réellement êtreconsidérée comme “disponible”. Cela souligne l’importance d’études régionales de ladétérioration de la qualité des eaux, la consommation réelle et l’accessibilité de l’eau.
Ce dernier facteur devrait être mis en rapport avec la façon dont de nombreux grands
schémas de prélèvements ont été mis en place régionalement. Il serait nécessaired’entreprendre des études complémentaires intégrées pour améliorer la précision desprojections concernant les aspects hydrologiques et sociaux des ressources en eau.

Open for discussion until 1 June 2002

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Taikan Oki et al.
Mots clefs évaluation des ressources en eau; disponibilité de l’eau; prélèvement;
demande en eau

INTRODUCTION
The water resources are seriously under pressure even at present in several regions of
the globe, and it is anticipated that the situation will be worse during the first half of
this century (Cosgrove & Rijsberman, 2000). The estimation of the current level ofwater stress is important for reliable projections of the severity of the coming watercrisis. However, most of the previous global analyses of water scarcity have been
carried out on a country or river basin basis. Among the exceptions are the works by
Takahashi et al. (2000), Alcamo et al. (2000), and Vörösmarty et al. (2000), who
presented water scarcities on a 0.5° × 0.5° grid for the globe. Considering the importance of global water scarcity, it is postulated that future projections should be
evaluated by multiple procedures, models and methods, used by different organizations, since the reliability of the estimates will be supported if similar results are
obtained from different scientific approaches, information and data processing.
For the estimation of the global supply of water, observed or simulated runoff data
are generally used. Shiklomanov (2000a,b) estimated the water availability for 26
regions of the world based on observed river discharge at 2500 stations. Takahashi etal. (2000) estimated the monthly water balance in 0.5° × 0.5° grids using a bucket
model (Manabe, 1969), with the potential evapotranspiration derived from the Penman
method using current and future climate projections (temperature, wind speed, andprecipitation) simulated by general circulation models (GCMs) at the Canadian
Climate Centre, the Max Planck Institute, Hamburg, and the Centre for Climate
System Research (CCSR) at the University of Tokyo. Vörösmarty et al. (2000) used asimilar approach, but their water supply estimates are linearly adjusted to observations
where discharge information is available, even though Alcamo et al. (2000) correlated
the calibration factor with (a) the ratio of potential evapotranspiration to the total
precipitation, (b) mean temperature, (c) open freshwater area as a ratio of the total
catchment area, and (d) the groundwater recharge factor. In this study, water balances
estimated by land surface models (LSMs) were used for the global estimates of water
availability. Such models were originally developed to be included in numericalatmospheric models to compute the lower boundary condition of the atmosphericcirculation. The bucket model (Manabe, 1969) is the first generation LSM and isrelatively simple. However, current LSMs consider detailed energy and water balancesat land surfaces and include hydrological, radiative, and even biogeochemical
processes (e.g. Sellers et al., 1996; Dickinson et al., 1998). All the GCMs that provide
future climate projections use some kind of LSM. Since current LSMs can simulatemonthly river runoff considerably well, provided that the precipitation and otherforcing input data for the LSMs are accurate enough (Oki et al., 1999), it is highly
possible that LSMs will be directly used for water resources projections in the futurewhen GCMs will simulate the hydrological cycle with enough accuracy. Therefore it isworthwhile to apply the current global water balances calculated by LSMs for current
water resources assessments. The methodology and results are presented in thefollowing sections.

Global assessment of current water resources using TRIP

985

For the demand side of water resources assessments, two distributed data sets canbe used globally in 0.5° × 0.5° longitude/latitude grid boxes, namely population andirrigated land area. In this study, country-based statistics, such as municipal watersupply and industrial water use, are assumed to be proportional to the population
distribution, and the agricultural intake is assumed to be proportional to the irrigatedland area. A global geographical information system (G-GIS) was developed toconvert country-based statistics into grid-based global distributions. Statistics were
given for various “current” years, and all of the statistics were linearly adjusted to
1995. The target year was selected partly in order to compare the results with previous
global estimates (WMO, 1997; Vörösmarty et al., 2000), but the most important reason
was that the latest information globally available was for 1995.
From these estimates of global annual water supply and demand, the global
distribution of water stress was estimated for every 0.5° × 0.5° grid globally, except for
those grids more than half of which are covered by ice or water. The result iscompared with previous estimates made on a country, river basin, and grid basis in thelast section.
WATER AVAILABILITY ESTIMATED BY TRIP
Simulated runoff values from offline runs by 10 different LSMs are used for
estimating water availability. Under the Global Soil Wetness Project (GSWP; IGPO,
1995), the global water balance was estimated by 11 LSMs using forcing data, such as
precipitation, incoming short-wave and long-wave radiation, wind speed, temperature,humidity and surface pressure, from the International Satellite Land Surface
Climatology Project (ISLSCP) (Meeson et al., 1995). The names of the LSMs,
references, and the groups handling each LSM for the GSWP are listed in Table 1.
Surface runoff values estimated by a bucket model of the Center for Climate System
Research, Univ. of Tokyo (CCSR) (Numaguti et al., 1997) and a SiB model of theJapan Meteorological Agency (JMA) (Sato et al., 1989) were sent to the GSWP Data
Center. Therefore the average values of these two estimates were used for the surface
runoff. As for the total runoff, the average values used were from the estimates of all
the 10 LSMs except for BATS. The target period of the GSWP was from 1987 to
1988, and the surface and total runoff were produced approximately every 10 days in
1° × 1° grid boxes.
To estimate the river discharge, the gridded surface and total runoff data weredivided into 0.5° longitude/latitude resolution and the total runoff integrating pathways(TRIP) network (Oki & Sud, 1998), corresponding to the horizontal resolution, wasused for the river routing calculations. The TRIP network is the global river routing
network similar to those developed by Vörösmarty et al. (1996) and Renssen & Knoop(2000). The outflow direction from each grid box is given in one of eight neighbouring
grid boxes, and the global river channel network is constructed virtually. The flow
direction in which the water will be routed was first determined from a digital
elevation model, and manually corrected in order to match the actual river channel
network. More detailed procedures are described in Okada (2000). The basinboundaries are also delineated from TRIP. Flow routing was improved from a simple
linear scheme (Oki et al., 1999) using a system similar to Arora et al. (1999). The

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Taikan Oki et al.

Table 1 Land surface models (LSMs) used to estimate the global water balance under the Global Soil
Wetness Project (Dirmeyer et al., 1999).
LSM
BATS
SSiB
SiB2
CAPS
SSiB
PLACE
MosaicJMA-SiB
BucketISBA
BASE

Affiliation of simulation group
University of Arizona, USA
Center for Ocean–Land–Atmosphere Studies, USA
Colorado State University, USA
National Center for Environmental Prediction, USA
GSFC* Climate & Radiation Branch, USA
GSFC Mesoscale Modeling Branch, USA
GSFC Hydrology Branch, USA
CCSR†, University of Tokyo, Japan
CCSR, University of Tokyo, Japan
Météo France, France
Macquarie University, Canada

Reference
Dickinson et al. (1986)
Sellers et al. (1986)
Sellers et al. (1996)
Chen et al. (1997)
Sud & Mocko (1999)
Wetzel & Boone (1995)
Koster & Suarez (1992)
Sato et al. (1989)
Manabe (1969)
Noilhan & Mahfouf (1996)
Desborough & Pitman(1998)* GSFC: Goddard Space Flight Center, National Aeronautics and Space Administration.
†
CCSR: Center for Climate System Research.

governing equations are the continuity equation of water mass in the grid box andManning’s equation.
Firstly, groundwater is represented by a simple linear reservoir:dS g
dt= DLSMg − DOUTg(1)where Sg, DLSMg, and DOUTg are groundwater storage, inflow from LSM substituted forthe amount of surface runoff, and the outflow from the groundwater reservoir,
respectively. The outflow is parameterized as:
DOUTg =

1
Sg
τ

(2)

where t is the time constant. Globally t = 30 (days) was assumed in this study.
The continuity equation for water in the river channel is:dS rc= DIN + DOUTg + DLSMs − DOUT
dt

(3)

where Src, DIN, DLSMs, and DOUT are water in the river channel, total inflow fromsurrounding grid boxes, surface runoff calculated by LSM, and outflow from the gridbox, respectively. Assuming the river channel is of width w (and depth d) outflow is
DOUT = hwv, where v is the flow velocity. For a large river channel, Manning’s
equation can be written as:2

1

1
v = h3I 2
n(4)

where n is Manning’s coefficient and I is the slope. In this study, n is assumed to be
0.045 globally, which is within the range of the values for meandering river channels.
The volume of the water in the river channel is:
S rc = w ⋅ h ⋅ l ⋅ rM

(5)

Global assessment of current water resources using TRIP

987

where l is the straight length of the river channel within the grid box calculated
geometrically and rM (= 1.4 globally) is the meandering ratio (Oki & Sud, 1998)
adjusting the river length to be realistic. To obtain h, information on w should begiven. Applying an empirical function to the relationship between the annual meandischarge, QM (m3 s-1) and river width, w (m) (Leopold, 1996):
w = (100.9 ⋅ QM ) 0.4856(6)

was obtained empirically. The tentative annual mean discharge, QM was calculatedwith effective velocity, ve = 0.5 (m s-1) globally, and w at each grid box wasdetermined by equation (6), setting the minimum value of w at 10 m. From thesecalculations, the maximum river width estimated was nearly 3 km. The slope, I wasdetermined from a digital elevation model by setting the minimum slope to 10-5.
Figure 1 illustrates the annual total river discharge in each 0.5° × 0.5° grid. The
distribution generally corresponds to annual precipitation and runoff patterns. However,
due to the effect of accumulation of water as it moves through the river channel network,
higher discharge values occur in the downstream reaches of large rivers.
To examine the reliability of the results, the annual runoff, before being routed
through the river network, was compared with other estimates (see Table 2). Detailed
comparisons with observed discharge can be found in a previous study (Oki et al.,1999) which shows that the accuracy of the discharge estimates depends on theaccuracy of forcing data, which can be inferred from the density of raingauges. Oki etal. (1999) also point out that most of the LSMs tend to underestimate the runoff inhigher latitudes. According to Table 2, estimates tend to be smaller compared to theprevious estimates by approximately 20%. Some part of this result should have comefrom the insufficiency of the forcing data and shortcomings of the physics consideredin LSMs. However, as Oki et al. (1995) demonstrated, regions where runoff data are
available have higher annual runoff than those areas without discharge observation,and the global runoff estimated by extrapolating from river discharge observations
may overestimate reality. Therefore, the uncorrected estimates of runoff are used inthis study, and the assessment results may have a severe bias due to the underestimatederror of the runoff.
From the runoff, R, estimated by the LSMs, and river discharge, D, from therouting scheme, the water availability, Q for each grid can be calculated as:

Q = R + α∑ Dup(7)

where Dup is river discharge from grid boxes upstream.
Table 2 Continental runoff (km3 year-1).
RegionAfricaAsia
EuropeOceania
North America
South America
Total

WMO (1997)
4 050
13 510
2 900
2 404
7 890
12 03042 784

Vörösmarty et al. (2000)
4 520
13 700
2 770
714
5 890
11 70039 394This study
3 616
9 385
2 191
1 680
3 824
8 789
29 485

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Taikan Oki et al.

The term α is the ratio of water from outside of the region (grid box) that
contributes to water resources within the grid box. When α = 1.0, all the water
generated upstream can be used in the downstream reach, and when α = 0.0, only therunoff generated within the region (grid box, country, or river basin) can be abstracted.
The value of α should be influenced by at least three factors: the water quality
deterioration upstream, the real consumption of water resources in the upstream reach,and the accessibility of water. The last factor is closely related to how many largescale water schemes are implemented in the region.
The same concept can be applied for the water availability estimation for
countries. In this case, ΣDup corresponds to the transboundary water moving throughthe natural river systems. Transboundary water from other river basins is notconsidered here.
The values in Table 2 correspond to the cases when α = 0.0, and discharge in
Fig. 1 at each grid box corresponds to α = 1.0. The sensitivity of how a affects water
stress assessments is examined in the last section below.
WATER DEMAND ESTIMATED USING G-GIS
Statistics on water abstractions (demand side) for each country were obtained from the
CD-ROM of the World Resources Institute (WRI , 1998). The data set is a synthesis ofglobal data sets from various data sources as can be seen in Table 3. The most serious
issue in the data is that only the statistics of water abstracted from rivers and aquifers
are available globally, and no information on the actual consumption of water isavailable.
As can be seen, the years of the statistics of total/sectoral abstraction were not thesame and some adjustment was required to standardize the year. In this study, linear
trends between 1970 and 1995 are assumed for each continent and estimated from
Shiklomanov (2000b). Due to the limitations of the available information, it was also
assumed that the share of abstraction water did not change during the period. Theinterannual variability of abstractions, including the high value in 1995 when the
climate anomaly was large, was eliminated by fitting a linear trend.
In order to distribute the annual total of abstractions by country statistics, a global
GIS-based approach was taken. The gridded (raster) data are summarized in Table 4.
Population data were taken from the Center for International Earth Science
Table 3 Country statistics used for estimating annual water abstractions (from WRI, 1998).
Data
Unit
Total abstractions
km3
Sectoral abstraction%
Desalinated water
106 m3Population
103
Irrigated area
103 haGDP per capita
US$ in 1995
* WRI: World Resources Institute;
FAO: Food and Agriculture Organization;
WB: World Bank.

Year
1970–1995
1970–1995 or 1987
1990
1950–1995
1961–1994
1970–1995

Source*Various
WRI
FAO
UN
FAO
WB

Global assessment of current water resources using TRIP

989

Table 4 Information used to estimate the global distribution of annual abstractions.
Data
Country boundary
Population (persons per grid)
Irrigated area (km2 per grid)

Resolution (min)
2.5
30

Year
1992
1990 and 1995
1995

SourceESRI
CIESIN
Kassel Univ.

Information Network (CIESIN, Columbia University, USA), the irrigated area from
Kassel University, Germany (Döll & Siebert, 1999), and the vector country boundaryfrom the Environmental Systems Research Institute (ESRI, USA). Cropland
distribution was taken from data of the Center for Sustainability and Global
Environment (SAGE, University of Wisconsin-Madison, USA) for use instead of the
distribution of irrigated land; however, it was judged that cropland distribution is not agood alternative for the spatial distribution pattern for irrigation, since it includes
rainwater harvested areas.
First, templates for each country were generated for 0.5° × 0.5° grid boxes using a
GIS, and small modifications were applied to fit the situation in 1995, since somenational boundaries had changed since the issuing of the data by ESRI. The country
area sizes were calculated on the 0.5° × 0.5°grid considering the ellipsoid nature of the
Earth (Oki & Sud, 1998) and compared with national statistics. The mean of the error
was 5% and the standard deviation (SD) was 32%. This error is mainly influenced by
the large error for small countries, such as Sao Tome and Principe (960 km2) andBahrain (690 km2), as they are each represented by a single grid box, which isapproximately 2500 km2. The error statistics for countries represented by more than
one grid box are 1.5% (mean) and 8.3% (SD).
The population distribution given on a 2.5′ global grid was transferred to a 0.5° × 0.5°
grid. However, it was found that the total population after the conversion was less thanreality by nearly 300 million people. This happened because each 0.5° × 0.5° grid box
was considered to be ocean if more than half of the grid box was ocean. However there
were some 2.5′ × 2.5′ grid boxes with inhabitants. This problem was solved by counting
the population in “sea” grid boxes in the nearest “land” grid boxes. Consequently the
result compared well with national statistics with a 1% mean error and 14% SD.
Irrigated area data were provided by Döll & Siebert (1999). The spatial resolution
and the target year correspond to those of the current study. Even though the data set
was estimated based on Food and Agriculture Organization (FAO) statistics, theaccumulated irrigated areas for each country were found to be less than those of FAO
with a mean bias error of 12%. Most of the error occurs in African countries. Further
examination of these data will be necessary, particularly for the assessment of future
abstractions.
Based on these global distributions of population and irrigated area, national-levelstatistics on municipal and industrial water use per capita, and agricultural water use
per irrigated area were used to estimate global distributions of abstractions.
The total annual abstraction (106 m3 year-1 per 0.5° × 0.5°) is illustrated in Fig. 2. It
is concentrated in urban areas in industrialized countries where the population density
is high. The principal irrigated area corresponds to the densely populated areas in
China and India, and the estimated abstractions in those countries are as large as thosein the United States and European countries.

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Taikan Oki et al.

Fig. 1 TRIP estimated annual total river discharge (106 m3 year-1) in each 0.5° × 0.5°
grid. The average of estimates for 1987 and 1988 are presented and considered as theavailable water resources for 1995.

Fig. 2 G-GIS estimated global distribution of annual abstraction in 1995.

Fig. 3 The global distribution of the ratio of abstraction to availability, (W – S)/Q, in
1995.

Global assessment of current water resources using TRIP

991

GLOBAL WATER RESOURCES ASSESMENT
For the assessment of water scarcity, an index was adopted based on the ratio of theannual abstraction, W, to the annual available water, Q, and used in the studies of
WMO (1997) and Vörösmarty et al. (2000). Following Heap et al. (1998), the desalinated water resource, S, was subtracted from W, and the water scarcity index, Rws(Falkenmark et al., 1989) was derived:
Rws =

W −S
Q

(8)

where Q and W are taken from the result described in the sections above, and illustrated in Figs 1 and 2, respectively. Desalinated water, S, is a minor resource on the
global scale, but it is important in some regions. For example, S = 385 × 106 m3 year-1
in the United Arab Emirates, where the annual abstraction is 2.11 km3 year-1 and S
represents 18% (WRI, 1998). The global distribution of estimated Rws is shown in
Fig. 3. The severity of water scarcity is ranked as:
Rws < 0.1
0.1 = Rws < 0.2
0.2 = Rws < 0.4
0.4 = Rws

no water stress
low water stressmoderate water stresshigh water stress

Following these criteria, it is evident from Fig. 3 that water scarcity is severe in the
Yellow River, Indus, Ganges, and Amu-Darya basins, and in the mid-west of the USA.
In these high stress regions, the share of agricultural water is generally high.
The estimated water scarcity was compared with those from previous studies(WMO, 1997; Vörösmarty et al., 2000) (Table 5). Even though the annual runoff
estimated by LSMs was lower than in previous studies (see Table 2), the current
assessment corresponds well with previous results (Vörösmarty et al., 2000) for gridbased results shown in Table 5. For country-based assessments, previous studies
shown in Table 5 assumed that all the runoff generated in the upstream can be used in
the down stream and these estimates should be compared with the case where α = 1.0
derived by TRIP. The discrepancy seems large; however, as the withdrawal toavailability ratio Rws for China is 0.26, it was classified in this study in the category of
0.2 = Rws < 0.4. China has a population of more than 1.2 billion, and the estimates inthis study are not robust enough for both the supply side and demand side. Therefore,
China could be classified as Rws < 0.2 with a slight change in the source of the dataset
and the way of extrapolation of national statistics. In that case (α = 1.0* in Table 5),
the estimated results correspond fairly well with previous estimates with a slightoverestimate of the stressed population due to the underestimate of the annual runoff.
This suggests the need for further refinements of the assessments, while, at the sametime, the country-based assessments with discrete classification of water stress could
lead to quite different results. It should be noted again that the grid-based assessment
results are similar to those by UNH (Vörösmarty et al., 2000) and TRIP (current
study).
Moreover, it is noticeable that the estimate is highly dependent on the a value, as
shown in Fig. 4, assessed by the grid-based method: the number of people affected bywater stress below the water withdrawal to availability ratio Rws clearly depends on the992

Taikan Oki et al.

Table 5 Comparisons of the population (108 capita) under levels of water scarcity on a grid, country,
and river basin basis.
Rws

Country based:

Grid based:

TRIP
UNH UN
TRIP UNH
0.0
0.5
1.0
1.0*
1.0
1.0
1.0
1.0
α
Rws < 0.1
15
17
18
18
20
17
28
32
0.1 = Rws < 0.2
2
3
3
15
17
21
6
4
0.2 = Rws < 0.4
17
27
27
15
15
14
6
4
0.4 = Rws
22
9
8
8
5
5
17
18
UN: UN system organizations (WMO, 1997);
UNH: University of New Hampshire (Vörösmarty et al., 2000);
TRIP: this study.
Rws: water scarcity index; a: availability of water resources from outside of the region.
α = 1.0* (TRIP): in this case China (Rws = 0.26) has been classified as 0.1 = Rws < 0.2.

River basin
based:
TRIP
0.0
12
5
12
27

8

Accumulated population (10 persons)availability of water from outside of the region. The maximum Rws was more than 20;
however, the range of the Figure is only 0 = Rws = 2.0. As suggested in the previous
section, the value of a is decreased by pollution and the actual consumption of water
resources upstream. In contrast, development of the infrastructure may increase thevalue of α; for example, the construction of a large reservoir may increase theavailable water if the reservoir can hold the flood water which would otherwise be lostto the ocean. Figure 4 implies that the availability of transboundary water has a great
influence on the global assessment of water scarcity, and more detailed studies should
be encouraged on this issue.
As pointed out by Vörösmarty et al. (2000), estimates of the population under highwater stress, and also the population under no water stress, are much higher using a

Withdrawal to availability ratio, Rws

Fig. 4 Effect of α on the grid-based water stress assessment.

Global assessment of current water resources using TRIP

993

0.5° grid, because averaging on a country basis eliminates the differences in abstractions and supply which exist spatially. Consequently Rws will be underestimated. The
evaluation of water scarcity in each river basin shows the largest population under
severe water stress because α is assumed to be zero in this case, and no water fromupstream is considered to be available in the downstream area.
For a more realistic estimate of the assessment, abstractions, as well as the return
flow and the recycling of water, should be considered in the simulation. The recycling
ratios of water for agriculture, industry and municipal use are said to be 25, 86 and60%, respectively. Considering the share of annual abstractions for these uses, whichare 69, 23 and 8%, respectively (WRI, 1998), the mean recycling ratio could be
estimated as 42% for the first order approximation. In order to assess future global
water resources more realistically, a new approach should be attempted to route thenatural runoff generated in each grid with estimates of abstractions and return flows.
SUMMARY AND FUTURE RESEARCH NEEDS
Water availability and abstractions are estimated globally in 0.5° × 0.5° grids using an
LSM, and the global distribution of those areas under water stress are discussed. A
summary for each continent is shown in Table 6. The delineation of the continents
follows that of Baumgartner & Reichel (1975).
Although the amount of available water in Asia is large, the population and the useof water, particularly agricultural water demand, are very high, and therefore the water
stress ratio is highest among the continents. The estimate of water availability is basedon the offline simulation by land surface models, and seems to be underestimated byapproximately 20% compared to previous estimates (Table 2). Therefore the assessment could exaggerate water scarcity compared to reality. Efforts to improve accuracy
should be carried out, including analyses carried out at a higher resolution.
It should be noted that the available global-scale information, particularly data on
water demand, is limited, and it is still possible that the results presented here may
have significant errors, especially if examined in detail at the local scale. Therefore,
the interpretation of the estimates presented requires care because of the representativeness of some of the input data and the assumptions made.
In order to improve the reliability of these estimates, critical examination of global
information on water demand is needed, correlated with crop calendars, real consumpTable 6 Summary of water resources in each continent (estimated for 1995).
Continent

Population
Q
D
I
A
W
Rws
(103)
(km3)
(km3)
(km3)
(km3)
(km3)
(%)
Africa690 5503616.5
13.9
9.1
136.1
159.1
4.4
Asia
3 469 180
9384.9142.4
203.8
1697.42043.7
21.8
Europe688 143
2190.9
59.7
233.4
139.2
432.3
19.7
Oceania
28 1641679.6
8.9
0.4
6.0
15.4
0.9
North America
454 926
3824.4
80.5
263.7
315.8
660.0
17.3
South America
319 2148789.3
22.2
13.1
102.1
137.4
1.6
Q: annual water availability; D: annual domestic abstraction; I: annual industrial abstraction; A: annual
agricultural abstraction; W: total annual abstraction (= D + I + A); Rws: ratio of abstraction toavailability.

994

Taikan Oki et al.

tion of water resources (particularly for agriculture), and reservoir operation. Modeldevelopment is also needed to improve the information handling in the calculation ofwater availability. The seasonal mismatch of water demand and water availability
should be considered in future water resources assessments, even on the global scale.
It is pointed out that the ratio α has significant influence on the assessment. It issuggested that the availability of transboundary water has a great influence on the
global assessment of water scarcity. Global assessments using a are necessary forrobust estimates of water scarcity, and the influence of pollution can be included in a,since polluted water should be excluded from “available” water resources in theassessments. For international rivers, where a legal agreement or a treaty between theriparian upstream, downstream or neighbouring countries may be in place to regulate
abstractions, legally available water may differ from physically available water, andthe available water amount, Q, may not simply be expressed in the form ofequation (7). Further studies focusing on the question “What are the available water
resources?” should be encouraged in this context.
The framework employed in this study complements other global water resources
assessments and those which may be undertaken under the influence of global
warming and population increase (Saruhashi, 2001). An integrated approach to
improve the accuracy of future projections, in terms of both the natural and social
aspects of the water resources, should be promoted in support of developing actionplans to mitigate the water crisis anticipated in the early stages of the 21st century.
Acknowledgments The authors wish to thank the World Resources Institute, theGlobal Runoff Data Center, the US Geological Survey, the Food and Agriculture
Organization, the Consortium for International Earth Science Information Networks,
and Dr Petra Döll, for providing their valuable data sets. We would also like toacknowledge all the participants of the GSWP, particularly Dr Teruyuki Nishimura for
his great efforts managing the data centre. Special thanks go to Prof. K. Takeuchi andtwo anonymous reviewers for their helpful comments. This study was supported by theJapan Society for the Promotion of Science (JSPS) “Research for the Future Program”,
Grant-in-Aid (no. 12490007) for scientific research (B) from JSPS, and Grant-in-Aid(no. 12490007) for scientific research from the Japanese Ministry of Education,
Culture, Sports, Science, and Technology.
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