HESSHydrology and Earth System SciencesHESSHydrol. Earth Syst. Sci.1607-7938Copernicus GmbHGöttingen, Germany10.5194/hess-19-3829-2015 GlobWat – a global water balance model to assess water use in irrigated agricultureHoogeveenJ.jippe.hoogeveen@fao.orgFaurèsJ.-M.PeiserL.https://orcid.org/0000-0001-8911-380XBurkeJ.van de GiesenN.https://orcid.org/0000-0002-7200-3353Food and Agriculture Organization of the United Nations, Rome, ItalyWorld Bank, Washington, D.C., USADelft University of Technology, Delft, the NetherlandsJ. Hoogeveen (jippe.hoogeveen@fao.org)10September2015199382938441December201420January20159August2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://hess.copernicus.org/articles/19/3829/2015/hess-19-3829-2015.htmlThe full text article is available as a PDF file from https://hess.copernicus.org/articles/19/3829/2015/hess-19-3829-2015.pdf
GlobWat is a freely distributed, global soil water balance model that is used
by the Food and Agriculture Organization (FAO) to assess water use in
irrigated agriculture, the main factor behind scarcity of freshwater in an
increasing number of regions. The model is based on spatially distributed
high-resolution data sets that are consistent at global level and calibrated
against values for internal renewable water resources, as published in
AQUASTAT, the FAO's global information system on water and agriculture.
Validation of the model is done against mean annual river basin outflows.
The water balance is calculated in two steps: first a “vertical” water
balance is calculated that includes evaporation from in situ rainfall
(“green” water) and incremental evaporation from irrigated crops. In a
second stage, a “horizontal” water balance is calculated to determine
discharges from river (sub-)basins, taking into account incremental
evaporation from irrigation, open water and wetlands (“blue” water). The
paper describes the methodology, input and output data, calibration and
validation of the model. The model results are finally compared with other
global water balance models to assess levels of accuracy and validity.
Introduction
Modelling of the world's hydrological cycle is important to assess, among
others, water resources availability and the sustainability of their use, the
impact of climate change, and the influence on global food production (Wood
et al., 2011). With regard to global food production, one of the major
questions on the future of irrigated agriculture is whether there will be
sufficient freshwater to satisfy the growing needs of agricultural and
non-agricultural users. Agriculture accounts for about 70 % of the
freshwater withdrawals in the world (FAO, 2013), while consumptive use of
water in agriculture (water that is evaporated on irrigated fields) accounts
for about 90 % of all of the water that is evaporated as a result of human
intervention. Irrigated agriculture is therefore the main component of water
demand and a driver of scarcity of freshwater in an increasing number of
regions.
AQUASTAT (FAO, 2013), the Food and Agriculture Organization's (FAO's)
information system on water and agriculture, collects its information mainly
from country statistics and grey literature. As part of the AQUASTAT
programme, FAO distributes and contributes to the maintenance and development
of the Global Map of Irrigation Areas (Siebert et al., 2007, 2010, 2013),
which is compatible with AQUASTAT country figures for areas equipped for
irrigation. Assessing the impact of irrigation on water scarcity requires
information about the geographical distribution of water use in irrigation.
The Global Map of Irrigation Areas provides information about the
distribution of land under irrigation, but data collection through AQUASTAT
country surveys has shown that country statistics for agricultural water
withdrawals are not always available, and when they exist, they are often
unreliable. In most cases they are rough estimates based on water use per
unit area of land equipped for irrigation.
Actual water use in annual water balances is changing over time as cropping
patterns and cropping intensities shift and change, sometimes in response to
available water resources. Therefore a consistent global picture of water
withdrawals and consumptive use in irrigated agriculture cannot be obtained
without some reference to modelled estimates. Simulation of water use in
irrigated agriculture at global scale at the highest available resolution
would therefore address a gap in hydrological understanding under
contemporary land and water use patterns.
A number of global models exist that simulate water use in agriculture.
WaterGAP (Döll and Fiedler, 2008; Hunger and Döll, 2008; Alcamo et
al., 2007), WBMplus (Wisser et al., 2010), GEPIC (Liu, 2009; Liu and Yang,
2010), LPJmL (Biemans, 2012; Rost et al., 2008) and PCR-GLOBWB (Wada et al.,
2014) are all global hydrological models that have been used to calculate
water use by irrigated agriculture. Most of these models are sophisticated
hydrological models that are used for detailed water balance analyses in
which consumptive water use in agriculture is only one of the components of
the water balance rather than the main focus of analysis. Almost all of them
are developed at a spatial disaggregation (30 arcmin or coarser) which is
not directly comparable with the heterogeneity and spatial variability of
irrigation as captured in the FAO's high-resolution (5 arcmin) Global Map of
Irrigation Areas or in other global agriculture land use data such as that
available through FAO and IIASA's Global Agro-Ecological Zones portal
(FAO/IIASA, 2012).
To overcome this incompatibility between global hydrological models and the
Global Map of Irrigation Areas, Siebert and Döll (2008, 2010) developed
the Global Crop Water Model (GCWM) to calculate irrigated crop water
requirements on a grid resolution of 5 arcmin by using the MIRCA2000 data
set (Portmann et al., 2008). However, contrary to the earlier mentioned
models, the GCWM does not simulate the influence of incremental crop
evapotranspiration on the hydrological cycle to the extent that model results
can be calibrated or validated on river discharges.
Therefore, in order to be able to estimate current and future water use in
agriculture objectively and the consequent impact on river basin mean annual
flow, a global water balance model, GlobWat, was developed. The defining
feature of the model is that it is based on a set of spatially differentiated
data sets at 5 arcmin resolution that are consistent at global level, and
that model outputs are validated against actual basin outflows.
The model is designed to complement other FAO data sets and models as used
for AQUASTAT (FAO, 2013), Global Agro-Ecological Zones (FAO/IIASA, 2012) and
Global Perspective Studies (FAO, 2006, 2011a).
Methodology
Precipitation provides part of the water crops' need to satisfy their
transpiration requirements. The soil stores part of the precipitation water,
which is later evaporated or transpired by plants. In humid climates, the
water stored by the soil is sufficient to ensure satisfactory growth in
rainfed agriculture. Instead, in climates with extended dry periods,
irrigation is necessary to compensate for the evaporation deficit due to
insufficient precipitation. The purpose of the modelling exercise is to
estimate net irrigation water requirements on irrigated land. In terms of a
hydrological balance, this is incremental evaporation due to the import of
water onto land. Net irrigation water requirements in irrigation are
calculated as the volume of water needed to compensate for the deficit
between potential crop evaporation and effective precipitation over the
growing period of the crop. This requirement varies considerably with
climatic conditions, seasons, crops and soil types. Following
Savenije (2004), the model description uses the term evaporation (E) to
describe evaporative processes originating from soil evaporation,
transpiration and interception. In agro-hydrology these processes are often
referred to as evapotranspiration (ET).
GlobWat has spatially distributed input and output layers consisting of
monthly precipitation, number of wet days per month, coefficient of variation
of precipitation, monthly reference evaporation, maximum soil moisture
storage capacity, maximum groundwater recharge flux, irrigated areas, land
use, and areas of open water and wetlands. All these input layers are based
on freely available spatial data sets with a resolution of 10 arcmin for the
climate data sets and 5 arcmin for all the terrain and land data sets
(details and references of the data sets are given in Table 3).
The global water balance is calculated in two steps. First a one-dimensional
“vertical” water balance is solved (in daily time steps, on a spatial grid
layer with a resolution of 5 arcmin) to calculate rainfed evaporation
(including rainfed evaporation over open water and wetlands) and evaporation
from irrigated areas. In a second step, a “horizontal” water balance for
surface water is calculated (in monthly time steps, on the basis of a spatial
layer with catchments) to correct for the incremental evaporation from open
water and wetlands and to calculate river discharges from (sub-)basins taking
into consideration the net water demand for irrigation. The monthly time step
for the horizontal balance is justified, on the basis of the hydrological lag
between daily rainfall events and outflows generated from runoff and effluent
groundwater discharge, in combination with the size of the river basins
considered. The use of vertical and horizontal water balances helps to
clarify discussions on “green” and “blue” water (Falkenmark and
Rockström, 2004), as well as on water footprints (Hoekstra and Chapagain,
2007), since it can distinguish between evaporation attributable to land
management (evaporation from in situ rainfall) and evaporation attributable
to water management (evaporation resulting from the lateral import of water).
The significance of open water and wetlands as evaporative “sinks” is also
made explicit. A schematic representation of these two calculation steps is
given in Fig. 1.
Schematic representation of modelling steps.
Under long-term, stationary conditions (therefore ignoring changes in
hydrological storage), the model is based on two simple equations. For the
vertical water balance,
P+Eincr-irr=Erain+Eincr-irr+RO+R,
and for the horizontal (surface) water balance,
Qout=Qin+RO+R-Eincr-irr-Eincr-OW-Eincr-wetl,
where P is precipitation (L3 T-1), Erain is rainfed
evaporation (L3 T-1), Qin is inflow
(L3 T-1), RO is (sub-)surface runoff
(L3 T-1), R is groundwater recharge (in step 1)/baseflow (in
step 2) (L3 T-1), Eincr-irr is incremental evaporation
from irrigation (L3 T-1), Eincr-OW is incremental
evaporation over open water (L3 T-1), Eincr-wetl is
incremental evaporation over wetlands (L3 T-1) and
Qout is outflow (L3 T-12), where L is length and T
is time.
The detailed calculations procedure for the respective model components is
explained below.
Soil water balance
The soil water balance model is similar to the Thornwaite and Mather
procedure (Steenhuis and Van der Moolen, 1986) adapted for daily time steps.
The basic soil water balance equation for this model is as follows:
P=Erain+R+RO+ΔS/Δt,
with P= precipitation (mm day-1),
Erain= rainfall-dependent evaporation (mm day-1),
R= groundwater recharge (mm day-1),
RO= (sub-)surface runoff (mm day-1),
ΔS= changes in soil moisture storage (mm) and
Δt= time step (day).
The computation of water balance is carried out on a spatial resolution of
5 arcmin grid cells and in daily time steps taking account of spatial
variations in rainfall, evaporative power of the atmosphere and soil
properties.
Precipitation
Daily precipitation is generated from monthly figures by using a mixed
Bernoulli gamma distribution function (Wilks and Wilby, 1999). First the
number of wet days is randomly distributed over the month by using a
Bernoulli distribution, and then the amount of monthly precipitation is
randomly distributed over the wet days by using a gamma distribution with
parameters derived from the data set with coefficients of variation of
precipitation.
Usually, precipitation generators use Markov chains to generate precipitation
events (Schoof and Pryor, 2008). However, the data sets used do not include
information to parameterise these Markov chains. To take into consideration
that the chance of rainfall after a wet day is higher than the chance of
rainfall after a dry day, the following simple adjustment was made:
Pwet wet=(1+corr)×Pwet mean
and
Pwet dry=Pwet mean×1-Pwet wet/1-Pwet mean,
with Pwet wet is the probability of a wet day after a wet day,
Pwet dry is the probability of a wet day after a dry day, and
Pwet mean is the average probability of a wet day calculated as
Pwet mean=wet days/days of the month.
corr = correction coefficient is calculated as
corr=0.5×(days of the month-wet days)/days of the month.
Applying this adjustment, the chance of a wet day after a wet day is almost
1.5 times as high when the number of wet days approaches 0, while the chance
of a wet day after a wet day approaches the average chance of a wet day if
the number of wet days is high.
The spatial distribution of all climate data is determined by the CRU CL 2.0
data set (New et al., 2002). This data set has been chosen to obtain maximum
consistency between precipitation and reference evaporation, and because it
describes average climatic conditions, which are comparable to the data
available in AQUASTAT.
Rainfall-dependent evaporation
Rainfall-dependent evaporation (Erain) is assumed to be equal to
the maximum evaporation of a land use or vegetation type when evaporation is
not hampered by water shortage. Maximum evaporation is calculated by
multiplying reference evaporation by a crop or land use factor (Allen et al.,
1998). In dry periods, when the available soil moisture is reduced below a
certain level, lack of water reduces evaporation to an extent proportional to
the available soil moisture.
Vegetation types derived from FAO's Global Agricultural Systems Map.
In equations:
Erain(t)=Kc×Eo(t)forSmax≥S(t-1)≥SeavErain(t)=Kc×Eo(t)×S(t-1)/SeavforS(t-1)<SeavSeav=0.5×SmaxSmax=Rtd×SCmax
with t the time step indicator, Erain(t) the rainfed
evaporation on t (mm day-1), Eo(t) the reference
evaporation on t (mm day-1), Kc the crop or land use
factor (–), S(t- 1) the available soil moisture on t- 1 (mm),
Smax the maximum soil moisture storage (mm), Seav the
easily available soil moisture (mm), Rtd the effective root depth
(m), and SCmax the maximum soil moisture storage capacity
(mm m-1).
The crop or land use factor Kc is not constant over the year. It
varies during the growing season, depending on the growing stage. However,
for rainfed conditions, differentiated Kc factors were not
applied, since no distinction was made between the different crops and their
cropping calendars. The Kc factors used were attributed to the
Global Agricultural Systems Map (FAO, 2011b) according to Table 1.
As can be seen from the equations above, the easily available soil moisture
(Seav) is defined as half the maximum soil moisture storage
(Smax). In reality, easily available moisture depends on the type
of plant, its growing stage and its soil type, and it varies from about 40 to
60 % of the maximum soil moisture storage capacity (SCmax)
(Raes et al., 2012). For a global model, 50 % can be considered a
reasonable approximation.
The spatial distribution of the maximum soil moisture storage capacity
(SCmax) was derived from the Harmonised World Soil Database (FAO, 2012).
The effective root depth of vegetation is the part of the root zone from
which the plant extracts the majority of the water it needs, and therefore it
depends both on soil and plant characteristics as well as the amount of water
available. There are different ways to estimate the effective root depth,
i.e. half the maximum root depth (Evans et al., 1996) or the soil depth in
which 90 % of the weight of the roots is found (Allen et al., 1998; FAO,
1978). However, for a global model, such information is not available and
therefore an initial effective rooting depth of 60 cm is assumed. Since the
results of model computation are very sensitive to effective rooting depth
and to soil moisture storage capacity (SCmax), both parameters
were used to calibrate the model.
Groundwater recharge, actual available soil moisture and (sub-)surface runoff
Groundwater recharge is assumed to occur only above a threshold level when
there is enough water available in the soil to percolate downward in the
model. The recharge rate depends on a maximum possible groundwater recharge
flux, which is derived from the Groundwater Resources of the World Map
provided by the World-wide Hydrogeological Mapping and Assessment Programme
(WHYMAP) (BGR and UNESCO, 2008), and is assumed to be proportional to the
available soil moisture. The recharge component is assumed to contribute to
the shallow groundwater circulation that appears as effluent seepage in the
annually renewable water resource account of the respective basins. Given
that all groundwater heads are generated by tectonic uplift and fluvial
erosion, all groundwater flows in the model are assumed to enter the annual
river basin balance and not enter permanent groundwater storage.
In equations:
R(t)=Rmax×S(t-1)-Seav/Smax-SeavforSmax≥S(t-1)≥Seav,R(t)=0forS(t-1)<Seav,
with R(t) the recharge flux on t (mm day-1) and Rmax
the maximum groundwater recharge flux (mm day-1).
The available soil moisture is calculated per day by adding ingoing and
outgoing fluxes to the available soil moisture of the day before. Runoff
occurs when the balance of the ingoing and outgoing fluxes exceeds the
maximum soil moisture storage capacity.
In equations:
B(t)=S(t-1)+P(t)-Erain(t)-RO(t)⋅Δt
If B(t)<Smax, then
S(t)=B(t),RO(t)=0.
If B(t)≥Smax, then
S(t)=Smax,RO(t)=B(t)-Smax/Δt,
with B(t) the balance on t (mm), RO(t) the (sub-)surface
runoff on t (mm day-1) and S(t) the available soil moisture on t
(mm).
Evaporation for crops under irrigation
Evaporation for crops under irrigation is calculated by multiplying reference
evaporation by a crop and growing stage specific factor according to the FAO
Penman–Monteith method (Allen et al., 1998). It is assumed that there is
always enough water available to ensure that crops under irrigation never
suffer water stress.
The evaporation of a crop under irrigation (Ec) is calculated as follows:
Ec(t)=Kc×Eo(t),
with Ec(t) crop evaporation under irrigation on t
(mm day-1), Eo(t) reference evaporation on t
(mm day-1) and Kc the rop or land use factor (–).
Crop coefficients (Kc) have been derived for four different
growing stages: the initial phase (just after sowing), the development phase,
the mid-phase and the late phase (when the crop is ripening to be harvested).
In general, these coefficients are low during the initial phase, after which
they increase during the development phase to high values in the mid-phase,
and again decrease in the late phase. It is assumed that the initial phase,
the development phase and the late phase each take 1 month for each crop,
while the duration of the mid-phase varies according to the type of crop. For
example, the growing season for wheat in Morocco starts in October and ends
in April, as follows: initial phase: October (Kc= 0.4);
development phase: November (Kc= 0.8); mid-phase:
December–March (Kc= 1.15); and late phase: April
(Kc= 0.3).
Evaporation requirements of crops in irrigated agriculture are calculated by
converting data of irrigated area by crop (at the national level) into a
cropping calendar with monthly occupation rates of the land equipped for
irrigation. Cropping calendars have been developed by AQUASTAT
(http://www.fao.org/nr/water/aquastat/water_use agr/index2.stm) for
each of the countries or country groups of the study (except for China, India
and the United States, which were divided into several zones of homogenous
cropping patterns). Table 2 presents the irrigation cropping calendar for
Morocco, derived from AQUASTAT data for the year 2004.
The rate of evaporation coming from the irrigated area per month and per grid
cell is calculated by multiplying the area equipped for irrigation as derived
from the Global Map of Irrigation Areas (Siebert et al., 2007) by cropping
intensity and crop evaporation for each crop:
Ec-total(t)=IA×ΣcCIc×Ec(t),
with Ec-total(t) total evaporation for all crops under irrigation
on t in mm day-1, IA the area equipped for irrigation as
percentage of cell area for the given grid cell, c crop under irrigation,
Σc the sum over the different crops, CIc the
cropping intensity for crop c and Ec(t) the crop evaporation on
t in mm day-1, varying for each crop and each growth stage.
Cropping calendar in irrigation for Morocco for the year 2004.
The difference between the calculated evaporation of the irrigated area,
Ec, and the evaporation provided by rainfall, Erain, is equal to the
incremental evaporation due to irrigation, which equals the net irrigation
water requirement:
Eincr-irr(t)=Ec-total(t)-Erain(t),
with Eincr-irr(t) the incremental evaporation due to irrigation
on t (mm day-1).
Evaporation from open water and swamps
A special correction is applied for grid cells existing of open water or
swamps. In these areas, the actual evaporation depends on heat storage in
lakes and reservoirs, which is related to the depth of the water bodies, and,
in the case of swamps and wetlands, the vegetation. In the model, evaporation
from open water is computed in a simplified manner as follows:
Eow(t)=Kow×Eo(t)
and
B(t)=P(t)-Eow(t)⋅Δt,
with Eow(t) actual evaporation over open water on t
(mm day-1), Kow the open water coefficient (–) and B(t) the
open water balance on t (mm).
If B(t)< 0, then
RO(t)=0,Erain(t)=P(t),Eincr-ow(t)=(-1×B(t))/Δt.
If B(t)≥ 0, then
RO(t)=B(t)/Δt,Erain(t)=Eow(t)/Δt,Eincr-ow(t)=0,
with Eincr-ow(t) the incremental evaporation over open water on
t (mm day-1).
Evaporation over swamps and wetlands is calculated separately, but in the
same way as evaporation over open water. For this study, open water
evaporation and evaporation over swamps and wetlands is assumed to be 10 %
higher than reference evaporation (Kow= 1.1).
The spatial distribution of open water and wetlands was derived from the
global map of lakes and wetlands (Lehner and Döll, 2004).
It was decided to make a distinction between the rainfall-dependent
evaporation over open water and incremental evaporation over open water, to
make it possible to distinguish between evaporation of water that is
available from the “vertical” water balance (the rainfall minus evaporation)
and the water that has to come from outside the spatial domain of
calculation (the incremental evaporation).
River basin discharges
To calculate discharges from river basins and sub-basins, a global map of
river basins was used which was developed in the framework of the FAO's study
“The state of the world's land and water resources for food and agriculture
– managing systems at risk” (FAO, 2011b). For this study, major river
basins and their sub-basins were delineated from the HydroSHEDS database.
Sub-basins were named and assigned a flow direction, indicating the sub-basin
directly downstream of each sub-basin. Aggregated water balances were
calculated on a monthly time step by subtracting all evaporation occurring
over the sub-basin from the sum of the total precipitation over the sub-basin
and the inflow from upstream basins (Eq. 30).
Bsb(t)=Qin(t)+∑P(t)-∑E(t)
with Bsb the water balance of aggregated grid cells in a
sub-basin (m3 month-1), t the time step indicator,
Qin the incoming flow in the sub-basin calculated as the sum of
the outflow from all upstream sub-basins (m3 month-1), ∑P
the precipitation summed over all grid cells in the sub-basin
(m3 month-1) and ∑E the total evaporation summed over all
grid cells in the sub-basin (m3 month-1).
There is a lag between the time water enters a sub-basin and the time it gets
out, and some of the inflow is trapped in storage in the sub-basin. To take
the time lag and storage effect into account, a simple linear reservoir model
(De Zeeuw, 1973) is used to calculate monthly values for river discharges and
the amount of water stored per sub-basin:
Ssb(t)=Ssb(t-1)+Bsb(t)-Qout(t-1)⋅Δt
and
Qout(t)=Ssb(t)×F
with Ssb the river sub-basin storage (m3), Δt the
time step (month), Qout the outflow from the sub-basin
(m3 month-1) and F the response factor
(1/month).
The response factor F in Eq. (32) depends on the size and the
characteristics of the sub-basin. It can be defined as the one over the
retention time of the water in the basin in months. For small, quickly
reacting sub-basins, the monthly outflow can be equal to the monthly inflow
and F will be 1. Large sub-basins or sub-basins with high storage capacity
have high retention times and therefore low values for the response factor F.
For all sub-basins in this study a response factor F of 0.3 was assumed. No
differentiation was made between different carry-over factors since
analysing monthly differences in stream flow was beyond the scope of the study.
Irrigation efficiencies
GlobWat calculates the incremental evaporation over areas under irrigation.
In the case of paddy rice, an additional volume of water is used for
flooding to control weeds. This volume of water can be calculated by
multiplying the area under irrigated paddy rice by a water layer of 20 cm.
The total irrigation requirements can then be calculated as follows:
Irrreq=Eincr-irr(yr)×Acell+0.2×Apaddy(yr)×10,
with Irrreq the total irrigation requirements per year (m3),
Eincr-irr(yr) the incremental evaporation due to irrigation per
year (mm), Acell the area of the grid cell (ha) and
Apaddy(yr) the harvested area under paddy irrigation per year
(ha).
Input data sets.
MapResolutionSourceGlobal maps of monthly precipitation10 minNew et al. (2002)Global maps of wet days per month10 minNew et al. (2002)Global maps of coefficient of variation of10 minNew et al. (2002)precipitation per monthGlobal maps of monthly reference10 minCalculated according to FAO (Allen et al., 1998)evaporationwith input data from New et al. (2002)Maximum soil moisture storage capacity5 minDerived from the Harmonised WorldSoil Database, FAO (2012)Maximum groundwater recharge flux5 minDerived from WHYMAP, BGR andUNESCO (2008)Land use or vegetation type coefficient (Kc)5 minDerived from FAO's GlobalAgricultural Systems Map, FAO (2011b)Global map of irrigation areas5 minSiebert et al. (2007)Global map of lakes and wetlands5 minDerived from Lehner and Döll (2004)Global map of river basins and sub-basinsFAO (2011b)
In order to calculate irrigation efficiencies, the total irrigation water
requirements were compared with the amount of water withdrawn for irrigation
as available in AQUASTAT (http://www.fao.org/nr/water/aquastat/data/query/index.html). Since the
years for which AQUASTAT data on water withdrawals generally do not concur
with the years for which the cropping calendars are derived, the most recent
country values for agricultural water withdrawal were extrapolated towards
the year for which cropping calendars are valid. This was done by using the
item “Total area equipped for irrigation” as available in FAOSTAT
(http://faostat.fao.org/site/377/default.aspx#ancor) in Eq. (34):
Qaww(yr-cc)=Qaww(yr-aww)⋅AEI(yr-cc)/AEI(yr-aww),
with Qaww the agricultural water withdrawal per country
(m3 yr-1), AEI the total area equipped for irrigation per country
(ha), yr-cc the year for which a cropping calendar is available, and yr-aww
the year with the latest available country values for agricultural water
withdrawal.
The average of the years for which cropping calendar data are available is
2004, and consequently the calculated outputs of GlobWat as presented here
are on average valid for that year. Water withdrawal data in AQUASTAT that
were estimated on the basis of earlier model calculations were excluded from
the exercise.
Irrigation efficiencies can be calculated by applying Eq. (35) per country
for those countries for which country data on agricultural water withdrawals
are available. For those countries for which no water withdrawal data are
available, irrigation efficiencies have been estimated based on countries
nearby with similar conditions with regard to climate and economic
development:
Irreff=Irrreq/Qaww,
with Irreff the irrigation efficiency (–), Irrreq
the total irrigation requirements (m3 yr-1) and Qaww
the agricultural water withdrawal (m3 yr-1).
Input and output data sets
The input data sets used are derived from public domain data sets and are
found in Table 3.
The results of the water balance calculations consist of monthly values by
grid cell for generated precipitation, actual evaporation, incremental
evaporation due to irrigated agriculture, surface runoff, groundwater
recharge and water stored as soil moisture. Aggregated annual water balances
can be calculated for any desired spatial domain (e.g. countries or river
basins) and include, apart from the above-mentioned variables, incremental
evaporation over open water and incremental evaporation over wetlands.
Model results, calibration and validation
Water balances have been generated by the model and aggregated for each
country to compare them with AQUASTAT data on Internal Renewable Water
Resources (IRWR) and “Internally produced groundwater”. The internal
renewable water resources of a country are defined as “Long-term average
annual flow of rivers and recharge of aquifers generated from endogenous
precipitation”. It corresponds to the sum of surface runoff and groundwater
recharge as calculated by the model. Internally produced groundwater is
defined as “Long-term annual average groundwater recharge, generated from
precipitation within the boundaries of the country”, which was compared to
the model-generated groundwater recharge (AQUASTAT datadase; FAO, 2013).
Calibration of the model was only undertaken for the “vertical”
water balance (step 1 in Fig. 1). Calibration factors were applied for
hydrologically more or less uniform AQUASTAT regions as described in “The
state of the world's land and water resources for food and agriculture –
Managing systems at risk” (FAO, 2011b). Two layers with calibration factors
were used, one to adjust (sub-)surface runoff fluxes by multiplying the
maximum soil moisture storage capacity, the effective rooting depth and the
number of wet days by a calibration factor (Calsw), and another
one to fine-tune groundwater recharge by multiplying the maximum groundwater
recharge flux by a groundwater calibration factor (Calgw).
Calsw varies from 0.55 to 2 and Calgw from 1 to 2.5.
The results of the calibration are presented in Table 4.
Validation of the model output was accomplished by comparing average
river discharges of the Global River Discharge Database (Center for
Sustainability and the Global Environment, 2014). The stations with discharge
data in the Global River Discharge Database are not always located at the
mouth of the river basin. Therefore the specific discharge, defined as the
total annual discharge divided by the area over which the discharge is
generated, was calculated for the stations situated as close as possible to
the mouth of the river. The specific discharges per station were then
compared with the specific discharge per river basin as derived from the
modelled data. Table 5 shows the result of this validation exercise for
51 river basins with an area greater than 100 000 km2. Figure 2 shows
the same results in a graph.
Graphic representation of validation results in which modelled
specific river discharges are compared to observed specific river
discharges.
The total area weighted average specific discharge as measured over the
above-listed river basins is 332 mm per year. The area weighted average
difference (observed minus simulated) in specific discharges is 2 mm, which
indicates that the model underestimates the total discharge over all river
basins by 0.7 %.
The model results have also been evaluated against the three quantitative
statistical indicators recommended by Moriasi et al. (2007): the above-listed
results have a Nash–Sutcliffe efficiency (NSE) of 0.90 (where 1 would be the
ideal model), a percent bias (PBIAS) of -3.0 % (not taking into account
the area of the basin, the model overestimates, on average per basin, the
discharge by 3.0 %), and a root mean square error–standard deviation
ratio (RSR) of 0.31 (the RSR can vary from the optimal value of 0 – no
residual variation, so perfect simulation – to a large positive value).
According to Moriasi et al. (2007), model simulation can be judged as
satisfactory if NSE > 0.50, PBIAS <±25 %, and RSR < 0.70.
The outputs of the model are global raster maps with a resolution of
5 arcmin containing monthly information on generated precipitation, total
actual evaporation from the soil water balance component, incremental
evaporation from irrigation, incremental evaporation from lakes and wetlands,
(sub-)surface runoff and groundwater recharge. The output maps are available
on FAO's AQUAMAPS website: http://www.fao.org/nr/water/aquamaps/.
As an example, Fig. 3 shows total yearly actual evaporation (the sum of
actual evaporation from the soil water balances and incremental evaporation
from irrigated areas, lakes and wetlands).
Figure 4 shows the calculated accumulated outflow per sub-basin. Since the
HydroSHEDS database does not provide data above 60∘ northern latitude,
and due to the fact that in these areas population densities are generally
low, the mapped sub-basins are of a lower spatial resolution, which can be
seen in Greenland and the northern-flowing river basins in Siberia (e.g. Ob,
Lena, Yenissey).
Apart from maps, the model also calculates aggregated results per country,
per major basin and per sub-basin. As an example, the total global water
balance as calculated by GlobWat is presented in Table 6.
In Table 7, incremental evaporation due to irrigation as calculated by
GlobWat; total irrigation water requirements; amounts of water withdrawn for
irrigation as available in AQUASTAT (http://www.fao.org/nr/water/aquastat/data/query/index.html); and
irrigation efficiencies, are presented for the hydrological regions.
Since more or less reliable water withdrawal data per country are
available for less than 50 % of the countries included in this analysis,
it was decided to present water use efficiency values for hydrological
regions only rather than for individual countries.
Global terrestrial water balance.
109 m3(mm)Precipitation105 316(805)Rainfed evaporation61 106(467)Renewable water resources44 211(338)Incremental evaporation over open water1184(9)Incremental evaporation over wetlands2899(22)Incremental evaporation from irrigation1268(10)Outflow to sea38 859(297)
Figure 5 shows the distribution of global water stress by major river basin
based on incremental evaporation caused by irrigation as a percentage of
total generated groundwater and surface water resources. Levels of water
stress are often classified by using the Millennium Development Goals –
Water Indicator. The MDG Water Indicator measures water stress per country on
the ratio between total water withdrawn and total renewable water resources
(UN, 2008; FAO, 2013). Using this indicator, it is estimated that a
withdrawal rate above 20 % of renewable water resources represents
“substantial” pressure on water resources, while more than 40 % is
considered “critical” (FAO 2011b). Other classifications use thresholds of
0–10 % no stress, 10–20 %, low stress, 20–40 % moderate stress, and
more than 40 % severe stress (UN, 1997). The mentioned stress
classifications include all water withdrawals. Taking into account that
agriculture accounts for more than 90 % of the consumptive use of global
water withdrawals (FAO, 2012), and that on average incremental evaporation
due to irrigation is about half of irrigation water withdrawals (Table 8), it
is possible to assume thresholds of water stress classes based on incremental
evaporation that are half of the thresholds based on water withdrawals. Water
stress can then be considered substantial when incremental evaporation caused
by irrigation exceeds 10 % of the generated water resources in a river
basin. River basins in which the incremental evaporation caused by irrigation
exceeds 20 % should be considered critically stressed.
Average global actual evaporation per year.
Average annual outflow per sub-basin.
Water stress per major river basin expressed as a percentage of
incremental evaporation due to irrigation over generated groundwater and
surface water resources.
Modelling exercises, performed with earlier versions of the model and with
other input data sets, have been used by FAO on several occasions and were
documented in the following FAO perspective studies: World agriculture:
towards 2015–30; an FAO perspective (Bruinsma, 2003), World Agriculture –
Towards 2030 and 2050 (FAO, 2006), World Agriculture – Towards 2050 and 2080
(FAO, 2011a), and the global assessment of water use and availability carried
out for The State of the World's Land and Water Resources for Food and
Agriculture (FAO, 2011b). However, the model results were never
systematically compared with the outputs of other models. For this article,
the output of GlobWat is compared with models WaterGAP, WBMplus, GEPIC,
LPJmL, PCR-GLOBWB and GCWM as mentioned in the introduction of this article.
In Table 8 the calculated amount of water used in agriculture for these
models is compared with the results of GlobWat. Values for the incremental
evaporation due to irrigation for all models except PCR-GLOBWB were found in
Hoff et al. (2010). Values for the incremental evaporation due to irrigation
PCR-GLOBWB, and values on water withdrawal for irrigation, were found in the
references mentioned above.
Table 8 shows that the values of “incremental evaporation due to
irrigation” are fairly similar among the different models (except for GEPIC,
which shows significantly lower results than the other models). With regard
to the “water withdrawals for irrigation”, the results of GlobWat and LPJmL
are very similar, and, to a lesser extent, so are the results from WaterGap2
and WBMplus. This implies that the irrigation efficiencies as assumed by
WaterGAP2 and WBMplus (around 38 %) are significantly lower than those from
GlobWat and LPJmL (around 50 %). Irrigation efficiencies for PCR-GLOBWB are
in between these values at around 41 %. The calculated irrigation
efficiency for GlobWat (55 %) is higher than the irrigation efficiencies
for all other models. This is mainly due to the fact that for GlobWat also the
water requirements for flooding paddy rice are incorporated into the
irrigation water requirements. If the irrigation efficiency of GlobWat would
be calculated as incremental evaporation divided by water withdrawals, it
would result in a value of 48 %.
In addition to the higher resolution as compared to most other global
(agro)hydrological models, GlobWat differs from these models in the explicit
differentiation between evaporation from in situ rainfall and incremental
evaporation occurring over wetlands, open water and irrigated areas of water
that is conveyed from elsewhere. This distinction is especially useful for
differentiating between the part of the evaporation that can be influenced
only through land management (evaporation from in situ rainfall or “green
water”) and the part of the evaporation that is influenced through water
management (incremental evaporation or “blue water”).
For “open water” and “swamps and wetlands” GlobWat makes, unlike other
models, a clear distinction between the “vertical” water balance,
attributable to in situ rainfall, and the “horizontal” balance,
attributable to lateral flow. This distinction is not only important in terms
of the internal consistency of the model concept over all land cover classes,
it is especially relevant for the calculation of renewable water resources.
In the model, renewable water resources are calculated as the sum of all
generated groundwater and surface water, which equals precipitation minus
evaporation from in situ precipitation. If the precipitation exceeds
evaporation, the precipitation surplus over open water and wetlands
contributes to the renewable water resources. However, if evaporation exceeds
precipitation over open water and wetlands, (the evaporation “surplus”),
the incremental evaporation over open water and wetlands is not incorporated
into the calculation of generated renewable water resources. This is
necessary to account for water resources generated in internal river basins,
which would otherwise be classified as having no renewable water resources at
all.
Excluding the incremental evaporation over wetlands and open water has as a
consequence that some river basins in Fig. 5 (e.g. the Nile River basin) seem
to be less stressed than is apparent. In the case of the Nile basin, the
lower than expected stress levels are due to water losses over wetlands and
open water. According to the GlobWat results, more than 50 % of all the
water resources that are generated in the Nile River basin evaporate as
incremental evaporation over open water and wetlands, specifically over the
Sudd wetlands.
In conclusion, GlobWat is a simple model that has been designed specifically
to assess the impact of irrigated agriculture on the global hydrological
cycle. The model was calibrated using country-level data on total internal
renewable water resources and groundwater resources, and validated with
discharge data from major river basins. The model has a high resolution of
5 min and distinguishes between evaporation from in situ rainfall and from
water transported in from elsewhere. Model outputs include estimates of
consumptive water use by agriculture which were compared with AQUASTAT
country data on water withdrawals for agriculture to calculate irrigation efficiencies.
GlobWat, including all input data sets, will be made freely available for
download at FAO's AquaMaps website:
http://www.fao.org/nr/water/aquamaps/.
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