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Hydrol. Earth Syst. Sci., 22, 6297–6321, 2018
https://doi.org/10.5194/hess-22-6297-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Climate change vs. socio-economic development:
understanding the future South Asian water gap
René Reijer Wijngaard1,4, Hester Biemans2, Arthur Friedrich Lutz1, Arun Bhakta Shrestha3, Philippus Wester3, and
Walter Willem Immerzeel1,4
1FutureWater, Costerweg 1V, 6702 AA, Wageningen, the Netherlands
2Water and Food research group, Wageningen University & Research, P.O. Box 47, 6700 AA Wageningen, the Netherlands
3International Centre for Integrated Mountain Development, G.P.O. Box 3226, Khumaltar, Kathmandu, Nepal
4Utrecht University, Department of Physical Geography, P.O. Box 80115, 3508 TC, Utrecht, the Netherlands
Correspondence: René Reijer Wijngaard (r.r.wijngaard.uu@gmail.com, r.wijngaard@futurewater.nl)
Received: 17 January 2018 – Discussion started: 19 January 2018
Revised: 16 October 2018 – Accepted: 19 November 2018 – Published: 6 December 2018
Abstract. The Indus, Ganges, and Brahmaputra (IGB) river
basins provide about 900 million people with water re-
sources used for agricultural, domestic, and industrial pur-
poses. These river basins are marked as “climate change
hotspots”, where climate change is expected to affect mon-
soon dynamics and the amount of meltwater from snow and
ice, and thus the amount of water available. Simultaneously,
rapid and continuous population growth as well as strong
economic development will likely result in a rapid increase
in water demand. Since quantification of these future trends
is missing, it is rather uncertain how the future South Asian
water gap will develop. To this end, we assess the combined
impacts of climate change and socio-economic development
on the future “blue” water gap in the IGB until the end of
the 21st century. We apply a coupled modelling approach
consisting of the distributed cryospheric–hydrological model
SPHY, which simulates current and future upstream wa-
ter supply, and the hydrology and crop production model
LPJmL, which simulates current and future downstream wa-
ter supply and demand. We force the coupled models with an
ensemble of eight representative downscaled general circula-
tion models (GCMs) that are selected from the RCP4.5 and
RCP8.5 scenarios, and a set of land use and socio-economic
scenarios that are consistent with the shared socio-economic
pathway (SSP) marker scenarios 1 and 3. The simulation out-
puts are used to analyse changes in the water availability,
supply, demand, and gap. The outcomes show an increase
in surface water availability towards the end of the 21st cen-
tury, which can mainly be attributed to increases in monsoon
precipitation. However, despite the increase in surface wa-
ter availability, the strong socio-economic development and
associated increase in water demand will likely lead to an in-
crease in the water gap during the 21st century. This indicates
that socio-economic development is the key driver in the evo-
lution of the future South Asian water gap. The transgression
of future environmental flows will likely be limited, with sus-
tained environmental flow requirements during the monsoon
season and unmet environmental flow requirements during
the low-flow season in the Indus and Ganges river basins.
1 Introduction
Freshwater resources are essential for hundreds of millions of
people living in South Asian river basins. The Indus, Ganges,
and Brahmaputra (IGB) river systems provide about 900 mil-
lion people and the world’s largest irrigation scheme (i.e. that
of the Indus Basin Irrigation System, IBIS) with water, which
is mainly used for agricultural (e.g. irrigation), domestic (e.g.
drinking water supply), and industrial purposes (FAO, 2012;
Klein Goldewijk et al., 2010; Rasul, 2014; Shrestha et al.,
2013).
The water supply in the IGB is mainly dominated by two
different components: locally pumped groundwater and sur-
face water supplied by irrigation canals. Groundwater is an
important water supplier for the agricultural sector, with con-
tributions of about 64 % and 33 % to the total irrigation wa-
ter supply in India and Pakistan, respectively (Biemans et al.,
Published by Copernicus Publications on behalf of the European Geosciences Union.
6298 R. R. Wijngaard et al.: Climate change vs. socio-economic development
2016; Siebert et al., 2010). Surface water is supplied by irri-
gation canals that are diverted from rivers and reservoirs and
consist of direct rainfall runoff, meltwater from upstream lo-
cated ice melt and snow reserves, and baseflow. Meltwater is
the largest constituent of the total annual surface flow in the
western part of the IGB, where the amount of winter precip-
itation is substantial and the largest ice reserves are present
(Bookhagen and Burbank, 2010; Immerzeel, 2008; Lutz et
al., 2014; Rees and Collins, 2006). In the eastern part of the
IGB, where monsoon systems are more dominant, the mon-
soon precipitation is the largest constituent of the total an-
nual surface flow (Immerzeel, 2008). It is expected that due
to projected rises in temperature and precipitation changes,
glaciers and seasonal snow cover will be affected, eventu-
ally affecting the amount of meltwater and thus the amount
of surface water supply from upstream mountainous basins,
especially in the western part of the IGB (Kraaijenbrink et
al., 2017; Viste and Sorteberg, 2015). Further, monsoon dy-
namics will likely change, resulting in a decreasing number
of rainy days, increasing intensity of precipitation, and in-
creasing mean monsoon precipitation (Kumar et al., 2011;
Lutz et al., 2018; Sharmila et al., 2015; Turner and Anna-
malai, 2012). This might eventually affect the water supply
patterns in the eastern part of the IGB. On top of that, long-
term precipitation changes may lead to changes in groundwa-
ter recharge and storage, which in turn will affect groundwa-
ter availability (Asoka et al., 2017). There are, however, large
uncertainties in the projected precipitation changes due to the
large spread among the different climate model runs (Arnell
and Lloyd-Hughes, 2014; Lutz et al., 2016b; Moors et al.,
2011; Wijngaard et al., 2017), which hampers the projection
of future water supply rates. In addition to climate-induced
changes in surface and groundwater supply, groundwater de-
pletion is expected to intensify over the next decades due
to socio-economic development (Rodell et al., 2009; Wada,
2016; Wada et al., 2010).
Simultaneous with changes in water supply under climate
change, rapid and continuous population growth and strong
economic development are expected to result in a rapid in-
crease in water demand over the coming decades (Biemans et
al., 2011; Rasul, 2014, 2016; Wada et al., 2016). The popula-
tion in the IGB is expected to grow from 900 million inhab-
itants in 2010 to 1.1–1.4 billion inhabitants in 2050, which
will likely be accompanied by rapid urbanization (Klein
Goldewijk et al., 2010; Rasul, 2016). For instance, in coun-
tries like India and Pakistan, the expectation is that by 2050
more than 50 % of the population will live in urban areas
(Mukherji et al., 2018; UN-DESA, 2018). The population
growth is also expected to be accompanied by continuing fast
economic growth (i.e. currently between 2.5 % and 7.3 % per
year; ADB, 2018), rapid industrialization, and an intensifica-
tion of water use in food production (e.g. due to expansion of
irrigated areas) (Biemans et al., 2013; Rasul, 2016). This will
likely result in a potential water gap and increasing pressure
on water resources, which in turn will affect food security,
safe access to drinking water, public health, and environmen-
tal well-being (Liu et al., 2017; Taylor, 2009).
The development of the future blue water gap in the IGB
is rather uncertain. Some (global) studies (e.g. Alcamo et al.,
2007; Arnell, 2004; Lutz et al., 2014) found that water avail-
ability is projected to increase due to climate change, indicat-
ing that the future (seasonal) blue water gap might decline.
Other studies (e.g. Barnett et al., 2005; Gain and Wada, 2014;
Hanasaki et al., 2013; Vörösmarty et al., 2000) found that
water demand is projected to increase due to socio-economic
changes, mainly resulting from population growth, or that
water availability is projected to decrease. Both the projected
increases and decreases in water demand and availability, re-
spectively, might eventually result in an increasing (seasonal)
blue water gap. The opposing trends in how the future South
Asian blue water gap will develop indicate that the uncer-
tainty is large and that an improved understanding of the de-
velopment of the regional blue water gap is needed. One of
the drawbacks in some of the cited studies is, for example,
that, in general, the selection of climate models, RCPs, and
SSPs (RCP – Representative Concentration Pathway; SSP
– Shared Socio-economic Pathway) was not tailored to the
representation of a wide range of possible futures in terms
of climate change and socio-economic development. Conse-
quently, a full picture of how future water availability, supply
or demand can change cannot be provided. Model selection
approaches (e.g. Lutz et al., 2016b) with a focus on a wide
range of possible futures in terms of climate change, and the
selection of contrasting RCP–SSP combinations according to
a RCP–SSP framework (van Vuuren et al., 2014), can for in-
stance be used to eliminate this drawback. Another drawback
is that no models were used with a sufficient representation
of cryospheric–hydrological processes. Therefore, the lack
of proper simulations of the evolution of mountain water re-
sources (e.g. glacier evolution) may have imposed uncertain-
ties in the outcomes of these studies. Models with a sufficient
representation of cryospheric–hydrological processes can be
used to eliminate this drawback.
(Blue) water availability, supply, and demand have been
assessed by different methodologies over recent decades.
One type of assessments relied on statistics of water use (e.g.
FAO AQUASTAT) and observations of meteorological and
hydrological variables (Bierkens, 2015). Others were con-
ducted by using several model types, such as global hydro-
logical models (e.g. H08, Hanasaki et al., 2008a, b; LPJmL,
Schewe et al., 2014; and PCR-GLOBWB, van Beek et al.,
2011; Wada et al., 2014) (Veldkamp et al., 2017). There are
several advantages of the use of hydrological models above
the use of statistics. One advantage is that water availability
or supply, the main types of water use (i.e. agricultural, do-
mestic, and industrial), and their relationships and feedbacks
can be considered on a high spatial and temporal resolution
(e.g. 5 arcmin and daily). Another advantage is that models
such as the LPJmL model can be used to assess the impacts
of human interventions (e.g. reservoirs) on water availability
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R. R. Wijngaard et al.: Climate change vs. socio-economic development 6299
and irrigation water supply (Biemans et al., 2011; Haddeland
et al., 2014).
Large-scale hydrological models that simulate water sup-
ply and demand have mostly been applied without making
an explicit distinction between up- and downstream domains
and their roles in water supply and demand. To make an ex-
plicit distinction between the dominant processes in the dif-
ferent domains, different tools are required to simulate the
domain-specific processes properly. For instance, in the up-
stream domains of the IGB, water availability is highly de-
pendent on natural factors, such as ice and snowmelt (e.g.
Lutz et al., 2014). Since cryospheric and hydrological pro-
cesses vary strongly over short distances in the upstream
mountainous areas, higher-resolution models with a robust
representation of mountain-specific cryospheric and hydro-
logical processes are required to simulate water availability
and supply in and from the upstream (mountainous) domains
accurately. In the downstream domains of the IGB, the hu-
man influence on the hydrological cycle is large, with large
irrigation canal systems and reservoirs (e.g. Tarbela Dam)
(Biemans et al., 2013). In addition, agricultural water use is
a very important topic in this region, which requires knowl-
edge of related processes, such as crop growth, and relations
between water availability and food production. In these do-
mains, therefore, a high-resolution model is required that
(a) has an explicit representation of human interventions in
the hydrological cycle and (b) can link hydrological pro-
cesses with crop processes.
Environmental flow requirements (EFRs) have not been
considered in most future assessments on climate change-
induced or socio-economic development-induced changes in
water supply and demand in the region. EFRs have so far
only been applied by Hanasaki et al. (2013) by using an
EFR module (i.e. part of the H08 model) that controls the
consumptive amount of water that is withdrawn from river
systems. This allows the prioritization of maintaining EFRs,
but also has the consequence that agricultural production
might be affected. According to Jägermeyr et al. (2017) up
to ∼30 % of the agricultural production in South Asia can
be lost when EFRs are considered. In the IGB, rapid and
continuous population growth is expected, which will most
likely be accompanied by an increase in food demand and
thus requires a higher agricultural production (Biemans et al.,
2013). Therefore, agricultural needs will probably be prior-
itized at the cost of environmental flows and water use will
most likely intensify, which subsequently might alter flow
regimes and the ecological health of a river system (Döll
et al., 2009; Pastor et al., 2014). To understand the impact
of blue water consumption on environmental flow transgres-
sions, it is therefore needed to estimate EFRs and to assess
whether (future) EFRs are met or not.
The main objective of this study is to assess the combined
impacts of climate change and socio-economic development
on the future “blue” water gap for the downstream flood-
plains of the IGB river basins until the end of the 21st cen-
tury. For the upstream mountainous domains, we apply a dis-
tributed model with a strong representation of cryospheric–
hydrological processes that explicitly simulates cryospheric
changes (i.e. glacier and snow cover) under climate change.
For the downstream domains, we apply a distributed hy-
drology and crop production model with an explicit repre-
sentation of human interventions in the hydrological cycle
to simulate downstream water supply and demand. We use
the RCP–SSP framework to include a wide range of possi-
ble futures in terms of climate change and socio-economic
development (van Vuuren et al., 2014). Both models are
forced with outputs of eight downscaled general circulation
models (GCMs) representing a region-specific wide range
of possible climate conditions (i.e. representing RCP4.5 and
RCP8.5) (Lutz et al., 2016b). In addition, we use a set of re-
gional land use scenarios and socio-economic scenarios (de-
rived from SSP1 and SSP3, Riahi et al., 2017) to force the
hydrology and crop production model. Water demand and
consumption are estimated in terms of the amount of water
that is required for withdrawal and that is consumed, respec-
tively, by the agricultural, domestic, and industrial sectors.
The blue water gap is estimated as the amount of unsustain-
able groundwater that is withdrawn to fulfil the blue water
demand. Finally, EFRs are estimated according to the vari-
able monthly flow (VMF) method (Pastor et al., 2014) to as-
sess the impact of (future) blue water consumption on envi-
ronmental flow transgressions, assuming that meeting EFRs
have the lowest priority.
This study stands out in comparison with previous work
in the region by means of multiple novelties. First, the main
novelty is in understanding and assessing the combined im-
pacts of climate change and socio-economic development
on the future “blue” water gap in the major South Asian
river basins. Second, the novelty of this study lies in the
application of a coupled modelling approach, including a
high-resolution cryospheric–hydrological model (5 ×5 km)
and a high-resolution hydrology and crop production model
(5 ×5 arcmin), that can simulate up- and downstream wa-
ter availability, the downstream water supply, demand, and
the gap in the entire IGB. This modelling approach takes
upstream–downstream links and lateral transport into con-
sideration, which enables the possibility to assess the effects
of changes in upstream water supply on downstream water
availability and to improve analyses on the regional “blue”
water gap. Third, the hydrology and crop production model
applied for downstream domains has been specially devel-
oped for this region in that it is able to (a) simulate water
distribution through extensive irrigation canal systems of the
Indus and Ganges river basins, (b) make improved simula-
tions of the timing of water demand for agriculture due to an
explicit representation of a multiple cropping system (Bie-
mans et al., 2016), and (c) simulate groundwater withdrawal
and depletion rates. Fourth, the high-resolution models are
forced with an ensemble of downscaled and bias-corrected
GCMs that were selected by using an advanced selection
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6300 R. R. Wijngaard et al.: Climate change vs. socio-economic development
approach and represent a wide range of possible futures in
terms of climate change for RCP4.5 and RCP8.5. Fifth, the
hydrology and crop production model is forced with a set
of gridded socio-economic and land use scenarios that are
most likely linked with the RCPs (i.e. according to the RCP–
SSP framework). Finally, the outcomes of the hydrology and
crop production model are used to assess the impact of (fu-
ture) blue water consumption on environmental flow trans-
gressions.
2 Study area
The future blue water gap is examined for three major
South Asian river basins, which are considered a “hotspot”
of climate and socio-economic changes: the Indus, Ganges,
and Brahmaputra (De Souza et al., 2015) (Fig. 1). The
Indus, Ganges, and Brahmaputra river basins are selected
as study area because these South Asian river basins de-
pend to varying degrees on water generated in the Hindu
Kush–Himalayan (HKH) mountain ranges and at the same
time have contrasting differences in terms of hydro-climatic
and socio-economic characteristics. In a geopolitically com-
plex region, the Indus (I), Ganges (G), and Brahmaputra
(B) drain surface areas of around 1 116 000, 1 001 000, and
528 000 km2, respectively, and traverse Afghanistan (I), Pak-
istan (I), India (I, G, B), China (I, G, B), Nepal (G), Bhutan
(B), and Bangladesh (G, B). In this study, the IGB river
system is subdivided into several upstream and downstream
domains: the Upper Indus Basin (UIB), Upper Ganges
Basin (UGB), Upper Brahmaputra Basin (UBB), Lower In-
dus Basin (LIB), Lower Ganges Basin (LGB), and Lower
Brahmaputra Basin (LBB). Thereby, the upstream domains
are dominated by the mountainous terrains of the Tibetan
Plateau and Hindu Kush–Himalayan mountain ranges, with
elevations up to 8850m above sea level, and the downstream
domains are dominated by hilly regions and floodplains that
are part of the Indo-Gangetic plains. The boundary between
upstream and downstream domains is located at the southern
margins of the Himalayan foothills and directly upstream of
large reservoirs, such as the Tarbela and Mangla Dam reser-
voirs.
The Ganges river basin is the most densely populated
basin, with a population density of about 580 inhabi-
tants km−2, and the Brahmaputra river basin is the least pop-
ulated basin, with 131 inhabitants km−2(2016; Klein Gold-
ewijk et al., 2010). India has the largest economy with a
nominal GDP per capita of 1604 USD yr−1, whereas Nepal
has the smallest economy with a nominal GDP per capita of
748 USD yr−1(International Monetary Fund, 2016). Water
withdrawal (i.e. in South Asia) is highest in the agricultural
sector (91 %, corresponding with 913 km3yr−1), followed by
the domestic (7 %, corresponding with 70 km3yr−1) and in-
dustrial sectors (2 %, corresponding with 20 km3yr−1) (FAO,
2012). Much of the water withdrawn is used for the irri-
gated agricultural areas that are present in the IGB. Among
the three river basins, the Ganges river basin has the largest
irrigated area, with 257 000 km2(i.e. situation in 2000),
followed by the Indus river basin (213000km2) and the
Brahmaputra river basin (27 000 km2) (Biemans et al., 2013).
In the irrigated areas of the Indus and Ganges river basins,
mainly cash crops such as sugarcane, wheat, and rice are cul-
tivated (FAO, 2012). Thereby, the annual production of sug-
arcane is highest with 431 Mt, followed by rice (233 Mt) and
wheat (138 Mt) (2016; FAO, 2017).
The climate of the IGB river systems is mainly dominated
by the East Asian and Indian monsoon systems, and the west-
erlies. Westerlies are most dominant in the western part of the
IGB, with significant precipitation during the winter period.
The East Asian and Indian monsoon systems become in-
creasingly dominant when moving eastward, causing most of
the precipitation to occur during the monsoon season (June–
September). In the Brahmaputra river basin, where the cli-
mate is mainly driven by the monsoon systems, 60%–70 %
of the annual precipitation occurs during the monsoon season
(Immerzeel, 2008). Annual precipitation amounts vary from
less than 200 mm in the Thar desert (LIB) and the Tibetan
Plateau (UIB) to more than 5000 mm in the floodplains of
the LBB (Lutz et al., 2018). The high-altitude regions of the
HKH experience a cold climate with annual average temper-
atures down to −19 ◦C in the Karakoram (UIB), whereas the
downstream domains experience mild winters and hot sum-
mers with annual average temperatures up to 28◦C at the
southern margins of the LGB (Cheema and Bastiaanssen,
2010; Lutz et al., 2018; Wijngaard et al., 2017). Within
the IGB two growing seasons are prevailing: the rabi sea-
son (November–April) and the kharif season (May–October)
(Cheema et al., 2014; Portmann et al., 2010).
3 Data and methods
3.1 Definitions
Throughout this study, we use several terms, which we define
as follows:
–Blue water is water that is withdrawn from surface wa-
ter and groundwater bodies (surface water is defined as
water withdrawn directly from rivers, lakes, and reser-
voirs, and groundwater is defined as water withdrawn
from both shallow and deep aquifers, using (artificial)
wells).
–Green water is water that is infiltrated into soils and that
originated directly from precipitation.
–Blue water availability is the total amount of water
available in rivers, reservoirs, and groundwater.
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R. R. Wijngaard et al.: Climate change vs. socio-economic development 6301
Figure 1. (a) Map of study area showing the sub-basins and the largest cities in the region, (b) the population density (inhabitantskm−2),
(c) the GDP (PPP) per capita per country (USD inhabitant−1), and (d) the fraction of irrigated cropland (%). The source of the background
imagery, the cities, and the political borders illustrated in the inlet is http://naturalearthdata.com/ (last access: 6 December 2017). The source
of the population density data is the HYDE v3.2 database (Klein Goldewijk et al., 2010). The GDP (PPP) per capita is derived from IIASA
SSP database (IIASA, 2017). The fraction of the irrigated cropland is derived from the MIRCA2000 dataset (Biemans et al., 2016; Portmann
et al., 2010).
–Blue water demand is the total amount of blue water that
is required for withdrawal by the agricultural, domestic,
and industrial sectors.
–Blue water consumption is the total amount of blue wa-
ter that is consumed (evapotranspiration in agriculture)
by the agricultural (evapotranspiration), domestic, and
industrial sectors (withdrawal minus return flows).
–Blue water gap is the amount of unsustainable ground-
water that is withdrawn to fulfil the blue water de-
mand. The blue water gap occurs when the mean an-
nual groundwater withdrawal exceeds the mean annual
groundwater recharge.
3.2 Modelling framework
We use a coupled modelling approach to simulate upstream
water availability and downstream water supply and demand.
To this end, two physically based fully distributed models are
used: the cryospheric–hydrological Spatial Processes in HY-
drology (SPHY) model (Terink et al., 2015) and an adjusted
version of the (eco-)hydrological Lund–Potsdam–Jena man-
aged Land (LPJmL) model (Biemans et al., 2013, 2016; Bon-
deau et al., 2007; Rost et al., 2008). SPHY and LPJmL are
set up for a reference period (1981–2010) and a future period
(2011–2100), and both run at a daily time step.
3.2.1 Upstream: SPHY
We use SPHY to simulate water availability from the up-
stream mountainous domains of the IGB. The SPHY model
is developed specifically for the high mountain environment
in Asia. The model runs at a spatial resolution of 5 km ×5 km
and reports on a daily time step. SPHY has been used to as-
sess climate change impacts for high mountain hydrology
in Asia before (Lutz et al., 2014, 2016a; Wijngaard et al.,
2017). The set-up used was calibrated and validated using
IceSat glacier mass balance data (Kääb et al., 2012), MODIS
snow cover data (Hall et al., 2002; Hall and Riggs, 2015),
and observed discharge in a study on the impacts of climate
change on hydrological extremes in the upstream domains of
the IGB (Wijngaard et al., 2017). The model simulates daily
discharge by calculating the amount of total runoff for each
grid cell, and subsequently by routing the total runoff down-
stream by means of a simplified routing scheme that requires
a digital elevation model (DEM) and a recession coefficient.
Thereby, the total runoff is the sum of glacier runoff, snow
runoff, surface runoff, lateral flow, and baseflow.
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6302 R. R. Wijngaard et al.: Climate change vs. socio-economic development
For the estimation of the contribution of glacier runoff,
sub-grid variability (i.e. 1 km2) is applied by determining the
fractional ice cover in each cell, where fractional ice cover
can range between 0 (no ice cover) and 1 (complete ice
cover). Changes in fractional ice cover over time are mod-
elled using an approach that considers mass conservation and
ice redistribution (Terink et al., 2017). In addition to the de-
termination of fractional ice cover, other information, such
as initial ice thickness and the type of glacier (i.e. debris-
free or debris-covered) is attributed to a unique identifier that
is created for (a part of) each glacier within a model cell.
The degree-day approach of Hock (2003) is used to simu-
late glacier melt, which is subsequently subdivided over the
surface runoff and baseflow pathways by a calibrated glacier
runoff fraction.
Those parts that are not covered by glaciers are cov-
ered by snow, bare soil, vegetation, or open water. For the
snow-covered parts, the model of Kokkonen et al. (2006) is
used to simulate snow storage dynamics. Snow accumula-
tion and snowmelt is simulated by the degree-day approach
of Hock (2003), whereas snow sublimation is estimated by
a simple elevation-dependent potential sublimation function
(Lutz et al., 2016a). Besides snow melt, accumulation, and
sublimation, refreezing of snowmelt and rain are included as
well. Rainfall runoff processes are simulated for those parts
that are free of snow. Rain is subdivided over two pathways:
(i) a direct transport to the river network by surface runoff,
or (ii) an indirect transport to the river network via lateral
flow or baseflow. For the simulation of soil water processes,
processes as evapotranspiration, infiltration, and percolation
are included. These processes are simulated for a topsoil and
subsoil layer. For a more detailed description of SPHY we
refer to Terink et al. (2015).
3.2.2 Downstream: LPJmL
The outflows of upstream domains that are simulated by
SPHY are input to the hydrology and crop production model
LPJmL, where water is withdrawn by users or continues its
way downstream towards the Arabian Sea or the Bay of Ben-
gal. LPJmL has an explicit representation of human inter-
ventions in the hydrological cycle that are relevant in the
downstream domain, such as dynamic calculations of irri-
gation demand, withdrawal, and supply (Rost et al., 2008),
as well as the operation of large reservoirs (Biemans et al.,
2011). LPJmL has been applied to South Asia before (Bie-
mans et al., 2013), but has recently been updated to represent
the agricultural practice of multiple cropping with monsoon-
dependent sowing dates (Biemans et al., 2016) and the dis-
tinction between different irrigation systems (Jägermeyr et
al., 2015). The LPJmL model has been tested and validated
for global applications, such as river discharge (Biemans et
al., 2009), irrigation requirements (Rost et al., 2008), crop
yields (Fader et al., 2010), and sowing dates (Waha et al.,
2012). On a regional level, irrigation water withdrawals have
been validated for India and Pakistan (Biemans et al., 2013,
2016). In this study, the model was further improved to repre-
sent groundwater withdrawal and depletion and the distribu-
tion of irrigation water through the extensive canal systems
in the Indus and Ganges basins. Moreover, the resolution was
increased to 5 ×5 arcmin.
LPJmL simulates daily discharge by (i) calculating the to-
tal amount of runoff generated for each grid cell as the sum of
surface runoff, subsurface runoff, and baseflow, and (ii) rout-
ing the total runoff downstream along a river network. Water
enters a grid cell by precipitation and/or irrigation water and
can be subdivided over two pathways: direct transport to the
river network by surface runoff and indirect transport via in-
filtration and subsurface runoff or baseflow (Schaphoff et al.,
2018). Groundwater reservoirs are recharged from the bot-
tom soil layers. Water can be withdrawn from the groundwa-
ter reservoirs directly, or they contribute to baseflow through
a delayed outflow parameterized by a linear reservoir model.
Water can be removed from the grid cell by soil evaporation,
plant transpiration, canopy interception, and percolation. Wa-
ter can also be removed from the river network by lake or
canal evaporation. For a more detailed description of LPJmL
we refer to Rost et al. (2008) and Schaphoff et al. (2018).
In LPJmL, the daily irrigation water consumption is calcu-
lated for each grid cell as the minimum amount of additional
water needed to fill the upper two soil layers to field capacity
and the amount needed to fulfil the atmospheric evaporative
demand (Rost et al., 2008). The gross irrigation demand (i.e.
withdrawal) depends on the soil and the type of irrigation
system that is installed. We assume that all irrigated areas in
the IGB rely on flood irrigation (AQUASTAT; FAO, 2016),
which is less efficient than sprinkler or drip irrigation sys-
tems (Jägermeyr et al., 2015). Daily water demand for other
users (i.e. households and industry) is assumed to be constant
throughout the year.
Water for irrigation and other uses can be withdrawn from
surface water in a grid cell, surface water from a neighbour-
ing grid cell or a canal system (i.e. if connected), an upstream
reservoir build for water supply (i.e. if in place), and ground-
water bodies. If long-term groundwater withdrawals exceed
long-term groundwater recharge, the withdrawal is defined
as unsustainable. In this study, we define the blue water gap
as the mean annual groundwater depletion rate. Not all water
that is withdrawn is consumed. Water can be lost during con-
veyance, by open water evaporation, or as a return flow into
the river network. After application to the field, again only
part of the water will be used for evapotranspiration (blue
water consumption), and the remaining part will recharge
groundwater or discharge as return flow to the river.
3.3 Data
SPHY and LPJmL are forced with daily air temperature
and precipitation fields from a dataset that is developed for
the Indus, Ganges, and Brahmaputra river basins (Lutz and
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R. R. Wijngaard et al.: Climate change vs. socio-economic development 6303
Immerzeel, 2015), which accounts for the underestimate of
high-altitude precipitation, which is common for gridded me-
teorological forcing datasets in the region (Immerzeel et al.,
2015). The datasets are based on the Watch Forcing ERA-
Interim (WFDEI) dataset (Weedon et al., 2014) and are bias-
corrected and downscaled from a resolution of 0.5◦×0.5◦to
a resolution of 5 km ×5 km and 10 km ×10 km for the up-
stream and downstream domains, respectively. The LPJmL
model is also forced with downward longwave and shortwave
radiation, besides daily air temperature and precipitation
fields. Downward shortwave radiation is not bias-corrected,
since these datasets are corrected to observed cloud cover
and by means of corrections for aerosol loadings (Weedon et
al., 2010, 2011, 2014). For the application of the meteoro-
logical forcings in LPJmL the datasets were resampled to a
resolution of 5 arcmin.
We use the 15 arcsec void-filled and hydrologically con-
ditioned HydroSHEDS DEM (Lehner et al., 2008). For
the use of the DEMs in SPHY the DEMs are resam-
pled to 5 km ×5 km. LPJmL uses the stream network from
HydroSHEDS at 5 arcmin ×5 arcmin. Land use informa-
tion in SPHY is extracted from the MERIS Globcover
product (Defourny et al., 2007). In LPJmL, gridded crop
fractions of 13 rainfed and irrigated crop classes for the
2 cropping seasons were derived from the MIRCA2000
dataset (Biemans et al., 2016; Portmann et al., 2010). For
SPHY, soil information from the HiHydroSoil database (de
Boer, 2016), which is a dataset of soil hydraulic prop-
erties derived from the Harmonized World Soil Database
(FAO/IIASA/ISRIC/ISSCAS/JRC, 2012) using pedotransfer
functions (Sarmadian and Keshavarzi, 2010). LPJmL soil
classes were derived from the HWSD (Schaphoff et al.,
2013).
Current 5 arcmin domestic and industrial water demand
datasets are extracted from the PCR-GLOBWB model. In
these datasets, water demands were estimated based on meth-
ods developed by Wada et al. (2011b, 2014). Domestic wa-
ter withdrawals were derived by combining decadal and
yearly population data (i.e. extracted from the HYDE v3.2.
database (Klein Goldewijk et al., 2010) and the FAOSTAT
database, respectively), country-specific per capita domestic
withdrawal data (i.e. extracted from the FAO AQUASTAT
database), and water use intensities. The water use inten-
sities take country-specific economic and technological de-
velopments into account (Wada et al., 2011b). Hence, eco-
nomic developments are based on changes in GDP, electric-
ity production, energy, and household consumption. Techno-
logical developments are derived as the energy consumption
per unit of electricity production and accounts for domes-
tic and industrial restructuring or improved water use effi-
ciency (Wada et al., 2011b). Water use intensities are also
used to derive industrial water withdrawal. Industrial wa-
ter demands are assumed to remain constant throughout the
year, whereas domestic water demands are assumed to vary
throughout the year, depending on air temperature (Wada et
al., 2010, 2011a). Not all the water that is withdrawn is con-
sumed. A part of the water withdrawn for domestic and in-
dustrial purposes returns to the river network as return flows.
The amount of return flow is calculated by means of recy-
cling ratios that is depending on the country-specific GDP
and level of economic development (Wada et al., 2011a).
3.4 Future climate and socio-economic development
To evaluate future changes in the water supply, demand, and
gap due to climate change combined with socio-economic
developments we use the RCP–SSP Framework (van Vuuren
et al., 2014). We force SPHY and LPJmL with an ensemble
of downscaled GCM runs from the medium stabilization sce-
nario RCP4.5 and the very high baseline emission scenario
RCP8.5 (van Vuuren et al., 2011). From the CMIP5 multi-
model ensemble (Taylor et al., 2012) we select four GCM
runs for each RCP that represent the full CMIP5 ensemble in
terms of projected ranges in the means and extremes of future
air temperature and precipitation over the IGB region and
have sufficient skill to simulate historical climate conditions
in the IGB (Lutz et al., 2016b). Subsequently, the selected
models are downscaled using the reference climate data by
applying a quantile mapping approach, which performs well
in downscaling climate model data for floodplains as well
as mountainous terrains (Themeßl et al., 2011). This method
scales future GCMs down and bias-corrects them by means
of empirical cumulative density functions that are calculated
for the reference climate dataset and historical GCM runs
(1981–2010).
For the representation of future socio-economic develop-
ment, we select two SSP storylines (O’Neill et al., 2014,
2015; Riahi et al., 2017) that represent a “sustainability”
scenario (SSP1) and a “fragmentation” scenario (SSP3).
We choose to select SSP1 and SSP3, because these SSPs
are most likely linked with RCP4.5 (i.e. RCP4.5-SSP1)
and RCP8.5 (i.e. RCP8.5-SSP3) (van Vuuren and Carter,
2014). Future 5 arcmin domestic and industrial water demand
datasets are extracted from the IMAGE v3.0 model (Stehfest
et al., 2014). Within the IMAGE model a sub-model (i.e. de-
veloped by Bijl et al., 2016) is included, which calculates the
future domestic and industrial water demands based on pro-
jections for population growth and economic development
(based on GDP per capita) that are consistent with the se-
lected SSPs. The projected population and GDP (PPP – pur-
chasing power parity) changes for the IGB are summarized
in Table 1 for SSP1 and SSP3.
Land use change scenarios that are consistent with the
SSP storylines are calculated by integrated assessment mod-
els like IMAGE (Stehfest et al., 2014). IMAGE calculates
land use change based on a set of SSP-specific assumptions
regarding dietary changes and resulting per capita food de-
mand, the level of intensification and potential yield increase
on existing cropland, and changes in import and export of
commodities. We use the SSP1 and SSP3 regional scale out-
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6304 R. R. Wijngaard et al.: Climate change vs. socio-economic development
Table 1. Projected basin-aggregated population counts and GDP (PPP =purchasing power parity) for SSP1 and SSP3. The population counts
are extracted from the HYDE v3.2 database (Klein Goldewijk et al., 2010). The GDP (PPP) is a product of the population counts and the
country-specific GDP (PPP) per capita, which is derived from the IIASA SSP database (IIASA, 2017) as the ensemble mean of the IIASA
GDP and OECD Environmental Growth models.
Basins Countries Population (×106) GDP (PPP) (×109USD 2005)
2010 2050 2100 2010 2050 2100
Indus AF, CN, IN, PK 245 346/469 289/725 631 5124/2894 14 574/7191
Ganges BD, CN, IN, NP 494 629/804 466/1073 1410 14 276/8782 28 796/15 198
Brahmaputra BD, BT, CN, IN 65 81/101 58/129 165 1601/952 3299/1689
comes of IMAGE (Doelman et al., 2018) to derive changes
in rainfed and irrigated cropland extents for Pakistan, In-
dia, Nepal, and Bangladesh between 2010 and 2100. Sub-
sequently, we project those changes on our gridded datasets
of current kharif and rabi cropped areas to construct tran-
sient datasets of land use change in the IGB. These grid-
ded datasets are used in combination with the climate change
datasets to estimate future water requirements for irrigation.
We assume that both the crop distribution and crop types
remain as they are. This implies that they are not adapted
when crop growth conditions become unfavourable (e.g. due
to changing climate conditions). It is beyond the scope of this
study to investigate the impact of climate change adaptation
of agricultural practices on irrigation water requirements and
related impacts on the blue water gap.
3.5 Analysis of environmental flows
To assess the impacts of (blue) water consumption on en-
vironmental flow transgressions we estimate EFRs accord-
ing to the variable monthly flow (VMF) method of Pastor et
al. (2014). The VMF method is a valid method that consid-
ers intra-annual variability in streamflow and correlates well
with locally calculated EFRs. The EFRs are calculated on a
monthly basis by using the discharge at the river outlets of the
Indus, Ganges, and Brahmaputra under naturalized condi-
tions (i.e. without withdrawals for irrigation and other users).
First, the mean annual flows (MAFs) and mean monthly
flows (MMFs) are calculated for the reference (1981–2010)
and far-future periods (2071–2100). The MAFs and MMFs
are then used to determine low-flow (MMF ≤0.4·MAF),
high-flow (MMF >0.8·MAF), and intermediate flow sea-
sons (MMF >0.4·MAF & MMF ≤0.8·MAF). Based on
the seasonal classification, EFRs are subsequently calculated
where the EFR is set equal to 60 %, 45 %, and 30 % of the
MMF during low, intermediate, and high-flow seasons, re-
spectively. Finally, the discharge impacted by anthropogenic
water withdrawals (i.e. with irrigation and full access to
groundwater) is compared with the EFRs to assess whether
environmental flows are met or not.
4 Results and discussion
4.1 Future climate change
In the IGB, both temperature and precipitation are projected
to change towards the end of the 21st century. Figure 2 shows
the projected annual and seasonal temperature and precipita-
tion changes in the IGB for RCP4.5 and RCP8.5, at the end of
the 21st century. On an annual basis, temperature is projected
to increase, with 1.5–2.9 ◦C for RCP4.5 and 2.8–5.2 ◦C for
RCP8.5, with respect to the reference period (1981–2010).
The largest increases are projected in the western and north-
western parts of the Indus river basin (i.e. in the Hindu
Kush and Karakoram mountain ranges) and on the Tibetan
Plateau. The large temperature increases in these regions
can most likely be attributed to elevation-dependent warm-
ing, which causes a stronger warming in the high-altitude
upstream regions in comparison with the lower-lying down-
stream regions (Palazzi et al., 2016; Pepin et al., 2015). Pre-
cipitation is, in general, projected to increase with increases
up to about 200 % for RCP4.5 and up to about 100 % for
RCP8.5. Thereby, the largest increases are projected in the
southernmost parts of the Indus river basin, which is a re-
gion where the amount of precipitation is relatively low (less
than 300 mm yr−1) and thus small absolute increases can re-
sult in large relative increases. In the same region, the range
in model projections is also large. Besides precipitation in-
creases, precipitation decreases are also projected. These de-
creases are mainly projected to occur in the westernmost
part of the Indus river basin. On a seasonal basis, the pro-
jected temperature changes do not show large seasonal dif-
ferences. The main difference can be found between the pro-
jections made for RCP4.5 and RCP8.5, with temperature dif-
ferences up to about 2 ◦C between RCP4.5 and RCP8.5. The
projected precipitation changes show large seasonal differ-
ences. For RCP4.5, the largest and smallest increases are, in
general, projected during post-monsoon and pre-monsoon–
winter periods, respectively. During the pre-monsoon and
winter seasons even a decrease in precipitation is projected
in the UIB (∼ −1 %) and UGB (∼ −5 %), respectively. For
RCP8.5, precipitation increases are, in general, largest dur-
ing the post-monsoon period. During pre-monsoon, precipi-
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R. R. Wijngaard et al.: Climate change vs. socio-economic development 6305
Figure 2. Maps showing the annual changes in temperature (a, b) and precipitation (c, d) between 2071–2100 and 1981–2010 for RCP4.5
and RCP8.5. The bar plots show seasonal changes in temperature (e) and precipitation (f) in the upstream and downstream domains of the
IGB for RCP4.5 and RCP8.5. The contour lines within the maps and the error bars within the bar plots denote the ensemble range of the
projections.
tation decreases are also projected in the UIB (∼ −4 %). The
range in model projections is especially large during the post-
monsoon and winter seasons.
4.2 Blue water availability
In the IGB, the seasonal and spatial variability of surface
water availability is quite large. Figure 3 shows the sea-
sonal surface water availability (i.e. natural runoff) for the
reference period (1981–2010) in the upstream and down-
stream domains of the IGB as simulated by SPHY and
LPJmL. The surface water availability is generally largest
during the monsoon season (Fig. 3c), varying from less than
100 mm yr−1in the floodplains of the Indus (LIB) to more
than 3500 mm yr−1in the mountainous upstream domains of
the Ganges and Brahmaputra. In these domains, the large sur-
face water availability can mainly be attributed to the com-
bined contributions from ice and snowmelt, and monsoon
precipitation that can reach amounts over 3000 mm yr−1at
the southern margins of the UGB and UBB (Wijngaard et
al., 2017). During the winter season (see Fig. 3a) the sur-
face water availability is generally lowest, with rates less
than 100 mm yr−1in most regions of the IGB. Water avail-
ability is generally higher than 100 mm yr−1in the LBB and
directly south of the Himalayan arc. The higher surface water
availability in these regions can likely be explained by the re-
lease of groundwater from aquifers that have been recharged
during the monsoon season. A similar pattern can also be
recognized for the same regions during the pre-monsoon
(Fig. 3b) and post-monsoon seasons (Fig. 3d). During the
pre-monsoon season, surface water availability can reach up
to about 1000–1500 mm yr−1in the HKH mountain ranges,
which can be attributed to snowmelt.
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6306 R. R. Wijngaard et al.: Climate change vs. socio-economic development
Figure 3. Maps showing the surface water availability in winter (a), pre-monsoon (b), monsoon (c), and post-monsoon (d) seasons.
Future water availability is expected to increase as a re-
sult of climate change. Figure 4 shows the current and fu-
ture monthly surface water availability for the up- and down-
stream domains of the IGB under current (1981–2010), mid-
future (2041–2070; MOC – mid-21st century), and far-future
(2071–2100; EOC – end of the 21st century) climate condi-
tions. Surface water availability is projected to increase for
both RCP4.5 and RCP8.5 in the entire IGB. Similar trends
have also been found in other studies conducted in (a part of)
the IGB (Immerzeel et al., 2010; Lutz et al., 2014; Masood et
al., 2015; Nepal, 2016). The increases in surface water avail-
ability are projected to be stronger during the monsoon sea-
son, which can likely be attributed to increases in monsoon
precipitation (Fig. 2) and increases in ice melt. The increases
in melt (i.e. especially ice melt) are a likely reason that the
natural runoff peaks in the upstream domains of the Ganges
and Brahmaputra are projected to shift from July to August.
Furthermore, increases are stronger for RCP8.5, with the ex-
ception of the Indus basin, where an opposite trend can be
observed. The opposite trend can mainly be attributed to the
reduction in snowmelt towards the end of the 21st century,
which is most likely caused by the stronger temperature in-
creases in the Indus basin (Fig. 2), leading to a higher frac-
tion of precipitation to fall as rain. The range among model
runs is large, especially for RCP8.5, which indicates that un-
certainty in future water availability projections is large, es-
pecially in the upstream mountainous domains. The graphs
further show that, under current and future conditions, there
is a clear upstream–downstream difference in the amount of
water that is available in the Indus and Ganges, with signifi-
cantly larger amounts of water available in the upstream do-
mains. In the Brahmaputra basin, the upstream–downstream
difference is smaller, which can be attributed to the East
Asian monsoon systems that have a high intensity in the
floodplains of the Brahmaputra. The upstream–downstream
differences in surface water availability indicate the signif-
icance of upstream water resources for the floodplains that
are located downstream. In the future, it is projected that the
upstream–downstream difference will be enhanced, implying
that the dependency on upstream mountain water resources
will increase.
4.3 Blue water consumption
Irrigation is by far the largest water consumer in the IGB.
Figure 5 shows the annual and seasonal blue water consump-
tion for irrigated croplands and the combined blue water con-
sumption for domestic and industrial sectors. The maps indi-
cate that the irrigation water consumption is largest in the
Punjab and Haryana provinces (i.e. in northern part of the
LIB and western part of the LGB), with consumption rates
that reach over 600 mm yr−1on an annual basis. In the Sindh
province (i.e. located in the delta plains of the Indus) and
along the Ganges river consumption rates are also high. The
difference in water consumption between the rabi (winter)
and kharif (monsoon) seasons is limited in the Indus river
basin, whereas in the Ganges and Brahmaputra river basins
the water consumption during the rabi season is significantly
higher at most of the croplands than during the kharif season.
The seasonal differences are a result of rainfall patterns in
the IGB. In the Ganges and Brahmaputra river basins, the In-
dian and East Asian monsoon systems prevail, which means
that sufficient green water is available and thus (blue water)
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R. R. Wijngaard et al.: Climate change vs. socio-economic development 6307
Figure 4. Plots showing the mean monthly blue water availability for the reference (1981–2010) and future periods (mid-century, MOC,
2041–2070; end of century, EOC, 2071–2100) under RCP4.5 (blue) and RCP8.5 (red). The coloured bands represent the range of ensemble
projections that are resulting from forcing the SPHY and LPJmL models with the different climate models.
irrigation is less concentrated during the kharif season (Bie-
mans et al., 2016). In the Indus river basin, the influence of
monsoon systems is smaller, which means more irrigation
is required to fulfil the crop demands. However, during the
rabi seasons the amount of precipitation is limited, which
also means (blue water) irrigation is required in the Ganges
and Brahmaputra river basins. In comparison to irrigation,
the water consumption in the domestic and industrial sectors
is almost negligible. In most areas, the consumption rates are
less than 100 mm yr−1. Only in the larger urban areas, such
as New Delhi, Islamabad, Lucknow, and Jaipur (location,
Fig. 1), the consumption rates can reach up to 380 mm yr−1.
As a result of climate change and/or socio-economic de-
velopments, blue water consumption is projected to change
into the future. Figure 6 shows the projected changes in the
annual blue water consumption for irrigated croplands and
other users (i.e. domestic and industrial sectors) for RCP4.5,
RCP8.5, RCP4.5-SSP1, and RCP8.5-SSP3. Under current
conditions (i.e. REF, 1981–2010), the total blue water con-
sumption is largest in the Indus river basin, with a total rate of
145 km3yr−1, of which 138 km3yr−1(∼95 %) is consumed
on irrigated croplands and 7 km3yr−1(∼5 %) is consumed
by domestic and industrial sectors. The total blue water con-
sumption is smallest in the Brahmaputra river basin, with
a total rate of 5 km3yr−1of which 4 km3yr−1(∼80 %) is
consumed on irrigated croplands and 1 km3yr−1(∼20 %)
is consumed by domestic and industrial sectors. The differ-
ences in total water consumption among the basins are due
to the Indus river basin agriculture being dominated by irri-
gated croplands (see Fig. 1d), whereas in the Brahmaputra
river basin agriculture is dominated by rainfed croplands. In
addition, the LIB covers a larger area than the LBB, which
eventually results in larger consumption rates when aggre-
gating the grid values within a basin. Future total water con-
sumption is projected to change. When only considering cli-
mate change, there will be no change in domestic and in-
dustrial water consumption. Irrigation water consumption is
projected to decrease from 138, 91, and 4 up to about 116,
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6308 R. R. Wijngaard et al.: Climate change vs. socio-economic development
Figure 5. Maps showing the blue water consumption for irrigated croplands (a–c) and other users (i.e. domestic+industrial) (d). The
irrigation water consumption is given on an annual basis (a), and for the rabi (b) and kharif seasons (c). The domestic+industrial water
consumption is given on an annual basis.
69, and 3 km3yr−1in the LIB, LGB, and LBB respectively
for RCP8.5, at the end of the 21st century. This trend can be
explained by growing seasons that become shorter for most
crops due to temperature increases. The shorter growing sea-
sons mean that less water is demanded and thus less water is
consumed. In addition, precipitation is projected to increase
(Fig. 2), which means more green water will be available and
less (blue water) irrigation is required. When considering fu-
ture climate change and socio-economic developments, an
increase in the total water consumption is projected, with
mean relative increases up to about 36%, 60 %, and 147 %
in the LIB, LGB, and LBB for RCP8.5-SSP3 respectively, at
the end of the 21st century. The increasing total water con-
sumption can mainly be attributed to increasing domestic and
industrial water consumption that emerge from population
growth and economic development. Their increase ranges
from 283 % to 311 % for RCP4.5-SSP1 and from 586 % to
715 % for RCP8.5-SSP3, at the end of the 21st century, in-
dicating that domestic and industrial water consumption will
be a significant component of the South Asian future water
balance. Compared to the reference period there is, however,
a slight decrease in irrigation water consumption projected,
although the decreases are smaller than those for the runs
considering climate change only, which is due to the expan-
sion of irrigated croplands under the SSPs. Only for RCP8.5-
SSP3 is a slight increase in the irrigation water consumption
projected at the end of the 21st century.
Figure 7 shows the monthly projected changes in the to-
tal blue water consumption for RCP4.5, RCP8.5, RCP4.5-
SSP1, and RCP8.5-SSP3. Under current climate conditions,
two peaks in the total water consumption can be recognized
in the Indus river basin, which coincide with the rabi and
kharif crop seasons. In the Ganges and Brahmaputra river
basins, the total water consumption is highest during the rabi
season, but also smaller peaks can be recognized that coin-
cide with the kharif season. Considering climate change only,
the total water consumption is projected to decrease slightly
throughout the entire year in the Indus river basin, with ex-
ception of the post-monsoon season, when a slight increase is
projected. In the Ganges river basin, the total water consump-
tion is projected to decrease during the second half of the rabi
season, whereas during the first half of the rabi and kharif
seasons the total water consumption is projected to increase
slightly. These trends are also projected for the Brahmaputra
river basin, with the exception of the second half of the kharif
season, where a slight increase in total water consumption
is also projected, though the projected increases are smaller
than for the first half of the kharif season. The projected in-
creases can most likely be explained by increasing temper-
atures (Fig. 2) that enhance the atmospheric evaporative de-
mand. The increasing atmospheric evaporative demand re-
sults in higher crop evapotranspiration and thus higher irri-
gation water consumption. Because growing seasons are pro-
jected to become shorter in the IGB and precipitation is pro-
jected to increase (see Fig. 2), total water consumption will
eventually decrease in the second half of the rabi season, and
also for RCP8.5 in the second half of the kharif season. The
projected increases during the second half of the kharif sea-
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R. R. Wijngaard et al.: Climate change vs. socio-economic development 6309
Figure 6. Projected changes in the annual blue water consumption for irrigated croplands and other users (i.e. domestic+industrial) for
RCP4.5, RCP8.5, RCP4.5-SSP1, and RCP8.5-SSP3. The projected changes are given for the mid-21st century and end of the 21st century
(MOC and EOC) and represent the ensemble mean. The error bars denote the range of the ensemble projections.
son in the Brahmaputra river basin can likely be explained by
increasing temperatures that are smaller in the downstream
domains of the Brahmaputra river basin than in other down-
stream domains (Fig. 2). Due to the smaller temperature in-
creases, the growing seasons show a smaller decline, and
therefore the higher evapotranspiration rates emerging from
temperature increases as well might outweigh the effect of
shorter growing seasons, which eventually results in a slight
increase in total water consumption. In the entire IGB, the
water consumption for RCP8.5 is projected to be lower than
for RCP4.5, which can most likely be attributed to the pre-
cipitation increases that are larger for RCP8.5 and thus cause
blue water irrigation to be lower for RCP8.5 than for RCP4.5.
When considering both climate change and socio-economic
development, the total water consumption is projected to in-
crease, where the largest increases are projected for RCP8.5-
SSP3. Thereby, the difference in projected increases between
the mid of the 21st century (MOC) and the end of the 21st
century (EOC) are especially large for RCP8.5-SSP3, which
can be explained by the extensive population growth that is
projected at the end of the 21st century for SSP3 (Table 1).
This eventually results in a larger increase in domestic water
consumption. Further, the difference in projected increases
between the RCP–SSP model runs and the reference model
runs is especially large in the Brahmaputra river basin, which
can be explained by the strong increases in domestic and in-
dustrial water consumption. For instance, for RCP8.5-SSP3
a relative increase of 619% is projected in domestic and in-
dustrial water consumption at the end of the 21st century.
Although the difference between projected relative increases
in the Indus and Ganges river basins (i.e. 715% and 586 %,
respectively) is not large, the impact is higher since the do-
mestic and industrial sectors have a higher contribution in
the total water consumption (i.e. ∼20 % for the reference pe-
riod) in comparison with the Indus and Ganges river basins
(i.e. ∼5 % and ∼12 %, respectively).
4.4 Blue water gap
Climate change is projected to have a mitigating effect on
the future South Asian water gap, whereas socio-economic
development is projected to have an enhancing effect on the
water gap. Figure 8 shows the projected changes in the an-
nual and seasonal blue water demand and supply for RCP4.5,
RCP8.5, RCP4.5-SSP1, and RCP8.5-SSP3. In addition, Ta-
ble 2 lists the ensemble mean and standard deviation of the
projected relative changes in the annual and seasonal blue
water gap for the end of the 21st century (i.e. EOC). Un-
der current climate conditions, the total demand is largest
in the Indus river basin, with 767km3yr−1, and smallest in
the Brahmaputra river basin, with 15km3yr−1. Most of the
blue water supply consists of surface water (∼67 % in the
Indus and ∼93 % in the Brahmaputra). The other part con-
sists of sustainable and unsustainable groundwater. The lat-
ter is defined as the blue water gap or the unmet demand,
assuming that any unmet demand is covered by additional
groundwater abstractions. The unmet demand is largest in
the Indus river basin with 83km3yr−1(∼11 % of total de-
mand), followed by the Ganges river basin with an unmet
demand of 35 km3yr−1(∼11 % of total demand) (Table 2).
The simulated unmet demand in the Ganges river basin falls
in range with reported historical values in other studies (Ja-
cob et al., 2012; Richey et al., 2015; Rodell et al., 2009; Ti-
wari et al., 2009). The simulated unmet demand in the Indus
river basin is more difficult to compare due to the limited
amount of studies reporting groundwater depletion. Cheema
et al. (2014) reports a groundwater depletion rate (i.e. unmet
demand) of 31 km3yr−1, which is lower than the simulated
groundwater depletion rate in our study. The difference can
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6310 R. R. Wijngaard et al.: Climate change vs. socio-economic development
Figure 7. Monthly projected changes in the total water consumption for RCP4.5, RCP8.5, RCP4.5-SSP1, and RCP8.5-SSP3. The projected
changes are given for the mid-21st century and end of the 21st century (MOC and EOC). The coloured bands represent the range of ensemble
projections that are resulting from forcing the LPJmL model with the different climate models.
Table 2. Projected changes in the annual and seasonal blue water gap of the Indus and Ganges river basins under present (1981–2010)
and far-future (2071–2100; EOC) conditions for RCP4.5, RCP8.5, RCP4.5-SSP1, and RCP8.5-SSP3. The values between the parentheses
represent the minimum and maximum projected changes in the blue water gap. The colours indicate the number of model runs (i.e. normal
font: 3 or more runs; bold font: 2 runs; bold italic font: 1 run) that project the same sign of change as the projected mean change.
Basin Scenario Annual Winter Pre-monsoon Monsoon Post-monsoon
Indus REF (km3) 83 19 27 28 10
RCP45 EOC (%) −36(−59/−19)−47(−70/−32)−35(−61/−18)−32(−53/−13)−33(−54/−13)
RCP85 EOC (%) −37(−52/−15)−52(−59/−35)−44(−49/−35)−23(−58/14)−29(−44/−10)
RCP45-SSP1 EOC (%) −21(−50/1)−31(−60/−11)−21(−53/0)−16(−42/7)−15(−42/9)
RCP85-SSP3 EOC (%) 7(−18/42)−11(−25/19)−9(−16/5)30(−27/91)18(−8/48)
Ganges REF (km3) 35 13 10 6 6
RCP45 EOC (%) −52(−85/−26)−61(−89/−41)−51(−86/−23)−44(−82/−19)−41(−81/−8)
RCP85 EOC (%) −55(−73/−23)−66(−78/−38)−63(−73/−45)−39(−72/14)−34(−64/10)
RCP45-SSP1 EOC (%) −23(−74/16)−37(−79/−4)−23(−74/19)−9(−66/28)−8(−67/41)
RCP85-SSP3 EOC (%) 14(−26/82)−11(−40/49)1(−23/44)55(−17/165)50(−11/131)
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R. R. Wijngaard et al.: Climate change vs. socio-economic development 6311
Figure 8. Projected changes in the annual and seasonal blue water demand and supply for RCP4.5, RCP8.5, RCP4.5-SSP1, and RCP8.5-
SSP3. The projected changes are given for the mid-21st century and end of the 21st century (MOC and EOC).
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6312 R. R. Wijngaard et al.: Climate change vs. socio-economic development
mainly be explained by the fact that in our study the domes-
tic and industrial sectors are also able to abstract groundwa-
ter, which consequently results in larger depletion rates. In
the Brahmaputra river basin, no blue water gap is simulated,
because all demands can be sustained by surface water and
renewable groundwater. In the Indus river basin, the seasonal
demand, supply, and gap are largest during the monsoon and
melting season, which coincides with the prevailing grow-
ing season, the kharif. In the Ganges and Brahmaputra river
basins, the seasonal demand, supply, and gap (i.e. only in the
Ganges river basin) are largest during the winter, which coin-
cides with the rabi season. Assuming climate change without
socio-economic development, demand and supply are pro-
jected to decrease in all basins on an annual basis, and in
general during the winter, pre-monsoon, and monsoon sea-
sons for RCP4.5 and RCP8.5. During the monsoon (i.e. only
in the Brahmaputra river basin) and post-monsoon seasons,
demand and supply are projected to increase. The water gap
is projected to decrease under all circumstances, with mean
annual relative decreases of up to 37% and 55 % (Table 2) in
the Indus and Ganges river basins, respectively, for RCP8.5,
at the end of the 21st century. On a seasonal basis, the largest
mean relative decreases are projected during the winter sea-
son, with relative decreases of up to 52% and 66 % (Ta-
ble 2) in the Indus and Ganges river basins, respectively, for
RCP8.5, at the end of the 21st century. The decreasing de-
mand (met and unmet) and supply can mainly be explained
by shorter growing seasons that emerge from temperature in-
creases, and increasing precipitation that result in a shift from
blue water irrigation to green water or rainfed irrigation. The
increases in monsoon and post-monsoon (i.e. first half of the
kharif (monsoon) and rabi (post-monsoon) seasons) seasons
can likely be explained by enhanced atmospheric evapora-
tive demands and resulting increases in crop evapotranspi-
ration that emerge from temperature increases. Despite the
increases in demand, the water gap is projected to decrease,
which can mainly be explained by the higher surface water
availability (Fig. 4) that eventually result in lower unsustain-
able groundwater withdrawals and thus a smaller water gap.
Climate change and socio-economic developments combined
result, on an annual basis, in increasing water supply and
demand in the Brahmaputra and Ganges river basins for all
RCP–SSP scenarios. In the Indus river basin, only increases
are projected for RCP8.5-SSP3. For RCP4.5-SSP1, demand
and supply slightly decrease. The reason for the decreasing
trend is that the (relative) increase in domestic and industrial
water consumption is limited in comparison with those pro-
jected under RCP8.5-SSP3 and other basins, which, in com-
bination with declining irrigation water demand, eventually
results in decreasing water demand and supply. The future
water gap tends to increase for RCP8.5-SSP3 in the Indus
and Ganges river basins with annual relative increases up to
7 % and 14%, respectively, at the end of the 21st century (Ta-
ble 2). On a seasonal basis, the relative increases are largest
during the monsoon season with increases up to 30 % and
55 % in the Indus and Ganges river basin, respectively. For
RCP4.5-SSP1 the gap decreases, since the declining irriga-
tion water withdrawals are not outweighed by the increases in
domestic and industrial water consumption. This might also
explain why the water gap for RCP8.5-SSP3 is projected to
decline during the winter season. Finally, the changing water
demands result in changing shares of the different sectors in
the total water demand, which is especially striking during
the pre-monsoon season in the Brahmaputra river basin. Due
to a combination of increasing domestic and industrial water
demand and declining irrigation water demand (which is es-
pecially large during pre-monsoon in this basin) the domestic
and industrial sectors are eventually projected to become the
largest contributors to the total water demand. The projected
mean relative changes in the blue water gap are accompanied
by a large range in model outcomes that are generated for the
different climate models, whether or not they are generated in
combination with SSP projections (Table 2). The large range
in outcomes can mainly be attributed to the large spread in
possible futures that are currently projected by climate mod-
els. For the RCP combinations and RCP4.5-SSP1, the de-
creasing trend is present. For RCP8.5-SSP3, however, the
trend is less clear with projected increases for some GCM-
SSP combinations and projected decreases for other combi-
nations. The mean of the outcomes for RCP8.5-SSP3 indi-
cate, however, that the blue water gap will increase and that
climate change cannot compensate for the large projected
changes in water demand anymore.
Figure 9 shows the spatial distribution of current ground-
water depletion (i.e. indicator for the blue water gap) and fu-
ture absolute changes in groundwater depletion for RCP4.5,
RCP8.5, RCP4.5-SSP1, and RCP8.5-SSP3. Under current
conditions, groundwater depletion is largest in the Pun-
jab and Haryana provinces, with depletion rates of around
1000 mm yr−1in the irrigated areas. In urban areas, such as
New Delhi, depletion rates can even reach up to about 2000–
2500 mm yr−1. Also in the Sindh province, the water gap is
large, with depletion rates in the range 300–350 mm yr−1.
The simulated depletion rates in the irrigated areas of the
Indus river basin are similar to those that were found by
Cheema et al. (2014). For RCP4.5 and RCP8.5, in general
less groundwater depletion is projected, which is mainly
caused by the declining irrigation blue water withdrawal and
consumption. For both RCP–SSP combinations, depletion is
expected to decrease in the irrigated croplands, whereas in
the urban areas (e.g. New Delhi) depletion is projected to in-
crease by more than 200 mm yr−1(i.e. corresponding with
a relative increase of more than 150%). For RCP8.5-SSP3,
areas located in the Sindh province, and west of the Indus
river are also expected to experience more depletion, due to
population growth and economic development.
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R. R. Wijngaard et al.: Climate change vs. socio-economic development 6313
Figure 9. Maps showing the annual groundwater depletion for the reference period (a) and the projected changes in groundwater depletion
for RCP4.5 (b), RCP8.5 (c), RCP4.5-SSP1 (d), and RCP8.5-SSP3 (e). The projected changes are given for the end of the 21st century. Green
indicates less depletion and red indicates more depletion.
4.5 Environmental flows
The future socio-economic developments and associated in-
creases in blue water consumption are expected to have a lim-
ited impact on environmental flow transgressions. Figure 10
shows the ensemble mean and range of the projected changes
in EFRs and anthropogenically influenced discharge at the
outlets of the Indus, Ganges, and Brahmaputra under present
and far-future (EOC) RCP–SSP conditions. Under current
conditions, EFRs in the Indus, Ganges, and Brahmaputra are
generally not met during the low-flow season (i.e. winter,
pre-monsoon, and post-monsoon), whereas during the mon-
soon season EFRs are met. The combination of high unmet
demands in the Indus river basin (Fig. 8) on the one hand and
sustained EFRs on the other can be explained by the absence
of water shortages during the monsoon season due to the
higher surface water availability. During the low-flow season,
however, the surface water availability is low, which eventu-
ally results in EFRs and water demands not being met and
high competition between different water users occurring.
Future projections indicate that both EFRs and anthropogeni-
cally influenced discharge will increase, which can most
likely be attributed to the increase in surface water avail-
ability (Fig. 4). Future EFRs are projected to be sustained
during high-flow seasons, whereas during low-flow seasons
EFRs remain unmet. However, due to low withdrawals in the
Brahmaputra river basin it is projected that EFRs can be sus-
tained all-year round. Further, the large uncertainty bands in
the model projections of the Indus indicate that, especially
for RCP8.5-SSP3, there is a probability that EFRs will not
be met either during the second half of the monsoon season.
4.6 Comparison with other studies
The projected changes in the future water demand are, in
general, in line with reported trends in other studies, although
different processes can be responsible for the changes. In
their global-scale study, Wada et al. (2013) also project,
for instance, decreases in the irrigation water demand for
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6314 R. R. Wijngaard et al.: Climate change vs. socio-economic development
Figure 10. Monthly projected changes in the environmental flow requirements (EFRs) and anthropogenically influenced discharge (IPOT)
at the outlets of the Indus, Ganges, and Brahmaputra rivers for RCP4.5-SSP1 (row a) and RCP8.5-SSP3 (row b). The projected changes are
given for the end of the 21st century (EOC). The coloured bands represent the range of ensemble projections that are resulting from forcing
the LPJmL model with the different climate models and SSP storylines.
RCP4.5 in the irrigated croplands of South Asia. Neverthe-
less, the authors project an increase in irrigation water de-
mand for RCP8.5. According to the authors, increases in pre-
cipitation are responsible for the decrease in irrigation wa-
ter demand for RCP4.5 and are outweighed by increases in
temperature for RCP8.5, which cause atmospheric evapora-
tive demand to be enhanced, eventually resulting in increas-
ing irrigation water demands. In our study, the seasonal in-
creases in irrigation water demand (i.e. during the monsoon
(partly) and post-monsoon seasons) can also be attributed to
enhanced atmospheric evaporative demands emerging from
temperature increases. Nevertheless, other processes are re-
sponsible for the decreases in irrigation water demand. Be-
sides increases in precipitation, shorter growing seasons as
a response to temperature increases, which are larger for
RCP8.5, lead to decreasing irrigation water demands. An-
other study of Hanasaki et al. (2013) shows similar trends
with decreasing irrigation water demands that are the result
of increasing precipitation too.
4.7 Uncertainties and limitations
The projections of future water availability, demand, and sup-
ply are subject to several uncertainties and limitations that are
mainly related to the climate change projections, the repre-
sentation of (physical) processes and non-stationarity in the
used hydrological models, and the land use change and socio-
economic scenarios.
To assess the impacts of climate change on the future wa-
ter gap, an ensemble of eight downscaled and bias-corrected
GCMs were used that cover a wide range of climate con-
ditions representative for RCP4.5 and RCP8.5. The GCMs
have limited skill in simulating the regional climate in the
complex (mountainous) terrains of Central and South Asia
(Lutz et al., 2016b; Seneviratne et al., 2012). Despite the se-
lection of GCMs based on their skill in simulating the re-
gional climate by using an advanced envelope-based selec-
tion approach (Lutz et al., 2016b), uncertainties can still be
introduced in assessments on future changes in water supply
and demand. For instance, uncertainties can be introduced
in the way in which GCM runs were selected. The mod-
els were selected in three consecutive steps that are based
on changes in climatic means and extremes, and the skill in
simulating the historical regional climate. Which method is
chosen to select the climate models dictates which models
are selected and therefore largely determine the outcomes of
climate change impact studies like ours. In addition to the
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R. R. Wijngaard et al.: Climate change vs. socio-economic development 6315
uncertainties, the variation in climate change projections be-
tween GCMs is large. Since the climate models we selected
cover a wide range of possible future climate models, the
large variation in climate change projections result in a large
spread among the climate models, which subsequently prop-
agates into the hydrological model outcomes. The upcoming
CMIP6 model archive (Eyring et al., 2016) might improve
the outcomes of the studies by reducing the variation in cli-
mate change projections between the different GCMs.
The LPJmL model version we used for our assessments
has a limitation in simulating domestic and industrial water
demand. In the current version, only annual values of domes-
tic and industrial demand could be included. Since domes-
tic water demand varies on a monthly basis with higher de-
mands during the summer–monsoon season (i.e. higher tem-
peratures during summer–monsoon result in higher demand)
and lower demands during the winter season. This means that
on a seasonal basis, the domestic water demand and conse-
quently the water gap can be overestimated during the win-
ter season, and underestimated during the summer–monsoon
season. Further, the model has the limitation that the impact
of water pollution on water availability cannot be simulated.
This means that surface water and sustainable groundwater
withdrawals can be overestimated and unsustainable ground-
water withdrawals (i.e. the water gap) can be underestimated.
Whereas the LPJmL model includes human interventions,
such as dam operations, and irrigation withdrawals and dis-
tribution through canals, the SPHY model that has been
used for our upstream assessments does not include them.
Since human interventions can influence the hydrological cy-
cle, uncertainties might be introduced in the outflows of the
upstream domains. Current impacts of dams and irrigation
withdrawals are, however, assumed to be small due to the
relative low number and total capacity of dams in the up-
stream domains compared to the number and total capacity of
dams in the downstream domains. For instance, Tarbela Dam
has a total capacity of 12 km3, whereas the total capacity of
dams in the upstream domains reach up to about 5.5 km3dis-
tributed over about 50 dams (FAO, 2016). Furthermore, most
dams are designed as hydropower dams with limited storage
or for run-off-the-river hydropower operations, which have
a low degree of regulation in the upstream domains of the
IGB (FAO, 2016; Lehner et al., 2011). The impact of agri-
culture is also assumed to be small due to the rather low ir-
rigation water demands (and cropping intensity) in upstream
domains (i.e. <100 mm yr−1) compared to the irrigation wa-
ter demands (and cropping intensity) in downstream domains
(Biemans et al., 2016).
The parameterization of the SPHY and LPJmL models are
based on present climatology, land use, and other physical
catchment characteristics and is assumed to be stationary.
Many hydrological parameters, such as parameters control-
ling snow processes, are, however, non-stationary and can
change due to possible changes in climate, land use, or other
characteristics (Brigode et al., 2013; Merz et al., 2011; Wes-
tra et al., 2014). According to several studies (e.g. Brigode et
al., 2013; Vaze et al., 2010; Westra et al., 2014) the impact
of non-stationarity is highly dependent on several factors, in-
cluding the length and variability of the period of parame-
terization, which are decisive for the robustness of the mod-
els and thus the magnitude of uncertainty in the model out-
comes. For instance, Vaze et al. (2010) indicated that models
can be used for climate impact studies when parameteriza-
tions are based on data records of 20 years and longer, and
for areas where future annual precipitation is not more than
15 % dryer or 20 % wetter than the mean annual precipita-
tion that is derived from the data records. Other studies (e.g.
Brigode et al., 2013) have indicated that shorter periods (e.g.
3 years) also can result in acceptable parameter sets. The
disadvantage remains, however, that in the IGB long data
records are scarce and future changes in climate and land
use can be more extreme, especially in the southern part of
the IGB, where precipitation increases over 100 % are pro-
jected for the end of the 21st century (Fig. 2). This indicates
that the non-stationarity of hydrological parameters can re-
sult in uncertainties in the (future) model outcomes, such as
hydrological flow predictions. To reduce the impact of non-
stationarity other calibration strategies, such as the general-
ized split sample test procedure (Coron et al., 2012), are rec-
ommended, which aim to test several possible combinations
of calibration-validation periods to test the model’s perfor-
mance under different climate conditions.
Land use change scenarios that are consistent with SSP1
and SSP3 were extracted from the IMAGE model (Doel-
man et al., 2018) and represent future changes in rainfed and
irrigated cropland extents. One limitation is that only out-
comes on future cropland extents were used as a representa-
tive for the land use change scenarios, whereas outcomes on
future intensification of current croplands were not consid-
ered. Consequently, the projected yield increases and related
increase in irrigation water consumption, though not linearly
related, were not accounted for. This might eventually result
in an underestimation of irrigation water demand. Further, fu-
ture irrigation water demand can be overestimated since any
future increases in irrigation efficiency were not included in
our modelling approach. Another limitation that might influ-
ence the projections on irrigation water demand is the way
how irrigation practices are reflected within our modelling
approach. In our approach, it is assumed that crop types are
not adapted over time, which consequently results in decreas-
ing irrigation water demands when growing seasons shorten.
The reality, however, is that farmers may adapt to changing
climate conditions (e.g. due to the higher risk for heat stress
that is a consequence of increased temperature, i.e. extremes)
by switching to different crop types that are more suitable for
the changed climate. This might eventually influence projec-
tions on future irrigation water demand.
The SSP storylines that are used to project future changes
in water demand do not account for potential feedbacks be-
tween climate change and socio-economic changes. For in-
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6316 R. R. Wijngaard et al.: Climate change vs. socio-economic development
stance, the impacts of climate change on the land system are
not included (Doelman et al., 2018). According to Nelson et
al. (2014), climate change has an impact on agro-economic
variables, such as agricultural area and production. The au-
thors found, for example, that under climate change agricul-
tural areas are projected to increase due to intensifying man-
agement practices that are induced by climate change. This
means that without taking potential feedbacks between cli-
mate change and socio-economic changes into account, any
future increases in cropland extents might be underestimated.
Finally, future changes in the water demand and gap that
have been assessed are based on selected climate change
scenarios and SSP storylines. The future changes that are
assessed do not, however, reflect the impact of adapta-
tion strategies. For instance, it is most likely that extra hy-
dropower dams and reservoirs will be developed in the fu-
ture (Mukherji et al., 2015). In the agricultural sector, it
is most likely that irrigation efficiencies will be improved
by changing irrigation systems or that crop types will be
changed to ones that are more climate-tolerant (e.g. Biemans
et al., 2013). Further, future developments, such as regional
or transboundary cooperation that improves water and en-
ergy sharing and thus optimizes water resources use (Molden
et al., 2017), and their impact on the water gap have not
been assessed. Follow-up studies including the simulation of
basin-scale effects of climate change adaptation measures are
needed to investigate the impacts of future adaptation strate-
gies and developments on the South Asian water gap and
their potential in closing the water gap.
5 Conclusions
The objective of this study is to assess the impacts of cli-
mate change and socio-economic developments on the fu-
ture blue water gap in the downstream domains of the Indus,
Ganges, and Brahmaputra river basins. To this end, we use
a coupled modelling system consisting of the cryospheric–
hydrological SPHY model and the global dynamic hydro-
logical and crop production model LPJmL. The models are
forced with an ensemble of eight bias-corrected downscaled
GCMs that represent a wide range of regional RCP4.5 and
RCP8.5 climate conditions in combination with and with-
out two socio-economic development scenarios (SSP1 and
SSP3) that are likely linked with these RCPs. The model out-
comes are analysed in terms of changes in the water availabil-
ity, demand, and gap.
The outcomes indicate that surface water availability will
increase towards the end of the 21st century with the largest
projected increases for RCP8.5. Thereby, increases are pro-
jected to be stronger during the monsoon season, which can
mainly be attributed to the increases in monsoon precipita-
tion and glacier melt. The upstream–downstream difference
in water availability is largest in the Indus and Ganges river
basins, whereas in the Brahmaputra river basin this differ-
ence is relatively small. This indicates that the dependency
on upstream water resources is large, especially in the Indus
and Ganges river basins. Future upstream–downstream dif-
ferences in water availability are projected to be enhanced,
implying that the dependency on upstream water resources
will increase.
Annual and seasonal water consumption are projected
to decrease when considering climate change only. This is
mainly caused by the shortening of growing seasons that
emerges from temperature increases, and precipitation in-
creases that result in a shift from blue water irrigation to
green water or rainfed irrigation and thus cause irrigation
water consumption to decline. Only in the monsoon (partly)
and post-monsoon seasons is water consumption expected to
increase, which can mainly be attributed to enhanced atmo-
spheric evaporative demand and resulting increases in crop
evapotranspiration that emerge from temperature increases.
The combination of climate change and socio-economic de-
velopment result in increasing annual and seasonal water
consumption for RCP4.5-SSP1 and RCP8.5-SSP3 due to
population growth and economic developments.
Due to declining water demand under climate change only,
the water gap is also expected to decrease with relative de-
creases up to 37 % and 55 % in the Indus and Ganges, re-
spectively, for RCP8.5, at the end of the 21st century. The
combination of climate change and socio-economic develop-
ment is expected to result in increasing water gaps, with rel-
ative increases up to 7% and 14 % in the Indus and Ganges,
respectively, for RCP8.5-SSP3, at the end of the 21st century.
Future EFRs are projected to be sustained during high-flow
seasons, whereas during low-flow seasons EFRs cannot be
met in the Indus and Ganges river basins. Based on the out-
comes it can be concluded that socio-economic development
is the key driver in the evolution of the South Asian water
gap, whereas climate change plays a role as a decelerator.
For the South Asian region, which is already facing water
stress in a geopolitically complex situation, our findings pro-
vide valuable insights into the future evolution of the regional
water gap, providing a scientific basis for the formulation of
transboundary climate change adaptation policies.
Code and data availability. The code of the SPHY model is pub-
licly available at https://github.com/FutureWater/SPHY (last ac-
cess: 3 December 2018). The code of the LPJmL model is avail-
able upon request. The datasets that are produced in this study are
available upon request.
Author contributions. The study is designed by AFL, HB, RRW,
and WWI. The model codes were adjusted by HB for the LPJmL
model. The models were run by RRW (SPHY) and HB (LPJmL).
The analyses on the simulation outcomes were performed by RRW.
RRW and HB prepared the paper. The figures were prepared by
RRW. Finally, the proof-reading was performed by all (co-)authors.
Hydrol. Earth Syst. Sci., 22, 6297–6321, 2018 www.hydrol-earth-syst-sci.net/22/6297/2018/
R. R. Wijngaard et al.: Climate change vs. socio-economic development 6317
Competing interests. The authors declare that they have no conflict
of interest.
Disclaimer. The views expressed in this work are those of the cre-
ators and do not necessarily represent those of the UK Govern-
ment’s Department for International Development, the International
Development Research Centre, Canada, or its Board of Governors,
and are not necessarily attributable to their organizations.
Special issue statement. This article is part of the special issue
“The changing water cycle of the Indo-Gangetic Plain”. It is not
associated with a conference.
Acknowledgements. This work was carried out as part of the
Himalayan Adaptation, Water and Resilience (HI-AWARE)
consortium under the Collaborative Adaptation Research Initiative
in Africa and Asia (CARIAA) with financial support from the
UK Government’s Department for International Development and
the International Development Research Centre, Ottawa, Canada.
This work was also partially supported by core funds of ICIMOD
contributed by the governments of Afghanistan, Australia, Austria,
Bangladesh, Bhutan, China, India, Myanmar, Nepal, Norway,
Pakistan, Switzerland, and the United Kingdom. This project has
received funding from the European Research Council (ERC)
under the European Union’s Horizon 2020 research and innovation
programme (grant agreement no. 676819). This work is part of the
research programme VIDI with project number 016.161.208, which
is (partly) financed by the Netherlands Organisation for Scientific
Research (NWO). DFID and IDRC funds the HI-AWARE consor-
tium, of which ICIMOD and FutureWater are consortium members.
We thank Rens van Beek for supporting us in making the domestic
and industrial water demands available, and Marc Bierkens for the
helpful discussions. We thank Sharachchandra Lele and the editor
for their constructive remarks and suggestions that helped us to
improve the paper significantly.
Edited by: Pradeep P. Mujumdar
Reviewed by: Sharachchandra Lele
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