UCL Institute for Sustainable Resources

Irrigation freshwater withdrawal stress in future

climate and socio-economic scenarios

Victor Nechifor-Vostinaru, Matthew Winning

Abstract

Future pressure over freshwater resources coming from irrigated crop production is captured by an Irrigation Withdrawal to Availability (IWA) indicator derived through a global Computable General Equilibrium framework. The metric is calculated for several socio-economic development pathways and considers technological evolution through differentiated irrigated and rainfed crop yield changes. The RESCU model employed explicitly uses freshwater as a factor in crop production, whilst clearly distinguishing between irrigated and rainfed production functions. Two scenarios are applied to three alternative SSPs (SSP1, SSP2, SSP5) – inherent yield improvements under a ‘no climate change’ assumption and yield changes due to climate change in the A1B carbon emissions pathway. Results show that freshwater withdrawals continue to expand in most of the regions that are currently water-stressed with the IWA increasing in some cases by more than 50% from 2004 levels. Other regions, such as China, benefit from yield improvements and thus shift from irrigated to rainfed crop production. Climate change leads to a further increase in the IWA for India and a decrease for Northern Africa, the rest of South Asia and the Middle East.

Key words: Computable General Equilibrium, Freshwater withdrawals, Crop production, Irrigation, Climate change, Water stress indicators JEL Classification: D58, Q25, Q54, Q56 Victor Nechifor-Vostinaru UCL Institute for Sustainable Resources victor.nechifor.13@ucl.ac.uk

Matthew Winning UCL Institute for Sustainable Resources m.winning@ucl.ac.uk

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1. Introduction

Freshwater demand in economic activities currently accounts for about 9% of the 43,000 billion

cubic metres (bcm) of renewable resources available at a global scale (FAO AQUASTAT). Even if this

figure may be perceived as low, it hides the large disparities in how freshwater endowments are

distributed between regions. Continued increases in population and economic growth in developing

countries, many of which are already water constrained, will very likely put further pressure on

freshwater resources. Accounting for population growth alone, in accordance to World Bank

demographic projections, 46% of the global population will be living in countries with severe water

scarcity by 20501 (see Figure 1).

From a use perspective, freshwater is usually divided into the blue and green water categories. Green water represents the volume that is naturally contained in the soils, and by being immobile it can only be used in crop production. Blue water consists of the volume of freshwater that is withdrawn by man from rivers, lakes and aquifers. Hence, this is the category that can be directed to the multiple types of freshwater uses (crop production, household consumption, power plant cooling etc.) and that can become subject to competition where total demand exceeds the available resources.

Irrigated agriculture is currently the largest blue water user, representing globally 70% of all

withdrawals and 40% of total crop production. Irrigated land is expected to increase by 11% by 2050

(Alexandratos & Bruinsma 2012), partially replacing rainfed production, partially expanding into

currently non-crop land. Due to the central role of irrigated crop production in freshwater

withdrawal and considering the multiple possible futures with regard to socio-economic

development and climate change incidence over crop yields, it is vital to have a better understanding

of how freshwater demand in irrigated agriculture will evolve over time by exploring the alternative

economic development, demographic evolution and projected climate change impacts.

Therefore in this research we seek to assess the future pressure on freshwater resources by focusing

on the blue water use in irrigated crop production. We do this by expressing withdrawal pressure

using the Irrigation Withdrawals to Availability (IWA) indicator i.e. irrigation withdrawals relative to

total internal renewable water resources available. Changes in irrigation blue water demand are

determined at a macro-regional level by conducting a global analysis for the 2004-2050 timeframe

using the CGE dynamic-recursive RESCU model. The model represents irrigation freshwater as an

explicit factor of production, whilst clearly distinguishing between rainfed and irrigated crops.

Irrigation withdrawal changes depend on the evolution of demand for crops but also on crop yield

gains. In a first stage, we embed yield changes under a ‘no climate change’ assumption to derive the

crop expansion and thus the IWA indicator under three Shared Socioeconomic Pathways (SSP1, SSP2

and SSP5). In a second stage, yield changes are specified for the A1B carbon emissions SRES scenario.

Uncertainty of the incidence of climate change over local temperature and precipitation patterns

and implicitly over yields is addressed through the use of data coming from two climate models.

The analysis is structured as follows. Section 2 provides an overview of past attempts in determining

freshwater uses at a global level. Section 3 makes a brief introduction to the RESCU model and

presents the SSP and technological shocks used. Findings are presented in Section 4 followed by a

discussion in Section 5. Conclusions are drawn in Section 6.

1 Judging through the Falkenmark index (Falkenmark & Widstrand 1992) which sets an annual freshwater availability of 1000m3/capita as a threshold for sever water scarcity.

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Figure 1 - Water scarcity levels up to 2050

Source: own calculation from World Bank population projection data

2. Literature review

Future freshwater use at a global scale has been given considerable attention in the past two

decades. Various approaches have been used in this respect with each brining new light into the

present and future state of freshwater resources, but also facing some methodological limitations.

Shiklomanov (1999) and Shen et al. (2010) determine future freshwater requirements by

extrapolating past uses combined with assumptions on future socio-economic and agricultural

changes. Another approach taken is spatial analysis (Alcamo et al. 2007; Flörke et al. 2013; Arnell

2004; Shen et al. 2014) which brings further insights into the freshwater demand-availability

relationship at a local level. Both strands of research provide a first order estimate of future uses at

different geographical levels, however the expansion of water demand by the different sectors is

treated individually thus lacking an economy-wide cohesion. Nevertheless, it is noteworthy that

some of these assessments have taken into account alternative socio-economic developments based

on the IPCC SRES scenarios (Arnell 2004; Alcamo et al. 2007).

In terms of freshwater use at a sectoral level, the IMPACT model (Rosegrant et al. 2002; Nelson et al.

2010) provides a detailed account of future irrigation water withdrawals covering the world major

river basins and 44 crop commodities. A distinct feature of the model is the introduction of

competition over freshwater uses which takes place in two stages. Firstly, agricultural water

availability is calculated by deducting all exogenous non-agricultural water uses from total

renewable resources. Secondly, the remainder is allocated among crop types based on their prices

and relative profitability. Demand for water is indirectly determined by the demand for food (subject

to commodity prices, GDP/capita and population changes), crush oilseeds, feed and biofuels (subject

to government policy). Whilst taking income effects over crop demand and commodity prices into

account, as a partial equilibrium-model, IMPACT does not relate agriculture to the overall economy;

hence any welfare effects induced by water limitations are not propagated to other sectors.

Crowding-out effects of factor uses in other sectors are also not considered.

While freshwater modelling in a CGE framework has been undertaken extensively at a country- or

sub-country-level (Dixon et al. 2011; van Heerden et al. 2008; Luckmann et al. 2014; Strzepek et al.

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2008; Hassan et al. 2008; Letsoalo et al. 2007), to the authors’ best knowledge, freshwater

representation in global CGE models has only materialised in three instances – EPPA-IRC (Baker

2011), GTAP-W (Calzadilla et al. 2011a) and GTAP-BIO-W (Taheripour et al. 2013). Limited to

introducing freshwater as an input factor only in agriculture and no other economic sectors, these

models have tackled the welfare and international trade implications of constrained freshwater

supply. Whilst EPPA-IRC is essentially a land-use model introducing freshwater use constraints

implicitly through land-use conversion constraints, the latter two models distinguish irrigation

freshwater as a production factor per se.

GTAP-W used in Calzadilla et al. (2013) is employed to link the mean annual run-off changes under

different SRES scenarios to irrigation water supply using regional supply elasticities. The model has

also been used to test the impact of irrigation water use efficiency (Calzadilla et al. 2010) and to

determine the interaction between the effects of trade agreements and climate change CO2

fertilisation (Calzadilla et al. 2011b) . The GTAP-BIO-W model is employed in Liu et al. (2013) for a

comparative static analysis of constrained water supply at a river basin level. Water supply is

reduced according to the changes in the ‘Irrigation Water Supply Reliability’ index (IWRS) as

provided by the IMPACT model in a business-as-usual scenario. The IWRS tracks the dynamic

changes in freshwater availability for crop production once all the non-agricultural uses have been

deducted. Hence, GTAP-BIO-W inherits the assumptions with regard to the allocation of freshwater

uses across sectors from IMPACT.

In all of the above mentioned research efforts using the GTAP-W and GTAP-BIO-W models, water

withdrawals are only treated indirectly and do not expand or contract as a function of market forces.

In GTAP-W, because of the factor market clearing condition, it is implicit that any change in run-off

incurs a change in withdrawals in the same direction. In GTAP-BIO-W, water withdrawals are decided

outside the model through the IWRS index changes, hence all regions for which the index remains

unchanged, withdrawal changes do not occur regardless of the pressures coming from an expansion

of crop demand. Hence we conclude that irrigation water withdrawal changes as a function of

economic growth and population count evolution cannot be properly accounted for using these

models.

Concerning the physical freshwater supply constraints, our take is that at a global scale it is difficult

to assess how much freshwater can be abstracted from the environment and even more so to

determine how much can be employed for irrigation. On the one hand, there is vast evidence that

some river basins are currently being overexploited with a considerable reliance on groundwater

pumping (Smakhtin et al. 2004; Döll et al. 2014; Long et al. 2015; Wada et al. 2010) thus no upper

withdrawal limit can be taken for granted. On the other hand, without considering the evolution of

other freshwater demand drivers (industry, services and households) by systematically using the

same assumptions with regard to socio-economic development it is not possible to ascertain the

amount that is available to crop production even when a withdrawal limit can be considered. Hence

the present assessment of future freshwater requirements does not take into account limitations in

irrigation freshwater supply. Nevertheless, we do acknowledge that it will become increasingly

challenging to source the required volumes as withdrawals get closer to the upper limit of renewable

freshwater resources available.

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3. Methodology

Given that almost all blue water is employed within multiple economic sectors (agriculture, energy,

industry, water provisioning services), total withdrawals become a function of demand for economic

goods. From here we derive the opportunity of employing economy-wide models to determine blue

water demand when socio-economic changes are factored. As an input to production, freshwater

needs to be represented in relation to the other factors in terms of substitution possibilities. For

instance, better crop management through the use of more skilled labour can lead to better

irrigation practises requiring less water and land inputs. At the same time, productivity gains of one

factor influences the demand for other inputs. In this research we capture the relationship between

arable land productivity gains through yield changes and the demand for irrigation water. Yield

growth leads to a reduction in land requirements and implicitly reduces the land market prices. This

then reduces the total cost of production resulting in a growth of demand for crops with a feedback

effect over the demand of non-land inputs among which irrigation freshwater.

The rationale for using a macro-economic model as opposed to a partial-equilibrium model consists

in its capacity to determine the economy-wide effects of the dynamics needed to be taken into

account, namely:

- GDP growth and investment with differentiated impacts on the expansion of the various

sectors considered, especially crop production

- Population growth with implications over labour supply and implicitly over costs of

production

Whilst a partial-equilibrium model can still capture the income effects over crop demand, it cannot

account for how endowments are being allocated within an economy given changes in relative

prices of all factors of production.

With crops being some of the most intensely internationally traded commodities, the extension of

the analysis at a global level enables us to include the effects of trade over withdrawal pressure.

Regions that are land or water constrained are likely to replace some of the domestic production of

water-intensive goods with imports and thus avoid further increases in pressure over its

endowments. This is done in our model using the Armington assumption for substitution of domestic

and foreign varieties of commodities.

3.1. The RESCU model

To determine the stress induced by irrigation freshwater withdrawal, we employ the RESCU

(RESources CGE UCL) model which is built on a global dynamic-recursive CGE framework. In this

research, RESCU uses the GTAP 8.1 database with a 20 region and 12 sector aggregation. The

regional aggregation is done to reflect differences in terms of agro-ecological conditions (see Table 6

in Appendix).

The model details irrigated and rainfed production distinctly for three crop groups (gra grains, v_f

veg&fruit and ocp other crops). At the top level, the rainfed and irrigated outputs of each crop type

are combined assuming perfect substitution of the two varieties. Both production functions assume

a Leontief nest (perfect complements) between value-added VA and the intermediate goods bundle

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INT. For irrigated production, value added VA is a CES nest composed of an Irrigation composite and

a capital-labour KL bundle. Irrigation is a Leontief composite of irrigable land IrrLand and irrigation

water IrrWater. In the rainfed production nesting, the Irrigation bundle is replaced by rainfed land

RfLand, allowing for substitution between this factor and the capital-labour KL composite.

Therefore, compared to the other global CGE models covering freshwater as distinct factor of

production (GTAP-W and GTAP-BIO-W), RESCU further specifies crop production nesting. In the

GTAP-W and GTAP-BIO-W models all land types were bundled with capital, labour and energy2,

allowing for direct substitution between any pair of factors. The isolation of the capital-labour

substitution in the RESCU model is based on the assumption that agricultural intensification,

especially when moving to modern agricultural practises, implies a shift from labour to capital use.

Furthermore, the labour and capital interaction is also of a particular interest in the present

research, bearing in mind that capital and labour supply have different dynamics in the socio-

economic pathways considered.

For each crop production function, the two land types considered have associated productivity

factors λIrrLand and λRfLand which are exogenously specified using data from crop models. The supply of

crop land is endogenous in the model. In a first stage, arable land supply is specified using a logistic

function. The function is calibrated by an upper arable land expansion limit derived from the GAEZ

database (Fischer et al. 2011). The availability of arable land thus reacts to market prices – when the

price of land exceeds the regional price index, additional supply is brought in to counter price

inflation. In the current version of the model, the other land endowments employed in the livestock

and forestry sectors are considered to be distinct from arable land and to be in a fixed supply. In a

second stage, arable land is split into rainfed and irrigated land using a CET function. With a Leontief

(no substitution) nesting of irrigated land (IrrLand) and irrigation water (IrrWater), the supply of

IrrWater is introduced to follow the expansion of IrrLand supply such that it would not impose a

constraint on the expansion or contraction in demand of the Irrigation bundle.

Figure 2 – RESCU Irrigated and rainfed crop production functions

2 both GTAP-W and GTAP-BIO-W are an evolution of the energy and environment GTAP-E model (Burniaux & Truong 2002)

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3.2. Irrigation water endowment value accounting

The research builds on the existing efforts to model freshwater uses through a CGE framework. The

GTAP-W model (Calzadilla et al. 2011a) is the first model to properly introduce freshwater as factor

of production into a global CGE model. This introduction is possible by splitting the ‘Land’

endowment from the GTAP database into ‘rainfed’ and ‘irrigated’ varieties. The split shares are

based on disaggregated crop rainfed and irrigated production values derived from the agricultural

partial-equilibrium IMPACT model (Rosegrant et al. 2002). Based on higher yields obtained on

irrigated land as calculated by IMPACT, the irrigation bundle is further split into ‘irrigated land’ and

‘irrigation water’ according to the rainfed/irrigated yield differences. Hence, the underlying

assumption is that in the absence of irrigation, irrigable land produces the same yields as rainfed

land. This last assumption is not in line with the observed climatic and soil conditions which may

cause irrigation to occur in areas where plants are water deficient. The GWCM model data (Siebert &

Doll 2008) shows that in the large majority of cases, the removal of irrigation leads to yields that are

different than those on rainfed land within the same region.

Taheripour et al. (2013) introduce through GTAP-BIO-W the next generation of global agricultural

freshwater CGE modelling where irrigated and rainfed crops are produced by two distinct sectors.

This model is an evolution of the land-use GTAP-BIO model which already disaggregates land

endowments at an agro-ecological zone level (AEZ). The split of value added to isolate the input of

freshwater into agricultural production is done in a similar fashion to GTAP-W. The value of irrigation

water is derived through yield differences between irrigated and rainfed land with data obtained

from the GCWM model.

While we acknowledge the advancements made in freshwater endowment accounting in the GTAP

database, we derive the value of irrigation freshwater based on production losses when irrigation

does not take place. In the GTAP-RESCU database, the value of lost production is derived from the

‘no irrigation’ scenario results from the GCWM model (Siebert & Döll 2010). GCWM is a crop

simulation model dedicated to calculating green and blue water consumption occurring through

crop evapotranspiration. To do this, it combines monthly gridded data (5 arc-min resolution) for

growing areas of 26 crop classes with national and subnational statistics covering irrigated and

rainfed production and yields. It then determines annual water consumption requirements based on

cropping patterns and seasons. The annual data available covers the 1998-2002 period.

In the GTAP-RESCU database, the mean GCWM crop production data is used to split the GTAP crop

production into distinct rainfed and irrigated sectors. Thus, the 26 GCWM crop classes are mapped

to the 8 GTAP classes. In this respect, representative FAO commodity prices are factored in to

convert GWCM production quantities to dollar values. Next, the ‘Land’ factor in GTAP is split to

match the split sectors. All land inputs used for irrigated and rainfed crops are converted into

irrigation (‘Irrigation’) and rainfed land (‘RfLand’) respectively. The land used in forestry and livestock

are thus considered a third and distinct type of land endowment.

Finally, we seek to isolate the contribution of irrigation water as a separate production factor by

dividing the ‘Irrigation’ bundle into irrigation water ‘IrrWater’ and irrigable land ‘IrrLand’. To derive

the value of irrigation water we calculate the share of production in total irrigated production that is

lost when the irrigation facility is absent. The ‘no irrigation’ production figures are taken from the

GCWM model which determines the crop response to a reduction in water application. The resulting

production loss shares are then used as ‘IrrWater’ shares in the ‘Irrigation’ bundle of each irrigated

crop sector.

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3.3. IWA Indicator

To determine the freshwater withdrawals in irrigation we use blue water consumption intensities

Φirc associated with the use of the irrigation water IrrWater factor in the production functions of

each irrigated crop irc. These intensities are crop-specific and are determined using the blue water

consumption data from the GCWM model. Total irrigation withdrawals are being calculated by

factoring in regional irrigation efficiencies3 ηr. Finally, the annual irrigation withdrawals to availability

(IWA) indicator is determined by dividing the total irrigation withdrawals by the total internal

renewable freshwater resources IRWR obtained from the FAO AQUASTAT database:

𝐼𝑊𝐴𝑟 = 𝜂𝑟 ∗ ∑ 𝛷𝑖𝑟𝑐,𝑟𝐼𝑟𝑟𝑊𝑎𝑡𝑒𝑟𝑖𝑟𝑐,𝑟𝑖𝑟𝑐,𝑟

𝐼𝑅𝑊𝑅𝑟

The total freshwater (blue and green) requirements are also calculated by factoring in the green

water consumption on both rainfed and irrigated land (precipitation on irrigated land is also

considered). The green water intensities are linked to the land factor use (IrrLand for irrigated and

RfLand for rainfed production) with values derived from the GCWM green water consumption data.

3.4. Scenarios considered

Several model shocks are used to determine a range of possible water withdrawals outcomes

induced by the changes in irrigated crop production. These changes can occur through different

channels:

- Expansion of demand for crop products through domestic or foreign GDP growth via

international trade

- Availability of other factors of production namely labour which may have a significant weight

in crop production costs

- Cost advantage of irrigated over rainfed production or vice versa – this is induced by

differentiated yield growth rates with implications over factor prices

- Arable land expansion and conversion of irrigated land to and from rainfed land

We employ a baseline and two yield change scenarios- the ‘no-climate change yield improvements’

and the ‘A1B climate change yield changes’. In the baseline and the yield changes scenarios, GDP

and labour supply growth are driven by the alternative SSP1, SSP2 and SSP5 socio-economic

developments (Moss et al. 2010; van Vuuren et al. 2014) with downscaled country data obtained

from the IIASA SSP database4. A snap-shot of the socio-economic pathways used is given in Table 1.

GDP growth targets are achieved in the RESCU model by endogenising a region-specific labour

productivity multiplier. In terms of factor supply specified outside the model, investment which

drives capital accumulation is determined through adjusted investment rates. These investment

adjustments are exogenously introduced by factoring in the investment dynamics for each SSP as

3 The difference between withdrawal and consumption is driven by losses that occur in irrigation practises – a water use efficiency for each RESCU region is calculated based on the LPJmL methodology and data as described in Rohwer et al. (2007) 4 https://tntcat.iiasa.ac.at/SspDb/

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computed by the MaGE model (Fouré et al. 2013). Labour supply follows the population count

evolution of the 15-65 years age groups at the level of each region.

Table 1 – SSP scenario description

SSP scenario Details

SSP1 – Sustainability

Rapid development of low-income countries, reduction of inequality between economies; globalised economy; reduced dependency on fossil fuels and reduced resource intensity; adoption of clean energy technologies awareness of environmental degradation

SSP2 – Middle of the Road

Same trends as in previous decades; disproportionate development of low-income economies; global income per capita increases at a medium pace; reduction of energy intensities; some decrease of dependency on fossil fuels;

SSP5 – Conventional Development

Orientation towards economic growth; energy systems dependent on fossil fuels; highly-engineered infrastructure

Baseline

The baseline is defined by crop expansion determined through the three SSP pathways considered.

Irrigated and rainfed land yields are considered to be constant (no yield gains from 2004 levels). The

baseline is therefore used to gage the impact of the technological change occurring in the no-climate

change scenario.

Scenario 1 - Yield improvements under no climate change

Expected intrinsic productivity gains (induced for example by further crop research and information

dissemination) are embedded into the model as land factor productivity gains. The yield gains

differentiated by irrigated and rainfed production are taken from the IMPACT model estimates

(Nelson et al. 2010) and mapped onto the GTAP RESCU sectors.

Scenario 2 - Yield changes under the A1B emissions scenario

Yield modifications induced by climate change is considered by taking into account the influence of

temperature and precipitation changes in one of the high emissions SRES pathways – A1B. The mean

global temperature in this pathway was projected to increase by about 1.4°C by 2050 compared to

2000, slightly higher than the A2 pathway and about 0.4°C higher than B1 (Meehl et al. 2007).

The corresponding yield deviation from the ‘no climate change’ scenario are calculated in the

IMPACT model using climate data from two global climate models (MIROC and CSIRO). The use of

two climate datasets enables the inclusion of uncertainty with regard to climate change incidence at

a regional level. As illustrated in Table 2, climate change can influence land productivity both

upwards and downwards depending on the region, crop class and land type (rainfed or irrigated).

Furthermore, the yield gain calculation can lead to contrasting results depending on the climate

model employed which translate emission concentration increases into different changes in

precipitation and temperature. Therefore, we run the RESCU model using the yield changes

associated with each climate data, however water withdrawals and the IWA indicator in the A1B

emissions scenario are reported through the mean values of the model results obtained.

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Table 2 - Annual yield growth rates for rainfed and irrigated production – selected regions

% Yield annual growth (rainfed/irrigated) RESCU region No climate

change (noCC) A1B

CSIRO MIROC

India Grains Veg&Fruits Other crops

1.29 / 0.94 0.79 / 1.02 0.99 / (0.04)

0.49/0.29 1.26/0.82 1.04/(0.01)

0.91/0.67 1.45/0.94 1.31/(0.04)

Northern Africa Grains Veg&Fruits Other crops

1.29/1.43 1.13/1.58 1.09/0.89

0.99/0.41 1.41/(0.02) 1.07/0.74

0.67/0.11 1.19/(0.08) 1.16/0.75

China Grains Veg&Fruits Other crops

0.01/0.86 1.19/0.67 0.96/0.16

0.08/1.38 0.44/0.19 0.99/0.1

0.26/0.36 1.42/0.68 0.99/0.14

4. Results

4.1. Scenario 1 – Yield improvements under no climate change

With yield improvements under no climate change, irrigation water withdrawals for crop production

at a global level grow from 2313 bcm in 2004 to 2510 bcm (SSP1), 2483 bcm (SSP2) and 2545 (SSP5)

by 2050, hence an increase of 7-10% from the base year. Despite its ‘middle of the road’ label, the

SSP2 generally represents an economic and population growth path that produces lower

withdrawals than the ‘sustainability’ SSP1 scenario, whereas SSP5, in line with expectations,

produces the most significant increases.

At a regional level the implications of socio-economic changes and yield improvements can be

significant especially in regions that are already subject to water stress (Central Africa Dry, Central

Asia, Middle East, Northern Africa, India, South Asia). These regions continue to have an important

expansion of irrigated crop production leading to further increases in water withdrawal pressure

(see Table 3). The IWA ratios in these cases start from a range of 20-60% in 2004 and reach almost

90% by 2050 in extreme instances (Middle East in SSP5). Figure 4 presents the heat maps of the IWA

at the regional level for 2004, 2025 and 2050 in the case of SSP2.

At the other end of the spectrum, the IWA in China by 2050 is reduced significantly down to a

negligible level of 2% in all three SSPs. To a large extent, this is due to higher yield improvements on

rainfed production which causes a shift from irrigated to rainfed production. Thus the declining blue

water demand is complemented by an increase in green water inputs (see Figure 6 in Appendix). At

the same time, a declining population in China with implications over labour supply leads to higher

costs of production and thus to a relative reduction in demand for crops. Lower economic growth

regions (North America, USA, Australia&New Zeeland, Northern Europe, NE Asia) also reduce their

reliance on irrigation which determines their IWA to contract. These dynamics share some of the

causes found in China i.e. better yield growth obtained on rainfed land.

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Thus, irrigation water withdrawal across regions can go in both directions (increase or decrease in

withdrawals) depending on the size of the yield improvement and on which of the two land types

(irrigated or rainfed) becomes more productive. The comparison of results in the ‘no climate

change’ scenario to the baseline where no yield changes are considered (Figure 3) enables us to

determine the contribution of yield changes to withdrawal pressure. Yield improvements lead to an

increase in withdrawal pressure in some water-challenged regions (Middle East, South Asia, Central

Africa Dry, Central Asia) but can also determine a reduction in withdrawals in others (India, Northern

Africa, Southern Africa).

Figure 3 – Irrigation (blue) water withdrawals changes relative to 2004 by SSP – no climate change scenario and baseline

Table 3 –IWA ratio by SSP in the ‘no climate change’ yield improvement scenario – 2004, 2025 and 2050

2004

SSP1 SSP2 SSP5

Region 2025 2050 2025 2050 2025 2050

Middle East ↗ 50.32% 63.68% 82.79% 63.18% 81.98% 64.75% 89.77%

Northern Africa ↗ 60.66% 57.97% 69.30% 57.72% 67.34% 57.72% 68.74%

India ↗ 31.74% 43.39% 45.47% 43.00% 44.42% 43.49% 46.43%

South Asia ↗ 21.32% 21.74% 24.28% 21.66% 24.70% 21.56% 22.19%

Central Africa Dry ↗ 12.80% 15.61% 24.96% 15.26% 22.98% 15.73% 27.56%

Central Asia ↗ 7.05% 10.72% 16.10% 10.67% 16.26% 10.80% 17.53%

China ↘ 13.20% 7.32% 2.05% 6.48% 2.04% 7.11% 2.06%

S Europe ↗ 4.70% 5.17% 5.73% 5.15% 5.61% 5.19% 6.02%

Southern Africa ↗ 4.44% 4.30% 5.14% 4.25% 4.79% 4.34% 5.65%

USA ↘ 5.34% 4.43% 3.42% 4.44% 3.50% 4.39% 3.34%

SE Asia ↗ 2.13% 2.32% 2.83% 2.32% 2.77% 2.33% 2.96%

Eurasia ↘ 1.84% 1.68% 1.59% 1.67% 1.60% 1.68% 1.55%

-100%

-75%

-50%

-25%

0%

25%

50%

75%

100%

125%

150%

175%

SSP1 noCC* yields SSP2 noCC* yields SSP5 noCC* yields

SSP1 baseline SSP2 baseline SSP5 baseline

*noCC - no climate change yield changes

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(continued) Region

2004 SSP1 SSP2 SSP3

2025 2050 2025 2050 2025 2050

North Latin America↗ 1.11% 1.13% 1.23% 1.12% 1.22% 1.12% 1.24%

South Latin America↗ 1.05% 1.11% 1.20% 1.11% 1.19% 1.11% 1.24%

NE Asia ↘ 1.24% 0.90% 0.65% 1.05% 0.95% 0.86% 0.42%

AU&NZ ↘ 0.85% 0.73% 0.64% 0.73% 0.64% 0.73% 0.68%

Central Africa Humid↘ 0.42% 0.30% 0.34% 0.30% 0.32% 0.30% 0.36%

Brazil ↗ 0.13% 0.13% 0.13% 0.13% 0.13% 0.13% 0.13%

N Europe↘ 0.07% 0.08% 0.09% 0.08% 0.09% 0.08% 0.10%

North America ↘ 0.08% 0.06% 0.05% 0.06% 0.05% 0.06% 0.06%

4.2. Scenario 2 - Yield changes under the A1B emissions scenario

Climate change induces changes in yields compared to the previous scenario. Once again, the

irrigation withdrawal pressures alterations depend on the region as temperature and precipitation

can be affected differently from one region to the other. Figure 5a ranks the regions according to

their IWA and also shows how the indicator values change from the ‘no climate change yield

improvement’ scenario and the baseline. In some water stressed regions the pressure is reduced

following a degradation of irrigated yields which is either more pronounced than that of rainfed

yields (Northern Africa) or even accompanied by an improvement of rainfed yields compared to ‘no

climate change’ (South Asia, Central Asia). The largest impact relative to total withdrawals is in South

Asia where a 47% reduction in withdrawal relative to the ‘no climate change’ scenario (Figure 5b)

leads to IWA values even lower than in the base year 2004. In other cases, the IWA increases as a

function of improved irrigated yields relative to those on rainfed land (China, India, Central Africa

Dry). China increases its irrigation withdrawals by 61% compared to the previous scenario.

Nevertheless, judging through the two climate data used (CSIRO and MIROC) the uncertainty

concerning climate change impacts in China is high.

Table 4 shows the accordance of the two climate models used by IMPACT to determine yield

changes. In some cases the climate model outputs lead to opposing effects over yield (one

suggesting an improvement whilst the other a deterioration). Differences between yield values can

therefore lead to significant discrepancies over freshwater withdrawal pressure. In Table 4 these

diverging results are quantified trough a ratio (Diff) which relates the freshwater withdrawal

differences produced by yields derived from each climate model to the withdrawals in the ‘no

climate change’ scenario:

𝐷𝑖𝑓𝑓 =|𝑊𝑖𝑡ℎ𝑑𝑟𝑎𝑤𝑎𝑙𝑠𝐶𝑆𝐼𝑅𝑂 − 𝑊𝑖𝑡ℎ𝑑𝑟𝑎𝑤𝑎𝑙𝑠𝑀𝐼𝑅𝑂𝐶 |

𝑊𝑖𝑡ℎ𝑑𝑟𝑎𝑤𝑎𝑙𝑠𝑛𝑜 𝑐𝑙𝑖𝑚𝑎𝑡𝑒 𝑐ℎ𝑎𝑛𝑔𝑒

The IWA results for the A1B scenario yield changes across the three SSPs are included in Table 5 in

the Appendix.

13

Figure 4 – No climate change yield changes - IWA in SSP2 - 2004, 2025, 2050

14

Figure 5 – IWA for 2050 in the A1B emissions scenario using mean irrigation withdrawal values

a) IWA by scenario (SSP2) b) changes in A1B withdrawals relative to ‘no climate’

levels (SSP2)

Table 4 - Accordance of freshwater withdrawals in SSP2 using the A1B climate data of the MIROC and CSIRO models

Region

A1B Direction of change

Diff in 2050

Australia&New Zeeland same 3.32%

Brazil same 36.09%

Central Africa Dry opposing 1.97%

Central Africa Humid same 2.24%

Central Asia same 1.87%

China opposing 124.27%

Eurasia same 1.04%

India same 6.68%

Middle East opposing 5.74%

Norther Africa same 1.07%

North East Asia same 0.93%

Northern Europe opposing 1.75%

North Latin America same 2.32%

North America opposing 13.57%

Southern Africa same 0.24%

South Asia same 3.94%

South East Asia same 5.47%

Southern Europe same 3.28%

South Latin America same 3.13%

United States opposing 26.34%

0%

10%

20%

30%

40%

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-1.7%-5.1%

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6.4% 7.4%

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24.2%

-0.2%

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-60.0%

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-20.0%

0.0%

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15

5. Discussion

Analysing the range of values produced by the three SSPs applied in our scenarios, future socio-

economic developments are a significant contributor to the pressure exerted by irrigation over

freshwater resources. Noteworthy is the SSP1 which, in spite of its lower carbon emissions, has

greater implications over withdrawals than the ‘middle of the road’ SSP2. The high withdrawal levels

in the SSP1 development pathway is explained by high growth rates of developing regions which are

already irrigation-intensive leading thus to a further expansion of irrigated crop demand and

consequently to that of blue water use.

Nevertheless the relationship between economic growth and the increase in crop demand is not

linear, as crop sectors grow at a slower pace than the other economic sectors. The model captures

the differentiated impact of economic growth over the expansion of individual economic sectors by

taking into account the allocation of factors between economic activities driven by relative price

changes. Due to the important weight of labour in the crop production cost structure, regions

experiencing economic growth but a stagnating or declining labour supply will face increases in

labour costs and implicitly a price disadvantage of crops relative to the other commodities.

With the distinct representation of rainfed and irrigated crop production in the model, the crop yield

changes occurring either inherently (Scenario 1) or being influenced by changes in climatic

conditions (Scenario 2) lead to complex interactions between the expansion of crop demand and

irrigated and rainfed production. Thus economic growth does not necessarily determine an increase

in irrigation water withdrawal. A case in point is China where rainfed production facing higher yield

improvements displaces irrigated production.

Climate change impacts over yields introduce a further complication in assessing irrigation pressure

as data from multiple climate models may lead to diverging yield-change values. For the two climate

datasets used (MIROC and CSIRO) climate change leads to opposing effects over irrigation water

withdrawals in six out of the twenty RESCU regions. Therefore a greater diversity of climate data

may lead to an increase in the results robustness.

As opposed to the other global CGE models which include freshwater as a factor of production in

agricultural sectors, the RESCU model allows for the assessment of the possible expansion of

withdrawal from 2004 levels as a consequence of growth in irrigated crop output stemming from

socio-economic development. However, the calculated expansion of blue water volumes should be

considered to the extent that the assumption of future unrestricted withdrawals is plausible, given

the land constraints embedded in RESCU. On the one hand, past macro-economic modelling

attempts have included exogenous supply constraints coming from either changes in annual run-off

(GTAP-W) or changes in freshwater demand in non-agricultural sectors for which freshwater use is

not considered within the model (GTAP-BIO-W). On the other hand, proofs of current river basin

overexploitation in water-scarce- but also under-nourished regions lead us to the conclusion that

withdrawals can expand although blue water volumes used are getting closer to the total renewable

resource available. Therefore, even though future irrigation water use may be partially restricted,

either prescriptively or due to technical difficulties (lowering groundwater levels for instance), the

relation between crop output growth and the demand for irrigation water begs for thorough

consideration given the weight of crop blue water requirements in total withdrawals. Hence, the

IWA should be treated at least as an indication of the size of future pressure coming from freshwater

irrigation withdrawal at a regional level.

16

6. Conclusions

Through the use of macro-economic modelling, our research adds new findings to the global

freshwater assessment literature by determining alternative futures for blue water demand in

irrigated crop production. The pressure induced by demand changes over freshwater resources is

determined through the Irrigation Withdrawal to Availability (IWA) indicator. The factors considered

influencing the range of outcomes are socio-economic developments and technological evolution

through crop yield improvements on one hand, and temperature and precipitation changes through

climate change on the other. The advantages of using a global CGE model come from the

framework’s capacity to determine the incidence of different socio-economic pathways over factor

allocation across economic activities and consequently over sectoral expansion when factor

availability and relative prices change.

The novelty of the RESCU model consists in a distinct representation of irrigated and rainfed crop

production complemented by an improved irrigation water accounting methodology. These

additions enable the consideration of impacts coming from irrigated and rainfed yield changes over

irrigation blue water use. The model is thus used to determine the future freshwater withdrawals in

two technological change scenarios and embeds the results into the IWA metric.

Economic growth plays an important role in the expansion of irrigation freshwater use. From the

three SSP pathways considered, ‘the middle of the road’ SSP2 leads to the lowest increase in global

irrigation water requirements, below the ‘sustainability’ SSP1 pathway. Compared to the baseline

where yields are held constant, the inherent yield improvements implemented in the ‘no climate

change’ scenario lead to both a relative increase in withdrawals in some water-scarce regions

(Middle East, Central Africa Dry, Central Asia, South Asia) and a decrease in others (India, Northern

Africa). Still, in this scenario the IWA for all these regions increases relative to the 2004 levels

indicating further pressure on freshwater resources. Among the regions with wide-spread irrigated

production, China and the USA decrease their dependency on blue water as a consequence on

higher yield gains on rainfed- compared to irrigated land.

Compared to the ‘no climate change’ scenario, yield changes in the A1B emissions pathway lead to a

reduction in the IWA for the Middle East, Northern Africa and South Asia following a shift of

irrigated- to rainfed crop production. An increase in IWA is determined for India, China, Southern

Europe, USA and Brazil. The IWA values are determined such that the issue of uncertainty of climate

change incidence is taken into account. This is done through the use of datasets coming from two

global climate models.

With irrigated crop production representing by far the largest share in global freshwater

withdrawals, the research indicates that withdrawals will be an increasing challenge for water

stressed regions. Very likely, high economic growth in these regions will also lead to an expansion of

blue water demand from other sectors, further amplifying stress. Thus, more economy-wide

modelling research should be directed to extending the analysis into other water-intensive economic

activities.

17

Appendix

Figure 6 - China total (green and blue) water consumption in SSP2 by yield scenario – in bcm

a) No climate change yields b) A1B yields

Table 5 - IWA by SSP in the A1B yield changes scenario – 2025 and 2050

2004

SSP1 SSP2 SSP5

Region 2025 2050 2025 2050 2025 2050

Middle East ↗ 50.32% 63.21% 81.21% 62.77% 80.58% 64.24% 87.83%

Northern Africa ↗ 60.66% 57.41% 65.96% 57.09% 63.92% 57.16% 65.64%

India ↗ 31.74% 44.62% 50.07% 44.26% 48.96% 44.73% 51.35%

South Asia ↗ 21.32% 15.64% 25.06% 15.29% 23.12% 15.75% 27.68%

Central Africa Dry ↗ 12.80% 18.25% 12.22% 18.31% 13.12% 18.06% 10.62%

Central Asia ↗ 7.05% 10.22% 14.54% 10.20% 14.70% 10.28% 15.65%

China ↘ 13.20% 9.12% 3.21% 8.68% 3.27% 8.93% 2.94%

S Europe ↗ 4.70% 5.27% 6.04% 5.24% 5.91% 5.28% 6.37%

Southern Africa ↗ 4.44% 4.30% 5.07% 4.25% 4.74% 4.34% 5.55%

USA ↘ 5.34% 4.62% 3.82% 4.62% 3.89% 4.59% 3.75%

SE Asia ↗ 2.13% 2.30% 2.69% 2.30% 2.64% 2.31% 2.80%

Eurasia ↘ 1.84% 1.64% 1.54% 1.64% 1.55% 1.64% 1.51%

North Lat America↗ 1.11% 1.14% 1.26% 1.14% 1.25% 1.14% 1.27%

South Lat America↗ 1.05% 1.10% 1.18% 1.10% 1.17% 1.10% 1.22%

NE Asia ↘ 1.24% 0.96% 0.82% 1.04% 1.01% 0.93% 0.58%

AU&NZ ↘ 0.85% 0.74% 0.69% 0.74% 0.69% 0.74% 0.73% Central Africa Humid↘ 0.42% 0.30% 0.33% 0.30% 0.31% 0.30% 0.35%

Brazil ↗ 0.13% 0.13% 0.16% 0.13% 0.16% 0.13% 0.17%

N Europe↘ 0.07% 0.08% 0.09% 0.08% 0.09% 0.08% 0.10%

North America ↘ 0.08% 0.06% 0.05% 0.06% 0.05% 0.06% 0.06%

18

Table 6 - RESCU region description

RESCU region Population -SSP2 GDP ($2005 bn) - SSP2 Relative GDP size IRWR

2005 2025 2050 2005 2025 2050 2025/2005 2025/2050 109m3/year m3/capita 2005

% of total

South Asia SAS 348.4 468.7 589.4 204.6 415.6 1016.1 2.03 2.44 338 970 0.8%

India IND 1147.2 1461.8 1733.8 866.7 4931.8 11999.2 5.69 2.43 1,446 1,260 3.4%

South East Asia SEA 591.5 718.0 792.0 886.1 2605.6 5532.7 2.94 2.12 5,191 8,777 12.1%

China CNA 1326.4 1393.1 1272.4 2875.2 14473.0 22893.6 5.03 1.58 2,813 2,121 6.6%

Central Asia CEA 22.9 27.5 30.4 62.9 222.2 340.3 3.53 1.53 148 6,468 0.3%

North East Asia NEA 174.8 172.5 154.8 5463.3 6009.2 6994.5 1.10 1.16 495 2,831 1.2%

Australia and New Zeeland AUZ 33.5 45.0 57.2 953.4 2021.2 3683.6 2.12 1.82 1,693 50,564 4.0%

Middle East MEA 261.4 367.0 471.3 1456.3 3085.3 6205.8 2.12 2.01 4,11 1,572 1.0%

Northern Africa NAF 154.2 199.1 233.9 272.5 536.5 1170.8 1.97 2.18 47 304 0.1%

Central Africa Dry CAFD 71.8 111.0 153.6 33.0 94.0 256.6 2.85 2.73 63 873 0.1%

Central Africa Humid CAFH 597.9 961.3 1465.7 280.7 989.4 3231.6 3.52 3.27 3,626 6,063 8.5%

Southern Africa SAF 91.8 121.2 157.1 222.6 469.4 1090.1 2.11 2.32 170 1,851 0.4%

Northern Europe NEU 251.3 266.4 276.8 8264.0 12003.7 19260.6 1.45 1.60 1,300 5,173 3.0%

Southern Europe SEU 279.6 294.4 302.3 6509.1 10273.6 18058.7 1.58 1.76 873 3,123 2.0%

Eurasia EUA 257.8 257.2 250.7 945.3 2158.8 3044.0 2.28 1.41 4,559 17,684 10.6%

Northern America NOA 32.3 39.6 47.6 1141.1 1807.0 3019.8 1.58 1.67 2,850 88,207 6.7%

United States USA 298.6 348.7 402.3 12162.7 16788.3 26352.9 1.38 1.57 2,818 9,437 6.6%

Northern Latin America NLAM 314.4 388.0 439.6 1570.3 3059.7 5703.9 1.95 1.86 7,057 22,450 16.5%

Brazil BRA 187.2 217.4 231.9 841.1 1662.7 2949.5 1.98 1.77 5,661 30,248 13.2%

Southern Latin America SLAM 58.6 67.6 73.2 277.4 592.7 1012.0 2.14 1.71 1,269 21,670 3.0%

19

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