Adaptation of surface water supply to climate change

in central Iran

Alireza Gohari, Ali Bozorgi, Kaveh Madani, Jeffrey Elledge

and Ronny Berndtsson

ABSTRACT

Optimal reservoir operation changes and adaptation strategies for the Zayandeh-Rud River Basin’s

surface water supply system are examined for a changing climate during the 2015–2044 period. On

average, the monthly temperature in the basin is expected to increase by 0.46–0.76W

C and annual

precipitation is expected to decrease by 14–38% with climate change, resulting in a reduction of the

Zayandeh-Rud’s peak stream flow and the amplitude of its seasonal range. Snowfall decrease in

winter months will generally lead to an 8–43% reduction in annual stream flow under climate change.

A reservoir operation model is developed and optimal reservoir operation strategies are identified for

adaptation of the basin’s surface water supply to climate change in the face of the increasing water

demand. Results indicate that the reservoir drawdown season starts 2 months earlier under climate

change. Smaller storage levels and greater water releases must occur to meet the increasing water

demand. The optimized water release can provide sufficient water for non-agricultural water

demand, but agriculture will experience more severe water shortage under a changing climate.

Having the highest vulnerability, the agricultural sector should be the main focus of regional

management plans to address the current water challenge and more severe water shortages under

climate change.

Alireza Gohari

Jeffrey Elledge

Department of Civil, Environmental and

Construction Engineering,

University of Central Florida,

Orlando, FL 32816,

USA

Ali Bozorgi

Department of Industrial Engineering and

Management Systems,

University of Central Florida,

Orlando, FL 32816,

USA

Kaveh Madani (corresponding author)

Centre for Environmental Policy,

Imperial College London,

London SW7 2AZ,

UK

E-mail: k.madani@imperial.ac.uk

Ronny Berndtsson

Department of Water Resources Engineering and

Centre for Middle Eastern Studies,

Lund University, Lund,

Sweden

Key words | Iran, optimization, reservoir operation, water resources management, water supply,

Zayandeh-Rud

INTRODUCTION

Expected impacts of climate change upon water availability

will have important implications for socioeconomic growth

in the future (Frederick & Schwarz ). Strategic adap-

tation strategies must be implemented to ensure sufficient

supply and efficient consumption of water in response to

new hydrological conditions. Adaptation can be defined

as the adjustments which are applied in natural and

human systems to exploit beneficial opportunities or

reduce risk and damage from current or future harms

(IPCC ). Water resources adaptations to cope with

water scarcity resulting from climate change and increasing

water supply demands of a growing population, include

(UNEP ): (1) supply oriented strategies: development

of new infrastructure, modification of existing infrastruc-

ture, and improved management of water supply systems;

and (2) demand oriented strategies: conservation and

improved water use efficiency, technological changes, and

water marketing and virtual water transfer between differ-

ent sectors.

Water managers should reevaluate the operation of

existing water resources systems to consider the wide

range of probable changes in climatic variables. By storing

water in times of surplus and releasing it in times of short-

age, reservoirs play an important role in regulating surface

water under climatic and hydrologic changes. Reduction in

stream flow and its pattern changes can significantly affect

391 © IWA Publishing 2014 Journal of Water and Climate Change | 05.3 | 2014

doi: 10.2166/wcc.2014.189

reservoir water supply, imposing additional water scarcity.

Many current reservoir operation plans are likely to be

inadequate to mitigate the negative impacts of climate

change on water supply reliability. Therefore, optimized

reservoir operation and adaptation has been the subject of

many studies aimed at accommodating the seasonal changes

of surface water under climate change and moderating the

impacts of climate change on water resources (e.g.

Medellin-Azuara et al. ; Adamec et al. ; Graham

& Georgakakos ; Madani & Lund ; Minville et al.

; Olsson et al. ; Raje & Mujumdar ; Connell-

Buck et al. ; Ncube et al. ; Vicuna et al. ;

Steinschneider & Brown ; Jamali et al. ).

Reservoir operation can be defined as the strategies of

reservoir water management to achieve different objectives,

such as providing water supply, hydropower production,

flood control, or recreational opportunities (Rani & Moreira

). Reservoir planning and management deal with com-

plex variables such as inflow, storage, release and different

water demands to satisfy often conflicting and unequally

weighted objectives. Optimization of reservoir operation to

maximize the beneficiaries’ utilities, subject to the mass bal-

ance equation and other hydrological and hydraulic

constraints, can help to determine balanced solutions for

reservoir operations planning and management challenges

(Ngo ; Stahl ; Wang et al. ).

Labadie () and Rani & Moreira () reviewed the

application of numerous optimization techniques in reser-

voir operation systems. Based on the forms of objective

function and constraints, the developed optimization

approaches have been classified into linear programming

(LP), non-linear programming (NLP) and dynamic program-

ming (DP) techniques (Simonovic ). LP is used for

problems with linear objective functions and constraints.

Even with the non-linear nature of problems in the real

world, LP is one of the most widely used optimization

approaches in water resources systems analysis (e.g. Need-

ham et al. ; Tu et al. ; Vedula et al. ; Vicuna

et al. , ; Madani & Lund , ; Yu ).

NLP is used to optimize problems with non-linear objective

functions and constraints (e.g. Caia et al. ; Teegavarapu

& Simonovic ; Chang et al. a, b; Reddy &

Kumar ; Afshar et al. ; Brandão ; Madani

). As another powerful optimization method, DP

(Bellman ) has been frequently used to exploit the con-

secutive decision structure in order to provide optimized

operation of water resources systems in non-linear optimiz-

ation problems (e.g. Chang et al. ; Teixeira & Marino

; Kumar & Baliarsingh ; Liu et al. ; Jothipra-

kash & Shanthi ; Kumar et al. ; Olivares & Lund

).

The Zayandeh-Rud River Basin is one of the most

studied basins in Iran (Ganji et al. (), Karamouz & Ara-

ghinejad (), Zahraie et al. (), Madani & Mariño

(), Homayoun-far et al. , Gohari et al. (a,

b), among others), due to its unique and complex

hydro-environmental and socioeconomic characteristics.

As the home to the historic city of Isfahan (former capital

of Iran), the internationally recognized Gav-Khouni Marsh

with a valuable and vulnerable ecosystem, major agriculture

in central Iran, and many inhabitants, the Zayandeh-Rud

River Basin is the most important basin in central Iran.

Due to watershed development and population growth,

increasing water demand and associated competition for

water have posed a great challenge for water resources man-

agement in the basin in the past decades (Madani ).

While the annual yield of the Zayandeh-Rud River has

been dramatically increased through major water transfers

in the past decades, the basin is still suffering from serious

water shortages, making the reliability of water transfer as

a solution to water shortage questionable (Madani &

Mariño ; Gohari et al. b).

Climate change projections have indicated a 1.1 to

1.5W

C increase in monthly temperature and 11–31%

reduction in annual precipitation in the basin during the

near-term future (2015–2044) (Gohari et al. a). The

negative impacts of climate change on water resources,

coupled with increasing water demand, will intensify the

current water scarcity in the basin. The appropriate oper-

ation of the Zayandeh-Rud Reservoir can play a key role

in surface water supply to moderate the negative impacts

of watershed development and global warming in the

future. Impacts of climate change and potential adaptation

strategies with respect to climate change risk and uncer-

tainty have not been investigated for the Zayandeh-Rud

River Basin. Therefore, this study tries to bridge the gap in

the decision makers’ understanding of climate change

effects on the Zayandeh-Rud River Basin’s surface water

392 A. Gohari et al. | Adaptation of surface water supply to climate change Journal of Water and Climate Change | 05.3 | 2014

system to identify effective climate change adaptation

strategies, considering the uncertainties involved in climate

change projections.

The paper is structured as follows. The following section

provides an overview of the case study and the Zayandeh-

Rud Reservoir. The selected climate change scenarios with

hydrological models and the optimization method are

described next. The subsequent section presents the results

of future hydrological regimes and optimized reservoir

operation, followed by a discussion of the major limitations

of this study. Finally, the main conclusions and recommen-

dations for adapting surface water supply to climate

change are presented.

CASE STUDY

The Zayandeh-Rud River Basin, with an area of about

26,917 km2, is located in central Iran (Figure 1). In recent

decades, due to high agricultural and industrial

development potential, the basin has witnessed economic

growth and increased population (Madani & Mariño

). Currently, more than 3.7 million people live in the

basin, making it the second most populated water basin in

Iran. As the major water consumer, the agricultural sector

uses more than 73% of water supply (Zayandab Consulting

Engineering Co. ). Cultivation of high water demand

crops (i.e. rice, corn, wheat and barley) and low irrigation

efficiency (low ratio of water stored in the soil depth inhab-

ited with active plant roots to water applied by the irrigation

system) of 34–42% contribute to the high agricultural water

demands.

The Zayandeh-Rud River, with an average flow of 1,400

million cubic meters (MCM) per year (44.39 cubic meters

per second), including 650 MCM of natural flow and 750

MCM of transferred flow (provided from neighbouring

water basins through water transfer projects), starts in the

Zagros Mountains to the west of the basin and flows into

the Gav-Khouni Marsh to the east of the basin. The river

has been tapped by increasing water consumption within

Figure 1 | The Zayandeh-Rud River Basin in central Iran.

393 A. Gohari et al. | Adaptation of surface water supply to climate change Journal of Water and Climate Change | 05.3 | 2014

and outside the basin. The Zayandeh-Rud River is the most

important water resource in the basin for its residents and

their urban, industrial and agricultural uses, as well as for

the survival of the ecosystem of the Gav-Khouni Marsh,

recognized by the Ramsar Convention ().

The Chadegan (Zayandeh-Rud) Dam, with physical

characteristics as listed in Table 1, was built in 1971

mainly for flood control, water supply and hydroelectricity

generation. In past decades, population growth and water-

shed development, coupled with the occurrence of severe

droughts, have intensified water scarcity in the basin.

During the recent water shortage, the water managers in

the basin decided to operate the Zayandeh-Rud Reservoir

only for water supply. The spillway gates of the reservoir

have been closed during spring and summer to store suffi-

cient water to supply the domestic and industrial sectors,

as the most important water user sectors to the operators.

Aggressive upstream water uses coupled with the current

operation of the reservoir have resulted in drying and parch-

ing of the Zayandeh-Rud riverbed during summer, which

has caused the Gav-Khouni Marsh to be considered by

many environmental activists as an already-dead wetland.

METHOD

This study uses the projected climate change variables for the

Zayandeh-Rud River Basin supplied by Gohari et al. (a),

based on their suggested probabilistic multi-model framework.

In this framework, the uncertainties of global climate models

(GCMs), emission scenarios and climate variability of daily

time series are handled by a combination of change factors

and the LARS-WG stochastic weather generator (Semenov

). In their study, monthly climate change scenarios of

temperature and precipitation were first developed, using 10

GCMs. Then, cumulative probability distributions for

climate change scenarios were developed and climate scen-

arios corresponding to the 25th, 50th and 75th probability

percentiles were extracted. This was followed by using the

LARS-WG stochastic downscaling method (Semenov &

Barrow ) to generate daily temperature and precipitation

time series at different probability percentiles. In this study,

monthly runoff at different probability percentiles is generated

by a lumped IHACRES model (Jakeman &Hornberger ).

Then, a non-linear reservoir operation model is developed to

optimize the operations of the Zayandeh-Rud Reservoir to

minimize water shortage under different climate change and

water demand scenarios. For better illustration, the approach

taken by Gohari et al. (a) to generate the input data

needed for this study is briefly explained below. Readers can

consult the original work for further details.

Daily climate variables’ time series under climate

change

Future daily time series of climate variables, namely precipi-

tation (mm) and maximum and minimum temperatures (W

C)

were generated for climate change scenarios within the study

region. The uncertainties of GCMs, emission scenarios and cli-

mate variability of daily time series were dealt with through a

combination of change factors and a weather generator.

Extraction of climate change scenarios

Monthly temperature and precipitation variables for

the baseline period (1971–2000) and future period

(2015–2044) were extracted from 10 GCMs (described in

Gohari et al. (a) for two emission scenarios (A2 and

B1) from the Fourth Intergovernmental Panel on Climate

Change (IPCC) Assessment Report (AR4)). The differences

of temperature and relative precipitation in the 30-year

monthly average future period (2015–2044) and baseline

period (1971–2000) were calculated for each month.

Probability distribution of climate change scenarios

The ranges of extracted climate change scenarios were not

the same for different GCMs, due to their dissimilar

Table 1 | The physical characteristics of Zayandeh-Rud Reservoir

Characteristic Descriptiona

Type of dam Concrete arch dam

Elevation (m) 100

Crest length (m) 450

Reservoir capacity (MCM) 1,470

Effective reservoir capacity (MCM) 1,250

aSource: Esfahan Regional Water Company website: http://www.esrw.ir.

394 A. Gohari et al. | Adaptation of surface water supply to climate change Journal of Water and Climate Change | 05.3 | 2014

capabilities in simulating the regional climate. Therefore,

each of the 10 GCMs was weighted based on the Mean

Observed Temperature-Precipitation method (Massah

Bavani & Morid ), which weighs GCMs based on

their ability to simulate the observed climate variables for

the baseline period.

In the next step, probability distribution functions

(PDFs) of climate change scenarios were developed for

each month. These PDFs associate the ‘30-year monthly

average of temperature and precipitation changes’ with the

‘weight of corresponding GCM’, helping with converting

discrete probability distributions of climate change scen-

arios to continuous probability distributions. The Gamma

distribution function with two parameters was then used

to develop continuous synthetic monthly PDFs, followed

by finding the monthly cumulative distribution functions

(CDFs) of climate change variables. The values of climate

change scenarios for temperature and precipitation were

extracted at three percentiles (25th, 50th and 75th) from

the developed CDFs, with the 25th probability percentile

representing low temperature and high precipitation

changes and the 75th probability percentile reflecting high

temperature and low precipitation changes.

Regional climatic variable time series

Stochastic Weather Generators (WGs) can generate daily

time series of climate variables. The LARS-WG (Semenov

& Barrow ) is a powerful stochastic WG that can gener-

ate synthetic daily time series of climatic variables from the

monthly data for the climate change scenarios. Gohari et al.

(a) used LARS-WG to generate daily precipitation, maxi-

mum and minimum temperatures, and precipitation time

series for the future study period (2015–2044), based on

the observed daily time series of the 1971–2000 period

and climate change scenarios at 25th, 50th and 75th prob-

ability percentiles at two stations, characterized in Table 2.

As their main drawback, WGs randomly produce var-

ious time series with similar monthly mean based on the

local observed times series and climate change scenarios.

To address this drawback and the uncertainty in the WGs’

outputs, LARS-WG was run to generate 300-year daily

time series for the future study period at different probability

percentiles under each emission scenario. Each one of the

generated 300-year time series was then broken into

30-year daily time series and the average daily values of

the resulting 10 time series (300/30) were calculated to

handle the uncertainty in the generated climatic variables.

More details about this process can be found in Gohari

et al. (a).

Simulation of monthly runoff

The IHACRES model (Jakeman & Hornberger ) was

used in this study to simulate rainfall-runoff processes in the

basin. This conceptual-metric model imposes more detailed

internal processes than a metric model to reduce the typical

uncertainty of the parameters that exist in a pure conceptual

model. Based on the concept of effective rainfall, a non-linear

loss module was used to estimate the effective rainfall amount

for each time step according to the rainfall depth and temp-

erature. The effective rainfall was then used as an input for

a linear unit hydrograph module to generate the runoff hydro-

graph. The IHACRES model was used to generate the runoff

time series based on the temperature and precipitation time

series for the future 2015–2044 period.

Reservoir operation model

A non-linear optimization model was formulated to adapt

the operations of the Zayandeh-Rud Reservoir to climate

change and increasing water demand in the future. The

defined constraints in this model include the physical con-

straints of the Zayandeh-Rud Reservoir and water

continuity equations. Given the existing water delivery pri-

orities for the water managers in the basin, it was assumed

that domestic and industrial water demand should be fully

supplied in the planning horizon. In other words, the

Table 2 | Brief description of the selected observation stations

Geographical position

Station name Longitude Latitude Elevation (m) Type of station

Dmaneh-

Fereydan

50W

290 33W

010 2,300 Climatological

station

Chelgerd 50W

050 32W

270 2,300 Rain gauge

station

395 A. Gohari et al. | Adaptation of surface water supply to climate change Journal of Water and Climate Change | 05.3 | 2014

minimum water release must satisfy both of the mentioned

water demands in each month. The objective function of

the model minimizes the sum of squared water shortage in

the basin during the future 2015–2044 period

minZ ¼X

30

j¼1

X

12

i¼1

Sh2i, j (1)

subject to

DTi,j� Ri,j � Shi,j ∀i, j (2)

0 � Shi,j ∀i, j (3)

DTi,j¼ DDi,j

þDIi,j þDEi,jþDAi,j

∀i, j (4)

Siþ1,j ¼ Si,j þ Ii,j � Ri,j i ¼ 1, 2, 3, . . . , 11; ∀jS1,jþ1 ¼ Sij þ Iij � Rij i ¼ 12; ∀j

� �

(5)

S1,1 ¼ S12,30 (6)

0 � Si,j � Smax ∀i, j (7)

DDi,jþDIi,j ≪ Ri,j � DDi,j

þDIi,j þDEi,jþDAi,j

∀i, j (8)

where in month i (i¼ 1, 2,…, 12) of year j ( j¼ 1, 2,…, 30):

Shij is water shortage; Rij is the water release from the reser-

voir; DTijis the total water demand; DDij

is the domestic

water demand; DIij is the industrial water demand; DEijis

environmental water demand; DAijis the agriculture water

demand; Si,j is the water storage in reservoir at beginning

of the month; Ii,j is the reservoir inflow; and Smax is the

maximum possible water storage in the reservoir

Smax ¼ 1250MCMð Þ. In this model, end-of-period storage

was set equal to the initial storage to avoid the full draw-

down of the reservoir (by the optimization model) at the

end of the planning horizon (Equation (6)).

The formulated optimization model is a monthly-step

reservoir operation model with a non-linear quadratic objec-

tive function and linear constraints. The model was solved

using the General Algebraic Modeling System (GAMS

23.5) software package to evaluate the impact of climate

change on the optimal operation of the reservoir during

the 2015–2044 period. To understand the effects of climatic

and water demand changes on the optimal operation of the

system, the optimization model was run for different combi-

nations of seven climate and three water demand scenarios,

described in Tables 3 and 4, respectively.

Indices

Indices or performance measures are helpful in assessing

the relative benefits of alternative water management strat-

egies. Here, three indices were used to better understand

the effects of climatic and demand changes on the

Zayandeh-Rud River surface water system.

Table 3 | Reservoir inflow scenarios

Scenario Description

HI Historical (1971–2000) flows

IA25 25th climate change probability percentile for the A2

emission scenario

IA50 50th climate change probability percentile for the A2

emission scenario

IA75 75th climate change probability percentile for the A2

emission scenario

IB25 25th climate change probability percentile for the B1

emission scenario

IB50 50th climate change probability percentile for the B1

emission scenario

IB75 75th climate change probability percentile for the B1

emission scenario

Table 4 | Water demand scenarios

Scenario Description

HD Historical (1971–2000) water demand

DF1 Water demand will increase with the historical growth

rate (domestic water demand growth rate of 1.7%; and

industrial water demand growth rate of 3.2%)

DF2 High water demand (water demand equal to the 2044

water demand under DF1)

396 A. Gohari et al. | Adaptation of surface water supply to climate change Journal of Water and Climate Change | 05.3 | 2014

Reliability index

Reliability is the likelihood of full satisfaction of water

demands during the planning period. The water reliability

index is formulated as (Hashimoto et al. )

Re ¼number of time� stepswith Sh ¼ 0

N(9)

where Sh is water shortage and N is the number of time-

steps (months here) in the planning period.

Vulnerability index

Vulnerability indicates the likely magnitude of water deficit,

if it occurs, and can be expressed as the average value of def-

icit (Loucks & van Beek ):

VuI ¼

PNj¼1 Shj

� �

=(number of time� stepswith Sh ≠ 0)

water demand

(10)

Resilience index

Resilience is defined as a system’s capacity to recover after a

failure, that is, the probability that a successful period occurs

after a period of failure (Moy et al. ). The resilience

index is defined as

Res ¼number of times Shj ¼ 0 follows Sh ≠ 0

Number of time� stepswithSh ≠ 0(11)

These indices can be calculated for the desired planning

time-steps (annual, monthly, or weekly). Here, these indices

were calculated on an annual basis.

RESULTS

Climate change impact assessment

Changes in temperature and precipitation

Figures 2 and 3, respectively, indicate the estimated monthly

temperature and precipitation for various climate change

scenarios based on Gohari et al. (a). In general, monthly

temperature increases are expected under all climate change

scenarios except in January and February. The values of pro-

jected temperature are larger under the B1 emission

scenario than under the A2 scenario due to more rapid

socioeconomic and population growth under this scenario.

The maximum temperature rises are expected in summer

and spring months. Similar to the temperature changes,

the values of estimated precipitation under the B1 emission

scenario are larger than the changes under the A2 emission

scenario. Except for the wet conditions projected by the A2–

25% scenario, the local precipitation changes show a gen-

eral decreasing trend. The results suggest that the

maximum reduction in monthly precipitation will happen

in January. The projected annual and seasonal changes of

the 30-year mean temperature and total precipitation were

calculated under different probability percentiles under cli-

mate change (Table 5). With an annual 14–38% decrease

in precipitation and an annual 0.46–0.76W

C increase in

temperature, the basin will face warmer and drier conditions

under climate change except in scenario A2–25%. The maxi-

mum seasonal changes are predicted for winter

precipitation (6–55% reduction) and spring temperature

(0.70–1.03W

C increase).

Changes in reservoir inflows

Figure 4 shows the average monthly reservoir inflows under

different climate change scenarios. Temperature rise will

Figure 2 | Expected average temperature changes under different climate change

scenarios. The horizontal dash–dot line shows the no temperature change

level.

397 A. Gohari et al. | Adaptation of surface water supply to climate change Journal of Water and Climate Change | 05.3 | 2014

lead to more precipitation falling as rain, instead of snow,

and the snowpack will melt earlier in the spring. The peak

inflow usually occurs 1 month earlier in the year under cli-

mate change. Reduced snowfall due to increasing

temperature in winter months will generally lead to a

reduction in the reservoir inflow under climate change.

The ranges of inflow reduction are larger under the B1 emis-

sion scenario than the A2 emission scenario. Global

warming reduces the value of peak inflow and the amplitude

of its seasonal range. The projected annual and seasonal

changes of 30-year mean inflows under different climate

scenarios are shown in Table 6. With the annual reduction

of 8–43% in reservoir inflows and reduction of 27–48% in

spring inflows, the current operation policies will be ineffec-

tive to minimize the negative impacts of climate change.

Optimal surface water operation under climate change

The reservoir operation model was run under different cli-

mate change and demand scenarios to find the optimal

Figure 3 | Average monthly precipitation for different climate change scenarios.

Table 5 | Summary of temperature and precipitation changes under different climate change scenarios

A2-25% A2-50% A2-75% B1-25% B1-50% B1-75% Maximum Minimum

Temperature change (W

C) Annual 0.46 0.49 0.54 0.66 0.70 0.76 0.76 0.46

Autumn 0.41 0.43 0.47 0.64 0.68 0.74 0.74 0.41

Winter 0.20 0.24 0.26 0.35 0.39 0.43 0.43 0.20

Spring 0.70 0.74 0.81 0.89 0.94 1.03 1.03 0.70

Summer 0.53 0.56 0.61 0.74 0.78 0.86 0.86 0.53

Precipitation change (%) Annual 3.12 � 14.07 � 30.00 � 11.91 � 32.31 � 38.02 � 38.02 3.12

Autumn � 3.92 � 14.92 � 28.43 � 22.77 � 32.74 � 39.70 � 39.70 � 3.92

Winter � 5.78 � 8.48 � 26.70 � 7.43 � 26.20 � 55.71 � 55.71 � 5.78

Spring � 3.78 � 25.40 � 30.84 � 17.10 � 34.46 � 41.95 � 41.95 � 3.78

Summer 20.60 14.42 � 19.37 9.91 � 26.51 � 59.13 � 59.13 20.60

398 A. Gohari et al. | Adaptation of surface water supply to climate change Journal of Water and Climate Change | 05.3 | 2014

storage operation, water release and water shortage during

the 2015–2044 period. The values of performance indicators

were also calculated to better understand the effects of

future climatic and socioeconomic changes on surface

water management in the basin.

Changes in storage operation

Figure 5 shows the average monthly water storage in the

Zayandeh-Rud Reservoir under climate change, assuming his-

torical water demand (HD) for the future 2015–2044 period.

Global warming will lead to a reduction in the amount of

peak-storage in the reservoir and the peak storage will occur

2months earlier in the springwith climate change. The general

seasonal cycle of storage illustrates that the reservoir refills par-

tially during autumn and winter months and then is drawn

down until late summer. Climate change decreases the ampli-

tude of seasonal cycle of storage in the reservoir. The storage

levels under the A2 emission scenario are projected to be

higher than the B1 scenario. The reservoir storage under cli-

mate change dominates historical storage levels during the

new refilling stage of the cycle (autumn and winter months),

but has lower levels until late summer.

The seasonal cycle of water storage with historical and

different climate change hydrological conditions under

DF1 and DF2 scenarios for the future 2015–2044 water

demand are shown in Figures 6 and 7, respectively. The

peak-storage will occur 1 month earlier under climate

change for both water demand scenarios. In comparison

with HD and DF2, increasing water demand under DF1

will lead to 1 month earlier peak-storage under historical

Figure 4 | Average reservoir inflow under different climate change scenarios.

Table 6 | Relative annual and seasonal changes (%) in the reservoir inflow under different

climate change scenarios

Scenario Annual Autumn Winter Spring Summer

IA25 �7.8 þ10.0 þ14.8 �27.3 �3.3

IA50 �24.8 �23.2 �8.2 �36.1 �21.7

IA75 �36.6 �36.0 �25.2 �43.1 �36.9

IB25 �34.0 �32.7 �25.1 �41.6 �29.0

IB50 �37.1 �38.1 �28.8 �44.1 �32.0

IB75 �42.8 �41.6 �36.2 �48.2 �40.2

Figure 5 | Average monthly water storage for the Zayandeh-Rud Reservoir under differ-

ent climate scenarios with HD. As a reference for comparison, the HI scenario

corresponds to optimal operations based the historical hydrology (1971–2000).

Figure 6 | Average monthly water storage for the Zayandeh-Rud Reservoir under differ-

ent climate scenarios with the DF1 water demand scenario. As a reference for

comparison, the HI scenario corresponds to optimal operations based the

historical hydrology (1971–2000).

399 A. Gohari et al. | Adaptation of surface water supply to climate change Journal of Water and Climate Change | 05.3 | 2014

hydrology. Global warming will lead to a reduction in the

amounts of peak-storage under DF1 and DF2 scenarios.

The lower levels of water storage overall are projected

under DF2 scenario compared with HD and DF1 water

demand scenarios. This is because further water release

during the drawdown period will occur to meet greater

water demand under DF2 scenarios.

Changes in water supply

Optimized water releases under different hydrological con-

ditions were compared to water delivery targets, which

include domestic, industrial, agricultural and environmental

water demands. The 30-year monthly average water release

from the Zayandeh-Rud Reservoir under different hydrologi-

cal conditions for the three water demand scenarios is shown

in Figures 8–10. The results indicate that climate change

decreases water releases and intensifies water scarcity in

the basin. Based on Figure 8, surface water is not sufficient

to even satisfy the historical demand in the basin. Therefore,

groundwater has been heavily used as a secondary water

supply source, resulting in strongly declining groundwater

levels in the basin. The current groundwater withdrawal has

been more than the safe groundwater yield and the basin is

experiencing groundwater shortage issues. Therefore, the

basin has no further opportunity for minimizing water deficit

in the future through aggressive groundwater withdrawal.

The ranges of monthly water release vary between the

historical and climate change scenarios for each water

demand scenario. Generally, global warming will lead to

a reduction in monthly water releases, especially under

the B2 emission scenario. The smaller variability in seaso-

nal water release amplitude under the B1 emission

scenario corresponds to insignificant variability in

annual stream flow in the Zayandeh-Rud River. There is

no water shortage for the months of December through

to February under climate change and historical hydrol-

ogy conditions. Comparison of the optimized water

release with water demand illustrates that non-agricultural

water demand can be fully satisfied under different water

demand scenarios, except for the B75 climate change

scenario under DF2 water demand.

Figure 7 | Average monthly water storage for the Zayandeh-Rud Reservoir under differ-

ent climate scenarios with the DF2 water demand scenario. As a reference for

comparison, the HI scenario corresponds to optimal operations based the

historical hydrology (1971–2000).

Figure 8 | Monthly water release from the Zayandeh-Rud Reservoir for different water demand levels under the historical hydrology. The circles, upper and lower whiskers, respectively,

show the mean, maximum, and minimum water release during the future 2015–2044 period.

400 A. Gohari et al. | Adaptation of surface water supply to climate change Journal of Water and Climate Change | 05.3 | 2014

Table 7 presents the values of three indices, calculated

to assess the effectiveness of changing reservoir operation

policies as an adaptation measure to mitigate the effects of

global warming. To better understand the climate change

effects on each water sector, these indices have been calcu-

lated for each sector separately. Given that domestic and

industrial water demands are fully satisfied in all cases

here, the corresponding index values are not reported.

Due to the extreme importance of the Gav-Khouni

Marsh (an internationally recognized marsh under the

Ramsar Convention on Wetlands of 1971) as the main eco-

logical resource of the basin, it is assumed that

environmental water demand has the third priority (after

domestic and industrial water demand), putting it above

agricultural water demand. Fortunately, the basin’s environ-

mental water demand can be fully satisfied under all

scenarios (except for IB75 with DF2), if the operators give

priority to ecosystem over agriculture in practice.

The reliability index values indicate that agricultural

water demand cannot be fully satisfied under the different

Figure 9 | Monthly water release from the Zayandeh-Rud Reservoir for different water demand levels under A2 climate change scenarios. The circles, upper, and lower whiskers,

respectively, show the mean, maximum, and minimum water release during the future 2015–2044 period.

401 A. Gohari et al. | Adaptation of surface water supply to climate change Journal of Water and Climate Change | 05.3 | 2014

hydrological scenarios. Lower values of the reliability

index show that climate change intensifies agricultural

water shortage in the future. The maximum and minimum

values of the agricultural reliability index are projected to

be 0.34 and 0, respectively, with the HD and IB75 hydro-

logical conditions under the DF2 water demand scenario.

The vulnerability index demonstrates that agriculture is

the most vulnerable water sector to climate change. The

optimized operation of the reservoir can provide sufficient

water for environmental water demand even under drier

conditions. The values of the resilience index suggest

that the Zayandeh-Rud’s surface water supply system’s

resiliency decreases with future socioeconomic and cli-

matic changes.

Water shortage

Figure 11 shows the frequency of monthly relative water

shortages (monthly water release/monthly water demand)

during the 30-year study period under different hydrologic

Figure 10 | Monthly water release from the Zayandeh-Rud Reservoir for different water demand levels under B1 climate change scenarios. The circles, upper and lower whiskers,

respectively, show the mean, maximum, and minimum water release during the future 2015–2044 period.

402 A. Gohari et al. | Adaptation of surface water supply to climate change Journal of Water and Climate Change | 05.3 | 2014

conditions withHD. The results indicate that water shortages

are likely in 65% of months, even with the historical hydrol-

ogy. Climate change will intensify water shortages,

increasing both shortage frequency and magnitude. Water

shortage intensities are generally expected to be greater

under the B1 than A2 emission scenarios. As indicated by

Figures 12 and 13, growingwater demand increases the short-

age frequency and magnitude. The situation is expected to

worsen by climate change. Under the extreme scenarios (e.

g. IB75-DF2), water shortages are expected 100% of the time.

LIMITATION

Similar to all modeling studies, our results are associated with

limitations which should not be overlooked when interpreting

the results and developing policy advice (Madani ). Four

specific limitations should be mentioned for this study. First,

this study attempts to adapt the Zayandeh-Rud’s surface water

supply to climate change. The operation of the Zayandeh-Rud

Reservoir is optimized tominimize the impact ofwater shortage

in the study region.However, groundwater is also recognized as

Table 7 | The values of reliability, vulnerability and resilience indices under different hydrological conditions and water demand scenarios

Scenario name

Reliability index Vulnerability index Resilience index

HD DF1 DF2 HD DF1 DF2 HD DF1 DF2

Agriculture HI 0.34 0.26 0.25 0.29 0.35 0.36 0.50 0.36 0.33

IA25 0.30 0.26 0.25 0.36 0.37 0.37 0.42 0.36 0.33

IA50 0.28 0.26 0.25 0.43 0.44 0.45 0.39 0.36 0.33

IA75 0.26 0.25 0.25 0.49 0.50 0.51 0.35 0.34 0.33

IB25 0.28 0.26 0.25 0.47 0.48 0.49 0.38 0.35 0.33

IB50 0.26 0.25 0.25 0.49 0.50 0.51 0.35 0.34 0.33

IB75 0.26 0.24 0.00 0.51 0.52 0.53 0.35 0.31 0.00

Environment HI 1.00 1.00 1.00 0.00 0.00 0.00 – – –

IA25 1.00 1.00 1.00 0.00 0.00 0.00 – – –

IA50 1.00 1.00 1.00 0.00 0.00 0.00 – – –

IA75 1.00 1.00 1.00 0.00 0.00 0.00 – – –

IB25 1.00 1.00 1.00 0.00 0.00 0.00 – – –

IB50 1.00 1.00 1.00 0.00 0.00 0.00 – – –

IB75 1.00 1.00 0.26 0.00 0.00 0.64 – – 0.34

Figure 11 | Frequency of monthly relative water shortage under different hydrologic

conditions with HD.Figure 12 | Frequency of monthly relative water shortage under different hydrologic

conditions with DF1 water demand.

403 A. Gohari et al. | Adaptation of surface water supply to climate change Journal of Water and Climate Change | 05.3 | 2014

a backup water resource for domestic and agricultural water

demand in the basin, while optimization of conjunctive surface

and ground water use was not considered in this study. Cur-

rently, groundwater is used as a substitute for surface water.

The high agricultural demand in the region has resulted in

serious groundwater overdraft. Conjunctive use of groundwater

with serious control of groundwater withdrawal becomesmore

important under climate change. Nevertheless, given that cur-

rent groundwater withdrawals exceed the safe groundwater

yield, withdrawal of additional groundwater to manage the

expected water shortages is not reasonable.

Second, limitations relate to the bias implicit in the pro-

jection of future hydrology under climate change. The land

use, soil moisture and soil physical characteristics are the

main parameters which affect timing and amount of infiltra-

tion and runoff in the basin. The impacts of such parameters

and their changes under climate warming and human devel-

opment have not been considered in the runoff projections.

Third, water availability and water demand growth have

a feedback relationship (Mirchi et al. ; Gohari et al.

b). So, the water demand scenarios of this study might

be unrealistic as they have been assumed to be independent

from water availability in the basin. Furthermore, here agri-

cultural water demand was assumed not to increase in the

basin in the future. Nevertheless, if agricultural activities

are continued in the basin at the current level, future agricul-

tural demand will be higher than the current rate due to

higher water requirements of crops under climate change

(Gohari et al. a).

Fourth, the developed optimization model is determinis-

tic with perfect foresight into future hydrologic conditions.

This makes the model optimistic, as the prescribed perform-

ance quality of the operations is not achievable in practice

given the uncertainties involved in reservoir operations.

Future studies might address these limitations.

CONCLUSIONS

This study used a combination of water demand and global

warming scenarios to study the impacts of future socioeco-

nomic and climatic changes on surface water supply in the

Zayandeh-Rud River Basin in central Iran. The IHACRES

model, as a lumped hydrological model, was applied to

simulate monthly reservoir inflow under climate change.

The results indicate that global warming reduces the value

of peak inflow and the amplitude of its seasonal range.

Reduced snowfall due to increasing temperature in winter

months will generally lead to a reduction in the reservoir

inflow under climate change. On an annual basis, inflow

will decrease by 8–43% under climate change. The maxi-

mum seasonal change is projected for spring inflow

(27–48% reduction). The peak inflow is projected to occur

1 month earlier in the water year due to temperature rise

and earlier snowmelt under climate change.

The Zayandeh-Rud River Basin with a semi-arid Medi-

terranean climate, as well as continuous watershed

development and growing water demand, is a region with

high vulnerability to climate change. Given the strategic

importance of water availability in the basin for sustainable

agriculture, socioeconomic development and ecosystem

health in central Iran, adaptation strategies must be ident-

ified and timely actions taken to minimize the impacts of

the expected climatic and socioeconomic changes in the

basin. As the first step in adaptation of surface water

supply to climate change in central Iran, this study focused

on optimization of the operation of the Zayandeh-Rud

Reservoir which serves as the major water supply source

in the region. Modeling results indicate that changes in

reservoir operations are required to minimize the undesir-

able impacts of future changes in the basin. In general,

water storage levels are expected to decrease and the

Figure 13 | Frequency of monthly relative water shortage under different hydrologic

conditions with DF2 water demand.

404 A. Gohari et al. | Adaptation of surface water supply to climate change Journal of Water and Climate Change | 05.3 | 2014

drawdown season is expected to start earlier under climatic

and water demand change.

Generally, non-agricultural water demand can be satis-

fied to a great degree in the future. As the most vulnerable

water use sector, agriculture will witness more water short-

age under climate change even if water demand remains

unchanged. This could lead to more groundwater withdra-

wals from groundwaters which are already experiencing

declining levels. Given the unavailability of sufficient

groundwater to compensate for the surface water losses

and inefficiency of supply oriented strategies, such as

water transfer, in satisfying the increasing water demand

in the basin (Gohari et al. b), it is necessary to

implement demand management strategies for sustainable

water management in the region. Water management in

the basin has no alternative other than limiting agricultural

development, discontinuing growing water-intensive crops

(e.g. rice, alfalfa and corn), expanding rainfed agriculture

(to replace the lost irrigated lands), and selecting an appro-

priate crop portfolio with special attention to water

availability in the basin. Furthermore, there is a serious

need to increase water use efficiency in irrigation and drai-

nage networks to reduce water waste in the existing

outdated irrigation systems.

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