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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: [email protected]
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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