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U N I V E R S I TAT I S O U L U E N S I SACTAC

TECHNICA


TECHNICA

OULU 2020

C 761

Nizar Abou Zaki

THE ROLE OF AGRICULTURE EXPANSION IN WATER RESOURCES DEPLETIONIN CENTRAL IRAN

UNIVERSITY OF OULU GRADUATE SCHOOL;UNIVERSITY OF OULU,FACULTY OF TECHNOLOGY

C 761

AC

TAN

izar Abou Z

aki

C761etukansi.fm Tuesday, September 8, 2020 11:27 AM

ACTA UNIVERS ITAT I S OULUENS I SC Te c h n i c a 7 6 1

NIZAR ABOU ZAKI

THE ROLE OF AGRICULTURE EXPANSION IN WATER RESOURCES DEPLETION IN CENTRAL IRAN

Academic dissertation to be presented with the assent ofthe Doctoral Training Committee of Technology andNatural Sciences of the University of Oulu for publicdefence in the OP auditorium (L10), Linnanmaa, on 15October 2020, at 1 p.m.

UNIVERSITY OF OULU, OULU 2020

Copyright © 2020Acta Univ. Oul. C 761, 2020

Supervised by

Professor Bjørn Kløve

Doctor Ali Torabi Haghighi

Doctor Pekka Rossi

Reviewed byDoctor Saeed GolianDoctor Pennan Chinnasamy

ISBN 978-952-62-2720-7 (Paperback)ISBN 978-952-62-2721-4 (PDF)

ISSN 0355-3213 (Printed)ISSN 1796-2226 (Online)

Cover DesignRaimo Ahonen

PUNAMUSTA

TAMPERE 2020

OpponentDoctor Hamed Ketabchi

Abou Zaki, Nizar, The role of agriculture expansion in water resources depletion incentral Iran. University of Oulu Graduate School; University of Oulu, Faculty of TechnologyActa Univ. Oul. C 761, 2020University of Oulu, P.O. Box 8000, FI-90014 University of Oulu, Finland

AbstractIn arid and semi-arid regions, water is considered as the main resource for agriculture and thereforefor the mainstay of rural societies. In this study, agricultural water usage sustainability in FarsProvince, Zayanderud and Bakhtegan Basins, in central Iran was evaluated. The study areas havewitnessed a depletion in main river flows and groundwater levels, directly affecting the watersecurity and the well-being on the local inhabitants. Available and developed drought indicators suchas: Standardized Precipitation Index (SPI), Stream Drought Index (SDI), Overall AgriculturalDrought Index (OADI) and Agricultural Drought Index (ADI) were used. This indicators in additionto remote sensed and in-situ data were used to monitor the water depletion in these areas. Resultsindicated that both Fars and Zayanderud went through three sustainability water usage stages duringa period of 40 year: 1) a sustainable phase, where the water usage matched the water renewabilitycapacity; 2) a transition phase, where water usage occasionally exceeds the water renewabilitycapacity; 3) an unsustainable phase, where there is a lack and depletion in water resources foragriculture and domestic usage. Climatically there was no significant distribution of meteorologicaldrought, and no negative trends in the annual precipitation, in Fars and Zayanderud. A hyper-aridclimate prevailed for an average of 32 percent of the Fars province spatio-temporal coverage duringthe study period. The area increased significantly from 30.6 percent in the first decade, 1977 till 1986to 44.4 percent in the last decade, 2007–2016. In Zayanderud the hyper-arid cold climate wasdominant in the study period, with an average 57.5 percent frequency of occurrence. Most of theyears, 86 percent of the period from 1977 till 2016, are considered to be wet and normal yearsmeteorologically, and the climatic diversity remained constant with no significant negative trend inFars. In contrary, the hydrological drought occurrence increased significantly from 30 to 73 percentof the years, especially after the 1980s when the irrigation expanded and 60 percent of the rainfedareas were converted to irrigated areas. This exerted a substantial pressure on surface andgroundwater resources for irrigation purposes, led to groundwater depletion in major aquifers in Farsand Zayanderud, reaching 50 meters in some aquifers, and zero flow in the downstream of mainrivers. This decrease in the downstream flow of the Zayanderud and Kor Rivers led to the decreasein the surface volume of Gavkhuni Wetland and Bakhtegan Lake. The wetland and the lake reachedto complete dryness in several occasion, exerting pressure on the environment and surroundingecology. The increase in water pressure and the depletion of its resources led to the decrease in theirrigated areas to half, after the farms doubled the irrigated areas in the 1990s. The remote senseddata confirmed this results were the Gravity Recovery and Climate Experiment (GRACE) and theGlobal Land Data Assimilation System (GLDAS) derived data showed the total water massdepletion in the Zayanderud and Bakhtegan Basins, and the groundwater levels depletion inBakhtegan. GRACE data showed an average water mass monthly depletion of 32 millimeters (mm)in Zayanderud and 17.5 mm in Bakhtegan. The Normalized Water Difference Index (NDWI),confirmed the surface area depletion in the Gavkhuni Wetland reaching total dryness in the year 2009onwards. As a main conclusion, the increase in the hydrological and agricultural droughts occurrencein Fars and Zayanderud seem to be directly related to human farming activities, even with theoccurrence of meteorological droughts.

Keywords: Bakhtegan, depletion, drought indices, drought severity, Fars, Gavkhuni, groundwater,remote sensing, river flow, water management, Zayanderud

Abou Zaki, Nizar, Maatalouden laajentumisen rooli Iranin keskiosien vesivarojenehtymisessä.

Oulun yliopiston tutkijakoulu; Oulun yliopisto, Teknillinen tiedekunta

Acta Univ. Oul. C 761, 2020

Oulun yliopisto, PL 8000, 90014 Oulun yliopisto

Tiivistelmä

Kuivilla seuduilla vesi on maanviljelyn tärkein resurssi ja siten myös maalaisyhteisöjen tukipila-

ri. Tässä tutkimuksessa arvioitiin Iranin keski- ja eteläosissa sijaitsevien Farsin provinssin sekä

Zayanderudin ja Bakhteganin valuma-alueiden maatalouden veden käytön kestävyyttä. Tutki-

musalueiden suurimpien jokien virtaamat ja pohjaveden tasot ovat ehtyneet, mikä vaikuttaa suo-

raan vedensaantiin ja paikallisten asukkaiden hyvinvointiin. Näillä alueilla veden ehtymistä seu-

rattiin olemassa olevilla ja kehitetyillä kuivuusindekseillä keräämällä dataa sekä kaukokartoituk-

sella että paikan päällä. Tuloksi havaittiin, että tutkimusalueilla ollaan käyty läpi kolme veden

kulutuksen vaihetta neljänkymmenen vuoden aikana: 1) Kulutusta kestävä vaihe, jolloin veden

käyttö oli suhteessa veden uusiutumiskapasiteettiin; 2) Siirtymävaihe, jolloin veden käyttö toisi-

naan ylittää veden uusiutumiskapasiteettiin; 3) Kestämätön vaihe, jolloin on vesivaroissa on

puutetta ja maatalouden ja kotitalouksien käyttöön varojen ehtyessä. Ilmastollisesti tutkimusalu-

eilla ei ollut merkittäviä säännöllisiä kuivuuskausia, eikä negatiivisia trendejä vuosittaisessa

sademäärissä. Suurin osa tarkastelluista vuosista 1977–2016 oli joko kosteita tai normaaleita

ilmastotieteellisessä mielessä. Ilmaston monimuotoisuus pysyi Farsissa tutkimusaikana muuttu-

mattomana, eikä siinä havaittu merkittäviä negatiivisia trendejä. Hydrologinen kuivuus kuiten-

kin lisääntyi merkittävästi eritoten 1980-luvun jälkeen, kun keinokastelu lisääntyi ja 60 prosent-

tia sadekastelun varassa olleista alueista muunnettiin keinokastelluiksi. Tämä aiheutti merkittä-

vää painetta pinta- ja pohjavesivaroihin, mikä johti pohjavesien ehtymiseen suurissa pohjavesi-

varannoissa Farsissa ja Zayanderudissa, sekä suurimpien jokien alavirtojen tyrehtymiseen. Tut-

kimusalueen Zayanderud- ja Kor-jokien alavirtojen valunnan väheneminen johti Gavkhunin

suon ja Bakhtegan-järven pinta-alojen pienenemiseen. Suo ja järvi kuivuivat useaan otteeseen

kokonaan, tuottaen suurta painetta ympäristölle ja ympäröivälle ekologialle. Veden käyttöpai-

neen kohoaminen ja alueen resurssien ehtyminen johtivat kasteltujen alueiden vähenemiseen

maatilojen lisättyä kasteltuja alueita 1990-luvulla. Kaukokartoituksella kerätty data vahvistaa

nämä löydöt. Painovoimaan perustuvalla GRACE-satelliitilla kerätty data osoitti Zayanderudin

ja Bakhteganin valuma-alueiden kokonaisvesimassan ehtymisen ja pohjaveden ehtymisen Bakh-

teganissa. Vesi-indeksitarkastelu (NDWI) vahvisti sekä Gavkhunin suon, että Bakhtegan-järven

pinta-alojen pienenemisen. Johtopäätelmänä tutkimuksesta oli, että vaikka alueella on ajoittaisia

kuivuuskausia, hydrologisen ja maataloudellisen kuivuuden esiintymisen lisääntyminen Farsissa

ja Zayanderudisda näyttää olevan suorassa yhteydessä ihmisten maanviljelytoimintaan.

Asiasanat: Bakhtegan, Fars, Gavkhuni, jokivirta, kaukokartoitus, kuivuusaste,kuivuusindeksit, pohjavesi, veden ehtyminen, vesivarojen hallinta, Zayanderud

Для моїх рідних

9

Acknowledgements

First I would like to thank Maa-ja vesitekniikan tuki ry (MVTT), University of

Oulu Graduate School, and Olvi Foundation for their continued financial support

during my doctoral studies. This life changing experience in Oulu would have never

been possible without their support.

I would like to express my sincere gratitude and appreciation for my principle

supervisor, Professor Bjørn Kløve, and my co-supervisors Dr. Ali Torabi Haghighi

and Dr. Pekka Rossi, for their support, patience and motivation during my doctoral

studies. Thank you for making me feel a part of the family rather than the classical

supervisor-student relationship. I would like to express my appreciation to my

follow-up group members Dr. Hannu Marttila and Dr. Anna-Kaisa Ronkanen for

guiding and supporting me during my doctoral studies. A special thanks for all my

colleagues at the Water, Energy and Environmental Engineering research unit. I

will always remember our individual talks in the coffee room, our mökki trips, and

our nights out in Oulu.

This thesis would have never been completed without the contribution of Dr.

Mary McAfee who checked the language of my thesis and articles, and ACTA

editor Dr. Jani Peräntie who carefully reviewed every detail in this thesis.

Furthermore I would like to thank Dr. Saeed Golian and Dr. Pennan Chinnasamy

for their efforts in pre-examining this thesis. A special thanks for my opponent Dr.

Hamed Ketabchi for his time in reviewing my work.

Last, but definitely not least, I want to thank my family: My mom Olha, my

father Issam, my sister Natalia, my brother-in-law Wassim, and my nephews Samir

and Ziad. This will have never been possible without you being a part of my life.

Thank you for all the times you were next to me, especially when I was going

through the worse period of my life. Thanks for your long phone calls and support,

and most importantly, thank you for believing in me. Mom and Dad I am always at

a loss of words when I want to describe my heartfelt appreciation for all what you

have done for me in my life…

20th of August 2020 Nizar Abou Zaki

11

Abbreviations

HIF Humidity influence factor

TIF Thermal influence factor

CIF Climate influence factor

OADI Overall agricultural drought index

ADI Agricultural drought index

AI Aridity index

SPI Standardized precipitation index

SDI Streamflow drought index

NDWI Normalized difference water index

NDVI Normalized difference vegetation index

GRACE Gravity Recovery and Climate Experiment

NIR Near-infrared spectroscopy GLADS Global Land Data Assimilation System

13

List of original publications

This thesis is based on the following publications, which are referred throughout

the text by their Roman numerals:

I Abou Zaki, N., Haghighi, A.T., Rossi, P.M., Tourian, M.J, & Kløve, B. (2019). Monitoring groundwater storage depletion using gravity recovery and climate experiment (GRACE) data in Bakhtegan catchment, Iran. Water 11(7), 1456. https://doi.org/10.3390/w11071456

II Abou Zaki, N., Haghighi, A. T., Rossi, P. M., Tourian, M. J., Bakhshaee, A., & Kløve, B. (2020). Evaluating impacts of irrigation and drought on river, groundwater and a terminal wetland in the Zayanderud basin, Iran. Water, 12(5), 1302. https://doi.org/10.3390/w12051302

III Haghighi, A.T., Abou Zaki, N., Rossi, P.M., Noori, R., Hekmatzadeh, A.A., Saremi, H. & Kløve, B. (2020). Unsustainability syndrome – from meteorological to agricultural drought in arid and semi-arid regions. Water 12(3), 838. https://doi.org/10.3390/w12030838

The author’s contribution to Papers I–III:

I Elaborated the research plan with Ali Torabi Haghighi and Pekka Rossi, performed the analyses and evaluated the results, and wrote the manuscript. Responded to comments from the co-authors and reviewers.

II Elaborated the research plan with Ali Torabi Haghighi and Pekka Rossi, performed the analyses and evaluated the results, and wrote the manuscript. Responded to comments from the co-authors.

III Elaborated the research plan with Ali Torabi Haghighi, contributed to data analysis and manuscript writing, and commented on all versions of the manuscript.

15

Contents

Abstract

Tiivistelmä

Acknowledgements 9

Abbreviations 11

List of original publications 13

Contents 15

1 Introduction 17

1.1 Overview ................................................................................................. 17

1.2 Remote sensing applications in hydrology .............................................. 19

1.3 Thesis objectives and hypothesis ............................................................ 20

2 Study area and data used 23

2.1 Water resources in Iran ............................................................................ 23

2.2 Fars province ........................................................................................... 24

2.3 Bakhtegan basin ...................................................................................... 27

2.4 Zayanderud basin in Esfahan province ................................................... 28

3 Methods 31

3.1 Climate variability analysis using the Emberger method ........................ 31

3.2 Analysis of metrological, hydrological, and agricultural droughts ......... 34

3.3 Using Normalized Difference Water Index (NDWI) to monitor

the fluctuation in surface area of Gavkhuni wetland and Lake

Bakhtegan ............................................................................................... 36

3.4 Monitoring total water mass depletion in Zayanderud and

Bakhtegan basins, and groundwater depletion in Bakhtegan

basin, using GRACE data ....................................................................... 38

4 Results 41

4.1 Spatiotemporal climate variability in Fars province and in

Zayanderud basin, Esfahan province ...................................................... 41

4.1.1 Fars province ................................................................................ 41

4.1.2 Zayanderud basin ......................................................................... 42

4.2 Monitoring meteorological drought in Fars province ............................. 44

4.3 Monitoring hydrological drought in Fars province ................................. 47

4.4 Monitoring meteorological and hydrological drought in


4.5 Monitoring the transformation from rainfed to irrigated farming

in Fars province ....................................................................................... 51

16

4.6 Monitoring groundwater depletion in Fars province and


4.6.1 Fars province ................................................................................ 54

4.6.2 Zayanderud basin .......................................................................... 54

4.7 Monitoring surface area change in Gavkhuni wetland using

Normalized Difference Water Index (NDWI) ......................................... 57

4.8 Monitoring total water storage depletion in Zayanderud and

Bakhtegan basins using GRACE data ..................................................... 60

4.9 Monitoring groundwater depletion in Bakhtegan basin using

GRACE data ........................................................................................... 62

5 Discussion 65

5.1 Fars province ........................................................................................... 65

5.2 Zayanderud basin .................................................................................... 67

5.3 Gavkhuni wetland area ............................................................................ 68

5.4 Bakhtegan basin ...................................................................................... 69

6 Conclusions and further research 71

List of references 75

Original publications 83

17

1 Introduction

1.1 Overview

Globally, the 20th century witnessed major developments in irrigation systems and

schemes, with approximately 53 million hectares in 1900 expanding to 285 million

hectares by 2000 (Wisser, Fekete, Vörösmarty, & Schumann, 2010). This

development has contributed to world food security, especially in arid and semi-

arid climate zones, as irrigated areas comprise 17% of total cropped land but

provide 40% of global agricultural production (Li & Troy, 2018). However, water

resources have decreased over the same period, which is evident as reduced river

flows and groundwater levels (Famiglietti, 2014; Konikow, 2011; Wada et al.,

2010). Extensive development of irrigation in arid regions has also resulted in

higher pollution and contamination, waterlogging, and salinity (Ghumman,

Ghazaw, Niazi, & Hashmi, 2011).

Sustainable water management is defined as the ability to meet current water

needs without compromising the water needs of future generations Poff et al., 2016;

Marino, Pernice, Marra, & Caruso, 2016). Achieving this requires an approach

addressing technical, environmental, economic, and social issues (Ong, Tan, Ng,

Yong, & Chai, 2016). The sustainable yield concept is defined as the rate of water

exploitation without endangering water quantity or quality (Yari, 2016).

Sustainable yield is estimated for every aquifer, and varies according to the

hydrogeological conditions (Chartzoulakis, 2015). Sustainable management of

aquifers in arid zones requires accurate data on recharge rates of groundwater

resources, however such data are often lacking for developing countries (Abou Zaki,

Haghighi, Rossi, Xenarios, & Kløve, 2018). When long term groundwater

abstraction exceeds long term groundwater recharge rates, overexploitation or

persistent groundwater depletion can occur (Doell, Mueller Schmied, Schuh,

Portmann, & Eicker, 2014). This can result in a decline in groundwater levels,

which can have devastating effects on natural streamflow in groundwater-fed

wetlands and related ecosystems (Davranche et al., 2015). Many well-known

hotspots of groundwater depletion have already appeared world-wide, including

Ogallala in USA (Wada et al., 2012), North-East China (Wada et al., 2012), Iran

(Khaki, 2020), Yemen (Taher et al., 2012), South-East Spain (Martínez Aldaya et

al., 2019), and Northwest India (Chen, Li, Zhang, & Ni, 2014).

18

Estimates indicate that the intensity and frequency of droughts in arid and

semi-arid climate zones are increasing due to climate change (Dashtpagerdi et al.,

2015). Research has confirmed that unsustainable usage of water resources leads

to increasing drought impacts (Cooley, Donnelly, Phurisamban, & Subramanian,

2015; Van Loon & Van Lanen, 2013). The overexploitation of water resources is

directly linked with decrease in precipitation volume, increase in temperature,

decreases in river flow (Wen, Rogers, Ling, & Saintilan, 2011), groundwater level

depletion (Castle et al., 2014), and ultimately decreases in irrigated farmed area

(Paper III). These factors increase the severity of droughts, as less water is available

to compensate water shortage during drought period.

Unlike other natural disasters, drought can affect large areas for a relatively

long time (Nafarzadegan et al., 2012). Droughts are characterized by an unexpected

reduction in precipitation volume (Shahabfar & Eitzinger, 2011), which can lead to

major economic and social challenges, especially in rural areas (Joshi, 2019).

Water-dependent sectors such as agriculture are critically impacted by droughts,

lowering crop yield (Karami & Keshavarz, 2010). There are four types of drought:

meteorological, hydrological, agricultural, and socioeconomic (Mishra & Singh,

2010). Meteorological drought occurs when there is a severe decrease in

precipitation volume (Xu, Wang, Zhao & Yang, 2018). Hydrological drought

occurs when there is a decrease in stream and reservoir discharge (Abdi, Shirvani,

& Buchroithner, 2019). Agricultural drought refers to the depletion of surface,

groundwater, and soil moisture levels due to overexploitation of water resources

for agricultural activities (Huang et al., 2015). Socioeconomic drought refers to the

societal, economic, and environmental impacts of the three other types of drought

(Guo et al., 2019). In arid and semi-arid climate zones, meteorological drought is

the trigger for the other drought types. Agricultural drought and socioeconomic

drought occur when usage of water resources exceeds the volume of renewable

surface and groundwater resources. There has been considerable progress in

understanding the impacts of drought in terms of engineering, economics, social

science, and geography, but the links between human activities and droughts still

need more investigation. In areas with a high dependency on agriculture, these links

are more complicated, as they affect the livelihoods of rural inhabitants.

Identification of the relationships between different types of drought could lead to

improvements in strategies for overcoming social challenges caused by droughts.

Understanding these links could also reveal the factors that determine the transition

from sustainability to unsustainability in water resources systems (Paper III).

19

1.2 Remote sensing applications in hydrology

Remote sensing monitoring of agriculture has been employed for various

applications, e.g., detection of land use change, yield production, irrigation, and

vegetation type (Kussul, Lavreniuk, Skakun, & Shelestov, 2017). Published

literature reflects increasing interest in remote sensing in agricultural research

(Weiss, Jacob, & Duveiller, 2020). This increased research has been enabled by

advances in relevant technologies, including sensors, spatial, temporal, and spectral

capacities, Nano-satellites, and unmanned aerial vehicles (UAV). Remote sensing

data have been used to date in monitoring crop canopy temperature, soil moisture,

evapotranspiration rate, crop density, and crop height (Zhao, Yang, Li, Zhao, &

Zhang, 2016; Huang, Qiang, Jianxia, Guoyong, & Li, 2015; Salamí, Barrado, &

Pastor, 2014). In combination, such data give farmers the ability to optimize

irrigation and water management schemes. This is important in arid and semi-arid

climate zones where precipitation volume is low, especially in the dry season, and

where irrigation water is used to compensate for precipitation shortages.

Remote sensing approaches have also been used for monitoring hydrological

variables and estimating hydrological model parameters (Tang, Gao, Lu, &

Lettenmaier, 2009). Remote sensing data obtained from various satellites covering

different spectral bands provide information on different catchment characteristics,

which can be used as parameters in hydrological models (Khan et al., 2010; Dabral,

Baithuri, & Pandey, 2008). Combining these data with other spatial information,

such as digital terrain and elevation models, allows spatial estimation of

hydrological model parameters. Remote sensing gives the ability to acquire data

for remote areas, where data are otherwise difficult to acquire due to accessibility

challenges and due to associated high costs. Remote sensing provides an

understanding of hydrological parameters by covering the whole globe. This is

important for the development of global hydrological models to forecast climate

change, floods, and weather conditions (Stisen, Jensen, Sandholt, & Grimes, 2008).

Historically, hydrological models have been data-limited, as all such models are

calibrated on observed data such as rainfall, runoff, and evaporation rates.

Remote sensing can be used for assessing watershed priority evaluation

problems, management requirements, and periodic monitoring (Butt, Shabbir,

Ahmad, & Aziz, 2015). Data on watershed characteristics, including area, size,

geometrical shape, topography, drainage pattern, and land forms, can be obtained

by remote sensing. The near infra-red (NIR), is a method that makes use of the near

infrared region of the electromagnetic spectrum (from 700 to 2500 nanometers).

20

By measuring the light scattered off and through a sample, this bands differ between

land and water, so remote sensing of these differences helps in mapping river

drainage and lake area changes, while differences in the red band help differentiate

between vegetated and non-vegetated areas (Hunt et al., 2010). Remote sensing can

also be used to monitor components of hydrological models, e.g., precipitation

volume can be obtained from weather radar (Chen et al., 2014). Elevation changes

in the land surface can be detected by airborne laser altimeters, which provide data

on topography, stream channel cross-section, soil surface roughness, and vegetation

height. Soil degradation, salinity, alkalinity, erosion, and desertification can be

evaluated by remote sensing measures (Hereher, & Ismael, 2016; Lamchin et al.,

2016).

Remote sensing has also proven to be a good tool for monitoring surface and

groundwater resources. Data from Gravity Recovery and Climate Experiment

(GRACE) mission satellites can be used to measure spatial and temporal variations

in the gravitational field, which can be used to estimate changes in groundwater

storage level (Landerer & Swenson, 2012). The normalized difference water index

(NDWI) monitors variations in the reflectance of surface water bodies, which can

be used to detect any changes in lakes and wetlands (Jawak & Luis, 2014). These

tools can provide a time series for measured parameters, as the analyst can

extrapolate data for any previous time frame. This helps in obtaining data for

remote areas, but also in filling data gaps and overcoming data shortages. As

hydrological data is essential for arid and semi-arid climatic zones, remote sensing

may provide needed data for the development of water management goals and

strategies. When understood and used correctly, remote sensing data can be an

important tool in such climate zones, especially following recent advances in

technology and research in the remote sensing field.

1.3 Thesis objectives and hypothesis

With the world’s growing population, especially in arid and semi-arid zones,

expansion of irrigated agriculture is a solution to maintain food security in these

areas. Agriculture is the main economic activity of arid and semi-arid communities,

making water management policies important for this sector, as an increase in the

irrigated area increases the stress on the water supply.

Water management in arid and semi-arid regions faces the challenge of

matching supply and demand, as the precipitation volume is already low. To face

the water demand challenges, water management systems must consider three main

21

factors: the water demand volume, geophysical parameters, and water management

objectives (Figure 1). The focus in this thesis was on the impacts of increasing

demand for water in agriculture, which is the main water demand sector, and water

resources depletion and drought occurrence frequencies in arid and semi-arid

regions. Two regions in central Iran, with similar climate and geophysical prospects,

were selected for analysis of the long-term impact of water overexploitation.

Fig. 1. Conceptual diagram of the water resources management framework.

Fars and Esfahan provinces, in central Iran, are important agricultural zones in the

country. The introduction of large-scale irrigation schemes in the second half of the

20th century increased the agricultural area and crop yield in both provinces (Paper

II; Paper III). However, the associated increase in exploitation of water in the two

provinces has increased the stress on their surface and groundwater resources.

Increased stress on the river water resources in the provinces has directly affected

the lake and wetland area in downstream river reaches (Paper I; Paper II). This has

resulted in shrinkage of surface areas of e.g., Lake Bakhtegan in Fars province and

the important Gavkhuni wetland in Esfahan province, affecting the ecological

biodiversity (Paper I). The aim of this thesis was to evaluate the role of irrigation-

based agriculture expansion for the frequency of occurrence of drought in the study

regions.

22

The first section of the thesis describes water mass depletion and the increase

in drought frequencies in Fars and Esfahan provinces and assesses whether this is

because of climate or human factors. Standardized precipitation index (SPI) was

used to determine the frequency of occurrence of meteorological drought, and

standardized drought index (SDI) to determine the frequency of occurrence of

hydrological drought. In normal conditions, meteorological and hydrological

drought occurrence is related to precipitation volume, and any increase in

hydrological droughts not related to dry years is directly related to overexploitation

of water resources. The increases in irrigated farmed area and in hydrological

drought occurrence were compared to assess whether water stress in the two

provinces is directly related to the farming activities. For this purpose, two new

agricultural drought indices were developed to connect water resources depletion

with irrigation expansion.

The second section of the thesis describes the use of remote sensing data to

monitor different hydrological parameters. Normalized differential water index

(NDWI), derived from satellite images, and was used to monitor surface area

changes in Lake Bakhtegan and Gavkhuni wetland. Satellite data from GRACE

were used to monitor groundwater reserves and the total water storage in Bakhtegan

basin and Zayanderud basin. In addition, remote sensing GLADS data were used

to derive evapotranspiration and soil moisture data for use in the water balance

equation.

The main hypothesis explored in this thesis was that agriculture expansion in

arid regions such as south and central Iran causes surface and groundwater

depletion. The following four research questions were addressed: Is

overexploitation of water resources the main trigger for groundwater depletion or

has climate change also contributed to this depletion? Is increased frequency of

occurrence of droughts related to human activities and irrigation? What are the

impacts of river flow depletion in terms of changes in flow and wetlands? Does

remote sensing data have sufficient reliability and coverage for use in monitoring

different hydrological cycle components, when compared with in-situ measured

data?

23

2 Study area and data used

2.1 Water resources in Iran

Water resources management in Iran developed rapidly since the mid of 20th century,

due to various economic and social factors. The Iranian population has increased

by eight-fold since 1900 (World Bank, 2020), with 75% of this population growth

occurring in rural areas (Torabi Farsani, Coelho, & Costa, 2012). The increase in

population increased the demand for potable and agricultural water. In the same

period, rapid urbanization and improvements in living standards increased the

demand for domestic water with improving living standards. This increase in water

demand, for domestic and agricultural needs, required major infrastructure and

system changes, which became more intense in the 1960s (Madani, 2014). Since

then, 88 major dams have been constructed and brought into operation, and 10 more

are under construction (Aquastat, 2015). These have increased Iran’s reservoir

capacity to 30 billion cubic meters (bcm), for the major purposes of irrigation,

domestic and industrial water supply, hydroelectricity, and flood control. In the

same period, the volume of groundwater extracted has increased via an increasing

number of deep and semi-deep wells, which replaced the traditional qanats

(Motagh et al., 2017). The increase in demand for water resources also required an

expansion in the water supply system and installation of transmission pipelines,

tunnels, and pumping stations (Motagh et al., 2017).

Mean annual precipitation in Iran as a whole is estimated to be 250 mm,

varying from 50 mm in the central arid areas to 1600 mm in the Northern Caspian

Sea coastal zone (Fahmi, 2012). Mean rainfall volume is estimated to be 400 bcm,

with a 68% evaporation rate removing 270 bcm (Valipour & Eslamian, 2014).

Mean volume of annual renewable water resources is estimated to be 130 bcm, with

92 bcm of this being lost as surface runoff and 38 bcm going to groundwater

recharge. The mean water volume usage per capita in 2017was 1688 m3, which is

much lower than the world average of 3980 m3. Iran is divided into six main

catchment areas: Central Plateau, Lake Urmia, Persian Gulf, Lake Hamoon, Kara-

Kum, and Caspian Sea (Nourani & Mano, 2007). Half of all renewable water

resources in the country are located in the Persian Gulf catchment in southern-

western Iran, which occupies a total area of 430,000 km2 (Madani, 2014). Potable

water quality in Iran is considered good, with 96% of the population having access

to safe water supply (Mohebbi et al., 2013). Water pollution in Iran is often due to

24

dumping of solid wastes, inputs of urban and industrial wastewater, and residues

from agricultural chemicals (Mohebbi et al., 2013).

The agriculture sector is considered the main water withdrawal sector in Iran,

using 92.2 percent of the total water consumption, of which 62 percent is from

groundwater and 38 percent is from surface water (Saatsaz, 2019). In terms of

actual volume, it is estimated that the annual exploitation volume for agriculture is

93 bcm, of which 53 bcm are from groundwater and 40 bcm are from surface water

(Madani, 2014). Most overexploitation of water resources occurs in the Central

Plateau catchment, where less surface water is available. This overexploitation has

led to an average 3.8 bcm annual depletion in groundwater resources (Keshavarz,

Karami, & Vanclay, 2013). The number of wells increased from 9000 in the 1970s

to 45,000 by 2010 (Keshavarz, Karami, & Lahsaeizadeh, 2013). The irrigated area

in Iran is estimated to be 8.7 million hectares, mostly surface irrigated (Aquastat,

2015). In terms of sources of water for the irrigated area, 5.2 million hectares are

irrigated using groundwater and 3.1 million hectares using surface water (Frenken,

2013). Wheat is the most widely irrigated crop in Iran, representing 31% of the total

irrigated area, followed by fodder crops, groundnuts, rice, and barley (Bannayan,

Sanjani, Alizadeh, Lotfabadi, & Mohamadian, 2010). Crop yields on irrigated land

are two- to three-fold higher than in rainfed areas, but the irrigation efficiency is

generally low, an estimated 33% as a national average (Bannayan, Lotfabadi,

Sanjani, Mohamadian, & Aghaalikhani, 2011).

2.2 Fars province

Fars province, the fourth largest province (122,608 km2), is located in central Iran.

Fars has a population of 4.85 million people, distributed between 68 percent urban

and 32 percent rural residents (Pishkar, 2015). Due to its geographical location

between the Zagros Mountains in the north-west, the Sirjan Desert in the north-east,

and the Persian Gulf in the south, Fars province contains different climate zones.

The variation in climate allows a variety of crops to be grown, including cereals,

beans, cotton, and maize. Orchard crops like almonds, apples, plums, citrus, and

palm date are also produced in the province (Pishkar, 2015). Fars is a leading

agricultural area in Iran and the agriculture sector in the province is important

economically, contributing 8.7% of total Iranian agricultural production (Pishkar,

2015). The agricultural sector provides 20.6% of jobs in the province (Pishkar,

2015). Fars had the highest cultivated area in Iran in the years 2005 and 2006, 1.08

and 1.06 million hectares, respectively (Ahmadi et al., 2015). Even with recent

25

droughts, Fars is still considered the leading province in agricultural production in

Iran (Paper III).

Although the climate in Fars province varies under the influence of

geographical factors, the temperate hyper-arid type is the dominant climate,

creating pressure on water resources with its high water dependency rate. Surface

water availability has been experiencing a continual reduction in recent years,

leading to major lakes drying up, because of agricultural expansion, river regulation,

and an increase in the frequency of meteorological droughts (Haghighi &

Keshtkaran, 2008). With surface water depletion, local people began to overexploit

the groundwater resources, leading to depletion in groundwater levels in the

province (Paper III).

For better drought analysis and assessment, in this thesis Fars province was

divided into six regions: southern, western, eastern, central, northwestern, and

northeastern (Figure 2). Monthly precipitation and temperature data for the period

1977–2016, obtained from 38 meteorological stations in Fars Province, were used

to assess the variations in meteorological drought occurrence. Stream flow data

obtained from 24 gauging stations on 17 rivers in the six main basins in Fars

province were used to assess hydrological drought intensity. The six main river

basins in Fars province are: Bakhtegan, Sirjan desert, Mond, Heleh, Kol-o-Mehran,

and Zohreh. Of these, Bakhtegan is the main basin in the province and the only

basin not shared with other provinces. The Kor River is the main river in Bakhtegan

basin and it discharges into Lake Bakhtegan downstream. The other five basins

discharge into the Persian Gulf and Sirjan Desert catchment areas.

The effect of repeated occurrence of meteorological and hydrological droughts,

in the period 1977–2016, on the occurrence of the agricultural droughts was

assessed in this thesis. Groundwater depletion in the period 1995–2015 in 12 major

aquifers in Fars province and fluctuations in the area of Lake Bakhtegan in the

period 1985–2015 were also assessed. Data on hydro-climatological factors, river

flow, groundwater levels, precipitation, and temperature data were provided by Fars

Regional Water Authority (FRRW, 2016). Data on farmed areas and the farming

methods used in the period 1977–2016 were provided by the Ministry of

Agricuclture, Jahad (MAJ).

26

Fig. 2. Maps showing (a) the location of Fars province in south-western Iran, (b) the six

main geographical regions in the province and the location of the meteorological

monitoring stations, (c) the six basins, major rivers, and 24 hydrological stations in Fars

province, and (d) province topography and the location of the 12 groundwater

monitoring wells (Reprinted [adapted] under CC BY license from Paper III © 2020

Authors).

27

2.3 Bakhtegan basin

Bakhtegan basin is located in the north-eastern part of Fars province (Figure 3).

The basin area is 31,511 km2, divided into 16,630 km2 of mountainous area and

14,881 km2 of plains (Hedayat, Zarei, Radmanesh, Mohammadi, 2017). The main

river in the province is the Kor, which rises in the Zagros Mountains and is 280 km

long (Haghighi & Klove, 2017). Lake Tashk and Lake Bakhtegan are the main lakes

in the province, with a total area of between 220 and 640 km2 depending on the

precipitation rate. Mean precipitation in the basin in the 43-year period between

1967 and 2009 was 270 mm (Choubin, Malekian, & Golshan, 2016). Thus most

areas in the basin belong to the hyper-arid climate type (Hojjati & Bostani, 2010).

Based on alluvial aquifer geometry and distribution, Bakhtegan basin is divided

into 27 groundwater zones (Figure 3). The total area of these aquifers is 10,564 km2

and the average thickness is between 30 and 50 m (Rasoulzadeh & Moosavi, 2007).

The aquifer sediments consist of rubble, stone, gravel, sand, silt, and clay, and most

of the aquifers are located in valleys between highlands. Groundwater is an

important water source in the basin, especially in the dry season when surface water

sources are depleted. The groundwater in Fars province is generally of good quality,

and low pumping costs and good availability make it the main water source used

for agricultural expansion in recent decades (Noshadi & Ghafourian, 2016).

However, overexploitation has led to depletion in groundwater levels in the

province, directly affecting surface water recharge (Hedayat et al., 2017). It is

estimated that 4000 million cubic meters (Mm3) are extracted every year from the

40,000 wells and boreholes in the province (Hokkati & Boustani, 2010).

Precipitation data from Bakhtegan basin were obtained from 22 local observation

stations for the period between 2002 and 2011 from Bureau for Design and

Development and Farmers Participation (BDDFP, 2011). Land use data and data on

average evapotranspiration were derived from USGS MODIS data and images

(Oak Ridge National Laboratory Distributed Active Archive Center [ORNL

DAAC], 2011). GRACE water mass data for the basin for the period 2002–2015

were provided by the German Research Center for Geosciences. Soil moisture data

were computed from the Global Land Data Assimilation System (GLDAS) datasets

available from EARTHDATA (Rodell et al., 2004).

28

Fig. 3. Main image: Map of Bakhtegan basin showing alluvial aquifers, observation wells,

and the location of Lake Bakhtegan at the mouth of the river Kor. Inserts: (A) location

of Fars province in south-west Iran and (B) location of Bakhtegan basin in Fars province

(Reprinted [adapted] under CC BY license from Paper I © 2019 Authors).

2.4 Zayanderud basin in Esfahan province

The Zayanderud river basin is considered the largest and most important

agricultural, industrial, and urban zone in central Iran (Madani & Marino, 2009).

The river rises in the Zagros Mountains and flows east for 400 km to discharge in

Gavkhuni wetland (Figure 4). Gavkhuni wetland is listed under the Ramsar

Convention of 1975 and is considered one of the most important aquatic

ecosystems in Iran (Gohari et al., 2013). The basin ranges in altitude from 1470 to

3974 m above sea level and has a total area of 41,524 km2. Mean annual

precipitation in the basin ranges between 50 and 1500 mm (Akbari et al., 2007).

The temperature ranges between 3 and 30 °C and annual potential

evapotranspiration is around 1500 mm (Madani & Marino, 2009). There are 4.5

million inhabitants in the basin and, because of the fertile basin soils, agriculture is

the largest economic (and water-consuming) sector (Zamani, Grundmann, Libra, &

29

Nikouei, 2019). Precipitation is low in eastern and central parts of the basin, so

irrigation is essential for cultivation, which consumes 90% of total water resources

(Bijani & Hayati, 2015). Until the 1950s, the water systems in the basin were

confined to small diversion structures that provided irrigation water. The water was

derived from springs, underground qanats and snowmelt, and was used in a small

number of fully irrigated systems (Harandi & De Vries, 2014). The Zayanderud

dam was constructed in 1972 and has a total capacity of 1500 Mm3 (Gohari et al.,

2013). Later, three transbasin diversions were constructed, in 1985, 2004, and 2007,

adding 152 Mm3 annually to cities in the Dasht-e Kavir desert facing water stress

(Madani & Marino, 2009).

Precipitation, evapotranspiration, and temperature data for the basin in the

period 1963–2012 were provided by the Iranian Meteorological Organization

(IRIMO). Groundwater level data from 35 observation wells for the period 1981–

2015 were provided by the Iranian Water Resources Management Company

(WRM). Data on monthly inflow volume from the Zayanderud river to Gavkhuni

wetland for the period 1963–2012 were obtained from Varzaneh gauging station.

Data on the total variation in water mass storage was derived from GRACE datasets

for the period 2002–2015 provided by the German Research Center for

Geosciences. The GRACE data were limited to the period 2002–2015 because of

monthly image availability. Monthly satellite images of Gavkhuni wetland for the

period (1985–2015) were downloaded from the Landsat 4/5/7/8 Surface

Reflectance database of the United States Geological Survey (USGS). Variations

in 50-year precipitation and stream flow data (1963–2012) were used in monitoring

meteorological and hydrological droughts on a longer temporal scale. This long

monitoring period covered any change in the frequency of drought occurrence

before and after the 1980s, when the irrigated area in Zayanderud basin expanded

greatly.

30

Fig. 4. Main image: Map of Zayanderud basin showing observation wells, the location

of Gavkhuni wetland at the end of the Zayanderud River, and the Varzaneh hydrometric

station. Insert: Location of Zayanderud basin in central Iran (Reprinted [adapted] under

CC BY license from Paper II © 2020 Authors).

31

3 Methods

3.1 Climate variability analysis using the Emberger method

Many factors affect the climate variability such as precipitation, temperature,

evapotranspiration etc. In this thesis, the Emberger method (Emberger, 1932) was

used for the analysis of the climate variation in the study areas. It combines

Emberger climate classification, Emberger aridity index, standardized precipitation

index (SPI), streamflow drought index (SDI), surface and groundwater volume and

fluctuations in level, and agricultural development (Emberger, 1932). It was used

here to identify agricultural development and direct impacts of climate change on

water resources in Fars province as a whole, in Bakhtegan basin in Fars province,

and in Zayanderud basin in Esfahan province. The non-parametric Mann-Kendall

test was used for assessing trends in different drought types. The Emberger method

is widely used for climate classification in climatology, hydrology, ecology, and

bioclimatic analysis. The aridity index (Q) considers two important climate

parameters, precipitation and the temperature:

𝑄

, (1)

where P is annual precipitation in mm, M is mean maximum temperature of the

warmest month in degrees Kelvin (K), and m is mean minimum temperature of the

coldest month in K. The equation considers the minimum temperature of the coldest

month in the year, since vegetation growth is strictly related to thermal limits

(Emberger, 1932). The rate of evaporation changes with temperature variations, so

the term (M – m) represents evaporation and the continentality of a climate. Based

on Q and m values variation, 30 different climate classes are defined and

represented in an Emberger climatogram (Emberger, 1932) as shown in (Figure 5).

32

Fig. 5. Emberger climatogram based on aridity index (Q) and mean temperature (m) in

the coldest month of the year (Reprinted [adapted] under CC BY license from Paper III

© 2020 Authors).

Emberger aridity index (Q) was calculated for each station in the study areas based

on annual data. The three Emberger influence factors (humidity influence factor

(HIF), thermal influence factor (TIF), and climate influence factor (CIF)) and the

dominant humidity, thermal, and climate groups were then defined for each basin

in Fars province and for Zayanderud basin in Esfahan province. The calculations

were performed on decade scale (1977–1986, 1987–1996, 1997–2006, 2007–2016)

and for the whole study period (1976–2016). The three Emberger influence factors

are discussed in detail below.

There are six HIF groups, which show the influence of different humidity groups

over an area or station. These six HIF types are shown on the y-axis of the Emberger

climatogram (Figure 5). They comprise: HA (hyper-arid), AR (arid), SA (semi-arid),

33

SU (sub-humid), HU (humid), and HH (hyper-humid), which are defined based on the

value of Q in Equation 1. Humidity influence factor is defined by equation

HIF∑ NY

∑ ∑ NY, (2)

where HIFx is humidity influence factor for group x (HA, AR, SA, SH, HU, or SU) in

a region or station, n is number of meteorological stations in each region, NYxi is

number of years in which station i has experienced humidity conditions of type x, and

NYHi is number of years in which station i has experienced humidity conditions of type

H.

There are 5 TIF groups, which show the influence of different thermal groups over

an area or station. These five TIF types are shown on the x-axis of the Emberger

climatogram (Figure 5). They comprise: VC (very cold), CD (cold), CL (cool), TE

(temperate), and HO (hot), which are defined based on the value of mean lowest

temperature in the coldest month (m). Thermal influence factor is defined by equation

TIF∑ NY

∑ ∑ NY, (3)

where TIFy is thermal influence factor for group y (VC, CD, CL, TE, or HO) in a region

or station, n is number of meteorological stations in each region, NYyi is number of

years in which station i has experienced thermal conditions of type y, and NYTi is

number of years in which station i has experienced thermal conditions of type T.

There are 30 CIF groups, which show the influence of each climate group over an

area or station. Each climate group has two terms, where the first term indicates

humidity group (HA, AR, SA, SH, HU, SU) and the second term indicates thermal

group (VC, CD, CL, TE, HO). Climate influence factor is defined by equation

CIF∑ NY

∑ ∑ ∑ NY, (4)

where CIFx-y is climate influence factor of x and y, x is a humidity group and y is a

thermal group, in a region or station, H is humidity group (HA, AR, SA, SH, HU, or

SU), T is thermal group (VC, CD, CL, TE, or HO), n is number of meteorological

stations in each region, NY(x-y)i is number of years in which station i has experienced

climate type x and y in the selected region, and NYHTi is number of years in which

station i has experienced humidity conditions of type H and thermal conditions of type

T. Details of the 30 CIFs are provided in Table 1.

34

Table 1. The 30 types of climate influence factor (CIF) defined in the Emberger

classification (Emberger, 1932).

Humidity group Thermal group

very gold cold cool temperate hot

hyper-arid VC-HA CD-HA CL-HA TE-HA HO-HA

arid VC-AR CD-AR CL-AR TE-AR HO-AR

semi-arid VC-SA CD-SA CL-SA TE-SA HO-SA

sub-humid VC-SH CD-SH CL-SH TE-SH HO-SH

humid VC-HU CD-HU CL-HU TE-HU HO-HU

hyper-humid VC-HH CD-HH CL-HH TE-HH HO-HH

3.2 Analysis of metrological, hydrological, and agricultural

droughts

Standardized precipitation index (SPI) is used for defining and monitoring

meteorological droughts. Zero and positive SPI values reflect normal and wet

conditions, while negative values indicate meteorological drought (Table 2). A detailed

definition of states, as suggested by McKee et al. (1993), is provided in Table 2. The

gamma distribution of monthly precipitation time series is calculated as suggested by

Lloyd and Saunders (2002) using equations

SPI 𝑡 (5)

and

SPI 𝑡 , (6)

where c0 = 2.515517, c1 = 0.802853, c2 = 0.010328, d1 = 1.432788, d2 = 0.189269, and

d3 = 0.001308, and t is average precipitation on the scale parameter. Equation 5 is used

for 0 < Hx ≤ 0.5 and Equation 6 is used for 0.5 < Hx < 1, where Hx is the cumulative

probability function for the gamma distribution.In this thesis, SPI was calculated

using the SPI Generator application provided by the National Drought Mitigation

Center (UNL) in Canada and USA, for aggregation time of 1, 3, 6 and 12 months

respectively. SPI is derived from the precipitation volume, without considering

important parameters like evapotranspiration and soil moisture, and thus a more

comprehensive index must be used for comparison.

35

Table 2. Standardized precipitation index (SPI) classification of states devised by McKee

et al. (1993).

Range of SPI Description of state

SPI ≥ 2.0 Extremely wet (EW)

1.5 ≤ SPI < 2.0 Severely wet (SW)

1.0 ≤ SPI < 1.5 Moderately wet (MW)

0 ≤ SPI < 1.0 Normally wet (Nw)

- 1.0 ≤ SPI < 0 Normally dry (ND)

- 1.5 ≤ SPI < - 1.0 Moderately dry (MD)

- 2.0 ≤ SPI < - 1.5 Severely dry (SD)

SPI < - 2.0 Extremely dry (ED)

Streamflow drought index (SDI) uses standardized annual streamflow volumes to

define values lower than the mean streamflow at least at standard deviation value,

meaning that it shows if the annual flow is lower or higher than the average for the

study period (Palmer, 1965). Based on SDI values, the state of hydrological

droughts is defined in a way close to that for SPI Table 3. Fieldhouse and Palmer

(1965) suggest that SDI can be calculated using the following equation:

SDI =

, (7)

where Fi is the annual stream flow for a given period 𝑖, and 𝐹 and 𝑆 are the mean

and standard deviation of the cumulative streamflow for the whole study period.

The SDI was calculated for an aggregation time of 1 month.

Table 3. Streamflow drought index classification of states (Palmer, 1965).

Criterion Description of state

SDI ≥ 0.0 No drought

- 1.0 ≤ SDI < 0.0 Mild drought

- 1.5 ≤ SDI < - 1.0 Moderate drought

- 2.0 ≤ SDI < - 1.5 Severe drought

SDI < - 2.0 Extreme drought

For monitoring agricultural drought, two indices were developed in this thesis,

overall agricultural drought index (OADI) and agricultural drought index (ADI), to

evaluate changes in agriculture and drought in the study area (Paper III). These

indices are defined by equations

OADI (8)

36

and

ADI , (9)

where

𝐴∑

. (10)

In Equations 8–10 Ay and Ai are annual farmed areas for a given year y and i,

respectively, A and S are mean and standard deviation of farmed area for the whole

study period, and A1–Y and S1–y are mean and standard deviation of farmed area between

the first year and year y in the study period. These indices were calculated for irrigated,

rainfed, and total farmed area. The OADI shows, in any year, whether the irrigated

or rainfed area of farmed land is below or above the overall average, while the ADI

shows whether it is below or above the average in a given period. OADI and ADI

annual values are directly related to the agricultural area farming type, either

irrigated or rainfed. This shows the direct annual impact of precipitation and water

availability on the irrigated area variability. If the study period is divided into

decade sub-periods, as in this thesis, ADI shows the change in farmed area in any

decade.

3.3 Using Normalized Difference Water Index (NDWI) to monitor the

fluctuation in surface area of Gavkhuni wetland and Lake

Bakhtegan

Landsat satellites have been operating since 1972, providing a continuous global

record of the Earth’s land surface. The imagery is currently available at no cost

through the United States Geological Survey (USGS). Monthly Landsat surface

reflectance images for Gavkhuni wetland and Lake Bakhtegan for the period from

January 1985 to December 2015 were downloaded from the Landsat database. For

this study period Landsat missions 5, 7 and 8 datasets are used. Landsat 5 images

consist of seven spectral bands with a spatial resolution of 30 meters for bands 1 to

5 and 7 (Chander et al., 2004). Spatial resolution for band 6 (thermal infrared) is

120 meters, but is resampled to 30 meter pixels (Chander et al., 2004). Landsat 7

images consist of eight spectral bands with a spatial resolution of 30 meters for

bands 1 to 7. The resolution for band 8, panchromatic, is 15 meters. Landsat 8

images consist of nine spectral bands with a spatial resolution of 30 meters for

bands 1 to 7 and 9 (Roy et al., 2014). New band 1, ultra-blue, is useful for coastal

37

and aerosol studies. New band 9 is useful for cirrus cloud detection (Roy et al.,

2014). The resolution for band 8, panchromatic is 15 meters (Roy et al., 2014).

Reflectance images show the reflectance capacity of different surfaces, including

water bodies. Most of the selected images were cloud-free or nearly cloud-free, and

were georeferenced with the topographical maps. In the electromagnetic spectrum,

water absorbs almost all of incident radiation in the near and mid-infrared bands,

while it shows a strong reflectance in visible bands. Based on the spectral analysis,

NDWI was used to extract the water body, employing a method by McFeeters

(1996). NDWI is a band ratio index between the green and near infrared (NIR)

spectral bands, which not only enhance water features, but also depress vegetation,

bare land, and other environmental information. NDWI is calculated by equation

NDWI =

, (11)

where (RGreen) is the top of atmospheric reflectance of the green band and (RNir)

represents that of the near-infrared band, which correspond to band 2 and band 4,

respectively, in Landsat surface reflectance. The NDWI value ranges from –1 to 1.

McFeeters (1996) also set zero as the threshold, where the cover type is water if

NDWI > 0 and non-water if NDWI ≤ 0. In order to indicate the best NDWI value

reflecting Gavkhuni wetland and Lake Bakhtegan, nine sub-thresholds were

defined, with values of 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, and 0.9 respectively, for

the positive NDWI values. The higher the NDWI value, the higher the reflection

rate, indicating higher probability of a water body. For different sub-threshold

values, the corresponding wetland area was obtained by image processing using

MATLAB. Starting with in-situ data for Gavkhuni wetland, the monthly variation

in wetland volume was calculated for each sub-threshold from the water balance

equation

ΔV Vi – Vi-1 Ai Pi – Ei QiΔt , (12)

where area Ai is equivalent to (An + An-1)/2, where n is any given month, and Pi, Ei,

and QiΔt are monthly precipitation, evapotranspiration, and inflow volume,

respectively. Both Pi and Ei are in mm and QiΔt is in m3 per month. Using equation

12 with every sub-threshold, nine different values of ΔV are obtained for each value

of Ai. The monthly variation in wetland water level ΔH can be expressed as

ΔH = . (13)

38

Using Equation 13 with every sub-threshold, nine different values of ΔH are

calculated for each value of Ai. But ΔH is also equivalent to

ΔH = Pi - Ei (14)

Using the values of ΔH, Pi, Ei, and Ai calculated previously, the value of QiΔt in

equation 12 can be expressed in mm for the nine different values of Ai. The real-

time value of QiΔt measured at a gauging station is then compared with the nine

different QiΔt values calculated. This can be done using the “coefficient of

determination” method by equation

R2 = ∑ ∑ ∑

∑ ∑ ∑ ∑ , (15)

where n is the number of observations, QiΔtobs is the real time monthly flow volume

and QiΔtmod is the flow volume calculated from Equation 12.

The coefficient of determination (R2) ranges from 0 to 1, with 0 meaning that

the calculated flow and the real flow volumes are independent, and 1 meaning both

values are related without errors. The area of the sub-threshold with the highest

value best fits the flow data available. This sub-threshold area is thus considered

the most similar area to the real-time wetland surface area. From the data produced,

an area Ai versus volume ΔV relationship can be obtained. This mathematical

relationship can be used to fill in missing wetland area data. In the present case,

data availability was restricted by climate factors, such as clouds and fogs, and

blurring of the images downloaded. Thus the sub-threshold with the highest

coefficient of determination was used to derive monthly area data for Lake

Bakhtegan. The water balance equation (Equation 12) can also be used to fill in

values missing due to unavailability or blurring of satellite images.

3.4 Monitoring total water mass depletion in Zayanderud and

Bakhtegan basins, and groundwater depletion in Bakhtegan

basin, using GRACE data

For more than 15 years of operations, from 2002 till 2017, the GRACE satellite

mission revolutionized our view of how water moves and is stored on Earth.

GRACE measured changes in the local pull of gravity as water shifts around Earth

due to changing seasons, weather and climate processes. In this study, GRACE data

for the period 2002 till 2015 were examined to determine the total water mass

variation in Bakhtegan basin and Zayanderud basin. GRACE data represent gravity

39

anomalies, showing the mass distribution variation on the planet. The data consist

of monthly images, which when analyzed reveal monthly anomalies in total water

storage. The GRACE data show mass change, or equivalent water height, which is

expressed as monthly variation WMI in mm. The GRACE data were provided by the

German Research Center for Geosciences (GFZ). In order to identify water storage

changes from the spherical harmonic coefficients provided by GFZ, the following

procedure was followed. First, we replaced C20 according to instructions outlined

in GRACE technical note 7 (Cheng, Tapley & Ries, 2013). We then added degree

1 according to the estimation by Swenson et al. (2008) to correct for geocentric

motion. The degree 1 Love number (K1 = 0.021) was taken into account. We then

carefully removed the primary tidal aliasing error of the tidal constituents S2, S1,

and P1, and the secondary tidal aliasing error of M2, O2, O1, and Q1 from the

spherical harmonics (Tourian, 2013). Next, we removed the Glacial Isostatic

Adjustment (GIA) according to the model provided by Wahr and Zhong (2013) and

filtered the coefficients by a Gaussian filter (450 km radius) and a destriping filter,

as proposed by Swenson and Wahr (2006). Finally, in order to account for leakage,

we applied the so-called data-driven method developed by Vishwakarma et al.

(2017). This method is able to restore the signal lost due to filtering, irrespective of

the catchment size.

The groundwater component can be isolated from the total GRACE data.

According to Rodell and Famiglietti (2002), this can be achieved by subtracting the

monthly fluctuation in snow, surface water, and soil moisture volume from the total

water mass by equation

GWMI = WMI – SW – SM, (16)

where GWMI is the groundwater storage thickness derived from the GRACE data,

WMI is the total water mass, SW is the surface water storage, and SM is soil moisture.

The change in surface water storage was calculated from flow data available for

Lake Bakhtegan and Lake Tashk, which are the only surface water bodies in the

study basins. The stream gauges are located in the lake inlets, where the flow data

are recorded. SW can be expressed in the following equation

SW = , (17)

where QiΔt is the monthly inflow volume, EiΔt is the monthly evapotranspiration

volume and AT is the total area of lakes Bakhtegan and Tashk in any given month.

Soil moisture values were computed from the GLDAS datasets available from

EARTHDATA (Rodell & Kato, 2007). The GLDAS can provide various continuous,

40

high-resolution estimates of land surface states and fluxes, such as soil moisture,

soil temperature, latent heat flux, and sensible heat flux. The GLDAS data represent

the water content in different soil layers, 0 to 10 cm, 10 to 40 cm, 40 to 100 cm,

100 to 200 cm. In this study, the 3 hourly 0.25o and 1.0o GLDAS Version 1 products

are used.

In order to compare the GRACE-derived data with the in-situ data, monthly

groundwater levels in 448 observation wells spread throughout Bakhtegan basin

were used. These wells depth vary from 30 to 50 meters depending on the aquifer

depth. The groundwater table levels are monitored on weekly bases. As GRACE

data show the monthly variation of groundwater levels in catchment scale, the

specific monthly groundwater volume variation ΔS in the total catchment area was

calculated by equation

ΔS = ∑ .

(18)

where Si is the groundwater level variation in a given aquifer i, Ai is the area of

aquifer i, and AT is the total catchment area. Here, ΔS is expressed in (mm). For any

given period, the accumulated groundwater volume in the basin ΔSi is equal to

ΔSi = ∑ ΔS. (19)

In this case, ΔSi shows the variation in groundwater storage volume. The

groundwater extracted in the catchment can be assumed to be used mostly by

agriculture, as more than 90 percent of the groundwater exploitation is for

agricultural purpose, and thus directly related to soil moisture. The remaining 10

percent is divided between domestic, 3 percent, and industrial 7 percent. Therefore

ΔSi can be compared with the groundwater volume fluctuation derived from

GRACE data.

41

4 Results

4.1 Spatiotemporal climate variability in Fars province and in

Zayanderud basin, Esfahan province

4.1.1 Fars province

For the study period from 1977–2016, 21 different climate types were detected in

Fars province based on the Emberger index (see Figure 5). It was observed that the

variety of climate types present decreased over time. It was at its peak in the period

from 1987–1996, with 20 different climate types, which decreased to 14 types in

the period from 2007–2016. The greatest climate variety was recorded in the central

region of the province, with 17 types, while the least variation was observed in

western and northeastern region, with eight types each. Hyper-arid and temperate

climate types were the most frequently occurring in the province, being detected in

14.9% of years in the study period. From the first decade in the study period to the

last decade, the frequency of hyper-arid and temperate climate types increased from

12.7 to 20.8%. Humid and very cold, and humid and cold climate types were the

least frequently occurring, in 0.4 and 0.9% of years respectively, and occurring only

in the northwest of the province. In general, except for the northeastern part, the

climate type diversity in Fars province decreased during the study period (1977–

2016).

Based on CIF, regionally the most frequent climate types were hyper-arid and

temperate in western and southern parts of Fars province (42.2 and 40.5% of years);

arid and very cold in northeastern parts (33.6%); semi-arid and very cold in

northwestern parts (26.2%); and semi-arid and cold in central parts (16%). It was

observed that, except for the southern region, the dominant climate type changed

during the study period.

Based on HIF, hyper-arid was the dominant humidity group, occurring in 32.3%

of years in the study period. The occurrence of the hyper-aridity type increased

from 30.6 to 44.4% during the 40-year period. Arid (31.4%), semi-arid (24.9%),

and sub-humid (11.1%) were the next most common humidity types during the

study period, with a noticeable decrease in frequency of occurrence.

Based on TIF, the very cold thermal group was the dominant group, with 28.7%

occurrence, followed temperate (26.5%), cool (21.3%), cold (19%), and hot (4.5%).

42

During the study period, it was noticed that the frequency of occurrence of the cold

thermal groups increased.

4.1.2 Zayanderud basin

Zayanderud basin in Esfahan province had less diverse climate types than Fars

province. Three climate types were detected, hyper-arid cold, hyper-arid very cold,

and arid cold, with CIF values of 57.5, 30, and 12.5%, respectively. Unlike in Fars

province, the climate type distribution in Zayanderud basin did not change much

during the 40-year study period, and the climate distribution remained constant.

Based on HIF, hyper-arid was the dominant humidity group, with 87.5%

occurrence, followed by arid (12.5%). The TIF calculations also showed two

thermal groups, cold (70%) and very cold (30%). The HIF and TIF distributions

did not indicate any major change during the study period. Detailed decadal CIF,

HIF, and TIF distributions for the period from 1977–2016 can be found in Figure

6.

43

Fig. 6. Results of Emberger climate classifications for the six different regions in Fars

Province (Central, North-eastern, Eastern, North-western, Western, and Central), and

for Zayanderud basin (Esfahan). Y-axis shows percentage occurrence of different

climate types (Papers II and III) during the study period from 1977 till 2016. The detailed

climate classification are found in (Table 1) (Under CC BY license from Paper III © 2020

Authors).

44

4.2 Monitoring meteorological drought in Fars province

In Fars province, the SPI calculation for the last decade of the study period from

1977–2016 showed the most extreme droughts in different regions, with 2008 being

the driest year (Figure 7). The years 2006 and 2007 had the longest drought periods

in eastern and southern regions, and in the whole province. It is noticeable that even

with latter decades of the study period having a majority of negative SPI values,

there was no recorded decrease or negative trend in the precipitation values. The

year 1990 was the start of the wettest decade in all of Fars province except the

eastern region, where it began in 1996. The year 1993 was considered extremely

wet and had the highest SPI value, while on a regional basis the wettest years were:

2005 in central, 2004 in northwestern, 1997 in western, 1999 in eastern, 2004 in

northeastern, and 1993 in southern. The year 1990 was a threshold in the SPI

calculations, with the SPI values randomly occurring before that. After 1990 there

was a wet period from 1990 to 2006, and then a dry period from 2006 to 2016.

“Normally wet” and “normally dry” conditions according to the SPI values were

the most commonly occurring conditions in 18% of the years. When “moderately

wet” conditions were included, the total occurrence increased to 25% of the years

(Figure 8). This shows that meteorological drought years were less frequent than

normal and wet years, and were more common in southern and eastern regions than

the other regions of Fars province. This results have been confirmed with similar

studies in the province (Moradi, Rajabi & Faragzadeh, 2011).

45

Fig

. 7. (

a)

Pre

cip

itati

on

vo

lum

e d

istr

ibu

tio

n i

n m

m (

Pap

er

III)

an

d (

b)

reg

ion

al

sta

nd

ard

ized

pre

cip

itati

on

in

de

x (

SP

I) i

n t

he

six

reg

ion

s o

f F

ars

pro

vin

ce i

n t

he

pe

rio

d 1

97

7–

201

6.

ED

: e

xtr

em

ely

dry

, S

D:

se

ve

rely

dry

, M

oD

: m

od

era

tely

dry

, M

iD:

mil

dly

dry

, N

o:

no

rmal,

MiW

: m

ild

ly w

et,

Mo

W:

mo

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SW

: se

ve

rely

we

t, a

nd

EW

: e

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em

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wet

(Un

der

CC

BY

lic

en

se f

rom

Pap

er

III

©

20

20 A

uth

ors

).

46

Fig. 8. Percentage occurrence of standardized precipitation index (SPI) values and

distribution in the six regions of Fars province and in the province as a whole. ED:

extremely drought, SD: severely drought, MoD: moderately drought, MiD: mildly

drought, No: normal, MiW: mildly wet, MoW: moderately wet, SW: severely wet, and EW:

extremely wet (Under CC BY license from Paper III © 2020 Authors).

47

4.3 Monitoring hydrological drought in Fars province

For the study period from 1977–2016, all 24 gauging stations on the main rivers

(Kor, Firouzabad, and Mond) in Fars province showed a significant negative trend,

with the year 1997 being the point of change to a significant negative trend. In the

period from 1977–1996, 17 out of the 24 stations (70%) showed a positive trend,

while the remaining 30% showed a non-significant negative trend (Figure 9). In the

second half of the study period (1997–2016), all stations showed a significant

negative trend. The stations were distributed along the river, from upstream to

downstream. In the second half of the study period, flow at many stations reached

zero or near zero (stations 4, 5, 6, 7, and 18). In this period, the mean reduction in

flow volume at the stations varied from 42% to 70% (station 24). These results

show that flow at the stations changed from being non-stationary, basically

depending on precipitation volume, to stationary negative flow over the 40 years

studied. Analysis of these results confirmed an extended hydrological drought after

1997, with a continuous negative trend. The stations that experienced mild droughts

from 1977 to 1996, such as stations 7, 14, and 24, in eastern and western areas,

witnessed an increase in drought frequencies reaching 73% in the period from

1997–2016 (Figure 10). Hydrological drought was most common in the eastern

zone, increasing from 50 to 77.5 % occurrence during the study period.

48

Fig. 9. Flow at 24 gauging stations on the Kor, Firouzabad, and Mond rivers in Fars

province in the period 1977–2016. The red lines indicate whether a station had a

negative or positive trend (Reprinted [adapted] under CC BY license from Paper III ©

2020 Authors).

49

Fig. 10. Variation in streamflow drought index (SDI) at 24 gauging stations on the Kor,

Firouzabad, and Mond rivers in Fars province in the period 1977–2016. C: central, NE:

north-eastern, E: eastern, NW: north-western, W: western, and C: central, NE: north-

eastern, E: eastern, NW: north-western, and W: western (Under CC BY license from

Paper III © 2020 Authors).

50

4.4 Monitoring meteorological and hydrological drought in


The SPI and SDI calculations showed that the occurrence of meteorological

droughts was less frequent than the occurrence of hydrological droughts in

Zayanderud basin (Figure 11). In the period from 1963–2012, 54% of years had

SPI values higher than 0, meaning that they can be considered wet years. Adding

“normal dry” years with SPI value between –1 and 0, 90% of the years can be

considered non-drought years. The remaining 10% of years in the study period can

be considered “moderately dry” years, and they were spread randomly throughout

the study period. There was no record in the basin of “severely or extremely” dry

years, even though Zayanderud basin is considered an arid zone. In contrast, based

on the SDI calculation, hydrological droughts (SDI values less than 0) were

recorded in 60% of the years (Figure 11). It was found that the intensity and

frequency of hydrological droughts increased after 1980. Between 1980 and 2012,

the frequency of occurrence of hydrological droughts increased to 75%, compared

with 35% in the period from 1963–1979. In the same period, the irrigated area in

the basin increased from 25,000 to 40,000 hectares. The hydrological droughts

before 1980 were directly affected by the precipitation volume (Figure 11), as their

occurrence was restricted to years with lower SPI value. There was a clear change

to constant positive values of SPI minus SDI in the basin in the 1990s and 2000s

(Figure 11). There were some positive SPI - SDI values, indicating hydrological

drought in years considered as being either wet or normally dry years based on the

SPI values. The best evidence for this was found in 1998 and 1999, as these were

considered to be years with the most severe hydrological drought, but were

“normally wet” based on SPI calculations.

51

Fig. 11. Change in monthly A) standardized precipitation index (SPI) and B) streamflow

drought index (SDI) values for Zayanderud basin, Esfahan province, and C) annual SPI,

SDI, and SPI - SDI values in the period 1963–2012 (Under CC BY license from Paper II ©

2020 Authors).

4.5 Monitoring the transformation from rainfed to irrigated farming

in Fars province

In the study period from 1977–2016, there was an increase and a positive trend in

irrigated area in Fars province, a decrease and negative trend in rainfed area, and

no significant trend in total farmed area (Figure 12). Based on the farmed area data,

provided from (MAJ) the study period was divided into two main periods: a period

of constant increase in farmed area until 2006, reaching a peak of 1,193,450

hectares, with an average increase of 11,650 hectares annually, and a period of

constant decrease in farmed area, to almost half the peak (631,000 hectares) by

2016 and an average decrease of 20,310 hectares annually. It is noticeable that the

expansion in farmed area between 1977 and 2006 was only in the irrigated area,

52

with a rate of increase of 14,079 hectares annually. The rainfed area decreased

significantly in the same period, at a rate of 2430 hectares annually. In the period

from 2007–2016, both irrigated and rainfed area showed a significant decrease, of

20,300 and 7260 hectares annually, respectively.

Overall agricultural drought index (OADI) values showed that in the period

from 1977–1996, the mean rainfed area exceeded the irrigated area (Figure 12).

The main change happened after 1997, when rainfed areas (with low water

requirements) were replaced by irrigated areas with higher water demand. The

irrigated area reached its peak (870,000 hectares) in 2006 and then kept on

decreasing gradually, reaching 397,000 hectares in 2016. The rainfed area reached

its peak (396,000 hectares) in 1983. The year 1983 was considered a “normal wet

year” according to the SPI calculation. The year with the lowest rainfed farmed

area (60,000 hectares) was 2000, a severe meteorological year according to the SPI

calculations. It is important also to mention that the rainfed contribution to

agriculture, on a yield basis, decreased from 41% in the first decade of the study

period to 20% in the last decade.

Agricultural drought index (ADI) values showed agricultural drought in Fars

province after the year 2006. The total farmed area before 2006 showed positive

ADI values, meaning no agricultural drought, excluding the years when the

province was affected by meteorological drought. With a deficit in rainfall volume,

which is considered a major limiting factor in rainfed agriculture, farmers

increasingly began to irrigate their farmed area. This increase in irrigated area led

to an increase in the ADI values over the years. The ADI values in irrigated areas

were found to be positive before the year 2006. This demonstrates that hydrological

and meteorological droughts did not drive the expansion in irrigated area, but rather

than farmers compensated for surface water depletion with groundwater

overexploitation.

53

Fig. 12. Change in agricultural land use in Fars province 1977–2016. (a1), (a2), and (a3)

show irrigated, rainfed, and total farmed area, respectively, while (b1) to (b3) show

overall agricultural drought index (OADI) values and (c1) to (c3) show agricultural

drought index (ADI) values. Solid lines show trend over the total study period and

dashed lines show sub-trend lines before and after the turning point (Under CC BY

license from Paper III © 2020 Authors).

54

4.6 Monitoring groundwater depletion in Fars province and


4.6.1 Fars province

Considering the hydrological drought analysis (SDI) and its negative trend,

meaning lack of surface water availability, it can be concluded that in Fars province

the expansion in irrigated area in the study period was based on groundwater

exploitation. This unsustainable exploitation of groundwater resources led to

groundwater depletion in the 12 major aquifers in the province (Figure 13). During

the period from 1995–2015 and based on available data, the greatest depletion in

groundwater level (50 m decline) was recorded in the Arsenjan aquifer in the

eastern region. Even the least depleted aquifers (Shiraz and Neyriz) recorded a 10

m depletion in a period of 20 years. The groundwater level depletion was directly

related to aquifer area, as smaller aquifers showed greater depletion. The Arsenjan

aquifer (area 101.6 km2) showed the greatest depletion volume (around 0.212 km3).

The lowest depletion volume (~0.123 km3) was in Shiraz aquifer (area 207 km2).

The ADI calculations in the previous section showed a decrease in farmed area after

the year 2006. Based on this finding, in combination with the results of

meteorological and hydrological drought analysis for the period after 2006 and the

groundwater level depletion, it can be concluded that Fars province faced a severe

agricultural drought from 2006.

4.6.2 Zayanderud basin

Zayanderud basin in Esfahan province showed a similar story, as the irrigated area

in the province doubled from 1981 to peak at 41,500 hectares in 2015. In this period,

the groundwater level was depleted in all major aquifers in Zayanderud basin

(Figure 14). On average, the annual groundwater depletion volume was estimated

to be 365 Mm3 and the average annual decline in the groundwater level was 0.6 m.

This decline was directly related to overexploitation of groundwater resources due

to expansion of the irrigated area. The most severe groundwater depletion was

recorded in North and South Mahyar, Damaneh, Isfahan, Murchehkhort, and

Ardestan, which are major agricultural zones in Zayanderud basin. Although in the

wet years 1986, 1993, and 2004 (according to SPI calculations) there was some

recharge in the groundwater level, the trend in the study period was negative. The

greatest depletion occurred in the 1990s and 2000s, in parallel with hydrological

55

drought in the same period. In years considered dry or moderately dry, it was

evident that the groundwater exploitation rate increased.

Fig. 13. Cumulative groundwater level fluctuation in the 12 major aquifers in Fars

province, 1995–2015 (Under CC BY license from Paper III © 2020 Authors).

56

Fig. 14. Cumulative groundwater level fluctuation in the 12 major aquifers in

Zayanderud basin, Esfahan province, 1981–2015 (Under CC BY license from Paper II ©

2020 Authors).

57

4.7 Monitoring surface area change in Gavkhuni wetland using

Normalized Difference Water Index (NDWI)

Fluctuations in the surface area of Gavkhuni wetland area were directly related to

the inflow volume from the Zayanderud River, which is mainly based on

precipitation and snowmelt volume. With the increase in the surface water and

groundwater exploitation along the river, the flow rate into the wetland reached

alarmingly low levels. Based on coefficient of determination (Equation 15), an

NDWI threshold of 0.5 showed the highest determination (0.75). This means that

the NDWI threshold of 0.5 was congruent in 75% of cases with the real wetland

area (Figure 15). Using the wetland surface area data from the NDWI and the water

balance equation (Equation 12), the area versus volume equation was obtained and

missing data for the wetland were interpolated. Missing NDWI data, marked with

red in (Figure 15), were due to blurred unavailable satellite images. In the period

1985–2015, the wetland area showed great fluctuation, in many cases annually. The

largest wetland area recorded was 480 km2, in the years 1993 and 1994. This can

be related to 1993 being a wet year with high precipitation (145 mm). The year

2009 had higher precipitation (218 mm), but the wetland area only reached 320

km2. This difference reflects the increase in surface water exploitation between the

two years. The smallest wetland area, 30 km2, was recorded in 2008, which was a

dry year with only 24 mm precipitation. Low SPI values were thus not the only

reason for surface water depletion, but rather decreased inflow volume to the

wetland (Figure 15). Even through 90% of years were normal years based on the

SPI values, mean inflow volume decreased from 226 M m3 in the 1960s to 5 Mm3

in 2010. The greatest reduction in inflow was recorded after the 1980s, with a

depleting volume of 100 Mm3 per decade. The NDWI-derived values for the

fluctuation in wetland surface area were confirmed by the real-time data on full and

dry status of Gavkhuni wetland in different years (Figure 16).

58

Fig

. 15.

Inte

rpo

late

d (

A)

infl

ow

to

Gavkh

un

i w

etl

an

d (

Mm

3)

an

d (

B)

su

rfa

ce a

rea (

km

2)

of

the w

etl

an

d b

ased

on

measu

red

an

d

no

rmali

zed

dif

fere

nce w

ate

r in

de

x (

ND

WI)

-deri

ved

in

terp

ola

ted

valu

es (

ND

WI)

-deri

ved

in

terp

ola

ted

valu

es (

Un

der

CC

BY

lic

en

se

fro

m P

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er

II ©

202

0 A

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ors

).

59

Fig. 16. Surface area of Gavkhuni wetland, based on real-time data, in (A) 1988, (B) 1993,

(C) 2009, and (D) 2010. (Reprinted with permission from Google Earth © 2020 Maxar

Technologies).

60

4.8 Monitoring total water storage depletion in Zayanderud and

Bakhtegan basins using GRACE data

The GRACE data available (2002–2015) showed depletion in total water storage

volume in both Zayanderud basin in Esfahan province and Bakhtegan basin in Fars

province (Figure 17). In the 14-year period, and taking 2002 as a baseline,

Zayanderud basin showed average depletion of 32 mm monthly and 385 mm

annually from the baseline. Despite the depletion in the study area, there was

average recharge in total water storage of 200 mm annually from 2002 till 2007.

The depletion started to be severe after 2008, a drought year based on SPI values.

Exploitation of water resources was so high that all months after 2008 showed a

negative total storage anomaly value, i.e., depletion. In this period, the basin

experienced total drying up of Gavkhuni wetland and severe declines in

groundwater level. The Zayanderud streamflow was zero downstream and at its

lowest rates upstream, and evapotranspiration levels were at their peak.

Similar findings were obtained for Bakhtegan basin, taking 2002 as a baseline,

an average total water storage depletion reached 17.5 mm monthly and 211 mm

annually (Figure 17). Positive values indicated recharge in the basin in the period

from 2002–2007, by an annual average of 168 mm. The highest recharge rate, 371

mm, was witnessed in 2005, a wet year in the basin. The depletion resumed in

Bakhtegan basin in 2008, a severe drought year based on SPI values. The inflow to

Lake Bakhtegan in this period reached zero and the lake dried up, while streamflow

in upstream reaches of the river was minimal. In-situ data showed a decline in

groundwater level as groundwater resources were overexploited.

61

Fig

. 17.

Ch

an

ge in

to

tal w

ate

r sto

rag

e, d

eri

ve

d fro

m G

RA

CE

data

, in

(m

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(A

) Z

aya

nd

eru

d b

asin

an

d (B

) B

akh

teg

an

basin

(R

ep

rin

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ted

] u

nd

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I ©

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an

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62

4.9 Monitoring groundwater depletion in Bakhtegan basin using

GRACE data

The relationship between available GRACE data on groundwater level and local

in-situ groundwater volume fluctuation was analyzed for the period 2002–2011.

The monthly GRACE groundwater data showed a direct response to the water

balance equation (Equation 12). The SPI values showed a decrease in precipitation

volume for the period, especially after the drought in 2007. Mean annual

precipitation decreased from 307 mm between 2002 and 2006 to 180 mm between

2007 and 2011 (Figure 18). The evapotranspiration rate was on average 480 mm

annually, meaning that groundwater resources were used to cover the water

shortage, and the water balance in Bakhtegan basin showed a negative trend.

Average soil moisture varied between –8 and 13 mm during the year, with the

highest values in February and March, and the lowest in June and July.

Fig. 18. Mean annual precipitation (mm) in Bakhtegan basin, 2002-2011, and distribution

of the 22 precipitation stations in the basin (Reprinted [adapted] under CC BY license

from Paper I © 2019 Authors).

The in-situ data showed that groundwater levels in the basin declined by an average

of 8 mm monthly, with the average increasing after 2007 to 13 mm monthly. The

highest exploitation rates were observed in the end of the summer season, in

September and October, and the lowest in April and May. The GRACE

groundwater anomaly data showed agreement with the net precipitation and the in-

situ collected data. Although differing in magnitude, the in-situ and GRACE data

showed similar trends (Figure 19). Having 2002 as a baseline, the GRACE data

showed average monthly depletion of 7 mm, which increased to 26 mm after 2007.

63

The in-situ data showed total depletion of 905 mm during the 10-year period 2002

till 2011, while the GRACE data showed a total depletion of 794 mm. This was

expected, as Bakhtegan basin is smaller than the minimum 200,000 km2

recommended area when using GRACE data to monitor groundwater depletion.

However, the results showed 0.6 congruence rate, meaning that GRACE data were

congruent with the in-situ data in 60% of cases.

64

Fig

. 19.

Net p

recip

itati

on

, in

-sit

u c

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d d

ata

, an

d G

RA

CE

gro

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, 2002–2011

. (A

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65

5 Discussion

5.1 Fars province

The number of different climate types in Fars province decreased during the 40-

year study period, which is a clear indication of climate change in the province.

The Emberger index showed that the occurrence of hyper-arid climate type

increased in the province over the period. Thus, the climate type analysis indicated

a reduction in renewable water availability, but there was no significant reduction

in precipitation volume in the corresponding period. The reduction in water

resources observed in the province could be related to meteorological droughts in

later years. Based on the SPI values, it was found that meteorological drought was

a non-stationary stochastic phenomenon in trend analysis for the province. There

was a clear negative trend in the streamflow data, indicating hydrological drought,

with a significant lowering of groundwater level in recent decades. Based on the

SDI values, there was a clear hydrological drought after 1997. The irrigated area

continued increasing, to peak in 2006 at the greatest area in the history of the

province (Paper III). Considering the surface water depletion observed, the

expansion of 400,000 hectares in the irrigated area between 1997 and 2006 was

based on groundwater resources. This overexploitation of groundwater resources,

with an average increase of 50 percent in all aquifers, exerted more pressure on the

overall resource, leading to declines in groundwater level in major aquifers. The

metrological droughts occurring from 2006 onwards led to further decreases in the

rainfed area in the province, reaching 20 percent of the total farmed area,

accelerating hydrological drought occurrence, reducing groundwater recharge by

half, and increasing groundwater overexploitation. For these reasons, the

agricultural drought that started in 2006 resulted in a significant decrease in farmed

area in the province in later years, with an average decrease of 20,000 hectares

annually.

Previous studies have shown that droughts are usually related to human

activities rather than a force majeure, and can be avoided with proper management

(Hervás-Gámez & Delgado-Ramos, 2019; Paper III). In Fars province,

overexploitation of water resources by 50 percent, led to increase occurrence of

hydrological drought starting from 1997 by 30 percent. In the period from 1997–

2016, hydrological drought was observed in 72% of years, while meteorological

drought was less frequent, occurring in 40% of years. In the earlier period 1977–

66

1996, hydrological drought occurred in 42% of years, and more than 75% of the

gauging stations in the province showed a positive trend in streamflow. Thus water

usage before 1997 was sustainable and did not exceed the renewal capacity of the

water resources, whereas it exceeded the renewal capacity after 1997 by 50 percent

and became unsustainable usage. Groundwater resources became the main water

resources in the province after 1997 and they started depleting due to overuse. With

severe depletion of surface water and groundwater resources, the farmed area

decreased after 2006. The decrease in rainfed farmed area, from 41% of total area

in the first decade of the study period to 20% in the last decade, is another indicator

of unsustainable use of water resources, increasing the occurrence of hydrological

drought. These factors led in turn to agricultural drought and a decrease in farmed

area. The increased occurrence of metrological drought in later years contributed

further to the water shortage, as drought years doubled in the last decade. This

meant that groundwater resources could not keep on compensating for the water

shortage in the same way as they did e.g., in the dry year of 1990, when the irrigated

area was smaller.

The increasingly dry climate, increased irrigated area, and excess of water

demand over water supply led to an unsustainable tipping point. The unsustainable

use of water resources was confirmed by depletion in surface water, as Bakhtegan

Lake reached complete dryness (Paper I) and groundwater resources reaching 50

meters depletion in some aquifers. As groundwater resources are considered to be

an important reserve for drought periods in arid and semi-arid climate zones, the

Overexploitation of these resources can lead to many socioeconomic and

environmental impacts. This impacts can be summarize as: land subsidence (Hojjati

& Bostani, 2010), dryness of major lakes (Paper I), abandonment of rural areas

(Bastani, Najafpour & Javani, 2016), depression in rural farmers (Zarafshani,

Gorgievski & Zamani, 2007) and food shortages for local households (Karpisheh,

2010).

Overall, three stages were identified in Fars province in south-western Iran in

the 40-year study period. The first stage, between 1977 and 1996, was a period of

sustainable usage of water resources and a sustainable ratio of 40:60 between

rainfed and irrigated areas. In this period, exploitation of groundwater resources

only occurred in periods of insecure water resources, i.e., meteorological droughts,

and groundwater reserves were replenished in wet years. During this period, the

farmed area decreased by an average of 40% during dry years. Water usage did not

exceed the renewable water resources volume, and hydrological drought was a

stochastic phenomenon that occurred randomly. The sustainable usage of water

67

resources was not affected by climate variability and meteorological droughts. The

second stage, between 1996 and 2006, saw a transition from sustainable to

unsustainable water resources usage. Total farmed area reached an all-time peak

and high-yielding irrigated area increased to 80% of the farmed area, mainly using

groundwater resources. The economic revenue of farmers increased, but average

water demand has doubled. This period witnessed a considerable decrease in

groundwater table level with an average of 1.2 meters annually, altering the

interaction between surface water and groundwater by surface water recharge from

groundwater. The reduction in streamflow by 45 percent, and increased intensity of

hydrological drought by 30 percent led to unsustainable water usage. In the third

stage, between 2006 and 2015, unsustainable usage of water resources culminated

in a reduction in farmed area due to water resources depletion and overexploitation

of groundwater with deeper wells and higher-energy pumping. River flow was at

its lowest, causing serious problems for the volume of hydraulic structures. The

insecurity in the agricultural and water sectors led to an increase in internal

migration in Iran from rural areas to cities (Keshavarz et al., 2013). The water

resource depletion also led to environmental impacts like lake and wetland

desiccation, land subsidence, and desertification (Hojjati & Bostani, 2010; Paper I).

While it cannot be stated that all of Fars province is now in stage three, many of its

regions are. Decision makers for the province should move water usage toward

sustainability at greater efficiency than the current 33 percent, as water usage for

irrigation must become effective and farmers should select crops more suitable for

arid zones, with lower water demand per hectare. Smallholder farmers should be

supplied with information about optimal seeding and harvesting times for each crop

type (Roozbeh, 2020). Groundwater must be reserved as a vital resources to cope

with climate variability.

5.2 Zayanderud basin

A similar pattern emerged in Zayanderud basin in Esfahan province, central Iran,

with discharge and groundwater levels declining considerably following the

introduction of full irrigation schemes in the 1970s and 1980s. From the year 1980

the groundwater exploitation reached an average of 365 Mm3 annually, with a

constant annual drop of 1 to 2.5 meters in the groundwater level. The Zayanderud

river discharge decreased 80 percent, reaching zero in many occasions. On many

occasions in recent decades, the water flow from the Zagros Mountains reached

zero and no water reached Gavkhuni wetland at the end of the Zayanderud River.

68

With the inflow volume reaching zero, Gavkhuni wetland dried up completely in

winter 2009. The average depletion in total water resources in the basin was

estimated to be 1350 Mm3 annually from 1981 till 2015. With the expansion of

irrigated area, water demand always exceeded the water available, even with a

constant increase in water supply through inter-basin transfer, where e.g., 590 Mm3

of water were diverted from the Kuhrang River to Zayanderud basin (Abrishamchi

& Tajrishy, 2005). Completion of the Behesht-Abad tunnel brought an extra 700

Mm3 downstream from the Chadgen dam (Doostmohammadi, Jafari & Asghari,

2015). Despite all these efforts, the demand for water outstripped the supply. There

was an initial plan for the irrigated area in the basin to reach 200,000 hectares by

the 1990s (Molle, 2009). The peak irrigated area achieved was 41,500 hectares. As

the agricultural expansion in the basin had a direct impact on surface water

resources, there was an increase in the hydraulic drought intensity. After 1980, the

occurrence of hydraulic drought increased by 40%. Negative SPI minus SDI values,

indicating more severe hydrological droughts compared with precipitation levels,

increased by 40% in the 1970s and 20% in the 1980s. On average, hydraulic

drought appeared in 60% of study years and this increased to 72% after 1980.

Hydrological drought had a direct environmental impact in Zayanderud basin, with

the constant dryness of the Gavkhuni wetland (Paper II). The expansion in irrigated

area also had a direct impact on groundwater levels, as groundwater became the

main source of water for irrigation, rather than the traditional qanats. Groundwater

drilling and pumping permits are issued by the central administration, which lacks

knowledge of the local hydrological situation (Molle, 2009). In period of 35 years,

1981 till 2015, groundwater data collected in-situ showed declines in the levels in

all major aquifers. The greatest depletion has been in the Najafabad aquifer (2.5 m

annually), because of large-scale conversion of undeveloped land to irrigated land.

In other major agricultural aquifers such as Isfahan, North Mayhar, and Damaneh,

the annual depletion has been between 1 and 1.5 m. On average, 365 Mm3 of

groundwater are pumped annually for irrigation purposes in Zayanderud basin. As

in Fars province, groundwater is used to compensate for deficiency of surface water

resources, as 60 percent of the irrigation water sources is from groundwater leading

to unsustainable exploitation of groundwater resources.

5.3 Gavkhuni wetland area

Normalized difference water index (NDWI) values proved in previous studies to

be reliable for monitoring surface water bodies (Nair & Babu, 2016; Sarp &

69

Ozcelik, 2017; Dai et al., 2020). For Gavkhuni wetland, the 0.5 NDWI threshold

was congruent with real inflow in 60% of cases. Although the highest recorded

capacity of the wetland was 480 km2, the average area during the study period was

350 km2. With frequent hydrological drought and intensive water usage reducing

streamflow, a minimal amount of river water reaches the wetland downstream, as

the flows downstream reached less than 5 percent of the upstream flow. According

to a previous study and indicator, the volume of water required for Gavkhuni

wetland to sustain its area is 244 Mm3 annually, including the evaporation rate

(Sarhadi & Soltani, 2013). At the Varzaneh hydrometric station located on the inlet

to Gavkhuni wetland, the annual inflow to the wetland between 1985 and 2015 was

only 117 Mm3. This resulted in a constant decrease in surface area and occasional

drying out of the wetland after the year 2009, which is considered an important site

for migratory birds and is registered under the Ramsar Convention (Gohari et al.,

2013).

5.4 Bakhtegan basin

GRACE satellite data can help reach general conclusions about total water storage

and aquifer conditions (Joodaki & Swenson, 2014). Based on GRACE data, the

estimated total water loss was 2.4 km3, or 7.6 mm annually, in Bakhtegan basin.

This total water loss was confirmed by previous studies regarding surface water

depletion values for Lake Bakhtegan (Paper I), and the SPI and SDI values. The

GRACE data also indicated a groundwater depletion scenario in the basin, which

was agreeable by the in-situ measurements, and showed similar trend.

Although in-situ data are still considered the most reliable data source for

monitoring surface water and groundwater depletion, this thesis demonstrated that

the GRACE data were able to confirm the water volume loss during the study

period. The GRACE and total water volume loss were coherent in most of the cases.

The monthly net precipitation data derived from the water balance equation showed

a good fit of 90 percent with the GRACE data. There were some discrepancies in

the monthly data, which can be related to the small basin area (31,511 km2),

hydraulic interactions with neighboring catchments, or changes in soil porosity,

which can delay the water mass change in the basin. Part of the variation may also

be attributable to the geometry and geology of the aquifer (Paper I). However, it

was still possible to draw general conclusions about the hydrological situation of

the basin, while considering the uncertainties summarized by Longuevergne et al.

(2010). They stated that to get precise groundwater depletion data, the basin area

70

must be higher than 200,000 km2 (Longuevergne, Scanlon & Wilson, 2010). In

Bakhtegan, even with smaller aquifer size, the water mass volume loss was

coherent with more than 90 percent of the cases, and the groundwater depth lost

was coherent in 60 percent of the cases. This coherence difference between

GRACE and in-situ data is related to the error in GRACE data derivation, scaled

GRACE measurement error, leakage correction error, and scaling uncertainty.

71

6 Conclusions and further research

This thesis assessed the effects of agriculture expansion and associated

unsustainable water resources exploitation in the agricultural sector in central Iran

on the occurrence and effects of meteorological, hydrological, and agricultural

droughts. The results showed that overexploitation of surface and groundwater

resources were directly linked to the increased occurrence of both hydrological and

agricultural droughts, and exacerbated the effects of meteorological droughts.

Surface and groundwater depletion have had direct effects on local residents

especially in downstream water bodies. Methodology was developed to evaluate

climate change and variability based on two main elements of the Emberger aridity

index, humidity influence factor (HIF) and climate influence factor (CIF). Two new

indices were developed for analysis of agricultural drought based on farming type:

overall agricultural drought index (OADI) and agricultural drought index (ADI).

The study areas were Fars province, with a detailed study in Bakhtegan basin, and

Zayanderud basin in Esfahan province, all of which are very important agricultural

regions in Iran. It was shown that the number of different climate types in Fars

province decreased from 20 in the period from 1987–1996 to 14 in the period from

2007–2016 due to change in precipitation volume. Over the 40-year period studied

in this thesis, the hyper-arid climate type increased in occurrence from 32.3% of

years in the first decade to 44.4% in the last decade due to increase in the mean

temperature.

In general, 75% of years between 1977 and 2016 were normal and wet years

meteorologically, distributed uniformly. The frequency of hydrological and

agricultural droughts increased in later years (between 2006 and 2015). Fars

province was identified as passing through three main stages in the 40-year study

period: a sustainable water usage phase where demand was less than the resource

renewal capacity; a transitional phase of water usage characterized by an increase

in irrigated area, groundwater depletion, and hydrological drought; and an

unsustainable phase where there is a lack of water resources and climatological,

hydrological, and agricultural drought.

Zayanderud basin showed less climate variation, with the cold and hyper-arid

climate type occurring in 57.5% of years and with 90% wet and normal years during

the study period. However, this did not reduce the water depletion in the basin,

which displayed a similar pattern as observed in Fars province. The water depletion

was again directly linked with the expansion of irrigated area, to 41,500 hectares in

year 1990 from 20,000 hectares in the 1970s. Water from the Zayanderud River

72

was extracted along its length, and the flow ultimately reached zero downstream,

resulting in complete drying of Gavkhuni wetland. The groundwater level depletion

reached 50 m in some aquifers with a constant level decrease of 0.6 m annually. In

Bakhtegan basin in Fars province, the groundwater level depletion reached an

average of 10 m in 10 years, with some smaller aquifers reaching 30 m. The

downstream flow in the basin’s main river (Kor River) fell to 60% lower than

upstream flow and reached zero on most occasions after 2006, leading to Lake

Bakhtegan drying up.

In general, it can be concluded that the high water consumption in Fars

province and in Zayanderud basin in Esfahan province, is due to the expansion of

the irrigated areas and the over exploitation of the available water resources. To

ease the situation, future research must focus on the following:

i. Future research should focus on increasing water productivity and

increasing yield per m3 water consumed in irrigation. It is also

important to understand what motivates farmers to adopt new

technologies and practices.

ii. Farmers must be educated about crop, land, and water management in

different circumstances, especially farmers with access to poor water

quality who risk decreasing yield rates and increasing soil salinity.

iii. Irrigation water use must be directly related to crop value, e.g.,

vegetables are a good irrigation investment because of higher quality

and price.

iv. In drought years, irrigation must be applied only to overcome the

direct effects of drought on crops.

v. Remote sensing data could become a major aid in water resources and

agricultural management, as they are cheaper to obtain and reliable.

Combined remote sensing and in-situ measured data could be used to

track changes in crop and water resources.

vi. Irrigation efficiency must be continually compared with that in other

irrigation projects in similar arid and semi-arid zones, for data

comparison and to learn from experiences elsewhere.

vii. As April is usually the month with the highest streamflow rate, annual

water allowance calendars must be drawn up depending on the

precipitation rate in the previous months and expected

evapotranspiration rate in summer. Water allowances must be

decreased in drought years.

73

viii. Basin-level surveys are required to determine the total number of

groundwater extraction wells and their depth, quality, and drilling date.

The data obtained can help optimize combined usage of surface and

groundwater, and map the current detailed groundwater situation.

Wells that reached alarming levels or quality must be closed,

especially in drought years.

ix. The technical and economic feasibility of transbasin water diversion

must be reconsidered. Planning more sustainable water usage will

have better environmental and economic impacts than water diversion.

Previous experiences show that increasing the water supply only

increases demand.

x. Salinity development in soil layers due to irrigation and drought

occurrence must be mapped and the effects of increased salinity on

groundwater quality must be determined.

xi. Water institutes must set water priorities for different economic sectors

in times of drought. The highest priority must be providing potable

water in rural areas. Water consumption in other industrial and

agricultural sectors must then be ranked from most to least important.

75

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83

Original publications

I Abou Zaki, N., Haghighi, A.T., Rossi, P.M., Tourian, M.J., & Kløve, B. (2019). Monitoring groundwater storage depletion using gravity recovery and climate experiment (GRACE) data in Bakhtegan catchment, Iran. Water 11(7), 1456. https://doi.org/10.3390/w11071456

II Abou Zaki, N., Haghighi, A.T., Rossi, P.M., Tourian, M.J., Bakhshaee A. & Kløve, B. (2020). Evaluating impacts of irrigation and drought on rivers, groundwater and a terminal wetland in the Zayanderud basin, Iran. Water 12(5), 1302. https://doi.org/10.3390/w12051302

III Haghighi, A.T., Abou Zaki, N., Rossi, P.M., Noori, R., Hekmatzadeh, A.A., Saremi, H. & Kløve, B. (2020). Unsustainability syndrome – from meteorological to agricultural drought in arid and semi-arid regions. Water 12(3), 838. https://doi.org/10.3390/w12030838

Reprinted with permission under CC BY license from Authors (Papers I, II, and

III).

Original publications are not included in the electronic version of the dissertation.


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