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Simulating the interactions between the water and the socio-economic system in a stressed endorheic basin

Enteshari, Sajad; Safavi, Hamid R.; van der Zaag, Pieter DOI 10.1080/02626667.2020.1802027 Publication date 2020 Document Version Final published version Published in Hydrological Sciences Journal

Citation (APA) Enteshari, S., Safavi, H. R., & van der Zaag, P. (2020). Simulating the interactions between the water and the socio-economic system in a stressed endorheic basin. Hydrological Sciences Journal, 65(13), 2159- 2174. https://doi.org/10.1080/02626667.2020.1802027 Important note To cite this publication, please use the final published version (if applicable). Please check the document version above.

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Simulating the interactions between the water and the socio-economic system in a stressed endorheic basin

Sajad Enteshari , Hamid R. Safavi & Pieter van der Zaag

To cite this article: Sajad Enteshari , Hamid R. Safavi & Pieter van der Zaag (2020) Simulating the interactions between the water and the socio-economic system in a stressed endorheic basin, Hydrological Sciences Journal, 65:13, 2159-2174, DOI: 10.1080/02626667.2020.1802027 To link to this article: https://doi.org/10.1080/02626667.2020.1802027

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Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=thsj20 HYDROLOGICAL SCIENCES JOURNAL 2020, VOL. 65, NO. 13, 2159–2174 https://doi.org/10.1080/02626667.2020.1802027

SPECIAL ISSUE: ADVANCING SOCIO-HYDROLOGY Simulating the interactions between the water and the socio-economic system in a stressed endorheic basin Sajad Enteshari a, Hamid R. Safavia and Pieter van der Zaag b,c

aDepartment of Civil Engineering, Isfahan University of Technology, Isfahan, Iran; bDepartment of Land and Water Management, IHE Delft Institute for Water Education, Delft, The Netherlands; cDepartment of Water Management, Delft University of Technology, Delft, The Netherlands

ABSTRACT ARTICLE HISTORY The endorheic basin of Zayandehrud in Iran suffers from environmental problems, social tensions, and Received 11 January 2020 economic instability. Lack of understanding how the water system and the socio-economic system Accepted 11 June 2020 interact may explain these challenges. A system dynamics model, being a holistic simulation tool, was EDITOR developed for the Zayandehrud basin and used to evaluate several policy scenarios. The indices of S. Archfield employment, gross regional product, the volume of groundwater and surface water stored, flow into the basin’s end lake, and the water flow in the river were used to evaluate the scenarios. The findings GUEST EDITOR demonstrate that focusing on supply-based activities or water demand management cannot solely S. Pande improve the condition of the Zayandehrud basin. It is required to reconsider the development policies KEYWORDS of the region in a broader context. Reducing the irrigated area by 15% and developing new industries up Zayandehrud; Iran; socio- to a certain limit may make the combined water and socio-economic system sustainable. hydrology; IWRM; scenario analysis

1 Introduction between systems (Winz et al. 2008, Mirchi et al. 2012, Kotir et al. 2017, Zomorodian et al. 2018). System dynamics models Water management is not an easy task owing to numerous aid to perceive cause and effect relationships between different consumers, diversity of consumption, the conflict between factors of system and feedbacks and subsequently understand stakeholders, and water scarcity, specifically in large-scale sys­ what had been the stimulus of the outputs (Walters and tems (Sandoval-Solis and McKinney 2014). Moreover, there is Javernick-will 2015). In recent years, system dynamics models an interaction between the water resources system and the have been used for water resources modelling in various sub­ socio-economic system, which makes water management jects. For example, participatory water resources modelling more complex (Sivapalan et al. 2012, Montanari 2015, (Butler and Adamowski 2015, Kotir et al. 2017), global water Ogilvie et al. 2019). The interaction between these two systems resources and climate change (Davies and Simonovic 2011), is reciprocal. On the one hand, the decisions made by man­ the interaction between human settlement and flooding (Di agers of water resources could produce both welfare or diffi­ Baldassarre et al. 2013), socio-economic sensitivity and collec­ culty for society and even lead the society towards social tive response to hydrology system (Elshafei et al. 2014), the tensions and movements (Walker et al. 2015). On the other interaction between the water resource and the agricultural hand, economic and social policies influence water consump­ production sector (Kotir et al. 2016), and integrated assess­ tion (Grigg 2016). The lack of heed to these complexities and ment of water security system (Su et al. 2019). the interaction between the water resources system and socio- In order to take account of the interaction between the economic systems could result in the failure of water manage­ water system and the socio-economic system in decisions, ment (Walters and Javernick-will 2015). a system dynamics model can be developed as a predictive Therefore, successful water management needs both under­ tool so that it can be utilized for evaluating scenarios. The standing the interactions between socio-economic and water evaluation of scenarios assists the decision-makers to recog­ resources systems and taking account of this interaction in nize the effects of different decisions and policies on a system decision-making. To understand this interaction, it is essential (Safavi et al. 2016). However, the criteria for the evaluation of to utilize holistic and integrated models that cover both sys­ scenarios should be determined before evaluating them. tems and the connections between them (Mirchi et al. 2012). The criteria, which have been used for evaluating policy Since the processes of water resources systems are not linear, scenarios of water resources management in previous studies, and there are intensifying feedbacks, these models should are diverse. Kotir et al. (2016) have used the amount of water consider the dynamics mode of the systems (Madani and consumption in agricultural, industrial, and domestic sectors, Mariño 2009, Mirchi et al. 2012). System dynamics is one of population, net income of agriculture sector, and the amount the modelling tools that can present a holistic and dynamic of agricultural production for the evaluation of scenarios. comprehension of the system and consider the interaction Chao et al. (2017) used the variables of water demand,

CONTACT Pieter van der Zaag [email protected] Department of Land and Water Management, IHE Delft Institute for Water Education, Delft, The Netherlands Supplemental data for this article can be accessed here. © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. 2160 S. ENTESHARI ET AL. agricultural water demand, water shortage, the ratio of surface socio-economic and water systems. These interactions are water to groundwater resources for choosing the scenarios. around the growth of industry and agriculture, employment, Bastan et al. (2018) assessed the scenarios by surveying the gross regional domestic product (GRP), population, water variables of water storage changes, areas of agricultural land, uses, and water resources. The simulated model is then used and areas of changed land-use. Su et al. (2019) chose factors of to evaluate several policy scenarios. In order to evaluate the gap between water resources and consumption, water scenarios, several indices have been used, which cover social, security, amount of sewage, water contamination, water economic, and environmental aspects. The main take-home damage, population, and gross domestic product (GDP) as message of this study is broadening our perspectives beyond criteria for evaluation of scenarios. managing the water supply and demand and to also consider The Zayandehrud River basin is one of the most important socio-economic development trajectories. basins of Iran which has faced several troubles in recent dec­ A description of the study area is given in Section 2. ades. The basin intensively suffers from a lack of equivalence Section 3 discusses the model development process including between resources and consumption, which means “the basin evaluation criteria, calibration process and designing policy is closing” (Molle and Mamanpoush 2012). Social tensions and scenarios. The results under different policy options for protests have proliferated about the water (Safavi and improving sustainability criteria in the basin and conclusions Enteshari 2016), and the costs of the water management sys­ are given in Sections 4 and 5. tem are not met due to inappropriate pricing of water. In other words, the water resources management in this basin is envir­ 2 Case study: Zayandehrud River basin onmentally, socially, and economically unsustainable. The lack of proper attention to the interactions between the water The Zayandehrud River basin is located in the central part of system and the socio-economic system is one of the most Iran, covering 27 000 km2 of land. Zayandehrud is a 350-km significant causes of this unsustainability. On the one side, endorheic river that originates from mountainous areas in the the policies of industrial and agricultural growth have aug­ west of the basin and flows into Gavkhuni Wetland in the east mented the water demands. On the other side, the water of basin after passing through several cities and villages. The allocation without considering its impacts on society has cre­ most important surface water resources of the basin are three ated difficulties for employment and livelihoods of a part of rivers Pelasjan, Zayandehrud, and Samandegan in the upper society. In some areas of the basin where the livelihood and side of this basin, and inter-basin water transfer tunnels, which employment of individuals (mostly farmers) have altered all flow into Zayandehrud Reservoir. Moreover, this basin owing to water allocation, social tensions and protests have consists of 16 sub-basins, 13 of which have aquifer and risen to the extent that the water system facilities have been groundwater resources (Safavi et al. 2015). An overall view of damaged (Fig. 1). However, most of the studies on the the location of the Zayandehrud basin in Iran and its sub- Zayandehrud basin have focused on water resources and con­ basins, as well as the locations of the river, Zayandehrud Dam, sumption (e.g. Safavi et al. 2015, 2016, Ahmadi et al. 2019), and aquifers, and hydrometric stations, is presented in Fig. 2. they have less dealt with community issues related to water In the Zayandehrud basin, industry and agriculture play an resources. Although employment and livelihood have been the important social and economic role. Owing to the diversity of most significantkeywords of farmers in social protests, none of climatic conditions in this basin, more than 50 different crops the studies on the Zayandehrud basin has examined the are planted; the most important annual crops are wheat, bar­ employment component. However, this issue has been ley, alfalfa, potato, onion, maize, and rice, and the most impor­ addressed in other basins (Roobavannan et al. 2017a, 2017b). tant perennial crops are almond, grape, pomegranate, walnut, The present study attempts to create a holistic model of and peach. The potential agricultural land of the basin is about the water resources system of the Zayandehrud basin using 400 000 hectares, 50 000 hectares of which are rain-fed system dynamics to simulate the interactions between the (Isfahan Agricultural Jihad Organization 2017). The

Figure 1. Social tensions over water: (a) farmers in the eastern stretches of the watershed holding a tractor lining protest to defy their reduced water allocation; and (b) water transfer pipeline broken by farmers (Safavi and Enteshari 2016). HYDROLOGICAL SCIENCES JOURNAL 2161

Figure 2. The Zayandehrud River basin and its surface and groundwater resources (Safavi et al. 2015).

Zayandehrud basin, as the second industrial basin of Iran, has centre of the basin, increase of illegal extractions, and drought. about 13 000 industrial units. The largest industries of this Moreover, climate change impacts have caused annual preci­ basin regarding water consumption are steel and iron indus­ pitation and temperature to increase, changing the temporal tries, power plant, refinery, and petrochemical respectively distribution of precipitation as well as converting part of it (Horlemann and Mohajeri 2017). In addition to water uses from snow to rain (Sarhadi and Soltani 2013, Madani et al. within the basin, on average 80 million m3/year is transferred 2016, Horlemann and Mohajeri 2017). Due to water scarcity in to the cities of Kashan, Yazd and Shahrekord in the adjacent these years, fewer water resources have been released towards basins. downstream of the basin, and it has caused the agricultural Data of water resources in this basin from 1991 to 2011 land on the east side of the basin to be arid and consequently demonstrates that average precipitation is 5787 million m3/ increased unemployment in downstream regions. The growth year, 997 million m3/year of which on average flows into the of unemployment is followed by other forms of social harm reservoir of Zayandehrud dam as runoff, and about like increased crimes and divorces. The lack of water allocation 933 million m3/year infiltrates and recharges groundwater to downstream and unemployment in recent years has caused resources. Moreover, on average, 564 million m3/year enters many social protests in this region (Safavi and Enteshari 2016) the basin through three tunnels of Koohrang I, Koohrang II, and expanded slums and cash jobs. and Cheshmeh-Langan from adjacent basins (Fig. 2). Overall, To solve the water scarcity in the Zayandehrud basin, the average renewable water resources are 2494 million m3/ structural solutions, such as the inter-basin transfer water year, while on average 2765 million m3/year is consumptively from adjacent basins, have been regarded in recent years. used (Safavi et al. 2015). The average annual water balance of Construction of three inter-basin water transfer tunnels during the Zayandehrud basin water resources is presented in Table 1. the recent six decades has compensated the water shortage to Several reasons have been cited for the scarcity of water in some extent. However, it has raised the tensions between the Zayandehrud basin such as the growth of population, inhabitants of the Zayandehrud basin and residents of neigh­ development of agriculture and industry in upstream and bouring basins (Madani and Mariño 2009). Furthermore, inter-basin water transfers have raised dependence on water resources, and the basin has become more vulnerable to Table 1. Average water balance in the Zayandehrud River basin in the period 1991–2011 (106 m3/year). drought (Gohari et al. 2013a). This effect may be likened to Renewable water resources Water Return Net water Water the “reservoir effect” (Di Baldassarre et al. 2018). Thus, water transfer cannot be regarded as the main solution. In addition Surface water withdrawal flow to consumption deficit (including aquifers to socio-economic issues, environmental problems are also inter-basin Ground serious in the Zayandehrud River basin. A sharp drop in transfer) water Sum groundwater level has occurred due to illegal extractions; 1 561 933 2 494 4 320 1 554 2 765 272 lack of contamination control, has declined the quality of 2162 S. ENTESHARI ET AL.

water resources. The Zayandehrud River has become intermit­ presumes that integrated water resources management tent, and a vast part of Gavkhuni Wetland, at the mouth of the (IWRM) is the creation of equivalency between social goals, Zayandehrud River, has dried out owing to lack of water economic development, and environmental conservation. In (Sarhadi and Soltani 2013). These socio-economic and envir­ some other studies, these three aspects are also titled expres­ onmental crises have arisen from problems of the administra­ sions like profit/planet/people or economy/ecology/equity tive-institutional system. The related administrative- (Guest et al. 2010, Tortajada 2014, Walters and Javernick-will institutional system of water resources does not have sufficient 2015, Chao et al. 2017). Hence, there is a general agreement efficiency due to some reasons such as prioritizing large-scale that any decision and any solution for water resources man­ infrastructures, conflict of interest, lack of transparency and agement, should be based on these three categories of criteria. participation (Molle and Mamanpoush 2012). The existence of However, it is not easy to determine a criterion for measuring all these complexities in the Zayandehrud basin and the rela­ these three categories, and it may vary for each region. The tion between societal issues and the system of water resources determination of evaluation criteria has its challenges like the call for a holistic understanding of the interactions between definition of criteria, quantification, the compromise between these systems and for a holistic model. This research is an stakeholders, and weighting the criteria (Peña 2011, Mooney effort to model a part of these complexities, so it would be et al. 2012, Walker et al. 2015). In this research, based on the possible to identify more feasible scenarios for the manage­ interviews with stakeholders, practitioners, and managers, the ment of this complicated system. following criteria are used for the evaluation of scenarios in Zayandehrud basin:

3 Processes of model development ● the most significant social factor influenced by water 3.1 System dynamics approach management in the basin is unemployment, which is expressed by the people in protests. Therefore, the The system dynamics, which is developed based on systemic employment rate is used as the most important social thinking, is a modelling method of complex systems (Forrester index. 1961). The water resources system is a complex system, that ● the GDP is one of the economic indices that is used for cannot be analysed by a linear and event-based thought measuring the economic activities of a country. The GDP (Simonovic 2009, Mirchi et al. 2012). Hence, the analyses represents the financial value of all provided products that do not consider the complexities of this system lead to and services in a country. The GRP is utilized to measure unstable strategies (Mirchi et al. 2012). System dynamics the value of produced merchandise and services across assists in recognizing water resources as a system consisting a region or subdivision (Gohari et al. 2013a). In this of various sections that have interactions with each other research, the obtained GRP from industry and agricul­ (Simonovic 2009). By using system dynamics, instead of con­ ture in the Zayandehrud basin is assessed as an economic sidering and modelling the factors separately, the focus would index. be on relations and processes, and also instead of concentrat­ ● In recent years, there has been land subsidence in some ing on the result of an issue, the behavioural patterns which areas due to the decrease in groundwater level in aquifers have produced the issue are considered. Nonetheless, the cap­ of the basin. Moreover, the stability of water consump­ ability of these models and their success rate depend on the tion, specifically drinking water, is threatened in years effective identification of main factors and their interrelations when the volume of existing surface water in the (Madani and Mariño 2009, Mirchi et al. 2012). Zayandehrud Reservoir falls below 300 million m3. The structure of system dynamics consists of some factors Thus, the volumes of groundwater and surface water and positive and negative relationships between them are considered the most important criteria of water (Sterman 2000). Some of the factors shape a feedback loop, resources stability and water consumption stability, which could be reinforcing or balancing. In order to com­ respectively. mence the modelling of system dynamics, a conceptual frame­ ● The level of inflow discharge to Gavkhuni Wetland on work is created first.Then, the stock and flowdiagram (SFD) is the east side of the basin has dramatically dropped since developed based on the conceptual framework. All factors in 1993, and, in some years, it has reached less than SFDs are defined as state variable (an indicator of system 5 million m3. However, this wetland needs 60, 140, and status), rate variable (an indicator of the change rate of state 175 million m3 of water per year for preserving the life of variables), and auxiliary variables. The system dynamics model birds, conserving the life of aquatic species, and main­ can be used for the evaluation of managerial scenarios after its taining all ecologic functions, respectively (Morid 2003, development and validation. Sarhadi and Soltani 2013). The discharge at Varzaneh gauging station (Fig. 2) is assumed as the amount of water inflow into this wetland and one of the environ­ 3.2 Evaluation criteria mental indices. In order to obtain sustainability in integrated studies of water ● Different stretches of the Zayandehrud River need resources management, it is emphasized that three categories a certain amount of water flow to keep its ecologic func­ of economic, social, and environmental criteria must be con­ tions working. The interval between hydrometric stations sidered (Rahaman and Varis 2005, Giordano and Shah 2014, of Lenj and Pole-Choom (Fig. 2) is one of the most Roozbahani et al. 2015). van der Zaag (2005) explicitly important stretches of the river where it passes through HYDROLOGICAL SCIENCES JOURNAL 2163

the most significant city of the basin (historical district of dynamics comprises four subsystems which are: population Isfahan). Therefore, Lenj hydrometric station is used as and drinking water subsystem, agricultural subsystem, indus­ an environmental index in which its required environ­ trial subsystem, and water resources subsystem. The water mental discharge to keep the ecologic function of the resources subsystem is the most important part of the model river is about 315 million m3/year (Isfahan University because of linking all resources and consumptions. of Technology and Shahrekord University 2018). Unemployment and GRP are calculated in agricultural and industrial subsystems. Some of these subsystems are designed with multi-layers, meaning each variable is divided into several 3.3 Model development sections. Most of the links between components in the system dynamics are simulated based on the experimental formulas. For a better understanding of the Zayandehrud basin and In some cases, links between two components have been designing a conceptual model, interviews were conducted simulated based on regression analyses in which plausible with 43 experts from governmental, civic, and academic orga­ causal relationships have been identifiedbetween components. nizations. The result of this step was a causal loop diagram, of The coefficient of determination (R2) was greater than 0.7 in which a part is shown in Fig. 3. To illustrate the interactions these cases (Fig. 4). The territorial boundaries of the model are and feedbacks between the socio-economic and the water based on the boundaries of the basin and the time horizon of system, only a part of the components are shown in this figure. the model is defined for a 35-year period (1991–2025) with Water resource storage is at the core of this causal loop dia­ a yearly time step. gram. Growth in agriculture, industry, and population increases water withdrawal and reduces water resource sto­ rage. Growth of industry and agriculture also increases GRP 3.3.1 Population and drinking water subsystem and employment, leading to an increase of the population The consumption of water for drinking and hygiene purposes through immigration. Water resources reduction provides is simulated in this subsystem (Fig. 5). The population-related some feedbacks on the socio-economic system. These feed­ data (Statistical Center of Iran 2018a), water consumption data backs are considered in the form of scenarios. Feedbacks (Isfahan RWW. Co. 2017, Isfahan WW. Co. 2017) and green increase the water resource storage in negative loops and space data (Zayandab Consulting Co. 2009) were used to find decrease the water resource storage in positive loops. One the relationships between variables and calibration of the other interaction is that increasing precipitation and inter- model. The population is regarded as a state variable, which basin water transfer will increase water resources directly and changes by birth, mortality, and immigration. The immigra­ on the other hand decrease water resources by increasing tion is also affected by water inflow to the basin, employment cultivation and immigration. SFDs were created after the rate, and GRP. The factors of domestic water consumption per development of the conceptual model. The developed system capita, public and commercial water consumption per capita,

Figure 3. Causal loop diagram of interactions between socio-economic and water system in Zayandehrud basin; Thick arrows are considered in the form of scenarios. 2164 S. ENTESHARI ET AL.

Figure 4. Analysis of regressions that used to simulate the relationships between some components in the system dynamics model: (a) cultivation area for rainfed crops; (b) cultivation area for irrigated crops; (c) evaporation; (d) percolation; and (e) runoff.

Figure 5. Stock and flow diagram (SFD) of population and drinking water subsystem. HYDROLOGICAL SCIENCES JOURNAL 2165

birth rate, mortality rate, and irrigation need for green areas separately. In this research, the large industries mean the ones that per square meter have a constant value throughout a particular demand more than 0.5 million m3 of water per year. At the period while other factors change over time. moment, there are about 30 large industrial units. Aquaculture, livestock, poultry, and greenhouse are the agricultural industries 3.3.2 Industry subsystem that have been included in the model. The data of industries (Isfahan Agricultural Jihad Organization 2017, Isfahan Industry and Trade Organisation 2018), water 3.3.3 Agriculture subsystem consumption of industries (Statistical Center of Iran 2004, To model the agriculture subsystem, data of crops (Isfahan Zayandab Consulting Co. 2009, Horlemann and Mohajeri Agricultural Jihad Organization 2017), GRP (Statistical Center 2017), the number of created jobs in industries on a yearly base of Iran 2016), and the created jobs in the agriculture sector (Statistical Center of Iran 2018b), and GRP resulted from the (Statistical Center of Iran 2018a) were used. The area under industries on the seasonal base (Statistical Center of Iran 2016) cultivation for crops is assumed as a state variable. The amount were used to simulate industry subsystem (Fig. 6). The subsystem of crop production, amount of water consumption, GRP, and includes three layers. Large industries, small and medium-size the created jobs in the agriculture sector are the most signifi­ industries, and agricultural industries are modelled in each layer cant factors of this subsystem (Fig. 7). The subsystem

Figure 6. SFD of industry subsystem.

Figure 7. SFD of agriculture subsystem. 2166 S. ENTESHARI ET AL. comprises three layers, in each of which a category of crop is effective factors on irrigation water need such as the required modelled. These categories are rain-fed annual crops, irrigated water for leaching. The irrigation efficiency can be calculated annual crops, and irrigated perennial crops. from the following equation (Brouwer et al. 1989): The incongruence of hydrologic boundaries and political ¼ � � boundaries is one of the challenges of water resources manage­ Ep Ec Ed Ea (5) ment. In the Zayandehrud basin, agricultural data are also in which, Ec is conveyance efficiency, Ed is distribution effi­ collected based on political units and are not adapted to ciency, and Ea is irrigation application efficiency. The convey­ hydrological units. It means that Agricultural Jihad ance efficiency is only applied for the use that withdraws water Organization gathers and presents the agricultural data includ­ from surface resources. Regarding the fact that the irrigation ing cultivation area and the yield of crops based on counties. In application efficiency is different in surface irrigation systems order to convert agricultural data from political-based bound­ and pressurized irrigation systems, GIWN was calculated for aries to hydrologic-based boundaries, it is possible to obtain surface irrigation systems and pressurized ones separately. some coefficients.For the firsttime, Sally et al. (2001) provided Based on previous studies, the irrigation efficiency is estimated some coefficient to convert cultivated land based on region as shown in Table 2 (Ataie 1997, Yekom Consulting unit into irrigation networks unit. Felmeden (2014) updated Engineering Co. 2013, Abbasi et al. 2017). these coefficients. In those studies, the lands with irrigation The number of created jobs in the agriculture sector networks were solely considered, and other agricultural lands depends on several factors, including the type of crop and that exist scattered in the area and nourish mostly from the type of cultivation (rain-fed or irrigated). On average, groundwater resources were ignored. Furthermore, these stu­ different types of cultivation activities create 85, 30, and 144 dies have only focused on irrigated farming land and neglected people day jobs per year in one hectare of irrigated annual rain-fed cultivation. To convert all agricultural data from poli­ crops, rain-fed annual crops, and perennial crops, respectively. tical-based boundaries to hydrologic-based boundaries, some During the calibration of the model, it was found that 155 days coefficients were obtained based on land-use maps in this of cultivation activities per year is equivalent to one fulltime study (see Tables S1–S3 in the Supplementary material). job on average. In the Zayandehrood Basin, farmers decide how much of their lands to cultivate based on the amount of precipitation. Therefore, there is a significant correlation between the area 3.3.4 Water resources subsystem under cultivation of crops per year and the amount of pre­ Two resources of surface water and groundwater are assumed cipitation. Hence, regression was used to simulate the culti­ as the state variables in the water resources subsystem. The vated land of crops (Fig. 4(a) and (b)). In order to calculate the outputs of water resources are outgoing inter-basin transfer amount of water consumption in agriculture sector, the fol­ flow, evaporation from surface water bodies, and withdraw for lowing equations were used: drinking, industrial, agricultural, and environmental purposes. The inputs of water resources are runoff, percolation, incom­ GIWN ¼ NIWN=Ep (1) ing inter-basin transfer flowand return water flow.The SFD of the water resources subsystem is illustrated in Fig. 8. ! Xn The data from the period 1991–2011, were used to simulate NIWN ¼ CWRc � Ac � 10 (2) runoffand percolation. For this purpose, the effectof tempera­ c ture on runoffwas omitted, and multiple regression analysis of a series of observed precipitation, simulated percolation in which, GIWN is gross irrigation water need of all agricul­ 3 (Safavi et al. 2015) and observed river discharge data were tural products (m ), Ep is irrigation efficiency (%), NIWN is 3 conducted (Fig. 4(c) and (d)). The incoming transfer flow, net irrigation water need of all agricultural products (m ), outgoing transfer flow, and the precipitation were entered CWRc is crop water requirement of crop c in cultivation period into the model in time series. To estimate evaporation from (mm), Ac is area under cultivation of crop c (ha), and n is the the surface water bodies, regression analysis was performed number of crops. The crop water requirement is gained from between evaporation and volume of surface water resources the following equations (Brouwer and Heibloem 1986, Allen (Fig. 4(e)). et al. 1998): A total of 80% of urban and rural drinking water demands (Isfahan WW. Co. 2017), 68% of industrial water demands CWRc ¼ ETc À Pe (3) (Yekom Consulting Engineering Co. 2013), and 30% of agri­ cultural water demands are supplied from surface water ¼ � ETC ETo Kc (4) resources, and the rest are provided from groundwater resources. During the calibration of the model, it was per­ In these equations, ETc is evapotranspiration of crop c during ceived that in years when the available volume of surface the cultivation period (mm), Pe is the effective precipitation during the cultivation period (mm), ETo is the reference eva­ potranspiration (mm), and Kc represents the crop coefficient. Table 2. Water consumption efficiency in different irrigation systems (%). ETo is calculated based on climatic parameters of the region, Irrigation Conveyance Distribution Application and Kc is obtained based on the type of crop. Since this system efficiency efficiency efficiency research aims at water management in the scale of the river Pressurized n.a. 88 67 basin and not based on a farm, it was decided to ignore other Surface 67 70 53 HYDROLOGICAL SCIENCES JOURNAL 2167

Figure 8. SFD of water resources subsystem.

water is low, less surface water is allocated to the agriculture account other economic impacts on the calculation of GRP, sector and the remaining demand for agriculture is provided the consumer price index (CPI) was utilized. via groundwater resources based on the following equation: To demonstrate the impact of inflation,the CPI is one of the � most informative indices. This index is measures consumer 0:30 � Awu if SWR > 300 expenses for purchasing a set of goods and services. If the CPI Awus ¼ (6) 0:30 � Awu � SWR=300 if SWR < 300 would be 112 for the current period, then it shows that 12% more should be paid for purchasing the set of goods at the In which Awu is the agricultural water demands, Awus is the moment compared to the time when the index has been 100. agricultural water demands covered by surface water The index of CPI in Iran and the exchange rate of USD to IRR resources, and SWR is the volume of surface water, all 3 are illustrated in Fig. 9. As it is shown, the exchange rate of in million m . USD and CPI did not grow synchronously. Therefore, there For the simulation of return flowprevious studies have been would be many errors if this issue is not considered. used. About 23% of withdrawal water for drinking use is consumed and the remaining 77% return to water resources of which 37% returns to the sewage treatment system and 3.4 Calibration and validation consequently to surface water resources (Isfahan WW. Co. 2017) and about 40% percolates to groundwater resources. In the first step of calibration, variables with deterministic and Moreover, 56% of industrial withdrawal water returns to linear relationships were calibrated without considering groundwater resources and 44% of it is consumed (Yekom dynamic feedbacks within the system. In this step, agricultural, Consulting Engineering Co. 2013). The return flow of the industrial and urban subsystems were calibrated separately agricultural sector is calculated based on the efficiency of different irrigation systems in the agriculture subsystem.

3.3.5 Calculation of GRP In general, GRP for an industrial or agricultural product is calculated as:

GRPi ¼ ðPri�PiÞ=Er (7)

So that Pri is the production of the product i (kg), Pi is the price of the product i (IRR/kg), Er is the exchange rate of US dollar (USD) to Iranian Rial (IRR), and GRPi is the created GRP through product i (USD). Owing to the economic conditions of Iran, the exchange rate of USD to IRR and the price of products has not been stable over time. In order to take into Figure 9. Exchange rate of USD and IRR, and Consumer Price Index (CPI) in Iran. 2168 S. ENTESHARI ET AL.

and the variables connected to other subsystems considered Scenario 2: Water demand management. According to the constant. For instance, employment is a variable affected by survey of Rastghalam (2013), it is possible to decrease non- the industry and agriculture subsystems and affects the popu­ revenue water up to 35%, per capita domestic and commercial lation. Thus, to calibrate the population and drinking water water consumption up to 11%, and per capita water consump­ subsystem, employment was entered in the model as a series of tion in large scale industries up to 10% by conducting techni­ observed data and other variables of drinking water subsystem cal, cultural, educational, economic, and legal activities. were calibrated. In the next step, the water resources subsystem Besides the reduction of drinking and industrial water con­ and all other feedback loops were calibrated. In this step, the sumption, it is assumed in this scenario that all agricultural variables within the feedback loops were examined one by one lands of the basin are equipped with pressurized irrigation and the mathematical relationships were replaced by observed systems. Other factors of this scenario are similar to those of data to reproduce historical trends. In the calibration process, Scenario 1 (business-as-usual). relationships between variables were obtained through empiri­ Scenario 3: Industry development. Right now, there are cal equations or regression from observational data. many requests for industry development in the Zayandehrud The level of accuracy certainty depends on the intended basin. In the case of industrial development based on those view and goals of the model-maker (Gohari et al. 2013b). For requests, fivelarge industries (Zayandab Consulting Co. 2009), assurance about the accuracy of the model, parameter- about 800 small and medium-sized industries, and 1200 agri­ confirmation test, dimensional consistency, and behavioural cultural industries will be created on average each year till pattern test, introduced by Sterman (2000), were utilized. In the year 2025 (Horlemann and Mohajeri 2017). Other factors the behavioural pattern test, the factors of groundwater of this scenario are similar to those of Scenario 1. resources, surface water resources, river water flow, popula­ Scenario 4: Development management. The scenario of tion, employment and GRP in agriculture and industry sec­ development management was designed as a mediating policy tors for the period 1990–2016 were used. The coefficient of after analysing the process and industrial development scenar­ determination of these factors was greater than 0.6, a value ios. In this scenario, it is assumed that the area under cultiva­ that was considered acceptable by e.g. Roobavannan et al. tion of irrigated lands reduces by 15%, and only 30% of new (2017a). industries that have requested a license, start to operate.

4 Results 3.5 Designing policy scenarios 4.1 Model validation As Gohari et al. (2013b) and Kotir et al. (2016) have shown, the design of scenarios is performed based on trial and error, and The results of testing the model for observed data and simu­ assumptions that are available for the efficiency of policies. lated data for the period 1991–2016 are presented in Fig. 10. However, proper professional judgment might reduce the There is a delay between observed and simulated data in some number of tentative scenarios. Regarding the situation of the cases. Nevertheless, almost everywhere the data have similar Zayandehrud basin and informed by qualitative interviews behaviours, and the model is well-calibrated for the anticipa­ conducted with stakeholders and experts, four scenarios were tion of the behaviour of various factors. Even though data of designed and implemented for the period 2016–2025 in this water resources, industries, agriculture, GRP, and employment research. were collected from independent sources, the model could A survey of climate change was carried out on the properly simulate the relationship between these variables. Zayandehrud basin based on the fifth report of the Intergovernmental Panel on Climate Change (IPCC 2015) to 4.2 Analysis of scenarios simulate the precipitation in the future (Sangestani 2019). To consider the impacts of climate change, the amount of pre­ To analyse the results of scenarios, social, economic, and cipitation in all scenarios was entered into the system environmental sustainability of the basin are assessed through dynamics model based on findings of the climate change the observation of determined indices. The results of all simu­ scenario of representative concentration pathway RCP2.6. lated scenarios are illustrated in Fig. 11. Each scenario is described below. The year 2019 was relatively wet, which caused an increase Scenario 1: Business-as-usual. This scenario is designed as in the volume of surface water at the beginning of the year a reference for comparing other scenarios. In this scenario, 2020 and the volume of groundwater at the beginning of population, and consequently urban and rural water consump­ the year 2021 in Scenario 1 (business-as-usual). In other tion, increase according to the existence trends. Furthermore, years, the volume of groundwater continuously declined, and CPI and exchange rate with a fixed increase rate and the the volume of surface water storage fluctuates around the percentage of non-revenue water with a fixed decrease rate critical limit (300 million m3). Water resources and their con­ were entered in the model. Other factors of the system includ­ sumption are not stable in this scenario. According to this ing birth rate, mortality rate, domestic, commercial and public scenario, the minimum environmental requirement was pro­ water consumption per capita, green areas per capita, indus­ vided for conservation of bird life in Gavkhuni Wetland only trial water consumption per capita, irrigation efficiency, crop in 2020; while in other years even the minimum water require­ yield, incoming transferred flow, and outgoing transfer flow ment of the wetland was not met. Nonetheless, the required have been deemed invariant as before for future years. minimum river flow for preserving its ecologic functions was HYDROLOGICAL SCIENCES JOURNAL 2169

Figure 10. Comparison between observed data and simulation results for main components for 1991–2016: (a) surface water resources; (b) groundwater resources; (c) population; (d) agricultural employment; (e) industrial employment; (f) agricultural gross regional product (GRP); (g) industrial GRP; (h) river flow at Lenj station. supplied in the area of Isfahan (Lenj station) in most of the In Scenario 2 (water demand management), socio- years. economic factors do not differ from the ones in Scenario 1 The population of the basin reaches about 4.7 million in 2025 and the minimum environmental requirement was provided in this scenario. On the one side, the growth of the population for conservation of bird life in Gavkhuni in most of the years. and on the other side the lack of industrial development causes In contrast, the water need of the river could not be fully the employment to decline from 0.89 to about 0.84%. This can satisfied owing to the reduction of water consumption and increase social tensions and augment the problems of society. consequently less release of water from the dam. Whereas the Due to the sudden growth of the exchange rate of USD to IRR in level of surface water resources increased, the instability of 2018, the GRP had a dramatic downfall. However, it has gradu­ groundwater resources is still apparent. Overall, in this sce­ ally returned to a normal situation. In general, it can be said that nario, the socio-economic condition did not change compared environmental sustainability has not been completely secured, to the business-as-usual scenario, the sustainability of water the social situation deteriorates, and the economic index of the resources and consumption slightly improved, while environ­ basin did not change in this scenario. mental sustainability was not fully achieved. 2170 S. ENTESHARI ET AL.

Figure 11. Behaviour of main model variables under four scenarios for 2016–2025: (a, b) baseline scenario; (c, d) water demand management scenario; (e, f) industrial development scenario; and (g, h) development management scenario.

In Scenario 3 (development of industry), the quantity of reduction of water resources occurred in this scenario and GRP nearly doubled in value, and the employment rate Gavkhuni Wetland received water only in wet years. In other reached about 99%, which caused an increase in migration to words, the sustainability of water resources and consumption the basin. Consequently, the population reached six million in and environmental indices intensively deteriorated in this 2025. Whereas the growth of GRP and employment is a sign of scenario. improvement in residents’ welfare, population growth might In Scenario 4 (development management), a 15% decrease produce undesirable effects on other aspects of residents’ life. in the irrigated area compared to Scenario 1 had a tremendous For instance, it could increase the number of people living in impact on water resources and stabilized these resources. On slums. Thus, it cannot be asserted that socio-economic sustain­ the other hand, 30% of industrial development improved ability was obtained in this scenario. The most considerable employment up to 94%. Industrial development was not HYDROLOGICAL SCIENCES JOURNAL 2171

enough to increase the population of the basin. However, the precisely from where the water resources into Zayandehrud minimum water requirement of the wetland for keeping birds’ basin were transferred. In other words, it can be claimed that life was provided. It can be observed that the environmental, population flows go along with the water flows. The rate of social and economic indices significantly improved. population growth in the Zayandehrud basin is about 1% Through the comparison of scenarios, it is apparent that per year, of which 20% could be attributed to the immigra­ surface water resources in Scenario 2 and groundwater tion caused by the inter-basin water transfer into the basin. resources in Scenario 4 improved the most. Its reason could This lesson, learned from socio-hydrologic studies, contains be that the industry and drinking water sectors depend on a warning that has been repeated frequently: remedial efforts surface water and major agricultural lands depend on ground­ to address water scarcity with a heavy focus on infrastructure water resources. Moreover, the leverage point for creating jobs solutions, may exacerbate the problem through unintended and an increase in GRP is the development of the industry, consequences that arise from neglect of social, economic, and whereas the leverage point for reduction of pressure on water environmental factors (Gohari et al. 2013a, Di Baldassarre resources is a decrease of irrigated agriculture. The identifica­ et al. 2018, Li et al. 2019). tion of these leverage points assists managers in achieving their Instead of infrastructure solutions, socio-hydrological stu­ intended goals with the least changes. In none of the scenarios, dies help to identify solutions from recognizing the relation­ the minimum environmental need for preserving all ecologic ships between the water system and the socio-economic functions was met for the Gavkhuni Wetland, because it is system. In this regard, socio-economic components such as located at the end of the river, and in the existing decision- industry, agriculture, environment, employment, GDP, popu­ making mechanism is the last receiver of water. To supply the lation, migration, settlement, social sensitivity, and awareness needs of this wetland, the rules governing the management have been considered in socio-hydrologic studies (Di system must be modified, whereby the environment obtains Baldassarre et al. 2013, Elshafei et al. 2014, Roobavannan a more prominent place in decision making. The amount of et al. 2017a, 2017b, Li et al. 2019). The solutions obtained in water extracted in the year 2025 is presented in Table 3 for this way change the lever points of the system generally each of the four scenarios. Since the average of renewable towards soft interventions, for instance, diversifying the econ­ water resources is about 2500 million m3/year in the omy (Roobavannan et al. 2017b, 2020), designing the location Zayandehrud basin and the net extraction of water is less of the settlement away from the river (Di Baldassarre et al. than this amount in Scenario 4, this scenario can lead to the 2013), limiting population expansion along with improving sustainability of water resources. water use efficiency (Li et al. 2019) and changing the reservoir operation policy (Garcia et al. 2016). Similarly, the core obtained solution in this study is devel­ 4.3 Discussion opment management. The findings show that it is possible to To create a balance between water resources and consump­ reduce the cultivated areas of agricultural lands and deal with tion in the Zayandehrud basin, it has been proposed that the development of new industries for increasing the sustain­ water demand management coincides with inter-basin water ability of water resources and consumptions and improving transfer (Safavi et al. 2016). However, the water transfer the social, economic, and environmental situation of the sys­ policy has many environmental and social challenges, of tem. In other words, the basin evolves toward industrializa­ which the most common challenge is population growth. tion. However, it is clear that the skills, local knowledge, and This interaction between the socio-economic system and infrastructure capitals dedicated to agriculture, which have the hydrology system was discovered in many prior socio- been formed during centuries, should not be neglected. hydrology studies around the world (Di Baldassarre et al. Therefore, the development of agricultural industries (green­ 2018, Li et al. 2019). In contrast, it has been proven that house, livestock, poultry, and aquaculture) must be prioritized reducing water allocation could cause emigration and popu­ compared to large and medium-sized industries. However, lation decline (Roobavannan et al. 2017a). The impact of how to reduce agricultural land and its localization requires inter-basin water transfer on the socio-economic system has further studies. also been reported in the Zayandehrud basin (Madani and The solutions obtained from socio-economic studies (like Mariño 2009, Gohari et al. 2013a), but the level of influence reducing agriculture and developing the industry in this study) had not yet been assessed. In this research, it was found that demonstrate that water resources management is not under­ most immigrants to the Zayandehrud basin came from two taken only by managers of the water sector, and that managers provinces, Chahar-Mahal & Bakhtiari and Khoozestan, from other sectors, such as the economy, agriculture, and industry directly influence water management. Managers of the water sector may concentrate on water supply manage­ Table 3. Water withdrawal and consumption in 2025 (106 m3/year). ment or water demand management through technical, legal, Scenario 1 Scenario 2 Scenario 3 Scenario 4 economic, pricing, and educational activities, while there is Urban water use 468 412 591 468 Industrial water use 205 185 400 264 a need to review the development policies in a larger context. Agricultural water use 3 516 2 933 3 516 2 850 The comparison of scenarios shows that choosing appro­ Environmental use 0 143 0 109 priate criteria strongly influences decisions. Although all three Surface water withdrawals 1 446 1 478 1 510 1 518 Groundwater withdrawals 2 743 2 194 2 997 2 174 social, economic, and environmental aspects were covered Return flow 1 503 1 066 1 706 1 291 here, there are still other criteria that are significant in the Net water consumption 2 687 2 607 2 801 2 401 Zayandehrud basin and must be considered in future research; 2172 S. 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