A system dynamics model of smart groundwater governance

Agricultural Water Management 221 (2019) 502–518

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Agricultural Water Management journal homepage: www.elsevier.com/locate/agwat

A system dynamics model of smart groundwater governance Ali Akbar Barati a b c

a,b,⁎

b,c

, Hossein Azadi , Jürgen Scheffran

b

T

Department of Agricultural Management and Development, University of Tehran, Iran Research Group Climate Change and Security, Institute of Geography, University of Hamburg, Germany Department of Geography, Ghent University, Belgium

ARTICLE INFO

ABSTRACT

Keywords: Groundwater crisis Water management Smart governance Water shortage Iran water crisis

Groundwater is one of the most important and vital resources in the world. In current decades, increased water demand and pollution threaten groundwater availability. Yet, groundwater governance is a serious challenge and despite many studies focusing on groundwater assessments, its governance has been largely neglected. Smart groundwater governance is one of the most critical areas to improve the sustainable use of this resource in societies and people’s livelihood. The main goal of this paper was to introduce a model to evaluate and measure the smartness level of any policy or action as an index. To do so, this study uses a system dynamics (SD) approach to develop a smart groundwater governance (SGG) model to assist policy and decision makers to better understand the short and long-term impacts of their actions, plans, and policies. For this purpose, the SGG index was introduced in the dynamic model, including four indicators (i.e., equitability, efficiency, sustainability, and democracy) and was applied in Iran. The results indicate that groundwater balance is critical (i.e. negative) and the current trend of groundwater governance is highly non-smart in the country. This study concluded that the best strategy to manage this situation and to govern the groundwater resources in a smarter manner includes both increasing the infiltration rate and decreasing the extraction rate (or increase the water efficiency) as the left and right based scenarios. The obtained results also demonstrate the benefits of the SGG index for policy and decision makers in SGG.

1. Introduction Water plays a critical role in sustainable development (Batchelor, 2007) and its scarcity is among the major global challenges today (Jacobson et al., 2013). Groundwater is one of the world’s most important water resources, accounts for over 98% of all liquid freshwater (Dunnivant and Anders, 2006; FAO, 2018). About one-third of humanity depends totally on groundwater for their daily needs. Also, 99% of the planet earth’s accessible freshwater is found in aquifers (FAO, 2015). According to the project of the groundwater governance of FAO (2018), groundwater extraction over the past 50 years (1960–2010) has increased by more than 300%. Recently, communities, agriculture and industry around the nation and the world have increased their use of groundwater (Megdal et al., 2015). In the future, the use of groundwater will continue to increase at higher rate than before, as there is a critical need to supply water for agriculture sector, urban areas, industry, and ecosystems. Groundwater provides drinking water to at least 50% of the world’s population and 43% of all of the water is used for irrigation (FAO, 2018). Therefore, groundwater is an essential natural resource to sustain life. It is also substantial to

⁎

agriculture and food security. Over the past 50 years, groundwater irrigation has grown rapidly. It supplies over one-third of the world’s irrigated area (Shah, 2014). Increased water demand and pollution threaten groundwater availability. In order to share groundwater resources equitably among nations, regions, consumers per sector and generations, and to maintain the groundwater availability and quality, informed decisions need to be made about allocation and protection of groundwater resources (IGRAC, 2017). Groundwater is a critical component of the water supply for agriculture, urban areas, industry, and ecosystems, but its managing is a challenge because it is difficult to map, quantify, and evaluate groundwater. Until recently, study and assessment of governance of this water resource has been largely neglected (Megdal et al., 2015). Globally, more than 150 million people live below the altitude of 1 m a.s.l. and 250 million live below the altitude of 5 m a.s.l (IGRAC, 2017). Some countries (e.g. Africa) are in an initial stage in terms of protection areas of groundwater sources or they have not yet started these practices because more stringent problems, such as the scarcity of water resources, have taken the priority (Doveri et al., 2015).The crisis of groundwater in Iran is multifold especially because a) it is the most important water resource for most regions of

Corresponding author at: Department of Agricultural Management and Development, University of Tehran, Iran. E-mail address: [email protected] (A.A. Barati).

https://doi.org/10.1016/j.agwat.2019.03.047 Received 19 December 2018; Received in revised form 27 March 2019; Accepted 28 March 2019 Available online 23 May 2019 0378-3774/ © 2019 Elsevier B.V. All rights reserved.

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Iran (Hojjati and Boustani, 2010) which means Iran is more dependent on groundwater. b) The average annual precipitation in Iran has been 228 mm/year (1994–2014) which means 6% less than the long term average (242 mm/year) (Moridi, 2017) which means water resources will be more limited. c) Iran's population (according to World Population Review) will reach 95 million over the next two decades (the current population of Iran is more than 80 million) and it means there will be more pressure on groundwater resources. d) Intensification of climate change and periodic droughts can worsen the quantity and quality condition of water resources. For example, in the last 50 years, Iran has faced ten severe and prolonged droughts, which have significantly threatened water availability in all sectors (Sadeghi, 2017). It is expected that climate change will further increase the risk of droughts in some parts of the country and cause floods in others. e) The recent acceleration of inland use and land cover changes, such as deforestation, destruction of rangelands and conversion of agricultural lands (Asadi et al., 2014; Azadi and Barati, 2013; Azadi et al., 2016; Barati et al., 2015) will increase natural disasters and climatic changes (e.g. flood, drought and frostbite) which will damage (i.e. drying up) the water resources more than before. f) Finally, mismanagement (Nabavi, 2017) and the lack of a good water management or governance (Hojjati and Boustani, 2010; Moridi, 2017) are at the top of all causes. For instance, low water efficiency in the agricultural sector (the biggest water consumer sector), dropping of the groundwater level and land subsidence1 are two main impacts of impractical water management and governance in Iran (Sadeghi, 2017). Therefore, it seems that the issue of water governance in Iran is a complex dynamic system; therefore, its study needs suitable methodologies and techniques. Groundwater governance could be defined as a procedure which determines who gets, when, and how much of the water (UNDP Water Governance Facility at SIWI, 2015). It is the overarching framework of groundwater use laws, regulations, and customs, as well as the processes of engaging the public sector, the private sector, and civil society (Shah, 2014). Since it relates not only to the state or government but also to civil society and the private sector, its development takes place within different constellations of these three entities. In this sense, improved governance is path dependent and needs to be linked to particular development goals in society, such as water services and sanitation for all, equitable reallocation of water between users, or any other goals such as food and energy for all, or conservation and restoration of ecosystems (UNDP et al., 2017). Now, despite the seriousness of the water crisis in most parts of the world and especially in arid and semiarid areas such as Iran (Madani, 2014; Nabavi, 2017), Middle East, and North Africa (FAO, 2012; Lezzaik et al., 2018; Mekki et al., 2017), the question is why the previous and current actions, plans, policies, and strategies have not been successful? Today increasing water scarcity is still one of the major global challenges. As local demand for water rises above available supply in many regions, the governance of available water resources becomes the key issue to achieve water security at the local, regional, and global level. Around the world, communities, agriculture, and industry are increasing their use of groundwater, sometimes with adverse impacts on riparian habitats and often with disregard for sustainability (Megdal et al., 2015). Poor and bad resource management, corruption, lack of appropriate institutions, bureaucratic inertia, insufficient capacity and a shortage of new investments undermine the effective governance of water in many places around the world (Jacobson et al., 2013).

After 113 years of the Persian constitution in 1906, the country is experiencing a serious water crisis (Nabavi, 2017). In the case of Iran, the over-use and abuse of increasingly precious water resources has been dramatically intensified over the recent decades. It is reaching a point where water shortages, water quality degradation, and aquatic ecosystem destruction are seriously affecting the prospects for socioeconomic development, political stability, ecosystem integrity (Batchelor, 2007), the health and the welfare of natural systems, and human communities (Moridi, 2017). Blame is often attributed to the government’s management (Nabavi, 2017) or misgovernance of water resources including both surface and groundwater. Then, apparently there is a need to be a new and different kind of view about groundwater governance. This view should be holistic, dynamic, and systematic to achieve smart groundwater governance. This paper aims to address this issue. To deal with this, at first, this paper will model the system dynamics (SD) of groundwater in Iran, then it will define smart groundwater governance, and finally it will analyze the operation of the smart groundwater governance SD. There are some previous studies that tried to analyze water systems in different ways. For example Madani (2010) illustrated the utility of game theory in water systems analysis and conflict resolution and Loáiciga (2004) studied the roles of cooperation and non-cooperation in the sustainable exploitation of a jointly used groundwater resource using an analytical game-theoretic formulation. Bhattacharjee et al. (2018) evaluated the existing water related policies and functions of multidimensional institutions, and discussed the key challenges of effective groundwater management. Also, Huo et al. (2016) used a simulation method to develop an operational water governance model for predicting the future water cycle. Although these studies tried to analyze and explain the governance of water and groundwater systems, they could not develop a smart system to explain the dynamics of groundwater resources over time and under different scenarios. In addition, they could not tell us whether the governance system is smart or how much it is equitable, efficient, sustainable, and democratic. Accordingly, this study tries to introduce new smart and dynamic models to meet groundwater management deficiencies. Therefore, the main aim of this study is to introduce a model to forecast not only the future trend of groundwater balance, but also to evaluate and measure the smartness level of any policies or actions as an index. It also aims at assisting policy and decision makers to realize the impacts of their decisions, to better understand the short and long-term impacts of their actions, plans, and policies and to ask themselves whether their decisions on groundwater are smart. 2. Methodology 2.1. Study area The site of this study was Iran that is located in the world’s dry belt. According to the last formal census of Iran (2016), the total population of the country is 79,926,270 persons. Iran is one of the largest countries in southwestern Asia with an area of 1,648,000 square kilometers. The average of Iran’s annual precipitation is about 240 mm that is about a third of the global average. The rainy period in most of the country is from November to May. In the dry period between May and October, rain is rare in most of the country. About 90% of total precipitation occurs in cold and humid seasons (in northern and western parts of the country) and only 10% occurs in warm and dry seasons (in central, southern and eastern parts). Moreover, about 52% of precipitation occurs in 25% of the area of the country (Sadeghi, 2017). Therefore, the temporal and spatial distribution of precipitation in Iran is volatile and uneven across the country (Fig. 1). While in many localities of Iran there is no rainfall, torrential rains cause floods and local damages in other locations. Hence, some parts of Iran are suffering lack of water resources and Iran is experiencing a serious water crisis.

1 Groundwater levels are dropping at an average of 2-4 meters per year, since the slow-flling aquifers have not been able to keep up with the growing number of water users and subscribers. This drop has caused unprecedented land subsidence of an average of 2-30cm in di?erent plains across the country and sinkhole formation. All of these factors are contributing to massive desertifcation (Sadeghi, 2017).

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Fig. 1. Precipitation distribution (in mm) across Iran’s provinces (Sadeghi, 2017).

2.2.3. Formulation of a Simulation Model It includes the specification of structure, decision rules, estimation of parameters, behavioral relationships, initial conditions and tests for consistency with the purpose and boundary. The structure, decision rules, parameters, behavioral relationships and initial conditions were estimated using: a) basic models, and b) analyzing the available timeseries datasets such as AQUASTAT (2018), Statistical Center of Iran (SCI), Ministry of Agriculture Jihad (MAJ) etc.

2.2. System dynamic modeling SD is an approach to policy analysis and design. It applies to dynamic problems arising in different complex systems (which are characterized by interdependence, mutual interaction, information feedback, and circular causality). Thus, this approach is one of the most appropriate ways to examine the groundwater governance complexity, especially in Iran. Therefore, the current study applied for the first time the SD modeling approach to deal with the problem of water crisis and its smart governance in Iran. Recently several studies (Li et al., 2018; Xiao-qing et al., 2012; Yang et al., 2019; Zomorodian et al., 2018) used or emphasized this approach to solve the water crisis problem. SD is an approach to understand and model the behavior of complex systems over time. To model the groundwater cycle, this paper applied both primary (collected by interviewing experts to develop the SGG model) and secondary (the databases such as AQUASTAT and Statistical Center of Iran to model the dynamic system of groundwater in Iran) data. According to the SD approach, the following steps have been proceeded to design the smart governance index (Sterman, 2000):

2.2.4. Testing the Model After formulating the model, it is necessary to test it. This phase includes some specific tests such as comparison to reference modes, robustness under extreme conditions, and sensitivity analysis. This study uses the calibration and sensitivity procedure of the Vensim software to test the model. During this process, the observed data were compared with the model data for a period of time according to the time series data. Then, the simulation is repeated many times in which parameters of the model have changed for each simulation. At the end, the software will propose the details of the best parameters for the tested variables.

2.2.1. Problem Articulation (Boundary Selection) It is a situation in which the problem, the key variables, the time horizon, and the dynamic problem should be defined and considered. In this study, the key variables of the system were identified using two main sources: a) interviews with filed experts, and b) literature review.

2.2.5. Policy Design and Evaluation During this phase, the capability of the model to develop scenarios and to design policies is evaluated, based on new decision rules, strategies, and structures on the ground. Decision making is the main object of modeling any phenomena or systems to solve the modeling problem (s); therefore, this phase is critical for managers and policy makers. The scenarios in this study were developed based on the main key variables of the model that have the greatest impacts on the groundwater balance. These impacts were determined by analyzing the sensitivity of the model to their changes. The number of these variables per each scenario was selected using experts’ opinions and monitoring the model behaviors under each condition.The software that used for SD modelling and simulation was Ventana Vensim® DDS for windows Version 6.4E. We

2.2.2. Formulation of Dynamic Hypothesis It includes a process that will be ended in the maps of causal structure based on initial hypotheses, reference modes, and other available data and tools. The base of causal structure in the groundwater system is the hydrological cycle which is relevant for smart groundwater governance (Jacobson et al., 2013; UNDP et al., 2017). Some data to formulate the model are adapted from AQUASTAT (2018) a Iranian government database. 504

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Fig. 2. Complexity of groundwater system dynamics (mapping of causal structures).

downward from the land surface. As a part of the water cycle, groundwater is a major contributor to flow in many streams and rivers, and it has a strong influence on river and wetland habitats for plants and animals. People have been using groundwater for thousands of years and continue to use it today, largely for irrigation and drinking. Life on Earth depends on groundwater just as surface water (USGS and Perlman, 2016). Wells, springs, and Qanats (especially in Iran) are three main ways to evacuate groundwater (Ahmadi et al., 2010; Hamidian et al., 2015). The balance of water that remains on the earth's surface is runoff, which is emptied into lakes, rivers, and streams and is carried back to the oceans, where the cycle begins again. Based on this causal model, we can map the main groundwater processes based on the concepts of stocks and flows which are a central idea in SD (see Fig. 3).

used the case of Iran for testing, policy design, and evaluation. 2.3. Elaboration of the specific smart groundwater governance model for Iran 2.3.1. The system dynamics of groundwater For smart governance of groundwater, this paper investigates a SD approach to expose the key variables and the dynamics of groundwater. Fig. 2 depicts part of a causal structure of a groundwater SD. This system includes two main parts. The left part indicates the inflow of groundwater using the hydrologic cycle and the right part includes the main outflows of groundwater resources and their main important variables and factors. The hydrologic cycle begins with the evaporation (90%) and evapotranspiration (10%) from the surface of water, plants, and lands. The moist air and water vapor condense to form clouds and return to the surface as precipitation. Once the water reaches the ground, one of the two processes may occur: 1) evaporates and transpires back into the atmosphere or 2) penetrates into the surface and becomes groundwater (infiltration) (Penuel et al., 2013). The amount of water that plants transpire varies geographically over time. There are a number of factors that determine transpiration rates including temperature, relative humidity, wind and air movement, soil-moisture availability, and type of plant (USGS and Perlman, 2016). Infiltration is known as the process of water entering the soil surface. When rain or irrigation water is supplied to a field, it seeps into the soil, called infiltration. The infiltration rate is the velocity at which water can seep into the soil. It is commonly measured by the depth (in mm) of the water layer that the soil can absorb in an hour. The infiltration rate of a soil depends on the factors that are constant, such as the soil texture, and it also depends on factors that vary, such as the soil moisture content and the soil structure. Infiltration rate is higher for coarse textured soils than for fine textured soils. The water infiltrates faster (higher infiltration rate) when the soil is dry, than when it is wet. Generally, water infiltrates quickly (high infiltration rate) into granular soils but very slowly (low infiltration rate) into massive and compact soils (Brouwer et al., 1985; Johnson, 1963). The head of the applied water, the depth to ground water, the length of time of application of water and biological activity are some of the other influencing variables (Johnson, 1963). Groundwater either seeps its way into the oceans, rivers, and streams, or is released back into the atmosphere through transpiration. Large amounts of water are stored in the ground. The water is still moving, possibly very slowly, and it is a part of the water cycle. Most of the water in the ground comes from precipitation that infiltrates

2.3.2. Governance of groundwater Governance is the activity of coordinating communications in order to achieve collective goals through collaboration (Willke, 2007). The need for governance not only arises as the population grows, but also with shortage of resources. In addition, with simultaneous increase in resources shortage, (such as water resource) the need for governance will increase and will be more complex. Governance is also obvious when there is an explicit goal to be implemented, resulting from a project, a plan or a strategy (Willke, 2007). Therefore, water governance is to determine who obtains when and how much water (UNDP Water Governance Facility at SIWI, 2015). Water governance also refers to the political, social, economic, and administrative systems that influence water use and resource management (Rogers and Hall, 2003).

Fig. 3. A general stock and flow model for groundwater. 505

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Water governance determines the equity and efficiency in water resource and services allocation and distribution. It balances water use between socio-economic activities and ecosystems. It determines who gets water, when and how, and who has the right to water and related services and their benefits. How the decisions are made and the roles of power and politics are important issues in governance (UNDP et al., 2017). Given the limitation of water resources, the competition for this resource has rapidly increased and there is a general agreement that improvements to water governance and collaboration among key stakeholders are a necessary part of the solution (Batchelor, 2007). On the whole, to analyze the dynamics of water resources and their governance, at least four fundamental dimensions including social, economic, political, and environmental should be considered (Jacobson et al., 2013; UNDP et al., 2017). In terms of social dimension, its distribution should be equitable among various socio-economic groups in both time (from now to the future) and space (from local to non-local). In economic terms, the allocation of water should be efficient which means it should be done in a way to have a suitable role in the overall economic growth and poverty reduction. Politically, water stakeholders should have the equal rights, opportunities, and facilities to play a bigger role in decision-making processes. It could greatly improve the short and the long outcomes and solve and decrease current and future conflicts. Finally, water governance should lead to sustainable use of water and related ecosystem services. Water resources need to be sufficiently safeguarded against the current agricultural, urban, and industrial unsustainable uses.

Fig. 4. The study proposed model about the smart groundwater governance and its dimensions. The dimensions of water governance were adapted from Jacobson et al. (2013) and UNDP et al. (2017).

2.3.3. Smart groundwater governance (SGG) Smart groundwater governance, especially in the present era and for the modern world is necessary due to the vital role of water for humanity. Also, water changes and shortages are of the main crises of the current world. All too often, well-intentioned efforts to solve pressing problems lead to policy resistance, where our policies are delayed, diluted, or defeated by the unforeseen reactions of other people or of nature. Then we need to have a new kind of smart governance which is an innovation of traditional concepts of municipality, from administrative approaches to different manager visions. Smart governance is using technology to facilitate and support better planning and decision-making. It is about improving democratic processes and transforming ways of delivering public services (Ioppolo, 2013). Also, smart governance is about the future and it should be transparent, accountable, responsive, and efficient In this study, smart groundwater governance (SGG) is defined as a SD including a range of political, social, economic, and administrative components. These components are responsible for developing and managing groundwater resources (their inflows and outflows) in an equitable, efficient, democratic, and sustainable way. In this regard, SGG, at least needs to indicate, who uses water, who decides about using water, how much water could be used, how to use water (using in a way that does not deal with the shortage), and when to use water (Fig. 4). . SGG aims to balance groundwater use between socio-economic activities and ecosystems through developing a smart and open model. Also, SGG would involve governmental institutions as well as stakeholder participation and collaboration at all levels and in all branches of the governing process. Smart groundwater governance needs to follow a SD approach because it contains several elements with a complex network of relationships. The networks affect smart groundwater governance performance and the bad governance would lead to many socio-economic, political, and environmental outputs (immediate results), outcomes (mid-term results) and impacts (long-term results), which will appear now and in the future. Furthermore, SSG needs the systems dynamics (SD) approach because there is an increasing competition between the agricultural, domestic, and industrial sectors, especially in arid and semiarid agro-systems (Mekki et al., 2017) that will increase its

complexity over time. Moreover, testing and assessing different ways and methods are time and resources consuming. Groundwater plays a pivotal role in sustaining human activities, especially in arid environments with scarce surface water resources (Giordano, 2009). Also, the bad governance of groundwater has countless challenges and crises for many countries including Iran (Madani, 2014; Nabavi, 2017). According to a global risk report, water crises have been identified as one of the four major risks most likely to impose devastating threats globally (Lezzaik et al., 2018). The SD approach could improve our understanding of how the governance performance is the result of its internal and external structure, including stakeholders, competitors, and suppliers. The approach could also help us use our understanding to design high leverage successful groundwater governance policies. Finally, it is a fundamental interdisciplinary method to solve important real world problems (Sterman, 2000). SSG’s conceptual model could be changed to a stock and flow model as represented in Figs. 5 and 6. In this model, we defined “Cumulative SGG index” as a stock and “SGG index” as a flow variable. The SGG index (as a flow variable) measures the average value of the SGG index for each main stockholder of groundwater resources (agriculture, municipal, industry, and ecosystem) over an interval of time. The Cumulative SGG index (as a stock variable) measures the aggregate value of total SGG indicators at one specific time. Since a flow variable is measured over an interval of time, The SGG index would be measured

Fig. 5. SGG index as a flow variable. 506

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Fig. 6. Cumulative SGG index as a stock variable.

per unit of time (such as a year or a month). A flow is roughly similar to rate or speed in this sense. The SGG index is defined as a function of four variables that indicate the value of equitability, efficiency, sustainability, and democracy indicators of each stockholder (Eq. (1)).

SGG indexi =

(equitable+ efficient+ democratic+ sustainable) 4

(1)

Fig. 7. A general shape of the stock and flow model for the SGG index.

Stocks are accumulations that characterize the state of the system and generate the information upon which decisions and actions are based (Sterman, 2000). They are measured at one specific time, and represent quantities existing at that point in time. The Cumulative SGG index aggregates or integrates their inflow or overall SGG index (Eq. (2)).

Cumulative SGG index=

t t0

overall SGG index+ SGG index(t 0)

country (it is equal to the total infiltration of water entering the soil surface). Fig. 7 depicts a general shape of the stock and flow model for SGG index. The value of each indicator will be between 0 and +1; where 0 means the subsector is completely non-equitable (or efficient, sustainable or democratic). In contrary, the value of +1 means it is quite equitable (or efficient, sustainable or democratic). As a result, the value of SGG index will also have a range between zero and one. Based on Figs. 4–7, the SD of SGG could be exposed as Fig. 8. In which “Cumulative SGG index” is accumulated over time by the “overall SGG index” which is a rate of four flow variables including “SGG index Muni.”, “SGG index Indu.”, “SGG index Eco.” and “SGG index Agri.”.

(2)

Eq. (2) (adapted from Sterman (2000)) determines the “Cumulative SGG index” which represents the value of the overall SGG index as a flow at any time between the initial time (to) and the current time (t). The “overall SGG index” is the weighted mean of SGG indicators. It could be calculated by Eq. (3) where AWS (Eq. (3)) is the share of stakeholder i of total accessible water (it could be a number between 0 and 1).

overall SGG index=

(SGG indexi × AWS i)

3. Results and discussion

(3)

3.1. Dynamic system of groundwater in Iran

We measure equitability, efficiency, sustainability, and democracy indicators by the following equations:

Equ i =

Population i

To introduce a model for smart groundwater governance in Iran, at first it needs to realize the dynamics of Iran groundwater. According to AQUASTAT (2018) the annual average precipitation of Iran over the past twenty years has been 397.9 km³/year or 228 mm/year, which is about 6% less than the long term average (242 mm/year). In addition, the average surface run-off during this period has been 52 km³/year (Moridi, 2017). It is about 44% less than the long-term average (97.3 km³/year) (AQUASTAT, 2018). The volume of groundwater that was produced internally is about 49.3 km³/year and there is an overlap of about 18.1 km³/year between surface water and groundwater. Total external renewable water resources of Iran are about 8.5 km³/year. Based on these data, total surface water, total groundwater and total renewable water resources of Iran are respectively about 105.8, 49.3 and 137 km³/year (see Table 1). Although the total renewable water of Iran is about 137 km³/year, but only about 30% of this amount is accessible (Postel et al., 1996). It means the total accessible renewable water of Iran is 41.1 km³/year. Total renewable water is equal to surface water plus groundwater minus the overlap between surface water and groundwater (Kohli and Frenken, 2015). Table 1 and the data mentioned above were used to model the dynamics of groundwater in Iran over the next years (Fig. 9). The left hand of this model shows the resources of groundwater (inflows). The trend of precipitation by 2050 was forecasted based on time-series data (1964–2017) (Fig. 10). The right hand of the model is about the extraction of water from resources of groundwater (outflows). Based on the observed and literature reviewed data, three main outflows including agriculture, municipality, and industry were considered in the model. It is also assumed that only 30% of renewable water is accessible

(4)

EedGSi

Where Equi is the equitability index of subsector or main stockholders i, Populationi is a proportion of the population of stockholders i who have access to extracted water and EedGSi is the share of subsector i of the total extracted groundwater of the country.

Effi =

GDPi EedGSi

(5)

Where Effi is efficiency index of subsector i, GDPi is the share of subsector i in total GDP and EedGSi is the share of the extracted groundwater of subsector i.

Demi =

RGC i EedGS

(6)

Where Demi is the democracy index of subsector i, in every country RGCi represents part of the regulations, government agencies and cooperatives, etc. that belong to a specific subsector, such as agriculture or industry (subsector i). EedGSi is the share of subsector i from the total extracted groundwater.

Susi =

EedGi TEableG TEedG × TEedG TEableG

+1

(7)

Where Susi is sustainability index of subsector i, EedGi is the extracted groundwater of subsector i, TEedG is the total extracted groundwater of the country, and TEableG is the total extractable groundwater of the 507

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Fig. 8. System dynamics model of SGG.

(which implies that extraction higher than this amount will damage the ecosystem). The agricultural water demand is dependent on agricultural land area and water efficiency in the agriculture sector. The agricultural land area decreased during recent years and its changes over time have been mainly related to population growth (Azadi and Barati, 2013; Azadi et al., 2015, 2016; Barati et al., 2015). The trends of the agricultural land area and population of Iran were forecasted based on MAJ and CSI (Computational Science and Informatics) time-series databases. This model has also included water efficiency in different sectors. Agricultural water efficiency in Iran is generally low (Frenken, 2009). According to Alizadeh and Keshavarz (2005) it is about 35% and based on Frenken (2009) it is about 33%. Also, about 62%–64% of water used in agriculture is withdrawn from groundwater. These shares for the industrial and municipal sectors from groundwater are about 50% and 25%. Then, the total withdrawal of water from these sectors (agriculture, industry and municipal) have been 86, 1.1, and 6.2 km3/ year, respectively (Frenken, 2009). Table 2 shows some other information (real or observed data) about the variables that is used in the groundwater dynamic model. Based on this model (the model data) the volume of precipitation, population, and irrigated land area (according to the current situations and changes) will reach 344.7 km3, 112,500 thousand-person and 2,470,000 ha by 2050, respectively. For the base model, the efficiency of water is supposed to be fixed as well.

3.2. Model calibration and testing results After formulating the model, it is necessary to validate the model. This phase includes some specific tests such as comparison to reference modes, robustness under extreme conditions, and sensitivity. To do so, at first, the modeler needs to evaluate the model calibration and then the validity of the calibration. This study used the Vensim software calibration and sensitivity procedure followed by the SD approach since it is an initial effort to measure the smartness level of the groundwater governance. Accordingly, the first step is to evaluate the model calibration. The term evaluation refers to the estimated parameters of the system models (Wallach et al., 2018). If the parameters are estimated within reasonable ranges, the modeling results are comparable with observed data (Oliva, 2003). In addition, the only real time series data in this study were precipitation, population and agricultural land areas to which the calibration and validation are limited. Fig. 10 shows the results of the calibrated model compared to the observed or actual data showing good consistency. In addition, Table 2 indicates the average of the long-term observed and resulting data of the groundwater dynamic model. These data are close to each other as well. Similarly, the calibration of variables relevant to the state variable's (groundwater) outflow side (extraction) was evaluated. The parameters of the model were estimated by fitting data method (Wallach et al., 2018). If the parameters are estimated within reasonable ranges, the modeling results are comparable with observed data (Oliva, 2003). Moreover, the only real

Table 1 Long-term annual renewable water resources of Iran under real data (AQUASTAT, 2018) and model results. Resource

Internal renewable water resources (IRW)

External renewable water resources (ERW)

Total renewable water resources RWR Total evapotranspiration Accessible renewable water (30% total renewable water)

Description

Volume (km³/year)

Precipitation Surface water: produced internally Groundwater: produced internally Overlap between surface water and groundwater Total internal renewable water resources Surface water leaving the country Surface water entering the country Inflow secured through treaties Total external renewable surface water Surface water Groundwater Total renewable water resources

* The average of long-term real data. ** The average of model data (1956–2016). 508

AQUASTAT

Model**

397.9 (359)* 97.3 49.3 18.1 128.5 18.67 7.77 2.355 8.545 105.845 49.3 137.045 269.4 41.1

360.1 94.7 44.6 16.4 122.9 16.7 7.32 1.87 7.52 94.7 44.6 123.9 244.9 37.17

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Fig. 9. System dynamics model of groundwater changes in Iran.

time-series data in this study were precipitation, population and agricultural land areas to which the calibration and the validation are limited. Fig. 10 indicates the results of the calibrated model in comparison to the observed or real data which demonstrates a good consistence. In addition, Table 1 indicates the average long-term data, reported by AQUASTAT and the same data resulted of the model. These

data are close to each other as well. In the same way, the calibration of the influencing variables, relevant to the outflow side (extraction) of the state variable (groundwater), was evaluated. The parameters of the model were estimated by the method of fitting the full system model to the data. In addition to calibration, verification and validation were used as a

Fig. 10. Comparison of real (observed) and model data for the three main model variables in the system dynamics of groundwater in Iran.

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Table 2 Current water demand and efficiency, agricultural land and population in Iran. Source of data: Frenken (2009); Kohli and Frenken (2015); Ministry of agriculture and Statistical Center of Iran. Variable Total water withdrawal

Water withdrawal from groundwater Irrigated agricultural area Agricultural water efficiency Agricultural transmission water loss Municipal water usage efficiency Standard water need per person Population

Municipal Industrial Agricultural Total Municipal Industrial Agricultural

Value

Unit

6.2 1.1 86 93.3 25 50 62–64 4800 35–40 25 24 50 79960

Km3/Year Km3/Year Km3/Year Km3/Year Percentage Percentage Percentage Tha*/Year Percentage Percentage Percentage Liter/Year Tperson*

Fig. 12. Iran’s groundwater resource changes over.1980–2050.

outputs of the model have been allocated to different sources of uncertainty in its inputs, demonstrating the confidence of the model.

* T means thousand.

3.3. The trends of variables

critical part of the model testing process. Generally, the SD model testing has two types: behavioral and structural (Elsawah et al., 2017; Sterman, 2000). This study uses sensitivity analysis as a valuable approach to behavioral and structural testing. It assesses the effects of variations in parameter values, boundary conditions and other model inputs on model output (Elsawah et al., 2017). Vensim has the capability of sensitivity analysis in which model parameters change for each simulation in an arbitrary or default variation range for each simulation (50%, 75%, 95% and 100%). The assumptions about the value of constants in the model were changed during the sensitivity testing process and then the resulting outputs were examined. This can be very helpful to understand the behavioral boundaries of a model and testing the robustness of model-based policies. Fig. 11 indicates the results of the sensitivity analysis for four main variables of the study based on four different ranges of variations (50%, 75%, 95% and 100%).These variables were selected based on both experts’ opinion and the sensitivity of the model to their changes. In regard to these results, the

3.3.1. Scenarios and simulation results When the dynamic model was formulated and the model validation, verification, and functions were checked, it can run. Fig. 12 depicts the changes of infiltration, extraction, and its impacts on the balance of groundwater resources. This base model indicates the balance between infiltration and extraction for groundwater. It was implemented under the conditions that the water efficiency and the rate of transmission loss will not change until 2050. Under the assumption that the accessible water is less than 30% of renewable water (Postel et al., 1996), it shows that there is a surplus extraction from groundwater (Fig. 12). The current balance between accessible water and extraction is about -21.2 km3/year. By 2042, the accumulated value of this over withdrawal will reach about -183.4 km3, and then the balance between accessible water and extraction will be established. Because of low water efficiency, this model also indicates that about 32 km3/year water would be wasted annually

Fig. 11. The confidence bounds of four main variables of the model (based on their parameters changes).

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Fig. 13. The Iran population, irrigated land and groundwater changes over next 100 years.

Fig. 15. Two different scenarios about infiltration (left) and water efficiency rate (right).

(from 2018 to 2050). If the current conditions continue as before, and the current government policies do not change, it will take more than 22 years until the extraction rate from groundwater reaches to less than its infiltration rate. As in the past, population and agriculture will be the two main influence factors that affect groundwater. As Fig. 13 indicates, the growing population increases the pressure on groundwater resources, and due to the decrease in the agricultural irrigated land area this pressure will begin to decrease and will gradually be intensified. According to the dynamic groundwater model, groundwater governance has at least two sides (extraction and infiltration). It means that not only the rapid increase of water withdrawal is a main cause of the (ground) water crisis, but also it is the result of low water infiltration. A decreasing infiltration is not only the result of a decline in precipitation, but is also the result of a lower infiltration rate, where land cover changes and soil erosion are the main reasons of lower infiltration rate (see Fig. 2). Therefore, any government who wants to manage groundwater resources should consider both sides. For example, if over 10 years, a) the current infiltration rate of internal renewable water (0.384) (using land cover and watershed projects) reaches 0.4 (as a left side base scenario) or b) agriculture water efficiency increases from 0.4 to 0.5 (as a right side base scenario) (see Fig. 14), then the groundwater balance will change (see Fig. 14). As Fig. 15 shows, the return time of the groundwater balance (equality of inflow and outflow) under the left scenario is about 25 years, but under the right scenario this return time will be about 16 years. It means the right hand policies and programs (extraction scenarios) will have a faster effect than the left hand scenarios (infiltration scenarios). The government can also apply an integrated scenario including both infiltration and extraction modifications which increase the infiltration rate of internal renewable water from 0.384 to 0.395

Table 3 The values of four main variables in three different scenarios (%). Variables namea

A (Pessimistic)

B (Probable)

C (Optimistic)

Agricultural water efficiency Agricultural transmission loss rate Municipal water efficiency Infiltration rate of IRW

40 25

45 15

50 10

24 38.4

40 40

50 41

a

The values of each of the variables for each scenario were obtained based on the experts’ opinion according to their realization of potential variables for Iran. Table 4 Common variables in all the scenarios. Variables name

Initial time

End time (2050 Years later)

Irrigated land area (hectare) Precipitation (km3/year) Population (Person) GDP share Agricultural GDP share Municipal GDP share Industrial GDP share Ecosystem.

4800 397.9 79960 11% 71.5% 17% 0.5%

2470 344.7 112.5 5% 74.5% 20 % 0.5%

and the water efficiency of agriculture from 0.4 to 0.45 during the next 10 years. Therefore, returning the balance to groundwater (equality of inflow and outflow) will take about 14 years (Fig. 15). Such dynamic model could help decision makers to test many scenarios and to apply smarter policies and programs. It also helps them to see the impacts of their programs and policies. It can also help them to see the impacts of their programs and policies. For example, incentive and punitive

Fig. 14. Two different scenarios: A) increase the infiltration rate of internal renewable water from 0.384 to 0.4 (left side base scenario), B) increase the agriculture water efficiency from 0.4 to 0.5 (right side base scenario).

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policies that prevent the agricultural land conversion. Other examples of policies include soil erosion through increasing the vegetative cover of the land, increasing soil organic matter, and preventing unnecessary use of chemical fertilizers that damage the soil structure can increase the infiltration rate of surface water to groundwater. Using a dynamic model can help policymakers realize the impacts of such policies and make the best decisions Since governments usually deal with their problems in the real world with a combination of different programs and policies, in this study three different scenarios (A = Pessimistic, B = Probable and C = Optimistic) were designed. The scenarios were based on four variables including agricultural water efficiency, agricultural transmission loss rate, municipal water efficiency, and infiltration rate of IRW (Table 3). In order to have logical and possible choices for each variable, the selections of variables is made based on the field observations and opinions of scientific experts in agriculture, irrigation,

Fig. 16. The groundwater balance changes under three main defined scenarios.

Fig. 17. Simplified dynamic model of smart groundwater governance in Iran.

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Fig. 18. The changes of the overall SGG index and their components under different scenarios.

Fig. 19. The impacts of different scenario (Scenario C2) on the SGG index.

soil and rangeland sciences. Moreover, the change in these variables is more likely for the government than other variables. To achieve the goals, a 30-year period was considered. Changes in the other variables, such as precipitation, population, and GDP share, are similar for all the scenarios (Table 4). Fig. 16 displays the groundwater balance changes under pessimistic (A), probable (B), and optimistic (C) scenarios. In fact, the pessimistic scenario is the base model. Under this scenario the current condition will continue to exist in the future. As said above, under this scenario, coming back to balance in groundwater will take about 24 years (by 2042). But under scenario B and C, reaching to the balance between infiltration and extraction will take respectively about 18 (by 2036) and 14 (by 2032) years. For example, under the probable scenario, it is

assumed that during a 30-year program the amounts of agricultural water efficiency, agricultural transmission loss rate, municipal water efficiency, and infiltration rate of IRW reaches 45%, 5%, 40%, and 40% respectively (see Table 3). 3.3.2. Integrated system for smart groundwater governance in Iran In this section at first a smart dynamic model for groundwater governance has been introduced and then the changes of the SGG index under three main scenarios (A, B and C) have been discussed. Fig. 17 shows a simplified smart dynamic model for groundwater governance in Iran. This model is linked to the Iran groundwater dynamic model (Fig. 9). It means that some inputs for this model are derived from the groundwater dynamic model.

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The changes in the overall SGG index and its components by 2050 based on the SGG and groundwater models are shown in Fig. 18. It seems that above scenarios not only will change the groundwater resource conditions but also will affect the SGG index. On the other hand, implementation of these scenarios (especially B and C), notably by increasing agriculture and ecosystem SGG indices, will affect and improve the overall SGG index a little. However, using scenarios B and specially C, at first will make this rising faster and then it will be stopped. What is the main cause of this behavior? It seems that smart groundwater governance, like other smart systems, is a complex phenomenon that requires more systematic attention. If the government wants to be smarter in this context, there should be more systematic plans. Moreover, this study assumed that the duration of each policy is 30 years. If the policy implementation continues after 30 years, the SGG index will certainly continue to increase. These plans and policy also should consider the equitability, efficiency, sustainability, and democratic aspects of smart groundwater governance. For example, in the agriculture sector, it is very important not only to increase the water efficiency, but also to make some efforts to increase the value added in the agricultural sector in order to increase its GDP share. In addition, the government needs to increase the share of agriculture in rules, regulations and organizations. For instance, if the government, under a different scenario (while other conditions are similar to Scenario C), tries to increase the “GDP share Agri” variable up to 15% and the “RGC Agri.” variable up to 40% (i.e. during 30 years (Scenario C2)), then the “SGG index Agri” will reach 0.491 (from 0.231) over this period. It will also increase the “overall SGG index” from 0.076 to 0.132 (Fig. 19). The model presented in this paper is capable not only to demonstrate the impacts of various actions, plans, and scenarios on the changes of groundwater resources but also to measure an index to determine the smart level of groundwater governance. There are many earlier and currently presented models and studies such as Madani (2010); Jacobson et al. (2013); UNDP et al. (2017); Mechlem (2016), and Bhattacharjee et al. (2018) that have looked at the groundwater governance from a specific angle. Although all these studies have been useful and could improve our awareness about the issue and crisis of water scarcity, we think they could not simulate the complexity of this issue as a dynamic system and its changes over a long-time. The presented model can help governments have a smart and systematic view about groundwater governance and it is the main contribution of this study.

acknowledged that effective governance is the prerequisite for any sound water resource management, little attention has been given to groundwater resource governance, conservation, and protection. Ineffective and non-smart groundwater governance systems are serious threats, such as depletion, quality degradation, and reduced replenishment of these vital resources. This paper assesses the emerging global concerns over increasingly unsustainable groundwater use and aquifer degradation. Since the groundwater system as a subpart of a water system is a part of a broader social, political, and economic system in any country, it is affected by actors’ decisions outside the water sector. Thus, this study uses a SD approach to address these concerns. It developed a smart system that can indicate the effects of different actions, plans, programs, and policies (or scenarios). It can do it with measuring the equitability, efficiency, sustainability, and democracy impacts of any decision. This smart dynamic system can help policy makers to know better about the short and long-term impacts of their decisions. Then, it also allows them to choose the best alternative scenarios about groundwater governance. More specifically, this study indicates that to govern groundwater resources in Iran, it is not sufficient to pay only attention to water efficiency (economic dimension). It also needs to pay attention to the other dimensions such as equity in access to resources (who to use? and how much to use?), equity in rights and opportunities (who decides), sustainable use (how much to use? and how to use?). In Iran, although the agricultural sector is the biggest consumer of the groundwater resources, it does not have the same big role in decision or policy-making. This study also shows that following multi-dimensional policies or scenarios would enable the government to overcome the water crisis much faster and smarter. Despite all the benefits, this primary smart system is still at the beginning. This model could be more developed and improved using more accurate data, completing the variables that affect it, and improving and validating its indicators and indexes. For example, this study suggests that some ecological (e.g. population trends of some key species linked to aquatic ecosystems, land cover types, temperature change, CO2 emissions, etc.) and socio-economic indicators (e.g. gross national income index, poverty index, education index, etc.) should be included and linked to the groundwater dynamics model in future works and model developments. In particular, in some of these indicators, there are generally quite direct or indirect relationships between groundwater dynamics and observed trends of them. Making such improvements and developments in the groundwater dynamics model or any other similar models requires more quantitative information (data series and variables) related to those aspects. Therefore, this study also suggests that local governments and international institutions develop some databases and complete the relevant existing databases. It would be very interesting to have them included in such models, in the frame of ecological, socio-economic, etc. indicators.

4. Conclusion Population growth, rapid urbanization, industrialization, economic development, food security and climate change are putting unprecedented pressure on groundwater consumption (Mechlem, 2016). In addition, the lack of precipitation as the main result of climate change has forced many nations to use groundwater resources more than in the past. While groundwater use has some short-term socioeconomic and political benefits, it has many long-term environmental, socioeconomic, and political costs and influences (America and Region, 2009; Kumar et al., 2001; Procházka et al., 2018). Although it is well

Acknowledgements This work was supported in part by the DFG Cluster of ExcellenceCliSAP.

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Appendix A. : Model Documentation (Vensim®DSS V.6.4E)

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United Nations, Rome, Italy. Doveri, M., Menichini, M., Scozzari, A., 2015. Protection of Groundwater Resources: Worldwide Regulations and Scientific Approaches. Threats to the Quality of Groundwater Resources, 13–30 (Part of the the Handbook of Environmental Chemistry Book Series (HEC, Volume 40)). Dunnivant, F.M., Anders, E., 2006. A Basic Introduction to Pollutant Fate and Transport: an Integrated Approach with Chemistry, Modeling, Risk Assessment, and Environmental Legislation. John Wiley & Sons, USA. Elsawah, S., Pierce, S.A., Hamilton, S.H., van Delden, H., Haase, D., Elmahdi, A., Jakeman, A.J., 2017. An overview of the system dynamics process for integrated modelling of socio-ecological systems: lessons on good modelling practice from five case studies. Environ. Model. Softw. 93, 127–145. FAO, 2015. Shared global vision for Groundwater Governance 2030 and A call-for-action. Groundwater Governance A Global Framework for Action. FAO, 2018. Groundwater Governance - a Global Framework for Action. FAO. FAO, 2012. Coping With Water Scarcity. An Action Framework for Agriculture and Food Security. FAO WATER REPORTS. FAO, Rome. Frenken, K., 2009. Irrigation in the Middle East Region in Figures AQUASTAT Survey2008. Water Reports. Giordano, M., 2009. Global groundwater? Issues and solutions. Annu. Rev. Environ. Resour. 34, 153–178. Hamidian, A., Ghorbani, M., Abdolshahnejad, M., Abdolshahnejad, A., 2015. RETRACTED: qanat, traditional eco-technology for irrigation and water management. Agric. Agric. Sci. Procedia 4, 119–125. Hojjati, M.H., Boustani, F., 2010. An assessment of groundwater crisis in Iran, case study: Fars province. World Acad. Sci. Eng. Technol. 70, 476–480. Huo, A.-D., Dang, J., Song, J.-X., Chen, X.H., Mao, H.-R., 2016. Simulation modeling for water governance in basins based on surface water and groundwater. Agric. Water Manag. 174, 22–29. IGRAC, 2017. Groundwater Governance. International Groundwater Resources Assessment Centre Netherlands. Ioppolo, G., 2013. Environment and Energy. First Issue. Franco Angeli. Jacobson, M., Meyer, F., Oia, I., Reddy, P., Tropp, H., 2013. User’s Guide on Assessing

References Ahmadi, H., Samani, A.N., Malekian, A., 2010. The Qanat: a Living History in Iran, Water and Sustainability in Arid Regions. Springer, pp. 125–138. Alizadeh, A., Keshavarz, A., 2005. Status of Agricultural Water Use in Iran, Water Conservation, Reuse, and Recycling: Proceedings of an Iranian-american Workshop. National Academies Press, Washington DC, USA, pp. 94–105. America, L., Region, C., 2009. Poverty and Social Impact Analysis of Groundwater Overexploitation in. The World Bank, Mexico, pp. 90. AQUASTAT, 2018. Computation of Long-term Annual Renewable Water Resources (RWR) by Country (in Km³/year, Average) Iran (Islamic Republic of). October 13 ed. FAO. Asadi, A., Barati, A.A., Kalantari, K., 2014. Analyzing and modeling the impacts of agricultural land conversion in Iran. Bus. Manag. Rev. 4, 110–118. Azadi, H., Barati, A.A., 2013. Agricultural Land Conversion Drivers in Northeast Iran, LDPI Working Papers. The Land Deal Politics Initiative. Working Paper 36, United Kingdom. . Azadi, H., Barati, A.A., Rafiaani, P., Raufirad, V., Zarafshani, K., Mamoorian, M., Van Passel, S., Lebailly, P., 2015. Agricultural land conversion drivers in northeast iran: application of structural equation model. Appl. Spat. Anal. Policy 1–19. Azadi, H., Barati, A.A., Rafiaani, P., Taheri, F., Gebrehiwot, K., Witlox, F., Lebailly, P., 2016. Evolution of land use-change modeling: routes of different schools of knowledge. Landsc. Ecol. Eng. 1–14. Barati, A.A., Asadi, A., Kalantari, K., Azadi, H., Mamoorian, M., 2015. Analyzing the impacts of agricultural land use change according to the experts opinion of agricultural land organization in Iran. IJAEDR 45, 639–650. Batchelor, C., 2007. Water Governance Literature Assessment. (IIED). pp. 2523. org/ pubs/pdfs G. http://www.iied. Bhattacharjee, S., Saha, B., Saha, B., Sadib Uddin, M., Panna, C.H., 2018. Groundwater governance in Bangladesh: established practices and recent trends. Groundw. Sustain. Dev. 8, 69–81. Brouwer, C., Goffeau, A., Heibloem, M., 1985. Irrigation Water Management: Training Manual No. 1-Introduction to Irrigation. Food and Agriculture Organization of the

517

Agricultural Water Management 221 (2019) 502–518

A.A. Barati, et al. Water Governance. The UNDP the United Nations Development Programme. Johnson, A.I., 1963. A Field Method for Measurement of Infiltration. US Government Printing Office. Kohli, A., Frenken, K., 2015. Renewable Water Resources Assessment – 2015 AQUASTAT Methodology Review: Elimination of ‘actual’ versus ‘natural’ Flow Distinction and Simplification of Border Flow Accounting FAO AQUASTAT Reports. FAO Rome. Kumar, M.D., Singh, O.P., Singh, K., 2001. Groundwater depletion and its socio-economic and ecological consequences in Sabarmati River Basin. Monograph. Lezzaik, K., Milewski, A., Mullen, J., 2018. The groundwater risk index: development and application in the Middle East and North Africa region. Sci. Total Environ. 628–629, 1149–1164. Li, Z., Li, C., Wang, X., Peng, C., Cai, Y., Huang, W., 2018. A hybrid system dynamics and optimization approach for supporting sustainable water resources planning in Zhengzhou City, China. J. Hydrol. 556, 50–60. Loáiciga, H.A., 2004. Analytic game—theoretic approach to ground-water extraction. J. Hydrol. 297, 22–33. Madani, K., 2010. Game theory and water resources. J. Hydrol. 381, 225–238. Madani, K., 2014. Water management in Iran: what is causing the looming crisis? JESS 4, 315–328. Mechlem, K., 2016. Groundwater governance: the role of legal frameworks at the local and national level—established practice and emerging trends. Water 8, 347. Megdal, S.B., Gerlak, A.K., Varady, R.G., Huang, L.Y., 2015. Groundwater governance in the United States: common priorities and challenges. Groundwater 53, 677–684. Mekki, I., Ghazouani, W., Closas, A., Molle, F., 2017. Perceptions of groundwater degradation and mitigation responses in the Haouaria region in Tunisia. Groundwater Sustain. Dev. 5, 101–110. Moridi, A., 2017. State of water resources in Iran. Int. J. Hydrol. 1, 00021. Nabavi, E., 2017. (Ground)Water governance and legal development in Iran, 1906–2016. Middle East Law Gov. 9, 43–70. Oliva, R., 2003. Model calibration as a testing strategy for system dynamics models. Eur. J. Oper. Res. 151, 552–568. Penuel, K.B., Statler, M., Hagen, R., 2013. Encyclopedia of Crisis Management. SAGE

Publications. Postel, S.L., Daily, G.C., Ehrlich, P.R., 1996. Human appropriation of renewable fresh water. Science 271, 785–788. Procházka, P., Hönig, V., Maitah, M., Pljučarská, I., Kleindienst, J.J.W., 2018. Evaluation of water scarcity in selected countries of the Middle East. Water 10, 1482. Rogers, P., Hall, A.W., 2003. Effective Water Governance, TEC Background Papers, no.7. Global Water Partnership/Swedish International Development Agency, Stockholm, Sweden. Sadeghi, N., 2017. Water Challenges of Iran. Fanack Water in the Middle East and North Africa. Shah, T., 2014. Groundwater Governance and Irrigated Agriculture. TEC BACKGROUND PAPERS Global Water Partnership Technical Committee (TEC) No. 19. Sterman, J., 2000. Business Dynamics: Systems Thinking and Modeling for a Complex World. Irwin/McGraw-Hill. UNDP, WGF, SIWI, 2017. Water Governance. Stockholm International Water Institute. UNDP Water Governance Facility at SIWI, 2015. Water Governance in Perspective – Water Governance Facility 10 Years 2005-2015. UNDP Water Governance Facility at SIWI. USGS, Perlman, H., 2016. What is the Water Cycle? USGS, USA. Wallach, D., Makowski, D., Jones, J.W., Brun, F., 2018. Working With Dynamic Crop Models: Methods, Tools and Examples for Agriculture and Environment. Academic Press. Willke, H., 2007. Smart governance: governing the global knowledge society. Campus. Xiao-qing, Z., Hui, R., Qi, Y., Chun-lan, H., Hong-hui, Y., 2012. Scenarios simulation on carrying capacity of water resources in Kunming City. Procedia Earth Planet. Sci. 5, 107–112. Yang, Z., Song, J., Cheng, D., Xia, J., Li, Q., Ahamad, M.I., 2019. Comprehensive evaluation and scenario simulation for the water resources carrying capacity in Xi’an city, China. J. Environ. Manag. 230, 221–233. Zomorodian, M., Lai, S.H., Homayounfar, M., Ibrahim, S., Fatemi, S.E., El-Shafie, A., 2018. The state-of-the-art system dynamics application in integrated water resources modeling. J. Environ. Manag. 227, 294–304.

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