Abstract

During the last decade, Urmia Lake has lost most of its surface area. As a result, finding management practices to restore the sustainable ecological status of Urmia Lake, the world's second largest hyper-saline lake, is imperative. In this study, the sustainability of different plans under climate change was assessed using system dynamics. The plans were evaluated with respect to sustainability criteria including reliability, resiliency, and vulnerability measures. According to the results due to different management practices, on average, water consumption should be reduced by at least 30% to restore the lake. The results revealed that only hybrid plans which incorporate multiple management practices, instead of focusing on just one approach, can be influential. Among the hybrid plans, that of increasing irrigation efficiency, reducing cultivated area, changing crop pattern, and inter-basin water transfer was identified as the most sustainable plan. About eight years after applying this plan, the lake will achieve its ecological level and will remain sustainable. Considering comprehensive factors, the proposed model can help watershed managers to take the necessary measures to restore this vital ecosystem. The results of this study can be applied to water resources systems with the same problem, especially those in semi-arid regions with multidisciplinary aspects.

INTRODUCTION

Despite global water resources limitations, increasing population growth and human activities have led to greater water demand. Among all natural resources, water is one of the most challenging issues. The main objective of sustainable environmental management is finding a proper balance between humans and their impact on the environment (Falkenmark 2003). Water management practices directly alter water availability and distribution, with feedbacks on land surface fluxes (Ferguson & Maxwell 2012).

Urmia Lake with a 5,200–6,000 km2 area is the largest lake in the northwestern region of Iran (Fathian et al. 2014). In past decades, the lake's water level has significantly dropped. Furthermore, the decreasing trend of Urmia Lake's level in recent years has become a challenge and one of the most problematic issues in the region. This ongoing reduction of water level has had critical impacts on agriculture, environment, and the economy (Fathian et al. 2014).

In the current decade, saving Urmia Lake is one of the most important priorities. Nevertheless, the absence of comprehensive research to evaluate the effect of adaptation/mitigation strategies on the lake is evident (Zarghami & AmirRahmani 2017). Irrigation accounts for ∼90% of water consumption in Iran (Iran Ministry of Energy 2012). Since the agricultural sector is known to be the main culprit of the basin's water shortage, it is vital to assess the impact of irrigation water consumption on the lake's restoration. In this study, some effective variables such as water demand (in millimeters per month, calculated using NETWAT software), cultivated area (hectare per month), and irrigation efficiency were used in crop and horticultural cultivated land modeling.

System dynamics (SD) models are tools to facilitate understanding of the interactions between diverse but interconnected sub-systems that drive the dynamic behavior of the larger system (Forrester 1968). However, a model's ability to provide proper insights into potential consequences of system perturbation depends on efficient recognition of the main constituents and feedback loops between them (Gohari et al. 2013).

Finding management practices to achieve sustainable ecological status of the lake is vital due to the socio-environmental consequences of the current status of Urmia Lake. Sustainable water resources management is complex and climate change brings more complexity to the problem. Hence, managing water resources considering climate change is crucial for determining the possible future challenges. To progress in sustainability, quantifiable indicators are key tools for managers (Aydin 2014; Graziano & Rizzi 2016).

A new set of emission scenarios are consistent with the new Representative Concentration Pathways (RCPs) and are different from the emission scenarios described in the IPCC Special Report on Emissions Scenarios (SRES) (Semenov & Stratonovitch 2015). The RCPs included mitigation measures to achieve specific emission targets. However, the Lars-WG model does not consider RCPs.

Hassanzadeh et al. (2012) assessed the dynamic status of Urmia Lake in the past. They showed that the effect of inflow variations were the result of from climate change and overuse of surface water resources by 65%, which is the main factor behind the lake's shrinkage. Zarghami & AmirRahmani (2017) produced an SD model with hydrological demands and percolation sub-models. They assessed the contribution of some plans to increase Urmia Lake's level.

Here, the developed SD model for Urmia Lake is the integration of simpler models made earlier (Hassanzadeh et al. 2012; Zarghami & AmirRahmani 2017). This model represents the complex reactions among the variables while taking public participation into account. The goals of this study are to observe Urmia Lake's dynamic status up to 2030, apply various restoration plans and assess their sustainability. To reach this end, a new holistic SD model consisting of hydrological and agricultural sub-models with consideration of climate change has been developed to achieve sustainable status in the lake ecosystem. Sustainability of the plans is evaluated by a sustainability index (SI) based on reliability, vulnerability, and resiliency performances. According to the results of this model, the following can be understood: how much water is required for Urmia Lake to achieve its ecological level; how vulnerable, resistant, and reliable are the restoration plans; and generally, which plan is the most sustainable one.

The case study is the Urmia Lake basin which is a closed drainage basin with an approximate area of 52,000 km2 in the northwest of Iran (Figure 1). Urmia Lake is the second largest hyper saline lake and has a unique ecosystem and important socio-economic and ecological role in the region (Ebrahimi & Zarghami 2018). Due to its unique ecological features, the lake has been designated as a UNESCO Biosphere Reserve (CIWP 2008). The lake basin has experienced extreme water shortages in recent years due to the dry climate (Alipour 2006). Inflow to the lake includes rainfall, surface water, and groundwater and the only outflow from the lake is evaporation (Hassanzadeh et al. 2012).

Figure 1

Geographic location of the stations used for assessing Urmia Lake in Iran.

Figure 1

Geographic location of the stations used for assessing Urmia Lake in Iran.

The required ecological water level for Urmia Lake is 1,274.1 m above sea level (Abbaspour & Nazaridoust 2007). This water level is calculated using ecological and water quality (NaCl concentration) indices. The ecological water level of Urmia Lake is evaluated considering the required ecological concentration of NaCl (i.e., 240 ppt). If the water level in Urmia Lake rises more than 1,274.1 m, Artemia (a species of brine shrimp) can tolerate the water quality index concentration (Abbaspour & Nazaridoust 2007). As shown in Figure 2, the lake's annual water level dropped below the ecological level from 2001. One of the socio-economic aspects of Urmia Lake is the use of Artemia fish and shrimp aquaculture which is commercially important, but which recently has been restricted and is becoming extinct. In addition, windblown salty dusts from dry areas of the lakebed can threaten the health of the resident population in the basin (Zarghami & AmirRahmani 2017).

Figure 2

Water level and ecological level of Urmia Lake (2000–2014).

Figure 2

Water level and ecological level of Urmia Lake (2000–2014).

According to the reports of the Iran Ministry of Energy (2012), approximately 70% of the Urmia Lake basin's cultivated lands are crop lands (Figure 3) and 30% of the area is dedicated to horticultural lands. The average irrigation efficiency of crop lands and horticultural lands were equal to 37% and 45%, respectively.

Figure 3

Curve fitting of crop cultivated lands in the Urmia Lake basin (2001–2012).

Figure 3

Curve fitting of crop cultivated lands in the Urmia Lake basin (2001–2012).

METHODOLOGY

In order to plan for sustainable water resource development, the SD model of Urmia Lake was developed. Consideration of hydrological factors, population, and water demand sectors was proposed to improve the previous Urmia Lake SD models. To achieve this, the first step was assessing the climate of the lake basin using LARS-WG (version 5.0) model. Then, effective factors about the basin were recognized and partitioned into the SD model incorporating climate change considerations. In order to investigate the impact of various restoration plans on Urmia Lake, VENSIM software was utilized.

Meteorological forcing

Rainfall

Data and statistics on monthly rainfall were collected from five stations (Figure 4) close to the lake. The average amount of these stations was considered as rainfall on the lake (Table 1).

Figure 4

Monthly rainfall using five stations in the Urmia Lake basin (2001–2015).

Figure 4

Monthly rainfall using five stations in the Urmia Lake basin (2001–2015).

Table 1

Rainfall stations used to model Urmia Lake

Location Longitude Latitude 
Urmia Lake 45-39-43 37-68-22 
Bonab 46-03-00 37-19-00 
Sharfkhaneh 45-28-00 38-11-00 
Dashkhaneh 45-41-00 37-01-00 
Abajalu Sofla 45-08-00 37-43-00 
Yalghuz Aghaj 44-56-00 38-14-00 
Location Longitude Latitude 
Urmia Lake 45-39-43 37-68-22 
Bonab 46-03-00 37-19-00 
Sharfkhaneh 45-28-00 38-11-00 
Dashkhaneh 45-41-00 37-01-00 
Abajalu Sofla 45-08-00 37-43-00 
Yalghuz Aghaj 44-56-00 38-14-00 

Evaporation

Evaporation intensity was calculated by collecting data and statistics on monthly pan evaporation from five stations (Figure 1) close to the lake (Figure 5).

Figure 5

Monthly evaporation using five stations in the Urmia Lake basin (2001–2015).

Figure 5

Monthly evaporation using five stations in the Urmia Lake basin (2001–2015).

Evaporation volume was calculated using a volume–area relationship, a pan coefficient, and a salt coefficient. Studies suggest that the pan coefficient could be considered from 0.6 (Nimmo 1964) to 0.94 (Garrett & Hoy 1978). Salt coefficient was calibrated to 0.93 (the range was 0.72 to 0.96) (Quants 2014). It is assumed that the evaporation will also keep this increasing trend in the future.

Climate change

The obtained data from eight synoptic stations of Urmia, Tabriz, Takab, Sarab, Salmas, Sardasht, Maragheh, and Mahabad (Figure 1) were downscaled by the LARS-WG model in order to study the climate change of the basin. A 25-year baseline of weather data (1991–2014) was used to generate the long-term weather series from 2014 to 2030.

The LARS-WG model uses predictions of 15 GCMs (general circulation models) approved by the IPCC (AR4) under three scenarios of SRES storylines of A1B, A2, and B1 (Arnell et al. 2004). These scenarios cover a range of future socio-economic, demographic, and technological storylines. LARS-WG uses a semi-empirical distribution (SED) to provide approximate probability distributions of precipitation, minimum and maximum temperatures, and solar radiation. The number of intervals (n) used in SED is 23. For each climatic variable v, a value of a climatic variable vi corresponding to the probability pi is calculated as Equation (1): 
formula
(1)
where P() denotes probability based on observed data {vobs}. For each climatic variable, two values, p0 and pn, are fixed as p0 = 0 and pn = 1, with corresponding values of v0 = min{vobs} and vn = max{vobs}. To approximate the extreme values of a climatic variable accurately, some pi are assigned close to 0 for extremely low values of the variable and close to 1 for extremely high values; the remaining values of pi are distributed evenly on the probability scale (Semenov & Stratonovitch 2010).

In order to assess the performance of the LARS-WG model, proper statistical tests were done to compare the observed and synthetic time series. Each test produces a p-value for measuring the probability that both sets of data come from the same distribution. The results of the t and F tests at 1% probability showed that the monthly rainfall predicted model means and standard deviations are in agreement with the observed series. A similar process was done for the maximum and minimum temperatures in the region.

Rainfall–runoff model

Seventeen hydrometric stations, which are located around the lake (Figure 1), were used to determine the surface water flow (Figure 6).

Figure 6

Monthly runoff using 17 hydrometric stations in the Urmia Lake basin (2001–2015).

Figure 6

Monthly runoff using 17 hydrometric stations in the Urmia Lake basin (2001–2015).

Average change of the different emission scenarios effect was in the range of 2 to 5%. To get more conservative results, the most pessimistic emission scenario (A2) was used to predict runoff. The runoff was calculated using rainfall (Figure 7) and runoff (Figure 8) of the basin. To do this, a linear regression model was developed relating rainfall and basin runoff. Runoff predictions were then calculated using predicted rainfall by the LARS-WG model. Most rainfall occurs between March and June and it is expected that the average lake runoff from 2015 to 2030 will be reduced by 15% compared with the period of 1996 to 2014.

Figure 7

Monthly rainfall prediction of the Urmia Lake basin (2015–2030).

Figure 7

Monthly rainfall prediction of the Urmia Lake basin (2015–2030).

Figure 8

Monthly runoff prediction of the Urmia Lake basin using a linear regression model between rainfall and runoff of the basin (2015–2030).

Figure 8

Monthly runoff prediction of the Urmia Lake basin using a linear regression model between rainfall and runoff of the basin (2015–2030).

System dynamics

System dynamics model

In the context of SD, variables are either stocks, flows, auxiliaries or constants. Stocks are state variables and start from an initial value and change by changing their inflows or outflows. Flow variables are used to define rates to change the stock variables. Information between levels (state variables) and flows are integrated with auxiliary variables; however, there are a few parameters with values that can be assumed as constant over the simulation horizon. The stock value at any time (t) when it has one inlet and one outlet is calculated using Equation (2): 
formula
(2)
where Stock(t) is stock at time t; Inflow(t) is inflow at time t; Outflow(t) is outflow at time t; and Stock(t0) is stock at time t0.

Key variables to develop SD model

In the agriculture sector, water demand (millimeters per month), cultivated area (hectare per month), and irrigation efficiency were used in crop and horticultural land modeling. Water demand was calculated using NETWAT software. This software uses the FAO–Penman–Monteith method on a 10-day and monthly basis. Water demand in the Urmia Lake basin is evaluated by considering a period of planting, harvesting, and growth and the required meteorological data were obtained from synoptic stations over a 30-year period.

Furthermore, lake water balance analysis showed that the amount of groundwater inflow to the lake is negligible (Hassanzadeh et al. 2012) and is considered equal to 1% of available surface water.

The causal loop diagrams (CLDs) of water resources system including the lake and its sub-systems are identified in Figure 9. Sub-systems include population, socio-economic, hydrologic, and agricultural parts. In these diagrams, elements of the system are connected by arrows with positive and negative polarities. A positive link indicates the parallel behavior of variables: in the case of an increase in the ‘cause’ variable, the variable that is affected also increases, while a decrease in the ‘cause’ variable implies a decrease in the affected one. A negative link indicates an inverse linkage between variables (Halbe et al. 2013).

Figure 9

Causal loop diagram for Urmia Lake.

Figure 9

Causal loop diagram for Urmia Lake.

The CLDs consist of various variables (lake balance, surface flow, the agriculture demand, population, and groundwater). The causal loops which have been added compared to the last publication of the Urmia SD model are displayed in Figure 10, however some parts of Figure 11 were published earlier (Hassanzadeh et al. 2012; Gohari et al. 2013). As mentioned in the Introduction, the agricultural sector is the main culprit causing shrinkage of Urmia Lake, and thus, assessing the agriculture in the basin in detail and considering the consequences of applying the related restoration plans is essential.

Figure 10

New key loops affecting the sustainability of the restoration plan for the Urmia Lake.

Figure 10

New key loops affecting the sustainability of the restoration plan for the Urmia Lake.

Figure 11

Stock and flow diagram for the Urmia Lake SD model.

Figure 11

Stock and flow diagram for the Urmia Lake SD model.

Using the CLDs, the stock flow diagram was developed in Vensim software, as shown in Figure 11, to characterize the system processes. In order to simulate the model, monthly data were entered and the required hydrologic equations were defined in the Vensim environment.

The variables used in the SD model, their sources and units, are listed in Table 2.

Table 2

Variables of the Urmia Lake SD model

Data Source(s) Units Data source type 
Total supply GW Iran Ministry of Energy MCM/month Modeled 
Natural recharge Survey data MCM/month Modeled 
Returned water Survey data MCM/month Modeled 
Groundwater extraction Iran Ministry of Energy MCM/month Modeled 
Natural discharge Hassanzadeh et al. (2012)  MCM/month Statistical 
Groundwater volume change Survey data MCM/month Modeled 
Wastewater percolation SW Iran Ministry of Energy MCM/month Statistical 
Precipitation volume Survey data MCM/month Modeled 
Perception height Iran Ministry of Energy Millimeter/month Statistical 
Margin area Survey data km2/month Modeled 
Margin precipitation height Survey data Millimeter/month Modeled 
Unmeasured surface inflow volume Survey data MCM/month Modeled 
Evaporation Survey data MCM/month Modeled 
Urmia Lake volume Survey data MCM Statistical/Modeled 
Population Statistical Center of Iran Dimensionless Statistical 
Domestic demand Iran Ministry of Energy MCM/month Modeled 
Mineral demand Iran Ministry of Energy MCM/month Modeled 
Industrial demand Iran Ministry of Energy MCM/month Modeled 
Horticultural demand Survey data MCM/month Modeled 
Crop demand Survey data MCM/month Modeled 
Agricultural demand Survey data MCM/month Modeled 
Total demand Survey data MCM/month Modeled 
Total supply SW Iran Ministry of Energy MCM/month Modeled 
Inflow to basin Survey data MCM/month Modeled 
Inflow Survey data MCM/month Modeled 
Surface water percolation Iran Ministry of Energy MCM/month Modeled 
Wastewater GW percolation Iran Ministry of Energy MCM/month Statistical 
Irrigation percolation Iran Ministry of Energy MCM/month Statistical 
Crop cultivated land Iran Ministry of Agriculture; ULRPa Ha/month Statistical 
Horticultural cultivated land Iran Ministry of Agriculture; ULRP Ha/month Statistical 
Crop demand average NETWAT software Millimeter/month Statistical 
Horticultural demand average NETWAT software Millimeter/month Statistical 
Runoff Iran Ministry of Energy MCM/month Statistical 
Evaporation intensity Iran Ministry of Energy Millimeter/month Statistical 
Lake's water level Survey data Meters/month Modeled 
Pan coef. Survey data Dimensionless Personal intuition 
Salt coef. Survey data Dimensionless Personal intuition 
Data Source(s) Units Data source type 
Total supply GW Iran Ministry of Energy MCM/month Modeled 
Natural recharge Survey data MCM/month Modeled 
Returned water Survey data MCM/month Modeled 
Groundwater extraction Iran Ministry of Energy MCM/month Modeled 
Natural discharge Hassanzadeh et al. (2012)  MCM/month Statistical 
Groundwater volume change Survey data MCM/month Modeled 
Wastewater percolation SW Iran Ministry of Energy MCM/month Statistical 
Precipitation volume Survey data MCM/month Modeled 
Perception height Iran Ministry of Energy Millimeter/month Statistical 
Margin area Survey data km2/month Modeled 
Margin precipitation height Survey data Millimeter/month Modeled 
Unmeasured surface inflow volume Survey data MCM/month Modeled 
Evaporation Survey data MCM/month Modeled 
Urmia Lake volume Survey data MCM Statistical/Modeled 
Population Statistical Center of Iran Dimensionless Statistical 
Domestic demand Iran Ministry of Energy MCM/month Modeled 
Mineral demand Iran Ministry of Energy MCM/month Modeled 
Industrial demand Iran Ministry of Energy MCM/month Modeled 
Horticultural demand Survey data MCM/month Modeled 
Crop demand Survey data MCM/month Modeled 
Agricultural demand Survey data MCM/month Modeled 
Total demand Survey data MCM/month Modeled 
Total supply SW Iran Ministry of Energy MCM/month Modeled 
Inflow to basin Survey data MCM/month Modeled 
Inflow Survey data MCM/month Modeled 
Surface water percolation Iran Ministry of Energy MCM/month Modeled 
Wastewater GW percolation Iran Ministry of Energy MCM/month Statistical 
Irrigation percolation Iran Ministry of Energy MCM/month Statistical 
Crop cultivated land Iran Ministry of Agriculture; ULRPa Ha/month Statistical 
Horticultural cultivated land Iran Ministry of Agriculture; ULRP Ha/month Statistical 
Crop demand average NETWAT software Millimeter/month Statistical 
Horticultural demand average NETWAT software Millimeter/month Statistical 
Runoff Iran Ministry of Energy MCM/month Statistical 
Evaporation intensity Iran Ministry of Energy Millimeter/month Statistical 
Lake's water level Survey data Meters/month Modeled 
Pan coef. Survey data Dimensionless Personal intuition 
Salt coef. Survey data Dimensionless Personal intuition 

aUrmia Lake Restoration Program.

To assess the model's performance, statistical measures such as coefficient of determination (R2) by Equation (3) and root mean square error (RMSE) by Equation (4) were used: 
formula
(3)
 
formula
(4)
where Qi is estimated water level; is average of estimated water levels; is observed water level; is average of observed water levels; and n is number of the months between 2001 until 2014.

Sensitivity analysis using Monte Carlo simulation

The Monte Carlo method is used to sample a set of numbers between bounded domains (distribution for each particular parameters is specified) (Kasperska et al. 2014). Vensim provides an infrastructure to apply Monte Carlo simulation on the model to examine its behavior under uncertainty.

There were few sensitive parameters in this model. Among all variables, ‘pan coef.’ and ‘salt coef.’ did not have a certain value. The effect of changing these parameters on the lake's water level was assessed using Monte Carlo simulation in Vensim software. As previously mentioned, pan coefficient could have the value from 0.6 to 0.94 and this range for salt coefficient varies from 0.72 to 0.96. The result of sensitivity analysis is shown in Figure 12.

Figure 12

Sensitivity analysis on the effect of uncertain parameters on the lake level: (a) pan coefficient and (b) salt coefficient.

Figure 12

Sensitivity analysis on the effect of uncertain parameters on the lake level: (a) pan coefficient and (b) salt coefficient.

A notable point is that changes in the pan coefficient had a considerable influence over Urmia Lake's water level. The sensitivity of the lake's water level to the pan coefficient grows throughout the test simulation, such that, by the end of the simulation, its influence on the lake's water level was considerably greater than the initial change made to the parameter. The effect of changing the pan coefficient is different from the effect of changing the salt coefficient on the lake's water level. The effect of changing the salt coefficient is greater in the median time and negligible in the initial time and at the end of the simulation. Furthermore, changes in the salt coefficient had less effect on the Urmia Lake level compared with changes in the pan coefficient. Pan coefficient and evaporation depend on various parameters, thus in order to valid prediction, it should have a wider range.

Simulation plans

Among the various plans to change the lake's level, five restoration plans were considered. These policies, strategies, and projects were selected among the suggestions of ULRP, which is responsible for saving the lake in Iran (ULRP 2014). The chosen plans as shown below are the most applicable and efficient ones.

Plan 1: impact of increasing the irrigation efficiency on the lake level

As the agriculture sector is a high water demand sector, managing water in this sector is crucial. Therefore, using an efficient irrigation plan is important to achieve sustainable agriculture, as from the farmers' point of view irrigation could be an economic concern (Faramarzi 2012). The irrigation efficiency for crop cultivated lands and horticultural cultivated lands is about 0.37 and 0.45, respectively, in the Urmia Lake basin. Efficiency could be increased up to about 0.90 by using mechanized or drip irrigation. Hence, changing the irrigation method in some of the cultivated lands will greatly reduce agricultural water consumption. It is assumed that the irrigation method in 50% of cultivated lands of the basin will be changed to mechanized irrigation from traditional ones like furrow and flood irrigation.

Plan 2: impact of reducing cultivated area on the lake level

According to Warren et al. (2016), it seems that the changes in farming systems are going to be an important policy option for assessing environmental sustainability. Rapid growth in farmland in the basin is known to be one of the main factors behind the Urmia Lake disaster. Therefore, stopping its growth and decreasing the area are followed in Plan 2. It is assumed to have a maximum reduction of about 10% considering social resistance (changing 10% of irrigated land into dry farming).

Overall, by increasing the agricultural yields, the prices drop and cultivated areas decrease. Canada, China, and the United States are examples of countries that have experienced a decline in cultivated areas (Rudel et al. 2009). It is crucial to motivate farmers to adopt restoration policies. As reduction of cultivated areas imposes significant social costs on small farmers, increasing farmers' participation is very important (Rudel et al. 2009).

Iran's government has paid attention to this plan. Some subsidies were considered to reduce the financial burden and damage to cultivated lands. In this way, farmers would tend to accept the changes of water allocation in the Urmia Lake basin (ULRP 2014). Changing 10% of irrigated cultivated land to dry farming lands is suggested; however, due to social resistance it is considered that this plan will take at least three years to affect the Urmia Lake basin.

Plan 3: impact of changing crop pattern on the Urmia Lake level

According to the local authorities and experts, farmers are not aware of the impacts of their practices on water consumption. For instance, if in one year, one of the crops, such as sugar beet, is economically profitable in West Azerbaijan, most farmers in the next harvesting year are willing to cultivate sugar beet. Due to the different water demands of crops, replacing crops with less water demand and more economic crops can be considered as a solution for conserving the water. In this study, it was assumed that planting alfalfa, watermelon, clover, sugar beet, and sainfoin in crop cultivated land and also planting apple, pear, walnut, and almond in the horticultural cultivated land section would be stopped or reduced significantly. Applying this plan may influence farmers' income, and thus, compensations for the damage caused by the crop pattern change were considered as subsidies.

Plan 4: impact of inter-basin water transfer on the Urmia Lake level

Another way of increasing water supply in a basin is water transferring. There are two major suggested water transfer projects for the Urmia Lake basin including Zaab and Aras. The Zaab basin was intended to transfer 700 MCM annually to the Urmia Lake basin to help with restoring the lake by supplying the demands partially. Also, 300 MCM is planned to be transferred annually from Aras basin into the lake.

Plan 5: impact of increasing irrigation efficiency and changing crop pattern on the Urmia Lake level

Simultaneously, increasing irrigation efficiency and changing crop pattern in the Urmia Lake basin was considered as a fifth plan.

Plan 6: impact of increasing irrigation efficiency, changing crop pattern, and inter-basin water transfer on the Urmia Lake level

The sixth plan involves increasing irrigation efficiency, changing crop pattern, and inter-basin water transfer in the Urmia Lake basin simultaneously.

Plan 7: impact of increasing irrigation efficiency, reducing cultivated area, and changing crop pattern on the Urmia Lake level

This plan is the accumulation of increasing irrigation efficiency, reducing the cultivated area, and changing crop pattern in the Urmia Lake basin.

Plan 8: impact of increasing irrigation efficiency, reducing cultivated area, changing crop pattern, and inter-basin water transfer on the Urmia Lake level

The simultaneous application of increasing irrigation efficiency, reducing the cultivated area, changing crop pattern, and inter-basin water transfer in the Urmia Lake basin is considered as an eighth plan.

Sustainability indices

Loucks et al. (2005) recommended the SI, including reliability, resiliency, and vulnerability performance criteria for quantifying and monitoring sustainability over time. In this study, SI is measured by these three indices. Reliability (Rel) is the probability that Urmia Lake's water level, X, is in a satisfactory state at time t, defined as Equation (5) (Aydin 2014): 
formula
(5)
Resiliency (Res) represents how fast the system recovers from a failure, defined as Equation (6) (Aydin 2014): 
formula
(6)
Vulnerability (Vul) is the magnitude or duration of an unacceptable state of the lake's water level in a certain time scale characterizing the average probability of failure of the water level to meet the ecological level, defined in Equation (7) (Aydin 2014): 
formula
(7)
where is the ecological water level of the lake and is the lake's level at time t. The definitions of the SI proposed by Sood & Ritter (2011) are used to calculate the SI (Equation (8)): 
formula
(8)

RESULTS AND DISCUSSION

According to the results of the LARS-WG model, the average temperature and precipitation changes of the watershed under the A1B, A2, and B1 scenarios are presented in Figures 13 and 14, respectively.

Figure 13

Temperature changes of the watershed under A1B, A2, and B1 scenarios (2011–2030).

Figure 13

Temperature changes of the watershed under A1B, A2, and B1 scenarios (2011–2030).

Figure 14

Precipitation changes of the watershed under A1B, A2, and B1 scenarios (2011–2030).

Figure 14

Precipitation changes of the watershed under A1B, A2, and B1 scenarios (2011–2030).

In developing the SD model, the calibrated pan coefficient was found to be 0.925. The SD model was simulated considering different plans and then the outcomes discussed. First, the model should be verified.

Verification and validation

Boundary-adequacy test

This test verifies whether the model structure is appropriate for the model purpose. In SD modeling, this test is done by evaluating the endogenous and exogenous variables of the model. As given in Table 3, the variables have been evaluated according to the model's purpose.

Table 3

Endogenous and exogenous variables of the Urmia Lake SD model

Endogenous variables
 
Exogenous variables 
Total supply GW Population Crop cultivated land 
Natural recharge Domestic demand Horticultural cultivated land 
Returned water Mineral demand Crop demand average 
Groundwater extraction Industrial demand Horticultural demand average 
Natural discharge Horticultural demand Number of mines 
Groundwater volume change Crop demand Number of industrial factories 
Wastewater percolation SW Agricultural demand Runoff 
Precipitation volume Total demand Volume–area relationship 
Perception height Total supply SW A1B scenario 
Margin area Inflow to basin A2 scenario 
Margin precipitation height Inflow B1 scenario 
Unmeasured surface inflow volume Surface water percolation Evaporation intensity 
Evaporation Wastewater GW percolation Lake's water level 
Urmia Lake volume Irrigation percolation Volume–elevation relationship 
Endogenous variables
 
Exogenous variables 
Total supply GW Population Crop cultivated land 
Natural recharge Domestic demand Horticultural cultivated land 
Returned water Mineral demand Crop demand average 
Groundwater extraction Industrial demand Horticultural demand average 
Natural discharge Horticultural demand Number of mines 
Groundwater volume change Crop demand Number of industrial factories 
Wastewater percolation SW Agricultural demand Runoff 
Precipitation volume Total demand Volume–area relationship 
Perception height Total supply SW A1B scenario 
Margin area Inflow to basin A2 scenario 
Margin precipitation height Inflow B1 scenario 
Unmeasured surface inflow volume Surface water percolation Evaporation intensity 
Evaporation Wastewater GW percolation Lake's water level 
Urmia Lake volume Irrigation percolation Volume–elevation relationship 

Extreme condition test

The structure in an SD model should allow the representation of an extreme combination of levels in the system. Examining the model structure for extreme conditions leads to increased confidence in a model's ability to behave rationally in a wide range of conditions.

To test the extreme condition in this SD model, the evaporation intensity (the only outflow of the lake) was assumed equal to zero. As shown in Figure 15, the lake's volume would rise progressively in the absence of evaporation.

Figure 15

Change of the Urmia Lake volume with and without evaporation.

Figure 15

Change of the Urmia Lake volume with and without evaporation.

Figure 16

Observed and estimated water level by the Urmia Lake SD model.

Figure 16

Observed and estimated water level by the Urmia Lake SD model.

Behavior-reproduction test

The generated model's behavior was investigated with the historical performance. Figure 16 shows the observed vs. estimated values for the lake's water level.

The values of R2 and RMSE were obtained as 0.90 and 0.37, respectively, which means that the simulation results were acceptable. After this successful model verification, the impact of restoration plans can be considered.

Impact of restoration plans

The impacts of these plans were considered once separately and then simultaneously to investigate if the lake could be saved by a combination of these plans. Table 4 summarizes the results.

Table 4

Impact of implying restoration plans on the Urmia Lake

Plans Description The effect of the plans (%) to achieve target ecological water level 
Plan 1 Increasing irrigation efficiency 11 
Plan 2 Reducing cultivated area 
Plan 3 Changing crop pattern 
Plan 4 Inter-basin water transfer 
Plan 5 Increasing irrigation efficiency and changing crop pattern 19 
Plan 6 Increasing irrigation efficiency, reducing cultivated area, changing crop pattern 24 
Plan 7 Increasing irrigation efficiency, reducing cultivated area, changing crop pattern 20 
Plan 8 Increasing irrigation efficiency, reducing cultivated area, changing crop pattern, inter-basin water transfer 25 
Plans Description The effect of the plans (%) to achieve target ecological water level 
Plan 1 Increasing irrigation efficiency 11 
Plan 2 Reducing cultivated area 
Plan 3 Changing crop pattern 
Plan 4 Inter-basin water transfer 
Plan 5 Increasing irrigation efficiency and changing crop pattern 19 
Plan 6 Increasing irrigation efficiency, reducing cultivated area, changing crop pattern 24 
Plan 7 Increasing irrigation efficiency, reducing cultivated area, changing crop pattern 20 
Plan 8 Increasing irrigation efficiency, reducing cultivated area, changing crop pattern, inter-basin water transfer 25 

Figure 17 shows every plan's impact on the Urmia Lake water level in the horizon of 2030.

Figure 17

Impact of restoration plans on the Urmia Lake water level in the period of 2016–2030.

Figure 17

Impact of restoration plans on the Urmia Lake water level in the period of 2016–2030.

Sustainability assessment

The lake sustainability was calculated based on the concepts of reliability (Equation (5)), resiliency (Equation (6)), and vulnerability (Equation (7)). The results of these evaluations are shown in Table 5.

Table 5

Watershed sustainability indices of considered restoration plans

Plan Vulnerability Reliability Resiliency Sustainability index 
Plan 1 2.17 
Plan 2 3.48 
Plan 3 2.78 
Plan 4 3.16 
Plan 5 1.10 
Plan 6 1.43 0.52 0.01 0.0021 
Plan 7 0.97 
Plan 8 0.72 0.50 0.02 0.0079 
Plan Vulnerability Reliability Resiliency Sustainability index 
Plan 1 2.17 
Plan 2 3.48 
Plan 3 2.78 
Plan 4 3.16 
Plan 5 1.10 
Plan 6 1.43 0.52 0.01 0.0021 
Plan 7 0.97 
Plan 8 0.72 0.50 0.02 0.0079 

The results showed that when implementing the individual plans none caused Urmia Lake to be restored. Due to different management practices, on average, at least 30% of water consumption should be reduced to restore the lake and make it sustainable. This result aligned with the ULRP research output (ULRP 2014).

According to Table 5, while the most vulnerable plans are reducing cultivated lands and inter-basin water transfer, respectively, among the hybrid plans, Plans 8 and 7 are the least vulnerable ones. The lake restoration was made possible by implementing the hybrid plans of increasing the irrigation efficiency, reducing cultivated area, changing crop pattern, and water transfer (Plan 6 and Plan 8). The results indicated that about eight years after applying Plan 8 (combination of the four individual plans mentioned above), the lake level will achieve the ecological level and remain sustainable in the future. According to Equation (8), the SI for Plans 6 and 8 were 0.0021 and 0.0079, respectively. Due to the higher resiliency and lower vulnerability of Plan 8, it is the most sustainable plan; by excluding this plan, Plan 6 was the most reliable one in restoring the lake.

The outcomes of the Drought Risk Management Plan for the Urmia Lake Basin were consistent with the results of the present study. According to the results of that research, in different levels of droughts, taking into account the parameters of a 50-year simulation and full water allocation, the lake level can reach 1,273.06 m. Based on the results, the impact of all management measures will lead to a 10 to 20% reduction in water consumption (CIWP & Tarbiat Modares University 2012). In fact, the combined restoration plans could be implemented along with the drought risk management measures to achieve the total 30% reduction in water consumption in the basin (with the assumption of the linear effect of plans) that is necessary for restoring the lake.

Also, the results of this research correspond with the study of Zarghami & AmirRahmani (2017). In their study, policies such as increasing irrigation efficiency, reducing agricultural area, seeding clouds, and water transfer were considered for restoring Urmia Lake. The study showed that among these policies, increasing irrigation efficiency and reducing agricultural area were the most effective policies, but none of them caused the lake to be restored (Zarghami & AmirRahmani 2017). As mentioned before, the agricultural sector is the most important part to manage. NGOs and local authorities should become involved and educate farmers about the impact of their essential role in saving the Urmia Lake ecosystem. In addition, government should consider paying some subsidies to farmers who have lost some of their income due to the management plans. These kinds of considerations may encourage farmers to adapt to restorations plans; however, ultimately, a facilitated process is vital for their cooperation.

CONCLUSION

Urmia Lake's SD model provided an integrated simulation of the complex case of the lake. In this way, the effect of various restoration plans could be seen before applying them to the basin. Therefore, this SD model can help all the managers and stakeholders to understand how to deal with the Urmia Lake crisis. Overall, it is hoped that the study will further demonstrate the value of SD modeling in similar management issues for environmental stewardship.

To improve the efficiency of the developed SD model, some general recommendations for effective management of Urmia Lake are listed here: constructing hydrometric stations near the lake to monitor the exact water inflow to the lake, considering the cooperation of stakeholders, and assessing the effect of climate change on the groundwater level variations. An assessment of the accuracy of the assumption that evaporation is the only outflow from the lake is suggested. A consideration of the salinity concentration effect on the water level and the impacts of this phenomenon are also suggested. Furthermore, the operation cost of plans should be considered and a comprehensive comparison including economical, technical, and operational aspects should be made in future studies.

Downscaling the results of GCMs using the LARS-WG model revealed that the Urmia Lake basin would have a warmer climate in the horizon of 2030, especially during summer. According to the results, precipitation will have an increasing trend in autumn and winter, and there will be a decreasing trend in spring and summer.

The Urmia Lake SD model describes variables that affect the lake level. Assessing the impact of various restoration plans revealed that, on average, water consumption should be reduced by at least 30% in order to restore the lake. Paying special attention to the agricultural sector as the most important influencing factor, the lake basin water balance is vital. The results revealed that no single restoration plan is sufficient to restore the lake. Among the hybrid restoration plans, that of increasing irrigation efficiency, reducing cultivated area, changing crop pattern, and inter-basin water transfer is the most sustainable plan. If this plan is adopted, the lake should achieve the desired ecological level after about eight years.

Through considering comprehensive factors, the proposed model can help watershed managers to take the necessary measures for sustainable restoration of this ecosystem. Therefore, the research provides an integrated simulation for this complex case that is also suggested for other cases where multidisciplinary water resources problems are concerned, especially those in semi-arid regions.

ACKNOWLEDGEMENTS

The authors would like to thank the Urmia Lake Restoration Program for providing watershed characteristics and required data. We also appreciate the comments of Dr. Masoud Bagherzadeh Karimi (Department of Environment), Dr. Nasim Saffari, Ata Akbari, Reza Samadzadeh Fahim, Mahsa Zarei (University of Tabriz), Mohammad Ali Olyaei (University of Tehran), and Kevin M. Smith (Tufts University).

REFERENCES

REFERENCES
Abbaspour
M.
&
Nazaridoust
A.
2007
Determination of environmental water requirements of Lake Urmia, Iran: an ecological approach
.
International Journal of Environmental Studies
6
,
161
169
.
http://dx.doi.org/10.1080/00207230701238416
.
Alipour
S
, .
2006
Hydrogeochemistry of seasonal variation of Urmia Salt Lake, Iran
.
Saline Systems
2
,
9
.
http://dx.doi.org/10.1186/1746-1448-2-9
.
Arnell
N. W.
,
Livermore
M. J. L.
,
Kovats
S.
,
Levy
P. E.
,
Nicholls
R.
,
Parry
M. L.
&
Gaffin
S. R.
2004
Climate and socio-economic scenarios for global-scale climate change impacts assessments: characterizing the SRES storylines
.
Global Environmental Change
14
(
1
),
3
20
.
http://dx.doi.org/10.1016/j.gloenvcha.2003.10.004
.
Aydin
Y. N.
2014
Scenario-based Sustainability Assessment to Provide Interactive Decision Support for the Long-Term Transition of Urban Water Supply Systems
.
PhD dissertation
,
University of Kaiserslautern
,
Kaiserslautern
,
Germany
.
CIWP
2008
Integrated management plan for Lake Urmia. Conservation of Iranian Wetlands Project. Department of Environment, Tehran
.
CIWP and Tarbiat Modares University
2012
Drought risk management plan for Lake Urmia basin, Conservation of Iranian Wetlands Project, Working Group on Sustainable Management of Water Resources and Agriculture, Regional Council of Lake Urmia Basin Management.
Ebrahimi Sarindizaj
E.
&
& Zarghami
M.
2018
Comparing effects of restoration policies under climate change by using system dynamics; Case study Urmia Lake ecosystem
.
Iran Water Resources Research
13
(
4
),
184
189
.
(in Persian)
.
Falkenmark
M.
2003
Water Management and Ecosystems: Living with Change
.
Global Water Partnership
,
Stockholm
,
Sweden
.
Faramarzi
N.
2012
Agricultural Water use in Lake Urmia Basin, Iran: an Approach to Adaptive Policies and Transition to Sustainable Irrigation Water use
.
Master's thesis
,
Uppsala University
,
Uppsala
,
Sweden
.
Fathian
F.
,
Dehghan
Z.
&
Eslamian
S.
2014
Analysis of water level changes in Lake Urmia based on data characteristics and non-parametric test
.
International Journal of Hydrology Science and Technology
4
,
18
38
.
http://dx.doi.org/10.1504/IJHST.2014.064398
.
Ferguson
I. M.
&
Maxwell
R. M.
2012
Human impacts on terrestrial hydrology: climate change versus pumping and irrigation
.
Environmental Research Letters
7
(
4
),
044022
.
Forrester
J. W.
1968
Principles of Systems
,
2nd edn
.
Productivity Press
,
MA, USA
.
Garrett
D. R.
&
Hoy
R. D.
1978
A study of monthly lake to pan coefficients using a numerical lake model
. In:
Proceedings of the Hydrology Symposium, 5–6 September
.
Institution of Engineers
,
Canberra
,
Australia, Barton
, pp.
145
149
.
Gohari
A.
,
Eslamian
S.
,
Mirchi
A.
,
Abedi-Koupaei
J.
,
Bavani
A. M.
&
Madani
K.
2013
Water transfer as a solution to water shortage: a fix that can backfire
.
Journal of Hydrology
491
,
23
39
.
http://dx.doi.org/10.1016/j.jhydrol.2013.03.021
.
Graziano
P.
&
Rizzi
P.
2016
Vulnerability and resilience in the local systems: the case of Italian provinces
.
Science of the Total Environment
553
,
211
222
.
http://dx.doi.org/10.1016/j.scitotenv.2016.02.051
.
Halbe
J.
,
Pahl-Wostl
C.
,
Sendzimir
J.
&
Adamowski
J.
2013
Towards adaptive and integrated management paradigms to meet the challenges of water governance
.
Water Science and Technology
67
(
11
),
2651
2660
.
Hassanzadeh
E.
,
Zarghami
M.
&
Hassanzadeh
Y.
2012
Determining the main factors in declining the Urmia Lake level by using system dynamics modeling
.
Water Resources Management
26
,
129
145
.
http://dx.doi.org/10.1007/s11269-011-9909-8
.
Iran Ministry of Energy, Water and Waste Water Macro Planning Bureau
.
2012
Kasperska
E.
,
Mateja-Losa
E.
&
Marjasz
R
, .
2014
Sensitivity analysis and optimization for selected supply chain management issues in the company – using system dynamics and Vensim
.
Journal of Entrepreneurship Management and Innovation (JEMI
),
9
(
2
),
29
44
.
Loucks
D. P.
,
Van Beek
E.
,
Stedinger
J. R.
,
Dijkman
J. P. M.
&
Villars
M. T.
2005
Water Resources Systems Planning and Management: an Introduction to Methods, Models and Applications
.
UNESCO
,
Paris
,
France
.
Nimmo
W. H. R.
1964
Measurement of evaporation by pans and tanks
.
Australian Meteorological Magazine
46
,
17
53
.
Quants
2014
Analysis of the Urmia Lake water balance: Part I – the amount of evaporation. http://www.quants.ir/
Rudel
T. K.
,
Schneider
L.
,
Uriarte
M.
,
Turner
B. L.
,
DeFries
R.
,
Lawrence
D.
,
Geoghegan
J.
,
Hecht
S.
,
Ickowitz
A.
,
Lambin
E. F.
&
Birkenholtz
T.
2009
Agricultural intensification and changes in cultivated areas, 1970–2005
.
Proceedings of the National Academy of Sciences
106
(
49
),
20675
20680
.
Semenov
M. A.
&
Stratonovitch
P.
2010
Use of multi-model ensembles from global climate models for assessment of climate change impacts
.
Climate Reseach
41
(
1
),
1
14
.
http://dx.doi.org/10.3354/cr00836
.
Semenov
M. A.
&
Stratonovitch
P.
2015
Adapting wheat ideotypes for climate change: accounting for uncertainties in CMIP5 climate projections
.
Climate Research
65
,
123
139
.
http://dx.doi.org/10.3354/cr01297
.
Sood
A.
&
Ritter
W. F.
2011
Developing a framework to measure watershed sustainability by using hydrological/water quality model
.
Water Resource and Protection
3
,
788
804
.
http://dx.doi.org/10.4236/jwarp.2011.311089
.
ULRP, Urmia Lake Restoration Programs
.
2014
Warren
C. R.
,
Burton
R.
,
Buchanan
O.
&
Birnie
R. V.
2016
Limited adoption of short rotation coppice: the role of farmers’ socio-cultural identity in influencing practice
.
Journal of Rural Studies
45
,
175
183
.
Zarghami
M.
&
AmirRahmani
M. A.
2017
System dynamics approach to simulate the restoration plans for Urmia Lake, Iran
. In:
Optimization and Dynamics with Their Applications
(
Akio
M.
ed.).
Springer, Singapore
.