Bankruptcy methods are straightforward practical methods to address the problem of allocating limited sources of water to different users in regions where claims exceed assets. In the present study, three levels of restoration for the Hawr-Al-Azim wetland (35, 50, and 100%) and two agricultural-related scenarios, maintaining the current cultivation area and decreasing the area under cultivation, were considered and assessed using classical bankruptcy methods. The results showed that, because of climatic conditions and agricultural demands, full wetland restoration was out of reach and led to minimum satisfaction levels for agricultural beneficiaries. The results also showed that the modified bankruptcy method led to the highest satisfaction levels for beneficiaries in the scenario of maintaining the current cultivation area. In addition, the percentage of the water supply was increased by applying the scenario of crop restriction in the conditions of the full restoration of the wetland; for example, in the Abbas Plain region, this increase was achieved by almost 10–15% in all methods. On the other hand, decreasing the area under cultivation shifted the allocation problem in the basin to a non-bankruptcy one.

  • Up to 50% of the wetland can be restored with the cooperation of all stakeholders and sustainable management of water resources.

  • Given the Gini coefficient, constrained equal award is the best way to achieve equitable distributions among all stakeholders.

  • The modified bankruptcy method is the best way to maximize the levels of stakeholder satisfaction.

Graphical Abstract

Graphical Abstract
Graphical Abstract

In recent years, due to the increasing scarcity of global freshwater resources, issues such as improving production methods to manage the water demand, developing new technologies and methods to make irrigation more efficient, and making water conservation a lifestyle and priority have become matters of great importance (Nelson et al. 2015; Okamoto et al. 2015; Sohn & Nam 2015). Alongside population growth, the competition over limited freshwater resources between agricultural consumption and environmental demands such as protecting freshwater ecosystems has intensified (Boehlert & Jaeger 2010). Such competitions could lead to serious political arguments and disputes among international players, highlighting the importance and value of using conflict management techniques to address and resolve water conflict issues (Mianabadi et al. 2014).

According to Acharya et al. (2006), Gardiner & Simmons (1992), and Fisher (2012), conflict, which does not always have negative effects on a system, occurs when there are differences in interests and goals or priorities between people, groups, or organizations. Conflict can be recognized as a part of the feedback loops of society and, if properly managed, can improve the performance of a social system. On the contrary, repressive responses can intensify conflicts and may lead to violent social crises (Pondy 1967). Although power and policy are important factors in resolving water conflicts, principled arguments play crucial roles in this regard. Resolving conflict is an attempt to manage disputes through constructive and collaborative consultation to accomplish an ultimate satisfactory compromise solution and achieve a long-term commitment to the solution (Gray 1989). An optimal resolution to a conflict is achieved when all players are satisfied with a just outcome (Fisher 2015).

Although methods based on the principles of modeling, simulation, and multi-objective optimization of complex systems are useful tools to address water allocation problems, there is no general agreement on the optimal approach (Reddy & Kumar 2009; Nouiri et al. 2015; Chang et al. 2016). The first necessary step to select an appropriate approach to resolve a conflict is finding out at which of the following five stages of development the conflict is – latent, perceived, felt, manifest, or aftermath. In this regard, the first stage of the conflict, which is recognized as the latent stage, is competing for rare resources (Pondy 1967).

Game theory, the study of mathematical models to resolve disputes among rational actors, consists of different techniques used to resolve a conflict based on the conditions of players and their strategic situation. In game theory, the bankruptcy problem is a fair allocation problem that arises when the demands of all actors exceed the available assets (Herrero & Villar 2001). Methods used to solve bankruptcy problems could be effective in different situations due to their cooperative nature and their attention to the benefits of all actors (Madani et al. 2014). Successful applications of bankruptcy theory in resolving water conflicts have been reported in the literature. Zarezadeh et al. (2012), using conventional bankruptcy rules such as proportional rule (PRO), constrained equal awards (CEA), constrained equal losses (CEL), and adjust proportional (AP), suggested allocation schemes for a transboundary river system supplying water to eight Iranian provinces. Madani et al. (2014) employed four conventional bankruptcy methods to develop a novel river bankruptcy approach and applied it to a transboundary river system in Iran. Results showed that the acceptability of solutions is sensitive to both water demand and availability. Sheikhmohammady et al. (2010) used bankruptcy procedures to predict the outcome of negotiations among the five rival states over the water, natural gas, and petroleum resources of the Caspian Sea. In a comprehensive study, Beard (2011) investigated the relationship between bankruptcy theory and the river-sharing problem. Li et al. (2018) developed a bankruptcy method to address the water allocation problem among stakeholders in a basin in China. They concluded that their proposed method prioritizes high-demand cities with higher water-use efficiency. To resolve the water disputes among the stakeholders in the Euphrates River, Mianabadi et al. (2014) introduced a new bankruptcy rule and compared it with four conventional methods. They found that their proposed rule, based on the UN Watercourses Convention (1997), is more effective in addressing river-sharing problems.

The mentioned studies collectively suggest that the water allocation problem in regions where water scarcity is a significant cause of social, environmental, and political disputes can be effectively addressed using bankruptcy methods. The present study investigates different water allocation schemes in the Karkheh River Basin, in southwestern Iran. This river provides water for both agricultural land in the basin and the ecosystem of the Hawr-Al-Azim Wetland, which is located at the outlet of the basin. The wetland is essentially the Iranian portion of the Hawizeh Wetlands, a complex of wetlands that straddle the border between Iran and Iraq. The Hawizeh Wetlands, the Central Wetlands, and the Hammar Wetlands combine to form the internationally well-known Mesopotamian Wetlands. The Karkheh River in Iran and the Tigris River in Iraq are the dominant sources of water for the Hawizeh Wetlands. In recent years, the poor management of water resources in both countries, diverting water from both rivers to irrigate farmland, and prolonged and intense droughts in the region have resulted in critical environmental issues threatening the wetland ecosystems in the area. Given the fact that all countries whose decisions and activities directly affect the flow of the rivers discharged into the Mesopotamian Wetlands are responsible for the emerging environmental issues, all riparian countries should consider sustainable water supply and demand management as a priority. The present study's focus is to use bankruptcy methods to find a satisfactory resolution to the water conflicts that may arise in the Karkheh River Basin in Iran between agricultural and environmental sectors. Furthermore, different bankruptcy methods are evaluated, and the best method in each situation is selected according to the circumstances and differences.

The Hawr-Al-Azim Wetland

The Hawr-Al-Azim Wetland is a portion of the Hawizeh Wetlands located within the territory of Iran, between 47° 57′ and 47° 16′ east longitude and 31° 53′ and 41° north latitude, at the outlet of the Karkheh River Basin in the border of the Azadegan plain (Figure 1). The Karkheh River, the third largest river in Iran with a length of about 900 km, rises in the mountains of western Iran and flows southwest toward the Iran–Iraq border where it ends at the Hawizeh Wetlands. The maximum height of the Karkheh river basin is 3,645 m in the Garin, and its minimum height is 3 m in the Hawr-Al-Azim Wetland. The average rainfall in this basin is 474.82 mm, which is equivalent to 24,406.37 million m3 of water. This area's highest volume of rainfall is 1,120.40 million m3 related to the Azadegan plain, and its lowest volume of rainfall is 35.63 million m3 related to the Shirvan plain. In 2001, an embankment dam was constructed to impound the Karkheh River to generate hydropower, reduce flood damages, and provide irrigation water to 62,267 ha of farmland. The Karkheh Dam caused a drastic reduction of freshwater in the Hawr-Al-Azim Wetland and, consequently, a significant decrease in the wetland area. During the Iran–Iraq war and because of military reasons, a series of dykes were constructed, and the wetland was divided into five separate parts. The first two parts are fed directly by the Karkheh River, and their excess water is then drained into the remaining three parts. The fourth and the fifth parts also receive the water drained from their adjacent agricultural land (Figure 2).
Figure 1

The Karkheh River Basin.

Figure 1

The Karkheh River Basin.

Close modal
Figure 2

Five parts of the wetland under study.

Figure 2

Five parts of the wetland under study.

Close modal

Crop data used in the study

The cropland irrigated with the water released by the Karkheh Dam consists of four plains and six irrigation schemes (Table 1). Wheat, paddy rice, watermelon, and maize account for 92.2% of the total cropland area. Other crops, such as alfalfa and cucumber, are also grown in the area to a lesser extent. The list of cultivated crops and the area allocated to each crop in 2016 were derived from the Iran Water Resources Management Company database. For each plain, the net irrigation requirements for each crop were extracted from the NETWAT software (APERDRI – Agricultural Planning 2016). NETWAT is essentially a database of net irrigation requirements for 609 agricultural plains in Iran. NETWAT is developed by the Iranian Agricultural Planning, Economic and Rural Development Research Institute (APERDRI) using the FAO-56 Penman-Monteith (FAO-PM) equation and long-term average meteorological data. NETWAT output is known as the ‘Iranian National Water Document’ and is considered a reliable reference in water resources management projects in Iran. Based on an irrigation efficiency of 38%, the gross irrigation requirements for each crop were estimated (Table 1). The crop yield, physical water productivity, and economic productivity data in the study area, provided by the Iranian Ministry of Agriculture, are also presented in Table 1. The gross irrigation requirements for each scheme are presented in Table 2.

Table 1

Irrigation requirements, yield, and productivity of crops in irrigation schemes

PlainIrrigation schemeCropCultivated area (ha)Irrigation requirements (m3/ha)Yield (ton/ha)Economic productivity (106 Iranian Rial/m3)Physical productivity (kg/m3)
Dasht-Azadegan Hamoudi Wheat 7,387 2,690 4,406 0.008 0.622 
Paddy rice 2,814 8,480 4,141 0.006 0.186 
Alfalfa 12,440 14,011 0.005 0.428 
Watermelon 41 4,370 29,456 0.003 2.561 
Cucumber 13 3,800 22,402 0.007 2.240 
Clover 38 6,550 3,500 0.002 0.203 
Potato 316 2,970 26,326 0.014 3.368 
Hamidiyeh Wheat 10,909 3,010 4,406 0.007 0.556 
Paddy rice 3,366 9,040 4,141 0.006 0.174 
Alfalfa 13,320 14,011 0.005 0.400 
Cucumber 12 4,140 22,402 0.006 2.056 
Arayez-Bagheh Dosalogh Wheat 7,807 3,160 4,406 0.007 0.530 
Paddy rice 42 10,050 4,141 0.005 0.157 
Alfalfa 14,590 14,011 0.005 0.365 
Barley 1,000 2,570 2,835 0.004 0.419 
Clover 7,080 7,170 0.005 0.385 
Onion 5,230 29,671 0.006 2.156 
Cucumber 21 4,410 22,402 0.006 1.930 
Watermelon 1,085 5,040 29,456 0.002 2.221 
Maize 600 6,870 6,868 0.004 0.380 
Potato 131 3,480 26,326 0.012 2.875 
Kosar Wheat 3,291 3,160 4,406 0.007 0.530 
Paddy rice 981 10,050 4,141 0.005 0.157 
Watermelon 5,040 29,456 0.002 2.221 
Maize 107 6,870 6,868 0.004 0.380 
Cucumber 4,410 22,402 0.006 1.930 
Evan Evan Paddy rice 7,340 4,141 0.007 0.214 
Maize 1,831 5,100 6,868 0.006 0.512 
Other products  3,260  0.025 0.000 
Dasht-Abbas Dasht-Abbas Wheat 13,443 2,250 4,406 0.010 0.803 
Watermelon 1,500 3,930 29,456 0.003 3.073 
Maize 4,500 5,100 6,868 0.006 0.552 
Cucumber 1,000 3,260 22,402 0.008 2.817 
PlainIrrigation schemeCropCultivated area (ha)Irrigation requirements (m3/ha)Yield (ton/ha)Economic productivity (106 Iranian Rial/m3)Physical productivity (kg/m3)
Dasht-Azadegan Hamoudi Wheat 7,387 2,690 4,406 0.008 0.622 
Paddy rice 2,814 8,480 4,141 0.006 0.186 
Alfalfa 12,440 14,011 0.005 0.428 
Watermelon 41 4,370 29,456 0.003 2.561 
Cucumber 13 3,800 22,402 0.007 2.240 
Clover 38 6,550 3,500 0.002 0.203 
Potato 316 2,970 26,326 0.014 3.368 
Hamidiyeh Wheat 10,909 3,010 4,406 0.007 0.556 
Paddy rice 3,366 9,040 4,141 0.006 0.174 
Alfalfa 13,320 14,011 0.005 0.400 
Cucumber 12 4,140 22,402 0.006 2.056 
Arayez-Bagheh Dosalogh Wheat 7,807 3,160 4,406 0.007 0.530 
Paddy rice 42 10,050 4,141 0.005 0.157 
Alfalfa 14,590 14,011 0.005 0.365 
Barley 1,000 2,570 2,835 0.004 0.419 
Clover 7,080 7,170 0.005 0.385 
Onion 5,230 29,671 0.006 2.156 
Cucumber 21 4,410 22,402 0.006 1.930 
Watermelon 1,085 5,040 29,456 0.002 2.221 
Maize 600 6,870 6,868 0.004 0.380 
Potato 131 3,480 26,326 0.012 2.875 
Kosar Wheat 3,291 3,160 4,406 0.007 0.530 
Paddy rice 981 10,050 4,141 0.005 0.157 
Watermelon 5,040 29,456 0.002 2.221 
Maize 107 6,870 6,868 0.004 0.380 
Cucumber 4,410 22,402 0.006 1.930 
Evan Evan Paddy rice 7,340 4,141 0.007 0.214 
Maize 1,831 5,100 6,868 0.006 0.512 
Other products  3,260  0.025 0.000 
Dasht-Abbas Dasht-Abbas Wheat 13,443 2,250 4,406 0.010 0.803 
Watermelon 1,500 3,930 29,456 0.003 3.073 
Maize 4,500 5,100 6,868 0.006 0.552 
Cucumber 1,000 3,260 22,402 0.008 2.817 
Table 2

Irrigation requirements of each irrigation scheme

PlainIrrigation schemeIrrigation requirements (million m3)
Dasht-Azadegan Hamoudi 118.89 
Hamidiyeh 167.10 
Arayez-Baghe Dosalogh 99.89 
Kosar 55.37 
Evan Even 54.69 
Dasht-Abbas Dasht-Abbas 152.08 
PlainIrrigation schemeIrrigation requirements (million m3)
Dasht-Azadegan Hamoudi 118.89 
Hamidiyeh 167.10 
Arayez-Baghe Dosalogh 99.89 
Kosar 55.37 
Evan Even 54.69 
Dasht-Abbas Dasht-Abbas 152.08 

Hawr-Al-Azim wetland environmental demands

The environmental demands of the wetland were estimated according to the available data on the area as well as the depth and volume of the five different parts of the wetland. Inverted pyramids were used to approximate the three-dimensional geometric shape of different lagoon parts. The dimensions of these pyramids are adjusted to simulate the total volume of the wetland (Table 3). Annual volumes of water released from the Karkheh River were also reported by the Iran Water Resources Management Corporation. The 2003–2017 time series is presented in Table 4.

Table 3

Dimensions of the inverted pyramids used to simulate the geometric shape of the wetland

PartLevel (m) Area ()Volume (million m3)Depth (m)
7.25 92 214 2.33 
7.00 281 1,102 3.92 
1 and 2 7.13 373 1,316 3.53 
4.00 147 245 1.67 
3.75 293 365 1.25 
3.75 182 223 1.23 
The whole wetland 2.16 995 2,149 2.16 
PartLevel (m) Area ()Volume (million m3)Depth (m)
7.25 92 214 2.33 
7.00 281 1,102 3.92 
1 and 2 7.13 373 1,316 3.53 
4.00 147 245 1.67 
3.75 293 365 1.25 
3.75 182 223 1.23 
The whole wetland 2.16 995 2,149 2.16 
Table 4

Annual outflow of the Karkheh Dam

Year2003–20042004–20052005–20062006–20072007–20082008–20092009–2010
Outlet (million m32,866.5 4,644.6 5,002.2 5,078.2 4,669.0 2,769.0 1,624.2 
Year 2010–2011 2011–2012 2012–2013 2013–2014 2014–2015 2015–2016 2016–2017 
Outlet (million m31,598.7 2,239.5 1,560.9 1,694.0 1,623.0 1,162.5 2,500.0 
Year2003–20042004–20052005–20062006–20072007–20082008–20092009–2010
Outlet (million m32,866.5 4,644.6 5,002.2 5,078.2 4,669.0 2,769.0 1,624.2 
Year 2010–2011 2011–2012 2012–2013 2013–2014 2014–2015 2015–2016 2016–2017 
Outlet (million m31,598.7 2,239.5 1,560.9 1,694.0 1,623.0 1,162.5 2,500.0 

Simulating the geometry of the wetland reveals that its total volume measures 2,149 million m3. The annual received precipitation and the daily evaporation from the wetland's surface are 212 and 6.68 mm, respectively, so the total annual environmental demands of the wetland would be 4,500 million m3. The wetland consists of five different parts, and its filling mechanism is as follows. The Karkheh River directly feeds the two first parts (Reservoirs 1 and 2), then the excess water is delivered to the third. Half of the two last parts (Reservoirs 4 and 5) are filled by the outflow of Reservoir 3 and the other half by the drained water from adjacent agricultural land. Based on precipitation and evaporation data and the filling mechanism of the wetland, the environmental demands are presented in Table 5.

Table 5

Total environmental demands of the wetland in different levels of restoration, taking into account the precipitation and evaporation

PartDepth (m)Area in each part (km2)The wetland area (km2)Restoration (%)Volume in each part (million m3)Wetland's water demands (million m3)
1 and 2 0.00 18.0 18.00 0.02 90.20 90.20 
0.40 27.0 27.00 0.03 118.28 118.28 
1.00 44.0 44.00 0.04 177.80 177.80 
2.00 80.0 80.00 0.08 326.00 326.00 
3.00 128.0 128.00 0.13 543.90 543.90 
4.00 187.0 187.00 0.18 874.40 874.40 
5.00 257.0 257.00 0.25 1,306.60 1,306.60 
6.00 338.0 338.00 0.33 1,868.40 1,868.40 
6.60 391.0 391.00 0.39 2,272.00 2,272.00 
0.00 5.0 396.00 0.39 19.94 2,291.94 
1.00 28.4 419.37 0.41 88.99 2,360.99 
2.00 70.8 461.83 0.46 242.98 2,514.98 
3.00 132.4 523.40 0.52 510.02 2,782.02 
3.20 147.0 538.00 0.53 579.04 2,851.04 
0.00 10.0 548.00 0.54 35.88 2,886.92 
1.00 81.0 619.00 0.61 238.22 3,089.26 
2.00 219.0 757.00 0.75 729.42 3,580.46 
0.00 7.0 764.00 0.75 23.52 3,603.98 
1.00 51.3 808.33 0.80 151.28 3,731.74 
1.80 116.2 873.20 0.86 377.25 3,957.71 
PartDepth (m)Area in each part (km2)The wetland area (km2)Restoration (%)Volume in each part (million m3)Wetland's water demands (million m3)
1 and 2 0.00 18.0 18.00 0.02 90.20 90.20 
0.40 27.0 27.00 0.03 118.28 118.28 
1.00 44.0 44.00 0.04 177.80 177.80 
2.00 80.0 80.00 0.08 326.00 326.00 
3.00 128.0 128.00 0.13 543.90 543.90 
4.00 187.0 187.00 0.18 874.40 874.40 
5.00 257.0 257.00 0.25 1,306.60 1,306.60 
6.00 338.0 338.00 0.33 1,868.40 1,868.40 
6.60 391.0 391.00 0.39 2,272.00 2,272.00 
0.00 5.0 396.00 0.39 19.94 2,291.94 
1.00 28.4 419.37 0.41 88.99 2,360.99 
2.00 70.8 461.83 0.46 242.98 2,514.98 
3.00 132.4 523.40 0.52 510.02 2,782.02 
3.20 147.0 538.00 0.53 579.04 2,851.04 
0.00 10.0 548.00 0.54 35.88 2,886.92 
1.00 81.0 619.00 0.61 238.22 3,089.26 
2.00 219.0 757.00 0.75 729.42 3,580.46 
0.00 7.0 764.00 0.75 23.52 3,603.98 
1.00 51.3 808.33 0.80 151.28 3,731.74 
1.80 116.2 873.20 0.86 377.25 3,957.71 

Bankruptcy methods

Disputes among actors involved in an allocation problem in which resources are insufficient to satisfy all demands can be modeled as a bankruptcy problem defined as follows:
(1)
(2)
(3)
(4)
where n is the number of beneficiaries, C is the total amount of demand, E is the total amount of available resources, ci is the demand of ith beneficiary, and xi is the number of resources allocated to the ith beneficiary. This problem can be addressed through different methods such as proportional rule (PRO), CEA, CEL, Talmud solution (TAL), and random arrival (RA). A brief explanation of these bankruptcy methods is presented here. More details of these methods can be found in Mianabadi et al. (2014), Thomson (2003), and Li et al. (2018).

Proportional rule (PRO)

This rule is among the most straightforward methods to solve a bankruptcy problem. In this method, an allocation coefficient, which is defined as the ratio of total resources to total demand, is used to determine the share of each beneficiary:
(5)
(6)
where γ is the allocation coefficient.

The CEA method

In this method, the share allocated to each beneficiary is determined as the minimum of its demand and the ratio of total resources to the number of all actors:
(7)
(8)

The CEA method allocates equal shares to each beneficiary, but none of the actors receives more than its demand.

The CEL method

Contrary to the CEA method, CEL determines the deficit (CE) and allocates equal shares of the deficit to each actor:
(9)
(10)
where γ is the average of all deficits.

The Talmud solution

This method, which is a combination of the CEA and CEL methods, uses the following procedure to determine the share of each player:
(11)
where CEA {0.5ci, E} denotes the CEA solution of the problem if all demands are reduced in half, and the total amount of available resources remains constant; and CEL{0.5ci, E − 0.5C} is the CEL solution of the problem if all demands are reduced in half and the total amount of available resources reduced to E − 0.5C.

The RA method

In this method, a specific arrangement of all beneficiaries is considered. Then, one by one, the demand of each beneficiary is completely satisfied until the supply runs out. This process is done for all possible permutations of the set of actors. Finally, the method allocates to each player the average value of the amounts received in each permutation:
(12)

The modified bankruptcy method

The total amount of available resources (E) and the demand claimed by each player (ci) are the main variables in an ordinary bankruptcy problem. However, in water conflict problems, other factors such as the contribution of agents to E can affect the level of satisfaction of players involved in the problem. Ansink (2009) proposed the sequential sharing rule (SSR) in which the flows generated within each player territory are considered the player's contribution to the total amount of available surface water resources. To resolve the river-sharing problem, the SSR is based on the conventional proportional rule (PRO) but assigns different values of allocation coefficient to players. A shortcoming of SSD is that it assigns greater allocation coefficients to upstream players (Ansink & Weikard 2012). To address this shortcoming, Mianabadi et al. (2014) and Li et al. (2018) proposed modifications to the SSR method, in which the total deficit (CE) is distributed to players according to their ci/C, contribution (fi), and efficiency (ei). In the modified bankruptcy method, the deficit assigned to each player is calculated as follows:
(13)
where D is the total deficit, and fi and ei are defined as follows:
(14)
(15)
where Qi is the mean annual flow generated in the territory of the ith player. Finally, the amount of surface water resources allocated to the ith beneficiary is calculated as follows:
(16)
where s is the minimum level of satisfaction. In the present study, s was considered 60%.

Allocation scenarios

The first scenario: not to impose limitations on the cultivation area

To satisfy the environmental demands of the Hawr-Al-Azim Wetland, in addition to managing the water resources in the Karkheh River Basin, it is essential to consider appropriate measures in both agricultural and industrial sectors. If the Iranian Ministry of Agriculture imposes no limitations on the cultivation area in the region and no measures are taken to reduce the agricultural water consumption, water volumes equal to the figures presented in Table 2, whose sum exceeds the total available water resources, should be allocated to the farmland, resulting in a bankruptcy problem. Three allocation levels for the wetland (35, 50, and 100%) were considered, and based on these levels, the water allocated to each beneficiary was calculated. The wetland-filling percentage is a function of its geometry as well as the volume of inflow during a year. In the present study, based on the water that the Karkheh Dam could provide, three precipitation year types, including wet year (more than 5,100 million m3), normal year (almost equal to 3,000 million m3), and drought year (less than 1,888 million m3), were considered. Given the fact that about 15% of the wetland (half of the fourth and fifth parts) is filled by the water drained from the upstream irrigated land, to fully (100%) restore the wetland, 3,957 million m3 of water, the volume that corresponds to 85% restoration in Table 5, is required. Accordingly, 1,893 and 1,100 million m3 of water would be required to restore 50 and 35% of the wetland, respectively.

The second scenario: imposing limitations on the cultivation area

The total gross irrigation requirements to meet agricultural needs in the region are about 648 million m3. As Table 1 suggests, the region's largest proportion of the arable land is dedicated to wheat production, a profitable crop with high productivity and low water requirements. On the contrary, although rice is among the most cultivated crops in the region, its physical water productivity is the least. The net irrigation requirements for rice range from 8,480 to 10,050 m3/ha (Table 1), which are relatively high. To improve water management in the Karkheh River Basin, local water authorities should impose restrictions on rice cultivation and encourage farmers to grow alternative crops with lower water requirements. In terms of economic productivity, watermelon is the worst crop to grow. The economic water productivity for watermelon ranges from US$0.022 to 0.030 per m3. The volume of water used to produce 1 kg of this crop ranges from 0.3 to 0.4 m3, consuming about 200 million m3 of water in a year. Watermelon is a good candidate to be eliminated from the cropping pattern of the region or to be replaced with crops that are more efficient in using water.

Bankruptcy is one of the most effective methods for water allocation under water shortage conditions. Li et al. (2018) used the weighting distribution method and productivity coefficients to achieve the best allocation. Prioritization was performed based on more water requirements, water resources, and productivity. Therefore, by weighing the two computational indicators, they provided a solution to create a negotiation between the stakeholders. Mianabadi et al. (2014) also used a similar method of bankruptcy, which resulted in the allocation of water with greater efficiency and satisfaction of the beneficiaries in the studied area. The results of using this approach in this study are given subsequently.

The first scenario

This scenario was analyzed according to three restoration levels for the Hawr-Al-Azim wetland, 35% in drought years, 50% in normal years, and 100% in wet years. Analyzing the present situation of the basin (Table 5) reveals that 1,100 million m3 of water is needed to meet 35% of the wetland's water demand (20% from the Karkheh River and 15% from drained agricultural water). Since the flow of the Karkheh River has largely declined in recent years, for the present study, an average value of 1,888 million m3 (corresponding to drought years) was considered for the annual river outflow. After subtracting the environmental water requirements of the river (235 million m3) and the amount of water allocated to the wetland (1,100 million m3), 553 million m3 of water remains to be allocated to the agricultural sector. However, according to the cultivation area and the cropping pattern of the region, the total gross irrigation requirement is 648 million m3. Using the five above-mentioned ordinary bankruptcy methods and the modified method, the water allocated to each beneficiary (i.e., each irrigating scheme) and the percentage of demand met were calculated (Table 6).

Table 6

The results produced by bankruptcy methods for a drought year in the first scenario

Irrigation schemeDemand (million m3)PRO
CEA
CEL
Talmud
RA
Modified method
xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)
Hamoudi 118.89 101.23 85.15 92.16 77.52 102.8 86.47 102.8 86.47 100.00.09 84.19 102.98 86 
Hamidiyeh 167.1 142.28 85.15 92.16 55.15 151.01 90.37 151.01 90.37 148.3 88.75 152.18 91 
Dosalogh 99.89 85.05 85.14 92.16 92.26 83.8 83.89 83.8 83.89 81.09 81.18 83.75 83 
Kosar 55.37 48.85 88.22 55.37 100 41.28 74.55 41.28 74.55 46.41 83.82 39.61 71 
Evan 54.69 46.57 85.15 54.69 100 38.6 70.58 38.6 70.58 44.27 80.95 38.78 70 
Dasht-Abbas 152.08 129.49 85.15 92.16 60.6 135.99 89.42 135.99 89.42 133.28 87.64 136.13 89 
Average of P (%) — 85.66 80.92 82.54 82.54 84.42 82.25 
Gini index — 0.22 0.10 0.26 0.26 0.24 0.26 
Irrigation schemeDemand (million m3)PRO
CEA
CEL
Talmud
RA
Modified method
xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)
Hamoudi 118.89 101.23 85.15 92.16 77.52 102.8 86.47 102.8 86.47 100.00.09 84.19 102.98 86 
Hamidiyeh 167.1 142.28 85.15 92.16 55.15 151.01 90.37 151.01 90.37 148.3 88.75 152.18 91 
Dosalogh 99.89 85.05 85.14 92.16 92.26 83.8 83.89 83.8 83.89 81.09 81.18 83.75 83 
Kosar 55.37 48.85 88.22 55.37 100 41.28 74.55 41.28 74.55 46.41 83.82 39.61 71 
Evan 54.69 46.57 85.15 54.69 100 38.6 70.58 38.6 70.58 44.27 80.95 38.78 70 
Dasht-Abbas 152.08 129.49 85.15 92.16 60.6 135.99 89.42 135.99 89.42 133.28 87.64 136.13 89 
Average of P (%) — 85.66 80.92 82.54 82.54 84.42 82.25 
Gini index — 0.22 0.10 0.26 0.26 0.24 0.26 

P, percent of demand allocated; PRO, proportional rule; CEA, constrained equal awards; CEL, constrained equal losses; RA, random arrival.

As Table 6 suggests, the CEA method leads to the lowest Gini coefficient, a factor measuring the equality of the distribution of available resources. It means that the available water in the basin is almost equally distributed if the CEA method is used to solve the conflict problem. However, the average percentage of the demand met in the CEA method is the lowest among all methods. It is also observed that similar results are produced by the CEL, TAL, and the modified method because they are designed to allocate equal shares of deficit to each actor. The modified bankruptcy method allocates greater levels of satisfaction to the actors who are more productive and have greater contributions to available resources, so, in the current situation, Hamidiyeh and Dasht-Abbas irrigation schemes receive the greatest levels of satisfaction.

If the wetland allocation level is 50% (35% from the Karkheh River and 15% from drained agricultural water), according to Table 5, 1,893 million m3 of water is needed to meet the wetland's water demand. In this case, the meteorological condition in the basin is assumed to be normal, and the amount of water provided by the Karkheh Dam is 3,000 million m3, so 633 million m3 of water can be supplied to the agricultural sector (the environmental water requirements of the river is assumed to be 473 million m3, i.e., 25% of the river flow). The results of the bankruptcy methods, in this case, are presented in Table 7.

Table 7

The results produced by bankruptcy methods for a normal year in the first scenario

Irrigation schemeDemand (million m3)PRO
CEA
CEL
Talmud
RA
Modified method
xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)
Hamoudi 118.89 115.79 97.39 105.51 88.75 116.07 97.63 116.07 97.63 116.07 97.63 116.36 97.0 
Hamidiyeh 167.1 162.74 97.39 105.51 63.14 164.28 98.31 164.28 98.31 164.28 98.31 164.73 98.0 
Dosalogh 99.89 97.29 97.4 99.89 100 97.07 97.18 97.07 97.18 97.07 97.18 97.32 97.0 
Kosar 55.37 55.37 100 55.37 100 54.55 98.52 54.55 98.52 54.55 98.52 52.86 95.0 
Evan 54.69 53.26 97.39 54.69 100 51.87 94.84 51.87 94.84 51.87 94.84 52.16 95.0 
Dasht-Abbas 152.08 148.11 97.39 105.51 69.38 149.26 98.15 149.26 98.15 149.26 98.15 149.54 98.0 
Average of P (%) — 97.83 86.87 97.44 97.44 97.44 96.7 
Gini index — 0.22 0.12 0.23 0.23 0.23 0.22 
Irrigation schemeDemand (million m3)PRO
CEA
CEL
Talmud
RA
Modified method
xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)
Hamoudi 118.89 115.79 97.39 105.51 88.75 116.07 97.63 116.07 97.63 116.07 97.63 116.36 97.0 
Hamidiyeh 167.1 162.74 97.39 105.51 63.14 164.28 98.31 164.28 98.31 164.28 98.31 164.73 98.0 
Dosalogh 99.89 97.29 97.4 99.89 100 97.07 97.18 97.07 97.18 97.07 97.18 97.32 97.0 
Kosar 55.37 55.37 100 55.37 100 54.55 98.52 54.55 98.52 54.55 98.52 52.86 95.0 
Evan 54.69 53.26 97.39 54.69 100 51.87 94.84 51.87 94.84 51.87 94.84 52.16 95.0 
Dasht-Abbas 152.08 148.11 97.39 105.51 69.38 149.26 98.15 149.26 98.15 149.26 98.15 149.54 98.0 
Average of P (%) — 97.83 86.87 97.44 97.44 97.44 96.7 
Gini index — 0.22 0.12 0.23 0.23 0.23 0.22 

P, percent of demand allocated; PRO, proportional rule; CEA, constrained equal awards; CEL, constrained equal losses; RA, random arrival.

The results presented in Table 7 suggest that if there is no increase in the area under cultivation, the average satisfaction level would be improved, and the Gini coefficient would decrease, showing more equality among beneficiaries.

According to Table 5, 3,957 million m3 of water is needed to completely (100%) meet the environmental need of the wetland (85% from the Karkheh River and 15% from drained agricultural water). In a wet year, the water provided by the Karkheh Dam would be 5,100 million m3, and 350.75 million m3 of water would be available to allocate to the agricultural sector (the environmental water requirements of the river are assumed to be 3,957 million m3). The results corresponding to this situation are presented in Table 8.

Table 8

The results produced by bankruptcy methods for a wet year in the first scenario

Irrigation schemeDemand (million m3)Pro
CEA
CEL
Talmud
RA
Modified method
xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)
Hamoudi 118.89 64.15 53.96 58.46 49.17 69.01 58.05 69.01 58.05 64.04 53.86 71.33 60.00 
Hamidiyeh 167.1 90.17 53.96 58.46 34.99 117.22 70.15 117.22 70.15 90.72 54.29 120.22 71.94 
Dosalogh 99.89 53.9 53.96 58.46 58.52 50.01 50.07 50.01 50.07 53.27 53.33 59.93 60.00 
Kosar 55.37 30.96 55.91 55.37 100 7.49 13.53 7.49 13.53 31.02 56.02 33.22 60.00 
Evan 54.69 29.51 53.96 54.69 100 4.81 8.8 27.34 49.99 29.5 53.94 32.81 59.99 
Dasht-Abbas 152.08 82.06 53.96 58.46 38.44 102.2 67.2 101.78 66.93 82.21 54.06 91.24 59.99 
Average of P (%) — 54.28 63.52 44.63 51.45 54.25 61.99 
Gini index — 0.22 0.01 0.41 0.35 0.22 0.25 
Irrigation schemeDemand (million m3)Pro
CEA
CEL
Talmud
RA
Modified method
xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)
Hamoudi 118.89 64.15 53.96 58.46 49.17 69.01 58.05 69.01 58.05 64.04 53.86 71.33 60.00 
Hamidiyeh 167.1 90.17 53.96 58.46 34.99 117.22 70.15 117.22 70.15 90.72 54.29 120.22 71.94 
Dosalogh 99.89 53.9 53.96 58.46 58.52 50.01 50.07 50.01 50.07 53.27 53.33 59.93 60.00 
Kosar 55.37 30.96 55.91 55.37 100 7.49 13.53 7.49 13.53 31.02 56.02 33.22 60.00 
Evan 54.69 29.51 53.96 54.69 100 4.81 8.8 27.34 49.99 29.5 53.94 32.81 59.99 
Dasht-Abbas 152.08 82.06 53.96 58.46 38.44 102.2 67.2 101.78 66.93 82.21 54.06 91.24 59.99 
Average of P (%) — 54.28 63.52 44.63 51.45 54.25 61.99 
Gini index — 0.22 0.01 0.41 0.35 0.22 0.25 

P, percent of demand allocated; PRO, proportional rule; CEA, constrained equal awards; CEL, constrained equal losses; RA, random arrival.

As Table 8 suggests, if the wetland restoration level is 100%, the level of satisfaction of agricultural stakeholders involved in the problem declines dramatically. The CEA method, which allocates equal shares to each beneficiary, leads to a Gini coefficient of 0.01, showing near-perfect equality in the distribution of available water. However, the level of satisfaction provided by all methods is very low. The modified bankruptcy method, in which the level of satisfaction is fixed at a predefined value, resulted in levels less than 60% (the predefined percent) for all irrigation schemes except Hamidiyeh; however, the method considered a minimum level of 60% for all schemes. It means that restoring the wetland to its full level could lead to dissatisfaction among agricultural stakeholders and even new social crises. Figure 3 shows the performance of different methods in supplying the water demand of each irrigation area as a percentage. Comparing the results of the first scenario showed that the highest percentage of water supply was observed in the condition of 50% of wetland restoration.
Figure 3

The percentage of water supply in different conditions of wetland restoration in the first scenario.

Figure 3

The percentage of water supply in different conditions of wetland restoration in the first scenario.

Close modal

The second scenario

The second scenario was defined to improve the current situation of the wetland in wet years when excess water is available. This scenario is based on reducing the area of the cultivated land in the study area and changing the cropping pattern of the region to replace crops with high water requirements with more efficient ones. Similar to the first scenario, three levels for the wetland restoration (35, 50, and 100%) were considered. The results are presented in Tables 9,1011.

Table 9

The results produced by bankruptcy methods for 35% of wetland restoration in the second scenario

Irrigation schemeDemand (million m3)PRO
CEA
CEL
Talmud
RA
Modified method
xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)
Hamoudi 82.25 83.71 100 82.25 100 83.86 100 83.86 100 82.25 100 83.83 100 
Hamidiyeh 127.06 129.32 100 92.86 73.08 128.67 100 128.67 100 127.06 100 128.73 100 
Dosalogh 92.14 93.78 100 92.14 100 93.75 100 93.75 100 92.14 100 93.88 100 
Kosar 42.37 43.12 100 42.37 100 43.98 100 43.98 100 42.37 100 44.05 100 
Evan 54.63 55.6 100 54.63 100 56.24 100 56.24 100 54.63 100 56.35 100 
Dasht-Abbas 144.88 147.46 100 92.86 64.09 146.49 100 146.49 100 144.88 100 146.62 100 
Average of P (%) — 100 89.53 100 100 100 100 
Gini index — 0.23 0.14 0.22 0.22 0.23 0.22 
Irrigation schemeDemand (million m3)PRO
CEA
CEL
Talmud
RA
Modified method
xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)
Hamoudi 82.25 83.71 100 82.25 100 83.86 100 83.86 100 82.25 100 83.83 100 
Hamidiyeh 127.06 129.32 100 92.86 73.08 128.67 100 128.67 100 127.06 100 128.73 100 
Dosalogh 92.14 93.78 100 92.14 100 93.75 100 93.75 100 92.14 100 93.88 100 
Kosar 42.37 43.12 100 42.37 100 43.98 100 43.98 100 42.37 100 44.05 100 
Evan 54.63 55.6 100 54.63 100 56.24 100 56.24 100 54.63 100 56.35 100 
Dasht-Abbas 144.88 147.46 100 92.86 64.09 146.49 100 146.49 100 144.88 100 146.62 100 
Average of P (%) — 100 89.53 100 100 100 100 
Gini index — 0.23 0.14 0.22 0.22 0.23 0.22 

P, percent of demand allocated; PRO, proportional rule; CEA, constrained equal awards; CEL, constrained equal losses; RA, random arrival.

Table 10

The results produced by bankruptcy methods for 50% of wetland restoration in the second scenario

Irrigation schemeDemand (million m3)PRO
CEA
CEL
Talmud
RA
Modified method
xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)
Hamoudi 82.25 95.83 100 82.25 100 97.21 100 97.21 100 82.25 100 96.20 100 
Hamidiyeh 127.06 148.05 100 105.51 83.04 142.02 100 142.02 100 127.06 100 142.00 100 
Dosalogh 92.14 107.36 100 92.14 100 107.10 100 107.10 100 92.14 100 107.00 100 
Kosar 42.37 49.37 100 42.37 100 57.33 100 57.33 100 42.37 100 57.20 100 
Evan 54.63 63.65 100 54.63 100 69.59 100 69.59 100 54.63 100 69.80 100 
Dasht-Abbas 144.88 168.81 100 105.51 72.83 159.84 100 159.84 100 144.88 100 161.00 100 
Average of P (%) — 100 92.64 100 100 100 100 
Gini index — 0.23 0.17 0.19 0.19 0.23 0.19 
Irrigation schemeDemand (million m3)PRO
CEA
CEL
Talmud
RA
Modified method
xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)
Hamoudi 82.25 95.83 100 82.25 100 97.21 100 97.21 100 82.25 100 96.20 100 
Hamidiyeh 127.06 148.05 100 105.51 83.04 142.02 100 142.02 100 127.06 100 142.00 100 
Dosalogh 92.14 107.36 100 92.14 100 107.10 100 107.10 100 92.14 100 107.00 100 
Kosar 42.37 49.37 100 42.37 100 57.33 100 57.33 100 42.37 100 57.20 100 
Evan 54.63 63.65 100 54.63 100 69.59 100 69.59 100 54.63 100 69.80 100 
Dasht-Abbas 144.88 168.81 100 105.51 72.83 159.84 100 159.84 100 144.88 100 161.00 100 
Average of P (%) — 100 92.64 100 100 100 100 
Gini index — 0.23 0.17 0.19 0.19 0.23 0.19 

P, percent of demand allocated; PRO, proportional rule; CEA, constrained equal awards; CEL, constrained equal losses; RA, random arrival.

Table 11

The results produced by bankruptcy methods for 100% of wetland restoration in the second scenario

Irrigation schemeDemand (million m3)PRO
CEA
CEL
Talmud
RA
Modified method
xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)
Hamoudi 82.25 52.98 64.41 58.45 71.06 50.03 60.83 50.03 60.83 52.6 63.95 52.3 63.59 
Hamidiyeh 127.06 81.85 64.42 58.45 46 94.84 74.64 94.84 74.64 82.55 64.97 95.22 74.94 
Dosalogh 92.14 59.35 64.41 58.45 63.44 59.92 65.03 59.92 65.03 59.2 64.25 59 64.03 
Kosar 42.37 27.29 64.41 42.37 100 10.15 23.96 10.15 23.96 26.32 62.12 25.42 60.00 
Evan 54.63 35.19 64.42 54.63 100 22.41 41.02 22.41 41.02 33.99 62.22 32.78 60.00 
Dasht-Abbas 144.88 93.33 64.42 58.45 40.34 112.66 77.76 112.66 77.76 95.34 65.81 111.85 77.20 
Average of P (%) — 64.41 70.14 57.20 57.20 63.88 57.42 
Gini index — 0.23 0.05 0.35 0.35 0.24 0.27 
Irrigation schemeDemand (million m3)PRO
CEA
CEL
Talmud
RA
Modified method
xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)xi (million m3)P (%)
Hamoudi 82.25 52.98 64.41 58.45 71.06 50.03 60.83 50.03 60.83 52.6 63.95 52.3 63.59 
Hamidiyeh 127.06 81.85 64.42 58.45 46 94.84 74.64 94.84 74.64 82.55 64.97 95.22 74.94 
Dosalogh 92.14 59.35 64.41 58.45 63.44 59.92 65.03 59.92 65.03 59.2 64.25 59 64.03 
Kosar 42.37 27.29 64.41 42.37 100 10.15 23.96 10.15 23.96 26.32 62.12 25.42 60.00 
Evan 54.63 35.19 64.42 54.63 100 22.41 41.02 22.41 41.02 33.99 62.22 32.78 60.00 
Dasht-Abbas 144.88 93.33 64.42 58.45 40.34 112.66 77.76 112.66 77.76 95.34 65.81 111.85 77.20 
Average of P (%) — 64.41 70.14 57.20 57.20 63.88 57.42 
Gini index — 0.23 0.05 0.35 0.35 0.24 0.27 

P, percent of demand allocated; PRO, proportional rule; CEA, constrained equal awards; CEL, constrained equal losses; RA, random arrival.

As stated previously, if the minimum level of restoration is reached (35%), 553 million m3 of water would be available to allocate to agriculture. Furthermore, imposing limitations on cultivation areas in the region could decrease the gross irrigation requirements for all six schemes from their current value (648 million m3) to 543 million m3, roughly equal to available water. As a result, we are not faced with a bankruptcy problem anymore, and all beneficiaries are expected to meet their water demand. The results of 50% restoration show that the available water exceeds the total demand, so employing the CEA method, which is based on the allocation of equal shares to beneficiaries, results in levels of satisfaction less than 100% for two irrigation schemes and also the CEL, TAL, and the modified bankruptcy method are not applicable. The PRO method is based on the ratio of total resources to total demand, and when the denominator is less than the numerator, excess water is allocated to each beneficiary, making PRO an inappropriate method. On the contrary, the RA method allocates optimal volumes of water to beneficiaries.

In the case of full restoration (100%), water scarcity is observed, but, compared to the first scenario in which the area under cultivation is not limited, more satisfaction is reached. In this case, just two beneficiaries, Kosar and Evan irrigation schemes, do not meet the minimum level of satisfaction (i.e., 60%). Figure 4 shows the performance of different methods in supplying the water demand for each irrigation area as a percentage. The second scenario investigates different restoration percentages for a wet year condition. Therefore, the different results presented in this scenario have shown that the lowest percentage of water supply in the condition of wetland restoration is 100%.
Figure 4

The percentage of water supply in different conditions of wetland restoration in the second scenario.

Figure 4

The percentage of water supply in different conditions of wetland restoration in the second scenario.

Close modal

In the present study, to resolve the water conflicts in the Karkheh River Basin, southwestern Iran, six bankruptcy methods, including proportional rule, CEA, CEL, Talmud, RA, and a modified bankruptcy method, which consider the productivity of agents and their contribution to available resources, were used. The main focus of our study was to supply the environmental demands of the Iranian portion of the Hawizeh Wetlands, the Hawr-Al-Azim Wetland, in different climatic conditions. The results showed that the full wetland restoration needs 3,957 million m3 volume of water, and considering the current water resource situation in the basin, supplying this amount of water is out of reach. To restore 50% of the wetland in the present climatic conditions, sustainable collaboration between all beneficiaries, plus appropriate management of the water resources in the basin, is necessary.

The results showed that higher levels of actors' satisfaction would be achieved if the modified bankruptcy method was used to resolve the bankruptcy problem, but in the case of the non-bankruptcy state, the RA method led to the best results. Two scenarios were analyzed: not to impose limitations on the cultivation area and to impose limitations on the cultivation area. Based on the total demand and available resources, the basin faces a bankruptcy problem in the first scenario. Three restoration levels for the Hawr-Al-Azim Wetland (35, 50, and 100%) were considered in this situation. For all restoration levels, the CEA method, which tries to allocate equal shares to all beneficiaries, generated lower satisfaction levels than the other methods. So, if the goal is to achieve a just distribution among all beneficiaries, CEA is the best method to solve the bankruptcy problem according to the Gini coefficient. However, the modified bankruptcy method is the best if the goal is to maximize satisfaction levels. The method generated the highest satisfaction levels for the stakeholders of the Hamidiyeh and Dasht-Abbas irrigation schemes, with the highest contribution to available resources. It is worth noting that the CEL and TAL methods generate similar results to the modified method. In the first scenario, when the wetland's restoration level is 100%, the levels of stakeholders' satisfaction are much lower than the other restoration levels (35 and 50%). This could lead to a crisis since the modified method generates the minimum satisfaction levels and the minimum demand cannot be met. In the second scenario (imposing limitations on the cultivation area) and when the wetland's restoration level is 35 or 50%, the total supply exceeds the total demand and the Karkheh River Basin is not faced with a bankruptcy problem. So, the CEL, TAL, and the modified method are not applicable, all based on the water supply deficit. Full demand satisfaction is obtained using the CEA method, except for the stakeholders of the Hamidiyeh and Dasht-Abbas irrigation schemes. It is concluded that the PRO and RA generate reasonable results in the mentioned situation. However, while PRO allocates excess water to the beneficiaries, it could be concluded that RA is the best method. In the second scenario and when the wetland's restoration level is 100%, the basin would face a bankruptcy problem; however, the satisfaction levels are higher than in the first scenario.

The results from both scenarios showed that restoring the Iranian portion of the Hawizeh Wetlands requires the cooperation of all the beneficiaries in the region. Due to precipitation and Karkheh Dam outflow declines, the agricultural requirements are not completely met. This could lead to conflicts among stakeholders in the agricultural sector. The results also showed that imposing limitations on cultivation areas and replacing crops with high water requirements with more efficient ones could be effective ways to address the problem of agricultural water scarcity in the region. However, full wetland restoration requires changing water management policies in the Karkheh River Basin to increase the volume of water that the Karkheh Dam could release.

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

All authors contributed to the study conception and design and material preparation, and data collection and analysis. All authors read and approved the final manuscript.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.

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