The Conceptual Socio-Hydrological Based Framework For Water, Energy and Food Nexus

23 The current study introduces a conceptual socio-hydrological based framework for water-energy- 24 food (WEF) nexus. The proposed conceptual framework aims to investigate how farmers' dynamic 25 agricultural activities under different socio-economic conditions affect the WEF systems. The 26 WEF nexus model has been integrated with an Agent- Based Model, reflecting the farmers’ 27 agricultural activities. Furthermore, the agent-based model benefits from Association Rule Mining 28 to define farmer agents’ agricultural decision -making in various conditions. The processes within 29 the WEF nexus are simultaneously physical, socio-economic, ecological, and political. Indeed, 30 there are interrelated interactions among the mentioned processes in ways that have not yet 31 properly delineated, mapped, or even perceived. Thus, for obtaining sustainable outcomes, the 32 current study attempts to investigate trade-offs among natural resources and social systems in the 33 WEF nexus approach. The proposed framework may provide more in-depth future insights for 34 policy-makers through capturing bidirectional feedbacks among farmers and WEF systems. 35 Furthermore, the proposed socio-hydrological WEF nexus framework can be adapted and applied 36 to various societies and environments to provide more in-depth future insights for policy-makers 37 through capturing bidirectional feedbacks among farmers and WEF systems. 38

Food production consumes about 90% of the freshwater resources, and approximately 30% of global energy use for production and related supply chains (Zhang and Vesselinov, 2017 have not yet delineated, mapped, or even perceived (Howells et al., 2013). In this regard, nexus 82 approaches require strong transdisciplinary and interdisciplinary joint efforts (Newell et al., 2019). 83 Thus, for obtaining sustainable outcomes, it is necessary to investigate socio-ecological resilience,  This research aims to deliver a WEF nexus framework for investigating how farmers' dynamic 89 decisions and activities can shape the co-evolutionary trajectories of humans and WEF systems. 90 As a novel strategy, this research benefits from the integration of a socio-hydrological model with 91 a WEF nexus model to incorporate farmers' dynamic activities within nexus in a more detailed 92 manner. One of the significant points of the proposed socio-hydrological WEF nexus framework 93 is its ability to be adapted and exercised for various societies and environments. Thus, the proposed 94 framework may open a window to have more precise future insights for policy-makers through 95 capturing bidirectional feedbacks among farmers and WEF systems. For assessing the efficiency given region, the crops being cultivated in almost 92% of the farms include wheat, barley, corn, 117 alfalfa, tomato, onion, and sugar beet. In many countries, agricultural activities have a significant role in enhancing the economy, and 121 providing food security of the societies. Therefore, one way to improve the economic condition is 122 through elevating agricultural productivity. As discussed earlier, studies on WEF nexus mainly 123 explore the optimized strategies leading to the most beneficial outcomes for all food, water, and 124 energy sectors. However, regardless of how dynamic farmers may behave, these entities mainly land-use change, and crop choice may alter the condition of water resources, it can be inferred that 129 farmers have the potential to affect the whole WEF nexus system as well (see Fig. 1).
130 Fig. 1 The conceptual framework of socio-hydrological WEF nexus system 132 133 As presented in Fig. 1, the socio-hydrological loop has been jointed within the WEF nexus 134 framework through available water resources. Socio-hydrological loops can contain a wide variety 135 of anthropogenic processes that may cause severe drought in available water resources. Therefore, 136 to capture anthropogenic dynamics, a hybrid socio-hydrological simulation model consisting of    161 The ARM is a data mining method, which aims to extract causal structures, frequent patterns,  (1)

Association Rule Mining (ARM)
Where C is confidence and S is support, and in which E, F ⊆ Y, where Y represents a dataset of 174 transactions.

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In this study, to have more accurate ABM, which can mimic farmers' decision-making based on Yekom, 2016a, 2016b). The mentioned questionnaire is shown in Table 1.  demand. And, the economic data contains the production costs and selling prices of the agricultural 196 products. The mentioned input data may be considered as the boundary conditions for the ABM 197 environment; therefore, they will be updated beginning each agricultural year, according to the 198 gathered data.

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In the 2nd phase, the agents initiate the decision-making processes for the upcoming agricultural Then, farmers try to obtain their remainder irrigation water need from surface water resources, 233 Zarrineh-Rud River. Also, it has been considered that farmers use groundwater as an additional 234 source when accessible surface water is less than their actual demand: Where, is the combined illegal and legal groundwater withdrawal for irrigation, is farm's 238 net monthly crop consumptive use minus effective precipitation, and is withdrawn surface 239 water.

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Meanwhile, agricultural expenditures based on the selected crop type and farm's size will be paid 241 from the agents' deposits: Where, 1 is the remaining money in the deposit after payment, 0 is the deposited money, and 245 is average agricultural expenditures of the crop i per hectare. 246 Next, the crop yield for the agents, , is determined as a function of supplied water per hectare 247 (ha) of farm as follow: where is the actual yield (kg/ha), is the maximum yield (kg/ha), is the yield response

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Using the social data gathered in the field studied, nine behavioral rules have been extrapolated by 261 ARM for the main crop types of the study area. As stated earlier, the agents choose their behavioral 262 profile from the rules listed in Table 2. Each of the rules has some degree of correlation with the characteristics of the farmer agents   As shown in Fig. 3, wheat is the most planted crop type in the simulation period. The underlying 274 reason for this matter may be due to its suitableness (i.e., less required characteristics (Table 2) been used for some agents to represents actual farmers farming dryland wheat averagely 23% of 286 total agricultural areas and an averagely 7% dryland corn every agricultural year.

287
As stated earlier, the agricultural activities in ABM affect the hydrological and energy systems.

288
The primary energy usages in agricultural activities, which have been considered for this study, 289 contain on-farm and off-farm consumptions (Fig. 4).

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A significant amount of energy is needed to utilize groundwater resources for irrigation. Required 305 fuel energy for irrigating one hectare of farmland depends on groundwater elevation, irrigation 306 system type, crop water need, and equipment's power consumption. Eq. 9 represents the required 307 energy for pumping water: For calculating the dynamic head, the required data have been provided through field studies. It 317 should be noted that for this case study, the Moody diagram has been used to calculate the friction 318 loss ( ). In the next step required energy for the pump ( ) may be calculated through Eq. 10:

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(10) = (( 2 ) + 0.5 2 2 + 2 ) − (( 1 ) + 0.5 1 2 + 1 ) + Where P is pressure, is specific weight, v is speed, z is height, and g is the gravitational 321 acceleration. Since flow speed in the given aquifer is negligible, it has been considered that 1 = 322 2 = 0. Groundwater irrigation required energy has been obtained for each crop type, considering 323 1 = 0.8 ‫و‬ 2 = 0.2 (see Table 3).        In this regard, the amount of consumed fuel in each operation have been calculated and presented 363 in Table 7. It should be noted the required fuel for harvesting machinery has been considered equal 364 to the relative operation according to the type of the employed tractor.

367
Based on the field studies and the farming system of the given region, the relative energy of the production of seed, pesticide, and 368 fertilizer have been determined and presented in Table 8. 369 Table 8. Energy inputs of seeds, pesticides, and fertilizers in the cultivation of one hectare of different crops in the study area  According to Fig. 6, 2014 was one of the aridest years of the study area. Respectively, the amount 374 of groundwater extractions was at its highest in the study period. Therefore, more energy has been 375 consumed for pumping groundwater, which significantly raised the energy consumption in 2014.

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On the other hand, energy consumption was at its lowest rate in 2011, which may be due to lower 377 groundwater extractions and lower cultivation areas of crop types such as alfalfa and tomato (see

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It has been argued, demand for freshwater, energy, and food will be significantly increased due to 406 population growth and following rapid anthropogenic changes and developments. Respectively, 407 the capacity of water resources to sustain environmental and human needs is of paramount concern 408 of policy-makers, as its condition, directly and indirectly, affects food production, energy systems, 409 and ecosystems. Thus, due to the interdependence of water-food-energy (WEF), many studies        The conceptual framework of socio-hydrological WEF nexus system The schematic overview of socio-hydrological WEF nexus simulation model The overview of energy nexus model The water need for 2009 to 2016 agricultural years Figure 6