The management of water resources requires a correct understanding of the simultaneous management of food and energy resources. The framework of water–food–energy correlation with the approach of sustainability of resources and uses analyzes the combined management and exploitation of water, food, and energy resources with the help of scenario planning. In addition to sustainability concepts, environmental costs such as the emission of carbon dioxide from fossil fuels and its impact on the environment are also discussed. In this research, according to the five defined indicators and based on the potential of using solar energy and the possibility of exploiting renewable energy sources such as solar energy, various management scenarios have been developed. After examining the virtual water management model developed in the Hunan basin as a case study, the development of the water–food–energy nexus model and its calibration, and four scenarios including improving water use efficiency, energy saving, increasing food productivity, and nexus sustainability were developed. The results showed that the nexus strategy can provide sustainability goals according to the weight of each component. After the combined scenario, improving the efficiency of water consumption can be the component with the highest priority in the decision-making model in dry areas.

  • The water–food–energy nexus is evaluated as a conceptual approach for achieving sustainable management.

  • Improving water use efficiency, energy saving, increasing food productivity, and nexus sustainability were considered.

  • The developed approach provides a significant contribution to achieving regional sustainable development goals.

The twenty-first century is witnessing an explosion in environmental changes, global population, agricultural land disintegration, and geopolitical instabilities (Salem et al. 2022). The development of industry, population growth, and increasing use of water, energy, and food resources has caused a challenge to sustainable management. In addition to this, climate change and the reduction of ecosystem services have caused problems in the use of water resources, food security, and energy supply (Xiang et al. 2016). The use of renewable energy such as solar energy instead of fossil fuels helps to better manage water and food resources. The use of this type of energy in the agricultural energy supply sector should be considered as an alternative policy for energy supply. Considering the increasing severity and extent of droughts in the world and the need to evaluate solutions to reduce the effects of drought on the production of agricultural products, as well as the reduction of surface water resources and the lowering of the underground water level (Cai & Rosegrant 2004; Liu et al. 2019), as well as its consequences on the economic and social life of the people, there is a need to propose solutions for sustainable development in energy, water, and food sectors (Chen et al. 2020).

Water–energy–food nexus

Water, energy, and food are three key factors in managing these critical conditions in the future, and improving decision-making systems is not possible without paying attention to them (Xia & Pahl-Wostl 2012; Li et al. 2020). Some researchers have reported that the frameworks developed so far have not been able to sufficiently ensure the goals of sustainable development (Biggs et al. 2015). To create a new approach for considering the dynamic behavior and evaluation of water–energy–food nexus, a system dynamic model platform was used by El Gafy et al. (2017) in Egypt under different scenarios and alternatives. The main goals were determination of water and energy footprints of crop production, estimating the virtual water and energy import and export and the national water and energy saving balance due to trade of agricultural commodities. The main finding of the study was the necessity of considering the water–food–energy nexus in developing national strategies. Biggs et al. (2015) provided a critical review of water–food–energy nexus approaches and identified potential linkages with sustainable livelihoods theory and practice to deepen the understanding of the interrelated dynamics between human populations and the natural environment. In this regard, the concept of ‘Environmental Livelihood Security,’ which includes the balance between the supply of natural resources and the human demand for the environment, was created to promote sustainability. The result of this structure was an integrated framework with the capacity to measure and monitor the environmental livelihood security of entire systems through accounting for water, energy, and food required for livelihoods at multiple spatial scales and organizational levels. Albrecht et al. (2018) provided a knowledge base of existing approaches and promoted further development of analytical methods that align with nexus thinking. The literature review in this research showed that the concept of linkage can be identified using approaches that focus on four key features including innovation, social and political context, collaboration, and implementation in policy and practice. The use of interdisciplinary and hybrid methods and the combination of transdisciplinary or collaborative approaches in this field have been suggested as promising approaches. Interdisciplinary and hybrid approaches that combine quantitative and qualitative methods from different disciplines can contribute to the physical and social aspects of water, energy, and food systems. It has been recommended that analysis should target policy and societal scales.

Katyaini et al. (2021) showed that the main priorities for managing the water–food nexus in arid and semi-arid regions of India are to reduce the overexploitation of groundwater and to investigate the uncertainty of rainfall, which directly affects agriculture. Cazcarro & Dilekli (2021) evaluated the strategies of future food and energy demands as well as direct and indirect resource uses to generate a substantial number of economic and environmental scenarios. The clear policy implication was that, in all scenarios, processes of energy transition, raw material use reduction, and recycling must be strengthened. Orimoloye (2022) evaluated the gaps and implementations of water, energy, and food nexus in published articles between 2015 and 2021. Findings showed that nexus modeling should be combined with influencing factors including population growth, environmental change impacts (including climate change), climate change adaptation and climate resilience regimes, biodiversity loss, and sustainable nature. Issues such as the protection of water resources and management strategies and tools or mechanisms for the use of water assets and agricultural innovations under the commitments of sustainable use should be considered (Salem et al. 2022). Norouzi (2022) presented a conceptual model of water–food–energy nexus using a dynamic system for Iran and analyze the factors affecting this interconnection. The developed model applied to improve the economic productivity index of water–energy–food for 2005–2018. Results showed that for reducing the risk of energy–water–food planning, these three parts should be examined in regular operation and coordination.

Virtual water

One of the emerging concepts in water-scarce countries is the concept of ‘virtual water’ to determine agricultural and industrial production strategies. As a new approach to addressing water scarcity and security issues, the concept of virtual water can be used to support sustainable development in water-scarce areas. With growing consumption, the virtual water trade has become an important element in the water sustainability of a nation (Goswami & Nishad 2015). Virtual water flow analysis based on water footprint has important insights for sustainable economic management in the agricultural sector and modification of water resources management patterns (Katyaini et al. (2021). The concept of virtual water was first proposed by Allen (1993) to refer to the amount of water consumed in the production of goods and services. Since 2002, this concept has gained widespread attention worldwide. In China, virtual water has been proposed as a potential approach for water resource conservation, especially in water-scarce areas such as the northwest.

Goswami & Nishad (2015) estimated the virtual water trades of two populous nations, India and China, to present certain quantitative measures and time scales. Results showed that the export of virtual water alone can lead to the loss of water sustainability. In general, water sustainability has emerged as a major global concern, with uncertainties and added vulnerability due to climate change. An emerging issue of growing importance and debate in the context of water and food sustainability is the virtual water trade. Virtual trade of water has become an important component of global fresh water demand and supply and has resulted in globalization of water resources. Wang et al. 2020 investigated interprovincial virtual water trade in Gansu Province, China, using an input–output method and spatial flow patterns. Based on the obtained results, it was found that in the current structure, virtual water is mainly exported to develop coastal areas and their adjacent provinces or other water-rich areas. Therefore, for sustainable development, the current business model should be adjusted to reduce virtual water output and at the same time increase its input to achieve balanced economic development and water resource security. Khaneiki et al. (2022) showed that virtual water has historically been an adaptation strategy that enabled some arid regions to develop a prosperous economy without putting pressure on their scarce water resources in arid central Iran. This article concluded that a similar model of virtual water can remedy the ongoing water crisis in central Iran, where groundwater reserves are overexploited, and many rural and urban centers are teetering on the edge of socio-ecological collapse.

Achieving water security to overcome water shortage has been the goal and subject of attention of researchers in recent years. In this process, it is necessary to evaluate the mutual connection and inherent dependencies between water and its related factors and systems. Past researches have provided programs for simulation, optimization, uncertainty analysis, and sensitivity analysis of water, food, and energy nexus, but their flexibility and reliability have not been paid enough attention. Based on the concept of virtual water, this article deals with the connection between water, food, and energy. Planning has been done based on the structure of nexus and virtual water in the form of five criteria of reliability, durability, vulnerability, adaptability, and resiliency.

Water–food–energy nexus description

Different interpretations of the water–food–energy nexus have been expressed in recent years, but in general, it is the reaction between the subsystems with respect to the larger system. In fact, this definition shows the interaction between the three parts of water, food, and energy to reach the complex characteristics of a universal integrated system. Nexus is considered as the factor of simultaneous dependence between the energy and water sectors and the simultaneous coupling of production, methods, distribution, and the way of using resources, and when it comes to food, it becomes a complete cycle. Evaluating system performance only by considering its sub-sections and considering each sub-section alone does not lead to improvement and sustainable development in the overall system. Therefore, there is a need to explain indicators to evaluate the performance of the system and the way of supply and demand in the water, energy, and food sectors. The correlation of water–energy–food was proposed as a new approach to issues related to the interconnected management of water-energy-food resources. Many efforts have been made to examine this model from various aspects, including resource consumption flow calculations and technology performance evaluation. Based on the predefined structure, five evaluation indicators have been considered to measure the efficiency of the system.

Resiliency

Resiliency refers to the possibility of the system returning to the desired state after a failure. Since the systems of various resources, including water resources, have a state of uncertainty and its conditions are not constant in many cases, reversibility is considered a statistical characteristic to check the flexibility of the system to changing conditions or even changing management policies. For a specific water resources system, the reversibility is equal to the number of time steps that the system returns from the failure state to the desired state during the simulation period and to the total number of steps in which the system is deficient.
(1)
where S is the reliability of the simulated model (0 ≤ S ≤ 1), SUPt is the water supplied at the time of t (m3), and DEMt is the amount of demand at the time of t (m3), and n is the number of time steps.

Durability

Durability shows how likely the water allocated to the consumer will meet its needs. It shows the number of time steps in which the consumer's needs are fully satisfied during the simulation period, out of the total number of the simulation period.
(2)
where D is the durability of the simulated model (0 ≤ D ≤ 1).

Reliability

The concept of reliability is equal to the probability that the water allocated to the consumer will completely satisfy its needs, or in other words, to what extent the system can continue to work without failure. Therefore, reliability can be defined as the total volume of water supplied to the total volume of water required for a consumer in the simulation time period.
(3)

where R is the reliability of the simulated model (0 ≤ R ≤ 1).

Vulnerability

This concept specifies the amount of shortages in a system and indicates the severity of failures in a system and can be used as the average of failures and the probability of the shortage in one or more periods exceeding a certain limit. In this article, it is defined as the ratio of total shortages to the number of steps in which shortages occurred divided by the total amount of demand that existed in a simulation period.
(4)
where V is the vulnerability of the simulated model (0 ≤ V ≤ 1).

Adaptability

A water allocation system should be able to adapt according to serious needs. Therefore, the following relationship is considered to address this concept.
(5)
where R is the adaptability of the simulated model (0 ≤ A ≤ 1).

Best–worst method

The best–worst method is based on pairwise comparisons and inspired by the hierarchical analysis method. In this method, the basis of work is choosing the best and worst criteria or options. If it is assumed that a decision matrix with n criteria is considered, in this matrix, a pairwise comparison between the criteria should be made and the relative importance of each of the indicators should be evaluated. Similar to the method of paired comparisons in hierarchical analysis, the equality of two indicators means equal importance of two criteria in relation to each other and the relative importance of two times, i.e., the first criterion is two times more important compared to the second criterion. Therefore, two concepts, (1) the principle of consistency of paired comparisons and (2) the principle of invertibility of the decision matrix, are the basis of weighting. Two factors of direction and intensity of preference of one criterion over another can be applied. The direction of preference is determined by the decision maker, but the main challenge is the intensity of preference and the superiority of one criterion over another, which causes inconsistency in paired comparisons.

In this article, sets of criteria for the problem were determined for weighting at the first step. The best and worst indicators were determined by the decision maker. Then, the preference of the best criterion over the rest of the indicators from numbers 1 to 9 was determined.
(6)
where is the preference of the best criterion a compared to the other criteria and .
The preference for other criteria over the worst criterion was determined using numbers between 1 and 9. Therefore, the vector of other measures relative to the worst measure is as follows:
(7)
where is an indicator of the preference of other criteria over the worst criterion and .
In this step, optimal weights are obtained by minimizing the following equation.
(8)
Subject to:
(9)
(10)
In this method, the inconsistency rate should be calculated so that the results can be evaluated better. If the matrix is completely consistent, the following equation is obtained
(11)
(12)
where is the preference of the best criterion over the worst criterion. According to the definition of complete consistency, it is clear that if the aforementioned relationship is established for all criteria, there is complete consistency, but if not, the matrix has some inconsistency. Inconsistency exists when the left side of the aforementioned equation is greater or less than its right side. Therefore, by combining Equations (8) and (12) and considering the highest level of inconsistency :
(13)
(14)
(15)
Finally, the consistency ratio (CR) is determined by solving Equation (15) based on different values of . Figure 1 shows the CR based on values.
Figure 1

Consistency ratio estimated by different values of .

Figure 1

Consistency ratio estimated by different values of .

Close modal

Agricultural virtual water

Virtual water in this study is referred to as the total amount of water that is consumed to produce agricultural yields. Two strategic crops including wheat and barley were simulated in 2021–2022 growing season in Hunan region, China. Field information was measured through a field experiment in the study area for three wheat fields with an area of 165 hectares and three barley fields with an area of 137 hectares. The required parameters regarding water balance and performance in the last 5 years (2018–2022) were recorded and used to calibrate water and energy and yield production by AquaCrop software. In addition, carbon dioxide concentration (CO2) is one of the primary parameters required by the model, which is summarized in Table 1.

Table 1

Required parameters for simulating water–food–energy nexus

YieldFactorUnit20182019202020212022
Wheat Virtual water m3·ton−1 2,825 2,940 2,812 3,120 3,080 
1,000 m3·ha−1 12.3 12.4 12.9 14.9 14.0 
Transpiration mm·year−1 379 396 364 417 409 
CO2 emission ton 128 142 156 167 161 
Production ton·ha−1 4.36 4.23 4.62 4.78 4.56 
Barley Virtual water m3·ton−1 2,814 2,910 2,823 3,048 3,034 
1,000 m3·ha−1 11.2 11.8 11.5 12.6 12.2 
Transpiration mm·year−1 352 338 348 379 373 
CO2 emission ton 129 140 137 152 146 
Production ton·ha−1 3.98 4.05 4.08 4.12 4.03 
YieldFactorUnit20182019202020212022
Wheat Virtual water m3·ton−1 2,825 2,940 2,812 3,120 3,080 
1,000 m3·ha−1 12.3 12.4 12.9 14.9 14.0 
Transpiration mm·year−1 379 396 364 417 409 
CO2 emission ton 128 142 156 167 161 
Production ton·ha−1 4.36 4.23 4.62 4.78 4.56 
Barley Virtual water m3·ton−1 2,814 2,910 2,823 3,048 3,034 
1,000 m3·ha−1 11.2 11.8 11.5 12.6 12.2 
Transpiration mm·year−1 352 338 348 379 373 
CO2 emission ton 129 140 137 152 146 
Production ton·ha−1 3.98 4.05 4.08 4.12 4.03 

Evaluation of nexus system

In the first step, it is necessary to evaluate the existing system based on the current conditions of water and energy consumption for food production, so that in the next stages, by improving the components, their positive impact can be observed in planning. Figure 2 compares planning systems based on water, energy, production, and nexus. Energy planning in the current conditions has the highest values compared to water, production, and nexus in the three criteria of durability, reliability, and resiliency. Maximum values of adaptability and vulnerability were estimated for nexus and production plans, respectively. The standard error for each calculation is shown in the figure. In the current situation, the energy planning which is calculated based on the extraction of groundwater has optimal conditions, but it needs to be combined with water and production to reach stainable conditions.
Figure 2

Evaluation of existing strategies for water (W), food (F), energy (E), and nexus.

Figure 2

Evaluation of existing strategies for water (W), food (F), energy (E), and nexus.

Close modal

Class-based improvement

Three levels of improvement of decision-making components (5, 10, and 25%) were evaluated to compare the progress of the five indicators. The results of the improvement in the decision-making components and the nexus model are summarized in Table 2. As shown in the table, the vulnerability of the system will increase in conditions of improvement of irrigation efficiency. The main reason for increased vulnerability is due to environmental stress for crop production, which reduces the range of soil moisture. Reliability and adaptability for barley have been more than wheat, which shows that the sensitivity of this product to deficit irrigation is less than that of wheat. In general, maximizing food productivity increases the criteria of reliability, resiliency, adaptability, and durability of the model compared to improving water use efficiency and energy saving. The use of the nexus system has improved the reliability and resiliency of the model by more than 80%. In addition, the results showed that using the nexus method can take into account the ability of all components to create optimal conditions and reduce vulnerability to less than 20%.

Table 2

Class-based estimation of evaluation criteria under optimal condition

Wheat
Barley
StrategiesLevelSDRVALevelSDRVA
Improving water use efficiency 5% 0.67 0.56 0.78 0.29 0.63 5% 0.64 0.57 0.79 0.31 0.66 
10% 0.61 0.52 0.73 0.31 0.58 10% 0.59 0.53 0.74 0.34 0.62 
25% 0.54 0.46 0.67 0.35 0.52 25% 0.52 0.47 0.68 0.37 0.58 
Energy saving 5% 0.63 0.47 0.65 0.26 0.66 5% 0.62 0.46 0.64 0.27 0.64 
10% 0.59 0.44 0.62 0.29 0.64 10% 0.59 0.43 0.61 0.29 0.60 
25% 0.53 0.39 0.56 0.34 0.59 25% 0.54 0.38 0.54 0.33 0.56 
Increasing food productivity 5% 0.74 0.61 0.79 0.18 0.64 5% 0.73 0.62 0.78 0.19 0.63 
10% 0.69 0.58 0.74 0.22 0.60 10% 0.68 0.59 0.73 0.23 0.61 
25% 0.61 0.52 0.68 0.26 0.57 25% 0.62 0.53 0.67 0.26 0.58 
Nexus sustainability 5% 0.81 0.63 0.82 0.12 0.71 5% 0.82 0.64 0.80 0.13 0.72 
10% 0.78 0.60 0.79 0.14 0.68 10% 0.79 0.60 0.77 0.15 0.69 
25% 0.72 0.53 0.74 0.17 0.63 25% 0.74 0.54 0.72 0.18 0.64 
Wheat
Barley
StrategiesLevelSDRVALevelSDRVA
Improving water use efficiency 5% 0.67 0.56 0.78 0.29 0.63 5% 0.64 0.57 0.79 0.31 0.66 
10% 0.61 0.52 0.73 0.31 0.58 10% 0.59 0.53 0.74 0.34 0.62 
25% 0.54 0.46 0.67 0.35 0.52 25% 0.52 0.47 0.68 0.37 0.58 
Energy saving 5% 0.63 0.47 0.65 0.26 0.66 5% 0.62 0.46 0.64 0.27 0.64 
10% 0.59 0.44 0.62 0.29 0.64 10% 0.59 0.43 0.61 0.29 0.60 
25% 0.53 0.39 0.56 0.34 0.59 25% 0.54 0.38 0.54 0.33 0.56 
Increasing food productivity 5% 0.74 0.61 0.79 0.18 0.64 5% 0.73 0.62 0.78 0.19 0.63 
10% 0.69 0.58 0.74 0.22 0.60 10% 0.68 0.59 0.73 0.23 0.61 
25% 0.61 0.52 0.68 0.26 0.57 25% 0.62 0.53 0.67 0.26 0.58 
Nexus sustainability 5% 0.81 0.63 0.82 0.12 0.71 5% 0.82 0.64 0.80 0.13 0.72 
10% 0.78 0.60 0.79 0.14 0.68 10% 0.79 0.60 0.77 0.15 0.69 
25% 0.72 0.53 0.74 0.17 0.63 25% 0.74 0.54 0.72 0.18 0.64 

Virtual water changes

Virtual water changes in 5, 10, and 25% scenarios are shown in Figure 3. In all scenarios, wheat virtual water is overestimated than barley. Virtual water volume has decreased by 24% with a 25% improvement in water consumption efficiency, which shows that it is linearly related to agricultural water consumption. In the energy-saving scenario, in the level of 25%, virtual water can be reduced by 12%. As expected, the improvement in production efficiency has led to an increase in virtual water, which contradicts the previous two goals. The sustainable nexus scenario, by combining the effects of the three evaluated components, has been able to reduce the volume of virtual water by 17 m3 per unit of increase in system sustainability. Due to the mutual effect of the components, the virtual water reduction slope in the nexus scenario was lower than other scenarios.
Figure 3

Virtual water content in sustainable scenarios.

Figure 3

Virtual water content in sustainable scenarios.

Close modal

CO2 emission

Carbon dioxide has been evaluated as an indicator of groundwater consumption. Figure 4 shows the value of CO2 emission as a ratio of changes compared to the initial value. The trend of CO2 changes also depends on the estimated fluctuations of virtual water. The slope of changes in this parameter is lower than virtual water and negative for food productivity. The results showed that the use of the nexus system in planning has helped sustainable development and can establish a balance between the components.
(16)
Figure 4

Comparison of CO2 emission in sustainable scenarios.

Figure 4

Comparison of CO2 emission in sustainable scenarios.

Close modal

The water–food–energy nexus is being promoted as a conceptual approach for achieving sustainable management. According to the concepts in the relationship between water, food, and energy in this study in three general parts of the analysis of the internal relationship between the components, the analysis of the influence of planning factors and the evaluation of the developed systems have been developed. We anticipate that the developed approach will make a significant contribution to achieving regional sustainable development goals and will be effective in improving the technical knowledge of the water, food, and energy relationship at the national and global levels. In future research, transdisciplinary and collaborative approaches can be addressed from the perspective of decision-makers, beneficiaries, and policy-makers in the fields of water, energy, and food. These components can help align relevant research with policy needs and support applied use.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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