Modeling the Sustainability of Water, Energy, and Food Based on the System Dynamics Approach in the Sarab Seyed Ali Watershed, Lorestan Province

Introduction and Goal
Water, energy, and food are the basis of the sustainable development of societies and they are inextricably and interrelated, such that changes in each of them cause direct and indirect consequences on the others. The objective of this study was to present and develop a new methodological approach that can efficiently support the decision-making and policy-making process in the supply and demand of a watershed’s resources, and to quantitatively model the sustainability of water, energy, and food based on the system dynamics approach across different scenarios. The structure of this framework can be used as an effective tool to improve policy-making and elevate the level of watershed sustainability, providing more efficient perspectives in resource management.
Materials and Methods
In this study, a quantitative and integrated framework based on system dynamics (SD) was developed and modeled, considering the interactions of resource supply and demand with a Water-Energy-Food nexus approach in the Sarab Sayed Ali watershed. Since accurate and reliable SD modeling for assessing resource sustainability requires a comprehensive and transparent database, the necessary data were collected from official and credible national authorities. For modeling and validation in the study area, historical data recorded between 2001 and 2023 were utilized as inputs for the Vensim software. Furthermore, the simulation time step was set annually, and the overall modeling horizon was determined from 2001 to 2046 considering technical aspects and the long-term policy orientations of the Iran Water Resources Management Company to enable the prediction of the system’s future behavior. After developing the model, its validation was performed using standard SD tests, such as the boundary adequacy test, dimensional consistency test, and behavior reproduction test. The results were analyzed based on feedback loops. By integrating the findings from the sensitivity analysis with the local characteristics of the region, various scenarios were defined and applied to the model. Ultimately, the most desirable scenario was ranked and selected using the Fuzzy TOPSIS Multi-Criteria Decision-Making method.
Results and Discussion
Considering the variables related to the water, energy, food, and social and economic sectors, the modeling process was carried out from 2001 to 2046. After model validation, the results showed that in 2001, the population living in the watershed was 56,400. Based on modeling conducted based on variables such as birth rate, death rate, and migration rate, the population in 2046 was predicted to be 85,770 people. This finding indicates a 52 increase in population. From 2001 to 2046, the reduction in water resources in the region will be significant. The decrease in surface water volume in the first period from 2001 to 2005 was about 21 million m³, and in the second period from 2006 to 2046, 2 million m3 was predicted. During the modeling period, the volume of groundwater decreased by about 1.77 billion m³. This is while the decrease in the sustainability of water resources (surface and underground) was estimated at 1.79 billion m³. The trend in energy sustainability (fossil and electrical) from 2001 to 2046, was an increase of 2.2 million barrels of oil. The energy trend during the period 2022 to 2040, was decreasing, but it still remained in a positive range, meaning it met the needs of the region. From 2041 to 2046, negative energy sustainability index values were predicted, and by 2046, it will reach a range of negative 1.8 million barrels of oil. This finding indicates a sharp decline in energy sustainability and the emergence of significant challenges in this sector. The trend of food resource sustainability, considering all four sources (agriculture, livestock and poultry, fisheries, and livestock and poultry feed of agricultural origin), was predicted to increase from 2001 to 2030, and food supply demand by about 597 thousand tonnes by 2030. ​From 2031 to 2046, this trend changed, and food reserves decreased to 390 thousand tonnes in 2046. This finding indicate a reduction in reserves and a weakening of the sustainability of food resources. The results of the sensitivity analysis showed that, in the water subsystem, the groundwater share of consumption and irrigation efficiency; in the energy sector, the fossil energy share and per capita energy consumption in the residential and commercial sector; and in the agricultural sector, the beet cultivation area and per capita food consumption are considered key variables. In this study, based on the findings of sensitivity analysis and the identification of effective leverage points in the model, the consumption pattern modification scenario with a proximity coefficient of 0.755 is introduced as the best option to improve resource sustainability. On the other hand, the least impact on improving resource sustainability was related to the base case scenario with a proximity coefficient of 0.189.
Conclusion and Suggestions
Comprehensive management and planning in watersheds have a complex and multidimensional nature. Because hydrological, social, economic, and environmental affect each other in an intertwined manner. Achieving effective management in such a system requires a scientific, accurate, and in‑depth understanding of the challenges and interactions among various components such as water, energy, and food resources. Despite the effectiveness of the integrated approach with a systems perspective, its implementation still faces challenges such as the lack of accurate data, the interdisciplinary nature of the subject, the complexity of economic and social structures, and existing gaps in policymaking. Based on the results of this study, it is suggested that by developing more comprehensive correlation models and taking into account the lack of certainties and climate change, the field of informed decision-making and sustainable resource management can be prepared by modifying consumption patterns, diversifying resources, utilizing new technologies, and strengthening risk management.