A novel modeling approach on the water–electricity–climate nexus in the context of resource sustainability

Abstract

Water and energy have become essential resources and must be wisely managed for a sustainable future. This paper explores the relationship between water consumption and electricity generation in hydropower plants with dams under different climate change scenarios to contribute to the policy perspectives with a new tool and method to sustain the future. Still, as a reliable forecasting tool, the evaluation of the adaptive neuro-fuzzy inference system model has not been tested for forecasting water consumption during electricity generation. Thus, this study uses this modeling approach to generate reliable water consumption estimates based on electricity generation. The operational data of 78 hydroelectric power plants with dams and meteorological parameters were used as input variables, while water consumption was the output parameter in the model. The dataset was randomly divided into training and testing sets, and 85–15% data splitting presented the best-fitted model. The lowest mean average percent error of the hydroelectric power plants' model resulted in 9.59%, and the coefficient of determination of the model was 0.97, which showed that the developed model presented acceptable prediction performance. Various climate change scenarios are applied to analyze the effects of climate parameters on the water consumption of hydropower plants. The annual hydroelectric power plant water consumption and water intensity were estimated between 2,609 million m3 and 4,393 million m3, and 50,768 m3/GWh and 85,487 m3/GWh, respectively, based on climate change scenarios. The study concludes with significant policy suggestions to endorse this approach. Graphical abstract