Understanding spatio-temporal variations in crop water consumption: Applying the WF Methodology Integrated with the SWAT Model

Agricultural activities account for a significant portion of global water consumption, emphasizing the need to improve water productivity in this sector. This can be achieved through the implementation of effective agricultural management practices, such as optimizing crop patterns, adjusting irrigation methods, and improving fertilization practices. The Water Footprint (WF) concept offers a comprehensive approach to assessing water consumption in agriculture, considering different types of water use. By integrating the WF methodology with hydrological modeling programs, a more detailed analysis of water consumption patterns can be achieved, overcoming previous limitations in understanding agricultural water use. This study aims to assess the water consumption patterns of maize under different spatio-temporal dynamics. The WF of maize, including blue, green, and grey components, was calculated using the Soil and Water Assessment Tool (SWAT) model in the Ceyhan Basin (Turkey) between 2011-2020. The study outputs indicated considerable spatial and temporal variations, with a total WF ranging from 350 to 1320 m³/t. Green WF (61-385 m³/t) is found to be less dominant in maize production across the basin, while blue water emerges as the primary component (25 to 870 m³/t). In this study, the utilization of the SWAT model provided detailed spatio-temporal results, allowing for adjustments in agricultural patterns. We obtained that, optimizing the cultivation regions of maize within the Ceyhan Basin has the potential to reduce the total WF by approximately 26% and the blue WF by 47%. This optimization could result in an annual saving of around 135 million m³ of irrigation water. Furthermore, the study also analyzed temporal water consumption patterns. The findings highlight the significant potential for water conservation in agricultural activities through the spatio-temporal optimization of blue and green water, taking into account hydrological characteristics.