Water Management Optimization in Agriculture: a Digital Model Development

Water scarcity is one of 21st century’s most pressing global issues. The anthropogenic pressure and climate change will be the main drivers of freshwater depletion in the coming decades. According to the FAO, the amount of water needed to support all human activities will be 20–30% higher by 2050. A closer look reveals how agriculture is a major contributor to water scarcity, with irrigation accounting for 70% of global water use. In this framework, the development of effective water management approaches is a key solution to turn the tide and change current patterns. Despite that, there still exists a gap in the scientific literature in the development and validation of innovative water management strategies using advanced technologies. This study aims to address this gap by developing a digital model of a real irrigation network able to accurately predict the water distribution across the network at different operating conditions. A living lab was used for the experimental activities, where a low-power wide-area network was used to acquire data from the system. For modeling purposes, the integration of the 1-D and 3-D simulation was leveraged to fluid-dynamically characterize all the components involved. The numerical model resulted to be accurate in predicting both pressure and velocity patterns (determination coefficient higher than 93%). The proposed model could be considered a starting point for the implementation of a digital twin to support agricultural water management in both the design and management of an irrigation network by defining the correct network configuration and detect anomalous conditions.