Digital twin–based adaptive irrigation management framework for enhancing water use efficiency and drought resilience in the Kaleshwaram Lift Irrigation System

A large multi-stage lift irrigation system in urban semi-arid regions is mainly affected by a persistent imbalance between supply and demand, driven by seasonal variations in hydrology, rising evapotranspiration, pipe losses, and the rigidity of operating it according to rules. These restrictions adversely affect water-use efficiency and reduce drought resistance. The basics of these operational issues in a large-scale irrigation scenario can be seen in the Kaleshwaram Lift Irrigation System (KLIS). This study aimed to introduce and test an Adaptive GDD framework based on a Digital Twin approach to enhance water use efficiency, water supply security, spatial equity, and drought resistance in KLIS. The performance was measured by comparing energy consumption in conventional (rule-based) operation and in Digital Twin adaptive operation under the same boundary conditions. Results indicated that the adaptive operation reduced losses occurring in the dry season by approximately 60% to 65%, augmented the water use efficiency by approximately 52% – 73%, raised the percentage of supply reliability by approximately 62% – 88%, reduced losses in the total drought season by approximately 51% and shortened recovery time by 1/2 of a year from about 8 – 9 months to 4 – 5 months. The novelty of the study was to provide a measurable increase in efficiency, equity and drought-resilience of such a large lift irrigation system without the need to build more infrastructure or take more water out of the irrigation system, and to showcase how this was achieved through the incorporation of forecasting, physics-based simulation and adaptive control.