A validated low-cost model for precise water leak detection in irrigation ponds (WLDIP)

• A low-cost water leak detection model for irrigation ponds is proposed. • The model determines the location and size of the water leak. • Applied by monitoring water height in ponds shallower than 10 m. • It has been validated for irrigation ponds between 150 to 5000 m 3 . Efficient water management in agriculture is crucial for global food security and environmental sustainability, especially in water-scarce regions. Undetected leaks in irrigation ponds lead to significant water losses and increased operational costs. This study aimed to propose a low-cost Water Leak Detection model for Irrigation Ponds (WLDIP), capable of estimating both the location (height) and size of leaks, thereby helping to reduce agricultural water losses. The methodology integrates continuous water level measurements with a detailed water balance model, considering variables such as inflows, outflows, evaporation, and thermal expansion of both the water and the reservoir structure. The model relies on measuring water height every 15 min over a week and uses meteorological data to account for evaporation and thermal expansion. Least squares optimization compares measured data with the model’s calculated values to determine leak parameters. Field validation was conducted on three irrigation ponds in southeastern Spain. The WLDIP model successfully identified leak locations and sizes in all three ponds, which were subsequently confirmed by physical inspections and repairs. Results showed that level variations exceeding 0.01 m within 15 min were due to normal pond use, while leaks exhibited a typical flow rate ten times lower. This demonstrates the model’s high accuracy in determining both the height (h LEAK ) and size (ω LEAK ) of the leaks. The WLDIP model is a reliable, scalable, and cost-effective solution for improving water use efficiency in agricultural systems. Its implementation aligns with sustainable development goals related to clean water and responsible production, and it helps prevent adverse effects on natural ecosystems, particularly in semi-arid climatic zones. Future research could focus on automating this system for real-time leak monitoring.