SCADA systems are widely used to monitor irrigation systems. They acquire real time data from gauging stations located in strategic points allowing the control over parameters such as instant flow or water level.

Maintaining water levels is crucial to ensure regulating capabilities and to obtain customer satisfaction by providing an optimal delivery of water. While receiving water levels everywhere in the scheme would be ideal for a Channel Operator, it is also true that it is not feasible to install monitoring sites throughout the channel.

Statistical techniques and algorithms such as the Kalman Filter are able to estimate site measurements based on observed data. The algorithm takes observed data and considers the existence of statistical noise and inaccuracies to produce precise estimates. As new data is received, analysis of noise values is performed in order to provide new estimates. In other words, it may be considered as a living algorithm that constantly learns as new data is received.

This approach can be applied to multiple situations in real life being one of them the estimation of water levels along a channel using observed data from monitored sites provided by SCADA. In order to achieve it, a gauging device should be placed between regulators, which are the ones defining the hydraulic behaviour of the reach. Data received at the actual monitoring site will be processed according to the Kalman Filter approach, which will define a new estimate and a noise value associated to the new reading. By means of previously calibrated hydraulic models or from data collected from calibrating campaigns, these new values of the estimation of water level and noise can be used to estimate water levels at other points of the reach following the hydraulic principles of conservation of continuity and momentum.

Obtaining real time data from SCADA remains a MUST in order to monitor any hydraulic system but frequently the benefits behind this data are not exploited to the maximum. Taking good care of this data and using it wisely is the next challenge for Rural Water Intelligence.