Presented at the 2002 USCID/EWRI conference, Energy, climate, environment and water - issues and opportunities for irrigation and drainage on July 9-12 in San Luis Obispo, California.Includes bibliographical references.Measurement network on hydraulic system includes many sensors subject to failure or deviation, and spread over a huge area. In addition discharge and volume measurements in open channel hydraulic networks are characterized by large uncertainties. To overcome this kind of problem, in process control industrial applications, data reconciliation is more and more used. The objective of the data reconciliation is to take advantage of information redundancy on a system to make a cross-checking of real-time measurements. Using this information redundancy, a data reconciliation module allows to detect inconsistent measurements, measurement deviations and provides corrected values whether the initial measurements are valid, biased or invalid. A derived consequence is to better schedule the maintenance of sensors. A data reconciliation module, based on the measurements from the hydraulic network, has been recently developed and implemented in the SCP's supervisory system. The software has initially been used on a daily basis to check the measured flow on the main canal. It has then been adapted in order to run every 15 minutes on a distribution network including pipes, canals, and tanks. The paper presents first the theory of the Canal de Provence data reconciliation application. The basic model is an hydraulic network with a series of nodes corresponding to balance equations (inflows, outflows, and storage). Constrained data reconciliation is used in order to satisfy the non-negativity of the hydraulic variables and the mass balance relations. The results are corrected values for measured variables and proposed values for non-measured quantities. A statistical analysis of the results is performed. This analysis allows to evaluate the uncertainties attached to the estimated flows and volume values. It allows also to detect invalid measurements, drift of sensors and to decide which maintenance operations to perform. Secondly, field examples are presented: measured and re-estimated flow values with their standard deviations, detection of invalid sensors, performed maintenance operation. The data reconciliation is situated just after the measurement process and takes place in the decision process for diagnosis, identification and control
