The Everglades Depth Estimation Network (EDEN) is an integrated network of real-time water-level gaging stations, ground-elevation models, and watersurface models designed to provide scientists, engineers, and water-resource managers with current (2000-present) water-depth information for the entire freshwater portion of the greater Everglades. The generation of EDEN waterlevel surfaces is derived from real-time data. Real-time data are automatically checked for outliers using minimum, maximum, and rate-of-change thresholds for each station. Smaller errors in the real-time data, such as gradual drift of malfunctioning pressure transducers, are more difficult to immediately identify with visual inspection of time-series plots and may only be identified during on-site inspections of the gages. Correcting smaller errors in the data often is time consuming and water-level data may not be finalized for several months. To provide water-level surfaces on a daily basis, EDEN needed an automated process to identify errors in water-level data and to provide estimates for missing or erroneous waterlevel data.

A technology often used for industrial applications is “inferential sensor.” Rather than installing a redundant sensor to measure a process, such as an additional waterlevel gage, an inferential sensor, or virtual sensor, is developed that estimates the processes measured by the physical sensor. The advantage of an inferential sensor is that it provides a redundant signal to the sensor in the field but without exposure to environmental threats. In the event that a gage does malfunction, the inferential sensor provides an estimate for the period of missing data. The inferential sensor also can be used in the quality assurance and quality control of the data. Inferential sensors for gages in the EDEN network are currently (2010) under development. The inferential sensors will be automated so that the real-time EDEN data will continuously be compared to the inferential sensor signal and digital reports of the status of the real-time data will be sent periodically to the appropriate support personnel. The development and application of inferential sensors is easily transferable to other real-time hydrologic monitoring networks.