As part of an environmental observation and forecasting system, sensors deployed in the Columbia RIver Estuary (CORIE) gather information on physical dynamics and changes in estuary habi- tat. Of these, salinity sensors are particularly susceptible to bio- fouling, which gradually degrades sensor response and corrupts crit- ical data. Automatic fault detectors have the capability to identify bio-fouling early and minimize data loss. Complicating the devel- opment of discriminatory classi(cid:12)ers is the scarcity of bio-fouling onset examples and the variability of the bio-fouling signature. To solve these problems, we take a novelty detection approach that incorporates a parameterized bio-fouling model. These detectors identify the occurrence of bio-fouling, and its onset time as reliably as human experts. Real-time detectors installed during the sum- mer of 2001 produced no false alarms, yet detected all episodes of sensor degradation before the (cid:12)eld sta(cid:11) scheduled these sensors for cleaning. From this initial deployment through February 2003, our bio-fouling detectors have essentially doubled the amount of useful data coming from the CORIE sensors.