Title - Innovative method to monitor groundwater and surface water interactions

ABSTRACT: Groundwater-surface water interactions can occur in a short period after meteorological events, making it difficult to manually obtain complete and timely hydrological data. An intelligent data capturing system that can recognize changing hydrologic parameters to trigger data collection may enhance information gathering. In this study, a prototype smart data logging system has been developed in cooperation with Solinst Canada Ltd. The system supports two-way, wireless communication between sensors and a home station and employs an editable triggering algorithm, which informs data collection protocols. Historical hydrologic and meteorological data are analyzed to determine thresholds for certain parameters that can define an event of interest. An example case involving the onset of groundwater recharge following a snow melt period within the Waterloo Moraine is used as an example. The thresholds for predictive triggers including snow cover, air and subsurface temperatures and groundwater levels, are established based on field data by excluding noise events and tracking groundwater recharge events. Combined trigger assessments provide more precise predictive capabilities. These data sets can be combined with an integrated watershed model to generate synthetic hydrological data and scenarios to train a threshold-targeted artificial narrow intelligence (ANI). The trained ANI will be able to determine the triggering algorithm quickly to help forecast and capture a hydrologic event of interest.