SS3.08 Application of Automated Technology to Detect Environmental Change
Date: Thursday, June 13, 2002
Time: 11:30:00 AM
Location: View Royal
 
GlibertPM, University of MD Center for Envir. Science, Cambridge, MD 21601, USA, glibert@hpl.umces.edu
Kelly, V, , University of MD Center for Envir. Science, Cambridge, MD 21601, USA, vkelly@hpl.umces.edu
Alexander, J, , University of MD Center for Envir. Science, Cambridge, MD 21601, USA, jalexand@hpl.umces.edu
Cooper, E, E, University of MD Center for Envir. Science, Cambridge, MD 21601, USA, ecooper@hpl.umces.edu
Codispoti, L, , University of MD Center for Envir. Science, Cambridge, MD 21601, USA, codispot@hpl.umces.edu
Boicourt, W, , University of MD Center for Envir. Science, Cambridge, MD 21601, USA, boicourt@hpl.umces.edu
 
EPHEMERAL NUTRIENT PULSES: THE VALUE OF IN SITU DETECTION
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In an attempt to better understand the relationship between nutrient availability and algal bloom events, we employed W.S. Ocean Systems automated nutrient analyzers to collect time-series reactive phosphorus and nitrate+nitrite data from 2 tributaries of Chesapeake Bay, the Choptank River and the Pocomoke River. Data were typically collected in replicate every 2 hours for several week periods. The nutrient data from both rivers reveal several-day time scale increases in nutrient concentrations following rain events, as well as a strong tidal component. What was most surprising were high-frequency (minutes scale) several-fold increases in reactive phosphorus coincident with the rain events. These nutrient pulses were attributed to runoff from the heavily agricultural watershed of these rivers. More conventional sampling, with samples collected every few days, missed all of the ephemeral pulses in phosphorus, particularly since they occurred in stormy periods, and many of the longer-lived increases in nitrogen and phosphorus. Chlorophyll increases were observed in the Pocomoke River following these runoff events, but showed no relation to the conventional nutrient data, only the high frequency data.