Water, Air, and Soil Pollution

, Volume 83, Issue 3–4, pp 263–284 | Cite as

Temporal variation in precipitation chemistry on the shore of the Chesapeake Bay

  • Thomas E. Jordan
  • David L. Correll
  • Donald E. Weller
  • Nancy M. Goff
Article
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Accepted:

Abstract

We studied precipitation chemistry at the Rhode River on the western shore of the Chesapeake Bay. We sampled on an event basis, beginning in 1973 for some constituents in bulk precipitation. Beginning in 1981, we also sampled wet precipitation separately from bulk precipitation. In this report, we examine temporal variability of precipitation chemistry at different time scales. Several constituents showed long-term trends. In bulk precipitation, hydronium concentration increased by 27% of its mean concentration per decade, calcium by 67%, ammonium by 28%, and nitrate by 25%, while organic nitrogen decreased by 41%, organic phosphorus by 31%, and organic carbon by 16%. In wet precipitation, ammonium increased by 33% and calcium by 100%, while magnesium decreased by 78% per decade. Concentrations differed greatly among precipitation events, increasing as the volume of precipitation decreased and as the interval since the previous event increased. Most constituents also showed marked seasonal variation. We used a regression model to predict concentrations for each event from month, precipitation volume, and the time since the previous event. We evaluated how much of the interannual variability could be explained by these factors. The event-scale model accounted for almost half of the variability among annual means for ammonium, sodium, and magnesium in bulk precipitation, and for potassium in wet precipitation. This suggests that much of the interannual variability of concentrations may result from interannual variation in the temporal distribution of precipitation.

Keywords

Precipitation Phosphorus Magnesium Organic Carbon Regression Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Kluwer Academic Publishers 1995

Authors and Affiliations

  • Thomas E. Jordan
    • 1
  • David L. Correll
    • 1
  • Donald E. Weller
    • 1
  • Nancy M. Goff
    • 1
  1. 1.Smithsonian Environmental Research Center EdgewaterUSA

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