Detecting water quality patterns in New Hampshire's estuaries using National Coastal Assessment probability-based survey data

Environ Monit Assess. 2009 Mar;150(1-4):129-42. doi: 10.1007/s10661-008-0683-x. Epub 2008 Dec 4.


Between 2000 and 2006, the New Hampshire Department of Environmental Services and the University of New Hampshire collected water quality samples at 25 to 40 stations per year in a 56.5-km(2) estuary as part of the Environmental Protection Agency's National Coastal Assessment program. Due to the high density of stations, probabilistic statistics for the estuary could be calculated with low uncertainty. The proportions of the estuary exceeding thresholds in each year were calculated for temperature, salinity, dissolved oxygen, chlorophyll a, nitrogen as nitrate and nitrite, nitrogen as ammonium, phosphorus as orthophosphate, total suspended solids, and fecal coliform bacteria. These values were tested for trends over time and correlations with climate variables. The same statistical tests were applied to monthly grab sample data from a representative station in the estuary. The outcomes of the statistical tests on the two datasets were compared to determine if they provided similar information to coastal managers. Trends and correlations were equally likely to be detected using the probability-based data and the fixed station data, but the results were different for the two datasets. The differences were likely due to the distributed nature of the probability-based sampling design, which places stations in all sections of the estuary. In addition, expressing the probabilistic datasets as estimated proportions reduced variability in volatile parameters, such as bacteria, relative to the grab sample dataset. It will be important to develop tools to rectify trends from probability-based surveys with fixed station monitoring to provide clear information to managers.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Animals
  • Data Collection / methods*
  • Environmental Monitoring / methods*
  • New Hampshire
  • Probability*
  • Water Pollutants, Chemical / analysis*
  • Water Supply*


  • Water Pollutants, Chemical