# Presentation: 514382

## Detecting Water Quality Patterns in New Hampshire’s Estuaries Using National Coastal Assessment Probability-Based Survey Data

- P.R. Trowbridge and S.H. Jones
- NEAEB 2008 – Bartlett, NH
- March 27, 2008

## Introduction

- Probability-based monitoring (PBM) of NH’s estuarine waters has been done annually since 2000.
- The small study area resulted in dense sampling network.
- Fixed station monitoring for trends was also conducted at representative station.
- Climate variables (temp, rainfall) recorded for Portsmouth NH.

## Research Questions

- Can probability-based sampling data, expressed as estimated proportions, be used for trend and correlation analysis in a small estuary?
- If so, how do the results of trend and correlation analyses with probability-based data compare to the results when the same tests are run using data from traditional fixed station monitoring?

## Study Area

## Methods

- Monitoring designs
- PBM: 25-40 station visits per year, stratified random (2000-2005), GRTS (2006)
- Fixed Station: Monthly visits on high and low tide in 2000-2006
- Index Period: July 1 to September 30
- Parameters
- Temperature, Salinity, Dissolved Oxygen, Chlorophyll-*a*, Nitrogen (NH4, NO23), Orthophosphate, Suspended Solids, Fecal Coliforms.
- Summer air temperature and rainfall in Portsmouth.

## Methods (cont.)

- Data Processing
- PBM: Estimated proportions of the estuary above thresholds using Horvitz-Thompson estimator method.
- Fixed Station: Compute median value for each parameter for each summer season.
- Statistical tests
- Mann-Kendall test for trends in both datasets.
- Spearman Rank Correlation test with climate variables.
- Equal number of values from each program were used to ensure equal statistical power.

## Results-Trend Analysis

- Significant trends were detected from both datasets (0.1 significance level) but they did not match.
- Two parameters, nitrogen as nitrate and nitrite and fecal coliform bacteria, showed significant trends using the probability-based dataset.
- Two different parameters, chlorophyll *a* and total suspended solids, showed significant trends using the fixed station dataset.

**Notes:**
Statistically significant trends in the probability-based estimates were detected for nitrogen as nitrate and nitrite and fecal coliform bacteria.  The proportion of the estuary with nitrogen as nitrate and nitrate greater than 0.1 mg N/L increased from 5% in 2000 to 16% in 2006 (Figure 7A). The Sen slope estimate for this trend was an increase in the estimated proportion by 2% per year. In contrast, no trend was evident in the grab samples collected at the fixed station (Figure 7B).  Figure 10A shows a sharp rise in the proportion of the estuary with fecal coliform bacteria concentrations greater than 14 cts/100ml.  The proportion grew from 5% in 2002 to 32% in 2006 (Sen slope estimate of 7% per year). There was not a corresponding trend in the grab sample concentrations for this parameter.
Significant trends were detected using the grab sample data for chlorophyll a and suspended solids.  The median summer chlorophyll a concentration at the fixed station increased by 0.6 ug/L/yr between 2000 and 2006 (Figure 5B).  A statistically significant trend was not detected for this parameter for the probability-based estimates for the 2000-2006 period. However, probability-based estimates from 2002-2006 show a strong upwards trend which is statistically significant (Figure 5A). The Sen slope estimate for this trend is 4% per year.  The concentration of suspended solids in the grab samples increased by 1.8 mg/L/yr, although the data were quite scattered (Figure 9B).  No coherent trend was evident in the probability-based estimates for this parameter.

## Nitrogen as Nitrate and Nitrite

**Notes:**
Threshold = 0.1 mg/L

## Fecal Coliform Bacteria

**Notes:**
Threshold=14

## Chlorophyll-a

**Notes:**
Threshold=5

## Suspended Solids

**Notes:**
Threshold=10

## Results-Climate Correlation

- Correlations detected for both datasets
- Probability-Based Monitoring:
- Salinity and Summer Rainfall (-)
- Fixed Station Monitoring:
- Water Temperature and Summer Rainfall (+)
- Chlorophyll-a and Summer Rainfall (+)
- Fecal Coliforms and Summer Rainfall (+)
- Chlorophyll-a and Air Temperature (+)

**Notes:**
Results not exceptional – except that they are different for the two programs.

## Conclusions

- Estimated proportions from PBM surveys were useful for detecting trends and correlations.
- However, the trends and correlations evident from probability-based surveys were different than those detected using data from monthly monitoring at a fixed station.
- Distributed nature of the PBM design, which places stations in all sections of the estuary
- Estimated proportions for PBM datasets reduces variability in volatile parameters

**Notes:**
An equal number of parameters had significant trends using the estimated proportions from NCA surveys as with the median concentrations at GRBAP.  Correlations with climate variables were detected using both datasets.  The probability-based survey data had the added advantages of representing the whole estuary and having known error in the estimates. 
This was especially true for fecal coliform bacteria, where an increase like that observed for the estimated proportions from the NCA surveys (Figure 10A) has significant and immediate management implications. In past reports for the Great Bay Estuary, the NHEP has reported significant decreases in fecal coliform bacteria concentrations at station GRBAP (NHEP, 2006). These differences are important to consider, as management decisions based only on traditional fixed station sampling results may not reflect the conditions of concern to managers for whole systems as accurately as probability-based monitoring results.  Generally speaking, probability-based monitoring may be more appropriate than fixed station monitoring for any area beyond the immediate vicinity of the fixed station.

## Challenges

- If trends detected by probability-based surveys differ from the trends that have been typically detected from fixed station monitoring, the results will be confusing to managers.
- Another challenge of using probabilistic monitoring data for trend detection is the lack of agreed-upon methods for calculating trend statistics.

**Notes:**
The difference in the trend analysis results for the two datasets highlights a challenge for using probability-based survey data for trend detection.  Coastal managers typically rely upon fixed station monitoring to detect trends in estuaries.  If trends detected by probability-based surveys differ from the trends that have been typically detected from fixed station monitoring, the results will be confusing to managers. It will be important to develop tools to rectify trends from probability-based surveys with fixed station monitoring to provide clear information to managers.
Another challenge of using probabilistic monitoring data for trend detection is the lack of agreed-upon methods for calculating trend statistics.  The methods used in this report provide one example. It is also possible to perform standard t-tests to compare the results of one survey to another to detect change following the example of EPA (2007). Uncertainty in proportions can be incorporated into regression equations using weighting factors. However, States and the federal government have not established the appropriate methods that should be used for comparison of multiple surveys over time to detect trends while accounting for the uncertainty in the probability-based estimates.  Probabilistic monitoring is being performed annually in states across the country. In several years, there will be an opportunity for trend analysis from these datasets.  EPA and others should develop statistical methods for trend analysis to maximize the utility of these datasets for documenting trends.

## Contact Information

- Philip Trowbridge, P.E.
- NH Department of Environmental Services
- Watershed Management Bureau
- Concord, NH 03302-0095
- Tel: (603) 271-8872
- Email: Philip.Trowbridge@des.nh.gov