GCRC Project Summary |
Development and Analysis of Coastal Water Quality Indicators
PIs: Joan Sheldon and Merryl Alber (Dept of Marine Sciences, Univ of Georgia, Athens, GA, USA)
Support: Georgia Coastal Management Program
Timeframe: Oct 2007 - Sept 2009
Project Overview:
Objective 1. Compile coastal water quality data collected by GA DNR CRD into an integrated database and analyze it for long-term and seasonal trends.
Objective 2. Choose an appropriate framework for classifying water quality status with respect to public and living resource health.
Objective 3. Identify an appropriate suite of indicator variables and use them to evaluate Georgia coastal waters.
Objective 4. Write a report on the status and trends of water quality using environmental indicators developed for Georgia coastal waters.
Objective 5. Work with CRD to develop recommendations for efficient continued monitoring of Georgia coastal waters.
Accomplishments:
We have obtained data from CRD for beach water quality, shellfish water quality, river nutrients, and sound nutrients. The data from these four monitoring programs have been compiled into a single integrated database and we have begun the analysis steps for this two-year project. A total of 186 sites are represented among the four programs, with some measurements spanning the years 1998-2007 but most, including nutrient observations, beginning in 2001 or later.
We have designed the database with a goal of flexible querying across programs while retaining as much detail as possible about changes in labs and methodologies in case these changes should prove to be important (e.g. breaks in trendlines). Metadata details (methods, labs) were requested and received from CRD and Marine Extension personnel. Although we are still in the process of correcting the data, all of the tables have been populated with data, including the table of the actual observations.
We have started evaluating the data with regard to sampling frequency, data gaps, and identification of outliers in preparation for selecting suitable analyses. All datasets have slightly irregular sampling frequency and some missing data (especially nutrients), and some have longer data gaps, which will have to be taken into consideration for time series analyses.
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