In this section
2014 Water quality status and trends report
- Summary table
- Summary table
- Surface water
- Summary tables
The St. Johns River Water Management District (the district) is responsible for assessing water quality throughout the district’s 18-county service area by sampling ambient water quality and maintaining an ambient water quality monitoring network. The fact pages found on this website summarize sample data from some of the sites in the network. This information is designed to provide basic water quality assessment and trend analysis throughout the district. These measurements and analyses are intended to help residents and concerned citizens acquire a basic knowledge about water quality for water bodies in which they have an interest.
Network design and sample collection
Fifty-eight of the 73 monitoring sites are funded by the district and are sampled every other month. Fourteen other sample sites, also known as temporal variability sites, are funded by DEP and are sampled on a monthly basis. Samples are collected in the field by trained technicians. The sites may be sampled from a bridge, boat, or stream bank. After collection and appropriate preservation, the samples are sent to an analytical laboratory. Currently, the district laboratory analyzes the 58 ambient samples, while the DEP laboratory, in Tallahassee, analyzes the temporal variability samples. Every effort is made to ensure that field samples are sent to the laboratory within holding time frames. After an appropriate quality assurance process, the data are uploaded to the district’s environmental database as well as the U.S. Environmental Protection Agency’s (EPA) national database known as STORET, which is located in Research Triangle Park, North Carolina.
The data used for the fact sheets were compiled from the district’s environmental database and span the time frame from 1990 to 2008.
All analyses employed user-written SAS programs. After the data were downloaded, associated remark codes were evaluated. Data values with remark codes that indicated a problem were rejected from the calculations. Excessive holding times, problems in the analytical methodology, or other problems can lead to questionable data. Data from different depths sampled on the same day were averaged. A range-check for obvious exceedences (e.g. very high numbers) was then performed. Total nitrogen was calculated from measured constituents, such as ammonia, nitrate, and total Kjehdahl nitrogen. Both the trophic state index (TSI) and the water quality index (WQI) were calculated similarly to the DEP method as presented in the 1996 305b report.
Analytical results are summarized in a paragraph or two at the bottom of each fact sheet. In addition, each station has an associated data table that contains a number of summary measurements for a series of water quality constituents. Median values in the fact sheet data table represent the median of daily values from all years of data. The minimum value, the 25th percentile (Q1), the 75th percentile (Q3), and the maximum value for each constituent are also presented in the table. The period of record for each constituent (Data Yrs) and the total number of data values that were available for the calculations (n Data) can also be found in the table. Each table also contains a “range” value. The range value is based on the result of a comparison between each water quality constituent median value and the range of data for that constituent for similar water body types. Using Big Davis Creek as an example, the median conductivity of 163 micromhos per centimeter (μmhos/cm) was compared to all daily conductivity values from all other ambient monitoring network streams. It is important to point out that other District monitoring and restoration programs (such as the Lower St. Johns Basin project) have ambient water quality monitoring networks. Although data from non-EAS network stations were not reported in the fact sheets, the data were included in the range analysis. In other words, data from Big Davis Creek (for example) was compared to all stream data from all ambient water quality monitoring network stations in the district. The result of the comparison was a percentile rank assigned to the compared median value. The range values were arbitrarily generated by dividing the percentile scale into quintiles.
0 – < 20
20 – < 40
40 – < 60
60 – < 80
80 – < 100
In the case of Big Davis Creek, the range value for conductivity is “low,” which means that the conductivity at Big Davis Creek was lower than 80 percent of the measured conductivity values in streams throughout the rest of the district.
Several water quality constituent results can be found in the table and described in the summary paragraph. Water temperature is usually one of the first characteristics described. In the summary paragraph, an interquartile range (Q1 to Q3) between 5 and 15 degrees was considered to be a typical range of water temperatures in the district. A range of temperatures less than 5 degrees (such as at Blue Spring) was considered a narrow range. Temperature variations greater than 15 degrees were considered as a wide temperature range.
Specific conductance is a measure of the water’s ability to conduct electrical current. Major ions concentrations such as calcium, magnesium, sodium, potassium, chloride, and sulfate affect the measured specific conductance. Higher concentrations of major ions usually result in a higher conductance in the water. Ions facilitate the flow of electricity in water, which is why salt water is a better electrical conductor than fresh water.
In the table, sampling station depth refers to the depth at which the sample was taken. The actual depth of the water body at the sample site is near the bottom of the table, and both measurements are in meters.
Dissolved oxygen concentration is a very important characteristic of the water, as it has a great effect on fish survival. Higher dissolved oxygen concentrations are usually found in colder water, or water with high amounts of algae. Lower oxygen concentrations are usually found in warmer waters, or water with high oxygen demand, such as those waters that drain swamps.
Hardness was also calculated for many of the sample sites. Although different hardness scales exist, the following scale was used:
75 – 150
150 – 300
Hardness was calculated from the concentrations of calcium and magnesium, in accordance with standard methods. Soft water is generally more suited to residential and industrial uses, as it is less likely to form scale and clog pipes and pumps.
Alkalinity was also measured at many sites and is a measure of the water’s ability to tolerate additional acidity without changing the pH significantly. Water with high alkalinity is referred to as well-buffered, because the addition of acidic rainfall (for example) will not lower the pH very much. Poorly buffered water, or water with a low alkalinity, will generally have a lower pH due to rainfall.
Total organic carbon
Total organic carbon was measured for most water bodies and is a good indication of the source of the water. Waters that drain swamps will usually have higher concentrations of total organic carbon than waters that originate from rainfall or springs.
Water with a high concentration of total organic carbon is usually highly colored also. Water with a high-enough color (>250 PCU) and a pH less than 6 is referred to as blackwater. Streams and blackwater streams comprise two different groups for the purpose of the water quality index analysis.
The Secchi depth is affected by turbidity, color, and total suspended solids concentrations (or total filterable residue in the tables). Waters with higher Secchi depths are considered to be less eutrophic than waters with low Secchi depths. Of course, waters that are naturally highly colored can give a misleading impression of quality based solely on Secchi depth.
Nutrients are considered to be total phosphorus and total nitrogen. Although total phosphorus was often measured directly in the laboratory, total nitrogen had to be calculated in most cases from concentrations of total Kjeldahl nitrogen (organic nitrogen and ammonia), nitrate, and nitrite. High concentrations of nutrients in lakes especially can lead to eutrophication. Most restoration projects at the district are concerned with reducing the concentration of nutrients in water bodies.
Chlorophyll concentrations provide an indication of primary productivity (or algal productivity) in the water body. High concentration of chlorophyll is associated with the eutrophication processes, while low concentrations of chlorophyll usually indicate an oligotrophic condition.
The summary paragraph also mentions exceedences of Florida water quality standards for some of the water quality constituents, such as dissolved oxygen, pH, and alkalinity. It is important to note that none of the calculated exceedences are based on the impaired waters rule, but are rather simply comparisons of median values to the state standard. The water was also qualitatively assessed based on the trophic state index (TSI) for lakes and estuaries and the water quality index for streams, blackwater streams, and springs. A good, fair, or poor rating was assigned to water bodies at the sample site depending on the outcome of the qualitative assessment. Additionally, Forsberg–Ryding criteria were used to rate lakes as oligotrophic, mesotrophic, eutrophic, or hypereutrophic. Other water quality monitoring organizations in Florida, such as LAKEWATCH, find that the Forsberg–Ryding criteria are a useful addition to TSI measurements to determine water quality. Forsberg–Ryding criteria were applied to Secchi depth, total nitrogen, total phosphorus, and chlorophyll concentrations to determine the condition of the water. One drawback of the method is that oftentimes the different constituents give different results. For example, a Secchi depth might indicate a hypereutrophic condition, while the nutrients and chlorophyll concentrations will indicate a eutrophic condition. In those cases, the Forsberg–Ryding criteria is mentioned for each constituent, but no overall rating for the lake is assigned. Forsberg–Ryding criteria results may not always agree with the TSI rating, primarily due to the way the two scales were developed. The TSI was based on regression analysis of Florida lakes, whereas the Forsberg–Ryding is based on studies done on Swedish lakes.