Learn More: Water Quality Contour Map

What does this mean?

sample contour map

In an open body of water such as a bay, water quality may vary considerably in different regions, and water quality also changes continually over time. Tidal influence, inflow from tributaries, seasonal temperature changes, and rainfall are the most important factors driving these differences. A Water Quality Contour Map (WQCM) shows the water quality in different parts of the bay at a single point in time. By creating and comparing multiple contour maps, it is possible to detect patterns in water quality changes from month to month or from season to season, and to identify water quality trends over a longer period.

How are Water Quality Contour Maps Generated?

Water Quality Contour Maps are generated using water quality sample results for a single parameter (nitrogen, phosphorus, etc.), taken from sample sites spread across an open area of water like a bay or inlet, where the sampling events occur at regular intervals and reasonably close to each other in time.

The use of the word "contour" is a hold-over from the original maps which used lines (called isolines) to demarcate zones with similar water quality parameter values. Instead, the current method employs a "raster" or spatial surface that uses colors to indicate incremental changes in a water quality parameter over space. The geographic area to be represented is divided into equal-sized cells. Colors are assigned to subsets of the entire anticipated range of sample values.

Each cell receives a color value corresponding to the parameter value at that location. Cells that contain sample sites are colored using actual data. For all other cells, parameter values are calculated using a geographic information system (GIS) interpolation method called Inverse Distance Weighting (IDW) that is described below, under "Calculations." Once parameter values are calculated for each cell and each has been assigned a corresponding color, the resulting continuous color field creates a visual approximation of spatial water quality differences over the selected geographic area.

Viewed individually, Water Quality Contour Maps do not reveal temporal patterns in water quality. For this reason, maps are produced at regular intervals for each period during which water quality samples are available. When viewed chronologically, the maps can be used to detect seasonal patterns and long-term trends in water quality.

How are the data collected? (Methods)

The data used to create water quality contour maps are supplied by multiple agencies, municipalities, and/or volunteer monitoring groups and are subject to rigorous quality assurance/quality control standards. These collected data are managed through the Water Atlas data management system and can be downloaded using the Water Atlas Data Download tool (popular tools Main Page). As sample data become available, they are fed into the WQCM model and displayed as spatial water quality maps for the desired parameters.

WQCM Parameters

Tampa Bay Charlotte Harbor Sarasota Bay

Chlorophyll a
Color
Dissolved Oxygen (Bottom)
Dissolved Oxygen (Surface)
Salinity (Bottom)
Salinity (Surface)
Secchi Depth

Chlorophyll a
Color
Dissolved Oxygen (Bottom)
Dissolved Oxygen (Surface)
Fecal Coliform
Salinity (Bottom)
Salinity (Surface)
Secchi Depth
Temperature
Total Nitrogen
Total Phosphorus
Turbidity


Chlorophyll a
Color
Dissolved Oxygen (Bottom)
Dissolved Oxygen (Surface)
Salinity (Bottom)
Salinity (Surface)
Secchi Depth
Total Nitrogen
Total Phosphorus
Turbidity

WQCM Data Sources

Tampa Bay Charlotte Harbor Sarasota Bay

Hillsborough County
Environmental Protection Division

Map of sampling locations

Coastal Charlotte Harbor
Monitoring Network (CCHMN)

CCHMN Procedures and Quality Standards

Sarasota County
Environmental Services Department

Calculations

With a proper sample density and spread, we can assume that the change in a parameter value is primarily a function of spatial change and that, in open water, this change can be modeled by weighting the initial (sampled) value by a factor that is driven by the inverse ratio of concentration with distance. This modeling approach is called Inverse Distance Weighting (IDW) and is a common technique used in spatial visualization. The model assigns a measured or interpolated parameter value to each cell within a sample matrix, making adjustments for samples taken from sites that are near a shoreline, based on certain conditions established in the model (boundary conditions).

The weighting function (wi) for a distance x (wi(x)) is determined by calculating the inverse of the distance between the sample site and a selected distance from that site. A smoothing factor (p) is used to account for the influences of values closer to the sample site. The simplest expression for this factor is:

The calculations are carried out for all samples and in all directions along a plane from the sample site. The combinations of all resulting values are used to create a raster surface. For more information on the method used, please see the following reference links:

Caveats and Limitations

The most important caveat is that a collection of Water Quality Contour Maps is an estimate, and only an estimate, of spatial change to allow a better understanding of general bay condition during the month that saples were taken. Although it uses "real" data, it does not take into consideration transitory environmental conditions around the time of sampling event(s) that may generate sampling data that is not representative of overall bay health-such as heavy rainfall or rough, storm-driven tides.

Water Quality Contour Maps should be used only as a way to understand the overall condition of a bay, and by comparing a map from one time period to another time period, to show how the bay's condition changes over time, in order to draw attention to changes that warrant further investigation. Because the water quality of a bay or inlet is highly variable and influenced by rainfall, land use, biological growth and the interchange of salt and fresh water, and other factors, no one visualization can accurately and completely show all these contributing factors.

The IDW method is well-established and great care has been taken to select sufficiently dense data to produce representative simulations. The primary limitations of the method are shown below, based on the ESRI IDW discussion at: http://webhelp.esri.com/arcgisdesktop/9.3/index.cfm?TopicName=IDW

  • The output value for a cell using IDW is limited to the range of the values used to interpolate. Because IDW is a weighted distance average, the average cannot be greater than the highest or less than the lowest input.
  • The best results from IDW are obtained when sampling is sufficiently dense with regard to the local variation you are attempting to simulate. If the sampling of input points is sparse or uneven, the results may not sufficiently represent the desired surface (Watson and Philip, 1985)[1].
  • The barriers option is used to specify the location of linear features known to interrupt the surface continuity. Cliffs, faults, and embankments are typical examples of barriers. Barriers limit the selected set of the input sample points used to interpolate output z-values to those samples on the same side of the barrier as the current processing cell. Separation by a barrier is determined by line-of-sight analysis between each pair of points. This means that topological separation is not required for two points to be excluded from each other's region of influence. Input sample points that lie exactly on the barrier line will be included in the selected sample set for both sides of the barrier.

[1] Watson, D.F., and G.M. Philip. A Refinement of Inverse Distance Weighted Interpolation. Geoprocessing, 2:315-327. 1985.

Additional Information