The Amy H Remley Foundation  
   
     
 

Citrus County

The following analysis is taken from:

Baker, A.E., Wood, A.R., and Cichon, J.R., 2008 (in review), The Citrus County Aquifer Vulnerability Assessment: Contract deliverable report prepared for the Florida Geological Survey - Florida Department of Environmental Protection.

THE CITRUS COUNTY AQUIFER VULNERABILITY ASSESSMENT

Part of Phase II of the Florida Aquifer Vulnerability Assessment (FAVA) Project, Florida Department of Environmental Protection Contract No. RM059, July, 2008.

Prepared for the Florida Department of Environmental Protection by Advanced GeoSpatial Inc.

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THE CITRUS COUNTY AQUIFER VULNERABILITY ASSESSMENT

NOTE: In discussion with the principal author of the Analysis, it was confirmed that no consideration was given to the effect of fault lines or fracture sets which are prevalent in the area covered by the analysis. [But see Fracture Sets Example]

Interpretation of Results in Context of FAVA

Results of the CAVA project have allowed delineation of new and unique zones of relative vulnerability for the FAS in Citrus County, based on the county-specific model boundary used, use of numerous well points for aquifer confinement characterization, incorporation of most recent soils data, and application of recently-developed approaches for karst estimation in a GIS. These new results, though refined and highly detailed, do not replace results of previous studies. In other words, the FDEP's regional FAVA results (Arthur et al., 2005) for the FAS indicate that the Citrus County study area occurs in primarily a "more vulnerable" zone relative to other areas in Florida (Figure 17); as a result the new CAVA model output should be interpreted in the context of this major regional project. The new zones delineated in the CAVA project are unique to the CAVA study area, and reveal more detailed information regarding aquifer vulnerability within the regional "more vulnerable", and "vulnerable" zones identified in the FAVA project.

Figure 17
Figure 17. Results of the Florida Aquifer Vulnerability Assessment project (Arthur et al., 2005) for the FAS in Citrus County. The CAVA model relative vulnerability zones, while based on more refined data than the FAVA project, occur within the context of this regional model.
Figure 1
Figure 1. Citrus County Aquifer Vulnerability Assessment project study area corresponds to the County’s political boundary.

Study Area

The political boundary of Citrus County was used as the CAVA model study area extent (shown in Figure 1).

Soil Hydraulic Conductivity and Soil Pedality Themes

The rate that water moves through soil is a critical component of any aquifer vulnerability analysis, as soil is literally an aquifer system’s first line of defense against potential contamination (Arthur et al., 2005). Two parameters of soils were evaluated for input into the CAVA model: soil hydraulic conductivity, which is the “amount of water that would move vertically through a unit area of saturated soil in unit time under unit hydraulic gradient” (U.S. Department of Agriculture, 2005); and soil pedality, which is calculated based on soil type, soil grade, and soil pedon size, and is a unitless parameter. Soil pedality is a relatively new concept used to estimate the hydrologic parameter of soil and is generated for CAVA using the pedality point method developed by Lin et al. (1999).

In 2006, Citrus County soils data were expanded for the study area and made available by the Natural Resources Conservation Service. This expansion included adaptation into ESRI geodatabase compatible format, and specific soils values were updated (U.S. Department of Agriculture, 2006). As a result, more detailed information is available for analysis for the CAVA project than during previous projects (e.g., Arthur et al., 2005). Countywide data sets representing soil hydraulic conductivity and soil pedality were developed for use as input into the CAVA model. This is completed for both hydraulic conductivity and soil pedality.

Weights calculated during sensitivity analysis for soil pedality were much stronger (i.e., had higher absolute value) than weights calculated for soil hydraulic conductivity. As a result, soil pedality was chosen as the better predictor of aquifer vulnerability because it shared the best association with training points.

Soil pedality, a unitless parameter, ranges from 0.0157 to 0.0533 across the study area. The analysis indicated that areas underlain by 0.0429 to 0.0533 were more associated with the training points, and therefore associated with higher aquifer vulnerability. Conversely, areas underlain by 0.0428 to 0.0157 were less associated with the training points, and therefore lower aquifer vulnerability. Based on this analysis, the evidential theme was generalized into two classes as displayed in Figure 10.

Figure 10
Figure 10. Generalized soil pedality evidential theme; based on calculated weights analysis blue areas share a weaker association with training points and thereby relatively lower aquifer vulnerability, whereas red areas share a stronger association with training points.

Recharge Potential

In Copeland et al. (1991), the area of the Brooksville Ridge in central Florida is defined as having higher recharge potential than adjacent areas. The Brooksville Ridge is chiefly composed of Undifferentiated Hawthorn Group sediments which are poorly to moderately consolidated clayey sands and silty clays (Scott et al., 2001). In Citrus County, these sediments reach a maximum calculated thickness of 199 feet, can be discontinuous, deeply weathered and highly perforated by karst features.

In Citrus County these sediments are generally highly weathered, leaky, thin and intensely breached by karst features. These factors combine to increase the recharge potential to the FAS in the study area.

Recharge potential values were calculated for the study area by subtracting the USGS 2000 potentiometric surfacean imaginary surface defined by the level to which water in an aquifer would rise in a well due to the natural pressure in the rocks. of the FAS (USGS, 2000) from land surface elevation derived from USGS 7.5” quadrangles. Resulting recharge potential values range from -18 ft to greater than 150 ft (relative to mean sea level). Negative values generally correspond to areas where the aquifer is estimated to be discharging while higher positive values are restricted to the more substantial hills located on the Brooksville Ridge.

Categories of recharge potential were derived from records of the potentiometric surface. A preliminary weights of evidence indicated a very strong relationship between training points and recharge potential. Categories of recharge potential were ranked as displayed in Figure 5. Use of recharge potential via this approach is restricted to areas of Florida where the FAS is not well confined (e.g., this layer may not be usable in areas which are also underlain by thicker, contiguous Intermediate confining unit sediments), and where there is not a laterally contiguous Surficial aquifer system present.

Figure 5
Figure 5. Recharge potential estimated from FAS potentiometric surfacean imaginary surface defined by the level to which water in an aquifer would rise in a well due to the natural pressure in the rocks. data, land surface elevation and estimates developed for Copeland et al., (1991).

Intermediate Confining Unit / Overburden Thickness Themes

Aquifer confinement – either in the form of overburden overlying the FAS, or the Intermediate confining unit (ICU) – is another critical layer in determining aquifer vulnerability. Where aquifer confinement is thick and the FAS is deeply buried, aquifer vulnerability is generally lower, whereas in areas of thin to absent confinement, the vulnerability of the FAS is generally higher.

To allow the calculation of aquifer confinement thickness in various study areas, surface models were developed using a dataset of borehole records supplemented with well gamma logs that contain descriptions of subsurface materials. AGI (the consultants) used these surfaces to calculate thickness of the ICU (Figure 6) and thickness of overburden overlying the FAS (Figure 7) in the study area. These two layers were tested for input in the model as described in Sensitivity Analysis.

Figure 6 Figure 7
Figure 6. Thickness of the ICU calculated by subtracting predicted surface of ICU (Figure 6) from predicted surface of FAS as generated by FGS/FDEP. Figure 7. Thickness of sediments overlying the FAS calculated by subtracting digital elevation data from predicted surface of FAS as generated by FGS/FDEP.

Potential Karst Feature Theme

Karst features, or sinkholes and depressions, can provide preferential pathways for movement of surface water into the underlying aquifer system and enhance an area’s aquifer vulnerability where present. The closer an area is to a karst feature, the more vulnerable it may be considered. Closed topographic depressions extracted from U.S. Geological Survey 7.5-minute quadrangle maps served as the initial dataset from which to estimate potential karst features in the study area. To supplement these data, the FGS/FDEP sinkhole database was included to identify karst features possibly not represented on USGS maps. These two data sources displayed in Figure 8 were combined and analyzed to develop a potential karst features evidential theme. See Figure 13 below.

Figure 13
Figure 13. Potential karst features evidential theme buffered into 100-ft zones for proximity analysis in the weights of evidence analysis.

Potential Karst Features

As mentioned above, areas closer to a potential karst feature are normally associated with higher aquifer vulnerability. Based on this, features were buffered into 100-ft zones to allow for a proximity analysis (Figure 13). The analysis indicated that areas within 3,346 feet of a karst feature were more associated with the training points, and therefore with higher aquifer vulnerability. Conversely, areas greater than 3,346 feet from a karst feature were less associated with the training points, and therefore lower aquifer vulnerability. Based on this analysis, the evidential theme was generalized into two classes as displayed in Figure 14.

Figure 14
Figure 14. Generalized potential karst feature evidential theme; based on calculated weights analysis blue areas share a weaker association with training points and thereby relatively lower aquifer vulnerability, whereas red areas share a stronger association with training points.

Response Theme

Using evidential themes representing soil pedality, recharge potential, ICU thickness, and potential karst, weights of evidence was applied to generate a response theme, which is a GIS raster consisting of posterior probability values ranging from 0.0002697 to 0.0343457 across the study area (Figure 15). These probability values describe the relative probability that a unit area of the model will contain a training point – i.e., a point of aquifer vulnerability as defined above in Training Points – with respect to the prior probability value of 0.01043. Prior probability is the probability that a training

Figure 15
Figure 15. Relative vulnerability map for the Citrus County Aquifer Vulnerability Assessment project. Classes of vulnerability are based on calculated probabilities of a unit area containing a training point, or a monitor well with water quality sample results indicative of vulnerability.
Figure 18
Figure 18. Confidence map for the CAVA model calculated by dividing the posterior probability values by the total uncertainty for each class to give an estimate of how well specific areas of the model are predicted.

Model Limitations and Scale of Use

When implementing the CAVA model results, it is vital to remember that aquifer systems in Florida are vulnerable to contamination; an invulnerable aquifer does not exist. Model results are based on features of the natural system that have significant association with the location of training points and thereby aquifer vulnerability. The CAVA project results provide a probability map that identifies zones of relative vulnerability in the study area based on input data; as a result the CAVA model output is an estimation of natural aquifer vulnerability and the results do not account for activities at land surface, contaminant type, groundwater flow paths or fate/transport of chemical constituents.

Derivative Products: Protection Zones

Relative vulnerability zones defined in this project may be applied to develop derivative maps, such as a protection-zone map (Cichon et al., 2005). Ideally, data layers not included as input in the aquifer vulnerability model would be considered to help in defining such protection zones and may include groundwater flow modeling, stream-sink features, induced drawdown areas from large well fields, and distribution of drainage wells. These layers, while important to aquifer vulnerability, do not form usable input into this aquifer vulnerability assessment project.

Confidence Map

As mentioned above, a confidence map of the model’s posterior probability values can be calculated by dividing the posterior probability by its total uncertainty. This essentially applies an informal student T-test (as in Table 2) to the posterior probability values. The higher the confidence values, the greater the certainty is with regard to the posterior probability. This map essentially indicates the degree of confidence to which the posterior probabilities are meaningful and should be referenced when interpreting and implementing the model results. In other words, the confidence map should be used to help guide implementation of the vulnerability map as it reveals the confidence level associated with each vulnerability class (Mihalasky and Moyer, 2004).

Surface Water Areas

In addition to large surface-water bodies omitted from the analysis, there are many other surface-water features which were not removed. Many of these features may represent areas of groundwater discharge; however, these discharging surface waters are not part of the aquifer, although they originate from it. Accordingly, the CAVA model is not intended to be used to assess contamination potential of surface waters, though the discharging surface waters are highly vulnerable to contamination.

Recommendations on Scale of Use

Use of highly detailed evidential theme data as model input results in highly resolute model output as can be seen in the model response theme. These resolute features are reflections of real data used as input; however, the final maps should not be applied to very large scales such as to compare adjacent small parcels.

CAVA model output is, in a sense, as accurate as the most detailed input layer, and as inaccurate as the least detailed layer. For example, wells used to define confinement thickness represent an area up to 25 square miles (mi 2 ), the potentiometric surfacean imaginary surface defined by the level to which water in an aquifer would rise in a well due to the natural pressure in the rocks. map used in the development of the recharge potential evidential theme was mapped at 1:500,000, and soils polygonal data represent an area as small as 19,375 ft 2 .

Every raster cell of the model output coverage has significance per the model input as discussed above. However, it is important to note that aquifer vulnerability assessments are predictive models and no assumptions are made that all input layers are accurate, precise or complete at a single-raster cell scale. As mentioned above, the confidence map, because it is an indicator of the meaningfulness of the vulnerability classes, should be used to help guide implementation of the vulnerability map. For example, in the CAVA confidence map (Figure 18), local-scale land-use decisions might be more defensible in with the higher vulnerability classes (more vulnerable and most vulnerable) as these areas are associated with highest confidence values.

Ultimately, accuracy of the maps does not allow for evaluation of aquifer vulnerability at a specific parcel or site location. It is the responsibility of the end-users of the CAVA model output to determine specific and appropriate applications of these maps. In no instance should use of aquifer vulnerability assessment results substitute for a detailed, site-specific hydrogeological analysis.

CONCLUSION

As demands for fresh groundwater from the Floridan aquifer system underlying Citrus County increase resulting from continued population growth, identification of zones of relative vulnerability becomes an increasingly important tool for implementation of a successful groundwater protection and management program.

The results of the CAVA project provide a science-based, water-resource management tool allowing for a pro-active approach to protection of the FAS, and, as a result, have the potential to increase the value of protection efforts.

Model results will enable improved decisions to be made about aquifer vulnerability based on the input selected, including focused protection of sensitive areas such as springsheds and ground-water recharge areas.

The results of the CAVA vulnerability model are useful for development and implementation of groundwater protection measures; however, the vulnerability output map included in this report should not be viewed as a static evaluation of the vulnerability of the FAS. Because the assessments are based on snapshots of best-available data, the results are static representations; however, a benefit of this methodology is the flexibility to easily update the response themes as more refined or new data becomes available. In other words, As the scientific body of knowledge grows regarding hydrogeologic systems, this methodology allows the ongoing incorporation and update of datasets to modernize vulnerability assessments thereby enabling end users to better meet their objectives of protecting these sensitive resources. The weights of evidence modeling approach to aquifer vulnerability is a highly adaptable and useful tool for implementing ongoing protection of Florida’s vulnerable groundwater resources.

NOTE: In discussion with the principal author of the Analysis, it was confirmed that no consideration was given to the effect of fault lines or fracture sets which are prevalent in the area covered by the analysis. [See Fracture Sets Example]

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