By Scott T. Prinos

CORRELATION ANALYSIS OF A GROUND-WATER LEVEL MONITORING NETWORK

Analytical Considerations

Spatial Relations

Complex water-supply and water-management systems could affect the extent to which water-level data from specific monitoring wells correlate. The effect that these hydrologic systems have on water levels in monitoring wells is determined by the proximity of the wells to the components of the systems and the transmissivity of the aquifers. In Miami-Dade County, an example of spatial influence could be poor correlation in water-level data from two shallow monitoring wells near separately regulated canals. Another example could be poor correlation in the water levels from monitoring wells near separate municipal well fields. In each example, the extent of correlation is influenced by one overriding component within the system. The water-level data from each monitoring well in the network, however, will likely represent the net effect of all components in these systems that are proximal to the well.

The water-supply and water-management systems are not the only spatial influences on water-level correlation between monitoring wells. Natural factors can affect spatial correlation in ground-water level data. One such natural factor, rainfall, can be intense and extremely localized in southern Florida (fig. 3A and 3B). Therefore, widely separated wells are less likely to show influence from the same rainfall events. Land use is another factor that could influence correlation. Rainfall runoff in urbanized areas is different than in natural settings. In urban settings, pavement and storm sewers direct water into streams and canals; in natural settings, rainfall can readily infiltrate the soil. Evapotranspiration in urban and natural settings also is likely to be different. Because of the combined effects of all of these factors, correlation in the water-level data in widely separated wells will generally be poor.

Conversely, water-level data from two wells will correlate with each other if both are predominately influenced by the same causal factors. For example, two wells influenced solely by pumpage from the same municipal water-supply well or recharge from the same section of a canal will generally indicate high correlation unless differences in the hydrogeology, such as separation by impermeable zones, create differing responses. If water-level data from two wells indicate high correlation, however, there is no certainty that these wells are being influenced by the same causal factors. If two separate canals or two separate municipal water-supply wells were managed in similar fashion, water-level data from the wells that monitor these two areas might indicate high correlation. Any change, however, in the management of either the canal or municipal water-supply well would create a change in the correlation between these monitoring wells. Therefore, although the correlation in water levels in these two areas may be high, it is not very useful for assessing the redundancy of the monitoring wells in these areas.

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