Geostatistics-based groundwater-level monitoring network design and its application to the Upper Floridan aquifer, USA.

A geostatistical method was applied to optimize an existing groundwater-level monitoring network in the Upper Floridan aquifer for the South Florida Water Management District in the southeastern United States. Analyses were performed to determine suitable numbers and locations of monitoring wells that will provide equivalent or better quality groundwater-level data compared to an existing monitoring network. Ambient, unadjusted groundwater heads were expressed as salinity-adjusted heads based on the density of freshwater, well screen elevations, and temperature-dependent saline groundwater density. The optimization of the numbers and locations of monitoring wells is based on a pre-defined groundwater-level prediction error. The newly developed network combines an existing network with the addition of new wells that will result in a spatial distribution of groundwater monitoring wells that better defines the regional potentiometric surface of the Upper Floridan aquifer in the study area. The network yields groundwater-level predictions that differ significantly from those produced using the existing network. The newly designed network will reduce the mean prediction standard error by 43% compared to the existing network. The adoption of a hexagonal grid network for the South Florida Water Management District is recommended to achieve both a uniform level of information about groundwater levels and the minimum required accuracy. It is customary to install more monitoring wells for observing groundwater levels and groundwater quality as groundwater development progresses. However, budget constraints often force water managers to implement cost-effective monitoring networks. In this regard, this study provides guidelines to water managers concerned with groundwater planning and monitoring.

A comparison of storm-based and annual-based indices of hydrologic variability: a case study in Fort Benning, Georgia.

The magnitude, frequency, duration, timing, and rate of change of hydrologic conditions regulate ecological processes in aquatic ecosystems. Conditions are typically characterized using annual-based hydrologic indices derived from daily and/or monthly stream flow data. In this study, we present an alternative approach to identify hydrologic indices based on storm hydrographs. Hydrologic indices derived from long-term daily flow data were compared to those from storm events for two headwater watersheds in Fort Benning, Georgia. Five hydrologic indices derived from daily flow data and storm events shared common features. Storm-based magnitude of mean peak discharge and mean response factor, frequency of bankfull discharge, rate of change in mean slopes of rising, and falling limb of the hydrograph were consistent with the results from long-term daily flow data. The annual flow increases and decreases were well matched by stormflow rising and falling. Both indicators showed one watershed having three times the response rates as compared to the other. Results suggested that select storm-based indices may be used as surrogates to the indices derived from long-term data.