Cost-effective monitoring strategies to estimate mean water table depth

Cost-effect monitoring strategies exist for estimating mean depth to the water table and for characterizing fluctuations about that mean. These strategies can also be used to evaluate the utility of sparse water level data sets for making such estimates. This type of information is needed to design or reduce costs of observation well networks, to characterize regional water-table fluctuations for use in maps or models, and to calibrate ground-water models using hydraulic head estimates. The study is based on a statistical analysis of hourly water levels measured at observations wells by the U.S. Geological Survey (USGS) over a five-year period. Using Monte Carlo simulation techniques, sampling results for different monitoring strategies were compared with parameters of the underlying distribution of hourly water levels. Monthly, bimonthly, quarterly, and even triannual water-level monitoring was found to effectively estimate mean water table depth and standard deviation to within 30 cm. Quarterly and triannual sampling were found more cost-effective than monthly observations, and electronic monitoring more effective than manual measurements for sampling made more frequently than monthly. Results have broad application for studies involving surficial aquifers in humid regions of the world where annual rainfall exceeds 50 cm and is reasonably distributed throughout the year. Governmental agencies and universities have a wealth of water table monitoring records, collected with through ambient monitoring or as part of a permit requirement or study. These sparse data sets hold the promise of significantly increasing the hydrogeologic knowledge of water table behavior in many regions of the world