Submitted to: American Nuclear Society
Publication Type: Government Publication
Publication Acceptance Date: August 1, 2001
Publication Date: N/A
Interpretive Summary: This report provides information and data bases for assessing analytic and field methods for estimating net infiltration and net ground-water recharge and their associated uncertainties. Uncertainty in this context refers to information loss due to intermittent and low frequency monitoring. Infrequent monitoring of highly transient events can lead to significant loss of information, e.g., timing and quantity of ground-water recharge. This research provided insights into data and conceptual model uncertainties at the site scale (hectare) for a shallow (less than 10 m) unsaturated zone. This report provides comparisons of "real-time" models against detailed, site specific water content data. Further comparisons of other infiltration models using these data sets are feasible. The datasets and the programs used in this study are available as computer readable files from the USDA-National Agriculture Library.
This study investigated field instrumentation [multi-sensor capacitance probes (MCP)] and analytical methods for estimating "real-time" infiltration and subsequent ground-water recharge and their attendant uncertainties. The MCP data allowed comparative estimates of ground-water recharge using near-continuous water content measurements to recharge estimates based on less frequent water content observations (e.g., hourly or daily), intermittently measured piezometric data or analytical models. Drainage was underestimated by only using changes in water contents measured by MCP. Differences in water content did not always accurately represent fluxes when the system was at steady state. The estimate of net ground-water recharge decreased as measurement frequency decreased. The MCP data provided better estimates of recharge and timing than the piezometer data. Estimates of ground-water recharge were also compared to simulated recharge using a PNNL water budget model. The optimization of data in combination with a model can significantly reduce errors associated with using changes in water contents alone.