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ORIGINAL RESEARCH

Susceptibility to Enhanced Chemical Migration from Depression-Focused Preferential Flow, High Plains Aquifer

U.S. Geological Survey, Denver Federal Center, Mail Stop 415, Lakewood, CO 80225. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government
^* Corresponding author (jjgurdak{at}usgs.gov).

Received 20 August 2007.

Aquifer susceptibility to contamination is controlled in part by the inherent hydrogeologic properties of the vadose zone, which includes preferential-flow pathways. The purpose of this study was to investigate the importance of seasonal ponding near leaky irrigation wells as a mechanism for depression-focused preferential flow and enhanced chemical migration through the vadose zone of the High Plains aquifer. Such a mechanism may help explain the widespread presence of agrichemicals in recently recharged groundwater despite estimates of advective chemical transit times through the vadose zone from diffuse recharge that exceed the historical period of agriculture. Using a combination of field observations, vadose zone flow and transport simulations, and probabilistic neural network modeling, we demonstrated that vadose zone transit times near irrigation wells range from 7 to 50 yr, which are one to two orders of magnitude faster than previous estimates based on diffuse recharge. These findings support the concept of fast and slow transport zones and help to explain the previous discordant findings of long vadose zone transit times and the presence of agrichemicals at the water table. Using predictions of aquifer susceptibility from probabilistic neural network models, we delineated approximately 20% of the areal extent of the aquifer to have conditions that may promote advective chemical transit times to the water table of <50 yr if seasonal ponding and depression-focused flow exist. This aquifer-susceptibility map may help managers prioritize areas for groundwater monitoring or implementation of best management practices.

Abbreviations: ANN, artificial neural network • GIS, geographic information system • PNN, probabilistic neural network