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MODELING CONCENTRATION VARIATIONS IN HIGH‐CAPACITY WELLS: IMPLICATIONS FOR GROUNDWATER SAMPLING 1
Author(s) -
Gosselin David C.,
Ayers Jerry F,
Zhang YouKuan
Publication year - 1994
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/j.1752-1688.1994.tb03316.x
Subject(s) - aquifer , groundwater , hydrogeology , water well , water table , environmental science , hydrology (agriculture) , sampling (signal processing) , soil science , water quality , geology , geotechnical engineering , ecology , filter (signal processing) , computer science , computer vision , biology
High‐capacity wells are used as a convenient and economical means of sampling groundwater quality. Although the inherent limitations of using these wells are generally recognized, little has been done to investigate how these wells actually sample groundwater. A semi‐analytical particle tracking model is used to illustrate the influence of variable vertical contaminant distributions and aquifer heterogeneity on the composition of water samples from these wells during short pumping periods. The hypothetical pumping well used in the simulations is located in an unconfined, alluvial aquifer with a shallow water table and concentration gradients of nitrate‐nitrogen contamination. This is a typical setting for many irrigated areas in the United States. The main conclusions are: (1) high‐capacity wells underestimate the average amount of contamination within an aquifer; (2) shapes of concentration‐time curves for high‐capacity wells appear to be governed by the distribution of the contaminant and travel times to the well; (3) variables such as well construction, pumping rate, and hydrogeologic properties contribute to the magnitude of the concentration‐time curves at individual high‐capacity wells; and (4) a sampling strategy using concentration‐time curves based on the behavioral characteristics of the well rather than individual samples will provide a much better framework for interpreting spatial contaminant distributions.