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Characterization of surface and ground water δ18O seasonal variation and its use for estimating groundwater residence times

✍ Scribed by Michael M. Reddy; Paul Schuster; Carol Kendall; Micaela B. Reddy


Publisher
John Wiley and Sons
Year
2006
Tongue
English
Weight
349 KB
Volume
20
Category
Article
ISSN
0885-6087

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✦ Synopsis


18

O is an ideal tracer for characterizing hydrological processes because it can be reliably measured in several watershed hydrological compartments. Here, we present multiyear isotopic data, i.e. 18 O variations (υ 18 O), for precipitation inputs, surface water and groundwater in the Shingobee River Headwaters Area (SRHA), a well-instrumented research catchment in north-central Minnesota. SRHA surface waters exhibit υ 18 O seasonal variations similar to those of groundwaters, and seasonal υ 18 O variations plotted versus time fit seasonal sine functions. These seasonal υ 18 O variations were interpreted to estimate surface water and groundwater mean residence times (MRTs) at sampling locations near topographically closed-basin lakes. MRT variations of about 1 to 16 years have been estimated over an area covering about 9 km 2 from the basin boundary to the most downgradient well. Estimated MRT error (š0Ð3 to š0Ð7 years) is small for short MRTs and is much larger (š10 years) for a well with an MRT (16 years) near the limit of the method. Groundwater transit time estimates based on Darcy's law, tritium content, and the seasonal υ 18 O amplitude approach appear to be consistent within the limits of each method. The results from this study suggest that use of the υ 18 O seasonal variation method to determine MRTs can help assess groundwater recharge areas in small headwaters catchments.


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Comment on ‘M. M. Reddy, P. Schuster, C.
✍ R. E. Criss; W. E. Winston 📂 Article 📅 2006 🏛 John Wiley and Sons 🌐 English ⚖ 137 KB

## Abstract Reddy __et al__. (2006, __Hydrological Processes__ **20**: 1753–1772) disregard germane published data and successful isotopic models, selectively depict data from their data sets, misplot many of their data, and use poorly correlated sinusoidal fits to their data to derive erroneous hy