HR: 14:40h
AN: H23G-05    [Abstracts]
TI: Application of Time-Series Method for Estimating Surface Water - Groundwater Interaction from Streambed Thermal Records
AU: * Hatch, C E
EM: chatch@es.ucsc.edu
AF: University of California, Santa Cruz, Earth Sciences, UCSC E&MS A232 1156 High Street, Santa Cruz, CA 95064 United States
AU: * Hatch, C E
EM: chatch@es.ucsc.edu
AF: U.S. Geological Survey, Mailstop 496 345 Middlefield Road, Menlo Park, CA 94025 United States
AU: Fisher, A T
EM: afisher@es.ucsc.edu
AF: University of California, Santa Cruz, Earth Sciences, UCSC E&MS A232 1156 High Street, Santa Cruz, CA 95064 United States
AU: Constantz, J
EM: jconstan@usgs.gov
AF: U.S. Geological Survey, Mailstop 496 345 Middlefield Road, Menlo Park, CA 94025 United States
AU: Revenaugh, J S
EM: justinr@tc.umn.edu
AF: Univeristy of Minnesota, Geology and Geophysics 310 Pillsbury Drive SE, Minneapolis, MN 55455-0219 United States
AU: Ruehl, C R
EM: cruehl@es.ucsc.edu
AF: University of California, Santa Cruz, Earth Sciences, UCSC E&MS A232 1156 High Street, Santa Cruz, CA 95064 United States
AB: Established methods for estimating seepage from streambed thermal methods, including the use of forward models, can be time consuming, generally include relatively short time periods, and may require independent determination or calibration of hydraulic properties. We apply a newly developed method for interpretation of streambed thermal data, using long records from multiple depths. We use forward models to define relationships between seepage rate and thermal response with depth, generate type curves, and apply modeled relationships to measured temperature records to generate long-term estimates of streambed seepage. Spectral analyses of streambed thermal records from different depths show clearly that the diurnal period has the greatest power, and that there is generally strong coherence between thermal records collected from different depths at a single location. The frequency content of propagating temperature signals does not change significantly with depth, but variations in the phase and amplitude of temperature changes are well explained by coupled heat and fluid flow across the streambed. Data are filtered with a cosine-taper, band-pass filter to extract the dominant diurnal signal prior to analysis. Data from models used to generate type curves are run through the same filter to minimize errors associated with filter leakage. We have completed a series of forward models under different conditions to assess the sensitivity of the method to relative and absolute sensor depth, sediment thermal properties, abrupt changes in seepage rate, irregular thermal variations at the stream-sediment interface, and multi-directional subsurface flow. For the smallest sensor spacing, the largest range of seepage rates can be derived using the amplitude ratio. In contrast, for the largest relative depth, the largest range of seepage rates can be derived using the phase shift. Abrupt changes in seepage rate that are shorter than one day tend to be smoothed during the filtering process, as expected, but longer, more gradual changes are well resolved. Given typical streambed thermal properties, the time series method for quantifying seepage works best for specific seepage rates on the order of -3.0 to 2.5 m/day (negative down), which should allow use in most natural systems.
DE: 1899 General or miscellaneous
DE: 1800 HYDROLOGY
DE: 1829 Groundwater hydrology
DE: 1832 Groundwater transport
DE: 1878 Water/energy interactions
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting