HR: 14:25h
AN: H13M-03    [Abstracts]
TI: Generality of Fractal 1/f Scaling in Catchment Tracer Time Series: Implications for Catchment Travel Time Distributions
AU: * Godsey, S E
EM: godseys@eps.berkeley.edu
AF: UC-Berkeley Dept. of Earth & Planetary Science, 307 McCone Hall #4767, Berkeley, CA 94720-4767, United States
AU: Palucis, M C
EM: mpalucis@calmail.berkeley.edu
AF: UC-Berkeley Dept. of Earth & Planetary Science, 307 McCone Hall #4767, Berkeley, CA 94720-4767, United States
AU: Kirchner, J W
EM: kirchner@seismo.berkeley.edu
AF: UC-Berkeley Dept. of Earth & Planetary Science, 307 McCone Hall #4767, Berkeley, CA 94720-4767, United States
AU: Kirchner, J W
EM: kirchner@seismo.berkeley.edu
AF: Swiss Federal Institute for Forest Snow and Landscape Research (WSL), Zuercherstrasse 111, Birmensdorf, CH-8903, Switzerland
AB: The mean travel time - the time that it takes a parcel of rainwater to reach the stream - is a basic parameter used to characterize catchments. More generally, a catchment is characterized by its travel-time distribution, which is described not only by its mean but also its shape. The travel time distribution of water in a catchment is typically inferred from passive tracer time series (typically water isotopes or chloride concentrations) in rainfall and streamflow. The catchment mixes precipitation inputs (and thus passive tracers) falling at different points in time; as a result, tracer fluctuations in streamflow are usually strongly damped relative to precipitation. Mathematically, this mixing of waters of different ages is represented by the convolution of the travel time distribution and the precipitation inputs to generate the stream outputs. Previous analyses of both rainfall and streamflow tracer time series from several catchments in Wales have demonstrated that rainfall chemistry spectra resemble white noise, whereas these same catchments exhibit fractal 1/f scaling in stream tracer chemistry over three orders of magnitude. These observations imply that these catchments have an approximate power-law distribution of travel times, and thus they retain a long memory of past inputs. The observed fractal scaling places strong constraints on possible models of catchment behavior: commonly-used exponential or advection-dispersion travel time distribution models do not exhibit fractal scaling. Here we test the generality of the observed fractal scaling of streamflow chemistry, by analyzing long-term tracer time series from 17 other catchments in North America and Europe. Special care is taken to account for the effects of spectral aliasing. We demonstrate that 1/f fractal scaling of stream chemistry is a common feature of these catchments and discuss the implications of this observation to catchment-scale hydrologic modeling. We then present the best-fit travel time distributions for each site and explain differences among the sites in light of available hydrometric information.
DE: 1804 Catchment
DE: 1854 Precipitation (3354)
DE: 1860 Streamflow
DE: 1872 Time series analysis (3270, 4277, 4475)
DE: 1879 Watershed
SC: Hydrology [H]
MN: 2007 Fall Meeting