|Pan, F -|
|Hill, R. -|
Submitted to: American Geophysical Union
Publication Type: Abstract Only
Publication Acceptance Date: January 28, 2011
Publication Date: December 10, 2011
Citation: Pan, F., Pachepsky, Y.A., Guber, A.K., Hill, R.L. 2011. Scale effects on information content and complexity of streamflows. [abstract] American Geophysical Union. H11C-0818. Technical Abstract: Understanding temporal and spatial variations of streamflows is important for flood forecasting, water resources management, and revealing interactions between hydrologic processes (e.g., precipitation, evapotranspiration, and soil water and groundwater flows.) The information theory has been used in several studies to assess the complexity of hydrological variables and to characterize patterns of their changes in space and time. The objective of this study was to apply information and complexity measures to characterize the temporal and spatial variations of streamflows at small agricultural watersheds. The study site was the USDA-ARS North Appalachian experimental watershed with more than 30-year continuous data records of precipitation and streamflow. Information content measures were the metric entropy and the mean information gain, and complexity measures were the fluctuation complexity and the effective measure complexity. These measures were computed based on the binary encoding of daily, half-daily and hourly time series of streamflow and precipitation at five catchments in the watershed. Patterns in streamflows were quantified using probabilities of joint or sequential appearances of the symbol sequences. The information content of streamflow time series was much smaller than the one of precipitation data, and had the higher complexity, indicating that watersheds acted as filters of information brought by precipitation. The information content and complexity measures of streamflow time series varied substantially depending on observation frequency and catchment location, size, and land use. The information content and complexity measures can provide useful complementary knowledge about the hydrologic system complexity and its variability in space and time.