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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Environmental Microbial & Food Safety Laboratory » Research » Publications at this Location » Publication #267923
Title: Scale effects on information theory-based measures applied to streamflow patterns in two rural watersheds

Author
item PAN, FENG - University Of Maryland
item Pachepsky, Yakov
item Guber, Andrey
item HILL, ROBERT - University Of Maryland

Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/1/2011
Publication Date: 2/1/2012
Citation: Pan, F., Pachepsky, Y.A., Guber, A.K., Hill, R. 2012. Scale effects on information theory-based measures applied to streamflow patterns in two rural watersheds. Journal of Hydrology. 414-415:99-107.

Interpretive Summary: Understanding streamflow patterns in space and time is important to improve flood and drought forecasting, water resources management, and predictions of ecological changes. An efficient method of streamflow pattern characterization and discrimination has previously been proposed that consists of representing streamflow variations with a small number of symbols, and using information theory parameters to characterize the randomness and the complexity of streamflow patterns. However, the effect of frequency of streamflow measurements and the area of the watershed on the information theory parameters has never been researched. We evaluated this effect with data from two USDA watersheds and found that measurement frequency had a pronounced effect on the complexity and information content measures, whereas the watershed area was only weakly related to the measures. Results of this study are of use for watershed modeling in that they identify the need to factor in measurement frequency in the performance evaluation of hydrological models with information theory parameters.

Technical Abstract: Understanding streamflow patterns in space and time is important to improve the flood and drought forecasting, water resources management, and predictions of ecological changes. The objectives of this work were (a) to characterize the spatial and temporal patterns of streamflow using information theory-based measures at two thoroughly monitored agricultural watersheds located in different hydroclimatic zones and having similar land use, and (b) to evaluate temporal and spatial scale effects on those measures. Little River (LREW), Tifton, Georgia and Sleepers River (SREW), North Danville, Vermont, USDA experimental watersheds were selected as the study sites. Both watersheds include several nested sub-watersheds with more than 30-year continuous data records of precipitation and streamflow. The information content measures (i.e., metric entropy and mean information gain in this study) and complexity measures (i.e., effective measure and fluctuation complexity) were computed based on the binary encoding of 5-year streamflow and precipitation time series. Patterns of streamflow were quantified using probabilities of joint or sequential appearances of the binary symbol sequences. The information content measures of streamflow time series were much smaller than the one of precipitation data, and had higher complexity, indicating that watersheds acted as filters of information brought by precipitation and imposed additional complexity to the streamflow. The correlation coefficients between the information content and complexity measures, and averaging time interval were close to 0.9, demonstrating the significant effects of temporal scale on the streamflow patterns. The moderate spatial scale effects on the streamflow patterns were observed with absolute values of correlation coefficients between the measures and sub-watershed area varying from 0.2 to 0.6 in the two watersheds. Temporal effects have to be taken in account when the information theory-based models are used in performance evaluation and comparison of hydrological models.