Time-scale dependence in numerical simulations: Assessment of physical, chemical, and biological predictions in a stratified lake at temporal scales of hours to months

  • Authors:

  • Emily L. Kara;Paul Hanson;David Hamilton;Matthew R. Hipsey;Katherine D. McMahon;Jordan S. Read;Luke Winslow;John Dedrick;Kevin Rose;Cayelan C. Carey;Stefan Bertilsson;David da Motta Marques;Lucas Beversdorf;Todd Miller;Chin Wu;Yi-Fang Hsieh;Evelyn Gaiser;Tim Kratz

  • Affiliations:

  • Civil and Environmental Engineering Department, University of Wisconsin-Madison, 1415 Engineering Dr., Madison, WI 53706, USA;Center for Limnology, University of Wisconsin-Madison, 680 N. Park St., Madison, WI 53706, USA;Department of Biological Sciences, University of Waikato, Gate 1 Knighton Road, Hamilton 3240, New Zealand;School of Earth and Environment, The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia;Civil and Environmental Engineering Department, University of Wisconsin-Madison, 1415 Engineering Dr., Madison, WI 53706, USA and Department of Bacteriology, University of Wisconsin-Madison, 1550 ...;Civil and Environmental Engineering Department, University of Wisconsin-Madison, 1415 Engineering Dr., Madison, WI 53706, USA;Center for Limnology, University of Wisconsin-Madison, 680 N. Park St., Madison, WI 53706, USA;Center for Limnology, University of Wisconsin-Madison, 680 N. Park St., Madison, WI 53706, USA;Smithsonian Environmental Research Center, 647 Contees Wharf Road, Edgewater, MD 21037, USA;Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY 14853, USA;Department of Ecology, Genetics, and Limnology, Uppsala University, Norbyv. 18 D, 75236 Uppsala, Sweden;Instituto de Pesquisa Hidráulicas, Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves 9500, Caixa Postal 15.029, 91501-970 Porto Alegre, RS, Brazil;Civil and Environmental Engineering Department, University of Wisconsin-Madison, 1415 Engineering Dr., Madison, WI 53706, USA;Civil and Environmental Engineering Department, University of Wisconsin-Madison, 1415 Engineering Dr., Madison, WI 53706, USA;Civil and Environmental Engineering Department, University of Wisconsin-Madison, 1415 Engineering Dr., Madison, WI 53706, USA;Civil and Environmental Engineering Department, University of Wisconsin-Madison, 1415 Engineering Dr., Madison, WI 53706, USA;Department of Biological Sciences, Southeast Environmental Research Center, Miami, FL 33199, USA;Center for Limnology, University of Wisconsin-Madison, 680 N. Park St., Madison, WI 53706, USA

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2012

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Abstract

We evaluated the predictive ability of a one-dimensional coupled hydrodynamic-biogeochemical model across multiple temporal scales using wavelet analysis and traditional goodness-of-fit metrics. High-frequency in situ automated sensor data and long-term manual observational data from Lake Mendota, Wisconsin, USA, were used to parameterize, calibrate, and evaluate model predictions. We focused specifically on short-term predictions of temperature, dissolved oxygen, and phytoplankton biomass over one season. Traditional goodness-of-fit metrics indicated more accurate prediction of physics than chemical or biological variables in the time domain. This was confirmed by wavelet analysis in both the time and frequency domains. For temperature, predicted and observed global wavelet spectra were closely related, while observed dissolved oxygen and chlorophyll fluorescence spectral characteristics were not reproduced by the model for key time scales, indicating that processes not modeled may be important drivers of the observed signal. Although the magnitude and timing of physical and biological changes were simulated adequately at the seasonal time scale through calibration, time scale-specific dynamics, for example short-term cycles, were difficult to reproduce, and were relatively insensitive to the effects of varying parameters. The use of wavelet analysis is novel to aquatic ecosystem modeling, is complementary to traditional goodness-of-fit metrics, and allows for assessment of variability at specific temporal scales. In this way, the effect of processes operating at distinct temporal scales can be isolated and better understood, both in situ and in silico. Wavelet transforms are particularly well suited for assessment of temporal and spatial heterogeneity when coupled to high-frequency data from automated in situ or remote sensing platforms.