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dc.contributor.authorKara, Emily L.
dc.contributor.authorHanson, Paul C.
dc.contributor.authorHamilton, David P.
dc.contributor.authorHipsey, Matthew R.
dc.contributor.authorMcMahon, Katherine D.
dc.contributor.authorRead, Jordan S.
dc.contributor.authorWinslow, Luke A.
dc.contributor.authorDedrick, John
dc.contributor.authorRose, Kevin
dc.contributor.authorCarey, Cayelan C.
dc.contributor.authorBertilsson, Stefan
dc.contributor.authorda Motta Marques, David
dc.contributor.authorBeversdorf, Lucas
dc.contributor.authorMiller, Todd
dc.contributor.authorWu, Chin H.
dc.contributor.authorHsieh, Yi-Fang
dc.contributor.authorGaiser, Evelyn
dc.contributor.authorKratz, Tim
dc.date.accessioned2012-07-16T04:45:27Z
dc.date.available2012-07-16T04:45:27Z
dc.date.copyright2012-07
dc.date.issued2012-07
dc.identifier.citationKara, E.L., Hanson, P., Hamilton, D., Hipsey, M.R., McMahon, K.D.,…, Kratz, T. (2012). Time-scale dependence in numerical simulations: Assessment of physical, chemical, and biological predictions in a stratified lake at temporal scales of hours to months. Environmental Modelling & Software, 35, 104-121.en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/6527
dc.description.abstractWe 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.en_NZ
dc.language.isoen
dc.publisherElsevieren_NZ
dc.relation.ispartofEnvironmental Modelling & Software
dc.relation.urihttp://www.sciencedirect.com/science/article/pii/S136481521200062Xen_NZ
dc.subjectecosystem modellingen_NZ
dc.subjectphytoplanktonen_NZ
dc.subjectspectral analysisen_NZ
dc.subjectwavelet analysisen_NZ
dc.subjectautomated observatoryen_NZ
dc.subjectsensor networken_NZ
dc.titleTime-scale dependence in numerical simulations: Assessment of physical, chemical, and biological predictions in a stratified lake at temporal scales of hours to monthsen_NZ
dc.typeJournal Articleen_NZ
dc.identifier.doi10.1016/j.envsoft.2012.02.014en_NZ
dc.relation.isPartOfEnvironmental Modelling & Softwareen_NZ
pubs.begin-page104en_NZ
pubs.elements-id37403
pubs.end-page121en_NZ
pubs.volume35en_NZ
uow.identifier.article-noCen_NZ


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