An Introduction to Wavelet Analysis in Oceanography and Meteorology: With Application to the Dispersion of Yanai Waves

Meyers, Steven D.; Kelly, Brian Gerard Joseph; O’Brien, James J.

doi:10.1175/1520-0493(1993)121<2858:aitwai>2.0.co;2

Cited by 459 publications

(270 citation statements)

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“…The average total annual precipitation in the Curuai floodplain is 2447 mm year -1 , which is much larger than the average potential evaporation of 1400 mm year -1 (average obtained by a time series from 1990 to 2001), with the wet season lasting from January the time-localized frequency content of the signals (Meyers et al, 1993;Kumar and Foufoula-Georgiou, 1997) but, on the other hand, the spatial variability patterns could be assessed through geostatistical analysis (Bellehumeur et al, 2000;Hedger et al, 2001). Here, we show that the combination of a high-resolution time series and spatial data allows for a better understanding of the turbidity dynamics in the Amazon floodplain.…”

Section: Introductionmentioning

confidence: 76%

A contribution to understanding the turbidity behaviour in an Amazon floodplain

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Stech

et al. 2010

Hydrol. Earth Syst. Sci.

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Abstract.Observations of turbidity provide quantitative information about water quality. However, the number of available in situ measurements for water quality determination is usually limited in time and space. Here, we present an analysis of the temporal and spatial variability of the turbidity of an Amazon floodplain lake using two approaches: (1) wavelet analysis of a turbidity time series measured by an automatic monitoring system, which should be improved/simplified, and (2) turbidity samples measured in different locations and then interpolated using an ordinary Kriging algorithm. The spatial and temporal variability of turbidity are clearly related to the Amazon River flood pulses in the floodplain. When the water level in the floodplain is rising or receding, the exchange between the Amazon River and the floodplain is the major driving force in turbidity variability. At high-water levels, turbidity variability is controlled by Lake Bathymetry. When the water level is low, wind action and Lake Morphometry are the main causes of turbidity variability. The combined use of temporal and spatial data shows a good potential for better understanding of the turbidity behaviour in a complex aquatic system such as the Amazon floodplain.

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Section: Introductionmentioning

confidence: 76%

A contribution to understanding the turbidity behaviour in an Amazon floodplain

Alcântara

Novo

Stech

et al. 2010

Hydrol. Earth Syst. Sci.

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“…For this wavelet, the ratio between the equivalent Fourier period (l), and the wavelet scales (s), F f exists and can be derived analytically (Meyers et al 1993); we find that it is…”

Section: ( ) C Smentioning

confidence: 94%

Fitting Ecological Process Models to Spatial Patterns Using Scalewise Variances and Moment Equations

Detto

Muller‐Landau

2013

The American Naturalist

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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. 7, 2012; Accepted October 29, 2012; Electronically published February 19, 2013 Online enhancement: zip file with appendixes, supplemental figure. abstract: Ecological spatial patterns are structured by a multiplicity of processes acting over a wide range of scales. We propose a new method, based on the scalewise variance-that is, the variance as a function of spatial scale, calculated here with wavelet kernel functions-to disentangle the signature of processes that act at different and similar scales on observed spatial patterns. We derive exact and approximate analytical solutions for the expected scalewise variance under different individual-based, spatially explicit models for sessile organisms (e.g., plants), using moment equations. We further determine the probability distribution of independently observed scalewise variances for a given expectation, including complete spatial randomness. Thus, we provide a new analytical test of the null model of spatial randomness to understand at which scales, if any, the variance departs significantly from randomness. We also derive the likelihood function that is needed to estimate parameters of spatial models and their uncertainties from observed patterns. The methods are demonstrated through numerical examples and case studies of four tropical tree species on Barro Colorado Island, Panama. The methods developed here constitute powerful new tools for investigating effects of ecological processes on spatial point patterns and for statistical inference of process models from spatial patterns.

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“…Further afield, wind waves, seiches, atmospherically forced continental shelf motions and equatorial waves [Meyers et al, 1993] are important, transient oceanographic phenomena susceptible to analysis with wavelets.…”

Section: Nonstationary Tidal Processesmentioning

confidence: 99%