Stream flow characterization and feature detection using a discrete wavelet transform

Abstract

Abstract:An exploration of the wavelet transform as applied to daily river discharge records demonstrates its strong potential for quantifying stream¯ow variability. Both periodic and non-periodic features are detected equally, and their locations in time preserved. Wavelet scalograms often reveal structures that are obscure in raw discharge data. Integration of transform magnitude vectors over time yields wavelet spectra that re¯ect the characteristic time-scales of a river's¯ow, which in turn are controlled by the hydroclimatic regime. For example, snowmelt rivers in Colorado possess maximum wavelet spectral energy at time-scales on the order of 4 months owing to sustained high summer¯ows; Hawaiian streams display high energies at time-scales of a few days, re¯ecting the domination of brief rainstorm events. Wavelet spectral analyses of daily discharge records for 91 rivers in the US and on tropical islands indicate that this is a simple and robust way to characterize stream¯ow variability. Wavelet spectral shape is controlled by the distribution of event time-scales, which in turn re¯ects the timing, variability and often the mechanism of water delivery to the river. Five hydroclimatic regions, listed here in order of decreasing seasonality and increasing pulsatory nature, are described from the wavelet spectral analysis: (a) western snowmelt, (b) north-eastern snowmelt, (c) mid-central humid, (d) southwestern arid and (e)`rainstorm island'. Spectral shape is qualitatively diagnostic for three of these regions. While more work is needed to establish the use of wavelets for hydrograph analysis, our results suggest that river¯ows may be eectively classi®ed into distinct hydroclimatic categories using this approach. # 1998 John

Wavelet Regression Technique for Streamflow Prediction

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Flow alteration impacts on Hudson Bay river discharge

“…Although the power spectra for the regulated and unregulated rivers appear nearly identical in the 1960s, the emergence thereafter of spikes at the weekly timescale and a levelling of the power spectra (identified by the lower values of B coefficients in the non‐linear regressions) mark departures in the hydrological regimes of the regulated rivers from their unregulated counterparts. This transition from a relatively strong red noise power spectrum in the 1960s to white noise power spectra in the following decades in the regulated rivers mimics differences between snow‐dominated rivers with highly periodic, annual spring freshets versus rainfall‐dominated rivers with highly irregular precipitation events (cf figure 4 of Smith et al, ). The reduction in values of the coefficient B for the non‐linear regressions applied to the power spectra of the regulated rivers reveals the cascade of energy to shorter timescales of variability associated with flow regulation with the loss of the robust seasonality typically seen in nival regimes.…”

Inertial Currents in the southern Gulf of Mexico

“…Usando una combinación de los métodos FFT, rotacionales, representaciones vectoriales y análisis wavelets, se pueden identificar los diferentes componentes espectrales en el domino de las frecuencias de las series. Además, el método de wavelets permite identificar eventos no estacionarios (Smith et al 1998). Finalmente se calcularon las frecuencias y periodos teóricos que correspondían a cada lugar de los anclajes y éstos se compararon con los obtenidos a partir del análisis espectral de las series de corrientes.…”