Author(s): Fernando J. González Villarreal, Victor Ignacio Mastache Mendoza
Linked Author(s): Victor Ignacio Masta Mendoza
Keywords: Colorado River Basin, Fast Fourier Transform, precipitation, frequency domain, periodic cycles.
Abstract: The Colorado River flows more than 2400 kilometers, from its source in the Rocky Mountains in the United States through deserts and canyons, to the wetlands of a delta into the Gulf of California in Mexico. Detection of variations over the long term for a series of hydrological variables is an important and critical issue, which is subjected to increasing interest because of the current topic of climate change. This study covers a 70-year time period from 1940 to 2010, using a Fast Fourier Transform (FFT) analysis of 118 daily precipitation sites located throughout the Colorado River basin. Tests for homogeneity and independence were applied to the data series; also a regression analysis was used just in specific cases. The data series of the stations was characterized as a function of frequency domain in order to identify a return rate of hydro-meteorological variables within the basin, verifying the existence of dominant periodic cycles in the data series. Different magnitudes in the precipitation periodicity were also examined. It is concluded that the precipitation of the Colorado River basin behaves in dominant periodic cycles of approximately 10. 7 or 12. 8 years. Nevertheless, there are three small areas in the basin which react in a different way: the mountains of Arizona showed a dominant period of 8 years; the higher elevations in the state of Colorado, 6. 4 years; and the peaks of Wyoming, 4. 6 years. These identified areas are the highest peaks where precipitation is more frequent. Besides, the moving average adjusts over a constant 13-year period for the data series of the stations. This suggests that precipitation in the basin completes a cycle every 13 years, verifying the FFT results. The FFT analysis may also be applied for frequency detection of other hydro-climatic variables such as temperature, humidity, streamflow and evapotranspiration
Year: 2017