Wavelet analysis of hydrological and water quality signals in an agricultural watershed

Studying temporal patterns of hydrology and water quality can assist in understanding hydrological processes, improving hydrological modeling, and water quality monitoring. Using wavelet analysis, we analyzed temporal patterns of three hydrological signals (precipitation, stream flow, and well water level) for three periods (15 years, 3 year, and a hydrological year). For three unevenly sampled water quality signals (nitrate, chloride, and sodium), the weighted wavelet Z-transform (WWZ) method was employed. The results showed that the wavelet analysis of hydrological signals showed advantages of detecting detailed temporal patterns compared to the classical Fourier analysis. No strong temporal pattern of precipitation was found for all three periods. For the 15 years’ continual monitoring datasets, strong consistent annual temporal pattern of well water level and an intermittent annual temporal pattern of stream flow were observed. For the relative short-time periods (three years and a hydrological year), strong seasonal patterns of stream flow and well water level were noticed. Using the WWZ method, seasonal patterns of the three stream water quality indicators can be associated with their seasonal shifts. Nitrate concentration showed stronger temporal patterns than chloride and sodium over longer time periods (15 and 3 years). In a hydrological year, temporal patterns of nitrate, chloride, and sodium shared some similarities as well as dissimilarities, as can be explained by their different transport pathways and sources. Sodium and chloride peak concentrations in the winter season were captured by the wavelet analysis. It is concluded that wavelet analysis can be a useful tool to analyze detailed temporal patterns of non-stationary hydrological and water quality signals over different temporal scales.