Application of the Least-Squares Wavelet software in hydrology: Athabasca River Basin

Study region: Athabasca River Basin (ARB) in Alberta, Canada. Study focus: Understanding the historical streamflow variability within basins is crucial to reduce the effect of utmost events, such as drought and floods on agriculture, fishery, and other human activities. The Least-Squares Wavelet software (LSWAVE) is applied to estimate the trend and seasonal components of sixty-year-long climate and discharge time series and to study the impact of climate change on streamflow over time. New hydrological insights for the region: The seasonal components of the discharge and precipitation time series including annual and semi-annual are coherent with phase discrepancy. The mean temperature has been gradually increasing since 1960, and it is projected to increase by approximately 2 degrees C during the mid-century which may reduce the snowpack volume during the spring. From the recurring pattern of spectral peaks in the spectrograms and jumps in the trend component of streamflow time series, the blue water is projected to increase during the midcentury, in particular in early 2030s. The results also highlight the potential of LSWAVE in analyzing climate and hydrological time series without any need for interpolation, gap-filling, and de-spiking.

Automated summary

The study focused on understanding historical streamflow variability in the Athabasca River Basin in Alberta, Canada, and the impact of climate change on streamflow over time using Least-Squares Wavelet software. The results showed that the seasonal components of discharge and precipitation time series were coherent with phase discrepancy, and mean temperature has been gradually increasing since 1960, projected to further increase by 2 degrees C during the mid-century. Spectral peaks and trend jumps in the streamflow time series suggest an increase in blue water during the midcentury, especially in the early 2030s. The study highlighted the potential of LSWAVE in analyzing climate and hydrological time series without the need for interpolation, gap-filling, or de-spiking.