Changes in Streamflow Statistical Structure across the United States due to Recent Climate Change

A variety of watershed responses to climate change are expected due to non-linear interactions between various hydrologic processes acting at different timescales that are modulated by watershed properties. Changes in statistical structure (spectral properties) of streamflow in the USA due to climate change were studied for water years 1980-2013. The Fractionally differenced Autoregressive Integrated Moving Average (FARIMA) model was fit to the deseasonalized streamflow time series to model its statistical structure. FARIMA allows the separation of streamflow into low- (slowly varying) and high-frequency (fast varying) components. Results show that in the snow-dominated watersheds, the contribution of low-frequency components to total streamflow variance decreased over the study period, and the contribution of high-frequency components increased. The change in the snow-dominated watersheds was primarily driven by changes in rainfall statistics and changes in snow water equivalent but also by changes in seasonal temperature statistics. Among the rain-driven watersheds, the contribution of high-frequency components generally increased in arid regions but decreased in humid regions. In both humid and arid rain-driven watersheds, increasing winter temperature appears to be responsible for the change in streamflow statistical structure. These results have consequences for predictability of streamflow in the presence of climate change. We expect that changes in the high-frequency component will result in decreased predictability of streamflow. Further, the analysis carried out in this study allows to understand the plausible changes in watershed hydrologic processes that affect streamflow without using process-based or conceptual models.