Probabilistic Assessment of Seasonality of Streamflow in the Northeastern United States

Understanding the changes in seasonality or timing of streamflow is crucial for water resource system planning and management. Climate induced changes in streamflow seasonality for the northeastern United States have received much less attention, despite observed and projected future snowpack declines. We evaluate the seasonality of daily streamflows (high, average, and low) for 158 unregulated river gaging stations in the northeastern United States for the period 1951-2010 (60 years). A probabilistic approach based on nonparametric circular density estimates was used to characterize seasonality. Circular density method can precisely detect the multimodal distribution of the calendar dates for streamflow. Temporal changes in seasonality was assessed to detect the sites with the weakening, strengthening, emergence or disappearance of seasonality modes in time. Results from our study provides opportunity to re-examine the timing of seasonality-dependent decisions on the repair and maintenance of flood controlling infrastructures. Additionally, our fresh statistical approach based on non-parametric circular density estimates reduces some of the limitations of trend analysis of previous studies to detect and model event timing distributions with multiple seasons and address issues of non-stationarity in the data records of extreme events.