Decoding Streamflow Patterns and Seasonal Timing with Data-Driven Techniques

Streamflow analysis is fundamental to effective water resource management, providing essential insights into the dynamics of water systems. This data-driven study is divided into two main components: elucidating patterns in streamflow data and analyzing changes in seasonal transitions, both of which are increasingly influenced by climate change. Recognizing patterns within streamflow data enhances understanding of seasonal transitions and supports decision-making related to water allocation and flood mitigation. A mathematical framework combining dynamic time warping (DTW) and hierarchical cluster analysis (HCA) is introduced to analyze time series streamflow data. DTW calculates the distance cost function between hydrological years, capturing similarities in streamflow patterns, while HCA clusters these years into groups with similar hydrological characteristics. This approach facilitates the identification of specific years that exhibit similar behaviors—critical in environments where inter-annual variability is heightened by climate variability. Moreover, identifying the change point in the transition from the dry to the wet season is essential for optimizing water resource allocation strategies during dry seasons and enhancing flood prediction and mitigation in wet seasons. The at most one change (AMOC) method is implemented to detect significant transitions. By employing ensemble empirical mode decomposition (EEMD), the trends of these change points are analyzed to better understand their timing and implications over time and across clusters. This analysis helps anticipate and manage the hydrological impacts associated with seasonal transitions and provides a clearer perspective on hydrological variability and the detection of change points, elucidating the complex interplays between hydrological patterns and climate change.