Characterization of Flood-Producing Mechanisms in Watersheds with a High-Elevation Area in Southeast Alaska and Hawaiʻi

Major floods in Southeast Alaska and Hawaiʻi that potentially threaten life, property, and culturally significant resources and ecosystems are caused by mechanisms related to intense precipitation for both locations as well as snow melt-based processes for Alaska. Small, high-gradient, and heavily vegetated watersheds with direct contribution to the ocean are common in both locations. To understand how climate change may affect flooding in these regions, an analysis of the underlying mechanisms that cause flooding is needed.  

The scope of this study includes an analysis of annual peak-streamflow records from long-term streamgages in Southeast Alaska and Hawaiʻi to determine whether the main flood-producing mechanisms can be identified or, for Southeast Alaska, further refined. This study will integrate the effects of watersheds covering a range of altitudes, from mountainous interior areas to near-coastal areas at lower altitudes, including precipitation, land-cover, soil type, and physical watershed characteristics, including area, slope, and stream length. The mechanisms that control flooding can be disproportionately affected by the high-elevation parts of a watershed, where precipitation intensity and rain/snow ratios can vary from lower elevations. Furthermore, because of the variability in precipitation and watershed characteristics within a region, the largest recorded peak discharges may occur in different years in different watersheds. Thus, this analysis must consider both spatial and temporal variations in flood-producing mechanisms and whether these stem from local or regional factors. 

The results of this study will pave the way toward developing a comprehensive dataset of peak streamflow characteristics, based on historical streamflow data, for small, steep, mountain-to-ocean streams in both Alaska and Hawaiʻi. Such a product would be a valuable contribution to both the communities and researchers as it could serve as a foundation for understanding the processes affecting these ecosystems and a calibration dataset for modeling efforts, thus forming an important step in predicting future floods.