19 May 2023

Summary of Ice Jams and Mitigation Techniques in Alaska

Abstract: Ice is an important part of the Alaska ecosystems and can form through dynamic (e.g., frazil) or static (e.g., thermal) processes. In Alaska, both freeze-up and breakup ice jams occur, however breakup jams during the spring snowmelt period are most common. Historically there have been many river systems in Alaska that have chronic ice jam issues. These ice jams have resulted in several significant ice jam floods. There are several ice jam mitigation techniques that can be used to either provide state and local emergency managers warnings of a potential ice jam or reduce the impacts of a jam. Common relatively low-cost mitigation methods that can be implemented prior to a jam forming are monitoring and detection of movement, mechanical or thermal weakening of the ice cover. Permanent measures are also effective and maybe the best option in specific locations. These measures include structures to keep flood waters from inundating areas (e.g., levee) or they can be designed to hold back ice fragments moving downstream (e.g., ice boom and pier structures). Climate change impacts to ice processes are important for Alaska and additional investigations will be needed to quantify the ecologic, hydrologic, and societal impacts.

21 Jul 2021

Machine Learning Analyses of Remote Sensing Measurements Establish Strong Relationships Between Vegetation and Snow Depth in the Boreal Forest of Interior Alaska

Abstract: The seasonal snowpack plays a critical role in Arctic and boreal hydrologic and ecologic processes. Though snow depth can be different from one season to another there are repeated relationships between ecotype and snowpack depth. Alterations to the seasonal snowpack, which plays a critical role in regulating wintertime soil thermal conditions, have major ramifications for near-surface permafrost. Therefore, relationships between vegetation and snowpack depth are critical for identifying how present and projected future changes in winter season processes or land cover will affect permafrost. Vegetation and snow cover areal extent can be assessed rapidly over large spatial scales with remote sensing methods, however, measuring snow depth remotely has proven difficult. This makes snow depth–vegetation relationships a potential means of assessing snowpack characteristics. In this study, we combined airborne hyperspectral and LiDAR data with machine learning methods to characterize relationships between ecotype and the end of winter snowpack depth. Our results show hyperspectral measurements account for two thirds or more of the variance in the relationship between ecotype and snow depth. An ensemble analysis of model outputs using hyperspectral and LiDAR measurements yields the strongest relationships between ecotype and snow depth. Our results can be applied across the boreal biome to model the coupling effects between vegetation and snowpack depth.

26 Jul 2019

PUBLICATION NOTICE: Ice Control to Prevent Flooding in Ship Creek, Alaska

The US Army Engineer Research and Development Center has published the report/note described and linked below. Approved for public release; distribution is unlimited.Report Number:ERDC/CRREL TR-19-11Title:Ice Control to Prevent Flooding in Ship Creek, AlaskaAuthor(s):Steven F. Daly, Joseph S. Rocks, Marina Reilly-Collette, and Arthur B.