Abstract

AbstractWithin Arctic deltas, surficial hydrologic connectivity of lakes to nearby river channels influences physical processes like sediment transport and ice phenology as well as biogeochemical processes such as photochemistry. As the Arctic hydrologic cycle is impacted by climate change, it is important to quantify temporal variability in connectivity. However, current connectivity detection methods are either spatially limited due to data availability constraints or have been applied at only a single time. Additionally, the relationship between connectivity and lake ice is still poorly quantified. In this study, we present a multitemporal classification of functional lake connectivity in the Colville River Delta, Alaska. We introduce a connectivity detection algorithm based on remote sensing of high sediment river water recharge that is expandable to other high sediment Arctic deltas. We compare results to three existing data sets, producing 64.4%, 75.8%, and 85.2% lake classification accuracy. Mismatches between the data sets are often due to inconsistencies in methodology or definition of connectivity. Connectivity varies temporally in about 10% of studied lakes and correlates strongly with discharge and lake elevation, supporting the idea that future changes in discharge will be a driver of changes in connectivity. Highly connected lakes start and end ice break up an average of 26 and 17 days earlier, respectively, compared to lakes that are poorly connected. Because spring and summer ice conditions drive Arctic lake photochemistry processes, our research suggests that surface connectivity is an important parameter to consider when studying biogeochemistry of Arctic delta lakes.

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