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Pacific salmon Oncorhynchus spp. face serious challenges from climate and landscape change, particularly in the southern portion of their native range. Conversely, climate warming appears to be allowing salmon to expand northwards into the Arctic. Between these geographic extremes, in the Gulf of Alaska region, salmon are at historically high abundances but face an uncertain future due to rapid environmental change. We examined changes in climate, hydrology, land cover, salmon populations, and fisheries over the past 30–70 years in this region. We focused on the Kenai River, which supports world‐famous fisheries but where Chinook Salmon O. tshawytscha populations have declined, raising concerns about their future resilience. The region is warming and experiencing drier summers and wetter autumns. The landscape is also changing, with melting glaciers, wetland loss, wildfires, and human development. This environmental transformation will likely harm some salmon populations while benefiting others. Lowland salmon streams are especially vulnerable, but retreating glaciers may allow production gains in other streams. Some fishing communities harvest a diverse portfolio of fluctuating resources, whereas others have specialized over time, potentially limiting their resilience. Maintaining diverse habitats and salmon runs may allow ecosystems and fisheries to continue to thrive amidst these changes.

Land surface temperature (LST) is one of the sources of input data for modeling land surface processes. The Landsat satellite series is the only operational mission with more than 30 years of archived thermal infrared imagery from which we can retrieve LST. Unfortunately, stray light artifacts were observed in Landsat-8 TIRS data, mostly affecting Band 11, currently making the split-window technique impractical for retrieving surface temperature without requiring atmospheric data. In this study, a single-channel methodology to retrieve surface temperature from Landsat TM and ETM+ was improved to retrieve LST from Landsat-8 TIRS Band 10 using near-surface air temperature (T a) and integrated atmospheric column water vapor (w) as input data. This improved methodology was parameterized and successfully evaluated with simulated data from a global and robust radiosonde database and validated with in situ data from four flux tower sites under different types of vegetation and snow cover in 44 Landsat-8 scenes. Evaluation results using simulated data showed that the inclusion of T a together with w within a single-channel scheme improves LST retrieval, yielding lower errors and less bias than models based only on w. The new proposed LST retrieval model, developed with both w and T a , yielded overall errors on the order of 1 K and a bias of −0.5 K validated against in situ data, providing a better performance than other models parameterized using w and T a or only w models that yielded higher error and bias.

A better understanding of water movement on hillslopes in Arctic environments is necessary for evaluating the effects of climate variability. Drainage networks include a range of features that vary in transport capacity from rills to water tracks to rivers. This research focuses on describing and classifying water tracks, which are saturated linear-curvilinear stripes that act as first-order pathways for transporting water off of hillslopes into valley bottoms and streams. Multiple factor analysis was used to develop five water tracks classes based on their geomorphic, soil, and vegetation characteristics. The water track classes were then validated using conditional inference trees, to verify that the classes were repeatable. Analysis of the classes and their characteristics indicate that water tracks cover a broad spectrum of patterns and processes primarily driven by surficial geology. This research demonstrates an improved approach to quantifying water track characteristics for specific areas, which is a major step toward understanding hydrological processes and feedbacks within a region.