Rebecca A. Bellmore | USGS Science

USGS2023

Modeling coho salmon (Oncorhynchus kisutch) population response to streamflow and water temperature extremes

Models that assess the vulnerability of freshwater species to shifting environmental conditions do not always account for short-duration extremes, which are increasingly common. Life cycle models for Pacific salmon ( Oncorhynchus spp.) generally focus on average conditions that fish experience during each life stage, yet many floods, low flows, and elevated water temperatures only last days to weeks. We developed a process-based life cycle model that links coho salmon ( Oncorhynchus kisutch ) abundance to daily streamflow and thermal regimes to assess: (1) “How does salmon abundance respond to short-duration floods, low flows, and high temperatures in glacier-, snow-, and rain-fed streams?” and (2) “How does the temporal resolution of flow and temperature data influence these responses?”. Our simulations indicate that short-duration extremes can reduce salmon abundance in some contexts. However, after daily flow and temperature data were aggregated into weekly and monthly averages, the impact of extreme events on populations declined. Our analysis demonstrates that novel modeling frameworks that capture daily variability in flow and temperature are needed to examine impacts of extreme events on Pacific salmon.

Article↗

USGS2020

A classification of streamflow patterns across the coastal Gulf of Alaska

Streamflow controls many freshwater and marine processes, including salinity profiles, sediment composition, fluxes of nutrients, and the timing of animal migrations. Watersheds that border the Gulf of Alaska (GOA) comprise over 400,000 km 2 of largely pristine freshwater habitats and provide ecosystem services such as reliable fisheries for local and global food production. Yet no comprehensive watershed-scale description of current temporal and spatial patterns of streamflow exists within the coastal GOA. This is an immediate need because the spatial distribution of future streamflow patterns may shift dramatically due to warming air temperature, increased rainfall, diminishing snowpack, and rapid glacial recession. Our primary goal was to describe variation in streamflow patterns across the coastal GOA using an objective set of descriptors derived from flow predictions at the downstream-most point within each watershed. We leveraged an existing hydrologic runoff model and Bayesian mixture model to classify 4,140 watersheds into 13 classes based on seven streamflow statistics. Maximum discharge timing (annual phase shift) and magnitude relative to mean discharge (amplitude) were the most influential attributes. Seventy-six percent of watersheds by number showed patterns consistent with rain or snow as dominant runoff sources, while the remaining watersheds were driven by rain-snow, glacier, or low-elevation wetland runoff. Streamflow classes exhibited clear mechanistic links to elevation, ice coverage, and other landscape features. Our classification identifies watersheds that might shift streamflow patterns in the near future and, importantly, will help guide the design of studies that evaluate how hydrologic change will influence coastal GOA ecosystems.

Alaska↗