Author: Mariska Obedzinski

Congratulations to Freshwater Lab postdoc Hank Baker on a new paper in Ecology Letters, Variation in salmon migration phenology bolsters population stability but is threatened by drought! The research team used over a decade of movement data to characterize variation in the rearing strategies of juvenile coho salmon, which are endangered on the central California coast. They found that some fish leave their natal habitat early to rear in the lower portion of the creek, while others stay in their natal habitat until they are ready to migrate to sea. This seemingly subtle variation in behavior dramatically improves population stability and may be an important feature in their recovery. However, variation is reduced or absent in low flow years, suggesting that the negative effects of drought on salmon may be compounded by loss of critical phenotypic variation. This work was funded by the NOAA Restoration Center as part of ongoing salmon recovery efforts in Willow Creek, a tributary to the Russian River in Sonoma County, CA. For more information on this work, see our news story.

A new study, Environmental planning and the evolution of inter-basin water transfers in the United States, led by postdoc Sooyeon Yi, was published in Frontiers in Environmental Science. The study provides a comprehensive analysis of inter-basin water transfers across the U.S., crucial for balancing water availability and demand. These projects can significantly alter river flows, affect water quality, and disrupt habitats. By categorizing projects from 1900 to 2020, the study reveals trends toward larger, energy-intensive systems and an evolving emphasis on environmental planning. The findings highlight the growing need for sustainable management, urging future projects to incorporate climate change vulnerability assessments to mitigate potential impacts effectively.

A new Applied Water Science study led by Berkeley Freshwater postdoc Sooyeon Yi highlights the importance of improving flood forecasting for reservoir-based systems, essential for effective flood management and community safety. By comparing advanced machine learning and deep learning techniques, the research provides valuable insights into optimizing prediction models, ultimately enhancing our ability to respond to extreme flood events. This work is crucial for informing decision-makers and improving early warning systems in regions vulnerable to flooding.