Daily Stream Temperature Predictions for Free-Flowing Streams in the Pacific Northwest, USA

Supporting sustainable lotic ecosystems and thermal habitats for cold-water species like salmonids requires estimates of stream temperature that are high in scope and resolution across space and time. We combined and enhanced elements of existing stream temperature models to produce a new statistical model to address this need. This model reflects mechanistic processes using publicly available climate and landscape covariates in a Generalized Additive Model (GAM) framework. We allowed covariates to interact while accounting for nonlinear relationships between temporal and spatial covariates to better capture seasonal patterns. Additionally, to represent variation in sensitivity to climate, we used a moving average of antecedent air temperatures over a variable duration linked to area-standardized streamflow. The moving average window size was longer for reaches classified as having a snow-dominated hydrology, especially at higher flows, whereas window size was relatively constant and low for reaches having rain-dominated hydrology. Our model’s ability to capture the temporally variable impact of snowmelt on stream sensitivity helped improve its capacity to predict stream temperature across diverse geography for multiple years. We fit the model to stream temperature data from 1993-2013 and used the model to predict daily stream temperatures for ~222,000 free-flowing stream reaches across the Pacific Northwest from 1990-2017. Our daily model fit well (RMSE = 1.76; MAE = 1.32 °C). Spatial and temporal cross-validation suggested that the model produced useful predictions at unsampled locations and across diverse landscapes and climate conditions. We produced stream temperature predictions that will be immediately useful to natural resource practitioners in the Pacific Northwest, USA, especially for effective conservation planning in lotic ecosystems and for managing species such as Pacific salmon. Our approach is straightforward and can be easily adapted to new spatial regions, time periods, or scenarios such as anticipated changing snowmelt patterns with climate change.