Machine learning unravels controls on river water temperature regime dynamics

Water temperature is vitally important to the health of rivers and streams, influencing the integrity of ecosystems, aquatic biogeochemistry, and the habitability of waterways for a variety of species. While climate is often regarded as the primary driver of stream temperature regimes, other factors -including hydrology, watershed characteristics, and human impacts -add substantial complexity to the variability of water temperatures. However, it remains challenging to disentangle the influence of these drivers through time and across rivers spanning diverse settings. To quantify the underlying controls on river thermal regimes, we applied conditional inference random forest models to predict maximum monthly stream temperatures and thermal sensitivities, averaged across a 4-year period (2016 to 2020), at 410 watersheds spanning the conterminous United States. Maximum stream temperatures were selected given their ecological relevance, while thermal sensitivity, which measures the relationship between air and water temperatures, was used to assess the responsiveness of stream temperatures to climate forcings. We interpreted these random forest models using variable importance rankings, describing seasonal and spatial variability in the dominant controls on water temperatures. Although our empirical results confirm that climate is indeed a primary control on temperature magnitude, our models highlight the diversity in drivers of water temperature variability across seasons, hydrologic regions, and between metrics. By combining random forest models with process-based understanding of stream thermal regimes, we provide new insights on the dynamic controls of water temperature variability across broad geographical domains, informing region-and season-specific controls for tailored thermal watershed management and guiding the framing of future water temperature modeling.