Forecasting hydrologic controls on juvenile anadromous fish out-migration with process-based modeling and machine learning

River herring (Alosa sp.) are ecologically and economically foundational species in freshwater streams, estuaries, and oceanic ecosystems. The migration between fresh and saltwater is a key life stage of river herring, where the timing and magnitude of out-migration by juveniles can be limited when streams dry and hydrologic connectivity is lost. Operational decisions by water managers (e.g., restricting community water use) can impact out-migration success; however, these decisions are largely made without reliable predictions of outmigration potential across the migration season. This research presents a model to generate short-term forecasts of the probability of herring out-migration loss. We monitored streamflow and herring out-migration for 2 years at three critical runs along Long Island Sound (CT, USA) to develop empirical understandings of the hydrologic controls on out-migration. We used calibrated Soil and Water Assessment Tool hydrologic models of each site to generate 10,000 years of daily synthetic meteorological and streamflow records. These synthetic meteorological and streamflow data were used to train random forest models to provide rapid within-season forecasts of out-migration loss from two simple predictors: current spawning reservoir depth and the previous 30-day precipitation total. The resulting models were approximately 60%–80% accurate with a 1.5-month lead time and 70–90% accurate within 2 weeks. We anticipate that this tool will support regional decisions on spawning reservoir operations and community water withdrawals. The architecture of this tool provides a framework to facilitate broader predictions of the ecological consequences of streamflow connectivity loss in human-impacted watersheds. •We developed a tool to predict juvenile river herring out-migration loss.•The framework involves random forest algorithms trained on hydrologic model outputs.•Out-migration loss can be reliably predicted 1.5 months before it occurs.