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Description: Wood jams in rivers and on floodplains play an essential role in shaping valley bottoms. Jams are inherently complex and difficult to measure and collecting data on wood jam dynamics during high flows is complicated by the recurrence interval between flows that measurably change or mobilize a jam. We present the Wood Jam Dynamics Database and Assessment Model (WooDDAM) to improve understanding and management of both natural and anthropogenic wood jams in rivers. WooDDAM provides a platform for building a wood jam dynamics database as well as an evolving, machine-learning statistical model for predicting wood jam dynamics during high flows. The tool includes a field data collection protocol, wood jam dynamics database, predictive statistical model, and an online user interface to facilitate collaborative data collection. This article provides the background and guidance necessary to utilize WooDDAM to make predictions of and contribute to the database describing wood jam dynamics. We present tests of interoperator variability to justify database variable selection. To refine the model predictions and improve the predictive power, users are encouraged to follow simple resurvey procedures and submit new data. This statistical model provides a management tool for the retention or reintroduction of wood jams in rivers and facilitates further research into the interactions between wood jam dynamics and fluvial or ecological processes.