This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1002/rra.3481. This is version 5 of this Preprint.
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Wood jams in rivers and on floodplains play an essential role in shaping valley bottoms, and their dynamics regulate the ecology and morphology of river systems. We present the Wood Jam Dynamics Database and Assessment Model (WooDDAM) to improve understanding and management of natural and anthropogenic wood jams in rivers. WooDDAM is comprised of a field data collection protocol, an open database of wood jam characteristics and dynamics, machine learning statistical models for predicting wood jam dynamics during high flows, and an online user interface to facilitate collaborative data collection and use. Here, we provide 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 model predictions and improve predictive power, we encourage users to follow simple resurvey procedures and submit observations of wood jam dynamics. WooDDAM provides a management and monitoring 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.
Earth Sciences, Geomorphology, Physical Sciences and Mathematics
machine learning, Rivers, Database, Wood, ELJ, engineered log jam, log jam, monitor, restoration, wood in rivers, wood jam
Published: 2018-09-21 05:16
Last Updated: 2019-07-30 23:10
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