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User Manual for a Beta Streamflow Duration Assessment Method (SDAM) for the Great Plains of the United States
Citation:
James, A., Tracie-Lynn Nadeau, K. Fritz, B. Topping, R. Fertik Edgerton, J. Kelso, AND R. Mazor. User Manual for a Beta Streamflow Duration Assessment Method (SDAM) for the Great Plains of the United States. U.S. Environmental Protection Agency, Washington, DC, EPA/840/B-22/009, 2022.
Impact/Purpose:
Flow duration classification is used to implement several federal, state and local stream management programs. Because the flow duration of streams via existing maps, remote sensing, and gauging is constrained, field-based tools are often needed by practitioners. The manual describes a beta method to rapidly classify stream reaches as ephemeral, intermittent, perennial, and at-least intermittent in the Great Plains of the United States.
Description:
This manual describes a beta Streamflow Duration Assessment Method (SDAM) that is intended to distinguish flow duration classes (ephemeral, intermittent, and perennial) of stream reaches in the Northern and Southern Great Plains (GP) regions of the United States. All indicators are quantified during a single field visit and across the entire assessment reach. It is anticipated that the beta method will be made available for one year to allow the user community to provide feedback before a final SDAM GP is produced. The manual provides an overview of the beta SDAM process, data collection, data interpretation, and preparation of a beta SDAM report. Four biological, four geomorphological indicators, and one geographical indicators form the basis of the beta SDAM GP. These indicators are evaluated together to assign a preliminary flow duration class to a stream reach. The beta SDAM GP assigns reaches to one of four possible classifications: ephemeral, intermittent, perennial, and at least intermittent. The latter classification occurs when an intermittent or perennial classification cannot be made with high confidence, but an ephemeral classification can be ruled out. The protocol uses a machine learning model known as random forest. Random forest models are increasingly common in the environmental sciences because of their superior performance in handling complex relationships among indicators used to predict classifications. In addition to describing the protocol for collecting field data, the manual describes the open-access, user-friendly web application for entering indicator data and running the developed random forest model to obtain the classification for individual assessment reaches.