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Classifying Streamflow Duration: The Scientific Basis and an Operational Framework for Method Development
Citation:
Fritz, K., Tracie-Lynn Nadeau, J. Kelso, W. Beck, R. Mazor, R. Harrington, AND B. Topping. Classifying Streamflow Duration: The Scientific Basis and an Operational Framework for Method Development. WATER. MDPI, Basel, Switzerland, 12(9):2545, (2020). https://doi.org/10.3390/w12092545
Impact/Purpose:
We intend for this paper to represent the scientific and operational foundation for the development of robust and accurate SDAMs being developed by USEPA. Our hope is that the paper will lend credibility to SDAMs as an agency tool and encourage 1) the development of data-driven SDAMs by other water resource agencies, 2) the application of SDAMs to support water resource decision-making, and 3) further research and monitoring of hydrology and ecology of non-perennial streams.
Description:
Streamflow duration is used to differentiate reaches into discrete classes (e.g., perennial, intermittent, and ephemeral) for water resource management. Because the depiction of the extent and flow duration of streams via existing maps, remote sensing, and gauging is constrained, field-based tools are needed for use by practitioners and to validate hydrography and modeling advances. Streamflow Duration Assessment Methods (SDAMs) are rapid, reach-scale indices or models that use physical and biological indicators to predict flow duration class. We review the scientific basis for indicators and present conceptual and operational frameworks for SDAM development. Indicators can be responses to or controls of flow duration. Aquatic and terrestrial responses can be integrated into SDAMs, reflecting concurrent increases and decreases along the flow duration gradient. The conceptual framework for data-driven SDAM development shows interrelationships among the key components: study reaches, hydrologic data, and indicators. We present a generalized operational framework for SDAM development that integrates the data-driven components through five process steps: preparation, data collection, data analysis, evaluation, and implementation. We highlight priorities for the advancement of SDAMs, including expansion of gauging of nonperennial reaches, use of citizen science data, adjusting for stressor gradients, and statistical and monitoring advances to improve indicator effectiveness.
URLs/Downloads:
DOI: Classifying Streamflow Duration: The Scientific Basis and an Operational Framework for Method Development