The fate of bioavailable nitrogen species transported through agricultural landscapes remains highly uncertain given complexities of measuring fluxes impacting the fluvial N cycle. We present and test a new numerical model named Technology for Removable Annual Nitrogen in Streams For Ecosystem Restoration (TRANSFER), which aims to reduce model uncertainty due to erroneous parameterization, i.e., equifinality, in stream nitrogen cycle assessment and quantify the significance of transient and permanent removal pathways. TRANSFER couples nitrogen elemental and stable isotope mass‐balance equations with existing hydrologic, hydraulic, sediment transport, algal biomass, and sediment organic matter mass‐balance subroutines and a robust GLUE‐like uncertainty analysis. We test the model in an agriculturally impacted, third‐order stream reach located in the Bluegrass Region of Central Kentucky. Results of the multiobjective model evaluation for the model application highlight the ability of sediment nitrogen fingerprints including elemental concentrations and stable N isotope signatures to reduce equifinality of the stream N model. Advancements in the numerical simulations allow for illumination of the significance of algal sloughing fluxes for the first time in relation to denitrification. Broadly, model estimates suggest that denitrification is slightly greater than algal N sloughing (10.7% and 6.3% of dissolved N load on average), highlighting the potential for overestimation of denitrification by 37%. We highlight the significance of the transient N pool given the potential for the N store to be regenerated to the water column in downstream reaches, leading to harmful and nuisance algal bloom development.

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Published in Water Resource Research, v. 53, issue 8, p. 6539-6561.

© 2017. American Geophysical Union. All Rights Reserved.

The copyright holder has granted the permission for posting the article here.

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We thank the University of Kentucky, Department of Civil Engineering for partial funding of the graduate student while at UK. We gratefully acknowledge financial support of this research under National Science Foundation award 0918856 and Kentucky Science and Engineering Foundation award 2687‐RDE‐015.

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This is publication 17–05‐018 of the Kentucky Agricultural Experiment Station and is published with the approval of the Director.

All data, including computer code, model calibration data, and generated model results will be stored and publicly available on the Aquavit data repository (a HUBzero platform) at the time of publication at the following link https://wvaquavit.marshall.edu/publications/3/1.