Improving Predictions of Fine Particle Immobilization in Streams. Issue 23 (13th December 2019)

• Record Type: Journal Article
• Authors: Drummond, Jennifer; Schmadel, Noah; Kelleher, Christa; Packman, Aaron; Ward, Adam
• Is Part Of: Geophysical research letters. Volume 46:Issue 23(2019)
• Page Start: 13853
• Page End: 13861
• Publication Date: 2019-12-13
• Subjects: fine particles -- mobile‐immobile model -- parameter uncertainty -- particle immobilization -- improved particle predictions -- river corridor; Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1029/2019GL085849
• Languages: English
• ISSNs: 0094-8276

Abstract

Abstract: Fine particles are critical to stream ecosystem functioning, influencing in‐stream processes from pathogen transmission to carbon cycling, all of which depend on particle immobilization. However, our ability to predict particle immobilization is limited by (1) availability of combined solute and particle tracer data and (2) identifying parameters that appropriately represent fine particle immobilization, due to the myriad of objective functions and model formulations. We found that improved predictions of the full distribution of possible fine particle residence times requires using an objective function that assesses both the peak and tailing of breakthrough curves together with solute tracers to constrain in‐stream transport processes. The representation of immobilization processes was significantly improved when solute tracer data were combined with a particle model, starkly contrasting the common assumption that fine particles transport as washload. We develop a clear strategy for improving fine particle transport predictions, reshaping the potential role of fine particles in water quality management. Plain Language Summary: Fine particles, a general term that can be used to describe inorganic material like clays, particulate organic carbon, and harmful bacteria (i.e., pathogens), are important to stream functioning and water quality. The time it takes fine particles to move through a stream is difficult to predict primarily because there is no general guidance regarding the required data types and modeling approaches. Through comprehensive computational experiments and data analysis, we found that fine particles remain in streams much longer than commonly assumed, reshaping understanding of the role of fine particles in stream functioning and how waterborne pathogens are transmitted. Key Points: Applying a balanced objective function and two tracers improves accuracy of particle retention up to 57% versus applying a washload assumption Improved parameter identifiability found with an objective function that considers both peak and tailing of breakthrough curves Particle immobilization can be predicted directly from solute data by adding particle transport terms to a model with hyporheic exchange