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Exploring Tracer Information and Model Framework Trade‐Offs to Improve Estimation of Stream Transient Storage Processes
Journal article   Open access   Peer reviewed

Exploring Tracer Information and Model Framework Trade‐Offs to Improve Estimation of Stream Transient Storage Processes

Christa Kelleher, Adam Ward, J. L. A. Knapp, P. J. Blaen, M. J. Kurz, J. D. Drummond, J. P. Zarnetske, D. M. Hannah, C. Mendoza‐Lera, N. M. Schmadel, …
Water resources research, v 55(4), pp 3481-3501
Apr 2019
DOI: https://doi.org/10.1029/2018WR023585
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1029/2018wr023585View
Published, Version of Record (VoR)Maybe Open Access (Publisher Bronze),  Open
url
https://doi.org/10.1029/2018WR023585View
Published, Version of Record (VoR) Open

Abstract

hyporheic zone model intercomparison resazurin smart tracer stream transient storage modeling
Novel observation techniques (e.g., smart tracers) for characterizing coupled hydrological and biogeochemical processes are improving understanding of stream network transport and transformation dynamics. In turn, these observations are thought to enable increasingly sophisticated representations within transient storage models (TSMs). However, TSM parameter estimation is prone to issues with insensitivity and equifinality, which grow as parameters are added to model formulations. Currently, it is unclear whether (or not) observations from different tracers may lead to greater process inference and reduced parameter uncertainty in the context of TSM. Herein, we aim to unravel the role of in‐stream processes alongside metabolically active (MATS) and inactive storage zones (MITS) using variable TSM formulations. Models with one (1SZ) and two storage zones (2SZ) and with and without reactivity were applied to simulate conservative and smart tracer observations obtained experimentally for two reaches with differing morphologies. As we show, smart tracers are unsurprisingly superior to conservative tracers when it comes to partitioning MITS and MATS. However, when transient storage is lumped within a 1SZ formulation, little improvement in parameter uncertainty is gained by using a smart tracer, suggesting the addition of observations should scale with model complexity. Importantly, our work identifies several inconsistencies and open questions related to reconciling time scales of tracer observation with conceptual processes (parameters) estimated within TSM. Approaching TSM with multiple models and tracer observations may be key to gaining improved insight into transient storage simulation as well as advancing feedback loops between models and observations within hydrologic science. Plain Language Summary Solute experiments and transport models, called commonly tracer experiments, are used to understand the relative importance of different stream processes, especially those that influence water, solutes, and nutrients as they move through a stream network. Within these tracer experiments, there are processes that exchange mass beyond the main stream channel to other parts of the river valley bottom environment. Sometimes, there are single or multiple types of tracers used and modeled to try to understand this exchange. There are also multiple models with different equations and structures to simulate these tracers. This study shows that what you can learn about these stream processes depends on experiment choices and which model you use. Hence, refining future multiple tracer experiments and models is needed to determine how we best obtain consistent measurements of key stream processes. Key Points TSM interpretation improved with analysis of multiple tracers but results in increased parameter uncertainty for a more complex model Nonconservative tracers enabled interpretation of parameters that were highly uncertain when estimated by conservative tracers alone Achieving reliable parameter estimates depends on choice of tracers and model framework and should be coupled with uncertainty assessment

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18 Record Views
30 citations in Web of Science
30 citations in Scopus

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#6 Clean Water and Sanitation

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Environmental Sciences
Limnology
Water Resources
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