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; T. Datry; J. Lewandowski; A. M. Milner; S. Krause

doi:10.1029/2018WR023585

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, T. Datry, J. Lewandowski, A. M. Milner, S. Krause

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Abstract

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.

Original language	English
Pages (from-to)	3481-3501
Number of pages	21
Journal	Water Resources Research
Volume	55
Issue number	4
DOIs	https://doi.org/10.1029/2018WR023585
State	Published - Apr 2019
Externally published	Yes

Funding

Funding for this research was provided by the Leverhulme Trust (Where rivers, groundwater, and disciplines meet: a hyporheic research network) and the U.K. Natural Environment Research Council (Large woody debris—A river restoration panacea for streambed nitrate attenuation? NERC NE/L003872/1). Data collection would not have been possible without the Leverhulme Hyporheic Zone Network Team, as well as funding from participating institutions. We also extend our thanks to Chithurst Buddhist Monastery for permitting access to their woodland. Additional support for Ward's time and computational infrastructure was provided by the Lilly Endowment, Inc., through its support for the Indiana University (IU) Pervasive Technology Institute, in part by the Indiana METACyt Initiative and by the University of Birmingham's Institute for Advanced Studies. Ward's time in development and implementation of Monte Carlo software and time series analysis was supported by National Science Foundation (NSF) grants EAR 1331906, EAR 1505309, and EAR 1652293. Several authors were also supported by the European Commission supported HiFreq: Smart high‐frequency environmental sensor networks for quantifying nonlinear hydrological process dynamics across spatial scales (project ID 734317). Field experiments were primarily led by Blaen, Kurz, and Knapp, with input and assistance from most coauthors. Kelleher and Ward primarily conceived of the analyses presented here and led the modeling and data analysis efforts. Kelleher and Ward led the writing of this manuscript, with input from all coauthors. Views expressed in this manuscript do not necessarily reflect those of any funding agencies or institutions. Experimental data are accessible via Hydroshare (Blaen et al., 2019). Monte Carlo and uncertainty analysis software are available as the Sensitivity and Uncertainty Analysis for Everyone (SAFE) package (https:// www.safetoolbox.info/). The authors especially wish to thank three anonymous reviewers who provided productive feedback to revising this manuscript.

Funders	Funder number
Lilly Endowment, Inc.
METACyt
University of Birmingham's Institute for Advanced Studies
National Science Foundation	1652293, EAR 1505309, EAR 1652293, EAR 1331906
Indiana University
Natural Environment Research Council	NE/L003872/1
Leverhulme Trust
European Commission	734317

Keywords

hyporheic zone
model intercomparison
resazurin
smart tracer
stream
transient storage modeling

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10.1029/2018WR023585

Cite this

Kelleher, C., Ward, A., Knapp, J. L. A., Blaen, P. J., Kurz, M. J., Drummond, J. D., Zarnetske, J. P., Hannah, D. M., Mendoza-Lera, C., Schmadel, N. M., Datry, T., Lewandowski, J., Milner, A. M., & Krause, S. (2019). Exploring Tracer Information and Model Framework Trade-Offs to Improve Estimation of Stream Transient Storage Processes. Water Resources Research, 55(4), 3481-3501. https://doi.org/10.1029/2018WR023585

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title = "Exploring Tracer Information and Model Framework Trade-Offs to Improve Estimation of Stream Transient Storage Processes",

abstract = "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.",

keywords = "hyporheic zone, model intercomparison, resazurin, smart tracer, stream, transient storage modeling",

author = "Christa Kelleher and Adam Ward and Knapp, {J. L.A.} and Blaen, {P. J.} and Kurz, {M. J.} and Drummond, {J. D.} and Zarnetske, {J. P.} and Hannah, {D. M.} and C. Mendoza-Lera and Schmadel, {N. M.} and T. Datry and J. Lewandowski and Milner, {A. M.} and S. Krause",

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Kelleher, C, Ward, A, Knapp, JLA, Blaen, PJ, Kurz, MJ, Drummond, JD, Zarnetske, JP, Hannah, DM, Mendoza-Lera, C, Schmadel, NM, Datry, T, Lewandowski, J, Milner, AM & Krause, S 2019, 'Exploring Tracer Information and Model Framework Trade-Offs to Improve Estimation of Stream Transient Storage Processes', Water Resources Research, vol. 55, no. 4, pp. 3481-3501. https://doi.org/10.1029/2018WR023585

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AU - Kelleher, Christa

AU - Ward, Adam

AU - Knapp, J. L.A.

AU - Blaen, P. J.

AU - Kurz, M. J.

AU - Drummond, J. D.

AU - Zarnetske, J. P.

AU - Hannah, D. M.

AU - Mendoza-Lera, C.

AU - Schmadel, N. M.

AU - Datry, T.

AU - Lewandowski, J.

AU - Milner, A. M.

AU - Krause, S.

PY - 2019/4

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N2 - 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.

AB - 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.

KW - hyporheic zone

KW - model intercomparison

KW - resazurin

KW - smart tracer

KW - stream

KW - transient storage modeling

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ER -

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

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