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A Synoptic System for Capturing Ecosystem Control Points Across Terrestrial‐Aquatic Interfaces.

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Title: A Synoptic System for Capturing Ecosystem Control Points Across Terrestrial‐Aquatic Interfaces.
Authors: Ward, Nicholas D.1 (AUTHOR) nicholas.ward@pnnl.gov, Megonigal, J. Patrick2 (AUTHOR), Weintraub, Michael N.1,3 (AUTHOR), Regier, Peter1 (AUTHOR), Pennington, Stephanie C.1 (AUTHOR), Peixoto, Roberta Bittencourt3 (AUTHOR), Bond‐Lamberty, Ben1 (AUTHOR), Chen, Xingyuan1 (AUTHOR), Doro, Kennedy O.3 (AUTHOR), Kemner, Kenneth M.4 (AUTHOR), Machado‐Silva, Fausto3 (AUTHOR), McDowell, Nate G.1 (AUTHOR), Myers‐Pigg, Allison N.1,3 (AUTHOR), Sandoval, Leticia3 (AUTHOR), Patel, Kaizad F.1 (AUTHOR), Thornton, Peter E.5 (AUTHOR), Wilson, Stephanie J.2 (AUTHOR), Bailey, Vanessa L.1,3 (AUTHOR), Rich, Roy L.2 (AUTHOR) richr@si.edu
Source: Journal of Geophysical Research. Biogeosciences. Oct2025, Vol. 130 Issue 10, p1-16. 16p.
Subject Terms: *Environmental monitoring, *Ecosystem management, *Biogeochemical cycles, *Riparian areas, Sensor networks, Data loggers
Geographic Terms: Chesapeake Bay (Md. & Va.), Lake Erie
Abstract: Interconnected landscape features such as terrestrial‐aquatic interfaces play an outsized role in biogeochemical cycles as ecosystem control points, but it is notoriously challenging to characterize these. Here, we document a synoptic sensor network design that is (a) flexible to accommodate diverse ecosystem interfaces and gradients, (b) adaptable to monitoring and modeling needs of small and large projects alike, (c) standardized for intercomparability across sites and field experiments, and (d) adequately replicated to capture heterogeneity of each parameter monitored. This real‐time monitoring of surface water, groundwater, soil, and vegetation supports configuration and evaluation of models that span upland, wetland, open water strata, and transitions between them. We established the network at seven sites along the Chesapeake Bay and Lake Erie coastlines, including large‐scale flood manipulation experiments in both regions. A central design element is "one data logger program to rule them all"—a collection of sensor‐specific modules deployed on 40 loggers controlling ∼2,000 sensors, with the goal of streamlining maintenance, debugging, and reproducible data processing. The network generates ∼6 M observations per month, capturing system dynamics at the broad spatial and fine temporal scales needed to initialize and benchmark models; measurement frequency can be modified remotely to capture events. This network design has also revealed behaviors not represented in Earth system models, such as transient groundwater oxygen pulses. Completely documented and open source, this standardized, flexible, and efficient sensor network design can reduce barriers to understanding environmental changes and ecosystem responses across systems and scales. Plain Language Summary: The investigation of how climate change and water level fluctuations impact variable and interconnected ecosystems, like the interfaces between terrestrial and aquatic environments, requires the collection and integration of many data types. We describe an integrative and autonomous environmental monitoring approach that uses environmental sensors and data loggers to monitor surface water, groundwater, soil, and vegetation changes to generate essential data for predictive models. We established the network at seven sites along the Chesapeake Bay and Lake Erie coastlines, including large‐scale flood manipulation experiments in both regions, collectively generating over 6 million observations per month. Such sensor networks hold great promise for tracking and comprehending environmental changes where land and water intersect. The sensor system and overall approach to sensor management that we have designed is intended to be widely accessible for research teams spanning in size from an individual investigator to large multi‐institution projects. Key Points: We describe a flexible approach for monitoring hydrological controls on the biogeochemistry and ecology of terrestrial aquatic interfacesManagement of ∼2,000 sensors is streamlined with a single modular data logging program deployed at seven coastal sites with unique featuresThe network provides foundational data for coastal model development and has enabled discovery of previously unknown system behaviors [ABSTRACT FROM AUTHOR]
Copyright of Journal of Geophysical Research. Biogeosciences is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A Synoptic System for Capturing Ecosystem Control Points Across Terrestrial‐Aquatic Interfaces.
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  Data: <searchLink fieldCode="AR" term="%22Ward%2C+Nicholas+D%2E%22">Ward, Nicholas D.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> nicholas.ward@pnnl.gov</i><br /><searchLink fieldCode="AR" term="%22Megonigal%2C+J%2E+Patrick%22">Megonigal, J. Patrick</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Weintraub%2C+Michael+N%2E%22">Weintraub, Michael N.</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Regier%2C+Peter%22">Regier, Peter</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pennington%2C+Stephanie+C%2E%22">Pennington, Stephanie C.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Peixoto%2C+Roberta+Bittencourt%22">Peixoto, Roberta Bittencourt</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bond‐Lamberty%2C+Ben%22">Bond‐Lamberty, Ben</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Xingyuan%22">Chen, Xingyuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Doro%2C+Kennedy+O%2E%22">Doro, Kennedy O.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kemner%2C+Kenneth+M%2E%22">Kemner, Kenneth M.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Machado‐Silva%2C+Fausto%22">Machado‐Silva, Fausto</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22McDowell%2C+Nate+G%2E%22">McDowell, Nate G.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Myers‐Pigg%2C+Allison+N%2E%22">Myers‐Pigg, Allison N.</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sandoval%2C+Leticia%22">Sandoval, Leticia</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Patel%2C+Kaizad+F%2E%22">Patel, Kaizad F.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Thornton%2C+Peter+E%2E%22">Thornton, Peter E.</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wilson%2C+Stephanie+J%2E%22">Wilson, Stephanie J.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bailey%2C+Vanessa+L%2E%22">Bailey, Vanessa L.</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rich%2C+Roy+L%2E%22">Rich, Roy L.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> richr@si.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Geophysical+Research%2E+Biogeosciences%22">Journal of Geophysical Research. Biogeosciences</searchLink>. Oct2025, Vol. 130 Issue 10, p1-16. 16p.
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  Data: *<searchLink fieldCode="DE" term="%22Environmental+monitoring%22">Environmental monitoring</searchLink><br />*<searchLink fieldCode="DE" term="%22Ecosystem+management%22">Ecosystem management</searchLink><br />*<searchLink fieldCode="DE" term="%22Biogeochemical+cycles%22">Biogeochemical cycles</searchLink><br />*<searchLink fieldCode="DE" term="%22Riparian+areas%22">Riparian areas</searchLink><br /><searchLink fieldCode="DE" term="%22Sensor+networks%22">Sensor networks</searchLink><br /><searchLink fieldCode="DE" term="%22Data+loggers%22">Data loggers</searchLink>
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– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Interconnected landscape features such as terrestrial‐aquatic interfaces play an outsized role in biogeochemical cycles as ecosystem control points, but it is notoriously challenging to characterize these. Here, we document a synoptic sensor network design that is (a) flexible to accommodate diverse ecosystem interfaces and gradients, (b) adaptable to monitoring and modeling needs of small and large projects alike, (c) standardized for intercomparability across sites and field experiments, and (d) adequately replicated to capture heterogeneity of each parameter monitored. This real‐time monitoring of surface water, groundwater, soil, and vegetation supports configuration and evaluation of models that span upland, wetland, open water strata, and transitions between them. We established the network at seven sites along the Chesapeake Bay and Lake Erie coastlines, including large‐scale flood manipulation experiments in both regions. A central design element is "one data logger program to rule them all"—a collection of sensor‐specific modules deployed on 40 loggers controlling ∼2,000 sensors, with the goal of streamlining maintenance, debugging, and reproducible data processing. The network generates ∼6 M observations per month, capturing system dynamics at the broad spatial and fine temporal scales needed to initialize and benchmark models; measurement frequency can be modified remotely to capture events. This network design has also revealed behaviors not represented in Earth system models, such as transient groundwater oxygen pulses. Completely documented and open source, this standardized, flexible, and efficient sensor network design can reduce barriers to understanding environmental changes and ecosystem responses across systems and scales. Plain Language Summary: The investigation of how climate change and water level fluctuations impact variable and interconnected ecosystems, like the interfaces between terrestrial and aquatic environments, requires the collection and integration of many data types. We describe an integrative and autonomous environmental monitoring approach that uses environmental sensors and data loggers to monitor surface water, groundwater, soil, and vegetation changes to generate essential data for predictive models. We established the network at seven sites along the Chesapeake Bay and Lake Erie coastlines, including large‐scale flood manipulation experiments in both regions, collectively generating over 6 million observations per month. Such sensor networks hold great promise for tracking and comprehending environmental changes where land and water intersect. The sensor system and overall approach to sensor management that we have designed is intended to be widely accessible for research teams spanning in size from an individual investigator to large multi‐institution projects. Key Points: We describe a flexible approach for monitoring hydrological controls on the biogeochemistry and ecology of terrestrial aquatic interfacesManagement of ∼2,000 sensors is streamlined with a single modular data logging program deployed at seven coastal sites with unique featuresThe network provides foundational data for coastal model development and has enabled discovery of previously unknown system behaviors [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Geophysical Research. Biogeosciences is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=8gh&AN=188962315
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        Value: 10.1029/2025JG009335
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        Text: English
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      – SubjectFull: Chesapeake Bay (Md. & Va.)
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      – SubjectFull: Lake Erie
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