A General Lake Model (GLM 2.4) for linking with high-frequency sensor data from the Global Lake Ecological Observatory Network (GLEON)

Hipsey, Matthew R.; Bruce, Louise; Boon, Casper; Busch, Brendan; Carey, Cayelan C.; Hamilton, David P.; Hanson, Paul C.; Read, Jordan S.; Sousa, Eduardo; Weber, Michael; Winslow, Luke

doi:10.5194/gmd-2017-257

Cited by 22 publications

(32 citation statements)

References 62 publications

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“…Popular approaches to couple sediment processes to lake water column models have included the incorporation of an empirical bottom flux boundary (Schmid et al, 2017) and vertically integrated submodules (e.g., oxic and anoxic layers; Janssen et al, 2015;Matzinger et al, 2010;Mooij et al, 2011;Schmid et al, 2017). Several well-established lake models, such as FABM-PCLake (Hu et al, 2016), DYRESM-CAEDYM (Trolle et al, 2008), CE-QUAL-W2 (Zhang et al, 2015), GLM (Hipsey et al, 2017), and DELWAQ (Smits & van Beek, 2013), were built on variations of those approaches in order to represent sediment-water interactions.…”

Section: Introductionmentioning

confidence: 99%

Coupling Water Column and Sediment Biogeochemical Dynamics: Modeling Internal Phosphorus Loading, Climate Change Responses, and Mitigation Measures in Lake Vansjø, Norway

et al. 2019

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We expanded the existing one‐dimensional MyLake model by incorporating a vertically resolved sediment diagenesis module and developing a reaction network that seamlessly couples the water column and sediment biogeochemistry. The application of the MyLake‐Sediment model to boreal Lake Vansjø illustrates the model's ability to reproduce daily water quality variables and predict sediment‐water column exchange fluxes over a long historical period. In prognostic scenarios, we assessed the importance of sediment processes and the effects of various climatic and anthropogenic drivers on the lake's biogeochemistry and phytoplankton dynamics. First, MyLake‐Sediment was used to simulate the potential impacts of increasing air temperature on algal growth and water quality. Second, the key role of ice cover in controlling water column mixing and biogeochemical cycles was analyzed in a series of scenarios that included a fully ice‐free end‐member. Third, in another end‐member scenario P loading from the watershed to the lake was abruptly halted. The model results suggest that remobilization of legacy P stored in the bottom sediments could sustain the lake's primary productivity on a time scale of several centuries. Finally, while the majority of management practices to reduce excessive algal growth in lakes focus on reducing external P loads, other efforts rely on the addition of reactive materials that sequester P in the sediment. Therefore, we investigated the effectiveness of ferric iron additions in decreasing the dissolved phosphate efflux from the sediment and, consequently, limit phytoplankton growth in Lake Vansjø.

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Section: Introductionmentioning

confidence: 99%

Coupling Water Column and Sediment Biogeochemical Dynamics: Modeling Internal Phosphorus Loading, Climate Change Responses, and Mitigation Measures in Lake Vansjø, Norway

et al. 2019

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“…General Lake Model is an open‐source hydrodynamic‐water quality model that is widely used to predict phytoplankton blooms and water quality (Hipsey et al. ). General Lake Model offers a modeling environment that balances water, mass, and energy budgets at sub‐daily time scales and includes biogeochemical cycling and the interaction of trophic levels within the lake.…”

Section: From Concept To Practice: Cnhs Components and Modelsmentioning

confidence: 99%

“…To capture the underlying dynamics that drive water quality outcomes, we chose the General Lake Model coupled with the Aquatic EcoDynamics library (GLM-AED, hereafter GLM). General Lake Model is an open-source hydrodynamic-water quality model that is widely used to predict phytoplankton blooms and water quality (Hipsey et al 2017). General Lake Model offers a modeling environment that balances water, mass, and energy budgets at sub-daily time scales and includes biogeochemical cycling and the interaction of trophic levels within the lake.…”

Section: From Concept To Practice: Cnhs Components and Modelsmentioning

confidence: 99%

From concept to practice to policy: modeling coupled natural and human systems in lake catchments

et al. 2018

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Abstract. Recent debate over the scope of the U.S. Clean Water Act underscores the need to develop a robust body of scientific work that defines the connectivity between freshwater systems and people. Coupled natural and human systems (CNHS) modeling is one tool that can be used to study the complex, reciprocal linkages between human actions and ecosystem processes. Well-developed CNHS models exist at a conceptual level, but the mapping of these system representations in practice is limited in capturing these feedbacks. This article presents a paired conceptual-empirical methodology for functionally capturing feedbacks between human and natural systems in freshwater lake catchments, from human actions to the ecosystem and from the ecosystem back to human actions. We address extant challenges in CNHS modeling, which arise from differences in disciplinary approach, model structure, and spatiotemporal resolution, to connect a suite of models. In doing so, we create an integrated, multi-disciplinary tool that captures diverse processes that operate at multiple scales, including land-management decision-making, hydrologic-solute transport, aquatic nutrient cycling, and civic engagement. In this article, we build on this novel framework to advance cross-disciplinary dialogue to move CNHS lake-catchment modeling in a systematic direction and, ultimately, provide a foundation for smart decision-making and policy.

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“…Human activities, however, as well as climate change impacts, have resulted in global‐scale vulnerability of lakes (Folke et al., ). Data analyses from a variety of lakes have provided a general scientific understanding of processes such as lake metabolism and carbon cycling (Hanson et al., ; Solomon et al., ), the effects of wind and heat exchange on lake dynamics (Kimura, Wu, Hoopes, & Tai, ; Woolway et al., ), interactions between climate change and lakes (Adrian et al., ; Jennings et al., ), the effects of changing climate on lake ice cover (Magee & Wu, ), model validations using high collection frequency data (Hamilton et al., ) and development of relevant models (Hipsey et al., ). It can be concluded, therefore, that lake monitoring and data analysis are beneficial for maintaining and managing aquatic ecosystems, their organisms and their environments.…”

Section: Introductionmentioning

confidence: 99%

“…frequency data and development of relevant models (Hipsey et al, 2017). It can be concluded, therefore, that lake monitoring and data analysis are beneficial for maintaining and managing aquatic ecosystems, their organisms and their environments.…”

mentioning

confidence: 99%

Using a nowcasting system to better understand lake water dynamics

Kimura

2018

Lakes & Reservoirs

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Constraints in conventional field observations, such as measurement stations and instruments, human resources and occasional inclement weather conditions, hinder our understanding of the biological, chemical and physical processes in aquatic systems. Accordingly, the present study developed an integrated monitoring and prediction system called WISconsin nowCASTing (WIS‐CAST) to better predict spatial and temporal physical lake dynamics (e.g., water movements and temperatures). The system uses either present or past observations from instrumented buoys equipped with flow, water temperature and meteorological measurement sensors, integrating the observed data (i.e., in situ data) with high‐tech data communication tools, databases, a three‐dimensional hydrodynamic model and visualization tools. Acoustic Doppler current profiler in the system with radio wave two‐way communication covering a distance of 1,000 m also was utilized in the present study. The WIS‐CAST was deployed in early August 2006 in Trout Lake, a mid‐sized lake Wisconsin. It was maintained until late October 2006 as a local‐level, compacted nowcasting system to show temporal and spatial distributions of water temperature and movement in the water column and the entire lake. The lake dynamics affected by the Coriolis force for a state of summer stratification, a unique event of upwelling/downwelling and a process of overturn, all monitored and predicted during the measurement period with lake current and water temperature distributions, is presented herein. The WIS‐CAST, which was used as a system to assist lake management (e.g., water quality control), indicated the chemical dynamics were caused by a physical process, such as nutrient upwelling and vertical mixing of nutrients.

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A General Lake Model (GLM 2.4) for linking with high-frequency sensor data from the Global Lake Ecological Observatory Network (GLEON)

Cited by 22 publications

References 62 publications

Coupling Water Column and Sediment Biogeochemical Dynamics: Modeling Internal Phosphorus Loading, Climate Change Responses, and Mitigation Measures in Lake Vansjø, Norway

Coupling Water Column and Sediment Biogeochemical Dynamics: Modeling Internal Phosphorus Loading, Climate Change Responses, and Mitigation Measures in Lake Vansjø, Norway

From concept to practice to policy: modeling coupled natural and human systems in lake catchments

Using a nowcasting system to better understand lake water dynamics

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