A benthic–pelagic coupled ecosystem model to estimate the hypoxic estuary including tidal flat—Model description and validation of seasonal/daily dynamics

Sohma, Akio; Sekiguchi, Yasuyuki; Kuwae, Tomohiro; Nakamura, Yoshiyuki

doi:10.1016/j.ecolmodel.2008.02.027

Cited by 75 publications

(44 citation statements)

References 39 publications

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“…In most models, respiration is simply parameterized as a loss term which is either a fraction of the growth rate (e.g. Sohma et al, 2008) mann, 2000). In nitrogen based models, the respiration loss term is represented by the excretion of ammonia.…”

Section: Water Columnmentioning

confidence: 99%

“…The oxygen dependence can be modeled as a linear function (e.g. Yakushev and Neretin, 1997) or as a non-linear function, Sohma et al, 2008). Many models simulate the remineralization of sinking organic matter throughout the water column without explicitly including the organisms involved (e.g.…”

Section: Water Columnmentioning

confidence: 99%

“…Biogeochemical models that include the oxygen cycle have contributed to improved understanding of diverse aspects of hypoxia, such as processes responsible for its development (Druon et al, 2004), controls on its extent and temporal variability (Sohma et al, 2008), its impact on biogeochemical cycles (Oguz et al, 2000), issues related to eutrophication (Grégoire et al, 2008), and effects of climate change on O 2 concentrations (Matear and Hirst, 2003). They also have been useful in providing guidelines for setting goals of nutrient reductions (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Modeling dissolved oxygen dynamics and hypoxia

et al. 2010

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Abstract. Hypoxia conditions are increasing throughout the world, influencing biogeochemical cycles of elements and marine life. Hypoxia results from complex interactions between physical and biogeochemical processes, which can not be understood by observations alone. Models are invaluable tools at studying system dynamics, generalizing discrete observations and predicting future states. They are also useful as management tools for evaluating site-specific responses to management scenarios. Here we review oxygen dynamics models that have significantly contributed to a better understanding of the effects of natural processes and human perturbations on the development of hypoxia, factors controlling the extent and temporal variability of coastal hypoxia, and the effects of oxygen depletion on biogeochemical cycles. Because hypoxia occurs in a variety of environments and can be persistent, periodic or episodic, models differ significantly in their complexity and temporal and spatial resolution. We discuss the progress in developing hypoxia models for benthic and pelagic systems that range from simple box models to three dimensional circulation models. Applications of these models in five major hypoxia regions are presented. In the last decades, substantial progress has been made towards the parameterization of biogeochemical processes in both hypoxic water columns and sediments. In coastal regions, semiempirical models have been used more frequently than mechanistic models to study nutrient enrichment and hypoxia relationships. Recent advances in three-dimensional coupled physical-ecological-biogeochemical models have allowed a better representation of physical-biological interactions in these systems. We discuss the remaining gaps in process deCorrespondence to: M. A. Peña (angelica.pena@dfo-mpo.gc.ca) scriptions and suggest directions for improvement. Better process representations in models will help us answer several important questions, such as those about the causes of the observed worldwide increase in hypoxic conditions, and future changes in the intensity and spread of coastal hypoxia. At the same time, quantitative model intercomparison studies suggest that the predictive ability of our models may be adversely affected by their increasing complexity, unless the models are properly constrained by observations.

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Section: Water Columnmentioning

confidence: 99%

Section: Water Columnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling dissolved oxygen dynamics and hypoxia

et al. 2010

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“…Amongst these are the European Regional Seas Ecosystem Model (ERSEM) (Allen et al 2012;Baretta et al 1995;BarettaBekker et al 1997;Butenschön et al 2016) and those reviewed by James (2002) and Moll and Radach (2003). Biogeochemical models for Japanese coastal waters have also been developed (Hata et al 2004;Sohma et al 2001Sohma et al , 2004Sohma et al , 2008. Of note, the BHI concept proposed by Hori (2008) focused more on the spatial heterogeneity of habitats within a coastal area, which is different from numerical studies of biogeochemical/material cycles at larger scales, often considering areas with a homogeneous benthic habitat (Cranford et al 2007;Lacroix et al 2007;Nobre et al 2010;Xu and Hood 2006).…”

Section: Concepts Coupling the Four Featuresmentioning

confidence: 99%

“…Although original versions of these models do not include benthic habitats, this approach coupling a biogeochemical model with an IBM is important, as these components are generally more accessible and easier to handle than Ecospace and Atlantis that are reviewed in the following subsection. It is also noted that some biogeochemical models coupled pelagic and benthic ecosystems (e.g., Sohma et al 2008;Butenschön et al 2016).…”

Section: Biogeochemical Model Coupled With Individual-based Modelmentioning

confidence: 99%

Modeling the coastal ecosystem complex: present situation and challenges

et al. 2018

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To enhance numerical modeling of the coastal ecosystem complex (CEC), we reviewed the CEC and related concepts along with the current coastal ecosystem model framework in this study. We identified two model implementation paths from the initial objectives to numerical models: specific model building, and the use of existing model frameworks. As the CEC is still at the conceptual stage, both paths are possible. Four important ecological features of CEC modeling (population connectivity, habitat heterogeneity, ontogeny of organisms, and trophic interactions) were also identified. Models for population connectivity, species distributions, life histories, and food webs were categorized using these features. We found that some previously established concepts (between-habitat interactions, coastal ecosystem mosaic, and seascape nursery) overlap with the CEC concept. Several existing integrated model frameworks were reviewed, focusing on their potential to simulate CEC processes. Building specific models for the CEC at the current conceptual stage will be challenging, and modification of existing models will be needed if they are to be used for CEC modeling. Habitat function, ontogenetic development in early life stages, and recruitment variability are important factors when modifying existing models for the development of CEC models. Although model complexity should become high to reproduce observed ecoclogical processes, an intermediate level of model ccomplexity is feasible to decrease parameter uncertainty in models for fisheries management.

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