A biophysical representation of seagrass growth for application in a complex shallow-water biogeochemical model

Baird, Mark E.; Adams, Matthew; Babcock, Russell C.; Oubelkheir, Kadija; Mongin, Mathieu; Wild-Allen, Karen; Skerratt, Jennifer; Robson, Barbara; Petrou, Katherina; Ralph, Peter J.; O’Brien, Katherine R.; Carter, Alex B.; Jarvis, Jessie C.; Rasheed, Michael A.

doi:10.1016/j.ecolmodel.2015.12.011

Cited by 42 publications

(63 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In equation (2), P 0 is the photosynthesis rate at the reference temperature T ref = 20 °C, following the convention of Baird et al 7…”

Section: Methodsmentioning

confidence: 99%

“…Parameters that are transferable have usage beyond the selected empirical model; they may be directly compared to experimentally measurable quantities to provide an indication of system state, and/or easily used in different model types. For example, the minimum light requirement (MLR) of seagrass is a transferable parameter, as comparison of local light levels to MLR indicates whether seagrass are at risk of loss due to light deprivation56, and MLR can be used to parameterise both mechanistic7 and statistical89 coastal ecosystem models. Model parameters that are (1) stable, (2) physically interpretable and (3) transferable have the greatest biological meaning, and therefore we define parameters that satisfy these three criteria as biologically-meaningful .…”

mentioning

confidence: 99%

See 1 more Smart Citation

Model fit versus biological relevance: Evaluating photosynthesis-temperature models for three tropical seagrass species

Adams

Collier

Uthicke

et al. 2017

Sci Rep

Self Cite

View full text Add to dashboard Cite

When several models can describe a biological process, the equation that best fits the data is typically considered the best. However, models are most useful when they also possess biologically-meaningful parameters. In particular, model parameters should be stable, physically interpretable, and transferable to other contexts, e.g. for direct indication of system state, or usage in other model types. As an example of implementing these recommended requirements for model parameters, we evaluated twelve published empirical models for temperature-dependent tropical seagrass photosynthesis, based on two criteria: (1) goodness of fit, and (2) how easily biologically-meaningful parameters can be obtained. All models were formulated in terms of parameters characterising the thermal optimum (Topt) for maximum photosynthetic rate (Pmax). These parameters indicate the upper thermal limits of seagrass photosynthetic capacity, and hence can be used to assess the vulnerability of seagrass to temperature change. Our study exemplifies an approach to model selection which optimises the usefulness of empirical models for both modellers and ecologists alike.

show abstract

“…In equation (2), P 0 is the photosynthesis rate at the reference temperature T ref = 20 °C, following the convention of Baird et al 7…”

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Model fit versus biological relevance: Evaluating photosynthesis-temperature models for three tropical seagrass species

Adams

Collier

Uthicke

et al. 2017

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is now a management priority to determine the impacts of catchment-derived sediments and nutrients on the inshore reef waters Brodie et al, 2017). The bio-optical properties of the waters of the GBR have been studied before (Blondeau-Patissier et al, 2009;Cherukuru et al, 2017;Oubelkheir et al, 2006;Qin et al, 2007;Schroeder, Nechad, et al, 2012) and used, for example, to parameterize of semianalytical optical models Schroeder, Devlin, et al, 2012) and in optical and coupled optical-biogeochemical modeling (Baird, Adams, et al, 2016;Jones et al, 2016) of the region. Perhaps the greatest weakness in the optical modeling undertaken is the poor resolution of spectral characteristics of inorganic suspended sediments.…”

Section: Great Barrier Reefmentioning

confidence: 99%

“…Inherent optical properties, and the concentration of optically significant constituents, can be derived from remote-sensing reflectance; however, in optically complex waters global empirical and globally parameterized semianalytical remotely sensed algorithms tend to fail (Darecki & Stramski, 2004;Kratzer et al, 2008;Woźniak et al, 2014;Qin et al, 2007). A better understanding of coastal bio-optical properties is crucial to the accurate estimation of the seawater components from optical remote sensing (Aurin & Dierssen, 2012;Babin et al, 2003;Miller et al, 2005;Odermatt et al, 2012;Reynolds et al, 2001;Sathyendranath et al, 1989Sathyendranath et al, , 2016Soja-Woźniak et al, 2017) and for parameterizing biogeochemical models (Baird, Adams, et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Particulate Backscattering Ratio as an Indicator of Changing Particle Composition in Coastal Waters: Observations From Great Barrier Reef Waters

et al. 2019

View full text Add to dashboard Cite

Particle scattering is an important process that determines both the light penetration through the water column and water‐leaving light. Backscattering, in combination with absorption, determines the remote‐sensing reflectance that is used in ocean color algorithms. Additionally, the wavelength dependence of the backscattering ratio can be related to the particle composition in seawater. Here, we examine the magnitude and the spectral behavior of the backscattering ratio against other bio‐optical properties based on a comprehensive set of continuous measurements collected in coastal waters of the Great Barrier Reef over 3 years. The site is located offshore of a major river, and close to the cross‐shelf transition of bottom sediments from terrigenous muds to marine carbonates. The backscattering ratio measured at 650 nm clearly clustered the data into two types. Type 1, which we identified as terrigenous mud with a low organic fraction, is characterized by backscattering ratio below 0.011 with a mean value of 0.005. Type 2, which we identified as marine carbonate with a higher organic fraction, has backscattering ratio above 0.011 with a mean value of 0.02. Within 3 years, study site was exposed to type 1‐dominated particles 13% of the time, and type 2 87%. The observed changes in the backscattering ratio at this one coastal site are as large as the variability seen throughout the global ocean. This work provides a better understanding of processes determining the optical characteristics and insights into optical parameterizations that can be used in process‐based optical modeling of the Great Barrier Reef.

show abstract

“…The GBR ecosystem, described as one of the seven natural wonders of the world, is under increasing pressure from local and global anthropogenic stressors (De'ath et al, 2012). Decreasing water clarity due to nutrient and sediment pollution is considered a serious threat to the GBR ecosystem (Thompson et al, 2014), with major concerns including the impact of reduced benthic light levels on coral and seagrass communities (Collier et al, 2012;Baird et al, 2016b) and the impacts of invasive species (e.g. Morello et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Use of remote-sensing reflectance to constrain a data assimilating marine biogeochemical model of the Great Barrier Reef

et al. 2016

Self Cite

View full text Add to dashboard Cite

Abstract. Skillful marine biogeochemical (BGC) models are required to understand a range of coastal and global phenomena such as changes in nitrogen and carbon cycles. The refinement of BGC models through the assimilation of variables calculated from observed in-water inherent optical properties (IOPs), such as phytoplankton absorption, is problematic. Empirically derived relationships between IOPs and variables such as chlorophyll-a concentration (Chl a), total suspended solids (TSS) and coloured dissolved organic matter (CDOM) have been shown to have errors that can exceed 100 % of the observed quantity. These errors are greatest in shallow coastal regions, such as the Great Barrier Reef (GBR), due to the additional signal from bottom reflectance. Rather than assimilate quantities calculated using IOP algorithms, this study demonstrates the advantages of assimilating quantities calculated directly from the less error-prone satellite remote-sensing reflectance (RSR). To assimilate the observed RSR, we use an in-water optical model to produce an equivalent simulated RSR and calculate the mismatch between the observed and simulated quantities to constrain the BGC model with a deterministic ensemble Kalman filter (DEnKF). The traditional assumption that simulated surface Chl a is equivalent to the remotely sensed OC3M estimate of Chl a resulted in a forecast error of approximately 75 %. We show this error can be halved by instead using simulated RSR to constrain the model via the assimilation system. When the analysis and forecast fields from the RSR-based assimilation system are compared with the non-assimilating model, a comparison against independent in situ observations of Chl a, TSS and dissolved inorganic nutrients (NO 3 , NH 4 and DIP) showed that errors are reduced by up to 90 %. In all cases, the assimilation system improves the simulation compared to the non-assimilating model. Our approach allows for the incorporation of vast quantities of remote-sensing observations that have in the past been discarded due to shallow water and/or artefacts introduced by terrestrially derived TSS and CDOM or the lack of a calibrated regional IOP algorithm.

show abstract

A biophysical representation of seagrass growth for application in a complex shallow-water biogeochemical model

Cited by 42 publications

References 60 publications

Model fit versus biological relevance: Evaluating photosynthesis-temperature models for three tropical seagrass species

Model fit versus biological relevance: Evaluating photosynthesis-temperature models for three tropical seagrass species

Particulate Backscattering Ratio as an Indicator of Changing Particle Composition in Coastal Waters: Observations From Great Barrier Reef Waters

Use of remote-sensing reflectance to constrain a data assimilating marine biogeochemical model of the Great Barrier Reef

Contact Info

Product

Resources

About