Towards evidence-based parameter values and priors for aquatic ecosystem modelling
Section snippets
Software and data availability
Software and data described in this paper are available from the Parameter Library Exploration Interface at http://shiny.csiro.au/CDM/parameterlibrary/latest/
Current state of the art
Parameterisation of aquatic ecosystem models has historically been a difficult and labour-intensive task. Parameter values are typically required for a range of model components, defining traits and responses of phytoplankton, zooplankton and other aquatic biota, characteristics of sediments and detrital material, physical and chemical process rates, and, more recently, bacterial traits and processes. Although mathematical methods have long been available to automate parameter estimation and
Collation of parameter values
There have been several efforts to collate and tabulate parameter values from across the literature. Some of these have been broadly focused on the most commonly used parameters in aquatic ecosystem models (e.g. Bowie et al., 1985) or eco-chemistry (Jorgensen et al., 2000), while others have comprehensively reviewed the literature for a narrower range of parameters relating to a particular component or process of an aquatic ecosystem (e.g. phytoplankton: Edwards et al., 2015, Eppley, 1985;
Online interface
We have implemented a preliminary online user interface to allow further exploration of the parameter library, which can be found at http://shiny.csiro.au/CDM/parameterlibrary/latest/. A screenshot is presented as Fig. 1. In addition to calculating and visualising probability distributions (gamma, normal, log-normal or uniform) fitted to each parameter, the interface allows users to filter datasets where relevant information exists in the database. In most cases, there is an option to restrict
Summary of parameter data
Table 1 summarises the data included in the parameter library as at May 2017. We anticipate that we will add to this resource over time, drawing on community contributions.
Making effective use of parameter prior distributions
The simplest approach to using the information presented via this online resource may be to select the subsample of data most relevant to the application at hand, plot a distribution, and calibrate within the range suggested by the 5th and 95th percentiles. After calibration, the final parameter set can be compared with the expected prior distributions. If more than a few calibrated parameter values fall close to the outer limits of the expected distribution, this may warrant further
Future development
The beta version of the parameter database presented here and currently available online is a static resource, manually updated. It is our intention (subject to funding) to maintain and update this to provide a modern data service. This will include not only expansion of the database itself and improvement of the online user interface, but also improvement of the underlying data structure, consideration of issues of semantics, inter-operability, provenance and data delivery, and provision of an
Conclusion
By presenting physiological trait and biogeochemical process rate information through an online tool that not only synthesises observations from multiple sources but also supports consistent and reproducable processing of these data (for instance, by applying a consistent temperature correction function to observed metabolic rates), we facilitate more evidence-based Bayesian parameterisation of aquatic systems models as well as improved model evaluation and development processes.
Acknowledgements
This work was funded in part through the eReefs project, a public-private collaboration between Australia's leading operational and scientific research agencies, government, and corporate Australia. JB and LG contributed to this work during internships at CSIRO Land and Water, completed as part of their studies. Thanks to Philip Balding for assisting with phytoplankton data entry.
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