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Modelling macronutrients in shelf sea sediments: fitting model output to experimental data using a genetic algorithm

  • SEDIMENTS, SEC 2 • PHYSICAL AND BIOGEOCHEMICAL PROCESSES • RESEARCH ARTICLE
  • Published:
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Abstract

Purpose

Diagenetic modelling, the mathematical simulation of the breakdown of sedimentary organic matter and subsequent fate of associated nutrients, has progressed to a point where complex, non-steady state environments can be accurately modelled. A genetic algorithm has never been used in conjunction with an early diagenetic model, and so we aim to discover whether this method is viable to determining a set of realistic model parameters, which itself is often a difficult task.

Materials and methods

A range of sensitivity analyses were conducted to establish the parameters for which the model was most sensitive before a micro-genetic algorithm (μGA) was used to fit an output from a previously published diagenetic model (OMEXDIA) to observational data, taken at the North Dogger site from a series of cruises in the North Sea. Profiles of carbon, oxygen, nitrate and ammonia were considered. The method allows a set of parameters to be determined in a manner analogous to natural selection. Each iteration of the genetic algorithm within each experiment decreases the variance between the observed profiles and those calculated by OMEXDIA.

Results and discussion

Despite some of the observed profiles, particularly for carbon, showing unusual patterns, the genetic algorithm was able to generate a set of parameters which was able to fit the observations. The genetic algorithm can therefore help to determine the values of other parameters used in the model, for which observational values are difficult to measure (e.g. the flux of organic matter to the sediment from the overlying water column and the rates of degradation of organic matter). We also show that the values of the parameters determined by the μGA technique are able to be used in a potentially temporally predictive manner.

Conclusions

The μGA used is a viable method to fit carbon and nutrient sedimentary profiles observed in complex, dynamic shelf sea systems, despite OMEXDIA originally being designed for a different sedimentary environment. The results therefore show that this novel use of a genetic algorithm is a suitable method for both model calibration and validation and that the technique may help in explaining processes which are poorly understood.

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Acknowledgments

The authors acknowledge the use of the IRIDIS High Performance Computing Facility and associated support services at the University of Southampton, in the completion of this work. Ruth Parker and Naomi Greenwood from the Centre for Environment, Fisheries and Aquaculture Science and Gary Fones and Fay Couceiro from the University of Portsmouth are thanked for providing observational data, as are Phil Owens, Ian Droppo and Katja Fennel for their constructive comments on the manuscript. The project was funded by the UK Natural Environment Research Council (NERC NE/F003552/1) as part of the Marine Ecosystem Connections project. The code used in the experiments is available by contacting the authors.

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Author notes

  1. Christopher C. Wood

    Present address: The Research Complex at Harwell, Rutherford Appleton Laboratory, Didcot, OX11 0FA, UK

  2. Boris A. Kelly-Gerreyn

    Present address: Australian Bureau of Meteorology, GPO Box 1289, Melbourne, VIC, 3001, Australia

Authors and Affiliations

  1. Ocean and Earth Science, National Oceanography Centre Southampton, University of Southampton Waterfront Campus, European Way, Southampton, SO14 3ZH, UK

    Christopher C. Wood & Peter J. Statham

  2. NERC Strategic Research Division, National Oceanography Centre Southampton, University of Southampton Waterfront Campus, European Way, Southampton, SO14 3ZH, UK

    Boris A. Kelly-Gerreyn & Adrian P. Martin

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  1. Christopher C. Wood
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Correspondence to Christopher C. Wood.

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Responsible editor: Ian G. Droppo

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Wood, C.C., Statham, P.J., Kelly-Gerreyn, B.A. et al. Modelling macronutrients in shelf sea sediments: fitting model output to experimental data using a genetic algorithm. J Soils Sediments 14, 218–229 (2014). https://doi.org/10.1007/s11368-013-0793-0

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  • DOI: https://doi.org/10.1007/s11368-013-0793-0

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