Elsevier

Ecological Modelling

Volume 247, December 2012, Pages 286-301
Ecological Modelling

Modelling of biogeochemical processes in fish earth ponds: Model development and calibration

https://doi.org/10.1016/j.ecolmodel.2012.08.021Get rights and content

Abstract

The biogeochemistry of fish earth ponds is a complex subject due to the interactions between several water column and sediment compounds, particularly nutrient species. Models can improve our ability to understand such complexity. This study combines existing knowledge on biogeochemical processes in earth ponds into a model that calculates the concentration of the compounds that are more likely to negatively affect fish production and cause undesirable environmental impacts, such as nitrogen, phosphorus and oxygen. Aside from inorganic nutrient forms, organic compounds were included in the model due to their relevance for the nutrient cycles in aquatic systems. The model couples the pelagic and benthic compartments, due to the importance of sediment–water interactions in shallow systems. In this first approach in modelling the fishpond environment, the feedbacks between cultivated species and the environment were not accounted for the model, to reduce its complexity and easily identify the interactions between water column and sediment variables and processes. The model was calibrated for an earth pond without fish, using data sets collected during a 2-year trial. The variability of water column compounds was generally well predicted (p < 0.01), however the model could not fully reproduce ammonium and dissolved organic phosphorus concentrations. In sediments, organic phosphorus was accurately simulated (p < 0.05) while nitrogen and carbon pools were occasionally over or underestimated. Model limitations regarding sediment variables are most likely related to the effects of benthic primary producers and macrofauna activity in earth ponds biogeochemistry. Upcoming applications of the model developed herein include its coupling to a fish Dynamic Energy Budget model to be used as a predictive tool for fishpond management. Nevertheless, the model may also be applied to other aquatic systems such as coastal systems or wastewater treatment ponds.

Highlights

► A biogeochemical model was developed for earth pond systems. ► The model couples a pelagic and benthic modules to reproduce sediment–water interactions. ► Inorganic and organic nutrient forms were considered due to their relevance in aquatic systems. ► The model reproduced reasonably well water column, porewater and sediment compounds. ► Models may be powerful tools for improving water quality in fishponds.

Introduction

The biogeochemical processes occurring in earth ponds are essentially the same as in other aquatic systems (Burford and Lorenzen, 2004, Chapelle, 1995, Kittiwanich et al., 2007, Serpa et al., 2007a, Serpa et al., 2007b, Wang et al., 2003). However, in shallow earth ponds, the interactions between pelagic and benthic systems are more intense because most autochthonous particulate organic matter is rapidly settled, being mineralized in the top sediment layer (Hargreaves, 1998, Serpa et al., 2007b). Organic matter decomposition generates a pool of organic and inorganic nutrients (Kittiwanich et al., 2007, Worsfold et al., 2008), which are intensely transported to the water column, becoming available for the biota (Kittiwanich et al., 2007, Worsfold et al., 2008).

Although several studies on earth pond biogeochemistry have been produced (Alongi et al., 1999, Boyd et al., 2006, Burford et al., 2003, Burford and Lorenzen, 2004, Hargreaves, 1998, Lefebvre et al., 2001, Montoya et al., 2000, Muendo, 2006, Mukherjee et al., 2008, Xinglong and Boyd, 2006), linkage between early diagenetic processes and the interactions between compounds are complex and poorly understood. Furthermore, biogeochemical processes are affected by abiotic (e.g. dissolved oxygen, temperature, pH and light intensity) and biotic parameters (e.g. structure of microbial and benthic macrofauna communities) that interact in a complex way (Hargreaves, 1998, Moriarty, 1997, Peng et al., 2007), making it difficult to predict the variability of the different compounds.

Mathematical models can improve our ability to understand the complexity of such systems by integrating physical, chemical and biological processes occurring in fish earth ponds. Models are also powerful tools to predict the effects of management strategies on pond biogeochemistry (Burford and Lorenzen, 2004, Li and Yakupitiyage, 2003, Montoya et al., 2000, Piedecausa et al., 2010), providing useful information on how to improve water quality and to reduce the environmental impacts of fish farms. Several mathematical models have been developed for aquaculture ponds (Burford and Lorenzen, 2004, Culberson and Piedrahita, 1996, Jiménez-Montealegre et al., 2002, Kochba et al., 1994, Lefebvre et al., 2001, Li and Yakupitiyage, 2003, Montoya et al., 2000, Mukherjee et al., 2008, Piedrahita et al., 1984). Some of these models were specifically used for analysing nitrogen (Burford and Lorenzen, 2004, Hargreaves, 1997, Jiménez-Montealegre et al., 2002, Kochba et al., 1994) and phosphorus dynamics (Montoya et al., 2000), while less effort has been made to develop more comprehensive predictive models (Lefebvre et al., 2001, Li and Yakupitiyage, 2003, Mukherjee et al., 2008, Piedrahita et al., 1984).

The general objective of this work was to develop a mathematical model describing the main biogeochemical processes in fish earth ponds, namely for the elements that are more likely to negatively affect fish production and cause undesirable environmental impacts due to their excess, such as nitrogen (N) and phosphorus (P), or deficit, such as oxygen (DO). Given the importance of sedimentation and diffusion processes in shallow aquatic systems, the model developed herein couples a pelagic and benthic module to simulate the interactions between the two compartments. Feedbacks between fish and the environment were not considered in this work because this would substantially increase model complexity, making it difficult to calibrate the model and evaluate its performance regarding the simulation of other biogeochemical processes. The specific objectives of this study were to:

  • (1)

    evaluate model sensitivity to changes in individual processes;

  • (2)

    identify the main sources and sinks of nutrients for the system;

  • (3)

    identify those processes needing further study.

The model described herein is the first step towards a complete fish pond model after its coupling with a fish Dynamic Energy Budget (DEB) model.

Section snippets

Description of the system

Data for model calibration was collected during a 2-year white seabream (Diplodus sargus) growth trial (18th June 2003–31st March 2005), carried out in the earth ponds of an Aquaculture Research Centre (ARC), located in the Ria Formosa lagoon (Southeast Portugal). In this trial, a rectangular earth pond with a surface area of 495 m2 (33 m × 15 m) and 1.5 m depth (height of the water column) was used as a control pond (without fish). The model was calibrated against water column and sediment data from

Water column variables

The comparison between model simulations and observations for water column variables is presented in Fig. 3. Model II regressions between predicted and measured values (Table 1) suggest that the model was able to accurately predict (p < 0.01) the variability of POMw and HPO42−w in pond water. Nevertheless, a systematic overestimation was found for these variables (Fig. 3) since the slope of the regressions was not significantly different from one but the y-intercept significantly differs from

Discussion

Like other mathematical models that have successfully predicted water quality in earth ponds (Burford and Lorenzen, 2004, Hargreaves, 1997, Jiménez-Montealegre et al., 2002, Piedrahita et al., 1984), the model developed herein was able to reproduce the variability of most water column variables, with the exception of NH4+w and DOPw (Fig. 3 and Table 1). As the variation pattern predicted by the model was principally determined by the concentrations of these compounds in inflowing water (Fig. 2

Conclusions

The model developed herein simulated fairly well the water and sediment quality in an earth pond without fish, constituting a basis for understanding the biogeochemistry of fishponds. During calibration it became clear that, in general, changes in model parameters would not substantially improve model performance, which suggests that further studies are needed on the effects of unaccounted processes such as sediment resuspension as well as primary producers and benthic fauna activity, on

Acknowledgements

This research was funded by a PhD fellowship from Fundação para a Ciência e Tecnologia (SFRH/BD/27643/2006). Special thanks to Maria de Lourdes Santos for her help in the laboratory work.

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