Modelling of biogeochemical processes in fish earth ponds: Model development and calibration
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.
References (58)
- et al.
Rates and pathways of benthic mineralization in extensive shrimp ponds of the Mekong delta, Vietnam
Aquaculture
(1999) Carbon/nitrogen ratio as a control element in aquaculture systems
Aquaculture
(1999)- et al.
Effects of aeration and mixing on nitrogen and organic matter transformations in simulated fish ponds
Aquacultural Engineering
(1992) - et al.
A synthesis of dominant ecological processes in intensive shrimp ponds and adjacent coastal environments in NE Australia
Marine Pollution Bulletin
(2003) - et al.
Modeling nitrogen dynamics in intensive shrimp ponds: the role of sediment remineralization
Aquaculture
(2004) A preliminary model of nutrient cycling in sediments of a Mediterranean lagoon
Ecological Modelling
(1995)- et al.
Modelling nitrogen, primary production and oxygen in a Mediterranean lagoon. Impact of oysters farming and inputs from the watershed
Ecological Modelling
(2000) - et al.
Aquaculture pond ecosystem model: temperature and dissolved oxygen prediction – mechanism and application
Ecological Modelling
(1996) - et al.
Effects of infauna harvesting on tidal flats of a coastal lagoon (Ria Formosa Portugal): implications on phosphorus dynamics
Marine Environmental Research
(2006) - et al.
Phosphorus exchange kinetics and exchangeable phosphorus form in sediments
Water Research
(1989)
A simulation model of ammonia dynamics in commercial catfish ponds in the southeastern United States
Aquacultural Engineering
Nitrogen biogeochemistry of aquaculture ponds
Aquaculture
Distribution and bioturbation effects of the tropical alpheid shrimp Alpheus macellarius in sediments impacted by milkfish farming
Estuarine Coastal and Shelf Science
An organic matter and nitrogen dynamics model for the ecological analysis of integrated aquaculture/agriculture systems: 1. model development and calibration
Environmental Modelling & Software
Conceptualization and validation of a dynamic model for the simulation of nitrogen transformations and fluxes in fishponds
Ecological Modelling
Analyses of phosphorus and nitrogen cyclings in the estuarine ecosystem of Hiroshima Bay by a pelagic and benthic coupled model
Estuarine Coastal and Shelf Science
Modeling of nitrogen transformations in intensively aerated fishponds
Aquaculture
Modelling approach of nitrogen and phosphorus exchanges at the sediment-water interface of an intensive fishpond system
Aquaculture
A model for food nutrient dynamics of semi-intensive pond fish culture
Aquacultural Engineering
Simulation of phosphorus dynamics in an intensive shrimp culture system: effects of feed formulations and feeding strategies
Ecological Modelling
The role of microorganisms in aquaculture ponds
Aquaculture
Modelling carbon and nutrient cycling in a simulated pond system at Ranchi
Ecological Modelling
Implications for oxygen, nutrient fluxes and denitrification rates during the early stage of sediment colonisation by the polychaete Nereis spp. in four estuaries
Estuarine Coastal and Shelf Science
Modelling N transformation and removal in a duckweed pond: model development and calibration
Ecological Modelling
Different modelling tools of aquatic ecosystems: a proposal for a unified approach
Ecological Informatics
Simulating the temporal pattern of waste production in farmed gilthead seabream (Sparus aurata) European seabass (Dicentrarchus labrax) and Atlantic bluefin tuna (Thunnus thynnus)
Ecological Modelling
Benthic metabolism and the effects of bioturbation in a fertilised polyculture fish pond in northeast Thailand
Aquaculture
A simple dynamic model for the simulation of the release of phosphorus from sediments in shallow eutrophic systems
Water Research
Phosphate sorption in superficial intertidal sediments
Marine Chemistry
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