Document Type

Journal Article

Publication Title

Science of the Total Environment

Volume

792

PubMed ID

34157534

Publisher

Elsevier

School

School of Engineering

Funders

Griffith University Australian Research Council

Grant Number

ARC Number : DP190101848

Grant Link

http://purl.org/au-research/grants/arc/DP190101848

Comments

Ranjbar, M. H., Hamilton, D. P., Etemad-Shahidi, A., & Helfer, F. (2021). Individual-based modelling of cyanobacteria blooms: Physical and physiological processes. Science of The Total Environment, 792, article 148418.https://doi.org/10.1016/j.scitotenv.2021.148418

Abstract

Lakes and reservoirs throughout the world are increasingly adversely affected by cyanobacterial harmful algal blooms (CyanoHABs). The development and spatiotemporal distributions of blooms are governed by complex physical mixing and transport processes that interact with physiological processes affecting the growth and loss of bloom-forming species. Individual-based models (IBMs) can provide a valuable tool for exploring and integrating some of these processes. Here we contend that the advantages of IBMs have not been fully exploited. The main reasons for the lack of progress in mainstreaming IBMs in numerical modelling are their complexity and high computational demand. In this review, we identify gaps and challenges in the use of IBMs for modelling CyanoHABs and provide an overview of the processes that should be considered for simulating the spatial and temporal distributions of cyanobacteria. Notably, important processes affecting cyanobacteria distributions, in particular their vertical passive movement, have not been considered in many existing lake ecosystem models. We identify the following research gaps that should be addressed in future studies that use IBMs: 1) effects of vertical movement and physiological processes relevant to cyanobacteria growth and accumulations, 2) effects and feedbacks of CyanoHABs on their environment; 3) inter and intra-specific competition of cyanobacteria species for nutrients and light; 4) use of high resolved temporal-spatial data for calibration and verification targets for IBMs; and 5) climate change impacts on the frequency, intensity and duration of CyanoHABs. IBMs are well adapted to incorporate these processes and should be considered as the next generation of models for simulating CyanoHABs.

DOI

10.1016/j.scitotenv.2021.148418

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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