Potamon: A dynamic model for predicting phytoplankton composition and biomass in lowland rivers

E. Everbecq, V. Gosselain, L. Viroux, J. P. Descy

Research output: Contribution to journalArticlepeer-review

Abstract

POTAMON is a unidimensional, non-stationary model, designed for simulating potamoplankton from source to mouth. The forcing variables are discharge, river morphology, water temperature, available light and nutrient inputs. Given the description of several algal categories, POTAMON allows to simulate algal "successions" at a particular site, as well as longitudinal changes of potamoplankton composition and biomass. The algal categories differ by their physiology, their loss rates, and their sensitivity to grazing by zooplankton. Two zooplankton categories were considered, Brachionus-like and Keratella-like, which differ by their clearance rate, their incipient limiting level, their selectivity towards phytoplankton, and their growth yield. The model simulates satisfactorily the onset and the magnitude of the phytoplankton spring bloom in the Belgian part of R. Meuse, the biomass decrease in early summer, and the autumn bloom. It also renders the major variations of algal assemblages along the river. The model allows to confirm that the main driving variables of potamoplankton dynamics in a eutrophic river are physical factors: discharge and related variables (e.g. retention time), light and temperature. In addition, the simulations confirm that the zooplankton-phytoplankton interaction may result in phytoplankton biomass fluctuations and compositional changes. POTAMON can be useful to explore plankton dynamics in a large river, and it may become a tool to test various management measures.

Original languageEnglish
Pages (from-to)901-912
Number of pages12
JournalWater Research
Volume35
Issue number4
DOIs
Publication statusPublished - 1 Mar 2001

Keywords

  • Eutrophication
  • Lowland river
  • Modelling
  • Phytoplankton
  • Zooplankton

Fingerprint

Dive into the research topics of 'Potamon: A dynamic model for predicting phytoplankton composition and biomass in lowland rivers'. Together they form a unique fingerprint.

Cite this