A simple metric for predicting the timing of river phytoplankton blooms

Journal Article (Journal Article)

In rivers, phytoplankton populations are continuously exported by unidirectional, advective flow. Both transport and growth conditions determine periods of excess phytoplankton growth, or blooms, in a given reach. Phytoplankton abundance, however, has mainly been compared to the state of either growth or transport conditions alone rather than in tandem. Previous studies have not yielded consistent driver–response relationships, limiting our ability to predict the timing of riverine phytoplankton blooms based on environmental factors. Here, we derive a simple joint metric that combines the state of growth and transport conditions, specifically the ratio of temperature and discharge ((Formula presented.)). We then compare the metric to biomass abundance data (daily, sensor-based chlorophyll a [chl a] data) from a mid-sized Great Plains river (the Kansas). (Formula presented.) was an excellent predictor of low to high biomass, outperforming either variable alone. However, it could not differentiate between very high biomass values, values well above the biomass threshold designating bloom conditions. Our findings of reduced performance at the highest values of T/Q indicated that (Formula presented.) could predict the occurrence (timing) but not magnitude of phytoplankton blooms, and we used T/Q to correctly predict 71% of days when bloom conditions occurred. Analyzing chl a abundance with (Formula presented.) also revealed likely switching from transport and temperature to nutrient control. (Formula presented.) offers a simple tool for (1) predicting the timing of river phytoplankton blooms, (2) forecasting how river ecosystems will respond to surrounding environmental changes, and (3) determining which environmental factors shape phytoplankton blooms at specific locations along a river.

Full Text

Duke Authors

Cited Authors

  • Bruns, NE; Heffernan, JB; Ross, MRV; Doyle, M

Published Date

  • December 1, 2022

Published In

Volume / Issue

  • 13 / 12

Electronic International Standard Serial Number (EISSN)

  • 2150-8925

Digital Object Identifier (DOI)

  • 10.1002/ecs2.4348

Citation Source

  • Scopus