Seasonal algal blooms support sediment release of phosphorus via positive feedback in a eutrophic lake: Insights from a nutrient flux tracking modeling

Rui Zou, Zhen Wu, Lei Zhao, James J. Elser, Yanhong Yu, Yihui Chen and Yong Liu

Ecological Modelling, 2020, vol. 416, issue C

Abstract: Despite the great effort of nutrient loading reduction, lake rehabilitation often suffers from the impacts of internal nutrient cycling. However, the mechanisms of internal nutrient cycling, specifically the feedback of algal blooms on nutrients cycling is still an open question. Management of Lake Dianchi, the most eutrophic lake in China, has involved a series of costly measures but without significant algal bloom decreasing. In view of the difficulty to evaluate the importance of internal cycling only by monitoring data, a three-dimensional model-based flux tracking approach was performed to identify the contributions of internal cycling. The results highlighted the role of nitrogen (N) and phosphorus (P) benthic fluxes with high seasonal fluctuations, contributing ∼29 %(N) and ∼18 %(P) of total input. The scenario analysis indicated that N loading reduction was more efficient for lake restoration. Furthermore, A positive feedback was detected between algae biomass and benthic P flux. Benthic P flux varied from adsorption to release when algae biomass increased. This phenomenon implied that a close link existed between N and P cycles with algae acting as a strong integrator. Due to the positive feedback loop between benthic P flux and algal blooms as well as the high efficiency of N loading reduction, controlling both N and P loadings will benefit lake restoration, especially eutrophic lakes with heavy sediment nutrient loadings.

Keywords: Internal nutrient cycling; Phosphorus release; Water quality model; Scenario analysis; Algae feedback; Lake Dianchi (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:416:y:2020:i:c:s0304380019303898

DOI: 10.1016/j.ecolmodel.2019.108881

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