Drivers of phytoplankton responses to summer wind events in a stratified lake: A modeling study

Abstract

Extreme wind events affect lake phytoplankton by deepening the mixed layer and increasing internal nutrient loading. Both increases and decreases in phytoplankton concentration after strong wind events have been observed, but the precise mechanisms driving these responses remain poorly understood or quantified. We coupled a one-dimensional physical model to a biogeochemical model to investigate the factors regulating short-term phytoplankton responses to summer wind events, now and under expected warmer future conditions. We simulated physical, chemical, and biological dynamics in Lake Erken, Sweden, and found that strong wind could increase or decrease the phytoplankton concentration in the euphotic zone 1 week after the event, depending on antecedent lake physical and chemical conditions. Wind had little effect on phytoplankton concentration if the mixed layer was deep prior to wind exposure. Higher incoming shortwave radiation and hypolimnetic nutrient concentration boosted phytoplankton concentration, whereas higher surface water temperatures decreased concentrations after wind events. Medium-intensity wind events resulted in more phytoplankton than high-intensity wind. Simulations under a future climate scenario did not show marked differences in the way wind events affect phytoplankton concentration. These findings help to better understand how wind impacts vary as a function of local environmental conditions and how climate warming and changing extreme weather dynamics will affect lake ecosystems.

Increasing air temperature relative to water temperature makes the mixed layer shallower, reducing phytoplankton biomass in a stratified lake

“…The high positive dependence of Chla area on Z mix found in this study is generally consistent with models that predict an increase in phytoplankton biomass with a deepening mixed layer (Berger et al, 2006; Diehl, 2002; Mesman et al, 2022). These findings are supported by results from enclosure experiments, where artificial deepening of the mixed layer typically increases phytoplankton growth and biomass (Cantin et al, 2011; Diehl et al, 2002; Giling et al, 2017).…”

A review of wind-driven hydrodynamics in large shallow lakes: Importance, process-based modeling and perspectives

“…Ecological activities in shallow lakes are also affected by winddriven lake environments. There are differences in the adaptability of phytoplankton to a series of changes in lake environments, resulting in an entirely new competition for nutrients, light energy and buoyancy regulation (Mesman et al, 2022). Therefore, biological species that can quickly absorb and store nutrients and grow well in low light can get growth advantages, and eventually, the biological community is restructured (Ptacnik et al, 2010).…”

“…Under the catastrophic events of short-term hurricanes, submersed and emergent macrophytes in shallow lakes and even in deep lakes were uprooted (James et al, 2008;Stockwell et al, 2020), the spatial distribution of both micro-and macro-zooplankton changed substantially, and even the fishery collapsed (Havens et al, 2011). Previous studies have shown that extreme wind events still have an increasing trend in various regions (e.g., Western Europe), including intensification of intensity, duration and frequency, which may have an aggressive impact on the structure and function of lake ecosystems (Mesman et al, 2022). Clearly, the global influence of extreme events on lake ecosystems warrants further study.…”

Prediction of algal blooms via data-driven machine learning models: an evaluation using data from a well-monitored mesotrophic lake

“…In this study, a PB hydrodynamic lake model, GOTM (General Ocean Turbulence Model; Burchard et al, 1999), was used to generate water temperature profiles and other hydrodynamic metrics. GOTM also served as the foundation of water quality simulations made with the SELMAPROT-BAS model (Mesman et al, 2022) that is coupled to GOTM through the Framework for Aquatic Biogeochemical Models (FABM; Bruggeman and Bolding, 2014).…”