A systematic literature review of forecasting and predictive models for cyanobacteria blooms in freshwater lakes

Rousso, Benny Zuse; Bertone, Edoardo; Stewart, Rodney Anthony; Hamilton, David P.

doi:10.1016/j.watres.2020.115959

Cited by 172 publications

(133 citation statements)

References 199 publications

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“…Forecasting applications in which data assimilation calibrates parameters as more data are collected allow a lake-specific parameterization to emerge, thus improving forecast skill over time (following Thomas et al 2020a). Coupled hydrodynamic-ecosystem models can forecast numerous variables and may be able to perform better than empirical models when initial conditions are outside the historical envelope (Dietze 2017) as well as be used for forecast scenario development (Rousso et al 2020).…”

Section: Lesson 3: Let Your Forecasting Goals Guide Your Modeling Appmentioning

confidence: 99%

Advancing lake and reservoir water quality management with near-term, iterative ecological forecasting

et al. 2021

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Near-term, iterative ecological forecasts with quantified uncertainty have great potential for improving lake and reservoir management. For example, if managers received a forecast indicating a high likelihood of impending impairment, they could make decisions today to prevent or mitigate poor water quality in the future. Increasing the number of automated, realtime freshwater forecasts used for management requires integrating interdisciplinary expertise to develop a framework that seamlessly links data, models, and cyberinfrastructure, as well as collaborations with managers to ensure that forecasts are embedded into decision-making workflows. The goal of this study is to advance the implementation of near-term, iterative ecological forecasts for freshwater management. We first provide an overview of FLARE (Forecasting Lake And Reservoir Ecosystems), a forecasting framework we developed and applied to a drinking water reservoir to assist water quality management, as a potential opensource option for interested users. We used FLARE to develop scenario forecasts simulating different water quality interventions to inform manager decision-making. Second, we share lessons learned from our experience developing and running FLARE over 2 years to inform other forecasting projects. We specifically focus on how to develop, implement, and maintain a forecasting system used for active management. Our goal is to break down the barriers to forecasting for freshwater researchers, with the aim of improving lake and reservoir management globally.

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Section: Lesson 3: Let Your Forecasting Goals Guide Your Modeling Appmentioning

confidence: 99%

Advancing lake and reservoir water quality management with near-term, iterative ecological forecasting

et al. 2021

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“…For the purpose of planning lake restoration, accounting for hydrodynamics, ecosystem functioning and sediment processes may be necessary (Trolle et al, 2011;Smits & van Beek, 2013;Hipsey et al, 2015;Zhang et al, 2015;Hu et al, 2016). Recently, the coupling of stateof-the-art lake models (Hipsey et al, 2020;Rousso et al, 2020) with sediment diagenetic models (e.g., Gudimov et al, 2016;Matisoff et al, 2016;Doan et al, 2018) has been promoted specifically to address gaps in lake resaturation planning (Markelov et al, 2019;Messina et al, 2020). A key future perspective is thus the improved integration of the required modeling infrastructure to test different restoration scenarios.…”

Section: Future Perspectivesmentioning

confidence: 99%

Preface: Restoration of eutrophic lakes: current practices and future challenges

et al. 2020

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“…Also, our model results pointed at average concentrations and responses, not at phytoplankton bloom or scum formation. Prediction of blooms with data-driven or process-based models still remains a challenge (Rousso et al 2020), and may require inclusion of processes that are not parameterised in our model, such as life cycles (Hense and Beckmann 2010) or selective grazing by zooplankton (Sommer et al 2012). This may be part of the reason for why the spring peak and occasional summer spikes in chlorophyll-a were missed by the model.…”

Section: Implications Beyond Lake Erkenmentioning

confidence: 99%

Drivers of phytoplankton responses to summer storms in a stratified lake: a modelling study

Mesman¹,

Ayala²,

Goyette³

et al. 2022

Preprint

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Extreme wind events affect lake phytoplankton amongst others by deepening the mixed layer and increasing internal nutrient loading. Both increases and decreases of phytoplankton biomass after storms have been observed, but the precise mechanisms driving these responses remain poorly understood or quantified. In this study, we coupled a one-dimensional physical model to a biogeochemical model to investigate the factors regulating short-term phytoplankton responses to summer storms, now and under expected high-temperature conditions. We simulated physical, chemical and biological dynamics in Lake Erken, Sweden, to take advantage of a long-term time series that we used to calibrate our model. We found that wind storms could increase or decrease the phytoplankton concentration one week after the storm, depending on antecedent lake physical and chemical conditions. Storms had little effect on phytoplankton biomass if the mixed layer was deep prior to storm exposure. Higher incoming shortwave radiation and hypolimnetic nutrient concentration boosted growth, whereas higher surface water temperatures decreased phytoplankton concentration after storms. Medium-intensity wind speeds resulted in more phytoplankton biomass after storms than high-intensity wind. Simulations under a future climate scenario did not show marked differences in the way wind affects phytoplankton growth following storms. Our study shows that storm impacts on lake phytoplankton are complex and likely to vary as a function of local environmental conditions.

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A systematic literature review of forecasting and predictive models for cyanobacteria blooms in freshwater lakes

Cited by 172 publications

References 199 publications

Advancing lake and reservoir water quality management with near-term, iterative ecological forecasting

Advancing lake and reservoir water quality management with near-term, iterative ecological forecasting

Preface: Restoration of eutrophic lakes: current practices and future challenges

Drivers of phytoplankton responses to summer storms in a stratified lake: a modelling study

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