How to model algal blooms in any lake on earth

Janssen, Annette B.G.; Janse, Jan H.; Beusen, Arthur; Chang, Manqi; Harrison, John; Huttunen, Inese; Kong, Xiangzhen; Rost, Jasmijn; Teurlincx, Sven; Troost, Tineke A.; Wijk, Dianneke van; Mooij, Wolf M.

doi:10.1016/j.cosust.2018.09.001

Cited by 68 publications

(31 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Applying a first preliminary SLR analysis between the cyanobacteria biovolume (LOG CyanoBV) and the measured water temperature at lake surface, we found the highest Pearson correlation coefficient (0.5), which was also expected from previous studies [9,[19][20][21][22][23][24]. For the average wind speeds, we observed negligible correlations with the thermocline slopes (data not shown), while strong correlations were found for the average surface temperatures and the thermocline slopes, as shown on Figure A3 in Appendix A.…”

Section: Relationships Between Cyanobacteria and Environmental Paramesupporting

confidence: 86%

“…The difficulty of predicting cyanobacteria blooms using physico-chemical environmental variables is a common problem highlighted also by previous studies [17,23,59]. On the other hand, as reported by Janssen et al, it is urgent and challenging to provide an algal bloom prediction at global level for the lakes [21]. The 14-days average air temperature together with total phosphorus can be, therefore, used to predict an algal outbreak two weeks in advance and, eventually, to adopt management actions to reduce their occurrence in monomictic and eutrophic shallow lakes.…”

Section: Discussionmentioning

confidence: 96%

See 1 more Smart Citation

Cyanobacterial Blooms in Lake Varese: Analysis and Characterization over Ten Years of Observations

Chirico

António

Pozzoli

et al. 2020

Water

View full text Add to dashboard Cite

Cyanobacteria blooms are a worldwide concern for water bodies and may be promoted by eutrophication and climate change. The prediction of cyanobacterial blooms and identification of the main triggering factors are of paramount importance for water management. In this study, we analyzed a comprehensive dataset including ten-years measurements collected at Lake Varese, an eutrophic lake in Northern Italy. Microscopic analysis of the water samples was performed to characterize the community distribution and dynamics along the years. We observed that cyanobacteria represented a significant fraction of the phytoplankton community, up to 60% as biovolume, and a shift in the phytoplankton community distribution towards cyanobacteria dominance onwards 2010 was detected. The relationships between cyanobacteria biovolume, nutrients, and environmental parameters were investigated through simple and multiple linear regressions. We found that 14-days average air temperature together with total phosphorus may only partly explain the cyanobacteria biovolume variance at Lake Varese. However, weather forecasts can be used to predict an algal outbreak two weeks in advance and, eventually, to adopt management actions. The prediction of cyanobacteria algal blooms remains challenging and more frequent samplings, combined with the microscopy analysis and the metagenomics technique, would allow a more conclusive analysis.

show abstract

Section: Relationships Between Cyanobacteria and Environmental Paramesupporting

confidence: 86%

Section: Discussionmentioning

confidence: 96%

Cyanobacterial Blooms in Lake Varese: Analysis and Characterization over Ten Years of Observations

Chirico

António

Pozzoli

et al. 2020

Water

View full text Add to dashboard Cite

show abstract

“…Currently, many people assume the growth of cyanobacteria is constrained to aquatic ecosystems. Cyanobacterial blooms have increased in frequency worldwide within the last decades, posing a threat to water supplies and recreational areas (Aguilera et al, 2018;Janssen et al, 2019;Picardo et al, 2019). These microorganisms are produced in most aquatic ecosystems, including lakes (Scavia et al, 2014;Zhang et al, 2012), reservoirs (Takahashi et al, 2014), ponds (Clark et al, 2017), lagoon (Paldavičiene et al, 2015), rivers (Picardo et al, 2019), estuarine (Preece et al, 2017), bay (Estepp & Reavie, 2015), and oceans (Bennett, 2017).…”

Section: Presence and Movementmentioning

confidence: 99%

Problems and use of cyanobacteria for environmental improvement – A Review

Kusumawati¹,

Mangkoedihardjo²

2021

jaa

View full text Add to dashboard Cite

Cyanobacteria were considered harmful to other living organisms, due to their microcystin content. In addition, uncontrolled populations of cyanobacteria, also known as algal blooms, occur both naturally and as a result of human activity, leading to the development of more complex problems. These microorganisms are currently receiving a lot of attention, as several investigations have considered successful applications of reducing exposure to nature, causing reduced risks to ecosystems, and providing benefits to human life. This study aims to provide answers to current problems, by providing a different perspective on cyanobacteria. Furthermore, it is for the benefits of cyanobacteria for the environment in accordance with the recommendations in some of the literature on previous research results.

show abstract

“…The response of lakes to concurrent drivers of eutrophication can be explored using 91 process-oriented models (Couture et al 2018; Page et al 2018;Janssen et al 2019). A variety of 92 lake ecosystem models exist that include physical processes and nutrient dynamics, varying in 93 modeling approach, spatial dimensions, and complexity of process representation (Robson 94 2014).…”

Section: Introduction 69mentioning

confidence: 99%

Long-term influence of climate and experimental eutrophication regimes on phytoplankton blooms

Salk

Venkiteswaran

Couture

et al. 2019

Preprint

View full text Add to dashboard Cite

20 Nicholas School of the Environment 21 Duke University 22 Durham, NC 27708 23 USA 24 25 Author Contribution Statement 26 KRS conducted modeling and analysis efforts. RMC provided expertise on model application 27 and validation. SLS and JJV provided guidance on the research questions. KRS, RMC, JJV, and 28 SLS analyzed model fit, guided scenario analysis, and provided interpretations of results. SNH 29 and MJP provided guidance on historical data analyses, model input data, and postprocessing 30 analyses. KRS wrote the manuscript, and all coauthors contributed edits to the manuscript. 31 32 Significance Statement 33 Harmful algal blooms and eutrophication are key water quality issues worldwide. 34 Managing algal blooms is often difficult because multiple drivers, such as climate change and 35 nutrient loading, act concurrently and potentially synergistically. Long-term datasets and 36 simulation models allow us to parse the effects of interacting drivers of blooms. The 37 performance of our model depended on the ratio of nitrogen to phosphorus inputs, suggesting 38 that complex biological dynamics control blooms under variable nutrient loads. We found that 39 blooms were dampened under a "no climate change" scenario, suggesting that the interaction of 40 nutrient loading and increased temperature intensifies blooms. Our results highlight successes 41 and gaps in our ability to model blooms, helping to establish future management 42 recommendations. 43 44 Data Availability Statement 45 Data and metadata will be made available in a GitHub repository 46 (https://github.comAbstract 53 Phytoplankton blooms respond to multiple drivers, including climate change and nutrient 54loading. Here we examine a long-term dataset from Lake 227, a site exposed to a fertilization 55 experiment (1969-present). Changes in nitrogen:phosphorus loading ratios (high N:P, low N:P, 56 P-only) did not impact mean annual biomass, but blooms exhibited substantial inter-and intra-57 annual variability. We used a process-oriented lake model, MyLake, to successfully reproduce 58 lake physics over 48 years and test if a P-limited model structure predicted blooms. The timing 59 and magnitude of blooms was reproduced during the P-only period but not for the high and low 60 N:P periods, perhaps due to N acquisition pathways not currently included in the model. A 61 model scenario with no experimental fertilization confirmed P loading is the major driver of 62 blooms, while a scenario that removed climate-driven temperature trends showed that increased 63 spring temperatures have exacerbated blooms beyond the effects of fertilization alone. 64 65

show abstract

How to model algal blooms in any lake on earth

Cited by 68 publications

References 60 publications

Cyanobacterial Blooms in Lake Varese: Analysis and Characterization over Ten Years of Observations

Cyanobacterial Blooms in Lake Varese: Analysis and Characterization over Ten Years of Observations

Problems and use of cyanobacteria for environmental improvement – A Review

Long-term influence of climate and experimental eutrophication regimes on phytoplankton blooms

Contact Info

Product

Resources

About