Abstract:Over the last 22 years significant phytoplankton changes in Hongfeng lake reservoir have been observed with multiple years of harmful cyanobacteria blooms (cHABs). Fish farming and other anthropogenic activities from 1994-2001 triggered the harmful blooms. Nine years after the cessation of aquaculture, a conversion from problematic species (Microcystis spp, Aphanizomenon flos-aquae) to the less problematic species P. limnetica and other associated non-cyanobacteria taxa was recorded. Through this period of cha… Show more
“…Microcystis spp. blooms for instance are favoured by higher temperatures, higher nutrient (TN, TP) contents, and reasonable light (Pick 2016;Long et al 2018), while Pseudanabaena limnetica is favoured by lower temperature (< 20°C) as observed during a monitoring in the Hongfeng Lake reservoir (Guizhou Province, China) (Long et al 2018). Moreover, Microcystis spp.…”
Section: Species Switch During the Cyanobacterial Bloom In Lake Tanga...mentioning
confidence: 94%
“…Strong SE trade winds in 2018 most likely induced a strong tilting of the thermocline and the Dolichospermum flosaquae bloom observed in September, just after the dry season winds, is likely the consequence of an upward movement of hypolimnion nutrient-rich water at this moment of the year as water layers oscillate to recover initial depths (Mortimer 1974;Plisnier et al 1999). Nutrient loading of anthropogenic impact or erosion are in this case not responsible for the Cocquyt et al, Phytoplankton bloom in Lake Tanganyika cyanobacteria bloom as often observed in aquatic ecosystems (Paerl & Huisman 2009;Long et al 2018). Rivers flowing into Lake Tanganyika are colder than the surface water of the lake and quickly sink into the hypolimnion (Capart 1949;Pierre-Denis Plisnier, pers.…”
Section: Secondary Upwelling In the North Of Lake Tanganyikamentioning
confidence: 98%
“…Species switches in cyanobacterial blooms are well known and documented (Long et al 2018). Microcystis spp.…”
Section: Species Switch During the Cyanobacterial Bloom In Lake Tanga...mentioning
confidence: 99%
“…An increase of cyanobacterial blooms in freshwater ecosystems has been reported during the last decades (e.g. Paerl & Huisman 2009;Long et al 2018). Anthropogenic nutrient loading and global warming with its impact on the vertical stratification in lakes and reservoirs seem to play an important role in this increase.…”
Background and aims – Massive algae growth resulting in a phytoplankton bloom is a very rare event in the meromictic and oligotrophic Lake Tanganyika. Such a bloom was observed in the north of the lake in September 2018. Phytoplankton species composition during this bloom is compared to a documented bloom in 1955, and to the composition in September 2011–2013. Meteorological observations suggest hydrodynamics could explain the occurrence of the 2018 bloom.Material and methods – Phytoplankton net samples were taken in the pelagic and littoral zone near Uvira during five consecutive days of the bloom in 2018. For the period 2011–2013, quantitative phytoplankton samples were obtained during a weekly sampling at the same sites. Samples were analysed with an inverted microscope and relative abundances of the algal species were compared. Key results – Dolichospermum flosaquae (Cyanobacteria) initially dominated the bloom followed by high relative abundance of Limnococcus limneticus (Cyanobacteria) on the third sampling day in September 2018. In the pelagic zone an increase of Nitzschia asterionelloides (Bacillariophyta), and Dictyosphaerium and Lobocystis (Chlorophyta) was observed while in the littoral zone increasing abundances of dinophytes were noted. Dolichospermum flosaquae was also responsible for the bloom reported in 1955, but was only sporadically observed in the 2011–2013 samples. Although Limnococcus limneticus was present in 2011–2013, it never reached relative abundances as high as during the 2018 bloom. Meteorological data indicate that 2018 experienced different conditions compared to previous years: strong south-east winds from May to September with a more eastern direction of the wind, and a well-marked drop in atmospheric pressure between August and September.Conclusion – After a very windy season, the combination of strong hydrodynamics, calmer lake conditions, and high solar radiation and air temperature in September 2018 was favourable for a massive Cyanobacteria bloom in the north of Lake Tanganyika.
“…Microcystis spp. blooms for instance are favoured by higher temperatures, higher nutrient (TN, TP) contents, and reasonable light (Pick 2016;Long et al 2018), while Pseudanabaena limnetica is favoured by lower temperature (< 20°C) as observed during a monitoring in the Hongfeng Lake reservoir (Guizhou Province, China) (Long et al 2018). Moreover, Microcystis spp.…”
Section: Species Switch During the Cyanobacterial Bloom In Lake Tanga...mentioning
confidence: 94%
“…Strong SE trade winds in 2018 most likely induced a strong tilting of the thermocline and the Dolichospermum flosaquae bloom observed in September, just after the dry season winds, is likely the consequence of an upward movement of hypolimnion nutrient-rich water at this moment of the year as water layers oscillate to recover initial depths (Mortimer 1974;Plisnier et al 1999). Nutrient loading of anthropogenic impact or erosion are in this case not responsible for the Cocquyt et al, Phytoplankton bloom in Lake Tanganyika cyanobacteria bloom as often observed in aquatic ecosystems (Paerl & Huisman 2009;Long et al 2018). Rivers flowing into Lake Tanganyika are colder than the surface water of the lake and quickly sink into the hypolimnion (Capart 1949;Pierre-Denis Plisnier, pers.…”
Section: Secondary Upwelling In the North Of Lake Tanganyikamentioning
confidence: 98%
“…Species switches in cyanobacterial blooms are well known and documented (Long et al 2018). Microcystis spp.…”
Section: Species Switch During the Cyanobacterial Bloom In Lake Tanga...mentioning
confidence: 99%
“…An increase of cyanobacterial blooms in freshwater ecosystems has been reported during the last decades (e.g. Paerl & Huisman 2009;Long et al 2018). Anthropogenic nutrient loading and global warming with its impact on the vertical stratification in lakes and reservoirs seem to play an important role in this increase.…”
Background and aims – Massive algae growth resulting in a phytoplankton bloom is a very rare event in the meromictic and oligotrophic Lake Tanganyika. Such a bloom was observed in the north of the lake in September 2018. Phytoplankton species composition during this bloom is compared to a documented bloom in 1955, and to the composition in September 2011–2013. Meteorological observations suggest hydrodynamics could explain the occurrence of the 2018 bloom.Material and methods – Phytoplankton net samples were taken in the pelagic and littoral zone near Uvira during five consecutive days of the bloom in 2018. For the period 2011–2013, quantitative phytoplankton samples were obtained during a weekly sampling at the same sites. Samples were analysed with an inverted microscope and relative abundances of the algal species were compared. Key results – Dolichospermum flosaquae (Cyanobacteria) initially dominated the bloom followed by high relative abundance of Limnococcus limneticus (Cyanobacteria) on the third sampling day in September 2018. In the pelagic zone an increase of Nitzschia asterionelloides (Bacillariophyta), and Dictyosphaerium and Lobocystis (Chlorophyta) was observed while in the littoral zone increasing abundances of dinophytes were noted. Dolichospermum flosaquae was also responsible for the bloom reported in 1955, but was only sporadically observed in the 2011–2013 samples. Although Limnococcus limneticus was present in 2011–2013, it never reached relative abundances as high as during the 2018 bloom. Meteorological data indicate that 2018 experienced different conditions compared to previous years: strong south-east winds from May to September with a more eastern direction of the wind, and a well-marked drop in atmospheric pressure between August and September.Conclusion – After a very windy season, the combination of strong hydrodynamics, calmer lake conditions, and high solar radiation and air temperature in September 2018 was favourable for a massive Cyanobacteria bloom in the north of Lake Tanganyika.
“…Dinoflagellates-based or diatoms blooms are not as harmful to citizens as cyanobacteriabased blooms, but their massive growth is as degrading to the freshwater quality and to the entire aquatic ecosystem as cyanobacteria blooms [3]. Additionally, Long et al [4] have reported a positive relationship between the biomass of phytoplankton as well as the concentration of their main pigment, Chlorophyll-a (chl_a), and the biomass of cyanobacteria. For these reasons, the World Health Organization (WHO) has listed chl_a as a mandatory parameter to measure in freshwater guidance level for cyanobacteriarelated risk (https://www.who.int/water_sanitation_health/bathing/srwe1-chap8.pdf, accessed on 4 February 2021).…”
Optical sensors are increasingly sought to estimate the amount of chlorophyll a (chl_a) in freshwater bodies. Most, whether empirical or semi-empirical, are data-oriented. Two main limitations are often encountered in the development of such models. The availability of data needed for model calibration, validation, and testing and the locality of the model developed—the majority need a re-parameterization from lake to lake. An Unmanned aerial vehicle (UAV) data-based model for chl_a estimation is developed in this work and tested on Sentinel-2 imagery without any re-parametrization. The Ensemble-based system (EBS) algorithm was used to train the model. The leave-one-out cross validation technique was applied to evaluate the EBS, at a local scale, where results were satisfactory (R2 = Nash = 0.94 and RMSE = 5.6 µg chl_a L−1). A blind database (collected over 89 lakes) was used to challenge the EBS’ Sentine-2-derived chl_a estimates at a regional scale. Results were relatively less good, yet satisfactory (R2 = 0.85, RMSE= 2.4 µg chl_a L−1, and Nash = 0.79). However, the EBS has shown some failure to correctly retrieve chl_a concentration in highly turbid waterbodies. This particularity nonetheless does not affect EBS performance, since turbid waters can easily be pre-recognized and masked before the chl_a modeling.
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