Abstract:Cyanobacterial blooms are a concern in oligotrophic lakes because these systems are often used for multiple purposes (i.e., recreation and public water supplies). Monitoring cyanobacteria impacts is challenging in such cases because of low and heterogeneous concentrations over time, space, and depth. Canandaigua Lake (New York State) is oligotrophic, but has been impacted by blooms since at least the early 2000s, with limited quantification. This study integrated data from established shoreline surveillance an… Show more
“…However, this does not imply that this pattern will occur every year. For instance, a study recently published on Canandaigua lake, indicates higher water clarity for October 2019 [64] which contradicts from our observations. However, it is important to note that the maps generated in our study are based on a single day and not representative of the monthly average.…”
Section: Spatio-temporal Variation Of Sddcontrasting
confidence: 99%
“…However, it is important to note that the maps generated in our study are based on a single day and not representative of the monthly average. The authors in [64] do report higher water clarity in June which aligns well with our observations. Whereas the lower water clarity reported in that study is on Aug 22 and Sep 02 for the year 2019.…”
Section: Spatio-temporal Variation Of Sddsupporting
Optical remote sensing of water quality poses challenges in small oligotrophic lakes due to the diminishing contribution of constituents to the water-leaving radiance as water clarity increases. For monitoring water clarity over such lakes, this study utilizes machine learning models and data from citizen science to develop effective models for retrieving Secchi disk depth (SDD) in Canandaigua Lake, USA. Using Sentinel-2 band ratios as input, we trained Random Forest (RF), Adaptive Boosting (AB), Extreme Gradient Boosting (XGB), and Support Vector Regression (SVR) models using spatiotemporally distributed in-situ data within 7 days of Senitnel-2 overpass. Each model was optimized using hyperparameter tuning, and cross-validation was used for accuracy assessment to compare the models' effectiveness in retrieving SDD. The results indicate the superior performance of RF with an R 2 of ~0.74 and an RMSE of ~0.72 m. A feature importance analysis for RF indicated high relevance of the blue and green bands ratio in estimation of SDD. The RF model was subsequently employed to generate temporal maps for Canandaigua Lake, indicating that water clarity tends to be higher during the early summer months (May and June) but declines during late summer and fall (September and October). This pattern can be closely associated with the increased algal presence in the lake. The spatial variability of the SDD indicated the possibility of greater sediments entering from the southern part of the lake. This study can be expanded to encompass other Finger Lakes, offering a comprehensive understanding of water clarity in these lake systems.
“…However, this does not imply that this pattern will occur every year. For instance, a study recently published on Canandaigua lake, indicates higher water clarity for October 2019 [64] which contradicts from our observations. However, it is important to note that the maps generated in our study are based on a single day and not representative of the monthly average.…”
Section: Spatio-temporal Variation Of Sddcontrasting
confidence: 99%
“…However, it is important to note that the maps generated in our study are based on a single day and not representative of the monthly average. The authors in [64] do report higher water clarity in June which aligns well with our observations. Whereas the lower water clarity reported in that study is on Aug 22 and Sep 02 for the year 2019.…”
Section: Spatio-temporal Variation Of Sddsupporting
Optical remote sensing of water quality poses challenges in small oligotrophic lakes due to the diminishing contribution of constituents to the water-leaving radiance as water clarity increases. For monitoring water clarity over such lakes, this study utilizes machine learning models and data from citizen science to develop effective models for retrieving Secchi disk depth (SDD) in Canandaigua Lake, USA. Using Sentinel-2 band ratios as input, we trained Random Forest (RF), Adaptive Boosting (AB), Extreme Gradient Boosting (XGB), and Support Vector Regression (SVR) models using spatiotemporally distributed in-situ data within 7 days of Senitnel-2 overpass. Each model was optimized using hyperparameter tuning, and cross-validation was used for accuracy assessment to compare the models' effectiveness in retrieving SDD. The results indicate the superior performance of RF with an R 2 of ~0.74 and an RMSE of ~0.72 m. A feature importance analysis for RF indicated high relevance of the blue and green bands ratio in estimation of SDD. The RF model was subsequently employed to generate temporal maps for Canandaigua Lake, indicating that water clarity tends to be higher during the early summer months (May and June) but declines during late summer and fall (September and October). This pattern can be closely associated with the increased algal presence in the lake. The spatial variability of the SDD indicated the possibility of greater sediments entering from the southern part of the lake. This study can be expanded to encompass other Finger Lakes, offering a comprehensive understanding of water clarity in these lake systems.
“…From a methodological perspective, incorporating cyanobacteria-specific spectral evaluations from satellite remote sensing , presents a promising approach for (re)constructing regional, long-term photochemical data sets at scales challenging for ground-based lake monitoring initiatives such as CSLAP. High-resolution sampling at specific lakes of interest, , on the other hand, should provide a more integrated picture of how transient spatiotemporal heterogeneity and cyanobacterial community succession shape the photoreactivity of DOM. Overall, our work represents a step forward in understanding the implications of cyanobacterial blooms for surface water photoreactivity in the context of a changing climate. , …”
Cyanobacterial blooms introduce autochthonous dissolved organic matter (DOM) into aquatic environments, but their impact on surface water photoreactivity has not been investigated through collaborative field sampling with comparative laboratory assessments. In this work, we quantified the apparent quantum yields (Φ app,RI ) of reactive intermediates (RIs), including excited triplet states of dissolved organic matter ( 3 DOM*), singlet oxygen ( 1 O 2 ), and hydroxyl radicals ( • OH), for whole water samples collected by citizen volunteers from more than 100 New York lakes. Multiple comparisons tests and orthogonal partial leastsquares analysis identified the level of cyanobacterial chlorophyll a as a key factor in explaining the enhanced photoreactivity of whole water samples sourced from bloomimpacted lakes. Laboratory recultivation of bloom samples in bloom-free lake water demonstrated that apparent increases in Φ app,RI during cyanobacterial growth were likely driven by the production of photoreactive moieties through the heterotrophic transformation of freshly produced labile bloom exudates. Cyanobacterial proliferation also altered the energy distribution of 3 DOM* and contributed to the accelerated transformation of protriptyline, a model organic micropollutant susceptible to photosensitized reactions, under simulated sunlight conditions. Overall, our study provides insights into the relationship between the photoreactivity of surface waters and the limnological characteristics and trophic state of lakes and highlights the relevance of cyanobacterial abundance in predicting the photoreactivity of bloom-impacted surface waters.
“…The study concluded that a combination of rapid bloom identification and effective treatment processes are needed to protect water quality for systems using sources vulnerable to algal blooms. Prestigiacomo et al (2023) conducted an extensive study in Canandaigua Lake, located in the state of New York, to quantify cyano-HABs indicators and understand the role of various factors on the formation and effects of cyano-HABs. Canandaigua Lake is an oligotrophic, thermally stratified lake that in recent years has experienced formation of HABs, which is a great concern since the lake is used for recreational activities and as a source of drinking water supply for thousands of people.…”
Section: Cyanobacterial Presence In Water Sourcesmentioning
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