Summer 2021 marine heat wave in the Gulf of Finland from the perspective of climate warming

Suursaar, Ülo

doi:10.3176/earth.2022.01

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…Water transparency (given as SD) was the lowest in April, which is typical of the spring months, confirming that CHL and CDOM concentrations have a strong impact on water clarity [128,129]. The mean water temperature was highest in June 2021, the same month that Estonia experienced the warmest June ever [130]. In April and May, the mean oxygen concentration was at its maximum.…”

Section: Discussionmentioning

confidence: 61%

Estimation of the Biogeochemical and Physical Properties of Lakes Based on Remote Sensing and Artificial Intelligence Applications

Toming,

Liu,

Soomets

et al. 2024

Remote Sensing

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Lakes play a crucial role in the global biogeochemical cycles through the transport, storage, and transformation of different biogeochemical compounds. Their regulatory service appears to be disproportionately important relative to their small areal extent, necessitating continuous monitoring. This study leverages the potential of optical remote sensing sensors, specifically Sentinel-2 Multispectral Imagery (MSI), to monitor and predict water quality parameters in lakes. Optically active parameters, such as chlorophyll a (CHL), total suspended matter (TSM), and colored dissolved matter (CDOM), can be directly detected using optical remote sensing sensors. However, the challenge lies in detecting non-optically active substances, which lack direct spectral characteristics. The capabilities of artificial intelligence applications can be used in the identification of optically non-active compounds from remote sensing data. This study aims to employ a machine learning approach (combining the Genetic Algorithm (GA) and Extreme Gradient Boost (XGBoost)) and in situ and Sentinel-2 Multispectral Imagery data to construct inversion models for 16 physical and biogeochemical water quality parameters including CHL, CDOM, TSM, total nitrogen (TN), total phosphorus (TP), phosphate (PO4), sulphate, ammonium nitrogen, 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), and the biomasses of phytoplankton and cyanobacteria, pH, dissolved oxygen (O2), water temperature (WT) and transparency (SD). GA_XGBoost exhibited strong predictive capabilities and it was able to accurately predict 10 biogeochemical and 2 physical water quality parameters. Additionally, this study provides a practical demonstration of the developed inversion models, illustrating their applicability in estimating various water quality parameters simultaneously across multiple lakes on five different dates. The study highlights the need for ongoing research and refinement of machine learning methodologies in environmental monitoring, particularly in remote sensing applications for water quality assessment. Results emphasize the need for broader temporal scopes, longer-term datasets, and enhanced model selection strategies to improve the robustness and generalizability of these models. In general, the outcomes of this study provide the basis for a better understanding of the role of lakes in the biogeochemical cycle and will allow the formulation of reliable recommendations for various applications used in the studies of ecology, water quality, the climate, and the carbon cycle.

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Section: Discussionmentioning

confidence: 61%

Estimation of the Biogeochemical and Physical Properties of Lakes Based on Remote Sensing and Artificial Intelligence Applications

Toming,

Liu,

Soomets

et al. 2024

Remote Sensing

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“…The nearbottom water layer below the halocline where seasonality has relatively low influence was selected for analysis. As the sampling procedure and analytical methods varied over the time period of 1970-2017, some variables estimated during the two historic subperiods (USSR and independent Estonia) are not directly comparable (Suursaar 1994). Therefore, only a few, relatively consistent longterm time series (temperature, salinity, oxygen and pH) were studied in this work.…”

Section: Sampling Routines and Analytical Methodsmentioning

confidence: 99%

“…The monitoring data for the Estonian coastal sea area was digitized and statistically analysed at the beginning of the 1990s (Suursaar 1992). Evaluation and comparison between international (BMP based) and national (USSR standards based) data sets revealed frequent incompati bility due to different sampling and analysis standards (Suursaar 1994).…”

Section: Historical Overview Of Estonian Monitoring Activitiesmentioning

confidence: 99%

Contemporary trends in hydrophysical and hydrochemical parameters in the NE Baltic Sea

Lainela¹,

Herkül²,

Leito³

et al. 2020

Estonian J. Earth Sci.

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The current study focuses on trends in hydrophysical and chemical parameters (e.g. temperature, salinity, dissolved oxygen, chlorophyll a (Chl a), pH and nutrients) in the Estonian coastal sea and offshore areas in relation to the biogeochemical processes and marine carbon dioxide system of the Baltic Sea. Analysis of 586 time series of these parameters, retrieved during national monitoring activities in 1993-2017, revealed a number of significant trends, which characterize the changes in the northeastern (NE) Baltic Sea. The number of significant trends in the surface layer was slightly higher in the coastal sea area than in the offshore area. No significant (e.g. climate changerelated) temperature trends were revealed in the surface layers of the Estonian offshore area. Over a longer time frame (since the 1970s-1980s), the trends in hydrochemical parameters have shown improved ecological conditions in the Estonian coastal waters, however, further improvement is not so obvious. In fact, most nutrient trends were positive over the last two decades. A positive Chl a trend was detected in the offshore area of the Baltic Proper. Dissolved oxygen trends in the bottom layers were all negative. So far, not enough parameters have been monitored for the evaluation of marine acidification processes. Several important recommendations for further improvement of monitoring programmes are suggested.

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Characteristics of Marine Heatwaves of the Baltic Sea for 1993−2022 and Their Driving Factors

Travkin,

Tikhonova,

Zakharchuk

2024

Pure Appl. Geophys.

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Summer 2021 marine heat wave in the Gulf of Finland from the perspective of climate warming

Cited by 6 publications

References 30 publications

Estimation of the Biogeochemical and Physical Properties of Lakes Based on Remote Sensing and Artificial Intelligence Applications

Estimation of the Biogeochemical and Physical Properties of Lakes Based on Remote Sensing and Artificial Intelligence Applications

Contemporary trends in hydrophysical and hydrochemical parameters in the NE Baltic Sea

Characteristics of Marine Heatwaves of the Baltic Sea for 1993−2022 and Their Driving Factors

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