Phenology of Phytoplankton Blooms in a Trophic Lake Observed from Long-Term MODIS Data

Monitoring phycocyanin in global inland waters by remote sensing: Progress and future developments

Cyanobacterial blooms are increasingly becoming major threats to global inland aquatic ecosystems. Phycocyanin (PC), a pigment unique to cyanobacteria, can provide important reference for the study of cyanobacterial blooms warning. New satellite technology and cloud computing platforms have greatly improved research on PC, with the average number of studies examining it having increased from 5 per year before 2018 to 17 per year thereafter. Many empirical, semi-empirical, semi-analytical, quasi-analytical algorithm (QAA) and machine learning (ML) algorithms have been developed based on unique absorption characteristics of PC at approximately 620 nm. However, most models have been developed for individual lakes or clusters of them in specific regions, and their applicability at greater spatial scales requires evaluation. A review of optical mechanisms, principles and advantages and disadvantages of different model types, performance advantages and disadvantages of mainstream sensors in PC remote sensing inversion, and an evaluation of global lacustrine PC datasets is needed. We examine 230 articles from the Web of Science citation database between 1900 and 2024, summarize 57 of them that deal with construction of PC inversion models, and compile a list of 6526 PC sampling sites worldwide. This review proposed the key to achieving global lacustrine PC remote sensing inversion and spatiotemporal evolution analysis is to fully use existing multi-source remote sensing big data platforms, and a deep combination of ML and optical mechanisms, to classify the object lakes in advance based on lake optical characteristics, eutrophication level, water depth, climate type, altitude, population density within the watershed. Additionally, integrating data from multi-source satellite sensors, ground-based observations, and unmanned aerial vehicles, will enable future development of global lacustrine PC remote estimation, and contribute to achieving United Nations Sustainable Development Goals inland water goals.

Mapping satellite-derived chlorophyll-a concentrations from 2013 to 2023 in Western Lake Ontario using Landsat 8 and 9 imagery

Algal blooms are a major environmental issue in many freshwater environments. While traditional in-situ measurements remain indispensable to monitor algal dynamics, they offer only limited spatiotemporal coverage, especially when dealing with large water bodies. Satellite remote sensing can help overcome this limitation. Here, a semi-empirical model for retrieving surface water Chlorophyll-a (Chl-a) concentrations, a proxy of phytoplankton biomass, was developed for the western basin of Lake Ontario, one of the Laurentian Great Lakes. ACOLITE-corrected Landsat 8 and 9 imagery between 2013 and 2023 was calibrated and verified with local in-situ Chl-a measurements. The nearshore areas of Western Lake Ontario, including the semi-enclosed Hamilton Harbour, are prone to algal blooms, while oligotrophic conditions prevail in the offshore areas. Three bloom indicators-intensity, extent, and severity-were used to characterize the variability and seasonality of algal blooms in different areas of the lake. Time-series analyses revealed contrasting temporal trends in Chl-a concentrations of the nearshore and offshore waters over the eleven-year period of observation. Analysis of external factors impacting algal blooms in Western Lake Ontario and Hamilton Harbour revealed temperature, wind speed, and cloud cover as the most influential, with around 80 % of blooms occurring under moderate conditions (temperature 4-26 °C and wind speed 2.5-5. m s-1). Overall, our research underlines the great potential for cost-effective monitoring of algal dynamics in large lakes, utilizing publicly available satellite imagery, in order to support eutrophication management.

Light limitation during a compound drought and heat event inhibited algal blooms in a nutrient-rich shallow lake

Extreme weather events caused by climate change have a significant impact on the lake ecosystems. While many researchers believe that heatwaves and droughts may enhance algal bloom, heatwaves and droughts reduced algal blooms in Lake Chaohu during the spring and summer of 2022. To investigate the mechanism behind this phenomenon, on-site sampling and online monitoring were conducted in Lake Chaohu from 2019 to 2022. The results showed that non-algal turbidity played a critical role in temporarily inhibiting algal blooms. The water level in spring and summer of the drought year was 1.2 m lower than that in conventional years, resulting in the area of shoals where resuspension could occur being nearly 4 times larger than in conventional years. Strong resuspension caused turbidity in spring and summer to be more than double that of conventional years, sharply reducing gross primary productivity by 39 %, which led to lower chlorophyll a concentration than in conventional years. These results indicate that drought does not necessarily exacerbate algal blooms, and changes in shoal area due to water level fluctuations are a key factor affecting algal blooms in shallow lakes prone to resuspension. Furthermore, these results suggest lake managers can control algal blooms by adjusting water level to increase turbidity during or before algal bloom seasons.

Predicting Chlorophyll-a Concentrations in the World's Largest Lakes Using Kolmogorov-Arnold Networks

Accurate prediction of chlorophyll-a (Chl-a) concentrations, a key indicator of eutrophication, is essential for the sustainable management of lake ecosystems. This study evaluated the performance of Kolmogorov-Arnold Networks (KANs) along with three neural network models (MLP-NN, LSTM, and GRU) and three traditional machine learning tools (RF, SVR, and GPR) for predicting time-series Chl-a concentrations in large lakes. Monthly remote-sensed Chl-a data derived from Aqua-MODIS spanning September 2002 to April 2024 were used. The models were evaluated based on their forecasting capabilities from March 2024 to August 2024. KAN consistently outperformed others in both test and forecast (unseen data) phases and demonstrated superior accuracy in capturing trends, dynamic fluctuations, and peak Chl-a concentrations. Statistical evaluation using ranking metrics and critical difference diagrams confirmed KAN's robust performance across diverse study sites, further emphasizing its predictive power. Our findings suggest that the KAN, which leverages the KA representation theorem, offers improved handling of nonlinearity and long-term dependencies in time-series Chl-a data, outperforming neural network models grounded in the universal approximation theorem and traditional machine learning algorithms.

Defining algal bloom phenology in Lake Erie

Elucidating the impact of global climate change on aquatic ecosystems, particularly through phenological shifts in primary producers, is critical for understanding ecological resilience. Here, we focus on the phenological shifts in chlorophyll as a proxy for algae biomass and primary production in aquatic ecosystems, specifically in Lake Erie as well as concentrations of the toxin microcystin. By tracking temporal changes in each, we identified key phenological phases important to estimate duration, magnitude, and intensity of harmful algal blooms (HABs). Determining which influential biotic and abiotic factors such as temperature, wind speed, nutrient availability, and climate change is most important, is a long-term management need for Lake Erie, which can be explored using our methodology. Our novel statistical framework employing Bayesian generalized additive mixed models described seasonal chlorophyll and particulate microcystin concentration from Lake Erie and our simple geometric method identified the start, peak, and end of algal blooms. This research enhances our understanding of the ecological effects of nutrient pollution on aquatic ecosystems and provides a repeatable method for determining phenological events without the need for user defined cutoffs which aids in the management and mitigation of HABs, safeguarding water quality in regions dependent on lakes for drinking water.

Particulate and water-mobilizable phosphorus from a watershed with a plain river network contributes equal amounts of algal available phosphorus to its downstream lake

This research was designed to estimate the contributions of phosphorus (P) in different factions from an upstream plain river network to algal growth in a downstream shallow eutrophic lake, Taihu Lake, in China. During three flow regimes, the P fractions in multiple phases (particulate, colloidal and dissolved phases) and their algal availabilities were assessed via bioassays with Dolichospermum flos-aquae as the test organism. The P partitioning patterns among multiple phases were strongly affected by the concentration of total suspended solids (TSS) that changed with the river flow regime, with stronger disturbance of sediments at lower water levels (low flow) and weaker disturbance of sediments at higher water levels (high flow) in the plain river network. The median TSS concentration across the river network decreased from 157.4 mg/L during low flow to 31.8 mg/L during high flow, and the median particulate P concentration decreased from 0.132 mg/L to 0.093 mg/L. The particulate P contributed equally to the amount of algal available P (AAP) as did the water-mobilizable P (colloidal plus dissolved phase) in the rivers flowing into Taihu Lake. The annual average concentrations of particulate algal available P (P-AAP), colloidal algal available P (C-AAP) and dissolved algal available P (D-AAP) were estimated to be 0.032 mg/L, 0.012 mg/L and 0.019 mg/L, respectively, during 2012-2018, accounting for 50.8 %, 19.0 % and 30.2 %, respectively, of the total AAP. At the watershed scale, controlling P drainage from downstream urbanized areas should be emphasized. Additionally, controlling sediment resuspension or reducing the TSS concentration in the inflowing rivers is important for decreasing the particulate P flux to downstream lakes.

Functional trait-based phytoplankton biomass and assemblage analyses in the pre-growing season for comprehensive algal bloom risk assessment

Algal bloom (AB) risk assessment is critical for maintaining ecosystem health and human sustainability. Previous AB risk assessments have focused on the potential occurrence of ABs and related factors in the growing season, whereas their hazards, especially in the pre-growing season, have attracted less attention. Here, we performed a comprehensive AB risk assessment, including water trophic levels, phytoplankton biomass, functional trait-based assemblages, and related environmental factors, in the pre-growing season in Dongting Lake, China. Although mesotrophic water and low phytoplankton biomass suggested low AB potential, toxic taxa, which constituted 13.28% of the phytoplankton biomass, indicated non-negligible AB hazards. NH4+ and water temperature were key factors affecting phytoplankton motility and toxicity. Our study establishes a new paradigm for quantitative AB risk assessment, including both potential AB occurrence and hazards. We emphasize the importance of phytoplankton functional traits for early AB warning and NH4+ reduction for AB control in the pre-growing season.

Dependence of evolution of Cyanobacteria superiority on temperature and nutrient use efficiency in a meso-eutrophic plateau lake

Algal blooms in lakes have been a challenging environmental issue globally under the dual influence of human activity and climate change. Considerable progress has been made in the study of phytoplankton dynamics in lakes; The long-term in situ evolution of dominant bloom-forming cyanobacteria in meso-eutrophic plateau lakes, however, lacks systematic research. Here, the monthly parameters from 12 sampling sites during the period of 1997-2022 were utilized to investigate the underlying mechanisms driving the superiority of bloom-forming cyanobacteria in Erhai, a representative meso-eutrophic plateau lake. The findings indicate that global warming will intensify the risk of cynaobacteria blooms, prolong Microcystis blooms in autumn to winter or even into the following year, and increase the superiority of filamentous Planktothrix and Cylindrospermum in summer and autumn. High RUETN (1.52 Biomass/TN, 0.95-3.04 times higher than other species) under N limitation (TN < 0.5 mg/L, TN/TP < 22.6) in the meso-eutrophic Lake Erhai facilitates the superiority of Dolichospermum. High RUETP (43.8 Biomass/TP, 2.1-10.2 times higher than others) in TP of 0.03-0.05 mg/L promotes the superiority of Planktothrix and Cylindrospermum. We provided a novel insight into the formation of Planktothrix and Cylindrospermum superiority in meso-eutrophic plateau lake with low TP (0.005-0.07 mg/L), which is mainly influenced by warming, high RUETP and their vertical migration characteristics. Therefore, we posit that although the obvious improvement of lake water quality is not directly proportional to the control efficacy of cyanobacterial blooms, the evolutionary shift in cyanobacteria population structure from Microcystis, which thrives under high nitrogen and phosphorus conditions, to filamentous cyanobacteria adapted to low nitrogen and phosphorus levels may serve as a significant indicator of water quality amelioration. Therefore, we suggest that the risk of filamentous cyanobacteria blooms in the meso-eutrophic plateau lake should be given attention, particularly in light of improving water quality and global warming, to ensure drinking water safety.

Satellite mapping reveals phytoplankton biomass's spatio-temporal dynamics and responses to environmental factors in a eutrophic inland lake

Chlorophyll a (Chla) concentration can be used as an indicator of algal biomass, and the accumulation of algal biomass in water column is essential for the emergence of surface blooms. By using Moderate Resolution Imaging Spectrometer (MODIS) data, a machine learning algorithm was previously developed to assess algal biomass within the euphotic depth (Beu). Here, a long-term Beu dataset of Lake Taihu from 2003 to 2020 was generated to examine its spatio-temporal dynamics, sensitivity to environmental factors, and variations in comparison to the surface algal bloom area. During this period, the daily Beu (total Beu within the whole lake) exhibited temporal fluctuations between 40 and 90 t Chla, with an annual average of 63.32 ± 5.23 t Chla. Notably, it reached its highest levels in 2007 (72.34 t Chla) and 2017 (73.57 t Chla). Moreover, it demonstrated a clear increasing trend of 0.197 t Chla/y from 2003 to 2007, followed by a slight decrease of 0.247 t Chla/y after 2017. Seasonal variation showed a bimodal annual cycle, characterized by a minor peak in March ∼ April and a major peak in July ∼ September. Spatially, the average pixel-based Beu (total Beu of a unit water column) ranged from 21.17 to 49.85 mg Chla, with high values predominantly distributed in the northwest region and low values in the central region. The sensitivity of Beu to environmental factors varies depending on regions and time scales. Temperature has a significant impact on monthly variation (65.73%), while the level of nutrient concentrations influences annual variation (55.06%). Wind speed, temperature, and hydrodynamic conditions collectively influence the spatial distribution of Beu throughout the entire lake. Algal bloom biomass can capture trend changes in two mutant years as well as bimodal phenological changes compared to surface algal bloom area. This study can provide a basis for scientific evaluation of water environment and a reference for monitoring algal biomass in other similar eutrophic lakes.

Combined Earth observations reveal the sequence of conditions leading to a large algal bloom in Lake Geneva

Freshwater algae exhibit complex dynamics, particularly in meso-oligotrophic lakes with sudden and dramatic increases in algal biomass following long periods of low background concentration. While the fundamental prerequisites for algal blooms, namely light and nutrient availability, are well-known, their specific causation involves an intricate chain of conditions. Here we examine a recent massive Uroglena bloom in Lake Geneva (Switzerland/France). We show that a certain sequence of meteorological conditions triggered this specific algal bloom event: heavy rainfall promoting excessive organic matter and nutrients loading, followed by wind-induced coastal upwelling, and a prolonged period of warm, calm weather. The combination of satellite remote sensing, in-situ measurements, ad-hoc biogeochemical analyses, and three-dimensional modeling proved invaluable in unraveling the complex dynamics of algal blooms highlighting the substantial role of littoral-pelagic connectivities in large low-nutrient lakes. These findings underscore the advantages of state-of-the-art multidisciplinary approaches for an improved understanding of dynamic systems as a whole.

A novel total phosphorus concentration retrieval method based on two-line classification in lakes and reservoirs across China

Phosphorus is widely recognized as a nutrient that restricts growth and is the primary contributor to eutrophication in 80 % of water bodies. Consequently, the Chinese government has consistently prioritized monitoring and controlling total phosphorus (TP) levels. The remote estimation of TP in lakes and reservoirs at a national scale is a challenging task due to TP being a non-optically active parameter. Currently, there is a lack of developed TP inversion models specifically designed for lakes and reservoirs in China. For solving this problem, a novel two-line classification method drawn on scatter plots based on the natural logarithm of TP (Ln(TP)) and B33/B9 was proposed and used to classify 1211 measured samples obtained from field cruises in 105 lakes and reservoirs across China from 2012 to 2022 into three categories, Class 1, Class 2, and Class 3. Results demonstrate that the proposed classification method has the ability to enhance the correlation between Ln(TP) and 43 basic potential single band and band combinations. Specifically, the correlation range improved from (-0.31,0.15) to (-0.77,0.24) in Class 1, (-0.81, 0.36) in Class 2, and (-0.74, 0.52) in Class 3. Additionally, the classification method also improved the correlation range between Ln(TP) and 820 band ratios, from (-0.32, 0.32) to (-0.83, 0.82) in Class 1, (-0.86, 0.86) in Class 2, and (-0.86, 0.86) in Class 3. These datasets were subsequently utilized as input for eXtreme Gradient Boosting (XGBoost) models. Finally, well performing XGBoost models in Class 1 (R2 = 0.76, RMSE = 0.3, MAPE = 12 %), Class 2 (R2 = 0.84, RMSE = 0.49, MAPE = 38 %), and Class 3 (R2 = 0.74, RMSE = 0.46, MAPE = 14 %) were used to map TP of 563 large lakes and reservoirs (≥20 km2) across China using MODIS images from 2005, 2010, 2015, and 2020. This study presents a novel approach for estimating non-optically active parameters through remote sensing on a national scale.

Deep-Learning-Based Automated Tracking and Counting of Living Plankton in Natural Aquatic Environments

Plankton are widely distributed in the aquatic environment and serve as an indicator of water quality. Monitoring the spatiotemporal variation in plankton is an efficient approach to forewarning environmental risks. However, conventional microscopy counting is time-consuming and laborious, hindering the application of plankton statistics for environmental monitoring. In this work, an automated video-oriented plankton tracking workflow (AVPTW) based on deep learning is proposed for continuous monitoring of living plankton abundance in aquatic environments. With automatic video acquisition, background calibration, detection, tracking, correction, and statistics, various types of moving zooplankton and phytoplankton were counted at a time scale. The accuracy of AVPTW was validated with conventional counting via microscopy. Since AVPTW is only sensitive to mobile plankton, the temperature- and wastewater-discharge-induced plankton population variations were monitored online, demonstrating the sensitivity of AVPTW to environmental changes. The robustness of AVPTW was also confirmed with natural water samples from a contaminated river and an uncontaminated lake. Notably, automated workflows are essential for generating large amounts of data, which are a prerequisite for available data set construction and subsequent data mining. Furthermore, data-driven approaches based on deep learning pave a novel way for long-term online environmental monitoring and elucidating the correlation underlying environmental indicators. This work provides a replicable paradigm to combine imaging devices with deep-learning algorithms for environmental monitoring.

MODIS Terra and Aqua images bring non-negligible effects to phytoplankton blooms derived from satellites in eutrophic lakes

Phytoplankton-induced lake eutrophication has drawn ongoing interest on a global scale. One of the most popular remote sensing satellite data for observing long-term dynamic changes in phytoplankton is Moderate-resolution Imaging Spectroradiometer (MODIS). However, it is worth noting that MODIS provides two images with different transit times: Terra (local time, about 10:30 am) and Aqua (local time, about 1:30 pm), which may result in a considerable bias in monitoring phytoplankton bloom areas due to the rapid migration of phytoplankton under wind or hydrodynamic conditions. To analyze this quantitatively, we selected MODIS Terra and Aqua images to generate datasets of phytoplankton bloom areas in Lake Taihu from 2003 to 2022. The results showed that Terra more frequently detected larger ranges of phytoplankton blooms than Aqua, whether on daily, monthly, or annual scales. In addition, long-term trend changes, seasonal characteristics, and abrupt years also varied with different transit times. Terra detected mutation years earlier, while Aqua displayed more pronounced seasonal characteristics. There were also differences in sensitivity to climate factors, with Terra being more responsive to temperature and wind speed on monthly and annual scales, while Aqua was more sensitive to nutrient and meteorological factors. These conclusions have also been further confirmed in Lake Chaohu, Lake Dianchi, and Lake Hulun. In conclusion, our findings strongly advocate for a linear relationship to fit Terra to Aqua results to mitigate long-term monitoring errors of phytoplankton blooms in inland lakes (R2 = 0.70, RMSE = 101.56). It is advised to utilize satellite data with transit times between 10 am and 1 pm to track phytoplankton bloom changes and to consider the diverse applications resulting from the transit times of Terra and Aqua.

The effects of nutrient loading from different sources on eutrophication in a large shallow lake in Southeast China

Lake water eutrophication has become one of the leading obstacles to sustainable economic development in China. Research on the effects of mainstream currents on reservoirs has been relatively underdeveloped compared with research on tributaries, though changes in the water-sediment transport regime in a downstream river may affect nutrient transport behavior in a lake connected to that river. This is particularly problematic because certain wastewater sources, including runoff from agricultural wastes and industrial discharges, adversely affect lake water. Our study focused on Sanshiliujiao Lake, a significant drinking water source in Fujian, Southeast China, that has suffered considerably from eutrophication over the past few decades. This study aimed to estimate the phosphorus and nitrogen loads to the lake, exploring their sources and their ecologic effects using in situ observation and the export coefficient model. Our results showed that the pollution loads of total phosphorus (TP) and total nitrogen (TN) were 2.390 and 46.040 t/year, respectively, most of which were derived from the water diversion (TP 45.7%, TN 29.2%) and non-point source (TP 30.2%, TN 41.6%). The TN input was the highest in East river (3.557 kg/d), followed by Red river (2.524 kg/d). During the wet season, the input of TP and TN increased by 14.6 and 18.7 times, respectively, but produced only slight variations in concentration. Water diversion enriched the nutrients inputs and altered the structure and abundance of phytoplankton communities. In addition, when water flows from the main river directly to Sanshiliujiao Lake, algal blooms in river-connected lakes are significantly exacerbated, so our study may also serve as a theoretical basis to regulate eutrophication in Sanshiliujiao Lake.

The unprecedented 2022 extreme summer heatwaves increased harmful cyanobacteria blooms

Heatwaves are increasing and expected to intensify in coming decades with global warming. However, direct evidence and knowledge of the mechanisms of the effects of heatwaves on harmful cyanobacteria blooms are limited and unclear. In 2022, we measured chlorophyll-a (Chla) at 20-s intervals based on a novel ground-based proximal sensing system (GBPSs) in the shallow eutrophic Lake Taihu and combined in situ Chla measurements with meteorological data to explore the impacts of heatwaves on cyanobacterial blooms and the potential relevant mechanisms. We found that three unprecedented summer heatwaves (July 4-15, July 22-August 16, and August 18-23) lasting a total of 44 days were observed with average maximum air temperatures (MATs) of 38.1 ± 1.9 °C, 38.7 ± 1.9 °C, and 40.2 ± 2.1 °C, respectively, and that these heatwaves were characterized by high air temperature, strong PAR, low wind speed and rainfall. The daily Chla significantly increased with increasing MAT and photosynthetically active radiation (PAR) and decreasing wind speed, revealing a clear promotion effect on harmful cyanobacteria blooms from the heatwaves. Moreover, the combined effects of high temperature, strong PAR and low wind, enhanced the stability of the water column, the light availability and the phosphorus release from the sediment which ultimately boosted cyanobacteria blooms. The projected increase in heatwave occurrence under future climate change underscores the urgency of reducing nutrient input to eutrophic lakes to combat cyanobacteria growth and of improving early warning systems to ensure secure water management.

Simultaneous increases of filter-feeding fish and bivalves are key for controlling cyanobacterial blooms in a shallow eutrophic lake

Eutrophication and cyanobacterial blooms have severely affected many freshwater ecosystems. We studied the effects of filter-feeding fish and bivalves on algal populations using a mesocosm experiment and long-term monitoring data from Lake Taihu (China). The mesocosm study, comprised of a two-way factorial design with the clam Corbicula fluminea and the fish Aristichthys nobilis at three biomass levels, resulted in lower chlorophyll a (Chl a) in high fish treatments, but no significant differences in the low and medium fish treatments. Chl a also decreased with an increase in clam biomass in the high fish treatments. Moreover, filter-feeding fish resulted in a decrease in algal sizes (e.g., the colony size of Microcystis aeruginosa was inversely related to fish biomass) which likely increased the filter-feeding efficiency of bivalves. Biomass of filter-feeding fish was found to be a key factor driving the synergistic effects of filter-feeding fish and bivalves in waters dominated by Microcystis colonies. Long-term monitoring revealed increasing trends in Chl a concentration, total fish catch per unit effort (TF-CPUE), and filter-feeding fish (FF-CPUE), and slightly decreasing trends in bivalve biomass and nitrogen to phosphorus ratios (N:P) from 2006 to 2016. Bivalve biomass and N:P were negatively correlated with Chl a, while FF-CPUE was not significantly related to Chl a. The current filter-feeding fish biomass in Lake Taihu is estimated to be too low to drive synergistic algal control effects together with bivalves. Furthermore, the lack of filter feeders in Lake Taihu may lead to top-down control by predators that cannot counteract the bottom-up effects of nutrients on phytoplankton. Collectively, these long-term monitoring and experimental data support the combined use filter-feeding fish and bivalves for managing cyanobacteria blooms in Lake Taihu.

Urbanization shifts long-term phenology and severity of phytoplankton blooms in an urban lake through different pathways

Climate change can induce phytoplankton blooms (PBs) in eutrophic lakes worldwide, and these blooms severely threaten lake ecosystems and human health. However, it is unclear how urbanization and its interaction with climate impact PBs, which has implications for the management of lakes. Here, we used multi-source remote sensing data and integrated the Virtual-Baseline Floating macroAlgae Height (VB-FAH) index and OTSU threshold automatic segmentation algorithm to extract the area of PBs in Lake Dianchi, China, which has been subjected to frequent PBs and rapid urbanization in its vicinity. We further explored long-term (2000-2021) trends in the phenological and severity metrics of PBs and quantified the contributions from urbanization, climate change, and also nutrient levels to these trends. When comparing data from 2011-2021 to 2000-2010, we found significantly advanced initiation of PBs (28.6 days) and noticeably longer duration (51.9 days) but an insignificant trend in time of disappearance. The enhancement of algal nutrient use efficiency, likely induced by increased water temperature and reduced nutrient concentrations, presumably contributed to an earlier initiation and longer duration of PBs, while there was a negative correlation between spring wind speed and the initiation of PBs. Fortunately, we found that both the area of the PBs and the frequency of severe blooms (covering more than 19.8 km2 ) demonstrated downward trends, which could be attributed to increased wind speed and/or reduced nutrient levels. Moreover, the enhanced land surface temperature caused by urbanization altered the thermodynamic characteristics between the land and the lake, which, in turn, possibly caused an increase in local wind speed and water temperature, suggesting that urbanization can differently regulate the phenology and severity of PBs. Our findings have significant implications for the understanding of the impacts of urbanization on PB dynamics and for improving lake management practices to promote sustainable urban development under global change.

How does Microcystis aeruginosa respond to elevated temperature?

Cyanobacteria and their toxins widely exist in freshwater ecosystems. Microcystis aeruginosa is among dominant bloom-forming cyanobacteria. Water temperature is a key factor influencing the life cycle of M. aeruginosa. We simulated elevated temperature (4-35 °C) experiment and cultured M. aeruginosa during the overwintering, recruitment and rapid growth phases. The results showed that M. aeruginosa recovered growth after overwintering at 4-8 °C and recruited at 16 °C. The total extracellular polymeric substance (TEPS) concentration increased rapidly at 15 °C. The actual quantum yield of photosystem II (Fv'/Fm') peaked at 20 °C during the rapid growth phase, and the optimum temperature of M. aeruginosa growth was 20-25 °C. Additionally, TEPS and microcystins (MCs) secretion peaked at 20-25 °C. The cell density accumulated rapidly from 26 °C to 35 °C. Furthermore, enzymes of RuBisCO and FBA related to photosynthetic activity were confirmed to contribute to the metabolism, as well as mcyB gene was affected by elevated temperature. Our results provide insights of the physiological effects and metabolic activity during annual cycle of M. aeruginosa. And it is predicted that global warming may promote the earlier recruitment of M. aeruginosa, extend the optimum growth period, enhance the toxicity, and finally intensify M. aeruginosa blooms.

Climate change drives rapid warming and increasing heatwaves of lakes

Climate change could seriously threaten global lake ecosystems by warming lake surface water and increasing the occurrence of lake heatwaves. Yet, there are great uncertainties in quantifying lake temperature changes globally due to a lack of accurate large-scale model simulations. Here, we integrated satellite observations and a numerical model to improve lake temperature modeling and explore the multifaceted characteristics of trends in surface temperatures and lake heatwave occurrence in Chinese lakes from 1980 to 2100. Our model-data integration approach revealed that the lake surface waters have warmed at a rate of 0.11 °C 10a-1 during the period 1980-2021, being only half of the pure model-based estimate. Moreover, our analysis suggested that an asymmetric seasonal warming rate has led to a reduced temperature seasonality in eastern plain lakes but an amplified one in alpine lakes. The durations of lake heatwaves have also increased at a rate of 7.7 d 10a-1. Under the high-greenhouse-gas-emission scenario, lake surface temperature and lake heatwave duration were projected to increase by 2.2 °C and 197 d at the end of the 21st century, respectively. Such drastic changes would worsen the environmental conditions of lakes subjected to high and increasing anthropogenic pressures, posing great threats to aquatic biodiversity and human health.

Eutrophication control of large shallow lakes in China

Large shallow lake refers to a polymictic system that is often well mixed without stratification during summer. Similar to a small and deep lake, a large and shallow lake has a high nutrient retention rate. Differing from a small and deep lake, it has an extensive sediment-water interface and internal loading from sediment, which has led to high susceptibility to eutrophication. There are many large and shallow freshwater lakes in the middle and lower Yangtze River (MLYR), China, experienced eutrophication and cyanobacteria blooms. To address this issue, a variety of methods focused on in-lake physical and biogeochemical processes was explored. The main gains of these studies included: (1) shallow lakes in the floodplain of the Yangtze River are prone to eutrophication because of their high trophic conditions; (2) wind-induced waves determine sediment resuspension, downward dissolved oxygen penetration, and upward soluble reactive nutrient mobilization, while wind-driven currents regulate the spatial distribution of water quality metrics and algal blooms; (3) the low P loss of shallow lakes via sedimentation and high N loss via denitrification lead to a low N:P ratio and N and P colimitation, which demonstrated the significance of dual N and P reduction for eutrophication control in shallow lakes; (4) extensive submerged macrophyte could suppress internal loading in large, shallow waters, but nutrient loading must be reduced and water clarity must be increased; and (5) climate warming promotes cyanobacterial blooms through positive feedback to exacerbate eutrophication in shallow lakes. The lack of action to address the challenges of non-point source pollution and internal loading from the sediment has led to limited effectiveness of eutrophication control in large shallow lakes under climate warming. In the future, the management of large shallow eutrophic lakes in China must combine social sciences (economic development) with natural technology (pollution reduction) to achieve sustainability.

Unbalanced impacts of nature and nurture factors on the phenology, area and intensity of algal blooms in global large lakes: MODIS observations

Under the influence of climate warming and human activities, many large lakes have experienced an increase in eutrophication and algal blooms. Although these trends have been identified using low temporal resolution (~16 days) satellites such as those of the Landsat missions, the opportunity to compare high-frequency spatiotemporal variations of algal bloom characteristics between lakes has not been explored. In the present study, we explore daily satellite observations by developing a universal, practical, and robust algorithm to identify the spatiotemporal distribution of algal bloom dynamics in large lakes (>500 km²) across the globe. Data from 161 lakes, taken from 2000 to 2020 showed an average accuracy of 79.9 %. Algal blooms were detected in 44 % of all lakes, with a higher incidence in temperate lakes (67 % of all temperate lakes), followed by tropical lakes (59 %) compared to lakes in arid climates (23 %). We found positive trends in bloom area and frequency (p < 0.05), as well as an earlier bloom time (p < 0.05). Climate factors were found to be linked to changes in annual initial bloom time (44 %); while an increase in human activities was associated to bloom duration (49 %), area (max percent: 53 %, mean percent: 45 %), and frequency (46 %). The study shows the evolution of daily algal blooms and their phenology in global large lakes for the first time. Such information enhances our understanding of algal bloom dynamics and their drivers, with important considerations to improve the management of large lake ecosystems.

Characterization of lacustrine harmful algal blooms using multiple biomarkers: Historical processes, driving synergy, and ecological shifts

Harmful algal blooms (HABs) producing toxic metabolites are increasingly threatening environmental and human health worldwide. Unfortunately, long-term process and mechanism triggering HABs remain largely unclear due to the scarcity of temporal monitoring. Retrospective analysis of sedimentary biomarkers using up-to-date chromatography and mass spectrometry techniques provide a potential means to reconstruct the past occurrence of HABs. By combining aliphatic hydrocarbons, photosynthetic pigments, and cyanotoxins, we quantified herein century-long changes in abundance, composition, and variability of phototrophs, particularly toxigenic algal blooms, in China's third largest freshwater Lake Taihu. Our multi-proxy limnological reconstruction revealed an abrupt ecological shift in the 1980s characterized by elevated primary production, Microcystis-dominated cyanobacterial blooms, and exponential microcystin production, in response to nutrient enrichment, climate change, and trophic cascades. The empirical results from ordination analysis and generalized additive models support climate warming and eutrophication synergy through nutrient recycling and their feedback through buoyant cyanobacterial proliferation, which sustain bloom-forming potential and further promote the occurrence of increasingly-toxic cyanotoxins (e.g., microcystin-LR) in Lake Taihu. Moreover, temporal variability of the lake ecosystem quantified using variance and rate of change metrics rose continuously after state change, indicating increased ecological vulnerability and declined resilience following blooms and warming. With the persistent legacy effects of lake eutrophication, nutrient reduction efforts mitigating toxic HABs probably be overwhelmed by climate change effects, emphasizing the need for more aggressive and integrated environmental strategies.

Intensified sensitivity and adaptability of zooplankton Bosminidae in subtropical shallow freshwater lakes with increasing trophic level

The deterioration in lake water environments, especially increasing lake eutrophication, is prevalent all over the world, which has seriously affected the balance and stability of the internal ecosystem of lakes. In this study, modern water and sediment samples were collected from three subtropical freshwater lakes with significant differences in nutrient levels to analyze the concentration of the zooplankton Cladocera Bosminidae and its relationship with lakes’ ecological changes. The results show that the deterioration in lake water environments caused by increasing eutrophication limits the survival of most zooplankton. However, the Bosminidae shows a positive adaptability to eutrophication and high sensitivity to the changes in the lake environment. In addition, the lake eutrophication process caused by the intensification of human activities enhances the survival advantage of Bosminidae with more food sources, which is more conducive to its rapid reproduction.

Warming of surface water in the large and shallow lakes across the Yangtze River Basin, China, and its driver analysis

A variety of physical, chemical, and biological processes within the lakes relies on the surface water temperature while the spatial pattern of large lakes of different warming trends and their connections with climate change remain unclear. Using correlation analysis, regression tree analysis (RTA), and general linear models (GLMs), we have estimated the warming trends of 192 lakes since 2000 in the populated Yangtze River Basin, China, to identify dominant climate drivers and quantify their contributions. The results show that surface water temperature has increased substantially in the majority of the investigated lakes (179 from a total of 192 lakes) at a rate of 0.29 (- 0.12 to 0.62) °C/decade (median and 95% confidence interval). The shallower lakes (< 13.1 m in depth) usually have the faster median warming rates than the deeper lakes (i.e., 0.37 °C/decade versus 0.16 °C/decade). We find that in the shallow lakes, rising air temperatures and declining wind speeds can explain the majority of variation in surface water temperature (i.e., 31.4‒80.3% and 13.0‒21.0%, respectively). In contrast, in deeper lakes, change of air temperatures plays a dominant role in water warming (75.4‒91.2%). This study has emphasized the importance of declining wind speed in water warming in large and shallow lakes and illustrated a difference of dominant climatic drivers in water warming between the shallow and deep lakes.

Ancient DNA reveals potentially toxic cyanobacteria increasing with climate change

Cyanobacterial blooms in freshwater systems are a global threat to human and aquatic ecosystem health, exhibiting particularly harmful effects when toxin-producing taxa are present. While climatic change and nutrient over-enrichment control the global expansion of total cyanobacterial blooms, it remains unknown to what extent this expansion reflected cyanobacterial assemblage due to the scarcity of long-term monitoring data. Here we use high-throughput sequencing of sedimentary DNA to track ∼100 years of changes in cyanobacterial community in hyper-eutrophic Lake Taihu, China's third largest freshwater lake and the key water source for ∼30 million people. A steady increase in the abundance of Microcystis (as potential toxin producers) during the past thirty years was correlated with increasing temperatures and declining wind speeds, but not with temporal trends in lakewater nutrient concentrations, highlighting recent climate effects on potentially increasing toxin-producing taxa. The socio-environmental repercussions of these findings are worrisome as continued anthropogenic climate change may counteract nutrient amelioration efforts in this critical freshwater resource.

A systematic review and quantitative meta-analysis of the relationships between driving forces and cyanobacterial blooms at global scale

The global expansion of cyanobacterial blooms poses a major risk to the safety of freshwater resources. As a result, many explorations have been performed at a regional scale to determine the underlying impact mechanism of cyanobacterial blooms for one or several waterbodies. However, two questions still need to be answered quantitatively at a global scale to assist the water management. One is to specify which factors were often selected as the driving forces of cyanobacterial blooms, and the other is to estimate their quantitative relationships. For that, this paper applied a systematic literature review for 41 peer-reviewed studies published before May 2021 and a statistical meta-analysis based on the Pearson's or Spearman's correlation coefficients from 27 studies. These results showed that the water quality, hydraulic conditions, meteorological conditions and nutrient levels were often considered the driving forces of cyanobacterial blooms in global freshwater systems. Among these, meteorological conditions and nutrient level had the highest probability of being chosen as the driving force. In addition, knowledge of the quantitative relationships between these driving forces and cyanobacterial blooms was newly synthesized based on the correlation coefficients. The results indicated that, at a global scale, meteorological conditions were negatively related to cyanobacterial blooms, and other driving forces, such as water quality, hydraulic conditions and nutrient levels, were positively related to cyanobacterial blooms. In addition, the measurement indicators of these driving forces had diverse forms. For example, the nutrient level can be measured by the concentration of different forms of nitrogen or phosphorus, which may lead to different results in correlation analysis. Thus, a subgroup meta-analysis was necessary for the subdivided driving forces and cyanobacterial blooms, which had a better accuracy. Overall, the synthesized knowledge can help guide advanced cyanobacteria-centered water management, especially when the necessary cyanobacterial data of targeting waterbodies are inaccessible.

Influence of cyanobacterial bloom accumulation and dissipation on underwater light attenuation in a large and shallow lake

Cyanobacterial bloom accumulation and dissipation frequently occur in Lake Taihu, a typically shallow, eutrophic lake due to wind wave disturbance. However, knowledge of the driving mechanisms of cyanobacterial blooms on underwater light attenuation is still limited. In this study, we collected a high-frequency in situ monitoring of the wind field, underwater light environment, and surface water quality to elucidate how cyanobacterial bloom accumulation and dissipation affect the variations in underwater light attenuation in the littoral zone of Lake Taihu. Results showed that cyanobacterial blooms significantly increased the diffuse attenuation coefficient of ultraviolet-B (K_d(313)), ultraviolet-A (K_d(340)), and photosynthetically active radiation (K_d(PAR)); the scattering of total suspended matter (b_bp(λ)); and the absorption of phytoplankton (a_ph(λ)) and chromophoric dissolved organic matter (CDOM, a_g(λ)) (p < 0.01). The K_d(PAR) decreased quickly during the processes of bloom dissipation, but the decrease of K_d(313) and K_d(340) lagged 0.5 day. Our results suggested that cyanobacterial blooms could increase particle matters and elevated the production of autochthonous CDOM, resulting in underwater light attenuation increase. Ultraviolet radiation (UVR) and PAR attenuation both have significant responses to cyanobacterial blooms, but the response processes were distinct due to the different changes of particle and dissolved organic matters. Our study unravels the driving mechanisms of cyanobacterial blooms on underwater light attenuation, improving lake ecosystem management and protection.

The variations of labile arsenic diffusion driven by algal bloom decomposition in eutrophic lake ecosystems

The vertical labile arsenic (As) concentration and diffusion pattern variations in eutrophic lakes were investigated using in situ techniques of diffusive gradients in thin films (DGT) and high-resolution dialysis (HR-Peeper) in the typical eutrophic system of Lake Taihu in China. In addition, simulation experiments were used to reveal labile As distributions in sediment profiles under the influence of algae blooms and wind fluctuations. Our results indicated that eutrophication could lead to the migration and transformation of As fractions, including increased As bioavailability, as well as varied diffusion patterns. The sulfate released from algae decomposition reduced to H₂S and formed FeS, which weak adsorbability contributed to the increased mobility of the As fractions. Meanwhile, further decomposition released a large quantity of algae-derived organic matter which competed with the adsorbed As, leading to more endogenous As migrating to the overlying water. Accordingly, the H₂S production presented a likely explanation for the changed distribution of labile As and contributed to labile As concentrations in the sediment profiles significantly increasing at depths of -20 mm to -60 mm in the early stages of the simulation experiment. Moreover, the areas of enhanced diffusion patterns with high concentrations of As obviously expanded. However, following the complete decomposition of the algae, the organic matter component significantly changed, suggesting an explanation for the variations in distribution of labile As. All the diffusion pattern variations showed similar trends. Consequently, variation of labile As diffusion patterns could indicate the decomposition and eutrophication levels of freshwater ecosystems.

Six decades of field observations reveal how anthropogenic pressure changes the coverage and community of submerged aquatic vegetation in a eutrophic lake

Six decades field observation data series on submerged aquatic vegetation (SAV), water level and water quality from Lake Taihu were compiled to reveal the dynamics in coverage and species composition of SAV and their anthropogenic drivers. We found that both SAV species composition and coverage area declined significantly in Lake Taihu during the period, and the increasing nutrient levels and water level as well as decreasing water clarity were responsible for these change trends. Specifically, the decrease in species richness could be particularly well predicted by total nitrogen (TN) and the ratio of water clarity (i.e., Secchi disk depth (SDD)) to water level (WL), contributing 47.3 % and 32.3 %, respectively, while the coverage of macrophytes was most strongly related to the water level, accounting for 70.1 % of the variation. A classification tree analysis revealed a threshold of TN of 3.2 mg/L and SDD/WL of 0.14 that caused a shift to a eutrophic low-macrophyte dominated state. Our results highlight that SDD/WL must be improved for SAV recolonization, rather than merely reducing nutrient input and regulating water level. Our findings provide scientific information for lake managers to prevent plant degradation in macrophyte-dominant lakes and facilitate a shift to a macrophyte-dominant state in eutrophic lakes.

A ground-based remote sensing system for high-frequency and real-time monitoring of phytoplankton blooms

The worldwide expansion of phytoplankton blooms has severely threatened water quality, food webs, habitat stability and human health. Due to the rapidity of phytoplankton migration and reproduction, high-frequency information on phytoplankton bloom dynamics is crucial for their forecasting, treatment, and management. While several approaches involving satellites, in situ observations and automated underwater monitoring stations have been widely used in the past several decades, they cannot fully provide high-frequency and continuous observations of phytoplankton blooms at low cost and with high accuracy. Thus, we propose a novel ground-based remote sensing system (GRSS) that can monitor real-time chlorophyll a concentrations (Chla) in inland waters with a high frequency. The GRSS mainly consists of three platforms: the spectral measurement platform, the data-processing platform, and the remote access control, display and storage platform. The GRSS is capable of obtaining a remote sensing irradiance ratio (R(λ)) of 400-1000 nm at a high frequency of 20 s. Eight different Chla retrieval algorithms were calibrated and validated using a dataset of 481 pairs of GRSS R(λ) and in situ Chla measurements collected from four inland waters. The results showed that random forest regression achieved the best performance in deriving Chla (R² = 0.95, root mean square error = 13.40 μg/L, and mean relative error = 25.7%). The GRSS successfully captured two typical phytoplankton bloom events in August 2021 with rapid changes in Chla from 20 μg/L to 325 μg/L at the minute level, highlighting the critical role that this GRSS can play in the high-frequency monitoring of phytoplankton blooms. Although the algorithm embedded into the GRSS may be limited by the size of the training dataset, the high-frequency, continuous and real-time data acquisition capabilities of the GRSS can effectively compensate for the limitations of traditional observations. The initial application demonstrated that the GRSS can capture rapid changes of phytoplankton blooms in a short time and thus will play a critical role in phytoplankton bloom management. From a broader perspective, this approach can be extended to other carriers, such as aircraft, ships and unmanned aerial vehicles, to achieve the networked monitoring of phytoplankton blooms.

Autochthonous sources and drought conditions drive anomalous oxygen-consuming pollution increase in a sluice-controlled reservoir in eastern China

Freshwater reservoirs are an important type of inland waterbody. However, they can suffer from oxygen-consuming pollution, which can seriously threaten drinking water safety and negatively impact the health of aquatic ecosystems. Oxygen-consuming pollutants originate from both allochthonous and autochthonous sources, and have temporally and spatially heterogeneous drivers. Datanggang Reservoir, China, is located in a small agricultural watershed; it is controlled by multiple sluice gates. Anomalously high oxygen consumption indicators were observed in this reservoir in March 2021. Here, it was hypothesized that autochthonous sources were the primary drivers of oxygen-consuming pollution in the reservoir under drought conditions. Datasets of water quality, precipitation, primary productivity, and sediment were used to analyze water quality trends in the reservoir and inflow rivers, demonstrating the effects of allochthonous inputs and autochthonous pollution. No correlation was found between reservoir oxygen consumption indicators and allochthonous inputs; reservoir oxygen consumption indicators and chlorophyll-a concentration were significantly positively correlated (p < 0.05). Substantially lower precipitation and higher water temperature and pH (compared to historical levels) were also observed before the pollution event. Therefore, during this period the hydrological conditions, water temperature, pH, and other variables caused by short-term drought conditions may have facilitated phytoplankton growth in the reservoir. This contributed to a large increase in autochthonous oxygen-consuming pollutants, as reflected by the abnormally high indicators. Sediments contaminated with organic matter may also have been an important contributor. As the effects of environmental management and pollution control continue to emerge, exogenous pollutants imported from the land to reservoirs are currently effectively controlled. However, endogenous pollutants driven by a variety of factors, such as meteorology and hydrology, will likely become the main drivers of short-term changes in oxygen-consuming pollution in freshwater reservoirs in the foreseeable future.

Monitoring and control methods of harmful algal blooms in Chinese freshwater system: a review

Harmful algal blooms (HABs) are a worldwide problem with substantial adverse effects on the aquatic environment as well as human health, which have prompted researchers to study measures to stem and control them. Meanwhile, it is key to research and develop monitoring methods to establish early warning HABs. However, both the current monitoring methods and control methods have some shortcomings, making the field application limited. Thus, we need to improve current approaches for monitoring and controlling HABs efficiently. Based on the freshwater system features in China, we review various monitoring and control methods of HABs, summarize and discuss the problems with these methods, and propose the future development direction of monitoring and control HABs. Finally, we envision that it can combine physical, chemical, and biological methods to inhibit HAB expansion in the future, complementing each other with advantages. Further, we promise to establish a long-term strategy of controlling HABs with various algicidal bacteria co-cultivate for field applications in China. Efforts in studying algicidal bacteria must be increased to better control HABs and mitigate the risks of aquatic ecosystems and human health in China.

The synergistic effect of rising temperature and declining light boosts the dominance of bloom-forming cyanobacteria in spring

Global warming and eutrophication result in rising temperature and declining underwater light, respectively, which affect the shift of the phytoplankton community in spring. However, knowledge of how temperature and light synergistically impact phytoplankton community shifts and cyanobacterial dominance is limited. In this study, we performed a long-term data analysis and an outdoor mesocosm experiment to detect the synergistic effect of temperature and light on shift of phytoplankton community and dominance of bloom-forming cyanobacteria in Lake Taihu, China. The results showed that cyanobacterial biomass was boosted alone and jointly by increased temperature and decreased light levels (sunshine hours and light intensity), and the interaction might be more important than temperature or light levels independently. Chlorophyta biomass was driven by the joint effect of temperature and light levels. Bacillariophyta biomass was mainly affected by light levels, and decreased with declining light levels. Our results emphasize that the interactions of temperature and light have an important impact on the shift of the phytoplankton community in spring. Increasing temperature and declining underwater light boosted the flourishing of cyanobacteria, especially Microcystis, and were adverse to the development of diatoms in spring. Our findings contribute to an increased understanding of the effects of temperature and light on phytoplankton composition shifts and the development of cyanobacterial dominance in spring.

Is there any difference on cyanobacterial blooms patterns between Lake Chaohu and Lake Taihu over the last 20 years?

Serious cyanobacterial blooms (CBs) caused by lake eutrophication have become a global ecological and environmental problem and have adversely affected the production, life, and health of human beings. Lake Chaohu and Lake Taihu are two large closed shallow eutrophication lakes in the Yangtze River Delta in China with frequent CBs. In this study, the floating algae index (FAI) algorithm was applied to detect a long-time CBs sequence using Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2000 to 2019. The common characteristics and differences of the CBs patterns were further explored in both lakes over the last 20 years. The results showed that the severity of CBs in Lakes Chaohu and Taihu presented a similar trend of decreasing and then increasing during the period of 2000-2004 and 2005-2007, respectively. Although the severity of CBs in the two lakes was alleviated after 2008, CBs in Lake Taihu has gradually increased since 2011 and severe CBs broke out again in 2017 and 2019. Meanwhile, the CBs in Lake Chaohu have varied significantly in different years, and severe CBs were observed in 2012, 2014-2015, and 2018-2019, while in other years, CBs remained relatively low level. The high-frequency regions of CBs were mainly concentrated in the western part in Lake Chaohu and in Zhushan Bay and Meilian Bay in Lake Taihu in the initial years of 2000. However, since 2005, the CBs in Lake Chaohu gradually expanded to the central and eastern parts, and to the northwestern and western shore in Lake Taihu. Furthermore, the relationship between the monthly mean area of CBs (CBs_mean) and environmental factors based on principal component analysis (PCA) indicated that temperature was the most important driving factor affecting CBs patterns. Compared to the period from 2001 to 2007, TP played a more important role in both lakes from 2008 to 2019. Various management measures have been adopted to reduce CBs in both lakes and these methods can effectively remove cyanobacteria in a short time, but they do not change CBs patterns in the long period.

Classifying diurnal changes of cyanobacterial blooms in Lake Taihu to identify hot patterns, seasons and hotspots based on hourly GOCI observations

Occurrence of cyanobacterial blooms in most lakes has dramatic changes in time and space. However, most current studies only focused on daily or seasonal scales to obtain a relatively coarse resolution result. To explore the possibility of fine changes occurring within a day in Lake Taihu (China), the area coverage of surface cyanobacterial blooms was quantified from the hourly Geostationary Ocean Color Imager (GOCI) data using a GOCI-derived cyanobacterial index. Based on that, diurnal change characteristics were explored at two scales, and the environmental impacts were investigated. For that, an classification method was first designed to identify the types of diurnal change patterns of cyanobacterial blooms automatically. This method classified the patterns into four types, including the decreasing (Type1), decreasing first and then increasing (Type2), increasing (Type3), increasing first and then decreasing (Type4). Based on that, the types of diurnal change patterns of blooms in Lake Taihu (from April 1, 2011 to October 31, 2020) were identified at pixel (500 m) and synoptic scales. Results indicated that Type1 and Type3 were two hot diurnal change patterns of blooms, and lakeshore was the hotspot occurring severe diurnal changes, and autumn was the hot season occurring frequent diurnal changes. Specifically, hotspot of Type1 was lakeshore, while hotspot of Type3 was Central Regions. Environmental impacts were analyzed at two scales. At pixel scale (500 m), diurnal variation of temperature affected the regional occurence of each type ofdiurnal changes patterns of blooms, and the afternoon temperature played the most critical role (p < 0.001, N = 8316). The occurrence frequency of Type1 was positively (R = 0.41) related with the afternoon temperature, and the occurrence frequency of Type3 was negatively (R = -0.37) related with it. Diurnal variation of wind speed was another key factor impacting the occurrence of obvious diurnal blooms changes, and the wind impacts should be distinguished when the wind speed was over or below 3.5 m/s. At synoptic scale, the interaction of multi environmental factors influenced the diurnal change degree of blooms area, and the environmental contributions were 71%.Comparing with the existing manual classifying workat synoptic scale, the designed classification method can identify the types of diurnal change patterns of blooms at a higher spatial resolution (500 m). These explorations on diurnal dynamics of cyanobacterial blooms in Lake Taihu provide a new insight for advanced cyanobacteria dynamics studies and regional water management.

Distinguishing Algal Blooms from Aquatic Vegetation in Chinese Lakes Using Sentinel 2 Image

Algal blooms frequently occur in numerous lakes in China, risking human health and the environment. In contrast, aquatic vegetation contributes to water purification. Due to the similar spectral characteristics shared by algal and aquatic vegetation, both are hardly distinguishable in remote sensing imaging, especially in turbid water bodies. To address this challenge, this study constructed a method to effectively extract algal blooms and aquatic vegetation from the turbid water bodies using Sentinel 2 images with high spatial resolution. Our results showed that the accuracy of the extraction of vegetation information could reach 96.1%. Since this method combined the vegetation extraction results from multiple indices, it effectively tackled the mis-extraction when only the Floating Algae Index (FAI) or the Normalized Difference Vegetation Index (NDVI) is used in water with high turbidity. By combining the image time series information with the natural phenological characteristics of the aquatic vegetation and algal blooms, an improved Vegetation Presence Frequency (VPF) was developed. It effectively distinguished algal blooms and aquatic vegetation without actual measurement data. Based on the above method and process, the information of algal blooms and aquatic vegetation was sufficiently distinguished in five typical lakes in China (Lake Hulun, Lake Hongze, Lake Chaohu, Lake Taihu, and Lake Dianchi), and the spatial distribution was reasonably mapped. The overall identification accuracy of aquatic vegetation and algal blooms using the improved VPF ranged 71.8–84.3%. The spatial transferability test of the method in the independent lakes with the various optical properties indicated the prospects of its application in other turbid water bodies. This study should provide strong methodological and theoretical support for future monitoring of algal blooms in turbid water bodies with vigorous aquatic vegetation, especially in the absence of actual measurement data. This should have practical relevance for water environment management and governance departments.

Global divergent trends of algal blooms detected by satellite during 1982-2018

Algal blooms (ABs) in inland lakes have caused adverse ecological effects, and health impairment of animals and humans. We used archived Landsat images to examine ABs in lakes (>1 km² ) around the globe over a 37-year time span (1982-2018). Out of the 176032 lakes with area >1 km² detected globally, 863 were impacted by ABs, 708 had sufficiently long records to define a trend, and 66% exhibited increasing trends in frequency ratio (FRQR, ratio of the number of ABs events observed in a year in a given lake to the number of available Landsat images for that lake) or area ratio (AR, ratio of annual maximum area covered by ABs observed in a lake to the surface area of that lake), while 34% showed a decreasing trend. Across North America, an intensification of ABs severity was observed for FRQR (p < .01) and AR (p < .01) before 1999, followed by a decrease in ABs FRQR (p < .01) and AR (p < .05) after the 2000s. The strongest intensification of ABs was observed in Asia, followed by South America, Africa, and Europe. No clear trend was detected for the Oceania. Across climatic zones, the contributions of anthropogenic factors to ABs intensification (16.5% for fertilizer, 19.4% for gross domestic product, and 18.7% for population) were slightly stronger than climatic drivers (10.1% for temperature, 11.7% for wind speed, 16.8% for pressure, and for 11.6% for rainfall). Collectively, these divergent trends indicate that consideration of anthropogenic factors as well as climate change should be at the forefront of management policies aimed at reducing the severity and frequency of ABs in inland waters.

Responses of steroid estrogen biodegradation to cyanobacterial organic matter biodegradability in the water column of a eutrophic lake

The co-occurrence of cyanobacterial harmful algal blooms and contaminants is an increasing environmental concern in freshwater worldwide. Our field investigations coupled with laboratory incubations demonstrated that the microbial degradation potential of 17β-estradiol (E2) with estrone as the intermediate was primarily driven by increased dissolved organic matter (DOM) in the water column of a cyanobacterial bloom. To explain the intrinsic contribution of cyanobacterial-derived DOM (C-DOM) to estrogen biodegradation, a combination of methods including bioassay, ultrahigh-resolution mass spectrometry, and microbial ecology were applied. The results showed that preferential assimilation of highly biodegradable structures, including protein-, carbohydrate-, and unsaturated hydrocarbon-like molecules sustained bacterial growth, selected for more diverse microbes, and resulted in greater estrogen biodegradation compared to less biodegradable molecules (lignin- and tannin-like molecules). The biodegradability of C-DOM decreased from 78% to 1%, whereas the E2 biodegradation rate decreased dramatically at first, then increased with the accumulation of recalcitrant, bio-produced lipid-like molecules in C-DOM. This change was linked to alternative substrate-induced selection of the bacterial community under highly refractory conditions, as suggested by the greater biomass-normalized E2 biodegradation rate after a 24-h lag phase. In addition to the increased frequency of potential degraders, such as Sphingobacterium, the network analysis revealed that C-DOM molecules distributed in high H/C (protein- and lipid-like molecules) were the main drivers structuring the bacterial community, inducing strong deterministic selection of the community assemblage and upregulating the metabolic capacity for contaminants. These findings provide strong evidence that estrogen biodegradation in eutrophic water may be facilitated by cyanobacterial blooms and provide a theoretical basis for ecological remediation of estrogen pollution.

Sensitivity of phytoplankton to climatic factors in a large shallow lake revealed by column-integrated algal biomass from long-term satellite observations

There are some uncertainties of using chlorophyll a (Chla) concentrations in water surface to address phytoplankton dynamics, especially in large shallow lakes, because of the dramatic vertical migration of phytoplankton. The column-integrated algal biomass (CAB) can reflect the whole water column information, so it is considered as a better indicator for phytoplankton total biomass. An algal biomass index (ABI) and an empirical algorithm were proposed previously to measure algal biomass inside and outside euphotic zone from the Moderate Resolution Imaging Spectrometer (MODIS) data. A long-term CAB time series was generated in this study to clarify the temporal and spatial changes in phytoplankton and address its sensitivity to climatic factors in Lake Chaohu, a shallow eutrophic lake in China, from 2000 to 2018. Overall, the CAB for Lake Chaohu showed significant temporal and spatial dynamics. Temporally, the annual average CAB (total CBA within the whole lake) was increased at rate of 0.569 t Chla/y, ranging from 62.06±8.89 t Chla to 76.03±10.01 t Chla during the 19-year period. Seasonal and periodic variations in total CAB presented a bimodal annual cycle every year, the total CAB was highest in summer, followed by that in autumn, and it was the lowest in winter. The pixel-based CAB (total CAB of a unit water column), ranging from 112.42 to 166.85 mg Chla, was the highest in the western segment, especially its northern part, and was the lowest in the central parts of eastern and central segments. The sensitivity of CAB dynamics to climatic conditions was found to vary by region and time scale. Specifically, the change of pixel-based algal biomass was more sensitive to the temperature change on the monthly and annual scales, while wind speed impacted directly on the short-term spatial-temporal redistribution of algal biomass. High temperature and low wind speed could prompt the growth of total CAB for the whole lake, and the hydrodynamic situations affected by wind and so on determined the spatial details. It also indicated that Lake Chaohu may face more severe challenges with the future climatic warming. This study may serve as a reference to support algal bloom forecasting and early warning management for other large eutrophic lakes with similar problems.

Long-term trend of heat waves and potential effects on phytoplankton blooms in Lake Qiandaohu, a key drinking water reservoir

Global warming is increasing the frequency and duration of heat waves, which is defined as when air temperature exceeds a threshold for more than specific consecutive days. Ecosystem around the globe will be impaired by heat waves just like the exposures to dangerously high temperatures as a public health threat to human. However, the knowledge of the response of lake and reservoir ecosystem to heat waves is largely unknown although it has been argued that climate warming may increase the incidence of harmful algal blooms. We examined the long-term trend of heat waves and how the variability of phytoplankton biomass responds to lake heat waves on a deep reservoir (Lake Qiandaohu). Long-term (1980-2020) meteorological observation in the lake watershed showed a significant warming trend of 0.36 °C per decade for the yearly average of daily average air temperature and the yearly average of daily maximum air temperature of 18.32 °C was observed in 2016. Meanwhile, a significant increasing number of heat wave events lasting longer was observed, and Lake Qiandaohu suffered an unusually severe lake heat wave in summer 2016. Significant correlations were found between the yearly average of daily maximum air temperature and heat days, heat wave events, and heat wave days. Nuisance phytoplankton bloom was found in Lake Qiandaohu by high frequency observation and remote sensing monitoring in summer 2016. Remote sensing estimation from two Landsat 8 Operational Land Imager (OLI) images showed that the average chlorophyll a (Chla) was 7.45 ± 4.89 μg/L on July 18 before heat wave and 18.96 ± 0.98 μg/L on August 19 during the heat wave. Two heat wave events lasting from July 20 to August 2 and August 11 to 26 with average surface water temperature of 29.93 and 31.99 °C promoted two marked phytoplankton blooms with average Chla concentrations of 11.75 ± 4.08 and 10.53 ± 1.65 μg/L in the central lake region, respectively, as evidenced by high-frequency buoy data. These findings suggest that heat waves are likely to yield an increased threat of harmful algal bloom in freshwater ecosystems. With lake heat waves projected to increase in frequency, duration, and spatial extent with global climate change, more studies are needed to improve our understanding of lake heat waves and their potential effects on the species, communities, frequency of phytoplankton bloom, and also help providing advanced schemes of water quality management.

Environmental controls of harmful cyanobacterial blooms in Chinese inland waters

Harmful cyanobacterial blooms (CyanoHABs) are expanding world-wide, adversely affecting aquatic food production, recreational and tourism activities and safe drinking water supplies. China's inland waters have been increasingly threatened by CyanoHABs during the past several decades. The environmental factors controlling CyanoHABs are highly variable in space and time in China due to significant variations in climate, geography, geological and geochemical conditions among its many regions. Here, we synthesize diverse examples among Chinese water bodies regarding interactive effects of anthropogenic, climatic and geographic drivers influencing CyanoHAB potentials and dynamics in lakes and reservoirs; in order to provide a perspective and integrative approach to mitigating CyanoHABs. In China's many shallow water bodies, water quality is highly susceptible to human activity and to changing climatic and hydrological conditions, when compared to deeper lakes. Rapid increases in population, economic activity, and wastewater have accelerated CyanoHABs in China since 1980s, especially in the heavily urbanized, agricultural and industrial regions in the middle and lower Yangtze River basins. Climatic changes have provided an additional catalyst for expansion of CyanoHABs. In particular, rising spring temperatures have accelerated the onset and proliferation of Microcystis spp, blooms in the middle and lower reaches of Yangtze River basin. Large hydroelectric and water supply projects, like the Three Gorges Reservoir (TGR), have altered hydrological regimes, and have led to an increase of CyanoHABs in reservoirs and tributaries due to increases in water residence times. Manipulating water level fluctuations in the TGR may prove useful for controlling CyanoHAB in its tributary bays. Overall,CyanoHAB mitigation strategies will have to incorporate both N and P input reductions in these shallow systems. Furthermore, nutrient reduction strategies must consider climate change-induced increases in extreme weather events, including more intense rainfall and protracted heat waves and droughts, which can extend the magnitudes and duration of CyanoHABs. Ensuring the maintenance of natural hydrologic connectivity between lakes and rivers is of utmost importance in mitigating CyanoHABs throughout China.

Research Trends in the Remote Sensing of Phytoplankton Blooms: Results from Bibliometrics

Phytoplankton blooms have caused many serious public safety incidents and eco-environmental problems worldwide and became a focus issue for research. Accurate and rapid monitoring of phytoplankton blooms is critical for forecasting, treating, and management. With the advantages of large spatial coverage and high temporal resolution, remote sensing has been widely used to monitor phytoplankton blooms. Numerous advances have been made in the remote sensing of phytoplankton blooms, biomass, and phenology over the past several decades. To fully understand the development history, research hotspots, and future trends of remote-sensing technology in the study of phytoplankton blooms, we conducted a comprehensive review to systematically analyze the research trends in the remote sensing of phytoplankton blooms through bibliometrics. Our findings showed that research on the use of remote-sensing technology in this field increased substantially in the past 30 years. “Oceanography,” “Environmental Sciences,” and “Remote Sensing” are the most popular subject categories. Remote Sensing of Environment, Journal of Geophysical Research: Oceans, and International Journal of Remote Sensing were the journals with the most published articles. The results of the analysis of international influence and cooperation showed that the United States had the greatest influence in this field and that the cooperation between China and the United States was the closest. The Chinese Academy of Sciences published the largest number of papers, reaching 542 articles. Keyword and topic analysis results showed that “phytoplankton,” “chlorophyll,” and “ocean” were the most frequently occurring keywords, while “eutrophication management and monitoring,” “climate change,” “lakes,” and “remote-sensing algorithms” were the most popular research topics in recent years. Researchers are now paying increasing attention to the phenological response of phytoplankton under the conditions of climate change and the application of new remote-sensing methods. With the development of new remote-sensing technology and the expansion of phytoplankton research, future research should focus on (1) accurate observation of phytoplankton blooms; (2) the traits of phytoplankton blooms; and (3) the drivers, early warning, and management of phytoplankton blooms. In addition, we discuss the future challenges and opportunities in the use of remote sensing in phytoplankton blooms. Our review will promote a deeper and wider understanding of the field.

Internal loop sustains cyanobacterial blooms in eutrophic lakes: Evidence from organic nitrogen and ammonium regeneration

Algal bloom species can live upon internal regenerated ammonium (NH₄⁺) for growth during the nitrogen-limited period. However, the linkages between NH₄⁺ regeneration and phytoplankton biomass and community composition dynamics remain largely unknown. To unravel the interactions between NH₄⁺ regeneration and phytoplankton community, we measured water column NH₄⁺ regeneration rates (REGs) during a continuous phytoplankton growing period and a contrast summer/winter turnover in eutrophic Lake Taihu. Measured REGs were higher in summer than in winter and significantly correlated to total phytoplankton biomass, Cyanophyta biomass and its biomass proportions, and the concentrations of particulate nitrogen and dissolved organic carbon as well as the relative abundance of labile components (proteins and lipids). Random forest regression analyses displayed that variation of REGs were mainly controlled by water temperature and algal-related parameters (including chlorophyll a, total phytoplankton biomass, and Cyanophyta biomass). Partial least squares path model further revealed that algal-related parameters were the direct and significant factors regulating REGs, and contributed to the largest effect of the variance in REGs. Of the algal community, Cyanophyta was the dominant phylum to accelerate REGs. Correspondingly, rapid internal NH₄⁺ turnover may strongly support the persistence of cyanobacterial blooms, thus forming a positive feedback between cyanobacterial blooms and REGs during the nitrogen-limited summer months. We therefore deduced that the internal loop between cyanobacterial blooms and REGs during summer may be a key self-maintenance mechanism of continuous cyanobacterial blooms.

Flexibility of Microcystis Overwintering Strategy in Response to Winter Temperatures

Microcystis is one of the most common bloom-forming cyanobacteria in freshwater ecosystems throughout the world. However, the underlying life history mechanism and distinct temporal dynamics (inter- and intra-annual) of Microcystis populations in different geographical locations and lakes remain unclear but is critical information needed for the development of robust prediction, prevention, and management strategies. Perennial observations indicate that temperature may be the key factor driving differences in the overwintering strategy. This study quantitatively compared the overwintering abilities of Microcystis aeruginosa (Ma) in both the water column and sediments under a gradient of overwintering water temperatures (i.e., 4, 8, and 12 °C) using the death and proliferation rates of Ma. The results show that the dynamics of the Microcystis overwintering strategy were significantly affected by water temperatures. At 4 and 8 °C, Ma mainly overwintered in sediments and disappeared from the water column after exposure to low temperatures for a long duration, although some Microcystis cells can overwinter in the water column for short durations at low temperatures. At 12 °C, most Ma can overwinter in the water column. Rising temperatures promoted the proliferation of pelagic Ma but accelerated the death of benthic Ma. With warmer winter temperatures, pelagic Microcystis might become the primary inoculum sources in the spring. Our study highlights the overwintering strategy flexibility in explaining temporal dynamics differences of Microcystis among in geographical locations and should be considered in the context of global warming.

A Meta-Analysis on Harmful Algal Bloom (HAB) Detection and Monitoring: A Remote Sensing Perspective

Algae serves as a food source for a wide range of aquatic species; however, a high concentration of inorganic nutrients under favorable conditions can result in the development of harmful algal blooms (HABs). Many studies have addressed HAB detection and monitoring; however, no global scale meta-analysis has specifically explored remote sensing-based HAB monitoring. Therefore, this manuscript elucidates and visualizes spatiotemporal trends in HAB detection and monitoring using remote sensing methods and discusses future insights through a meta-analysis of 420 journal articles. The results indicate an increase in the quantity of published articles which have facilitated the analysis of sensors, software, and HAB proxy estimation methods. The comparison across multiple studies highlighted the need for a standardized reporting method for HAB proxy estimation. Research gaps include: (1) atmospheric correction methods, particularly for turbid waters, (2) the use of analytical-based models, (3) the application of machine learning algorithms, (4) the generation of harmonized virtual constellation and data fusion for increased spatial and temporal resolutions, and (5) the use of cloud-computing platforms for large scale HAB detection and monitoring. The planned hyperspectral satellites will aid in filling these gaps to some extent. Overall, this review provides a snapshot of spatiotemporal trends in HAB monitoring to assist in decision making for future studies.

Variations in the light absorption coefficients of phytoplankton, non-algal particles and dissolved organic matter in reservoirs across China

Reservoirs were critical sources of drinking water for many large cities around the world, but progress in the development of large-scale monitoring protocols to obtain timely information about water quality had been hampered by the complex nature of inland waters and the various optical conditions exhibited by these aquatic ecosystems. In this study, we systematically investigated the absorption coefficient of different optically-active constituents (OACs) in 120 reservoirs of different trophic states across five eco-regions in China. The relationships were found between phytoplankton absorption coefficient at 675 nm (a_ph (675)) and Chlorophyll a (Chla) concentration in different regions (R²:0.60-0.82). The non-algal particle (NAP) absorption coefficient (a_NAP) showed an increasing trend for reservoirs with trophic states. Significant correlation (p < 0.05) was observed between chromophoric dissolved organic matter (CDOM) absorption and water chemical parameters. The influencing factors for contributing the relative proportion of OACs absorption including the hydrological factors and water quality factors were analyzed. The non-water absorption budget from our data showed the variations of the dominant absorption types which underscored the need to develop and parameterize region-specific bio-optical models for large-scale assessment in water reservoirs.

High-resolution distribution of internal phosphorus release by the influence of harmful algal blooms (HABs) in Lake Taihu

The Mechanisms driving phosphorus (P) release in sediment of shallow lakes is essential for managing harmful algal blooms (HABs). Accordingly, this study conducted field monitoring of labile P, iron (Fe), sulfur (S), and dissolved manganese (Mn) in different biomass of algae in Lake Taihu. The in-situ technique of ZrO-Chelex-AgI (ZrO-CA) diffusive gradients in thin-films (DGT) and high-resolution dialysis sampler (high resolution-Peeper (HR-Peeper)) were used to measure labile P, Fe, S, and dissolved Mn, as well as their apparent diffusion fluxes at the sediment-water interface (SWI). In addition, the distribution of iron-reducing bacteria (IRB) and sulfate-reducing bacteria (SRB) in sediments was also detected. Results showed that high HABs biomass promoted the reduction of sulfate into labile S, however, IRB is the dominant species. Thus, labile Fe concentrations greatly exceeded labile S concentrations across all sites, indicating that microbial iron reduction (MIR) is the principal pathway for ferric iron reduction. Furthermore, the simple relationship analysis revealed the principal influence P migration and transformation is the Fe-P in high algal biomass sites, while Fe and Mn redox reactions did not significantly influence labile P mobilization in low algal areas.

Response of community composition and biomass of submerged macrophytes to variation in underwater light, wind and trophic status in a large eutrophic shallow lake

Light climate is of key importance for the growth, community composition of submerged macrophytes in lakes and, they, in turn, are affected by lake depth and the degree of eutrophication. To test the relationships between submerged macrophyte presence and the ratio of Secchi disk depth (SDD) to water depth, i.e. SDD/depth, nutrients and wind, we conducted an extensive sampling campaign in a macrophyte-dominated area of the eastern region (n = 36) in 2016 in Lake Taihu, China, and combined the data gathered with results from extensive physico-chemical monitoring data from the entire lake. We confirmed that SDD/Depth is the primary factor controlling the community composition of macrophytes and showed that plant abundance increased with increasing SDD/Depth ratio (p < 0.01), but that only SDD/Depth > 0.4 ensured growth of submerged macrophytes. Total phosphorus and total nitrogen also influenced the growth and community composition of macrophytes (p < 0.01), while Chla was an indirectly affecting factor by reducing underwater light penetration. Wave height significantly influenced plant abundance (p < 0.01), whereas it had little effect on the biomass (p > 0.05). The key to restore the macrophyte beds in the lake is to reduce the nutrient loading. A decrease of the water level may contribute as well in the shallow bays but will not bring plants back in the main part of the lake. As the tolerance of shade and nutrients varied among the species studied, this should be taken into account in the restoration of lakes by addition of plants.

Hot spots and hot moments of nitrogen removal from hyporheic and riparian zones: A review

The Earth is experiencing excessive nitrogen (N) input to its various ecosystems due to human activities. How to effectively and efficiently remove N from ecosystems has been, is and will be at the center of attention in N research. Hyporheic and riparian zones are widely acknowledged for their buffering capacity to reduce contaminants (especially N) transport downstream. However, these zones are usually misunderstood that they can remove N at all spots and at any moments. Here pathways of N removal from hyporheic and riparian zones are reviewed and summarized with an emphasize on their hot spots and hot moments. N is biogeochemically removed by denitrification, anammox, nitrifier denitrification, denitrifying anaerobic methane oxidation, Feammox and Sulfammox. Hot moments of N removal are mainly triggered by precipitation, fire and snowmelt. Finally, some research needs are outlined and discussed, such as developing approaches for multiscale sampling and monitoring, quantifying the effects of hot spots and hot moments at hyporheic and riparian zones and evaluating the impacts of human activities on hot spots and hot moments, to inspire more research on hot spots and hot moments of N removal. By this review, we hope to bring awareness of the heterogeneity of hyporheic and riparian zones to catchment managers and policy makers when tackling N pollution problems.

Climatic versus Anthropogenic Controls of Decadal Trends (1983-2017) in Algal Blooms in Lakes and Reservoirs across China

The proliferation of algal blooms (ABs) in lakes and reservoirs (L&Rs) poses a threat to water quality and the ecological health of aquatic communities. With global climate change, there is a concern that the frequency and geographical expansion of ABs in L&Rs could increase. China has experienced rapid economic growth and major land-use changes over the last several decades and therefore provides an excellent context for such an analysis. About 289,600 Landsat images were used to examine the spatiotemporal distribution of ABs in L&Rs (>1 km²) across China (1983-2017). Results showed significant changes in the temporal slope of the sum of normalized area (0.26), frequency (2.28), duration (6.14), and early outbreak (-3.48) of AB events in L&Rs across China. Specifically, AB-impacted water bodies expanded longitudinally, and the time range of AB observation has expanded starting in the 2000s. Spearman correlation and random forest regression analyses further indicated that, among climatic factors, wind speed and temperature contributed the most to AB expansion. Overall, anthropogenic forces have overridden the imprints of climatic factors on the temporal evolution of ABs in China's L&Rs and therefore could inform policy decisions for the management of these resources.