Understanding the long-term trend of particulate phosphorus in a cyanobacteria-dominated lake using MODIS-Aqua observations

Spatiotemporal variation reconstruction of total phosphorus in the Great Lakes since 2002 using remote sensing and deep neural network

Total phosphorus (TP) is non-optically active, thus TP concentration (CTP) estimation using remote sensing still exists grand challenge. This study developed a deep neural network model (DNN) for CTP estimation with synchronous in-situ measurements and MODIS-derived remote sensing reflectance (Rrs) (N = 3916). Using DNN, the annual and intra-annual CTP spatial distributions of the Great Lakes since 2002 were reconstructed. Then, the reconstructions were correlated to nine potential factors, e.g., Chlorophyll-a, snowmelt, and cropland, to explain seasonal and long-term CTP variations. The results showed that DNN reliably estimated CTP from MODIS Rrs, with R2, mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and root mean squared logarithmic error (RMSLE) of 0.83, 1.05 μg/L, 2.95 μg/L, 9.92%, and 0.13 on the test set. The near-surface CTP in the Great Lakes decreased significantly (p < 0.05) during 2002 - 2022, primarily attributed to cropland reduction, coupled with improvements in basin natural ecosystems. The sensitivity analysis verified the model robustness when confronted with input feature changes < 35%. This result along with the marginal difference between CTP derived from two sensors (R2 = 0.76, MAE = 2.12 μg/L, RMSE = 2.51 μg/L, MAPE = 11.52%, RMSLE = 0.24) suggested the model transferability from MODIS to VIIRS. This transformation facilitated optimal usage of MODIS-related archive and enhanced the continuity of CTP estimation at moderate resolution. This study presents a practical method for spatiotemporal reconstruction of CTP using remote sensing, and contributes to better understandings of driving factors behind CTP variations in the Great Lakes.

A novel chlorophyll-a retrieval model based on suspended particulate matter classification and different machine learning

Chlorophyll-a (Chla) in inland waters is one of the most significant optical parameters of aquatic ecosystem assessment, and long-term and daily Chla concentration monitoring has the potential to facilitate in early warning of algal blooms. MOD09 products have multiple observation advantages (higher temporal, spatial resolution and signal-to-noise ratio), and play an extremely important role in the remote sensing of water color. For developing a high accuracy machine learning model of remotely estimating Chla concentration in inland waters based on MOD09 products, this study proposed an assumption that the accuracy of Chla concentration retrieval will be improved after classifying water bodies into three groups by suspended particulate matter (SPM) concentration. A total of 10 commonly used machine learning models were compared and evaluated in this study, including random forest regressor (RFR), deep neural networks (DNN), extreme gradient boosting (XGBoost), and convolutional neural network (CNN). Altogether, 41 basic bands and 820 band ratios between the 41 bands were filtered by measuring their correlation with Ln(Chla) and several bands brought into different machine learning models. Results demonstrated that the construction of Chla concentration remote estimation model based on SPM classification could significantly improve the correlation between Ln(Chla) and 41 basic spectral band combinations, the correlation between Ln(Chla) and 820 band ratios, and the model verification R2 from 0.41 to 0.83. Furthermore, B3, B20, and B32 were finally selected based on correlation with SPM to classify SPM and the classification accuracy could reach 0.9. Finally, we concluded that RFR model performed best via comparing the R2, RMSE, and MAPE. By comparing the relative contribution of input bands in different groups, B3 contributed most to three groups. The model constructed in this study has promising prospects for promotion and application in other inland waters, and could provide systematic research reference for subsequent research.

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.

Regulating phytoplankton-available suspended particulate phosphorus (P) to control internal P pollution in lake: Conclusion from a short review

The necessity on controlling internal P pollution has been widely reported for lake restoration; thus far, cutting the migrations of soluble P from sediment to overlying water, especially under anoxic condition, is the main target of the internal P pollution control to achieve favorable ecological responses in lake. Here, according to the types of P directly available by phytoplankton, phytoplankton-available suspended particulate P (SPP) pollution, which mainly occurs under aerobic condition and due to sediment resuspension and soluble P adsorption by suspended particle, is found to be the other kind of internal P pollution. The SPP has long been a key index for environmental quality assessment, which could be indirectly reflected by the developed various methods for phytoplankton-available P pool analysis; also, the P has been demonstrated to be a major cause of phytoplankton breeding, typically in shallow lakes. Importantly, compared to the soluble P, SPP pollution clearly has more complicated loading pathways and P activation mechanisms and involves in different fractions of P, even part of which are with relatively high stability in sediment and suspended particle, leading to the potential control measures for the pollution being more complex. Considering the potential differences of internal P pollution among various lakes, this study is therefore calling for more research to focus on regulating phytoplankton-available SPP pollution. Recommendations are also offered to bridge knowledge gap of the regulation to design proper measures for lake restoration.

Long-term remote observations of particulate organic phosphorus concentration in eutrophic Lake Taihu based on a novel algorithm

Monitoring the long-term spatiotemporal variations in particulate organic phosphorus concentration (C_POP) is imperative for clarifying the phosphorus cycle and its biogeochemical behavior in waters. However, little attention has been devoted to this owing to a lack of suitable bio-optical algorithms that allow the application of remote sensing data. In this study, based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, a novel absorption-based algorithm of C_POP was developed for eutrophic Lake Taihu, China. The algorithm yielded a promising performance with a mean absolute percentage error of 27.75% and root mean square error of 21.09 μg/L. The long-term MODIS-derived C_POP demonstrated an overall increasing pattern over the past 19 years (2003-2021) and a significant temporal heterogeneity in Lake Taihu, with higher value in summer (82.06 ± 3.81 μg/L) and autumn (78.74 ± 3.8 μg/L), and lower C_POP in spring (79.52 ± 3.81 μg/L) and winter (81.97 ± 3.8 μg/L). Spatially, relatively higher C_POP was observed in the Zhushan Bay (85.87 ± 7.5 μg/L), whereas the lower value was observed in the Xukou Bay (78.95 ± 3.48 μg/L). In addition, significant correlations (r > 0.6, P < 0.05) were observed between C_POP and air temperature, chlorophyll-a concentration and cyanobacterial blooms areas, demonstrating that C_POP was greatly influenced by air temperature and algal metabolism. This study provides the first record of the spatial-temporal characteristics of C_POP in Lake Taihu over the past 19 years, and the C_POP results and regulatory factors analyses could provide valuable insights for aquatic ecosystem conservation.

Long-term dynamics and drivers of particulate phosphorus concentration in eutrophic lake Chaohu, China

Particulate phosphorus (PP) plays an important biological role in the eutrophication process, and is thus an important water quality parameter for assessing climatic change and anthropogenic activity factors that affect aquatic ecosystems. Here, we used 20-year Moderate Resolution Imaging Spectroradiometer (MODIS) data to explore the patterns and trends of PP concentration (C_PP) in eutrophic Lake Chaohu based on a new empirical model. The validation results indicated that the developed model performed satisfactorily in estimating C_PP, with a mean absolute percentage error of 31.89% and root mean square error of 0.022 mg/L. Long-term MODIS observations (2000-2019) revealed that the C_PP of Lake Chaohu has experienced an overall increasing trend and distinct spatiotemporal heterogeneity. The driving factor analysis revealed that the chemical fertilizer consumption, municipal wastewater, industrial sewage, precipitation, and air temperature were the five potential driving factors and collectively explained more than 81% of the long-term variation in C_PP. This study provides the long-term datasets of C_PP in inland waters and new insights for future water eutrophication control and restoration efforts.

Multivariable integrated risk assessment for cyanobacterial blooms in eutrophic lakes and its spatiotemporal characteristics

Climate change has catalyzed the global expansion of cyanobacterial blooms in eutrophic , lakes and threatens water security. In most studies, the cyanobacterial bloom risk levels in lakes were evaluated using field-collected data from multiple indicators or spatially continuous data from one cyanobacteria-related indicator. Nevertheless, the occurrence of cyanobacterial blooms in lakes has clear spatial heterogeneity and is affected by numerous factors. Therefore, we developed a multivariable integrated risk assessment framework for cyanobacterial blooms in lakes using five spatially continuous datasets to estimate the risk level of cyanobacterial blooms at the pixel scale (250 m). The spatial and temporal variations in cyanobacterial bloom risk levels from May 1, 2002, to October 31, 2020, were investigated for three typical eutrophic lakes in China: Lakes Taihu, Chaohu, and Dianchi. Seasons and regions of high cyanobacterial bloom risk were identified for each lake. Environmental characteristics were discussed. A long-term investigation revealed that owing to its warm climate, the cyanobacterial risk levels in summer and autumn were much higher than those in the other two seasons. At the synoptic scale, Lake Taihu had a lower cyanobacterial bloom risk than Lakes Chaohu and Dianchi. A further comparison found that precipitation, wind speed, and temperature were responsible for the differences in cyanobacterial bloom risk levels among the three lakes. At the pixel scale, the risk map indicated that the cyanobacterial bloom risk levels of Lake Taihu were unevenly distributed, and the cyanobacterial bloom risk of the lakeshore was higher than that of the other subregions. Nutrient levels played the most critical role in the regional differences in cyanobacterial bloom risk levels in a lake. While the differences of cyanobacterial bloom risk levels in three lakes were resulted by the climates. Bloom events were defined and classified as "long-term bloom" or "flash bloom" according to their duration (over or below a year). Overall, this study can assist in advanced water management with a pixel-scale evaluation of cyanobacterial bloom risk levels.

Machine learning predictions of chlorophyll-a in the Han river basin, Korea

This study developed a model to predict concentrations of chlorophyll-a ([Chl-a]) as a proxy for algal population with data from multiple monitoring stations in the Han river basin, by using machine-learning predictive models, then analyzed the relationship between [Chl-a] and the input variables of the optimized model. Daily water quality and meteorological data from 2012 to 2020 were collected from the real-time water quality information system and the meteorological administration of Korea. To quantify model accuracy, the coefficient of determination, root mean square error, and mean absolute error were applied. Among random forest (RF), support vector machine, and artificial neural network, the RF with random dataset showed the highest accuracy. The RF was optimized when 78 trees were applied to the model. Input variables for the best RF model were total organic carbon (feature importance: 27%), total nitrogen (19%), pH (13%), water temperature (8%), total phosphorus (8%), electrical conductivity (7%), dissolved oxygen (6%), minimum air temperature (AT) (4%), mean AT (3%), and maximum AT (3%). The feature-importance analysis showed that total organic carbon was the most important variable to predict [Chl-a] in the Han river basin. Total nitrogen was a more important variable than total phosphorus.

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.

High-resolution characteristics and mechanisms of endogenous phosphorus migration and transformation impacted by algal blooms decomposition

Extremely high phosphorus (P) concentrations can be found in eutrophic freshwater sediments during algal blooms (ABs). However, few investigations have revealed the mechanism of labile P production in anoxic sediments following ABs decomposition. This limits our understanding of P cycling and mitigation of ABs in aquatic ecosystems. To identify such a mechanism, we conducted a microcosm experiment to identify how ABs decomposition enhances endogenous P release, using the combined techniques of diffusive gradients in thin films, high-resolution dialysis, and 16S rRNA amplicon sequencing. We show the concentrations of labile iron, manganese, sulfide, and P can be well predicted by quality and quantity of algal biomass. The relative abundance of iron reduction bacteria positively correlated with the decrease of pH induced by ABs decomposition, suggesting that this decomposition facilitates microbial iron and manganese reduction. In addition, the reductive dissolution of iron and manganese oxides leads to the labile P release, resulting in higher concentrations of labile P in those sediments affected by ABs compared with those not affected. The P fluxes in the algae-dominated regions exhibited higher values in the algae group than in the control group, with gains of 14.07-100.04%. Furthermore, endogenous P release is strongly controlled by Mn when the Fe(II):Mn(II) ratio is low (below 0.47), and by both Fe and Mn when the Fe(II):Mn(II) ratio is high (above 0.63). Our results quantify the endogenous P diffusion fluxes across the sediment-water interface can be attributed to ABs decomposition, and are therefore useful for further understanding of P cycling in freshwater.

Hysteresis effects of meteorological variation-induced algal blooms: A case study based on satellite-observed data from Dianchi Lake, China (1988-2020)

As one of three top-priority eutrophic lakes in China, Dianchi Lake has received national attention due to its severe eutrophication in recent decades. Meteorological factors are the main factors driving the formation and persistence of algae blooms. In addition, meteorological variation-induced algal blooms usually have a hysteresis effect. However, there have been few quantitative studies on this hysteresis effect. In the present study, Landsat images were used to extract the dynamic characteristics of changes in algal blooms in Dianchi Lake from 1988 to 2020. The hysteresis effect of meteorological factors driving algal blooms was studied by employing the modified lag-correlation method. The results showed that the algal blooms in Dianchi Lake were most severe between 1998 and 2008. During the periods of algal blooms, the values of air temperature (AT) and precipitation (PP) were significantly higher, while those wind velocity (WV) and sunshine duration (SSD) were obviously lower, than the corresponding annual mean values. AT and PP were significantly positively correlated with algal bloom factors in both the formation and persistence stages of algal blooms, while SSD and WV both promoted their regression, but these effects were less significant in the persistence period than in the formation period. Moreover, rainfall led to a decrease in SSD and WV, indirectly contributing to algal blooms. Furthermore, AT, PP and SSD are the main factors impacting the duration of persistent blooms. The time periods during which each meteorological factor was most influential were as follows: 1) AT - 25-30 days before the maximum bloom. 2) PP - within the first 10 days before the maximum bloom. 3) Both SSD and WV - 15-20 days before the maximum bloom. The results of this study support the prediction of algal blooms in Dianchi Lake.

Remote monitoring of total dissolved phosphorus in eutrophic Lake Taihu based on a novel algorithm: Implications for contributing factors and lake management

Understanding the spatiotemporal dynamics of total dissolved phosphorus concentration (C_TDP) and its regulatory factors is essential to improving our understanding of its impact on inland water eutrophication, but few studies have assessed this in eutrophic inland lakes due to a lack of suitable bio-optical algorithms allowing the use of remote sensing data. We developed a novel semi-analytical algorithm for this purpose and tested it in the eutrophic Lake Taihu, China. Our algorithm produced robust results with a mean absolute square percentage error of 29.65% and root mean square error of 9.54 μg/L. Meanwhile, the new algorithm demonstrates good portability to other waters with different optical properties and could be applied to various image data, including Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer (MERIS), and Ocean and Land Color Instrument (OLCI). Further analysis based on Geostationary Ocean Color Imager observations from 2011 to 2020 revealed a significant spatiotemporal heterogeneity of C_TDP in Lake Taihu. Correlation analysis of the long-term trend between C_TDP and driving factors demonstrated that air temperature is the dominant regulating factor in variations of C_TDP. This study provides a novel algorithm allowing remote-sensing monitoring of C_TDP in eutrophic lakes and can lead to new insights into the role of dissolved phosphorus in water eutrophication.

Monitoring the particulate phosphorus concentration of inland waters on the Yangtze Plain and understanding its relationship with driving factors based on OLCI data

Tracking the spatiotemporal dynamics of particulate phosphorus concentration (C_PP) and understanding its regulating factors is essential to improve our understanding of its impact on inland water eutrophication. However, few studies have assessed this in eutrophic inland lakes, owing to a lack of suitable bio-optical algorithms allowing the use of remote sensing data. Herein, a novel semi-analytical algorithm of C_PP was developed to estimate C_PP in lakes on the Yangtze Plain, China. The independent validations of the proposed algorithm showed a satisfying performance with the mean absolute percentage error and root mean square error less than 27% and 27 μg/L, respectively. The Ocean and Land Color Instrument observations revealed a remarkable spatiotemporal heterogeneity of C_PP in 23 lakes on the Yangtze Plain from 2016 to 2020, with the lowest value in December (62.91 ± 34.59 μg/L) and the highest C_PP in August (114.9 ± 51.69 μg/L). Among the 23 examined lakes, the highest mean C_PP was found in Lake Poyang (124.58 ± 44.71 μg/L), while the lowest value was found in Lake Qiandao (33.51 ± 4.71 μg/L). Additionally, 13 lakes demonstrated significant decreasing or increasing trends (P < 0.05) of annual mean C_PP during the observation period. The driving factor analysis revealed that four natural factors (wind speed, air temperature, precipitation, and sunshine duration) and two anthropogenic factors (the normalized difference vegetation index and nighttime light) combined explained more than 91% of the variation in C_PP, while the impacts of these factors on C_PP showed considerable differences among lakes. This study offered a novel and scalable algorithm for the study of the spatiotemporal variation of C_PP in inland waters and provided new insights into the regulating factors in water eutrophication.

Phosphorus compounds in the dissolved and particulate phases in urban rivers and a downstream eutrophic lake as analyzed using 31P NMR

Phosphorus (P) discharges from human activities result in eutrophication of lakes. We investigated whether the forms of phosphorus (P) in rivers with high effluent loads flowing through urban areas of Sapporo, Japan, were transformed when transported downstream into a eutrophic lake, namely Lake Barato. We hypothesized that the inorganic P supplied from the rivers might be transformed to organic forms in the lake. The results showed that soluble reactive phosphorus (SRP) and particulate inorganic phosphorus (PIP) dominated in the river discharge to the lake. Suspended solids in the rivers were rich in iron (Fe) so PIP was associated with Fe. A comparison of the concentrations at the river mouth and 4.5 km downstream showed that the concentrations of SRP and PIP were lower at 4.5 km downstream than at the river mouth, whereas the concentrations of organic P (i.e., dissolved organic phosphorus and particulate organic phosphorus) were similar. The results from solution ³¹P nuclear magnetic resonance spectroscopy of lake water showed that pyrophosphate was only present in the particulate fraction, while orthophosphate diesters (DNA-P) were only present in the dissolved fraction. Riverine samples contained orthophosphate (ortho-P) only, while lake samples contained ortho-P, orthophosphate monoesters, and DNA-P. The results suggest that the P forms, particularly those of dissolved P, shifted from inorganic to organic forms as the water was discharged from the river to the lake.

Ecological impacts of freshwater algal blooms on water quality, plankton biodiversity, structure, and ecosystem functioning

Harmful algal blooms are among the emerging threats to freshwater biodiversity that need to be studied further in the Anthropocene. Here, we studied freshwater plankton communities in ten tropical reservoirs to record the impact of algal blooms, comprising different phytoplankton taxa, on water quality, plankton biodiversity, and ecosystem functioning. We compared water quality parameters (water transparency, mixing depth, pH, electrical conductivity, dissolved inorganic nitrogen, total dissolved phosphorus, total phosphorus, chlorophyll-a, and trophic state), plankton structure (composition and biomass), biodiversity (species richness, diversity, and evenness), and ecosystem functioning (phytoplankton:phosphorus and zooplankton:phytoplankton ratios as a metric of resource use efficiency) through univariate and multivariate analysis of variance, and generalized additive mixed models in five different bloom categories. Most of the bloom events were composed of Cyanobacteria, followed by Dinophyta and Chlorophyta. Mixed blooms were composed of Cyanobacteria plus Bacillariophyta, Chlorophyta, and/or Dinophyta, while non-bloom communities presented phytoplankton biomass below the threshold for bloom development (10 mg L^-1, WHO alert level 2). Higher phytoplankton biomasses were recorded during Cyanobacteria blooms (15.87-273.82 mg L^-1) followed by Dinophyta blooms (18.86-196.41 mg L^-1). An intense deterioration of water quality, including higher pH, eutrophication, stratification, and lower water transparency, was verified during Cyanobacteria and mixed blooms, while Chlorophyta and Dinophyta blooms presented lower pH, eutrophication, stratification, and higher water transparency. All bloom categories significantly impacted phytoplankton and zooplankton structure, changing the composition and dominance patterns. Bloom intensity positively influenced phytoplankton resource use efficiency (R² = 0.25; p < 0.001), while decreased zooplankton resource acquisition (R² = 0.51; p < 0.001). Moreover, Cyanobacteria and Chlorophyta blooms negatively impacted zooplankton species richness, while Dinophyta blooms decreased phytoplankton richness. In general, Cyanobacteria blooms presented low water quality and major threats to plankton biodiversity, and ecosystem functioning. Moreover, we demonstrated that biodiversity losses decrease ecosystem functioning, with cascading effects on plankton dynamics.