A space-time geostatistical model for probabilistic estimation of harmful algal bloom biomass and areal extent

Estimating the Amount of the Wild Artemisia annua in China Based on the MaxEnt Model and Spatio-Temporal Kriging Interpolation

In order to determine the distribution area and amount of Artemisia annua Linn. (A. annua) in China, this study estimated the current amount of A. annua specimens based on the field survey sample data obtained from the Fourth National Census of Chinese Medicinal Resources. The amount was calculated using the maximum entropy model (MaxEnt model) and spatio-temporal kriging interpolation. The influencing factors affecting spatial variations in the amount were studied using geographic probes. The results indicated that the amount of A. annua in China was about 700 billion in 2019. A. annua was mainly distributed in the circular coastal belt of Shandong Peninsula, central Hebei, Tianjin, western Liaoning, and along the Yangtze River and in the middle and lower reaches of Jiangsu, Anhui, and the northern Chongqing provinces. The main factors affecting the amount are the precipitation in the wettest and the warmest seasons, the average annual precipitation, and the average temperature in the coldest and the driest seasons. The results show that the amount of A. annua is strongly influenced by precipitation and temperature.

Current status and prospects of algal bloom early warning technologies: A Review

In recent years, frequent occurrences of algal blooms due to environmental changes have posed significant threats to the environment and human health. This paper analyzes the reasons of algal bloom from the perspective of environmental factors such as nutrients, temperature, light, hydrodynamics factors and others. Various commonly used algal bloom monitoring methods are discussed, including traditional field monitoring methods, remote sensing techniques, molecular biology-based monitoring techniques, and sensor-based real-time monitoring techniques. The advantages and limitations of each method are summarized. Existing algal bloom prediction models, including traditional models and machine learning (ML) models, are introduced. Support Vector Machine (SVM), deep learning (DL), and other ML models are discussed in detail, along with their strengths and weaknesses. Finally, this paper provides an outlook on the future development of algal bloom warning techniques, proposing to combine various monitoring methods and prediction models to establish a multi-level and multi-perspective algal bloom monitoring system, further improving the accuracy and timeliness of early warning, and providing more effective safeguards for environmental protection and human health.

Short-term Lake Erie algal bloom prediction by classification and regression models

The recent outbreaks of harmful algal blooms in the western Lake Erie Basin (WLEB) have drawn tremendous attention to bloom prediction for better control and management. Many weekly to annual bloom prediction models have been reported, but they only employ small datasets, have limited types of input features, build linear regression or probabilistic models, or require complex process-based computations. To address these limitations, we conducted a comprehensive literature review, complied a large dataset containing chlorophyll-a index (from 2002 to 2019) as the output and a novel combination of riverine (the Maumee & Detroit Rivers) and meteorological (WLEB) features as the input, and built machine learning-based classification and regression models for 10-d scale bloom predictions. By analyzing the feature importance, we identified 8 most important features for the HAB control, including nitrogen loads, time, water levels, soluble reactive phosphorus load, and solar irradiance. Here, both long- and short-term nitrogen loads were for the first time considered in HAB models for Lake Erie. Based on these features, the 2-, 3-, and 4-level random forest classification models achieved an accuracy of 89.6%, 77.0%, and 66.7%, respectively, and the regression model achieved an R² value of 0.69. In addition, long-short term memory (LSTM) was implemented to predict temporal trends of four short-term features (N, solar irradiance, and two water levels) and achieved the Nash-Sutcliffe efficiency of 0.12-0.97. Feeding the LSTM model predictions for these features into the 2-level classification model reached an accuracy of 86.0% for predicting the HABs in 2017-2018, suggesting that we can provide short-term HAB forecasts even when the feature values are not available.

Spatiotemporal analysis of lake chlorophyll-a with combined in situ and satellite data

We estimated chlorophyll-a (Chl-a) concentration using various combinations of routine sampling, automatic station measurements, and MERIS satellite images. Our study site was the northern part of the large, shallow, mesotrophic Lake Pyhäjärvi located in southwestern Finland. Various combinations of measurements were interpolated spatiotemporally using a data fusion system (DFS) based on an ensemble Kalman filter and smoother algorithms. The estimated concentrations together with corresponding 68% confidence intervals are presented as time series at routine sampling and automated stations, as maps and as mean values over the EU Water Framework Directive monitoring period, to evaluate the efficiency of various monitoring methods. The mean Chl-a calculated with DFS in June-September was 6.5-7.5 µg/l, depending on the observations used as input. At the routine monitoring station where grab samples were used, the average uncertainty (standard deviation, SD) decreased from 2.7 to 1.6 µg/l when EO data were also included in the estimation. At the automatic station, located 0.9 km from the routine monitoring site, the SD was 0.7 µg/l. The SD of spatial mean concentration decreased from 6.7 to 2.9 µg/l when satellite observations were included in June-September, in addition to in situ monitoring data. This demonstrates the high value of the information derived from satellite observations. The conclusion is that the confidence of Chl-a monitoring could be increased by deploying spatially extensive measurements in the form of satellite imaging or transects conducted with flow-through sensors installed on a boat and spatiotemporal interpolation of the multisource data.

Advancing freshwater ecological forecasts: Harmful algal blooms in Lake Erie

Ecological models help provide forecasts of ecosystem responses to natural and anthropogenic stresses. However, their ability to create reliable predictions requires forecasts with track records sufficiently long to build confidence, skill assessments, and treating uncertainty quantitatively. We use Lake Erie harmful algal blooms as a case study to help formalize ecological forecasting. Key challenges for models include uncertainty in the deterministic structure of the load-bloom relationship and the need to assess alternative drivers (e.g., biologically available phosphorus load, spring load, longer term cumulative load) with a larger dataset. We enhanced a Bayesian model considering new information and an expanded data set, test it through cross validation and blind forecasts, quantify and discuss its uncertainties, and apply it for assessing historical and future scenarios. Allowing a segmented relationship between bloom size and spring load indicates that loading above 0.15 Gg/month will have a substantially higher marginal impact on bloom size. The new model explains 84 % of interannual variability (9.09 Gg RMSE) when calibrated to the 19-year data set and 66 % of variability in cross validation (12.58 Gg RMSE). Blind forecasts explain 84 % of HAB variability between 2014 and 2020, which is substantially better than the actual forecast track record (R² = 0.32) over this same period. Because of internal phosphorus recycling, represented by the long-term cumulative load, it could take over a decade for HABs to fully respond to loading reductions, depending on the pace of those reductions. Thus, the desired speed and endpoint of the lake's recovery should be considered when updating and adaptively managing load reduction targets. Results are discussed in the context of ecological forecasting best pactices: incorporate new knowledge and data in model construction; account for multiple sources of uncertainty; evaluate predictive skill through validation and hindcasting; and answer management questions related to both short-term forecasts and long-term scenarios.

A multivariate Chain-Bernoulli-based prediction model for cyanobacteria algal blooms at multiple stations in South Korea

Predicting the occurrence of algal blooms is of great importance in managing water quality. Moreover, the demand for predictive models, which are essential tools for understanding the drivers of algal blooms, is increasing with global warming. However, modeling cyanobacteria dynamics is a challenging task. We developed a multivariate Chain-Bernoulli-based prediction model to effectively forecast the monthly sequences of algal blooms considering hydro-environmental predictors (water temperature, total phosphorus, total nitrogen, and water velocity) at a network of stations. The proposed model effectively predicts the risk of harmful algal blooms, according to performance measures based on categorical metrics of a contingency table. More specifically, the model performance assessed by the LOO cross-validation and the skill score for the POD and CSI during the calibration period was over 0.8; FAR and MR were less than 0.15. We also explore the relationship between hydro-environmental predictors and algal blooms (based on cyanobacteria cell count) to understand the dynamics of algal blooms and the relative contribution of each potential predictor. A support vector machine is applied to delineate a plane separating the presence and absence of algal bloom occurrences determined by stochastic simulations using different combinations of predictors. The multivariate Chain-Bernoulli-based prediction model proposed here offers effective, scenario-based, and strategic options and remedies (e.g., controlling the governing environmental predictors) to relieve or reduce increases in cyanobacteria concentration and enable the development of water quality management and planning in river systems.

Quantification of microcystin production and biodegradation rates in the western basin of Lake Erie

Cyanobacterial biomass forecasts currently cannot predict the concentrations of microcystin, one of the most ubiquitous cyanotoxins that threaten human and wildlife health globally. Mechanistic insights into how microcystin production and biodegradation by heterotrophic bacteria change spatially and throughout the bloom season can aid in toxin concentration forecasts. We quantified microcystin production and biodegradation during two growth seasons in two western Lake Erie sites with different physicochemical properties commonly plagued by summer Microcystis blooms. Microcystin production rates were greater with elevated nutrients than under ambient conditions and were highest nearshore during the initial phases of the bloom, and production rates were lower in later bloom phases. We examined biodegradation rates of the most common and toxic microcystin by adding extracellular stable isotope-labeled microcystin-LR (1 μg L-1), which remained stable in the abiotic treatment (without bacteria) with minimal adsorption onto sediment, but strongly decreased in all unaltered biotic treatments, suggesting biodegradation. Greatest biodegradation rates (highest of -8.76 d-1, equivalent to the removal of 99.98% in 18 h) were observed during peak bloom conditions, while lower rates were observed with lower cyanobacteria biomass. Cell-specific nitrogen incorporation from microcystin-LR by nanoscale imaging mass spectrometry showed that a small percentage of the heterotrophic bacterial community actively degraded microcystin-LR. Microcystin production and biodegradation rates, combined with the microcystin incorporation by single cells, suggest that microcystin predictive models could be improved by incorporating toxin production and biodegradation rates, which are influenced by cyanobacterial bloom stage (early vs. late bloom), nutrient availability, and bacterial community composition.

Salty Twins: Salt-Tolerance of Terrestrial Cyanocohniella Strains (Cyanobacteria) and Description of C. rudolphia sp. nov. Point towards a Marine Origin of the Genus and Terrestrial Long Distance Dispersal Patterns

The ability to adapt to wide ranges of environmental conditions coupled with their long evolution has allowed cyanobacteria to colonize almost every habitat on Earth. Modern taxonomy tries to track not only this diversification process but also to assign individual cyanobacteria to specific niches. It was our aim to work out a potential niche concept for the genus Cyanocohniella in terms of salt tolerance. We used a strain based on the description of C. rudolphia sp. nov. isolated from a potash tailing pile (Germany) and for comparison C. crotaloides that was isolated from sandy beaches (The Netherlands). The taxonomic position of C. rudolphia sp. nov. was evaluated by phylogenetic analysis and morphological descriptions of its life cycle. Salt tolerance of C. rudolphia sp. nov. and C. crotaloides was monitored with cultivation assays in liquid medium and on sand under salt concentrations ranging from 0% to 12% (1500 mM) NaCl. Optimum growth conditions were detected for both strains at 4% (500 mM) NaCl based on morpho-anatomical and physiological criteria such as photosynthetic yield by chlorophyll a fluorescence measurements. Taking into consideration that all known strains of this genus colonize salty habitats supports our assumption that the genus might have a marine origin but also expands colonization to salty terrestrial habitats. This aspect is further discussed, including the ecological and biotechnological relevance of the data presented.

Advancing estuarine ecological forecasts: seasonal hypoxia in Chesapeake Bay

Ecological forecasts are quantitative tools that can guide ecosystem management. The coemergence of extensive environmental monitoring and quantitative frameworks allows for widespread development and continued improvement of ecological forecasting systems. We use a relatively simple estuarine hypoxia model to demonstrate advances in addressing some of the most critical challenges and opportunities of contemporary ecological forecasting, including predictive accuracy, uncertainty characterization, and management relevance. We explore the impacts of different combinations of forecast metrics, drivers, and driver time windows on predictive performance. We also incorporate multiple sets of state-variable observations from different sources and separately quantify model prediction error and measurement uncertainty through a flexible Bayesian hierarchical framework. Results illustrate the benefits of (1) adopting forecast metrics and drivers that strike an optimal balance between predictability and relevance to management, (2) incorporating multiple data sources in the calibration data set to separate and propagate different sources of uncertainty, and (3) using the model in scenario mode to probabilistically evaluate the effects of alternative management decisions on future ecosystem state. In the Chesapeake Bay, the subject of this case study, we find that average summer or total annual hypoxia metrics are more predictable than monthly metrics and that measurement error represents an important source of uncertainty. Application of the model in scenario mode suggests that absent watershed management actions over the past decades, long-term average hypoxia would have increased by 7% compared to 1985. Conversely, the model projects that if management goals currently in place to restore the Bay are met, long-term average hypoxia would eventually decrease by 32% with respect to the mid-1980s.

The Lake Erie HABs Grab: A binational collaboration to characterize the western basin cyanobacterial harmful algal blooms at an unprecedented high-resolution spatial scale

Monitoring of cyanobacterial bloom biomass in large lakes at high resolution is made possible by remote sensing. However, monitoring cyanobacterial toxins is only feasible with grab samples, which, with only sporadic sampling, results in uncertainties in the spatial distribution of toxins. To address this issue, we conducted two intensive "HABs Grabs" of microcystin (MC)-producing Microcystis blooms in the western basin of Lake Erie. These were one-day sampling events during August of 2018 and 2019 in which 100 and 172 grab samples were collected, respectively, within a six-hour window covering up to 2,270 km² and analyzed using consistent methods to estimate the total mass of MC. The samples were analyzed for 57 parameters, including toxins, nutrients, chlorophyll, and genomics. There were an estimated 11,513 kg and 30,691 kg of MCs in the western basin during the 2018 and 2019 HABs Grabs, respectively. The bloom boundary poses substantial issues for spatial assessments because MC concentration varied by nearly two orders of magnitude over very short distances. The MC to chlorophyll ratio (MC:chl) varied by a factor up to 5.3 throughout the basin, which creates challenges for using MC:chl to predict MC concentrations. Many of the biomass metrics strongly correlated (r > 0.70) with each other except chlorophyll fluorescence and phycocyanin concentration. While MC and chlorophyll correlated well with total phosphorus and nitrogen concentrations, MC:chl correlated with dissolved inorganic nitrogen. More frequent MC data collection can overcome these issues, and models need to account for the MC:chl spatial heterogeneity when forecasting MCs.

Selection of water source for water transfer based on algal growth potential to prevent algal blooms

Water transfer is becoming a popular method for solving the problems of water quality deterioration and water level drawdown in lakes. However, the principle of choosing water sources for water transfer projects has mainly been based on the effects on water quality, which neglects the influence in the variation of phytoplankton community and the risk of algal blooms. In this study, algal growth potential (AGP) test was applied to predict changes in the phytoplankton community caused by water transfer projects. The feasibility of proposed water transfer sources (Baqing River and Jinsha River) was assessed through the changes in both water quality and phytoplankton community in Chenghai Lake, Southwest China. The results showed that the concentration of total nitrogen (TN) and total phosphorus (TP) in Chenghai Lake could be decreased to 0.52 mg/L and 0.02 mg/L respectively with the simulated water transfer source of Jinsha River. The algal cell density could be reduced by 60%, and the phytoplankton community would become relatively stable with the Jinsha River water transfer project, and the dominant species of Anabaena cylindrica evolved into Anabaenopsis arnoldii due to the species competition. However, the risk of algal blooms would be increased after the Baqing River water transfer project even with the improved water quality. Algae gained faster proliferation with the same dominant species in water transfer source. Therefore, water transfer projects should be assessed from not only the variation of water quality but also the risk of algal blooms.

Quantifying uncertainty cascading from climate, watershed, and lake models in harmful algal bloom predictions

In response to increased harmful algal blooms (HABs), hypoxia, and nearshore algae growth in Lake Erie, the United States and Canada agreed to phosphorus load reduction targets. While the load targets were guided by an ensemble of models, none of them considered the effects of climate change. Some watershed models developed to guide load reduction strategies have simulated climate effects, but without extending the resulting loads or their uncertainties to HAB projections. In this study, we integrated an ensemble of four climate models, three watershed models, and four HAB models. Nutrient loads and HAB predictions were generated for historical (1985-1999), current (2002-2017), and mid-21st-century (2051-2065) periods. For the current and historical periods, modeled loads and HABs are comparable to observations but exhibit less interannual variability. Our results show that climate impacts on watershed processes are likely to lead to reductions in future loading, assuming land use and watershed management practices are unchanged. This reduction in load should help reduce the magnitude of future HABs, although increases in lake temperature could mitigate that decrease. Using Monte-Carlo analysis to attribute sources of uncertainty from this cascade of models, we show that the uncertainty associated with each model is significant, and that improvements in all three are needed to build confidence in future projections.

Elucidating controls on cyanobacteria bloom timing and intensity via Bayesian mechanistic modeling

The adverse impacts of harmful algal blooms (HABs) are increasing worldwide. Lake Erie is a North American Great Lake highly affected by cultural eutrophication and summer cyanobacterial HABs. While phosphorus loading is a known driver of bloom size, more nuanced yet crucial questions remain. For example, it is unclear what mechanisms are primarily responsible for initiating cyanobacterial dominance and subsequent biomass accumulation. To address these questions, we develop a mechanistic model describing June-October dynamics of chlorophyll a, nitrogen, and phosphorus near the Maumee River outlet, where blooms typically initiate and are most severe. We calibrate the model to a new, geostatistically-derived dataset of daily water quality spanning 2008-2017. A Bayesian framework enables us to embed prior knowledge on system characteristics and test alternative model formulations. Overall, the best model formulation explains 42% of the variability in chlorophyll a and 83% of nitrogen, and better captures bloom timing than previous models. Our results, supported by cross validation, show that onset of the major midsummer bloom is associated with about a month of water temperatures above 20 °C (occurring 19 July to 6 August), consistent with when cyanobacteria dominance is usually reported. Decreased phytoplankton loss rate is the main factor enabling biomass accumulation, consistent with reduced zooplankton grazing on cyanobacteria. The model also shows that phosphorus limitation is most severe in August, and nitrogen limitation tends to occur in early autumn. Our results highlight the role of temperature in regulating bloom initiation and subsequent loss rates, and suggest that a 2 °C increase could lead to blooms that start about 10 days earlier and grow 23% more intense.

Exploring nutrient and light limitation of algal production in a shallow turbid reservoir

Harmful algal blooms are increasingly recognized as a threat to the integrity of freshwater reservoirs, which serve as water supplies, wildlife habitats, and recreational attractions. While algal growth and accumulation is controlled by many environmental factors, the relative importance of these factors is unclear, particularly for turbid eutrophic systems. Here we develop and compare two models that test the relative importance of vertical mixing, light, and nutrients for explaining chlorophyll-a variability in shallow (2-3 m) embayments of a eutrophic reservoir, Jordan Lake, North Carolina. One is a multiple linear regression (statistical) model and the other is a process-based (mechanistic) model. Both models are calibrated using a 15-year data record of chlorophyll-a concentration (2003-2018) for the seasonal period of cyanobacteria dominance (June-October). The mechanistic model includes a novel representation of vertical mixing and is calibrated in a Bayesian framework, which allows for data-driven inference of important process rates. Both models show that chlorophyll-a concentration is much more responsive to nutrient variability than mixing, light, or temperature. While both models explain approximately 60% of the variability in chlorophyll-a, the mechanistic model is more robust in cross-validation and provides a more comprehensive assessment of algal drivers. Overall, these models indicate that nutrient reductions, rather than changes in mixing or background turbidity, are critical to controlling cyanobacteria in a shallow eutrophic freshwater system.

Photocatalytic degradation of microcystin-LR by modified TiO2 photocatalysis: A review

Microcystin-LR (MC-LR), the most toxic and commonly encountered cyanotoxin, is produced by harmful cyanobacterial blooms and potentially threatens human and ecosystems health. Titanium dioxide (TiO₂) photocatalysis is attracting growing attention and has been considered as an efficient, environmentally friendly and promising solution to eliminate MC-LR in the aquatic ecosystems. Over recent decades, scientific efforts have been directed towards the understanding of fundamentals, modification strategies, and application potentials of TiO₂ photocatalysis in degrading MC-LR. In this article, recent reports have been reviewed and progress has been summarized in the development of heterogeneous TiO₂-based photocatalysts for MC-LR photodegradation under visible, UV, or solar light. The proposed photocatalytic principles of TiO₂ and destruction of MC-LR have been thoroughly discussed. Specifically, some main modification methods for improving the drawbacks and performance of TiO₂ nanoparticle were highlighted, including element doping, semiconductor coupling, immobilization, floatability amelioration and magnetic separation. Moreover, the performance evaluation metrics quantum yield (QY) and figure of merit (FOM) were used to compare different photocatalysts in MC-LR degradation. The best performance was seen in N-TiO₂ with QY and FOM values of 2.20E-07 molecules/photon and 1.00E-11 mol·L/(g·J·h). N-TiO₂ or N-TiO₂-based materials may be excellent options for photocatalyst design in terms of MC-LR degradation. Finally, a summary of the remaining challenges and perspectives on new tendencies in this exciting frontier and still an emerging area of research were addressed accordingly. Overall, the present review will offer a deep insight for understanding the photodegradation of MC-LR with modified TiO₂ to further inspire researchers that work in associated fields.

Fusion-Based Hypoxia Estimates: Combining Geostatistical and Mechanistic Models of Dissolved Oxygen Variability

The need to characterize and track coastal hypoxia has led to the development of geostatistical models based on in situ observations of dissolved oxygen (DO) and mechanistic models based on a representation of biophysical processes. To integrate the benefits of these two distinct modeling approaches, we develop a space-time geostatistical framework for synthesizing DO observations with hydrodynamic-biogeochemical model simulations and meteorological time series (as covariates). This fusion-based approach is used to estimate hypoxia in the northern Gulf of Mexico across summers from 1985 to 2017. Deterministic trends with dynamic covariates explain over 35% of the variability in DO. Moreover, cross-validation results indicate that 58% of DO variability is explained when combining these trends with spatiotemporal interpolation, which is substantially better than mechanistic or conventional geostatistical hypoxia modeling alone. The fusion-based approach also reduces hypoxic area uncertainties by 11% on average and up to 40% in months with sparse sampling. Moreover, our new estimates of mean summer hypoxic area changed by >10% in a majority of years, relative to previous geostatistical estimates. These fusion-based estimates can be a valuable resource when assessing the influence of hypoxia on the coastal ecosystem.

Effectiveness of a fixed-depth sensor deployed from a buoy to estimate water-column cyanobacterial biomass depends on wind speed

Water quality sondes have the advantage of containing multiple sensors, extended deployment times, high temporal resolution, and telecommunication with stakeholder accessible data portals. However, sondes that are part of buoy deployments often suffer from typically being fixed at one depth. Because water treatment plants are interested in water quality at a depth of the water intake and other stakeholders (ex. boaters and swimmers) are interested in the surface, we examined whether a fixed depth of approximately 1 m could cause over- or under-estimation of cyanobacterial biomass. We sampled the vertical distribution of cyanobacteria adjacent to a water quality sonde buoy in the western basin of Lake Erie during the summers of 2015-2017. A comparison of buoy cyanobacteria RFU (Relative Fluorescence Unit) at 1 m to cyanobacteria chlorophyll a (chla) measured throughout the water column showed occurrences when the buoy both under and overestimated the cyanobacteria chla at specific depths. Largest differences between buoy measurements and at-depth grab samples occurred during low wind speeds (< 4.5 m/sec) because low winds allowed cyanobacteria to accumulate at the surface above the buoy's sonde. Higher wind speeds (> 4.5 m/sec) resulted in better agreement between the buoy and at-depth measurements. Averaging wind speeds 12 hr before sample collection decreased the difference between the buoy and at-depth samples for high wind speeds but not low speeds. We suggest that sondes should be placed at a depth of interest for the appropriate stakeholder group or deploy sondes with the ability to sample at various depths.

Preliminary analysis to estimate the spatial distribution of benefits of P load reduction: Identifying the spatial influence of phosphorus loading from the Maumee River (USA) in western Lake Erie

Since the early 2000s, Lake Erie has been experiencing annual cyanobacterial blooms that often cover large portions of the western basin and even reach into the central basin. These blooms have affected several ecosystem services provided by Lake Erie to surrounding communities (notably drinking water quality). Several modeling efforts have identified the springtime total bioavailable phosphorus (TBP) load as a major driver of maximum cyanobacterial biomass in western Lake Erie, and on this basis, international water management bodies have set a phosphorus (P) reduction goal. This P reduction goal is intended to reduce maximum cyanobacterial biomass, but there has been very limited effort to identify the specific locations within the western basin of Lake Erie that will likely experience the most benefits. Here, we used pixel-specific linear regression to identify where annual variation in spring TBP loads is most strongly associated with cyanobacterial abundance, as inferred from satellite imagery. Using this approach, we find that annual TBP loads are most strongly associated with cyanobacterial abundance in the central and southern areas of the western basin. At the location of the Toledo water intake, the association between TBP load and cyanobacterial abundance is moderate, and in Maumee Bay (near Toledo, Ohio), the association between TBP and cyanobacterial abundance is no better than a null model. Both of these locations are important for the delivery of specific ecosystem services, but this analysis indicates that P load reductions would not be expected to substantially improve maximum annual cyanobacterial abundance in these locations. These results are preliminary in the sense that only a limited set of models were tested in this analysis, but these results illustrate the importance of identifying whether the spatial distribution of management benefits (in this case P load reduction) matches the spatial distribution of management goals (reducing the effects of cyanobacteria on important ecosystem services).

Impact factors and mechanisms of dissolved reactive phosphorus (DRP) losses from agricultural fields: A review and synthesis study in the Lake Erie basin

Dissolved Reactive Phosphorus (DRP) losses from agricultural fields promote algae growth in water bodies, and may increase the risk of Harmful Algal Blooms (HABs). Using existing data from the Lake Erie Basin, we applied multiple regression analysis to better understand the impacts of both site-specific conditions (e.g., soil types/properties) and management practices (e.g., Agricultural Conservation Practices [ACP]) on annual DRP losses in subsurface and surface runoff. Results showed that soil properties significantly impact DRP losses. Greater DRP losses were associated with increased soil pH and Soil Test Phosphorus (STP). By contrast, soil organic matter (SOM) was inversely correlated with DRP losses. Soil clay content was also inversely correlated with DRP subsurface losses, but had no impact on DRP surface losses. The ACPs evaluated had varied effectiveness on DRP loss reduction. Cropping systems involving soybean could reduce DRP subsurface losses, whereas winter cover crops could cause unintended DRP subsurface losses. Cropping systems involving soybean and cover crops, however, had no impact on DRP surface losses. In addition, no-till and conservation tillage also enhanced DRP losses compared to conventional tillage, particularly for soils with high SOM and/or high clay content. Precipitation amount and fertilizer application rate significantly increased DRP surface losses as expected. Fertilizer application rate, however, had no impact on DRP subsurface losses. The impact of precipitation amount on DRP subsurface losses depends on STP levels. Precipitation amount significantly increases DRP subsurface losses when STP is higher (>41 mg kg-1 in this analysis). The optimal STP level for crop growth is 30 to 50 mg kg-1. Results from this study help us to better understand DRP losses and the effectiveness of ACPs for controlling them. We suggest taking soil surveys and soil tests into consideration when designing and/or implementing ACPs to manage DRP losses. Furthermore, the method we used for this study could be applied to other agricultural regions to investigate impacts of site-specific conditions and management practices on water quality.

The Use of Multisource Optical Sensors to Study Phytoplankton Spatio-Temporal Variation in a Shallow Turbid Lake

Lake water quality monitoring has the potential to be improved through integrating detailed spatial information from new generation remote sensing satellites with high frequency observations from in situ optical sensors (WISPstation). We applied this approach for Lake Trasimeno with the aim of increasing knowledge of phytoplankton dynamics at different temporal and spatial scales. High frequency chlorophyll-a data from the WISPstation was modeled using non-parametric multiplicative regression. The ‘day of year’ was the most important factor, reflecting the seasonal progression of a phytoplankton bloom from July to September. In addition, weather factors such as the east–west wind component were also significant in predicting phytoplankton seasonal and diurnal patterns. Sentinel 3-OLCI and Sentinel 2-MSI satellites delivered 42 images in 2018 that successfully mapped the spatial and seasonal change in chlorophyll-a. The potential influence of localized inflows in contributing to increased chlorophyll-a in mid-summer was visualized. The satellite data also allowed an estimation of quality status at a much finer scale than traditional manual methods. Good correspondence was found with manually collected field data but more significantly, the greatly increased spatial and temporal resolution provided by satellite and WISPstation sensors clearly offers an unprecedented resource in the research and management of aquatic resources.