YoonKyung Cha

Long-term and seasonal trend decomposition of Maumee River nutrient inputs to western Lake Erie.

Cyanobacterial blooms in western Lake Erie have recently garnered widespread attention. Current evidence indicates that a major source of the nutrients that fuel these blooms is the Maumee River. We applied a seasonal trend decomposition technique to examine long-term and seasonal changes in Maumee River discharge and nutrient concentrations and loads. Our results indicate similar long-term increases in both regional precipitation and Maumee River discharge (1975-2013), although changes in the seasonal cycles are less pronounced. Total and dissolved phosphorus concentrations declined from the 1970s into the 1990s; since then, total phosphorus concentrations have been relatively stable, while dissolved phosphorus concentrations have increased. However, both total and dissolved phosphorus loads have increased since the 1990s because of the Maumee River discharge increases. Total nitrogen and nitrate concentrations and loads exhibited patterns that were almost the reverse of those of phosphorus, with increases into the 1990s and decreases since then. Seasonal changes in concentrations and loads were also apparent with increases since approximately 1990 in March phosphorus concentrations and loads. These documented changes in phosphorus, nitrogen, and suspended solids likely reflect changing land-use practices. Knowledge of these patterns should facilitate efforts to better manage ongoing eutrophication problems in western Lake Erie.

An evaluation of automated structure learning with Bayesian networks: An application to estuarine chlorophyll dynamics

We develop a Bayesian network (BN) model that describes estuarine chlorophyll dynamics in the upper section of the Neuse River Estuary in North Carolina, using automated constraint based structure learning algorithms. We examine the functionality and usefulness of the structure learning algorithms in building model topology with real-time data under different scenarios. Generated BN models are evaluated and a final model is selected. Model results indicate that although the effect of water temperature and river flow on chlorophyll dynamics has remained unchanged following the implementation of the nitrogen Total Maximum Daily Load (TMDL) program; the response of chlorophyll levels to nutrient concentrations has been altered. The results stress the importance of incorporating expert defined constraints and links in conjunction with the automated structure learning algorithms to generate more plausible structures and minimize the sensitivity of the learning algorithms. This hybrid approach towards structure learning allows for the incorporation of existing knowledge while limiting the scope of the learning algorithms to defining the links between environmental variables for which the expert has little or no information.

Do Invasive Mussels Restrict Offshore Phosphorus Transport in Lake Huron

Dreissenid mussels were first documented in the Laurentian Great Lakes in the late 1980s. Zebra mussels (Dreissena polymorpha) spread quickly into shallow, hard-substrate areas; quagga mussels (Dreissena rostriformis bugensis) spread more slowly and are currently colonizing deep, offshore areas. These mussels occur at high densities, filter large water volumes while feeding on suspended materials, and deposit particulate waste on the lake bottom. This filtering activity has been hypothesized to sequester tributary phosphorus in nearshore regions reducing offshore primary productivity. We used a mass balance model to estimate the phosphorus sedimentation rate in Saginaw Bay, a shallow embayment of Lake Huron, before and after the mussel invasion. Our results indicate that the proportion of tributary phosphorus retained in Saginaw Bay increased from approximately 46-70% when dreissenids appeared, reducing phosphorus export to the main body of Lake Huron. The combined effects of increased phosphorus retention and decreased phosphorus loading have caused an approximate 60% decrease in phosphorus export from Saginaw Bay to Lake Huron. Our results support the hypothesis that the ongoing decline of preyfish and secondary producers including diporeia (Diporeia spp.) in Lake Huron is a bottom-up phenomenon associated with decreased phosphorus availability in the offshore to support primary production.

Phosphorus load estimation in the Saginaw River, MI using a Bayesian hierarchical/multilevel model.

We propose the use of Bayesian hierarchical/multilevel ratio approach to estimate the annual riverine phosphorus loads in the Saginaw River, Michigan, from 1968 to 2008. The ratio estimator is known to be an unbiased, precise approach for differing flow-concentration relationships and sampling schemes. A Bayesian model can explicitly address the uncertainty in prediction by using a posterior predictive distribution, while in comparison, a Bayesian hierarchical technique can overcome the limitation of interpreting the estimated annual loads inferred from small sample sizes by borrowing strength from the underlying population shared by the years of interest. Thus, by combining the ratio estimator with the Bayesian hierarchical modeling framework, long-term loads estimation can be addressed with explicit quantification of uncertainty. Our study results indicate a slight decrease in total phosphorus load early in the series. The estimated ratio parameter, which can be interpreted as flow-weighted concentration, shows a clearer decrease, damping the noise that yearly flow variation adds to the load. Despite the reductions, it is not likely that Saginaw Bay meets with its target phosphorus load, 440 tonnes/yr. Throughout the decades, the probabilities of the Saginaw Bay not complying with the target load are estimated as 1.00, 0.50, 0.57 and 0.36 in 1977, 1987, 1997, and 2007, respectively. We show that the Bayesian hierarchical model results in reasonable goodness-of-fits to the observations whether or not individual loads are aggregated. Also, this modeling approach can substantially reduce uncertainties associated with small sample sizes both in the estimated parameters and loads.

Mining web-based data to assess public response to environmental events

We explore how the analysis of web-based data, such as Twitter and Google Trends, can be used to assess the social relevance of an environmental accident. The concept and methods are applied in the shutdown of drinking water supply at the city of Toledo, Ohio, USA. Toledos notice, which persisted from August 1 to 4, 2014, is a high-profile event that directly influenced approximately half a million people and received wide recognition. The notice was given when excessive levels of microcystin, a byproduct of cyanobacteria blooms, were discovered at the drinking water treatment plant on Lake Erie. Twitter mining results illustrated an instant response to the Toledo incident, the associated collective knowledge, and public perception. The results from Google Trends, on the other hand, revealed how the Toledo event raised public attention on the associated environmental issue, harmful algal blooms, in a long-term context. Thus, when jointly applied, Twitter and Google Trend analysis results offer complementary perspectives. Web content aggregated through mining approaches provides a social standpoint, such as public perception and interest, and offers context for establishing and evaluating environmental management policies.

Probabilistic prediction of cyanobacteria abundance in a Korean reservoir using a Bayesian Poisson model

There have been increasing reports of harmful algal blooms (HABs) worldwide. However, the factors that influence cyanobacteria dominance and HAB formation can be site-specific and idiosyncratic, making prediction challenging. The drivers of cyanobacteria blooms in Lake Paldang, South Korea, the summer climate of which is strongly affected by the East Asian monsoon, may differ from those in well-studied North American lakes. Using the observational data sampled during the growing season in 2007–2011, a Bayesian hurdle Poisson model was developed to predict cyanobacteria abundance in the lake. The model allowed cyanobacteria absence (zero count) and nonzero cyanobacteria counts to be modeled as functions of different environmental factors. The model predictions demonstrated that the principal factor that determines the success of cyanobacteria was temperature. Combined with high temperature, increased residence time indicated by low outflow rates appeared to increase the probability of cyanobacteria occurrence. A stable water column, represented by low suspended solids, and high temperature were the requirements for high abundance of cyanobacteria. Our model results had management implications; the model can be used to forecast cyanobacteria watch or alert levels probabilistically and develop mitigation strategies of cyanobacteria blooms.

Are chlorophyll a-total phosphorus correlations useful for inference and prediction?

Correlations between chlorophyll a and total phosphorus in freshwater ecosystems were first documented in the 1960s and have been used since then to infer phosphorus limitation, build simple models, and develop management targets. Often these correlations are considered indicative of a cause-effect relationship. However, many scientists regard the use of these associations for modeling and inference to be misleading due to their potentially spurious nature. Using data from Saginaw Bay, Lake Huron, we examine the relationship among chlorophyll a, total phosphorus, and algal biomass measurements. We apply graphical models and recently developed structure learning principles that use conditional dependencies to help identify causal relationships among observational data. The spurious relationship suspected by some is not supported by our data, whereas a direct relationship between chlorophyll a and total phosphorus is always supported, and an additional indirect relationship with an algal biomass intermediary is plausible under some circumstances. Thus, we conclude that these correlations are useful for simple model building but encourage the use of modern statistical methods to avoid common model-assumption violations.

Implications of Stein’s Paradox for Environmental Standard Compliance Assessment

The implications of Steins paradox stirred considerable debate in statistical circles when the concept was first introduced in the 1950s. The paradox arises when we are interested in estimating the means of several variables simultaneously. In this situation, the best estimator for an individual mean, the sample average, is no longer the best. Rather, a shrinkage estimator, which shrinks individual sample averages toward the overall average is shown to have improved overall accuracy. Although controversial at the time, the concept of shrinking toward overall average is now widely accepted as a good practice for improving statistical stability and reducing error, not only in simple estimation problems, but also in complicated modeling problems. However, the utility of Steins insights are not widely recognized in the environmental management community, where mean pollutant concentrations of multiple waters are routinely estimated for management decision-making. In this essay, we introduce Steins paradox and its modern generalization, the Bayesian hierarchical model, in the context of environmental standard compliance assessment. Using simulated data and nutrient monitoring data from wadeable streams around the Great Lakes, we show that a Bayesian hierarchical model can improve overall estimation accuracy, thereby improving our confidence in the assessment results, especially for standard compliance assessment of waters with small sample sizes.

A Bayesian network incorporating observation error to predict phosphorus and chlorophyll a in Saginaw Bay

Empirical relationships between lake chlorophyll a and total phosphorus concentrations are widely used to develop predictive models. These models are often estimated using sample averages as implicit surrogates for unknown lake-wide means, a practice than can result in biased parameter estimation and inaccurate predictive uncertainty. We develop a Bayesian network model based on empirical chlorophyll-phosphorus relationships for Saginaw Bay, an embayment on Lake Huron. The model treats the means as unknown parameters, and includes structure to accommodate the observation error associated with estimating those means. Compared with results from an analogous simple model using sample averages, the observation error model has a lower predictive uncertainty and predicts lower chlorophyll and phosphorus concentrations under contemporary lake conditions. These models will be useful to guide pending decision-making pursuant to the 2012 Great Lakes Water Quality Agreement.

A Bayesian hierarchical approach to model seasonal algal variability along an upstream to downstream river gradient

Modeling to accurately predict river phytoplankton distribution and abundance is important in water quality and resource management. Nevertheless, the complex nature of eutrophication processes in highly connected river systems makes the task challenging. To model dynamics of river phytoplankton, represented by chlorophyll a (Chl a) concentration, we propose a Bayesian hierarchical model that explicitly accommodates seasonality and upstream-downstream spatial gradient in the structure. The utility of our model is demonstrated with an application to the Nakdong River (South Korea), which is a eutrophic, intensively regulated river, but functions as an irreplaceable water source for more than 13 million people. Chl a is modeled with two manageable factors, river flow, and total phosphorus (TP) concentration. Our model results highlight the importance of taking seasonal and spatial context into account when describing flow regimes and phosphorus delivery in rivers. A contrasting positive Chl a-flow relationship across stations versus negative Chl a-flow slopes that arose when Chl a was modeled on a station-month basis is an illustration of Simpsons paradox, which necessitates modeling Chl a-flow relationships decomposed into seasonal and spatial components. Similar Chl a-TP slopes among stations and months suggest that, with the flow effect removed, positive TP effects on Chl a are uniform regardless of the season and station in the river. Our model prediction successfully captured the shift in the spatial and monthly patterns of Chl a.