Abstract
The reoccurrence of algal blooms in western Lake Erie (WLE) since the mid-1990s, under increased system stress from climate change and excessive nutrients, has shown the need for developing management tools to predict water quality. In this study, process-based model GLM-AED (General Lake Model-Aquatic Ecosystem Dynamics) and statistical model ANN (artificial neural network) were developed with meteorological forcing derived from surface buoys, airports, and land-based stations and historical monitoring nutrients, to predict water quality in WLE from 2002 to 2015. GLM-AED was calibrated with observed water temperature and chlorophyll a (Chl-a) from 2002 to 2015. For ANN, during the training period (2002–2010), the inputs included meteorological forcing and nutrient concentrations, and the target was Chl-a simulated by calibrated GLM-AED due to the lack of continuously daily measured Chl-a concentrations. During the testing period (2011–2015), the predicted Chl-a concentrations were compared with the observations. The results showed that the ANN model has higher accuracy with lower Chl-a RMSE and MAE values than GLM-AED during 2011 and 2015. Lastly, we applied the established ANN model to predict the future 10-year water quality of WLE, which showed that the probability of adverse health effects would be moderate, so more intense water resources management should be implemented.
Highlights
Eutrophication has been a serious global environmental problem in large lakes [1,2,3], including Lake Victoria in Africa, Lake Loosdrecht in Europe, and Lake Taihu in Asia
The results showed that the ANN model has higher accuracy with lower chlorophyll a (Chl-a) root mean square error (RMSE) and MAE values than GLM-AED during 2011 and 2015
With the implementation of phosphorus (P) abatement programs based on the Great Lakes Water Quality Agreement (GLWQA) in 1972 and the Lake Erie total phosphorus (TP) target load of 11,000 MTA in the 1978 Amendment to the GLWQA, P loads entering into Western Lake Erie (WLE) reduced, resulting in a decrease in algal blooms in WLE [12,13]
Summary
Eutrophication has been a serious global environmental problem in large lakes [1,2,3], including Lake Victoria in Africa, Lake Loosdrecht in Europe, and Lake Taihu in Asia. To investigate this water quality problem in these regions, different approaches have been applied including sampling field data, lab methods, and remote sensing [4,5,6]. There are two commonly applied approaches to predict water quality, namely, process-based [17] and data-driven [18] models. The artificial neural network method, one of the statistical methods, has been widely used in previous prediction cases [26,27], showing the capability of this method in analyzing biogeochemical monitoring data [28,29] and providing improved results compared to other modeling methods [30,31]
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