Mathematical Modeling of Trophic State and Nutrient Flows of Lake Karla using the PCLake Model

Abstract

In the present article, we simulate Lake Karla, an important natural ecosystem under restoration in Greece, operating also as a reservoir. The lake trophic state is characterized as hypertrophic with the expected negative effects on biodiversity. The simulation of Lake Karla is a significant tool in terms of understanding, predicting and managing the ecosystem. We perform simulations using PCLake, a software package for shallow lakes, which provides a full set of parameters, modeling a wide range of physical, chemical and biological variables. The model is calibrated based on the time series of six variables for year 2012 and validated using data of years 2014-2015. We present the nutrient flow dynamics for year 2012 on a trimester basis, and investigate the interrelations of nutrient cycling and trophic state, through observed variables such as phycocyanin and chlorophyll-a concentrations, Carlson Trophic State Index and ratio of Dissolved Inorganic Nitrogen to Soluble Reactive Phosphorus.

Machine Learning Approaches for Predicting Health Risk of Cyanobacterial Blooms in Northern European Lakes

“…In our case, Chl-a, TN, TP along with MeanATemp were used to construct models under various scenarios that explore all possibilities, with different combinations of direct and indirect effects of the predictor variables on CBB. All variables were chosen as the strongest predictors resulting from the literature [15], but also from the stepwise multiple regression performed. Validation of the produced models was carried out by examining the z-values and Pr (>|z|); the former is computed as the test statistic for the hypothesis test that the true corresponding regression coefficient β of variable X (Chl-a, TN, TP and MeanATemp) in predicting variable Y (CBB) is 0.…”

“…Examples of such activities include changes in hydrological flow pathways, excessive use of fertilizers and the gradual removal of natural buffering zones between terrestrial and freshwater ecosystems [10]. On the contrary, there are some anthropogenic changes, such as flooding and flushing, that tend to reduce the growth of cyanobacteria more than other algae [11].Empirical modeling has recognized the fundamental effect of phosphorus and nitrogen on the fluctuation of cyanobacterial biomass, incriminating over-enrichment of lakes with nutrients as a major driver of cyanobacterial blooms [12][13][14][15][16]. In addition, high air or water temperature [11,17], calm weather (low wind speed) [18], high water residence time [19,20], low nitrogen-to-phosphorus ratios [21,22] and low light availability [23,24] are documented as significant factors and possible predictors that determine the dominance of cyanobacteria.…”

Use of Optimally Pruned Extreme Learning Machine (OP-ELM) in Forecasting Dissolved Oxygen Concentration (DO) Several Hours in Advance: a Case Study from the Klamath River, Oregon, USA

“…DO is used to assess the trophic state of rivers, canals and lakes (Gikas 2014;Chamoglou et al 2014;Mellios et al 2015). During the last decade, much effort has been devoted to the modelling of DO in river, lake and stream ecosystems using artificial intelligence (AI) techniques, and much work has been done on this subject.…”

Editorial

“…They emphasize the necessity that the critical relationships concerning the ecosystem, the perception of the landscape and the socio-economic spatial values should be identified and marked. Mellios et al (2015) present mathematical modeling of the trophic state and nutrient flows of a lake using the PCLake model. Nutrient flow dynamics were simulated for the year 2012 on a trimester basis in Lake Karla, Greece, which is an important natural ecosystem under restoration, also operating as a reservoir.…”