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Alexandria Lake Maryut: Integrated Environmental Management
Published in Brian D. Fath, Sven E. Jørgensen, Megan Cole, Managing Water Resources and Hydrological Systems, 2020
Using the data set out in Tables 1–5, a dynamic 0-dimension ecological model was built for the different basins of Lake Maryut. The software PCLAKE describes the dominant ecological interaction in a shallow lake ecosystem.[23] This model was chosen for its ability to model the complex processes and allow a number of different proposed actions to be modeled, including vegetation management on a seasonal basis, dredging, wetland installation, and improvements to discharges. It should be noted that the model assumes mixing across a basin, which, for Lake Maryut, is a simplification; however, the model is detailed enough to give an indication of the potential change caused by an action.
Phosphorus balance in a tropical shallow urban pond in Southeast Brazil: implications for eutrophication management
Published in Inland Waters, 2022
Marcela Miranda, Marcelo Manzi Marinho, Natália Noyma, Vera L. M. Huszar, Frank van Oosterhout, Miquel Lürling, Jean P. Ometto, Felipe S. Pacheco
PCLake is an ecosystem model developed for shallow lakes that simulates the dynamics of an aquatic food web that includes, among other variables, phytoplankton, macrophytes, and zooplankton. This model is commonly used to estimate critical nutrient loads at which a waterbody will transition between a stable state (e.g., clear and non-dominating phytoplankton) to another (e.g., turbid and abundant phytoplankton; Janse and Aldenberg 1990a, 1990b). PCLake simulates the influence of P on lakes based on water and sediment P, transparency, amount of aquatic vegetation, phytoplankton concentration, and residence time while taking into consideration soil type, size, and lake depth. PCLake was run to estimate the critical P load at which the pond shifts to a clear-water state, considering the current turbid pond state as the initial condition in the simulation (Supplemental Table S1). Estimates of annual average values for all parameters were used to train the model (Table 1). The parameters that define the physical characteristics of the pond were average depth of 1 m, no marsh area, fetch of 100 m, discharge based on the water balance, background light extinction = 0.5 m−1, and sand soil type with the parameter values proposed by Janse (2005). Because data to calibrate the model’s parameters to the Mapro Pond were lacking, the parameter values of the best run found by Janse et al. (2010) for shallow ponds were used (Supplemental Table S2). PCLake was used to simulate 21 internal P loading scenarios ranging from 8 to 20 mg m−2 d−1. For each simulation, the yearly averages of the total chlorophyll a concentration (Chl-a) after a period of 10 years from the beginning of the simulation were used for the analysis. Equilibrium situations for Chl-a and TP were observed in the simulations up to 7 years following the start of the simulation.