Ecological Study and Restoration Planning in Eutrophic Lakes

DIAZ, M. S.; DI MAGGIO, J.; SINISCALCHI, A.; ESTRADA, V.; FRITZ, L.; GROSMAN, F.; SANZANO, P.; COLASURDO, V.; GUERRERO, J. M.; BAGLIVI, J. C.; CRISAFULLI, M.; POLONIOLI, M.; PETIGROSSO, R. ; HOFFMEYER, M.

Congreso; 10th World Congress of Chemical Engineering; 2017

Cyanobacterial algal blooms are mainly associated to anthropogenic activities that include nitrogen and phosphorus loading to lakes from point and nonpoint sources, such as industrial and domestic wastewater and agricultural activities. Bloom conditions prediction and control methods constitute a priority in freshwater sources. While mechanistic models are powerful tools to describe freshwater ecosystems and to evaluate and plan water restoration strategies, they must be calibrated with field data from the lake under study. In previouswork, Estrada et al. (2011, 2015) propose a mechanistic ecological model that includes dynamic mass balances for phytoplankton groups; zooplankton groups and local zooplanktivorous fish, as well as dissolved oxygen and main nutrients. Algebraic equations stand for forcing functions profiles, such as temperature, solar radiation, river inflows and concentrations. In this work, we address monitoring, parameter estimation, validation and restoration planning for Paso de las Piedras Reservoir, a non-stratified lake which is the drinking water source for two cities in Argentina. Field data were collected during the period 2014-2016 and include nutrient, phytoplankton; dissolved oxygen, macro and microzooplankton, as well as both zooplanktivorous and piscivorous fish biomass concentrations. Phytoplankton and zooplankton data were collected weekly and twice a month, respectively, while fish data are collected bimonthly. The study of the phytoplankton community allows the identification of 189 taxa, three of six groups were determined as the most important in terms of number of species and biomass. Zooplankton is represented by cladocera and copepoda groups. In the case of fish community, experimental data show that it is represented by ten taxa, from which the most representative in terms of biomass are Odontesthes bonariensis (zooplanktivorous) and Oligosarcus jenynsi (piscivorous).Based on this ecological study, we extend the model with dynamic mass balances for Oligosarcus jenynsi, which is a predator of Odontesthes bonariensis. This is the first time an integral monitoring project is carried out, including fish and zooplankton population. The inclusion of main fish dynamics is relevant to evaluate the application of biomanipulation, a well-known restoration strategy (Søndergaard et al., 2007). This technique can be complemented with bottom-up strategies like wetland construction. Collected data during 2015 have been used to calibrate the ecological model and data from 2016 have been used for validation. Both parameter estimation and dynamic optimization problems have been formulated within an equation oriented optimization framework in gPROMS (PSEnterprise, 2015). The re-calibrated model provides a reliable tool for restoration planning.ReferencesDi Maggio, J., Estrada V., M.S. Diaz, Water Resources Management with Dynamic Optimization Strategies and Integrated Models of Lakes and Artificial Wetlands, PSE2015 ? ESCAPE25, 12th International Symposium on Process Systems Engineering and 25th European Symposium on Computer Aided Process Engineering , 31 May - =4 June 2015, Copenhagen, DenmarkEstrada V., J. Di Maggio, M.S. Diaz (2011), Water Sustainability: A Process Systems Engineering approach to the restoration of eutrophic lakes, Computers and Chemical Engineering, 35, 8, 1598-1613Jeppesen, E., Søndergaard, M., Lauridsen, T., Davidson, T., Liu, Z., Mazzeo, N., Trochine, C., Özkan, K., Jensen, H., Tolle, D., Starling, F, Lazzaro, X., Johansson, L., Bjerring, R., Liboriussen, L., Larsen, S.,Landkildehus, F., Egemose, S. & Meerhoff, M. (2012). Biomanipulation as a Restoration Tool to Combat Eutrophication: Recent Advances and Future Challenges. Advances in Ecological Research, 47.Paerl, H. and Otten, T. (2013) Harmful Cyanobacterial Blooms: Causes, Consequences, and Controls. Environmental microbiology, 65, 995-1010.Process Systems Enterprise (2013), gPROMS, www.psenterprise.com/gpromsSøndergaard, M., Jeppesen, E., Lauridsen, T. L., Skov, C., van Nes, E. H., Roijackers, R., Lammens, E. & Portielje, R. (2007). Lake restoration: successes, failures and long-term effects. Journal of Applied Ecology,44, 1095?1105.