| Authors |
Methods |
Advantages |
Disadvantages/Limitations |
| Anderson et al. (1998) |
Use of a Hybridized Monte Carlo finite segment model to predict the pathogen concentration in the Eastside Reservoir, considering the spatial and temporal variability. |
Assessment of the recreational activities impacts on water quality; simulation of pathogen concentration; prediction of annual mean values, considering acceptable levels and treatment practices; comparison of the results with available sampling data; peak events identification. |
Requirement of sampling data to identify peak events; lack of mechanistic information; simplification of assumptions. |
| Westphal et al. (2004) |
Quasi-mechanistic approach that resulted in a mass balance model with equations to simulate TOC. |
Use of historical input series for calibration; simulation of mechanistic elements (e.g. diffusion); the predictive strength can be improved using updated data; credibility to be extended into long planning and operational strategies; characterization of thermal/seasonal structure. |
Calibration demands large amounts of data to improve the accuracy; simplification of elements; underprediction/overprediction due to TOC sources; eventual multivariate dependencies in the variables. |
| Thibodeaux and Aguilar (2005) |
Quantification of the DOC in the bed and water column based on a mathematical model, using differential equations, algebraic expressions and transport kinetics in laboratory conditions. |
It was possible to obtain an algorithm that predicts the DOC in a hypothetical reservoir. The steps led to the understanding of DOC release, considering basic mechanisms and resulting in the generalization of results. |
Assumptions related to temperature are necessary. Also, uncertain intervals need to be considered. Sufficient data are necessary for the modeling. Differential equations are necessary to describe microbial processes. |
| Cunha-Santino et al. (2013) |
The CPOM mineralization was described by equations and non-linear regression with an iterative algorithm (Levenberg-Marquardt). The oxygen consumption was described using a first-order kinetics model. |
The mathematical model of decomposition kinetics allowed us to verify the half-life of CPOM and the effects on water quality from short to long-term, including oxygen consumption and implications to eutrophication. |
Not clear in the text. |
| Zhou et al. (2016) |
Use of Dyna-CLUE model, grey relational analysis (GRA), and grey model (GM) to simulate P levels |
Prediction of P concentration changes associated with land use; identification of the ecological and environmental effects of changes in land use. Less data requirement. |
There is a need to adapt the modeling approach for other watersheds. |
| Harris and Graham (2017) |
Use of training functions and random data to verify the temporal variation patterns and the abundance of cyanobacteria. The predictive model included (non)linear regression and five repetitions. |
Prediction of cyanobacterial, geosmin, and microcystin abundance. It was possible to verify important predictor variables. Also, the model demanded fewer explanatory variables. Improvements are possible adjusting the models. |
The cubist model was more robust in the cases of larger cyanobacterial abundances or geosmin concentrations. The maxima concentration was not predicted due to seasonal changes (environmental variation was not captured). The models do not distinguish inter-annual/intra-annual differences. Long-term data are necessary. |
| Xu et al. (2017) |
A 3D-emerging model (Delft3D-FLOW/Delft3D-WAQ) was used, the numerical hydrodynamic simulates some parameters (transport, flow, water temperature) and counts with an orthogonal curvilinear coordinate |
The modeling was capable of describing the patterns of water temperature, salinity and emerging contaminants over time and space. It was possible to verify the transport and biodegradation of the contaminants, insight into risk of contaminants and base for decision-making. Observational data was employed to adjust the model. |
The complications of the water quality model demand different criteria of goodness-of-fit. May high input data and long intervals ignore important variations. Low observed that it can generate uncertainty in prediction. |
| Crespo et al. (2018) |
A mathematical model was performed considering numerical simulations and the problem optimization, based on an algorithm and refiling process. |
Optimal strategies were obtained for refilling water, and optimal locations were identified ensuring water quality. The generic pollutant distribution associated with refilling location was obtained, as well as prospective refilling. |
The model assumes that water volume is constant, the pollutant remains at the water surface, and its distribution is influenced by wind and water currents. |
| Siniscalchi et al. (2018) |
Differential algebraic equations (Kinect, mass balance, evaporation) represented the ecohydrological models. A dataset (10 years) was used for calibration. |
Optimal control problems were possible; the modeling addressed salinity and flooding issues. Also, eutrophication problems were considered, as well as restoration profiles, biomanipulation processes, and useful elements for decision-making were generated. |
The entire model has 110 algebraic equations, as well as 42 differential equations. |
| Bianchini Jr. et al. (2019) |
Description of limnological mass balance from, using two equations. |
The model allowed us to verify the retention capacity of the reservoir, showing the numbers of retentions and inferences about the physical processes, using an alpha parameter. It is a feasible approach in water monitoring. |
The model had some premises: the reservoir is a completely mixed system; the system can be represented by a zero-dimensional model and is in a steady state. |
| Chen et al. (2019) |
A three-dimensional ecological model was employed to simulate the carbon dynamics, the climate conditions, nutrients scenarios, algal blooms, and the systematic carbon transformations. |
Scenarios were evaluated, considering climate and nutrients, including the systematic carbon transformations. The model allowed us to verify changes in the trophic state associated with CO2 and water volume. Simulations (algal blooms, carbon dynamics) were possible. The model can be coupled with other models (e.g. watersheds). |
A negative bias of surface CO2was verified; but the model was validated. Simulated CO2concentrations were obtained using derived data from semiempirical equations, so uncertain and potential sources of error can be verified. The atmospheric contribution was not considered. The parameters in the Wanninkhof equation have 20% of uncertainty. |
| Absalon et al. (2020) |
The Aquatic Ecosystem 3D Model (AEM3D) was used to assess variability in water quality and flow, considering many parameters (water flow, retention time, transport, temperature, plankton, and nutrients concentration). |
Impacts on water quality, flow and limnological variables over time were verified based on the model, including scenarios formulation related to pollutants, climate change and algal bloom. Actions formulation was possible. Also, the main source of problems was identified. |
The hydrodynamics and thermodynamics of the reservoir were represented by > 100 equations that represent the system processes. No sufficient available data for 2016 compromised the parameterization of water quality and inflows. |
| Siniscalchi et al. (2020) |
It involves an integration of mechanistic models and partial differential-algebraic equations. The analysis considered water variables, mass balance of biogeochemical variables (including taxonomic groups), inflows and outflows. |
The approach provided temporal profiles for biogeochemical variables, contributing to planning and restoration measures. Experimental data calibrated the model. Furthermore, optimal control and problem design were considered. The study showed as advantage the information on gradients and the variables optimization. |
Not clear in the text. |
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