# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Deterministic Models
Some uncertainties can be removed, such as epistemic uncertainty, which arises from missing or imperfect knowledge [609, 610, 611, 612]. Sources of uncertainty include: input data (from input data and external forces on the system), technical (related to codes, software constraints, etc. ), structural (related to the conceptual model and simplification of the system), parameters (related to parameter distribution, parameter estimation, and constraint variables), prediction uncertainty, and calibration data uncertainty [613, 614]. Uncertainty can be reduced by adding additional data on current and historical system behaviour (data assimilation, e.g., through high-resolution monitoring and hydro-geophysical screening [615]), by comparing different parameterisation methods and models, and by changing model resolution and other modelling measures based on experience. Statistical methods dedicated to the broad field of uncertainty analysis can be divided into six classes: Monte Carlo sampling, response surface-based methods including polynomial chaos expansion and machine learning, multi-model approaches, Bayesian statistics, multi-criteria analysis, and least-squares-based inverse modelling [614]. Methods successfully applied in water-related studies include generalised likelihood uncertainty estimation (GLUE) [616, 617], differential adaptive evolution (DREAM) [532, 618], parameter estimation code (PEST) [619, 620], the Bayesian approach including total error analysis (BATEA) [621, 622, 623, 624, 625] and multi-objective analysis [626, 627], machine learning methods [628], and the Null-Space Monte Carlo method (NSMC) [629, 630]. Some helpful guidelines for successful calibration and uncertainty analysis, even under the pressure of climate change, can be found in the work of Mai [631]. * Sensitivity analysis (SA) aims to examine how uncertainty in the results of a model or performance measure is distributed across different sources of uncertainty in the inputs [632, 633, 634, 635]. Some researchers define SA as drawing a conclusion when/where uncertainty has an impact [636]. Therefore, sensitivity analysis helps to focus efforts on the most critical parameters when collecting additional data or refining parameter estimates. When conducting a SA, the objective should be defined first. Depending on this, samples for selected parameters/factors (probabilistic approach) or discrete values for the entire parameter range (global methods) are selected. Local methods investigate the effects of variability of the input data around the nominal values, while global methods aim to investigate the uncertainty of the results caused by changes in the input data over the entire range [637]. The model results are evaluated by examining, for example, the variance or distribution of the model results, which should allow the identification of the parameters that influence the results the most. The uncertainty analysis is not part of the sensitivity analysis, but both should be performed in parallel, as they are essential parts of the model development. In the best case, the uncertainty analysis should precede the sensitivity analysis, as the uncertainties should be estimated before linking to input data and parameters [638, 639]. Reviews of SA methods in environmental and water modelling can be found in the following works: Saltelli et al. [640], Hall et al. [641], Perz et al. [642], Gao et al. [643], Pianosi et al. [644], Koo et al. [645], and Razavi et al. [646];
* Maintenance and preservation: Knowledge is a resource, so if we consider models as a source of knowledge, we should use and protect them carefully [647]. Once a model exists and has been validated and verified, it can be used in the future for different purposes and for conditions that are now unknown and go beyond those for which the model was developed. Therefore, it is important to maintain it until it can be replaced or rewritten [648, 649]. It should be noted that many of the models mentioned in this overview have been changed or overwritten over time.