Abstract
The release of certain substances into surface waters (lakes, rivers, estuaries and oceans) to the point where they interfere with beneficial use of water or with natural functioning of ecosystems defines the phenomenon of pollution. When stating aspects of pollution modelling, we refer to constitutive equations of the model, which may involve different values, so that the shape of equation is flexible while maintaining its structure. Quantifying the phenomenon of water pollution through simulation and spatial-temporal modelling requires the use of hydrological models that use characteristic parameters such as: bathymetry, hydrodynamic roughness, speed, Model Boundary Conditions, etc. The current paper is driven by lack of clear performance guidelines for pollution models for software users trying to demonstrate to customers and/or end users that a model is fit for purpose. Thus, common problems associated with data availability, errors and uncertainty as well as model examination will be addressed, including its calibration and validation on a case study materialized on a watercourse located in the Jiu Valley, Romania. The scientific article is intended to be a point of reference both for software users (numerical modelers) and for specialists in charge of interpreting the accuracy and validity of results from hydrodynamic models.
Highlights
The important issues of river pollution and spread of pollutants must be addressed with the help of prediction tools to develop systems for assessing pollution, counteracting it and making the right management decisions
Fig. 6. 2D mesh configurated with the help of quadrilateral elements d) Input data used for the RMA2 subprogram For running, the RMA2 sub-routine needed the input data measured in situ for the watercourse under study (Table 3): Table 3
While these guidelines naturally remain open to challenges from software users who require more accurate model performance, the statistical processing of input data adapts the models used to a step closer to calibrating the defined model
Summary
The important issues of river pollution and spread of pollutants must be addressed with the help of prediction tools (models for pollutants transport) to develop systems for assessing pollution, counteracting it and making the right management decisions. Consultants and teachers use a wide variety of different modelling practices and frequently pay insufficient attention to potential errors associated with the measured (and modelled) data used to calibrate and validate the model This can lead to poor model performance and uncertain model predictions. The second step in the calibration process is to reduce uncertainty in model predictions It uses carefully selected values for the model input parameters and compares model predictions with observed data, for the same conditions. This is often done iteratively, without any fixed rules and is guided by user’s experience and knowledge of the processes to be modelled. Increasing complexity of model functionality and their use by end-users, requiring information on model accuracy to reduce risks, has led to an increasing need for guidelines regarding quantification and evaluation of model performance
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