Complexity Reduction in Environmental Models Using Cascading Simulation Framework

Environmental decision making requires data about current status of the environment andpossible environmental states after anthropogenic impact. Environmental models are aimedto produce the results which complement observations on environmental indicators whenthe latter cannot be obtained directly, e.g. missing observations or predictions. One of theapproaches to environmental modeling rests on process-based models. Environmentalindicators relevant to an investigated case study are selected and their spatial and/ortemporal dynamics is imitated by describing natural processes affecting the indicatorsbased on mathematical formulae. The set of indicators determines the number of modelstate variables and processes which must be taken into account. The processes whichsignificantly contribute to indicators’ variability are included into a model using balanceequations. It may call for additional processes to be added to the model and each process tobe described by a particular mathematical term. Alternatively, several natural processes canbe described by one aggregated term.