Identifiability analysis enables one the quantification of the number of modelparameters that can be assessed by calibration with respect to a data set. Such amethodology is based on the appraisal of sensitivity coefficients of the modelparameters by means of Monte Carlo runs. By employing the Fisher InformationMatrix, the methodology is able to gain insights with respect to the number of modelparameters that can be reliably assessed. The paper presents a study whereidentifiability analysis is used as a tool for the setting up of measuring campaignsfor integrated water quality modelling. The analysis has been applied to a real casestudy characterized by a partially urbanized catchment represented by two sewersystems, two wastewater treatment plants and a river. Several scenarios ofmeasuring campaigns have been considered; each scenario was characterized bydifferent monitoring stations for the gathering of quantity and quality data. Theresults enabled us to assess the maximum number of model parameter quantifiablefor each scenario i.e. for each data set. The methodology resulted to be a powerfultool for designing measuring campaign for integrated water quality modelling.Indeed, the crucial cross sections throughout the integrated wastewater system weredetected optimizing both human and economic efforts in the gathering of field data.
|Numero di pagine||12|
|Stato di pubblicazione||Published - 2010|