Presenter/Author Information

A. Candela
G. Aronica
G. Viviani

Keywords

water quality modelling, non-point source pollution, uncertainty

Start Date

1-7-2004 12:00 AM

Description

Water quality impacts due to non-point source pollution can be significant particularly in environmentally sensitive areas. They may, however, be difficult to quantify, since the magnitude is heavily influenced by climatic, geomorphologic, lithologic and pedologic characteristics. A conceptual model for continuous daily simulation is proposed to reproduce the quali-quantitative response of a Sicilian catchment. Short-term water quality monitoring is necessary to assess the hydrological response of catchments characterised by hot dry summers and rainfalls with short duration and high intensity. The quantitative sub-model comprises two modules: a non linear loss model, to transform total rainfall in effective rainfall, which involves calculation of an index of catchment storage based upon a non-linear triggered exponentially decreasing weighting of precipitation and temperature; a linear convolution of effective rainfall with the total unit hydrograph with a configuration of one parallel channel and reservoir, corresponding to ‘quick’ and ‘slow’ components of runoff. The qualitative sub-model here presented deals with a conceptual form of the unit-mass response function of non-point source pollutants runoff. It connects flow discharges to concentrations of pollutants, as nitrates and orthophosphates by means of components of IUH (Instantaneous Unit Hydrograph) describing the quantitative response of the system. This paper explores how the limitations inherent in the modelling processes can be reflected in the estimation of predictive uncertainty. The Generalised Likelihood Uncertainty Estimation (GLUE) approach is used here in the estimation of predictive uncertainty of both, quantitative and qualitative, sub-models. With this methodology it is possible to make an assessment of the likelihood of a parameter set being an acceptable simulator of a system when model predictions are compared to measured field data.

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Jul 1st, 12:00 AM

Uncertainty in Quali-Quantitative Response of a Natural Catchment on a Daily Basis

Water quality impacts due to non-point source pollution can be significant particularly in environmentally sensitive areas. They may, however, be difficult to quantify, since the magnitude is heavily influenced by climatic, geomorphologic, lithologic and pedologic characteristics. A conceptual model for continuous daily simulation is proposed to reproduce the quali-quantitative response of a Sicilian catchment. Short-term water quality monitoring is necessary to assess the hydrological response of catchments characterised by hot dry summers and rainfalls with short duration and high intensity. The quantitative sub-model comprises two modules: a non linear loss model, to transform total rainfall in effective rainfall, which involves calculation of an index of catchment storage based upon a non-linear triggered exponentially decreasing weighting of precipitation and temperature; a linear convolution of effective rainfall with the total unit hydrograph with a configuration of one parallel channel and reservoir, corresponding to ‘quick’ and ‘slow’ components of runoff. The qualitative sub-model here presented deals with a conceptual form of the unit-mass response function of non-point source pollutants runoff. It connects flow discharges to concentrations of pollutants, as nitrates and orthophosphates by means of components of IUH (Instantaneous Unit Hydrograph) describing the quantitative response of the system. This paper explores how the limitations inherent in the modelling processes can be reflected in the estimation of predictive uncertainty. The Generalised Likelihood Uncertainty Estimation (GLUE) approach is used here in the estimation of predictive uncertainty of both, quantitative and qualitative, sub-models. With this methodology it is possible to make an assessment of the likelihood of a parameter set being an acceptable simulator of a system when model predictions are compared to measured field data.