Role of modeling uncertainty in the estimation of climate and socioeconomic impact on river water quality. J. of Water Resources Planning and Management – Asce, 138(5), 479–490.

Climate is one of the most important factors leading to changes in the hydrologic and environmental characteristics of river basins. When considering water quality, the natural factors should be weighed against anthropogenic factors (such as urbanization, increased population, and higher water demand) that may increase or decrease the effect of climatic modifications. Any prediction of future climatic and anthropogenic scenarios is affected by uncertainty and the modelling tools that are used for evaluating their impact on receiving water bodies. The present paper is aimed at investigating the complexity of such analyses and the uncertainty related to future impact predictions based on limited databases. Specifically, the study is focused on quantifying the impact of urbanization and social changes coupled with climate on river water quality under uncertainty. The study was carried out at a small catchment scale (less than 100 km2), an analysis that has been lacking from the field so far. The analysis took into account both anthropogenic change and climate determined from observed data. Because of inherent uncertainty in the model caused by the complexity of the model and its calibration, the analysis was performed considering the effect of such uncertainty on reliability by means of Monte Carlo simulations. This analysis has been applied to a monitored semiurbanized basin in Sicily (Italy). The study showed that the proposed analyses can useful for planners and urban managers; however, the uncertainty connected with mathematical modeling and small available databases has to be taken into account because it can affect decision making. The impacts of anthropogenic changes and climate can interact, thus requiring that such factors be jointly analyzed. A larger effort in monitoring and characterization of natural and urban water systems is needed to increase the reliability of models and to improve the effectiveness of decision making based on such models