Bayesian inference analysis of the uncertainty linked to the evaluation of potential flood damage in urban areas.

Flood damage in urbanized watersheds may be assessed by combining the flood depth-damage curves and the outputs of urban flood models. The complexity of the physical processes that must be simulated and the limited amount of data available for model calibration may lead to high uncertainty in the model results and consequently in damage estimation. Moreover depth-damage functions are usually affected by significant uncertainty related to the collected data and to the simplified structure of the regression law that is used. The present paper carries out the analysis of the uncertainty connected to the flood damage estimate obtained combining the use of hydraulic models and depth-damage curves. A Bayesian inference analysis was proposed along with a probabilistic approach for the parameters estimating. The analysis demonstrated that the Bayesian approach is very effective considering that the available databases are usually short.

Uncertainty evaluation of design rainfall for urban flood risk analysis.

A reliable and long dataset describing urban flood locations, volumes and depths would be an ideal prerequisite for assessing flood frequency distributions. However, data are often piecemeal and long-term hydraulic modelling is often adopted to estimate floods from historical rainfall series. Long-term modelling approaches are time- and resource-consuming, and synthetically designed rainfalls are often used to estimate flood frequencies. The present paper aims to assess the uncertainty of such an approach and for suggesting improvements in the definition of synthetic rainfall data for flooding frequency analysis. According to this aim, a multivariate statistical analysis based on a copula method was applied to rainfall features (total depth, duration and maximum intensity) to generate synthetic rainfalls that are more consistent with historical events. The procedure was applied to a real case study, and the results were compared with those obtained by simulating other typical synthetic rainfall events linked to intensity-duration-frequency (IDF) curves. The copula-based multi-variate analysis is more robust and adapts well to experimental flood locations even if it is more complex and time-consuming. This study demonstrates that statistical correlations amongst rainfall frequency, duration, volume and peak intensity can partially explain the weak reliability of flood-frequency analyses based on synthetic rainfall events.

Evaluation of the apparent losses caused by water meter under-registration in intermittent water supply.

Apparent losses are usually caused by water theft, billing errors, or revenue meter under-registration. While the first two causes are directly related to water utility management and may be reduced by improving company procedures, water meter inaccuracies are considered to be the most significant and hardest to quantify. Water meter errors are amplified in networks subjected to water scarcity, where users adopt private storage tanks to cope with the intermittent water supply. The aim of this paper is to analyse the role of two variables influencing the apparent losses: water meter age and the private storage tank effect on meter performance. The study was carried out in Palermo (Italy). The impact of water meter ageing was evaluated in laboratory by testing 180 revenue meters, ranging from 0 to 45 years in age. The effects of the private water tanks were determined via field monitoring of real users and a mathematical model. This study demonstrates that the impact on apparent losses from the meter starting flow rapidly increases with meter age. Private water tanks, usually fed by a float valve, overstate meter under-registration, producing additional apparent losses between 15% and 40% for the users analysed in this study.