Vincenza Notaro

Uncertainty in urban flood damage assessment due to urban drainage modelling and depth-damage curve estimation.

Due to the increased occurrence of flooding events in urban areas, many procedures for flood damage quantification have been defined in recent decades. The lack of large databases in most cases is overcome by combining the output of urban drainage models and damage curves linking flooding to expected damage. The application of advanced hydraulic models as diagnostic, design and decision-making support tools has become a standard practice in hydraulic research and application. Flooding damage functions are usually evaluated by a priori estimation of potential damage (based on the value of exposed goods) or by interpolating real damage data (recorded during historical flooding events). Hydraulic models have undergone continuous advancements, pushed forward by increasing computer capacity. The details of the flooding propagation process on the surface and the details of the interconnections between underground and surface drainage systems have been studied extensively in recent years, resulting in progressively more reliable models. The same level of was advancement has not been reached with regard to damage curves, for which improvements are highly connected to data availability; this remains the main bottleneck in the expected flooding damage estimation. Such functions are usually affected by significant uncertainty intrinsically related to the collected data and to the simplified structure of the adopted functional relationships. The present paper aimed to evaluate this uncertainty by comparing the intrinsic uncertainty connected to the construction of the damage-depth function to the hydraulic model uncertainty. In this way, the paper sought to evaluate the role of hydraulic model detail level in the wider context of flood damage estimation. This paper demonstrated that the use of detailed hydraulic models might not be justified because of the higher computational cost and the significant uncertainty in damage estimation curves. This uncertainty occurs mainly because a large part of the total uncertainty is dependent on depth-damage curves. Improving the estimation of these curves may provide better results in term of uncertainty reduction than the adoption of detailed hydraulic models.

A model of the filling process of an intermittent distribution network

In many countries, private tanks are acquired by users to reduce their vulnerability to intermittent supply. The presence of these local reservoirs modifies the user demand pattern and usually increases user water demand at the beginning of the service period depending on the tank filling process. This practice is thus responsible for the inequality that occurs among users: those located in advantaged positions of the network are able to obtain water resources soon after the service period begins, while disadvantaged users have to wait much longer, after the network is full. This dynamic process requires the development of ad hoc models in order to obtain reliable results. This paper discusses a numerical model used for evaluating this complex process as well as the application of model to an Italian case study. The model agreed with calibration data and provided interesting insights into the network filling process.

Impact of rainfall data resolution in time and space on the urban flooding evaluation

Climate change and modification of the urban environment increase the frequency and the negative effects of flooding, increasing the interest of researchers and practitioners in this topic. Usually, flood frequency analysis in urban areas is indirectly carried out by adopting advanced hydraulic models to simulate long historical rainfall series or design storms. However, their results are affected by a level of uncertainty which has been extensively investigated in recent years. A major source of uncertainty inherent to hydraulic model results is linked to the imperfect knowledge of the rainfall input data both in time and space. Several studies show that hydrological modelling in urban areas requires rainfall data with fine resolution in time and space. The present paper analyses the effect of rainfall knowledge on urban flood modelling results. A mathematical model of urban flooding propagation was applied to a real case study and the maximum efficiency conditions for the model and the uncertainty affecting the results were evaluated by means of generalised likelihood uncertainty estimation (GLUE) analysis. The added value provided by the adoption of finer temporal and spatial resolution of the rainfall was assessed.

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.

The apparent losses due to metering errors: a proactive approach to predict losses and schedule maintenance

The effects of water meter age and private tanks on the apparent losses due to metering errors were evaluated by experimental and theoretical analyses. A monitoring campaign on a small district metered area (DMA) was carried out to determine the causes of apparent losses and implement a numerical model. Metering errors are affected by the flow rate passing through the meter, which is dependent on the network pressure and water level of the private tank. A node model that reproduces the effect of private tanks was coupled with EPANET and was applied to the DMA. The proposed modelling approach was used to identify where apparent losses are higher and to schedule maintenance. The model predicted the results of the installation of a device that minimises the effect of private tanks on apparent losses, the unmeasured flow reducer (UFR), the economic impact of losses and substitution programmes.

Identification of the best flood retrofitting scenario in an urban watershed by means of a Bayesian Decision Network

Urban resilience to floods can be defined as a city’s capacity to avoid damage through the implementation of structural and non-structural measures, to reduce damage in the case of a flood that exceeds a desired threshold, to recover quickly to the same or an equivalent state, and to adapt to an uncertain future. To build flood resilience, planners need to identify and analyse risk, to understand the impacts of flooding, and how they cope with these impacts by means of innovative and adaptable strategies and measures. The number of possible retrofitting scenarios to cope with flooding problems in an urban watershed could be greatly increased by the combination of several stormwater management practices. Therefore, the present study aims to develop an expert system in the form of a Bayesian Decision Network (BDN) able to evaluate the efficiency of some possible urban flood retrofitting scenario by examining all significant water management variables and their inherent uncertainty. The methodology was applied to an urbanized area of the city of Palermo (Italy).

Urban Drainage And Sustainable Cities:How To Achieve Flood Resilient Societies?

This paper tries to describe the main developments of urban flood forecasting and modelling. Currently, several new technologies are available for flood monitoring, modelling and mitigation and several paradigms suggest the adoption of greener approaches to urban storm water management. These tools and new approaches can be easily adaptable to new developments where the entire urban drainage system can be suited to follow a more sustainable way to drain storm water. The challenge for the future is instead aimed to apply this new philosophy to existing urban areas where the application of new tools and technologies requires high costs and such approaches have to be prepared by constructing a flood resilient society by means of education and capillary information.

Flood frequency analysis for an urban watershed: comparison between several statistical methodologies simulating synthetic rainfall events

To obtain the flooding frequency distribution for an urban watershed, different methods based on simulations of synthetic rainfall events were compared with an empirical analysis of the flooding data and with the results of long-term simulations. A copula-based multivariate statistical analysis of the main hydrological variables was proposed to generate synthetic hyetographs. Two different approaches were adopted to assess a temporal pattern to the synthetic rainfall: one analyses all available historical rainfall patterns, and another adopts the cluster analysis in three different variants to reduce the computational effort of the analysis. To test the methodology reliability, the analysis was carried out for a real urban watershed. To carry out the flooding frequency analysis, all generated synthetic hyetographs were used as input of a dual drainage mathematical model of the analysed drainage system. Results showed that the method based on the analysis of all historical rainfall patterns was efficient in the estimation of flooding frequency, especially for higher return periods and approximately halved the computational costs of ordinary long-term analysis. Regarding clustering approaches, although attractive for their computational efficiency, their adoption must be carefully evaluated because it could neglect relevant information and result in a less accurate flood frequency analysis.

Uncertainty related to climate change in the assessment of the DDF curve parameters

In the context of climate change, the evaluation of the parameters of Depth-Duration-Frequency (DDF) curves has become a critical issue. Neglecting future rainfall variations could result in an overestimation/underestimation of DDF parameters and, consequently, of the design storm. In this study, uncertainty analysis was integrated into trend analysis to provide an estimate of trends that cannot actually be rigorously verified. A Bayesian procedure was suggested for the updating of DDF curve parameters and to evaluate the uncertainty related to their assessment. The proposed procedure also allowed identification of the years of a series that contributed most to the overall uncertainty related to the parameter estimation. The methodology was implemented to estimate the DDF parameters for 65 sites in Sicily (Southern Italy). The results showed that the DDF parameters were affected by increases and decreases over the 1950–2008 period, with different levels of uncertainty.

Statistical analysis of the uncertainty related to flood hazard appraisal

The estimation of flood hazard frequency statistics for an urban catchment is of great interest in practice. It provides the evaluation of potential flood risk and related damage and supports decision making for flood risk management. Flood risk is usually defined as function of the probability, that a system deficiency can cause flooding (hazard), and the expected damage, due to the flooding magnitude (damage), taking into account both the exposure and the vulnerability of the goods at risk. The expected flood damage can be evaluated by an a priori estimation of potential damage caused by flooding or by interpolating real damage data. With regard to flood hazard appraisal several procedures propose to identify some hazard indicator (HI) such as flood depth or the combination of flood depth and velocity and to assess the flood hazard corresponding to the analyzed area comparing the HI variables with user-defined threshold values or curves (penalty curves or matrixes). However, flooding data are usually un...