The efficiency of asset management strategies to reduce urban flood risk.

In this study, three asset management strategies were compared with respect to their efficiency to reduce flood risk. Data from call centres at two municipalities were used to quantify urban flood risks associated with three causes of urban flooding: gully pot blockage, sewer pipe blockage and sewer overloading. The efficiency of three flood reduction strategies was assessed based on their effect on the causes contributing to flood risk. The sensitivity of the results to uncertainty in the data source, citizens' calls, was analysed through incorporation of uncertainty ranges taken from customer complaint literature. Based on the available data it could be shown that increasing gully pot blockage is the most efficient action to reduce flood risk, given data uncertainty. If differences between cause incidences are large, as in the presented case study, call data are sufficient to decide how flood risk can be most efficiently reduced. According to the results of this analysis, enlargement of sewer pipes is not an efficient strategy to reduce flood risk, because flood risk associated with sewer overloading is small compared to other failure mechanisms.

Flood risk modelling based on tangible and intangible urban flood damage quantification.

The usual way to quantify flood damage is by application stage-damage functions. Urban flood incidents in flat areas mostly result in intangible damages like traffic disturbance and inconvenience for pedestrians caused by pools at building entrances, on sidewalks and parking spaces. Stage-damage functions are not well suited to quantify damage for these floods. This paper presents an alternative method to quantify flood damage that uses data from a municipal call centre. The data cover a period of 10 years and contain detailed information on consequences of urban flood incidents. Call data are linked to individual flood incidents and then assigned to specific damage classes. The results are used to draw risk curves for a range of flood incidents of increasing damage severity. Risk curves for aggregated groups of damage classes show that total flood risk related to traffic disturbance is larger than risk of damage to private properties, which in turn is larger than flood risk related to human health. Risk curves for detailed damage classes show how distinctions can be made between flood risks related to many types of occupational use in urban areas. This information can be used to support prioritisation of actions for flood risk reduction. Since call data directly convey how citizens are affected by urban flood incidents, they provide valuable information that complements flood risk analysis based on hydraulic models.

Fault tree analysis for data-loss in long-term monitoring networks.

Prevention of data-loss is an important aspect in the design as well as the operational phase of monitoring networks since data-loss can seriously limit intended information yield. In the literature limited attention has been paid to the origin of unreliable or doubtful data from monitoring networks. Better understanding of causes of data-loss points out effective solutions to increase data yield. This paper introduces FTA as a diagnostic tool to systematically deduce causes of data-loss in long-term monitoring networks in urban drainage systems. In order to illustrate the effectiveness of FTA, a fault tree is developed for a monitoring network and FTA is applied to analyze the data yield of a UV/VIS submersible spectrophotometer. Although some of the causes of data-loss cannot be recovered because the historical database of metadata has been updated infrequently, the example points out that FTA still is a powerful tool to analyze the causes of data-loss and provides useful information on effective data-loss prevention.

Fault tree analysis for urban flooding.

Traditional methods to evaluate flood risk generally focus on heavy storm events as the principal cause of flooding. Conversely, fault tree analysis is a technique that aims at modelling all potential causes of flooding. It quantifies both overall flood probability and relative contributions of individual causes of flooding. This paper presents a fault model for urban flooding and an application to the case of Haarlem, a city of 147,000 inhabitants. Data from a complaint register, rainfall gauges and hydrodynamic model calculations are used to quantify probabilities of basic events in the fault tree. This results in a flood probability of 0.78/week for Haarlem. It is shown that gully pot blockages contribute to 79% of flood incidents, whereas storm events contribute only 5%. This implies that for this case more efficient gully pot cleaning is a more effective strategy to reduce flood probability than enlarging drainage system capacity. Whether this is also the most cost-effective strategy can only be decided after risk assessment has been complemented with a quantification of consequences of both types of events. To do this will be the next step in this study.

Microbial risks associated with exposure to pathogens in contaminated urban flood water.

Urban flood incidents induced by heavy rainfall in many cases entail flooding of combined sewer systems. These flood waters are likely to be contaminated and may pose potential health risks to citizens exposed to pathogens in these waters. The purpose of this study was to evaluate the microbial risk associated with sewer flooding incidents. Concentrations of Escherichia coli, intestinal enterococci and Campylobacter were measured in samples from 3 sewer flooding incidents. The results indicate faecal contamination: faecal indicator organism concentrations were similar to those found in crude sewage under high-flow conditions and Campylobacter was detected in all samples. Due to infrequent occurrence of such incidents only a small number of samples could be collected; additional data were collected from controlled flooding experiments and analyses of samples from combined sewers. The results were used for a screening-level quantitative microbial risk assessment (QMRA). Calculated annual risks values vary from 5 x 10(-6) for Cryptosporidium assuming a low exposure scenario to 0.03 for Giardia assuming a high exposure scenario. The results of this screening-level risk assessment justify further research and data collection to allow more reliable quantitative assessment of health risks related to contaminated urban flood waters.