J.A.E. Ten Veldhuis

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.

On the sensitivity of urban hydrodynamic modelling to rainfall spatial and temporal resolution

Cities are increasingly vulnerable to floods generated by intense rainfall, because of urbanisation of floodprone areas and ongoing urban densification. Accurate information of convective storm characteristics at high spatial and temporal resolution is a crucial input for urban hydrological models to be able to simulate fast runoff processes and enhance flood prediction in cities. In this paper, a detailed study of the sensitivity of urban hydrodynamic response to high resolution radar rainfall was conducted. Rainfall rates derived from X-band dual polarimetric weather radar were used as input into a detailed hydrodynamic sewer model for an urban catchment in the city of Rotterdam, the Netherlands. The aim was to characterise how the effect of space and time aggregation on rainfall structure affects hydrodynamic modelling of urban catchments, for resolutions ranging from 100 to 2000 m and from 1 to 10 min. Dimensionless parameters were derived to compare results between different storm conditions and to describe the effect of rainfall spatial resolution in relation to storm characteristics and hydrodynamic model properties: rainfall sampling number (rainfall resolution vs. storm size), catchment sampling number (rainfall resolution vs. catchment size), runoff and sewer sampling number (rainfall resolution vs. runoff and sewer model resolution respectively). Results show that for rainfall resolution lower than half the catchment size, rainfall volumes mean and standard deviations decrease as a result of smoothing of rainfall gradients. Moreover, deviations in maximum water depths, from 10 to 30 % depending on the storm, occurred for rainfall resolution close to storm size, as a result of rainfall aggregation. Model results also showed that modelled runoff peaks are more sensitive to rainfall resolution than maximum insewer water depths as flow routing has a damping effect on in-sewer water level variations. Temporal resolution aggregation of rainfall inputs led to increase in de-correlation lengths and resulted in time shift in modelled flow peaks by several minutes. Sensitivity to temporal resolution of rainfall inputs was low compared to spatial resolution, for the storms analysed in this study.

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.

Can urban pluvial flooding be predicted by open spatial data and weather data

Cities are increasingly prone to urban flooding due to heavier rainfall, denser populations, augmenting imperviousness, and infrastructure aging. Urban pluvial flooding causes damage to buildings and contents, and disturbs stormwater drainage, transportation, and electricity provision. Designing and implementing efficient adaptation measures requires proper understanding of the urban response to heavy rainfall. However, implemented stormwater drainage models lack flood impact data for calibration, which results in poor flood predictions. Moreover, such models only consider rainfall and hydraulic parameters, neglecting the role of other natural, built, and social conditions in flooding mechanisms. This paper explores the potential of open spatial datasets to explain the occurrence of citizen-reported flood incidents during a heavy rain event. After a dimensionality reduction, imperviousness and proximity to watershed outflow point were found to significantly explain up to half of the flooding incidents variability, proving the usefulness of the proposed approach for urban flood modelling and management. Occurrence of flooding incident reports is used as a response variable in an urban pluvial flooding analysis.Information in open spatial datasets explains up to half of response variability.Physical variables significantly explain response variability.Rainfall and population density do not explain incident reports distribution.Results doubled the explaining power achieved by previous related studies.

Automatic classification of municipal call data to support quantitative risk analysis of urban drainage systems

Quantitative analyses of urban flood risks are often limited by lack of data on flood incidents. Call data are a valuable source of information about urban flood incidents, yet the unstructured nature of call information results in large time investments to prepare the data for application in quantitative analyses. Consequently, the existing call databases are not used for this purpose. If automatic classification routines can be applied to transfer unstructured call data into a quantitative data source, large stores of currently unused data can be made available for quantitative risk analysis of urban infrastructure systems. This article aims to assess whether automatic classification of calls from municipal call centres can reach sufficient accuracy to allow for use of the results in quantitative risk analysis. This is illustrated by the application of automatic classification results in quantitative fault tree analysis for urban flooding, for two cases with datasets of approximately 6000 calls. The results show that the obtained classification accuracy is sufficient to correctly rank failure mechanisms according to their contributions to the overall failure probability. This is a promising first result that shows the potential of automatic call classification to obtain data about failure incidents that are otherwise hard to find.

Potential of cost-efficient single frequency GNSS receivers for water vapor monitoring

Dual-frequency Global Navigation Satellite Systems (GNSSs) enable the estimation of Zenith Tropospheric Delay (ZTD) which can be converted to PrecipitableWater Vapor (PWV). The density of existing GNSS monitoring networks is insufficient to capture small-scale water vapor variations that are especially important for extreme weather forecasting. A densification with geodetic-grade dual-frequency receivers is not economically feasible. Cost-efficient single-frequency receivers offer a possible alternative. This paper studies the feasibility of using low-cost receivers to increase the density of GNSS networks for retrieval of PWV. We processed one year of GNSS data from an IGS station and two co-located single-frequency stations. Additionally, in another experiment, the Radio Frequency (RF) signal from a geodetic-grade dual-frequency antenna was split to a geodetic receiver and two low-cost receivers. To process the single-frequency observations in Precise Point Positioning (PPP) mode, we apply the Satellite-specific Epoch-differenced IonosphericDelay (SEID)model using two different reference network configurations of 50-80 km and 200-300 km mean station distances, respectively. Our research setup can distinguish between the antenna, ionospheric interpolation, and software-related impacts on the quality of PWV retrievals. The study shows that single-frequency GNSS receivers can achieve a quality similar to that of geodetic receivers in terms of RMSE for ZTD estimations. We demonstrate that modeling of the ionosphere and the antenna type are the main sources influencing the ZTD precision.