Stochastic urban pluvial flood hazard maps based upon a spatial-temporal rainfall generator

It is a common practice to assign the return period of a given storm event to the urban pluvial flood event that such storm generates. However, this approach may be inappropriate as rainfall events with the same return period can produce different urban pluvial flooding events, i.e., with different associated flood extent, water levels and return periods. This depends on the characteristics of the rainfall events, such as spatial variability, and on other characteristics of the sewer system and the catchment. To address this, the paper presents an innovative contribution to produce stochastic urban pluvial flood hazard maps. A stochastic rainfall generator for urban-scale applications was employed to generate an ensemble of spatially—and temporally—variable design storms with similar return period. These were used as input to the urban drainage model of a pilot urban catchment (~9 km2) located in London, UK. Stochastic flood hazard maps were generated through a frequency analysis of the flooding generated by the various storm events. The stochastic flood hazard maps obtained show that rainfall spatial-temporal variability is an important factor in the estimation of flood likelihood in urban areas. Moreover, as compared to the flood hazard maps obtained by using a single spatially-uniform storm event, the stochastic maps generated in this study provide a more comprehensive assessment of flood hazard which enables better informed flood risk management decisions

A joint probability approach to flood frequency estimation using Monte Carlo simulation

In the UK, flood estimation using event based rainfall–runoff modelling currently assigns pre-defined design values to the input variables which control the size of the flow events, apart from the rainfall magnitude which is treated as a random variable. The use of design values, rather than allowing the variables to be described by their full probability distribution, is a practical simplification but may lead to biases in the output flood magnitudes. The present study simulates a large number of flow events using sets of input variables from distributions fitted to observed event data, taking into account seasonality. These simulated datasets are used for running a rainfall-runoff model, and a frequency analysis is applied to the peaks of the output flow hydrographs. The simulated inputs are the rainfall intensity and duration, and the soil moisture deficit (SMD) and initial river flow at the beginning of the rainfall event. An inter-event arrival time is simulated so that a series of events is obtained. The initial conditions of SMD and river flow of each event are made dependent on the (simulated) time elapsed since the previous event, and on the SMD at the end of the previous event

Correction of upstream flow and hydraulic state with data assimilation in the context of flood forecasting

The present study describes the assimilation of river water level observations and the resulting improvement in flood forecasting. The Kalman Filter algorithm was built on top of a one-dimensional hydraulic model which describes the Saint-Venant equations. The assimilation algorithm folds in two steps: the first one was based on the assumption that the upstream flow can be adjusted using a three-parameter correction; the second one consisted of directly correcting the hydraulic state. This procedure was applied using a four- day sliding window over the flood event. The background error covariances for water level and discharge were repre- sented with anisotropic correlation functions where the cor- relation length upstream of the observation points is larger than the correlation length downstream of the observation points. This approach was motivated by the implementation of a Kalman Filter algorithm on top of a diffusive flood wave propagation model. The study was carried out on the Adour and the Marne Vallage (France) catchments. The correction of the upstream flow as well as the control of the hydraulic state during the flood event leads to a significant improve- ment in the water level and discharge in both analysis and forecast modes

Comprehensive flood mitigation and management in the Chi River Basin, Thailand

Severe flooding of the flat downstream area of the Chi River Basin occurs frequently. This flooding is causing catastrophic loss of human lives, damage and economic loss. Effective flood management requires a broad and practical approach. Although flood disasters cannot completely be prevented, major part of potential loss of lives and damages can be reduced by comprehensive mitigation measures. In this paper, the effects of river normalisation, reservoir operation, green river (bypass), and retention have been analysed by using integrated hydrologic and hydraulic modelling. Every tributary has been simulated by a process-based hydrological model (SWAT) coupled with the 1D/2D SOBEK river routing model. Model simulation results under the design rainfall event, i.e. flood depth, flood extent, and damages for the situation with and without flood mitigation measures have been compared and evaluated to determine an optimal set of mitigation measures. The results reveal that a combination of river normalisation, reservoir operation, and green river (bypass) is most effective as it can decrease the extent of the 100-year flood event by approximately 24% and 31% for the economic damage. The results of this study will be useful for improving the present flood defence practice in the Chi River Basi

Groundwater flooding within an urbanised flood plain

In Europe in recent years, there has been recognition of the need to better understand the risk from groundwater flooding. This recognition has been due both to the occurrence of major flooding events clearly attributable to groundwater and the inclusion of groundwater flooding in European and national legislation. The case study of the city of Oxford on the River Thames flood plain in UK is used to examine the mechanisms for groundwater flooding in urbanised flood plain settings. Reference is made to an extensive data set gathered during a major flood event in 2007. Groundwater flooding of a significant number of properties is shown to occur in areas isolated from fluvial flooding because of high ground created historically to protect property and the transport network from flood inundation. The options for mitigating this form of flooding are discussed; measures to increase the rate of conveyance of flood waters through Oxford, designed to reduce fluvial flood risk, have also been recognised as a means for reducing groundwater flood risk within the city

The impact of uncertainty in satellite data on the assessment of flood inundation models

The performance of flood inundation models is often assessed using satellite observed data; however these data have inherent uncertainty. In this study we assess the impact of this uncertainty when calibrating a flood inundation model (LISFLOOD-FP) for a flood event in December 2006 on the River Dee, North Wales, UK. The flood extent is delineated from an ERS-2 SAR image of the event using an active contour model (snake), and water levels at the flood margin calculated through intersection of the shoreline vector with LiDAR topographic data. Gauged water levels are used to create a reference water surface slope for comparison with the satellite-derived water levels. Residuals between the satellite observed data points and those from the reference line are spatially clustered into groups of similar values. We show that model calibration achieved using pattern matching of observed and predicted flood extent is negatively influenced by this spatial dependency in the data. By contrast, model calibration using water elevations produces realistic calibrated optimum friction parameters even when spatial dependency is present. To test the impact of removing spatial dependency a new method of evaluating flood inundation model performance is developed by using multiple random subsamples of the water surface elevation data points. By testing for spatial dependency using Moran’s I, multiple subsamples of water elevations that have no significant spatial dependency are selected. The model is then calibrated against these data and the results averaged. This gives a near identical result to calibration using spatially dependent data, but has the advantage of being a statistically robust assessment of model performance in which we can have more confidence. Moreover, by using the variations found in the subsamples of the observed data it is possible to assess the effects of observational uncertainty on the assessment of flooding risk

Testing the use of viscous remanent magnetisation to date flood events

© 2015 Muxworthy, Williams and Heslop. Using erratics associated with large flood events, this paper assesses whether their viscous remanent magnetisation (VRM) can be used to date the flood events. We tested this method using flood erratics from three large events: (1) the Late Pleistocene Bonneville mega-flood in Idaho, USA, (~14–18 ka), (2) the 1918 A.D. Mt. Katla, Iceland, eruption and associated jökulhaup (meltwater flood) at Mýrdalssandur, and (3) the Markarfljót jökulhaup due to an earlier eruption of Mt. Katla (~2.5 ka). We measured 236 specimens, 66 of which yielded clear identifiable and measurable viscous magnetisation signals from erratics with clustered VRM directions. From the VRM unblocking temperatures, age estimates were made. The age estimate for the most recent event (Mýrdalssandur) worked well, with a median estimated age of 80 years (with individual erratic estimates distributed between 61–105 years) compared to the known age of 91 years. The ages of the other two events were over-estimated. The estimates for Markarfljót [15 ka (7–33 ka)] were based on the results of just one erratic. For the Bonneville flood the estimates were too old, however, this locality had the largest uncertainty in the ambient temperature used in the age determination; the VRM acquired is strongly dependent on the ambient temperature, the older the event the greater the uncertainty. Southern Idaho currently has hot summers, with average summer maximum temperatures of ~31°C, but a mean annual temperature of only ~ 9°C. It is suggested that the VRM dating method works best for recent events (<2–3 ka) where the ambient temperature history can be constrained

Probabilistic modeling of flood characterizations with parametric and minimum information pair-copula model

This paper highlights the usefulness of the minimum information and parametric pair-copula construction (PCC) to model the joint distribution of flood event properties. Both of these models outperform other standard multivariate copula in modeling multivariate flood data that exhibiting complex patterns of dependence, particularly in the tails. In particular, the minimum information pair-copula model shows greater flexibility and produces better approximation of the joint probability density and corresponding measures have capability for effective hazard assessments. The study demonstrates that any multivariate density can be approximated to any degree of desired precision using minimum information pair-copula model and can be practically used for probabilistic flood hazard assessment

Exploring the experience of insured UK homeowners in flood disasters

The frequency of flooding and the number of properties at risk of flooding in the UK are forecast to increase. Costs associated with flooding are usually significant and include for the provision of adequate flood defences, emergency services as well as for the repair of flood-damaged property. Although floods are known for their devastating effects often manifested in visible physical damage to property, the ‘human side’ of the impact of floods is often overlooked. At present there is a dearth of research with regards to the experience of homeowners following flood damage to their property. Findings of exploratory in-depth interviews with homeowners who have recently experienced a flood event to their insured property are presented and classified into five dimensions, namely: economic aspects, emotional aspects, service-related aspects, social aspects and physical characteristics. It is argued that a greater understanding of the ‘human side’ of flood disasters would be beneficial to all stakeholders involved in the damage management supply chain and should lead to improved services for insured flood victims thereby minimising the impact of flooding events on households