Managing sewer flood risk

Managing sewer flood risk J. Ryu 1 *, D. Butler 2 1 Environmental and Water Resource Engineering, Department of Civil and Environmental Engineering, Imperial College, London, SW7 2AZ, UK 2 Centre for Water Systems, School of Engineering, Computing and Mathematics, University of Exeter, North Park Road, Exeter, EX4 4QF, UK *Corresponding author, e-mail jaena.ryu@imperial.ac.uk ABSTRACT As recent UK floods have shown, serious inundation can still occur even if river discharge has not exceeded the functional flood plain capacity due to pluvial or sewer flooding. This has further highlighted the issue of apportionment of responsibility of individual stakeholders in the urban environment, and the need for a robust risk management method to assist in setting a consistent and transparent management plan. This study aims to develop a methodology to support effective sewer flood risk management. Flood stage at property boundaries has been determined by coupling a long term flow time series generated from an urban drainage model and a flood catchment delineation method applied on a digital elevation model. Flood consequence was obtained from the relationship between the flood stage and the corresponding damage cost. Annual average flood risk for the property was identified by linking flood probability, stage and damage. Within a cost benefit framework in sewerage rehabilitation and management, setting up different scenarios gave various outcomes for the risk alleviation. The tool developed allows a rational comparison of alternatives, thereby assisting stakeholders make more informed and robust decisions. KEYWORDS Decision support; flood risk assessment; flood risk management; sewer flooding INTRODUCTION Sewer flooding is arguably the second most serious issue facing UK water companies after drinking water quality (OFWAT, 2002) an estimated investment requirement of 970 million by 2010 (OFWAT, 2005). Data indicates that some 11,600 properties, in England and Wales, were at risk of sewer flooding at least once in ten years (NAO, 2004), and the risk would increase over time with climate change, likewise shown in the 2007 summer flooding that was partially attributed to the unprecedented heavy rainfall (EA, 2007). The increasing reality of frequent urban flooding requires a robust risk assessment method that can essentially assist water companies, policy makers and other stakeholders in setting a consistent and transparent plan. A method is needed that allows flood risk (rather than merely frequency) to be reduced, over time and in a cost-effective manner. This requires tools to assess flood location, frequency and severity coupled with a means to estimate the associated costs and predicted benefits of options to reduce comprehensive flood risk. Such a framework and its associated tools are demonstrated in this paper. Ryu and Butler. 1

RISK ASSESSMENT Frequency of flooding can be determined either by a statistical analysis of historical flood discharge where available, or a model simulation can be used to generate flood discharge. Certainly, no historic flood data is available for every site of interest and hence in a number of cases, the estimation of flood discharges must rely on the outputs of a model simulation, either event based or continuous. In this work, we argue continuous simulation is more appropriate for the drainage model simulation due to the advantages in achieving reliable flood discharges consequently leading to a more inclusive risk graph creation. Several statistical distributions could be utilised to analyse the flood discharges, and the one with the best fit would be applied for analysing the discharge data to obtain the flood probability. Flood consequence is quantified using depth-damage curves that represent the flood cost against the percentage damage of total value of the flooded property, depending on property type. Flood risk can be quantified from a risk curve which combines the flood stage, probability and damage relationship (Penning-Rowsell and Chatterton, 1977). COST BENEFIT ANALYSIS When deciding measures to reduce flood risk, there is no one solution that can be applied to all problems and situations. Whilst several decision criteria based on many types of information are available, cost benefit analysis (CBA) is a widely used technique already is used in intervention justification for flood risk management. For flood risk alleviation, the objective is, in general, to reduce the probability and extent of damage resulting from flooding. Therefore benefits arising from the project are the net difference between the total present value of damage with and without the proposed works. The greatest benefit for sewer flood risk management is hence associated with a reduction of the highest risk and the cost for option implementation. Once a certain option is taken, a reduced flood stage and damage will be achieved (grey lines in Figure 1). flood stage flood stage probability probability damage damage Figure 1 (a) Figure 1 (b) Figure 1 (c) Figure 1. Interrelation of flood probability, flood stage and flood damage The initial flood risk before implementing a certain option (R i ) will be the area below the black line, and the flood risk after implementing a certain option (R a ) is the area below the grey line. The level of the risk reduction is the difference between the initial risk and the alleviated risk and translated into a sum of monetary value. Therefore the benefits expected to be maximised are the lowest sum of the difference between the alleviated risk and the initial risk (R i -R a ). Although it certainly will be reduced, the flood risk in most cases will still exist (R a, Figure 1(c), area below the grey line) after implementing the scheme selected. The associated cost for reducing the risk, and the option cost (C o ), should also be counted. Hence the sum of the alleviated flood risk and the options implementation cost (R a +C o ) is the total cost to be minimized in the CBA. 2 Managing sewer flood risk

RISK ASSESSMENT MODEL A tool has been developed, which serves as the underlying structure for the management of risk by providing the assessment of annual average flood risk. A schematic of this is in Figure 2. Figure 2. Model structure of sewer flood risk assessment A brief illustration of the modules is given and more details of the model flows are explored in the following sections with a description of the case study: Ryu and Butler. 3

Network module : to generate flood volume time series from a continuous simulation GIS module : to produce a raster map of a catchment area from GIS model with DEM (Digital Elevation Model) FC module : to recognise flood extents and property locations by collating information after the two above modules and estimate flood stages Risk module : to assess flood risk at each property. The study area is a part of Leiston, situated in south east of England. Every node in the network has its contribution area assuming to contain 4 subcatchments which cover in total 6.131 ha with 73 residential properties. All four subcatchments have 75 % imperviousness, with pervious retained. The sewer system includes 5 manholes including 1 outfall connected by 255 mm circular concrete pipes of combined types. Network A hydraulic/hydrologic simulation has been performed using InfoWorks so as to produce surcharging and surface flooding on the area. A 10 year historical times series rainfall data, measured at 5 minute intervals, was used. A time series of flood volumes recorded over the 10 year simulation for each node and X and Y coordinates of the nodes were exported. GIS The address points of properties were available from Digimap and used to assign the properties into a synthetically generated DEM. The DEM has changed into a raster grid file format in the GIS module and exported to a matrix array of Matlab as it is an ideal platform for analysing ground elevation data incorporating into other datasets. The generated array has been the base array for the remaining steps of the modelling work. Flood Catchment Whilst there are four geographical catchments in the area, this is not the physical extent to which the flooded water could spread (i.e. Flood Catchment, FC). Hence a realistic flood catchment, where the exceedance flow would actually spread, has to be defined. FC delineation. The FC module firstly imports the ground elevation database created from the GIS module and generates a raster array which covers the whole catchment area, for storing the elevation data. The array is assigned the same grid and height scale to that of the raster map in the GIS module. Based on the ground elevations, FCs are recognised by applying several developed algorithms: (1) Finding flow direction, (2) Recognising lowest cells, (3) Assigning cells into the lowest cells and (4) Delineating catchments, mainly adopting a conventional catchment delineation method. The nodes are located in the raster array following the coordinates exported from the drainage model network. The time series flood volume produced after the continuous simulation is distributed throughout the FCs. The number of flood cell is counted depending on pre-defined unit levels of the Z axis. After the catchment delineation method developed by Jenson and Domingue (1988), every cell in the FC raster array apart from the lowest cells has flow directions assigned. In total, 6 groups of lowest cells were identified and the address of the lowest cell was converted into the raster array scale alike, then other cells are assigned to the lowest cells following encoded direction values. After the catchment delineation, 6 FCs were recognised. 5 nodes were in the case study area but the outfall node has been excluded in the delineation. The addresses of nodes exported from the Network module were assigned to the FC raster array (Figure 3). 4 Managing sewer flood risk

FC 5 FC 6 19.0 18.8 FC 3 Level (m) 18.6 18.4 18.2 18.0 17.8 17.6 FC 4 FC 2 FC 1 44627703 44627704 44627701 44627705 44627702 Figure 3. 3-Dimensional view of FC raster map Property assigning. The point source database exported from the GIS module facilitates properties to be assigned into the raster array according to the coordinates. Among 73 properties, 10, 9, 2, 25, 15 and 12 properties were allocated to the FC 1, 2, 3, 4, 5 and 6 respectively. Flood depth generation. The distributed flood volume data is converted into the flood depths for each property by incorporating the flood cell information and storage volumes of the FCs. Any flood catchment recognised from the FC delineation procedure can be recognised as a kind of flood storage. Since every cell in any flood catchment has the same square grid, the volume inside the FC can be filled with a number of flood cells, if the same height axis is assigned. Risk The calculated depths were statistically analysed to identify the flood stage and probability relationship. The relationships, developed by Penning-Rowsell et al. (2003), were chosen for flood damage estimation due to its reliability. The estimated baseline flood risk for all 73 properties is mapped in Figure 4. Among the 73 properties, 2 and 6 properties in the FC 3 and 4 respectively have experienced flooding more than once in 10 years with no flooding for the remained properties. The risk estimated in total was 90,312. Ryu and Butler. 5

Node Property FC Boundary Ground Elevation (m) 17.6 17.8 18.0 18.2 18.4 18.6 18.8 19.0 Flood Risk ( ) - 10,000 10,000-15,000 15,000-20,000 FC 4 FC 5 FC 6 46 47 48 49 50 40 39 FC 3 37 FC 2 FC 1 Figure 4. Flood risk map RISK MANAGEMENT Options Four options have been proposed to potentially improve the hydraulic performance of the prevailing circumstance: reduce inflow, maximise use of existing sewer system capacity, attenuate peak flows and enhance existing system capacity. HYD 1 models a reduction of hydraulic inputs by diverting them to other catchments. FC 3, where the majority of frequently flooded houses are, has the runoff from two catchments. Hence node sealing is tested for node 44627701. HYD 2 applies cleaning of all pipes as a part of regular maintenance work and the effects of flow attenuation due to provision of additional storage volume is modelled in HYD 3. An increase in the storage volume by about 30 m 3 is examined at node 44627703. HYD 4 models a replacement of sewers from 225 mm to 450 mm in diameter. After HYD 1, the number of flooding events occurring at properties 37 and 50 in FC 3 almost halved and the flood risks were reduced by 400 % at each property. However, the total flood risk for all properties has only reduced from 90,312 to 79,072. This is because two other properties located in FC 5, where no flooding occurred in the baseline case, experienced inundation due to the diverted runoff. Although the number of flooding events for the 8 properties experiencing flooding events in the baseline case was reduced after HYD 2, the flood risks for each house suggested a slight increase for 3 properties with a small reduction for the remaining 5 properties. The total flood risk was reduced by just 3% to 90,058. The number of flooding events for the 6 properties in FC 4, where the storage volume was examined, was considerably reduced after HYD 3. However, the total risk was only modestly reduced to 88,427. The reason is that the estimation of annual average flood risk does not entirely depend on the number of flooding events. Even though the total number of flooding events decreases, the probability and damage graph could increase as the frequencies of flooding events which generated the same stages could increase. Hence the risk could be higher than the baseline case. After HYD 4 no above ground flooding occurred as all pipes 6 Managing sewer flood risk

were sized to be large enough to accommodate the maximum flow rate found under the baseline model condition. Therefore the flood risk estimated was zero. Decision making According to the CBA procedure adopted, the objective is to maximise the benefits from the risk management and to minimise the flood costs. The flood risk before and after implementing any option and the options cost is considered in a single term and hence is used to compare alternative solutions to define the preferred option in a convenient way. The associated costs for implementing options are summarised in Table 1. Table 1. List of costs for options implementation Options Details Costs HYD 1 Sealing node 164 HYD 2 Cleaning pipe 12.76/m HYD 3 Additional storage 7,021 for 30 m 3 HYD 4 Enhancing pipe diameter 59.08/m for 450 mm diameter The annual average flood risks for the four options are shown in Figure 5 with the baseline flood risk and Figure 6 shows the estimated benefits and Benefit-Cost Ratio (BC Ratio) for each option. 120x10 3 100x10 3 Flood Cost Option Cost 100x10 3 80x10 3 BC Ratio Benefits 8 6 Flood Risk ( ) 80x10 3 60x10 3 40x10 3 Baseline Benefits ( ) 60x10 3 40x10 3 4 2 Benefit-Cost Ratio 20x10 3 20x10 3 0 0 HYD 1 HYD 2 HYD 3 HYD 4 0 HYD 1 HYD 2 HYD 3 HYD 4-2 Figure 5. Flood risk Figure 6. Benefit Cost Ratio The black and white bars in Figure 5 show estimated flood costs and the capital costs respectively after the various options have been implemented. The flood cost before any option is implemented (baseline) is the horizontal dashed line. All four options are reasonable management candidates as the flood costs assessed after the options are lower than the baseline, with HYD 4 giving the highest benefits due to the zero flood cost. With regard to the option cost, HYD 1 is the lowest, but there was a large associated flood cost. Total costs are the summed amounts of the black and white bars in Figure 5. When compared, HYD 4 is Ryu and Butler. 7

substantially lower than the other 3 options and HYD 2 and 3 can be eliminated as being poorer than a do nothing option. The BC Ratio is generally accepted to provide a means of selecting the most cost effective measures overall (Snell, 1997). It is determined by dividing the annual benefit by the annual cost. Options with a BC Ratio lower than one can therefore be regarded as uneconomic (the grey dashed line in Figure 6). Hence HYD 4 is clearly identified as having the highest BC ratio, and in fact, the only one above unity. Therefore, the HYD 4 option is recommended in this case as the most economically worthwhile scheme. Of course, although HYD 4 has the highest BC Ratio, it has to be stressed that the costs and benefits are effectively accrued by different stakeholders. CONCLUSIONS This study has presented a methodology and a tool developed for sewer flood risk management with a case study. The main achievement can be summarised as follows: The use of continuous simulation (rather than design storms) analysis provides reliable flood probability assessments. The algorithms developed for the flood stage generation by coupling the flood discharge and the DEM do not explicitly replicate all the natural surface flow phenomena, but are an improvement on conventional urban drainage model methods (e.g. a virtual reservoir) and avoid the computational burden of more complex methods (e.g. 2-D surface flow models). The results have demonstrated how the quantification of annual average flood risk by combining the probability and consequence into the single monetary term can be used to identify effective and cost-beneficial flood management solutions. The framework developed could of course be used to evaluate many more options for risk alleviation, allowing more robust and comprehensive decisions to be supported. The method could be extended to allocate risk costs and benefits to different stakeholders. REFERENCES EA (2007). Review of 2007 summer floods, Environment Agency, Bristol, UK. Jenson S.K. and Domingue J.O. (1988). Extracting topographic structure from Digital Elevation Data for Geographic Information System analysis. Photogrammetric Engineering and Remote Sensing, 54, 1593-1600. NAO (2004). Out of sight- not out of mind: Ofwat and the public sewer network in England and Wales, The National Audit Office, London, UK. OFWAT (2002). Flooding from sewers, a way forward: consultation, Report 18/20, Office of Science and Technology, London, UK. OFWAT (2005). Levels of service for the water industry in England and Wales, 2004-2005 report, Office of Science and Technology, London, UK. Penning-Rowsell E., Johnson C., Tunstall S., Tapsell S., Morris J., Chatterton J., Coker A. and Green C. (2003). The benefits of flood and coastal defence: techniques and data for 2003. Flood Hazard Research Centre, Middlesex University. Penning-Rowsell E.C. and Chatterton J.B. (1977). The benefits of flood alleviation: a manual of assessment techniques, Saxon House, Farnborough. Snell M. (1997). Cost-benefit analysis, for engineers and planners. Thomas Telford, London, UK. 8 Managing sewer flood risk