overland flow and pathway analysis for modelling of

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Overland flow and pathway analysis for modelling ofurban pluvial floodingProfessor edo Maksimovi a , Associate Professor Duan Prodanovi b , ResearcherSurajate Boonya-Aroonnet c , Research Student Joo P. Leito IAHR Member d , SeniorLecturer Slobodan Djordjevi IAHR Member e & Director Richard Allitt fa Department of Civil and Environmental Engineering, Imperial College London, London,SW7 2AZ, UK E-mail:b Institute for Hydraulic and Environmental Engineering, Faculty of Civil Engineering,University of Belgrade, Serbia E-mail:c Hydro and Agro Informatics Institute, Ministry of Science and Technology, Thailandd Department of Civil and Environmental Engineering, Imperial College, London, SW72AZ, UK E-mail:e Centre for Water Systems, University of Exeter, North Park Road, Exeter, EX4 4QF, UK E-mail:f Richard Allitt Associates Ltd., Suite 3, The Forge Offices, Cuckfield Road, Staplefield,Haywards, West Sussex, RH17 6ET, UK E-mail:

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To cite this article: Professor edo Maksimovi, Associate Professor Duan Prodanovi, Researcher Surajate Boonya-Aroonnet, Research Student Joo P. Leito IAHR Member, Senior Lecturer Slobodan Djordjevi IAHR Member & DirectorRichard Allitt (2009): Overland flow and pathway analysis for modelling of urban pluvial flooding, Journal of HydraulicResearch, 47:4, 512-523

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Journal of Hydraulic Research Vol. 47, No. 4 (2009), pp. 512523doi:10.3826/jhr.2009.3361 2009 International Association of Hydraulic Engineering and Research

Overland flow and pathway analysis for modelling of urban pluvial floodingDrainage superficiel et analyse de chemins prfrentiels pour le modelagedinondations dorigine pluviale en milieu urbainCEDO MAKSIMOVI C, IAHR Member, Professor, Department of Civil and Environmental Engineering, Imperial College London,London, SW7 2AZ, UK. E-mail: [email protected]

DUAN PRODANOVI C, IAHR Member, Associate Professor, Institute for Hydraulic and Environmental Engineering,Faculty of Civil Engineering, University of Belgrade, Serbia. E-mail: [email protected] BOONYA-AROONNET, Researcher, Hydro and Agro Informatics Institute, Ministry of Science and Technology,Thailand. E-mail: [email protected] (author for correspondence)

JOO P. LEITO, IAHR Member, Research Student, Department of Civil and Environmental Engineering, Imperial CollegeLondon, London, SW7 2AZ, UK. E-mail: [email protected] DJORDJEVI C, IAHR Member, Senior Lecturer, Centre for Water Systems, University of Exeter, North Park Road,Exeter, EX4 4QF, UK. E-mail: [email protected] ALLITT, Director, Richard Allitt Associates Ltd., Suite 3, The Forge Offices, Cuckfield Road, Staplefield, HaywardsHeath, West Sussex, RH17 6ET, UK. E-mail: [email protected]

ABSTRACTResearch on improving an overland flow model is presented for urban pluvial flooding under the dual-drainage concept where sewer flow dynamicallyinteracts with overland flow. This occurs during heavy storms when the sewer system is surcharged. The system becomes pressurised and overlandflow increases by the additional volume flowing out from the sewer. To represent the overland flow realistically, a new methodology was developedto automatically create the overland flow network which can interact with the drainage system. Use is made of high-resolution, accurate DigitalElevation Model data collected by the LiDAR technique. This approach updates the current urban drainage models to urban flood models with detailedrepresentation of overland flow processes such as pond forming, flow through preferential surface pathways and surface drainage capacity. This workadvances new areas of urban flood management including improvement in real-time control and of links with rainfall now-casting, and short termurban flood forecasting. The dual-drainage approach is appropriate for real-time applications.

RSUMCette tude prsente les rsultats dun travail de recherche concernant lamlioration du modle de drainage superficiel en milieu urbain daprs leconcept Dual Drainage. Dans ce concept, le drainage dans les collecteurs interagit de faon dynamique avec le drainage la surface. Les interactionsse produisent pendant de forts vnements de prcipitation qui surchargent les collecteurs. Lorsque le rseau de collecteurs est sous pression, unepartie du volume deau peut sortir du rseau de collecteurs et est achemine vers le rseau superficiel. Une nouvelle mthodologie est en train dtredveloppe afin de reprsenter de faon relle lcoulement superficiel; celle-ci cre automatiquement le rseau dcoulement superficiel, ainsi que lesinteractions de ce dernier avec le rseau de collecteurs. Des Modles Digitaux de Terrain de haute rsolution et prcision, rassembls principalementpar la technique LiDAR, sont utiliss pour la conception du rseau superficiel de drainage. La mthodologie dveloppe prsente une nouvelleopportunit damliorer les actuels modles de drainage urbain, surtout dans la reprsentation dtaille des processus dcoulement superficiel, telsque la reprsentation de zones de dpression du terrain, lidentification des chemins prfrentiels dcoulement et la dtermination de la capacitdcoulement. Ce travail ouvre chemin de nouveaux secteurs en rapport avec un modelage plus volu dans la gestion dinondations en temps relen milieu urbain. Lapproche Dual Drainage ci prsente est considre efficace dans les applications de prvision en temps rel.

Keywords: GIS, LiDAR, Modelling, Overland flow, Urban pluvial flooding

1 Introduction

Urban flooding is a costly environmental hazard. To minimisethe risk from flood events, improvement in prediction and

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quantification of the flood risk is needed. Urban floods may becaused by a number of factors. One of the main causes is the lim-ited capacity of the drainage system which, during extreme wetweather conditions, may result in sewer flow being discharged

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Journal of Hydraulic Research Vol. 47, No. 4 (2009) Overland flow and pathway analysis for modelling of urban pluvial flooding 513

to the catchment surface where it interacts with the incomingoverland flow. The flood water either fills natural or constructedsurface storage or subsequently travels across the terrain throughpreferential pathways that create a surface flow network typicallycalled the major system, while the minor system refers to anunderground sewer network.

Major systems in urban areas typically comprise roads, foot-paths and natural ground depressions as well as small watercourses. The major system can transfer flood water over sig-nificant distances causing flooding at locations remote from thepoint at which the drainage system capacity is exceeded. Sur-face runoff from adjacent areas that have no direct connectionto the sewer system also contributes to flood flow. Therefore,urban drainage modelling requires a detailed representation ofthe overland flow network of ponds and pathways to reliably rep-resent surface retention storage, flow paths and volume conveyed.Although surface pathways over urban areas are mainly directedby buildings and streets, water often flows elsewhere throughgardens and other open spaces. It is therefore important to have arealistic depiction of terrain and urban structures on the surface.

Use of sophisticated hydraulic models as diagnostic, designand decision-support tools has become a standard practice in thewater industry. Widely used conventional urban drainage modelscan deal with rainfall-runoff analysis within the minor systemonly under the effects of local storms. Significant progress hasbeen made in wrapping conventional urban drainage models withsophisticated interfaces and gluing routines (Elgy et al. 1993,Fuchs et al. 1994), to link them with a Geographical Infor-mation System (GIS) for the automatic creation of input files(Lhomme et al. 2004). Additionally, results presented recentlyby Ball and Alexander (2006), Mignot et al. (2008) and Voji-novic et al. (2006) contribute to the general understanding ofaspects of urban flooding processes. An integrated approach tomodelling with minor/major system integration has been advo-cated as a more realistic method of flooding simulation (Markand Parkinson 2005, Schmitt et al. 2004).

However, to date, urban drainage simulation models did notaccurately represent several important features of surface flow,including:

overland flow taking place during heavy storms, flow into and from the underground drainage network, flow along streets (as primary preferential paths), surface ponds, and flow across the urban catchment along preferential paths

different from streets (Prodanovic et al. 1998).Detailed problem identification and concerns were presented byMaksimovic and Prodanovic (2001) who claim that to increasereliability, modelling practices have to realistically representsurface flow processes and their interactions with flow in sewers.

High-resolution Digital Elevation Models (DEM) generatedby the LIght Detection And Ranging (LiDAR) technique make adetailed analysis of overland flow achievable, although improve-ments are still needed to fully utilise this technology in the contextof urban flood management. Conventional model concepts ofsurface flow do not benefit from these newly available features.

This opens a new possibility to enhance model capability for thenext generation of urban flood modelling beyond the limitationsreported by Mark et al. (2004).

Originally described by Prodanovic (1999) and Djordjevic(2001) to model overland flow in the urban environment causedby extreme rainfall, surface flow processes and modelling con-cepts that generate a temporary network of ponds and pathwaysare described herein with a key role in routing overland flow dur-ing heavy storms. Flow in this network interacts with the flowin a surcharged sewer network. Based on the dual-drainageapproach (Djordjevic et al. 1999), or 1D-1D approach asreferred to by other researchers, vertical interactions are identifiedbetween major and minor systems through manholes or groups ofseveral gully inlets as a dynamic link between 1D flow in pipesand 1D flow in surface pathways and ponds. Carr and Smith(2006), Chen et al. (2005), Dey and Kamioka (2006) Chen et al.(2007) have described the 1D-2D approach, where 1D sewerflow is integrated with 2D surface flow simulation. Interactionsbetween the two models take place between underground net-work nodes and surface computational grid cells. This approachenables more realistic analysis of overland flows than the 1D-1D approach, especially in extreme events in which flood flowsare not confined to street/road profiles. Also, treatment of build-ings and other urban structures is more exact, which was studiedboth experimentally (Testa et al. 2007) and numerically (Soares-Frazo et al. 2008). However, 2D models require a higher levelof spatial detail and much shorter time steps than 1D models,hence they are computationally more demanding. This approachis impractical for real-time representation or rapid forecasting ofthe flooding process.

A set of GIS modules was developed and used to define thevarious phases of overland flow and its interaction with time-dependent water bodies, created as ponds, and the computational(and physical) inlets to the sewer network. The sections belowdescribe the concept and modelling processes and outline the soft-ware system developed to perform these tasks. Also described arethe results of a sensitivity analysis and of testing the methodologyon a selected case study at Town A in south UK. The initial ver-sion of the tool was reported by Boonya-aroonnet et al. (2007).It is emphasised however that the methodology for overland flowmodelling can be coupled with other physically-based urbandrainage simulation software packages which use quality GIS fordefinition of processes on the surface.

2 Problem description and research aims

2.1 Pluvial urban floodingPluvial flooding is caused by extreme local storms. To reliablymodel urban pluvial flooding, it is necessary to realistically repre-sent the urban fabric (both land use and terrain) in its complexity.Physically-based concepts in rainfall-runoff modelling have tobe implemented in both the overland and the sewer network partof the system and the interaction of these two systems should beanalysed throughout the storm event and between events. The crit-ical phase occurs when the sewer network capacity is exceeded.

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514 C. Maksimovic et al. Journal of Hydraulic Research Vol. 47, No. 4 (2009)

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(a) (b) (c)Figure 1 Interactions through drainage inlets (manholes) between surface and subsurface systems during storm events (a) free inflow, inlet as a weir,(b) submerged inflow, inlet as an orifice, and (c) outflow (Djordjevic et al. 2005)

Some parts of the network become surcharged and flow throughinlets change direction in the cases (a) to (c) (Fig. 1). Duringheavy storms pluvial flooding can take place even if flow in thesewer network is with a free surface, i.e. the sewer network capac-ity is not exceeded, if inlet capacity is insufficient to capturesurface run-off.

Conventional modelling of overland flow assumes thin sheetflow over a plane surface with an area equivalent to the subcatch-ment area. Although this assumption can yield acceptable results,during heavy storms it may lead to false predictions because inflood vulnerable areas the actual flow pattern is significantly dif-ferent from the simplified sheet flow. Water tends to pond and flowalong preferential paths not only along streets but also betweenbuildings and through other open spaces, and it interacts withoutflows from the pressurised sewer network. Conventional com-mercial urban drainage simulation packages are not developedto appropriately consider such situations. They cannot automati-cally create overland flow features such as ponds and preferentialpaths, thus they cannot represent the extent of pluvial floodingwithin the 1D-1D framework. The approach presented hereinovercomes this difficulty by introducing an innovative analysis ofterrain and flood pattern which enables more reliable and realisticanalysis of the surface flooding process.

2.2 Modelling approaches and limitations

To enable minimising of flood risk in urban areas, modellingapproaches to improve the prediction and quantification of floodrisk have evolved significantly in recent years. Default approachin some conventional urban drainage models is to assume virtualreservoirs on top of the manholes in which surcharged floodwa-ter from the manhole is temporarily detained and then returnedto the sewer if the conditions in the sewer network permit. Thismethod cannot be used for realistic assessment of flood extentand flood damage.

A significant step forward has been made by the dual-drainageconcept (1D-1D), where the urban surface is treated as a net-work of open channels and ponds (major system) connected tothe sewer system (minor system). These systems are linked viaweir/orifice-type elements representing inlets and holes on man-hole covers, through which direct interaction between the twosystems takes place (Djordjevic 2001, Mark et al. 2004, Okamoto

et al. 2007, Leandro et al. 2009). Current software packages(InfoWorks CS, MOUSE, SOBEK, SWMM) are also capableof simulating flooding under 1D-1D, but their methodology toestimate overland flow assumes manual (hence subjective) def-inition of surface flow paths, which is laborious and does notnecessarily allow for a realistic representation of surface flowprocesses. Using high resolution and accurate LiDAR DEMs,automatic creation of the surface flow network is achievable.However, some of the limitations of this method are inherentin its 1D-1D nature (Djordjevic et al. 2005). The obtained local(surface) flood flow depths and velocities enable analysis of dif-ferent flood mitigation schemes, damage evaluation, or flood riskmapping.

2.3 Research aims

The principal aim of the research presented herein was to developand test a new surface flow generation tool to be used inurban drainage simulation. The potential of a 1D-1D model wasenhanced by more accurate GIS-based automatic generation ofthe surface flow network to achieve this goal.

LiDAR based DEMs have been used to identify the locationand characteristics of ponds, the definition of flow paths and theircross-sectional geometry and surface flow network connectivity.A GIS-based tool customised for urban areas has been producedand linked with an existing simulation model. Hence, the inter-actions between the two systems can be modelled reliably, andthe performance of the underground sewer system can be betterassessed. Subsequently, the developed tools were analysed andtested using real data sets from a selected case study site.

The approach is based on the spatial analysis of DEM data andthe creation of separate layers for surface ponds and preferentialsurface flow pathways. The main steps of the procedure are:

Preparation of DEM data: DEM has to be hydraulicallyappropriate; i.e. without large number of flat areas and sinks(pits) and also with the correct alignment of slopes. In thisphase, data reduction is also needed to reduce the number ofpoints in the LiDAR raster image thereby avoiding the use ofextremely large data files.

Identification of ponds and flood vulnerable areas: Themodified DEM is subsequently used to identify the loca-tion of depressions or ponds, and to define their depth

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(elevation)-volume relationships. These ponds define possibleflood vulnerable areas.

Connectivity analysis: The DEM which also includes urban(man-made) features such as streets and buildings is used withan algorithm for defining surface pathways. These pathwaysconnect the previously identified ponds in order to form asurface flow network.

Automatic subcatchment delineation: Sub-areas (initiallymodelled as sheet flow) contributing flow to individualdrainage elements are determined from the DEM and intro-duced to the model via nodes and/or links (depending on whichmodel is used).

Assessment of pathway geometry: Suitable prismatic shapes,representative of channel cross-sections are determined fromthe DEM in the vicinity of the pathway. The cross-sectionquantifies hydraulic capacity of surface flow paths.

Assessment of roughness coefficients: Based on the imper-viousness of surface cover where a pathway is located, theroughness coefficients are estimated as relatively low valuescorresponding to roads and paved surfaces and higher valuesfor green areas.

The results of the above analyses are introduced into a modelfor the integrated hydraulic analysis of the interactions of theburied drainage network and the surface network of ponds (stor-age nodes) and pathways. In this way, temporal and spatialextent of surface flooding can be modelled and analysed morereliably.

3 Concepts to improve overland flow modelling

3.1 Physically-based modelling

Traditional conceptual model methods of analysis based onconcepts such as the rational formula, time of concentration, lin-ear reservoirs, or regression analysis fail in analysing flows onurban catchment during floods. Clearly such methodologies havelimited capacity to reliably model flow dynamics. In physically-based modelling approaches, water movement over the surface(as well as in sewer pipes) is modelled by solving the appropriateapproximation of mass and momentum conservation equations.It is then feasible to simulate the features of urban areas morerealistically.

To prepare the input data for improved overland flow mod-elling, the six activities identified in the previous section have tobe carried out. The surface runoff has to be routed by the appli-cation of the full Saint-Venant equations simultaneously with theunderground sewer network (dual-drainage system). The advan-tage of physically based approaches is that once the model hasbeen calibrated, any changes in physical characteristics of thecatchment (e.g. increased imperviousness due to urbanization),change of network topology, or adding new pipes can be reliablydescribed by updating subcatchment characteristics but withoutthe need for re-calibration of surface run-off model parametersas it would be necessary with conceptual models.

3.2 DEM enhancement for hydraulic modellingPerformance and reliability of overland flow models are highlydependent on DEM quality in terms of accuracy and resolution.Physical processes such as surface flow, surface retention, andsurface conveyance along preferential pathways are essential ele-ments in modelling urban flooding processes. These require fineand accurate terrain representation data; e.g. DEM with hori-zontal resolution less than 5 m. To properly represent the urbanfeatures such as houses, buildings or streets and DEM verticalaccuracy preferably 5 cm to adequately distinguish sidewalkfrom street.

Imperfections in the DEM may directly compromise theresults of the surface network delineation model, so it is importantto have a DEM with as little noise as possible. Since the qualityof DEM mostly depends on the source of data, a detailed DEManalysis and pre-processing are required. The best approach isto produce a custom tailored DEM with a pre-specified resolu-tion (Garbrecht and Martz 2000). However, in the majority of thecases, this solution is cost prohibitive, and a DEM data set thatis already available is used. Therefore, the usual procedure is tocorrect the existing DEM, as described below.

It is becoming common in urban areas that DEM data areavailable from more than one source. Usually each source hasa different resolution and accuracy. Thus, detailed analysis ofthe various sources of DEM data and uncertainty quantificationis necessary. Another problem occurs if the area of interest isonly partly covered by a high resolution data set (LiDAR forexample) and the remaining area covered by a low resolutionDEM. One option is to use only the low resolution data set. Withthis approach, no discrepancies are identified, however, the highresolution data set which is of paramount importance to surfaceflow modelling is not used. A second option is to merge the twodata sets. In this case, several solutions can be used.

Pit cells and flat areas can be considered as errors associatedwith DEM representations of low relief areas. Pit cells do nothave lower adjacent cells; consequently, there is no downslopeflow path to an adjacent cell. Flat areas are characterised by aset of adjacent cells with the same elevation. These problems areusually inherent to the acquisition processes or are generated byinterpolation processes.

Freeman (1991) presents two views in dealing with pit cellsand flat areas in DEM. The first assumes pit cells and flat areasare real terrain features that need to be considered as such duringdrainage analysis. The second considers them as spurious featuresthat should be corrected or removed prior to drainage analysis, soas to create a depressionless DEM. An intermediate approach,known as conditional DEM processing, has produced acceptableresults in this study.

Assuming that small corrections of the DEM are needed toguarantee that flow pathway algorithms run with no problems,changes (depression filling and artificial sloping of flat terrain)and smoothing techniques should be kept to a minimum. Poten-tial methods to solve these problems were presented by Band(1986), Garbrecht and Martz (1996), Jenson and Domingue(1988), Martz and De Jong (1988), Martz and Garbrecht (1995),

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OCallaghan and Mark (1984) and Prodanovic (1999). Thesetechniques include frequency analysis of DEM data and low-passfiltering. These techniques have been enhanced by improvingexisting de-flattening routines and combining current pit cellsremoval algorithms (Leito et al. 2006).

3.3 Identification of ponds and flood vulnerable areasIn most cases, flood events occur during extreme rainfall whensurface runoff is combined with water flowing out from the sur-charged underground drainage system. These two quantities ofwater, which would normally blend and flow, are routed alongthe preferential surface pathways (including streets) and theysubsequently fill local depressions (ponds). Ponds have theirown characteristics and flow dynamics. They can be isolatedor mutually connected, and the flow pattern into and out of apond may change quickly in time. To highlight flood-vulnerableareas with the DEM, it is necessary to identify and characteriseponds.

The DEM raster image is utilised to identify and analyseflood vulnerable areas. The algorithm developed for this pur-pose searches the entire DEM and identifies the local points withelevation lower than surrounding areas. Based on the DEM, thepond boundary and storage volume for each low point is delin-eated using an iterative grow-up routine. The natural exit pointis identified as the termination criterion for the pond delineation(Fig. 2). The exit point is the first location where the water insidethe pond would start to overflow. It acts as the starting point forthe flood pathway over the catchment surface. Hence, it is nec-essary to define the hydraulic characteristics of the outlet at thispoint such that the discharge capacity of the pond outflow hydro-graph can be computed as realistically as possible for a range offlood depths.

3.4 Interaction between ponds and sewer network

The major and minor systems are physically linked at man-holes, gullies or inlets as shown in Fig. 3. Points of verticalflow exchange are located by the GIS tool where manholes stayinside the analysed ponds polygon. The connection type has to beidentified and its potential interaction quantified (Leandro et al.2007). Flow out from the manhole is a function of the difference

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Figure 2 Pond delineation (numbers in cells are elevations)

manhole

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Figure 3 Interaction between manholes and surface pond

in piezometric level in the manhole and the water sheet abovethe manhole. If the manhole is within the pond boundary, thesurcharged flow starts filling the pond. When the pond is full, theexcess flow leaves the pond and flows over the catchment sur-face. After or during the storm, flood water that remained in thepond returns to the drainage system through the same manholes.The rest of the water flows downstream along the appropriatepreferential pathways. The model has been developed such thatthe identified interactions and mutual movements of flood waterbetween the two systems can be represented realistically.

3.5 Connectivity analysis

The urban surface is a complex array of different types of perme-able and impermeable surfaces that typically comprise roadwaysand footpaths which are generally lower than the surroundingareas. Such pathways can transfer flow over significant distancesso that flooding can occur at locations that are remote from thesource of the flood water. Overland flow accumulates in depres-sions, and once the depression is filled, it will overtop and createa surface flow. This flow will either overflow directly into an adja-cent depression or will flow along a connecting pathway until itenters another depression or the sewer network via a gulley inletor manhole. It may also leave the catchment, and so this vol-ume of water has to be subtracted from the water balance of thecatchment.

The rolling ball technique (using flow direction image todetermine flow path to the next surrounding cell) is implementedto trace the water path and delineate all processes (Fig. 4). Start-ing at the natural exit points of the identified ponds or surchargedmanholes, the analysis determines pathways by preferential flowdirections based on terrain slope, taking into account the pres-ence of buildings and other features that are included in the DEM.In cases where insufficiently high DEM resolution prevents therolling ball algorithm from capturing all relevant small featureson the surface, a generated network of pathways may have tobe enhanced manually. Thus the ponds and pathways are rep-resentative of the major flooding related features of the urbansurface.

From Fig. 4, the following types of surface pathways in anurban area can be summarised as:

(i) from pond to downstream pond via pond link;(ii) from pond to downstream manhole or gully;

(iii) from pond out of the catchment;(iv) spillway between two mutually connected ponds;(v) from surcharged manhole to downstream manhole;

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Pond 2

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Figure 4 Types of surface pathways calculated from DEM

(vi) from manhole to downstream pond, and(vii) from surcharged manhole to the outlet of the catchment.

3.6 Automatic sub-catchment delineation

Automatic sub-catchment delineation is the process of automaticpartitioning of its surface into smaller areas that are to be drainedby each computational node (or each pipe/pathway, depending onthe urban drainage model used). The procedure takes into accountthe DEM and the surface features described in the cover images(land uses). The sub-catchments are delineated to comply withsurface flow patterns and are created for nodes (manholes and/orsurface ponds) or links (pipes and/or surface pathways). The pro-cedure uses a raster-based analysis of terrain slope with robustupward search algorithm for contributing pixels (Prodanovicet al. 1998). The procedure is designed to account for variabil-ity of flow angles over different types of land use and artificialobjects (streets, buildings, fences). Grid cells with slope smallerthan a defined threshold are taken as horizontal.

In an urban system there would be some bifurcations whereflow could partition between the major and minor systems. Thedelineation firstly identifies sub-catchments for the minor sys-tem and marks this area as sewered. Then the sub-catchmentsfor the major system will be delineated. The GIS tool sub-sequently extracts the parameters (for example areas, averageslope or weighted slope, percentages of different cover areaswithin the subcatchment) required for computing inflow hydro-graphs for both major and minor systems. Having hydrographsfor the major system (ponds or pathways) is essential duringintensive storm conditions if the overland flow originating fromthe upper part of the catchment outside sewered area can sig-nificantly contribute to the runoff in drainage system in thelower part.

Sub-catchment parameters are estimated from attributes avail-able for each kind of cover object (type of area, imperviousness,surface storage retention, connectivity with sewer system, popu-lation density, property value for flood damage assessment, etc.).By overlapping cover image with the subcatchment image, theGIS tool computes the parameters needed by the model for eachdelineated subcatchment: area, slope (average or weighted), per-centages of different cover areas within subcatchment, or shape(Prodanovic et al. 1998).

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5 Calculated shape

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(b)

Figure 5 Estimation of pathway geometry (a) 3D DEM showing iden-tified flow path, (b) number of cross-section lines drawn perpendicularlyto path, (c) arbitrary shapes of cross sections plotted as found from DEM,and (d) averaged output with two choicestrapezoidal or arbitraryshapes

3.7 Estimation of pathway geometrySurface pathways are approximated by prismatic open chan-nels. To model flow in pathways, the following informationis required: geometry of open-channel, upstream/downstreamelevations, roughness and actual length between two ponds orsurface nodes. The process of the approximation is presentedin Fig. 5. The algorithm uses the previously extracted pathwaysand draws equi-distant cross-sections along each pathway length(Fig. 5b). It then uses the surrounding DEM to estimate andaverage the areas of each cross-section (Fig. 5c). Finally, thealgorithm allows users to select the shape of the channel cross-section which can be either an arbitrary set of points (irregularshape) or e.g. trapezoidal (Fig. 5d).

If an arbitrary shape is selected, the algorithm will determinethe average elevation of the entire pathway at each offset distancefrom the centreline (Fig. 5c). If the trapezoidal shape is selected,the algorithm will compute the average flow areas at differentdepths along the length of each pathway (so called stage-flowarea curve) and then will find the geometry of a trapezoidalshape that fits the stage-flow area curve. The calculation is doneby recognising that the relation between flow area A and depthH of trapezoidal shape is quadratic. The channels bottom widthB and the slope of channels sides 1/m are the unknowns to becalculated. Least-square for the polynomial regression is used tofind these two unknown variables.

4 Specific details of GIS procedures for automaticsurface network creation

4.1 Removal of small pondsWhen analysing a high resolution DEM (e.g. on a 1 m 1 mgrid), it is likely that a large number of small ponds will begenerated. They result either from existing pit cells or errors

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Depth = 0.05 m YesVolume = 10 m3 No

Depth =0.09 m YesVolume = 7 m3 No

Depth = 0.05 m YesVolume = 4 m3 Yes

Depth = 0.25 m NoVolume = 25 m3 No

Depth = 0.05 mVolume = 10 m3

Depth =0.10 mVolume = 7 m3

Depth = 0.25 mVolume=25 m3

Before removal After removal

Figure 6 Pond removal with 0.10 m depth and 5 m3 volume thresholds

in the DEM. Analysis of small pond removal is required toreduce the computational burden by keeping the removal undercontrol, otherwise large amounts of storage will be ignored in thesimulation.

The conventional method for pit cell removal in GIS is to fillall small ponds (sinks) in the DEM with a threshold depth. How-ever, filling DEM will create flat areas which are unfavourable fordetermination of flow direction, as discussed previously. Herein acombination of threshold volume and depth of delineated storagesis employed. Ponds smaller and shallower than the thresholds areremoved and the DEM remains unaltered to preserve slope fea-tures required for the pathway delineation procedure. An exampleof pond removal is shown in Fig. 6. The ponds to be discardedhave to satisfy both thresholds of depth and volume to ensure thatshallow ponds with large storage volumes and deep ponds withsmall surface areas are not excluded.

4.2 Removal of ponds inside buildingsDEM analysis for ponds can identify depressions located insidethe building area. This particular case occurs if there are gardenor roof storage features constructed inside the building perimeter.These storages can be modelled as initial losses from the effec-tive rainfall surface retention, but in this model consideration theyhave no surface linkage to the overland drainage network. There-fore they are to be discarded from the surface runoff network.The developed tool recognises buildings by the raster image thatwas pre-processed from an OS master map by common GIS prac-tices. Buildings and pond layers are overlaid to identify ponds tobe removed.

4.3 Discontinuity of pathways during delineation processFlow pathways have their points of origin and termination (exit).Several terminating conditions for pathway delineation wereidentified previously (Fig. 4). However, the delineation processoccasionally stops without satisfying any of the defined terminat-ing conditions (for example if a pathway enters a pit cell or a flatarea). This problem is common for raster-based algorithms andcan be severe with low quality DEM data. An automatic searchmethod from the path stop point within a buffer region is usedto trace an exit from the problematic point and to continue thepathway delineation. The criteria to determine the exit are eleva-tion, distance and presence of buildings. The algorithm selects anexit by comparing the heights between the stop and potential exit

Path 1

Path 2 Path 3

Path 1

Path 2 Path 3

Path 1001Joining cell

Before After

Figure 7 Procedure for merging pathways at a joining grid cell

points within a user defined surrounding area (buffer radius). Theexit point must be lower with height difference greater than theelevation threshold, usually 1 to 2 cm for a fine DEM, dependingon the decimal precision of the height data. A reasonable bufferradius is about 20 to 50 m.

4.4 Surface junctionsPathways identified using the procedure described in Sec. 3.5form a surface network in which it is likely that parts of two ormore pathways will come close to each other and possibly over-lap over a certain distance. In reality, these parts of pathways willmerge and function as a single channel. During surface networkcreation, this situation is identified by looking at the proxim-ity of pathways such that if parts of two or more pathways arecloser than the specified threshold (usually DEM cell size), theyare merged forming a new computational node (termed surfacejunction) at the meeting point. From that point downstream, sev-eral pathways are combined into one pathway. Figure 7 gives anexample of this procedure. Path 1 and path 2 come close and anew pathway (path 1001) replaces the downstream portions ofthe original paths.

4.5 Pathway cross-section analysis

This analysis is used to quantify the carrying capacity of the path-ways by calculating the hydraulically equivalent cross-sectionalshape. In this analysis, the parameters involved are (Fig. 8):

(i) cross-section interval: this parameter should be equal to orlarger than the DEM cell size,

(ii) buffer radius: pathways along streets should have enoughradius to capture both sides of street curbs,

(iii) maximum depth: this is to define where to stop whenbuildings are on the side of pathways (usually 1 m),

CL Buffer radius

Depth

Cross section interval

Figure 8 Parameters required for shape analysis of cross section

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(iv) minimum depth: this is to define that this pathway isconsidered to be flat, and

(v) distance between two cross-sections along pathways: thiswill result in a number of cross-sections along each pathwayto be analysed and averaged.

5 Tools developed for surface flow delineationin urban areas

Overland flow sub-system modelling is organised in a seriesof modules including DEM analysis, data preparation, inputto hydrodynamic models, post-processing and graphical (GISbased) presentation of results. All modules are wrapped togetherwithin one GIS tool for automatic generation of the surface path-way network across an urban area for the links with hydraulicsimulators.

The tool usage can be divided into five main steps: (1) sub-catchment delineation, (2) pond delineation (also identifyingmajor-minor interactions), (3) pathway delineation (connectiv-ity analysis and locating surface junction nodes), (4) pathwaygeometry extraction, and (5) generation of output files. Cur-rently, the results are exported as an ASCII text file. It containsall the information needed by the hydraulic model for running1D-1D flood simulation. Figure 9 illustrates the developed toolinterface.

The SIPSON 1D/1D dual-drainage simulation model(Djordjevic 2001) was integrated with the developed tool pre-sented herein into a methodological framework for advancedmodelling of urban pluvial flooding (Fig. 10). The algorithms areused to automatically generate the sets of data; i.e. the surfacenetwork with full topology (as nodes and links) with simula-tion parameters and DEM-dependent parameters used for therainfall-runoff model including sub-catchment areas, percentageof pervious and impervious covers, and shape of sub-catchments.

Figure 9 Tool based on described concepts for generating surfaceoverland flow network and simulation inputs. Given values are examples

DelineatedPonds

Sinks & Exits

Connecting Paths

Approximate Geometry

1D Surface Network (Nodes & Links)

1. Surface System

Sewer Network (Manhole & Pipes)

Subcatchment Delineation

SeweredareasUndrained Areas

Out of Catch

R-R Model

2. Drainage System

Majorminor systemmodel (SIPSON)1D surface pathway + 1D sewer network

DTM Enhancement

R-R model parameters parameters

Sewer network

Pond catchment

Reducedpond

Catchment

Interactions

Figure 10 Methodological framework for advanced modelling of urbanpluvial flooding

6 Case study

This section was to trial the use of a 1D surface flow model in con-junction with an InfoWorks model in a sub-catchment of TownA. The study area is a location where two valleys combine toform a single valley. There are four particular locations withinthe catchment which experience frequent flooding due to a combi-nation of hydraulic incapacity in minor system, major system andoperational problems such as root ingress and pumping stationfailure due to power supply failure. The four locations (Fig. 11)with notable historical flooding are described as follows:

Street-1 is a road running along the valley side. There is alow point in the middle of the road which fills up with water toa depth of 150 to 250 mm until the water reaches the level ofa spill point into a narrow alleyway between houses (Fig. 12).The water from here contributes to the flooding in Street-2.

Figure 11 Surface flow network produced, an example of Town A inthe south UK

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Figure 12 Alleyway between houses known to be in operation whenflooding

Street-2 has flooding primarily from severe root ingress ata number of locations. The worst affected sections of thissewer have been replaced. Flooding is no longer reported as aproblem but any flooding would run down into Street-3 andcontribute to the flooding there.

Street-3 is a road traversing across the main valley at ninetydegrees to the valley direction. The flooding location is at thelowest point of Street-3 at the valley bottom where there isalso a row of terraced houses on the downhill side of the road.The flooding fills the low point of the road to a depth of 300 to400 mm until the water reaches the level of a spill point into adownstream road which then flows down towards Street-4.

Street-4 is in the flat coastal area where the road runs at 90to the line of the valley has petered out by this point. Floodinghere is due to overland flow from upstream road which is eitherbecause of runoff from extreme storms or because of powerfailure at pumping station.

There were two different versions of the model used herein asdescribed below:

The Base Model is the conventional InfoWorks verifiedmodel which assumes storage of the outflow from the sur-charged sewers in the hypothetical flood cones, thus havingno overland flow routing. The contributing areas in this modelwere assigned to the modelled manholes in the conventional

Table 1 Simulated flow volumes for storms

Storms 19th October 2004 22nd July 2006 Synthetic M10-60

Basic Enhanced Basic Enhanced Basic model (m3) Enhancedmodel (m3) model (m3) model (m3) model (m3) model (m3) model (m3)

Street-1Minor flows in 652 646 305 297 241 235Street-1Major flows in n/a 0 n/a 0 n/a 0Street-1Minor flows out 682 680 310 300 242 236Street-1Major flows out n/a 0 n/a 0 n/a 0Street-3Minor flows in 4705 4659 1220 1175 905 842Street-3Major flows in n/a 12 n/a 62 n/a 62Street-3Minor flows out 4739 4692 1227 1180 905 842Street-3Major flows out n/a 0 n/a 0 n/a 43

manner. The Base Model comprises 363 nodes, 359 man-holes, 4 orifices, 12 weirs, 9 pumps, 14,263 m of sewer, pipediameters of 150 to 900 mm, 254 sub-catchments with 118 hatotal area. Manhole cover levels vary between +51.34 m and+1.24 m.

The Enhanced Model is a derivative of the Base Model inwhich the surface runoff and flooding (in ponds) has beenbased on the surface flood module developed by the UWRGand described herein. The overland flow routes were identi-fied by the developed GIS tool and the LiDAR DEM of thecatchment was used. No changes were made to the contribut-ing areas which remained allocated to modelled nodes. Thecross-sections of the overland flow links were confined toopen rectangular or open trapezoidal channels. The EnhancedModel contains the sewer and manhole data in the samestructure as the previous model. Importantly the routing ofthe drainage pathways is based on the DEM rather than themodellers time consuming walk around the catchment. Inthe Enhanced Model the drainage pathways used were thosedefined from the estimated channel data and therefore com-prised a mixture of open rectangular and open trapezoidalchannels. In addition to the basic sewer data the final versionof the model (after data clean up) included 192 break nodes(to join channels), 507 channels, 15 weirs, 16 outfalls and 80storage nodes.

Simulations were carried out for the two historical storms(19th October 2004 and 22nd July 2006) and for a syntheticdesign storm of 10 year return periods with duration of 60 min-utes (Synthetic M10-60). The results were compared to assesswhether any of the models (Base and Enhanced) provided abetter or worse representation of the flooding mechanism andextents.

As the models are different and because the overland flow path-ways convey the floodwater away from the flooding locations, itis not appropriate to simply compare flood volumes. Instead, thetotal volumes of flow to and from the Street-1 and the Street-3flooding areas have been extracted from the simulation results andare summarized in Table 1. The minor flow is through the sewersand the major flow is along the overland drainage pathways.

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From these results it appears that overland flows do not play alarge part in the flooding at Street-1, since there are no simulatedoverland inflows along the passageway between houses (Fig. 12).This is due to the fact that pathway in the Enhanced Model hasa higher invert level because the GIS tool picked the level fromLiDAR DEM. It is known that LiDAR DEM at this particularlocation has problems, i.e. it does not represent correctly theheights of the alleyway.

It is clear from the Enhanced Model that overland flows intothe Street-3 flooding area underlines an important aspect in theflooding mechanism. It may be that this is also the case with theSimplified Model but in that model the drainage pathway alongStreet-2 is routed directly into Street-4, effectively bypassingStreet-3. The relatively high proportion of flow into and out ofStreet-3via overland pathways is particularly well illustrated inTable 1 (Synthetic M10-60). With more extreme storms than the10 year return periods used, the minor flows will remain similarbut the major flows will increase significantly and will become ahigher proportion of the total flow.

Another simulation of a more extreme synthetic rainfall of 50years return periods with durations of 60 minutes was carried out.Comparison between hydrographs obtained by the Base Modeland Enhanced Model for the pipe just downstream from Street-3 is shown in Fig. 13. For the Base Model the hydrograph is forthe sewer flow, since (according to the model concept) there is nooverland flow of flood water and it stays in the upstream conesas long as the sewer is surcharged. It has quite a long almost flatpeak because of the gradual release of the volumes stored inthe upstream cones.

For the Enhanced Model the flow is presented separately forthe pipe and surface runoff (broken lines in Fig. 13). It can beseen that the Enhanced Model has identical flows as the BaseModel up to about 30 minutes (free surface flow in sewer net-work, both models produce identical results). Once the sewersystem is surcharged, the Enhanced Model simulates the over-land flow closer to reality. It enables transfer of volumes betweensub-catchments, therefore volumes can differ significantly. Inthis case there is more volume for the two graphs of the EnhancedModel than the single graph of the Base Model. This is becauseadditional overland flows contribute to this location by flowingfrom uphill flooded areas.

Figure 13 clearly presents also the problem of storage conesconcept, as used in the Base Model: since certain volume ofwater is stored in artificial cones and returned later through thesewer network, it will produce a relative large discharge afterthe storm (from 00:50 till 01:02). Consequently the EnhancedModel has a shorter peak because the overland flow channel (inthe road immediately above) is conveying the very high flows ofover 250 l/s.

7 Conclusions

An innovative method for the analysis of the overland flow com-ponent during pluvial flooding in urban areas is presented. Theconcept is based on the use of Detailed High Resolution DEM

0

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Ra

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0 9030 60

Figure 13 Comparison of hydrographs for Base Model andEnhanced Model for pipe just downstream from Street-3

that supports creation of the surface runoff pathways network.The surface network of ponds and preferential paths (alongthe streets originating from surcharged manholes or across theurban catchment from other remote areas) are created by auto-matic catchment delineation. The methodology for obtaining theinformation required for flood simulation such as pond storageand pathway geometries is also presented and uncertainties arequantified. The mutual interactions between surface and under-ground sewer networks are established through inlets or manholeslocated on the bottom of ponds and through predefined surfacepathways that can deliver to the sewer network at downstreaminlets. A detailed description of the methodology is presentedand its applications shown using a case study. This is an impor-tant breakthrough in the long-awaited solution of urban floodmodelling.

The work presented herein enables a fully integrated urbanpluvial flood to be modelled by the 1D-1D approach. However,it should be noted that full success in implementing this conceptdepends on the quality of the DEM. Detailed analysis is requiredto improve DEM quality in complex urban areas for purposes ofmodelling urban flooding. Completion of this tool creates sev-eral new opportunities for advanced urban flood management,including improvements in Real-Time Control, improvementof links between hydraulic modelling and quick precipitationforecasting (nowcasting), and development of short term urbanflood forecasting. Future research will include comparisons ofresults obtained by the presented methodology and emergingcoupled 1D-2D models. Additionally, it is expected that devel-opment of this methodology will shift the overall perception ofurban drainage flooding modelling and will create the need forupgrading commercial 1D packages.

Acknowledgements

The present research presented has been carried out under coor-dination of the first author by the Urban Water Research Group(UWRG), Environmental and Water Resources Engineering(EWRE) Section, Department of Civil and Environmental Engi-neering, Imperial College London, UK within the Flood RiskManagement Research Consortium (FRMRC) project. Financial

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support of the UK Engineering and Physical Sciences ResearchCouncil (EPSRC) and other funding organisations is acknowl-edged. Additionally the support of UK Water Industry Research(UKWIR) and Environmental Agency in funding the case studymodelling exercise is acknowledged and appreciated. The fourthauthor was financially supported by the Portuguese ResearchCouncil Fundao para a Cincia e a Tecnologia (FCT). Thesupport is highly appreciated. Authors also acknowledge contri-butions to discussions of the case study by Mr Oluseyi Adeyemo.

Notation

A = Flow cross-sectional areaB = Width of trapezoidal channelH = Flow depthm = Side slope (H:V) of trapezoidal channel

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overland flow and pathway analysis for modelling of

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