Urban Flooding Assesment-v4 - LNH waterlnhwater.dk/pdf/Artikler/Urban Flooding Assesment.pdf ·...

11th International Conference on Urban Drainage, Edinburgh, Scotland, UK, 2008

Nielsen et al. 1

Urban Flooding Assessment

Nanna H. Nielsen1*, Lina N. Jensen1, Jens J. Linde1 and Per Hallager3

1 PH-Consult, Gladsaxevej 363, 2860 Soeborg, Denmark 2 Odense Water LTD, Vandvaerksvej 7, 5100 Odense, Denmark

*Corresponding author, e-mail [email protected]

ABSTRACT Predicted increase in extreme rainfall due to climate change combined with rapid urbanization can lead to an increase in urban flooding. In order to minimize the risk of flooding and locate high flood risk areas, tools for flood management are needed. In the last couple of years several damaging floods have occurred in the city of Odense, Denmark. In order to better understand the reasons behind this and find adaptive solutions to reduce the impact of urban flooding, an integrated urban flood study was initiated. In the study numerical coupled 1D-2D models were developed for two catchments. The models simulate the drainage conditions in the sewer network and overland flow and proved useful in urban flood management. The 1D model for the collection network was calibrated using measured water levels in the sewage system. The coupled model was verified using photos taken during a flooding event. The model was able to simulate the observed overland flow for an extreme rainfall event. The model was then used to test and optimize regulation of terrain for control of water pathways of overland flow. The use of integrated 1D-2D models for assessing urban flooding is a useful tool for detailed analysis of the overland flow. The models can also be used for locating flood risk areas and developing strategies for minimizing the consequences of hazardous flooding. KEYWORDS Urban flooding management; coupled models; overland flow; extreme rainfall INTRODUCTION Urban flooding is expected to increase due to climate changes and rapid urbanization. In order to minimize the risk of flooding and locate high risk flood areas, tools for flood management are needed. When flooding occurs overland flow tends to run on a complex terrain with many flow paths in close connection with the collection system. It is in a physical sense difficult to separate these two flow systems. Usually, a one way flow connection is applied, allowing water to enter the collection system but overflow from the drainage system is not routed on the surface. In recent years there have been advances in modelling the interaction between sewer models and models describing the flow on the surface. Two dimensional models originally developed for use in estuaries and oceanic modelling has been modified to interact with the drainage system, i.e. MIKE 21 (DHI, 2008), TUFLOW (Sume, 2001) and RMA-2 (King, 2006). Other models have been developed specifically to describe overland flooding, such as DELFT-FLS (Deltares, 2008) and GIS based pathway models (Boonya-aroonnet et al, 2007). This will allow for more complex systems to be better described and modeled.


2 Urban Flooding Assessment

METHODS In order to describe overland flow in interaction with the collection system a number of basic data are needed. First of all, a detailed description of the collection system with validated data on the physics of the system is needed. Furthermore a terrain model of the contributing catchment area including surface runoff parameters, i.e. time of concentration and the time area curve is required. In this process, it is of significant importance to obtain the correct volume in the hydrograph. Besides obtaining the correct volume of rainwater and hydrograph it is important to obtain an accurate description of waterways and estimation of the storage volume on the surface. A fully integrated software system, MIKE FLOOD, was used to model the pipe flow, the surface flow and the interaction between them (DHI, 2008). MIKE FLOOD consists of a two- dimensional hydrodynamic surface model (MIKE 21) with a dynamic link to a fully dynamic one-dimensional collection model (MOUSE). MIKE FLOOD couple the two models with a link flow equation describing the bidirectional flow through the manholes, see figure 1. To describe the exchange of water between surface and the manholes three equations are implemented in the model; orifice equation, weir equation and exponential function. In general, it requires an unrealistic amount of detailed data on every manhole in order to apply physical dimensions for the orifice and the weir equation, so from an engineering point of view the physic of the equation is unimportant. It was found that the exponential function was easy to use and produced reliable results in respect to differences between water levels in the two models (the surface model and the collection model).

Figure 1. The two dimensional hydrodynamic surface model is linked to the collection model (left). Illustration of the dynamic flow between the surface and the collection system (right). The surface model Relatively new techniques make it possible to obtain high quality digital terrain models (DTM) from digital surface models (DSM), i.e. (Baillard, 2003). The DSM model is the observed surface with houses, trees, parked cars, etc. In the digital terrain model houses, trees and cars are removed leaving a model of the “untouched” ground. On top of the DTM, registered houses were added with an additional elevation height of 4 meters assuming that buildings are impenetrable. The DTM model used in this study has a rather rough density of 5-10 meters resulting in an inaccurate description of roads. In the case area, roads are typically located 10-30 cm lower than the walkways and garden areas. To compensate, the roads were digitised and lowered 20 cm, see figure 2. For terrain models with higher digital densities, this should not be necessary.


Nielsen et al. 3

Figure 2. The surface model is generated from the digital terrain model and houses and roads polygons (left). 3D image of the generated surface model (right). The MIKE 21 surface model solves the equations for conservation of mass and the momentum equation in a squared grid. In this type of model the grid size should be small enough to simulate the flow around buildings and on the streets. Furthermore, flow diagonal to the squared grids tends to have a high numerical resistance: in some cases higher than the physical resistance. The numerical resistance decreases with decreased grid size. For numerical reasons it is therefore an advantage to decrease the grid size to a minimum, but the computational time will increase drastically. Having in mind that the model should be applied as an ordinary engineering tool for larger areas, it was found that a grid size between 1 and 4 meters was most optimal. When using a 4 meter grid for calculating surface flow, some manual adjustments to the terrain model were necessary in order to insure flow around buildings, etc. Only minor adjustments had to be done using a 1 meter grid. Tests were made on grid size, resistance and runoff reduction factor. It was found that the grid size should not be higher than 4 meters in suburban catchments. For dense city areas the grid size should be lower. With a grid size of 1-2 meters a sufficient level of detail of urban morphological features is obtained. The resistance on the surface and in the collection pipes was found to have minor influence on the result during extreme rainfall runoff, while the reduction factor and the volume in the rain had a high influence on the results. The collection model To describe the collection system a calibrated MOUSE model was used. The physical sewer data were well registered, i.e. lengths, inverts levels and materials. The rain was applied to each manhole as runoff hydrographs taking runoff time, reduction factor and initial losses into account. METHOD EVALUATION The method was applied on two urban catchments within the city of Odense, Denmark, which has been flooded in the recent past. Five RIMCO tipping bucket rain gauges are located in the city area. The rain gauges are used by the Danish Water Pollution Control Committee and connected to a national database (DMI, 2007). Case area 1 is located closest to gauge 28181 and 28182 while case area 2 is between gauge 28181 and 28186, see figure 3.



Figure 3. Location of case areas and rain gauges. Case study 1 The initial case study was carried out in a suburb with a mix of an industrial and a residential area. The residential area consists of single family houses and apartment buildings. Rainwater collected from an impervious area of 74 hectares is lead to an open catch basin of only 1600 m3. The basin is connected to the downstream system by a single pipe with a capacity of 800 l/s. The pipes coming into the basin area have a capacity of 1600 l/s and, consequently, the capacity of the basin will be exceeded and flooding in the area will occur at certain rain events, see figure 4.

Figure 4. The catchment area covers 74 hectares with just one single downstream pipe connection. The capacity of the outlet is 800 l/s, but with an inlet of 1600 l/s there is high risk of flooding. In the vicinity of the catch basin there is a local depression in the terrain where water may pond. This area has been developed with single family houses and flooding resulting in damaged buildings has occurred 3 times during the last 5 years. One of the incidents was July 23th 2006 where 21.8 mm of rain was measured at gauge 28182 within 40 minutes and 21.2 mm at gauge 28181 within 50 minutes. The rain was moving from gauge 28181 toward gauge 28182. A linear relation for the rain was assumed weighting gauge 28181 with one third and gauge 28182 with two thirds as shown on figure 5.


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Figure 5. Rainfall of July 23th 2006 at gauge 28181 and 28182. The area is located between the two gauges. The rainfall is moving from gauge 28181 to 28182. During the flooding event 73 pictures were taken by one of the residents, resulting in high quality data for model validation. Figure 6-9 shows the simulated water levels and photos taken during the event at exactly the same time. The water levels are visualised in a GIS environment.

Waterlevel [m]Above 0.70

0.65 - 0.700.60 - 0.650.55 - 0.600.50 - 0.550.45 - 0.500.40 - 0.450.35 - 0.400.30 - 0.350.25 - 0.300.20 - 0.250.15 - 0.200.10 - 0.150.05 - 0.100.02 - 0.05Below 0.02


0.65 - 0.700.60 - 0.650.55 - 0.600.50 - 0.550.45 - 0.500.40 - 0.450.35 - 0.400.30 - 0.350.25 - 0.300.20 - 0.250.15 - 0.200.10 - 0.150.05 - 0.100.02 - 0.05Below 0.02

Figure 6. 04:43 pm. The road is flooded caused by backwater effect and water level is rising toward the front yard.


0.65 - 0.700.60 - 0.650.55 - 0.600.50 - 0.550.45 - 0.500.40 - 0.450.35 - 0.400.30 - 0.350.25 - 0.300.20 - 0.250.15 - 0.200.10 - 0.150.05 - 0.100.02 - 0.05Below 0.02


0.65 - 0.700.60 - 0.650.55 - 0.600.50 - 0.550.45 - 0.500.40 - 0.450.35 - 0.400.30 - 0.350.25 - 0.300.20 - 0.250.15 - 0.200.10 - 0.150.05 - 0.100.02 - 0.05Below 0.02

Figure 7. 04:57 pm. The flooding covers the front yard and water level is rising toward the plinth.




0.65 - 0.700.60 - 0.650.55 - 0.600.50 - 0.550.45 - 0.500.40 - 0.450.35 - 0.400.30 - 0.350.25 - 0.300.20 - 0.250.15 - 0.200.10 - 0.150.05 - 0.100.02 - 0.05Below 0.02


0.65 - 0.700.60 - 0.650.55 - 0.600.50 - 0.550.45 - 0.500.40 - 0.450.35 - 0.400.30 - 0.350.25 - 0.300.20 - 0.250.15 - 0.200.10 - 0.150.05 - 0.100.02 - 0.05Below 0.02

Figure 8. 05:07 pm. Water level exceeds the edge of the storage basin. Waterlevel [m]

Above 0.700.65 - 0.700.60 - 0.650.55 - 0.600.50 - 0.550.45 - 0.500.40 - 0.450.35 - 0.400.30 - 0.350.25 - 0.300.20 - 0.250.15 - 0.200.10 - 0.150.05 - 0.100.02 - 0.05Below 0.02


0.65 - 0.700.60 - 0.650.55 - 0.600.50 - 0.550.45 - 0.500.40 - 0.450.35 - 0.400.30 - 0.350.25 - 0.300.20 - 0.250.15 - 0.200.10 - 0.150.05 - 0.100.02 - 0.05Below 0.02

Figure 9. 05:46 pm. Maximum water levels approximately 45 minutes after the rain stops. During the end of the most intensive part of the rainfall the capacity in the pipes is exceeded resulting in backwater effect from the downstream system. The backwater effect generated a water flow of 400-600 l/s resulting in flooded roads within 5-10 minutes (see figure 6). 10 minutes later, the water level reaches the plinths of several houses (see figure 7). After 30 minutes with backwater effect, the water exceeds the edge of the catch basin (see figure 8). The use of photos compared to the simulated water levels shows that the model complex can be used for reliable modeling of overland flow. At the moment no solution to reduce the flooding has been agreed on. After validation of the model the method was used to identify possible solutions to a flooding problem in another area in Odense. Case study 2 The second case study was carried out in a sports arena close to downtown Odense. The arena consists of a gymnasium, an athletic stadium, an ice rink, a cycle-racing track and a soccer stadium. In the area, a newly build gymnasium had been subject to flooding. Since construction finished in 2005 the building has been flooded 3 times. The cost of restoration was approximately 150,000 euro, including new flooring and drying of the building. Furthermore, the building was out of use several months after each flooding. The causes of the flooding events were analyzed and 3 major sources contributing to the flooding were identified. It was concluded that the cause of the flooding was a combination of heavy rainfall, hydraulic insufficiency of the drainage system and the fact that gymnasium was build in a local depression in the terrain where the overland flow tends to accumulate.


Nielsen et al. 7

Comparing the rain event resulting in flooding with statistical data from 6 Danish rain gauges from 1933-1962, indicates that the area are flooded with a return period of 1-2 years (maximum rain intensity in a 10 minute interval). Table 1. Rain statistic of events that caused flooding of the gymnasium. I5-180 is maximum rain intensity in µm/s in intervals from 5-180 minutes. Rain event I5 I10 I20 I30 I60 I180 13th Aug. 2006 (34.4mm/296min) 13.33 12.33 9.00 7.17 5.22 2.51 Gauge 28186, return period (year) 0.5-1 1-2 1-2 1-2 3-5 4-5 Nationally, return period (year) 0.5-1 1-2 1-2 2-5 2-5 5-10 17th of Aug. 2006 (30.8mm/47min) 33.33 31.00 22.33 16.56 8.56 2.85 Gauge 28186, return period (year) >25.6 >25.6 >25.6 >25.6 >25.6 10-15 Nationally, return period (year) 10-20 20-50 20-50 20-50 10-20 5-10 30th June 2007 (19.0mm/150min) 15.33 13.67 11.17 8.00 4.23 1.76 Gauge 28186, return period (year) 0.5-1 1-2 3-4 2-3 1-2 1-2 Nationally, return period (year) 1-2 1-2 2-5 2-5 1-2 1-2

Proposed solutions A traditional approach to the flooding problem would be to modify the drainage net. But the modification needed to achieve adequate capacity is so extensive that it would not be carried out in the near future. Furthermore, due to the topography of the area, the building would eventually be flooded for events exceeding the design criteria. An alternative flooding control solution was therefore proposed consisting of open channels that lead excess overland flow around the building towards the athletic stadium. A cost/benefit analysis indicated that the post-event recovery at the stadium had the lowest cost. Cleaning of this area after a flood event would be in the range of 4,000 euro. The model complex was then used to identify obvious pathways and optimize design of the channels. A detailed MOUSE model for the collection system was used. The impervious catchment area is approximately 74 ha containing a mix of combined and separate sewage systems. To the north and west the area contains mostly residential housing with dense buildings, while the area to the south is a commercial area. The model was calibrated using data from 6 water level sensors placed in combined overflow structures and in the separate system. Data obtained from the original DTM with a grid size of approx. 5-10 m was resampled to include buildings and roads as a 1m x 1m grid resolution. To minimize numerical resistance, the grid was rotated to fit the flow angle along the gymnasium. (See figure 10). A time step of 0.2 sec was used in the simulations.



Figure 10. The two-dimensional hydrodynamic model linked to the collection model. Zoom on the sports arena.

Model Results A simulation of the rain event that caused flooding on August the 17th 2006 was carried out. Rain data was collected from rain gauge 28186 (see table 1). A maximum water depth of 48 cm was simulated in front of the gymnasium (see figure 11). Comparing the result to photos taken after the event validated this (see figure 11). The simulation clearly indicates how overland flow is accumulated in the vicinity of the gymnasium where the deepest water levels were calculated. The overland water tends to flow along the building toward the stadium.

Figure 11. Maximum simulated water depth and pictures taken after the event. Pathways differing in width, depth, and slope and Manning number were inserted into the DTM and tested in the one-dimensional pipe model, see figure 12. The channels were simulated as open channels in connection with manholes. Maximum water levels were calculated for different return periods. This was done in order to test different scenarios before modeling the best alternatives in MIKE FLOOD, which have a much longer computational time.

Above 2827 - 2826 - 2725 - 2624 - 2523 - 2422 - 2321 - 2220 - 2119 - 2018 - 1917 - 1816 - 1715 - 1614 - 1513 - 1412 - 1311 - 1210 - 119 - 108 - 97 - 86 - 75 - 6

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Figure 12. Sketch of proposed alterations in the terrain. Maximum simulated water levels with two open channels on each side of the building are shown in figure 13. Simulation is done for the rain event August the 17th 2006. Water is lead around the building toward the athletic stadium. The maximum water levels in the channel are approximately 40 cm, which is below the plinth of the building.

Figure 13. Maximum simulated water levels in front of the gymnasium. Simulation is done with a channel on each side of the building. The maximum water levels in the channel are approximately 40 cm, which is below the plinth of the building. The proposed solution will prevent flooding of the gymnasium for rain events with a return period of approximately 10 years. For return periods up to 100 years, water depths of 20 cm above the plinth can be expected. At the stadium a maximum of 6300 m3 will be stored during a rain event with a 100 year return period.

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CONCLUSIONS In this study a method combining a two-dimensional MIKE 21 model, describing overland flow, with a one-dimensional MOUSE model, describing pipe flow, was used to describe urban flooding. The method was applied on two areas in the city of Odense, Denmark. The simulations were validated using photos taken during an extreme rain event. The results obtained shows that the method is capable of successfully describing behavior across an urban flood plain. The model was used to identify and optimize different solutions to reduce flooding of a gymnasium. Flooding of the area is inevitable due to the topography. Open channels were proposed to protect the buildings and reduce the economic consequence of flooding events. Water will be routed towards an athletic stadium where the post recovery cost of the flood event is expected to be much lower. To obtain plausible results, detailed knowledge of data from the collection system and high- quality digital terrain model is needed. However, if this data is available the method is useful for engineering purposes. To decrease the computational time a grid size of 1-4 meters was found to be sufficient. Furthermore a one-dimensional collection model can be used as a first step to describe the interaction between the pipes and overland flow via open channel flow. Rainfall was not applied to the surface model, but to the manholes in the collection model. This simplified method proved sufficient in this study. The model complex will be a good tool when assessing urban flooding or making emergency plans, i.e. regulating the terrain. The approach can also be used to generate flood risk maps visualized in a GIS-environment. ACKNOWLEDGEMENT Special thanks to Søren F. Rasmussen PH-Consult for modelling and input to this study. REFERENCES Baillard, C. (2003). Production of urban DSMs combining 3D vector data and stereo aerial imagery. In: Heinrich

Ebner H., Heipke C., Mayer H. and Pakzad K. (eds): Photogrammetric Image Analysis. Proc. ISPRS Workshop, Vol. XXXIV, Part 3/W8, Munich 17.-19. Sept. 2003. Institute of Photogrammetry and GeoInformation, University of Hannover, Germany

Boonya-aroonnet S, Maksimović Č, Prodanović D, Djordjević S (2007). Urban pluvial flooding : development

of GIS based pathway model for surface flooding and interface with surcharged sewer model. In: Desbordes M. and B. Chocat (eds): Sustainable Techniques and Strategies in Urban Water Management. Proc. 6th Int. Conf. Novatech 2007, Volume 1, Lyon June 2007, 481-488. GRAIE, France

Danish Metrological Institute (DMI) (2007) rain data, Lyngbyvej 100, DK-2100 Copenhagen Oe, Denmark Deltares (2008), Reference Manual DELFT-FLS, http://www.wldelft.nl, visited Marts 2008. DHI Water and Environment (2008), Reference Manual Mike Flood. http://www.dhigroup.com, visited Marts

2008. King, I. P., Donnel B.P. (eds) (2006). Users Guide to RMA2 WES Version 4.5. US Army, Engineer research and

Development Center. Waterways Experiment Station and Hydraulics Laboratory. USA Sume W.J. (2001). Modelling of Bends and Hydraulic Structures in a Two-Dimensional Scheme. Conference on

Hydraulics in Civil Engineering, Hobart, 28 –30 November 2001. The Institution of Engineers, Australia.

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