CityFIT Urban Guide: Modelling and Deploying indicators of Property Exposure to Flooding in Lagos using LIDAR DEM and DSM data

Sulaiman Mosuro


The prevalence of flood following heavy precipitation is a vivid and daunting problem in the coastal city of Lagos mostly exacerbated by restricted drainage channels, poorly engineered structures, urbanization processes and the uncontrolled siting of buildings on flood plains. An understanding of floodplain characteristics and storm water flow mechanisms is essential in the efficient management of urbanizing cities. Flood prediction models benefits in giving informed guidance aimed towards ameliorating the impacts of inundation hazards in urbanizing areas as it aids in the capacity for timely public enlightenment on potential flood hazards raising preparedness and minimizing loss.

The propagation of flood inundation in urban catchments is highly dependent on the surface topography, land cover and the representation of structure in elevation data used in modelling. Components of flood prediction modelling addressed by this project include determining:

  • How well LIDAR derived digital elevation data captures floodplain surface forms, their utility in profiling cross-sections for flow hydraulics modelling and how well it can be used in determining structural influences on flood propagation, velocities and depths
  • How might flood exposure indicators be modelled and promptly disseminated to assist in flood response, preparedness, urban planning and in model validation and refinement

The following objectives are set for this research:

  • To develop a flood propagation model consisting of an Hydrologic model of discharge rates and a flow hydraulics model using elevation, land cover and precipitation data
  • To assess the impacts and susceptibility of structures to flood dynamics by utilizing both DEM and DSM in models
  • To map risk levels associated to properties based on their exposure level to flood depths and velocities
  • To prototype a portable application for disseminating flood warning levels, time-series charts and maps of at-risk areas and an event reporting interface for recording flood event details



A case study area of 132km2­­ lying between 3.22° - 3.23° E and 6.42° – 6.52° N in Lagos Nigeria is selected for this research.

Flood inundation is simulated using two modelling components namely the Hydrologic Model a rainfall-runoff model that transforms precipitation data into overland discharge and the Hydraulic Model used to determine water surface levels, flooding depth, extents and flow distribution velocities.

Datasets used for the study include LIDAR DEM and DSM data, Land use, soil and precipitation data


Description: Nigeria.emf

Figure 1: Map of Nigeria showing Lagos State


Description: M:\Dissertation\data\map\LAGMap.png

Figure 2: LIDAR DSM of Study site in context of the whole State

Modelling processes are executed using HEC Tools including HMS, RAS, GeoHMS and GeoRAS. Intermediate procedures including the creation of steady flow cross-section geometric data, composite land use etc. were automated using scripts (python and perl) developed in this research. Thw modelling workflow is outlined in the Figure below

Figure 3: Workflow chart for flood inundation modelling, analysis and deployment



Figure 4: Map of flood depths over time for the July 10, 2011 event, overlaid on DSM


Property risk levels were mapped by associating exposure indicators of flood depth and flow velocities to units of the address parcel layer. The ratings were based on the magnitudes of these flood characterisitics in the neighbourhood of the affected units.

Description: M:\Dissertation\Work\Infrastructure_ZoneStats\Infra Risk Map -Depth.png

Figure 5: Map showing risk levels associated to Properties and Infrastructure based of Flood depth

Inundation extents and flood depths for DSM profiled models cover larger areas than that of their counterpart DEM runs. This is as a result of the fact that structure present in the DSM reduces area that can be occupied by flood water thereby increasing depth and coverage

Figure 6: Comparison of water stages derived from DEM and DSM model simulations for the same rainfall volume

A symmetric difference of the DEM profiled floodplain, with that of the DSM resulted in a layer showing areas potentially protected by presence of structures and the zones that are potential receptors of diverted flood due to structural influence on the flow propagation

Description: M:\Dissertation\images\Structure Influence.png

Figure 7: Map showing structural influence on flood propagation created by a symmetric difference of the flood extents maps from DEM and DSM models


A portable android application was prototyped for disseminating time-series flood model information and for reporting details of flood events as they occur to serve for model calibration and enhancement, thereby completing the flood modelling lifecycle.

  Mobile Texhnology

Figure 8 and 9: Flood Modelling Lifecycle, showing the utility of mobile devices for dissemination and reporting; Architecture of developed mobile application showing the methods and data it utilizes for result visualisation and event reporting

A graphing activity displays charts of discharge rates, precipitation volume and flood depths over the time period of the event. The map interface contain flood extent layers simulated at specific time steps navigable using the seek bar and overlaid on a google satellite base map.


Figure 10 and 11: Android activities showing graph plots of discharge rate, rainfall and flood depths; flood map overlays and event reporting forms. A touch motion on the graph changes the text displays to those of the current time position. The map activity shows an overlay of flood extent navigable over time using the seek bar/time glider. Map tools allow toggling layer and interactive selection on map to reveal additional information


This research saw the creation of a floodplain inundation model suitable for predicting the propagation of flood and the influence of structural form to the flow dynamics of inundation in the study site. The model consists of a hydrologic component that returns the discharge hydrograph of the runoff from precipitation events factoring processes of infiltration loss, concentration lag and topography. The hydraulic component determines water surface levels, flow velocities and flood depths of the inundated area over time.
A comparative analysis of the results of simulation by DEM and DSM allowed for the identification of areas that may benefit from flood defence structures and the consequence of such to other locations. Flood model characteristics of depth and velocity have shown good promise in the assignment of risk levels to properties for evaluating their exposure and vulnerability.
A portable android application was prototyped for disseminating time-series flood model information and for reporting details of flood events as they occur to serve for model calibration and enhancement, thereby completing the flood modelling lifecycle.


ACKERMAN, C. 2011. HEC-GeoRAS–GIS Tools for Support of HEC-RAS using ArcGIS, User's Manual. US Army Corps of Engineers, Hydrologic Engineering Center (HEC), Davis, CA.
ARONICA, G. & LANZA, L. 2005. Drainage efficiency in urban areas: a case study. Hydrological Processes, 19, 1105-1119.
COOK, A. & MERWADE, V. 2009. Effect of topographic data, geometric configuration and modeling approach on flood inundation mapping. Journal of Hydrology, 377, 131-142.
FELDMAN, A. D. 2000. Hydrologic Modeling System HEC-HMS: Technical Reference Manual, US Army Corps of Engineers, Hydrologic Engineering Center.
FEWTRELL, T. J., DUNCAN, A., SAMPSON, C. C., NEAL, J. C. & BATES, P. D. 2011. Benchmarking urban flood models of varying complexity and scale using high resolution terrestrial LiDAR data. Physics and Chemistry of the Earth, Parts A/B/C, 36, 281-291.
HERNANDEZ, M., MILLER, S. N., GOODRICH, D. C., GOFF, B. F., KEPNER, W. G., EDMONDS, C. M. & JONES, K. B. 2000. Modeling runoff response to land cover and rainfall spatial variability in semi-arid watersheds. Environmental Monitoring and Assessment, 64, 285-298.
HORRITT, M. S. & BATES, P. D. 2002. Evaluation of 1D and 2D numerical models for predicting river flood inundation. Journal of Hydrology, 268, 87-99.
MARKS, K. & BATES, P. 2000. Integration of high-resolution topographic data with floodplain flow models. Hydrological Processes, 14, 2109-2122.
MEIERDIERCKS, K. L., SMITH, J. A., BAECK, M. L. & MILLER, A. J. 2010. Analyses of Urban Drainage Network Structure and its Impact on Hydrologic Response1. JAWRA Journal of the American Water Resources Association, 46, 932-943.
MESSNER, F. & MEYER, V. 2006. Flood damage, vulnerability and risk perception - Challenges for flood damage research. In: SCHANZE, J., ZEMAN, E. & MARSALEK, J. (eds.) Flood Risk Management: Hazards, Vulnerability and Mitigation Measures. Springer Netherlands.
PENNING-ROWSELL, E. C., TUNSTALL, S. M., TAPSELL, S. M. & PARKER, D. J. 2000. The Benefits of Flood Warnings: Real But Elusive, and Politically Significant. Water and Environment Journal, 14, 7-14.
WEBSTER, T. L., FORBES, D. L., DICKIE, S. & SHREENAN, R. 2004. Using topographic lidar to map flood risk from storm-surge events for Charlottetown, Prince Edward Island, Canada. Canadian Journal of Remote Sensing, 30, 64-76.
WILSON, M. & ATKINSON, P. 2005. The use of elevation data in flood inundation modelling: a comparison of ERS interferometric SAR and combined contour and differential GPS data. International Journal of River Basin Management, 3, 3-20.

Designed by Sulaiman Mosuro (s1062870). Dissertation in GIS, School of GeoSciences, University of Edinburgh.
Contact: August 2012. All rights reserved.