Delineation of flooding risk hotspots based on digital elevation model, calculated and historical flooding extents: the case of Ouagadougou

Abstract

Delineation of flood risk hotspots can be considered as one of the first steps in an integrated methodology for urban flood risk management and mitigation. This paper presents a step-by-step methodology in a GISbased framework for identifying flooding risk hotspots for residential buildings. This is done by overlaying a map of potentially flood-prone areas [estimated through the topographic wetness index (TWI)], a map of residential areas [extracted from a city-wide assessment of urban morphology types (UMT)], and a geo-spatial census dataset. The novelty of this paper consists in the fact that the floodprone areas (the TWI thresholds) are identified through a maximum likelihood method (MLE) based both on inundation profiles calculated for a specific return period (T R ), and on information about the extent of historical flooding in the area of interest. Furthermore, Bayesian parameter updating is employed in order to estimate the TWI threshold by employing the historical extent as prior information and the inundation map for calculating the likelihood function. For different statistics of the TWI threshold, the map of potentially flood-prone areas is overlaid with the map of residential urban morphology units in order to delineate the residential flooding risk urban hotspots. Overlaying the delineated urban hotspots with geo-spatial census datasets, the number of people affected by flooding is estimated. These kind of screening procedures are particularly useful for locations where there is a lack of detailed data or where it is difficult to perform accurate flood risk assessment. In fact, an application of the proposed procedure is demonstrated for the identification of urban flooding risk hotspots in the city of Ouagadougou, capital of Burkina Faso, a city for which the observed spatial extent of a major flood event in 2009 and a calculated inundation map for a return period of 300 years are both available.

Flood-prone areas assessment using linear binary classifiers based on flood maps obtained from 1D and 2D hydraulic models

“…In this framework, the research has recently shown that the delineation of flood-prone areas can be carried out using simplified methods that rely on basin geomorphologic features (e.g., Nardi et al 2006;Manfreda et al 2011Manfreda et al , 2014aDegiorgis et al 2012;Jalayer et al 2014;De Risi et al 2014;Papaioannou et al 2014). Such innovative procedures may provide a preliminary delineation of the flood-prone areas useful for the planning of numerical analyses, and for insurance companies that have a growing interest toward the identification of the assets and population at risk.…”

A multi-dimensional assessment of urban vulnerability to climate change in Sub-Saharan Africa

“…Fourth, the systematic engagement of a wide range of stakeholders in indicator selection as well as in discussions on how to plan for climate change adaptation was also a new and innovative method (Nyed and Herslund 2013) The project 'cocreated' data and learning for the use of local authorities in the years to come-data which are relatively simple to update, e.g., through vulnerability mapping which combined different data types and sets in GIS layers. Fifth, a probabilistic flood hot spot methodology was developed and applied to several case study cities, providing a method for flood risk screening which has strong potential for application in other Sub-Saharan urban contexts (Jalayer et al 2014b;De Risi et al 2014). Sixth, the integration of the different assessment methods was attempted through multiple studies undertaken at the same case study sites and involving experts in engineering, social science, climate science, geography, ecology and urban and green infrastructure planning.…”

Delineation of flood-prone areas using modified topographic index for a river basin

“…De Risi et al (2014) studied DEM as well as a probabilistic framework for Ouagadougou, Burkina Faso, Africa for the year 2009. They concluded that the Bayesian updating procedure was useful for computing the topographic wetness index (TWI) threshold.…”