Probabilistic GIS-based method for delineation of urban flooding risk hotspots

Jalayer, Fatemeh; Risi, Raffaele De; Paola, Francesco De; Giugni, Maurizio; Manfredi, Gaetano; Gasparini, Paolo; Topa, Maria Elena; Yonas, Nebyou; Yeshitela, Kumelachew; Nebebe, Alemu; Cavan, Gina; Lindley, Sarah; Printz, Andreas; Renner, Florian

doi:10.1007/s11069-014-1119-2

Cited by 59 publications

(52 citation statements)

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“…The maximum likelihood procedure employed for calibrating the TWI threshold is described in detail in Jalayer et al (2014) andDe Risi et al (2014). Let W represent the spatial window of a zone of interest (within the basin) containing historical flooding extent information.…”

Section: Maximum Likelihood Estimation Of Twi Thresholdmentioning

confidence: 99%

“…This index allows for the delineation of a portion of hydrographic basin potentially exposed to flood inundation, by identifying all the areas characterized by a TWI that exceeds a given threshold; this threshold is strictly dependent on the geomorphology of the hydrographic basin. The threshold value can be calibrated based on results of detailed delineation of the inundation profile for selected zones (Motevalli and Vafakhah 2016;Jalayer et al 2014;Degiorgis et al 2012;Manfreda et al 2008Manfreda et al , 2014Manfreda et al , 2015. High exposure areas are identified by using a map of urban morphology types (UMTs), from which residential UMTs are extracted, and a geo-spatial census dataset for demographic information (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…The novelty in the approach proposed herein lies in the use of Bayesian parameter estimation for the characterization of uncertainties in delineating the potentially flood-prone areas. More specifically, in two previous works Jalayer et al 2014) the authors demonstrate how the potentially flood-prone areas can be delineated through a Maximum Likelihood Estimation (MLE) procedure applied to a spatial window in microscale based on historical and calculated flooding extents, respectively. In this work, these two approaches are compared and integrated in a Bayesian framework.…”

Section: Introductionmentioning

confidence: 99%

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

Risi

Jalayer

Paola

et al. 2017

Stoch Environ Res Risk Assess

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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.

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Section: Maximum Likelihood Estimation Of Twi Thresholdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Risi

Jalayer

Paola

et al. 2017

Stoch Environ Res Risk Assess

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“…Vulnerability is the degree of loss to a given element or set of elements at risk resulting from the occurrence of a natural phenomenon of a given magnitude (Mundhe, 2008). For urban planning purposes, a hazard map is not completely useful (Jalayer et al, 2014). The location of the areas where the largest economical losses due to natural disasters could occur is of invaluable importance.…”

Section: Introductionmentioning

confidence: 99%

GIS Based Urban Flood Vulnerability Analysis in Western Zone of Ahmedabad City

Mundhe¹,

Deshmukh²,

Vyas³

2017

International Journal of Research in Geography

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“…Urban sprawl, a serious problem for many rapidly developing countries, refers to the uncontrolled growth of an urban area resulting from poorly or totally unplanned urbanization [2]. Considering that around half of the world's population lives in urban areas [3], and the encroachment of urban land uses on non-urban land has become a common phenomenon throughout developing countries [4], this is a notable issue that requires further study.…”

Section: Introductionmentioning

confidence: 99%

Identifying the Risk Areas and Urban Growth by ArcGIS-Tools

et al. 2016

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Abstract:Abouelreesh is one of the most at risk areas in Aswan, Egypt, which suffers from storms, poor drainage, and flash flooding. These phenomena affect the urban areas and cause a lot of damage to buildings and infrastructure. Moreover, the potential for the further realization of dangerous situations increased when the urban areas of Abouelreesh extended towards the risk areas. In an effort to ameliorate the danger, two key issues for urban growth management were studied, namely: (i) estimations regarding the pace of urban sprawl, and (ii) the identification of urban areas located in regions that would be affected by flash floods. Analyzing these phenomena require a lot of data in order to obtain good results, but in our case, the official data or field data was limited so we tried to obtain it by accessing two kinds of free sources of satellite data. First, we used Arc GIS tools to analyze (digital elevation model (DEM)) files in order to study the watershed and better identify the risk area. Second, we studied historical imagery in Google Earth to determine the age of each urban block. The urban growth rate in the risk areas had risen to 63.31% in 2001. Urban growth in the case study area had been influenced by house sizes, because most people were looking to live in bigger houses. The aforementioned problem can be observed by considering the increasing average house sizes from 2001 until 2013, where, especially in risky areas, the average of house sizes had grown from 223 m 2 in 2001 to 318 m 2 in 2013. The findings from this study would be useful to urban planners and government officials in helping them to make informed decisions on urban development to benefit the community, especially those living in areas at risk from flash flooding from heavy rain events.

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Probabilistic GIS-based method for delineation of urban flooding risk hotspots

Cited by 59 publications

References 11 publications

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

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

GIS Based Urban Flood Vulnerability Analysis in Western Zone of Ahmedabad City

Identifying the Risk Areas and Urban Growth by ArcGIS-Tools

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