|
|||||
|
|
Probabilistic GIS-based method for delineation of urban flooding risk hotspots |
|
| Authors: | Fatemeh Jalayer Raffaele De Risi Francesco De Paola Maurizio Giugni Gaetano Manfredi Paolo Gasparini Maria Elena Topa Nebyou Yonas Kumelachew Yeshitela Alemu Nebebe Gina Cavan Sarah Lindley Andreas Printz Florian Renner |
| Institution: | 1. Department of Structures for Engineering and Architecture, University of Naples Federico II, Via Claudio 21, 80125, Naples, Italy 3. Analysis and Monitoring of Environmental Risks (AMRA), Scarl, Naples, Italy 2. Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Naples, Italy 4. Ethiopian Institute of Architecture, Building Construction and City Development, Addis Ababa, Ethiopia 5. Division of Geography and Environmental Management, Manchester Metropolitan University, Manchester, UK 6. School of Environment and Development, The University of Manchester, Manchester, UK 7. Chair for Strategic Landscape Planning and Management, Technical University of Munich, Munich, Germany |
| Abstract: | Identifying urban flooding risk hotspots is one of the first steps in an integrated methodology for urban flood risk assessment and mitigation. This work employs three GIS-based frameworks for identifying urban flooding risk hotspots for residential buildings and urban corridors. This is done by overlaying a map of potentially flood-prone areas estimated through the topographic wetness index (TWI)], a map of residential areas and urban corridors extracted from a city-wide assessment of urban morphology types (UMT)], and a geo-spatial census dataset. A maximum likelihood method (MLE) is employed for estimating the threshold used for identifying the flood-prone areas (the TWI threshold) based on the inundation profiles calculated for various return periods within a given spatial window. Furthermore, Bayesian parameter estimation is employed in order to estimate the TWI threshold based on inundation profiles calculated for more than one spatial window. For different statistics of the TWI threshold (e.g. MLE estimate, 16th percentile, 50th percentile), the map of the potentially flood-prone areas is overlaid with the map of urban morphology units, identified as residential and urban corridors, in order to delineate the urban hotspots for both UMT. Moreover, information related to population density is integrated by overlaying geo-spatial census datasets in order to estimate the number of people affected by flooding. Differences in exposure characteristics have been assessed for a range of different residential types. As a demonstration, urban flooding risk hotspots are delineated for different percentiles of the TWI value for the city of Addis Ababa, Ethiopia. |
| Keywords: | |
| 本文献已被 SpringerLink 等数据库收录！ | |