Flood Risk Assessment in Housing under an Urban Development Scheme Simulating Water Flow in Plains

Abstract

Floods are increasingly occurring around the world more often, this implies analysing the risks connected to both human health and the environment, and to infrastructure and properties. The objective is to establish areas susceptible to flooding and their impact on the population through the effects on the unit of analysis "housing". To simulate the floods and map the affected areas, the FluBiDi 2D model was used. Two conditions for one urban zone analysed within the Mexico Valley were compared: (a) with the current hydraulic infrastructure and (b) with the application of rectification of channels. The available information was the discharge getting into the catchment and the total of homes in 2015. Projections for 20-year and 50-year planning horizon were considered, and for the 50 years, an evaluation of a non-structural measure was applied. Results show that under the current infrastructure, the flood simulated had a flow depth of 20 cm, decreasing to 5 cm average with rectification of channels, and a decrement of 45% of the cost of housing risk. Applying the both structural and non-structural measures, the cost of vulnerable housing was reduced until 94%, thus, this a trustworthy tool for decision-making in urban developments.

Flood Resilience of Housing Infrastructure Modeling and Quantification Using a Bayesian Belief Network

“…As resilience depends on the two key factors of reliability and recovery, 14 resilience parameters for housing infrastructure against flood hazards were selected based on the experts' knowledge and a literature review. The selected resilience parameters were as follows: (i) Type_of_house (robust types of houses are expected to perform better during floods) [31,65]; (ii) Wall_thickness (increasing wall thickness increases the resistance capability and reliability) [31,66]; (iii) Building_age (newly constructed buildings were found to be in better condition than older building) [31,67]; (iv) Number_of_stories (as stories increase, casualties decrease during floods) [31,67]; (v) Drainage (adequate drainage systems prevent damage to infrastructure) [31,68]; (vi) Flood_depth (this produces water pressure, which reduces the reliability of the infrastructure) [31,68]; (vii) Foundation_type [65,69]; (viii) Plinth_level (increasing the plinth height of the house to the top level of the road reduces vulnerability) [31]; (ix) Insurance (insurance enhances the recovery rate as the insurer can pay off the insurance claims) [31,70]; (x) Income (for households with higher income levels, the recovery process will be faster after a disaster) [31,69,71]; (xi) Education (education enhances the preparedness against disaster) [67,69,72]; (xii) Relief_received (whether during/after the disaster relief is received or not) [31,73,74]; (xiii) Approachability (whether connectivity from resource location to vulnerable site is disturbed or not) [31,69]; (xiv) Resource_availability (whether during/after the disaster raw materials for construction are available locally or not) [31].…”