Using Multi-Stakeholder Causal Mapping to Explore Priorities for Infrastructure Resilience to Flooding

Pregnolato, Maria; West, Charles P.; Evans, Barry; Lam, Auguste; Chen, Albert S.; Ahmadian, Reza; Djordjević, Slobodan

doi:10.2139/ssrn.4511344

Cited by 1 publication

(1 citation statement)

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“…The importance of flood modeling and mapping associated with rainfall return periods is evident for instances such as aiding in the development of flood insurance plans [8] or even by being used to define multifaceted risk areas that would depend not only on hydrological but also socioeconomic information [9][10][11]. Even though multiple applications use products derived from flood simulation results, a large uncertainty can arise from not clearly defining the proper duration and temporal distribution of the rainfall that would maximize the expected flood hazard (e.g., maximum water surface depth).…”

Section: Introductionmentioning

confidence: 99%

Exploring the Impact of Rainfall Temporal Distribution and Critical Durations on Flood Hazard Modeling

Gomes,

Jalihal,

Mendiondo

2024

Preprint

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Urbanization and climate change amplify challenges posed by floods for both city dwellers and planners. Flood modeling, rooted in geosciences and atmospheric sciences, operates in a non-linear and multi-modal fashion, marked by uncertainties from factors like soil infiltration characteristics, floodplain roughness, and spatio-temporal variations in rainfall volume, distribution, and intensities. This paper addresses these challenges by introducing a flood mapping methodology that incorporates synthetic design storms and compares them with a fitted median rainfall distribution derived from high-resolution observed rainfall data in the catchment. The flood hazard effect of choosing different rainfall temporal distribution methods is investigated. The Alternating Blocks Method and the Huff curves method, one of the most widely used methods in engineering hydrology, were chosen as representative synthetic rainfall methods for flood mapping assessment and compared against the measured median rainfall distribution. The framework was applied in a 131 km2 flood-prone urban catchment in Bangalore, India. Evaluation of different rainfall distributions reveals a potential 50% smaller areas with flood hazard, for the same return period and duration, simply by selecting a specific rainfall distribution compared to the expected fitted median rainfall distribution based on observed data. This research not only underscores the importance of appropriate rainfall distribution selection and critical rainfall duration, but also highlights the need for accurate data-driven methodologies in flood mapping, particularly in the face of urbanization and climate-induced complexities.

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