Quantitative flood hazard assessment methods: A review

Maranzoni, Andrea; D’Oria, Marco; Rizzo, Carmine

doi:10.1111/jfr3.12855

Cited by 53 publications

(31 citation statements)

References 132 publications

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“…Therefore, there are still many studies trying to find ways to reduce the impact of floods. Maranzoni et al [20] carried out a flood hazard classification based on the underlying approach (heuristic, conceptual, empirical), the open elements designed for it (humans, buildings, vehicles, and more. ), and the area of application (rivers overflowing, dam breaking, embankments, breach, debris flow).…”

Section: Discussionmentioning

confidence: 99%

Analysis of Time of Concentration Estimates Using Some Methods and HEC RAS

Biantoro¹,

Wahyudi²,

Ni’am³

2023

cea

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Floods are natural disasters which cause losses that often occur in coastal cities such as Jakarta, Indonesia. Paying attention to rainfall and water flow is in the form of predicting floods. The research goal is to determine the best concentration time method, predict water level and flood inundation, and reduce the impact of flooding for people in the downstream area of the river, Jakarta. The method used in this research is quantitative descriptive analysis, rational approach, time of concentration, artificial neural network (ANN), and the use of the HEC RAS application. The results show that the arrival time of the flood using the Kirpich method shows a concentration time of 12 hours 5 minutes, Travel Time of 12 hours 10 minutes, Velocity of 11 hours 21 minutes and Nakayasu of 9 hours 1 minute. Modelling using an artificial neural network shows that the modelling results can function to predict water levels in the future. The results of the artificial neural network that estimates the water level for the 2020 period are 5.1 m (January), 4.94 m (February), 3.89 m (March), 3.52 m (April), 3.71 m (May), 2.58 m (June), 2.59 m (July), 24.1 m (August), 2.97 m (September), 3.32 m (October), 3.07 m (November) and 3.68 m (December).

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Section: Discussionmentioning

confidence: 99%

Analysis of Time of Concentration Estimates Using Some Methods and HEC RAS

Biantoro¹,

Wahyudi²,

Ni’am³

2023

cea

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“…An important driver for uncertainties is climate change, which, to some extent, is addressed in the reports by using IPCC's climate scenario RCP 8.5 for modelling floods. Another way to address uncertainties would be to move from deterministic to probabilistic hazard maps, which are based on various flood scenarios with different likelihoods instead of one fixed scenario (Maranzoni et al, 2023).…”

Section: Summary and Implication Of Findingsmentioning

confidence: 99%

Flood risk assessments—Exploring maturity and challenges in Sweden

Arvidsson,

Johansson

2024

J Flood Risk Management

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Floods are destructive to society. Hence, to assess and manage flood risk is imperative. Flood risk assessments require several layers of analysis, from flood hazard to societal impact. This paper explores the maturity level and challenges related to three components of a comprehensive risk assessment: flood hazard, direct consequences, and indirect consequences. This is achieved by introducing and applying a maturity framework on flood hazard reports and flood risk management plans in Sweden, as required by the EU Floods Directive (FD). A longitudinal analysis is conducted over two FD cycles (2009–2015 and 2016–2021). Complementary interviews with county administrative boards and responsible authorities provide deeper insights into processes and practical challenges. The results reveal that the maturity level of flood hazard assessment is high, while direct and, in particular, indirect consequences assessments need increased attention and substantial improvements. Further, there is no significant increase in maturity between the FD cycles, indicating fundamental challenges towards achieving this. Critical steps forward include developing applicable methods for analysing both direct and indirect consequences of floods, improving data availability on functionality and interdependency of critical infrastructure and society at large, and creating incentives for a broader range of societal actors to participate in flood risk management.

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“… Average Hazard Rate (HR ave ) and Maximum Hazard Rate (HR max ). The flood HR is defined as: HR = h × ( V + 0.5); and, it has been used to provide more information on velocity impacts to assets including structural damage to residential buildings, damages to road infrastructures, and risks to people's life and injury (Kreibich et al., 2009; Lumbroso & Davison, 2018; Maranzoni et al., 2022; Ramsbottom et al., 2006; Shirvani & Kesserwani, 2021; Shirvani et al., 2021). In this study, HR ave and HR max quantify the average and maximum flood HR over the computational area. Floodtiming .…”

Section: Uq Analysis Frameworkmentioning

confidence: 99%

Sampling‐Based Methods for Uncertainty Propagation in Flood Modeling Under Multiple Uncertain Inputs: Finding Out the Most Efficient Choice

Hajihassanpour

Kesserwani

Pettersson

et al. 2023

Water Resources Research

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In probabilistic flood modeling, uncertainty manifests in frequency of occurrence, or histograms, for quantities of interest, including the Flood Extent and hazard rating (HR). Such modeling at the field‐scale requires the identification of a more efficient alternative to the Standard Monte Carlo (SMC) method that can reproduce comparable output probability distributions with a relatively reduced sample size, including detailed histograms of quantities of interest. Latin hypercube sampling (LHS) is the most evaluated alternative for fluvial floods but yields no considerable sample size reduction. Potentially better alternatives include adaptive stratified sampling (ASS), Quasi Monte Carlo (QMC) and Haar‐wavelet expansion (HWE), which are yet unevaluated for probabilistic flood modeling. To fulfill this gap, LHS, ASS, QMC, and HWE are compared to quantify sample size reduction to reproduce output detailed histograms—for Flood Extent, and average and maximum HR—while keeping the difference below 10% to the reference SMC prediction. The comparison is done for two test cases with two (i.e., inflow discharge and Manning's coefficient) and three (i.e., further including the ground elevation) input random variables, and a real case with five input random variables. With two input random variables, all four alternatives yield sample size reductions, with QMC and HWE considerably outperforming the others; with three and more input random variables, HWE becomes inflexible and LHS underperforms. Still, QMC is a better choice than ASS to boost sample size reduction for the real case and shall be preferred in probabilistic flood modeling. Accompanying research codes are openly available online.

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Quantitative flood hazard assessment methods: A review

Cited by 53 publications

References 132 publications

Analysis of Time of Concentration Estimates Using Some Methods and HEC RAS

Analysis of Time of Concentration Estimates Using Some Methods and HEC RAS

Flood risk assessments—Exploring maturity and challenges in Sweden

Sampling‐Based Methods for Uncertainty Propagation in Flood Modeling Under Multiple Uncertain Inputs: Finding Out the Most Efficient Choice

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