Flood risk mapping using uncertainty propagation analysis on a peak discharge: case study of the Mille Iles River in Quebec

Zokagoa, Jean-Marie; Soulaïmani, Azzeddine; Dupuis, Pierre

doi:10.1007/s11069-021-04583-2

Cited by 7 publications

(5 citation statements)

References 24 publications

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“…These marginal samples are then randomly combined, one from each dimension, into multivariate samples that preserve the space‐filling property of the marginal distribution (McKay et al., 1979). LHS is the most explored alternative‐to‐SMC (Pharr et al., 2016; Zhu et al., 2017; Zokagoa et al., 2021) for probabilistic modeling of fluvial floods (Aitken et al., 2022; Beevers et al., 2020). Here, LHS is also explored for rapid floods to identify whether any of HWE, QMC or ASS can be a better alternative‐to‐SMC.…”

Section: Uq Analysis Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Accelerated Monte Carlo methods with random sampling rely on variance reduction techniques to reduce N s over SMC (James, 1985;Owen, 2013). From this category, LHS is a popular alternative (McKay et al, 1979;Pharr et al, 2016;Zhu et al, 2017;Zokagoa et al, 2021), which has been widely studied in many research areas (Kalagnanam & Diwekar, 1997;Yu et al, 2001). Kucherenko et al (2015) analyzed the performance of LHS to SMC to estimate three types of synthetic response functions, concluding that LHS can only reduce N s , by 10 times, for one function type featured by low sensitivity to the variations in the input variables, retrieving the findings in Yu et al (2001).…”

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confidence: 99%

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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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Section: Uq Analysis Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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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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“…The flood risk map results from the multiplication of flood hazard and vulnerability maps. Zokagoa et al (2021) produced a probabilistic flood map using uncertainty propagation in real flood events in Quebec. They adopted a Monte Carlo method to generate an ensemble of results from different combinations of uncertain input parameters.…”

Section: Theore Ti C Al Backg Roundmentioning

confidence: 99%

“…Ramkar and Yadav (2021) created a flood risk index in data‐scarce river basins using an Analytical Hierarchical Process and GIS approach. Zokagoa et al (2021) mapped the flood risk using uncertainty propagation analysis on a peak discharge in Quebec.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Flood risk map from hydrological and mobility data: A case study in São Paulo (Brazil)

Tomás

Guarnieri

Jorge

et al. 2022

Transactions in GIS

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Cities increasingly face flood risk primarily due to extensive changes of the natural land cover to built‐up areas with impervious surfaces. In urban areas, flood impacts come mainly from road interruption. This article proposes an urban flood risk map from hydrological and mobility data, considering the megacity of São Paulo, Brazil, as a case study. We estimate the flood susceptibility through the Height Above the Nearest Drainage algorithm; and the potential impact through the exposure and vulnerability components. We aggregate all variables into a regular grid and then classify the cells of each component into three classes: Moderate, High, and Very High. All components, except the flood susceptibility, have few cells in the Very High class. The flood susceptibility component reflects the presence of watercourses, and it has a strong influence on the location of those cells classified as Very High.

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Flood inundation mapping and hazard assessment of Baitarani River basin using hydrologic and hydraulic model

2021

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Flood risk mapping using uncertainty propagation analysis on a peak discharge: case study of the Mille Iles River in Quebec

Cited by 7 publications

References 24 publications

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

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

Flood risk map from hydrological and mobility data: A case study in São Paulo (Brazil)

Flood inundation mapping and hazard assessment of Baitarani River basin using hydrologic and hydraulic model

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