Neglecting uncertainties surrounding model parameters can drastically underestimate flood risks

Sharma, Sanjib; Lee, Benjamin Seiyon; Hosseini‐Shakib, Iman; Haran, Murali; Keller, Klaus

doi:10.1002/essoar.10510275.1

Cited by 3 publications

(3 citation statements)

References 116 publications

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“…Accordingly, we call the 10 m resolution model runs “expensive” and the 50 m resolution model runs “cheap.” We use the river discharge corresponding to the 2011 Tropical Storm Lee as the inflow boundary condition (13,110.7 m

{}^3

/s) (USGS, 2022). This was one of the largest flood events in Selinsgrove in recent decades (Gitro et al, 2014; Sharma et al, 2022).…”

Section: Data and Flood Hazard Model Descriptionmentioning

confidence: 99%

Flood hazard model calibration using multiresolution model output

et al. 2022

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Riverine floods pose a considerable risk to many communities. Improving flood hazard projections has the potential to inform the design and implementation of flood risk management strategies. Current flood hazard projections are uncertain, especially due to uncertain model parameters. Calibration methods use observations to quantify model parameter uncertainty. With limited computational resources, researchers typically calibrate models using either relatively few expensive model runs at high spatial resolutions or many cheaper runs at lower spatial resolutions. This leads to an open question: is it possible to effectively combine information from the high and low resolution model runs? We propose a Bayesian emulation-calibration approach that assimilates model outputs and observations at multiple resolutions. As a case study for a riverine community in Pennsylvania, we demonstrate our approach using the LISFLOOD-FP flood hazard model. The multiresolution approach results in improved parameter inference over the single resolution approach in multiple scenarios. Results vary based on the parameter values and the number of available models runs. Our method is general and can be used to calibrate other high dimensional computer models to improve projections.

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{}^3

/s) (USGS, 2022). This was one of the largest flood events in Selinsgrove in recent decades (Gitro et al, 2014; Sharma et al, 2022).…”

Section: Data and Flood Hazard Model Descriptionmentioning

confidence: 99%

Flood hazard model calibration using multiresolution model output

et al. 2022

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“…Uncertainties surrounding flood exposure and vulnerability can contribute additional uncertainty to flood risk projections (Zarekarizi et al, 2020). Neglecting important uncertainties in flood risk projections can lead to overconfidence in risk projections, resulting in an underestimation of flood risks (Sharma et al, 2022;Zarekarizi et al, 2020). Neglecting uncertainties in flood hazard projections can lead to suboptimal decisions about infrastructure designs (Sharma et al, 2021), where to acquire land (Sharma et al, 2021), and how to reduce vulnerability (Zarekarizi et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

What Drives Uncertainty Surrounding Riverine Flood Risks?

Hosseini-Shakib,

Alipour,

Lee

et al. 2023

Preprint

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Flooding drives considerable risks. Designing strategies to manage these risks is complicated by the often large uncertainty surrounding flood risk projections. Uncertainty surrounding riverine flood risks can stem from choices regarding boundary conditions, model structures, and parameters as well as interactions among hazards, exposures, and vulnerabilities. Flood risk assessment involves multiple research disciplines including the fields of atmospheric science, hydrology, and socioeconomics. A quantitative understanding of which factors contribute most to uncertainties surrounding flood hazards and risks can inform the design of mission-oriented research. Here we analyze a case study to rank the drivers of uncertainties surrounding riverine flood hazards and risks. We find that the projected flood risk is most sensitive to factors associated with flood hazards, rather than exposure and vulnerability: upstream discharge, river bed elevation, channel roughness, and the digital elevation model resolution. Our framework and results can help to improve flood-hazard and risk projections.

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“…Uncertainties surrounding flood exposure and vulnerability can further add uncertainty to flood risk projections 14 . Neglecting important uncertainties in flood risk projections can lead to overconfidence in risk projections and often underestimate flood risks [14][15][16] . Neglecting uncertainties in flood hazard projections can lead to poor decisions about infrastructure designs 16 , where to acquire land 15 , and how to reduce exposure 14 .…”

Section: Introductionmentioning

confidence: 99%

Hazards are the key drivers of uncertainties surrounding riverine flood risks

Hosseini‐Shakib

Alipour

Lee

et al. 2023

Preprint

View full text Add to dashboard Cite

Flooding drives considerable risks. Designing strategies to manage these risks is complicated by the often-large uncertainty surrounding flood risk projections. Uncertainty surrounding riverine flood risks can stem, for example, from choices regarding boundary conditions, model structures, and parameters as well as interactions among hazards, exposures, and vulnerabilities. A quantitative understanding of which factors drive uncertainties surrounding flood hazards and risks can inform the design of mission-oriented research. Here we analyze a case study to (i) characterize key uncertainties impacting flood risk projections and (ii) characterize the most important drivers of the uncertainties surrounding riverine flood hazards and risks. We find that the projected flood risk is most sensitive to factors associated with flood hazards: upstream discharge, riverbed elevation, channel roughness, and the digital elevation model resolution. Our framework and results can help to improve flood-hazard and risk projections.

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Neglecting uncertainties surrounding model parameters can drastically underestimate flood risks

Abstract: This a preprint and has not been peer reviewed. Data may be preliminary.

Cited by 3 publications

References 116 publications

Flood hazard model calibration using multiresolution model output

Flood hazard model calibration using multiresolution model output

What Drives Uncertainty Surrounding Riverine Flood Risks?

Hazards are the key drivers of uncertainties surrounding riverine flood risks

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