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Watershed Planning within a Quantitative Scenario Analysis Framework
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Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting.
Peyman Abbaszadeh1, David F Muñoz1, Hamed Moftakhari1
1Department of Civil, Construction and Environmental Engineering, Center for Complex Hydrosystems Research, Tuscaloosa, AL, USA.
Iscience
|October 11, 2022
Summary
Understanding compound flooding (CF) uncertainties is crucial for accurate forecasting. Addressing these uncertainties, from hydrometeorological models to data science, improves predictions and risk management for this escalating natural hazard.
Area of Science:
- Hydrology
- Environmental Science
- Climate Science
Background:
- Compound flooding (CF) is a significant natural hazard resulting from multiple flood drivers, causing substantial economic, societal, and environmental impacts.
- The frequency and severity of CF events are projected to increase due to climate change and urbanization.
- Accurate forecasting of CF is essential for effective risk management and governance.
Purpose of the Study:
- To highlight the critical need for characterizing and quantifying uncertainties in hydrometeorological and hydrodynamic model simulations for compound flooding.
- To discuss the propagation and cascading effects of these uncertainties in forecasting CF events.
- To explore how recent advances in remote sensing and data science can mitigate predictive uncertainties and enhance CF predictability.
Main Methods:
- Review of uncertainty sources in hydrometeorological and hydrodynamic models.
- Analysis of uncertainty propagation and cascading effects in compound flooding simulations.
- Exploration of remote sensing and data science applications for uncertainty reduction.
Main Results:
- Uncertainties in model simulations significantly impact compound flooding forecasts.
- Interactions and cascading effects of uncertainties exacerbate prediction challenges.
- Advances in data science and remote sensing offer promising avenues for improving CF predictability.
Conclusions:
- Characterizing and quantifying uncertainties in CF modeling is paramount.
- Integrating advanced data science and remote sensing techniques can enhance forecasting accuracy.
- Improved predictability of CF events is vital for informed decision-making and sustainable risk governance.
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