Skill of ensemble flood inundation forecasts at short- to medium-range timescales

Gomez, Michael; Sharma, Sanjib; Reed, Seann; Mejía, Alfonso

doi:10.1016/j.jhydrol.2018.10.063

Cited by 27 publications

(11 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The sensitive parameters are associated with different hydrodynamic processes related to baseflow, percolation, evaporation, snowfall, storm runoff, and channel routing (Table S1). These parameters are also suggested by several other studies (Gomez et al, 2019;Sharma et al, 2021;Siddique & Mejia, 2017;Zarzar et al, 2018) as the most sensitive parameters in the Susquehanna river basin.…”

Section: Experimental Designsupporting

confidence: 72%

Neglecting model parametric uncertainty can drastically underestimate flood risks

Sharma

Lee

Hosseini‐Shakib

et al. 2022

Preprint

View full text Add to dashboard Cite

Floods drive dynamic and deeply uncertain risks for people and infrastructures. Uncertainty characterization is a crucial step in improving the predictive understanding of multi-sector dynamics and the design of risk-management strategies. Current approaches to estimate flood hazards often sample only a relatively small subset of the known unknowns, for example the uncertainties surrounding the model parameters. This approach neglects the impacts of key uncertainties on hazards and system dynamics. Here we mainstream a recently developed method for Bayesian inference to calibrate a computationally expensive distributed hydrologic model. We compare three different calibration approaches: (1) stepwise line search, (2) precalibration or screening, and (3) the new Fast Model Calibrations (FaMoS) approach. FaMoS deploys a particle-based approach that takes advantage of the massive parallelization afforded by modern high-performance computing systems. We quantify how neglecting parametric uncertainty and data discrepancy can drastically underestimate extreme flood events and risks. Precalibration improves prediction skill score over a stepwise line search. The Bayesian calibration improves the uncertainty characterization of model parameters and flood risk projections.

show abstract

Section: Experimental Designsupporting

confidence: 72%

Neglecting model parametric uncertainty can drastically underestimate flood risks

Sharma

Lee

Hosseini‐Shakib

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The BayesGLM model has never been used for flood susceptibility mapping or any other hydrological modeling up until now. But the popularity of boosting in the advancement of ensemble machine learning methods for hydrological modeling including the flood prediction has been fast-growing due to their accuracy (Antonetti et al, 2019;Berkhahn et al, 2019;Gomez et al, 2019;Lee et al, 2017;Peng et al, 2019;Tian et al, 2019).…”

Section: Model Evaluation Resultsmentioning

confidence: 99%

Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method

Hosseini

Choubin

Mosavi

et al. 2020

Science of The Total Environment

264

View full text Add to dashboard Cite

“…Recent advances in hydrodynamic modeling alongside computing (e.g., parallel computing, open-source code) have led to vast improvements in flood inundation modeling. With ongoing research and model development it will become more common for variables beyond river flow, such as inundation and water level, which are directly related to flood impacts, to be included in ensemble flood forecasts (Cooten et al, 2011;Georgas et al, 2016;Gomez et al, 2019). This is particularly promising in the near future with forecasting services worldwide moving more and more toward the provision of impact-based forecasts (World Meteorological Organisation (WMO), 2018).…”

Section: Extending Flood Forecast Variablesmentioning

confidence: 99%

Ensemble flood forecasting: Current status and future opportunities

et al. 2020

View full text Add to dashboard Cite

Ensemble flood forecasting has gained significant momentum over the past decade due to the growth of ensemble numerical weather and climate prediction, expansion in high performance computing, growing interest in shifting from deterministic to risk-based decision-making that accounts for forecast uncertainty, and the efforts of communities such as the international Hydrologic Ensemble Prediction Experiment (HEPEX), which focuses on advancing relevant ensemble forecasting capabilities and fostering its adoption. With this shift, comes the need to understand the current state of ensemble flood forecasting, in order to provide insights into current capabilities and areas for improvement, thus identifying future research opportunities to allow for better allocation of research resources. In this article, we provide an overview of current research activities in ensemble flood forecasting and discuss knowledge gaps and future research opportunities, based on a review of 70 papers focusing on various aspects of ensemble flood forecasting around the globe. Future research directions include opportunities to improve technical aspects of ensemble flood forecasting, such as data assimilation techniques and methods to account for more sources of uncertainty, and developing ensemble forecasts for more variables, for example, flood inundation, by applying techniques such as machine learning. Further to this, we conclude that there is a need to not only improve technical aspects of flood forecasting, but also to bridge the gap between scientific research and hydrometeorological model development, and real-world flood management using probabilistic ensemble forecasts, especially through effective communication.

show abstract

Skill of ensemble flood inundation forecasts at short- to medium-range timescales

Cited by 27 publications

References 62 publications

Neglecting model parametric uncertainty can drastically underestimate flood risks

Neglecting model parametric uncertainty can drastically underestimate flood risks

Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method

Ensemble flood forecasting: Current status and future opportunities

Contact Info

Product

Resources

About