Preprints
https://doi.org/10.5194/egusphere-2023-2088
https://doi.org/10.5194/egusphere-2023-2088
09 Oct 2023
 | 09 Oct 2023

Barriers to operational flood forecasting in complex terrain: from precipitation forecasts to probabilistic flood forecast mapping at short lead times

Luiz Bacelar, Arezoo ReifeeiNasab, Nathaniel Chaney, and Ana Barros

Abstract. As flood alert systems move towards higher spatial resolutions, there is a continued need to enable approaches that provide robust predictions of flood extent that adequately account for the uncertainties from meteorological forcing, hydrologic and hydraulic model structure, and parameter uncertainty. In flood forecasting, two primary sources of uncertainty are the quantitative precipitation forecasts (QPF) and the representation of the channel and floodplain geometry. This is especially relevant as simple approaches (e.g., HAND) are being used to map floods operationally at field scales (< 10 m). This article investigates the benefits of using a computationally efficient probabilistic precipitation forecast (PPF) approach to generate multiple flood extension scenarios over a region of complex terrain prone to flash floods. First, we assess the limitations of using a calibrated version of the gridded version of the WRF-Hydro model to predict an extreme flash flood event in the Greenbrier River Basin (West Virginia) on 24 June 2016. We investigated an ensemble methodology to combine operational High-Resolution Rapid Refresh (HRRR) QPF with radar-based Quantitative Precipitation Estimates, specifically MRMS QPE products. This approach was most effective to increase the headwaters streamflow accuracy in the first hour lead time, which is still insufficient to issue actionable flood warnings in operational applications. At longer lead-times, success was elusive due to epistemic uncertainties in MRMS rainfall intensity and HRRR rainfall spatial patterns. Furthermore, a QPF ensemble was used to generate an ensemble of flood heights using the HAND flood mapping methodology at different spatial resolutions. Results revealed a scale-dependency with increasing dispersion among the predicted flooded areas with increasing spatial resolution down to 1 meter. We hypothesize the overprediction of flooded areas at higher spatial resolutions reflects the increasing number of river reaches and the need for scale-aware representation of river hydraulics that impacts flood propagation in the river network.

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Luiz Bacelar, Arezoo ReifeeiNasab, Nathaniel Chaney, and Ana Barros

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2088', Anonymous Referee #1, 22 Dec 2023
    • AC1: 'Reply on RC1', Luiz Bacelar, 06 Mar 2024
  • RC2: 'Comment on egusphere-2023-2088', Anonymous Referee #2, 21 Jan 2024
    • AC2: 'Reply on RC2', Luiz Bacelar, 06 Mar 2024
  • RC3: 'Comment on egusphere-2023-2088', Anonymous Referee #3, 26 Jan 2024
    • AC3: 'Reply on RC3', Luiz Bacelar, 06 Mar 2024
Luiz Bacelar, Arezoo ReifeeiNasab, Nathaniel Chaney, and Ana Barros
Luiz Bacelar, Arezoo ReifeeiNasab, Nathaniel Chaney, and Ana Barros

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Short summary
The study explores a computationally efficient probabilistic precipitation forecast approach to generate multiple flood scenarios. It reveals the limitations in predicting flash floods accurately and the need for advanced ensemble methodologies to combine different sources of precipitation forecasts. It highlights the scale-dependency of flood predictions at higher spatial resolutions, shedding light on the relationship between river hydraulics and flood propagation in the river network.