Flash Flood Forecasting Based on Rainfall Thresholds

Alfieri, Lorenzo; Berenguer, Marc; Knechtl, Valentin; Liechti, Katharina; Sempere‐Torres, Daniel; Zappa, Massimiliano

doi:10.1007/978-3-642-40457-3_49-1

Cited by 14 publications

(8 citation statements)

References 69 publications

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“…Therefore, probabilistic approaches are applied to produce forecast ensembles (Ben Bouallègue and Theis, 2014;Clark et al, 2016). Forecast ensembles have proven to yield better results than deterministic ones (Alfieri et al, 2015;Liechti and Zappa, 2019), and are in operational use in the meteorological offices of several countries (DWD, 2020; Met Office, 2020; MeteoSwiss, 2020). Forecast ensemble members are created by applying small changes to initial conditions or by varying the description of the physical process of the weather model (Hagelin et al, 2017).…”

Section: The Cosmo Numerical Weather-prediction Modelmentioning

confidence: 99%

Hydrometeorological analysis and forecasting of a 3-day flash-flood-triggering desert rainstorm

Rinat¹,

Marra²,

Armon³

et al. 2020

Preprint

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Abstract. Flash floods are among the most devastating and lethal natural hazards. In 2018, three flash-flood episodes resulted in 46 casualties in the deserts of Israel and Jordan alone. This paper presents the hydrometeorological analysis and forecasting of a substantial storm (25–27 Apr 2018) that hit an arid desert basin (Zin, ~ 1400 km2, southern Israel), claiming 12 human lives. Our aim was to: (a) spatially assess the severity of the storm, (b) quantify the time scale of the hydrological response, and (c) evaluate the available operational precipitation forecasting. Return periods of the storm's maximal rain intensities were derived locally, at 1-km2 resolution, using weather radar data and a novel statistical methodology. A high-resolution grid-based hydrological model was used to study the intra-basin flash-flood magnitudes, which were consistent with direct information from witnesses. The model was further used to examine the hydrological response to different forecast scenarios. A small portion of the basin (1–20 %) experienced extreme precipitation intensities (75- to 100-year return period), resulting in a local hydrological response of a high magnitude (10- to 50-year return period). Hillslope runoff, initiated minutes after the intense rainfall occurred, reached the streams and resulted in peak discharge within tens of minutes. Available deterministic operational precipitation forecasts poorly predicted the hydrological response in the studied basins (tens to hundreds of km2) mostly due to location inaccuracy. There was no gain from assimilating radar estimates in the numerical weather-prediction model. Therefore, we suggest using deterministic forecasts with caution as it might lead to fatal decision making. To cope with such errors a novel cost-effective methodology is applied by spatially shifting the forecasted precipitation fields. In this way, flash-flood occurrences were captured in most of the sub-basins, resulting in few false alarms.

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Section: The Cosmo Numerical Weather-prediction Modelmentioning

confidence: 99%

Hydrometeorological analysis and forecasting of a 3-day flash-flood-triggering desert rainstorm

Rinat¹,

Marra²,

Armon³

et al. 2020

Preprint

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“…At that scale, operational EWS for surface water flooding rely on simplified forecast indices representative of extreme rainfall, for which there is no need for additional calibration parameters (Alfieri et al ., ). The extreme rainfall alert (Hurford et al ., ) (ERA) of the British Flood Forecasting Centre (FFC) and the Swiss warning system for point precipitation (Alfieri et al ., ) are two examples of such systems, both designed to give early indications on upcoming severe rainfall events potentially leading to surface water flooding, including in urban environments (Alfieri et al ., ). However, the large data requirement for continent‐scale EWS limits their current application, with the European Flood Awareness System (EFAS) (Thielen et al ., ) and flash flood guidance (FFG) (Ntelekos et al ., ) being prominent examples for application in Europe and the world (Gourley et al ., ).…”

Section: Introductionmentioning

confidence: 97%

Flood hazard risk forecasting index (FHRFI) for urban areas: The Hurricane Harvey case study

Jurlina

Baugh

Pappenberger

et al. 2019

Meteorological Applications

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Hurricane Harvey caused at least 70 confirmed deaths, with estimated losses in the Houston urban area of Texas reaching above US$150 billion, making it one of the costliest natural disasters ever in the United States. The study tests two types of forecast index to provide surface flooding (inundation) warning over the Houston area: a meteorological index based on a global numerical weather prediction (NWP) system, and a new combined meteorological and land surface index, the flood hazard risk forecasting index (FHRFI), where land surface is used to condition the meteorological forecast. Both indices use the total precipitation extreme forecast index (EFI) and shift of tails (SoT) products from the European Centre for Medium‐Range Weather Forecasts (ECMWF) medium‐range ensemble forecasting system (ENS). Forecasts at the medium range (3–14 days ahead) were assessed against 153 observed National Weather Service (NWS) urban flood reports over the Houston urban area between August 26 and 29, 2017. It is shown that the method provides skilful forecasts up to four days ahead using both approaches. Moreover, the FHRFI combined index has a hit ratio of up to 74% at 72 hr lead time, with a false‐alarm ratio of only 45%. This amounts to a statistically significant 20% increase in performance compared with the meteorological indices. This first study demonstrates the importance of including land‐surface information to improve the quality of the flood forecasts over meteorological indices only, and that skilful flood warning in urban areas can be obtained from the NWP using the FHRFI.

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“…These quantities describe the likelihood of different flood magnitudes occurring locally based on a "climatological" data set over a long period of time (preferably 30 years or more; World Meteorological Organisation [WMO], 2017). Traditionally, flood thresholds are produced from observations or deterministic model reanalysis (Alfieri et al, 2015). River discharge observations can provide a solution only at certain locations, whereas hydrological model simulations, forced with meteorological observations, can cover a whole geographical domain, delivering flood thresholds at every model river point or catchment.…”

Section: Introductionmentioning

confidence: 99%

Using ensemble reforecasts to generate flood thresholds for improved global flood forecasting

Zsótér

Prudhomme

Stephens

et al. 2020

J Flood Risk Management

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Global flood forecasting systems rely on predefining flood thresholds to highlight potential upcoming flood events. Existing methods for flood threshold definition are often based on reanalysis datasets using a single threshold across all forecast lead times, such as in the Global Flood Awareness System. This leads to inconsistencies between how the extreme flood events are represented in the flood thresholds and the ensemble forecasts. This paper explores the potential benefits of using river flow ensemble reforecasts to generate flood thresholds that can deliver improved reliability and skill, increasing the confidence in the forecasts for humanitarian and civil protection partners. The choice of dataset and methods used to sample annual maxima in the threshold computation, both for reanalysis and reforecast, is analysed in terms of threshold magnitude, forecast reliability, and skill for different flood severity levels and lead times. The variability of threshold magnitudes, when estimated from the different annual maxima samples, can be extremely large, as can the subsequent impact on forecast skill. Reanalysis-based thresholds should only be used for the first few days, after which ensemble-reforecast-based thresholds, that vary with forecast lead time and can account for the forecast bias trends, provide more reliable and skilful flood forecasts.

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Flash Flood Forecasting Based on Rainfall Thresholds

Cited by 14 publications

References 69 publications

Hydrometeorological analysis and forecasting of a 3-day flash-flood-triggering desert rainstorm

Hydrometeorological analysis and forecasting of a 3-day flash-flood-triggering desert rainstorm

Flood hazard risk forecasting index (FHRFI) for urban areas: The Hurricane Harvey case study

Using ensemble reforecasts to generate flood thresholds for improved global flood forecasting

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