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Comparison of methodologies for flood rainfall thresholds estimation

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  • V. Montesarchio
  • F. Napolitano
  • M. Rianna
  • E. Ridolfi
  • F. Russo
  • S. Sebastianelli

Abstract

A flood warning system based on rainfall thresholds makes it possible to issue alarms via an off-line approach. This technique is useful for mitigating the effects of flooding in small-to-medium-sized basins characterized by an extremely rapid response to rainfall. Rainfall threshold values specify the amount of precipitation that occurs over a given period of time and are dependent on both the amount of soil moisture and the spatiotemporal distribution of the rainfall. The precipitation generates a critical discharge in a particular river cross section. Exceeding these values can produce a critical situation in river sites that make them susceptible to flooding. In this work, we present a comparison of methodologies for estimating rainfall thresholds. Critical precipitation amounts are evaluated using empirical data, hydrological simulations and probabilistic methods. The study focuses on three small-to-medium-sized basins located in central Italy. For each catchment, historical data are first used to theoretically evaluate the empirical rainfall thresholds. Next, we calibrate a semi-distributed hydrological model that is validated using rain gauge and weather radar data. Critical rainfall depths over 30 min and 1, 3, 6, 12 and 24 h durations are then evaluated using the hydrological simulation. In the probabilistic approach, rainfall threshold values result from a minimization of two different functions, one following the Bayesian decision theory and the other following the informative entropy concept. In order to implement both functions, it is necessary to evaluate the joint probability function. The joint probability function is built up as a bivariate distribution of rainfall depth for a given duration with the corresponding flow peak value. Finally, in order to assess the performance of each methodology, we construct contingency tables to highlight the system performance. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • V. Montesarchio & F. Napolitano & M. Rianna & E. Ridolfi & F. Russo & S. Sebastianelli, 2015. "Comparison of methodologies for flood rainfall thresholds estimation," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(1), pages 909-934, January.
    • Handle: RePEc:spr:nathaz:v:75:y:2015:i:1:p:909-934
    DOI: 10.1007/s11069-014-1357-3
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    References listed on IDEAS

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    1. Yang, Jiping & Qiu, Wanhua, 2005. "A measure of risk and a decision-making model based on expected utility and entropy," European Journal of Operational Research, Elsevier, vol. 164(3), pages 792-799, August.
    2. Saeed Golian & Bahram Saghafian & Reza Maknoon, 2010. "Derivation of Probabilistic Thresholds of Spatially Distributed Rainfall for Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(13), pages 3547-3559, October.
    3. Michalis Diakakis, 2012. "Rainfall thresholds for flood triggering. The case of Marathonas in Greece," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 60(3), pages 789-800, February.
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    Cited by:

    1. Wenlin Yuan & Meiqi Liu & Fang Wan, 2019. "Calculation of Critical Rainfall for Small-Watershed Flash Floods Based on the HEC-HMS Hydrological Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(7), pages 2555-2575, May.

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