Evaluation of the Uncertainty of Flash Flood Prediction Using the RRI Model in Mountainous Rivers

9 pages•Published: September 20, 2018

Yosuke Nakamura, Koji Ikeuchi, Shiori Abe, Toshio Koike and Shinji Egashira

Abstract

In recent years, flood damage caused by flash floods in mountainous rivers has been frequently reported in Japan. In order to ensure a sufficient lead time for safe evacuation, it is necessary to predict river water levels in real time utilizing a hydrological model. In this study, we conducted flood prediction using the RRI model and rainfall forecasted for the next 6 hours in the Kagetsu River basin (136.1 km2) in July 2017, evaluated the uncertainty regarding the prediction, and illustrated the results using a box-plot. The evaluation found that the mean error of the forecasted water level was approximately - 0.3 m in the prediction for the initial 3 hours and -0.97 m at the 6th hour. Also, the study investigated the possibility of correcting water levels forecasted by clarifying an uncertainty distribution. As a result, the water level forecasted was found to be underestimated because it was predicted to rise as high as Warning Level 2, while the water level forecasted with bias correction was predicted to reach Warning Level 4. Moreover, the lead time was estimated to prolong by 2 hours. Overall, the study suggested that flood forecasting can be improved by considering the uncertainty involved in prediction.

Keyphrases: flash flood simulation, lead time for evacuation, rri model, uncertainty of forecasting

In: Goffredo La Loggia, Gabriele Freni, Valeria Puleo and Mauro De Marchis (editors). HIC 2018. 13th International Conference on Hydroinformatics, vol 3, pages 1486-1494.

Links:	https://easychair.org/publications/paper/LVs6
	https://doi.org/10.29007/n72w

BibTeX entry

@inproceedings{HIC2018:Evaluation_Uncertainty_Flash_Flood,
  author    = {Yosuke Nakamura and Koji Ikeuchi and Shiori Abe and Toshio Koike and Shinji Egashira},
  title     = {Evaluation of the Uncertainty of Flash Flood Prediction Using the RRI Model in Mountainous Rivers},
  booktitle = {HIC 2018. 13th International Conference on Hydroinformatics},
  editor    = {Goffredo La Loggia and Gabriele Freni and Valeria Puleo and Mauro De Marchis},
  series    = {EPiC Series in Engineering},
  volume    = {3},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2516-2330},
  url       = {/publications/paper/LVs6},
  doi       = {10.29007/n72w},
  pages     = {1486-1494},
  year      = {2018}}

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