A method for probabilistic flash flood forecasting

JOURNAL OF HYDROLOGY

卷:541

Article

Hardy, Jill¹  Gourley, Jonathan J.²  Kirstetter, Pierre-Emmanuel³  Hong, Yang⁴  Kong, Fanyou⁵  Flamig, Zachary L.⁶ 

[1] Univ Oklahoma, Sch Meteorol, 120 David L Boren Blvd, Norman, OK 73072 USA

[2] NOAA, Natl Severe Storms Lab, 120 David L Boren Blvd, Norman, OK 73072 USA

[3] Univ Oklahoma, Adv Radar Res Ctr, NOAA, Natl Severe Storms Lab, 120 David L Boren Blvd, Norman, OK 73072 USA

[4] Univ Oklahoma, Adv Radar Res Ctr, Sch Civil Engn & Environm Sci, 120 David L Boren Blvd, Norman, OK 73072 USA

[5] Univ Oklahoma, Ctr Anal & Predict Storms, 120 David L Boren Blvd, Norman, OK 73072 USA

[6] Univ Oklahoma, Cooperat Inst Mesoscale Meteorol Studies, Adv Radar Res Ctr, NOAA,Natl Severe Storms Lab, 120 David L Boren Blvd, Norman, OK 73072 USA

关键词: Flash flood;    Probabilistic;    NWP;    Distributed modeling;   

DOI  :  10.1016/j.jhydrol.2016.04.007

来源: Elsevier

【 摘 要 】

Flash flooding is one of the most costly and deadly natural hazards in the United States and across the globe. This study advances the use of high-resolution quantitative precipitation forecasts (QPFs) for flash flood forecasting. The QPFs are derived from a stormscale ensemble prediction system, and used within a distributed hydrological model framework to yield basin-specific, probabilistic flash flood forecasts (PFFFs). Before creating the PFFFs, it is important to characterize QPF uncertainty, particularly in terms of location which is the most problematic for hydrological use of QPFs. The SAL methodology (Wernli et al., 2008), which stands for structure, amplitude, and location, is used for this error quantification, with a focus on location. Finally, the PFFF methodology is proposed that produces probabilistic hydrological forecasts. The main advantages of this method are: (1) identifying specific basin scales that are forecast to be impacted by flash flooding; (2) yielding probabilistic information about the forecast hydrologic response that accounts for the locational uncertainties of the QPFs; (3) improving lead time by using stormscale NWP ensemble forecasts; and (4) not requiring multiple simulations, which are computationally demanding. Published by Elsevier B.V.

【 授权许可】

Free