Six decades of rainfall and flood frequency analysis using stochastic storm transposition: Review, progress, and prospects

JOURNAL OF HYDROLOGY

卷:585

Review

Wright, Daniel B.¹  Yu, Guo¹  England, John F.² 

[1] Univ Wisconsin, Madison, WI 53706 USA

[2] US Army Corps Engineers, Denver, CO USA

关键词: Extreme rainfall;    Floods;    Rainfall frequency analysis;    Flood frequency analysis;    Rainfall remote sensing;    Stochastic hydrology;   

DOI  :  10.1016/j.jhydrol.2020.124816

来源: Elsevier

【 摘 要 】

Stochastic Storm Transposition (SST) involves resampling and random geospatial shifting (i.e. transposition) of observed storm events to generate hypothetical but realistic rainstorms. Though developed as a probabilistic alternative to probable maximum precipitation (PMP) and sharing PMP's storm transposition characteristic, SST can also be used in more typical rainfall frequency analysis (RFA) and flood frequency analysis (FFA) applications. This paper explains the method, discusses its origins and linkages to both PMP and RFA/FFA, and reviews the development of SST research over the past six decades. Discussion topics includes: the relevance of recent advances in precipitation remote sensing to frequency analysis, numerical weather prediction, and distributed rainfall-runoff modeling; uncertainty and boundedness in rainfall and floods; the flood frequency challenges posed by climatic and land use change; and the concept of mull-scale flood frequency. Recent literature has shown that process-based multiscale FFA, in which the joint distributions of flood-producing meteorological and hydrological processes are synthesized and resolved using distributed physics-based rainfall-runoff models, provides a useful framework for translating nonstationary hydroclimatic conditions into flood frequency estimates. SST pairs well with the process-based approaches. This pairing is promising because it can leverage advances from other branches of hydrology and hydrometeorology that appear to be difficult to integrate into better-known RFA and FFA approaches. The paper closes with several recommendations for future SST research and applications.

【 授权许可】

Free