Ensemble-based flood vulnerability assessment for probable maximum flood in a changing environment

JOURNAL OF HYDROLOGY

卷:576

Article

Gangrade, Sudershan^1,2,3  Kao, Shih-Chieh^1,2,3  Dullo, Tigstu T.⁴  Kalyanapu, Alfred J.⁴  Preston, Benjamin L.⁵ 

[1] Univ Tennessee, Bredesen Ctr, Knoxville, TN 37996 USA

[2] Oak Ridge Natl Lab, Environm Sci Div, POB 2009, Oak Ridge, TN 37831 USA

[3] Oak Ridge Natl Lab, Climate Change Sci Inst, POB 2009, Oak Ridge, TN 37831 USA

[4] Tennessee Technol Univ, Dept Civil & Environm Engn, Cookeville, TN 38505 USA

[5] RAND Corp, POB 2138, Santa Monica, CA 90407 USA

关键词: Flood modeling;    Graphics Processing Units (GPU);    Probable maximum precipitation (PMP);    Probable maximum flood (PMF);    Probabilistic flood maps (PFMs);   

DOI  :  10.1016/j.jhydrol.2019.06.027

来源: Elsevier

【 摘 要 】

The magnitude and frequency of hydro-meteorological extremes are expected to increase in a changing environment in ways that threaten the security of US energy-water assets. These include probable maximum precipitation (PMP) and probable maximum flood (PMF), which are used as hydraulic design standards for highly sensitive infrastructures such as nuclear power plants and main dams. To assess the flood vulnerability due to PMP/PMF, an integrated high-resolution process-based hydro-meteorologic modeling framework was used to develop ensemble-based probabilistic flood maps based on best-available historic observations and future climate projections. A graphics processing unit-accelerated 2-dimensional hydrodynamic model was used to simulate the surface inundation areas corresponding to a total of 120 PMF hydrographs. These ensemble-based PMF maps were compared with flood maps obtained from the conventional deterministic PMP/PMF approach, revealing added information about conditional probability of flooding. Further, a relative sensitivity test was conducted to explore the effects of various factors in the framework, such as meteorological forcings, antecedent hydrologic conditions, reservoir storage, and flood model input resolution and parameters. The proposed framework better illustrates the uncertainties associated with model inputs, parameterization, and hydro-meteorological factors, allowing more informed decision-making for future emergency preparation.

【 授权许可】

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