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

Sudershan Gangrade, Shih Chieh Kao, Tigstu T. Dullo, Alfred J. Kalyanapu, Benjamin L. Preston

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)342-355
Number of pages14
JournalJournal of Hydrology
Volume576
DOIs
StatePublished - Sep 2019

Funding

SG and SCK were supported by the Hydro Research Foundation and US Department of Energy ( DOE ) Water Power Technologies Office under Award Number DE-00006506. SCK was also partially supported by the U.S. Air Force Numerical Weather Modeling Program. AJK and TTD are supported by the Tennessee Technological University Center for the Management, Utilization and Protection of Water Resources. The research used resources of the Oak Ridge Leadership Computing Facility. Some of the co-authors are employees of UT-Battelle, LLC, under contract DE-AC05-00OR22725 with DOE. Accordingly, the US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US Government purposes. The input data sets are cited throughout the paper, as appropriate. The meteorological data was acquired from Rastogi et al, (2017), while the hydrologic model setup was acquired from Gangrade et al, (2018). The open source version of hydrological model (DHSVM) is available at https://dhsvm.pnnl.gov/code.stm . Any data questions can be directed to S.-C. Kao ( [email protected] ) at ORNL.

FundersFunder number
Tennessee Technological University Center for the Management, Utilization and Protection of Water Resources
U.S. Air Force Numerical Weather Modeling Program
US Department of Energy
U.S. Department of Energy
Hydro Research Foundation
Water Power Technologies OfficeDE-00006506

    Keywords

    • Flood modeling
    • Graphics Processing Units (GPU)
    • Probabilistic flood maps (PFMs)
    • Probable maximum flood (PMF)
    • Probable maximum precipitation (PMP)

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