Sensitivity of Probable Maximum Flood in a Changing Environment

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Abstract

With likely increases in probable maximum precipitation (PMP) in a changing environment, critical infrastructures such as major reservoirs and nuclear power plants are subject to elevated risk. To understand how factors such as PMP variability, climate change, land use land cover (LULC) change, antecedent soil moisture conditions, and reservoir storage may individually or jointly affect the magnitude of probable maximum flood (PMF), we conducted integrated hydrometeorological simulations involving both the Weather Research Forecasting model and the distributed hydrologic model (DHSVM) over the Alabama-Coosa-Tallapoosa (ACT) River Basin in the southeastern United States. A total of 120 relative humidity-maximized PMP storms under historic and projected future climate conditions were used to drive DHSVM in current and projected future LULC conditions. Overall, PMP and PMF are projected to increase significantly over the ACT River Basin. Sources of meteorological forcing data sets and climate change were found to be the most sensitive factors affecting PMF, followed by antecedent soil moisture, reservoir storage, and then LULC change. The ensemble of PMP and PMF simulations, along with their sensitivity, allows us to better quantify the potential risks associated with hydroclimatic extreme events to critical infrastructures for energy-water security.

Original languageEnglish
Pages (from-to)3913-3936
Number of pages24
JournalWater Resources Research
Volume54
Issue number6
DOIs
StatePublished - Jun 2018

Funding

This study was supported by the US Department of Energy, Office of Science, Biological and Environmental Research, Integrated Assessment Program, and by Oak Ridge National Laboratory’s (ORNL) Laboratory Directed Research and Development Program. The research used resources of the Oak Ridge Leadership Computing Facility at ORNL. The authors are employees of UT-Battelle, LLC, under contract DE-AC05- 00OR22725 with the US Department of Energy. 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 sources of the input data sets used in the study are provided in the paper. The meteorological data used in this study were obtained from Rastogi et al. (2017). The hydrologic model DHSVM v3.1.1 used in this study is open source and available at https://dhsvm.pnnl. gov/code.stm. Other data questions can be directed to S.-C. Kao (kaos@ ornl.gov) at the ORNL Climate Change Science Institute. Notice: This manuscript has been authored by UT- Battelle, LLC, under contract DE-AC05- 00OR22725 with the US Department of Energy (DOE). DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http:// energy.gov/downloads/doe-public- access-plan).

FundersFunder number
US Department of Energy
U.S. Department of Energy
Office of Science
Biological and Environmental Research
Oak Ridge National Laboratory
Laboratory Directed Research and DevelopmentDE-AC05- 00OR22725

    Keywords

    • ACT
    • DHSVM
    • climate change
    • land use land cover change
    • probable maximum flood

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