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

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

doi:10.1016/j.jhydrol.2019.06.027

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

Journal Article · Mon Jun 10 00:00:00 EDT 2019 · Journal of Hydrology

DOI:https://doi.org/10.1016/j.jhydrol.2019.06.027· OSTI ID:1530096

^[1]; ^[1]; Dullo, Tigstu T. ^[2]; Kalyanapu, Alfred J. ^[2]; Preston, Benjamin L. ^[3]

Univ. of Tennessee, Knoxville, TN (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Tennessee Technological Univ., Cookeville, TN (United States)
RAND Corp., Santa Monica, CA (United States)

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. In conclusion, 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.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE; USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1530096

Alternate ID(s):: OSTI ID: 1562776

Journal Information:: Journal of Hydrology, Journal Name: Journal of Hydrology Journal Issue: C Vol. 576; ISSN 0022-1694

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (39)

Application of a GIS-based distributed hydrology model for prediction of forest harvest effects on peak stream flow in the Pacific Northwest Storck, Pascal; Bowling, Laura; Wetherbee, Paul Hydrological Processes, Vol. 12, Issue 6 https://doi.org/10.1002/(SICI)1099-1085(199805)12:6<889::AID-HYP661>3.0.CO;2-P	journal	May 1998
The role of tributary relative timing and sequencing in controlling large floods Pattison, Ian; Lane, Stuart N.; Hardy, Richard J. Water Resources Research, Vol. 50, Issue 7 https://doi.org/10.1002/2013WR014067	journal	July 2014
Probabilistic Flood Maps to support decision‐making: Mapping the Value of Information Alfonso, L.; Mukolwe, M. M.; Di Baldassarre, G. Water Resources Research, Vol. 52, Issue 2 https://doi.org/10.1002/2015WR017378	journal	February 2016
Effects of climate change on probable maximum precipitation: A sensitivity study over the Alabama-Coosa-Tallapoosa River Basin: EFFECTS OF CLIMATE CHANGE ON PMP Rastogi, Deeksha; Kao, Shih-Chieh; Ashfaq, Moetasim Journal of Geophysical Research: Atmospheres, Vol. 122, Issue 9 https://doi.org/10.1002/2016JD026001	journal	May 2017
Validation of a 30 m resolution flood hazard model of the conterminous United States: 30 m RESOLUTION FLOOD MODEL OF CONUS Wing, Oliver E. J.; Bates, Paul D.; Sampson, Christopher C. Water Resources Research, Vol. 53, Issue 9 https://doi.org/10.1002/2017WR020917	journal	September 2017
Probable maximum precipitation and climate change: PMP AND CLIMATE CHANGE Kunkel, Kenneth E.; Karl, Thomas R.; Easterling, David R. Geophysical Research Letters, Vol. 40, Issue 7 https://doi.org/10.1002/grl.50334	journal	April 2013
Probabilistic flood risk mapping including spatial dependence: PROBABILISTIC FLOOD RISK MAPPING INCLUDING SPATIAL DEPENDENCE Neal, Jeffrey; Keef, Caroline; Bates, Paul Hydrological Processes, Vol. 27, Issue 9 https://doi.org/10.1002/hyp.9572	journal	November 2012
Advances in pan-European flood hazard mapping: ADVANCES IN PAN-EUROPEAN FLOOD HAZARD MAPPING Alfieri, Lorenzo; Salamon, Peter; Bianchi, Alessandra Hydrological Processes, Vol. 28, Issue 13 https://doi.org/10.1002/hyp.9947	journal	July 2013
Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States Daly, Christopher; Halbleib, Michael; Smith, Joseph I. International Journal of Climatology, Vol. 28, Issue 15 https://doi.org/10.1002/joc.1688	journal	December 2008
Estimation of the summer-fall PMP and PMF of a northern watershed under a changed climate: PMP and PMF Estimation Under Climate Change Beauchamp, Josée; Leconte, Robert; Trudel, Mélanie Water Resources Research, Vol. 49, Issue 6 https://doi.org/10.1002/wrcr.20336	journal	June 2013
Flood inundation mapping sensitivity to riverine spatial resolution and modelling approach Papaioannou, G.; Loukas, A.; Vasiliades, L. Natural Hazards, Vol. 83, Issue S1 https://doi.org/10.1007/s11069-016-2382-1	journal	May 2016
A simple raster-based model for flood inundation simulation Bates, P. D.; De Roo, A. P. J. Journal of Hydrology, Vol. 236, Issue 1-2 https://doi.org/10.1016/S0022-1694(00)00278-X	journal	September 2000
Generating surfaces of daily meteorological variables over large regions of complex terrain Thornton, Peter E.; Running, Steven W.; White, Michael A. Journal of Hydrology, Vol. 190, Issue 3-4 https://doi.org/10.1016/S0022-1694(96)03128-9	journal	March 1997
Flood inundation uncertainty: The case of a 0.5% annual probability flood event Prime, Thomas; Brown, Jennifer M.; Plater, Andrew J. Environmental Science & Policy, Vol. 59 https://doi.org/10.1016/j.envsci.2016.01.018	journal	May 2016
Assessment of GPU computational enhancement to a 2D flood model Kalyanapu, Alfred J.; Shankar, Siddharth; Pardyjak, Eric R. Environmental Modelling & Software, Vol. 26, Issue 8 https://doi.org/10.1016/j.envsoft.2011.02.014	journal	August 2011
Development of a methodology to evaluate probable maximum precipitation (PMP) under changing climate conditions: Application to southern Quebec, Canada Rousseau, Alain N.; Klein, Iris M.; Freudiger, Daphné Journal of Hydrology, Vol. 519 https://doi.org/10.1016/j.jhydrol.2014.10.053	journal	November 2014
Uncertainty analysis for Probable Maximum Precipitation estimates Micovic, Zoran; Schaefer, Melvin G.; Taylor, George H. Journal of Hydrology, Vol. 521 https://doi.org/10.1016/j.jhydrol.2014.12.033	journal	February 2015
Evaluation of probable maximum snow accumulation: Development of a methodology for climate change studies Klein, Iris M.; Rousseau, Alain N.; Frigon, Anne Journal of Hydrology, Vol. 537 https://doi.org/10.1016/j.jhydrol.2016.03.031	journal	June 2016
Sensitivity of Probable Maximum Flood in a Changing Environment Gangrade, Sudershan; Kao, Shih-Chieh; Naz, Bibi S. Water Resources Research, Vol. 54, Issue 6 https://doi.org/10.1029/2017WR021987	journal	June 2018
A distributed hydrology-vegetation model for complex terrain Wigmosta, Mark S.; Vail, Lance W.; Lettenmaier, Dennis P. Water Resources Research, Vol. 30, Issue 6 https://doi.org/10.1029/94WR00436	journal	June 1994
Physically Based Estimation of Maximum Precipitation over American River Watershed, California Ohara, N.; Kavvas, M. L.; Kure, S. Journal of Hydrologic Engineering, Vol. 16, Issue 4 https://doi.org/10.1061/(ASCE)HE.1943-5584.0000324	journal	April 2011
Probable Maximum Precipitation in a Changing Climate: Implications for Dam Design Stratz, Steven A.; Hossain, Faisal Journal of Hydrologic Engineering, Vol. 19, Issue 12 https://doi.org/10.1061/(ASCE)HE.1943-5584.0001021	journal	December 2014
Physically Based Estimation of Maximum Precipitation over Three Watersheds in Northern California: Relative Humidity Maximization Method Ishida, K.; Kavvas, M. L.; Jang, S. Journal of Hydrologic Engineering, Vol. 20, Issue 10 https://doi.org/10.1061/(ASCE)HE.1943-5584.0001175	journal	October 2015
Hurricane Harvey Highlights: Need to Assess the Adequacy of Probable Maximum Precipitation Estimation Methods Kao, Shih-Chieh; DeNeale, Scott T.; Watson, David B. Journal of Hydrologic Engineering, Vol. 24, Issue 4 https://doi.org/10.1061/(ASCE)HE.1943-5584.0001768	journal	April 2019
Flood-plain mapping: a critical discussion of deterministic and probabilistic approaches Di Baldassarre, Giuliano; Schumann, Guy; Bates, Paul D. Hydrological Sciences Journal, Vol. 55, Issue 3 https://doi.org/10.1080/02626661003683389	journal	April 2010
Probabilistic Flood Mapping Using Synthetic Aperture Radar Data Giustarini, Laura; Hostache, Renaud; Kavetski, Dmitri IEEE Transactions on Geoscience and Remote Sensing, Vol. 54, Issue 12 https://doi.org/10.1109/TGRS.2016.2592951	journal	December 2016
Demonstrating Floodplain Uncertainty Using Flood Probability Maps Smemoe, Christopher M.; Nelson, E. James; Zundel, Alan K. Journal of the American Water Resources Association, Vol. 43, Issue 2 https://doi.org/10.1111/j.1752-1688.2007.00028.x	journal	April 2007
Monte Carlo-based flood modelling framework for estimating probability weighted flood risk: Monte Carlo-based flood modelling framework Kalyanapu, A. J.; Judi, D. R.; McPherson, T. N. Journal of Flood Risk Management, Vol. 5, Issue 1 https://doi.org/10.1111/j.1753-318X.2011.01123.x	journal	October 2011
Estimation of probabilistic flood inundation maps for an extreme event: Pánuco River, México: Probabilistic flood inundation maps Pedrozo-Acuña, A.; Rodríguez-Rincón, J. P.; Arganis-Juárez, M. Journal of Flood Risk Management, Vol. 8, Issue 2 https://doi.org/10.1111/jfr3.12067	journal	September 2013
Probabilistic flood hazard maps for Jakarta derived from a stochastic rain-storm generator: Probabilistic flood hazard maps for Jakarta Nuswantoro, R.; Diermanse, F.; Molkenthin, F. Journal of Flood Risk Management, Vol. 9, Issue 2 https://doi.org/10.1111/jfr3.12114	journal	May 2014
Generating precipitation ensembles for flood alert and risk management: Precipitation ensembles for flood alert Caseri, A.; Javelle, P.; Ramos, M. H. Journal of Flood Risk Management, Vol. 9, Issue 4 https://doi.org/10.1111/jfr3.12203	journal	October 2015
A Conterminous United States Multilayer Soil Characteristics Dataset for Regional Climate and Hydrology Modeling Miller, Douglas A.; White, Richard A. Earth Interactions, Vol. 2, Issue 2 https://doi.org/10.1175/1087-3562(1998)002<0001:ACUSMS>2.3.CO;2	journal	January 1998
The NCEP Climate Forecast System Reanalysis Saha, Suranjana; Moorthi, Shrinivas; Pan, Hua-Lu Bulletin of the American Meteorological Society, Vol. 91, Issue 8 https://doi.org/10.1175/2010BAMS3001.1	journal	August 2010
The Community Climate System Model Version 4 Gent, Peter R.; Danabasoglu, Gokhan; Donner, Leo J. Journal of Climate, Vol. 24, Issue 19 https://doi.org/10.1175/2011JCLI4083.1	journal	October 2011
Performance Evaluation and Enhancements of a Flood Simulator Application for Heterogeneous HPC Environments Marshall, Ryan; Ghafoor, Sheikh; Rogers, Mike International Journal of Networking and Computing, Vol. 8, Issue 2 https://doi.org/10.15803/ijnc.8.2_387	journal	January 2018
Probabilistic flood inundation mapping at ungauged streams due to roughness coefficient uncertainty in hydraulic modelling Papaioannou, George; Vasiliades, Lampros; Loukas, Athanasios Advances in Geosciences, Vol. 44 https://doi.org/10.5194/adgeo-44-23-2017	journal	January 2017
Probabilistic flood hazard mapping: effects of uncertain boundary conditions Domeneghetti, A.; Vorogushyn, S.; Castellarin, A. Hydrology and Earth System Sciences, Vol. 17, Issue 8 https://doi.org/10.5194/hess-17-3127-2013	journal	January 2013
Effects of variability in probable maximum precipitation patterns on flood losses Zischg, Andreas Paul; Felder, Guido; Weingartner, Rolf Hydrology and Earth System Sciences, Vol. 22, Issue 5 https://doi.org/10.5194/hess-22-2759-2018	journal	January 2018
Flood-risk mapping: contributions towards an enhanced assessment of extreme events and associated risks Büchele, B.; Kreibich, H.; Kron, A. Natural Hazards and Earth System Sciences, Vol. 6, Issue 4 https://doi.org/10.5194/nhess-6-485-2006	journal	January 2006

Cited By (2)

Multi-model Hydroclimate Projections for the Alabama-Coosa-Tallapoosa River Basin in the Southeastern United States Gangrade, Sudershan; Kao, Shih-Chieh; McManamay, Ryan A. Scientific Reports, Vol. 10, Issue 1 https://doi.org/10.1038/s41598-020-59806-6	journal	February 2020
Flood Inundation Mapping at Ungauged Basins Using Coupled Hydrometeorological–Hydraulic Modelling: The Catastrophic Case of the 2006 Flash Flood in Volos City, Greece Papaioannou, George; Varlas, George; Terti, Galateia Water, Vol. 11, Issue 11 https://doi.org/10.3390/w11112328	journal	November 2019

Similar Records

DEVELOPMENT OF ENSEMBLE-BASED PROBABILISTIC FLOOD MAPS FOR PROBABLE MAXIMUM FLOOD

Other · Mon Sep 24 00:00:00 EDT 2018 · OSTI ID:1630932

Sensitivity of Probable Maximum Flood in a Changing Environment

Journal Article · Thu Apr 26 20:00:00 EDT 2018 · Water Resources Research · OSTI ID:1462900

Probable maximum flood analysis, Richton Dome, Mississippi-Phase I: Technical report

Technical Report · Sat Feb 28 23:00:00 EST 1987 · OSTI ID:6304411

Related Subjects

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

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

Citation Formats

References (39)

Cited By (2)

Similar Records

Related Subjects