Technical Papers
May 2, 2017

Distributed Hydrologic Modeling of Coastal Flood Inundation and Damage: Nonstationary Approach

Publication: Journal of Irrigation and Drainage Engineering
Volume 143, Issue 8

Abstract

Superstorm Sandy and Hurricane Irene on the East Coast of the United States were wake-up calls that the floodplain delineation and flood damage estimation models need major overhaul. The first step in flood-related studies is frequency analysis. A great challenge has emerged on the validity of the data stationarity assumption. In hydrologic studies, data stationarity has been doubted due to climate change and variability, and sea-level rise. This paper presents an integrated scheme for floodplain delineation and flood damage estimation in coastal areas, considering the combined effect of inland and coastal flooding. For this purpose, time series of rainfall and water level and surge are tested for stationarity. Then, nonstationary frequency analysis is performed to determine the extreme flood events. Considering the coincidence of heavy rainfalls and storm surges as a serious concern in coastal regions, the joint probability distribution of rainfall and storm surge data is also investigated. Consequently, three flood scenarios are defined. A distributed hydrologic model is then utilized for floodplain delineation. A geographic information system (GIS)–based model, using depth-damage functions, land use data, digital elevation model (DEM), and raster maps, is used to estimate flood damage for Manhattan in New York City. Results showed that by incorporating nonstationarity in frequency analysis, design values of rainfall and extreme water levels could be significantly different than those obtained under the assumption of data stationarity. The results also indicated that floodplain extent and estimation of flood damage are increased when data nonstationarity is considered. However, these values are decreased when the joint probability of rainfall and water level is incorporated into defining flood scenarios.

Get full access to this article

View all available purchase options and get full access to this article.

References

Aissaoui-Fqayeh, I., El-Adlouni, S., Ouarda, T. B. M. J., and St-Hilaire, A. (2009). “Non-stationary lognormal model development and comparison with non-stationary GEV model.” Hydrol. Sci. J., 54(6), 1141–1156.
Alves, I. F., and Neves, C. (2011). “Extreme value distributions.” International encyclopedia of statistical science, Springer, Berlin, 493–496.
ArcGIS [Computer software]. ESRI, Redlands, CA.
Bowman, A. W., and Azzalini, A. (1997). Applied smoothing techniques for data analysis: The kernel approach with S-Plus illustrations, OUP Oxford, Oxford, U.K.
Burn, D. H. (1990). “Evaluation of regional flood frequency analysis with a region of influence approach.” Water Resour. Res., 26(10), 2257–2265.
City of New York Department of City Planning. (2014). “MapPLUTO-Manhattan.” ⟨https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page⟩ (Nov. 10, 2014).
Coles, S. (2001). “An introduction to statistical modelling of extreme values.” Springer series in statistics, Springer, London.
Coles, S., and Davison, A. (2013). “Statistical modelling of extreme values.” ⟨http://www.cces.ethz.ch/projects/hazri/EXTREMES/talks/colesDavisonDavosJan08.pdf⟩ (Feb. 4, 2013).
Cong, R. G., and Brady, M. (2012). “The interdependence between rainfall and temperature: Copula analyses.” Sci. World J., 2012, 1–11.
Cunderlik, J. M., and Burn, D. H. (2003). “Non-stationary pooled flood frequency analysis.” J. Hydrol., 276(1), 210–223.
Downer, C., and Ogden, F. (2004). “GSSHA: Model to simulate diverse stream flow producing processes.” J. Hydrol. Eng., 161–174.
Downer, C. W., and Ogden, F. L. (2012). GSSHA user’s manual, U.S. Army Engineer Research and Development Center, Vicksburg, MS.
Dutta, D., Herath, S., and Musiake, K. (2003). “A mathematical model for flood loss estimation.” J. Hydrol., 277(1), 24–49.
Gilroy, K. L., and McCuen, R. H. (2012). “A non-stationary flood frequency analysis method to adjust for future climate change and urbanization.” J Hydrol., 414(1), 40–48.
Golian, S., Saghafian, B., and Farokhnia, A. (2012). “Copula-based interpretation of continuous rainfall-runoff simulations of a watershed in northern Iran.” Can. J. Earth Sci., 49(5), 681–691.
Hanel, M., Buishand, T. A., and Ferro, C. A. T. (2009). “A non-stationary index-flood model for precipitation extremes in transient regional climate model simulations.” J. Geophys. Res. Atmos., 114(D15), 1–61.
Haq, M., Akhtar, M., Muhammad, S., Paras, S., and Rahmatullah, J. (2012). “Techniques of remote sensing and GIS for flood monitoring and damage assessment: A case study of Sindh province, Pakistan.” Egypt. J. Remote Sens. Space Sci., 15(2), 135–141.
Hawkes, P. J., Gonzalez-Marco, D., Sánchez-Arcilla, A., and Prinos, P. (2008). “Best practice for the estimation of extremes: A review.” J. Hydraul. Res., 46(S2), 324–332.
HAZUS [Computer software]. Federal Emergency Management Agency FEMA, Washington, DC.
Hosking, J. R. (1990). “L-moments: Analysis and estimation of distributions using linear combinations of order statistics.” J. Royal Stat. Soc., 52(1), 105–124.
Islam, K. M. N. (2000). “Impact of floods in Bangladesh.” Floods, Routledge hazards and disasters series, D. J. Parker, ed., Routledge, London, 156–171.
Jonge, T. D., Matthijs, K., and Hogeweg, M. (1996). Modeling floods and damage assessment using GIS, IAHS, Vienna, Austria, 299–306.
Jongman, B., et al. (2014). “Increasing stress on disaster-risk finance due to large floods.” Nat. Clim. Change, 4(4), 264–268.
Karamouz, M., Fereshtehpour, M., Ahmadvand, F., and Zahmatkesh, Z. (2016). “Coastal flood damage estimator: An alternative to FEMA’s HAZUS platform.” J. Irrig. Drain. Eng., 04016016.
Karamouz, M., Zahmatkesh, Z., Goharian, E., and Nazif, S. (2014a). “Combined impact of inland and coastal floods: Mapping knowledge base for development of planning strategies.” J. Water Resour. Plann. Manage., 04014098.
Karamouz, M., Zahmatkesh, Z., Nazif, S., and Razmi, A. (2014b). “An evaluation of climate change impacts on extreme sea level variability: Coastal area of New York City.” Water Resour. Manage., 28(11), 3697–3714.
Katz, R. W., Parlange, M. B., and Naveau, P. (2002). “Statistics of extremes in hydrology.” Adv. Water Resour., 25(8), 1287–1304.
Kendall, M. G. (1976). Rank correlation methods, 4th Ed., Griffin, London.
Khaliq, M. N., Ouarda, T. B. M. J., Ondo, J. C., Gachon, P., and Bobée, B. (2006). “Frequency analysis of a sequence of dependent and/or non-stationary hydro-meteorological observations: A review.” J. Hydrol., 329(3–4), 534–552.
Klein, B., Pahlow, M., Hundecha, Y., and Schumann, A. (2009). “Probability analysis of hydrological loads for the design of flood control systems using copulas.” J. Hydrol. Eng., 360–369.
Kvam, P. H., and Vidakovic, B. (2007). Nonparametric statistics with applications to science and engineering, Wiley, Hoboken, NJ.
Leclerc, M., and Ouarda, T. B. M. J. (2007). “Non-stationary regional flood frequency analysis at ungauged sites.” J. Hydrol., 343(3), 254–265.
Li, F., Van Gelder, P. H. A. J. M., Ranasinghe, R., Callaghan, D. P., and Jongejan, R. B. (2014). “Probabilistic modelling of extreme storms along the Dutch coast.” Coastal Eng., 86, 1–13.
Li, T., Guo, S., Chen, L., and Guo, J. (2012). “Bivariate flood frequency analysis with historical information based on copula.” J. Hydrol. Eng., 1018–1030.
Lilliefors, H. W. (1967). “On the Kolmogorov-Smirnov test for normality with mean and variance unknown.” J. Am. Stat. Assoc., 62(318), 399–402.
Lopez, J., and Frances, F. (2013). “Non-stationary flood frequency analysis in continental Spanish rivers, using climate and reservoir indices as external covariates.” Hydrol. Earth Syst. Sci., 10(8), 3103–3142.
MacKinnon, J. G., Haug, A. A., and Michelis, L. (1999). “Numerical distribution functions of likelihood ratio tests for cointegration.” J. Appl. Econ., 14(5), 563–577.
Mann, H. B. (1945). “Nonparametric tests against trend. Econometrica.” J. Econ. Soc., 13(3), 245–259.
McElroy, F. W. (1967). “A necessary and sufficient condition that ordinary least-squares estimators be best linear unbiased.” J. Am. Stat. Assoc., 62(320), 1302–1304.
Mohammadi, S. A., Nazariha, M., and Mehrdadi, N. (2014). “Flood damage estimate (quantity), using HEC-FDA model. Case study: The Neka river.” Procedia Eng., 70, 1173–1182.
Mudersbach, C., and Jensen, J. (2010). “Nonstationary extreme value analysis of annual maximum water levels for designing coastal structures on the German North Sea coastline.” J. Flood Risk Manage., 3(1), 52–62.
NOAA (National Oceanic and Atmospheric Administration). (2017a). “Daily summaries station details at central park.” ⟨https://www.ncdc.noaa.gov/cdo-web/datasets/GHCND/stations/GHCND:USW00094728/detail⟩ (Apr. 5, 2016).
NOAA (National Oceanic and Atmospheric Administration). (2017b). “Observed water levels at battery park.” ⟨https://tidesandcurrents.noaa.gov/waterlevels.html?id=8518750⟩ (Apr. 5, 2016).
NOAA (National Oceanic and Atmospheric Administration). (2017c). “State of the coast.” ⟨http://oceanservice.noaa.gov/facts/population.html⟩ (Jun. 6, 2014).
Parker, D. J. (1992). “The assessment of the economic and social impacts of natural hazards.” Int. Conf. on Preparedness and Mitigation for Natural Disasters, Reykjavik, Iceland.
Rootzén, H., and Katz, R. W. (2013). “Design life level: Quantifying risk in a changing climate.” Water Resour. Res., 49(9), 5964–5972.
Roussas, G. (2014). “Joint probability density function of two random variables and related quantities.” Introduction to probability, 2nd Ed., Academic Press (Elsevier), Cambridge, MA, 137–161.
RS Means. (2006). Means square foot costs, 27th Ed., RS Means Company, New York.
Said, S. E., and Dickey, D. A. (1984). “Testing for unit roots in autoregressive-moving average models of unknown order.” Biometrika, 71(3), 599–607.
Salas, J. D. (1993). “Analysis and modelling of hydrologic timeseries.” Handbook of hydrology, McGraw-Hill, New York, 19.1–19.72.
Salas, J. D., and Obeysekera, J. (2013). “Revisiting the concepts of return period and risk for nonstationary hydrologic extreme events.” J. Hydrol. Eng., 554–568.
Scawthorn, C., et al. (2006). “HAZUS-MH flood loss estimation methodology. II: Damage and loss assessment.” Nat. Hazard. Rev., 72–81.
Serinaldi, F., and Kilsby, C. G. (2015). “Stationarity is undead: Uncertainty dominates the distribution of extremes.” Adv. Water Resour., 77, 17–36.
Sharif, H. O., Sparks, L., Hassan, A. A., Zeitler, J., and Xie, H. (2010). “Application of a distributed hydrologic model to the November 17, 2004, flood of Bull Creek watershed, Austin, Texas.” J. Hydrol. Eng., 651–657.
Sklar, M. (1959). Fonctions de répartition à n dimensions et leurs marges, Univ. of Vincennes, Paris.
Song, J. B., Wei, E., and Hou, Y. J. (2004). “Joint statistical distribution of two-point sea surface elevations in finite water depth.” Coastal Eng., 50(4), 169–179.
Strupczewski, W. G., and Kaczmarek, Z. (2001). “Non-stationary approach to at-site flood frequency modeling. II: Weighted least squares estimation.” J. Hydrol., 248(1–4), 143–151.
Svensson, C., Kundzewicz, W. Z., and Maurer, T. (2005). “Trend detection in river flow series. 2: Flood and low-flow index series [Détection de tendancedans des séries de débit fluvial. 2: Sériesd’indices de crueetd’étiage].” Hydrol. Sci. J., 50(5), 824.
USACE (U.S. Army Corps of Engineers). (2003). “Economic guidance memorandum (EGM) 04-01: Generic depth damage relationships for residential structures with basements.” Washington, DC.
Vasiliades, L., Galiatsatou, P., and Loukas, A. (2015). “Nonstationary frequency analysis of annual maximum rainfall using climate covariates.” Water Resour. Manage., 29(2), 339–358.
Vogel, R. M., and Fennessey, N. M. (1993). “L moment diagrams should replace product moment diagrams.” Water Resour. Res., 29(6), 1745–1752.
Wahl, T., Jain, S., Bender, J., Meyers, S. D., and Luther, M. E. (2015). “Increasing risk of compound flooding from storm surge and rainfall for major US cities.” Nat. Clim. Change, 5(12), 1093–1097.
Xu, K., Ma, C., Lian, J., and Bin, L. (2014). “Joint probability analysis of extreme precipitation and storm tide in a coastal city under changing environment.” PloS One, 9(10), e109341.
Yang, J., Townsend, R. D., and Daneshfar, B. (2006). “Applying the HEC-RAS model and GIS techniques in river network floodplain delineation.” Can. J. Civil. Eng., 33(1), 19–28.
Zahmatkesh, Z., Karamouz, M., Goharian, E., and Burian, S. (2014). “Analysis of the effects of climate change on urban storm water runoff using statistically downscaled precipitation data and a change factor approach.” J. Hydrol. Eng., 05014022.
Zhang, H. D., Cherneva, Z., and Guedes Soares, C. (2013). “Joint distributions of wave height and period in laboratory generated nonlinear sea states.” Ocean Eng., 74, 72–80.

Information & Authors

Information

Published In

Go to Journal of Irrigation and Drainage Engineering
Journal of Irrigation and Drainage Engineering
Volume 143Issue 8August 2017

History

Received: Jul 6, 2016
Accepted: Nov 15, 2016
Published online: May 2, 2017
Published in print: Aug 1, 2017
Discussion open until: Oct 2, 2017

Permissions

Request permissions for this article.

Authors

Affiliations

Mohammad Karamouz, F.ASCE [email protected]
Professor, School of Civil Engineering, Univ. of Tehran, 14174 Tehran, Iran (corresponding author). E-mail: [email protected]
Forough Ahmadvand [email protected]
Research Assistant, School of Civil Engineering, Univ. of Tehran, 14174 Tehran, Iran. E-mail: [email protected]
Zahra Zahmatkesh [email protected]
Research Associate, Faculty of Engineering, Dept. of Civil Engineering, Univ. of Manitoba, Winnipeg, MB, Canada R3T 2N2. E-mail: [email protected]

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited by

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share