Abstract
Flooding poses a serious public health hazard throughout the world. Flood modeling is an important tool for emergency preparedness and response, but some common methods require a high degree of expertise or may be unworkable due to poor data quality or data availability issues. The conceptually simple method of inverse distance weight modeling offers an alternative. Using stream gauges as inputs, this study interpolated stream elevation via inverse distance weight modeling under 15 different model input parameter scenarios for Harris County, Texas, USA, from August 25th to September 15th, 2017 (before, during, and after Hurricane Harvey inundated the county). A digital elevation model was used to identify areas where modeled stream elevation exceeded ground elevation, indicating flooding. Imagery and observed high water marks were used to validate the models’ outputs. There was a high degree of agreement (between 79 and 88%) between imagery and model outputs of parameterizations visually validated. Quantitative validations based on high water marks were also positive, with a Nash–Sutcliffe efficiency of in excess of .6 for all parameterizations relative to a Nash–Sutcliffe efficiency of the benchmark of 0.56. Inverse distance weight modeling offers a simple, accurate method for first-order estimations of riverine flooding in near real-time using readily available data, and outputs are robust to some alterations to input parameters.
Similar content being viewed by others
References
Ahern M, Kovats RS, Wilkinson P, Few R, Matthies F (2005) Global health impacts of floods: epidemiologic evidence. Epidemiol Rev 27:36–46. https://doi.org/10.1093/epirev/mxi004
Al-Sabhan W, Mulligan M, Blackburn GA (2003) A real-time hydrological model for flood prediction using GIS and the WWW. Comput Environ Urban Syst 27(1):9–32. https://doi.org/10.1016/S0198-9715(01)00010-2
Cann KF, Thomas DR, Salmon RL, Wyn-Jones AP, Kay D (2013) Extreme water-related weather events and waterborne disease. Epidemiol Infect 141(4):671–686. https://doi.org/10.1017/S0950268812001653
Center for Research on the Epidemiology of Disasters (2016) Poverty & Death: Disaster Mortality 1996–2015. United Nations Office for Disaster Risk Reduction. https://www.unisdr.org/we/inform/publications/50589. Accessed 16 Sept 2019
Centers for Disease Control and Prevention (2006) Health concerns associated with mold in water-damaged homes after Hurricanes Katrina and Rita-New Orleans area, Louisiana, October 2005. MMWR Morb Mortal Wkly Rep 55(2):41–44
Chen J, Hill AA, Urbano LD (2009) A GIS-based model for urban flood inundation. J Hydrol 373(1–2):184–192. https://doi.org/10.1016/j.jhydrol.2009.04.021
Chew GL, Wilson J, Rabito FA, Grimsley F, Iqbal S, Reponen T, Muilenberg ML, Thorne PS, Dearborn DG, Morley RL (2006) Mold and endotoxin levels in the aftermath of Hurricane Katrina: a pilot project of homes in New Orleans undergoing renovation. Environ Health Perspect 114(12):1883–1889. https://doi.org/10.1289/ehp.9258
Diem JE (2003) A critical examination of ozone mapping from a spatial-scale perspective. Environ Pollut 125(3):369–383. https://doi.org/10.1016/s0269-7491(03)00110-6
Gallien TW, Barnard PL, van Ormondt M, Foxgrover AC, Sanders BF (2013) A parcel-scale coastal flood forecasting prototype for a southern California urbanized embayment. J Coast Res 29(3):642–656. https://doi.org/10.2112/JCOASTRES-D-12-00114.1
Horritt MS, Bates PD (2002) Evaluation of 1D and 2D numerical models for predicting river flood inundation. J Hydrol 268:87–99. https://doi.org/10.1016/S0022-1694(02)00121-X
Ivers LC, Ryan ET (2006) Infectious diseases of severe weather-related and flood-related natural disasters. Curr Opin Infect Dis 19(5):408–414. https://doi.org/10.1097/01.qco.0000244044.85393.9e
Jonkman SN, Kelman I (2005) An analysis of the causes and circumstances of flood disaster deaths. Disasters 29(1):75–97. https://doi.org/10.1111/j.0361-3666.2005.00275.x
Kia MB, Pirasteh S, Pradhan B, Mahmud AR, Sulaiman WNA, Moradi A (2012) An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environ Earth Sci 67:251–264. https://doi.org/10.1007/s12665-011-1504-z
Lillesand TM, Kiefer RW, Chipman JW (2008) Remote sensing and image interpretation, 6th edn. Wiley, Hoboken
Longenecker HE, Graeden E, Kluskiewicz D, Zuzak C, Rozelle J, Aziz AL (2019) A rapid flood risk assessment method for response operations and nonsubject-matter-expert community planning. J Flood Risk Manag 13(1):1–20. https://doi.org/10.1111/jfr3.12579
Moriasi DN, Arnold JG, van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans Am Soc Agric Biol Eng 50(3):885–900. https://doi.org/10.13031/2013.23153
Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part—a discussion of principles. J Hydrol 10(3):282–290
Rabie A, Peterson EW, Kostelnick J, Rowley RJ (2017) Optimizing digital elevation model resolution inputs and number of stream gauges in geographic information system predictions of flood inundation: a case study along the Illinois River, USA. Environ Eng Geosci 23(4):345–357. https://doi.org/10.2113/gseegeosci.23.4.345
Rao CY, Riggs MA, Chew GL, Muilenberg ML, Thorne PS, Van Sickle D, Dunn KH, Brown C (2007) Characterization of airborne molds, endotoxins, and glucans in homes in New Orleans after Hurricanes Katrina and Rita. Appl Environ Microbiol 73(5):1630–1634. https://doi.org/10.1128/AEM.01973-06
Riggs MA, Rao CY, Brown CM, Van Sickle D, Cummings KJ, Dunn KH, Deddens JA, Ferdinands J, Callahan D, Moolenaar RL, Pinkerton LE (2008) Resident cleanup activities, characteristics of flood-damaged homes and airborne microbial concentrations in New Orleans, Louisiana, October 2005. Environ Res 106(3):401–409. https://doi.org/10.1016/j.envres.2007.11.004
Schaefli B, Gupta HV (2007) Do Nash values have value? Hydrol Process 21:2075–2080. https://doi.org/10.1002/hyp.6825
Schumann GJP, Neal JC, Mason DC, Bates PD (2011) The accuracy of sequential aerial photography and SAR data for observing urban flood dynamics, a case study of the UK summer 2007 floods. Remote Sens Environ 115(10):2536–2546. https://doi.org/10.1016/j.rse.2011.04.039
Taylor J, Biddulph P, Davies M, Lai KM (2013) Predicting the microbial exposure risks in urban floods using GIS, building simulation, and microbial models. Environ Int 51:182–195. https://doi.org/10.1016/j.envint.2012.10.006
ten Veldhuis JAE, Clemens FHLR, Sterk G, Berends BR (2010) Microbial risks associated with exposure to pathogens in contaminated urban flood water. Water Res 44(9):2910–2918. https://doi.org/10.1016/j.watres.2010.02.009
Waring S, Zakos-Feliberti A, Wood R, Stone M, Padgett P, Arafat R (2005) The utility of geographic information systems (GIS) in rapid epidemiological assessments following weather-related disasters: methodological issues based on the Tropical Storm Allison Experience. Int J Hyg Environ Health 208(1–2):109–116. https://doi.org/10.1016/j.ijheh.2005.01.020
Watson DF, Philip GM (1985) A refinement of inverse distance weighted interpolation. Geoprocessing 2:315–327
Willmott CJ (1982) Some comments on the evaluation of model performance. Bull Am Meteorol Soc 63(11):1309–1313. https://doi.org/10.1175/1520-0477(1982)063%3C1309:SCOTEO%3E2.0.CO;2
Yard EE, Murphy MW, Schneeberger C, Narayanan J, Hoo E, Freiman A, Lewis LS (2011) Hill VR (2014) Microbial and chemical contamination during and after flooding in the Ohio River—Kentucky. J Environ Sci Health A Tox Hazard Subst Environ Eng 49(11):1236–1243. https://doi.org/10.1080/10934529.2014.910036
Funding
Not applicable.
Author information
Authors and Affiliations
Contributions
Andrew Berens was the project leader and lead author. Tess Palmer, Nina Dutton, and Amy Lavery contributed to the data acquisition, analysis, validation of models, and writing of the paper. Mark Moore accessed and prepared the stream gauge data for analysis and offered a county-level perspective to the manuscript development. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Human and animal rights
Not applicable.
Informed consent
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The findings and conclusions in this study are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention or the Agency for Toxic Substances and Disease Registry.
Rights and permissions
About this article
Cite this article
Berens, A.S., Palmer, T., Dutton, N.D. et al. Using search-constrained inverse distance weight modeling for near real-time riverine flood modeling: Harris County, Texas, USA before, during, and after Hurricane Harvey. Nat Hazards 105, 277–292 (2021). https://doi.org/10.1007/s11069-020-04309-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11069-020-04309-w