An integrated web framework for HAZUS-MH flood loss estimation analysis

  • Enes YildirimEmail author
  • Ibrahim Demir
Original Paper


Flood emergency management practices cover various aspects of flooding, such as demography, infrastructure, economy, transportation, and agriculture. Emergency managers and local authorities work to understand existing and potential impacts of flooding in their communities. HAZUS is one of the most widely used GIS-based desktop software packages designed to help emergency managers to simulate floods and observe their possible effects in their communities. Using HAZUS, emergency managers can prioritize regions to receive help, allocate resources, and plan mitigation measures in the disaster area. However, the system has limitations in terms of its technical requirements, the number of flood scenarios and data options, accessibility, and performance. In this study, we present an integrated and scalable web framework for HAZUS-MH flood loss estimation analysis. By taking advantage of the Iowa Flood Center’s extensive flood inundation map repository for Iowa, we could enable interactive analysis between flood map raster and census data to demonstrate flooding impacts in Iowa communities. High-resolution pre-computed flood inundation maps allowed us to execute damage and loss analyses within seconds. Using visualization techniques on the web, users can access damage and loss analysis without specific software or technical expertise. Moreover, by connecting new data sources, we also enabled many different analyses through the web-based system, including agricultural damage and loss analysis and flooded transportation segments. The generalized architecture of the system allows the framework to analyze any region and community.


Flood damage Flood loss HAZUS Loss estimation Flood hazard 



  1. Apel H, Aronica GT, Kreibich H, Thieken AH (2009) Flood risk analyses—how detailed do we need to be? Nat Hazards 49(1):79–98CrossRefGoogle Scholar
  2. Baas S, Trujillo M, Lombardi N (2015) The impact of disasters on agriculture and food security. Food and Agriculture Organization of the United Nations. Retrieved from
  3. Banks JC, Camp JV, Abkowitz MD (2014) Adaptation planning for floods: a review of available tools. Nat Hazards 70(2):1327–1337CrossRefGoogle Scholar
  4. Carson A, Windsor M, Hill H, Haigh T, Wall N, Smith J, Olsen R, Bathke D, Demir I, Muste M (2018) Serious gaming for participatory planning of multi-hazard mitigation. Int J River Basin Manag 16(3):379–391CrossRefGoogle Scholar
  5. Correia FN, Fordham M, Saraiva MG, Bernardo F (1998) Flood hazard assessment and management: interface with the public. Water Resour Manag 12:209–227CrossRefGoogle Scholar
  6. Davis S, Skaggs LL (1992) Catalog of residential depth-damage functions used by the army corps of engineers in flood damage estimation. USACE (United States Army Corps of Engineers), VirginiaGoogle Scholar
  7. Demir I, Beck MB (2009) GWIS: a prototype information system for Georgia watersheds. In: Proceedings of Georgia water resources conference: regional water management opportunities, Paper 6.6.4, 27–29 AprGoogle Scholar
  8. Demir I, Krajewski WF (2013) Towards an integrated flood information system: centralized data access, analysis, and visualization. Environ Model Softw 50:77–84CrossRefGoogle Scholar
  9. Demir I, Jiang F, Walker RV, Parker AK, Beck MB (2009) Information systems and social legitimacy: scientific visualization of water quality. In: Proceedings of IEEE international conference on systems, man, and cybernetics, 11–14 Oct, San Antonio, TX, pp 1093–1098Google Scholar
  10. Demir I, Conover H, Krajewski W, Seo B, Goska R, He Y, McEniry MF, Graves SJ, Peterson W (2015) Data enabled field experiment planning, management, and research using cyberinfrastructure. J Hydrometeorol 16(3):1155–1170CrossRefGoogle Scholar
  11. Demir I, Yildirim E, Sermet Y, Sit MA (2018) FLOODSS: Iowa flood information system as a generalized flood cyberinfrastructure. Int J River Basin Manag 16(3):393–400CrossRefGoogle Scholar
  12. Downton MW, Pielke RA (2005) How accurate are disaster loss data? The case of US flood damage. Nat Hazards 35(2):211–228CrossRefGoogle Scholar
  13. Dutta D, Herath S, Musiake K (2003) A mathematical model for flood loss estimation. J Hydrol 277(1–2):24–49CrossRefGoogle Scholar
  14. Gilles D, Young N, Schroeder H, Piotrowski J, Chang YJ (2012) Inundation mapping initiatives of the Iowa Flood Center: statewide coverage and detailed urban flooding analysis. Water 4(1):85–106CrossRefGoogle Scholar
  15. Hirabayashi Y, Mahendran R, Koirala S, Konoshima L, Yamazaki D, Watanabe S, Kim H, Kanae S (2013) Global flood risk under climate change. Nat Clim Change 3(9):816–821CrossRefGoogle Scholar
  16. Holz KP, Hildebrandt G, Weber L (2006) Concept for a web-based information system for flood management. Nat Hazards 38(1–2):121–140CrossRefGoogle Scholar
  17. Jonkman SN (2005) Global perspectives on loss of human life caused by floods. Nat Hazards 34(2):151–175CrossRefGoogle Scholar
  18. Katuk N, Ruhana Ku-Mahamud K, Norwawi N, Deris S (2009) Web-based support system for flood response operation in Malaysia. Disaster Prev Manag Int J 18(3):327–337CrossRefGoogle Scholar
  19. Krajewski WF, Ceynar D, Demir I, Goska R, Kruger A, Langel C, Mantilla R, Niemeier J, Quintero F, Seo BC, Small SJ (2017) Real-time flood forecasting and information system for the state of Iowa. Bull Am Meteorol Soc 98(3):539–554CrossRefGoogle Scholar
  20. Kreibich H et al (2009) Is flow velocity a significant parameter in flood damage modelling? Nat Hazards Earth Syst Sci 9(5):1679–1692CrossRefGoogle Scholar
  21. Levy JK, Hartmann J, Li KW, An Y, Asgary A (2007) Multi-criteria decision support systems for flood hazard mitigation and emergency response in urban watersheds. JAWRA J Am Water Resour As 43(2):346–358CrossRefGoogle Scholar
  22. Moffatt S, Laefer D (2009) An open-source vision for HAZUSGoogle Scholar
  23. NIBS F (2003) Multi-hazard loss estimation methodology. Flood model. HAZUS-MH Technical Manual, National Institute of Building Sciences and Federal Emergency Management Agency, Washington, DC, 3-3Google Scholar
  24. Penning-Rowsell E, Wilson T (2006) Gauging the impact of natural hazards: the pattern and cost of emergency response during flood events. Trans Inst Br Geogr 31(2):99–115CrossRefGoogle Scholar
  25. Pistrika A, Jonkman S (2010) Damage to residential buildings due to flooding of New Orleans after Hurricane Katrina. Nat Hazards 54(2):413–434CrossRefGoogle Scholar
  26. Postgresql (2015) About PostgreSQL. Retrieved on 1 Apr 2018
  27. Sermet Y, Demir I (2018a) An intelligent system on knowledge generation and communication about flooding. Environ Model Softw 108:51–60CrossRefGoogle Scholar
  28. Sermet Y, Demir I (2018b). Flood action VR: a virtual reality framework for disaster awareness and emergency response training. In: 15th International conference on modeling, simulation and visualization methods (MSV’18), Las Vegas, NV, 30 July–2 AugGoogle Scholar
  29. Tullos D, Byron E, Galloway G, Obeysekera J, Prakash O, Sun YH (2016) Review of challenges of and practices for sustainable management of mountain flood hazards. Nat Hazards 83(3):1763–1797Google Scholar
  30. U.S. Army Corps of Engineers (USACE) (2003) Economic guidance memorandum (EGM) 04-01: generic depth-damage relationships for residential structures with basements. Washington, DCGoogle Scholar
  31. USACE (2006) Depth-damage relationships for structures, contents, and vehicle and content-to-structure value ratios (CSVR) in support of the Donaldsville to the golf, Louisiana, feasibility study. Final Report to the New Orleans District, 2Google Scholar
  32. Vanderkimpen P, Melger E, Peeters P (2008) Flood modeling for risk evaluation–a MIKE FLOOD vs. SOBEK 1D2D benchmark study. In: Flood risk management: research and practice. Extended abstracts volume (332 pp)+ full paper CD-ROM, 23Google Scholar
  33. Weber LJ, Muste M, Bradley AA, Amado AA, Demir I, Drake CW, Krajewski WF, Loeser TJ, Politano MS, Shea BR, Thomas NW (2018) The Iowa Watersheds Project: Iowa’s prototype for engaging communities and professionals in watershed hazard mitigation. Int J River Basin Manag 16(3):315–328CrossRefGoogle Scholar
  34. Xu H, Hameed H, Windsor M, Muste M, Demir I, Smith J, Hunemuller T, Stevenson MB (2017) Decision-support system for underpinning collaborative planning for multi-hazard mitigation. In: Proceedings of 37th IAHR world congress, Kuala Lumpur, MalaysiaGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringUniversity of IowaIowa CityUSA

Personalised recommendations