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Water Resources Management

Erschienen in:

06.04.2022

A Novel Framework for Urban Flood damage Assessment

verfasst von: Fatemeh Yavari, Seyyed Ali Salehi Neyshabouri, Jafar Yazdi, Amir Molajou, Ph.D., Adam Brysiewicz

Erschienen in: Water Resources Management | Ausgabe 6/2022

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Abstract

In the current study, a novel method is proposed to analyze the simultaneous impacts of non-stationarity in hydrological time series and land-use changes in urban areas to predict future floods and probable damage. For this purpose, rainfall frequency and land-use changes analyses were conducted for two time periods (first: 1979–2009 and second: 1979–2019), and the results were compared. Then, hydrologic modeling of the catchment was carried out using the HEC-HMS model, and obtained hydrographs were fed to the HEC-RAS2D model for estimating flood inundation areas. Using the financial information of assets and their damage functions, flood damages related to these two periods were evaluated through the HEC-FIA model. The results indicated that in the low return periods (e.g., 2-year flood), the damage in the second period was decreased with respect to the first one but increased for the return periods of 5 to 100 years. In the surface runoff, a 4.65% increase due to land-use change and a 12% increase due to rainfall non-stationarity signed the important role the hydrologic condition plays compared to land-use changes in flood modeling. Moreover, flood damage showed a 136% increase on average, and among the two studied factors, the non-stationarity of rainfalls is considerably more effective on flood intensification. All the points show that the studied socio-hydrological system is completely dynamic.

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Arrighi C, Campo L (2019) Effects of digital terrain model uncertainties on high-resolution urban flood damage assessment.Journal of Flood Risk Management, 12(S2), e12530

Awadallah MOM, Juárez A, Alfredsen K (2022) Comparison between Topographic and Bathymetric LiDAR Terrain Models in Flood Inundation Estimations. Remote Sens 14(1):227CrossRef

Bhuyian MN, Kalyanapu A (2018) Accounting digital elevation uncertainty for flood consequence assessment. J Flood Risk Manag 11:S1051–S1062CrossRef

Chen AS, Hammond MJ, Djordjević S, Butler D, Khan DM, Veerbeek W (2016) From hazard to impact: Flood damage assessment tools for mega cities. Nat Hazards 82(2):857–890CrossRef

Chow VT, Maidment DR, Mays LW (1962) Applied hydrology. Journal of Engineering Education, 308, 1959

Cutter SL, Emrich CT, Gall M, Reeves R (2018) Flash flood risk and the paradox of urban development. Nat Hazards Rev 19(1):05017005CrossRef

Dutta D, Herath S, Musiake K (2003) A mathematical model for flood loss estimation. J Hydrol 277(1–2):24–49CrossRef

El Alfy M (2016) Assessing the impact of arid area urbanization on flash floods using GIS, remote sensing, and HEC-HMS rainfall–runoff modeling. Hydrol Res 47(6):1142–1160CrossRef

Farooq M, Shafique M, Khattak MS (2019) Flood hazard assessment and mapping of River Swat using HEC-RAS 2D model and high-resolution 12-m TanDEM-X DEM (WorldDEM). Nat Hazards 97(2):477–492CrossRef

Feng B, Zhang Y, Bourke R (2021) Urbanization impacts on flood risks based on urban growth data and coupled flood models. Nat Hazards 106(1):613–627CrossRef

Freni G, La Loggia G, Notaro V (2010) Uncertainty in urban flood damage assessment due to urban drainage modelling and depth-damage curve estimation. Water Sci Technol 61(12):2979–2993CrossRef

Hallegatte S, Green C, Nicholls RJ, Corfee-Morlot J (2013) Future flood losses in major coastal cities. Nat Clim change 3(9):802–806CrossRef

Hammond MJ, Chen AS, Djordjević S, Butler D, Mark O (2015) Urban flood impact assessment: A state-of-the-art review. Urban Water Journal 12(1):14–29CrossRef

Han Y, Mozumder P (2022) Risk-based flood adaptation assessment for large-scale buildings in coastal cities using cloud computing. Sustainable Cities and Society 76:103415CrossRef

Hawkins RH (1993) Asymptotic determination of runoff curve numbers from data. J Irrig Drain Eng 119(2):334–345CrossRef

HEC-RAS (2016) River Analysis System: Hydraulic reference manual. USACE version: 5.0. US Army Corps of Engineers. CPD-68

Heydari Mofrad H, Yazdi J (2022) An enhanced multi-objective evolutionary algorithm for the rehabilitation of urban drainage systems. Eng Optim 54(2):349–367CrossRef

Henonin J, Russo B, Mark O, Gourbesville P (2013) Real-time urban flood forecasting and modelling–a state of the art. J Hydroinformatics 15(3):717–736CrossRef

Hidayah E, Wiyono RUA, Wicaksono AD (2021) Development of the flood vulnerability index using a multi-element approach.Journal of Water and Land Development,255–264

Hodgkins GA, Dudley RW, Archfield SA, Renard B (2019) Effects of climate, regulation, and urbanization on historical flood trends in the United States. J Hydrol 573:697–709CrossRef

Jamali B, Löwe R, Bach PM, Urich C, Arnbjerg-Nielsen K, Deletic A (2018) A rapid urban flood inundation and damage assessment model. J Hydrol 564:1085–1098CrossRef

Jiménez-Jiménez SI, Ojeda-Bustamante W, Ontiveros-Capurata RE, Marcial-Pablo MDJ (2020) Rapid urban flood damage assessment using high resolution remote sensing data and an object-based approach. Geomatics Nat Hazards Risk 11(1):906–927CrossRef

Knebl MR, Yang ZL, Hutchison K, Maidment DR (2005) Regional scale flood modeling using NEXRAD rainfall, GIS, and HEC-HMS/RAS: a case study for the San Antonio River Basin Summer 2002 storm event. J Environ Manage 75(4):325–336CrossRef

Liu YB, De Smedt F, Hoffmann L, Pfister L (2005) Assessing land use impacts on flood processes in complex terrain by using GIS and modeling approach. Environ Model Assess 9(4):227–235CrossRef

Lørup JK, Refsgaard JC, Mazvimavi D (1998) Assessing the effect of land use change on catchment runoff by combined use of statistical tests and hydrological modelling: case studies from Zimbabwe. J Hydrol 205(3–4):147–163CrossRef

Maksimović Č, Prodanović D, Boonya-Aroonnet S, Leitao JP, Djordjević S, Allitt R (2009) Overland flow and pathway analysis for modelling of urban pluvial flooding. J Hydraul Res 47(4):512–523CrossRef

Mark O, Weesakul S, Apirumanekul C, Aroonnet SB, Djordjević S (2004) Potential and limitations of 1D modelling of urban flooding. J Hydrol 299(3–4):284–299CrossRef

Martins R, Leandro J, Djordjević S (2018) Influence of sewer network models on urban flood damage assessment based on coupled 1D/2D models. J Flood Risk Manag 11:S717–S728CrossRef

Merz B, Kreibich H, Schwarze R, Thieken A (2010) Review article” Assessment of economic flood damage”. Nat Hazards Earth Syst Sci 10(8):1697–1724CrossRef

Miller JD, Kim H, Kjeldsen TR, Packman J, Grebby S, Dearden R (2014) Assessing the impact of urbanization on storm runoff in a peri-urban catchment using historical change in impervious cover. J Hydrol 515:59–70CrossRef

Moftakhari HR, AghaKouchak A, Sanders BF, Matthew RA (2017) Cumulative hazard: The case of nuisance flooding. Earths Future 5(2):214–223CrossRef

Ministry of Energy (2016) The report of “Flood Damage Consideration”, The Office of Water and Wastewater Designs and Standards, Report No. 164, (in Farsi)

Mokhtari F, Soltani S, Mousavi SA (2017) Assessment of flood damage on humans, infrastructure, and agriculture in the Ghamsar Watershed using HEC-FIA software. Nat Hazards Rev 18(3):04017006CrossRef

Molajou A, Afshar A, Khosravi M, Soleimanian E, Vahabzadeh M, Variani HA (2021a) A new paradigm of water, food, and energy nexus. Environmental Science and Pollution Research, pp 1–11

Molajou A, Nourani V, Afshar A, Khosravi M, Brysiewicz A (2021b) Optimal design and feature selection by genetic algorithm for emotional artificial neural network (EANN) in rainfall-runoff modeling. Water Resour Manage 35(8):2369–2384CrossRef

Mostafiz RB, Friedland CJ, Rahman MA, Rohli RV, Tate E, Bushra N, Taghinezhad A (2021) Comparison of neighborhood-scale, residential property flood-loss assessment methodologies.Frontiers in Environmental Science,448

Mubeen A, Ruangpan L, Vojinovic Z, Sanchez Torrez A, Plavšić J (2021) Planning and suitability assessment of large-scale nature-based solutions for flood-risk reduction. Water Resour Manage 35(10):3063–3081CrossRef

Myronidis D, Ivanova E (2020) Generating regional models for estimating the peak flows and environmental flows magnitude for the Bulgarian-Greek Rhodope mountain range torrential watersheds. Water 12(3):784CrossRef

Nirupama N, Simonovic SP (2007) Increase of flood risk due to urbanisation: A Canadian example. Nat Hazards 40(1):25–41CrossRef

Nofal OM, Van De Lindt JW (2020) Understanding flood risk in the context of community resilience modeling for the built environment: Research needs and trends.Sustainable and Resilient Infrastructure,1–17

Nourani V, Molajou A, Najafi H, Danandeh Mehr A (2019) Emotional ANN (EANN): a new generation of neural networks for hydrological modeling in IoT. In: Fadi Al-Turjman (ed) Artificial intelligence in IoT. Springer, Cham, pp 45–61CrossRef

Nourani V, Rouzegari N, Molajou A, Baghanam AH (2020) An integrated simulation-optimization framework to optimize the reservoir operation adapted to climate change scenarios. J Hydrol 587:125018CrossRef

Park K, Lee MH (2019) The development and application of the urban flood risk assessment model for reflecting upon urban planning elements. Water 11(5):920CrossRef

Quirogaa VM, Kurea S, Udoa K, Manoa A (2016) Application of 2D numerical simulation for the analysis of the February 2014 Bolivian Amazonia flood: Application of the new HEC-RAS version 5. Ribagua 3(1):25–33CrossRef

Rangari VA, Umamahesh NV, Bhatt CM (2019) Assessment of inundation risk in urban floods using HEC RAS 2D. Model Earth Syst Environ 5(4):1839–1851CrossRef

Ranzi R, Bochicchio M, Bacchi B (2002) Effects on floods of recent afforestation and urbanisation in the Mella River (Italian Alps). Hydrol Earth Syst Sci 6(2):239–254CrossRef

Saghafian B, Farazjoo H, Bozorgy B, Yazdandoost F (2008) Flood intensification due to changes in land use. Water Resour Manage 22(8):1051–1067CrossRef

Salas JD, Obeysekera J (2014) Revisiting the concepts of return period and risk for nonstationary hydrologic extreme events. J Hydrol Eng 19(3):554–568CrossRef

Seyoum SD, Vojinovic Z, Price RK, Weesakul S (2012) Coupled 1D and noninertia 2D flood inundation model for simulation of urban flooding. J Hydraul Eng 138(1):23–34CrossRef

Sharghi E, Nourani V, Najafi H, Molajou A (2018) Emotional ANN (EANN) and wavelet-ANN (WANN) approaches for Markovian and seasonal based modeling of rainfall-runoff process. Water Resour Manage 32(10):3441–3456CrossRef

Singh A, Sarma AK, Hack J (2020) Cost-effective optimization of nature-based solutions for reducing urban floods considering limited space availability. Environ Processes 7(1):297–319CrossRef

Skougaard Kaspersen P, Høegh Ravn N, Arnbjerg-Nielsen K, Madsen H, Drews M (2017) Comparison of the impacts of urban development and climate change on exposing European cities to pluvial flooding. Hydrol Earth Syst Sci 21(8):4131–4147CrossRef

Söderholm K, Pihlajamäki M, Dubrovin T, Veijalainen N, Vehviläinen B, Marttunen M (2018) Collaborative planning in adaptive flood risk management under climate change. Water Resour Manage 32(4):1383–1397CrossRef

Song JY, Chung ES (2016) Robustness, uncertainty and sensitivity analyses of the TOPSIS method for quantitative climate change vulnerability: a case study of flood damage. Water Resour Manage 30(13):4751–4771CrossRef

Suriya S, Mudgal BV (2012) Impact of urbanization on flooding: The Thirusoolam sub watershed–A case study. J Hydrol 412:210–219CrossRef

Tang Y, Leon AS, Kavvas ML (2020) Impact of size and location of wetlands on watershed-scale flood control. Water Resour Manage 34(5):1693–1707CrossRef

Tate E, Muñoz C, Suchan J (2015) Uncertainty and sensitivity analysis of the HAZUS-MH flood model. Nat Hazards Rev 16(3):04014030CrossRef

Ten Veldhuis JAE (2011) How the choice of flood damage metrics influences urban flood risk assessment. J Flood Risk Manag 4(4):281–287CrossRef

Thakur B, Parajuli R, Kalra A, Ahmad S, Gupta R (2017) Coupling HEC-RAS and HEC-HMS in precipitation runoff modelling and evaluating flood plain inundation map. In World Environmental and Water Resources Congress 2017 (pp. 240–251)

Tsakiris G (2014) Flood risk assessment: concepts, modelling, applications. Nat Hazards Earth Syst Sci 14(5):1361–1369CrossRef

Tripathi G, Pandey AC, Parida BR, Kumar A (2020) Flood inundation mapping and impact assessment using multi-temporal optical and SAR satellite data: a case study of 2017 Flood in Darbhanga district, Bihar, India. Water Resour Manage 34(6):1871–1892CrossRef

Uddin MJ, Hasan MM, Eisenreich SJ, Quevauviller P (2020) Correction to: Strengthening Pluvial Flood Risk Management in the Southeast Region of Bangladesh: Lessons Learnt from the EU Policy and Practice. Environ Processes 7(1):383–386CrossRef

USACE HEC-RAS (2016) 5.0 User’s Manual,

US Army Corps of Engineers (USACE) (2012) HEC-FIA. flood impact analysis: User’s manual,

U.S. Army Corps of Engineers (USACE) (2003) Economic Guidance Memorandum (EGM) 04 – 01, Generic Depth-Damage Relationships for Residential Structures with Basements; U.S. Army Corps of Engineers (USACE): Washington, DC, USA,

USDA (2004) Hydrology. National Engineering Handbook. United States Department of Agriculture (USDA) Soil Conservation Service, Washington, DC, USA., Chap. 10

Vasiliades L, Galiatsatou P, Loukas AJWRM (2015) Nonstationary frequency analysis of annual maximum rainfall using climate covariates. Water Resour Manage 29(2):339–358CrossRef

Vogel RM, Yaindl C, Walter M (2011) Nonstationarity: flood magnification and recurrence reduction factors in the United States 1. JAWRA J Am Water Resour Association 47(3):464–474CrossRef

Walega A, Amatya DM, Caldwell P, Marion D, Panda S (2020) Assessment of storm direct runoff and peak flow rates using improved SCS-CN models for selected forested watersheds in the Southeastern United States. J Hydrology: Reg Stud 27:100645

Wedawatta G, Ingirige B, Proverbs D (2014) Small businesses and flood impacts: the case of the 2009 flood event in C ockermouth. J Flood Risk Manag 7(1):42–53CrossRef

World Meteorological Organization (1969) Manual for Depth-Area-Duration Analysis of Storm Precipitation, No. 237, Geneva

Wojnowska-Heciak M, Heciak J, Kłak A (2020) Flood resilient streetscape. Journal of Water and Land Development

Wu Z, Lv H, Meng Y, Guan X, Zang Y (2021) The determination of flood damage curve in areas lacking disaster data based on the optimization principle of variation coefficient and beta distribution. Sci Total Environ 750:142277CrossRef

Wu X, Guo J (2021) Urban flood depth-economic loss curves and their amendment based on resilience: evidence from Lizhong Town in Lixia River and Houbai Town in Jurong River of China. In: Wu X and Guo J (eds) Economic Impacts and Emergency Management of Disasters in China. Springer, Singapore, pp 191–219CrossRef

Yalcin E (2020) Assessing the impact of topography and land cover data resolutions on two-dimensional HEC-RAS hydrodynamic model simulations for urban flood hazard analysis. Nat Hazards 101(3):995–1017CrossRef

Yang L, Li J, Kang A, Li S, Feng P (2020) The Effect of Nonstationarity in Rainfall on Urban Flooding Based on Coupling SWMM and MIKE21.Water Resources Management, 34(4)

Zhang W, Li J, Chen Y, Li Y (2019) A surrogate-based optimization design and uncertainty analysis for urban flood mitigation. Water Resour Manage 33(12):4201–4214CrossRef

Zhou Q, Leng G, Su J, Ren Y (2019) Comparison of urbanization and climate change impacts on urban flood volumes: Importance of urban planning and drainage adaptation. Sci Total Environ 658:24–33CrossRef

Titel: A Novel Framework for Urban Flood damage Assessment
verfasst von: Fatemeh Yavari
Seyyed Ali Salehi Neyshabouri
Jafar Yazdi
Amir Molajou, Ph.D.
Adam Brysiewicz
Publikationsdatum: 06.04.2022
Verlag: Springer Netherlands
Erschienen in: Water Resources Management / Ausgabe 6/2022
Print ISSN: 0920-4741
Elektronische ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-022-03122-3

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