Skip to main content
SpringerLink
Log in

Estimation of rainfall threshold for flood warning for small urban watersheds based on the 1D–2D drainage model simulation

  • Original Paper
  • Published:
Stochastic Environmental Research and Risk Assessment Aims and scope Submit manuscript

Abstract

Flood warning systems can provide an effective and low-cost solution to protect urban residents especially in developing countries suffering from severe flood damages caused by weak capacity of flood defense structures. Rainfall Threshold for Flood Warning (RTFW) is an essential key of accurate flood warning system. This study proposed an easy-to-use mathematical equation for RTFW for small urban watersheds based on computer simulations. First, a coupled 1D–2D dual-drainage model was used to simulate the flooded area in nine watersheds of Seoul, Korea corresponding to 540 scenarios of various synthetic rainfall events and watershed imperviousness. Then, the results of the 101 simulations that caused the critical flooded depth (0.25 m–0.35 m) were used to develop the equation that relates the value of RTFW to the rainfall event temporal variability (represented as coefficient of variation or CV) and the watershed imperviousness (represented as NRCS Curve Number or CN). The results suggest that (1) RTFW exponentially decreases as the rainfall CV increases; (2) RTFW linearly decreases as the watershed CN increases; and that (3) RTFW is dominated by CV when the rainfall has low temporal variability (e.g., CV < 0.2) while RTFW is dominated by CN when the rainfall has high temporal variability (e.g., CV > 0.4). For validation, the proposed equation was applied for the flood warning system with two storm events occurred in 2010 and 2011 over 239 watersheds in Seoul. The system showed the hit, false and missed alarm rates at 69% (48%), 31% (52%) and 6.7% (4.5%), respectively for the 2010 (2011) event.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Availability of data and material

Rainfall data that was used in this study is available over the internet website of Korea Meteorological Administration (www.kma.go.kr). The drainage pipe network data that was used in this study is available upon request to Seoul Metropolitan Government (www.seoul.go.kr). The land use and the DEM data will be available upon request to National Geographic Information Institute of Korean Government (www.ngii.go.kr).

References

  • Anquetin S, Braud I, Vannier O, Viallet P, Boudevillain B, Creutin J et al (2010) Sensitivity of the hydrological response to the variability of rainfall fields and soils for the gard 2002 flash-flood event. J Hydrol 394(1–2):134–147

    Article  Google Scholar 

  • Blanc J, Hall J, Roche N, Dawson R, Cesses Y, Burton A et al (2012) Enhanced efficiency of pluvial flood risk estimation in urban areas using spatial–temporal rainfall simulations. J Flood Risk Manag 5(2):143–152

    Article  Google Scholar 

  • Bedient PB, Hoblit BC, Gladwell DC, Vieux BE (2000) NEXRAD radar for flood prediction in houston. J Hydrol Eng 5(3):269–277

    Article  Google Scholar 

  • Brath A, Montanari A, Toth E (2004) Analysis of the effects of different scenarios of historical data availability on the calibration of a spatially-distributed hydrological model. J Hydrol 291(3–4):232–253

    Article  Google Scholar 

  • Caine N (1980) The rainfall intensity-duration control of shallow landslides and debris flows. Geogr Ann Ser B 62:23–27

    Google Scholar 

  • Cannon SH, Gartner JE, Wilson RC, Bowers JC, Laber JL (2008) Storm rainfall conditions for floods and debris flows from recently burned areas in southwestern Colorado and southern California. Geomorphology 96:250–269

    Article  Google Scholar 

  • Chang T, Wang C, Chen AS (2015) A novel approach to model dynamic flow interactions between storm sewer system and overland surface for different land covers in urban areas. J Hydrol 524:662–679

    Article  Google Scholar 

  • Chen J, Hill AA, Urbano LD (2009) A GIS-based model for urban flood inundation. J Hydrol 373:184–192

    Article  Google Scholar 

  • Chen Y, Zhou H, Zhang H, Du G, Zhou J (2015) Urban flood risk warning under rapid urbanization. Environ Res 139:3–10

    Article  CAS  Google Scholar 

  • Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Masuda T, Nishino K (2009) Failure characteristics of rainfall-induced shallow landslides in granitic terrains of Shikoku Island of Japan. Environ Geol 56:1295–1310

    Article  CAS  Google Scholar 

  • Dao DA, Kim D, Kim S, Park J (2020a) Determination of flood-inducing rainfall and runoff for highly urbanized area based on high-resolution radar-gauge composite rainfall data and flooded area GIS data. J Hydrol 584:124704

    Article  Google Scholar 

  • Dao DA, Kim D, Park J, Kim T (2020b) Precipitation threshold for urban flood warning-an analysis using the satellite-based flooded area and radar-gauge composite rainfall data. J Hydro Environ Res 32:48–61

    Article  Google Scholar 

  • Drainage U (2016) Flood control district (UDFCD): Denver. CO, USA

    Google Scholar 

  • Duncan A, Chen AS, Keedwell E, Djordjevic S, Savic D (2011) Urban flood prediction in real-time from weather radar and rainfall data using artificial neural networks.

  • Einfalt T, Arnbjerg-Nielsen K, Golz C, Jensen N, Quirmbach M, Vaes G et al (2004) Towards a roadmap for use of radar rainfall data in urban drainage. J Hydrol 299:186–202

    Article  Google Scholar 

  • Emmanuel I, Andrieu H, Leblois E, Flahaut B (2012) Temporal and spatial variability of rainfall at the urban hydrological scale. J Hydrol 430:162–172

    Article  Google Scholar 

  • Environmental Geographic Information Service, EGIS (2019). http://www.neins.go.kr

  • Fan Y, Ao T, Yu H, Huang G, Li X (2017) A coupled 1D-2D hydrodynamic model for urban flood inundation. Adv Meteorol

  • Glade T, Crozier M, Smith P (2000) Applying probability determination to refine landslide-triggering rainfall thresholds using an empirical “Antecedent Daily Rainfall Model.” Pure Appl Geophys 157:1059–1079

    Article  Google Scholar 

  • Golian S, Saghafian B, Maknoon R (2010) Derivation of probabilistic thresholds of spatially distributed rainfall for flood forecasting. Water Resour Manag 24:3547–3559

    Article  Google Scholar 

  • Guzzetti F, Peruccacci S, Rossi M, Stark CP (2007) Rainfall thresholds for the initiation of landslides in central and southern Europe. Meteorol Atmos Phys 98:239–267

    Article  Google Scholar 

  • Haiden T, Magnusson L, Tsonevsky I, Wetterhall F, Alfieri L, Pappenberger F, et al (2014) ECMWF forecast performance during the June 2013 flood in central Europe. European Centre for Medium-Range Weather Forecasts Reading, MA

  • Han J, Olivera F, Kim D (2021) An algorithm of spatial composition of hourly rainfall fields for improved high rainfall value estimation. KSCE J Civ Eng 25(1):356–368

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Hong Y, Hiura H, Shino K, Sassa K, Suemine A, Fukuoka H et al (2005) The influence of intense rainfall on the activity of large-scale crystalline schist landslides in Shikoku Island. Jpn Landslides 2:97–105

    Article  Google Scholar 

  • Jahanbazi M, Egger U (2014) Application and comparison of two different dual drainage models to assess urban flooding. Urban Water J 11(7):584–595

    Article  Google Scholar 

  • Jang J (2015) An advanced method to apply multiple rainfall thresholds for urban flood warnings. Water 7:6056–6078

    Article  Google Scholar 

  • Jha AK, Bloch R, Lamond J (2012) Cities and flooding: a guide to integrated urban flood risk management for the 21st century, The World Bank

  • Kaczmarska J, Isham V, Onof C (2014) Point process models for fine-resolution rainfall. Hydrol Sci J 59(11):1972–1991

    Article  Google Scholar 

  • Kim D, Onof C (2020) A stochastic rainfall model that can reproduce important rainfall properties across the timescales from several minutes to a decade. J Hydrol 589:125150

    Article  Google Scholar 

  • Kim D, Lee J, Kim H, Choi M (2016a) Spatial composition of AMSR2 soil moisture products by conditional merging technique with ground soil moisture data. Stoch Env Res Risk Assess 30(8):2109–2126

    Article  Google Scholar 

  • Kim D, Kwon H, Lee S, Kim S (2016b) Regionalization of the modified Bartlett-Lewis rectangular pulse stochastic rainfall model across the Korean Peninsula. J Hydro Environ Res 11:123–137

    Article  Google Scholar 

  • Kim D, Olivera F, Cho H, Socolofsky SA (2013) Regionalization of the modified Bartlett-Lewis rectangular pulse stochastic rainfall model. TAO Terr Atmos Ocean Sci 24(3):421

    Article  Google Scholar 

  • Kim J, Lee J, Kim D, Kang B (2019) The role of rainfall spatial variability in estimating areal reduction factors. J Hydrol 568(1):416–426

    Article  Google Scholar 

  • Kramer KS (2016) Safety criteria for the trafficability of inundated roads in urban floodings

  • Leandro J, Chen AS, Djordjević S, Savić DA (2009) Comparison of 1D/1D and 1D/2D coupled (sewer/surface) hydraulic models for urban flood simulation. J Hydraul Eng 135(6):495–504

    Article  Google Scholar 

  • Metropolitan Government of Seoul, MGS (2019). https://opengov.seoul.go.kr/sanction/2175286

  • Miao Q, Yang D, Yang H, Li Z (2016) Establishing a rainfall threshold for flash flood warnings in China’s mountainous areas based on a distributed hydrological model. J Hydrol 541:371–386

    Article  Google Scholar 

  • Min SK, Zhang X, Zwiers FW, Hegerl GC (2011) Human contribution to more-intense precipitation extremes. Nature 470:378–381

    Article  CAS  Google Scholar 

  • Miller JD, Hutchins M (2017) The impacts of urbanization and climate change on urban flooding and urban water quality: a review of the evidence concerning the United Kingdom. J Hydrol Regional Stud 12:345–362

    Article  Google Scholar 

  • 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 515:59–70

    Article  Google Scholar 

  • Milly PCD, Wetherald RT, Dunne KA, Delworth TL (2002) Increasing risk of great floods in a changing climate. Nature 415:514e517

  • Mogil HM, Monro JC, Groper HS (1978) NWS’s flash flood warning and disaster preparedness programs. Bull Am Meteorol Soc 59:690–699

    Article  Google Scholar 

  • Nicótina L, Alessi Celegon E, Rinaldo A, Marani M (2008) On the impact of rainfall patterns on the hydrologic response. Water Resour Res 44(12)

  • Oh M, Lee D, Kwon H, Kim D (2016) Development of flood inundation area GIS database for Samsung-1 drainage sector, Seoul, Korea. J Korea Water Resour Assoc 49:981–993

    Article  Google Scholar 

  • O’Driscoll M, Clinton S, Jefferson A, Manda A, McMillan S (2010) Urbanization effects on watershed hydrology and in-stream processes in the southern united states. Water 2(3):605–648

    Article  Google Scholar 

  • Olivera F, Choi J, Kim D, Li MH (2008) Estimation of average rainfall areal reduction factors in Texas using NEXRAD data. J Hydrol Eng 13(6):438–448

    Article  Google Scholar 

  • Onof C, Wang L (2020) Modelling rainfall with a Bartlett-Lewis process: new developments. Hydrol Earth Syst Sci 24:2791–2815

    Article  Google Scholar 

  • Onof C, Wheater HS (1994) Improvements to the modelling of British rainfall using a modified random parameter Bartlett-Lewis rectangular pulse model. J Hydrol 157(1–4):177–195

    Article  Google Scholar 

  • Pall P, Aina T, Stone DA, Stott PA, Nozawa T, Hilberts AGJ, Lohmann D, Allen MR (2011) Anthropogenic greenhouse gas contribution to flood risk in England and Wales in autumn 2000. Nature 470:382–385

    Article  CAS  Google Scholar 

  • Pappenberger F, Cloke HL, Parker DJ, Wetterhall F, Richardson DS, Thielen J (2015) The monetary benefit of early flood warnings in Europe. Environ Sci Policy 51:278–291

    Article  Google Scholar 

  • Paprotny D, Sebastian A, Morales-Nápoles O, Jonkman SN (2018) Trends in flood losses in Europe over the past 150 years. Nat Commun 9(1):1985

    Article  Google Scholar 

  • Priest SJ, Parker DJ, Hurford A, Walker J, Evans K (2011) Assessing options for the development of surface water flood warning in england and wales. J Environ Manag 92(12):3038–3048

    Article  CAS  Google Scholar 

  • Reed S, Schaake J, Zhang Z (2007) A distributed hydrologic model and threshold frequency-based method for flash flood forecasting at ungauged locations. J Hydrol 337:402–420

    Article  Google Scholar 

  • Rodriguez-Iturbe I, Cox DR, Isham V (1988) A point process model for rainfall: further developments. Proc R Soc Lond A Math Phys Sci 417(1853):283–298

    Google Scholar 

  • Sarmah T, Das S (2018) Urban flood mitigation planning for guwahati: A case of bharalu basin. J Environ Manag 206:1155–1165

    Article  Google Scholar 

  • Saito H, Nakayama D, Matsuyama H (2010) Relationship between the initiation of a shallow landslide and rainfall intensity—duration thresholds in Japan. Geomorphology 118:167–175

    Article  Google Scholar 

  • Sene K (2008) Flood warning, forecasting and emergency response. Springer, Berlin

    Book  Google Scholar 

  • Seo Y, Hwang J, Noh SJ (2015) Analysis of urban drainage networks using Gibbs’ model: A case study in Seoul, South Korea. Water 7(8):4129–4143

    Article  Google Scholar 

  • Seoul Open Data Platform, SODP (2019) http://data.seoul.go.kr/dataList/OA-2527/S/1/datasetView.do#

  • Seyoum SD, Vojinovic Z, Price RK, Weesakul S (2011) Coupled 1D and noninertia 2D flood inundation model for simulation of urban flooding. J Hydraul Eng 138(1):23–34

    Article  Google Scholar 

  • Smith MB, Koren VI, Zhang Z, Reed SM, Pan J, Moreda F (2004) Runoff response to spatial variability in precipitation: an analysis of observed data. J Hydrol 298(1–4):267–286

    Article  Google Scholar 

  • Stott PA, Stone DA, Allen MR (2004) Human contribution to the European heatwave of 2003. Nature 432:610–614

    Article  CAS  Google Scholar 

  • Tarolli M, Borga M, Zoccatelli D, Bernhofer C, Jatho N, Janabi FA (2013) Rainfall space-time organization and orographic control on flash flood response: the Weisseritz event of august 13, 2002. J Hydrol Eng 18(2):183–193

    Article  Google Scholar 

  • Thomas MA, Mirus BB, Collins BD (2018) Identifying physics-based thresholds for rainfall-induced landsliding. Geophys Res Lett 45(18):9651–9661

    Article  Google Scholar 

  • USDA S (1986) Urban hydrology for small watersheds. Tech Release 55:2–6

    Google Scholar 

  • UNEP (2002) Early warning, forecasting and operational flood risk monitoring in Asia (Bangladesh, China and India)

  • United Nations (2008) United Nations expert group meeting on Population Distribution, Urbanization, Internal Migration and Development. United Nations Population Division. http://sustainabledevelopment.un.org/content/documents/2529P01_UNPopDiv.pdf

  • Weijian G, Chuanhai W, Xianmin Z, Tengfei M, Hai Y (2015) Quantifying the spatial variability of rainfall and flow routing on flood response across scales. Environ Earth Sci 74(8):6421–6430

    Article  Google Scholar 

  • WMO W (2011) Manual on flood forecasting and warning. WMO, No.1072

  • Yoon DK (2014) Disaster and development examining global issues and cases. In: Kapucu N, Liou KT (eds) Disaster policies and emergency management in Korea. Springer, Cham, pp 149–164

    Google Scholar 

  • Yoon S, Lee B (2017) Effects of using high-density rain gauge networks and weather radar data on urban hydrological analyses. Water 9(12):931

    Article  Google Scholar 

  • Zêzere J, Vaz T, Pereira S, Oliveira S, Marques R, Garcia RA (2015) Rainfall thresholds for landslide activity in Portugal: a state of the art. Environ Earth Sci 73:2917–2936

    Article  Google Scholar 

  • Zhang S, Pan B (2014) An urban storm-inundation simulation method based on GIS. J Hydrol 517:260–268

    Article  Google Scholar 

Download references

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2021R1A2C2003471).

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Hongik University, Seoul, Korea

    Duc Anh Dao, Dongkyun Kim & Dang Hai Ha Tran

Authors
  1. Duc Anh Dao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dongkyun Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dang Hai Ha Tran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dongkyun Kim.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dao, D.A., Kim, D. & Tran, D.H.H. Estimation of rainfall threshold for flood warning for small urban watersheds based on the 1D–2D drainage model simulation. Stoch Environ Res Risk Assess 36, 735–752 (2022). https://doi.org/10.1007/s00477-021-02049-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00477-021-02049-2

Keywords

Search

Navigation