JDR Vol.10 No.2 pp. 308-318
doi: 10.20965/jdr.2015.p0308


Understanding Flood Risks for Better Planning and Resilience: Novel Stochastic Models and Methods for South-East Asia

Julien Oliver*, Ole Larsen**, Mads Rasmussen***, Erickson Lanuza*, and Avinash Chakravarthy*

*DHI Water & Environment, Singapore, 1 Cleantech Loop, #03-05 CleanTech One, Singapore 637141

**DHI Water & Environment, Denmark

***DHI-GRAS, Denmark

October 24, 2014
January 28, 2015
April 1, 2015
quasi 2D flood model, flood risk, flood simulation, DEM enhancement, probabilistic structure failure, probabilistic flood modelling
Throughout history, human beings have been attracted to waterfront living. Today, most residents live in cities, most of which, in turn, are built on flood plains and in coastal areas – areas often threatened by floods. Physical changes to the environment have changed the response of catchments and rivers to heavy rainfall. Despite attempts to control the size of floods, economic growth – especially as experienced in Asia – has led to an explosion in exposure to floods. The most integrated, cost-effective method for disaster reduction and prevention requires that risk be assessed purposefully and adequately. Disaster risk is captured in two major components: occurrence probability and event intensity and reach, and its consequences. Understanding the risks associated with floods in Asia has been hindered by the complexity of flood dynamics in large river basins and in existing or unreliable datasets. With calculation power increasingly available, the development of flexible modeling systems and the appearance of new datasets, so-called probabilistic flood models can now be developed for large areas to quantify risks. A flexible modeling framework has been developed at DHI to better characterize flood plains and complex hydraulic systems in datapoor and highly exposed areas in Asia. The model relies on automated processes merging freely available datasets such as HydroSHEDS, WorldPop, crowd-sourced data available in OpenStreet Map and Landsat 7 and 8 satellite imagery. The combination of spatial data sources provides opportunities to optimize the hydrodynamic model domain and to improve the lowresolution digital elevation model. Such methods enhance flood hazard information conventionally derived from deterministic models by taking a full probabilistic approach considering source loading conditions, e.g., weather events and sea level rise, and the performance of existing and planned mitigation measures and failures of control structures such as dykes. With risks better quantified, new opportunities arise for cost-effective mitigation and resilience measures and for the development of novel risk transfer schemes through the use of insurance and capital markets.
Cite this article as:
J. Oliver, O. Larsen, M. Rasmussen, E. Lanuza, and A. Chakravarthy, “Understanding Flood Risks for Better Planning and Resilience: Novel Stochastic Models and Methods for South-East Asia,” J. Disaster Res., Vol.10 No.2, pp. 308-318, 2015.
Data files:
  1. [1] D. Guha-Sapir, R. Below, and Ph. Hoyois, “EM-DAT: International Disaster Database,”, Universite Catholique de Louvain, Brussels, Belgium.
  2. [2] P. D. Bates, M. S. Horritt, G. Aronica, and K. Beven, “Bayesian updating of flood inundation likelihoods conditioned on flood extent data,” Hydrol Process, Vol.18, pp. 3347-3370, 2004.
  3. [3] G. Baldassarre, G. Schumann, P. D. Bates, J. E. Freer, and K. J. Beven, “Flood-plain mapping: a critical discussion of deterministic and probabilistic approaches,” Hydrol Sci. J., Vol.55, No.3, pp. 364-376, 2010.
  4. [4] F. Pappenberger, K. Beven, M. Horritt, and S. Blazkova, “Uncertainty in the calibration of effective roughness parameters in HEC-RAS using inundation and downstream level observations,” J. Hydrol., Vol.302, No.1-4, pp. 46-69, 2005.
  5. [5] DHI, “MIKE 11 – Reference Manual,” DHI Software, DHI Water & Environment, Horsholm, Denmark, 2014a.
  6. [6] National Research Council (NRC), Mapping the zone: improving flood map accuracy, Committee on FEMA Flood Maps, National Research Council, National Academies Press, Washington, DC, 2009.
  7. [7] DHI, “MIKE 21 – Reference Manual,” DHI Software, DHI Water & Environment, Horsholm, Denmark, 2014b.
  8. [8] DHI, “MIKE FLOOD – Reference Manual,” DHI Software, DHI Water & Environment, Horsholm, Denmark, 2014c.
  9. [9] D. Komori, S. Nakamura, M. Kiguchi, A. Nishijima, D. Yamazaki, S. Suzuki, A. Kawasaki, K. Oki, and T. Oki, “Characteristics of the 2011 Chao Phraya River flood in Central Thailand,” Hydrological Research Letters 6, pp. 41-46, 2012.
  10. [10] E. P. Crist and R. C. Cicone, “A physically-based transformation of Thematic Mapper data-The TM Tasseled Cap,” Geoscience and Remote Sensing, IEEE Transactions On 3, pp. 256-263, 1984.
  11. [11] C. Huang, B. Wylie, L. Yang, C. Homer, and G. Zylstra, “Derivation of a tasselled cap transformation based on Landsat 7 at-satellite reflectance,” International Journal of Remote Sensing, Vol.23, Iss. 8, 2002.
  12. [12] C. E. Division, Urban Hydrology for Small Watersheds, TR-55, U. S. D. o. Agriculture, 1986.

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