Natural Hazards

, Volume 86, Issue 3, pp 1039–1058 | Cite as

Basinwide disaster loss assessments under extreme climate scenarios: a case study of the Kaoping River basin

  • Hsin-Chi Li
  • Tingyeh Wu
  • Hsiao-Ping Wei
  • Hung-Ju Shih
  • Yi-Chiung Chao
Original Paper


This study examined the Kaoping River basin, Taiwan, an area severely destroyed by Typhoon Morakot in 2009. Dynamically downscaled data were applied to simulate extreme typhoon precipitation events for facilitating future preparation efforts (2075–2099) under climate change conditions. Models were used to simulate possible impacts in upstream and downstream areas for basinwide disaster loss assessment purposes. The Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability and FLO-2D models were applied to simulate slope-land disaster impacts and sediment volume in the upstream area. The sediment delivery ratio was used to calculate the valid sediment amount delivered downstream and the riverbed uplift altitude. SOBEK was used to build a flood impact model for the Kaoping River basin, and the model was used to simulate potential flooding caused by future extreme typhoon events. The Taiwan Typhoon Loss Assessment System established by the National Science and Technology Center for Disaster Reduction was used to evaluate the potential loss associated with extreme events. The property loss calculation included 32 land-use categories, including agriculture, forestry, fishery, and animal husbandry losses; industrial and commercial service losses; public building losses; and traffic and hydraulic facility losses. One of the Kaoping River basin townships, Daliao District, had the highest flood depth increase ratio (12.6%), and the losses were 1.5 times the original situation. This was much worse than were the losses suffered during Typhoon Morakot. These results also show that sediment delivered from the upstream areas had a significant influence on the downstream areas. This is a critical issue for future flood mitigation under climate change conditions.


Extreme climate TRIGRS FLO-2D SOBEK TLAS Loss assessment 



The authors extend their deep appreciation to Dr. Akio Kitoh of the Japan Meteorological Research Institute for the provision of MRI data and to the anonymous reviewer for their helpful comments. This project was funded by the Taiwan National Science Council (MOST 103-2621-M-865-001).


  1. Chen SC, Lai YC (1999) Estimating the sediment delivery ratio in rivers and watersheds. J Chin Soil Water Conserv 30(1):47–57Google Scholar
  2. Chen C, Beardsley RC, Cowles G (2006) An unstructured grid, finite-volume coastal ocean model (FVCOM) system. Oceanography 19(1):78–89CrossRefGoogle Scholar
  3. Chen WF, Lee DE, Chen YC (2011) The sediment cleanness work for sustainable management- a case study of Tseng-Wen reservoir watershed. In: Chinese soil and water conservation society annual conference, Taipei, TaiwanGoogle Scholar
  4. Chen YH, Tan CH, Chen MM, Su TW (2013) Estimation of rainfall threshold for regional shallow landslide in a watershed. J Chin Soil Water Conserv 44(1):87–96Google Scholar
  5. FLO Engineering Inc (2006) FLO-2D users’ manualGoogle Scholar
  6. Ho HC, Lin BS, Chang YL, Chi SY, Chien YD, Tsai MD, Fei LY (2013) Effectiveness assessment of hill slope conservation on Zheng-Wen reservoir after typhoon Morakot. J Sinotech 118:21–30Google Scholar
  7. Hsu MH (1996) North embankment flooding and a flood forecast model for the Ba-Zhang River Basin (2). Research Program Report, National Science CouncilGoogle Scholar
  8. Hsu SM, Chiou LB, Lin GF, Chao CH, Wen HY, Ku CY (2010) Applications of simulation technique on debris-flow hazard zone delineation: a case study in Hualien County, Taiwan. Nat Hazards Earth Syst Sci 10:535–545CrossRefGoogle Scholar
  9. Hsu H-H, Chou C, Wu Y-c, Lu M-M, Chen C-T, Chen Y-M (2011) Climate change in Taiwan: scientific report 2011 (Summary). National Science Council, Taipei, Taiwan, R.O.CGoogle Scholar
  10. Huang JC, Kao SJ (2006) Optimal estimator for assessing landslide model performance. Hydrol Earth Syst 10:957–965CrossRefGoogle Scholar
  11. Ikeya HA (1998) A method of designation for area in danger of debris flow. Erosion and sediment transport in Pacific Rim Steeplands. Int Assoc Hydrol Sci Publ 132:576–588Google Scholar
  12. Iverson RM (2000) Landslide triggering by rain infiltration. Water Resour Res 36(7):1897–1910CrossRefGoogle Scholar
  13. Ko TF, Yeh CT, Wu CH, Wang YM (2006) Identifying potential floods in low-lying urban areas and operating flood detention ponds. J Univ Sci Technol Beijing 28(S2):148–151Google Scholar
  14. Lai YC (2011) Evaluation of sediment delivery ratio and completeness ratio of the reservoir watershed, Doctoral Dissertation, National Chung-Hsin UniversityGoogle Scholar
  15. Li HC, Kuo YL, Shaw D, Huang TH (2008) The household benefits assessment of the flood reduction plan in a flood-prone area: a case study of Sinwen, Chiayi, Taiwan. Agric Resour Econ 5(2):41–58Google Scholar
  16. Li WS, Yeh KC, Lin CC, Hsieh CL, Wen CC, Yeh YL, Shie LS, Chen LG, Li SJ, Wang YW (2010) Exploration and analysis regarding the aftermath of typhoon Morakot research project report. National Science Council, Taipei City (NSC 98-2625-M-492 -010)Google Scholar
  17. Li H-C, Chen Y-C, Guo M-J (2013) The development and application of Taiwan typhoon loss assessment system (TLAS). J Taiwan Agric Eng 59(4):42–55 (In Chinese) Google Scholar
  18. Li H-C, Wei S-P, Cheng C-T, Liou J-J, Chan Y-M, Yeh K-C (2015) Applying risk analysis to disaster impact of extreme typhoon events under climate change. J Disaster Res 10(3):513–526CrossRefGoogle Scholar
  19. Liu KF, Huang MCh (2006) Numerical simulation of debris flow with application on hazard area mapping. Comput Geosci 10(2):221–240CrossRefGoogle Scholar
  20. Liu K-F, Li H-C, Hsu Y-C (2009) Debris flow hazard assessment with numerical simulation. Nat Hazards 49(1):137–161CrossRefGoogle Scholar
  21. Mizuta R, Yoshimura H, Murakami H, Matsueda M, Endo H, Ose T, Kamiguchi K, Hosaka M, Sugi M, Yukimoto S, Kusunoki S, Kitohj A (2012) Climate simulations using the improved MRI-AGCM with 20-km grid. J Meteor Soc Japan 90(A):235–260Google Scholar
  22. O’brien JS, Julian PY, Fullerton WT (1993) Two-dimensional water flood and mudflow simulation. J Hydraul Eng 119(2):244–261CrossRefGoogle Scholar
  23. Quan Luna B, Blahut J, van Westen CJ, Sterlacchini S, van Asch TWJ, Akbas SO (2011) The application of numerical debris flow modelling for the generation of physical vulnerability curves. Nat Hazards Earth Syst Sci 11:2047–2060CrossRefGoogle Scholar
  24. Salciarini D, Godt JW, Savage WZ, Conversini P, Baum RL, Michael JA (2006) Modeling regional initiation of rainfallinduced shallow landslides in the eastern Umbria Regional of Central Italy. Landslides 3:181–194CrossRefGoogle Scholar
  25. Shih MW (2006) Simulation study on applying 2D inundation model for three drainage systems in Chang-Hua County. Master’s Thesis, Department of Soil and Water Conservation, National Chung Hsing UniversityGoogle Scholar
  26. Tang CY, Hsieh PC, Lin LL (2006) The application of FLO-2D and HEC-GeoRAS for simulating the flooding in Na-Hu Creek caused by typhoon Mindulle. J Soil Water Conserv 40(4):455–467Google Scholar
  27. Vieira BC, Fernandes NF, Filho OA (2010) Shallow landslide prediction in the Serra do Mar, São Paulo, Brazil. Nat Hazards Earth Syst Sci 10:1829–1837CrossRefGoogle Scholar
  28. WL | Delft Hydraulics (2006) SOBEK software user’s manual. delft, the NetherlandsGoogle Scholar
  29. Wu CH, Chen SC, Chou HT (2011) Geomorphologic characteristics of catastrophic landslides during typhoon Morakot in the Kaoping Watershed, Taiwan. Eng Geol 123:13–21CrossRefGoogle Scholar
  30. Yen CL, Tsai YP, Chen LC, Hsu MS, Lin ML, Lo CH (1997) National disaster prevention technology program—plan reportGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.National Science and Technology Center for Disaster ReductionNew Taipei CityTaiwan, People’s Republic of China

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