Distributed Assessment of Sediment Dynamics in Central Vietnam

  • Manfred Fink
  • Christian Fischer
  • Patrick Laux
  • Hannes Tünschel
  • Markus Meinhardt
Part of the Water Resources Development and Management book series (WRDM)


Central Vietnam is located within the Southeast Asia monsoon . It is affected by extreme climatic phenomena, like Typhoon storm events. This combined with the deeply weathered bedrock, typical for subtropical regions, resulting in higher vulnerability for erosion and therefore resulting high sediment rates. Additionally, local human activities and the effects of global climate change amplify this higher vulnerability. The presented analysis contains the assessment of sheet erosion by means of the J2000-S eco-hydrological model, where the effect of climate and land use scenarios were analyzed. The results show that land use change has a higher effect then the climate chance on sheet erosion in the analyzed future period. Additionally, the landslide activity in the area was assessed, using a landslide inventory and bivariate statistical analysis. A landslide susceptibility map was the result of this assessment.


  1. Arnold JG, Fohrer N (2005) SWAT2000: current capabilities and research opportunities in applied watershed modelling. Hydrol Process 19(3):563–572. doi: 10.1002/hyp.5611 CrossRefGoogle Scholar
  2. Bingner RL, Theurer FD, Yuan Y (2015) AnnAGNPS Technical Processes: Technical_Documentation. Version 5.4Google Scholar
  3. Bui DT, Lofman O, Revhaug I, Dick O (2011) Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Nat Hazards 59(3):1413–1444. doi: 10.1007/s11069-011-9844-2 CrossRefGoogle Scholar
  4. Carrara A (1983) Multivariate models for landslide hazard evaluation. Math Geol 15(3):403–426. doi: 10.1007/BF01031290 CrossRefGoogle Scholar
  5. Claessens L, Schoorl JM, Veldkamp A (2007) Modelling the location of shallow landslides and their effects on landscape dynamics in large watersheds: an application for Northern New Zealand. Geomorphology 87(1–2):16–27. doi: 10.1016/j.geomorph.2006.06.039 CrossRefGoogle Scholar
  6. Crosta G (1998) Regionalization of rainfall thresholds: an aid to landslide hazard evaluation. Environ Geol 35(2–3):131–145. doi: 10.1007/s002540050300 CrossRefGoogle Scholar
  7. Duong DQ, Stolpe H, Jolk C, Greassidis S, Führer N, Zindler B, Fink Vo, M DP (2014) Standardize geodata management in Vietnam—an urgent need. International Symposium on Geoinformatics for Spatial, Da NangGoogle Scholar
  8. Fink M, Fischer C, Führer N, Firoz, AMB, Viet TQ, Laux P, Flügel W (2013) Distributive hydrological modeling of a monsoon dominated river system in central Vietnam. International Congress on Modelling and Simulation (20th). pp 1826–1832Google Scholar
  9. Flügel W (1996) Hydrological response units (HRU’s) as modelling entities for hydrological river basin simulation and their methodological potential for modelling complex environmental process systems—results from the Sieg catchment. Die Erde 127:43–62Google Scholar
  10. IPPC (2012) Managing the risks of extreme events and disasters to advance climate change adaptation: a special report of Working Groups I and II of the Intergovernmental Panel on Climate Change. Cambridge, New YorkGoogle Scholar
  11. Kanungo DP, Arora MK, Sarkar S, Gupta RP (2006) A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Eng Geol 85(3–4):347–366. doi: 10.1016/j.enggeo.2006.03.004 CrossRefGoogle Scholar
  12. Kralisch S, Krause P (2006) JAMS—A framework for natural resource model development and application. In: Proceedings of the iEMSs Third Biannual Meeting.Google Scholar
  13. Kralisch S, Krause P, Fink M, Fischer C, Flügel W-A (2007) Component based environmental modelling using the JAMS framework. In: Proceedings of the MODSIM 2007 International Congress on Modelling and Simulation, 812–818Google Scholar
  14. Labrière N, Locatelli B, Laumonier Y, Freycon V, Bernoux M (2015) Soil erosion in the humid tropics: a systematic quantitative review. Agric Ecosyst Environ 203:127–139. doi: 10.1016/j.agee.2015.01.027 CrossRefGoogle Scholar
  15. Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4(1):33–41. doi: 10.1007/s10346-006-0047-y CrossRefGoogle Scholar
  16. Meinhardt M, Fink M, Tünschel H (2015) Landslide susceptibility analysis in central Vietnam based on an incomplete landslide inventory: comparison of a new method to calculate weighting factors by means of bivariate statistics. Geomorphology 234:80–97. doi: 10.1016/j.geomorph.2014.12.042 CrossRefGoogle Scholar
  17. Miola A, Simonet C (2014) Concepts and metrics for climate change risk and development: towards an index for climate resilient development. EUR, Scientific and technical research series, vol 26587. Publications Office, LuxembourgGoogle Scholar
  18. Montgomery DR, Dietrich WE (1994) A physically based model for the topographic control on shallow landsliding. Water Resour Res 30(4):1153–1171. doi: 10.1029/93WR02979 CrossRefGoogle Scholar
  19. Morgan RPC, Quinton JN, Smith RE, Govers G, Poesen JWA, Auerswald K, Chisci G, Torri D, Styczen ME (1998) The European soil erosion model (EUROSEM): a dynamic approach for predicting sediment transport from fields and small catchments. Earth Surf Proc Land 23:527–544Google Scholar
  20. Nearing MA, Foster GR, Lane LJ, Finkner SC (1989) A process-based soil erosion model for USDA-Water Erosion Prediction Project technology. Trans ASAE 32(5):1587–1593. doi: 10.13031/2013.31195 CrossRefGoogle Scholar
  21. Park S, Choi C, Kim B, Kim J (2013) Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environ Earth Sci 68(5):1443–1464. doi: 10.1007/s12665-012-1842-5 CrossRefGoogle Scholar
  22. Pfennig B, Kipka H, Wolf M, Fink M, Krause P, Flügel W (2009) Development of an extended routing scheme in reference to consideration of multi-dimensional flow relations between hydrological model entities. In: Proceedings of the 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation, 1972–1978Google Scholar
  23. Pieri L, Bittelli M, Wu JQ, Dun S, Flanagan DC, Pisa PR, Ventura F, Salvatorelli F (2007) Using the Water Erosion Prediction Project (WEPP) model to simulate field-observed runoff and erosion in the Apennines mountain range, Italy. J Hydrol 336(1–2):84–97. doi: 10.1016/j.jhydrol.2006.12.014 CrossRefGoogle Scholar
  24. Roeckner E, Bäuml G, Bonaventura L, Brokopf R, Esch M, Giorgetta M, Hagemann S, Kirchner I, Kornblueh L, Manzini E, Rhodin A, Schlese U, Schulzweida U, Tompkins A (2003) The atmospheric general circulation model ECHAM5—Part I: Model description. Max-Planck-Institut für Meteorologie, Hamburg (Report No. 349)Google Scholar
  25. Salmi T, Maatta A, Anttila P, Ruoho-Airola T, Amnell T (2002) Detecting trends of annual values of atmospheric pollutants by the Mann–Kendall test and Sen’s slope estimatesGoogle Scholar
  26. Skamarock WC, Klemp JB (2008) A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. J Comput Phys 227(7):3465–3485. doi: 10.1016/ CrossRefGoogle Scholar
  27. Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012a) Landslide susceptibility assessment in the Hoa Binh province of Vietnam: A comparison of the Levenberg–Marquardt and Bayesian regularized neural networks. Geomorphology 171–172:12–29. doi: 10.1016/j.geomorph.2012.04.023 CrossRefGoogle Scholar
  28. Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012b) Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput Geosci 45:199–211. doi: 10.1016/j.cageo.2011.10.031 CrossRefGoogle Scholar
  29. Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012c) Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. CATENA 96:28–40. doi: 10.1016/j.catena.2012.04.001 CrossRefGoogle Scholar
  30. Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick ØB (2013) Regional prediction of landslide hazard using probability analysis of intense rainfall in the Hoa Binh province, Vietnam. Nat Hazards 66(2):707–730. doi: 10.1007/s11069-012-0510-0 CrossRefGoogle Scholar
  31. Williams JR (1975) Sediment—yield prediction with universal equation using runoff energy factor. Proceedings of the sediment—Yield Workshop, USDA Sedimentation LaboratoryGoogle Scholar
  32. Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses—a guide to conservation planning. Agriculture Handbook (537)Google Scholar
  33. Yin KL, Yan TZ (1988) Statistical prediction models for slope instability of metamorphosed rocks. Landslides. In: Proceedings of the Fifth International Symposium on Landslides, vol 2. pp 1269–1272Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  • Manfred Fink
    • 1
  • Christian Fischer
    • 1
  • Patrick Laux
    • 2
  • Hannes Tünschel
    • 1
  • Markus Meinhardt
    • 1
  1. 1.Department of Geography Friedrich-Schiller University JenaJenaGermany
  2. 2.Regional Climate and HydrologyKarlsruhe Institute of TechnologyKarlsruheGermany

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