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Assessing the Hydrological Response of Ayamama Watershed from Urbanization Predicted under Various Landuse Policy Scenarios

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Abstract

This study investigated the effects of urbanization predicted using the SLEUTH urban growth model (an acronym taken from Slope, Landuse, Exclusion, Urban extent, Transportation and Hillshade) under four landuse policy scenarios on the hydrological response of Ayamama watershed using the Hydrologic Engineering Center Release 1 (HEC-1) hydrological model. The SLEUTH model was calibrated based on the Brute Force Monte Carlo iteration technique using the urban extents of Istanbul in 1987, 2000, 2009 and 2013 and was verified by considering Kappa coefficient as evaluation criteria. HEC-1 was calibrated and verified using observed rainfall-runoff event and based on the coefficient of determination (R2), Nash-Sutcliffe coefficient of efficiency (CE) and percentage of bias (PBIAS) as performance indicators. The urbanization prediction results showed that the urban extent of Ayamama watershed would reach 50.3 km2, 44 km2, 63 km2 and 60 km2 under Scenarios 1, 2, 3 and 4, respectively, in 2050. The hydrological simulation results under these urban extents showed that the urban extent of Ayamama watershed under Scenario-3, a scenario that allows unrestricted growth with the implementation of Project Canal Istanbul (PCI), resulted in the highest peak discharge and the shortest time to peak. Such an increase in the peak discharge and reduction in the time to peak will increase the risk of flooding and, therefore, extreme care needs to be taken before and during the implementation of PCI.

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References

  1. Advanced Space-borne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER-GDEM).(2014) http://gdem.ersdac.jspacesystems.or.jp/search.jsp. Accessed 12 October 2014

  2. Akin A, Berberoglu, S (2012) Modelling urban growth of Adana city under different policy scenarios. IV. Semposseum on Remote Sensing and Geographic Information System (UZAL-CBS 2012), 16–19 October 2012, Zonguldak, Turkey

  3. Bardossy A (2007) Calibration of hydrological model parameters for ungauged catchments. Hydrol Earth Syst Sc 11:703–710

  4. Bates PD (2004) Remote sensing and flood inundation modelling. Hydrol Process 18:2593–2597

  5. Chow VT, Maidment DR, Mays LW (1988) A general theory of the unit hydrograph theory. J Geophy Res 64:241–256

  6. Ciavola SJ, Jantz CA, Reilly J, Moglen GE (2014) Forecast changes in runoff quality and quantity from urbanization in the DelMarVa peninsula. J Hydrol Eng 19:1–9

  7. Clarke KC (2005) The limits of simplicity: toward geo-computational honesty in urban modeling. In: Atkinson P, Foody G, Darby S, Wu F (eds) GeoDynamics. CRC Press, Boca Raton, pp. 215–232

  8. Clarke KC, Gaydos L (1998) Loose coupling a cellular automaton model and GIS: long-term growth prediction for San Francisco and Washington/Baltimore. Int J Geogr Inf Sci 12:699–714

  9. Clarke KC, Hoppen S, Gaydos L (1996) Methods and techniques for rigorous calibration of a cellular automaton model of urban growth http://www.ncgia.ucsb.edu/projects/gig/Pub/SLEUTHPapers_Nov24/Clarke_Hoppen_Gaydos_1996.pdf. Accessed 12 October 2014

  10. Crochemore L, Perrin C, Andreassian V, Ehret U, Seibert SP, Grimaldi S, Gupta H, Paturel JE (2015) Comparing expert judgment and numerical criteria for hydrograph evaluation. Hydrolog Sci J 60:402–423

  11. Demissie M, Soong TW, Camacho R (1990) Cache River Basin: Hydrology, Hydraulics, and Sediment Transport, Vol. 2: Mathematical Modeling. Illinois State Water Survey Contract Report 485

  12. Dietzel C, Clarke KC (2007) Toward optimal calibration of the SLEUTH land use change model. Trans GIS 11:29–45

  13. Fleiss JL, Levin B, Paik MC (2003) Statistical methods for rates and proportions, 3rd edn. John Wiley & Sons, Hoboken

  14. Goetz SJ, Jantz CA, Sun M (2011) Forecasting future land use and its hydrologic implications: a case study of the upper Delaware River watershed. Watershed Science Bulletin, Association of Watershed and Stormwater Professionals (AWSPs). http://www.whrc.org/resources/publications/pdf/GoetzetalWatershedSci.11.pdf. Accessed 12 September 2014

  15. Gulbaz S, Kazezyilmaz-Alhan CM (2013) Calibrated hydrodynamic model for Sazlıdere watershed in Istanbul and investigation of urbanization effects. J Hydrol Eng 18:75–84

  16. Inceoglu A, Yurekli I (2011) Urban transformation in Istanbul: potential for a better city. ENHR Conference, 5–8 July, 2011, Toulouse

  17. Istanbul Metropolitan Municipality (IMM) (2011) Istanbul Metropolitan Area Urban Transportation Master Plan (in Turkish). http://www.ibb.gov.tr/tr-TR/kurumsal/Birimler/ulasimPlanlama/Documents/%C4%B0UAP_Ana_Raporu.pdf. Accessed 10 January 2015

  18. Jantz CA, Goetz SJ, Shelley MK (2004) Using SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Baltimore-Washington metropolitan area. Environ Plann B 30:251–271

  19. Jantz CA, Goetz SJ, Donato D, Claggett P (2010) Designing and implementing a regional urban modeling system using the SLEUTH cellular urban model. Comput Environ Urban 34:1–16

  20. Kundak S, Baypinar M (2011) The crazy project–canal Istanbul. Trimestrale del Laboratorio Territorio Mobilità e Ambiente 4:53–63

  21. Lalozaee A, Bahreini F, Dahmardeh MR, Akbarpour A, Moghaddamnia AR (2013) Efficiency comparison HEC-1 and TR-20 methods in flood hydrograph simulation. Technical Journal of Engineering and Applied Sciences 3:1719–1729

  22. Laouacheria F, Mansouri R (2015) Comparison of WBNM and HEC-HMS for runoff hydrograph prediction in a small urban catchment. Water Resour Manag 29:2485–2501

  23. Loukas A, Vasiliades L (2014) Streamflow simulation methods for ungauged and poorly gauged watersheds. Nat Hazards Earth Syst Sci 14:1641–1661

  24. Mega Istanbul.(2016) http://megaprojeleristanbul.com/. Accessed 28 September 2015

  25. Merz R, Bloschl G (2004) Regionalization of catchment model parameters. J Hydrol 287:95–123

  26. 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

  27. Misra AK (2011) Impact of urbanization on the hydrology of Ganga Basin (India). Water Resour Manag 25:705–719

  28. Mohan SM, Kumar SA, Reddy DC, Prasad SDV, Raja EG (2012) Analysis of various urban growth models based on cellular automata. International Journal of Engineering Science and Advanced Technology 2:453–460

  29. Moriasi DN, Arnold JG, Van Liew MW, Binger RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. T ASABE 50:885–900

  30. Project Gigalopolis. (2016) http://www.ncgia.ucsb.edu/projects/gig/. Accessed 12 December 2014

  31. Ramanarayanan TS, Williams JR, Dugas WA, Hauck LM, McFarland AMS (1997) Using APEX to identify alternative practices for animal waste management: Part II. Model application. ASAE Paper 97–2209. ASAE. http://apex.tamu.edu/media/16391/ramana.pdf. Accessed 18 October 2015

  32. Rawls WJ, Brakensiek DL, Miller N (1983) Green-Ampt infiltration parameters from soils data. J Hydraul Eng-ASCE 109:62–70

  33. Samela C, Manfreda S, De Paola F, Giugni M, Sole A, Fiorentino M (2015) DEM-based approaches for the delineation of flood-prone areas in an ungauged basin in Africa. J Hydrol Eng 06015010:1–10

  34. Sarma B, Sarma AK, Singh VP (2013) Optimal ecological management practices (EMPs) for minimizing the impact of climate change and watershed degradation due to urbanization. Water Resour Manag 27:4069–4082

  35. Sharifi F, Safapoor S, Ayoobzade S (2004) Evaluation of AWBM 2002 model in simulation of hydrological processes some of watersheds in Iran. Journal of Research and Constructional 63:35–42

  36. Solomatine DP, Dulal KN (2003) Model tree as an alternative to neural network in rainfall–runoff modelling. Hydrolog Sci J 48: 399–411

  37. Turkish Statistical Institute (TUIK) (2016). http://www.tuik.gov.tr/UstMenu.do?metod=temelist. Accessed 25 March 2016

  38. Turoglu H (2011) Flash floods and floods in Istanbul. Journal of Ecology of Ankara University 3:39–46

  39. United States Geological Survey Global Visualization Viewer. (2016) http://glovis.usgs.gov/. Accessed 13 December 2014

  40. Wang Y, Li Y, Cheng S, Yang F, Chen Y (2015) Effects of spatial-temporal imperviousness on hydrological responses of various areas in an urbanized watershed. Water Resour Manag 29:3551–3567

  41. Yeo IY, Gordon SI, Guldmann JM (2004) Optimizing patterns of landuse to reduce peak runoff flow and nonpoint source pollution with an integrated hydrological and landuse model. Earth Interactions 8:1–20

  42. Zhang G, Guhathakurta S, Lee S, Moore A, Yan L (2014) Grid-based land-use composition and configuration optimization for watershed stormwater management. Water Resour Manag 28:2867–2883

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Correspondence to Tewodros Assefa Nigussie.

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Nigussie, T.A., Altunkaynak, A. Assessing the Hydrological Response of Ayamama Watershed from Urbanization Predicted under Various Landuse Policy Scenarios. Water Resour Manage 30, 3427–3441 (2016). https://doi.org/10.1007/s11269-016-1360-4

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Keywords

  • Urbanization
  • Hydrologic response
  • Ayamama
  • Prediction
  • SLEUTH