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Surface runoff prediction regarding LULC and climate dynamics using coupled LTM, optimized ARIMA, and GIS-based SCS-CN models in tropical region

  • Hossein Mojaddadi Rizeei
  • Biswajeet Pradhan
  • Maryam Adel Saharkhiz
GCEC 2017
Part of the following topical collections:
  1. Global Sustainability through Geosciences and Civil Engineering

Abstract

The effects of climate and land use/land cover (LULC) dynamics have directly affected the surface runoff and flooding events. Hence, current study proposes a full-packaged model to monitor the changes in surface runoff in addition to forecast of the future surface runoff based on LULC and precipitation variations. On one hand, six different LULC classes were extracted from Spot-5 satellite image. Conjointly, land transformation model (LTM) was used to detect the LULC pixel changes from 2000 to 2010 as well as predict the 2020 ones. On the other hand, the time series-autoregressive integrated moving average (ARIMA) model was applied to forecast the amount of rainfall in 2020. The ARIMA parameters were calibrated and fitted by latest Taguchi method. To simulate the maximum probable surface runoff, distributed soil conservation service-curve number (SCS-CN) model was applied. The comparison results showed that firstly, deforestation and urbanization have been occurred upon the given time, and they are anticipated to increase as well. Secondly, the amount of rainfall has non-stationary declined since 2000 till 2015 and this trend is estimated to continue by 2020. Thirdly, due to damaging changes in LULC, the surface runoff has been also increased till 2010 and it is forecasted to gradually exceed by 2020. Generally, model calibrations and accuracy assessments have been indicated, using distributed-GIS-based SCS-CN model in combination with the LTM and ARIMA models are an efficient and reliable approach for detecting, monitoring, and forecasting surface runoff.

Keywords

Land use/land cover GIS Land transformation model ARIMA SCS-CN Runoff simulation 

References

  1. Aal-shamkhi, ADS, Mojaddadi H, Pradhan B, & Abdullahi S (2017). Extraction and modeling of urban sprawl development in Karbala City using VHR satellite imagery. In Spatial Modeling and Assessment of Urban Form (pp. 281-296). Springer International PublishingGoogle Scholar
  2. Abbas ABD, Allah Ibrahim TMM (2013) Time series analysis of Baghdad rainfall using ARIMA method. SUST J Eng Comp Sci 54(4):1136–1142Google Scholar
  3. Abdullahi, S., Pradhan, B., & Mojaddadi, H. (2017a). Assessing the relationship between city compactness and residential land use growth. In Spatial Modeling and Assessment of Urban Form (pp. 139-153). Springer International PublishingGoogle Scholar
  4. Abdullahi S, Pradhan B, Mojaddadi H (2017b) City compactness: assessing the influence of the growth of residential land use. J Urban Technol:1–26.  https://doi.org/10.1080/10630732.2017.1390299
  5. Adnan NA, Ariffin SDS, Asmat A and Mansor S (2016). Rainfall trend analysis and geospatial mapping of the Kelantan River basin Springer Singapore In ISFRAM Springer Singapore, 237–247Google Scholar
  6. Andréassian V (2004) Waters and forests: from historical controversy to scientific debate. J Hydrol 291(1–2):1–27.  https://doi.org/10.1016/j.jhydrol.2003.12.015 CrossRefGoogle Scholar
  7. Blaschke T (2010) Object based image analysis for remote sensing. ISPRS J Photogramm Remote Sens 65(1):2–16.  https://doi.org/10.1016/j.isprsjprs.2009.06.004 CrossRefGoogle Scholar
  8. Blaschke, T., Burnett, C., & Pekkarinen, A. (2004). Image segmentation methods for object-based analysis and classification. In Remote sensing image analysis: Including the spatial domain (Vol. 211–236). Remote sensing image analysis: Including the spatial domain. doi:  https://doi.org/10.1007/978-1-4020-2560-0
  9. Blaschke T, Lang S, Lorup E, Strobl J, Zeil P (2000) Object-oriented image processing in an integrated GIS/remote sensing environment and perspectives for environmental applications. Environ Inform Plan Polit Public 1995:555–570Google Scholar
  10. Briassoulis, Helen. (2000). Analysis of land use change: theoretical and modeling approachesGoogle Scholar
  11. Brocklebank J, Dickey DA (2003) SAS for forecasting time series. SAS Institute:191–299Google Scholar
  12. Bronstert A (2003) Floods and climate change: interactions and impacts. Risk Anal 23(3):545–557.  https://doi.org/10.1111/1539-6924.00335 CrossRefGoogle Scholar
  13. Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2(2):121–167.  https://doi.org/10.1023/A:1009715923555 CrossRefGoogle Scholar
  14. Calder IR (2007) Forests and water-ensuring forest benefits outweigh water costs. For Ecol Manag 251(1–2):110–120.  https://doi.org/10.1016/j.foreco.2007.06.015 CrossRefGoogle Scholar
  15. Chu X, Steinman A (2009) Event and continuous hydrologic modeling with HEC-HMS. J Irrig Drain Eng 135(1):119–124.  https://doi.org/10.1061/(ASCE)0733-9437(2009)135:1(119) CrossRefGoogle Scholar
  16. Du Q, Younan NH, King R, Shah VP (2007) On the performance evaluation of pan-sharpening techniques. IEEE Geosci Remote Sens Lett 4(4):518–522.  https://doi.org/10.1109/LGRS.2007.896328 CrossRefGoogle Scholar
  17. Durdu ÖF (2010) Application of linear stochastic models for drought forecasting in the Büyük Menderes river basin, western Turkey. Stoch Env Res Risk A 24(8):1145–1162.  https://doi.org/10.1007/s00477-010-0366-3 CrossRefGoogle Scholar
  18. Faizalhakim AS, Nurhidayu, S, Norizah, K., Shamsuddin, I, Hakeem, KR & Ismail, A. (2016). Curve number determination for, (September). doi:  https://doi.org/10.13140/RG.2.2.13722.85440
  19. Farley KA, Jobbágy EG, Jackson RB (2005) Effects of afforestation on water yield: a global synthesis with implications for policy. Glob Chang Biol 11(10):1565–1576.  https://doi.org/10.1111/j.1365-2486.2005.01011.x CrossRefGoogle Scholar
  20. Hamedianfar A, Shafri HZM (2016) Integrated approach using data mining-based decision tree and object-based image analysis for high-resolution urban mapping of WorldView-2 satellite sensor data. J Appl Remote Sens 10(2):25001.  https://doi.org/10.1117/1.JRS.10.025001 CrossRefGoogle Scholar
  21. Heistermann M, Müller C, Ronneberger K (2006) Land in sight? Achievements, deficits and potentials of continental to global scale land-use modelling. Agric Ecosyst Environ 114(2–4):141–158.  https://doi.org/10.1016/j.agee.2005.11.015 CrossRefGoogle Scholar
  22. Jebur MN, Pradhan B, Tehrany MS (2014) Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale. Remote Sens Environ 152:150–165.  https://doi.org/10.1016/j.rse.2014.05.013 CrossRefGoogle Scholar
  23. Kahya E, Kalayci S (2004) Trend analysis of streamflow in Turkey. J Hydrol 289(1–4):128–144.  https://doi.org/10.1016/j.jhydrol.2003.11.006 CrossRefGoogle Scholar
  24. Kay S, Hedley JD, Lavender S (2009) Sun glint correction of high and low spatial resolution images of aquatic scenes: a review of methods for visible and near-infrared wavelengths. Remote Sens 1(4):697–730.  https://doi.org/10.3390/rs1040697 CrossRefGoogle Scholar
  25. Kim, M., Madden, M., & Warner, T. (2008). Capítulo 3.2 estimation of optimal image object size for the segmentation of forest stands with multispectral IKONOS imagery. Object-based image analysis: spatial concepts for knowledge-driven remote sensing applications, 291–307. doi:  https://doi.org/10.1007/978-3-540-77058-9_16
  26. Koomen Eric, & Stillwell J. (2007). Modelling land-use change: SpringerGoogle Scholar
  27. Lambin EF, Geist HJ, Lepers E (2003) Dynamics of land-use and land-cover change in tropical regions. Annu Rev Environ Resour 28(1):205–241.  https://doi.org/10.1146/annurev.energy.28.050302.105459 CrossRefGoogle Scholar
  28. Liu, Z., Yao, Z., Huang, H., Wu, S., & Liu, G. (2012). Land use and climate changes and their impacts on runoff in the Yarlung Zangbo River Basin, China. doi:  https://doi.org/10.1002/ldr.1159
  29. Bartlett MS, Parolari AJ, McDonnell JJ, Porporato A (2016) Beyond the SCS-CN method: a theoretical framework for spatially lumped rainfall-runoff response. Water Resour Res 52(6):4608–4627.  https://doi.org/10.1002/2015WR018439 CrossRefGoogle Scholar
  30. Makridakis, S. (2000). The M3-competition : results, conclusions and implications, 16, 451–476Google Scholar
  31. Li M, Ma L, Blaschke T, Liang Cheng DT (2016) A systematic comparison of different object- based classification techniques using high spatial resolution imagery in agricultural environments. Int J Appl Earth Obs Geoinf 49(April):87–98.  https://doi.org/10.1016/j.jag.2016.01.011 CrossRefGoogle Scholar
  32. Mishra SK, Pandey A, Singh VP (2012) Special issue on soil conservation service curve number (SCS-CN) methodology. J Hydrol Eng 17(11):1157–1157.  https://doi.org/10.1061/(ASCE)HE.1943-5584.0000694 CrossRefGoogle Scholar
  33. Mojaddadi, H., Habibnejad M, & Mahdavi M (2012). Determining the role of rainfall time intervals in accuracy of SCS synthetic unit hydrograph (case study: Tehran and Alborz provinces)Google Scholar
  34. Mojaddadi, H., Pradhan, B., Nampak, H., Ahmad, N., & Halim, A. (2017). Ensemble machine-learning-based geospatial approach for flood risk assessment using multi- sensor remote-sensing data and GIS, 5705(March). doi:  https://doi.org/10.1080/19475705.2017.1294113
  35. Momani PENM (2009) Time series analysis model for rainfall data in Jordan: case study for using time series analysis. Am J Environ Sci 5(5):599–604 Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-70350129727&partnerID=40&md5=5e356da8cc4381cf6d1ee25214abe8b0 CrossRefGoogle Scholar
  36. Nagarjun, P.A., Rao, R.S., Rajesham, S. and Rao, L. (2005). Optimization of lactic acid production in SSF by lactobacillus amylovorus NRRL B-4542 using Taguchi methodology optimization of lactic acid production in SSF by Lactobacillus amylovorus NRRL B-4542 using Taguchi methodology, (march)Google Scholar
  37. Nguyen L (2016) Tutorial on support vector machine Special Issue “Some Novel Algorithms for Global Optimization and Relevant Subjects”. Appl Comp Math (ACM) 6(4–1):1–15.  https://doi.org/10.11648/j.acm.s.2017060401.11 Google Scholar
  38. Nikfar M, Valadan Zoej MJ, Mokhtarzade M, Shoorehdeli MA (2015) Designing a new framework using type-2 FLS and cooperative-competitive genetic algorithms for road detection from IKONOS satellite imagery. Remote Sens 7(7):8271–8299.  https://doi.org/10.3390/rs70708271 CrossRefGoogle Scholar
  39. Nonglait TL, Tiwari BK (2016) Application of SCS-CN method for estimation of runoff in a humid micro watershed. Int J Curr Agric Sci 6(10):121–127Google Scholar
  40. Omrani H, Tayyebi A, Pijanowski B (2017) Integrating the multi-label land-use concept and cellular automata with the artificial neural network-based land transformation model: an integrated ML-CA-LTM modeling framework. GISci Remote Sensing 54(3):1–22.  https://doi.org/10.1080/15481603.2016.1265706 Google Scholar
  41. Pflug, B., Main-Knorn, M., Makarau, A., & Richter, R. (2015). Validation of aerosol estimation in atmospheric correction algorithm ATCOR. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, XL (May), 11–15. doi:  https://doi.org/10.5194/isprsarchives-XL-7-W3-677-2015
  42. Pijanowski BC, Brown DG, Shellito BA, Manik GA (2002a) Using neural networks and GIS to forecast land use changes: a land transformation model. Comput Environ Urban Syst 26(6):553–575.  https://doi.org/10.1016/S0198-9715(01)00015-1 CrossRefGoogle Scholar
  43. Pijanowski BC, Shellito B, Pithadia S, Alexandridis K (2002b) Forecasting and assessing the impact of urban sprawl in coastal watersheds along eastern Lake Michigan. Lakes Reserv Res Manag 7(3):271–285.  https://doi.org/10.1046/j.1440-1770.2002.00203.x CrossRefGoogle Scholar
  44. Pradhan, B., Jebur, M. N., Zulhaidi, H., Shafri, M., & Tehrany, M. S. (2015). Data fusion technique using wavelet transform and Taguchi methods for automatic landslide detection from airborne laser scanning data and QuickBird satellite imagery, 1–13. doi:  https://doi.org/10.1109/TGRS.2015.2484325
  45. Rizeei HM, Saharkhiz MA, Pradhan B, Ahmad N (2016) Soil erosion prediction based on land cover dynamics at the Semenyih watershed in Malaysia using LTM and USLE models. Geocarto Int 6049(April 2016):1–20.  https://doi.org/10.1080/10106049.2015.1120354 Google Scholar
  46. Rosenzweig C, Casassa G, Karoly DJ, Imeson A, Liu C, Menzel A, Rawlins S, Root TL, Seguin B, Tryjanowski P (2007). Assessment of observed changes and responses in natural and managed systems. In Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Climate Change. Impacts, Adaptation and Vulnerability. Cambridge University Press: Cambridge, UKGoogle Scholar
  47. Taneja K, Ahmad S, Ahmad K, Attri SD (2016) Time series analysis of aerosol optical depth over New Delhi using box???Jenkins ARIMA modelling approach. Atmos Poll Res 7(4):585–596.  https://doi.org/10.1016/j.apr.2016.02.004 CrossRefGoogle Scholar
  48. Ward, A.D. and Trimble, S. W. (2003). Environmental hydrology. (C. Press, Ed.). CRC PressGoogle Scholar
  49. Wilder MG (1985) Site and situation determinants of land use change: an empirical example. Econ Geogr 61(4):332–344.  https://doi.org/10.2307/144053 CrossRefGoogle Scholar
  50. Woodward DE, Hawkins RH, Jiang R, Hjelmfelt Jr, AT, Van Mullem JA and Quan QD (2003). Runoff curve number method: examination of the initial abstraction ratio. In World Water Environ Res Congress, 1–10Google Scholar
  51. Yin, J., He, F., Xiong, Y., & Qiu, G. (2016). Effect of land use/land cover and climate changes on surface runoff in a semi-humid and semi-arid transition zone in northwest China. Hydrology and earth system sciences discussions, (June), 1–23. doi:  https://doi.org/10.5194/hess-2016-212

Copyright information

© Saudi Society for Geosciences 2018

Authors and Affiliations

  • Hossein Mojaddadi Rizeei
    • 1
  • Biswajeet Pradhan
    • 1
  • Maryam Adel Saharkhiz
    • 1
  1. 1.School of Systems, Management and Leadership, Faculty of Engineering and ITUniversity Technology SydneyUltimoAustralia

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