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Modeling Earth Systems and Environment

, Volume 5, Issue 1, pp 257–273 | Cite as

Modelling of river flow in ungauged catchment using remote sensing data: application of the empirical (SCS-CN), Artificial Neural Network (ANN) and Hydrological Model (HEC-HMS)

  • Hadush MeresaEmail author
Original Article
  • 42 Downloads

Abstract

In the present study an attempt is made to provide empirical and deterministic modelling approach for deriving flood frequency curve in ungauged Keseke river catchment, South Nation Nationality and People (SNNP)-Ethiopia. The research work consists of (i) extracting of remote sensing data; (ii) evaluation and validation of remote sensing data; (iii) modelling of river flow using remote sensing data (climate and physiographic data) of the river catchment; (ii) three types of hydrological models validation and evaluation; (iv) developing of flood frequency model for each sub-catchment. The evaluation and validation of remote sensing data and river flow prediction is carried out on eight selected rivers in Keseke River catchment. The single gamma distribution quantile mapping is a good approximation to adjust satellite precipitation product data and the Pearson correlation function has shown a good correlation, mainly on heavy rain events. Results reveals that the SCS-CN and ANN approaches are suitable to predict river runoff in ungauged with reasonable accuracy in the investigated sub-catchments, and appears acceptable correlation between estimated and corrected satellite rainfall. A field campaign to obtain possible data was executed via interview and river cross section measures. The flood quantiles are compared with one time flow observation from field measured value (which is estimated from the river cross-section size) to identify the most representative hydrological model structure.

Keywords

Ungauged catchment SCS-CN HEC-HMS ANN GIS Remote sensing Modelling Keseke catchment. 

Notes

Acknowledgements

Support for this work was provided by the Ethiopia Construction Design and Supervision Work Corporation (ECDSWC). Author is thankful to the National Meteorology Agency (NMA) for providing me Meteorological data of the study area.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

References

  1. Adib A, Salarijazi M, Najafpour K (2010) Evaluation of synthetic outlet runoff assessment models. J Appl Sci Environ Manag 14(3):13–18Google Scholar
  2. Asati SR, Rathore SS (2012) Stream flow prediction using soft computing technique. Int J Lakes River 5(1):7–22Google Scholar
  3. Besaw LE, Rizzo DM, Bierman PR et al (2010) Advances in ungauged stream flow prediction using artificial neural networks. J Hydrol 386: 27–37.  https://doi.org/10.1016/j.jhydrol.2010.02.037 CrossRefGoogle Scholar
  4. Beven KJ (2001) Rainfall-runoff modelling: the primer. Wiley, LancasterGoogle Scholar
  5. Choudhari K, Panigrahi, B, Paul JC (2014) Simulation of rainfall-runoff process using HEC-HMS model for Balijore Nala watershed, Odisha, India. Int J Geom Geosci 5(2). ISSN 0976–4380Google Scholar
  6. Coles S (2001) An introduction to statistical modeling of extreme values. Springer, HeidelbergCrossRefGoogle Scholar
  7. Dastorani MT, Moghadamnia A, Piri J, Rico-Ramirez M (2009) Application of ANN and ANFIS Models for reconstructing missing flow data. J Environ Monit Assess 166:421–434CrossRefGoogle Scholar
  8. Demirel MC, Anabela Venancio MC, Ercan Kahya MC (2009) Flow forecast by SWAT model and ANN in Pracana basin, Portugal. Adv Eng Softw 40:467–473CrossRefGoogle Scholar
  9. Dube T, Gumindoga W, Chawira M (2014) Detection of land cover changes around Lake Mutirikwi, Zimbabwe, based on traditional remote sensing image classification techniques. Afr J Aquat Sci 39(1):89–95CrossRefGoogle Scholar
  10. Gibbs MS, Maier HR, Dandy GC (2012) A generic framework for regression regionalization in ungauged catchments. Environ Model Softw 27:1–14CrossRefGoogle Scholar
  11. Gumindoga W, Rwasoka DT, Murwira A (2011) Simulation of streamflow using TOPMODEL in the Save River catchment of Zimbabwe. Physics and Chemistry of the Earth, Parts A/B/C In PressGoogle Scholar
  12. Gumindoga W, Makurira H, Phiri M, Nhapi I (2015) Estimating runoff from ungauged catchments for reservoir water balance in the Lower Middle Zambezi Basin. Water SA 42(4):641–649CrossRefGoogle Scholar
  13. Gunter Bloschl (2005) Rainfall-runoff modeling of ungauged catchments. Encyclopedia of hydrological sciences. Edited by M G Anderson. Wiley, AmsterdamGoogle Scholar
  14. He Y, Bardossy A, Zehe E (2011) A review of regionalisation for continuous streamflow simulation. Hydrol Earth syst Sci 15:3539–3553.  https://doi.org/10.5194/hess-15-3539-2011 CrossRefGoogle Scholar
  15. Hengl T, Maathuis BHP, Wang L (2007) Terrain parameterization in ILWIS. Chapter 3. In: Hengel, Hannes R (ed) ‘Geomorphometry’ the textbook. European Commission. DG Joint Research Centre, Institute for Environment and Sustainability, Land Management and Natural Hazards Unit, Ispra, pp 29–48Google Scholar
  16. Hrachowitz M, Savenije HHG, Blöschl G, McDonnell JJ, Sivapalan M, Pomeroy JW, Arheimer B, Blume T, Clark MP, Ehret U, Fenicia F, Freer JE, Gelfan A, Gupta HV, Hughes DA, Hut RW, Montanari A, Pande S, Tetzlaff D, Troch PA, Uhlenbrook S, Wagener T, Winsemius HC, Woods RA, Zehe E, Cudennec C (2013) A decade of predictions in ungauged basins (PUB)—a review. Hydrol Sci J 58(6):1198–1255.  https://doi.org/10.1080/02626667.2013.803183 CrossRefGoogle Scholar
  17. Kalteh M (2013) Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform. Comput Geosci 54:1–8CrossRefGoogle Scholar
  18. Khosravi A, Nahavandi S, Creighton D, Atiya A (2011) Comprehensive review of neural network-based prediction intervals and new advances. IEEE Trans Neural Netw 22(9):1341–1356CrossRefGoogle Scholar
  19. Maathuis BHP (2007) DEM based hydro-processing: introduction to the tools developed, tutorial with exercises version 1. Department of Water Resources, ITC, EnshedeGoogle Scholar
  20. Maier HR, Jain A, Dandy GC et al (2010) Methods used for the development of neural networks for prediction of water resource variables in river systems: current status and future directions. Environ Model Softw 25:891–909.  https://doi.org/10.1016/j.envsoft.2010.02.003 CrossRefGoogle Scholar
  21. Maraun D (2012) Nonstationarities of regional climate model biases in European seasonal mean temperature and precipitation sums. Geophys Res Lett 39:L06706.  https://doi.org/10.1029/2012GL051210 CrossRefGoogle Scholar
  22. Maraun D et al (2010) Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Rev Geophys 48:RG3003.  https://doi.org/10.1029/2009RG000314 CrossRefGoogle Scholar
  23. Meresa HK, Gatachew MT (2018) Climate change impact on river flow extremes in the Upper Blue Nile River Basin. Water Clim Change.  https://doi.org/10.2166/wcc.2018.154 Google Scholar
  24. Meresa HK, Marzena O, Renata R (2016) Hydro-meteorological drought projections into the 21-st century for selected polish catchments. Water 8(5):206.  https://doi.org/10.3390/w8050206 CrossRefGoogle Scholar
  25. Meresa HK, Renata RJ, Napiorkowski JJ (2017) Understanding changes and trends in projected hydroclimatic indices in selected Norwegian and Polish catchments. Acta Geophys.  https://doi.org/10.1007/s11600-017-0062-5 Google Scholar
  26. Mondal NC, Singh VP, Ahmed S (2012) Entropy-based approach for assessing natural recharge in unconfined aquifers from southern India. Water Res Manag 26(9):2715–2732CrossRefGoogle Scholar
  27. Moretti G, Montanari A (2008) Inferring the flood frequency distribution for an ungauged basin using a spatially distributed rainfall-runoff model. Hydrol Earth Syst Sci 12:1141–1152CrossRefGoogle Scholar
  28. Nayak TR, Jaiswal RK (2003) Rainfall-runoff modeling using satellite data and GIS for bebas river in Madhya Pradesh. Inst Eng 84:47–50Google Scholar
  29. Patil S, Stieglitz M (2012) Controls on hydrologic similarity: role of nearby gauged catchments for prediction at an ungauged catchment. Hydrol Earth Syst Sci 16(2):551–562.  https://doi.org/10.5194/hess-16-551-2012 CrossRefGoogle Scholar
  30. Randrianasolo A, Ramos M, Andréassian V (2011) Hydrological ensemble forecasting at ungauged basins: using neighbour catchments for model setup and updating. Adv Geosci 29:1–11CrossRefGoogle Scholar
  31. Sabouri F, Gharabaghi B, Mahboubi A, McBean E (2013) Impervious surfaces and sewer pipe effects on stormwater runoff temperature. J Hydrol 502:10–17CrossRefGoogle Scholar
  32. Saliha A, Awulachew S, Cullmann J, Hans -B (2011) Estimation of flow in ungauged catchments by coupling a hydrological model and neural networks: case study. Hydrol Res 42(5):386–400CrossRefGoogle Scholar
  33. Schaefli B, Harman CJ, Sivapalan M, Schymanski SJ (2011) HESS opinions: hydrologic predictions in a changing environment: behavioral modeling. Hydrol Earth Syst Sci 15:635–646.  https://doi.org/10.5194/hess-15-635-2011 CrossRefGoogle Scholar
  34. Sivapalan M, Takeuchi K, Franks SW, Gupta VK, Karambiri H, Lakshmi V, Liang X, McDonnell JJ, Mendiondo EM, O’Connell PE, Oki T, Pomeroy JW, Schertzer D, Uhlenbrook S, Zehe E (2003) IAHS decade on predictions in ungauged basins (PUB), 2003–2012: shaping an exciting future for the hydrological sciences. Hydrol Sci J 48(6):857–880.  https://doi.org/10.1623/hysj.48.6.857.51421 CrossRefGoogle Scholar
  35. Solomatine DP, Shrestha DL (2009) A novel method to estimate model uncertainty using machine learning techniques. Water Resour Res 45:W00B11.  https://doi.org/10.1029/2008WR006839 CrossRefGoogle Scholar
  36. Sreenivasulu V, Bhaskar PU (2010) Estimation of catchment characteristics using remote sensing and GIS techniques international. J Eng Sci Technol 2(12):7763–7770Google Scholar
  37. Srinivasan V, Gorelick SM, Goulder L (2010) A hydrologic-economic modeling approach for analysis of urban water supply dynamics in Chennai, India. Water Resour Res 46:W07540.  https://doi.org/10.1029/2009WR008693 Google Scholar
  38. Srivastav RK, Sudheer KP, Chaubey IA (2007) Simplified approach to quantifying predictive and parametric uncertainty in artificial neural network hydrologic models. Water Resour Res 43(10):W10407CrossRefGoogle Scholar
  39. Talebizadeh M, Ayyoubzadeh S, Ghasemzadeh M (2010) Uncertainty analysis in sediment load modeling using ANN and SWAT model. Water Resour Manag 24(9):1747–1761CrossRefGoogle Scholar
  40. Wale A, Rientjes T, Gieske A, Getachew H (2009) Ungauged catchment contributions to Lake Tana’s water balance. Hydrol Process 23(26):3682–3693Google Scholar
  41. Yadav M, Wagener T, Gupta H (2007) Regionalization of constraints on expected watershed response behavior for improved predictions in ungauged basins. Adv Water Resour 30:1756–1774CrossRefGoogle Scholar
  42. Yaghoubi M, Massah A (2014) Sensitivity analysis and comparison of capability of three conceptual models HEC-HMS, HBV and IHACRES in simulating continuous rainfall-runoff in semi-arid basins. J Earth Space Phys 2:153–172Google Scholar
  43. Zhand Y, Zeng H, Chiew F, Csiro J (2015) Evaluating regional and global hydrological models against streamflow and evapotranspiration measurements. J Am Meteorol 17(3):995–1010Google Scholar
  44. Zhao F, Chiew FHS, Zhang L, Vaze J, Perraud J-M, Li M (2012) Application of a macroscale hydrologic model to estimate streamflow across southeast Australia. J Hydrometeorol 13:1233–1250.  https://doi.org/10.1175/JHM-D-11-0114.1 CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Ethiopian Construction Design and Supervision Work CorporationAddis AbabaEthiopia

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