Hydrological Assessment of Daily Satellite Precipitation Products over a Basin in Iran

Document Type: Research Paper

Authors

1 Environmental Sciences Research Center, Department of Civil Engineering, Islamshahr Branch, Islamic Azad University, Islamshahr, Tehran, Iran

2 Department of Civil Engineering, Roudehen Branch, Islamic Azad university, Roudehen, Tehran, Iran

3 Department of Civil Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

Abstract

In order to measure precipitation as the main variable for estimating the runoff and designing hydraulic structures, the satellite algorithm products that have the proper spatial and temporal coverage, can be used. In this study, at first, the daily streamflow simulation of Sarough-Cahy River from the Zarinehroud basin was conducted through the artificial neural network (ANN) and ground data of daily precipitation, temperature and discharge for the years of 1988 to 2008. The developed network was trained, validated and tested. Then, in order to evaluate the products of satellite precipitation algorithms in streamflow simulation which is the aim of this study, daily satellite rainfall data of PERSIANN, TMPA-3B42V7, TMPA-3B42RT and CMORPH between 2003 and 2008 were used as an input data to the trained ANN model. Considering indices of R2, RMSE and MAE implemented for evaluations, the results indicated that satellite rainfall algorithms are able to simulate runoff efficiently over the study area.

Keywords


  1. Beven, K. J. (2001). "Rainfall-runoff modelling: The Primer, John Wiley and Sons Press, Department of   Geography Royal Holloway", University of London Egham, Surrey.
  2. Tokar, A.S. and Markus M. (2000). "Precipitation-runoff modeling using artificial neural networks and conceptual models", Journal of Hydrologic Engineering, Vol.5, April, PP.156-161.
  3. Li, X and Zhang Q, Xu Ch. (2013). "Assessing the performance of satellite based precipitation products and its dependence on topography over Poyang Lake basin", Theor Appl Climatol, 115:713–729.
  4. Sadeghi, S. H. R., Yasrebi, B., Noormohammadi F., (2005). "Preparation and analysis of precipitation - runoff models, Haraz catchment monthly in Mazandaran", Journal of Agricultural Sciences and Natural Resources of the Caspian Sea, 3 (1): 1-12. (In Persian)
  5. Moazami S, Golian S, Kavianpour M R and Hong Y. (2013). "Comparison of PERSIANN and V7 TRMM Multi-satellite Precipitation Analysis (TMPA) products with rain gauge data over Iran", International Journal.
  6. Katiraie Boroujerdi, P. S. (2013). "Comparison of monthly satellite and ground precipitation data in a network of high-resolution on Iran," Iranian Geophysical Journal, Volume 7, Issue 4, Pp. 149-160. (In Persian)
  7. Baranizadeh, A., Behiar, M. B., Javanmard, S., Abedini, Y. (2011). "Validation of precipitation algorithm of PERSIANN satellite through (APHRODITE) reticulated ground precipitation in Iran", the article of physics conference in Iran, interdisciplinary physics, 2615-2618. (In Persian)
  8. Collischonn B, Collischonn W and Morelli Tucci C E, (2008). "Daily hydrological modeling in the Amazon basin using TRMM rainfall estimates", Journal of Hydrology, 360: 207-216.
  9. Stisen S and Sandholt I, (2010). "Evaluation of remote-sensing-based rainfall products through predictive capability in hydrological runoff modeling", Journal of Hydrology Process, 24(7):879–891.
  10. Behrangi A, Khakbaz B, Jaw TC, AghaKouchak A, Hsu K and Sorooshian S, (2011). "Hydrologic evaluation of satellite precipitation products over a mid-size basin", Journal of Hydrology, 397:225–237.
  11. Li X and Zhang Q, Xu Ch, (2012). "Suitability of the TRMM satellite rainfalls in driving a distributed hydrological model for water balance computations in Xinjiang catchment, Poyang lake basin", Journal of Hydrology, 426- 427:28-38.
  12. Dawson C.W. and Wilby R., (1988). "An artificial neural network approach for rainfall-runoff modeling", Hydrological Sciences Journal, Vol.43, February, PP. 47-66.
  13. Tokar A.S. and Johnson P.A., (1999). "Rainfall-runoff modeling using artificial neural networks", Journal of Hydrologic Engineering, Vol.4, July , PP.232-239.
  14. Zhang B. and Govindaraju R.S. (2003). "Geomorphology-based artificial neural networks (GANNs) for estimation of direct runoff over watersheds", Journal of Hydrologic Engineering, Vol.273, October, PP. 18-34.
  15. Soltani, S., (2002), "Comparison of conceptual models with Artificial Neural Networks in simulating precipitation - runoff", Master Thesis, Tarbiat Modarres University, Tehran. (In Persian)
  16. Haykin, S., 1999. Neural Networks : a Comprehensive Foundation, second ed. Prentice Hall, Upper Saddle River, N.J., USA, p. 842.
  17. Mulia, I.E., Tay, H., Roopsekhar, K., Tkalich, P., 2013. Hybrid ANN-GA model for predicting turbidity and chlorophyll-a concentration. J. Hydro environ. Res. 7, 279-299.
  18. Zamani, A., Azimian, A., Heemink, A., Solomatine, D., 2009. Wave height prediction at the Caspian Sea using a data-driven model and ensemble-based data assimilation methods. J. Hydroinform. 11 (2), 154-164.
  19. Sung, E. K. and Il Won, S. (2015). “Artificial Neural Network ensemble modeling with conjunctive data clustering for water quality prediction in rivers”, Journal of Hydro-environment Research 9, 325-339.
  20. Ebert, E. E., Janowiak, J.E., and Kidd, C. (2007). "Comparison of near-real-time precipitation estimates from satellite observations and numerical models", Bull. Amer. Meteor. Soc., 88, 47-64.
  21. Joyce, R. J., Janowiak, J. E., Arkin, P. A. and Xie, P. (2004), "CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution", Journal of Hydrometeorol, 5:487-503.
  22. Romilly T.G., Gebremichael M., (2011), Evaluation of satellite rainfall estimates over Ethiopian river basins, EGU, Hydrology and Earth System Sciences, 15:1505-1514.