The Combined Effect of Seasonal Fluctuations of Persian Gulf and Mediterranean Sea Surface Temperature on Monthly Streamflow Forecasting of Karkheh River, Iran

Document Type : Research Paper

Authors

1 PhD Candidate, Water Resources Eng, Department of Irrigation and Reclamation Eng., Faculty of Agricultural Engineering & Technology, University College of Agriculture & Natural Resources, University of Tehran, Iran.

2 Associate Professor, Department of Irrigation and Reclamation Eng., Faculty of Agricultural Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Iran

3 Associate Professor, Department of Irrigation and Reclamation Eng., Faculty of Agricultural Engineering & Technology, University College of Agriculture & Natural Resources, University of Tehran, Iran

Abstract

In the current paper, the combined effect of seasonal fluctuations of Persian Gulf and Mediterranean Sea Surface Temperatures (SSTS) on the forecast of monthly streamflow of Karkheh River has been investigated. To follow the purpose, Singular Value Decomposition method (SVD) has been made use of to determine the effective nodes of the seas on the climate of the region and to produce the correlated series of sea surface temperatures vs streamflow’s. Moreover, Generalized Regression Neural Network method (GRNN) based on cross-validation technique has been applied to determine the most appropriate predictors from same several combinations of predictors for each month. Results for the forecast of the inflow in to Garsha dam show that the Mediterranean sea SST, during autumn, affects the streamflow from February to April, and while summer and autumn SSTs of Persian Gulf affect the streamflow in April and May such that applying these two indices for streamflow forecast in April and May results in an average increase of 118% vs 282% in Nash-Sutcliff index during calibration vs validation phases, respectively.

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References

Araghinejad S. (2014). Data-Deriven Modeling: Using MATLAB in Water Resources and Environmental Engineering. NewYork: Springer.
Borhani Dariane, A. and Fatehi Marj, A. (2008). Application of artificial neural network in stream flow forecasting using climatic indices (Case study: Nazloochay River Basin). Journal of Faculty of Eng, 35 (3) (Civil Eng), 25-36. (In Farsi)
 Bretherton C.S., Smith C. and Wallace J.M. (1992). An intercomparison of methods for finding coupled patterns in climate data. J. Climate, 5: 541–560.
Chen, L., Ye, L., Singh, V., Zhou, J. and Guo, S. (2014). Determination of input for artificial neural networks for flood forecasting using the copula entropy method. J. Hydrol. Eng, 19(11), 04014021.
Cigizoglu, H. K. and Alp, M. (2004). Rainfall-runoff modelling using three neural network methods. ‌J. Artificial Intelligence and Soft Computing,‌‌ 3070, 166-171.
Cigizoglu, H. K. (2005). Generalized regression neural network in monthly flow forecasting. ‌J. Civil Engineering and Environmental Systems, ‌22 (2), 71-81.
Gray Robert.M. (2013). Entropy and Information Theory. NewYork: Springer-Verlag.
 Kassomenos P. A. and McGregor G. R. (2006). The interannual variability and trend of Precipitable Water over Southern Greece. J. Hydrometeorol, 7: 271-284.
Kişi, Ö. (2008). River flow forecasting and estimation using different artificial neural network techniques. Hydrol. Res, 39(1), 27–40.
Kutiel, H., Hirsch-Eshkol, T. R. and Turkes, M. (2001). Sea level pressure patterns associated with dry and wet monthly rainfall conditions in Turkey. Theor. Appl. Climatol., 69, 39-67.
Meidani E., and Araghinejad S. (2014). Long-lead streamflow forecasting in southwest of Iran by the Sea Surface Temperature of Mediterranean Sea. J. Hydrol.Eng,19(8), 05014005.
Nazemosadat  M.J.  (1998). The Persian Gulf Sea Surface Temperature as a drought diagnostic for southern parts of Iran. Drought News Network, 10:12-14.
Nazemosadat, M. J.  (2008). Improving neural network models for forecasting seasonal precipitation in southwestern Iran: The evaluation of oceanic-atmospheric indices. Advances in Geosciences, 16, 133-145.
Nazemosadat, M.J., Ghasemi, A.R., Amin, S.A. and Soltani, A.R. (2008). The simultaneous effect of ENSO and Persian Gulf SSTs on the occurrence of the drought and wet condition over the western and northwestern parts of Iran. Journal of Agricultural Science (University of Tabriz), 18(3), 1-17. (In Farsi)
NOAA_OI_SST_V2 data Available at
http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.html)(visited 26 May 2015).
Mahab Ghodss Consulting Engineering Company. (2010). Report of water resources planning and management for Karkheh basin. (In Farsi)
Rezayi Banafsheh, M., Jahanbakhsh, S., Bayati Khatibi, M. and Zeinali, B. (2010). Forecast of autumn and winter precipitation of west Iran by use from summer and autumn Mediterranean sea surface temprature. Physycal Geography Research Quarterly, 24, 47-62. (In Farsi)
Rowell D. P.  (2003). The impact of Mediterranean SSTs on the Sahelian rainfall seasonal. J. Climate, 16(5): 849–862.
 Soukup T.L., Aziz O.A., Tootle G.A., Piechota T.C., and Wulff, S.S. (2009). Long lead-time streamflow forecasting of the North Platte River incorporating ocean-atmospheric climate variability.  J.  Hydrology, 368: 131-142.
Uvo C.B., Repelli C.A., Zebiak S.E., and Kushnir Y. (1998). The relationship between tropical Pacific and Atlantic SST and northeast Brazil monthly precipitation. J. Climate, 11:551-562.
 Wallace J.M., Smith C., and Bretherton C.S. (1992). Singular value decomposition of wintertime sea surface temperature and 500-mb height anomalies. J. Climate, 5:561-576.
Wang, W.C., Chau, K.W., Cheng, C.T. and Qui, L. (2009). A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. ‌J. Hydrology, ‌374, 294-306.
Wu, C. L., Chau, K.W. and Fan, C. (2010). Prediction of rainfall time series using modular artificial neural networks coupled with data preprocessing techniques. ‌J. Hydrology, ‌389, 146-167.
Iranian Journal of Soil and Water Research
Volume 46, Issue 4 - Serial Number 4
January 2016
Pages 597-607

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