Identification of Influential Sea Surface Temperature Locations and Predicting Streamflow for Six Months Using Bayesian Machine Learning Regression

N. K. Shrestha^1*, G. Urroz²

¹Department of Agricultural and Biological Engineering, University of Florida, Immokalee, USA.
²Department of Civil and Environmental Engineering, Utah State University, Logan, USA.

DOI: 10.4236/jwarp.2015.73016                

Abstract

Sea surface temperature (SST) has significant influence in the hydrological cycle and affects the discharge in the stream. SST is an atmospheric circulation indicator which provides the predictive information about the hydrologic variability in the region around the world. Use of right location of SST for a given location of stream gage can capture the effect of oceanic-atmospheric interaction, improving the predictive ability of the model. This study aims on identifying the best locations of SST at the selected stream gage in the state of Utah that spatially covers the state from south to north, and use them for next six-month streamflow volume predictions. The data-driven model derived from the statistical learning theory was used in this study. Using an appropriate location of SST together with local climatic conditions and state of basin, an accurate and reliable streamflow was predicted for next six months. Influence of Pacific Ocean SST was observed to be stronger than that of Atlantic Ocean SST in the state of Utah. The SST of North Pacific developed the best model in most of the selected stream gages. Each model was ensured to be robust by the bootstrap analysis. The long-term streamflow prediction is important for water resource planning and management in the river basin scale and is a key step for successful water resource management in arid regions.

Keywords

Streamflow, Prediction, SWE, Temperature, RVM

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Sivakumar, B. (2003) Forecasting Monthly Streamflow Dynamics in the Western United States: A Nonlinear Dynamical Approach. Environmental Modelling & Software, 18, 721-728. http://dx.doi.org/10.1016/S1364-8152(03)00074-4
[2]	Asefa, T., Kemblowski, M., McKee, M. and Khalil, A. (2006) Multi-Time Scale Stream Flow Predictions: The Support Vector Machines Approach. Journal of Hydrology, 318, 7-16. http://dx.doi.org/10.1016/j.jhydrol.2005.06.001
[3]	Khalil, A.F., McKee, M., Kemblowski, M. and Asefa, T. (2005) Basin Scale Water Management and Forecasting Using Artificial Neural Networks. JAWRA Journal of the American Water Resources Association, 41, 195-208. http://dx.doi.org/10.1111/j.1752-1688.2005.tb03728.x
[4]	Kalra, A. and Ahmad, S. (2011) Improving Streamflow Forecast Using Predefined Seas Surface Temperature. American Geophysical Meeting, San Francisco, 5-9 December 2011.
[5]	Tootle, G.A. and Piechota, T.C. (2006) Relationships between Pacific and Atlantic Ocean Sea Surface Temperatures and U.S. Streamflow Variability. Water Resources Research, 42, W07411. http://dx.doi.org/10.1029/2005WR004184
[6]	Shrestha, N.K. (2014) Long Lead-Time Streamflow Forecasting Using Oceanic-Atmospheric Oscillation Indices. Journal of Water Resources and Protection, 6, 635-653. http://dx.doi.org/10.4236/jwarp.2014.66062
[7]	Shrestha, N.K. and Shukla, S. (2014) Basal Crop Coefficient for Vine and Erect Crops with Plastic Mulch in a Sub-Tropical Region. Agricultural Water Management, 143, 29-37. http://dx.doi.org/10.1016/j.agwat.2014.05.011
[8]	Shukla, S., Shrestha, N.K. and Goswami, D. (2014) Evapotranspiration and Crop Coefficient for Seepage-Irrigated Watermelon with Plastic Mulch in a Sub-Tropical Region. Transactions of the ASABE, 57, 1017-1028.
[9]	Shukla, S., Shrestha, N.K., Jaber, F.H., Srivastava, S., Obreza, T.A. and Boman, B.J. (2014) Evapotranspiration and Crop Coefficient for Watermelon Grown under Plastic Mulched Conditions in Sub-Tropical Florida. Agricultural Water Management, 132, 1-9. http://dx.doi.org/10.1016/j.agwat.2013.09.019
[10]	Hamlet, A.F. and Lettenmaier, D.P. (1999) Columbia River Streamflow Forecasting Based on ENSO and PDO Climate Signals. Journal of Water Resources Planning and Management, 125, 333-341. http://dx.doi.org/10.1061/(ASCE)0733-9496(1999)125:6(333)
[11]	Khalil, A.F., McKee, M., Kemblowski, M., Asefa, T. and Bastidas, L. (2006) Multiobjective Analysis of Chaotic Dynamic Systems with Sparse Learning Machines. Advances in Water Resources, 29, 72-88. http://dx.doi.org/10.1016/j.advwatres.2005.05.011
[12]	Shrestha, N.K. and Shukla, S. (2015) Support Vector Machine Based Modeling of Evapotranspiration Using Hydro-Climatic Variables in a Sub-Tropical Environment. Agricultural and Forest Meteorology, 200, 172-184. http://dx.doi.org/10.1016/j.agrformet.2014.09.025
[13]	Tipping, M. (2001) Sparse Bayesian Learning and the Relevance Vector Machine. Journal of Machine Learning Research, 1, 211-244.
[14]	Ticlavilca, A. (2010) Multivariate Bayesian Machine Learning Regression for Operation and Management of Multiple Reservoir, Irrigation Canal, and River Systems. Ph.D. Dissertation, Utah State University, Logan.
[15]	Perica, S. and Stayner, M. (2004) Regional Flood Frequency Analysis for Selected Basins in Utah. Utah Department of Transportation Research and Development Division, Salt Lake City.
[16]	Vapnik, V.N. (1995) The Nature of Statistical Learning Theory. Springer Verlag, New York. http://dx.doi.org/10.1007/978-1-4757-2440-0
[17]	Vapnik, V.N. (1998) The Nature of Statistical Learning Theory. Springer Verlag, New York.
[18]	Tipping, M. (2000) The Relevance Vector Machine. Proceeding of Advances in Neural Information Processing Systems, the MIT Press, 652-658.
[19]	Thayananthan, A. (2005) Template-Based Pose Estimation and Tracking of 3D Hand Motion. PhD Dissertation, University of Cambridge, Cambridge.
[20]	Tipping, M.E. and Faul, A.C. (2003) Fast Marginal Likelihood Maximization for Sparse Bayesian Models. Proceedings of the 9th International Workshop on Artificial Intelligence and Statistics, Key West, 3-6 January 2003.
[21]	Dibike, Y., Velickov, S., Solomatine, D. and Abbott, M. (2001) Model Induction with Support Vector Machines: Introduction and Applications. Journal of Computing in Civil Engineering, 15, 208-216. http://dx.doi.org/10.1061/(ASCE)0887-3801(2001)15:3(208)
[22]	Scholkopf, B. and Smola, A.J. (2002) Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge.
[23]	Smith, T.M. and Reynolds, R.W. (2003) Extended Reconstruction of Global Sea Surface Temperatures Based on COADS Data (1854-1997). Journal of Climate, 16, 1495-1510. http://dx.doi.org/10.1175/1520-0442-16.10.1495
[24]	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 Oceanic-Atmospheric Climate Variability. Journal of Hydrology, 368, 131-142. http://dx.doi.org/10.1016/j.jhydrol.2008.11.047
[25]	Efron, B. and Tibshirani, R.J. (1998) An Introduction of the Bootstrap, Monographs on Statistics and Applied Probability. CRC Press LLC, Boca Raton.
[26]	Ting, M. and Wang, H. (1997) Summertime U.S. Precipitation Variability and Its Relation to Pacific Sea Surface Temperature. Journal of Climate, 10, 1853-1873. http://dx.doi.org/10.1175/1520-0442(1997)010<1853:SUSPVA>2.0.CO;2
[27]	Wang, H. and Ting, M. (2000) Covariabilities of Winter U.S. Precipitation and Pacific Sea Surface Temperatures. Journal of Climate, 13, 3711-3719. http://dx.doi.org/10.1175/1520-0442(2000)013<3711:COWUSP>2.0.CO;2