Articles | Volume 23, issue 4
Hydrol. Earth Syst. Sci., 23, 1905–1929, 2019
https://doi.org/10.5194/hess-23-1905-2019
Hydrol. Earth Syst. Sci., 23, 1905–1929, 2019
https://doi.org/10.5194/hess-23-1905-2019

Research article 09 Apr 2019

Research article | 09 Apr 2019

Identifying El Niño–Southern Oscillation influences on rainfall with classification models: implications for water resource management of Sri Lanka

Thushara De Silva M. and George M. Hornberger

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Cited articles

Amarasekera, K. N., Lee, R. F., Williams, E. R., and Eltahir, E. A. B.: ENSO and the natural variability in the flow tropical rivers, J. Hydrol., 200, 24–39, https://doi.org/10.1016/S0022-1694(96)03340-9, 1997. 
Analytical Vidhya Team: Tunning the parameters of your Random Forest model, available at: https://www.analyticsvidhya.com/blog/2015/06/tuning-random-forest-model/ (last access: 12 March 2018), 2015. 
Analytical Vidhya Team: A Complete Tutorial on Tree Based Modeling from Scratch, available at: https://www.analyticsvidhya.com/blog/2016/04/complete-tutorial-tree-based-modeling-scratch-in-python/ (last access: 12 March 2018), 2016. 
Block, P. and Goddard, L.: Statistical and Dynamical Climate Predictions to Guide Water Resources in Ethiopia, J. Water Resour. Plan. Manage., 138, 287–298, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000181, 2012. 
Breiman, L.: Randomforest2001, Mach. Learn., 45, 5–32, https://doi.org/10.1017/CBO9781107415324.004, 2001. 
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Short summary
Season-ahead rainfall forecast is very important for water resource management. Classification methods are used to identify the extreme rainfall classes dry and wet using climate teleconnections. These models can be used for river basin areal rainfall forecast and water resources and power generation planning for climate uncertainty. Water resource management decisions are informed by forecasts of El Niño–Southern Oscillation and Indian Ocean Dipole phenomena.