Articles | Volume 23, issue 4
Hydrol. Earth Syst. Sci., 23, 1905–1929, 2019
Hydrol. Earth Syst. Sci., 23, 1905–1929, 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

Related authors

Combined impact of local climate and soil properties on soil moisture patterns
Thushara Gunda, Udeni P. Nawagamuwa, and George M. Hornberger
Hydrol. Earth Syst. Sci. Discuss.,,, 2017
Manuscript not accepted for further review
Short summary

Related subject area

Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
A history of TOPMODEL
Keith J. Beven, Mike J. Kirkby, Jim E. Freer, and Rob Lamb
Hydrol. Earth Syst. Sci., 25, 527–549,,, 2021
Short summary
Progressive water deficits during multiyear droughts in basins with long hydrological memory in Chile
Camila Alvarez-Garreton, Juan Pablo Boisier, René Garreaud, Jan Seibert, and Marc Vis
Hydrol. Earth Syst. Sci., 25, 429–446,,, 2021
Short summary
A comparison of catchment travel times and storage deduced from deuterium and tritium tracers using StorAge Selection functions
Nicolas Björn Rodriguez, Laurent Pfister, Erwin Zehe, and Julian Klaus
Hydrol. Earth Syst. Sci., 25, 401–428,,, 2021
Short summary
The role and value of distributed precipitation data in hydrological models
Ralf Loritz, Markus Hrachowitz, Malte Neuper, and Erwin Zehe
Hydrol. Earth Syst. Sci., 25, 147–167,,, 2021
Short summary
Flood spatial coherence, triggers, and performance in hydrological simulations: large-sample evaluation of four streamflow-calibrated models
Manuela I. Brunner, Lieke A. Melsen, Andrew W. Wood, Oldrich Rakovec, Naoki Mizukami, Wouter J. M. Knoben, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 25, 105–119,,, 2021
Short summary

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,, 1997. 
Analytical Vidhya Team: Tunning the parameters of your Random Forest model, available at: (last access: 12 March 2018), 2015. 
Analytical Vidhya Team: A Complete Tutorial on Tree Based Modeling from Scratch, available at: (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,, 2012. 
Breiman, L.: Randomforest2001, Mach. Learn., 45, 5–32,, 2001. 
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.