Identifying ENSO Influences on Rainfall with Classification Models: Implications for Water Resource Management of Sri Lanka

Seasonal to annual forecasts of precipitation patterns are very important for water infrastructure management. In particular, such forecasts can be used to inform decisions about the operation of multipurpose reservoir systems in the face of changing climate conditions. Success in making useful forecasts often is achieved by considering climate teleconnections such as the El-Nino-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) as related to sea surface temperature variations. We present a statistical analysis to explore the utility of using rainfall relationships in Sri Lanka with ENSO and IOD to predict rainfall to Mahaweli and Kelani, river basins of the country. Forecasting of rainfall as classes; flood, drought and normal are helpful for the water resource management decision making. Results of these models give better accuracy than a prediction of absolute values. Quadratic discrimination analysis (QDA) and classification tree models are used to identify the patterns of rainfall classes with respect to ENSO and IOD indices. Ensemble modeling tool Random Forest is also used to predict the rainfall classes as drought and not drought with higher skill. These models can be used to forecast the areal rainfall using predicted climate indices. Results from these models are not very accurate; however, the patterns recognized are useful input to the water resources management and adaptation the climate variability of agriculture and energy sectors.