Identifying El Niño–Southern Oscillation influences on rainfall with classification models: implications for water resource management of Sri Lanka
2019
Abstract. 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 is often achieved by considering climate
teleconnections such as the El Nino–Southern Oscillation (ENSO) and 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 the Mahaweli
and Kelani River basins of the country. Forecasting of rainfall as the classes flood, drought, and normal is helpful
for 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 provide useful input to water resource managers as they plan for
adaptation of agriculture and energy sectors in response to climate variability.
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