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Paddy harvest is extremely vulnerable to climate change and climate variations. It is a well-known fact that climate change has been accelerated over the past decades due to various human induced activities. In addition, demand for the food is increasing day-by-day due to the rapid growth of population. Therefore, understanding the relationships between climatic factors and paddy production has become crucial for the sustainability of the agriculture sector. However, these relationships are usually complex nonlinear relationships. Artificial Neural Networks (ANNs) are extensively used in obtaining these complex, nonlinear relationships. However, these relationships are not yet obtained in the context of Sri Lanka; a country where its staple food is rice. Therefore, this research presents an attempt in obtaining the relationships between the paddy yield and climatic parameters for several paddy grown areas (Ampara, Batticaloa, Badulla, Bandarawela, Hambantota, Trincomalee, Kurunegala, and Puttalam) with available data. Three training algorithms (Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugated Gradient (SCG)) are used to train the developed neural network model, and they are compared against each other to find the better training algorithm. Correlation coefficient (R) and Mean Squared Error (MSE) were used as the performance indicators to evaluate the performance of the developed ANN models. The results obtained from this study reveal that LM training algorithm has outperformed the other two algorithms in determining the relationships between climatic factors and paddy yield with less computational time. In addition, in the absence of seasonal climate data, annual prediction process is understood as an efficient prediction process. However, the results reveal that there is an error threshold in the prediction. Nevertheless, the obtained results are stable and acceptable under the highly unpredicted climate scenarios. The ANN relationships developed can be used to predict the future paddy yields in corresponding areas with the future climate data from various climate models.

The effect of climate variability on the rainfall pattern is canvassed on the Uma Oya river basin, Sri Lanka, consisting of 5 rainfall gauging stations. The Uma Oya basin (720 km2) is given utmost precedence due to environmental concerns seen in the ongoing Uma Oya multipurpose development project (529 million USD worth) which is expected to divert water to the southeast dry zone of the country while adding 231 GWh/year electricity to the national grid. The rainfall data for a period of 26 years (1992–2017) were analysed using Mann–Kendall’s test and Sen’s slope estimator test to identify the rainfall trends. Both of these trend analysis test results depict only one negative trend for Hilpankandura Estate for the month of June; however, the seasonal trend analysis and annual trend analysis do not support this observation. Nevertheless, Mann–Kendall’s test showed potential positive trends for the 3 rainfall gauging stations Kirklees Estate, Ledgerwatte Estate, and Welimada Group only in the 1st intermediate period (March-April), and this is well supported by the monthly trend analysis. Other than these trends, the results do not show any significant negative trends in the Uma Oya catchment. Therefore, the results vividly explain that there is no threat of water scarcity to the catchment area being resistant to changing global climate for the past 26 years.

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