Abstract
This study was performed to further understanding of the variations in hydrology and rice crop productivity during different El Niño/Southern Oscillation (ENSO) events in the Cauvery River Basin of Tamil Nadu, India using the Soil and Water Assessment Tool (SWAT). The entire Cauvery Basin was divided into 301 sub-basins and further subdivided into 3,601 Hydrological Response Units (HRU). Based on the National Oceanic and Atmospheric Administration (NOAA) official website, information on El Niño (1972, 1982, 1987, 1991, 1997, 2002 and 2004) and La Niña (1970, 1971, 1973, 1974, 1975, 1988, 1998, 1999 and 2000) years were obtained. The SWAT model was continuously run from 1970 to 2008, and a composite for El Niño, La Niña and normal years was constructed to understand their influence on hydrology and rice crop productivity in the study area. From the analysis, it was clear that an El Niño episode is correlated with rainfall, hydrology and rice productivity in the Cauvery river basin. The validation of the SWAT model indicated the capability of SWAT to accurately predict stream flow and rice productivity. It was evident from the investigation that the quantum of rainfall was more during El Niño years with high inter-annual rainfall variability (809.3mm to 2,366mm) compared with La Niña and normal years. As a result, the soil water recharge, including percolation and soil water availability in the surface layers, was increased in the El Niño years. Simulated rice productivity over 39 years in the Cauvery Basin ranged between 1,137 and 7,865kgha−1 with a mean productivity of 3,955kgha−1. The coefficient of variation in rice productivity was higher during La Niña (21.4%) years compared with El Niño (14.7%) and normal years (14.6%). The mean rice productivity was increased in both El Niño and normal years, indicating the possibility of higher yields than those in La Niña years. An analysis of the hydrological data and rice productivity showed that the risk of failure was low during El Niño years compared with normal or La Niña years. This behavior could be utilized for forecasting rice crop productivity under different ENSO conditions and can provide information for policy makers when deciding on water allocation and import / export policies.
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