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

Accurate and consistent information on the area and production of field crops is vital for national and state planning and ensuring food security in India. Satellite-based remote sensing offers a suitable and cost-effective technique for regional- and national-scale crop monitoring. The use of remote sensing data for crop yield estimation has been demonstrated using a semi-physical approach with reasonable success. Assimilating remote sensing data with the DSSAT model and spectral indices-based regression analysis are promising methods for spatially estimating rice crop yields. Rice area and yield in the Cauvery delta zone of Tamil Nadu, India was estimated during samba (August–January) season in the years 2020–2021 using Sentinel 1A Synthetic Aperture Radar satellite data with three different spatial yield estimation methods, namely a spectral indices-based regression analysis, semi-physical approach, and integrating remote products with DSSAT crop growth model. A rice area map was generated for the study area using a rule-based classifier approach utilizing parameterization with a classification accuracy of 94.5% and a kappa score of 0.89. The total classified rice area in Cauvery Delta Region was 379,767 ha, and the Start of Season (SoS) maps for samba season revealed that the major planting period for rice was between 22 September and 9 November in 2020. The study also aimed to identify promising spatial yield estimation techniques for optimal rice yield prediction over large areas. Regression models resulted in rice yields of 3234 to 3905 kg ha⁻¹ with a mean of 3654 kg ha⁻¹. The net primary product was computed using the periodical PAR, fAPAR, Wstress, Tstress, and maximum radiation use efficiency in a semi-physical approach. The resultant rice yields ranged between 2652 and 3438 kg ha⁻¹ with the mean of 3076 kg ha⁻¹. During the integration of remote sensing products with crop growth models, LAI values were extracted from dB images and utilized to simulate rice yields in the range of 3684 to 4012 kg ha⁻¹ with the mean of 3855 kg ha⁻¹. When compared to the semi-physical approach, both integrating remote sensing products with the DSSAT crop growth model and spectral indices-based regression analysis had R² greater than 0.80, NRMSE of less than 10%, and agreement of more than 90%, indicating that these two approaches could be used for spatial rice yield estimation.

Coastal zone is exposed to various natural forces including cyclones and tsunamis, which are constantly affecting the shorelines, beaches and headlands, causing storm surges, erosion/accretion, landslides, and coastal flooding. Magnitude and risk of disasters are directly proportional to the sensitivity and inversely proportional to degree of resilience of exposed community. To mitigate the ill effects of hazards, a thorough understanding of the vulnerability causing factors and coping capabilities is required for which vulnerability analysis is essential. A study was undertaken in the most vulnerable coastal zone in Cuddalore District of Tamil Nadu, with a goal to draw a comprehensive vulnerability framework combining Geo-Physical–Natural factors with Socio-Economic-Institutional factors responsible for causing vulnerability at habitation levels and to construct composite vulnerability index (CVI) and dimensional indices. Analysis on changes along the shoreline using the information extracted from the satellite imageries between the years 1972 and 2011 indicated that the average net rate of shoreline change was +0.15myear−1. Of the total length of 42km studied for shoreline changes, about 40.5% of the coastline is accreting, 15.72% is medium to highly eroded and 18.23% is classified under low erosion zone. The flood hazard mapping study undertaken for a stretch of ~14km along the Cuddalore coastline for 1-in-100-year extreme flood level, including local mean sea level and global sea-level rise, indicated maximum inundation level to be 3.62m form MSL for the Cuddalore coastal region. The composite hazard line drawn on the GIS map shows that in the study area seventeen habitations (coastal settlements) are vulnerable to storm surge coastal flooding generated by one in 100 year return period storm surge (3.62m height). CVI of 17 habitations in study area was developed on a scale of ‘one’ to ‘five’ by considering nine broad dimensions of vulnerability viz., geographic, demographic, institutional, natural, social, safety infrastructure, physical, livelihood and economic, each expressed by five indicators, using a total of seventy five variables of vulnerability, with weightage of 22.20%, 13.19%, 13.34%, 13.35%, 9.20%, 6.24%,5.89%, 9.83% and 6.77% respectively, arrived through Analytic Hierarchy Process (AHP). The results indicated that two habitations viz. Samiyarpettai (3.18) and C. Pudupettai (3.10) have CVI in acutely vulnerable (level 3-CVI between 3 and 4) category and rest of the 15 habitations are in the highly vulnerable (level 2-CVI between 2 and 3) category. Dimension wise vulnerability indices appear to differ considerably among different habitations. Institutional vulnerability is in a lower range owing to a better prepared coastal community after 2004 Tsunami. CVI construction enables the policy makers to devise a suitable strategy for vulnerability reduction. The habitation vulnerability mapping provides information for prioritisation of the vulnerability dimensions and is a very useful tool for developing effective policy to reduce vulnerability at habitation level

Field experiments were conducted during summer (March–July) and kharif (June–September), 2008 at the wetland farm, Tamil Nadu Agricultural University, Coimbatore, India, to study the performance of different rice cultivation methods on productivity and water usage using the hybrid CORH-3 as a test crop. Treatments consisted of different rice cultivation methods, namely, transplanted rice (conventional), direct sown rice (wet seeded), alternate wetting and drying method (AWD), system of rice intensification (SRI) and aerobic rice cultivation. Results revealed that maximum number of tillers m⁻², higher shoot and root length at maturity were recorded under SRI followed by transplanted rice, while aerobic rice produced lower growth parameters in both the seasons. Chlorophyll content at flowering was higher under SRI in two seasons studied (42.74 and 39.48 SPAD value, respectively) and transplanted rice compared to aerobic rice and AWD. In both summer and kharif seasons, SRI produced higher grain yield (6014 and 6682 kg ha⁻¹), followed by transplanted rice (5732 and 6262 kg ha⁻¹), while the lowest grain yield (3582 and 3933 kg ha⁻¹) was recorded under aerobic rice cultivation. Under SRI, 5 and 6.7% increase in grain yield and 12.6 and 14.8% water saving were noticed compared to transplanted rice, respectively, during summer and kharif seasons. In respect to water productivity, the SRI method of rice cultivation registered the highest water productivity (0.43and 0.47 kg m⁻³), followed by AWD and aerobic rice cultivation. The conventional rice cultivation and direct sown rice produced lower grain yield per unit quantity of water used.