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Climate variability is directly or indirectly affecting crop production and threatens the food security of livelihood. The Cauvery delta region (CDR) is the agricultural protective zone of Tamil Nadu state, the crop production in this region is vulnerable to climate change impacts. In this study, the future climate change impact of black gram yield has been assessed using the DSSAT-CROPGRO model in the CDR. The model was calibrated during the 2018 sowing period and validated during 2019 cultivation practices for determining the genetic coefficient of the black gram cultivar VBN6. The RMSE values of observed and simulated yield were 910 and 890 kg ha−1 respectively. RegCM4 model is used for the simulation of Vigna mungo yield under RCP 4.5 and 8.5 scenarios during the 1985–2015 (baseline), 2020–2039 (near-century), 2040–2069 (mid-century), and 2070–2099 (end-century). The results reveal that simulation of black gram yield change could be around 34%, 52%, and 25% during the near-century, mid-century, and end-century respectively for RCP 4.5 based on the baseline period. Adjusting the sowing date is evaluated as an adaptation option in future climate change scenarios, and results revealed that early sowing in the winter season could be the best suitable growing period for better yield in this region. [ABSTRACT FROM AUTHOR]

This paper describes the projection of climate change scenarios under increased greenhouse gas emissions, using the results of atmospheric-ocean general circulation models in the Coupled Model Intercomparison Project phase 3 dataset. A score is given to every model based on global and regional performance. Four out of 20 general circulation models (GCMs) were selected based on skill in predicting observed annual temperature and precipitation conditions. The ensemble of these four models shows superiority over the individual model scores. These models were subjected to increases in future anthropogenic radiative forcings for constructing climate change scenarios. Future climate scenarios for Tamil Nadu were developed with MAGICC/SCENGEN software. Model results show both temperature and precipitation increases under increased greenhouse gas scenarios. Northeast and northwest parts of Tamil Nadu show a greater increase in temperature and precipitation. Seasonally, the maximum rise in temperature occurred during the MAM season, followed by DJF, JJA, and SON. Decreasing trends of precipitation were observed during DJF and MAM. [ABSTRACT FROM AUTHOR]

Climate change is a terrible global concern and one of the greatest future threats to societal development as a whole. The accelerating pace of climate change is becoming a major challenge for agricultural production and food security everywhere. The present study uses the midcentury climate derived from the ensemble of 29 general circulation models (GCMs) on a spatial grid to quantify the anticipated climate change impacts on rice, maize, black gram, and red gram productivity over Tamil Nadu state in India under RCP 4.5 and RCP 8.5 scenarios. The future climate projections show an unequivocal increase of annual maximum temperature varying from 0.9 to 2.2°C for RCP 4.5 and 1.4 to 2.7°C in RCP 8.5 scenario by midcentury, centered around 2055 compared to baseline (1981–2020). The projected rise in minimum temperature ranges from 1.0 to 2.2°C with RCP 4.5 and 1.8 to 2.7°C under RCP 8.5 scenario. Among the monsoons, the southwest monsoon (SWM) is expected to be warmer than the northeast monsoon (NEM). Annual rainfall is predicted to increase up to 20% under RCP 4.5 scenario in two-third of the area over Tamil Nadu. Similarly, RCP 8.5 scenario indicates the possibility of an increase in rainfall in the midcentury with higher magnitude than RCP 4.5. Both SWM and NEM seasons are expected to receive higher rainfall during midcentury under RCP 4.5 and RCP 8.5 than the baseline. In the midcentury, climate change is likely to pose a negative impact on the productivity of rice, maize, black gram, and red gram with both RCP 4.5 and RCP 8.5 scenarios in most places of Tamil Nadu. The magnitude of the decline in yield of all four crops would be more with RCP 8.5 over RCP 4.5 scenario in Tamil Nadu. Future climate projections made through multi-climate model ensemble could increase the plausibility of future climate change impact assessment on crop productivity. The adverse effects of climate change on cereal and legume crop productivity entail the potential adaptation options to ensure food security. [ABSTRACT FROM AUTHOR]

This article examines the structural aspects of climate vulnerabilities in the context of monsoon failures. The paper is based on a unique household survey, conducted in Tamil Nadu, India. The study uses a rural differentiation framework to interrogate unequal vulnerabilities to monsoon failures, based on measures such as Gini coefficients and Lorenz curves of monetary losses. Results show that negative consequences of climate change in general, and monsoon failures in particular, intensify pre-existing socio-economic disparities. When the rural differentiation theory is applied in a broader sense, the analysis shows that landed and farming households have greater exposure and losses. When we move beyond these aggregate categories, the revelation is that households with pre-existing disadvantages such as marginal landholders, subsistence farmers and agricultural workers are more vulnerable. • Global warming affects agriculture in South Asia in a major way through monsoon failures. • Vulnerability to monsoon failures can be measured by associated monetary losses. • These losses are highly unevenly distributed, pointing to significant inequality in impacts. • Landless households, marginal holders and subsistence farmers have higher losses. • Climate change intensifies existing structural disadvantages. [ABSTRACT FROM AUTHOR]

Background: The urban water system is the worst hit in global climate change. Water resilience is the system's ability to retaliate and recover from various water-related disruptions. The present study aims to delineate the water resilience zones in Chennai city, Tamil Nadu, India, by effectively integrating the geographic information system, remote sensing, and analytical hierarchy process (AHP). Methods: The methodology incorporated 15 vital factors. A multi-criteria decision analysis technique was adopted to assign a weight to each parameter using the AHP. A pairwise decision matrix was constructed, parameter's relative importance and the consistency ratio were established. Integration of all maps by weighted overlay analysis technique depicted water resilience intensities of five different classes. Results: Very low, low and moderate water resilience areas accounted for more than three-fourth of the study area. Area Under Curve score (80.12%) depicted the accuracy of the developed model. Sensitivity analysis determined the significance of the parameters in the delineation. The logical structural approach can be employed in other parts of India or elsewhere with modifications. Conclusion: This study is novel in its approach by holistically analyzing water resilience by integrating disruptions related to flood, drought and the city's water infrastructure system's adequacy and efficiency. Researchers and planners can effectively use the study results to ensure resilience as a new perspective on effective water resource management and climate change mitigation. It becomes a decision aid mechanism identifying where the system is vulnerable to potential water-related risks for employing resilience measures. [ABSTRACT FROM AUTHOR]

This study has been undertaken to examine the occurrence of climate change in Tamil Nadu, the southernmost state of India and its impact on rainfall pattern which is a primary constraint for agricultural production. Among the five sample stations examined across the state, the minimum temperature has increased significantly in Coimbatore while the same has decreased significantly in Vellore whereas both minimum and maximum temperatures have increased significantly in Madurai since 1969 with climate change occurring between late 1980s and early 1990s. As a result, the south-west monsoon has been disturbed with August rainfall increasing with more dispersion while September rainfall decreasing with less dispersion. Thus, September, the peak rainfall month of south-west monsoon before climate change, has become the monsoon receding month after climate change. Though there has been no change in the trend of the north-east monsoon, the quantity of October and November rainfall has considerably increased with increased dispersion after climate change. On the whole, south-west monsoon has decreased with decreased dispersion while north-east monsoon has increased with increased dispersion. Consequently, the season window for south-west monsoon crops has shortened while the north-east monsoon crops are left to fend against flood risk during their initial stages. Further, the incoherence in warming, climate change and rainfall impact seen across the state necessitates devising different indigenous and institutional adaptation strategies for different regions to overcome the adverse impacts of climate change on agriculture. [ABSTRACT FROM AUTHOR]