Coral diseases are major contributors to coral mortality in Shingle Island, Gulf of Mannar, southeastern India

The present study reports coral mortality, driven primarily by coral diseases, around Shingle Island, Gulf of Mannar (GOM), Indian Ocean. In total, 2910 colonies were permanently monitored to assess the incidence of coral diseases and consequent mortality for 2 yr. Four types of lesions consistent with white band disease (WBD), black disease (BD), white plaque disease (WPD), and pink spot disease (PSD) were recorded from 4 coral genera: Montipora, Pocillopora, Acropora, and Porites. Porites were affected by 2 disease types, while the other 3 genera were affected by only 1 disease type. Overall disease prevalence increased from 8% (n = 233 colonies) to 41.9% (n = 1219) over the 2 yr study period. BD caused an unprecedented 100% mortality in Pocillopora, followed by 20.4 and 13.1% mortality from WBD in Montipora and Acropora, respectively. Mean disease progression rates of 0.8 +/- 1.0 and 0.6 +/- 0.5 cm mo(-1) over live coral colonies were observed for BD and WBD. Significant correlations between temperature and disease progression were observed for BD (r = 0.86, R-2 = 0.75, p < 0.001) and WBD (R-2 = 0.76, p < 0.001). This study revealed the increasing trend of disease prevalence and progression of disease over live coral in a relatively limited study area; further study should investigate the status of the entire coral reef in the GOM and the role of diseases in reef dynamics.

Multi-site downscaling of maximum and minimum daily temperature using support vector machine

Climate change impact assessment studies involve downscaling large-scale atmospheric predictor variables (LSAPVs) simulated by general circulation models (GCMs) to site-scale meteorological variables. This article presents a least-square support vector machine (LS-SVM)-based methodology for multi-site downscaling of maximum and minimum daily temperature series. The methodology involves (1) delineation of sites in the study area into clusters based on correlation structure of predictands, (2) downscaling LSAPVs to monthly time series of predictands at a representative site identified in each of the clusters, (3) translation of the downscaled information in each cluster from the representative site to that at other sites using LS-SVM inter-site regression relationships, and (4) disaggregation of the information at each site from monthly to daily time scale using k-nearest neighbour disaggregation methodology. Effectiveness of the methodology is demonstrated by application to data pertaining to four sites in the catchment of Beas river basin, India. Simulations of Canadian coupled global climate model (CGCM3.1/T63) for four IPCC SRES scenarios namely A1B, A2, B1 and COMMIT were downscaled to future projections of the predictands in the study area. Comparison of results with those based on recently proposed multivariate multiple linear regression (MMLR) based downscaling method and multi-site multivariate statistical downscaling (MMSD) method indicate that the proposed method is promising and it can be considered as a feasible choice in statistical downscaling studies. The performance of the method in downscaling daily minimum temperature was found to be better when compared with that in downscaling daily maximum temperature. Results indicate an increase in annual average maximum and minimum temperatures at all the sites for A1B, A2 and B1 scenarios. The projected increment is high for A2 scenario, and it is followed by that for A1B, B1 and COMMIT scenarios. Projections, in general, indicated an increase in mean monthly maximum and minimum temperatures during January to February and October to December.

Performance evaluation of AR4 Climate Models in simulating daily precipitation over the Indian region using skill scores

The ability of Coupled General Circulation Models (CGCMs) participating in the Intergovernmental Panel for Climate Change's fourth assessment report (IPCC AR4) for the 20th century climate (20C3M scenario) to simulate the daily precipitation over the Indian region is explored. The skill is evaluated on a 2.5A degrees x 2.5A degrees grid square compared with the Indian Meteorological Department's (IMD) gridded dataset, and every GCM is ranked for each of these grids based on its skill score. Skill scores (SSs) are estimated from the probability density functions (PDFs) obtained from observed IMD datasets and GCM simulations. The methodology takes into account (high) extreme precipitation events simulated by GCMs. The results are analyzed and presented for three categories and six zones. The three categories are the monsoon season (JJASO - June to October), non-monsoon season (JFMAMND - January to May, November, December) and for the entire year (''Annual''). The six precipitation zones are peninsular, west central, northwest, northeast, central northeast India, and the hilly region. Sensitivity analysis was performed for three spatial scales, 2.5A degrees grid square, zones, and all of India, in the three categories. The models were ranked based on the SS. The category JFMAMND had a higher SS than the JJASO category. The northwest zone had higher SSs, whereas the peninsular and hilly regions had lower SS. No single GCM can be identified as the best for all categories and zones. Some models consistently outperformed the model ensemble, and one model had particularly poor performance. Results show that most models underestimated the daily precipitation rates in the 0-1 mm/day range and overestimated it in the 1-15 mm/day range.