http://nopr.niscpr.res.in/handle/123456789/65417 Mon, 12 Oct 2026 16:14:10 GMT 2026-10-12T16:14:10Z http://nopr.niscpr.res.in/handle/123456789/65425 Title: Impact of changing climate on wind power potential at proposed Indian offshore sites using quantile mapping Authors: Wadalkar, A S; Deo, M C Abstract: Energy extraction from offshore wind is of high priority in India due to the vigorous efforts of the government to replace fossil fuels with renewable ones. There is, however concern about the future sustainability of such power projects against the backdrop of climate change. It is therefore necessary to assess likely changes in the wind potential of identified Indian coastal areas. While previous studies do exist, an update is needed given the availability of more advanced General Circulation Models (GCMs), reanalysis wind data, and LiDAR (Light Detection And Ranging)-based wind speed measurements. This study, therefore, aims to understand future changes in the monthly wind potential at identified offshore sites off the coasts of Gujarat and Tamil Nadu based on the latest GCM suite of models from the Climate Model Inter-comparison Project (CMIP) as well as a high-resolution reanalysis wind data. It compares the results obtained from the CMIP6 suite of models with those from earlier CMIP5 models across three time slices representing the past (1989-2005), near future (2017-2033), and far future (2034-2050) conditions. The seasonal and spatial variations of wind power are explored. The multi-model ensemble median of CMIP6 GCMs, which demonstrates greater agreement with in-situ winds than that of CMIP5, suggests an increase in wind power density during the monsoon season and a decrease in winter at Gujarat. However, at Tamil Nadu, it shows a declining trend from 2017-2050, though winter densities are expected to exceed historical values. Page(s): 95-108 Fri, 01 Mar 2024 00:00:00 GMT http://nopr.niscpr.res.in/handle/123456789/65425 2024-03-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/65424 Title: Semiarid mangrove species classification using machine learning algorithms and visible UAV data Authors: Torres-Aguirre, E; Flores-de-Santiago, F; Valderrama-Landeros, L; Amezcua, F; Flores-Verdugo, F Abstract: Remote sensing studies have emerged as a crucial tool for monitoring and managing mangrove forests, with Unmanned Aerial Vehicles (UAV) being a popular platform for data collection. UAV surveys offer a non-invasive and efficient means of gathering high-resolution data on mangrove forests, enabling detailed analysis of their structure and health. However, the cost of UAV platforms and software can be a major drawback, particularly for small-scale projects. Despite these difficulties, this research provides valuable insights as it aimed to assess seasonal variations in mangrove forest vegetation at a local scale during the winter, dry, and rainy seasons utilizing solely visible data from a conventional UAV camera and processed through three machine learning algorithms and an 11×11 Lee Sigma speckle filter. The Support Vector Machine (SVM) algorithm outperformed the Random Forest (RF) and Neural Network (NN) algorithms in terms of overall accuracy during the dry season. Specifically, the SVM algorithm achieved an accuracy of 85 % for the dry, 61 % for the winter, and 57 % for the rainy season. In comparison, the RF algorithm achieved accuracies of 80 % for the dry, 77 % for the rainy, and 63 % for the winter season, and the NN algorithm recorded accuracies of 71 % for the dry, 65 % for the rainy, and 57 % for the winter season. These findings indicate that the SVM algorithm is a more effective approach for the dry season. Importantly, this study demonstrates the potential of machine learning algorithms in classifying mangrove species using visible UAV data, which could substantially enhance the efficiency and cost-effectiveness of mangrove forest management. Page(s): 109-118 Fri, 01 Mar 2024 00:00:00 GMT http://nopr.niscpr.res.in/handle/123456789/65424 2024-03-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/65423 Title: Phytohormones for enhanced biomass production of marine microalgae Authors: Subramanian, A; Joseph, Imelda Abstract: Microalgae, abundant in essential nutrients like vitamins, proteins, lipids, and pigments, hold immense potential for diverse applications spanning food, biofuels, pharmaceuticals, and environmental solutions. Present study focuses assessment of cell multiplication in microalgae Chlorella vulgaris, Nannochloropsis salina and Isochrysis galbana, widely used in aquaculture hatcheries with Indole Acetic Acid (IAA), Gibberellic Acid (GA), and zeatin at 1.0 mg/L, 0.1 mg/L, and 0.01 mg/L in 16 days culture period. The study results revealed that the microalgal biomass can be increased by inducing phytohormones at different concentrations. With zeatin, the cell count observed in C. vulgaris and N. salina was more than that in control at all three concentrations. For I. galbana, except in 1.0 mg/L, the cell count was higher than that in control. Chlorella vulgaris attained the maximum cell density at 0.1 mg/L of zeatin (2.5×105 cells/ml) and with GA, the cell count attained was highest at 0.01 mg/L (2.25×105 cells/ml). Nannochloropsis salina also showed higher cell count than the control at all three concentrations of GA. Isochrysis galbana showed 1.43×105 & 0.61×105 cells/ml cell count with 0.01 mg/L of GA. The maximum cell count recorded for C. vulgaris was 2.29×105 cells/ml at 0.01 mg/L of IAA. Similarly, N. salina showed the highest growth in 0.01 mg/L of IAA (2.16×105 cells/ml), and the growth was lower than in control at 1 mg/L (1.25×105 cells/ml). Isochrysis galbana recorded higher cell count than in control (0.57×105 cells/ml) in 0.1 mg/L (1.9×105 cells/ml) and 0.01 mg/L concentrations of IAA. Study suggests the possibility and potential of phytohormones in the enhanced production of microalgae for various applications. Page(s): 119-125 Fri, 01 Mar 2024 00:00:00 GMT http://nopr.niscpr.res.in/handle/123456789/65423 2024-03-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/65422 Title: Bloom of Trichodesmium species and its impact on coastal water quality and plankton dynamics at Kalpakkam, Southeast coast of India Authors: Sahu, G; Sathishkumar, R S; Mohanty, A K; Suriyaprakash, R; Arunachalam, K D; Sarangi, R K; Subramanian, V; Venkatesan, R Abstract: A massive bloom of Trichodesmium (T. erythraeum and T. thiebautii) was observed in the coastal waters of Kalpakkam, southeast India. The bloom formed thick and greenish-yellow patches on the sea surface during the post-monsoon season (March 2020). Water and plankton samples were collected in Coastal Near-shore (CNW) and Coastal Offshore Waters (COW) at 12 stations. Nitrate and phosphate concentrations were found distributed evenly in the entire study site. Concentration of ammonia and total nitrogen found increased significantly during the bloom period as compared to non-bloom periods. The blooming species T. erythraeum and T. thiebautii contributed 53.98 % and 43.82 % to the region’s phytoplankton population density. Phytoplankton and zooplankton species richness was relatively low during the study. Results indicated that the higher temperature (≥ 29 °C) and constant salinity (≥ 33 PSU) are the conducive factors for Trichodesmium growth. The MODIS Aqua satellite-derived Sea Surface Temperature (SST) and chlorophyll data further supported the in-situ observations. Page(s): 126-141 Fri, 01 Mar 2024 00:00:00 GMT http://nopr.niscpr.res.in/handle/123456789/65422 2024-03-01T00:00:00Z