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In the face of rising greenhouse gas concentrations, our study investigates the intricate regional dynamics of hydrological responses across three vital river basins of the Western Ghats of India. Employing advanced eXtreme Gradient Boosting (XGBoost) ensemble models based on Coupled Model Intercomparison Project (CMIP6) data, the article explores the anticipated changes in the climate variables under two future scenarios. The findings reveal a compelling narrative of temperature fluctuations, with increased warming in future decades from November to June ushering in warmer winters and extended summer seasons. These climatic shifts carry profound implications for rainfall patterns, potentially disrupting rainfall during the pivotal months of June and July up to the decade 2030s, with a more pronounced increase in the Purna River Basin (PRB) after the decade 2050s. The projected future climate scenarios indicate that the Vamanapuram River Basin (VRB) and PRB will experience contrasting patterns of dry and wet events, with the VRB facing severe to extreme dry and the PRB witnessing increased moderate to extreme wet events under high-emission scenarios. Additionally, the PRB may experience the paradox of increasing wetness and aridity. These insights provide crucial guidance for policy formulation and adaptation measures to safeguard agriculture and other vital sectors in the face of evolving climate conditions. [ABSTRACT FROM AUTHOR]

The sun is an abundant source of energy, and solar energy has been at the forefront of the renewable energy sector for years. A way to convert it into electricity is by the use of solar cells. Multiple solar cells, connected to each other, create solar panels, which in their turn, are connected in a solar string, and they create solar farms. These structures are extremely efficient in electricity production, but also, cells are fragile in nature and delicate to environmental conditions, which is the reason why some of them show discrepancies and are called defective. In this research, a thermal camera mounted on a drone has been used for the first time in the solar farm operating conditions of India in order to capture images of the solar field and investigate solar panels for defective cells and create an orthomosaic image of the entire area. This procedure next year will be established on an international scale as a best practice example for commercialization, providing effortless photovoltaic monitoring and maintenance planning. For this process, an open source software WebODM has been used, and the entire field was digitized so as to identify the location of defective panels in the field. This software was the base in order to provide and analyze a digital twin of the studied area and the included photovoltaic panels. The defects on solar cells were identified with the use of thermal bands, which record and point out their temperature of them, whereas anomalies in the detected temperature in defective solar cells were captured using thermal electromagnetic waves, and these areas are mentioned as hotspots. In this research, a total number of 232.934 solar panels were identified, and 2481 defective solar panels were automatically indicated. The majority of the defects were due to manufacturing failure and normal aging, but also due to persistent shadowing and soiling from aerosols and especially dust transport, as well as from extreme weather conditions, including hail. The originality of this study relies on the application of the proposed under development technology to the specific conditions of India, including high photovoltaic panels wear rates due to extreme aerosol loads (India presents one of the highest aerosol levels worldwide) and the monsoon effects. The ability to autonomously monitor solar farms in such conditions has a strong energy and economic benefit for production management and for long-term optimization purposes.

Coastal erosion is a global concern that has been augmenting due to the natural evolution of beaches, human activities and sea-level rise. One of the eco-friendly shore protection methods is to dissipate the wave energy by constructing offshore breakwaters. Conical pile head breakwater (CPHB) is one of the eco-friendly innovative offshore structures consisting of closely spaced piles with an enlarged cross-sectional area (conical pile head) in the vicinity of the free surface. In the present study, perforations are incorporated over the conical pile head to achieve higher efficiency by promoting energy dissipation. The influence of the perforations on the performance characteristics, namely wave transmission (Kt), wave reflection (Kr) and energy dissipation (Kd) of the perforated CPHB is comprehensively investigated through physical model studies. The effect of perforations and their distribution around the pile head (Pa), percentage of perforation (P) and size of perforations (S/D) on the wave attenuation characteristics are evaluated to arrive at an optimum configuration. The study is carried out under monochromatic waves of varying wave height (0.06–0.16 m) and wave period (1.4–2 s) at different depths of water (0.35, 0.40 and 0.45 m). A minimum Kt of 0.58 associated with Kr of 0.26 and Kd of 0.78 is obtained with an optimum configuration of Pa = 50%, P = 19.2% and S/D = 0.25. The Kt of the proposed CPHB is about 19 to 35% lesser than that of the perforated hollow pile breakwater under matching test conditions. Overall, providing the perforations is found to be effective in enhancing the wave attenuation capability by up to 12.4%. Further, empirical equations are formulated and validated with the experimental data. The empirical equations estimate the Kt and Kr values accurately with a high coefficient of determination (R 2 ≥ 0.90). [ABSTRACT FROM AUTHOR]

The popularity of cutting‐edge machine learning ensemble approaches has solved many climate change research and prediction issues. The six top‐performing GCMs obtained from Technique for Order Preference by Similarity to an Ideal Solution were ensembled using seven machine learning ensemble methods such as Random Forest Regressor (RFR), Support Vector Regressor (SVR), Linear Regression (LR), Adaptive Boosting Regressor (AdaBoost), Extreme Gradient Boosting Regressor (XGBR), Extra Tree Regressor (ETR), Multi‐Layer Perceptron neural network (MLP) and simple Arithmetic Mean (AM) over the diverse geo‐climatic basins. Precipitation is best simulated by EC‐Earth3 and BCC‐CSM2‐MR. Maximum temperature by MPI‐ESM1‐2‐HR, EC‐Earth3‐Veg, INM‐CM5‐0 and MPI‐ESM1‐2‐LR. Minimum temperature by INM‐CM5‐0 and MPI‐ESM1‐2‐LR model. The MME of XGBR and RFR stand out for their superior performance across all six basins, with exceptional performance over the per‐humid basins, while AdaBoost, SVR and the AM underperform. Examining the interseasonal variability of the simulated MMEs over the basins highlights the reliability of these MME models. The anticipated change in maximum and minimum temperature in the SSP245 and SSP585 in the future horizon corroborates the undeniable rise in temperature by all the MMEs with a dramatic change in future temperature in AM and AdaBoost in precipitation with a factor of two rises in the far future over the recent past. Though climate change is expected to increase precipitation, atmospheric stabilization over the Ghats will affect the spatiotemporal distribution of precipitation. We recommend a comprehensive testing and validation approach to generate ensembles in regional investigations involving complicated and diverse precipitation mechanisms. [ABSTRACT FROM AUTHOR]