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Climatic condition is triggering human health emergencies and earth's surface changes. Anthropogenic activities, such as built-up expansion, transportation development, industrial works, and some extreme phases, are the main reason for climate change and global warming. Air pollutants are increased gradually due to anthropogenic activities and triggering the earth's health. Nitrogen Dioxide (NO2), Carbon Monoxide (CO), and Aerosol Optical Depth (AOD) are truthfully important for air quality measurement because those air pollutants are more harmful to the environment and human's health. Earth observational Sentinel-5P is applied for monitoring the air pollutant and chemical conditions in the atmosphere from 2018 to 2021. The cloud computing-based Google Earth Engine (GEE) platform is applied for monitoring those air pollutants and chemical components in the atmosphere. The NO2 variation indicates high during the time because of the anthropogenic activities. Carbon Monoxide (CO) is also located high between two 1-month different maps. The 2020 and 2021 results indicate AQI change is high where 2018 and 2019 indicates low AQI throughout the year. The Kolkata have seven AQI monitoring station where high nitrogen dioxide recorded 102 (2018), 48 (2019), 26 (2020) and 98 (2021), where Delhi AQI stations recorded 99 (2018), 49 (2019), 37 (2020), and 107 (2021). Delhi, Kolkata, Mumbai, Pune, and Chennai recorded huge fluctuations of air pollutants during the study periods, where ~ 50–60% NO2 was recorded as high in the recent time. The AOD was noticed high in Uttar Pradesh in 2020. These results indicate that air pollutant investigation is much necessary for future planning and management otherwise; our planet earth is mostly affected by the anthropogenic and climatic conditions where maybe life does not exist. [ABSTRACT FROM AUTHOR]

Water scarcity is the most obstacle faced by irrigation water requirements, likewise, limited available meteorological data to calculate reference evapotranspiration. Consequently, the focal aims of the investigation are to assess the potential of machine learning models in forecasting irrigation water requirements (IWR) of snap beans by evolving multi-scenarios of inputs parameters to figure out the impact of meteorological, crop, and soil parameters on IWR. Six models were applied, support vector regressor (SVR), random forest (RF), deep neural networks (DNN), convolutional neural networks (CNN), long short-term memory (LSTM), and Hybrid CNN-LSTM. Ten variables including maximum and minimum temperature, Relative humidity, wind speed, precipitation, root depth, basal crop coefficient, soil evaporation, a fraction of surface wetted and, exposed and soil wetted fraction were used as the input data for models with their combination, 8 input scenarios were designed. Overall models, the best scenario was scenario 4 (relative humidity, wind speed, basal crop coefficient, soil evaporation), however, the best scenario for DNN and RF model was scenario 7 (root depth, basal crop coefficient, soil evaporation, fraction of surface wetted, exposed and soil wetted fraction). While the weakest one was the group of climatic factors in scenario 6 (maximum temperature, minimum temperature, relative humidity, wind speed, and precipitation). Among the models, the hybrid LTSM & CNN was the most accurate and the SVR model had the lowest estimation accuracy. The outcomes of this research work could set up a modeling strategy that would set in motion the improvement of efforts to identify the shortages in IWR forecasting, which sequentially may support alleviation strategies such as policies for sustainable water use and water resources management. The current approach was promising and has research value for other similar regions. [ABSTRACT FROM AUTHOR]

Groundwater is the second largest store of freshwater in the world. The sustainability of the ecosystem is largely dependent on groundwater availability, and groundwater has already been under tremendous pressure to fulfill human needs owing to anthropogenic activities around various parts of the world. The footprints of human activities can be witnessed in terms of looming climate change, water pollution, and changes in available water resources. This paper provides a comprehensive view of the linkage between groundwater, climate system, and anthropogenic activities, with a focus on the Indian region. The significant prior works addressing the groundwater-induced response on the climatic system and the impacts of climate on groundwater through natural and human-instigated processes are reviewed. The condition of groundwater quality in India with respect to various physicochemical, heavy metal and biological contamination is discussed. The utility of remote sensing and GIS in groundwater-related studies is discussed, focusing on Gravity Recovery and Climate Experiment (GRACE) applications over the Indian region. GRACE-based estimates of terrestrial water storage have been instrumental in numerous groundwater studies in recent times. Based on the literature review, the sustainable practices adopted for optimum utilization of groundwater for different purposes and the possible groundwater-based adaptation strategies for climate change are also enunciated. [ABSTRACT FROM AUTHOR]

The increases in extreme rainfall could increase the probable maximum flood (PMF) and pose a severe threat to the critical hydraulic infrastructure such as dams and flood protection structures. This study is conducted to assess the impact of climate change on PMF in a tropical catchment. Climate and inflow data of the Tenmengor reservoir, located in the state of Perak in Malaysia, have been used to calibrate and validate the hydrological model. The projected rainfall from regional climate model is used to generate probable maximum precipitation (PMP) for future periods. A hydrological model was used to simulate PMF from PMP estimated for the historical and two future periods, early (2031 − 2045) and late (2060 − 2075). The results revealed good performance of the hydrological model with Nash–Sutcliffe efficiency, 0.74, and the relative standard error, 0.51, during validation. The estimated rainfall depths were 89.5 mm, 106.3 mm, and 143.3 mm, respectively, for 5, 10, and 50 years of the return period. The study indicated an increase in PMP by 162% to 507% and 259% to 487% during early and late periods for different return periods ranging from 5 to 1000 years. This would cause an increase in PMF by 48.9% and 122.6% during early and late periods. A large increase in PMF indicates the possibility of devastating floods in the future in his tropical catchment due to climate change. [ABSTRACT FROM AUTHOR]

A noticeable increase in drought frequency and severity has been observed across the globe due to climate change, which attracted scientists in development of drought prediction models for mitigation of impacts. Droughts are usually monitored using drought indices (DIs), most of which are probabilistic and therefore, highly stochastic and non-linear. The current research investigated the capability of different versions of relatively well-explored machine learning (ML) models including random forest (RF), minimum probability machine regression (MPMR), M5 Tree (M5tree), extreme learning machine (ELM) and online sequential-ELM (OSELM) in predicting the most widely used DI known as standardized precipitation index (SPI) at multiple month horizons (i.e., 1, 3, 6 and 12). Models were developed using monthly rainfall data for the period of 1949–2013 at four meteorological stations namely, Barisal, Bogra, Faridpur and Mymensingh, each representing a geographical region of Bangladesh which frequently experiences droughts. The model inputs were decided based on correlation statistics and the prediction capability was evaluated using several statistical metrics including mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R), Willmott's Index of agreement (WI), Nash Sutcliffe efficiency (NSE), and Legates and McCabe Index (LM). The results revealed that the proposed models are reliable and robust in predicting droughts in the region. Comparison of the models revealed ELM as the best model in forecasting droughts with minimal RMSE in the range of 0.07–0.85, 0.08–0.76, 0.062–0.80 and 0.042–0.605 for Barisal, Bogra, Faridpur and Mymensingh, respectively for all the SPI scales except one-month SPI for which the RF showed the best performance with minimal RMSE of 0.57, 0.45, 0.59 and 0.42, respectively. [ABSTRACT FROM AUTHOR]