Water is of vital and critical importance to ecosystems and human societies. The effects of human activities on land and water are now large and extensive. These reflect physical changes to the environment. Global change such as urbanization, population growth, socioeconomic change, evolving energy needs, and climate change have put unprecedented pressure on water resources systems. It is argued that achieving water security throughout the world is the key to sustainable development. Studies on holistic view with persistently changing dimensions is in its infancy. This study focuses on narrative review work for giving a comprehensive insight on the concept of water security, its evolution with recent environmental changes (e.g., urbanization, socioeconomic, etc.) and various implications. Finally, it presents different sustainable solutions to achieve water security. Broadly, water security evolves from ensuring reliable access of enough safe water for every person (at an affordable price where market mechanisms are involved) to lead a healthy and productive life, including that of future generations. The constraints on water availability and water quality threaten secured access to water resources for different uses. Despite recent progress in developing new strategies, practices and technologies for water resource management, their dissemination and implementation has been limited. A comprehensive sustainable approach to address water security challenges requires connecting social, economic, and environmental systems at multiple scales. This paper captures the persistently changing dimensions and new paradigms of water security providing a holistic view including a wide range of sustainable solutions to address the water challenges.

Energy expansion and security in the current world scenario focuses on increasing the energy generation capacity and if possible, adopting cleaner and greener energy in that development process. However, too often this expansion and planning alters the landscape and human influence on its surroundings through a very complex mechanism. Resource extraction and land management activity involved in energy infrastructure development and human management of such development systems have long-term and sometimes unforeseen consequences. Although alternative energy sources are being explored, energy production is still highly dependent on fossil fuel, especially in most developing countries. Further, energy production can potentially affect land productivity, land cover, human migration, and other factors involved in running an energy production system, which presents a complex integration of these factors. Thus, land use, energy choices, infrastructure development and the population for which such facilities are being developed must be cognizant of each other, and the interactions between them need to be studied and understood closely. This study strives to analyze the implications of linkages between the energy industry, urbanization, and population and especially highlights processes that can be affected by their interaction. It is found that despite advancement in scientific tools, each of the three components, i.e., population growth, urbanization, and energy production, operates in silos, especially in developing countries, and that this complex issue of nexus is not dealt with in a comprehensive way.

Land use and land cover change (LULCC) is among the most apparent natural landscape processes impacted by anthropogenic activities, particularly in fast-growing regions. In India, at present, due to the impacts of anthropogenic climate change, supplemented by the fast pace of developmental activities, the areas providing the highest agricultural yields are facing the threat of either extinction or change in land use. This study assesses the LULCC in the fastest-changing landscape region of the Indian state of Bihar, District Muzaffarpur. This district is known for its litchi cultivation, which, over the last few years, has been observed to be increasing in acreage at the behest of a decrease in natural vegetation. In this study, we aim to assess the past, present and future changes in LULC of the Muzaffarpur district using support vector classification and CA-ANN (cellular automata-artificial neural network) algorithms. For assessing the present and past LULC of the study area, we used Landsat Satellite data for 1990, 2000, 2010, and 2020. It was observed that between 1990 and 2020, the area under vegetation, wetlands, water body, and fallow land decreased by 44.28%, 34.82%, 25.56%, and 5.63%, respectively. At the same time, the area under built-up, litchi plantation, and cropland increased by 1451.30%, 181.91%, and 5.66%, respectively. Extensive ground truthing was carried out to assess the accuracy of the LULC for 2020, whereas historical google earth images were used for 1990, 2000, and 2010, through the use of overall accuracy and kappa coefficient indices. The kappa coefficients for the final LULC for the years 1990, 2000, 2010, and 2020 were 0.79, 0.75, 0.87, and 0.85, respectively. For forecasting the future LULC, first, the LULC of 1990 and 2010 were used to predict the landscape for 2020 using the CA-ANN model. After calibrating and validating the CA-ANN outputs, LULC for 2030 and 2050 were generated. The generated future LULC scenarios were validated using kappa index statistics by comparing the forecast outcomes with the original LULC data for 2020. It was observed that in both 2030 and 2050, built-up and vegetation would be the major transitioning LULC. In 2030 and 2050, built-up will increase by 13.15% and 108.69%, respectively, compared to its area in 2020; whereas vegetation is expected to decrease by 14.30% in 2030 and 32.84% in 2050 compared to its area in 2020. Overall, this study depicted a decline in the natural landscape and a sudden increase in the built-up and cash-crop area. If such trends continue, the future scenario of LULC will also demonstrate the same pattern. This study will help formulate better land use management policy in the study area, and the overall state of Bihar, which is considered to be the poorest state of India and the most vulnerable to natural calamities. It also demonstrates the ability of the CA-ANN model to forecast future events and comprehend spatiotemporal LULC dynamics.