The Earth’s water resources, totalling 1.386 billion cubic kilometres, predominantly consist of saltwater in oceans. Groundwater plays a pivotal role, with 99% of usable freshwater supporting 1.5–3 billion people as a drinking water source and 60–70% for irrigation. Climate change, with temperature increases and altered precipitation patterns, directly impacts groundwater systems, affecting recharge, discharge, and temperature. Hydrological models are crucial for assessing climate change effects on groundwater, aiding in management decisions. Advanced hydrological models, incorporating data assimilation and improved process representation, contribute to understanding complex systems. Recent studies employ numerical models to assess climate change impacts on groundwater recharge that could help in the management of groundwater. Groundwater vulnerability assessments vary with the spatial and temporal considerations, as well as assumptions in modelling groundwater susceptibility. This review assesses the vulnerability of groundwater to climate change and stresses the importance of accurate assessments for sustainable water resource management. It highlights challenges in assumptions related to soil and aquifer properties, multiple stressors, adaptive capacity, topography and groundwater contamination processes, gradual sea level rise scenarios, and realistic representations of the region of study. With the advancements in hydrological modelling, including the integration of uncertainty quantification and remote sensing data, artificial intelligence could assist in the efforts to improve models for assessing the impacts of climate change on hydrological modelling.

The aim of this study was to determine the elemental and nutritive values of leaf parts of 10 selected wild medicinal plants, Acer pictum, Acer caecium, Betula utilis, Oxalis corniculata, Euphorbia pilosa, Heracleum lanatum, Urtica dioica, Berberis lycium, Berberis asiaticaand, and Quercus ilex, collected from the high hills of the Chitkul range in district Kinnaur, Western Himalaya. The nutritional characteristics of medicinal plant species were analyzed by using muffle furnace and micro-Kjeldahl methods, and the mineral content in plants was analyzed through atomic absorption spectrometry. The highest percentage of used value was reported in Betula utilis (0.42) and the lowest in Quercus ilex (0.17). In this study, it was found that new generations are not much interested in traditional knowledge of ethnomedicinal plants due to modernization in society. Therefore, there is an urgent need to document ethnomedicinal plants along with their phytochemical and minerals analysis in study sites. It was found that rural people in western Himalaya are dependent on wild medicinal plants, and certain steps must be taken to conserve these plants from extinction in the cold desert of Himalayan region. They are an alternative source of medicine because they contain saponin, alkaloid, and flavonoid etc. as well as minerals. The leaves used for analysis possesses good mineral content, such as Na, N, K, P, Zn, Fe, Cu, Mn, Ca, Mg, and S. Hence, in the current study it was observed that medicinal plants are not only used for therapeutic purposes, but they can also be used as nutritional supplements.

Predicting crop yield at preharvest is pivotal for agricultural policy and strategic decision making. Despite global agricultural targets, labour-intensive surveys for yield estimation pose challenges. Using unmanned aerial vehicle (UAV)-based multispectral sensors, this study assessed crop phenology and biotic stress conditions using various spectral vegetation indices. The goal was to enhance the accuracy of predicting key agricultural parameters, such as leaf area index (LAI), soil and plant analyser development (SPAD) chlorophyll, and grain yield of maize. The study’s findings demonstrate that during the kharif season, the wide dynamic range vegetation index (WDRVI) showcased superior correlation coefficients (R), coefficients of determination (R²), and the lowest root mean square errors (RMSEs) of 0.92, 0.86, and 0.14, respectively. However, during the rabi season, the atmospherically resistant vegetation index (ARVI) achieved the highest R and R² and the lowest RMSEs of 0.83, 0.79, and 0.15, respectively, indicating better accuracy in predicting LAI. Conversely, the normalised difference red-edge index (NDRE) during the kharif season and the modified chlorophyll absorption ratio index (MCARI) during the rabi season were identified as the predictors with the highest accuracy for SPAD chlorophyll prediction. Specifically, R values of 0.91 and 0.94, R² values of 0.83 and 0.82, and RMSE values of 2.07 and 3.10 were obtained, respectively. The most effective indices for LAI prediction during the kharif season (WDRVI and NDRE) and for SPAD chlorophyll prediction during the rabi season (ARVI and MCARI) were further utilised to construct a yield model using stepwise regression analysis. Integrating the predicted LAI and SPAD chlorophyll values into the model resulted in higher accuracy compared to individual predictions. More exactly, the R² values were 0.51 and 0.74, while the RMSE values were 9.25 and 6.72, during the kharif and rabi seasons, respectively. These findings underscore the utility of UAV-based multispectral imaging in predicting crop yields, thereby aiding in sustainable crop management practices and benefiting farmers and policymakers alike.