Summary: Deserts represent key carbon reservoirs, yet as these systems are threatened this has implications for biodiversity and climate change. This review focuses on how these changes affect desert ecosystems, particularly plant root systems and their impact on carbon and mineral nutrient stocks. Desert plants have diverse root architectures shaped by water acquisition strategies, affecting plant biomass and overall carbon and nutrient stocks. Climate change can disrupt desert plant communities, with droughts impacting both shallow and deep‐rooted plants as groundwater levels fluctuate. Vegetation management practices, like grazing, significantly influence plant communities, soil composition, root microorganisms, biomass, and nutrient stocks. Shallow‐rooted plants are particularly susceptible to climate change and human interference. To safeguard desert ecosystems, understanding root architecture and deep soil layers is crucial. Implementing strategic management practices such as reducing grazing pressure, maintaining moderate harvesting levels, and adopting moderate fertilization can help preserve plant–soil systems. Employing socio‐ecological approaches for community restoration enhances carbon and nutrient retention, limits desert expansion, and reduces CO₂ emissions. This review underscores the importance of investigating belowground plant processes and their role in shaping desert landscapes, emphasizing the urgent need for a comprehensive understanding of desert ecosystems.

Global drylands, covering about 41% of Earth’s surface and inhabited by 38% of the world’s population, are facing the stark challenges of water scarcity, low water productivity, and food insecurity. This paper highlights the major constraints to agricultural productivity, traditional irrigation scheduling methods, and associated challenges, efforts, and progress to enhance water use efficiency (WUE), conserve water, and guarantee food security by overviewing different smart irrigation approaches. Widely used traditional irrigation scheduling methods (based on weather, plant, and soil moisture conditions) usually lack important information needed for precise irrigation, which leads to over- or under-irrigation of fields. On the other hand, by using several factors, including soil and climate variation, soil properties, plant responses to water deficits, and changes in weather factors, smart irrigation can drive better irrigation decisions that can help save water and increase yields. Various smart irrigation approaches, such as artificial intelligence and deep learning (artificial neural network, fuzzy logic, expert system, hybrid intelligent system, and deep learning), model predictive irrigation systems, variable rate irrigation (VRI) technology, and unmanned aerial vehicles (UAVs) could ensure high water use efficiency in water-scarce regions. These smart irrigation technologies can improve water management and accelerate the progress in achieving multiple Sustainable Development Goals (SDGs), where no one gets left behind.

The paucity of in-situ records, particularly in the glaciated mountainous region, is an obstacle in cryosphere ecology and environmental studies. Generally, available gauge station data is fragmented and covers valleys; thus, the use of gridded precipitation products (GPPs) is crucial in such complex terrains. However, these GPPs suffer from systematic biases and uncertainties owing to parameterization deficiencies. Therefore, the main goal of this research is to systematically evaluate the long-term performance and differences of the newly launched MSWEP in comparison to APHRO, CHIRPS, ERA-5, and PGMFD over the transboundary region of Indo-Pak (1981–2009) at spatial (whole to sub-basins) and temporal (daily to seasonal) scales. Findings reveal (1) overall, five GPPs produced well annual spatial precipitation variability with high magnitudes in the northwestern and low in the northeastern region. (2) The estimations from GPPs also divulged better correlation with in-situ observations (MSWEP = 0.86, APHRO = 0.76, ERA-5 = 0.81, CHIRPS = 0.57 and PGMFD = 0.68) at daily span. Better performance was observed during the monsoon compared to winter and pre-monsoon seasons. (3) Lately, estimates from MSWEP are more reliable for all the seasons, especially in the winter season, with the highest CC (0.90) and lowest relative bias (3.03%). (4) All GPPs (excluding ERA-5) overestimated light precipitation (0–1 mm/day) and underestimated moderate to heavy precipitation, in contrast to the ERA-5 that tended to underestimate the light but overestimate moderate (1–20 mm/day) and heavy precipitation (>20 mm/day) events. The CHIRPS was less accurate in detecting most of the precipitation events. The MSWEP product captured all precipitation intensities more accurately than other GPPs. The current research indicates considerable implications for product improvement and data users for choosing better alternative precipitation data at a local scale.

Groundwater is a precious natural resource that is vital to various aspects of life. Punjab is experiencing groundwater stress due to urbanization and population growth, leading to overuse and reduced aquifer recharge. Sustainable groundwater supplies can only be created through better management and artificial recharge techniques. This study uses multi-influencing factor, literature-based, and combined techniques to identify and characterize groundwater-managed aquifer recharge potential sites (GWMARPSs) in Punjab. There are limitations to the previous work in this field, and these factors have not been used to estimate GWRPSs in the study area. The study uses GIS and RS techniques to overlay twelve geo-informative layers, with rainfall being the most significant factor. High-quality data and observations from the field are incorporated into the model. The study classifies the GWMARPSs into five categories, with Punjab having 0.34%, 13.29%, 60.68%, 25.26%, and 0.43% of the least, poorly, moderately, well-, and highly suitable sites. Punjab’s southern regions are least suitable for recharge, while some areas in eastern and northern Punjab are well-suited for recharge. Alluvial plains, valleys, low-lying areas, and areas with volcanic landforms are classified as least to poorly suitable zones. Model predictions are validated using piezometric level data and ROC and exhibit good performance (AUC, 0.74). This study could serve as a baseline for future groundwater research.