Recent environmental and hydrological research in arid and semi-arid regions, such as Jordan, increasingly emphasizes sustainable water and land management amid increasing pressures from climate change, demographic expansion, and water resources scarcity. Researchers are adopting interdisciplinary approach that combine hydrological modeling, remote sensing, artificial intelligence (AI), and policy analysis to address these challenges effectively.
Hydrological modeling is the cornerstone the interdisciplinary approach. Given the limited surface water and overexploited groundwater in the region, it is important to accurately simulate water movement through the soil system. Tools such as HYDRUS and SWAT are frequently used to model the movement of water and solutes through variably saturated soils. These models help assess risks to groundwater quality, especially in soils macropores and cracks that provide preferential flow pathways, which is essential for the sustainable use of alternative water sources in agriculture.
Remote sensing plays a central role in drought monitoring, climate assessment, and land surface analysis. Satellite-derived datasets, including NASA's POWER data, provide rainfall, temperature, and solar radiation estimates that supplement the limited ground station networks. These data enable the calculation of drought indices like SPI and SPEI across multiple timescales, supporting early warning systems and resource planning. While accuracy varies by climatic zone, particularly under dry conditions, ongoing advancements in remote sensing continue to improve both spatial and temporal resolution.
AI and machine learning are increasingly integrated into environmental studies, offering exciting opportunities, particularly for land use projections and soil property estimation. Algorithms such as random forests, support vector machines, and neural networks classify land cover and predict future land use changes based on historical trends and spatial drivers. These models simulate scenarios of urban and agricultural expansion or deforestation, informing spatial planning and conservation strategies. AI also enhances remote sensing applications, enabling the inference of soil characteristics, such as texture, moisture, and salinity, by analyzing spectral signatures and multi-temporal satellite imagery. These techniques significantly improve the mapping and monitoring of soil health, particularly in data-scarce regions.
Climate change's impact on water infrastructure is another major research focus. Many reservoirs and dams, originally designed based on past hydrological conditions, are becoming less reliable due to shifting rainfall patterns and rising temperatures. Researchers are modeling the performance of these structures under future climate scenarios, highlighting the need for adaptive water storage solutions and improved operational strategies to enhance sustainability.
Environmental governance and regulation are also receiving increased attention. Studies have examined the effects of policies on land and water use, particularly in areas affected by mining or industrial activities. Findings often reveal gaps between environmental legislation and practical enforcement, stemming from weak institutional capacity and low public awareness. Strengthening policy implementation and stakeholder engagement is essential for effective environmental management.
Long-term climate and hydrological data analyses reveal changing rainfall patterns and rising temperatures, which have significant implications for crop cycles, groundwater recharge, and ecosystem stability. Combined with AI-driven scenario modeling, such data support proactive planning in agriculture, urban development, and water allocation.
The growing use of open-source datasets and tools has democratized environmental analysis. Freely accessible climate data, geospatial platforms, and machine learning libraries enable broader participation in research and capacity building, particularly within local universities and government agencies.
Collaborative research, particularly between local and international institutions, has become a hallmark of progress in this field. These partnerships foster technical training, graduate mentorship, and co-developed solutions tailored to regional needs.
Overall, environmental research in water-scarce regions is evolving toward integrated, technology-driven strategies for land and water sustainability. The fusion of AI, remote sensing, hydrological modeling, and governance analysis is fostering more resilient systems that can adapt to future environmental and socio-economic pressures.