Recently, the use and management of water resources have become a critical issue, especially in arid and semi-arid surroundings. In such areas, the importance of mapping groundwater potential zones is unmeasurable since water is a critical human resource. Focused on the semi-arid region of Kuwait, our research integrates Google Earth Engine and multi-criteria decision-making (MCDM), offering a robust admission to identifying groundwater potential zones. To this end, thirteen criteria including geology, slope, rainfall, elevation, Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Land Surface Temperature (LST), drainage density, Land Use and Land Cover (LULC), lineament density, topographic wetness index (TWI), geomorphology, and the water table were integrated to reach a wise decision through AHP-MCDM. Our findings revealed that Kuwait’s groundwater potential zones are extremely well suited to the country’s north and east. Appropriate potential zones with high and moderate degrees encompass 12% and 37%, respectively, while extremely high suitability is less than 1.5%. These findings are based on the input data (mainly rainfall, NDVI, NDWI, and LST). The likelihood of groundwater potential is higher in the lower regions of the country. Over 7500 km²—nearly 50% of the total area—are classified as low potential. Furthermore, Our research shows that combining more factors can increase precision while ensuring the stability of the detection process of GWPZs. Around 242 km² as promising targets for future groundwater exploitation within Kuwait. Rigorously validated through on-site field observations and corroborated by maps showing the distribution of productive groundwater wells in Kuwait, our results exhibit a considered alignment. It is recommended that we extend the application of our methodology beyond the borders of the studied terrain and urge decision-makers to consider our findings in their strategic planning processes.

Flash floods from rainstorms pose a severe natural hazard, especially dangerous in arid regions. This has occurred multiple times in Kuwait: in 1967, 1997, 2009, 2013, 2018, and 2020. For example, about 300 mm of rain beset the State of Kuwait on the 14th and 15th of November 2018. Subsequently, Sabah Al-Ahmad City was declared an environmental disaster zone, grappling with extensive water accumulation in its roads, urban zones, and surrounding valleys. This study focuses on (i) monitoring the hydro-geomorphological impacts of the floods on Sabah Al-Ahmad City and (ii) assessing the strategic plan to diminish flood risks, aiming to prevent a recurrence akin to the catastrophic events during rainstorms. (iii) proposing a protection plan for the city. The research employs various methodologies, including drones (UAV), field surveys, hydrological modelling, digital elevation model analysis, and spatial analysis of multi-spectral satellite imagery. These methods are instrumental in comprehending how flash floods impact the sustainable development of urban areas in Kuwait. Our approach is designed to support decision-makers and urban planners, enabling them to proactively identify flood-prone zones before future urban development initiatives in Kuwait.

Understanding land-use and land-cover (LULC) dynamics within protected areas (PAs) is fundamental for assessing conservation effectiveness and ecosystem resilience under increasing anthropogenic and climatic pressures. This study examines the spatio-temporal evolution of LULC across Bavaria’s protected areas between 2000 and 2023 by integrating categorical land-cover data, satellite-derived vegetation indices, and environmental drivers. Annual LULC changes were first quantified using MODIS MCD12Q1 land-cover classifications to evaluate class persistence, transitions, and area trajectories and were subsequently interpreted alongside 16-day MODIS NDVI and SAVI composites to assess associated vegetation greening and browning trends. Ecological stability was characterized by using class-level persistence indicators, coefficients of variation (CVs), and linear trend slopes. The results reveal a marked greening signal after 2010, coinciding with pronounced land-cover transitions, including a decline in evergreen needleleaf forests (−480.6 km²; −32.2%) and substantial expansion of deciduous broadleaf forests (+390.8 km²; +106.1%) and grasslands (+275.8 km²; +28.4%), while wetlands experienced a severe contraction (−203.4 km²; −73.7%), indicating heightened hydrological sensitivity within protected ecosystems. Correlation analysis further indicates that anthropogenic pressure, quantified using the human footprint index, remains a dominant driver of change in croplands and urban areas, even within legally protected boundaries. Overall, this study demonstrates that vegetation trends, land-cover transitions, climatic exposure, and human pressure jointly shape ecological stability in protected areas, highlighting the value of an integrated indicator-based framework.

In response to global energy depletion, this study addresses the critical challenge of selecting optimal wind farm sites in Kuwait, a key pillar of the “New Kuwait” 2035 vision for 15% renewable energy integration. A robust, hybrid Multi-Criteria Decision-Making (MCDM) framework was developed, integrating Fuzzy Analytical Hierarchy Process (FAHP) with Entropy-based objective weighting and Type-2 Fuzzy logic within a GIS environment. This approach effectively mitigates expert subjectivity and manages the high “Footprint of Uncertainty” inherent in complex desert environments. Twenty-six technical, environmental, and socioeconomic criteria—including wind power density, proximity to the national grid, and oil field restrictions—were analyzed at a modern utility-scale hub height of 100 m. The spatial results identified approximately 1444 km² (8.6%) of Kuwait’s territory as ' Highly Suitable’ for wind development, primarily concentrated in the western (Al-Jahra) and northern (Al-Abali) corridors. Validation against existing Shagaya Renewable Energy Park and Global Wind Atlas data confirmed a strong spatial correlation (R ² = 0.84). The identified zones represent a potential generation capacity of 2500 MW, providing a data-driven roadmap for national energy planners to meet sustainability targets while minimizing land-use conflicts with oil production and urban expansion.