Non-stationary drought patterns in hyper-arid regions: Spatiotemporal and multi-timescale drought analysis.

Droughts rank among the most devastating natural disasters, particularly in arid regions such as Oman. However, traditional drought assessment based on stationarity may not be applicable under climate change. Moreover, most previous studies have been point-based, relying on station observations without capturing the spatial variability of drought.

Climate-driven projections of cyanobacterial harmful algal bloom expansion in coastal waters.

Cyanobacterial harmful algal blooms (CyanoHABs) in coastal waters are a growing ecological and environmental concern, especially in climate-vulnerable regions. While many studies have explored historical variations and short-term forecasting of CyanoHABs, this study extends projections into the coming decades, focusing on Oman's vulnerable coastal areas under future climate change scenarios. By integrating General Circulation Models (GCMs) outputs with machine learning and deep learning models, this research aims to enhance predictive accuracy and assess long-term CyanoHAB impacts.

Future flood susceptibility mapping under climate and land use change.

Floods are a significant natural hazard, causing severe damage. Understanding how climate change and land use and land cover (LULC) changes influence flood patterns is crucial for developing sustainable management strategies. This research aims to develop flood susceptibility maps considering the impacts of climate change and land use changes, providing insights into risks from urbanization and climate shifts.

Improving index-based coastal vulnerability assessment using machine learning in Oman.

Coastal vulnerability assessments are crucial for evaluating the potential impacts of environmental hazards. Traditional methods that typically rely on index-based approaches are often limited by their inability to account for the relative importance of individual parameters. This study integrated machine learning models (Random Forest and XGBoost), which were optimized through Particle Swarm Optimization, with an index-based method to determine the weights of vulnerability parameters using feature importance analysis.

Hybrid deep learning downscaling of GCMs for climate impact assessment and future projections in Oman.

Accurate downscaling of global circulation models (GCMs) is critical for assessing the impacts of climate change and water resources management. In this research, Fourteen GCMs were evaluated through a Taylor diagram, including EC-Earth3-CC, ACCESS-CM2, AWI-ESM-1-1-LR, BCC-ESM1, CanESM5, IITM-ESM, MPI ESM1-2HR, INM-CM5-0, IPSL-CM5A2-INCA, KIOST-ESM, NorCPM1, NorESM2-MM, TaiESM1, and ACCESS-ESM1-5. IITM-ESM showed the best performance, making it the preferred model for future climate studies.