Uzma Sarfraz

The Arabian Gulf (Persian Gulf) and the Gulf of Oman are two basins, physically linked but comprising different hydrodynamic features. The shape of the basins, the weather, and the exchange through the Strait of Hormuz all affect circulation in the system. This review synthesizes current knowledge of hydrodynamic processes leading to water circulation in both basins, including wind forcing, density-driven exchange, tidal dynamics, and mesoscale changes. The Arabian Gulf has shallow depths, intense evaporation, and limited exchange, which promote hypersaline conditions, long residence times, and greater sensitivity to environmental stress. In comparison, the Gulf of Oman has a deeper, more exposed system, governed by monsoon-driven circulation, upwelling, and mesoscale processes. The present review highlights how various hydrodynamic regimes influence stratification, changes in temperature and salinity, and exchange processes, with direct impacts on nutrient transfer, oxygen delivery, and ecosystem dynamics. An important control point, the Strait of Hormuz, connects the Arabian Gulf and the Gulf of Oman. This review also stresses the importance of observational data and modeling capacity, as well as their limitations, for subsurface processes and connections across scales. It also provides an inclusive assessment of hydrodynamics and water circulation in the two basins. Moreover, it underscores the need for coordinated observational and modeling approaches to improve understanding and enhance management of these globally sensitive aquatic systems.

Flash floods pose serious risks to human life and infrastructure, leading to significant economic losses. While traditional conceptual models have long been used for runoff estimation, recent advancements in artificial intelligence have introduced machine learning and deep learning models for more accurate predictions. This study presents a deep learning framework that integrates Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Neural Ordinary Differential Equations (Neural ODEs) to enhance precipitation-induced runoff forecasting. A six-year dataset (2016–2022) from Al Ain, United Arab Emirates (UAE), was employed for model training, with validation conducted using data from a severe April 2024 flash flood. The proposed framework was compared against standalone CNN, RNN, and Neural ODE models to evaluate its predictive performance. Results show that the combination of the CNN’s feature extraction, the RNN’s temporal analysis, and the Neural ODE’s continuous-time modeling achieves superior accuracy, with an R² value of 0.98, RMSE = 2.87 × 10⁶, MAE = 1.13 × 10⁶, and PBIAS of −8.38. These findings highlight the model’s ability to effectively capture complex hydrological dynamics. The framework provides a valuable tool for improving flash-flood forecasting and water resource management, especially in arid regions like the UAE. Future work may explore its application in different climates and integration with real-time monitoring systems.