Mohamad Alkhalidi

Accurate wind speed and direction data are vital for coastal engineering, renewable energy, and climate resilience, particularly in regions with sparse observational datasets. This study evaluates the ERA5 reanalysis model’s performance in predicting wind speeds and directions at ten coastal and offshore stations in Kuwait from 2010 to 2017. This analysis reveals that ERA5 effectively captures general wind speed patterns, with offshore stations demonstrating stronger correlations (up to 0.85) and higher Perkins Skill Score (PSS) values (up to 0.94). However, the model consistently underestimates wind variability and extreme wind events, especially at coastal stations, where correlation coefficients dropped to 0.35. Wind direction analysis highlighted ERA5’s ability to replicate dominant northwest wind patterns. However, it reveals notable biases and underrepresented variability during transitional seasons. Taylor diagrams and error metrics further emphasize ERA5’s challenges in capturing localized dynamics influenced by land-sea interactions. Enhancements such as localized calibration using high-resolution datasets, hybrid models incorporating machine learning techniques, and long-term monitoring networks are recommended to improve accuracy. By addressing these limitations, ERA5 can more effectively support engineering applications, including coastal infrastructure design and renewable energy development, while advancing Kuwait’s sustainable development goals. This study provides valuable insights into refining reanalysis model performance in complex coastal environments.

Coastal marinas are particularly susceptible to pollution due to their limited flushing capabilities and ineffective management practices. Therefore, it is necessary to implement measures that promote enhanced sustainability. This study aims to explore the intricate relationship between tidal flushing characteristics and water quality within marinas to foster sustainable development and management practices that mitigate environmental impacts. The research scrutinized seven marinas along the Kuwait coastline, each exhibiting unique hydrodynamic conditions and geometric configurations. Water quality indicators such as BOD, COD, DO, and SO₄²⁻ were evaluated concerning each marina’s flushing efficiency through field assessments and hydrodynamic numerical modeling. An empirical formula was developed to predict and optimize flushing mechanisms, which provided critical insights into the design and management of marinas to enhance water quality. The study revealed significant disparities in water quality across the examined marinas as the hydrodynamic and geometric conditions differed. The empirical formula developed offers a novel approach to quantitatively assessing flushing efficiency, which is valuable for marina designers and managers, facilitating informed decisions, and promoting environmental sustainability. This study underscores the critical importance of integrating hydrodynamic and geometric considerations in the design and management of marinas to improve water quality and sustainability. It advocates for a multifaceted strategy that includes advanced design solutions, rigorous policy implementation, and active community engagement to safeguard coastal marine environments. The findings emphasize the need for comprehensive environmental management plans.

Creating an efficient model for predicting sea level fluctuations is essential for climate change research. This study examined the effectiveness of utilizing Artificial Neural Networks (ANNs), particularly the recurrent network approach. ANNs were chosen for their capacity to learn from extensive and intricate data and their ability to handle nonlinear correlations. The Long Short-Term Memory (LSTM) algorithm was employed to fill data gaps and predict future sea level records in the Arabian Gulf, especially in Mina Salman. The results were promising, with LSTM successfully filling a 6-year data gap while maintaining low Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values. The first phase of the model yielded a RMSE value of 63.4 mm and a MAPE value of 3.14%. The same approach was used to retrain the model with a mix of real and predicted values, preserving historical patterns and yearly rates with an RMSE of 66.5 mm and a MAPE of 3.07%. These findings highlight LSTM’s advantages when considering only historical information for predicting the future sea level changes. The research provides valuable insights into predicting sea level changes in regions with limited field data, such as the Arabian Gulf, and emphasizes the potential for further research to enhance sea level prediction models through improved optimization techniques.

This study investigates the seasonal and spatial trends in Kuwait’s coastal water’s physical, chemical, and biological parameters by applying multivariate statistical techniques, including cluster analysis (CA), principal component/factor analysis (PCA/FA), and the Pearson correlation (PC) method to the average daily reading of water quality parameters from fifteen stations over one year. The investigated parameters are pH, turbidity, chlorophyll-a, conductivity, dissolved oxygen (DO), phycoerythrin, salinity, and temperature. The results show that the coastal water of Kuwait is subjected to high environmental pressure due to natural and human interferences. During 2017, the DO levels were below the threshold limit, and at the same time, the water temperature and salinity were very high, causing a series of fish death events. CA resulted in three different regions based on the turbidity, including high, moderate, and low regions, and three seasons (winter, summer, and autumn). Spring is very short and overlaps with winter and summer. PCA/FA applied on the datasets assisted in extracting and identifying parameters responsible for the variations in the seasons and regions obtained from CA. Additionally, Pearson’s correlation resulted in a strong positive relation between chlorophyll and phycoerythrin in 7 out of the 15 stations. However, at high turbidity regions (stations 1 and 2), chlorophyll concentration was low. Additionally, the negative correlation between DO and temperature was observed at stations with rare human activities.

The interaction between waves and slotted vertical walls was experimentally studied in this research to examine the performance of the structure in terms of wave transmission, reflection, and energy dissipation. Single and twin slotted barriers of different slopes and porosities were tested under random wave conditions. A parametric analysis was performed to understand the effect of wall porosity and slope, the number of walls, and the incoming relative wave height and period on the structure performance. The main focus of the study was on wave transmission, which is the main parameter required for coastal engineering applications. The results show that reducing wall porosity from 30% to 10% decreases the wave transmission by a maximum of 35.38% and 38.86% for single and twin walls, respectively, increases the wave reflection up to 47.6%, and increases the energy dissipation by up to 23.7% on average for single walls. For twin-walls, the reduction in wall porosity decreases the wave transmission up to 26.3%, increases the wave reflection up to 40.5%, and the energy dissipation by 13.3%. The addition of a second wall is more efficient in reducing the transmission coefficient than the other wall parameters. The reflection and the energy dissipation coefficients are more affected by the wall porosity than the wall slope or the existence of a second wall. The results show that as the relative wave height increases from 0.1284 to 0.2593, the transmission coefficient decreases by 21.2%, the reflection coefficient decreases by 15.5%, and the energy dissipation coefficient increases by 18.4% on average. Both the transmission and the reflection coefficients increase as the relative wave length increases while the energy dissipation coefficient decreases. The variation in the three coefficients is more significant in deep water than in shallower water.

The statistical distribution describing nonlinear narrowband waves characterized with second-order nonlinearities is simplified to an explicit closed form amenable to theoretical and practical applications. The approximate form and its salient features are examined and compared with oceanic measurements in deep and shallower water depths. The approximate form developed here provides certain advantages in terms its algebraic simplicity, accuracy and a somewhat wider domain of validity, in particular, relative to a previous approximation derived from the same second-order narrowband model. Comparisons with the oceanic measurements in deep and shallow water depths also yield good agreement with the approximate form over the range of surface elevations where the second-order theory provides a relatively valid representation of nonlinear waves.