Modeling Physical-Biogeochemical Interactions in the Marine System of the Arabian Gulf

Abstract

The Arabian Gulf is a substantial and unique source of water to many desalination plants across the Gulf basin. It experiences significant biogeochemical changes, anthropogenic activities, and is susceptible to the impacts of climate change. Biogeochemical models have been developed primarily to examine the primary production and variability of carbon fixation by the ocean, as well as the impact of physical, biological, and geochemical processes on ecosystems and human activities. These models have been widely applied to numerous oceans and ecosystems; however, studies on the biogeochemistry of the Arabian Gulf waters are lacking. Therefore, in this thesis, a coupled physical-biogeochemical model that incorporates the influence of advective-diffusive-mixing processes driven by tides, winds, internal density gradients, dust deposition, species types, light field is developed and calibrated for the Arabian Gulf in order to study and examine the biogeochemical properties of the Arabian Gulf.

This work is divided into three distinct studies, the first study examines the vertical and horizontal distribution of nutrients and its response to seasonal upwelling using existing datasets from the World Ocean Atlas (WOA) as well as Ekman transport estimation. Nutrient’s dynamics are correlated with the upwelling, with strong upwelling regions capable of transporting more nutrients to subsurface waters, alleviating nutrient limitation during low nutrient periods. Following the first study and to further expand our understanding of the nutrient cycling, a biogeochemical model configured for the Arabian Gulf and Sea of Oman is utilised to validate key biogeochemical variables such as chlorophyll (Chl-a) and nutrient content. Validation is carried out by comparing model outputs with the existing in-situ measurements and satellite data. This comparison exhibits the model’s ability to depict the Chl-a concentrations after improvement with a RMSE of 0.48, bias of -0.28, and Willmott's index of 0.7. Despite underestimation in most of the cases, the model has demonstrated its ability to reproduce most of the patterns observed in satellite data. However, comparisons between nutrient and in situ observations indicate that the model metrics show different performance for each variable, with nitrate exhibiting some predictability and phosphate exhibiting good predictability, but silicate not being adequately represented by the model. Thus, further model evaluation and adjustments are required to improve the model's overall predictive performance.

The final work aims at exploring the effect of atmospheric dust deposition on the biogeochemical properties such as Chl-a, primary production, and phytoplankton composition using the aforementioned model. The results show that atmospheric dust deposition is a key source of iron to the phytoplankton in the Arabian Gulf with concentrations vary according to the season and predominant winds. Chl-a and primary production are enhanced at extreme deposition particularly during the summer season. This suggests that iron is a key limiting nutrient to the phytoplankton. However, a negative impact associated with increased iron deposition is found in the winter as a result of the changes in the composition of phytoplankton species, nutrient and carbon cycling. This indicate that phytoplankton may be limited by other nutrient such nitrate or phosphate during the winter. In the winter, in addition, iron deposition can stimulate the growth of phytoplankton groups that are adapted to low iron concentrations which can be the non-dominant groups resulting in a shift in phytoplankton composition, thus, decreasing Chl-a and primary production. Overall, this thesis contributes to the better understanding of the Arabian Gulf’s peculiar ecosystem and its response to the anthropogenic stressors to develop efficient management and conservation strategies to preserve the region's biodiversity, food security, as well as economic development.

Date of Award	Apr 2023
Original language	American English
Supervisor	Maryam Alshehhi (Supervisor)