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Red Tide Development Modelling by MIKE Software (in Persian Gulf)
, M.Sc. Thesis Sharif University of Technology ; Abasspour, Majid (Supervisor)
Abstract
Land sourced marine pollution, overexploitation of living marine resources, destruction of habitat and introduction of harmful aquatic organisms and pathogens to new environment had been identified as the four greatest threats to the world’s oceans. Since invasive species can not be cleaned or absorbed in the ocean so, their effects on the environment are irreversible. Red tide is one of the invasive spieces and is a colloquial term used to refer to one of a variety of natural phenomena known as algal bloom. This phenomenon occur in estuarine, marine, or fresh water and algae accumulate rapidly in the water column and resulting in coloration of the surface water, varying in colour normally...
Evaluation of Water Quality of Anzali Lagoon by Developing a Numerical Model
, M.Sc. Thesis Sharif University of Technology ; Safaie, Ammar (Supervisor)
Abstract
Wetlands are valuable ecosystems that have a wide variety of functions to protect biodiversity, natural, economic and social values. Hydrological changes and nutrient enrichment, resulting from population growth, economic development and climate change are threatening the wetlands. Recently, assessing spatial and temporal variations of water quality has become an important aspect of the physical and chemical characterization of aquatic environments. Anzali wetland is one of the international wetlands in Iran, located in the southern part of the Caspian Sea in Gilan province. This wetland has been exposed to many pollution sources including agricultural, industrial, and municipal wastewater...
Seasonal and interannual cycles of total phytoplankton phenology metrics in the Persian Gulf using ocean color remote sensing
, Article Continental Shelf Research ; Volume 237 , 2022 ; 02784343 (ISSN) ; Moradi, M ; Moradi, N ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
Spatial and temporal patterns of climatological seasonality, interannual variability, and phytoplankton phenology were estimated using satellite-derived ocean color chlorophyll-a data (Chl-a) 1998 to 2020 in the Persian Gulf from. Biogeography of phytoplankton seasonal and interannual climatology was determined using k-means multivariate clustering analysis applied on the Chl-a time-series data. As a result, two distinct regions were identified: one in the deep north and middle area (DZC) with a minimum value of Chl-a in April–July (0.62–0.76 mg m−3) and maximum in December–February (1.07–1.59 mg m−3), and the other in the north–west coastal areas and along the southwest-southern area (SZC)...
Correlation between concentrations of chlorophyll-a and satellite derived climatic factors in the Persian Gulf
, Article Marine Pollution Bulletin ; Volume 161, Part A , December , 2020 ; Moradi, N ; Sharif University of Technology
Elsevier Ltd
2020
Abstract
Monthly mean satellite derived Chl-a, aerosols, wind, SST, PAR, and turbidity datasets were used to investigate the possible factors regulating phytoplankton variability in the Persian Gulf. The spatial correlation analysis revealed two distinct regions of SST and PAR, and a relatively uniform spatial correlation pattern of the other parameters. The cross correlation between aeolian dusts and Chl-a was significantly positive with 1–3 months offset. The pattern of spatial correlation between Chl-a and SST was positive in the shallow regions without time lag, and was negative with time offset of 3–5 months in deeper regions. The cross correlation between Chl-a and north-ward winds were...
Application of the Active Learning Method for the estimation of geophysical variables in the Caspian Sea from satellite ocean colour observations
, Article International Journal of Remote Sensing ; Volume 28, Issue 20 , 2007 , Pages 4677-4683 ; 01431161 (ISSN) ; Schaale, M ; Fell, F ; Fischer, J ; Preusker, R ; Vatandoust, M ; Shouraki, B ; Tajrishy, M ; Khodaparast, H ; Tavakoli, A ; Sharif University of Technology
Taylor and Francis Ltd
2007
Abstract
Remotely sensed data inherently contain noise. The development of inverse modelling methods with a low sensitivity to noise is in demand for the estimation of geophysical variables from remotely sensed data. The Active Learning Method (ALM) is well known to have a low sensitivity to noise. For the first time, ALM was utilized for the inversion of radiative transfer calculations with the aim of estimating chlorophyll a (Chl a), coloured dissolved organic matter (CDOM), and suspended particulate matter (SPM) in the Caspian Sea using MERIS (MEdium Resolution Imaging Spectrometer) data. ALM training is straightforward and fast. The ALM inversion models revealed the most relevant variables and...