Spatio-temporal variability of SST and Chlorophyll-a from MODIS data in the Persian Gulf.

Marine Pollution Bulletin xxx (2015) xxx–xxx

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Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul

Spatio-temporal variability of SST and Chlorophyll-a from MODIS data in the Persian Gulf Masoud Moradi ⇑, Keivan Kabiri Iranian National Institute of Oceanography and Atmospheric Science (INIOAS), Tehran, Iran

a r t i c l e

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Article history: Received 20 January 2015 Revised 7 July 2015 Accepted 9 July 2015 Available online xxxx Keywords: Chlorophyll-a Sea surface temperature Wavelet Transform MODIS imagery Remote sensing

a b s t r a c t Spatio-temporal variability of SST and Chl-a evaluated using MODIS products from 2002 to 2013 in the Persian Gulf. Wavelet Transform was utilized to analyze the spatio-temporal stability and abnormality of MODIS SST and Chl-a. The stationary level of SST decreases from west to the east during summer to early autumn, and increases from late autumn to spring. The stationary level of Chl-a is higher in the coastal areas, while its average ranged from 0.1 to 0.5 mg m 3. No meaningful major oscillating period observed in the abnormal variability of SST and Chl-a. The winter and summer peaks of SST and Chl-a were observed in the central parts and north-west regions. The timing of maximum SST was observed in August, which is not correlated with Chl-a maxima. The variability of SST and Chl-a in the whole Persian Gulf is seasonal, and related to river outflows, water circulation and climate regimes. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction Knowledge of the spatial and temporal variability in Chlorophyll-a (Chl-a) and Sea Surface Temperature (SST) assists in a more comprehensive understanding of biological and physical oceanography prospective of the marine ecosystems. The Chl-a concentrations as an index of phytoplankton pigment are considered an important indicator of eutrophication in marine ecosystems that may affect human life (Smith, 2006; Werdell et al., 2009). Additionally, it can be used to analyze the comprehensive dynamics of phytoplankton biomass (Muller-Karger et al., 2005). On the other hand, SST anomalies indicate water column stratification, which is closely related to light and nutrient loads of phytoplankton growth (He et al., 2010). Satellite remote sensing allows information on Chl-a concentrations and SST to be collected at moderate resolution, over large areas and long periods of time. The effective methodologies to analyze and describe the spatial and temporal variations of satellite driven oceanographic parameters have always been the subjects of interest in seas and oceans (Bruzzone et al., 2003; Werdell et al., 2009; Xu et al., 2011; Zhang et al., 2011). In this regards, statistical techniques have been widely used to analyze the spatial and temporal variations of SST and Chl-a from both in situ and remotely sensed data (Yoder et al., 2001; Carder et al., 2004; Zhang et al., 2006; Iida and Saitoh, 2007; Williams et al., 2013). Most of these studies have ⇑ Corresponding author. E-mail address: [email protected] (M. Moradi).

been focused on synchronous variations of SST and Chl-a by applying several methods such as the Empirical Orthogonal Function (EOF), which decomposes the spatio-temporal variability into a set of orthogonal functions (Brickley and Thomas, 2004; Iida and Saitoh, 2007; Figueiredo et al., 2010; Williams et al., 2013). The recent and most effective methodologies focus on abnormal and dominant variations (Liu et al., 2014). The abnormal or non-stationary variation shows the localized abrupt changes or discontinuities that result from disturbance events. The dominant or stationary level of SST or Chl-a eliminates the noise and anomalous variations, and can provide the average patterns, such as the seasonal mean, monthly mean and annual mean (Liu et al., 2014). Therefore, comprehensive understanding of the spatio-temporal variations of SST and Chl-a can be obtained by detecting of stationary and non-stationary variations. The wavelet Transform (WT) is a well-known methodology for stationary and non-stationary analysis of SST and Chl-a concentrations (Baliunas et al., 1997; Saco and Kumar, 2000; Belonenko, 2005; Zhang et al., 2012; Bashmachnikov et al., 2013; Liu et al., 2014;). Wavelet functions decompose a complex signal into component sub-signals. The recent studies demonstrated the capabilities of wavelet analysis for identifying the stability and abnormality of SST and Chl-a variations (Zhang et al., 2012; Bashmachnikov et al., 2013; Liu et al., 2014). In this study, wavelet analysis was utilized to analyze the spatio-temporal stability and abnormality of SST and Chl-a in the Persian Gulf area, where it has been subject to high biological productivity. Seasonal phytoplankton growth is controlled in different

http://dx.doi.org/10.1016/j.marpolbul.2015.07.018 0025-326X/Ó 2015 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Moradi, M., Kabiri, K. Spatio-temporal variability of SST and Chlorophyll-a from MODIS data in the Persian Gulf. Mar. Pollut. Bull. (2015), http://dx.doi.org/10.1016/j.marpolbul.2015.07.018

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M. Moradi, K. Kabiri / Marine Pollution Bulletin xxx (2015) xxx–xxx

regions by hydrodynamics, bathymetry, stratification, mixing processes, and nutrient uptake (Rao and Al-Yamani, 1998; Heil et al., 2001; Glibert et al., 2002; Moradi and Kabiri, 2012). Researches on plankton bloom and Chl-a variations have not been widely reported, and studies to date have relied largely on episodic surveys (Alsahli et al., 2012; Nezlin et al., 2007; Hassanzadeh et al., 2007). Specifically, a practical limitation of this approach is data gaps on how phytoplankton abundance varies spatially, and how this relates to biological processes in the water column. One possible reason why there is limited information on these topics is the lack of suitable required data. Early studies using remote sensing in the Persian Gulf area have been done for ocean environmental variability, habitat mapping, coastal, and hydro-biological processes (Al-Ghadban, 2004; Alsahli et al., 2012). The only attempt for processing of meso-scale satellite data over the whole Persian Gulf for analyzing the chlorophyll distribution has been done by Nezlin et al. (2007). They have divided the Persian Gulf area into six several sub-regions (Fig. 1) according to general water circulation patterns (Michael Reynolds, 1993). Afterward, they analyzed satellite driven monthly averaged SST, Chl-a, and Photosynthetically Available Radiation (PAR) concentrations in each above mentioned sub-regions from 1997 to 2006. They utilized Sea-viewing Field-of-View Sensor (SeaWiFS), Advanced Very High Resolution Radiometer (AVHRR), and Moderate Resolution Imaging Spectroradiometer (MODIS). Here, we have used concurrent SST and ocean color observations obtained from MODIS to investigate simultaneous variations of these two parameters in the Persian Gulf. Plankton biomass, measured as the concentration of Chl-a, can be accurately estimated from ocean color data (Gordon et al., 1983; O’Reilly et al., 1998; Carder et al., 1999). The surface pigment concentrations extracted from remotely sensed data are not identical with total pigment concentrations in water column, but are correlated (Platt and Herman, 1983). The standard Chl-a calculation algorithms from MODIS data were developed for open ocean waters, where the color of ocean surface results mainly from chlorophyll concentrations (O’Reilly et al., 2000). The Persian Gulf is influenced by rivers discharge, coastal processes, and pollutants that increase

the suspended loads. Therefore, it should be classified as coastal waters, where the sea surface color depends on the dissolved and suspended matter concentrations (Siegel et al., 2005). It means the standard algorithms developed for open oceans are not applicable here and may overestimate chlorophyll concentrations in the Persian Gulf. In such cases, regional algorithms based on in situ measurements provide more reliable results (Nezlin et al., 2007). So far, these studies have not carried out in the study region, and would be target for future studies. However, in this study the absolute values of satellite driven data were not considered. Instead, the variations of satellite driven SST and Chl-a were considered as indicators of hydro-biological processes and dynamics of phytoplankton biomass in the study region. 2. Study area Persian Gulf is a semi-enclosed marginal sea, 1000 km long, 200–300 km wide, with a mean depth of 35 m and a total volume of 6000 km3 located in south of Iran and is connected to the Gulf of Oman through the narrow Strait of Hormuz (Fig. 1). The number of regional oceanographic observation from this region is rare, and investigations have been undertaken by individual countries in their own coastal waters. Regional circulation, hydrodynamic modeling, and pollution have been studied in the past (Nezlin et al., 2010; Hunter, 1983; Kämpf and Sadrinasab, 2006; Swift and Bower, 2003; Fatemi et al., 2012; Richlen et al., 2010). The mean circulation is cyclonic, and inflow current extends to the head of the Persian Gulf in summer, and weakened by shamal winds along the Iranian coasts in the winter (Michael Reynolds, 1993). The basin-scale circulation consists of two components, one from the Strait of Hormuz northwesterly, and the other southeastern in the southern part (Michael Reynolds, 1993; Swift and Bower, 2003). The flow is predominantly density driven with surface flow from the Strait of Hormuz and adjacent to the Iranian coasts. A southward coastal flow is present along the entire southern coasts (Swift and Bower, 2003). A northwest-to-southeast gradient exists, from high salinity and lower temperature in the northern area to lower salinity and higher temperature at the Strait of Hormuz

Fig. 1. Location of the study area. Regions from Nezlin et al. (2007). 1: River Plume, 2: North-West, 3: Southeast, 4: Central, 5: South, 6: East.



(Halim, 1984). The dense water formed by winter cooling and evaporation sinks to the bottom in the central area and eventually flows out through the Straits of Hormuz as a thin layer into the Gulf of Oman (Grasshoff, 1976; Halim, 1984). The adjacent rivers mainly in the north and northwest discharge annually 1.1 108 m3 of water and 4.8 l06 tones of sediments (Michael Reynolds, 1993). Because of the surrounding arid desert and enormous loss of water due to evaporation that does not exceed the precipitation and river run-off; surface coastal waters attain a maximum temperature of 32 °C and salinity of 44.3 (Saad, 1976; Jacob and Al-Muzaini, 1990). 3. Data and methods The concurrent daily 4-micron day time MODIS Level-2 data from August 2002 to December 2013 were used in this study. The MODIS data downloaded from NASA ocean color website (http://oceancolor.gsfc.nasa.gov). These data products have been processed by the NASA OBPG (Ocean Biology Processing Group) using the most recent updates in algorithms and instrument calibration (O’Reilly et al., 2000). Land and clouds were masked using Standard Level-2 flags. The images with over 98% cloud coverage for both SST and Chl-a were removed from further processing (Alvera-Azcárate et al., 2007). To reduce errors from satellite sensor digitization noise, a median value from a 3 3 box was used on the monthly composite images data (Hu et al., 2001). Fig. 2 shows the total number of images collected for each month during 2002–2013. The available images range from 18 to 26 days in summer months to over 22 days in winter months. There are some gaps in the MODIS Level-2 products data grid, resulting from the absence of observations due to cloudy weather or dust events and inappropriate satellite view angles. Fig. 3 shows the percentage of data availability in different months from 2002 to 2013 in the study region. It was observed that the higher percentage of data gaps is in the middle parts of the Persian Gulf and Strait of Hormuz spatially, with the minimum value in the summer months. The study of stationary and non-stationary variations requires a complete time series of input gridded data without data voids. To overcome the data gaps problem, the Data Interpolating Empirical Orthogonal Function (DINEOF) method was performed for reconstructing the daily SST and Chl-a data (Beckers and Rixen, 2003; Alvera-Azcárate et al., 2007; Miles et al., 2009; Miles and He, 2010). This approach identifies spatial

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and temporal patterns and fills missing pixels. It should be noted that similar approach has been successfully applied to study ocean color and SST variability in the South Atlantic Bight (Miles et al., 2009), the North Sea (Sirjacobs et al., 2011) and Mediterranean Sea (Volpe et al., 2012). This technique presents some advantages over more classical approaches (such as optimal interpolation) when working on SST and Chl-a data sets (Zhao and He, 2012). Additionally, it provides an accurate datasets allowing to study SST and Chl-a co-variability, and explore their relations with surface forcing and basin-scale deep ocean forcing (Li and He, 2014). Afterward, the WT analysis were used to assess the spatio-temporal stability and abnormality of SST and Chl-a in the study region. This method decomposes a signal into a hierarchical structure of detail and approximations at limited levels. The wavelet approximation, (a), and detail coefficient, (d), vectors are generated at each level of decomposition of an original signal (Percival and Walden, 2006). The approximation coefficients represent the high-scale, and low-frequency components of a signal, whereas detailed coefficients represent the low-scale, and high-frequency components. WT is performed using dilated and shifted version of the mother wavelet to produce a set of wavelet basis function. In this study, the Daubechies wavelet (‘db5’) used as mother wavelet, because it has been used by many researches and yielded a satisfactory results for marine data analysis (Domingues et al., 2005; Belonenko, 2005; Andreo et al., 2006; Belonenko et al., 2009; Liu et al., 2014). 4. Result and discussion The scale and frequency of WT must be considered to obtain successful results for the spatio-temporal variations of SST and Chl-a. However, the monthly averaged SST and Chl-a concentrations between 2002 and 2013 from reconstructed daily images for whole region of the Persian Gulf were estimated by WT using ‘db5’ mother wavelet (Fig. 4). For all possible decomposition levels, the approximation coefficients and detail coefficients at levels j = 1, 2, 3, 4 have shown by a1, a2, a3, a4 and d1, d2, d3, d4, respectively. The stationary level of the signal is given by the approximation series related to the low-frequency components, and the noise or abnormal signal is given by the detail series related to the high-frequency components. The approximation components for level 2 (a2) and level 4 (a4), and the detail components for the first two levels (d1 + d2) and the fourth level (d4) provide information

Fig. 2. Number of retained monthly images used during 2002–2013. Only images with less than 98% cloud coverage were selected.


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Fig. 3. Monthly spatial distribution of valuable pixels of MODIS data during 2002–2013.

Fig. 4. Wavelet Transform for MODIS SST and Chl-a time series data using the db5 wavelet function.



about the seasonal, and annual average level, as well as monthly fluctuation and inter-annual abnormality over the considered period (i.e., 12 years), respectively (Liu et al., 2014). 4.1. Stable levels of SST and Chl-a 4.1.1. Monthly average of SST and Chl-a Fig. 5 shows the monthly distribution of averaged SST and Chl-a concentrations from 2002 to 2013. The results revealed that the Chl-a concentration is relatively low in the central parts of the Persian Gulf, and higher values were observed along coasts.

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The relative higher values of Chl-a (>0.5 mg m 3) extended to the eastern parts of the Persian Gulf toward the Oman Sea from November to March, while SST decreased from north-west to the east during this time. On the other hand, during May–October, the mean Chl-a value decreased distinctly from the eastern parts, and the mean value (0.5 mg m 3) increased in the central and coastal areas. From mid spring to late summer, SST increased from east to the head of Persian Gulf at north-west. Fig. 6 shows the variation of the monthly area-averaged of SST and Chl-a concentrations of different sub-areas of the Persian Gulf over time. The highest Chl-a concentration was observed in regions 1 and 3, where

Fig. 5. Monthly average of (a) Chl-a concentration, and (b) SST during 2002–2013 from MODIS images.


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Fig. 6. Seasonal variations of (a) Chl-a, and (b) SST in six regions of the Persian Gulf. 1: River Plume, 2: North-West, 3: Southeast, 4: Central, 5: South, 6: East.

river plumes penetrate sea waters. Chl-a concentrations gradually increased in late summer, with maxima in September–October. In region 6, a maximum peak was observed in February–March. Regions 2, 4 and 6 are not influenced by river discharge and their characteristics are controlled by hydrodynamic properties of the Persian Gulf. The minimum Chl-a was observed in spring and early summer. Chl-a concentrations gradually increased in late summer, with maxima in September–October and smaller peaks in November and February. Monthly variability of SST is characterized by a well-defined sinusoidal seasonal cycle (Fig. 6b). SST increases from north to the south at winter. Fig. 7 shows the percentage of the average monthly SST and Chl-a concentrations by area for each class values. The maximum coverage area of Chl-a values for 1–2 mg m 3 was observed during September–November. In the other months, the most areas were covered by Chl-a values less than 1 mg m 3. The maximum area coverage for SST in warmer months (20–25 and 25–30 °C) was in August–September and July. The maximum area coverage of SST in cold months was in December. In addition, 90% of the area showed Chl-a concentrations less than 2 mg m 3, and SST between 15 and 25 °C for each month. Particularly, in the Strait of Hormuz (region 6), Chl-a shows spring bloom in Febuary–March and minimum in July–August, which is opposite to SST oscillation (Fig. 6). Nezlin et al. (2007) have interpreted that the reasons of this incident is complex and related to the combined effects of the seasonal upwelling and water exchange between Persian Gulf and Oman Sea. The high values of pollutant materials and river outflow from the northern coast can affect the chlorophyll dynamics in the Strait of Hormuz.

4.1.2. Seasonal average of SST and Chl-a Fig. 8 shows the distribution of seasonally averaged SST and Chl-a concentrations during the study period. This figure shows the high seasonal and spatial variability in Chl-a values. High Chl-a concentrations were observed in winter, while low Chl-a concentrations were found in spring in the Persian Gulf main body. The distribution of SST values shows warming from spring to summer, and then cooling from summer to winter. The highest Chl-a was observed in regions 1, 3 and 6. The regions 1 and 3 represent the zone of river plume and the region of cyclonic circulation of river waters. Chl-a concentrations in the southern coastal region (region 5) were significantly lower, and are not influenced by river discharge. This suggests that different factors may control the Chl-a seasonal variability in this region. Fig. 9 shows the average seasonal SST and Chl-a concentrations by area for each class values. These values show a seasonal pattern for almost each value category. The coverage area of Chl-a was maximized for values less than 2 mg m 3 for all seasons, except autumn. In addition, the regions of Chl-a concentrations 1–2 mg m 3 increased from spring-summer to autumn and then decreased. The maximum area coverage for Chl-a concentrations less than 1 mg m 3 increased from spring to summer, and decreased from summer to autumn. The maximum area coverage of SST between 20 and 25 °C was in summer. The colder value classes from 10 to 20 °C, had maximum area coverage in spring. The maximum area coverage of SST 20–25 °C was in autumn, and then in winter. The regions 2 and 4 show a seasonal cycle with maximum value in winter and minimum value in spring. This cycle is typical for

Fig. 7. The area percentage of monthly averaged (a) Chl-a, and (b) SST during 2002–2013.



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Fig. 8. Seasonal average of Chl-a concentration (top), and SST (bottom) during 2002–2013 from MODIS images.

Fig. 9. The area percentage of seasonal averaged (a) Chl-a, and (b) SST during 2002–2013.

tropical and subtropical ocean, where phytoplankton growth is limited by lack of nutrients, separated from euphotic zone by strong Pycnocline (Muller et al., 2005). This is in agreement with the Michael Reynolds (1993) findings that showed Pycnocline in the wintertime in north of Qatar (i.e. region 6) is absent and the water column is almost perfectly mixed to bottom and stimulating nutrient flux to the surface and phytoplankton growth. 4.1.3. Annual average of SST and Chl-a The annual average of SST and Chl-a concentrations from 2002 to 2013 is shown in Fig. 10. The result shows that the Chl-a concentration was relatively low in the middle part, and high in the coastal areas. The spatial variations of the annual average SST resembled that of the Summer-autumn seasonal average. Fig. 11 represents the inter-annual anomalies and WT a4 level for total and sub-regions of the Persian Gulf over time. Inter-annual variation of the Chl-a concentration was relatively subtle, except in 2008 and 2009, which is coincident with tremendous red tide occurrences in the Persian Gulf, especially in northern parts along Iranian coasts (Moradi and Kabiri, 2012; Zhao and Ghedira, 2014). The variation of SST was relatively subtle, while an increased value

of SST was observed in 2007 and 2010. Fig. 12 shows the coverage areas of the average annual SST and Chl-a concentrations for each year. The maximum Chl-a concentrations by area were less than 1 mg m 3, which closely associated with the monthly average Chl-a values. The maximum coverage areas of Chl-a concentrations less than 1 mg m 3 were observed from 2011 to 2013, and for values greater than 2 mg m 3 were happened in 2008 and 2009. In addition, more than 70% of the area showed Chl-a concentrations less than 1 mg m 3. The second maximum Chl-a concentration coverage areas were 1–2 mg m 3. The maximum SST coverage areas were for values of 25–30 °C. The maximum SST coverage areas for values between 30 and 35 °C were observed in 2007 and 2010. In summary, the stationary levels of SST and Chl-a concentrations were stable in the study area irrespective of the monthly, seasonal or annual cycles. The stationary level (WT approximation coefficient) of SST and Chl-a in the Persian Gulf revealed four points: (i) The average Chl-a concentration decreased from coastal area to central part. (ii) The average Chl-a concentration in the central part primary ranged from 0.1 to 0.5 mg m 3. (iii) The average SST decreased from central parts toward the head (north-west)


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Fig. 10. Annual averaged of (a) Chl-a concentration, and (b) SST during 2002–2013 from MODIS data.

and coasts, and (iv) The average SST decreased from west to east (entrance of Oman Sea) from summer to early autumn, and increased from late autumn to spring. Fig. 12 shows the comparison of the mean SST and Chl-a concentrations derived from low-frequency components and from original data. It reveals the ability of WT stationary level for studying the spatio-temporal variations of these parameters, and indicates that the mean values before and after WT have similar distribution. This expression demonstrates that the approximation coefficients of WT are able to illustrate the stationary level (stability variations) of SST and Chl-a in the study area.

4.2. Fluctuation levels of SST and Chl-a The detail components for the first two levels (d1 + d2) represent the distribution of the monthly fluctuation level of SST and Chl-a concentration. The inter-annual variations of SST and Chl-a is shown in Fig. 13. The result showed that the monthly fluctuation level of SST and Chl-a were different between months. The monthly fluctuation level of Chl-a was positive in February, March and negative in November and December for most regions, and were concentrated between 0.32 and +0.52 mg m 3 while the other values scattered as anomalous peaks. The monthly



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Fig. 11. Time series of spatial mean Chl-a (left), and SST (right) anomalies of the whole and sub-regions of the Persian Gulf during 2002–2013.

fluctuation levels of SST were positive in October–November, while it was negative in June-July, January–February, and were concentrated between 2.4 to +3.2 °C. The monthly fluctuation of the wavelet is generally clear in Fig. 13, and it is equal to 6 and 12 months for SST and Chl-a oscillating period, respectively. This pattern indicated that the Chl-a concentration was higher in summer and autumn and lower in winter and spring, and SST variation increased in summer and decreased in winter.

The timing of d4 modulus extreme maximum represents the inter-annual singularity (Liu et al., 2014). The SST and Chl-a inter-annual singularity may be associated with anthropological changes, climate oscillations, climate change and extreme atmospheric anomalies, such as the global heat wave in 2003 and the abnormal SST in 2007 (Volpe et al., 2012). The abnormal Chl-a concentrations appeared in September 2004, July 2006, August 2007, July 2008, August 2009, July 2010 and September 2011 (Fig. 4a).


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Fig. 12. The area percentage of annual averaged (a) Chl-a, and (b) SST during 2002–2013.

Fig. 13. Time series variability of (a) Chl-a, and (b) SST corresponding to the sum of detail components d1 + d2 as an amplitude of monthly abnormality during 2002–2013 from MODIS data.

The abnormality of SST was observed in August 2003, September 2004, August 2005, August 2007, August 2010, and September 2011 (Fig. 4b). This abnormal pattern does not follow a regular cycle from 2002 to 2013, and mainly refers to catastrophic events such as red tide blooms (Zhao and Ghedira, 2014). The timing and spatial distribution of SST and Chl-a maximum value can provide information about the effects of various oceanic environments, such as chlorophyll blooms, heat flux and wind mixing (Peñaflor et al., 2007; Liu et al., 2014). Fig. 14 shows the timing and spatial distribution of maximum SST and Chl-a during the study period. Fig. 14a(i) indicates that the spatial distribution of maximum Chl-a concentrations in central body (zones 2, 4 and 5) is lower than along the north-west and southern coasts and nearby strait of Hormoz (zones 1, 3 and 6). The largest coverage area of maximum Chl-a distribution is for values less than

2 mg m 3, and then followed by 2–3 mg m 3, 5–10 mg m 3. The winter peak of Chl-a concentrations was mainly located in the central parts of the Persian Gulf and near the Iranian coastal areas, and summer peak was mainly located in the southern and north-west (river plume) regions. The largest area percentage of Chl-a maximum timing increases from spring to autumn and then decreases in winter (Fig. 14b(i)). This finding indicated that the different parts of the study area have maximum Chl-a concentrations at different time. Fig. 14a(ii) shows that the spatial distribution of maximum SST is more homogenous than Chl-a concentrations, with higher value in central of southern parts (zone 5). The largest coverage area of maximum SST distribution is >80% for value 25–30 °C, and then followed by 30–35 °C. The timing of maximum SST distributions was in August (90%) and in September (eastern part) (Fig. 14b(ii)). This shows that the timing and spatial distribution



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Fig. 14. Spatio-temporal distribution of the timing of Chl-a (left), and SST (right) maxima and maximum distribution during 2002–2013 from MODIS data.

of maximum SST in the study region is relatively homogenous and different spatial and temporal pattern relative to maximum Chl-a distribution. 5. Conclusion The SST and Chl-a concentration maps in the Persian Gulf were generated using MODIS data products during 2002–2013 and then analyzed using WT method. The wavelet analysis revealed the spatio-temporal stability and abnormality of these surface parameters. The approximation and detail components of WT method are considered to be a source of stationary level (stability) and anomalous variability. The stability and abnormality of Chl-a concentrations refer to phytoplankton dynamics at different time scales. Monthly, seasonal, and annual stability of SST and Chl-a were derived from approximation components and considered as the stationary level of these parameters. The variability of SST and Chl-a in the whole Persian Gulf is seasonal, and related to river outflows (regions 1 and 6), water circulation and climate regimes (Nezlin et al., 2007; Michael Reynolds, 1993). In this study several areas with different seasonality of Chl-a development were distinguished, whereas SST stationary variability showed continuous changes over these regions. The anomalous variability of SST and Chl-a were obtained from the detailed components of WT. No meaningful major cycles were found for Chl-a abnormality in the Persian Gulf area, which suggests the abnormality of phytoplankton growth is related to

catastrophic events such as red tide blooms. The timing and spatial distribution of SST and Chl-a maxima showed different values in each region. In general, the winter peak of Chl-a concentrations has been located mainly in the central parts near the Iranian coasts, and the summer peak was located in the southern and north-west regions. The timing of maximum SST distributions was in August, and values of 25–30 °C. In summary, WT method can be considered as a valuable tool for assessment of the spatio-temporal stability and abnormality of SST and Chl-a concentrations in the study area. Moreover, the findings of this study revealed that the different parts of the Persian Gulf have different phytoplankton growth dynamics. It is clear that more detailed analysis is required for better understanding of the Persian Gulf ecosystem, and more researches are needed to be done for better understanding the influence of the water mass properties, horizontal circulation, deep mixing events and their effects on plankton growths.

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Monitoring and trend mapping of sea surface temperature (SST) from MODIS data: a case study of Mumbai coast.

Data of heavy metals biosorption onto Sargassum oligocystum collected from the northern coast of Persian Gulf.

Fighting words: new medical terms from the Persian Gulf War.

Persian gulf bioactive natural drugs.

Gracilariopsis persica from Persian Gulf Contains Bioactive Sterols.

Total petroleum hydrocarbons in sediments from the coastline and mangroves of the northern Persian Gulf.

Spatiotemporal and variability gait data in community-dwelling elderly women from Brazil.

Biological activity and chemical constituents of red and brown algae from the persian gulf.

Cytotoxicity and antimicrobial activity of marine macro algae (Dictyotaceae and Ulvaceae) from the Persian Gulf.

Medical support for American troops in the Persian Gulf.

Methamphetamine use and treatment in Iran: A systematic review from the most populated Persian Gulf country.

An investigation on the mercury contamination of Persian Gulf fish.

Environmental health concerns of the Persian Gulf War.

Antifungal and antibacterial activity of Haliclona sp. from the Persian Gulf, Iran.

Antioxidant and anticancer activities of selected persian gulf algae.

Arabian Gulf.

Antioxidant Properties of two Edible Green Seaweeds From Northern Coasts of the Persian Gulf.

Antibacterial and Antifungal Activity of Holothuria leucospilota Isolated From Persian Gulf and Oman Sea.

Heavy metal in water and aquatic organisms from different intertidal ecosystems, Persian Gulf.

A checklist of the barnacles (Crustacea: Cirripedia: Thoracica) of the Persian Gulf and Gulf of Oman with nine new records.

Dermatology in a war zone: a Persian Gulf experience.

Mercury distribution in contaminated surface sediments from four estuaries, Khuzestan shore, north part of Persian Gulf.

First time isolation of Photobacterium damselae subsp. damselae from Caranx sexfasciatus in Persian Gulf, Iran.

Analgesic effect of Persian Gulf Conus textile venom.