Relationship Between the Persian Gulf Sea-Level Fluctuations and Meteorological Forcing

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Relationship Between the Persian Gulf Sea-Level Fluctuations and Meteorological Forcing Journal of Marine Science and Engineering Article Relationship between the Persian Gulf Sea-Level Fluctuations and Meteorological Forcing Naghmeh Afshar-Kaveh 1,* , Mostafa Nazarali 2 and Charitha Pattiaratchi 3 1 School of Civil Engineering, Iran University of science and technology, Tehran 16846-13114, Iran 2 Department of Coastal Engineering, Pouya Tarh Pars Consulting Engineers Company, Tehran 14376-15613, Iran; [email protected] 3 Oceans Graduate School & the UWA Oceans Institute, The University of Western Australia, Perth 6009, Australia; [email protected] * Correspondence: [email protected]; Tel.: +98-91-2325-5481 Received: 14 February 2020; Accepted: 13 April 2020; Published: 16 April 2020 Abstract: Sea-level data from six tide gauge stations along the northern coast of the Persian Gulf were analyzed both in time and frequency domain to evaluate meteorological forcing. Spectral analyses indicated that mixed, predominantly semi-diurnal tides were dominant at all stations, but low-frequency fluctuations correlated well with atmospheric pressure and wind components. Non-tidal sea-level fluctuations up to 0.75 m were observed along the northern coasts of the Gulf due to the combined action of lower atmospheric pressure and cross-shore wind. Coherency between low-frequency sea-level records and mean sea-level pressure indicated that the latter usually leads to sea-level fluctuations between 1 and 6.4 days. In contrast, the same analysis on the wind velocity and sea level revealed that the former lags between 3 and 13 days. The effect of wind stress on coastal sea-level variations was higher compared with the effect of atmospheric pressure. Concurrent analysis of low-pass-filtered sea-level records proved that the non-tidal wave moves from west to east along the northern coasts of the Persian Gulf. Keywords: sea level; wind velocity; atmospheric pressure; spectral analysis 1. Introduction Coastal regions experience sea-level fluctuations occurring over different time scales from seconds to centuries. Globally, astronomical forces of the Sun and the Moon result in tidal variability with periods of 12 and 24 h as well as tidal modulations with periods up to 18.6 years [1,2]. In many regions, the effects of the tides dominate the water-level variability. However, in regions where the tidal effects are smaller, other processes, such as atmospheric forcing, are important for determining the local water level. The relative importance of air pressure and winds in sea-level variability depends on the location and time scale [3]. The sea surface will rise in response to a drop in local air pressure, and vice versa, which is called the local inverse barometer (LIB). In the ideal LIB model a rise/fall of 1 mbar in air pressure results in a fall/rise of 1 cm in sea level [1]. However, this isostatic condition rarely applies to coastal seas [4]. In addition, atmospheric pressure is not the only meteorological forcing responsible for sea-level variability. Winds blowing over the shallow waters result in wind set-up/set-down. Sea-level variability (SLV) in response to wind and atmospheric pressure change has been extensively studied for different water bodies around the world [5–7]. They are considered to be meso-scale phenomena both temporally and spatially [8]. In these studies, observed records of sea-level fluctuations were compared with atmospheric pressure and wind observations both in time and frequency domains. In contrast, some authors employed soft computing methods [9,10] or ocean J. Mar. Sci. Eng. 2020, 8, 285; doi:10.3390/jmse8040285 www.mdpi.com/journal/jmse J. Mar. Sci. Eng. 2020, 8, 285 2 of 16 circulation models [11–13] to investigate the relationship between meteorological forces and the sea-level fluctuations. However, these types of sea-level fluctuations due to atmospheric forcing have received less attention in the Persian Gulf. Although occurrences of tropical cyclones have never been recorded in the Persian Gulf, there may be such events in the future associated with global warming [14]. One of the most recent destructive events in the region was recorded on 19 March 2017. This was a meteorological tsunami [15] that hit the shorelines of Dayyer coastal city along the northern coast of the Gulf [16]. During this event, the maximum run-up of 3 m and inundation of about one kilometer resulted in five people dying. Atmospheric processes that generate SLV encompass a variety of temporal and spatial scales, and their contribution to sea-level extremes may vary spatially and temporally. The response of sea level to meteorological forces can be described by means of depth-integrated equations of motions. The change in sea level, Dη, in response to an atmospheric pressure change DPa and wind can be written as: DPa Dη = − (1) ρg Dη τy = (2) Dy ρg(η + h) where Pa is atmospheric pressure, τy is wind stress, ρ and g are seawater density and gravitational acceleration, respectively. h is the water depth. In this paper, we examine the SLV along the northern coasts of the Persian Gulf, with the aim of identifying the contribution of atmospheric pressure and wind to low-frequency sea-level fluctuations. Sea-level data from 6 tide gauges along the northern Persian Gulf coasts were used to evaluate different types of sea-level fluctuations in the region. In addition, frequency analysis on the meteorological forces, including atmospheric pressure, and wind velocity components are presented in this paper. The coherency between these forces is also described both in time and frequency domains. 2. Materials and Methods 2.1. Study Area The study area is the Persian Gulf, a semi-enclosed basin with the maximum depth of 90 m and area of 251,000 km2. This Gulf is connected to the Gulf of Oman in the east by the Strait of Hormuz. The sea-level data were available at 6 stations along the northern coast of the Persian Gulf (Figure1). Some of these stations are operated by the National Cartographic Center of Iran and the others were installed during temporary projects by Port and Maritime Organization of Iran. The sampling interval of sea-level observations at Kangan and Bushehr was 30 min and 10 min in others (Table1). The mean tidal range varied from 1.5 m in Bushehr to 0.9 m in Javad al-Aemmeh. The maximum tidal range (difference between the highest and the lowest astronomical tide) at these stations were 3.26 m and 1.65 m, respectively. J. Mar. Sci. Eng. 2020, 8, 285 3 of 16 J. Mar. Sci. Eng. 2020, 8, x FOR PEER REVIEW 3 of 16 Figure 1.1. Location of sea-levelsea‐level observations (red triangles) and meteorology stations (yellow boxes) locatedlocated alongalong the the northern northern coasts ofcoasts the Persian of the Gulf Persian (Image fromGulf Google (Image Map: from https: Google//goo.gl /mapsMap:/ MAU6TJCNbFGXoDzU6https://goo.gl/maps/MAU6TJCNbFGXoDzU6).). Table 1.1. Sea-levelSea‐level data along northern Persian GulfGulf coasts.coasts. StationStation NameName Start Start Finish Finish Time Time Step Step (min) Duration Duration (days) (days) DeylamDeylam 11 11 July July 2010 2010 11 11 February February 2011 2011 10 10 215 215 BushehrBushehr 3030 September September 2008 2008 30 30 September September 2010 2010 3030 729 729 LavarLavar 24 24 June June 2010 2010 3 3 October October 2010 2010 10 10 101 101 KanganKangan 3030 September September 2008 2008 30 30 September September 2010 2010 3030 729 729 JavadJavad al-Aemmehal‐Aemmeh 14 14 April April 2010 2010 5 5 July July 2010 2010 10 10 81 81 BostanehBostaneh 2525 November November 2009 2009 1818 August August 2010 2010 10 10 265 265 2.2. Data Sources Data on atmosphericatmospheric parameters including winds (speed and direction) and MeanMean Sea-LevelSea‐Level Pressure (MSLP)(MSLP) were were obtained obtained from from two two sources: sources: (1) coastal(1) coastal meteorology meteorology stations stations [17]; and, [17]; (2) and, global (2) weatherglobal weather prediction prediction model, model, European European Center Center for Medium-Range for Medium‐Range Weather Weather Forecasts Forecasts (ECMWF) (ECMWF) [18]. In[18]. this In study,this study, the latterthe latter set wereset were chosen chosen for for further further analysis. analysis. This This was was because because location location ofof coastalcoastal meteorology stations (Figure 11)) were were far far away away from from the the tide tide gauge gauge stations. stations. Also, Also, the the sampling sampling intervalinterval at these these stations stations was was three three hours hours with with many many gaps gaps in the in the records records and and were were located located inland. inland. The TheECMWF ECMWF ERA5 ERA5 data set data were set available were available at hourly at hourly intervals intervals over a 0.25° over grid. a 0.25 Although◦ grid. Although these data these may dataunderestimate may underestimate some peak some values peak due values to the due temporal to the temporal and spatial and averaging. spatial averaging. Comparison Comparison between betweenERA5 and ERA5 meteorology and meteorology station wind station speeds wind indicated speeds that indicated for both that wind for components both wind components at all stations, at allthe stations, mean correlation the mean coefficient correlation was coe 0.68.fficient The was Root 0.68. Mean The Square Root Mean Error Square(RMSE) Error in these (RMSE) stations in thesewere −1 1 stationsbetween were 2 and between 2.75 ms 2 and. The 2.75 same ms− comparisons. The same comparisonswere performed were for performed MSLP data for MSLPof ERA5 data and of ERA5meteorology and meteorology stations and stations the mean and correlation the mean correlationcoefficient and coe ffiRMSEcient at and the RMSE stations at thewere stations 0.99 and were 0.6 0.99hPa, and respectively.
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