{"paper_id":"0c3ef290-d963-487e-be83-9f896b9302dc","body_text":"Decadal Sea level patterns around Arabian Peninsula at its impact to ENSO events | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Decadal Sea level patterns around Arabian Peninsula at its impact to ENSO events Kutubuddin Ansari, Muhammad Zainuddin Lubis, Mery Biswas This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6440771/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract The study investigates three decades (1990–2020) of sea-level variation across the Arabian Peninsula using Permanent Service for Mean Sea Level (PSMSL) and satellite-based Sea Surface Height (SSH) data. PSMSL and SSH observations are used over six stations, and their linear trends and accelerations along the coast are estimated. The results show a positive trend for both data, points out the rise of sea level, possibly because of land sinking due to oceanic plate subduction. The study also discussed a critical connection between the El Niño-Southern Oscillation (ENSO) event and the cause of large sea-level rise. The SSH data from 1993 to 2020 reveals significant fluctuations attributed to the ENSO phenomenon over the Arabian Peninsula. Based on RMSE statistics, locations exhibiting greater volatility, such as SALALAH and ADEN, demonstrate inferior performance compared to MANAMA and MUSCAT. The average SSH trend reflects the impacts of ENSO, exhibiting negative anomalies during La Niña event and positive anomalies during El Niño events. While locations such as ADEN and SALALAH demonstrate a decrease in SSH, MANAMA often displays a favorable trend of rising SSH. This indicates that local ocean dynamics are significantly affected by global climate. The discrepancies in SSH measurements at each site underscore the necessity of accounting for regional and local variability when assessing sea level change. MANAMA exhibits a robust positive correlation, but ADEN demonstrates a notable negative association with regional SSH patterns. Finally, the marine topography of the Arabian Peninsula has been disclosed with an elevation of onshore and offshore bathymetry alteration and changes in the sea floor. The contour spacing and alignment identified the major and minor faults, and the Aden ridge is noticeable due to the contour pattern where transverse faults to the ridge are accompanied by opposite movement of plates. Arabian Peninsula PSMSL Satellite Altimetry El Niño-Southern Oscillation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 1. Introduction Sea-level change is a critical indication of climate change, which affects global and local coastal areas (Boretti, 2024 ). In recent years, the rise of sea levels has become a dominant issue, giving it the potential to impose severe costs on the public living in coastal areas and ecosystems (Nicholls and Cazenave, 2010 ). Moreover, the global increment and local sea-level variation are influenced by several geographical, oceanographic, and meteorological factors, regularly deviating from the global trend (Ponte et al., 2019 ; Pérez Gómez et al., 2022 ). These sea level factors include the combination of regional and global issues such as ocean currents, tides, wind patterns, and anonymous local topographies. Atmospheric and oceanic circulation also has a significant effect on the sea-level pattern (Boretti, 2024 ). It has been recorded that the global sea level has risen an average of about 21 cm in the last 130 years and is expected to rise continuously during the 21st century or more with a rate of acceleration (Meehl et al., 2007 ; Church and White, 2011 ). Several studies have been reported that the many coastal region cities all over the world are affected because of subsidence (Bell et al., 2002 ; Burbey et al., 2006 ; Abidin et al., 2008; Blewitt et al., 2009 ; Baldi et al., 2009 ; Kolker et al., 2011 ; Bock et al., 2012 ; Featherstone et al., 2012 ; Khan et al., 2014; Chang et al., 2014 ; Wang and Soler, 2015 ; Karegar et al., 2016 ; Parker et al., 2017 ; Hammond et al., 2018 ; Minderhoud et al., 2018 ; Garthwaite and Fuhrmann, 2020 ; Rateb and Abotalib, 2020 ; Ansari and Bae 2021 ; Ansari 2022 ; Wu et al., 2022 ; Ansari et al, 2024 ). Precise prediction of sea-level fluctuation is necessary for understanding the fundamental variability and pattern, adaptation measurements, and simplifying effective planning (Nieves et al., 2023 ). The Arabian Peninsula is surrounded by the crowded cities and infrastructures in Yemen, Oman, Saudi Arabia, Bahrain, Qatar, United Arab Emirates, Kuwait, and Iraq. Power plants, seaports, airports, desalination plants, and oil refineries play an important role in both global and local trade and economy. It is essential to study sea-level rising in the region, with specific importance on low-lying coastal zones of the Arabian Peninsula. Such type of research is crucial to understand the cause of sea-level rising, impacts, and develop the effective strategies to adapt and mitigate the global challenges. Numerous studies have investigated the sea-level rise surrounding the Arabian Peninsula and noticed the substantial changes before its present shape (Babu et al. 2011 ; Al-Subhi et al. 2021; Bakhamis et al. 2024 ). Babu et al. ( 2011 ) discussed the scenarios of sea-level rising due to loss of land, degradation of environment, and infrastructure destruction in the Saudi coastal area of the Arabian Gulf. They created three kinds of present mean sea level rising simulation scenarios concerning 1 m, 2 m, and 3 m and assessed their impacts. Analysis of these scenarios provided the infrastructure and coastal resources spatial vulnerability around 2000 km long coastal line. Al-Subhi et al. (2021) estimated around three decades (1993–2021) of availability by using satellite-derived sea-surface height (SSH) data and tried to understand the climatology of sea level and long-term variability in the Gulf of Arabia in comparison with the global sea level. Their results showed that the Arabian Gulf is categorized by a higher sea level from September to December and a lower sea level from February to May, with a minimum value in April and maximum value in November. It has also been noticed that the sea-level variability in the Arabian Gulf is significantly different and almost opposite to the change of sea-level pattern in the adjacent Red Sea marginal basin. Bakhamis et al ( 2024 ) reviewed the decadal activity in terms of main drivers which affects the sea level changes within the semi-closed water body such as rise of sea-level, thickness of seawater equivalent from climate and gravity experiment, trend of sea surface, trend of terrestrial temperature, salinity levels, precipitation, erosion of coastal region and sedimentation. This study focuses on and examines the sea-level changes on the coast of the Arabian Peninsula. This region is significantly impacted by climate factors, and ongoing research is important for further understanding and mitigating the impact of rising sea levels. The data collection and method of modelling have been written in Section 2. Results and their outcomes are discussed in Section 3, and finally, a summary of obtained results is given in Section 4. 2. Data Collection and Method of modelling The Permanent Service for Mean Sea Level (PSMSL; https://psmsl.org/ ) data provides a standard procedure for the analysis of sea-level measurements (Pugh et al., 2014 ; Woodworth and Player, 2003 ). The PSMSL data is a kind of tide gauge data that measures the sea level concerning an instrument and is somehow able to provide vertical land motion (Wöppelmann and Marcos, 2016 .; Boretti, 2024 ). The study uses data from PSMSL from 19990 to 2020 around the coast of the Arabian Peninsula, which includes the six locations shown in Fig. 1 . The understanding of sea-level around the Peninsula is crucial for the long-term sea-level trend. Hence, we tried to fit them with an equation of linear fit. If the tide gauge measurements at time t are taken with an absolute reference frame of z, then as a linear fit, they can be considered as: $$z=a\\;+\\;bt$$ 1 The component of sea-level variations is characterized by an accelerating pattern because of global warming (Marcos and Amores, 2014 ; Antonov et al., 2005 ; Douglas, 1997 ). Such kind of acceleration at time t relative to z is approximated by a parabola. $$z=a\\;+\\;bt\\;+\\;c{t^2}$$ 2 Here a, b and c are constants, the b in Eq. ( 1 ) indicates the linear trend of absolute sea-level and c in Eq. ( 2 ) point out natural oscillation. The study is also conducted using the sea surface height (SSH) measurements from the GLORYS12V1 dataset, which is part of the Copernicus Marine Environment Monitoring Service (CMEMS) (Internet Ref 1). This service provides a sophisticated global ocean reanalysis characterized by an impressive horizontal resolution of 1/12° and 50 vertical levels, covering the period from 1993 to 2020. The boundary of our focus is from longitude 30–62°E and latitude 12–36°N. The SSH data from dataset ID cmems_mod_glo_phy_my_0.083deg_P1D-m (Internet Ref-2), with SSH represented by the variable zones with a minimum depth of 0 m and a maximum depth of 3 m. This extensive analysis offers significant insights into oceanic processes and climate variability in the Arabian Peninsula. The CMEMS provided the SSH data from January 1993 to December 2020. We get the SST anomaly with a time range of 1993–2020 (Nino 3.4 index) from Internet Ref-3. Tide gauges and satellite altimetry sea-level measurements are elemental to verify our observations and better understand the dynamics of regional oceans (Cipollini et al., 2017). Tide gauges record changes in sea level relative to fixed points of land, which can be altered by vertical movements of land. Satellite altimetry, on the other hand, yields absolute sea-level change data referred to a geocentric reference frame (Wöppelmann and Marcos, 2016). Hence, for comparison analysis, the mean of PSMSL and SSH data are subtracted from their original time-series, and new time-series are generated, which are the fundamental deviation from their average. The mathematical equations that have been used are as follows (Ansari et al., 2022 ): $$PSMS{L_{New}}=PSMSL - mean\\;(PSMSL)$$ 3 $$Satellite\\;Altimetr{y_{New}}=Satellite\\;Altimetry - mean\\;(Satellite\\;Altimetry)$$ 4 These newly obtained time-series are compared, and their correlation coefficients are estimated. Finally, the prediction of the bathymetry method is used to map the ocean of the Arabian Peninsula. The data set is based on the General Bathymetric Chart of the Oceans (GEBCO) global bathymetric grids developed since 2019 through The Nippon Foundation-GEBCO Seabed 2030 Project (Internet ref-1). The data was used to assess the slope direction of the topographic slope and undulating terrain to determine the deposition of the marine depression pattern in topography, faults, and ridges. In this work, we specify the offshore topographic slope direction with slope ranges and contour spacing from the coastline with major and minor faults. Bathymetric data were downloaded from the GEBCO website (Internet ref-1) at an extension of 10.90°N–34.60°N and 32.24°E– 62.24°E. This 3D data is processed in the Arc GIS 10.8.1 (2020) platform. 3. Result and Discussion We used PSMSL and SSH observations from 1991 to 2020 over six stations and estimated linear trends and accelerations along the coast of the Arabian Peninsula, as shown in Fig. 2 and Fig. 3 , respectively. The gap in Fig. 2 occurred because of missing PSMSL data, and similarly, SSH data was available from 1993, and hence, there are also two years (1991–1992) of gaps in the figure. A minimum least square method is used to approximate the curve in linear and quadratic form by using Eq. 1 and Eq. 2 . Decadal sea level patterns in terms of velocity and acceleration concerning time are analyzed, and their graphical representation as the time in years is given by the X-axis and sea level in the Y-axis. The results show a positive trend for both data and all selected sites (except ADEN because of data issues), indicating sea-level rising with higher than 0.20 (x coefficient in Eq. 1 ). This kind of trend happened possibly because of land sinking due to subduction of oceanic plate (Ansari and Bae 2021 ). The tide gauge plot for ELAIT for PSMSL and SSH data shows a rising trend of 3 and 4 mm/yr, respectively. The difference between these two data occurred because PSMSL data have a very short time window. A linear fitting for a short time window can show overestimate or overestimate relative rese of sea-level compared to the results showed by using full-time window (Parker and Ollier; 2015). Other sites such as EDEN, MASIRAH, MANAMA, and MUSCAT show some differences in their trend. The site of SALALAH shows a similar trend of around 3.7 mm/yr for both data sets. This is because PSMSL data was continuously available. In order to see the differences between PSMSL and SSH, a comparative analysis was performed by using Eqs. ( 3 ) and ( 4 ) as shown in Fig. 4 . Results in both time series, the physical trend appears similar, but there are periods of low agreement and significant differences between the tide gauge and satellite altimetry. This included the projection of the sea-level trends for every tide gauge station and the nearest satellite altimetry grid point (Dean and Houston, 2013). The PSMSL and SSH correlation coefficients for the PSMSL are not nearly the same for all stations, as a high correlation for some (e.g., ADEN and SALALAH) is close to 0.92 (> 0.92), while MANAMA has a lower relative value (0.32). Such discrepancies can be due to several reasons, which include the poor precision of satellite data in coastal areas, the effective data coverage of the satellite altimetry, and the possible effect of the distance between the tide gauge station and the nearest satellite grid point (Vignudelli et al., 2019). It is thus worth keeping this in mind when interpreting the comparative analysis and taking into account the differences between the two datasets as a means to explain regional sea-level trends and their drivers. We took annual mean of SSH during 1993–2020 and plotted their outcomes for the different sites as shown in Fig. 5. The observed patterns exhibit strong correlations with the El Niño-Southern Oscillation (ENSO) phenomenon, a major driver of global climate variability. The results show that the SSH values across the Peninsula tend to be elevated during the El Niño events when sea surface temperatures are warmer than average in the equatorial Pacific. This is evident in the peak values observed in 1993–1994 and 1997–1998. During these El Niño, MANAMA reached a maximum SSH of 0.29 m in 1993. MUSCAT recorded a maximum SSH of 0.28 m in 1993. MASIRAH had a maximum SSH of 0.24 m in 1993. SALALAH reached a peak SSH of 0.30 m in 1993, and ADEN recorded a maximum SSH of 0.36 m in 1993. EILAT had a maximum SSH of 0.17 m in 1993. Conversely, during La Niña events, which feature cooler than average equatorial Pacific temperatures, the SSH values generally decrease. This can be seen in the lower values recorded in 1999–2000 and 2010–2011, where some locations even experienced negative SSH anomalies, such as SALALAH reaching a minimum of -0.04 m in 2011 and EILAT dropping to a minimum of -0.13 m in 2010. This strong correlation between El Niño events and the regional SSH patterns highlights the teleconnections between the Pacific and Indian Ocean basins, where large-scale climate patterns can significantly influence ocean dynamics. The yearly root means square error (RMSE) of SSH at various locations across the Arabian Peninsula from 1993 to 2020 has been shown in Fig. 6 . The data exhibits significant variability, with clear correlations to the El Niño-Southern Oscillation (ENSO) phenomenon. In the MANAMA station, it has a minimum RMSE of 0.05 m, a maximum RMSE of 0.16 m, and a mean RMSE of 0.07 m. In the MUSCAT, it has a minimum RMSE of 0.04 m, a maximum RMSE of 0.12 m, and a mean RMSE of 0.06 m. In the MASIRAH, it has a minimum RMSE of 0.05 m, a maximum RMSE of 0.15 m, and a mean RMSE of 0.07 m. SALALAH has a minimum RMSE of 0.09 m, a maximum RMSE of 0.25 m, and a mean RMSE of 0.13 m. In the ADEN, it has a minimum RMSE of 0.11 m, a maximum RMSE of 0.24 m, and a mean RMSE of 0.16 m. In the EILAT, it has a minimum RMSE of 0.08 m, a maximum RMSE of 0.17 m, and a mean RMSE of 0.12 m. These variations in RMSE values across the different locations and over time highlight the complex regional ocean dynamics influenced by large-scale climate patterns in ENSO events. The average SSH anomalies with trends at various locations across the Arabian Peninsula from 1993 to 2020 have been shown in Fig. 7 . The results exhibit a clear correlation with the El Niño-Southern Oscillation (ENSO) phenomenon. During El Niño events, the region experienced positive SSH anomalies, with the maximum values reaching 0.15 m in 1993 at both ADEN and EILAT, while La Niña events, such as in 2011, were characterized by negative anomalies, with the minimum values reaching − 0.24 m in SALALAH and − 0.22 m in ADEN. The average SSH values and trends were as follows: MANAMA (0.1487 m, 0.0038 m/year), MUSCAT (0.1770 m, -0.0007 m/year), MASIRAH (0.1635 m, -0.0029 m/year), SALALAH (0.1747 m, -0.0054 m/year), ADEN (0.1931 m, -0.0097 m/year), and EILAT (0.0173 m, -0.0031 m/year), highlighting the complex regional ocean dynamics influenced by large-scale climate patterns across the Arabian Peninsula. The positive trend in MANAMA indicates a gradual increase in sea level over the years, while the negative trends in other locations suggest a decline, underscoring the intricate regional oceanographic processes driven by global-scale ENSO phenomena. The total average SSH value (1993–2020) for all locations has been described in Fig. 8 . The results shows that the average SSH value is maximum (0.19 m) and is found in all coastal stations located at ADEN in the South Arabian Peninsula. The minimum average values of SSH are at the north, down to 0.02 m at EILAT, in the northern Red Sea. The average SSH values in the coastal locations of MANAMA (0.15 m), MUSCAT (0.18 m), MASIRAH (0.16 m), SALALAH (0.17 m), ADEN (0.19 m)) are slightly more elevated when compared to the more inland or open ocean locations. This means that the coastal domain could be more affected by changes in sea level and related processes like tides, storm surges, and coastal flooding. The SSH values essentially rise from the north of the EILAT region to the south of the ADEN region. Among other factors, this may have to do with its location within the regional ocean currents which are themselves influenced by, among other things, the response of the Indian Ocean to the Indian Ocean Dipole and ENSO events. Such variability in SSH values highlights the importance of considering local and regional differences when assessing the impact of sea seal-level rise and associated phenomena in this dynamic and heterogeneous marine region. The correlation coefficient of total average SSH values for specified locations (1993–2020) has been shown in Fig. 9 . MANAMA exhibits the highest positive correlation coefficient of 0.56, indicating a strong positive relationship between the average SSH and the time series at this location. This suggests that the SSH values at MANAMA tend to increase or decrease with the overall regional trends. In contrast, the locations of MUSCAT, MASIRAH, SALALAH, and ADEN show negative correlation coefficients, ranging from − 0.12 to -0.67. This implies that the average SSH values at these locations have an inverse relationship with the regional SSH patterns over the 1993–2020 period. ADEN, in particular, has the strongest negative correlation coefficient of -0.67, suggesting a significant divergence from the broader regional SSH dynamics. EILAT, located in the northern Red Sea, has a moderate negative correlation coefficient of -0.29, indicating a weaker, but still inverse, relationship with the overall SSH trends across the Peninsula. These variations in the correlation coefficients highlight the complex and heterogeneous nature of SSH variability across the different coastal and near-shore locations within the study area. Understanding these spatial patterns of correlation can provide valuable insights into the regional ocean dynamics and assist in the development of more accurate models and forecasting systems for the Peninsula. The average SSH trends at various locations across the Peninsula from 1993 to 2020, with a clear connection to the ENSO events phenomenon has been shown in Fig. 10 . We use a color scale where negative trends (in blue) indicate a net increase in SSH due to ocean dynamics and positive trends (in red) indicate a net decrease in SSH either from ocean dynamics as well as the replenishment caused by overlying water mass. These data provide important information on regional ocean dynamics and their relationship with large-scale climate phenomena like ENSO events, which influence SSH patterns across the Peninsula. The trends of SSH show major spatial differences, mainly with positive trends in the northern region of SSH, and more negative trends tend to be located around the southwestern region flowing the Horn of Africa. Positive SSH anomalies were observed during El Niño events, peaking at 0.15 m in 1993 at ADEN and EILAT, while La Niña events were characterized by negative anomalies down to − 0.24 m in SALALAH and − 0.22 m at ADEN. Average SSH values and trends were heterogeneous among the sites, with MANAMA exhibiting the most positive trend (0.0038 m/year), placing it at the upper end, while ADEN had the most negative trend (− 0.0097 m/year), placing it at the lower end of the scale, demonstrating the complexity of regional oceanography. In which the strong influence of large-scale climate models such as ENSO in the surrounded ocean dynamics. The highest positive trend of SSH at 0.01500 m/year is found in MANAMA, which is located in the northern part of the region, while the lowest negative trend of -0.01227 m/year is recorded in ADEN, which is in the southwestern part of the Arabian Peninsula. The under marine topography map with elevation of both onshore and offshore disclose the bathymetry alteration and changes in the sea floor has been shown in Fig. 11 . The aspect NE and E directions are shown about the presence of ridges, depression and trances in Fig. 12 . The contour map of 250 m interval discloses the under marine structure and shelf displacement which are also defined by Bollino et al, in 2022 especially in the Gulf of Aden. The Aden ridge is composed of multi-slope directions with intermediate flat bases and intersected by SW-NE extended major faults as SSFZ: Shukra-el-Sheik break zone. XAMFZ: Al Mukalla break zone. AFFZ: Alula-Fartakbreak zone. SHFZ: Socotra-Hadbeen break zone (Fig. 13 ) (Bollino et al., 2022 ). In the offshore region, contour spacing and alignment identify the major and minor faults and the Aden ridge is noticeable due to the contour pattern where transverse faults to the ridge are accompanied by opposite movement of plates; e. g. near Shukra-el-Sheik break zone it is 13mm/yr, Alula-Fartakbreak zone 23mm/yr (Bollino et al., 2022 ). This kind of movement is also in the Red Sea area at an average of 15mm/yr. The degree of slope varies from 0 to 90. The depressed land may be indicated by the flat topography (Fig. 14 ). The slope changes to moderate to steep, and structural aspects of the Aden Gulf are visible with 0º − 89.99º very steep slope boundaries. Because of such under marine structural control, there may be some oblique rifting on the fault geometry along with crustal necking and thermo mechanical deformation. Conclusion This study focuses on and examines the sea-level changes on the coast of the Arabian Peninsula using six tide gauge sites across the Arabian Peninsula. Decadal sea level patterns in terms of velocity and acceleration with respect to time show a positive trend for PSMSL and SSH data of sea-level rising with higher than 0.20. The PSMSL and SSH correlation coefficients for the PSMSL shows highest correlation for some (e.g., ADEN and SALALAH) is close to 0.92, while MANAMA has a lower relative value (0.32). The results based on SSH reported the highest levels of sea at ADEN (0.36 m) and SALALAH (0.30 m) in 1993. During La Niña, on the other hand, SSH values tend to go down. In 2010, negative anomalies were seen in SALALAH (-0.04 m) and EILAT (-0.13 m). The RMSE of SSH shows clear variability: MANAMA has a minimum RMSE of 0.05 m and a maximum of 0.16 m, while EILAT shows a minimum RMSE of 0.08 m and a maximum of 0.17 m. The average SSH trend across locations shows an increase in MANAMA (0.0038 m/yr) and a decrease in ADEN (-0.0097 m/yr), with MASIRAH (-0.0029 m/yr) and SALALAH (-0.0054 m/yr) also showing negative trends. Changes in SSH show how different places react to ENSO events. The bathymetry contour map of 250 m interval discloses the under marine structure and shelf displacement especially in the Gulf of Aden. The slope changes to moderate to steep and structural aspects of the Aden Gulf are visible with 0º − 89.99º very steep slope boundaries. Declarations Ethical approval and consent to participate: This study does not involve human participants, animal experiments, or clinical trials. Human ethics: No human related data or biological materials were used in this research. Consent for publication: All authors have reviewed the final version of the manuscript and consent to its submission for publication Funding: Not applicable Authors' contributions : Kutubuddin Ansari and Muhammad Zainuddin Lubis wrote the main manuscript text; Mery Biswas prepared the figures Availability of supporting data: The datasets used in this study include PSMSLdownloaded from PSMSL site(https://psmsl.org/data/).The study is also conducted using the sea surface height (SSH) measurements from the GLORYS12V1 dataset (https://data.marine.copernicus.eu/products). We get the SST anomaly (Nino 3.4 index) from (https://www.ncei.noaa.gov/pub/data/cmb/ersst/v5/netcdf/) Competing interests: The authors declare no competing interests. Clinical Trial Number in the manuscript. Not Applicable References Al-Subhi, A.M. and Abdulla, C.P., 2021. Sea-level variability in the Arabian Gulf in comparison with global oceans. 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Internet Ref-1: https://data.marine.copernicus.eu/products Internet Ref-2: https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_001_030/ download?dataset=cmems_mod_glo_phy_my_0.083deg_P1D-m_202311 Internet Ref-3: https://www.ncei.noaa.gov/pub/data/cmb/ersst/v5/netcdf/. Hammond, W.C., Burgette, R.J., Johnson, K.M. and Blewitt, G., 2018. Uplift of the western transverse ranges and Ventura area of Southern California: A four‐technique geodetic study combining GPS, InSAR, leveling, and tide gauges. Journal of Geophysical Research: Solid Earth, 123(1), pp.836-858; https://doi.org/10.1002/2017JB014499 Karegar, M.A., Dixon, T.H. and Engelhart, S.E., 2016. Subsidence along the Atlantic Coast of North America: Insights from GPS and late Holocene relative sea level data. Geophysical Research Letters, 43(7), pp.3126-3133; https://doi.org/10.1002/2016GL068015 Khan, S.D., Huang, Z. and Karacay, A., 2014. Study of ground subsidence in northwest Harris county using GPS, LiDAR, and InSAR techniques. Natural hazards, 73, pp.1143-1173; ; https://doi.org/10.1007/s11069-014-1067-x Kolker, A.S., Allison, M.A. and Hameed, S., 2011. An evaluation of subsidence rates and sea‐level variability in the northern Gulf of Mexico. Geophysical Research Letters, 38(21). https://doi.org/10.1029/2011GL049458 Minderhoud, P.S.J., Coumou, L., Erban, L.E., Middelkoop, H., Stouthamer, E. and Addink, E.A., 2018. The relation between land use and subsidence in the Vietnamese Mekong delta. Science of the Total Environment, 634, pp.715-726; https://doi.org/10.1016/j.scitotenv.2018.03.372 Marcos, M. and Amores, A., 2014. Quantifying anthropogenic and natural contributions to thermosteric sea level rise. Geophysical Research Letters, 41(7), pp.2502-2507; https://doi.org/10.1002/2014GL059766 Meehl, G.A., Stocker, T.F., Collins, W.D., Friedlingstein, P., Gaye, A.T., Gregory, J.M., Kitoh, A., Knutti, R., Murphy, J.M., Noda, A., Raper, S.C.B., Watterson, I.G., Weaver, A.J., Zhao, Z.-C., 2007. Global climate projections. In: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M., Miller, H.L. (Eds.), Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Nicholls, R.J. and Cazenave, A., 2010. Sea-level rise and its impact on coastal zones. science, 328(5985), pp.1517-1520; https://doi.org/10.1126/science.1185782 Nieves, V., Ruescas, A. and Sauzède, R., 2023. AI for marine, ocean and climate change monitoring. Remote Sensing, 16(1), p.15; https://doi.org/10.3390/rs16010015 Parker, A.L., Filmer, M.S. and Featherstone, W.E., 2017. First results from Sentinel-1A InSAR over Australia: application to the Perth Basin. Remote Sensing, 9(3), p.299; https://doi.org/10.3390/rs9030299 Pugh, D., Woodworth, P.L. and Woodworth, P., 2014. Sea-level science: understanding tides, surges, tsunamis and mean sea-level changes. Cambridge University Press. Ponte, R.M., Carson, M., Cirano, M., Domingues, C.M., Jevrejeva, S., Marcos, M., Mitchum, G., Van De Wal, R.S.W., Woodworth, P.L., Ablain, M. and Ardhuin, F., 2019. Towards comprehensive observing and modeling systems for monitoring and predicting regional to coastal sea level. Frontiers in Marine Science, 6, p.437; https://doi.org/10.3389/fmars.2019.00437 Pérez Gómez, B., Vilibić, I., Šepić, J., Međugorac, I., Ličer, M., Testut, L., Fraboul, C., Marcos, M., Abdellaoui, H., Álvarez Fanjul, E. and Barbalić, D., et al, 2022. Coastal sea level monitoring in the Mediterranean and Black seas. Ocean science, 18(4), pp.997-1053. https://doi.org/10.5194/os-18-997-2022 Rateb, A. and Abotalib, A.Z., 2020. Inferencing the land subsidence in the Nile Delta using Sentinel-1 satellites and GPS between 2015 and 2019. Science of the Total Environment, 729, p.138868; https://doi.org/10.1016/j.scitotenv.2020.138868 Wang, G. and Soler, T., 2015. Measuring land subsidence using GPS: Ellipsoid height versus orthometric height. Journal of surveying engineering, 141(2), p.05014004; https://doi.org/10.1061/(ASCE)SU.1943-5428.0000137 Wu, P.C., Wei, M. and D’Hondt, S., 2022. Subsidence in coastal cities throughout the world observed by InSAR. Geophysical Research Letters, 49(7), p.e2022GL098477; https://doi.org/10.1029/2022GL098477 Woodworth, P.L., Player, R., 2003. The permanent service for mean sea level: An update to the 21stCentury. J. Coast. Res. 287–295 Wöppelmann, G., & Marcos, M. (2016). Vertical land motion as a key to understanding sea level change and variability. Reviews of Geophysics, 54(1), 64-92. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 Aug, 2025 Reviews received at journal 27 Aug, 2025 Reviewers agreed at journal 10 Aug, 2025 Reviewers invited by journal 18 Apr, 2025 Editor assigned by journal 16 Apr, 2025 Submission checks completed at journal 16 Apr, 2025 First submitted to journal 13 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6440771\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":444698766,\"identity\":\"6abb3556-3a4f-4c52-9a30-96d23f02d77d\",\"order_by\":0,\"name\":\"Kutubuddin Ansari\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBACCYYEMM3DwN4ApAwsSNHCcwCkRYJ4LUBWAoRPEEi2Jz97+KPmnozBzedXN/wokGDgb+9OwKtFmueZuTHPsWIeydk5ZTd7gA6TOHN2A14tchIJZtIMbAk8/NI5aTd4gFoMJHIJaUn/JvnjXwIPm+SZtJt/iNEiLZFjJsHbBrRFgv3YbaJskex5UybN25fAI9mTw3ZbxkCCh6BfJI6nb5P88S3B3uD48Wc33/yxkeNv78WvBQnwGIBJYpWDAPsDUlSPglEwCkbBCAIAYvhAuG0GLpMAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Integrated Geoinformation (IntGeo) Solution Private Limited\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Kutubuddin\",\"middleName\":\"\",\"lastName\":\"Ansari\",\"suffix\":\"\"},{\"id\":444698767,\"identity\":\"098ec16a-8171-44e1-a5c5-4f00ce2e2d28\",\"order_by\":1,\"name\":\"Muhammad Zainuddin Lubis\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shanghai Ocean University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Muhammad\",\"middleName\":\"Zainuddin\",\"lastName\":\"Lubis\",\"suffix\":\"\"},{\"id\":444698768,\"identity\":\"300b109b-6825-4291-9d53-054358a43b2e\",\"order_by\":2,\"name\":\"Mery Biswas\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Presidency University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Mery\",\"middleName\":\"\",\"lastName\":\"Biswas\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-04-13 18:53:09\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6440771/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6440771/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":81398315,\"identity\":\"d33bf8dd-1841-429e-9c84-5844b7e755c4\",\"added_by\":\"auto\",\"created_at\":\"2025-04-25 15:57:05\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":165117,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe six selected locations around the coast of the Arabian Peninsula from where the PSMSL data has been studied during 1990-2020.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6440771/v1/22956ede981536a7575f1728.jpg\"},{\"id\":81398314,\"identity\":\"e0938468-7fe8-4b90-8abf-d8c9e934613c\",\"added_by\":\"auto\",\"created_at\":\"2025-04-25 15:57:05\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":141230,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePSMSL data have been fitted in linear and quadratic equations over six stations located at the Arabian Peninsula.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6440771/v1/42a59130c9d12470ba0bd3b7.jpg\"},{\"id\":81399410,\"identity\":\"c0e861ca-9a34-4c08-aadf-5e0e0aa471be\",\"added_by\":\"auto\",\"created_at\":\"2025-04-25 16:13:05\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":185887,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSSH data have been fitted in linear and quadratic equations over six stations located at the Arabian Peninsula.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6440771/v1/6fc980d5e651adaeed674e63.jpg\"},{\"id\":81398318,\"identity\":\"50f3b25f-8f9c-4321-9fbe-60b746031d41\",\"added_by\":\"auto\",\"created_at\":\"2025-04-25 15:57:05\",\"extension\":\"jpg\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":200359,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe differences between PSMSL and SSH, a comparative analysis was performed by using Eq. (3) and (4).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6440771/v1/cef57c1554599066e12c30fc.jpg\"},{\"id\":81398329,\"identity\":\"c80e9ae3-8d38-40cf-b327-7be3bacf2cb1\",\"added_by\":\"auto\",\"created_at\":\"2025-04-25 15:57:05\",\"extension\":\"jpg\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":86172,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAnnual average of SSH at specified location (1993-2020)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6440771/v1/7d84194e931351991c344246.jpg\"},{\"id\":81398322,\"identity\":\"c1e0da7d-a0ee-49bb-9836-cb14d1428023\",\"added_by\":\"auto\",\"created_at\":\"2025-04-25 15:57:05\",\"extension\":\"jpg\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":61094,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAnnual RMSE of SSH at specified location (1993-2020)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6440771/v1/c52d4793776d769b0d1ca293.jpg\"},{\"id\":81398708,\"identity\":\"9153912c-d79f-4f1c-a63b-845aa624fd23\",\"added_by\":\"auto\",\"created_at\":\"2025-04-25 16:05:05\",\"extension\":\"jpg\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":89020,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAnnual average SSH anomalies at specified location (1993-2020)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"7.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6440771/v1/caeeeb7bad163b4fb75db11b.jpg\"},{\"id\":81398710,\"identity\":\"674a0cc3-f7bd-43ba-bb73-f816fdd522b6\",\"added_by\":\"auto\",\"created_at\":\"2025-04-25 16:05:05\",\"extension\":\"jpg\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":77628,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eTotal average of SSH value at specified location (1993-2020)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"8.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6440771/v1/5acbd5c2ff8b0ec8979efeac.jpg\"},{\"id\":81399411,\"identity\":\"4fb09580-50a0-4ca8-96e2-a552a262f98c\",\"added_by\":\"auto\",\"created_at\":\"2025-04-25 16:13:05\",\"extension\":\"jpg\",\"order_by\":9,\"title\":\"Figure 9\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":42807,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCorrelation coefficient of total average SSH values for specified locations (1993-2020)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"9.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6440771/v1/bfa4520696b558628f555ad4.jpg\"},{\"id\":81398333,\"identity\":\"f736c019-467d-4bfc-aa55-f943e8ebf883\",\"added_by\":\"auto\",\"created_at\":\"2025-04-25 15:57:06\",\"extension\":\"jpg\",\"order_by\":10,\"title\":\"Figure 10\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":78395,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eTotal average SSH trend for specified locations (1993-2020)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"10.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6440771/v1/e4f39e70434dea675c3c3e06.jpg\"},{\"id\":81398325,\"identity\":\"0ad98c50-42a9-4104-8e62-2b096010cbdf\",\"added_by\":\"auto\",\"created_at\":\"2025-04-25 15:57:05\",\"extension\":\"jpg\",\"order_by\":11,\"title\":\"Figure 11\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":182362,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eUnder marine topography map based on under marine DEM data with six under marine elevation classification.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"11.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6440771/v1/fc05b224860fed68f28045a8.jpg\"},{\"id\":81398331,\"identity\":\"dd783bf3-99ee-4557-a593-7443b14e5319\",\"added_by\":\"auto\",\"created_at\":\"2025-04-25 15:57:05\",\"extension\":\"jpg\",\"order_by\":12,\"title\":\"Figure 12\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":191470,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eTen slope directions are mapped both on land and bottom sea topography based on bathymetric data set.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"12.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6440771/v1/98495552e27369f749677e5f.jpg\"},{\"id\":81398346,\"identity\":\"ce6d1c54-d733-4c73-bdd0-f2fbebd128fe\",\"added_by\":\"auto\",\"created_at\":\"2025-04-25 15:57:06\",\"extension\":\"jpg\",\"order_by\":13,\"title\":\"Figure 13\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":229847,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDiagram outline appearing the most structural highlights of the Inlet of Aden and encompassing zones utilizing distributed information (Bollino et al., 2022; Nonn et al., 2019). Expansive dark bolts demonstrate the directions of plate movement within the locale. SSFZ: Shukra-el-Sheik break zone. XAMFZ: Al Mukalla break zone. AFFZ: Alula-Fartak break zone. SHFZ: Socotra-Hadbeen break zone. Cyan, light yellow and light ruddy colors demonstrate the western, central and eastern divisions, separately.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"13.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6440771/v1/1a6ead97d6c11e3ea3e02c5e.jpg\"},{\"id\":81398341,\"identity\":\"50a67c7a-3a1e-4053-a906-ee0eaf24811e\",\"added_by\":\"auto\",\"created_at\":\"2025-04-25 15:57:06\",\"extension\":\"jpg\",\"order_by\":14,\"title\":\"Figure 14\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":157700,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSlope map of land and under marine part.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"14.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6440771/v1/0e709c23021ab102e0e29161.jpg\"},{\"id\":81399646,\"identity\":\"e68d9987-04b3-4088-b802-e634afb29df3\",\"added_by\":\"auto\",\"created_at\":\"2025-04-25 16:21:06\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2380108,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6440771/v1/7c0d1f87-e3d3-4c9a-9c65-a7d5c0b994c1.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Decadal Sea level patterns around Arabian Peninsula at its impact to ENSO events\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eSea-level change is a critical indication of climate change, which affects global and local coastal areas (Boretti, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). In recent years, the rise of sea levels has become a dominant issue, giving it the potential to impose severe costs on the public living in coastal areas and ecosystems (Nicholls and Cazenave, \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e). Moreover, the global increment and local sea-level variation are influenced by several geographical, oceanographic, and meteorological factors, regularly deviating from the global trend (Ponte et al., \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; P\\u0026eacute;rez G\\u0026oacute;mez et al., \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). These sea level factors include the combination of regional and global issues such as ocean currents, tides, wind patterns, and anonymous local topographies. Atmospheric and oceanic circulation also has a significant effect on the sea-level pattern (Boretti, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). It has been recorded that the global sea level has risen an average of about 21 cm in the last 130 years and is expected to rise continuously during the 21st century or more with a rate of acceleration (Meehl et al., \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e; Church and White, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e). Several studies have been reported that the many coastal region cities all over the world are affected because of subsidence (Bell et al., \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e; Burbey et al., \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e; Abidin et al., 2008; Blewitt et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e; Baldi et al., \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e; Kolker et al., \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Bock et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Featherstone et al., \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Khan et al., 2014; Chang et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Wang and Soler, \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Karegar et al., \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Parker et al., \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Hammond et al., \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Minderhoud et al., \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Garthwaite and Fuhrmann, \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Rateb and Abotalib, \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Ansari and Bae \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Ansari \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Wu et al., \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Ansari et al, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Precise prediction of sea-level fluctuation is necessary for understanding the fundamental variability and pattern, adaptation measurements, and simplifying effective planning (Nieves et al., \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe Arabian Peninsula is surrounded by the crowded cities and infrastructures in Yemen, Oman, Saudi Arabia, Bahrain, Qatar, United Arab Emirates, Kuwait, and Iraq. Power plants, seaports, airports, desalination plants, and oil refineries play an important role in both global and local trade and economy. It is essential to study sea-level rising in the region, with specific importance on low-lying coastal zones of the Arabian Peninsula. Such type of research is crucial to understand the cause of sea-level rising, impacts, and develop the effective strategies to adapt and mitigate the global challenges. Numerous studies have investigated the sea-level rise surrounding the Arabian Peninsula and noticed the substantial changes before its present shape (Babu et al. \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Al-Subhi et al. 2021; Bakhamis et al. \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Babu et al. (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e) discussed the scenarios of sea-level rising due to loss of land, degradation of environment, and infrastructure destruction in the Saudi coastal area of the Arabian Gulf. They created three kinds of present mean sea level rising simulation scenarios concerning 1 m, 2 m, and 3 m and assessed their impacts. Analysis of these scenarios provided the infrastructure and coastal resources spatial vulnerability around 2000 km long coastal line. Al-Subhi et al. (2021) estimated around three decades (1993\\u0026ndash;2021) of availability by using satellite-derived sea-surface height (SSH) data and tried to understand the climatology of sea level and long-term variability in the Gulf of Arabia in comparison with the global sea level. Their results showed that the Arabian Gulf is categorized by a higher sea level from September to December and a lower sea level from February to May, with a minimum value in April and maximum value in November. It has also been noticed that the sea-level variability in the Arabian Gulf is significantly different and almost opposite to the change of sea-level pattern in the adjacent Red Sea marginal basin. Bakhamis et al (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e) reviewed the decadal activity in terms of main drivers which affects the sea level changes within the semi-closed water body such as rise of sea-level, thickness of seawater equivalent from climate and gravity experiment, trend of sea surface, trend of terrestrial temperature, salinity levels, precipitation, erosion of coastal region and sedimentation.\\u003c/p\\u003e \\u003cp\\u003eThis study focuses on and examines the sea-level changes on the coast of the Arabian Peninsula. This region is significantly impacted by climate factors, and ongoing research is important for further understanding and mitigating the impact of rising sea levels. The data collection and method of modelling have been written in Section 2. Results and their outcomes are discussed in Section 3, and finally, a summary of obtained results is given in Section 4.\\u003c/p\\u003e\"},{\"header\":\"2. Data Collection and Method of modelling\",\"content\":\"\\u003cp\\u003eThe Permanent Service for Mean Sea Level (PSMSL; \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://psmsl.org/\\u003c/span\\u003e\\u003cspan address=\\\"https://psmsl.org/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) data provides a standard procedure for the analysis of sea-level measurements (Pugh et al., \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Woodworth and Player, \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e). The PSMSL data is a kind of tide gauge data that measures the sea level concerning an instrument and is somehow able to provide vertical land motion (Wöppelmann and Marcos, \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e.; Boretti, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). The study uses data from PSMSL from 19990 to 2020 around the coast of the Arabian Peninsula, which includes the six locations shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003eThe understanding of sea-level around the Peninsula is crucial for the long-term sea-level trend. Hence, we tried to fit them with an equation of linear fit. If the tide gauge measurements at time t are taken with an absolute reference frame of z, then as a linear fit, they can be considered as:\\u003c/p\\u003e\\u003cdiv id=\\\"Equ1\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equ1\\\" name=\\\"EquationSource\\\"\\u003e\\n$$z=a\\\\;+\\\\;bt$$\\u003c/div\\u003e\\u003cdiv class=\\\"EquationNumber\\\"\\u003e1\\u003c/div\\u003e\\u003c/div\\u003e\\u003cp\\u003e\\u003c/p\\u003e \\u003cp\\u003eThe component of sea-level variations is characterized by an accelerating pattern because of global warming (Marcos and Amores, \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Antonov et al., \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e; Douglas, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e1997\\u003c/span\\u003e). Such kind of acceleration at time t relative to z is approximated by a parabola.\\u003c/p\\u003e\\u003cdiv id=\\\"Equ2\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equ2\\\" name=\\\"EquationSource\\\"\\u003e\\n$$z=a\\\\;+\\\\;bt\\\\;+\\\\;c{t^2}$$\\u003c/div\\u003e\\u003cdiv class=\\\"EquationNumber\\\"\\u003e2\\u003c/div\\u003e\\u003c/div\\u003e\\u003cp\\u003e\\u003c/p\\u003e \\u003cp\\u003eHere a, b and c are constants, the b in Eq.\\u0026nbsp;(\\u003cspan refid=\\\"Equ1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e) indicates the linear trend of absolute sea-level and c in Eq.\\u0026nbsp;(\\u003cspan refid=\\\"Equ2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e) point out natural oscillation.\\u003c/p\\u003e \\u003cp\\u003eThe study is also conducted using the sea surface height (SSH) measurements from the GLORYS12V1 dataset, which is part of the Copernicus Marine Environment Monitoring Service (CMEMS) (Internet Ref 1). This service provides a sophisticated global ocean reanalysis characterized by an impressive horizontal resolution of 1/12° and 50 vertical levels, covering the period from 1993 to 2020. The boundary of our focus is from longitude 30–62°E and latitude 12–36°N. The SSH data from dataset ID cmems_mod_glo_phy_my_0.083deg_P1D-m (Internet Ref-2), with SSH represented by the variable zones with a minimum depth of 0 m and a maximum depth of 3 m. This extensive analysis offers significant insights into oceanic processes and climate variability in the Arabian Peninsula. The CMEMS provided the SSH data from January 1993 to December 2020. We get the SST anomaly with a time range of 1993–2020 (Nino 3.4 index) from Internet Ref-3.\\u003c/p\\u003e \\u003cp\\u003eTide gauges and satellite altimetry sea-level measurements are elemental to verify our observations and better understand the dynamics of regional oceans (Cipollini et al., 2017). Tide gauges record changes in sea level relative to fixed points of land, which can be altered by vertical movements of land. Satellite altimetry, on the other hand, yields absolute sea-level change data referred to a geocentric reference frame (Wöppelmann and Marcos, 2016). Hence, for comparison analysis, the mean of PSMSL and SSH data are subtracted from their original time-series, and new time-series are generated, which are the fundamental deviation from their average. The mathematical equations that have been used are as follows (Ansari et al., \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e):\\u003c/p\\u003e\\u003cdiv id=\\\"Equ3\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equ3\\\" name=\\\"EquationSource\\\"\\u003e\\n$$PSMS{L_{New}}=PSMSL - mean\\\\;(PSMSL)$$\\u003c/div\\u003e\\u003cdiv class=\\\"EquationNumber\\\"\\u003e3\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Equ4\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equ4\\\" name=\\\"EquationSource\\\"\\u003e\\n$$Satellite\\\\;Altimetr{y_{New}}=Satellite\\\\;Altimetry - mean\\\\;(Satellite\\\\;Altimetry)$$\\u003c/div\\u003e\\u003cdiv class=\\\"EquationNumber\\\"\\u003e4\\u003c/div\\u003e\\u003c/div\\u003e\\u003cp\\u003e\\u003c/p\\u003e \\u003cp\\u003eThese newly obtained time-series are compared, and their correlation coefficients are estimated.\\u003c/p\\u003e \\u003cp\\u003eFinally, the prediction of the bathymetry method is used to map the ocean of the Arabian Peninsula. The data set is based on the General Bathymetric Chart of the Oceans (GEBCO) global bathymetric grids developed since 2019 through The Nippon Foundation-GEBCO Seabed 2030 Project (Internet ref-1). The data was used to assess the slope direction of the topographic slope and undulating terrain to determine the deposition of the marine depression pattern in topography, faults, and ridges. In this work, we specify the offshore topographic slope direction with slope ranges and contour spacing from the coastline with major and minor faults. Bathymetric data were downloaded from the GEBCO website (Internet ref-1) at an extension of 10.90°N–34.60°N and 32.24°E– 62.24°E. This 3D data is processed in the Arc GIS 10.8.1 (2020) platform.\\u003c/p\\u003e \"},{\"header\":\"3. Result and Discussion\",\"content\":\"\\u003cp\\u003eWe used PSMSL and SSH observations from 1991 to 2020 over six stations and estimated linear trends and accelerations along the coast of the Arabian Peninsula, as shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, respectively. The gap in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e occurred because of missing PSMSL data, and similarly, SSH data was available from 1993, and hence, there are also two years (1991–1992) of gaps in the figure. A minimum least square method is used to approximate the curve in linear and quadratic form by using Eq.\\u0026nbsp;\\u003cspan refid=\\\"Equ1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e and Eq.\\u0026nbsp;\\u003cspan refid=\\\"Equ2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. Decadal sea level patterns in terms of velocity and acceleration concerning time are analyzed, and their graphical representation as the time in years is given by the X-axis and sea level in the Y-axis. The results show a positive trend for both data and all selected sites (except ADEN because of data issues), indicating sea-level rising with higher than 0.20 (x coefficient in Eq.\\u0026nbsp;\\u003cspan refid=\\\"Equ1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). This kind of trend happened possibly because of land sinking due to subduction of oceanic plate (Ansari and Bae \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). The tide gauge plot for ELAIT for PSMSL and SSH data shows a rising trend of 3 and 4 mm/yr, respectively. The difference between these two data occurred because PSMSL data have a very short time window. A linear fitting for a short time window can show overestimate or overestimate relative rese of sea-level compared to the results showed by using full-time window (Parker and Ollier; 2015). Other sites such as EDEN, MASIRAH, MANAMA, and MUSCAT show some differences in their trend. The site of SALALAH shows a similar trend of around 3.7 mm/yr for both data sets. This is because PSMSL data was continuously available.\\u003c/p\\u003e\\u003cp\\u003eIn order to see the differences between PSMSL and SSH, a comparative analysis was performed by using Eqs.\\u0026nbsp;(\\u003cspan refid=\\\"Equ3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e) and (\\u003cspan refid=\\\"Equ4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e) as shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e. Results in both time series, the physical trend appears similar, but there are periods of low agreement and significant differences between the tide gauge and satellite altimetry. This included the projection of the sea-level trends for every tide gauge station and the nearest satellite altimetry grid point (Dean and Houston, 2013). The PSMSL and SSH correlation coefficients for the PSMSL are not nearly the same for all stations, as a high correlation for some (e.g., ADEN and SALALAH) is close to 0.92 (\\u0026gt; 0.92), while MANAMA has a lower relative value (0.32). Such discrepancies can be due to several reasons, which include the poor precision of satellite data in coastal areas, the effective data coverage of the satellite altimetry, and the possible effect of the distance between the tide gauge station and the nearest satellite grid point (Vignudelli et al., 2019). It is thus worth keeping this in mind when interpreting the comparative analysis and taking into account the differences between the two datasets as a means to explain regional sea-level trends and their drivers.\\u003c/p\\u003e\\u003cp\\u003eWe took annual mean of SSH during 1993–2020 and plotted their outcomes for the different sites as shown in Fig.\\u0026nbsp;5. The observed patterns exhibit strong correlations with the El Niño-Southern Oscillation (ENSO) phenomenon, a major driver of global climate variability. The results show that the SSH values across the Peninsula tend to be elevated during the El Niño events when sea surface temperatures are warmer than average in the equatorial Pacific. This is evident in the peak values observed in 1993–1994 and 1997–1998. During these El Niño, MANAMA reached a maximum SSH of 0.29 m in 1993. MUSCAT recorded a maximum SSH of 0.28 m in 1993. MASIRAH had a maximum SSH of 0.24 m in 1993. SALALAH reached a peak SSH of 0.30 m in 1993, and ADEN recorded a maximum SSH of 0.36 m in 1993. EILAT had a maximum SSH of 0.17 m in 1993. Conversely, during La Niña events, which feature cooler than average equatorial Pacific temperatures, the SSH values generally decrease. This can be seen in the lower values recorded in 1999–2000 and 2010–2011, where some locations even experienced negative SSH anomalies, such as SALALAH reaching a minimum of -0.04 m in 2011 and EILAT dropping to a minimum of -0.13 m in 2010. This strong correlation between El Niño events and the regional SSH patterns highlights the teleconnections between the Pacific and Indian Ocean basins, where large-scale climate patterns can significantly influence ocean dynamics.\\u003c/p\\u003e\\u003cp\\u003eThe yearly root means square error (RMSE) of SSH at various locations across the Arabian Peninsula from 1993 to 2020 has been shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e. The data exhibits significant variability, with clear correlations to the El Niño-Southern Oscillation (ENSO) phenomenon. In the MANAMA station, it has a minimum RMSE of 0.05 m, a maximum RMSE of 0.16 m, and a mean RMSE of 0.07 m. In the MUSCAT, it has a minimum RMSE of 0.04 m, a maximum RMSE of 0.12 m, and a mean RMSE of 0.06 m. In the MASIRAH, it has a minimum RMSE of 0.05 m, a maximum RMSE of 0.15 m, and a mean RMSE of 0.07 m. SALALAH has a minimum RMSE of 0.09 m, a maximum RMSE of 0.25 m, and a mean RMSE of 0.13 m. In the ADEN, it has a minimum RMSE of 0.11 m, a maximum RMSE of 0.24 m, and a mean RMSE of 0.16 m. In the EILAT, it has a minimum RMSE of 0.08 m, a maximum RMSE of 0.17 m, and a mean RMSE of 0.12 m. These variations in RMSE values across the different locations and over time highlight the complex regional ocean dynamics influenced by large-scale climate patterns in ENSO events.\\u003c/p\\u003e\\u003cp\\u003eThe average SSH anomalies with trends at various locations across the Arabian Peninsula from 1993 to 2020 have been shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e. The results exhibit a clear correlation with the El Niño-Southern Oscillation (ENSO) phenomenon. During El Niño events, the region experienced positive SSH anomalies, with the maximum values reaching 0.15 m in 1993 at both ADEN and EILAT, while La Niña events, such as in 2011, were characterized by negative anomalies, with the minimum values reaching − 0.24 m in SALALAH and − 0.22 m in ADEN. The average SSH values and trends were as follows: MANAMA (0.1487 m, 0.0038 m/year), MUSCAT (0.1770 m, -0.0007 m/year), MASIRAH (0.1635 m, -0.0029 m/year), SALALAH (0.1747 m, -0.0054 m/year), ADEN (0.1931 m, -0.0097 m/year), and EILAT (0.0173 m, -0.0031 m/year), highlighting the complex regional ocean dynamics influenced by large-scale climate patterns across the Arabian Peninsula. The positive trend in MANAMA indicates a gradual increase in sea level over the years, while the negative trends in other locations suggest a decline, underscoring the intricate regional oceanographic processes driven by global-scale ENSO phenomena.\\u003c/p\\u003e\\u003cp\\u003eThe total average SSH value (1993–2020) for all locations has been described in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e. The results shows that the average SSH value is maximum (0.19 m) and is found in all coastal stations located at ADEN in the South Arabian Peninsula. The minimum average values of SSH are at the north, down to 0.02 m at EILAT, in the northern Red Sea. The average SSH values in the coastal locations of MANAMA (0.15 m), MUSCAT (0.18 m), MASIRAH (0.16 m), SALALAH (0.17 m), ADEN (0.19 m)) are slightly more elevated when compared to the more inland or open ocean locations. This means that the coastal domain could be more affected by changes in sea level and related processes like tides, storm surges, and coastal flooding. The SSH values essentially rise from the north of the EILAT region to the south of the ADEN region. Among other factors, this may have to do with its location within the regional ocean currents which are themselves influenced by, among other things, the response of the Indian Ocean to the Indian Ocean Dipole and ENSO events. Such variability in SSH values highlights the importance of considering local and regional differences when assessing the impact of sea seal-level rise and associated phenomena in this dynamic and heterogeneous marine region.\\u003c/p\\u003e\\u003cp\\u003eThe correlation coefficient of total average SSH values for specified locations (1993–2020) has been shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003e. MANAMA exhibits the highest positive correlation coefficient of 0.56, indicating a strong positive relationship between the average SSH and the time series at this location. This suggests that the SSH values at MANAMA tend to increase or decrease with the overall regional trends. In contrast, the locations of MUSCAT, MASIRAH, SALALAH, and ADEN show negative correlation coefficients, ranging from − 0.12 to -0.67. This implies that the average SSH values at these locations have an inverse relationship with the regional SSH patterns over the 1993–2020 period. ADEN, in particular, has the strongest negative correlation coefficient of -0.67, suggesting a significant divergence from the broader regional SSH dynamics. EILAT, located in the northern Red Sea, has a moderate negative correlation coefficient of -0.29, indicating a weaker, but still inverse, relationship with the overall SSH trends across the Peninsula. These variations in the correlation coefficients highlight the complex and heterogeneous nature of SSH variability across the different coastal and near-shore locations within the study area. Understanding these spatial patterns of correlation can provide valuable insights into the regional ocean dynamics and assist in the development of more accurate models and forecasting systems for the Peninsula.\\u003c/p\\u003e\\u003cp\\u003eThe average SSH trends at various locations across the Peninsula from 1993 to 2020, with a clear connection to the ENSO events phenomenon has been shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e10\\u003c/span\\u003e. We use a color scale where negative trends (in blue) indicate a net increase in SSH due to ocean dynamics and positive trends (in red) indicate a net decrease in SSH either from ocean dynamics as well as the replenishment caused by overlying water mass. These data provide important information on regional ocean dynamics and their relationship with large-scale climate phenomena like ENSO events, which influence SSH patterns across the Peninsula. The trends of SSH show major spatial differences, mainly with positive trends in the northern region of SSH, and more negative trends tend to be located around the southwestern region flowing the Horn of Africa. Positive SSH anomalies were observed during El Niño events, peaking at 0.15 m in 1993 at ADEN and EILAT, while La Niña events were characterized by negative anomalies down to − 0.24 m in SALALAH and − 0.22 m at ADEN. Average SSH values and trends were heterogeneous among the sites, with MANAMA exhibiting the most positive trend (0.0038 m/year), placing it at the upper end, while ADEN had the most negative trend (− 0.0097 m/year), placing it at the lower end of the scale, demonstrating the complexity of regional oceanography. In which the strong influence of large-scale climate models such as ENSO in the surrounded ocean dynamics. The highest positive trend of SSH at 0.01500 m/year is found in MANAMA, which is located in the northern part of the region, while the lowest negative trend of -0.01227 m/year is recorded in ADEN, which is in the southwestern part of the Arabian Peninsula.\\u003c/p\\u003e\\u003cp\\u003eThe under marine topography map with elevation of both onshore and offshore disclose the bathymetry alteration and changes in the sea floor has been shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig10\\\" class=\\\"InternalRef\\\"\\u003e11\\u003c/span\\u003e. The aspect NE and E directions are shown about the presence of ridges, depression and trances in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e12\\u003c/span\\u003e. The contour map of 250 m interval discloses the under marine structure and shelf displacement which are also defined by Bollino et al, in 2022 especially in the Gulf of Aden. The Aden ridge is composed of multi-slope directions with intermediate flat bases and intersected by SW-NE extended major faults as SSFZ: Shukra-el-Sheik break zone. XAMFZ: Al Mukalla break zone. AFFZ: Alula-Fartakbreak zone. SHFZ: Socotra-Hadbeen break zone (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig12\\\" class=\\\"InternalRef\\\"\\u003e13\\u003c/span\\u003e) (Bollino et al., \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). In the offshore region, contour spacing and alignment identify the major and minor faults and the Aden ridge is noticeable due to the contour pattern where transverse faults to the ridge are accompanied by opposite movement of plates; e. g. near Shukra-el-Sheik break zone it is 13mm/yr, Alula-Fartakbreak zone 23mm/yr (Bollino et al., \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). This kind of movement is also in the Red Sea area at an average of 15mm/yr. The degree of slope varies from 0 to 90. The depressed land may be indicated by the flat topography (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig13\\\" class=\\\"InternalRef\\\"\\u003e14\\u003c/span\\u003e). The slope changes to moderate to steep, and structural aspects of the Aden Gulf are visible with 0º − 89.99º very steep slope boundaries. Because of such under marine structural control, there may be some oblique rifting on the fault geometry along with crustal necking and thermo mechanical deformation.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThis study focuses on and examines the sea-level changes on the coast of the Arabian Peninsula using six tide gauge sites across the Arabian Peninsula. Decadal sea level patterns in terms of velocity and acceleration with respect to time show a positive trend for PSMSL and SSH data of sea-level rising with higher than 0.20. The PSMSL and SSH correlation coefficients for the PSMSL shows highest correlation for some (e.g., ADEN and SALALAH) is close to 0.92, while MANAMA has a lower relative value (0.32). The results based on SSH reported the highest levels of sea at ADEN (0.36 m) and SALALAH (0.30 m) in 1993. During La Niña, on the other hand, SSH values tend to go down. In 2010, negative anomalies were seen in SALALAH (-0.04 m) and EILAT (-0.13 m). The RMSE of SSH shows clear variability: MANAMA has a minimum RMSE of 0.05 m and a maximum of 0.16 m, while EILAT shows a minimum RMSE of 0.08 m and a maximum of 0.17 m. The average SSH trend across locations shows an increase in MANAMA (0.0038 m/yr) and a decrease in ADEN (-0.0097 m/yr), with MASIRAH (-0.0029 m/yr) and SALALAH (-0.0054 m/yr) also showing negative trends. Changes in SSH show how different places react to ENSO events. The bathymetry contour map of 250 m interval discloses the under marine structure and shelf displacement especially in the Gulf of Aden. The slope changes to moderate to steep and structural aspects of the Aden Gulf are visible with 0º − 89.99º very steep slope boundaries.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthical approval and consent to participate:\\u003c/strong\\u003e This study does not involve human participants, animal experiments, or clinical trials.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eHuman ethics:\\u0026nbsp;\\u003c/strong\\u003eNo human related data or biological materials were used in this research.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication:\\u0026nbsp;\\u003c/strong\\u003eAll authors have reviewed the final version of the manuscript and consent to its submission for publication\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding:\\u0026nbsp;\\u003c/strong\\u003eNot applicable\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors' contributions\\u003c/strong\\u003e: Kutubuddin Ansari and Muhammad Zainuddin Lubis wrote the main manuscript text; Mery Biswas prepared the figures\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of supporting data:\\u0026nbsp;\\u003c/strong\\u003eThe datasets used in this study include PSMSLdownloaded from PSMSL site(https://psmsl.org/data/).The study is also conducted using the sea surface height (SSH) measurements from the GLORYS12V1 dataset (https://data.marine.copernicus.eu/products). We get the SST anomaly (Nino 3.4 index) from (https://www.ncei.noaa.gov/pub/data/cmb/ersst/v5/netcdf/)\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests:\\u003c/strong\\u003e The authors declare no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eClinical Trial Number in the manuscript.\\u003c/strong\\u003e Not Applicable\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAl-Subhi, A.M. and Abdulla, C.P., 2021. Sea-level variability in the Arabian Gulf in comparison with global oceans. Remote Sensing, 13(22), p.4524; https://doi.org/10.3390/rs13224524\\u003c/li\\u003e\\n\\u003cli\\u003eAntonov, J.I., Levitus, S. and Boyer, T.P., 2005. Thermosteric sea level rise, 1955\\u0026ndash;2003. Geophysical Research Letters, 32(12); https://doi.org/10.1029/2005GL023112\\u003c/li\\u003e\\n\\u003cli\\u003eAnsari, K. and Bae, T. S. 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Reviews of Geophysics, 54(1), 64-92.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"ocean-dynamics\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"odyn\",\"sideBox\":\"Learn more about [Ocean Dynamics](https://link.springer.com/journal/10236)\",\"snPcode\":\"10236\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/10236/3\",\"title\":\"Ocean Dynamics\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Arabian Peninsula, PSMSL, Satellite Altimetry, El Niño-Southern Oscillation\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6440771/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6440771/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThe study investigates three decades (1990\\u0026ndash;2020) of sea-level variation across the Arabian Peninsula using Permanent Service for Mean Sea Level (PSMSL) and satellite-based Sea Surface Height (SSH) data. PSMSL and SSH observations are used over six stations, and their linear trends and accelerations along the coast are estimated. The results show a positive trend for both data, points out the rise of sea level, possibly because of land sinking due to oceanic plate subduction. The study also discussed a critical connection between the El Ni\\u0026ntilde;o-Southern Oscillation (ENSO) event and the cause of large sea-level rise. The SSH data from 1993 to 2020 reveals significant fluctuations attributed to the ENSO phenomenon over the Arabian Peninsula. Based on RMSE statistics, locations exhibiting greater volatility, such as SALALAH and ADEN, demonstrate inferior performance compared to MANAMA and MUSCAT. The average SSH trend reflects the impacts of ENSO, exhibiting negative anomalies during La Ni\\u0026ntilde;a event and positive anomalies during El Ni\\u0026ntilde;o events. While locations such as ADEN and SALALAH demonstrate a decrease in SSH, MANAMA often displays a favorable trend of rising SSH. This indicates that local ocean dynamics are significantly affected by global climate. The discrepancies in SSH measurements at each site underscore the necessity of accounting for regional and local variability when assessing sea level change. MANAMA exhibits a robust positive correlation, but ADEN demonstrates a notable negative association with regional SSH patterns. Finally, the marine topography of the Arabian Peninsula has been disclosed with an elevation of onshore and offshore bathymetry alteration and changes in the sea floor. The contour spacing and alignment identified the major and minor faults, and the Aden ridge is noticeable due to the contour pattern where transverse faults to the ridge are accompanied by opposite movement of plates.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Decadal Sea level patterns around Arabian Peninsula at its impact to ENSO events\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-04-25 15:57:00\",\"doi\":\"10.21203/rs.3.rs-6440771/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-08-28T14:45:05+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-08-27T17:00:08+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"145256056402747168855209185415029501179\",\"date\":\"2025-08-11T02:51:20+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-04-18T06:28:00+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-04-16T04:10:17+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-04-16T04:09:58+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Ocean Dynamics\",\"date\":\"2025-04-13T18:42:33+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"ocean-dynamics\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"odyn\",\"sideBox\":\"Learn more about [Ocean Dynamics](https://link.springer.com/journal/10236)\",\"snPcode\":\"10236\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/10236/3\",\"title\":\"Ocean Dynamics\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"e6466cfb-b9d1-4520-a8dc-742f70d79e38\",\"owner\":[],\"postedDate\":\"April 25th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-05-10T16:38:59+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-04-25 15:57:00\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6440771\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6440771\",\"identity\":\"rs-6440771\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}