Modeling Sea Level Rise Impacts on Western Arabian Gulf Cities Using Nighttime Lights and LULC-Driven Cellular Automata

Modeling Sea Level Rise Impacts on Western Arabian Gulf Cities Using Nighttime Lights and LULC-Driven Cellular Automata | 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 Modeling Sea Level Rise Impacts on Western Arabian Gulf Cities Using Nighttime Lights and LULC-Driven Cellular Automata Azher Hussain Syed, Bijoy Mitra, Mohammad Shahedur Rahman, Omer Rehman Reshi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7126987/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Dec, 2025 Read the published version in Natural Hazards → Version 1 posted 5 You are reading this latest preprint version Abstract Sea-level rise (SLR) poses a major global risk to the populated coastal zones, with recent assessments indicating significant acceleration due to climate change. In this study, we integrate nighttime light (NTL) data and Land Use and Land Cover (LULC) modeling within a cellular automata framework to project SLR impacts on six major coastal cities of Saudi Arabia’s Eastern Province along the Arabian Gulf. Historical sea-level records (1979–2020) reveal an annual mean rise of 7.9 mm, corroborating global trends. To forecast future inundation, we applied a GIS-based “bathtub” approach using sea-level scenarios from the Intergovernmental Panel on Climate Change Sixth Assessment Report (IPCC AR6) and digital elevation models. Concurrently, LULC transitions for 1973–2020 were derived from Landsat images and extrapolated to future time slices (2070, 2100, and 2130) via cellular automata-based approach. Nighttime light data served as a proxy for economic and urban activity, allowing refined mapping of vulnerable coastal development zones. Results show spatially heterogeneous, yet markedly increasing, inundation exposure by mid- to late-century. Jubail, Qatif, and Ras Tanura emerge as high-risk areas, with up to 40% or more of coastal lands threatened under the worst-case SLR scenario by 2130. Rapidly growing built-up areas and reclaimed lands in Dammam and Khobar also face significant flood hazard. These findings highlight the need for proactive planning and adaptation measures to reduce the economic and ecological impacts of rising seas in the Arabian Gulf region. Sea Level Rise IPCC Saudi Arabia MOLUSCE GIS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Highlights This research projects the impacts of sea level rise on major coastal governorates of Saudi Arabia along the western Arabian Gulf. This study employed GIS and AI based approach to project sea level rise, predict land cover loss and predict spatiotemporal vulnerability economic zones for the years 2070, 2100 and 2130 using scenarios from the IPCC report. The research also shows spatiotemporal distribution of LULC from 1973 to 2020 and historical changes in the shoreline from 1973 to 2022. Jubail, Qatif, and Ras Tanura have 40% of coastal areas threatened by the worst sea level rise scenario by 2130 and are high risk areas. The findings urge proactive planning and adaptation to mitigate the economic and ecological impacts. 1. Introduction The global concern surrounding climate change, namely the impact of sea level rise (SLR) on coastal areas with low elevation, has garnered significant attention in recent decades. It is expected that coastal erosion and accretion will be exacerbated by the anticipated rise in sea levels and increased storm surge effects in the next several decades (IPCC, 2019 ). This will pose a significant risk to the overall health and stability of coastal ecosystems in the near, intermediate, and far future (Djouder & Boutiba, 2017 ; Ghoussein et al., 2018 ; Almaliki et al., 2023 ). About one billion individuals, accounting for 13% of the worldwide population, reside in coastal areas with elevations within 10 meters of sea level (Griggs & Reguero, 2021 ). Due to population growth, approximately 70% of the global population lives in coastal plain areas (Alfarrah & Walraevens, 2018 ). Consequently, the SLR poses a significant peril, as it has the potential to result in the submergence of low-lying coastal regions, the depletion of wetlands, and the erosion of shorelines. The submergence of coastal areas at lower elevations is a highly notable and direct consequence of SLR. This phenomenon leads to the infiltration of saltwater into nearby coastal regions, the flooding of deltaic areas and numerous urban centers, and the disruption of transportation systems (CCPO, 2018 ). The Intergovernmental Panel on Climate Change (IPCC) projected an increase in global mean sea level (GMSL) ranging from 0.52 to 0.98 meters by the year 2100, as stated in its 5th Assessment Report (IPCC, 2018 ). Nevertheless, the IPCC revised its previous estimation in the latest report, increasing the projected SLR to 2 meters by the year 2100 (IPCC, 2021 ; Mortillaro, 2019 ). It is anticipated that in the forthcoming decades, the acceleration of SLR resulting from persistent global warming will render several low-lying, densely inhabited coastal areas across the globe increasingly susceptible to adverse impacts. As components of climate modes of variability, regional sea-level fluctuations are linked to dynamic fluctuations in ocean circulation, alterations in wind patterns, and an isostatic adjustment of the Earth's crust in response to historical and ongoing modifications in polar ice masses and continental water storage (Stammer et al., 2013 ; Magnan et al., 2023). The alteration of land use and land cover (LULC) has emerged as a significant issue in numerous coastal ecosystems worldwide (Zhu et al., 2022 ; Hasan et al., 2023 ). According to Han et al. ( 2015 ), alterations in LULC along the coast have significant impacts on hydrological and sedimentary processes, resulting in constraints on the structure and productivity of coastal ecosystems (Han et al., 2015 ). The scholarly literature acknowledges that substantial alterations in LULC in coastal areas can exert a considerable influence on the local climate, water distribution, socioeconomic patterns, ecological resilience, and biodiversity (Abdullah et al., 2022 ; Alam et al., 2020 ). The phenomenon of climate change has been found to have a significant impact on ocean circulation and coastal risks, resulting in a reduction in global land area, potential productivity, and ecosystem health (Sajjad et al., 2020 ; Zhang et al., 2019 ). Further, activities such as groundwater pumping (Galloway & Burbey, 2011 ) or mining (Jones et al., 2016 ), can contribute to land subsidence (Tzampoglou et al., 2023 ). This phenomenon has the potential to inflict harm on the environment, hence posing a significant concern from various perspectives, encompassing social, environmental, and protective considerations (Stouthamer et al., 2020 ). In the context of future projections, the issue at hand has greater significance due to the phenomenon of SLR (Erkens et al., 2015 ), particularly in regions experiencing a quicker rate of subsidence relative to the rate of sea level increase (Buffardi & Ruberti, 2023 ). The utilization of satellite imagery to view the Earth's surface offers significant opportunities for the monitoring, analysis, evaluation, and prediction of notable transformations occurring on the Earth's surface. This capability enables the quantification and tracking of the dynamic nature of human activity and its associated environmental effects (Chuvieco, 2008 ). For example, monitoring LULC over a specific period will significantly contribute to monitoring the recent alterations of coastal belts, changes in urban activities, and even the submergence of low-lying islands. Further, the measurement of Night Time Light (NTL) from space is considered a significant indicator of human presence and activity on the Earth's surface (Elvidge et al., 2012 ). Satellite-based observations of NTL have emerged as a prominent tool for assessing the intensity of human activities, surpassing other satellite products that rely on visible, near-infrared, or radar sensors (Zhao et al., 2018 ). It also offers distinct viewpoints that can shed light on environmental and socioeconomic concerns, presenting valuable opportunities for monitoring human activities and comprehending their associated environmental consequences (Ch et al., 2021 ; Sanders et al., 2021 ). Regional sea level fluctuations may exhibit significant deviations from the GMSL and may adhere to a distinct regional pattern, with certain areas witnessing noteworthy deviations from the average global SLR (IPCC, 2018 ). For instance, the Arabian Gulf (AG) exhibits a seasonal variation in sea level, with a decrease observed from the months of February to May and an increase from September to December. The highest sea level is typically recorded in November, while the lowest is observed in April (Al-Subhi & Abdulla, 2021 ). Previous tidal gauge studies in the AG found varied long-term sea level trends due to the variation in the research period (Hassanzadeh et al., 2007 ; Sultan et al., 1995 ). Two tide gauges' 11-year record analysis showed a 2.1 mm/year sea-level trend (Sultan et al., 1995 ). Another study using 1990–1999 altimetry data found 2.8 mm/year sea-level increase in the Northern AG (Hassanzadeh et al., 2007 ). Moreover, Hosseinibalam et al. ( 2007 ) mentioned a SLR of 2.34 mm/year for the entire gulf. Global satellite altimetry records show a quicker sea-level increase of 3.3 ± 0.5 mm/year from 1993 to 2017 (Antonov et al., 2005 ; Cazenave et al., 2019 ). Al-Subhi and Abdulla ( 2021 ) analyzed almost 30 years of satellite altimetry data and reported the expected SLR in the AG for different scenarios. They estimated the rise to be 1.3 to 8.1 cm by 2050 and 16.9 to 39.1 cm by 2100. Saudi Arabia’s Eastern Province hosts a growing population and economic centers, characterized by expanding urbanization, land reclamation, and heavy industrial activities. The coastal cities of Ad Dammam, Al Jubail, Al Khafji, Al Khobar, Qatif, and Ras Tanura marked by historical and cultural significance are especially at risk from SLR due to their low-lying topography, ongoing economic globalization, and increasing maritime traffic. The eastern province's shoreline is seeing some of the fastest rates of population and economic expansion. The energy exchange between the land and the atmosphere is impacted by rising urbanization, the conversion of seawater to reclaimed land, heavy industrial activity, and oil refining processes, which particularly promote a major warming at the regional scale. Saudi Arabia's coastal cities will probably continue to see population growth due to economic globalization and rising shipping traffic. Therefore, it is crucial to study the effect of SLR on the major coastal cities of the province. While several studies have examined SLR in the coasts of AG, few have incorporated socioeconomic metrics to quantify the spatial distribution of urban and industrial activities. This omission leaves uncertainties about how rapidly growing urban and industrial zones are exposed to climate-driven coastal hazards. In addition, existing regional or global projections often treat the AG’s coastal areas with broad assumptions. Detailed, localized modeling specific to Saudi Arabia’s Eastern Province is lacking, which hinders accurate risk assessments for critical infrastructure and rapidly expanding cities. Therefore, we integrate the IPCC AR6 SLR scenarios with a GIS-based “bathtub” approach provides inundation estimates that capture local conditions across the Eastern Province. Our study focuses on the major coastal cities of Saudi Arabia’s Eastern Province. We further incorporated urban development with multi-year LULC information in a Cellular Automata (CA) approach to generate projections of future landcover which are potentially vulnerable for sea flooding. Additionally, spatially explicit mapping of economic hotspots using NTL data highlights where industrial and infrastructural assets face the greatest flood exposure. This approach closes a gap in arid coastal risk research and informs adaptation measures, thereby supporting more resilient coastal management strategies. This study's major contribution lies in the innovative methodological approach that combines remote sensing and geospatial modeling to generate high-resolution, future-oriented urban vulnerability maps. The study’s findings have significant implications for urban planning and climate resilience, particularly in data-scarce regions like the Western Arabian Gulf, by providing actionable insights for policymakers and stakeholders to prioritize adaptation strategies. The research is significant as it addresses a critical knowledge gap in SLR impact modeling for arid coastal cities, which are rapidly urbanizing and highly exposed to climate-induced risks. Its novelty stems from the use of nighttime light data as a proxy for human activity and urban expansion, offering a dynamic and scalable tool for anticipating future risk zones under different SLR scenarios. 2. Methodology 2.1 Study Area The Arabian Gulf is a shallow inland sea with a mean depth of 50 meters; its coastal regions are also shallow (5 to 15 meters). The Arabian Gulf is connected to the Indian Ocean and the Sea of Oman; therefore, the inputs from river systems, evaporation, and water exchange with the Sea of Oman through the Strait of Hormuz influence the Arabian Gulf's water budget (Hereher, 2020 ). The scorching desert region of Arabia, where it is located, is nearby, and the hot climate there is mirrored in the hot surface water temperatures (up to 34°C). It is further distinguished by having water that is more salinized than typical seawater, which has a salinity of 35 parts per thousand (ppt) on average (Buchanan et al., 2016 ). 2.2 Data 2.2.1 Digital Elevation Model Derived from data acquired by the Shuttle Radar Topography Mission (SRM), NASADEM (NASA Digital Elevation Model) is a high-resolution worldwide digital elevation dataset. It was released in 2020 as an improved version of the original SRTM dataset, created by reprocessing of the SRTM interferometric SAR Data and merging it with DEM datasets such as ASTER, ICESat and GLAS. Its main objective was to eliminate voids and other limitations that were present in the SRTM dataset (Crippen et al., 2016 ). The NASADEM is supposed to be the successor of the SRTM Data Dataset (Gesch, 2018 ). In this study, NASADEM with 30m digital elevation model of year 2000 (NASA JPL, 2020 ) is used for the entire east coast of Saudi Arabia. Accessible through NASA's data archives, including the Earth data portal, it is freely available to the public. It was considered for this study because of its improved processing and higher quality also thus seen as a huge leap forward over previous elevation datasets. 2.2.2 IPCC SLR Statistics It is important to note that rising sea levels are not uniform globally. Over the last 170 years, the global sea level has risen ∼20 cm. Through each passing year, the rate of change has increased; however, in the early twenty-first century, it is ∼3.2 mm/year and growing at a rate of ∼0.8 mm/year per decade (Nerem et al., 2018 ). The “NASA Sea Level Projection Tool” from IPCC AR6 was used to estimate the future SLR projections in the designated area of interest in the Kingdom of Saudi Arabia (KSA). Moreover, the IPCC statistics are a numerical method employed to examine a range of parameters, including both low and high-emission possibilities (van Vuuren et al., 2011 ). Our study represents the sea level rise projections for both regional and local scales from 2020 to 2150. One of the main factors affecting sea level rise at coastal areas in the KSA is due to the changes in the elevation of coastal land and may arise from plate tectonic processes, ongoing isostatic response to past changes in loads such as removal of the weight of the ice-age ice sheets, and other processes, including groundwater/fossil-fuel extraction and compaction of coastal sediments (Mitrovica et al., 2011 ). 2.2.3 LULC data In this study, we have categorized Land use and Land cover in four classes viz Water, Land, Built-up and Vegetation. All the datasets used for the study were downloaded from USGS Earth explorer for the multiple years from 1973 to 2020 of varying satellite sensors. The data is processed in ArcGIS for layer stacking of all the bands present in each dataset. Once the images are ready by combining multiple scenes for the area of interest through Mosaicking, and Classification algorithm is used to each image from 2000 to 2020. The priority for classifying the image in different classes is creating training samples for each feature classes, which is more than 500 samples and stored as signature input file for the classification Algorithm. The Maximum Likelihood classification algorithm was used for this study, which shows different classes in the produced output raster image and area was calculated for each class from 2000 to 2020. The supervised classification algorithm is used for differentiating land use land cover features. The statistics in square kilometers calculated from the Supervised classification for different land use land cover features in mentioned in Table 1 : Table 1 Land use land cover statistics of different land covers in square kilometres. Year Built up Vegetation Water Land 1973 101.23 1075.27 13568.12 17397.84 1990 659.85 729.24 13871.49 17062.97 2000 1064.36 370.14 14115.71 16773.34 2015 1787.13 590.94 13919.35 16026.79 2020 4393.34 272.02 14203.87 13456.69 From the above mentioned the statistics of the above classes are discussed for built up, vegetation, water and land. 2.2.4 Nighttime Light from Visible Infrared Imaging Radiometer Suite (VIIRS) Economic clusters were identified using NPP-VIIRS data as a stand-in. It is used by many academics to calculate its economic impacts at both regional and national levels (Ch et al., 2021 ; D. Sanders et al., 2021 ). Monthly mean NPP-VIIRS pictures are generated by the National Centers for Environmental Information (NCEI) Earth Observations Group (EOG). The resolution of this instrument is 15 arc seconds. The Suomi National Polar-Orbiting Partnership (NPP) satellite's VIIRS sensor examines data in 22 different wavelength bands, including the DNB (Day/Night Band). VIIRS DNB data have been used to monitor natural dangers and disharmony, evaluate the population, evaluate working conditions in rural modernization, and understand the biological implications of light pollution (Ch et al., 2021 ; Mahmud et al., 2023 ; D. Sanders et al., 2021 ). The VIIRS Day/Night Band data is used to create mean radiance composite pictures based on nighttime brightness. 2.3 Classification and Simulation Models 2.3.1 Bathtub Model The GIS-based bathtub model is more advanced than the traditional bathtub model because it uses a GIS approach to map, analyze, and visualize spatial data in the context of environmental, hydrological, and climate-related studies by incorporating spatial data such as Digital elevation models (DEMS), land use land cover and hydrological data to model how rising sea levels, storm surges or heavily precipitation affect coastal areas. This model is mainly and widely used to study for managing and analyzing water resources, flooding, coastal inundation, and climate change impacts (Sanders et al., 2024 ; Williams & Lück-Vogel, 2020 ). Moreover, the IPCC employs and makes the bathtub technique a foundational model for simulating potential inundation under different scenarios and levels of flooding (Almaliki et al., 2023 ; Mitra et al., 2023 , 2024 ). The bathtub model uses the principle that the earth's surface is flat. Subsequently, the bathtub model cannot measure the height obstacle accurately or use the nearby topography to create detailed flood mapping. This model was developed using the SSP scenarios analyzed during the study period to evaluate the potential extent of flooding near the coastal areas resulting from rising sea levels. 2.3.2 Landsat image classification and validation This study employs a Supervised classification algorithm in ArcMap to identify and evaluate LULC patterns. The principle for applying Supervised classification on any satellite image is first to create a signature file with a training sample for each feature class. The training samples for Land use land cover features were chosen from a multispectral band combination using polygon construction to classify the provided dataset. The signature file is input for this algorithm to classify land use land cover features. Once the satellite image is classified into its respective classes, post-classification accuracy measurement is crucial for validating the accuracy of land use and land cover maps generated by models (Mitra et al., 2024 ). Therefore, Kappa statistics was used to assess the accuracy levels of the different samples. Thus, to check the accuracy of supervised classification images that separate the different land use land cover features, we correlate or validate the samples taken from multiple satellite datasets used for the study. The formula for calculating kappa statistics is as follows: where, r = the error matrix's rows and columns, N = total number of pixels, X ii = observation in row i and column i, and X + i = marginal total of column i. Additionally, the root mean square error (RMSE) was also calculated. A smaller RMSE matrix indicates a higher level of accuracy in the prediction of LULC. Table 3 displays the kappa statistics and RMSE value obtained from the evaluation of the classified images. Table 2 LULC accuracy assessment. Year Kappa(K) RMSE 1973 0.918919 0.63 1990 0.918919 0.63 2000 1 0.72 2015 0.918919 0.63 2020 0.756757 0.78 2.3.3 Detecting the land cover changes This study employed the MOLUSCE plugin of QGIS, an open-source software specifically used for the spatial and temporal changes in LULC, which uses cellular automata (CA) data to generate potential outcomes based on a given training dataset This model effectively differentiates nonlinear spatial, sequential LULC change by estimating pixel present values from beginning and neighboring pixels. Meanwhile, this investigation used a neighborhood value of 30*30 m to account for spatial interactions among cells [0000]. Moreover, the MOLUSCE QGIS validation assessed the accuracy of the predicted LULC raster by simulating and validating the MP-ANN model. Maithani (Maithani, 2009 ) employed nonlinear statistical analysis to examine the complex underlying variables and models for the drivers of urban development. The logistic regression shown in Eq. 3 is used to identify variations in the training dataset. Here, argmax j = category that amplifies the consequence of conversion probability and current land-use pattern, LUPt(i,j) = present land-use trends value for a change from land-use class i to j at time t, P (LU t+1 = j | LU t = i) = evolution likelihood from land-use category i to j at time t + 1, LU t+1 = land-use class at t + 1 time. The MOLUSCE QGIS validation assessed the accuracy of the predicted LULC raster by simulating and validating the MP-ANN model. Maithani ( 2009 ) (Maithani, 2009 ) employs non-linear statistical analysis to examine the complex underlying variables and models the drivers of urban development. The MOLUSCE system utilizes the ANN, which integrates the Cellular-Automata (CA) simulation method. This method employs the Monte Carlo computational approach (Lin et al., 2010 ). In Eq. 4, the Monte Carlo algorithm generates distinct samples from the probability distributions of the system's unexpected variables or features. Here, Y = estimated outcome, N = number of samples, f(x i ) = value of the function at the i th random sample, x i . The overall kappa (k i ) (Eq. 3) and % of correctness (C) (Eq. 4) were determined as follows: In Eqs. 5 and 6, P o = observed percentage of settlement, P e = percentage anticipated by casual, n ij = transverse fundamentals in the fault matrix, k = overall quantity of modules, n = overall quantity of examples in the fault matrix. Furthermore, the result of the training dataset will demonstrate the kappa validation value in Table 1 and Fig. 3 . The overall kappa (ki) (Eq. 4) and % of correctness (C) (Eq. 5) were determined as follows: Table 3 Validation parameters (K parameters) and % correctness of the CA-ANN model in QGIS software. Dataset Predicted Year for Validation % Correctness K histogram K location Overall kappa LULC 2021 87.482 0.678 0.692 0.671 2022 85.931 0.652 0.689 0.663 NTL 2021 96.752 0.812 0.884 0.856 2022 97.103 0.841 0.879 0.868 3. Results 3.1 Historical and Future SLH changes Sea level height (SLH) increase, will be a critical concern at the bank of AG-coast in KSA. Although the historical station-based tide-gauge data are not continuous, insights from stations, e.g., the Abu Ali Pier, Masirah, and Mina Sulman, indicate an annual increase from January 1979 to December 2020 (Table 4 ). With a mean SLH of 7.9 ± 31.8 mm, it rose 0.0026 m yearly from 1979 to 2020 with a statistically significant trend (Sen’s slope of 0.002 m/year, p < 0.005). While five- and ten-year averages show short-term volatility, the overall trend implies a steady sea level increase. No significant seasonal fluctuations were observed, but the long-term trend dominates reported changes with a statistically significant moderate model coefficient (R 2 = 0.4694, p < 0.0001) observed for time-dependent SLH. Although the SLR dropped by 0.0079 m from 1999 through 2004, there has been a pronounced upsurge of 0.0303 m in recent decades. Table 4 Statistical summary of SLH trends, seasonal components, residuals, and long-term changes (1979–2020), including OLS regression and Sen’s Slope analysis. Metric Value SLH Trend Overview SLH Mean (1979–2020) 0.0079 m SLH Standard Deviation 0.0318 m Total SLH Increase (1979–2020) 0.1046 m Seasonal Component Seasonal Component Mean -0.0000 m Seasonal Component Std Dev 0.0215 m Seasonal Component Min -0.0251 m Seasonal Component Max 0.0391 m Residuals (Noise after removing seasonality & trend) Residuals Mean 0.0001 m Residuals Std Dev 0.0437 m Residuals Min -0.1278 m Residuals Max 0.1479 m Five-Year SLH Changes 1979–1984 0.027 m 1984–1989 0.0148 m 1989–1994 0.0257 m 1994–1999 0.0083 m 1999–2004 -0.0079 m 2004–2009 0.0095 m 2009–2014 0.0271 m 2014–2019 0.0032 m Ten-Year SLH Changes 1979–1989 0.0418 m 1989–1999 0.034 m 1999–2009 0.0017 m 2009–2019 0.0303 m Ordinary Least Squares (OLS) Regression Slope (Trend) 0.0018 m/year R-squared 0.4694 *** Sen's Slope Analysis Sen’s Slope Estimate 0.002 m/year ** Annual and Decadal Trend Analysis Average Annual Increase 0.0026 m/year Average 5-Year Increase 0.0053 m/5year Average 10-Year Increase 0.0194 m/10year Here , ** indicates p < 0.005, and *** indicates p < 0.0001 Although a sudden plummet is visible just before 2016, IPCC AR6-based SLR prediction reveals a steady increase in the following years (Fig. 4 ). For this study, we incorporated two of the most studied socioeconomic pathways: SSP2-4.5 (medium confidence) and SSP5-8.5 (low confidence). Here, we modeled future SLR based on SSP2-4.5 as the maximum likelihood scenario (MLS) and SSP5-8.5 as the worst-case scenario (WCS) based on anthropogenic activities. The IPCC-derived AR-6 report predicted a future SLR of 0.754 ± 0.458 m and 1.893 ± 1.509 m in the AG-coast in KSA by 2130 based on the MLS and WCS, respectively. From the MLS, the mean annual increase rate of SLR is expected to be 0.377 ± 0.224 m, while its 95% confidence interval indicates a potential SLR of 1.351 m by 2130. On the contrary, SSP5-8.5 indicates a pronounced increase after 2060, where from a 95% confidence interval, the expected SLR is expected to reach 4.02 m in 2130. The annual mean sea level rise is almost 0.603 ± 0.479 m, with a high deviation indicating exponential growth after 2100. 3.2 SLR Inundation Based on the IPCC-derived SLR statistics, we evaluated the potential spatiotemporal distribution of SLR in the AG-coast in KSA for 2070, 2100, and 2130 (Fig. 5 ). However, we subdivided our area of interest into nine central governorates from the Eastern Province of KSA, bordering the AG. The governorates are Abqaiq, Al Khafji, Al Khobar, Al Qatif, Al-Ahsa, Dammam, Jubail, Ras Tanura, and Khawr al Udayd. From 2070 to 2130, inundation patterns evolve spatially heterogeneously in Saudi Arabia's eastern coastlines (Fig. 5 ). From our GIS-based Bathtub model, Al Qatif and Ras Tanura are highly vulnerable, with huge flooding potential throughout the study period. Initial forecasts for 2070 show mild flooding hazards, but the WCS for 2130 shows significant flood-prone area growth, notably in low-lying coastal zones. The coastal flooding is expected to evolve in a south-westerly direction in Ras Tanura, where, starting in 2070, significant land areas are expected to be submerged on MLS. On the other hand, Al Qatif will have an inland flood inundation of all governorates by 2130 in the WCS. Dammam and Al Khobar have complicated flooding patterns where a centroid joining is evident at the central Dammam, which has high coastal inundation potential in the southern part of the region. Northern locations like Al Khafji indicate minor flooding in early forecasts but increased vulnerability by 2130, especially near the coast. Although coastal vulnerability exists throughout the anticipated period, southern places like Al Udayd have more confined flooding patterns. Al Ahsa, Abqaiq, and Al Udayad comprised comparatively linear inundation patterns concentrated at the eastern coastland. Statistically, the highest landcover loss is expected to be from the Jubail governorate, whereby by 2070, the sea inundation will be roughly 409.694 km² and 578.316 km² for MLS and WCS, respectively (Fig. 6 ). This, however, is expected to rise to 741.860 km² and 1481.712 km² for respective scenarios by 2130. Al Khafji has the most significant rise in potential inundation in the Northern Region, rising from 13.61 km² (2070 ML) to 279.86 km² (2130 WCS), the most significant proportionate increase among the governorates. The geographical distribution shows enhanced coastal vulnerability in the WCS. Al Qatif's flooding area grows from 107.39 km² to 443.42 km² under WCSs, while Ras Tanura's flooding area increases from 128.61 km² to 278.01 km². Moreover, Ras Tanura is expected to see almost 77.34–97.53% inundation by 2130. The inundation area in Dammam increased from 128.36 km² (2070 MLS) to 596.51 km² (2130 WCS) (Fig. 6 ). In contrast, Al Khobar showed a more moderate but substantial rise from 66.19 km² to 161.07 km². Here, coastal developments and industrial locations with low elevations are the most vulnerable for future SLR scenarios. Abqaiq, Al Ahsa, and Al Udayd show lesser initial inundation areas but a significant percentage rise over time. Under WCSs, Abqaiq's inundation area grows from 144.82 km² to 380.22 km², Al Ahsa from 39.47 to 198.32 km², and Al Udayd from 28.89 to 249.51 km². Al Khafji and Al Ahsa are also the least vulnerable to SLR, with 3.408% and 0.0822% of their governorate area by 2130 (WCS). For SLR, while the projected SLR in 2070 is expected to rise by 1067.04 km² and 1498.13 km² for MLS and WCS, respectively, this trend may increase to 1860.27 km² and 4068.639 km² in 2130 for respective scenarios. 3.3 Vulnerability to future LULC’s and Economic Potential Zones To capture the SLR vulnerability at future LULC, we incorporated historical LULC across the region for 1973, 1990, 2000, 2015, 2020, and 2022. From a historical perspective, our observation demonstrates significant belt alteration throughout the coast of AG across the governorates in Eastern Province owing to tidal activities, sediment transport, SLR, or even human activities (Fig. 7 ). However, land reclamation and infrastructure development were some of the significant activities reforming the coastal belt of Ad Dammam and Al Qatif. In contrast, the long-term coastal belt alteration demonstrates a significant abatement for some regions, e.g., Al Jubail, Ras Tanura, Al Khobar, and Al Ahsa. In Ras Tanura, the mid-section observed a substantial shoreline change where it retreated compared to the 1972 shoreline. A similar loss near the SLR vulnerable zones was evident on the entire coast of Al Jubail and in the northern part of Al Qatif. However, Al Khafji and Al Khobar had the least shoreline change throughout the observation period, with Al Khafji the noteworthy land reclamation at the southern part of the governorate. Utilizing the historical LULC’s we predicted future LULC’s for the years 2070, 2100, and 2130 to understand the spatiotemporal inundation vulnerability for specific land cover in eastern province (Fig. 8 ). Throughout the research period (2070–2130), SLR-induced land cover changes along the AG coast showed considerable variances in inundation patterns and their direction. The trajectory of SLR inundation and corresponding LULC loss reveal significant long-term floods in anthropogenic built-ups and vegetation cover across the coasts. Our model demonstrates Jubail will witness the most intensified SLR consequences at an exponential rate by 2130, followed by Dammam and Al Qatif. On the contrary, governorates like Abqaiq and Ras Tanura may face severe inundation in early 2070, although their inundation trend will decline in the coming years. Nevertheless, flooding patterns mostly move inland from the shore, with varied intensities between governorates. The northern (Al Khafji) and southernmost (Al Ahsa and Al Udayad) regions were mostly inundated along the coast. In contrast, SLR inundation spread inland in the central coastal districts, notably Jubail and Ras Tanura, with the built-up areas by 2130 (WCS). Coastal regions with recent built-up were the most vulnerable regions to SLR flooding in AG Coast, where these regions were inundated in an east-to-west direction, starting on the eastern coastline borders (Fig. 8 ). Although limited, plant cover exhibited different degrees of susceptibility, especially near Al Khafji, where tiny patches of vegetation correspond with forecasted flood zones. Inundation scenarios intensified, with the 2130 estimates exhibiting the most significant inland reach for WCS. The Dammam-Al Khobar urban region showed considerable extension of waterlogged areas from the coast westward in both MLS and WCS. The MLS predicted modest inland penetration, mostly on built-up eastern edges, while on the contrary, the WCS showed widespread flooding and westward migration into developed zones between 2100 and 2130. This trend was constant throughout governorates, although inland flooding varied by terrain and land cover. The central coastal region, especially Jubail, saw the most severe consequences, and here the built-up area losses expected to reach 436.22 km² and 1112.25 km² by 2130 under MLS and WCS respectively (Fig. 9 ). The Dammam region is also vulnerable, with built-up area inundation anticipated to rise from 97.69 km² (2070) to 144.17 km² (2130) under MLS and to 423.12 km² under WCS in 2130. Moreover, Al Khafji in the northern sector saw minor losses in a similar patter to Dammam. Here the potential inundation for built-up area rising from 11.52 km² (2070) to 23.68 km² (2130) under MLS and 165.19 km² under WCSs by 2130. Rapid flooding losses were expected between 2100 and 2130, especially in the WCS scenario and vegetation cover was lost correspondingly. Jubail had significant consequences, with vegetation loss estimated to rise from 8.98 km² to 13.47 km² (MLS) and 21.52 km² (WCS) by 2130. Abqaiq saw significant land category losses, rising from 72.12 km² to 110.38 km² (MLS) and 180.13 km² (WCS) by 2130. The spatiotemporal distribution of future economic potential zones on the coast of Eastern Provinces and its vulnerability to future SLR consequences showed differential sensitivity patterns during the study period (Fig. 10 ). We incorporated VIIRS-derived NTL data to capture the historical economic potential zones across the study region and predict future economic potential zones for the years 2070, 2100, and 2130. Medium economic potential zones were concentrated in coastal proximity, especially Dammam, Al Khobar, and Jubail industrial cities. On the contrary, high economic potential locations are more concentrated in city areas like Dammam, Al Khobar, and Al Qatif. However, the medium economic potential zones were the most vulnerable to flooding, particularly under the WCS, and anticipated flood regions overlapped along the central coastal corridor from Jubail to Ras Tanura to Al Qatif. However, Al Khafji had limited potential economic zone exposure in the north and low inundation risk until 2070 (MLS), but vulnerability increased at a exponential rate by 2130. Inundation and highly potential economic interest areas overlapped significantly by 2130, indicating increasing vulnerability in cities like Dammam and Al Khobar where also land reclamation rates are increasing at an alarming rate. Inundation risks increased in the Dammam-Al Khobar urban region, within medium potential economic zones. The MLS predicted minor consequences until 2100, whereas the WCS predicted widespread economic zone flooding by 2130, notably in coastal and industrial districts. The southern areas (Abqaiq-Al Ahsa and Al Udayd) had scant potential economic zone distribution, but increased coastal flooding threats, especially under the WCS, and it further validates the finding from LULC projections. Regions like Al Ahsa and Al Udayad remained in low vulnerability owing to their low economic potential. 4. Discussion The spatiotemporal distribution of historical and projected SLR was assessed across Eastern Province, KSA's AG coast. While incorporating the SLH throughout the coast, this study evaluated the estimated land area loss across the entire western coast of AG utilizing a GIS-based bathtub model for two distinct socioeconomic scenarios. We further predicted the LULC and future economic potential zones using the MOLUSCE model and overlayed the spatiotemporal distributions of sea inundation with the landcover outputs. The Arabian Gulf (AG) is a low-lying and hyper-arid coastal area, especially the eastern Arabian Peninsula, which includes populated urban areas and crucial infrastructure in Iraq, Kuwait, KSA, Bahrain, Qatar, and UAE. These areas are potentially vulnerable to SLR, and the main drivers are precipitation, sea surface trends, terrestrial temperature trends, salinity levels, seawater thickness, sedimentation, and coastal erosion (Bakhamis et al., 2024 ). Validating previous studies on SLH in the Eastern Province of KSA (Alothman et al., 2014 ; Bakhamis et al., 2024 ; Parker et al., 2020 ), our study demonstrates a clear relationship between the time-series SLH trend and the mean SLH rise of 0.0026 m annually, with no significant seasonal fluctuations and the long-term trend dominating reported changes, with a moderate model coefficient (Table 4 ). The associated absolute SLR, using the land subsidence considered at six GPS stations within 100 km of the tide gauges as an indicator of vertical land motion, is 1.5 ± 0.8 mm/year, which is consistent with the global projection of 1.9 ± 0.1 mm/year (Alothman et al., 2014 ) for the period 1979–2007. The OLS outputs and Sen’s slope estimator further indicate the strength of annual SLH increase with high probability during our observation period, while a sudden plummet of SLH was also evident during 1999–2004. Further, the IPCC AR6-based SLR prediction shows a steady rise in the following years, where the annual mean SLR is almost 0.603 ± 0.479 m, and the deviation indicates exponential growth after 2080. To capture the sensitivity of future SLR at corresponding LULC, the study also utilized past LULC in 1973, 1990, 2000, 2015, 2020, and 2022 in the Eastern Province of KSA (Fig. 2 and Fig. 7 ). The observation showed a significant variation in modifying the coastal belt along the AG coast in all governorates due to sediment transport, tides, SLR, or human activity. However, major projects involving land reclamation and infrastructure development changed the face of the Ad Dammam and Al Qatif coastal areas. In addition, long-term coastal belt change indicates a significant shoreline retreat for numerous regions such as Al Jubail, Ras Tanura, Al Khobar, and Al Ahsa between 1973 and 2022. While studying the Yanbu coastal zone from 1965 to 2019 Niang ( 2020 ) mentioned the greatest accretion was 1655.9 m, while the maximum erosion was − 1484.8 m (Niang, 2020 ). Moreover, based on two RCP scenarios (RCP 4.5 and RCP 8.5), the AG coast of the KSA shoreline is expected to undergo regional mean retreats of around 30 meters by 2050 and 130 meters by 2100 (Luijendijk et al., 2022 ). However, from our literature review, while study related to SLH and shoreline change are evident (Alothman et al., 2014 ; Bakhamis et al., 2024 ; Luijendijk et al., 2022 ; Niang, 2020 ; Parker et al., 2020 ), no quantitative studies were conducted that analyzed the potential inundation utilizing IPCC scenarios and demonstrated vulnerability to economic potential zones. Therefore, utilizing a GIS-based bathtub model, we aggregated SRTM DEM data and incorporated catchment characteristics to anticipate the future inundation pattern and estimated land cover loss across the study area (Fig. 5 ). In our model output, we have mentioned that for the specific nine governorates, there is a complex inundation pattern. For most of the regions, linear upward trend for sea inundation from 2070 was evident, while for areas, such as at Khafji and Al Ahsa post-2100, the trajectory was reduced significantly. Flood-prone governorates have environmentally diverse and delicate systems that are vulnerable to SLR-induced disruption. Such salinity incursion and benthic habitat fragmentation can threaten mangroves and intertidal wetlands in Tarout Bay at Al Qatif and Ras Tanura (Al-Ali et al., 2015 ). Despite land reclamation, which is common in the Dammam and Al Qatif coasts, the degree of land cover loss and seawater inundation in the Jubail governorate, a booming industrial zone of such operation, is alarming. Here, the rate is projected to increase as much as 741.860 km² and 1481.712 km² by 2130 (Fig. 6 ). Under maritime retreat, Half Moon Bay in Dammam and Al Khobar may face increased littoral erosion, seagrass attrition, and coral deterioration. Biodiverse intertidal Al Khafji Coastal Flats faces rising submersion threats, endangering migrating birds and benthic trophic webs. However, Abqaiq, Al Ahsa, and Al Udayd show lesser initial inundation areas but a significant percentage rise over time. Al Asfar Lake and Al Ahsa Oasis have significant freshwater habitats but are threatened by hydrological instability, desiccation, and saltwater intrusion (Alqahtany, 2023 ; Chouari, 2021 ). The isolated marine embayment Khawr al Udayd Lagoon confronts hydrodynamic disequilibrium, with advancing dune fields increasing coastal flooding risk. These ecological upheavals will greatly impact biodiversity, ecosystem services, and anthropogenic resilience along Saudi Arabia's eastern coast. Nevertheless, KSA is one of the leading economies in the Middle East, with high urbanization and industrial potential in governorates like Ad Dammam, Jubail, and Al Qatif. Between 1992 and 2013, urban areas grew by 31% between 1992 and 1999, 19% between 1999 and 2006, and 37% between 2006 and 2013 on the whole eastern coast of KSA (Alahmadi & Atkinson, 2019 ). Here, Dammam, Al Khobar, and Al Qatif took charge of such enormous anthropogenic infrastructure with a massive land reclamation project at the coast of AG. Due to Dammam's geographical limitations between the Gulf Saihat to the north and Al Khobar and Dhahran to the south, its urban expansion pattern has expanded substantially inland towards the west. While Dammam saw a total urban expansion of about 42.5 km² over 34 years (1985–2019), with more than 25 km² occurring during the last 4 years interval (2015–2019), Al-Khobar saw a built-up area increase of 117% over 11 years (1990–2001) and 43.5% over the next 12 years (2001–2013) (Aljaddani et al., 2022 ; Rahman et al., 2017 ). Consequently, one of our objectives was to quantitatively comprehend the plausible impact of SLR inundation on future anthropogenic activities. Therefore, we predicted the future LULCs and economic concentration utilizing the complex CA-ANN approach, the MOLUSCE plugin from QGIS for 2070, 2100, and 2130. The SLR inundation track and the following LULC loss show significant long-term floods in manmade built-ups and plant cover along the coastlines (Fig. 8 and Fig. 10 ). By 2130, Jubail will have the most severe SLR effects at an exponential rate, followed by Dammam and Al Qatif. Although their inundation tendency will decrease in the following years, governorates like Abqaiq and Ras Tanura may see significant flooding in the early years of 2070. 5. Conclusion This study combines NTL data with LULC modeling using a cellular automata framework to forecast the impacts of sea level rise on six major coastal cities in Saudi Arabia’s Eastern Province along the Arabian Gulf. The study found a clear relationship between the time-series SLH trend and the mean SLH rise of 0.0026 m annually, with no significant seasonal fluctuations. The associated absolute SLR is 1.5 ± 0.8 mm/year, consistent with the global projection of 1.9 ± 0.1 mm/year for 1979–2007. The study also utilized past LULC in the Eastern Province of KSA. The observation showed significant variation in modifying the coastal belt along the AG coast in all governorates due to sediment transport, tides, SLR, or human activity. An approximate 1075.7021 km 2 area is expected to be inundated based on the IPCC’s ML scenario by 2070, which can rise by 3833.017 km 2 in the WS scenario by 2130, with Ras Tanura and Al Qatif as the most vulnerable zones. The study findings present critical implications for urban planning and climate adaptation in Saudi Arabia. By integrating nighttime light data with LULC patterns through cellular automata modeling, the study offers a nuanced projection of how SLR may affect major urban centers along the Western Arabian Gulf. The findings underscore the vulnerability of densely populated and rapidly urbanizing coastal areas to inundation, highlighting the potential loss of infrastructure, economic assets, and residential zones. This research provides a valuable decision-support tool for policymakers, emphasizing the urgent need for proactive coastal management, sustainable development strategies, and investment in climate-resilient infrastructure to mitigate the long-term risks associated with SLR in one of the world’s most economically and strategically significant regions. While previous studies have identified the recent fluctuations in SLH on the coast of AG, no studies have incorporated the future vulnerability to LULCs in the Easter Providences. Moreover, growing economic cities like Dammam and Al Qatif are at a very high risk of coastal inundation in the upcoming years. This study addresses the limitations of past literature in assessing the LULC and economic vulnerability in the Eastern Province, KSA, which are expected to be potentially posed by climate change. Therefore, it will help the local government and corresponding authority to reassess the initiatives to build necessary damming and SLR-resilient infrastructure and enhance emergency preparedness for potential migrations. We, however, acknowledge that future humanitarian responses are still unpredictable due to the degree of potential climate repercussions. Moreover, climatic circumstances may change with time. Therefore, future studies should include scenarios with enhanced infrastructures and ecological settings that can minimize the effect of SLR in the Eastern Province, KSA, and locations for urgent migrations. Declarations Declaration of conflict of interest: The authors declare no conflict of interest. Acknowledgments: The authors gratefully acknowledge the support provided by King Fahd University of Petroleum & Minerals (KFUPM) for facilitating this research. References Abdullah, S., Adnan, M. S. G., Barua, D., Murshed, M. M., Kabir, Z., Chowdhury, M. B. H., Hassan, Q. K., & Dewan, A. (2022). Urban green and blue space changes: A spatiotemporal evaluation of impacts on ecosystem service value in Bangladesh. Ecological Informatics , 70 , 101730. https://doi.org/10.1016/j.ecoinf.2022.101730 Alahmadi, M., & Atkinson, P. M. (2019). Three-Fold Urban Expansion in Saudi Arabia from 1992 to 2013 Observed Using Calibrated DMSP-OLS Night-Time Lights Imagery. Remote Sensing , 11 (19), 2266. https://doi.org/10.3390/RS11192266 Al-Ali, A. M., Del Campo, A. G., & Rocha, C. (2015). Environmental assessment of Mangrove communities in Tarut Bay, Eastern Arabian Peninsula, based on multidisciplinary approach. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences , XL-7-W3 (7W3), 269–276. https://doi.org/10.5194/ISPRSARCHIVES-XL-7-W3-269-2015 Alam, A., Bhat, M. S., & Maheen, M. (2020). Using Landsat satellite data for assessing the land use and land cover change in Kashmir valley. GeoJournal , 85 (6), 1529–1543. https://doi.org/10.1007/s10708-019-10037-x Alfarrah, N., & Walraevens, K. (2018). Groundwater Overexploitation and Seawater Intrusion in Coastal Areas of Arid and Semi-Arid Regions. Water, 10 (2). https://doi.org/10.3390/w10020143 Aljaddani, A. H., Song, X. P., & Zhu, Z. (2022). Characterizing the Patterns and Trends of Urban Growth in Saudi Arabia’s 13 Capital Cities Using a Landsat Time Series. Remote Sensing , 14 (10), 2382. https://doi.org/10.3390/RS14102382 Almaliki, A. H., Zerouali, B., Santos, C. A. G., Almaliki, A. A., Silva, R. M. da, Ghoneim, S. S. M., & Ali, E. (2023). Assessing coastal vulnerability and land use to sea level rise in Jeddah province, Kingdom of Saudi Arabia. Heliyon , 9 (8), e18508. https://doi.org/10.1016/j.heliyon.2023.e18508 Alothman, A. O., Bos, M. S., Fernandes, R. M. S., & Ayhan, M. E. (2014). Sea level rise in the north-western part of the Arabian Gulf. Journal of Geodynamics , 81 , 105–110. https://doi.org/10.1016/J.JOG.2014.09.002 Alqahtany, A. (2023). GIS-based assessment of land use for predicting increase in settlements in Al Ahsa Metropolitan Area, Saudi Arabia for the year 2032. Alexandria Engineering Journal , 62 , 269–277. https://doi.org/10.1016/J.AEJ.2022.07.020 Al-Subhi, A. M., & Abdulla, C. P. (2021). Sea-Level Variability in the Arabian Gulf in Comparison with Global Oceans. Remote Sensing, 13 (22), 4524. https://doi.org/10.3390/rs13224524 Antonov, J. I., Levitus, S., & Boyer, T. P. (2005). Thermosteric sea level rise, 1955–2003. Geophysical Research Letters , 32 (12), 1-4. https://doi.org/10.1029/2005GL023112 Bakhamis, A. N., Bilal, H., Heggy, E., Al-Kuwari, M. S., & Al-Ansari, T. (2024). On the drivers, forecasts, and uncertainties of relative sea level rise in the Eastern Arabian Peninsula: A review. Regional Studies in Marine Science , 73 , 103503. https://doi.org/10.1016/J.RSMA.2024.103503 Buchanan, J. R., Krupp, F., Burt, J. A., Feary, D. A., Ralph, G. M., & Carpenter, K. E. (2016). Living on the edge: Vulnerability of coral-dependent fishes in the Gulf. Marine Pollution Bulletin , 105 (2), 480–488. https://doi.org/10.1016/j.marpolbul.2015.11.033 Buffardi, C., & Ruberti, D. (2023). The Issue of Land Subsidence in Coastal and Alluvial Plains: A Bibliometric Review. Remote Sensing, 15 (9), 2409. https://doi.org/10.3390/rs15092409 Cazenave, A., Hamlington, B., Horwath, M., Barletta, V. R., Benveniste, J., Chambers, D., Döll, P., Hogg, A. E., Legeais, J. F., Merrifield, M., Meyssignac, B., Mitchum, G., Nerem, S., Pail, R., Palanisamy, H., Paul, F., von Schuckmann, K., & Thompson, P. (2019). Observational Requirements for Long-Term Monitoring of the Global Mean Sea Level and Its Components Over the Altimetry Era. Frontiers in Marine Science , 6 , 582. https://doi.org/10.3389/fmars.2019.00582 CCPO. (2018). Urban Areas in Coastal Zones. In Climate Change and Cities: Second Assessment Report of the Urban Climate Change Research Network (pp. 319–362). Cambridge University Press. Ch, R., Martin, D. A., & Vargas, J. F. (2021). Measuring the size and growth of cities using nighttime light. Journal of Urban Economics , 125 , 103254. https://doi.org/10.1016/j.jue.2020.103254 Chouari, W. (2021). Wetland land cover change detection using multitemporal Landsat data: a case study of the Al-Asfar wetland, Kingdom of Saudi Arabia. Arabian Journal of Geosciences , 14 (6), 1–14. https://doi.org/10.1007/S12517-021-06815-Y/METRICS Chuvieco, E. (2008). Earth Observation of Global Change: The Role of Satellite Remote Sensing in Monitoring the Global Environment . https://doi.org/10.1007/978-1-4020-6358-9 Crippen, R., Buckley, S., Agram, P., Belz, E., Gurrola, E., Hensley, S., Kobrick, M., Lavalle, M., Martin, J., Neumann, M., Nguyen, Q., Rosen, P., Shimada, J., Simard, M., & Tung, W. (2016). Nasadem global elevation model: Methods and progress. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives , 41 , 125–128. https://doi.org/10.5194/isprsarchives-XLI-B4-125-2016 Djouder, F., & Boutiba, M. (2017). Vulnerability assessment of coastal areas to sea level rise from the physical and socioeconomic parameters: case of the Gulf Coast of Bejaia, Algeria. Arabian Journal of Geosciences , 10 (14), 299. https://doi.org/10.1007/s12517-017-3062-5 Elvidge, C. D., Baugh, K. E., Anderson, S. J., Sutton, P. C., & Ghosh, T. (2012). The Night Light Development Index (NLDI): a spatially explicit measure of human development from satellite data. Social Geography , 7 (1), 23–35. https://doi.org/10.5194/sg-7-23-2012 Erkens, G., Bucx, T., Dam, R., de Lange, G., & Lambert, J. (2015). Sinking coastal cities. Proceedings of the International Association of Hydrological Sciences , 372 , 189–198. https://doi.org/10.5194/piahs-372-189-2015 Galloway, D. L., & Burbey, T. J. (2011). Review: Regional land subsidence accompanying groundwater extraction. Hydrogeology Journal , 19 (8), 1459–1486. https://doi.org/10.1007/s10040-011-0775-5 Gesch, D. B. (2018). Best practices for elevation-based assessments of sea-level rise and coastal flooding exposure. Frontiers in Earth Science , 6 , 230. https://doi.org/10.3389/feart.2018.00230 Ghoussein, Y., Mhawej, M., Jaffal, A., Fadel, A., El Hourany, R., & Faour, G. (2018). Vulnerability assessment of the South-Lebanese coast: A GIS-based approach. Ocean & Coastal Management , 158 , 56–63. https://doi.org/10.1016/j.ocecoaman.2018.03.028 Griggs, G., & Reguero, B. G. (2021). Coastal adaptation to climate change and sea- level rise. Water, 13 (16), 2151. https://doi.org/10.3390/w13162151 Han, H., Yang, C., & Song, J. (2015). Scenario Simulation and the Prediction of Land Use and Land Cover Change in Beijing, China. Sustainability, 7 (4), 4260–4279. https://doi.org/10.3390/su7044260 Hasan, M. H., Newton, I. H., Chowdhury, M. A., Esha, A. A., Razzaque, S., & Hossain, M. J. (2023). Land use land cover change and related drivers have livelihood consequences in coastal Bangladesh. Earth Systems and Environment, 7 (2), 541-559. https://doi.org/10.1007/s41748-023-00339-z Hassanzadeh, S., Kiasatpour, A., & Hosseinibalam, F. (2007). Sea-level response to atmospheric forcing along the north coast of Persian Gulf. Meteorology and Atmospheric Physics , 95 (3), 223–237. https://doi.org/10.1007/s00703-006-0213-8 Hereher, M. E. (2020). Assessment of Climate Change Impacts on Sea Surface Temperatures and Sea Level Rise—The Arabian Gulf. Climate , 8 (4), 50. https://doi.org/10.3390/cli8040050 Hosseinibalam, F., Hassanzadeh, S., & Kiasatpour, A. (2007). Interannual variability and seasonal contribution of thermal expansion to sea level in the Persian Gulf. Deep Sea Research Part I: Oceanographic Research Papers , 54 (9), 1474–1485. https://doi.org/10.1016/j.dsr.2007.05.005 IPCC. (2018). Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change . Cambridge University Press, 1535 pp. IPCC. (2019). Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. Cambridge University Press, pp. 321-445. https://doi.org/10.1017/9781009157964.006 IPCC. (2021). Climate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report . Cambridge University Press. https://doi.org/10.1017/9781009157896 Jones, C. E., An, K., Blom, R. G., Kent, J. D., Ivins, E. R., & Bekaert, D. (2016). Anthropogenic and geologic influences on subsidence in the vicinity of New Orleans, Louisiana. Journal of Geophysical Research: Solid Earth , 121 (5), 3867–3887. https://doi.org/https://doi.org/10.1002/2015JB012636 Lin, Y. P., Chu, H. J., Wu, C. F., & Verburg, P. H. (2010). Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling – a case study. International Journal of Geographical Information Science , 25 (1), 65–87. https://doi.org/10.1080/13658811003752332 Luijendijk, A. P., Kras, E., Dagalaki, V., Morelissen, R., Hoteit, I., & Ranasinghe, R. (2022). Regime Shifts in Future Shoreline Dynamics of Saudi Arabia. Frontiers in Marine Science , 8 , 798657. https://doi.org/10.3389/FMARS.2021.798657/BIBTEX Mahmud, K., Mitra, B., Uddin, M. S., Hridoy, A.-E. E., Aina, Y. A., Abubakar, I. R., Rahman, S. M., Tan, M. L., & Rahman, M. M. (2023). Temporal assessment of air quality in major cities in Nigeria using satellite data. Atmospheric Environment: X ,. 20 , 100227. https://doi.org/10.1016/j.aeaoa.2023.100227 Maithani, S. (2009). A Neural Network based Urban Growth Model of an Indian City. Journal of the Indian Society of Remote Sensing, 37 , 363–376. https://doi.org/10.1007/s12524-009-0041-7 Magnan, A. K., Oppenheimer, M., Garschagen, M., Buchanan, M. K., Duvat, V. K., Forbes, D. L., ... & Pörtner, H. O. (2022). Sea level rise risks and societal adaptation benefits in low-lying coastal areas. Scientific Reports, 12 (1), 10677. https://doi.org/10.1038/s41598-022-14303-w Mitra, B., Rahman, M. M., Khan, A. A., & Rahman, S. M. (2024). Assessing the Impact of Sea Level Rise on the Indus Delta in Pakistan: A Comprehensive Analysis of Flooded Areas and Future Vulnerabilities. Heliyon, 10 (12), e33120. https://doi.org/10.1016/j.heliyon.2024.e33120 Mitra, B., Rahman, S. M., Uddin, M. S., Mahmud, K., Islam, M. K., Arifuzzaman, M., Hafizur Rahman, M. M., & Rahman, M. M. (2023). Assessing demographic and economic vulnerabilities to sea level rise in Bangladesh via a nighttime light-based cellular automata model. Scientific Reports , 13 (1), 1–14. https://doi.org/10.1038/s41598-023-40329-9 Mitrovica, J. X., Gomez, N., Morrow, E., Hay, C., Latychev, K., & Tamisiea, M. E. (2011). On the robustness of predictions of sea level fingerprints. Geophysical Journal International , 187 (2), 729–742. https://doi.org/10.1111/j.1365-246X.2011.05090.x Mortillaro, N. (2019). Sea levels could rise by up to 2 metres by 2100, new study finds . CBC News. https://www.cbc.ca/news/science/sea-level-rise-climate-change-1.5144739 NASA JPL. (2020). NASADEM Merged DEM Global 1 arc second V001 [Data set] . NASA EOSDIS Land Processes Distributed Active Archive Center. Nerem, R. S., Beckley, B. D., Fasullo, J. T., Hamlington, B. D., Masters, D., & Mitchum, G. T. (2018). Climate-change–driven accelerated sea-level rise detected in the altimeter era. Proceedings of the National Academy of Sciences of the United States of America , 115 (9), 2022–2025. https://doi.org/10.1073/pnas.1717312115 Niang, A. J. (2020). Monitoring long-term shoreline changes along Yanbu, Kingdom of Saudi Arabia using remote sensing and GIS techniques. Journal of Taibah University for Science , 14 (1), 762–776. https://doi.org/10.1080/16583655.2020.1773623 Parker, A. G., Morley, M. W., Armitage, S. J., Engel, M., Parton, A., Preston, G. W., Russ, H., & Drechsler, P. (2020). Palaeoenvironmental and sea level changes during the Holocene in eastern Saudi Arabia and their implications for Neolithic populations. Quaternary Science Reviews , 249 , 106618. https://doi.org/10.1016/J.QUASCIREV.2020.106618 Rahman, M. T., Aldosary, A. S., & Mortoja, M. G. (2017). Modeling Future Land Cover Changes and Their Effects on the Land Surface Temperatures in the Saudi Arabian Eastern Coastal City of Dammam. Land , 6 (2), 36. https://doi.org/10.3390/LAND6020036 Sajjad, M., Chan, J. C. L., & Lin, N. (2020). Incorporating natural habitats into coastal risk assessment frameworks. Environmental Science & Policy , 106 , 99–110. https://doi.org/10.1016/j.envsci.2020.01.004 Sanders, B. F., Wing, O. E. J., & Bates, P. D. (2024). Flooding is Not Like Filling a Bath. Earth’s Future , 12 (12), e2024EF005164. https://doi.org/10.1029/2024EF005164 Sanders, D., Frago, E., Kehoe, R., Patterson, C., & Gaston, K. J. (2021). A meta-analysis of biological impacts of artificial light at night. Nature Ecology & Evolution , 5 (1), 74–81. https://doi.org/10.1038/s41559-020-01322-x Stammer, D., Cazenave, A., Ponte, R. M., & Tamisiea, M. E. (2013). Causes for contemporary regional sea level changes. Annual Review of Marine Science , 5 , 21–46. https://doi.org/10.1146/annurev-marine-121211-172406 Stouthamer, E., Erkens, G., Cohen, K., Hegger, D., Driessen, P., Weikard, H. P., Hefting, M., Hanssen, R., Fokker, P., van den Akker, J., Groothuijse, F., & van Rijswick, M. (2020). Dutch national scientific research program on land subsidence: Living on soft soils -- subsidence and society. Proceedings of the International Association of Hydrological Sciences , 382 , 815–819. https://doi.org/10.5194/piahs-382-815-2020 Sultan, S. A. R., Ahmad, F., Elghribi, N. M., & Al-Subhi, A. M. (1995). An analysis of Arabian Gulf monthly mean sea level. Continental Shelf Research , 15 (11), 1471–1482. https://doi.org/10.1016/0278-4343(94)00081-W Tzampoglou, P., Ilia, I., Karalis, K., Tsangaratos, P., Zhao, X., & Chen, W. (2023). Selected Worldwide Cases of Land Subsidence Due to Groundwater Withdrawal. Water, 15 (6), 1094. https://doi.org/10.3390/w15061094 van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J. F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S. J., & Rose, S. K. (2011). The representative concentration pathways: An overview. Climatic Change , 109 (1), 5–31. https://doi.org/10.1007/s10584-011-0148-z Williams, L. L., & Lück-Vogel, M. (2020). Comparative assessment of the GIS based bathtub model and an enhanced bathtub model for coastal inundation. Journal of Coastal Conservation , 24 (2), 1–15. https://doi.org/10.1007/S11852-020-00735-X/METRICS Zhang et al. (2019). Spatial and temporal changes of habitat quality in Jiangsu Yancheng wetland national nature reserve - Rare birds of China. Applied Ecology and Environmental Research , 17 , 4807–4821. https://doi.org/10.15666/aeer/1702_48074821 Zhao, M., Cheng, W., Zhou, C., Li, M., Huang, K., & Wang, N. (2018). Assessing Spatiotemporal Characteristics of Urbanization Dynamics in Southeast Asia Using Time Series of DMSP/OLS Nighttime Light Data. Remote Sensing, 10 (1), 47. https://doi.org/10.3390/rs10010047 Zhu, L., Song, R., Sun, S., Li, Y., & Hu, K. (2022). Land use/land cover change and its impact on ecosystem carbon storage in coastal areas of China from 1980 to 2050. Ecological Indicators, 142, 109178. https://doi.org/10.1016/j.ecolind.2022.109178 Supplementary Files GraphicalAbstract.docx Cite Share Download PDF Status: Published Journal Publication published 26 Dec, 2025 Read the published version in Natural Hazards → Version 1 posted Reviewers agreed at journal 07 Aug, 2025 Reviewers invited by journal 22 Jul, 2025 Editor invited by journal 17 Jul, 2025 Editor assigned by journal 16 Jul, 2025 First submitted to journal 15 Jul, 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7126987","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":488969534,"identity":"476d8a7e-1298-43a8-9a4c-6cf5991c64a7","order_by":0,"name":"Azher Hussain Syed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYFACHhgj+TBDBYRlQKyWtGSGMyRqyTEmTgt//9mDjysY6hL72XM+GxxsuxPNwN68TYLhjx1OLRIHziUbnmE4nDiz5+3mhINtz3IbeI6VSTC2JeO25mCPmWQDw4HEDTdyNx/+2HY4t0Eix0yCsYEZpw75wzzmPxuADtt/I+fxgYMgLfJvzIAOq8epxeAYjxljAwNz4gaJHOYEsBYJHqAWtsM4tRie4TGWbDA4bDzjzDNjgwPnnuW28aQVWyS2HcepRe78GcOPDRV1sv3tyY8lDpTdye1nP7zxxoc/1bi9D3Eeg2MDhHWAgQ1EJRDQAAL2DDAto2AUjIJRMArQAQAID1s0tW0+LQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-1386-1091","institution":"King Fahd University of Petroleum \u0026 Minerals","correspondingAuthor":true,"prefix":"","firstName":"Azher","middleName":"Hussain","lastName":"Syed","suffix":""},{"id":488969535,"identity":"865ded98-21d4-4d9b-9d2a-1c6762c0e9f6","order_by":1,"name":"Bijoy Mitra","email":"","orcid":"","institution":"University of Chittagong","correspondingAuthor":false,"prefix":"","firstName":"Bijoy","middleName":"","lastName":"Mitra","suffix":""},{"id":488969536,"identity":"726248e0-147c-46a2-a5ea-eda6e5f6fec0","order_by":2,"name":"Mohammad Shahedur Rahman","email":"","orcid":"","institution":"Imam Muhammad Ibn Saud Islamic University","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Shahedur","lastName":"Rahman","suffix":""},{"id":488969537,"identity":"71aa74c3-f037-4c4d-a78c-d23e0d17b4c7","order_by":3,"name":"Omer Rehman Reshi","email":"","orcid":"","institution":"King Fahd University of Petroleum \u0026 Minerals","correspondingAuthor":false,"prefix":"","firstName":"Omer","middleName":"Rehman","lastName":"Reshi","suffix":""},{"id":488969538,"identity":"2f1ecbea-412d-41f6-8f0e-5030194b2d35","order_by":4,"name":"Syed Masiur Rahman","email":"","orcid":"","institution":"King Fahd University of Petroleum \u0026 Minerals","correspondingAuthor":false,"prefix":"","firstName":"Syed","middleName":"Masiur","lastName":"Rahman","suffix":""},{"id":488969539,"identity":"abe88a93-43df-4abd-ae8e-c7bde6587221","order_by":5,"name":"Asif Raihan","email":"","orcid":"","institution":"King Fahd University of Petroleum \u0026 Minerals","correspondingAuthor":false,"prefix":"","firstName":"Asif","middleName":"","lastName":"Raihan","suffix":""}],"badges":[],"createdAt":"2025-07-15 06:33:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7126987/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7126987/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11069-025-07740-z","type":"published","date":"2025-12-26T15:57:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87553849,"identity":"6f3f5c17-5fa4-45da-aecb-fa7e9666a6fd","added_by":"auto","created_at":"2025-07-25 06:38:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":521289,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Area Map and the Digital Elevation Map (DEM) from Nasa DEM database.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7126987/v1/a21808c92500d0d29d054f93.png"},{"id":87552672,"identity":"7a19f40a-2995-4284-bc24-f79ec52137d0","added_by":"auto","created_at":"2025-07-25 06:30:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":398517,"visible":true,"origin":"","legend":"\u003cp\u003eSpatiotemporal distribution of LULC across the coastal belt of Eastern Province, KSA.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7126987/v1/eb0f81239955e8476cf003c0.png"},{"id":87551299,"identity":"64997c9c-0fc5-4ddc-b74f-539e4df8d186","added_by":"auto","created_at":"2025-07-25 06:22:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":90687,"visible":true,"origin":"","legend":"\u003cp\u003eLearning Curve plot for predicting future (a) LULC and (b) NTL images.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7126987/v1/5f7e53bc71827847cdf4842f.png"},{"id":87551305,"identity":"08752ae3-a1e5-4353-bd29-5cf6ec3e227b","added_by":"auto","created_at":"2025-07-25 06:22:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":114335,"visible":true,"origin":"","legend":"\u003cp\u003eHistorical and IPCC projected future sea-level changes along the AG coast KSA.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7126987/v1/30f060b6e469e4310a5d02f9.png"},{"id":87551321,"identity":"282d76b3-868a-4b63-a504-225802f8525a","added_by":"auto","created_at":"2025-07-25 06:22:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":504427,"visible":true,"origin":"","legend":"\u003cp\u003eSpatiotemporal distribution of coastal inundation along the AG coast of KSA under projected sea-level rise scenarios.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7126987/v1/7167127d691ba2218a68b986.png"},{"id":87552669,"identity":"0db67db3-6843-450f-8389-fcab63ef8ab6","added_by":"auto","created_at":"2025-07-25 06:30:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":288114,"visible":true,"origin":"","legend":"\u003cp\u003eTime-series representation of the expected extent of coastal inundation along the AG coast of KSA under (a) the maximum likelihood scenario (MLS) and (b) the worst-case scenario (WCS).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7126987/v1/2683858dedebdc60d61a3949.png"},{"id":87551306,"identity":"3fb1f971-6ff6-4ecc-bf14-0a0f3f801698","added_by":"auto","created_at":"2025-07-25 06:22:40","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":291203,"visible":true,"origin":"","legend":"\u003cp\u003eHistorical changes in the shoreline of the study area.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7126987/v1/cbc537f22fa15ceaeb6a0ae6.png"},{"id":87551310,"identity":"e46c6d40-1be5-493c-9035-1c30174b4f75","added_by":"auto","created_at":"2025-07-25 06:22:40","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":811409,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted spatiotemporal distribution of land cover loss along the AG coast of KSA due to projected sea-level rise.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7126987/v1/97885676f2e16ba3ecf369cd.png"},{"id":87551311,"identity":"cb60da4e-3640-4f66-9ae3-d006d7238865","added_by":"auto","created_at":"2025-07-25 06:22:41","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":164671,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted land cover loss along the AG coast of KSA under future inundation scenarios for (a) Abqaiq, (b) Al Khafji, (c) Al Khobar, (d) Al Qatif, (e) Al-Ahsa, (f) Dammam, (g) Jubail, (h) Ras Tanura, and (i) Khawr al Udayd.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7126987/v1/87937c2a60d2733f44f73de7.png"},{"id":87551325,"identity":"928826d3-a517-4cac-9f37-68eb072a6080","added_by":"auto","created_at":"2025-07-25 06:22:41","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":563284,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted spatiotemporal vulnerability of future economic potential along the AG coast of KSA under projected sea-level rise.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-7126987/v1/e3153be200e8638e2bf61ba1.png"},{"id":99172460,"identity":"0ae56289-0efb-4024-9742-713770e38a5b","added_by":"auto","created_at":"2025-12-29 16:09:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4420329,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7126987/v1/9db6362c-3121-41d7-b2cd-54ea1ccd7772.pdf"},{"id":87551300,"identity":"cf76e705-9747-43a0-8366-8e79e38b04f0","added_by":"auto","created_at":"2025-07-25 06:22:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1131473,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.docx","url":"https://assets-eu.researchsquare.com/files/rs-7126987/v1/af589f9fcffdfdf9f4c3899a.docx"}],"financialInterests":"","formattedTitle":"Modeling Sea Level Rise Impacts on Western Arabian Gulf Cities Using Nighttime Lights and LULC-Driven Cellular Automata","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003eThis research projects the impacts of sea level rise on major coastal governorates of Saudi Arabia along the western Arabian Gulf.\u003c/li\u003e\n \u003cli\u003eThis study employed\u0026nbsp;GIS and AI based approach to project sea level rise, predict land cover loss and predict spatiotemporal vulnerability economic zones for the years 2070, 2100 and 2130 using scenarios from the IPCC report.\u003c/li\u003e\n \u003cli\u003eThe research also shows spatiotemporal distribution of LULC from 1973 to 2020 and historical changes in the shoreline from 1973 to 2022.\u003c/li\u003e\n \u003cli\u003eJubail, Qatif, and Ras Tanura have 40% of coastal areas threatened by the worst sea level rise scenario by 2130 and are high risk areas.\u003c/li\u003e\n \u003cli\u003eThe findings urge proactive planning and adaptation to mitigate the economic and ecological impacts.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eThe global concern surrounding climate change, namely the impact of sea level rise (SLR) on coastal areas with low elevation, has garnered significant attention in recent decades. It is expected that coastal erosion and accretion will be exacerbated by the anticipated rise in sea levels and increased storm surge effects in the next several decades (IPCC, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This will pose a significant risk to the overall health and stability of coastal ecosystems in the near, intermediate, and far future (Djouder \u0026amp; Boutiba, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ghoussein et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Almaliki et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). About one billion individuals, accounting for 13% of the worldwide population, reside in coastal areas with elevations within 10 meters of sea level (Griggs \u0026amp; Reguero, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Due to population growth, approximately 70% of the global population lives in coastal plain areas (Alfarrah \u0026amp; Walraevens, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Consequently, the SLR poses a significant peril, as it has the potential to result in the submergence of low-lying coastal regions, the depletion of wetlands, and the erosion of shorelines.\u003c/p\u003e\u003cp\u003eThe submergence of coastal areas at lower elevations is a highly notable and direct consequence of SLR. This phenomenon leads to the infiltration of saltwater into nearby coastal regions, the flooding of deltaic areas and numerous urban centers, and the disruption of transportation systems (CCPO, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The Intergovernmental Panel on Climate Change (IPCC) projected an increase in global mean sea level (GMSL) ranging from 0.52 to 0.98 meters by the year 2100, as stated in its 5th Assessment Report (IPCC, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Nevertheless, the IPCC revised its previous estimation in the latest report, increasing the projected SLR to 2 meters by the year 2100 (IPCC, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mortillaro, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It is anticipated that in the forthcoming decades, the acceleration of SLR resulting from persistent global warming will render several low-lying, densely inhabited coastal areas across the globe increasingly susceptible to adverse impacts. As components of climate modes of variability, regional sea-level fluctuations are linked to dynamic fluctuations in ocean circulation, alterations in wind patterns, and an isostatic adjustment of the Earth's crust in response to historical and ongoing modifications in polar ice masses and continental water storage (Stammer et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Magnan et al., 2023).\u003c/p\u003e\u003cp\u003eThe alteration of land use and land cover (LULC) has emerged as a significant issue in numerous coastal ecosystems worldwide (Zhu et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hasan et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). According to Han et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), alterations in LULC along the coast have significant impacts on hydrological and sedimentary processes, resulting in constraints on the structure and productivity of coastal ecosystems (Han et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The scholarly literature acknowledges that substantial alterations in LULC in coastal areas can exert a considerable influence on the local climate, water distribution, socioeconomic patterns, ecological resilience, and biodiversity (Abdullah et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Alam et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The phenomenon of climate change has been found to have a significant impact on ocean circulation and coastal risks, resulting in a reduction in global land area, potential productivity, and ecosystem health (Sajjad et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Further, activities such as groundwater pumping (Galloway \u0026amp; Burbey, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) or mining (Jones et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), can contribute to land subsidence (Tzampoglou et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This phenomenon has the potential to inflict harm on the environment, hence posing a significant concern from various perspectives, encompassing social, environmental, and protective considerations (Stouthamer et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the context of future projections, the issue at hand has greater significance due to the phenomenon of SLR (Erkens et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), particularly in regions experiencing a quicker rate of subsidence relative to the rate of sea level increase (Buffardi \u0026amp; Ruberti, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe utilization of satellite imagery to view the Earth's surface offers significant opportunities for the monitoring, analysis, evaluation, and prediction of notable transformations occurring on the Earth's surface. This capability enables the quantification and tracking of the dynamic nature of human activity and its associated environmental effects (Chuvieco, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). For example, monitoring LULC over a specific period will significantly contribute to monitoring the recent alterations of coastal belts, changes in urban activities, and even the submergence of low-lying islands. Further, the measurement of Night Time Light (NTL) from space is considered a significant indicator of human presence and activity on the Earth's surface (Elvidge et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Satellite-based observations of NTL have emerged as a prominent tool for assessing the intensity of human activities, surpassing other satellite products that rely on visible, near-infrared, or radar sensors (Zhao et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It also offers distinct viewpoints that can shed light on environmental and socioeconomic concerns, presenting valuable opportunities for monitoring human activities and comprehending their associated environmental consequences (Ch et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sanders et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRegional sea level fluctuations may exhibit significant deviations from the GMSL and may adhere to a distinct regional pattern, with certain areas witnessing noteworthy deviations from the average global SLR (IPCC, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For instance, the Arabian Gulf (AG) exhibits a seasonal variation in sea level, with a decrease observed from the months of February to May and an increase from September to December. The highest sea level is typically recorded in November, while the lowest is observed in April (Al-Subhi \u0026amp; Abdulla, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Previous tidal gauge studies in the AG found varied long-term sea level trends due to the variation in the research period (Hassanzadeh et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Sultan et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Two tide gauges' 11-year record analysis showed a 2.1 mm/year sea-level trend (Sultan et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Another study using 1990\u0026ndash;1999 altimetry data found 2.8 mm/year sea-level increase in the Northern AG (Hassanzadeh et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Moreover, Hosseinibalam et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) mentioned a SLR of 2.34 mm/year for the entire gulf. Global satellite altimetry records show a quicker sea-level increase of 3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 mm/year from 1993 to 2017 (Antonov et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Cazenave et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Al-Subhi and Abdulla (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) analyzed almost 30 years of satellite altimetry data and reported the expected SLR in the AG for different scenarios. They estimated the rise to be 1.3 to 8.1 cm by 2050 and 16.9 to 39.1 cm by 2100.\u003c/p\u003e\u003cp\u003eSaudi Arabia\u0026rsquo;s Eastern Province hosts a growing population and economic centers, characterized by expanding urbanization, land reclamation, and heavy industrial activities. The coastal cities of Ad Dammam, Al Jubail, Al Khafji, Al Khobar, Qatif, and Ras Tanura marked by historical and cultural significance are especially at risk from SLR due to their low-lying topography, ongoing economic globalization, and increasing maritime traffic. The eastern province's shoreline is seeing some of the fastest rates of population and economic expansion. The energy exchange between the land and the atmosphere is impacted by rising urbanization, the conversion of seawater to reclaimed land, heavy industrial activity, and oil refining processes, which particularly promote a major warming at the regional scale. Saudi Arabia's coastal cities will probably continue to see population growth due to economic globalization and rising shipping traffic. Therefore, it is crucial to study the effect of SLR on the major coastal cities of the province.\u003c/p\u003e\u003cp\u003eWhile several studies have examined SLR in the coasts of AG, few have incorporated socioeconomic metrics to quantify the spatial distribution of urban and industrial activities. This omission leaves uncertainties about how rapidly growing urban and industrial zones are exposed to climate-driven coastal hazards. In addition, existing regional or global projections often treat the AG\u0026rsquo;s coastal areas with broad assumptions. Detailed, localized modeling specific to Saudi Arabia\u0026rsquo;s Eastern Province is lacking, which hinders accurate risk assessments for critical infrastructure and rapidly expanding cities. Therefore, we integrate the IPCC AR6 SLR scenarios with a GIS-based \u0026ldquo;bathtub\u0026rdquo; approach provides inundation estimates that capture local conditions across the Eastern Province. Our study focuses on the major coastal cities of Saudi Arabia\u0026rsquo;s Eastern Province. We further incorporated urban development with multi-year LULC information in a Cellular Automata (CA) approach to generate projections of future landcover which are potentially vulnerable for sea flooding. Additionally, spatially explicit mapping of economic hotspots using NTL data highlights where industrial and infrastructural assets face the greatest flood exposure. This approach closes a gap in arid coastal risk research and informs adaptation measures, thereby supporting more resilient coastal management strategies.\u003c/p\u003e\u003cp\u003eThis study's major contribution lies in the innovative methodological approach that combines remote sensing and geospatial modeling to generate high-resolution, future-oriented urban vulnerability maps. The study\u0026rsquo;s findings have significant implications for urban planning and climate resilience, particularly in data-scarce regions like the Western Arabian Gulf, by providing actionable insights for policymakers and stakeholders to prioritize adaptation strategies. The research is significant as it addresses a critical knowledge gap in SLR impact modeling for arid coastal cities, which are rapidly urbanizing and highly exposed to climate-induced risks. Its novelty stems from the use of nighttime light data as a proxy for human activity and urban expansion, offering a dynamic and scalable tool for anticipating future risk zones under different SLR scenarios.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study Area\u003c/h2\u003e\n \u003cp\u003eThe Arabian Gulf is a shallow inland sea with a mean depth of 50 meters; its coastal regions are also shallow (5 to 15 meters). The Arabian Gulf is connected to the Indian Ocean and the Sea of Oman; therefore, the inputs from river systems, evaporation, and water exchange with the Sea of Oman through the Strait of Hormuz influence the Arabian Gulf\u0026apos;s water budget (Hereher, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The scorching desert region of Arabia, where it is located, is nearby, and the hot climate there is mirrored in the hot surface water temperatures (up to 34\u0026deg;C). It is further distinguished by having water that is more salinized than typical seawater, which has a salinity of 35 parts per thousand (ppt) on average (Buchanan et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Data\u003c/h2\u003e\n \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.1 Digital Elevation Model\u003c/h2\u003e\n \u003cp\u003eDerived from data acquired by the Shuttle Radar Topography Mission (SRM), NASADEM (NASA Digital Elevation Model) is a high-resolution worldwide digital elevation dataset. It was released in 2020 as an improved version of the original SRTM dataset, created by reprocessing of the SRTM interferometric SAR Data and merging it with DEM datasets such as ASTER, ICESat and GLAS. Its main objective was to eliminate voids and other limitations that were present in the SRTM dataset (Crippen et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). The NASADEM is supposed to be the successor of the SRTM Data Dataset (Gesch, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). In this study, NASADEM with 30m digital elevation model of year 2000 (NASA JPL, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) is used for the entire east coast of Saudi Arabia. Accessible through NASA\u0026apos;s data archives, including the Earth data portal, it is freely available to the public. It was considered for this study because of its improved processing and higher quality also thus seen as a huge leap forward over previous elevation datasets.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.2 IPCC SLR Statistics\u003c/h2\u003e\n \u003cp\u003eIt is important to note that rising sea levels are not uniform globally. Over the last 170 years, the global sea level has risen \u0026sim;20 cm. Through each passing year, the rate of change has increased; however, in the early twenty-first century, it is \u0026sim;3.2 mm/year and growing at a rate of \u0026sim;0.8 mm/year per decade (Nerem et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). The \u0026ldquo;NASA Sea Level Projection Tool\u0026rdquo; from IPCC AR6 was used to estimate the future SLR projections in the designated area of interest in the Kingdom of Saudi Arabia (KSA). Moreover, the IPCC statistics are a numerical method employed to examine a range of parameters, including both low and high-emission possibilities (van Vuuren et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). Our study represents the sea level rise projections for both regional and local scales from 2020 to 2150. One of the main factors affecting sea level rise at coastal areas in the KSA is due to the changes in the elevation of coastal land and may arise from plate tectonic processes, ongoing isostatic response to past changes in loads such as removal of the weight of the ice-age ice sheets, and other processes, including groundwater/fossil-fuel extraction and compaction of coastal sediments (Mitrovica et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.3 LULC data\u003c/h2\u003e\n \u003cp\u003eIn this study, we have categorized Land use and Land cover in four classes viz Water, Land, Built-up and Vegetation. All the datasets used for the study were downloaded from USGS Earth explorer for the multiple years from 1973 to 2020 of varying satellite sensors. The data is processed in ArcGIS for layer stacking of all the bands present in each dataset. Once the images are ready by combining multiple scenes for the area of interest through Mosaicking, and Classification algorithm is used to each image from 2000 to 2020. The priority for classifying the image in different classes is creating training samples for each feature classes, which is more than 500 samples and stored as signature input file for the classification Algorithm. The Maximum Likelihood classification algorithm was used for this study, which shows different classes in the produced output raster image and area was calculated for each class from 2000 to 2020. The supervised classification algorithm is used for differentiating land use land cover features.\u003c/p\u003e\n \u003cp\u003eThe statistics in square kilometers calculated from the Supervised classification for different land use land cover features in mentioned in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e:\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLand use land cover statistics of different land covers in square kilometres.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBuilt up\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWater\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLand\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1075.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13568.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17397.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e659.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e729.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13871.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17062.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1064.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e370.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14115.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16773.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1787.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e590.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13919.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16026.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4393.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e272.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14203.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13456.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFrom the above mentioned the statistics of the above classes are discussed for built up, vegetation, water and land.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.4 Nighttime Light from Visible Infrared Imaging Radiometer Suite (VIIRS)\u003c/h2\u003e\n \u003cp\u003eEconomic clusters were identified using NPP-VIIRS data as a stand-in. It is used by many academics to calculate its economic impacts at both regional and national levels (Ch et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; D. Sanders et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Monthly mean NPP-VIIRS pictures are generated by the National Centers for Environmental Information (NCEI) Earth Observations Group (EOG). The resolution of this instrument is 15 arc seconds. The Suomi National Polar-Orbiting Partnership (NPP) satellite\u0026apos;s VIIRS sensor examines data in 22 different wavelength bands, including the DNB (Day/Night Band). VIIRS DNB data have been used to monitor natural dangers and disharmony, evaluate the population, evaluate working conditions in rural modernization, and understand the biological implications of light pollution (Ch et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mahmud et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; D. Sanders et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The VIIRS Day/Night Band data is used to create mean radiance composite pictures based on nighttime brightness.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Classification and Simulation Models\u003c/h2\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.1 Bathtub Model\u003c/h2\u003e\n \u003cp\u003eThe GIS-based bathtub model is more advanced than the traditional bathtub model because it uses a GIS approach to map, analyze, and visualize spatial data in the context of environmental, hydrological, and climate-related studies by incorporating spatial data such as Digital elevation models (DEMS), land use land cover and hydrological data to model how rising sea levels, storm surges or heavily precipitation affect coastal areas. This model is mainly and widely used to study for managing and analyzing water resources, flooding, coastal inundation, and climate change impacts (Sanders et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Williams \u0026amp; L\u0026uuml;ck-Vogel, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, the IPCC employs and makes the bathtub technique a foundational model for simulating potential inundation under different scenarios and levels of flooding (Almaliki et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mitra et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). The bathtub model uses the principle that the earth\u0026apos;s surface is flat. Subsequently, the bathtub model cannot measure the height obstacle accurately or use the nearby topography to create detailed flood mapping. This model was developed using the SSP scenarios analyzed during the study period to evaluate the potential extent of flooding near the coastal areas resulting from rising sea levels.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.2 Landsat image classification and validation\u003c/h2\u003e\n \u003cp\u003eThis study employs a Supervised classification algorithm in ArcMap to identify and evaluate LULC patterns. The principle for applying Supervised classification on any satellite image is first to create a signature file with a training sample for each feature class. The training samples for Land use land cover features were chosen from a multispectral band combination using polygon construction to classify the provided dataset. The signature file is input for this algorithm to classify land use land cover features. Once the satellite image is classified into its respective classes, post-classification accuracy measurement is crucial for validating the accuracy of land use and land cover maps generated by models (Mitra et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, Kappa statistics was used to assess the accuracy levels of the different samples. Thus, to check the accuracy of supervised classification images that separate the different land use land cover features, we correlate or validate the samples taken from multiple satellite datasets used for the study. The formula for calculating kappa statistics is as follows:\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere, r\u0026thinsp;=\u0026thinsp;the error matrix\u0026apos;s rows and columns, N\u0026thinsp;=\u0026thinsp;total number of pixels, X\u003csub\u003eii\u003c/sub\u003e = observation in row i and column i, and X\u003csub\u003e+\u0026thinsp;i\u003c/sub\u003e = marginal total of column i. Additionally, the root mean square error (RMSE) was also calculated. A smaller RMSE matrix indicates a higher level of accuracy in the prediction of LULC. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e displays the kappa statistics and RMSE value obtained from the evaluation of the classified images.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLULC accuracy assessment.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKappa(K)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.918919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.918919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.918919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.756757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.3 Detecting the land cover changes\u003c/h2\u003e\n \u003cp\u003eThis study employed the MOLUSCE plugin of QGIS, an open-source software specifically used for the spatial and temporal changes in LULC, which uses cellular automata (CA) data to generate potential outcomes based on a given training dataset This model effectively differentiates nonlinear spatial, sequential LULC change by estimating pixel present values from beginning and neighboring pixels. Meanwhile, this investigation used a neighborhood value of 30*30 m to account for spatial interactions among cells [0000]. Moreover, the MOLUSCE QGIS validation assessed the accuracy of the predicted LULC raster by simulating and validating the MP-ANN model. Maithani (Maithani, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) employed nonlinear statistical analysis to examine the complex underlying variables and models for the drivers of urban development.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003eThe logistic regression shown in Eq. 3 is used to identify variations in the training dataset. Here, argmax\u003csub\u003ej\u003c/sub\u003e = category that amplifies the consequence of conversion probability and current land-use pattern, LUPt(i,j)\u0026thinsp;=\u0026thinsp;present land-use trends value for a change from land-use class i to j at time t, P (LU\u003csub\u003et+1\u003c/sub\u003e = j | LU\u003csub\u003et\u003c/sub\u003e = i)\u0026thinsp;=\u0026thinsp;evolution likelihood from land-use category i to j at time t\u0026thinsp;+\u0026thinsp;1, LU\u003csub\u003et+1\u003c/sub\u003e = land-use class at t\u0026thinsp;+\u0026thinsp;1 time.\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe MOLUSCE QGIS validation assessed the accuracy of the predicted LULC raster by simulating and validating the MP-ANN model. Maithani (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) (Maithani, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) employs non-linear statistical analysis to examine the complex underlying variables and models the drivers of urban development. The MOLUSCE system utilizes the ANN, which integrates the Cellular-Automata (CA) simulation method. This method employs the Monte Carlo computational approach (Lin et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eIn Eq. 4, the Monte Carlo algorithm generates distinct samples from the probability distributions of the system\u0026apos;s unexpected variables or features. Here, Y\u0026thinsp;=\u0026thinsp;estimated outcome, N\u0026thinsp;=\u0026thinsp;number of samples, f(x\u003csub\u003ei\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;value of the function at the i\u003csup\u003eth\u003c/sup\u003e random sample, x\u003csub\u003ei\u003c/sub\u003e. The overall kappa (k\u003csub\u003ei\u003c/sub\u003e) (Eq. 3) and % of correctness (C) (Eq. 4) were determined as follows:\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAl0AAACWCAYAAADkMj11AAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAABZKSURBVHhe7d3/a1vX/cfxlz6/d40k/1TGCLoqrGzFppHj0DiBBGKpSSiBNVj5Qhl0tJE3+kN/sKmVMEYbd9JoB2FR6pJBGR2Wkwb2i1XbP3gQK2WxVbBoQgLxFWMr+0k3SvYP3M8Pn+h+fI+dOPGXa8V6PuBC9D7nJkqbRC+dc+45Idd1XQEAAGBT/Y9ZAAAAwMYjdAEAAASA0AUAABAAQhcAAEAACF0AAAABIHQBAAAEgNAFAAAQAEIXAABAAAhdAAAAASB0AQAABIDQBQAAEABCFwAAQAAIXc+BUCi04tXd3a18Pm9233LlcnnZe21eqVRKpVLJvAUAgG2P0PUcWFhYkCRZliXXdeW6rmzbVjQa1dDQUMsFr97eXn3++eeSpEwm473n2dlZLS4u6siRIyqXy+ZtAABsa4Su50hfX5/341gspkKhIEmamZlZ0qu17N+/3/txb2+vPv74Y0nSt99+u6QXAOBZ5PP5ZTMJj7uC/JJbq9UUiURabjCgVRC6ngN37tyRJHV2dvrqsVjM97qVVKtVSdIrr7ziq//4xz/2vQYAPLvBwUHV63UlEglJUi6X82YVls6INNuDMjAwoEajYZbxCKHrOfD9999Lkvbs2eOrN7+97Nixw1dvBfPz8wqHw8uCYnOE68UXX/TVAQDPJhqN6vjx42bZE4vFdPnyZbO8afL5fEt+HrUSQtdzYHp6esUA86c//UmS9Pbbb/vqW81xHFUqFd90aLP+xRdfKBwO69ChQ742AMDG6+zsVG9vr1necNVqVTMzM/rNb35jNmEJQleLWynAVKtVpdNpjY+PK5PJ6PDhw757ttrNmzclSQcOHPBqpVJJqVRKtm2rUCi09NQoAGwHj1tXVavVNDAwoEgk4j1Vns1mlU6nza5PxXEc/epXv/LWGePxCF0trhlgxsfHvUWRXV1dkqSJiYmW/EN+48YNSdKZM2e893z69Gl1d3drYWFhzX+xAQBPp1ar6erVq2ZZ1WpViURCO3fu1L1791Sv1xWLxTQyMqIHDx6Y3Z/KZ599pt/97nd8mX4KhK4W1wwwExMTvgWSxWJxQ0a4zKdcVrtSqZT5UywzPT0tSarX6977vX//vgqFwrIpUgDA+g0NDfn+rbYsS5VKxeymX/ziF0qn0xocHFQ0GlU0Gl3Xl/dSqaRGo7Ehn0ftgNDV4poBpqenx2zyqVar6u7uVigUUiQS0ejoqNllRebTLqtdk5OT5k/h05wOTSQSikajZjMAYBOYTy+u9ORiqVSSbds6efKkr75WjuPot7/9rT766COzCY9B6GpxTxNgHMfRgQMHdPz4cbmuq0KhoDNnzmzJzu/N7S3MRfQAgODEYrFlTzbeunXL93q9Tp06pcuXLz/x8wl+hK4W1gxNqwWYr7/+Wo1GQ4ODg5KkdDoty7L0l7/8xey66b755htJ0t69e80mn3K5rHQ67Q2FP27BJwBgbZqfCZuhXC5rampKXV1dvmnNffv2SUumO4PcmPV5QOhqYc3QZNu22eTTXBi5VDweX/OiyLVyHEfj4+OSpH//+99ms6dYLOrNN9/UsWPH5LquhoeHNTQ0ZHYDAGyAUqnkbVgtSbdv3/a1r8VLL72kXC637MpkMpKkZDKpXC6nl156yby1vbloSZlMxpXkXcPDw2YXTzKZdJPJ5Kq1zWZZlu89T0xMmF3cer3uhsNhd2xszK3X6+7ExISbSCTcTCZjdgUArCKXy7mS3FwuZza57qN/c5ufBQsLC64k17Ist16ve32adfMzY2FhwbVt21dbzezs7BPfT7tjpKtFFQoF36LI8+fPm108O3bs0OLiolkO3OLiou89r/Q0S3Mq9MSJE+ro6NCFCxfY3wUA1qC54bQkPXz40GxWrVbTqVOnvK0cOjs7lclkZNu2Xn75ZWWzWWWzWV25csW8VdVqVV1dXctmUbA+bRW6UqmUN++8FYvMN8trr70m27blOI5XcxynJfdMefjwoRKJhO9pSHanB4Bnk8/n1dHR4S0/GRkZWbbFj2VZmpqa0tGjR737CoWChoeH1Wg0dOnSJb344osrfql/4YUXFA6HZVmW2YR1CLmu65rF7ay7u1uVSkW2bbdkKFmLWq0my7I0PDys8+fPq1Qq6ciRI5qdnQ3k+IdnMTo6qjNnznj//YvFos6ePavp6elt8/8DAJ43oVBIyWRy1W2BsD5tNdKlRweEWpa1rT7gY7GYJiYmvF3r33//fY2NjbVc4JKkt956S/39/bIsS5FIRNevX9fNmze31f8PAABW0nYjXaFQSP39/SoWi2YTAABtp7l+y7Is3bx5k323NlFbjXQ1H5ldehAzAADtKp/Pe+f52ratjo4O9tbaRG0Vuv7xj39Ikvbs2ePVmkfnbLfF9QAArGZwcND31Lnrui25NGW7aKvQ1RzpWnrocl9fn8LhsMbGxlbc4uBplMvlZU+NrHaxAzsAAO2lrdZ0xeNxxeNx7+mMYrGoTz/9VJcvX/YFMQAAgI3WNiNdjuPItm0dPHhQtVpN6XRa169f1+TkZEsGLnNkrJ0uAAC2o7YZ6WruXbXUxMTEE6cUQ6GQcrncph4aCgAA2kPbjHTduHFDkrSwsKDZ2VlfbSXN9V+vv/662bQMa7oAAMBq2makK5VKaWpqSs3fbjwelx6dFwgAALDZ2maka2pqSslk0nvd19cn27ZVq9V8/SQpnU4rFAoplUqZTQAAAGvSFqGrudHbwYMHvdr+/fslSXNzc6pWq0qn015bsViUZVm+/gAAAOvRFqHr9u3bkqSf//znXu3QoUNKJpM6ceKERkdHdfHixSV3/N/OvE+zngsAAOBptM2armfRfNKx1f/TlEolnT59Wvfv3zebApXP5zU0NGSWFQ6H1dfXpw8//LAlt+UAACBIbTHS9axu3LihRCJhlluG4zjKZrM6cuSIGo2G2Ry4wcFB9ff3S5LGxsa8oyQKhYLGx8d14MABOY5j3gYAQFshdK3gu+++U3d3t6rVqrLZrNm8pcrlsnp6emTbtsLhsNm85Xbv3u39OJ1OK5PJqNFo6M6dO75+AAC0G0LXCnbt2qVLly5paGhIH3zwgdm8pT7++GNdu3ZNxWLRF3C2WqVSUTgcViwW89V37tzpew0AQLsidK3g/Pnzcl1Xk5OTikajZvOWasVji5pHLPX19ZlNmpmZkSS98MILZhMAAG2F0IV1u3nzpiTpwIEDvnq1WtXU1JQSiUTLBUUAAIJG6MK6NY9T2rNnj/Ro5KtYLOrAgQMKh8O6fPmycQcAAO2H0IV1m56eliR1dXUpFAqpo6NDZ8+eVSaT0b179xjlAgCA0NVe8vn8soO3V7uau/k/juM4qlQqSiQS3lYRrutqcXFR58+fb7k1cQAAbBVCVxsZHBz0BaOnuXp7e82fxqe5nmulRfQAAOD/EbqwLrdu3ZIk7d2712wCAABLELqwLlevXpUk9fT0mE0+xWJR3d3dCoVCikQiKpVKZhcAALY1QtdzynEcLS4uSo92qd8K1WpVlUpFkvTDDz+YzZ6BgQF9+umn+uMf/yjXdWVZli5cuGB2AwBgW+PA6+dMNpvVd999p6mpKV89kUgoGo1qcnLSV98stVpNlmX5avV6fdnC+XK5rH379sm2bUnS3NycBgYGVCgUlE6nfX0BANjOCF3YVOl0WuPj45KkcDisvr4+vf322zp8+LDZFQCAbY3pRWyqBw8eaHh4WK7r6v79+yoWi/rpT39qdgMAYNsjdGFT7dixQ9PT03IcR47jKJ/Pq7+/3+wGAMC2R+jCpvrwww8lSR0dHd4TjvPz80YvAAC2P9Z0AQAABICRLgAAgAAQugAAAAJA6AIAAAgAoQsAACAAhC4AAIAAELoAAAACQOgCAAAIAKELAAAgAIQuAACAABC6AAAAAkDoAgAACAChCwAAIACELgAAgAAQugAAAAJA6AIAAAgAoQsAACAAhC4AAIAAELoAAAACQOgCAAAIAKELAAAgAIQuAACAABC6AAAAAkDoAgAACAChCwAAIACELgAAgAAQugAAAAJA6AIAAAgAoQsAACAAhC4AAIAAELoAAAACQOgCAAAIAKELAAAgAIQuAACAABC6AAAAAkDoAgAACAChaxuIx+MKhUIaGBgwmwAAQIsgdG0DN2/elCR1dnaaTQAAoEUQuraBhw8fSpL27NljNgEAtqF8Pq9QKPRUV7lcNm/fcAMDA8t+3WKxaHZre4SuFlAsFpf9YV3tyufz3v1zc3PSkpGuZp9UKuX1AQBsH4ODg6rX60okEpKkXC4n13V9l23bXvtmqtVqmp6eVjKZ9K7+/n6l02mza9sjdLWAdDqtZDIpSbIsS/V6fdlfHtd1NTs7a94qSbp+/bp3vyT19/crmUzq7Nmzvn4AgO0jGo3q+PHjZtkTi8V0+fJls7zhvv76a124cEGTk5PexSjXyghdLaJQKCgcDsu2bX322WdmsySpt7dXmUzGLGt+fl67du1StVpVNpvVsWPHNDk5qd7eXrMrAKCNdHZ2bupngeM4unr1qg4fPmw2YQWErhYRi8X0ySefSJJGRkYeOwd/8uRJ32vHcVSpVDQyMqKuri7Zts2QLgDAtwxlqVqtpoGBAUUiEW8pSjabXdNnx5///GdVKhXF43ENDAyoVquZXbAEoauFvPfee+rv75ckvfnmm3Icx+yy7BtL88nFhYUF5XI5jY+Pr3gfAKB91Go1Xb161SyrWq0qkUho586dunfvnur1umKxmEZGRvTgwQOz+6r++c9/yrIs2batS5cuybIsZbNZsxuaXLSUer3uhsNhV5Lb399vNi8zPDzsWpbluq7r2rbtSnJzuZy7sLDgfv7552Z3AMA2ksvlXEmPvUyWZbmZTMYsu5LcZDJplp9avV53c7mc9/k1PDxsdoHruox0tZhoNKqvvvpKkjQ+Pq5SqWR28ZmenlZfX5/0aIoyk8loaGhIV65c0VtvvWV2XzfHcbxHg+PxuGq1mhzHUTabVSQSUSQSYQElAATMfHpxpScXS6WSbNtetkxlI0SjUQ0ODurvf/+7wuGwRkZGmGpcAaGrBR0+fNibZjx9+vQT/+DOz8+rUCh4rwuFglzX1fnz5xWNRn19N8K5c+d08uRJTUxMyLZt3b17V6dOndIbb7yhe/fuKRKJsDM+AGyxWCy27MnGW7du+V5vhs7OTu8z6e7du2Zz2yN0taiLFy8qHA6r0WhoeHjYbN4yhUJBvb29+tGPfiRJev/995XL5dTb26toNKp4PK5Go2HeBgAI2ODgoFkKRHNB/n//+1+zqe0RulpUNBr1toc4duyY2bzlvv32W0nSu+++6zt+aG5ubtmQNgBg65RKJVWrVe/17du3fe2bZffu3Wap7RG6WlStVtOlS5eUyWTW9BjvZpuZmZEkvfPOO16tVqup0Wh4a8yWKpfLCj3hOIpQKKTR0VGzDABYB8dxdOHCBXV2dnqbaP/hD3/wPeW+NJAtVa1Wn7i85XGKxaKGh4cVi8XMprZH6GpR/f39ikQi+uijj8ymFaVSqUCP/Zmbm1MymfStG2seR7R3794lPf9P85uVueWFHgUySfrZz35mNgEAHsNxHH3xxRfSkjN4l6rVajp16pQXfjo7O5XJZGTbtl5++WVls1lls1lduXLFvFXValVdXV1PnLkYHR31jqVrhrhisai//e1v+uCDD8zu0ErPk2LLNR8BXlhYMJseKxwOu7lcziw/k7GxMdeyLNe2bbPJZ2FhYcVHgjOZjCvJrdfrvjoAYGOttlXE0mtiYsJ37/DwsCvJ97khY8sI27bdcDjsJhKJJXf6LSwsuIlEwvt1ksmkOzY2ZnbDEoSuFjM7O+vq0V5bQZmdnXX7+/tdy7JcPUUOHxsbcyUt+8tlWZa3Z9hSzX1bVvo9JZNJV0+5JxkAYHOYoQubg+nFFuI4jn75y18qmUw+8amTVCrlHe/QHN4NhUJmt6f2ww8/6OLFi/ryyy/NphVdv35dWmGRpG3bCofD3l5ezWnDSqUiSXr99dd9/SVpcnJSkvTaa6+ZTQAAbCuErhby61//Wvfv3/ftu2VyHEdTU1Pe6/fee0+ZTMZbINmUSqW8MLbStXT9VzqdfqY9vebn5xUOh5ctkszlcqpUKurp6dHRo0e99Vv/+c9/pMes52quA1gpkAEANl9zIf3i4iLHyG0yQleLKBaLGh8f11dffbUszDQ5jqNz586ZZdVqNR08eNBXm5yc9O1ObF7NEaa1mJ+f1/37982yBgcH5bquFhcXfSfOf/PNN8tCYVPz7MhXXnnFbAIAbLJ8Pq+uri7p0WxFR0fHY58yx/oRulpAtVr1dnE/cuTIslGp5tXR0aFLly757m2OfLXySNH09LR27dplliVJN27cUCKReKaRNgDAxmh+WV56rTQrgY3RlqGrVqtpYGBA8XjcCzTxeFzpdHpNe5Ks1yeffPLMu7g3Q9bjRoqeZXpxs1UqFb366qsqFovLzmWcnp5Wd3e3rwYAwHbUdqGrVCopkUhofn5eX375pdxHB4Pq0QHTj5va20zFYnHZN43VruY3kVu3bsmyLGnJ0Qta4/Ric5f51Q7ZflaJREInTpzQ999/73uPjuOoUqlo//79vv4AAGxHIdd1XbO4XS3d7G1yctI3pZXP5zUzM7NiGGllpVJJR44ckWVZunbtmu9InqeVz+c1NDRkljU7O7upw8zN927b9paEXQAAgtRWoSsej8u2bT7kW4DjOEqlUopGo89d0AUAYC3aZnqxWCzKtm1lMhkC1xZLpVLq6OiQZVn661//ajYDALAttc1IV3d3tyqVihYWFtY0BddULpe1b98+s/xEuVzuiZudAgCA7a8tQletVpNlWbIsS4uLi2YzAADApmuL6cW5uTlJUl9fn9nUEsztHLjWfqQRAACtqi1C17/+9S9J0s6dO82mlmBu58C17QdfAQBtqC1CV9NG7NpeLpeXjcqsdjUPpwYAAO2rrULXRujt7V02KrPaxSJ6AADQFqHrJz/5ibRkx/WlisWi4vG4WQYAANhQbRG6Dh06JMuy9Pvf/947Pd1xHI2OjurEiRMtu8AeAABsH22xZYQebRsxPDys8fFxr2ZZluLxuM6ePbupx90AAAC0TegCAADYSm0xvQgAALDVCF0AAAABIHQBAAAEgNDVhhzH0cDAgEKhkOLxuGq1mhzHUTabVSQSUSQSUbFYNG8DAADrQOhqQ+fOndPJkyc1MTEh27Z19+5dnTp1Sm+88Ybu3bunSCSigYEB8zYAALAOPL3Yxsrlsvbt2yfLsnTt2jV1dnZKklKplKampjgDEQCADcRIVxtr7tD/7rvveoFLkubm5pRIJJb0BAAA60XoamMzMzOSpHfeecer1Wo1NRoNdukHAGCDEbra2NzcnJLJpKLRqK8mSXv37l3SEwAArBehq01Vq1U1Gg3t2rXLV79+/bokqaenx1cHAADrQ+hqU3fu3JEkvfrqq7769PS0LMvyjX4BAID1I3S1qeaI1u7du31127YVDoe9vbzK5bKvHQAArA2hq03Nz88rHA4rFov56rlcTpVKRT09PTp69Kh6e3t97QAAYG3YpwsAACAAjHQBAAAEgNAFAAAQAEIXAABAAP4X6B+gMUqAg+gAAAAASUVORK5CYII=\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eIn Eqs. 5 and 6, P\u003csub\u003eo\u003c/sub\u003e = observed percentage of settlement, P\u003csub\u003ee\u003c/sub\u003e = percentage anticipated by casual, n\u003csub\u003eij\u003c/sub\u003e = transverse fundamentals in the fault matrix, k\u0026thinsp;=\u0026thinsp;overall quantity of modules, n\u0026thinsp;=\u0026thinsp;overall quantity of examples in the fault matrix.\u003c/p\u003e\n \u003cp\u003eFurthermore, the result of the training dataset will demonstrate the kappa validation value in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The overall kappa (ki) (Eq. 4) and % of correctness (C) (Eq. 5) were determined as follows:\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eValidation parameters (K parameters) and % correctness of the CA-ANN model in QGIS software.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDataset\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredicted Year for Validation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% Correctness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eK histogram\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eK location\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverall kappa\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLULC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85.931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNTL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.856\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Historical and Future SLH changes\u003c/h2\u003e\u003cp\u003eSea level height (SLH) increase, will be a critical concern at the bank of AG-coast in KSA. Although the historical station-based tide-gauge data are not continuous, insights from stations, e.g., the Abu Ali Pier, Masirah, and Mina Sulman, indicate an annual increase from January 1979 to December 2020 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). With a mean SLH of 7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;31.8 mm, it rose 0.0026 m yearly from 1979 to 2020 with a statistically significant trend (Sen\u0026rsquo;s slope of 0.002 m/year, p\u0026thinsp;\u0026lt;\u0026thinsp;0.005). While five- and ten-year averages show short-term volatility, the overall trend implies a steady sea level increase. No significant seasonal fluctuations were observed, but the long-term trend dominates reported changes with a statistically significant moderate model coefficient (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.4694, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) observed for time-dependent SLH. Although the SLR dropped by 0.0079 m from 1999 through 2004, there has been a pronounced upsurge of 0.0303 m in recent decades.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStatistical summary of SLH trends, seasonal components, residuals, and long-term changes (1979\u0026ndash;2020), including OLS regression and Sen\u0026rsquo;s Slope analysis.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMetric\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003eSLH Trend Overview\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSLH Mean (1979\u0026ndash;2020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0079 m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSLH Standard Deviation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0318 m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal SLH Increase (1979\u0026ndash;2020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1046 m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cem\u003eSeasonal Component\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSeasonal Component Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0000 m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSeasonal Component Std Dev\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0215 m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSeasonal Component Min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0251 m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSeasonal Component Max\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0391 m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cem\u003eResiduals (Noise after removing seasonality \u0026amp; trend)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResiduals Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0001 m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResiduals Std Dev\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0437 m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResiduals Min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.1278 m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResiduals Max\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1479 m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003e\u003cem\u003eFive-Year SLH Changes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1979\u0026ndash;1984\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.027 m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1984\u0026ndash;1989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0148 m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1989\u0026ndash;1994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0257 m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1994\u0026ndash;1999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0083 m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1999\u0026ndash;2004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0079 m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2004\u0026ndash;2009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0095 m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2009\u0026ndash;2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0271 m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2014\u0026ndash;2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0032 m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cem\u003eTen-Year SLH Changes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1979\u0026ndash;1989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0418 m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1989\u0026ndash;1999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.034 m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1999\u0026ndash;2009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0017 m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2009\u0026ndash;2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0303 m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eOrdinary Least Squares (OLS) Regression\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSlope (Trend)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0018 m/year\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4694 \u003cb\u003e***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSen's Slope Analysis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSen\u0026rsquo;s Slope Estimate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002 m/year \u003cb\u003e**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003eAnnual and Decadal Trend Analysis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage Annual Increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0026 m/year\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage 5-Year Increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0053 m/5year\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage 10-Year Increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0194 m/10year\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHere\u003c/em\u003e, \u003cb\u003e**\u003c/b\u003e \u003cem\u003eindicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.005, and\u003c/em\u003e \u003cb\u003e***\u003c/b\u003e \u003cem\u003eindicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAlthough a sudden plummet is visible just before 2016, IPCC AR6-based SLR prediction reveals a steady increase in the following years (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For this study, we incorporated two of the most studied socioeconomic pathways: SSP2-4.5 (medium confidence) and SSP5-8.5 (low confidence). Here, we modeled future SLR based on SSP2-4.5 as the maximum likelihood scenario (MLS) and SSP5-8.5 as the worst-case scenario (WCS) based on anthropogenic activities.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe IPCC-derived AR-6 report predicted a future SLR of 0.754\u0026thinsp;\u0026plusmn;\u0026thinsp;0.458 m and 1.893\u0026thinsp;\u0026plusmn;\u0026thinsp;1.509 m in the AG-coast in KSA by 2130 based on the MLS and WCS, respectively. From the MLS, the mean annual increase rate of SLR is expected to be 0.377\u0026thinsp;\u0026plusmn;\u0026thinsp;0.224 m, while its 95% confidence interval indicates a potential SLR of 1.351 m by 2130. On the contrary, SSP5-8.5 indicates a pronounced increase after 2060, where from a 95% confidence interval, the expected SLR is expected to reach 4.02 m in 2130. The annual mean sea level rise is almost 0.603\u0026thinsp;\u0026plusmn;\u0026thinsp;0.479 m, with a high deviation indicating exponential growth after 2100.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.2 SLR Inundation\u003c/h2\u003e\u003cp\u003eBased on the IPCC-derived SLR statistics, we evaluated the potential spatiotemporal distribution of SLR in the AG-coast in KSA for 2070, 2100, and 2130 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). However, we subdivided our area of interest into nine central governorates from the Eastern Province of KSA, bordering the AG. The governorates are Abqaiq, Al Khafji, Al Khobar, Al Qatif, Al-Ahsa, Dammam, Jubail, Ras Tanura, and Khawr al Udayd.\u003c/p\u003e\u003cp\u003eFrom 2070 to 2130, inundation patterns evolve spatially heterogeneously in Saudi Arabia's eastern coastlines (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). From our GIS-based Bathtub model, Al Qatif and Ras Tanura are highly vulnerable, with huge flooding potential throughout the study period. Initial forecasts for 2070 show mild flooding hazards, but the WCS for 2130 shows significant flood-prone area growth, notably in low-lying coastal zones. The coastal flooding is expected to evolve in a south-westerly direction in Ras Tanura, where, starting in 2070, significant land areas are expected to be submerged on MLS. On the other hand, Al Qatif will have an inland flood inundation of all governorates by 2130 in the WCS. Dammam and Al Khobar have complicated flooding patterns where a centroid joining is evident at the central Dammam, which has high coastal inundation potential in the southern part of the region. Northern locations like Al Khafji indicate minor flooding in early forecasts but increased vulnerability by 2130, especially near the coast. Although coastal vulnerability exists throughout the anticipated period, southern places like Al Udayd have more confined flooding patterns. Al Ahsa, Abqaiq, and Al Udayad comprised comparatively linear inundation patterns concentrated at the eastern coastland.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eStatistically, the highest landcover loss is expected to be from the Jubail governorate, whereby by 2070, the sea inundation will be roughly 409.694 km\u0026sup2; and 578.316 km\u0026sup2; for MLS and WCS, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This, however, is expected to rise to 741.860 km\u0026sup2; and 1481.712 km\u0026sup2; for respective scenarios by 2130. Al Khafji has the most significant rise in potential inundation in the Northern Region, rising from 13.61 km\u0026sup2; (2070 ML) to 279.86 km\u0026sup2; (2130 WCS), the most significant proportionate increase among the governorates. The geographical distribution shows enhanced coastal vulnerability in the WCS. Al Qatif's flooding area grows from 107.39 km\u0026sup2; to 443.42 km\u0026sup2; under WCSs, while Ras Tanura's flooding area increases from 128.61 km\u0026sup2; to 278.01 km\u0026sup2;. Moreover, Ras Tanura is expected to see almost 77.34\u0026ndash;97.53% inundation by 2130.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe inundation area in Dammam increased from 128.36 km\u0026sup2; (2070 MLS) to 596.51 km\u0026sup2; (2130 WCS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In contrast, Al Khobar showed a more moderate but substantial rise from 66.19 km\u0026sup2; to 161.07 km\u0026sup2;. Here, coastal developments and industrial locations with low elevations are the most vulnerable for future SLR scenarios. Abqaiq, Al Ahsa, and Al Udayd show lesser initial inundation areas but a significant percentage rise over time. Under WCSs, Abqaiq's inundation area grows from 144.82 km\u0026sup2; to 380.22 km\u0026sup2;, Al Ahsa from 39.47 to 198.32 km\u0026sup2;, and Al Udayd from 28.89 to 249.51 km\u0026sup2;. Al Khafji and Al Ahsa are also the least vulnerable to SLR, with 3.408% and 0.0822% of their governorate area by 2130 (WCS). For SLR, while the projected SLR in 2070 is expected to rise by 1067.04 km\u0026sup2; and 1498.13 km\u0026sup2; for MLS and WCS, respectively, this trend may increase to 1860.27 km\u0026sup2; and 4068.639 km\u0026sup2; in 2130 for respective scenarios.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Vulnerability to future LULC\u0026rsquo;s and Economic Potential Zones\u003c/h2\u003e\u003cp\u003eTo capture the SLR vulnerability at future LULC, we incorporated historical LULC across the region for 1973, 1990, 2000, 2015, 2020, and 2022. From a historical perspective, our observation demonstrates significant belt alteration throughout the coast of AG across the governorates in Eastern Province owing to tidal activities, sediment transport, SLR, or even human activities (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). However, land reclamation and infrastructure development were some of the significant activities reforming the coastal belt of Ad Dammam and Al Qatif. In contrast, the long-term coastal belt alteration demonstrates a significant abatement for some regions, e.g., Al Jubail, Ras Tanura, Al Khobar, and Al Ahsa. In Ras Tanura, the mid-section observed a substantial shoreline change where it retreated compared to the 1972 shoreline. A similar loss near the SLR vulnerable zones was evident on the entire coast of Al Jubail and in the northern part of Al Qatif. However, Al Khafji and Al Khobar had the least shoreline change throughout the observation period, with Al Khafji the noteworthy land reclamation at the southern part of the governorate.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eUtilizing the historical LULC\u0026rsquo;s we predicted future LULC\u0026rsquo;s for the years 2070, 2100, and 2130 to understand the spatiotemporal inundation vulnerability for specific land cover in eastern province (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Throughout the research period (2070\u0026ndash;2130), SLR-induced land cover changes along the AG coast showed considerable variances in inundation patterns and their direction. The trajectory of SLR inundation and corresponding LULC loss reveal significant long-term floods in anthropogenic built-ups and vegetation cover across the coasts. Our model demonstrates Jubail will witness the most intensified SLR consequences at an exponential rate by 2130, followed by Dammam and Al Qatif. On the contrary, governorates like Abqaiq and Ras Tanura may face severe inundation in early 2070, although their inundation trend will decline in the coming years. Nevertheless, flooding patterns mostly move inland from the shore, with varied intensities between governorates. The northern (Al Khafji) and southernmost (Al Ahsa and Al Udayad) regions were mostly inundated along the coast. In contrast, SLR inundation spread inland in the central coastal districts, notably Jubail and Ras Tanura, with the built-up areas by 2130 (WCS).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCoastal regions with recent built-up were the most vulnerable regions to SLR flooding in AG Coast, where these regions were inundated in an east-to-west direction, starting on the eastern coastline borders (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Although limited, plant cover exhibited different degrees of susceptibility, especially near Al Khafji, where tiny patches of vegetation correspond with forecasted flood zones. Inundation scenarios intensified, with the 2130 estimates exhibiting the most significant inland reach for WCS. The Dammam-Al Khobar urban region showed considerable extension of waterlogged areas from the coast westward in both MLS and WCS. The MLS predicted modest inland penetration, mostly on built-up eastern edges, while on the contrary, the WCS showed widespread flooding and westward migration into developed zones between 2100 and 2130. This trend was constant throughout governorates, although inland flooding varied by terrain and land cover.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe central coastal region, especially Jubail, saw the most severe consequences, and here the built-up area losses expected to reach 436.22 km\u0026sup2; and 1112.25 km\u0026sup2; by 2130 under MLS and WCS respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The Dammam region is also vulnerable, with built-up area inundation anticipated to rise from 97.69 km\u0026sup2; (2070) to 144.17 km\u0026sup2; (2130) under MLS and to 423.12 km\u0026sup2; under WCS in 2130. Moreover, Al Khafji in the northern sector saw minor losses in a similar patter to Dammam. Here the potential inundation for built-up area rising from 11.52 km\u0026sup2; (2070) to 23.68 km\u0026sup2; (2130) under MLS and 165.19 km\u0026sup2; under WCSs by 2130. Rapid flooding losses were expected between 2100 and 2130, especially in the WCS scenario and vegetation cover was lost correspondingly. Jubail had significant consequences, with vegetation loss estimated to rise from 8.98 km\u0026sup2; to 13.47 km\u0026sup2; (MLS) and 21.52 km\u0026sup2; (WCS) by 2130. Abqaiq saw significant land category losses, rising from 72.12 km\u0026sup2; to 110.38 km\u0026sup2; (MLS) and 180.13 km\u0026sup2; (WCS) by 2130.\u003c/p\u003e\u003cp\u003eThe spatiotemporal distribution of future economic potential zones on the coast of Eastern Provinces and its vulnerability to future SLR consequences showed differential sensitivity patterns during the study period (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). We incorporated VIIRS-derived NTL data to capture the historical economic potential zones across the study region and predict future economic potential zones for the years 2070, 2100, and 2130. Medium economic potential zones were concentrated in coastal proximity, especially Dammam, Al Khobar, and Jubail industrial cities. On the contrary, high economic potential locations are more concentrated in city areas like Dammam, Al Khobar, and Al Qatif.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eHowever, the medium economic potential zones were the most vulnerable to flooding, particularly under the WCS, and anticipated flood regions overlapped along the central coastal corridor from Jubail to Ras Tanura to Al Qatif. However, Al Khafji had limited potential economic zone exposure in the north and low inundation risk until 2070 (MLS), but vulnerability increased at a exponential rate by 2130. Inundation and highly potential economic interest areas overlapped significantly by 2130, indicating increasing vulnerability in cities like Dammam and Al Khobar where also land reclamation rates are increasing at an alarming rate. Inundation risks increased in the Dammam-Al Khobar urban region, within medium potential economic zones. The MLS predicted minor consequences until 2100, whereas the WCS predicted widespread economic zone flooding by 2130, notably in coastal and industrial districts. The southern areas (Abqaiq-Al Ahsa and Al Udayd) had scant potential economic zone distribution, but increased coastal flooding threats, especially under the WCS, and it further validates the finding from LULC projections. Regions like Al Ahsa and Al Udayad remained in low vulnerability owing to their low economic potential.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe spatiotemporal distribution of historical and projected SLR was assessed across Eastern Province, KSA's AG coast. While incorporating the SLH throughout the coast, this study evaluated the estimated land area loss across the entire western coast of AG utilizing a GIS-based bathtub model for two distinct socioeconomic scenarios. We further predicted the LULC and future economic potential zones using the MOLUSCE model and overlayed the spatiotemporal distributions of sea inundation with the landcover outputs.\u003c/p\u003e\u003cp\u003eThe Arabian Gulf (AG) is a low-lying and hyper-arid coastal area, especially the eastern Arabian Peninsula, which includes populated urban areas and crucial infrastructure in Iraq, Kuwait, KSA, Bahrain, Qatar, and UAE. These areas are potentially vulnerable to SLR, and the main drivers are precipitation, sea surface trends, terrestrial temperature trends, salinity levels, seawater thickness, sedimentation, and coastal erosion (Bakhamis et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Validating previous studies on SLH in the Eastern Province of KSA (Alothman et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Bakhamis et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Parker et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), our study demonstrates a clear relationship between the time-series SLH trend and the mean SLH rise of 0.0026 m annually, with no significant seasonal fluctuations and the long-term trend dominating reported changes, with a moderate model coefficient (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The associated absolute SLR, using the land subsidence considered at six GPS stations within 100 km of the tide gauges as an indicator of vertical land motion, is 1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 mm/year, which is consistent with the global projection of 1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 mm/year (Alothman et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) for the period 1979\u0026ndash;2007. The OLS outputs and Sen\u0026rsquo;s slope estimator further indicate the strength of annual SLH increase with high probability during our observation period, while a sudden plummet of SLH was also evident during 1999\u0026ndash;2004. Further, the IPCC AR6-based SLR prediction shows a steady rise in the following years, where the annual mean SLR is almost 0.603\u0026thinsp;\u0026plusmn;\u0026thinsp;0.479 m, and the deviation indicates exponential growth after 2080.\u003c/p\u003e\u003cp\u003eTo capture the sensitivity of\u0026ensp;future SLR at corresponding LULC, the study also utilized past LULC in 1973, 1990, 2000, 2015, 2020, and 2022 in the Eastern Province of KSA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The observation showed a significant variation in modifying the coastal belt along the AG coast in all governorates due to sediment transport, tides, SLR,\u0026ensp;or human activity. However,\u0026ensp;major projects involving land reclamation and infrastructure development changed the face of the Ad Dammam and Al Qatif coastal areas. In addition, long-term coastal belt change indicates a significant shoreline retreat\u0026ensp;for numerous regions such as Al Jubail, Ras Tanura, Al Khobar, and Al Ahsa between 1973 and 2022. While studying the Yanbu coastal zone from 1965 to 2019 Niang (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) mentioned the greatest accretion was 1655.9 m, while the maximum erosion was \u0026minus;\u0026thinsp;1484.8 m (Niang, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, based on two RCP scenarios (RCP 4.5 and RCP 8.5), the AG coast of the KSA shoreline is expected to undergo regional mean retreats of around 30 meters by 2050 and 130 meters by 2100 (Luijendijk et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, from our literature review, while study related to SLH and shoreline change are evident (Alothman et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Bakhamis et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Luijendijk et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Niang, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Parker et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), no quantitative studies were conducted that analyzed the potential inundation utilizing IPCC scenarios and demonstrated vulnerability to economic potential zones. Therefore, utilizing a\u0026ensp;GIS-based bathtub model, we aggregated SRTM DEM data and incorporated catchment characteristics to anticipate the future inundation pattern and estimated land cover loss across the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In our model output, we have mentioned that for the specific nine governorates, there is a complex inundation pattern. For most of the regions, linear upward trend for sea inundation\u0026ensp;from 2070 was evident, while for areas, such as at Khafji and Al Ahsa post-2100, the trajectory was reduced significantly. Flood-prone governorates have environmentally diverse and delicate systems that are vulnerable to SLR-induced disruption. Such salinity incursion and benthic habitat fragmentation can threaten mangroves and intertidal wetlands in Tarout Bay at Al Qatif and Ras Tanura (Al-Ali et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Despite land reclamation, which is common in the Dammam and Al Qatif coasts, the degree of land cover loss and seawater inundation in the Jubail governorate, a booming industrial zone of such operation, is alarming. Here, the rate is\u0026ensp;projected to increase as much as 741.860 km\u0026sup2; and 1481.712 km\u0026sup2; by 2130 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Under maritime retreat, Half Moon Bay in Dammam and Al Khobar may face increased littoral erosion, seagrass attrition, and coral deterioration. Biodiverse intertidal Al Khafji Coastal Flats faces rising submersion threats, endangering migrating birds and benthic trophic webs. However, Abqaiq, Al Ahsa, and Al Udayd show lesser initial inundation areas but a significant percentage rise over time. Al Asfar Lake and Al Ahsa Oasis have significant freshwater habitats but are threatened by hydrological instability, desiccation, and saltwater intrusion (Alqahtany, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chouari, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The isolated marine embayment Khawr al Udayd Lagoon confronts hydrodynamic disequilibrium, with advancing dune fields increasing coastal flooding risk. These ecological upheavals will greatly impact biodiversity, ecosystem services, and anthropogenic resilience along Saudi Arabia's eastern coast.\u003c/p\u003e\u003cp\u003eNevertheless, KSA is one of the leading economies in the Middle East, with high urbanization and industrial potential in governorates like Ad Dammam, Jubail, and Al Qatif. Between 1992 and 2013, urban areas grew by 31% between 1992 and 1999, 19% between 1999 and 2006, and 37% between 2006 and 2013 on the whole eastern coast of KSA (Alahmadi \u0026amp; Atkinson, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Here, Dammam, Al Khobar, and Al Qatif took charge of such enormous anthropogenic infrastructure with a massive land reclamation project at the coast of AG. Due to Dammam's geographical limitations between the Gulf Saihat to the north and Al Khobar and Dhahran to the south, its urban expansion pattern has expanded substantially inland towards the west. While Dammam saw a total urban expansion of about 42.5 km\u0026sup2; over 34 years (1985\u0026ndash;2019), with more than 25 km\u0026sup2; occurring during the last 4 years interval (2015\u0026ndash;2019), Al-Khobar saw a built-up area increase of 117% over 11 years (1990\u0026ndash;2001) and 43.5% over the next 12 years (2001\u0026ndash;2013) (Aljaddani et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rahman et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Consequently, one of our objectives was to quantitatively comprehend the plausible impact of SLR inundation on future anthropogenic activities. Therefore, we predicted the future LULCs and economic concentration utilizing the complex CA-ANN approach, the MOLUSCE plugin from QGIS for 2070, 2100, and 2130. The SLR inundation track and the following LULC loss show significant long-term floods in manmade built-ups and plant cover along the coastlines (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). By 2130, Jubail will have the most severe SLR effects at an exponential rate, followed by Dammam and Al Qatif. Although their inundation tendency will decrease in the following years, governorates like Abqaiq and Ras Tanura may see significant flooding in the early years of 2070.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study combines NTL data with LULC modeling using a cellular automata framework to forecast the impacts of sea level rise on six major coastal cities in Saudi Arabia\u0026rsquo;s Eastern Province along the Arabian Gulf. The study found a clear relationship between the time-series SLH trend and the mean SLH rise of 0.0026 m annually, with no significant seasonal fluctuations. The associated absolute SLR is 1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 mm/year, consistent with the global projection of 1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 mm/year for 1979\u0026ndash;2007. The study also utilized past LULC in the Eastern Province of KSA. The observation showed significant variation in modifying the coastal belt along the AG coast in all governorates due to sediment transport, tides, SLR, or human activity. An approximate 1075.7021 km\u003csup\u003e2\u003c/sup\u003e area is expected to be inundated based on the IPCC\u0026rsquo;s ML scenario by 2070, which can rise by 3833.017 km\u003csup\u003e2\u003c/sup\u003e in the WS scenario by 2130, with Ras Tanura and Al Qatif as the most vulnerable zones.\u003c/p\u003e\u003cp\u003eThe study findings present critical implications for urban planning and climate adaptation in Saudi Arabia. By integrating nighttime light data with LULC patterns through cellular automata modeling, the study offers a nuanced projection of how SLR may affect major urban centers along the Western Arabian Gulf. The findings underscore the vulnerability of densely populated and rapidly urbanizing coastal areas to inundation, highlighting the potential loss of infrastructure, economic assets, and residential zones. This research provides a valuable decision-support tool for policymakers, emphasizing the urgent need for proactive coastal management, sustainable development strategies, and investment in climate-resilient infrastructure to mitigate the long-term risks associated with SLR in one of the world\u0026rsquo;s most economically and strategically significant regions.\u003c/p\u003e\u003cp\u003eWhile previous studies have identified the recent fluctuations in SLH on the coast of AG, no studies have incorporated the future vulnerability to LULCs in the Easter Providences. Moreover, growing economic cities like Dammam and Al Qatif are at a very high risk of coastal inundation in the upcoming years. This study addresses the limitations of past literature in assessing the LULC and economic vulnerability in the Eastern Province, KSA, which are expected to be potentially posed by climate change. Therefore, it will help the local government and corresponding authority to reassess the initiatives to build necessary damming and SLR-resilient infrastructure and enhance emergency preparedness for potential migrations. We, however, acknowledge that future humanitarian responses are still unpredictable due to the degree of potential climate repercussions. Moreover, climatic circumstances may change with time. Therefore, future studies should include scenarios with enhanced infrastructures and ecological settings that can minimize the effect of SLR in the Eastern Province, KSA, and locations for urgent migrations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eDeclaration of conflict of interest:\u003c/h2\u003e\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e\u003cp\u003eThe authors gratefully acknowledge the support provided by King Fahd University of Petroleum \u0026amp; Minerals (KFUPM) for facilitating this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbdullah, S., Adnan, M. S. G., Barua, D., Murshed, M. M., Kabir, Z., Chowdhury, M. B. H., Hassan, Q. K., \u0026amp; Dewan, A. (2022). Urban green and blue space changes: A spatiotemporal evaluation of impacts on ecosystem service value in Bangladesh. \u003cem\u003eEcological Informatics\u003c/em\u003e, \u003cem\u003e70\u003c/em\u003e, 101730. https://doi.org/10.1016/j.ecoinf.2022.101730\u003c/li\u003e\n \u003cli\u003eAlahmadi, M., \u0026amp; Atkinson, P. M. (2019). Three-Fold Urban Expansion in Saudi Arabia from 1992 to 2013 Observed Using Calibrated DMSP-OLS Night-Time Lights Imagery. \u003cem\u003eRemote Sensing\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(19), 2266. https://doi.org/10.3390/RS11192266\u003c/li\u003e\n \u003cli\u003eAl-Ali, A. M., Del Campo, A. G., \u0026amp; Rocha, C. (2015). Environmental assessment of Mangrove communities in Tarut Bay, Eastern Arabian Peninsula, based on multidisciplinary approach. \u003cem\u003eThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences\u003c/em\u003e, \u003cem\u003eXL-7-W3\u003c/em\u003e(7W3), 269\u0026ndash;276. https://doi.org/10.5194/ISPRSARCHIVES-XL-7-W3-269-2015\u003c/li\u003e\n \u003cli\u003eAlam, A., Bhat, M. S., \u0026amp; Maheen, M. (2020). Using Landsat satellite data for assessing the land use and land cover change in Kashmir valley. \u003cem\u003eGeoJournal\u003c/em\u003e, \u003cem\u003e85\u003c/em\u003e(6), 1529\u0026ndash;1543. https://doi.org/10.1007/s10708-019-10037-x\u003c/li\u003e\n \u003cli\u003eAlfarrah, N., \u0026amp; Walraevens, K. (2018). Groundwater Overexploitation and Seawater Intrusion in Coastal Areas of Arid and Semi-Arid Regions. \u003cem\u003eWater, 10\u003c/em\u003e(2). https://doi.org/10.3390/w10020143\u003c/li\u003e\n \u003cli\u003eAljaddani, A. H., Song, X. P., \u0026amp; Zhu, Z. (2022). Characterizing the Patterns and Trends of Urban Growth in Saudi Arabia\u0026rsquo;s 13 Capital Cities Using a Landsat Time Series.\u0026nbsp;\u003cem\u003eRemote Sensing\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(10), 2382. https://doi.org/10.3390/RS14102382\u003c/li\u003e\n \u003cli\u003eAlmaliki, A. H., Zerouali, B., Santos, C. A. G., Almaliki, A. A., Silva, R. M. da, Ghoneim, S. S. M., \u0026amp; Ali, E. (2023). Assessing coastal vulnerability and land use to sea level rise in Jeddah province, Kingdom of Saudi Arabia.\u0026nbsp;\u003cem\u003eHeliyon\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(8), e18508. https://doi.org/10.1016/j.heliyon.2023.e18508\u003c/li\u003e\n \u003cli\u003eAlothman, A. O., Bos, M. S., Fernandes, R. M. S., \u0026amp; Ayhan, M. E. (2014).\u0026nbsp;Sea level rise in the north-western part of the Arabian Gulf. \u003cem\u003eJournal of Geodynamics\u003c/em\u003e, \u003cem\u003e81\u003c/em\u003e, 105\u0026ndash;110. https://doi.org/10.1016/J.JOG.2014.09.002\u003c/li\u003e\n \u003cli\u003eAlqahtany, A. (2023). GIS-based assessment of land use for predicting increase in settlements in Al Ahsa Metropolitan Area, Saudi Arabia for the year 2032. \u003cem\u003eAlexandria Engineering Journal\u003c/em\u003e, \u003cem\u003e62\u003c/em\u003e, 269\u0026ndash;277. https://doi.org/10.1016/J.AEJ.2022.07.020\u003c/li\u003e\n \u003cli\u003eAl-Subhi, A. M., \u0026amp; Abdulla, C. P. (2021). Sea-Level Variability in the Arabian Gulf in Comparison with Global Oceans. \u003cem\u003eRemote Sensing, 13\u003c/em\u003e(22), 4524. https://doi.org/10.3390/rs13224524\u003c/li\u003e\n \u003cli\u003eAntonov, J. I., Levitus, S., \u0026amp; Boyer, T. P. (2005). Thermosteric sea level rise, 1955\u0026ndash;2003. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(12), 1-4. https://doi.org/10.1029/2005GL023112\u003c/li\u003e\n \u003cli\u003eBakhamis, A. N., Bilal, H., Heggy, E., Al-Kuwari, M. S., \u0026amp; Al-Ansari, T. (2024). On the drivers, forecasts, and uncertainties of relative sea level rise in the Eastern Arabian Peninsula: A review. \u003cem\u003eRegional Studies in Marine Science\u003c/em\u003e, \u003cem\u003e73\u003c/em\u003e, 103503. https://doi.org/10.1016/J.RSMA.2024.103503\u003c/li\u003e\n \u003cli\u003eBuchanan, J. R., Krupp, F., Burt, J. A., Feary, D. A., Ralph, G. M., \u0026amp; Carpenter, K. E. (2016). Living on the edge: Vulnerability of coral-dependent fishes in the Gulf.\u0026nbsp;\u003cem\u003eMarine Pollution Bulletin\u003c/em\u003e, \u003cem\u003e105\u003c/em\u003e(2), 480\u0026ndash;488. https://doi.org/10.1016/j.marpolbul.2015.11.033\u003c/li\u003e\n \u003cli\u003eBuffardi, C., \u0026amp; Ruberti, D. (2023). The Issue of Land Subsidence in Coastal and Alluvial Plains: A Bibliometric Review. \u003cem\u003eRemote Sensing, 15\u003c/em\u003e (9), 2409. https://doi.org/10.3390/rs15092409\u003c/li\u003e\n \u003cli\u003eCazenave, A., Hamlington, B., Horwath, M., Barletta, V. R., Benveniste, J., Chambers, D., D\u0026ouml;ll, P., Hogg, A. E., Legeais, J. F., Merrifield, M., Meyssignac, B., Mitchum, G., Nerem, S., Pail, R., Palanisamy, H., Paul, F., von Schuckmann, K., \u0026amp; Thompson, P. (2019). Observational Requirements for Long-Term Monitoring of the Global Mean Sea Level and Its Components Over the Altimetry Era. \u003cem\u003eFrontiers in Marine Science\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e, 582. https://doi.org/10.3389/fmars.2019.00582\u003c/li\u003e\n \u003cli\u003eCCPO. (2018). Urban Areas in Coastal Zones. In \u003cem\u003eClimate Change and Cities: Second Assessment Report of the Urban Climate Change Research Network\u003c/em\u003e (pp. 319\u0026ndash;362). Cambridge University Press.\u003c/li\u003e\n \u003cli\u003eCh, R., Martin, D. A., \u0026amp; Vargas, J. F. (2021). Measuring the size and growth of cities using nighttime light. \u003cem\u003eJournal of Urban Economics\u003c/em\u003e, \u003cem\u003e125\u003c/em\u003e, 103254. https://doi.org/10.1016/j.jue.2020.103254\u003c/li\u003e\n \u003cli\u003eChouari, W. (2021). Wetland land cover change detection using multitemporal Landsat data: a case study of the Al-Asfar wetland, Kingdom of Saudi Arabia. \u003cem\u003eArabian Journal of Geosciences\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(6), 1\u0026ndash;14. https://doi.org/10.1007/S12517-021-06815-Y/METRICS\u003c/li\u003e\n \u003cli\u003eChuvieco, E. (2008). \u003cem\u003eEarth Observation of Global Change: The Role of Satellite Remote Sensing in Monitoring the Global Environment\u003c/em\u003e. https://doi.org/10.1007/978-1-4020-6358-9\u003c/li\u003e\n \u003cli\u003eCrippen, R., Buckley, S., Agram, P., Belz, E., Gurrola, E., Hensley, S., Kobrick, M., Lavalle, M., Martin, J., Neumann, M., Nguyen, Q., Rosen, P., Shimada, J., Simard, M., \u0026amp; Tung, W. (2016). Nasadem global elevation model: Methods and progress. \u003cem\u003eInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e, 125\u0026ndash;128. https://doi.org/10.5194/isprsarchives-XLI-B4-125-2016\u003c/li\u003e\n \u003cli\u003eDjouder, F., \u0026amp; Boutiba, M. (2017). Vulnerability assessment of coastal areas to sea level rise from the physical and socioeconomic parameters: case of the Gulf Coast of Bejaia, Algeria. \u003cem\u003eArabian Journal of Geosciences\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(14), 299. https://doi.org/10.1007/s12517-017-3062-5\u003c/li\u003e\n \u003cli\u003eElvidge, C. D., Baugh, K. E., Anderson, S. J., Sutton, P. C., \u0026amp; Ghosh, T. (2012). The Night Light Development Index (NLDI): a spatially explicit measure of human development from satellite data.\u0026nbsp;\u003cem\u003eSocial Geography\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(1), 23\u0026ndash;35. https://doi.org/10.5194/sg-7-23-2012\u003c/li\u003e\n \u003cli\u003eErkens, G., Bucx, T., Dam, R., de Lange, G., \u0026amp; Lambert, J. (2015). Sinking coastal cities. \u003cem\u003eProceedings of the International Association of Hydrological Sciences\u003c/em\u003e, \u003cem\u003e372\u003c/em\u003e, 189\u0026ndash;198. https://doi.org/10.5194/piahs-372-189-2015\u003c/li\u003e\n \u003cli\u003eGalloway, D. L., \u0026amp; Burbey, T. J. (2011). Review: Regional land subsidence accompanying groundwater extraction. \u003cem\u003eHydrogeology Journal\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(8), 1459\u0026ndash;1486. https://doi.org/10.1007/s10040-011-0775-5\u003c/li\u003e\n \u003cli\u003eGesch, D. B. (2018). Best practices for elevation-based assessments of sea-level rise and coastal flooding exposure. \u003cem\u003eFrontiers in Earth Science\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e, 230. https://doi.org/10.3389/feart.2018.00230\u003c/li\u003e\n \u003cli\u003eGhoussein, Y., Mhawej, M., Jaffal, A., Fadel, A., El Hourany, R., \u0026amp; Faour, G. (2018). Vulnerability assessment of the South-Lebanese coast: A GIS-based approach. \u003cem\u003eOcean \u0026amp; Coastal Management\u003c/em\u003e, \u003cem\u003e158\u003c/em\u003e, 56\u0026ndash;63. https://doi.org/10.1016/j.ocecoaman.2018.03.028\u003c/li\u003e\n \u003cli\u003eGriggs, G., \u0026amp; Reguero, B. G. (2021). Coastal adaptation to climate change and sea-\u003cem\u003elevel rise. Water, 13\u003c/em\u003e(16), 2151. https://doi.org/10.3390/w13162151\u003c/li\u003e\n \u003cli\u003eHan, H., Yang, C., \u0026amp; Song, J. (2015). Scenario Simulation and the Prediction of Land Use and Land Cover Change in Beijing, China. \u003cem\u003eSustainability, 7\u003c/em\u003e(4), 4260\u0026ndash;4279. https://doi.org/10.3390/su7044260\u003c/li\u003e\n \u003cli\u003eHasan, M. H., Newton, I. H., Chowdhury, M. A., Esha, A. A., Razzaque, S., \u0026amp; Hossain, M. J. (2023). Land use land cover change and related drivers have livelihood consequences in coastal Bangladesh. \u003cem\u003eEarth Systems and Environment, 7\u003c/em\u003e(2), 541-559. https://doi.org/10.1007/s41748-023-00339-z\u003c/li\u003e\n \u003cli\u003eHassanzadeh, S., Kiasatpour, A., \u0026amp; Hosseinibalam, F. (2007). Sea-level response to atmospheric forcing along the north coast of Persian Gulf. \u003cem\u003eMeteorology and Atmospheric Physics\u003c/em\u003e, \u003cem\u003e95\u003c/em\u003e(3), 223\u0026ndash;237. https://doi.org/10.1007/s00703-006-0213-8\u003c/li\u003e\n \u003cli\u003eHereher, M. E. (2020). Assessment of Climate Change Impacts on Sea Surface Temperatures and Sea Level Rise\u0026mdash;The Arabian Gulf.\u0026nbsp;\u003cem\u003eClimate\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(4), 50. https://doi.org/10.3390/cli8040050\u003c/li\u003e\n \u003cli\u003eHosseinibalam, F., Hassanzadeh, S., \u0026amp; Kiasatpour, A. (2007). Interannual variability and seasonal contribution of thermal expansion to sea level in the Persian Gulf. \u003cem\u003eDeep Sea Research Part I: Oceanographic Research Papers\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e(9), 1474\u0026ndash;1485. https://doi.org/10.1016/j.dsr.2007.05.005\u003c/li\u003e\n \u003cli\u003eIPCC. (2018). \u003cem\u003eClimate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change\u003c/em\u003e. Cambridge University Press, 1535 pp.\u003c/li\u003e\n \u003cli\u003eIPCC. (2019). Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities. In:\u003cem\u003e\u0026nbsp;IPCC Special Report on the Ocean and Cryosphere in a Changing Climate.\u0026nbsp;\u003c/em\u003eCambridge University Press, pp. 321-445. https://doi.org/10.1017/9781009157964.006\u003c/li\u003e\n \u003cli\u003eIPCC. (2021). \u003cem\u003eClimate Change 2021: The Physical Science Basis. Working Group I Contribution to the IPCC Sixth Assessment Report\u003c/em\u003e. Cambridge University Press. https://doi.org/10.1017/9781009157896\u003c/li\u003e\n \u003cli\u003eJones, C. E., An, K., Blom, R. G., Kent, J. D., Ivins, E. R., \u0026amp; Bekaert, D. (2016). Anthropogenic and geologic influences on subsidence in the vicinity of New Orleans, Louisiana. \u003cem\u003eJournal of Geophysical Research: Solid Earth\u003c/em\u003e, \u003cem\u003e121\u003c/em\u003e(5), 3867\u0026ndash;3887. https://doi.org/https://doi.org/10.1002/2015JB012636\u003c/li\u003e\n \u003cli\u003eLin, Y. P., Chu, H. J., Wu, C. F., \u0026amp; Verburg, P. H. (2010). Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling \u0026ndash; a case study. \u003cem\u003eInternational Journal of Geographical Information Science\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(1), 65\u0026ndash;87. https://doi.org/10.1080/13658811003752332\u003c/li\u003e\n \u003cli\u003eLuijendijk, A. P., Kras, E., Dagalaki, V., Morelissen, R., Hoteit, I., \u0026amp; Ranasinghe, R. (2022). Regime Shifts in Future Shoreline Dynamics of Saudi Arabia. \u003cem\u003eFrontiers in Marine Science\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e, 798657. https://doi.org/10.3389/FMARS.2021.798657/BIBTEX\u003c/li\u003e\n \u003cli\u003eMahmud, K., Mitra, B., Uddin, M. S., Hridoy, A.-E. E., Aina, Y. A., Abubakar, I. R., Rahman, S. M., Tan, M. L., \u0026amp; Rahman, M. M. (2023). Temporal assessment of air quality in major cities in Nigeria using satellite data. \u003cem\u003eAtmospheric Environment: X\u003c/em\u003e,. \u003cem\u003e20\u003c/em\u003e, 100227. https://doi.org/10.1016/j.aeaoa.2023.100227\u003c/li\u003e\n \u003cli\u003eMaithani, S. (2009). A Neural Network based Urban Growth Model of an Indian City. \u003cem\u003eJournal of the Indian Society of Remote Sensing,\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003e37\u003c/em\u003e, 363\u0026ndash;376. https://doi.org/10.1007/s12524-009-0041-7\u003c/li\u003e\n \u003cli\u003eMagnan, A. K., Oppenheimer, M., Garschagen, M., Buchanan, M. K., Duvat, V. K., Forbes, D. L., ... \u0026amp; P\u0026ouml;rtner, H. O. (2022). Sea level rise risks and societal adaptation benefits in low-lying coastal areas. \u003cem\u003eScientific Reports, 12\u003c/em\u003e(1), 10677. https://doi.org/10.1038/s41598-022-14303-w\u003c/li\u003e\n \u003cli\u003eMitra, B., Rahman, M. M., Khan, A. A., \u0026amp; Rahman, S. M. (2024). Assessing the Impact of Sea Level Rise on the Indus Delta in Pakistan: A Comprehensive Analysis of Flooded Areas and Future Vulnerabilities. \u003cem\u003eHeliyon,\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003e10\u003c/em\u003e(12), e33120. https://doi.org/10.1016/j.heliyon.2024.e33120\u003c/li\u003e\n \u003cli\u003eMitra, B., Rahman, S. M., Uddin, M. S., Mahmud, K., Islam, M. K., Arifuzzaman, M., Hafizur Rahman, M. M., \u0026amp; Rahman, M. M. (2023). Assessing demographic and economic vulnerabilities to sea level rise in Bangladesh via a nighttime light-based cellular automata model. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(1), 1\u0026ndash;14. https://doi.org/10.1038/s41598-023-40329-9\u003c/li\u003e\n \u003cli\u003eMitrovica, J. X., Gomez, N., Morrow, E., Hay, C., Latychev, K., \u0026amp; Tamisiea, M. E. (2011). On the robustness of predictions of sea level fingerprints. \u003cem\u003eGeophysical Journal International\u003c/em\u003e, \u003cem\u003e187\u003c/em\u003e(2), 729\u0026ndash;742. https://doi.org/10.1111/j.1365-246X.2011.05090.x\u003c/li\u003e\n \u003cli\u003eMortillaro, N. (2019). \u003cem\u003eSea levels could rise by up to 2 metres by 2100, new study finds\u003c/em\u003e. CBC News. https://www.cbc.ca/news/science/sea-level-rise-climate-change-1.5144739\u003c/li\u003e\n \u003cli\u003eNASA JPL. (2020). \u003cem\u003eNASADEM Merged DEM Global 1 arc second V001 [Data set]\u003c/em\u003e. NASA EOSDIS Land Processes Distributed Active Archive Center.\u003c/li\u003e\n \u003cli\u003eNerem, R. S., Beckley, B. D., Fasullo, J. T., Hamlington, B. D., Masters, D., \u0026amp; Mitchum, G. T. (2018). Climate-change\u0026ndash;driven accelerated sea-level rise detected in the altimeter era. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e, \u003cem\u003e115\u003c/em\u003e(9), 2022\u0026ndash;2025. https://doi.org/10.1073/pnas.1717312115\u003c/li\u003e\n \u003cli\u003eNiang, A. J. (2020). Monitoring long-term shoreline changes along Yanbu, Kingdom of Saudi Arabia using remote sensing and GIS techniques. \u003cem\u003eJournal of Taibah University for Science\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), 762\u0026ndash;776. https://doi.org/10.1080/16583655.2020.1773623\u003c/li\u003e\n \u003cli\u003eParker, A. G., Morley, M. W., Armitage, S. J., Engel, M., Parton, A., Preston, G. W., Russ, H., \u0026amp; Drechsler, P. (2020). Palaeoenvironmental and sea level changes during the Holocene in eastern Saudi Arabia and their implications for Neolithic populations. \u003cem\u003eQuaternary Science Reviews\u003c/em\u003e, \u003cem\u003e249\u003c/em\u003e, 106618. https://doi.org/10.1016/J.QUASCIREV.2020.106618\u003c/li\u003e\n \u003cli\u003eRahman, M. T., Aldosary, A. S., \u0026amp; Mortoja, M. G. (2017). Modeling Future Land Cover Changes and Their Effects on the Land Surface Temperatures in the Saudi Arabian Eastern Coastal City of Dammam. \u003cem\u003eLand\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(2), 36. https://doi.org/10.3390/LAND6020036\u003c/li\u003e\n \u003cli\u003eSajjad, M., Chan, J. C. L., \u0026amp; Lin, N. (2020). Incorporating natural habitats into coastal risk assessment frameworks. \u003cem\u003eEnvironmental Science \u0026amp; Policy\u003c/em\u003e, \u003cem\u003e106\u003c/em\u003e, 99\u0026ndash;110. https://doi.org/10.1016/j.envsci.2020.01.004\u003c/li\u003e\n \u003cli\u003eSanders, B. F., Wing, O. E. J., \u0026amp; Bates, P. D. (2024). Flooding is Not Like Filling a Bath. \u003cem\u003eEarth\u0026rsquo;s Future\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(12), e2024EF005164. https://doi.org/10.1029/2024EF005164\u003c/li\u003e\n \u003cli\u003eSanders, D., Frago, E., Kehoe, R., Patterson, C., \u0026amp; Gaston, K. J. (2021). A meta-analysis of biological impacts of artificial light at night. \u003cem\u003eNature Ecology \u0026amp; Evolution\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(1), 74\u0026ndash;81. https://doi.org/10.1038/s41559-020-01322-x\u003c/li\u003e\n \u003cli\u003eStammer, D., Cazenave, A., Ponte, R. M., \u0026amp; Tamisiea, M. E. (2013). Causes for contemporary regional sea level changes. \u003cem\u003eAnnual Review of Marine Science\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e, 21\u0026ndash;46. https://doi.org/10.1146/annurev-marine-121211-172406\u003c/li\u003e\n \u003cli\u003eStouthamer, E., Erkens, G., Cohen, K., Hegger, D., Driessen, P., Weikard, H. P., Hefting, M., Hanssen, R., Fokker, P., van den Akker, J., Groothuijse, F., \u0026amp; van Rijswick, M. (2020). Dutch national scientific research program on land subsidence: Living on soft soils -- subsidence and society. \u003cem\u003eProceedings of the International Association of Hydrological Sciences\u003c/em\u003e, \u003cem\u003e382\u003c/em\u003e, 815\u0026ndash;819. https://doi.org/10.5194/piahs-382-815-2020\u003c/li\u003e\n \u003cli\u003eSultan, S. A. R., Ahmad, F., Elghribi, N. M., \u0026amp; Al-Subhi, A. M. (1995). An analysis of Arabian Gulf monthly mean sea level. \u003cem\u003eContinental Shelf Research\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(11), 1471\u0026ndash;1482. https://doi.org/10.1016/0278-4343(94)00081-W\u003c/li\u003e\n \u003cli\u003eTzampoglou, P., Ilia, I., Karalis, K., Tsangaratos, P., Zhao, X., \u0026amp; Chen, W. (2023). Selected Worldwide Cases of Land Subsidence Due to Groundwater Withdrawal. \u003cem\u003eWater, 15\u003c/em\u003e (6), 1094. https://doi.org/10.3390/w15061094\u003c/li\u003e\n \u003cli\u003evan Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J. F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S. J., \u0026amp; Rose, S. K. (2011). The representative concentration pathways: An overview. \u003cem\u003eClimatic Change\u003c/em\u003e, \u003cem\u003e109\u003c/em\u003e(1), 5\u0026ndash;31. https://doi.org/10.1007/s10584-011-0148-z\u003c/li\u003e\n \u003cli\u003eWilliams, L. L., \u0026amp; L\u0026uuml;ck-Vogel, M. (2020). Comparative assessment of the GIS based bathtub model and an enhanced bathtub model for coastal inundation. \u003cem\u003eJournal of Coastal Conservation\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(2), 1\u0026ndash;15. https://doi.org/10.1007/S11852-020-00735-X/METRICS\u003c/li\u003e\n \u003cli\u003eZhang et al. (2019). Spatial and temporal changes of habitat quality in Jiangsu Yancheng wetland national nature reserve - Rare birds of China. \u003cem\u003eApplied Ecology and Environmental Research\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e, 4807\u0026ndash;4821. https://doi.org/10.15666/aeer/1702_48074821\u003c/li\u003e\n \u003cli\u003eZhao, M., Cheng, W., Zhou, C., Li, M., Huang, K., \u0026amp; Wang, N. (2018). Assessing Spatiotemporal Characteristics of Urbanization Dynamics in Southeast Asia Using Time Series of DMSP/OLS Nighttime Light Data. \u003cem\u003eRemote Sensing, 10\u003c/em\u003e (1), 47. https://doi.org/10.3390/rs10010047\u003c/li\u003e\n \u003cli\u003eZhu, L., Song, R., Sun, S., Li, Y., \u0026amp; Hu, K. (2022). Land use/land cover change and its impact on ecosystem carbon storage in coastal areas of China from 1980 to 2050. Ecological Indicators, 142, 109178. https://doi.org/10.1016/j.ecolind.2022.109178\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Sea Level Rise, IPCC, Saudi Arabia, MOLUSCE, GIS","lastPublishedDoi":"10.21203/rs.3.rs-7126987/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7126987/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSea-level rise (SLR) poses a major global risk to the populated coastal zones, with recent assessments indicating significant acceleration due to climate change. In this study, we integrate nighttime light (NTL) data and Land Use and Land Cover (LULC) modeling within a cellular automata framework to project SLR impacts on six major coastal cities of Saudi Arabia’s Eastern Province along the Arabian Gulf. Historical sea-level records (1979–2020) reveal an annual mean rise of 7.9 mm, corroborating global trends. To forecast future inundation, we applied a GIS-based “bathtub” approach using sea-level scenarios from the Intergovernmental Panel on Climate Change Sixth Assessment Report (IPCC AR6) and digital elevation models. Concurrently, LULC transitions for 1973–2020 were derived from Landsat images and extrapolated to future time slices (2070, 2100, and 2130) via cellular automata-based approach. Nighttime light data served as a proxy for economic and urban activity, allowing refined mapping of vulnerable coastal development zones. Results show spatially heterogeneous, yet markedly increasing, inundation exposure by mid- to late-century. Jubail, Qatif, and Ras Tanura emerge as high-risk areas, with up to 40% or more of coastal lands threatened under the worst-case SLR scenario by 2130. Rapidly growing built-up areas and reclaimed lands in Dammam and Khobar also face significant flood hazard. These findings highlight the need for proactive planning and adaptation measures to reduce the economic and ecological impacts of rising seas in the Arabian Gulf region.\u003c/p\u003e","manuscriptTitle":"Modeling Sea Level Rise Impacts on Western Arabian Gulf Cities Using Nighttime Lights and LULC-Driven Cellular Automata","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-25 06:22:36","doi":"10.21203/rs.3.rs-7126987/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-08-07T14:43:23+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-22T06:47:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Natural Hazards","date":"2025-07-17T09:54:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-17T02:43:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Natural Hazards","date":"2025-07-16T02:36:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"27906127-b570-4134-b13a-d668fd8f406a","owner":[],"postedDate":"July 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T16:04:36+00:00","versionOfRecord":{"articleIdentity":"rs-7126987","link":"https://doi.org/10.1007/s11069-025-07740-z","journal":{"identity":"natural-hazards","isVorOnly":false,"title":"Natural Hazards"},"publishedOn":"2025-12-26 15:57:50","publishedOnDateReadable":"December 26th, 2025"},"versionCreatedAt":"2025-07-25 06:22:36","video":"","vorDoi":"10.1007/s11069-025-07740-z","vorDoiUrl":"https://doi.org/10.1007/s11069-025-07740-z","workflowStages":[]},"version":"v1","identity":"rs-7126987","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7126987","identity":"rs-7126987","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}