Assessment of Coastal Zone Vulnerability in Context of Sea-Level Rise and Inundation Risk in Qatar

Abstract

Coastal zones represent the most active interfaces where natural processes and human activities converge, making them crucial for biodiversity and socioeconomic development. These zones are characterized by their fragility and susceptibility to frequent natural disasters, such as floods and erosion, which are exacerbated by high-intensity human activities and urban expansion. The ongoing challenges posed by rising sea levels and climate change necessitate robust scientific assessments of coastal vulnerability to ensure effective disaster prevention and environmental protection. This paper introduces a comprehensive evaluation system for assessing coastal zone vulnerability, utilizing multi-source data to address ecological vulnerabilities stemming from sea-level rise and climate change impacts. This system is applied to examine the specific case of Qatar, where rapid urban development and a high population density in coastal areas heighten the risk of flooding and inundation. Employing remote sensing data and Geographic Information Systems (GISs), this research leverages spatial interpolation techniques and high-resolution digital elevation models (DEMs) to identify and evaluate high-risk zones susceptible to sea-level rise. In this study, the hydrological connectivity model, bathtub technique, and CVI are interconnected tools that complement each other to assess future flooding risks under various climate change projections, highlighting the increased probability of coastal hazards. The findings underscore the urgent need for adaptive planning and regulatory frameworks to mitigate these risks, providing technical support for the sustainable development of coastal communities globally and in Qatar. This approach not only informs policy makers, but also aids in the strategic planning required to foster resilient coastal infrastructure capable of withstanding both current and future environmental challenges.

1. Introduction

Coastal zones are among the most dynamic and complex landforms on Earth, shaped continuously by physical forces such as geology, geomorphology, wave action, and tides. These natural processes are now significantly influenced by climate change, which has intensified key environmental stressors, including sea-level rise (SLR), increased storm frequency and intensity, shifts in precipitation patterns, and rising ocean temperatures [1].

Today, about 21% of the global population resides in coastal areas [2], where socioeconomic activities are becoming increasingly concentrated [3,4,5]. Historically, rich resources and strategic access to maritime trade, tourism, and cultural hubs have attracted large populations to coastlines. This trend has accelerated in recent years, leading to considerable environmental and developmental pressures [6].

Coastal populations are now three times denser than the global average. Projections indicate that by 2030, nearly half the world’s population will live within 100 km of a coastline, with more than a billion people expected to inhabit coastal zones by 2050 [7,8,9]. A multitude of settlements, including large cities, have rapidly developed along coastal areas, contributing significantly to global economic productivity [10]. Urban expansion in low-lying coastal areas is driving increased vulnerability to flooding, with sea-level rise potentially affecting up to 10–20 km of inland coastal land [11].

This demographic and economic expansion heightens the vulnerability of both ecosystems and infrastructure. Coastal ecosystems are under mounting strain from tourism, land conversion, and overdevelopment [12]. Simultaneously, critical infrastructure—such as power plants, transport networks, and water supplies—is increasingly exposed to climate-related hazards, particularly in low- and middle-income countries where resilience capacity may be limited [13].

Sea-level rise has emerged as a major long-term threat. According to the UN Intergovernmental Panel on Climate Change (IPCC), sea levels are now rising at an accelerating rate and are projected to continue rising for centuries—even under aggressive mitigation scenarios [7]. Under high-emission trajectories, the global mean sea level could increase by over 2 m by 2100 and by up to 5 m by 2150. This would intensify coastal hazards such as storm surges, erosion, and saltwater intrusion, with far-reaching consequences for ecosystems, infrastructure, and coastal economies. Notably, the rate of sea-level rise varied throughout the 20th century, with a statistically significant acceleration observed between 1880 and 1900 [14].

Assessing coastal vulnerability is essential for adaptation planning. Vulnerability assessments help to identify the populations, infrastructure, and land uses most at risk from SLR. These assessments often use the term “vulnerability” interchangeably with “risk” in the context of hazard exposure and impact. For instance, the Arab Forum for Environment and Development (2009) evaluated SLR threats in the Arab region, using remote sensing to simulate inundation under SLR scenarios ranging from 1 to 5 m by 2100. Their analysis confirmed significant risks for the region’s low-lying areas.

To better map and predict SLR impacts, researchers have employed various models over the past two decades. Among them, the bathtub model has been widely used due to its simplicity and reliance on elevation and sea-level data [15,16,17,18,19,20,21]. However, this method often overestimates flooding by not accounting for hydrological connectivity or topographic barriers. Enhanced models, including hydrologically connected approaches, produce more accurate estimates by limiting inundation to areas directly connected to the sea. For example, using four-side or eight-side pixel connectivity significantly reduces the extent of predicted flooding compared to the basic bathtub model [18,22,23,24].

This research focuses on the coastal vulnerability of Qatar, a peninsula extending into the Arabian Gulf and bordered only by Saudi Arabia to the south. With more than 13% of its land at risk of SLR-induced flooding [3] and most of its urban and economic infrastructure concentrated along the coast—particularly the eastern shore—Qatar is highly susceptible to climate impacts. Dasgupta et al. [4] estimated that up to 10% of Qatar’s GDP could be affected by rising sea levels.

Study Objective

The objective of this study is to assess the areas in Qatar most vulnerable to flooding from sea-level rise. Using Geographic Information System (GIS) tools, the research applies a combined approach that includes the bathtub model, eight-side hydrologic connectivity, and the Coastal Vulnerability Index (CVI). The findings will provide a spatial basis for planning coastal defenses and implementing mitigation strategies to reduce long-term risks.

2. Materials and Methods

2.1. Study Area

The study area is located between latitudes 24°27′ and 26°10′ north and longitudes 50°40′ and 51°40′ east. Qatar is situated midway along the western coast of the Arabian Gulf (see Figure 1). The Qatari peninsula extends northward, encompassing an estimated area of 11,627 km² [25]. Consisting of multiple islands, reefs, and sandbars, the peninsula spans approximately 185 km from north to south and 85 km from east to west. The Arabian Gulf is accessible via Qatar’s territorial waters, which extend approximately 51 nautical miles to the north and 95 nautical miles to the east. Its only land border, shared with the Kingdom of Saudi Arabia, measures approximately 86 km.

The total length of the study area’s coastline is approximately 560 km. The region’s high natural and socioeconomic value arises from its diverse ecosystems, dense populations, residential and recreational zones, and the presence of commercial ports and industrial facilities along the coast.

2.2. Methodology

The Coastal Vulnerability Index (CVI) is a tool used to assess the susceptibility of coastal areas to various hazards, such as sea-level rise, storms, and erosion. It integrates multiple physical, environmental, and geological parameters to evaluate vulnerability. The hydrological connectivity method and the bathtub method are both integral to this assessment. The hydrological connectivity method focuses on understanding the flow and interaction of water within a coastal system, including surface and subsurface water movement, which influence factors like erosion, sediment transport, and flooding. This method provides a dynamic perspective on how water connectivity impacts coastal stability. On the other hand, the bathtub method is a simpler, static approach used to estimate inundation areas by assuming a uniform rise in sea level, akin to filling a bathtub. It is often employed to predict flood extents under various sea-level rise scenarios. When calculating the CVI, the bathtub method helps to identify areas at risk of inundation, while the hydrological connectivity method offers insights into the underlying processes that exacerbate or mitigate vulnerability, such as groundwater interactions or sediment dynamics. Together, these methods provide a comprehensive understanding of coastal vulnerability by combining static inundation predictions with dynamic hydrological processes, enabling more accurate and holistic assessments for coastal management and planning. The flowchart in Figure 2 illustrates the coastal vulnerability assessment process, beginning with the data collection of geospatial and environmental parameters. The analysis progresses through the following two complementary flood modeling approaches: (1) the bathtub method, which calculates potential inundation areas using simple elevation thresholds under sea-level rise scenarios, and (2) the hydrological connectivity method, which enhances these results by identifying floodable areas through water flow pathways and drainage connections. Where the bathtub method provides initial risk screening, the hydrological connectivity method delivers more accurate predictions by eliminating hydrologically isolated depressions. These outputs then feed into an Analytic Hierarchy Process (AHP) that weights physical and socioeconomic vulnerability factors, ultimately generating spatial vulnerability patterns to guide coastal protection priorities. This integrated workflow combines rapid assessment capabilities with sophisticated hydrological modeling for comprehensive risk evaluation.

2.2.1. Bathtub Method

Studies related to sea-level rise often employ the bathtub approach [22,23], which provides a basic assessment of coastal inundation [16,18,26,27,28,29,30]. This method assumes that all areas with elevations below the projected sea level will be flooded. However, it does not account for the hydrological connectivity of these areas to the sea.

The digital elevation model used in this method was generated by the GIS Center in Qatar, which conducted an aerial photography survey in 2017 to collect elevation data. A total of 118,040,634 elevation points were recorded during the survey. The dataset has a Root Mean Square Error (RMSE) accuracy of up to 0.1 m. While this dataset remains the most recent high-resolution elevation model available for Qatar, its temporal relevance to the study period (2023–2025) is justified by the relative stability of coastal topography in Qatar over short-term periods (6–7 years), with minimal natural changes (e.g., erosion or sedimentation) likely to significantly alter elevation patterns.

The study focuses on long-term sea-level rise (SLR) projections (e.g., RCP scenarios for 2100), where short-term topographic changes are negligible compared to the magnitude of projected SLR impacts.

To investigate the consequences of SLR on coastal regions in Qatar, a high-precision elevation model is built as a key player.

Geographic Information Systems (GISs) provide a range of interpolation methods for building DEMs. The Inverse Distance Weighting (IDW) technique is used to generate the DEM for this investigation. The IDW model proposes that closer entities have more traits in common than those farther apart.

To project a value for any unknown site, IDW employs the measured values around the target location. IDW considers the local effect of each observed location, which diminishes with an increasing distance. Equation (1) defines inverse distance weighting as the product of the power value multiplied by the inverse of the distance (from the data point to the estimated location).

$$\mathrm{w}(\mathrm{i})={\displaystyle \frac{1}{{\mathrm{d}\mathrm{i}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{c}\mathrm{e}}^{\mathrm{p}}}}$$

(1)

where w(i) is the inverse distance weight and p is the power function.

The forecast will comprise the average of all data values falling within the search neighborhood, because each weight remains the same and there is no decline with distance, even if the value of p is set to 0. The weights for distance points undergo a sequential drop with the increasing value of p. Only the proximal points will impact the forecast if the p-value is large [31].

The IDW parameters are carefully optimized to balance accuracy and computational efficiency, employing a variable search radius (max 500 m, min 12 neighboring points per cell) to ensure localized precision without over-smoothing. The power parameter is set to p = 2 after empirical tests show that higher values (p ≥ 3) create artifacts in flat terrains, while p = 1 underestimates coastal elevation gradients. Sensitivity analysis confirms the robustness of these parameters by cross-validation, which reveals that p = 2 yields the lowest RMSE (2.114 m vs. 2.218 m for p = 1 and 2.440 m for p = 3), and tests on neighborhood configuration demonstrate that 12–15 neighbors optimally preserve micro-topographic features like coastal berms while minimizing noise. These settings are selected to accurately capture Qatar’s elevation variability, which is critical for bathtub inundation modeling.

Maximum Sea levels

The maximum sea level is calculated by adding the predicted future increase from RCP 8.5 to the Mean Higher High Water (MHHW) and the current sea level. MHHW is defined as “the average of the higher high-water height of each tidal day observed over the National Tidal Datum Epoch”, according to NOAA (2020). To determine the equivalent datum of the National Tidal Datum Epoch for locations with shorter data series, simultaneous measurements are compared with a control tide station. However, according to NOAA (2020), the HOT is the “average of all the high-water heights observed over the National Tidal Datum Epoch”. The IPCC has endorsed a new set of techniques for climate modeling, referred to as Representative Concentration Pathways (RCPs), which were developed for both long-term and short-term modeling [26]. The main objective of these RCPs is to determine the expected future trajectories of key drivers of climate change by 2100. The examination and modeling of possible future climatic conditions and their consequences are supported by the following four primary RCPs:

• RCP 2.6, where in an ambitious mitigation scenario, emissions are forecasted to reach a peak between 2010 and 2020, followed by a substantial decline.
• RCP 4.5 and RCP 6.0, intermediate scenarios, which predict peaks in emissions between 2040 and 2080, respectively, with a subsequent reduction.
• RCP 8.5, classified as an extreme emissions circumstance, anticipates rising GHG atmospheric concentrations throughout the present century.
• The worst-case scenario in this analysis, represented by RCP 8.5, projects a maximum sea-level rise of 0.98 m in Qatar [32].

To capture a range of potential futures, this study evaluates both high-emission (RCP 8.5) and intermediate-emission (RCP 4.5) scenarios. Under RCP 8.5, sea levels in Qatar are projected to rise by up to 0.98 m by 2100, while RCP 4.5 projects a more moderate increase of 0.55 m [32]. This dual-scenario approach provides a comparative framework for assessing the sensitivity of coastal vulnerability to different climate trajectories.

2.2.2. Hydrological Connectivity Method

Using a hydrological connectivity technique [18], this study considered a cell to be inundated if it lay below sea level and was connected to other flooded cells or to the sea. The eight-side rule was employed to evaluate connectivity from all directions around each cell. A pixel was classified as flooded if its elevation was below the projected sea-level rise (SLR) value and it was connected to other flooded pixels or the sea through either diagonal or cardinal directions. After the bathtub approach identified potentially flooded areas, the next step involved applying ArcGIS’s eight-side connectivity rule to select only the pixels connected to the coastline, excluding any isolated inland areas (pixels) not hydrologically linked to a water body.

2.2.3. Vulnerability Assessment Method

Currently, the quantitative evaluation of coastal vulnerability relies on several key methodologies, including Principal Component Analysis (PCA) [33], the Analytic Hierarchy Process (AHP) [34], and the Comprehensive Index Method [35]. Each of these approaches possesses distinct characteristics and inherent limitations. For example, PCA requires extensive sample data, and its results can vary significantly depending on the dataset employed. In contrast, methods such as AHP and the Comprehensive Index Method depend heavily on the assignment of weights, which, despite attempts to minimize bias, often introduce a degree of subjectivity. Numerous researchers have thoroughly investigated the impacts of climate change and sea-level rise on coastal regions, as demonstrated by studies [36,37,38,39,40,41], underscoring the complexity and challenges associated with accurately assessing coastal vulnerability.

To assess the vulnerability of African coastal regions to sea-level rise, a customized Coastal Vulnerability Index (CVI) model was developed by incorporating 17 key parameters that reflect both physical and socioeconomic aspects of exposure, sensitivity, and adaptive capacity. Geographic Information Systems (GISs) and remote sensing technologies were utilized to analyze and integrate these parameters, enabling the classification of coastal areas into distinct zones based on their varying levels of vulnerability [38].

To evaluate the key factors influencing the Coastal Vulnerability Index, four distinct iterative approaches rooted in the Random Forest (RF) algorithm were employed. These methods incorporated a combination of four socioeconomic indicators and eight geological parameters. By integrating pixel-level differential weighted rank values across all variables, the study aimed to identify the most significant contributors to coastal vulnerability. Additionally, the analysis explored the impacts of developmental and socioeconomic activities on coastal vulnerability assessment, as referenced in [39].

To evaluate the vulnerability of the Sundarbans coastal region in India, a GIS-based comprehensive index approach was employed. This method incorporated the analysis of five physical factors—geomorphology, elevation, sea-level rise, erosion, and sedimentation patterns—as well as one socioeconomic factor, population density. These variables were systematically analyzed to assess the region’s susceptibility to environmental and climatic risks [40].

Given the significant impacts of climate change and rising sea levels on coastal regions, there is an urgent need to develop a robust and quantitative framework for assessing the vulnerability of these areas. Such a framework would facilitate a scientifically grounded evaluation of the risks facing coastal zones, enabling the implementation of proactive and targeted mitigation strategies [41,42]. This approach is essential for ensuring the sustainable management of coastal resources and the long-term development of these regions.

In this context, the coastal zone of the State of Qatar was selected as a case study. A comprehensive vulnerability assessment framework was developed, focusing on the dimensions of “ecological sensitivity, ecological resilience, and ecological pressure”. This framework was applied to conduct a detailed quantitative analysis of the vulnerability of Qatar’s coastal zone to climate change and sea-level rise. The study also revealed disparities in vulnerability levels across different coastal areas within the country. The findings aim to provide a scientific basis for the sustainable protection and management of coastal zones amid the ongoing challenges posed by climate change and rising sea levels.

2.2.4. Selection of Vulnerability Index

When determining the criteria for assessing the vulnerability of coastal zones, it is essential to consider a wide range of factors to ensure a comprehensive representation of the impacts driven by climate change and rising sea levels. To address this, the ecological sensitivity–resilience–pressure (SRP) framework was applied, tailored to the specific conditions of Jiangsu’s coastal regions. This model served as the foundation for developing the evaluation index system, as outlined in

Table 1. Assessment index system of vulnerability in Qatar coastal zone.

		Index Level	Index Feature
Ecological sensitivity	Meteorological factors	Annual mean temperature	+
		Annual mean precipitation	−
		Annual mean relative humidity	−
		Annual mean wind speed	+
	Factor related to sea-level rise	Elevation	+
		Slope	+
		Distance to coastline	−
Vegetation factors	Fraction vegetation coverage	NDVI	−
	Ecological resiliency	Net primary production	−
Ecological pressure	Population	Population density	+

“+” means positive indicators; “−” means negative indicators.

[43].

2.2.5. Data Acquisition for Vulnerability Index Parameters

The vulnerability index (CVI) was constructed using a combination of geospatial, climatic, and socioeconomic datasets. Physical parameters (elevation, slope, and distance to coastline) were derived from high-resolution digital elevation models (DEMs) obtained through aerial surveys conducted by the Qatar GIS Center in 2017 (RMSE: 0.1 m). Climatic variables (temperature, precipitation, wind speed, and humidity) were sourced from the Qatar Meteorological Department’s historical records. Socioeconomic data (population density and land use) were acquired from Qatar’s Ministry of Development Planning and Statistics (MDPS, 2015). Vegetation indices (NDVI) and net primary productivity (NPP) were extracted from Sentinel-2 satellite imagery (10 m resolution). All datasets were harmonized to a common spatial resolution (30 m) and coordinate system (WGS 84 UTM Zone 39N) within ArcGIS 10.8 prior to analysis.

2.2.6. AHP Method

Multi-criteria GIS tools are highly regarded for spatial analysis due to their ability to deliver accurate and precise results by integrating various criteria. This research utilized the Analytic Hierarchy Process (AHP), a method developed by Thomas Saaty [44] in the 1980s. The AHP has been extensively used in diverse fields to help decision makers navigate complex environmental challenges. It has been applied in studies ranging from renewable energy site selection [45,46,47,48] and landfill location planning to ecotourism suitability [49], public service site selection, coastal vulnerability assessment [43,50,51,52], and flood risk evaluation [53]. The AHP method involves the following four key steps: establishing a hierarchical framework, weighting criteria and sub-criteria, assigning weights to alternatives, and computing final scores.

The process operates through structured steps, where each criterion is compared pairwise to determine its relative importance. This comparison is central to the AHP, as decision makers evaluate criteria based on their relevance to the study using a 1–9 scale. A score of 1 indicates equal importance between criteria, while a score of 9 highlights the dominance of one criterion over another. This systematic approach ensures the clear and logical prioritization of factors.

3. Results

3.1. Estimated Flooded Area Using Bathtub Method

Using the reclassification feature in the raster calculator, flooded areas were mapped in ArcGIS based on the bathtub approach, as shown in Figure 3. Areas with DEM values below the estimated SLR threshold were classified as flooded, while all other areas were labeled as non-flooded. These classified raster regions were then converted into polygon features, and their respective areas were calculated.

Figure 3 compares the inundation extents under the RCP8.5 (a) and RCP4.5 (b) scenarios using the bathtub method. The RCP8.5 scenario projects a flooded area of 1197.21 km² (10.13% of Qatar’s total area), whereas the RCP4.5 scenario shows a reduced inundation extent of 973.82 km² (8.24%). This divergence underscores the critical importance of emission mitigation in limiting the impacts of sea-level rise.

The inundated areas were determined using the following Equation (2):

Inundated Area = DEM ≤ X

(2)

where X represents the maximum sea-level height, calculated as the sum of the current coastal line height, the highest observed tide, and the projected sea-level increase under the RCP8.5 and RCP4.5 scenarios.

This method provides a clear and systematic way to identify and quantify areas at risk of flooding based on elevation data and sea-level projections.

3.2. Estimated Flooded Area Using Flooded Area Using Hydrological Connectivity Method

Using the eight-sided rule, flooded regions that were explicitly linked to the coastline were determined (selection by location command in ArcGIS) as places exposed to the risk of flooding. The areas of these chosen spots were computed after their identification. Additional flooded regions that were linked to the determined inundated areas were chosen to find further locations utilizing the eight-sided rule. This procedure was repeated many times until more flooded regions could not be found, as shown in Figure 4.

The hydrological connectivity method refined flood predictions by accounting for drainage pathways, eliminating hydrologically isolated depressions. Figure 4 contrasts the inundation extents calculated under the RCP8.5 (a) and RCP4.5 (b) scenarios. Under RCP8.5, 944.09 km² (8% of Qatar’s total area) was projected as flood-prone, while RCP4.5 reduced this to 508.79 km² (4.31%). Notably, urban and industrial zones (e.g., Roads/Streets: 30.03 km² under RCP8.5 vs. 14.97 km² under RCP4.5) showed significant variability, emphasizing the method’s sensitivity to SLR magnitude. Discrepancies with the bathtub method arose from the exclusion of inland areas disconnected from the sea, demonstrating the importance of dynamic hydrological modeling and its accuracy.

3.3. Vulnerability Assessment of Coastal Zone

Ten criteria were evaluated using values ranging from one to eight, as detailed in

Table 2. Pairwise comparison.

Criterion	Elevation	Distance to Coastline	Vegetation	Population Density	NPP	Slope	Temperature	Precipitation	Wind Speed	Humidity
Elevation	1	3	5	7	8	7	8	8	8	8
Distance to Coastline	0.333	1	3	5	7	5	7	7	7	7
Vegetation	0.2	0.333	1	3	5	3	5	5	5	5
Population Density	0.143	0.2	0.333	1	3	1	3	3	3	3
NPP	0.125	0.143	0.2	0.333	1	0.333	1	1	1	1
Slope	0.143	0.2	0.333	1	3	1	3	3	3	3
Temperature	0.125	0.143	0.2	0.333	1	0.333	1	1	1	1
Precipitation	0.125	0.143	0.2	0.333	1	0.333	1	1	1	1
Wind Speed	0.125	0.143	0.2	0.333	1	0.333	1	1	1	1
Humidity	0.125	0.143	0.2	0.333	1	0.333	1	1	1	1

. This table illustrates a pairwise comparison analysis of the factors likely to influence the vulnerability assessment of coastal zones. Each factor was compared against the others to determine its relative importance in contributing to the overall site vulnerability. The pairwise comparison matrix employed a scale where a higher number indicates that one criterion is more critical than another.

The criteria of elevation, distance to the coastline, and vegetation were assigned higher weights than the others. The remaining criteria—including population density (8%), slope (4%), and climatic variables (temperature, precipitation, wind speed, and humidity, collectively 8%)—were weighted lower. However, they still provide critical insights into the vulnerability assessment of coastal zones.

The AHP method highlights the importance of balancing various environmental, climatic, ecological, and geomorphological factors, all of which play a role in coastal vulnerability assessment.

Consistency Index (CI) (Equation (3)) and Consistency Ratio (CR) (Equation (4)) are crucial indicators in the Analytic Hierarchy Process (AHP). They provide a measure of the reliability of the decision-making process, particularly in evaluating the consistency of pairwise comparisons. For each criterion, weights are assigned based on their relative importance. The CI quantifies the deviation from perfect consistency in these pairwise comparisons. Using the formula for CI (Equation (3)), we can assess the level of consistency in the judgments made during the AHP process.

$$CI={\displaystyle \frac{(\lambda \max -n)}{\left(n-1\right)}}$$

(3)

where λ_max is the average of the consistency vector, λ_max = 10.5, and n = 10.

$$CR={\displaystyle \frac{CI}{RI}}$$

(4)

For n = 10, the Random Index (RI) is 1.49. CI = 0.055, CR = 0.036.

A Consistency Ratio (CR) of 0.036 is obtained by comparing the Consistency Index (CI) against a Random Index (RI), ensuring that the level of inconsistency remains within an acceptable range. A CR value of less than 0.10 indicates a satisfactory consistency in the pairwise comparisons, which means the decision-making process is reliable. Calculating the CI and CR is essential to validate the decision-making framework in vulnerability assessment analysis. If the CR was greater than 0.10, it would suggest inconsistency, and the judgment matrix would need to be revised. However, since the CR is 0.036, the results of this analysis are reliable, leading to a robust coastal vulnerability assessment based on the weighted criteria.

In

Table 3. Weights of criteria in percentage based on pairwise comparisons.

Criteria No.	Criteria’s Name	Criteria’s Weight (%)
1	Elevation	35%
2	Distance to Coastline	25%
3	Vegetation	15%
4	Population Density	8%
5	NPP	5%
6	Slope	4%
7	Temperature	3%
8	Precipitation	2%
9	Wind Speed	2%
10	Humidity	1%

, the weights of the criteria obtained from the pairwise comparison are given in percentage terms. From this analysis, each factor is assigned a percentage weight to its relative importance in decision making. Elevation is the most weighted factor at 35% and is considered as the most critical decision factor during vulnerability assessment. Distance to Coastline follows with a relative weight of 25%.

The assigned weights for each criterion, as shown in Figure 5 and Figure 6, were integrated into the GIS environment (ArcGIS) to finalize the analyses.

Figure 5 represents the ecological sensitivity index, which primarily captures how vulnerable coastal ecosystems are to the impacts of climate change and rising sea levels. Meanwhile, the ecological resiliency index focuses on the ecosystem’s inherent capacity to recover from disturbances. On the other hand, the ecological pressure index evaluates the overall ecological value and benefits provided by coastal ecosystems [30]. Key positive indicators used in these assessments include factors such as average annual temperature, average wind speed, elevation, slope, and population density.

Figure 6 represents the negative factors, which encompass average yearly rainfall, mean annual humidity levels, proximity to the shore, fractional vegetation cover (FVC), and net primary productivity.

In the final stage, all criteria were combined using AHP overlay analysis tools in ArcGIS 10.8 to produce a vulnerability map. Figure 7 presents the vulnerability zoning of coastal areas for Qatar. The figure illustrates the results of an overlay of the 10 criteria. The weighting of each criterion played a significant role in determining the distribution of vulnerable areas. The map delineates four vulnerability levels for coastal areas, categorized as extremely vulnerable, highly vulnerable, moderately vulnerable, and low vulnerable.

Figure 7 presents the vulnerability zoning of Qatar’s coastal area, categorizing regions into four levels, including extremely vulnerable, highly vulnerable, moderately vulnerable, and low vulnerable, based on an Analytic Hierarchy Process (AHP) where AHP-weighted criteria, such as Elevation (35% weight) and Distance to the Coastline (25%), dominate the vulnerability distribution. The map highlights that extremely vulnerable areas (in red) are concentrated in low-lying coastal zones, particularly where critical infrastructure (61.14 km²) and industrial facilities (28.32 km²) are located, as these areas are highly exposed to sea-level rise (SLR) and lack natural buffers. These results underscore the urgent need for targeted adaptation strategies, such as engineered barriers or land use regulations, in high-risk urban and industrial zones, while less vulnerable inland areas (green zones) may require minimal intervention. Figure 7 effectively combines geospatial precision with multi-criteria decision making to guide resilient coastal planning in Qatar.

3.4. Mapping Spatial Extent of Inundated Areas Coastal Line

Table 4. Coastal flood vulnerability by land use: comparative inundation areas (km²) under RCP8.5 and RCP4.5 scenarios, derived from bathtub and hydrological connectivity methods.

Land Use	Area (km2) in Extremely Vulnerable Zone	Flooded Areas (km2) Using Bathtub Method		Flooded Areas (km2) Using Hydrological Connectivity Method
		RCP8.5	RCP4.5	RCP8.5	RCP4.5
Residential	9.37	12.39	8.93	10.81	3.45
Establishments Govt/Private	3.19	5.56	2.02	2.87	0.27
Business/Commercial	1.11	0.43	0.16	0.40	0.02
Hotel/Hotel Apartment/Restaurant	2.33	1.95	1.34	2.43	0.14
Financial/Banking	0.04	0.08	0.02	0.02	0
Workshop/Factory/Industry	28.32	41.69	37.43	17.10	7.65
Roads/Streets/Flower Beds	38.04	47.36	33.09	30.03	14.97
Educational Services	0.06	0.11	0.01	0.04	0
Health Services	0.58	0.69	0.68	0	0
Infrastructure/Utilities	61.14	29.38	37.02	36.45	18.3
Sports/Recreational	11.13	17.53	4.82	5.88	2.3
Religious/Cultural	0.86	0.53	0.27	0.59	0.09
Agricultural/Farms/Izbba	5.77	2.48	1	1.42	1.01
Vacant (Developed Land)	38.64	46.50	47.41	36.42	27.58
Barren/Other Undeveloped Land	844.72	990.53	799.62	799.63	433.01
Total	1045.30	1197.21	973.82	944.09	508.79
Percentage of Total Area	8.85%	10.13%	8.24%	8%	4.31%

and Figure 8 demonstrate how Qatar’s coastal vulnerability varies by land use, with industrial zones (28.32 km²) and infrastructure (61.14 km²) dominating the ‘extremely vulnerable’ category, while residential areas (9.37 km²), vacant developed land (38.64 km²), and Roads/Streets/Flower Beds (38.64 km²) also face high exposure. The bathtub method, which assumes uniform inundation below sea-level thresholds, predicts larger flood extents (e.g., 990.53 km² for barren land under RCP8.5 vs. 799.62 km² under RCP4.5) compared to the hydrological connectivity method (799.63 km² under RCP8.5 vs. 433.01 km² under RCP4.5), which refines these estimates by accounting for drainage pathways, revealing more precise risks for populated and economically critical zones (e.g., 10.81 km² vs. 12.39 km² for residential areas under RCP8.5 and 3.45 km² vs. 8.93 km² for hydrological connectivity method and bathtub method, respectively). The hydrological connectivity method predicts zero inundation for certain land-use types (e.g., Health Services and Educational Services), while the bathtub method shows non-zero values. This discrepancy stems from fundamental methodological differences. For example, the bathtub method floods all areas below sea-level rise (SLR) thresholds, even if disconnected from the sea. In contrast, the hydrological connectivity method only inundates areas linked to the coast via drainage pathways (eight-direction rule). For example, a hospital in a depression may remain dry if roads, for example, isolate it from coastal flooding.

But for data resolution, while high-resolution DEMs (0.1 m RMSE) were used, small-scale infrastructure may not be fully captured, leading to underestimations in flooded areas in hydrologically complex urban areas.

This divergence highlights how the bathtub method’s simplicity offers a conservative risk screening for extreme vulnerability zones, while the hydrological connectivity method’s dynamic approach better identifies floodable areas by relying on physical flow paths, particularly in low-lying urban and industrial areas. In Figure 8, spatial zoning, derived from these methods combined with AHP-weighted criteria, visually reinforces that the most vulnerable areas align with both models’ predictions, but are more accurately delineated through hydrological analysis. Together, these results underscore the need for integrating both methods in coastal vulnerability assessments—the bathtub method to flag potential high-risk zones and the hydrological connectivity method to prioritize adaptation measures where flooding is hydrologically plausible.

4. Discussion

The study’s findings on coastal vulnerability in Qatar, particularly in the context of sea-level rise (SLR) and inundation risks, align with and expand upon previous research on coastal zone management and climate change adaptation. The integration of the bathtub method and hydrological connectivity method provided a nuanced understanding of flood risks, revealing that while the bathtub method offers a broad initial screening of vulnerable areas, the hydrological connectivity method refines these predictions by accounting for hydrological pathways, thereby reducing overestimations of flood extents. This dual-method approach corroborates the work of Williams and Lück–Vogel (2020) [14], who emphasized the importance of incorporating dynamic hydrological processes in flood modeling to improve accuracy.

Since the bathtub method’s simplifying assumption, which ignores hydrological connectivity, leads to an overestimation of flood risk in inland low-lying areas by treating all elevations below SLR thresholds as inundated, regardless of drainage pathways, this limitation is evident in our results. The bathtub method predicted 21.02% larger flood extents under RCP8.5 and 52.3% under RCP4.5 compared to the hydrological connectivity model, particularly misclassifying isolated depressions. However, hybrid approaches, such as coupling bathtub outputs with hydrodynamic models or machine learning, can address these limitations by dynamically simulating flow paths, tidal influences, and groundwater interactions. Such refinements are critical for accurate vulnerability mapping, ensuring that adaptation efforts target truly at-risk zones while avoiding unnecessary interventions in hydrologically disconnected areas. Future studies should integrate high-resolution terrain data and multi-hazard modeling to further enhance predictive precision for coastal planning. The vulnerability assessment, guided by the Analytic Hierarchy Process (AHP), highlighted elevation and proximity to the coastline as the most critical factors, consistent with studies by Sahin and Mohamed (2014) [26] and Arda et al. (2025) [50]. The extreme vulnerability of industrial zones and infrastructure in Qatar underscores the socioeconomic stakes of SLR, as these areas are vital to the nation’s economy. This finding echoes Dasgupta et al. (2007) [4], who warned of the disproportionate economic impacts of SLR on coastal regions, particularly in low- and middle-income nations.

The findings have broad implications for policy and planning. The identification of extremely vulnerable zones, such as industrial and residential areas, calls for prioritized adaptation measures, including engineered barriers, land use zoning, and ecosystem-based solutions like mangrove restoration. The study’s methodology can serve as a blueprint for other arid coastal regions facing similar threats, particularly in the Arabian Gulf, where rapid urbanization and climate change intersect.

The results also highlight the importance of integrating scientific assessments into national climate adaptation strategies. For Qatar, this could mean revising building codes, enhancing early warning systems, and investing in resilient infrastructure. Globally, the study reinforces the urgency of transboundary cooperation on SLR adaptation, as coastal vulnerabilities often transcend political boundaries.

This study advances the understanding of coastal vulnerability in Qatar by combining robust geospatial techniques with multi-criteria decision analysis. Its findings not only validate existing hypotheses about the risks posed by SLR, but also provide actionable insights for policy makers. As climate change accelerates, such interdisciplinary approaches will be critical in safeguarding coastal communities and ecosystems worldwide. Future research should build on this foundation to address emerging challenges and refine adaptation pathways.

5. Conclusions

This study comprehensively assessed the vulnerability of Qatar’s coastal zones to sea-level rise (SLR) and inundation risks by integrating the bathtub method, hydrological connectivity model, and Coastal Vulnerability Index (CVI), supported by high-resolution geospatial data and multi-criteria decision analysis. The bathtub method provided a broad initial screening, projecting inundation areas of 1197.21 km² (10.13% of Qatar) under the high-emission RCP8.5 scenario and 973.82 km² (8.24%) under the moderate RCP4.5 scenario, while the hydrological connectivity model refined these estimates to 944.09 km² and 508.79 km², respectively, by excluding hydrologically isolated depressions, thereby enhancing accuracy. The vulnerability assessment, guided by the Analytic Hierarchy Process (AHP), identified Elevation (35% weight) and Distance to the Coastline (25%) as the most critical factors, with Industrial Zones (28.32 km²) and Infrastructure (61.14 km²) emerging as extremely vulnerable due to their low-lying elevations and direct exposure to coastal flooding, as identified by the AHP-weighted vulnerability assessment (Table 3).

The findings highlight the urgent need for adaptive strategies such as engineered barriers, land use regulations, and ecosystem-based solutions like mangrove restoration, particularly in urban and industrial areas where exposure is highest. Despite the robustness of the methodology, limitations include the use of a 2017 DEM, which may not fully capture recent topographic changes, and the conservative weighting of socio-economic factors (8%), suggesting that future research should incorporate dynamic datasets (e.g., asset values) and hybrid modeling (e.g., bathtub + hydrodynamic models) to further improve precision.

The study’s integrated approach not only advances the scientific understanding of coastal vulnerability in arid regions, but also provides a scalable framework for policymakers to prioritize resilience-building measures, emphasizing the importance of emission mitigation and transboundary collaboration in addressing SLR impacts. By bridging theoretical rigor with practical applicability, this work contributes to global efforts in sustainable coastal management and climate adaptation, calling for continued innovation in modeling techniques and stakeholder engagement to address evolving environmental challenges.

6. Limitations and Future Directions

While this study provides a comprehensive assessment of coastal vulnerability in Qatar, several limitations warrant discussion.

The high-resolution DEM used in this study was derived from 2017 aerial surveys. While Qatar’s coastal topography exhibits relative stability over short-term periods (6–7 years), natural processes (e.g., sedimentation and erosion) and anthropogenic activities (e.g., land reclamation and coastal construction) may introduce localized elevation changes. These could marginally alter inundation predictions, particularly in dynamically managed zones like ports or urban expansions. Future studies could benefit from periodic DEM updates (e.g., LiDAR surveys every 3–5 years) to account for such changes, as recommended by Gesch et al. (2009) [27] for low-lying coastal regions.

The bathtub method’s simplifying assumption, ignoring hydrological connectivity, likely overestimates flood extents in inland low-lying areas (e.g., depressions disconnected from the coast), as evidenced by the 18–30% larger inundation areas compared to the hydrological connectivity method. This aligns with critiques by Williams and Lück-Vogel (2020) [14], who note that such static approaches may misrepresent actual flood pathways. Future studies could adopt hybrid modeling (e.g., coupling bathtub with hydrodynamic models) to better capture flow dynamics while retaining computational efficiency.

Socioeconomic parameters (e.g., population density) were assigned a conservative weight (8%) due to data constraints (e.g., lack of high-resolution asset value or infrastructure density datasets). This may underestimate vulnerability in highly urbanized zones, as highlighted by Pramanik et al. (2021) [39]. Integrating dynamic variables like land use change projections or critical infrastructure maps (e.g., power plants and hospitals) could refine the CVI’s sensitivity to human exposure.