Introduction

Coastal cities are already being impacted by rising sea levels, alongside other natural-driven changes in the ocean and harmful effects from human activities on marine, coastal and terrestrial environments. Attributing these impacts directly to sea level rise (SLR) is complex, as they arise from climate-related factors and other influences like coastal infrastructure expansion and habitat degradation caused by human activities1. Coastal zones face numerous constant influences, including wave height and direction, water depth, wind speed, sediment dynamics, tidal range, and changes in relative sea level. Additionally, rainfall and the intensity of extreme weather events, such as storm surges, play crucial roles. The rapid SLR is expected to increase the occurrence and the intensity of marine storm surge events1,2,3. As sea levels rise, we can anticipate accelerated shoreline erosion, increased intensity of marine storms, and more frequent flooding, leading to significant changes in natural environments and the destruction of human infrastructure in coastal areas4,5.

The projected SLR scenario over the next centuries6 in the coastal communities are likely to face increasingly severe impacts as a result of rising sea levels. These risks will manifest across a range of timescales, from sporadic dangerous events to interannual and centennial shifts linked to climatic drivers’ variability7,8,9. Over time, the influence of exposure trends stemming from coastal migration, urbanization, and rising asset values gradually become less pronounced10. Coastal cities are growing more urbanized than inland areas, marked by accelerated population growth and urbanization rates. This trend is concentrating vital economic assets and critical infrastructure in these zones, heightening their exposure to risks such as climate hazards and resource pressures11.

Low elevation coastal zone (LECZ) is defined as a vulnerable area near the coast and less than 10 m of elevation12,13. By 2100, the projected population in low-lying coastal areas (below 10 m in elevation) is expected to increase by 85 to 239 million people14. Under the Shared Socioeconomic Pathways (SSP), the population living in these regions is anticipated to grow from 640 million in 2000 to between 700 million and over one billion by 205014,15. Considering population density and urbanization, by 2060, a global mean SLR of 21 cm could increase the population density on low-lying coastal zones (less than 10 m) from 189 M in 2000 to 316 M and 411 M in 2060. The most significant relative increases would be seen in the African coastal countries, while the largest absolute rises would occur in Asia (South and Southeast countries)16.

Coastal zones hold immense economic value, which means the losses from coastal inundation risks are significant17. Currently, Europe faces expected annual damages from coastal inundation of about 1.25 B €, but this amount can rise by 2 to 3 orders of magnitude if adaptation strategies are not improved10. Marine floods are among the four main climate-related hazards identified for Europe countries, affecting people, infrastructure, and the economy18. Furthermore, SLR and the increasing frequency and intensity of dangerous weather events leading to flooding and erosion could collectively devastate existing infrastructure and disrupt the supply, transportation, and storage processes of coastal energy systems19,20,21.

Energy security is vital for the functioning of modern societies22. Sustainable infrastructure and energy-resilient solutions are at the heart of the smart city model developed by23,24. The global energy infrastructure is located along coastlines, making it especially vulnerable to the effects of climate change, extreme marine events, and SLR. These factors pose significant risks to energy supply chains22,25. To safeguard coastal communities and the infrastructure essential for their survival, it is crucial to develop adaptive management tools and models to address these complexities26,27,28. Effective management strategies must be capable of monitoring and predicting a range of interrelated natural and socioeconomic drivers, such as SLR and storm surge events29, as well as trends in population and economic activity25. This will assist in ensuring the long-term sustainability of coastal energy infrastructure.

While previous research has examined the relationship between energy infrastructure and its environmental context21,30,31, including the vulnerability of industrial plants to floods and production effects due to extreme weather events, there has been limited analysis on the relationship between future SLR and coastal energy infrastructure in concentrated areas like the Qatar coast. The IPCC Sixth Assessment Report (AR6) illustrates the impact of the flood risks on infrastructure caused by climate change, especially in low-lying coastal arid areas, and advocates for region-specific resilience measures32. Around the world, research like Koks et al.33 used GIS-based multi-model frameworks to assess the flood exposure of energy assets, while Tellman et al.34 leveraged Sentinel-1 satellite data to identify vulnerabilities in infrastructure, showcasing the versatility of geospatial tools. Following similar strategies, a recent study of Al-Dosari35 in the Arabian region implemented high-resolution flood modeling for Qatar’s 2022 World Cup infrastructure, incorporating AR6 climate predictions to enhance resilience in the hydrocarbon and renewable energy sectors. Collectively, these studies emphasize the beneficial relationship between global frameworks and localized geospatial analyses in addressing flood risks to energy infrastructure amid changing climate conditions. Unlike prior studies offering general assessments of infrastructure vulnerability, this paper presents a high-resolution GIS-based flood analysis tailored specifically to Qatar’s energy infrastructure. By combining tide gauge data, Shared Socioeconomic Pathways (SSP) scenarios, and geospatial modeling, in this work, a data-driven framework is proposed to assess long-term SLR-related coastal flooding and its potential impact on disruptions in energy security. Specifically, this study aims to evaluate the impacts of future SLR on coastal residential power infrastructure under various fixed scenarios in Doha, Qatar. However, the objectives of this paper are:

  • To develop GIS-based coastal flooding models for IPCC future scenarios in 2030, 2050, and 2100.

  • To quantify the exposure of Qatar’s coastal residential infrastructure and energy networks’ vulnerability to SLR-induced flooding.

  • To examine the implications of SLR on urban resilience and energy security within the context of Qatar.

  • To assess the socioeconomic and environmental consequences of coastal flooding in the urban Qatari environment.

  • To develop policy recommendations for adaptive strategies that enhance the infrastructure for energy in terms of climate resilience.

Our research addresses critical gaps in climate resilience science by providing the first high-resolution, localized evaluation of flooding risks due to accelerated sea-level rise (SLR) and storm surge, affecting coastal energy infrastructure and communities in Qatar. By employing GIS and machine learning to project exposure scenarios for 2030, 2050, and 2100, we strengthen predictive models for assessing vulnerability specific to infrastructure, linking physical risks to socio-economic impacts. Furthermore, this research not only enhances global SLR risk modeling approaches but also introduces new adaptation strategies and policy regulations for safeguarding coastal infrastructure in rapidly developing arid and hyper-arid regions that have received less attention.

Study site

Doha, the capital city of Qatar, is situated on the central-eastern Qatari peninsula coast along the Persian Gulf, as illustrated in Fig. 1. The city’s geography, characterized by predominantly flat terrain and an average elevation of approximately 10 m above the coastline, makes it particularly vulnerable to SLR, coastal erosion, and extreme marine storm events36. A combination of natural geomorphological processes and extensive human modifications to the landscape exacerbates these vulnerabilities37. The coastal landscape includes low-lying sandy shores, tidal flats, and mudflats. The coastline features a mix of rocky outcrops and sandy beaches, but significant human-induced changes, such as land reclamation, have occurred38. Rapid urbanization and infrastructure development have affected natural processes like coastal erosion and sediment deposition, including extensive port and marina construction39.

Fig. 1
figure 1

Location and characteristics of the study of Doha’s coastal areas. This figure was generated using ArcGIS Pro 3.4.3 (https://www.esri.com/en-us/arcgis/products/arcgis-pro).

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Coastal erosion is a direct natural consequence of rising sea levels and climate change with a rate in Doha of − 2.94 m/y40; however, human activities have intensified its effect. The loss of natural coastal barriers such as mangroves, dunes, and wetlands exacerbates this issue. The degradation of these ecosystems diminishes the coastline’s ability to withstand rising tides, storm surges, and strong winds41. Qatar’s geology is predominantly composed of sedimentary rock formations, including limestone and dolomite, with many areas characterized by sand and gravel. These materials are highly susceptible to erosion, further accelerating land degradation when exposed to intensifying climate pressures.

Doha’s hyper-arid climate42 further compounds these challenges. The city receives an average annual precipitation of 7.7 cm, with maximum rainfall occurring between November and April43. In the southern Qatari Peninsula, air temperatures vary from 17 °C in winter and 36 °C in summer, while relative humidity varies from 32 and 65%44. During the summer months, average temperatures often exceed 40 °C, with daily maximums reaching up to 45 °C. The high humidity levels in coastal areas amplify the sensation of heat, creating a challenging living environment. Overall, rainfall is scarce, averaging only 76 mm annually, with most precipitation occurring during the cooler months from October to March45.

Qatar is characterized by the presence of two dominant wind directions: northwesterly “shamal” winds, which are intense in late spring and summer46. In the summer, these dominant winds are amplified by the southwest monsoons47. In the southern Qatari Peninsula, the measured wind speeds ranged from 2 to 4 m/s between 2016 and 2017, primarily generated from the N/NW. During storm events, wind speeds can go above 50 knots (25 m/s), predominantly from NW and associated with the arrival of extra-tropical cold fronts48. Qatar’s eastern coast is characterized by a mixed diurnal tide, with values ranging from 1.1 to 2.3 m49.

With the increase in intensity and frequency of extreme marine events, the combined effects of storm surges and high tides are expected to result in more severe coastal flooding, further threatening Doha’s built environment and infrastructure resilience. The interaction of these factors poses a significant challenge for coastal planning, necessitating immediate adaptation strategies to mitigate future risks. Doha’s rapid urban development and population growth have played a key role in shaping its coastal vulnerability. Since the discovery of oil, the country has witnessed exponential population increases, with its share rising sharply from 37.5% in 1959 to over 70% in subsequent decades50. Today, more than 90% of the nation’s residents are concentrated within Doha Municipality, marked by sprawling low-density suburbs that extend across a vast area. This urban model, averaging around 3,700 inhabitants per square kilometer, places significant pressure on resource efficiency, land use planning, and infrastructure capacity, fueling ongoing debates about the sustainability of the city’s expansive growth patterns51.

Furthermore, with rapid development, unplanned urbanization exacerbated conventional environment-related issues, such as increased run-off over the surface and reduced recharging of groundwater, and increased susceptibility of Doha to extreme weather impacts. The expansion of impervious surfaces and lack of adequate drainage infrastructure further exacerbate the effects of flooding, requiring immediate intervention through urban resilience improvement policies. Given the intersection of sea level rise-related vulnerabilities, urban development pressures, and Doha’s coastal location, it is critical to implement resilient urban planning strategies in an attempt to mitigate sea level rise and extreme weather-related effects on the city’s infrastructure and larger urban environment.

Methods

Future SLR projections

The AR6 IPCC Sea Level Projection Tool, provided in Box TS.4 and Sect. 9.6 of the IPCC Working Group 1 contribution to the Sixth Assessment Report, is based on processes with at least standard confidence and compared to the 1995–2014 period. These projections cover five Shared Socioeconomic Pathway (SSP) forecasting scenarios as illustrated in Fig. 2. The scenarios include SSP1-1.9, which limits heating to about 1.5 °C (1850–1900) with a small overshoot and carbon neutrality by the middle of the century; SSP1-2.6, which stays under 2 °C archiving net zero emissions by the second half of the century; SSP2-4.5, aligning with the upper range of current Nationally Determined Contributions and projecting approximately 2.7 °C warming; SSP3-7.0, an average-extreme reference projection reflecting no additional environment strategy and elevated non-CO₂ emissions; and SSP5-8.5, a high-emission scenario unique to a fossil-fueled development pathway with no additional climate policy52.

Fig. 2
figure 2

Future SLR Scenarios for the Arabian Gulf according to AR6 Sea Level Projection Tool (Adapted from52). This figure was generated using Adobe Illustrator CC 18 (Vector Graphics Software—Adobe Illustrator).

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SSP3 was chosen as the objective scenario in this study because it represents a real forecasting scenario characterized by regional rivalry, limited economic growth, and minimal international cooperation situations that lead to higher climate-altering emissions and reduced capacities for effective adaptation. This scenario offers a plausible "worst-case" context, which is essential for measuring the potential effects of SLR on coastal hazards under challenging socio-economic conditions and for developing robust strategies for risk management and adaptation52. This pathway also faces barriers to trade and slow technological advancements. Compared to other scenarios, SSP3 presents significant challenges for both mitigation and adaptation53. A timeline was selected from The AR6 Sea Level Projection Tool, and the SLR was projected for 3 periods: 2030, 2050, and 2100, as mentioned in Fig. 2.

Digital elevation model

The elevation data points were collected from the Center for Geographic Information Systems (CGIS), Ministry of Municipality and Environment (MME). The Digital Elevation Model (DEM) was established from LIDAR data. As a result, this elevation model features data points with a horizontal pixel count of 10 m and a vertical precision of 10 cm. The accuracy of the LIDAR DEM can be evaluated using the root mean square error (RMSE), which serves as a general measure of uncertainty54. In our study, the observed RMSE is below 0.15 pixels, varying between 0.05 and 0.15 pixels, corresponding to an uncertainty margin of ± 0.15 m.

Total inundation level

To evaluate the frequency and intensity of episodic coastal inundation, it is crucial first to determine sea levels during extreme storm events. The resulting inundation levels are generally composed of three main variables: tide (Mean Higher High Water) (\({\beta }_{t}\)), storm surge (αt), and regional sea level rise (SLRt) projections based on SSP3 scenarios for 2030, 2050, and 2100, as detailed in Table 1. The Total Inundation Level (m) across the three future timeframes within the SSP climate scenario combines various factors contributing to coastal flooding. Firstly, the Mean Higher High Water (MHHW) indicates the baseline average highest tide level. Secondly, the Sea Level Rise (SLR) figures signify anticipated increases due to climate change under SSP3. Finally, the storm surge heights consider temporary water level rises caused by extreme weather events (Table 1).

Table 1 Inundation level (m) in Doha based on Future SSP3 SLR scenarios, Mean Higher High, Storm Surge10,36,51,54.
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Accurately predicting future coastal flooding needs a systematic estimation of the magnitude of these physical processes, their temporal interactions, and the likelihood of extreme events. Recent studies have explored these aspects, with varying levels of validation against observed data10,55,56). In line with these global-scale studies, the total inundation level (TIL) is assumed to be reasonably approximated by a linear summation, as described in Eq. 1.

$${\text{TIL}} = \beta_{t} + \, \alpha_{{\text{t}}} + {\text{ SLR}}_{{\text{t}}}$$
(1)

The total inundation level (TIL) is determined by the observed tidal data (βt) measured in meters, which depend on time (t). Additionally, αt represents the forecasted storm surge, a function of time (t), while SLRt denotes the projected sea level rise at (t) years (Table 1). Historical TIL estimates have been thoroughly validated using extensive global tide gauge records and statistical extreme value analysis at the Doha station. For our study area, the Mean Higher High.

Water levels have been calculated from tide observation stations from 2003 to 2015 and were analyzed over the National Tidal Datum Epoch36. Coastal storm surge time series for the period 1979–2014 were derived from the GTSR (Global Tide and Surge Reanalysis) dataset and generated using the Global Tide and Surge Model (GTSM)49,57). For the Arabian Gulf the GTSM model has been validated from tide stations observations, and the representation is generally a good agreement between modelled and observed storm surge. The accuracy of the model, based on a temporal resolution of 10 min, is performed in 80% of the stations57.

Mapping the delineation of this inundation surface and the extraction of data from the global database are conducted using spatial analysis functions of Geographic Information System (ArcGIS Pro) with the Spatial Analyst extension. This process depends on three main inputs: a raster DEM that represents elevation values, tidal water levels, and a numerical SLR value indicating the expected rise in sea levels above the shoreline. Additionally, raster-to-vector conversion can extract inundation polygons, facilitating vector-based spatial analysis and visualization. This process typically begins with a high-resolution raster dataset representing modelled inundation extents, where each cell indicates the presence or absence of flooding based on a specified threshold. Once the inundated areas are delineated, machine learning neural network algorithms are applied to identify contiguous flooded regions as illustrated in Fig. 3. Training and testing flood level models involved using various ML algorithms. KNN classifies data points by the majority of their k-nearest neighbors in the feature space. This makes it a straightforward yet powerful method for non-linear and multi-class classification challenges. A supervised binary classification approach was employed. Initially, the dataset was divided into 95% (950 samples) for training the ML model and 68% (680 samples) for testing, utilizing ArcGIS Pro.

Fig. 3
figure 3

The model structure applied to assess the coastal inundation level for different SLR projections. This figure was generated using Adobe Illustrator CC 18 (Vector Graphics Software—Adobe Illustrator).

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Energy infrastructure assessment

This study focuses on residential energy infrastructure as the critical economic sub-sector. For each increment in inundation level, a GIS mapping tool was used to assess coastal inundation level maps with the locations of existing electric stations, providing insight into the residential energy infrastructure at risk from storm surges and SLR. These facilities were selected due to their high concentration, reliance on specific energy sources, and crucial role in the energy supply chain. Data on residential power units, obtained from KAHRAMAA, includes 1,000 buildings of various types, such as commercial, residential, and retail structures, as shown in Fig. 1. The location of each residential energy infrastructure was analyzed under the projected inundation levels for 2030 (2 m), 2050 (3 m), and 2100 (5 m).

Accounting for uncertainty in inundation assessments

Recognizing uncertainty is crucial in coastal flooding assessment, particularly when evaluating SLR vertical accuracy and coastal inundation studies. Extensive research has addressed uncertainty in geospatial data, with a strong focus on digital elevation models (DEM) and related elevation datasets58,59,60.

Minimum sea level rise increase

In coastal inundation assessments, the increase in sea level from its existing rise to a forecasted elevation in the future is similar to the contour interval (CI). In the future, this increase will surpass the inherent vertical error of the LIDAR Digital Elevation Model (DEM). The confidence interval indicates the degree of certainty that the actual extent of the impact zone lies within the specified range.

To improve the accuracy of elevation contour lines, Gesch61 developed a method to determine the minimum inundation level increase required to achieve a specific confidence level62. This approach, known as the Vertical Map Precision (VMP) at a 90% certainty or “Proportional Error” (PE90%), assesses DEM elevation errors using 10 references observed elevation points located within a range of the CI. The standard error for contours is defined by Eq. 2:

$${\text{VMP}} = {\text{PE}}90{\text{\% }} = \frac{{{\text{CI}}}}{2}$$
(2)

To clarify, we can rearrange Eq. 3 as follows:

$${\text{CI}} = {\text{PE}}90{\text{\% }} \times 2$$
(3)

The CI is calculated using the precision of the elevation data points63. Two commonly used metrics for assessing errors in DEM are the RMSE and LE95%, which indicate potential error at a 95% confidence level. When errors follow an unbiased normal distribution, RMSE can be converted into PE90% and PE95%63. TheDEM of Qatar is obtained from aerial lidar data provided by the Ministry of Municipality of Qatar with an RMSE of ± 0.15 m. This error is normally distributed and can be converted to Error Level (EL) at the 95% confidence level (EL95%) using a specific formula, as described in Eq. 463.

$${\text{EL}}_{{{95}\% }} = {\text{ RMSE}} \times {1}.{96}$$
(4)

The calculated EL95 value is 0.29 m. Using the outlined procedure, the minimum sea-level rise increment (SLRImin) is 0.58 m and 0.3 m at a 95% and 68% confidence level, respectively.

$${\text{SLRI}}_{{{\text{min95}}\% }} = \, \left( {{\text{RMSE }} \times {1}.{96}} \right) \, \times { 2}$$
(5)

SLRImin indicates the reliability of the CL’s vertical placement, which defines zones with elevations equal to or lower than the forecasted sea level. For instance, 95% of the time, the CL is expected to be located within ± 0.15 m of its real position. Since SLRImin is associated with the DEM’s accuracy, models with lower accuracy will need larger water level increments to maintain the same level of reliability.

Cumulative vertical uncertainty

An important assessment consideration is the planning horizon, which concerns the time interval used to project rising water levels and identify potential risk zones. The SLRImin and TLmin (minimum planning timeline) are directly linked to the vertical uncertainty of the DEM input on GIS software. These factors also influence the projected rate of SLR over the relevant period. Assuming a linear change of SLR, the TLmin can be determined using Eq. 6:

$${\text{TL}}_{{{\text{min}}}} = \frac{{{\text{SLRI}}_{{{\text{min}}}} }}{{{\text{ SLR }}\left( {{\text{Annual}}\;{\text{Rate}}} \right)}}$$
(6)

For our case study, the maximum and minimum values of global SLR scenarios were obtained from the AR6 Assessment Report established by the Intergovernmental Panel on Climate Change (IPCC), ranging from 0.28 to 1.01 m by 210032. These reports represent the annual SLR between 2000 and 2100, yielding annual increments of 2.8 mm/year for the minimum scenario and 10.1 mm/year for the maximum scenario. Given a Digital Elevation Model with an RMSE of 0.15 m, the minimum timeline (TLmin) for the lowest projected SLR extends over 107 years and 50 years under an extreme forecasting scenario. In the low scenario, projecting a potential damage area for any year before 2107. Conversely, in the high projected scenario, assessing a coastal inundation zone would be valid beyond 2050. While this example assumes a linear rise of sea levels, TLmin could be determined for non-linear trends. Similar to SLRImin, TLmin is associated with a confidence level, as it depends on the DEM accuracy. For instance, using SLRImin95% to determine the minimum planning timeline results in TLmin95%, ensuring a 95% confidence level.

TLmin and SLRImin are indispensable frameworks for guiding parameter selection in evaluative processes, particularly within managerial contexts64. These metrics enable practitioners to define actionable increments and timeframes for reliable projections when applied to a DEM of verified precision. Alternatively, by predefining objectives for incremental thresholds, temporal scopes, and SLR projections, these tools facilitate calibrating elevation data standards required to achieve predetermined confidence benchmarks.

Results

Inundation risk assessment

Coastal inundation analysis shows that 6.6 km2 of the Doha coast is vulnerable to flooding due to a 2 m inundation surface, as illustrated in Fig. 4a. This represents a 5% increase in the land surface that will fall within the coastal risk zone by 2030 under the SPP3 SLR scenario. By 2050, the inundation area is projected to expand by an additional 52.8 km2 at a 3 m inundation level, affecting 40% of the total area of Doha Municipality, as shown in Fig. 4b. At a 5 m inundation level, the area at risk will increase by 79 km2, which corresponds to 60% of the municipality. The analysis recognizes land surface with a high possibility of inundation from those with lower certainty, aiding in the identification of at-risk facilities, as shown in Fig. 4c.

Fig. 4
figure 4

Illustrative inundation levels at a: 2 m (2030); b: 3 m (2050) and c: 5 m (2100) with affected residential power units and population. This figure was generated using ArcGIS Pro 3.4.3 (https://www.esri.com/en-us/arcgis/products/arcgis-pro).

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Potential effects on electric stations

The potential risk of inundation and flooding map, as mentioned in Fig. 4a, indicates that by 2030, only 5% of the generation power will be located directly in the risk zone associated with a 2 m inundation level. However, by 2050, as inundations rise to 3 m, a larger proportion of residential power units will be within the 1 km to 5 km risk zones, compared to lower SLR projections, as shown in Fig. 4b. It is estimated that 20% of residential power units will be at risk due to their proximity to the receding shoreline. Figure 4c shows that by 2100, 39% of residential power units in Doha are projected to be within the risk zone of a 5 m inundation level.

Projected population displacement and infrastructure vulnerability due to SLR

The population data have been obtained from the Planning and Statistics Authority of Qatar. Flood risk assessment for Doha’s coastline reveals alarming population exposure trends. By 2030, approximately 78,000 residents (5% of Doha’s population) will reside in high-risk inundation zones, as mentioned in Fig. 4a. This exposure is anticipated to increase significantly by 2050, with the affected population projected to surge by 8.5%, disproportionately impacting critical infrastructure clusters, including energy facilities, transportation hubs, and densely populated residential districts, as announced in Fig. 4b. By 2100, the compounded effects of accelerated SLR and intensifying storm surges could result in the displacement or severe disruption of livelihoods for over 1.87 million people, representing more than half of Qatar’s current population, as presented in Fig. 4c.

Validation of the inundation assessment

Table 2 illustrates the results from the direct comparison between the SRTM anglobal DEMs and the local LIDAR DEM. The results provide context for the effectiveness of global DEMs in coastal inundation assessments, showing that the local LIDAR DEM of Qatar offers a very high level of accuracy when compared to the global DEMs. The derived variables SLRImin and TLmin have been calculated to confirm the accuracy of the flood model. These two parameters are presented at the 68% and 95% confidence levels. In this case study, the mean error of the Qtari DEM is 0.05 m. The findings in Table 2 reveal that only high spatial resolution (10 m), high-accuracy LIDAR DEM can achieve an SLRImin of less than 1 m and a TLmin of under 100 years at high confidence levels, whereas global DEMs are inadequate for supporting such parameters. The SRTM and ASTER global DEMs are no longer suitable for detecting inundation levels at the 95% confidence level.

Table 2 DEM accuracy and corresponding SLRImin for coastal areas.
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Discussion

Economic costs of SLR

The future increase of coastal flooding is specifically concerning for populated cities in the Arabian Gulf. This region is vulnerable, yet metropolitan areas are growing at a pace several times faster than the global average65,66. With ongoing coastal urban expansion, decision-makers strive for greater development and prosperity. Consequently, effectively addressing the interconnected challenges of urban expansion, climate change, and inundation risk within these areas is crucial. Most urban development in the Arabian Gulf occurs along coastal areas, making these regions especially exposed to the effects of SLR and other climate and non-climate drivers12,67. By 2100, 200–630 million people globally could face annual flooding under high SLR scenarios68. In the high-emissions SSP5-8.5 scenario, Qatar is expected to experience considerable sea level rise (SLR) impacts, with estimates suggesting a global SLR of 0.6 to 2 m by 210032. This situation is worsened locally by marine storms, land subsidence, and rapid warming in the Persian Gulf10. Such changes threaten essential coastal infrastructure, including LNG terminals like Ras Laffan, which accounts for about 70% of GDP, and desalination plants that supply 99% of the region’s freshwater needs. Additionally, SSP5-8.5 heightens social inequities, placing migrant workers in low-lying coastal areas at greater risk of flooding, while adaptive capacity remains uneven12.

Unless proactive measures for coastal inundation and SLR control and resilience are implemented, the environmental costs associated with urban expansion and global climate change will escalate, potentially leading to critical tipping points within the region’s physical systems65,66. For example, in San Diego, California, the economic impact on customers from unserved energy due to service disruptions caused by exposed substations could exceed $300,000 under a 2-m sea-level rise scenario with periodic tidal flooding. In extreme cases, this could escalate to approximately $25 billion69.

Globally, under AR4 sea-level rise (SLR) scenarios and without accounting for adaptation efforts, studies estimate significant coastal impacts. Hinkel et al.70 project that 6,000 -17,000 km2 of land surface might be submerged by 2100 due to accelerated coastal erosion driven by SLR and other climatic and non-climatic variables. This may induce the migration of 1.6 to 5.3 M individuals with related increasing costs between 300 and $1000 $B. The total cost of constant coastal inundation for 1 m of SLR is estimated to be 193.8 $B71. The total inundation damage costs to coastal infrastructure under projected SLR (+ 1.34 m) were estimated to be 11 $B72. In Oman, the total coastal flooding damage due to the severe tropical storm Gonu is estimated at 4 $B and 216 $M for Iran73.

The adaptation goals for mitigating projected coastal flooding risk within essential actions have been examined, and a benefit–cost analysis has been conducted at the sub-national scale worldwide74. Additionally, adaptation costs have been allocated to various flood risk drivers, including SLR, marine storms, socioeconomic changes, and optimization for current conditions. Hallegatte et al.75 assessed future flood risk for 136 major coastal cities, estimating expected annual damages at 6 $B. In contrast, Tiggeloven et al.74 reported a higher estimate of 19.6 $B, with projections indicating an increase to 84 $B by 2050. Without adaptation measures, Hallegatte et al.75 estimates that expected annual damages could exceed 1 $T by 2050.

Inundation level and SLR at the regional scale

The densely populated, low-lying coastal Gulf cities are at extreme risk of SLR due to direct flooding. The assessment of inundation levels in the southern Arabian Gulf regions highlights the significant threat that sea level rise poses to Bahrain, Qatar, and the United Arab Emirates (UAE). By 2030, it is projected that 10% of Bahrain’s land, 5% of Qatar’s surface area, and 6% of the UAE’s territory will be submerged, as shown in Figs. 5a and 6a. The situation will deteriorate further by 2050, with Qatar losing 10% of its land (115,710 ha), Bahrain losing 15% (11,850 ha), and the UAE losing 10% (9,864,800 ha), as mentioned in Figs. 5b and 6a. By 2100, the anticipated inundation will be even more severe, with 30% of Bahrain’s territory, 20% of Qatar’s land, and 19% of the UAE’s surface area expected to be submerged, as mapped in Figs. 5c and 6a. This progressive trend underscores a growing regional crisis, threatening critical infrastructure, economic centers, and residential areas. The potential losses may lead to mass displacement, disruption of coastal ecosystems, and increased economic vulnerability in key industries such as tourism and energy. The regional flood risk assessment for Qatar’s coastline reveals significant population exposure to rising sea levels. By 2030, around 4% of the population will be in high-risk inundation zones, increasing to 6% by 2050 and 14% by 2100, as shown in Fig. 6b. Similarly, in Bahrain, the proportion of the population vulnerable to accelerated SLR is expected to reach 4% in 2030, 5% in 2050, and 11% in 2100. In the UAE, the combined impacts of accelerated SLR and intensifying storm surges could lead to displacement or severe disruption of livelihoods for more than 16% of the total population by 2100.

Fig. 5
figure 5

Coastal inundation map under SLR scenario in the southeastern Arabian countries by 2030 (a), 2050 (b), and 2100 (c). This figure was generated using ArcGIS Pro 3.4.3 (https://www.esri.com/en-us/arcgis/products/arcgis-pro).

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Fig. 6
figure 6

Submerged land surface (a) and the total affected population (b) under the SLR scenario in the southeastern Arabian countries. This figure was generated using Adobe Illustrator CC 18 (Vector Graphics Software—Adobe Illustrator).

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Impact of SLR on energy infrastructure in Qatar

Coastal communities in the Gulf rely heavily on energy infrastructure, making adaptive measures critical to improving resilience against climate-driven threats such as sea level rise (SLR). Increasingly frequent hurricane storm surges and coastal flooding pose escalating risks to vital facilities. Regional and local governments are adopting more coordinated strategies to modernize vulnerable infrastructure and integrate SLR adaptation into long-term planning frameworks to mitigate these challenges.

In Qatar, SLR has become a primary government concern due to densely populated coastal areas being vulnerable to flooding, especially during extreme marine events. The country’s natural resources, coastal infrastructure, and social systems face significant risks from climate change impacts. These vulnerabilities extend to critical coastal and offshore infrastructure, including electric power and water cogeneration plants and petroleum stations76. Qatar is also among the largest exporters of liquefied natural gas in the world and is rich in natural gas supplies, which can be extracted with minimal costs. The petroleum sector has significantly contributed to the country’s socioeconomic growth, accounting for 44% of the national GDP with a gain of $237B in 202277.

Electricity demand in Qatar is projected to rise dramatically due to economic growth and demographic changes. Recent integrated renewable energy assessments have incorporated climate-driven impacts on projected energy demand growth in Qatar, evaluating feasibility and performance under evolving environmental conditions78. According to the Qatar National Renewable Energy Strategy (QNRES) projections, electricity demand is expected to increase from approximately 51 terawatt-hours (TWh) in 2021 to about 80 TWh by 2040. However, considerable growth in energy demand underscores the need for a stable and efficient coastal energy infrastructure to satisfy the country’s growing demands. The inundation analysis reveals that approximately 20% of Qatar’s coastline is vulnerable to land surface inundation under the SSP3 climate scenario. This forecasting poses a significant threat to coastal infrastructure, natural ecosystems, and urban cities as rising sea levels and extreme marine events are becoming increasingly frequent over time.

Furthermore, the analysis indicates that by the year 2100, all power generation facilities in Qatar will be vulnerable to rising sea levels and potential flooding, as mentioned in Fig. 7. This presents a severe challenge to national energy security and economic stability. Given Qatar’s reliance on coastal infrastructure for power generation, urgent measures are needed to enhance resilience. The vulnerability of energy facilities emphasizes the urgent need to implement resilience measures, such as relocating key infrastructure, investing in renewable energy sources, and enhancing disaster preparedness to ensure an uninterrupted power supply in the face of climatic drivers.

Fig. 7
figure 7

(Adapted from KAHRAMAA77). This figure was generated using ArcGIS Pro 3.4.3 ( https://www.esri.com/en-us/arcgis/products/arcgis-pro).

Qatar’s energy infrastructure under future SLR inundation (2100)

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Impact of SLR on other coastal infrastructure sectors in Qatar

Coastal infrastructure in Qatar is vital to the well-being of local communities. Residents rely on an interconnected network of roads, rail corridors, and bike and pedestrian paths for daily transportation, while essential water systems supply drinking water, manage wastewater, and control stormwater drainage. Damage to any part of these systems can compromise public safety, disrupt everyday activities, impact properties even those outside traditional flood zones, and trigger far-reaching economic consequences across the country (Fig. 8). Understanding the critical role of this infrastructure is key to navigating the challenges of maintaining and adapting it for future use.

Fig. 8
figure 8

Qatar’s coastal infrastructure under future SLR inundation (2100). This figure was generated using ArcGIS Pro 3.4.3 ( https://www.esri.com/en-us/arcgis/products/arcgis-pro).

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Similarly, if water infrastructure vulnerabilities remain unaddressed, service disruptions could severely impact daily life. A power outage or damage to key components of the wastewater or drinking water system might halt the collection, pumping, processing, or treatment of water and sewage for hours, days, or even weeks, depending on the extent of the damage. Notably, flooding at a wastewater treatment plant can have consequences far beyond the immediate area. Hummel et al.79 suggest that the number of people affected by a loss of wastewater services due to SLR could be up to five times higher than the estimates of those experiencing direct flooding. Moreover, damage to water infrastructure can also pose significant public health risks, not only by disrupting services but also by mobilizing pollutants.

In Qatar, port, natural green park, and railways are closely intertwined with land use patterns, serving both to support and to promote development in specific areas (Fig. 8). Many of these coastal infrastructures have been established for decades, deeply embedding them into long-standing networks of infrastructure and land use. Consequently, implementing successful sea-level rise adaptation strategies often require proactive regional transportation planning, thoughtful policymaking, and the initiation of targeted projects.

Resilience and adaptation strategies for Qatar

Coastal flooding driven by SLR represents a critical and escalating threat to Qatar’s low-lying coastal regions, carrying cascading risks to human populations, infrastructure, and socio-economic stability. Under the SSP3 scenario, global mean sea levels are estimated to increase by 1.5 m by 2100, exacerbating vulnerabilities in densely populated coastal cities like Doha54. This escalation will amplify exposure to permanent land submergence, recurrent episodic flooding, and storm surge events, all of which will have profound consequences for urban communities and economic stability.

To address the high risks associated with long-term SLR, the Ministry of Environment and Climate Change and the Ministry of Municipality of Qatar must propose several initiatives under the 2030 Vision. Firstly, it is crucial to revise the Qatar National Spatial Development Plan to establish a Vulnerable Coastal Zone (VCZ) that prohibits new coastal infrastructure in low-lying areas. Secondly, improving coastal infrastructure resilience, planning, and regulation, a new policy should be created to mandate coastal flood risk assessments and implement flood accommodation measures. Third, authorities must evaluate the cost-effectiveness of coastal protection strategies and consider strategic retreat options. The coast should be divided into management units to facilitate targeted solutions. Finally, a strategic retreat framework should be developed to assist in relocating assets from flood-prone areas, which may include land acquisition and leasehold strategies to minimize future costs. Furthermore, it is crucial to require Emergency Management Plans (EMP) for development in high-risk zones to enhance prevention, preparedness, and response efforts.

To improve coastal resilience against flooding and reduce future risks, we recommend implementing nature-based solutions to dissipate wave energy and mitigate storm surges80,81 in conjunction with effective catchment management and arranging reactions82,83. These solutions include restoring and protecting wetlands, tidal marshes, seagrass ecosystems, and coral habitats, natural buffers against coastal flooding84,85. Restoring dunes and wetlands can help absorb excess water, decrease erosion, and enhance biodiversity. Integrating these nature-based approaches with traditional engineering methods can create a more sustainable and adaptive coastal protection strategy, ensuring long-term resilience against rising sea levels and extreme marine events. Haloxylon persicum Zygophyllum qatarense, heat and salt tolerant species, can be used for the Qatar coast. These species thrive in extreme temperatures (up to 50 °C) and saline soils, anchoring sand and reducing the impact of SLR and erosion. By integrating these strategies, Qatar can future-proof its coastal energy infrastructure, sustain its hydrocarbon-driven economy, and lead Gulf Cooperation Council (GCC) climate adaptation efforts.

Limitations and future work

Assessing the flooding level using DEM data with a spatial resolution of 10 m faces challenges, which limits the ability to resolve fine scale topographic features smaller than a pixel. This limitation affects the accuracy of flood model extent and depth estimations in each pixel, especially in low-lying developed areas where minor variation of the topography influence water flow. The approach used in this study offers an initial, practical way to estimate which existing coastal facilities could face higher inundation risks in the future due to SLR. By focusing on varying hurricane intensities, this method helps identify which energy infrastructure might be most vulnerable. However, it doesn’t account for several critical factors. For instance, it doesn’t consider how likely hurricanes are to hit specific areas, how deep flooding might get, or how much damage facilities could sustain, whether from water, wind, or other storm-related forces. The analysis also overlooks factors that might either lower or heighten risks. For example, facility owners might already be taking steps like building protective barriers, raising equipment, or planning future upgrades to reduce vulnerability. This means actual risks for the facilities flagged as “exposed” could vary widely.

Rising sea levels will almost certainly leave coastal infrastructure more susceptible to storm surges. Petroleum facilities, in particular, seem disproportionately at risk, largely because many are clustered along Qatar’s coastlines and in the Persian Gulf, where their proximity to the shore increases exposure. The real-world impact will hinge on the specifics of future storms and the design of the infrastructure they strike. Still, on balance, SLR is expected to amplify these threats. More research is essential to better grasp what this means for energy resilience. Key questions remain: How severely would expose facilities be damaged? How might energy infrastructure evolve in the coming decades through upgrades, relocations, or new protective measures? How would disruptions to these facilities affect energy supplies, and for how long? Answering these questions will require detailed modeling of energy systems and a deeper analysis of how vulnerabilities translate into real-world consequences. Only then can we design strategies to safeguard critical infrastructure and ensure reliable energy access in a changing climate. For future efforts, we recommend enhancing stakeholder engagement in the resilience of coastal energy infrastructure by integrating participatory frameworks tailored to regional strategies. This approach empowers local communities and industries in Qatar. Canu et al.86. highlights the importance of establishing science-policy interfaces to incorporate IPCC AR6 SLR projections into governance practices. By embracing these methods, focusing on equity, and utilizing widespread global knowledge sharing on climate change, Qatar can strengthen its adaptive capacity, safeguard critical assets, and promote inclusive resilience within its vulnerable coastal energy infrastructure.

Conclusion

This study evaluates the risks of coastal inundation posed by sea-level rise (SLR) to critical energy infrastructure along the Qatar Coast, which exhibits unusually high SLR rates. The GIS assessment indicates that residential power units and power generation facilities, often located near the coastline, are disproportionately exposed to inundation driven by SLR. Under projected SLR scenarios, a significant portion of critical infrastructure will be affected, with 39% of residential power units in Doha and 60% of their land surface likely to be situated within future inundation zones. This will increase their vulnerability to storm surge propagation, even at moderate SLR levels. By 2030, more than 78,000 residents, comprising 5% of Doha’s population, will inhabit high-risk inundation zones. This exposure is expected to rise dramatically by 2050, with the number of affected individuals anticipated to increase by 8.5%. This surge will disproportionately affect key infrastructure, such as energy facilities, transportation hubs, and crowded residential areas. By 2100, an estimated 1.87 million residents could face displacement or significant disruption to their livelihoods due to accelerated sea level rise and increasingly intense storm surges.

The GIS assessment for the southern Arabian Gulf also reveals a significant threat due to the inundation level. By 2030, projections indicate that 10% of Bahrain’s land, 5% of Qatar’s area, and 6% of the UAE’s coastal territory could be submerged. The situation worsens by 2050, with expected losses of 11,850 ha for Bahrain, 115,710 ha for Qatar, and 9,864,800 ha for the UAE. Looking further ahead to 2100, the inundation is projected to reach 30% for Bahrain, 20% for Qatar, and 19% for the UAE low-lying coasts. These findings underscore the potential for SLR to disrupt interconnected energy systems, leading to cascading consequences for national energy security. The concentration of infrastructure in high-risk zones, along with accelerating SLR rates, highlights the urgent need to integrate climate adaptation into energy sector planning in Qatar. The increased exposure of Qatar’s infrastructure for energy to SLR-related marine hazards underlines the urgency for a comprehensive adaptation framework. Our study highlights the key factors driving coastal inundation in the western Arabian Gulf and presents targeted mitigation strategies rooted in Nature-Based Solutions (NBS) include restoring and protecting wetlands, tidal marshes, seagrass ecosystems, and coral habitats. Integrating these solutions into coastal management policies offers a sustainable, cost-effective means to protect vulnerable areas, safeguard energy infrastructure, and support local communities. Future research should expand upon this work by quantifying the economic and operational impacts of SLR on energy networks and evaluating the efficacy of proposed resilience measures under dynamic climatic conditions.