Sulaimani et al. (2026) Flood Hazard Assessment Under Subsidence-Influenced Terrain Using Deformation-Adjusted DEM in an Oil and Gas Field

Identification

• Journal: Hydrology
• Year: 2026
• Date: 2026-01-04
• Authors: Mohammed Al Sulaimani, Rifaat Abdalla, Mohammed El-Diasty, Amani Humaid Al Abri, Mohamed A. K. El-Ghali, Ahmed Tabook
• DOI: 10.3390/hydrology13010018

Research Groups

• Department of Earth Sciences, College of Science, Sultan Qaboos University, Muscat, Oman
• Department of Civil and Architectural Engineering, College of Engineering, Sultan Qaboos University, Muscat, Oman
• Geomatics Department, Exploration Directorate, Petroleum Development Oman, Muscat, Oman

Short Summary

This study developed a deformation-adjusted flood hazard assessment framework by integrating subsidence-corrected Digital Elevation Models (DEMs) with hydrological indicators in the Yibal oil field, Oman. It found that even moderate ground subsidence significantly alters hydrological susceptibility, redistributing flood-prone areas into newly formed depressions and emphasizing the need for dynamic terrain modeling in hazard assessments.

Objective

• To quantify how subsidence-induced terrain deformation modifies DEM-derived hydrological indicators and reshapes flood-susceptibility patterns in an arid oil-producing region.
• To develop a transferable methodological framework for deformation-adjusted flood hazard assessment by integrating pre- and post-subsidence terrain models with hydrological and geomorphic indicators.

Study Configuration

• Spatial Scale: Yibal field, Al Dhahirah Governorate, northern Oman. Data standardized to 30 m spatial resolution. Original DEM at 5 m, InSAR at 20–30 m, ERA5-Land at approximately 11 km, HYSOGs250m at 250 m.
• Temporal Scale: Analysis comparing 2013 (baseline terrain) and 2023 (subsidence-adjusted terrain). PS-InSAR observations spanned 2013–2023. Precipitation data covered 2010–2023.

Methodology and Data

• Models used:
  • Analytic Hierarchy Process (AHP) for multi-criteria flood hazard modeling.
  • Geomorphic Flood Index (GFI) for geomorphic diagnostic analysis.
  • Soil Conservation Service Curve Number (SCS-CN) concept for runoff estimation.
  • D8 algorithm for flow direction.
  • Weighted overlay procedure for hazard map generation.
• Data sources:
  • Elevation: 2013 photogrammetric Digital Elevation Model (DEM) from the National Survey and Geospatial Information Authority (NSGIA) of Oman.
  • Surface Deformation: Multi-temporal Persistent Scatterer-Interferometric Synthetic Aperture Radar (PS-InSAR) data from RADARSAT-2 (2013–2020) and TerraSAR-X (2020–2023).
  • Land Use/Land Cover (LULC): Sentinel-2 multispectral imagery (2023) from the European Space Agency.
  • Precipitation: ERA5-Land reanalysis product (2010–2023) from the Copernicus Climate Data Store.
  • Soil: HYSOGs250m global Hydrologic Soil Group dataset (2017) from the National Aeronautics and Space Administration (NASA) / U.S. Geological Survey.
  • Derived Hydrological/Geomorphic Indicators: Slope, Flow Direction, Flow Accumulation, Height Above Nearest Drainage (HAND), Floodplain Depth, Stream Proximity (flow-path-based distance), Curve Number (CN), Effective Precipitation.

Main Results

• The total area of high- and very-high-hazard zones changed only slightly (within ±6%) across most scenarios, but these zones spatially shifted into subsidence-affected depressions.
• Low-hazard zones significantly increased, particularly in Scenarios S2–S4, showing increases of 160–320% compared to 2013. Moderate-hazard areas exhibited smaller but consistent growth.
• Floodplain-dominated conditions (Scenario S5) resulted in the most pronounced nonlinear response, with a substantial increase in very low hazard and localized concentration of very high hazard in areas of deepest subsidence.
• Geomorphic analysis using the Geomorphic Flood Index (GFI) indicated a clear shift towards higher susceptibility, with High-GFI areas increasing by approximately 42 square kilometers between 2013 and 2023, reflecting deepening of flow pathways and expansion of geomorphic depressions.
• The findings demonstrate that even moderate subsidence can significantly alter hydrological susceptibility and redistribute flood-prone areas.

Contributions

• Developed a transferable methodological framework for deformation-adjusted flood hazard assessment, integrating subsidence-corrected DEMs with consistently recalculated hydrological indicators.
• Introduced a scenario-based multi-criteria Analytic Hierarchy Process (AHP) framework to evaluate flood susceptibility under varying hydrological dominance assumptions, enhancing robustness.
• Provided a dual-epoch analysis (2013 vs. 2023) to explicitly quantify the impact of subsidence-induced terrain changes on flood susceptibility patterns, addressing a significant research gap.
• Demonstrated the critical importance of incorporating dynamic landscape representation (subsidence data) into flood hazard assessments, particularly in petroleum fields and other subsidence-prone arid environments.

Funding

This research received no external funding. Sultan Qaboos University funded the Article Processing Charge (APC).

Citation

@article{Sulaimani2026Flood,
  author = {Sulaimani, Mohammed Al and Abdalla, Rifaat and El-Diasty, Mohammed and Abri, Amani Humaid Al and El-Ghali, Mohamed A. K. and Tabook, Ahmed},
  title = {Flood Hazard Assessment Under Subsidence-Influenced Terrain Using Deformation-Adjusted DEM in an Oil and Gas Field},
  journal = {Hydrology},
  year = {2026},
  doi = {10.3390/hydrology13010018},
  url = {https://doi.org/10.3390/hydrology13010018}
}