Dash et al. (2025) Sedimentation in Saudi Arabia’s 574 reservoirs: Nationwide assessment using remote sensing and erosion modeling

Identification

• Journal: Journal of Environmental Management
• Year: 2025
• Date: 2025-09-16
• Authors: Sonam Sandeep Dash, Nikola Ivanović, Raied Saad Alharbi, Gregory Hancock, Yoshihide Wada, Matthew F. McCabe, Debasish Pal, Hannu Marttila, Hylke E. Beck
• DOI: 10.1016/j.jenvman.2025.127199

Research Groups

• Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
• Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
• Department of Civil Engineering, College of Engineering, King Saud University, Riyadh, Saudi Arabia
• School of Environmental and Life Sciences, University of Newcastle, Callaghan, NSW, Australia
• Water, Energy and Environmental Engineering Research Unit, University of Oulu, Oulu, Finland

Short Summary

This study presents the first nationwide assessment of sedimentation across 574 reservoirs in Saudi Arabia, combining long-term Landsat imagery (1986–2024) with erosion modeling. It reveals a median annual water extent decline of –1.5% per year and estimates a 32% reduction in total usable storage capacity, highlighting a critical threat to the nation's water security.

Objective

• To conduct the first nationwide assessment of reservoir sedimentation across 574 reservoirs in Saudi Arabia, quantifying the scope and drivers of satellite-derived trends in water extent from 1986 to 2024.

Study Configuration

• Spatial Scale: Nationwide assessment across 574 reservoirs in Saudi Arabia, focusing on 488 reservoirs with capacities greater than 0.1 million cubic meters.
• Temporal Scale: Long-term analysis from 1986 to 2024 (39 years).

Methodology and Data

• Models used:
  • Revised Universal Soil Loss Equation (RUSLE) for soil erosion and sediment yield.
  • Penman approach for open water evaporation (OWE) estimation.
  • Sen’s slope estimator for trend analysis.
  • Mann–Kendall test for statistical significance of trends.
  • Boyce (1974) equation for sediment delivery ratio (SDR).
  • Brune’s equation (Heinemarm, 1981) for sediment trapping efficiency (TE).
  • Empirical power-law level-storage relationship for in-situ volume estimation.
• Data sources:
  • Landsat imagery (Landsat 5, 7, 8, 9) from U.S. Geological Survey Level-2 Surface Reflectance product (1986–2024).
  • Normalized Difference Water Index (NDWI) for water extent.
  • ERA5 (0.25° resolution) for precipitation (1940–present) and Penman inputs (1986–2024).
  • IMERG-Final V7 (0.1° resolution) for precipitation (2000–present) and rainfall erosivity factor (R-factor).
  • Multi-Error-Removed Improved-Terrain (MERIT) Digital Elevation Model (DEM) (90 m resolution) for topographic factors and catchment delineation.
  • Soil-Grids250m 2.0 (250 m resolution) for soil erodibility factor (K-factor) inputs (texture, structure, permeability).
  • FAO-UNESCO Soil Map of the World for organic matter content (K-factor input).
  • ESA WorldCover 10 m v200 (10 m resolution) for cover-management factor (C-factor).
  • Reports from the Ministry of Environment, Water and Agriculture (MEWA) and Abderrahman (2006) for national water use data.
  • Global Lithological Map (GLiM) for surface geology.
  • Field observations and staff gauge measurements in the Wadi Baysh Basin (July 2024).

Main Results

• The median annual water extent decline across 488 analyzed reservoirs was –1.5% per year, with 63% (307 reservoirs) showing decreasing trends and 30% (147 reservoirs) exhibiting statistically significant reductions (p < 0.05).
• 26% (126 reservoirs) experienced at least five consecutive years of negligible water presence, indicating severe sediment accumulation.
• Climate variability (precipitation and open water evaporation trends) was not the dominant driver, showing weak and counter-expected spatial correlations with water extent trends.
• The median RUSLE-based reservoir infill time was estimated at 43 years, which reasonably aligned with satellite-inferred estimates (median 66 years, assuming a linear relationship between water extent and storage capacity).
• Siliciclastic sedimentary rocks showed the strongest negative spatial correlation (–0.38, p = 0.00001) with standardized water extent trends, suggesting their higher susceptibility to erosion influences sedimentation.
• In-situ measurements in the Wadi Baysh Basin indicated remaining storage capacities from 34% to 54% (mean 45%), which were consistent with RUSLE-based (mean 52%) and satellite-based (mean 26%) estimates.
• By 2024, the cumulative usable storage capacity of Saudi Arabia’s 574 reservoirs is estimated to have decreased by approximately 32% from their original design capacity of 2.58 billion cubic meters, reducing it to 1.77 billion cubic meters.

Contributions

• Provides the first comprehensive, national-scale assessment of reservoir sedimentation in Saudi Arabia, integrating long-term remote sensing data with erosion modeling and field validation.
• Quantifies the significant impact of sedimentation on national water storage capacity, estimating a 32% reduction in usable volume.
• Systematically evaluates the roles of climate variability, water use, and geological factors as drivers of reservoir water extent trends across an arid region.
• Identifies critical research and data gaps, offering actionable recommendations for sustainable reservoir and catchment management in Saudi Arabia, especially in light of planned dam construction.

Funding

• This research used Shaheen II managed by the Supercomputing Core Laboratory at KAUST for some data processing. No specific funding projects or reference codes were listed.

Citation

@article{Dash2025Sedimentation,
  author = {Dash, Sonam Sandeep and Ivanović, Nikola and Alharbi, Raied Saad and Hancock, Gregory and Wada, Yoshihide and McCabe, Matthew F. and Pal, Debasish and Marttila, Hannu and Beck, Hylke E.},
  title = {Sedimentation in Saudi Arabia’s 574 reservoirs: Nationwide assessment using remote sensing and erosion modeling},
  journal = {Journal of Environmental Management},
  year = {2025},
  doi = {10.1016/j.jenvman.2025.127199},
  url = {https://doi.org/10.1016/j.jenvman.2025.127199}
}