Optimization of Sediment Management in Dam Reservoirs: A Comprehensive Study of Dohuk Dam, Iraq

Abstract

The long-term viability of water infrastructure throughout the world is seriously threatened by reservoir sedimentation; the Dohuk Dam in northern Iraq is already losing a significant amount of storage capacity as a result of deposited sediments.  Sedimentation compromises downstream ecosystem integrity, water supply dependability, and hydropower production in addition to decreasing reservoir volume.  Traditional mitigating techniques are still dispersed and inadequate, especially in semi-arid areas where land-use demands and climate change are intensifying.  The study presents a thorough methodology for assessing and improving sediment management choices under the unique hydrological and environmental conditions of Dohuk Dam.The study evaluates sediment dynamics and management options by combining government information, satellite images, and published hydrological records from 1988 to 2024. Critical erosion zones that provide more than 60% of the total sediment load from less than 20% of the catchment area were identified by a calibrated sediment transport model based on Yang's unit stream power method and backed by SWAT watershed simulations. Continuous turbidity monitoring with high predicted accuracy was made possible by remote sensing analysis of multi-temporal Sentinel-2 data, and reliable predictions of sediment load variations were produced using machine learning models, such as Long Short-Term Memory networks. Economic analysis conducted over a 100-year period showed that integrated solutions, which combined seasonal hydraulic flushing, turbidity current venting, and targeted watershed interventions, may achieve favorable benefit-cost ratios and lower sedimentation rates by up to 80%. Monte Carlo simulations confirmed the validity of the suggested framework by further quantifying uncertainty under anticipated climate change scenarios. The results demonstrate how an optimization framework integrating hydrological modeling, remote sensing, and machine learning may promote sustainable reservoir management in semi-arid areas.