Reservoir Water Quality Improvement Using Optimized Strategic Selective Withdrawal and Hypolimnetic Aeration Considering Climate Changes: A Case Study, Wadi Dayqah Dam, Oman

Project: HM Grants ( Strategic)

Project Details

Description

A novel risk-based multi-objective compromise methodology is proposed in this study for selective reservoir withdrawal and hypolimnetic aeration considering climate change. Climate changes, population growth, and urbanization impose uncertainties on water supply and demand. Therefore, the established strategies for the IWRM should consider the uncertainties in future conditions to select the most effective strategy for long-term water supply and demands management. Hence, this research proposal presents a first attempt to explore the best suitable way to solve thermal stratification in Wadi Dayqah considering climate change that imposes uncertainties in two independent phases. The first phase addresses the capability of optimum performance of TIBEANTM aerator plants considering the optimum number of plants, place, depth, rate, and duration of aeration, and it should minimize the aeration duration, the number of TIBEAN plants, determine the optimum segment, depth, and speed of aeration. For the second phase, the implementation of selective withdrawal and risk-based conflict resolution methods considering climate change would be examined using the two-dimensional CE-QUAL-W2 model to provide sufficient health for the downstream's ecosystem considering temperature and quality of the water using the following objectives and resolution of conflicts between stakeholders.

Key findings

Wadi Dayqah dam, Selective withdrawal, Aeration, Climate Change, Conditional Value at Risk (CVaR)
Short titleReservoir Water Quality Improvement Using Optimized Strategic Selective Withdrawal and Hypolimnetic Aeration Considering Climate Changes: A Case Study, Wadi Dayqah Dam, Oman
StatusActive
Effective start/end date9/1/2212/31/24

Fingerprint

Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.