Satellite-based algae estimation in reservoirs integrating basin-reservoir modeling

Area of Science:

• Environmental Science
• Hydrology
• Water Resource Management

Background:

• The Mamloo basin and reservoir, east of Tehran, Iran, face water quality challenges.
• Understanding nutrient dynamics (nitrate and phosphate) is crucial for managing this vital water resource.

Purpose of the Study:

• To assess nitrate and phosphate levels in the Mamloo basin and reservoir.
• To evaluate the effectiveness of a coupled basin-reservoir modeling system combined with satellite imagery for water quality assessment.
• To identify key sources influencing nutrient loads.

Main Methods:

• Employed a coupled basin-reservoir modeling system: SWAT for basin processes and CE-Qual-W2 for reservoir processes.
• Utilized satellite imagery and Artificial Neural Networks (ANN) to extract chlorophyll a and algal data.
• Calibrated and validated models using hydrometric data and in-situ measurements, achieving high Nash-Sutcliffe Efficiency (NSE) values.

Main Results:

• Simulated streamflow showed strong agreement with observed data (NSE 0.89-0.88).
• Simulated nitrate (9.6 mg/L) and phosphate (0.19 mg/L) concentrations closely matched observed values (9.1 mg/L and 0.21 mg/L).
• ANN achieved high accuracy (NSE 0.9) in predicting chlorophyll a and algal data.

Conclusions:

• The reservoir effectively reduces nitrate concentrations.
• Nitrate loads are primarily from point sources and fertilizers; phosphate levels are influenced by soil content and pollutants.
• A comprehensive basin-wide management strategy is essential for effective water quality control.