Identifying and assessing pond best management practice under future climate scenarios

Area of Science:

• Environmental Science
• Hydrology
• Remote Sensing

Background:

• Climate change significantly influences watershed hydrological processes.
• Ponds are crucial Best Management Practices (BMPs) for mitigating these impacts.
• Limited research exists on pond BMPs' effectiveness against climate change in Mississippi watersheds.

Purpose of the Study:

• To classify ponds using Google Earth Engine (GEE) in the Big Sunflower River Watershed (BSRW).
• To integrate pond data into the Soil and Water Assessment Tool (SWAT) model.
• To evaluate future hydrological and water quality impacts under climate change scenarios.

Main Methods:

• Remote sensing (GEE) for pond identification and classification.
• SWAT model for hydrological and sediment load simulation.
• Climate change scenarios (RCP 4.5, 8.5) for future projections.

Main Results:

• Projected increase in average annual flow (26-46%) and sediment load (107-150%) by late century.
• Pond BMPs could decrease average annual sediment load by 44-46% under both RCP scenarios.
• Seasonal sediment load reductions projected across spring, summer, and fall due to pond BMPs.

Conclusions:

• Pond BMPs are effective in mitigating climate change-driven increases in sediment load.
• Integration of remote sensing and hydrological models enhances watershed management strategies.
• Findings support the development of climate-smart management for agricultural watersheds.