Inferring floodplain bathymetry using inundation frequency.

Area of Science:

• Earth and Planetary Sciences
• Environmental Sciences
• Remote Sensing

Background:

• Accurate bathymetry is crucial for understanding aquatic ecosystems and managing water resources.
• Traditional bathymetric surveys are often costly and logistically challenging, especially in remote or large floodplains.
• Optical remote sensing offers a potential alternative for large-scale depth mapping.

Purpose of the Study:

• To develop and validate a new method for retrieving bathymetry in turbid-water floodplains using inundation frequency (IF) data.
• To assess the accuracy and sensitivity of the proposed method against established bathymetric data and digital elevation models (DEMs).
• To evaluate the method's applicability in permanent water bodies and its sensitivity to sedimentation processes.

Main Methods:

• Utilized over 32 years of composite optical remote sensing data to derive inundation frequency (IF).
• Developed a relationship between IF and surveyed depth data for bathymetric estimation.
• Validated the method on the Curuai floodplain, Amazon River, using historical bathymetry and water level data.
• Compared results with bathymetry derived from bare-Earth DEM and analyzed sensitivity in permanent water bodies.

Main Results:

• The proposed method demonstrated high accuracy in estimating bathymetry from IF data.
• The model proved sensitive to IF variations, even in permanent water body areas (~8m depth).
• Gradual sedimentation effects on lake bathymetry were successfully assessed.

Conclusions:

• The IF-based bathymetry retrieval method is accurate and reliable for turbid-water floodplains.
• This approach offers a cost-effective and feasible alternative to traditional bathymetric surveys in seasonal lakes worldwide.
• The method's sensitivity to IF changes highlights its potential for monitoring dynamic aquatic environments.