Runoff potentiality of a watershed through SCS and functional data analysis technique

Area of Science:

• Hydrology
• Environmental Science
• Statistical Modeling

Background:

• Understanding watershed runoff potential is crucial for water resource management.
• Traditional methods may not fully capture the dynamic patterns of surface runoff.
• Accurate hydrological modeling requires reliable input data on watershed characteristics.

Purpose of the Study:

• To assess the runoff potentiality of watersheds using advanced statistical techniques.
• To transform discrete runoff data into smooth curves for pattern representation.
• To provide essential input data for hydrological modeling and water resource planning.

Main Methods:

• Utilized curve number (CN) and Soil Conservation Service (SCS) techniques.
• Applied Functional Data Analysis (FDA) for data interpretation.
• Employed the Lowess method for smoothing rainfall data and Fourier series for fitting runoff patterns.

Main Results:

• Identified specific watersheds (1, 2, 3, 6, 7, and 8) with significant monthly mean runoffs (22-29 mm).
• Determined the optimal number of Fourier series terms for accurate data fitting across different watersheds.
• Demonstrated the effectiveness of statistical methods in characterizing surface runoff patterns.

Conclusions:

• The study successfully characterized runoff potentiality and surface runoff patterns in the analyzed watersheds.
• The findings provide valuable data for hydrological modeling and informed water management decisions.
• Statistical approaches offer a robust framework for analyzing complex hydrological processes.