Regression modeling of streamflow, baseflow, and runoff using geographic information systems.

Area of Science:

• Hydrology
• Water Resource Management
• Geographic Information Systems (GIS)

Background:

• Accurate prediction of streamflow components (total streamflow, baseflow, storm runoff) is crucial for effective water resource planning and management.
• Baseflow is often used as a surrogate for groundwater recharge, highlighting its importance in hydrological assessments.
• Existing methods for streamflow prediction may require refinement to incorporate diverse basin characteristics.

Purpose of the Study:

• To develop and evaluate regression models for predicting total streamflow (TSF), baseflow (TBF), and storm runoff (TRO).
• To assess the influence of different sets of predictor variables (geomorphological, geological, soil, climatic) on model accuracy.
• To provide reliable predictive tools for water resource managers.

Main Methods:

• Utilized streamflow data from 54 gaging stations in Pennsylvania (1971-2001).
• Partitioned total streamflow into baseflow and storm runoff components.
• Developed regression models using basin characteristics derived from GIS under three distinct scenarios.

Main Results:

• All developed regression models achieved high coefficients of determination (R² > 0.94) with acceptable prediction errors.
• The best models for TSF and TBF showed similar predictive performance, offering flexibility in variable selection.
• The storm runoff model under scenario 1 outperformed scenario 2, while simplified Area-alone models provided lower accuracy.

Conclusions:

• Regression models incorporating basin characteristics provide accurate predictions of TSF, TBF, and TRO.
• Model selection for TSF and TBF can be flexible based on data availability.
• Simplified models may be useful when detailed data are unavailable, but with a trade-off in accuracy.