Summary
The article examines the efficacy of various gridded precipitation datasets—Tropical Rainfall Measuring Mission (TRMM), Climate Forecast System Reanalysis (CFSR), and Parameter-elevation Relationships on Independent Slopes Model (PRISM)—in hydrological modeling, specifically focusing on streamflow simulations in the Leon Creek Watershed (LCW) in San Antonio, Texas.
The study establishes that precipitation quality is critical for effective hydrological modeling. Using conventional gauge data as a benchmark, it finds that TRMM underestimates rainfall volume, while PRISM closely aligns with gauge observations. Hydrological simulations indicate that models utilizing PRISM and TRMM data yield better results than those driven by CFSR or gauge data.
Both the Soil and Water Assessment Tool (SWAT) and Artificial Neural Network (ANN) models were used, revealing consistent performance patterns across both frameworks when the same data source was applied. In calibration, models showed satisfactory to very good performances, whereas validation results, particularly for CFSR and gauge data, fell short, likely due to spatial inadequacies.
The findings suggest that PRISM is a superior data source for hydrological simulations, while TRMM also performs satisfactorily. The work underscores the importance of quality precipitation inputs in streamflow modeling and sets the groundwork for future studies on alternative weather data sources.
Key Points
- Importance of Precipitation: Quality of precipitation input is critical for hydrological models.
- Data Comparison: TRMM significantly underestimates rainfall, while PRISM closely matches gauge measurements.
- Model Performance: PRISM and TRMM yield better hydrological outcomes than CFSR and conventional gauges.
- SWAT and ANN Consistency: Both models show similar performance trends depending on the precipitation dataset used.
- Future Research Directions: Highlights the need for further evaluation of alternative precipitation data sources.
