Enhancing Streamflow Simulations Through Input Data Denoising

Hydrological models are vital for understanding water resources and their responses to environmental and climatic changes, but their accuracy depends strongly on input data quality. This study evaluates how noise reduction in meteorological inputs influences the performance of the SWAT hydrological model for the lower Columbia River basin. Wavelet Transform (WT) was applied for partial denoising, while Singular Spectrum Analysis (SSA) was used for both partial and full noise removal. SSA allows extraction of trend, periodic, and noise components individually from time series data. Results indicate that partial denoising using WT significantly improves model performance, increasing the correlation coefficient (r) and Nash–Sutcliffe Efficiency (NSE) by 2 to 5%, Kling-Gupta Efficiency (KGE) by 16%, and reducing RSR by 4%, along with a notable reduction in PBIAS (from −4.7 to +1.3). The partially denoised WT model achieved r = 0.91, NSE = 0.81, PBIAS = 1.30, KGE = 0.88, and RSR = 0.45, outperforming both the base and fully denoised models. The comparative analysis shows that completely removing noise offers limited benefits and may suppress natural variability, while partial denoising provides an optimal balance between data reliability and model precision. These findings highlight the importance of appropriate input-data preprocessing in improving hydrological model performance and reducing uncertainty in water resource assessments.