Integrating Meteorological Forcing from Ground Observations and MSWX Dataset for Streamflow Prediction under Multiple Parameterization Scenarios

Abstract

Precipitation and near-surface air temperatures are significant meteorological forcing for streamflow prediction where most basins are partially or fully data-scarce in many parts of the world. This study aims to evaluate the consistency of MSWXv100-based precipitation, temperatures, and estimated potential evapotranspiration (PET) by direct comparison with observed measurements and by utilizing an independent combination of MSWXv100 dataset and observed data for streamflow prediction under four distinct scenarios considering model parameter and output uncertainties. Initially, the model is calibrated/validated entirely based on observed data (Scenario 1), where for the second calibration/validation, the observed precipitation is replaced by MSWXv100 precipitation and the daily observed temperature and PET remained unchanged (Scenario 2). Furthermore, the model calibration/validation is done by considering observed precipitation and MSWXv100-based temperature and PET (Scenario 3), and finally, the model is calibrated/validated entirely based on the MSWXv100 dataset (Scenario 4). The Kling–Gupta Efficiency (KGE) and its components (correlation, ratio of bias, and variability ratio) are utilized for direct comparison, and the Hanssen–Kuiper (HK) skill score is employed to evaluate the detectability strength of MSWXv100 precipitation for different precipitation intensities. Moreover, the hydrologic utility of MSWXv100 dataset under four distinct scenarios is tested by exploiting a conceptual rainfall-runoff model under KGE and Nash–Sutcliffe Efficiency (NSE) metrics. The results indicate that each scenario depicts high streamflow reproducibility where, regardless of other meteorological forcing, utilizing observed precipitation (Scenario 1 and 3) as one of the model inputs, shows better model performance (KGE = 0.85) than MSWXv100-based precipitation, such as Scenario 2 and 4 (KGE = 0.78–0.80).