The accuracy of soil databases is essential in hydrological modeling, yet limited studies have evaluated the implications of using emerging soil datasets like POLARIS compared to traditional ones such as SSURGO. This study evaluates the performance of POLARIS soil data for simulating the streamflow and sediment yield at both the sub-basin and field scales within the Big Muddy Watershed (BMW), Illinois, U.S.A., using a soft-calibrated SWAT+ model. The field-scale analysis focused on cropland-dominated HRUs from two sub-basins with contrasting POLARIS-SSURGO similarities at the sub-basin scale, optimizing computational efficiency. POLARIS results were compared to those derived from the widely used SSURGO soil database using a soft-calibrated SWAT+ model. At the sub-basin scale, the two datasets showed strong overall agreement for the streamflow and sediment yield over the 81 BMW sub-basins, with minor discrepancies, especially in sediment yield predictions, which exhibited more variability. At the field scale, the agreement between POLARIS and SSURGO was good for both variables, streamflow and sediment yield, though the sediment yield showed greater variability as shown at the sub-basin level. At both scales, the POLARIS and SSURGO outcomes for the streamflow and sediment yield did not always follow the same trend, with discrepancies observed in some sub-basins and HRUs. This suggested that while POLARIS can replicate SSURGO's streamflow outcomes, this similarity does not always extend to sediment yield predictions and vice versa. At the sub-basin scale, the POLARIS and SSURGO outcomes showed strong alignment (88.9% in "very good" agreement). However, at the field scale, this alignment decreased to 42.9% and 33.3% in specific sub-basins. This indicates that sub-basin aggregation reduces local variability, while finer scales reveal greater sensitivity to soil and hydrological differences. This study highlights POLARIS as a robust alternative to SSURGO for hydrological modeling. Future research should explore its broader application across diverse conditions.
