摘要：Core Ideas Saturated hydraulic conductivity ( K sat ) was measured with different methods. K sat was upscaled from point to catchment to watershed scales. Upscaled K sat predicted streamflow at a large watershed without model calibration. A soil system approach was used to successfully upscale K sat for streamflow predictions Successful hydrological model predictions depend on appropriate framing of scale and the spatial‐temporal accuracy of input parameters describing soil hydraulic properties. Saturated soil hydraulic conductivity ( K sat ) is one of the most important properties influencing water movement through soil under saturated conditions. It is also one of the most expensive to measure and is highly variable. The objectives of this research were (i) to assess the ability of Amoozemeters, wells, piezometers, and flumes to accurately represent K sat at a small catchment scale and (ii) to extrapolate K sat to a larger watershed based on available soil data and soil landscape models for simulating streamflow using the Distributed Hydrological Soil Vegetation Model. The mean K sat between Amoozemeters, wells, and flumes varied from 2.4 to 4.9 × 10 −7 m s −1 , and differences were not significant. Mixed trends in mean K sat for slope positions and soil series were observed. The strongest significant and consistent trend in mean K sat was observed for soil depth. The mean K sat decreased exponentially with depth, from 6.51 × 10 6 m s −1 for upper horizons to 2.37 × 10 −7 m s −1 for bottom horizons. Recognizing the significantly decreasing trend of K sat with soil depth and the lack of consistent trends between soils and slope positions for small catchments, K sat values were extrapolated from the small catchments occurring in Dillon Creek to another large watershed (Hall Creek) based on soil similarity and distribution. The Nash–Sutcliffe model overall efficiency of 0.52 indicated a good performance in simulating streamflows without model calibration. Combining K sat measurement methods in small catchments with an understanding of soil landscapes and soil distribution relationships allowed successful upscaling of localized soil hydraulic properties for streamflow predictions to larger watersheds.
