Implications of a priori parameters on calibration in conditions of varying terrain characteristics: Case study of the SAC-SMA model in eastern United States
This study seeks to advance the knowledge about the effect of the Sacramento Soil Moisture counting Model (SAC-SMA) a priori parameters on calibration. We investigated the catchment characteristics where calibration is most affected by the limitations in the a priori parameters and we studied the effect on the modeled processes. The a priori SAC-SMA model parameters were determined from soil-derived physical expressions that make use of the soil’s physical properties. The study employed 63 catchments from the eastern United States (US). The model calibration employed the Shuffle-Complex algorithm (SCE-UA) and used the a priori parameters as default allowing for 35% as a range of deviation. The model efficiency after calibration was sensitive to the catchment landscape properties, particularly the soil texture and topography. The highest efficiency was obtained in conditions of well-drained soils and flat topography where the saturation excess overland flow is predominant. Most of the catchments with smaller efficiency had poorly drained soils where mountainous and forested catchments of predominant subsurface stormflow had the lowest efficiency. The current regional study shows that improvements of SAC-SMA a priori
parameters are crucial to foster their operational use for calibration and prediction at ungauged catchments.