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• First validation of a daily large-scale sediment model using remote-sensing data. • Remotely-sensed reflectance can help sediment dynamics modeling in ungauged basins. • Better spatiotemporal sediment patterns using surrogate observations. Sediment models are important for estimating soil erosion, sediment transport, and deposition at different scales and for disparate scenarios; however, the availability of in-situ measurements may not be sufficient for the appropriate evaluation of model performance. For this reason, this study investigated how surrogate data (water quality [WQ] and remote sensing) could be used as a proxy for suspended sediment concentration (SSC) in order to evaluate the performance of a large-scale erosion and sediment transport model. To achieve this, the daily SSC was simulated, using the Large Scale Sediment Model (MGB-SED), a semi-distributed and conceptual model in which the Modified Universal Soil Loss Equation (MUSLE) is used to estimate sediment yield and an advection-diffusion equation is used to route suspended loads along rivers. Four experiments were performed that compared simulated SSC against: (1) in-situ SSC; (2) turbidity; (3) total suspended solids (TSS); and (4) surface spectral reflectance (SSR). Correlation analysis showed that r -values above 0.5 were found in 62%, 91%, 89% and 83% of the SSC, turbidity, TSS and SSR stations, respectively. Spatially, the model was evaluated in smaller drainage areas, using WQ data. Temporally, the number of SSR observations was greater than the number of in-situ measurements, allowing a better analysis of the temporal dynamics of the simulated SSC. Also, the SSR data improved the spatial coverage and could be employed for even large-scale, ungauged basins. These results bring a new perspective to the application of sediment models in poorly or ungauged basins, since surrogate data can improve the spatial and temporal suspended sediment coverage when compared to traditional SSC measurements, which is a common issue in large-scale sediment modeling. [ABSTRACT FROM AUTHOR]