This paper describes and evaluates a procedure that integrates radar altimetry data into the automatic calibration of large-scale flow routing schemes (LFRS). The Hydrological Modeling and Analysis Platform, coupled in off-line mode with the Interactions between Soil, Biosphere, and Atmosphere land surface model, is used to simulate daily surface water dynamics of the Amazon basin at a 0.25° spatial resolution. The Multiobjective Complex Evolution optimization algorithm is used to optimize one parameter (subsurface runoff time delay) and other three parameter multiplier factors (Manning roughness coefficient for rivers, river width, and bankfull height) by minimizing two objective functions for the 2002 to 2006 period. Four calibration experiments are performed by combining water discharge observations and Envisat data to evaluate the potential of using radar altimetry in the automatic calibration of LFRS. One experiment is based on daily discharge observations, other combines discharge with altimetric data, and the other two ones are driven exclusively by radar altimetry data, at 16 or four virtual stations, depending on the experiment. The calibration process is validated against discharge observations at five gauging stations located on the main tributaries. This study shows the feasibility of calibrating LFRS using radar altimetry data. Results demonstrate that reasonable parameters can be obtained by using radar altimetry in an optimization procedure with competitive computational costs. However, there is evidence of equifinality among model parameters. Furthermore, the automatic calibration driven by altimetric data can reliably reproduce discharges time series, and significant improvements are noticed in simulated water level variations.