The seasonal-to-decadal terrestrial water balance on river basin scales depends on several well-characterized but uncertain soil physical processes, including soil moisture, plant available water, rooting depth, and recharge to lower soil layers. Reducing uncertainties in these quantities using observations is a key step toward improving the data fidelity and skill of land surface models. In this study, we quantitatively characterize the capability of Gravity Recovery and Climate Experiment (NASA-GRACE) measurements – a key constraint on total water storage (TWS) – to inform and constrain these processes. We use a reduced-complexity physically based model capable of simulating the hydrologic cycle, and we apply Bayesian inference on the model parameters using a Markov chain Monte Carlo algorithm, to minimize mismatches between model-simulated and GRACE-observed TWS anomalies. Based on the prior and posterior model parameter distributions, we further quantify information gain with regard to terrestrial water states, associated fluxes, and time-invariant process parameters. We show that the data-constrained terrestrial water storage model can capture basic physics of the hydrologic cycle for a watershed in the western Amazon during the period January 2003 through December 2012, with an r2 of 0.98 and root mean square error of 30.99 mm between observed and simulated TWS. Furthermore, we show a reduction of uncertainty in many of the parameters and state variables, ranging from a 2 % reduction in uncertainty for the porosity parameter to an 85 % reduction for the rooting depth parameter. The annual and interannual variability of the system are also simulated accurately, with the model simulations capturing the impacts of the 2005–2006 and 2010–2011 South American droughts. The results shown here suggest the potential of using gravimetric observations of TWS to identify and constrain key parameters in soil hydrologic models.