It is well known that one of the major problems in the application of land surface models is the determination of the various model parameters. In most cases, only one or a limited number of variables are used to estimate these parameters. This study evaluates the use of two fundamentally different global optimization methods, multistart weight-adaptive recursive parameter estimation (MWARPE) and particle swarm optimization (PSO), for the estimation of hydrologic model parameters on the basis of data for multiple variables. MWARPE iteratively uses the linear recursive filter equations in a Monte Carlo setting and therefore does not rely on the explicit minimization of an objective function. However, a major drawback of the MWARPE method is the high dimensionality, determined by the number of observations, of the matrix to be inverted. On the other hand, PSO is a stochastic optimization method based on the collective strength of a population of individuals with flocking or herding behavior, as observed in a wide number of biological systems. In situ observations of net radiation; latent, sensible, and ground heat fluxes; and the soil moisture profile are used to determine the parameters of a simplified water and energy balance model. Both optimization methods are analyzed in terms of model performance and computational efficiency. Comparable results, expressed in terms of the root mean square error values, were obtained for both methods. However, it was found that MWARPE tends to slightly overfit the data.