Uncertainty quantification seeks to provide a quantitative means to understand complex systems that are impacted by uncertainty in their parameters. The polynomial chaos method is a computational approach to solve stochastic partial differential equations (SPDE) by projecting the solution onto a space of orthogonal polynomials of the stochastic variables and solving for the deterministic coefficients. Polynomial chaos can be more efficient than Monte Carlo methods when the number of stochastic variables is low, and the integration time is not too large. When performing long-term integration, however, achieving accurate solutions often requires the space of polynomial functions to become unacceptably large. This dissertation presents an alternative approach, where sets of empirical basis functions are constructed by examining the behavior of the solution for fixed values of the random variables. The empirical basis functions are evolved over time, which means that the total number can be kept small, even when performing long-term integration. We introduce this method of empirical chaos expansion, and apply it to a number of model equations, demonstrating that the computational time scales linearly with the final integration time. That is not the case for polynomial chaos in general, since achieving accuracy for long-term integration usually requires larger polynomial bases, causing a nonlinear scaling with the final integration time. We also present an analytical method that uses the dynamics of the SPDE to predict the evolution of the empirical basis functions and demonstrate how it can be applied to evolve the empirical basis functions without needing to resample realizations of the original SPDE.