There is increasing consensus in the hydrologic literature that an appropriate framework for streamflow forecasting and simulation should include explicit recognition of forcing, parameter and model structural error. In this talk, I will present a methodology to analyze and estimate areal average rainfall using streamflow data from two different catchments. This approach combines recent advances in nonlinear uncertainty estimation using Markov Chain Monte Carlo sampling and high performance computing. Explicit treatment of precipitation error during hydrologic model calibration not only results in prediction uncertainty bounds that are more appropriate, but also significantly alters the posterior distribution of the watershed model parameters. This has significant implications for regionalization studies. The approach also provides important new ways to estimate areal average atershed precipitation, information that is of utmost mportance to test hydrologic theory, diagnose structural errors in models, and appropriately benchmark rainfall measurement devices.