Hydrologic parameters and state variables estimation for the purpose of forecasting hydrologic system dynamics have been among the most challenging tasks in the filed of hydrologic modeling. Most of the efforts in this area can be categorized broadly into deterministic and stochastic approaches. The former approaches generally adopt the paradigm as an optimization problem and attempt to minimize an error cost function for estimating the optimum parameter set. The latter approach analyzes the problem in the discourse of estimation theory. Because of existing uncertainties associated with model structure, measurement and initial boundary condition, forecasts using the deterministic approaches lack the ability to explicitly address the mentioned uncertainties and so one can not relay only on the results of the first approach. Although several attempts have been made to develop a well scientifically accepted and robust framework to estimate hydrologic parameters in a probabilistic context, several open questions still exist. In this paper we introduce a new approach to employ Block Bootstrap resampling coupled with a global optimization algorithm to quantify probabilistic structure of hydrologic parameter space using nonparametric confidence interval analysis.