Stream flow predictions in ungauged basins are one of the most challenging tasks in surface water hydrology because of nonavailability of data and system heterogeneity. This study proposes a method to quantify stream flow predictive uncertainty of distributed hydrologic models for ungauged basins. The method is based on the concepts of deriving probability distribution of model's sensitive parameters by using measured data from a gauged basin and transferring the distribution to hydrologically similar ungauged basins for stream flow predictions. A Monte Carlo simulation of the hydrologic model using sampled parameter sets with assumed probability distribution is conducted. The posterior probability distributions of the sensitive parameters are then computed using a Bayesian approach. In addition, preselected threshold values of likelihood measure of simulations are employed for sizing the parameter range, which helps reduce the predictive uncertainty. The proposed method is illustrated through two case studies using two hydrologically independent sub-basins in the Cedar Creek watershed located in Texas, USA, using the Soil and Water Assessment Tool (SWAT) model. The probability distribution of the SWAT parameters is derived from the data from one of the sub-basins and is applied for simulation in the other sub-basin considered as pseudo-ungauged. In order to assess the robustness of the method, the numerical exercise is repeated by reversing the gauged and pseudo-ungauged basins. The results are subsequently compared with the measured stream flow from the sub-basins. It is observed that the measured stream flow in the pseudo-ungauged basin lies well within the estimated confidence band of predicted stream flow.
All Science Journal Classification (ASJC) codes
- Water Science and Technology