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In hydrologic modeling, various uncertainty sources may arise due to simplification/representation of real-world spatially distributed processes into the modeling framework, such as uncertainty due to model structure, initial conditions and input errors. One approach that is currently gaining attention to reduce model uncertainty is by optimally combining multiple models. The rationale behind this approach is that optimal weights could be derived for each model during the model combination process so that the developed multimodel predictions will result in improved predictability. Another approach-data assimilation-is gaining popularity in reducing uncertainty by deriving updated initial conditions recursively from the current available observations to reduce overall uncertainty by minimizing the error covariance matrix of state variables. In this paper, an experimental design is proposed to test the performance of both approaches, multimodel combination and data assimilation, in improving the hydrologic prediction at daily and monthly time scales. The experimental design is constructed on a synthetic basis such that the 'true' model structure and streamflow values are known. We evaluated the performance of multimodel combination and data assimilation through the experimental design at monthly and daily time scales, then compare how uncertainty due to initial conditions and hydrologic model can be dominant at the respective time scales. For the multimodel combination, we combined the models by evaluating the model performance conditioned on the predictor state. For data assimilation, the Ensemble Kalman Filter (EnKF) was adopted to test its usefulness through the same experimental design. Results from the synthetic study showed that under increased model uncertainty, the multimodel algorithm consistently performed better than the single model predictions and the EnKF algorithm in terms of all performance measures at monthly time scale. However, under daily time scale, the multimodel algorithm did not performing better than the EnKF algorithm in most of the model uncertainty cases. Findings from the synthetic study was also consistent upon application in predicting streamflow at daily and monthly time scales for a watershed in North Carolina. [ABSTRACT FROM AUTHOR]

Regional water supply systems undergo surplus and deficit conditions due to differences in inflow characteristics as well as due to their seasonal demand patterns. This study proposes a framework for regional water management by proposing an interbasin transfer (IBT) model that uses climate-information-based inflow forecast for minimizing the deviations from the end-of-season target storage across the participating pools. Using the ensemble streamflow forecast, the IBT water allocation model was applied for two reservoir systems in the North Carolina Triangle Area. Results show that interbasin transfers initiated by the ensemble streamflow forecast could potentially improve the overall water supply reliability as the demand continues to grow in the Triangle Area. To further understand the utility of climate forecasts in facilitating IBT under different spatial correlation structures between inflows and between the initial storages of the two systems, a synthetic experiment was designed to evaluate the framework under inflow forecast having different skills. Findings from the synthetic study can be summarized as follows: (a) inflow forecasts combined with the proposed IBT optimization model provide improved allocation in comparison to the allocations obtained under the no-transfer scenario as well as under transfers obtained with climatology; (b) spatial correlations between inflows and between initial storages among participating reservoirs could also influence the potential benefits that could be achieved through IBT; (c) IBT is particularly beneficial for systems that experience low correlations between inflows or between initial storages or on both attributes of the regional water supply system. Thus, if both infrastructure and permitting structures exist for promoting interbasin transfers, season-ahead inflow forecasts could provide added benefits in forecasting surplus/deficit conditions among the participating pools in the regional water supply system. [ABSTRACT FROM AUTHOR]