The precise reproduction of different flow regimes in both gauged and ungauged watersheds is crucial for managing environmental flow and water quality requirements. However, the ability of hydrological models to reproduce flow quantiles (FQs) is often influenced by the process of calibrating the most dominant parameters through traditional parameter estimation methods. This research proposes a systematic parameter estimation approach to improve the credibility of the hydrologic model in reproducing FQs in gauged and ungauged watersheds through the following steps: (a) implementation of parameter sensitivity analysis to identify the dominant parameters, (b) initial estimation of the sensitive parameter values, (c) an iterative search for the optimal value of the dominant parameter in reproducing FQs and (d) regionalization of parameters to estimate FQs in the ungauged watersheds. The analysis shows the highest sensitivity of the runoff curve number (CN2) in simulating the hydrologic process in all test watersheds. Moreover, the best value of CN2 was found to be different for each flow quantile. Therefore, CN2 was updated for reproduction of FQs, which resulted in an overall average improvement of the regionalized model accuracy (across all test watersheds and flow quantiles) by 37 and 46% during the calibration and validation periods, respectively. The spatiotemporal dynamics of the water balance components that often control the behaviours of FQs in both the gauged and ungauged watersheds were also quantified. The results show a wide range of spatiotemporal variations for the majority of the water balance components.

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This study aims compares how different formulations of a reservoir operation problem with conflicting objectives affect the quality of the generated solution set. Six models were developed for comparative analysis: three using dynamic programming (DP) and three using the evolutionary multi-objective direct policy search (EMODPS) algorithm. Afterward, to improve the quality of the generated solution set, an EMODPS model was selected and coupled with zone-based hedging policy that is currently being applied in real-world reservoir operations. The solutions generated by each model were then evaluated regarding proximity to the ideal and three eminent performance indices (risk, resiliency, and vulnerability). The proposed methodology was applied to a multi-purpose reservoir located in South Korea, Boryeong Dam, which had suffered a multi-year drought recently. Consequently, the solution sets from the EMODPS model yielded closer results than those of the stochastic DP model for optimality and diversity. Although the solutions from the algorithm performed better than actual operation results under normal conditions, the actual operations executed based on the zone-based hedging rule outperformed the other two in case of droughts. Among the EMODPS models, one with the fewest parameters, the EMODPS-Gaussian model, resulted in better solutions for all cases. Finally, coupling the real-world policy with the optimally derived solutions in the case of droughts improved the frequency, duration, and magnitude of the water supplies whereas the water users experienced an improvement in scale at the expense of more recurrent failures.