The paper "Multi-objective global optimization for hydrologic models" by Patrice Ogou Yapo, Hoshin Vijai Gupta, and Soroosh Sorooshian discusses the development and application of a multi-objective global optimization algorithm, MOCOM-UA, for calibrating hydrologic models. The authors argue that single-objective optimization methods, which focus on minimizing the distance between model outputs and observed data, are inadequate for capturing the complex relationships between model parameters and observed data, especially when multiple output fluxes (such as water, energy, and chemical constituents) need to be simulated. The MOCOM-UA algorithm is an extension of the successful SCE-UA single-objective global optimization algorithm, designed to handle multiple objectives simultaneously. The method combines controlled random search, competitive evolution, Pareto ranking, and a multi-objective downhill simplex search strategy. The paper demonstrates the effectiveness of MOCOM-UA through a calibration study of the Sacramento Soil Moisture Accounting model (SAC-SMA) using historical data from the Leaf River watershed. The results show that MOCOM-UA can generate a uniform set of solutions spanning the Pareto space, providing a trade-off between different objectives. The study also highlights the importance of proper selection of objective functions and the sensitivity of calibration results to population size. The authors conclude that MOCOM-UA is a promising approach for calibrating complex hydrologic models and warrant further research to address theoretical and practical issues.The paper "Multi-objective global optimization for hydrologic models" by Patrice Ogou Yapo, Hoshin Vijai Gupta, and Soroosh Sorooshian discusses the development and application of a multi-objective global optimization algorithm, MOCOM-UA, for calibrating hydrologic models. The authors argue that single-objective optimization methods, which focus on minimizing the distance between model outputs and observed data, are inadequate for capturing the complex relationships between model parameters and observed data, especially when multiple output fluxes (such as water, energy, and chemical constituents) need to be simulated. The MOCOM-UA algorithm is an extension of the successful SCE-UA single-objective global optimization algorithm, designed to handle multiple objectives simultaneously. The method combines controlled random search, competitive evolution, Pareto ranking, and a multi-objective downhill simplex search strategy. The paper demonstrates the effectiveness of MOCOM-UA through a calibration study of the Sacramento Soil Moisture Accounting model (SAC-SMA) using historical data from the Leaf River watershed. The results show that MOCOM-UA can generate a uniform set of solutions spanning the Pareto space, providing a trade-off between different objectives. The study also highlights the importance of proper selection of objective functions and the sensitivity of calibration results to population size. The authors conclude that MOCOM-UA is a promising approach for calibrating complex hydrologic models and warrant further research to address theoretical and practical issues.