This paper presents a new framework for calibrating computationally expensive watershed models with multi-objective optimization methods and hydrological consistency analysis. The analysis evaluates different algorithms' efficiencies for finding watershed model calibration solutions within a limited budget. Two surrogate multi-objective algorithms GOMORS and ParEGO are compared to five evolutionary algorithms without surrogates on two watershed models. We test the algorithms’ performance with two multi-objective formulations (i.e., threshold-based flow separation and decomposition of the Nash-Sutcliffe Efficiency (NSE)). Results indicate that the surrogate-based GOMORS is the most computationally efficient overall. We also propose a framework to select among the calibration solutions obtained from multi-objective optimization using different hydrologic signatures. GOMORS is assessed for its ability to identify hydrologically acceptable calibrations. The decomposition of NSE is the most effective calibration formulation in terms of hydraulic consistency analysis. In addition, hydrologic signatures could be used effectively to filter non-dominated solutions obtained from multi-objective optimization.

中文翻译：

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增强流域模型评估，将水文特征和一致性纳入有效的替代多目标优化中

本文提出了一个新的框架，用于通过多目标优化方法和水文一致性分析来校准计算量大的流域模型。该分析评估了不同算法在有限预算内寻找分水岭模型校准解决方案的效率。将两种代理多目标算法 GOMORS 和 ParEGO 与两个分水岭模型上没有代理的五种进化算法进行了比较。我们使用两个多目标公式（即基于阈值的流分离和纳什-萨特克利夫效率（NSE）分解）来测试算法的性能。结果表明，基于代理的 GOMORS 总体上计算效率最高。我们还提出了一个框架，用于在使用不同水文特征的多目标优化获得的校准解决方案中进行选择。GOMORS 因其识别水文上可接受的校准的能力而受到评估。NSE 的分解是水力一致性分析中最有效的校准公式。此外，水文特征可以有效地用于过滤从多目标优化中获得的非支配解。