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A new fractal-theory-based criterion for hydrological model calibration

机译：一种新的基于分形理论的水文模型校准标准

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Fractality has been found in many areas and has been used to describe the internal features of time series. But is it possible to use fractal theory to improve the performance of hydrological models? This study aims at investigating the potential benefits of applying fractal theory in model calibration. A new criterion named the ratio of fractal dimensions?(RD) is defined as the ratio of the fractal dimensions of simulated and observed streamflow series. To combine the advantages of fractal theory with classical criteria based on squared residuals, a multi-objective calibration strategy is designed. The selected classical criterion is the Nash–Sutcliffe efficiency?( E ). The E –RD strategy is tested in three study cases with different climates and geographies. The results reveal that, in most aspects, introducing?RD into model calibration makes the simulation of streamflow components more reasonable. Also, pursuing a better?RD during calibration leads to only a small decrease in? E . We therefore recommend choosing the parameter set with the best? E among the parameter sets with RD?values of around?1.
机译：在许多领域发现了地位，并且已被用于描述时间序列的内部特征。但是否有可能使用分形理论来提高水文模型的性能？本研究旨在研究应用分形理论在模型校准中的潜在好处。命名为分形尺寸的比率的新标准被定义为模拟和观察到的流流量系列的分形尺寸的比率。将分形理论与基于平方残留的经典标准的优势相结合，设计了一种多目标校准策略。所选的经典标准是NASH-SUTCLIFFE效率？（e）。 E -RD策略在三个研究案例中测试了不同的气候和地理位置。结果表明，在大多数方面，引入模型校准的介绍使得流出组件的模拟更合理。此外，在校准期间追求更好的？RD只能减少一次？ e。因此，我们建议选择最佳参数集？ e参数集中的rd？围绕？1的值。

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