STATUS OF AUTOMATIC CALIBRATION FOR HYDROLOGIC MODELS: COMPARISON WITH MULTILEVEL EXPERT CALIBRATION
STATUS OF AUTOMATIC CALIBRATION FOR HYDROLOGIC MODELS: COMPARISON WITH MULTILEVEL EXPERT CALIBRATION
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DOI：
10.1061/(asce)1084-0699(1999)4:2(135
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发表时间：
1999-04-01
影响因子：
2.4
通讯作者：
Yapo, Patrice Ogou
中科院分区：
文献类型：
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作者：
Gupta, Hoshin Vijai;Sorooshian, Soroosh;Yapo, Patrice Ogou
水文模型的有用性取决于模型的校准程度。因此，必须仔细进行校准过程，以最大限度地提高模型的可靠性。一般来说，手动校准程序可能非常耗时且令人沮丧，这已成为阻碍更复杂的水文模型广泛使用的主要因素。最近开发了一种名为“混洗复杂进化”的全局优化算法，该算法已被证明在定位水文模型的全局最优模型参数方面是一致、有效和高效的。本文将混洗复杂演化自动程序的能力与交互式多级校准多级半自动方法进行了比较，该方法是为校准美国国家气象局萨克拉门托土壤湿度核算水流预报模型而开发的。结果表明，目前最先进的自动校准技术的技术水平有望接近训练有素的水文学家的水平。这使得水文学家能够利用自动化方法的力量来获得与历史数据一致的良好参数估计，然后使用个人判断来完善这些估计，并考虑其他不易纳入自动化程序的因素和知识。分析还表明，简单的模型性能分割样本测试无法可靠地表明模型分歧的存在，需要更稳健的性能评估标准。
The usefulness of a hydrologic model depends on how well the model is calibrated. Therefore, the calibration procedure must be conducted carefully to maximize the reliability of the model. In general, manual procedures for calibration can be extremely time-consuming and frustrating, and this has been a major factor inhibiting the widespread use of the more sophisticated and complex hydrologic models. A global optimization algorithm entitled shuffled complex evolution recently was developed that has proved to be consistent, effective, and efficient in locating the globally optimal model parameters of a hydrologic model. In this paper, the capability of the shuffled complex evolution automatic procedure is compared with the interactive multilevel calibration multistage semiautomated method developed for calibration of the Sacramento soil moisture accounting streamflow forecasting model of the U.S. National Weather Service. The results suggest that the state of the art in automatic calibration now can be expected to perform with a level of skill approaching that of a well-trained hydrologist. This enables the hydrologist to take advantage of the power of automated methods to obtain good parameter estimates that are consistent with the historical data and to then use personal judgment to refine these estimates and account for other factors and knowledge not incorporated easily into the automated procedure. The analysis also suggests that simple split-sample testing of model performance is not capable of reliably indicating the existence of model divergence and that more robust performance evaluation criteria are needed.