The Quest for Hydrological Signatures: Effects of Data Transformation on Bayesian Inference of Watershed Models
The Quest for Hydrological Signatures: Effects of Data Transformation on Bayesian Inference of Watershed Models
复制标题
寻找水文特征：数据转换对流域模型贝叶斯推理的影响
DOI：
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发表时间：
2018
影响因子：
4.3
通讯作者：
A. Haghighi
中科院分区：
文献类型：
--
作者：
M. Sadegh;Morteza Shakeri Majd;Jairo E. Hernandez;A. Haghighi
水文模型包含参数，其值无法在实地直接测量，因此需要根据历史记录通过校准进行有意义的推断。虽然在模型推断文献中已经取得了很大的进展，相对较少的是已知的转换校准数据（或误差残差）的模型参数的可识别性和模型预测的可靠性的影响。本文使用两个水文模型和三个流域分析了这种影响。我们的研究结果表明，校准数据转换显着影响参数和预测不确定性估计。这些变换，扭曲的时间分布的校准数据，如流量持续时间曲线，正常分位数变换，傅立叶变换，大大恶化的可识别性的模型参数导出一个正式的贝叶斯框架与残差为基础的似然函数。其他变换，如小波，BoxCox和平方根，虽然在确定特定的模型参数表现出一定的优点，不会始终如一地提高水文模型的预测能力，在一个单一的目标反问题。然而，多目标优化方案可以提供更严格的基础，以从不同的数据转换中提取几条独立的信息。最后，数据转换可能提供更大的潜力来评估模型性能和评估模型行为的特定部分，而不是在单个目标框架中校准模型。这项研究的结果揭示了数据转换在寻找水文特征的重要性和影响。
Hydrological models contain parameters, values of which cannot be directly measured in the field, and hence need to be meaningfully inferred through calibration against historical records. Although much progress has been made in the model inference literature, relatively little is known about the effects of transforming calibration data (or error residual) on the identifiability of model parameters and reliability of model predictions. Such effects are analyzed herein using two hydrological models and three watersheds. Our results depict that calibration data transformations significantly influence parameter and predictive uncertainty estimates. Those transformations that distort the temporal distribution of calibration data, such as flow duration curve, normal quantile transform, and Fourier transform, considerably deteriorate the identifiability of model parameters derived in a formal Bayesian framework with a residual-based likelihood function. Other transformations, such as wavelet, BoxCox and square root, while demonstrating some merits in identifying specific model parameters, would not consistently improve predictive capability of hydrological models in a single objective inverse problem. Multi-objective optimization schemes, however, may present a more rigorous basis to extract several independent pieces of information from different data transformations. Finally, data transformations might offer a greater potential to evaluate model performance and assess specific sections of model behavior, rather than to calibrate models in a single objective framework. Findings of this study shed light on the importance and impacts of data transformations in search of hydrological signatures.
影响因子：
1
作者：
A. Aghakouchak;E. Habib
通讯作者：
A. Aghakouchak;E. Habib