Barnhart Bradley L, Sawicz Keith A, Ficklin Darren L, Whittaker Gerald W
Oak Ridge Institute for Science and Education (ORISE) Post-Doctoral Appointee, U.S. EPA National Health and Environmental Effects Research Laboratory, Western Ecology Division, Corvallis, Oregon.
Department of Geography, Indiana University, Bloomington, Indiana.
Trans ASABE. 2017;60(4):1259-1269. doi: 10.13031/trans.12179.
Characterization of the uncertainty and sensitivity of model parameters is an essential facet of hydrologic modeling. This article introduces the multi-objective evolutionary sensitivity handling algorithm (MOESHA) that combines input parameter uncertainty and sensitivity analyses with a genetic algorithm calibration routine to dynamically sample the parameter space. This novel algorithm serves as an alternative to traditional static space-sampling methods, such as stratified sampling or Latin hypercube sampling. In addition to calibrating model parameters to a hydrologic model, MOESHA determines the optimal distribution of model parameters that maximizes model robustness and minimizes error, and the results provide an estimate for model uncertainty due to the uncertainty in model parameters. Subsequently, we compare the model parameter distributions to the distribution of a dummy variable (i.e., a variable that does not affect model output) to differentiate between impactful (i.e., sensitive) and non-impactful parameters. In this way, an optimally calibrated model is produced, and estimations of model uncertainty as well as the relative impact of model parameters on model output (i.e., sensitivity) are determined. A case study using a single-cell hydrologic model (EXP-HYDRO) is used to test the method using river discharge data from the Dee River catchment in Wales. We compare the results of MOESHA with Sobol's global sensitivity analysis method and demonstrate that the algorithm is able to pinpoint non-impactful parameters, demonstrate the uncertainty of model results with respect to uncertainties in model parameters, and achieve excellent calibration results. A major drawback of the algorithm is that it is computationally expensive; therefore, parallelized methods should be used to reduce the computational burden.
模型参数不确定性和敏感性的表征是水文建模的一个重要方面。本文介绍了多目标进化敏感性处理算法（MOESHA），该算法将输入参数不确定性和敏感性分析与遗传算法校准程序相结合，以动态采样参数空间。这种新颖的算法可作为传统静态空间采样方法（如分层采样或拉丁超立方采样）的替代方法。除了对水文模型的参数进行校准外，MOESHA还能确定使模型稳健性最大化和误差最小化的模型参数最优分布，其结果提供了因模型参数不确定性导致的模型不确定性估计。随后，我们将模型参数分布与虚拟变量（即不影响模型输出的变量）的分布进行比较，以区分有影响（即敏感）参数和无影响参数。通过这种方式，生成了一个经过最优校准的模型，并确定了模型不确定性估计以及模型参数对模型输出的相对影响（即敏感性）。使用一个单单元水文模型（EXP - HYDRO）的案例研究，利用威尔士迪伊河流域的河流流量数据对该方法进行了测试。我们将MOESHA的结果与索博尔全局敏感性分析方法的结果进行了比较，证明该算法能够找出无影响参数，展示模型结果相对于模型参数不确定性的不确定性，并取得了优异的校准结果。该算法的一个主要缺点是计算成本高；因此，应使用并行化方法来减轻计算负担。
