Calibration of rainfall‐runoff models: Application of global optimization to the Sacramento Soil Moisture Accounting Model
Calibration of rainfall‐runoff models: Application of global optimization to the Sacramento Soil Moisture Accounting Model
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DOI：
10.1029/92wr02617
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发表时间：
1993-04
影响因子：
5.4
通讯作者：
S. Sorooshian;Q. Duan;V. Gupta
中科院分区：
文献类型：
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作者：
S. Sorooshian;Q. Duan;V. Gupta
降雨径流概念模型很难通过自动方法进行校准；造成这种情况的一个主要原因是传统程序无法找到全局最优的参数集。本文研究了两种全局优化方法，即混洗复数演化 (SCE-UA) 方法（由作者开发）和多起点单纯形 (MSX) 方法，在国家气象局河流预报系统 (NWSRFS) 萨克拉门托土壤湿度核算模型 (SAC-SMA) 校准期间能够找到最佳参数集的一致性。在本研究的第一阶段，使用无误差的合成数据对“理想”条件下的算法进行比较评估。在每种算法的 10 次独立试验中，同时优化 SAC-SMA 模型的 13 个参数，SCE-UA 方法在定位精确的全局最优值（即“真实”参数值）方面实现了 100% 的成功率，而 MSX 方法在所有试验中都失败了，即使函数评估次数是两倍以上。第二阶段，利用叶河流域的历史数据，使用DRMS和HMLE两种不同的估计标准，对“真实”条件下的算法进行比较评估； SCE-UA 算法始终获得较低的函数值和更紧密分组的参数估计，同时使用比 MSX 算法少三分之一的函数评估。
Conceptual rainfall-runoff models are difficult to calibrate by means of automatic methods; one major reason for this is the inability of conventional procedures to locate the globally optimal set of parameters. This paper investigates the consistency with which two global optimization methods, the shuffled complex evolution (SCE-UA) method (developed by the authors) and the multistart simplex (MSX) method, are able to find the optimal parameter set during calibration of the Sacramento soil moisture accounting model (SAC-SMA) of the National Weather Service River Forecast System (NWSRFS). In the first phase of this study, error-free synthetic data are used to conduct a comparative evaluation of the algorithms under “ideal” conditions. In 10 independent trials of each algorithm in which 13 parameters of the SAC-SMA model were optimized simultaneously, the SCE-UA method achieved a 100% success rate in locating the precise global optimum (i.e., the “true” parameter values) while the MSX method failed in all trials even with more than twice the number of function evaluations. In the second phase, historical data from the Leaf River watershed are used to conduct a comparative evaluation of the algorithms under “real” conditions, using two different estimation criteria, DRMS and HMLE; the SCE-UA algorithm obtained consistently lower function values and more closely grouped parameter estimates, while using one-third fewer function evaluations than the MSX algorithm.