Beck Marcus W, Lehrter John C, Lowe Lisa L, Jarvis Brandon M
USEPA National Health and Environmental Effects Research Laboratory, Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, FL 32561.
University of South Alabama, Dauphin Island Sea Lab, Dauphin Island, AL 36528.
Ecol Modell. 2017 Nov 10;363:17-30. doi: 10.1016/j.ecolmodel.2017.08.020.
Local sensitivity analyses and identifiable parameter subsets were used to describe numerical constraints of a hypoxia model for bottom waters of the northern Gulf of Mexico. The sensitivity of state variables differed considerably with parameter changes, although most variables were responsive to changes in parameters that influenced planktonic growth rates and less sensitive to physical or chemical parameters. Variation in sensitivity had a direct correspondence with identifiability, such that only small subsets of the complete parameter set had unique effects on the model output. Selecting parameters by decreasing sensitivity demonstrated that only eight of 51 total parameters had a sufficiently unique effect on model output for accurate calibration. As a result, parameter selection heuristics were used to identify parameters for model calibration that depended on combined effects on output, relative sensitivity of each parameter, and ecological categories for the biogeochemical equations. The calibrated zero-dimensional (0-D) unit of the hypoxia model had improved fit to the observed data if sensitive phytoplankton parameters were included in an identifiable subset. Extension of results to a three-dimensional grid of the Gulf of Mexico showed that sensitive parameters for the 0-D model translated to non-trivial changes in the areal estimates of hypoxia.
局部敏感性分析和可识别参数子集被用于描述墨西哥湾北部底层水域缺氧模型的数值约束。状态变量的敏感性随参数变化有很大差异，尽管大多数变量对影响浮游生物生长速率的参数变化有响应，而对物理或化学参数不太敏感。敏感性的变化与可识别性直接相关，以至于完整参数集中只有小部分子集对模型输出有独特影响。通过降低敏感性来选择参数表明，51个参数中只有8个对模型输出有足够独特的影响以进行精确校准。因此，参数选择启发法被用于识别模型校准参数，这些参数取决于对输出的综合影响、每个参数的相对敏感性以及生物地球化学方程的生态类别。如果将敏感的浮游植物参数包含在可识别子集中，缺氧模型的校准零维（0-D）单元对观测数据的拟合会得到改善。将结果扩展到墨西哥湾的三维网格表明，0-D模型的敏感参数转化为缺氧面积估计的显著变化。
