基于过程模型CROBAS的全局灵敏度分析方法比较

过程机理模型在开发过程中常受限于生理学参数无法直接或准确测量。全局灵敏度分析可以评估模型预测结果对于生理学参数变化的响应,为模型结构改进、数据收集和参数校准提供参考。本研究基于过程模型CROBAS,以华山松为例,选取模型中描述树木结构关系的10个参数,以树高和各器官生物量的Nash-Sutcliffe效率(NSE)为目标函数,比较了3种应用较广泛的全局灵敏度分析方法,即Morris筛选法、基于方差的Sobol指数法和扩展的傅里叶幅度检验法(EFAST)。结果表明: 参数灵敏度排序在不同方法中仅略微有所变化,但对于不同目标函数则区别明显。对算法耗时和收敛效率而言,Morris和EFAST性能较高,Sobol效率相对较低。所有模型输出变量均对单位面积年最大光合速率、比叶面积、消光系数敏感,林冠光截留状态对于林木生长量有着关键性影响,意味着光合固碳量是CROBAS在模型校正和林木生长动态模拟中需要优先进行数据收集、验证与测试的模块。灵敏度分析同时表明,碳平衡理论在林木生物量模拟中最为核心部分是树叶生物量模块的计算与验证。对于复杂过程模型的参数灵敏度分析,如需定性研究可选Morris,而量化评估采用EFAST更适合。     

Sensitivity Analysis of Environmental Process Modeling in a Life Cycle Context: A Case Study of Hemp Crop Production

The aim of this article is to develop a methodological approach allowing to assess the influence of parameters of one or more elementary processes in the foreground system, on the outcomes of a life cycle assessment (LCA) study. From this perspective, the method must be able to: (1) include foreground process modeling in order to avoid the assumption of proportionality between inventory data and reference flows; (2) quantify influences of foreground processes’ parameters (and, possibly, interactions between parameters); and (3) identify trends (either increasing or decreasing) for each parameter on each indicator in order to determine the most favorable direction for parametric variation. These objectives can be reached by combining foreground system modeling, a set of two different sensitivity analysis methods (each one providing different and complementary information), and LCA. The proposed method is applied to a case study of hemp‐based insulation materials for buildings. The present study will focus on the agricultural stage as a foreground system and as a first step encompassing the entire life cycle. A set of technological recommendations were identified for hemp farmers in order to reduce the crop's environmental impacts (from –11% to –89% according to the considered impact category). One of the main limitations of the approach is the need for a detailed model of the foreground process. Further, the method is, at present, rather time‐consuming. However, it offers long‐term advantages given that the higher level of model detail adds robustness to the LCA results.     

Evaluation of habitat suitability index models by global sensitivity and uncertainty analyses: a case study for submerged aquatic vegetation

Habitat suitability index (HSI) models are commonly used to predict habitat quality and species distributions and are used to develop biological surveys, assess reserve and management priorities, and anticipate possible change under different management or climate change scenarios. Important management decisions may be based on model results, often without a clear understanding of the level of uncertainty associated with model outputs. We present an integrated methodology to assess the propagation of uncertainty from both inputs and structure of the HSI models on model outputs (uncertainty analysis: UA) and relative importance of uncertain model inputs and their interactions on the model output uncertainty (global sensitivity analysis: GSA). We illustrate the GSA/UA framework using simulated hydrology input data from a hydrodynamic model representing sea level changes and HSI models for two species of submerged aquatic vegetation (SAV) in southwest Everglades National Park: Vallisneria americana (tape grass) and Halodule wrightii (shoal grass). We found considerable spatial variation in uncertainty for both species, but distributions of HSI scores still allowed discrimination of sites with good versus poor conditions. Ranking of input parameter sensitivities also varied spatially for both species, with high habitat quality sites showing higher sensitivity to different parameters than low‐quality sites. HSI models may be especially useful when species distribution data are unavailable, providing means of exploiting widely available environmental datasets to model past, current, and future habitat conditions. The GSA/UA approach provides a general method for better understanding HSI model dynamics, the spatial and temporal variation in uncertainties, and the parameters that contribute most to model uncertainty. Including an uncertainty and sensitivity analysis in modeling efforts as part of the decision‐making framework will result in better‐informed, more robust decisions.