A General Probabilistic Framework for uncertainty and global sensitivity analysis of deterministic models: A hydrological case study
A General Probabilistic Framework for uncertainty and global sensitivity analysis of deterministic models: A hydrological case study
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DOI：
10.1016/j.envsoft.2013.09.022
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发表时间：
2014-01-01
影响因子：
4.9
通讯作者：
Tarantola, S.
中科院分区：
文献类型：
--
作者：
Baroni, G.;Tarantola, S.
本研究提出了一个通用的概率框架（GPF）的不确定性和全局灵敏度分析的确定性模型，其中，除了标量输入，非标量和相关的输入可以考虑以及。进行分析的方差为基础的方法Sobol/Saltelli的第一和总的敏感性指数估计。该框架的结果可以用于模型改进、参数估计或模型简化的循环中。该框架适用于SWAP，一个113水文模型的水，溶质和热量在非饱和和饱和土壤中的运输。不确定性的来源分为五大类：模型结构（土壤离散化），输入（天气数据），随时间变化的（作物）参数，标量参数（土壤特性）和观测（测量土壤湿度）。对于每一个不确定性的来源，根据直接的监测活动创建不同的实现。分析中考虑了蒸散量、根区土壤水分和根区以下底层通量的不确定性。结果表明，不确定性的来源是不同的每个输出考虑，它是必要的，考虑多个输出变量的模型进行适当的评估。通过减少观测、土壤参数和天气数据的不确定性，可以提高模型的性能。总体而言，研究表明，GPF有能力量化不同来源的不确定性的相对贡献，并确定提高模型性能所需的优先事项。所提出的框架可以扩展到各种各样的建模应用程序，也当模型输出的直接测量是不可用的。(C)2013爱思唯尔有限公司保留所有权利。
The present study proposes a General Probabilistic Framework (GPF) for uncertainty and global sensitivity analysis of deterministic models in which, in addition to scalar inputs, non-scalar and correlated inputs can be considered as well. The analysis is conducted with the variance-based approach of Sobol/Saltelli where first and total sensitivity indices are estimated. The results of the framework can be used in a loop for model improvement, parameter estimation or model simplification. The framework is applied to SWAP, a 113 hydrological model for the transport of water, solutes and heat in unsaturated and saturated soils. The sources of uncertainty are grouped in five main classes: model structure (soil discretization), input (weather data), time-varying (crop) parameters, scalar parameters (soil properties) and observations (measured soil moisture). For each source of uncertainty, different realizations are created based on direct monitoring activities. Uncertainty of evapotranspiration, soil moisture in the root zone and bottom fluxes below the root zone are considered in the analysis. The results show that the sources of uncertainty are different for each output considered and it is necessary to consider multiple output variables for a proper assessment of the model. Improvements on the performance of the model can be achieved reducing the uncertainty in the observations, in the soil parameters and in the weather data. Overall, the study shows the capability of the GPF to quantify the relative contribution of the different sources of uncertainty and to identify the priorities required to improve the performance of the model. The proposed framework can be extended to a wide variety of modelling applications, also when direct measurements of model output are not available. (C) 2013 Elsevier Ltd. All rights reserved.