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Sensitivity analysis and uncertainty estimation for tephra dispersal models

机译:Tephra扩散模型的灵敏度分析和不确定性估计

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Sensitivity analysis and uncertainty estimation are crucial to the validation and calibration of numerical models. In this paper we present the application of sensitivity analyses, parameter estimations and Monte-Carlo uncertainty analyses on TEPHRA, an advection-diffusion model for the description of particle dispersion and sedimentation from volcanic plumes. The model and the related sensitivity analysis are tested on two sub-plinian eruptions: the 22 July 1998 eruption of Etna volcano (Italy) and the 17 June 1996 eruption of Ruapehu volcano (New Zealand). Sensitivity analyses are key to (1) constrain crucial eruption parameters (e.g., total erupted mass) (2) reduce the number of variables by eliminating non-influential parameters (e.g., particle density) and (3) investigate the interactions among all input parameters (plume height, total grain-size distribution, diffusion coefficient, fall-time threshold and mass-distribution parameter). For the two test cases, we found that the total erupted mass significantly affects the model outputs and, therefore, it can be accurately estimated from field data of the fallout deposit, whereas the particle density can be fixed at its nominal value because it has negligible effects on the model predictions.
机译:灵敏度分析和不确定性估计对于数值模型的验证和校准至关重要。在本文中,我们介绍了灵敏度分析,参数估计和蒙特卡洛不确定性分析在TEPHRA上的应用,TEPHRA是对流扩散模型,用于描述火山羽中颗粒的弥散和沉降。该模型和相关的敏感性分析在两次亚平山爆发中进行了测试:1998年7月22日埃特纳火山爆发(意大利)和1996年6月17日鲁阿佩胡火山爆发(新西兰)。敏感性分析对于以下方面至关重要:(1)限制关键的喷发参数(例如,总喷发质量)(2)通过消除无影响的参数(例如,颗粒密度)减少变量的数量,以及(3)研究所有输入参数之间的相互作用(粉尘高度,总晶粒尺寸分布,扩散系数,下降时间阈值和质量分布参数)。对于这两个测试案例,我们发现总喷发质量显着影响了模型输出,因此可以从沉降沉积物的现场数据中准确估算出喷发质量,而颗粒密度可以固定为标称值,因为它可以忽略不计对模型预测的影响。

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