The use of mass flow simulations in volcanic hazard zonation and mapping is often limited by model complexity (i.e. uncertainty in correct values of model parameters), a lack of model uncertainty quantification, and limited approaches to incorporate this uncertainty into hazard maps. When quantified, mass flow simulation errors are typically evaluated on a pixel-pair basis, using the difference between simulated and observed (“actual”) map-cell values to evaluate the performance of a model. However, these comparisons conflate location and quantification errors, neglecting possible spatial autocorrelation of evaluated errors. As a result, model performance assessments typically yield moderate accuracy values. In this paper, similarly moderate accuracy values were found in a performance assessment of three depth-averaged numerical models using the 2012 debris avalanche from the Upper Te Maari crater, Tongariro Volcano, as a benchmark. To provide a fairer assessment of performance and evaluate spatial covariance of errors, we use a fuzzy set approach to indicate the proximity of similarly valued map cells. This “fuzzification” of simulated results yields improvements in targeted performance metrics relative to a length scale parameter at the expense of decreases in opposing metrics (e.g. fewer false negatives result in more false positives) and a reduction in resolution. The use of this approach to generate hazard zones incorporating the identified uncertainty and associated trade-offs is demonstrated and indicates a potential use for informed stakeholders by reducing the complexity of uncertainty estimation and supporting decision-making from simulated data.

中文翻译：

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量化位置误差以定义火山质量流灾害模拟中的不确定性

在火山灾害区划和绘图中使用质量流量模拟通常受到模型复杂性（即模型参数正确值的不确定性）、模型不确定性量化的缺乏以及将这种不确定性纳入灾害地图的有限方法的限制。量化后，质量流量模拟误差通常以像素对为基础进行评估，使用模拟和观察（“实际”）映射单元值之间的差异来评估模型的性能。然而，这些比较将位置和量化误差混为一谈，忽略了评估误差的可能空间自相关。因此，模型性能评估通常会产生中等准确度值。在这篇论文中，使用 2012 年来自汤加里罗火山上蒂马里火山口的碎片雪崩作为基准，在对三个深度平均数值模型的性能评估中发现了类似的中等精度值。为了提供更公平的性能评估并评估误差的空间协方差，我们使用模糊集方法来指示类似值的地图单元格的接近度。模拟结果的这种“模糊化”产生了相对于长度尺度参数的目标性能指标的改进，代价是相反指标的减少（例如，更少的假阴性导致更多的假阳性）和分辨率的降低。